diff --git a/.gitattributes b/.gitattributes index 1f127616a744dd758363474542a7493adc28f6b7..2e175b1a31d5b783050694060d4ef676dfd33e48 100644 --- a/.gitattributes +++ b/.gitattributes @@ -33,7 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text *.zip filter=lfs diff=lfs merge=lfs -text *.zst filter=lfs diff=lfs merge=lfs -text *tfevents* filter=lfs diff=lfs merge=lfs -text -EMAGE/test_sequences/wave16k/2_scott_0_1_1.wav filter=lfs diff=lfs merge=lfs -text -EMAGE/test_sequences/wave16k/2_scott_0_2_2.wav filter=lfs diff=lfs merge=lfs -text -EMAGE/test_sequences/wave16k/2_scott_0_3_3.wav filter=lfs diff=lfs merge=lfs -text -EMAGE/test_sequences/wave16k/2_scott_0_4_4.wav filter=lfs diff=lfs merge=lfs -text +*.wav filter=lfs diff=lfs merge=lfs -text \ No newline at end of file diff --git a/.ipynb_checkpoints/README-checkpoint.md b/.ipynb_checkpoints/README-checkpoint.md deleted file mode 100644 index 290eb050c2de0c8a7305fe604ca61d5cda6a61a9..0000000000000000000000000000000000000000 --- a/.ipynb_checkpoints/README-checkpoint.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: EMAGE -emoji: ⚡ -colorFrom: yellow -colorTo: green -sdk: gradio -sdk_version: 4.24.0 -app_file: app.py -pinned: false -license: apache-2.0 ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/.ipynb_checkpoints/app-checkpoint.py b/.ipynb_checkpoints/app-checkpoint.py deleted file mode 100644 index 11a141959a6f3b4638139e16772a0fa4431e69e4..0000000000000000000000000000000000000000 --- a/.ipynb_checkpoints/app-checkpoint.py +++ /dev/null @@ -1,664 +0,0 @@ -import spaces -import os -# os.system("Xvfb :99 -ac &") -# os.environ["DISPLAY"] = ":99" -import OpenGL.GL as gl -os.environ["PYOPENGL_PLATFORM"] = "egl" -os.environ["MESA_GL_VERSION_OVERRIDE"] = "4.1" -import signal -import time -import csv -import sys -import warnings -import random -import gradio as gr -import torch -import torch.nn as nn -import torch.nn.functional as F -import torch.distributed as dist -from torch.nn.parallel import DistributedDataParallel as DDP -import torch.multiprocessing as mp -import numpy as np -import time -import pprint -from loguru import logger -import smplx -from torch.utils.tensorboard import SummaryWriter -import wandb -import matplotlib.pyplot as plt -from utils import config, logger_tools, other_tools_hf, metric, data_transfer -from dataloaders import data_tools -from dataloaders.build_vocab import Vocab -from optimizers.optim_factory import create_optimizer -from optimizers.scheduler_factory import create_scheduler -from optimizers.loss_factory import get_loss_func -from dataloaders.data_tools import joints_list -from utils import rotation_conversions as rc -import soundfile as sf -import librosa - -def inverse_selection_tensor(filtered_t, selection_array, n): - selection_array = torch.from_numpy(selection_array).cuda() - original_shape_t = torch.zeros((n, 165)).cuda() - selected_indices = torch.where(selection_array == 1)[0] - for i in range(n): - original_shape_t[i, selected_indices] = filtered_t[i] - return original_shape_t - -@spaces.GPU(duration=120) -def test_demo_gpu( - model, vq_model_face, vq_model_upper, vq_model_hands, vq_model_lower, global_motion, smplx_model, - dict_data, - args, - joints, joint_mask_upper, joint_mask_lower, joint_mask_hands, - log_softmax, -): - rank = 0 - other_tools_hf.load_checkpoints(vq_model_face, args.data_path_1 + "pretrained_vq/last_790_face_v2.bin", args.e_name) - other_tools_hf.load_checkpoints(vq_model_upper, args.data_path_1 + "pretrained_vq/upper_vertex_1layer_710.bin", args.e_name) - other_tools_hf.load_checkpoints(vq_model_hands, args.data_path_1 + "pretrained_vq/hands_vertex_1layer_710.bin", args.e_name) - other_tools_hf.load_checkpoints(vq_model_lower, args.data_path_1 + "pretrained_vq/lower_foot_600.bin", args.e_name) - other_tools_hf.load_checkpoints(global_motion, args.data_path_1 + "pretrained_vq/last_1700_foot.bin", args.e_name) - other_tools_hf.load_checkpoints(model, args.test_ckpt, args.g_name) - model.to(rank).eval() - smplx_model.to(rank).eval() - vq_model_face.to(rank).eval() - vq_model_upper.to(rank).eval() - vq_model_hands.to(rank).eval() - vq_model_lower.to(rank).eval() - global_motion.to(rank).eval() - - with torch.no_grad(): - tar_pose_raw = dict_data["pose"] - tar_pose = tar_pose_raw[:, :, :165].to(rank) - tar_contact = tar_pose_raw[:, :, 165:169].to(rank) - tar_trans = dict_data["trans"].to(rank) - tar_exps = dict_data["facial"].to(rank) - in_audio = dict_data["audio"].to(rank) - in_word = None# dict_data["word"].to(rank) - tar_beta = dict_data["beta"].to(rank) - tar_id = dict_data["id"].to(rank).long() - bs, n, j = tar_pose.shape[0], tar_pose.shape[1], joints - - tar_pose_jaw = tar_pose[:, :, 66:69] - tar_pose_jaw = rc.axis_angle_to_matrix(tar_pose_jaw.reshape(bs, n, 1, 3)) - tar_pose_jaw = rc.matrix_to_rotation_6d(tar_pose_jaw).reshape(bs, n, 1*6) - tar_pose_face = torch.cat([tar_pose_jaw, tar_exps], dim=2) - - tar_pose_hands = tar_pose[:, :, 25*3:55*3] - tar_pose_hands = rc.axis_angle_to_matrix(tar_pose_hands.reshape(bs, n, 30, 3)) - tar_pose_hands = rc.matrix_to_rotation_6d(tar_pose_hands).reshape(bs, n, 30*6) - - tar_pose_upper = tar_pose[:, :, joint_mask_upper.astype(bool)] - tar_pose_upper = rc.axis_angle_to_matrix(tar_pose_upper.reshape(bs, n, 13, 3)) - tar_pose_upper = rc.matrix_to_rotation_6d(tar_pose_upper).reshape(bs, n, 13*6) - - tar_pose_leg = tar_pose[:, :, joint_mask_lower.astype(bool)] - tar_pose_leg = rc.axis_angle_to_matrix(tar_pose_leg.reshape(bs, n, 9, 3)) - tar_pose_leg = rc.matrix_to_rotation_6d(tar_pose_leg).reshape(bs, n, 9*6) - tar_pose_lower = torch.cat([tar_pose_leg, tar_trans, tar_contact], dim=2) - - # tar_pose = rc.axis_angle_to_matrix(tar_pose.reshape(bs, n, j, 3)) - # tar_pose = rc.matrix_to_rotation_6d(tar_pose).reshape(bs, n, j*6) - tar4dis = torch.cat([tar_pose_jaw, tar_pose_upper, tar_pose_hands, tar_pose_leg], dim=2) - - tar_index_value_face_top = vq_model_face.map2index(tar_pose_face) # bs*n/4 - tar_index_value_upper_top = vq_model_upper.map2index(tar_pose_upper) # bs*n/4 - tar_index_value_hands_top = vq_model_hands.map2index(tar_pose_hands) # bs*n/4 - tar_index_value_lower_top = vq_model_lower.map2index(tar_pose_lower) # bs*n/4 - - latent_face_top = vq_model_face.map2latent(tar_pose_face) # bs*n/4 - latent_upper_top = vq_model_upper.map2latent(tar_pose_upper) # bs*n/4 - latent_hands_top = vq_model_hands.map2latent(tar_pose_hands) # bs*n/4 - latent_lower_top = vq_model_lower.map2latent(tar_pose_lower) # bs*n/4 - - latent_in = torch.cat([latent_upper_top, latent_hands_top, latent_lower_top], dim=2) - - index_in = torch.stack([tar_index_value_upper_top, tar_index_value_hands_top, tar_index_value_lower_top], dim=-1).long() - - tar_pose_6d = rc.axis_angle_to_matrix(tar_pose.reshape(bs, n, 55, 3)) - tar_pose_6d = rc.matrix_to_rotation_6d(tar_pose_6d).reshape(bs, n, 55*6) - latent_all = torch.cat([tar_pose_6d, tar_trans, tar_contact], dim=-1) - - loaded_data = { - "tar_pose_jaw": tar_pose_jaw, - "tar_pose_face": tar_pose_face, - "tar_pose_upper": tar_pose_upper, - "tar_pose_lower": tar_pose_lower, - "tar_pose_hands": tar_pose_hands, - 'tar_pose_leg': tar_pose_leg, - "in_audio": in_audio, - "in_word": in_word, - "tar_trans": tar_trans, - "tar_exps": tar_exps, - "tar_beta": tar_beta, - "tar_pose": tar_pose, - "tar4dis": tar4dis, - "tar_index_value_face_top": tar_index_value_face_top, - "tar_index_value_upper_top": tar_index_value_upper_top, - "tar_index_value_hands_top": tar_index_value_hands_top, - "tar_index_value_lower_top": tar_index_value_lower_top, - "latent_face_top": latent_face_top, - "latent_upper_top": latent_upper_top, - "latent_hands_top": latent_hands_top, - "latent_lower_top": latent_lower_top, - "latent_in": latent_in, - "index_in": index_in, - "tar_id": tar_id, - "latent_all": latent_all, - "tar_pose_6d": tar_pose_6d, - "tar_contact": tar_contact, - } - - mode = 'test' - bs, n, j = loaded_data["tar_pose"].shape[0], loaded_data["tar_pose"].shape[1], joints - tar_pose = loaded_data["tar_pose"] - tar_beta = loaded_data["tar_beta"] - in_word =None# loaded_data["in_word"] - tar_exps = loaded_data["tar_exps"] - tar_contact = loaded_data["tar_contact"] - in_audio = loaded_data["in_audio"] - tar_trans = loaded_data["tar_trans"] - - remain = n%8 - if remain != 0: - tar_pose = tar_pose[:, :-remain, :] - tar_beta = tar_beta[:, :-remain, :] - tar_trans = tar_trans[:, :-remain, :] - # in_word = in_word[:, :-remain] - tar_exps = tar_exps[:, :-remain, :] - tar_contact = tar_contact[:, :-remain, :] - n = n - remain - - tar_pose_jaw = tar_pose[:, :, 66:69] - tar_pose_jaw = rc.axis_angle_to_matrix(tar_pose_jaw.reshape(bs, n, 1, 3)) - tar_pose_jaw = rc.matrix_to_rotation_6d(tar_pose_jaw).reshape(bs, n, 1*6) - tar_pose_face = torch.cat([tar_pose_jaw, tar_exps], dim=2) - - tar_pose_hands = tar_pose[:, :, 25*3:55*3] - tar_pose_hands = rc.axis_angle_to_matrix(tar_pose_hands.reshape(bs, n, 30, 3)) - tar_pose_hands = rc.matrix_to_rotation_6d(tar_pose_hands).reshape(bs, n, 30*6) - - tar_pose_upper = tar_pose[:, :, joint_mask_upper.astype(bool)] - tar_pose_upper = rc.axis_angle_to_matrix(tar_pose_upper.reshape(bs, n, 13, 3)) - tar_pose_upper = rc.matrix_to_rotation_6d(tar_pose_upper).reshape(bs, n, 13*6) - - tar_pose_leg = tar_pose[:, :, joint_mask_lower.astype(bool)] - tar_pose_leg = rc.axis_angle_to_matrix(tar_pose_leg.reshape(bs, n, 9, 3)) - tar_pose_leg = rc.matrix_to_rotation_6d(tar_pose_leg).reshape(bs, n, 9*6) - tar_pose_lower = torch.cat([tar_pose_leg, tar_trans, tar_contact], dim=2) - - tar_pose_6d = rc.axis_angle_to_matrix(tar_pose.reshape(bs, n, 55, 3)) - tar_pose_6d = rc.matrix_to_rotation_6d(tar_pose_6d).reshape(bs, n, 55*6) - latent_all = torch.cat([tar_pose_6d, tar_trans, tar_contact], dim=-1) - - rec_index_all_face = [] - rec_index_all_upper = [] - rec_index_all_lower = [] - rec_index_all_hands = [] - - roundt = (n - args.pre_frames) // (args.pose_length - args.pre_frames) - remain = (n - args.pre_frames) % (args.pose_length - args.pre_frames) - round_l = args.pose_length - args.pre_frames - - for i in range(0, roundt): - # in_word_tmp = in_word[:, i*(round_l):(i+1)*(round_l)+args.pre_frames] - # audio fps is 16000 and pose fps is 30 - in_audio_tmp = in_audio[:, i*(16000//30*round_l):(i+1)*(16000//30*round_l)+16000//30*args.pre_frames] - in_id_tmp = loaded_data['tar_id'][:, i*(round_l):(i+1)*(round_l)+args.pre_frames] - mask_val = torch.ones(bs, args.pose_length, args.pose_dims+3+4).float().cuda() - mask_val[:, :args.pre_frames, :] = 0.0 - if i == 0: - latent_all_tmp = latent_all[:, i*(round_l):(i+1)*(round_l)+args.pre_frames, :] - else: - latent_all_tmp = latent_all[:, i*(round_l):(i+1)*(round_l)+args.pre_frames, :] - # print(latent_all_tmp.shape, latent_last.shape) - latent_all_tmp[:, :args.pre_frames, :] = latent_last[:, -args.pre_frames:, :] - - net_out_val = model( - in_audio = in_audio_tmp, - in_word=None, #in_word_tmp, - mask=mask_val, - in_motion = latent_all_tmp, - in_id = in_id_tmp, - use_attentions=True,) - - if args.cu != 0: - rec_index_upper = log_softmax(net_out_val["cls_upper"]).reshape(-1, args.vae_codebook_size) - _, rec_index_upper = torch.max(rec_index_upper.reshape(-1, args.pose_length, args.vae_codebook_size), dim=2) - #rec_upper = vq_model_upper.decode(rec_index_upper) - else: - _, rec_index_upper, _, _ = vq_model_upper.quantizer(net_out_val["rec_upper"]) - #rec_upper = vq_model_upper.decoder(rec_index_upper) - if args.cl != 0: - rec_index_lower = log_softmax(net_out_val["cls_lower"]).reshape(-1, args.vae_codebook_size) - _, rec_index_lower = torch.max(rec_index_lower.reshape(-1, args.pose_length, args.vae_codebook_size), dim=2) - #rec_lower = vq_model_lower.decode(rec_index_lower) - else: - _, rec_index_lower, _, _ = vq_model_lower.quantizer(net_out_val["rec_lower"]) - #rec_lower = vq_model_lower.decoder(rec_index_lower) - if args.ch != 0: - rec_index_hands = log_softmax(net_out_val["cls_hands"]).reshape(-1, args.vae_codebook_size) - _, rec_index_hands = torch.max(rec_index_hands.reshape(-1, args.pose_length, args.vae_codebook_size), dim=2) - #rec_hands = vq_model_hands.decode(rec_index_hands) - else: - _, rec_index_hands, _, _ = vq_model_hands.quantizer(net_out_val["rec_hands"]) - #rec_hands = vq_model_hands.decoder(rec_index_hands) - if args.cf != 0: - rec_index_face = log_softmax(net_out_val["cls_face"]).reshape(-1, args.vae_codebook_size) - _, rec_index_face = torch.max(rec_index_face.reshape(-1, args.pose_length, args.vae_codebook_size), dim=2) - #rec_face = vq_model_face.decoder(rec_index_face) - else: - _, rec_index_face, _, _ = vq_model_face.quantizer(net_out_val["rec_face"]) - #rec_face = vq_model_face.decoder(rec_index_face) - - if i == 0: - rec_index_all_face.append(rec_index_face) - rec_index_all_upper.append(rec_index_upper) - rec_index_all_lower.append(rec_index_lower) - rec_index_all_hands.append(rec_index_hands) - else: - rec_index_all_face.append(rec_index_face[:, args.pre_frames:]) - rec_index_all_upper.append(rec_index_upper[:, args.pre_frames:]) - rec_index_all_lower.append(rec_index_lower[:, args.pre_frames:]) - rec_index_all_hands.append(rec_index_hands[:, args.pre_frames:]) - - if args.cu != 0: - rec_upper_last = vq_model_upper.decode(rec_index_upper) - else: - rec_upper_last = vq_model_upper.decoder(rec_index_upper) - if args.cl != 0: - rec_lower_last = vq_model_lower.decode(rec_index_lower) - else: - rec_lower_last = vq_model_lower.decoder(rec_index_lower) - if args.ch != 0: - rec_hands_last = vq_model_hands.decode(rec_index_hands) - else: - rec_hands_last = vq_model_hands.decoder(rec_index_hands) - # if args.cf != 0: - # rec_face_last = vq_model_face.decode(rec_index_face) - # else: - # rec_face_last = vq_model_face.decoder(rec_index_face) - - rec_pose_legs = rec_lower_last[:, :, :54] - bs, n = rec_pose_legs.shape[0], rec_pose_legs.shape[1] - rec_pose_upper = rec_upper_last.reshape(bs, n, 13, 6) - rec_pose_upper = rc.rotation_6d_to_matrix(rec_pose_upper)# - rec_pose_upper = rc.matrix_to_axis_angle(rec_pose_upper).reshape(bs*n, 13*3) - rec_pose_upper_recover = inverse_selection_tensor(rec_pose_upper, joint_mask_upper, bs*n) - rec_pose_lower = rec_pose_legs.reshape(bs, n, 9, 6) - rec_pose_lower = rc.rotation_6d_to_matrix(rec_pose_lower) - rec_pose_lower = rc.matrix_to_axis_angle(rec_pose_lower).reshape(bs*n, 9*3) - rec_pose_lower_recover = inverse_selection_tensor(rec_pose_lower, joint_mask_lower, bs*n) - rec_pose_hands = rec_hands_last.reshape(bs, n, 30, 6) - rec_pose_hands = rc.rotation_6d_to_matrix(rec_pose_hands) - rec_pose_hands = rc.matrix_to_axis_angle(rec_pose_hands).reshape(bs*n, 30*3) - rec_pose_hands_recover = inverse_selection_tensor(rec_pose_hands, joint_mask_hands, bs*n) - rec_pose = rec_pose_upper_recover + rec_pose_lower_recover + rec_pose_hands_recover - rec_pose = rc.axis_angle_to_matrix(rec_pose.reshape(bs, n, j, 3)) - rec_pose = rc.matrix_to_rotation_6d(rec_pose).reshape(bs, n, j*6) - rec_trans_v_s = rec_lower_last[:, :, 54:57] - rec_x_trans = other_tools_hf.velocity2position(rec_trans_v_s[:, :, 0:1], 1/args.pose_fps, tar_trans[:, 0, 0:1]) - rec_z_trans = other_tools_hf.velocity2position(rec_trans_v_s[:, :, 2:3], 1/args.pose_fps, tar_trans[:, 0, 2:3]) - rec_y_trans = rec_trans_v_s[:,:,1:2] - rec_trans = torch.cat([rec_x_trans, rec_y_trans, rec_z_trans], dim=-1) - latent_last = torch.cat([rec_pose, rec_trans, rec_lower_last[:, :, 57:61]], dim=-1) - - rec_index_face = torch.cat(rec_index_all_face, dim=1) - rec_index_upper = torch.cat(rec_index_all_upper, dim=1) - rec_index_lower = torch.cat(rec_index_all_lower, dim=1) - rec_index_hands = torch.cat(rec_index_all_hands, dim=1) - if args.cu != 0: - rec_upper = vq_model_upper.decode(rec_index_upper) - else: - rec_upper = vq_model_upper.decoder(rec_index_upper) - if args.cl != 0: - rec_lower = vq_model_lower.decode(rec_index_lower) - else: - rec_lower = vq_model_lower.decoder(rec_index_lower) - if args.ch != 0: - rec_hands = vq_model_hands.decode(rec_index_hands) - else: - rec_hands = vq_model_hands.decoder(rec_index_hands) - if args.cf != 0: - rec_face = vq_model_face.decode(rec_index_face) - else: - rec_face = vq_model_face.decoder(rec_index_face) - - rec_exps = rec_face[:, :, 6:] - rec_pose_jaw = rec_face[:, :, :6] - rec_pose_legs = rec_lower[:, :, :54] - bs, n = rec_pose_jaw.shape[0], rec_pose_jaw.shape[1] - rec_pose_upper = rec_upper.reshape(bs, n, 13, 6) - rec_pose_upper = rc.rotation_6d_to_matrix(rec_pose_upper)# - rec_pose_upper = rc.matrix_to_axis_angle(rec_pose_upper).reshape(bs*n, 13*3) - rec_pose_upper_recover = inverse_selection_tensor(rec_pose_upper, joint_mask_upper, bs*n) - rec_pose_lower = rec_pose_legs.reshape(bs, n, 9, 6) - rec_pose_lower = rc.rotation_6d_to_matrix(rec_pose_lower) - rec_lower2global = rc.matrix_to_rotation_6d(rec_pose_lower.clone()).reshape(bs, n, 9*6) - rec_pose_lower = rc.matrix_to_axis_angle(rec_pose_lower).reshape(bs*n, 9*3) - rec_pose_lower_recover = inverse_selection_tensor(rec_pose_lower, joint_mask_lower, bs*n) - rec_pose_hands = rec_hands.reshape(bs, n, 30, 6) - rec_pose_hands = rc.rotation_6d_to_matrix(rec_pose_hands) - rec_pose_hands = rc.matrix_to_axis_angle(rec_pose_hands).reshape(bs*n, 30*3) - rec_pose_hands_recover = inverse_selection_tensor(rec_pose_hands, joint_mask_hands, bs*n) - rec_pose_jaw = rec_pose_jaw.reshape(bs*n, 6) - rec_pose_jaw = rc.rotation_6d_to_matrix(rec_pose_jaw) - rec_pose_jaw = rc.matrix_to_axis_angle(rec_pose_jaw).reshape(bs*n, 1*3) - rec_pose = rec_pose_upper_recover + rec_pose_lower_recover + rec_pose_hands_recover - rec_pose[:, 66:69] = rec_pose_jaw - - to_global = rec_lower - to_global[:, :, 54:57] = 0.0 - to_global[:, :, :54] = rec_lower2global - rec_global = global_motion(to_global) - - rec_trans_v_s = rec_global["rec_pose"][:, :, 54:57] - rec_x_trans = other_tools_hf.velocity2position(rec_trans_v_s[:, :, 0:1], 1/args.pose_fps, tar_trans[:, 0, 0:1]) - rec_z_trans = other_tools_hf.velocity2position(rec_trans_v_s[:, :, 2:3], 1/args.pose_fps, tar_trans[:, 0, 2:3]) - rec_y_trans = rec_trans_v_s[:,:,1:2] - rec_trans = torch.cat([rec_x_trans, rec_y_trans, rec_z_trans], dim=-1) - tar_pose = tar_pose[:, :n, :] - tar_exps = tar_exps[:, :n, :] - tar_trans = tar_trans[:, :n, :] - tar_beta = tar_beta[:, :n, :] - - rec_pose = rc.axis_angle_to_matrix(rec_pose.reshape(bs*n, j, 3)) - rec_pose = rc.matrix_to_rotation_6d(rec_pose).reshape(bs, n, j*6) - tar_pose = rc.axis_angle_to_matrix(tar_pose.reshape(bs*n, j, 3)) - tar_pose = rc.matrix_to_rotation_6d(tar_pose).reshape(bs, n, j*6) - - net_out = { - 'rec_pose': rec_pose, - 'rec_trans': rec_trans, - 'tar_pose': tar_pose, - 'tar_exps': tar_exps, - 'tar_beta': tar_beta, - 'tar_trans': tar_trans, - 'rec_exps': rec_exps, - } - - - tar_pose = net_out['tar_pose'] - rec_pose = net_out['rec_pose'] - tar_exps = net_out['tar_exps'] - tar_beta = net_out['tar_beta'] - rec_trans = net_out['rec_trans'] - tar_trans = net_out['tar_trans'] - rec_exps = net_out['rec_exps'] - # print(rec_pose.shape, tar_pose.shape) - bs, n, j = tar_pose.shape[0], tar_pose.shape[1], joints - # interpolate to 30fps - if (30/args.pose_fps) != 1: - assert 30%args.pose_fps == 0 - n *= int(30/args.pose_fps) - tar_pose = torch.nn.functional.interpolate(tar_pose.permute(0, 2, 1), scale_factor=30/args.pose_fps, mode='linear').permute(0,2,1) - rec_pose = torch.nn.functional.interpolate(rec_pose.permute(0, 2, 1), scale_factor=30/args.pose_fps, mode='linear').permute(0,2,1) - - # print(rec_pose.shape, tar_pose.shape) - rec_pose = rc.rotation_6d_to_matrix(rec_pose.reshape(bs*n, j, 6)) - rec_pose = rc.matrix_to_axis_angle(rec_pose).reshape(bs*n, j*3) - - tar_pose = rc.rotation_6d_to_matrix(tar_pose.reshape(bs*n, j, 6)) - tar_pose = rc.matrix_to_axis_angle(tar_pose).reshape(bs*n, j*3) - - return tar_pose, rec_pose, tar_exps, tar_beta, rec_trans, tar_trans, rec_exps, bs, n, j - - -class BaseTrainer(object): - def __init__(self, args, sp, ap, tp): - hf_dir = "hf" - if not os.path.exists(args.out_path + "custom/" + hf_dir + "/"): - os.makedirs(args.out_path + "custom/" + hf_dir + "/") - sf.write(args.out_path + "custom/" + hf_dir + "/tmp.wav", ap[1][:ap[0]*8], ap[0]) - self.audio_path = args.out_path + "custom/" + hf_dir + "/tmp.wav" - audio, ssr = librosa.load(self.audio_path) - ap = (ssr, audio) - self.args = args - self.rank = 0 # dist.get_rank() - - #self.checkpoint_path = args.out_path + "custom/" + args.name + args.notes + "/" #wandb.run.dir #args.cache_path+args.out_path+"/"+args.name - self.checkpoint_path = args.out_path + "custom/" + hf_dir + "/" - if self.rank == 0: - self.test_data = __import__(f"dataloaders.{args.dataset}", fromlist=["something"]).CustomDataset(args, "test", smplx_path=sp, audio_path=ap, text_path=tp) - self.test_loader = torch.utils.data.DataLoader( - self.test_data, - batch_size=1, - shuffle=False, - num_workers=args.loader_workers, - drop_last=False, - ) - logger.info(f"Init test dataloader success") - model_module = __import__(f"models.{args.model}", fromlist=["something"]) - - if args.ddp: - self.model = getattr(model_module, args.g_name)(args).to(self.rank) - process_group = torch.distributed.new_group() - self.model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(self.model, process_group) - self.model = DDP(self.model, device_ids=[self.rank], output_device=self.rank, - broadcast_buffers=False, find_unused_parameters=False) - else: - self.model = torch.nn.DataParallel(getattr(model_module, args.g_name)(args), args.gpus).cpu() - - if self.rank == 0: - logger.info(self.model) - logger.info(f"init {args.g_name} success") - - self.smplx = smplx.create( - self.args.data_path_1+"smplx_models/", - model_type='smplx', - gender='NEUTRAL_2020', - use_face_contour=False, - num_betas=300, - num_expression_coeffs=100, - ext='npz', - use_pca=False, - ) - - self.args = args - self.joints = self.test_data.joints - self.ori_joint_list = joints_list[self.args.ori_joints] - self.tar_joint_list_face = joints_list["beat_smplx_face"] - self.tar_joint_list_upper = joints_list["beat_smplx_upper"] - self.tar_joint_list_hands = joints_list["beat_smplx_hands"] - self.tar_joint_list_lower = joints_list["beat_smplx_lower"] - - self.joint_mask_face = np.zeros(len(list(self.ori_joint_list.keys()))*3) - self.joints = 55 - for joint_name in self.tar_joint_list_face: - self.joint_mask_face[self.ori_joint_list[joint_name][1] - self.ori_joint_list[joint_name][0]:self.ori_joint_list[joint_name][1]] = 1 - self.joint_mask_upper = np.zeros(len(list(self.ori_joint_list.keys()))*3) - for joint_name in self.tar_joint_list_upper: - self.joint_mask_upper[self.ori_joint_list[joint_name][1] - self.ori_joint_list[joint_name][0]:self.ori_joint_list[joint_name][1]] = 1 - self.joint_mask_hands = np.zeros(len(list(self.ori_joint_list.keys()))*3) - for joint_name in self.tar_joint_list_hands: - self.joint_mask_hands[self.ori_joint_list[joint_name][1] - self.ori_joint_list[joint_name][0]:self.ori_joint_list[joint_name][1]] = 1 - self.joint_mask_lower = np.zeros(len(list(self.ori_joint_list.keys()))*3) - for joint_name in self.tar_joint_list_lower: - self.joint_mask_lower[self.ori_joint_list[joint_name][1] - self.ori_joint_list[joint_name][0]:self.ori_joint_list[joint_name][1]] = 1 - - self.tracker = other_tools_hf.EpochTracker(["fid", "l1div", "bc", "rec", "trans", "vel", "transv", 'dis', 'gen', 'acc', 'transa', 'exp', 'lvd', 'mse', "cls", "rec_face", "latent", "cls_full", "cls_self", "cls_word", "latent_word","latent_self"], [False,True,True, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False,False,False,False]) - - vq_model_module = __import__(f"models.motion_representation", fromlist=["something"]) - self.args.vae_layer = 2 - self.args.vae_length = 256 - self.args.vae_test_dim = 106 - self.vq_model_face = getattr(vq_model_module, "VQVAEConvZero")(self.args).cpu() - # print(self.vq_model_face) - # other_tools_hf.load_checkpoints(self.vq_model_face, self.args.data_path_1 + "pretrained_vq/last_790_face_v2.bin", args.e_name) - self.args.vae_test_dim = 78 - self.vq_model_upper = getattr(vq_model_module, "VQVAEConvZero")(self.args).cpu() - # other_tools_hf.load_checkpoints(self.vq_model_upper, self.args.data_path_1 + "pretrained_vq/upper_vertex_1layer_710.bin", args.e_name) - self.args.vae_test_dim = 180 - self.vq_model_hands = getattr(vq_model_module, "VQVAEConvZero")(self.args).cpu() - # other_tools_hf.load_checkpoints(self.vq_model_hands, self.args.data_path_1 + "pretrained_vq/hands_vertex_1layer_710.bin", args.e_name) - self.args.vae_test_dim = 61 - self.args.vae_layer = 4 - self.vq_model_lower = getattr(vq_model_module, "VQVAEConvZero")(self.args).cpu() - # other_tools_hf.load_checkpoints(self.vq_model_lower, self.args.data_path_1 + "pretrained_vq/lower_foot_600.bin", args.e_name) - self.args.vae_test_dim = 61 - self.args.vae_layer = 4 - self.global_motion = getattr(vq_model_module, "VAEConvZero")(self.args).cpu() - # other_tools_hf.load_checkpoints(self.global_motion, self.args.data_path_1 + "pretrained_vq/last_1700_foot.bin", args.e_name) - self.args.vae_test_dim = 330 - self.args.vae_layer = 4 - self.args.vae_length = 240 - - # self.cls_loss = nn.NLLLoss().to(self.rank) - # self.reclatent_loss = nn.MSELoss().to(self.rank) - # self.vel_loss = torch.nn.L1Loss(reduction='mean').to(self.rank) - # self.rec_loss = get_loss_func("GeodesicLoss").to(self.rank) - self.log_softmax = nn.LogSoftmax(dim=2) - - - def inverse_selection(self, filtered_t, selection_array, n): - original_shape_t = np.zeros((n, selection_array.size)) - selected_indices = np.where(selection_array == 1)[0] - for i in range(n): - original_shape_t[i, selected_indices] = filtered_t[i] - return original_shape_t - - def inverse_selection_tensor(self, filtered_t, selection_array, n): - selection_array = torch.from_numpy(selection_array).cuda() - original_shape_t = torch.zeros((n, 165)).cuda() - selected_indices = torch.where(selection_array == 1)[0] - for i in range(n): - original_shape_t[i, selected_indices] = filtered_t[i] - return original_shape_t - - - def test_demo(self, epoch): - ''' - input audio and text, output motion - do not calculate loss and metric - save video - ''' - results_save_path = self.checkpoint_path + f"/{epoch}/" - if os.path.exists(results_save_path): - import shutil - shutil.rmtree(results_save_path) - os.makedirs(results_save_path) - start_time = time.time() - total_length = 0 - test_seq_list = self.test_data.selected_file - align = 0 - latent_out = [] - latent_ori = [] - l2_all = 0 - lvel = 0 - for its, batch_data in enumerate(self.test_loader): - tar_pose, rec_pose, tar_exps, tar_beta, rec_trans, tar_trans, rec_exps, bs, n, j = test_demo_gpu( - self.model, self.vq_model_face, self.vq_model_upper, self.vq_model_hands, self.vq_model_lower, self.global_motion, self.smplx, - batch_data, - self.args, - self.joints, self.joint_mask_upper, self.joint_mask_lower, self.joint_mask_hands, - self.log_softmax, - ) - - tar_pose_np = tar_pose.detach().cpu().numpy() - rec_pose_np = rec_pose.detach().cpu().numpy() - rec_trans_np = rec_trans.detach().cpu().numpy().reshape(bs*n, 3) - rec_exp_np = rec_exps.detach().cpu().numpy().reshape(bs*n, 100) - tar_exp_np = tar_exps.detach().cpu().numpy().reshape(bs*n, 100) - tar_trans_np = tar_trans.detach().cpu().numpy().reshape(bs*n, 3) - #''' - # its = 0 - gt_npz = np.load(self.args.data_path+self.args.pose_rep +"/"+test_seq_list.iloc[its]['id']+".npz", allow_pickle=True) - np.savez(results_save_path+"gt_"+test_seq_list.iloc[its]['id']+'.npz', - betas=gt_npz["betas"], - poses=tar_pose_np, - expressions=tar_exp_np, - trans=tar_trans_np, - model='smplx2020', - gender='neutral', - mocap_frame_rate = 30, - ) - np.savez(results_save_path+"res_"+test_seq_list.iloc[its]['id']+'.npz', - betas=gt_npz["betas"], - poses=rec_pose_np, - expressions=rec_exp_np, - trans=rec_trans_np, - model='smplx2020', - gender='neutral', - mocap_frame_rate = 30, - ) - - total_length += n - render_vid_path = other_tools_hf.render_one_sequence_no_gt( - results_save_path+"res_"+test_seq_list.iloc[its]['id']+'.npz', - # results_save_path+"gt_"+test_seq_list.iloc[its]['id']+'.npz', - results_save_path, - self.audio_path, - self.args.data_path_1+"smplx_models/", - use_matplotlib = False, - args = self.args, - ) - result = gr.Video(value=render_vid_path, visible=True) - end_time = time.time() - start_time - logger.info(f"total inference time: {int(end_time)} s for {int(total_length/self.args.pose_fps)} s motion") - return result - - -@logger.catch -def emage(audio_path): - smplx_path = None - text_path = None - rank = 0 - world_size = 1 - args = config.parse_args() - #os.environ['TRANSFORMERS_CACHE'] = args.data_path_1 + "hub/" - if not sys.warnoptions: - warnings.simplefilter("ignore") - # dist.init_process_group(backend="gloo", rank=rank, world_size=world_size) - - #logger_tools.set_args_and_logger(args, rank) - other_tools_hf.set_random_seed(args) - other_tools_hf.print_exp_info(args) - - # return one intance of trainer - trainer = BaseTrainer(args, sp = smplx_path, ap = audio_path, tp = text_path) - result = trainer.test_demo(999) - return result - -examples = [ - ["./EMAGE/test_sequences/wave16k/2_scott_0_1_1.wav"], - ["./EMAGE/test_sequences/wave16k/2_scott_0_2_2.wav"], - ["./EMAGE/test_sequences/wave16k/2_scott_0_3_3.wav"], -] - -demo = gr.Interface( - emage, # function - inputs=[ - # gr.File(label="Please upload SMPL-X file with npz format here.", file_types=["npz", "NPZ"]), - gr.Audio(), - # gr.File(label="Please upload textgrid format file here.", file_types=["TextGrid", "Textgrid", "textgrid"]) - ], # input type - outputs=gr.Video(format="mp4", visible=True), - title='\ -
\ - EMAGE: Towards Unified Holistic Co-Speech Gesture Generation via Expressive Masked Audio Gesture Modeling
\ - CVPR 2024
\ -
', - description='\ -
\ - Haiyang Liu1*, Zihao Zhu2*, Giorgio Becherini3, Yichen Peng4, Mingyang Su5,
\ - You Zhou, Xuefei Zhe, Naoya Iwamoto, Bo Zheng, Michael J. Black3
\ - (*Equal Contribution)
\ - 1The University of Tokyo, 2Keio University, 4Japan Advanced Institute of Science and Technology,
\ - 3Max Planck Institute for Intelligent Systems, 5Tsinghua University
\ -
\ - ', - article="\ - Due to the limited resources in this space, we process the first 8s of your uploaded audio.
\ - Try to develop this space locally for longer motion generation, e.g., 60s.
\ - Relevant links: [Project Page (https://pantomatrix.github.io/EMAGE/)\ - ", - examples=examples, -) - - -if __name__ == "__main__": - os.environ["MASTER_ADDR"]='127.0.0.1' - os.environ["MASTER_PORT"]='8675' - #os.environ["TORCH_DISTRIBUTED_DEBUG"] = "DETAIL" - demo.launch(share=True) \ No newline at end of file diff --git a/.ipynb_checkpoints/packages-checkpoint.txt b/.ipynb_checkpoints/packages-checkpoint.txt deleted file mode 100644 index 9695433780ba56a2bbc4a9ba13e2725d6f9bbb7a..0000000000000000000000000000000000000000 --- a/.ipynb_checkpoints/packages-checkpoint.txt +++ /dev/null @@ -1,4 +0,0 @@ -libgl1-mesa-dev -libglu1-mesa-dev -freeglut3-dev -mesa-common-dev \ No newline at end of file diff --git a/.ipynb_checkpoints/requirements-checkpoint.txt b/.ipynb_checkpoints/requirements-checkpoint.txt deleted file mode 100644 index 924c282b799630240602eb639d8fb25d0505d74c..0000000000000000000000000000000000000000 --- a/.ipynb_checkpoints/requirements-checkpoint.txt +++ /dev/null @@ -1,39 +0,0 @@ -ffmpeg -ConfigArgParse==1.7 -fasttext==0.9.2 -h5py==3.10.0 -imageio==2.31.4 -ipython==8.12.3 -joblib==1.3.2 -librosa==0.10.1 -lmdb==1.4.1 -loguru==0.7.2 -matplotlib==3.7.3 -moviepy==1.0.3 -gradio -fasttext-wheel -opencv_contrib_python==4.8.1.78 -opencv_python==4.8.1.78 -pandas==1.5.3 -peakutils==1.3.4 -ptflops==0.7.1.2 -python_igraph==0.11.3 -pyvirtualdisplay==3.0 -PyYAML==6.0.1 -replicate==0.15.4 -scikit_learn==1.3.2 -scipy -soundfile==0.12.1 -termcolor==2.4.0 -textgrid==1.5 -torch==2.1.0 -torchvision -tqdm==4.66.1 -transformers==4.35.2 -trimesh==3.23.5 -wandb==0.16.0 -pyglet<2 -smplx -tensorboard -pyrender -pyarrow \ No newline at end of file diff --git a/.ipynb_checkpoints/test_demo-checkpoint.py b/.ipynb_checkpoints/test_demo-checkpoint.py deleted file mode 100644 index 6ad0b83c01947ccba896e8dd4d20a5b36860a060..0000000000000000000000000000000000000000 --- a/.ipynb_checkpoints/test_demo-checkpoint.py +++ /dev/null @@ -1,581 +0,0 @@ -import os -import signal -import time -import csv -import sys -import warnings -import random -import torch -import torch.nn as nn -import torch.nn.functional as F -import torch.distributed as dist -from torch.nn.parallel import DistributedDataParallel as DDP -import torch.multiprocessing as mp -import numpy as np -import time -import pprint -from loguru import logger -import smplx -from torch.utils.tensorboard import SummaryWriter -import wandb -import matplotlib.pyplot as plt -from utils import config, logger_tools, other_tools, metric, data_transfer -from dataloaders import data_tools -from dataloaders.build_vocab import Vocab -from optimizers.optim_factory import create_optimizer -from optimizers.scheduler_factory import create_scheduler -from optimizers.loss_factory import get_loss_func -from dataloaders.data_tools import joints_list -from utils import rotation_conversions as rc - -class BaseTrainer(object): - def __init__(self, args): - self.args = args - self.rank = dist.get_rank() - self.checkpoint_path = args.out_path + "custom/" + args.name + args.notes + "/" #wandb.run.dir #args.cache_path+args.out_path+"/"+args.name - if self.rank == 0: - self.test_data = __import__(f"dataloaders.{args.dataset}", fromlist=["something"]).CustomDataset(args, "test") - self.test_loader = torch.utils.data.DataLoader( - self.test_data, - batch_size=1, - shuffle=False, - num_workers=args.loader_workers, - drop_last=False, - ) - logger.info(f"Init test dataloader success") - model_module = __import__(f"models.{args.model}", fromlist=["something"]) - - if args.ddp: - self.model = getattr(model_module, args.g_name)(args).to(self.rank) - process_group = torch.distributed.new_group() - self.model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(self.model, process_group) - self.model = DDP(self.model, device_ids=[self.rank], output_device=self.rank, - broadcast_buffers=False, find_unused_parameters=False) - else: - self.model = torch.nn.DataParallel(getattr(model_module, args.g_name)(args), args.gpus).cuda() - - if self.rank == 0: - logger.info(self.model) - logger.info(f"init {args.g_name} success") - - self.smplx = smplx.create( - self.args.data_path_1+"smplx_models/", - model_type='smplx', - gender='NEUTRAL_2020', - use_face_contour=False, - num_betas=300, - num_expression_coeffs=100, - ext='npz', - use_pca=False, - ).to(self.rank).eval() - - self.args = args - self.joints = self.test_data.joints - self.ori_joint_list = joints_list[self.args.ori_joints] - self.tar_joint_list_face = joints_list["beat_smplx_face"] - self.tar_joint_list_upper = joints_list["beat_smplx_upper"] - self.tar_joint_list_hands = joints_list["beat_smplx_hands"] - self.tar_joint_list_lower = joints_list["beat_smplx_lower"] - - self.joint_mask_face = np.zeros(len(list(self.ori_joint_list.keys()))*3) - self.joints = 55 - for joint_name in self.tar_joint_list_face: - self.joint_mask_face[self.ori_joint_list[joint_name][1] - self.ori_joint_list[joint_name][0]:self.ori_joint_list[joint_name][1]] = 1 - self.joint_mask_upper = np.zeros(len(list(self.ori_joint_list.keys()))*3) - for joint_name in self.tar_joint_list_upper: - self.joint_mask_upper[self.ori_joint_list[joint_name][1] - self.ori_joint_list[joint_name][0]:self.ori_joint_list[joint_name][1]] = 1 - self.joint_mask_hands = np.zeros(len(list(self.ori_joint_list.keys()))*3) - for joint_name in self.tar_joint_list_hands: - self.joint_mask_hands[self.ori_joint_list[joint_name][1] - self.ori_joint_list[joint_name][0]:self.ori_joint_list[joint_name][1]] = 1 - self.joint_mask_lower = np.zeros(len(list(self.ori_joint_list.keys()))*3) - for joint_name in self.tar_joint_list_lower: - self.joint_mask_lower[self.ori_joint_list[joint_name][1] - self.ori_joint_list[joint_name][0]:self.ori_joint_list[joint_name][1]] = 1 - - self.tracker = other_tools.EpochTracker(["fid", "l1div", "bc", "rec", "trans", "vel", "transv", 'dis', 'gen', 'acc', 'transa', 'exp', 'lvd', 'mse', "cls", "rec_face", "latent", "cls_full", "cls_self", "cls_word", "latent_word","latent_self"], [False,True,True, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False,False,False,False]) - - vq_model_module = __import__(f"models.motion_representation", fromlist=["something"]) - self.args.vae_layer = 2 - self.args.vae_length = 256 - self.args.vae_test_dim = 106 - self.vq_model_face = getattr(vq_model_module, "VQVAEConvZero")(self.args).to(self.rank) - # print(self.vq_model_face) - other_tools.load_checkpoints(self.vq_model_face, self.args.data_path_1 + "pretrained_vq/last_790_face_v2.bin", args.e_name) - self.args.vae_test_dim = 78 - self.vq_model_upper = getattr(vq_model_module, "VQVAEConvZero")(self.args).to(self.rank) - other_tools.load_checkpoints(self.vq_model_upper, self.args.data_path_1 + "pretrained_vq/upper_vertex_1layer_710.bin", args.e_name) - self.args.vae_test_dim = 180 - self.vq_model_hands = getattr(vq_model_module, "VQVAEConvZero")(self.args).to(self.rank) - other_tools.load_checkpoints(self.vq_model_hands, self.args.data_path_1 + "pretrained_vq/hands_vertex_1layer_710.bin", args.e_name) - self.args.vae_test_dim = 61 - self.args.vae_layer = 4 - self.vq_model_lower = getattr(vq_model_module, "VQVAEConvZero")(self.args).to(self.rank) - other_tools.load_checkpoints(self.vq_model_lower, self.args.data_path_1 + "pretrained_vq/lower_foot_600.bin", args.e_name) - self.args.vae_test_dim = 61 - self.args.vae_layer = 4 - self.global_motion = getattr(vq_model_module, "VAEConvZero")(self.args).to(self.rank) - other_tools.load_checkpoints(self.global_motion, self.args.data_path_1 + "pretrained_vq/last_1700_foot.bin", args.e_name) - self.args.vae_test_dim = 330 - self.args.vae_layer = 4 - self.args.vae_length = 240 - - self.vq_model_face.eval() - self.vq_model_upper.eval() - self.vq_model_hands.eval() - self.vq_model_lower.eval() - self.global_motion.eval() - - self.cls_loss = nn.NLLLoss().to(self.rank) - self.reclatent_loss = nn.MSELoss().to(self.rank) - self.vel_loss = torch.nn.L1Loss(reduction='mean').to(self.rank) - self.rec_loss = get_loss_func("GeodesicLoss").to(self.rank) - self.log_softmax = nn.LogSoftmax(dim=2).to(self.rank) - - - def inverse_selection(self, filtered_t, selection_array, n): - original_shape_t = np.zeros((n, selection_array.size)) - selected_indices = np.where(selection_array == 1)[0] - for i in range(n): - original_shape_t[i, selected_indices] = filtered_t[i] - return original_shape_t - - def inverse_selection_tensor(self, filtered_t, selection_array, n): - selection_array = torch.from_numpy(selection_array).cuda() - original_shape_t = torch.zeros((n, 165)).cuda() - selected_indices = torch.where(selection_array == 1)[0] - for i in range(n): - original_shape_t[i, selected_indices] = filtered_t[i] - return original_shape_t - - def _load_data(self, dict_data): - tar_pose_raw = dict_data["pose"] - tar_pose = tar_pose_raw[:, :, :165].to(self.rank) - tar_contact = tar_pose_raw[:, :, 165:169].to(self.rank) - tar_trans = dict_data["trans"].to(self.rank) - tar_exps = dict_data["facial"].to(self.rank) - in_audio = dict_data["audio"].to(self.rank) - in_word = dict_data["word"].to(self.rank) - tar_beta = dict_data["beta"].to(self.rank) - tar_id = dict_data["id"].to(self.rank).long() - bs, n, j = tar_pose.shape[0], tar_pose.shape[1], self.joints - - tar_pose_jaw = tar_pose[:, :, 66:69] - tar_pose_jaw = rc.axis_angle_to_matrix(tar_pose_jaw.reshape(bs, n, 1, 3)) - tar_pose_jaw = rc.matrix_to_rotation_6d(tar_pose_jaw).reshape(bs, n, 1*6) - tar_pose_face = torch.cat([tar_pose_jaw, tar_exps], dim=2) - - tar_pose_hands = tar_pose[:, :, 25*3:55*3] - tar_pose_hands = rc.axis_angle_to_matrix(tar_pose_hands.reshape(bs, n, 30, 3)) - tar_pose_hands = rc.matrix_to_rotation_6d(tar_pose_hands).reshape(bs, n, 30*6) - - tar_pose_upper = tar_pose[:, :, self.joint_mask_upper.astype(bool)] - tar_pose_upper = rc.axis_angle_to_matrix(tar_pose_upper.reshape(bs, n, 13, 3)) - tar_pose_upper = rc.matrix_to_rotation_6d(tar_pose_upper).reshape(bs, n, 13*6) - - tar_pose_leg = tar_pose[:, :, self.joint_mask_lower.astype(bool)] - tar_pose_leg = rc.axis_angle_to_matrix(tar_pose_leg.reshape(bs, n, 9, 3)) - tar_pose_leg = rc.matrix_to_rotation_6d(tar_pose_leg).reshape(bs, n, 9*6) - tar_pose_lower = torch.cat([tar_pose_leg, tar_trans, tar_contact], dim=2) - - # tar_pose = rc.axis_angle_to_matrix(tar_pose.reshape(bs, n, j, 3)) - # tar_pose = rc.matrix_to_rotation_6d(tar_pose).reshape(bs, n, j*6) - tar4dis = torch.cat([tar_pose_jaw, tar_pose_upper, tar_pose_hands, tar_pose_leg], dim=2) - - tar_index_value_face_top = self.vq_model_face.map2index(tar_pose_face) # bs*n/4 - tar_index_value_upper_top = self.vq_model_upper.map2index(tar_pose_upper) # bs*n/4 - tar_index_value_hands_top = self.vq_model_hands.map2index(tar_pose_hands) # bs*n/4 - tar_index_value_lower_top = self.vq_model_lower.map2index(tar_pose_lower) # bs*n/4 - - latent_face_top = self.vq_model_face.map2latent(tar_pose_face) # bs*n/4 - latent_upper_top = self.vq_model_upper.map2latent(tar_pose_upper) # bs*n/4 - latent_hands_top = self.vq_model_hands.map2latent(tar_pose_hands) # bs*n/4 - latent_lower_top = self.vq_model_lower.map2latent(tar_pose_lower) # bs*n/4 - - latent_in = torch.cat([latent_upper_top, latent_hands_top, latent_lower_top], dim=2) - - index_in = torch.stack([tar_index_value_upper_top, tar_index_value_hands_top, tar_index_value_lower_top], dim=-1).long() - - tar_pose_6d = rc.axis_angle_to_matrix(tar_pose.reshape(bs, n, 55, 3)) - tar_pose_6d = rc.matrix_to_rotation_6d(tar_pose_6d).reshape(bs, n, 55*6) - latent_all = torch.cat([tar_pose_6d, tar_trans, tar_contact], dim=-1) - # print(tar_index_value_upper_top.shape, index_in.shape) - return { - "tar_pose_jaw": tar_pose_jaw, - "tar_pose_face": tar_pose_face, - "tar_pose_upper": tar_pose_upper, - "tar_pose_lower": tar_pose_lower, - "tar_pose_hands": tar_pose_hands, - 'tar_pose_leg': tar_pose_leg, - "in_audio": in_audio, - "in_word": in_word, - "tar_trans": tar_trans, - "tar_exps": tar_exps, - "tar_beta": tar_beta, - "tar_pose": tar_pose, - "tar4dis": tar4dis, - "tar_index_value_face_top": tar_index_value_face_top, - "tar_index_value_upper_top": tar_index_value_upper_top, - "tar_index_value_hands_top": tar_index_value_hands_top, - "tar_index_value_lower_top": tar_index_value_lower_top, - "latent_face_top": latent_face_top, - "latent_upper_top": latent_upper_top, - "latent_hands_top": latent_hands_top, - "latent_lower_top": latent_lower_top, - "latent_in": latent_in, - "index_in": index_in, - "tar_id": tar_id, - "latent_all": latent_all, - "tar_pose_6d": tar_pose_6d, - "tar_contact": tar_contact, - } - - def _g_test(self, loaded_data): - mode = 'test' - bs, n, j = loaded_data["tar_pose"].shape[0], loaded_data["tar_pose"].shape[1], self.joints - tar_pose = loaded_data["tar_pose"] - tar_beta = loaded_data["tar_beta"] - in_word = loaded_data["in_word"] - tar_exps = loaded_data["tar_exps"] - tar_contact = loaded_data["tar_contact"] - in_audio = loaded_data["in_audio"] - tar_trans = loaded_data["tar_trans"] - - remain = n%8 - if remain != 0: - tar_pose = tar_pose[:, :-remain, :] - tar_beta = tar_beta[:, :-remain, :] - tar_trans = tar_trans[:, :-remain, :] - in_word = in_word[:, :-remain] - tar_exps = tar_exps[:, :-remain, :] - tar_contact = tar_contact[:, :-remain, :] - n = n - remain - - tar_pose_jaw = tar_pose[:, :, 66:69] - tar_pose_jaw = rc.axis_angle_to_matrix(tar_pose_jaw.reshape(bs, n, 1, 3)) - tar_pose_jaw = rc.matrix_to_rotation_6d(tar_pose_jaw).reshape(bs, n, 1*6) - tar_pose_face = torch.cat([tar_pose_jaw, tar_exps], dim=2) - - tar_pose_hands = tar_pose[:, :, 25*3:55*3] - tar_pose_hands = rc.axis_angle_to_matrix(tar_pose_hands.reshape(bs, n, 30, 3)) - tar_pose_hands = rc.matrix_to_rotation_6d(tar_pose_hands).reshape(bs, n, 30*6) - - tar_pose_upper = tar_pose[:, :, self.joint_mask_upper.astype(bool)] - tar_pose_upper = rc.axis_angle_to_matrix(tar_pose_upper.reshape(bs, n, 13, 3)) - tar_pose_upper = rc.matrix_to_rotation_6d(tar_pose_upper).reshape(bs, n, 13*6) - - tar_pose_leg = tar_pose[:, :, self.joint_mask_lower.astype(bool)] - tar_pose_leg = rc.axis_angle_to_matrix(tar_pose_leg.reshape(bs, n, 9, 3)) - tar_pose_leg = rc.matrix_to_rotation_6d(tar_pose_leg).reshape(bs, n, 9*6) - tar_pose_lower = torch.cat([tar_pose_leg, tar_trans, tar_contact], dim=2) - - tar_pose_6d = rc.axis_angle_to_matrix(tar_pose.reshape(bs, n, 55, 3)) - tar_pose_6d = rc.matrix_to_rotation_6d(tar_pose_6d).reshape(bs, n, 55*6) - latent_all = torch.cat([tar_pose_6d, tar_trans, tar_contact], dim=-1) - - rec_index_all_face = [] - rec_index_all_upper = [] - rec_index_all_lower = [] - rec_index_all_hands = [] - - roundt = (n - self.args.pre_frames) // (self.args.pose_length - self.args.pre_frames) - remain = (n - self.args.pre_frames) % (self.args.pose_length - self.args.pre_frames) - round_l = self.args.pose_length - self.args.pre_frames - - for i in range(0, roundt): - in_word_tmp = in_word[:, i*(round_l):(i+1)*(round_l)+self.args.pre_frames] - # audio fps is 16000 and pose fps is 30 - in_audio_tmp = in_audio[:, i*(16000//30*round_l):(i+1)*(16000//30*round_l)+16000//30*self.args.pre_frames] - in_id_tmp = loaded_data['tar_id'][:, i*(round_l):(i+1)*(round_l)+self.args.pre_frames] - mask_val = torch.ones(bs, self.args.pose_length, self.args.pose_dims+3+4).float().cuda() - mask_val[:, :self.args.pre_frames, :] = 0.0 - if i == 0: - latent_all_tmp = latent_all[:, i*(round_l):(i+1)*(round_l)+self.args.pre_frames, :] - else: - latent_all_tmp = latent_all[:, i*(round_l):(i+1)*(round_l)+self.args.pre_frames, :] - # print(latent_all_tmp.shape, latent_last.shape) - latent_all_tmp[:, :self.args.pre_frames, :] = latent_last[:, -self.args.pre_frames:, :] - - net_out_val = self.model( - in_audio = in_audio_tmp, - in_word=in_word_tmp, - mask=mask_val, - in_motion = latent_all_tmp, - in_id = in_id_tmp, - use_attentions=True,) - - if self.args.cu != 0: - rec_index_upper = self.log_softmax(net_out_val["cls_upper"]).reshape(-1, self.args.vae_codebook_size) - _, rec_index_upper = torch.max(rec_index_upper.reshape(-1, self.args.pose_length, self.args.vae_codebook_size), dim=2) - #rec_upper = self.vq_model_upper.decode(rec_index_upper) - else: - _, rec_index_upper, _, _ = self.vq_model_upper.quantizer(net_out_val["rec_upper"]) - #rec_upper = self.vq_model_upper.decoder(rec_index_upper) - if self.args.cl != 0: - rec_index_lower = self.log_softmax(net_out_val["cls_lower"]).reshape(-1, self.args.vae_codebook_size) - _, rec_index_lower = torch.max(rec_index_lower.reshape(-1, self.args.pose_length, self.args.vae_codebook_size), dim=2) - #rec_lower = self.vq_model_lower.decode(rec_index_lower) - else: - _, rec_index_lower, _, _ = self.vq_model_lower.quantizer(net_out_val["rec_lower"]) - #rec_lower = self.vq_model_lower.decoder(rec_index_lower) - if self.args.ch != 0: - rec_index_hands = self.log_softmax(net_out_val["cls_hands"]).reshape(-1, self.args.vae_codebook_size) - _, rec_index_hands = torch.max(rec_index_hands.reshape(-1, self.args.pose_length, self.args.vae_codebook_size), dim=2) - #rec_hands = self.vq_model_hands.decode(rec_index_hands) - else: - _, rec_index_hands, _, _ = self.vq_model_hands.quantizer(net_out_val["rec_hands"]) - #rec_hands = self.vq_model_hands.decoder(rec_index_hands) - if self.args.cf != 0: - rec_index_face = self.log_softmax(net_out_val["cls_face"]).reshape(-1, self.args.vae_codebook_size) - _, rec_index_face = torch.max(rec_index_face.reshape(-1, self.args.pose_length, self.args.vae_codebook_size), dim=2) - #rec_face = self.vq_model_face.decoder(rec_index_face) - else: - _, rec_index_face, _, _ = self.vq_model_face.quantizer(net_out_val["rec_face"]) - #rec_face = self.vq_model_face.decoder(rec_index_face) - - if i == 0: - rec_index_all_face.append(rec_index_face) - rec_index_all_upper.append(rec_index_upper) - rec_index_all_lower.append(rec_index_lower) - rec_index_all_hands.append(rec_index_hands) - else: - rec_index_all_face.append(rec_index_face[:, self.args.pre_frames:]) - rec_index_all_upper.append(rec_index_upper[:, self.args.pre_frames:]) - rec_index_all_lower.append(rec_index_lower[:, self.args.pre_frames:]) - rec_index_all_hands.append(rec_index_hands[:, self.args.pre_frames:]) - - if self.args.cu != 0: - rec_upper_last = self.vq_model_upper.decode(rec_index_upper) - else: - rec_upper_last = self.vq_model_upper.decoder(rec_index_upper) - if self.args.cl != 0: - rec_lower_last = self.vq_model_lower.decode(rec_index_lower) - else: - rec_lower_last = self.vq_model_lower.decoder(rec_index_lower) - if self.args.ch != 0: - rec_hands_last = self.vq_model_hands.decode(rec_index_hands) - else: - rec_hands_last = self.vq_model_hands.decoder(rec_index_hands) - # if self.args.cf != 0: - # rec_face_last = self.vq_model_face.decode(rec_index_face) - # else: - # rec_face_last = self.vq_model_face.decoder(rec_index_face) - - rec_pose_legs = rec_lower_last[:, :, :54] - bs, n = rec_pose_legs.shape[0], rec_pose_legs.shape[1] - rec_pose_upper = rec_upper_last.reshape(bs, n, 13, 6) - rec_pose_upper = rc.rotation_6d_to_matrix(rec_pose_upper)# - rec_pose_upper = rc.matrix_to_axis_angle(rec_pose_upper).reshape(bs*n, 13*3) - rec_pose_upper_recover = self.inverse_selection_tensor(rec_pose_upper, self.joint_mask_upper, bs*n) - rec_pose_lower = rec_pose_legs.reshape(bs, n, 9, 6) - rec_pose_lower = rc.rotation_6d_to_matrix(rec_pose_lower) - rec_pose_lower = rc.matrix_to_axis_angle(rec_pose_lower).reshape(bs*n, 9*3) - rec_pose_lower_recover = self.inverse_selection_tensor(rec_pose_lower, self.joint_mask_lower, bs*n) - rec_pose_hands = rec_hands_last.reshape(bs, n, 30, 6) - rec_pose_hands = rc.rotation_6d_to_matrix(rec_pose_hands) - rec_pose_hands = rc.matrix_to_axis_angle(rec_pose_hands).reshape(bs*n, 30*3) - rec_pose_hands_recover = self.inverse_selection_tensor(rec_pose_hands, self.joint_mask_hands, bs*n) - rec_pose = rec_pose_upper_recover + rec_pose_lower_recover + rec_pose_hands_recover - rec_pose = rc.axis_angle_to_matrix(rec_pose.reshape(bs, n, j, 3)) - rec_pose = rc.matrix_to_rotation_6d(rec_pose).reshape(bs, n, j*6) - rec_trans_v_s = rec_lower_last[:, :, 54:57] - rec_x_trans = other_tools.velocity2position(rec_trans_v_s[:, :, 0:1], 1/self.args.pose_fps, tar_trans[:, 0, 0:1]) - rec_z_trans = other_tools.velocity2position(rec_trans_v_s[:, :, 2:3], 1/self.args.pose_fps, tar_trans[:, 0, 2:3]) - rec_y_trans = rec_trans_v_s[:,:,1:2] - rec_trans = torch.cat([rec_x_trans, rec_y_trans, rec_z_trans], dim=-1) - latent_last = torch.cat([rec_pose, rec_trans, rec_lower_last[:, :, 57:61]], dim=-1) - - rec_index_face = torch.cat(rec_index_all_face, dim=1) - rec_index_upper = torch.cat(rec_index_all_upper, dim=1) - rec_index_lower = torch.cat(rec_index_all_lower, dim=1) - rec_index_hands = torch.cat(rec_index_all_hands, dim=1) - if self.args.cu != 0: - rec_upper = self.vq_model_upper.decode(rec_index_upper) - else: - rec_upper = self.vq_model_upper.decoder(rec_index_upper) - if self.args.cl != 0: - rec_lower = self.vq_model_lower.decode(rec_index_lower) - else: - rec_lower = self.vq_model_lower.decoder(rec_index_lower) - if self.args.ch != 0: - rec_hands = self.vq_model_hands.decode(rec_index_hands) - else: - rec_hands = self.vq_model_hands.decoder(rec_index_hands) - if self.args.cf != 0: - rec_face = self.vq_model_face.decode(rec_index_face) - else: - rec_face = self.vq_model_face.decoder(rec_index_face) - - rec_exps = rec_face[:, :, 6:] - rec_pose_jaw = rec_face[:, :, :6] - rec_pose_legs = rec_lower[:, :, :54] - bs, n = rec_pose_jaw.shape[0], rec_pose_jaw.shape[1] - rec_pose_upper = rec_upper.reshape(bs, n, 13, 6) - rec_pose_upper = rc.rotation_6d_to_matrix(rec_pose_upper)# - rec_pose_upper = rc.matrix_to_axis_angle(rec_pose_upper).reshape(bs*n, 13*3) - rec_pose_upper_recover = self.inverse_selection_tensor(rec_pose_upper, self.joint_mask_upper, bs*n) - rec_pose_lower = rec_pose_legs.reshape(bs, n, 9, 6) - rec_pose_lower = rc.rotation_6d_to_matrix(rec_pose_lower) - rec_lower2global = rc.matrix_to_rotation_6d(rec_pose_lower.clone()).reshape(bs, n, 9*6) - rec_pose_lower = rc.matrix_to_axis_angle(rec_pose_lower).reshape(bs*n, 9*3) - rec_pose_lower_recover = self.inverse_selection_tensor(rec_pose_lower, self.joint_mask_lower, bs*n) - rec_pose_hands = rec_hands.reshape(bs, n, 30, 6) - rec_pose_hands = rc.rotation_6d_to_matrix(rec_pose_hands) - rec_pose_hands = rc.matrix_to_axis_angle(rec_pose_hands).reshape(bs*n, 30*3) - rec_pose_hands_recover = self.inverse_selection_tensor(rec_pose_hands, self.joint_mask_hands, bs*n) - rec_pose_jaw = rec_pose_jaw.reshape(bs*n, 6) - rec_pose_jaw = rc.rotation_6d_to_matrix(rec_pose_jaw) - rec_pose_jaw = rc.matrix_to_axis_angle(rec_pose_jaw).reshape(bs*n, 1*3) - rec_pose = rec_pose_upper_recover + rec_pose_lower_recover + rec_pose_hands_recover - rec_pose[:, 66:69] = rec_pose_jaw - - to_global = rec_lower - to_global[:, :, 54:57] = 0.0 - to_global[:, :, :54] = rec_lower2global - rec_global = self.global_motion(to_global) - - rec_trans_v_s = rec_global["rec_pose"][:, :, 54:57] - rec_x_trans = other_tools.velocity2position(rec_trans_v_s[:, :, 0:1], 1/self.args.pose_fps, tar_trans[:, 0, 0:1]) - rec_z_trans = other_tools.velocity2position(rec_trans_v_s[:, :, 2:3], 1/self.args.pose_fps, tar_trans[:, 0, 2:3]) - rec_y_trans = rec_trans_v_s[:,:,1:2] - rec_trans = torch.cat([rec_x_trans, rec_y_trans, rec_z_trans], dim=-1) - tar_pose = tar_pose[:, :n, :] - tar_exps = tar_exps[:, :n, :] - tar_trans = tar_trans[:, :n, :] - tar_beta = tar_beta[:, :n, :] - - rec_pose = rc.axis_angle_to_matrix(rec_pose.reshape(bs*n, j, 3)) - rec_pose = rc.matrix_to_rotation_6d(rec_pose).reshape(bs, n, j*6) - tar_pose = rc.axis_angle_to_matrix(tar_pose.reshape(bs*n, j, 3)) - tar_pose = rc.matrix_to_rotation_6d(tar_pose).reshape(bs, n, j*6) - - return { - 'rec_pose': rec_pose, - 'rec_trans': rec_trans, - 'tar_pose': tar_pose, - 'tar_exps': tar_exps, - 'tar_beta': tar_beta, - 'tar_trans': tar_trans, - 'rec_exps': rec_exps, - } - - - def test_demo(self, epoch): - ''' - input audio and text, output motion - do not calculate loss and metric - save video - ''' - results_save_path = self.checkpoint_path + f"/{epoch}/" - if os.path.exists(results_save_path): - return 0 - os.makedirs(results_save_path) - start_time = time.time() - total_length = 0 - test_seq_list = self.test_data.selected_file - align = 0 - latent_out = [] - latent_ori = [] - l2_all = 0 - lvel = 0 - self.model.eval() - self.smplx.eval() - # self.eval_copy.eval() - with torch.no_grad(): - for its, batch_data in enumerate(self.test_loader): - loaded_data = self._load_data(batch_data) - net_out = self._g_test(loaded_data) - tar_pose = net_out['tar_pose'] - rec_pose = net_out['rec_pose'] - tar_exps = net_out['tar_exps'] - tar_beta = net_out['tar_beta'] - rec_trans = net_out['rec_trans'] - tar_trans = net_out['tar_trans'] - rec_exps = net_out['rec_exps'] - # print(rec_pose.shape, tar_pose.shape) - bs, n, j = tar_pose.shape[0], tar_pose.shape[1], self.joints - - # interpolate to 30fps - if (30/self.args.pose_fps) != 1: - assert 30%self.args.pose_fps == 0 - n *= int(30/self.args.pose_fps) - tar_pose = torch.nn.functional.interpolate(tar_pose.permute(0, 2, 1), scale_factor=30/self.args.pose_fps, mode='linear').permute(0,2,1) - rec_pose = torch.nn.functional.interpolate(rec_pose.permute(0, 2, 1), scale_factor=30/self.args.pose_fps, mode='linear').permute(0,2,1) - - # print(rec_pose.shape, tar_pose.shape) - rec_pose = rc.rotation_6d_to_matrix(rec_pose.reshape(bs*n, j, 6)) - rec_pose = rc.matrix_to_axis_angle(rec_pose).reshape(bs*n, j*3) - - tar_pose = rc.rotation_6d_to_matrix(tar_pose.reshape(bs*n, j, 6)) - tar_pose = rc.matrix_to_axis_angle(tar_pose).reshape(bs*n, j*3) - - tar_pose_np = tar_pose.detach().cpu().numpy() - rec_pose_np = rec_pose.detach().cpu().numpy() - rec_trans_np = rec_trans.detach().cpu().numpy().reshape(bs*n, 3) - rec_exp_np = rec_exps.detach().cpu().numpy().reshape(bs*n, 100) - tar_exp_np = tar_exps.detach().cpu().numpy().reshape(bs*n, 100) - tar_trans_np = tar_trans.detach().cpu().numpy().reshape(bs*n, 3) - - gt_npz = np.load(self.args.data_path+self.args.pose_rep +"/"+test_seq_list.iloc[its]['id']+".npz", allow_pickle=True) - np.savez(results_save_path+"gt_"+test_seq_list.iloc[its]['id']+'.npz', - betas=gt_npz["betas"], - poses=tar_pose_np, - expressions=tar_exp_np, - trans=tar_trans_np, - model='smplx2020', - gender='neutral', - mocap_frame_rate = 30 , - ) - np.savez(results_save_path+"res_"+test_seq_list.iloc[its]['id']+'.npz', - betas=gt_npz["betas"], - poses=rec_pose_np, - expressions=rec_exp_np, - trans=rec_trans_np, - model='smplx2020', - gender='neutral', - mocap_frame_rate = 30, - ) - total_length += n - # other_tools.render_one_sequence( - # results_save_path+"res_"+test_seq_list.iloc[its]['id']+'.npz', - # results_save_path+"gt_"+test_seq_list.iloc[its]['id']+'.npz', - # results_save_path, - # self.args.data_path+"wave16k/"+test_seq_list.iloc[its]['id']+".wav", - # self.args.data_path_1+"smplx_models/", - # use_matplotlib = False, - # args = self.args, - # ) - end_time = time.time() - start_time - logger.info(f"total inference time: {int(end_time)} s for {int(total_length/self.args.pose_fps)} s motion") - -@logger.catch -def main_worker(rank, world_size, args): - #os.environ['TRANSFORMERS_CACHE'] = args.data_path_1 + "hub/" - if not sys.warnoptions: - warnings.simplefilter("ignore") - dist.init_process_group(backend="nccl", rank=rank, world_size=world_size) - - logger_tools.set_args_and_logger(args, rank) - other_tools.set_random_seed(args) - other_tools.print_exp_info(args) - - # return one intance of trainer - other_tools.write_wav_names_to_csv(args.data_path, args.data_path+"test.csv") - trainer = BaseTrainer(args) - other_tools.load_checkpoints(trainer.model, args.test_ckpt, args.g_name) - trainer.test_demo(999) - - - -if __name__ == "__main__": - os.environ["MASTER_ADDR"]='127.0.0.1' - os.environ["MASTER_PORT"]='8675' - #os.environ["TORCH_DISTRIBUTED_DEBUG"] = "DETAIL" - args = config.parse_args() - if args.ddp: - mp.set_start_method("spawn", force=True) - mp.spawn( - main_worker, - args=(len(args.gpus), args,), - nprocs=len(args.gpus), - ) - else: - main_worker(0, 1, args) \ No newline at end of file diff --git a/EMAGE/emage_audio_175.bin b/EMAGE/emage_audio_175.bin deleted file mode 100644 index a65200bc53a2212e78567c7bbda12ec2e6e8dc46..0000000000000000000000000000000000000000 --- a/EMAGE/emage_audio_175.bin +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:b19f845300e7f52c77eddfb6307f48c8fd2766edada3efa8ad1973a87990c1ea -size 556333206 diff --git a/EMAGE/pretrained_vq/.DS_Store b/EMAGE/pretrained_vq/.DS_Store deleted file mode 100644 index 5008ddfcf53c02e82d7eee2e57c38e5672ef89f6..0000000000000000000000000000000000000000 Binary files a/EMAGE/pretrained_vq/.DS_Store and /dev/null differ diff --git a/EMAGE/pretrained_vq/hands_vertex_1layer_710.bin b/EMAGE/pretrained_vq/hands_vertex_1layer_710.bin deleted file mode 100644 index da28ca39541d2811d9753c84bee2d8ead465826e..0000000000000000000000000000000000000000 --- a/EMAGE/pretrained_vq/hands_vertex_1layer_710.bin +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:1595a13fbdf38b95da2baf6a4ba9f0c62cd6af8b8f537da12c1c90321affa3b3 -size 9644516 diff --git a/EMAGE/pretrained_vq/last_1700_foot.bin b/EMAGE/pretrained_vq/last_1700_foot.bin deleted file mode 100644 index 06d2da3a17f60955e3e78333d0c3a2e3e99acb86..0000000000000000000000000000000000000000 --- a/EMAGE/pretrained_vq/last_1700_foot.bin +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:f293265b828c6b45e12068c9b7956283c92b40cfdc9dd56ae960bbeb7bba1ad6 -size 14611444 diff --git a/EMAGE/pretrained_vq/last_790_face_v2.bin b/EMAGE/pretrained_vq/last_790_face_v2.bin deleted file mode 100644 index 427de62ce83e964d2628d8b56105f80719c8ae21..0000000000000000000000000000000000000000 --- a/EMAGE/pretrained_vq/last_790_face_v2.bin +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:13ff79afef2c3209804c0cae2b9a7c467c1a39268efa87a637e860b8e6b1b4c0 -size 8935204 diff --git a/EMAGE/pretrained_vq/lower_foot_600.bin b/EMAGE/pretrained_vq/lower_foot_600.bin deleted file mode 100644 index 0270a5e73202732adde4e6b88cb7d24cf8adc3b2..0000000000000000000000000000000000000000 --- a/EMAGE/pretrained_vq/lower_foot_600.bin +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:0e323ed5f7014957433b59249497188656811b76952d666eae5f4affdc341786 -size 14873924 diff --git a/EMAGE/pretrained_vq/upper_vertex_1layer_710.bin b/EMAGE/pretrained_vq/upper_vertex_1layer_710.bin deleted file mode 100644 index 33ccfbdf3aaf92df0e06bf3779ba7730bfb75503..0000000000000000000000000000000000000000 --- a/EMAGE/pretrained_vq/upper_vertex_1layer_710.bin +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:58ffcb34ff18f3aeaf53898980ef623ea9ce36f0302a005b5f95ceef1a206a8f -size 8701092 diff --git a/EMAGE/smplx_models/.DS_Store b/EMAGE/smplx_models/.DS_Store deleted file mode 100644 index 64c4cc33f2dfef96d20152797da59df9627ab464..0000000000000000000000000000000000000000 Binary files a/EMAGE/smplx_models/.DS_Store and /dev/null differ diff --git a/EMAGE/smplx_models/smplx/SMPLX_NEUTRAL_2020.npz b/EMAGE/smplx_models/smplx/SMPLX_NEUTRAL_2020.npz deleted file mode 100644 index 998c299e1ea055a6091e87edb92774c888940cd7..0000000000000000000000000000000000000000 --- a/EMAGE/smplx_models/smplx/SMPLX_NEUTRAL_2020.npz +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:bdf06146e27d92022fe5dadad3b9203373f6879eca8e4d8235359ee3ec6a5a74 -size 167264530 diff --git a/EMAGE/test_sequences/smplxflame_30/2_scott_0_1_1.npz b/EMAGE/test_sequences/smplxflame_30/2_scott_0_1_1.npz deleted file mode 100644 index 4d96616f0a09bd6df0971b9547d2e8376b078fcf..0000000000000000000000000000000000000000 --- a/EMAGE/test_sequences/smplxflame_30/2_scott_0_1_1.npz +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:37b112fd59fcabb09270d6ca3c74e7459cc5b9729564bcacf1f75609f3999592 -size 2831524 diff --git a/EMAGE/test_sequences/smplxflame_30/2_scott_0_2_2.npz b/EMAGE/test_sequences/smplxflame_30/2_scott_0_2_2.npz deleted file mode 100644 index f52cab82761973524dbd96ba8d442591779073bd..0000000000000000000000000000000000000000 --- a/EMAGE/test_sequences/smplxflame_30/2_scott_0_2_2.npz +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:5875f768aa4600af7d767625e0d87941b1cca9555855d8c6b509004116790f7d -size 2754356 diff --git a/EMAGE/test_sequences/smplxflame_30/2_scott_0_3_3.npz b/EMAGE/test_sequences/smplxflame_30/2_scott_0_3_3.npz deleted file mode 100644 index 67846e0f8dd34b32f72946fff008c3ba3598ef0b..0000000000000000000000000000000000000000 --- a/EMAGE/test_sequences/smplxflame_30/2_scott_0_3_3.npz +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:23ace88c7ff0288af83cc30d2428e0cb70c3d92bce981a67a5811cd53ab96db4 -size 3021476 diff --git a/EMAGE/test_sequences/smplxflame_30/2_scott_0_4_4.npz b/EMAGE/test_sequences/smplxflame_30/2_scott_0_4_4.npz deleted file mode 100644 index a7f4b518c86337929556e42a2f79cae7bdfd66f2..0000000000000000000000000000000000000000 --- a/EMAGE/test_sequences/smplxflame_30/2_scott_0_4_4.npz +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:ede3993db9565b7b3a945532def69d617d6b2338f488a746a7be998f3b0685d8 -size 2976956 diff --git a/EMAGE/test_sequences/test.csv b/EMAGE/test_sequences/test.csv deleted file mode 100644 index 1f33f8b45bcd303b94c113e4865ab89ae14cbd24..0000000000000000000000000000000000000000 --- a/EMAGE/test_sequences/test.csv +++ /dev/null @@ -1,5 +0,0 @@ -id,type -2_scott_0_3_3,test -2_scott_0_2_2,test -2_scott_0_1_1,test -2_scott_0_4_4,test diff --git a/EMAGE/test_sequences/textgrid/2_scott_0_1_1.TextGrid b/EMAGE/test_sequences/textgrid/2_scott_0_1_1.TextGrid deleted file mode 100644 index abd0228f0e219b86cc370b11c03712d85afa21cc..0000000000000000000000000000000000000000 --- a/EMAGE/test_sequences/textgrid/2_scott_0_1_1.TextGrid +++ /dev/null @@ -1,3636 +0,0 @@ -File type = "ooTextFile" -Object class = "TextGrid" - -xmin = 0 -xmax = 64.097375 -tiers? -size = 2 -item []: - item [1]: - class = "IntervalTier" - name = "words" - xmin = 0 - xmax = 64.097375 - intervals: size = 220 - intervals [1]: - xmin = 0 - xmax = 1.42 - text = "" - intervals [2]: - xmin = 1.42 - xmax = 1.52 - text = "the" - intervals [3]: - xmin = 1.52 - xmax = 1.78 - text = "first" - intervals [4]: - xmin = 1.78 - xmax = 1.97 - text = "thing" - intervals [5]: - xmin = 1.97 - xmax = 2.04 - text = "i" - intervals [6]: - xmin = 2.04 - xmax = 2.21 - text = "like" - intervals [7]: - xmin = 2.21 - xmax = 2.28 - text = "to" - intervals [8]: - xmin = 2.28 - xmax = 2.47 - text = "do" - intervals [9]: - xmin = 2.47 - xmax = 2.63 - text = "on" - intervals [10]: - xmin = 2.63 - xmax = 3.32 - text = "weekends" - intervals [11]: - xmin = 3.32 - xmax = 3.58 - text = "is" - intervals [12]: - xmin = 3.58 - xmax = 4.41 - text = "relaxing" - intervals [13]: - xmin = 4.41 - xmax = 4.52 - text = "" - intervals [14]: - xmin = 4.52 - xmax = 5.05 - text = "and" - intervals [15]: - xmin = 5.05 - xmax = 5.14 - text = "i" - intervals [16]: - xmin = 5.14 - xmax = 5.33 - text = "think" - intervals [17]: - xmin = 5.33 - xmax = 5.41 - text = "i'll" - intervals [18]: - xmin = 5.41 - xmax = 5.5 - text = "go" - intervals [19]: - xmin = 5.5 - xmax = 6 - text = "shopping" - intervals [20]: - xmin = 6 - xmax = 6.11 - text = "if" - intervals [21]: - xmin = 6.11 - xmax = 6.29 - text = "i'm" - intervals [22]: - xmin = 6.29 - xmax = 6.54 - text = "not" - intervals [23]: - xmin = 6.54 - xmax = 6.7 - text = "that" - intervals [24]: - xmin = 6.7 - xmax = 7.19 - text = "tired" - intervals [25]: - xmin = 7.19 - xmax = 7.45 - text = "" - intervals [26]: - xmin = 7.45 - xmax = 7.62 - text = "so" - intervals [27]: - xmin = 7.62 - xmax = 7.74 - text = "that" - intervals [28]: - xmin = 7.74 - xmax = 7.85 - text = "you" - intervals [29]: - xmin = 7.85 - xmax = 8.14 - text = "started" - intervals [30]: - xmin = 8.14 - xmax = 8.24 - text = "by" - intervals [31]: - xmin = 8.24 - xmax = 8.52 - text = "job" - intervals [32]: - xmin = 8.52 - xmax = 8.59 - text = "i" - intervals [33]: - xmin = 8.59 - xmax = 8.75 - text = "think" - intervals [34]: - xmin = 8.75 - xmax = 8.88 - text = "it's" - intervals [35]: - xmin = 8.88 - xmax = 9.35 - text = "very" - intervals [36]: - xmin = 9.35 - xmax = 9.8 - text = "important" - intervals [37]: - xmin = 9.8 - xmax = 9.87 - text = "to" - intervals [38]: - xmin = 9.87 - xmax = 9.99 - text = "get" - intervals [39]: - xmin = 9.99 - xmax = 10.03 - text = "a" - intervals [40]: - xmin = 10.03 - xmax = 10.17 - text = "good" - intervals [41]: - xmin = 10.17 - xmax = 10.56 - text = "sleep" - intervals [42]: - xmin = 10.56 - xmax = 11.14 - text = "during" - intervals [43]: - xmin = 11.14 - xmax = 11.32 - text = "your" - intervals [44]: - xmin = 11.32 - xmax = 11.77 - text = "weekend" - intervals [45]: - xmin = 11.77 - xmax = 12.4 - text = "because" - intervals [46]: - xmin = 12.4 - xmax = 12.95 - text = "when" - intervals [47]: - xmin = 12.95 - xmax = 13.04 - text = "you" - intervals [48]: - xmin = 13.04 - xmax = 13.19 - text = "have" - intervals [49]: - xmin = 13.19 - xmax = 13.27 - text = "to" - intervals [50]: - xmin = 13.27 - xmax = 13.44 - text = "work" - intervals [51]: - xmin = 13.44 - xmax = 13.58 - text = "on" - intervals [52]: - xmin = 13.58 - xmax = 13.96 - text = "monday" - intervals [53]: - xmin = 13.96 - xmax = 14.1 - text = "through" - intervals [54]: - xmin = 14.1 - xmax = 14.75 - text = "friday" - intervals [55]: - xmin = 14.75 - xmax = 15.41 - text = "" - intervals [56]: - xmin = 15.41 - xmax = 15.53 - text = "the" - intervals [57]: - xmin = 15.53 - xmax = 15.75 - text = "whole" - intervals [58]: - xmin = 15.75 - xmax = 16.09 - text = "week" - intervals [59]: - xmin = 16.09 - xmax = 16.28 - text = "" - intervals [60]: - xmin = 16.28 - xmax = 16.42 - text = "you" - intervals [61]: - xmin = 16.42 - xmax = 16.49 - text = "are" - intervals [62]: - xmin = 16.49 - xmax = 16.73 - text = "very" - intervals [63]: - xmin = 16.73 - xmax = 17.59 - text = "tired" - intervals [64]: - xmin = 17.59 - xmax = 17.83 - text = "" - intervals [65]: - xmin = 17.83 - xmax = 18.29 - text = "so" - intervals [66]: - xmin = 18.29 - xmax = 18.55 - text = "getting" - intervals [67]: - xmin = 18.55 - xmax = 18.61 - text = "a" - intervals [68]: - xmin = 18.61 - xmax = 18.78 - text = "good" - intervals [69]: - xmin = 18.78 - xmax = 19.08 - text = "rest" - intervals [70]: - xmin = 19.08 - xmax = 19.21 - text = "is" - intervals [71]: - xmin = 19.21 - xmax = 19.3 - text = "as" - intervals [72]: - xmin = 19.3 - xmax = 19.77 - text = "important" - intervals [73]: - xmin = 19.77 - xmax = 20.16 - text = "as" - intervals [74]: - xmin = 20.16 - xmax = 20.3 - text = "" - intervals [75]: - xmin = 20.3 - xmax = 20.66 - text = "complain" - intervals [76]: - xmin = 20.66 - xmax = 20.75 - text = "to" - intervals [77]: - xmin = 20.75 - xmax = 21.09 - text = "jaw" - intervals [78]: - xmin = 21.09 - xmax = 21.3 - text = "or" - intervals [79]: - xmin = 21.3 - xmax = 21.79 - text = "completing" - intervals [80]: - xmin = 21.79 - xmax = 21.9 - text = "an" - intervals [81]: - xmin = 21.9 - xmax = 22.23 - text = "excellent" - intervals [82]: - xmin = 22.23 - xmax = 22.64 - text = "job" - intervals [83]: - xmin = 22.64 - xmax = 23.04 - text = "" - intervals [84]: - xmin = 23.04 - xmax = 23.17 - text = "in" - intervals [85]: - xmin = 23.17 - xmax = 23.29 - text = "my" - intervals [86]: - xmin = 23.29 - xmax = 23.56 - text = "spare" - intervals [87]: - xmin = 23.56 - xmax = 23.8 - text = "time" - intervals [88]: - xmin = 23.8 - xmax = 23.88 - text = "if" - intervals [89]: - xmin = 23.88 - xmax = 23.98 - text = "i" - intervals [90]: - xmin = 23.98 - xmax = 24.18 - text = "feel" - intervals [91]: - xmin = 24.18 - xmax = 24.84 - text = "okay" - intervals [92]: - xmin = 24.84 - xmax = 25.07 - text = "i" - intervals [93]: - xmin = 25.07 - xmax = 25.1 - text = "" - intervals [94]: - xmin = 25.1 - xmax = 25.38 - text = "like" - intervals [95]: - xmin = 25.38 - xmax = 25.44 - text = "to" - intervals [96]: - xmin = 25.44 - xmax = 25.55 - text = "go" - intervals [97]: - xmin = 25.55 - xmax = 25.79 - text = "for" - intervals [98]: - xmin = 25.79 - xmax = 25.83 - text = "a" - intervals [99]: - xmin = 25.83 - xmax = 26.12 - text = "hike" - intervals [100]: - xmin = 26.12 - xmax = 26.21 - text = "in" - intervals [101]: - xmin = 26.21 - xmax = 26.81 - text = "nature" - intervals [102]: - xmin = 26.81 - xmax = 27.11 - text = "" - intervals [103]: - xmin = 27.11 - xmax = 27.45 - text = "sometimes" - intervals [104]: - xmin = 27.45 - xmax = 27.51 - text = "i" - intervals [105]: - xmin = 27.51 - xmax = 27.74 - text = "try" - intervals [106]: - xmin = 27.74 - xmax = 27.88 - text = "to" - intervals [107]: - xmin = 27.88 - xmax = 28.37 - text = "organize" - intervals [108]: - xmin = 28.37 - xmax = 28.94 - text = "something" - intervals [109]: - xmin = 28.94 - xmax = 28.98 - text = "" - intervals [110]: - xmin = 28.98 - xmax = 29.19 - text = "for" - intervals [111]: - xmin = 29.19 - xmax = 29.32 - text = "my" - intervals [112]: - xmin = 29.32 - xmax = 29.89 - text = "friends" - intervals [113]: - xmin = 29.89 - xmax = 29.92 - text = "" - intervals [114]: - xmin = 29.92 - xmax = 29.95 - text = "i" - intervals [115]: - xmin = 29.95 - xmax = 30.2 - text = "" - intervals [116]: - xmin = 30.2 - xmax = 30.73 - text = "volunteer" - intervals [117]: - xmin = 30.73 - xmax = 30.86 - text = "at" - intervals [118]: - xmin = 30.86 - xmax = 30.97 - text = "the" - intervals [119]: - xmin = 30.97 - xmax = 31.38 - text = "buddhist" - intervals [120]: - xmin = 31.38 - xmax = 31.83 - text = "temple" - intervals [121]: - xmin = 31.83 - xmax = 31.94 - text = "on" - intervals [122]: - xmin = 31.94 - xmax = 32.01 - text = "the" - intervals [123]: - xmin = 32.01 - xmax = 32.6 - text = "weekend" - intervals [124]: - xmin = 32.6 - xmax = 33.01 - text = "or" - intervals [125]: - xmin = 33.01 - xmax = 33.24 - text = "i" - intervals [126]: - xmin = 33.24 - xmax = 33.62 - text = "can" - intervals [127]: - xmin = 33.62 - xmax = 33.91 - text = "just" - intervals [128]: - xmin = 33.91 - xmax = 34.3 - text = "walk" - intervals [129]: - xmin = 34.3 - xmax = 34.69 - text = "around" - intervals [130]: - xmin = 34.69 - xmax = 35.08 - text = "enjoying" - intervals [131]: - xmin = 35.08 - xmax = 35.17 - text = "the" - intervals [132]: - xmin = 35.17 - xmax = 35.87 - text = "sunshine" - intervals [133]: - xmin = 35.87 - xmax = 36.15 - text = "" - intervals [134]: - xmin = 36.15 - xmax = 36.34 - text = "i'd" - intervals [135]: - xmin = 36.34 - xmax = 36.52 - text = "like" - intervals [136]: - xmin = 36.52 - xmax = 36.59 - text = "to" - intervals [137]: - xmin = 36.59 - xmax = 36.74 - text = "have" - intervals [138]: - xmin = 36.74 - xmax = 36.79 - text = "a" - intervals [139]: - xmin = 36.79 - xmax = 37.06 - text = "healthy" - intervals [140]: - xmin = 37.06 - xmax = 37.66 - text = "lifestyle" - intervals [141]: - xmin = 37.66 - xmax = 38.06 - text = "considering" - intervals [142]: - xmin = 38.06 - xmax = 38.17 - text = "how" - intervals [143]: - xmin = 38.17 - xmax = 38.38 - text = "much" - intervals [144]: - xmin = 38.38 - xmax = 38.74 - text = "time" - intervals [145]: - xmin = 38.74 - xmax = 38.81 - text = "i" - intervals [146]: - xmin = 38.81 - xmax = 39.18 - text = "spend" - intervals [147]: - xmin = 39.18 - xmax = 39.29 - text = "at" - intervals [148]: - xmin = 39.29 - xmax = 39.84 - text = "work" - intervals [149]: - xmin = 39.84 - xmax = 40.29 - text = "" - intervals [150]: - xmin = 40.29 - xmax = 40.52 - text = "i" - intervals [151]: - xmin = 40.52 - xmax = 40.79 - text = "always" - intervals [152]: - xmin = 40.79 - xmax = 41.28 - text = "try" - intervals [153]: - xmin = 41.28 - xmax = 41.47 - text = "to" - intervals [154]: - xmin = 41.47 - xmax = 41.85 - text = "move" - intervals [155]: - xmin = 41.85 - xmax = 42 - text = "as" - intervals [156]: - xmin = 42 - xmax = 42.22 - text = "much" - intervals [157]: - xmin = 42.22 - xmax = 42.31 - text = "as" - intervals [158]: - xmin = 42.31 - xmax = 42.4 - text = "i" - intervals [159]: - xmin = 42.4 - xmax = 42.76 - text = "can" - intervals [160]: - xmin = 42.76 - xmax = 42.89 - text = "when" - intervals [161]: - xmin = 42.89 - xmax = 42.98 - text = "i'm" - intervals [162]: - xmin = 42.98 - xmax = 43.18 - text = "not" - intervals [163]: - xmin = 43.18 - xmax = 43.76 - text = "working" - intervals [164]: - xmin = 43.76 - xmax = 44.5 - text = "" - intervals [165]: - xmin = 44.5 - xmax = 45.19 - text = "and" - intervals [166]: - xmin = 45.19 - xmax = 45.32 - text = "on" - intervals [167]: - xmin = 45.32 - xmax = 45.49 - text = "other" - intervals [168]: - xmin = 45.49 - xmax = 45.82 - text = "days" - intervals [169]: - xmin = 45.82 - xmax = 45.96 - text = "when" - intervals [170]: - xmin = 45.96 - xmax = 46.16 - text = "i'm" - intervals [171]: - xmin = 46.16 - xmax = 46.65 - text = "free" - intervals [172]: - xmin = 46.65 - xmax = 46.86 - text = "i" - intervals [173]: - xmin = 46.86 - xmax = 47.16 - text = "like" - intervals [174]: - xmin = 47.16 - xmax = 47.39 - text = "to" - intervals [175]: - xmin = 47.39 - xmax = 47.86 - text = "listen" - intervals [176]: - xmin = 47.86 - xmax = 48.03 - text = "to" - intervals [177]: - xmin = 48.03 - xmax = 48.41 - text = "music" - intervals [178]: - xmin = 48.41 - xmax = 48.73 - text = "and" - intervals [179]: - xmin = 48.73 - xmax = 48.76 - text = "" - intervals [180]: - xmin = 48.76 - xmax = 49.01 - text = "we're" - intervals [181]: - xmin = 49.01 - xmax = 49.3 - text = "watch" - intervals [182]: - xmin = 49.3 - xmax = 49.38 - text = "a" - intervals [183]: - xmin = 49.38 - xmax = 50.05 - text = "documentary" - intervals [184]: - xmin = 50.05 - xmax = 50.51 - text = "movies" - intervals [185]: - xmin = 50.51 - xmax = 50.82 - text = "on" - intervals [186]: - xmin = 50.82 - xmax = 51.11 - text = "my" - intervals [187]: - xmin = 51.11 - xmax = 51.81 - text = "laptop" - intervals [188]: - xmin = 51.81 - xmax = 52.14 - text = "" - intervals [189]: - xmin = 52.14 - xmax = 52.44 - text = "but" - intervals [190]: - xmin = 52.44 - xmax = 52.86 - text = "sometimes" - intervals [191]: - xmin = 52.86 - xmax = 52.93 - text = "it" - intervals [192]: - xmin = 52.93 - xmax = 53.13 - text = "just" - intervals [193]: - xmin = 53.13 - xmax = 53.61 - text = "sleep" - intervals [194]: - xmin = 53.61 - xmax = 53.65 - text = "" - intervals [195]: - xmin = 53.65 - xmax = 53.83 - text = "i" - intervals [196]: - xmin = 53.83 - xmax = 54.27 - text = "especially" - intervals [197]: - xmin = 54.27 - xmax = 54.61 - text = "liked" - intervals [198]: - xmin = 54.61 - xmax = 55.01 - text = "watching" - intervals [199]: - xmin = 55.01 - xmax = 55.62 - text = "japanese" - intervals [200]: - xmin = 55.62 - xmax = 55.91 - text = "anime" - intervals [201]: - xmin = 55.91 - xmax = 56.33 - text = "i" - intervals [202]: - xmin = 56.33 - xmax = 56.85 - text = "" - intervals [203]: - xmin = 56.85 - xmax = 57.12 - text = "think" - intervals [204]: - xmin = 57.12 - xmax = 57.43 - text = "watching" - intervals [205]: - xmin = 57.43 - xmax = 57.62 - text = "a" - intervals [206]: - xmin = 57.62 - xmax = 57.79 - text = "me" - intervals [207]: - xmin = 57.79 - xmax = 58.09 - text = "is" - intervals [208]: - xmin = 58.09 - xmax = 58.39 - text = "anime" - intervals [209]: - xmin = 58.39 - xmax = 59.06 - text = "is" - intervals [210]: - xmin = 59.06 - xmax = 59.31 - text = "very" - intervals [211]: - xmin = 59.31 - xmax = 59.67 - text = "helpful" - intervals [212]: - xmin = 59.67 - xmax = 59.81 - text = "for" - intervals [213]: - xmin = 59.81 - xmax = 59.98 - text = "me" - intervals [214]: - xmin = 59.98 - xmax = 60.28 - text = "to" - intervals [215]: - xmin = 60.28 - xmax = 60.69 - text = "learn" - intervals [216]: - xmin = 60.69 - xmax = 60.78 - text = "and" - intervals [217]: - xmin = 60.78 - xmax = 61.21 - text = "express" - intervals [218]: - xmin = 61.21 - xmax = 61.89 - text = "japanese" - intervals [219]: - xmin = 61.89 - xmax = 62.42 - text = "better" - intervals [220]: - xmin = 62.42 - xmax = 64.097375 - text = "" - item [2]: - class = "IntervalTier" - name = "phones" - xmin = 0 - xmax = 64.097375 - intervals: size = 684 - intervals [1]: - xmin = 0 - xmax = 1.42 - text = "" - intervals [2]: - xmin = 1.42 - xmax = 1.48 - text = "DH" - intervals [3]: - xmin = 1.48 - xmax = 1.52 - text = "AH0" - intervals [4]: - xmin = 1.52 - xmax = 1.62 - text = "F" - intervals [5]: - xmin = 1.62 - xmax = 1.72 - text = "ER1" - intervals [6]: - xmin = 1.72 - xmax = 1.75 - text = "S" - intervals [7]: - xmin = 1.75 - xmax = 1.78 - text = "T" - intervals [8]: - xmin = 1.78 - xmax = 1.81 - text = "TH" - intervals [9]: - xmin = 1.81 - xmax = 1.88 - text = "IH1" - intervals [10]: - xmin = 1.88 - xmax = 1.97 - text = "NG" - intervals [11]: - xmin = 1.97 - xmax = 2.04 - text = "AY1" - intervals [12]: - xmin = 2.04 - xmax = 2.08 - text = "L" - intervals [13]: - xmin = 2.08 - xmax = 2.17 - text = "AY1" - intervals [14]: - xmin = 2.17 - xmax = 2.21 - text = "K" - intervals [15]: - xmin = 2.21 - xmax = 2.24 - text = "T" - intervals [16]: - xmin = 2.24 - xmax = 2.28 - text = "IH0" - intervals [17]: - xmin = 2.28 - xmax = 2.34 - text = "D" - intervals [18]: - xmin = 2.34 - xmax = 2.47 - text = "UW1" - intervals [19]: - xmin = 2.47 - xmax = 2.58 - text = "AA1" - intervals [20]: - xmin = 2.58 - xmax = 2.63 - text = "N" - intervals [21]: - xmin = 2.63 - xmax = 2.68 - text = "W" - intervals [22]: - xmin = 2.68 - xmax = 2.78 - text = "IY1" - intervals [23]: - xmin = 2.78 - xmax = 2.88 - text = "K" - intervals [24]: - xmin = 2.88 - xmax = 3.01 - text = "EH2" - intervals [25]: - xmin = 3.01 - xmax = 3.14 - text = "N" - intervals [26]: - xmin = 3.14 - xmax = 3.2 - text = "D" - intervals [27]: - xmin = 3.2 - xmax = 3.32 - text = "Z" - intervals [28]: - xmin = 3.32 - xmax = 3.47 - text = "IH1" - intervals [29]: - xmin = 3.47 - xmax = 3.58 - text = "Z" - intervals [30]: - xmin = 3.58 - xmax = 3.64 - text = "R" - intervals [31]: - xmin = 3.64 - xmax = 3.7 - text = "IY0" - intervals [32]: - xmin = 3.7 - xmax = 3.8 - text = "L" - intervals [33]: - xmin = 3.8 - xmax = 3.96 - text = "AE1" - intervals [34]: - xmin = 3.96 - xmax = 4.02 - text = "K" - intervals [35]: - xmin = 4.02 - xmax = 4.11 - text = "S" - intervals [36]: - xmin = 4.11 - xmax = 4.2 - text = "IH0" - intervals [37]: - xmin = 4.2 - xmax = 4.41 - text = "NG" - intervals [38]: - xmin = 4.41 - xmax = 4.52 - text = "" - intervals [39]: - xmin = 4.52 - xmax = 4.97 - text = "AH0" - intervals [40]: - xmin = 4.97 - xmax = 5.01 - text = "N" - intervals [41]: - xmin = 5.01 - xmax = 5.05 - text = "D" - intervals [42]: - xmin = 5.05 - xmax = 5.14 - text = "AY1" - intervals [43]: - xmin = 5.14 - xmax = 5.19 - text = "TH" - intervals [44]: - xmin = 5.19 - xmax = 5.25 - text = "IH1" - intervals [45]: - xmin = 5.25 - xmax = 5.29 - text = "NG" - intervals [46]: - xmin = 5.29 - xmax = 5.33 - text = "K" - intervals [47]: - xmin = 5.33 - xmax = 5.36 - text = "AY1" - intervals [48]: - xmin = 5.36 - xmax = 5.41 - text = "L" - intervals [49]: - xmin = 5.41 - xmax = 5.44 - text = "G" - intervals [50]: - xmin = 5.44 - xmax = 5.5 - text = "OW1" - intervals [51]: - xmin = 5.5 - xmax = 5.68 - text = "SH" - intervals [52]: - xmin = 5.68 - xmax = 5.87 - text = "AA1" - intervals [53]: - xmin = 5.87 - xmax = 5.92 - text = "P" - intervals [54]: - xmin = 5.92 - xmax = 5.96 - text = "IH0" - intervals [55]: - xmin = 5.96 - xmax = 6 - text = "NG" - intervals [56]: - xmin = 6 - xmax = 6.06 - text = "IH0" - intervals [57]: - xmin = 6.06 - xmax = 6.11 - text = "F" - intervals [58]: - xmin = 6.11 - xmax = 6.16 - text = "AY1" - intervals [59]: - xmin = 6.16 - xmax = 6.29 - text = "M" - intervals [60]: - xmin = 6.29 - xmax = 6.35 - text = "N" - intervals [61]: - xmin = 6.35 - xmax = 6.48 - text = "AA1" - intervals [62]: - xmin = 6.48 - xmax = 6.54 - text = "T" - intervals [63]: - xmin = 6.54 - xmax = 6.58 - text = "DH" - intervals [64]: - xmin = 6.58 - xmax = 6.64 - text = "AE1" - intervals [65]: - xmin = 6.64 - xmax = 6.7 - text = "T" - intervals [66]: - xmin = 6.7 - xmax = 6.78 - text = "T" - intervals [67]: - xmin = 6.78 - xmax = 6.93 - text = "AY1" - intervals [68]: - xmin = 6.93 - xmax = 7.08 - text = "ER0" - intervals [69]: - xmin = 7.08 - xmax = 7.19 - text = "D" - intervals [70]: - xmin = 7.19 - xmax = 7.45 - text = "" - intervals [71]: - xmin = 7.45 - xmax = 7.59 - text = "S" - intervals [72]: - xmin = 7.59 - xmax = 7.62 - text = "OW1" - intervals [73]: - xmin = 7.62 - xmax = 7.66 - text = "DH" - intervals [74]: - xmin = 7.66 - xmax = 7.71 - text = "AE1" - intervals [75]: - xmin = 7.71 - xmax = 7.74 - text = "T" - intervals [76]: - xmin = 7.74 - xmax = 7.77 - text = "Y" - intervals [77]: - xmin = 7.77 - xmax = 7.85 - text = "UW1" - intervals [78]: - xmin = 7.85 - xmax = 7.92 - text = "S" - intervals [79]: - xmin = 7.92 - xmax = 7.97 - text = "T" - intervals [80]: - xmin = 7.97 - xmax = 8.02 - text = "AA1" - intervals [81]: - xmin = 8.02 - xmax = 8.05 - text = "R" - intervals [82]: - xmin = 8.05 - xmax = 8.08 - text = "T" - intervals [83]: - xmin = 8.08 - xmax = 8.11 - text = "AH0" - intervals [84]: - xmin = 8.11 - xmax = 8.14 - text = "D" - intervals [85]: - xmin = 8.14 - xmax = 8.17 - text = "B" - intervals [86]: - xmin = 8.17 - xmax = 8.24 - text = "AY1" - intervals [87]: - xmin = 8.24 - xmax = 8.35 - text = "JH" - intervals [88]: - xmin = 8.35 - xmax = 8.48 - text = "AA1" - intervals [89]: - xmin = 8.48 - xmax = 8.52 - text = "B" - intervals [90]: - xmin = 8.52 - xmax = 8.59 - text = "AY1" - intervals [91]: - xmin = 8.59 - xmax = 8.64 - text = "TH" - intervals [92]: - xmin = 8.64 - xmax = 8.69 - text = "IH1" - intervals [93]: - xmin = 8.69 - xmax = 8.72 - text = "NG" - intervals [94]: - xmin = 8.72 - xmax = 8.75 - text = "K" - intervals [95]: - xmin = 8.75 - xmax = 8.79 - text = "IH1" - intervals [96]: - xmin = 8.79 - xmax = 8.84 - text = "T" - intervals [97]: - xmin = 8.84 - xmax = 8.88 - text = "S" - intervals [98]: - xmin = 8.88 - xmax = 9.08 - text = "V" - intervals [99]: - xmin = 9.08 - xmax = 9.2 - text = "EH1" - intervals [100]: - xmin = 9.2 - xmax = 9.28 - text = "R" - intervals [101]: - xmin = 9.28 - xmax = 9.35 - text = "IY0" - intervals [102]: - xmin = 9.35 - xmax = 9.4 - text = "IH0" - intervals [103]: - xmin = 9.4 - xmax = 9.46 - text = "M" - intervals [104]: - xmin = 9.46 - xmax = 9.55 - text = "P" - intervals [105]: - xmin = 9.55 - xmax = 9.63 - text = "AO1" - intervals [106]: - xmin = 9.63 - xmax = 9.68 - text = "R" - intervals [107]: - xmin = 9.68 - xmax = 9.71 - text = "T" - intervals [108]: - xmin = 9.71 - xmax = 9.74 - text = "AH0" - intervals [109]: - xmin = 9.74 - xmax = 9.77 - text = "N" - intervals [110]: - xmin = 9.77 - xmax = 9.8 - text = "T" - intervals [111]: - xmin = 9.8 - xmax = 9.83 - text = "T" - intervals [112]: - xmin = 9.83 - xmax = 9.87 - text = "IH0" - intervals [113]: - xmin = 9.87 - xmax = 9.93 - text = "G" - intervals [114]: - xmin = 9.93 - xmax = 9.96 - text = "EH1" - intervals [115]: - xmin = 9.96 - xmax = 9.99 - text = "T" - intervals [116]: - xmin = 9.99 - xmax = 10.03 - text = "AH0" - intervals [117]: - xmin = 10.03 - xmax = 10.07 - text = "G" - intervals [118]: - xmin = 10.07 - xmax = 10.1 - text = "IH0" - intervals [119]: - xmin = 10.1 - xmax = 10.17 - text = "D" - intervals [120]: - xmin = 10.17 - xmax = 10.35 - text = "S" - intervals [121]: - xmin = 10.35 - xmax = 10.43 - text = "L" - intervals [122]: - xmin = 10.43 - xmax = 10.53 - text = "IY1" - intervals [123]: - xmin = 10.53 - xmax = 10.56 - text = "P" - intervals [124]: - xmin = 10.56 - xmax = 10.8 - text = "D" - intervals [125]: - xmin = 10.8 - xmax = 10.92 - text = "ER1" - intervals [126]: - xmin = 10.92 - xmax = 10.99 - text = "IH0" - intervals [127]: - xmin = 10.99 - xmax = 11.14 - text = "NG" - intervals [128]: - xmin = 11.14 - xmax = 11.2 - text = "Y" - intervals [129]: - xmin = 11.2 - xmax = 11.23 - text = "UH1" - intervals [130]: - xmin = 11.23 - xmax = 11.32 - text = "R" - intervals [131]: - xmin = 11.32 - xmax = 11.4 - text = "W" - intervals [132]: - xmin = 11.4 - xmax = 11.51 - text = "IY1" - intervals [133]: - xmin = 11.51 - xmax = 11.6 - text = "K" - intervals [134]: - xmin = 11.6 - xmax = 11.68 - text = "EH2" - intervals [135]: - xmin = 11.68 - xmax = 11.74 - text = "N" - intervals [136]: - xmin = 11.74 - xmax = 11.77 - text = "D" - intervals [137]: - xmin = 11.77 - xmax = 11.8 - text = "B" - intervals [138]: - xmin = 11.8 - xmax = 11.88 - text = "IH0" - intervals [139]: - xmin = 11.88 - xmax = 12 - text = "K" - intervals [140]: - xmin = 12 - xmax = 12.26 - text = "AH1" - intervals [141]: - xmin = 12.26 - xmax = 12.4 - text = "Z" - intervals [142]: - xmin = 12.4 - xmax = 12.6 - text = "W" - intervals [143]: - xmin = 12.6 - xmax = 12.88 - text = "EH1" - intervals [144]: - xmin = 12.88 - xmax = 12.95 - text = "N" - intervals [145]: - xmin = 12.95 - xmax = 12.99 - text = "Y" - intervals [146]: - xmin = 12.99 - xmax = 13.04 - text = "UW1" - intervals [147]: - xmin = 13.04 - xmax = 13.07 - text = "HH" - intervals [148]: - xmin = 13.07 - xmax = 13.16 - text = "AE1" - intervals [149]: - xmin = 13.16 - xmax = 13.19 - text = "V" - intervals [150]: - xmin = 13.19 - xmax = 13.22 - text = "T" - intervals [151]: - xmin = 13.22 - xmax = 13.27 - text = "UW1" - intervals [152]: - xmin = 13.27 - xmax = 13.32 - text = "W" - intervals [153]: - xmin = 13.32 - xmax = 13.4 - text = "ER1" - intervals [154]: - xmin = 13.4 - xmax = 13.44 - text = "K" - intervals [155]: - xmin = 13.44 - xmax = 13.51 - text = "AA1" - intervals [156]: - xmin = 13.51 - xmax = 13.58 - text = "N" - intervals [157]: - xmin = 13.58 - xmax = 13.66 - text = "M" - intervals [158]: - xmin = 13.66 - xmax = 13.76 - text = "AH1" - intervals [159]: - xmin = 13.76 - xmax = 13.81 - text = "N" - intervals [160]: - xmin = 13.81 - xmax = 13.85 - text = "D" - intervals [161]: - xmin = 13.85 - xmax = 13.96 - text = "EY2" - intervals [162]: - xmin = 13.96 - xmax = 14.01 - text = "TH" - intervals [163]: - xmin = 14.01 - xmax = 14.04 - text = "R" - intervals [164]: - xmin = 14.04 - xmax = 14.1 - text = "UW1" - intervals [165]: - xmin = 14.1 - xmax = 14.17 - text = "F" - intervals [166]: - xmin = 14.17 - xmax = 14.26 - text = "R" - intervals [167]: - xmin = 14.26 - xmax = 14.4 - text = "AY1" - intervals [168]: - xmin = 14.4 - xmax = 14.45 - text = "D" - intervals [169]: - xmin = 14.45 - xmax = 14.75 - text = "EY2" - intervals [170]: - xmin = 14.75 - xmax = 15.41 - text = "" - intervals [171]: - xmin = 15.41 - xmax = 15.49 - text = "DH" - intervals [172]: - xmin = 15.49 - xmax = 15.53 - text = "AH1" - intervals [173]: - xmin = 15.53 - xmax = 15.62 - text = "HH" - intervals [174]: - xmin = 15.62 - xmax = 15.67 - text = "OW1" - intervals [175]: - xmin = 15.67 - xmax = 15.75 - text = "L" - intervals [176]: - xmin = 15.75 - xmax = 15.8 - text = "W" - intervals [177]: - xmin = 15.8 - xmax = 15.94 - text = "IY1" - intervals [178]: - xmin = 15.94 - xmax = 16.09 - text = "K" - intervals [179]: - xmin = 16.09 - xmax = 16.28 - text = "" - intervals [180]: - xmin = 16.28 - xmax = 16.38 - text = "Y" - intervals [181]: - xmin = 16.38 - xmax = 16.42 - text = "UW1" - intervals [182]: - xmin = 16.42 - xmax = 16.49 - text = "ER0" - intervals [183]: - xmin = 16.49 - xmax = 16.55 - text = "V" - intervals [184]: - xmin = 16.55 - xmax = 16.58 - text = "EH1" - intervals [185]: - xmin = 16.58 - xmax = 16.65 - text = "R" - intervals [186]: - xmin = 16.65 - xmax = 16.73 - text = "IY0" - intervals [187]: - xmin = 16.73 - xmax = 16.92 - text = "T" - intervals [188]: - xmin = 16.92 - xmax = 17.08 - text = "AY1" - intervals [189]: - xmin = 17.08 - xmax = 17.22 - text = "ER0" - intervals [190]: - xmin = 17.22 - xmax = 17.59 - text = "D" - intervals [191]: - xmin = 17.59 - xmax = 17.83 - text = "" - intervals [192]: - xmin = 17.83 - xmax = 18.02 - text = "S" - intervals [193]: - xmin = 18.02 - xmax = 18.29 - text = "OW1" - intervals [194]: - xmin = 18.29 - xmax = 18.37 - text = "G" - intervals [195]: - xmin = 18.37 - xmax = 18.42 - text = "IH1" - intervals [196]: - xmin = 18.42 - xmax = 18.46 - text = "T" - intervals [197]: - xmin = 18.46 - xmax = 18.5 - text = "IH0" - intervals [198]: - xmin = 18.5 - xmax = 18.55 - text = "NG" - intervals [199]: - xmin = 18.55 - xmax = 18.61 - text = "EY1" - intervals [200]: - xmin = 18.61 - xmax = 18.67 - text = "G" - intervals [201]: - xmin = 18.67 - xmax = 18.73 - text = "UH1" - intervals [202]: - xmin = 18.73 - xmax = 18.78 - text = "D" - intervals [203]: - xmin = 18.78 - xmax = 18.86 - text = "R" - intervals [204]: - xmin = 18.86 - xmax = 18.97 - text = "EH1" - intervals [205]: - xmin = 18.97 - xmax = 19.05 - text = "S" - intervals [206]: - xmin = 19.05 - xmax = 19.08 - text = "T" - intervals [207]: - xmin = 19.08 - xmax = 19.13 - text = "IH0" - intervals [208]: - xmin = 19.13 - xmax = 19.21 - text = "Z" - intervals [209]: - xmin = 19.21 - xmax = 19.24 - text = "EH1" - intervals [210]: - xmin = 19.24 - xmax = 19.3 - text = "Z" - intervals [211]: - xmin = 19.3 - xmax = 19.34 - text = "IH0" - intervals [212]: - xmin = 19.34 - xmax = 19.38 - text = "M" - intervals [213]: - xmin = 19.38 - xmax = 19.48 - text = "P" - intervals [214]: - xmin = 19.48 - xmax = 19.55 - text = "AO1" - intervals [215]: - xmin = 19.55 - xmax = 19.59 - text = "R" - intervals [216]: - xmin = 19.59 - xmax = 19.62 - text = "T" - intervals [217]: - xmin = 19.62 - xmax = 19.65 - text = "AH0" - intervals [218]: - xmin = 19.65 - xmax = 19.68 - text = "N" - intervals [219]: - xmin = 19.68 - xmax = 19.77 - text = "T" - intervals [220]: - xmin = 19.77 - xmax = 19.94 - text = "AE1" - intervals [221]: - xmin = 19.94 - xmax = 20.16 - text = "Z" - intervals [222]: - xmin = 20.16 - xmax = 20.3 - text = "" - intervals [223]: - xmin = 20.3 - xmax = 20.39 - text = "K" - intervals [224]: - xmin = 20.39 - xmax = 20.43 - text = "AH0" - intervals [225]: - xmin = 20.43 - xmax = 20.46 - text = "M" - intervals [226]: - xmin = 20.46 - xmax = 20.53 - text = "P" - intervals [227]: - xmin = 20.53 - xmax = 20.59 - text = "L" - intervals [228]: - xmin = 20.59 - xmax = 20.63 - text = "EY1" - intervals [229]: - xmin = 20.63 - xmax = 20.66 - text = "N" - intervals [230]: - xmin = 20.66 - xmax = 20.69 - text = "T" - intervals [231]: - xmin = 20.69 - xmax = 20.75 - text = "AH0" - intervals [232]: - xmin = 20.75 - xmax = 20.87 - text = "JH" - intervals [233]: - xmin = 20.87 - xmax = 21.09 - text = "AO1" - intervals [234]: - xmin = 21.09 - xmax = 21.3 - text = "ER0" - intervals [235]: - xmin = 21.3 - xmax = 21.44 - text = "K" - intervals [236]: - xmin = 21.44 - xmax = 21.47 - text = "AH0" - intervals [237]: - xmin = 21.47 - xmax = 21.5 - text = "M" - intervals [238]: - xmin = 21.5 - xmax = 21.53 - text = "P" - intervals [239]: - xmin = 21.53 - xmax = 21.6 - text = "L" - intervals [240]: - xmin = 21.6 - xmax = 21.63 - text = "IY1" - intervals [241]: - xmin = 21.63 - xmax = 21.66 - text = "T" - intervals [242]: - xmin = 21.66 - xmax = 21.72 - text = "IH0" - intervals [243]: - xmin = 21.72 - xmax = 21.79 - text = "NG" - intervals [244]: - xmin = 21.79 - xmax = 21.83 - text = "AH0" - intervals [245]: - xmin = 21.83 - xmax = 21.9 - text = "N" - intervals [246]: - xmin = 21.9 - xmax = 21.98 - text = "EH1" - intervals [247]: - xmin = 21.98 - xmax = 22.03 - text = "K" - intervals [248]: - xmin = 22.03 - xmax = 22.07 - text = "S" - intervals [249]: - xmin = 22.07 - xmax = 22.11 - text = "AH0" - intervals [250]: - xmin = 22.11 - xmax = 22.14 - text = "L" - intervals [251]: - xmin = 22.14 - xmax = 22.17 - text = "AH0" - intervals [252]: - xmin = 22.17 - xmax = 22.2 - text = "N" - intervals [253]: - xmin = 22.2 - xmax = 22.23 - text = "T" - intervals [254]: - xmin = 22.23 - xmax = 22.34 - text = "JH" - intervals [255]: - xmin = 22.34 - xmax = 22.5 - text = "AA1" - intervals [256]: - xmin = 22.5 - xmax = 22.64 - text = "B" - intervals [257]: - xmin = 22.64 - xmax = 23.04 - text = "" - intervals [258]: - xmin = 23.04 - xmax = 23.14 - text = "IH0" - intervals [259]: - xmin = 23.14 - xmax = 23.17 - text = "N" - intervals [260]: - xmin = 23.17 - xmax = 23.2 - text = "M" - intervals [261]: - xmin = 23.2 - xmax = 23.29 - text = "AY1" - intervals [262]: - xmin = 23.29 - xmax = 23.36 - text = "S" - intervals [263]: - xmin = 23.36 - xmax = 23.41 - text = "P" - intervals [264]: - xmin = 23.41 - xmax = 23.52 - text = "EH1" - intervals [265]: - xmin = 23.52 - xmax = 23.56 - text = "R" - intervals [266]: - xmin = 23.56 - xmax = 23.65 - text = "T" - intervals [267]: - xmin = 23.65 - xmax = 23.76 - text = "AY1" - intervals [268]: - xmin = 23.76 - xmax = 23.8 - text = "M" - intervals [269]: - xmin = 23.8 - xmax = 23.85 - text = "IH0" - intervals [270]: - xmin = 23.85 - xmax = 23.88 - text = "F" - intervals [271]: - xmin = 23.88 - xmax = 23.98 - text = "AY1" - intervals [272]: - xmin = 23.98 - xmax = 24.04 - text = "F" - intervals [273]: - xmin = 24.04 - xmax = 24.13 - text = "IY1" - intervals [274]: - xmin = 24.13 - xmax = 24.18 - text = "L" - intervals [275]: - xmin = 24.18 - xmax = 24.26 - text = "OW2" - intervals [276]: - xmin = 24.26 - xmax = 24.39 - text = "K" - intervals [277]: - xmin = 24.39 - xmax = 24.84 - text = "EY1" - intervals [278]: - xmin = 24.84 - xmax = 25.07 - text = "AY1" - intervals [279]: - xmin = 25.07 - xmax = 25.1 - text = "" - intervals [280]: - xmin = 25.1 - xmax = 25.29 - text = "L" - intervals [281]: - xmin = 25.29 - xmax = 25.35 - text = "AY1" - intervals [282]: - xmin = 25.35 - xmax = 25.38 - text = "K" - intervals [283]: - xmin = 25.38 - xmax = 25.41 - text = "T" - intervals [284]: - xmin = 25.41 - xmax = 25.44 - text = "IH0" - intervals [285]: - xmin = 25.44 - xmax = 25.5 - text = "G" - intervals [286]: - xmin = 25.5 - xmax = 25.55 - text = "OW1" - intervals [287]: - xmin = 25.55 - xmax = 25.59 - text = "F" - intervals [288]: - xmin = 25.59 - xmax = 25.79 - text = "ER0" - intervals [289]: - xmin = 25.79 - xmax = 25.83 - text = "AH0" - intervals [290]: - xmin = 25.83 - xmax = 25.94 - text = "HH" - intervals [291]: - xmin = 25.94 - xmax = 26.06 - text = "AY1" - intervals [292]: - xmin = 26.06 - xmax = 26.12 - text = "K" - intervals [293]: - xmin = 26.12 - xmax = 26.17 - text = "IH1" - intervals [294]: - xmin = 26.17 - xmax = 26.21 - text = "N" - intervals [295]: - xmin = 26.21 - xmax = 26.27 - text = "N" - intervals [296]: - xmin = 26.27 - xmax = 26.4 - text = "EY1" - intervals [297]: - xmin = 26.4 - xmax = 26.53 - text = "CH" - intervals [298]: - xmin = 26.53 - xmax = 26.81 - text = "ER0" - intervals [299]: - xmin = 26.81 - xmax = 27.11 - text = "" - intervals [300]: - xmin = 27.11 - xmax = 27.21 - text = "S" - intervals [301]: - xmin = 27.21 - xmax = 27.25 - text = "AH1" - intervals [302]: - xmin = 27.25 - xmax = 27.28 - text = "M" - intervals [303]: - xmin = 27.28 - xmax = 27.31 - text = "T" - intervals [304]: - xmin = 27.31 - xmax = 27.38 - text = "AY2" - intervals [305]: - xmin = 27.38 - xmax = 27.41 - text = "M" - intervals [306]: - xmin = 27.41 - xmax = 27.45 - text = "Z" - intervals [307]: - xmin = 27.45 - xmax = 27.51 - text = "AY1" - intervals [308]: - xmin = 27.51 - xmax = 27.6 - text = "T" - intervals [309]: - xmin = 27.6 - xmax = 27.67 - text = "R" - intervals [310]: - xmin = 27.67 - xmax = 27.74 - text = "AY1" - intervals [311]: - xmin = 27.74 - xmax = 27.77 - text = "T" - intervals [312]: - xmin = 27.77 - xmax = 27.88 - text = "AH0" - intervals [313]: - xmin = 27.88 - xmax = 28.02 - text = "AO1" - intervals [314]: - xmin = 28.02 - xmax = 28.07 - text = "R" - intervals [315]: - xmin = 28.07 - xmax = 28.12 - text = "G" - intervals [316]: - xmin = 28.12 - xmax = 28.15 - text = "AH0" - intervals [317]: - xmin = 28.15 - xmax = 28.18 - text = "N" - intervals [318]: - xmin = 28.18 - xmax = 28.3 - text = "AY2" - intervals [319]: - xmin = 28.3 - xmax = 28.37 - text = "Z" - intervals [320]: - xmin = 28.37 - xmax = 28.42 - text = "S" - intervals [321]: - xmin = 28.42 - xmax = 28.47 - text = "AH1" - intervals [322]: - xmin = 28.47 - xmax = 28.5 - text = "M" - intervals [323]: - xmin = 28.5 - xmax = 28.53 - text = "TH" - intervals [324]: - xmin = 28.53 - xmax = 28.61 - text = "IH0" - intervals [325]: - xmin = 28.61 - xmax = 28.94 - text = "NG" - intervals [326]: - xmin = 28.94 - xmax = 28.98 - text = "" - intervals [327]: - xmin = 28.98 - xmax = 29.08 - text = "F" - intervals [328]: - xmin = 29.08 - xmax = 29.13 - text = "AO1" - intervals [329]: - xmin = 29.13 - xmax = 29.19 - text = "R" - intervals [330]: - xmin = 29.19 - xmax = 29.23 - text = "M" - intervals [331]: - xmin = 29.23 - xmax = 29.32 - text = "AY1" - intervals [332]: - xmin = 29.32 - xmax = 29.41 - text = "F" - intervals [333]: - xmin = 29.41 - xmax = 29.49 - text = "R" - intervals [334]: - xmin = 29.49 - xmax = 29.6 - text = "EH1" - intervals [335]: - xmin = 29.6 - xmax = 29.65 - text = "N" - intervals [336]: - xmin = 29.65 - xmax = 29.7 - text = "D" - intervals [337]: - xmin = 29.7 - xmax = 29.89 - text = "Z" - intervals [338]: - xmin = 29.89 - xmax = 29.92 - text = "" - intervals [339]: - xmin = 29.92 - xmax = 29.95 - text = "AY1" - intervals [340]: - xmin = 29.95 - xmax = 30.2 - text = "" - intervals [341]: - xmin = 30.2 - xmax = 30.26 - text = "V" - intervals [342]: - xmin = 30.26 - xmax = 30.39 - text = "AA2" - intervals [343]: - xmin = 30.39 - xmax = 30.45 - text = "L" - intervals [344]: - xmin = 30.45 - xmax = 30.48 - text = "AH0" - intervals [345]: - xmin = 30.48 - xmax = 30.51 - text = "N" - intervals [346]: - xmin = 30.51 - xmax = 30.6 - text = "T" - intervals [347]: - xmin = 30.6 - xmax = 30.67 - text = "IH1" - intervals [348]: - xmin = 30.67 - xmax = 30.73 - text = "R" - intervals [349]: - xmin = 30.73 - xmax = 30.77 - text = "AE1" - intervals [350]: - xmin = 30.77 - xmax = 30.86 - text = "T" - intervals [351]: - xmin = 30.86 - xmax = 30.91 - text = "DH" - intervals [352]: - xmin = 30.91 - xmax = 30.97 - text = "AH1" - intervals [353]: - xmin = 30.97 - xmax = 31.13 - text = "B" - intervals [354]: - xmin = 31.13 - xmax = 31.19 - text = "UW1" - intervals [355]: - xmin = 31.19 - xmax = 31.24 - text = "D" - intervals [356]: - xmin = 31.24 - xmax = 31.3 - text = "AH0" - intervals [357]: - xmin = 31.3 - xmax = 31.35 - text = "S" - intervals [358]: - xmin = 31.35 - xmax = 31.38 - text = "T" - intervals [359]: - xmin = 31.38 - xmax = 31.41 - text = "T" - intervals [360]: - xmin = 31.41 - xmax = 31.47 - text = "EH1" - intervals [361]: - xmin = 31.47 - xmax = 31.52 - text = "M" - intervals [362]: - xmin = 31.52 - xmax = 31.56 - text = "P" - intervals [363]: - xmin = 31.56 - xmax = 31.61 - text = "AH0" - intervals [364]: - xmin = 31.61 - xmax = 31.83 - text = "L" - intervals [365]: - xmin = 31.83 - xmax = 31.9 - text = "AO1" - intervals [366]: - xmin = 31.9 - xmax = 31.94 - text = "N" - intervals [367]: - xmin = 31.94 - xmax = 31.97 - text = "DH" - intervals [368]: - xmin = 31.97 - xmax = 32.01 - text = "AH1" - intervals [369]: - xmin = 32.01 - xmax = 32.08 - text = "W" - intervals [370]: - xmin = 32.08 - xmax = 32.17 - text = "IY1" - intervals [371]: - xmin = 32.17 - xmax = 32.26 - text = "K" - intervals [372]: - xmin = 32.26 - xmax = 32.45 - text = "EH2" - intervals [373]: - xmin = 32.45 - xmax = 32.51 - text = "N" - intervals [374]: - xmin = 32.51 - xmax = 32.6 - text = "D" - intervals [375]: - xmin = 32.6 - xmax = 32.88 - text = "AO1" - intervals [376]: - xmin = 32.88 - xmax = 33.01 - text = "R" - intervals [377]: - xmin = 33.01 - xmax = 33.24 - text = "AY1" - intervals [378]: - xmin = 33.24 - xmax = 33.36 - text = "K" - intervals [379]: - xmin = 33.36 - xmax = 33.51 - text = "AE1" - intervals [380]: - xmin = 33.51 - xmax = 33.62 - text = "N" - intervals [381]: - xmin = 33.62 - xmax = 33.7 - text = "JH" - intervals [382]: - xmin = 33.7 - xmax = 33.77 - text = "IH0" - intervals [383]: - xmin = 33.77 - xmax = 33.8 - text = "S" - intervals [384]: - xmin = 33.8 - xmax = 33.91 - text = "T" - intervals [385]: - xmin = 33.91 - xmax = 33.96 - text = "W" - intervals [386]: - xmin = 33.96 - xmax = 34.2 - text = "AO1" - intervals [387]: - xmin = 34.2 - xmax = 34.3 - text = "K" - intervals [388]: - xmin = 34.3 - xmax = 34.42 - text = "ER0" - intervals [389]: - xmin = 34.42 - xmax = 34.63 - text = "AW1" - intervals [390]: - xmin = 34.63 - xmax = 34.69 - text = "N" - intervals [391]: - xmin = 34.69 - xmax = 34.76 - text = "IH0" - intervals [392]: - xmin = 34.76 - xmax = 34.8 - text = "N" - intervals [393]: - xmin = 34.8 - xmax = 34.9 - text = "JH" - intervals [394]: - xmin = 34.9 - xmax = 34.99 - text = "OY1" - intervals [395]: - xmin = 34.99 - xmax = 35.03 - text = "IH0" - intervals [396]: - xmin = 35.03 - xmax = 35.08 - text = "NG" - intervals [397]: - xmin = 35.08 - xmax = 35.12 - text = "DH" - intervals [398]: - xmin = 35.12 - xmax = 35.17 - text = "AH0" - intervals [399]: - xmin = 35.17 - xmax = 35.26 - text = "S" - intervals [400]: - xmin = 35.26 - xmax = 35.33 - text = "AH1" - intervals [401]: - xmin = 35.33 - xmax = 35.4 - text = "N" - intervals [402]: - xmin = 35.4 - xmax = 35.53 - text = "SH" - intervals [403]: - xmin = 35.53 - xmax = 35.69 - text = "AY2" - intervals [404]: - xmin = 35.69 - xmax = 35.87 - text = "N" - intervals [405]: - xmin = 35.87 - xmax = 36.15 - text = "" - intervals [406]: - xmin = 36.15 - xmax = 36.3 - text = "AY1" - intervals [407]: - xmin = 36.3 - xmax = 36.34 - text = "D" - intervals [408]: - xmin = 36.34 - xmax = 36.38 - text = "L" - intervals [409]: - xmin = 36.38 - xmax = 36.49 - text = "AY1" - intervals [410]: - xmin = 36.49 - xmax = 36.52 - text = "K" - intervals [411]: - xmin = 36.52 - xmax = 36.56 - text = "T" - intervals [412]: - xmin = 36.56 - xmax = 36.59 - text = "AH0" - intervals [413]: - xmin = 36.59 - xmax = 36.62 - text = "HH" - intervals [414]: - xmin = 36.62 - xmax = 36.7 - text = "AE1" - intervals [415]: - xmin = 36.7 - xmax = 36.74 - text = "V" - intervals [416]: - xmin = 36.74 - xmax = 36.79 - text = "AH0" - intervals [417]: - xmin = 36.79 - xmax = 36.83 - text = "HH" - intervals [418]: - xmin = 36.83 - xmax = 36.88 - text = "EH1" - intervals [419]: - xmin = 36.88 - xmax = 36.93 - text = "L" - intervals [420]: - xmin = 36.93 - xmax = 37.01 - text = "TH" - intervals [421]: - xmin = 37.01 - xmax = 37.06 - text = "IY0" - intervals [422]: - xmin = 37.06 - xmax = 37.12 - text = "L" - intervals [423]: - xmin = 37.12 - xmax = 37.23 - text = "AY1" - intervals [424]: - xmin = 37.23 - xmax = 37.27 - text = "F" - intervals [425]: - xmin = 37.27 - xmax = 37.34 - text = "S" - intervals [426]: - xmin = 37.34 - xmax = 37.39 - text = "T" - intervals [427]: - xmin = 37.39 - xmax = 37.56 - text = "AY2" - intervals [428]: - xmin = 37.56 - xmax = 37.66 - text = "L" - intervals [429]: - xmin = 37.66 - xmax = 37.73 - text = "K" - intervals [430]: - xmin = 37.73 - xmax = 37.77 - text = "AH0" - intervals [431]: - xmin = 37.77 - xmax = 37.82 - text = "N" - intervals [432]: - xmin = 37.82 - xmax = 37.87 - text = "S" - intervals [433]: - xmin = 37.87 - xmax = 37.91 - text = "IH1" - intervals [434]: - xmin = 37.91 - xmax = 37.94 - text = "D" - intervals [435]: - xmin = 37.94 - xmax = 37.98 - text = "ER0" - intervals [436]: - xmin = 37.98 - xmax = 38.02 - text = "IH0" - intervals [437]: - xmin = 38.02 - xmax = 38.06 - text = "NG" - intervals [438]: - xmin = 38.06 - xmax = 38.13 - text = "HH" - intervals [439]: - xmin = 38.13 - xmax = 38.17 - text = "AW1" - intervals [440]: - xmin = 38.17 - xmax = 38.23 - text = "M" - intervals [441]: - xmin = 38.23 - xmax = 38.27 - text = "AH1" - intervals [442]: - xmin = 38.27 - xmax = 38.38 - text = "CH" - intervals [443]: - xmin = 38.38 - xmax = 38.5 - text = "T" - intervals [444]: - xmin = 38.5 - xmax = 38.67 - text = "AY1" - intervals [445]: - xmin = 38.67 - xmax = 38.74 - text = "M" - intervals [446]: - xmin = 38.74 - xmax = 38.81 - text = "AY1" - intervals [447]: - xmin = 38.81 - xmax = 38.95 - text = "S" - intervals [448]: - xmin = 38.95 - xmax = 39.02 - text = "P" - intervals [449]: - xmin = 39.02 - xmax = 39.09 - text = "EH1" - intervals [450]: - xmin = 39.09 - xmax = 39.12 - text = "N" - intervals [451]: - xmin = 39.12 - xmax = 39.18 - text = "D" - intervals [452]: - xmin = 39.18 - xmax = 39.21 - text = "AE1" - intervals [453]: - xmin = 39.21 - xmax = 39.29 - text = "T" - intervals [454]: - xmin = 39.29 - xmax = 39.47 - text = "W" - intervals [455]: - xmin = 39.47 - xmax = 39.69 - text = "ER1" - intervals [456]: - xmin = 39.69 - xmax = 39.84 - text = "K" - intervals [457]: - xmin = 39.84 - xmax = 40.29 - text = "" - intervals [458]: - xmin = 40.29 - xmax = 40.52 - text = "AY1" - intervals [459]: - xmin = 40.52 - xmax = 40.56 - text = "AO1" - intervals [460]: - xmin = 40.56 - xmax = 40.59 - text = "L" - intervals [461]: - xmin = 40.59 - xmax = 40.66 - text = "W" - intervals [462]: - xmin = 40.66 - xmax = 40.7 - text = "IY0" - intervals [463]: - xmin = 40.7 - xmax = 40.79 - text = "Z" - intervals [464]: - xmin = 40.79 - xmax = 40.94 - text = "T" - intervals [465]: - xmin = 40.94 - xmax = 41.05 - text = "R" - intervals [466]: - xmin = 41.05 - xmax = 41.28 - text = "AY1" - intervals [467]: - xmin = 41.28 - xmax = 41.38 - text = "T" - intervals [468]: - xmin = 41.38 - xmax = 41.47 - text = "IH0" - intervals [469]: - xmin = 41.47 - xmax = 41.7 - text = "M" - intervals [470]: - xmin = 41.7 - xmax = 41.77 - text = "UW1" - intervals [471]: - xmin = 41.77 - xmax = 41.85 - text = "V" - intervals [472]: - xmin = 41.85 - xmax = 41.9 - text = "EH1" - intervals [473]: - xmin = 41.9 - xmax = 42 - text = "Z" - intervals [474]: - xmin = 42 - xmax = 42.08 - text = "M" - intervals [475]: - xmin = 42.08 - xmax = 42.13 - text = "AH1" - intervals [476]: - xmin = 42.13 - xmax = 42.22 - text = "CH" - intervals [477]: - xmin = 42.22 - xmax = 42.26 - text = "EH1" - intervals [478]: - xmin = 42.26 - xmax = 42.31 - text = "Z" - intervals [479]: - xmin = 42.31 - xmax = 42.4 - text = "AY1" - intervals [480]: - xmin = 42.4 - xmax = 42.51 - text = "K" - intervals [481]: - xmin = 42.51 - xmax = 42.64 - text = "AE1" - intervals [482]: - xmin = 42.64 - xmax = 42.76 - text = "N" - intervals [483]: - xmin = 42.76 - xmax = 42.81 - text = "W" - intervals [484]: - xmin = 42.81 - xmax = 42.84 - text = "EH1" - intervals [485]: - xmin = 42.84 - xmax = 42.89 - text = "N" - intervals [486]: - xmin = 42.89 - xmax = 42.95 - text = "AH0" - intervals [487]: - xmin = 42.95 - xmax = 42.98 - text = "M" - intervals [488]: - xmin = 42.98 - xmax = 43.03 - text = "N" - intervals [489]: - xmin = 43.03 - xmax = 43.12 - text = "AA1" - intervals [490]: - xmin = 43.12 - xmax = 43.18 - text = "T" - intervals [491]: - xmin = 43.18 - xmax = 43.28 - text = "W" - intervals [492]: - xmin = 43.28 - xmax = 43.42 - text = "ER1" - intervals [493]: - xmin = 43.42 - xmax = 43.49 - text = "K" - intervals [494]: - xmin = 43.49 - xmax = 43.53 - text = "IH0" - intervals [495]: - xmin = 43.53 - xmax = 43.76 - text = "NG" - intervals [496]: - xmin = 43.76 - xmax = 44.5 - text = "" - intervals [497]: - xmin = 44.5 - xmax = 44.86 - text = "AH0" - intervals [498]: - xmin = 44.86 - xmax = 45.15 - text = "N" - intervals [499]: - xmin = 45.15 - xmax = 45.19 - text = "D" - intervals [500]: - xmin = 45.19 - xmax = 45.27 - text = "AA1" - intervals [501]: - xmin = 45.27 - xmax = 45.32 - text = "N" - intervals [502]: - xmin = 45.32 - xmax = 45.4 - text = "AH1" - intervals [503]: - xmin = 45.4 - xmax = 45.46 - text = "DH" - intervals [504]: - xmin = 45.46 - xmax = 45.49 - text = "ER0" - intervals [505]: - xmin = 45.49 - xmax = 45.55 - text = "D" - intervals [506]: - xmin = 45.55 - xmax = 45.74 - text = "EY1" - intervals [507]: - xmin = 45.74 - xmax = 45.82 - text = "Z" - intervals [508]: - xmin = 45.82 - xmax = 45.89 - text = "W" - intervals [509]: - xmin = 45.89 - xmax = 45.92 - text = "EH1" - intervals [510]: - xmin = 45.92 - xmax = 45.96 - text = "N" - intervals [511]: - xmin = 45.96 - xmax = 46.09 - text = "AY1" - intervals [512]: - xmin = 46.09 - xmax = 46.16 - text = "M" - intervals [513]: - xmin = 46.16 - xmax = 46.29 - text = "F" - intervals [514]: - xmin = 46.29 - xmax = 46.39 - text = "R" - intervals [515]: - xmin = 46.39 - xmax = 46.65 - text = "IY1" - intervals [516]: - xmin = 46.65 - xmax = 46.86 - text = "AY1" - intervals [517]: - xmin = 46.86 - xmax = 46.94 - text = "L" - intervals [518]: - xmin = 46.94 - xmax = 47.08 - text = "AY1" - intervals [519]: - xmin = 47.08 - xmax = 47.16 - text = "K" - intervals [520]: - xmin = 47.16 - xmax = 47.25 - text = "T" - intervals [521]: - xmin = 47.25 - xmax = 47.39 - text = "UW1" - intervals [522]: - xmin = 47.39 - xmax = 47.48 - text = "L" - intervals [523]: - xmin = 47.48 - xmax = 47.53 - text = "IH1" - intervals [524]: - xmin = 47.53 - xmax = 47.6 - text = "S" - intervals [525]: - xmin = 47.6 - xmax = 47.64 - text = "AH0" - intervals [526]: - xmin = 47.64 - xmax = 47.86 - text = "N" - intervals [527]: - xmin = 47.86 - xmax = 47.93 - text = "T" - intervals [528]: - xmin = 47.93 - xmax = 48.03 - text = "IH0" - intervals [529]: - xmin = 48.03 - xmax = 48.07 - text = "M" - intervals [530]: - xmin = 48.07 - xmax = 48.15 - text = "Y" - intervals [531]: - xmin = 48.15 - xmax = 48.2 - text = "UW1" - intervals [532]: - xmin = 48.2 - xmax = 48.27 - text = "Z" - intervals [533]: - xmin = 48.27 - xmax = 48.35 - text = "IH0" - intervals [534]: - xmin = 48.35 - xmax = 48.41 - text = "K" - intervals [535]: - xmin = 48.41 - xmax = 48.48 - text = "AH0" - intervals [536]: - xmin = 48.48 - xmax = 48.56 - text = "N" - intervals [537]: - xmin = 48.56 - xmax = 48.73 - text = "D" - intervals [538]: - xmin = 48.73 - xmax = 48.76 - text = "" - intervals [539]: - xmin = 48.76 - xmax = 48.91 - text = "W" - intervals [540]: - xmin = 48.91 - xmax = 49.01 - text = "ER1" - intervals [541]: - xmin = 49.01 - xmax = 49.13 - text = "W" - intervals [542]: - xmin = 49.13 - xmax = 49.23 - text = "AA1" - intervals [543]: - xmin = 49.23 - xmax = 49.3 - text = "CH" - intervals [544]: - xmin = 49.3 - xmax = 49.38 - text = "AH0" - intervals [545]: - xmin = 49.38 - xmax = 49.46 - text = "D" - intervals [546]: - xmin = 49.46 - xmax = 49.56 - text = "AA2" - intervals [547]: - xmin = 49.56 - xmax = 49.62 - text = "K" - intervals [548]: - xmin = 49.62 - xmax = 49.66 - text = "Y" - intervals [549]: - xmin = 49.66 - xmax = 49.7 - text = "AH0" - intervals [550]: - xmin = 49.7 - xmax = 49.76 - text = "M" - intervals [551]: - xmin = 49.76 - xmax = 49.81 - text = "EH1" - intervals [552]: - xmin = 49.81 - xmax = 49.85 - text = "N" - intervals [553]: - xmin = 49.85 - xmax = 49.98 - text = "ER0" - intervals [554]: - xmin = 49.98 - xmax = 50.05 - text = "IY0" - intervals [555]: - xmin = 50.05 - xmax = 50.17 - text = "M" - intervals [556]: - xmin = 50.17 - xmax = 50.2 - text = "UW1" - intervals [557]: - xmin = 50.2 - xmax = 50.28 - text = "V" - intervals [558]: - xmin = 50.28 - xmax = 50.38 - text = "IY0" - intervals [559]: - xmin = 50.38 - xmax = 50.51 - text = "Z" - intervals [560]: - xmin = 50.51 - xmax = 50.75 - text = "AA1" - intervals [561]: - xmin = 50.75 - xmax = 50.82 - text = "N" - intervals [562]: - xmin = 50.82 - xmax = 50.9 - text = "M" - intervals [563]: - xmin = 50.9 - xmax = 51.11 - text = "AY1" - intervals [564]: - xmin = 51.11 - xmax = 51.22 - text = "L" - intervals [565]: - xmin = 51.22 - xmax = 51.39 - text = "AE1" - intervals [566]: - xmin = 51.39 - xmax = 51.44 - text = "P" - intervals [567]: - xmin = 51.44 - xmax = 51.49 - text = "T" - intervals [568]: - xmin = 51.49 - xmax = 51.66 - text = "AA2" - intervals [569]: - xmin = 51.66 - xmax = 51.81 - text = "P" - intervals [570]: - xmin = 51.81 - xmax = 52.14 - text = "" - intervals [571]: - xmin = 52.14 - xmax = 52.2 - text = "B" - intervals [572]: - xmin = 52.2 - xmax = 52.33 - text = "AH1" - intervals [573]: - xmin = 52.33 - xmax = 52.44 - text = "T" - intervals [574]: - xmin = 52.44 - xmax = 52.51 - text = "S" - intervals [575]: - xmin = 52.51 - xmax = 52.59 - text = "AH1" - intervals [576]: - xmin = 52.59 - xmax = 52.64 - text = "M" - intervals [577]: - xmin = 52.64 - xmax = 52.67 - text = "T" - intervals [578]: - xmin = 52.67 - xmax = 52.77 - text = "AY2" - intervals [579]: - xmin = 52.77 - xmax = 52.82 - text = "M" - intervals [580]: - xmin = 52.82 - xmax = 52.86 - text = "Z" - intervals [581]: - xmin = 52.86 - xmax = 52.9 - text = "IH1" - intervals [582]: - xmin = 52.9 - xmax = 52.93 - text = "T" - intervals [583]: - xmin = 52.93 - xmax = 52.98 - text = "JH" - intervals [584]: - xmin = 52.98 - xmax = 53.07 - text = "IH0" - intervals [585]: - xmin = 53.07 - xmax = 53.1 - text = "S" - intervals [586]: - xmin = 53.1 - xmax = 53.13 - text = "T" - intervals [587]: - xmin = 53.13 - xmax = 53.18 - text = "S" - intervals [588]: - xmin = 53.18 - xmax = 53.26 - text = "L" - intervals [589]: - xmin = 53.26 - xmax = 53.35 - text = "IY1" - intervals [590]: - xmin = 53.35 - xmax = 53.61 - text = "P" - intervals [591]: - xmin = 53.61 - xmax = 53.65 - text = "" - intervals [592]: - xmin = 53.65 - xmax = 53.83 - text = "AY1" - intervals [593]: - xmin = 53.83 - xmax = 53.88 - text = "AH0" - intervals [594]: - xmin = 53.88 - xmax = 53.95 - text = "S" - intervals [595]: - xmin = 53.95 - xmax = 54 - text = "P" - intervals [596]: - xmin = 54 - xmax = 54.09 - text = "EH1" - intervals [597]: - xmin = 54.09 - xmax = 54.19 - text = "SH" - intervals [598]: - xmin = 54.19 - xmax = 54.22 - text = "L" - intervals [599]: - xmin = 54.22 - xmax = 54.27 - text = "IY0" - intervals [600]: - xmin = 54.27 - xmax = 54.33 - text = "L" - intervals [601]: - xmin = 54.33 - xmax = 54.43 - text = "AY1" - intervals [602]: - xmin = 54.43 - xmax = 54.57 - text = "K" - intervals [603]: - xmin = 54.57 - xmax = 54.61 - text = "T" - intervals [604]: - xmin = 54.61 - xmax = 54.69 - text = "W" - intervals [605]: - xmin = 54.69 - xmax = 54.79 - text = "AA1" - intervals [606]: - xmin = 54.79 - xmax = 54.85 - text = "CH" - intervals [607]: - xmin = 54.85 - xmax = 54.89 - text = "IH0" - intervals [608]: - xmin = 54.89 - xmax = 55.01 - text = "NG" - intervals [609]: - xmin = 55.01 - xmax = 55.12 - text = "JH" - intervals [610]: - xmin = 55.12 - xmax = 55.25 - text = "AE2" - intervals [611]: - xmin = 55.25 - xmax = 55.3 - text = "P" - intervals [612]: - xmin = 55.3 - xmax = 55.35 - text = "AH0" - intervals [613]: - xmin = 55.35 - xmax = 55.4 - text = "N" - intervals [614]: - xmin = 55.4 - xmax = 55.59 - text = "IY1" - intervals [615]: - xmin = 55.59 - xmax = 55.62 - text = "Z" - intervals [616]: - xmin = 55.62 - xmax = 55.77 - text = "AE1" - intervals [617]: - xmin = 55.77 - xmax = 55.83 - text = "N" - intervals [618]: - xmin = 55.83 - xmax = 55.87 - text = "AH0" - intervals [619]: - xmin = 55.87 - xmax = 55.91 - text = "M" - intervals [620]: - xmin = 55.91 - xmax = 56.33 - text = "AY1" - intervals [621]: - xmin = 56.33 - xmax = 56.85 - text = "" - intervals [622]: - xmin = 56.85 - xmax = 56.99 - text = "TH" - intervals [623]: - xmin = 56.99 - xmax = 57.05 - text = "IH1" - intervals [624]: - xmin = 57.05 - xmax = 57.09 - text = "NG" - intervals [625]: - xmin = 57.09 - xmax = 57.12 - text = "K" - intervals [626]: - xmin = 57.12 - xmax = 57.2 - text = "W" - intervals [627]: - xmin = 57.2 - xmax = 57.27 - text = "AA1" - intervals [628]: - xmin = 57.27 - xmax = 57.35 - text = "CH" - intervals [629]: - xmin = 57.35 - xmax = 57.4 - text = "IH0" - intervals [630]: - xmin = 57.4 - xmax = 57.43 - text = "NG" - intervals [631]: - xmin = 57.43 - xmax = 57.62 - text = "EY1" - intervals [632]: - xmin = 57.62 - xmax = 57.69 - text = "M" - intervals [633]: - xmin = 57.69 - xmax = 57.79 - text = "IY1" - intervals [634]: - xmin = 57.79 - xmax = 57.92 - text = "IH0" - intervals [635]: - xmin = 57.92 - xmax = 58.09 - text = "Z" - intervals [636]: - xmin = 58.09 - xmax = 58.12 - text = "AE1" - intervals [637]: - xmin = 58.12 - xmax = 58.19 - text = "N" - intervals [638]: - xmin = 58.19 - xmax = 58.23 - text = "AH0" - intervals [639]: - xmin = 58.23 - xmax = 58.39 - text = "M" - intervals [640]: - xmin = 58.39 - xmax = 58.97 - text = "IH1" - intervals [641]: - xmin = 58.97 - xmax = 59.06 - text = "Z" - intervals [642]: - xmin = 59.06 - xmax = 59.11 - text = "V" - intervals [643]: - xmin = 59.11 - xmax = 59.15 - text = "EH1" - intervals [644]: - xmin = 59.15 - xmax = 59.24 - text = "R" - intervals [645]: - xmin = 59.24 - xmax = 59.31 - text = "IY0" - intervals [646]: - xmin = 59.31 - xmax = 59.38 - text = "HH" - intervals [647]: - xmin = 59.38 - xmax = 59.43 - text = "EH1" - intervals [648]: - xmin = 59.43 - xmax = 59.52 - text = "L" - intervals [649]: - xmin = 59.52 - xmax = 59.55 - text = "P" - intervals [650]: - xmin = 59.55 - xmax = 59.58 - text = "F" - intervals [651]: - xmin = 59.58 - xmax = 59.61 - text = "AH0" - intervals [652]: - xmin = 59.61 - xmax = 59.67 - text = "L" - intervals [653]: - xmin = 59.67 - xmax = 59.72 - text = "F" - intervals [654]: - xmin = 59.72 - xmax = 59.75 - text = "R" - intervals [655]: - xmin = 59.75 - xmax = 59.81 - text = "ER0" - intervals [656]: - xmin = 59.81 - xmax = 59.88 - text = "M" - intervals [657]: - xmin = 59.88 - xmax = 59.98 - text = "IY1" - intervals [658]: - xmin = 59.98 - xmax = 60.08 - text = "T" - intervals [659]: - xmin = 60.08 - xmax = 60.28 - text = "UW1" - intervals [660]: - xmin = 60.28 - xmax = 60.42 - text = "L" - intervals [661]: - xmin = 60.42 - xmax = 60.63 - text = "ER1" - intervals [662]: - xmin = 60.63 - xmax = 60.69 - text = "N" - intervals [663]: - xmin = 60.69 - xmax = 60.72 - text = "AE1" - intervals [664]: - xmin = 60.72 - xmax = 60.75 - text = "N" - intervals [665]: - xmin = 60.75 - xmax = 60.78 - text = "D" - intervals [666]: - xmin = 60.78 - xmax = 60.84 - text = "IH0" - intervals [667]: - xmin = 60.84 - xmax = 60.88 - text = "K" - intervals [668]: - xmin = 60.88 - xmax = 60.95 - text = "S" - intervals [669]: - xmin = 60.95 - xmax = 61.01 - text = "P" - intervals [670]: - xmin = 61.01 - xmax = 61.09 - text = "R" - intervals [671]: - xmin = 61.09 - xmax = 61.14 - text = "EH1" - intervals [672]: - xmin = 61.14 - xmax = 61.21 - text = "S" - intervals [673]: - xmin = 61.21 - xmax = 61.33 - text = "JH" - intervals [674]: - xmin = 61.33 - xmax = 61.45 - text = "AE2" - intervals [675]: - xmin = 61.45 - xmax = 61.51 - text = "P" - intervals [676]: - xmin = 61.51 - xmax = 61.55 - text = "AH0" - intervals [677]: - xmin = 61.55 - xmax = 61.59 - text = "N" - intervals [678]: - xmin = 61.59 - xmax = 61.75 - text = "IY1" - intervals [679]: - xmin = 61.75 - xmax = 61.89 - text = "Z" - intervals [680]: - xmin = 61.89 - xmax = 62.02 - text = "B" - intervals [681]: - xmin = 62.02 - xmax = 62.11 - text = "EH1" - intervals [682]: - xmin = 62.11 - xmax = 62.19 - text = "T" - intervals [683]: - xmin = 62.19 - xmax = 62.42 - text = "ER0" - intervals [684]: - xmin = 62.42 - xmax = 64.097375 - text = "" diff --git a/EMAGE/test_sequences/textgrid/2_scott_0_2_2.TextGrid b/EMAGE/test_sequences/textgrid/2_scott_0_2_2.TextGrid deleted file mode 100644 index faef4cc5a37423c8f9ad3a32097bb27a75cd9d0e..0000000000000000000000000000000000000000 --- a/EMAGE/test_sequences/textgrid/2_scott_0_2_2.TextGrid +++ /dev/null @@ -1,3716 +0,0 @@ -File type = "ooTextFile" -Object class = "TextGrid" - -xmin = 0.0 -xmax = 62 -tiers? -size = 2 -item []: - item [1]: - class = "IntervalTier" - name = "words" - xmin = 0.0 - xmax = 62 - intervals: size = 223 - intervals [1]: - xmin = 0.0 - xmax = 1.45 - text = "" - intervals [2]: - xmin = 1.45 - xmax = 1.87 - text = "so" - intervals [3]: - xmin = 1.87 - xmax = 2.02 - text = "when" - intervals [4]: - xmin = 2.02 - xmax = 2.13 - text = "i" - intervals [5]: - xmin = 2.13 - xmax = 2.35 - text = "have" - intervals [6]: - xmin = 2.35 - xmax = 2.57 - text = "time" - intervals [7]: - xmin = 2.57 - xmax = 2.65 - text = "to" - intervals [8]: - xmin = 2.65 - xmax = 3.18 - text = "kill" - intervals [9]: - xmin = 3.18 - xmax = 3.22 - text = "" - intervals [10]: - xmin = 3.22 - xmax = 3.41 - text = "i" - intervals [11]: - xmin = 3.41 - xmax = 3.6 - text = "like" - intervals [12]: - xmin = 3.6 - xmax = 3.68 - text = "to" - intervals [13]: - xmin = 3.68 - xmax = 3.88 - text = "play" - intervals [14]: - xmin = 3.88 - xmax = 3.96 - text = "on" - intervals [15]: - xmin = 3.96 - xmax = 4.08 - text = "the" - intervals [16]: - xmin = 4.08 - xmax = 4.5 - text = "internet" - intervals [17]: - xmin = 4.5 - xmax = 4.66 - text = "and" - intervals [18]: - xmin = 4.66 - xmax = 4.87 - text = "play" - intervals [19]: - xmin = 4.87 - xmax = 5.19 - text = "close" - intervals [20]: - xmin = 5.19 - xmax = 5.67 - text = "attention" - intervals [21]: - xmin = 5.67 - xmax = 6.0 - text = "to" - intervals [22]: - xmin = 6.0 - xmax = 6.26 - text = "new" - intervals [23]: - xmin = 6.26 - xmax = 6.71 - text = "fashion" - intervals [24]: - xmin = 6.71 - xmax = 7.17 - text = "events" - intervals [25]: - xmin = 7.17 - xmax = 7.43 - text = "" - intervals [26]: - xmin = 7.43 - xmax = 7.76 - text = "such" - intervals [27]: - xmin = 7.76 - xmax = 8.14 - text = "as" - intervals [28]: - xmin = 8.14 - xmax = 8.19 - text = "" - intervals [29]: - xmin = 8.19 - xmax = 8.34 - text = "the" - intervals [30]: - xmin = 8.34 - xmax = 8.47 - text = "new" - intervals [31]: - xmin = 8.47 - xmax = 8.68 - text = "york" - intervals [32]: - xmin = 8.68 - xmax = 9.12 - text = "fashion" - intervals [33]: - xmin = 9.12 - xmax = 9.42 - text = "week" - intervals [34]: - xmin = 9.42 - xmax = 9.49 - text = "the" - intervals [35]: - xmin = 9.49 - xmax = 9.87 - text = "paris" - intervals [36]: - xmin = 9.87 - xmax = 10.25 - text = "fashion" - intervals [37]: - xmin = 10.25 - xmax = 10.56 - text = "week" - intervals [38]: - xmin = 10.56 - xmax = 10.66 - text = "the" - intervals [39]: - xmin = 10.66 - xmax = 11.07 - text = "london" - intervals [40]: - xmin = 11.07 - xmax = 11.51 - text = "fashion" - intervals [41]: - xmin = 11.51 - xmax = 11.78 - text = "week" - intervals [42]: - xmin = 11.78 - xmax = 12.17 - text = "and" - intervals [43]: - xmin = 12.17 - xmax = 12.21 - text = "" - intervals [44]: - xmin = 12.21 - xmax = 12.83 - text = "milan" - intervals [45]: - xmin = 12.83 - xmax = 13.24 - text = "fashion" - intervals [46]: - xmin = 13.24 - xmax = 13.62 - text = "week" - intervals [47]: - xmin = 13.62 - xmax = 14.03 - text = "" - intervals [48]: - xmin = 14.03 - xmax = 14.15 - text = "the" - intervals [49]: - xmin = 14.15 - xmax = 14.35 - text = "rest" - intervals [50]: - xmin = 14.35 - xmax = 14.43 - text = "of" - intervals [51]: - xmin = 14.43 - xmax = 14.49 - text = "the" - intervals [52]: - xmin = 14.49 - xmax = 14.8 - text = "time" - intervals [53]: - xmin = 14.8 - xmax = 14.87 - text = "i" - intervals [54]: - xmin = 14.87 - xmax = 15.2 - text = "usually" - intervals [55]: - xmin = 15.2 - xmax = 15.3 - text = "go" - intervals [56]: - xmin = 15.3 - xmax = 15.36 - text = "to" - intervals [57]: - xmin = 15.36 - xmax = 15.44 - text = "the" - intervals [58]: - xmin = 15.44 - xmax = 15.93 - text = "library" - intervals [59]: - xmin = 15.93 - xmax = 16.04 - text = "and" - intervals [60]: - xmin = 16.04 - xmax = 16.25 - text = "find" - intervals [61]: - xmin = 16.25 - xmax = 16.35 - text = "some" - intervals [62]: - xmin = 16.35 - xmax = 16.71 - text = "interesting" - intervals [63]: - xmin = 16.71 - xmax = 17.19 - text = "books" - intervals [64]: - xmin = 17.19 - xmax = 17.31 - text = "and" - intervals [65]: - xmin = 17.31 - xmax = 17.51 - text = "then" - intervals [66]: - xmin = 17.51 - xmax = 17.63 - text = "go" - intervals [67]: - xmin = 17.63 - xmax = 17.7 - text = "to" - intervals [68]: - xmin = 17.7 - xmax = 17.78 - text = "a" - intervals [69]: - xmin = 17.78 - xmax = 18.08 - text = "park" - intervals [70]: - xmin = 18.08 - xmax = 18.17 - text = "and" - intervals [71]: - xmin = 18.17 - xmax = 18.75 - text = "relax" - intervals [72]: - xmin = 18.75 - xmax = 19.04 - text = "" - intervals [73]: - xmin = 19.04 - xmax = 19.22 - text = "there" - intervals [74]: - xmin = 19.22 - xmax = 19.27 - text = "are" - intervals [75]: - xmin = 19.27 - xmax = 19.5 - text = "many" - intervals [76]: - xmin = 19.5 - xmax = 19.78 - text = "books" - intervals [77]: - xmin = 19.78 - xmax = 19.93 - text = "that" - intervals [78]: - xmin = 19.93 - xmax = 20.11 - text = "i" - intervals [79]: - xmin = 20.11 - xmax = 20.4 - text = "find" - intervals [80]: - xmin = 20.4 - xmax = 20.92 - text = "interesting" - intervals [81]: - xmin = 20.92 - xmax = 21.15 - text = "such" - intervals [82]: - xmin = 21.15 - xmax = 21.3 - text = "as" - intervals [83]: - xmin = 21.3 - xmax = 21.62 - text = "fashion" - intervals [84]: - xmin = 21.62 - xmax = 22.19 - text = "magazines" - intervals [85]: - xmin = 22.19 - xmax = 22.8 - text = "inspirational" - intervals [86]: - xmin = 22.8 - xmax = 23.15 - text = "books" - intervals [87]: - xmin = 23.15 - xmax = 23.44 - text = "and" - intervals [88]: - xmin = 23.44 - xmax = 24.04 - text = "professional" - intervals [89]: - xmin = 24.04 - xmax = 24.46 - text = "books" - intervals [90]: - xmin = 24.46 - xmax = 24.83 - text = "" - intervals [91]: - xmin = 24.83 - xmax = 25.06 - text = "these" - intervals [92]: - xmin = 25.06 - xmax = 25.37 - text = "books" - intervals [93]: - xmin = 25.37 - xmax = 25.54 - text = "can" - intervals [94]: - xmin = 25.54 - xmax = 25.66 - text = "give" - intervals [95]: - xmin = 25.66 - xmax = 25.76 - text = "me" - intervals [96]: - xmin = 25.76 - xmax = 25.86 - text = "the" - intervals [97]: - xmin = 25.86 - xmax = 26.85 - text = "motivation" - intervals [98]: - xmin = 26.85 - xmax = 26.88 - text = "" - intervals [99]: - xmin = 26.88 - xmax = 27.07 - text = "to" - intervals [100]: - xmin = 27.07 - xmax = 27.37 - text = "be" - intervals [101]: - xmin = 27.37 - xmax = 28.01 - text = "healthier" - intervals [102]: - xmin = 28.01 - xmax = 28.18 - text = "and" - intervals [103]: - xmin = 28.18 - xmax = 28.9 - text = "energetic" - intervals [104]: - xmin = 28.9 - xmax = 29.1 - text = "" - intervals [105]: - xmin = 29.1 - xmax = 29.3 - text = "and" - intervals [106]: - xmin = 29.3 - xmax = 29.37 - text = "the" - intervals [107]: - xmin = 29.37 - xmax = 29.74 - text = "last" - intervals [108]: - xmin = 29.74 - xmax = 29.94 - text = "thing" - intervals [109]: - xmin = 29.94 - xmax = 30.14 - text = "i" - intervals [110]: - xmin = 30.14 - xmax = 30.42 - text = "like" - intervals [111]: - xmin = 30.42 - xmax = 30.53 - text = "to" - intervals [112]: - xmin = 30.53 - xmax = 30.84 - text = "do" - intervals [113]: - xmin = 30.84 - xmax = 31.22 - text = "when" - intervals [114]: - xmin = 31.22 - xmax = 31.43 - text = "i'm" - intervals [115]: - xmin = 31.43 - xmax = 31.87 - text = "free" - intervals [116]: - xmin = 31.87 - xmax = 31.99 - text = "is" - intervals [117]: - xmin = 31.99 - xmax = 32.11 - text = "it" - intervals [118]: - xmin = 32.11 - xmax = 32.23 - text = "out" - intervals [119]: - xmin = 32.23 - xmax = 32.35 - text = "with" - intervals [120]: - xmin = 32.35 - xmax = 32.48 - text = "my" - intervals [121]: - xmin = 32.48 - xmax = 32.86 - text = "family" - intervals [122]: - xmin = 32.86 - xmax = 33.33 - text = "members" - intervals [123]: - xmin = 33.33 - xmax = 33.51 - text = "" - intervals [124]: - xmin = 33.51 - xmax = 33.89 - text = "you" - intervals [125]: - xmin = 33.89 - xmax = 34.11 - text = "would" - intervals [126]: - xmin = 34.11 - xmax = 34.29 - text = "be" - intervals [127]: - xmin = 34.29 - xmax = 35.07 - text = "surprised" - intervals [128]: - xmin = 35.07 - xmax = 35.16 - text = "to" - intervals [129]: - xmin = 35.16 - xmax = 35.36 - text = "know" - intervals [130]: - xmin = 35.36 - xmax = 35.5 - text = "that" - intervals [131]: - xmin = 35.5 - xmax = 35.64 - text = "i" - intervals [132]: - xmin = 35.64 - xmax = 35.84 - text = "have" - intervals [133]: - xmin = 35.84 - xmax = 36.3 - text = "tried" - intervals [134]: - xmin = 36.3 - xmax = 36.57 - text = "" - intervals [135]: - xmin = 36.57 - xmax = 36.99 - text = "all" - intervals [136]: - xmin = 36.99 - xmax = 37.08 - text = "the" - intervals [137]: - xmin = 37.08 - xmax = 37.68 - text = "restaurants" - intervals [138]: - xmin = 37.68 - xmax = 37.71 - text = "" - intervals [139]: - xmin = 37.71 - xmax = 37.83 - text = "in" - intervals [140]: - xmin = 37.83 - xmax = 37.95 - text = "our" - intervals [141]: - xmin = 37.95 - xmax = 38.5 - text = "huge" - intervals [142]: - xmin = 38.5 - xmax = 39.07 - text = "community" - intervals [143]: - xmin = 39.07 - xmax = 39.23 - text = "" - intervals [144]: - xmin = 39.23 - xmax = 39.6 - text = "i" - intervals [145]: - xmin = 39.6 - xmax = 40.09 - text = "actually" - intervals [146]: - xmin = 40.09 - xmax = 40.32 - text = "give" - intervals [147]: - xmin = 40.32 - xmax = 40.61 - text = "each" - intervals [148]: - xmin = 40.61 - xmax = 41.08 - text = "restaurant" - intervals [149]: - xmin = 41.08 - xmax = 41.15 - text = "a" - intervals [150]: - xmin = 41.15 - xmax = 41.55 - text = "score" - intervals [151]: - xmin = 41.55 - xmax = 41.82 - text = "based" - intervals [152]: - xmin = 41.82 - xmax = 41.89 - text = "on" - intervals [153]: - xmin = 41.89 - xmax = 42.05 - text = "how" - intervals [154]: - xmin = 42.05 - xmax = 42.17 - text = "good" - intervals [155]: - xmin = 42.17 - xmax = 42.23 - text = "the" - intervals [156]: - xmin = 42.23 - xmax = 42.51 - text = "food" - intervals [157]: - xmin = 42.51 - xmax = 42.85 - text = "is" - intervals [158]: - xmin = 42.85 - xmax = 43.13 - text = "" - intervals [159]: - xmin = 43.13 - xmax = 43.36 - text = "how" - intervals [160]: - xmin = 43.36 - xmax = 43.51 - text = "good" - intervals [161]: - xmin = 43.51 - xmax = 43.62 - text = "the" - intervals [162]: - xmin = 43.62 - xmax = 44.1 - text = "environment" - intervals [163]: - xmin = 44.1 - xmax = 44.4 - text = "is" - intervals [164]: - xmin = 44.4 - xmax = 44.49 - text = "" - intervals [165]: - xmin = 44.49 - xmax = 44.98 - text = "and" - intervals [166]: - xmin = 44.98 - xmax = 45.34 - text = "at" - intervals [167]: - xmin = 45.34 - xmax = 45.62 - text = "the" - intervals [168]: - xmin = 45.62 - xmax = 45.91 - text = "same" - intervals [169]: - xmin = 45.91 - xmax = 46.29 - text = "time" - intervals [170]: - xmin = 46.29 - xmax = 46.42 - text = "i" - intervals [171]: - xmin = 46.42 - xmax = 46.54 - text = "will" - intervals [172]: - xmin = 46.54 - xmax = 46.74 - text = "write" - intervals [173]: - xmin = 46.74 - xmax = 46.94 - text = "down" - intervals [174]: - xmin = 46.94 - xmax = 47.02 - text = "the" - intervals [175]: - xmin = 47.02 - xmax = 47.24 - text = "type" - intervals [176]: - xmin = 47.24 - xmax = 47.39 - text = "of" - intervals [177]: - xmin = 47.39 - xmax = 47.8 - text = "food" - intervals [178]: - xmin = 47.8 - xmax = 48.03 - text = "" - intervals [179]: - xmin = 48.03 - xmax = 48.24 - text = "they" - intervals [180]: - xmin = 48.24 - xmax = 48.76 - text = "serve" - intervals [181]: - xmin = 48.76 - xmax = 49.42 - text = "" - intervals [182]: - xmin = 49.42 - xmax = 49.9 - text = "so" - intervals [183]: - xmin = 49.9 - xmax = 50.46 - text = "when" - intervals [184]: - xmin = 50.46 - xmax = 50.49 - text = "" - intervals [185]: - xmin = 50.49 - xmax = 50.85 - text = "you're" - intervals [186]: - xmin = 50.85 - xmax = 50.98 - text = "so" - intervals [187]: - xmin = 50.98 - xmax = 51.13 - text = "when" - intervals [188]: - xmin = 51.13 - xmax = 51.35 - text = "each" - intervals [189]: - xmin = 51.35 - xmax = 51.55 - text = "time" - intervals [190]: - xmin = 51.55 - xmax = 51.62 - text = "a" - intervals [191]: - xmin = 51.62 - xmax = 51.91 - text = "friend" - intervals [192]: - xmin = 51.91 - xmax = 52.32 - text = "comes" - intervals [193]: - xmin = 52.32 - xmax = 52.46 - text = "to" - intervals [194]: - xmin = 52.46 - xmax = 52.59 - text = "the" - intervals [195]: - xmin = 52.59 - xmax = 52.9 - text = "city" - intervals [196]: - xmin = 52.9 - xmax = 53.07 - text = "to" - intervals [197]: - xmin = 53.07 - xmax = 53.35 - text = "enjoy" - intervals [198]: - xmin = 53.35 - xmax = 53.62 - text = "time" - intervals [199]: - xmin = 53.62 - xmax = 53.74 - text = "with" - intervals [200]: - xmin = 53.74 - xmax = 54.02 - text = "me" - intervals [201]: - xmin = 54.02 - xmax = 54.31 - text = "" - intervals [202]: - xmin = 54.31 - xmax = 54.54 - text = "i" - intervals [203]: - xmin = 54.54 - xmax = 54.69 - text = "will" - intervals [204]: - xmin = 54.69 - xmax = 54.84 - text = "give" - intervals [205]: - xmin = 54.84 - xmax = 54.97 - text = "them" - intervals [206]: - xmin = 54.97 - xmax = 55.07 - text = "the" - intervals [207]: - xmin = 55.07 - xmax = 55.38 - text = "top" - intervals [208]: - xmin = 55.38 - xmax = 55.53 - text = "5" - intervals [209]: - xmin = 55.53 - xmax = 56.1 - text = "restaurants" - intervals [210]: - xmin = 56.1 - xmax = 56.44 - text = "based" - intervals [211]: - xmin = 56.44 - xmax = 56.68 - text = "on" - intervals [212]: - xmin = 56.68 - xmax = 56.99 - text = "this" - intervals [213]: - xmin = 56.99 - xmax = 57.35 - text = "ranking" - intervals [214]: - xmin = 57.35 - xmax = 57.53 - text = "and" - intervals [215]: - xmin = 57.53 - xmax = 57.86 - text = "every" - intervals [216]: - xmin = 57.86 - xmax = 58.44 - text = "time" - intervals [217]: - xmin = 58.44 - xmax = 59.02 - text = "" - intervals [218]: - xmin = 59.02 - xmax = 59.2 - text = "you're" - intervals [219]: - xmin = 59.2 - xmax = 59.72 - text = "satisfied" - intervals [220]: - xmin = 59.72 - xmax = 59.85 - text = "with" - intervals [221]: - xmin = 59.85 - xmax = 60.1 - text = "these" - intervals [222]: - xmin = 60.1 - xmax = 60.81 - text = "restaurants" - intervals [223]: - xmin = 60.81 - xmax = 62 - text = "" - item [2]: - class = "IntervalTier" - name = "phones" - xmin = 0.0 - xmax = 62 - intervals: size = 701 - intervals [1]: - xmin = 0.0 - xmax = 1.45 - text = "" - intervals [2]: - xmin = 1.45 - xmax = 1.64 - text = "S" - intervals [3]: - xmin = 1.64 - xmax = 1.87 - text = "OW1" - intervals [4]: - xmin = 1.87 - xmax = 1.94 - text = "W" - intervals [5]: - xmin = 1.94 - xmax = 1.97 - text = "EH1" - intervals [6]: - xmin = 1.97 - xmax = 2.02 - text = "N" - intervals [7]: - xmin = 2.02 - xmax = 2.13 - text = "AY1" - intervals [8]: - xmin = 2.13 - xmax = 2.21 - text = "HH" - intervals [9]: - xmin = 2.21 - xmax = 2.29 - text = "AE1" - intervals [10]: - xmin = 2.29 - xmax = 2.35 - text = "V" - intervals [11]: - xmin = 2.35 - xmax = 2.43 - text = "T" - intervals [12]: - xmin = 2.43 - xmax = 2.52 - text = "AY1" - intervals [13]: - xmin = 2.52 - xmax = 2.57 - text = "M" - intervals [14]: - xmin = 2.57 - xmax = 2.6 - text = "T" - intervals [15]: - xmin = 2.6 - xmax = 2.65 - text = "AH0" - intervals [16]: - xmin = 2.65 - xmax = 2.75 - text = "K" - intervals [17]: - xmin = 2.75 - xmax = 2.81 - text = "IH1" - intervals [18]: - xmin = 2.81 - xmax = 3.18 - text = "L" - intervals [19]: - xmin = 3.18 - xmax = 3.22 - text = "" - intervals [20]: - xmin = 3.22 - xmax = 3.41 - text = "AY1" - intervals [21]: - xmin = 3.41 - xmax = 3.46 - text = "L" - intervals [22]: - xmin = 3.46 - xmax = 3.57 - text = "AY1" - intervals [23]: - xmin = 3.57 - xmax = 3.6 - text = "K" - intervals [24]: - xmin = 3.6 - xmax = 3.63 - text = "T" - intervals [25]: - xmin = 3.63 - xmax = 3.68 - text = "IH0" - intervals [26]: - xmin = 3.68 - xmax = 3.75 - text = "P" - intervals [27]: - xmin = 3.75 - xmax = 3.83 - text = "L" - intervals [28]: - xmin = 3.83 - xmax = 3.88 - text = "EY1" - intervals [29]: - xmin = 3.88 - xmax = 3.93 - text = "AA1" - intervals [30]: - xmin = 3.93 - xmax = 3.96 - text = "N" - intervals [31]: - xmin = 3.96 - xmax = 4.01 - text = "DH" - intervals [32]: - xmin = 4.01 - xmax = 4.08 - text = "IY0" - intervals [33]: - xmin = 4.08 - xmax = 4.13 - text = "IH1" - intervals [34]: - xmin = 4.13 - xmax = 4.16 - text = "N" - intervals [35]: - xmin = 4.16 - xmax = 4.19 - text = "T" - intervals [36]: - xmin = 4.19 - xmax = 4.25 - text = "ER0" - intervals [37]: - xmin = 4.25 - xmax = 4.29 - text = "N" - intervals [38]: - xmin = 4.29 - xmax = 4.42 - text = "EH2" - intervals [39]: - xmin = 4.42 - xmax = 4.5 - text = "T" - intervals [40]: - xmin = 4.5 - xmax = 4.58 - text = "AE1" - intervals [41]: - xmin = 4.58 - xmax = 4.62 - text = "N" - intervals [42]: - xmin = 4.62 - xmax = 4.66 - text = "D" - intervals [43]: - xmin = 4.66 - xmax = 4.71 - text = "P" - intervals [44]: - xmin = 4.71 - xmax = 4.8 - text = "L" - intervals [45]: - xmin = 4.8 - xmax = 4.87 - text = "EY1" - intervals [46]: - xmin = 4.87 - xmax = 4.97 - text = "K" - intervals [47]: - xmin = 4.97 - xmax = 5.02 - text = "L" - intervals [48]: - xmin = 5.02 - xmax = 5.09 - text = "OW1" - intervals [49]: - xmin = 5.09 - xmax = 5.19 - text = "S" - intervals [50]: - xmin = 5.19 - xmax = 5.23 - text = "AH0" - intervals [51]: - xmin = 5.23 - xmax = 5.32 - text = "T" - intervals [52]: - xmin = 5.32 - xmax = 5.36 - text = "EH1" - intervals [53]: - xmin = 5.36 - xmax = 5.42 - text = "N" - intervals [54]: - xmin = 5.42 - xmax = 5.49 - text = "SH" - intervals [55]: - xmin = 5.49 - xmax = 5.55 - text = "AH0" - intervals [56]: - xmin = 5.55 - xmax = 5.67 - text = "N" - intervals [57]: - xmin = 5.67 - xmax = 5.8 - text = "T" - intervals [58]: - xmin = 5.8 - xmax = 6.0 - text = "UW1" - intervals [59]: - xmin = 6.0 - xmax = 6.03 - text = "N" - intervals [60]: - xmin = 6.03 - xmax = 6.15 - text = "Y" - intervals [61]: - xmin = 6.15 - xmax = 6.26 - text = "UW1" - intervals [62]: - xmin = 6.26 - xmax = 6.41 - text = "F" - intervals [63]: - xmin = 6.41 - xmax = 6.54 - text = "AE1" - intervals [64]: - xmin = 6.54 - xmax = 6.63 - text = "SH" - intervals [65]: - xmin = 6.63 - xmax = 6.66 - text = "AH0" - intervals [66]: - xmin = 6.66 - xmax = 6.71 - text = "N" - intervals [67]: - xmin = 6.71 - xmax = 6.75 - text = "IH0" - intervals [68]: - xmin = 6.75 - xmax = 6.81 - text = "V" - intervals [69]: - xmin = 6.81 - xmax = 6.93 - text = "EH1" - intervals [70]: - xmin = 6.93 - xmax = 6.97 - text = "N" - intervals [71]: - xmin = 6.97 - xmax = 7.02 - text = "T" - intervals [72]: - xmin = 7.02 - xmax = 7.17 - text = "S" - intervals [73]: - xmin = 7.17 - xmax = 7.43 - text = "" - intervals [74]: - xmin = 7.43 - xmax = 7.55 - text = "S" - intervals [75]: - xmin = 7.55 - xmax = 7.63 - text = "AH1" - intervals [76]: - xmin = 7.63 - xmax = 7.76 - text = "CH" - intervals [77]: - xmin = 7.76 - xmax = 7.94 - text = "EH1" - intervals [78]: - xmin = 7.94 - xmax = 8.14 - text = "Z" - intervals [79]: - xmin = 8.14 - xmax = 8.19 - text = "" - intervals [80]: - xmin = 8.19 - xmax = 8.28 - text = "DH" - intervals [81]: - xmin = 8.28 - xmax = 8.34 - text = "AH0" - intervals [82]: - xmin = 8.34 - xmax = 8.44 - text = "N" - intervals [83]: - xmin = 8.44 - xmax = 8.47 - text = "UW1" - intervals [84]: - xmin = 8.47 - xmax = 8.52 - text = "Y" - intervals [85]: - xmin = 8.52 - xmax = 8.56 - text = "AO1" - intervals [86]: - xmin = 8.56 - xmax = 8.62 - text = "R" - intervals [87]: - xmin = 8.62 - xmax = 8.68 - text = "K" - intervals [88]: - xmin = 8.68 - xmax = 8.79 - text = "F" - intervals [89]: - xmin = 8.79 - xmax = 8.93 - text = "AE1" - intervals [90]: - xmin = 8.93 - xmax = 9.03 - text = "SH" - intervals [91]: - xmin = 9.03 - xmax = 9.07 - text = "AH0" - intervals [92]: - xmin = 9.07 - xmax = 9.12 - text = "N" - intervals [93]: - xmin = 9.12 - xmax = 9.19 - text = "W" - intervals [94]: - xmin = 9.19 - xmax = 9.33 - text = "IY1" - intervals [95]: - xmin = 9.33 - xmax = 9.42 - text = "K" - intervals [96]: - xmin = 9.42 - xmax = 9.46 - text = "DH" - intervals [97]: - xmin = 9.46 - xmax = 9.49 - text = "AH0" - intervals [98]: - xmin = 9.49 - xmax = 9.57 - text = "P" - intervals [99]: - xmin = 9.57 - xmax = 9.64 - text = "EH1" - intervals [100]: - xmin = 9.64 - xmax = 9.75 - text = "R" - intervals [101]: - xmin = 9.75 - xmax = 9.8 - text = "IH0" - intervals [102]: - xmin = 9.8 - xmax = 9.87 - text = "S" - intervals [103]: - xmin = 9.87 - xmax = 9.93 - text = "F" - intervals [104]: - xmin = 9.93 - xmax = 10.09 - text = "AE1" - intervals [105]: - xmin = 10.09 - xmax = 10.19 - text = "SH" - intervals [106]: - xmin = 10.19 - xmax = 10.22 - text = "AH0" - intervals [107]: - xmin = 10.22 - xmax = 10.25 - text = "N" - intervals [108]: - xmin = 10.25 - xmax = 10.32 - text = "W" - intervals [109]: - xmin = 10.32 - xmax = 10.49 - text = "IY1" - intervals [110]: - xmin = 10.49 - xmax = 10.56 - text = "K" - intervals [111]: - xmin = 10.56 - xmax = 10.6 - text = "DH" - intervals [112]: - xmin = 10.6 - xmax = 10.66 - text = "AH0" - intervals [113]: - xmin = 10.66 - xmax = 10.76 - text = "L" - intervals [114]: - xmin = 10.76 - xmax = 10.81 - text = "AH1" - intervals [115]: - xmin = 10.81 - xmax = 10.87 - text = "N" - intervals [116]: - xmin = 10.87 - xmax = 10.92 - text = "D" - intervals [117]: - xmin = 10.92 - xmax = 10.97 - text = "AH0" - intervals [118]: - xmin = 10.97 - xmax = 11.07 - text = "N" - intervals [119]: - xmin = 11.07 - xmax = 11.18 - text = "F" - intervals [120]: - xmin = 11.18 - xmax = 11.31 - text = "AE1" - intervals [121]: - xmin = 11.31 - xmax = 11.42 - text = "SH" - intervals [122]: - xmin = 11.42 - xmax = 11.46 - text = "AH0" - intervals [123]: - xmin = 11.46 - xmax = 11.51 - text = "N" - intervals [124]: - xmin = 11.51 - xmax = 11.56 - text = "W" - intervals [125]: - xmin = 11.56 - xmax = 11.69 - text = "IY1" - intervals [126]: - xmin = 11.69 - xmax = 11.78 - text = "K" - intervals [127]: - xmin = 11.78 - xmax = 11.84 - text = "AE1" - intervals [128]: - xmin = 11.84 - xmax = 11.99 - text = "N" - intervals [129]: - xmin = 11.99 - xmax = 12.17 - text = "D" - intervals [130]: - xmin = 12.17 - xmax = 12.21 - text = "" - intervals [131]: - xmin = 12.21 - xmax = 12.36 - text = "M" - intervals [132]: - xmin = 12.36 - xmax = 12.48 - text = "IH0" - intervals [133]: - xmin = 12.48 - xmax = 12.55 - text = "L" - intervals [134]: - xmin = 12.55 - xmax = 12.73 - text = "AA1" - intervals [135]: - xmin = 12.73 - xmax = 12.83 - text = "N" - intervals [136]: - xmin = 12.83 - xmax = 12.92 - text = "F" - intervals [137]: - xmin = 12.92 - xmax = 13.06 - text = "AE1" - intervals [138]: - xmin = 13.06 - xmax = 13.16 - text = "SH" - intervals [139]: - xmin = 13.16 - xmax = 13.21 - text = "AH0" - intervals [140]: - xmin = 13.21 - xmax = 13.24 - text = "N" - intervals [141]: - xmin = 13.24 - xmax = 13.29 - text = "W" - intervals [142]: - xmin = 13.29 - xmax = 13.41 - text = "IY1" - intervals [143]: - xmin = 13.41 - xmax = 13.62 - text = "K" - intervals [144]: - xmin = 13.62 - xmax = 14.03 - text = "" - intervals [145]: - xmin = 14.03 - xmax = 14.11 - text = "DH" - intervals [146]: - xmin = 14.11 - xmax = 14.15 - text = "AH1" - intervals [147]: - xmin = 14.15 - xmax = 14.22 - text = "R" - intervals [148]: - xmin = 14.22 - xmax = 14.26 - text = "EH1" - intervals [149]: - xmin = 14.26 - xmax = 14.31 - text = "S" - intervals [150]: - xmin = 14.31 - xmax = 14.35 - text = "T" - intervals [151]: - xmin = 14.35 - xmax = 14.4 - text = "AH0" - intervals [152]: - xmin = 14.4 - xmax = 14.43 - text = "V" - intervals [153]: - xmin = 14.43 - xmax = 14.46 - text = "DH" - intervals [154]: - xmin = 14.46 - xmax = 14.49 - text = "AH0" - intervals [155]: - xmin = 14.49 - xmax = 14.55 - text = "T" - intervals [156]: - xmin = 14.55 - xmax = 14.73 - text = "AY1" - intervals [157]: - xmin = 14.73 - xmax = 14.8 - text = "M" - intervals [158]: - xmin = 14.8 - xmax = 14.87 - text = "AY1" - intervals [159]: - xmin = 14.87 - xmax = 14.96 - text = "Y" - intervals [160]: - xmin = 14.96 - xmax = 14.99 - text = "UW1" - intervals [161]: - xmin = 14.99 - xmax = 15.08 - text = "ZH" - intervals [162]: - xmin = 15.08 - xmax = 15.11 - text = "AH0" - intervals [163]: - xmin = 15.11 - xmax = 15.14 - text = "L" - intervals [164]: - xmin = 15.14 - xmax = 15.2 - text = "IY0" - intervals [165]: - xmin = 15.2 - xmax = 15.23 - text = "G" - intervals [166]: - xmin = 15.23 - xmax = 15.3 - text = "OW1" - intervals [167]: - xmin = 15.3 - xmax = 15.33 - text = "T" - intervals [168]: - xmin = 15.33 - xmax = 15.36 - text = "AH0" - intervals [169]: - xmin = 15.36 - xmax = 15.39 - text = "DH" - intervals [170]: - xmin = 15.39 - xmax = 15.44 - text = "AH1" - intervals [171]: - xmin = 15.44 - xmax = 15.51 - text = "L" - intervals [172]: - xmin = 15.51 - xmax = 15.67 - text = "AY1" - intervals [173]: - xmin = 15.67 - xmax = 15.71 - text = "B" - intervals [174]: - xmin = 15.71 - xmax = 15.74 - text = "R" - intervals [175]: - xmin = 15.74 - xmax = 15.83 - text = "EH2" - intervals [176]: - xmin = 15.83 - xmax = 15.9 - text = "R" - intervals [177]: - xmin = 15.9 - xmax = 15.93 - text = "IY0" - intervals [178]: - xmin = 15.93 - xmax = 15.96 - text = "AH0" - intervals [179]: - xmin = 15.96 - xmax = 15.99 - text = "N" - intervals [180]: - xmin = 15.99 - xmax = 16.04 - text = "D" - intervals [181]: - xmin = 16.04 - xmax = 16.11 - text = "F" - intervals [182]: - xmin = 16.11 - xmax = 16.18 - text = "AY1" - intervals [183]: - xmin = 16.18 - xmax = 16.21 - text = "N" - intervals [184]: - xmin = 16.21 - xmax = 16.25 - text = "D" - intervals [185]: - xmin = 16.25 - xmax = 16.29 - text = "S" - intervals [186]: - xmin = 16.29 - xmax = 16.32 - text = "AH1" - intervals [187]: - xmin = 16.32 - xmax = 16.35 - text = "M" - intervals [188]: - xmin = 16.35 - xmax = 16.38 - text = "IH1" - intervals [189]: - xmin = 16.38 - xmax = 16.41 - text = "N" - intervals [190]: - xmin = 16.41 - xmax = 16.46 - text = "T" - intervals [191]: - xmin = 16.46 - xmax = 16.49 - text = "R" - intervals [192]: - xmin = 16.49 - xmax = 16.53 - text = "IH0" - intervals [193]: - xmin = 16.53 - xmax = 16.57 - text = "S" - intervals [194]: - xmin = 16.57 - xmax = 16.6 - text = "T" - intervals [195]: - xmin = 16.6 - xmax = 16.64 - text = "IH0" - intervals [196]: - xmin = 16.64 - xmax = 16.71 - text = "NG" - intervals [197]: - xmin = 16.71 - xmax = 16.78 - text = "B" - intervals [198]: - xmin = 16.78 - xmax = 17.0 - text = "UH1" - intervals [199]: - xmin = 17.0 - xmax = 17.09 - text = "K" - intervals [200]: - xmin = 17.09 - xmax = 17.19 - text = "S" - intervals [201]: - xmin = 17.19 - xmax = 17.25 - text = "AH0" - intervals [202]: - xmin = 17.25 - xmax = 17.28 - text = "N" - intervals [203]: - xmin = 17.28 - xmax = 17.31 - text = "D" - intervals [204]: - xmin = 17.31 - xmax = 17.34 - text = "DH" - intervals [205]: - xmin = 17.34 - xmax = 17.42 - text = "EH1" - intervals [206]: - xmin = 17.42 - xmax = 17.51 - text = "N" - intervals [207]: - xmin = 17.51 - xmax = 17.58 - text = "G" - intervals [208]: - xmin = 17.58 - xmax = 17.63 - text = "OW1" - intervals [209]: - xmin = 17.63 - xmax = 17.67 - text = "T" - intervals [210]: - xmin = 17.67 - xmax = 17.7 - text = "AH0" - intervals [211]: - xmin = 17.7 - xmax = 17.78 - text = "AH0" - intervals [212]: - xmin = 17.78 - xmax = 17.89 - text = "P" - intervals [213]: - xmin = 17.89 - xmax = 17.95 - text = "AA1" - intervals [214]: - xmin = 17.95 - xmax = 18.04 - text = "R" - intervals [215]: - xmin = 18.04 - xmax = 18.08 - text = "K" - intervals [216]: - xmin = 18.08 - xmax = 18.11 - text = "AH0" - intervals [217]: - xmin = 18.11 - xmax = 18.14 - text = "N" - intervals [218]: - xmin = 18.14 - xmax = 18.17 - text = "D" - intervals [219]: - xmin = 18.17 - xmax = 18.2 - text = "R" - intervals [220]: - xmin = 18.2 - xmax = 18.25 - text = "IH0" - intervals [221]: - xmin = 18.25 - xmax = 18.33 - text = "L" - intervals [222]: - xmin = 18.33 - xmax = 18.53 - text = "AE1" - intervals [223]: - xmin = 18.53 - xmax = 18.58 - text = "K" - intervals [224]: - xmin = 18.58 - xmax = 18.75 - text = "S" - intervals [225]: - xmin = 18.75 - xmax = 19.04 - text = "" - intervals [226]: - xmin = 19.04 - xmax = 19.14 - text = "DH" - intervals [227]: - xmin = 19.14 - xmax = 19.18 - text = "EH1" - intervals [228]: - xmin = 19.18 - xmax = 19.22 - text = "R" - intervals [229]: - xmin = 19.22 - xmax = 19.27 - text = "ER0" - intervals [230]: - xmin = 19.27 - xmax = 19.34 - text = "M" - intervals [231]: - xmin = 19.34 - xmax = 19.39 - text = "EH1" - intervals [232]: - xmin = 19.39 - xmax = 19.43 - text = "N" - intervals [233]: - xmin = 19.43 - xmax = 19.5 - text = "IY0" - intervals [234]: - xmin = 19.5 - xmax = 19.56 - text = "B" - intervals [235]: - xmin = 19.56 - xmax = 19.66 - text = "UH1" - intervals [236]: - xmin = 19.66 - xmax = 19.72 - text = "K" - intervals [237]: - xmin = 19.72 - xmax = 19.78 - text = "S" - intervals [238]: - xmin = 19.78 - xmax = 19.81 - text = "DH" - intervals [239]: - xmin = 19.81 - xmax = 19.84 - text = "AH0" - intervals [240]: - xmin = 19.84 - xmax = 19.93 - text = "T" - intervals [241]: - xmin = 19.93 - xmax = 20.11 - text = "AY1" - intervals [242]: - xmin = 20.11 - xmax = 20.22 - text = "F" - intervals [243]: - xmin = 20.22 - xmax = 20.3 - text = "AY1" - intervals [244]: - xmin = 20.3 - xmax = 20.37 - text = "N" - intervals [245]: - xmin = 20.37 - xmax = 20.4 - text = "D" - intervals [246]: - xmin = 20.4 - xmax = 20.52 - text = "IH1" - intervals [247]: - xmin = 20.52 - xmax = 20.55 - text = "N" - intervals [248]: - xmin = 20.55 - xmax = 20.59 - text = "T" - intervals [249]: - xmin = 20.59 - xmax = 20.62 - text = "R" - intervals [250]: - xmin = 20.62 - xmax = 20.67 - text = "AH0" - intervals [251]: - xmin = 20.67 - xmax = 20.74 - text = "S" - intervals [252]: - xmin = 20.74 - xmax = 20.78 - text = "T" - intervals [253]: - xmin = 20.78 - xmax = 20.85 - text = "IH0" - intervals [254]: - xmin = 20.85 - xmax = 20.92 - text = "NG" - intervals [255]: - xmin = 20.92 - xmax = 21.02 - text = "S" - intervals [256]: - xmin = 21.02 - xmax = 21.06 - text = "AH1" - intervals [257]: - xmin = 21.06 - xmax = 21.15 - text = "CH" - intervals [258]: - xmin = 21.15 - xmax = 21.2 - text = "EH1" - intervals [259]: - xmin = 21.2 - xmax = 21.3 - text = "Z" - intervals [260]: - xmin = 21.3 - xmax = 21.36 - text = "F" - intervals [261]: - xmin = 21.36 - xmax = 21.47 - text = "AE1" - intervals [262]: - xmin = 21.47 - xmax = 21.56 - text = "SH" - intervals [263]: - xmin = 21.56 - xmax = 21.59 - text = "AH0" - intervals [264]: - xmin = 21.59 - xmax = 21.62 - text = "N" - intervals [265]: - xmin = 21.62 - xmax = 21.68 - text = "M" - intervals [266]: - xmin = 21.68 - xmax = 21.76 - text = "AE1" - intervals [267]: - xmin = 21.76 - xmax = 21.81 - text = "G" - intervals [268]: - xmin = 21.81 - xmax = 21.85 - text = "AH0" - intervals [269]: - xmin = 21.85 - xmax = 21.9 - text = "Z" - intervals [270]: - xmin = 21.9 - xmax = 22.0 - text = "IY2" - intervals [271]: - xmin = 22.0 - xmax = 22.1 - text = "N" - intervals [272]: - xmin = 22.1 - xmax = 22.19 - text = "Z" - intervals [273]: - xmin = 22.19 - xmax = 22.22 - text = "IH2" - intervals [274]: - xmin = 22.22 - xmax = 22.29 - text = "N" - intervals [275]: - xmin = 22.29 - xmax = 22.34 - text = "S" - intervals [276]: - xmin = 22.34 - xmax = 22.38 - text = "P" - intervals [277]: - xmin = 22.38 - xmax = 22.48 - text = "ER0" - intervals [278]: - xmin = 22.48 - xmax = 22.55 - text = "EY1" - intervals [279]: - xmin = 22.55 - xmax = 22.64 - text = "SH" - intervals [280]: - xmin = 22.64 - xmax = 22.67 - text = "AH0" - intervals [281]: - xmin = 22.67 - xmax = 22.7 - text = "N" - intervals [282]: - xmin = 22.7 - xmax = 22.73 - text = "AH0" - intervals [283]: - xmin = 22.73 - xmax = 22.8 - text = "L" - intervals [284]: - xmin = 22.8 - xmax = 22.88 - text = "B" - intervals [285]: - xmin = 22.88 - xmax = 23.03 - text = "UH1" - intervals [286]: - xmin = 23.03 - xmax = 23.09 - text = "K" - intervals [287]: - xmin = 23.09 - xmax = 23.15 - text = "S" - intervals [288]: - xmin = 23.15 - xmax = 23.24 - text = "AH0" - intervals [289]: - xmin = 23.24 - xmax = 23.35 - text = "N" - intervals [290]: - xmin = 23.35 - xmax = 23.44 - text = "D" - intervals [291]: - xmin = 23.44 - xmax = 23.5 - text = "P" - intervals [292]: - xmin = 23.5 - xmax = 23.55 - text = "R" - intervals [293]: - xmin = 23.55 - xmax = 23.59 - text = "AH0" - intervals [294]: - xmin = 23.59 - xmax = 23.69 - text = "F" - intervals [295]: - xmin = 23.69 - xmax = 23.76 - text = "EH1" - intervals [296]: - xmin = 23.76 - xmax = 23.87 - text = "SH" - intervals [297]: - xmin = 23.87 - xmax = 23.9 - text = "AH0" - intervals [298]: - xmin = 23.9 - xmax = 23.94 - text = "N" - intervals [299]: - xmin = 23.94 - xmax = 23.98 - text = "AH0" - intervals [300]: - xmin = 23.98 - xmax = 24.04 - text = "L" - intervals [301]: - xmin = 24.04 - xmax = 24.12 - text = "B" - intervals [302]: - xmin = 24.12 - xmax = 24.24 - text = "UH1" - intervals [303]: - xmin = 24.24 - xmax = 24.32 - text = "K" - intervals [304]: - xmin = 24.32 - xmax = 24.46 - text = "S" - intervals [305]: - xmin = 24.46 - xmax = 24.83 - text = "" - intervals [306]: - xmin = 24.83 - xmax = 24.91 - text = "DH" - intervals [307]: - xmin = 24.91 - xmax = 24.98 - text = "IY1" - intervals [308]: - xmin = 24.98 - xmax = 25.06 - text = "Z" - intervals [309]: - xmin = 25.06 - xmax = 25.13 - text = "B" - intervals [310]: - xmin = 25.13 - xmax = 25.23 - text = "UH1" - intervals [311]: - xmin = 25.23 - xmax = 25.3 - text = "K" - intervals [312]: - xmin = 25.3 - xmax = 25.37 - text = "S" - intervals [313]: - xmin = 25.37 - xmax = 25.44 - text = "K" - intervals [314]: - xmin = 25.44 - xmax = 25.51 - text = "AH0" - intervals [315]: - xmin = 25.51 - xmax = 25.54 - text = "N" - intervals [316]: - xmin = 25.54 - xmax = 25.59 - text = "G" - intervals [317]: - xmin = 25.59 - xmax = 25.63 - text = "IH1" - intervals [318]: - xmin = 25.63 - xmax = 25.66 - text = "V" - intervals [319]: - xmin = 25.66 - xmax = 25.71 - text = "M" - intervals [320]: - xmin = 25.71 - xmax = 25.76 - text = "IY1" - intervals [321]: - xmin = 25.76 - xmax = 25.82 - text = "DH" - intervals [322]: - xmin = 25.82 - xmax = 25.86 - text = "AH0" - intervals [323]: - xmin = 25.86 - xmax = 25.95 - text = "M" - intervals [324]: - xmin = 25.95 - xmax = 26.01 - text = "OW2" - intervals [325]: - xmin = 26.01 - xmax = 26.06 - text = "T" - intervals [326]: - xmin = 26.06 - xmax = 26.1 - text = "AH0" - intervals [327]: - xmin = 26.1 - xmax = 26.19 - text = "V" - intervals [328]: - xmin = 26.19 - xmax = 26.32 - text = "EY1" - intervals [329]: - xmin = 26.32 - xmax = 26.42 - text = "SH" - intervals [330]: - xmin = 26.42 - xmax = 26.51 - text = "AH0" - intervals [331]: - xmin = 26.51 - xmax = 26.85 - text = "N" - intervals [332]: - xmin = 26.85 - xmax = 26.88 - text = "" - intervals [333]: - xmin = 26.88 - xmax = 27.0 - text = "T" - intervals [334]: - xmin = 27.0 - xmax = 27.07 - text = "IH0" - intervals [335]: - xmin = 27.07 - xmax = 27.13 - text = "B" - intervals [336]: - xmin = 27.13 - xmax = 27.37 - text = "IY1" - intervals [337]: - xmin = 27.37 - xmax = 27.5 - text = "HH" - intervals [338]: - xmin = 27.5 - xmax = 27.55 - text = "EH1" - intervals [339]: - xmin = 27.55 - xmax = 27.68 - text = "L" - intervals [340]: - xmin = 27.68 - xmax = 27.72 - text = "TH" - intervals [341]: - xmin = 27.72 - xmax = 27.86 - text = "IY0" - intervals [342]: - xmin = 27.86 - xmax = 28.01 - text = "ER0" - intervals [343]: - xmin = 28.01 - xmax = 28.09 - text = "AE1" - intervals [344]: - xmin = 28.09 - xmax = 28.12 - text = "N" - intervals [345]: - xmin = 28.12 - xmax = 28.18 - text = "D" - intervals [346]: - xmin = 28.18 - xmax = 28.25 - text = "EH2" - intervals [347]: - xmin = 28.25 - xmax = 28.32 - text = "N" - intervals [348]: - xmin = 28.32 - xmax = 28.41 - text = "ER0" - intervals [349]: - xmin = 28.41 - xmax = 28.51 - text = "JH" - intervals [350]: - xmin = 28.51 - xmax = 28.59 - text = "EH1" - intervals [351]: - xmin = 28.59 - xmax = 28.62 - text = "T" - intervals [352]: - xmin = 28.62 - xmax = 28.71 - text = "IH0" - intervals [353]: - xmin = 28.71 - xmax = 28.9 - text = "K" - intervals [354]: - xmin = 28.9 - xmax = 29.1 - text = "" - intervals [355]: - xmin = 29.1 - xmax = 29.24 - text = "AE1" - intervals [356]: - xmin = 29.24 - xmax = 29.27 - text = "N" - intervals [357]: - xmin = 29.27 - xmax = 29.3 - text = "D" - intervals [358]: - xmin = 29.3 - xmax = 29.33 - text = "DH" - intervals [359]: - xmin = 29.33 - xmax = 29.37 - text = "AH0" - intervals [360]: - xmin = 29.37 - xmax = 29.47 - text = "L" - intervals [361]: - xmin = 29.47 - xmax = 29.62 - text = "AE1" - intervals [362]: - xmin = 29.62 - xmax = 29.74 - text = "S" - intervals [363]: - xmin = 29.74 - xmax = 29.8 - text = "TH" - intervals [364]: - xmin = 29.8 - xmax = 29.86 - text = "IH1" - intervals [365]: - xmin = 29.86 - xmax = 29.94 - text = "NG" - intervals [366]: - xmin = 29.94 - xmax = 30.14 - text = "AY1" - intervals [367]: - xmin = 30.14 - xmax = 30.23 - text = "L" - intervals [368]: - xmin = 30.23 - xmax = 30.38 - text = "AY1" - intervals [369]: - xmin = 30.38 - xmax = 30.42 - text = "K" - intervals [370]: - xmin = 30.42 - xmax = 30.48 - text = "T" - intervals [371]: - xmin = 30.48 - xmax = 30.53 - text = "IH0" - intervals [372]: - xmin = 30.53 - xmax = 30.59 - text = "D" - intervals [373]: - xmin = 30.59 - xmax = 30.84 - text = "UW1" - intervals [374]: - xmin = 30.84 - xmax = 30.97 - text = "W" - intervals [375]: - xmin = 30.97 - xmax = 31.03 - text = "EH1" - intervals [376]: - xmin = 31.03 - xmax = 31.22 - text = "N" - intervals [377]: - xmin = 31.22 - xmax = 31.35 - text = "AY1" - intervals [378]: - xmin = 31.35 - xmax = 31.43 - text = "M" - intervals [379]: - xmin = 31.43 - xmax = 31.55 - text = "F" - intervals [380]: - xmin = 31.55 - xmax = 31.65 - text = "R" - intervals [381]: - xmin = 31.65 - xmax = 31.87 - text = "IY1" - intervals [382]: - xmin = 31.87 - xmax = 31.91 - text = "IH1" - intervals [383]: - xmin = 31.91 - xmax = 31.99 - text = "Z" - intervals [384]: - xmin = 31.99 - xmax = 32.06 - text = "IH1" - intervals [385]: - xmin = 32.06 - xmax = 32.11 - text = "T" - intervals [386]: - xmin = 32.11 - xmax = 32.2 - text = "AW1" - intervals [387]: - xmin = 32.2 - xmax = 32.23 - text = "T" - intervals [388]: - xmin = 32.23 - xmax = 32.27 - text = "W" - intervals [389]: - xmin = 32.27 - xmax = 32.32 - text = "IH0" - intervals [390]: - xmin = 32.32 - xmax = 32.35 - text = "TH" - intervals [391]: - xmin = 32.35 - xmax = 32.39 - text = "M" - intervals [392]: - xmin = 32.39 - xmax = 32.48 - text = "AY1" - intervals [393]: - xmin = 32.48 - xmax = 32.61 - text = "F" - intervals [394]: - xmin = 32.61 - xmax = 32.72 - text = "AE1" - intervals [395]: - xmin = 32.72 - xmax = 32.76 - text = "M" - intervals [396]: - xmin = 32.76 - xmax = 32.81 - text = "L" - intervals [397]: - xmin = 32.81 - xmax = 32.86 - text = "IY0" - intervals [398]: - xmin = 32.86 - xmax = 32.92 - text = "M" - intervals [399]: - xmin = 32.92 - xmax = 32.97 - text = "EH1" - intervals [400]: - xmin = 32.97 - xmax = 33.0 - text = "M" - intervals [401]: - xmin = 33.0 - xmax = 33.05 - text = "B" - intervals [402]: - xmin = 33.05 - xmax = 33.16 - text = "ER0" - intervals [403]: - xmin = 33.16 - xmax = 33.33 - text = "Z" - intervals [404]: - xmin = 33.33 - xmax = 33.51 - text = "" - intervals [405]: - xmin = 33.51 - xmax = 33.75 - text = "Y" - intervals [406]: - xmin = 33.75 - xmax = 33.89 - text = "UW1" - intervals [407]: - xmin = 33.89 - xmax = 33.97 - text = "W" - intervals [408]: - xmin = 33.97 - xmax = 34.02 - text = "UH1" - intervals [409]: - xmin = 34.02 - xmax = 34.11 - text = "D" - intervals [410]: - xmin = 34.11 - xmax = 34.16 - text = "B" - intervals [411]: - xmin = 34.16 - xmax = 34.29 - text = "IY1" - intervals [412]: - xmin = 34.29 - xmax = 34.36 - text = "S" - intervals [413]: - xmin = 34.36 - xmax = 34.42 - text = "AH0" - intervals [414]: - xmin = 34.42 - xmax = 34.53 - text = "P" - intervals [415]: - xmin = 34.53 - xmax = 34.63 - text = "R" - intervals [416]: - xmin = 34.63 - xmax = 34.91 - text = "AY1" - intervals [417]: - xmin = 34.91 - xmax = 35.02 - text = "Z" - intervals [418]: - xmin = 35.02 - xmax = 35.07 - text = "D" - intervals [419]: - xmin = 35.07 - xmax = 35.1 - text = "T" - intervals [420]: - xmin = 35.1 - xmax = 35.16 - text = "IH0" - intervals [421]: - xmin = 35.16 - xmax = 35.26 - text = "N" - intervals [422]: - xmin = 35.26 - xmax = 35.36 - text = "OW1" - intervals [423]: - xmin = 35.36 - xmax = 35.44 - text = "DH" - intervals [424]: - xmin = 35.44 - xmax = 35.47 - text = "AE1" - intervals [425]: - xmin = 35.47 - xmax = 35.5 - text = "T" - intervals [426]: - xmin = 35.5 - xmax = 35.64 - text = "AY1" - intervals [427]: - xmin = 35.64 - xmax = 35.7 - text = "HH" - intervals [428]: - xmin = 35.7 - xmax = 35.75 - text = "AE1" - intervals [429]: - xmin = 35.75 - xmax = 35.84 - text = "V" - intervals [430]: - xmin = 35.84 - xmax = 35.9 - text = "T" - intervals [431]: - xmin = 35.9 - xmax = 35.98 - text = "R" - intervals [432]: - xmin = 35.98 - xmax = 36.15 - text = "AY1" - intervals [433]: - xmin = 36.15 - xmax = 36.3 - text = "D" - intervals [434]: - xmin = 36.3 - xmax = 36.57 - text = "" - intervals [435]: - xmin = 36.57 - xmax = 36.92 - text = "AO1" - intervals [436]: - xmin = 36.92 - xmax = 36.99 - text = "L" - intervals [437]: - xmin = 36.99 - xmax = 37.03 - text = "DH" - intervals [438]: - xmin = 37.03 - xmax = 37.08 - text = "AH1" - intervals [439]: - xmin = 37.08 - xmax = 37.17 - text = "R" - intervals [440]: - xmin = 37.17 - xmax = 37.28 - text = "EH1" - intervals [441]: - xmin = 37.28 - xmax = 37.34 - text = "S" - intervals [442]: - xmin = 37.34 - xmax = 37.39 - text = "T" - intervals [443]: - xmin = 37.39 - xmax = 37.44 - text = "R" - intervals [444]: - xmin = 37.44 - xmax = 37.55 - text = "AA2" - intervals [445]: - xmin = 37.55 - xmax = 37.6 - text = "N" - intervals [446]: - xmin = 37.6 - xmax = 37.64 - text = "T" - intervals [447]: - xmin = 37.64 - xmax = 37.68 - text = "S" - intervals [448]: - xmin = 37.68 - xmax = 37.71 - text = "" - intervals [449]: - xmin = 37.71 - xmax = 37.77 - text = "IH0" - intervals [450]: - xmin = 37.77 - xmax = 37.83 - text = "N" - intervals [451]: - xmin = 37.83 - xmax = 37.87 - text = "AA1" - intervals [452]: - xmin = 37.87 - xmax = 37.95 - text = "R" - intervals [453]: - xmin = 37.95 - xmax = 38.12 - text = "HH" - intervals [454]: - xmin = 38.12 - xmax = 38.2 - text = "Y" - intervals [455]: - xmin = 38.2 - xmax = 38.33 - text = "UW1" - intervals [456]: - xmin = 38.33 - xmax = 38.5 - text = "JH" - intervals [457]: - xmin = 38.5 - xmax = 38.53 - text = "K" - intervals [458]: - xmin = 38.53 - xmax = 38.59 - text = "AH0" - intervals [459]: - xmin = 38.59 - xmax = 38.64 - text = "M" - intervals [460]: - xmin = 38.64 - xmax = 38.67 - text = "Y" - intervals [461]: - xmin = 38.67 - xmax = 38.7 - text = "UW1" - intervals [462]: - xmin = 38.7 - xmax = 38.76 - text = "N" - intervals [463]: - xmin = 38.76 - xmax = 38.79 - text = "AH0" - intervals [464]: - xmin = 38.79 - xmax = 38.82 - text = "T" - intervals [465]: - xmin = 38.82 - xmax = 39.07 - text = "IY0" - intervals [466]: - xmin = 39.07 - xmax = 39.23 - text = "" - intervals [467]: - xmin = 39.23 - xmax = 39.6 - text = "AY1" - intervals [468]: - xmin = 39.6 - xmax = 39.81 - text = "AE1" - intervals [469]: - xmin = 39.81 - xmax = 39.86 - text = "K" - intervals [470]: - xmin = 39.86 - xmax = 39.93 - text = "SH" - intervals [471]: - xmin = 39.93 - xmax = 39.97 - text = "AH0" - intervals [472]: - xmin = 39.97 - xmax = 40.0 - text = "L" - intervals [473]: - xmin = 40.0 - xmax = 40.09 - text = "IY0" - intervals [474]: - xmin = 40.09 - xmax = 40.17 - text = "G" - intervals [475]: - xmin = 40.17 - xmax = 40.26 - text = "IH1" - intervals [476]: - xmin = 40.26 - xmax = 40.32 - text = "V" - intervals [477]: - xmin = 40.32 - xmax = 40.5 - text = "IY1" - intervals [478]: - xmin = 40.5 - xmax = 40.61 - text = "CH" - intervals [479]: - xmin = 40.61 - xmax = 40.7 - text = "R" - intervals [480]: - xmin = 40.7 - xmax = 40.78 - text = "EH1" - intervals [481]: - xmin = 40.78 - xmax = 40.83 - text = "S" - intervals [482]: - xmin = 40.83 - xmax = 40.9 - text = "T" - intervals [483]: - xmin = 40.9 - xmax = 40.94 - text = "R" - intervals [484]: - xmin = 40.94 - xmax = 41.02 - text = "AA2" - intervals [485]: - xmin = 41.02 - xmax = 41.05 - text = "N" - intervals [486]: - xmin = 41.05 - xmax = 41.08 - text = "T" - intervals [487]: - xmin = 41.08 - xmax = 41.15 - text = "AH0" - intervals [488]: - xmin = 41.15 - xmax = 41.29 - text = "S" - intervals [489]: - xmin = 41.29 - xmax = 41.33 - text = "K" - intervals [490]: - xmin = 41.33 - xmax = 41.44 - text = "AO1" - intervals [491]: - xmin = 41.44 - xmax = 41.55 - text = "R" - intervals [492]: - xmin = 41.55 - xmax = 41.61 - text = "B" - intervals [493]: - xmin = 41.61 - xmax = 41.73 - text = "EY1" - intervals [494]: - xmin = 41.73 - xmax = 41.77 - text = "S" - intervals [495]: - xmin = 41.77 - xmax = 41.82 - text = "T" - intervals [496]: - xmin = 41.82 - xmax = 41.85 - text = "AA1" - intervals [497]: - xmin = 41.85 - xmax = 41.89 - text = "N" - intervals [498]: - xmin = 41.89 - xmax = 41.98 - text = "HH" - intervals [499]: - xmin = 41.98 - xmax = 42.05 - text = "AW1" - intervals [500]: - xmin = 42.05 - xmax = 42.11 - text = "G" - intervals [501]: - xmin = 42.11 - xmax = 42.14 - text = "IH0" - intervals [502]: - xmin = 42.14 - xmax = 42.17 - text = "D" - intervals [503]: - xmin = 42.17 - xmax = 42.2 - text = "DH" - intervals [504]: - xmin = 42.2 - xmax = 42.23 - text = "IY0" - intervals [505]: - xmin = 42.23 - xmax = 42.35 - text = "F" - intervals [506]: - xmin = 42.35 - xmax = 42.48 - text = "UW1" - intervals [507]: - xmin = 42.48 - xmax = 42.51 - text = "D" - intervals [508]: - xmin = 42.51 - xmax = 42.71 - text = "IH1" - intervals [509]: - xmin = 42.71 - xmax = 42.85 - text = "Z" - intervals [510]: - xmin = 42.85 - xmax = 43.13 - text = "" - intervals [511]: - xmin = 43.13 - xmax = 43.28 - text = "HH" - intervals [512]: - xmin = 43.28 - xmax = 43.36 - text = "AW1" - intervals [513]: - xmin = 43.36 - xmax = 43.43 - text = "G" - intervals [514]: - xmin = 43.43 - xmax = 43.46 - text = "IH0" - intervals [515]: - xmin = 43.46 - xmax = 43.51 - text = "D" - intervals [516]: - xmin = 43.51 - xmax = 43.56 - text = "DH" - intervals [517]: - xmin = 43.56 - xmax = 43.62 - text = "IY0" - intervals [518]: - xmin = 43.62 - xmax = 43.65 - text = "IH0" - intervals [519]: - xmin = 43.65 - xmax = 43.69 - text = "N" - intervals [520]: - xmin = 43.69 - xmax = 43.78 - text = "V" - intervals [521]: - xmin = 43.78 - xmax = 43.89 - text = "AY1" - intervals [522]: - xmin = 43.89 - xmax = 43.92 - text = "R" - intervals [523]: - xmin = 43.92 - xmax = 43.95 - text = "AH0" - intervals [524]: - xmin = 43.95 - xmax = 43.98 - text = "N" - intervals [525]: - xmin = 43.98 - xmax = 44.01 - text = "M" - intervals [526]: - xmin = 44.01 - xmax = 44.04 - text = "AH0" - intervals [527]: - xmin = 44.04 - xmax = 44.07 - text = "N" - intervals [528]: - xmin = 44.07 - xmax = 44.1 - text = "T" - intervals [529]: - xmin = 44.1 - xmax = 44.25 - text = "IH1" - intervals [530]: - xmin = 44.25 - xmax = 44.4 - text = "Z" - intervals [531]: - xmin = 44.4 - xmax = 44.49 - text = "" - intervals [532]: - xmin = 44.49 - xmax = 44.79 - text = "AE1" - intervals [533]: - xmin = 44.79 - xmax = 44.9 - text = "N" - intervals [534]: - xmin = 44.9 - xmax = 44.98 - text = "D" - intervals [535]: - xmin = 44.98 - xmax = 45.26 - text = "AE1" - intervals [536]: - xmin = 45.26 - xmax = 45.34 - text = "T" - intervals [537]: - xmin = 45.34 - xmax = 45.39 - text = "DH" - intervals [538]: - xmin = 45.39 - xmax = 45.62 - text = "AH1" - intervals [539]: - xmin = 45.62 - xmax = 45.75 - text = "S" - intervals [540]: - xmin = 45.75 - xmax = 45.87 - text = "EY1" - intervals [541]: - xmin = 45.87 - xmax = 45.91 - text = "M" - intervals [542]: - xmin = 45.91 - xmax = 46.01 - text = "T" - intervals [543]: - xmin = 46.01 - xmax = 46.19 - text = "AY1" - intervals [544]: - xmin = 46.19 - xmax = 46.29 - text = "M" - intervals [545]: - xmin = 46.29 - xmax = 46.42 - text = "AY1" - intervals [546]: - xmin = 46.42 - xmax = 46.45 - text = "W" - intervals [547]: - xmin = 46.45 - xmax = 46.48 - text = "AH0" - intervals [548]: - xmin = 46.48 - xmax = 46.54 - text = "L" - intervals [549]: - xmin = 46.54 - xmax = 46.62 - text = "R" - intervals [550]: - xmin = 46.62 - xmax = 46.69 - text = "AY1" - intervals [551]: - xmin = 46.69 - xmax = 46.74 - text = "T" - intervals [552]: - xmin = 46.74 - xmax = 46.82 - text = "D" - intervals [553]: - xmin = 46.82 - xmax = 46.91 - text = "AW1" - intervals [554]: - xmin = 46.91 - xmax = 46.94 - text = "N" - intervals [555]: - xmin = 46.94 - xmax = 46.97 - text = "DH" - intervals [556]: - xmin = 46.97 - xmax = 47.02 - text = "AH1" - intervals [557]: - xmin = 47.02 - xmax = 47.1 - text = "T" - intervals [558]: - xmin = 47.1 - xmax = 47.19 - text = "AY1" - intervals [559]: - xmin = 47.19 - xmax = 47.24 - text = "P" - intervals [560]: - xmin = 47.24 - xmax = 47.29 - text = "AH0" - intervals [561]: - xmin = 47.29 - xmax = 47.39 - text = "V" - intervals [562]: - xmin = 47.39 - xmax = 47.45 - text = "F" - intervals [563]: - xmin = 47.45 - xmax = 47.64 - text = "UW1" - intervals [564]: - xmin = 47.64 - xmax = 47.8 - text = "D" - intervals [565]: - xmin = 47.8 - xmax = 48.03 - text = "" - intervals [566]: - xmin = 48.03 - xmax = 48.1 - text = "DH" - intervals [567]: - xmin = 48.1 - xmax = 48.24 - text = "EY1" - intervals [568]: - xmin = 48.24 - xmax = 48.37 - text = "S" - intervals [569]: - xmin = 48.37 - xmax = 48.58 - text = "ER1" - intervals [570]: - xmin = 48.58 - xmax = 48.76 - text = "V" - intervals [571]: - xmin = 48.76 - xmax = 49.42 - text = "" - intervals [572]: - xmin = 49.42 - xmax = 49.61 - text = "S" - intervals [573]: - xmin = 49.61 - xmax = 49.9 - text = "OW1" - intervals [574]: - xmin = 49.9 - xmax = 50.09 - text = "W" - intervals [575]: - xmin = 50.09 - xmax = 50.22 - text = "EH1" - intervals [576]: - xmin = 50.22 - xmax = 50.46 - text = "N" - intervals [577]: - xmin = 50.46 - xmax = 50.49 - text = "" - intervals [578]: - xmin = 50.49 - xmax = 50.58 - text = "Y" - intervals [579]: - xmin = 50.58 - xmax = 50.67 - text = "UW1" - intervals [580]: - xmin = 50.67 - xmax = 50.85 - text = "R" - intervals [581]: - xmin = 50.85 - xmax = 50.94 - text = "S" - intervals [582]: - xmin = 50.94 - xmax = 50.98 - text = "OW1" - intervals [583]: - xmin = 50.98 - xmax = 51.03 - text = "W" - intervals [584]: - xmin = 51.03 - xmax = 51.06 - text = "EH1" - intervals [585]: - xmin = 51.06 - xmax = 51.13 - text = "N" - intervals [586]: - xmin = 51.13 - xmax = 51.24 - text = "IY1" - intervals [587]: - xmin = 51.24 - xmax = 51.35 - text = "CH" - intervals [588]: - xmin = 51.35 - xmax = 51.41 - text = "T" - intervals [589]: - xmin = 51.41 - xmax = 51.49 - text = "AY1" - intervals [590]: - xmin = 51.49 - xmax = 51.55 - text = "M" - intervals [591]: - xmin = 51.55 - xmax = 51.62 - text = "AH0" - intervals [592]: - xmin = 51.62 - xmax = 51.69 - text = "F" - intervals [593]: - xmin = 51.69 - xmax = 51.74 - text = "R" - intervals [594]: - xmin = 51.74 - xmax = 51.8 - text = "EH1" - intervals [595]: - xmin = 51.8 - xmax = 51.85 - text = "N" - intervals [596]: - xmin = 51.85 - xmax = 51.91 - text = "D" - intervals [597]: - xmin = 51.91 - xmax = 51.98 - text = "K" - intervals [598]: - xmin = 51.98 - xmax = 52.03 - text = "AH1" - intervals [599]: - xmin = 52.03 - xmax = 52.14 - text = "M" - intervals [600]: - xmin = 52.14 - xmax = 52.32 - text = "Z" - intervals [601]: - xmin = 52.32 - xmax = 52.37 - text = "T" - intervals [602]: - xmin = 52.37 - xmax = 52.46 - text = "UW1" - intervals [603]: - xmin = 52.46 - xmax = 52.53 - text = "DH" - intervals [604]: - xmin = 52.53 - xmax = 52.59 - text = "AH0" - intervals [605]: - xmin = 52.59 - xmax = 52.68 - text = "S" - intervals [606]: - xmin = 52.68 - xmax = 52.74 - text = "IH1" - intervals [607]: - xmin = 52.74 - xmax = 52.77 - text = "T" - intervals [608]: - xmin = 52.77 - xmax = 52.9 - text = "IY0" - intervals [609]: - xmin = 52.9 - xmax = 52.97 - text = "T" - intervals [610]: - xmin = 52.97 - xmax = 53.07 - text = "UW1" - intervals [611]: - xmin = 53.07 - xmax = 53.12 - text = "IH0" - intervals [612]: - xmin = 53.12 - xmax = 53.17 - text = "N" - intervals [613]: - xmin = 53.17 - xmax = 53.26 - text = "JH" - intervals [614]: - xmin = 53.26 - xmax = 53.35 - text = "OY1" - intervals [615]: - xmin = 53.35 - xmax = 53.46 - text = "T" - intervals [616]: - xmin = 53.46 - xmax = 53.59 - text = "AY1" - intervals [617]: - xmin = 53.59 - xmax = 53.62 - text = "M" - intervals [618]: - xmin = 53.62 - xmax = 53.65 - text = "W" - intervals [619]: - xmin = 53.65 - xmax = 53.7 - text = "IH1" - intervals [620]: - xmin = 53.7 - xmax = 53.74 - text = "DH" - intervals [621]: - xmin = 53.74 - xmax = 53.81 - text = "M" - intervals [622]: - xmin = 53.81 - xmax = 54.02 - text = "IY1" - intervals [623]: - xmin = 54.02 - xmax = 54.31 - text = "" - intervals [624]: - xmin = 54.31 - xmax = 54.54 - text = "AY1" - intervals [625]: - xmin = 54.54 - xmax = 54.6 - text = "W" - intervals [626]: - xmin = 54.6 - xmax = 54.66 - text = "AH0" - intervals [627]: - xmin = 54.66 - xmax = 54.69 - text = "L" - intervals [628]: - xmin = 54.69 - xmax = 54.77 - text = "G" - intervals [629]: - xmin = 54.77 - xmax = 54.81 - text = "IH1" - intervals [630]: - xmin = 54.81 - xmax = 54.84 - text = "V" - intervals [631]: - xmin = 54.84 - xmax = 54.87 - text = "DH" - intervals [632]: - xmin = 54.87 - xmax = 54.94 - text = "AH0" - intervals [633]: - xmin = 54.94 - xmax = 54.97 - text = "M" - intervals [634]: - xmin = 54.97 - xmax = 55.0 - text = "DH" - intervals [635]: - xmin = 55.0 - xmax = 55.07 - text = "AH0" - intervals [636]: - xmin = 55.07 - xmax = 55.17 - text = "T" - intervals [637]: - xmin = 55.17 - xmax = 55.27 - text = "AA1" - intervals [638]: - xmin = 55.27 - xmax = 55.38 - text = "P" - intervals [639]: - xmin = 55.38 - xmax = 55.53 - text = "spn" - intervals [640]: - xmin = 55.53 - xmax = 55.62 - text = "R" - intervals [641]: - xmin = 55.62 - xmax = 55.7 - text = "EH1" - intervals [642]: - xmin = 55.7 - xmax = 55.76 - text = "S" - intervals [643]: - xmin = 55.76 - xmax = 55.83 - text = "T" - intervals [644]: - xmin = 55.83 - xmax = 55.86 - text = "R" - intervals [645]: - xmin = 55.86 - xmax = 55.97 - text = "AA2" - intervals [646]: - xmin = 55.97 - xmax = 56.01 - text = "N" - intervals [647]: - xmin = 56.01 - xmax = 56.04 - text = "T" - intervals [648]: - xmin = 56.04 - xmax = 56.1 - text = "S" - intervals [649]: - xmin = 56.1 - xmax = 56.17 - text = "B" - intervals [650]: - xmin = 56.17 - xmax = 56.32 - text = "EY1" - intervals [651]: - xmin = 56.32 - xmax = 56.37 - text = "S" - intervals [652]: - xmin = 56.37 - xmax = 56.44 - text = "T" - intervals [653]: - xmin = 56.44 - xmax = 56.64 - text = "AA1" - intervals [654]: - xmin = 56.64 - xmax = 56.68 - text = "N" - intervals [655]: - xmin = 56.68 - xmax = 56.72 - text = "DH" - intervals [656]: - xmin = 56.72 - xmax = 56.9 - text = "IH0" - intervals [657]: - xmin = 56.9 - xmax = 56.99 - text = "S" - intervals [658]: - xmin = 56.99 - xmax = 57.05 - text = "R" - intervals [659]: - xmin = 57.05 - xmax = 57.11 - text = "AE1" - intervals [660]: - xmin = 57.11 - xmax = 57.16 - text = "NG" - intervals [661]: - xmin = 57.16 - xmax = 57.22 - text = "K" - intervals [662]: - xmin = 57.22 - xmax = 57.29 - text = "IH0" - intervals [663]: - xmin = 57.29 - xmax = 57.35 - text = "NG" - intervals [664]: - xmin = 57.35 - xmax = 57.4 - text = "AH0" - intervals [665]: - xmin = 57.4 - xmax = 57.43 - text = "N" - intervals [666]: - xmin = 57.43 - xmax = 57.53 - text = "D" - intervals [667]: - xmin = 57.53 - xmax = 57.7 - text = "EH1" - intervals [668]: - xmin = 57.7 - xmax = 57.76 - text = "V" - intervals [669]: - xmin = 57.76 - xmax = 57.82 - text = "R" - intervals [670]: - xmin = 57.82 - xmax = 57.86 - text = "IY0" - intervals [671]: - xmin = 57.86 - xmax = 57.95 - text = "T" - intervals [672]: - xmin = 57.95 - xmax = 58.19 - text = "AY1" - intervals [673]: - xmin = 58.19 - xmax = 58.44 - text = "M" - intervals [674]: - xmin = 58.44 - xmax = 59.02 - text = "" - intervals [675]: - xmin = 59.02 - xmax = 59.12 - text = "Y" - intervals [676]: - xmin = 59.12 - xmax = 59.15 - text = "UH1" - intervals [677]: - xmin = 59.15 - xmax = 59.2 - text = "R" - intervals [678]: - xmin = 59.2 - xmax = 59.32 - text = "S" - intervals [679]: - xmin = 59.32 - xmax = 59.41 - text = "AE1" - intervals [680]: - xmin = 59.41 - xmax = 59.44 - text = "T" - intervals [681]: - xmin = 59.44 - xmax = 59.49 - text = "AH0" - intervals [682]: - xmin = 59.49 - xmax = 59.55 - text = "S" - intervals [683]: - xmin = 59.55 - xmax = 59.62 - text = "F" - intervals [684]: - xmin = 59.62 - xmax = 59.69 - text = "AY2" - intervals [685]: - xmin = 59.69 - xmax = 59.72 - text = "D" - intervals [686]: - xmin = 59.72 - xmax = 59.77 - text = "W" - intervals [687]: - xmin = 59.77 - xmax = 59.82 - text = "IH0" - intervals [688]: - xmin = 59.82 - xmax = 59.85 - text = "DH" - intervals [689]: - xmin = 59.85 - xmax = 59.88 - text = "DH" - intervals [690]: - xmin = 59.88 - xmax = 60.0 - text = "IY1" - intervals [691]: - xmin = 60.0 - xmax = 60.1 - text = "Z" - intervals [692]: - xmin = 60.1 - xmax = 60.15 - text = "R" - intervals [693]: - xmin = 60.15 - xmax = 60.23 - text = "EH1" - intervals [694]: - xmin = 60.23 - xmax = 60.28 - text = "S" - intervals [695]: - xmin = 60.28 - xmax = 60.34 - text = "T" - intervals [696]: - xmin = 60.34 - xmax = 60.4 - text = "R" - intervals [697]: - xmin = 60.4 - xmax = 60.52 - text = "AA2" - intervals [698]: - xmin = 60.52 - xmax = 60.57 - text = "N" - intervals [699]: - xmin = 60.57 - xmax = 60.62 - text = "T" - intervals [700]: - xmin = 60.62 - xmax = 60.81 - text = "S" - intervals [701]: - xmin = 60.81 - xmax = 62 - text = "" diff --git a/EMAGE/test_sequences/textgrid/2_scott_0_3_3.TextGrid b/EMAGE/test_sequences/textgrid/2_scott_0_3_3.TextGrid deleted file mode 100644 index 42b1321f18dcafe25ab172fe1607812a4c5c8eb9..0000000000000000000000000000000000000000 --- a/EMAGE/test_sequences/textgrid/2_scott_0_3_3.TextGrid +++ /dev/null @@ -1,3676 +0,0 @@ -File type = "ooTextFile" -Object class = "TextGrid" - -xmin = 0.0 -xmax = 68 -tiers? -size = 2 -item []: - item [1]: - class = "IntervalTier" - name = "words" - xmin = 0.0 - xmax = 68 - intervals: size = 213 - intervals [1]: - xmin = 0.0 - xmax = 1.47 - text = "" - intervals [2]: - xmin = 1.47 - xmax = 2.36 - text = "well" - intervals [3]: - xmin = 2.36 - xmax = 2.56 - text = "" - intervals [4]: - xmin = 2.56 - xmax = 3.05 - text = "in" - intervals [5]: - xmin = 3.05 - xmax = 3.43 - text = "my" - intervals [6]: - xmin = 3.43 - xmax = 4.29 - text = "opinion" - intervals [7]: - xmin = 4.29 - xmax = 4.43 - text = "i" - intervals [8]: - xmin = 4.43 - xmax = 4.7 - text = "think" - intervals [9]: - xmin = 4.7 - xmax = 4.77 - text = "the" - intervals [10]: - xmin = 4.77 - xmax = 5.06 - text = "best" - intervals [11]: - xmin = 5.06 - xmax = 5.31 - text = "job" - intervals [12]: - xmin = 5.31 - xmax = 5.41 - text = "for" - intervals [13]: - xmin = 5.41 - xmax = 5.62 - text = "me" - intervals [14]: - xmin = 5.62 - xmax = 5.76 - text = "is" - intervals [15]: - xmin = 5.76 - xmax = 5.84 - text = "to" - intervals [16]: - xmin = 5.84 - xmax = 6.11 - text = "become" - intervals [17]: - xmin = 6.11 - xmax = 6.2 - text = "a" - intervals [18]: - xmin = 6.2 - xmax = 6.93 - text = "journalist" - intervals [19]: - xmin = 6.93 - xmax = 7.3 - text = "cuz" - intervals [20]: - xmin = 7.3 - xmax = 7.53 - text = "this" - intervals [21]: - xmin = 7.53 - xmax = 7.63 - text = "is" - intervals [22]: - xmin = 7.63 - xmax = 7.79 - text = "my" - intervals [23]: - xmin = 7.79 - xmax = 8.14 - text = "dream" - intervals [24]: - xmin = 8.14 - xmax = 8.53 - text = "job" - intervals [25]: - xmin = 8.53 - xmax = 8.71 - text = "i've" - intervals [26]: - xmin = 8.71 - xmax = 9.13 - text = "always" - intervals [27]: - xmin = 9.13 - xmax = 9.53 - text = "wanted" - intervals [28]: - xmin = 9.53 - xmax = 9.6 - text = "to" - intervals [29]: - xmin = 9.6 - xmax = 9.77 - text = "be" - intervals [30]: - xmin = 9.77 - xmax = 9.84 - text = "a" - intervals [31]: - xmin = 9.84 - xmax = 10.29 - text = "journalist" - intervals [32]: - xmin = 10.29 - xmax = 10.48 - text = "since" - intervals [33]: - xmin = 10.48 - xmax = 10.54 - text = "i" - intervals [34]: - xmin = 10.54 - xmax = 10.71 - text = "was" - intervals [35]: - xmin = 10.71 - xmax = 10.78 - text = "in" - intervals [36]: - xmin = 10.78 - xmax = 11.01 - text = "middle" - intervals [37]: - xmin = 11.01 - xmax = 11.4 - text = "school" - intervals [38]: - xmin = 11.4 - xmax = 11.96 - text = "" - intervals [39]: - xmin = 11.96 - xmax = 12.92 - text = "journalists" - intervals [40]: - xmin = 12.92 - xmax = 13.73 - text = "never" - intervals [41]: - xmin = 13.73 - xmax = 13.81 - text = "" - intervals [42]: - xmin = 13.81 - xmax = 14.38 - text = "tell" - intervals [43]: - xmin = 14.38 - xmax = 15.08 - text = "lies" - intervals [44]: - xmin = 15.08 - xmax = 15.19 - text = "and" - intervals [45]: - xmin = 15.19 - xmax = 15.38 - text = "are" - intervals [46]: - xmin = 15.38 - xmax = 15.75 - text = "always" - intervals [47]: - xmin = 15.75 - xmax = 16.07 - text = "seeking" - intervals [48]: - xmin = 16.07 - xmax = 16.17 - text = "the" - intervals [49]: - xmin = 16.17 - xmax = 16.69 - text = "truth" - intervals [50]: - xmin = 16.69 - xmax = 16.85 - text = "" - intervals [51]: - xmin = 16.85 - xmax = 17.09 - text = "i" - intervals [52]: - xmin = 17.09 - xmax = 17.27 - text = "want" - intervals [53]: - xmin = 17.27 - xmax = 17.33 - text = "to" - intervals [54]: - xmin = 17.33 - xmax = 17.45 - text = "be" - intervals [55]: - xmin = 17.45 - xmax = 17.73 - text = "just" - intervals [56]: - xmin = 17.73 - xmax = 18.0 - text = "like" - intervals [57]: - xmin = 18.0 - xmax = 18.36 - text = "that" - intervals [58]: - xmin = 18.36 - xmax = 18.73 - text = "" - intervals [59]: - xmin = 18.73 - xmax = 19.54 - text = "i" - intervals [60]: - xmin = 19.54 - xmax = 19.71 - text = "" - intervals [61]: - xmin = 19.71 - xmax = 20.27 - text = "usually" - intervals [62]: - xmin = 20.27 - xmax = 20.52 - text = "feel" - intervals [63]: - xmin = 20.52 - xmax = 20.96 - text = "shy" - intervals [64]: - xmin = 20.96 - xmax = 21.14 - text = "when" - intervals [65]: - xmin = 21.14 - xmax = 21.26 - text = "i" - intervals [66]: - xmin = 21.26 - xmax = 21.38 - text = "am" - intervals [67]: - xmin = 21.38 - xmax = 21.75 - text = "talking" - intervals [68]: - xmin = 21.75 - xmax = 21.86 - text = "to" - intervals [69]: - xmin = 21.86 - xmax = 22.22 - text = "others" - intervals [70]: - xmin = 22.22 - xmax = 22.37 - text = "and" - intervals [71]: - xmin = 22.37 - xmax = 22.46 - text = "i" - intervals [72]: - xmin = 22.46 - xmax = 22.8 - text = "know" - intervals [73]: - xmin = 22.8 - xmax = 23.1 - text = "that" - intervals [74]: - xmin = 23.1 - xmax = 23.79 - text = "journalists" - intervals [75]: - xmin = 23.79 - xmax = 23.99 - text = "are" - intervals [76]: - xmin = 23.99 - xmax = 24.73 - text = "very" - intervals [77]: - xmin = 24.73 - xmax = 25.29 - text = "good" - intervals [78]: - xmin = 25.29 - xmax = 25.44 - text = "" - intervals [79]: - xmin = 25.44 - xmax = 25.7 - text = "at" - intervals [80]: - xmin = 25.7 - xmax = 26.41 - text = "communicating" - intervals [81]: - xmin = 26.41 - xmax = 26.94 - text = "because" - intervals [82]: - xmin = 26.94 - xmax = 26.98 - text = "" - intervals [83]: - xmin = 26.98 - xmax = 27.2 - text = "good" - intervals [84]: - xmin = 27.2 - xmax = 27.95 - text = "communication" - intervals [85]: - xmin = 27.95 - xmax = 28.36 - text = "skills" - intervals [86]: - xmin = 28.36 - xmax = 28.51 - text = "are" - intervals [87]: - xmin = 28.51 - xmax = 28.75 - text = "very" - intervals [88]: - xmin = 28.75 - xmax = 29.22 - text = "important" - intervals [89]: - xmin = 29.22 - xmax = 29.39 - text = "when" - intervals [90]: - xmin = 29.39 - xmax = 29.51 - text = "you're" - intervals [91]: - xmin = 29.51 - xmax = 29.85 - text = "doing" - intervals [92]: - xmin = 29.85 - xmax = 30.43 - text = "interviews" - intervals [93]: - xmin = 30.43 - xmax = 30.71 - text = "" - intervals [94]: - xmin = 30.71 - xmax = 30.99 - text = "i" - intervals [95]: - xmin = 30.99 - xmax = 31.21 - text = "want" - intervals [96]: - xmin = 31.21 - xmax = 31.3 - text = "to" - intervals [97]: - xmin = 31.3 - xmax = 31.71 - text = "possess" - intervals [98]: - xmin = 31.71 - xmax = 31.82 - text = "the" - intervals [99]: - xmin = 31.82 - xmax = 32.16 - text = "skill" - intervals [100]: - xmin = 32.16 - xmax = 32.93 - text = "myself" - intervals [101]: - xmin = 32.93 - xmax = 33.0 - text = "" - intervals [102]: - xmin = 33.0 - xmax = 33.53 - text = "so" - intervals [103]: - xmin = 33.53 - xmax = 33.56 - text = "" - intervals [104]: - xmin = 33.56 - xmax = 33.95 - text = "that's" - intervals [105]: - xmin = 33.95 - xmax = 34.31 - text = "why" - intervals [106]: - xmin = 34.31 - xmax = 34.53 - text = "i" - intervals [107]: - xmin = 34.53 - xmax = 34.83 - text = "want" - intervals [108]: - xmin = 34.83 - xmax = 34.89 - text = "to" - intervals [109]: - xmin = 34.89 - xmax = 35.42 - text = "become" - intervals [110]: - xmin = 35.42 - xmax = 35.46 - text = "" - intervals [111]: - xmin = 35.46 - xmax = 35.59 - text = "a" - intervals [112]: - xmin = 35.59 - xmax = 36.37 - text = "journalist" - intervals [113]: - xmin = 36.37 - xmax = 36.74 - text = "" - intervals [114]: - xmin = 36.74 - xmax = 37.02 - text = "other" - intervals [115]: - xmin = 37.02 - xmax = 37.18 - text = "than" - intervals [116]: - xmin = 37.18 - xmax = 37.73 - text = "that" - intervals [117]: - xmin = 37.73 - xmax = 37.76 - text = "" - intervals [118]: - xmin = 37.76 - xmax = 38.72 - text = "photography" - intervals [119]: - xmin = 38.72 - xmax = 38.96 - text = "" - intervals [120]: - xmin = 38.96 - xmax = 39.4 - text = "often" - intervals [121]: - xmin = 39.4 - xmax = 39.66 - text = "makes" - intervals [122]: - xmin = 39.66 - xmax = 39.8 - text = "me" - intervals [123]: - xmin = 39.8 - xmax = 40.39 - text = "feel" - intervals [124]: - xmin = 40.39 - xmax = 40.72 - text = "like" - intervals [125]: - xmin = 40.72 - xmax = 41.42 - text = "i'm" - intervals [126]: - xmin = 41.42 - xmax = 41.77 - text = "" - intervals [127]: - xmin = 41.77 - xmax = 42.06 - text = "doing" - intervals [128]: - xmin = 42.06 - xmax = 42.13 - text = "a" - intervals [129]: - xmin = 42.13 - xmax = 42.41 - text = "job" - intervals [130]: - xmin = 42.41 - xmax = 42.58 - text = "full" - intervals [131]: - xmin = 42.58 - xmax = 42.64 - text = "of" - intervals [132]: - xmin = 42.64 - xmax = 42.99 - text = "design" - intervals [133]: - xmin = 42.99 - xmax = 43.12 - text = "and" - intervals [134]: - xmin = 43.12 - xmax = 43.25 - text = "in" - intervals [135]: - xmin = 43.25 - xmax = 43.52 - text = "for" - intervals [136]: - xmin = 43.52 - xmax = 44.31 - text = "innovation" - intervals [137]: - xmin = 44.31 - xmax = 45.18 - text = "" - intervals [138]: - xmin = 45.18 - xmax = 45.73 - text = "because" - intervals [139]: - xmin = 45.73 - xmax = 45.89 - text = "for" - intervals [140]: - xmin = 45.89 - xmax = 45.99 - text = "the" - intervals [141]: - xmin = 45.99 - xmax = 46.35 - text = "same" - intervals [142]: - xmin = 46.35 - xmax = 46.88 - text = "scenery" - intervals [143]: - xmin = 46.88 - xmax = 47.01 - text = "we" - intervals [144]: - xmin = 47.01 - xmax = 47.12 - text = "can" - intervals [145]: - xmin = 47.12 - xmax = 47.34 - text = "use" - intervals [146]: - xmin = 47.34 - xmax = 47.61 - text = "different" - intervals [147]: - xmin = 47.61 - xmax = 48.12 - text = "angles" - intervals [148]: - xmin = 48.12 - xmax = 48.21 - text = "and" - intervals [149]: - xmin = 48.21 - xmax = 48.48 - text = "different" - intervals [150]: - xmin = 48.48 - xmax = 49.32 - text = "compositions" - intervals [151]: - xmin = 49.32 - xmax = 49.68 - text = "" - intervals [152]: - xmin = 49.68 - xmax = 49.98 - text = "for" - intervals [153]: - xmin = 49.98 - xmax = 50.4 - text = "example" - intervals [154]: - xmin = 50.4 - xmax = 50.91 - text = "people" - intervals [155]: - xmin = 50.91 - xmax = 51.42 - text = "being" - intervals [156]: - xmin = 51.42 - xmax = 51.91 - text = "shifted" - intervals [157]: - xmin = 51.91 - xmax = 52.06 - text = "from" - intervals [158]: - xmin = 52.06 - xmax = 52.15 - text = "the" - intervals [159]: - xmin = 52.15 - xmax = 52.47 - text = "center" - intervals [160]: - xmin = 52.47 - xmax = 52.54 - text = "of" - intervals [161]: - xmin = 52.54 - xmax = 52.63 - text = "the" - intervals [162]: - xmin = 52.63 - xmax = 52.95 - text = "frame" - intervals [163]: - xmin = 52.95 - xmax = 53.1 - text = "to" - intervals [164]: - xmin = 53.1 - xmax = 53.32 - text = "left" - intervals [165]: - xmin = 53.32 - xmax = 53.54 - text = "side" - intervals [166]: - xmin = 53.54 - xmax = 53.6 - text = "of" - intervals [167]: - xmin = 53.6 - xmax = 53.69 - text = "the" - intervals [168]: - xmin = 53.69 - xmax = 54.17 - text = "frame" - intervals [169]: - xmin = 54.17 - xmax = 54.62 - text = "" - intervals [170]: - xmin = 54.62 - xmax = 54.75 - text = "it" - intervals [171]: - xmin = 54.75 - xmax = 54.91 - text = "can" - intervals [172]: - xmin = 54.91 - xmax = 55.13 - text = "make" - intervals [173]: - xmin = 55.13 - xmax = 55.22 - text = "a" - intervals [174]: - xmin = 55.22 - xmax = 55.56 - text = "different" - intervals [175]: - xmin = 55.56 - xmax = 56.05 - text = "feeling" - intervals [176]: - xmin = 56.05 - xmax = 56.41 - text = "" - intervals [177]: - xmin = 56.41 - xmax = 56.69 - text = "when" - intervals [178]: - xmin = 56.69 - xmax = 57.25 - text = "we" - intervals [179]: - xmin = 57.25 - xmax = 57.5 - text = "" - intervals [180]: - xmin = 57.5 - xmax = 57.82 - text = "when" - intervals [181]: - xmin = 57.82 - xmax = 58.07 - text = "seen" - intervals [182]: - xmin = 58.07 - xmax = 58.17 - text = "in" - intervals [183]: - xmin = 58.17 - xmax = 58.77 - text = "context" - intervals [184]: - xmin = 58.77 - xmax = 58.86 - text = "with" - intervals [185]: - xmin = 58.86 - xmax = 58.93 - text = "the" - intervals [186]: - xmin = 58.93 - xmax = 59.61 - text = "background" - intervals [187]: - xmin = 59.61 - xmax = 59.96 - text = "" - intervals [188]: - xmin = 59.96 - xmax = 60.31 - text = "when" - intervals [189]: - xmin = 60.31 - xmax = 60.79 - text = "everyone's" - intervals [190]: - xmin = 60.79 - xmax = 61.08 - text = "taking" - intervals [191]: - xmin = 61.08 - xmax = 61.14 - text = "a" - intervals [192]: - xmin = 61.14 - xmax = 61.46 - text = "picture" - intervals [193]: - xmin = 61.46 - xmax = 61.53 - text = "of" - intervals [194]: - xmin = 61.53 - xmax = 61.6 - text = "the" - intervals [195]: - xmin = 61.6 - xmax = 61.98 - text = "exact" - intervals [196]: - xmin = 61.98 - xmax = 62.19 - text = "same" - intervals [197]: - xmin = 62.19 - xmax = 62.64 - text = "scenery" - intervals [198]: - xmin = 62.64 - xmax = 62.67 - text = "" - intervals [199]: - xmin = 62.67 - xmax = 62.89 - text = "i'm" - intervals [200]: - xmin = 62.89 - xmax = 63.28 - text = "very" - intervals [201]: - xmin = 63.28 - xmax = 63.75 - text = "happy" - intervals [202]: - xmin = 63.75 - xmax = 63.9 - text = "when" - intervals [203]: - xmin = 63.9 - xmax = 64.35 - text = "people" - intervals [204]: - xmin = 64.35 - xmax = 64.6 - text = "say" - intervals [205]: - xmin = 64.6 - xmax = 64.84 - text = "my" - intervals [206]: - xmin = 64.84 - xmax = 65.29 - text = "photos" - intervals [207]: - xmin = 65.29 - xmax = 65.47 - text = "look" - intervals [208]: - xmin = 65.47 - xmax = 66.11 - text = "better" - intervals [209]: - xmin = 66.11 - xmax = 66.43 - text = "" - intervals [210]: - xmin = 66.43 - xmax = 66.56 - text = "than" - intervals [211]: - xmin = 66.56 - xmax = 66.7 - text = "the" - intervals [212]: - xmin = 66.7 - xmax = 67.19 - text = "others" - intervals [213]: - xmin = 67.19 - xmax = 68 - text = "" - item [2]: - class = "IntervalTier" - name = "phones" - xmin = 0.0 - xmax = 68 - intervals: size = 701 - intervals [1]: - xmin = 0.0 - xmax = 1.47 - text = "" - intervals [2]: - xmin = 1.47 - xmax = 1.59 - text = "W" - intervals [3]: - xmin = 1.59 - xmax = 1.99 - text = "EH1" - intervals [4]: - xmin = 1.99 - xmax = 2.36 - text = "L" - intervals [5]: - xmin = 2.36 - xmax = 2.56 - text = "" - intervals [6]: - xmin = 2.56 - xmax = 2.88 - text = "IH0" - intervals [7]: - xmin = 2.88 - xmax = 3.05 - text = "N" - intervals [8]: - xmin = 3.05 - xmax = 3.18 - text = "M" - intervals [9]: - xmin = 3.18 - xmax = 3.43 - text = "AY1" - intervals [10]: - xmin = 3.43 - xmax = 3.53 - text = "AH0" - intervals [11]: - xmin = 3.53 - xmax = 3.6 - text = "P" - intervals [12]: - xmin = 3.6 - xmax = 3.7 - text = "IH1" - intervals [13]: - xmin = 3.7 - xmax = 3.77 - text = "N" - intervals [14]: - xmin = 3.77 - xmax = 3.85 - text = "Y" - intervals [15]: - xmin = 3.85 - xmax = 3.92 - text = "AH0" - intervals [16]: - xmin = 3.92 - xmax = 4.29 - text = "N" - intervals [17]: - xmin = 4.29 - xmax = 4.43 - text = "AY1" - intervals [18]: - xmin = 4.43 - xmax = 4.5 - text = "TH" - intervals [19]: - xmin = 4.5 - xmax = 4.57 - text = "IH1" - intervals [20]: - xmin = 4.57 - xmax = 4.67 - text = "NG" - intervals [21]: - xmin = 4.67 - xmax = 4.7 - text = "K" - intervals [22]: - xmin = 4.7 - xmax = 4.73 - text = "DH" - intervals [23]: - xmin = 4.73 - xmax = 4.77 - text = "AH0" - intervals [24]: - xmin = 4.77 - xmax = 4.84 - text = "B" - intervals [25]: - xmin = 4.84 - xmax = 4.96 - text = "EH1" - intervals [26]: - xmin = 4.96 - xmax = 5.02 - text = "S" - intervals [27]: - xmin = 5.02 - xmax = 5.06 - text = "T" - intervals [28]: - xmin = 5.06 - xmax = 5.17 - text = "JH" - intervals [29]: - xmin = 5.17 - xmax = 5.25 - text = "AA1" - intervals [30]: - xmin = 5.25 - xmax = 5.31 - text = "B" - intervals [31]: - xmin = 5.31 - xmax = 5.35 - text = "F" - intervals [32]: - xmin = 5.35 - xmax = 5.41 - text = "ER0" - intervals [33]: - xmin = 5.41 - xmax = 5.49 - text = "M" - intervals [34]: - xmin = 5.49 - xmax = 5.62 - text = "IY1" - intervals [35]: - xmin = 5.62 - xmax = 5.69 - text = "IH1" - intervals [36]: - xmin = 5.69 - xmax = 5.76 - text = "Z" - intervals [37]: - xmin = 5.76 - xmax = 5.8 - text = "T" - intervals [38]: - xmin = 5.8 - xmax = 5.84 - text = "IH0" - intervals [39]: - xmin = 5.84 - xmax = 5.88 - text = "B" - intervals [40]: - xmin = 5.88 - xmax = 5.94 - text = "IH0" - intervals [41]: - xmin = 5.94 - xmax = 6.01 - text = "K" - intervals [42]: - xmin = 6.01 - xmax = 6.06 - text = "AH1" - intervals [43]: - xmin = 6.06 - xmax = 6.11 - text = "M" - intervals [44]: - xmin = 6.11 - xmax = 6.2 - text = "AH0" - intervals [45]: - xmin = 6.2 - xmax = 6.42 - text = "JH" - intervals [46]: - xmin = 6.42 - xmax = 6.52 - text = "ER1" - intervals [47]: - xmin = 6.52 - xmax = 6.57 - text = "N" - intervals [48]: - xmin = 6.57 - xmax = 6.63 - text = "AH0" - intervals [49]: - xmin = 6.63 - xmax = 6.71 - text = "L" - intervals [50]: - xmin = 6.71 - xmax = 6.77 - text = "AH0" - intervals [51]: - xmin = 6.77 - xmax = 6.87 - text = "S" - intervals [52]: - xmin = 6.87 - xmax = 6.93 - text = "T" - intervals [53]: - xmin = 6.93 - xmax = 7.04 - text = "K" - intervals [54]: - xmin = 7.04 - xmax = 7.18 - text = "UW0" - intervals [55]: - xmin = 7.18 - xmax = 7.3 - text = "Z" - intervals [56]: - xmin = 7.3 - xmax = 7.37 - text = "DH" - intervals [57]: - xmin = 7.37 - xmax = 7.44 - text = "IH1" - intervals [58]: - xmin = 7.44 - xmax = 7.53 - text = "S" - intervals [59]: - xmin = 7.53 - xmax = 7.58 - text = "IH0" - intervals [60]: - xmin = 7.58 - xmax = 7.63 - text = "Z" - intervals [61]: - xmin = 7.63 - xmax = 7.71 - text = "M" - intervals [62]: - xmin = 7.71 - xmax = 7.79 - text = "AY1" - intervals [63]: - xmin = 7.79 - xmax = 7.9 - text = "D" - intervals [64]: - xmin = 7.9 - xmax = 7.97 - text = "R" - intervals [65]: - xmin = 7.97 - xmax = 8.07 - text = "IY1" - intervals [66]: - xmin = 8.07 - xmax = 8.14 - text = "M" - intervals [67]: - xmin = 8.14 - xmax = 8.26 - text = "JH" - intervals [68]: - xmin = 8.26 - xmax = 8.44 - text = "AA1" - intervals [69]: - xmin = 8.44 - xmax = 8.53 - text = "B" - intervals [70]: - xmin = 8.53 - xmax = 8.65 - text = "AY1" - intervals [71]: - xmin = 8.65 - xmax = 8.71 - text = "V" - intervals [72]: - xmin = 8.71 - xmax = 8.82 - text = "AO1" - intervals [73]: - xmin = 8.82 - xmax = 8.88 - text = "L" - intervals [74]: - xmin = 8.88 - xmax = 8.98 - text = "W" - intervals [75]: - xmin = 8.98 - xmax = 9.03 - text = "IY0" - intervals [76]: - xmin = 9.03 - xmax = 9.13 - text = "Z" - intervals [77]: - xmin = 9.13 - xmax = 9.3 - text = "W" - intervals [78]: - xmin = 9.3 - xmax = 9.42 - text = "AO1" - intervals [79]: - xmin = 9.42 - xmax = 9.46 - text = "N" - intervals [80]: - xmin = 9.46 - xmax = 9.5 - text = "IH0" - intervals [81]: - xmin = 9.5 - xmax = 9.53 - text = "D" - intervals [82]: - xmin = 9.53 - xmax = 9.56 - text = "T" - intervals [83]: - xmin = 9.56 - xmax = 9.6 - text = "AH0" - intervals [84]: - xmin = 9.6 - xmax = 9.66 - text = "B" - intervals [85]: - xmin = 9.66 - xmax = 9.77 - text = "IY1" - intervals [86]: - xmin = 9.77 - xmax = 9.84 - text = "AH0" - intervals [87]: - xmin = 9.84 - xmax = 9.95 - text = "JH" - intervals [88]: - xmin = 9.95 - xmax = 10.0 - text = "ER1" - intervals [89]: - xmin = 10.0 - xmax = 10.03 - text = "N" - intervals [90]: - xmin = 10.03 - xmax = 10.1 - text = "AH0" - intervals [91]: - xmin = 10.1 - xmax = 10.18 - text = "L" - intervals [92]: - xmin = 10.18 - xmax = 10.23 - text = "AH0" - intervals [93]: - xmin = 10.23 - xmax = 10.26 - text = "S" - intervals [94]: - xmin = 10.26 - xmax = 10.29 - text = "T" - intervals [95]: - xmin = 10.29 - xmax = 10.33 - text = "S" - intervals [96]: - xmin = 10.33 - xmax = 10.37 - text = "IH1" - intervals [97]: - xmin = 10.37 - xmax = 10.42 - text = "N" - intervals [98]: - xmin = 10.42 - xmax = 10.48 - text = "S" - intervals [99]: - xmin = 10.48 - xmax = 10.54 - text = "AY1" - intervals [100]: - xmin = 10.54 - xmax = 10.6 - text = "W" - intervals [101]: - xmin = 10.6 - xmax = 10.64 - text = "AH0" - intervals [102]: - xmin = 10.64 - xmax = 10.71 - text = "Z" - intervals [103]: - xmin = 10.71 - xmax = 10.75 - text = "IH0" - intervals [104]: - xmin = 10.75 - xmax = 10.78 - text = "N" - intervals [105]: - xmin = 10.78 - xmax = 10.84 - text = "M" - intervals [106]: - xmin = 10.84 - xmax = 10.87 - text = "IH1" - intervals [107]: - xmin = 10.87 - xmax = 10.9 - text = "D" - intervals [108]: - xmin = 10.9 - xmax = 10.95 - text = "AH0" - intervals [109]: - xmin = 10.95 - xmax = 11.01 - text = "L" - intervals [110]: - xmin = 11.01 - xmax = 11.11 - text = "S" - intervals [111]: - xmin = 11.11 - xmax = 11.16 - text = "K" - intervals [112]: - xmin = 11.16 - xmax = 11.21 - text = "UW1" - intervals [113]: - xmin = 11.21 - xmax = 11.4 - text = "L" - intervals [114]: - xmin = 11.4 - xmax = 11.96 - text = "" - intervals [115]: - xmin = 11.96 - xmax = 12.2 - text = "JH" - intervals [116]: - xmin = 12.2 - xmax = 12.28 - text = "ER1" - intervals [117]: - xmin = 12.28 - xmax = 12.34 - text = "N" - intervals [118]: - xmin = 12.34 - xmax = 12.38 - text = "AH0" - intervals [119]: - xmin = 12.38 - xmax = 12.5 - text = "L" - intervals [120]: - xmin = 12.5 - xmax = 12.64 - text = "AH0" - intervals [121]: - xmin = 12.64 - xmax = 12.83 - text = "S" - intervals [122]: - xmin = 12.83 - xmax = 12.86 - text = "T" - intervals [123]: - xmin = 12.86 - xmax = 12.92 - text = "S" - intervals [124]: - xmin = 12.92 - xmax = 13.14 - text = "N" - intervals [125]: - xmin = 13.14 - xmax = 13.34 - text = "EH1" - intervals [126]: - xmin = 13.34 - xmax = 13.46 - text = "V" - intervals [127]: - xmin = 13.46 - xmax = 13.73 - text = "ER0" - intervals [128]: - xmin = 13.73 - xmax = 13.81 - text = "" - intervals [129]: - xmin = 13.81 - xmax = 14.0 - text = "T" - intervals [130]: - xmin = 14.0 - xmax = 14.17 - text = "EH1" - intervals [131]: - xmin = 14.17 - xmax = 14.38 - text = "L" - intervals [132]: - xmin = 14.38 - xmax = 14.43 - text = "L" - intervals [133]: - xmin = 14.43 - xmax = 14.95 - text = "AY1" - intervals [134]: - xmin = 14.95 - xmax = 15.08 - text = "Z" - intervals [135]: - xmin = 15.08 - xmax = 15.13 - text = "AE1" - intervals [136]: - xmin = 15.13 - xmax = 15.16 - text = "N" - intervals [137]: - xmin = 15.16 - xmax = 15.19 - text = "D" - intervals [138]: - xmin = 15.19 - xmax = 15.38 - text = "ER0" - intervals [139]: - xmin = 15.38 - xmax = 15.43 - text = "AO1" - intervals [140]: - xmin = 15.43 - xmax = 15.48 - text = "L" - intervals [141]: - xmin = 15.48 - xmax = 15.57 - text = "W" - intervals [142]: - xmin = 15.57 - xmax = 15.61 - text = "IY0" - intervals [143]: - xmin = 15.61 - xmax = 15.75 - text = "Z" - intervals [144]: - xmin = 15.75 - xmax = 15.8 - text = "S" - intervals [145]: - xmin = 15.8 - xmax = 15.89 - text = "IY1" - intervals [146]: - xmin = 15.89 - xmax = 15.96 - text = "K" - intervals [147]: - xmin = 15.96 - xmax = 16.03 - text = "IH0" - intervals [148]: - xmin = 16.03 - xmax = 16.07 - text = "NG" - intervals [149]: - xmin = 16.07 - xmax = 16.12 - text = "DH" - intervals [150]: - xmin = 16.12 - xmax = 16.17 - text = "AH0" - intervals [151]: - xmin = 16.17 - xmax = 16.3 - text = "T" - intervals [152]: - xmin = 16.3 - xmax = 16.36 - text = "R" - intervals [153]: - xmin = 16.36 - xmax = 16.48 - text = "UW1" - intervals [154]: - xmin = 16.48 - xmax = 16.69 - text = "TH" - intervals [155]: - xmin = 16.69 - xmax = 16.85 - text = "" - intervals [156]: - xmin = 16.85 - xmax = 17.09 - text = "AY1" - intervals [157]: - xmin = 17.09 - xmax = 17.18 - text = "W" - intervals [158]: - xmin = 17.18 - xmax = 17.21 - text = "AA1" - intervals [159]: - xmin = 17.21 - xmax = 17.24 - text = "N" - intervals [160]: - xmin = 17.24 - xmax = 17.27 - text = "T" - intervals [161]: - xmin = 17.27 - xmax = 17.3 - text = "T" - intervals [162]: - xmin = 17.3 - xmax = 17.33 - text = "AH0" - intervals [163]: - xmin = 17.33 - xmax = 17.37 - text = "B" - intervals [164]: - xmin = 17.37 - xmax = 17.45 - text = "IY0" - intervals [165]: - xmin = 17.45 - xmax = 17.57 - text = "JH" - intervals [166]: - xmin = 17.57 - xmax = 17.64 - text = "IH0" - intervals [167]: - xmin = 17.64 - xmax = 17.7 - text = "S" - intervals [168]: - xmin = 17.7 - xmax = 17.73 - text = "T" - intervals [169]: - xmin = 17.73 - xmax = 17.81 - text = "L" - intervals [170]: - xmin = 17.81 - xmax = 17.91 - text = "AY1" - intervals [171]: - xmin = 17.91 - xmax = 18.0 - text = "K" - intervals [172]: - xmin = 18.0 - xmax = 18.06 - text = "DH" - intervals [173]: - xmin = 18.06 - xmax = 18.23 - text = "AE1" - intervals [174]: - xmin = 18.23 - xmax = 18.36 - text = "T" - intervals [175]: - xmin = 18.36 - xmax = 18.73 - text = "" - intervals [176]: - xmin = 18.73 - xmax = 19.54 - text = "AY1" - intervals [177]: - xmin = 19.54 - xmax = 19.71 - text = "" - intervals [178]: - xmin = 19.71 - xmax = 19.92 - text = "Y" - intervals [179]: - xmin = 19.92 - xmax = 20.03 - text = "UW1" - intervals [180]: - xmin = 20.03 - xmax = 20.11 - text = "ZH" - intervals [181]: - xmin = 20.11 - xmax = 20.14 - text = "AH0" - intervals [182]: - xmin = 20.14 - xmax = 20.17 - text = "L" - intervals [183]: - xmin = 20.17 - xmax = 20.27 - text = "IY0" - intervals [184]: - xmin = 20.27 - xmax = 20.37 - text = "F" - intervals [185]: - xmin = 20.37 - xmax = 20.45 - text = "IY1" - intervals [186]: - xmin = 20.45 - xmax = 20.52 - text = "L" - intervals [187]: - xmin = 20.52 - xmax = 20.73 - text = "SH" - intervals [188]: - xmin = 20.73 - xmax = 20.96 - text = "AY1" - intervals [189]: - xmin = 20.96 - xmax = 21.06 - text = "W" - intervals [190]: - xmin = 21.06 - xmax = 21.09 - text = "EH1" - intervals [191]: - xmin = 21.09 - xmax = 21.14 - text = "N" - intervals [192]: - xmin = 21.14 - xmax = 21.26 - text = "AY1" - intervals [193]: - xmin = 21.26 - xmax = 21.31 - text = "AE1" - intervals [194]: - xmin = 21.31 - xmax = 21.38 - text = "M" - intervals [195]: - xmin = 21.38 - xmax = 21.5 - text = "T" - intervals [196]: - xmin = 21.5 - xmax = 21.57 - text = "AO1" - intervals [197]: - xmin = 21.57 - xmax = 21.64 - text = "K" - intervals [198]: - xmin = 21.64 - xmax = 21.69 - text = "IH0" - intervals [199]: - xmin = 21.69 - xmax = 21.75 - text = "NG" - intervals [200]: - xmin = 21.75 - xmax = 21.8 - text = "T" - intervals [201]: - xmin = 21.8 - xmax = 21.86 - text = "AH0" - intervals [202]: - xmin = 21.86 - xmax = 21.96 - text = "AH1" - intervals [203]: - xmin = 21.96 - xmax = 22.02 - text = "DH" - intervals [204]: - xmin = 22.02 - xmax = 22.13 - text = "ER0" - intervals [205]: - xmin = 22.13 - xmax = 22.22 - text = "Z" - intervals [206]: - xmin = 22.22 - xmax = 22.29 - text = "AE1" - intervals [207]: - xmin = 22.29 - xmax = 22.32 - text = "N" - intervals [208]: - xmin = 22.32 - xmax = 22.37 - text = "D" - intervals [209]: - xmin = 22.37 - xmax = 22.46 - text = "AY1" - intervals [210]: - xmin = 22.46 - xmax = 22.6 - text = "N" - intervals [211]: - xmin = 22.6 - xmax = 22.8 - text = "OW1" - intervals [212]: - xmin = 22.8 - xmax = 22.85 - text = "DH" - intervals [213]: - xmin = 22.85 - xmax = 22.98 - text = "AH0" - intervals [214]: - xmin = 22.98 - xmax = 23.1 - text = "T" - intervals [215]: - xmin = 23.1 - xmax = 23.27 - text = "JH" - intervals [216]: - xmin = 23.27 - xmax = 23.35 - text = "ER1" - intervals [217]: - xmin = 23.35 - xmax = 23.39 - text = "N" - intervals [218]: - xmin = 23.39 - xmax = 23.46 - text = "AH0" - intervals [219]: - xmin = 23.46 - xmax = 23.54 - text = "L" - intervals [220]: - xmin = 23.54 - xmax = 23.61 - text = "AH0" - intervals [221]: - xmin = 23.61 - xmax = 23.69 - text = "S" - intervals [222]: - xmin = 23.69 - xmax = 23.72 - text = "T" - intervals [223]: - xmin = 23.72 - xmax = 23.79 - text = "S" - intervals [224]: - xmin = 23.79 - xmax = 23.89 - text = "AA1" - intervals [225]: - xmin = 23.89 - xmax = 23.99 - text = "R" - intervals [226]: - xmin = 23.99 - xmax = 24.22 - text = "V" - intervals [227]: - xmin = 24.22 - xmax = 24.49 - text = "EH1" - intervals [228]: - xmin = 24.49 - xmax = 24.64 - text = "R" - intervals [229]: - xmin = 24.64 - xmax = 24.73 - text = "IY0" - intervals [230]: - xmin = 24.73 - xmax = 24.9 - text = "G" - intervals [231]: - xmin = 24.9 - xmax = 25.12 - text = "UH1" - intervals [232]: - xmin = 25.12 - xmax = 25.29 - text = "D" - intervals [233]: - xmin = 25.29 - xmax = 25.44 - text = "" - intervals [234]: - xmin = 25.44 - xmax = 25.61 - text = "AE1" - intervals [235]: - xmin = 25.61 - xmax = 25.7 - text = "T" - intervals [236]: - xmin = 25.7 - xmax = 25.73 - text = "K" - intervals [237]: - xmin = 25.73 - xmax = 25.77 - text = "AH0" - intervals [238]: - xmin = 25.77 - xmax = 25.83 - text = "M" - intervals [239]: - xmin = 25.83 - xmax = 25.89 - text = "Y" - intervals [240]: - xmin = 25.89 - xmax = 25.93 - text = "UW1" - intervals [241]: - xmin = 25.93 - xmax = 25.98 - text = "N" - intervals [242]: - xmin = 25.98 - xmax = 26.05 - text = "AH0" - intervals [243]: - xmin = 26.05 - xmax = 26.19 - text = "K" - intervals [244]: - xmin = 26.19 - xmax = 26.25 - text = "EY2" - intervals [245]: - xmin = 26.25 - xmax = 26.28 - text = "T" - intervals [246]: - xmin = 26.28 - xmax = 26.35 - text = "IH0" - intervals [247]: - xmin = 26.35 - xmax = 26.41 - text = "NG" - intervals [248]: - xmin = 26.41 - xmax = 26.46 - text = "B" - intervals [249]: - xmin = 26.46 - xmax = 26.54 - text = "IH0" - intervals [250]: - xmin = 26.54 - xmax = 26.64 - text = "K" - intervals [251]: - xmin = 26.64 - xmax = 26.84 - text = "AH0" - intervals [252]: - xmin = 26.84 - xmax = 26.94 - text = "Z" - intervals [253]: - xmin = 26.94 - xmax = 26.98 - text = "" - intervals [254]: - xmin = 26.98 - xmax = 27.07 - text = "G" - intervals [255]: - xmin = 27.07 - xmax = 27.15 - text = "UH1" - intervals [256]: - xmin = 27.15 - xmax = 27.2 - text = "D" - intervals [257]: - xmin = 27.2 - xmax = 27.27 - text = "K" - intervals [258]: - xmin = 27.27 - xmax = 27.32 - text = "AH0" - intervals [259]: - xmin = 27.32 - xmax = 27.38 - text = "M" - intervals [260]: - xmin = 27.38 - xmax = 27.43 - text = "Y" - intervals [261]: - xmin = 27.43 - xmax = 27.46 - text = "UW2" - intervals [262]: - xmin = 27.46 - xmax = 27.49 - text = "N" - intervals [263]: - xmin = 27.49 - xmax = 27.54 - text = "AH0" - intervals [264]: - xmin = 27.54 - xmax = 27.65 - text = "K" - intervals [265]: - xmin = 27.65 - xmax = 27.75 - text = "EY1" - intervals [266]: - xmin = 27.75 - xmax = 27.84 - text = "SH" - intervals [267]: - xmin = 27.84 - xmax = 27.89 - text = "AH0" - intervals [268]: - xmin = 27.89 - xmax = 27.95 - text = "N" - intervals [269]: - xmin = 27.95 - xmax = 28.03 - text = "S" - intervals [270]: - xmin = 28.03 - xmax = 28.08 - text = "K" - intervals [271]: - xmin = 28.08 - xmax = 28.15 - text = "IH1" - intervals [272]: - xmin = 28.15 - xmax = 28.27 - text = "L" - intervals [273]: - xmin = 28.27 - xmax = 28.36 - text = "Z" - intervals [274]: - xmin = 28.36 - xmax = 28.42 - text = "AA1" - intervals [275]: - xmin = 28.42 - xmax = 28.51 - text = "R" - intervals [276]: - xmin = 28.51 - xmax = 28.56 - text = "V" - intervals [277]: - xmin = 28.56 - xmax = 28.61 - text = "EH1" - intervals [278]: - xmin = 28.61 - xmax = 28.72 - text = "R" - intervals [279]: - xmin = 28.72 - xmax = 28.75 - text = "IY0" - intervals [280]: - xmin = 28.75 - xmax = 28.8 - text = "IH0" - intervals [281]: - xmin = 28.8 - xmax = 28.86 - text = "M" - intervals [282]: - xmin = 28.86 - xmax = 28.97 - text = "P" - intervals [283]: - xmin = 28.97 - xmax = 29.01 - text = "AO1" - intervals [284]: - xmin = 29.01 - xmax = 29.07 - text = "R" - intervals [285]: - xmin = 29.07 - xmax = 29.11 - text = "T" - intervals [286]: - xmin = 29.11 - xmax = 29.14 - text = "AH0" - intervals [287]: - xmin = 29.14 - xmax = 29.18 - text = "N" - intervals [288]: - xmin = 29.18 - xmax = 29.22 - text = "T" - intervals [289]: - xmin = 29.22 - xmax = 29.29 - text = "W" - intervals [290]: - xmin = 29.29 - xmax = 29.32 - text = "EH1" - intervals [291]: - xmin = 29.32 - xmax = 29.39 - text = "N" - intervals [292]: - xmin = 29.39 - xmax = 29.43 - text = "Y" - intervals [293]: - xmin = 29.43 - xmax = 29.46 - text = "UH1" - intervals [294]: - xmin = 29.46 - xmax = 29.51 - text = "R" - intervals [295]: - xmin = 29.51 - xmax = 29.6 - text = "D" - intervals [296]: - xmin = 29.6 - xmax = 29.7 - text = "UW1" - intervals [297]: - xmin = 29.7 - xmax = 29.76 - text = "IH0" - intervals [298]: - xmin = 29.76 - xmax = 29.85 - text = "NG" - intervals [299]: - xmin = 29.85 - xmax = 29.88 - text = "IH1" - intervals [300]: - xmin = 29.88 - xmax = 29.99 - text = "N" - intervals [301]: - xmin = 29.99 - xmax = 30.06 - text = "ER0" - intervals [302]: - xmin = 30.06 - xmax = 30.1 - text = "V" - intervals [303]: - xmin = 30.1 - xmax = 30.21 - text = "Y" - intervals [304]: - xmin = 30.21 - xmax = 30.28 - text = "UW2" - intervals [305]: - xmin = 30.28 - xmax = 30.43 - text = "Z" - intervals [306]: - xmin = 30.43 - xmax = 30.71 - text = "" - intervals [307]: - xmin = 30.71 - xmax = 30.99 - text = "AY1" - intervals [308]: - xmin = 30.99 - xmax = 31.11 - text = "W" - intervals [309]: - xmin = 31.11 - xmax = 31.15 - text = "AA1" - intervals [310]: - xmin = 31.15 - xmax = 31.18 - text = "N" - intervals [311]: - xmin = 31.18 - xmax = 31.21 - text = "T" - intervals [312]: - xmin = 31.21 - xmax = 31.24 - text = "T" - intervals [313]: - xmin = 31.24 - xmax = 31.3 - text = "AH0" - intervals [314]: - xmin = 31.3 - xmax = 31.35 - text = "P" - intervals [315]: - xmin = 31.35 - xmax = 31.42 - text = "AH0" - intervals [316]: - xmin = 31.42 - xmax = 31.51 - text = "Z" - intervals [317]: - xmin = 31.51 - xmax = 31.62 - text = "EH1" - intervals [318]: - xmin = 31.62 - xmax = 31.71 - text = "S" - intervals [319]: - xmin = 31.71 - xmax = 31.75 - text = "DH" - intervals [320]: - xmin = 31.75 - xmax = 31.82 - text = "AH0" - intervals [321]: - xmin = 31.82 - xmax = 31.97 - text = "S" - intervals [322]: - xmin = 31.97 - xmax = 32.02 - text = "K" - intervals [323]: - xmin = 32.02 - xmax = 32.09 - text = "IH1" - intervals [324]: - xmin = 32.09 - xmax = 32.16 - text = "L" - intervals [325]: - xmin = 32.16 - xmax = 32.23 - text = "M" - intervals [326]: - xmin = 32.23 - xmax = 32.31 - text = "AY2" - intervals [327]: - xmin = 32.31 - xmax = 32.46 - text = "S" - intervals [328]: - xmin = 32.46 - xmax = 32.53 - text = "EH1" - intervals [329]: - xmin = 32.53 - xmax = 32.76 - text = "L" - intervals [330]: - xmin = 32.76 - xmax = 32.93 - text = "F" - intervals [331]: - xmin = 32.93 - xmax = 33.0 - text = "" - intervals [332]: - xmin = 33.0 - xmax = 33.22 - text = "S" - intervals [333]: - xmin = 33.22 - xmax = 33.53 - text = "OW1" - intervals [334]: - xmin = 33.53 - xmax = 33.56 - text = "" - intervals [335]: - xmin = 33.56 - xmax = 33.67 - text = "DH" - intervals [336]: - xmin = 33.67 - xmax = 33.82 - text = "AE1" - intervals [337]: - xmin = 33.82 - xmax = 33.89 - text = "T" - intervals [338]: - xmin = 33.89 - xmax = 33.95 - text = "S" - intervals [339]: - xmin = 33.95 - xmax = 34.09 - text = "W" - intervals [340]: - xmin = 34.09 - xmax = 34.31 - text = "AY1" - intervals [341]: - xmin = 34.31 - xmax = 34.53 - text = "AY1" - intervals [342]: - xmin = 34.53 - xmax = 34.67 - text = "W" - intervals [343]: - xmin = 34.67 - xmax = 34.77 - text = "AO1" - intervals [344]: - xmin = 34.77 - xmax = 34.8 - text = "N" - intervals [345]: - xmin = 34.8 - xmax = 34.83 - text = "T" - intervals [346]: - xmin = 34.83 - xmax = 34.86 - text = "T" - intervals [347]: - xmin = 34.86 - xmax = 34.89 - text = "IH0" - intervals [348]: - xmin = 34.89 - xmax = 34.93 - text = "B" - intervals [349]: - xmin = 34.93 - xmax = 35.0 - text = "IH0" - intervals [350]: - xmin = 35.0 - xmax = 35.08 - text = "K" - intervals [351]: - xmin = 35.08 - xmax = 35.19 - text = "AH1" - intervals [352]: - xmin = 35.19 - xmax = 35.42 - text = "M" - intervals [353]: - xmin = 35.42 - xmax = 35.46 - text = "" - intervals [354]: - xmin = 35.46 - xmax = 35.59 - text = "AH0" - intervals [355]: - xmin = 35.59 - xmax = 35.68 - text = "JH" - intervals [356]: - xmin = 35.68 - xmax = 35.79 - text = "ER1" - intervals [357]: - xmin = 35.79 - xmax = 35.84 - text = "N" - intervals [358]: - xmin = 35.84 - xmax = 35.89 - text = "AH0" - intervals [359]: - xmin = 35.89 - xmax = 35.97 - text = "L" - intervals [360]: - xmin = 35.97 - xmax = 36.07 - text = "AH0" - intervals [361]: - xmin = 36.07 - xmax = 36.27 - text = "S" - intervals [362]: - xmin = 36.27 - xmax = 36.37 - text = "T" - intervals [363]: - xmin = 36.37 - xmax = 36.74 - text = "" - intervals [364]: - xmin = 36.74 - xmax = 36.92 - text = "AH1" - intervals [365]: - xmin = 36.92 - xmax = 36.96 - text = "DH" - intervals [366]: - xmin = 36.96 - xmax = 37.02 - text = "ER0" - intervals [367]: - xmin = 37.02 - xmax = 37.05 - text = "DH" - intervals [368]: - xmin = 37.05 - xmax = 37.13 - text = "AE1" - intervals [369]: - xmin = 37.13 - xmax = 37.18 - text = "N" - intervals [370]: - xmin = 37.18 - xmax = 37.23 - text = "DH" - intervals [371]: - xmin = 37.23 - xmax = 37.47 - text = "AE1" - intervals [372]: - xmin = 37.47 - xmax = 37.73 - text = "T" - intervals [373]: - xmin = 37.73 - xmax = 37.76 - text = "" - intervals [374]: - xmin = 37.76 - xmax = 37.8 - text = "F" - intervals [375]: - xmin = 37.8 - xmax = 37.85 - text = "AH0" - intervals [376]: - xmin = 37.85 - xmax = 37.96 - text = "T" - intervals [377]: - xmin = 37.96 - xmax = 38.08 - text = "AA1" - intervals [378]: - xmin = 38.08 - xmax = 38.13 - text = "G" - intervals [379]: - xmin = 38.13 - xmax = 38.2 - text = "R" - intervals [380]: - xmin = 38.2 - xmax = 38.26 - text = "AH0" - intervals [381]: - xmin = 38.26 - xmax = 38.35 - text = "F" - intervals [382]: - xmin = 38.35 - xmax = 38.72 - text = "IY0" - intervals [383]: - xmin = 38.72 - xmax = 38.96 - text = "" - intervals [384]: - xmin = 38.96 - xmax = 39.25 - text = "AO1" - intervals [385]: - xmin = 39.25 - xmax = 39.3 - text = "F" - intervals [386]: - xmin = 39.3 - xmax = 39.36 - text = "AH0" - intervals [387]: - xmin = 39.36 - xmax = 39.4 - text = "N" - intervals [388]: - xmin = 39.4 - xmax = 39.47 - text = "M" - intervals [389]: - xmin = 39.47 - xmax = 39.54 - text = "EY1" - intervals [390]: - xmin = 39.54 - xmax = 39.59 - text = "K" - intervals [391]: - xmin = 39.59 - xmax = 39.66 - text = "S" - intervals [392]: - xmin = 39.66 - xmax = 39.7 - text = "M" - intervals [393]: - xmin = 39.7 - xmax = 39.8 - text = "IY1" - intervals [394]: - xmin = 39.8 - xmax = 39.94 - text = "F" - intervals [395]: - xmin = 39.94 - xmax = 40.09 - text = "IY1" - intervals [396]: - xmin = 40.09 - xmax = 40.39 - text = "L" - intervals [397]: - xmin = 40.39 - xmax = 40.48 - text = "L" - intervals [398]: - xmin = 40.48 - xmax = 40.62 - text = "AY1" - intervals [399]: - xmin = 40.62 - xmax = 40.72 - text = "K" - intervals [400]: - xmin = 40.72 - xmax = 40.97 - text = "AY1" - intervals [401]: - xmin = 40.97 - xmax = 41.42 - text = "M" - intervals [402]: - xmin = 41.42 - xmax = 41.77 - text = "" - intervals [403]: - xmin = 41.77 - xmax = 41.85 - text = "D" - intervals [404]: - xmin = 41.85 - xmax = 41.95 - text = "UW1" - intervals [405]: - xmin = 41.95 - xmax = 42.02 - text = "IH0" - intervals [406]: - xmin = 42.02 - xmax = 42.06 - text = "NG" - intervals [407]: - xmin = 42.06 - xmax = 42.13 - text = "EY1" - intervals [408]: - xmin = 42.13 - xmax = 42.25 - text = "JH" - intervals [409]: - xmin = 42.25 - xmax = 42.36 - text = "AA1" - intervals [410]: - xmin = 42.36 - xmax = 42.41 - text = "B" - intervals [411]: - xmin = 42.41 - xmax = 42.47 - text = "F" - intervals [412]: - xmin = 42.47 - xmax = 42.51 - text = "UH1" - intervals [413]: - xmin = 42.51 - xmax = 42.58 - text = "L" - intervals [414]: - xmin = 42.58 - xmax = 42.61 - text = "AH0" - intervals [415]: - xmin = 42.61 - xmax = 42.64 - text = "V" - intervals [416]: - xmin = 42.64 - xmax = 42.68 - text = "D" - intervals [417]: - xmin = 42.68 - xmax = 42.74 - text = "IH0" - intervals [418]: - xmin = 42.74 - xmax = 42.83 - text = "Z" - intervals [419]: - xmin = 42.83 - xmax = 42.96 - text = "AY1" - intervals [420]: - xmin = 42.96 - xmax = 42.99 - text = "N" - intervals [421]: - xmin = 42.99 - xmax = 43.03 - text = "AE1" - intervals [422]: - xmin = 43.03 - xmax = 43.07 - text = "N" - intervals [423]: - xmin = 43.07 - xmax = 43.12 - text = "D" - intervals [424]: - xmin = 43.12 - xmax = 43.19 - text = "IH0" - intervals [425]: - xmin = 43.19 - xmax = 43.25 - text = "N" - intervals [426]: - xmin = 43.25 - xmax = 43.3 - text = "F" - intervals [427]: - xmin = 43.3 - xmax = 43.52 - text = "ER0" - intervals [428]: - xmin = 43.52 - xmax = 43.68 - text = "IH2" - intervals [429]: - xmin = 43.68 - xmax = 43.74 - text = "N" - intervals [430]: - xmin = 43.74 - xmax = 43.8 - text = "AH0" - intervals [431]: - xmin = 43.8 - xmax = 43.87 - text = "V" - intervals [432]: - xmin = 43.87 - xmax = 44.01 - text = "EY1" - intervals [433]: - xmin = 44.01 - xmax = 44.09 - text = "SH" - intervals [434]: - xmin = 44.09 - xmax = 44.16 - text = "AH0" - intervals [435]: - xmin = 44.16 - xmax = 44.31 - text = "N" 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"IY1" - intervals [452]: - xmin = 46.59 - xmax = 46.65 - text = "N" - intervals [453]: - xmin = 46.65 - xmax = 46.74 - text = "ER0" - intervals [454]: - xmin = 46.74 - xmax = 46.88 - text = "IY0" - intervals [455]: - xmin = 46.88 - xmax = 46.94 - text = "W" - intervals [456]: - xmin = 46.94 - xmax = 47.01 - text = "IY1" - intervals [457]: - xmin = 47.01 - xmax = 47.06 - text = "K" - intervals [458]: - xmin = 47.06 - xmax = 47.09 - text = "AH0" - intervals [459]: - xmin = 47.09 - xmax = 47.12 - text = "N" - intervals [460]: - xmin = 47.12 - xmax = 47.22 - text = "Y" - intervals [461]: - xmin = 47.22 - xmax = 47.27 - text = "UW1" - intervals [462]: - xmin = 47.27 - xmax = 47.34 - text = "Z" - intervals [463]: - xmin = 47.34 - xmax = 47.4 - text = "D" - intervals [464]: - xmin = 47.4 - xmax = 47.45 - text = "IH1" - intervals [465]: - xmin = 47.45 - xmax = 47.49 - text = "F" - intervals [466]: - xmin = 47.49 - xmax = 47.52 - text = "R" - intervals [467]: - xmin = 47.52 - xmax = 47.55 - text = "AH0" - intervals [468]: - xmin = 47.55 - xmax = 47.58 - text = "N" - intervals [469]: - xmin = 47.58 - xmax = 47.61 - text = "T" - intervals [470]: - xmin = 47.61 - xmax = 47.74 - text = "AE1" - intervals [471]: - xmin = 47.74 - xmax = 47.82 - text = "NG" - intervals [472]: - xmin = 47.82 - xmax = 47.85 - text = "G" - intervals [473]: - xmin = 47.85 - xmax = 47.89 - text = "AH0" - intervals [474]: - xmin = 47.89 - xmax = 48.04 - text = "L" - intervals [475]: - xmin = 48.04 - xmax = 48.12 - text = "Z" - intervals [476]: - xmin = 48.12 - xmax = 48.15 - text = "AH0" - intervals [477]: - xmin = 48.15 - xmax = 48.18 - text = "N" - intervals [478]: - xmin = 48.18 - xmax = 48.21 - text = "D" - intervals [479]: - xmin = 48.21 - xmax = 48.26 - text = "D" - intervals [480]: - xmin = 48.26 - xmax = 48.31 - text = "IH1" - intervals [481]: - xmin = 48.31 - xmax = 48.36 - text = "F" - intervals [482]: - xmin = 48.36 - xmax = 48.39 - text = "R" - intervals [483]: - xmin = 48.39 - xmax = 48.42 - text = "AH0" - intervals [484]: - xmin = 48.42 - xmax = 48.45 - text = "N" - intervals [485]: - xmin = 48.45 - xmax = 48.48 - text = "T" - intervals [486]: - xmin = 48.48 - xmax = 48.53 - text = "K" - intervals [487]: - xmin = 48.53 - xmax = 48.6 - text = "AA2" - intervals [488]: - xmin = 48.6 - xmax = 48.64 - text = "M" - intervals [489]: - xmin = 48.64 - xmax = 48.67 - text = "P" - intervals [490]: - xmin = 48.67 - xmax = 48.72 - text = "AH0" - intervals [491]: - xmin = 48.72 - xmax = 48.8 - text = "Z" - intervals [492]: - xmin = 48.8 - xmax = 48.88 - text = "IH1" - intervals [493]: - xmin = 48.88 - xmax = 48.95 - text = "SH" - intervals [494]: - xmin = 48.95 - xmax = 49.02 - text = "AH0" - intervals [495]: - xmin = 49.02 - xmax = 49.12 - text = "N" - intervals [496]: - xmin = 49.12 - xmax = 49.32 - text = "Z" - intervals [497]: - xmin = 49.32 - xmax = 49.68 - text = "" - intervals [498]: - xmin = 49.68 - xmax = 49.91 - text = "F" - intervals [499]: - xmin = 49.91 - xmax = 49.98 - text = "ER0" - intervals [500]: - xmin = 49.98 - xmax = 50.02 - text = "IH0" - intervals [501]: - xmin = 50.02 - xmax = 50.06 - text = "G" - intervals [502]: - xmin = 50.06 - xmax = 50.13 - text = "Z" - intervals [503]: - xmin = 50.13 - xmax = 50.22 - text = "AE1" - intervals [504]: - xmin = 50.22 - xmax = 50.27 - text = "M" - intervals [505]: - xmin = 50.27 - xmax = 50.31 - text = "P" - intervals [506]: - xmin = 50.31 - xmax = 50.34 - text = "AH0" - intervals [507]: - xmin = 50.34 - xmax = 50.4 - text = "L" - intervals [508]: - xmin = 50.4 - xmax = 50.48 - text = "P" - intervals [509]: - xmin = 50.48 - xmax = 50.58 - text = "IY1" - intervals [510]: - xmin = 50.58 - xmax = 50.63 - text = "P" - intervals [511]: - xmin = 50.63 - xmax = 50.69 - text = "AH0" - intervals [512]: - xmin = 50.69 - xmax = 50.91 - text = "L" - intervals [513]: - xmin = 50.91 - xmax = 51.21 - text = "B" - intervals [514]: - xmin = 51.21 - xmax = 51.29 - text = "IY1" - intervals [515]: - xmin = 51.29 - xmax = 51.34 - text = "IH0" - intervals [516]: - xmin = 51.34 - xmax = 51.42 - text = "NG" - intervals [517]: - xmin = 51.42 - xmax = 51.57 - text = "SH" - intervals [518]: - xmin = 51.57 - xmax = 51.62 - text = "IH1" - intervals [519]: - xmin = 51.62 - xmax = 51.68 - text = "F" - intervals [520]: - xmin = 51.68 - xmax = 51.74 - text = "T" - intervals [521]: - xmin = 51.74 - xmax = 51.79 - text = "IH0" - intervals [522]: - xmin = 51.79 - xmax = 51.91 - text = "D" - intervals [523]: - xmin = 51.91 - xmax = 51.94 - text = "F" - intervals [524]: - xmin = 51.94 - xmax = 52.02 - text = "ER0" - intervals [525]: - xmin = 52.02 - xmax = 52.06 - text = "M" - intervals [526]: - xmin = 52.06 - xmax = 52.1 - text = "DH" - intervals [527]: - xmin = 52.1 - xmax = 52.15 - text = "AH0" - intervals [528]: - xmin = 52.15 - xmax = 52.27 - text = "S" - intervals [529]: - xmin = 52.27 - xmax = 52.32 - text = "EH1" - intervals [530]: - xmin = 52.32 - xmax = 52.38 - text = "N" - intervals [531]: - xmin = 52.38 - xmax = 52.47 - text = "ER0" - intervals [532]: - xmin = 52.47 - xmax = 52.51 - text = "AH0" - intervals [533]: - xmin = 52.51 - xmax = 52.54 - text = "V" - intervals [534]: - xmin = 52.54 - xmax = 52.57 - text = "DH" - intervals [535]: - xmin = 52.57 - xmax = 52.63 - text = "AH0" - intervals [536]: - xmin = 52.63 - xmax = 52.72 - text = "F" - intervals [537]: - xmin = 52.72 - xmax = 52.8 - text = "R" - intervals [538]: - xmin = 52.8 - xmax = 52.89 - text = "EY1" - intervals [539]: - xmin = 52.89 - xmax = 52.95 - text = "M" - intervals [540]: - xmin = 52.95 - xmax = 53.01 - text = "T" - intervals [541]: - xmin = 53.01 - xmax = 53.1 - text = "AH0" - intervals [542]: - xmin = 53.1 - xmax = 53.19 - text = "L" - intervals [543]: - xmin = 53.19 - xmax = 53.26 - text = "EH1" - intervals [544]: - xmin = 53.26 - xmax = 53.29 - text = "F" - intervals [545]: - xmin = 53.29 - xmax = 53.32 - text = "T" - intervals [546]: - xmin = 53.32 - xmax = 53.4 - text = "S" - intervals [547]: - xmin = 53.4 - xmax = 53.5 - text = "AY1" - intervals [548]: - xmin = 53.5 - xmax = 53.54 - text = "D" - intervals [549]: - xmin = 53.54 - xmax = 53.57 - text = "AH0" - intervals [550]: - xmin = 53.57 - xmax = 53.6 - text = "V" - intervals [551]: - xmin = 53.6 - xmax = 53.63 - text = "DH" - intervals [552]: - xmin = 53.63 - xmax = 53.69 - text = "AH0" - intervals [553]: - xmin = 53.69 - xmax = 53.78 - text = "F" - intervals [554]: - xmin = 53.78 - xmax = 53.92 - text = "R" - intervals [555]: - xmin = 53.92 - xmax = 54.03 - text = "EY1" - intervals [556]: - xmin = 54.03 - xmax = 54.17 - text = "M" - intervals [557]: - xmin = 54.17 - xmax = 54.62 - text = "" - intervals [558]: - xmin = 54.62 - xmax = 54.72 - text = "IH0" - intervals [559]: - xmin = 54.72 - xmax = 54.75 - text = "T" - intervals [560]: - xmin = 54.75 - xmax = 54.82 - text = "K" - intervals [561]: - xmin = 54.82 - xmax = 54.88 - text = "AH0" - intervals [562]: - xmin = 54.88 - xmax = 54.91 - text = "N" - intervals [563]: - xmin = 54.91 - xmax = 54.97 - text = "M" - intervals [564]: - xmin = 54.97 - xmax = 55.1 - text = "EY1" - intervals [565]: - xmin = 55.1 - xmax = 55.13 - text = "K" - intervals [566]: - xmin = 55.13 - xmax = 55.22 - text = "EY1" - intervals [567]: - xmin = 55.22 - xmax = 55.29 - text = "D" - intervals [568]: - xmin = 55.29 - xmax = 55.35 - text = "IH1" - intervals [569]: - xmin = 55.35 - xmax = 55.43 - text = "F" - intervals [570]: - xmin = 55.43 - xmax = 55.46 - text = "R" - intervals [571]: - xmin = 55.46 - xmax = 55.49 - text = "AH0" - intervals [572]: - xmin = 55.49 - xmax = 55.52 - text = "N" - intervals [573]: - xmin = 55.52 - xmax = 55.56 - text = "T" - intervals [574]: - xmin = 55.56 - xmax = 55.64 - text = "F" - intervals [575]: - xmin = 55.64 - xmax = 55.77 - text = "IY1" - intervals [576]: - xmin = 55.77 - xmax = 55.82 - text = "L" - intervals [577]: - xmin = 55.82 - xmax = 55.88 - text = "IH0" - intervals [578]: - xmin = 55.88 - xmax = 56.05 - text = "NG" - intervals [579]: - xmin = 56.05 - xmax = 56.41 - text = "" - intervals [580]: - xmin = 56.41 - xmax = 56.48 - text = "W" - intervals [581]: - xmin = 56.48 - xmax = 56.66 - text = "EH1" - intervals [582]: - xmin = 56.66 - xmax = 56.69 - text = "N" - intervals [583]: - xmin = 56.69 - xmax = 57.1 - text = "W" - intervals [584]: - xmin = 57.1 - xmax = 57.25 - text = "IY1" - intervals [585]: - xmin = 57.25 - xmax = 57.5 - text = "" - intervals [586]: - xmin = 57.5 - xmax = 57.68 - text = "W" - intervals [587]: - xmin = 57.68 - xmax = 57.72 - text = "EH1" - intervals [588]: - xmin = 57.72 - xmax = 57.82 - text = "N" - intervals [589]: - xmin = 57.82 - xmax = 57.96 - text = "S" - intervals [590]: - xmin = 57.96 - xmax = 58.02 - text = "IY1" - intervals [591]: - xmin = 58.02 - xmax = 58.07 - text = "N" - intervals [592]: - xmin = 58.07 - xmax = 58.11 - text = "IH1" - intervals [593]: - xmin = 58.11 - xmax = 58.17 - text = "N" - intervals [594]: - xmin = 58.17 - xmax = 58.25 - text = "K" - intervals [595]: - xmin = 58.25 - xmax = 58.41 - text = "AA1" - intervals [596]: - xmin = 58.41 - xmax = 58.47 - text = "N" - intervals [597]: - xmin = 58.47 - xmax = 58.53 - text = "T" - intervals [598]: - xmin = 58.53 - xmax = 58.65 - text = "EH0" - intervals [599]: - xmin = 58.65 - xmax = 58.69 - text = "K" - intervals [600]: - xmin = 58.69 - xmax = 58.74 - text = "S" - intervals [601]: - xmin = 58.74 - xmax = 58.77 - text = "T" - intervals [602]: - xmin = 58.77 - xmax = 58.8 - text = "W" - intervals [603]: - xmin = 58.8 - xmax = 58.83 - text = "IH0" - intervals [604]: - xmin = 58.83 - xmax = 58.86 - text = "DH" - intervals [605]: - xmin = 58.86 - xmax = 58.9 - text = "DH" - intervals [606]: - xmin = 58.9 - xmax = 58.93 - text = "AH1" - intervals [607]: - xmin = 58.93 - xmax = 59.03 - text = "B" - intervals [608]: - xmin = 59.03 - xmax = 59.2 - text = "AE1" - intervals [609]: - xmin = 59.2 - xmax = 59.25 - text = "K" - intervals [610]: - xmin = 59.25 - xmax = 59.29 - text = "G" - intervals [611]: - xmin = 59.29 - xmax = 59.33 - text = "R" - intervals [612]: - xmin = 59.33 - xmax = 59.45 - text = "AW2" - intervals [613]: - xmin = 59.45 - xmax = 59.52 - text = "N" - intervals [614]: - xmin = 59.52 - xmax = 59.61 - text = "D" - intervals [615]: - xmin = 59.61 - xmax = 59.96 - text = "" - intervals [616]: - xmin = 59.96 - xmax = 60.13 - text = "W" - intervals [617]: - xmin = 60.13 - xmax = 60.17 - text = "EH1" - intervals [618]: - xmin = 60.17 - xmax = 60.31 - text = "N" - intervals [619]: - xmin = 60.31 - xmax = 60.41 - text = "EH1" - intervals [620]: - xmin = 60.41 - xmax = 60.45 - text = "V" - intervals [621]: - xmin = 60.45 - xmax = 60.48 - text = "R" - intervals [622]: - xmin = 60.48 - xmax = 60.51 - text = "IY0" - intervals [623]: - xmin = 60.51 - xmax = 60.58 - text = "W" - intervals [624]: - xmin = 60.58 - xmax = 60.65 - text = "AH2" - intervals [625]: - xmin = 60.65 - xmax = 60.69 - text = "N" - intervals [626]: - xmin = 60.69 - xmax = 60.79 - text = "Z" - intervals [627]: - xmin = 60.79 - xmax = 60.86 - text = "T" - intervals [628]: - xmin = 60.86 - xmax = 60.92 - text = "EY1" - intervals [629]: - xmin = 60.92 - xmax = 60.95 - text = "K" - intervals [630]: - xmin = 60.95 - xmax = 61.0 - text = "IH0" - intervals [631]: - xmin = 61.0 - xmax = 61.08 - text = "NG" - intervals [632]: - xmin = 61.08 - xmax = 61.14 - text = "AH0" - intervals [633]: - xmin = 61.14 - xmax = 61.2 - text = "P" - intervals [634]: - xmin = 61.2 - xmax = 61.28 - text = "IH1" - intervals [635]: - xmin = 61.28 - xmax = 61.32 - text = "K" - intervals [636]: - xmin = 61.32 - xmax = 61.43 - text = "CH" - intervals [637]: - xmin = 61.43 - xmax = 61.46 - text = "ER0" - intervals [638]: - xmin = 61.46 - xmax = 61.49 - text = "AH0" - intervals [639]: - xmin = 61.49 - xmax = 61.53 - text = "V" - intervals [640]: - xmin = 61.53 - xmax = 61.56 - text = "DH" - intervals [641]: - xmin = 61.56 - xmax = 61.6 - text = "AH0" - intervals [642]: - xmin = 61.6 - xmax = 61.64 - text = "IH0" - intervals [643]: - xmin = 61.64 - xmax = 61.69 - text = "G" - intervals [644]: - xmin = 61.69 - xmax = 61.76 - text = "Z" - intervals [645]: - xmin = 61.76 - xmax = 61.9 - text = "AE1" - intervals [646]: - xmin = 61.9 - xmax = 61.94 - text = "K" - intervals [647]: - xmin = 61.94 - xmax = 61.98 - text = "T" - intervals [648]: - xmin = 61.98 - xmax = 62.05 - text = "S" - intervals [649]: - xmin = 62.05 - xmax = 62.13 - text = "EY1" - intervals [650]: - xmin = 62.13 - xmax = 62.19 - text = "M" - intervals [651]: - xmin = 62.19 - xmax = 62.3 - text = "S" - intervals [652]: - xmin = 62.3 - xmax = 62.34 - text = "IY1" - intervals [653]: - xmin = 62.34 - xmax = 62.39 - text = "N" - intervals [654]: - xmin = 62.39 - xmax = 62.47 - text = "ER0" - intervals [655]: - xmin = 62.47 - xmax = 62.64 - text = "IY0" - intervals [656]: - xmin = 62.64 - xmax = 62.67 - text = "" - intervals [657]: - xmin = 62.67 - xmax = 62.8 - text = "AY1" - intervals [658]: - xmin = 62.8 - xmax = 62.89 - text = "M" - intervals [659]: - xmin = 62.89 - xmax = 63.03 - text = "V" - intervals [660]: - xmin = 63.03 - xmax = 63.12 - text = "EH1" - intervals [661]: - xmin = 63.12 - xmax = 63.22 - text = "R" - intervals [662]: - xmin = 63.22 - xmax = 63.28 - text = "IY0" - intervals [663]: - xmin = 63.28 - xmax = 63.39 - text = "HH" - intervals [664]: - xmin = 63.39 - xmax = 63.56 - text = "AE1" - intervals [665]: - xmin = 63.56 - xmax = 63.62 - text = "P" - intervals [666]: - xmin = 63.62 - xmax = 63.75 - text = "IY0" - intervals [667]: - xmin = 63.75 - xmax = 63.81 - text = "W" - intervals [668]: - xmin = 63.81 - xmax = 63.85 - text = "IH1" - intervals [669]: - xmin = 63.85 - xmax = 63.9 - text = "N" - intervals [670]: - xmin = 63.9 - xmax = 64.03 - text = "P" - intervals [671]: - xmin = 64.03 - xmax = 64.14 - text = "IY1" - intervals [672]: - xmin = 64.14 - xmax = 64.19 - text = "P" - intervals [673]: - xmin = 64.19 - xmax = 64.23 - text = "AH0" - intervals [674]: - xmin = 64.23 - xmax = 64.35 - text = "L" - intervals [675]: - xmin = 64.35 - xmax = 64.48 - text = "S" - intervals [676]: - xmin = 64.48 - xmax = 64.6 - text = "EY1" - intervals [677]: - xmin = 64.6 - xmax = 64.69 - text = "M" - intervals [678]: - xmin = 64.69 - xmax = 64.84 - text = "AY1" - intervals [679]: - xmin = 64.84 - xmax = 64.99 - text = "F" - intervals [680]: - xmin = 64.99 - xmax = 65.07 - text = "OW1" - intervals [681]: - xmin = 65.07 - xmax = 65.1 - text = "T" - intervals [682]: - xmin = 65.1 - xmax = 65.18 - text = "OW2" - intervals [683]: - xmin = 65.18 - xmax = 65.29 - text = "Z" - intervals [684]: - xmin = 65.29 - xmax = 65.37 - text = "L" - intervals [685]: - xmin = 65.37 - xmax = 65.42 - text = "UH1" - intervals [686]: - xmin = 65.42 - xmax = 65.47 - text = "K" - intervals [687]: - xmin = 65.47 - xmax = 65.67 - text = "B" - intervals [688]: - xmin = 65.67 - xmax = 65.79 - text = "EH1" - intervals [689]: - xmin = 65.79 - xmax = 65.88 - text = "T" - intervals [690]: - xmin = 65.88 - xmax = 66.11 - text = "ER0" - intervals [691]: - xmin = 66.11 - xmax = 66.43 - text = "" - intervals [692]: - xmin = 66.43 - xmax = 66.5 - text = "DH" - intervals [693]: - xmin = 66.5 - xmax = 66.53 - text = "AH0" - intervals [694]: - xmin = 66.53 - xmax = 66.56 - text = "N" - intervals [695]: - xmin = 66.56 - xmax = 66.6 - text = "DH" - intervals [696]: - xmin = 66.6 - xmax = 66.7 - text = "IY0" - intervals [697]: - xmin = 66.7 - xmax = 66.76 - text = "AH1" - intervals [698]: - xmin = 66.76 - xmax = 66.82 - text = "DH" - intervals [699]: - xmin = 66.82 - xmax = 66.95 - text = "ER0" - intervals [700]: - xmin = 66.95 - xmax = 67.19 - text = "Z" - intervals [701]: - xmin = 67.19 - xmax = 68 - text = "" diff --git a/EMAGE/test_sequences/textgrid/2_scott_0_4_4.TextGrid b/EMAGE/test_sequences/textgrid/2_scott_0_4_4.TextGrid deleted file mode 100644 index 017574fad30ad90188cab3099371d11ff28c71a2..0000000000000000000000000000000000000000 --- a/EMAGE/test_sequences/textgrid/2_scott_0_4_4.TextGrid +++ /dev/null @@ -1,3844 +0,0 @@ -File type = "ooTextFile" -Object class = "TextGrid" - -xmin = 0.0 -xmax = 67 -tiers? -size = 2 -item []: - item [1]: - class = "IntervalTier" - name = "words" - xmin = 0.0 - xmax = 67 - intervals: size = 235 - intervals [1]: - xmin = 0.0 - xmax = 0.53 - text = "" - intervals [2]: - xmin = 0.53 - xmax = 0.93 - text = "my" - intervals [3]: - xmin = 0.93 - xmax = 1.34 - text = "favorite" - intervals [4]: - xmin = 1.34 - xmax = 1.57 - text = "kind" - intervals [5]: - xmin = 1.57 - xmax = 1.65 - text = "of" - intervals [6]: - xmin = 1.65 - xmax = 2.2 - text = "movies" - intervals [7]: - xmin = 2.2 - xmax = 2.45 - text = "are" - intervals [8]: - xmin = 2.45 - xmax = 3.2 - text = "romantic" - intervals [9]: - xmin = 3.2 - xmax = 3.72 - text = "movies" - intervals [10]: - xmin = 3.72 - xmax = 3.75 - text = "" - intervals [11]: - xmin = 3.75 - xmax = 4.1 - text = "such" - intervals [12]: - xmin = 4.1 - xmax = 4.33 - text = "as" - intervals [13]: - xmin = 4.33 - xmax = 5.23 - text = "titanic" - intervals [14]: - xmin = 5.23 - xmax = 5.78 - text = "" - intervals [15]: - xmin = 5.78 - xmax = 6.19 - text = "it's" - intervals [16]: - xmin = 6.19 - xmax = 6.46 - text = "a" - intervals [17]: - xmin = 6.46 - xmax = 6.49 - text = "" - intervals [18]: - xmin = 6.49 - xmax = 7.13 - text = "fantastic" - intervals [19]: - xmin = 7.13 - xmax = 7.56 - text = "film" - intervals [20]: - xmin = 7.56 - xmax = 7.77 - text = "it" - intervals [21]: - xmin = 7.77 - xmax = 8.28 - text = "captured" - intervals [22]: - xmin = 8.28 - xmax = 8.73 - text = "many" - intervals [23]: - xmin = 8.73 - xmax = 9.1 - text = "young" - intervals [24]: - xmin = 9.1 - xmax = 9.44 - text = "people's" - intervals [25]: - xmin = 9.44 - xmax = 9.79 - text = "hearts" - intervals [26]: - xmin = 9.79 - xmax = 10.02 - text = "with" - intervals [27]: - xmin = 10.02 - xmax = 10.17 - text = "it's" - intervals [28]: - xmin = 10.17 - xmax = 10.65 - text = "amazing" - intervals [29]: - xmin = 10.65 - xmax = 11.12 - text = "music" - intervals [30]: - xmin = 11.12 - xmax = 11.36 - text = "and" - intervals [31]: - xmin = 11.36 - xmax = 11.92 - text = "sentimental" - intervals [32]: - xmin = 11.92 - xmax = 12.47 - text = "plots" - intervals [33]: - xmin = 12.47 - xmax = 12.84 - text = "" - intervals [34]: - xmin = 12.84 - xmax = 12.98 - text = "when" - intervals [35]: - xmin = 12.98 - xmax = 13.12 - text = "i" - intervals [36]: - xmin = 13.12 - xmax = 13.28 - text = "think" - intervals [37]: - xmin = 13.28 - xmax = 13.35 - text = "of" - intervals [38]: - xmin = 13.35 - xmax = 13.42 - text = "the" - intervals [39]: - xmin = 13.42 - xmax = 13.62 - text = "movie" - intervals [40]: - xmin = 13.62 - xmax = 14.2 - text = "titanic" - intervals [41]: - xmin = 14.2 - xmax = 14.23 - text = "" - intervals [42]: - xmin = 14.23 - xmax = 14.42 - text = "the" - intervals [43]: - xmin = 14.42 - xmax = 14.92 - text = "word" - intervals [44]: - xmin = 14.92 - xmax = 15.06 - text = "that" - intervals [45]: - xmin = 15.06 - xmax = 15.3 - text = "comes" - intervals [46]: - xmin = 15.3 - xmax = 15.39 - text = "to" - intervals [47]: - xmin = 15.39 - xmax = 15.5 - text = "my" - intervals [48]: - xmin = 15.5 - xmax = 15.91 - text = "mind" - intervals [49]: - xmin = 15.91 - xmax = 16.06 - text = "mind" - intervals [50]: - xmin = 16.06 - xmax = 16.41 - text = "" - intervals [51]: - xmin = 16.41 - xmax = 16.6 - text = "to" - intervals [52]: - xmin = 16.6 - xmax = 17.07 - text = "mises" - intervals [53]: - xmin = 17.07 - xmax = 17.15 - text = "the" - intervals [54]: - xmin = 17.15 - xmax = 17.39 - text = "whole" - intervals [55]: - xmin = 17.39 - xmax = 17.94 - text = "film" - intervals [56]: - xmin = 17.94 - xmax = 17.97 - text = "" - intervals [57]: - xmin = 17.97 - xmax = 18.18 - text = "would" - intervals [58]: - xmin = 18.18 - xmax = 18.62 - text = "be" - intervals [59]: - xmin = 18.62 - xmax = 19.09 - text = "" - intervals [60]: - xmin = 19.09 - xmax = 19.94 - text = "love" - intervals [61]: - xmin = 19.94 - xmax = 20.07 - text = "" - intervals [62]: - xmin = 20.07 - xmax = 20.27 - text = "it's" - intervals [63]: - xmin = 20.27 - xmax = 20.36 - text = "a" - intervals [64]: - xmin = 20.36 - xmax = 20.83 - text = "kind" - intervals [65]: - xmin = 20.83 - xmax = 20.98 - text = "of" - intervals [66]: - xmin = 20.98 - xmax = 21.25 - text = "thing" - intervals [67]: - xmin = 21.25 - xmax = 21.43 - text = "that" - intervals [68]: - xmin = 21.43 - xmax = 21.8 - text = "makes" - intervals [69]: - xmin = 21.8 - xmax = 22.28 - text = "you" - intervals [70]: - xmin = 22.28 - xmax = 22.31 - text = "" - intervals [71]: - xmin = 22.31 - xmax = 22.8 - text = "makes" - intervals [72]: - xmin = 22.8 - xmax = 22.91 - text = "the" - intervals [73]: - xmin = 22.91 - xmax = 23.21 - text = "world" - intervals [74]: - xmin = 23.21 - xmax = 23.38 - text = "go" - intervals [75]: - xmin = 23.38 - xmax = 23.87 - text = "round" - intervals [76]: - xmin = 23.87 - xmax = 24.08 - text = "" - intervals [77]: - xmin = 24.08 - xmax = 24.6 - text = "watching" - intervals [78]: - xmin = 24.6 - xmax = 24.8 - text = "these" - intervals [79]: - xmin = 24.8 - xmax = 25.18 - text = "kinds" - intervals [80]: - xmin = 25.18 - xmax = 25.29 - text = "of" - intervals [81]: - xmin = 25.29 - xmax = 25.83 - text = "romantic" - intervals [82]: - xmin = 25.83 - xmax = 26.23 - text = "movies" - intervals [83]: - xmin = 26.23 - xmax = 26.43 - text = "is" - intervals [84]: - xmin = 26.43 - xmax = 26.86 - text = "just" - intervals [85]: - xmin = 26.86 - xmax = 27.07 - text = "like" - intervals [86]: - xmin = 27.07 - xmax = 27.49 - text = "reading" - intervals [87]: - xmin = 27.49 - xmax = 27.56 - text = "a" - intervals [88]: - xmin = 27.56 - xmax = 27.98 - text = "book" - intervals [89]: - xmin = 27.98 - xmax = 28.11 - text = "" - intervals [90]: - xmin = 28.11 - xmax = 28.29 - text = "that" - intervals [91]: - xmin = 28.29 - xmax = 28.65 - text = "teaches" - intervals [92]: - xmin = 28.65 - xmax = 28.78 - text = "me" - intervals [93]: - xmin = 28.78 - xmax = 28.99 - text = "how" - intervals [94]: - xmin = 28.99 - xmax = 29.19 - text = "to" - intervals [95]: - xmin = 29.19 - xmax = 29.61 - text = "love" - intervals [96]: - xmin = 29.61 - xmax = 29.93 - text = "and" - intervals [97]: - xmin = 29.93 - xmax = 30.09 - text = "be" - intervals [98]: - xmin = 30.09 - xmax = 30.53 - text = "loved" - intervals [99]: - xmin = 30.53 - xmax = 30.96 - text = "" - intervals [100]: - xmin = 30.96 - xmax = 31.68 - text = "moreover" - intervals [101]: - xmin = 31.68 - xmax = 31.81 - text = "we" - intervals [102]: - xmin = 31.81 - xmax = 32.01 - text = "" - intervals [103]: - xmin = 32.01 - xmax = 32.51 - text = "can" - intervals [104]: - xmin = 32.51 - xmax = 32.56 - text = "" - intervals [105]: - xmin = 32.56 - xmax = 32.72 - text = "learn" - intervals [106]: - xmin = 32.72 - xmax = 33.09 - text = "we" - intervals [107]: - xmin = 33.09 - xmax = 33.25 - text = "can" - intervals [108]: - xmin = 33.25 - xmax = 34.05 - text = "learn" - intervals [109]: - xmin = 34.05 - xmax = 34.2 - text = "" - intervals [110]: - xmin = 34.2 - xmax = 35.12 - text = "more" - intervals [111]: - xmin = 35.12 - xmax = 35.44 - text = "from" - intervals [112]: - xmin = 35.44 - xmax = 35.66 - text = "it" - intervals [113]: - xmin = 35.66 - xmax = 35.98 - text = "such" - intervals [114]: - xmin = 35.98 - xmax = 36.35 - text = "things" - intervals [115]: - xmin = 36.35 - xmax = 36.69 - text = "as" - intervals [116]: - xmin = 36.69 - xmax = 36.89 - text = "" - intervals [117]: - xmin = 36.89 - xmax = 37.59 - text = "loyalty" - intervals [118]: - xmin = 37.59 - xmax = 37.76 - text = "and" - intervals [119]: - xmin = 37.76 - xmax = 37.88 - text = "what" - intervals [120]: - xmin = 37.88 - xmax = 37.99 - text = "we" - intervals [121]: - xmin = 37.99 - xmax = 38.47 - text = "treasure" - intervals [122]: - xmin = 38.47 - xmax = 38.58 - text = "in" - intervals [123]: - xmin = 38.58 - xmax = 38.71 - text = "our" - intervals [124]: - xmin = 38.71 - xmax = 39.11 - text = "lives" - intervals [125]: - xmin = 39.11 - xmax = 39.4 - text = "" - intervals [126]: - xmin = 39.4 - xmax = 39.8 - text = "another" - intervals [127]: - xmin = 39.8 - xmax = 40.13 - text = "movie" - intervals [128]: - xmin = 40.13 - xmax = 40.51 - text = "about" - intervals [129]: - xmin = 40.51 - xmax = 40.83 - text = "love" - intervals [130]: - xmin = 40.83 - xmax = 41.08 - text = "is" - intervals [131]: - xmin = 41.08 - xmax = 41.24 - text = "the" - intervals [132]: - xmin = 41.24 - xmax = 41.3 - text = "" - intervals [133]: - xmin = 41.3 - xmax = 41.9 - text = "secret" - intervals [134]: - xmin = 41.9 - xmax = 42.13 - text = "" - intervals [135]: - xmin = 42.13 - xmax = 42.47 - text = "the" - intervals [136]: - xmin = 42.47 - xmax = 43.01 - text = "movie" - intervals [137]: - xmin = 43.01 - xmax = 43.58 - text = "secret" - intervals [138]: - xmin = 43.58 - xmax = 43.71 - text = "is" - intervals [139]: - xmin = 43.71 - xmax = 44.1 - text = "about" - intervals [140]: - xmin = 44.1 - xmax = 44.15 - text = "a" - intervals [141]: - xmin = 44.15 - xmax = 44.88 - text = "story" - intervals [142]: - xmin = 44.88 - xmax = 44.95 - text = "" - intervals [143]: - xmin = 44.95 - xmax = 45.14 - text = "of" - intervals [144]: - xmin = 45.14 - xmax = 45.21 - text = "a" - intervals [145]: - xmin = 45.21 - xmax = 45.56 - text = "musical" - intervals [146]: - xmin = 45.56 - xmax = 45.96 - text = "prodigy" - intervals [147]: - xmin = 45.96 - xmax = 46.04 - text = "do" - intervals [148]: - xmin = 46.04 - xmax = 46.17 - text = "that" - intervals [149]: - xmin = 46.17 - xmax = 46.42 - text = "falls" - intervals [150]: - xmin = 46.42 - xmax = 46.49 - text = "in" - intervals [151]: - xmin = 46.49 - xmax = 46.62 - text = "love" - intervals [152]: - xmin = 46.62 - xmax = 46.72 - text = "with" - intervals [153]: - xmin = 46.72 - xmax = 46.85 - text = "a" - intervals [154]: - xmin = 46.85 - xmax = 46.91 - text = "" - intervals [155]: - xmin = 46.91 - xmax = 47.21 - text = "girl" - intervals [156]: - xmin = 47.21 - xmax = 47.46 - text = "who's" - intervals [157]: - xmin = 47.46 - xmax = 48.01 - text = "dying" - intervals [158]: - xmin = 48.01 - xmax = 49.08 - text = "" - intervals [159]: - xmin = 49.08 - xmax = 49.33 - text = "there" - intervals [160]: - xmin = 49.33 - xmax = 49.39 - text = "are" - intervals [161]: - xmin = 49.39 - xmax = 49.46 - text = "a" - intervals [162]: - xmin = 49.46 - xmax = 49.82 - text = "lot" - intervals [163]: - xmin = 49.82 - xmax = 50.2 - text = "of" - intervals [164]: - xmin = 50.2 - xmax = 50.29 - text = "" - intervals [165]: - xmin = 50.29 - xmax = 50.88 - text = "enviable" - intervals [166]: - xmin = 50.88 - xmax = 51.3 - text = "moments" - intervals [167]: - xmin = 51.3 - xmax = 51.37 - text = "in" - intervals [168]: - xmin = 51.37 - xmax = 51.53 - text = "this" - intervals [169]: - xmin = 51.53 - xmax = 51.77 - text = "film" - intervals [170]: - xmin = 51.77 - xmax = 52.01 - text = "such" - intervals [171]: - xmin = 52.01 - xmax = 52.2 - text = "as" - intervals [172]: - xmin = 52.2 - xmax = 52.3 - text = "the" - intervals [173]: - xmin = 52.3 - xmax = 52.57 - text = "simple" - intervals [174]: - xmin = 52.57 - xmax = 52.74 - text = "love" - intervals [175]: - xmin = 52.74 - xmax = 53.06 - text = "between" - intervals [176]: - xmin = 53.06 - xmax = 53.26 - text = "high" - intervals [177]: - xmin = 53.26 - xmax = 53.52 - text = "school" - intervals [178]: - xmin = 53.52 - xmax = 54.09 - text = "students" - intervals [179]: - xmin = 54.09 - xmax = 54.32 - text = "" - intervals [180]: - xmin = 54.32 - xmax = 54.56 - text = "every" - intervals [181]: - xmin = 54.56 - xmax = 54.93 - text = "time" - intervals [182]: - xmin = 54.93 - xmax = 54.96 - text = "" - intervals [183]: - xmin = 54.96 - xmax = 55.08 - text = "i" - intervals [184]: - xmin = 55.08 - xmax = 55.45 - text = "watch" - intervals [185]: - xmin = 55.45 - xmax = 55.68 - text = "this" - intervals [186]: - xmin = 55.68 - xmax = 55.89 - text = "movie" - intervals [187]: - xmin = 55.89 - xmax = 55.98 - text = "it" - intervals [188]: - xmin = 55.98 - xmax = 56.44 - text = "reminds" - intervals [189]: - xmin = 56.44 - xmax = 56.55 - text = "me" - intervals [190]: - xmin = 56.55 - xmax = 56.63 - text = "of" - intervals [191]: - xmin = 56.63 - xmax = 56.7 - text = "a" - intervals [192]: - xmin = 56.7 - xmax = 56.99 - text = "time" - intervals [193]: - xmin = 56.99 - xmax = 57.08 - text = "that" - intervals [194]: - xmin = 57.08 - xmax = 57.13 - text = "i" - intervals [195]: - xmin = 57.13 - xmax = 57.23 - text = "was" - intervals [196]: - xmin = 57.23 - xmax = 57.29 - text = "in" - intervals [197]: - xmin = 57.29 - xmax = 57.53 - text = "high" - intervals [198]: - xmin = 57.53 - xmax = 57.88 - text = "school" - intervals [199]: - xmin = 57.88 - xmax = 58.03 - text = "and" - intervals [200]: - xmin = 58.03 - xmax = 58.25 - text = "" - intervals [201]: - xmin = 58.25 - xmax = 58.4 - text = "you" - intervals [202]: - xmin = 58.4 - xmax = 58.55 - text = "might" - intervals [203]: - xmin = 58.55 - xmax = 59.1 - text = "remember" - intervals [204]: - xmin = 59.1 - xmax = 59.29 - text = "" - intervals [205]: - xmin = 59.29 - xmax = 59.44 - text = "the" - intervals [206]: - xmin = 59.44 - xmax = 59.83 - text = "crush" - intervals [207]: - xmin = 59.83 - xmax = 59.94 - text = "you" - intervals [208]: - xmin = 59.94 - xmax = 60.16 - text = "had" - intervals [209]: - xmin = 60.16 - xmax = 60.27 - text = "in" - intervals [210]: - xmin = 60.27 - xmax = 60.74 - text = "school" - intervals [211]: - xmin = 60.74 - xmax = 60.9 - text = "" - intervals [212]: - xmin = 60.9 - xmax = 61.12 - text = "and" - intervals [213]: - xmin = 61.12 - xmax = 61.24 - text = "how" - intervals [214]: - xmin = 61.24 - xmax = 61.36 - text = "you" - intervals [215]: - xmin = 61.36 - xmax = 61.48 - text = "would" - intervals [216]: - xmin = 61.48 - xmax = 61.7 - text = "look" - intervals [217]: - xmin = 61.7 - xmax = 61.77 - text = "at" - intervals [218]: - xmin = 61.77 - xmax = 62.16 - text = "him" - intervals [219]: - xmin = 62.16 - xmax = 62.37 - text = "" - intervals [220]: - xmin = 62.37 - xmax = 62.54 - text = "while" - intervals [221]: - xmin = 62.54 - xmax = 62.74 - text = "he's" - intervals [222]: - xmin = 62.74 - xmax = 63.02 - text = "at" - intervals [223]: - xmin = 63.02 - xmax = 63.61 - text = "in" - intervals [224]: - xmin = 63.61 - xmax = 64.04 - text = "class" - intervals [225]: - xmin = 64.04 - xmax = 64.38 - text = "without" - intervals [226]: - xmin = 64.38 - xmax = 64.83 - text = "thinking" - intervals [227]: - xmin = 64.83 - xmax = 64.95 - text = "or" - intervals [228]: - xmin = 64.95 - xmax = 64.98 - text = "" - intervals [229]: - xmin = 64.98 - xmax = 65.27 - text = "wanting" - intervals [230]: - xmin = 65.27 - xmax = 65.36 - text = "to" - intervals [231]: - xmin = 65.36 - xmax = 65.54 - text = "go" - intervals [232]: - xmin = 65.54 - xmax = 65.95 - text = "places" - intervals [233]: - xmin = 65.95 - xmax = 66.12 - text = "with" - intervals [234]: - xmin = 66.12 - xmax = 66.38 - text = "him" - intervals [235]: - xmin = 66.38 - xmax = 67 - text = "" - item [2]: - class = "IntervalTier" - name = "phones" - xmin = 0.0 - xmax = 67 - intervals: size = 721 - intervals [1]: - xmin = 0.0 - xmax = 0.53 - text = "" - intervals [2]: - xmin = 0.53 - xmax = 0.75 - text = "M" - intervals [3]: - xmin = 0.75 - xmax = 0.93 - text = "AY1" - intervals [4]: - xmin = 0.93 - xmax = 1.06 - text = "F" - intervals [5]: - xmin = 1.06 - xmax = 1.16 - text = "EY1" - intervals [6]: - xmin = 1.16 - xmax = 1.23 - text = "V" - intervals [7]: - xmin = 1.23 - xmax = 1.26 - text = "ER0" - intervals [8]: - xmin = 1.26 - xmax = 1.31 - text = "IH0" - intervals [9]: - xmin = 1.31 - xmax = 1.34 - text = "T" - intervals [10]: - xmin = 1.34 - xmax = 1.42 - text = "K" - intervals [11]: - xmin = 1.42 - xmax = 1.51 - text = "AY1" - intervals [12]: - xmin = 1.51 - xmax = 1.54 - text = "N" - intervals [13]: - xmin = 1.54 - xmax = 1.57 - text = "D" - intervals [14]: - xmin = 1.57 - xmax = 1.61 - text = "AH0" - intervals [15]: - xmin = 1.61 - xmax = 1.65 - text = "V" - intervals [16]: - xmin = 1.65 - xmax = 1.74 - text = "M" - intervals [17]: - xmin = 1.74 - xmax = 1.8 - text = "UW1" - intervals [18]: - xmin = 1.8 - xmax = 1.9 - text = "V" - intervals [19]: - xmin = 1.9 - xmax = 2.01 - text = "IY0" - intervals [20]: - xmin = 2.01 - xmax = 2.2 - text = "Z" - intervals [21]: - xmin = 2.2 - xmax = 2.35 - text = "AA1" - intervals [22]: - xmin = 2.35 - xmax = 2.45 - text = "R" - intervals [23]: - xmin = 2.45 - xmax = 2.55 - text = "R" - intervals [24]: - xmin = 2.55 - xmax = 2.6 - text = "OW0" - intervals [25]: - xmin = 2.6 - xmax = 2.81 - text = "M" - intervals [26]: - xmin = 2.81 - xmax = 2.93 - text = "AE1" - intervals [27]: - xmin = 2.93 - xmax = 2.99 - text = "N" - intervals [28]: - xmin = 2.99 - xmax = 3.06 - text = "T" - intervals [29]: - xmin = 3.06 - xmax = 3.13 - text = "IH0" - intervals [30]: - xmin = 3.13 - xmax = 3.2 - text = "K" - intervals [31]: - xmin = 3.2 - xmax = 3.28 - text = "M" - intervals [32]: - xmin = 3.28 - xmax = 3.38 - text = "UW1" - intervals [33]: - xmin = 3.38 - xmax = 3.42 - text = "V" - intervals [34]: - xmin = 3.42 - xmax = 3.55 - text = "IY0" - intervals [35]: - xmin = 3.55 - xmax = 3.72 - text = "Z" - intervals [36]: - xmin = 3.72 - xmax = 3.75 - text = "" - intervals [37]: - xmin = 3.75 - xmax = 3.92 - text = "S" - intervals [38]: - xmin = 3.92 - xmax = 3.99 - text = "AH1" - intervals [39]: - xmin = 3.99 - xmax = 4.1 - text = "CH" - intervals [40]: - xmin = 4.1 - xmax = 4.22 - text = "EH1" - intervals [41]: - xmin = 4.22 - xmax = 4.33 - text = "Z" - intervals [42]: - xmin = 4.33 - xmax = 4.47 - text = "T" - intervals [43]: - xmin = 4.47 - xmax = 4.58 - text = "AY0" - intervals [44]: - xmin = 4.58 - xmax = 4.77 - text = "T" - intervals [45]: - xmin = 4.77 - xmax = 4.88 - text = "AE1" - intervals [46]: - xmin = 4.88 - xmax = 4.94 - text = "N" - intervals [47]: - xmin = 4.94 - xmax = 5.04 - text = "IH0" - intervals [48]: - xmin = 5.04 - xmax = 5.23 - text = "K" - intervals [49]: - xmin = 5.23 - xmax = 5.78 - text = "" - intervals [50]: - xmin = 5.78 - xmax = 5.95 - text = "IH1" - intervals [51]: - xmin = 5.95 - xmax = 6.05 - text = "T" - intervals [52]: - xmin = 6.05 - xmax = 6.19 - text = "S" - intervals [53]: - xmin = 6.19 - xmax = 6.46 - text = "AH0" - intervals [54]: - xmin = 6.46 - xmax = 6.49 - text = "" - intervals [55]: - xmin = 6.49 - xmax = 6.55 - text = "F" - intervals [56]: - xmin = 6.55 - xmax = 6.6 - text = "AE0" - intervals [57]: - xmin = 6.6 - xmax = 6.66 - text = "N" - intervals [58]: - xmin = 6.66 - xmax = 6.79 - text = "T" - intervals [59]: - xmin = 6.79 - xmax = 6.92 - text = "AE1" - intervals [60]: - xmin = 6.92 - xmax = 6.99 - text = "S" - intervals [61]: - xmin = 6.99 - xmax = 7.03 - text = "T" - intervals [62]: - xmin = 7.03 - xmax = 7.08 - text = "IH0" - intervals [63]: - xmin = 7.08 - xmax = 7.13 - text = "K" - intervals [64]: - xmin = 7.13 - xmax = 7.21 - text = "F" - intervals [65]: - xmin = 7.21 - xmax = 7.31 - text = "IH1" - intervals [66]: - xmin = 7.31 - xmax = 7.51 - text = "L" - intervals [67]: - xmin = 7.51 - xmax = 7.56 - text = "M" - intervals [68]: - xmin = 7.56 - xmax = 7.71 - text = "IH1" - intervals [69]: - xmin = 7.71 - xmax = 7.77 - text = "T" - intervals [70]: - xmin = 7.77 - xmax = 7.86 - text = "K" - intervals [71]: - xmin = 7.86 - xmax = 7.96 - text = "AE1" - intervals [72]: - xmin = 7.96 - xmax = 8.02 - text = "P" - intervals [73]: - xmin = 8.02 - xmax = 8.1 - text = "CH" - intervals [74]: - xmin = 8.1 - xmax = 8.16 - text = "ER0" - intervals [75]: - xmin = 8.16 - xmax = 8.28 - text = "D" - intervals [76]: - xmin = 8.28 - xmax = 8.4 - text = "M" - intervals [77]: - xmin = 8.4 - xmax = 8.46 - text = "EH1" - intervals [78]: - xmin = 8.46 - xmax = 8.53 - text = "N" - intervals [79]: - xmin = 8.53 - xmax = 8.73 - text = "IY0" - intervals [80]: - xmin = 8.73 - xmax = 8.81 - text = "Y" - intervals [81]: - xmin = 8.81 - xmax = 8.99 - text = "AH1" - intervals [82]: - xmin = 8.99 - xmax = 9.1 - text = "NG" - intervals [83]: - xmin = 9.1 - xmax = 9.18 - text = "P" - intervals [84]: - xmin = 9.18 - xmax = 9.26 - text = "IY1" - intervals [85]: - xmin = 9.26 - xmax = 9.31 - text = "P" - intervals [86]: - xmin = 9.31 - xmax = 9.34 - text = "AH0" - intervals [87]: - xmin = 9.34 - xmax = 9.39 - text = "L" - intervals [88]: - xmin = 9.39 - xmax = 9.44 - text = "Z" - intervals [89]: - xmin = 9.44 - xmax = 9.5 - text = "HH" - intervals [90]: - xmin = 9.5 - xmax = 9.57 - text = "AA1" - intervals [91]: - xmin = 9.57 - xmax = 9.68 - text = "R" - intervals [92]: - xmin = 9.68 - xmax = 9.73 - text = "T" - intervals [93]: - xmin = 9.73 - xmax = 9.79 - text = "S" - intervals [94]: - xmin = 9.79 - xmax = 9.88 - text = "W" - intervals [95]: - xmin = 9.88 - xmax = 9.94 - text = "IH1" - intervals [96]: - xmin = 9.94 - xmax = 10.02 - text = "DH" - intervals [97]: - xmin = 10.02 - xmax = 10.1 - text = "IH0" - intervals [98]: - xmin = 10.1 - xmax = 10.13 - text = "T" - intervals [99]: - xmin = 10.13 - xmax = 10.17 - text = "S" - intervals [100]: - xmin = 10.17 - xmax = 10.28 - text = "AH0" - intervals [101]: - xmin = 10.28 - xmax = 10.36 - text = "M" - intervals [102]: - xmin = 10.36 - xmax = 10.47 - text = "EY1" - intervals [103]: - xmin = 10.47 - xmax = 10.53 - text = "Z" - intervals [104]: - xmin = 10.53 - xmax = 10.59 - text = "IH0" - intervals [105]: - xmin = 10.59 - xmax = 10.65 - text = "NG" - intervals [106]: - xmin = 10.65 - xmax = 10.7 - text = "M" - intervals [107]: - xmin = 10.7 - xmax = 10.78 - text = "Y" - intervals [108]: - xmin = 10.78 - xmax = 10.81 - text = "UW1" - intervals [109]: - xmin = 10.81 - xmax = 10.9 - text = "Z" - intervals [110]: - xmin = 10.9 - xmax = 10.98 - text = "IH0" - intervals [111]: - xmin = 10.98 - xmax = 11.12 - text = "K" - intervals [112]: - xmin = 11.12 - xmax = 11.21 - text = "AE1" - intervals [113]: - xmin = 11.21 - xmax = 11.27 - text = "N" - intervals [114]: - xmin = 11.27 - xmax = 11.36 - text = "D" - intervals [115]: - xmin = 11.36 - xmax = 11.47 - text = "S" - intervals [116]: - xmin = 11.47 - xmax = 11.52 - text = "EH2" - intervals [117]: - xmin = 11.52 - xmax = 11.59 - text = "N" - intervals [118]: - xmin = 11.59 - xmax = 11.64 - text = "T" - intervals [119]: - xmin = 11.64 - xmax = 11.69 - text = "AH0" - intervals [120]: - xmin = 11.69 - xmax = 11.74 - text = "M" - intervals [121]: - xmin = 11.74 - xmax = 11.78 - text = "EH1" - intervals [122]: - xmin = 11.78 - xmax = 11.81 - text = "N" - intervals [123]: - xmin = 11.81 - xmax = 11.85 - text = "T" - intervals [124]: - xmin = 11.85 - xmax = 11.88 - text = "AH0" - intervals [125]: - xmin = 11.88 - xmax = 11.92 - text = "L" - intervals [126]: - xmin = 11.92 - xmax = 11.99 - text = "P" - intervals [127]: - xmin = 11.99 - xmax = 12.04 - text = "L" - intervals [128]: - xmin = 12.04 - xmax = 12.23 - text = "AA1" - intervals [129]: - xmin = 12.23 - xmax = 12.31 - text = "T" - intervals [130]: - xmin = 12.31 - xmax = 12.47 - text = "S" - intervals [131]: - xmin = 12.47 - xmax = 12.84 - text = "" - intervals [132]: - xmin = 12.84 - xmax = 12.92 - text = "W" - intervals [133]: - xmin = 12.92 - xmax = 12.95 - text = "EH1" - intervals [134]: - xmin = 12.95 - xmax = 12.98 - text = "N" - intervals [135]: - xmin = 12.98 - xmax = 13.12 - text = "AY1" - intervals [136]: - xmin = 13.12 - xmax = 13.16 - text = "TH" - intervals [137]: - xmin = 13.16 - xmax = 13.22 - text = "IH1" - intervals [138]: - xmin = 13.22 - xmax = 13.25 - text = "NG" - intervals [139]: - xmin = 13.25 - xmax = 13.28 - text = "K" - intervals [140]: - xmin = 13.28 - xmax = 13.32 - text = "AH0" - intervals [141]: - xmin = 13.32 - xmax = 13.35 - text = "V" - intervals [142]: - xmin = 13.35 - xmax = 13.38 - text = "DH" - intervals [143]: - xmin = 13.38 - xmax = 13.42 - text = "AH0" - intervals [144]: - xmin = 13.42 - xmax = 13.46 - text = "M" - intervals [145]: - xmin = 13.46 - xmax = 13.52 - text = "UW1" - intervals [146]: - xmin = 13.52 - xmax = 13.58 - text = "V" - intervals [147]: - xmin = 13.58 - xmax = 13.62 - text = "IY0" - intervals [148]: - xmin = 13.62 - xmax = 13.69 - text = "T" - intervals [149]: - xmin = 13.69 - xmax = 13.79 - text = "AY0" - intervals [150]: - xmin = 13.79 - xmax = 13.89 - text = "T" - intervals [151]: - xmin = 13.89 - xmax = 13.96 - text = "AE1" - intervals [152]: - xmin = 13.96 - xmax = 14.02 - text = "N" - intervals [153]: - xmin = 14.02 - xmax = 14.17 - text = "IH0" - intervals [154]: - xmin = 14.17 - xmax = 14.2 - text = "K" - intervals [155]: - xmin = 14.2 - xmax = 14.23 - text = "" - intervals [156]: - xmin = 14.23 - xmax = 14.34 - text = "DH" - intervals [157]: - xmin = 14.34 - xmax = 14.42 - text = "AH1" - intervals [158]: - xmin = 14.42 - xmax = 14.61 - text = "W" - intervals [159]: - xmin = 14.61 - xmax = 14.85 - text = "ER1" - intervals [160]: - xmin = 14.85 - xmax = 14.92 - text = "D" - intervals [161]: - xmin = 14.92 - xmax = 14.95 - text = "DH" - intervals [162]: - xmin = 14.95 - xmax = 15.02 - text = "AH0" - intervals [163]: - xmin = 15.02 - xmax = 15.06 - text = "T" - intervals [164]: - xmin = 15.06 - xmax = 15.13 - text = "K" - intervals [165]: - xmin = 15.13 - xmax = 15.19 - text = "AH1" - intervals [166]: - xmin = 15.19 - xmax = 15.24 - text = "M" - intervals [167]: - xmin = 15.24 - xmax = 15.3 - text = "Z" - intervals [168]: - xmin = 15.3 - xmax = 15.35 - text = "T" - intervals [169]: - xmin = 15.35 - xmax = 15.39 - text = "AH0" - intervals [170]: - xmin = 15.39 - xmax = 15.44 - text = "M" - intervals [171]: - xmin = 15.44 - xmax = 15.5 - text = "AY1" - intervals [172]: - xmin = 15.5 - xmax = 15.61 - text = "M" - intervals [173]: - xmin = 15.61 - xmax = 15.85 - text = "AY1" - intervals [174]: - xmin = 15.85 - xmax = 15.88 - text = "N" - intervals [175]: - xmin = 15.88 - xmax = 15.91 - text = "D" - intervals [176]: - xmin = 15.91 - xmax = 15.94 - text = "M" - intervals [177]: - xmin = 15.94 - xmax = 15.97 - text = "AY1" - intervals [178]: - xmin = 15.97 - xmax = 16.0 - text = "N" - intervals [179]: - xmin = 16.0 - xmax = 16.06 - text = "D" - intervals [180]: - xmin = 16.06 - xmax = 16.41 - text = "" - intervals [181]: - xmin = 16.41 - xmax = 16.54 - text = "T" - intervals [182]: - xmin = 16.54 - xmax = 16.6 - text = "IH0" - intervals [183]: - xmin = 16.6 - xmax = 16.69 - text = "M" - intervals [184]: - xmin = 16.69 - xmax = 16.85 - text = "AY1" - intervals [185]: - xmin = 16.85 - xmax = 16.95 - text = "Z" - intervals [186]: - xmin = 16.95 - xmax = 16.99 - text = "IH0" - intervals [187]: - xmin = 16.99 - xmax = 17.07 - text = "Z" - intervals [188]: - xmin = 17.07 - xmax = 17.11 - text = "DH" - intervals [189]: - xmin = 17.11 - xmax = 17.15 - text = "AH1" - intervals [190]: - xmin = 17.15 - xmax = 17.24 - text = "HH" - intervals [191]: - xmin = 17.24 - xmax = 17.32 - text = "OW1" - intervals [192]: - xmin = 17.32 - xmax = 17.39 - text = "L" - intervals [193]: - xmin = 17.39 - xmax = 17.48 - text = "F" - intervals [194]: - xmin = 17.48 - xmax = 17.57 - text = "IH1" - intervals [195]: - xmin = 17.57 - xmax = 17.68 - text = "L" - intervals [196]: - xmin = 17.68 - xmax = 17.94 - text = "M" - intervals [197]: - xmin = 17.94 - xmax = 17.97 - text = "" - intervals [198]: - xmin = 17.97 - xmax = 18.06 - text = "W" - intervals [199]: - xmin = 18.06 - xmax = 18.12 - text = "UH1" - intervals [200]: - xmin = 18.12 - xmax = 18.18 - text = "D" - intervals [201]: - xmin = 18.18 - xmax = 18.24 - text = "B" - intervals [202]: - xmin = 18.24 - xmax = 18.62 - text = "IY1" - intervals [203]: - xmin = 18.62 - xmax = 19.09 - text = "" - intervals [204]: - xmin = 19.09 - xmax = 19.34 - text = "L" - intervals [205]: - xmin = 19.34 - xmax = 19.51 - text = "AH1" - intervals [206]: - xmin = 19.51 - xmax = 19.94 - text = "V" - intervals [207]: - xmin = 19.94 - xmax = 20.07 - text = "" - intervals [208]: - xmin = 20.07 - xmax = 20.18 - text = "IH1" - intervals [209]: - xmin = 20.18 - xmax = 20.24 - text = "T" - intervals [210]: - xmin = 20.24 - xmax = 20.27 - text = "S" - intervals [211]: - xmin = 20.27 - xmax = 20.36 - text = "AH0" - intervals [212]: - xmin = 20.36 - xmax = 20.59 - text = "K" - intervals [213]: - xmin = 20.59 - xmax = 20.74 - text = "AY1" - intervals [214]: - xmin = 20.74 - xmax = 20.79 - text = "N" - intervals [215]: - xmin = 20.79 - xmax = 20.83 - text = "D" - intervals [216]: - xmin = 20.83 - xmax = 20.87 - text = "AH0" - intervals [217]: - xmin = 20.87 - xmax = 20.98 - text = "V" - intervals [218]: - xmin = 20.98 - xmax = 21.04 - text = "TH" - intervals [219]: - xmin = 21.04 - xmax = 21.13 - text = "IH1" - intervals [220]: - xmin = 21.13 - xmax = 21.25 - text = "NG" - intervals [221]: - xmin = 21.25 - xmax = 21.31 - text = "DH" - intervals [222]: - xmin = 21.31 - xmax = 21.37 - text = "AE1" - intervals [223]: - xmin = 21.37 - xmax = 21.43 - text = "T" - intervals [224]: - xmin = 21.43 - xmax = 21.54 - text = "M" - intervals [225]: - xmin = 21.54 - xmax = 21.64 - text = "EY1" - intervals [226]: - xmin = 21.64 - xmax = 21.68 - text = "K" - intervals [227]: - xmin = 21.68 - xmax = 21.8 - text = "S" - intervals [228]: - xmin = 21.8 - xmax = 21.87 - text = "Y" - intervals [229]: - xmin = 21.87 - xmax = 22.28 - text = "UW1" - intervals [230]: - xmin = 22.28 - xmax = 22.31 - text = "" - intervals [231]: - xmin = 22.31 - xmax = 22.63 - text = "M" - intervals [232]: - xmin = 22.63 - xmax = 22.7 - text = "EY1" - intervals [233]: - xmin = 22.7 - xmax = 22.75 - text = "K" - intervals [234]: - xmin = 22.75 - xmax = 22.8 - text = "S" - intervals [235]: - xmin = 22.8 - xmax = 22.84 - text = "DH" - intervals [236]: - xmin = 22.84 - xmax = 22.91 - text = "AH0" - intervals [237]: - xmin = 22.91 - xmax = 23.0 - text = "W" - intervals [238]: - xmin = 23.0 - xmax = 23.08 - text = "ER1" - intervals [239]: - xmin = 23.08 - xmax = 23.18 - text = "L" - intervals [240]: - xmin = 23.18 - xmax = 23.21 - text = "D" - intervals [241]: - xmin = 23.21 - xmax = 23.27 - text = "G" - intervals [242]: - xmin = 23.27 - xmax = 23.38 - text = "OW1" - intervals [243]: - xmin = 23.38 - xmax = 23.48 - text = "R" - intervals [244]: - xmin = 23.48 - xmax = 23.68 - text = "AW1" - intervals [245]: - xmin = 23.68 - xmax = 23.75 - text = "N" - intervals [246]: - xmin = 23.75 - xmax = 23.87 - text = "D" - intervals [247]: - xmin = 23.87 - xmax = 24.08 - text = "" - intervals [248]: - xmin = 24.08 - xmax = 24.27 - text = "W" - intervals [249]: - xmin = 24.27 - xmax = 24.36 - text = "AA1" - intervals [250]: - xmin = 24.36 - xmax = 24.46 - text = "CH" - intervals [251]: - xmin = 24.46 - xmax = 24.54 - text = "IH0" - intervals [252]: - xmin = 24.54 - xmax = 24.6 - text = "NG" - intervals [253]: - xmin = 24.6 - xmax = 24.65 - text = "DH" - intervals [254]: - xmin = 24.65 - xmax = 24.72 - text = "IY1" - intervals [255]: - xmin = 24.72 - xmax = 24.8 - text = "Z" - intervals [256]: - xmin = 24.8 - xmax = 24.9 - text = "K" - intervals [257]: - xmin = 24.9 - xmax = 25.05 - text = "AY1" - intervals [258]: - xmin = 25.05 - xmax = 25.12 - text = "N" - intervals [259]: - xmin = 25.12 - xmax = 25.18 - text = "Z" - intervals [260]: - xmin = 25.18 - xmax = 25.21 - text = "AH0" - intervals [261]: - xmin = 25.21 - xmax = 25.29 - text = "V" - intervals [262]: - xmin = 25.29 - xmax = 25.36 - text = "R" - intervals [263]: - xmin = 25.36 - xmax = 25.39 - text = "OW0" - intervals [264]: - xmin = 25.39 - xmax = 25.5 - text = "M" - intervals [265]: - xmin = 25.5 - xmax = 25.56 - text = "AE1" - intervals [266]: - xmin = 25.56 - xmax = 25.6 - text = "N" - intervals [267]: - xmin = 25.6 - xmax = 25.65 - text = "T" - intervals [268]: - xmin = 25.65 - xmax = 25.75 - text = "IH0" - intervals [269]: - xmin = 25.75 - xmax = 25.83 - text = "K" - intervals [270]: - xmin = 25.83 - xmax = 25.9 - text = "M" - intervals [271]: - xmin = 25.9 - xmax = 25.99 - text = "UW1" - intervals [272]: - xmin = 25.99 - xmax = 26.06 - text = "V" - intervals [273]: - xmin = 26.06 - xmax = 26.14 - text = "IY0" - intervals [274]: - xmin = 26.14 - xmax = 26.23 - text = "Z" - intervals [275]: - xmin = 26.23 - xmax = 26.34 - text = "IH1" - intervals [276]: - xmin = 26.34 - xmax = 26.43 - text = "Z" - intervals [277]: - xmin = 26.43 - xmax = 26.64 - text = "JH" - intervals [278]: - xmin = 26.64 - xmax = 26.73 - text = "IH0" - intervals [279]: - xmin = 26.73 - xmax = 26.82 - text = "S" - intervals [280]: - xmin = 26.82 - xmax = 26.86 - text = "T" - intervals [281]: - xmin = 26.86 - xmax = 26.92 - text = "L" - intervals [282]: - xmin = 26.92 - xmax = 27.04 - text = "AY1" - intervals [283]: - xmin = 27.04 - xmax = 27.07 - text = "K" - intervals [284]: - xmin = 27.07 - xmax = 27.26 - text = "R" - intervals [285]: - xmin = 27.26 - xmax = 27.33 - text = "IY1" - intervals [286]: - xmin = 27.33 - xmax = 27.36 - text = "D" - intervals [287]: - xmin = 27.36 - xmax = 27.43 - text = "IH0" - intervals [288]: - xmin = 27.43 - xmax = 27.49 - text = "NG" - intervals [289]: - xmin = 27.49 - xmax = 27.56 - text = "AH0" - intervals [290]: - xmin = 27.56 - xmax = 27.63 - text = "B" - intervals [291]: - xmin = 27.63 - xmax = 27.77 - text = "UH1" - intervals [292]: - xmin = 27.77 - xmax = 27.98 - text = "K" - intervals [293]: - xmin = 27.98 - xmax = 28.11 - text = "" - intervals [294]: - xmin = 28.11 - xmax = 28.21 - text = "DH" - intervals [295]: - xmin = 28.21 - xmax = 28.25 - text = "AH0" - intervals [296]: - xmin = 28.25 - xmax = 28.29 - text = "T" - intervals [297]: - xmin = 28.29 - xmax = 28.38 - text = "T" - intervals [298]: - xmin = 28.38 - xmax = 28.44 - text = "IY1" - intervals [299]: - xmin = 28.44 - xmax = 28.52 - text = "CH" - intervals [300]: - xmin = 28.52 - xmax = 28.57 - text = "IH0" - intervals [301]: - xmin = 28.57 - xmax = 28.65 - text = "Z" - intervals [302]: - xmin = 28.65 - xmax = 28.69 - text = "M" - intervals [303]: - xmin = 28.69 - xmax = 28.78 - text = "IY1" - intervals [304]: - xmin = 28.78 - xmax = 28.91 - text = "HH" - intervals [305]: - xmin = 28.91 - xmax = 28.99 - text = "AW1" - intervals [306]: - xmin = 28.99 - xmax = 29.08 - text = "T" - intervals [307]: - xmin = 29.08 - xmax = 29.19 - text = "AH0" - intervals [308]: - xmin = 29.19 - xmax = 29.27 - text = "L" - intervals [309]: - xmin = 29.27 - xmax = 29.52 - text = "AH1" - intervals [310]: - xmin = 29.52 - xmax = 29.61 - text = "V" - intervals [311]: - xmin = 29.61 - xmax = 29.78 - text = "AE1" - intervals [312]: - xmin = 29.78 - xmax = 29.86 - text = "N" - intervals [313]: - xmin = 29.86 - xmax = 29.93 - text = "D" - intervals [314]: - xmin = 29.93 - xmax = 29.97 - text = "B" - intervals [315]: - xmin = 29.97 - xmax = 30.09 - text = "IY1" - intervals [316]: - xmin = 30.09 - xmax = 30.21 - text = "L" - intervals [317]: - xmin = 30.21 - xmax = 30.33 - text = "AH1" - intervals [318]: - xmin = 30.33 - xmax = 30.43 - text = "V" - intervals [319]: - xmin = 30.43 - xmax = 30.53 - text = "D" - intervals [320]: - xmin = 30.53 - xmax = 30.96 - text = "" - intervals [321]: - xmin = 30.96 - xmax = 31.09 - text = "M" - intervals [322]: - xmin = 31.09 - xmax = 31.14 - text = "AO0" - intervals [323]: - xmin = 31.14 - xmax = 31.22 - text = "R" - intervals [324]: - xmin = 31.22 - xmax = 31.5 - text = "OW1" - intervals [325]: - xmin = 31.5 - xmax = 31.53 - text = "V" - intervals [326]: - xmin = 31.53 - xmax = 31.68 - text = "ER0" - intervals [327]: - xmin = 31.68 - xmax = 31.74 - text = "W" - intervals [328]: - xmin = 31.74 - xmax = 31.81 - text = "IY1" - intervals [329]: - xmin = 31.81 - xmax = 32.01 - text = "" - intervals [330]: - xmin = 32.01 - xmax = 32.13 - text = "K" - intervals [331]: - xmin = 32.13 - xmax = 32.39 - text = "AE1" - intervals [332]: - xmin = 32.39 - xmax = 32.51 - text = "N" - intervals [333]: - xmin = 32.51 - xmax = 32.56 - text = "" - intervals [334]: - xmin = 32.56 - xmax = 32.65 - text = "L" - intervals [335]: - xmin = 32.65 - xmax = 32.68 - text = "ER1" - intervals [336]: - xmin = 32.68 - xmax = 32.72 - text = "N" - intervals [337]: - xmin = 32.72 - xmax = 33.02 - text = "W" - intervals [338]: - xmin = 33.02 - xmax = 33.09 - text = "IY1" - intervals [339]: - xmin = 33.09 - xmax = 33.16 - text = "K" - intervals [340]: - xmin = 33.16 - xmax = 33.21 - text = "AH0" - intervals [341]: - xmin = 33.21 - xmax = 33.25 - text = "N" - intervals [342]: - xmin = 33.25 - xmax = 33.4 - text = "L" - intervals [343]: - xmin = 33.4 - xmax = 33.58 - text = "ER1" - intervals [344]: - xmin = 33.58 - xmax = 34.05 - text = "N" - intervals [345]: - xmin = 34.05 - xmax = 34.2 - text = "" - intervals [346]: - xmin = 34.2 - xmax = 34.91 - text = "M" - intervals [347]: - xmin = 34.91 - xmax = 35.05 - text = "AO1" - intervals [348]: - xmin = 35.05 - xmax = 35.12 - text = "R" - intervals [349]: - xmin = 35.12 - xmax = 35.23 - text = "F" - intervals [350]: - xmin = 35.23 - xmax = 35.32 - text = "R" - intervals [351]: - xmin = 35.32 - xmax = 35.36 - text = "AH1" - intervals [352]: - xmin = 35.36 - xmax = 35.44 - text = "M" - intervals [353]: - xmin = 35.44 - xmax = 35.56 - text = "IH0" - intervals [354]: - xmin = 35.56 - xmax = 35.66 - text = "T" - intervals [355]: - xmin = 35.66 - xmax = 35.76 - text = "S" - intervals [356]: - xmin = 35.76 - xmax = 35.84 - text = "AH1" - intervals [357]: - xmin = 35.84 - xmax = 35.98 - text = "CH" - intervals [358]: - xmin = 35.98 - xmax = 36.06 - text = "TH" - intervals [359]: - xmin = 36.06 - xmax = 36.16 - text = "IH1" - intervals [360]: - xmin = 36.16 - xmax = 36.24 - text = "NG" - intervals [361]: - xmin = 36.24 - xmax = 36.35 - text = "Z" - intervals [362]: - xmin = 36.35 - xmax = 36.56 - text = "AE1" - intervals [363]: - xmin = 36.56 - xmax = 36.69 - text = "Z" - intervals [364]: - xmin = 36.69 - xmax = 36.89 - text = "" - intervals [365]: - xmin = 36.89 - xmax = 37.03 - text = "L" - intervals [366]: - xmin = 37.03 - xmax = 37.16 - text = "OY1" - intervals [367]: - xmin = 37.16 - xmax = 37.23 - text = "AH0" - intervals [368]: - xmin = 37.23 - xmax = 37.32 - text = "L" - intervals [369]: - xmin = 37.32 - xmax = 37.4 - text = "T" - intervals [370]: - xmin = 37.4 - xmax = 37.59 - text = "IY0" - intervals [371]: - xmin = 37.59 - xmax = 37.68 - text = "AE1" - intervals [372]: - xmin = 37.68 - xmax = 37.73 - text = "N" - intervals [373]: - xmin = 37.73 - xmax = 37.76 - text = "D" - intervals [374]: - xmin = 37.76 - xmax = 37.8 - text = "W" - intervals [375]: - xmin = 37.8 - xmax = 37.83 - text = "AH1" - intervals [376]: - xmin = 37.83 - xmax = 37.88 - text = "T" - intervals [377]: - xmin = 37.88 - xmax = 37.94 - text = "W" - intervals [378]: - xmin = 37.94 - xmax = 37.99 - text = "IY1" - intervals [379]: - xmin = 37.99 - xmax = 38.15 - text = "T" - intervals [380]: - xmin = 38.15 - xmax = 38.21 - text = "R" - intervals [381]: - xmin = 38.21 - xmax = 38.26 - text = "EH1" - intervals [382]: - xmin = 38.26 - xmax = 38.38 - text = "ZH" - intervals [383]: - xmin = 38.38 - xmax = 38.47 - text = "ER0" - intervals [384]: - xmin = 38.47 - xmax = 38.53 - text = "IH0" - intervals [385]: - xmin = 38.53 - xmax = 38.58 - text = "N" - intervals [386]: - xmin = 38.58 - xmax = 38.64 - text = "AA1" - intervals [387]: - xmin = 38.64 - xmax = 38.71 - text = "R" - intervals [388]: - xmin = 38.71 - xmax = 38.77 - text = "L" - intervals [389]: - xmin = 38.77 - xmax = 38.96 - text = "AY1" - intervals [390]: - xmin = 38.96 - xmax = 39.02 - text = "V" - intervals [391]: - xmin = 39.02 - xmax = 39.11 - text = "Z" - intervals [392]: - xmin = 39.11 - xmax = 39.4 - text = "" - intervals [393]: - xmin = 39.4 - xmax = 39.57 - text = "AH0" - intervals [394]: - xmin = 39.57 - xmax = 39.63 - text = "N" - intervals [395]: - xmin = 39.63 - xmax = 39.69 - text = "AH1" - intervals [396]: - xmin = 39.69 - xmax = 39.73 - text = "DH" - intervals [397]: - xmin = 39.73 - xmax = 39.8 - text = "ER0" - intervals [398]: - xmin = 39.8 - xmax = 39.85 - text = "M" - intervals [399]: - xmin = 39.85 - xmax = 39.98 - text = "UW1" - intervals [400]: - xmin = 39.98 - xmax = 40.06 - text = "V" - intervals [401]: - xmin = 40.06 - xmax = 40.13 - text = "IY0" - intervals [402]: - xmin = 40.13 - xmax = 40.25 - text = "AH0" - intervals [403]: - xmin = 40.25 - xmax = 40.31 - text = "B" - intervals [404]: - xmin = 40.31 - xmax = 40.46 - text = "AW1" - intervals [405]: - xmin = 40.46 - xmax = 40.51 - text = "T" - intervals [406]: - xmin = 40.51 - xmax = 40.62 - text = "L" - intervals [407]: - xmin = 40.62 - xmax = 40.76 - text = "AH1" - intervals [408]: - xmin = 40.76 - xmax = 40.83 - text = "V" - intervals [409]: - xmin = 40.83 - xmax = 41.01 - text = "IH1" - intervals [410]: - xmin = 41.01 - xmax = 41.08 - text = "Z" - intervals [411]: - xmin = 41.08 - xmax = 41.11 - text = "DH" - intervals [412]: - xmin = 41.11 - xmax = 41.24 - text = "AH0" - intervals [413]: - xmin = 41.24 - xmax = 41.3 - text = "" - intervals [414]: - xmin = 41.3 - xmax = 41.47 - text = "S" - intervals [415]: - xmin = 41.47 - xmax = 41.53 - text = "IY1" - intervals [416]: - xmin = 41.53 - xmax = 41.61 - text = "K" - intervals [417]: - xmin = 41.61 - xmax = 41.64 - text = "R" - intervals [418]: - xmin = 41.64 - xmax = 41.75 - text = "IH0" - intervals [419]: - xmin = 41.75 - xmax = 41.9 - text = "T" - intervals [420]: - xmin = 41.9 - xmax = 42.13 - text = "" - intervals [421]: - xmin = 42.13 - xmax = 42.35 - text = "DH" - intervals [422]: - xmin = 42.35 - xmax = 42.47 - text = "AH1" - intervals [423]: - xmin = 42.47 - xmax = 42.56 - text = "M" - intervals [424]: - xmin = 42.56 - xmax = 42.62 - text = "UW1" - intervals [425]: - xmin = 42.62 - xmax = 42.71 - text = "V" - intervals [426]: - xmin = 42.71 - xmax = 43.01 - text = "IY0" - intervals [427]: - xmin = 43.01 - xmax = 43.19 - text = "S" - intervals [428]: - xmin = 43.19 - xmax = 43.26 - text = "IY1" - intervals [429]: - xmin = 43.26 - xmax = 43.31 - text = "K" - intervals [430]: - xmin = 43.31 - xmax = 43.37 - text = "R" - intervals [431]: - xmin = 43.37 - xmax = 43.53 - text = "IH0" - intervals [432]: - xmin = 43.53 - xmax = 43.58 - text = "T" - intervals [433]: - xmin = 43.58 - xmax = 43.65 - text = "IH1" - intervals [434]: - xmin = 43.65 - xmax = 43.71 - text = "Z" - intervals [435]: - xmin = 43.71 - xmax = 43.77 - text = "AH0" - intervals [436]: - xmin = 43.77 - xmax = 43.84 - text = "B" - intervals [437]: - xmin = 43.84 - xmax = 44.03 - text = "AW1" - intervals [438]: - xmin = 44.03 - xmax = 44.1 - text = "T" - intervals [439]: - xmin = 44.1 - xmax = 44.15 - text = "AH0" - intervals [440]: - xmin = 44.15 - xmax = 44.33 - text = "S" - intervals [441]: - xmin = 44.33 - xmax = 44.4 - text = "T" - intervals [442]: - xmin = 44.4 - xmax = 44.51 - text = "AO1" - intervals [443]: - xmin = 44.51 - xmax = 44.65 - text = "R" - intervals [444]: - xmin = 44.65 - xmax = 44.88 - text = "IY0" - intervals [445]: - xmin = 44.88 - xmax = 44.95 - text = "" - intervals [446]: - xmin = 44.95 - xmax = 45.09 - text = "AH0" - intervals [447]: - xmin = 45.09 - xmax = 45.14 - text = "V" - intervals [448]: - xmin = 45.14 - xmax = 45.21 - text = "AH0" - intervals [449]: - xmin = 45.21 - xmax = 45.27 - text = "M" - intervals [450]: - xmin = 45.27 - xmax = 45.31 - text = "Y" - intervals [451]: - xmin = 45.31 - xmax = 45.34 - text = "UW1" - intervals [452]: - xmin = 45.34 - xmax = 45.41 - text = "Z" - intervals [453]: - xmin = 45.41 - xmax = 45.45 - text = "IH0" - intervals [454]: - xmin = 45.45 - xmax = 45.48 - text = "K" - intervals [455]: - xmin = 45.48 - xmax = 45.51 - text = "AH0" - intervals [456]: - xmin = 45.51 - xmax = 45.56 - text = "L" - intervals [457]: - xmin = 45.56 - xmax = 45.62 - text = "P" - intervals [458]: - xmin = 45.62 - xmax = 45.69 - text = "R" - intervals [459]: - xmin = 45.69 - xmax = 45.81 - text = "AA1" - intervals [460]: - xmin = 45.81 - xmax = 45.84 - text = "D" - intervals [461]: - xmin = 45.84 - xmax = 45.87 - text = "AH0" - intervals [462]: - xmin = 45.87 - xmax = 45.91 - text = "JH" - intervals [463]: - xmin = 45.91 - xmax = 45.96 - text = "IY0" - intervals [464]: - xmin = 45.96 - xmax = 45.99 - text = "D" - intervals [465]: - xmin = 45.99 - xmax = 46.04 - text = "UW1" - intervals [466]: - xmin = 46.04 - xmax = 46.09 - text = "DH" - intervals [467]: - xmin = 46.09 - xmax = 46.12 - text = "AH0" - intervals [468]: - xmin = 46.12 - xmax = 46.17 - text = "T" - intervals [469]: - xmin = 46.17 - xmax = 46.24 - text = "F" - intervals [470]: - xmin = 46.24 - xmax = 46.29 - text = "AO1" - intervals [471]: - xmin = 46.29 - xmax = 46.36 - text = "L" - intervals [472]: - xmin = 46.36 - xmax = 46.42 - text = "Z" - intervals [473]: - xmin = 46.42 - xmax = 46.46 - text = "IH0" - intervals [474]: - xmin = 46.46 - xmax = 46.49 - text = "N" - intervals [475]: - xmin = 46.49 - xmax = 46.53 - text = "L" - intervals [476]: - xmin = 46.53 - xmax = 46.59 - text = "AH1" - intervals [477]: - xmin = 46.59 - xmax = 46.62 - text = "V" - intervals [478]: - xmin = 46.62 - xmax = 46.65 - text = "W" - intervals [479]: - xmin = 46.65 - xmax = 46.68 - text = "IH0" - intervals [480]: - xmin = 46.68 - xmax = 46.72 - text = "DH" - intervals [481]: - xmin = 46.72 - xmax = 46.85 - text = "AH0" - intervals [482]: - xmin = 46.85 - xmax = 46.91 - text = "" - intervals [483]: - xmin = 46.91 - xmax = 47.04 - text = "G" - intervals [484]: - xmin = 47.04 - xmax = 47.13 - text = "ER1" - intervals [485]: - xmin = 47.13 - xmax = 47.21 - text = "L" - intervals [486]: - xmin = 47.21 - xmax = 47.27 - text = "HH" - intervals [487]: - xmin = 47.27 - xmax = 47.38 - text = "UW1" - intervals [488]: - xmin = 47.38 - xmax = 47.46 - text = "Z" - intervals [489]: - xmin = 47.46 - xmax = 47.55 - text = "D" - intervals [490]: - xmin = 47.55 - xmax = 47.73 - text = "AY1" - intervals [491]: - xmin = 47.73 - xmax = 47.84 - text = "IH0" - intervals [492]: - xmin = 47.84 - xmax = 48.01 - text = "NG" - intervals [493]: - xmin = 48.01 - xmax = 49.08 - text = "" - intervals [494]: - xmin = 49.08 - xmax = 49.15 - text = "DH" - intervals [495]: - xmin = 49.15 - xmax = 49.18 - text = "EH1" - intervals [496]: - xmin = 49.18 - xmax = 49.33 - text = "R" - intervals [497]: - xmin = 49.33 - xmax = 49.36 - text = "AA1" - intervals [498]: - xmin = 49.36 - xmax = 49.39 - text = "R" - intervals [499]: - xmin = 49.39 - xmax = 49.46 - text = "AH0" - intervals [500]: - xmin = 49.46 - xmax = 49.52 - text = "L" - intervals [501]: - xmin = 49.52 - xmax = 49.69 - text = "AA1" - intervals [502]: - xmin = 49.69 - xmax = 49.82 - text = "T" - intervals [503]: - xmin = 49.82 - xmax = 49.98 - text = "AH1" - intervals [504]: - xmin = 49.98 - xmax = 50.2 - text = "V" - intervals [505]: - xmin = 50.2 - xmax = 50.29 - text = "" - intervals [506]: - xmin = 50.29 - xmax = 50.43 - text = "EH1" - intervals [507]: - xmin = 50.43 - xmax = 50.52 - text = "N" - intervals [508]: - xmin = 50.52 - xmax = 50.56 - text = "V" - intervals [509]: - xmin = 50.56 - xmax = 50.65 - text = "IY0" - intervals [510]: - xmin = 50.65 - xmax = 50.7 - text = "AH0" - intervals [511]: - xmin = 50.7 - xmax = 50.75 - text = "B" - intervals [512]: - xmin = 50.75 - xmax = 50.78 - text = "AH0" - intervals [513]: - xmin = 50.78 - xmax = 50.88 - text = "L" - intervals [514]: - xmin = 50.88 - xmax = 50.94 - text = "M" - intervals [515]: - xmin = 50.94 - xmax = 51.05 - text = "OW1" - intervals [516]: - xmin = 51.05 - xmax = 51.12 - text = "M" - intervals [517]: - xmin = 51.12 - xmax = 51.16 - text = "AH0" - intervals [518]: - xmin = 51.16 - xmax = 51.19 - text = "N" - intervals [519]: - xmin = 51.19 - xmax = 51.23 - text = "T" - intervals [520]: - xmin = 51.23 - xmax = 51.3 - text = "S" - intervals [521]: - xmin = 51.3 - xmax = 51.34 - text = "IH0" - intervals [522]: - xmin = 51.34 - xmax = 51.37 - text = "N" - intervals [523]: - xmin = 51.37 - xmax = 51.4 - text = "DH" - intervals [524]: - xmin = 51.4 - xmax = 51.46 - text = "IH0" - intervals [525]: - xmin = 51.46 - xmax = 51.53 - text = "S" - intervals [526]: - xmin = 51.53 - xmax = 51.58 - text = "F" - intervals [527]: - xmin = 51.58 - xmax = 51.62 - text = "IH1" - intervals [528]: - xmin = 51.62 - xmax = 51.72 - text = "L" - intervals [529]: - xmin = 51.72 - xmax = 51.77 - text = "M" - intervals [530]: - xmin = 51.77 - xmax = 51.86 - text = "S" - intervals [531]: - xmin = 51.86 - xmax = 51.91 - text = "AH1" - intervals [532]: - xmin = 51.91 - xmax = 52.01 - text = "CH" - intervals [533]: - xmin = 52.01 - xmax = 52.09 - text = "EH1" - intervals [534]: - xmin = 52.09 - xmax = 52.2 - text = "Z" - intervals [535]: - xmin = 52.2 - xmax = 52.23 - text = "DH" - intervals [536]: - xmin = 52.23 - xmax = 52.3 - text = "AH0" - intervals [537]: - xmin = 52.3 - xmax = 52.39 - text = "S" - intervals [538]: - xmin = 52.39 - xmax = 52.43 - text = "IH1" - intervals [539]: - xmin = 52.43 - xmax = 52.46 - text = "M" - intervals [540]: - xmin = 52.46 - xmax = 52.5 - text = "P" - intervals [541]: - xmin = 52.5 - xmax = 52.53 - text = "AH0" - intervals [542]: - xmin = 52.53 - xmax = 52.57 - text = "L" - intervals [543]: - xmin = 52.57 - xmax = 52.66 - text = "L" - intervals [544]: - xmin = 52.66 - xmax = 52.71 - text = "AH1" - intervals [545]: - xmin = 52.71 - xmax = 52.74 - text = "V" - intervals [546]: - xmin = 52.74 - xmax = 52.79 - text = "B" - intervals [547]: - xmin = 52.79 - xmax = 52.83 - text = "IH0" - intervals [548]: - xmin = 52.83 - xmax = 52.91 - text = "T" - intervals [549]: - xmin = 52.91 - xmax = 52.99 - text = "W" - intervals [550]: - xmin = 52.99 - xmax = 53.03 - text = "IY1" - intervals [551]: - xmin = 53.03 - xmax = 53.06 - text = "N" - intervals [552]: - xmin = 53.06 - xmax = 53.18 - text = "HH" - intervals [553]: - xmin = 53.18 - xmax = 53.26 - text = "AY1" - intervals [554]: - xmin = 53.26 - xmax = 53.34 - text = "S" - intervals [555]: - xmin = 53.34 - xmax = 53.42 - text = "K" - intervals [556]: - xmin = 53.42 - xmax = 53.45 - text = "UW1" - intervals [557]: - xmin = 53.45 - xmax = 53.52 - text = "L" - intervals [558]: - xmin = 53.52 - xmax = 53.63 - text = "S" - intervals [559]: - xmin = 53.63 - xmax = 53.68 - text = "T" - intervals [560]: - xmin = 53.68 - xmax = 53.77 - text = "UW1" - intervals [561]: - xmin = 53.77 - xmax = 53.8 - text = "D" - intervals [562]: - xmin = 53.8 - xmax = 53.84 - text = "AH0" - intervals [563]: - xmin = 53.84 - xmax = 53.89 - text = "N" - intervals [564]: - xmin = 53.89 - xmax = 53.95 - text = "T" - intervals [565]: - xmin = 53.95 - xmax = 54.09 - text = "S" - intervals [566]: - xmin = 54.09 - xmax = 54.32 - text = "" - intervals [567]: - xmin = 54.32 - xmax = 54.42 - text = "EH1" - intervals [568]: - xmin = 54.42 - xmax = 54.45 - text = "V" - intervals [569]: - xmin = 54.45 - xmax = 54.51 - text = "R" - intervals [570]: - xmin = 54.51 - xmax = 54.56 - text = "IY0" - intervals [571]: - xmin = 54.56 - xmax = 54.65 - text = "T" - intervals [572]: - xmin = 54.65 - xmax = 54.83 - text = "AY1" - intervals [573]: - xmin = 54.83 - xmax = 54.93 - text = "M" - intervals [574]: - xmin = 54.93 - xmax = 54.96 - text = "" - intervals [575]: - xmin = 54.96 - xmax = 55.08 - text = "AY1" - intervals [576]: - xmin = 55.08 - xmax = 55.28 - text = "W" - intervals [577]: - xmin = 55.28 - xmax = 55.36 - text = "AA1" - intervals [578]: - xmin = 55.36 - xmax = 55.45 - text = "CH" - intervals [579]: - xmin = 55.45 - xmax = 55.53 - text = "DH" - intervals [580]: - xmin = 55.53 - xmax = 55.59 - text = "IH0" - intervals [581]: - xmin = 55.59 - xmax = 55.68 - text = "S" - intervals [582]: - xmin = 55.68 - xmax = 55.73 - text = "M" - intervals [583]: - xmin = 55.73 - xmax = 55.76 - text = "UW1" - intervals [584]: - xmin = 55.76 - xmax = 55.84 - text = "V" - intervals [585]: - xmin = 55.84 - xmax = 55.89 - text = "IY0" - intervals [586]: - xmin = 55.89 - xmax = 55.92 - text = "IH1" - intervals [587]: - xmin = 55.92 - xmax = 55.98 - text = "T" - intervals [588]: - xmin = 55.98 - xmax = 56.05 - text = "R" - intervals [589]: - xmin = 56.05 - xmax = 56.12 - text = "IY0" - intervals [590]: - xmin = 56.12 - xmax = 56.2 - text = "M" - intervals [591]: - xmin = 56.2 - xmax = 56.34 - text = "AY1" - intervals [592]: - xmin = 56.34 - xmax = 56.38 - text = "N" - intervals [593]: - xmin = 56.38 - xmax = 56.41 - text = "D" - intervals [594]: - xmin = 56.41 - xmax = 56.44 - text = "Z" - intervals [595]: - xmin = 56.44 - xmax = 56.49 - text = "M" - intervals [596]: - xmin = 56.49 - xmax = 56.55 - text = "IY1" - intervals [597]: - xmin = 56.55 - xmax = 56.6 - text = "AH0" - intervals [598]: - xmin = 56.6 - xmax = 56.63 - text = "V" - intervals [599]: - xmin = 56.63 - xmax = 56.7 - text = "AH0" - intervals [600]: - xmin = 56.7 - xmax = 56.82 - text = "T" - intervals [601]: - xmin = 56.82 - xmax = 56.94 - text = "AY1" - intervals [602]: - xmin = 56.94 - xmax = 56.99 - text = "M" - intervals [603]: - xmin = 56.99 - xmax = 57.02 - text = "DH" - intervals [604]: - xmin = 57.02 - xmax = 57.05 - text = "AH0" - intervals [605]: - xmin = 57.05 - xmax = 57.08 - text = "T" - intervals [606]: - xmin = 57.08 - xmax = 57.13 - text = "AY1" - intervals [607]: - xmin = 57.13 - xmax = 57.17 - text = "W" - intervals [608]: - xmin = 57.17 - xmax = 57.2 - text = "AH0" - intervals [609]: - xmin = 57.2 - xmax = 57.23 - text = "Z" - intervals [610]: - xmin = 57.23 - xmax = 57.26 - text = "IH0" - intervals [611]: - xmin = 57.26 - xmax = 57.29 - text = "N" - intervals [612]: - xmin = 57.29 - xmax = 57.41 - text = "HH" - intervals [613]: - xmin = 57.41 - xmax = 57.53 - text = "AY1" - intervals [614]: - xmin = 57.53 - xmax = 57.66 - text = "S" - intervals [615]: - xmin = 57.66 - xmax = 57.69 - text = "K" - intervals [616]: - xmin = 57.69 - xmax = 57.84 - text = "UW1" - intervals [617]: - xmin = 57.84 - xmax = 57.88 - text = "L" - intervals [618]: - xmin = 57.88 - xmax = 57.91 - text = "AH0" - intervals [619]: - xmin = 57.91 - xmax = 57.94 - text = "N" - intervals [620]: - xmin = 57.94 - xmax = 58.03 - text = "D" - intervals [621]: - xmin = 58.03 - xmax = 58.25 - text = "" - intervals [622]: - xmin = 58.25 - xmax = 58.36 - text = "Y" - intervals [623]: - xmin = 58.36 - xmax = 58.4 - text = "UW1" - intervals [624]: - xmin = 58.4 - xmax = 58.45 - text = "M" - intervals [625]: - xmin = 58.45 - xmax = 58.51 - text = "AY1" - intervals [626]: - xmin = 58.51 - xmax = 58.55 - text = "T" - intervals [627]: - xmin = 58.55 - xmax = 58.59 - text = "R" - intervals [628]: - xmin = 58.59 - xmax = 58.66 - text = "IH0" - intervals [629]: - xmin = 58.66 - xmax = 58.7 - text = "M" - intervals [630]: - xmin = 58.7 - xmax = 58.76 - text = "EH1" - intervals [631]: - xmin = 58.76 - xmax = 58.79 - text = "M" - intervals [632]: - xmin = 58.79 - xmax = 58.84 - text = "B" - intervals [633]: - xmin = 58.84 - xmax = 59.1 - text = "ER0" - intervals [634]: - xmin = 59.1 - xmax = 59.29 - text = "" - intervals [635]: - xmin = 59.29 - xmax = 59.38 - text = "DH" - intervals [636]: - xmin = 59.38 - xmax = 59.44 - text = "AH0" - intervals [637]: - xmin = 59.44 - xmax = 59.56 - text = "K" - intervals [638]: - xmin = 59.56 - xmax = 59.63 - text = "R" - intervals [639]: - xmin = 59.63 - xmax = 59.69 - text = "AH1" - intervals [640]: - xmin = 59.69 - xmax = 59.83 - text = "SH" - intervals [641]: - xmin = 59.83 - xmax = 59.89 - text = "Y" - intervals [642]: - xmin = 59.89 - xmax = 59.94 - text = "UW1" - intervals [643]: - xmin = 59.94 - xmax = 60.03 - text = "HH" - intervals [644]: - xmin = 60.03 - xmax = 60.12 - text = "AE1" - intervals [645]: - xmin = 60.12 - xmax = 60.16 - text = "D" - intervals [646]: - xmin = 60.16 - xmax = 60.2 - text = "IH0" - intervals [647]: - xmin = 60.2 - xmax = 60.27 - text = "N" - intervals [648]: - xmin = 60.27 - xmax = 60.37 - text = "S" - intervals [649]: - xmin = 60.37 - xmax = 60.43 - text = "K" - intervals [650]: - xmin = 60.43 - xmax = 60.54 - text = "UW1" - intervals [651]: - xmin = 60.54 - xmax = 60.74 - text = "L" - intervals [652]: - xmin = 60.74 - xmax = 60.9 - text = "" - intervals [653]: - xmin = 60.9 - xmax = 61.06 - text = "AE1" - intervals [654]: - xmin = 61.06 - xmax = 61.09 - text = "N" - intervals [655]: - xmin = 61.09 - xmax = 61.12 - text = "D" - intervals [656]: - xmin = 61.12 - xmax = 61.2 - text = "HH" - intervals [657]: - xmin = 61.2 - xmax = 61.24 - text = "AW1" - intervals [658]: - xmin = 61.24 - xmax = 61.29 - text = "Y" - intervals [659]: - xmin = 61.29 - xmax = 61.36 - text = "UW1" - intervals [660]: - xmin = 61.36 - xmax = 61.4 - text = "W" - intervals [661]: - xmin = 61.4 - xmax = 61.43 - text = "UH1" - intervals [662]: - xmin = 61.43 - xmax = 61.48 - text = "D" - intervals [663]: - xmin = 61.48 - xmax = 61.57 - text = "L" - intervals [664]: - xmin = 61.57 - xmax = 61.63 - text = "UH1" - intervals [665]: - xmin = 61.63 - xmax = 61.7 - text = "K" - intervals [666]: - xmin = 61.7 - xmax = 61.73 - text = "AE1" - intervals [667]: - xmin = 61.73 - xmax = 61.77 - text = "T" - intervals [668]: - xmin = 61.77 - xmax = 61.84 - text = "HH" - intervals [669]: - xmin = 61.84 - xmax = 61.98 - text = "IH1" - intervals [670]: - xmin = 61.98 - xmax = 62.16 - text = "M" - intervals [671]: - xmin = 62.16 - xmax = 62.37 - text = "" - intervals [672]: - xmin = 62.37 - xmax = 62.42 - text = "HH" - intervals [673]: - xmin = 62.42 - xmax = 62.46 - text = "W" - intervals [674]: - xmin = 62.46 - xmax = 62.49 - text = "AY1" - intervals [675]: - xmin = 62.49 - xmax = 62.54 - text = "L" - intervals [676]: - xmin = 62.54 - xmax = 62.59 - text = "HH" - intervals [677]: - xmin = 62.59 - xmax = 62.64 - text = "IY1" - intervals [678]: - xmin = 62.64 - xmax = 62.74 - text = "Z" - intervals [679]: - xmin = 62.74 - xmax = 62.93 - text = "AE1" - intervals [680]: - xmin = 62.93 - xmax = 63.02 - text = "T" - intervals [681]: - xmin = 63.02 - xmax = 63.5 - text = "IH1" - intervals [682]: - xmin = 63.5 - xmax = 63.61 - text = "N" - intervals [683]: - xmin = 63.61 - xmax = 63.7 - text = "K" - intervals [684]: - xmin = 63.7 - xmax = 63.77 - text = "L" - intervals [685]: - xmin = 63.77 - xmax = 63.93 - text = "AE1" - intervals [686]: - xmin = 63.93 - xmax = 64.04 - text = "S" - intervals [687]: - xmin = 64.04 - xmax = 64.13 - text = "W" - intervals [688]: - xmin = 64.13 - xmax = 64.16 - text = "IH0" - intervals [689]: - xmin = 64.16 - xmax = 64.2 - text = "DH" - intervals [690]: - xmin = 64.2 - xmax = 64.3 - text = "AW1" - intervals [691]: - xmin = 64.3 - xmax = 64.38 - text = "T" - intervals [692]: - xmin = 64.38 - xmax = 64.45 - text = "TH" - intervals [693]: - xmin = 64.45 - xmax = 64.52 - text = "IH1" - intervals [694]: - xmin = 64.52 - xmax = 64.58 - text = "NG" - intervals [695]: - xmin = 64.58 - xmax = 64.64 - text = "K" - intervals [696]: - xmin = 64.64 - xmax = 64.76 - text = "IH0" - intervals [697]: - xmin = 64.76 - xmax = 64.83 - text = "NG" - intervals [698]: - xmin = 64.83 - xmax = 64.88 - text = "AO1" - intervals [699]: - xmin = 64.88 - xmax = 64.95 - text = "R" - intervals [700]: - xmin = 64.95 - xmax = 64.98 - text = "" - intervals [701]: - xmin = 64.98 - xmax = 65.13 - text = "W" - intervals [702]: - xmin = 65.13 - xmax = 65.17 - text = "AA1" - intervals [703]: - xmin = 65.17 - xmax = 65.21 - text = "N" - intervals [704]: - xmin = 65.21 - xmax = 65.24 - text = "IH0" - intervals [705]: - xmin = 65.24 - xmax = 65.27 - text = "NG" - intervals [706]: - xmin = 65.27 - xmax = 65.31 - text = "T" - intervals [707]: - xmin = 65.31 - xmax = 65.36 - text = "AH0" - intervals [708]: - xmin = 65.36 - xmax = 65.42 - text = "G" - intervals [709]: - xmin = 65.42 - xmax = 65.54 - text = "OW1" - intervals [710]: - xmin = 65.54 - xmax = 65.63 - text = "P" - intervals [711]: - xmin = 65.63 - xmax = 65.7 - text = "L" - intervals [712]: - xmin = 65.7 - xmax = 65.76 - text = "EY1" - intervals [713]: - xmin = 65.76 - xmax = 65.83 - text = "S" - intervals [714]: - xmin = 65.83 - xmax = 65.88 - text = "IH0" - intervals [715]: - xmin = 65.88 - xmax = 65.95 - text = "Z" - intervals [716]: - xmin = 65.95 - xmax = 66.0 - text = "W" - intervals [717]: - xmin = 66.0 - xmax = 66.03 - text = "IH1" - intervals [718]: - xmin = 66.03 - xmax = 66.12 - text = "DH" - intervals [719]: - xmin = 66.12 - xmax = 66.2 - text = "IH0" - intervals [720]: - xmin = 66.2 - xmax = 66.38 - text = "M" - intervals [721]: - xmin = 66.38 - xmax = 67 - text = "" diff --git a/EMAGE/test_sequences/wave16k/2_scott_0_1_1.wav b/EMAGE/test_sequences/wave16k/2_scott_0_1_1.wav deleted file mode 100644 index 73affec9c06df23e103e8cd718cba17c00c4a8e8..0000000000000000000000000000000000000000 --- a/EMAGE/test_sequences/wave16k/2_scott_0_1_1.wav +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:0c113fe8c698ffd6772a3f9e110024fac486ea5cddc6fada8d7987ff1f32901d -size 481240 diff --git a/EMAGE/test_sequences/wave16k/2_scott_0_2_2.wav b/EMAGE/test_sequences/wave16k/2_scott_0_2_2.wav deleted file mode 100644 index 8dcbcdf83eba99f4fe1f58ebbcfa510940e5d3bc..0000000000000000000000000000000000000000 --- a/EMAGE/test_sequences/wave16k/2_scott_0_2_2.wav +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:8c389ae95861164fef0b1c9aab923cc4af6043b25655868db0a1164db236ba0b -size 235254 diff --git a/EMAGE/test_sequences/wave16k/2_scott_0_3_3.wav b/EMAGE/test_sequences/wave16k/2_scott_0_3_3.wav deleted file mode 100644 index a9154ffc043563d4f14667ffa55464b4cf315183..0000000000000000000000000000000000000000 --- a/EMAGE/test_sequences/wave16k/2_scott_0_3_3.wav +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:55b4083dc7f5f548eba4b720b2fe2dbadc02dbda8f6d1a2cd2e058b5098a45b9 -size 231396 diff --git a/EMAGE/test_sequences/wave16k/2_scott_0_4_4.wav b/EMAGE/test_sequences/wave16k/2_scott_0_4_4.wav deleted file mode 100644 index 602baa191890dbf7a7579b8efba5e0f808d689d2..0000000000000000000000000000000000000000 --- a/EMAGE/test_sequences/wave16k/2_scott_0_4_4.wav +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:b4d3052dc05e85222169441f5d487edd7106188cdb340f2f24b1a9197bae8ef7 -size 124100 diff --git a/EMAGE/test_sequences/weights/AESKConv_240_100.bin b/EMAGE/test_sequences/weights/AESKConv_240_100.bin deleted file mode 100644 index 1d1ea36ecd9582802176c499eba43969144ad9fe..0000000000000000000000000000000000000000 --- a/EMAGE/test_sequences/weights/AESKConv_240_100.bin +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:5cd9566b24264f34d44003b3de62cdfd50aa85b7cdde2d369214599023c40f55 -size 17558653 diff --git a/EMAGE/test_sequences/weights/mean_vel_smplxflame_30.npy b/EMAGE/test_sequences/weights/mean_vel_smplxflame_30.npy deleted file mode 100644 index 0789238537103f051a6c51a3c4725e8afe85e140..0000000000000000000000000000000000000000 --- a/EMAGE/test_sequences/weights/mean_vel_smplxflame_30.npy +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:53b5e48f2a7bf78c41a6de6395d6bb4f29018465ca5d0ee2820a2be3eebb7137 -size 348 diff --git a/EMAGE/test_sequences/weights/vocab.pkl b/EMAGE/test_sequences/weights/vocab.pkl deleted file mode 100644 index 3daf14aa7f32d7823b19fd765ff5739bb9a1bd32..0000000000000000000000000000000000000000 --- a/EMAGE/test_sequences/weights/vocab.pkl +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:54fbcea7b19e0ee9b5c5836c85087a682d3a9513041091ce3e95d83eed0b2acd -size 13821361 diff --git a/README.md b/README.md index 59fc532076a75d4da93fbc577f6740dd33aaa2ed..8b21b9d0d81875215eda0be2eb31fff9bd420d12 100644 --- a/README.md +++ b/README.md @@ -1,24 +1,13 @@ --- -title: EMAGE -emoji: 🐑 -colorFrom: green -colorTo: gray +title: Emagedev +emoji: 👁 +colorFrom: red +colorTo: indigo sdk: gradio -sdk_version: 4.24.0 +sdk_version: 4.44.1 +python_version: 3.9.20 app_file: app.py pinned: false -license: cc-by-nc-4.0 -short_description: Co-Speech Gesture 3D Motion Generation --- -This is a demo is a audio-only version of the approach described in the paper, ["EMAGE: Towards Unified Holistic Co-Speech Gesture Generation via Expressive Masked Audio Gesture Modeling"](https://arxiv.org/abs/2401.00374) -``` -@misc{liu2023emage, - title={EMAGE: Towards Unified Holistic Co-Speech Gesture Generation via Expressive Masked Audio Gesture Modeling}, - author={Liu, Haiyang and Zhu, Zihao and Becherini, Giorgio and Peng, Yichen and Su, Mingyang and Zhou, You and Zhe, Xuefei and Iwamoto, Naoya and Zheng, Bo and Black, Michael J}, - year={2023}, - eprint={2401.00374}, - archivePrefix={arXiv}, - primaryClass={cs.CV} -} -``` \ No newline at end of file +Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/ae_trainer.py b/ae_trainer.py deleted file mode 100644 index fcc27c34aa8c3544671cc3db0a529d73dab3134f..0000000000000000000000000000000000000000 --- a/ae_trainer.py +++ /dev/null @@ -1,375 +0,0 @@ -import train -import os -import time -import csv -import sys -import warnings -import random -import numpy as np -import time -import pprint -import pickle - -import torch -import torch.nn as nn -import torch.nn.functional as F -from torch.utils.tensorboard import SummaryWriter -from torch.nn.parallel import DistributedDataParallel as DDP -from loguru import logger -import smplx - -from utils import config, logger_tools, other_tools, metric -from utils import rotation_conversions as rc -from dataloaders import data_tools -from optimizers.optim_factory import create_optimizer -from optimizers.scheduler_factory import create_scheduler -from optimizers.loss_factory import get_loss_func -from scipy.spatial.transform import Rotation - - -class CustomTrainer(train.BaseTrainer): - """ - motion representation learning - """ - def __init__(self, args): - super().__init__(args) - self.joints = self.train_data.joints - self.smplx = smplx.create( - self.args.data_path_1+"smplx_models/", - model_type='smplx', - gender='NEUTRAL_2020', - use_face_contour=False, - num_betas=300, - num_expression_coeffs=100, - ext='npz', - use_pca=False, - ).cuda().eval() - self.tracker = other_tools.EpochTracker(["rec", "vel", "ver", "com", "kl", "acc"], [False, False, False, False, False, False]) - if not self.args.rot6d: #"rot6d" not in args.pose_rep: - logger.error(f"this script is for rot6d, your pose rep. is {args.pose_rep}") - self.rec_loss = get_loss_func("GeodesicLoss") - self.vel_loss = torch.nn.L1Loss(reduction='mean') - self.vectices_loss = torch.nn.MSELoss(reduction='mean') - - def inverse_selection(self, filtered_t, selection_array, n): - # 创建一个全为零的数组，形状为 n*165 - original_shape_t = np.zeros((n, selection_array.size)) - - # 找到选择数组中为1的索引位置 - selected_indices = np.where(selection_array == 1)[0] - - # 将 filtered_t 的值填充到 original_shape_t 中相应的位置 - for i in range(n): - original_shape_t[i, selected_indices] = filtered_t[i] - - return original_shape_t - - def inverse_selection_tensor(self, filtered_t, selection_array, n): - # 创建一个全为零的数组，形状为 n*165 - selection_array = torch.from_numpy(selection_array).cuda() - original_shape_t = torch.zeros((n, 165)).cuda() - - # 找到选择数组中为1的索引位置 - selected_indices = torch.where(selection_array == 1)[0] - - # 将 filtered_t 的值填充到 original_shape_t 中相应的位置 - for i in range(n): - original_shape_t[i, selected_indices] = filtered_t[i] - - return original_shape_t - - def train(self, epoch): - self.model.train() - t_start = time.time() - self.tracker.reset() - for its, dict_data in enumerate(self.train_loader): - tar_pose = dict_data["pose"] - tar_beta = dict_data["beta"].cuda() - tar_trans = dict_data["trans"].cuda() - tar_pose = tar_pose.cuda() - bs, n, j = tar_pose.shape[0], tar_pose.shape[1], self.joints - tar_exps = torch.zeros((bs, n, 100)).cuda() - tar_pose = rc.axis_angle_to_matrix(tar_pose.reshape(bs, n, j, 3)) - tar_pose = rc.matrix_to_rotation_6d(tar_pose).reshape(bs, n, j*6) - t_data = time.time() - t_start - - self.opt.zero_grad() - g_loss_final = 0 - net_out = self.model(tar_pose) - rec_pose = net_out["rec_pose"] - rec_pose = rec_pose.reshape(bs, n, j, 6) - rec_pose = rc.rotation_6d_to_matrix(rec_pose)# - tar_pose = rc.rotation_6d_to_matrix(tar_pose.reshape(bs, n, j, 6)) - loss_rec = self.rec_loss(rec_pose, tar_pose) * self.args.rec_weight * self.args.rec_pos_weight - self.tracker.update_meter("rec", "train", loss_rec.item()) - g_loss_final += loss_rec - - velocity_loss = self.vel_loss(rec_pose[:, 1:] - rec_pose[:, :-1], tar_pose[:, 1:] - tar_pose[:, :-1]) * self.args.rec_weight - acceleration_loss = self.vel_loss(rec_pose[:, 2:] + rec_pose[:, :-2] - 2 * rec_pose[:, 1:-1], tar_pose[:, 2:] + tar_pose[:, :-2] - 2 * tar_pose[:, 1:-1]) * self.args.rec_weight - self.tracker.update_meter("vel", "train", velocity_loss.item()) - self.tracker.update_meter("acc", "train", acceleration_loss.item()) - g_loss_final += velocity_loss - g_loss_final += acceleration_loss - # vertices loss - if self.args.rec_ver_weight > 0: - tar_pose = rc.matrix_to_axis_angle(tar_pose).reshape(bs*n, j*3) - rec_pose = rc.matrix_to_axis_angle(rec_pose).reshape(bs*n, j*3) - rec_pose = self.inverse_selection_tensor(rec_pose, self.train_data.joint_mask, rec_pose.shape[0]) - tar_pose = self.inverse_selection_tensor(tar_pose, self.train_data.joint_mask, tar_pose.shape[0]) - vertices_rec = self.smplx( - betas=tar_beta.reshape(bs*n, 300), - transl=tar_trans.reshape(bs*n, 3), - expression=tar_exps.reshape(bs*n, 100), - jaw_pose=rec_pose[:, 66:69], - global_orient=rec_pose[:,:3], - body_pose=rec_pose[:,3:21*3+3], - left_hand_pose=rec_pose[:,25*3:40*3], - right_hand_pose=rec_pose[:,40*3:55*3], - return_verts=True, - return_joints=True, - leye_pose=tar_pose[:, 69:72], - reye_pose=tar_pose[:, 72:75], - ) - vertices_tar = self.smplx( - betas=tar_beta.reshape(bs*n, 300), - transl=tar_trans.reshape(bs*n, 3), - expression=tar_exps.reshape(bs*n, 100), - jaw_pose=tar_pose[:, 66:69], - global_orient=tar_pose[:,:3], - body_pose=tar_pose[:,3:21*3+3], - left_hand_pose=tar_pose[:,25*3:40*3], - right_hand_pose=tar_pose[:,40*3:55*3], - return_verts=True, - return_joints=True, - leye_pose=tar_pose[:, 69:72], - reye_pose=tar_pose[:, 72:75], - ) - vectices_loss = self.vectices_loss(vertices_rec['vertices'], vertices_tar['vertices']) - self.tracker.update_meter("ver", "train", vectices_loss.item()*self.args.rec_weight * self.args.rec_ver_weight) - g_loss_final += vectices_loss*self.args.rec_weight*self.args.rec_ver_weight - - vertices_vel_loss = self.vel_loss(vertices_rec['vertices'][:, 1:] - vertices_rec['vertices'][:, :-1], vertices_tar['vertices'][:, 1:] - vertices_tar['vertices'][:, :-1]) * self.args.rec_weight - vertices_acc_loss = self.vel_loss(vertices_rec['vertices'][:, 2:] + vertices_rec['vertices'][:, :-2] - 2 * vertices_rec['vertices'][:, 1:-1], vertices_tar['vertices'][:, 2:] + vertices_tar['vertices'][:, :-2] - 2 * vertices_tar['vertices'][:, 1:-1]) * self.args.rec_weight - g_loss_final += vertices_vel_loss * self.args.rec_weight * self.args.rec_ver_weight - g_loss_final += vertices_acc_loss * self.args.rec_weight * self.args.rec_ver_weight - - # if self.args.vel_weight > 0: - # pos_rec_vel = other_tools.estimate_linear_velocity(vertices_rec['joints'], 1/self.pose_fps) - # pos_tar_vel = other_tools.estimate_linear_velocity(vertices_tar['joints'], 1/self.pose_fps) - # vel_rec_loss = self.vel_loss(pos_rec_vel, pos_tar_vel) - # tar_pose = rc.axis_angle_to_matrix(tar_pose.reshape(bs, n, j, 3)) - # rec_pose = rc.axis_angle_to_matrix(rec_pose.reshape(bs, n, j, 3)) - # rot_rec_vel = other_tools.estimate_angular_velocity(rec_pose, 1/self.pose_fps) - # rot_tar_vel = other_tools.estimate_angular_velocity(tar_pose, 1/self.pose_fps) - # vel_rec_loss += self.vel_loss(pos_rec_vel, pos_tar_vel) - # self.tracker.update_meter("vel", "train", vel_rec_loss.item()*self.args.vel_weight) - # loss += (vel_rec_loss*self.args.vel_weight) - - # ---------------------- vae -------------------------- # - if "VQVAE" in self.args.g_name: - loss_embedding = net_out["embedding_loss"] - g_loss_final += loss_embedding - self.tracker.update_meter("com", "train", loss_embedding.item()) - # elif "VAE" in self.args.g_name: - # pose_mu, pose_logvar = net_out["pose_mu"], net_out["pose_logvar"] - # KLD = -0.5 * torch.sum(1 + pose_logvar - pose_mu.pow(2) - pose_logvar.exp()) - # if epoch < 0: - # KLD_weight = 0 - # else: - # KLD_weight = min(1.0, (epoch - 0) * 0.05) * 0.01 - # loss += KLD_weight * KLD - # self.tracker.update_meter("kl", "train", KLD_weight * KLD.item()) - g_loss_final.backward() - if self.args.grad_norm != 0: - torch.nn.utils.clip_grad_norm_(self.model.parameters(), self.args.grad_norm) - self.opt.step() - t_train = time.time() - t_start - t_data - t_start = time.time() - mem_cost = torch.cuda.memory_cached() / 1E9 - lr_g = self.opt.param_groups[0]['lr'] - if its % self.args.log_period == 0: - self.train_recording(epoch, its, t_data, t_train, mem_cost, lr_g) - if self.args.debug: - if its == 1: break - self.opt_s.step(epoch) - - def val(self, epoch): - self.model.eval() - t_start = time.time() - with torch.no_grad(): - for its, dict_data in enumerate(self.val_loader): - tar_pose = dict_data["pose"] - tar_beta = dict_data["beta"].cuda() - tar_trans = dict_data["trans"].cuda() - tar_pose = tar_pose.cuda() - bs, n, j = tar_pose.shape[0], tar_pose.shape[1], self.joints - tar_exps = torch.zeros((bs, n, 100)).cuda() - tar_pose = rc.axis_angle_to_matrix(tar_pose.reshape(bs, n, j, 3)) - tar_pose = rc.matrix_to_rotation_6d(tar_pose).reshape(bs, n, j*6) - t_data = time.time() - t_start - - #self.opt.zero_grad() - #g_loss_final = 0 - net_out = self.model(tar_pose) - rec_pose = net_out["rec_pose"] - rec_pose = rec_pose.reshape(bs, n, j, 6) - rec_pose = rc.rotation_6d_to_matrix(rec_pose)# - tar_pose = rc.rotation_6d_to_matrix(tar_pose.reshape(bs, n, j, 6)) - loss_rec = self.rec_loss(rec_pose, tar_pose) * self.args.rec_weight * self.args.rec_pos_weight - self.tracker.update_meter("rec", "val", loss_rec.item()) - #g_loss_final += loss_rec - - # vertices loss - if self.args.rec_ver_weight > 0: - tar_pose = rc.matrix_to_axis_angle(tar_pose).reshape(bs*n, j*3) - rec_pose = rc.matrix_to_axis_angle(rec_pose).reshape(bs*n, j*3) - rec_pose = self.inverse_selection_tensor(rec_pose, self.train_data.joint_mask, rec_pose.shape[0]) - tar_pose = self.inverse_selection_tensor(tar_pose, self.train_data.joint_mask, tar_pose.shape[0]) - vertices_rec = self.smplx( - betas=tar_beta.reshape(bs*n, 300), - transl=tar_trans.reshape(bs*n, 3), - expression=tar_exps.reshape(bs*n, 100), - jaw_pose=rec_pose[:, 66:69], - global_orient=rec_pose[:,:3], - body_pose=rec_pose[:,3:21*3+3], - left_hand_pose=rec_pose[:,25*3:40*3], - right_hand_pose=rec_pose[:,40*3:55*3], - return_verts=True, - leye_pose=tar_pose[:, 69:72], - reye_pose=tar_pose[:, 72:75], - ) - vertices_tar = self.smplx( - betas=tar_beta.reshape(bs*n, 300), - transl=tar_trans.reshape(bs*n, 3), - expression=tar_exps.reshape(bs*n, 100), - jaw_pose=tar_pose[:, 66:69], - global_orient=tar_pose[:,:3], - body_pose=tar_pose[:,3:21*3+3], - left_hand_pose=tar_pose[:,25*3:40*3], - right_hand_pose=tar_pose[:,40*3:55*3], - return_verts=True, - leye_pose=tar_pose[:, 69:72], - reye_pose=tar_pose[:, 72:75], - ) - vectices_loss = self.vectices_loss(vertices_rec['vertices'], vertices_tar['vertices']) - self.tracker.update_meter("ver", "val", vectices_loss.item()*self.args.rec_weight * self.args.rec_ver_weight) - if "VQVAE" in self.args.g_name: - loss_embedding = net_out["embedding_loss"] - self.tracker.update_meter("com", "val", loss_embedding.item()) - #g_loss_final += vectices_loss*self.args.rec_weight*self.args.rec_ver_weight - self.val_recording(epoch) - - def test(self, epoch): - results_save_path = self.checkpoint_path + f"/{epoch}/" - if os.path.exists(results_save_path): - return 0 - os.makedirs(results_save_path) - start_time = time.time() - total_length = 0 - test_seq_list = self.test_data.selected_file - self.model.eval() - with torch.no_grad(): - for its, dict_data in enumerate(self.test_loader): - tar_pose = dict_data["pose"] - tar_pose = tar_pose.cuda() - bs, n, j = tar_pose.shape[0], tar_pose.shape[1], self.joints - tar_pose = rc.axis_angle_to_matrix(tar_pose.reshape(bs, n, j, 3)) - tar_pose = rc.matrix_to_rotation_6d(tar_pose).reshape(bs, n, j*6) - remain = n%self.args.pose_length - tar_pose = tar_pose[:, :n-remain, :] - #print(tar_pose.shape) - if True: - net_out = self.model(tar_pose) - rec_pose = net_out["rec_pose"] - n = rec_pose.shape[1] - tar_pose = tar_pose[:, :n, :] - rec_pose = rec_pose.reshape(bs, n, j, 6) - rec_pose = rc.rotation_6d_to_matrix(rec_pose)# - rec_pose = rc.matrix_to_axis_angle(rec_pose).reshape(bs*n, j*3) - rec_pose = rec_pose.cpu().numpy() - else: - pass -# for i in range(tar_pose.shape[1]//(self.args.vae_test_len)): -# tar_pose_new = tar_pose[:,i*(self.args.vae_test_len):i*(self.args.vae_test_len)+self.args.vae_test_len,:] -# net_out = self.model(**dict(inputs=tar_pose_new)) -# rec_pose = net_out["rec_pose"] -# rec_pose = (rec_pose.reshape(rec_pose.shape[0], rec_pose.shape[1], -1, 6) * self.joint_level_mask_cuda).reshape(rec_pose.shape[0], rec_pose.shape[1], -1) -# if "rot6d" in self.args.pose_rep: -# rec_pose = data_transfer.rotation_6d_to_matrix(rec_pose.reshape(tar_pose.shape[0], self.args.vae_test_len, -1, 6)) -# rec_pose = data_transfer.matrix_to_euler_angles(rec_pose, "XYZ").reshape(rec_pose.shape[0], rec_pose.shape[1], -1) -# if "smplx" not in self.args.pose_rep: -# rec_pose = torch.rad2deg(rec_pose) -# rec_pose = rec_pose * self.joint_mask_cuda - -# out_sub = rec_pose.cpu().numpy().reshape(-1, rec_pose.shape[2]) -# if i != 0: -# out_final = np.concatenate((out_final,out_sub), 0) -# else: -# out_final = out_sub - - tar_pose = rc.rotation_6d_to_matrix(tar_pose.reshape(bs, n, j, 6)) - tar_pose = rc.matrix_to_axis_angle(tar_pose).reshape(bs*n, j*3) - tar_pose = tar_pose.cpu().numpy() - - total_length += n - # --- save --- # - if 'smplx' in self.args.pose_rep: - gt_npz = np.load(self.args.data_path+self.args.pose_rep+"/"+test_seq_list.iloc[its]['id']+'.npz', allow_pickle=True) - stride = int(30 / self.args.pose_fps) - tar_pose = self.inverse_selection(tar_pose, self.test_data.joint_mask, tar_pose.shape[0]) - np.savez(results_save_path+"gt_"+test_seq_list.iloc[its]['id']+'.npz', - betas=gt_npz["betas"], - poses=tar_pose[:n], - expressions=gt_npz["expressions"]-gt_npz["expressions"], - trans=gt_npz["trans"][::stride][:n] - gt_npz["trans"][::stride][:n], - model='smplx2020', - gender='neutral', - mocap_frame_rate = 30 , - ) - rec_pose = self.inverse_selection(rec_pose, self.test_data.joint_mask, rec_pose.shape[0]) - np.savez(results_save_path+"res_"+test_seq_list.iloc[its]['id']+'.npz', - betas=gt_npz["betas"], - poses=rec_pose, - expressions=gt_npz["expressions"]-gt_npz["expressions"], - trans=gt_npz["trans"][::stride][:n] - gt_npz["trans"][::stride][:n], - model='smplx2020', - gender='neutral', - mocap_frame_rate = 30 , - ) - else: - rec_pose = rc.axis_angle_to_matrix(torch.from_numpy(rec_pose.reshape(bs*n, j, 3))) - rec_pose = np.rad2deg(rc.matrix_to_euler_angles(rec_pose, "XYZ")).reshape(bs*n, j*3).numpy() - tar_pose = rc.axis_angle_to_matrix(torch.from_numpy(tar_pose.reshape(bs*n, j, 3))) - tar_pose = np.rad2deg(rc.matrix_to_euler_angles(tar_pose, "XYZ")).reshape(bs*n, j*3).numpy() - #trans="0.000000 0.000000 0.000000" - - with open(f"{self.args.data_path}{self.args.pose_rep}/{test_seq_list.iloc[its]['id']}.bvh", "r") as f_demo: - with open(results_save_path+"gt_"+test_seq_list.iloc[its]['id']+'.bvh', 'w+') as f_gt: - with open(results_save_path+"res_"+test_seq_list.iloc[its]['id']+'.bvh', 'w+') as f_real: - for i, line_data in enumerate(f_demo.readlines()): - if i < 431: - f_real.write(line_data) - f_gt.write(line_data) - else: break - for line_id in range(n): #,args.pre_frames, args.pose_length - line_data = np.array2string(rec_pose[line_id], max_line_width=np.inf, precision=6, suppress_small=False, separator=' ') - f_real.write(line_data[1:-2]+'\n') - for line_id in range(n): #,args.pre_frames, args.pose_length - line_data = np.array2string(tar_pose[line_id], max_line_width=np.inf, precision=6, suppress_small=False, separator=' ') - f_gt.write(line_data[1:-2]+'\n') - # with open(results_save_path+"gt_"+test_seq_list[its]+'.pkl', 'wb') as fw: - # pickle.dump(new_dict, fw) - # #new_dict2["fullpose"] = out_final - # with open(results_save_path+"res_"+test_seq_list[its]+'.pkl', 'wb') as fw1: - # pickle.dump(new_dict2, fw1) - - # other_tools.render_one_sequence( - # results_save_path+"res_"+test_seq_list[its]+'.pkl', - # results_save_path+"gt_"+test_seq_list[its]+'.pkl', - # results_save_path, - # self.args.data_path + self.args.test_data_path + 'wave16k/' + test_seq_list[its]+'.npy', - # ) - - #if its == 1:break - end_time = time.time() - start_time - logger.info(f"total inference time: {int(end_time)} s for {int(total_length/self.args.pose_fps)} s motion") \ No newline at end of file diff --git a/aeface_trainer.py b/aeface_trainer.py deleted file mode 100644 index 0c174bc5c94d1240b4b1494ee57922a3c903ae73..0000000000000000000000000000000000000000 --- a/aeface_trainer.py +++ /dev/null @@ -1,388 +0,0 @@ -import train -import os -import time -import csv -import sys -import warnings -import random -import numpy as np -import time -import pprint -import pickle - -import torch -import torch.nn as nn -import torch.nn.functional as F -from torch.utils.tensorboard import SummaryWriter -from torch.nn.parallel import DistributedDataParallel as DDP -from loguru import logger -import smplx - -from utils import config, logger_tools, other_tools, metric -from utils import rotation_conversions as rc -from dataloaders import data_tools -from optimizers.optim_factory import create_optimizer -from optimizers.scheduler_factory import create_scheduler -from optimizers.loss_factory import get_loss_func -from scipy.spatial.transform import Rotation - - -class CustomTrainer(train.BaseTrainer): - """ - motion representation learning - """ - def __init__(self, args): - super().__init__(args) - self.joints = self.train_data.joints - self.tracker = other_tools.EpochTracker(["rec", "vel", "acc", "com", "face", "face_vel", "face_acc", "ver", "ver_vel", "ver_acc"], [False, False, False, False, False, False, False, False, False, False]) - self.rec_loss = get_loss_func("GeodesicLoss") - self.mse_loss = torch.nn.MSELoss(reduction='mean') - self.vel_loss = torch.nn.MSELoss(reduction='mean') #torch.nn.L1Loss(reduction='mean') - self.vectices_loss = torch.nn.MSELoss(reduction='mean') - - def inverse_selection(self, filtered_t, selection_array, n): - # 创建一个全为零的数组，形状为 n*165 - original_shape_t = np.zeros((n, selection_array.size)) - - # 找到选择数组中为1的索引位置 - selected_indices = np.where(selection_array == 1)[0] - - # 将 filtered_t 的值填充到 original_shape_t 中相应的位置 - for i in range(n): - original_shape_t[i, selected_indices] = filtered_t[i] - - return original_shape_t - - def train(self, epoch): - self.model.train() - t_start = time.time() - self.tracker.reset() - for its, dict_data in enumerate(self.train_loader): - tar_pose = dict_data["pose"] - tar_beta = dict_data["beta"].cuda() - tar_trans = dict_data["trans"].cuda() - tar_pose = tar_pose.cuda() - bs, n, j = tar_pose.shape[0], tar_pose.shape[1], self.joints - tar_exps = dict_data["facial"].to(self.rank) - tar_pose = rc.axis_angle_to_matrix(tar_pose.reshape(bs, n, j, 3)) - tar_pose = rc.matrix_to_rotation_6d(tar_pose).reshape(bs, n, j*6) - in_tar_pose = torch.cat([tar_pose, tar_exps], -1) # 103 - t_data = time.time() - t_start - - self.opt.zero_grad() - g_loss_final = 0 - net_out = self.model(in_tar_pose) - # jaw open 6d loss - rec_pose = net_out["rec_pose"][:, :, :j*6] - rec_pose = rec_pose.reshape(bs, n, j, 6) - rec_pose = rc.rotation_6d_to_matrix(rec_pose)# - tar_pose = rc.rotation_6d_to_matrix(tar_pose.reshape(bs, n, j, 6)) - loss_rec = self.rec_loss(rec_pose, tar_pose) * self.args.rec_weight * self.args.rec_pos_weight - self.tracker.update_meter("rec", "train", loss_rec.item()) - g_loss_final += loss_rec - # jaw open 6d vel and acc loss - velocity_loss = self.vel_loss(rec_pose[:, 1:] - rec_pose[:, :-1], tar_pose[:, 1:] - tar_pose[:, :-1]) * self.args.rec_weight - acceleration_loss = self.vel_loss(rec_pose[:, 2:] + rec_pose[:, :-2] - 2 * rec_pose[:, 1:-1], tar_pose[:, 2:] + tar_pose[:, :-2] - 2 * tar_pose[:, 1:-1]) * self.args.rec_weight - self.tracker.update_meter("vel", "train", velocity_loss.item()) - self.tracker.update_meter("acc", "train", acceleration_loss.item()) - g_loss_final += velocity_loss - g_loss_final += acceleration_loss - # face parameter l1 loss - rec_exps = net_out["rec_pose"][:, :, j*6:] - loss_face = self.mse_loss(rec_exps, tar_exps) * self.args.rec_weight - self.tracker.update_meter("face", "train", loss_face.item()) - g_loss_final += loss_face - # face parameter l1 vel and acc loss - face_velocity_loss = self.vel_loss(rec_exps[:, 1:] - rec_exps[:, :-1], tar_exps[:, 1:] - tar_exps[:, :-1]) * self.args.rec_weight - face_acceleration_loss = self.vel_loss(rec_exps[:, 2:] + rec_exps[:, :-2] - 2 * rec_exps[:, 1:-1], tar_exps[:, 2:] + tar_exps[:, :-2] - 2 * tar_exps[:, 1:-1]) * self.args.rec_weight - self.tracker.update_meter("face_vel", "train", face_velocity_loss.item()) - self.tracker.update_meter("face_acc", "train", face_acceleration_loss.item()) - g_loss_final += face_velocity_loss - g_loss_final += face_acceleration_loss - - # vertices loss - if self.args.rec_ver_weight > 0: - tar_pose = rc.matrix_to_axis_angle(tar_pose).reshape(bs*n, j*3) - rec_pose = rc.matrix_to_axis_angle(rec_pose).reshape(bs*n, j*3) - vertices_rec = self.smplx( - betas=tar_beta.reshape(bs*n, 300), - transl=tar_trans.reshape(bs*n, 3)-tar_trans.reshape(bs*n, 3), - expression=tar_exps.reshape(bs*n, 100), - jaw_pose=rec_pose, - global_orient=torch.zeros(bs*n, 3).cuda(), - body_pose=torch.zeros(bs*n, 21*3).cuda(), - left_hand_pose=torch.zeros(bs*n, 15*3).cuda(), - right_hand_pose=torch.zeros(bs*n, 15*3).cuda(), - return_verts=True, - # return_joints=True, - leye_pose=torch.zeros(bs*n, 3).cuda(), - reye_pose=torch.zeros(bs*n, 3).cuda(), - ) - vertices_tar = self.smplx( - betas=tar_beta.reshape(bs*n, 300), - transl=tar_trans.reshape(bs*n, 3)-tar_trans.reshape(bs*n, 3), - expression=rec_exps.reshape(bs*n, 100), - jaw_pose=tar_pose, - global_orient=torch.zeros(bs*n, 3).cuda(), - body_pose=torch.zeros(bs*n, 21*3).cuda(), - left_hand_pose=torch.zeros(bs*n, 15*3).cuda(), - right_hand_pose=torch.zeros(bs*n, 15*3).cuda(), - return_verts=True, - # return_joints=True, - leye_pose=torch.zeros(bs*n, 3).cuda(), - reye_pose=torch.zeros(bs*n, 3).cuda(), - ) - vectices_loss = self.mse_loss(vertices_rec['vertices'], vertices_tar['vertices']) - self.tracker.update_meter("ver", "train", vectices_loss.item()*self.args.rec_weight * self.args.rec_ver_weight) - g_loss_final += vectices_loss*self.args.rec_weight*self.args.rec_ver_weight - # vertices vel and acc loss - vert_velocity_loss = self.vel_loss(vertices_rec['vertices'][:, 1:] - vertices_rec['vertices'][:, :-1], vertices_tar['vertices'][:, 1:] - vertices_tar['vertices'][:, :-1]) * self.args.rec_weight * self.args.rec_ver_weight - vert_acceleration_loss = self.vel_loss(vertices_rec['vertices'][:, 2:] + vertices_rec['vertices'][:, :-2] - 2 * vertices_rec['vertices'][:, 1:-1], vertices_tar['vertices'][:, 2:] + vertices_tar['vertices'][:, :-2] - 2 * vertices_tar['vertices'][:, 1:-1]) * self.args.rec_weight * self.args.rec_ver_weight - self.tracker.update_meter("ver_vel", "train", vert_velocity_loss.item()) - self.tracker.update_meter("ver_acc", "train", vert_acceleration_loss.item()) - g_loss_final += vert_velocity_loss - g_loss_final += vert_acceleration_loss - - # ---------------------- vae -------------------------- # - if "VQVAE" in self.args.g_name: - loss_embedding = net_out["embedding_loss"] - g_loss_final += loss_embedding - self.tracker.update_meter("com", "train", loss_embedding.item()) - # elif "VAE" in self.args.g_name: - # pose_mu, pose_logvar = net_out["pose_mu"], net_out["pose_logvar"] - # KLD = -0.5 * torch.sum(1 + pose_logvar - pose_mu.pow(2) - pose_logvar.exp()) - # if epoch < 0: - # KLD_weight = 0 - # else: - # KLD_weight = min(1.0, (epoch - 0) * 0.05) * 0.01 - # loss += KLD_weight * KLD - # self.tracker.update_meter("kl", "train", KLD_weight * KLD.item()) - g_loss_final.backward() - if self.args.grad_norm != 0: - torch.nn.utils.clip_grad_norm_(self.model.parameters(), self.args.grad_norm) - self.opt.step() - t_train = time.time() - t_start - t_data - t_start = time.time() - mem_cost = torch.cuda.memory_cached() / 1E9 - lr_g = self.opt.param_groups[0]['lr'] - if its % self.args.log_period == 0: - self.train_recording(epoch, its, t_data, t_train, mem_cost, lr_g) - if self.args.debug: - if its == 1: break - self.opt_s.step(epoch) - - def val(self, epoch): - self.model.eval() - t_start = time.time() - with torch.no_grad(): - for its, dict_data in enumerate(self.val_loader): - tar_pose = dict_data["pose"] - tar_beta = dict_data["beta"].cuda() - tar_trans = dict_data["trans"].cuda() - tar_pose = tar_pose.cuda() - bs, n, j = tar_pose.shape[0], tar_pose.shape[1], self.joints - tar_exps = dict_data["facial"].to(self.rank) - tar_pose = rc.axis_angle_to_matrix(tar_pose.reshape(bs, n, j, 3)) - tar_pose = rc.matrix_to_rotation_6d(tar_pose).reshape(bs, n, j*6) - in_tar_pose = torch.cat([tar_pose, tar_exps], -1) # 103 - # print(tar_pose.shape, in_tar_pose.shape, tar_exps.shape) - t_data = time.time() - t_start - - #self.opt.zero_grad() - #g_loss_final = 0 - net_out = self.model(in_tar_pose) - # jaw open 6d loss - rec_pose = net_out["rec_pose"][:, :, :j*6] - rec_pose = rec_pose.reshape(bs, n, j, 6) - rec_pose = rc.rotation_6d_to_matrix(rec_pose)# - tar_pose = rc.rotation_6d_to_matrix(tar_pose.reshape(bs, n, j, 6)) - loss_rec = self.rec_loss(rec_pose, tar_pose) * self.args.rec_weight * self.args.rec_pos_weight - self.tracker.update_meter("rec", "val", loss_rec.item()) - # g_loss_final += loss_rec - # jaw open 6d vel and acc loss - velocity_loss = self.vel_loss(rec_pose[:, 1:] - rec_pose[:, :-1], tar_pose[:, 1:] - tar_pose[:, :-1]) * self.args.rec_weight - acceleration_loss = self.vel_loss(rec_pose[:, 2:] + rec_pose[:, :-2] - 2 * rec_pose[:, 1:-1], tar_pose[:, 2:] + tar_pose[:, :-2] - 2 * tar_pose[:, 1:-1]) * self.args.rec_weight - self.tracker.update_meter("vel", "val", velocity_loss.item()) - self.tracker.update_meter("acc", "val", acceleration_loss.item()) - # g_loss_final += velocity_loss - # g_loss_final += acceleration_loss - # face parameter l1 loss - rec_exps = net_out["rec_pose"][:, :, j*6:] - loss_face = self.vel_loss(rec_exps, tar_exps) * self.args.rec_weight - self.tracker.update_meter("face", "val", loss_face.item()) - # g_loss_final += loss_face - # face parameter l1 vel and acc loss - face_velocity_loss = self.vel_loss(rec_exps[:, 1:] - rec_exps[:, :-1], tar_exps[:, 1:] - tar_exps[:, :-1]) * self.args.rec_weight - face_acceleration_loss = self.vel_loss(rec_exps[:, 2:] + rec_exps[:, :-2] - 2 * rec_exps[:, 1:-1], tar_exps[:, 2:] + tar_exps[:, :-2] - 2 * tar_exps[:, 1:-1]) * self.args.rec_weight - self.tracker.update_meter("face_vel", "val", face_velocity_loss.item()) - self.tracker.update_meter("face_acc", "val", face_acceleration_loss.item()) - # g_loss_final += face_velocity_loss - # g_loss_final += face_acceleration_loss - - # vertices loss - if self.args.rec_ver_weight > 0: - tar_pose = rc.matrix_to_axis_angle(tar_pose).reshape(bs*n, j*3) - rec_pose = rc.matrix_to_axis_angle(rec_pose).reshape(bs*n, j*3) - vertices_rec = self.smplx( - betas=tar_beta.reshape(bs*n, 300), - transl=tar_trans.reshape(bs*n, 3)-tar_trans.reshape(bs*n, 3), - expression=tar_exps.reshape(bs*n, 100), - jaw_pose=rec_pose, - global_orient=torch.zeros(bs*n, 3).cuda(), - body_pose=torch.zeros(bs*n, 21*3).cuda(), - left_hand_pose=torch.zeros(bs*n, 15*3).cuda(), - right_hand_pose=torch.zeros(bs*n, 15*3).cuda(), - return_verts=True, - # return_joints=True, - leye_pose=torch.zeros(bs*n, 3).cuda(), - reye_pose=torch.zeros(bs*n, 3).cuda(), - ) - vertices_tar = self.smplx( - betas=tar_beta.reshape(bs*n, 300), - transl=tar_trans.reshape(bs*n, 3)-tar_trans.reshape(bs*n, 3), - expression=rec_exps.reshape(bs*n, 100), - jaw_pose=tar_pose, - global_orient=torch.zeros(bs*n, 3).cuda(), - body_pose=torch.zeros(bs*n, 21*3).cuda(), - left_hand_pose=torch.zeros(bs*n, 15*3).cuda(), - right_hand_pose=torch.zeros(bs*n, 15*3).cuda(), - return_verts=True, - # return_joints=True, - leye_pose=torch.zeros(bs*n, 3).cuda(), - reye_pose=torch.zeros(bs*n, 3).cuda(), - ) - vectices_loss = self.mse_loss(vertices_rec['vertices'], vertices_tar['vertices']) - self.tracker.update_meter("ver", "val", vectices_loss.item()*self.args.rec_weight * self.args.rec_ver_weight) - # g_loss_final += vectices_loss*self.args.rec_weight*self.args.rec_ver_weight - # vertices vel and acc loss - vert_velocity_loss = self.vel_loss(vertices_rec['vertices'][:, 1:] - vertices_rec['vertices'][:, :-1], vertices_tar['vertices'][:, 1:] - vertices_tar['vertices'][:, :-1]) * self.args.rec_weight * self.args.rec_ver_weight - vert_acceleration_loss = self.vel_loss(vertices_rec['vertices'][:, 2:] + vertices_rec['vertices'][:, :-2] - 2 * vertices_rec['vertices'][:, 1:-1], vertices_tar['vertices'][:, 2:] + vertices_tar['vertices'][:, :-2] - 2 * vertices_tar['vertices'][:, 1:-1]) * self.args.rec_weight * self.args.rec_ver_weight - self.tracker.update_meter("ver_vel", "val", vert_velocity_loss.item()) - self.tracker.update_meter("ver_acc", "val", vert_acceleration_loss.item()) - # g_loss_final += vert_velocity_loss - # g_loss_final += vert_acceleration_loss - if "VQVAE" in self.args.g_name: - loss_embedding = net_out["embedding_loss"] - self.tracker.update_meter("com", "val", loss_embedding.item()) - #g_loss_final += vectices_loss*self.args.rec_weight*self.args.rec_ver_weight - self.val_recording(epoch) - - def test(self, epoch): - results_save_path = self.checkpoint_path + f"/{epoch}/" - if os.path.exists(results_save_path): - return 0 - os.makedirs(results_save_path) - start_time = time.time() - total_length = 0 - test_seq_list = self.test_data.selected_file - self.model.eval() - with torch.no_grad(): - for its, dict_data in enumerate(self.test_loader): - tar_pose = dict_data["pose"] - tar_pose = tar_pose.cuda() - tar_exps = dict_data["facial"].to(self.rank) - bs, n, j = tar_pose.shape[0], tar_pose.shape[1], self.joints - tar_pose = rc.axis_angle_to_matrix(tar_pose.reshape(bs, n, j, 3)) - tar_pose = rc.matrix_to_rotation_6d(tar_pose).reshape(bs, n, j*6) - remain = n%self.args.pose_length - tar_pose = tar_pose[:, :n-remain, :] - # print(tar_exps.shape) - in_tar_pose = torch.cat([tar_pose, tar_exps[:, :n-remain, :]], -1) # 103 - #print(tar_pose.shape) - if True: - net_out = self.model(in_tar_pose) - rec_pose = net_out["rec_pose"][:, :, :j*6] - n = rec_pose.shape[1] - tar_pose = tar_pose[:, :n, :] - rec_pose = rec_pose.reshape(bs, n, j, 6) - rec_pose = rc.rotation_6d_to_matrix(rec_pose)# - rec_pose = rc.matrix_to_axis_angle(rec_pose).reshape(bs*n, j*3) - rec_pose = rec_pose.cpu().numpy() - rec_exps = net_out["rec_pose"][:, :, j*6:] - rec_exps = rec_exps.cpu().numpy().reshape(bs*n, 100) - else: - pass -# for i in range(tar_pose.shape[1]//(self.args.vae_test_len)): -# tar_pose_new = tar_pose[:,i*(self.args.vae_test_len):i*(self.args.vae_test_len)+self.args.vae_test_len,:] -# net_out = self.model(**dict(inputs=tar_pose_new)) -# rec_pose = net_out["rec_pose"] -# rec_pose = (rec_pose.reshape(rec_pose.shape[0], rec_pose.shape[1], -1, 6) * self.joint_level_mask_cuda).reshape(rec_pose.shape[0], rec_pose.shape[1], -1) -# if "rot6d" in self.args.pose_rep: -# rec_pose = data_transfer.rotation_6d_to_matrix(rec_pose.reshape(tar_pose.shape[0], self.args.vae_test_len, -1, 6)) -# rec_pose = data_transfer.matrix_to_euler_angles(rec_pose, "XYZ").reshape(rec_pose.shape[0], rec_pose.shape[1], -1) -# if "smplx" not in self.args.pose_rep: -# rec_pose = torch.rad2deg(rec_pose) -# rec_pose = rec_pose * self.joint_mask_cuda - -# out_sub = rec_pose.cpu().numpy().reshape(-1, rec_pose.shape[2]) -# if i != 0: -# out_final = np.concatenate((out_final,out_sub), 0) -# else: -# out_final = out_sub - - tar_pose = rc.rotation_6d_to_matrix(tar_pose.reshape(bs, n, j, 6)) - tar_pose = rc.matrix_to_axis_angle(tar_pose).reshape(bs*n, j*3) - tar_pose = tar_pose.cpu().numpy() - - total_length += n - # --- save --- # - if 'smplx' in self.args.pose_rep: - gt_npz = np.load(self.args.data_path+self.args.pose_rep+"/"+test_seq_list.iloc[its]['id']+'.npz', allow_pickle=True) - stride = int(30 / self.args.pose_fps) - tar_pose = self.inverse_selection(tar_pose, self.test_data.joint_mask, tar_pose.shape[0]) - np.savez(results_save_path+"gt_"+test_seq_list.iloc[its]['id']+'.npz', - betas=gt_npz["betas"], - poses=tar_pose[:n], - expressions=gt_npz["expressions"], - trans=gt_npz["trans"][::stride][:n] - gt_npz["trans"][::stride][:n], - model='smplx2020', - gender='neutral', - mocap_frame_rate = 30 , - ) - rec_pose = self.inverse_selection(rec_pose, self.test_data.joint_mask, rec_pose.shape[0]) - np.savez(results_save_path+"res_"+test_seq_list.iloc[its]['id']+'.npz', - betas=gt_npz["betas"], - poses=rec_pose, - expressions=rec_exps, - trans=gt_npz["trans"][::stride][:n] - gt_npz["trans"][::stride][:n], - model='smplx2020', - gender='neutral', - mocap_frame_rate = 30 , - ) - else: - rec_pose = rc.axis_angle_to_matrix(torch.from_numpy(rec_pose.reshape(bs*n, j, 3))) - rec_pose = np.rad2deg(rc.matrix_to_euler_angles(rec_pose, "XYZ")).reshape(bs*n, j*3).numpy() - tar_pose = rc.axis_angle_to_matrix(torch.from_numpy(tar_pose.reshape(bs*n, j, 3))) - tar_pose = np.rad2deg(rc.matrix_to_euler_angles(tar_pose, "XYZ")).reshape(bs*n, j*3).numpy() - #trans="0.000000 0.000000 0.000000" - - with open(f"{self.args.data_path}{self.args.pose_rep}/{test_seq_list.iloc[its]['id']}.bvh", "r") as f_demo: - with open(results_save_path+"gt_"+test_seq_list.iloc[its]['id']+'.bvh', 'w+') as f_gt: - with open(results_save_path+"res_"+test_seq_list.iloc[its]['id']+'.bvh', 'w+') as f_real: - for i, line_data in enumerate(f_demo.readlines()): - if i < 431: - f_real.write(line_data) - f_gt.write(line_data) - else: break - for line_id in range(n): #,args.pre_frames, args.pose_length - line_data = np.array2string(rec_pose[line_id], max_line_width=np.inf, precision=6, suppress_small=False, separator=' ') - f_real.write(line_data[1:-2]+'\n') - for line_id in range(n): #,args.pre_frames, args.pose_length - line_data = np.array2string(tar_pose[line_id], max_line_width=np.inf, precision=6, suppress_small=False, separator=' ') - f_gt.write(line_data[1:-2]+'\n') - # with open(results_save_path+"gt_"+test_seq_list[its]+'.pkl', 'wb') as fw: - # pickle.dump(new_dict, fw) - # #new_dict2["fullpose"] = out_final - # with open(results_save_path+"res_"+test_seq_list[its]+'.pkl', 'wb') as fw1: - # pickle.dump(new_dict2, fw1) - - # other_tools.render_one_sequence( - # results_save_path+"res_"+test_seq_list[its]+'.pkl', - # results_save_path+"gt_"+test_seq_list[its]+'.pkl', - # results_save_path, - # self.args.data_path + self.args.test_data_path + 'wave16k/' + test_seq_list[its]+'.npy', - # ) - - #if its == 1:break - end_time = time.time() - start_time - logger.info(f"total inference time: {int(end_time)} s for {int(total_length/self.args.pose_fps)} s motion") \ No newline at end of file diff --git a/aelower_trainer.py b/aelower_trainer.py deleted file mode 100644 index 3af3d647bcf63cd15e859cff3e619ac441e087e5..0000000000000000000000000000000000000000 --- a/aelower_trainer.py +++ /dev/null @@ -1,494 +0,0 @@ -import train -import os -import time -import csv -import sys -import warnings -import random -import numpy as np -import time -import pprint -import pickle - -import torch -import torch.nn as nn -import torch.nn.functional as F -from torch.utils.tensorboard import SummaryWriter -from torch.nn.parallel import DistributedDataParallel as DDP -from loguru import logger -import smplx - -from utils import config, logger_tools, other_tools, metric -from utils import rotation_conversions as rc -from dataloaders import data_tools -from optimizers.optim_factory import create_optimizer -from optimizers.scheduler_factory import create_scheduler -from optimizers.loss_factory import get_loss_func -from scipy.spatial.transform import Rotation - - -class CustomTrainer(train.BaseTrainer): - """ - motion representation learning - """ - def __init__(self, args): - super().__init__(args) - self.joints = self.train_data.joints - self.smplx = smplx.create( - self.args.data_path_1+"smplx_models/", - model_type='smplx', - gender='NEUTRAL_2020', - use_face_contour=False, - num_betas=300, - num_expression_coeffs=100, - ext='npz', - use_pca=False, - ).cuda().eval() - self.tracker = other_tools.EpochTracker(["rec", "contact", "vel", "foot", "ver", "com", "kl", "acc", "trans", "transv"], [False,False, False, False, False, False, False, False, False, False]) - if not self.args.rot6d: #"rot6d" not in args.pose_rep: - logger.error(f"this script is for rot6d, your pose rep. is {args.pose_rep}") - self.rec_loss = get_loss_func("GeodesicLoss") - self.vel_loss = torch.nn.L1Loss(reduction='mean') - self.vectices_loss = torch.nn.MSELoss(reduction='mean') - - def inverse_selection(self, filtered_t, selection_array, n): - # 创建一个全为零的数组，形状为 n*165 - original_shape_t = np.zeros((n, selection_array.size)) - - # 找到选择数组中为1的索引位置 - selected_indices = np.where(selection_array == 1)[0] - - # 将 filtered_t 的值填充到 original_shape_t 中相应的位置 - for i in range(n): - original_shape_t[i, selected_indices] = filtered_t[i] - - return original_shape_t - - def inverse_selection_tensor(self, filtered_t, selection_array, n): - # 创建一个全为零的数组，形状为 n*165 - selection_array = torch.from_numpy(selection_array).cuda() - original_shape_t = torch.zeros((n, 165)).cuda() - - # 找到选择数组中为1的索引位置 - selected_indices = torch.where(selection_array == 1)[0] - - # 将 filtered_t 的值填充到 original_shape_t 中相应的位置 - for i in range(n): - original_shape_t[i, selected_indices] = filtered_t[i] - - return original_shape_t - - - def train(self, epoch): - self.model.train() - t_start = time.time() - self.tracker.reset() - for its, dict_data in enumerate(self.train_loader): - tar_pose_raw = dict_data["pose"] - tar_beta = dict_data["beta"].cuda() - tar_trans = dict_data["trans"].cuda() - tar_trans_vel_x = other_tools.estimate_linear_velocity(tar_trans[:, :, 0:1], dt=1/self.args.pose_fps) - tar_trans_vel_z = other_tools.estimate_linear_velocity(tar_trans[:, :, 2:3], dt=1/self.args.pose_fps) - tar_pose = tar_pose_raw[:, :, :27].cuda() - tar_contact = tar_pose_raw[:, :, 27:31].cuda() - bs, n, j = tar_pose.shape[0], tar_pose.shape[1], self.joints - tar_exps = torch.zeros((bs, n, 100)).cuda() - tar_pose = rc.axis_angle_to_matrix(tar_pose.reshape(bs, n, j, 3)) - tar_pose = rc.matrix_to_rotation_6d(tar_pose).reshape(bs, n, j*6) - tar_trans_copy = tar_trans-tar_trans - tar_contact_copy = tar_contact-tar_contact - in_tar_pose = torch.cat((tar_pose, tar_trans_copy, tar_contact_copy), dim=-1) - - t_data = time.time() - t_start - - self.opt.zero_grad() - g_loss_final = 0 - net_out = self.model(in_tar_pose) - rec_pose = tar_pose#net_out["rec_pose"][:, :, :j*6] - rec_pose = rec_pose.reshape(bs, n, j, 6) - rec_pose = rc.rotation_6d_to_matrix(rec_pose)# - tar_pose = rc.rotation_6d_to_matrix(tar_pose.reshape(bs, n, j, 6)) - # loss_rec = self.rec_loss(rec_pose, tar_pose) * self.args.rec_weight * self.args.rec_pos_weight - # self.tracker.update_meter("rec", "train", loss_rec.item()) - # g_loss_final += loss_rec - - rec_contact = net_out["rec_pose"][:, :, j*6+3:j*6+7] - loss_contact = self.vectices_loss(rec_contact, tar_contact) * self.args.rec_weight * self.args.rec_pos_weight - self.tracker.update_meter("contact", "train", loss_contact.item()) - g_loss_final += loss_contact - - # velocity_loss = self.vel_loss(rec_pose[:, 1:] - rec_pose[:, :-1], tar_pose[:, 1:] - tar_pose[:, :-1]) * self.args.rec_weight - # acceleration_loss = self.vel_loss(rec_pose[:, 2:] + rec_pose[:, :-2] - 2 * rec_pose[:, 1:-1], tar_pose[:, 2:] + tar_pose[:, :-2] - 2 * tar_pose[:, 1:-1]) * self.args.rec_weight - # self.tracker.update_meter("vel", "train", velocity_loss.item()) - # self.tracker.update_meter("acc", "train", acceleration_loss.item()) - # g_loss_final += velocity_loss - # g_loss_final += acceleration_loss - - rec_trans = net_out["rec_pose"][:, :, j*6:j*6+3] - rec_x_trans = other_tools.velocity2position(rec_trans[:, :, 0:1], 1/self.args.pose_fps, tar_trans[:, 0, 0:1]) - rec_z_trans = other_tools.velocity2position(rec_trans[:, :, 2:3], 1/self.args.pose_fps, tar_trans[:, 0, 2:3]) - rec_y_trans = rec_trans[:,:,1:2] - rec_xyz_trans = torch.cat([rec_x_trans, rec_y_trans, rec_z_trans], dim=-1) - loss_trans_vel = self.vel_loss(rec_trans[:, :, 0:1], tar_trans_vel_x) * self.args.rec_weight \ - + self.vel_loss(rec_trans[:, :, 2:3], tar_trans_vel_z) * self.args.rec_weight - v3 = self.vel_loss(rec_trans[:, :, 0:1][:, 1:] - rec_trans[:, :, 0:1][:, :-1], tar_trans_vel_x[:, 1:] - tar_trans_vel_x[:, :-1]) * self.args.rec_weight \ - + self.vel_loss(rec_trans[:, :, 2:3][:, 1:] - rec_trans[:, :, 2:3][:, :-1], tar_trans_vel_z[:, 1:] - tar_trans_vel_z[:, :-1]) * self.args.rec_weight - a3 = self.vel_loss(rec_trans[:, :, 0:1][:, 2:] + rec_trans[:, :, 0:1][:, :-2] - 2 * rec_trans[:, :, 0:1][:, 1:-1], tar_trans_vel_x[:, 2:] + tar_trans_vel_x[:, :-2] - 2 * tar_trans_vel_x[:, 1:-1]) * self.args.rec_weight \ - + self.vel_loss(rec_trans[:, :, 2:3][:, 2:] + rec_trans[:, :, 2:3][:, :-2] - 2 * rec_trans[:, :, 2:3][:, 1:-1], tar_trans_vel_z[:, 2:] + tar_trans_vel_z[:, :-2] - 2 * tar_trans_vel_z[:, 1:-1]) * self.args.rec_weight - g_loss_final += 5*v3 - g_loss_final += 5*a3 - v2 = self.vel_loss(rec_xyz_trans[:, 1:] - rec_xyz_trans[:, :-1], tar_trans[:, 1:] - tar_trans[:, :-1]) * self.args.rec_weight - a2 = self.vel_loss(rec_xyz_trans[:, 2:] + rec_xyz_trans[:, :-2] - 2 * rec_xyz_trans[:, 1:-1], tar_trans[:, 2:] + tar_trans[:, :-2] - 2 * tar_trans[:, 1:-1]) * self.args.rec_weight - g_loss_final += 5*v2 - g_loss_final += 5*a2 - self.tracker.update_meter("transv", "train", loss_trans_vel.item()) - g_loss_final += loss_trans_vel - loss_trans = self.vel_loss(rec_xyz_trans, tar_trans) * self.args.rec_weight - self.tracker.update_meter("trans", "train", loss_trans.item()) - g_loss_final += loss_trans - - # vertices loss - if self.args.rec_ver_weight > 0: - # print(tar_pose.shape, j) - tar_pose = rc.matrix_to_axis_angle(tar_pose).reshape(bs*n, j*3) - rec_pose = rc.matrix_to_axis_angle(rec_pose).reshape(bs*n, j*3) - rec_pose = self.inverse_selection_tensor(rec_pose, self.train_data.joint_mask, rec_pose.shape[0]) - tar_pose = self.inverse_selection_tensor(tar_pose, self.train_data.joint_mask, tar_pose.shape[0]) - vertices_rec = self.smplx( - betas=tar_beta.reshape(bs*n, 300), - transl=rec_xyz_trans.reshape(bs*n, 3), - expression=tar_exps.reshape(bs*n, 100), - jaw_pose=rec_pose[:, 66:69], - global_orient=rec_pose[:,:3], - body_pose=rec_pose[:,3:21*3+3], - left_hand_pose=rec_pose[:,25*3:40*3], - right_hand_pose=rec_pose[:,40*3:55*3], - return_verts=True, - return_joints=True, - leye_pose=tar_pose[:, 69:72], - reye_pose=tar_pose[:, 72:75], - ) - vertices_tar = self.smplx( - betas=tar_beta.reshape(bs*n, 300), - transl=tar_trans.reshape(bs*n, 3), - expression=tar_exps.reshape(bs*n, 100), - jaw_pose=tar_pose[:, 66:69], - global_orient=tar_pose[:,:3], - body_pose=tar_pose[:,3:21*3+3], - left_hand_pose=tar_pose[:,25*3:40*3], - right_hand_pose=tar_pose[:,40*3:55*3], - return_verts=True, - return_joints=True, - leye_pose=tar_pose[:, 69:72], - reye_pose=tar_pose[:, 72:75], - ) - joints_rec = vertices_rec['joints'] - # print(joints_rec.shape) - joints_rec = joints_rec.reshape(bs, n, -1, 3) - vectices_loss = self.vectices_loss(vertices_rec['vertices'], vertices_tar['vertices']) - vertices_vel_loss = self.vectices_loss( - vertices_rec['vertices'][:, 1:] - vertices_rec['vertices'][:, :-1], - vertices_tar['vertices'][:, 1:] - vertices_tar['vertices'][:, :-1]) - vertices_acc_loss = self.vectices_loss( - vertices_rec['vertices'][:, 2:] + vertices_rec['vertices'][:, :-2] - 2 * vertices_rec['vertices'][:, 1:-1], - vertices_tar['vertices'][:, 2:] + vertices_tar['vertices'][:, :-2] - 2 * vertices_tar['vertices'][:, 1:-1]) - foot_idx = [7, 8, 10, 11] - model_contact = net_out["rec_pose"][:, :, j*6+3:j*6+7] - # find static indices consistent with model's own predictions - static_idx = model_contact > 0.95 # N x S x 4 - # print(model_contact,static_idx) - model_feet = joints_rec[:, :, foot_idx] # foot positions (N, S, 4, 3) - model_foot_v = torch.zeros_like(model_feet) - model_foot_v[:, :-1] = ( - model_feet[:, 1:, :, :] - model_feet[:, :-1, :, :] - ) # (N, S-1, 4, 3) - model_foot_v[~static_idx] = 0 - foot_loss = self.vel_loss( - model_foot_v, torch.zeros_like(model_foot_v) - ) - self.tracker.update_meter("foot", "train", foot_loss.item()*self.args.rec_weight * self.args.rec_ver_weight*1000) - self.tracker.update_meter("ver", "train", vectices_loss.item()*self.args.rec_weight * self.args.rec_ver_weight) - g_loss_final += (vectices_loss+5*vertices_vel_loss+5*vertices_acc_loss)*self.args.rec_weight*self.args.rec_ver_weight - g_loss_final += foot_loss*self.args.rec_weight*self.args.rec_ver_weight*20 - - # ---------------------- vae -------------------------- # - if "VQVAE" in self.args.g_name: - loss_embedding = net_out["embedding_loss"] - g_loss_final += loss_embedding - self.tracker.update_meter("com", "train", loss_embedding.item()) - # elif "VAE" in self.args.g_name: - # pose_mu, pose_logvar = net_out["pose_mu"], net_out["pose_logvar"] - # KLD = -0.5 * torch.sum(1 + pose_logvar - pose_mu.pow(2) - pose_logvar.exp()) - # if epoch < 0: - # KLD_weight = 0 - # else: - # KLD_weight = min(1.0, (epoch - 0) * 0.05) * 0.01 - # loss += KLD_weight * KLD - # self.tracker.update_meter("kl", "train", KLD_weight * KLD.item()) - g_loss_final.backward() - if self.args.grad_norm != 0: - torch.nn.utils.clip_grad_norm_(self.model.parameters(), self.args.grad_norm) - self.opt.step() - t_train = time.time() - t_start - t_data - t_start = time.time() - mem_cost = torch.cuda.memory_cached() / 1E9 - lr_g = self.opt.param_groups[0]['lr'] - if its % self.args.log_period == 0: - self.train_recording(epoch, its, t_data, t_train, mem_cost, lr_g) - if self.args.debug: - if its == 1: break - self.opt_s.step(epoch) - - def val(self, epoch): - self.model.eval() - t_start = time.time() - with torch.no_grad(): - for its, dict_data in enumerate(self.val_loader): - tar_pose_raw = dict_data["pose"] - tar_beta = dict_data["beta"].cuda() - tar_trans = dict_data["trans"].cuda() - tar_trans_vel_x = other_tools.estimate_linear_velocity(tar_trans[:, :, 0:1], dt=1/self.args.pose_fps) - tar_trans_vel_z = other_tools.estimate_linear_velocity(tar_trans[:, :, 2:3], dt=1/self.args.pose_fps) - #print(tar_pose.shape) - tar_pose = tar_pose_raw[:, :, :27].cuda() - - tar_contact = tar_pose_raw[:, :, 27:31].cuda() - bs, n, j = tar_pose.shape[0], tar_pose.shape[1], self.joints - tar_exps = torch.zeros((bs, n, 100)).cuda() - tar_pose = rc.axis_angle_to_matrix(tar_pose.reshape(bs, n, j, 3)) - tar_pose = rc.matrix_to_rotation_6d(tar_pose).reshape(bs, n, j*6) - tar_trans_copy = tar_trans-tar_trans - tar_contact_copy = tar_contact-tar_contact - in_tar_pose = torch.cat((tar_pose, tar_trans_copy, tar_contact_copy), dim=-1) - t_data = time.time() - t_start - - #self.opt.zero_grad() - #g_loss_final = 0 - net_out = self.model(in_tar_pose) - rec_pose = tar_pose - rec_pose = rec_pose.reshape(bs, n, j, 6) - rec_pose = rc.rotation_6d_to_matrix(rec_pose)# - tar_pose = rc.rotation_6d_to_matrix(tar_pose.reshape(bs, n, j, 6)) - # loss_rec = self.rec_loss(rec_pose, tar_pose) * self.args.rec_weight * self.args.rec_pos_weight - # self.tracker.update_meter("rec", "val", loss_rec.item()) - rec_contact = net_out["rec_pose"][:, :, j*6+3:j*6+7] - # print(rec_contact.shape, tar_contact.shape) - loss_contact = self.vel_loss(rec_contact, tar_contact) * self.args.rec_weight * self.args.rec_pos_weight - self.tracker.update_meter("contact", "val", loss_contact.item()) - #g_loss_final += loss_rec - # rec_trans = net_out["rec_pose"][:, :, j*6:j*6+3] - # rec_x_trans = other_tools.velocity2position(rec_trans[:, :, 0:1], 1/self.args.pose_fps, tar_trans[:, 0, 0:1]) - # rec_z_trans = other_tools.velocity2position(rec_trans[:, :, 2:3], 1/self.args.pose_fps, tar_trans[:, 0, 2:3]) - # rec_y_trans = rec_trans[:,:,1:2] - # rec_xyz_trans = torch.cat([rec_x_trans, rec_y_trans, rec_z_trans], dim=-1) - - rec_trans = net_out["rec_pose"][:, :, j*6:j*6+3] - rec_x_trans = other_tools.velocity2position(rec_trans[:, :, 0:1], 1/self.args.pose_fps, tar_trans[:, 0, 0:1]) - rec_z_trans = other_tools.velocity2position(rec_trans[:, :, 2:3], 1/self.args.pose_fps, tar_trans[:, 0, 2:3]) - rec_y_trans = rec_trans[:,:,1:2] - rec_xyz_trans = torch.cat([rec_x_trans, rec_y_trans, rec_z_trans], dim=-1) - loss_trans_vel = self.vel_loss(rec_trans[:, :, 0:1], tar_trans_vel_x) * self.args.rec_weight \ - + self.vel_loss(rec_trans[:, :, 2:3], tar_trans_vel_z) * self.args.rec_weight - # v3 = self.vel_loss(rec_trans[:, :, 0:1][:, 1:] - rec_trans[:, :, 0:1][:, :-1], tar_trans_vel_x[:, 1:] - tar_trans_vel_x[:, :-1]) * self.args.rec_weight \ - # + self.vel_loss(rec_trans[:, :, 2:3][:, 1:] - rec_trans[:, :, 2:3][:, :-1], tar_trans_vel_z[:, 1:] - tar_trans_vel_z[:, :-1]) * self.args.rec_weight - # a3 = self.vel_loss(rec_trans[:, :, 0:1][:, 2:] + rec_trans[:, :, 0:1][:, :-2] - 2 * rec_trans[:, :, 0:1][:, 1:-1], tar_trans_vel_x[:, 2:] + tar_trans_vel_x[:, :-2] - 2 * tar_trans_vel_x[:, 1:-1]) * self.args.rec_weight \ - # + self.vel_loss(rec_trans[:, :, 2:3][:, 2:] + rec_trans[:, :, 2:3][:, :-2] - 2 * rec_trans[:, :, 2:3][:, 1:-1], tar_trans_vel_z[:, 2:] + tar_trans_vel_z[:, :-2] - 2 * tar_trans_vel_z[:, 1:-1]) * self.args.rec_weight - # #g_loss_final += 5*v3 - # #g_loss_final += 5*a3 - # v2 = self.vel_loss(rec_xyz_trans[:, 1:] - rec_xyz_trans[:, :-1], tar_trans[:, 1:] - tar_trans[:, :-1]) * self.args.rec_weight - # a2 = self.vel_loss(rec_xyz_trans[:, 2:] + rec_xyz_trans[:, :-2] - 2 * rec_xyz_trans[:, 1:-1], tar_trans[:, 2:] + tar_trans[:, :-2] - 2 * tar_trans[:, 1:-1]) * self.args.rec_weight - #g_loss_final += 5*v2 - #g_loss_final += 5*a2 - self.tracker.update_meter("transv", "val", loss_trans_vel.item()) - #g_loss_final += loss_trans_vel - loss_trans = self.vel_loss(rec_xyz_trans, tar_trans) * self.args.rec_weight - self.tracker.update_meter("trans", "val", loss_trans.item()) - #g_loss_final += loss_trans - - # vertices loss - if self.args.rec_ver_weight > 0: - tar_pose = rc.matrix_to_axis_angle(tar_pose).reshape(bs*n, j*3) - rec_pose = rc.matrix_to_axis_angle(rec_pose).reshape(bs*n, j*3) - rec_pose = self.inverse_selection_tensor(rec_pose, self.train_data.joint_mask, rec_pose.shape[0]) - tar_pose = self.inverse_selection_tensor(tar_pose, self.train_data.joint_mask, tar_pose.shape[0]) - vertices_rec = self.smplx( - betas=tar_beta.reshape(bs*n, 300), - transl=rec_xyz_trans.reshape(bs*n, 3), - expression=tar_exps.reshape(bs*n, 100), - jaw_pose=rec_pose[:, 66:69], - global_orient=rec_pose[:,:3], - body_pose=rec_pose[:,3:21*3+3], - left_hand_pose=rec_pose[:,25*3:40*3], - right_hand_pose=rec_pose[:,40*3:55*3], - return_verts=False, - return_joints=True, - leye_pose=tar_pose[:, 69:72], - reye_pose=tar_pose[:, 72:75], - ) - vertices_tar = self.smplx( - betas=tar_beta.reshape(bs*n, 300), - transl=tar_trans.reshape(bs*n, 3), - expression=tar_exps.reshape(bs*n, 100), - jaw_pose=tar_pose[:, 66:69], - global_orient=tar_pose[:,:3], - body_pose=tar_pose[:,3:21*3+3], - left_hand_pose=tar_pose[:,25*3:40*3], - right_hand_pose=tar_pose[:,40*3:55*3], - return_verts=False, - return_joints=True, - leye_pose=tar_pose[:, 69:72], - reye_pose=tar_pose[:, 72:75], - ) - joints_rec = vertices_rec['joints'] - joints_rec = joints_rec.reshape(bs, n, -1, 3) - vectices_loss = self.vectices_loss(vertices_rec['joints'], vertices_tar['joints']) - foot_idx = [7, 8, 10, 11] - model_contact = net_out["rec_pose"][:, :, j*6+3:j*6+7] - # find static indices consistent with model's own predictions - static_idx = model_contact > 0.95 # N x S x 4 - # print(model_contact) - model_feet = joints_rec[:, :, foot_idx] # foot positions (N, S, 4, 3) - model_foot_v = torch.zeros_like(model_feet) - model_foot_v[:, :-1] = ( - model_feet[:, 1:, :, :] - model_feet[:, :-1, :, :] - ) # (N, S-1, 4, 3) - model_foot_v[~static_idx] = 0 - foot_loss = self.vectices_loss( - model_foot_v, torch.zeros_like(model_foot_v) - ) - self.tracker.update_meter("foot", "val", foot_loss.item()*self.args.rec_weight * self.args.rec_ver_weight) - self.tracker.update_meter("ver", "val", vectices_loss.item()*self.args.rec_weight * self.args.rec_ver_weight) - if "VQVAE" in self.args.g_name: - loss_embedding = net_out["embedding_loss"] - self.tracker.update_meter("com", "val", loss_embedding.item()) - #g_loss_final += vectices_loss*self.args.rec_weight*self.args.rec_ver_weight - self.val_recording(epoch) - - def test(self, epoch): - results_save_path = self.checkpoint_path + f"/{epoch}/" - if os.path.exists(results_save_path): - return 0 - os.makedirs(results_save_path) - start_time = time.time() - total_length = 0 - test_seq_list = self.test_data.selected_file - self.model.eval() - with torch.no_grad(): - for its, dict_data in enumerate(self.test_loader): - tar_pose_raw = dict_data["pose"] - tar_trans = dict_data["trans"].to(self.rank) - tar_pose = tar_pose_raw[:, :, :27].cuda() - tar_contact = tar_pose_raw[:, :, 27:31].cuda() - # tar_pose = tar_pose.cuda() - bs, n, j = tar_pose.shape[0], tar_pose.shape[1], self.joints - tar_pose = rc.axis_angle_to_matrix(tar_pose.reshape(bs, n, j, 3)) - tar_pose = rc.matrix_to_rotation_6d(tar_pose).reshape(bs, n, j*6) - remain = n%self.args.pose_length - tar_pose = tar_pose[:, :n-remain, :] - tar_contact = tar_contact[:, :n-remain, :] - tar_trans_copy = tar_trans[:, :n-remain, :]-tar_trans[:, :n-remain, :] - tar_contact_copy = tar_contact-tar_contact - in_tar_pose = torch.cat([tar_pose, tar_trans_copy, tar_contact_copy], dim=-1) - #print(tar_pose.shape) - if True: - net_out = self.model(in_tar_pose) - rec_pose = tar_pose #net_out["rec_pose"][:, :, :j*6] - rec_trans = net_out["rec_pose"][:, :, j*6:j*6+3] - # print(rec_trans.shape) - rec_x_trans = other_tools.velocity2position(rec_trans[:, :, 0:1], 1/self.args.pose_fps, tar_trans[:, 0, 0:1]) - rec_z_trans = other_tools.velocity2position(rec_trans[:, :, 2:3], 1/self.args.pose_fps, tar_trans[:, 0, 2:3]) - rec_y_trans = rec_trans[:,:,1:2] - rec_trans = torch.cat([rec_x_trans, rec_y_trans, rec_z_trans], dim=-1) - n = rec_pose.shape[1] - rec_trans = rec_trans.cpu().numpy().reshape(bs*n, 3) - tar_pose = tar_pose[:, :n, :] - rec_pose = rec_pose.reshape(bs, n, j, 6) - rec_pose = rc.rotation_6d_to_matrix(rec_pose)# - rec_pose = rc.matrix_to_axis_angle(rec_pose).reshape(bs*n, j*3) - rec_pose = rec_pose.cpu().numpy() - else: - pass -# for i in range(tar_pose.shape[1]//(self.args.vae_test_len)): -# tar_pose_new = tar_pose[:,i*(self.args.vae_test_len):i*(self.args.vae_test_len)+self.args.vae_test_len,:] -# net_out = self.model(**dict(inputs=tar_pose_new)) -# rec_pose = net_out["rec_pose"] -# rec_pose = (rec_pose.reshape(rec_pose.shape[0], rec_pose.shape[1], -1, 6) * self.joint_level_mask_cuda).reshape(rec_pose.shape[0], rec_pose.shape[1], -1) -# if "rot6d" in self.args.pose_rep: -# rec_pose = data_transfer.rotation_6d_to_matrix(rec_pose.reshape(tar_pose.shape[0], self.args.vae_test_len, -1, 6)) -# rec_pose = data_transfer.matrix_to_euler_angles(rec_pose, "XYZ").reshape(rec_pose.shape[0], rec_pose.shape[1], -1) -# if "smplx" not in self.args.pose_rep: -# rec_pose = torch.rad2deg(rec_pose) -# rec_pose = rec_pose * self.joint_mask_cuda - -# out_sub = rec_pose.cpu().numpy().reshape(-1, rec_pose.shape[2]) -# if i != 0: -# out_final = np.concatenate((out_final,out_sub), 0) -# else: -# out_final = out_sub - - tar_pose = rc.rotation_6d_to_matrix(tar_pose.reshape(bs, n, j, 6)) - tar_pose = rc.matrix_to_axis_angle(tar_pose).reshape(bs*n, j*3) - tar_pose = tar_pose.cpu().numpy() - - total_length += n - # --- save --- # - if 'smplx' in self.args.pose_rep: - gt_npz = np.load(self.args.data_path+self.args.pose_rep+"/"+test_seq_list.iloc[its]['id']+'.npz', allow_pickle=True) - stride = int(30 / self.args.pose_fps) - tar_pose = self.inverse_selection(tar_pose, self.test_data.joint_mask, tar_pose.shape[0]) - np.savez(results_save_path+"gt_"+test_seq_list.iloc[its]['id']+'.npz', - betas=gt_npz["betas"], - poses=tar_pose[:n], - expressions=gt_npz["expressions"]-gt_npz["expressions"], - trans=gt_npz["trans"][::stride][:n], - model='smplx2020', - gender='neutral', - mocap_frame_rate = 30 , - ) - rec_pose = self.inverse_selection(rec_pose, self.test_data.joint_mask, rec_pose.shape[0]) - np.savez(results_save_path+"res_"+test_seq_list.iloc[its]['id']+'.npz', - betas=gt_npz["betas"], - poses=rec_pose, - expressions=gt_npz["expressions"]-gt_npz["expressions"], - trans=rec_trans, - model='smplx2020', - gender='neutral', - mocap_frame_rate = 30 , - ) - else: - rec_pose = rc.axis_angle_to_matrix(torch.from_numpy(rec_pose.reshape(bs*n, j, 3))) - rec_pose = np.rad2deg(rc.matrix_to_euler_angles(rec_pose, "XYZ")).reshape(bs*n, j*3).numpy() - tar_pose = rc.axis_angle_to_matrix(torch.from_numpy(tar_pose.reshape(bs*n, j, 3))) - tar_pose = np.rad2deg(rc.matrix_to_euler_angles(tar_pose, "XYZ")).reshape(bs*n, j*3).numpy() - #trans="0.000000 0.000000 0.000000" - - with open(f"{self.args.data_path}{self.args.pose_rep}/{test_seq_list.iloc[its]['id']}.bvh", "r") as f_demo: - with open(results_save_path+"gt_"+test_seq_list.iloc[its]['id']+'.bvh', 'w+') as f_gt: - with open(results_save_path+"res_"+test_seq_list.iloc[its]['id']+'.bvh', 'w+') as f_real: - for i, line_data in enumerate(f_demo.readlines()): - if i < 431: - f_real.write(line_data) - f_gt.write(line_data) - else: break - for line_id in range(n): #,args.pre_frames, args.pose_length - line_data = np.array2string(rec_pose[line_id], max_line_width=np.inf, precision=6, suppress_small=False, separator=' ') - f_real.write(line_data[1:-2]+'\n') - for line_id in range(n): #,args.pre_frames, args.pose_length - line_data = np.array2string(tar_pose[line_id], max_line_width=np.inf, precision=6, suppress_small=False, separator=' ') - f_gt.write(line_data[1:-2]+'\n') - # with open(results_save_path+"gt_"+test_seq_list[its]+'.pkl', 'wb') as fw: - # pickle.dump(new_dict, fw) - # #new_dict2["fullpose"] = out_final - # with open(results_save_path+"res_"+test_seq_list[its]+'.pkl', 'wb') as fw1: - # pickle.dump(new_dict2, fw1) - - # other_tools.render_one_sequence( - # results_save_path+"res_"+test_seq_list[its]+'.pkl', - # results_save_path+"gt_"+test_seq_list[its]+'.pkl', - # results_save_path, - # self.args.data_path + self.args.test_data_path + 'wave16k/' + test_seq_list[its]+'.npy', - # ) - - #if its == 1:break - end_time = time.time() - start_time - logger.info(f"total inference time: {int(end_time)} s for {int(total_length/self.args.pose_fps)} s motion") \ No newline at end of file diff --git a/aelowerfoot_trainer.py b/aelowerfoot_trainer.py deleted file mode 100644 index 38c5f11e2dcebea01aae5a049eaa96b5ad327bb8..0000000000000000000000000000000000000000 --- a/aelowerfoot_trainer.py +++ /dev/null @@ -1,491 +0,0 @@ -import train -import os -import time -import csv -import sys -import warnings -import random -import numpy as np -import time -import pprint -import pickle - -import torch -import torch.nn as nn -import torch.nn.functional as F -from torch.utils.tensorboard import SummaryWriter -from torch.nn.parallel import DistributedDataParallel as DDP -from loguru import logger -import smplx - -from utils import config, logger_tools, other_tools, metric -from utils import rotation_conversions as rc -from dataloaders import data_tools -from optimizers.optim_factory import create_optimizer -from optimizers.scheduler_factory import create_scheduler -from optimizers.loss_factory import get_loss_func -from scipy.spatial.transform import Rotation - - -class CustomTrainer(train.BaseTrainer): - """ - motion representation learning - """ - def __init__(self, args): - super().__init__(args) - self.joints = self.train_data.joints - self.smplx = smplx.create( - self.args.data_path_1+"smplx_models/", - model_type='smplx', - gender='NEUTRAL_2020', - use_face_contour=False, - num_betas=300, - num_expression_coeffs=100, - ext='npz', - use_pca=False, - ).cuda().eval() - self.tracker = other_tools.EpochTracker(["rec", "contact", "vel", "foot", "ver", "com", "kl", "acc", "trans", "transv"], [False,False, False, False, False, False, False, False, False, False]) - if not self.args.rot6d: #"rot6d" not in args.pose_rep: - logger.error(f"this script is for rot6d, your pose rep. is {args.pose_rep}") - self.rec_loss = get_loss_func("GeodesicLoss") - self.vel_loss = torch.nn.L1Loss(reduction='mean') - self.vectices_loss = torch.nn.MSELoss(reduction='mean') - - def inverse_selection(self, filtered_t, selection_array, n): - # 创建一个全为零的数组，形状为 n*165 - original_shape_t = np.zeros((n, selection_array.size)) - - # 找到选择数组中为1的索引位置 - selected_indices = np.where(selection_array == 1)[0] - - # 将 filtered_t 的值填充到 original_shape_t 中相应的位置 - for i in range(n): - original_shape_t[i, selected_indices] = filtered_t[i] - - return original_shape_t - - def inverse_selection_tensor(self, filtered_t, selection_array, n): - # 创建一个全为零的数组，形状为 n*165 - selection_array = torch.from_numpy(selection_array).cuda() - original_shape_t = torch.zeros((n, 165)).cuda() - - # 找到选择数组中为1的索引位置 - selected_indices = torch.where(selection_array == 1)[0] - - # 将 filtered_t 的值填充到 original_shape_t 中相应的位置 - for i in range(n): - original_shape_t[i, selected_indices] = filtered_t[i] - - return original_shape_t - - - def train(self, epoch): - self.model.train() - t_start = time.time() - self.tracker.reset() - for its, dict_data in enumerate(self.train_loader): - tar_pose_raw = dict_data["pose"] - tar_beta = dict_data["beta"].cuda() - tar_trans = dict_data["trans"].cuda() - # tar_trans_vel_x = other_tools.estimate_linear_velocity(tar_trans[:, :, 0:1], dt=1/self.args.pose_fps) - # tar_trans_vel_z = other_tools.estimate_linear_velocity(tar_trans[:, :, 2:3], dt=1/self.args.pose_fps) - tar_pose = tar_pose_raw[:, :, :27].cuda() - tar_contact = tar_pose_raw[:, :, 27:31].cuda() - bs, n, j = tar_pose.shape[0], tar_pose.shape[1], self.joints - tar_exps = torch.zeros((bs, n, 100)).cuda() - tar_pose = rc.axis_angle_to_matrix(tar_pose.reshape(bs, n, j, 3)) - tar_pose = rc.matrix_to_rotation_6d(tar_pose).reshape(bs, n, j*6) - tar_trans_copy = tar_trans - tar_contact_copy = tar_contact - in_tar_pose = torch.cat((tar_pose, tar_trans_copy, tar_contact_copy), dim=-1) - - t_data = time.time() - t_start - - self.opt.zero_grad() - g_loss_final = 0 - net_out = self.model(in_tar_pose) - rec_pose = net_out["rec_pose"][:, :, :j*6] - rec_pose = rec_pose.reshape(bs, n, j, 6) - rec_pose = rc.rotation_6d_to_matrix(rec_pose)# - tar_pose = rc.rotation_6d_to_matrix(tar_pose.reshape(bs, n, j, 6)) - loss_rec = self.rec_loss(rec_pose, tar_pose) * self.args.rec_weight * self.args.rec_pos_weight - self.tracker.update_meter("rec", "train", loss_rec.item()) - g_loss_final += loss_rec - - rec_contact = net_out["rec_pose"][:, :, j*6+3:j*6+7] - loss_contact = self.vectices_loss(rec_contact, tar_contact) * self.args.rec_weight * self.args.rec_pos_weight - self.tracker.update_meter("contact", "train", loss_contact.item()) - g_loss_final += loss_contact - - velocity_loss = self.vel_loss(rec_pose[:, 1:] - rec_pose[:, :-1], tar_pose[:, 1:] - tar_pose[:, :-1]) * self.args.rec_weight - acceleration_loss = self.vel_loss(rec_pose[:, 2:] + rec_pose[:, :-2] - 2 * rec_pose[:, 1:-1], tar_pose[:, 2:] + tar_pose[:, :-2] - 2 * tar_pose[:, 1:-1]) * self.args.rec_weight - self.tracker.update_meter("vel", "train", velocity_loss.item()) - self.tracker.update_meter("acc", "train", acceleration_loss.item()) - g_loss_final += velocity_loss - g_loss_final += acceleration_loss - - # rec_trans = net_out["rec_pose"][:, :, j*6:j*6+3] - # rec_x_trans = other_tools.velocity2position(rec_trans[:, :, 0:1], 1/self.args.pose_fps, tar_trans[:, 0, 0:1]) - # rec_z_trans = other_tools.velocity2position(rec_trans[:, :, 2:3], 1/self.args.pose_fps, tar_trans[:, 0, 2:3]) - # rec_y_trans = rec_trans[:,:,1:2] - # rec_xyz_trans = torch.cat([rec_x_trans, rec_y_trans, rec_z_trans], dim=-1) - # loss_trans_vel = self.vel_loss(rec_trans[:, :, 0:1], tar_trans_vel_x) * self.args.rec_weight \ - # + self.vel_loss(rec_trans[:, :, 2:3], tar_trans_vel_z) * self.args.rec_weight - # v3 = self.vel_loss(rec_trans[:, :, 0:1][:, 1:] - rec_trans[:, :, 0:1][:, :-1], tar_trans_vel_x[:, 1:] - tar_trans_vel_x[:, :-1]) * self.args.rec_weight \ - # + self.vel_loss(rec_trans[:, :, 2:3][:, 1:] - rec_trans[:, :, 2:3][:, :-1], tar_trans_vel_z[:, 1:] - tar_trans_vel_z[:, :-1]) * self.args.rec_weight - # a3 = self.vel_loss(rec_trans[:, :, 0:1][:, 2:] + rec_trans[:, :, 0:1][:, :-2] - 2 * rec_trans[:, :, 0:1][:, 1:-1], tar_trans_vel_x[:, 2:] + tar_trans_vel_x[:, :-2] - 2 * tar_trans_vel_x[:, 1:-1]) * self.args.rec_weight \ - # + self.vel_loss(rec_trans[:, :, 2:3][:, 2:] + rec_trans[:, :, 2:3][:, :-2] - 2 * rec_trans[:, :, 2:3][:, 1:-1], tar_trans_vel_z[:, 2:] + tar_trans_vel_z[:, :-2] - 2 * tar_trans_vel_z[:, 1:-1]) * self.args.rec_weight - # g_loss_final += 5*v3 - # g_loss_final += 5*a3 - # v2 = self.vel_loss(rec_xyz_trans[:, 1:] - rec_xyz_trans[:, :-1], tar_trans[:, 1:] - tar_trans[:, :-1]) * self.args.rec_weight - # a2 = self.vel_loss(rec_xyz_trans[:, 2:] + rec_xyz_trans[:, :-2] - 2 * rec_xyz_trans[:, 1:-1], tar_trans[:, 2:] + tar_trans[:, :-2] - 2 * tar_trans[:, 1:-1]) * self.args.rec_weight - # g_loss_final += 5*v2 - # g_loss_final += 5*a2 - # self.tracker.update_meter("transv", "train", loss_trans_vel.item()) - # g_loss_final += loss_trans_vel - # loss_trans = self.vel_loss(rec_xyz_trans, tar_trans) * self.args.rec_weight - # self.tracker.update_meter("trans", "train", loss_trans.item()) - # g_loss_final += loss_trans - - # vertices loss - if self.args.rec_ver_weight > 0: - # print(tar_pose.shape, bs, n, j) - tar_pose = rc.matrix_to_axis_angle(tar_pose).reshape(bs*n, j*3) - rec_pose = rc.matrix_to_axis_angle(rec_pose).reshape(bs*n, j*3) - rec_pose = self.inverse_selection_tensor(rec_pose, self.train_data.joint_mask, rec_pose.shape[0]) - tar_pose = self.inverse_selection_tensor(tar_pose, self.train_data.joint_mask, tar_pose.shape[0]) - vertices_rec = self.smplx( - betas=tar_beta.reshape(bs*n, 300), - transl=tar_trans.reshape(bs*n, 3)-tar_trans.reshape(bs*n, 3), - expression=tar_exps.reshape(bs*n, 100), - jaw_pose=rec_pose[:, 66:69], - global_orient=rec_pose[:,:3], - body_pose=rec_pose[:,3:21*3+3], - left_hand_pose=rec_pose[:,25*3:40*3], - right_hand_pose=rec_pose[:,40*3:55*3], - return_verts=False, - return_joints=True, - leye_pose=tar_pose[:, 69:72], - reye_pose=tar_pose[:, 72:75], - ) - vertices_tar = self.smplx( - betas=tar_beta.reshape(bs*n, 300), - transl=tar_trans.reshape(bs*n, 3)-tar_trans.reshape(bs*n, 3), - expression=tar_exps.reshape(bs*n, 100), - jaw_pose=tar_pose[:, 66:69], - global_orient=tar_pose[:,:3], - body_pose=tar_pose[:,3:21*3+3], - left_hand_pose=tar_pose[:,25*3:40*3], - right_hand_pose=tar_pose[:,40*3:55*3], - return_verts=False, - return_joints=True, - leye_pose=tar_pose[:, 69:72], - reye_pose=tar_pose[:, 72:75], - ) - joints_rec = vertices_rec['joints'] - # print(joints_rec.shape) - joints_rec = joints_rec.reshape(bs, n, -1, 3) - vectices_loss = self.vectices_loss(vertices_rec['joints'], vertices_tar['joints']) - foot_idx = [7, 8, 10, 11] - model_contact = net_out["rec_pose"][:, :, j*6+3:j*6+7] - # find static indices consistent with model's own predictions - static_idx = model_contact > 0.95 # N x S x 4 - # print(model_contact,static_idx) - model_feet = joints_rec[:, :, foot_idx] # foot positions (N, S, 4, 3) - model_foot_v = torch.zeros_like(model_feet) - model_foot_v[:, :-1] = ( - model_feet[:, 1:, :, :] - model_feet[:, :-1, :, :] - ) # (N, S-1, 4, 3) - model_foot_v[~static_idx] = 0 - foot_loss = self.vel_loss( - model_foot_v, torch.zeros_like(model_foot_v) - ) - self.tracker.update_meter("foot", "train", foot_loss.item()*self.args.rec_weight * self.args.rec_ver_weight*20) - self.tracker.update_meter("ver", "train", vectices_loss.item()*self.args.rec_weight * self.args.rec_ver_weight) - g_loss_final += (vectices_loss)*self.args.rec_weight*self.args.rec_ver_weight - g_loss_final += foot_loss*self.args.rec_weight*self.args.rec_ver_weight*20 - - # ---------------------- vae -------------------------- # - if "VQVAE" in self.args.g_name: - loss_embedding = net_out["embedding_loss"] - g_loss_final += loss_embedding - self.tracker.update_meter("com", "train", loss_embedding.item()) - # elif "VAE" in self.args.g_name: - # pose_mu, pose_logvar = net_out["pose_mu"], net_out["pose_logvar"] - # KLD = -0.5 * torch.sum(1 + pose_logvar - pose_mu.pow(2) - pose_logvar.exp()) - # if epoch < 0: - # KLD_weight = 0 - # else: - # KLD_weight = min(1.0, (epoch - 0) * 0.05) * 0.01 - # loss += KLD_weight * KLD - # self.tracker.update_meter("kl", "train", KLD_weight * KLD.item()) - g_loss_final.backward() - if self.args.grad_norm != 0: - torch.nn.utils.clip_grad_norm_(self.model.parameters(), self.args.grad_norm) - self.opt.step() - t_train = time.time() - t_start - t_data - t_start = time.time() - mem_cost = torch.cuda.memory_cached() / 1E9 - lr_g = self.opt.param_groups[0]['lr'] - if its % self.args.log_period == 0: - self.train_recording(epoch, its, t_data, t_train, mem_cost, lr_g) - if self.args.debug: - if its == 1: break - self.opt_s.step(epoch) - - def val(self, epoch): - self.model.eval() - t_start = time.time() - with torch.no_grad(): - for its, dict_data in enumerate(self.val_loader): - tar_pose_raw = dict_data["pose"] - tar_beta = dict_data["beta"].cuda() - tar_trans = dict_data["trans"].cuda() - tar_trans_vel_x = other_tools.estimate_linear_velocity(tar_trans[:, :, 0:1], dt=1/self.args.pose_fps) - tar_trans_vel_z = other_tools.estimate_linear_velocity(tar_trans[:, :, 2:3], dt=1/self.args.pose_fps) - #print(tar_pose.shape) - tar_pose = tar_pose_raw[:, :, :27].cuda() - - tar_contact = tar_pose_raw[:, :, 27:31].cuda() - bs, n, j = tar_pose.shape[0], tar_pose.shape[1], self.joints - tar_exps = torch.zeros((bs, n, 100)).cuda() - tar_pose = rc.axis_angle_to_matrix(tar_pose.reshape(bs, n, j, 3)) - tar_pose = rc.matrix_to_rotation_6d(tar_pose).reshape(bs, n, j*6) - tar_trans_copy = tar_trans - tar_contact_copy = tar_contact - in_tar_pose = torch.cat((tar_pose, tar_trans_copy, tar_contact_copy), dim=-1) - t_data = time.time() - t_start - - #self.opt.zero_grad() - #g_loss_final = 0 - net_out = self.model(in_tar_pose) - rec_pose = net_out["rec_pose"][:, :, :j*6] - rec_pose = rec_pose.reshape(bs, n, j, 6) - rec_pose = rc.rotation_6d_to_matrix(rec_pose)# - tar_pose = rc.rotation_6d_to_matrix(tar_pose.reshape(bs, n, j, 6)) - loss_rec = self.rec_loss(rec_pose, tar_pose) * self.args.rec_weight * self.args.rec_pos_weight - self.tracker.update_meter("rec", "val", loss_rec.item()) - rec_contact = net_out["rec_pose"][:, :, j*6+3:j*6+7] - # print(rec_contact.shape, tar_contact.shape) - loss_contact = self.vel_loss(rec_contact, tar_contact) * self.args.rec_weight * self.args.rec_pos_weight - self.tracker.update_meter("contact", "val", loss_contact.item()) - #g_loss_final += loss_rec - rec_trans = net_out["rec_pose"][:, :, j*6:j*6+3] - rec_x_trans = other_tools.velocity2position(rec_trans[:, :, 0:1], 1/self.args.pose_fps, tar_trans[:, 0, 0:1]) - rec_z_trans = other_tools.velocity2position(rec_trans[:, :, 2:3], 1/self.args.pose_fps, tar_trans[:, 0, 2:3]) - rec_y_trans = rec_trans[:,:,1:2] - rec_xyz_trans = torch.cat([rec_x_trans, rec_y_trans, rec_z_trans], dim=-1) - - # rec_trans = net_out["rec_pose"][:, :, j*6:j*6+3] - # rec_x_trans = other_tools.velocity2position(rec_trans[:, :, 0:1], 1/self.args.pose_fps, tar_trans[:, 0, 0:1]) - # rec_z_trans = other_tools.velocity2position(rec_trans[:, :, 2:3], 1/self.args.pose_fps, tar_trans[:, 0, 2:3]) - # rec_y_trans = rec_trans[:,:,1:2] - # rec_xyz_trans = torch.cat([rec_x_trans, rec_y_trans, rec_z_trans], dim=-1) - # loss_trans_vel = self.vel_loss(rec_trans[:, :, 0:1], tar_trans_vel_x) * self.args.rec_weight \ - # + self.vel_loss(rec_trans[:, :, 2:3], tar_trans_vel_z) * self.args.rec_weight - # v3 = self.vel_loss(rec_trans[:, :, 0:1][:, 1:] - rec_trans[:, :, 0:1][:, :-1], tar_trans_vel_x[:, 1:] - tar_trans_vel_x[:, :-1]) * self.args.rec_weight \ - # + self.vel_loss(rec_trans[:, :, 2:3][:, 1:] - rec_trans[:, :, 2:3][:, :-1], tar_trans_vel_z[:, 1:] - tar_trans_vel_z[:, :-1]) * self.args.rec_weight - # a3 = self.vel_loss(rec_trans[:, :, 0:1][:, 2:] + rec_trans[:, :, 0:1][:, :-2] - 2 * rec_trans[:, :, 0:1][:, 1:-1], tar_trans_vel_x[:, 2:] + tar_trans_vel_x[:, :-2] - 2 * tar_trans_vel_x[:, 1:-1]) * self.args.rec_weight \ - # + self.vel_loss(rec_trans[:, :, 2:3][:, 2:] + rec_trans[:, :, 2:3][:, :-2] - 2 * rec_trans[:, :, 2:3][:, 1:-1], tar_trans_vel_z[:, 2:] + tar_trans_vel_z[:, :-2] - 2 * tar_trans_vel_z[:, 1:-1]) * self.args.rec_weight - # #g_loss_final += 5*v3 - # #g_loss_final += 5*a3 - # v2 = self.vel_loss(rec_xyz_trans[:, 1:] - rec_xyz_trans[:, :-1], tar_trans[:, 1:] - tar_trans[:, :-1]) * self.args.rec_weight - # a2 = self.vel_loss(rec_xyz_trans[:, 2:] + rec_xyz_trans[:, :-2] - 2 * rec_xyz_trans[:, 1:-1], tar_trans[:, 2:] + tar_trans[:, :-2] - 2 * tar_trans[:, 1:-1]) * self.args.rec_weight - #g_loss_final += 5*v2 - #g_loss_final += 5*a2 - # self.tracker.update_meter("transv", "val", loss_trans_vel.item()) - # #g_loss_final += loss_trans_vel - # loss_trans = self.vel_loss(rec_xyz_trans, tar_trans) * self.args.rec_weight - # self.tracker.update_meter("trans", "val", loss_trans.item()) - #g_loss_final += loss_trans - - # vertices loss - if self.args.rec_ver_weight > 0: - # print(tar_pose.shape, bs, n, j) - tar_pose = rc.matrix_to_axis_angle(tar_pose).reshape(bs*n, j*3) - rec_pose = rc.matrix_to_axis_angle(rec_pose).reshape(bs*n, j*3) - rec_pose = self.inverse_selection_tensor(rec_pose, self.train_data.joint_mask, rec_pose.shape[0]) - tar_pose = self.inverse_selection_tensor(tar_pose, self.train_data.joint_mask, tar_pose.shape[0]) - vertices_rec = self.smplx( - betas=tar_beta.reshape(bs*n, 300), - transl=tar_trans.reshape(bs*n, 3)-tar_trans.reshape(bs*n, 3), - expression=tar_exps.reshape(bs*n, 100), - jaw_pose=rec_pose[:, 66:69], - global_orient=rec_pose[:,:3], - body_pose=rec_pose[:,3:21*3+3], - left_hand_pose=rec_pose[:,25*3:40*3], - right_hand_pose=rec_pose[:,40*3:55*3], - return_verts=False, - return_joints=True, - leye_pose=tar_pose[:, 69:72], - reye_pose=tar_pose[:, 72:75], - ) - vertices_tar = self.smplx( - betas=tar_beta.reshape(bs*n, 300), - transl=tar_trans.reshape(bs*n, 3)-tar_trans.reshape(bs*n, 3), - expression=tar_exps.reshape(bs*n, 100), - jaw_pose=tar_pose[:, 66:69], - global_orient=tar_pose[:,:3], - body_pose=tar_pose[:,3:21*3+3], - left_hand_pose=tar_pose[:,25*3:40*3], - right_hand_pose=tar_pose[:,40*3:55*3], - return_verts=False, - return_joints=True, - leye_pose=tar_pose[:, 69:72], - reye_pose=tar_pose[:, 72:75], - ) - joints_rec = vertices_rec['joints'] - joints_rec = joints_rec.reshape(bs, n, -1, 3) - vectices_loss = self.vectices_loss(vertices_rec['joints'], vertices_tar['joints']) - foot_idx = [7, 8, 10, 11] - model_contact = net_out["rec_pose"][:, :, j*6+3:j*6+7] - # find static indices consistent with model's own predictions - static_idx = model_contact > 0.95 # N x S x 4 - # print(model_contact) - model_feet = joints_rec[:, :, foot_idx] # foot positions (N, S, 4, 3) - model_foot_v = torch.zeros_like(model_feet) - model_foot_v[:, :-1] = ( - model_feet[:, 1:, :, :] - model_feet[:, :-1, :, :] - ) # (N, S-1, 4, 3) - model_foot_v[~static_idx] = 0 - foot_loss = self.vectices_loss( - model_foot_v, torch.zeros_like(model_foot_v) - ) - self.tracker.update_meter("foot", "val", foot_loss.item()*self.args.rec_weight * self.args.rec_ver_weight) - self.tracker.update_meter("ver", "val", vectices_loss.item()*self.args.rec_weight * self.args.rec_ver_weight) - if "VQVAE" in self.args.g_name: - loss_embedding = net_out["embedding_loss"] - self.tracker.update_meter("com", "val", loss_embedding.item()) - #g_loss_final += vectices_loss*self.args.rec_weight*self.args.rec_ver_weight - if self.args.debug: - if its == 1: break - self.val_recording(epoch) - - def test(self, epoch): - results_save_path = self.checkpoint_path + f"/{epoch}/" - if os.path.exists(results_save_path): - return 0 - os.makedirs(results_save_path) - start_time = time.time() - total_length = 0 - test_seq_list = self.test_data.selected_file - self.model.eval() - with torch.no_grad(): - for its, dict_data in enumerate(self.test_loader): - tar_pose_raw = dict_data["pose"] - tar_trans = dict_data["trans"].to(self.rank) - tar_pose = tar_pose_raw[:, :, :27].cuda() - tar_contact = tar_pose_raw[:, :, 27:31].cuda() - # tar_pose = tar_pose.cuda() - bs, n, j = tar_pose.shape[0], tar_pose.shape[1], self.joints - tar_pose = rc.axis_angle_to_matrix(tar_pose.reshape(bs, n, j, 3)) - tar_pose = rc.matrix_to_rotation_6d(tar_pose).reshape(bs, n, j*6) - remain = n%self.args.pose_length - tar_pose = tar_pose[:, :n-remain, :] - tar_contact = tar_contact[:, :n-remain, :] - tar_trans_copy = tar_trans[:, :n-remain, :] - tar_contact_copy = tar_contact - in_tar_pose = torch.cat([tar_pose, tar_trans_copy, tar_contact_copy], dim=-1) - #print(tar_pose.shape) - if True: - net_out = self.model(in_tar_pose) - rec_pose = net_out["rec_pose"][:, :, :j*6] - rec_trans = net_out["rec_pose"][:, :, j*6:j*6+3] - net_out["rec_pose"][:, :, j*6:j*6+3] - # print(rec_trans.shape) - rec_x_trans = other_tools.velocity2position(rec_trans[:, :, 0:1], 1/self.args.pose_fps, tar_trans[:, 0, 0:1]) - rec_z_trans = other_tools.velocity2position(rec_trans[:, :, 2:3], 1/self.args.pose_fps, tar_trans[:, 0, 2:3]) - rec_y_trans = rec_trans[:,:,1:2] - rec_trans = torch.cat([rec_x_trans, rec_y_trans, rec_z_trans], dim=-1) - n = rec_pose.shape[1] - rec_trans = rec_trans.cpu().numpy().reshape(bs*n, 3) - tar_pose = tar_pose[:, :n, :] - rec_pose = rec_pose.reshape(bs, n, j, 6) - rec_pose = rc.rotation_6d_to_matrix(rec_pose)# - rec_pose = rc.matrix_to_axis_angle(rec_pose).reshape(bs*n, j*3) - rec_pose = rec_pose.cpu().numpy() - else: - pass -# for i in range(tar_pose.shape[1]//(self.args.vae_test_len)): -# tar_pose_new = tar_pose[:,i*(self.args.vae_test_len):i*(self.args.vae_test_len)+self.args.vae_test_len,:] -# net_out = self.model(**dict(inputs=tar_pose_new)) -# rec_pose = net_out["rec_pose"] -# rec_pose = (rec_pose.reshape(rec_pose.shape[0], rec_pose.shape[1], -1, 6) * self.joint_level_mask_cuda).reshape(rec_pose.shape[0], rec_pose.shape[1], -1) -# if "rot6d" in self.args.pose_rep: -# rec_pose = data_transfer.rotation_6d_to_matrix(rec_pose.reshape(tar_pose.shape[0], self.args.vae_test_len, -1, 6)) -# rec_pose = data_transfer.matrix_to_euler_angles(rec_pose, "XYZ").reshape(rec_pose.shape[0], rec_pose.shape[1], -1) -# if "smplx" not in self.args.pose_rep: -# rec_pose = torch.rad2deg(rec_pose) -# rec_pose = rec_pose * self.joint_mask_cuda - -# out_sub = rec_pose.cpu().numpy().reshape(-1, rec_pose.shape[2]) -# if i != 0: -# out_final = np.concatenate((out_final,out_sub), 0) -# else: -# out_final = out_sub - - tar_pose = rc.rotation_6d_to_matrix(tar_pose.reshape(bs, n, j, 6)) - tar_pose = rc.matrix_to_axis_angle(tar_pose).reshape(bs*n, j*3) - tar_pose = tar_pose.cpu().numpy() - - total_length += n - # --- save --- # - if 'smplx' in self.args.pose_rep: - gt_npz = np.load(self.args.data_path+self.args.pose_rep+"/"+test_seq_list.iloc[its]['id']+'.npz', allow_pickle=True) - stride = int(30 / self.args.pose_fps) - tar_pose = self.inverse_selection(tar_pose, self.test_data.joint_mask, tar_pose.shape[0]) - np.savez(results_save_path+"gt_"+test_seq_list.iloc[its]['id']+'.npz', - betas=gt_npz["betas"], - poses=tar_pose[:n], - expressions=gt_npz["expressions"]-gt_npz["expressions"], - trans=rec_trans-rec_trans, - model='smplx2020', - gender='neutral', - mocap_frame_rate = 30 , - ) - rec_pose = self.inverse_selection(rec_pose, self.test_data.joint_mask, rec_pose.shape[0]) - np.savez(results_save_path+"res_"+test_seq_list.iloc[its]['id']+'.npz', - betas=gt_npz["betas"], - poses=rec_pose, - expressions=gt_npz["expressions"]-gt_npz["expressions"], - trans=rec_trans-rec_trans, - model='smplx2020', - gender='neutral', - mocap_frame_rate = 30 , - ) - else: - rec_pose = rc.axis_angle_to_matrix(torch.from_numpy(rec_pose.reshape(bs*n, j, 3))) - rec_pose = np.rad2deg(rc.matrix_to_euler_angles(rec_pose, "XYZ")).reshape(bs*n, j*3).numpy() - tar_pose = rc.axis_angle_to_matrix(torch.from_numpy(tar_pose.reshape(bs*n, j, 3))) - tar_pose = np.rad2deg(rc.matrix_to_euler_angles(tar_pose, "XYZ")).reshape(bs*n, j*3).numpy() - #trans="0.000000 0.000000 0.000000" - - with open(f"{self.args.data_path}{self.args.pose_rep}/{test_seq_list.iloc[its]['id']}.bvh", "r") as f_demo: - with open(results_save_path+"gt_"+test_seq_list.iloc[its]['id']+'.bvh', 'w+') as f_gt: - with open(results_save_path+"res_"+test_seq_list.iloc[its]['id']+'.bvh', 'w+') as f_real: - for i, line_data in enumerate(f_demo.readlines()): - if i < 431: - f_real.write(line_data) - f_gt.write(line_data) - else: break - for line_id in range(n): #,args.pre_frames, args.pose_length - line_data = np.array2string(rec_pose[line_id], max_line_width=np.inf, precision=6, suppress_small=False, separator=' ') - f_real.write(line_data[1:-2]+'\n') - for line_id in range(n): #,args.pre_frames, args.pose_length - line_data = np.array2string(tar_pose[line_id], max_line_width=np.inf, precision=6, suppress_small=False, separator=' ') - f_gt.write(line_data[1:-2]+'\n') - # with open(results_save_path+"gt_"+test_seq_list[its]+'.pkl', 'wb') as fw: - # pickle.dump(new_dict, fw) - # #new_dict2["fullpose"] = out_final - # with open(results_save_path+"res_"+test_seq_list[its]+'.pkl', 'wb') as fw1: - # pickle.dump(new_dict2, fw1) - - # other_tools.render_one_sequence( - # results_save_path+"res_"+test_seq_list[its]+'.pkl', - # results_save_path+"gt_"+test_seq_list[its]+'.pkl', - # results_save_path, - # self.args.data_path + self.args.test_data_path + 'wave16k/' + test_seq_list[its]+'.npy', - # ) - - #if its == 1:break - end_time = time.time() - start_time - logger.info(f"total inference time: {int(end_time)} s for {int(total_length/self.args.pose_fps)} s motion") \ No newline at end of file diff --git a/app.py b/app.py index 035454b056d577a15b31ed5be7458b439e354add..424e08ccc58230c635ab5991b8b53a6e0e92d2d5 100644 --- a/app.py +++ b/app.py @@ -5,675 +5,231 @@ import os import OpenGL.GL as gl os.environ["PYOPENGL_PLATFORM"] = "egl" os.environ["MESA_GL_VERSION_OVERRIDE"] = "4.1" -import signal -import time -import csv -import sys -import warnings -import random import gradio as gr import torch -import torch.nn as nn -import torch.nn.functional as F -import torch.distributed as dist -from torch.nn.parallel import DistributedDataParallel as DDP -import torch.multiprocessing as mp import numpy as np -import time -import pprint -from loguru import logger -import smplx -from torch.utils.tensorboard import SummaryWriter -import wandb -import matplotlib.pyplot as plt -from utils import config, logger_tools, other_tools_hf, metric, data_transfer -from dataloaders import data_tools -from dataloaders.build_vocab import Vocab -from optimizers.optim_factory import create_optimizer -from optimizers.scheduler_factory import create_scheduler -from optimizers.loss_factory import get_loss_func -from dataloaders.data_tools import joints_list -from utils import rotation_conversions as rc import soundfile as sf -import librosa - -def inverse_selection_tensor(filtered_t, selection_array, n): - selection_array = torch.from_numpy(selection_array).cuda() - original_shape_t = torch.zeros((n, 165)).cuda() - selected_indices = torch.where(selection_array == 1)[0] - for i in range(n): - original_shape_t[i, selected_indices] = filtered_t[i] - return original_shape_t - -@spaces.GPU(duration=120) -def test_demo_gpu( - model, vq_model_face, vq_model_upper, vq_model_hands, vq_model_lower, global_motion, smplx_model, - dict_data, - args, - joints, joint_mask_upper, joint_mask_lower, joint_mask_hands, - log_softmax, -): - rank = 0 - other_tools_hf.load_checkpoints(vq_model_face, args.data_path_1 + "pretrained_vq/last_790_face_v2.bin", args.e_name) - other_tools_hf.load_checkpoints(vq_model_upper, args.data_path_1 + "pretrained_vq/upper_vertex_1layer_710.bin", args.e_name) - other_tools_hf.load_checkpoints(vq_model_hands, args.data_path_1 + "pretrained_vq/hands_vertex_1layer_710.bin", args.e_name) - other_tools_hf.load_checkpoints(vq_model_lower, args.data_path_1 + "pretrained_vq/lower_foot_600.bin", args.e_name) - other_tools_hf.load_checkpoints(global_motion, args.data_path_1 + "pretrained_vq/last_1700_foot.bin", args.e_name) - other_tools_hf.load_checkpoints(model, args.test_ckpt, args.g_name) - model.to(rank).eval() - smplx_model.to(rank).eval() - vq_model_face.to(rank).eval() - vq_model_upper.to(rank).eval() - vq_model_hands.to(rank).eval() - vq_model_lower.to(rank).eval() - global_motion.to(rank).eval() - - with torch.no_grad(): - tar_pose_raw = dict_data["pose"] - tar_pose = tar_pose_raw[:, :, :165].to(rank) - tar_contact = tar_pose_raw[:, :, 165:169].to(rank) - tar_trans = dict_data["trans"].to(rank) - tar_exps = dict_data["facial"].to(rank) - in_audio = dict_data["audio"].to(rank) - in_word = None# dict_data["word"].to(rank) - tar_beta = dict_data["beta"].to(rank) - tar_id = dict_data["id"].to(rank).long() - bs, n, j = tar_pose.shape[0], tar_pose.shape[1], joints - - tar_pose_jaw = tar_pose[:, :, 66:69] - tar_pose_jaw = rc.axis_angle_to_matrix(tar_pose_jaw.reshape(bs, n, 1, 3)) - tar_pose_jaw = rc.matrix_to_rotation_6d(tar_pose_jaw).reshape(bs, n, 1*6) - tar_pose_face = torch.cat([tar_pose_jaw, tar_exps], dim=2) - - tar_pose_hands = tar_pose[:, :, 25*3:55*3] - tar_pose_hands = rc.axis_angle_to_matrix(tar_pose_hands.reshape(bs, n, 30, 3)) - tar_pose_hands = rc.matrix_to_rotation_6d(tar_pose_hands).reshape(bs, n, 30*6) - - tar_pose_upper = tar_pose[:, :, joint_mask_upper.astype(bool)] - tar_pose_upper = rc.axis_angle_to_matrix(tar_pose_upper.reshape(bs, n, 13, 3)) - tar_pose_upper = rc.matrix_to_rotation_6d(tar_pose_upper).reshape(bs, n, 13*6) - - tar_pose_leg = tar_pose[:, :, joint_mask_lower.astype(bool)] - tar_pose_leg = rc.axis_angle_to_matrix(tar_pose_leg.reshape(bs, n, 9, 3)) - tar_pose_leg = rc.matrix_to_rotation_6d(tar_pose_leg).reshape(bs, n, 9*6) - tar_pose_lower = torch.cat([tar_pose_leg, tar_trans, tar_contact], dim=2) - - # tar_pose = rc.axis_angle_to_matrix(tar_pose.reshape(bs, n, j, 3)) - # tar_pose = rc.matrix_to_rotation_6d(tar_pose).reshape(bs, n, j*6) - tar4dis = torch.cat([tar_pose_jaw, tar_pose_upper, tar_pose_hands, tar_pose_leg], dim=2) - - tar_index_value_face_top = vq_model_face.map2index(tar_pose_face) # bs*n/4 - tar_index_value_upper_top = vq_model_upper.map2index(tar_pose_upper) # bs*n/4 - tar_index_value_hands_top = vq_model_hands.map2index(tar_pose_hands) # bs*n/4 - tar_index_value_lower_top = vq_model_lower.map2index(tar_pose_lower) # bs*n/4 - - latent_face_top = vq_model_face.map2latent(tar_pose_face) # bs*n/4 - latent_upper_top = vq_model_upper.map2latent(tar_pose_upper) # bs*n/4 - latent_hands_top = vq_model_hands.map2latent(tar_pose_hands) # bs*n/4 - latent_lower_top = vq_model_lower.map2latent(tar_pose_lower) # bs*n/4 - - latent_in = torch.cat([latent_upper_top, latent_hands_top, latent_lower_top], dim=2) - - index_in = torch.stack([tar_index_value_upper_top, tar_index_value_hands_top, tar_index_value_lower_top], dim=-1).long() - - tar_pose_6d = rc.axis_angle_to_matrix(tar_pose.reshape(bs, n, 55, 3)) - tar_pose_6d = rc.matrix_to_rotation_6d(tar_pose_6d).reshape(bs, n, 55*6) - latent_all = torch.cat([tar_pose_6d, tar_trans, tar_contact], dim=-1) - - loaded_data = { - "tar_pose_jaw": tar_pose_jaw, - "tar_pose_face": tar_pose_face, - "tar_pose_upper": tar_pose_upper, - "tar_pose_lower": tar_pose_lower, - "tar_pose_hands": tar_pose_hands, - 'tar_pose_leg': tar_pose_leg, - "in_audio": in_audio, - "in_word": in_word, - "tar_trans": tar_trans, - "tar_exps": tar_exps, - "tar_beta": tar_beta, - "tar_pose": tar_pose, - "tar4dis": tar4dis, - "tar_index_value_face_top": tar_index_value_face_top, - "tar_index_value_upper_top": tar_index_value_upper_top, - "tar_index_value_hands_top": tar_index_value_hands_top, - "tar_index_value_lower_top": tar_index_value_lower_top, - "latent_face_top": latent_face_top, - "latent_upper_top": latent_upper_top, - "latent_hands_top": latent_hands_top, - "latent_lower_top": latent_lower_top, - "latent_in": latent_in, - "index_in": index_in, - "tar_id": tar_id, - "latent_all": latent_all, - "tar_pose_6d": tar_pose_6d, - "tar_contact": tar_contact, - } - - mode = 'test' - bs, n, j = loaded_data["tar_pose"].shape[0], loaded_data["tar_pose"].shape[1], joints - tar_pose = loaded_data["tar_pose"] - tar_beta = loaded_data["tar_beta"] - in_word =None# loaded_data["in_word"] - tar_exps = loaded_data["tar_exps"] - tar_contact = loaded_data["tar_contact"] - in_audio = loaded_data["in_audio"] - tar_trans = loaded_data["tar_trans"] - - remain = n%8 - if remain != 0: - tar_pose = tar_pose[:, :-remain, :] - tar_beta = tar_beta[:, :-remain, :] - tar_trans = tar_trans[:, :-remain, :] - # in_word = in_word[:, :-remain] - tar_exps = tar_exps[:, :-remain, :] - tar_contact = tar_contact[:, :-remain, :] - n = n - remain - - tar_pose_jaw = tar_pose[:, :, 66:69] - tar_pose_jaw = rc.axis_angle_to_matrix(tar_pose_jaw.reshape(bs, n, 1, 3)) - tar_pose_jaw = rc.matrix_to_rotation_6d(tar_pose_jaw).reshape(bs, n, 1*6) - tar_pose_face = torch.cat([tar_pose_jaw, tar_exps], dim=2) - - tar_pose_hands = tar_pose[:, :, 25*3:55*3] - tar_pose_hands = rc.axis_angle_to_matrix(tar_pose_hands.reshape(bs, n, 30, 3)) - tar_pose_hands = rc.matrix_to_rotation_6d(tar_pose_hands).reshape(bs, n, 30*6) - - tar_pose_upper = tar_pose[:, :, joint_mask_upper.astype(bool)] - tar_pose_upper = rc.axis_angle_to_matrix(tar_pose_upper.reshape(bs, n, 13, 3)) - tar_pose_upper = rc.matrix_to_rotation_6d(tar_pose_upper).reshape(bs, n, 13*6) - - tar_pose_leg = tar_pose[:, :, joint_mask_lower.astype(bool)] - tar_pose_leg = rc.axis_angle_to_matrix(tar_pose_leg.reshape(bs, n, 9, 3)) - tar_pose_leg = rc.matrix_to_rotation_6d(tar_pose_leg).reshape(bs, n, 9*6) - tar_pose_lower = torch.cat([tar_pose_leg, tar_trans, tar_contact], dim=2) - - tar_pose_6d = rc.axis_angle_to_matrix(tar_pose.reshape(bs, n, 55, 3)) - tar_pose_6d = rc.matrix_to_rotation_6d(tar_pose_6d).reshape(bs, n, 55*6) - latent_all = torch.cat([tar_pose_6d, tar_trans, tar_contact], dim=-1) - - rec_index_all_face = [] - rec_index_all_upper = [] - rec_index_all_lower = [] - rec_index_all_hands = [] - - roundt = (n - args.pre_frames) // (args.pose_length - args.pre_frames) - remain = (n - args.pre_frames) % (args.pose_length - args.pre_frames) - round_l = args.pose_length - args.pre_frames - - for i in range(0, roundt): - # in_word_tmp = in_word[:, i*(round_l):(i+1)*(round_l)+args.pre_frames] - # audio fps is 16000 and pose fps is 30 - in_audio_tmp = in_audio[:, i*(16000//30*round_l):(i+1)*(16000//30*round_l)+16000//30*args.pre_frames] - in_id_tmp = loaded_data['tar_id'][:, i*(round_l):(i+1)*(round_l)+args.pre_frames] - mask_val = torch.ones(bs, args.pose_length, args.pose_dims+3+4).float().cuda() - mask_val[:, :args.pre_frames, :] = 0.0 - if i == 0: - latent_all_tmp = latent_all[:, i*(round_l):(i+1)*(round_l)+args.pre_frames, :] - else: - latent_all_tmp = latent_all[:, i*(round_l):(i+1)*(round_l)+args.pre_frames, :] - # print(latent_all_tmp.shape, latent_last.shape) - latent_all_tmp[:, :args.pre_frames, :] = latent_last[:, -args.pre_frames:, :] - - net_out_val = model( - in_audio = in_audio_tmp, - in_word=None, #in_word_tmp, - mask=mask_val, - in_motion = latent_all_tmp, - in_id = in_id_tmp, - use_attentions=True,) - - if args.cu != 0: - rec_index_upper = log_softmax(net_out_val["cls_upper"]).reshape(-1, args.vae_codebook_size) - _, rec_index_upper = torch.max(rec_index_upper.reshape(-1, args.pose_length, args.vae_codebook_size), dim=2) - #rec_upper = vq_model_upper.decode(rec_index_upper) - else: - _, rec_index_upper, _, _ = vq_model_upper.quantizer(net_out_val["rec_upper"]) - #rec_upper = vq_model_upper.decoder(rec_index_upper) - if args.cl != 0: - rec_index_lower = log_softmax(net_out_val["cls_lower"]).reshape(-1, args.vae_codebook_size) - _, rec_index_lower = torch.max(rec_index_lower.reshape(-1, args.pose_length, args.vae_codebook_size), dim=2) - #rec_lower = vq_model_lower.decode(rec_index_lower) - else: - _, rec_index_lower, _, _ = vq_model_lower.quantizer(net_out_val["rec_lower"]) - #rec_lower = vq_model_lower.decoder(rec_index_lower) - if args.ch != 0: - rec_index_hands = log_softmax(net_out_val["cls_hands"]).reshape(-1, args.vae_codebook_size) - _, rec_index_hands = torch.max(rec_index_hands.reshape(-1, args.pose_length, args.vae_codebook_size), dim=2) - #rec_hands = vq_model_hands.decode(rec_index_hands) - else: - _, rec_index_hands, _, _ = vq_model_hands.quantizer(net_out_val["rec_hands"]) - #rec_hands = vq_model_hands.decoder(rec_index_hands) - if args.cf != 0: - rec_index_face = log_softmax(net_out_val["cls_face"]).reshape(-1, args.vae_codebook_size) - _, rec_index_face = torch.max(rec_index_face.reshape(-1, args.pose_length, args.vae_codebook_size), dim=2) - #rec_face = vq_model_face.decoder(rec_index_face) - else: - _, rec_index_face, _, _ = vq_model_face.quantizer(net_out_val["rec_face"]) - #rec_face = vq_model_face.decoder(rec_index_face) - - if i == 0: - rec_index_all_face.append(rec_index_face) - rec_index_all_upper.append(rec_index_upper) - rec_index_all_lower.append(rec_index_lower) - rec_index_all_hands.append(rec_index_hands) - else: - rec_index_all_face.append(rec_index_face[:, args.pre_frames:]) - rec_index_all_upper.append(rec_index_upper[:, args.pre_frames:]) - rec_index_all_lower.append(rec_index_lower[:, args.pre_frames:]) - rec_index_all_hands.append(rec_index_hands[:, args.pre_frames:]) - - if args.cu != 0: - rec_upper_last = vq_model_upper.decode(rec_index_upper) - else: - rec_upper_last = vq_model_upper.decoder(rec_index_upper) - if args.cl != 0: - rec_lower_last = vq_model_lower.decode(rec_index_lower) - else: - rec_lower_last = vq_model_lower.decoder(rec_index_lower) - if args.ch != 0: - rec_hands_last = vq_model_hands.decode(rec_index_hands) - else: - rec_hands_last = vq_model_hands.decoder(rec_index_hands) - # if args.cf != 0: - # rec_face_last = vq_model_face.decode(rec_index_face) - # else: - # rec_face_last = vq_model_face.decoder(rec_index_face) - - rec_pose_legs = rec_lower_last[:, :, :54] - bs, n = rec_pose_legs.shape[0], rec_pose_legs.shape[1] - rec_pose_upper = rec_upper_last.reshape(bs, n, 13, 6) - rec_pose_upper = rc.rotation_6d_to_matrix(rec_pose_upper)# - rec_pose_upper = rc.matrix_to_axis_angle(rec_pose_upper).reshape(bs*n, 13*3) - rec_pose_upper_recover = inverse_selection_tensor(rec_pose_upper, joint_mask_upper, bs*n) - rec_pose_lower = rec_pose_legs.reshape(bs, n, 9, 6) - rec_pose_lower = rc.rotation_6d_to_matrix(rec_pose_lower) - rec_pose_lower = rc.matrix_to_axis_angle(rec_pose_lower).reshape(bs*n, 9*3) - rec_pose_lower_recover = inverse_selection_tensor(rec_pose_lower, joint_mask_lower, bs*n) - rec_pose_hands = rec_hands_last.reshape(bs, n, 30, 6) - rec_pose_hands = rc.rotation_6d_to_matrix(rec_pose_hands) - rec_pose_hands = rc.matrix_to_axis_angle(rec_pose_hands).reshape(bs*n, 30*3) - rec_pose_hands_recover = inverse_selection_tensor(rec_pose_hands, joint_mask_hands, bs*n) - rec_pose = rec_pose_upper_recover + rec_pose_lower_recover + rec_pose_hands_recover - rec_pose = rc.axis_angle_to_matrix(rec_pose.reshape(bs, n, j, 3)) - rec_pose = rc.matrix_to_rotation_6d(rec_pose).reshape(bs, n, j*6) - rec_trans_v_s = rec_lower_last[:, :, 54:57] - rec_x_trans = other_tools_hf.velocity2position(rec_trans_v_s[:, :, 0:1], 1/args.pose_fps, tar_trans[:, 0, 0:1]) - rec_z_trans = other_tools_hf.velocity2position(rec_trans_v_s[:, :, 2:3], 1/args.pose_fps, tar_trans[:, 0, 2:3]) - rec_y_trans = rec_trans_v_s[:,:,1:2] - rec_trans = torch.cat([rec_x_trans, rec_y_trans, rec_z_trans], dim=-1) - latent_last = torch.cat([rec_pose, rec_trans, rec_lower_last[:, :, 57:61]], dim=-1) - - rec_index_face = torch.cat(rec_index_all_face, dim=1) - rec_index_upper = torch.cat(rec_index_all_upper, dim=1) - rec_index_lower = torch.cat(rec_index_all_lower, dim=1) - rec_index_hands = torch.cat(rec_index_all_hands, dim=1) - if args.cu != 0: - rec_upper = vq_model_upper.decode(rec_index_upper) - else: - rec_upper = vq_model_upper.decoder(rec_index_upper) - if args.cl != 0: - rec_lower = vq_model_lower.decode(rec_index_lower) - else: - rec_lower = vq_model_lower.decoder(rec_index_lower) - if args.ch != 0: - rec_hands = vq_model_hands.decode(rec_index_hands) - else: - rec_hands = vq_model_hands.decoder(rec_index_hands) - if args.cf != 0: - rec_face = vq_model_face.decode(rec_index_face) - else: - rec_face = vq_model_face.decoder(rec_index_face) +import librosa +from torchvision.io import write_video +from emage_utils.motion_io import beat_format_save +from emage_utils import fast_render +from emage_utils.npz2pose import render2d +from models.camn_audio import CamnAudioModel +from models.disco_audio import DiscoAudioModel +from models.emage_audio import EmageAudioModel, EmageVQVAEConv, EmageVAEConv, EmageVQModel +import torch.nn.functional as F - rec_exps = rec_face[:, :, 6:] - rec_pose_jaw = rec_face[:, :, :6] - rec_pose_legs = rec_lower[:, :, :54] - bs, n = rec_pose_jaw.shape[0], rec_pose_jaw.shape[1] - rec_pose_upper = rec_upper.reshape(bs, n, 13, 6) - rec_pose_upper = rc.rotation_6d_to_matrix(rec_pose_upper)# - rec_pose_upper = rc.matrix_to_axis_angle(rec_pose_upper).reshape(bs*n, 13*3) - rec_pose_upper_recover = inverse_selection_tensor(rec_pose_upper, joint_mask_upper, bs*n) - rec_pose_lower = rec_pose_legs.reshape(bs, n, 9, 6) - rec_pose_lower = rc.rotation_6d_to_matrix(rec_pose_lower) - rec_lower2global = rc.matrix_to_rotation_6d(rec_pose_lower.clone()).reshape(bs, n, 9*6) - rec_pose_lower = rc.matrix_to_axis_angle(rec_pose_lower).reshape(bs*n, 9*3) - rec_pose_lower_recover = inverse_selection_tensor(rec_pose_lower, joint_mask_lower, bs*n) - rec_pose_hands = rec_hands.reshape(bs, n, 30, 6) - rec_pose_hands = rc.rotation_6d_to_matrix(rec_pose_hands) - rec_pose_hands = rc.matrix_to_axis_angle(rec_pose_hands).reshape(bs*n, 30*3) - rec_pose_hands_recover = inverse_selection_tensor(rec_pose_hands, joint_mask_hands, bs*n) - rec_pose_jaw = rec_pose_jaw.reshape(bs*n, 6) - rec_pose_jaw = rc.rotation_6d_to_matrix(rec_pose_jaw) - rec_pose_jaw = rc.matrix_to_axis_angle(rec_pose_jaw).reshape(bs*n, 1*3) - rec_pose = rec_pose_upper_recover + rec_pose_lower_recover + rec_pose_hands_recover - rec_pose[:, 66:69] = rec_pose_jaw +device = torch.device("cuda" if torch.cuda.is_available() else "cpu") +save_folder = "./gradio_results" +os.makedirs(save_folder, exist_ok=True) +print(device) - to_global = rec_lower - to_global[:, :, 54:57] = 0.0 - to_global[:, :, :54] = rec_lower2global - rec_global = global_motion(to_global) +if not os.path.exists("./emage_evaltools/smplx_models"): + import subprocess + subprocess.run(["git", "clone", "https://huggingface.co/H-Liu1997/emage_evaltools"]) - rec_trans_v_s = rec_global["rec_pose"][:, :, 54:57] - rec_x_trans = other_tools_hf.velocity2position(rec_trans_v_s[:, :, 0:1], 1/args.pose_fps, tar_trans[:, 0, 0:1]) - rec_z_trans = other_tools_hf.velocity2position(rec_trans_v_s[:, :, 2:3], 1/args.pose_fps, tar_trans[:, 0, 2:3]) - rec_y_trans = rec_trans_v_s[:,:,1:2] - rec_trans = torch.cat([rec_x_trans, rec_y_trans, rec_z_trans], dim=-1) - tar_pose = tar_pose[:, :n, :] - tar_exps = tar_exps[:, :n, :] - tar_trans = tar_trans[:, :n, :] - tar_beta = tar_beta[:, :n, :] +model_camn = CamnAudioModel.from_pretrained("H-Liu1997/camn_audio").to(device).eval() +model_disco = DiscoAudioModel.from_pretrained("H-Liu1997/disco_audio").to(device).eval() - rec_pose = rc.axis_angle_to_matrix(rec_pose.reshape(bs*n, j, 3)) - rec_pose = rc.matrix_to_rotation_6d(rec_pose).reshape(bs, n, j*6) - tar_pose = rc.axis_angle_to_matrix(tar_pose.reshape(bs*n, j, 3)) - tar_pose = rc.matrix_to_rotation_6d(tar_pose).reshape(bs, n, j*6) - - net_out = { - 'rec_pose': rec_pose, - 'rec_trans': rec_trans, - 'tar_pose': tar_pose, - 'tar_exps': tar_exps, - 'tar_beta': tar_beta, - 'tar_trans': tar_trans, - 'rec_exps': rec_exps, - } - +face_motion_vq = EmageVQVAEConv.from_pretrained("H-Liu1997/emage_audio", subfolder="emage_vq/face").to(device).eval() +upper_motion_vq = EmageVQVAEConv.from_pretrained("H-Liu1997/emage_audio", subfolder="emage_vq/upper").to(device).eval() +lower_motion_vq = EmageVQVAEConv.from_pretrained("H-Liu1997/emage_audio", subfolder="emage_vq/lower").to(device).eval() +hands_motion_vq = EmageVQVAEConv.from_pretrained("H-Liu1997/emage_audio", subfolder="emage_vq/hands").to(device).eval() +global_motion_ae = EmageVAEConv.from_pretrained("H-Liu1997/emage_audio", subfolder="emage_vq/global").to(device).eval() - tar_pose = net_out['tar_pose'] - rec_pose = net_out['rec_pose'] - tar_exps = net_out['tar_exps'] - tar_beta = net_out['tar_beta'] - rec_trans = net_out['rec_trans'] - tar_trans = net_out['tar_trans'] - rec_exps = net_out['rec_exps'] - # print(rec_pose.shape, tar_pose.shape) - bs, n, j = tar_pose.shape[0], tar_pose.shape[1], joints - # interpolate to 30fps - if (30/args.pose_fps) != 1: - assert 30%args.pose_fps == 0 - n *= int(30/args.pose_fps) - tar_pose = torch.nn.functional.interpolate(tar_pose.permute(0, 2, 1), scale_factor=30/args.pose_fps, mode='linear').permute(0,2,1) - rec_pose = torch.nn.functional.interpolate(rec_pose.permute(0, 2, 1), scale_factor=30/args.pose_fps, mode='linear').permute(0,2,1) - - # print(rec_pose.shape, tar_pose.shape) - rec_pose = rc.rotation_6d_to_matrix(rec_pose.reshape(bs*n, j, 6)) - rec_pose = rc.matrix_to_axis_angle(rec_pose).reshape(bs*n, j*3) +emage_vq_model = EmageVQModel( + face_model=face_motion_vq, + upper_model=upper_motion_vq, + lower_model=lower_motion_vq, + hands_model=hands_motion_vq, + global_model=global_motion_ae +).to(device).eval() - tar_pose = rc.rotation_6d_to_matrix(tar_pose.reshape(bs*n, j, 6)) - tar_pose = rc.matrix_to_axis_angle(tar_pose).reshape(bs*n, j*3) - - return tar_pose, rec_pose, tar_exps, tar_beta, rec_trans, tar_trans, rec_exps, bs, n, j +model_emage = EmageAudioModel.from_pretrained("H-Liu1997/emage_audio").to(device).eval() - -class BaseTrainer(object): - def __init__(self, args, sp, ap, tp): - hf_dir = "hf" - if not os.path.exists(args.out_path + "custom/" + hf_dir + "/"): - os.makedirs(args.out_path + "custom/" + hf_dir + "/") - sf.write(args.out_path + "custom/" + hf_dir + "/tmp.wav", ap[1], ap[0]) - self.audio_path = args.out_path + "custom/" + hf_dir + "/tmp.wav" - audio, ssr = librosa.load(self.audio_path) - ap = (ssr, audio) - self.args = args - self.rank = 0 # dist.get_rank() - - #self.checkpoint_path = args.out_path + "custom/" + args.name + args.notes + "/" #wandb.run.dir #args.cache_path+args.out_path+"/"+args.name - self.checkpoint_path = args.out_path + "custom/" + hf_dir + "/" - if self.rank == 0: - self.test_data = __import__(f"dataloaders.{args.dataset}", fromlist=["something"]).CustomDataset(args, "test", smplx_path=sp, audio_path=ap, text_path=tp) - self.test_loader = torch.utils.data.DataLoader( - self.test_data, - batch_size=1, - shuffle=False, - num_workers=args.loader_workers, - drop_last=False, - ) - logger.info(f"Init test dataloader success") - model_module = __import__(f"models.{args.model}", fromlist=["something"]) - - if args.ddp: - self.model = getattr(model_module, args.g_name)(args).to(self.rank) - process_group = torch.distributed.new_group() - self.model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(self.model, process_group) - self.model = DDP(self.model, device_ids=[self.rank], output_device=self.rank, - broadcast_buffers=False, find_unused_parameters=False) - else: - self.model = torch.nn.DataParallel(getattr(model_module, args.g_name)(args), args.gpus).cpu() - - if self.rank == 0: - logger.info(self.model) - logger.info(f"init {args.g_name} success") - self.smplx = smplx.create( - self.args.data_path_1+"smplx_models/", - model_type='smplx', - gender='NEUTRAL_2020', - use_face_contour=False, - num_betas=300, - num_expression_coeffs=100, - ext='npz', - use_pca=False, +def inference_camn(audio_path, sr_model, pose_fps, seed_frames): + audio_loaded, _ = librosa.load(audio_path, sr=sr_model) + audio_t = torch.from_numpy(audio_loaded).float().unsqueeze(0).to(device) + sid = torch.zeros(1, 1).long().to(device) + with torch.no_grad(): + motion_pred = model_camn(audio_t, sid, seed_frames=seed_frames)["motion_axis_angle"] + t = motion_pred.shape[1] + motion_pred = motion_pred.cpu().numpy().reshape(t, -1) + npz_path = os.path.join(save_folder, "camn_output.npz") + beat_format_save(npz_path, motion_pred, upsample=30 // pose_fps) + return npz_path + +def inference_disco(audio_path, sr_model, pose_fps, seed_frames): + audio_loaded, _ = librosa.load(audio_path, sr=sr_model) + audio_t = torch.from_numpy(audio_loaded).float().unsqueeze(0).to(device) + sid = torch.zeros(1, 1).long().to(device) + with torch.no_grad(): + motion_pred = model_disco(audio_t, sid, seed_frames=seed_frames, seed_motion=None)["motion_axis_angle"] + t = motion_pred.shape[1] + motion_pred = motion_pred.cpu().numpy().reshape(t, -1) + npz_path = os.path.join(save_folder, "disco_output.npz") + beat_format_save(npz_path, motion_pred, upsample=30 // pose_fps) + return npz_path + +def inference_emage(audio_path, sr_model, pose_fps): + audio_loaded, _ = librosa.load(audio_path, sr=sr_model) + audio_t = torch.from_numpy(audio_loaded).float().unsqueeze(0).to(device) + sid = torch.zeros(1, 1).long().to(device) + with torch.no_grad(): + latent_dict = model_emage.inference(audio_t, sid, emage_vq_model, masked_motion=None, mask=None) + face_latent = latent_dict["rec_face"] if model_emage.cfg.lf > 0 and model_emage.cfg.cf == 0 else None + upper_latent = latent_dict["rec_upper"] if model_emage.cfg.lu > 0 and model_emage.cfg.cu == 0 else None + hands_latent = latent_dict["rec_hands"] if model_emage.cfg.lh > 0 and model_emage.cfg.ch == 0 else None + lower_latent = latent_dict["rec_lower"] if model_emage.cfg.ll > 0 and model_emage.cfg.cl == 0 else None + + face_index = torch.max(F.log_softmax(latent_dict["cls_face"], dim=2), dim=2)[1] if model_emage.cfg.cf > 0 else None + upper_index = torch.max(F.log_softmax(latent_dict["cls_upper"], dim=2), dim=2)[1] if model_emage.cfg.cu > 0 else None + hands_index = torch.max(F.log_softmax(latent_dict["cls_hands"], dim=2), dim=2)[1] if model_emage.cfg.ch > 0 else None + lower_index = torch.max(F.log_softmax(latent_dict["cls_lower"], dim=2), dim=2)[1] if model_emage.cfg.cl > 0 else None + + ref_trans = torch.zeros(1, 1, 3).to(device) + all_pred = emage_vq_model.decode( + face_latent=face_latent, + upper_latent=upper_latent, + lower_latent=lower_latent, + hands_latent=hands_latent, + face_index=face_index, + upper_index=upper_index, + lower_index=lower_index, + hands_index=hands_index, + get_global_motion=True, + ref_trans=ref_trans[:, 0] ) - - self.args = args - self.joints = self.test_data.joints - self.ori_joint_list = joints_list[self.args.ori_joints] - self.tar_joint_list_face = joints_list["beat_smplx_face"] - self.tar_joint_list_upper = joints_list["beat_smplx_upper"] - self.tar_joint_list_hands = joints_list["beat_smplx_hands"] - self.tar_joint_list_lower = joints_list["beat_smplx_lower"] - - self.joint_mask_face = np.zeros(len(list(self.ori_joint_list.keys()))*3) - self.joints = 55 - for joint_name in self.tar_joint_list_face: - self.joint_mask_face[self.ori_joint_list[joint_name][1] - self.ori_joint_list[joint_name][0]:self.ori_joint_list[joint_name][1]] = 1 - self.joint_mask_upper = np.zeros(len(list(self.ori_joint_list.keys()))*3) - for joint_name in self.tar_joint_list_upper: - self.joint_mask_upper[self.ori_joint_list[joint_name][1] - self.ori_joint_list[joint_name][0]:self.ori_joint_list[joint_name][1]] = 1 - self.joint_mask_hands = np.zeros(len(list(self.ori_joint_list.keys()))*3) - for joint_name in self.tar_joint_list_hands: - self.joint_mask_hands[self.ori_joint_list[joint_name][1] - self.ori_joint_list[joint_name][0]:self.ori_joint_list[joint_name][1]] = 1 - self.joint_mask_lower = np.zeros(len(list(self.ori_joint_list.keys()))*3) - for joint_name in self.tar_joint_list_lower: - self.joint_mask_lower[self.ori_joint_list[joint_name][1] - self.ori_joint_list[joint_name][0]:self.ori_joint_list[joint_name][1]] = 1 - - self.tracker = other_tools_hf.EpochTracker(["fid", "l1div", "bc", "rec", "trans", "vel", "transv", 'dis', 'gen', 'acc', 'transa', 'exp', 'lvd', 'mse', "cls", "rec_face", "latent", "cls_full", "cls_self", "cls_word", "latent_word","latent_self"], [False,True,True, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False,False,False,False]) - - vq_model_module = __import__(f"models.motion_representation", fromlist=["something"]) - self.args.vae_layer = 2 - self.args.vae_length = 256 - self.args.vae_test_dim = 106 - self.vq_model_face = getattr(vq_model_module, "VQVAEConvZero")(self.args).cpu() - # print(self.vq_model_face) - # other_tools_hf.load_checkpoints(self.vq_model_face, self.args.data_path_1 + "pretrained_vq/last_790_face_v2.bin", args.e_name) - self.args.vae_test_dim = 78 - self.vq_model_upper = getattr(vq_model_module, "VQVAEConvZero")(self.args).cpu() - # other_tools_hf.load_checkpoints(self.vq_model_upper, self.args.data_path_1 + "pretrained_vq/upper_vertex_1layer_710.bin", args.e_name) - self.args.vae_test_dim = 180 - self.vq_model_hands = getattr(vq_model_module, "VQVAEConvZero")(self.args).cpu() - # other_tools_hf.load_checkpoints(self.vq_model_hands, self.args.data_path_1 + "pretrained_vq/hands_vertex_1layer_710.bin", args.e_name) - self.args.vae_test_dim = 61 - self.args.vae_layer = 4 - self.vq_model_lower = getattr(vq_model_module, "VQVAEConvZero")(self.args).cpu() - # other_tools_hf.load_checkpoints(self.vq_model_lower, self.args.data_path_1 + "pretrained_vq/lower_foot_600.bin", args.e_name) - self.args.vae_test_dim = 61 - self.args.vae_layer = 4 - self.global_motion = getattr(vq_model_module, "VAEConvZero")(self.args).cpu() - # other_tools_hf.load_checkpoints(self.global_motion, self.args.data_path_1 + "pretrained_vq/last_1700_foot.bin", args.e_name) - self.args.vae_test_dim = 330 - self.args.vae_layer = 4 - self.args.vae_length = 240 - - # self.cls_loss = nn.NLLLoss().to(self.rank) - # self.reclatent_loss = nn.MSELoss().to(self.rank) - # self.vel_loss = torch.nn.L1Loss(reduction='mean').to(self.rank) - # self.rec_loss = get_loss_func("GeodesicLoss").to(self.rank) - self.log_softmax = nn.LogSoftmax(dim=2) - - - def inverse_selection(self, filtered_t, selection_array, n): - original_shape_t = np.zeros((n, selection_array.size)) - selected_indices = np.where(selection_array == 1)[0] - for i in range(n): - original_shape_t[i, selected_indices] = filtered_t[i] - return original_shape_t - - def inverse_selection_tensor(self, filtered_t, selection_array, n): - selection_array = torch.from_numpy(selection_array).cuda() - original_shape_t = torch.zeros((n, 165)).cuda() - selected_indices = torch.where(selection_array == 1)[0] - for i in range(n): - original_shape_t[i, selected_indices] = filtered_t[i] - return original_shape_t - - def test_demo(self, epoch): - ''' - input audio and text, output motion - do not calculate loss and metric - save video - ''' - results_save_path = self.checkpoint_path + f"/{epoch}/" - if os.path.exists(results_save_path): - import shutil - shutil.rmtree(results_save_path) - os.makedirs(results_save_path) - start_time = time.time() - total_length = 0 - test_seq_list = self.test_data.selected_file - align = 0 - latent_out = [] - latent_ori = [] - l2_all = 0 - lvel = 0 - for its, batch_data in enumerate(self.test_loader): - tar_pose, rec_pose, tar_exps, tar_beta, rec_trans, tar_trans, rec_exps, bs, n, j = test_demo_gpu( - self.model, self.vq_model_face, self.vq_model_upper, self.vq_model_hands, self.vq_model_lower, self.global_motion, self.smplx, - batch_data, - self.args, - self.joints, self.joint_mask_upper, self.joint_mask_lower, self.joint_mask_hands, - self.log_softmax, - ) - - tar_pose_np = tar_pose.detach().cpu().numpy() - rec_pose_np = rec_pose.detach().cpu().numpy() - rec_trans_np = rec_trans.detach().cpu().numpy().reshape(bs*n, 3) - rec_exp_np = rec_exps.detach().cpu().numpy().reshape(bs*n, 100) - tar_exp_np = tar_exps.detach().cpu().numpy().reshape(bs*n, 100) - tar_trans_np = tar_trans.detach().cpu().numpy().reshape(bs*n, 3) - #''' - # its = 0 - gt_npz = np.load(self.args.data_path+self.args.pose_rep +"/"+test_seq_list.iloc[its]['id']+".npz", allow_pickle=True) - np.savez(results_save_path+"gt_"+test_seq_list.iloc[its]['id']+'.npz', - betas=gt_npz["betas"], - poses=tar_pose_np, - expressions=tar_exp_np, - trans=tar_trans_np, - model='smplx2020', - gender='neutral', - mocap_frame_rate = 30, - ) - np.savez(results_save_path+"res_"+test_seq_list.iloc[its]['id']+'.npz', - betas=gt_npz["betas"], - poses=rec_pose_np, - expressions=rec_exp_np, - trans=rec_trans_np, - model='smplx2020', - gender='neutral', - mocap_frame_rate = 30, - ) - - total_length += n - # render_vid_path = other_tools_hf.render_one_sequence_no_gt( - # results_save_path+"res_"+test_seq_list.iloc[its]['id']+'.npz', - # # results_save_path+"gt_"+test_seq_list.iloc[its]['id']+'.npz', - # results_save_path, - # self.audio_path, - # self.args.data_path_1+"smplx_models/", - # use_matplotlib = False, - # args = self.args, - # ) - render_vid_path = other_tools_hf.render_one_sequence_with_face( - results_save_path+"res_"+test_seq_list.iloc[its]['id']+'.npz', - results_save_path+"gt_"+test_seq_list.iloc[its]['id']+'.npz', - results_save_path, - self.audio_path, - self.args.data_path_1+"smplx_models/", - use_matplotlib = False, - args = self.args, - ) - result = [ - gr.Video(value=render_vid_path, visible=True), - gr.File(value=results_save_path+"res_"+test_seq_list.iloc[its]['id']+'.npz', visible=True), - ] - - end_time = time.time() - start_time - logger.info(f"total inference time: {int(end_time)} s for {int(total_length/self.args.pose_fps)} s motion") - return result - - -@logger.catch -def emage(audio_path): - smplx_path = None - text_path = None - rank = 0 - world_size = 1 - args = config.parse_args() - #os.environ['TRANSFORMERS_CACHE'] = args.data_path_1 + "hub/" - if not sys.warnoptions: - warnings.simplefilter("ignore") - # dist.init_process_group(backend="gloo", rank=rank, world_size=world_size) - - #logger_tools.set_args_and_logger(args, rank) - other_tools_hf.set_random_seed(args) - other_tools_hf.print_exp_info(args) - - # return one intance of trainer - trainer = BaseTrainer(args, sp = smplx_path, ap = audio_path, tp = text_path) - result = trainer.test_demo(999) - return result + motion_pred = all_pred["motion_axis_angle"] + t = motion_pred.shape[1] + motion_pred = motion_pred.cpu().numpy().reshape(t, -1) + face_pred = all_pred["expression"].cpu().numpy().reshape(t, -1) + trans_pred = all_pred["trans"].cpu().numpy().reshape(t, -1) + npz_path = os.path.join(save_folder, "emage_output.npz") + beat_format_save(npz_path, motion_pred, upsample=30 // pose_fps, expressions=face_pred, trans=trans_pred) + return npz_path + + +def inference_app(audio, model_type, render_mesh=False, render_face=False, render_mesh_face=False): + if audio is None: + return [None, None, None, None, None] + + sr_in, audio_data = audio + # --- TRUNCATE to 60 seconds if longer --- + max_len = int(60 * sr_in) + if len(audio_data) > max_len: + audio_data = audio_data[:max_len] + # ---------------------------------------- + + tmp_audio_path = os.path.join(save_folder, "tmp_input.wav") + sf.write(tmp_audio_path, audio_data, sr_in) + + if model_type == "CaMN (Upper only)": + sr_model, pose_fps, seed_frames = model_camn.cfg.audio_sr, model_camn.cfg.pose_fps, model_camn.cfg.seed_frames + npz_path = inference_camn(tmp_audio_path, sr_model, pose_fps, seed_frames) + elif model_type == "DisCo (Upper only)": + sr_model, pose_fps, seed_frames = model_disco.cfg.audio_sr, model_disco.cfg.pose_fps, model_disco.cfg.seed_frames + npz_path = inference_disco(tmp_audio_path, sr_model, pose_fps, seed_frames) + else: + sr_model, pose_fps = model_emage.cfg.audio_sr, model_emage.cfg.pose_fps + npz_path = inference_emage(tmp_audio_path, sr_model, pose_fps) + + motion_dict = np.load(npz_path, allow_pickle=True) + v2d_body = render2d(motion_dict, (720, 480), face_only=False, remove_global=True) + out_2d_body = npz_path.replace(".npz", "_2dbody.mp4") + write_video(out_2d_body, v2d_body.permute(0, 2, 3, 1), fps=30) + final_2d_body = out_2d_body.replace(".mp4", "_audio.mp4") + fast_render.add_audio_to_video(out_2d_body, tmp_audio_path, final_2d_body) + + final_mesh_video = None + final_meshface_video = None + if render_mesh: + mesh_vid = fast_render.render_one_sequence_no_gt( + npz_path, save_folder, tmp_audio_path, "./emage_evaltools/smplx_models/" + ) + final_mesh_video = mesh_vid -examples = [ - ["./EMAGE/test_sequences/wave16k/2_scott_0_1_1.wav"], - ["./EMAGE/test_sequences/wave16k/2_scott_0_2_2.wav"], - ["./EMAGE/test_sequences/wave16k/2_scott_0_3_3.wav"], + if render_mesh_face and render_mesh: + meshface_vid = fast_render.render_one_sequence_face_only( + npz_path, save_folder, tmp_audio_path, "./emage_evaltools/smplx_models/" + ) + final_meshface_video = meshface_vid + + final_face_video = None + if render_face: + v2d_face = render2d(motion_dict, (720, 480), face_only=True, remove_global=True) + out_2d_face = npz_path.replace(".npz", "_2dface.mp4") + write_video(out_2d_face, v2d_face.permute(0, 2, 3, 1), fps=30) + final_face_video = out_2d_face.replace(".mp4", "_audio.mp4") + fast_render.add_audio_to_video(out_2d_face, tmp_audio_path, final_face_video) + + return [final_2d_body, final_mesh_video, final_face_video, final_meshface_video, npz_path] + +examples_data = [ + ["./examples/audio/2_scott_0_103_103_10s.wav", "DisCo (Upper only)", True, True, True], + ["./examples/audio/2_scott_0_103_103_10s.wav", "CaMN (Upper only)", True, True, True], + ["./examples/audio/2_scott_0_103_103_10s.wav", "EMAGE (Full body + Face)", True, True, True], ] -demo = gr.Interface( - emage, # function - inputs=[ - # gr.File(label="Please upload SMPL-X file with npz format here.", file_types=["npz", "NPZ"]), - gr.Audio(), - # gr.File(label="Please upload textgrid format file here.", file_types=["TextGrid", "Textgrid", "textgrid"]) - ], # input type - outputs=[ - gr.Video(format="mp4", visible=True), - gr.File(label="download motion and visualize in blender"), - ], - title='\ -
\ - EMAGE: Towards Unified Holistic Co-Speech Gesture Generation via Expressive Masked Audio Gesture Modeling
\ - CVPR 2024
\ -
', - description='\ -
\ - Haiyang Liu1*, Zihao Zhu2*, Giorgio Becherini3, Yichen Peng4, Mingyang Su5,
\ - You Zhou, Xuefei Zhe, Naoya Iwamoto, Bo Zheng, Michael J. Black3
\ - (*Equal Contribution)
\ - 1The University of Tokyo, 2Keio University, 4Japan Advanced Institute of Science and Technology,
\ - 3Max Planck Institute for Intelligent Systems, 5Tsinghua University
\ -
\ - ', - article="\ - For appling motion on your avatar: download npz file and blender v3.3 add-on on our project page, then retarget the motion.
\ - Due to the limited resources in this space, we process the first 60s of your uploaded audio，try to develop this space locally for longer motion generation, \[Project Page](https://pantomatrix.github.io/EMAGE/)\ - ", - examples=examples, -) +with gr.Blocks() as demo: + with gr.Column(): + gr.Markdown( + """ +
+
+

EMAGE

+ Generating Face and Body Animation from Speech +
+
+ Project Page +   + +   + Stars +
+
+
+ """ + ) + with gr.Row(): + input_audio = gr.Audio(type="numpy", label="Upload Audio") + with gr.Column(): + model_type = gr.Radio( + choices=["DisCo (Upper only)", "CaMN (Upper only)", "EMAGE (Full body + Face)"], + value="CaMN (Upper only)", + label="Select Model: DisCo/CaMN for Upper, EMAGE for Full Body+Face" + ) + render_face = gr.Checkbox(value=False, label="Render 2D Face Landmark (Fast ~4s for 7s)") + render_mesh = gr.Checkbox(value=False, label="Render Mesh Body (Slow ~1min for 7s)") + render_mesh_face = gr.Checkbox(value=False, label="Render Mesh Face (Extra Slow)") + + btn = gr.Button("Run Inference") + + with gr.Row(): + vid_body = gr.Video(label="2D Body Video") + vid_mesh = gr.Video(label="Mesh Body Video (optional)") + vid_face = gr.Video(label="2D Face Video (optional)") + vid_meshface = gr.Video(label="Mesh Face Video (optional)") + + with gr.Column(): + gr.Markdown("Download Motion NPZ, Use Our [Blender Add-on](https://huggingface.co/datasets/H-Liu1997/BEAT2_Tools/blob/main/smplx_blender_addon_20230921.zip) for Visualization. [Demo](https://github.com/PantoMatrix/PantoMatrix/issues/178) of how to install on blender.") + file_npz = gr.File(label="Motion NPZ") + + btn.click( + fn=inference_app, + inputs=[input_audio, model_type, render_mesh, render_face, render_mesh_face], + outputs=[vid_body, vid_mesh, vid_face, vid_meshface, file_npz] + ) + + gr.Examples( + examples=examples_data, + inputs=[input_audio, model_type, render_mesh, render_face, render_mesh_face], + outputs=[vid_body, vid_mesh, vid_face, vid_meshface, file_npz], + fn=inference_app, + cache_examples=True + ) - if __name__ == "__main__": - os.environ["MASTER_ADDR"]='127.0.0.1' - os.environ["MASTER_PORT"]='8675' - #os.environ["TORCH_DISTRIBUTED_DEBUG"] = "DETAIL" - demo.launch(share=True) \ No newline at end of file + demo.launch(share=True) diff --git a/camn_trainer.py b/camn_trainer.py deleted file mode 100644 index c62fc207d2e58d27669a476ec90cadc10958baad..0000000000000000000000000000000000000000 --- a/camn_trainer.py +++ /dev/null @@ -1,361 +0,0 @@ -import train -import os -import time -import csv -import sys -import warnings -import random -import numpy as np -import time -import pprint -import pickle - -import torch -import torch.nn as nn -import torch.nn.functional as F -from torch.utils.tensorboard import SummaryWriter -from torch.nn.parallel import DistributedDataParallel as DDP -from loguru import logger -import smplx -import librosa - -from utils import config, logger_tools, other_tools, metric -from utils import rotation_conversions as rc -from dataloaders import data_tools -from optimizers.optim_factory import create_optimizer -from optimizers.scheduler_factory import create_scheduler -from optimizers.loss_factory import get_loss_func -from scipy.spatial.transform import Rotation - - -class CustomTrainer(train.BaseTrainer): - def __init__(self, args): - super().__init__(args) - self.joints = self.train_data.joints - self.tracker = other_tools.EpochTracker(["fid", "l1div", "bc", "rec", "trans", "vel", "transv", 'dis', 'gen', 'acc', 'transa', 'div_reg', "kl"], [False,True,True, False, False, False, False, False, False, False, False, False, False]) - if not self.args.rot6d: #"rot6d" not in args.pose_rep: - logger.error(f"this script is for rot6d, your pose rep. is {args.pose_rep}") - self.rec_loss = get_loss_func("GeodesicLoss").to(self.rank) - self.vel_loss = torch.nn.L1Loss(reduction='mean').to(self.rank) - - def _load_data(self, dict_data): - tar_pose = dict_data["pose"].to(self.rank) - tar_trans = dict_data["trans"].to(self.rank) - tar_exps = dict_data["facial"].to(self.rank) - tar_beta = dict_data["beta"].to(self.rank) - tar_id = dict_data["id"].to(self.rank).long() - tar_word = dict_data["word"].to(self.rank) - in_audio = dict_data["audio"].to(self.rank) - in_emo = dict_data["emo"].to(self.rank) - #in_sem = dict_data["sem"].to(self.rank) - bs, n, j = tar_pose.shape[0], tar_pose.shape[1], self.joints - tar_pose = rc.axis_angle_to_matrix(tar_pose.reshape(bs, n, j, 3)) - tar_pose = rc.matrix_to_rotation_6d(tar_pose).reshape(bs, n, j*6) - in_pre_pose_cat = torch.cat([tar_pose[:, 0:self.args.pre_frames], tar_trans[:, :self.args.pre_frames]], dim=2).to(self.rank) - - in_pre_pose = tar_pose.new_zeros((bs, n, j*6+1+3)).to(self.rank) - in_pre_pose[:, 0:self.args.pre_frames, :-1] = in_pre_pose_cat[:, 0:self.args.pre_frames] - in_pre_pose[:, 0:self.args.pre_frames, -1] = 1 - return { - "tar_pose": tar_pose, - "in_audio": in_audio, - "in_motion": in_pre_pose, - "tar_trans": tar_trans, - "tar_exps": tar_exps, - "tar_beta": tar_beta, - "tar_word": tar_word, - 'tar_id': tar_id, - 'in_emo': in_emo, - #'in_sem': in_sem, - } - - def _d_training(self, loaded_data): - bs, n, j = loaded_data["tar_pose"].shape[0], loaded_data["tar_pose"].shape[1], self.joints - net_out = self.model(in_audio = loaded_data['in_audio'], pre_seq = loaded_data["in_motion"], in_text=loaded_data["tar_word"], in_id=loaded_data["tar_id"], in_emo=loaded_data["in_emo"], in_facial = loaded_data["tar_exps"]) - rec_pose = net_out["rec_pose"][:, :, :j*6] - # rec_trans = net_out["rec_pose"][:, :, j*6:j*6+3] - - rec_pose = rec_pose.reshape(bs, n, j, 6) - rec_pose = rc.rotation_6d_to_matrix(rec_pose) - rec_pose = rc.matrix_to_rotation_6d(rec_pose).reshape(bs, n, j*6) - tar_pose = rc.rotation_6d_to_matrix(loaded_data["tar_pose"].reshape(bs, n, j, 6)) - tar_pose = rc.matrix_to_rotation_6d(tar_pose).reshape(bs, n, j*6) - out_d_fake = self.d_model(rec_pose) - out_d_real = self.d_model(tar_pose) - - d_loss_adv = torch.sum(-torch.mean(torch.log(out_d_real + 1e-8) + torch.log(1 - out_d_fake + 1e-8))) - self.tracker.update_meter("dis", "train", d_loss_adv.item()) - return d_loss_adv - - def _g_training(self, loaded_data, use_adv, mode="train"): - bs, n, j = loaded_data["tar_pose"].shape[0], loaded_data["tar_pose"].shape[1], self.joints - net_out = self.model(in_audio = loaded_data['in_audio'], pre_seq = loaded_data["in_motion"], in_text=loaded_data["tar_word"], in_id=loaded_data["tar_id"], in_emo=loaded_data["in_emo"], in_facial = loaded_data["tar_exps"]) - rec_pose = net_out["rec_pose"][:, :, :j*6] - rec_trans = net_out["rec_pose"][:, :, j*6:j*6+3] - # print(rec_pose.shape, bs, n, j, loaded_data['in_audio'].shape, loaded_data["in_motion"].shape) - rec_pose = rec_pose.reshape(bs, n, j, 6) - rec_pose = rc.rotation_6d_to_matrix(rec_pose) - tar_pose = rc.rotation_6d_to_matrix(loaded_data["tar_pose"].reshape(bs, n, j, 6)) - - rec_loss = self.rec_loss(tar_pose, rec_pose) - rec_loss *= self.args.rec_weight - self.tracker.update_meter("rec", mode, rec_loss.item()) - # rec_loss_vel = self.vel_loss(rec_pose[:, 1:] - rec_pose[:, :-1], tar_pose[:, 1:] - tar_pose[:, :-1]) - # self.tracker.update_meter("vel", mode, rec_loss_vel.item()) - # rec_loss_acc = self.vel_loss(rec_pose[:, 2:] - 2*rec_pose[:, 1:-1] + rec_pose[:, :-2], tar_pose[:, 2:] - 2*tar_pose[:, 1:-1] + tar_pose[:, :-2]) - # self.tracker.update_meter("acc", mode, rec_loss_acc.item()) - - rec_pose = rc.matrix_to_rotation_6d(rec_pose).reshape(bs, n, j*6) - tar_pose = rc.matrix_to_rotation_6d(tar_pose).reshape(bs, n, j*6) - if self.args.pose_dims < 330 and mode != "train": - rec_pose = rc.rotation_6d_to_matrix(rec_pose.reshape(bs, n, j, 6)) - rec_pose = rc.matrix_to_axis_angle(rec_pose).reshape(bs, n, j*3) - rec_pose = self.inverse_selection_tensor(rec_pose, self.train_data.joint_mask, rec_pose.shape[0]) - rec_pose = rc.axis_angle_to_matrix(rec_pose.reshape(bs, n, 55, 3)) - rec_pose = rc.matrix_to_rotation_6d(rec_pose).reshape(bs, n, 55*6) - - tar_pose = rc.rotation_6d_to_matrix(tar_pose.reshape(bs, n, j, 6)) - tar_pose = rc.matrix_to_axis_angle(tar_pose).reshape(bs, n, j*3) - tar_pose = self.inverse_selection_tensor(tar_pose, self.train_data.joint_mask, tar_pose.shape[0]) - tar_pose = rc.axis_angle_to_matrix(tar_pose.reshape(bs, n, 55, 3)) - tar_pose = rc.matrix_to_rotation_6d(tar_pose).reshape(bs, n, 55*6) - if use_adv and mode == 'train': - out_d_fake = self.d_model(rec_pose) - d_loss_adv = -torch.mean(torch.log(out_d_fake + 1e-8)) - self.tracker.update_meter("gen", mode, d_loss_adv.item()) - else: - d_loss_adv = 0 - - if self.args.train_trans: - trans_loss = self.vel_loss(rec_trans, loaded_data["tar_trans"]) - trans_loss *= self.args.rec_weight - self.tracker.update_meter("trans", mode, trans_loss.item()) - else: - trans_loss = 0 - # trans_loss_vel = self.vel_loss(rec_trans[:, 1:] - rec_trans[:, :-1], loaded_data["tar_trans"][:, 1:] - loaded_data["tar_trans"][:, :-1]) - # self.tracker.update_meter("transv", mode, trans_loss_vel.item()) - # trans_loss_acc = self.vel_loss(rec_trans[:, 2:] - 2*rec_trans[:, 1:-1] + rec_trans[:, :-2], loaded_data["tar_trans"][:, 2:] - 2*loaded_data["tar_trans"][:, 1:-1] + loaded_data["tar_trans"][:, :-2]) - # self.tracker.update_meter("transa", mode, trans_loss_acc.item()) - - if mode == 'train': - return d_loss_adv + rec_loss + trans_loss # + rec_loss_vel + rec_loss_acc + trans_loss_vel + trans_loss_acc - elif mode == 'val': - return { - 'rec_pose': rec_pose, - 'rec_trans': rec_trans, - 'tar_pose': tar_pose, - } - else: - return { - 'rec_pose': rec_pose, - 'rec_trans': rec_trans, - 'tar_pose': tar_pose, - 'tar_exps': loaded_data["tar_exps"], - 'tar_beta': loaded_data["tar_beta"], - 'tar_trans': loaded_data["tar_trans"], - } - - def train(self, epoch): - use_adv = bool(epoch>=self.args.no_adv_epoch) - self.model.train() - self.d_model.train() - self.tracker.reset() - t_start = time.time() - for its, batch_data in enumerate(self.train_loader): - loaded_data = self._load_data(batch_data) - t_data = time.time() - t_start - - if use_adv: - d_loss_final = 0 - self.opt_d.zero_grad() - d_loss_adv = self._d_training(loaded_data) - d_loss_final += d_loss_adv - d_loss_final.backward() - self.opt_d.step() - - self.opt.zero_grad() - g_loss_final = 0 - g_loss_final += self._g_training(loaded_data, use_adv, 'train') - g_loss_final.backward() - self.opt.step() - - mem_cost = torch.cuda.memory_cached() / 1E9 - lr_g = self.opt.param_groups[0]['lr'] - lr_d = self.opt_d.param_groups[0]['lr'] - t_train = time.time() - t_start - t_data - t_start = time.time() - if its % self.args.log_period == 0: - self.train_recording(epoch, its, t_data, t_train, mem_cost, lr_g, lr_d=lr_d) - if self.args.debug: - if its == 1: break - self.opt_s.step(epoch) - self.opt_d_s.step(epoch) - - - def val(self, epoch): - self.model.eval() - self.d_model.eval() - with torch.no_grad(): - for its, batch_data in enumerate(self.train_loader): - loaded_data = self._load_data(batch_data) - net_out = self._g_training(loaded_data, False, 'val') - tar_pose = net_out['tar_pose'] - rec_pose = net_out['rec_pose'] - n = tar_pose.shape[1] - if (30/self.args.pose_fps) != 1: - assert 30%self.args.pose_fps == 0 - n *= int(30/self.args.pose_fps) - tar_pose = torch.nn.functional.interpolate(tar_pose.permute(0, 2, 1), scale_factor=30/self.args.pose_fps, mode='linear').permute(0,2,1) - rec_pose = torch.nn.functional.interpolate(rec_pose.permute(0, 2, 1), scale_factor=30/self.args.pose_fps, mode='linear').permute(0,2,1) - n = tar_pose.shape[1] - remain = n%self.args.vae_test_len - tar_pose = tar_pose[:, :n-remain, :] - rec_pose = rec_pose[:, :n-remain, :] - latent_out = self.eval_copy.map2latent(rec_pose).reshape(-1, self.args.vae_length).cpu().numpy() - latent_ori = self.eval_copy.map2latent(tar_pose).reshape(-1, self.args.vae_length).cpu().numpy() - if its == 0: - latent_out_motion_all = latent_out - latent_ori_all = latent_ori - else: - latent_out_motion_all = np.concatenate([latent_out_motion_all, latent_out], axis=0) - latent_ori_all = np.concatenate([latent_ori_all, latent_ori], axis=0) - if self.args.debug: - if its == 1: break - fid_motion = data_tools.FIDCalculator.frechet_distance(latent_out_motion_all, latent_ori_all) - self.tracker.update_meter("fid", "val", fid_motion) - self.val_recording(epoch) - - def test(self, epoch): - results_save_path = self.checkpoint_path + f"/{epoch}/" - if os.path.exists(results_save_path): - return 0 - os.makedirs(results_save_path) - start_time = time.time() - total_length = 0 - test_seq_list = self.test_data.selected_file - align = 0 - latent_out = [] - latent_ori = [] - self.model.eval() - self.smplx.eval() - self.eval_copy.eval() - with torch.no_grad(): - for its, batch_data in enumerate(self.test_loader): - loaded_data = self._load_data(batch_data) - net_out = self._g_training(loaded_data, False, 'test') - tar_pose = net_out['tar_pose'] - rec_pose = net_out['rec_pose'] - tar_exps = net_out['tar_exps'] - tar_beta = net_out['tar_beta'] - rec_trans = net_out['rec_trans'] - tar_trans = net_out['tar_trans'] - bs, n, j = tar_pose.shape[0], tar_pose.shape[1], 55 - if (30/self.args.pose_fps) != 1: - assert 30%self.args.pose_fps == 0 - n *= int(30/self.args.pose_fps) - tar_pose = torch.nn.functional.interpolate(tar_pose.permute(0, 2, 1), scale_factor=30/self.args.pose_fps, mode='linear').permute(0,2,1) - rec_pose = torch.nn.functional.interpolate(rec_pose.permute(0, 2, 1), scale_factor=30/self.args.pose_fps, mode='linear').permute(0,2,1) - tar_beta = torch.nn.functional.interpolate(tar_beta.permute(0, 2, 1), scale_factor=30/self.args.pose_fps, mode='linear').permute(0,2,1) - tar_exps = torch.nn.functional.interpolate(tar_exps.permute(0, 2, 1), scale_factor=30/self.args.pose_fps, mode='linear').permute(0,2,1) - tar_trans = torch.nn.functional.interpolate(tar_trans.permute(0, 2, 1), scale_factor=30/self.args.pose_fps, mode='linear').permute(0,2,1) - rec_trans = torch.nn.functional.interpolate(rec_trans.permute(0, 2, 1), scale_factor=30/self.args.pose_fps, mode='linear').permute(0,2,1) - - # print(rec_pose.shape, tar_pose.shape) - # rec_pose = rc.rotation_6d_to_matrix(rec_pose.reshape(bs*n, j, 6)) - # rec_pose = rc.matrix_to_rotation_6d(rec_pose).reshape(bs, n, j*6) - # tar_pose = rc.rotation_6d_to_matrix(tar_pose.reshape(bs*n, j, 6)) - # tar_pose = rc.matrix_to_rotation_6d(tar_pose).reshape(bs, n, j*6) - remain = n%self.args.vae_test_len - latent_out.append(self.eval_copy.map2latent(rec_pose[:, :n-remain]).reshape(-1, self.args.vae_length).detach().cpu().numpy()) # bs * n/8 * 240 - latent_ori.append(self.eval_copy.map2latent(tar_pose[:, :n-remain]).reshape(-1, self.args.vae_length).detach().cpu().numpy()) - - rec_pose = rc.rotation_6d_to_matrix(rec_pose.reshape(bs*n, j, 6)) - rec_pose = rc.matrix_to_axis_angle(rec_pose).reshape(bs*n, j*3) - tar_pose = rc.rotation_6d_to_matrix(tar_pose.reshape(bs*n, j, 6)) - tar_pose = rc.matrix_to_axis_angle(tar_pose).reshape(bs*n, j*3) - - vertices_rec = self.smplx( - betas=tar_beta.reshape(bs*n, 300), - transl=rec_trans.reshape(bs*n, 3)-rec_trans.reshape(bs*n, 3), - expression=tar_exps.reshape(bs*n, 100)-tar_exps.reshape(bs*n, 100), - jaw_pose=rec_pose[:, 66:69], - global_orient=rec_pose[:,:3], - body_pose=rec_pose[:,3:21*3+3], - left_hand_pose=rec_pose[:,25*3:40*3], - right_hand_pose=rec_pose[:,40*3:55*3], - return_joints=True, - leye_pose=rec_pose[:, 69:72], - reye_pose=rec_pose[:, 72:75], - ) - # vertices_tar = self.smplx( - # betas=tar_beta.reshape(bs*n, 300), - # transl=rec_trans.reshape(bs*n, 3)-rec_trans.reshape(bs*n, 3), - # expression=tar_exps.reshape(bs*n, 100)-tar_exps.reshape(bs*n, 100), - # jaw_pose=tar_pose[:, 66:69], - # global_orient=tar_pose[:,:3], - # body_pose=tar_pose[:,3:21*3+3], - # left_hand_pose=tar_pose[:,25*3:40*3], - # right_hand_pose=tar_pose[:,40*3:55*3], - # return_joints=True, - # leye_pose=tar_pose[:, 69:72], - # reye_pose=tar_pose[:, 72:75], - # ) - joints_rec = vertices_rec["joints"].detach().cpu().numpy().reshape(1, n, 127*3)[0, :n, :55*3] - # joints_tar = vertices_tar["joints"].detach().cpu().numpy().reshape(1, n, 127*3)[0, :n, :55*3] - _ = self.l1_calculator.run(joints_rec) - if self.alignmenter is not None: - in_audio_eval, sr = librosa.load(self.args.data_path+"wave16k/"+test_seq_list.iloc[its]['id']+".wav") - in_audio_eval = librosa.resample(in_audio_eval, orig_sr=sr, target_sr=self.args.audio_sr) - a_offset = int(self.align_mask * (self.args.audio_sr / self.args.pose_fps)) - onset_bt = self.alignmenter.load_audio(in_audio_eval[:int(self.args.audio_sr / self.args.pose_fps*n)], a_offset, len(in_audio_eval)-a_offset, True) - beat_vel = self.alignmenter.load_pose(joints_rec, self.align_mask, n-self.align_mask, 30, True) - # print(beat_vel) - align += (self.alignmenter.calculate_align(onset_bt, beat_vel, 30) * (n-2*self.align_mask)) - - tar_pose_axis_np = tar_pose.detach().cpu().numpy() - rec_pose_axis_np = rec_pose.detach().cpu().numpy() - rec_trans_np = rec_trans.detach().cpu().numpy().reshape(bs*n, 3) - rec_exp_np = tar_exps.detach().cpu().numpy().reshape(bs*n, 100) - tar_exps.detach().cpu().numpy().reshape(bs*n, 100) - tar_exp_np = tar_exps.detach().cpu().numpy().reshape(bs*n, 100) - tar_exps.detach().cpu().numpy().reshape(bs*n, 100) - tar_trans_np = tar_trans.detach().cpu().numpy().reshape(bs*n, 3) - gt_npz = np.load(self.args.data_path+self.args.pose_rep +"/"+test_seq_list.iloc[its]['id']+".npz", allow_pickle=True) - if not self.args.train_trans: - tar_trans_np = tar_trans_np - tar_trans_np - rec_trans_np = rec_trans_np - rec_trans_np - np.savez(results_save_path+"gt_"+test_seq_list.iloc[its]['id']+'.npz', - betas=gt_npz["betas"], - poses=tar_pose_axis_np, - expressions=tar_exp_np, - trans=tar_trans_np, - model='smplx2020', - gender='neutral', - mocap_frame_rate = 30 , - ) - np.savez(results_save_path+"res_"+test_seq_list.iloc[its]['id']+'.npz', - betas=gt_npz["betas"], - poses=rec_pose_axis_np, - expressions=rec_exp_np, - trans=rec_trans_np, - model='smplx2020', - gender='neutral', - mocap_frame_rate = 30, - ) - total_length += n - - latent_out_all = np.concatenate(latent_out, axis=0) - latent_ori_all = np.concatenate(latent_ori, axis=0) - fid = data_tools.FIDCalculator.frechet_distance(latent_out_all, latent_ori_all) - logger.info(f"fid score: {fid}") - self.test_recording("fid", fid, epoch) - - align_avg = align/(total_length-2*len(self.test_loader)*self.align_mask) - logger.info(f"align score: {align_avg}") - self.test_recording("bc", align_avg, epoch) - - l1div = self.l1_calculator.avg() - logger.info(f"l1div score: {l1div}") - self.test_recording("l1div", l1div, epoch) - - # data_tools.result2target_vis(self.args.pose_version, results_save_path, results_save_path, self.test_demo, False) - end_time = time.time() - start_time - logger.info(f"total inference time: {int(end_time)} s for {int(total_length/self.args.pose_fps)} s motion") \ No newline at end of file diff --git a/configs/.ipynb_checkpoints/emage_test_hf-checkpoint.yaml b/configs/.ipynb_checkpoints/emage_test_hf-checkpoint.yaml deleted file mode 100644 index 564aaf431479e2d25825507d2819b8e247e857a1..0000000000000000000000000000000000000000 --- a/configs/.ipynb_checkpoints/emage_test_hf-checkpoint.yaml +++ /dev/null @@ -1,101 +0,0 @@ -is_train: True -ddp: False -stat: ts -root_path: ./ -out_path: ./outputs/audio2pose/ -project: s2g -data_path: ./EMAGE/test_sequences/ -e_path: weights/AESKConv_240_100.bin -eval_model: motion_representation -e_name: VAESKConv -test_ckpt: ./EMAGE/emage_audio_175.bin -data_path_1: ./EMAGE/ -vae_test_len: 32 -vae_test_dim: 330 -vae_test_stride: 20 -vae_length: 240 -vae_codebook_size: 256 -vae_layer: 4 -vae_grow: [1,1,2,1] -variational: False - -# data config -training_speakers: [2] #[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30] -additional_data: False -cache_path: ./datasets/beat_cache/beat_smplx_en_emage_test/ -dataset: beat_testonly_hf -new_cache: True - -# motion config -ori_joints: beat_smplx_joints -tar_joints: beat_smplx_full -pose_rep: smplxflame_30 -pose_norm: False -pose_fps: 30 -rot6d: True -pre_frames: 4 -pose_dims: 330 -pose_length: 64 -stride: 20 -test_length: 64 -motion_f: 256 -m_pre_encoder: null -m_encoder: null -m_fix_pre: False - -# audio config -audio_rep: wave16k -audio_sr: 16000 -audio_fps: 16000 -audio_norm: False -audio_f: 256 -# a_pre_encoder: tcn_camn -# a_encoder: none -# a_fix_pre: False - -# text config -# word_rep: textgrid -# word_index_num: 11195 -# word_dims: 300 -# freeze_wordembed: False -# word_f: 256 -# t_pre_encoder: fasttext -# t_encoder: null -# t_fix_pre: False - -# facial config -facial_rep: smplxflame_30 -facial_dims: 100 -facial_norm: False -facial_f: 0 -f_pre_encoder: null -f_encoder: null -f_fix_pre: False - -# speaker config -id_rep: onehot -speaker_f: 0 - -# model config -batch_size: 64 -# warmup_epochs: 1 -# warmup_lr: 1e-6 -lr_base: 5e-4 -model: emage_audio -g_name: MAGE_Transformer -trainer: emage -hidden_size: 768 -n_layer: 1 - -rec_weight: 1 -grad_norm: 0.99 -epochs: 400 -test_period: 20 -ll: 3 -lf: 3 -lu: 3 -lh: 3 -cl: 1 -cf: 0 -cu: 1 -ch: 1 diff --git a/configs/camn.yaml b/configs/camn.yaml deleted file mode 100644 index 177f4864bc8a5d8c1bb50f89675f463646133342..0000000000000000000000000000000000000000 --- a/configs/camn.yaml +++ /dev/null @@ -1,101 +0,0 @@ -is_train: True -ddp: False -stat: ts -root_path: ./ -out_path: ./outputs/audio2pose/ -project: s2g -data_path: ./BEAT2/beat_english_v2.0.0/ -e_path: weights/AESKConv_240_100.bin -eval_model: motion_representation -e_name: VAESKConv -test_ckpt: ./EMAGE/camn.bin -data_path_1: ./EMAGE/ -vae_test_len: 64 -vae_test_dim: 330 -vae_test_stride: 20 -vae_length: 240 -vae_codebook_size: 256 -vae_layer: 4 -vae_grow: [1,1,2,1] -variational: False - -# data config -training_speakers: [2] #[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30] -additional_data: False -cache_path: datasets/beat_cache/beat_smplx_en_camn/ -dataset: beat_sep -new_cache: False - -# motion config -ori_joints: beat_smplx_joints -tar_joints: beat_smplx_full -pose_rep: smplxflame_30 -pose_norm: False -pose_fps: 15 -rot6d: True -pre_frames: 4 -pose_dims: 330 -pose_length: 32 -stride: 10 -test_length: 32 -motion_f: 256 -m_pre_encoder: null -m_encoder: null -m_fix_pre: False - -# audio config -audio_rep: wave16k -audio_sr: 16000 -audio_fps: 16000 -audio_norm: False -audio_f: 128 -# a_pre_encoder: tcn_camn -# a_encoder: none -# a_fix_pre: False - -# text config -word_rep: textgrid -word_index_num: 11195 -word_dims: 300 -freeze_wordembed: False -word_f: 128 -t_pre_encoder: fasttext -t_encoder: null -t_fix_pre: False - -# facial config -facial_rep: smplxflame_30 -facial_dims: 100 -facial_norm: False -facial_f: 64 -f_pre_encoder: null -f_encoder: null -f_fix_pre: False - -# speaker config -id_rep: onehot -speaker_f: 16 -emo_rep: emo -emotion_f: 8 -# sem_rep: sem - - -# model config -batch_size: 128 -# warmup_epochs: 1 -# warmup_lr: 1e-6 -lr_base: 3e-4 -model: camn -g_name: CaMN -d_name: ConvDiscriminator -trainer: camn -hidden_size: 512 -n_layer: 4 -rec_weight: 500 -no_adv_epoch: 999 -# rec_pos_weight: 1 -# rec_ver_weight: 0 -# rec_fac_weight: 1 -# grad_norm: 1 -epochs: 100 -test_period: 20 \ No newline at end of file diff --git a/configs/camn_audio.yaml b/configs/camn_audio.yaml new file mode 100644 index 0000000000000000000000000000000000000000..d4cd9f71943aa193dc948b9c685684afcd8273fa --- /dev/null +++ b/configs/camn_audio.yaml @@ -0,0 +1,71 @@ +wandb_project: 'EMAGE++' +exp_name: 'debug' +debug: False +wandb_entity: '' +wandb_key: "" +# wandb_log_dir: '/content/outputs/wandb' +output_dir: "../outputs/" +log_period: 1 +seed: 42 +resume_from_checkpoint: null +test: False + + +data: + name_pyfile: "datasets.beat2" + class_name: "BEAT2Dataset" + train_bs: 64 + meta_paths: + - "./datasets/data_json/beat2_s20_l128_speaker2.json" + test_meta_paths: + - "./datasets/data_json/beat2_s20_l128_speaker2.json" + pose_norm: False + pose_length: 128 + stride: 20 + test_length: 128 + +model: + name_pyfile: "models.camn_audio.modeling_camn_audio" + class_name: "CamnAudioModel" + pose_fps: 15 + motion_f: 256 + pose_dims: 258 + pose_rep: "smplx" + body_dims: 78 + hands_dims: 180 + audio_rep: wave16k + audio_sr: 16000 + audio_fps: 16000 + audio_norm: False + audio_f: 128 + speaker_f: 16 + speaker_dims: 1 + hidden_size: 512 + n_layer: 4 + dropout_prob: 0.1 + seed_frames: 4 + joint_mask: "local_upper" + +validation: + validation_steps: 500 + test_steps: 500 + visualization: False + evaluation: False + wandb: False + +solver: + gradient_accumulation_steps: 1 + gradient_checkpointing: False + max_train_steps: 100000 + max_grad_norm: 0.0 + # lr + learning_rate: 3e-4 + scale_lr: False + lr_warmup_steps: 0 + lr_scheduler: 'constant' + # optimizer + use_8bit_adam: False + adam_beta1: 0.9 + adam_beta2: 0.999 + adam_weight_decay: 0.0 + adam_epsilon: 1.0e-8 \ No newline at end of file diff --git a/configs/cnn_vqvae_face_30.yaml b/configs/cnn_vqvae_face_30.yaml deleted file mode 100644 index 0f05b872ac57106cb57184397a6da5ebb016dffd..0000000000000000000000000000000000000000 --- a/configs/cnn_vqvae_face_30.yaml +++ /dev/null @@ -1,82 +0,0 @@ -is_train: True -ddp: False -stat: ts -training_speakers: [2] #[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30] -root_path: ./ -out_path: ./outputs/audio2pose/ -cache_path: datasets/beat_cache/beat_smplx_en_face/ -project: mage_smplx -data_path: ./BEAT2/beat_english_v2.0.0/ -e_path: weights/AESKConv_240_100.bin -test_ckpt: weights/multi.bin -data_path_1: ./EMAGE/ -#torch_hub_path: datasets/hub/ -additional_data: False -dataset: beat_sep -new_cache: False -ori_joints: beat_smplx_joints -tar_joints: beat_smplx_face -pose_rep: smplxflame_30 -facial_rep: smplxflame_30 -pose_norm: False -pose_fps: 30 - - -vae_test_len: 64 -vae_test_dim: 106 -vae_test_stride: 20 -vae_length: 256 -vae_codebook_size: 256 -vae_layer: 2 -vae_grow: [1,1,2,1] -variational: False - -pose_dims: 106 -pose_length: 64 -stride: 20 -facial_dims: 100 -word_index_num: 11195 -word_dims: 300 -batch_size: 64 -lr_base: 3e-4 -model: motion_representation -g_name: VQVAEConvZero -#eval_model: motion_autoencoder -#e_name: HalfEmbeddingNet -trainer: aeface -decay_epochs: 780 -# audio_f: 256 -# a_pre_encoder: tcn_camn -# a_encoder: lp -# a_fix_pre: False - -# freeze_wordembed: False -# word_f: 128 -# t_pre_encoder: fasttext -# t_encoder: lp -# t_fix_pre: False - -# motion_f: 256 -# m_pre_encoder: lp -# m_encoder: lp -# m_fix_pre: False - -# facial_f: 128 -# f_pre_encoder: lp -# f_encoder: lp -# f_fix_pre: False - -#m_decoder: lstm -#decode_fusion: cat -#n_layer: 2 -#hidden_size: 512 -rec_weight: 1 -rec_pos_weight: 1 -rec_ver_weight: 1 -# rec_fac_weight: 1 -#ita_weight: 0 -#iwa_weight: 0 -#fusion_mode: sum -# grad_norm: 1 -epochs: 800 -test_period: 100 \ No newline at end of file diff --git a/configs/cnn_vqvae_hands_30.yaml b/configs/cnn_vqvae_hands_30.yaml deleted file mode 100644 index e909d39d618bb89cd19333bb5eb427cfa937fc26..0000000000000000000000000000000000000000 --- a/configs/cnn_vqvae_hands_30.yaml +++ /dev/null @@ -1,81 +0,0 @@ -is_train: True -ddp: False -stat: ts -training_speakers: [2] #[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30] -root_path: ./ -out_path: ./outputs/audio2pose/ -cache_path: datasets/beat_cache/beat_smplx_en_hands/ -project: mage_smplx -data_path: ./BEAT2/beat_english_v2.0.0/ -e_path: weights/AESKConv_240_100.bin -test_ckpt: weights/multi.bin -data_path_1: ./EMAGE/ -#torch_hub_path: datasets/hub/ -additional_data: False -dataset: beat_sep -new_cache: False -ori_joints: beat_smplx_joints -tar_joints: beat_smplx_hands -pose_rep: smplxflame_30 -pose_norm: False -pose_fps: 30 - - -vae_test_len: 64 -vae_test_dim: 180 -vae_test_stride: 20 -vae_length: 256 -vae_codebook_size: 256 -vae_layer: 2 -vae_grow: [1,1,2,1] -variational: False - -pose_dims: 180 -pose_length: 64 -stride: 20 -facial_dims: 100 -word_index_num: 11195 -word_dims: 300 -batch_size: 64 -lr_base: 3e-4 -model: motion_representation -g_name: VQVAEConvZero -#eval_model: motion_autoencoder -#e_name: HalfEmbeddingNet -trainer: ae -decay_epochs: 780 -# audio_f: 256 -# a_pre_encoder: tcn_camn -# a_encoder: lp -# a_fix_pre: False - -# freeze_wordembed: False -# word_f: 128 -# t_pre_encoder: fasttext -# t_encoder: lp -# t_fix_pre: False - -# motion_f: 256 -# m_pre_encoder: lp -# m_encoder: lp -# m_fix_pre: False - -# facial_f: 128 -# f_pre_encoder: lp -# f_encoder: lp -# f_fix_pre: False - -#m_decoder: lstm -#decode_fusion: cat -#n_layer: 2 -#hidden_size: 512 -rec_weight: 1 -rec_pos_weight: 1 -rec_ver_weight: 1 -# rec_fac_weight: 1 -#ita_weight: 0 -#iwa_weight: 0 -#fusion_mode: sum -# grad_norm: 1 -epochs: 800 -test_period: 100 \ No newline at end of file diff --git a/configs/cnn_vqvae_lower_30.yaml b/configs/cnn_vqvae_lower_30.yaml deleted file mode 100644 index b7c61a8cdc9f0ca6e16d13259285b36c9bd83309..0000000000000000000000000000000000000000 --- a/configs/cnn_vqvae_lower_30.yaml +++ /dev/null @@ -1,81 +0,0 @@ -is_train: True -ddp: False -stat: ts -training_speakers: [2] #[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30] -root_path: ./ -out_path: ./outputs/audio2pose/ -cache_path: datasets/beat_cache/beat_smplx_en_lower/ -project: mage_smplx -data_path: ./BEAT2/beat_english_v2.0.0/ -e_path: weights/AESKConv_240_100.bin -test_ckpt: weights/multi.bin -data_path_1: ./EMAGE/ -#torch_hub_path: datasets/hub/ -additional_data: False -dataset: beat_sep_lower -new_cache: False -ori_joints: beat_smplx_joints -tar_joints: beat_smplx_lower -pose_rep: smplxflame_30 -pose_norm: False -pose_fps: 30 - - -vae_test_len: 64 -vae_test_dim: 61 -vae_test_stride: 20 -vae_length: 256 -vae_codebook_size: 256 -vae_layer: 4 -vae_grow: [1,1,2,1] -variational: False - -pose_dims: 61 -pose_length: 64 -stride: 20 -facial_dims: 100 -word_index_num: 11195 -word_dims: 300 -batch_size: 64 -lr_base: 3e-4 -model: motion_representation -g_name: VAEConvZero -#eval_model: motion_autoencoder -#e_name: HalfEmbeddingNet -trainer: aelower -decay_epochs: 780 -# audio_f: 256 -# a_pre_encoder: tcn_camn -# a_encoder: lp -# a_fix_pre: False - -# freeze_wordembed: False -# word_f: 128 -# t_pre_encoder: fasttext -# t_encoder: lp -# t_fix_pre: False - -# motion_f: 256 -# m_pre_encoder: lp -# m_encoder: lp -# m_fix_pre: False - -# facial_f: 128 -# f_pre_encoder: lp -# f_encoder: lp -# f_fix_pre: False - -#m_decoder: lstm -#decode_fusion: cat -#n_layer: 2 -#hidden_size: 512 -rec_weight: 1 -rec_pos_weight: 1 -rec_ver_weight: 1 -# rec_fac_weight: 1 -#ita_weight: 0 -#iwa_weight: 0 -#fusion_mode: sum -# grad_norm: 1 -epochs: 800 -test_period: 100 \ No newline at end of file diff --git a/configs/cnn_vqvae_lower_foot_30.yaml b/configs/cnn_vqvae_lower_foot_30.yaml deleted file mode 100644 index 161befcd0344ee38cff19e7e6a09fa26523b083f..0000000000000000000000000000000000000000 --- a/configs/cnn_vqvae_lower_foot_30.yaml +++ /dev/null @@ -1,81 +0,0 @@ -is_train: True -ddp: False -stat: ts -training_speakers: [2] -root_path: ./ -out_path: ./outputs/audio2pose/ -cache_path: datasets/beat_cache/beat_smplx_en_lower/ -project: mage_smplx -data_path: ./BEAT2/beat_english_v2.0.0/ -e_path: weights/AESKConv_240_100.bin -test_ckpt: weights/multi.bin -data_path_1: ./EMAGE/ -#torch_hub_path: datasets/hub/ -additional_data: False -dataset: beat_sep_lower -new_cache: False -ori_joints: beat_smplx_joints -tar_joints: beat_smplx_lower -pose_rep: smplxflame_30 -pose_norm: False -pose_fps: 30 - - -vae_test_len: 64 -vae_test_dim: 61 -vae_test_stride: 20 -vae_length: 256 -vae_codebook_size: 256 -vae_layer: 4 -vae_grow: [1,1,2,1] -variational: False - -pose_dims: 61 -pose_length: 64 -stride: 20 -facial_dims: 100 -word_index_num: 11195 -word_dims: 300 -batch_size: 64 -lr_base: 3e-4 -model: motion_representation -g_name: VQVAEConvZero -#eval_model: motion_autoencoder -#e_name: HalfEmbeddingNet -trainer: aelowerfoot -decay_epochs: 780 -# audio_f: 256 -# a_pre_encoder: tcn_camn -# a_encoder: lp -# a_fix_pre: False - -# freeze_wordembed: False -# word_f: 128 -# t_pre_encoder: fasttext -# t_encoder: lp -# t_fix_pre: False - -# motion_f: 256 -# m_pre_encoder: lp -# m_encoder: lp -# m_fix_pre: False - -# facial_f: 128 -# f_pre_encoder: lp -# f_encoder: lp -# f_fix_pre: False - -#m_decoder: lstm -#decode_fusion: cat -#n_layer: 2 -#hidden_size: 512 -rec_weight: 1 -rec_pos_weight: 1 -rec_ver_weight: 1 -# rec_fac_weight: 1 -#ita_weight: 0 -#iwa_weight: 0 -#fusion_mode: sum -# grad_norm: 1 -epochs: 800 -test_period: 100 \ No newline at end of file diff --git a/configs/cnn_vqvae_upper_30.yaml b/configs/cnn_vqvae_upper_30.yaml deleted file mode 100644 index db52e20b72ec2f18c81be58bf5414b99f8315fe5..0000000000000000000000000000000000000000 --- a/configs/cnn_vqvae_upper_30.yaml +++ /dev/null @@ -1,82 +0,0 @@ -is_train: True -ddp: False -stat: ts -training_speakers: [2] -root_path: ./ -out_path: ./outputs/audio2pose/ -cache_path: datasets/beat_cache/beat_smplx_en_upper/ -project: mage_smplx -data_path: ./BEAT2/beat_english_v2.0.0/ -e_path: weights/AESKConv_240_100.bin -test_ckpt: weights/multi.bin -data_path_1: ./EMAGE/ -#torch_hub_path: datasets/hub/ -additional_data: False -dataset: beat_sep -new_cache: False -ori_joints: beat_smplx_joints -tar_joints: beat_smplx_upper -pose_rep: smplxflame_30 -pose_norm: False -pose_fps: 30 - - -vae_test_len: 64 -vae_test_dim: 78 -vae_test_stride: 20 -vae_length: 256 -vae_codebook_size: 256 -vae_layer: 2 -vae_grow: [1,1,2,1] -variational: False - -pose_dims: 78 -pose_length: 64 -stride: 20 -facial_dims: 100 -word_index_num: 11195 -word_dims: 300 -batch_size: 64 -lr_base: 3e-4 -decay_epochs: 9999 -model: motion_representation -g_name: VQVAEConvZero -#eval_model: motion_autoencoder -#e_name: HalfEmbeddingNet -trainer: ae - -# audio_f: 256 -# a_pre_encoder: tcn_camn -# a_encoder: lp -# a_fix_pre: False - -# freeze_wordembed: False -# word_f: 128 -# t_pre_encoder: fasttext -# t_encoder: lp -# t_fix_pre: False - -# motion_f: 256 -# m_pre_encoder: lp -# m_encoder: lp -# m_fix_pre: False - -# facial_f: 128 -# f_pre_encoder: lp -# f_encoder: lp -# f_fix_pre: False - -#m_decoder: lstm -#decode_fusion: cat -#n_layer: 2 -#hidden_size: 512 -rec_weight: 1 -rec_pos_weight: 1 -rec_ver_weight: 1 -# rec_fac_weight: 1 -#ita_weight: 0 -#iwa_weight: 0 -#fusion_mode: sum -# grad_norm: 1 -epochs: 500 -test_period: 100 \ No newline at end of file diff --git a/configs/disco_audio.yaml b/configs/disco_audio.yaml new file mode 100644 index 0000000000000000000000000000000000000000..b3d0412a89acdfaafed3ca01adb908c9ce6adc96 --- /dev/null +++ b/configs/disco_audio.yaml @@ -0,0 +1,70 @@ +wandb_project: 'EMAGE++' +exp_name: 'debug' +debug: False +wandb_entity: '' +wandb_key: "" +# wandb_log_dir: '/content/outputs/wandb' +output_dir: "../outputs/" +log_period: 1 +seed: 42 +resume_from_checkpoint: null +test: False + +data: + name_pyfile: "datasets.beat2_disco" + class_name: "BEAT2DatasetDisco" + train_bs: 64 + meta_paths: + - "./datasets/data_json/beat2_s20_l128_speaker2_disco.json" + test_meta_paths: + - "./datasets/data_json/beat2_s20_l128_speaker2.json" + pose_norm: False + pose_length: 128 + stride: 20 + test_length: 128 + +model: + name_pyfile: "models.disco_audio.modeling_disco_audio" + class_name: "DiscoAudioModel" + pose_fps: 15 + motion_f: 256 + pose_dims: 258 + pose_rep: "smplx" + body_dims: 78 + hands_dims: 180 + audio_rep: wave16k + audio_sr: 16000 + audio_fps: 16000 + audio_norm: False + audio_f: 128 + speaker_f: 16 + speaker_dims: 1 + hidden_size: 512 + n_layer: 4 + dropout_prob: 0.1 + seed_frames: 4 + joint_mask: "local_upper" + +validation: + validation_steps: 500 + test_steps: 500 + visualization: False + evaluation: False + wandb: False + +solver: + gradient_accumulation_steps: 1 + gradient_checkpointing: False + max_train_steps: 14500 + max_grad_norm: 0.0 + # lr + learning_rate: 3e-4 + scale_lr: False + lr_warmup_steps: 0 + lr_scheduler: 'constant' + # optimizer + use_8bit_adam: False + adam_beta1: 0.9 + adam_beta2: 0.999 + adam_weight_decay: 0.0 + adam_epsilon: 1.0e-8 \ No newline at end of file diff --git a/configs/emage.yaml b/configs/emage.yaml deleted file mode 100644 index 1160bde10ed93a591ab2fc8903255d08391d9ff7..0000000000000000000000000000000000000000 --- a/configs/emage.yaml +++ /dev/null @@ -1,101 +0,0 @@ -is_train: True -ddp: False -stat: ts -root_path: ./ -out_path: ./outputs/audio2pose/ -project: s2g -data_path: ./BEAT2/beat_english_v2.0.0/ -e_path: weights/AESKConv_240_100.bin -eval_model: motion_representation -e_name: VAESKConv -test_ckpt: ./EMAGE/emage_240.bin -data_path_1: ./EMAGE/ -vae_test_len: 32 -vae_test_dim: 330 -vae_test_stride: 20 -vae_length: 240 -vae_codebook_size: 256 -vae_layer: 4 -vae_grow: [1,1,2,1] -variational: False - -# data config -training_speakers: [2] #[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30] -additional_data: False -cache_path: datasets/beat_cache/beat_smplx_en_emage/ -dataset: beat_sep_lower -new_cache: False - -# motion config -ori_joints: beat_smplx_joints -tar_joints: beat_smplx_full -pose_rep: smplxflame_30 -pose_norm: False -pose_fps: 30 -rot6d: True -pre_frames: 4 -pose_dims: 330 -pose_length: 64 -stride: 20 -test_length: 64 -motion_f: 256 -m_pre_encoder: null -m_encoder: null -m_fix_pre: False - -# audio config -audio_rep: onset+amplitude -audio_sr: 16000 -audio_fps: 16000 -audio_norm: False -audio_f: 256 -# a_pre_encoder: tcn_camn -# a_encoder: none -# a_fix_pre: False - -# text config -word_rep: textgrid -word_index_num: 11195 -word_dims: 300 -freeze_wordembed: False -word_f: 256 -t_pre_encoder: fasttext -t_encoder: null -t_fix_pre: False - -# facial config -facial_rep: smplxflame_30 -facial_dims: 100 -facial_norm: False -facial_f: 0 -f_pre_encoder: null -f_encoder: null -f_fix_pre: False - -# speaker config -id_rep: onehot -speaker_f: 0 - -# model config -batch_size: 64 -# warmup_epochs: 1 -# warmup_lr: 1e-6 -lr_base: 5e-4 -model: emage -g_name: MAGE_Transformer -trainer: emage -hidden_size: 768 -n_layer: 1 - -rec_weight: 1 -grad_norm: 0.99 -epochs: 400 -test_period: 20 -ll: 3 -lf: 3 -lu: 3 -lh: 3 -cl: 1 -cf: 0 -cu: 1 -ch: 1 diff --git a/configs/emage_audio.yaml b/configs/emage_audio.yaml new file mode 100644 index 0000000000000000000000000000000000000000..0520436cf1866c28aee7f70f0727838ae9cd487c --- /dev/null +++ b/configs/emage_audio.yaml @@ -0,0 +1,78 @@ +wandb_project: 'EMAGE++' +exp_name: 'debug' +debug: False +wandb_entity: '' +wandb_key: "" +# wandb_log_dir: '/content/outputs/wandb' +output_dir: /content/outputs/ +log_period: 1 +seed: 222 +resume_from_checkpoint: null +test: False + +data: + name_pyfile: "datasets.beat2" + class_name: "BEAT2DatasetEamgeFootContact" + train_bs: 56 + meta_paths: + - "/content/drive/MyDrive/my-codes/PantoMatrix/datasets/data_json/beat2_s20_l64_speaker2.json" + test_meta_paths: + - "/content/drive/MyDrive/my-codes/PantoMatrix/datasets/data_json/beat2_s20_l64_speaker2.json" + pose_norm: False + + +model: + name_pyfile: "models.emage_audio.modeling_emage_audio" + class_name: "EmageAudioModel" + pose_fps: 30 + motion_f: 256 + pose_dims: 330 + pose_rep: "smplx" + audio_rep: wave16k + audio_sr: 16000 + audio_fps: 16000 + audio_norm: False + audio_f: 256 + speaker_f: 768 + speaker_dims: 1 + hidden_size: 768 + seed_frames: 4 + pose_length: 64 + stride: 20 + test_length: 64 + joint_mask: null + vae_codebook_size: 256 + ll: 3 + lf: 3 + lu: 3 + lh: 3 + cl: 1 + cf: 0 + cu: 1 + ch: 1 + + +validation: + validation_steps: 500 + test_steps: 1000 + visualization: False + evaluation: False + wandb: False + + +solver: + gradient_accumulation_steps: 1 + gradient_checkpointing: False + max_train_steps: 100000 + max_grad_norm: 0.99 + # lr + learning_rate: 1.5e-4 + scale_lr: False + lr_warmup_steps: 0 + lr_scheduler: 'constant' + # optimizer + use_8bit_adam: False + adam_beta1: 0.9 + adam_beta2: 0.999 + adam_weight_decay: 0.0 + adam_epsilon: 1.0e-8 diff --git a/configs/emage_test.yaml b/configs/emage_test.yaml deleted file mode 100644 index ca29c88ba4cfe028f73a9f7ae3636b1e82be2a39..0000000000000000000000000000000000000000 --- a/configs/emage_test.yaml +++ /dev/null @@ -1,101 +0,0 @@ -is_train: True -ddp: False -stat: ts -root_path: ./ -out_path: ./outputs/audio2pose/ -project: s2g -data_path: ./EMAGE/test_sequences/ -e_path: weights/AESKConv_240_100.bin -eval_model: motion_representation -e_name: VAESKConv -test_ckpt: ./EMAGE/emage_240.bin -data_path_1: ./EMAGE/ -vae_test_len: 32 -vae_test_dim: 330 -vae_test_stride: 20 -vae_length: 240 -vae_codebook_size: 256 -vae_layer: 4 -vae_grow: [1,1,2,1] -variational: False - -# data config -training_speakers: [2] #[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30] -additional_data: False -cache_path: ./datasets/beat_cache/beat_smplx_en_emage_test/ -dataset: beat_testonly -new_cache: True - -# motion config -ori_joints: beat_smplx_joints -tar_joints: beat_smplx_full -pose_rep: smplxflame_30 -pose_norm: False -pose_fps: 30 -rot6d: True -pre_frames: 4 -pose_dims: 330 -pose_length: 64 -stride: 20 -test_length: 64 -motion_f: 256 -m_pre_encoder: null -m_encoder: null -m_fix_pre: False - -# audio config -audio_rep: onset+amplitude -audio_sr: 16000 -audio_fps: 16000 -audio_norm: False -audio_f: 256 -# a_pre_encoder: tcn_camn -# a_encoder: none -# a_fix_pre: False - -# text config -word_rep: textgrid -word_index_num: 11195 -word_dims: 300 -freeze_wordembed: False -word_f: 256 -t_pre_encoder: fasttext -t_encoder: null -t_fix_pre: False - -# facial config -facial_rep: smplxflame_30 -facial_dims: 100 -facial_norm: False -facial_f: 0 -f_pre_encoder: null -f_encoder: null -f_fix_pre: False - -# speaker config -id_rep: onehot -speaker_f: 0 - -# model config -batch_size: 64 -# warmup_epochs: 1 -# warmup_lr: 1e-6 -lr_base: 5e-4 -model: emage -g_name: MAGE_Transformer -trainer: emage -hidden_size: 768 -n_layer: 1 - -rec_weight: 1 -grad_norm: 0.99 -epochs: 400 -test_period: 20 -ll: 3 -lf: 3 -lu: 3 -lh: 3 -cl: 1 -cf: 0 -cu: 1 -ch: 1 diff --git a/configs/emage_test_colab.yaml b/configs/emage_test_colab.yaml deleted file mode 100644 index 79d64b05931bcba2291bea6199c8fff2a1557d71..0000000000000000000000000000000000000000 --- a/configs/emage_test_colab.yaml +++ /dev/null @@ -1,101 +0,0 @@ -is_train: True -ddp: False -stat: ts -root_path: ./ -out_path: ./outputs/audio2pose/ -project: s2g -data_path: ./EMAGE/test_sequences/ -e_path: weights/AESKConv_240_100.bin -eval_model: motion_representation -e_name: VAESKConv -test_ckpt: ./EMAGE/emage_240.bin -data_path_1: ./EMAGE/ -vae_test_len: 32 -vae_test_dim: 330 -vae_test_stride: 20 -vae_length: 240 -vae_codebook_size: 256 -vae_layer: 4 -vae_grow: [1,1,2,1] -variational: False - -# data config -training_speakers: [2] #[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30] -additional_data: False -cache_path: ./datasets/beat_cache/beat_smplx_en_emage_test/ -dataset: beat_testonly_colab -new_cache: True - -# motion config -ori_joints: beat_smplx_joints -tar_joints: beat_smplx_full -pose_rep: smplxflame_30 -pose_norm: False -pose_fps: 30 -rot6d: True -pre_frames: 4 -pose_dims: 330 -pose_length: 64 -stride: 20 -test_length: 64 -motion_f: 256 -m_pre_encoder: null -m_encoder: null -m_fix_pre: False - -# audio config -audio_rep: onset+amplitude -audio_sr: 16000 -audio_fps: 16000 -audio_norm: False -audio_f: 256 -# a_pre_encoder: tcn_camn -# a_encoder: none -# a_fix_pre: False - -# text config -word_rep: textgrid -word_index_num: 11195 -word_dims: 300 -freeze_wordembed: False -word_f: 256 -t_pre_encoder: fasttext -t_encoder: null -t_fix_pre: False - -# facial config -facial_rep: smplxflame_30 -facial_dims: 100 -facial_norm: False -facial_f: 0 -f_pre_encoder: null -f_encoder: null -f_fix_pre: False - -# speaker config -id_rep: onehot -speaker_f: 0 - -# model config -batch_size: 64 -# warmup_epochs: 1 -# warmup_lr: 1e-6 -lr_base: 5e-4 -model: emage -g_name: MAGE_Transformer -trainer: emage -hidden_size: 768 -n_layer: 1 - -rec_weight: 1 -grad_norm: 0.99 -epochs: 400 -test_period: 20 -ll: 3 -lf: 3 -lu: 3 -lh: 3 -cl: 1 -cf: 0 -cu: 1 -ch: 1 diff --git a/configs/emage_test_hf.yaml b/configs/emage_test_hf.yaml deleted file mode 100644 index 564aaf431479e2d25825507d2819b8e247e857a1..0000000000000000000000000000000000000000 --- a/configs/emage_test_hf.yaml +++ /dev/null @@ -1,101 +0,0 @@ -is_train: True -ddp: False -stat: ts -root_path: ./ -out_path: ./outputs/audio2pose/ -project: s2g -data_path: ./EMAGE/test_sequences/ -e_path: weights/AESKConv_240_100.bin -eval_model: motion_representation -e_name: VAESKConv -test_ckpt: ./EMAGE/emage_audio_175.bin -data_path_1: ./EMAGE/ -vae_test_len: 32 -vae_test_dim: 330 -vae_test_stride: 20 -vae_length: 240 -vae_codebook_size: 256 -vae_layer: 4 -vae_grow: [1,1,2,1] -variational: False - -# data config -training_speakers: [2] #[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30] -additional_data: False -cache_path: ./datasets/beat_cache/beat_smplx_en_emage_test/ -dataset: beat_testonly_hf -new_cache: True - -# motion config -ori_joints: beat_smplx_joints -tar_joints: beat_smplx_full -pose_rep: smplxflame_30 -pose_norm: False -pose_fps: 30 -rot6d: True -pre_frames: 4 -pose_dims: 330 -pose_length: 64 -stride: 20 -test_length: 64 -motion_f: 256 -m_pre_encoder: null -m_encoder: null -m_fix_pre: False - -# audio config -audio_rep: wave16k -audio_sr: 16000 -audio_fps: 16000 -audio_norm: False -audio_f: 256 -# a_pre_encoder: tcn_camn -# a_encoder: none -# a_fix_pre: False - -# text config -# word_rep: textgrid -# word_index_num: 11195 -# word_dims: 300 -# freeze_wordembed: False -# word_f: 256 -# t_pre_encoder: fasttext -# t_encoder: null -# t_fix_pre: False - -# facial config -facial_rep: smplxflame_30 -facial_dims: 100 -facial_norm: False -facial_f: 0 -f_pre_encoder: null -f_encoder: null -f_fix_pre: False - -# speaker config -id_rep: onehot -speaker_f: 0 - -# model config -batch_size: 64 -# warmup_epochs: 1 -# warmup_lr: 1e-6 -lr_base: 5e-4 -model: emage_audio -g_name: MAGE_Transformer -trainer: emage -hidden_size: 768 -n_layer: 1 - -rec_weight: 1 -grad_norm: 0.99 -epochs: 400 -test_period: 20 -ll: 3 -lf: 3 -lu: 3 -lh: 3 -cl: 1 -cf: 0 -cu: 1 -ch: 1 diff --git a/configs/skcnn_ae.yaml b/configs/skcnn_ae.yaml deleted file mode 100644 index 19430f8e617d1b069c023684e46eaceabe6fd795..0000000000000000000000000000000000000000 --- a/configs/skcnn_ae.yaml +++ /dev/null @@ -1,80 +0,0 @@ -is_train: True -ddp: False -stat: ts -training_speakers: [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30] -root_path: /home/s24273/ -out_path: /home/s24273/outputs/audio2pose/ -cache_path: datasets/beat_cache/beat_smplx_en/ -project: mage_smplx -data_path: /home/s24273/datasets/beat_v2.0.0/beat_english_v2.0.0/ -e_path: weights/AESKConv_240_100.bin -test_ckpt: weights/multi.bin -data_path_1: /home/s24273/datasets/hub/ -#torch_hub_path: datasets/hub/ -additional_data: False -dataset: beat_smplx2020 -new_cache: False -ori_joints: beat_smplx_joints -tar_joints: beat_smplx_full -pose_rep: smplxflame_30 -pose_norm: False -pose_fps: 30 - - -vae_test_len: 64 -vae_test_dim: 330 -vae_test_stride: 20 -vae_length: 240 -vae_layer: 2 -vae_grow: [1,2] -variational: False - -pose_dims: 330 -pose_length: 64 -stride: 20 -facial_dims: 100 -word_index_num: 11195 -word_dims: 300 -batch_size: 32 -lr_base: 1e-4 -model: motion_representation -g_name: VAESKConv -#eval_model: motion_autoencoder -#e_name: HalfEmbeddingNet -trainer: ae -decay_epochs: 950 -# audio_f: 256 -# a_pre_encoder: tcn_camn -# a_encoder: lp -# a_fix_pre: False - -# freeze_wordembed: False -# word_f: 128 -# t_pre_encoder: fasttext -# t_encoder: lp -# t_fix_pre: False - -# motion_f: 256 -# m_pre_encoder: lp -# m_encoder: lp -# m_fix_pre: False - -# facial_f: 128 -# f_pre_encoder: lp -# f_encoder: lp -# f_fix_pre: False - -#m_decoder: lstm -#decode_fusion: cat -#n_layer: 2 -#hidden_size: 512 -rec_weight: 1 -rec_pos_weight: 10 -rec_ver_weight: 0 -# rec_fac_weight: 1 -#ita_weight: 0 -#iwa_weight: 0 -#fusion_mode: sum -# grad_norm: 1 -epochs: 1000 -test_period: 100 \ No newline at end of file diff --git a/dataloaders/.ipynb_checkpoints/beat_testonly_hf-checkpoint.py b/dataloaders/.ipynb_checkpoints/beat_testonly_hf-checkpoint.py deleted file mode 100644 index 17393517d0d6f662114c19b009fa3471d7c1e91b..0000000000000000000000000000000000000000 --- a/dataloaders/.ipynb_checkpoints/beat_testonly_hf-checkpoint.py +++ /dev/null @@ -1,740 +0,0 @@ -import os -import pickle -import math -import shutil -import numpy as np -import lmdb as lmdb -import textgrid as tg -import pandas as pd -import torch -import glob -import json -from termcolor import colored -from loguru import logger -from collections import defaultdict -from torch.utils.data import Dataset -import torch.distributed as dist -import pyarrow -import librosa -import smplx - -from .build_vocab import Vocab -from .utils.audio_features import Wav2Vec2Model -from .data_tools import joints_list -from .utils import rotation_conversions as rc -from .utils import other_tools_hf - -class CustomDataset(Dataset): - def __init__(self, args, loader_type, smplx_path=None, audio_path=None, text_path=None, augmentation=None, kwargs=None, build_cache=True): - self.args = args - self.loader_type = loader_type - self.smplx_path = "./EMAGE/test_sequences/smplxflame_30/2_scott_0_1_1.npz" - self.audio_path = audio_path - self.text_path = "./EMAGE/test_sequences/textgrid/2_scott_0_1_1.TextGrid" - self.rank = 0 # dist.get_rank() - self.ori_stride = self.args.stride - self.ori_length = self.args.pose_length - self.alignment = [0,0] # for trinity - - self.ori_joint_list = joints_list[self.args.ori_joints] - self.tar_joint_list = joints_list[self.args.tar_joints] - if 'smplx' in self.args.pose_rep: - self.joint_mask = np.zeros(len(list(self.ori_joint_list.keys()))*3) - self.joints = len(list(self.tar_joint_list.keys())) - for joint_name in self.tar_joint_list: - self.joint_mask[self.ori_joint_list[joint_name][1] - self.ori_joint_list[joint_name][0]:self.ori_joint_list[joint_name][1]] = 1 - else: - self.joints = len(list(self.ori_joint_list.keys()))+1 - self.joint_mask = np.zeros(self.joints*3) - for joint_name in self.tar_joint_list: - if joint_name == "Hips": - self.joint_mask[3:6] = 1 - else: - self.joint_mask[self.ori_joint_list[joint_name][1] - self.ori_joint_list[joint_name][0]:self.ori_joint_list[joint_name][1]] = 1 - # select trainable joints - self.smplx = smplx.create( - self.args.data_path_1+"smplx_models/", - model_type='smplx', - gender='NEUTRAL_2020', - use_face_contour=False, - num_betas=300, - num_expression_coeffs=100, - ext='npz', - use_pca=False, - ).eval() - - split_rule = pd.read_csv(args.data_path+"test.csv") - self.selected_file = split_rule - self.data_dir = args.data_path - - if loader_type == "test": - self.args.multi_length_training = [1.0] - self.max_length = int(args.pose_length * self.args.multi_length_training[-1]) - self.max_audio_pre_len = math.floor(args.pose_length / args.pose_fps * self.args.audio_sr) - if self.max_audio_pre_len > self.args.test_length*self.args.audio_sr: - self.max_audio_pre_len = self.args.test_length*self.args.audio_sr - - if args.word_rep is not None: - with open(f"{args.data_path}weights/vocab.pkl", 'rb') as f: - self.lang_model = pickle.load(f) - - preloaded_dir = self.args.root_path + self.args.cache_path + loader_type + f"/{args.pose_rep}_cache" - if build_cache and self.rank == 0: - self.build_cache(preloaded_dir) - self.lmdb_env = lmdb.open(preloaded_dir, readonly=True, lock=False) - with self.lmdb_env.begin() as txn: - self.n_samples = txn.stat()["entries"] - - - def build_cache(self, preloaded_dir): - logger.info(f"Audio bit rate: {self.args.audio_fps}") - logger.info("Reading data '{}'...".format(self.data_dir)) - logger.info("Creating the dataset cache...") - if self.args.new_cache: - if os.path.exists(preloaded_dir): - shutil.rmtree(preloaded_dir) - if os.path.exists(preloaded_dir): - logger.info("Found the cache {}".format(preloaded_dir)) - elif self.loader_type == "test": - self.cache_generation( - preloaded_dir, True, - 0, 0, - is_test=True) - else: - self.cache_generation( - preloaded_dir, self.args.disable_filtering, - self.args.clean_first_seconds, self.args.clean_final_seconds, - is_test=False) - - - def __len__(self): - return self.n_samples - - - def cache_generation(self, out_lmdb_dir, disable_filtering, clean_first_seconds, clean_final_seconds, is_test=False): - self.n_out_samples = 0 - # create db for samples - if not os.path.exists(out_lmdb_dir): os.makedirs(out_lmdb_dir) - if len(self.args.training_speakers) == 1: - #dst_lmdb_env = lmdb.open(out_lmdb_dir, map_size= int(1024 ** 3 * 50))# 50G - dst_lmdb_env = lmdb.open(out_lmdb_dir, map_size= int(1024 ** 3 * 0.5))# 500M - else: - dst_lmdb_env = lmdb.open(out_lmdb_dir, map_size= int(1024 ** 3 * 200))# 200G - n_filtered_out = defaultdict(int) - - #for index, file_name in self.selected_file.iterrows(): - #f_name = file_name["id"] - ext = ".npz" if "smplx" in self.args.pose_rep else ".bvh" - pose_file = self.smplx_path#self.data_dir + self.args.pose_rep + "/" + f_name + ext - pose_each_file = [] - trans_each_file = [] - shape_each_file = [] - audio_each_file = [] - facial_each_file = [] - word_each_file = [] - emo_each_file = [] - sem_each_file = [] - vid_each_file = [] - id_pose = "dummy 2nd"#f_name - - logger.info(colored(f"# ---- Building cache for Pose {id_pose} ---- #", "blue")) - if "smplx" in self.args.pose_rep: - pose_data = np.load(pose_file, allow_pickle=True) - assert 30%self.args.pose_fps == 0, 'pose_fps should be an aliquot part of 30' - stride = int(30/self.args.pose_fps) - pose_each_file = pose_data["poses"][::stride] - trans_each_file = pose_data["trans"][::stride] - shape_each_file = np.repeat(pose_data["betas"].reshape(1, 300), pose_each_file.shape[0], axis=0) - - assert self.args.pose_fps == 30, "should 30" - m_data = np.load(pose_file, allow_pickle=True) - betas, poses, trans, exps = m_data["betas"], m_data["poses"], m_data["trans"], m_data["expressions"] - n, c = poses.shape[0], poses.shape[1] - betas = betas.reshape(1, 300) - betas = np.tile(betas, (n, 1)) - betas = torch.from_numpy(betas).float() - poses = torch.from_numpy(poses.reshape(n, c)).float() - exps = torch.from_numpy(exps.reshape(n, 100)).float() - trans = torch.from_numpy(trans.reshape(n, 3)).float() - max_length = 128 - s, r = n//max_length, n%max_length - #print(n, s, r) - all_tensor = [] - for i in range(s): - with torch.no_grad(): - joints = self.smplx( - betas=betas[i*max_length:(i+1)*max_length], - transl=trans[i*max_length:(i+1)*max_length], - expression=exps[i*max_length:(i+1)*max_length], - jaw_pose=poses[i*max_length:(i+1)*max_length, 66:69], - global_orient=poses[i*max_length:(i+1)*max_length,:3], - body_pose=poses[i*max_length:(i+1)*max_length,3:21*3+3], - left_hand_pose=poses[i*max_length:(i+1)*max_length,25*3:40*3], - right_hand_pose=poses[i*max_length:(i+1)*max_length,40*3:55*3], - return_verts=True, - return_joints=True, - leye_pose=poses[i*max_length:(i+1)*max_length, 69:72], - reye_pose=poses[i*max_length:(i+1)*max_length, 72:75], - )['joints'][:, (7,8,10,11), :].reshape(max_length, 4, 3).cpu() - all_tensor.append(joints) - if r != 0: - with torch.no_grad(): - joints = self.smplx( - betas=betas[s*max_length:s*max_length+r], - transl=trans[s*max_length:s*max_length+r], - expression=exps[s*max_length:s*max_length+r], - jaw_pose=poses[s*max_length:s*max_length+r, 66:69], - global_orient=poses[s*max_length:s*max_length+r,:3], - body_pose=poses[s*max_length:s*max_length+r,3:21*3+3], - left_hand_pose=poses[s*max_length:s*max_length+r,25*3:40*3], - right_hand_pose=poses[s*max_length:s*max_length+r,40*3:55*3], - return_verts=True, - return_joints=True, - leye_pose=poses[s*max_length:s*max_length+r, 69:72], - reye_pose=poses[s*max_length:s*max_length+r, 72:75], - )['joints'][:, (7,8,10,11), :].reshape(r, 4, 3).cpu() - all_tensor.append(joints) - joints = torch.cat(all_tensor, axis=0) # all, 4, 3 - # print(joints.shape) - feetv = torch.zeros(joints.shape[1], joints.shape[0]) - joints = joints.permute(1, 0, 2) - #print(joints.shape, feetv.shape) - feetv[:, :-1] = (joints[:, 1:] - joints[:, :-1]).norm(dim=-1) - #print(feetv.shape) - contacts = (feetv < 0.01).numpy().astype(float) - # print(contacts.shape, contacts) - contacts = contacts.transpose(1, 0) - pose_each_file = pose_each_file * self.joint_mask - pose_each_file = pose_each_file[:, self.joint_mask.astype(bool)] - pose_each_file = np.concatenate([pose_each_file, contacts], axis=1) - # print(pose_each_file.shape) - - - if self.args.facial_rep is not None: - logger.info(f"# ---- Building cache for Facial {id_pose} and Pose {id_pose} ---- #") - facial_each_file = pose_data["expressions"][::stride] - if self.args.facial_norm: - facial_each_file = (facial_each_file - self.mean_facial) / self.std_facial - - else: - assert 120%self.args.pose_fps == 0, 'pose_fps should be an aliquot part of 120' - stride = int(120/self.args.pose_fps) - with open(pose_file, "r") as pose_data: - for j, line in enumerate(pose_data.readlines()): - if j < 431: continue - if j%stride != 0:continue - data = np.fromstring(line, dtype=float, sep=" ") - rot_data = rc.euler_angles_to_matrix(torch.from_numpy(np.deg2rad(data)).reshape(-1, self.joints,3), "XYZ") - rot_data = rc.matrix_to_axis_angle(rot_data).reshape(-1, self.joints*3) - rot_data = rot_data.numpy() * self.joint_mask - - pose_each_file.append(rot_data) - trans_each_file.append(data[:3]) - - pose_each_file = np.array(pose_each_file) - trans_each_file = np.array(trans_each_file) - shape_each_file = np.repeat(np.array(-1).reshape(1, 1), pose_each_file.shape[0], axis=0) - if self.args.facial_rep is not None: - logger.info(f"# ---- Building cache for Facial {id_pose} and Pose {id_pose} ---- #") - facial_file = pose_file.replace(self.args.pose_rep, self.args.facial_rep).replace("bvh", "json") - assert 60%self.args.pose_fps == 0, 'pose_fps should be an aliquot part of 120' - stride = int(60/self.args.pose_fps) - if not os.path.exists(facial_file): - logger.warning(f"# ---- file not found for Facial {id_pose}, skip all files with the same id ---- #") - #self.selected_file = self.selected_file.drop(self.selected_file[self.selected_file['id'] == id_pose].index) - #continue - with open(facial_file, 'r') as facial_data_file: - facial_data = json.load(facial_data_file) - for j, frame_data in enumerate(facial_data['frames']): - if j%stride != 0:continue - facial_each_file.append(frame_data['weights']) - facial_each_file = np.array(facial_each_file) - if self.args.facial_norm: - facial_each_file = (facial_each_file - self.mean_facial) / self.std_facial - - if self.args.id_rep is not None: - int_value = 1 - vid_each_file = np.repeat(np.array(int_value).reshape(1, 1), pose_each_file.shape[0], axis=0) - - if self.args.audio_rep is not None: - logger.info(f"# ---- Building cache for Audio {id_pose} and Pose {id_pose} ---- #") - audio_file = self.audio_path[1]#pose_file.replace(self.args.pose_rep, 'wave16k').replace(ext, ".wav") - sr = self.audio_path[0] - print(sr) - #if not os.path.exists(audio_file): - # logger.warning(f"# ---- file not found for Audio {id_pose}, skip all files with the same id ---- #") - #self.selected_file = self.selected_file.drop(self.selected_file[self.selected_file['id'] == id_pose].index) - #continue - #audio_each_file, sr = librosa.load(audio_file) - audio_each_file = audio_file.astype(np.float32) - print(audio_each_file.shape) - audio_each_file = librosa.resample(audio_each_file, orig_sr=sr, target_sr=self.args.audio_sr) - print(audio_each_file.shape) - if self.args.audio_rep == "onset+amplitude": - from numpy.lib import stride_tricks - frame_length = 1024 - # hop_length = 512 - shape = (audio_each_file.shape[-1] - frame_length + 1, frame_length) - strides = (audio_each_file.strides[-1], audio_each_file.strides[-1]) - rolling_view = stride_tricks.as_strided(audio_each_file, shape=shape, strides=strides) - amplitude_envelope = np.max(np.abs(rolling_view), axis=1) - # pad the last frame_length-1 samples - amplitude_envelope = np.pad(amplitude_envelope, (0, frame_length-1), mode='constant', constant_values=amplitude_envelope[-1]) - audio_onset_f = librosa.onset.onset_detect(y=audio_each_file, sr=self.args.audio_sr, units='frames') - onset_array = np.zeros(len(audio_each_file), dtype=float) - onset_array[audio_onset_f] = 1.0 - # print(amplitude_envelope.shape, audio_each_file.shape, onset_array.shape) - audio_each_file = np.concatenate([amplitude_envelope.reshape(-1, 1), onset_array.reshape(-1, 1)], axis=1) - elif self.args.audio_rep == "mfcc": - audio_each_file = librosa.feature.melspectrogram(y=audio_each_file, sr=self.args.audio_sr, n_mels=128, hop_length=int(self.args.audio_sr/self.args.audio_fps)) - audio_each_file = audio_each_file.transpose(1, 0) - # print(audio_each_file.shape, pose_each_file.shape) - if self.args.audio_norm and self.args.audio_rep == "wave16k": - audio_each_file = (audio_each_file - self.mean_audio) / self.std_audio - - time_offset = 0 - if self.args.word_rep is not None: - logger.info(f"# ---- Building cache for Word {id_pose} and Pose {id_pose} ---- #") - word_file = self.text_path#f"{self.data_dir}{self.args.word_rep}/{id_pose}.TextGrid" - if not os.path.exists(word_file): - logger.warning(f"# ---- file not found for Word {id_pose}, skip all files with the same id ---- #") - #self.selected_file = self.selected_file.drop(self.selected_file[self.selected_file['id'] == id_pose].index) - #continue - tgrid = tg.TextGrid.fromFile(word_file) - if self.args.t_pre_encoder == "bert": - from transformers import AutoTokenizer, BertModel - tokenizer = AutoTokenizer.from_pretrained(self.args.data_path_1 + "hub/bert-base-uncased", local_files_only=True) - model = BertModel.from_pretrained(self.args.data_path_1 + "hub/bert-base-uncased", local_files_only=True).eval() - list_word = [] - all_hidden = [] - max_len = 400 - last = 0 - word_token_mapping = [] - first = True - for i, word in enumerate(tgrid[0]): - last = i - if (i%max_len != 0) or (i==0): - if word.mark == "": - list_word.append(".") - else: - list_word.append(word.mark) - else: - max_counter = max_len - str_word = ' '.join(map(str, list_word)) - if first: - global_len = 0 - end = -1 - offset_word = [] - for k, wordvalue in enumerate(list_word): - start = end+1 - end = start+len(wordvalue) - offset_word.append((start, end)) - #print(offset_word) - token_scan = tokenizer.encode_plus(str_word, return_offsets_mapping=True)['offset_mapping'] - #print(token_scan) - for start, end in offset_word: - sub_mapping = [] - for i, (start_t, end_t) in enumerate(token_scan[1:-1]): - if int(start) <= int(start_t) and int(end_t) <= int(end): - #print(i+global_len) - sub_mapping.append(i+global_len) - word_token_mapping.append(sub_mapping) - #print(len(word_token_mapping)) - global_len = word_token_mapping[-1][-1] + 1 - list_word = [] - if word.mark == "": - list_word.append(".") - else: - list_word.append(word.mark) - - with torch.no_grad(): - inputs = tokenizer(str_word, return_tensors="pt") - outputs = model(**inputs) - last_hidden_states = outputs.last_hidden_state.reshape(-1, 768).cpu().numpy()[1:-1, :] - all_hidden.append(last_hidden_states) - - #list_word = list_word[:10] - if list_word == []: - pass - else: - if first: - global_len = 0 - str_word = ' '.join(map(str, list_word)) - end = -1 - offset_word = [] - for k, wordvalue in enumerate(list_word): - start = end+1 - end = start+len(wordvalue) - offset_word.append((start, end)) - #print(offset_word) - token_scan = tokenizer.encode_plus(str_word, return_offsets_mapping=True)['offset_mapping'] - #print(token_scan) - for start, end in offset_word: - sub_mapping = [] - for i, (start_t, end_t) in enumerate(token_scan[1:-1]): - if int(start) <= int(start_t) and int(end_t) <= int(end): - sub_mapping.append(i+global_len) - #print(sub_mapping) - word_token_mapping.append(sub_mapping) - #print(len(word_token_mapping)) - with torch.no_grad(): - inputs = tokenizer(str_word, return_tensors="pt") - outputs = model(**inputs) - last_hidden_states = outputs.last_hidden_state.reshape(-1, 768).cpu().numpy()[1:-1, :] - all_hidden.append(last_hidden_states) - last_hidden_states = np.concatenate(all_hidden, axis=0) - - for i in range(pose_each_file.shape[0]): - found_flag = False - current_time = i/self.args.pose_fps + time_offset - j_last = 0 - for j, word in enumerate(tgrid[0]): - word_n, word_s, word_e = word.mark, word.minTime, word.maxTime - if word_s<=current_time and current_time<=word_e: - if self.args.word_cache and self.args.t_pre_encoder == 'bert': - mapping_index = word_token_mapping[j] - #print(mapping_index, word_s, word_e) - s_t = np.linspace(word_s, word_e, len(mapping_index)+1) - #print(s_t) - for tt, t_sep in enumerate(s_t[1:]): - if current_time <= t_sep: - #if len(mapping_index) > 1: print(mapping_index[tt]) - word_each_file.append(last_hidden_states[mapping_index[tt]]) - break - else: - if word_n == " ": - word_each_file.append(self.lang_model.PAD_token) - else: - word_each_file.append(self.lang_model.get_word_index(word_n)) - found_flag = True - j_last = j - break - else: continue - if not found_flag: - if self.args.word_cache and self.args.t_pre_encoder == 'bert': - word_each_file.append(last_hidden_states[j_last]) - else: - word_each_file.append(self.lang_model.UNK_token) - word_each_file = np.array(word_each_file) - #print(word_each_file.shape) - - if self.args.emo_rep is not None: - logger.info(f"# ---- Building cache for Emo {id_pose} and Pose {id_pose} ---- #") - rtype, start = int(id_pose.split('_')[3]), int(id_pose.split('_')[3]) - if rtype == 0 or rtype == 2 or rtype == 4 or rtype == 6: - if start >= 1 and start <= 64: - score = 0 - elif start >= 65 and start <= 72: - score = 1 - elif start >= 73 and start <= 80: - score = 2 - elif start >= 81 and start <= 86: - score = 3 - elif start >= 87 and start <= 94: - score = 4 - elif start >= 95 and start <= 102: - score = 5 - elif start >= 103 and start <= 110: - score = 6 - elif start >= 111 and start <= 118: - score = 7 - else: pass - else: - # you may denote as unknown in the future - score = 0 - emo_each_file = np.repeat(np.array(score).reshape(1, 1), pose_each_file.shape[0], axis=0) - #print(emo_each_file) - - if self.args.sem_rep is not None: - logger.info(f"# ---- Building cache for Sem {id_pose} and Pose {id_pose} ---- #") - sem_file = f"{self.data_dir}{self.args.sem_rep}/{id_pose}.txt" - sem_all = pd.read_csv(sem_file, - sep='\t', - names=["name", "start_time", "end_time", "duration", "score", "keywords"]) - # we adopt motion-level semantic score here. - for i in range(pose_each_file.shape[0]): - found_flag = False - for j, (start, end, score) in enumerate(zip(sem_all['start_time'],sem_all['end_time'], sem_all['score'])): - current_time = i/self.args.pose_fps + time_offset - if start<=current_time and current_time<=end: - sem_each_file.append(score) - found_flag=True - break - else: continue - if not found_flag: sem_each_file.append(0.) - sem_each_file = np.array(sem_each_file) - #print(sem_each_file) - - filtered_result = self._sample_from_clip( - dst_lmdb_env, - audio_each_file, pose_each_file, trans_each_file, shape_each_file, facial_each_file, word_each_file, - vid_each_file, emo_each_file, sem_each_file, - disable_filtering, clean_first_seconds, clean_final_seconds, is_test, - ) - for type in filtered_result.keys(): - n_filtered_out[type] += filtered_result[type] - - with dst_lmdb_env.begin() as txn: - logger.info(colored(f"no. of samples: {txn.stat()['entries']}", "cyan")) - n_total_filtered = 0 - for type, n_filtered in n_filtered_out.items(): - logger.info("{}: {}".format(type, n_filtered)) - n_total_filtered += n_filtered - logger.info(colored("no. of excluded samples: {} ({:.1f}%)".format( - n_total_filtered, 100 * n_total_filtered / (txn.stat()["entries"] + n_total_filtered)), "cyan")) - dst_lmdb_env.sync() - dst_lmdb_env.close() - - def _sample_from_clip( - self, dst_lmdb_env, audio_each_file, pose_each_file, trans_each_file, shape_each_file, facial_each_file, word_each_file, - vid_each_file, emo_each_file, sem_each_file, - disable_filtering, clean_first_seconds, clean_final_seconds, is_test, - ): - """ - for data cleaning, we ignore the data for first and final n s - for test, we return all data - """ - # audio_start = int(self.alignment[0] * self.args.audio_fps) - # pose_start = int(self.alignment[1] * self.args.pose_fps) - #logger.info(f"before: {audio_each_file.shape} {pose_each_file.shape}") - # audio_each_file = audio_each_file[audio_start:] - # pose_each_file = pose_each_file[pose_start:] - # trans_each_file = - #logger.info(f"after alignment: {audio_each_file.shape} {pose_each_file.shape}") - #print(pose_each_file.shape) - round_seconds_skeleton = pose_each_file.shape[0] // self.args.pose_fps # assume 1500 frames / 15 fps = 100 s - print(pose_each_file.shape[0]) - #print(round_seconds_skeleton) - #if audio_each_file != []: - if self.args.audio_rep != "wave16k": - round_seconds_audio = len(audio_each_file) // self.args.audio_fps # assume 16,000,00 / 16,000 = 100 s - elif self.args.audio_rep == "mfcc": - round_seconds_audio = audio_each_file.shape[0] // self.args.audio_fps - else: - round_seconds_audio = audio_each_file.shape[0] // self.args.audio_sr - # if facial_each_file != []: - round_seconds_facial = facial_each_file.shape[0] // self.args.pose_fps - logger.info(f"audio: {round_seconds_audio}s, pose: {round_seconds_skeleton}s, facial: {round_seconds_facial}s") - round_seconds_skeleton = min(round_seconds_audio, round_seconds_skeleton, round_seconds_facial) - max_round = max(round_seconds_audio, round_seconds_skeleton, round_seconds_facial) - if round_seconds_skeleton != max_round: - logger.warning(f"reduce to {round_seconds_skeleton}s, ignore {max_round-round_seconds_skeleton}s") - # else: - # logger.info(f"pose: {round_seconds_skeleton}s, audio: {round_seconds_audio}s") - # round_seconds_skeleton = min(round_seconds_audio, round_seconds_skeleton) - # max_round = max(round_seconds_audio, round_seconds_skeleton) - # if round_seconds_skeleton != max_round: - # logger.warning(f"reduce to {round_seconds_skeleton}s, ignore {max_round-round_seconds_skeleton}s") - - clip_s_t, clip_e_t = clean_first_seconds, round_seconds_skeleton - clean_final_seconds # assume [10, 90]s - clip_s_f_audio, clip_e_f_audio = self.args.audio_fps * clip_s_t, clip_e_t * self.args.audio_fps # [160,000,90*160,000] - clip_s_f_pose, clip_e_f_pose = clip_s_t * self.args.pose_fps, clip_e_t * self.args.pose_fps # [150,90*15] - - - for ratio in self.args.multi_length_training: - if is_test:# stride = length for test - cut_length = clip_e_f_pose - clip_s_f_pose - self.args.stride = cut_length - self.max_length = cut_length - else: - self.args.stride = int(ratio*self.ori_stride) - cut_length = int(self.ori_length*ratio) - - num_subdivision = math.floor((clip_e_f_pose - clip_s_f_pose - cut_length) / self.args.stride) + 1 - logger.info(f"pose from frame {clip_s_f_pose} to {clip_e_f_pose}, length {cut_length}") - logger.info(f"{num_subdivision} clips is expected with stride {self.args.stride}") - - # if audio_each_file != []: - audio_short_length = math.floor(cut_length / self.args.pose_fps * self.args.audio_fps) - logger.info(f"audio from frame {clip_s_f_audio} to {clip_e_f_audio}, length {audio_short_length}") - - n_filtered_out = defaultdict(int) - sample_pose_list = [] - sample_audio_list = [] - sample_facial_list = [] - sample_shape_list = [] - sample_word_list = [] - sample_emo_list = [] - sample_sem_list = [] - sample_vid_list = [] - sample_trans_list = [] - - for i in range(num_subdivision): # cut into around 2s chip, (self npose) - start_idx = clip_s_f_pose + i * self.args.stride - fin_idx = start_idx + cut_length - sample_pose = pose_each_file[start_idx:fin_idx] - - sample_trans = trans_each_file[start_idx:fin_idx] - sample_shape = shape_each_file[start_idx:fin_idx] - # print(sample_pose.shape) - if self.args.audio_rep is not None: - audio_start = clip_s_f_audio + math.floor(i * self.args.stride * self.args.audio_fps / self.args.pose_fps) - audio_end = audio_start + audio_short_length - sample_audio = audio_each_file[audio_start:audio_end] - else: - sample_audio = np.array([-1]) - sample_facial = facial_each_file[start_idx:fin_idx] if self.args.facial_rep is not None else np.array([-1]) - sample_word = word_each_file[start_idx:fin_idx] if self.args.word_rep is not None else np.array([-1]) - sample_emo = emo_each_file[start_idx:fin_idx] if self.args.emo_rep is not None else np.array([-1]) - sample_sem = sem_each_file[start_idx:fin_idx] if self.args.sem_rep is not None else np.array([-1]) - sample_vid = vid_each_file[start_idx:fin_idx] if self.args.id_rep is not None else np.array([-1]) - - if sample_pose.any() != None: - # filtering motion skeleton data - sample_pose, filtering_message = MotionPreprocessor(sample_pose).get() - is_correct_motion = True #(sample_pose != []) - if is_correct_motion or disable_filtering: - sample_pose_list.append(sample_pose) - sample_audio_list.append(sample_audio) - sample_facial_list.append(sample_facial) - sample_shape_list.append(sample_shape) - sample_word_list.append(sample_word) - sample_vid_list.append(sample_vid) - sample_emo_list.append(sample_emo) - sample_sem_list.append(sample_sem) - sample_trans_list.append(sample_trans) - else: - n_filtered_out[filtering_message] += 1 - - if len(sample_pose_list) > 0: - with dst_lmdb_env.begin(write=True) as txn: - for pose, audio, facial, shape, word, vid, emo, sem, trans in zip( - sample_pose_list, - sample_audio_list, - sample_facial_list, - sample_shape_list, - sample_word_list, - sample_vid_list, - sample_emo_list, - sample_sem_list, - sample_trans_list,): - k = "{:005}".format(self.n_out_samples).encode("ascii") - v = [pose, audio, facial, shape, word, emo, sem, vid, trans] - # v = pyarrow.serialize(v).to_buffer() - # txn.put(k, v) - # self.n_out_samples += 1 - v = pickle.dumps(v) - txn.put(k, v) - self.n_out_samples += 1 - return n_filtered_out - - def __getitem__(self, idx): - with self.lmdb_env.begin(write=False) as txn: - key = "{:005}".format(idx).encode("ascii") - sample = txn.get(key) - # sample = pyarrow.deserialize(sample) - if sample is not None: - sample = pickle.loads(sample) - tar_pose, in_audio, in_facial, in_shape, in_word, emo, sem, vid, trans = sample - #print(in_shape) - #vid = torch.from_numpy(vid).int() - emo = torch.from_numpy(emo).int() - sem = torch.from_numpy(sem).float() - in_audio = torch.from_numpy(in_audio).float() - in_word = torch.from_numpy(in_word).float() if self.args.word_cache else torch.from_numpy(in_word).int() - if self.loader_type == "test": - tar_pose = torch.from_numpy(tar_pose).float() - trans = torch.from_numpy(trans).float() - in_facial = torch.from_numpy(in_facial).float() - vid = torch.from_numpy(vid).float() - in_shape = torch.from_numpy(in_shape).float() - else: - in_shape = torch.from_numpy(in_shape).reshape((in_shape.shape[0], -1)).float() - trans = torch.from_numpy(trans).reshape((trans.shape[0], -1)).float() - vid = torch.from_numpy(vid).reshape((vid.shape[0], -1)).float() - tar_pose = torch.from_numpy(tar_pose).reshape((tar_pose.shape[0], -1)).float() - in_facial = torch.from_numpy(in_facial).reshape((in_facial.shape[0], -1)).float() - return {"pose":tar_pose, "audio":in_audio, "facial":in_facial, "beta": in_shape, "word":in_word, "id":vid, "emo":emo, "sem":sem, "trans":trans} - - -class MotionPreprocessor: - def __init__(self, skeletons): - self.skeletons = skeletons - #self.mean_pose = mean_pose - self.filtering_message = "PASS" - - def get(self): - assert (self.skeletons is not None) - - # filtering - # if self.skeletons != []: - # if self.check_pose_diff(): - # self.skeletons = [] - # self.filtering_message = "pose" - # elif self.check_spine_angle(): - # self.skeletons = [] - # self.filtering_message = "spine angle" - # elif self.check_static_motion(): - # self.skeletons = [] - # self.filtering_message = "motion" - - # if self.skeletons != []: - # self.skeletons = self.skeletons.tolist() - # for i, frame in enumerate(self.skeletons): - # assert not np.isnan(self.skeletons[i]).any() # missing joints - - return self.skeletons, self.filtering_message - - def check_static_motion(self, verbose=True): - def get_variance(skeleton, joint_idx): - wrist_pos = skeleton[:, joint_idx] - variance = np.sum(np.var(wrist_pos, axis=0)) - return variance - - left_arm_var = get_variance(self.skeletons, 6) - right_arm_var = get_variance(self.skeletons, 9) - - th = 0.0014 # exclude 13110 - # th = 0.002 # exclude 16905 - if left_arm_var < th and right_arm_var < th: - if verbose: - print("skip - check_static_motion left var {}, right var {}".format(left_arm_var, right_arm_var)) - return True - else: - if verbose: - print("pass - check_static_motion left var {}, right var {}".format(left_arm_var, right_arm_var)) - return False - - - def check_pose_diff(self, verbose=False): -# diff = np.abs(self.skeletons - self.mean_pose) # 186*1 -# diff = np.mean(diff) - -# # th = 0.017 -# th = 0.02 #0.02 # exclude 3594 -# if diff < th: -# if verbose: -# print("skip - check_pose_diff {:.5f}".format(diff)) -# return True -# # th = 3.5 #0.02 # exclude 3594 -# # if 3.5 < diff < 5: -# # if verbose: -# # print("skip - check_pose_diff {:.5f}".format(diff)) -# # return True -# else: -# if verbose: -# print("pass - check_pose_diff {:.5f}".format(diff)) - return False - - - def check_spine_angle(self, verbose=True): - def angle_between(v1, v2): - v1_u = v1 / np.linalg.norm(v1) - v2_u = v2 / np.linalg.norm(v2) - return np.arccos(np.clip(np.dot(v1_u, v2_u), -1.0, 1.0)) - - angles = [] - for i in range(self.skeletons.shape[0]): - spine_vec = self.skeletons[i, 1] - self.skeletons[i, 0] - angle = angle_between(spine_vec, [0, -1, 0]) - angles.append(angle) - - if np.rad2deg(max(angles)) > 30 or np.rad2deg(np.mean(angles)) > 20: # exclude 4495 - # if np.rad2deg(max(angles)) > 20: # exclude 8270 - if verbose: - print("skip - check_spine_angle {:.5f}, {:.5f}".format(max(angles), np.mean(angles))) - return True - else: - if verbose: - print("pass - check_spine_angle {:.5f}".format(max(angles))) - return False \ No newline at end of file diff --git a/dataloaders/__pycache__/beat_testonly_hf.cpython-310.pyc b/dataloaders/__pycache__/beat_testonly_hf.cpython-310.pyc deleted file mode 100644 index a6606ef23e6ff89c968415cbd5938753b2e7b450..0000000000000000000000000000000000000000 Binary files a/dataloaders/__pycache__/beat_testonly_hf.cpython-310.pyc and /dev/null differ diff --git a/dataloaders/__pycache__/beat_testonly_hf.cpython-38.pyc b/dataloaders/__pycache__/beat_testonly_hf.cpython-38.pyc deleted file mode 100644 index 4b4442886fefd170a44a27598eab176fc9abaad5..0000000000000000000000000000000000000000 Binary files a/dataloaders/__pycache__/beat_testonly_hf.cpython-38.pyc and /dev/null differ diff --git a/dataloaders/__pycache__/build_vocab.cpython-310.pyc b/dataloaders/__pycache__/build_vocab.cpython-310.pyc deleted file mode 100644 index ed333dec7d794bddf8216370abad83cdcd6bbbb6..0000000000000000000000000000000000000000 Binary files a/dataloaders/__pycache__/build_vocab.cpython-310.pyc and /dev/null differ diff --git a/dataloaders/__pycache__/build_vocab.cpython-38.pyc b/dataloaders/__pycache__/build_vocab.cpython-38.pyc deleted file mode 100644 index f391643b5e18cafcb145e2d8049141c978a0e26a..0000000000000000000000000000000000000000 Binary files a/dataloaders/__pycache__/build_vocab.cpython-38.pyc and /dev/null differ diff --git a/dataloaders/__pycache__/data_tools.cpython-310.pyc b/dataloaders/__pycache__/data_tools.cpython-310.pyc deleted file mode 100644 index cdf63580251ed8ad599b546f3defd04e1491f38c..0000000000000000000000000000000000000000 Binary files a/dataloaders/__pycache__/data_tools.cpython-310.pyc and /dev/null differ diff --git a/dataloaders/__pycache__/data_tools.cpython-38.pyc b/dataloaders/__pycache__/data_tools.cpython-38.pyc deleted file mode 100644 index 6ebb215579d4b2073878f92efe3fef3604c2fe00..0000000000000000000000000000000000000000 Binary files a/dataloaders/__pycache__/data_tools.cpython-38.pyc and /dev/null differ diff --git a/dataloaders/beat_sep.py b/dataloaders/beat_sep.py deleted file mode 100644 index e5dc7dff9f52f0d8c9762946006fb6dd32540f23..0000000000000000000000000000000000000000 --- a/dataloaders/beat_sep.py +++ /dev/null @@ -1,771 +0,0 @@ -import os -import pickle -import math -import shutil -import numpy as np -import lmdb as lmdb -import textgrid as tg -import pandas as pd -import torch -import glob -import json -from termcolor import colored -from loguru import logger -from collections import defaultdict -from torch.utils.data import Dataset -import torch.distributed as dist -import pyarrow -import librosa -import smplx - -from .build_vocab import Vocab -from .utils.audio_features import Wav2Vec2Model -from .data_tools import joints_list -from .utils import rotation_conversions as rc -from .utils import other_tools - -class CustomDataset(Dataset): - def __init__(self, args, loader_type, augmentation=None, kwargs=None, build_cache=True): - self.args = args - self.loader_type = loader_type - - self.rank = dist.get_rank() - self.ori_stride = self.args.stride - self.ori_length = self.args.pose_length - self.alignment = [0,0] # for trinity - - self.ori_joint_list = joints_list[self.args.ori_joints] - self.tar_joint_list = joints_list[self.args.tar_joints] - if 'smplx' in self.args.pose_rep: - self.joint_mask = np.zeros(len(list(self.ori_joint_list.keys()))*3) - self.joints = len(list(self.tar_joint_list.keys())) - for joint_name in self.tar_joint_list: - self.joint_mask[self.ori_joint_list[joint_name][1] - self.ori_joint_list[joint_name][0]:self.ori_joint_list[joint_name][1]] = 1 - else: - self.joints = len(list(self.ori_joint_list.keys()))+1 - self.joint_mask = np.zeros(self.joints*3) - for joint_name in self.tar_joint_list: - if joint_name == "Hips": - self.joint_mask[3:6] = 1 - else: - self.joint_mask[self.ori_joint_list[joint_name][1] - self.ori_joint_list[joint_name][0]:self.ori_joint_list[joint_name][1]] = 1 - # select trainable joints - - split_rule = pd.read_csv(args.data_path+"train_test_split.csv") - self.selected_file = split_rule.loc[(split_rule['type'] == loader_type) & (split_rule['id'].str.split("_").str[0].astype(int).isin(self.args.training_speakers))] - if args.additional_data and loader_type == 'train': - split_b = split_rule.loc[(split_rule['type'] == 'additional') & (split_rule['id'].str.split("_").str[0].astype(int).isin(self.args.training_speakers))] - #self.selected_file = split_rule.loc[(split_rule['type'] == 'additional') & (split_rule['id'].str.split("_").str[0].astype(int).isin(self.args.training_speakers))] - self.selected_file = pd.concat([self.selected_file, split_b]) - if self.selected_file.empty: - logger.warning(f"{loader_type} is empty for speaker {self.args.training_speakers}, use train set 0-8 instead") - self.selected_file = split_rule.loc[(split_rule['type'] == 'train') & (split_rule['id'].str.split("_").str[0].astype(int).isin(self.args.training_speakers))] - self.selected_file = self.selected_file.iloc[0:8] - self.data_dir = args.data_path - - if loader_type == "test": - self.args.multi_length_training = [1.0] - self.max_length = int(args.pose_length * self.args.multi_length_training[-1]) - self.max_audio_pre_len = math.floor(args.pose_length / args.pose_fps * self.args.audio_sr) - if self.max_audio_pre_len > self.args.test_length*self.args.audio_sr: - self.max_audio_pre_len = self.args.test_length*self.args.audio_sr - - if args.word_rep is not None: - with open(f"{args.data_path}weights/vocab.pkl", 'rb') as f: - self.lang_model = pickle.load(f) - - preloaded_dir = self.args.root_path + self.args.cache_path + loader_type + f"/{args.pose_rep}_cache" - # if args.pose_norm: - # # careful for rotation vectors - # if not os.path.exists(args.data_path+args.mean_pose_path+f"{args.pose_rep.split('_')[0]}/bvh_mean.npy"): - # self.calculate_mean_pose() - # self.mean_pose = np.load(args.data_path+args.mean_pose_path+f"{args.pose_rep.split('_')[0]}/bvh_mean.npy") - # self.std_pose = np.load(args.data_path+args.mean_pose_path+f"{args.pose_rep.split('_')[0]}/bvh_std.npy") - # if args.audio_norm: - # if not os.path.exists(args.data_path+args.mean_pose_path+f"{args.audio_rep.split('_')[0]}/bvh_mean.npy"): - # self.calculate_mean_audio() - # self.mean_audio = np.load(args.data_path+args.mean_pose_path+f"{args.audio_rep.split('_')[0]}/npy_mean.npy") - # self.std_audio = np.load(args.data_path+args.mean_pose_path+f"{args.audio_rep.split('_')[0]}/npy_std.npy") - # if args.facial_norm: - # if not os.path.exists(args.data_path+args.mean_pose_path+f"{args.pose_rep.split('_')[0]}/bvh_mean.npy"): - # self.calculate_mean_face() - # self.mean_facial = np.load(args.data_path+args.mean_pose_path+f"{args.facial_rep}/json_mean.npy") - # self.std_facial = np.load(args.data_path+args.mean_pose_path+f"{args.facial_rep}/json_std.npy") - if self.args.beat_align: - if not os.path.exists(args.data_path+f"weights/mean_vel_{args.pose_rep}.npy"): - self.calculate_mean_velocity(args.data_path+f"weights/mean_vel_{args.pose_rep}.npy") - self.avg_vel = np.load(args.data_path+f"weights/mean_vel_{args.pose_rep}.npy") - - if build_cache and self.rank == 0: - self.build_cache(preloaded_dir) - self.lmdb_env = lmdb.open(preloaded_dir, readonly=True, lock=False) - with self.lmdb_env.begin() as txn: - self.n_samples = txn.stat()["entries"] - - - def calculate_mean_velocity(self, save_path): - self.smplx = smplx.create( - self.args.data_path_1+"smplx_models/", - model_type='smplx', - gender='NEUTRAL_2020', - use_face_contour=False, - num_betas=300, - num_expression_coeffs=100, - ext='npz', - use_pca=False, - ).cuda().eval() - dir_p = self.data_dir + self.args.pose_rep + "/" - all_list = [] - from tqdm import tqdm - for tar in tqdm(os.listdir(dir_p)): - if tar.endswith(".npz"): - m_data = np.load(dir_p+tar, allow_pickle=True) - betas, poses, trans, exps = m_data["betas"], m_data["poses"], m_data["trans"], m_data["expressions"] - n, c = poses.shape[0], poses.shape[1] - betas = betas.reshape(1, 300) - betas = np.tile(betas, (n, 1)) - betas = torch.from_numpy(betas).cuda().float() - poses = torch.from_numpy(poses.reshape(n, c)).cuda().float() - exps = torch.from_numpy(exps.reshape(n, 100)).cuda().float() - trans = torch.from_numpy(trans.reshape(n, 3)).cuda().float() - max_length = 128 - s, r = n//max_length, n%max_length - #print(n, s, r) - all_tensor = [] - for i in range(s): - with torch.no_grad(): - joints = self.smplx( - betas=betas[i*max_length:(i+1)*max_length], - transl=trans[i*max_length:(i+1)*max_length], - expression=exps[i*max_length:(i+1)*max_length], - jaw_pose=poses[i*max_length:(i+1)*max_length, 66:69], - global_orient=poses[i*max_length:(i+1)*max_length,:3], - body_pose=poses[i*max_length:(i+1)*max_length,3:21*3+3], - left_hand_pose=poses[i*max_length:(i+1)*max_length,25*3:40*3], - right_hand_pose=poses[i*max_length:(i+1)*max_length,40*3:55*3], - return_verts=True, - return_joints=True, - leye_pose=poses[i*max_length:(i+1)*max_length, 69:72], - reye_pose=poses[i*max_length:(i+1)*max_length, 72:75], - )['joints'][:, :55, :].reshape(max_length, 55*3) - all_tensor.append(joints) - if r != 0: - with torch.no_grad(): - joints = self.smplx( - betas=betas[s*max_length:s*max_length+r], - transl=trans[s*max_length:s*max_length+r], - expression=exps[s*max_length:s*max_length+r], - jaw_pose=poses[s*max_length:s*max_length+r, 66:69], - global_orient=poses[s*max_length:s*max_length+r,:3], - body_pose=poses[s*max_length:s*max_length+r,3:21*3+3], - left_hand_pose=poses[s*max_length:s*max_length+r,25*3:40*3], - right_hand_pose=poses[s*max_length:s*max_length+r,40*3:55*3], - return_verts=True, - return_joints=True, - leye_pose=poses[s*max_length:s*max_length+r, 69:72], - reye_pose=poses[s*max_length:s*max_length+r, 72:75], - )['joints'][:, :55, :].reshape(r, 55*3) - all_tensor.append(joints) - joints = torch.cat(all_tensor, axis=0) - joints = joints.permute(1, 0) - dt = 1/30 - # first steps is forward diff (t+1 - t) / dt - init_vel = (joints[:, 1:2] - joints[:, :1]) / dt - # middle steps are second order (t+1 - t-1) / 2dt - middle_vel = (joints[:, 2:] - joints[:, 0:-2]) / (2 * dt) - # last step is backward diff (t - t-1) / dt - final_vel = (joints[:, -1:] - joints[:, -2:-1]) / dt - #print(joints.shape, init_vel.shape, middle_vel.shape, final_vel.shape) - vel_seq = torch.cat([init_vel, middle_vel, final_vel], dim=1).permute(1, 0).reshape(n, 55, 3) - #print(vel_seq.shape) - #.permute(1, 0).reshape(n, 55, 3) - vel_seq_np = vel_seq.cpu().numpy() - vel_joints_np = np.linalg.norm(vel_seq_np, axis=2) # n * 55 - all_list.append(vel_joints_np) - avg_vel = np.mean(np.concatenate(all_list, axis=0),axis=0) # 55 - np.save(save_path, avg_vel) - - - def build_cache(self, preloaded_dir): - logger.info(f"Audio bit rate: {self.args.audio_fps}") - logger.info("Reading data '{}'...".format(self.data_dir)) - logger.info("Creating the dataset cache...") - if self.args.new_cache: - if os.path.exists(preloaded_dir): - shutil.rmtree(preloaded_dir) - if os.path.exists(preloaded_dir): - logger.info("Found the cache {}".format(preloaded_dir)) - elif self.loader_type == "test": - self.cache_generation( - preloaded_dir, True, - 0, 0, - is_test=True) - else: - self.cache_generation( - preloaded_dir, self.args.disable_filtering, - self.args.clean_first_seconds, self.args.clean_final_seconds, - is_test=False) - - def __len__(self): - return self.n_samples - - - def cache_generation(self, out_lmdb_dir, disable_filtering, clean_first_seconds, clean_final_seconds, is_test=False): - # if "wav2vec2" in self.args.audio_rep: - # self.wav2vec_model = Wav2Vec2Model.from_pretrained(f"{self.args.data_path_1}/hub/transformer/wav2vec2-base-960h") - # self.wav2vec_model.feature_extractor._freeze_parameters() - # self.wav2vec_model = self.wav2vec_model.cuda() - # self.wav2vec_model.eval() - - self.n_out_samples = 0 - # create db for samples - if not os.path.exists(out_lmdb_dir): os.makedirs(out_lmdb_dir) - dst_lmdb_env = lmdb.open(out_lmdb_dir, map_size= int(1024 ** 3 * 50))# 50G - n_filtered_out = defaultdict(int) - - for index, file_name in self.selected_file.iterrows(): - f_name = file_name["id"] - ext = ".npz" if "smplx" in self.args.pose_rep else ".bvh" - pose_file = self.data_dir + self.args.pose_rep + "/" + f_name + ext - pose_each_file = [] - trans_each_file = [] - shape_each_file = [] - audio_each_file = [] - facial_each_file = [] - word_each_file = [] - emo_each_file = [] - sem_each_file = [] - vid_each_file = [] - id_pose = f_name #1_wayne_0_1_1 - - logger.info(colored(f"# ---- Building cache for Pose {id_pose} ---- #", "blue")) - if "smplx" in self.args.pose_rep: - pose_data = np.load(pose_file, allow_pickle=True) - assert 30%self.args.pose_fps == 0, 'pose_fps should be an aliquot part of 30' - stride = int(30/self.args.pose_fps) - pose_each_file = pose_data["poses"][::stride] * self.joint_mask - pose_each_file = pose_each_file[:, self.joint_mask.astype(bool)] - # print(pose_each_file.shape) - trans_each_file = pose_data["trans"][::stride] - shape_each_file = np.repeat(pose_data["betas"].reshape(1, 300), pose_each_file.shape[0], axis=0) - if self.args.facial_rep is not None: - logger.info(f"# ---- Building cache for Facial {id_pose} and Pose {id_pose} ---- #") - facial_each_file = pose_data["expressions"][::stride] - if self.args.facial_norm: - facial_each_file = (facial_each_file - self.mean_facial) / self.std_facial - - else: - assert 120%self.args.pose_fps == 0, 'pose_fps should be an aliquot part of 120' - stride = int(120/self.args.pose_fps) - with open(pose_file, "r") as pose_data: - for j, line in enumerate(pose_data.readlines()): - if j < 431: continue - if j%stride != 0:continue - data = np.fromstring(line, dtype=float, sep=" ") - rot_data = rc.euler_angles_to_matrix(torch.from_numpy(np.deg2rad(data)).reshape(-1, self.joints,3), "XYZ") - rot_data = rc.matrix_to_axis_angle(rot_data).reshape(-1, self.joints*3) - rot_data = rot_data.numpy() * self.joint_mask - - pose_each_file.append(rot_data) - trans_each_file.append(data[:3]) - - pose_each_file = np.array(pose_each_file) - # print(pose_each_file.shape) - trans_each_file = np.array(trans_each_file) - shape_each_file = np.repeat(np.array(-1).reshape(1, 1), pose_each_file.shape[0], axis=0) - if self.args.facial_rep is not None: - logger.info(f"# ---- Building cache for Facial {id_pose} and Pose {id_pose} ---- #") - facial_file = pose_file.replace(self.args.pose_rep, self.args.facial_rep).replace("bvh", "json") - assert 60%self.args.pose_fps == 0, 'pose_fps should be an aliquot part of 120' - stride = int(60/self.args.pose_fps) - if not os.path.exists(facial_file): - logger.warning(f"# ---- file not found for Facial {id_pose}, skip all files with the same id ---- #") - self.selected_file = self.selected_file.drop(self.selected_file[self.selected_file['id'] == id_pose].index) - continue - with open(facial_file, 'r') as facial_data_file: - facial_data = json.load(facial_data_file) - for j, frame_data in enumerate(facial_data['frames']): - if j%stride != 0:continue - facial_each_file.append(frame_data['weights']) - facial_each_file = np.array(facial_each_file) - if self.args.facial_norm: - facial_each_file = (facial_each_file - self.mean_facial) / self.std_facial - - if self.args.id_rep is not None: - vid_each_file = np.repeat(np.array(int(f_name.split("_")[0])-1).reshape(1, 1), pose_each_file.shape[0], axis=0) - - if self.args.audio_rep is not None: - logger.info(f"# ---- Building cache for Audio {id_pose} and Pose {id_pose} ---- #") - audio_file = pose_file.replace(self.args.pose_rep, 'wave16k').replace(ext, ".wav") - if not os.path.exists(audio_file): - logger.warning(f"# ---- file not found for Audio {id_pose}, skip all files with the same id ---- #") - self.selected_file = self.selected_file.drop(self.selected_file[self.selected_file['id'] == id_pose].index) - continue - audio_each_file, sr = librosa.load(audio_file) - audio_each_file = librosa.resample(audio_each_file, orig_sr=sr, target_sr=self.args.audio_sr) - if self.args.audio_rep == "onset+amplitude": - from numpy.lib import stride_tricks - frame_length = 1024 - # hop_length = 512 - shape = (audio_each_file.shape[-1] - frame_length + 1, frame_length) - strides = (audio_each_file.strides[-1], audio_each_file.strides[-1]) - rolling_view = stride_tricks.as_strided(audio_each_file, shape=shape, strides=strides) - amplitude_envelope = np.max(np.abs(rolling_view), axis=1) - # pad the last frame_length-1 samples - amplitude_envelope = np.pad(amplitude_envelope, (0, frame_length-1), mode='constant', constant_values=amplitude_envelope[-1]) - audio_onset_f = librosa.onset.onset_detect(y=audio_each_file, sr=self.args.audio_sr, units='frames') - onset_array = np.zeros(len(audio_each_file), dtype=float) - onset_array[audio_onset_f] = 1.0 - # print(amplitude_envelope.shape, audio_each_file.shape, onset_array.shape) - audio_each_file = np.concatenate([amplitude_envelope.reshape(-1, 1), onset_array.reshape(-1, 1)], axis=1) - elif self.args.audio_rep == "mfcc": - audio_each_file = librosa.feature.melspectrogram(y=audio_each_file, sr=self.args.audio_sr, n_mels=128, hop_length=int(self.args.audio_sr/self.args.audio_fps)) - audio_each_file = audio_each_file.transpose(1, 0) - # print(audio_each_file.shape, pose_each_file.shape) - if self.args.audio_norm and self.args.audio_rep == "wave16k": - audio_each_file = (audio_each_file - self.mean_audio) / self.std_audio - # print(audio_each_file.shape) - time_offset = 0 - if self.args.word_rep is not None: - logger.info(f"# ---- Building cache for Word {id_pose} and Pose {id_pose} ---- #") - word_file = f"{self.data_dir}{self.args.word_rep}/{id_pose}.TextGrid" - if not os.path.exists(word_file): - logger.warning(f"# ---- file not found for Word {id_pose}, skip all files with the same id ---- #") - self.selected_file = self.selected_file.drop(self.selected_file[self.selected_file['id'] == id_pose].index) - continue - tgrid = tg.TextGrid.fromFile(word_file) - if self.args.t_pre_encoder == "bert": - from transformers import AutoTokenizer, BertModel - tokenizer = AutoTokenizer.from_pretrained(self.args.data_path_1 + "hub/bert-base-uncased", local_files_only=True) - model = BertModel.from_pretrained(self.args.data_path_1 + "hub/bert-base-uncased", local_files_only=True).eval() - list_word = [] - all_hidden = [] - max_len = 400 - last = 0 - word_token_mapping = [] - first = True - for i, word in enumerate(tgrid[0]): - last = i - if (i%max_len != 0) or (i==0): - if word.mark == "": - list_word.append(".") - else: - list_word.append(word.mark) - else: - max_counter = max_len - str_word = ' '.join(map(str, list_word)) - if first: - global_len = 0 - end = -1 - offset_word = [] - for k, wordvalue in enumerate(list_word): - start = end+1 - end = start+len(wordvalue) - offset_word.append((start, end)) - #print(offset_word) - token_scan = tokenizer.encode_plus(str_word, return_offsets_mapping=True)['offset_mapping'] - #print(token_scan) - for start, end in offset_word: - sub_mapping = [] - for i, (start_t, end_t) in enumerate(token_scan[1:-1]): - if int(start) <= int(start_t) and int(end_t) <= int(end): - #print(i+global_len) - sub_mapping.append(i+global_len) - word_token_mapping.append(sub_mapping) - #print(len(word_token_mapping)) - global_len = word_token_mapping[-1][-1] + 1 - list_word = [] - if word.mark == "": - list_word.append(".") - else: - list_word.append(word.mark) - - with torch.no_grad(): - inputs = tokenizer(str_word, return_tensors="pt") - outputs = model(**inputs) - last_hidden_states = outputs.last_hidden_state.reshape(-1, 768).cpu().numpy()[1:-1, :] - all_hidden.append(last_hidden_states) - - #list_word = list_word[:10] - if list_word == []: - pass - else: - if first: - global_len = 0 - str_word = ' '.join(map(str, list_word)) - end = -1 - offset_word = [] - for k, wordvalue in enumerate(list_word): - start = end+1 - end = start+len(wordvalue) - offset_word.append((start, end)) - #print(offset_word) - token_scan = tokenizer.encode_plus(str_word, return_offsets_mapping=True)['offset_mapping'] - #print(token_scan) - for start, end in offset_word: - sub_mapping = [] - for i, (start_t, end_t) in enumerate(token_scan[1:-1]): - if int(start) <= int(start_t) and int(end_t) <= int(end): - sub_mapping.append(i+global_len) - #print(sub_mapping) - word_token_mapping.append(sub_mapping) - #print(len(word_token_mapping)) - with torch.no_grad(): - inputs = tokenizer(str_word, return_tensors="pt") - outputs = model(**inputs) - last_hidden_states = outputs.last_hidden_state.reshape(-1, 768).cpu().numpy()[1:-1, :] - all_hidden.append(last_hidden_states) - last_hidden_states = np.concatenate(all_hidden, axis=0) - - for i in range(pose_each_file.shape[0]): - found_flag = False - current_time = i/self.args.pose_fps + time_offset - j_last = 0 - for j, word in enumerate(tgrid[0]): - word_n, word_s, word_e = word.mark, word.minTime, word.maxTime - if word_s<=current_time and current_time<=word_e: - if self.args.word_cache and self.args.t_pre_encoder == 'bert': - mapping_index = word_token_mapping[j] - #print(mapping_index, word_s, word_e) - s_t = np.linspace(word_s, word_e, len(mapping_index)+1) - #print(s_t) - for tt, t_sep in enumerate(s_t[1:]): - if current_time <= t_sep: - #if len(mapping_index) > 1: print(mapping_index[tt]) - word_each_file.append(last_hidden_states[mapping_index[tt]]) - break - else: - if word_n == " ": - word_each_file.append(self.lang_model.PAD_token) - else: - word_each_file.append(self.lang_model.get_word_index(word_n)) - found_flag = True - j_last = j - break - else: continue - if not found_flag: - if self.args.word_cache and self.args.t_pre_encoder == 'bert': - word_each_file.append(last_hidden_states[j_last]) - else: - word_each_file.append(self.lang_model.UNK_token) - word_each_file = np.array(word_each_file) - #print(word_each_file.shape) - - if self.args.emo_rep is not None: - logger.info(f"# ---- Building cache for Emo {id_pose} and Pose {id_pose} ---- #") - rtype, start = int(id_pose.split('_')[3]), int(id_pose.split('_')[3]) - if rtype == 0 or rtype == 2 or rtype == 4 or rtype == 6: - if start >= 1 and start <= 64: - score = 0 - elif start >= 65 and start <= 72: - score = 1 - elif start >= 73 and start <= 80: - score = 2 - elif start >= 81 and start <= 86: - score = 3 - elif start >= 87 and start <= 94: - score = 4 - elif start >= 95 and start <= 102: - score = 5 - elif start >= 103 and start <= 110: - score = 6 - elif start >= 111 and start <= 118: - score = 7 - else: pass - else: - # you may denote as unknown in the future - score = 0 - emo_each_file = np.repeat(np.array(score).reshape(1, 1), pose_each_file.shape[0], axis=0) - #print(emo_each_file) - - if self.args.sem_rep is not None: - logger.info(f"# ---- Building cache for Sem {id_pose} and Pose {id_pose} ---- #") - sem_file = f"{self.data_dir}{self.args.sem_rep}/{id_pose}.txt" - sem_all = pd.read_csv(sem_file, - sep='\t', - names=["name", "start_time", "end_time", "duration", "score", "keywords"]) - # we adopt motion-level semantic score here. - for i in range(pose_each_file.shape[0]): - found_flag = False - for j, (start, end, score) in enumerate(zip(sem_all['start_time'],sem_all['end_time'], sem_all['score'])): - current_time = i/self.args.pose_fps + time_offset - if start<=current_time and current_time<=end: - sem_each_file.append(score) - found_flag=True - break - else: continue - if not found_flag: sem_each_file.append(0.) - sem_each_file = np.array(sem_each_file) - #print(sem_each_file) - - filtered_result = self._sample_from_clip( - dst_lmdb_env, - audio_each_file, pose_each_file, trans_each_file, shape_each_file, facial_each_file, word_each_file, - vid_each_file, emo_each_file, sem_each_file, - disable_filtering, clean_first_seconds, clean_final_seconds, is_test, - ) - for type in filtered_result.keys(): - n_filtered_out[type] += filtered_result[type] - - with dst_lmdb_env.begin() as txn: - logger.info(colored(f"no. of samples: {txn.stat()['entries']}", "cyan")) - n_total_filtered = 0 - for type, n_filtered in n_filtered_out.items(): - logger.info("{}: {}".format(type, n_filtered)) - n_total_filtered += n_filtered - logger.info(colored("no. of excluded samples: {} ({:.1f}%)".format( - n_total_filtered, 100 * n_total_filtered / (txn.stat()["entries"] + n_total_filtered)), "cyan")) - dst_lmdb_env.sync() - dst_lmdb_env.close() - - def _sample_from_clip( - self, dst_lmdb_env, audio_each_file, pose_each_file, trans_each_file, shape_each_file, facial_each_file, word_each_file, - vid_each_file, emo_each_file, sem_each_file, - disable_filtering, clean_first_seconds, clean_final_seconds, is_test, - ): - """ - for data cleaning, we ignore the data for first and final n s - for test, we return all data - """ - # audio_start = int(self.alignment[0] * self.args.audio_fps) - # pose_start = int(self.alignment[1] * self.args.pose_fps) - #logger.info(f"before: {audio_each_file.shape} {pose_each_file.shape}") - # audio_each_file = audio_each_file[audio_start:] - # pose_each_file = pose_each_file[pose_start:] - # trans_each_file = - #logger.info(f"after alignment: {audio_each_file.shape} {pose_each_file.shape}") - #print(pose_each_file.shape) - round_seconds_skeleton = pose_each_file.shape[0] // self.args.pose_fps # assume 1500 frames / 15 fps = 100 s - #print(round_seconds_skeleton) - if audio_each_file != []: - if self.args.audio_rep != "wave16k": - round_seconds_audio = len(audio_each_file) // self.args.audio_fps # assume 16,000,00 / 16,000 = 100 s - elif self.args.audio_rep == "mfcc": - round_seconds_audio = audio_each_file.shape[0] // self.args.audio_fps - else: - round_seconds_audio = audio_each_file.shape[0] // self.args.audio_sr - if facial_each_file != []: - round_seconds_facial = facial_each_file.shape[0] // self.args.pose_fps - logger.info(f"audio: {round_seconds_audio}s, pose: {round_seconds_skeleton}s, facial: {round_seconds_facial}s") - round_seconds_skeleton = min(round_seconds_audio, round_seconds_skeleton, round_seconds_facial) - max_round = max(round_seconds_audio, round_seconds_skeleton, round_seconds_facial) - if round_seconds_skeleton != max_round: - logger.warning(f"reduce to {round_seconds_skeleton}s, ignore {max_round-round_seconds_skeleton}s") - else: - logger.info(f"pose: {round_seconds_skeleton}s, audio: {round_seconds_audio}s") - round_seconds_skeleton = min(round_seconds_audio, round_seconds_skeleton) - max_round = max(round_seconds_audio, round_seconds_skeleton) - if round_seconds_skeleton != max_round: - logger.warning(f"reduce to {round_seconds_skeleton}s, ignore {max_round-round_seconds_skeleton}s") - - clip_s_t, clip_e_t = clean_first_seconds, round_seconds_skeleton - clean_final_seconds # assume [10, 90]s - clip_s_f_audio, clip_e_f_audio = self.args.audio_fps * clip_s_t, clip_e_t * self.args.audio_fps # [160,000,90*160,000] - clip_s_f_pose, clip_e_f_pose = clip_s_t * self.args.pose_fps, clip_e_t * self.args.pose_fps # [150,90*15] - - - for ratio in self.args.multi_length_training: - if is_test:# stride = length for test - cut_length = clip_e_f_pose - clip_s_f_pose - self.args.stride = cut_length - self.max_length = cut_length - else: - self.args.stride = int(ratio*self.ori_stride) - cut_length = int(self.ori_length*ratio) - - num_subdivision = math.floor((clip_e_f_pose - clip_s_f_pose - cut_length) / self.args.stride) + 1 - logger.info(f"pose from frame {clip_s_f_pose} to {clip_e_f_pose}, length {cut_length}") - logger.info(f"{num_subdivision} clips is expected with stride {self.args.stride}") - - if audio_each_file != []: - audio_short_length = math.floor(cut_length / self.args.pose_fps * self.args.audio_fps) - """ - for audio sr = 16000, fps = 15, pose_length = 34, - audio short length = 36266.7 -> 36266 - this error is fine. - """ - logger.info(f"audio from frame {clip_s_f_audio} to {clip_e_f_audio}, length {audio_short_length}") - - n_filtered_out = defaultdict(int) - sample_pose_list = [] - sample_audio_list = [] - sample_facial_list = [] - sample_shape_list = [] - sample_word_list = [] - sample_emo_list = [] - sample_sem_list = [] - sample_vid_list = [] - sample_trans_list = [] - - for i in range(num_subdivision): # cut into around 2s chip, (self npose) - start_idx = clip_s_f_pose + i * self.args.stride - fin_idx = start_idx + cut_length - sample_pose = pose_each_file[start_idx:fin_idx] - sample_trans = trans_each_file[start_idx:fin_idx] - sample_shape = shape_each_file[start_idx:fin_idx] - # print(sample_pose.shape) - if self.args.audio_rep is not None: - audio_start = clip_s_f_audio + math.floor(i * self.args.stride * self.args.audio_fps / self.args.pose_fps) - audio_end = audio_start + audio_short_length - sample_audio = audio_each_file[audio_start:audio_end] - else: - sample_audio = np.array([-1]) - sample_facial = facial_each_file[start_idx:fin_idx] if self.args.facial_rep is not None else np.array([-1]) - sample_word = word_each_file[start_idx:fin_idx] if self.args.word_rep is not None else np.array([-1]) - sample_emo = emo_each_file[start_idx:fin_idx] if self.args.emo_rep is not None else np.array([-1]) - sample_sem = sem_each_file[start_idx:fin_idx] if self.args.sem_rep is not None else np.array([-1]) - sample_vid = vid_each_file[start_idx:fin_idx] if self.args.id_rep is not None else np.array([-1]) - - if sample_pose.any() != None: - # filtering motion skeleton data - sample_pose, filtering_message = MotionPreprocessor(sample_pose).get() - is_correct_motion = (sample_pose != []) - if is_correct_motion or disable_filtering: - sample_pose_list.append(sample_pose) - sample_audio_list.append(sample_audio) - sample_facial_list.append(sample_facial) - sample_shape_list.append(sample_shape) - sample_word_list.append(sample_word) - sample_vid_list.append(sample_vid) - sample_emo_list.append(sample_emo) - sample_sem_list.append(sample_sem) - sample_trans_list.append(sample_trans) - else: - n_filtered_out[filtering_message] += 1 - - if len(sample_pose_list) > 0: - with dst_lmdb_env.begin(write=True) as txn: - for pose, audio, facial, shape, word, vid, emo, sem, trans in zip( - sample_pose_list, - sample_audio_list, - sample_facial_list, - sample_shape_list, - sample_word_list, - sample_vid_list, - sample_emo_list, - sample_sem_list, - sample_trans_list,): - k = "{:005}".format(self.n_out_samples).encode("ascii") - v = [pose, audio, facial, shape, word, emo, sem, vid, trans] - v = pyarrow.serialize(v).to_buffer() - txn.put(k, v) - self.n_out_samples += 1 - return n_filtered_out - - def __getitem__(self, idx): - with self.lmdb_env.begin(write=False) as txn: - key = "{:005}".format(idx).encode("ascii") - sample = txn.get(key) - sample = pyarrow.deserialize(sample) - tar_pose, in_audio, in_facial, in_shape, in_word, emo, sem, vid, trans = sample - #print(in_shape) - #vid = torch.from_numpy(vid).int() - emo = torch.from_numpy(emo).int() - sem = torch.from_numpy(sem).float() - in_audio = torch.from_numpy(in_audio).float() - in_word = torch.from_numpy(in_word).float() if self.args.word_cache else torch.from_numpy(in_word).int() - if self.loader_type == "test": - tar_pose = torch.from_numpy(tar_pose).float() - trans = torch.from_numpy(trans).float() - in_facial = torch.from_numpy(in_facial).float() - vid = torch.from_numpy(vid).float() - in_shape = torch.from_numpy(in_shape).float() - else: - in_shape = torch.from_numpy(in_shape).reshape((in_shape.shape[0], -1)).float() - trans = torch.from_numpy(trans).reshape((trans.shape[0], -1)).float() - vid = torch.from_numpy(vid).reshape((vid.shape[0], -1)).float() - tar_pose = torch.from_numpy(tar_pose).reshape((tar_pose.shape[0], -1)).float() - in_facial = torch.from_numpy(in_facial).reshape((in_facial.shape[0], -1)).float() - return {"pose":tar_pose, "audio":in_audio, "facial":in_facial, "beta": in_shape, "word":in_word, "id":vid, "emo":emo, "sem":sem, "trans":trans} - - -class MotionPreprocessor: - def __init__(self, skeletons): - self.skeletons = skeletons - #self.mean_pose = mean_pose - self.filtering_message = "PASS" - - def get(self): - assert (self.skeletons is not None) - - # filtering - if self.skeletons != []: - if self.check_pose_diff(): - self.skeletons = [] - self.filtering_message = "pose" - # elif self.check_spine_angle(): - # self.skeletons = [] - # self.filtering_message = "spine angle" - # elif self.check_static_motion(): - # self.skeletons = [] - # self.filtering_message = "motion" - - # if self.skeletons != []: - # self.skeletons = self.skeletons.tolist() - # for i, frame in enumerate(self.skeletons): - # assert not np.isnan(self.skeletons[i]).any() # missing joints - - return self.skeletons, self.filtering_message - - def check_static_motion(self, verbose=True): - def get_variance(skeleton, joint_idx): - wrist_pos = skeleton[:, joint_idx] - variance = np.sum(np.var(wrist_pos, axis=0)) - return variance - - left_arm_var = get_variance(self.skeletons, 6) - right_arm_var = get_variance(self.skeletons, 9) - - th = 0.0014 # exclude 13110 - # th = 0.002 # exclude 16905 - if left_arm_var < th and right_arm_var < th: - if verbose: - print("skip - check_static_motion left var {}, right var {}".format(left_arm_var, right_arm_var)) - return True - else: - if verbose: - print("pass - check_static_motion left var {}, right var {}".format(left_arm_var, right_arm_var)) - return False - - - def check_pose_diff(self, verbose=False): -# diff = np.abs(self.skeletons - self.mean_pose) # 186*1 -# diff = np.mean(diff) - -# # th = 0.017 -# th = 0.02 #0.02 # exclude 3594 -# if diff < th: -# if verbose: -# print("skip - check_pose_diff {:.5f}".format(diff)) -# return True -# # th = 3.5 #0.02 # exclude 3594 -# # if 3.5 < diff < 5: -# # if verbose: -# # print("skip - check_pose_diff {:.5f}".format(diff)) -# # return True -# else: -# if verbose: -# print("pass - check_pose_diff {:.5f}".format(diff)) - return False - - - def check_spine_angle(self, verbose=True): - def angle_between(v1, v2): - v1_u = v1 / np.linalg.norm(v1) - v2_u = v2 / np.linalg.norm(v2) - return np.arccos(np.clip(np.dot(v1_u, v2_u), -1.0, 1.0)) - - angles = [] - for i in range(self.skeletons.shape[0]): - spine_vec = self.skeletons[i, 1] - self.skeletons[i, 0] - angle = angle_between(spine_vec, [0, -1, 0]) - angles.append(angle) - - if np.rad2deg(max(angles)) > 30 or np.rad2deg(np.mean(angles)) > 20: # exclude 4495 - # if np.rad2deg(max(angles)) > 20: # exclude 8270 - if verbose: - print("skip - check_spine_angle {:.5f}, {:.5f}".format(max(angles), np.mean(angles))) - return True - else: - if verbose: - print("pass - check_spine_angle {:.5f}".format(max(angles))) - return False \ No newline at end of file diff --git a/dataloaders/beat_sep_lower.py b/dataloaders/beat_sep_lower.py deleted file mode 100644 index 01c8b228d0436cfbf3b988e8443460af702c02e8..0000000000000000000000000000000000000000 --- a/dataloaders/beat_sep_lower.py +++ /dev/null @@ -1,855 +0,0 @@ -import os -import pickle -import math -import shutil -import numpy as np -import lmdb as lmdb -import textgrid as tg -import pandas as pd -import torch -import glob -import json -from termcolor import colored -from loguru import logger -from collections import defaultdict -from torch.utils.data import Dataset -import torch.distributed as dist -import pyarrow -import librosa -import smplx - -from .build_vocab import Vocab -from .utils.audio_features import Wav2Vec2Model -from .data_tools import joints_list -from .utils import rotation_conversions as rc -from .utils import other_tools - -class CustomDataset(Dataset): - def __init__(self, args, loader_type, augmentation=None, kwargs=None, build_cache=True): - self.args = args - self.loader_type = loader_type - - self.rank = dist.get_rank() - self.ori_stride = self.args.stride - self.ori_length = self.args.pose_length - self.alignment = [0,0] # for trinity - - self.ori_joint_list = joints_list[self.args.ori_joints] - self.tar_joint_list = joints_list[self.args.tar_joints] - if 'smplx' in self.args.pose_rep: - self.joint_mask = np.zeros(len(list(self.ori_joint_list.keys()))*3) - self.joints = len(list(self.tar_joint_list.keys())) - for joint_name in self.tar_joint_list: - self.joint_mask[self.ori_joint_list[joint_name][1] - self.ori_joint_list[joint_name][0]:self.ori_joint_list[joint_name][1]] = 1 - else: - self.joints = len(list(self.ori_joint_list.keys()))+1 - self.joint_mask = np.zeros(self.joints*3) - for joint_name in self.tar_joint_list: - if joint_name == "Hips": - self.joint_mask[3:6] = 1 - else: - self.joint_mask[self.ori_joint_list[joint_name][1] - self.ori_joint_list[joint_name][0]:self.ori_joint_list[joint_name][1]] = 1 - # select trainable joints - self.smplx = smplx.create( - self.args.data_path_1+"smplx_models/", - model_type='smplx', - gender='NEUTRAL_2020', - use_face_contour=False, - num_betas=300, - num_expression_coeffs=100, - ext='npz', - use_pca=False, - ).cuda().eval() - - split_rule = pd.read_csv(args.data_path+"train_test_split.csv") - self.selected_file = split_rule.loc[(split_rule['type'] == loader_type) & (split_rule['id'].str.split("_").str[0].astype(int).isin(self.args.training_speakers))] - if args.additional_data and loader_type == 'train': - split_b = split_rule.loc[(split_rule['type'] == 'additional') & (split_rule['id'].str.split("_").str[0].astype(int).isin(self.args.training_speakers))] - #self.selected_file = split_rule.loc[(split_rule['type'] == 'additional') & (split_rule['id'].str.split("_").str[0].astype(int).isin(self.args.training_speakers))] - self.selected_file = pd.concat([self.selected_file, split_b]) - if self.selected_file.empty: - logger.warning(f"{loader_type} is empty for speaker {self.args.training_speakers}, use train set 0-8 instead") - self.selected_file = split_rule.loc[(split_rule['type'] == 'train') & (split_rule['id'].str.split("_").str[0].astype(int).isin(self.args.training_speakers))] - self.selected_file = self.selected_file.iloc[0:8] - self.data_dir = args.data_path - - if loader_type == "test": - self.args.multi_length_training = [1.0] - self.max_length = int(args.pose_length * self.args.multi_length_training[-1]) - self.max_audio_pre_len = math.floor(args.pose_length / args.pose_fps * self.args.audio_sr) - if self.max_audio_pre_len > self.args.test_length*self.args.audio_sr: - self.max_audio_pre_len = self.args.test_length*self.args.audio_sr - - if args.word_rep is not None: - with open(f"{args.data_path}weights/vocab.pkl", 'rb') as f: - self.lang_model = pickle.load(f) - - preloaded_dir = self.args.root_path + self.args.cache_path + loader_type + f"/{args.pose_rep}_cache" - # if args.pose_norm: - # # careful for rotation vectors - # if not os.path.exists(args.data_path+args.mean_pose_path+f"{args.pose_rep.split('_')[0]}/bvh_mean.npy"): - # self.calculate_mean_pose() - # self.mean_pose = np.load(args.data_path+args.mean_pose_path+f"{args.pose_rep.split('_')[0]}/bvh_mean.npy") - # self.std_pose = np.load(args.data_path+args.mean_pose_path+f"{args.pose_rep.split('_')[0]}/bvh_std.npy") - # if args.audio_norm: - # if not os.path.exists(args.data_path+args.mean_pose_path+f"{args.audio_rep.split('_')[0]}/bvh_mean.npy"): - # self.calculate_mean_audio() - # self.mean_audio = np.load(args.data_path+args.mean_pose_path+f"{args.audio_rep.split('_')[0]}/npy_mean.npy") - # self.std_audio = np.load(args.data_path+args.mean_pose_path+f"{args.audio_rep.split('_')[0]}/npy_std.npy") - # if args.facial_norm: - # if not os.path.exists(args.data_path+args.mean_pose_path+f"{args.pose_rep.split('_')[0]}/bvh_mean.npy"): - # self.calculate_mean_face() - # self.mean_facial = np.load(args.data_path+args.mean_pose_path+f"{args.facial_rep}/json_mean.npy") - # self.std_facial = np.load(args.data_path+args.mean_pose_path+f"{args.facial_rep}/json_std.npy") - if self.args.beat_align: - if not os.path.exists(args.data_path+f"weights/mean_vel_{args.pose_rep}.npy"): - self.calculate_mean_velocity(args.data_path+f"weights/mean_vel_{args.pose_rep}.npy") - self.avg_vel = np.load(args.data_path+f"weights/mean_vel_{args.pose_rep}.npy") - - if build_cache and self.rank == 0: - self.build_cache(preloaded_dir) - self.lmdb_env = lmdb.open(preloaded_dir, readonly=True, lock=False) - with self.lmdb_env.begin() as txn: - self.n_samples = txn.stat()["entries"] - - - def calculate_mean_velocity(self, save_path): - self.smplx = smplx.create( - self.args.data_path_1+"smplx_models/", - model_type='smplx', - gender='NEUTRAL_2020', - use_face_contour=False, - num_betas=300, - num_expression_coeffs=100, - ext='npz', - use_pca=False, - ).cuda().eval() - dir_p = self.data_dir + self.args.pose_rep + "/" - all_list = [] - from tqdm import tqdm - for tar in tqdm(os.listdir(dir_p)): - if tar.endswith(".npz"): - m_data = np.load(dir_p+tar, allow_pickle=True) - betas, poses, trans, exps = m_data["betas"], m_data["poses"], m_data["trans"], m_data["expressions"] - n, c = poses.shape[0], poses.shape[1] - betas = betas.reshape(1, 300) - betas = np.tile(betas, (n, 1)) - betas = torch.from_numpy(betas).cuda().float() - poses = torch.from_numpy(poses.reshape(n, c)).cuda().float() - exps = torch.from_numpy(exps.reshape(n, 100)).cuda().float() - trans = torch.from_numpy(trans.reshape(n, 3)).cuda().float() - max_length = 128 - s, r = n//max_length, n%max_length - #print(n, s, r) - all_tensor = [] - for i in range(s): - with torch.no_grad(): - joints = self.smplx( - betas=betas[i*max_length:(i+1)*max_length], - transl=trans[i*max_length:(i+1)*max_length], - expression=exps[i*max_length:(i+1)*max_length], - jaw_pose=poses[i*max_length:(i+1)*max_length, 66:69], - global_orient=poses[i*max_length:(i+1)*max_length,:3], - body_pose=poses[i*max_length:(i+1)*max_length,3:21*3+3], - left_hand_pose=poses[i*max_length:(i+1)*max_length,25*3:40*3], - right_hand_pose=poses[i*max_length:(i+1)*max_length,40*3:55*3], - return_verts=True, - return_joints=True, - leye_pose=poses[i*max_length:(i+1)*max_length, 69:72], - reye_pose=poses[i*max_length:(i+1)*max_length, 72:75], - )['joints'][:, :55, :].reshape(max_length, 55*3) - all_tensor.append(joints) - if r != 0: - with torch.no_grad(): - joints = self.smplx( - betas=betas[s*max_length:s*max_length+r], - transl=trans[s*max_length:s*max_length+r], - expression=exps[s*max_length:s*max_length+r], - jaw_pose=poses[s*max_length:s*max_length+r, 66:69], - global_orient=poses[s*max_length:s*max_length+r,:3], - body_pose=poses[s*max_length:s*max_length+r,3:21*3+3], - left_hand_pose=poses[s*max_length:s*max_length+r,25*3:40*3], - right_hand_pose=poses[s*max_length:s*max_length+r,40*3:55*3], - return_verts=True, - return_joints=True, - leye_pose=poses[s*max_length:s*max_length+r, 69:72], - reye_pose=poses[s*max_length:s*max_length+r, 72:75], - )['joints'][:, :55, :].reshape(r, 55*3) - all_tensor.append(joints) - joints = torch.cat(all_tensor, axis=0) - joints = joints.permute(1, 0) - dt = 1/30 - # first steps is forward diff (t+1 - t) / dt - init_vel = (joints[:, 1:2] - joints[:, :1]) / dt - # middle steps are second order (t+1 - t-1) / 2dt - middle_vel = (joints[:, 2:] - joints[:, 0:-2]) / (2 * dt) - # last step is backward diff (t - t-1) / dt - final_vel = (joints[:, -1:] - joints[:, -2:-1]) / dt - #print(joints.shape, init_vel.shape, middle_vel.shape, final_vel.shape) - vel_seq = torch.cat([init_vel, middle_vel, final_vel], dim=1).permute(1, 0).reshape(n, 55, 3) - #print(vel_seq.shape) - #.permute(1, 0).reshape(n, 55, 3) - vel_seq_np = vel_seq.cpu().numpy() - vel_joints_np = np.linalg.norm(vel_seq_np, axis=2) # n * 55 - all_list.append(vel_joints_np) - avg_vel = np.mean(np.concatenate(all_list, axis=0),axis=0) # 55 - np.save(save_path, avg_vel) - - - def build_cache(self, preloaded_dir): - logger.info(f"Audio bit rate: {self.args.audio_fps}") - logger.info("Reading data '{}'...".format(self.data_dir)) - logger.info("Creating the dataset cache...") - if self.args.new_cache: - if os.path.exists(preloaded_dir): - shutil.rmtree(preloaded_dir) - if os.path.exists(preloaded_dir): - logger.info("Found the cache {}".format(preloaded_dir)) - elif self.loader_type == "test": - self.cache_generation( - preloaded_dir, True, - 0, 0, - is_test=True) - else: - self.cache_generation( - preloaded_dir, self.args.disable_filtering, - self.args.clean_first_seconds, self.args.clean_final_seconds, - is_test=False) - - def __len__(self): - return self.n_samples - - def idmapping(self, id): - # map 1,2,3,4,5, 6,7,9,10,11, 12,13,15,16,17, 18,20,21,22,23, 24,25,27,28,30 to 0-24 - if id == 30: id = 8 - if id == 28: id = 14 - if id == 27: id = 19 - return id - 1 - - def cache_generation(self, out_lmdb_dir, disable_filtering, clean_first_seconds, clean_final_seconds, is_test=False): - # if "wav2vec2" in self.args.audio_rep: - # self.wav2vec_model = Wav2Vec2Model.from_pretrained(f"{self.args.data_path_1}/hub/transformer/wav2vec2-base-960h") - # self.wav2vec_model.feature_extractor._freeze_parameters() - # self.wav2vec_model = self.wav2vec_model.cuda() - # self.wav2vec_model.eval() - - self.n_out_samples = 0 - # create db for samples - if not os.path.exists(out_lmdb_dir): os.makedirs(out_lmdb_dir) - if len(self.args.training_speakers) == 1: - dst_lmdb_env = lmdb.open(out_lmdb_dir, map_size= int(1024 ** 3 * 50))# 50G - else: - dst_lmdb_env = lmdb.open(out_lmdb_dir, map_size= int(1024 ** 3 * 200))# 200G - n_filtered_out = defaultdict(int) - - for index, file_name in self.selected_file.iterrows(): - f_name = file_name["id"] - ext = ".npz" if "smplx" in self.args.pose_rep else ".bvh" - pose_file = self.data_dir + self.args.pose_rep + "/" + f_name + ext - pose_each_file = [] - trans_each_file = [] - shape_each_file = [] - audio_each_file = [] - facial_each_file = [] - word_each_file = [] - emo_each_file = [] - sem_each_file = [] - vid_each_file = [] - id_pose = f_name #1_wayne_0_1_1 - - logger.info(colored(f"# ---- Building cache for Pose {id_pose} ---- #", "blue")) - if "smplx" in self.args.pose_rep: - pose_data = np.load(pose_file, allow_pickle=True) - assert 30%self.args.pose_fps == 0, 'pose_fps should be an aliquot part of 30' - stride = int(30/self.args.pose_fps) - pose_each_file = pose_data["poses"][::stride] - trans_each_file = pose_data["trans"][::stride] - shape_each_file = np.repeat(pose_data["betas"].reshape(1, 300), pose_each_file.shape[0], axis=0) - - assert self.args.pose_fps == 30, "should 30" - m_data = np.load(pose_file, allow_pickle=True) - betas, poses, trans, exps = m_data["betas"], m_data["poses"], m_data["trans"], m_data["expressions"] - n, c = poses.shape[0], poses.shape[1] - betas = betas.reshape(1, 300) - betas = np.tile(betas, (n, 1)) - betas = torch.from_numpy(betas).cuda().float() - poses = torch.from_numpy(poses.reshape(n, c)).cuda().float() - exps = torch.from_numpy(exps.reshape(n, 100)).cuda().float() - trans = torch.from_numpy(trans.reshape(n, 3)).cuda().float() - max_length = 128 - s, r = n//max_length, n%max_length - #print(n, s, r) - all_tensor = [] - for i in range(s): - with torch.no_grad(): - joints = self.smplx( - betas=betas[i*max_length:(i+1)*max_length], - transl=trans[i*max_length:(i+1)*max_length], - expression=exps[i*max_length:(i+1)*max_length], - jaw_pose=poses[i*max_length:(i+1)*max_length, 66:69], - global_orient=poses[i*max_length:(i+1)*max_length,:3], - body_pose=poses[i*max_length:(i+1)*max_length,3:21*3+3], - left_hand_pose=poses[i*max_length:(i+1)*max_length,25*3:40*3], - right_hand_pose=poses[i*max_length:(i+1)*max_length,40*3:55*3], - return_verts=True, - return_joints=True, - leye_pose=poses[i*max_length:(i+1)*max_length, 69:72], - reye_pose=poses[i*max_length:(i+1)*max_length, 72:75], - )['joints'][:, (7,8,10,11), :].reshape(max_length, 4, 3).cpu() - all_tensor.append(joints) - if r != 0: - with torch.no_grad(): - joints = self.smplx( - betas=betas[s*max_length:s*max_length+r], - transl=trans[s*max_length:s*max_length+r], - expression=exps[s*max_length:s*max_length+r], - jaw_pose=poses[s*max_length:s*max_length+r, 66:69], - global_orient=poses[s*max_length:s*max_length+r,:3], - body_pose=poses[s*max_length:s*max_length+r,3:21*3+3], - left_hand_pose=poses[s*max_length:s*max_length+r,25*3:40*3], - right_hand_pose=poses[s*max_length:s*max_length+r,40*3:55*3], - return_verts=True, - return_joints=True, - leye_pose=poses[s*max_length:s*max_length+r, 69:72], - reye_pose=poses[s*max_length:s*max_length+r, 72:75], - )['joints'][:, (7,8,10,11), :].reshape(r, 4, 3).cpu() - all_tensor.append(joints) - joints = torch.cat(all_tensor, axis=0) # all, 4, 3 - # print(joints.shape) - feetv = torch.zeros(joints.shape[1], joints.shape[0]) - joints = joints.permute(1, 0, 2) - #print(joints.shape, feetv.shape) - feetv[:, :-1] = (joints[:, 1:] - joints[:, :-1]).norm(dim=-1) - #print(feetv.shape) - contacts = (feetv < 0.01).numpy().astype(float) - # print(contacts.shape, contacts) - contacts = contacts.transpose(1, 0) - pose_each_file = pose_each_file * self.joint_mask - pose_each_file = pose_each_file[:, self.joint_mask.astype(bool)] - pose_each_file = np.concatenate([pose_each_file, contacts], axis=1) - # print(pose_each_file.shape) - - - if self.args.facial_rep is not None: - logger.info(f"# ---- Building cache for Facial {id_pose} and Pose {id_pose} ---- #") - facial_each_file = pose_data["expressions"][::stride] - if self.args.facial_norm: - facial_each_file = (facial_each_file - self.mean_facial) / self.std_facial - - else: - assert 120%self.args.pose_fps == 0, 'pose_fps should be an aliquot part of 120' - stride = int(120/self.args.pose_fps) - with open(pose_file, "r") as pose_data: - for j, line in enumerate(pose_data.readlines()): - if j < 431: continue - if j%stride != 0:continue - data = np.fromstring(line, dtype=float, sep=" ") - rot_data = rc.euler_angles_to_matrix(torch.from_numpy(np.deg2rad(data)).reshape(-1, self.joints,3), "XYZ") - rot_data = rc.matrix_to_axis_angle(rot_data).reshape(-1, self.joints*3) - rot_data = rot_data.numpy() * self.joint_mask - - pose_each_file.append(rot_data) - trans_each_file.append(data[:3]) - - pose_each_file = np.array(pose_each_file) - # print(pose_each_file.shape) - trans_each_file = np.array(trans_each_file) - shape_each_file = np.repeat(np.array(-1).reshape(1, 1), pose_each_file.shape[0], axis=0) - if self.args.facial_rep is not None: - logger.info(f"# ---- Building cache for Facial {id_pose} and Pose {id_pose} ---- #") - facial_file = pose_file.replace(self.args.pose_rep, self.args.facial_rep).replace("bvh", "json") - assert 60%self.args.pose_fps == 0, 'pose_fps should be an aliquot part of 120' - stride = int(60/self.args.pose_fps) - if not os.path.exists(facial_file): - logger.warning(f"# ---- file not found for Facial {id_pose}, skip all files with the same id ---- #") - self.selected_file = self.selected_file.drop(self.selected_file[self.selected_file['id'] == id_pose].index) - continue - with open(facial_file, 'r') as facial_data_file: - facial_data = json.load(facial_data_file) - for j, frame_data in enumerate(facial_data['frames']): - if j%stride != 0:continue - facial_each_file.append(frame_data['weights']) - facial_each_file = np.array(facial_each_file) - if self.args.facial_norm: - facial_each_file = (facial_each_file - self.mean_facial) / self.std_facial - - if self.args.id_rep is not None: - int_value = self.idmapping(int(f_name.split("_")[0])) - vid_each_file = np.repeat(np.array(int_value).reshape(1, 1), pose_each_file.shape[0], axis=0) - - if self.args.audio_rep is not None: - logger.info(f"# ---- Building cache for Audio {id_pose} and Pose {id_pose} ---- #") - audio_file = pose_file.replace(self.args.pose_rep, 'wave16k').replace(ext, ".wav") - if not os.path.exists(audio_file): - logger.warning(f"# ---- file not found for Audio {id_pose}, skip all files with the same id ---- #") - self.selected_file = self.selected_file.drop(self.selected_file[self.selected_file['id'] == id_pose].index) - continue - audio_each_file, sr = librosa.load(audio_file) - audio_each_file = librosa.resample(audio_each_file, orig_sr=sr, target_sr=self.args.audio_sr) - if self.args.audio_rep == "onset+amplitude": - from numpy.lib import stride_tricks - frame_length = 1024 - # hop_length = 512 - shape = (audio_each_file.shape[-1] - frame_length + 1, frame_length) - strides = (audio_each_file.strides[-1], audio_each_file.strides[-1]) - rolling_view = stride_tricks.as_strided(audio_each_file, shape=shape, strides=strides) - amplitude_envelope = np.max(np.abs(rolling_view), axis=1) - # pad the last frame_length-1 samples - amplitude_envelope = np.pad(amplitude_envelope, (0, frame_length-1), mode='constant', constant_values=amplitude_envelope[-1]) - audio_onset_f = librosa.onset.onset_detect(y=audio_each_file, sr=self.args.audio_sr, units='frames') - onset_array = np.zeros(len(audio_each_file), dtype=float) - onset_array[audio_onset_f] = 1.0 - # print(amplitude_envelope.shape, audio_each_file.shape, onset_array.shape) - audio_each_file = np.concatenate([amplitude_envelope.reshape(-1, 1), onset_array.reshape(-1, 1)], axis=1) - elif self.args.audio_rep == "mfcc": - audio_each_file = librosa.feature.melspectrogram(y=audio_each_file, sr=self.args.audio_sr, n_mels=128, hop_length=int(self.args.audio_sr/self.args.audio_fps)) - audio_each_file = audio_each_file.transpose(1, 0) - # print(audio_each_file.shape, pose_each_file.shape) - if self.args.audio_norm and self.args.audio_rep == "wave16k": - audio_each_file = (audio_each_file - self.mean_audio) / self.std_audio - - time_offset = 0 - if self.args.word_rep is not None: - logger.info(f"# ---- Building cache for Word {id_pose} and Pose {id_pose} ---- #") - word_file = f"{self.data_dir}{self.args.word_rep}/{id_pose}.TextGrid" - if not os.path.exists(word_file): - logger.warning(f"# ---- file not found for Word {id_pose}, skip all files with the same id ---- #") - self.selected_file = self.selected_file.drop(self.selected_file[self.selected_file['id'] == id_pose].index) - continue - tgrid = tg.TextGrid.fromFile(word_file) - if self.args.t_pre_encoder == "bert": - from transformers import AutoTokenizer, BertModel - tokenizer = AutoTokenizer.from_pretrained(self.args.data_path_1 + "hub/bert-base-uncased", local_files_only=True) - model = BertModel.from_pretrained(self.args.data_path_1 + "hub/bert-base-uncased", local_files_only=True).eval() - list_word = [] - all_hidden = [] - max_len = 400 - last = 0 - word_token_mapping = [] - first = True - for i, word in enumerate(tgrid[0]): - last = i - if (i%max_len != 0) or (i==0): - if word.mark == "": - list_word.append(".") - else: - list_word.append(word.mark) - else: - max_counter = max_len - str_word = ' '.join(map(str, list_word)) - if first: - global_len = 0 - end = -1 - offset_word = [] - for k, wordvalue in enumerate(list_word): - start = end+1 - end = start+len(wordvalue) - offset_word.append((start, end)) - #print(offset_word) - token_scan = tokenizer.encode_plus(str_word, return_offsets_mapping=True)['offset_mapping'] - #print(token_scan) - for start, end in offset_word: - sub_mapping = [] - for i, (start_t, end_t) in enumerate(token_scan[1:-1]): - if int(start) <= int(start_t) and int(end_t) <= int(end): - #print(i+global_len) - sub_mapping.append(i+global_len) - word_token_mapping.append(sub_mapping) - #print(len(word_token_mapping)) - global_len = word_token_mapping[-1][-1] + 1 - list_word = [] - if word.mark == "": - list_word.append(".") - else: - list_word.append(word.mark) - - with torch.no_grad(): - inputs = tokenizer(str_word, return_tensors="pt") - outputs = model(**inputs) - last_hidden_states = outputs.last_hidden_state.reshape(-1, 768).cpu().numpy()[1:-1, :] - all_hidden.append(last_hidden_states) - - #list_word = list_word[:10] - if list_word == []: - pass - else: - if first: - global_len = 0 - str_word = ' '.join(map(str, list_word)) - end = -1 - offset_word = [] - for k, wordvalue in enumerate(list_word): - start = end+1 - end = start+len(wordvalue) - offset_word.append((start, end)) - #print(offset_word) - token_scan = tokenizer.encode_plus(str_word, return_offsets_mapping=True)['offset_mapping'] - #print(token_scan) - for start, end in offset_word: - sub_mapping = [] - for i, (start_t, end_t) in enumerate(token_scan[1:-1]): - if int(start) <= int(start_t) and int(end_t) <= int(end): - sub_mapping.append(i+global_len) - #print(sub_mapping) - word_token_mapping.append(sub_mapping) - #print(len(word_token_mapping)) - with torch.no_grad(): - inputs = tokenizer(str_word, return_tensors="pt") - outputs = model(**inputs) - last_hidden_states = outputs.last_hidden_state.reshape(-1, 768).cpu().numpy()[1:-1, :] - all_hidden.append(last_hidden_states) - last_hidden_states = np.concatenate(all_hidden, axis=0) - - for i in range(pose_each_file.shape[0]): - found_flag = False - current_time = i/self.args.pose_fps + time_offset - j_last = 0 - for j, word in enumerate(tgrid[0]): - word_n, word_s, word_e = word.mark, word.minTime, word.maxTime - if word_s<=current_time and current_time<=word_e: - if self.args.word_cache and self.args.t_pre_encoder == 'bert': - mapping_index = word_token_mapping[j] - #print(mapping_index, word_s, word_e) - s_t = np.linspace(word_s, word_e, len(mapping_index)+1) - #print(s_t) - for tt, t_sep in enumerate(s_t[1:]): - if current_time <= t_sep: - #if len(mapping_index) > 1: print(mapping_index[tt]) - word_each_file.append(last_hidden_states[mapping_index[tt]]) - break - else: - if word_n == " ": - word_each_file.append(self.lang_model.PAD_token) - else: - word_each_file.append(self.lang_model.get_word_index(word_n)) - found_flag = True - j_last = j - break - else: continue - if not found_flag: - if self.args.word_cache and self.args.t_pre_encoder == 'bert': - word_each_file.append(last_hidden_states[j_last]) - else: - word_each_file.append(self.lang_model.UNK_token) - word_each_file = np.array(word_each_file) - #print(word_each_file.shape) - - if self.args.emo_rep is not None: - logger.info(f"# ---- Building cache for Emo {id_pose} and Pose {id_pose} ---- #") - rtype, start = int(id_pose.split('_')[3]), int(id_pose.split('_')[3]) - if rtype == 0 or rtype == 2 or rtype == 4 or rtype == 6: - if start >= 1 and start <= 64: - score = 0 - elif start >= 65 and start <= 72: - score = 1 - elif start >= 73 and start <= 80: - score = 2 - elif start >= 81 and start <= 86: - score = 3 - elif start >= 87 and start <= 94: - score = 4 - elif start >= 95 and start <= 102: - score = 5 - elif start >= 103 and start <= 110: - score = 6 - elif start >= 111 and start <= 118: - score = 7 - else: pass - else: - # you may denote as unknown in the future - score = 0 - emo_each_file = np.repeat(np.array(score).reshape(1, 1), pose_each_file.shape[0], axis=0) - #print(emo_each_file) - - if self.args.sem_rep is not None: - logger.info(f"# ---- Building cache for Sem {id_pose} and Pose {id_pose} ---- #") - sem_file = f"{self.data_dir}{self.args.sem_rep}/{id_pose}.txt" - sem_all = pd.read_csv(sem_file, - sep='\t', - names=["name", "start_time", "end_time", "duration", "score", "keywords"]) - # we adopt motion-level semantic score here. - for i in range(pose_each_file.shape[0]): - found_flag = False - for j, (start, end, score) in enumerate(zip(sem_all['start_time'],sem_all['end_time'], sem_all['score'])): - current_time = i/self.args.pose_fps + time_offset - if start<=current_time and current_time<=end: - sem_each_file.append(score) - found_flag=True - break - else: continue - if not found_flag: sem_each_file.append(0.) - sem_each_file = np.array(sem_each_file) - #print(sem_each_file) - - filtered_result = self._sample_from_clip( - dst_lmdb_env, - audio_each_file, pose_each_file, trans_each_file, shape_each_file, facial_each_file, word_each_file, - vid_each_file, emo_each_file, sem_each_file, - disable_filtering, clean_first_seconds, clean_final_seconds, is_test, - ) - for type in filtered_result.keys(): - n_filtered_out[type] += filtered_result[type] - - with dst_lmdb_env.begin() as txn: - logger.info(colored(f"no. of samples: {txn.stat()['entries']}", "cyan")) - n_total_filtered = 0 - for type, n_filtered in n_filtered_out.items(): - logger.info("{}: {}".format(type, n_filtered)) - n_total_filtered += n_filtered - logger.info(colored("no. of excluded samples: {} ({:.1f}%)".format( - n_total_filtered, 100 * n_total_filtered / (txn.stat()["entries"] + n_total_filtered)), "cyan")) - dst_lmdb_env.sync() - dst_lmdb_env.close() - - def _sample_from_clip( - self, dst_lmdb_env, audio_each_file, pose_each_file, trans_each_file, shape_each_file, facial_each_file, word_each_file, - vid_each_file, emo_each_file, sem_each_file, - disable_filtering, clean_first_seconds, clean_final_seconds, is_test, - ): - """ - for data cleaning, we ignore the data for first and final n s - for test, we return all data - """ - # audio_start = int(self.alignment[0] * self.args.audio_fps) - # pose_start = int(self.alignment[1] * self.args.pose_fps) - #logger.info(f"before: {audio_each_file.shape} {pose_each_file.shape}") - # audio_each_file = audio_each_file[audio_start:] - # pose_each_file = pose_each_file[pose_start:] - # trans_each_file = - #logger.info(f"after alignment: {audio_each_file.shape} {pose_each_file.shape}") - #print(pose_each_file.shape) - round_seconds_skeleton = pose_each_file.shape[0] // self.args.pose_fps # assume 1500 frames / 15 fps = 100 s - #print(round_seconds_skeleton) - if audio_each_file != []: - if self.args.audio_rep != "wave16k": - round_seconds_audio = len(audio_each_file) // self.args.audio_fps # assume 16,000,00 / 16,000 = 100 s - elif self.args.audio_rep == "mfcc": - round_seconds_audio = audio_each_file.shape[0] // self.args.audio_fps - else: - round_seconds_audio = audio_each_file.shape[0] // self.args.audio_sr - if facial_each_file != []: - round_seconds_facial = facial_each_file.shape[0] // self.args.pose_fps - logger.info(f"audio: {round_seconds_audio}s, pose: {round_seconds_skeleton}s, facial: {round_seconds_facial}s") - round_seconds_skeleton = min(round_seconds_audio, round_seconds_skeleton, round_seconds_facial) - max_round = max(round_seconds_audio, round_seconds_skeleton, round_seconds_facial) - if round_seconds_skeleton != max_round: - logger.warning(f"reduce to {round_seconds_skeleton}s, ignore {max_round-round_seconds_skeleton}s") - else: - logger.info(f"pose: {round_seconds_skeleton}s, audio: {round_seconds_audio}s") - round_seconds_skeleton = min(round_seconds_audio, round_seconds_skeleton) - max_round = max(round_seconds_audio, round_seconds_skeleton) - if round_seconds_skeleton != max_round: - logger.warning(f"reduce to {round_seconds_skeleton}s, ignore {max_round-round_seconds_skeleton}s") - - clip_s_t, clip_e_t = clean_first_seconds, round_seconds_skeleton - clean_final_seconds # assume [10, 90]s - clip_s_f_audio, clip_e_f_audio = self.args.audio_fps * clip_s_t, clip_e_t * self.args.audio_fps # [160,000,90*160,000] - clip_s_f_pose, clip_e_f_pose = clip_s_t * self.args.pose_fps, clip_e_t * self.args.pose_fps # [150,90*15] - - - for ratio in self.args.multi_length_training: - if is_test:# stride = length for test - cut_length = clip_e_f_pose - clip_s_f_pose - self.args.stride = cut_length - self.max_length = cut_length - else: - self.args.stride = int(ratio*self.ori_stride) - cut_length = int(self.ori_length*ratio) - - num_subdivision = math.floor((clip_e_f_pose - clip_s_f_pose - cut_length) / self.args.stride) + 1 - logger.info(f"pose from frame {clip_s_f_pose} to {clip_e_f_pose}, length {cut_length}") - logger.info(f"{num_subdivision} clips is expected with stride {self.args.stride}") - - if audio_each_file != []: - audio_short_length = math.floor(cut_length / self.args.pose_fps * self.args.audio_fps) - """ - for audio sr = 16000, fps = 15, pose_length = 34, - audio short length = 36266.7 -> 36266 - this error is fine. - """ - logger.info(f"audio from frame {clip_s_f_audio} to {clip_e_f_audio}, length {audio_short_length}") - - n_filtered_out = defaultdict(int) - sample_pose_list = [] - sample_audio_list = [] - sample_facial_list = [] - sample_shape_list = [] - sample_word_list = [] - sample_emo_list = [] - sample_sem_list = [] - sample_vid_list = [] - sample_trans_list = [] - - for i in range(num_subdivision): # cut into around 2s chip, (self npose) - start_idx = clip_s_f_pose + i * self.args.stride - fin_idx = start_idx + cut_length - sample_pose = pose_each_file[start_idx:fin_idx] - - sample_trans = trans_each_file[start_idx:fin_idx] - sample_shape = shape_each_file[start_idx:fin_idx] - # print(sample_pose.shape) - if self.args.audio_rep is not None: - audio_start = clip_s_f_audio + math.floor(i * self.args.stride * self.args.audio_fps / self.args.pose_fps) - audio_end = audio_start + audio_short_length - sample_audio = audio_each_file[audio_start:audio_end] - else: - sample_audio = np.array([-1]) - sample_facial = facial_each_file[start_idx:fin_idx] if self.args.facial_rep is not None else np.array([-1]) - sample_word = word_each_file[start_idx:fin_idx] if self.args.word_rep is not None else np.array([-1]) - sample_emo = emo_each_file[start_idx:fin_idx] if self.args.emo_rep is not None else np.array([-1]) - sample_sem = sem_each_file[start_idx:fin_idx] if self.args.sem_rep is not None else np.array([-1]) - sample_vid = vid_each_file[start_idx:fin_idx] if self.args.id_rep is not None else np.array([-1]) - - if sample_pose.any() != None: - # filtering motion skeleton data - sample_pose, filtering_message = MotionPreprocessor(sample_pose).get() - is_correct_motion = (sample_pose != []) - if is_correct_motion or disable_filtering: - sample_pose_list.append(sample_pose) - sample_audio_list.append(sample_audio) - sample_facial_list.append(sample_facial) - sample_shape_list.append(sample_shape) - sample_word_list.append(sample_word) - sample_vid_list.append(sample_vid) - sample_emo_list.append(sample_emo) - sample_sem_list.append(sample_sem) - sample_trans_list.append(sample_trans) - else: - n_filtered_out[filtering_message] += 1 - - if len(sample_pose_list) > 0: - with dst_lmdb_env.begin(write=True) as txn: - for pose, audio, facial, shape, word, vid, emo, sem, trans in zip( - sample_pose_list, - sample_audio_list, - sample_facial_list, - sample_shape_list, - sample_word_list, - sample_vid_list, - sample_emo_list, - sample_sem_list, - sample_trans_list,): - k = "{:005}".format(self.n_out_samples).encode("ascii") - v = [pose, audio, facial, shape, word, emo, sem, vid, trans] - v = pyarrow.serialize(v).to_buffer() - txn.put(k, v) - self.n_out_samples += 1 - return n_filtered_out - - def __getitem__(self, idx): - with self.lmdb_env.begin(write=False) as txn: - key = "{:005}".format(idx).encode("ascii") - sample = txn.get(key) - sample = pyarrow.deserialize(sample) - tar_pose, in_audio, in_facial, in_shape, in_word, emo, sem, vid, trans = sample - #print(in_shape) - #vid = torch.from_numpy(vid).int() - emo = torch.from_numpy(emo).int() - sem = torch.from_numpy(sem).float() - in_audio = torch.from_numpy(in_audio).float() - in_word = torch.from_numpy(in_word).float() if self.args.word_cache else torch.from_numpy(in_word).int() - if self.loader_type == "test": - tar_pose = torch.from_numpy(tar_pose).float() - trans = torch.from_numpy(trans).float() - in_facial = torch.from_numpy(in_facial).float() - vid = torch.from_numpy(vid).float() - in_shape = torch.from_numpy(in_shape).float() - else: - in_shape = torch.from_numpy(in_shape).reshape((in_shape.shape[0], -1)).float() - trans = torch.from_numpy(trans).reshape((trans.shape[0], -1)).float() - vid = torch.from_numpy(vid).reshape((vid.shape[0], -1)).float() - tar_pose = torch.from_numpy(tar_pose).reshape((tar_pose.shape[0], -1)).float() - in_facial = torch.from_numpy(in_facial).reshape((in_facial.shape[0], -1)).float() - return {"pose":tar_pose, "audio":in_audio, "facial":in_facial, "beta": in_shape, "word":in_word, "id":vid, "emo":emo, "sem":sem, "trans":trans} - - -class MotionPreprocessor: - def __init__(self, skeletons): - self.skeletons = skeletons - #self.mean_pose = mean_pose - self.filtering_message = "PASS" - - def get(self): - assert (self.skeletons is not None) - - # filtering - if self.skeletons != []: - if self.check_pose_diff(): - self.skeletons = [] - self.filtering_message = "pose" - # elif self.check_spine_angle(): - # self.skeletons = [] - # self.filtering_message = "spine angle" - # elif self.check_static_motion(): - # self.skeletons = [] - # self.filtering_message = "motion" - - # if self.skeletons != []: - # self.skeletons = self.skeletons.tolist() - # for i, frame in enumerate(self.skeletons): - # assert not np.isnan(self.skeletons[i]).any() # missing joints - - return self.skeletons, self.filtering_message - - def check_static_motion(self, verbose=True): - def get_variance(skeleton, joint_idx): - wrist_pos = skeleton[:, joint_idx] - variance = np.sum(np.var(wrist_pos, axis=0)) - return variance - - left_arm_var = get_variance(self.skeletons, 6) - right_arm_var = get_variance(self.skeletons, 9) - - th = 0.0014 # exclude 13110 - # th = 0.002 # exclude 16905 - if left_arm_var < th and right_arm_var < th: - if verbose: - print("skip - check_static_motion left var {}, right var {}".format(left_arm_var, right_arm_var)) - return True - else: - if verbose: - print("pass - check_static_motion left var {}, right var {}".format(left_arm_var, right_arm_var)) - return False - - - def check_pose_diff(self, verbose=False): -# diff = np.abs(self.skeletons - self.mean_pose) # 186*1 -# diff = np.mean(diff) - -# # th = 0.017 -# th = 0.02 #0.02 # exclude 3594 -# if diff < th: -# if verbose: -# print("skip - check_pose_diff {:.5f}".format(diff)) -# return True -# # th = 3.5 #0.02 # exclude 3594 -# # if 3.5 < diff < 5: -# # if verbose: -# # print("skip - check_pose_diff {:.5f}".format(diff)) -# # return True -# else: -# if verbose: -# print("pass - check_pose_diff {:.5f}".format(diff)) - return False - - - def check_spine_angle(self, verbose=True): - def angle_between(v1, v2): - v1_u = v1 / np.linalg.norm(v1) - v2_u = v2 / np.linalg.norm(v2) - return np.arccos(np.clip(np.dot(v1_u, v2_u), -1.0, 1.0)) - - angles = [] - for i in range(self.skeletons.shape[0]): - spine_vec = self.skeletons[i, 1] - self.skeletons[i, 0] - angle = angle_between(spine_vec, [0, -1, 0]) - angles.append(angle) - - if np.rad2deg(max(angles)) > 30 or np.rad2deg(np.mean(angles)) > 20: # exclude 4495 - # if np.rad2deg(max(angles)) > 20: # exclude 8270 - if verbose: - print("skip - check_spine_angle {:.5f}, {:.5f}".format(max(angles), np.mean(angles))) - return True - else: - if verbose: - print("pass - check_spine_angle {:.5f}".format(max(angles))) - return False \ No newline at end of file diff --git a/dataloaders/beat_smplx2020.py b/dataloaders/beat_smplx2020.py deleted file mode 100644 index 7647c73d5962dd8856584ebdd6cfe26902ef5070..0000000000000000000000000000000000000000 --- a/dataloaders/beat_smplx2020.py +++ /dev/null @@ -1,763 +0,0 @@ -import os -import pickle -import math -import shutil -import numpy as np -import lmdb as lmdb -import textgrid as tg -import pandas as pd -import torch -import glob -import json -from termcolor import colored -from loguru import logger -from collections import defaultdict -from torch.utils.data import Dataset -import torch.distributed as dist -import pyarrow -import librosa -import smplx - -from .build_vocab import Vocab -from .utils.audio_features import Wav2Vec2Model -from .data_tools import joints_list -from .utils import rotation_conversions as rc -from .utils import other_tools - -class CustomDataset(Dataset): - def __init__(self, args, loader_type, augmentation=None, kwargs=None, build_cache=True): - self.args = args - self.loader_type = loader_type - - self.rank = dist.get_rank() - self.ori_stride = self.args.stride - self.ori_length = self.args.pose_length - self.alignment = [0,0] # for trinity - - self.ori_joint_list = joints_list[self.args.ori_joints] - self.tar_joint_list = joints_list[self.args.tar_joints] - if 'smplx' in self.args.pose_rep: - self.joint_mask = np.zeros(len(list(self.ori_joint_list.keys()))*3) - self.joints = len(list(self.ori_joint_list.keys())) - for joint_name in self.tar_joint_list: - self.joint_mask[self.ori_joint_list[joint_name][1] - self.ori_joint_list[joint_name][0]:self.ori_joint_list[joint_name][1]] = 1 - else: - self.joints = len(list(self.ori_joint_list.keys()))+1 - self.joint_mask = np.zeros(self.joints*3) - for joint_name in self.tar_joint_list: - if joint_name == "Hips": - self.joint_mask[3:6] = 1 - else: - self.joint_mask[self.ori_joint_list[joint_name][1] - self.ori_joint_list[joint_name][0]:self.ori_joint_list[joint_name][1]] = 1 - # select trainable joints - - split_rule = pd.read_csv(args.data_path+"train_test_split.csv") - self.selected_file = split_rule.loc[(split_rule['type'] == loader_type) & (split_rule['id'].str.split("_").str[0].astype(int).isin(self.args.training_speakers))] - if args.additional_data and loader_type == 'train': - split_b = split_rule.loc[(split_rule['type'] == 'additional') & (split_rule['id'].str.split("_").str[0].astype(int).isin(self.args.training_speakers))] - #self.selected_file = split_rule.loc[(split_rule['type'] == 'additional') & (split_rule['id'].str.split("_").str[0].astype(int).isin(self.args.training_speakers))] - self.selected_file = pd.concat([self.selected_file, split_b]) - if self.selected_file.empty: - logger.warning(f"{loader_type} is empty for speaker {self.args.training_speakers}, use train set 0-8 instead") - self.selected_file = split_rule.loc[(split_rule['type'] == 'train') & (split_rule['id'].str.split("_").str[0].astype(int).isin(self.args.training_speakers))] - self.selected_file = self.selected_file.iloc[0:8] - self.data_dir = args.data_path - - if loader_type == "test": - self.args.multi_length_training = [1.0] - self.max_length = int(args.pose_length * self.args.multi_length_training[-1]) - self.max_audio_pre_len = math.floor(args.pose_length / args.pose_fps * self.args.audio_sr) - if self.max_audio_pre_len > self.args.test_length*self.args.audio_sr: - self.max_audio_pre_len = self.args.test_length*self.args.audio_sr - - if args.word_rep is not None: - with open(f"{args.data_path}weights/vocab.pkl", 'rb') as f: - self.lang_model = pickle.load(f) - - preloaded_dir = self.args.root_path + self.args.cache_path + loader_type + f"/{args.pose_rep}_cache" - # if args.pose_norm: - # # careful for rotation vectors - # if not os.path.exists(args.data_path+args.mean_pose_path+f"{args.pose_rep.split('_')[0]}/bvh_mean.npy"): - # self.calculate_mean_pose() - # self.mean_pose = np.load(args.data_path+args.mean_pose_path+f"{args.pose_rep.split('_')[0]}/bvh_mean.npy") - # self.std_pose = np.load(args.data_path+args.mean_pose_path+f"{args.pose_rep.split('_')[0]}/bvh_std.npy") - # if args.audio_norm: - # if not os.path.exists(args.data_path+args.mean_pose_path+f"{args.audio_rep.split('_')[0]}/bvh_mean.npy"): - # self.calculate_mean_audio() - # self.mean_audio = np.load(args.data_path+args.mean_pose_path+f"{args.audio_rep.split('_')[0]}/npy_mean.npy") - # self.std_audio = np.load(args.data_path+args.mean_pose_path+f"{args.audio_rep.split('_')[0]}/npy_std.npy") - # if args.facial_norm: - # if not os.path.exists(args.data_path+args.mean_pose_path+f"{args.pose_rep.split('_')[0]}/bvh_mean.npy"): - # self.calculate_mean_face() - # self.mean_facial = np.load(args.data_path+args.mean_pose_path+f"{args.facial_rep}/json_mean.npy") - # self.std_facial = np.load(args.data_path+args.mean_pose_path+f"{args.facial_rep}/json_std.npy") - if self.args.beat_align: - if not os.path.exists(args.data_path+f"weights/mean_vel_{args.pose_rep}.npy"): - self.calculate_mean_velocity(args.data_path+f"weights/mean_vel_{args.pose_rep}.npy") - self.avg_vel = np.load(args.data_path+f"weights/mean_vel_{args.pose_rep}.npy") - - if build_cache and self.rank == 0: - self.build_cache(preloaded_dir) - self.lmdb_env = lmdb.open(preloaded_dir, readonly=True, lock=False) - with self.lmdb_env.begin() as txn: - self.n_samples = txn.stat()["entries"] - - - def calculate_mean_velocity(self, save_path): - self.smplx = smplx.create( - self.args.data_path_1+"smplx_models/", - model_type='smplx', - gender='NEUTRAL_2020', - use_face_contour=False, - num_betas=300, - num_expression_coeffs=100, - ext='npz', - use_pca=False, - ).cuda().eval() - dir_p = self.data_dir + self.args.pose_rep + "/" - all_list = [] - from tqdm import tqdm - for tar in tqdm(os.listdir(dir_p)): - if tar.endswith(".npz"): - m_data = np.load(dir_p+tar, allow_pickle=True) - betas, poses, trans, exps = m_data["betas"], m_data["poses"], m_data["trans"], m_data["expressions"] - n, c = poses.shape[0], poses.shape[1] - betas = betas.reshape(1, 300) - betas = np.tile(betas, (n, 1)) - betas = torch.from_numpy(betas).cuda().float() - poses = torch.from_numpy(poses.reshape(n, c)).cuda().float() - exps = torch.from_numpy(exps.reshape(n, 100)).cuda().float() - trans = torch.from_numpy(trans.reshape(n, 3)).cuda().float() - max_length = 128 - s, r = n//max_length, n%max_length - #print(n, s, r) - all_tensor = [] - for i in range(s): - with torch.no_grad(): - joints = self.smplx( - betas=betas[i*max_length:(i+1)*max_length], - transl=trans[i*max_length:(i+1)*max_length], - expression=exps[i*max_length:(i+1)*max_length], - jaw_pose=poses[i*max_length:(i+1)*max_length, 66:69], - global_orient=poses[i*max_length:(i+1)*max_length,:3], - body_pose=poses[i*max_length:(i+1)*max_length,3:21*3+3], - left_hand_pose=poses[i*max_length:(i+1)*max_length,25*3:40*3], - right_hand_pose=poses[i*max_length:(i+1)*max_length,40*3:55*3], - return_verts=True, - return_joints=True, - leye_pose=poses[i*max_length:(i+1)*max_length, 69:72], - reye_pose=poses[i*max_length:(i+1)*max_length, 72:75], - )['joints'][:, :55, :].reshape(max_length, 55*3) - all_tensor.append(joints) - if r != 0: - with torch.no_grad(): - joints = self.smplx( - betas=betas[s*max_length:s*max_length+r], - transl=trans[s*max_length:s*max_length+r], - expression=exps[s*max_length:s*max_length+r], - jaw_pose=poses[s*max_length:s*max_length+r, 66:69], - global_orient=poses[s*max_length:s*max_length+r,:3], - body_pose=poses[s*max_length:s*max_length+r,3:21*3+3], - left_hand_pose=poses[s*max_length:s*max_length+r,25*3:40*3], - right_hand_pose=poses[s*max_length:s*max_length+r,40*3:55*3], - return_verts=True, - return_joints=True, - leye_pose=poses[s*max_length:s*max_length+r, 69:72], - reye_pose=poses[s*max_length:s*max_length+r, 72:75], - )['joints'][:, :55, :].reshape(r, 55*3) - all_tensor.append(joints) - joints = torch.cat(all_tensor, axis=0) - joints = joints.permute(1, 0) - dt = 1/30 - # first steps is forward diff (t+1 - t) / dt - init_vel = (joints[:, 1:2] - joints[:, :1]) / dt - # middle steps are second order (t+1 - t-1) / 2dt - middle_vel = (joints[:, 2:] - joints[:, 0:-2]) / (2 * dt) - # last step is backward diff (t - t-1) / dt - final_vel = (joints[:, -1:] - joints[:, -2:-1]) / dt - #print(joints.shape, init_vel.shape, middle_vel.shape, final_vel.shape) - vel_seq = torch.cat([init_vel, middle_vel, final_vel], dim=1).permute(1, 0).reshape(n, 55, 3) - #print(vel_seq.shape) - #.permute(1, 0).reshape(n, 55, 3) - vel_seq_np = vel_seq.cpu().numpy() - vel_joints_np = np.linalg.norm(vel_seq_np, axis=2) # n * 55 - all_list.append(vel_joints_np) - avg_vel = np.mean(np.concatenate(all_list, axis=0),axis=0) # 55 - np.save(save_path, avg_vel) - - - def build_cache(self, preloaded_dir): - logger.info(f"Audio bit rate: {self.args.audio_fps}") - logger.info("Reading data '{}'...".format(self.data_dir)) - logger.info("Creating the dataset cache...") - if self.args.new_cache: - if os.path.exists(preloaded_dir): - shutil.rmtree(preloaded_dir) - if os.path.exists(preloaded_dir): - logger.info("Found the cache {}".format(preloaded_dir)) - elif self.loader_type == "test": - self.cache_generation( - preloaded_dir, True, - 0, 0, - is_test=True) - else: - self.cache_generation( - preloaded_dir, self.args.disable_filtering, - self.args.clean_first_seconds, self.args.clean_final_seconds, - is_test=False) - - def __len__(self): - return self.n_samples - - - def cache_generation(self, out_lmdb_dir, disable_filtering, clean_first_seconds, clean_final_seconds, is_test=False): - # if "wav2vec2" in self.args.audio_rep: - # self.wav2vec_model = Wav2Vec2Model.from_pretrained(f"{self.args.data_path_1}/hub/transformer/wav2vec2-base-960h") - # self.wav2vec_model.feature_extractor._freeze_parameters() - # self.wav2vec_model = self.wav2vec_model.cuda() - # self.wav2vec_model.eval() - - self.n_out_samples = 0 - # create db for samples - if not os.path.exists(out_lmdb_dir): os.makedirs(out_lmdb_dir) - dst_lmdb_env = lmdb.open(out_lmdb_dir, map_size= int(1024 ** 3 * 50))# 50G - n_filtered_out = defaultdict(int) - - for index, file_name in self.selected_file.iterrows(): - f_name = file_name["id"] - ext = ".npz" if "smplx" in self.args.pose_rep else ".bvh" - pose_file = self.data_dir + self.args.pose_rep + "/" + f_name + ext - pose_each_file = [] - trans_each_file = [] - shape_each_file = [] - audio_each_file = [] - facial_each_file = [] - word_each_file = [] - emo_each_file = [] - sem_each_file = [] - vid_each_file = [] - id_pose = f_name #1_wayne_0_1_1 - - logger.info(colored(f"# ---- Building cache for Pose {id_pose} ---- #", "blue")) - if "smplx" in self.args.pose_rep: - pose_data = np.load(pose_file, allow_pickle=True) - assert 30%self.args.pose_fps == 0, 'pose_fps should be an aliquot part of 30' - stride = int(30/self.args.pose_fps) - pose_each_file = pose_data["poses"][::stride] * self.joint_mask - trans_each_file = pose_data["trans"][::stride] - shape_each_file = np.repeat(pose_data["betas"].reshape(1, 300), pose_each_file.shape[0], axis=0) - if self.args.facial_rep is not None: - logger.info(f"# ---- Building cache for Facial {id_pose} and Pose {id_pose} ---- #") - facial_each_file = pose_data["expressions"][::stride] - if self.args.facial_norm: - facial_each_file = (facial_each_file - self.mean_facial) / self.std_facial - - else: - assert 120%self.args.pose_fps == 0, 'pose_fps should be an aliquot part of 120' - stride = int(120/self.args.pose_fps) - with open(pose_file, "r") as pose_data: - for j, line in enumerate(pose_data.readlines()): - if j < 431: continue - if j%stride != 0:continue - data = np.fromstring(line, dtype=float, sep=" ") - rot_data = rc.euler_angles_to_matrix(torch.from_numpy(np.deg2rad(data)).reshape(-1, self.joints,3), "XYZ") - rot_data = rc.matrix_to_axis_angle(rot_data).reshape(-1, self.joints*3) - rot_data = rot_data.numpy() * self.joint_mask - - pose_each_file.append(rot_data) - trans_each_file.append(data[:3]) - - pose_each_file = np.array(pose_each_file) - # print(pose_each_file.shape) - trans_each_file = np.array(trans_each_file) - shape_each_file = np.repeat(np.array(-1).reshape(1, 1), pose_each_file.shape[0], axis=0) - if self.args.facial_rep is not None: - logger.info(f"# ---- Building cache for Facial {id_pose} and Pose {id_pose} ---- #") - facial_file = pose_file.replace(self.args.pose_rep, self.args.facial_rep).replace("bvh", "json") - assert 60%self.args.pose_fps == 0, 'pose_fps should be an aliquot part of 120' - stride = int(60/self.args.pose_fps) - if not os.path.exists(facial_file): - logger.warning(f"# ---- file not found for Facial {id_pose}, skip all files with the same id ---- #") - self.selected_file = self.selected_file.drop(self.selected_file[self.selected_file['id'] == id_pose].index) - continue - with open(facial_file, 'r') as facial_data_file: - facial_data = json.load(facial_data_file) - for j, frame_data in enumerate(facial_data['frames']): - if j%stride != 0:continue - facial_each_file.append(frame_data['weights']) - facial_each_file = np.array(facial_each_file) - if self.args.facial_norm: - facial_each_file = (facial_each_file - self.mean_facial) / self.std_facial - - if self.args.id_rep is not None: - vid_each_file = np.repeat(np.array(int(f_name.split("_")[0])-1).reshape(1, 1), pose_each_file.shape[0], axis=0) - - if self.args.audio_rep is not None: - logger.info(f"# ---- Building cache for Audio {id_pose} and Pose {id_pose} ---- #") - audio_file = pose_file.replace(self.args.pose_rep, 'wave16k').replace(ext, ".wav") - if not os.path.exists(audio_file): - logger.warning(f"# ---- file not found for Audio {id_pose}, skip all files with the same id ---- #") - self.selected_file = self.selected_file.drop(self.selected_file[self.selected_file['id'] == id_pose].index) - continue - audio_each_file, sr = librosa.load(audio_file) - audio_each_file = librosa.resample(audio_each_file, orig_sr=sr, target_sr=self.args.audio_sr) - if self.args.audio_rep == "onset+amplitude": - from numpy.lib import stride_tricks - frame_length = 1024 - # hop_length = 512 - shape = (audio_each_file.shape[-1] - frame_length + 1, frame_length) - strides = (audio_each_file.strides[-1], audio_each_file.strides[-1]) - rolling_view = stride_tricks.as_strided(audio_each_file, shape=shape, strides=strides) - amplitude_envelope = np.max(np.abs(rolling_view), axis=1) - # pad the last frame_length-1 samples - amplitude_envelope = np.pad(amplitude_envelope, (0, frame_length-1), mode='constant', constant_values=amplitude_envelope[-1]) - audio_onset_f = librosa.onset.onset_detect(y=audio_each_file, sr=self.args.audio_sr, units='frames') - onset_array = np.zeros(len(audio_each_file), dtype=float) - onset_array[audio_onset_f] = 1.0 - # print(amplitude_envelope.shape, audio_each_file.shape, onset_array.shape) - audio_each_file = np.concatenate([amplitude_envelope.reshape(-1, 1), onset_array.reshape(-1, 1)], axis=1) - elif self.args.audio_rep == "mfcc": - audio_each_file = librosa.feature.mfcc(audio_each_file, sr=self.args.audio_sr, n_mfcc=13, hop_length=int(self.args.audio_sr/self.args.audio_fps)) - - if self.args.audio_norm and self.args.audio_rep == "wave16k": - audio_each_file = (audio_each_file - self.mean_audio) / self.std_audio - - time_offset = 0 - if self.args.word_rep is not None: - logger.info(f"# ---- Building cache for Word {id_pose} and Pose {id_pose} ---- #") - word_file = f"{self.data_dir}{self.args.word_rep}/{id_pose}.TextGrid" - if not os.path.exists(word_file): - logger.warning(f"# ---- file not found for Word {id_pose}, skip all files with the same id ---- #") - self.selected_file = self.selected_file.drop(self.selected_file[self.selected_file['id'] == id_pose].index) - continue - tgrid = tg.TextGrid.fromFile(word_file) - if self.args.t_pre_encoder == "bert": - from transformers import AutoTokenizer, BertModel - tokenizer = AutoTokenizer.from_pretrained(self.args.data_path_1 + "hub/bert-base-uncased", local_files_only=True) - model = BertModel.from_pretrained(self.args.data_path_1 + "hub/bert-base-uncased", local_files_only=True).eval() - list_word = [] - all_hidden = [] - max_len = 400 - last = 0 - word_token_mapping = [] - first = True - for i, word in enumerate(tgrid[0]): - last = i - if (i%max_len != 0) or (i==0): - if word.mark == "": - list_word.append(".") - else: - list_word.append(word.mark) - else: - max_counter = max_len - str_word = ' '.join(map(str, list_word)) - if first: - global_len = 0 - end = -1 - offset_word = [] - for k, wordvalue in enumerate(list_word): - start = end+1 - end = start+len(wordvalue) - offset_word.append((start, end)) - #print(offset_word) - token_scan = tokenizer.encode_plus(str_word, return_offsets_mapping=True)['offset_mapping'] - #print(token_scan) - for start, end in offset_word: - sub_mapping = [] - for i, (start_t, end_t) in enumerate(token_scan[1:-1]): - if int(start) <= int(start_t) and int(end_t) <= int(end): - #print(i+global_len) - sub_mapping.append(i+global_len) - word_token_mapping.append(sub_mapping) - #print(len(word_token_mapping)) - global_len = word_token_mapping[-1][-1] + 1 - list_word = [] - if word.mark == "": - list_word.append(".") - else: - list_word.append(word.mark) - - with torch.no_grad(): - inputs = tokenizer(str_word, return_tensors="pt") - outputs = model(**inputs) - last_hidden_states = outputs.last_hidden_state.reshape(-1, 768).cpu().numpy()[1:-1, :] - all_hidden.append(last_hidden_states) - - #list_word = list_word[:10] - if list_word == []: - pass - else: - if first: - global_len = 0 - str_word = ' '.join(map(str, list_word)) - end = -1 - offset_word = [] - for k, wordvalue in enumerate(list_word): - start = end+1 - end = start+len(wordvalue) - offset_word.append((start, end)) - #print(offset_word) - token_scan = tokenizer.encode_plus(str_word, return_offsets_mapping=True)['offset_mapping'] - #print(token_scan) - for start, end in offset_word: - sub_mapping = [] - for i, (start_t, end_t) in enumerate(token_scan[1:-1]): - if int(start) <= int(start_t) and int(end_t) <= int(end): - sub_mapping.append(i+global_len) - #print(sub_mapping) - word_token_mapping.append(sub_mapping) - #print(len(word_token_mapping)) - with torch.no_grad(): - inputs = tokenizer(str_word, return_tensors="pt") - outputs = model(**inputs) - last_hidden_states = outputs.last_hidden_state.reshape(-1, 768).cpu().numpy()[1:-1, :] - all_hidden.append(last_hidden_states) - last_hidden_states = np.concatenate(all_hidden, axis=0) - - for i in range(pose_each_file.shape[0]): - found_flag = False - current_time = i/self.args.pose_fps + time_offset - j_last = 0 - for j, word in enumerate(tgrid[0]): - word_n, word_s, word_e = word.mark, word.minTime, word.maxTime - if word_s<=current_time and current_time<=word_e: - if self.args.word_cache and self.args.t_pre_encoder == 'bert': - mapping_index = word_token_mapping[j] - #print(mapping_index, word_s, word_e) - s_t = np.linspace(word_s, word_e, len(mapping_index)+1) - #print(s_t) - for tt, t_sep in enumerate(s_t[1:]): - if current_time <= t_sep: - #if len(mapping_index) > 1: print(mapping_index[tt]) - word_each_file.append(last_hidden_states[mapping_index[tt]]) - break - else: - if word_n == " ": - word_each_file.append(self.lang_model.PAD_token) - else: - word_each_file.append(self.lang_model.get_word_index(word_n)) - found_flag = True - j_last = j - break - else: continue - if not found_flag: - if self.args.word_cache and self.args.t_pre_encoder == 'bert': - word_each_file.append(last_hidden_states[j_last]) - else: - word_each_file.append(self.lang_model.UNK_token) - word_each_file = np.array(word_each_file) - #print(word_each_file.shape) - - if self.args.emo_rep is not None: - logger.info(f"# ---- Building cache for Emo {id_pose} and Pose {id_pose} ---- #") - rtype, start = int(id_pose.split('_')[3]), int(id_pose.split('_')[3]) - if rtype == 0 or rtype == 2 or rtype == 4 or rtype == 6: - if start >= 1 and start <= 64: - score = 0 - elif start >= 65 and start <= 72: - score = 1 - elif start >= 73 and start <= 80: - score = 2 - elif start >= 81 and start <= 86: - score = 3 - elif start >= 87 and start <= 94: - score = 4 - elif start >= 95 and start <= 102: - score = 5 - elif start >= 103 and start <= 110: - score = 6 - elif start >= 111 and start <= 118: - score = 7 - else: pass - else: - # you may denote as unknown in the future - score = 0 - emo_each_file = np.repeat(np.array(score).reshape(1, 1), pose_each_file.shape[0], axis=0) - #print(emo_each_file) - - if self.args.sem_rep is not None: - logger.info(f"# ---- Building cache for Sem {id_pose} and Pose {id_pose} ---- #") - sem_file = f"{self.data_dir}{self.args.sem_rep}/{id_pose}.txt" - sem_all = pd.read_csv(sem_file, - sep='\t', - names=["name", "start_time", "end_time", "duration", "score", "keywords"]) - # we adopt motion-level semantic score here. - for i in range(pose_each_file.shape[0]): - found_flag = False - for j, (start, end, score) in enumerate(zip(sem_all['start_time'],sem_all['end_time'], sem_all['score'])): - current_time = i/self.args.pose_fps + time_offset - if start<=current_time and current_time<=end: - sem_each_file.append(score) - found_flag=True - break - else: continue - if not found_flag: sem_each_file.append(0.) - sem_each_file = np.array(sem_each_file) - #print(sem_each_file) - - filtered_result = self._sample_from_clip( - dst_lmdb_env, - audio_each_file, pose_each_file, trans_each_file, shape_each_file, facial_each_file, word_each_file, - vid_each_file, emo_each_file, sem_each_file, - disable_filtering, clean_first_seconds, clean_final_seconds, is_test, - ) - for type in filtered_result.keys(): - n_filtered_out[type] += filtered_result[type] - - with dst_lmdb_env.begin() as txn: - logger.info(colored(f"no. of samples: {txn.stat()['entries']}", "cyan")) - n_total_filtered = 0 - for type, n_filtered in n_filtered_out.items(): - logger.info("{}: {}".format(type, n_filtered)) - n_total_filtered += n_filtered - logger.info(colored("no. of excluded samples: {} ({:.1f}%)".format( - n_total_filtered, 100 * n_total_filtered / (txn.stat()["entries"] + n_total_filtered)), "cyan")) - dst_lmdb_env.sync() - dst_lmdb_env.close() - - def _sample_from_clip( - self, dst_lmdb_env, audio_each_file, pose_each_file, trans_each_file, shape_each_file, facial_each_file, word_each_file, - vid_each_file, emo_each_file, sem_each_file, - disable_filtering, clean_first_seconds, clean_final_seconds, is_test, - ): - """ - for data cleaning, we ignore the data for first and final n s - for test, we return all data - """ - # audio_start = int(self.alignment[0] * self.args.audio_fps) - # pose_start = int(self.alignment[1] * self.args.pose_fps) - #logger.info(f"before: {audio_each_file.shape} {pose_each_file.shape}") - # audio_each_file = audio_each_file[audio_start:] - # pose_each_file = pose_each_file[pose_start:] - # trans_each_file = - #logger.info(f"after alignment: {audio_each_file.shape} {pose_each_file.shape}") - #print(pose_each_file.shape) - round_seconds_skeleton = pose_each_file.shape[0] // self.args.pose_fps # assume 1500 frames / 15 fps = 100 s - #print(round_seconds_skeleton) - if audio_each_file != []: - round_seconds_audio = len(audio_each_file) // self.args.audio_fps # assume 16,000,00 / 16,000 = 100 s - if facial_each_file != []: - round_seconds_facial = facial_each_file.shape[0] // self.args.pose_fps - logger.info(f"audio: {round_seconds_audio}s, pose: {round_seconds_skeleton}s, facial: {round_seconds_facial}s") - round_seconds_skeleton = min(round_seconds_audio, round_seconds_skeleton, round_seconds_facial) - max_round = max(round_seconds_audio, round_seconds_skeleton, round_seconds_facial) - if round_seconds_skeleton != max_round: - logger.warning(f"reduce to {round_seconds_skeleton}s, ignore {max_round-round_seconds_skeleton}s") - else: - logger.info(f"pose: {round_seconds_skeleton}s, audio: {round_seconds_audio}s") - round_seconds_skeleton = min(round_seconds_audio, round_seconds_skeleton) - max_round = max(round_seconds_audio, round_seconds_skeleton) - if round_seconds_skeleton != max_round: - logger.warning(f"reduce to {round_seconds_skeleton}s, ignore {max_round-round_seconds_skeleton}s") - - clip_s_t, clip_e_t = clean_first_seconds, round_seconds_skeleton - clean_final_seconds # assume [10, 90]s - clip_s_f_audio, clip_e_f_audio = self.args.audio_fps * clip_s_t, clip_e_t * self.args.audio_fps # [160,000,90*160,000] - clip_s_f_pose, clip_e_f_pose = clip_s_t * self.args.pose_fps, clip_e_t * self.args.pose_fps # [150,90*15] - - - for ratio in self.args.multi_length_training: - if is_test:# stride = length for test - cut_length = clip_e_f_pose - clip_s_f_pose - self.args.stride = cut_length - self.max_length = cut_length - else: - self.args.stride = int(ratio*self.ori_stride) - cut_length = int(self.ori_length*ratio) - - num_subdivision = math.floor((clip_e_f_pose - clip_s_f_pose - cut_length) / self.args.stride) + 1 - logger.info(f"pose from frame {clip_s_f_pose} to {clip_e_f_pose}, length {cut_length}") - logger.info(f"{num_subdivision} clips is expected with stride {self.args.stride}") - - if audio_each_file != []: - audio_short_length = math.floor(cut_length / self.args.pose_fps * self.args.audio_fps) - """ - for audio sr = 16000, fps = 15, pose_length = 34, - audio short length = 36266.7 -> 36266 - this error is fine. - """ - logger.info(f"audio from frame {clip_s_f_audio} to {clip_e_f_audio}, length {audio_short_length}") - - n_filtered_out = defaultdict(int) - sample_pose_list = [] - sample_audio_list = [] - sample_facial_list = [] - sample_shape_list = [] - sample_word_list = [] - sample_emo_list = [] - sample_sem_list = [] - sample_vid_list = [] - sample_trans_list = [] - - for i in range(num_subdivision): # cut into around 2s chip, (self npose) - start_idx = clip_s_f_pose + i * self.args.stride - fin_idx = start_idx + cut_length - sample_pose = pose_each_file[start_idx:fin_idx] - sample_trans = trans_each_file[start_idx:fin_idx] - sample_shape = shape_each_file[start_idx:fin_idx] - # print(sample_pose.shape) - if self.args.audio_rep is not None: - audio_start = clip_s_f_audio + math.floor(i * self.args.stride * self.args.audio_fps / self.args.pose_fps) - audio_end = audio_start + audio_short_length - sample_audio = audio_each_file[audio_start:audio_end] - else: - sample_audio = np.array([-1]) - sample_facial = facial_each_file[start_idx:fin_idx] if self.args.facial_rep is not None else np.array([-1]) - sample_word = word_each_file[start_idx:fin_idx] if self.args.word_rep is not None else np.array([-1]) - sample_emo = emo_each_file[start_idx:fin_idx] if self.args.emo_rep is not None else np.array([-1]) - sample_sem = sem_each_file[start_idx:fin_idx] if self.args.sem_rep is not None else np.array([-1]) - sample_vid = vid_each_file[start_idx:fin_idx] if self.args.id_rep is not None else np.array([-1]) - - if sample_pose.any() != None: - # filtering motion skeleton data - sample_pose, filtering_message = MotionPreprocessor(sample_pose).get() - is_correct_motion = (sample_pose != []) - if is_correct_motion or disable_filtering: - sample_pose_list.append(sample_pose) - sample_audio_list.append(sample_audio) - sample_facial_list.append(sample_facial) - sample_shape_list.append(sample_shape) - sample_word_list.append(sample_word) - sample_vid_list.append(sample_vid) - sample_emo_list.append(sample_emo) - sample_sem_list.append(sample_sem) - sample_trans_list.append(sample_trans) - else: - n_filtered_out[filtering_message] += 1 - - if len(sample_pose_list) > 0: - with dst_lmdb_env.begin(write=True) as txn: - for pose, audio, facial, shape, word, vid, emo, sem, trans in zip( - sample_pose_list, - sample_audio_list, - sample_facial_list, - sample_shape_list, - sample_word_list, - sample_vid_list, - sample_emo_list, - sample_sem_list, - sample_trans_list,): - k = "{:005}".format(self.n_out_samples).encode("ascii") - v = [pose, audio, facial, shape, word, emo, sem, vid, trans] - v = pyarrow.serialize(v).to_buffer() - txn.put(k, v) - self.n_out_samples += 1 - return n_filtered_out - - def __getitem__(self, idx): - with self.lmdb_env.begin(write=False) as txn: - key = "{:005}".format(idx).encode("ascii") - sample = txn.get(key) - sample = pyarrow.deserialize(sample) - tar_pose, in_audio, in_facial, in_shape, in_word, emo, sem, vid, trans = sample - #print(in_shape) - #vid = torch.from_numpy(vid).int() - emo = torch.from_numpy(emo).int() - sem = torch.from_numpy(sem).float() - in_audio = torch.from_numpy(in_audio).float() - in_word = torch.from_numpy(in_word).float() if self.args.word_cache else torch.from_numpy(in_word).int() - if self.loader_type == "test": - tar_pose = torch.from_numpy(tar_pose).float() - trans = torch.from_numpy(trans).float() - in_facial = torch.from_numpy(in_facial).float() - vid = torch.from_numpy(vid).float() - in_shape = torch.from_numpy(in_shape).float() - else: - in_shape = torch.from_numpy(in_shape).reshape((in_shape.shape[0], -1)).float() - trans = torch.from_numpy(trans).reshape((trans.shape[0], -1)).float() - vid = torch.from_numpy(vid).reshape((vid.shape[0], -1)).float() - tar_pose = torch.from_numpy(tar_pose).reshape((tar_pose.shape[0], -1)).float() - in_facial = torch.from_numpy(in_facial).reshape((in_facial.shape[0], -1)).float() - return {"pose":tar_pose, "audio":in_audio, "facial":in_facial, "beta": in_shape, "word":in_word, "id":vid, "emo":emo, "sem":sem, "trans":trans} - - -class MotionPreprocessor: - def __init__(self, skeletons): - self.skeletons = skeletons - #self.mean_pose = mean_pose - self.filtering_message = "PASS" - - def get(self): - assert (self.skeletons is not None) - - # filtering - if self.skeletons != []: - if self.check_pose_diff(): - self.skeletons = [] - self.filtering_message = "pose" - # elif self.check_spine_angle(): - # self.skeletons = [] - # self.filtering_message = "spine angle" - # elif self.check_static_motion(): - # self.skeletons = [] - # self.filtering_message = "motion" - - # if self.skeletons != []: - # self.skeletons = self.skeletons.tolist() - # for i, frame in enumerate(self.skeletons): - # assert not np.isnan(self.skeletons[i]).any() # missing joints - - return self.skeletons, self.filtering_message - - def check_static_motion(self, verbose=True): - def get_variance(skeleton, joint_idx): - wrist_pos = skeleton[:, joint_idx] - variance = np.sum(np.var(wrist_pos, axis=0)) - return variance - - left_arm_var = get_variance(self.skeletons, 6) - right_arm_var = get_variance(self.skeletons, 9) - - th = 0.0014 # exclude 13110 - # th = 0.002 # exclude 16905 - if left_arm_var < th and right_arm_var < th: - if verbose: - print("skip - check_static_motion left var {}, right var {}".format(left_arm_var, right_arm_var)) - return True - else: - if verbose: - print("pass - check_static_motion left var {}, right var {}".format(left_arm_var, right_arm_var)) - return False - - - def check_pose_diff(self, verbose=False): -# diff = np.abs(self.skeletons - self.mean_pose) # 186*1 -# diff = np.mean(diff) - -# # th = 0.017 -# th = 0.02 #0.02 # exclude 3594 -# if diff < th: -# if verbose: -# print("skip - check_pose_diff {:.5f}".format(diff)) -# return True -# # th = 3.5 #0.02 # exclude 3594 -# # if 3.5 < diff < 5: -# # if verbose: -# # print("skip - check_pose_diff {:.5f}".format(diff)) -# # return True -# else: -# if verbose: -# print("pass - check_pose_diff {:.5f}".format(diff)) - return False - - - def check_spine_angle(self, verbose=True): - def angle_between(v1, v2): - v1_u = v1 / np.linalg.norm(v1) - v2_u = v2 / np.linalg.norm(v2) - return np.arccos(np.clip(np.dot(v1_u, v2_u), -1.0, 1.0)) - - angles = [] - for i in range(self.skeletons.shape[0]): - spine_vec = self.skeletons[i, 1] - self.skeletons[i, 0] - angle = angle_between(spine_vec, [0, -1, 0]) - angles.append(angle) - - if np.rad2deg(max(angles)) > 30 or np.rad2deg(np.mean(angles)) > 20: # exclude 4495 - # if np.rad2deg(max(angles)) > 20: # exclude 8270 - if verbose: - print("skip - check_spine_angle {:.5f}, {:.5f}".format(max(angles), np.mean(angles))) - return True - else: - if verbose: - print("pass - check_spine_angle {:.5f}".format(max(angles))) - return False \ No newline at end of file diff --git a/dataloaders/beat_testonly.py b/dataloaders/beat_testonly.py deleted file mode 100644 index d6d8d8fd524d63754ae68ba999f12085208f91bb..0000000000000000000000000000000000000000 --- a/dataloaders/beat_testonly.py +++ /dev/null @@ -1,731 +0,0 @@ -import os -import pickle -import math -import shutil -import numpy as np -import lmdb as lmdb -import textgrid as tg -import pandas as pd -import torch -import glob -import json -from termcolor import colored -from loguru import logger -from collections import defaultdict -from torch.utils.data import Dataset -import torch.distributed as dist -import pyarrow -import librosa -import smplx - -from .build_vocab import Vocab -from .utils.audio_features import Wav2Vec2Model -from .data_tools import joints_list -from .utils import rotation_conversions as rc -from .utils import other_tools - -class CustomDataset(Dataset): - def __init__(self, args, loader_type, augmentation=None, kwargs=None, build_cache=True): - self.args = args - self.loader_type = loader_type - - self.rank = dist.get_rank() - self.ori_stride = self.args.stride - self.ori_length = self.args.pose_length - self.alignment = [0,0] # for trinity - - self.ori_joint_list = joints_list[self.args.ori_joints] - self.tar_joint_list = joints_list[self.args.tar_joints] - if 'smplx' in self.args.pose_rep: - self.joint_mask = np.zeros(len(list(self.ori_joint_list.keys()))*3) - self.joints = len(list(self.tar_joint_list.keys())) - for joint_name in self.tar_joint_list: - self.joint_mask[self.ori_joint_list[joint_name][1] - self.ori_joint_list[joint_name][0]:self.ori_joint_list[joint_name][1]] = 1 - else: - self.joints = len(list(self.ori_joint_list.keys()))+1 - self.joint_mask = np.zeros(self.joints*3) - for joint_name in self.tar_joint_list: - if joint_name == "Hips": - self.joint_mask[3:6] = 1 - else: - self.joint_mask[self.ori_joint_list[joint_name][1] - self.ori_joint_list[joint_name][0]:self.ori_joint_list[joint_name][1]] = 1 - # select trainable joints - self.smplx = smplx.create( - self.args.data_path_1+"smplx_models/", - model_type='smplx', - gender='NEUTRAL_2020', - use_face_contour=False, - num_betas=300, - num_expression_coeffs=100, - ext='npz', - use_pca=False, - ).cuda().eval() - - split_rule = pd.read_csv(args.data_path+"test.csv") - self.selected_file = split_rule - self.data_dir = args.data_path - - if loader_type == "test": - self.args.multi_length_training = [1.0] - self.max_length = int(args.pose_length * self.args.multi_length_training[-1]) - self.max_audio_pre_len = math.floor(args.pose_length / args.pose_fps * self.args.audio_sr) - if self.max_audio_pre_len > self.args.test_length*self.args.audio_sr: - self.max_audio_pre_len = self.args.test_length*self.args.audio_sr - - if args.word_rep is not None: - with open(f"{args.data_path}weights/vocab.pkl", 'rb') as f: - self.lang_model = pickle.load(f) - - preloaded_dir = self.args.root_path + self.args.cache_path + loader_type + f"/{args.pose_rep}_cache" - if build_cache and self.rank == 0: - self.build_cache(preloaded_dir) - self.lmdb_env = lmdb.open(preloaded_dir, readonly=True, lock=False) - with self.lmdb_env.begin() as txn: - self.n_samples = txn.stat()["entries"] - - - def build_cache(self, preloaded_dir): - logger.info(f"Audio bit rate: {self.args.audio_fps}") - logger.info("Reading data '{}'...".format(self.data_dir)) - logger.info("Creating the dataset cache...") - if self.args.new_cache: - if os.path.exists(preloaded_dir): - shutil.rmtree(preloaded_dir) - if os.path.exists(preloaded_dir): - logger.info("Found the cache {}".format(preloaded_dir)) - elif self.loader_type == "test": - self.cache_generation( - preloaded_dir, True, - 0, 0, - is_test=True) - else: - self.cache_generation( - preloaded_dir, self.args.disable_filtering, - self.args.clean_first_seconds, self.args.clean_final_seconds, - is_test=False) - - - def __len__(self): - return self.n_samples - - - def cache_generation(self, out_lmdb_dir, disable_filtering, clean_first_seconds, clean_final_seconds, is_test=False): - self.n_out_samples = 0 - # create db for samples - if not os.path.exists(out_lmdb_dir): os.makedirs(out_lmdb_dir) - if len(self.args.training_speakers) == 1: - dst_lmdb_env = lmdb.open(out_lmdb_dir, map_size= int(1024 ** 3 * 50))# 50G - else: - dst_lmdb_env = lmdb.open(out_lmdb_dir, map_size= int(1024 ** 3 * 200))# 200G - n_filtered_out = defaultdict(int) - - for index, file_name in self.selected_file.iterrows(): - f_name = file_name["id"] - ext = ".npz" if "smplx" in self.args.pose_rep else ".bvh" - pose_file = self.data_dir + self.args.pose_rep + "/" + f_name + ext - pose_each_file = [] - trans_each_file = [] - shape_each_file = [] - audio_each_file = [] - facial_each_file = [] - word_each_file = [] - emo_each_file = [] - sem_each_file = [] - vid_each_file = [] - id_pose = f_name #1_wayne_0_1_1 - - logger.info(colored(f"# ---- Building cache for Pose {id_pose} ---- #", "blue")) - if "smplx" in self.args.pose_rep: - pose_data = np.load(pose_file, allow_pickle=True) - assert 30%self.args.pose_fps == 0, 'pose_fps should be an aliquot part of 30' - stride = int(30/self.args.pose_fps) - pose_each_file = pose_data["poses"][::stride] - trans_each_file = pose_data["trans"][::stride] - shape_each_file = np.repeat(pose_data["betas"].reshape(1, 300), pose_each_file.shape[0], axis=0) - - assert self.args.pose_fps == 30, "should 30" - m_data = np.load(pose_file, allow_pickle=True) - betas, poses, trans, exps = m_data["betas"], m_data["poses"], m_data["trans"], m_data["expressions"] - n, c = poses.shape[0], poses.shape[1] - betas = betas.reshape(1, 300) - betas = np.tile(betas, (n, 1)) - betas = torch.from_numpy(betas).cuda().float() - poses = torch.from_numpy(poses.reshape(n, c)).cuda().float() - exps = torch.from_numpy(exps.reshape(n, 100)).cuda().float() - trans = torch.from_numpy(trans.reshape(n, 3)).cuda().float() - max_length = 128 - s, r = n//max_length, n%max_length - #print(n, s, r) - all_tensor = [] - for i in range(s): - with torch.no_grad(): - joints = self.smplx( - betas=betas[i*max_length:(i+1)*max_length], - transl=trans[i*max_length:(i+1)*max_length], - expression=exps[i*max_length:(i+1)*max_length], - jaw_pose=poses[i*max_length:(i+1)*max_length, 66:69], - global_orient=poses[i*max_length:(i+1)*max_length,:3], - body_pose=poses[i*max_length:(i+1)*max_length,3:21*3+3], - left_hand_pose=poses[i*max_length:(i+1)*max_length,25*3:40*3], - right_hand_pose=poses[i*max_length:(i+1)*max_length,40*3:55*3], - return_verts=True, - return_joints=True, - leye_pose=poses[i*max_length:(i+1)*max_length, 69:72], - reye_pose=poses[i*max_length:(i+1)*max_length, 72:75], - )['joints'][:, (7,8,10,11), :].reshape(max_length, 4, 3).cpu() - all_tensor.append(joints) - if r != 0: - with torch.no_grad(): - joints = self.smplx( - betas=betas[s*max_length:s*max_length+r], - transl=trans[s*max_length:s*max_length+r], - expression=exps[s*max_length:s*max_length+r], - jaw_pose=poses[s*max_length:s*max_length+r, 66:69], - global_orient=poses[s*max_length:s*max_length+r,:3], - body_pose=poses[s*max_length:s*max_length+r,3:21*3+3], - left_hand_pose=poses[s*max_length:s*max_length+r,25*3:40*3], - right_hand_pose=poses[s*max_length:s*max_length+r,40*3:55*3], - return_verts=True, - return_joints=True, - leye_pose=poses[s*max_length:s*max_length+r, 69:72], - reye_pose=poses[s*max_length:s*max_length+r, 72:75], - )['joints'][:, (7,8,10,11), :].reshape(r, 4, 3).cpu() - all_tensor.append(joints) - joints = torch.cat(all_tensor, axis=0) # all, 4, 3 - # print(joints.shape) - feetv = torch.zeros(joints.shape[1], joints.shape[0]) - joints = joints.permute(1, 0, 2) - #print(joints.shape, feetv.shape) - feetv[:, :-1] = (joints[:, 1:] - joints[:, :-1]).norm(dim=-1) - #print(feetv.shape) - contacts = (feetv < 0.01).numpy().astype(float) - # print(contacts.shape, contacts) - contacts = contacts.transpose(1, 0) - pose_each_file = pose_each_file * self.joint_mask - pose_each_file = pose_each_file[:, self.joint_mask.astype(bool)] - pose_each_file = np.concatenate([pose_each_file, contacts], axis=1) - # print(pose_each_file.shape) - - - if self.args.facial_rep is not None: - logger.info(f"# ---- Building cache for Facial {id_pose} and Pose {id_pose} ---- #") - facial_each_file = pose_data["expressions"][::stride] - if self.args.facial_norm: - facial_each_file = (facial_each_file - self.mean_facial) / self.std_facial - - else: - assert 120%self.args.pose_fps == 0, 'pose_fps should be an aliquot part of 120' - stride = int(120/self.args.pose_fps) - with open(pose_file, "r") as pose_data: - for j, line in enumerate(pose_data.readlines()): - if j < 431: continue - if j%stride != 0:continue - data = np.fromstring(line, dtype=float, sep=" ") - rot_data = rc.euler_angles_to_matrix(torch.from_numpy(np.deg2rad(data)).reshape(-1, self.joints,3), "XYZ") - rot_data = rc.matrix_to_axis_angle(rot_data).reshape(-1, self.joints*3) - rot_data = rot_data.numpy() * self.joint_mask - - pose_each_file.append(rot_data) - trans_each_file.append(data[:3]) - - pose_each_file = np.array(pose_each_file) - trans_each_file = np.array(trans_each_file) - shape_each_file = np.repeat(np.array(-1).reshape(1, 1), pose_each_file.shape[0], axis=0) - if self.args.facial_rep is not None: - logger.info(f"# ---- Building cache for Facial {id_pose} and Pose {id_pose} ---- #") - facial_file = pose_file.replace(self.args.pose_rep, self.args.facial_rep).replace("bvh", "json") - assert 60%self.args.pose_fps == 0, 'pose_fps should be an aliquot part of 120' - stride = int(60/self.args.pose_fps) - if not os.path.exists(facial_file): - logger.warning(f"# ---- file not found for Facial {id_pose}, skip all files with the same id ---- #") - self.selected_file = self.selected_file.drop(self.selected_file[self.selected_file['id'] == id_pose].index) - continue - with open(facial_file, 'r') as facial_data_file: - facial_data = json.load(facial_data_file) - for j, frame_data in enumerate(facial_data['frames']): - if j%stride != 0:continue - facial_each_file.append(frame_data['weights']) - facial_each_file = np.array(facial_each_file) - if self.args.facial_norm: - facial_each_file = (facial_each_file - self.mean_facial) / self.std_facial - - if self.args.id_rep is not None: - int_value = 1 - vid_each_file = np.repeat(np.array(int_value).reshape(1, 1), pose_each_file.shape[0], axis=0) - - if self.args.audio_rep is not None: - logger.info(f"# ---- Building cache for Audio {id_pose} and Pose {id_pose} ---- #") - audio_file = pose_file.replace(self.args.pose_rep, 'wave16k').replace(ext, ".wav") - if not os.path.exists(audio_file): - logger.warning(f"# ---- file not found for Audio {id_pose}, skip all files with the same id ---- #") - self.selected_file = self.selected_file.drop(self.selected_file[self.selected_file['id'] == id_pose].index) - continue - audio_each_file, sr = librosa.load(audio_file) - audio_each_file = librosa.resample(audio_each_file, orig_sr=sr, target_sr=self.args.audio_sr) - if self.args.audio_rep == "onset+amplitude": - from numpy.lib import stride_tricks - frame_length = 1024 - # hop_length = 512 - shape = (audio_each_file.shape[-1] - frame_length + 1, frame_length) - strides = (audio_each_file.strides[-1], audio_each_file.strides[-1]) - rolling_view = stride_tricks.as_strided(audio_each_file, shape=shape, strides=strides) - amplitude_envelope = np.max(np.abs(rolling_view), axis=1) - # pad the last frame_length-1 samples - amplitude_envelope = np.pad(amplitude_envelope, (0, frame_length-1), mode='constant', constant_values=amplitude_envelope[-1]) - audio_onset_f = librosa.onset.onset_detect(y=audio_each_file, sr=self.args.audio_sr, units='frames') - onset_array = np.zeros(len(audio_each_file), dtype=float) - onset_array[audio_onset_f] = 1.0 - # print(amplitude_envelope.shape, audio_each_file.shape, onset_array.shape) - audio_each_file = np.concatenate([amplitude_envelope.reshape(-1, 1), onset_array.reshape(-1, 1)], axis=1) - elif self.args.audio_rep == "mfcc": - audio_each_file = librosa.feature.melspectrogram(y=audio_each_file, sr=self.args.audio_sr, n_mels=128, hop_length=int(self.args.audio_sr/self.args.audio_fps)) - audio_each_file = audio_each_file.transpose(1, 0) - # print(audio_each_file.shape, pose_each_file.shape) - if self.args.audio_norm and self.args.audio_rep == "wave16k": - audio_each_file = (audio_each_file - self.mean_audio) / self.std_audio - - time_offset = 0 - if self.args.word_rep is not None: - logger.info(f"# ---- Building cache for Word {id_pose} and Pose {id_pose} ---- #") - word_file = f"{self.data_dir}{self.args.word_rep}/{id_pose}.TextGrid" - if not os.path.exists(word_file): - logger.warning(f"# ---- file not found for Word {id_pose}, skip all files with the same id ---- #") - self.selected_file = self.selected_file.drop(self.selected_file[self.selected_file['id'] == id_pose].index) - continue - tgrid = tg.TextGrid.fromFile(word_file) - if self.args.t_pre_encoder == "bert": - from transformers import AutoTokenizer, BertModel - tokenizer = AutoTokenizer.from_pretrained(self.args.data_path_1 + "hub/bert-base-uncased", local_files_only=True) - model = BertModel.from_pretrained(self.args.data_path_1 + "hub/bert-base-uncased", local_files_only=True).eval() - list_word = [] - all_hidden = [] - max_len = 400 - last = 0 - word_token_mapping = [] - first = True - for i, word in enumerate(tgrid[0]): - last = i - if (i%max_len != 0) or (i==0): - if word.mark == "": - list_word.append(".") - else: - list_word.append(word.mark) - else: - max_counter = max_len - str_word = ' '.join(map(str, list_word)) - if first: - global_len = 0 - end = -1 - offset_word = [] - for k, wordvalue in enumerate(list_word): - start = end+1 - end = start+len(wordvalue) - offset_word.append((start, end)) - #print(offset_word) - token_scan = tokenizer.encode_plus(str_word, return_offsets_mapping=True)['offset_mapping'] - #print(token_scan) - for start, end in offset_word: - sub_mapping = [] - for i, (start_t, end_t) in enumerate(token_scan[1:-1]): - if int(start) <= int(start_t) and int(end_t) <= int(end): - #print(i+global_len) - sub_mapping.append(i+global_len) - word_token_mapping.append(sub_mapping) - #print(len(word_token_mapping)) - global_len = word_token_mapping[-1][-1] + 1 - list_word = [] - if word.mark == "": - list_word.append(".") - else: - list_word.append(word.mark) - - with torch.no_grad(): - inputs = tokenizer(str_word, return_tensors="pt") - outputs = model(**inputs) - last_hidden_states = outputs.last_hidden_state.reshape(-1, 768).cpu().numpy()[1:-1, :] - all_hidden.append(last_hidden_states) - - #list_word = list_word[:10] - if list_word == []: - pass - else: - if first: - global_len = 0 - str_word = ' '.join(map(str, list_word)) - end = -1 - offset_word = [] - for k, wordvalue in enumerate(list_word): - start = end+1 - end = start+len(wordvalue) - offset_word.append((start, end)) - #print(offset_word) - token_scan = tokenizer.encode_plus(str_word, return_offsets_mapping=True)['offset_mapping'] - #print(token_scan) - for start, end in offset_word: - sub_mapping = [] - for i, (start_t, end_t) in enumerate(token_scan[1:-1]): - if int(start) <= int(start_t) and int(end_t) <= int(end): - sub_mapping.append(i+global_len) - #print(sub_mapping) - word_token_mapping.append(sub_mapping) - #print(len(word_token_mapping)) - with torch.no_grad(): - inputs = tokenizer(str_word, return_tensors="pt") - outputs = model(**inputs) - last_hidden_states = outputs.last_hidden_state.reshape(-1, 768).cpu().numpy()[1:-1, :] - all_hidden.append(last_hidden_states) - last_hidden_states = np.concatenate(all_hidden, axis=0) - - for i in range(pose_each_file.shape[0]): - found_flag = False - current_time = i/self.args.pose_fps + time_offset - j_last = 0 - for j, word in enumerate(tgrid[0]): - word_n, word_s, word_e = word.mark, word.minTime, word.maxTime - if word_s<=current_time and current_time<=word_e: - if self.args.word_cache and self.args.t_pre_encoder == 'bert': - mapping_index = word_token_mapping[j] - #print(mapping_index, word_s, word_e) - s_t = np.linspace(word_s, word_e, len(mapping_index)+1) - #print(s_t) - for tt, t_sep in enumerate(s_t[1:]): - if current_time <= t_sep: - #if len(mapping_index) > 1: print(mapping_index[tt]) - word_each_file.append(last_hidden_states[mapping_index[tt]]) - break - else: - if word_n == " ": - word_each_file.append(self.lang_model.PAD_token) - else: - word_each_file.append(self.lang_model.get_word_index(word_n)) - found_flag = True - j_last = j - break - else: continue - if not found_flag: - if self.args.word_cache and self.args.t_pre_encoder == 'bert': - word_each_file.append(last_hidden_states[j_last]) - else: - word_each_file.append(self.lang_model.UNK_token) - word_each_file = np.array(word_each_file) - #print(word_each_file.shape) - - if self.args.emo_rep is not None: - logger.info(f"# ---- Building cache for Emo {id_pose} and Pose {id_pose} ---- #") - rtype, start = int(id_pose.split('_')[3]), int(id_pose.split('_')[3]) - if rtype == 0 or rtype == 2 or rtype == 4 or rtype == 6: - if start >= 1 and start <= 64: - score = 0 - elif start >= 65 and start <= 72: - score = 1 - elif start >= 73 and start <= 80: - score = 2 - elif start >= 81 and start <= 86: - score = 3 - elif start >= 87 and start <= 94: - score = 4 - elif start >= 95 and start <= 102: - score = 5 - elif start >= 103 and start <= 110: - score = 6 - elif start >= 111 and start <= 118: - score = 7 - else: pass - else: - # you may denote as unknown in the future - score = 0 - emo_each_file = np.repeat(np.array(score).reshape(1, 1), pose_each_file.shape[0], axis=0) - #print(emo_each_file) - - if self.args.sem_rep is not None: - logger.info(f"# ---- Building cache for Sem {id_pose} and Pose {id_pose} ---- #") - sem_file = f"{self.data_dir}{self.args.sem_rep}/{id_pose}.txt" - sem_all = pd.read_csv(sem_file, - sep='\t', - names=["name", "start_time", "end_time", "duration", "score", "keywords"]) - # we adopt motion-level semantic score here. - for i in range(pose_each_file.shape[0]): - found_flag = False - for j, (start, end, score) in enumerate(zip(sem_all['start_time'],sem_all['end_time'], sem_all['score'])): - current_time = i/self.args.pose_fps + time_offset - if start<=current_time and current_time<=end: - sem_each_file.append(score) - found_flag=True - break - else: continue - if not found_flag: sem_each_file.append(0.) - sem_each_file = np.array(sem_each_file) - #print(sem_each_file) - - filtered_result = self._sample_from_clip( - dst_lmdb_env, - audio_each_file, pose_each_file, trans_each_file, shape_each_file, facial_each_file, word_each_file, - vid_each_file, emo_each_file, sem_each_file, - disable_filtering, clean_first_seconds, clean_final_seconds, is_test, - ) - for type in filtered_result.keys(): - n_filtered_out[type] += filtered_result[type] - - with dst_lmdb_env.begin() as txn: - logger.info(colored(f"no. of samples: {txn.stat()['entries']}", "cyan")) - n_total_filtered = 0 - for type, n_filtered in n_filtered_out.items(): - logger.info("{}: {}".format(type, n_filtered)) - n_total_filtered += n_filtered - logger.info(colored("no. of excluded samples: {} ({:.1f}%)".format( - n_total_filtered, 100 * n_total_filtered / (txn.stat()["entries"] + n_total_filtered)), "cyan")) - dst_lmdb_env.sync() - dst_lmdb_env.close() - - def _sample_from_clip( - self, dst_lmdb_env, audio_each_file, pose_each_file, trans_each_file, shape_each_file, facial_each_file, word_each_file, - vid_each_file, emo_each_file, sem_each_file, - disable_filtering, clean_first_seconds, clean_final_seconds, is_test, - ): - """ - for data cleaning, we ignore the data for first and final n s - for test, we return all data - """ - # audio_start = int(self.alignment[0] * self.args.audio_fps) - # pose_start = int(self.alignment[1] * self.args.pose_fps) - #logger.info(f"before: {audio_each_file.shape} {pose_each_file.shape}") - # audio_each_file = audio_each_file[audio_start:] - # pose_each_file = pose_each_file[pose_start:] - # trans_each_file = - #logger.info(f"after alignment: {audio_each_file.shape} {pose_each_file.shape}") - #print(pose_each_file.shape) - round_seconds_skeleton = pose_each_file.shape[0] // self.args.pose_fps # assume 1500 frames / 15 fps = 100 s - #print(round_seconds_skeleton) - if audio_each_file != []: - if self.args.audio_rep != "wave16k": - round_seconds_audio = len(audio_each_file) // self.args.audio_fps # assume 16,000,00 / 16,000 = 100 s - elif self.args.audio_rep == "mfcc": - round_seconds_audio = audio_each_file.shape[0] // self.args.audio_fps - else: - round_seconds_audio = audio_each_file.shape[0] // self.args.audio_sr - if facial_each_file != []: - round_seconds_facial = facial_each_file.shape[0] // self.args.pose_fps - logger.info(f"audio: {round_seconds_audio}s, pose: {round_seconds_skeleton}s, facial: {round_seconds_facial}s") - round_seconds_skeleton = min(round_seconds_audio, round_seconds_skeleton, round_seconds_facial) - max_round = max(round_seconds_audio, round_seconds_skeleton, round_seconds_facial) - if round_seconds_skeleton != max_round: - logger.warning(f"reduce to {round_seconds_skeleton}s, ignore {max_round-round_seconds_skeleton}s") - else: - logger.info(f"pose: {round_seconds_skeleton}s, audio: {round_seconds_audio}s") - round_seconds_skeleton = min(round_seconds_audio, round_seconds_skeleton) - max_round = max(round_seconds_audio, round_seconds_skeleton) - if round_seconds_skeleton != max_round: - logger.warning(f"reduce to {round_seconds_skeleton}s, ignore {max_round-round_seconds_skeleton}s") - - clip_s_t, clip_e_t = clean_first_seconds, round_seconds_skeleton - clean_final_seconds # assume [10, 90]s - clip_s_f_audio, clip_e_f_audio = self.args.audio_fps * clip_s_t, clip_e_t * self.args.audio_fps # [160,000,90*160,000] - clip_s_f_pose, clip_e_f_pose = clip_s_t * self.args.pose_fps, clip_e_t * self.args.pose_fps # [150,90*15] - - - for ratio in self.args.multi_length_training: - if is_test:# stride = length for test - cut_length = clip_e_f_pose - clip_s_f_pose - self.args.stride = cut_length - self.max_length = cut_length - else: - self.args.stride = int(ratio*self.ori_stride) - cut_length = int(self.ori_length*ratio) - - num_subdivision = math.floor((clip_e_f_pose - clip_s_f_pose - cut_length) / self.args.stride) + 1 - logger.info(f"pose from frame {clip_s_f_pose} to {clip_e_f_pose}, length {cut_length}") - logger.info(f"{num_subdivision} clips is expected with stride {self.args.stride}") - - if audio_each_file != []: - audio_short_length = math.floor(cut_length / self.args.pose_fps * self.args.audio_fps) - """ - for audio sr = 16000, fps = 15, pose_length = 34, - audio short length = 36266.7 -> 36266 - this error is fine. - """ - logger.info(f"audio from frame {clip_s_f_audio} to {clip_e_f_audio}, length {audio_short_length}") - - n_filtered_out = defaultdict(int) - sample_pose_list = [] - sample_audio_list = [] - sample_facial_list = [] - sample_shape_list = [] - sample_word_list = [] - sample_emo_list = [] - sample_sem_list = [] - sample_vid_list = [] - sample_trans_list = [] - - for i in range(num_subdivision): # cut into around 2s chip, (self npose) - start_idx = clip_s_f_pose + i * self.args.stride - fin_idx = start_idx + cut_length - sample_pose = pose_each_file[start_idx:fin_idx] - - sample_trans = trans_each_file[start_idx:fin_idx] - sample_shape = shape_each_file[start_idx:fin_idx] - # print(sample_pose.shape) - if self.args.audio_rep is not None: - audio_start = clip_s_f_audio + math.floor(i * self.args.stride * self.args.audio_fps / self.args.pose_fps) - audio_end = audio_start + audio_short_length - sample_audio = audio_each_file[audio_start:audio_end] - else: - sample_audio = np.array([-1]) - sample_facial = facial_each_file[start_idx:fin_idx] if self.args.facial_rep is not None else np.array([-1]) - sample_word = word_each_file[start_idx:fin_idx] if self.args.word_rep is not None else np.array([-1]) - sample_emo = emo_each_file[start_idx:fin_idx] if self.args.emo_rep is not None else np.array([-1]) - sample_sem = sem_each_file[start_idx:fin_idx] if self.args.sem_rep is not None else np.array([-1]) - sample_vid = vid_each_file[start_idx:fin_idx] if self.args.id_rep is not None else np.array([-1]) - - if sample_pose.any() != None: - # filtering motion skeleton data - sample_pose, filtering_message = MotionPreprocessor(sample_pose).get() - is_correct_motion = (sample_pose != []) - if is_correct_motion or disable_filtering: - sample_pose_list.append(sample_pose) - sample_audio_list.append(sample_audio) - sample_facial_list.append(sample_facial) - sample_shape_list.append(sample_shape) - sample_word_list.append(sample_word) - sample_vid_list.append(sample_vid) - sample_emo_list.append(sample_emo) - sample_sem_list.append(sample_sem) - sample_trans_list.append(sample_trans) - else: - n_filtered_out[filtering_message] += 1 - - if len(sample_pose_list) > 0: - with dst_lmdb_env.begin(write=True) as txn: - for pose, audio, facial, shape, word, vid, emo, sem, trans in zip( - sample_pose_list, - sample_audio_list, - sample_facial_list, - sample_shape_list, - sample_word_list, - sample_vid_list, - sample_emo_list, - sample_sem_list, - sample_trans_list,): - k = "{:005}".format(self.n_out_samples).encode("ascii") - v = [pose, audio, facial, shape, word, emo, sem, vid, trans] - v = pyarrow.serialize(v).to_buffer() - txn.put(k, v) - self.n_out_samples += 1 - return n_filtered_out - - def __getitem__(self, idx): - with self.lmdb_env.begin(write=False) as txn: - key = "{:005}".format(idx).encode("ascii") - sample = txn.get(key) - sample = pyarrow.deserialize(sample) - tar_pose, in_audio, in_facial, in_shape, in_word, emo, sem, vid, trans = sample - #print(in_shape) - #vid = torch.from_numpy(vid).int() - emo = torch.from_numpy(emo).int() - sem = torch.from_numpy(sem).float() - in_audio = torch.from_numpy(in_audio).float() - in_word = torch.from_numpy(in_word).float() if self.args.word_cache else torch.from_numpy(in_word).int() - if self.loader_type == "test": - tar_pose = torch.from_numpy(tar_pose).float() - trans = torch.from_numpy(trans).float() - in_facial = torch.from_numpy(in_facial).float() - vid = torch.from_numpy(vid).float() - in_shape = torch.from_numpy(in_shape).float() - else: - in_shape = torch.from_numpy(in_shape).reshape((in_shape.shape[0], -1)).float() - trans = torch.from_numpy(trans).reshape((trans.shape[0], -1)).float() - vid = torch.from_numpy(vid).reshape((vid.shape[0], -1)).float() - tar_pose = torch.from_numpy(tar_pose).reshape((tar_pose.shape[0], -1)).float() - in_facial = torch.from_numpy(in_facial).reshape((in_facial.shape[0], -1)).float() - return {"pose":tar_pose, "audio":in_audio, "facial":in_facial, "beta": in_shape, "word":in_word, "id":vid, "emo":emo, "sem":sem, "trans":trans} - - -class MotionPreprocessor: - def __init__(self, skeletons): - self.skeletons = skeletons - #self.mean_pose = mean_pose - self.filtering_message = "PASS" - - def get(self): - assert (self.skeletons is not None) - - # filtering - if self.skeletons != []: - if self.check_pose_diff(): - self.skeletons = [] - self.filtering_message = "pose" - # elif self.check_spine_angle(): - # self.skeletons = [] - # self.filtering_message = "spine angle" - # elif self.check_static_motion(): - # self.skeletons = [] - # self.filtering_message = "motion" - - # if self.skeletons != []: - # self.skeletons = self.skeletons.tolist() - # for i, frame in enumerate(self.skeletons): - # assert not np.isnan(self.skeletons[i]).any() # missing joints - - return self.skeletons, self.filtering_message - - def check_static_motion(self, verbose=True): - def get_variance(skeleton, joint_idx): - wrist_pos = skeleton[:, joint_idx] - variance = np.sum(np.var(wrist_pos, axis=0)) - return variance - - left_arm_var = get_variance(self.skeletons, 6) - right_arm_var = get_variance(self.skeletons, 9) - - th = 0.0014 # exclude 13110 - # th = 0.002 # exclude 16905 - if left_arm_var < th and right_arm_var < th: - if verbose: - print("skip - check_static_motion left var {}, right var {}".format(left_arm_var, right_arm_var)) - return True - else: - if verbose: - print("pass - check_static_motion left var {}, right var {}".format(left_arm_var, right_arm_var)) - return False - - - def check_pose_diff(self, verbose=False): -# diff = np.abs(self.skeletons - self.mean_pose) # 186*1 -# diff = np.mean(diff) - -# # th = 0.017 -# th = 0.02 #0.02 # exclude 3594 -# if diff < th: -# if verbose: -# print("skip - check_pose_diff {:.5f}".format(diff)) -# return True -# # th = 3.5 #0.02 # exclude 3594 -# # if 3.5 < diff < 5: -# # if verbose: -# # print("skip - check_pose_diff {:.5f}".format(diff)) -# # return True -# else: -# if verbose: -# print("pass - check_pose_diff {:.5f}".format(diff)) - return False - - - def check_spine_angle(self, verbose=True): - def angle_between(v1, v2): - v1_u = v1 / np.linalg.norm(v1) - v2_u = v2 / np.linalg.norm(v2) - return np.arccos(np.clip(np.dot(v1_u, v2_u), -1.0, 1.0)) - - angles = [] - for i in range(self.skeletons.shape[0]): - spine_vec = self.skeletons[i, 1] - self.skeletons[i, 0] - angle = angle_between(spine_vec, [0, -1, 0]) - angles.append(angle) - - if np.rad2deg(max(angles)) > 30 or np.rad2deg(np.mean(angles)) > 20: # exclude 4495 - # if np.rad2deg(max(angles)) > 20: # exclude 8270 - if verbose: - print("skip - check_spine_angle {:.5f}, {:.5f}".format(max(angles), np.mean(angles))) - return True - else: - if verbose: - print("pass - check_spine_angle {:.5f}".format(max(angles))) - return False \ No newline at end of file diff --git a/dataloaders/beat_testonly_hf.py b/dataloaders/beat_testonly_hf.py deleted file mode 100644 index 17393517d0d6f662114c19b009fa3471d7c1e91b..0000000000000000000000000000000000000000 --- a/dataloaders/beat_testonly_hf.py +++ /dev/null @@ -1,740 +0,0 @@ -import os -import pickle -import math -import shutil -import numpy as np -import lmdb as lmdb -import textgrid as tg -import pandas as pd -import torch -import glob -import json -from termcolor import colored -from loguru import logger -from collections import defaultdict -from torch.utils.data import Dataset -import torch.distributed as dist -import pyarrow -import librosa -import smplx - -from .build_vocab import Vocab -from .utils.audio_features import Wav2Vec2Model -from .data_tools import joints_list -from .utils import rotation_conversions as rc -from .utils import other_tools_hf - -class CustomDataset(Dataset): - def __init__(self, args, loader_type, smplx_path=None, audio_path=None, text_path=None, augmentation=None, kwargs=None, build_cache=True): - self.args = args - self.loader_type = loader_type - self.smplx_path = "./EMAGE/test_sequences/smplxflame_30/2_scott_0_1_1.npz" - self.audio_path = audio_path - self.text_path = "./EMAGE/test_sequences/textgrid/2_scott_0_1_1.TextGrid" - self.rank = 0 # dist.get_rank() - self.ori_stride = self.args.stride - self.ori_length = self.args.pose_length - self.alignment = [0,0] # for trinity - - self.ori_joint_list = joints_list[self.args.ori_joints] - self.tar_joint_list = joints_list[self.args.tar_joints] - if 'smplx' in self.args.pose_rep: - self.joint_mask = np.zeros(len(list(self.ori_joint_list.keys()))*3) - self.joints = len(list(self.tar_joint_list.keys())) - for joint_name in self.tar_joint_list: - self.joint_mask[self.ori_joint_list[joint_name][1] - self.ori_joint_list[joint_name][0]:self.ori_joint_list[joint_name][1]] = 1 - else: - self.joints = len(list(self.ori_joint_list.keys()))+1 - self.joint_mask = np.zeros(self.joints*3) - for joint_name in self.tar_joint_list: - if joint_name == "Hips": - self.joint_mask[3:6] = 1 - else: - self.joint_mask[self.ori_joint_list[joint_name][1] - self.ori_joint_list[joint_name][0]:self.ori_joint_list[joint_name][1]] = 1 - # select trainable joints - self.smplx = smplx.create( - self.args.data_path_1+"smplx_models/", - model_type='smplx', - gender='NEUTRAL_2020', - use_face_contour=False, - num_betas=300, - num_expression_coeffs=100, - ext='npz', - use_pca=False, - ).eval() - - split_rule = pd.read_csv(args.data_path+"test.csv") - self.selected_file = split_rule - self.data_dir = args.data_path - - if loader_type == "test": - self.args.multi_length_training = [1.0] - self.max_length = int(args.pose_length * self.args.multi_length_training[-1]) - self.max_audio_pre_len = math.floor(args.pose_length / args.pose_fps * self.args.audio_sr) - if self.max_audio_pre_len > self.args.test_length*self.args.audio_sr: - self.max_audio_pre_len = self.args.test_length*self.args.audio_sr - - if args.word_rep is not None: - with open(f"{args.data_path}weights/vocab.pkl", 'rb') as f: - self.lang_model = pickle.load(f) - - preloaded_dir = self.args.root_path + self.args.cache_path + loader_type + f"/{args.pose_rep}_cache" - if build_cache and self.rank == 0: - self.build_cache(preloaded_dir) - self.lmdb_env = lmdb.open(preloaded_dir, readonly=True, lock=False) - with self.lmdb_env.begin() as txn: - self.n_samples = txn.stat()["entries"] - - - def build_cache(self, preloaded_dir): - logger.info(f"Audio bit rate: {self.args.audio_fps}") - logger.info("Reading data '{}'...".format(self.data_dir)) - logger.info("Creating the dataset cache...") - if self.args.new_cache: - if os.path.exists(preloaded_dir): - shutil.rmtree(preloaded_dir) - if os.path.exists(preloaded_dir): - logger.info("Found the cache {}".format(preloaded_dir)) - elif self.loader_type == "test": - self.cache_generation( - preloaded_dir, True, - 0, 0, - is_test=True) - else: - self.cache_generation( - preloaded_dir, self.args.disable_filtering, - self.args.clean_first_seconds, self.args.clean_final_seconds, - is_test=False) - - - def __len__(self): - return self.n_samples - - - def cache_generation(self, out_lmdb_dir, disable_filtering, clean_first_seconds, clean_final_seconds, is_test=False): - self.n_out_samples = 0 - # create db for samples - if not os.path.exists(out_lmdb_dir): os.makedirs(out_lmdb_dir) - if len(self.args.training_speakers) == 1: - #dst_lmdb_env = lmdb.open(out_lmdb_dir, map_size= int(1024 ** 3 * 50))# 50G - dst_lmdb_env = lmdb.open(out_lmdb_dir, map_size= int(1024 ** 3 * 0.5))# 500M - else: - dst_lmdb_env = lmdb.open(out_lmdb_dir, map_size= int(1024 ** 3 * 200))# 200G - n_filtered_out = defaultdict(int) - - #for index, file_name in self.selected_file.iterrows(): - #f_name = file_name["id"] - ext = ".npz" if "smplx" in self.args.pose_rep else ".bvh" - pose_file = self.smplx_path#self.data_dir + self.args.pose_rep + "/" + f_name + ext - pose_each_file = [] - trans_each_file = [] - shape_each_file = [] - audio_each_file = [] - facial_each_file = [] - word_each_file = [] - emo_each_file = [] - sem_each_file = [] - vid_each_file = [] - id_pose = "dummy 2nd"#f_name - - logger.info(colored(f"# ---- Building cache for Pose {id_pose} ---- #", "blue")) - if "smplx" in self.args.pose_rep: - pose_data = np.load(pose_file, allow_pickle=True) - assert 30%self.args.pose_fps == 0, 'pose_fps should be an aliquot part of 30' - stride = int(30/self.args.pose_fps) - pose_each_file = pose_data["poses"][::stride] - trans_each_file = pose_data["trans"][::stride] - shape_each_file = np.repeat(pose_data["betas"].reshape(1, 300), pose_each_file.shape[0], axis=0) - - assert self.args.pose_fps == 30, "should 30" - m_data = np.load(pose_file, allow_pickle=True) - betas, poses, trans, exps = m_data["betas"], m_data["poses"], m_data["trans"], m_data["expressions"] - n, c = poses.shape[0], poses.shape[1] - betas = betas.reshape(1, 300) - betas = np.tile(betas, (n, 1)) - betas = torch.from_numpy(betas).float() - poses = torch.from_numpy(poses.reshape(n, c)).float() - exps = torch.from_numpy(exps.reshape(n, 100)).float() - trans = torch.from_numpy(trans.reshape(n, 3)).float() - max_length = 128 - s, r = n//max_length, n%max_length - #print(n, s, r) - all_tensor = [] - for i in range(s): - with torch.no_grad(): - joints = self.smplx( - betas=betas[i*max_length:(i+1)*max_length], - transl=trans[i*max_length:(i+1)*max_length], - expression=exps[i*max_length:(i+1)*max_length], - jaw_pose=poses[i*max_length:(i+1)*max_length, 66:69], - global_orient=poses[i*max_length:(i+1)*max_length,:3], - body_pose=poses[i*max_length:(i+1)*max_length,3:21*3+3], - left_hand_pose=poses[i*max_length:(i+1)*max_length,25*3:40*3], - right_hand_pose=poses[i*max_length:(i+1)*max_length,40*3:55*3], - return_verts=True, - return_joints=True, - leye_pose=poses[i*max_length:(i+1)*max_length, 69:72], - reye_pose=poses[i*max_length:(i+1)*max_length, 72:75], - )['joints'][:, (7,8,10,11), :].reshape(max_length, 4, 3).cpu() - all_tensor.append(joints) - if r != 0: - with torch.no_grad(): - joints = self.smplx( - betas=betas[s*max_length:s*max_length+r], - transl=trans[s*max_length:s*max_length+r], - expression=exps[s*max_length:s*max_length+r], - jaw_pose=poses[s*max_length:s*max_length+r, 66:69], - global_orient=poses[s*max_length:s*max_length+r,:3], - body_pose=poses[s*max_length:s*max_length+r,3:21*3+3], - left_hand_pose=poses[s*max_length:s*max_length+r,25*3:40*3], - right_hand_pose=poses[s*max_length:s*max_length+r,40*3:55*3], - return_verts=True, - return_joints=True, - leye_pose=poses[s*max_length:s*max_length+r, 69:72], - reye_pose=poses[s*max_length:s*max_length+r, 72:75], - )['joints'][:, (7,8,10,11), :].reshape(r, 4, 3).cpu() - all_tensor.append(joints) - joints = torch.cat(all_tensor, axis=0) # all, 4, 3 - # print(joints.shape) - feetv = torch.zeros(joints.shape[1], joints.shape[0]) - joints = joints.permute(1, 0, 2) - #print(joints.shape, feetv.shape) - feetv[:, :-1] = (joints[:, 1:] - joints[:, :-1]).norm(dim=-1) - #print(feetv.shape) - contacts = (feetv < 0.01).numpy().astype(float) - # print(contacts.shape, contacts) - contacts = contacts.transpose(1, 0) - pose_each_file = pose_each_file * self.joint_mask - pose_each_file = pose_each_file[:, self.joint_mask.astype(bool)] - pose_each_file = np.concatenate([pose_each_file, contacts], axis=1) - # print(pose_each_file.shape) - - - if self.args.facial_rep is not None: - logger.info(f"# ---- Building cache for Facial {id_pose} and Pose {id_pose} ---- #") - facial_each_file = pose_data["expressions"][::stride] - if self.args.facial_norm: - facial_each_file = (facial_each_file - self.mean_facial) / self.std_facial - - else: - assert 120%self.args.pose_fps == 0, 'pose_fps should be an aliquot part of 120' - stride = int(120/self.args.pose_fps) - with open(pose_file, "r") as pose_data: - for j, line in enumerate(pose_data.readlines()): - if j < 431: continue - if j%stride != 0:continue - data = np.fromstring(line, dtype=float, sep=" ") - rot_data = rc.euler_angles_to_matrix(torch.from_numpy(np.deg2rad(data)).reshape(-1, self.joints,3), "XYZ") - rot_data = rc.matrix_to_axis_angle(rot_data).reshape(-1, self.joints*3) - rot_data = rot_data.numpy() * self.joint_mask - - pose_each_file.append(rot_data) - trans_each_file.append(data[:3]) - - pose_each_file = np.array(pose_each_file) - trans_each_file = np.array(trans_each_file) - shape_each_file = np.repeat(np.array(-1).reshape(1, 1), pose_each_file.shape[0], axis=0) - if self.args.facial_rep is not None: - logger.info(f"# ---- Building cache for Facial {id_pose} and Pose {id_pose} ---- #") - facial_file = pose_file.replace(self.args.pose_rep, self.args.facial_rep).replace("bvh", "json") - assert 60%self.args.pose_fps == 0, 'pose_fps should be an aliquot part of 120' - stride = int(60/self.args.pose_fps) - if not os.path.exists(facial_file): - logger.warning(f"# ---- file not found for Facial {id_pose}, skip all files with the same id ---- #") - #self.selected_file = self.selected_file.drop(self.selected_file[self.selected_file['id'] == id_pose].index) - #continue - with open(facial_file, 'r') as facial_data_file: - facial_data = json.load(facial_data_file) - for j, frame_data in enumerate(facial_data['frames']): - if j%stride != 0:continue - facial_each_file.append(frame_data['weights']) - facial_each_file = np.array(facial_each_file) - if self.args.facial_norm: - facial_each_file = (facial_each_file - self.mean_facial) / self.std_facial - - if self.args.id_rep is not None: - int_value = 1 - vid_each_file = np.repeat(np.array(int_value).reshape(1, 1), pose_each_file.shape[0], axis=0) - - if self.args.audio_rep is not None: - logger.info(f"# ---- Building cache for Audio {id_pose} and Pose {id_pose} ---- #") - audio_file = self.audio_path[1]#pose_file.replace(self.args.pose_rep, 'wave16k').replace(ext, ".wav") - sr = self.audio_path[0] - print(sr) - #if not os.path.exists(audio_file): - # logger.warning(f"# ---- file not found for Audio {id_pose}, skip all files with the same id ---- #") - #self.selected_file = self.selected_file.drop(self.selected_file[self.selected_file['id'] == id_pose].index) - #continue - #audio_each_file, sr = librosa.load(audio_file) - audio_each_file = audio_file.astype(np.float32) - print(audio_each_file.shape) - audio_each_file = librosa.resample(audio_each_file, orig_sr=sr, target_sr=self.args.audio_sr) - print(audio_each_file.shape) - if self.args.audio_rep == "onset+amplitude": - from numpy.lib import stride_tricks - frame_length = 1024 - # hop_length = 512 - shape = (audio_each_file.shape[-1] - frame_length + 1, frame_length) - strides = (audio_each_file.strides[-1], audio_each_file.strides[-1]) - rolling_view = stride_tricks.as_strided(audio_each_file, shape=shape, strides=strides) - amplitude_envelope = np.max(np.abs(rolling_view), axis=1) - # pad the last frame_length-1 samples - amplitude_envelope = np.pad(amplitude_envelope, (0, frame_length-1), mode='constant', constant_values=amplitude_envelope[-1]) - audio_onset_f = librosa.onset.onset_detect(y=audio_each_file, sr=self.args.audio_sr, units='frames') - onset_array = np.zeros(len(audio_each_file), dtype=float) - onset_array[audio_onset_f] = 1.0 - # print(amplitude_envelope.shape, audio_each_file.shape, onset_array.shape) - audio_each_file = np.concatenate([amplitude_envelope.reshape(-1, 1), onset_array.reshape(-1, 1)], axis=1) - elif self.args.audio_rep == "mfcc": - audio_each_file = librosa.feature.melspectrogram(y=audio_each_file, sr=self.args.audio_sr, n_mels=128, hop_length=int(self.args.audio_sr/self.args.audio_fps)) - audio_each_file = audio_each_file.transpose(1, 0) - # print(audio_each_file.shape, pose_each_file.shape) - if self.args.audio_norm and self.args.audio_rep == "wave16k": - audio_each_file = (audio_each_file - self.mean_audio) / self.std_audio - - time_offset = 0 - if self.args.word_rep is not None: - logger.info(f"# ---- Building cache for Word {id_pose} and Pose {id_pose} ---- #") - word_file = self.text_path#f"{self.data_dir}{self.args.word_rep}/{id_pose}.TextGrid" - if not os.path.exists(word_file): - logger.warning(f"# ---- file not found for Word {id_pose}, skip all files with the same id ---- #") - #self.selected_file = self.selected_file.drop(self.selected_file[self.selected_file['id'] == id_pose].index) - #continue - tgrid = tg.TextGrid.fromFile(word_file) - if self.args.t_pre_encoder == "bert": - from transformers import AutoTokenizer, BertModel - tokenizer = AutoTokenizer.from_pretrained(self.args.data_path_1 + "hub/bert-base-uncased", local_files_only=True) - model = BertModel.from_pretrained(self.args.data_path_1 + "hub/bert-base-uncased", local_files_only=True).eval() - list_word = [] - all_hidden = [] - max_len = 400 - last = 0 - word_token_mapping = [] - first = True - for i, word in enumerate(tgrid[0]): - last = i - if (i%max_len != 0) or (i==0): - if word.mark == "": - list_word.append(".") - else: - list_word.append(word.mark) - else: - max_counter = max_len - str_word = ' '.join(map(str, list_word)) - if first: - global_len = 0 - end = -1 - offset_word = [] - for k, wordvalue in enumerate(list_word): - start = end+1 - end = start+len(wordvalue) - offset_word.append((start, end)) - #print(offset_word) - token_scan = tokenizer.encode_plus(str_word, return_offsets_mapping=True)['offset_mapping'] - #print(token_scan) - for start, end in offset_word: - sub_mapping = [] - for i, (start_t, end_t) in enumerate(token_scan[1:-1]): - if int(start) <= int(start_t) and int(end_t) <= int(end): - #print(i+global_len) - sub_mapping.append(i+global_len) - word_token_mapping.append(sub_mapping) - #print(len(word_token_mapping)) - global_len = word_token_mapping[-1][-1] + 1 - list_word = [] - if word.mark == "": - list_word.append(".") - else: - list_word.append(word.mark) - - with torch.no_grad(): - inputs = tokenizer(str_word, return_tensors="pt") - outputs = model(**inputs) - last_hidden_states = outputs.last_hidden_state.reshape(-1, 768).cpu().numpy()[1:-1, :] - all_hidden.append(last_hidden_states) - - #list_word = list_word[:10] - if list_word == []: - pass - else: - if first: - global_len = 0 - str_word = ' '.join(map(str, list_word)) - end = -1 - offset_word = [] - for k, wordvalue in enumerate(list_word): - start = end+1 - end = start+len(wordvalue) - offset_word.append((start, end)) - #print(offset_word) - token_scan = tokenizer.encode_plus(str_word, return_offsets_mapping=True)['offset_mapping'] - #print(token_scan) - for start, end in offset_word: - sub_mapping = [] - for i, (start_t, end_t) in enumerate(token_scan[1:-1]): - if int(start) <= int(start_t) and int(end_t) <= int(end): - sub_mapping.append(i+global_len) - #print(sub_mapping) - word_token_mapping.append(sub_mapping) - #print(len(word_token_mapping)) - with torch.no_grad(): - inputs = tokenizer(str_word, return_tensors="pt") - outputs = model(**inputs) - last_hidden_states = outputs.last_hidden_state.reshape(-1, 768).cpu().numpy()[1:-1, :] - all_hidden.append(last_hidden_states) - last_hidden_states = np.concatenate(all_hidden, axis=0) - - for i in range(pose_each_file.shape[0]): - found_flag = False - current_time = i/self.args.pose_fps + time_offset - j_last = 0 - for j, word in enumerate(tgrid[0]): - word_n, word_s, word_e = word.mark, word.minTime, word.maxTime - if word_s<=current_time and current_time<=word_e: - if self.args.word_cache and self.args.t_pre_encoder == 'bert': - mapping_index = word_token_mapping[j] - #print(mapping_index, word_s, word_e) - s_t = np.linspace(word_s, word_e, len(mapping_index)+1) - #print(s_t) - for tt, t_sep in enumerate(s_t[1:]): - if current_time <= t_sep: - #if len(mapping_index) > 1: print(mapping_index[tt]) - word_each_file.append(last_hidden_states[mapping_index[tt]]) - break - else: - if word_n == " ": - word_each_file.append(self.lang_model.PAD_token) - else: - word_each_file.append(self.lang_model.get_word_index(word_n)) - found_flag = True - j_last = j - break - else: continue - if not found_flag: - if self.args.word_cache and self.args.t_pre_encoder == 'bert': - word_each_file.append(last_hidden_states[j_last]) - else: - word_each_file.append(self.lang_model.UNK_token) - word_each_file = np.array(word_each_file) - #print(word_each_file.shape) - - if self.args.emo_rep is not None: - logger.info(f"# ---- Building cache for Emo {id_pose} and Pose {id_pose} ---- #") - rtype, start = int(id_pose.split('_')[3]), int(id_pose.split('_')[3]) - if rtype == 0 or rtype == 2 or rtype == 4 or rtype == 6: - if start >= 1 and start <= 64: - score = 0 - elif start >= 65 and start <= 72: - score = 1 - elif start >= 73 and start <= 80: - score = 2 - elif start >= 81 and start <= 86: - score = 3 - elif start >= 87 and start <= 94: - score = 4 - elif start >= 95 and start <= 102: - score = 5 - elif start >= 103 and start <= 110: - score = 6 - elif start >= 111 and start <= 118: - score = 7 - else: pass - else: - # you may denote as unknown in the future - score = 0 - emo_each_file = np.repeat(np.array(score).reshape(1, 1), pose_each_file.shape[0], axis=0) - #print(emo_each_file) - - if self.args.sem_rep is not None: - logger.info(f"# ---- Building cache for Sem {id_pose} and Pose {id_pose} ---- #") - sem_file = f"{self.data_dir}{self.args.sem_rep}/{id_pose}.txt" - sem_all = pd.read_csv(sem_file, - sep='\t', - names=["name", "start_time", "end_time", "duration", "score", "keywords"]) - # we adopt motion-level semantic score here. - for i in range(pose_each_file.shape[0]): - found_flag = False - for j, (start, end, score) in enumerate(zip(sem_all['start_time'],sem_all['end_time'], sem_all['score'])): - current_time = i/self.args.pose_fps + time_offset - if start<=current_time and current_time<=end: - sem_each_file.append(score) - found_flag=True - break - else: continue - if not found_flag: sem_each_file.append(0.) - sem_each_file = np.array(sem_each_file) - #print(sem_each_file) - - filtered_result = self._sample_from_clip( - dst_lmdb_env, - audio_each_file, pose_each_file, trans_each_file, shape_each_file, facial_each_file, word_each_file, - vid_each_file, emo_each_file, sem_each_file, - disable_filtering, clean_first_seconds, clean_final_seconds, is_test, - ) - for type in filtered_result.keys(): - n_filtered_out[type] += filtered_result[type] - - with dst_lmdb_env.begin() as txn: - logger.info(colored(f"no. of samples: {txn.stat()['entries']}", "cyan")) - n_total_filtered = 0 - for type, n_filtered in n_filtered_out.items(): - logger.info("{}: {}".format(type, n_filtered)) - n_total_filtered += n_filtered - logger.info(colored("no. of excluded samples: {} ({:.1f}%)".format( - n_total_filtered, 100 * n_total_filtered / (txn.stat()["entries"] + n_total_filtered)), "cyan")) - dst_lmdb_env.sync() - dst_lmdb_env.close() - - def _sample_from_clip( - self, dst_lmdb_env, audio_each_file, pose_each_file, trans_each_file, shape_each_file, facial_each_file, word_each_file, - vid_each_file, emo_each_file, sem_each_file, - disable_filtering, clean_first_seconds, clean_final_seconds, is_test, - ): - """ - for data cleaning, we ignore the data for first and final n s - for test, we return all data - """ - # audio_start = int(self.alignment[0] * self.args.audio_fps) - # pose_start = int(self.alignment[1] * self.args.pose_fps) - #logger.info(f"before: {audio_each_file.shape} {pose_each_file.shape}") - # audio_each_file = audio_each_file[audio_start:] - # pose_each_file = pose_each_file[pose_start:] - # trans_each_file = - #logger.info(f"after alignment: {audio_each_file.shape} {pose_each_file.shape}") - #print(pose_each_file.shape) - round_seconds_skeleton = pose_each_file.shape[0] // self.args.pose_fps # assume 1500 frames / 15 fps = 100 s - print(pose_each_file.shape[0]) - #print(round_seconds_skeleton) - #if audio_each_file != []: - if self.args.audio_rep != "wave16k": - round_seconds_audio = len(audio_each_file) // self.args.audio_fps # assume 16,000,00 / 16,000 = 100 s - elif self.args.audio_rep == "mfcc": - round_seconds_audio = audio_each_file.shape[0] // self.args.audio_fps - else: - round_seconds_audio = audio_each_file.shape[0] // self.args.audio_sr - # if facial_each_file != []: - round_seconds_facial = facial_each_file.shape[0] // self.args.pose_fps - logger.info(f"audio: {round_seconds_audio}s, pose: {round_seconds_skeleton}s, facial: {round_seconds_facial}s") - round_seconds_skeleton = min(round_seconds_audio, round_seconds_skeleton, round_seconds_facial) - max_round = max(round_seconds_audio, round_seconds_skeleton, round_seconds_facial) - if round_seconds_skeleton != max_round: - logger.warning(f"reduce to {round_seconds_skeleton}s, ignore {max_round-round_seconds_skeleton}s") - # else: - # logger.info(f"pose: {round_seconds_skeleton}s, audio: {round_seconds_audio}s") - # round_seconds_skeleton = min(round_seconds_audio, round_seconds_skeleton) - # max_round = max(round_seconds_audio, round_seconds_skeleton) - # if round_seconds_skeleton != max_round: - # logger.warning(f"reduce to {round_seconds_skeleton}s, ignore {max_round-round_seconds_skeleton}s") - - clip_s_t, clip_e_t = clean_first_seconds, round_seconds_skeleton - clean_final_seconds # assume [10, 90]s - clip_s_f_audio, clip_e_f_audio = self.args.audio_fps * clip_s_t, clip_e_t * self.args.audio_fps # [160,000,90*160,000] - clip_s_f_pose, clip_e_f_pose = clip_s_t * self.args.pose_fps, clip_e_t * self.args.pose_fps # [150,90*15] - - - for ratio in self.args.multi_length_training: - if is_test:# stride = length for test - cut_length = clip_e_f_pose - clip_s_f_pose - self.args.stride = cut_length - self.max_length = cut_length - else: - self.args.stride = int(ratio*self.ori_stride) - cut_length = int(self.ori_length*ratio) - - num_subdivision = math.floor((clip_e_f_pose - clip_s_f_pose - cut_length) / self.args.stride) + 1 - logger.info(f"pose from frame {clip_s_f_pose} to {clip_e_f_pose}, length {cut_length}") - logger.info(f"{num_subdivision} clips is expected with stride {self.args.stride}") - - # if audio_each_file != []: - audio_short_length = math.floor(cut_length / self.args.pose_fps * self.args.audio_fps) - logger.info(f"audio from frame {clip_s_f_audio} to {clip_e_f_audio}, length {audio_short_length}") - - n_filtered_out = defaultdict(int) - sample_pose_list = [] - sample_audio_list = [] - sample_facial_list = [] - sample_shape_list = [] - sample_word_list = [] - sample_emo_list = [] - sample_sem_list = [] - sample_vid_list = [] - sample_trans_list = [] - - for i in range(num_subdivision): # cut into around 2s chip, (self npose) - start_idx = clip_s_f_pose + i * self.args.stride - fin_idx = start_idx + cut_length - sample_pose = pose_each_file[start_idx:fin_idx] - - sample_trans = trans_each_file[start_idx:fin_idx] - sample_shape = shape_each_file[start_idx:fin_idx] - # print(sample_pose.shape) - if self.args.audio_rep is not None: - audio_start = clip_s_f_audio + math.floor(i * self.args.stride * self.args.audio_fps / self.args.pose_fps) - audio_end = audio_start + audio_short_length - sample_audio = audio_each_file[audio_start:audio_end] - else: - sample_audio = np.array([-1]) - sample_facial = facial_each_file[start_idx:fin_idx] if self.args.facial_rep is not None else np.array([-1]) - sample_word = word_each_file[start_idx:fin_idx] if self.args.word_rep is not None else np.array([-1]) - sample_emo = emo_each_file[start_idx:fin_idx] if self.args.emo_rep is not None else np.array([-1]) - sample_sem = sem_each_file[start_idx:fin_idx] if self.args.sem_rep is not None else np.array([-1]) - sample_vid = vid_each_file[start_idx:fin_idx] if self.args.id_rep is not None else np.array([-1]) - - if sample_pose.any() != None: - # filtering motion skeleton data - sample_pose, filtering_message = MotionPreprocessor(sample_pose).get() - is_correct_motion = True #(sample_pose != []) - if is_correct_motion or disable_filtering: - sample_pose_list.append(sample_pose) - sample_audio_list.append(sample_audio) - sample_facial_list.append(sample_facial) - sample_shape_list.append(sample_shape) - sample_word_list.append(sample_word) - sample_vid_list.append(sample_vid) - sample_emo_list.append(sample_emo) - sample_sem_list.append(sample_sem) - sample_trans_list.append(sample_trans) - else: - n_filtered_out[filtering_message] += 1 - - if len(sample_pose_list) > 0: - with dst_lmdb_env.begin(write=True) as txn: - for pose, audio, facial, shape, word, vid, emo, sem, trans in zip( - sample_pose_list, - sample_audio_list, - sample_facial_list, - sample_shape_list, - sample_word_list, - sample_vid_list, - sample_emo_list, - sample_sem_list, - sample_trans_list,): - k = "{:005}".format(self.n_out_samples).encode("ascii") - v = [pose, audio, facial, shape, word, emo, sem, vid, trans] - # v = pyarrow.serialize(v).to_buffer() - # txn.put(k, v) - # self.n_out_samples += 1 - v = pickle.dumps(v) - txn.put(k, v) - self.n_out_samples += 1 - return n_filtered_out - - def __getitem__(self, idx): - with self.lmdb_env.begin(write=False) as txn: - key = "{:005}".format(idx).encode("ascii") - sample = txn.get(key) - # sample = pyarrow.deserialize(sample) - if sample is not None: - sample = pickle.loads(sample) - tar_pose, in_audio, in_facial, in_shape, in_word, emo, sem, vid, trans = sample - #print(in_shape) - #vid = torch.from_numpy(vid).int() - emo = torch.from_numpy(emo).int() - sem = torch.from_numpy(sem).float() - in_audio = torch.from_numpy(in_audio).float() - in_word = torch.from_numpy(in_word).float() if self.args.word_cache else torch.from_numpy(in_word).int() - if self.loader_type == "test": - tar_pose = torch.from_numpy(tar_pose).float() - trans = torch.from_numpy(trans).float() - in_facial = torch.from_numpy(in_facial).float() - vid = torch.from_numpy(vid).float() - in_shape = torch.from_numpy(in_shape).float() - else: - in_shape = torch.from_numpy(in_shape).reshape((in_shape.shape[0], -1)).float() - trans = torch.from_numpy(trans).reshape((trans.shape[0], -1)).float() - vid = torch.from_numpy(vid).reshape((vid.shape[0], -1)).float() - tar_pose = torch.from_numpy(tar_pose).reshape((tar_pose.shape[0], -1)).float() - in_facial = torch.from_numpy(in_facial).reshape((in_facial.shape[0], -1)).float() - return {"pose":tar_pose, "audio":in_audio, "facial":in_facial, "beta": in_shape, "word":in_word, "id":vid, "emo":emo, "sem":sem, "trans":trans} - - -class MotionPreprocessor: - def __init__(self, skeletons): - self.skeletons = skeletons - #self.mean_pose = mean_pose - self.filtering_message = "PASS" - - def get(self): - assert (self.skeletons is not None) - - # filtering - # if self.skeletons != []: - # if self.check_pose_diff(): - # self.skeletons = [] - # self.filtering_message = "pose" - # elif self.check_spine_angle(): - # self.skeletons = [] - # self.filtering_message = "spine angle" - # elif self.check_static_motion(): - # self.skeletons = [] - # self.filtering_message = "motion" - - # if self.skeletons != []: - # self.skeletons = self.skeletons.tolist() - # for i, frame in enumerate(self.skeletons): - # assert not np.isnan(self.skeletons[i]).any() # missing joints - - return self.skeletons, self.filtering_message - - def check_static_motion(self, verbose=True): - def get_variance(skeleton, joint_idx): - wrist_pos = skeleton[:, joint_idx] - variance = np.sum(np.var(wrist_pos, axis=0)) - return variance - - left_arm_var = get_variance(self.skeletons, 6) - right_arm_var = get_variance(self.skeletons, 9) - - th = 0.0014 # exclude 13110 - # th = 0.002 # exclude 16905 - if left_arm_var < th and right_arm_var < th: - if verbose: - print("skip - check_static_motion left var {}, right var {}".format(left_arm_var, right_arm_var)) - return True - else: - if verbose: - print("pass - check_static_motion left var {}, right var {}".format(left_arm_var, right_arm_var)) - return False - - - def check_pose_diff(self, verbose=False): -# diff = np.abs(self.skeletons - self.mean_pose) # 186*1 -# diff = np.mean(diff) - -# # th = 0.017 -# th = 0.02 #0.02 # exclude 3594 -# if diff < th: -# if verbose: -# print("skip - check_pose_diff {:.5f}".format(diff)) -# return True -# # th = 3.5 #0.02 # exclude 3594 -# # if 3.5 < diff < 5: -# # if verbose: -# # print("skip - check_pose_diff {:.5f}".format(diff)) -# # return True -# else: -# if verbose: -# print("pass - check_pose_diff {:.5f}".format(diff)) - return False - - - def check_spine_angle(self, verbose=True): - def angle_between(v1, v2): - v1_u = v1 / np.linalg.norm(v1) - v2_u = v2 / np.linalg.norm(v2) - return np.arccos(np.clip(np.dot(v1_u, v2_u), -1.0, 1.0)) - - angles = [] - for i in range(self.skeletons.shape[0]): - spine_vec = self.skeletons[i, 1] - self.skeletons[i, 0] - angle = angle_between(spine_vec, [0, -1, 0]) - angles.append(angle) - - if np.rad2deg(max(angles)) > 30 or np.rad2deg(np.mean(angles)) > 20: # exclude 4495 - # if np.rad2deg(max(angles)) > 20: # exclude 8270 - if verbose: - print("skip - check_spine_angle {:.5f}, {:.5f}".format(max(angles), np.mean(angles))) - return True - else: - if verbose: - print("pass - check_spine_angle {:.5f}".format(max(angles))) - return False \ No newline at end of file diff --git a/dataloaders/build_vocab.py b/dataloaders/build_vocab.py deleted file mode 100644 index 649204d0d502eaf671e40d2bbb08ca324e5e02f3..0000000000000000000000000000000000000000 --- a/dataloaders/build_vocab.py +++ /dev/null @@ -1,199 +0,0 @@ -import numpy as np -import glob -import os -import pickle -import lmdb -import pyarrow -import fasttext -from loguru import logger -from scipy import linalg - - -class Vocab: - PAD_token = 0 - SOS_token = 1 - EOS_token = 2 - UNK_token = 3 - - def __init__(self, name, insert_default_tokens=True): - self.name = name - self.trimmed = False - self.word_embedding_weights = None - self.reset_dictionary(insert_default_tokens) - - def reset_dictionary(self, insert_default_tokens=True): - self.word2index = {} - self.word2count = {} - if insert_default_tokens: - self.index2word = {self.PAD_token: "", self.SOS_token: "", - self.EOS_token: "", self.UNK_token: ""} - else: - self.index2word = {self.UNK_token: ""} - self.n_words = len(self.index2word) # count default tokens - - def index_word(self, word): - if word not in self.word2index: - self.word2index[word] = self.n_words - self.word2count[word] = 1 - self.index2word[self.n_words] = word - self.n_words += 1 - else: - self.word2count[word] += 1 - - def add_vocab(self, other_vocab): - for word, _ in other_vocab.word2count.items(): - self.index_word(word) - - # remove words below a certain count threshold - def trim(self, min_count): - if self.trimmed: - return - self.trimmed = True - - keep_words = [] - - for k, v in self.word2count.items(): - if v >= min_count: - keep_words.append(k) - - print(' word trimming, kept %s / %s = %.4f' % ( - len(keep_words), len(self.word2index), len(keep_words) / len(self.word2index) - )) - - # reinitialize dictionary - self.reset_dictionary() - for word in keep_words: - self.index_word(word) - - def get_word_index(self, word): - if word in self.word2index: - return self.word2index[word] - else: - return self.UNK_token - - def load_word_vectors(self, pretrained_path, embedding_dim=300): - print(" loading word vectors from '{}'...".format(pretrained_path)) - - # initialize embeddings to random values for special words - init_sd = 1 / np.sqrt(embedding_dim) - weights = np.random.normal(0, scale=init_sd, size=[self.n_words, embedding_dim]) - weights = weights.astype(np.float32) - - # read word vectors - word_model = fasttext.load_model(pretrained_path) - for word, id in self.word2index.items(): - vec = word_model.get_word_vector(word) - weights[id] = vec - self.word_embedding_weights = weights - - def __get_embedding_weight(self, pretrained_path, embedding_dim=300): - """ function modified from http://ronny.rest/blog/post_2017_08_04_glove/ """ - print("Loading word embedding '{}'...".format(pretrained_path)) - cache_path = pretrained_path - weights = None - - # use cached file if it exists - if os.path.exists(cache_path): # - with open(cache_path, 'rb') as f: - print(' using cached result from {}'.format(cache_path)) - weights = pickle.load(f) - if weights.shape != (self.n_words, embedding_dim): - logging.warning(' failed to load word embedding weights. reinitializing...') - weights = None - - if weights is None: - # initialize embeddings to random values for special and OOV words - init_sd = 1 / np.sqrt(embedding_dim) - weights = np.random.normal(0, scale=init_sd, size=[self.n_words, embedding_dim]) - weights = weights.astype(np.float32) - - with open(pretrained_path, encoding="utf-8", mode="r") as textFile: - num_embedded_words = 0 - for line_raw in textFile: - # extract the word, and embeddings vector - line = line_raw.split() - try: - word, vector = (line[0], np.array(line[1:], dtype=np.float32)) - # if word == 'love': # debugging - # print(word, vector) - - # if it is in our vocab, then update the corresponding weights - id = self.word2index.get(word, None) - if id is not None: - weights[id] = vector - num_embedded_words += 1 - except ValueError: - print(' parsing error at {}...'.format(line_raw[:50])) - continue - print(' {} / {} word vectors are found in the embedding'.format(num_embedded_words, len(self.word2index))) - - with open(cache_path, 'wb') as f: - pickle.dump(weights, f) - return weights - - -def build_vocab(name, data_path, cache_path, word_vec_path=None, feat_dim=None): - print(' building a language model...') - #if not os.path.exists(cache_path): - lang_model = Vocab(name) - print(' indexing words from {}'.format(data_path)) - index_words_from_textgrid(lang_model, data_path) - - if word_vec_path is not None: - lang_model.load_word_vectors(word_vec_path, feat_dim) - else: - print(' loaded from {}'.format(cache_path)) - with open(cache_path, 'rb') as f: - lang_model = pickle.load(f) - if word_vec_path is None: - lang_model.word_embedding_weights = None - elif lang_model.word_embedding_weights.shape[0] != lang_model.n_words: - logging.warning(' failed to load word embedding weights. check this') - assert False - - with open(cache_path, 'wb') as f: - pickle.dump(lang_model, f) - - - return lang_model - - -def index_words(lang_model, data_path): - #index words form text - with open(data_path, "r") as f: - for line in f.readlines(): - line = line.replace(",", " ") - line = line.replace(".", " ") - line = line.replace("?", " ") - line = line.replace("!", " ") - for word in line.split(): - lang_model.index_word(word) - print(' indexed %d words' % lang_model.n_words) - -def index_words_from_textgrid(lang_model, data_path): - import textgrid as tg - from tqdm import tqdm - #trainvaltest=os.listdir(data_path) - # for loadtype in trainvaltest: - # if "." in loadtype: continue #ignore .ipynb_checkpoints - texts = os.listdir(data_path+"/textgrid/") - #print(texts) - for textfile in tqdm(texts): - tgrid = tg.TextGrid.fromFile(data_path+"/textgrid/"+textfile) - for word in tgrid[0]: - word_n, word_s, word_e = word.mark, word.minTime, word.maxTime - word_n = word_n.replace(",", " ") - word_n = word_n.replace(".", " ") - word_n = word_n.replace("?", " ") - word_n = word_n.replace("!", " ") - #print(word_n) - lang_model.index_word(word_n) - print(' indexed %d words' % lang_model.n_words) - print(lang_model.word2index, lang_model.word2count) - -if __name__ == "__main__": - # 11195 for all, 5793 for 4 speakers - # build_vocab("beat_english_15_141", "/home/ma-user/work/datasets/beat_cache/beat_english_15_141/", "/home/ma-user/work/datasets/beat_cache/beat_english_15_141/vocab.pkl", "/home/ma-user/work/datasets/cc.en.300.bin", 300) - build_vocab("beat_chinese_v1.0.0", "/data/datasets/beat_chinese_v1.0.0/", "/data/datasets/beat_chinese_v1.0.0/weights/vocab.pkl", "/home/ma-user/work/cc.zh.300.bin", 300) - - \ No newline at end of file diff --git a/dataloaders/data_tools.py b/dataloaders/data_tools.py deleted file mode 100644 index f7388daf0f9ed7620ba98ccf964d30dc901a7737..0000000000000000000000000000000000000000 --- a/dataloaders/data_tools.py +++ /dev/null @@ -1,1756 +0,0 @@ -import numpy as np -import glob -import os -import pickle -import lmdb -import pyarrow -import fasttext -from loguru import logger -from scipy import linalg -from .pymo.parsers import BVHParser -from .pymo.viz_tools import * -from .pymo.preprocessing import * - - - - -# pose version fpsxx_trinity/japanese_joints(_xxx) -joints_list = { - "trinity_joints":{ - 'Hips': [6,6], - 'Spine': [3,9], - 'Spine1': [3,12], - 'Spine2': [3,15], - 'Spine3': [3,18], - 'Neck': [3,21], - 'Neck1': [3,24], - 'Head': [3,27], - 'RShoulder': [3,30], - 'RArm': [3,33], - 'RArm1': [3,36], - 'RHand': [3,39], - 'RHandT1': [3,42], - 'RHandT2': [3,45], - 'RHandT3': [3,48], - 'RHandI1': [3,51], - 'RHandI2': [3,54], - 'RHandI3': [3,57], - 'RHandM1': [3,60], - 'RHandM2': [3,63], - 'RHandM3': [3,66], - 'RHandR1': [3,69], - 'RHandR2': [3,72], - 'RHandR3': [3,75], - 'RHandP1': [3,78], - 'RHandP2': [3,81], - 'RHandP3': [3,84], - 'LShoulder': [3,87], - 'LArm': [3,90], - 'LArm1': [3,93], - 'LHand': [3,96], - 'LHandT1': [3,99], - 'LHandT2': [3,102], - 'LHandT3': [3,105], - 'LHandI1': [3,108], - 'LHandI2': [3,111], - 'LHandI3': [3,114], - 'LHandM1': [3,117], - 'LHandM2': [3,120], - 'LHandM3': [3,123], - 'LHandR1': [3,126], - 'LHandR2': [3,129], - 'LHandR3': [3,132], - 'LHandP1': [3,135], - 'LHandP2': [3,138], - 'LHandP3': [3,141], - 'RUpLeg': [3,144], - 'RLeg': [3,147], - 'RFoot': [3,150], - 'RFootF': [3,153], - 'RToeBase': [3,156], - 'LUpLeg': [3,159], - 'LLeg': [3,162], - 'LFoot': [3,165], - 'LFootF': [3,168], - 'LToeBase': [3,171],}, - "trinity_joints_123":{ - 'Spine': 3 , - 'Neck': 3 , - 'Neck1': 3 , - 'RShoulder': 3 , - 'RArm': 3 , - 'RArm1': 3 , - 'RHand': 3 , - 'RHandT1': 3 , - 'RHandT2': 3 , - 'RHandT3': 3 , - 'RHandI1': 3 , - 'RHandI2': 3 , - 'RHandI3': 3 , - 'RHandM1': 3 , - 'RHandM2': 3 , - 'RHandM3': 3 , - 'RHandR1': 3 , - 'RHandR2': 3 , - 'RHandR3': 3 , - 'RHandP1': 3 , - 'RHandP2': 3 , - 'RHandP3': 3 , - 'LShoulder': 3 , - 'LArm': 3 , - 'LArm1': 3 , - 'LHand': 3 , - 'LHandT1': 3 , - 'LHandT2': 3 , - 'LHandT3': 3 , - 'LHandI1': 3 , - 'LHandI2': 3 , - 'LHandI3': 3 , - 'LHandM1': 3 , - 'LHandM2': 3 , - 'LHandM3': 3 , - 'LHandR1': 3 , - 'LHandR2': 3 , - 'LHandR3': 3 , - 'LHandP1': 3 , - 'LHandP2': 3 , - 'LHandP3': 3 ,}, - "trinity_joints_168":{ - 'Hips': 3 , - 'Spine': 3 , - 'Spine1': 3 , - 'Spine2': 3 , - 'Spine3': 3 , - 'Neck': 3 , - 'Neck1': 3 , - 'Head': 3 , - 'RShoulder': 3 , - 'RArm': 3 , - 'RArm1': 3 , - 'RHand': 3 , - 'RHandT1': 3 , - 'RHandT2': 3 , - 'RHandT3': 3 , - 'RHandI1': 3 , - 'RHandI2': 3 , - 'RHandI3': 3 , - 'RHandM1': 3 , - 'RHandM2': 3 , - 'RHandM3': 3 , - 'RHandR1': 3 , - 'RHandR2': 3 , - 'RHandR3': 3 , - 'RHandP1': 3 , - 'RHandP2': 3 , - 'RHandP3': 3 , - 'LShoulder': 3 , - 'LArm': 3 , - 'LArm1': 3 , - 'LHand': 3 , - 'LHandT1': 3 , - 'LHandT2': 3 , - 'LHandT3': 3 , - 'LHandI1': 3 , - 'LHandI2': 3 , - 'LHandI3': 3 , - 'LHandM1': 3 , - 'LHandM2': 3 , - 'LHandM3': 3 , - 'LHandR1': 3 , - 'LHandR2': 3 , - 'LHandR3': 3 , - 'LHandP1': 3 , - 'LHandP2': 3 , - 'LHandP3': 3 , - 'RUpLeg': 3 , - 'RLeg': 3 , - 'RFoot': 3 , - 'RFootF': 3 , - 'RToeBase': 3 , - 'LUpLeg': 3 , - 'LLeg': 3 , - 'LFoot': 3 , - 'LFootF': 3 , - 'LToeBase': 3 ,}, - "trinity_joints_138":{ - "Hips": 3 , - 'Spine': 3 , - 'Spine1': 3 , - 'Spine2': 3 , - 'Spine3': 3 , - 'Neck': 3 , - 'Neck1': 3 , - 'Head': 3 , - 'RShoulder': 3 , - 'RArm': 3 , - 'RArm1': 3 , - 'RHand': 3 , - 'RHandT1': 3 , - 'RHandT2': 3 , - 'RHandT3': 3 , - 'RHandI1': 3 , - 'RHandI2': 3 , - 'RHandI3': 3 , - 'RHandM1': 3 , - 'RHandM2': 3 , - 'RHandM3': 3 , - 'RHandR1': 3 , - 'RHandR2': 3 , - 'RHandR3': 3 , - 'RHandP1': 3 , - 'RHandP2': 3 , - 'RHandP3': 3 , - 'LShoulder': 3 , - 'LArm': 3 , - 'LArm1': 3 , - 'LHand': 3 , - 'LHandT1': 3 , - 'LHandT2': 3 , - 'LHandT3': 3 , - 'LHandI1': 3 , - 'LHandI2': 3 , - 'LHandI3': 3 , - 'LHandM1': 3 , - 'LHandM2': 3 , - 'LHandM3': 3 , - 'LHandR1': 3 , - 'LHandR2': 3 , - 'LHandR3': 3 , - 'LHandP1': 3 , - 'LHandP2': 3 , - 'LHandP3': 3 ,}, - "beat_smplx_joints": { - 'pelvis': [3,3], - 'left_hip': [3,6], - 'right_hip': [3,9], - 'spine1': [3,12], - 'left_knee': [3,15], - 'right_knee': [3,18], - 'spine2': [3,21], - 'left_ankle': [3,24], - 'right_ankle': [3,27], - - 'spine3': [3,30], - 'left_foot': [3,33], - 'right_foot': [3,36], - 'neck': [3,39], - 'left_collar': [3,42], - 'right_collar': [3,45], - 'head': [3,48], - 'left_shoulder': [3,51], - - 'right_shoulder': [3,54], - 'left_elbow': [3,57], - 'right_elbow': [3,60], - 'left_wrist': [3,63], - 'right_wrist': [3,66], - - 'jaw': [3,69], - 'left_eye_smplhf': [3,72], - 'right_eye_smplhf': [3,75], - 'left_index1': [3,78], - 'left_index2': [3,81], - - 'left_index3': [3,84], - 'left_middle1': [3,87], - 'left_middle2': [3,90], - 'left_middle3': [3,93], - 'left_pinky1': [3,96], - - 'left_pinky2': [3,99], - 'left_pinky3': [3,102], - 'left_ring1': [3,105], - 'left_ring2': [3,108], - - 'left_ring3': [3,111], - 'left_thumb1': [3,114], - 'left_thumb2': [3,117], - 'left_thumb3': [3,120], - 'right_index1': [3,123], - 'right_index2': [3,126], - 'right_index3': [3,129], - 'right_middle1': [3,132], - - 'right_middle2': [3,135], - 'right_middle3': [3,138], - 'right_pinky1': [3,141], - 'right_pinky2': [3,144], - 'right_pinky3': [3,147], - - 'right_ring1': [3,150], - 'right_ring2': [3,153], - 'right_ring3': [3,156], - 'right_thumb1': [3,159], - 'right_thumb2': [3,162], - 'right_thumb3': [3,165], - -# 'nose': [3,168], -# 'right_eye': [3,171], -# 'left_eye': [3,174], -# 'right_ear': [3,177], - -# 'left_ear': [3,180], -# 'left_big_toe': [3,183], -# 'left_small_toe': [3,186], -# 'left_heel': [3,189], - -# 'right_big_toe': [3,192], -# 'right_small_toe': [3,195], -# 'right_heel': [3,198], -# 'left_thumb': [3,201], -# 'left_index': [3,204], -# 'left_middle': [3,207], - -# 'left_ring': [3,210], -# 'left_pinky': [3,213], -# 'right_thumb': [3,216], -# 'right_index': [3,219], -# 'right_middle': [3,222], -# 'right_ring': [3,225], - -# 'right_pinky': [3,228], -# 'right_eye_brow1': [3,231], -# 'right_eye_brow2': [3,234], -# 'right_eye_brow3': [3,237], - -# 'right_eye_brow4': [3,240], -# 'right_eye_brow5': [3,243], -# 'left_eye_brow5': [3,246], -# 'left_eye_brow4': [3,249], - -# 'left_eye_brow3': [3,252], -# 'left_eye_brow2': [3,255], -# 'left_eye_brow1': [3,258], -# 'nose1': [3,261], -# 'nose2': [3,264], -# 'nose3': [3,267], - -# 'nose4': [3,270], -# 'right_nose_2': [3,273], -# 'right_nose_1': [3,276], -# 'nose_middle': [3,279], -# 'left_nose_1': [3,282], -# 'left_nose_2': [3,285], - -# 'right_eye1': [3,288], -# 'right_eye2': [3,291], -# 'right_eye3': [3,294], -# 'right_eye4': [3,297], - -# 'right_eye5': [3,300], -# 'right_eye6': [3,303], -# 'left_eye4': [3,306], -# 'left_eye3': [3,309], - -# 'left_eye2': [3,312], -# 'left_eye1': [3,315], -# 'left_eye6': [3,318], -# 'left_eye5': [3,321], -# 'right_mouth_1': [3,324], -# 'right_mouth_2': [3,327], -# 'right_mouth_3': [3,330], -# 'mouth_top': [3,333], -# 'left_mouth_3': [3,336], -# 'left_mouth_2': [3,339], -# 'left_mouth_1': [3,342], -# 'left_mouth_5': [3,345], -# 'left_mouth_4': [3,348], -# 'mouth_bottom': [3,351], -# 'right_mouth_4': [3,354], -# 'right_mouth_5': [3,357], -# 'right_lip_1': [3,360], -# 'right_lip_2': [3,363], -# 'lip_top': [3,366], -# 'left_lip_2': [3,369], - -# 'left_lip_1': [3,372], -# 'left_lip_3': [3,375], -# 'lip_bottom': [3,378], -# 'right_lip_3': [3,381], -# 'right_contour_1': [3,384], -# 'right_contour_2': [3,387], -# 'right_contour_3': [3,390], -# 'right_contour_4': [3,393], -# 'right_contour_5': [3,396], -# 'right_contour_6': [3,399], -# 'right_contour_7': [3,402], -# 'right_contour_8': [3,405], -# 'contour_middle': [3,408], -# 'left_contour_8': [3,411], -# 'left_contour_7': [3,414], -# 'left_contour_6': [3,417], -# 'left_contour_5': [3,420], -# 'left_contour_4': [3,423], -# 'left_contour_3': [3,426], -# 'left_contour_2': [3,429], -# 'left_contour_1': [3,432], - }, - - "beat_smplx_no_eyes": { - "pelvis":3, - "left_hip":3, - "right_hip":3, - "spine1":3, - "left_knee":3, - "right_knee":3, - "spine2":3, - "left_ankle":3, - "right_ankle":3, - "spine3":3, - "left_foot":3, - "right_foot":3, - "neck":3, - "left_collar":3, - "right_collar":3, - "head":3, - "left_shoulder":3, - "right_shoulder":3, - "left_elbow":3, - "right_elbow":3, - "left_wrist":3, - "right_wrist":3, - "jaw":3, - # "left_eye_smplhf":3, - # "right_eye_smplhf":3, - "left_index1":3, - "left_index2":3, - "left_index3":3, - "left_middle1":3, - "left_middle2":3, - "left_middle3":3, - "left_pinky1":3, - "left_pinky2":3, - "left_pinky3":3, - "left_ring1":3, - "left_ring2":3, - "left_ring3":3, - "left_thumb1":3, - "left_thumb2":3, - "left_thumb3":3, - "right_index1":3, - "right_index2":3, - "right_index3":3, - "right_middle1":3, - "right_middle2":3, - "right_middle3":3, - "right_pinky1":3, - "right_pinky2":3, - "right_pinky3":3, - "right_ring1":3, - "right_ring2":3, - "right_ring3":3, - "right_thumb1":3, - "right_thumb2":3, - "right_thumb3":3, - }, - - "beat_smplx_full": { - "pelvis":3, - "left_hip":3, - "right_hip":3, - "spine1":3, - "left_knee":3, - "right_knee":3, - "spine2":3, - "left_ankle":3, - "right_ankle":3, - "spine3":3, - "left_foot":3, - "right_foot":3, - "neck":3, - "left_collar":3, - "right_collar":3, - "head":3, - "left_shoulder":3, - "right_shoulder":3, - "left_elbow":3, - "right_elbow":3, - "left_wrist":3, - "right_wrist":3, - "jaw":3, - "left_eye_smplhf":3, - "right_eye_smplhf":3, - "left_index1":3, - "left_index2":3, - "left_index3":3, - "left_middle1":3, - "left_middle2":3, - "left_middle3":3, - "left_pinky1":3, - "left_pinky2":3, - "left_pinky3":3, - "left_ring1":3, - "left_ring2":3, - "left_ring3":3, - "left_thumb1":3, - "left_thumb2":3, - "left_thumb3":3, - "right_index1":3, - "right_index2":3, - "right_index3":3, - "right_middle1":3, - "right_middle2":3, - "right_middle3":3, - "right_pinky1":3, - "right_pinky2":3, - "right_pinky3":3, - "right_ring1":3, - "right_ring2":3, - "right_ring3":3, - "right_thumb1":3, - "right_thumb2":3, - "right_thumb3":3, - }, - - "beat_smplx_upall": { - # "pelvis":3, - # "left_hip":3, - # "right_hip":3, - "spine1":3, - # "left_knee":3, - # "right_knee":3, - "spine2":3, - # "left_ankle":3, - # "right_ankle":3, - "spine3":3, - # "left_foot":3, - # "right_foot":3, - "neck":3, - "left_collar":3, - "right_collar":3, - "head":3, - "left_shoulder":3, - "right_shoulder":3, - "left_elbow":3, - "right_elbow":3, - "left_wrist":3, - "right_wrist":3, - # "jaw":3, - # "left_eye_smplhf":3, - # "right_eye_smplhf":3, - "left_index1":3, - "left_index2":3, - "left_index3":3, - "left_middle1":3, - "left_middle2":3, - "left_middle3":3, - "left_pinky1":3, - "left_pinky2":3, - "left_pinky3":3, - "left_ring1":3, - "left_ring2":3, - "left_ring3":3, - "left_thumb1":3, - "left_thumb2":3, - "left_thumb3":3, - "right_index1":3, - "right_index2":3, - "right_index3":3, - "right_middle1":3, - "right_middle2":3, - "right_middle3":3, - "right_pinky1":3, - "right_pinky2":3, - "right_pinky3":3, - "right_ring1":3, - "right_ring2":3, - "right_ring3":3, - "right_thumb1":3, - "right_thumb2":3, - "right_thumb3":3, - }, - - "beat_smplx_upper": { - #"pelvis":3, - # "left_hip":3, - # "right_hip":3, - "spine1":3, - # "left_knee":3, - # "right_knee":3, - "spine2":3, - # "left_ankle":3, - # "right_ankle":3, - "spine3":3, - # "left_foot":3, - # "right_foot":3, - "neck":3, - "left_collar":3, - "right_collar":3, - "head":3, - "left_shoulder":3, - "right_shoulder":3, - "left_elbow":3, - "right_elbow":3, - "left_wrist":3, - "right_wrist":3, - # "jaw":3, - # "left_eye_smplhf":3, - # "right_eye_smplhf":3, - # "left_index1":3, - # "left_index2":3, - # "left_index3":3, - # "left_middle1":3, - # "left_middle2":3, - # "left_middle3":3, - # "left_pinky1":3, - # "left_pinky2":3, - # "left_pinky3":3, - # "left_ring1":3, - # "left_ring2":3, - # "left_ring3":3, - # "left_thumb1":3, - # "left_thumb2":3, - # "left_thumb3":3, - # "right_index1":3, - # "right_index2":3, - # "right_index3":3, - # "right_middle1":3, - # "right_middle2":3, - # "right_middle3":3, - # "right_pinky1":3, - # "right_pinky2":3, - # "right_pinky3":3, - # "right_ring1":3, - # "right_ring2":3, - # "right_ring3":3, - # "right_thumb1":3, - # "right_thumb2":3, - # "right_thumb3":3, - }, - - "beat_smplx_hands": { - #"pelvis":3, - # "left_hip":3, - # "right_hip":3, - # "spine1":3, - # "left_knee":3, - # "right_knee":3, - # "spine2":3, - # "left_ankle":3, - # "right_ankle":3, - # "spine3":3, - # "left_foot":3, - # "right_foot":3, - # "neck":3, - # "left_collar":3, - # "right_collar":3, - # "head":3, - # "left_shoulder":3, - # "right_shoulder":3, - # "left_elbow":3, - # "right_elbow":3, - # "left_wrist":3, - # "right_wrist":3, - # "jaw":3, - # "left_eye_smplhf":3, - # "right_eye_smplhf":3, - "left_index1":3, - "left_index2":3, - "left_index3":3, - "left_middle1":3, - "left_middle2":3, - "left_middle3":3, - "left_pinky1":3, - "left_pinky2":3, - "left_pinky3":3, - "left_ring1":3, - "left_ring2":3, - "left_ring3":3, - "left_thumb1":3, - "left_thumb2":3, - "left_thumb3":3, - "right_index1":3, - "right_index2":3, - "right_index3":3, - "right_middle1":3, - "right_middle2":3, - "right_middle3":3, - "right_pinky1":3, - "right_pinky2":3, - "right_pinky3":3, - "right_ring1":3, - "right_ring2":3, - "right_ring3":3, - "right_thumb1":3, - "right_thumb2":3, - "right_thumb3":3, - }, - - "beat_smplx_lower": { - "pelvis":3, - "left_hip":3, - "right_hip":3, - # "spine1":3, - "left_knee":3, - "right_knee":3, - # "spine2":3, - "left_ankle":3, - "right_ankle":3, - # "spine3":3, - "left_foot":3, - "right_foot":3, - # "neck":3, - # "left_collar":3, - # "right_collar":3, - # "head":3, - # "left_shoulder":3, - # "right_shoulder":3, - # "left_elbow":3, - # "right_elbow":3, - # "left_wrist":3, - # "right_wrist":3, - # "jaw":3, - # "left_eye_smplhf":3, - # "right_eye_smplhf":3, - # "left_index1":3, - # "left_index2":3, - # "left_index3":3, - # "left_middle1":3, - # "left_middle2":3, - # "left_middle3":3, - # "left_pinky1":3, - # "left_pinky2":3, - # "left_pinky3":3, - # "left_ring1":3, - # "left_ring2":3, - # "left_ring3":3, - # "left_thumb1":3, - # "left_thumb2":3, - # "left_thumb3":3, - # "right_index1":3, - # "right_index2":3, - # "right_index3":3, - # "right_middle1":3, - # "right_middle2":3, - # "right_middle3":3, - # "right_pinky1":3, - # "right_pinky2":3, - # "right_pinky3":3, - # "right_ring1":3, - # "right_ring2":3, - # "right_ring3":3, - # "right_thumb1":3, - # "right_thumb2":3, - # "right_thumb3":3, - }, - - "beat_smplx_face": { - # "pelvis":3, - # "left_hip":3, - # "right_hip":3, - # # "spine1":3, - # "left_knee":3, - # "right_knee":3, - # # "spine2":3, - # "left_ankle":3, - # "right_ankle":3, - # # "spine3":3, - # "left_foot":3, - # "right_foot":3, - # "neck":3, - # "left_collar":3, - # "right_collar":3, - # "head":3, - # "left_shoulder":3, - # "right_shoulder":3, - # "left_elbow":3, - # "right_elbow":3, - # "left_wrist":3, - # "right_wrist":3, - "jaw":3, - # "left_eye_smplhf":3, - # "right_eye_smplhf":3, - # "left_index1":3, - # "left_index2":3, - # "left_index3":3, - # "left_middle1":3, - # "left_middle2":3, - # "left_middle3":3, - # "left_pinky1":3, - # "left_pinky2":3, - # "left_pinky3":3, - # "left_ring1":3, - # "left_ring2":3, - # "left_ring3":3, - # "left_thumb1":3, - # "left_thumb2":3, - # "left_thumb3":3, - # "right_index1":3, - # "right_index2":3, - # "right_index3":3, - # "right_middle1":3, - # "right_middle2":3, - # "right_middle3":3, - # "right_pinky1":3, - # "right_pinky2":3, - # "right_pinky3":3, - # "right_ring1":3, - # "right_ring2":3, - # "right_ring3":3, - # "right_thumb1":3, - # "right_thumb2":3, - # "right_thumb3":3, - }, - - "beat_joints": { - 'Hips': [6,6], - 'Spine': [3,9], - 'Spine1': [3,12], - 'Spine2': [3,15], - 'Spine3': [3,18], - 'Neck': [3,21], - 'Neck1': [3,24], - 'Head': [3,27], - 'HeadEnd': [3,30], - - 'RShoulder': [3,33], - 'RArm': [3,36], - 'RArm1': [3,39], - 'RHand': [3,42], - 'RHandM1': [3,45], - 'RHandM2': [3,48], - 'RHandM3': [3,51], - 'RHandM4': [3,54], - - 'RHandR': [3,57], - 'RHandR1': [3,60], - 'RHandR2': [3,63], - 'RHandR3': [3,66], - 'RHandR4': [3,69], - - 'RHandP': [3,72], - 'RHandP1': [3,75], - 'RHandP2': [3,78], - 'RHandP3': [3,81], - 'RHandP4': [3,84], - - 'RHandI': [3,87], - 'RHandI1': [3,90], - 'RHandI2': [3,93], - 'RHandI3': [3,96], - 'RHandI4': [3,99], - - 'RHandT1': [3,102], - 'RHandT2': [3,105], - 'RHandT3': [3,108], - 'RHandT4': [3,111], - - 'LShoulder': [3,114], - 'LArm': [3,117], - 'LArm1': [3,120], - 'LHand': [3,123], - 'LHandM1': [3,126], - 'LHandM2': [3,129], - 'LHandM3': [3,132], - 'LHandM4': [3,135], - - 'LHandR': [3,138], - 'LHandR1': [3,141], - 'LHandR2': [3,144], - 'LHandR3': [3,147], - 'LHandR4': [3,150], - - 'LHandP': [3,153], - 'LHandP1': [3,156], - 'LHandP2': [3,159], - 'LHandP3': [3,162], - 'LHandP4': [3,165], - - 'LHandI': [3,168], - 'LHandI1': [3,171], - 'LHandI2': [3,174], - 'LHandI3': [3,177], - 'LHandI4': [3,180], - - 'LHandT1': [3,183], - 'LHandT2': [3,186], - 'LHandT3': [3,189], - 'LHandT4': [3,192], - - 'RUpLeg': [3,195], - 'RLeg': [3,198], - 'RFoot': [3,201], - 'RFootF': [3,204], - 'RToeBase': [3,207], - 'RToeBaseEnd': [3,210], - - 'LUpLeg': [3,213], - 'LLeg': [3,216], - 'LFoot': [3,219], - 'LFootF': [3,222], - 'LToeBase': [3,225], - 'LToeBaseEnd': [3,228],}, - - "beat_full":{ - 'Hips': 3, - 'Spine': 3 , - 'Spine1': 3 , - 'Spine2': 3 , - 'Spine3': 3 , - 'Neck': 3 , - 'Neck1': 3 , - 'Head' : 3, - 'HeadEnd' : 3, - 'RShoulder': 3 , - 'RArm': 3 , - 'RArm1': 3 , - 'RHand': 3 , - 'RHandM1': 3 , - 'RHandM2': 3 , - 'RHandM3': 3 , - 'RHandM4': 3 , - 'RHandR': 3 , - 'RHandR1': 3 , - 'RHandR2': 3 , - 'RHandR3': 3 , - 'RHandR4': 3 , - 'RHandP': 3 , - 'RHandP1': 3 , - 'RHandP2': 3 , - 'RHandP3': 3 , - 'RHandP4': 3 , - 'RHandI': 3 , - 'RHandI1': 3 , - 'RHandI2': 3 , - 'RHandI3': 3 , - 'RHandI4': 3 , - 'RHandT1': 3 , - 'RHandT2': 3 , - 'RHandT3': 3 , - 'RHandT4': 3 , - 'LShoulder': 3 , - 'LArm': 3 , - 'LArm1': 3 , - 'LHand': 3 , - 'LHandM1': 3 , - 'LHandM2': 3 , - 'LHandM3': 3 , - 'LHandM4': 3 , - 'LHandR': 3 , - 'LHandR1': 3 , - 'LHandR2': 3 , - 'LHandR3': 3 , - 'LHandR4': 3 , - 'LHandP': 3 , - 'LHandP1': 3 , - 'LHandP2': 3 , - 'LHandP3': 3 , - 'LHandP4': 3 , - 'LHandI': 3 , - 'LHandI1': 3 , - 'LHandI2': 3 , - 'LHandI3': 3 , - 'LHandI4': 3 , - 'LHandT1': 3 , - 'LHandT2': 3 , - 'LHandT3': 3 , - 'LHandT4': 3 , - 'RUpLeg': 3, - 'RLeg': 3, - 'RFoot': 3, - 'RFootF': 3, - 'RToeBase': 3, - 'RToeBaseEnd': 3, - 'LUpLeg': 3, - 'LLeg': 3, - 'LFoot': 3, - 'LFootF': 3, - 'LToeBase': 3, - 'LToeBaseEnd': 3, - }, - - "japanese_joints":{ - 'Hips': [6,6], - 'Spine': [6,12], - 'Spine1': [6,18], - 'Spine2': [6,24], - 'Spine3': [6,30], - 'Neck': [6,36], - 'Neck1': [6,42], - 'Head': [6,48], - 'RShoulder': [6,54], - 'RArm': [6,60], - 'RArm1': [6,66], - 'RHand': [6,72], - 'RHandM1': [6,78], - 'RHandM2': [6,84], - 'RHandM3': [6,90], - 'RHandR': [6,96], - 'RHandR1': [6,102], - 'RHandR2': [6,108], - 'RHandR3': [6,114], - 'RHandP': [6,120], - 'RHandP1': [6,126], - 'RHandP2': [6,132], - 'RHandP3': [6,138], - 'RHandI': [6,144], - 'RHandI1': [6,150], - 'RHandI2': [6,156], - 'RHandI3': [6,162], - 'RHandT1': [6,168], - 'RHandT2': [6,174], - 'RHandT3': [6,180], - 'LShoulder': [6,186], - 'LArm': [6,192], - 'LArm1': [6,198], - 'LHand': [6,204], - 'LHandM1': [6,210], - 'LHandM2': [6,216], - 'LHandM3': [6,222], - 'LHandR': [6,228], - 'LHandR1': [6,234], - 'LHandR2': [6,240], - 'LHandR3': [6,246], - 'LHandP': [6,252], - 'LHandP1': [6,258], - 'LHandP2': [6,264], - 'LHandP3': [6,270], - 'LHandI': [6,276], - 'LHandI1': [6,282], - 'LHandI2': [6,288], - 'LHandI3': [6,294], - 'LHandT1': [6,300], - 'LHandT2': [6,306], - 'LHandT3': [6,312], - 'RUpLeg': [6,318], - 'RLeg': [6,324], - 'RFoot': [6,330], - 'RFootF': [6,336], - 'RToeBase': [6,342], - 'LUpLeg': [6,348], - 'LLeg': [6,354], - 'LFoot': [6,360], - 'LFootF': [6,366], - 'LToeBase': [6,372],}, - - "yostar":{ - 'Hips': [6,6], - 'Spine': [3,9], - 'Spine1': [3,12], - 'Bone040': [3,15], - 'Bone041': [3,18], - - 'Bone034': [3,21], - 'Bone035': [3,24], - 'Bone036': [3,27], - 'Bone037': [3,30], - 'Bone038': [3,33], - 'Bone039': [3,36], - - 'RibbonL1': [3,39], - 'RibbonL1_end': [3,42], - - 'Chest': [3,45], - 'L_eri': [3,48], - 'R_eri': [3,51], - 'Neck': [3,54], - 'Head': [3,57], - 'Head_end': [3,60], - - 'RBackHair_1': [3,63], - 'RBackHair_2': [3,66], - 'RBackHair_3': [3,69], - 'RBackHair_4': [3,72], - 'RBackHair_end': [3,75], - - 'RFrontHair': [3,78], - 'CFrontHair_1': [3,81], - 'CFrontHair_2': [3,84], - 'CFrontHair_3': [3,87], - 'CFrontHair_emd': [3,90], - - 'LFrontHair_1': [3,93], - 'LFrontHair_2': [3,96], - 'LFrontHair_3': [3,99], - - 'LBackHair_1': [3,102], - 'LBackHair_2': [3,105], - 'LBackHair_3': [3,108], - 'LBackHair_4': [3,111], - 'LBackHair_end': [3,114], - - 'LSideHair_1': [3,117], - 'LSideHair_2': [3,120], - 'LSideHair_3': [3,123], - 'LSideHair_4': [3,126], - 'LSideHair_5': [3,129], - 'LSideHair_6': [3,132], - 'LSideHair_7': [3,135], - 'LSideHair_end': [3,138], - - 'CBackHair_1': [3,141], - 'CBackHair_2': [3,144], - 'CBackHair_3': [3,147], - 'CBackHair_4': [3,150], - 'CBackHair_end': [3,153], - - 'RSideHair_1': [3,156], - 'RSideHair_2': [3,159], - 'RSideHair_3': [3,162], - 'RSideHair_4': [3,165], - - 'RibbonR_1': [3,168], - 'RibbonR_2': [3,171], - 'RibbonR_3': [3,174], - - 'RibbonL_1': [3,177], - 'RibbonL_2': [3,180], - 'RibbonL_3': [3,183], - - 'LeftEye': [3,186], - 'LeftEye_end': [3,189], - 'RightEye': [3,192], - 'RightEye_end': [3,195], - - 'LeftShoulder': [3,198], - 'LeftArm': [3,201], - 'LeftForearm': [3,204], - 'LeftHand': [3,207], - 'LeftHandThumb1': [3,210], - 'LeftHandThumb2': [3,213], - 'LeftHandThumb3': [3,216], - 'LeftHandThumb_end': [3,219], - - 'LeftHandIndex1': [3,222], - 'LeftHandIndex2': [3,225], - 'LeftHandIndex3': [3,228], - 'LeftHandIndex_end': [3,231], - - 'LeftHandMiddle1': [3,234], - 'LeftHandMiddle2': [3,237], - 'LeftHandMiddle3': [3,240], - 'LeftHandMiddle_end': [3,243], - - 'LeftHandRing1': [3,246], - 'LeftHandRing2': [3,249], - 'LeftHandRing3': [3,252], - 'LeftHandRing_end': [3,255], - - 'LeftHandPinky1': [3,258], - 'LeftHandPinky2': [3,261], - 'LeftHandPinky3': [3,264], - 'LeftHandPinky_end': [3,267], - - 'RightShoulder': [3,270], - 'RightArm': [3,273], - 'RightForearm': [3,276], - 'RightHand': [3,279], - 'RightHandThumb1': [3,282], - 'RightHandThumb2': [3,285], - 'RightHandThumb3': [3,288], - 'RightHandThumb_end': [3,291], - - 'RightHandIndex1': [3,294], - 'RightHandIndex2': [3,297], - 'RightHandIndex3': [3,300], - 'RightHandIndex_end': [3,303], - - 'RightHandMiddle1': [3,306], - 'RightHandMiddle2': [3,309], - 'RightHandMiddle3': [3,312], - 'RightHandMiddle_end': [3,315], - - 'RightHandRing1': [3,318], - 'RightHandRing2': [3,321], - 'RightHandRing3': [3,324], - 'RightHandRing_end': [3,327], - - 'RightHandPinky1': [3,330], - 'RightHandPinky2': [3,333], - 'RightHandPinky3': [3,336], - 'RightHandPinky_end': [3,339], - - 'RibbonR1': [3,342], - 'RibbonR1_end': [3,345], - 'RibbonR2': [3,348], - 'RibbonR2_end': [3,351], - 'RibbonL2': [3,354], - 'RibbonL2_end': [3,357], - - 'LeftUpLeg': [3,360], - 'LeftLeg': [3,363], - 'LeftFoot': [3,366], - 'LeftToe': [3,369], - 'LeftToe_end': [3,372], - - 'RightUpLeg': [3,375], - 'RightLEg': [3,378], - 'RightFoot': [3,381], - 'RightToe': [3,384], - 'RightToe_end': [3,387], - - 'bone_skirtF00': [3, 390], - 'bone_skirtF01': [3, 393], - 'bone_skirtF02': [3, 396], - 'bone_skirtF03': [3, 399], - 'Bone020': [3, 402], - 'Bone026': [3, 405], - - 'bone_skirtF_R_00': [3, 408], - 'bone_skirtF_R_01': [3, 411], - 'bone_skirtF_R_02': [3, 414], - 'bone_skirtF_R_03': [3, 417], - 'Bone019': [3, 420], - 'Bone028': [3, 423], - - 'bone_skirtR00': [3, 426], - 'bone_skirtR01': [3, 429], - 'bone_skirtR02': [3, 432], - 'bone_skirtR03': [3, 435], - 'Bone018': [3, 438], - 'Bone029': [3, 441], - - 'bone_skirtF_L_00': [3, 444], - 'bone_skirtF_L_01': [3, 447], - 'bone_skirtF_L_02': [3, 450], - 'bone_skirtF_L_03': [3, 453], - 'Bone021': [3, 456], - 'Bone027': [3, 459], - - 'bone_skirtL00': [3, 462], - 'bone_skirtL01': [3, 465], - 'bone_skirtL02': [3, 468], - 'bone_skirtL03': [3, 471], - 'Bone022': [3, 474], - 'Bone033': [3, 477], - - 'bone_skirtB_L_00': [3, 480], - 'bone_skirtB_L_01': [3, 483], - 'bone_skirtB_L_02': [3, 486], - 'bone_skirtB_L_03': [3, 489], - 'Bone023': [3, 492], - 'Bone032': [3, 495], - - 'bone_skirtB00': [3, 498], - 'bone_skirtB01': [3, 501], - 'bone_skirtB02': [3, 504], - 'bone_skirtB03': [3, 507], - 'Bone024': [3, 510], - 'Bone031': [3, 513], - - 'bone_skirtB_R_00': [3, 516], - 'bone_skirtB_R_01': [3, 519], - 'bone_skirtB_R_02': [3, 521], - 'bone_skirtB_R_03': [3, 524], - 'Bone025': [3, 527], - 'Bone030': [3, 530], - }, - - "yostar_fullbody_213":{ - 'Hips': 3 , - 'Spine': 3 , - 'Spine1': 3 , - 'Chest': 3 , - 'L_eri': 3 , - 'R_eri': 3 , - 'Neck': 3 , - 'Head': 3 , - 'Head_end': 3 , - - 'LeftEye': 3, - 'LeftEye_end': 3, - 'RightEye': 3, - 'RightEye_end': 3, - - 'LeftShoulder': 3, - 'LeftArm': 3, - 'LeftForearm': 3, - 'LeftHand': 3, - 'LeftHandThumb1': 3, - 'LeftHandThumb2': 3, - 'LeftHandThumb3': 3, - 'LeftHandThumb_end': 3, - - 'LeftHandIndex1': 3, - 'LeftHandIndex2': 3, - 'LeftHandIndex3': 3, - 'LeftHandIndex_end': 3, - - 'LeftHandMiddle1': 3, - 'LeftHandMiddle2': 3, - 'LeftHandMiddle3': 3, - 'LeftHandMiddle_end': 3, - - 'LeftHandRing1': 3, - 'LeftHandRing2': 3, - 'LeftHandRing3': 3, - 'LeftHandRing_end': 3, - - 'LeftHandPinky1': 3, - 'LeftHandPinky2': 3, - 'LeftHandPinky3': 3, - 'LeftHandPinky_end':3, - - 'RightShoulder': 3, - 'RightArm': 3, - 'RightForearm': 3, - 'RightHand': 3, - 'RightHandThumb1': 3, - 'RightHandThumb2': 3, - 'RightHandThumb3': 3, - 'RightHandThumb_end': 3, - - 'RightHandIndex1': 3, - 'RightHandIndex2': 3, - 'RightHandIndex3': 3, - 'RightHandIndex_end': 3, - - 'RightHandMiddle1': 3, - 'RightHandMiddle2': 3, - 'RightHandMiddle3': 3, - 'RightHandMiddle_end': 3, - - 'RightHandRing1': 3, - 'RightHandRing2': 3, - 'RightHandRing3': 3, - 'RightHandRing_end': 3, - - 'RightHandPinky1': 3, - 'RightHandPinky2': 3, - 'RightHandPinky3': 3, - 'RightHandPinky_end': 3, - - 'LeftUpLeg': 3, - 'LeftLeg': 3, - 'LeftFoot': 3, - 'LeftToe': 3, - 'LeftToe_end': 3, - - 'RightUpLeg': 3, - 'RightLEg': 3, - 'RightFoot': 3, - 'RightToe': 3, - 'RightToe_end': 3, - }, - "yostar_mainbody_48": { - #'Hips': 3 , - 'Spine': 3 , - 'Spine1': 3 , - 'Chest': 3 , - 'L_eri': 3 , - 'R_eri': 3 , - 'Neck': 3 , - 'Head': 3 , - 'Head_end': 3 , - - 'LeftShoulder': 3, - 'LeftArm': 3, - 'LeftForearm': 3, - 'LeftHand': 3, - - 'RightShoulder': 3, - 'RightArm': 3, - 'RightForearm': 3, - 'RightHand': 3, - }, - "yostar_mainbody_69": { - 'Hips': 3 , - 'Spine': 3 , - 'Spine1': 3 , - 'Chest': 3 , - 'L_eri': 3 , - 'R_eri': 3 , - 'Neck': 3 , - 'Head': 3 , - 'Head_end': 3 , - - 'LeftShoulder': 3, - 'LeftArm': 3, - 'LeftForearm': 3, - 'LeftHand': 3, - - 'RightShoulder': 3, - 'RightArm': 3, - 'RightForearm': 3, - 'RightHand': 3, - - 'LeftUpLeg': 3, - 'LeftLeg': 3, - 'LeftFoot': 3, - - 'RightUpLeg': 3, - 'RightLEg': 3, - 'RightFoot': 3, - }, - - "yostar_upbody_168": { - #'Hips': 3 , - 'Spine': 3 , - 'Spine1': 3 , - 'Chest': 3 , - 'L_eri': 3 , - 'R_eri': 3 , - 'Neck': 3 , - 'Head': 3 , - 'Head_end': 3 , - - 'LeftShoulder': 3, - 'LeftArm': 3, - 'LeftForearm': 3, - 'LeftHand': 3, - 'LeftHandThumb1': 3, - 'LeftHandThumb2': 3, - 'LeftHandThumb3': 3, - 'LeftHandThumb_end': 3, - - 'LeftHandIndex1': 3, - 'LeftHandIndex2': 3, - 'LeftHandIndex3': 3, - 'LeftHandIndex_end': 3, - - 'LeftHandMiddle1': 3, - 'LeftHandMiddle2': 3, - 'LeftHandMiddle3': 3, - 'LeftHandMiddle_end': 3, - - 'LeftHandRing1': 3, - 'LeftHandRing2': 3, - 'LeftHandRing3': 3, - 'LeftHandRing_end': 3, - - 'LeftHandPinky1': 3, - 'LeftHandPinky2': 3, - 'LeftHandPinky3': 3, - 'LeftHandPinky_end':3, - - 'RightShoulder': 3, - 'RightArm': 3, - 'RightForearm': 3, - 'RightHand': 3, - 'RightHandThumb1': 3, - 'RightHandThumb2': 3, - 'RightHandThumb3': 3, - 'RightHandThumb_end': 3, - - 'RightHandIndex1': 3, - 'RightHandIndex2': 3, - 'RightHandIndex3': 3, - 'RightHandIndex_end': 3, - - 'RightHandMiddle1': 3, - 'RightHandMiddle2': 3, - 'RightHandMiddle3': 3, - 'RightHandMiddle_end': 3, - - 'RightHandRing1': 3, - 'RightHandRing2': 3, - 'RightHandRing3': 3, - 'RightHandRing_end': 3, - - 'RightHandPinky1': 3, - 'RightHandPinky2': 3, - 'RightHandPinky3': 3, - 'RightHandPinky_end': 3, - }, - "spine_neck_141":{ - 'Spine': 3 , - 'Neck': 3 , - 'Neck1': 3 , - 'RShoulder': 3 , - 'RArm': 3 , - 'RArm1': 3 , - 'RHand': 3 , - 'RHandM1': 3 , - 'RHandM2': 3 , - 'RHandM3': 3 , - 'RHandR': 3 , - 'RHandR1': 3 , - 'RHandR2': 3 , - 'RHandR3': 3 , - 'RHandP': 3 , - 'RHandP1': 3 , - 'RHandP2': 3 , - 'RHandP3': 3 , - 'RHandI': 3 , - 'RHandI1': 3 , - 'RHandI2': 3 , - 'RHandI3': 3 , - 'RHandT1': 3 , - 'RHandT2': 3 , - 'RHandT3': 3 , - 'LShoulder': 3 , - 'LArm': 3 , - 'LArm1': 3 , - 'LHand': 3 , - 'LHandM1': 3 , - 'LHandM2': 3 , - 'LHandM3': 3 , - 'LHandR': 3 , - 'LHandR1': 3 , - 'LHandR2': 3 , - 'LHandR3': 3 , - 'LHandP': 3 , - 'LHandP1': 3 , - 'LHandP2': 3 , - 'LHandP3': 3 , - 'LHandI': 3 , - 'LHandI1': 3 , - 'LHandI2': 3 , - 'LHandI3': 3 , - 'LHandT1': 3 , - 'LHandT2': 3 , - 'LHandT3': 3 ,}, -} - - -class FIDCalculator(object): - ''' - todo - ''' - def __init__(self): - self.gt_rot = None # pandas dataframe for n frames * joints * 6 - self.gt_pos = None # n frames * (joints + 13) * 3 - self.op_rot = None # pandas dataframe for n frames * joints * 6 - self.op_pos = None # n frames * (joints + 13) * 3 - - - def load(self, path, load_type, save_pos=False): - ''' - select gt or op for load_type - ''' - parser = BVHParser() - parsed_data = parser.parse(path) - if load_type == 'gt': - self.gt_rot = parsed_data.values - elif load_type == 'op': - self.op_rot = parsed_data.values - else: print('error, select gt or op for load_type') - - if save_pos: - mp = MocapParameterizer('position') - positions = mp.fit_transform([parsed_data]) - if load_type == 'gt': - self.gt_pos = positions[0].values - elif load_type == 'op': - self.op_pos = positions[0].values - else: print('error, select gt or op for load_type') - - - def _joint_selector(self, selected_joints, ori_data): - selected_data = pd.DataFrame(columns=[]) - - for joint_name in selected_joints: - selected_data[joint_name] = ori_data[joint_name] - return selected_data.to_numpy() - - - def cal_vol(self, dtype): - if dtype == 'pos': - gt = self.gt_pos - op = self.op_pos - else: - gt = self.gt_rot - op = self.op_rot - - gt_v = gt.to_numpy()[1:, :] - gt.to_numpy()[0:-1, :] - op_v = op.to_numpy()[1:, :] - op.to_numpy()[0:-1, :] - if dtype == 'pos': - self.gt_vol_pos = pd.DataFrame(gt_v, columns = gt.columns.tolist()) - self.op_vol_pos = pd.DataFrame(op_v, columns = gt.columns.tolist()) - else: - self.gt_vol_rot = pd.DataFrame(gt_v, columns = gt.columns.tolist()) - self.op_vol_rot = pd.DataFrame(op_v, columns = gt.columns.tolist()) - - - @staticmethod - def frechet_distance(samples_A, samples_B): - A_mu = np.mean(samples_A, axis=0) - A_sigma = np.cov(samples_A, rowvar=False) - B_mu = np.mean(samples_B, axis=0) - B_sigma = np.cov(samples_B, rowvar=False) - try: - frechet_dist = FIDCalculator.calculate_frechet_distance(A_mu, A_sigma, B_mu, B_sigma) - except ValueError: - frechet_dist = 1e+10 - return frechet_dist - - - @staticmethod - def calculate_frechet_distance(mu1, sigma1, mu2, sigma2, eps=1e-6): - """ from https://github.com/mseitzer/pytorch-fid/blob/master/fid_score.py """ - """Numpy implementation of the Frechet Distance. - The Frechet distance between two multivariate Gaussians X_1 ~ N(mu_1, C_1) - and X_2 ~ N(mu_2, C_2) is - d^2 = ||mu_1 - mu_2||^2 + Tr(C_1 + C_2 - 2*sqrt(C_1*C_2)). - Stable version by Dougal J. Sutherland. - Params: - -- mu1 : Numpy array containing the activations of a layer of the - inception net (like returned by the function 'get_predictions') - for generated samples. - -- mu2 : The sample mean over activations, precalculated on an - representative data set. - -- sigma1: The covariance matrix over activations for generated samples. - -- sigma2: The covariance matrix over activations, precalculated on an - representative data set. - Returns: - -- : The Frechet Distance. - """ - - mu1 = np.atleast_1d(mu1) - mu2 = np.atleast_1d(mu2) - #print(mu1[0], mu2[0]) - sigma1 = np.atleast_2d(sigma1) - sigma2 = np.atleast_2d(sigma2) - #print(sigma1[0], sigma2[0]) - assert mu1.shape == mu2.shape, \ - 'Training and test mean vectors have different lengths' - assert sigma1.shape == sigma2.shape, \ - 'Training and test covariances have different dimensions' - - diff = mu1 - mu2 - - # Product might be almost singular - covmean, _ = linalg.sqrtm(sigma1.dot(sigma2), disp=False) - #print(diff, covmean[0]) - if not np.isfinite(covmean).all(): - msg = ('fid calculation produces singular product; ' - 'adding %s to diagonal of cov estimates') % eps - print(msg) - offset = np.eye(sigma1.shape[0]) * eps - covmean = linalg.sqrtm((sigma1 + offset).dot(sigma2 + offset)) - - # Numerical error might give slight imaginary component - if np.iscomplexobj(covmean): - if not np.allclose(np.diagonal(covmean).imag, 0, atol=1e-3): - m = np.max(np.abs(covmean.imag)) - raise ValueError('Imaginary component {}'.format(m)) - covmean = covmean.real - - tr_covmean = np.trace(covmean) - - return (diff.dot(diff) + np.trace(sigma1) + - np.trace(sigma2) - 2 * tr_covmean) - - - def calculate_fid(self, cal_type, joint_type, high_level_opt): - - if cal_type == 'pos': - if self.gt_pos.shape != self.op_pos.shape: - min_val = min(self.gt_pos.shape[0],self.op_pos.shape[0]) - gt = self.gt_pos[:min_val] - op = self.op_pos[:min_val] - else: - gt = self.gt_pos - op = self.op_pos - full_body = gt.columns.tolist() - elif cal_type == 'rot': - if self.gt_rot.shape != self.op_rot.shape: - min_val = min(self.gt_rot.shape[0],self.op_rot.shape[0]) - gt = self.gt_rot[:min_val] - op = self.op_rot[:min_val] - else: - gt = self.gt_rot - op = self.op_rot - full_body_with_offset = gt.columns.tolist() - full_body = [o for o in full_body_with_offset if ('position' not in o)] - elif cal_type == 'pos_vol': - assert self.gt_vol_pos.shape == self.op_vol_pos.shape - gt = self.gt_vol_pos - op = self.op_vol_pos - full_body_with_offset = gt.columns.tolist() - full_body = gt.columns.tolist() - elif cal_type == 'rot_vol': - assert self.gt_vol_rot.shape == self.op_vol_rot.shape - gt = self.gt_vol_rot - op = self.op_vol_rot - full_body_with_offset = gt.columns.tolist() - full_body = [o for o in full_body_with_offset if ('position' not in o)] - #print(f'full_body contains {len(full_body)//3} joints') - - if joint_type == 'full_upper_body': - selected_body = [o for o in full_body if ('Leg' not in o) and ('Foot' not in o) and ('Toe' not in o)] - elif joint_type == 'upper_body': - selected_body = [o for o in full_body if ('Hand' not in o) and ('Leg' not in o) and ('Foot' not in o) and ('Toe' not in o)] - elif joint_type == 'fingers': - selected_body = [o for o in full_body if ('Hand' in o)] - elif joint_type == 'indivdual': - pass - else: print('error, plz select correct joint type') - #print(f'calculate fid for {len(selected_body)//3} joints') - - gt = self._joint_selector(selected_body, gt) - op = self._joint_selector(selected_body, op) - - if high_level_opt == 'fid': - fid = FIDCalculator.frechet_distance(gt, op) - return fid - elif high_level_opt == 'var': - var_gt = gt.var() - var_op = op.var() - return var_gt, var_op - elif high_level_opt == 'mean': - mean_gt = gt.mean() - mean_op = op.mean() - return mean_gt, mean_op - else: return 0 - - -def result2target_vis(pose_version, res_bvhlist, save_path, demo_name, verbose=True): - if "trinity" in pose_version: - ori_list = joints_list[pose_version[6:-4]] - target_list = joints_list[pose_version[6:]] - file_content_length = 336 - elif "beat" in pose_version or "spine_neck_141" in pose_version: - ori_list = joints_list["beat_joints"] - target_list = joints_list["spine_neck_141"] - file_content_length = 431 - elif "yostar" in pose_version: - ori_list = joints_list["yostar"] - target_list = joints_list[pose_version] - file_content_length = 1056 - else: - ori_list = joints_list["japanese_joints"] - target_list = joints_list[pose_version] - file_content_length = 366 - - bvh_files_dirs = sorted(glob.glob(f'{res_bvhlist}*.bvh'), key=str) - #test_seq_list = os.list_dir(demo_name).sort() - - counter = 0 - if not os.path.exists(save_path): - os.makedirs(save_path) - for i, bvh_file_dir in enumerate(bvh_files_dirs): - short_name = bvh_file_dir.split("/")[-1][11:] - #print(short_name) - wirte_file = open(os.path.join(save_path, f'res_{short_name}'),'w+') - with open(f"{demo_name}{short_name}",'r') as pose_data_pre: - pose_data_pre_file = pose_data_pre.readlines() - for j, line in enumerate(pose_data_pre_file[0:file_content_length]): - wirte_file.write(line) - offset_data = pose_data_pre_file[file_content_length] - offset_data = np.fromstring(offset_data, dtype=float, sep=' ') - wirte_file.close() - - wirte_file = open(os.path.join(save_path, f'res_{short_name}'),'r') - ori_lines = wirte_file.readlines() - with open(bvh_file_dir, 'r') as pose_data: - pose_data_file = pose_data.readlines() - ori_lines[file_content_length-2] = 'Frames: ' + str(len(pose_data_file)-1) + '\n' - wirte_file.close() - - wirte_file = open(os.path.join(save_path, f'res_{short_name}'),'w+') - wirte_file.writelines(i for i in ori_lines[:file_content_length]) - wirte_file.close() - - with open(os.path.join(save_path, f'res_{short_name}'),'a+') as wirte_file: - with open(bvh_file_dir, 'r') as pose_data: - data_each_file = [] - pose_data_file = pose_data.readlines() - for j, line in enumerate(pose_data_file): - if not j: - pass - else: - data = np.fromstring(line, dtype=float, sep=' ') - data_rotation = offset_data.copy() - for iii, (k, v) in enumerate(target_list.items()): # here is 147 rotations by 3 - #print(data_rotation[ori_list[k][1]-v:ori_list[k][1]], data[iii*3:iii*3+3]) - data_rotation[ori_list[k][1]-v:ori_list[k][1]] = data[iii*3:iii*3+3] - data_each_file.append(data_rotation) - - for line_data in data_each_file: - line_data = np.array2string(line_data, max_line_width=np.inf, precision=6, suppress_small=False, separator=' ') - wirte_file.write(line_data[1:-2]+'\n') - - counter += 1 - if verbose: - logger.info('data_shape:', data_rotation.shape, 'process:', counter, '/', len(bvh_files_dirs)) \ No newline at end of file diff --git a/dataloaders/pymo/Quaternions.py b/dataloaders/pymo/Quaternions.py deleted file mode 100644 index d4b754871310a264e2bd2675479db9a79d24358e..0000000000000000000000000000000000000000 --- a/dataloaders/pymo/Quaternions.py +++ /dev/null @@ -1,468 +0,0 @@ -import numpy as np - -class Quaternions: - """ - Quaternions is a wrapper around a numpy ndarray - that allows it to act as if it were an narray of - a quaternion data type. - - Therefore addition, subtraction, multiplication, - division, negation, absolute, are all defined - in terms of quaternion operations such as quaternion - multiplication. - - This allows for much neater code and many routines - which conceptually do the same thing to be written - in the same way for point data and for rotation data. - - The Quaternions class has been desgined such that it - should support broadcasting and slicing in all of the - usual ways. - """ - - def __init__(self, qs): - if isinstance(qs, np.ndarray): - - if len(qs.shape) == 1: qs = np.array([qs]) - self.qs = qs - return - - if isinstance(qs, Quaternions): - self.qs = qs.qs - return - - raise TypeError('Quaternions must be constructed from iterable, numpy array, or Quaternions, not %s' % type(qs)) - - def __str__(self): return "Quaternions("+ str(self.qs) + ")" - def __repr__(self): return "Quaternions("+ repr(self.qs) + ")" - - """ Helper Methods for Broadcasting and Data extraction """ - - @classmethod - def _broadcast(cls, sqs, oqs, scalar=False): - - if isinstance(oqs, float): return sqs, oqs * np.ones(sqs.shape[:-1]) - - ss = np.array(sqs.shape) if not scalar else np.array(sqs.shape[:-1]) - os = np.array(oqs.shape) - - if len(ss) != len(os): - raise TypeError('Quaternions cannot broadcast together shapes %s and %s' % (sqs.shape, oqs.shape)) - - if np.all(ss == os): return sqs, oqs - - if not np.all((ss == os) | (os == np.ones(len(os))) | (ss == np.ones(len(ss)))): - raise TypeError('Quaternions cannot broadcast together shapes %s and %s' % (sqs.shape, oqs.shape)) - - sqsn, oqsn = sqs.copy(), oqs.copy() - - for a in np.where(ss == 1)[0]: sqsn = sqsn.repeat(os[a], axis=a) - for a in np.where(os == 1)[0]: oqsn = oqsn.repeat(ss[a], axis=a) - - return sqsn, oqsn - - """ Adding Quaterions is just Defined as Multiplication """ - - def __add__(self, other): return self * other - def __sub__(self, other): return self / other - - """ Quaterion Multiplication """ - - def __mul__(self, other): - """ - Quaternion multiplication has three main methods. - - When multiplying a Quaternions array by Quaternions - normal quaternion multiplication is performed. - - When multiplying a Quaternions array by a vector - array of the same shape, where the last axis is 3, - it is assumed to be a Quaternion by 3D-Vector - multiplication and the 3D-Vectors are rotated - in space by the Quaternions. - - When multipplying a Quaternions array by a scalar - or vector of different shape it is assumed to be - a Quaternions by Scalars multiplication and the - Quaternions are scaled using Slerp and the identity - quaternions. - """ - - """ If Quaternions type do Quaternions * Quaternions """ - if isinstance(other, Quaternions): - - sqs, oqs = Quaternions._broadcast(self.qs, other.qs) - - q0 = sqs[...,0]; q1 = sqs[...,1]; - q2 = sqs[...,2]; q3 = sqs[...,3]; - r0 = oqs[...,0]; r1 = oqs[...,1]; - r2 = oqs[...,2]; r3 = oqs[...,3]; - - qs = np.empty(sqs.shape) - qs[...,0] = r0 * q0 - r1 * q1 - r2 * q2 - r3 * q3 - qs[...,1] = r0 * q1 + r1 * q0 - r2 * q3 + r3 * q2 - qs[...,2] = r0 * q2 + r1 * q3 + r2 * q0 - r3 * q1 - qs[...,3] = r0 * q3 - r1 * q2 + r2 * q1 + r3 * q0 - - return Quaternions(qs) - - """ If array type do Quaternions * Vectors """ - if isinstance(other, np.ndarray) and other.shape[-1] == 3: - vs = Quaternions(np.concatenate([np.zeros(other.shape[:-1] + (1,)), other], axis=-1)) - return (self * (vs * -self)).imaginaries - - """ If float do Quaternions * Scalars """ - if isinstance(other, np.ndarray) or isinstance(other, float): - return Quaternions.slerp(Quaternions.id_like(self), self, other) - - raise TypeError('Cannot multiply/add Quaternions with type %s' % str(type(other))) - - def __div__(self, other): - """ - When a Quaternion type is supplied, division is defined - as multiplication by the inverse of that Quaternion. - - When a scalar or vector is supplied it is defined - as multiplicaion of one over the supplied value. - Essentially a scaling. - """ - - if isinstance(other, Quaternions): return self * (-other) - if isinstance(other, np.ndarray): return self * (1.0 / other) - if isinstance(other, float): return self * (1.0 / other) - raise TypeError('Cannot divide/subtract Quaternions with type %s' + str(type(other))) - - def __eq__(self, other): return self.qs == other.qs - def __ne__(self, other): return self.qs != other.qs - - def __neg__(self): - """ Invert Quaternions """ - return Quaternions(self.qs * np.array([[1, -1, -1, -1]])) - - def __abs__(self): - """ Unify Quaternions To Single Pole """ - qabs = self.normalized().copy() - top = np.sum(( qabs.qs) * np.array([1,0,0,0]), axis=-1) - bot = np.sum((-qabs.qs) * np.array([1,0,0,0]), axis=-1) - qabs.qs[top < bot] = -qabs.qs[top < bot] - return qabs - - def __iter__(self): return iter(self.qs) - def __len__(self): return len(self.qs) - - def __getitem__(self, k): return Quaternions(self.qs[k]) - def __setitem__(self, k, v): self.qs[k] = v.qs - - @property - def lengths(self): - return np.sum(self.qs**2.0, axis=-1)**0.5 - - @property - def reals(self): - return self.qs[...,0] - - @property - def imaginaries(self): - return self.qs[...,1:4] - - @property - def shape(self): return self.qs.shape[:-1] - - def repeat(self, n, **kwargs): - return Quaternions(self.qs.repeat(n, **kwargs)) - - def normalized(self): - return Quaternions(self.qs / self.lengths[...,np.newaxis]) - - def log(self): - norm = abs(self.normalized()) - imgs = norm.imaginaries - lens = np.sqrt(np.sum(imgs**2, axis=-1)) - lens = np.arctan2(lens, norm.reals) / (lens + 1e-10) - return imgs * lens[...,np.newaxis] - - def constrained(self, axis): - - rl = self.reals - im = np.sum(axis * self.imaginaries, axis=-1) - - t1 = -2 * np.arctan2(rl, im) + np.pi - t2 = -2 * np.arctan2(rl, im) - np.pi - - top = Quaternions.exp(axis[np.newaxis] * (t1[:,np.newaxis] / 2.0)) - bot = Quaternions.exp(axis[np.newaxis] * (t2[:,np.newaxis] / 2.0)) - img = self.dot(top) > self.dot(bot) - - ret = top.copy() - ret[ img] = top[ img] - ret[~img] = bot[~img] - return ret - - def constrained_x(self): return self.constrained(np.array([1,0,0])) - def constrained_y(self): return self.constrained(np.array([0,1,0])) - def constrained_z(self): return self.constrained(np.array([0,0,1])) - - def dot(self, q): return np.sum(self.qs * q.qs, axis=-1) - - def copy(self): return Quaternions(np.copy(self.qs)) - - def reshape(self, s): - self.qs.reshape(s) - return self - - def interpolate(self, ws): - return Quaternions.exp(np.average(abs(self).log, axis=0, weights=ws)) - - def euler(self, order='xyz'): - - q = self.normalized().qs - q0 = q[...,0] - q1 = q[...,1] - q2 = q[...,2] - q3 = q[...,3] - es = np.zeros(self.shape + (3,)) - - if order == 'xyz': - es[...,0] = np.arctan2(2 * (q0 * q1 + q2 * q3), 1 - 2 * (q1 * q1 + q2 * q2)) - es[...,1] = np.arcsin((2 * (q0 * q2 - q3 * q1)).clip(-1,1)) - es[...,2] = np.arctan2(2 * (q0 * q3 + q1 * q2), 1 - 2 * (q2 * q2 + q3 * q3)) - elif order == 'yzx': - es[...,0] = np.arctan2(2 * (q1 * q0 - q2 * q3), -q1 * q1 + q2 * q2 - q3 * q3 + q0 * q0) - es[...,1] = np.arctan2(2 * (q2 * q0 - q1 * q3), q1 * q1 - q2 * q2 - q3 * q3 + q0 * q0) - es[...,2] = np.arcsin((2 * (q1 * q2 + q3 * q0)).clip(-1,1)) - else: - raise NotImplementedError('Cannot convert from ordering %s' % order) - - """ - - # These conversion don't appear to work correctly for Maya. - # http://bediyap.com/programming/convert-quaternion-to-euler-rotations/ - - if order == 'xyz': - es[...,0] = np.arctan2(2 * (q0 * q3 - q1 * q2), q0 * q0 + q1 * q1 - q2 * q2 - q3 * q3) - es[...,1] = np.arcsin((2 * (q1 * q3 + q0 * q2)).clip(-1,1)) - es[...,2] = np.arctan2(2 * (q0 * q1 - q2 * q3), q0 * q0 - q1 * q1 - q2 * q2 + q3 * q3) - elif order == 'yzx': - es[...,0] = np.arctan2(2 * (q0 * q1 - q2 * q3), q0 * q0 - q1 * q1 + q2 * q2 - q3 * q3) - es[...,1] = np.arcsin((2 * (q1 * q2 + q0 * q3)).clip(-1,1)) - es[...,2] = np.arctan2(2 * (q0 * q2 - q1 * q3), q0 * q0 + q1 * q1 - q2 * q2 - q3 * q3) - elif order == 'zxy': - es[...,0] = np.arctan2(2 * (q0 * q2 - q1 * q3), q0 * q0 - q1 * q1 - q2 * q2 + q3 * q3) - es[...,1] = np.arcsin((2 * (q0 * q1 + q2 * q3)).clip(-1,1)) - es[...,2] = np.arctan2(2 * (q0 * q3 - q1 * q2), q0 * q0 - q1 * q1 + q2 * q2 - q3 * q3) - elif order == 'xzy': - es[...,0] = np.arctan2(2 * (q0 * q2 + q1 * q3), q0 * q0 + q1 * q1 - q2 * q2 - q3 * q3) - es[...,1] = np.arcsin((2 * (q0 * q3 - q1 * q2)).clip(-1,1)) - es[...,2] = np.arctan2(2 * (q0 * q1 + q2 * q3), q0 * q0 - q1 * q1 + q2 * q2 - q3 * q3) - elif order == 'yxz': - es[...,0] = np.arctan2(2 * (q1 * q2 + q0 * q3), q0 * q0 - q1 * q1 + q2 * q2 - q3 * q3) - es[...,1] = np.arcsin((2 * (q0 * q1 - q2 * q3)).clip(-1,1)) - es[...,2] = np.arctan2(2 * (q1 * q3 + q0 * q2), q0 * q0 - q1 * q1 - q2 * q2 + q3 * q3) - elif order == 'zyx': - es[...,0] = np.arctan2(2 * (q0 * q1 + q2 * q3), q0 * q0 - q1 * q1 - q2 * q2 + q3 * q3) - es[...,1] = np.arcsin((2 * (q0 * q2 - q1 * q3)).clip(-1,1)) - es[...,2] = np.arctan2(2 * (q0 * q3 + q1 * q2), q0 * q0 + q1 * q1 - q2 * q2 - q3 * q3) - else: - raise KeyError('Unknown ordering %s' % order) - - """ - - # https://github.com/ehsan/ogre/blob/master/OgreMain/src/OgreMatrix3.cpp - # Use this class and convert from matrix - - return es - - - def average(self): - - if len(self.shape) == 1: - - import numpy.core.umath_tests as ut - system = ut.matrix_multiply(self.qs[:,:,np.newaxis], self.qs[:,np.newaxis,:]).sum(axis=0) - w, v = np.linalg.eigh(system) - qiT_dot_qref = (self.qs[:,:,np.newaxis] * v[np.newaxis,:,:]).sum(axis=1) - return Quaternions(v[:,np.argmin((1.-qiT_dot_qref**2).sum(axis=0))]) - - else: - - raise NotImplementedError('Cannot average multi-dimensionsal Quaternions') - - def angle_axis(self): - - norm = self.normalized() - s = np.sqrt(1 - (norm.reals**2.0)) - s[s == 0] = 0.001 - - angles = 2.0 * np.arccos(norm.reals) - axis = norm.imaginaries / s[...,np.newaxis] - - return angles, axis - - - def transforms(self): - - qw = self.qs[...,0] - qx = self.qs[...,1] - qy = self.qs[...,2] - qz = self.qs[...,3] - - x2 = qx + qx; y2 = qy + qy; z2 = qz + qz; - xx = qx * x2; yy = qy * y2; wx = qw * x2; - xy = qx * y2; yz = qy * z2; wy = qw * y2; - xz = qx * z2; zz = qz * z2; wz = qw * z2; - - m = np.empty(self.shape + (3,3)) - m[...,0,0] = 1.0 - (yy + zz) - m[...,0,1] = xy - wz - m[...,0,2] = xz + wy - m[...,1,0] = xy + wz - m[...,1,1] = 1.0 - (xx + zz) - m[...,1,2] = yz - wx - m[...,2,0] = xz - wy - m[...,2,1] = yz + wx - m[...,2,2] = 1.0 - (xx + yy) - - return m - - def ravel(self): - return self.qs.ravel() - - @classmethod - def id(cls, n): - - if isinstance(n, tuple): - qs = np.zeros(n + (4,)) - qs[...,0] = 1.0 - return Quaternions(qs) - - if isinstance(n, int) or isinstance(n, long): - qs = np.zeros((n,4)) - qs[:,0] = 1.0 - return Quaternions(qs) - - raise TypeError('Cannot Construct Quaternion from %s type' % str(type(n))) - - @classmethod - def id_like(cls, a): - qs = np.zeros(a.shape + (4,)) - qs[...,0] = 1.0 - return Quaternions(qs) - - @classmethod - def exp(cls, ws): - - ts = np.sum(ws**2.0, axis=-1)**0.5 - ts[ts == 0] = 0.001 - ls = np.sin(ts) / ts - - qs = np.empty(ws.shape[:-1] + (4,)) - qs[...,0] = np.cos(ts) - qs[...,1] = ws[...,0] * ls - qs[...,2] = ws[...,1] * ls - qs[...,3] = ws[...,2] * ls - - return Quaternions(qs).normalized() - - @classmethod - def slerp(cls, q0s, q1s, a): - - fst, snd = cls._broadcast(q0s.qs, q1s.qs) - fst, a = cls._broadcast(fst, a, scalar=True) - snd, a = cls._broadcast(snd, a, scalar=True) - - len = np.sum(fst * snd, axis=-1) - - neg = len < 0.0 - len[neg] = -len[neg] - snd[neg] = -snd[neg] - - amount0 = np.zeros(a.shape) - amount1 = np.zeros(a.shape) - - linear = (1.0 - len) < 0.01 - omegas = np.arccos(len[~linear]) - sinoms = np.sin(omegas) - - amount0[ linear] = 1.0 - a[linear] - amount1[ linear] = a[linear] - amount0[~linear] = np.sin((1.0 - a[~linear]) * omegas) / sinoms - amount1[~linear] = np.sin( a[~linear] * omegas) / sinoms - - return Quaternions( - amount0[...,np.newaxis] * fst + - amount1[...,np.newaxis] * snd) - - @classmethod - def between(cls, v0s, v1s): - a = np.cross(v0s, v1s) - w = np.sqrt((v0s**2).sum(axis=-1) * (v1s**2).sum(axis=-1)) + (v0s * v1s).sum(axis=-1) - return Quaternions(np.concatenate([w[...,np.newaxis], a], axis=-1)).normalized() - - @classmethod - def from_angle_axis(cls, angles, axis): - axis = axis / (np.sqrt(np.sum(axis**2, axis=-1)) + 1e-10)[...,np.newaxis] - sines = np.sin(angles / 2.0)[...,np.newaxis] - cosines = np.cos(angles / 2.0)[...,np.newaxis] - return Quaternions(np.concatenate([cosines, axis * sines], axis=-1)) - - @classmethod - def from_euler(cls, es, order='xyz', world=False): - - axis = { - 'x' : np.array([1,0,0]), - 'y' : np.array([0,1,0]), - 'z' : np.array([0,0,1]), - } - - q0s = Quaternions.from_angle_axis(es[...,0], axis[order[0]]) - q1s = Quaternions.from_angle_axis(es[...,1], axis[order[1]]) - q2s = Quaternions.from_angle_axis(es[...,2], axis[order[2]]) - - return (q2s * (q1s * q0s)) if world else (q0s * (q1s * q2s)) - - @classmethod - def from_transforms(cls, ts): - - d0, d1, d2 = ts[...,0,0], ts[...,1,1], ts[...,2,2] - - q0 = ( d0 + d1 + d2 + 1.0) / 4.0 - q1 = ( d0 - d1 - d2 + 1.0) / 4.0 - q2 = (-d0 + d1 - d2 + 1.0) / 4.0 - q3 = (-d0 - d1 + d2 + 1.0) / 4.0 - - q0 = np.sqrt(q0.clip(0,None)) - q1 = np.sqrt(q1.clip(0,None)) - q2 = np.sqrt(q2.clip(0,None)) - q3 = np.sqrt(q3.clip(0,None)) - - c0 = (q0 >= q1) & (q0 >= q2) & (q0 >= q3) - c1 = (q1 >= q0) & (q1 >= q2) & (q1 >= q3) - c2 = (q2 >= q0) & (q2 >= q1) & (q2 >= q3) - c3 = (q3 >= q0) & (q3 >= q1) & (q3 >= q2) - - q1[c0] *= np.sign(ts[c0,2,1] - ts[c0,1,2]) - q2[c0] *= np.sign(ts[c0,0,2] - ts[c0,2,0]) - q3[c0] *= np.sign(ts[c0,1,0] - ts[c0,0,1]) - - q0[c1] *= np.sign(ts[c1,2,1] - ts[c1,1,2]) - q2[c1] *= np.sign(ts[c1,1,0] + ts[c1,0,1]) - q3[c1] *= np.sign(ts[c1,0,2] + ts[c1,2,0]) - - q0[c2] *= np.sign(ts[c2,0,2] - ts[c2,2,0]) - q1[c2] *= np.sign(ts[c2,1,0] + ts[c2,0,1]) - q3[c2] *= np.sign(ts[c2,2,1] + ts[c2,1,2]) - - q0[c3] *= np.sign(ts[c3,1,0] - ts[c3,0,1]) - q1[c3] *= np.sign(ts[c3,2,0] + ts[c3,0,2]) - q2[c3] *= np.sign(ts[c3,2,1] + ts[c3,1,2]) - - qs = np.empty(ts.shape[:-2] + (4,)) - qs[...,0] = q0 - qs[...,1] = q1 - qs[...,2] = q2 - qs[...,3] = q3 - - return cls(qs) - - - \ No newline at end of file diff --git a/dataloaders/pymo/__init__.py b/dataloaders/pymo/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/dataloaders/pymo/__pycache__/Quaternions.cpython-310.pyc b/dataloaders/pymo/__pycache__/Quaternions.cpython-310.pyc deleted file mode 100644 index c442d4bb4e465e0b2ac01b857277011dc21b1a0a..0000000000000000000000000000000000000000 Binary files a/dataloaders/pymo/__pycache__/Quaternions.cpython-310.pyc and /dev/null differ diff --git a/dataloaders/pymo/__pycache__/Quaternions.cpython-38.pyc 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differ diff --git a/dataloaders/pymo/__pycache__/viz_tools.cpython-38.pyc b/dataloaders/pymo/__pycache__/viz_tools.cpython-38.pyc deleted file mode 100644 index 317a125318df8ac36572f0fd96fabeb43e484b09..0000000000000000000000000000000000000000 Binary files a/dataloaders/pymo/__pycache__/viz_tools.cpython-38.pyc and /dev/null differ diff --git a/dataloaders/pymo/data.py b/dataloaders/pymo/data.py deleted file mode 100644 index 7be4f0a819aa041218b8a3d78e700017253d277c..0000000000000000000000000000000000000000 --- a/dataloaders/pymo/data.py +++ /dev/null @@ -1,53 +0,0 @@ -import numpy as np - -class Joint(): - def __init__(self, name, parent=None, children=None): - self.name = name - self.parent = parent - self.children = children - -class MocapData(): - def __init__(self): - self.skeleton = {} - self.values = None - self.channel_names = [] - self.framerate = 0.0 - self.root_name = '' - - def traverse(self, j=None): - stack = [self.root_name] - while stack: - joint = stack.pop() - yield joint - for c in self.skeleton[joint]['children']: - stack.append(c) - - def clone(self): - import copy - new_data = MocapData() - new_data.skeleton = copy.copy(self.skeleton) - new_data.values = copy.copy(self.values) - new_data.channel_names = copy.copy(self.channel_names) - new_data.root_name = copy.copy(self.root_name) - new_data.framerate = copy.copy(self.framerate) - return new_data - - def get_all_channels(self): - '''Returns all of the channels parsed from the file as a 2D numpy array''' - - frames = [f[1] for f in self.values] - return np.asarray([[channel[2] for channel in frame] for frame in frames]) - - def get_skeleton_tree(self): - tree = [] - root_key = [j for j in self.skeleton if self.skeleton[j]['parent']==None][0] - - root_joint = Joint(root_key) - - def get_empty_channels(self): - #TODO - pass - - def get_constant_channels(self): - #TODO - pass diff --git a/dataloaders/pymo/features.py b/dataloaders/pymo/features.py deleted file mode 100644 index fec29ed5758f79b61f296e01e9b077cba573f495..0000000000000000000000000000000000000000 --- a/dataloaders/pymo/features.py +++ /dev/null @@ -1,43 +0,0 @@ -''' -A set of mocap feature extraction functions - -Created by Omid Alemi | Nov 17 2017 - -''' -import numpy as np -import pandas as pd -import peakutils -import matplotlib.pyplot as plt - -def get_foot_contact_idxs(signal, t=0.02, min_dist=120): - up_idxs = peakutils.indexes(signal, thres=t/max(signal), min_dist=min_dist) - down_idxs = peakutils.indexes(-signal, thres=t/min(signal), min_dist=min_dist) - - return [up_idxs, down_idxs] - - -def create_foot_contact_signal(mocap_track, col_name, start=1, t=0.02, min_dist=120): - signal = mocap_track.values[col_name].values - idxs = get_foot_contact_idxs(signal, t, min_dist) - - step_signal = [] - - c = start - for f in range(len(signal)): - if f in idxs[1]: - c = 0 - elif f in idxs[0]: - c = 1 - - step_signal.append(c) - - return step_signal - -def plot_foot_up_down(mocap_track, col_name, t=0.02, min_dist=120): - - signal = mocap_track.values[col_name].values - idxs = get_foot_contact_idxs(signal, t, min_dist) - - plt.plot(mocap_track.values.index, signal) - plt.plot(mocap_track.values.index[idxs[0]], signal[idxs[0]], 'ro') - plt.plot(mocap_track.values.index[idxs[1]], signal[idxs[1]], 'go') diff --git a/dataloaders/pymo/mocapplayer/data-template.js b/dataloaders/pymo/mocapplayer/data-template.js deleted file mode 100644 index 68a51392fb7d2458487eae2a00a3ed03c1e7153a..0000000000000000000000000000000000000000 --- a/dataloaders/pymo/mocapplayer/data-template.js +++ /dev/null @@ -1,3 +0,0 @@ -var dataBuffer = `$$DATA$$`; - -start(dataBuffer); \ No newline at end of file diff --git a/dataloaders/pymo/mocapplayer/js/skeletonFactory.js b/dataloaders/pymo/mocapplayer/js/skeletonFactory.js deleted file mode 100644 index e1d072b7df2fb40772e93f2dee595e467744e36b..0000000000000000000000000000000000000000 --- a/dataloaders/pymo/mocapplayer/js/skeletonFactory.js +++ /dev/null @@ -1,233 +0,0 @@ -bm_v = new THREE.MeshPhongMaterial({ - color: 0x08519c, - emissive: 0x08306b, - specular: 0x08519c, - shininess: 10, - side: THREE.DoubleSide -}); - -jm_v = new THREE.MeshPhongMaterial({ - color: 0x08306b, - emissive: 0x000000, - specular: 0x111111, - shininess: 90, - side: THREE.DoubleSide -}); - -bm_a = new THREE.MeshPhongMaterial({ - color: 0x980043, - emissive: 0x67001f, - specular: 0x6a51a3, - shininess: 10, - side: THREE.DoubleSide -}); - -jm_a = new THREE.MeshPhongMaterial({ - color: 0x67001f, - emissive: 0x000000, - specular: 0x111111, - shininess: 90, - side: THREE.DoubleSide -}); - -bm_b = new THREE.MeshPhongMaterial({ - color: 0x3f007d, - emissive: 0x3f007d, - specular: 0x807dba, - shininess: 2, - side: THREE.DoubleSide -}); - -jm_b = new THREE.MeshPhongMaterial({ - color: 0x3f007d, - emissive: 0x000000, - specular: 0x807dba, - shininess: 90, - side: THREE.DoubleSide -}); - -//------------------ - - -jointmaterial = new THREE.MeshLambertMaterial({ - color: 0xc57206, - emissive: 0x271c18, - side: THREE.DoubleSide, - // shading: THREE.FlatShading, - wireframe: false, - shininess: 90, -}); - -bonematerial = new THREE.MeshPhongMaterial({ - color: 0xbd9a6d, - emissive: 0x271c18, - side: THREE.DoubleSide, - // shading: THREE.FlatShading, - wireframe: false -}); - -jointmaterial2 = new THREE.MeshPhongMaterial({ - color: 0x1562a2, - emissive: 0x000000, - specular: 0x111111, - shininess: 30, - side: THREE.DoubleSide -}); - -bonematerial2 = new THREE.MeshPhongMaterial({ - color: 0x552211, - emissive: 0x882211, - // emissive: 0x000000, - specular: 0x111111, - shininess: 30, - side: THREE.DoubleSide -}); - -bonematerial3 = new THREE.MeshPhongMaterial({ - color: 0x176793, - emissive: 0x000000, - specular: 0x111111, - shininess: 90, - side: THREE.DoubleSide -}); - - - -jointmaterial4 = new THREE.MeshPhongMaterial({ - color: 0xFF8A00, - emissive: 0x000000, - specular: 0x111111, - shininess: 90, - side: THREE.DoubleSide -}); - - -bonematerial4 = new THREE.MeshPhongMaterial({ - color: 0x53633D, - emissive: 0x000000, - specular: 0xFFC450, - shininess: 90, - side: THREE.DoubleSide -}); - - - -bonematerial44 = new THREE.MeshPhongMaterial({ - color: 0x582A72, - emissive: 0x000000, - specular: 0xFFC450, - shininess: 90, - side: THREE.DoubleSide -}); - -jointmaterial5 = new THREE.MeshPhongMaterial({ - color: 0xAA5533, - emissive: 0x000000, - specular: 0x111111, - shininess: 30, - side: THREE.DoubleSide -}); - -bonematerial5 = new THREE.MeshPhongMaterial({ - color: 0x552211, - emissive: 0x772211, - specular: 0x111111, - shininess: 30, - side: THREE.DoubleSide -}); - - -markermaterial = new THREE.MeshPhongMaterial({ - color: 0xc57206, - emissive: 0x271c18, - side: THREE.DoubleSide, - // shading: THREE.FlatShading, - wireframe: false, - shininess: 20, -}); - -markermaterial2 = new THREE.MeshPhongMaterial({ - color: 0x1562a2, - emissive: 0x271c18, - side: THREE.DoubleSide, - // shading: THREE.FlatShading, - wireframe: false, - shininess: 20, -}); - -markermaterial3 = new THREE.MeshPhongMaterial({ - color: 0x555555, - emissive: 0x999999, - side: THREE.DoubleSide, - // shading: THREE.FlatShading, - wireframe: false, - shininess: 20, -}); - - -var makeMarkerGeometry_Sphere10 = function(markerName, scale) { - return new THREE.SphereGeometry(10, 60, 60); -}; - -var makeMarkerGeometry_Sphere3 = function(markerName, scale) { - return new THREE.SphereGeometry(3, 60, 60); -}; - -var makeMarkerGeometry_SphereX = function(markerName, scale) { - return new THREE.SphereGeometry(5, 60, 60); -}; - -var makeJointGeometry_SphereX = function(X) { - return function(jointName, scale) { - return new THREE.SphereGeometry(X, 60, 60); - }; -}; - - -var makeJointGeometry_Sphere1 = function(jointName, scale) { - return new THREE.SphereGeometry(2 / scale, 60, 60); -}; - -var makeJointGeometry_Sphere2 = function(jointName, scale) { - return new THREE.SphereGeometry(1 / scale, 60, 60); -}; - -var makeJointGeometry_Dode = function(jointName, scale) { - return new THREE.DodecahedronGeometry(1 / scale, 0); -}; - -var makeBoneGeometry_Cylinder1 = function(joint1Name, joint2Name, length, scale) { - return new THREE.CylinderGeometry(1.5 / scale, 0.7 / scale, length, 40); -}; - -var makeBoneGeometry_Cylinder2 = function(joint1Name, joint2Name, length, scale) { - // if (joint1Name.includes("LeftHip")) - // length = 400; - return new THREE.CylinderGeometry(1.5 / scale, 0.2 / scale, length, 40); -}; - -var makeBoneGeometry_Cylinder3 = function(joint1Name, joint2Name, length, scale) { - var c1 = new THREE.CylinderGeometry(1.5 / scale, 0.2 / scale, length / 1, 20); - var c2 = new THREE.CylinderGeometry(0.2 / scale, 1.5 / scale, length / 1, 40); - - var material = new THREE.MeshPhongMaterial({ - color: 0xF7FE2E - }); - var mmesh = new THREE.Mesh(c1, material); - mmesh.updateMatrix(); - c2.merge(mmesh.geometry, mmesh.matrix); - return c2; -}; - -var makeBoneGeometry_Box1 = function(joint1Name, joint2Name, length, scale) { - return new THREE.BoxGeometry(1 / scale, length, 1 / scale, 40); -}; - - -var makeJointGeometry_Empty = function(jointName, scale) { - return new THREE.SphereGeometry(0.001, 60, 60); -}; - -var makeBoneGeometry_Empty = function(joint1Name, joint2Name, length, scale) { - return new THREE.CylinderGeometry(0.001, 0.001, 0.001, 40); -}; diff --git a/dataloaders/pymo/mocapplayer/libs/jquery.min.js b/dataloaders/pymo/mocapplayer/libs/jquery.min.js deleted file mode 100644 index b8c4187de18dd413ad3029839ce0773549e92a14..0000000000000000000000000000000000000000 --- a/dataloaders/pymo/mocapplayer/libs/jquery.min.js +++ /dev/null @@ -1,4 +0,0 @@ -/*! jQuery v2.2.3 | (c) jQuery Foundation | jquery.org/license */ -!function(a,b){"object"==typeof module&&"object"==typeof module.exports?module.exports=a.document?b(a,!0):function(a){if(!a.document)throw new Error("jQuery requires a window with a document");return b(a)}:b(a)}("undefined"!=typeof window?window:this,function(a,b){var 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str(camera_z)) - data_assigned = data_assigned.replace('$$SCALE$$', str(scale)) - data_assigned = data_assigned.replace('$$FRAMETIME$$', str(frame_time)) - - else: - return - - - - with open(os.path.join(dir_path, 'mocapplayer/data.js'), 'w') as oFile: - oFile.write(data_assigned) - - url = '%s?&cz=200&order=xzyi&frame_time=%f&scale=%f'%(base_url, frame_time, scale) - iframe = '' - link = 'New Window'%url - return IPython.display.HTML(iframe+link) diff --git a/dataloaders/pymo/writers.py b/dataloaders/pymo/writers.py deleted file mode 100644 index 834ef639bb3c86e7ca94a0c6de2fa868a48c3ff9..0000000000000000000000000000000000000000 --- a/dataloaders/pymo/writers.py +++ /dev/null @@ -1,55 +0,0 @@ -import numpy as np -import pandas as pd - -class BVHWriter(): - def __init__(self): - pass - - def write(self, X, ofile): - - # Writing the skeleton info - ofile.write('HIERARCHY\n') - - self.motions_ = [] - self._printJoint(X, X.root_name, 0, ofile) - - # Writing the motion header - ofile.write('MOTION\n') - ofile.write('Frames: %d\n'%X.values.shape[0]) - ofile.write('Frame Time: %f\n'%X.framerate) - - # Writing the data - self.motions_ = np.asarray(self.motions_).T - lines = [" ".join(item) for item in self.motions_.astype(str)] - ofile.write("".join("%s\n"%l for l in lines)) - - def _printJoint(self, X, joint, tab, ofile): - - if X.skeleton[joint]['parent'] == None: - ofile.write('ROOT %s\n'%joint) - elif len(X.skeleton[joint]['children']) > 0: - ofile.write('%sJOINT %s\n'%('\t'*(tab), joint)) - else: - ofile.write('%sEnd site\n'%('\t'*(tab))) - - ofile.write('%s{\n'%('\t'*(tab))) - - ofile.write('%sOFFSET %3.5f %3.5f %3.5f\n'%('\t'*(tab+1), - X.skeleton[joint]['offsets'][0], - X.skeleton[joint]['offsets'][1], - X.skeleton[joint]['offsets'][2])) - channels = X.skeleton[joint]['channels'] - n_channels = len(channels) - - if n_channels > 0: - for ch in channels: - self.motions_.append(np.asarray(X.values['%s_%s'%(joint, ch)].values)) - - if len(X.skeleton[joint]['children']) > 0: - ch_str = ''.join(' %s'*n_channels%tuple(channels)) - ofile.write('%sCHANNELS %d%s\n' %('\t'*(tab+1), n_channels, ch_str)) - - for c in X.skeleton[joint]['children']: - self._printJoint(X, c, tab+1, ofile) - - ofile.write('%s}\n'%('\t'*(tab))) diff --git a/dataloaders/utils/__pycache__/audio_features.cpython-310.pyc b/dataloaders/utils/__pycache__/audio_features.cpython-310.pyc deleted file mode 100644 index d221146c7895dbdba76987d632b873d3152584c4..0000000000000000000000000000000000000000 Binary files a/dataloaders/utils/__pycache__/audio_features.cpython-310.pyc and /dev/null differ diff --git a/dataloaders/utils/__pycache__/audio_features.cpython-38.pyc b/dataloaders/utils/__pycache__/audio_features.cpython-38.pyc deleted file mode 100644 index 9f48142fa047687a562f49e7868276b4ba004dbf..0000000000000000000000000000000000000000 Binary files a/dataloaders/utils/__pycache__/audio_features.cpython-38.pyc and /dev/null differ diff --git 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/dev/null @@ -1,209 +0,0 @@ -"""modified from https://github.com/yesheng-THU/GFGE/blob/main/data_processing/audio_features.py""" -import numpy as np -import librosa -import math -import os -import scipy.io.wavfile as wav -import torch -import torch.nn as nn -import torch.nn.functional as F -import copy -from tqdm import tqdm -from transformers import Wav2Vec2Model, Wav2Vec2Config -from transformers.modeling_outputs import BaseModelOutput -from typing import Optional, Tuple -_CONFIG_FOR_DOC = "Wav2Vec2Config" - -# the implementation of Wav2Vec2Model is borrowed from https://huggingface.co/transformers/_modules/transformers/models/wav2vec2/modeling_wav2vec2.html#Wav2Vec2Model -# initialize our encoder with the pre-trained wav2vec 2.0 weights. -def _compute_mask_indices( - shape: Tuple[int, int], - mask_prob: float, - mask_length: int, - attention_mask: Optional[torch.Tensor] = None, - min_masks: int = 0, -) -> np.ndarray: - bsz, all_sz = shape - mask = np.full((bsz, all_sz), False) - - all_num_mask = int( - mask_prob * all_sz / float(mask_length) - + np.random.rand() - ) - all_num_mask = max(min_masks, all_num_mask) - mask_idcs = [] - padding_mask = attention_mask.ne(1) if attention_mask is not None else None - for i in range(bsz): - if padding_mask is not None: - sz = all_sz - padding_mask[i].long().sum().item() - num_mask = int( - mask_prob * sz / float(mask_length) - + np.random.rand() - ) - num_mask = max(min_masks, num_mask) - else: - sz = all_sz - num_mask = all_num_mask - - lengths = np.full(num_mask, mask_length) - - if sum(lengths) == 0: - lengths[0] = min(mask_length, sz - 1) - - min_len = min(lengths) - if sz - min_len <= num_mask: - min_len = sz - num_mask - 1 - - mask_idc = np.random.choice(sz - min_len, num_mask, replace=False) - mask_idc = np.asarray([mask_idc[j] + offset for j in range(len(mask_idc)) for offset in range(lengths[j])]) - mask_idcs.append(np.unique(mask_idc[mask_idc < sz])) - - min_len = min([len(m) for m in mask_idcs]) - for i, mask_idc in enumerate(mask_idcs): - if len(mask_idc) > min_len: - mask_idc = np.random.choice(mask_idc, min_len, replace=False) - mask[i, mask_idc] = True - return mask - -# linear interpolation layer -def linear_interpolation(features, input_fps, output_fps, output_len=None): - features = features.transpose(1, 2) - seq_len = features.shape[2] / float(input_fps) - if output_len is None: - output_len = int(seq_len * output_fps) - output_features = F.interpolate(features,size=output_len,align_corners=True,mode='linear') - return output_features.transpose(1, 2) - -class Wav2Vec2Model(Wav2Vec2Model): - def __init__(self, config): - super().__init__(config) - self.audio_fps = 15 #args.audio_fps - #input_values 16K hz, 49fps, 20ms overlap, 25ms recepion field - def forward( - self, - input_values, - dataset="beat", - attention_mask=None, - output_attentions=None, - output_hidden_states=None, - return_dict=None, - frame_num=None - ): - #print(input_values.shape) - self.config.output_attentions = True - output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions - output_hidden_states = ( - output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states - ) - return_dict = return_dict if return_dict is not None else self.config.use_return_dict - - hidden_states = self.feature_extractor(input_values) - hidden_states = hidden_states.transpose(1, 2) - #print(hidden_states.shape) - if dataset == "beat": - hidden_states = linear_interpolation(hidden_states, 49, self.audio_fps, output_len=frame_num) - #print(hidden_states.shape) - if attention_mask is not None: - output_lengths = self._get_feat_extract_output_lengths(attention_mask.sum(-1)) - attention_mask = torch.zeros( - hidden_states.shape[:2], dtype=hidden_states.dtype, device=hidden_states.device - ) - attention_mask[ - (torch.arange(attention_mask.shape[0], device=hidden_states.device), output_lengths - 1) - ] = 1 - attention_mask = attention_mask.flip([-1]).cumsum(-1).flip([-1]).bool() - - hidden_states = self.feature_projection(hidden_states)[0] - #print(hidden_states.shape) - if self.config.apply_spec_augment and self.training: - batch_size, sequence_length, hidden_size = hidden_states.size() - if self.config.mask_time_prob > 0: - mask_time_indices = _compute_mask_indices( - (batch_size, sequence_length), - self.config.mask_time_prob, - self.config.mask_time_length, - attention_mask=attention_mask, - min_masks=2, - ) - hidden_states[torch.from_numpy(mask_time_indices)] = self.masked_spec_embed.to(hidden_states.dtype) - if self.config.mask_feature_prob > 0: - mask_feature_indices = _compute_mask_indices( - (batch_size, hidden_size), - self.config.mask_feature_prob, - self.config.mask_feature_length, - ) - mask_feature_indices = torch.from_numpy(mask_feature_indices).to(hidden_states.device) - hidden_states[mask_feature_indices[:, None].expand(-1, sequence_length, -1)] = 0 - encoder_outputs = self.encoder( - hidden_states, - attention_mask=attention_mask, - output_attentions=output_attentions, - output_hidden_states=output_hidden_states, - return_dict=return_dict, - ) - hidden_states = encoder_outputs[0] - #print(encoder_outputs.shape) - if not return_dict: - return (hidden_states,) + encoder_outputs[1:] - - return BaseModelOutput( - last_hidden_state=hidden_states, - hidden_states=encoder_outputs.hidden_states, - attentions=encoder_outputs.attentions, - ) - - -def extract_wav2vec2(file_folder, destpath, fps, inference_length=16000*20): - wav2vec_model = Wav2Vec2Model.from_pretrained("/home/ma-user/work/datasets/hub/transformer/wav2vec2-base-960h") - wav2vec_model.feature_extractor._freeze_parameters() - wav2vec_model = wav2vec_model.cuda() - wav2vec_model.eval() - audio_mean = np.load("/home/ma-user/work/datasets/beat_cache/beat_english_15_141/train/wave16k/npy_mean.npy") - audio_std = np.load("/home/ma-user/work/datasets/beat_cache/beat_english_15_141/train/wave16k/npy_std.npy") - if not os.path.exists(destpath): os.mkdir(destpath) - with torch.no_grad(): - for file_name in tqdm(os.listdir(file_folder)): - if "mean" in file_name or "std" in file_name or "pynb" in file_name: continue - audio_np = np.load(file_folder+file_name) - audio_np = (audio_np-audio_mean)/audio_std - audio_torch = torch.from_numpy(audio_np).cuda() - audio_torch = audio_torch.reshape(1, -1) - #print(audio_torch.shape, audio_np.shape) - - if audio_torch.shape[1] > inference_length: - num_div = audio_torch.shape[1] // inference_length - remain = audio_torch.shape[1] % inference_length - for i in range(num_div): - audio_feat = wav2vec_model(audio_torch[:, i*inference_length:(i+1)*inference_length]).last_hidden_state.cpu().numpy().reshape(-1, 768) - if i == 0: - audio_feat_all = audio_feat - else: - audio_feat_all = np.concatenate((audio_feat_all, audio_feat), 0) - if remain > 1600: #0.25s - audio_feat = wav2vec_model(audio_torch[:, num_div*inference_length:num_div*inference_length+remain]).last_hidden_state.cpu().numpy().reshape(-1, 768) - audio_feat_all = np.concatenate((audio_feat_all, audio_feat), 0) - else: - audio_feat_all = wav2vec_model(audio_torch).last_hidden_state.cpu().numpy().reshape(-1, 768) - #print(audio_feat_all.shape, audio_np.shape[0]/16000*15, torch.cuda.memory_cached() / 1E9) - np.save(destpath+file_name, audio_feat_all) - -def extract_melspec(file, destpath, fps, n_mels=128): - fs,X = wav.read(file) - X = X.astype(float)/math.pow(2,15) - target_sr = 48000 - X_48k = librosa.resample(X, orig_sr=fs, target_sr=target_sr, res_type="kaiser_best") - n_fft=int(target_sr*0.13) - hop_len=int(target_sr/fps) - C = librosa.feature.melspectrogram(y=X_48k, sr=target_sr, n_fft=n_fft, hop_length=hop_len, n_mels=n_mels, fmin=0.0, fmax=8000) - #C2 = librosa.feature.melspectrogram(y=X, sr=fs, n_fft=1024, hop_length=512) - #print(C.shape, C2.shape) - C = np.log(C) - np.save(destpath,np.transpose(C)) - - -if __name__ == "__main__": - #calculate mean and build cache for data. - target_fps = 15 - ori_data_path = f"/home/ma-user/work/datasets/beat_cache/beat_english_{target_fps}_141/" - for data_type in ["train", "val", "test"]: - extract_wav2vec2(ori_data_path+data_type+"/wave16k/", ori_data_path+data_type+f"/wav2vec2_{target_fps}/", target_fps) \ No newline at end of file diff --git a/dataloaders/utils/other_tools.py b/dataloaders/utils/other_tools.py deleted file mode 100644 index 08ce836b36ef1148f7d0eabaed131ac10f287020..0000000000000000000000000000000000000000 --- a/dataloaders/utils/other_tools.py +++ /dev/null @@ -1,676 +0,0 @@ -import os -import numpy as np -import random -import torch -import shutil -import csv -import pprint -import pandas as pd -from loguru import logger -from collections import OrderedDict -import matplotlib.pyplot as plt -import pickle -import time - -import numpy as np - -def adjust_array(x, k): - len_x = len(x) - len_k = len(k) - - # If x is shorter than k, pad with zeros - if len_x < len_k: - return np.pad(x, (0, len_k - len_x), 'constant') - - # If x is longer than k, truncate x - elif len_x > len_k: - return x[:len_k] - - # If both are of same length - else: - return x - -def onset_to_frame(onset_times, audio_length, fps): - # Calculate total number of frames for the given audio length - total_frames = int(audio_length * fps) - - # Create an array of zeros of shape (total_frames,) - frame_array = np.zeros(total_frames, dtype=np.int32) - - # For each onset time, calculate the frame number and set it to 1 - for onset in onset_times: - frame_num = int(onset * fps) - # Check if the frame number is within the array bounds - if 0 <= frame_num < total_frames: - frame_array[frame_num] = 1 - - return frame_array - -def smooth_animations(animation1, animation2, blend_frames): - """ - Smoothly transition between two animation clips using linear interpolation. - - Parameters: - - animation1: The first animation clip, a numpy array of shape [n, k]. - - animation2: The second animation clip, a numpy array of shape [n, k]. - - blend_frames: Number of frames over which to blend the two animations. - - Returns: - - A smoothly blended animation clip of shape [2n, k]. - """ - - # Ensure blend_frames doesn't exceed the length of either animation - blend_frames = min(blend_frames, len(animation1), len(animation2)) - - # Extract overlapping sections - overlap_a1 = animation1[-blend_frames:-blend_frames+1, :] - overlap_a2 = animation2[blend_frames-1:blend_frames, :] - - # Create blend weights for linear interpolation - alpha = np.linspace(0, 1, 2 * blend_frames).reshape(-1, 1) - - # Linearly interpolate between overlapping sections - blended_overlap = overlap_a1 * (1 - alpha) + overlap_a2 * alpha - - # Extend the animations to form the result with 2n frames - if blend_frames == len(animation1) and blend_frames == len(animation2): - result = blended_overlap - else: - before_blend = animation1[:-blend_frames] - after_blend = animation2[blend_frames:] - result = np.vstack((before_blend, blended_overlap, after_blend)) - return result - - -def interpolate_sequence(quaternions): - bs, n, j, _ = quaternions.shape - new_n = 2 * n - new_quaternions = torch.zeros((bs, new_n, j, 4), device=quaternions.device, dtype=quaternions.dtype) - - for i in range(n): - q1 = quaternions[:, i, :, :] - new_quaternions[:, 2*i, :, :] = q1 - - if i < n - 1: - q2 = quaternions[:, i + 1, :, :] - new_quaternions[:, 2*i + 1, :, :] = slerp(q1, q2, 0.5) - else: - # For the last point, duplicate the value - new_quaternions[:, 2*i + 1, :, :] = q1 - - return new_quaternions - -def quaternion_multiply(q1, q2): - w1, x1, y1, z1 = q1 - w2, x2, y2, z2 = q2 - w = w1 * w2 - x1 * x2 - y1 * y2 - z1 * z2 - x = w1 * x2 + x1 * w2 + y1 * z2 - z1 * y2 - y = w1 * y2 + y1 * w2 + z1 * x2 - x1 * z2 - z = w1 * z2 + z1 * w2 + x1 * y2 - y1 * x2 - return w, x, y, z - -def quaternion_conjugate(q): - w, x, y, z = q - return (w, -x, -y, -z) - -def slerp(q1, q2, t): - dot = torch.sum(q1 * q2, dim=-1, keepdim=True) - - flip = (dot < 0).float() - q2 = (1 - flip * 2) * q2 - dot = dot * (1 - flip * 2) - - DOT_THRESHOLD = 0.9995 - mask = (dot > DOT_THRESHOLD).float() - - theta_0 = torch.acos(dot) - theta = theta_0 * t - - q3 = q2 - q1 * dot - q3 = q3 / torch.norm(q3, dim=-1, keepdim=True) - - interpolated = (torch.cos(theta) * q1 + torch.sin(theta) * q3) - - return mask * (q1 + t * (q2 - q1)) + (1 - mask) * interpolated - -def estimate_linear_velocity(data_seq, dt): - ''' - Given some batched data sequences of T timesteps in the shape (B, T, ...), estimates - the velocity for the middle T-2 steps using a second order central difference scheme. - The first and last frames are with forward and backward first-order - differences, respectively - - h : step size - ''' - # first steps is forward diff (t+1 - t) / dt - init_vel = (data_seq[:, 1:2] - data_seq[:, :1]) / dt - # middle steps are second order (t+1 - t-1) / 2dt - middle_vel = (data_seq[:, 2:] - data_seq[:, 0:-2]) / (2 * dt) - # last step is backward diff (t - t-1) / dt - final_vel = (data_seq[:, -1:] - data_seq[:, -2:-1]) / dt - - vel_seq = torch.cat([init_vel, middle_vel, final_vel], dim=1) - return vel_seq - - -def estimate_angular_velocity(rot_seq, dt): - ''' - Given a batch of sequences of T rotation matrices, estimates angular velocity at T-2 steps. - Input sequence should be of shape (B, T, ..., 3, 3) - ''' - # see https://en.wikipedia.org/wiki/Angular_velocity#Calculation_from_the_orientation_matrix - dRdt = estimate_linear_velocity(rot_seq, dt) - R = rot_seq - RT = R.transpose(-1, -2) - # compute skew-symmetric angular velocity tensor - w_mat = torch.matmul(dRdt, RT) - # pull out angular velocity vector by averaging symmetric entries - w_x = (-w_mat[..., 1, 2] + w_mat[..., 2, 1]) / 2.0 - w_y = (w_mat[..., 0, 2] - w_mat[..., 2, 0]) / 2.0 - w_z = (-w_mat[..., 0, 1] + w_mat[..., 1, 0]) / 2.0 - w = torch.stack([w_x, w_y, w_z], axis=-1) - return w - -import matplotlib.image as mpimg -from io import BytesIO - -def image_from_bytes(image_bytes): - return mpimg.imread(BytesIO(image_bytes), format='PNG') - - - -def process_frame(i, vertices_all, vertices1_all, faces, output_dir, use_matplotlib, filenames, camera_params, camera_params1): - import matplotlib - matplotlib.use('Agg') - import matplotlib.pyplot as plt - import trimesh - import pyvirtualdisplay as Display - - vertices = vertices_all[i] - vertices1 = vertices1_all[i] - filename = f"{output_dir}frame_{i}.png" - filenames.append(filename) - if i%100 == 0: - print('processed', i, 'frames') - #time_s = time.time() - #print(vertices.shape) - if use_matplotlib: - fig = plt.figure(figsize=(20, 10)) - ax = fig.add_subplot(121, projection="3d") - fig.subplots_adjust(left=0, right=1, bottom=0, top=1) - #ax.view_init(elev=0, azim=90) - x = vertices[:, 0] - y = vertices[:, 1] - z = vertices[:, 2] - ax.scatter(x, y, z, s=0.5) - ax.set_xlim([-1.0, 1.0]) - ax.set_ylim([-0.5, 1.5])#heigth - ax.set_zlim([-0, 2])#depth - ax.set_box_aspect((1,1,1)) - else: - mesh = trimesh.Trimesh(vertices, faces) - scene = mesh.scene() - scene.camera.fov = camera_params['fov'] - scene.camera.resolution = camera_params['resolution'] - scene.camera.z_near = camera_params['z_near'] - scene.camera.z_far = camera_params['z_far'] - scene.graph[scene.camera.name] = camera_params['transform'] - fig, ax =plt.subplots(1,2, figsize=(16, 6)) - image = scene.save_image(resolution=[640, 480], visible=False) - im0 = ax[0].imshow(image_from_bytes(image)) - ax[0].axis('off') - - if use_matplotlib: - ax2 = fig.add_subplot(122, projection="3d") - ax2.set_box_aspect((1,1,1)) - fig.subplots_adjust(left=0, right=1, bottom=0, top=1) - x1 = vertices1[:, 0] - y1 = vertices1[:, 1] - z1 = vertices1[:, 2] - ax2.scatter(x1, y1, z1, s=0.5) - ax2.set_xlim([-1.0, 1.0]) - ax2.set_ylim([-0.5, 1.5])#heigth - ax2.set_zlim([-0, 2]) - plt.savefig(filename, bbox_inches='tight') - plt.close(fig) - else: - mesh1 = trimesh.Trimesh(vertices1, faces) - scene1 = mesh1.scene() - scene1.camera.fov = camera_params1['fov'] - scene1.camera.resolution = camera_params1['resolution'] - scene1.camera.z_near = camera_params1['z_near'] - scene1.camera.z_far = camera_params1['z_far'] - scene1.graph[scene1.camera.name] = camera_params1['transform'] - image1 = scene1.save_image(resolution=[640, 480], visible=False) - im1 = ax[1].imshow(image_from_bytes(image1)) - ax[1].axis('off') - plt.savefig(filename, bbox_inches='tight') - plt.close(fig) - -def generate_images(frames, vertices_all, vertices1_all, faces, output_dir, use_matplotlib, filenames): - import multiprocessing - import trimesh - num_cores = multiprocessing.cpu_count() # This will get the number of cores on your machine. - mesh = trimesh.Trimesh(vertices_all[0], faces) - scene = mesh.scene() - camera_params = { - 'fov': scene.camera.fov, - 'resolution': scene.camera.resolution, - 'focal': scene.camera.focal, - 'z_near': scene.camera.z_near, - "z_far": scene.camera.z_far, - 'transform': scene.graph[scene.camera.name][0] - } - mesh1 = trimesh.Trimesh(vertices1_all[0], faces) - scene1 = mesh1.scene() - camera_params1 = { - 'fov': scene1.camera.fov, - 'resolution': scene1.camera.resolution, - 'focal': scene1.camera.focal, - 'z_near': scene1.camera.z_near, - "z_far": scene1.camera.z_far, - 'transform': scene1.graph[scene1.camera.name][0] - } - # Use a Pool to manage the processes - # print(num_cores) - progress = multiprocessing.Value('i', 0) - lock = multiprocessing.Lock() - with multiprocessing.Pool(num_cores) as pool: - pool.starmap(process_frame, [(i, vertices_all, vertices1_all, faces, output_dir, use_matplotlib, filenames, camera_params, camera_params1) for i in range(frames)]) - -def render_one_sequence( - res_npz_path, - gt_npz_path, - output_dir, - audio_path, - model_folder="/data/datasets/smplx_models/", - model_type='smplx', - gender='NEUTRAL_2020', - ext='npz', - num_betas=300, - num_expression_coeffs=100, - use_face_contour=False, - use_matplotlib=False, - args=None): - import smplx - import matplotlib.pyplot as plt - import imageio - from tqdm import tqdm - import os - import numpy as np - import torch - import moviepy.editor as mp - import librosa - - model = smplx.create(model_folder, model_type=model_type, - gender=gender, use_face_contour=use_face_contour, - num_betas=num_betas, - num_expression_coeffs=num_expression_coeffs, - ext=ext, use_pca=False).cuda() - - #data_npz = np.load(f"{output_dir}{res_npz_path}.npz") - data_np_body = np.load(res_npz_path, allow_pickle=True) - gt_np_body = np.load(gt_npz_path, allow_pickle=True) - - if not os.path.exists(output_dir): os.makedirs(output_dir) - filenames = [] - if not use_matplotlib: - import trimesh - #import pyrender - from pyvirtualdisplay import Display - display = Display(visible=0, size=(640, 480)) - display.start() - faces = np.load(f"{model_folder}/smplx/SMPLX_NEUTRAL_2020.npz", allow_pickle=True)["f"] - seconds = 1 - #data_npz["jaw_pose"].shape[0] - n = data_np_body["poses"].shape[0] - beta = torch.from_numpy(data_np_body["betas"]).to(torch.float32).unsqueeze(0).cuda() - beta = beta.repeat(n, 1) - expression = torch.from_numpy(data_np_body["expressions"][:n]).to(torch.float32).cuda() - jaw_pose = torch.from_numpy(data_np_body["poses"][:n, 66:69]).to(torch.float32).cuda() - pose = torch.from_numpy(data_np_body["poses"][:n]).to(torch.float32).cuda() - transl = torch.from_numpy(data_np_body["trans"][:n]).to(torch.float32).cuda() - # print(beta.shape, expression.shape, jaw_pose.shape, pose.shape, transl.shape, pose[:,:3].shape) - output = model(betas=beta, transl=transl, expression=expression, jaw_pose=jaw_pose, - global_orient=pose[:,:3], body_pose=pose[:,3:21*3+3], left_hand_pose=pose[:,25*3:40*3], right_hand_pose=pose[:,40*3:55*3], - leye_pose=pose[:, 69:72], - reye_pose=pose[:, 72:75], - return_verts=True) - vertices_all = output["vertices"].cpu().detach().numpy() - - beta1 = torch.from_numpy(gt_np_body["betas"]).to(torch.float32).unsqueeze(0).cuda() - expression1 = torch.from_numpy(gt_np_body["expressions"][:n]).to(torch.float32).cuda() - jaw_pose1 = torch.from_numpy(gt_np_body["poses"][:n,66:69]).to(torch.float32).cuda() - pose1 = torch.from_numpy(gt_np_body["poses"][:n]).to(torch.float32).cuda() - transl1 = torch.from_numpy(gt_np_body["trans"][:n]).to(torch.float32).cuda() - output1 = model(betas=beta1, transl=transl1, expression=expression1, jaw_pose=jaw_pose1, global_orient=pose1[:,:3], body_pose=pose1[:,3:21*3+3], left_hand_pose=pose1[:,25*3:40*3], right_hand_pose=pose1[:,40*3:55*3], - leye_pose=pose1[:, 69:72], - reye_pose=pose1[:, 72:75],return_verts=True) - vertices1_all = output1["vertices"].cpu().detach().numpy() - if args.debug: - seconds = 1 - else: - seconds = vertices_all.shape[0]//30 - # camera_settings = None - time_s = time.time() - generate_images(int(seconds*30), vertices_all, vertices1_all, faces, output_dir, use_matplotlib, filenames) - filenames = [f"{output_dir}frame_{i}.png" for i in range(int(seconds*30))] - # print(time.time()-time_s) - # for i in tqdm(range(seconds*30)): - # vertices = vertices_all[i] - # vertices1 = vertices1_all[i] - # filename = f"{output_dir}frame_{i}.png" - # filenames.append(filename) - # #time_s = time.time() - # #print(vertices.shape) - # if use_matplotlib: - # fig = plt.figure(figsize=(20, 10)) - # ax = fig.add_subplot(121, projection="3d") - # fig.subplots_adjust(left=0, right=1, bottom=0, top=1) - # #ax.view_init(elev=0, azim=90) - # x = vertices[:, 0] - # y = vertices[:, 1] - # z = vertices[:, 2] - # ax.scatter(x, y, z, s=0.5) - # ax.set_xlim([-1.0, 1.0]) - # ax.set_ylim([-0.5, 1.5])#heigth - # ax.set_zlim([-0, 2])#depth - # ax.set_box_aspect((1,1,1)) - # else: - # mesh = trimesh.Trimesh(vertices, faces) - # if i == 0: - # scene = mesh.scene() - # camera_params = { - # 'fov': scene.camera.fov, - # 'resolution': scene.camera.resolution, - # 'focal': scene.camera.focal, - # 'z_near': scene.camera.z_near, - # "z_far": scene.camera.z_far, - # 'transform': scene.graph[scene.camera.name][0] - # } - # else: - # scene = mesh.scene() - # scene.camera.fov = camera_params['fov'] - # scene.camera.resolution = camera_params['resolution'] - # scene.camera.z_near = camera_params['z_near'] - # scene.camera.z_far = camera_params['z_far'] - # scene.graph[scene.camera.name] = camera_params['transform'] - # fig, ax =plt.subplots(1,2, figsize=(16, 6)) - # image = scene.save_image(resolution=[640, 480], visible=False) - # #print((time.time()-time_s)) - # im0 = ax[0].imshow(image_from_bytes(image)) - # ax[0].axis('off') - - # # beta1 = torch.from_numpy(gt_np_body["betas"]).to(torch.float32).unsqueeze(0) - # # expression1 = torch.from_numpy(gt_np_body["expressions"][i]).to(torch.float32).unsqueeze(0) - # # jaw_pose1 = torch.from_numpy(gt_np_body["poses"][i][66:69]).to(torch.float32).unsqueeze(0) - # # pose1 = torch.from_numpy(gt_np_body["poses"][i]).to(torch.float32).unsqueeze(0) - # # transl1 = torch.from_numpy(gt_np_body["trans"][i]).to(torch.float32).unsqueeze(0) - # # #print(beta.shape, expression.shape, jaw_pose.shape, pose.shape, transl.shape)global_orient=pose[0:1,:3], - # # output1 = model(betas=beta1, transl=transl1, expression=expression1, jaw_pose=jaw_pose1, global_orient=pose1[0:1,:3], body_pose=pose1[0:1,3:21*3+3], left_hand_pose=pose1[0:1,25*3:40*3], right_hand_pose=pose1[0:1,40*3:55*3], return_verts=True) - # # vertices1 = output1["vertices"].cpu().detach().numpy()[0] - - # if use_matplotlib: - # ax2 = fig.add_subplot(122, projection="3d") - # ax2.set_box_aspect((1,1,1)) - # fig.subplots_adjust(left=0, right=1, bottom=0, top=1) - # #ax2.view_init(elev=0, azim=90) - # x1 = vertices1[:, 0] - # y1 = vertices1[:, 1] - # z1 = vertices1[:, 2] - # ax2.scatter(x1, y1, z1, s=0.5) - # ax2.set_xlim([-1.0, 1.0]) - # ax2.set_ylim([-0.5, 1.5])#heigth - # ax2.set_zlim([-0, 2]) - # plt.savefig(filename, bbox_inches='tight') - # plt.close(fig) - # else: - # mesh1 = trimesh.Trimesh(vertices1, faces) - # if i == 0: - # scene1 = mesh1.scene() - # camera_params1 = { - # 'fov': scene1.camera.fov, - # 'resolution': scene1.camera.resolution, - # 'focal': scene1.camera.focal, - # 'z_near': scene1.camera.z_near, - # "z_far": scene1.camera.z_far, - # 'transform': scene1.graph[scene1.camera.name][0] - # } - # else: - # scene1 = mesh1.scene() - # scene1.camera.fov = camera_params1['fov'] - # scene1.camera.resolution = camera_params1['resolution'] - # scene1.camera.z_near = camera_params1['z_near'] - # scene1.camera.z_far = camera_params1['z_far'] - # scene1.graph[scene1.camera.name] = camera_params1['transform'] - # image1 = scene1.save_image(resolution=[640, 480], visible=False) - # im1 = ax[1].imshow(image_from_bytes(image1)) - # ax[1].axis('off') - # plt.savefig(filename, bbox_inches='tight') - # plt.close(fig) - - # display.stop() - # print(filenames) - images = [imageio.imread(filename) for filename in filenames] - imageio.mimsave(f"{output_dir}raw_{res_npz_path.split('/')[-1][:-4]}.mp4", images, fps=30) - for filename in filenames: - os.remove(filename) - - video = mp.VideoFileClip(f"{output_dir}raw_{res_npz_path.split('/')[-1][:-4]}.mp4") - # audio, sr = librosa.load(audio_path) - # audio = audio[:seconds*sr] - # print(audio.shape, seconds, sr) - # import soundfile as sf - # sf.write(f"{output_dir}{res_npz_path.split('/')[-1][:-4]}.wav", audio, 16000, 'PCM_24') - # audio_tmp = librosa.output.write_wav(f"{output_dir}{res_npz_path.split('/')[-1][:-4]}.wav", audio, sr=16000) - audio = mp.AudioFileClip(audio_path) - if audio.duration > video.duration: - audio = audio.subclip(0, video.duration) - final_clip = video.set_audio(audio) - final_clip.write_videofile(f"{output_dir}{res_npz_path.split('/')[-1][4:-4]}.mp4") - os.remove(f"{output_dir}raw_{res_npz_path.split('/')[-1][:-4]}.mp4") - -def print_exp_info(args): - logger.info(pprint.pformat(vars(args))) - logger.info(f"# ------------ {args.name} ----------- #") - logger.info("PyTorch version: {}".format(torch.__version__)) - logger.info("CUDA version: {}".format(torch.version.cuda)) - logger.info("{} GPUs".format(torch.cuda.device_count())) - logger.info(f"Random Seed: {args.random_seed}") - -def args2csv(args, get_head=False, list4print=[]): - for k, v in args.items(): - if isinstance(args[k], dict): - args2csv(args[k], get_head, list4print) - else: list4print.append(k) if get_head else list4print.append(v) - return list4print - -class EpochTracker: - def __init__(self, metric_names, metric_directions): - assert len(metric_names) == len(metric_directions), "Metric names and directions should have the same length" - - - self.metric_names = metric_names - self.states = ['train', 'val', 'test'] - self.types = ['last', 'best'] - - - self.values = {name: {state: {type_: {'value': np.inf if not is_higher_better else -np.inf, 'epoch': 0} - for type_ in self.types} - for state in self.states} - for name, is_higher_better in zip(metric_names, metric_directions)} - - self.loss_meters = {name: {state: AverageMeter(f"{name}_{state}") - for state in self.states} - for name in metric_names} - - - self.is_higher_better = {name: direction for name, direction in zip(metric_names, metric_directions)} - self.train_history = {name: [] for name in metric_names} - self.val_history = {name: [] for name in metric_names} - - - def update_meter(self, name, state, value): - self.loss_meters[name][state].update(value) - - - def update_values(self, name, state, epoch): - value_avg = self.loss_meters[name][state].avg - new_best = False - - - if ((value_avg < self.values[name][state]['best']['value'] and not self.is_higher_better[name]) or - (value_avg > self.values[name][state]['best']['value'] and self.is_higher_better[name])): - self.values[name][state]['best']['value'] = value_avg - self.values[name][state]['best']['epoch'] = epoch - new_best = True - self.values[name][state]['last']['value'] = value_avg - self.values[name][state]['last']['epoch'] = epoch - return new_best - - - def get(self, name, state, type_): - return self.values[name][state][type_] - - - def reset(self): - for name in self.metric_names: - for state in self.states: - self.loss_meters[name][state].reset() - - - def flatten_values(self): - flat_dict = {} - for name in self.metric_names: - for state in self.states: - for type_ in self.types: - value_key = f"{name}_{state}_{type_}" - epoch_key = f"{name}_{state}_{type_}_epoch" - flat_dict[value_key] = self.values[name][state][type_]['value'] - flat_dict[epoch_key] = self.values[name][state][type_]['epoch'] - return flat_dict - - def update_and_plot(self, name, epoch, save_path): - new_best_train = self.update_values(name, 'train', epoch) - new_best_val = self.update_values(name, 'val', epoch) - - - self.train_history[name].append(self.loss_meters[name]['train'].avg) - self.val_history[name].append(self.loss_meters[name]['val'].avg) - - - train_values = self.train_history[name] - val_values = self.val_history[name] - epochs = list(range(1, len(train_values) + 1)) - - - plt.figure(figsize=(10, 6)) - plt.plot(epochs, train_values, label='Train') - plt.plot(epochs, val_values, label='Val') - plt.title(f'Train vs Val {name} over epochs') - plt.xlabel('Epochs') - plt.ylabel(name) - plt.legend() - plt.savefig(save_path) - plt.close() - - - return new_best_train, new_best_val - - - - -def record_trial(args, tracker): - """ - 1. record notes, score, env_name, experments_path, - """ - csv_path = args.out_path + "custom/" +args.csv_name+".csv" - all_print_dict = vars(args) - all_print_dict.update(tracker.flatten_values()) - if not os.path.exists(csv_path): - pd.DataFrame([all_print_dict]).to_csv(csv_path, index=False) - else: - df_existing = pd.read_csv(csv_path) - df_new = pd.DataFrame([all_print_dict]) - df_aligned = df_existing.append(df_new).fillna("") - df_aligned.to_csv(csv_path, index=False) - - -def set_random_seed(args): - os.environ['PYTHONHASHSEED'] = str(args.random_seed) - random.seed(args.random_seed) - np.random.seed(args.random_seed) - torch.manual_seed(args.random_seed) - torch.cuda.manual_seed_all(args.random_seed) - torch.cuda.manual_seed(args.random_seed) - torch.backends.cudnn.deterministic = args.deterministic #args.CUDNN_DETERMINISTIC - torch.backends.cudnn.benchmark = args.benchmark - torch.backends.cudnn.enabled = args.cudnn_enabled - - -def save_checkpoints(save_path, model, opt=None, epoch=None, lrs=None): - if lrs is not None: - states = { 'model_state': model.state_dict(), - 'epoch': epoch + 1, - 'opt_state': opt.state_dict(), - 'lrs':lrs.state_dict(),} - elif opt is not None: - states = { 'model_state': model.state_dict(), - 'epoch': epoch + 1, - 'opt_state': opt.state_dict(),} - else: - states = { 'model_state': model.state_dict(),} - torch.save(states, save_path) - - -def load_checkpoints(model, save_path, load_name='model'): - states = torch.load(save_path) - new_weights = OrderedDict() - flag=False - for k, v in states['model_state'].items(): - #print(k) - if "module" not in k: - break - else: - new_weights[k[7:]]=v - flag=True - if flag: - try: - model.load_state_dict(new_weights) - except: - #print(states['model_state']) - model.load_state_dict(states['model_state']) - else: - model.load_state_dict(states['model_state']) - logger.info(f"load self-pretrained checkpoints for {load_name}") - - -def model_complexity(model, args): - from ptflops import get_model_complexity_info - flops, params = get_model_complexity_info(model, (args.T_GLOBAL._DIM, args.TRAIN.CROP, args.TRAIN), - as_strings=False, print_per_layer_stat=False) - logging.info('{:<30} {:<8} BFlops'.format('Computational complexity: ', flops / 1e9)) - logging.info('{:<30} {:<8} MParams'.format('Number of parameters: ', params / 1e6)) - - -class AverageMeter(object): - """Computes and stores the average and current value""" - def __init__(self, name, fmt=':f'): - self.name = name - self.fmt = fmt - self.reset() - - def reset(self): - self.val = 0 - self.avg = 0 - self.sum = 0 - self.count = 0 - - def update(self, val, n=1): - self.val = val - self.sum += val * n - self.count += n - self.avg = self.sum / self.count - - def __str__(self): - fmtstr = '{name} {val' + self.fmt + '} ({avg' + self.fmt + '})' - return fmtstr.format(**self.__dict__) \ No newline at end of file diff --git a/dataloaders/utils/other_tools_hf.py b/dataloaders/utils/other_tools_hf.py deleted file mode 100644 index 90ee9fcc909fbd0f3da6129bc90732c0d168d5aa..0000000000000000000000000000000000000000 --- a/dataloaders/utils/other_tools_hf.py +++ /dev/null @@ -1,991 +0,0 @@ -import os -import numpy as np -import random -import torch -import shutil -import csv -import pprint -import pandas as pd -from loguru import logger -from collections import OrderedDict -import matplotlib.pyplot as plt -import pickle -import time -import hashlib -from scipy.spatial.transform import Rotation as R -from scipy.spatial.transform import Slerp -import cv2 - -def write_wav_names_to_csv(folder_path, csv_path): - """ - Traverse a folder and write the base names of all .wav files to a CSV file. - - :param folder_path: Path to the folder to traverse. - :param csv_path: Path to the CSV file to write. - """ - # Open the CSV file for writing - with open(csv_path, mode='w', newline='') as file: - writer = csv.writer(file) - # Write the header - writer.writerow(['id', 'type']) - - # Walk through the folder - for root, dirs, files in os.walk(folder_path): - for file in files: - # Check if the file ends with .wav - if file.endswith('.wav'): - # Extract the base name without the extension - base_name = os.path.splitext(file)[0] - # Write the base name and type to the CSV - writer.writerow([base_name, 'test']) - -def resize_motion_sequence_tensor(sequence, target_frames): - """ - Resize a batch of 8-frame motion sequences to a specified number of frames using interpolation. - - :param sequence: A (bs, 8, 165) tensor representing a batch of 8-frame motion sequences - :param target_frames: An integer representing the desired number of frames in the output sequences - :return: A (bs, target_frames, 165) tensor representing the resized motion sequences - """ - bs, _, _ = sequence.shape - - # Create a time vector for the original and target sequences - original_time = torch.linspace(0, 1, 8, device=sequence.device).view(1, -1, 1) - target_time = torch.linspace(0, 1, target_frames, device=sequence.device).view(1, -1, 1) - - # Permute the dimensions to (bs, 165, 8) for interpolation - sequence = sequence.permute(0, 2, 1) - - # Interpolate each joint's motion to the target number of frames - resized_sequence = torch.nn.functional.interpolate(sequence, size=target_frames, mode='linear', align_corners=True) - - # Permute the dimensions back to (bs, target_frames, 165) - resized_sequence = resized_sequence.permute(0, 2, 1) - - return resized_sequence - -def adjust_speed_according_to_ratio_tensor(chunks): - """ - Adjust the playback speed within a batch of 32-frame chunks according to random intervals. - - :param chunks: A (bs, 32, 165) tensor representing a batch of motion chunks - :return: A (bs, 32, 165) tensor representing the motion chunks after speed adjustment - """ - bs, _, _ = chunks.shape - - # Step 1: Divide the chunk into 4 equal intervals of 8 frames - equal_intervals = torch.chunk(chunks, 4, dim=1) - - # Step 2: Randomly sample 3 points within the chunk to determine new intervals - success = 0 - all_success = [] - #sample_points = torch.sort(torch.randint(1, 32, (bs, 3), device=chunks.device), dim=1).values - # new_intervals_boundaries = torch.cat([torch.zeros((bs, 1), device=chunks.device, dtype=torch.long), sample_points, 32*torch.ones((bs, 1), device=chunks.device, dtype=torch.long)], dim=1) - while success != 1: - sample_points = sorted(random.sample(range(1, 32), 3)) - new_intervals_boundaries = [0] + sample_points + [32] - new_intervals = [chunks[0][new_intervals_boundaries[i]:new_intervals_boundaries[i+1]] for i in range(4)] - speed_ratios = [8 / len(new_interval) for new_interval in new_intervals] - # if any of the speed ratios is greater than 3 or less than 0.33, resample - if all([0.33 <= speed_ratio <= 3 for speed_ratio in speed_ratios]): - success += 1 - all_success.append(new_intervals_boundaries) - new_intervals_boundaries = torch.from_numpy(np.array(all_success)) - # print(new_intervals_boundaries) - all_shapes = new_intervals_boundaries[:, 1:] - new_intervals_boundaries[:, :-1] - # Step 4: Adjust the speed of each new interval - adjusted_intervals = [] - # print(equal_intervals[0].shape) - for i in range(4): - adjusted_interval = resize_motion_sequence_tensor(equal_intervals[i], all_shapes[0, i]) - adjusted_intervals.append(adjusted_interval) - - # Step 5: Concatenate the adjusted intervals - adjusted_chunk = torch.cat(adjusted_intervals, dim=1) - - return adjusted_chunk - -def compute_exact_iou(bbox1, bbox2): - x1 = max(bbox1[0], bbox2[0]) - y1 = max(bbox1[1], bbox2[1]) - x2 = min(bbox1[0] + bbox1[2], bbox2[0] + bbox2[2]) - y2 = min(bbox1[1] + bbox1[3], bbox2[1] + bbox2[3]) - - intersection_area = max(0, x2 - x1) * max(0, y2 - y1) - bbox1_area = bbox1[2] * bbox1[3] - bbox2_area = bbox2[2] * bbox2[3] - union_area = bbox1_area + bbox2_area - intersection_area - - if union_area == 0: - return 0 - - return intersection_area / union_area - -def compute_iou(mask1, mask2): - # Compute the intersection - intersection = np.logical_and(mask1, mask2).sum() - - # Compute the union - union = np.logical_or(mask1, mask2).sum() - - # Compute the IoU - iou = intersection / union - - return iou - -def blankblending(all_frames, x, n): - return all_frames[x:x+n+1] - -def synthesize_intermediate_frames_FILM(frame1, frame2, t, name, save_path): - import replicate - from urllib.request import urlretrieve - import os - cv2.imwrite(save_path[:-9]+name+"_frame1.png", frame1) - cv2.imwrite(save_path[:-9]+name+"_frame2.png", frame2) - os.environ["REPLICATE_API_TOKEN"] = "r8_He1rkPk9GAxNQ3LpOohK8sYw1SUfMYV3Fxk9b" - output = replicate.run( - "google-research/frame-interpolation:4f88a16a13673a8b589c18866e540556170a5bcb2ccdc12de556e800e9456d3d", - input={ - "frame1": open(save_path[:-9]+name+"_frame1.png", "rb"), - "frame2": open(save_path[:-9]+name+"_frame2.png", "rb"), - "times_to_interpolate": t, - } - ) - print(output) - urlretrieve(output, save_path[:-9]+name+"_inter.mp4") - return load_video_as_numpy_array(save_path[:-9]+name+"_inter.mp4") - -def load_video_as_numpy_array(video_path): - cap = cv2.VideoCapture(video_path) - - # Using list comprehension to read frames and store in a list - frames = [frame for ret, frame in iter(lambda: cap.read(), (False, None)) if ret] - - cap.release() - - return np.array(frames) - -def synthesize_intermediate_frames_bidirectional(all_frames, x, n): - frame1 = all_frames[x] - frame2 = all_frames[x + n] - - # Convert the frames to grayscale - gray1 = cv2.cvtColor(frame1, cv2.COLOR_BGR2GRAY) - gray2 = cv2.cvtColor(frame2, cv2.COLOR_BGR2GRAY) - - # Calculate the forward and backward optical flow - forward_flow = cv2.calcOpticalFlowFarneback(gray1, gray2, None, 0.5, 3, 15, 3, 5, 1.2, 0) - backward_flow = cv2.calcOpticalFlowFarneback(gray2, gray1, None, 0.5, 3, 15, 3, 5, 1.2, 0) - - synthesized_frames = [] - for i in range(1, n): # For each intermediate frame between x and x + n - alpha = i / n # Interpolation factor - - # Compute the intermediate forward and backward flow - intermediate_forward_flow = forward_flow * alpha - intermediate_backward_flow = backward_flow * (1 - alpha) - - # Warp the frames based on the intermediate flow - h, w = frame1.shape[:2] - flow_map = np.column_stack((np.repeat(np.arange(h), w), np.tile(np.arange(w), h))) - forward_displacement = flow_map + intermediate_forward_flow.reshape(-1, 2) - backward_displacement = flow_map - intermediate_backward_flow.reshape(-1, 2) - - # Use cv2.remap for efficient warping - remap_x_forward, remap_y_forward = np.clip(forward_displacement[:, 1], 0, w - 1), np.clip(forward_displacement[:, 0], 0, h - 1) - remap_x_backward, remap_y_backward = np.clip(backward_displacement[:, 1], 0, w - 1), np.clip(backward_displacement[:, 0], 0, h - 1) - - warped_forward = cv2.remap(frame1, remap_x_forward.reshape(h, w).astype(np.float32), remap_y_forward.reshape(h, w).astype(np.float32), interpolation=cv2.INTER_LINEAR) - warped_backward = cv2.remap(frame2, remap_x_backward.reshape(h, w).astype(np.float32), remap_y_backward.reshape(h, w).astype(np.float32), interpolation=cv2.INTER_LINEAR) - - # Blend the warped frames to generate the intermediate frame - intermediate_frame = cv2.addWeighted(warped_forward, 1 - alpha, warped_backward, alpha, 0) - synthesized_frames.append(intermediate_frame) - - return synthesized_frames # Return n-2 synthesized intermediate frames - - -def linear_interpolate_frames(all_frames, x, n): - frame1 = all_frames[x] - frame2 = all_frames[x + n] - - synthesized_frames = [] - for i in range(1, n): # For each intermediate frame between x and x + n - alpha = i / (n) # Correct interpolation factor - inter_frame = cv2.addWeighted(frame1, 1 - alpha, frame2, alpha, 0) - synthesized_frames.append(inter_frame) - return synthesized_frames[:-1] - -def warp_frame(src_frame, flow): - h, w = flow.shape[:2] - flow_map = np.column_stack((np.repeat(np.arange(h), w), np.tile(np.arange(w), h))) - displacement = flow_map + flow.reshape(-1, 2) - - # Extract x and y coordinates of the displacement - x_coords = np.clip(displacement[:, 1], 0, w - 1).reshape(h, w).astype(np.float32) - y_coords = np.clip(displacement[:, 0], 0, h - 1).reshape(h, w).astype(np.float32) - - # Use cv2.remap for efficient warping - warped_frame = cv2.remap(src_frame, x_coords, y_coords, interpolation=cv2.INTER_LINEAR) - - return warped_frame - -def synthesize_intermediate_frames(all_frames, x, n): - # Calculate Optical Flow between the first and last frame - frame1 = cv2.cvtColor(all_frames[x], cv2.COLOR_BGR2GRAY) - frame2 = cv2.cvtColor(all_frames[x + n], cv2.COLOR_BGR2GRAY) - flow = cv2.calcOpticalFlowFarneback(frame1, frame2, None, 0.5, 3, 15, 3, 5, 1.2, 0) - - synthesized_frames = [] - for i in range(1, n): # For each intermediate frame - alpha = i / (n) # Interpolation factor - intermediate_flow = flow * alpha # Interpolate the flow - intermediate_frame = warp_frame(all_frames[x], intermediate_flow) # Warp the first frame - synthesized_frames.append(intermediate_frame) - - return synthesized_frames - - -def map2color(s): - m = hashlib.md5() - m.update(s.encode('utf-8')) - color_code = m.hexdigest()[:6] - return '#' + color_code - -def euclidean_distance(a, b): - return np.sqrt(np.sum((a - b)**2)) - -def adjust_array(x, k): - len_x = len(x) - len_k = len(k) - - # If x is shorter than k, pad with zeros - if len_x < len_k: - return np.pad(x, (0, len_k - len_x), 'constant') - - # If x is longer than k, truncate x - elif len_x > len_k: - return x[:len_k] - - # If both are of same length - else: - return x - -def onset_to_frame(onset_times, audio_length, fps): - # Calculate total number of frames for the given audio length - total_frames = int(audio_length * fps) - - # Create an array of zeros of shape (total_frames,) - frame_array = np.zeros(total_frames, dtype=np.int32) - - # For each onset time, calculate the frame number and set it to 1 - for onset in onset_times: - frame_num = int(onset * fps) - # Check if the frame number is within the array bounds - if 0 <= frame_num < total_frames: - frame_array[frame_num] = 1 - - return frame_array - -# def np_slerp(q1, q2, t): -# dot_product = np.sum(q1 * q2, axis=-1) -# q2_flip = np.where(dot_product[:, None] < 0, -q2, q2) # Flip quaternions where dot_product is negative -# dot_product = np.abs(dot_product) - -# angle = np.arccos(np.clip(dot_product, -1, 1)) -# sin_angle = np.sin(angle) - -# t1 = np.sin((1.0 - t) * angle) / sin_angle -# t2 = np.sin(t * angle) / sin_angle - -# return t1 * q1 + t2 * q2_flip - - -def smooth_rotvec_animations(animation1, animation2, blend_frames): - """ - Smoothly transition between two animation clips using SLERP. - - Parameters: - - animation1: The first animation clip, a numpy array of shape [n, k]. - - animation2: The second animation clip, a numpy array of shape [n, k]. - - blend_frames: Number of frames over which to blend the two animations. - - Returns: - - A smoothly blended animation clip of shape [2n, k]. - """ - - # Ensure blend_frames doesn't exceed the length of either animation - n1, k1 = animation1.shape - n2, k2 = animation2.shape - animation1 = animation1.reshape(n1, k1//3, 3) - animation2 = animation2.reshape(n2, k2//3, 3) - blend_frames = min(blend_frames, len(animation1), len(animation2)) - all_int = [] - for i in range(k1//3): - # Convert rotation vectors to quaternion for the overlapping part - q = R.from_rotvec(np.concatenate([animation1[0:1, i], animation2[-2:-1, i]], axis=0))#.as_quat() - # q2 = R.from_rotvec()#.as_quat() - times = [0, blend_frames * 2 - 1] - slerp = Slerp(times, q) - interpolated = slerp(np.arange(blend_frames * 2)) - interpolated_rotvecs = interpolated.as_rotvec() - all_int.append(interpolated_rotvecs) - interpolated_rotvecs = np.concatenate(all_int, axis=1) - # result = np.vstack((animation1[:-blend_frames], interpolated_rotvecs, animation2[blend_frames:])) - result = interpolated_rotvecs.reshape(2*n1, k1) - return result - -def smooth_animations(animation1, animation2, blend_frames): - """ - Smoothly transition between two animation clips using linear interpolation. - - Parameters: - - animation1: The first animation clip, a numpy array of shape [n, k]. - - animation2: The second animation clip, a numpy array of shape [n, k]. - - blend_frames: Number of frames over which to blend the two animations. - - Returns: - - A smoothly blended animation clip of shape [2n, k]. - """ - - # Ensure blend_frames doesn't exceed the length of either animation - blend_frames = min(blend_frames, len(animation1), len(animation2)) - - # Extract overlapping sections - overlap_a1 = animation1[-blend_frames:-blend_frames+1, :] - overlap_a2 = animation2[blend_frames-1:blend_frames, :] - - # Create blend weights for linear interpolation - alpha = np.linspace(0, 1, 2 * blend_frames).reshape(-1, 1) - - # Linearly interpolate between overlapping sections - blended_overlap = overlap_a1 * (1 - alpha) + overlap_a2 * alpha - - # Extend the animations to form the result with 2n frames - if blend_frames == len(animation1) and blend_frames == len(animation2): - result = blended_overlap - else: - before_blend = animation1[:-blend_frames] - after_blend = animation2[blend_frames:] - result = np.vstack((before_blend, blended_overlap, after_blend)) - return result - -def interpolate_sequence(quaternions): - bs, n, j, _ = quaternions.shape - new_n = 2 * n - new_quaternions = torch.zeros((bs, new_n, j, 4), device=quaternions.device, dtype=quaternions.dtype) - - for i in range(n): - q1 = quaternions[:, i, :, :] - new_quaternions[:, 2*i, :, :] = q1 - - if i < n - 1: - q2 = quaternions[:, i + 1, :, :] - new_quaternions[:, 2*i + 1, :, :] = slerp(q1, q2, 0.5) - else: - # For the last point, duplicate the value - new_quaternions[:, 2*i + 1, :, :] = q1 - - return new_quaternions - -def quaternion_multiply(q1, q2): - w1, x1, y1, z1 = q1 - w2, x2, y2, z2 = q2 - w = w1 * w2 - x1 * x2 - y1 * y2 - z1 * z2 - x = w1 * x2 + x1 * w2 + y1 * z2 - z1 * y2 - y = w1 * y2 + y1 * w2 + z1 * x2 - x1 * z2 - z = w1 * z2 + z1 * w2 + x1 * y2 - y1 * x2 - return w, x, y, z - -def quaternion_conjugate(q): - w, x, y, z = q - return (w, -x, -y, -z) - -def slerp(q1, q2, t): - dot = torch.sum(q1 * q2, dim=-1, keepdim=True) - - flip = (dot < 0).float() - q2 = (1 - flip * 2) * q2 - dot = dot * (1 - flip * 2) - - DOT_THRESHOLD = 0.9995 - mask = (dot > DOT_THRESHOLD).float() - - theta_0 = torch.acos(dot) - theta = theta_0 * t - - q3 = q2 - q1 * dot - q3 = q3 / torch.norm(q3, dim=-1, keepdim=True) - - interpolated = (torch.cos(theta) * q1 + torch.sin(theta) * q3) - - return mask * (q1 + t * (q2 - q1)) + (1 - mask) * interpolated - -def estimate_linear_velocity(data_seq, dt): - ''' - Given some batched data sequences of T timesteps in the shape (B, T, ...), estimates - the velocity for the middle T-2 steps using a second order central difference scheme. - The first and last frames are with forward and backward first-order - differences, respectively - - h : step size - ''' - # first steps is forward diff (t+1 - t) / dt - init_vel = (data_seq[:, 1:2] - data_seq[:, :1]) / dt - # middle steps are second order (t+1 - t-1) / 2dt - middle_vel = (data_seq[:, 2:] - data_seq[:, 0:-2]) / (2 * dt) - # last step is backward diff (t - t-1) / dt - final_vel = (data_seq[:, -1:] - data_seq[:, -2:-1]) / dt - - vel_seq = torch.cat([init_vel, middle_vel, final_vel], dim=1) - return vel_seq - -def velocity2position(data_seq, dt, init_pos): - res_trans = [] - for i in range(data_seq.shape[1]): - if i == 0: - res_trans.append(init_pos.unsqueeze(1)) - else: - res = data_seq[:, i-1:i] * dt + res_trans[-1] - res_trans.append(res) - return torch.cat(res_trans, dim=1) - -def estimate_angular_velocity(rot_seq, dt): - ''' - Given a batch of sequences of T rotation matrices, estimates angular velocity at T-2 steps. - Input sequence should be of shape (B, T, ..., 3, 3) - ''' - # see https://en.wikipedia.org/wiki/Angular_velocity#Calculation_from_the_orientation_matrix - dRdt = estimate_linear_velocity(rot_seq, dt) - R = rot_seq - RT = R.transpose(-1, -2) - # compute skew-symmetric angular velocity tensor - w_mat = torch.matmul(dRdt, RT) - # pull out angular velocity vector by averaging symmetric entries - w_x = (-w_mat[..., 1, 2] + w_mat[..., 2, 1]) / 2.0 - w_y = (w_mat[..., 0, 2] - w_mat[..., 2, 0]) / 2.0 - w_z = (-w_mat[..., 0, 1] + w_mat[..., 1, 0]) / 2.0 - w = torch.stack([w_x, w_y, w_z], axis=-1) - return w - -def image_from_bytes(image_bytes): - import matplotlib.image as mpimg - from io import BytesIO - return mpimg.imread(BytesIO(image_bytes), format='PNG') - -def process_frame(i, vertices_all, vertices1_all, faces, output_dir, filenames): - import matplotlib - matplotlib.use('Agg') - import matplotlib.pyplot as plt - import trimesh - import pyrender - - def deg_to_rad(degrees): - return degrees * np.pi / 180 - - uniform_color = [220, 220, 220, 255] - resolution = (1000, 1000) - figsize = (10, 10) - - fig, axs = plt.subplots( - nrows=1, - ncols=2, - figsize=(figsize[0] * 2, figsize[1] * 1) - ) - axs = axs.flatten() - - vertices = vertices_all[i] - vertices1 = vertices1_all[i] - filename = f"{output_dir}frame_{i}.png" - filenames.append(filename) - if i%100 == 0: - print('processed', i, 'frames') - #time_s = time.time() - #print(vertices.shape) - angle_rad = deg_to_rad(-2) - pose_camera = np.array([ - [1.0, 0.0, 0.0, 0.0], - [0.0, np.cos(angle_rad), -np.sin(angle_rad), 1.0], - [0.0, np.sin(angle_rad), np.cos(angle_rad), 5.0], - [0.0, 0.0, 0.0, 1.0] - ]) - angle_rad = deg_to_rad(-30) - pose_light = np.array([ - [1.0, 0.0, 0.0, 0.0], - [0.0, np.cos(angle_rad), -np.sin(angle_rad), 0.0], - [0.0, np.sin(angle_rad), np.cos(angle_rad), 3.0], - [0.0, 0.0, 0.0, 1.0] - ]) - - for vtx_idx, vtx in enumerate([vertices, vertices1]): - trimesh_mesh = trimesh.Trimesh( - vertices=vtx, - faces=faces, - vertex_colors=uniform_color - ) - mesh = pyrender.Mesh.from_trimesh( - trimesh_mesh, smooth=True - ) - scene = pyrender.Scene() - scene.add(mesh) - camera = pyrender.OrthographicCamera(xmag=1.0, ymag=1.0) - scene.add(camera, pose=pose_camera) - light = pyrender.DirectionalLight(color=[1.0, 1.0, 1.0], intensity=4.0) - scene.add(light, pose=pose_light) - renderer = pyrender.OffscreenRenderer(*resolution) - color, _ = renderer.render(scene) - axs[vtx_idx].imshow(color) - axs[vtx_idx].axis('off') - renderer.delete() - - plt.savefig(filename, bbox_inches='tight') - plt.close(fig) - -def generate_images(frames, vertices_all, vertices1_all, faces, output_dir, filenames): - import multiprocessing - # import trimesh - num_cores = multiprocessing.cpu_count() - 1 # This will get the number of cores on your machine. - # mesh = trimesh.Trimesh(vertices_all[0], faces) - # scene = mesh.scene() - # fov = scene.camera.fov.copy() - # fov[0] = 80.0 - # fov[1] = 60.0 - # camera_params = { - # 'fov': fov, - # 'resolution': scene.camera.resolution, - # 'focal': scene.camera.focal, - # 'z_near': scene.camera.z_near, - # "z_far": scene.camera.z_far, - # 'transform': scene.graph[scene.camera.name][0] - # } - # mesh1 = trimesh.Trimesh(vertices1_all[0], faces) - # scene1 = mesh1.scene() - # camera_params1 = { - # 'fov': fov, - # 'resolution': scene1.camera.resolution, - # 'focal': scene1.camera.focal, - # 'z_near': scene1.camera.z_near, - # "z_far": scene1.camera.z_far, - # 'transform': scene1.graph[scene1.camera.name][0] - # } - # Use a Pool to manage the processes - # print(num_cores) - # for i in range(frames): - # process_frame(i, vertices_all, vertices1_all, faces, output_dir, use_matplotlib, filenames, camera_params, camera_params1) - for i in range(frames): - process_frame(i*3, vertices_all, vertices1_all, faces, output_dir, filenames) - - # progress = multiprocessing.Value('i', 0) - # lock = multiprocessing.Lock() - # with multiprocessing.Pool(num_cores) as pool: - # # pool.starmap(process_frame, [(i, vertices_all, vertices1_all, faces, output_dir, use_matplotlib, filenames, camera_params, camera_params1) for i in range(frames)]) - # pool.starmap( - # process_frame, - # [ - # (i, vertices_all, vertices1_all, faces, output_dir, filenames) - # for i in range(frames) - # ] - # ) - - # progress = multiprocessing.Value('i', 0) - # lock = multiprocessing.Lock() - # with multiprocessing.Pool(num_cores) as pool: - # # pool.starmap(process_frame, [(i, vertices_all, vertices1_all, faces, output_dir, use_matplotlib, filenames, camera_params, camera_params1) for i in range(frames)]) - # pool.starmap( - # process_frame, - # [ - # (i, vertices_all, vertices1_all, faces, output_dir, filenames) - # for i in range(frames) - # ] - # ) - -def render_one_sequence( - res_npz_path, - gt_npz_path, - output_dir, - audio_path, - model_folder="/data/datasets/smplx_models/", - model_type='smplx', - gender='NEUTRAL_2020', - ext='npz', - num_betas=300, - num_expression_coeffs=100, - use_face_contour=False, - use_matplotlib=False, - args=None): - import smplx - import matplotlib.pyplot as plt - import imageio - from tqdm import tqdm - import os - import numpy as np - import torch - import moviepy.editor as mp - import librosa - - model = smplx.create(model_folder, model_type=model_type, - gender=gender, use_face_contour=use_face_contour, - num_betas=num_betas, - num_expression_coeffs=num_expression_coeffs, - ext=ext, use_pca=False).cuda() - - #data_npz = np.load(f"{output_dir}{res_npz_path}.npz") - data_np_body = np.load(res_npz_path, allow_pickle=True) - gt_np_body = np.load(gt_npz_path, allow_pickle=True) - - if not os.path.exists(output_dir): os.makedirs(output_dir) - filenames = [] - # if not use_matplotlib: - # import trimesh - #import pyrender - from pyvirtualdisplay import Display - #''' - #display = Display(visible=0, size=(1000, 1000)) - #display.start() - faces = np.load(f"{model_folder}/smplx/SMPLX_NEUTRAL_2020.npz", allow_pickle=True)["f"] - seconds = 1 - #data_npz["jaw_pose"].shape[0] - n = data_np_body["poses"].shape[0] - beta = torch.from_numpy(data_np_body["betas"]).to(torch.float32).unsqueeze(0).cuda() - beta = beta.repeat(n, 1) - expression = torch.from_numpy(data_np_body["expressions"][:n]).to(torch.float32).cuda() - jaw_pose = torch.from_numpy(data_np_body["poses"][:n, 66:69]).to(torch.float32).cuda() - pose = torch.from_numpy(data_np_body["poses"][:n]).to(torch.float32).cuda() - transl = torch.from_numpy(data_np_body["trans"][:n]).to(torch.float32).cuda() - # print(beta.shape, expression.shape, jaw_pose.shape, pose.shape, transl.shape, pose[:,:3].shape) - output = model(betas=beta, transl=transl, expression=expression, jaw_pose=jaw_pose, - global_orient=pose[:,:3], body_pose=pose[:,3:21*3+3], left_hand_pose=pose[:,25*3:40*3], right_hand_pose=pose[:,40*3:55*3], - leye_pose=pose[:, 69:72], - reye_pose=pose[:, 72:75], - return_verts=True) - vertices_all = output["vertices"].cpu().detach().numpy() - - beta1 = torch.from_numpy(gt_np_body["betas"]).to(torch.float32).unsqueeze(0).cuda() - expression1 = torch.from_numpy(gt_np_body["expressions"][:n]).to(torch.float32).cuda() - jaw_pose1 = torch.from_numpy(gt_np_body["poses"][:n,66:69]).to(torch.float32).cuda() - pose1 = torch.from_numpy(gt_np_body["poses"][:n]).to(torch.float32).cuda() - transl1 = torch.from_numpy(gt_np_body["trans"][:n]).to(torch.float32).cuda() - output1 = model(betas=beta1, transl=transl1, expression=expression1, jaw_pose=jaw_pose1, global_orient=pose1[:,:3], body_pose=pose1[:,3:21*3+3], left_hand_pose=pose1[:,25*3:40*3], right_hand_pose=pose1[:,40*3:55*3], - leye_pose=pose1[:, 69:72], - reye_pose=pose1[:, 72:75],return_verts=True) - vertices1_all = output1["vertices"].cpu().detach().numpy() - if args.debug: - seconds = 1 - else: - seconds = vertices_all.shape[0]//30 - # camera_settings = None - time_s = time.time() - generate_images(int(seconds*10), vertices_all, vertices1_all, faces, output_dir, filenames) - filenames = ["{}frame_{}.png".format(output_dir, i*3) for i in range(int(seconds*10))] - # print(time.time()-time_s) - # for i in tqdm(range(seconds*10)): - # vertices = vertices_all[i] - # vertices1 = vertices1_all[i] - # filename = f"{output_dir}frame_{i}.png" - # filenames.append(filename) - # #time_s = time.time() - # #print(vertices.shape) - # if use_matplotlib: - # fig = plt.figure(figsize=(20, 10)) - # ax = fig.add_subplot(121, projection="3d") - # fig.subplots_adjust(left=0, right=1, bottom=0, top=1) - # #ax.view_init(elev=0, azim=90) - # x = vertices[:, 0] - # y = vertices[:, 1] - # z = vertices[:, 2] - # ax.scatter(x, y, z, s=0.5) - # ax.set_xlim([-1.0, 1.0]) - # ax.set_ylim([-0.5, 1.5])#heigth - # ax.set_zlim([-0, 2])#depth - # ax.set_box_aspect((1,1,1)) - # else: - # mesh = trimesh.Trimesh(vertices, faces) - # if i == 0: - # scene = mesh.scene() - # camera_params = { - # 'fov': scene.camera.fov, - # 'resolution': scene.camera.resolution, - # 'focal': scene.camera.focal, - # 'z_near': scene.camera.z_near, - # "z_far": scene.camera.z_far, - # 'transform': scene.graph[scene.camera.name][0] - # } - # else: - # scene = mesh.scene() - # scene.camera.fov = camera_params['fov'] - # scene.camera.resolution = camera_params['resolution'] - # scene.camera.z_near = camera_params['z_near'] - # scene.camera.z_far = camera_params['z_far'] - # scene.graph[scene.camera.name] = camera_params['transform'] - # fig, ax =plt.subplots(1,2, figsize=(16, 6)) - # image = scene.save_image(resolution=[640, 480], visible=False) - # #print((time.time()-time_s)) - # im0 = ax[0].imshow(image_from_bytes(image)) - # ax[0].axis('off') - - # # beta1 = torch.from_numpy(gt_np_body["betas"]).to(torch.float32).unsqueeze(0) - # # expression1 = torch.from_numpy(gt_np_body["expressions"][i]).to(torch.float32).unsqueeze(0) - # # jaw_pose1 = torch.from_numpy(gt_np_body["poses"][i][66:69]).to(torch.float32).unsqueeze(0) - # # pose1 = torch.from_numpy(gt_np_body["poses"][i]).to(torch.float32).unsqueeze(0) - # # transl1 = torch.from_numpy(gt_np_body["trans"][i]).to(torch.float32).unsqueeze(0) - # # #print(beta.shape, expression.shape, jaw_pose.shape, pose.shape, transl.shape)global_orient=pose[0:1,:3], - # # output1 = model(betas=beta1, transl=transl1, expression=expression1, jaw_pose=jaw_pose1, global_orient=pose1[0:1,:3], body_pose=pose1[0:1,3:21*3+3], left_hand_pose=pose1[0:1,25*3:40*3], right_hand_pose=pose1[0:1,40*3:55*3], return_verts=True) - # # vertices1 = output1["vertices"].cpu().detach().numpy()[0] - - # if use_matplotlib: - # ax2 = fig.add_subplot(122, projection="3d") - # ax2.set_box_aspect((1,1,1)) - # fig.subplots_adjust(left=0, right=1, bottom=0, top=1) - # #ax2.view_init(elev=0, azim=90) - # x1 = vertices1[:, 0] - # y1 = vertices1[:, 1] - # z1 = vertices1[:, 2] - # ax2.scatter(x1, y1, z1, s=0.5) - # ax2.set_xlim([-1.0, 1.0]) - # ax2.set_ylim([-0.5, 1.5])#heigth - # ax2.set_zlim([-0, 2]) - # plt.savefig(filename, bbox_inches='tight') - # plt.close(fig) - # else: - # mesh1 = trimesh.Trimesh(vertices1, faces) - # if i == 0: - # scene1 = mesh1.scene() - # camera_params1 = { - # 'fov': scene1.camera.fov, - # 'resolution': scene1.camera.resolution, - # 'focal': scene1.camera.focal, - # 'z_near': scene1.camera.z_near, - # "z_far": scene1.camera.z_far, - # 'transform': scene1.graph[scene1.camera.name][0] - # } - # else: - # scene1 = mesh1.scene() - # scene1.camera.fov = camera_params1['fov'] - # scene1.camera.resolution = camera_params1['resolution'] - # scene1.camera.z_near = camera_params1['z_near'] - # scene1.camera.z_far = camera_params1['z_far'] - # scene1.graph[scene1.camera.name] = camera_params1['transform'] - # image1 = scene1.save_image(resolution=[640, 480], visible=False) - # im1 = ax[1].imshow(image_from_bytes(image1)) - # ax[1].axis('off') - # plt.savefig(filename, bbox_inches='tight') - # plt.close(fig) - - #display.stop() - #''' - # print(filenames) - images = [imageio.imread(filename) for filename in filenames] - imageio.mimsave(f"{output_dir}raw_{res_npz_path.split('/')[-1][:-4]}.mp4", images, fps=10) - for filename in filenames: - os.remove(filename) - - video = mp.VideoFileClip(f"{output_dir}raw_{res_npz_path.split('/')[-1][:-4]}.mp4") - # audio, sr = librosa.load(audio_path) - # audio = audio[:seconds*sr] - # print(audio.shape, seconds, sr) - # import soundfile as sf - # sf.write(f"{output_dir}{res_npz_path.split('/')[-1][:-4]}.wav", audio, 16000, 'PCM_24') - # audio_tmp = librosa.output.write_wav(f"{output_dir}{res_npz_path.split('/')[-1][:-4]}.wav", audio, sr=16000) - audio = mp.AudioFileClip(audio_path) - if audio.duration > video.duration: - audio = audio.subclip(0, video.duration) - final_clip = video.set_audio(audio) - final_clip.write_videofile(f"{output_dir}{res_npz_path.split('/')[-1][4:-4]}.mp4") - os.remove(f"{output_dir}raw_{res_npz_path.split('/')[-1][:-4]}.mp4") - -def print_exp_info(args): - logger.info(pprint.pformat(vars(args))) - logger.info(f"# ------------ {args.name} ----------- #") - logger.info("PyTorch version: {}".format(torch.__version__)) - logger.info("CUDA version: {}".format(torch.version.cuda)) - logger.info("{} GPUs".format(torch.cuda.device_count())) - logger.info(f"Random Seed: {args.random_seed}") - -def args2csv(args, get_head=False, list4print=[]): - for k, v in args.items(): - if isinstance(args[k], dict): - args2csv(args[k], get_head, list4print) - else: list4print.append(k) if get_head else list4print.append(v) - return list4print - -class EpochTracker: - def __init__(self, metric_names, metric_directions): - assert len(metric_names) == len(metric_directions), "Metric names and directions should have the same length" - - - self.metric_names = metric_names - self.states = ['train', 'val', 'test'] - self.types = ['last', 'best'] - - - self.values = {name: {state: {type_: {'value': np.inf if not is_higher_better else -np.inf, 'epoch': 0} - for type_ in self.types} - for state in self.states} - for name, is_higher_better in zip(metric_names, metric_directions)} - - self.loss_meters = {name: {state: AverageMeter(f"{name}_{state}") - for state in self.states} - for name in metric_names} - - - self.is_higher_better = {name: direction for name, direction in zip(metric_names, metric_directions)} - self.train_history = {name: [] for name in metric_names} - self.val_history = {name: [] for name in metric_names} - - - def update_meter(self, name, state, value): - self.loss_meters[name][state].update(value) - - - def update_values(self, name, state, epoch): - value_avg = self.loss_meters[name][state].avg - new_best = False - - - if ((value_avg < self.values[name][state]['best']['value'] and not self.is_higher_better[name]) or - (value_avg > self.values[name][state]['best']['value'] and self.is_higher_better[name])): - self.values[name][state]['best']['value'] = value_avg - self.values[name][state]['best']['epoch'] = epoch - new_best = True - self.values[name][state]['last']['value'] = value_avg - self.values[name][state]['last']['epoch'] = epoch - return new_best - - - def get(self, name, state, type_): - return self.values[name][state][type_] - - - def reset(self): - for name in self.metric_names: - for state in self.states: - self.loss_meters[name][state].reset() - - - def flatten_values(self): - flat_dict = {} - for name in self.metric_names: - for state in self.states: - for type_ in self.types: - value_key = f"{name}_{state}_{type_}" - epoch_key = f"{name}_{state}_{type_}_epoch" - flat_dict[value_key] = self.values[name][state][type_]['value'] - flat_dict[epoch_key] = self.values[name][state][type_]['epoch'] - return flat_dict - - def update_and_plot(self, name, epoch, save_path): - new_best_train = self.update_values(name, 'train', epoch) - new_best_val = self.update_values(name, 'val', epoch) - - - self.train_history[name].append(self.loss_meters[name]['train'].avg) - self.val_history[name].append(self.loss_meters[name]['val'].avg) - - - train_values = self.train_history[name] - val_values = self.val_history[name] - epochs = list(range(1, len(train_values) + 1)) - - - plt.figure(figsize=(10, 6)) - plt.plot(epochs, train_values, label='Train') - plt.plot(epochs, val_values, label='Val') - plt.title(f'Train vs Val {name} over epochs') - plt.xlabel('Epochs') - plt.ylabel(name) - plt.legend() - plt.savefig(save_path) - plt.close() - - - return new_best_train, new_best_val - -def record_trial(args, tracker): - """ - 1. record notes, score, env_name, experments_path, - """ - csv_path = args.out_path + "custom/" +args.csv_name+".csv" - all_print_dict = vars(args) - all_print_dict.update(tracker.flatten_values()) - if not os.path.exists(csv_path): - pd.DataFrame([all_print_dict]).to_csv(csv_path, index=False) - else: - df_existing = pd.read_csv(csv_path) - df_new = pd.DataFrame([all_print_dict]) - df_aligned = df_existing.append(df_new).fillna("") - df_aligned.to_csv(csv_path, index=False) - -def set_random_seed(args): - os.environ['PYTHONHASHSEED'] = str(args.random_seed) - random.seed(args.random_seed) - np.random.seed(args.random_seed) - torch.manual_seed(args.random_seed) - torch.cuda.manual_seed_all(args.random_seed) - torch.cuda.manual_seed(args.random_seed) - torch.backends.cudnn.deterministic = args.deterministic #args.CUDNN_DETERMINISTIC - torch.backends.cudnn.benchmark = args.benchmark - torch.backends.cudnn.enabled = args.cudnn_enabled - -def save_checkpoints(save_path, model, opt=None, epoch=None, lrs=None): - if lrs is not None: - states = { 'model_state': model.state_dict(), - 'epoch': epoch + 1, - 'opt_state': opt.state_dict(), - 'lrs':lrs.state_dict(),} - elif opt is not None: - states = { 'model_state': model.state_dict(), - 'epoch': epoch + 1, - 'opt_state': opt.state_dict(),} - else: - states = { 'model_state': model.state_dict(),} - torch.save(states, save_path) - -def load_checkpoints(model, save_path, load_name='model'): - states = torch.load(save_path) - new_weights = OrderedDict() - flag=False - for k, v in states['model_state'].items(): - #print(k) - if "module" not in k: - break - else: - new_weights[k[7:]]=v - flag=True - if flag: - try: - model.load_state_dict(new_weights) - except: - #print(states['model_state']) - model.load_state_dict(states['model_state']) - else: - model.load_state_dict(states['model_state']) - logger.info(f"load self-pretrained checkpoints for {load_name}") - -def model_complexity(model, args): - from ptflops import get_model_complexity_info - flops, params = get_model_complexity_info(model, (args.T_GLOBAL._DIM, args.TRAIN.CROP, args.TRAIN), - as_strings=False, print_per_layer_stat=False) - logging.info('{:<30} {:<8} BFlops'.format('Computational complexity: ', flops / 1e9)) - logging.info('{:<30} {:<8} MParams'.format('Number of parameters: ', params / 1e6)) - -class AverageMeter(object): - """Computes and stores the average and current value""" - def __init__(self, name, fmt=':f'): - self.name = name - self.fmt = fmt - self.reset() - - def reset(self): - self.val = 0 - self.avg = 0 - self.sum = 0 - self.count = 0 - - def update(self, val, n=1): - self.val = val - self.sum += val * n - self.count += n - self.avg = self.sum / self.count - - def __str__(self): - fmtstr = '{name} {val' + self.fmt + '} ({avg' + self.fmt + '})' - return fmtstr.format(**self.__dict__) diff --git a/dataloaders/utils/rotation_conversions.py b/dataloaders/utils/rotation_conversions.py deleted file mode 100644 index f2bfaa1b2247622bff35d3f9b15e8eb84064aa53..0000000000000000000000000000000000000000 --- a/dataloaders/utils/rotation_conversions.py +++ /dev/null @@ -1,550 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved. - -import functools -from typing import Optional - -import torch -import torch.nn.functional as F - - -""" -The transformation matrices returned from the functions in this file assume -the points on which the transformation will be applied are column vectors. -i.e. the R matrix is structured as - - R = [ - [Rxx, Rxy, Rxz], - [Ryx, Ryy, Ryz], - [Rzx, Rzy, Rzz], - ] # (3, 3) - -This matrix can be applied to column vectors by post multiplication -by the points e.g. - - points = [[0], [1], [2]] # (3 x 1) xyz coordinates of a point - transformed_points = R * points - -To apply the same matrix to points which are row vectors, the R matrix -can be transposed and pre multiplied by the points: - -e.g. - points = [[0, 1, 2]] # (1 x 3) xyz coordinates of a point - transformed_points = points * R.transpose(1, 0) -""" - - -def quaternion_to_matrix(quaternions): - """ - Convert rotations given as quaternions to rotation matrices. - - Args: - quaternions: quaternions with real part first, - as tensor of shape (..., 4). - - Returns: - Rotation matrices as tensor of shape (..., 3, 3). - """ - r, i, j, k = torch.unbind(quaternions, -1) - two_s = 2.0 / (quaternions * quaternions).sum(-1) - - o = torch.stack( - ( - 1 - two_s * (j * j + k * k), - two_s * (i * j - k * r), - two_s * (i * k + j * r), - two_s * (i * j + k * r), - 1 - two_s * (i * i + k * k), - two_s * (j * k - i * r), - two_s * (i * k - j * r), - two_s * (j * k + i * r), - 1 - two_s * (i * i + j * j), - ), - -1, - ) - return o.reshape(quaternions.shape[:-1] + (3, 3)) - - -def _copysign(a, b): - """ - Return a tensor where each element has the absolute value taken from the, - corresponding element of a, with sign taken from the corresponding - element of b. This is like the standard copysign floating-point operation, - but is not careful about negative 0 and NaN. - - Args: - a: source tensor. - b: tensor whose signs will be used, of the same shape as a. - - Returns: - Tensor of the same shape as a with the signs of b. - """ - signs_differ = (a < 0) != (b < 0) - return torch.where(signs_differ, -a, a) - - -def _sqrt_positive_part(x): - """ - Returns torch.sqrt(torch.max(0, x)) - but with a zero subgradient where x is 0. - """ - ret = torch.zeros_like(x) - positive_mask = x > 0 - ret[positive_mask] = torch.sqrt(x[positive_mask]) - return ret - - -def matrix_to_quaternion(matrix): - """ - Convert rotations given as rotation matrices to quaternions. - - Args: - matrix: Rotation matrices as tensor of shape (..., 3, 3). - - Returns: - quaternions with real part first, as tensor of shape (..., 4). - """ - if matrix.size(-1) != 3 or matrix.size(-2) != 3: - raise ValueError(f"Invalid rotation matrix shape f{matrix.shape}.") - m00 = matrix[..., 0, 0] - m11 = matrix[..., 1, 1] - m22 = matrix[..., 2, 2] - o0 = 0.5 * _sqrt_positive_part(1 + m00 + m11 + m22) - x = 0.5 * _sqrt_positive_part(1 + m00 - m11 - m22) - y = 0.5 * _sqrt_positive_part(1 - m00 + m11 - m22) - z = 0.5 * _sqrt_positive_part(1 - m00 - m11 + m22) - o1 = _copysign(x, matrix[..., 2, 1] - matrix[..., 1, 2]) - o2 = _copysign(y, matrix[..., 0, 2] - matrix[..., 2, 0]) - o3 = _copysign(z, matrix[..., 1, 0] - matrix[..., 0, 1]) - return torch.stack((o0, o1, o2, o3), -1) - - -def _axis_angle_rotation(axis: str, angle): - """ - Return the rotation matrices for one of the rotations about an axis - of which Euler angles describe, for each value of the angle given. - - Args: - axis: Axis label "X" or "Y or "Z". - angle: any shape tensor of Euler angles in radians - - Returns: - Rotation matrices as tensor of shape (..., 3, 3). - """ - - cos = torch.cos(angle) - sin = torch.sin(angle) - one = torch.ones_like(angle) - zero = torch.zeros_like(angle) - - if axis == "X": - R_flat = (one, zero, zero, zero, cos, -sin, zero, sin, cos) - if axis == "Y": - R_flat = (cos, zero, sin, zero, one, zero, -sin, zero, cos) - if axis == "Z": - R_flat = (cos, -sin, zero, sin, cos, zero, zero, zero, one) - - return torch.stack(R_flat, -1).reshape(angle.shape + (3, 3)) - - -def euler_angles_to_matrix(euler_angles, convention: str): - """ - Convert rotations given as Euler angles in radians to rotation matrices. - - Args: - euler_angles: Euler angles in radians as tensor of shape (..., 3). - convention: Convention string of three uppercase letters from - {"X", "Y", and "Z"}. - - Returns: - Rotation matrices as tensor of shape (..., 3, 3). - """ - if euler_angles.dim() == 0 or euler_angles.shape[-1] != 3: - raise ValueError("Invalid input euler angles.") - if len(convention) != 3: - raise ValueError("Convention must have 3 letters.") - if convention[1] in (convention[0], convention[2]): - raise ValueError(f"Invalid convention {convention}.") - for letter in convention: - if letter not in ("X", "Y", "Z"): - raise ValueError(f"Invalid letter {letter} in convention string.") - matrices = map(_axis_angle_rotation, convention, torch.unbind(euler_angles, -1)) - return functools.reduce(torch.matmul, matrices) - - -def _angle_from_tan( - axis: str, other_axis: str, data, horizontal: bool, tait_bryan: bool -): - """ - Extract the first or third Euler angle from the two members of - the matrix which are positive constant times its sine and cosine. - - Args: - axis: Axis label "X" or "Y or "Z" for the angle we are finding. - other_axis: Axis label "X" or "Y or "Z" for the middle axis in the - convention. - data: Rotation matrices as tensor of shape (..., 3, 3). - horizontal: Whether we are looking for the angle for the third axis, - which means the relevant entries are in the same row of the - rotation matrix. If not, they are in the same column. - tait_bryan: Whether the first and third axes in the convention differ. - - Returns: - Euler Angles in radians for each matrix in data as a tensor - of shape (...). - """ - - i1, i2 = {"X": (2, 1), "Y": (0, 2), "Z": (1, 0)}[axis] - if horizontal: - i2, i1 = i1, i2 - even = (axis + other_axis) in ["XY", "YZ", "ZX"] - if horizontal == even: - return torch.atan2(data[..., i1], data[..., i2]) - if tait_bryan: - return torch.atan2(-data[..., i2], data[..., i1]) - return torch.atan2(data[..., i2], -data[..., i1]) - - -def _index_from_letter(letter: str): - if letter == "X": - return 0 - if letter == "Y": - return 1 - if letter == "Z": - return 2 - - -def matrix_to_euler_angles(matrix, convention: str): - """ - Convert rotations given as rotation matrices to Euler angles in radians. - - Args: - matrix: Rotation matrices as tensor of shape (..., 3, 3). - convention: Convention string of three uppercase letters. - - Returns: - Euler angles in radians as tensor of shape (..., 3). - """ - if len(convention) != 3: - raise ValueError("Convention must have 3 letters.") - if convention[1] in (convention[0], convention[2]): - raise ValueError(f"Invalid convention {convention}.") - for letter in convention: - if letter not in ("X", "Y", "Z"): - raise ValueError(f"Invalid letter {letter} in convention string.") - if matrix.size(-1) != 3 or matrix.size(-2) != 3: - raise ValueError(f"Invalid rotation matrix shape f{matrix.shape}.") - i0 = _index_from_letter(convention[0]) - i2 = _index_from_letter(convention[2]) - tait_bryan = i0 != i2 - if tait_bryan: - central_angle = torch.asin( - matrix[..., i0, i2] * (-1.0 if i0 - i2 in [-1, 2] else 1.0) - ) - else: - central_angle = torch.acos(matrix[..., i0, i0]) - - o = ( - _angle_from_tan( - convention[0], convention[1], matrix[..., i2], False, tait_bryan - ), - central_angle, - _angle_from_tan( - convention[2], convention[1], matrix[..., i0, :], True, tait_bryan - ), - ) - return torch.stack(o, -1) - - -def random_quaternions( - n: int, dtype: Optional[torch.dtype] = None, device=None, requires_grad=False -): - """ - Generate random quaternions representing rotations, - i.e. versors with nonnegative real part. - - Args: - n: Number of quaternions in a batch to return. - dtype: Type to return. - device: Desired device of returned tensor. Default: - uses the current device for the default tensor type. - requires_grad: Whether the resulting tensor should have the gradient - flag set. - - Returns: - Quaternions as tensor of shape (N, 4). - """ - o = torch.randn((n, 4), dtype=dtype, device=device, requires_grad=requires_grad) - s = (o * o).sum(1) - o = o / _copysign(torch.sqrt(s), o[:, 0])[:, None] - return o - - -def random_rotations( - n: int, dtype: Optional[torch.dtype] = None, device=None, requires_grad=False -): - """ - Generate random rotations as 3x3 rotation matrices. - - Args: - n: Number of rotation matrices in a batch to return. - dtype: Type to return. - device: Device of returned tensor. Default: if None, - uses the current device for the default tensor type. - requires_grad: Whether the resulting tensor should have the gradient - flag set. - - Returns: - Rotation matrices as tensor of shape (n, 3, 3). - """ - quaternions = random_quaternions( - n, dtype=dtype, device=device, requires_grad=requires_grad - ) - return quaternion_to_matrix(quaternions) - - -def random_rotation( - dtype: Optional[torch.dtype] = None, device=None, requires_grad=False -): - """ - Generate a single random 3x3 rotation matrix. - - Args: - dtype: Type to return - device: Device of returned tensor. Default: if None, - uses the current device for the default tensor type - requires_grad: Whether the resulting tensor should have the gradient - flag set - - Returns: - Rotation matrix as tensor of shape (3, 3). - """ - return random_rotations(1, dtype, device, requires_grad)[0] - - -def standardize_quaternion(quaternions): - """ - Convert a unit quaternion to a standard form: one in which the real - part is non negative. - - Args: - quaternions: Quaternions with real part first, - as tensor of shape (..., 4). - - Returns: - Standardized quaternions as tensor of shape (..., 4). - """ - return torch.where(quaternions[..., 0:1] < 0, -quaternions, quaternions) - - -def quaternion_raw_multiply(a, b): - """ - Multiply two quaternions. - Usual torch rules for broadcasting apply. - - Args: - a: Quaternions as tensor of shape (..., 4), real part first. - b: Quaternions as tensor of shape (..., 4), real part first. - - Returns: - The product of a and b, a tensor of quaternions shape (..., 4). - """ - aw, ax, ay, az = torch.unbind(a, -1) - bw, bx, by, bz = torch.unbind(b, -1) - ow = aw * bw - ax * bx - ay * by - az * bz - ox = aw * bx + ax * bw + ay * bz - az * by - oy = aw * by - ax * bz + ay * bw + az * bx - oz = aw * bz + ax * by - ay * bx + az * bw - return torch.stack((ow, ox, oy, oz), -1) - - -def quaternion_multiply(a, b): - """ - Multiply two quaternions representing rotations, returning the quaternion - representing their composition, i.e. the versor with nonnegative real part. - Usual torch rules for broadcasting apply. - - Args: - a: Quaternions as tensor of shape (..., 4), real part first. - b: Quaternions as tensor of shape (..., 4), real part first. - - Returns: - The product of a and b, a tensor of quaternions of shape (..., 4). - """ - ab = quaternion_raw_multiply(a, b) - return standardize_quaternion(ab) - - -def quaternion_invert(quaternion): - """ - Given a quaternion representing rotation, get the quaternion representing - its inverse. - - Args: - quaternion: Quaternions as tensor of shape (..., 4), with real part - first, which must be versors (unit quaternions). - - Returns: - The inverse, a tensor of quaternions of shape (..., 4). - """ - - return quaternion * quaternion.new_tensor([1, -1, -1, -1]) - - -def quaternion_apply(quaternion, point): - """ - Apply the rotation given by a quaternion to a 3D point. - Usual torch rules for broadcasting apply. - - Args: - quaternion: Tensor of quaternions, real part first, of shape (..., 4). - point: Tensor of 3D points of shape (..., 3). - - Returns: - Tensor of rotated points of shape (..., 3). - """ - if point.size(-1) != 3: - raise ValueError(f"Points are not in 3D, f{point.shape}.") - real_parts = point.new_zeros(point.shape[:-1] + (1,)) - point_as_quaternion = torch.cat((real_parts, point), -1) - out = quaternion_raw_multiply( - quaternion_raw_multiply(quaternion, point_as_quaternion), - quaternion_invert(quaternion), - ) - return out[..., 1:] - - -def axis_angle_to_matrix(axis_angle): - """ - Convert rotations given as axis/angle to rotation matrices. - - Args: - axis_angle: Rotations given as a vector in axis angle form, - as a tensor of shape (..., 3), where the magnitude is - the angle turned anticlockwise in radians around the - vector's direction. - - Returns: - Rotation matrices as tensor of shape (..., 3, 3). - """ - return quaternion_to_matrix(axis_angle_to_quaternion(axis_angle)) - - -def matrix_to_axis_angle(matrix): - """ - Convert rotations given as rotation matrices to axis/angle. - - Args: - matrix: Rotation matrices as tensor of shape (..., 3, 3). - - Returns: - Rotations given as a vector in axis angle form, as a tensor - of shape (..., 3), where the magnitude is the angle - turned anticlockwise in radians around the vector's - direction. - """ - return quaternion_to_axis_angle(matrix_to_quaternion(matrix)) - - -def axis_angle_to_quaternion(axis_angle): - """ - Convert rotations given as axis/angle to quaternions. - - Args: - axis_angle: Rotations given as a vector in axis angle form, - as a tensor of shape (..., 3), where the magnitude is - the angle turned anticlockwise in radians around the - vector's direction. - - Returns: - quaternions with real part first, as tensor of shape (..., 4). - """ - angles = torch.norm(axis_angle, p=2, dim=-1, keepdim=True) - half_angles = 0.5 * angles - eps = 1e-6 - small_angles = angles.abs() < eps - sin_half_angles_over_angles = torch.empty_like(angles) - sin_half_angles_over_angles[~small_angles] = ( - torch.sin(half_angles[~small_angles]) / angles[~small_angles] - ) - # for x small, sin(x/2) is about x/2 - (x/2)^3/6 - # so sin(x/2)/x is about 1/2 - (x*x)/48 - sin_half_angles_over_angles[small_angles] = ( - 0.5 - (angles[small_angles] * angles[small_angles]) / 48 - ) - quaternions = torch.cat( - [torch.cos(half_angles), axis_angle * sin_half_angles_over_angles], dim=-1 - ) - return quaternions - - -def quaternion_to_axis_angle(quaternions): - """ - Convert rotations given as quaternions to axis/angle. - - Args: - quaternions: quaternions with real part first, - as tensor of shape (..., 4). - - Returns: - Rotations given as a vector in axis angle form, as a tensor - of shape (..., 3), where the magnitude is the angle - turned anticlockwise in radians around the vector's - direction. - """ - norms = torch.norm(quaternions[..., 1:], p=2, dim=-1, keepdim=True) - half_angles = torch.atan2(norms, quaternions[..., :1]) - angles = 2 * half_angles - eps = 1e-6 - small_angles = angles.abs() < eps - sin_half_angles_over_angles = torch.empty_like(angles) - sin_half_angles_over_angles[~small_angles] = ( - torch.sin(half_angles[~small_angles]) / angles[~small_angles] - ) - # for x small, sin(x/2) is about x/2 - (x/2)^3/6 - # so sin(x/2)/x is about 1/2 - (x*x)/48 - sin_half_angles_over_angles[small_angles] = ( - 0.5 - (angles[small_angles] * angles[small_angles]) / 48 - ) - return quaternions[..., 1:] / sin_half_angles_over_angles - - -def rotation_6d_to_matrix(d6: torch.Tensor) -> torch.Tensor: - """ - Converts 6D rotation representation by Zhou et al. [1] to rotation matrix - using Gram--Schmidt orthogonalisation per Section B of [1]. - Args: - d6: 6D rotation representation, of size (*, 6) - - Returns: - batch of rotation matrices of size (*, 3, 3) - - [1] Zhou, Y., Barnes, C., Lu, J., Yang, J., & Li, H. - On the Continuity of Rotation Representations in Neural Networks. - IEEE Conference on Computer Vision and Pattern Recognition, 2019. - Retrieved from http://arxiv.org/abs/1812.07035 - """ - - a1, a2 = d6[..., :3], d6[..., 3:] - b1 = F.normalize(a1, dim=-1) - b2 = a2 - (b1 * a2).sum(-1, keepdim=True) * b1 - b2 = F.normalize(b2, dim=-1) - b3 = torch.cross(b1, b2, dim=-1) - return torch.stack((b1, b2, b3), dim=-2) - - -def matrix_to_rotation_6d(matrix: torch.Tensor) -> torch.Tensor: - """ - Converts rotation matrices to 6D rotation representation by Zhou et al. [1] - by dropping the last row. Note that 6D representation is not unique. - Args: - matrix: batch of rotation matrices of size (*, 3, 3) - - Returns: - 6D rotation representation, of size (*, 6) - - [1] Zhou, Y., Barnes, C., Lu, J., Yang, J., & Li, H. - On the Continuity of Rotation Representations in Neural Networks. - IEEE Conference on Computer Vision and Pattern Recognition, 2019. - Retrieved from http://arxiv.org/abs/1812.07035 - """ - return matrix[..., :2, :].clone().reshape(*matrix.size()[:-2], 6) diff --git a/datasets/beat2.py b/datasets/beat2.py new file mode 100644 index 0000000000000000000000000000000000000000..0246223c8b9e157e031a57af589c3ec26a4f50a7 --- /dev/null +++ b/datasets/beat2.py @@ -0,0 +1,129 @@ +import json +import torch +from torch.utils import data +import numpy as np +import librosa +import os +import sys + +sys.path.append(os.path.join(os.path.dirname(__file__), "..")) +from emage_utils.motion_io import beat_format_load, MASK_DICT + +class BEAT2Dataset(data.Dataset): + def __init__(self, cfg, split): + vid_meta = [] + for data_meta_path in cfg.data.meta_paths: + vid_meta.extend(json.load(open(data_meta_path, "r"))) + self.vid_meta = [item for item in vid_meta if item.get("mode") == split] + self.mean = 0 + self.std = 1 + self.joint_mask = MASK_DICT[cfg.model.joint_mask] if cfg.model.joint_mask is not None else None + self.data_list = self.vid_meta + self.fps = cfg.model.pose_fps + self.audio_sr = cfg.model.audio_sr + + def __len__(self): + return len(self.data_list) + + @staticmethod + def normalize(motion, mean, std): + return (motion - mean) / (std + 1e-7) + + @staticmethod + def inverse_normalize(motion, mean, std): + return motion * std + mean + + def __getitem__(self, item): + data_item = self.data_list[item] + smplx_data = beat_format_load(data_item["motion_path"], mask=self.joint_mask) + sdx, edx = data_item["start_idx"], data_item["end_idx"] + motion = smplx_data["poses"][sdx:edx] + SMPLX_FPS = 30 + downsample_factor = SMPLX_FPS // self.fps + motion = motion[::downsample_factor] + motion = self.normalize(motion, self.mean, self.std) + + audio, _ = librosa.load(data_item["audio_path"], sr=self.audio_sr) + sdx_audio = sdx * int((1 / SMPLX_FPS) * self.audio_sr) + edx_audio = edx * int((1 / SMPLX_FPS) * self.audio_sr) + audio = audio[sdx_audio:edx_audio] + + motion_tensor = torch.from_numpy(motion).float() + audio_tensor = torch.from_numpy(audio).float() + + return dict( + motion=motion_tensor, + audio=audio_tensor, + ) + +class BEAT2DatasetEamge(BEAT2Dataset): + def __init__(self, cfg, split): + super().__init__(cfg, split) + + def __getitem__(self, item): + data_item = self.data_list[item] + smplx_data = beat_format_load(data_item["motion_path"], mask=None) + sdx, edx = data_item["start_idx"], data_item["end_idx"] + motion = smplx_data["poses"][sdx:edx] + expressions = smplx_data["expressions"][sdx:edx] + trans = smplx_data["trans"][sdx:edx] + SMPLX_FPS = 30 + downsample_factor = SMPLX_FPS // self.fps + motion = motion[::downsample_factor] + motion = self.normalize(motion, self.mean, self.std) + + audio, _ = librosa.load(data_item["audio_path"], sr=self.audio_sr) + sdx_audio = sdx * int((1 / SMPLX_FPS) * self.audio_sr) + edx_audio = edx * int((1 / SMPLX_FPS) * self.audio_sr) + audio = audio[sdx_audio:edx_audio] + + motion_tensor = torch.from_numpy(motion).float() + audio_tensor = torch.from_numpy(audio).float() + expressions_tesnor = torch.from_numpy(expressions).float() + trans_tensor = torch.from_numpy(trans).float() + + return dict( + motion=motion_tensor, + audio=audio_tensor, + expressions=expressions_tesnor, + trans=trans_tensor, + ) + + +class BEAT2DatasetEamgeFootContact(BEAT2Dataset): + def __init__(self, cfg, split): + super().__init__(cfg, split) + + def __getitem__(self, item): + data_item = self.data_list[item] + smplx_data = beat_format_load(data_item["motion_path"], mask=None) + sdx, edx = data_item["start_idx"], data_item["end_idx"] + motion = smplx_data["poses"][sdx:edx] + expressions = smplx_data["expressions"][sdx:edx] + trans = smplx_data["trans"][sdx:edx] + foot_contact = np.load(data_item["motion_path"].replace("smplxflame_30", "footcontact").replace(".npz", ".npy"))[sdx:edx] + + SMPLX_FPS = 30 + downsample_factor = SMPLX_FPS // self.fps + motion = motion[::downsample_factor] + motion = self.normalize(motion, self.mean, self.std) + + audio, _ = librosa.load(data_item["audio_path"], sr=self.audio_sr) + sdx_audio = sdx * int((1 / SMPLX_FPS) * self.audio_sr) + edx_audio = edx * int((1 / SMPLX_FPS) * self.audio_sr) + audio = audio[sdx_audio:edx_audio] + + motion_tensor = torch.from_numpy(motion).float() + audio_tensor = torch.from_numpy(audio).float() + expressions_tesnor = torch.from_numpy(expressions).float() + trans_tensor = torch.from_numpy(trans).float() + foot_contact_tensor = torch.from_numpy(foot_contact).float() + # print(trans_tensor.shape, foot_contact_tensor.shape) + + return dict( + motion=motion_tensor, + audio=audio_tensor, + expressions=expressions_tesnor, + trans=trans_tensor, + foot_contact=foot_contact_tensor, + ) diff --git a/datasets/beat2_disco.py b/datasets/beat2_disco.py new file mode 100644 index 0000000000000000000000000000000000000000..ef3d66db3d1d84dd0bc7f18c6480c7cf68b8dde0 --- /dev/null +++ b/datasets/beat2_disco.py @@ -0,0 +1,84 @@ +''' +content +0 1269 0.13 +1 1282 0.14 +2 1142 0.12 +3 974 0.1 +4 859 0.09 +5 519 0.05 +6 909 0.1 +7 819 0.09 +8 803 0.08 +9 909 0.1 + +rhythm +0 798 0.08 +1 1195 0.12 +2 1007 0.1 +3 831 0.08 +4 1153 0.12 +5 1228 0.12 +6 985 0.1 +7 852 0.09 +8 1139 0.12 +9 654 0.07 +''' + +import json +import torch +from torch.utils.data import Dataset, DataLoader +import numpy as np +import librosa +import os +import sys +sys.path.append(os.path.join(os.path.dirname(__file__), "..")) +from emage_utils.motion_io import beat_format_load, MASK_DICT + +class BEAT2DatasetDisco(Dataset): + def __init__(self, cfg, split): + vid_meta = [] + for data_meta_path in cfg.data.meta_paths: + vid_meta.extend(json.load(open(data_meta_path, "r"))) + self.vid_meta = [i for i in vid_meta if i.get("mode") == split] + self.mean = 0 + self.std = 1 + self.joint_mask = MASK_DICT[cfg.model.joint_mask] if cfg.model.joint_mask else None + self.data_list = self.vid_meta + self.fps = cfg.model.pose_fps + self.audio_sr = cfg.model.audio_sr + + def __len__(self): + return len(self.data_list) + + @staticmethod + def normalize(motion, mean, std): + return (motion - mean) / (std + 1e-7) + + @staticmethod + def inverse_normalize(motion, mean, std): + return motion * std + mean + + def __getitem__(self, item): + data_item = self.data_list[item] + smplx_data = beat_format_load(data_item["motion_path"], mask=self.joint_mask) + sdx, edx = data_item["start_idx"], data_item["end_idx"] + motion = smplx_data["poses"][sdx:edx] + SMPLX_FPS = 30 + downsample_factor = SMPLX_FPS // self.fps + motion = motion[::downsample_factor] + motion = self.normalize(motion, self.mean, self.std) + + audio, _ = librosa.load(data_item["audio_path"], sr=self.audio_sr) + sdx_audio = sdx * int((1 / SMPLX_FPS) * self.audio_sr) + edx_audio = edx * int((1 / SMPLX_FPS) * self.audio_sr) + audio = audio[sdx_audio:edx_audio] + + content_label = torch.tensor(data_item["content_label"]).long() + rhythm_label = torch.tensor(data_item["rhythm_label"]).long() + + return dict( + motion=torch.from_numpy(motion).float(), + audio=torch.from_numpy(audio).float(), + content_label=content_label, + rhythm_label=rhythm_label + ) \ No newline at end of file diff --git a/datasets/beat_cache/beat_smplx_en_emage_test/test/smplxflame_30_cache/data.mdb b/datasets/beat_cache/beat_smplx_en_emage_test/test/smplxflame_30_cache/data.mdb deleted file mode 100644 index 0d662d186ed0e4ef934911bfc5b984880db9a384..0000000000000000000000000000000000000000 Binary files a/datasets/beat_cache/beat_smplx_en_emage_test/test/smplxflame_30_cache/data.mdb and /dev/null differ diff --git a/datasets/beat_cache/beat_smplx_en_emage_test/test/smplxflame_30_cache/lock.mdb b/datasets/beat_cache/beat_smplx_en_emage_test/test/smplxflame_30_cache/lock.mdb deleted file mode 100644 index f1ae3c350a05efc4c1a6f29555ac22620def584c..0000000000000000000000000000000000000000 Binary files a/datasets/beat_cache/beat_smplx_en_emage_test/test/smplxflame_30_cache/lock.mdb and /dev/null differ diff --git a/datasets/clustering.py b/datasets/clustering.py new file mode 100644 index 0000000000000000000000000000000000000000..0fd6d270474ba097aea4aea40c28cd909ec390bb --- /dev/null +++ b/datasets/clustering.py @@ -0,0 +1,47 @@ +import os +import json +import numpy as np +from sklearn.cluster import KMeans +from tqdm import tqdm + +json_path = "./datasets/data_json/beat2_s20_l128_speaker2.json" +with open(json_path, 'r') as f: + data = json.load(f) + +arr = [] +for d in tqdm(data): + m = np.load(d["motion_path"].replace("/content/beat_v2.0.0/", "./BEAT2/"))["poses"][d["start_idx"]:d["end_idx"]] + arr.append(m) +arr = np.array(arr).reshape(len(arr), 128, 55, 3)[:, :, :21] + +X_content = arr.reshape(len(arr), -1) +content_km = KMeans(n_clusters=10, random_state=0).fit(X_content) +content_labels = content_km.labels_ +for i, d in tqdm(enumerate(data)): + d["content_label"] = int(content_labels[i]) + +unique_c, counts_c = np.unique(content_labels, return_counts=True) +for uc, cc in zip(unique_c, counts_c): + print(uc, cc, round(cc/len(content_labels), 2)) + +vel = np.diff(arr, axis=1) +mag = np.linalg.norm(vel, axis=-1) +beat = np.zeros_like(mag) +w = 5 +for i in tqdm(range(beat.shape[0])): + for j in range(beat.shape[2]): + for t in range(w, beat.shape[1]-w): + if mag[i, t, j] == np.min(mag[i, t-w:t+w+1, j]): + beat[i, t, j] = 1 +X_rhythm = beat.reshape(len(beat), -1) +rhythm_km = KMeans(n_clusters=10, random_state=0).fit(X_rhythm) +rhythm_labels = rhythm_km.labels_ +for i, d in enumerate(data): + d["rhythm_label"] = int(rhythm_labels[i]) + +unique_r, counts_r = np.unique(rhythm_labels, return_counts=True) +for ur, cr in zip(unique_r, counts_r): + print(ur, cr, round(cr/len(rhythm_labels), 2)) + +with open("./datasets/data_json/beat2_s20_l128_speaker2_disco.json", 'w') as f: + json.dump(data, f) diff --git a/datasets/data_json/beat2_s20_l128_speaker2.json b/datasets/data_json/beat2_s20_l128_speaker2.json new file mode 100644 index 0000000000000000000000000000000000000000..cb7c6732b51c3afaeac3445352b12825d16de577 --- /dev/null +++ b/datasets/data_json/beat2_s20_l128_speaker2.json @@ -0,0 +1,75882 @@ +[ + { + "video_id": "2_scott_0_100_100", + "motion_path": "./BEAT2/beat_english_v2.0.0/smplxflame_30/2_scott_0_100_100.npz", + "audio_path": "./BEAT2/beat_english_v2.0.0/wave16k/2_scott_0_100_100.wav", + "mode": "train", + "start_idx": 0, + "end_idx": 128 + }, + { + "video_id": "2_scott_0_100_100", + "motion_path": "./BEAT2/beat_english_v2.0.0/smplxflame_30/2_scott_0_100_100.npz", + "audio_path": "./BEAT2/beat_english_v2.0.0/wave16k/2_scott_0_100_100.wav", + "mode": "train", + "start_idx": 20, + "end_idx": 148 + }, + { + "video_id": "2_scott_0_100_100", + "motion_path": "./BEAT2/beat_english_v2.0.0/smplxflame_30/2_scott_0_100_100.npz", + "audio_path": "./BEAT2/beat_english_v2.0.0/wave16k/2_scott_0_100_100.wav", + "mode": "train", + "start_idx": 40, + "end_idx": 168 + }, + { + "video_id": "2_scott_0_100_100", + "motion_path": "./BEAT2/beat_english_v2.0.0/smplxflame_30/2_scott_0_100_100.npz", + "audio_path": "./BEAT2/beat_english_v2.0.0/wave16k/2_scott_0_100_100.wav", + "mode": "train", + "start_idx": 60, + "end_idx": 188 + }, + { + "video_id": "2_scott_0_100_100", + "motion_path": "./BEAT2/beat_english_v2.0.0/smplxflame_30/2_scott_0_100_100.npz", + "audio_path": "./BEAT2/beat_english_v2.0.0/wave16k/2_scott_0_100_100.wav", + "mode": "train", + "start_idx": 80, + "end_idx": 208 + }, + { + "video_id": "2_scott_0_100_100", + "motion_path": 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"2_scott_0_9_9", + "motion_path": "./BEAT2/beat_english_v2.0.0/smplxflame_30/2_scott_0_9_9.npz", + "audio_path": "./BEAT2/beat_english_v2.0.0/wave16k/2_scott_0_9_9.wav", + "mode": "val", + "start_idx": 1560, + "end_idx": 1624 + }, + { + "video_id": "2_scott_0_9_9", + "motion_path": "./BEAT2/beat_english_v2.0.0/smplxflame_30/2_scott_0_9_9.npz", + "audio_path": "./BEAT2/beat_english_v2.0.0/wave16k/2_scott_0_9_9.wav", + "mode": "val", + "start_idx": 1580, + "end_idx": 1644 + }, + { + "video_id": "2_scott_0_9_9", + "motion_path": "./BEAT2/beat_english_v2.0.0/smplxflame_30/2_scott_0_9_9.npz", + "audio_path": "./BEAT2/beat_english_v2.0.0/wave16k/2_scott_0_9_9.wav", + "mode": "val", + "start_idx": 1600, + "end_idx": 1664 + } +] \ No newline at end of file diff --git a/datasets/foot_contact.py b/datasets/foot_contact.py new file mode 100644 index 0000000000000000000000000000000000000000..36ea45fd901fbeb652dcb2115928b6dfab95174a --- /dev/null +++ b/datasets/foot_contact.py @@ -0,0 +1,82 @@ +import os +import numpy as np +import torch +import smplx +from tqdm import tqdm + +root_dir = './BEAT2/beat_english_v2.0.0/smplxflame_30' +output_dir = "./BEAT2/beat_english_v2.0.0/footcontact" +os.makedirs(output_dir, exist_ok=True) + +device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') +model = smplx_model = smplx.create( + "./emage_evaltools/smplx_models/", + model_type='smplx', + gender='NEUTRAL_2020', + use_face_contour=False, + num_betas=300, + num_expression_coeffs=100, + ext='npz', + use_pca=False, +).eval().to(device) + +max_length = 128 +for data_file in tqdm(os.listdir(root_dir)): + if not data_file.endswith(".npz"): + continue + data = np.load(os.path.join(root_dir, data_file), allow_pickle=True) + betas = data["betas"] + poses = data["poses"] + trans = data["trans"] + if "expressions" in data: + exps = data["expressions"] + else: + exps = np.zeros((poses.shape[0], 100)) + n, c = poses.shape + betas = betas.reshape(1, 300) + betas = np.tile(betas, (n, 1)) + betas = torch.from_numpy(betas).float().to(device) + poses = torch.from_numpy(poses.reshape(n, c)).float().to(device) + exps = torch.from_numpy(exps.reshape(n, 100)).float().to(device) + trans = torch.from_numpy(trans.reshape(n, 3)).float().to(device) + s, r = n//max_length, n%max_length + all_tensor = [] + for i in range(s): + with torch.no_grad(): + joints = model( + betas=betas[i*max_length:(i+1)*max_length], + transl=trans[i*max_length:(i+1)*max_length], + expression=exps[i*max_length:(i+1)*max_length], + jaw_pose=poses[i*max_length:(i+1)*max_length,66:69], + global_orient=poses[i*max_length:(i+1)*max_length,:3], + body_pose=poses[i*max_length:(i+1)*max_length,3:66], + left_hand_pose=poses[i*max_length:(i+1)*max_length,75:120], + right_hand_pose=poses[i*max_length:(i+1)*max_length,120:165], + leye_pose=poses[i*max_length:(i+1)*max_length,69:72], + reye_pose=poses[i*max_length:(i+1)*max_length,72:75], + return_joints=True + )['joints'][:, (7,8,10,11), :].reshape(max_length, 4, 3).cpu() + all_tensor.append(joints) + if r != 0: + with torch.no_grad(): + joints = model( + betas=betas[s*max_length:s*max_length+r], + transl=trans[s*max_length:s*max_length+r], + expression=exps[s*max_length:s*max_length+r], + jaw_pose=poses[s*max_length:s*max_length+r,66:69], + global_orient=poses[s*max_length:s*max_length+r,:3], + body_pose=poses[s*max_length:s*max_length+r,3:66], + left_hand_pose=poses[s*max_length:s*max_length+r,75:120], + right_hand_pose=poses[s*max_length:s*max_length+r,120:165], + leye_pose=poses[s*max_length:s*max_length+r,69:72], + reye_pose=poses[s*max_length:s*max_length+r,72:75], + return_joints=True + )['joints'][:, (7,8,10,11), :].reshape(r, 4, 3).cpu() + all_tensor.append(joints) + joints = torch.cat(all_tensor, axis=0) + feetv = torch.zeros(joints.shape[1], joints.shape[0]) + joints = joints.permute(1, 0, 2) + feetv[:, :-1] = (joints[:, 1:] - joints[:, :-1]).norm(dim=-1) + contacts = (feetv < 0.01).numpy().astype(float) + contacts = contacts.transpose(1, 0) + np.save(os.path.join(output_dir, data_file.replace(".npz", ".npy")), contacts) diff --git a/datasets/process_testdata.py b/datasets/process_testdata.py new file mode 100644 index 0000000000000000000000000000000000000000..f58df1d019616673108f4502d8bf52b392f901f5 --- /dev/null +++ b/datasets/process_testdata.py @@ -0,0 +1,55 @@ +import os +import json +import numpy as np +import pandas as pd +from tqdm import tqdm +import librosa + +# Example parameters +stride = 20 +motion_length = 64 +speaker_target = 2 +use_additional = False + +root_dir = './beat_english_v2.0.0/' +output_dir = "./datasets/data_json/" +os.makedirs(output_dir, exist_ok=True) +train_test_split_path = './beat_english_v2.0.0/train_test_split.csv' +df = pd.read_csv(train_test_split_path) + +filtered_df = df[(df['id'].str.split('_').str[0].astype(int) == speaker_target) & (df['type'] != 'additional')] +clips = [] +for idx, row_item in tqdm(filtered_df.iterrows()): + video_id = row_item['id'] + mode = row_item['type'] + # check exist + npz_path = os.path.join(root_dir, "smplxflame_30", video_id + ".npz") + wav_path = os.path.join(root_dir, "wave16k", video_id + ".wav") + + try: + motion_data = np.load(npz_path, allow_pickle=True) + except: + print(f"cant open {npz_path}") + + try: + wave_data, _ = librosa.load(wav_path, sr=None) + except: + print(f"cant open {wav_path}") + + motion = motion_data['poses'] + total_len = motion.shape[0] + + for i in range(0, total_len - motion_length, stride): + clip = { + "video_id": video_id, + "motion_path": npz_path, + "audio_path": wav_path, + "mode": mode, + "start_idx": i, + "end_idx": i + motion_length + } + clips.append(clip) + +output_json = os.path.join(output_dir, f"beat2_s{stride}_l{motion_length}_speaker{speaker_target}.json") +with open(output_json, 'w') as f: + json.dump(clips, f, indent=4) diff --git a/emage_trainer.py b/emage_trainer.py deleted file mode 100644 index 292bbf6f797ed3a2a8afa41d20cd5619f15bf98e..0000000000000000000000000000000000000000 --- a/emage_trainer.py +++ /dev/null @@ -1,907 +0,0 @@ -import train -import os -import time -import csv -import sys -import warnings -import random -import torch -import torch.nn as nn -import torch.nn.functional as F -from torch.utils.tensorboard import SummaryWriter -from torch.nn.parallel import DistributedDataParallel as DDP -import numpy as np -import time -import pprint -from loguru import logger -from utils import rotation_conversions as rc -import smplx -from utils import config, logger_tools, other_tools, metric, data_transfer -from dataloaders import data_tools -from optimizers.optim_factory import create_optimizer -from optimizers.scheduler_factory import create_scheduler -from optimizers.loss_factory import get_loss_func -from dataloaders.data_tools import joints_list -import librosa - -class CustomTrainer(train.BaseTrainer): - def __init__(self, args): - super().__init__(args) - self.args = args - self.joints = self.train_data.joints - self.ori_joint_list = joints_list[self.args.ori_joints] - self.tar_joint_list_face = joints_list["beat_smplx_face"] - self.tar_joint_list_upper = joints_list["beat_smplx_upper"] - self.tar_joint_list_hands = joints_list["beat_smplx_hands"] - self.tar_joint_list_lower = joints_list["beat_smplx_lower"] - - self.joint_mask_face = np.zeros(len(list(self.ori_joint_list.keys()))*3) - self.joints = 55 - for joint_name in self.tar_joint_list_face: - self.joint_mask_face[self.ori_joint_list[joint_name][1] - self.ori_joint_list[joint_name][0]:self.ori_joint_list[joint_name][1]] = 1 - self.joint_mask_upper = np.zeros(len(list(self.ori_joint_list.keys()))*3) - for joint_name in self.tar_joint_list_upper: - self.joint_mask_upper[self.ori_joint_list[joint_name][1] - self.ori_joint_list[joint_name][0]:self.ori_joint_list[joint_name][1]] = 1 - self.joint_mask_hands = np.zeros(len(list(self.ori_joint_list.keys()))*3) - for joint_name in self.tar_joint_list_hands: - self.joint_mask_hands[self.ori_joint_list[joint_name][1] - self.ori_joint_list[joint_name][0]:self.ori_joint_list[joint_name][1]] = 1 - self.joint_mask_lower = np.zeros(len(list(self.ori_joint_list.keys()))*3) - for joint_name in self.tar_joint_list_lower: - self.joint_mask_lower[self.ori_joint_list[joint_name][1] - self.ori_joint_list[joint_name][0]:self.ori_joint_list[joint_name][1]] = 1 - - self.tracker = other_tools.EpochTracker(["fid", "l1div", "bc", "rec", "trans", "vel", "transv", 'dis', 'gen', 'acc', 'transa', 'exp', 'lvd', 'mse', "cls", "rec_face", "latent", "cls_full", "cls_self", "cls_word", "latent_word","latent_self"], [False,True,True, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False,False,False,False]) - - vq_model_module = __import__(f"models.motion_representation", fromlist=["something"]) - self.args.vae_layer = 2 - self.args.vae_length = 256 - self.args.vae_test_dim = 106 - self.vq_model_face = getattr(vq_model_module, "VQVAEConvZero")(self.args).to(self.rank) - # print(self.vq_model_face) - other_tools.load_checkpoints(self.vq_model_face, self.args.data_path_1 + "pretrained_vq/last_790_face_v2.bin", args.e_name) - self.args.vae_test_dim = 78 - self.vq_model_upper = getattr(vq_model_module, "VQVAEConvZero")(self.args).to(self.rank) - other_tools.load_checkpoints(self.vq_model_upper, self.args.data_path_1 + "pretrained_vq/upper_vertex_1layer_710.bin", args.e_name) - self.args.vae_test_dim = 180 - self.vq_model_hands = getattr(vq_model_module, "VQVAEConvZero")(self.args).to(self.rank) - other_tools.load_checkpoints(self.vq_model_hands, self.args.data_path_1 + "pretrained_vq/hands_vertex_1layer_710.bin", args.e_name) - self.args.vae_test_dim = 61 - self.args.vae_layer = 4 - self.vq_model_lower = getattr(vq_model_module, "VQVAEConvZero")(self.args).to(self.rank) - other_tools.load_checkpoints(self.vq_model_lower, self.args.data_path_1 + "pretrained_vq/lower_foot_600.bin", args.e_name) - self.args.vae_test_dim = 61 - self.args.vae_layer = 4 - self.global_motion = getattr(vq_model_module, "VAEConvZero")(self.args).to(self.rank) - other_tools.load_checkpoints(self.global_motion, self.args.data_path_1 + "pretrained_vq/last_1700_foot.bin", args.e_name) - self.args.vae_test_dim = 330 - self.args.vae_layer = 4 - self.args.vae_length = 240 - - self.vq_model_face.eval() - self.vq_model_upper.eval() - self.vq_model_hands.eval() - self.vq_model_lower.eval() - self.global_motion.eval() - - self.cls_loss = nn.NLLLoss().to(self.rank) - self.reclatent_loss = nn.MSELoss().to(self.rank) - self.vel_loss = torch.nn.L1Loss(reduction='mean').to(self.rank) - self.rec_loss = get_loss_func("GeodesicLoss").to(self.rank) - self.log_softmax = nn.LogSoftmax(dim=2).to(self.rank) - - - def inverse_selection(self, filtered_t, selection_array, n): - original_shape_t = np.zeros((n, selection_array.size)) - selected_indices = np.where(selection_array == 1)[0] - for i in range(n): - original_shape_t[i, selected_indices] = filtered_t[i] - return original_shape_t - - def inverse_selection_tensor(self, filtered_t, selection_array, n): - selection_array = torch.from_numpy(selection_array).cuda() - original_shape_t = torch.zeros((n, 165)).cuda() - selected_indices = torch.where(selection_array == 1)[0] - for i in range(n): - original_shape_t[i, selected_indices] = filtered_t[i] - return original_shape_t - - def _load_data(self, dict_data): - tar_pose_raw = dict_data["pose"] - tar_pose = tar_pose_raw[:, :, :165].to(self.rank) - tar_contact = tar_pose_raw[:, :, 165:169].to(self.rank) - tar_trans = dict_data["trans"].to(self.rank) - tar_exps = dict_data["facial"].to(self.rank) - in_audio = dict_data["audio"].to(self.rank) - in_word = dict_data["word"].to(self.rank) - tar_beta = dict_data["beta"].to(self.rank) - tar_id = dict_data["id"].to(self.rank).long() - bs, n, j = tar_pose.shape[0], tar_pose.shape[1], self.joints - - tar_pose_jaw = tar_pose[:, :, 66:69] - tar_pose_jaw = rc.axis_angle_to_matrix(tar_pose_jaw.reshape(bs, n, 1, 3)) - tar_pose_jaw = rc.matrix_to_rotation_6d(tar_pose_jaw).reshape(bs, n, 1*6) - tar_pose_face = torch.cat([tar_pose_jaw, tar_exps], dim=2) - - tar_pose_hands = tar_pose[:, :, 25*3:55*3] - tar_pose_hands = rc.axis_angle_to_matrix(tar_pose_hands.reshape(bs, n, 30, 3)) - tar_pose_hands = rc.matrix_to_rotation_6d(tar_pose_hands).reshape(bs, n, 30*6) - - tar_pose_upper = tar_pose[:, :, self.joint_mask_upper.astype(bool)] - tar_pose_upper = rc.axis_angle_to_matrix(tar_pose_upper.reshape(bs, n, 13, 3)) - tar_pose_upper = rc.matrix_to_rotation_6d(tar_pose_upper).reshape(bs, n, 13*6) - - tar_pose_leg = tar_pose[:, :, self.joint_mask_lower.astype(bool)] - tar_pose_leg = rc.axis_angle_to_matrix(tar_pose_leg.reshape(bs, n, 9, 3)) - tar_pose_leg = rc.matrix_to_rotation_6d(tar_pose_leg).reshape(bs, n, 9*6) - tar_pose_lower = torch.cat([tar_pose_leg, tar_trans, tar_contact], dim=2) - - # tar_pose = rc.axis_angle_to_matrix(tar_pose.reshape(bs, n, j, 3)) - # tar_pose = rc.matrix_to_rotation_6d(tar_pose).reshape(bs, n, j*6) - tar4dis = torch.cat([tar_pose_jaw, tar_pose_upper, tar_pose_hands, tar_pose_leg], dim=2) - - tar_index_value_face_top = self.vq_model_face.map2index(tar_pose_face) # bs*n/4 - tar_index_value_upper_top = self.vq_model_upper.map2index(tar_pose_upper) # bs*n/4 - tar_index_value_hands_top = self.vq_model_hands.map2index(tar_pose_hands) # bs*n/4 - tar_index_value_lower_top = self.vq_model_lower.map2index(tar_pose_lower) # bs*n/4 - - latent_face_top = self.vq_model_face.map2latent(tar_pose_face) # bs*n/4 - latent_upper_top = self.vq_model_upper.map2latent(tar_pose_upper) # bs*n/4 - latent_hands_top = self.vq_model_hands.map2latent(tar_pose_hands) # bs*n/4 - latent_lower_top = self.vq_model_lower.map2latent(tar_pose_lower) # bs*n/4 - - latent_in = torch.cat([latent_upper_top, latent_hands_top, latent_lower_top], dim=2) - - index_in = torch.stack([tar_index_value_upper_top, tar_index_value_hands_top, tar_index_value_lower_top], dim=-1).long() - - tar_pose_6d = rc.axis_angle_to_matrix(tar_pose.reshape(bs, n, 55, 3)) - tar_pose_6d = rc.matrix_to_rotation_6d(tar_pose_6d).reshape(bs, n, 55*6) - latent_all = torch.cat([tar_pose_6d, tar_trans, tar_contact], dim=-1) - # print(tar_index_value_upper_top.shape, index_in.shape) - return { - "tar_pose_jaw": tar_pose_jaw, - "tar_pose_face": tar_pose_face, - "tar_pose_upper": tar_pose_upper, - "tar_pose_lower": tar_pose_lower, - "tar_pose_hands": tar_pose_hands, - 'tar_pose_leg': tar_pose_leg, - "in_audio": in_audio, - "in_word": in_word, - "tar_trans": tar_trans, - "tar_exps": tar_exps, - "tar_beta": tar_beta, - "tar_pose": tar_pose, - "tar4dis": tar4dis, - "tar_index_value_face_top": tar_index_value_face_top, - "tar_index_value_upper_top": tar_index_value_upper_top, - "tar_index_value_hands_top": tar_index_value_hands_top, - "tar_index_value_lower_top": tar_index_value_lower_top, - "latent_face_top": latent_face_top, - "latent_upper_top": latent_upper_top, - "latent_hands_top": latent_hands_top, - "latent_lower_top": latent_lower_top, - "latent_in": latent_in, - "index_in": index_in, - "tar_id": tar_id, - "latent_all": latent_all, - "tar_pose_6d": tar_pose_6d, - "tar_contact": tar_contact, - } - - def _g_training(self, loaded_data, use_adv, mode="train", epoch=0): - bs, n, j = loaded_data["tar_pose"].shape[0], loaded_data["tar_pose"].shape[1], self.joints - # ------ full generatation task ------ # - mask_val = torch.ones(bs, n, self.args.pose_dims+3+4).float().cuda() - mask_val[:, :self.args.pre_frames, :] = 0.0 - - net_out_val = self.model( - loaded_data['in_audio'], loaded_data['in_word'], mask=mask_val, - in_id = loaded_data['tar_id'], in_motion = loaded_data['latent_all'], - use_attentions = True) - g_loss_final = 0 - loss_latent_face = self.reclatent_loss(net_out_val["rec_face"], loaded_data["latent_face_top"]) - loss_latent_lower = self.reclatent_loss(net_out_val["rec_lower"], loaded_data["latent_lower_top"]) - loss_latent_hands = self.reclatent_loss(net_out_val["rec_hands"], loaded_data["latent_hands_top"]) - loss_latent_upper = self.reclatent_loss(net_out_val["rec_upper"], loaded_data["latent_upper_top"]) - loss_latent = self.args.lf*loss_latent_face + self.args.ll*loss_latent_lower + self.args.lh*loss_latent_hands + self.args.lu*loss_latent_upper - self.tracker.update_meter("latent", "train", loss_latent.item()) - g_loss_final += loss_latent - - rec_index_face_val = self.log_softmax(net_out_val["cls_face"]).reshape(-1, self.args.vae_codebook_size) - rec_index_upper_val = self.log_softmax(net_out_val["cls_upper"]).reshape(-1, self.args.vae_codebook_size) - rec_index_lower_val = self.log_softmax(net_out_val["cls_lower"]).reshape(-1, self.args.vae_codebook_size) - rec_index_hands_val = self.log_softmax(net_out_val["cls_hands"]).reshape(-1, self.args.vae_codebook_size) - tar_index_value_face_top = loaded_data["tar_index_value_face_top"].reshape(-1) - tar_index_value_upper_top = loaded_data["tar_index_value_upper_top"].reshape(-1) - tar_index_value_lower_top = loaded_data["tar_index_value_lower_top"].reshape(-1) - tar_index_value_hands_top = loaded_data["tar_index_value_hands_top"].reshape(-1) - loss_cls = self.args.cf*self.cls_loss(rec_index_face_val, tar_index_value_face_top)\ - + self.args.cu*self.cls_loss(rec_index_upper_val, tar_index_value_upper_top)\ - + self.args.cl*self.cls_loss(rec_index_lower_val, tar_index_value_lower_top)\ - + self.args.ch*self.cls_loss(rec_index_hands_val, tar_index_value_hands_top) - self.tracker.update_meter("cls_full", "train", loss_cls.item()) - g_loss_final += loss_cls - - if mode == 'train': - # # ------ masked gesture moderling------ # - mask_ratio = (epoch / self.args.epochs) * 0.95 + 0.05 - mask = torch.rand(bs, n, self.args.pose_dims+3+4) < mask_ratio - mask = mask.float().cuda() - net_out_self = self.model( - loaded_data['in_audio'], loaded_data['in_word'], mask=mask, - in_id = loaded_data['tar_id'], in_motion = loaded_data['latent_all'], - use_attentions = True, use_word=False) - - loss_latent_face_self = self.reclatent_loss(net_out_self["rec_face"], loaded_data["latent_face_top"]) - loss_latent_lower_self = self.reclatent_loss(net_out_self["rec_lower"], loaded_data["latent_lower_top"]) - loss_latent_hands_self = self.reclatent_loss(net_out_self["rec_hands"], loaded_data["latent_hands_top"]) - loss_latent_upper_self = self.reclatent_loss(net_out_self["rec_upper"], loaded_data["latent_upper_top"]) - loss_latent_self = self.args.lf*loss_latent_face_self + self.args.ll*loss_latent_lower_self + self.args.lh*loss_latent_hands_self + self.args.lu*loss_latent_upper_self - self.tracker.update_meter("latent_self", "train", loss_latent_self.item()) - g_loss_final += loss_latent_self - rec_index_face_self = self.log_softmax(net_out_self["cls_face"]).reshape(-1, self.args.vae_codebook_size) - rec_index_upper_self = self.log_softmax(net_out_self["cls_upper"]).reshape(-1, self.args.vae_codebook_size) - rec_index_lower_self = self.log_softmax(net_out_self["cls_lower"]).reshape(-1, self.args.vae_codebook_size) - rec_index_hands_self = self.log_softmax(net_out_self["cls_hands"]).reshape(-1, self.args.vae_codebook_size) - index_loss_top_self = self.cls_loss(rec_index_face_self, tar_index_value_face_top) + self.cls_loss(rec_index_upper_self, tar_index_value_upper_top) + self.cls_loss(rec_index_lower_self, tar_index_value_lower_top) + self.cls_loss(rec_index_hands_self, tar_index_value_hands_top) - self.tracker.update_meter("cls_self", "train", index_loss_top_self.item()) - g_loss_final += index_loss_top_self - - # ------ masked audio gesture moderling ------ # - net_out_word = self.model( - loaded_data['in_audio'], loaded_data['in_word'], mask=mask, - in_id = loaded_data['tar_id'], in_motion = loaded_data['latent_all'], - use_attentions = True, use_word=True) - - loss_latent_face_word = self.reclatent_loss(net_out_word["rec_face"], loaded_data["latent_face_top"]) - loss_latent_lower_word = self.reclatent_loss(net_out_word["rec_lower"], loaded_data["latent_lower_top"]) - loss_latent_hands_word = self.reclatent_loss(net_out_word["rec_hands"], loaded_data["latent_hands_top"]) - loss_latent_upper_word = self.reclatent_loss(net_out_word["rec_upper"], loaded_data["latent_upper_top"]) - loss_latent_word = self.args.lf*loss_latent_face_word + self.args.ll*loss_latent_lower_word + self.args.lh*loss_latent_hands_word + self.args.lu*loss_latent_upper_word - self.tracker.update_meter("latent_word", "train", loss_latent_word.item()) - g_loss_final += loss_latent_word - - rec_index_face_word = self.log_softmax(net_out_word["cls_face"]).reshape(-1, self.args.vae_codebook_size) - rec_index_upper_word = self.log_softmax(net_out_word["cls_upper"]).reshape(-1, self.args.vae_codebook_size) - rec_index_lower_word = self.log_softmax(net_out_word["cls_lower"]).reshape(-1, self.args.vae_codebook_size) - rec_index_hands_word = self.log_softmax(net_out_word["cls_hands"]).reshape(-1, self.args.vae_codebook_size) - index_loss_top_word = self.cls_loss(rec_index_face_word, tar_index_value_face_top) + self.cls_loss(rec_index_upper_word, tar_index_value_upper_top) + self.cls_loss(rec_index_lower_word, tar_index_value_lower_top) + self.cls_loss(rec_index_hands_word, tar_index_value_hands_top) - self.tracker.update_meter("cls_word", "train", index_loss_top_word.item()) - g_loss_final += index_loss_top_word - - if mode != 'train': - if self.args.cu != 0: - _, rec_index_upper = torch.max(rec_index_upper_val.reshape(-1, self.args.pose_length, self.args.vae_codebook_size), dim=2) - rec_upper = self.vq_model_upper.decode(rec_index_upper) - else: - _, rec_index_upper, _, _ = self.vq_model_upper.quantizer(net_out_val["rec_upper"]) - rec_upper = self.vq_model_upper.decoder(rec_index_upper) - if self.args.cl != 0: - _, rec_index_lower = torch.max(rec_index_lower_val.reshape(-1, self.args.pose_length, self.args.vae_codebook_size), dim=2) - rec_lower = self.vq_model_lower.decode(rec_index_lower) - else: - _, rec_index_lower, _, _ = self.vq_model_lower.quantizer(net_out_val["rec_lower"]) - rec_lower = self.vq_model_lower.decoder(rec_index_lower) - if self.args.ch != 0: - _, rec_index_hands = torch.max(rec_index_hands_val.reshape(-1, self.args.pose_length, self.args.vae_codebook_size), dim=2) - rec_hands = self.vq_model_hands.decode(rec_index_hands) - else: - _, rec_index_hands, _, _ = self.vq_model_hands.quantizer(net_out_val["rec_hands"]) - rec_hands = self.vq_model_hands.decoder(rec_index_hands) - if self.args.cf != 0: - _, rec_index_face = torch.max(rec_index_face_val.reshape(-1, self.args.pose_length, self.args.vae_codebook_size), dim=2) - rec_face = self.vq_model_face.decode(rec_index_face) - else: - _, rec_index_face, _, _ = self.vq_model_face.quantizer(net_out_val["rec_face"]) - rec_face = self.vq_model_face.decoder(rec_index_face) - - rec_pose_jaw = rec_face[:, :, :6] - rec_pose_legs = rec_lower[:, :, :54] - - rec_pose_upper = rec_upper.reshape(bs, n, 13, 6) - rec_pose_upper = rc.rotation_6d_to_matrix(rec_pose_upper)# - rec_pose_upper = rc.matrix_to_axis_angle(rec_pose_upper).reshape(bs*n, 13*3) - rec_pose_upper_recover = self.inverse_selection_tensor(rec_pose_upper, self.joint_mask_upper, bs*n) - rec_pose_lower = rec_pose_legs.reshape(bs, n, 9, 6) - rec_pose_lower = rc.rotation_6d_to_matrix(rec_pose_lower) - rec_pose_lower = rc.matrix_to_axis_angle(rec_pose_lower).reshape(bs*n, 9*3) - rec_pose_lower_recover = self.inverse_selection_tensor(rec_pose_lower, self.joint_mask_lower, bs*n) - rec_pose_hands = rec_hands.reshape(bs, n, 30, 6) - rec_pose_hands = rc.rotation_6d_to_matrix(rec_pose_hands) - rec_pose_hands = rc.matrix_to_axis_angle(rec_pose_hands).reshape(bs*n, 30*3) - rec_pose_hands_recover = self.inverse_selection_tensor(rec_pose_hands, self.joint_mask_hands, bs*n) - rec_pose_jaw = rec_pose_jaw.reshape(bs*n, 6) - rec_pose_jaw = rc.rotation_6d_to_matrix(rec_pose_jaw) - rec_pose_jaw = rc.matrix_to_axis_angle(rec_pose_jaw).reshape(bs*n, 1*3) - rec_pose = rec_pose_upper_recover + rec_pose_lower_recover + rec_pose_hands_recover - rec_pose[:, 66:69] = rec_pose_jaw - # print(rec_pose.shape, tar_pose.shape) - - # tar_trans = loaded_data["tar_trans"] - # rec_trans_v_s = rec_lower[:, :, 54:57] - # rec_x_trans = other_tools.velocity2position(rec_trans_v_s[:, :, 0:1], 1/self.args.pose_fps, tar_trans[:, 0, 0:1]) - # rec_z_trans = other_tools.velocity2position(rec_trans_v_s[:, :, 2:3], 1/self.args.pose_fps, tar_trans[:, 0, 2:3]) - # rec_y_trans = rec_trans_v_s[:,:,1:2] - # rec_trans = torch.cat([rec_x_trans, rec_y_trans, rec_z_trans], dim=-1) - # tar_pose = loaded_data["tar_pose"] - # tar_pose = rc.axis_angle_to_matrix(tar_pose.reshape(bs, n, j, 3)) - # tar_pose = rc.matrix_to_rotation_6d(tar_pose).reshape(bs, n, j*6) - rec_pose = rc.axis_angle_to_matrix(rec_pose.reshape(bs, n, j, 3)) - rec_pose = rc.matrix_to_rotation_6d(rec_pose).reshape(bs, n, j*6) - - - if mode == 'train': - return g_loss_final - elif mode == 'val': - return { - 'rec_pose': rec_pose, - # rec_trans': rec_pose_trans, - 'tar_pose': loaded_data["tar_pose_6d"], - } - else: - return { - 'rec_pose': rec_pose, - # 'rec_trans': rec_trans, - 'tar_pose': loaded_data["tar_pose"], - 'tar_exps': loaded_data["tar_exps"], - 'tar_beta': loaded_data["tar_beta"], - 'tar_trans': loaded_data["tar_trans"], - # 'rec_exps': rec_exps, - } - - - def _g_test(self, loaded_data): - mode = 'test' - bs, n, j = loaded_data["tar_pose"].shape[0], loaded_data["tar_pose"].shape[1], self.joints - tar_pose = loaded_data["tar_pose"] - tar_beta = loaded_data["tar_beta"] - in_word = loaded_data["in_word"] - tar_exps = loaded_data["tar_exps"] - tar_contact = loaded_data["tar_contact"] - in_audio = loaded_data["in_audio"] - tar_trans = loaded_data["tar_trans"] - - remain = n%8 - if remain != 0: - tar_pose = tar_pose[:, :-remain, :] - tar_beta = tar_beta[:, :-remain, :] - tar_trans = tar_trans[:, :-remain, :] - in_word = in_word[:, :-remain] - tar_exps = tar_exps[:, :-remain, :] - tar_contact = tar_contact[:, :-remain, :] - n = n - remain - - tar_pose_jaw = tar_pose[:, :, 66:69] - tar_pose_jaw = rc.axis_angle_to_matrix(tar_pose_jaw.reshape(bs, n, 1, 3)) - tar_pose_jaw = rc.matrix_to_rotation_6d(tar_pose_jaw).reshape(bs, n, 1*6) - tar_pose_face = torch.cat([tar_pose_jaw, tar_exps], dim=2) - - tar_pose_hands = tar_pose[:, :, 25*3:55*3] - tar_pose_hands = rc.axis_angle_to_matrix(tar_pose_hands.reshape(bs, n, 30, 3)) - tar_pose_hands = rc.matrix_to_rotation_6d(tar_pose_hands).reshape(bs, n, 30*6) - - tar_pose_upper = tar_pose[:, :, self.joint_mask_upper.astype(bool)] - tar_pose_upper = rc.axis_angle_to_matrix(tar_pose_upper.reshape(bs, n, 13, 3)) - tar_pose_upper = rc.matrix_to_rotation_6d(tar_pose_upper).reshape(bs, n, 13*6) - - tar_pose_leg = tar_pose[:, :, self.joint_mask_lower.astype(bool)] - tar_pose_leg = rc.axis_angle_to_matrix(tar_pose_leg.reshape(bs, n, 9, 3)) - tar_pose_leg = rc.matrix_to_rotation_6d(tar_pose_leg).reshape(bs, n, 9*6) - tar_pose_lower = torch.cat([tar_pose_leg, tar_trans, tar_contact], dim=2) - - tar_pose_6d = rc.axis_angle_to_matrix(tar_pose.reshape(bs, n, 55, 3)) - tar_pose_6d = rc.matrix_to_rotation_6d(tar_pose_6d).reshape(bs, n, 55*6) - latent_all = torch.cat([tar_pose_6d, tar_trans, tar_contact], dim=-1) - - rec_index_all_face = [] - rec_index_all_upper = [] - rec_index_all_lower = [] - rec_index_all_hands = [] - # rec_index_all_face_bot = [] - # rec_index_all_upper_bot = [] - # rec_index_all_lower_bot = [] - # rec_index_all_hands_bot = [] - - roundt = (n - self.args.pre_frames) // (self.args.pose_length - self.args.pre_frames) - remain = (n - self.args.pre_frames) % (self.args.pose_length - self.args.pre_frames) - round_l = self.args.pose_length - self.args.pre_frames - - # pad latent_all_9 to the same length with latent_all - # if n - latent_all_9.shape[1] >= 0: - # latent_all = torch.cat([latent_all_9, torch.zeros(bs, n - latent_all_9.shape[1], latent_all_9.shape[2]).cuda()], dim=1) - # else: - # latent_all = latent_all_9[:, :n, :] - - for i in range(0, roundt): - in_word_tmp = in_word[:, i*(round_l):(i+1)*(round_l)+self.args.pre_frames] - # audio fps is 16000 and pose fps is 30 - in_audio_tmp = in_audio[:, i*(16000//30*round_l):(i+1)*(16000//30*round_l)+16000//30*self.args.pre_frames] - in_id_tmp = loaded_data['tar_id'][:, i*(round_l):(i+1)*(round_l)+self.args.pre_frames] - mask_val = torch.ones(bs, self.args.pose_length, self.args.pose_dims+3+4).float().cuda() - mask_val[:, :self.args.pre_frames, :] = 0.0 - if i == 0: - latent_all_tmp = latent_all[:, i*(round_l):(i+1)*(round_l)+self.args.pre_frames, :] - else: - latent_all_tmp = latent_all[:, i*(round_l):(i+1)*(round_l)+self.args.pre_frames, :] - # print(latent_all_tmp.shape, latent_last.shape) - latent_all_tmp[:, :self.args.pre_frames, :] = latent_last[:, -self.args.pre_frames:, :] - - net_out_val = self.model( - in_audio = in_audio_tmp, - in_word=in_word_tmp, - mask=mask_val, - in_motion = latent_all_tmp, - in_id = in_id_tmp, - use_attentions=True,) - - if self.args.cu != 0: - rec_index_upper = self.log_softmax(net_out_val["cls_upper"]).reshape(-1, self.args.vae_codebook_size) - _, rec_index_upper = torch.max(rec_index_upper.reshape(-1, self.args.pose_length, self.args.vae_codebook_size), dim=2) - #rec_upper = self.vq_model_upper.decode(rec_index_upper) - else: - _, rec_index_upper, _, _ = self.vq_model_upper.quantizer(net_out_val["rec_upper"]) - #rec_upper = self.vq_model_upper.decoder(rec_index_upper) - if self.args.cl != 0: - rec_index_lower = self.log_softmax(net_out_val["cls_lower"]).reshape(-1, self.args.vae_codebook_size) - _, rec_index_lower = torch.max(rec_index_lower.reshape(-1, self.args.pose_length, self.args.vae_codebook_size), dim=2) - #rec_lower = self.vq_model_lower.decode(rec_index_lower) - else: - _, rec_index_lower, _, _ = self.vq_model_lower.quantizer(net_out_val["rec_lower"]) - #rec_lower = self.vq_model_lower.decoder(rec_index_lower) - if self.args.ch != 0: - rec_index_hands = self.log_softmax(net_out_val["cls_hands"]).reshape(-1, self.args.vae_codebook_size) - _, rec_index_hands = torch.max(rec_index_hands.reshape(-1, self.args.pose_length, self.args.vae_codebook_size), dim=2) - #rec_hands = self.vq_model_hands.decode(rec_index_hands) - else: - _, rec_index_hands, _, _ = self.vq_model_hands.quantizer(net_out_val["rec_hands"]) - #rec_hands = self.vq_model_hands.decoder(rec_index_hands) - if self.args.cf != 0: - rec_index_face = self.log_softmax(net_out_val["cls_face"]).reshape(-1, self.args.vae_codebook_size) - _, rec_index_face = torch.max(rec_index_face.reshape(-1, self.args.pose_length, self.args.vae_codebook_size), dim=2) - #rec_face = self.vq_model_face.decoder(rec_index_face) - else: - _, rec_index_face, _, _ = self.vq_model_face.quantizer(net_out_val["rec_face"]) - #rec_face = self.vq_model_face.decoder(rec_index_face) - - if i == 0: - rec_index_all_face.append(rec_index_face) - rec_index_all_upper.append(rec_index_upper) - rec_index_all_lower.append(rec_index_lower) - rec_index_all_hands.append(rec_index_hands) - else: - rec_index_all_face.append(rec_index_face[:, self.args.pre_frames:]) - rec_index_all_upper.append(rec_index_upper[:, self.args.pre_frames:]) - rec_index_all_lower.append(rec_index_lower[:, self.args.pre_frames:]) - rec_index_all_hands.append(rec_index_hands[:, self.args.pre_frames:]) - - if self.args.cu != 0: - rec_upper_last = self.vq_model_upper.decode(rec_index_upper) - else: - rec_upper_last = self.vq_model_upper.decoder(rec_index_upper) - if self.args.cl != 0: - rec_lower_last = self.vq_model_lower.decode(rec_index_lower) - else: - rec_lower_last = self.vq_model_lower.decoder(rec_index_lower) - if self.args.ch != 0: - rec_hands_last = self.vq_model_hands.decode(rec_index_hands) - else: - rec_hands_last = self.vq_model_hands.decoder(rec_index_hands) - # if self.args.cf != 0: - # rec_face_last = self.vq_model_face.decode(rec_index_face) - # else: - # rec_face_last = self.vq_model_face.decoder(rec_index_face) - - rec_pose_legs = rec_lower_last[:, :, :54] - bs, n = rec_pose_legs.shape[0], rec_pose_legs.shape[1] - rec_pose_upper = rec_upper_last.reshape(bs, n, 13, 6) - rec_pose_upper = rc.rotation_6d_to_matrix(rec_pose_upper)# - rec_pose_upper = rc.matrix_to_axis_angle(rec_pose_upper).reshape(bs*n, 13*3) - rec_pose_upper_recover = self.inverse_selection_tensor(rec_pose_upper, self.joint_mask_upper, bs*n) - rec_pose_lower = rec_pose_legs.reshape(bs, n, 9, 6) - rec_pose_lower = rc.rotation_6d_to_matrix(rec_pose_lower) - rec_pose_lower = rc.matrix_to_axis_angle(rec_pose_lower).reshape(bs*n, 9*3) - rec_pose_lower_recover = self.inverse_selection_tensor(rec_pose_lower, self.joint_mask_lower, bs*n) - rec_pose_hands = rec_hands_last.reshape(bs, n, 30, 6) - rec_pose_hands = rc.rotation_6d_to_matrix(rec_pose_hands) - rec_pose_hands = rc.matrix_to_axis_angle(rec_pose_hands).reshape(bs*n, 30*3) - rec_pose_hands_recover = self.inverse_selection_tensor(rec_pose_hands, self.joint_mask_hands, bs*n) - rec_pose = rec_pose_upper_recover + rec_pose_lower_recover + rec_pose_hands_recover - rec_pose = rc.axis_angle_to_matrix(rec_pose.reshape(bs, n, j, 3)) - rec_pose = rc.matrix_to_rotation_6d(rec_pose).reshape(bs, n, j*6) - rec_trans_v_s = rec_lower_last[:, :, 54:57] - rec_x_trans = other_tools.velocity2position(rec_trans_v_s[:, :, 0:1], 1/self.args.pose_fps, tar_trans[:, 0, 0:1]) - rec_z_trans = other_tools.velocity2position(rec_trans_v_s[:, :, 2:3], 1/self.args.pose_fps, tar_trans[:, 0, 2:3]) - rec_y_trans = rec_trans_v_s[:,:,1:2] - rec_trans = torch.cat([rec_x_trans, rec_y_trans, rec_z_trans], dim=-1) - latent_last = torch.cat([rec_pose, rec_trans, rec_lower_last[:, :, 57:61]], dim=-1) - - rec_index_face = torch.cat(rec_index_all_face, dim=1) - rec_index_upper = torch.cat(rec_index_all_upper, dim=1) - rec_index_lower = torch.cat(rec_index_all_lower, dim=1) - rec_index_hands = torch.cat(rec_index_all_hands, dim=1) - if self.args.cu != 0: - rec_upper = self.vq_model_upper.decode(rec_index_upper) - else: - rec_upper = self.vq_model_upper.decoder(rec_index_upper) - if self.args.cl != 0: - rec_lower = self.vq_model_lower.decode(rec_index_lower) - else: - rec_lower = self.vq_model_lower.decoder(rec_index_lower) - if self.args.ch != 0: - rec_hands = self.vq_model_hands.decode(rec_index_hands) - else: - rec_hands = self.vq_model_hands.decoder(rec_index_hands) - if self.args.cf != 0: - rec_face = self.vq_model_face.decode(rec_index_face) - else: - rec_face = self.vq_model_face.decoder(rec_index_face) - - rec_exps = rec_face[:, :, 6:] - rec_pose_jaw = rec_face[:, :, :6] - rec_pose_legs = rec_lower[:, :, :54] - bs, n = rec_pose_jaw.shape[0], rec_pose_jaw.shape[1] - rec_pose_upper = rec_upper.reshape(bs, n, 13, 6) - rec_pose_upper = rc.rotation_6d_to_matrix(rec_pose_upper)# - rec_pose_upper = rc.matrix_to_axis_angle(rec_pose_upper).reshape(bs*n, 13*3) - rec_pose_upper_recover = self.inverse_selection_tensor(rec_pose_upper, self.joint_mask_upper, bs*n) - rec_pose_lower = rec_pose_legs.reshape(bs, n, 9, 6) - rec_pose_lower = rc.rotation_6d_to_matrix(rec_pose_lower) - rec_lower2global = rc.matrix_to_rotation_6d(rec_pose_lower.clone()).reshape(bs, n, 9*6) - rec_pose_lower = rc.matrix_to_axis_angle(rec_pose_lower).reshape(bs*n, 9*3) - rec_pose_lower_recover = self.inverse_selection_tensor(rec_pose_lower, self.joint_mask_lower, bs*n) - rec_pose_hands = rec_hands.reshape(bs, n, 30, 6) - rec_pose_hands = rc.rotation_6d_to_matrix(rec_pose_hands) - rec_pose_hands = rc.matrix_to_axis_angle(rec_pose_hands).reshape(bs*n, 30*3) - rec_pose_hands_recover = self.inverse_selection_tensor(rec_pose_hands, self.joint_mask_hands, bs*n) - rec_pose_jaw = rec_pose_jaw.reshape(bs*n, 6) - rec_pose_jaw = rc.rotation_6d_to_matrix(rec_pose_jaw) - rec_pose_jaw = rc.matrix_to_axis_angle(rec_pose_jaw).reshape(bs*n, 1*3) - rec_pose = rec_pose_upper_recover + rec_pose_lower_recover + rec_pose_hands_recover - rec_pose[:, 66:69] = rec_pose_jaw - - to_global = rec_lower - to_global[:, :, 54:57] = 0.0 - to_global[:, :, :54] = rec_lower2global - rec_global = self.global_motion(to_global) - - rec_trans_v_s = rec_global["rec_pose"][:, :, 54:57] - rec_x_trans = other_tools.velocity2position(rec_trans_v_s[:, :, 0:1], 1/self.args.pose_fps, tar_trans[:, 0, 0:1]) - rec_z_trans = other_tools.velocity2position(rec_trans_v_s[:, :, 2:3], 1/self.args.pose_fps, tar_trans[:, 0, 2:3]) - rec_y_trans = rec_trans_v_s[:,:,1:2] - rec_trans = torch.cat([rec_x_trans, rec_y_trans, rec_z_trans], dim=-1) - tar_pose = tar_pose[:, :n, :] - tar_exps = tar_exps[:, :n, :] - tar_trans = tar_trans[:, :n, :] - tar_beta = tar_beta[:, :n, :] - - rec_pose = rc.axis_angle_to_matrix(rec_pose.reshape(bs*n, j, 3)) - rec_pose = rc.matrix_to_rotation_6d(rec_pose).reshape(bs, n, j*6) - tar_pose = rc.axis_angle_to_matrix(tar_pose.reshape(bs*n, j, 3)) - tar_pose = rc.matrix_to_rotation_6d(tar_pose).reshape(bs, n, j*6) - - return { - 'rec_pose': rec_pose, - 'rec_trans': rec_trans, - 'tar_pose': tar_pose, - 'tar_exps': tar_exps, - 'tar_beta': tar_beta, - 'tar_trans': tar_trans, - 'rec_exps': rec_exps, - } - - - def train(self, epoch): - #torch.autograd.set_detect_anomaly(True) - use_adv = bool(epoch>=self.args.no_adv_epoch) - self.model.train() - # self.d_model.train() - t_start = time.time() - self.tracker.reset() - for its, batch_data in enumerate(self.train_loader): - loaded_data = self._load_data(batch_data) - t_data = time.time() - t_start - - self.opt.zero_grad() - g_loss_final = 0 - g_loss_final += self._g_training(loaded_data, use_adv, 'train', epoch) - #with torch.autograd.detect_anomaly(): - g_loss_final.backward() - if self.args.grad_norm != 0: - torch.nn.utils.clip_grad_norm_(self.model.parameters(), self.args.grad_norm) - self.opt.step() - - mem_cost = torch.cuda.memory_cached() / 1E9 - lr_g = self.opt.param_groups[0]['lr'] - # lr_d = self.opt_d.param_groups[0]['lr'] - t_train = time.time() - t_start - t_data - t_start = time.time() - if its % self.args.log_period == 0: - self.train_recording(epoch, its, t_data, t_train, mem_cost, lr_g) - if self.args.debug: - if its == 1: break - self.opt_s.step(epoch) - # self.opt_d_s.step(epoch) - - def val(self, epoch): - self.model.eval() - # self.d_model.eval() - with torch.no_grad(): - for its, batch_data in enumerate(self.train_loader): - loaded_data = self._load_data(batch_data) - net_out = self._g_training(loaded_data, False, 'val', epoch) - tar_pose = net_out['tar_pose'] - rec_pose = net_out['rec_pose'] - if (30/self.args.pose_fps) != 1: - assert 30%self.args.pose_fps == 0 - n *= int(30/self.args.pose_fps) - tar_pose = torch.nn.functional.interpolate(tar_pose.permute(0, 2, 1), scale_factor=30/self.args.pose_fps, mode='linear').permute(0,2,1) - rec_pose = torch.nn.functional.interpolate(rec_pose.permute(0, 2, 1), scale_factor=30/self.args.pose_fps, mode='linear').permute(0,2,1) - n = tar_pose.shape[1] - remain = n%self.args.vae_test_len - tar_pose = tar_pose[:, :n-remain, :] - rec_pose = rec_pose[:, :n-remain, :] - latent_out = self.eval_copy.map2latent(rec_pose).reshape(-1, self.args.vae_length).cpu().numpy() - latent_ori = self.eval_copy.map2latent(tar_pose).reshape(-1, self.args.vae_length).cpu().numpy() - if its == 0: - latent_out_motion_all = latent_out - latent_ori_all = latent_ori - else: - latent_out_motion_all = np.concatenate([latent_out_motion_all, latent_out], axis=0) - latent_ori_all = np.concatenate([latent_ori_all, latent_ori], axis=0) - if self.args.debug: - if its == 1: break - fid_motion = data_tools.FIDCalculator.frechet_distance(latent_out_motion_all, latent_ori_all) - self.tracker.update_meter("fid", "val", fid_motion) - self.val_recording(epoch) - - - def test(self, epoch): - - results_save_path = self.checkpoint_path + f"/{epoch}/" - if os.path.exists(results_save_path): - return 0 - os.makedirs(results_save_path) - start_time = time.time() - total_length = 0 - test_seq_list = self.test_data.selected_file - align = 0 - latent_out = [] - latent_ori = [] - l2_all = 0 - lvel = 0 - self.model.eval() - self.smplx.eval() - self.eval_copy.eval() - with torch.no_grad(): - for its, batch_data in enumerate(self.test_loader): - loaded_data = self._load_data(batch_data) - net_out = self._g_test(loaded_data) - tar_pose = net_out['tar_pose'] - rec_pose = net_out['rec_pose'] - tar_exps = net_out['tar_exps'] - tar_beta = net_out['tar_beta'] - rec_trans = net_out['rec_trans'] - tar_trans = net_out['tar_trans'] - rec_exps = net_out['rec_exps'] - # print(rec_pose.shape, tar_pose.shape) - bs, n, j = tar_pose.shape[0], tar_pose.shape[1], self.joints - if (30/self.args.pose_fps) != 1: - assert 30%self.args.pose_fps == 0 - n *= int(30/self.args.pose_fps) - tar_pose = torch.nn.functional.interpolate(tar_pose.permute(0, 2, 1), scale_factor=30/self.args.pose_fps, mode='linear').permute(0,2,1) - rec_pose = torch.nn.functional.interpolate(rec_pose.permute(0, 2, 1), scale_factor=30/self.args.pose_fps, mode='linear').permute(0,2,1) - - # print(rec_pose.shape, tar_pose.shape) - rec_pose = rc.rotation_6d_to_matrix(rec_pose.reshape(bs*n, j, 6)) - rec_pose = rc.matrix_to_rotation_6d(rec_pose).reshape(bs, n, j*6) - tar_pose = rc.rotation_6d_to_matrix(tar_pose.reshape(bs*n, j, 6)) - tar_pose = rc.matrix_to_rotation_6d(tar_pose).reshape(bs, n, j*6) - remain = n%self.args.vae_test_len - latent_out.append(self.eval_copy.map2latent(rec_pose[:, :n-remain]).reshape(-1, self.args.vae_length).detach().cpu().numpy()) # bs * n/8 * 240 - latent_ori.append(self.eval_copy.map2latent(tar_pose[:, :n-remain]).reshape(-1, self.args.vae_length).detach().cpu().numpy()) - - rec_pose = rc.rotation_6d_to_matrix(rec_pose.reshape(bs*n, j, 6)) - rec_pose = rc.matrix_to_axis_angle(rec_pose).reshape(bs*n, j*3) - tar_pose = rc.rotation_6d_to_matrix(tar_pose.reshape(bs*n, j, 6)) - tar_pose = rc.matrix_to_axis_angle(tar_pose).reshape(bs*n, j*3) - - vertices_rec = self.smplx( - betas=tar_beta.reshape(bs*n, 300), - transl=rec_trans.reshape(bs*n, 3)-rec_trans.reshape(bs*n, 3), - expression=tar_exps.reshape(bs*n, 100)-tar_exps.reshape(bs*n, 100), - jaw_pose=rec_pose[:, 66:69], - global_orient=rec_pose[:,:3], - body_pose=rec_pose[:,3:21*3+3], - left_hand_pose=rec_pose[:,25*3:40*3], - right_hand_pose=rec_pose[:,40*3:55*3], - return_joints=True, - leye_pose=rec_pose[:, 69:72], - reye_pose=rec_pose[:, 72:75], - ) - # vertices_tar = self.smplx( - # betas=tar_beta.reshape(bs*n, 300), - # transl=rec_trans.reshape(bs*n, 3)-rec_trans.reshape(bs*n, 3), - # expression=tar_exps.reshape(bs*n, 100)-tar_exps.reshape(bs*n, 100), - # jaw_pose=tar_pose[:, 66:69], - # global_orient=tar_pose[:,:3], - # body_pose=tar_pose[:,3:21*3+3], - # left_hand_pose=tar_pose[:,25*3:40*3], - # right_hand_pose=tar_pose[:,40*3:55*3], - # return_joints=True, - # leye_pose=tar_pose[:, 69:72], - # reye_pose=tar_pose[:, 72:75], - # ) - vertices_rec_face = self.smplx( - betas=tar_beta.reshape(bs*n, 300), - transl=rec_trans.reshape(bs*n, 3)-rec_trans.reshape(bs*n, 3), - expression=rec_exps.reshape(bs*n, 100), - jaw_pose=rec_pose[:, 66:69], - global_orient=rec_pose[:,:3]-rec_pose[:,:3], - body_pose=rec_pose[:,3:21*3+3]-rec_pose[:,3:21*3+3], - left_hand_pose=rec_pose[:,25*3:40*3]-rec_pose[:,25*3:40*3], - right_hand_pose=rec_pose[:,40*3:55*3]-rec_pose[:,40*3:55*3], - return_verts=True, - return_joints=True, - leye_pose=rec_pose[:, 69:72]-rec_pose[:, 69:72], - reye_pose=rec_pose[:, 72:75]-rec_pose[:, 72:75], - ) - vertices_tar_face = self.smplx( - betas=tar_beta.reshape(bs*n, 300), - transl=tar_trans.reshape(bs*n, 3)-tar_trans.reshape(bs*n, 3), - expression=tar_exps.reshape(bs*n, 100), - jaw_pose=tar_pose[:, 66:69], - global_orient=tar_pose[:,:3]-tar_pose[:,:3], - body_pose=tar_pose[:,3:21*3+3]-tar_pose[:,3:21*3+3], - left_hand_pose=tar_pose[:,25*3:40*3]-tar_pose[:,25*3:40*3], - right_hand_pose=tar_pose[:,40*3:55*3]-tar_pose[:,40*3:55*3], - return_verts=True, - return_joints=True, - leye_pose=tar_pose[:, 69:72]-tar_pose[:, 69:72], - reye_pose=tar_pose[:, 72:75]-tar_pose[:, 72:75], - ) - joints_rec = vertices_rec["joints"].detach().cpu().numpy().reshape(1, n, 127*3)[0, :n, :55*3] - # joints_tar = vertices_tar["joints"].detach().cpu().numpy().reshape(1, n, 127*3)[0, :n, :55*3] - facial_rec = vertices_rec_face['vertices'].reshape(1, n, -1)[0, :n] - facial_tar = vertices_tar_face['vertices'].reshape(1, n, -1)[0, :n] - face_vel_loss = self.vel_loss(facial_rec[1:, :] - facial_tar[:-1, :], facial_tar[1:, :] - facial_tar[:-1, :]) - l2 = self.reclatent_loss(facial_rec, facial_tar) - l2_all += l2.item() * n - lvel += face_vel_loss.item() * n - - _ = self.l1_calculator.run(joints_rec) - if self.alignmenter is not None: - in_audio_eval, sr = librosa.load(self.args.data_path+"wave16k/"+test_seq_list.iloc[its]['id']+".wav") - in_audio_eval = librosa.resample(in_audio_eval, orig_sr=sr, target_sr=self.args.audio_sr) - a_offset = int(self.align_mask * (self.args.audio_sr / self.args.pose_fps)) - onset_bt = self.alignmenter.load_audio(in_audio_eval[:int(self.args.audio_sr / self.args.pose_fps*n)], a_offset, len(in_audio_eval)-a_offset, True) - beat_vel = self.alignmenter.load_pose(joints_rec, self.align_mask, n-self.align_mask, 30, True) - # print(beat_vel) - align += (self.alignmenter.calculate_align(onset_bt, beat_vel, 30) * (n-2*self.align_mask)) - - tar_pose_np = tar_pose.detach().cpu().numpy() - rec_pose_np = rec_pose.detach().cpu().numpy() - rec_trans_np = rec_trans.detach().cpu().numpy().reshape(bs*n, 3) - rec_exp_np = rec_exps.detach().cpu().numpy().reshape(bs*n, 100) - tar_exp_np = tar_exps.detach().cpu().numpy().reshape(bs*n, 100) - tar_trans_np = tar_trans.detach().cpu().numpy().reshape(bs*n, 3) - gt_npz = np.load(self.args.data_path+self.args.pose_rep +"/"+test_seq_list.iloc[its]['id']+".npz", allow_pickle=True) - np.savez(results_save_path+"gt_"+test_seq_list.iloc[its]['id']+'.npz', - betas=gt_npz["betas"], - poses=tar_pose_np, - expressions=tar_exp_np, - trans=tar_trans_np, - model='smplx2020', - gender='neutral', - mocap_frame_rate = 30 , - ) - np.savez(results_save_path+"res_"+test_seq_list.iloc[its]['id']+'.npz', - betas=gt_npz["betas"], - poses=rec_pose_np, - expressions=rec_exp_np, - trans=rec_trans_np, - model='smplx2020', - gender='neutral', - mocap_frame_rate = 30, - ) - total_length += n - - logger.info(f"l2 loss: {l2_all/total_length}") - logger.info(f"lvel loss: {lvel/total_length}") - - latent_out_all = np.concatenate(latent_out, axis=0) - latent_ori_all = np.concatenate(latent_ori, axis=0) - fid = data_tools.FIDCalculator.frechet_distance(latent_out_all, latent_ori_all) - logger.info(f"fid score: {fid}") - self.test_recording("fid", fid, epoch) - - align_avg = align/(total_length-2*len(self.test_loader)*self.align_mask) - logger.info(f"align score: {align_avg}") - self.test_recording("bc", align_avg, epoch) - - l1div = self.l1_calculator.avg() - logger.info(f"l1div score: {l1div}") - self.test_recording("l1div", l1div, epoch) - - # data_tools.result2target_vis(self.args.pose_version, results_save_path, results_save_path, self.test_demo, False) - end_time = time.time() - start_time - logger.info(f"total inference time: {int(end_time)} s for {int(total_length/self.args.pose_fps)} s motion") - - - def test_demo(self, epoch): - ''' - input audio and text, output motion - do not calculate loss and metric - save video - ''' - results_save_path = self.checkpoint_path + f"/{epoch}/" - if os.path.exists(results_save_path): - return 0 - os.makedirs(results_save_path) - start_time = time.time() - total_length = 0 - test_seq_list = self.test_data.selected_file - align = 0 - latent_out = [] - latent_ori = [] - l2_all = 0 - lvel = 0 - self.model.eval() - self.smplx.eval() - # self.eval_copy.eval() - with torch.no_grad(): - for its, batch_data in enumerate(self.test_loader): - loaded_data = self._load_data(batch_data) - net_out = self._g_test(loaded_data) - tar_pose = net_out['tar_pose'] - rec_pose = net_out['rec_pose'] - tar_exps = net_out['tar_exps'] - tar_beta = net_out['tar_beta'] - rec_trans = net_out['rec_trans'] - tar_trans = net_out['tar_trans'] - rec_exps = net_out['rec_exps'] - # print(rec_pose.shape, tar_pose.shape) - bs, n, j = tar_pose.shape[0], tar_pose.shape[1], self.joints - - # interpolate to 30fps - if (30/self.args.pose_fps) != 1: - assert 30%self.args.pose_fps == 0 - n *= int(30/self.args.pose_fps) - tar_pose = torch.nn.functional.interpolate(tar_pose.permute(0, 2, 1), scale_factor=30/self.args.pose_fps, mode='linear').permute(0,2,1) - rec_pose = torch.nn.functional.interpolate(rec_pose.permute(0, 2, 1), scale_factor=30/self.args.pose_fps, mode='linear').permute(0,2,1) - - # print(rec_pose.shape, tar_pose.shape) - rec_pose = rc.rotation_6d_to_matrix(rec_pose.reshape(bs*n, j, 6)) - rec_pose = rc.matrix_to_axis_angle(rec_pose).reshape(bs*n, j*3) - - tar_pose = rc.rotation_6d_to_matrix(tar_pose.reshape(bs*n, j, 6)) - tar_pose = rc.matrix_to_axis_angle(tar_pose).reshape(bs*n, j*3) - - tar_pose_np = tar_pose.detach().cpu().numpy() - rec_pose_np = rec_pose.detach().cpu().numpy() - rec_trans_np = rec_trans.detach().cpu().numpy().reshape(bs*n, 3) - rec_exp_np = rec_exps.detach().cpu().numpy().reshape(bs*n, 100) - tar_exp_np = tar_exps.detach().cpu().numpy().reshape(bs*n, 100) - tar_trans_np = tar_trans.detach().cpu().numpy().reshape(bs*n, 3) - - gt_npz = np.load(self.args.data_path+self.args.pose_rep +"/"+test_seq_list.iloc[its]['id']+".npz", allow_pickle=True) - np.savez(results_save_path+"gt_"+test_seq_list.iloc[its]['id']+'.npz', - betas=gt_npz["betas"], - poses=tar_pose_np, - expressions=tar_exp_np, - trans=tar_trans_np, - model='smplx2020', - gender='neutral', - mocap_frame_rate = 30 , - ) - np.savez(results_save_path+"res_"+test_seq_list.iloc[its]['id']+'.npz', - betas=gt_npz["betas"], - poses=rec_pose_np, - expressions=rec_exp_np, - trans=rec_trans_np, - model='smplx2020', - gender='neutral', - mocap_frame_rate = 30, - ) - total_length += n - - data_tools.result2target_vis(self.args.pose_version, results_save_path, results_save_path, self.test_demo, False) - end_time = time.time() - start_time - logger.info(f"total inference time: {int(end_time)} s for {int(total_length/self.args.pose_fps)} s motion") diff --git a/emage_utils/__pycache__/fast_render.cpython-39.pyc b/emage_utils/__pycache__/fast_render.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..1867948814d6fff2359bc8aaef945fa171e7632a Binary files /dev/null and b/emage_utils/__pycache__/fast_render.cpython-39.pyc differ diff --git a/emage_utils/__pycache__/motion_io.cpython-39.pyc b/emage_utils/__pycache__/motion_io.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..90d1a0d87e83d1cae1df0c78f14bba10eec2b6ae Binary files /dev/null and b/emage_utils/__pycache__/motion_io.cpython-39.pyc differ diff --git a/emage_utils/__pycache__/npz2pose.cpython-39.pyc b/emage_utils/__pycache__/npz2pose.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..8d7d36e57ff67ac25b9d34d4c6ee698b169e429e Binary files /dev/null and b/emage_utils/__pycache__/npz2pose.cpython-39.pyc differ diff --git a/emage_utils/fast_render.py b/emage_utils/fast_render.py new file mode 100644 index 0000000000000000000000000000000000000000..f8b9cf32e8bc464515ca30ad82a71f44a3f498fe --- /dev/null +++ b/emage_utils/fast_render.py @@ -0,0 +1,429 @@ +import os +import time +import numpy as np +os.environ['PYOPENGL_PLATFORM'] = 'egl' # or 'osmesa' +import pyrender +import trimesh +import queue +import imageio +import threading +import multiprocessing +import glob +import subprocess +import matplotlib +matplotlib.use('Agg') +import matplotlib.pyplot as plt + +args = { + 'render_video_fps': 30, + 'render_video_width': 480, + 'render_video_height': 720, + 'render_concurrent_num': max(1, multiprocessing.cpu_count() - 1) , + 'render_tmp_img_filetype': 'bmp', + 'debug': False +} + +def deg_to_rad(degrees): + return degrees * np.pi / 180 + +def create_pose_camera(angle_deg): + angle_rad = deg_to_rad(angle_deg) + return np.array([ + [1.0, 0.0, 0.0, 0.0], + [0.0, np.cos(angle_rad), -np.sin(angle_rad), 1.0], + [0.0, np.sin(angle_rad), np.cos(angle_rad), 5.0], + [0.0, 0.0, 0.0, 1.0] + ]) + +def create_pose_light(angle_deg): + angle_rad = deg_to_rad(angle_deg) + return np.array([ + [1.0, 0.0, 0.0, 0.0], + [0.0, np.cos(angle_rad), -np.sin(angle_rad), 0.0], + [0.0, np.sin(angle_rad), np.cos(angle_rad), 3.0], + [0.0, 0.0, 0.0, 1.0] + ]) + +def create_scene_with_mesh(vertices, faces, uniform_color, pose_camera, pose_light): + trimesh_mesh = trimesh.Trimesh(vertices=vertices, faces=faces, vertex_colors=uniform_color) + mesh = pyrender.Mesh.from_trimesh(trimesh_mesh, smooth=True) + scene = pyrender.Scene(bg_color=[0, 0, 0, 0]) + scene.add(mesh) + camera = pyrender.OrthographicCamera(xmag=1.0, ymag=1.0) + scene.add(camera, pose=pose_camera) + light = pyrender.DirectionalLight(color=[1.0, 1.0, 1.0], intensity=4.0) + scene.add(light, pose=pose_light) + return scene + +def do_render_one_frame(renderer, frame_idx, vertices, vertices1, faces): + if frame_idx % 100 == 0: + print('processed', frame_idx, 'frames') + uniform_color = [220, 220, 220, 255] + pose_camera = create_pose_camera(angle_deg=-2) + pose_light = create_pose_light(angle_deg=-30) + figs = [] + for vtx in [vertices, vertices1]: + scene = create_scene_with_mesh(vtx, faces, uniform_color, pose_camera, pose_light) + fig, _ = renderer.render(scene) + figs.append(fig) + return figs[0], figs[1] + +def do_render_one_frame_no_gt(renderer, frame_idx, vertices, faces): + if frame_idx % 100 == 0: + print('processed', frame_idx, 'frames') + uniform_color = [220, 220, 220, 255] + pose_camera = create_pose_camera(angle_deg=-2) + pose_light = create_pose_light(angle_deg=-30) + scene = create_scene_with_mesh(vertices, faces, uniform_color, pose_camera, pose_light) + fig, _ = renderer.render(scene) + return fig + +def write_images_from_queue(fig_queue, output_dir, img_filetype): + while True: + e = fig_queue.get() + if e is None: + break + fid, fig1, fig2 = e + fn = os.path.join(output_dir, f"frame_{fid}.{img_filetype}") + merged_fig = np.hstack((fig1, fig2)) + try: + imageio.imwrite(fn, merged_fig) + except Exception as ex: + print(f"Error writing image {fn}: {ex}") + raise ex + +def write_images_from_queue_no_gt(fig_queue, output_dir, img_filetype): + while True: + e = fig_queue.get() + if e is None: + break + fid, fig1 = e + fn = os.path.join(output_dir, f"frame_{fid}.{img_filetype}") + try: + imageio.imwrite(fn, fig1) + except Exception as ex: + print(f"Error writing image {fn}: {ex}") + raise ex + +def render_frames_and_enqueue(fids, frame_vertex_pairs, faces, render_width, render_height, fig_queue): + fig_resolution = (render_width, render_height) + renderer = pyrender.OffscreenRenderer(*fig_resolution) + for idx, fid in enumerate(fids): + fig1, fig2 = do_render_one_frame(renderer, fid, frame_vertex_pairs[idx][0], frame_vertex_pairs[idx][1], faces) + fig_queue.put((fid, fig1, fig2)) + renderer.delete() + +def render_frames_and_enqueue_no_gt(fids, frame_vertex_pairs, faces, render_width, render_height, fig_queue): + fig_resolution = (render_width, render_height) + renderer = pyrender.OffscreenRenderer(*fig_resolution) + for idx, fid in enumerate(fids): + fig1 = do_render_one_frame_no_gt(renderer, fid, frame_vertex_pairs[idx][0], faces) + fig_queue.put((fid, fig1)) + renderer.delete() + +def sub_process_process_frame(subprocess_index, render_video_width, render_video_height, render_tmp_img_filetype, fids, frame_vertex_pairs, faces, output_dir): + t0 = time.time() + print(f"subprocess_index={subprocess_index} begin_ts={t0}") + fig_queue = queue.Queue() + render_frames_and_enqueue(fids, frame_vertex_pairs, faces, render_video_width, render_video_height, fig_queue) + fig_queue.put(None) + t1 = time.time() + thr = threading.Thread(target=write_images_from_queue, args=(fig_queue, output_dir, render_tmp_img_filetype)) + thr.start() + thr.join() + t2 = time.time() + print(f"subprocess_index={subprocess_index} render={t1 - t0:.2f} all={t2 - t0:.2f}") + +def sub_process_process_frame_no_gt(subprocess_index, render_video_width, render_video_height, render_tmp_img_filetype, fids, frame_vertex_pairs, faces, output_dir): + t0 = time.time() + print(f"subprocess_index={subprocess_index} begin_ts={t0}") + fig_queue = queue.Queue() + render_frames_and_enqueue_no_gt(fids, frame_vertex_pairs, faces, render_video_width, render_video_height, fig_queue) + fig_queue.put(None) + t1 = time.time() + thr = threading.Thread(target=write_images_from_queue_no_gt, args=(fig_queue, output_dir, render_tmp_img_filetype)) + thr.start() + thr.join() + t2 = time.time() + print(f"subprocess_index={subprocess_index} render={t1 - t0:.2f} all={t2 - t0:.2f}") + +def distribute_frames(frames, vertices_all, vertices1_all): + sample_interval = max(1, int(30 // args['render_video_fps'])) + subproc_frame_ids = [[] for _ in range(args['render_concurrent_num'])] + subproc_vertices = [[] for _ in range(args['render_concurrent_num'])] + sid = 0 + for i in range(frames): + if i % sample_interval != 0: + continue + idx = sid % args['render_concurrent_num'] + subproc_frame_ids[idx].append(sid) + subproc_vertices[idx].append((vertices_all[i], vertices1_all[i])) + sid += 1 + return subproc_frame_ids, subproc_vertices + +def distribute_frames_no_gt(frames, vertices_all): + sample_interval = max(1, int(30 // args['render_video_fps'])) + subproc_frame_ids = [[] for _ in range(args['render_concurrent_num'])] + subproc_vertices = [[] for _ in range(args['render_concurrent_num'])] + sid = 0 + for i in range(frames): + if i % sample_interval != 0: + continue + idx = sid % args['render_concurrent_num'] + subproc_frame_ids[idx].append(sid) + subproc_vertices[idx].append((vertices_all[i], vertices_all[i])) + sid += 1 + return subproc_frame_ids, subproc_vertices + +def generate_silent_videos(frames, vertices_all, vertices1_all, faces, output_dir): + ids, verts = distribute_frames(frames, vertices_all, vertices1_all) + with multiprocessing.Pool(args['render_concurrent_num']) as pool: + pool.starmap(sub_process_process_frame, [ + ( + i, + args['render_video_width'], + args['render_video_height'], + args['render_tmp_img_filetype'], + ids[i], + verts[i], + faces, + output_dir + ) + for i in range(args['render_concurrent_num']) + ]) + out_file = os.path.join(output_dir, "silence_video.mp4") + convert_img_to_mp4(os.path.join(output_dir, f"frame_%d.{args['render_tmp_img_filetype']}"), out_file, args['render_video_fps']) + for fn in glob.glob(os.path.join(output_dir, f"*.{args['render_tmp_img_filetype']}")): + os.remove(fn) + return out_file + +def generate_silent_videos_no_gt(frames, vertices_all, faces, output_dir): + ids, verts = distribute_frames_no_gt(frames, vertices_all) + with multiprocessing.Pool(args['render_concurrent_num']) as pool: + pool.starmap(sub_process_process_frame_no_gt, [ + ( + i, + args['render_video_width'], + args['render_video_height'], + args['render_tmp_img_filetype'], + ids[i], + verts[i], + faces, + output_dir + ) + for i in range(args['render_concurrent_num']) + ]) + out_file = os.path.join(output_dir, "silence_video.mp4") + convert_img_to_mp4(os.path.join(output_dir, f"frame_%d.{args['render_tmp_img_filetype']}"), out_file, args['render_video_fps']) + for fn in glob.glob(os.path.join(output_dir, f"*.{args['render_tmp_img_filetype']}")): + os.remove(fn) + return out_file + +def add_audio_to_video(silent_video_path, audio_path, output_video_path): + cmd = [ + 'ffmpeg','-y','-i', silent_video_path,'-i', audio_path,'-map','0:v','-map','1:a','-c:v','copy','-shortest',output_video_path + ] + try: + subprocess.run(cmd, check=True) + print(f"Video with audio generated: {output_video_path}") + except subprocess.CalledProcessError as e: + print(f"Error: {e}") + +def convert_img_to_mp4(input_pattern, output_file, framerate=30): + cmd = ['ffmpeg','-framerate', str(framerate),'-i', input_pattern,'-c:v','libx264','-pix_fmt','yuv420p',output_file,'-y'] + try: + subprocess.run(cmd, check=True) + print(f"Video conversion: {output_file}") + except subprocess.CalledProcessError as e: + print(f"Error: {e}") + +def process_frame(i, vertices_all, vertices1_all, faces, output_dir, filenames): + uniform_color = [220, 220, 220, 255] + reso = (1000, 1000) + fig, axs = plt.subplots(1, 2, figsize=(20,10)) + axs = axs.flatten() + vertices = vertices_all[i] + vertices1 = vertices1_all[i] + fn = f"{output_dir}frame_{i}.png" + if i % 100 == 0: + print('processed', i, 'frames') + angle_rad = deg_to_rad(-2) + pose_camera = np.array([ + [1.0, 0.0, 0.0, 0.0], + [0.0, np.cos(angle_rad), -np.sin(angle_rad), 1.0], + [0.0, np.sin(angle_rad), np.cos(angle_rad), 5.0], + [0.0, 0.0, 0.0, 1.0] + ]) + angle_rad = deg_to_rad(-30) + pose_light = np.array([ + [1.0, 0.0, 0.0, 0.0], + [0.0, np.cos(angle_rad), -np.sin(angle_rad), 0.0], + [0.0, np.sin(angle_rad), np.cos(angle_rad), 3.0], + [0.0, 0.0, 0.0, 1.0] + ]) + for idx, vtx in enumerate([vertices, vertices1]): + tm = trimesh.Trimesh(vertices=vtx, faces=faces, vertex_colors=uniform_color) + mesh = pyrender.Mesh.from_trimesh(tm, smooth=True) + scene = pyrender.Scene() + scene.add(mesh) + cam = pyrender.OrthographicCamera(xmag=1.0, ymag=1.0) + scene.add(cam, pose=pose_camera) + light = pyrender.DirectionalLight(color=[1.0, 1.0, 1.0], intensity=4.0) + scene.add(light, pose=pose_light) + r = pyrender.OffscreenRenderer(*reso) + color, _ = r.render(scene) + axs[idx].imshow(color) + axs[idx].axis('off') + r.delete() + plt.savefig(fn, bbox_inches='tight') + plt.close(fig) + +def generate_images(frames, vertices_all, vertices1_all, faces, output_dir, filenames): + nc = multiprocessing.cpu_count() - 1 + for i in range(frames): + process_frame(i*3, vertices_all, vertices1_all, faces, output_dir, filenames) + +def render_one_sequence_with_face(res_npz_path, output_dir, audio_path, model_folder="/data/datasets/smplx_models/", model_type='smplx', gender='NEUTRAL_2020', ext='npz', num_betas=300, num_expression_coeffs=100, use_face_contour=False, use_matplotlib=False, remove_transl=True): + import smplx + import torch + data_np_body = np.load(res_npz_path, allow_pickle=True) + if not os.path.exists(output_dir): + os.makedirs(output_dir) + faces = np.load(f"{model_folder}/smplx/SMPLX_NEUTRAL_2020.npz", allow_pickle=True)["f"] + n = data_np_body["poses"].shape[0] + model = smplx.create(model_folder, model_type=model_type, gender=gender, use_face_contour=use_face_contour, num_betas=num_betas, num_expression_coeffs=num_expression_coeffs, ext=ext, use_pca=False).cuda() + beta = torch.from_numpy(data_np_body["betas"]).to(torch.float32).unsqueeze(0).cuda() + beta = beta.repeat(n, 1) + expression = torch.from_numpy(data_np_body["expressions"][:n]).to(torch.float32).cuda() + jaw_pose = torch.from_numpy(data_np_body["poses"][:n, 66:69]).to(torch.float32).cuda() + pose = torch.from_numpy(data_np_body["poses"][:n]).to(torch.float32).cuda() + transl = torch.from_numpy(data_np_body["trans"][:n]).to(torch.float32).cuda() + if remove_transl: + transl = transl[0:1].repeat(n, 1) + output = model(betas=beta, transl=transl, expression=expression, jaw_pose=jaw_pose, global_orient=pose[:,:3], body_pose=pose[:,3:21*3+3], left_hand_pose=pose[:,25*3:40*3], right_hand_pose=pose[:,40*3:55*3], leye_pose=pose[:,69:72], reye_pose=pose[:,72:75], return_verts=True) + vertices_all = output["vertices"].cpu().numpy() + + pose1 = torch.zeros_like(pose).to(torch.float32).cuda() + output1 = model(betas=beta, transl=transl, expression=expression, jaw_pose=jaw_pose, global_orient=pose1[:,:3], body_pose=pose1[:,3:21*3+3], left_hand_pose=pose1[:,25*3:40*3], right_hand_pose=pose1[:,40*3:55*3], leye_pose=pose1[:,69:72], reye_pose=pose1[:,72:75], return_verts=True) + v1 = output1["vertices"].cpu().numpy()*7 + td = np.zeros_like(v1) + td[:, :, 1] = 10 + vertices1_all = v1 - td + if args['debug']: + seconds = 1 + else: + seconds = vertices_all.shape[0]//30 + sfile = generate_silent_videos(int(seconds*args['render_video_fps']), vertices1_all, vertices_all, faces, output_dir) + base = os.path.splitext(os.path.basename(res_npz_path))[0] + final_clip = os.path.join(output_dir, f"{base}.mp4") + add_audio_to_video(sfile, audio_path, final_clip) + os.remove(sfile) + return final_clip + +def render_one_sequence(res_npz_path, gt_npz_path, output_dir, audio_path, model_folder="/data/datasets/smplx_models/", model_type='smplx', gender='NEUTRAL_2020', ext='npz', num_betas=300, num_expression_coeffs=100, use_face_contour=False, use_matplotlib=False, remove_transl=True): + import smplx + import torch + data_np_body = np.load(res_npz_path, allow_pickle=True) + gt_np_body = np.load(gt_npz_path, allow_pickle=True) + if not os.path.exists(output_dir): + os.makedirs(output_dir) + faces = np.load(f"{model_folder}/smplx/SMPLX_NEUTRAL_2020.npz", allow_pickle=True)["f"] + n = data_np_body["poses"].shape[0] + model = smplx.create(model_folder, model_type=model_type, gender=gender, use_face_contour=use_face_contour, num_betas=num_betas, num_expression_coeffs=num_expression_coeffs, ext=ext, use_pca=False).cuda() + beta = torch.from_numpy(data_np_body["betas"]).to(torch.float32).unsqueeze(0).cuda() + beta = beta.repeat(n, 1) + expression = torch.from_numpy(data_np_body["expressions"][:n]).to(torch.float32).cuda() + jaw_pose = torch.from_numpy(data_np_body["poses"][:n, 66:69]).to(torch.float32).cuda() + pose = torch.from_numpy(data_np_body["poses"][:n]).to(torch.float32).cuda() + transl = torch.from_numpy(data_np_body["trans"][:n]).to(torch.float32).cuda() + if remove_transl: + transl = transl[0:1].repeat(n, 1) + output = model(betas=beta, transl=transl, expression=expression, jaw_pose=jaw_pose, global_orient=pose[:,:3], body_pose=pose[:,3:21*3+3], left_hand_pose=pose[:,25*3:40*3], right_hand_pose=pose[:,40*3:55*3], leye_pose=pose[:,69:72], reye_pose=pose[:,72:75], return_verts=True) + vertices_all = output["vertices"].cpu().numpy() + beta1 = torch.from_numpy(gt_np_body["betas"]).to(torch.float32).unsqueeze(0).cuda() + expression1 = torch.from_numpy(gt_np_body["expressions"][:n]).to(torch.float32).cuda() + jaw_pose1 = torch.from_numpy(gt_np_body["poses"][:n,66:69]).to(torch.float32).cuda() + pose1 = torch.from_numpy(gt_np_body["poses"][:n]).to(torch.float32).cuda() + transl1 = torch.from_numpy(gt_np_body["trans"][:n]).to(torch.float32).cuda() + if remove_transl: + transl1 = transl1[0:1].repeat(n, 1) + output1 = model(betas=beta1, transl=transl1, expression=expression1, jaw_pose=jaw_pose1, global_orient=pose1[:,:3], body_pose=pose1[:,3:21*3+3], left_hand_pose=pose1[:,25*3:40*3], right_hand_pose=pose1[:,40*3:55*3], leye_pose=pose1[:,69:72], reye_pose=pose1[:,72:75], return_verts=True) + vertices1_all = output1["vertices"].cpu().numpy() + if args['debug']: + seconds = 1 + else: + seconds = vertices_all.shape[0]//30 + sfile = generate_silent_videos(int(seconds*args['render_video_fps']), vertices_all, vertices1_all, faces, output_dir) + base = os.path.splitext(os.path.basename(res_npz_path))[0] + final_clip = os.path.join(output_dir, f"{base}.mp4") + add_audio_to_video(sfile, audio_path, final_clip) + os.remove(sfile) + return final_clip + +def render_one_sequence_no_gt(res_npz_path, output_dir, audio_path, model_folder="/data/datasets/smplx_models/", model_type='smplx', gender='NEUTRAL_2020', ext='npz', num_betas=300, num_expression_coeffs=100, use_face_contour=False, use_matplotlib=False, remove_transl=True): + import smplx + import torch + data_np_body = np.load(res_npz_path, allow_pickle=True) + if not os.path.exists(output_dir): + os.makedirs(output_dir) + faces = np.load(f"{model_folder}/smplx/SMPLX_NEUTRAL_2020.npz", allow_pickle=True)["f"] + n = data_np_body["poses"].shape[0] + model = smplx.create(model_folder, model_type=model_type, gender=gender, use_face_contour=use_face_contour, num_betas=num_betas, num_expression_coeffs=num_expression_coeffs, ext=ext, use_pca=False).cuda() + beta = torch.from_numpy(data_np_body["betas"]).to(torch.float32).unsqueeze(0).cuda() + beta = beta.repeat(n, 1) + expression = torch.from_numpy(data_np_body["expressions"][:n]).to(torch.float32).cuda() + jaw_pose = torch.from_numpy(data_np_body["poses"][:n, 66:69]).to(torch.float32).cuda() + pose = torch.from_numpy(data_np_body["poses"][:n]).to(torch.float32).cuda() + transl = torch.from_numpy(data_np_body["trans"][:n]).to(torch.float32).cuda() + if remove_transl: + transl = transl[0:1].repeat(n, 1) + output = model(betas=beta, transl=transl, expression=expression, jaw_pose=jaw_pose, global_orient=pose[:,:3], body_pose=pose[:,3:21*3+3], left_hand_pose=pose[:,25*3:40*3], right_hand_pose=pose[:,40*3:55*3], leye_pose=pose[:,69:72], reye_pose=pose[:,72:75], return_verts=True) + vertices_all = output["vertices"].cpu().numpy() + if args['debug']: + seconds = 1 + else: + seconds = vertices_all.shape[0]//30 + sfile = generate_silent_videos_no_gt(int(seconds*args['render_video_fps']), vertices_all, faces, output_dir) + base = os.path.splitext(os.path.basename(res_npz_path))[0] + final_clip = os.path.join(output_dir, f"{base}.mp4") + add_audio_to_video(sfile, audio_path, final_clip) + os.remove(sfile) + return final_clip + +def render_one_sequence_face_only(res_npz_path, output_dir, audio_path, model_folder="/data/datasets/smplx_models/", model_type='smplx', gender='NEUTRAL_2020', ext='npz', num_betas=300, num_expression_coeffs=100, use_face_contour=False, use_matplotlib=False, remove_transl=True): + import smplx + import torch + data_np_body = np.load(res_npz_path, allow_pickle=True) + if not os.path.exists(output_dir): + os.makedirs(output_dir) + faces = np.load(f"{model_folder}/smplx/SMPLX_NEUTRAL_2020.npz", allow_pickle=True)["f"] + n = data_np_body["poses"].shape[0] + model = smplx.create(model_folder, model_type=model_type, gender=gender, use_face_contour=use_face_contour, num_betas=num_betas, num_expression_coeffs=num_expression_coeffs, ext=ext, use_pca=False).cuda() + beta = torch.from_numpy(data_np_body["betas"]).to(torch.float32).unsqueeze(0).cuda() + beta = beta.repeat(n, 1) + expression = torch.from_numpy(data_np_body["expressions"][:n]).to(torch.float32).cuda() + jaw_pose = torch.from_numpy(data_np_body["poses"][:n, 66:69]).to(torch.float32).cuda() + pose = torch.from_numpy(data_np_body["poses"][:n]).to(torch.float32).cuda() + transl = torch.from_numpy(data_np_body["trans"][:n]).to(torch.float32).cuda() + if remove_transl: + transl = transl[0:1].repeat(n, 1) + output = model(betas=beta, transl=transl, expression=expression, jaw_pose=jaw_pose, global_orient=pose[:,:3], body_pose=pose[:,3:21*3+3], left_hand_pose=pose[:,25*3:40*3], right_hand_pose=pose[:,40*3:55*3], leye_pose=pose[:,69:72], reye_pose=pose[:,72:75], return_verts=True) + vertices_all = output["vertices"].cpu().numpy() + + pose1 = torch.zeros_like(pose).to(torch.float32).cuda() + output1 = model(betas=beta, transl=transl, expression=expression, jaw_pose=jaw_pose, global_orient=pose1[:,:3], body_pose=pose1[:,3:21*3+3], left_hand_pose=pose1[:,25*3:40*3], right_hand_pose=pose1[:,40*3:55*3], leye_pose=pose1[:,69:72], reye_pose=pose1[:,72:75], return_verts=True) + v1 = output1["vertices"].cpu().numpy()*7 + td = np.zeros_like(v1) + td[:, :, 1] = 10 + vertices_all = v1 - td + + if args['debug']: + seconds = 1 + else: + seconds = vertices_all.shape[0]//30 + sfile = generate_silent_videos_no_gt(int(seconds*args['render_video_fps']), vertices_all, faces, output_dir) + base = os.path.splitext(os.path.basename(res_npz_path))[0] + final_clip = os.path.join(output_dir, f"{base}_face.mp4") + add_audio_to_video(sfile, audio_path, final_clip) + os.remove(sfile) + return final_clip \ No newline at end of file diff --git a/emage_utils/motion_io.py b/emage_utils/motion_io.py new file mode 100644 index 0000000000000000000000000000000000000000..5a917cca04b4569ce0b97761e30236f490f1822e --- /dev/null +++ b/emage_utils/motion_io.py @@ -0,0 +1,180 @@ +import numpy as np +import torch +import smplx + +MASK_DICT = { + "local_upper": [ + False, False, False, True, False, False, True, False, False, True, + False, False, True, True, True, True, True, True, True, True, + True, True, False, False, False, True, True, True, True, True, + True, True, True, True, True, True, True, True, True, True, + True, True, True, True, True, True, True, True, True, True, + True, True, True, True, True + ], + "local_full": [False] + [True]*54 +} + +def select_with_mask(motion: np.ndarray, mask: list[bool]) -> np.ndarray: + mask_arr = np.array(mask, dtype=bool) + j = len(mask_arr) + c_channels = motion.shape[-1] // j + new_shape = motion.shape[:-1] + (j, c_channels) + motion = motion.reshape(new_shape) + selected_motion = motion[..., mask_arr, :] + final_shape = selected_motion.shape[:-2] + (selected_motion.shape[-2]*selected_motion.shape[-1],) + selected_motion = selected_motion.reshape(final_shape) + return selected_motion + +def recover_from_mask(selected_motion: np.ndarray, mask: list[bool]) -> np.ndarray: + mask_arr = np.array(mask, dtype=bool) + j = len(mask_arr) + # Infer c_channels from selected_motion's last dimension and sum(mask) + c_channels = selected_motion.shape[-1] // mask_arr.sum() + new_shape = selected_motion.shape[:-1] + (mask_arr.sum(), c_channels) + selected_motion = selected_motion.reshape(new_shape) + out_shape = selected_motion.shape[:-2] + (j, c_channels) + recovered = np.zeros(out_shape, dtype=selected_motion.dtype) + recovered[..., mask_arr, :] = selected_motion + final_shape = recovered.shape[:-2] + (j * c_channels,) + recovered = recovered.reshape(final_shape) + return recovered + +def select_with_mask_ts(motion: torch.Tensor, mask: list[bool]) -> torch.Tensor: + mask_arr = torch.tensor(mask, dtype=torch.bool, device=motion.device) + j = len(mask_arr) + c_channels = motion.shape[-1] // j + new_shape = motion.shape[:-1] + (j, c_channels) + motion = motion.reshape(new_shape) + selected_motion = motion[..., mask_arr, :] + final_shape = selected_motion.shape[:-2] + (selected_motion.shape[-2]*selected_motion.shape[-1],) + selected_motion = selected_motion.reshape(final_shape) + return selected_motion + +def recover_from_mask_ts(selected_motion: torch.Tensor, mask: list[bool]) -> torch.Tensor: + device = selected_motion.device + dtype = selected_motion.dtype + mask_arr = torch.tensor(mask, dtype=torch.bool, device=device) + j = len(mask_arr) + sum_mask = mask_arr.sum().item() + c_channels = selected_motion.shape[-1] // sum_mask + new_shape = selected_motion.shape[:-1] + (sum_mask, c_channels) + selected_motion = selected_motion.reshape(new_shape) + out_shape = list(selected_motion.shape[:-2]) + [j, c_channels] + recovered = torch.zeros(out_shape, dtype=dtype, device=device) + recovered[..., mask_arr, :] = selected_motion + final_shape = list(recovered.shape[:-2]) + [j * c_channels] + recovered = recovered.reshape(final_shape) + return recovered + +def time_upsample_numpy(data: np.ndarray, k: int) -> np.ndarray: + # data: (..., t, c) + # output: (..., k*t, c) + if k == 1: + return data.copy() + shape = data.shape + t = shape[-2] + c = shape[-1] + # original and new time indices + original_t = np.arange(t) + new_t = np.linspace(0, t - 1, k * t) + + # reshape to (M, c, t) + reshaped = data.reshape(-1, t, c).transpose(0, 2, 1) + M = reshaped.shape[0] + reshaped = reshaped.reshape(M * c, t) + + # find interpolation indices + idx = np.searchsorted(original_t, new_t, side='right') - 1 + idx = np.clip(idx, 0, t - 2) + idx1 = idx + 1 + + x0 = original_t[idx] + x1 = original_t[idx1] + w = (new_t - x0) / (x1 - x0) + + f0 = reshaped[:, idx] + f1 = reshaped[:, idx1] + + out = f0 + (f1 - f0) * w + out = out.reshape(M, c, k * t).transpose(0, 2, 1) + final_shape = shape[:-2] + (k * t, c) + return out.reshape(final_shape) + +def beat_format_save( + save_path: str, + motion_data: np.ndarray, + mask: list[bool] = None, + betas: np.ndarray = None, + expressions: np.ndarray = None, + trans: np.ndarray = None, + upsample: int = None, +): + if betas is None: + betas = np.zeros((motion_data.shape[0], 300), dtype=motion_data.dtype) + if expressions is None: + expressions = np.zeros((motion_data.shape[0], 100), dtype=motion_data.dtype) + if trans is None: + smplx_model = smplx.create( + "./emage_evaltools/smplx_models/", + model_type='smplx', + gender='NEUTRAL_2020', + use_face_contour=False, + num_betas=300, + num_expression_coeffs=100, + ext='npz', + use_pca=False + ).eval() + betas_ts = torch.from_numpy(betas[0:1]).float() + output = smplx_model( + betas=betas_ts, + transl=torch.zeros(1, 3), + expression=torch.zeros(1, 100), + jaw_pose=torch.zeros(1, 3), + global_orient=torch.zeros(1, 3), + body_pose=torch.zeros(1, 63), + left_hand_pose=torch.zeros(1, 45), + right_hand_pose=torch.zeros(1, 45), + return_joints=True, + leye_pose=torch.zeros(1, 3), + reye_pose=torch.zeros(1, 3) + ) + trans = (output["joints"][:, 10, :] + output["joints"][:, 11, :]) / 2 + # print(trans) + trans = -trans.repeat(motion_data.shape[0], 1).numpy() + + if mask is not None: + motion_data = recover_from_mask(motion_data, mask) + + if upsample is not None and upsample > 1: + motion_data = time_upsample_numpy(motion_data, upsample) + betas = time_upsample_numpy(betas, upsample) + expressions = time_upsample_numpy(expressions, upsample) + trans = time_upsample_numpy(trans, upsample) + + np.savez( + save_path, + betas=betas[0], + poses=motion_data, + expressions=expressions, + trans=trans, + model='smplx2020', + gender='neutral', + mocap_frame_rate=30 + ) + +def beat_format_load(load_path: str, mask: list[bool] = None): + data = np.load(load_path, allow_pickle=True) + poses = data['poses'] + betas = data['betas'] + expressions = data['expressions'] + trans = data['trans'] + + if mask is not None: + poses = select_with_mask(poses, mask) + + return { + "poses": poses, + "betas": betas, + "expressions": expressions, + "trans": trans + } \ No newline at end of file diff --git a/emage_utils/motion_rep_transfer.py b/emage_utils/motion_rep_transfer.py new file mode 100644 index 0000000000000000000000000000000000000000..96fccd8229e9b24f58cb44b41f6614538a5d5464 --- /dev/null +++ b/emage_utils/motion_rep_transfer.py @@ -0,0 +1,150 @@ +import smplx +import torch +import numpy as np +from . import rotation_conversions as rc +import os +import wget + +download_path = "./emage_evaltools/" +smplx_model_dir = os.path.join(download_path, "smplx_models", "smplx") +if not os.path.exists(smplx_model_dir): + smplx_model_file_path = os.path.join(smplx_model_dir, "SMPLX_NEUTRAL_2020.npz") + os.makedirs(smplx_model_dir, exist_ok=True) + if not os.path.exists(smplx_model_file_path): + print(f"Downloading {smplx_model_file_path}") + wget.download( + "https://huggingface.co/spaces/H-Liu1997/EMAGE/resolve/main/EMAGE/smplx_models/smplx/SMPLX_NEUTRAL_2020.npz", + smplx_model_file_path, + ) + +smplx_model = smplx.create( + "./emage_evaltools/smplx_models/", + model_type='smplx', + gender='NEUTRAL_2020', + use_face_contour=False, + num_betas=300, + num_expression_coeffs=100, + ext='npz', + use_pca=False, +).eval() + +def get_motion_rep_tensor(motion_tensor, pose_fps=30, device="cuda", betas=None): + global smplx_model + smplx_model = smplx_model.to(device) + bs, n, _ = motion_tensor.shape + motion_tensor = motion_tensor.float().to(device) + motion_tensor_reshaped = motion_tensor.reshape(bs * n, 165) + betas = torch.zeros(n, 300, device=device) if betas is None else betas.to(device).unsqueeze(0).repeat(n, 1) + output = smplx_model( + betas=torch.zeros(bs * n, 300, device=device), + transl=torch.zeros(bs * n, 3, device=device), + expression=torch.zeros(bs * n, 100, device=device), + jaw_pose=torch.zeros(bs * n, 3, device=device), + global_orient=torch.zeros(bs * n, 3, device=device), + body_pose=motion_tensor_reshaped[:, 3:21 * 3 + 3], + left_hand_pose=motion_tensor_reshaped[:, 25 * 3:40 * 3], + right_hand_pose=motion_tensor_reshaped[:, 40 * 3:55 * 3], + return_joints=True, + leye_pose=torch.zeros(bs * n, 3, device=device), + reye_pose=torch.zeros(bs * n, 3, device=device), + ) + joints = output['joints'].reshape(bs, n, 127, 3)[:, :, :55, :] + dt = 1 / pose_fps + init_vel = (joints[:, 1:2] - joints[:, 0:1]) / dt + middle_vel = (joints[:, 2:] - joints[:, :-2]) / (2 * dt) + final_vel = (joints[:, -1:] - joints[:, -2:-1]) / dt + vel = torch.cat([init_vel, middle_vel, final_vel], dim=1) + position = joints + rot_matrices = rc.axis_angle_to_matrix(motion_tensor.reshape(bs, n, 55, 3)) + rot6d = rc.matrix_to_rotation_6d(rot_matrices).reshape(bs, n, 55, 6) + init_vel_ang = (motion_tensor[:, 1:2] - motion_tensor[:, 0:1]) / dt + middle_vel_ang = (motion_tensor[:, 2:] - motion_tensor[:, :-2]) / (2 * dt) + final_vel_ang = (motion_tensor[:, -1:] - motion_tensor[:, -2:-1]) / dt + angular_velocity = torch.cat([init_vel_ang, middle_vel_ang, final_vel_ang], dim=1).reshape(bs, n, 55, 3) + rep15d = torch.cat([position, vel, rot6d, angular_velocity], dim=3).reshape(bs, n, 55 * 15) + return { + "position": position, + "velocity": vel, + "rotation": rot6d, + "axis_angle": motion_tensor, + "angular_velocity": angular_velocity, + "rep15d": rep15d, + } + +def get_motion_rep_numpy(poses_np, pose_fps=30, device="cuda", expressions=None, expression_only=False, betas=None): + # motion["poses"] is expected to be numpy array of shape (n, 165) + # (n, 55*3), axis-angle for 55 joints + global smplx_model + smplx_model = smplx_model.to(device) + n = poses_np.shape[0] + + # Convert numpy to torch tensor for SMPL-X forward pass + poses_ts = torch.from_numpy(poses_np).float().to(device).unsqueeze(0) # (1, n, 165) + poses_ts_reshaped = poses_ts.reshape(-1, 165) # (n, 165) + betas = torch.zeros(n, 300, device=device) if betas is None else torch.from_numpy(betas).to(device).unsqueeze(0).repeat(n, 1) + if expressions is not None and expression_only: + # print("xx") + expressions = torch.from_numpy(expressions).float().to(device) + output = smplx_model( + betas=betas, + transl=torch.zeros(n, 3, device=device), + expression=expressions, + jaw_pose=poses_ts_reshaped[:, 22 * 3:23 * 3], + global_orient=torch.zeros(n, 3, device=device), + body_pose=torch.zeros(n, 21*3, device=device), + left_hand_pose=torch.zeros(n, 15*3, device=device), + right_hand_pose=torch.zeros(n, 15*3, device=device), + return_joints=True, + leye_pose=torch.zeros(n, 3, device=device), + reye_pose=torch.zeros(n, 3, device=device), + ) + joints = output["vertices"].detach().cpu().numpy().reshape(n, -1) + return {"vertices": joints} + + # Run smplx model to get joints + output = smplx_model( + betas=betas, + transl=torch.zeros(n, 3, device=device), + expression=torch.zeros(n, 100, device=device), + jaw_pose=torch.zeros(n, 3, device=device), + global_orient=torch.zeros(n, 3, device=device), + body_pose=poses_ts_reshaped[:, 3:21 * 3 + 3], + left_hand_pose=poses_ts_reshaped[:, 25 * 3:40 * 3], + right_hand_pose=poses_ts_reshaped[:, 40 * 3:55 * 3], + return_joints=True, + leye_pose=torch.zeros(n, 3, device=device), + reye_pose=torch.zeros(n, 3, device=device), + ) + joints = output["joints"].detach().cpu().numpy().reshape(n, 127, 3)[:, :55, :] + + dt = 1 / pose_fps + # Compute linear velocity + init_vel = (joints[1:2] - joints[0:1]) / dt + middle_vel = (joints[2:] - joints[:-2]) / (2 * dt) + final_vel = (joints[-1:] - joints[-2:-1]) / dt + vel = np.concatenate([init_vel, middle_vel, final_vel], axis=0) + + position = joints + + # Compute rotation 6D from axis-angle + poses_ts_reshaped_aa = poses_ts.reshape(1, n, 55, 3) + rot_matrices = rc.axis_angle_to_matrix(poses_ts_reshaped_aa)[0] # (n, 55, 3, 3) + rot6d = rc.matrix_to_rotation_6d(rot_matrices).reshape(n, 55, 6).cpu().numpy() + + # Compute angular velocity + init_vel_ang = (poses_np[1:2] - poses_np[0:1]) / dt + middle_vel_ang = (poses_np[2:] - poses_np[:-2]) / (2 * dt) + final_vel_ang = (poses_np[-1:] - poses_np[-2:-1]) / dt + angular_velocity = np.concatenate([init_vel_ang, middle_vel_ang, final_vel_ang], axis=0).reshape(n, 55, 3) + + # rep15d: position(55*3), vel(55*3), rot6d(55*6), angular_velocity(55*3) => total 55*(3+3+6+3)=55*15 + rep15d = np.concatenate([position, vel, rot6d, angular_velocity], axis=2).reshape(n, 55 * 15) + + return { + "position": position, + "velocity": vel, + "rotation": rot6d, + "axis_angle": poses_np, + "angular_velocity": angular_velocity, + "rep15d": rep15d, + } diff --git a/emage_utils/npz2pose.py b/emage_utils/npz2pose.py new file mode 100644 index 0000000000000000000000000000000000000000..7945a19239ff3a06cba3767d93669e9761779b0f --- /dev/null +++ b/emage_utils/npz2pose.py @@ -0,0 +1,359 @@ +""" +Thanks to the author of this API +Tomoya Akiyama: https://research.cyberagent.ai/people/tomoya_akiyama/ +""" + +import math +import cv2 +import numpy as np +import torch +import smplx +from pytorch3d.renderer import PerspectiveCameras +from torchvision.io import write_video +from torchvision.transforms.functional import convert_image_dtype + +SMPLX_BODY_JOINT_EDGES = [ + {"indices": [12, 17], "color": [255, 0, 0]}, + {"indices": [12, 16], "color": [255, 85, 0]}, + {"indices": [17, 19], "color": [255, 170, 0]}, + {"indices": [19, 21], "color": [255, 255, 0]}, + {"indices": [16, 18], "color": [170, 255, 0]}, + {"indices": [18, 20], "color": [85, 255, 0]}, + {"indices": [2, 12], "color": [0, 255, 0]}, + {"indices": [2, 5], "color": [0, 255, 85]}, + {"indices": [5, 8], "color": [0, 255, 170]}, + {"indices": [1, 12], "color": [0, 255, 255]}, + {"indices": [1, 4], "color": [0, 170, 255]}, + {"indices": [4, 7], "color": [0, 85, 255]}, + {"indices": [12, 55], "color": [0, 0, 255]}, + {"indices": [55, 56], "color": [85, 0, 255]}, + {"indices": [56, 58], "color": [170, 0, 255]}, + {"indices": [55, 57], "color": [255, 0, 255]}, + {"indices": [57, 59], "color": [255, 0, 170]}, +] +SMPLX_BODY_JOINTS = [ + {"index": 55, "color": [255, 0, 0]}, + {"index": 12, "color": [255, 85, 0]}, + {"index": 17, "color": [255, 170, 0]}, + {"index": 19, "color": [255, 255, 0]}, + {"index": 21, "color": [170, 255, 0]}, + {"index": 16, "color": [85, 255, 0]}, + {"index": 18, "color": [0, 255, 0]}, + {"index": 20, "color": [0, 255, 85]}, + {"index": 2, "color": [0, 255, 170]}, + {"index": 5, "color": [0, 255, 255]}, + {"index": 8, "color": [0, 170, 255]}, + {"index": 1, "color": [0, 85, 255]}, + {"index": 4, "color": [0, 0, 255]}, + {"index": 7, "color": [85, 0, 255]}, + {"index": 56, "color": [170, 0, 255]}, + {"index": 57, "color": [255, 0, 255]}, + {"index": 58, "color": [255, 0, 170]}, + {"index": 59, "color": [255, 0, 85]}, +] +SMPLX_HAND_JOINT_EDGES = [ + {"indices": [21, 52], "color": [255, 0, 0]}, + {"indices": [52, 53], "color": [255, 76, 0]}, + {"indices": [53, 54], "color": [255, 153, 0]}, + {"indices": [54, 71], "color": [255, 229, 0]}, + {"indices": [21, 40], "color": [204, 255, 0]}, + {"indices": [40, 41], "color": [128, 255, 0]}, + {"indices": [41, 42], "color": [51, 255, 0]}, + {"indices": [42, 72], "color": [0, 255, 26]}, + {"indices": [21, 43], "color": [0, 255, 102]}, + {"indices": [43, 44], "color": [0, 255, 179]}, + {"indices": [44, 45], "color": [0, 255, 255]}, + {"indices": [45, 73], "color": [0, 179, 255]}, + {"indices": [21, 49], "color": [0, 102, 255]}, + {"indices": [49, 50], "color": [0, 26, 255]}, + {"indices": [50, 51], "color": [51, 0, 255]}, + {"indices": [51, 74], "color": [128, 0, 255]}, + {"indices": [21, 46], "color": [204, 0, 255]}, + {"indices": [46, 47], "color": [255, 0, 230]}, + {"indices": [47, 48], "color": [255, 0, 153]}, + {"indices": [48, 75], "color": [255, 0, 77]}, + {"indices": [20, 37], "color": [255, 0, 0]}, + {"indices": [37, 38], "color": [255, 76, 0]}, + {"indices": [38, 39], "color": [255, 153, 0]}, + {"indices": [39, 66], "color": [255, 229, 0]}, + {"indices": [20, 25], "color": [204, 255, 0]}, + {"indices": [25, 26], "color": [128, 255, 0]}, + {"indices": [26, 27], "color": [51, 255, 0]}, + {"indices": [27, 67], "color": [0, 255, 26]}, + {"indices": [20, 28], "color": [0, 255, 102]}, + {"indices": [28, 29], "color": [0, 255, 179]}, + {"indices": [29, 30], "color": [0, 255, 255]}, + {"indices": [30, 68], "color": [0, 179, 255]}, + {"indices": [20, 34], "color": [0, 102, 255]}, + {"indices": [34, 35], "color": [0, 26, 255]}, + {"indices": [35, 36], "color": [51, 0, 255]}, + {"indices": [36, 69], "color": [128, 0, 255]}, + {"indices": [20, 31], "color": [204, 0, 255]}, + {"indices": [31, 32], "color": [255, 0, 230]}, + {"indices": [32, 33], "color": [255, 0, 153]}, + {"indices": [33, 70], "color": [255, 0, 77]}, +] +SMPLX_HAND_JOINTS = [20, 21] + list(range(25, 55)) + list(range(66, 76)) +SMPLX_FACE_LANDMARKS = list(range(76, 144)) + +def _draw_bodypose(canvas, joints_np): + c = canvas.copy() + for edge_dict in SMPLX_BODY_JOINT_EDGES: + i = edge_dict["indices"] + color = edge_dict["color"] + xy = joints_np[i] + center = np.mean(xy, axis=0).astype(int) + length = np.linalg.norm(xy[0] - xy[1]) + angle = math.degrees(math.atan2(xy[0, 1] - xy[1, 1], xy[0, 0] - xy[1, 0])) + polygon = cv2.ellipse2Poly(center, (int(length / 2), 4), int(angle), 0, 360, 1) + cv2.fillConvexPoly(c, polygon, color) + c = (c * 0.6).astype(np.uint8) + for j_info in SMPLX_BODY_JOINTS: + center = joints_np[j_info["index"]].astype(int) + cv2.circle(c, tuple(center), 4, (255, 255, 255), -1) + return c + +def _draw_handpose(canvas, joints_np): + c = canvas.copy() + for edge_dict in SMPLX_HAND_JOINT_EDGES: + i = edge_dict["indices"] + color = edge_dict["color"] + xy = joints_np[i].astype(int) + if xy.min() > 0: + cv2.line(c, tuple(xy[0]), tuple(xy[1]), color, 2) + for j_idx in SMPLX_HAND_JOINTS: + center = joints_np[j_idx].astype(int) + if center.min() > 0: + cv2.circle(c, tuple(center), 4, (0, 0, 255), -1) + return c + +def _draw_facepose(canvas, joints_np): + c = canvas.copy() + for j_idx in SMPLX_FACE_LANDMARKS: + center = joints_np[j_idx].astype(int) + if center.min() > 0: + cv2.circle(c, tuple(center), 3, (255, 255, 255), -1) + return c + +def _draw_joints_2d(joints_2d, height, width, face_only): + outputs = [] + for j2d in joints_2d: + # Convert each frame's joints to NumPy + j2d_np = j2d.detach().cpu().numpy() + c = np.zeros((height, width, 3), dtype=np.uint8) + if face_only: + c = _draw_facepose(c, j2d_np) + else: + c = _draw_bodypose(c, j2d_np) + c = _draw_handpose(c, j2d_np) + c = _draw_facepose(c, j2d_np) + outputs.append(convert_image_dtype(torch.tensor(c, dtype=torch.uint8), torch.uint8)) + return torch.stack(outputs).permute(0, 3, 1, 2) + +def _draw_joints_3d(joints_3d, height, width, face_only): + outputs = [] + for j3d in joints_3d: + xy = j3d[:, :2].detach().cpu().numpy().copy() + z = j3d[:, 2].detach().cpu().numpy().copy() + z_min, z_max = z.min(), z.max() + z_norm = (z - z_min) / (z_max - z_min + 1e-8) + + # Normalize XY to fit in the image + xy[:, 0] = (xy[:, 0] - xy[:, 0].min()) / (xy[:, 0].max() - xy[:, 0].min() + 1e-8) * (width - 1) + xy[:, 1] = (xy[:, 1] - xy[:, 1].min()) / (xy[:, 1].max() - xy[:, 1].min() + 1e-8) * (height - 1) + + c = np.zeros((height, width, 3), dtype=np.uint8) + # j2d: [num_joints, 3], last dim is the normalized z + j2d = np.hstack([xy, z_norm.reshape(-1, 1)]) + if face_only: + c = _draw_facepose(c, j2d) + else: + c = _draw_bodypose(c, j2d) + c = _draw_handpose(c, j2d) + c = _draw_facepose(c, j2d) + + outputs.append(convert_image_dtype(torch.tensor(c, dtype=torch.uint8), torch.uint8)) + return torch.stack(outputs).permute(0, 3, 1, 2) + +def _load_motion_dict( + motion_dict, + device, + remove_global=False, + face_only=False +): + n = motion_dict["poses"].shape[0] + smplx_inputs = { + "betas": torch.tensor(motion_dict["betas"]).view(1, -1), + "global_orient": torch.tensor(motion_dict["poses"][:, :3]), + "body_pose": torch.tensor(motion_dict["poses"][:, 3 : 22 * 3]), + "left_hand_pose": torch.tensor(motion_dict["poses"][:, 25 * 3 : 40 * 3]), + "right_hand_pose": torch.tensor(motion_dict["poses"][:, 40 * 3 : 55 * 3]), + "transl": torch.tensor(motion_dict["trans"]), + "expression": torch.tensor(motion_dict["expressions"]), + "jaw_pose": torch.tensor(motion_dict["poses"][:, 22 * 3 : 23 * 3]), + "leye_pose": torch.tensor(motion_dict["poses"][:, 23 * 3 : 24 * 3]), + "reye_pose": torch.tensor(motion_dict["poses"][:, 24 * 3 : 25 * 3]), + } + # Move everything to device + for k, v in smplx_inputs.items(): + smplx_inputs[k] = v.to(device=device, dtype=torch.float32) + + # 1) If remove_global == True, keep 'transl' at the first frame's value for all frames + if remove_global: + first_frame_trans = smplx_inputs["transl"][0].clone() + smplx_inputs["transl"][:] = first_frame_trans + + # 2) If face_only == True, zero out everything but the jaw pose + if face_only: + smplx_inputs["global_orient"][:] = 0.0 + smplx_inputs["body_pose"][:] = 0.0 + smplx_inputs["left_hand_pose"][:] = 0.0 + smplx_inputs["right_hand_pose"][:] = 0.0 + smplx_inputs["leye_pose"][:] = 0.0 + smplx_inputs["reye_pose"][:] = 0.0 + # The jaw_pose and expression remain as is (allowing mouth movements), + # so the head is "frozen" in place except for jaw animation. + + return n, smplx_inputs + +def _get_smplx_model(smplx_folder, batch_size, device): + smplx_model = smplx.create( + model_path=smplx_folder, + model_type="smplx", + gender="NEUTRAL_2020", + create_global_orient=True, + create_body_pose=True, + create_betas=True, + create_left_hand_pose=True, + create_right_hand_pose=True, + create_expression=True, + create_jaw_pose=True, + create_leye_pose=True, + create_reye_pose=True, + create_transl=True, + use_face_contour=True, + use_pca=False, + flat_hand_mean=False, + use_hands=True, + use_face=True, + num_betas=300, + num_expression_coeffs=100, + batch_size=batch_size, + dtype=torch.float32, + ).to(device) + return smplx_model.eval() + +def _get_cameras( + batch_size, + height, + width, + focal_length, + camera_transl, + device +): + r = torch.tensor( + [[-1, 0, 0], + [ 0, 1, 0], + [ 0, 0, 1]], + device=device, dtype=torch.float32 + ) + t = torch.tensor(camera_transl, device=device, dtype=torch.float32) + cameras = PerspectiveCameras( + focal_length=focal_length, + principal_point=((width / 2, height / 2),), + in_ndc=False, + R=r.expand(batch_size, -1, -1), + T=t.expand(batch_size, -1), + image_size=((height, width),), + device=device, + ) + return cameras + +# New fix code snippet (inside render2d or render3d): +def render2d( + motion_dict, + resolution=(512, 512), + face_only=False, + remove_global=False, + smplx_folder="./emage_evaltools/smplx_models/", + focal_length=5000.0, + camera_transl=(0.0, -0.8, 16.0), + device=torch.device("cuda"), +): + h, w = resolution + # for face-only, override camera to zoom in + if face_only: + camera_transl = (0.0, -1.55, 6.0) + focal_length = 10000.0 + n, smplx_inputs = _load_motion_dict( + motion_dict, device, remove_global=remove_global, face_only=face_only + ) + model = _get_smplx_model(smplx_folder, n, device) + out = model(**smplx_inputs) + cams = _get_cameras(n, h, w, focal_length, camera_transl, device) + j2d = cams.transform_points_screen(out.joints)[:, :, :2] + frames_2d = _draw_joints_2d(j2d, h, w, face_only) + return frames_2d + +def render3d( + motion_dict, + resolution=(512, 512), + face_only=False, + remove_global=False, + smplx_folder="./emage_evaltools/smplx_models/", + device=torch.device("cuda"), +): + h, w = resolution + n, smplx_inputs = _load_motion_dict( + motion_dict, + device, + remove_global=remove_global, + face_only=face_only + ) + model = _get_smplx_model(smplx_folder, n, device) + out = model(**smplx_inputs) + frames_3d = _draw_joints_3d(out.joints, h, w, face_only) + return frames_3d + +def example_usage(): + # Suppose we have an NPZ with "poses", "trans", "betas", "expressions", etc. + motion_dict = np.load("/result_motion.npz", allow_pickle=True) + + # 2D face (freeze body, remove global motion) + v2d_face = render2d( + motion_dict, + resolution=(512, 512), + face_only=True, + remove_global=True + ) + write_video("/save_path_face_2d.mp4", v2d_face.permute(0, 2, 3, 1), fps=30) + + # 2D body (show entire body, keep global motion) + v2d_body = render2d( + motion_dict, + resolution=(1080, 1920), + face_only=False, + remove_global=False + ) + write_video("/save_path_body_2d.mp4", v2d_body.permute(0, 2, 3, 1), fps=30) + + # 3D face (freeze body, remove global motion) + v3d_face = render3d( + motion_dict, + resolution=(512, 512), + face_only=True, + remove_global=True + ) + write_video("/save_path_face_3d.mp4", v3d_face.permute(0, 2, 3, 1), fps=30) + + # 3D body (show entire body, keep global motion) + v3d_body = render3d( + motion_dict, + resolution=(1080, 1920), + face_only=False, + remove_global=False + ) + write_video("/save_path_body_3d.mp4", v3d_body.permute(0, 2, 3, 1), fps=30) + +if __name__ == "__main__": + example_usage() diff --git a/models/utils/rotation_conversions.py b/emage_utils/rotation_conversions.py similarity index 94% rename from models/utils/rotation_conversions.py rename to emage_utils/rotation_conversions.py index f2bfaa1b2247622bff35d3f9b15e8eb84064aa53..24f27b0ff5b185f80acc8149e31c7a6e709bf548 100644 --- a/models/utils/rotation_conversions.py +++ b/emage_utils/rotation_conversions.py @@ -548,3 +548,35 @@ def matrix_to_rotation_6d(matrix: torch.Tensor) -> torch.Tensor: Retrieved from http://arxiv.org/abs/1812.07035 """ return matrix[..., :2, :].clone().reshape(*matrix.size()[:-2], 6) + + +def axis_angle_to_rotation_6d(axis_angle): + """ + Convert rotations given as axis/angle to 6D rotation representation. + + Args: + axis_angle: Rotations given as a vector in axis angle form, + as a tensor of shape (..., 3), where the magnitude is + the angle turned anticlockwise in radians around the + vector's direction. + + Returns: + 6D rotation representation, of size (..., 6). + """ + return matrix_to_rotation_6d(axis_angle_to_matrix(axis_angle)) + + +def rotation_6d_to_axis_angle(rot6d): + """ + Convert rotations given as 6D rotation representation to axis/angle. + + Args: + rot6d: 6D rotation representation, of size (..., 6). + + Returns: + Rotations given as a vector in axis angle form, as a tensor + of shape (..., 3), where the magnitude is the angle + turned anticlockwise in radians around the vector's + direction. + """ + return matrix_to_axis_angle(rotation_6d_to_matrix(rot6d)) diff --git a/examples/audio/2_scott_0_103_103_10s.wav b/examples/audio/2_scott_0_103_103_10s.wav new file mode 100644 index 0000000000000000000000000000000000000000..447b2234e1b9a807227c508c9259213151675ae6 --- /dev/null +++ b/examples/audio/2_scott_0_103_103_10s.wav @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b5eded210269d4734198413658a512d914027a4295000f41eaaee869e7b0c056 +size 1749440 diff --git a/models/.ipynb_checkpoints/emage_audio-checkpoint.py b/models/.ipynb_checkpoints/emage_audio-checkpoint.py deleted file mode 100644 index 06d43a28284c4b4d339c38efeb44c4a131e3e62d..0000000000000000000000000000000000000000 --- a/models/.ipynb_checkpoints/emage_audio-checkpoint.py +++ /dev/null @@ -1,243 +0,0 @@ -import copy -import math -import pickle -import numpy as np -import torch -import torch.nn as nn -from .utils.layer import BasicBlock -from .motion_encoder import * - - -class WavEncoder(nn.Module): - def __init__(self, out_dim, audio_in=1): - super().__init__() - self.out_dim = out_dim - self.feat_extractor = nn.Sequential( - BasicBlock(audio_in, out_dim//4, 15, 5, first_dilation=1600, downsample=True), - BasicBlock(out_dim//4, out_dim//4, 15, 6, first_dilation=0, downsample=True), - BasicBlock(out_dim//4, out_dim//4, 15, 1, first_dilation=7, ), - BasicBlock(out_dim//4, out_dim//2, 15, 6, first_dilation=0, downsample=True), - BasicBlock(out_dim//2, out_dim//2, 15, 1, first_dilation=7), - BasicBlock(out_dim//2, out_dim, 15, 3, first_dilation=0,downsample=True), - ) - def forward(self, wav_data): - # print(wav_data.shape) - if wav_data.dim() == 2: - wav_data = wav_data.unsqueeze(1) - else: - wav_data = wav_data.transpose(1, 2) - out = self.feat_extractor(wav_data) - return out.transpose(1, 2) - - -class MLP(nn.Module): - def __init__(self, in_dim, hidden_size, out_dim): - super().__init__() - self.mlp = nn.Sequential( - nn.Linear(in_dim, hidden_size), - nn.LeakyReLU(0.2, True), - nn.Linear(hidden_size, out_dim) - ) - def forward(self, inputs): - out = self.mlp(inputs) - return out - - -class PeriodicPositionalEncoding(nn.Module): - def __init__(self, d_model, dropout=0.1, period=15, max_seq_len=60): - super(PeriodicPositionalEncoding, self).__init__() - self.dropout = nn.Dropout(p=dropout) - pe = torch.zeros(period, d_model) - position = torch.arange(0, period, dtype=torch.float).unsqueeze(1) - div_term = torch.exp(torch.arange(0, d_model, 2).float() * (-math.log(10000.0) / d_model)) - pe[:, 0::2] = torch.sin(position * div_term) - pe[:, 1::2] = torch.cos(position * div_term) - pe = pe.unsqueeze(0) # (1, period, d_model) - repeat_num = (max_seq_len//period) + 1 - pe = pe.repeat(1, repeat_num, 1) # (1, repeat_num, period, d_model) - self.register_buffer('pe', pe) - def forward(self, x): - # print(self.pe.shape, x.shape) - x = x + self.pe[:, :x.size(1), :] - return self.dropout(x) - - -class MAGE_Transformer(nn.Module): - def __init__(self, args): - super(MAGE_Transformer, self).__init__() - self.args = args - # with open(f"{args.data_path}weights/vocab.pkl", 'rb') as f: - # self.lang_model = pickle.load(f) - # pre_trained_embedding = self.lang_model.word_embedding_weights - # self.text_pre_encoder_face = nn.Embedding.from_pretrained(torch.FloatTensor(pre_trained_embedding),freeze=args.t_fix_pre) - # self.text_encoder_face = nn.Linear(300, args.audio_f) - # self.text_encoder_face = nn.Linear(300, args.audio_f) - # self.text_pre_encoder_body = nn.Embedding.from_pretrained(torch.FloatTensor(pre_trained_embedding),freeze=args.t_fix_pre) - # self.text_encoder_body = nn.Linear(300, args.audio_f) - # self.text_encoder_body = nn.Linear(300, args.audio_f) - - self.audio_pre_encoder_face = WavEncoder(args.audio_f, audio_in=1) - self.audio_pre_encoder_body = WavEncoder(args.audio_f, audio_in=1) - - # self.at_attn_face = nn.Linear(args.audio_f*2, args.audio_f*2) - # self.at_attn_body = nn.Linear(args.audio_f*2, args.audio_f*2) - - args_top = copy.deepcopy(self.args) - args_top.vae_layer = 3 - args_top.vae_length = args.motion_f - args_top.vae_test_dim = args.pose_dims+3+4 - self.motion_encoder = VQEncoderV6(args_top) # masked motion to latent bs t 333 to bs t 256 - - # face decoder - self.feature2face = nn.Linear(args.audio_f*2, args.hidden_size) - self.face2latent = nn.Linear(args.hidden_size, args.vae_codebook_size) - self.transformer_de_layer = nn.TransformerDecoderLayer( - d_model=self.args.hidden_size, - nhead=4, - dim_feedforward=self.args.hidden_size*2, - batch_first=True - ) - self.face_decoder = nn.TransformerDecoder(self.transformer_de_layer, num_layers=4) - self.position_embeddings = PeriodicPositionalEncoding(self.args.hidden_size, period=self.args.pose_length, max_seq_len=self.args.pose_length) - - # motion decoder - self.transformer_en_layer = nn.TransformerEncoderLayer( - d_model=self.args.hidden_size, - nhead=4, - dim_feedforward=self.args.hidden_size*2, - batch_first=True - ) - self.motion_self_encoder = nn.TransformerEncoder(self.transformer_en_layer, num_layers=1) - self.audio_feature2motion = nn.Linear(args.audio_f, args.hidden_size) - self.feature2motion = nn.Linear(args.motion_f, args.hidden_size) - - self.bodyhints_face = MLP(args.motion_f, args.hidden_size, args.motion_f) - self.bodyhints_body = MLP(args.motion_f, args.hidden_size, args.motion_f) - self.motion2latent_upper = MLP(args.hidden_size, args.hidden_size, self.args.hidden_size) - self.motion2latent_hands = MLP(args.hidden_size, args.hidden_size, self.args.hidden_size) - self.motion2latent_lower = MLP(args.hidden_size, args.hidden_size, self.args.hidden_size) - self.wordhints_decoder = nn.TransformerDecoder(self.transformer_de_layer, num_layers=8) - - self.upper_decoder = nn.TransformerDecoder(self.transformer_de_layer, num_layers=1) - self.hands_decoder = nn.TransformerDecoder(self.transformer_de_layer, num_layers=1) - self.lower_decoder = nn.TransformerDecoder(self.transformer_de_layer, num_layers=1) - - self.face_classifier = MLP(self.args.vae_codebook_size, args.hidden_size, self.args.vae_codebook_size) - self.upper_classifier = MLP(self.args.vae_codebook_size, args.hidden_size, self.args.vae_codebook_size) - self.hands_classifier = MLP(self.args.vae_codebook_size, args.hidden_size, self.args.vae_codebook_size) - self.lower_classifier = MLP(self.args.vae_codebook_size, args.hidden_size, self.args.vae_codebook_size) - - self.mask_embeddings = nn.Parameter(torch.zeros(1, 1, self.args.pose_dims+3+4)) - self.motion_down_upper = nn.Linear(args.hidden_size, self.args.vae_codebook_size) - self.motion_down_hands = nn.Linear(args.hidden_size, self.args.vae_codebook_size) - self.motion_down_lower = nn.Linear(args.hidden_size, self.args.vae_codebook_size) - self.motion_down_upper = nn.Linear(args.hidden_size, self.args.vae_codebook_size) - self.motion_down_hands = nn.Linear(args.hidden_size, self.args.vae_codebook_size) - self.motion_down_lower = nn.Linear(args.hidden_size, self.args.vae_codebook_size) - self._reset_parameters() - - self.spearker_encoder_body = nn.Embedding(25, args.hidden_size) - self.spearker_encoder_face = nn.Embedding(25, args.hidden_size) - - def _reset_parameters(self): - nn.init.normal_(self.mask_embeddings, 0, self.args.hidden_size ** -0.5) - - def forward(self, in_audio=None, in_word=None, mask=None, is_test=None, in_motion=None, use_attentions=True, use_word=True, in_id = None): - # in_word_face = self.text_pre_encoder_face(in_word) - # in_word_face = self.text_encoder_face(in_word_face) - # in_word_body = self.text_pre_encoder_body(in_word) - # in_word_body = self.text_encoder_body(in_word_body) - # bs, t, c = in_word_face.shape - in_audio_face = self.audio_pre_encoder_face(in_audio) - in_audio_body = self.audio_pre_encoder_body(in_audio) - bs, t, c = in_audio_body.shape - # if in_audio_face.shape[1] != in_motion.shape[1]: - # diff_length = in_motion.shape[1]- in_audio_face.shape[1] - # if diff_length < 0: - # in_audio_face = in_audio_face[:, :diff_length, :] - # in_audio_body = in_audio_body[:, :diff_length, :] - # else: - # in_audio_face = torch.cat((in_audio_face, in_audio_face[:,-diff_length:]),1) - # in_audio_body = torch.cat((in_audio_body, in_audio_body[:,-diff_length:]),1) - - # if use_attentions: - # alpha_at_face = torch.cat([in_word_face, in_audio_face], dim=-1).reshape(bs, t, c*2) - # alpha_at_face = self.at_attn_face(alpha_at_face).reshape(bs, t, c, 2) - # alpha_at_face = alpha_at_face.softmax(dim=-1) - # fusion_face = in_word_face * alpha_at_face[:,:,:,1] + in_audio_face * alpha_at_face[:,:,:,0] - # alpha_at_body = torch.cat([in_word_body, in_audio_body], dim=-1).reshape(bs, t, c*2) - # alpha_at_body = self.at_attn_body(alpha_at_body).reshape(bs, t, c, 2) - # alpha_at_body = alpha_at_body.softmax(dim=-1) - # fusion_body = in_word_body * alpha_at_body[:,:,:,1] + in_audio_body * alpha_at_body[:,:,:,0] - # else: - fusion_face = in_audio_face - fusion_body = in_audio_body - - masked_embeddings = self.mask_embeddings.expand_as(in_motion) - masked_motion = torch.where(mask == 1, masked_embeddings, in_motion) # bs, t, 256 - body_hint = self.motion_encoder(masked_motion) # bs t 256 - speaker_embedding_face = self.spearker_encoder_face(in_id).squeeze(2) - speaker_embedding_body = self.spearker_encoder_body(in_id).squeeze(2) - - # decode face - use_body_hints = True - if use_body_hints: - body_hint_face = self.bodyhints_face(body_hint) - fusion_face = torch.cat([fusion_face, body_hint_face], dim=2) - a2g_face = self.feature2face(fusion_face) - face_embeddings = speaker_embedding_face - face_embeddings = self.position_embeddings(face_embeddings) - decoded_face = self.face_decoder(tgt=face_embeddings, memory=a2g_face) - face_latent = self.face2latent(decoded_face) - cls_face = self.face_classifier(face_latent) - - # motion spatial encoder - body_hint_body = self.bodyhints_body(body_hint) - motion_embeddings = self.feature2motion(body_hint_body) - motion_embeddings = speaker_embedding_body + motion_embeddings - motion_embeddings = self.position_embeddings(motion_embeddings) - - # bi-directional self-attention - motion_refined_embeddings = self.motion_self_encoder(motion_embeddings) - - # audio to gesture cross-modal attention - if use_word: - a2g_motion = self.audio_feature2motion(fusion_body) - motion_refined_embeddings_in = motion_refined_embeddings + speaker_embedding_body - motion_refined_embeddings_in = self.position_embeddings(motion_refined_embeddings) - word_hints = self.wordhints_decoder(tgt=motion_refined_embeddings_in, memory=a2g_motion) - motion_refined_embeddings = motion_refined_embeddings + word_hints - - # feedforward - upper_latent = self.motion2latent_upper(motion_refined_embeddings) - hands_latent = self.motion2latent_hands(motion_refined_embeddings) - lower_latent = self.motion2latent_lower(motion_refined_embeddings) - - upper_latent_in = upper_latent + speaker_embedding_body - upper_latent_in = self.position_embeddings(upper_latent_in) - hands_latent_in = hands_latent + speaker_embedding_body - hands_latent_in = self.position_embeddings(hands_latent_in) - lower_latent_in = lower_latent + speaker_embedding_body - lower_latent_in = self.position_embeddings(lower_latent_in) - - # transformer decoder - motion_upper = self.upper_decoder(tgt=upper_latent_in, memory=hands_latent+lower_latent) - motion_hands = self.hands_decoder(tgt=hands_latent_in, memory=upper_latent+lower_latent) - motion_lower = self.lower_decoder(tgt=lower_latent_in, memory=upper_latent+hands_latent) - upper_latent = self.motion_down_upper(motion_upper+upper_latent) - hands_latent = self.motion_down_hands(motion_hands+hands_latent) - lower_latent = self.motion_down_lower(motion_lower+lower_latent) - cls_lower = self.lower_classifier(lower_latent) - cls_upper = self.upper_classifier(upper_latent) - cls_hands = self.hands_classifier(hands_latent) - - return { - "rec_face":face_latent, - "rec_upper":upper_latent, - "rec_lower":lower_latent, - "rec_hands":hands_latent, - "cls_face":cls_face, - "cls_upper":cls_upper, - "cls_lower":cls_lower, - "cls_hands":cls_hands, - } \ No newline at end of file diff --git a/models/__pycache__/emage_audio.cpython-310.pyc b/models/__pycache__/emage_audio.cpython-310.pyc deleted file mode 100644 index 2a8b3e3900da28a70bbc073f0bb594b4a51bd8ea..0000000000000000000000000000000000000000 Binary files a/models/__pycache__/emage_audio.cpython-310.pyc and /dev/null differ diff --git a/models/__pycache__/emage_audio.cpython-38.pyc b/models/__pycache__/emage_audio.cpython-38.pyc deleted file mode 100644 index beb71c71308d3293fab5de822488d0379016820d..0000000000000000000000000000000000000000 Binary files a/models/__pycache__/emage_audio.cpython-38.pyc and /dev/null differ diff --git a/models/__pycache__/motion_encoder.cpython-310.pyc b/models/__pycache__/motion_encoder.cpython-310.pyc deleted file mode 100644 index e935774f62f0c45067cc41b10b65750945b89252..0000000000000000000000000000000000000000 Binary files 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OrderedDict + +from transformers.configuration_utils import PretrainedConfig +from transformers.utils import logging + +from ...models.camn_audio.configuration_camn_audio import CamnAudioConfig + +logger = logging.get_logger(__name__) + +CONFIG_MAPPING = OrderedDict( + [ + ("camn_audio", CamnAudioConfig), + # Add other model configurations here if needed + ] +) + + +class AutoConfig: + r""" + AutoConfig is a generic configuration class to instantiate a model configuration. + It is designed to be instantiated using the `from_pretrained` method: + config = AutoConfig.from_pretrained("model_name_or_path") + """ + + @classmethod + def from_pretrained(cls, pretrained_model_name_or_path, **kwargs): + from transformers import PretrainedConfig + config_dict, _ = PretrainedConfig.get_config_dict(pretrained_model_name_or_path, **kwargs) + + if "model_type" in config_dict: + model_type = config_dict["model_type"] + if model_type in CONFIG_MAPPING: + config_class = CONFIG_MAPPING[model_type] + return config_class.from_dict(config_dict, **kwargs) + else: + raise ValueError(f"Unrecognized model type {model_type} in config.json.") + else: + # Fallback: If no model_type, try all config classes and see if any matches + for pattern, config_class in CONFIG_MAPPING.items(): + if pattern in pretrained_model_name_or_path: + return config_class.from_dict(config_dict, **kwargs) + # If still not found + raise ValueError("Config file is missing the `model_type` field and no default model type could be inferred.") diff --git a/models/auto/modeling_auto.py b/models/auto/modeling_auto.py new file mode 100644 index 0000000000000000000000000000000000000000..25d69d7a53d3c7b37847874154ba15e50092885d --- /dev/null +++ b/models/auto/modeling_auto.py @@ -0,0 +1,38 @@ +# modeling_auto.py +from collections import OrderedDict + +from transformers.configuration_utils import PretrainedConfig +from transformers.utils import logging + +from ...models.camn_audio.modeling_camn_audio import CamnAudioModel +from ...models.camn_audio.configuration_camn_audio import CamnAudioConfig + +logger = logging.get_logger(__name__) + +MODEL_MAPPING = OrderedDict( + [ + (CamnAudioConfig, CamnAudioModel), + # Add other model mappings here if needed + ] +) + + +class AutoModel: + r""" + AutoModel is a generic model class to instantiate a model from a configuration. + It is designed to be instantiated using the `from_pretrained` method: + model = AutoModel.from_pretrained("model_name_or_path") + """ + + @classmethod + def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs): + from transformers import PretrainedConfig + config = PretrainedConfig.from_pretrained(pretrained_model_name_or_path, **kwargs) + for config_class, model_class in MODEL_MAPPING.items(): + if isinstance(config, config_class): + return model_class.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs) + + raise ValueError( + f"Unrecognized configuration class {config.__class__} for this kind of AutoModel: {cls.__name__}.\n" + f"Model type should be one of {', '.join(c.__name__ for c in MODEL_MAPPING.keys())}." + ) diff --git a/models/camn.py b/models/camn.py deleted file mode 100644 index 5b9cd7f9f70447fd64a8376203a2a22925991c16..0000000000000000000000000000000000000000 --- a/models/camn.py +++ /dev/null @@ -1,449 +0,0 @@ -import torch -import torch.nn as nn -import os -import pickle -import numpy as np -from torch.nn.utils import weight_norm -from .utils.build_vocab import Vocab - -class Chomp1d(nn.Module): - def __init__(self, chomp_size): - super(Chomp1d, self).__init__() - self.chomp_size = chomp_size - - def forward(self, x): - return x[:, :, :-self.chomp_size].contiguous() - - -class TemporalBlock(nn.Module): - def __init__(self, n_inputs, n_outputs, kernel_size, stride, dilation, padding, dropout=0.2): - super(TemporalBlock, self).__init__() - self.conv1 = weight_norm(nn.Conv1d(n_inputs, n_outputs, kernel_size, - stride=stride, padding=padding, dilation=dilation)) - self.chomp1 = Chomp1d(padding) - self.relu1 = nn.ReLU() - self.dropout1 = nn.Dropout(dropout) - - self.conv2 = weight_norm(nn.Conv1d(n_outputs, n_outputs, kernel_size, - stride=stride, padding=padding, dilation=dilation)) - self.chomp2 = Chomp1d(padding) - self.relu2 = nn.ReLU() - self.dropout2 = nn.Dropout(dropout) - - self.net = nn.Sequential(self.conv1, self.chomp1, self.relu1, self.dropout1, - self.conv2, self.chomp2, self.relu2, self.dropout2) - self.downsample = nn.Conv1d(n_inputs, n_outputs, 1) if n_inputs != n_outputs else None - self.relu = nn.ReLU() - self.init_weights() - - def init_weights(self): - self.conv1.weight.data.normal_(0, 0.01) - self.conv2.weight.data.normal_(0, 0.01) - if self.downsample is not None: - self.downsample.weight.data.normal_(0, 0.01) - - def forward(self, x): - out = self.net(x) - res = x if self.downsample is None else self.downsample(x) - return self.relu(out + res) - - -class TemporalConvNet(nn.Module): - def __init__(self, num_inputs, num_channels, kernel_size=2, dropout=0.2): - super(TemporalConvNet, self).__init__() - layers = [] - num_levels = len(num_channels) - for i in range(num_levels): - dilation_size = 2 ** i - in_channels = num_inputs if i == 0 else num_channels[i-1] - out_channels = num_channels[i] - layers += [TemporalBlock(in_channels, out_channels, kernel_size, stride=1, dilation=dilation_size, - padding=(kernel_size-1) * dilation_size, dropout=dropout)] - - self.network = nn.Sequential(*layers) - - def forward(self, x): - return self.network(x) - - -class TextEncoderTCN(nn.Module): - """ based on https://github.com/locuslab/TCN/blob/master/TCN/word_cnn/model.py """ - def __init__(self, args, n_words, embed_size=300, pre_trained_embedding=None, - kernel_size=2, dropout=0.3, emb_dropout=0.1, word_cache=False): - super(TextEncoderTCN, self).__init__() - if word_cache: - self.embedding = None - else: - if pre_trained_embedding is not None: # use pre-trained embedding (fasttext) - #print(pre_trained_embedding.shape) - assert pre_trained_embedding.shape[0] == n_words - assert pre_trained_embedding.shape[1] == embed_size - self.embedding = nn.Embedding.from_pretrained(torch.FloatTensor(pre_trained_embedding), - freeze=args.freeze_wordembed) - else: - self.embedding = nn.Embedding(n_words, embed_size) - - num_channels = [args.hidden_size] * args.n_layer - self.tcn = TemporalConvNet(embed_size, num_channels, kernel_size, dropout=dropout) - - self.decoder = nn.Linear(num_channels[-1], args.word_f) - self.drop = nn.Dropout(emb_dropout) - self.emb_dropout = emb_dropout - self.init_weights() - - def init_weights(self): - self.decoder.bias.data.fill_(0) - self.decoder.weight.data.normal_(0, 0.01) - - def forward(self, input): - #print(input.shape) - if self.embedding is None: - emb = self.drop(input) - else: - emb = self.drop(self.embedding(input)) - y = self.tcn(emb.transpose(1, 2)).transpose(1, 2) - y = self.decoder(y) - return y.contiguous(), 0 - - -class BasicBlock(nn.Module): - """ based on timm: https://github.com/rwightman/pytorch-image-models """ - def __init__(self, inplanes, planes, ker_size, stride=1, downsample=None, cardinality=1, base_width=64, - reduce_first=1, dilation=1, first_dilation=None, act_layer=nn.LeakyReLU, norm_layer=nn.BatchNorm1d, attn_layer=None, aa_layer=None, drop_block=None, drop_path=None): - super(BasicBlock, self).__init__() - - self.conv1 = nn.Conv1d( - inplanes, planes, kernel_size=ker_size, stride=stride, padding=first_dilation, - dilation=dilation, bias=True) - self.bn1 = norm_layer(planes) - self.act1 = act_layer(inplace=True) - self.conv2 = nn.Conv1d( - planes, planes, kernel_size=ker_size, padding=ker_size//2, dilation=dilation, bias=True) - self.bn2 = norm_layer(planes) - self.act2 = act_layer(inplace=True) - if downsample is not None: - self.downsample = nn.Sequential( - nn.Conv1d(inplanes, planes, stride=stride, kernel_size=ker_size, padding=first_dilation, dilation=dilation, bias=True), - norm_layer(planes), - ) - else: self.downsample=None - self.stride = stride - self.dilation = dilation - self.drop_block = drop_block - self.drop_path = drop_path - - def zero_init_last_bn(self): - nn.init.zeros_(self.bn2.weight) - - def forward(self, x): - shortcut = x - x = self.conv1(x) - x = self.bn1(x) - x = self.act1(x) - x = self.conv2(x) - x = self.bn2(x) - if self.downsample is not None: - shortcut = self.downsample(shortcut) - x += shortcut - x = self.act2(x) - return x - - -class WavEncoder(nn.Module): - def __init__(self, out_dim): - super().__init__() - self.out_dim = out_dim - self.feat_extractor = nn.Sequential( - BasicBlock(1, 32, 15, 5, first_dilation=1600, downsample=True), - BasicBlock(32, 32, 15, 6, first_dilation=0, downsample=True), - BasicBlock(32, 32, 15, 1, first_dilation=7, ), - BasicBlock(32, 64, 15, 6, first_dilation=0, downsample=True), - BasicBlock(64, 64, 15, 1, first_dilation=7), - BasicBlock(64, 128, 15, 6, first_dilation=0,downsample=True), - ) - - def forward(self, wav_data): - wav_data = wav_data.unsqueeze(1) - out = self.feat_extractor(wav_data) - return out.transpose(1, 2) - - -class PoseGenerator(nn.Module): - """ - End2End model - audio, text and speaker ID encoder are customized based on Yoon et al. SIGGRAPH ASIA 2020 - """ - def __init__(self, args): - super().__init__() - self.args = args - self.pre_length = args.pre_frames - self.gen_length = args.pose_length - args.pre_frames - self.pose_dims = args.pose_dims - self.facial_f = args.facial_f - self.speaker_f = args.speaker_f - self.audio_f = args.audio_f - self.word_f = args.word_f - self.emotion_f = args.emotion_f - self.facial_dims = args.facial_dims - self.args.speaker_dims = args.speaker_dims - self.emotion_dims = args.emotion_dims - - self.in_size = self.audio_f + self.pose_dims + self.facial_f + self.word_f + 1 - self.audio_encoder = WavEncoder(self.audio_f) - self.hidden_size = args.hidden_size - self.n_layer = args.n_layer - - if self.facial_f is not 0: - self.facial_encoder = nn.Sequential( - BasicBlock(self.facial_dims, self.facial_f//2, 7, 1, first_dilation=3, downsample=True), - BasicBlock(self.facial_f//2, self.facial_f//2, 3, 1, first_dilation=1, downsample=True), - BasicBlock(self.facial_f//2, self.facial_f//2, 3, 1, first_dilation=1, ), - BasicBlock(self.facial_f//2, self.facial_f, 3, 1, first_dilation=1, downsample=True), - ) - else: - self.facial_encoder = None - - self.text_encoder = None - if self.word_f is not 0: - if args.word_cache: - self.text_encoder = TextEncoderTCN(args, args.word_index_num, args.word_dims, pre_trained_embedding=None, - dropout=args.dropout_prob, word_cache=True) - else: - with open(f"{args.data_path}weights/vocab.pkl", 'rb') as f: - self.lang_model = pickle.load(f) - pre_trained_embedding = self.lang_model.word_embedding_weights - self.text_encoder = TextEncoderTCN(args, args.word_index_num, args.word_dims, pre_trained_embedding=pre_trained_embedding, - dropout=args.dropout_prob) - - self.speaker_embedding = None - if self.speaker_f is not 0: - self.in_size += self.speaker_f - self.speaker_embedding = nn.Sequential( - nn.Embedding(self.args.speaker_dims, self.speaker_f), - nn.Linear(self.speaker_f, self.speaker_f), - nn.LeakyReLU(True) - ) - - - self.emotion_embedding = None - if self.emotion_f is not 0: - self.in_size += self.emotion_f - - self.emotion_embedding = nn.Sequential( - nn.Embedding(self.emotion_dims, self.emotion_f), - nn.Linear(self.emotion_f, self.emotion_f) - ) - - # self.emotion_embedding_tail = nn.Sequential( - # nn.Conv1d(self.emotion_f, 8, 9, 1, 4), - # nn.BatchNorm1d(8), - # nn.LeakyReLU(0.3, inplace=True), - # nn.Conv1d(8, 16, 9, 1, 4), - # nn.BatchNorm1d(16), - # nn.LeakyReLU(0.3, inplace=True), - # nn.Conv1d(16, 16, 9, 1, 4), - # nn.BatchNorm1d(16), - # nn.LeakyReLU(0.3, inplace=True), - # nn.Conv1d(16, self.emotion_f, 9, 1, 4), - # nn.BatchNorm1d(self.emotion_f), - # nn.LeakyReLU(0.3, inplace=True), - # ) - - self.LSTM = nn.LSTM(self.in_size+3, hidden_size=self.hidden_size, num_layers=args.n_layer, batch_first=True, - bidirectional=True, dropout=args.dropout_prob) - self.out = nn.Sequential( - nn.Linear(self.hidden_size, self.hidden_size//2), - nn.LeakyReLU(True), - nn.Linear(self.hidden_size//2, 330-180) - ) - - self.LSTM_hands = nn.LSTM(self.in_size+150+3, hidden_size=self.hidden_size, num_layers=args.n_layer, batch_first=True, - bidirectional=True, dropout=args.dropout_prob) - self.out_hands = nn.Sequential( - nn.Linear(self.hidden_size, self.hidden_size//2), - nn.LeakyReLU(True), - nn.Linear(self.hidden_size//2, 180+3) - ) - - self.do_flatten_parameters = False - if torch.cuda.device_count() > 1: - self.do_flatten_parameters = True - - - def forward(self, pre_seq, in_audio=None, in_facial=None, in_text=None, in_id=None, in_emo=None, is_test=False): - if self.do_flatten_parameters: - self.LSTM.flatten_parameters() - - text_feat_seq = audio_feat_seq = None - if in_audio is not None: - audio_feat_seq = self.audio_encoder(in_audio) - if in_text is not None: - text_feat_seq, _ = self.text_encoder(in_text) - assert(audio_feat_seq.shape[1] == text_feat_seq.shape[1]) - - if self.facial_f is not 0: - face_feat_seq = self.facial_encoder(in_facial.permute([0, 2, 1])) - face_feat_seq = face_feat_seq.permute([0, 2, 1]) - speaker_feat_seq = None - if self.speaker_embedding: - speaker_feat_seq = self.speaker_embedding(in_id) - emo_feat_seq = None - if self.emotion_embedding: - emo_feat_seq = self.emotion_embedding(in_emo) - emo_feat_seq = emo_feat_seq.permute([0,2,1]) - emo_feat_seq = self.emotion_embedding_tail(emo_feat_seq) - emo_feat_seq = emo_feat_seq.permute([0,2,1]) - - if audio_feat_seq.shape[1] != pre_seq.shape[1]: - diff_length = pre_seq.shape[1] - audio_feat_seq.shape[1] - audio_feat_seq = torch.cat((audio_feat_seq, audio_feat_seq[:,-diff_length:, :].reshape(1,diff_length,-1)),1) - - if self.audio_f is not 0 and self.facial_f is 0: - in_data = torch.cat((pre_seq, audio_feat_seq), dim=2) - elif self.audio_f is not 0 and self.facial_f is not 0: - in_data = torch.cat((pre_seq, audio_feat_seq, face_feat_seq), dim=2) - else: pass - - if text_feat_seq is not None: - in_data = torch.cat((in_data, text_feat_seq), dim=2) - if emo_feat_seq is not None: - in_data = torch.cat((in_data, emo_feat_seq), dim=2) - - if speaker_feat_seq is not None: - repeated_s = speaker_feat_seq - if len(repeated_s.shape) == 2: - repeated_s = repeated_s.reshape(1, repeated_s.shape[1], repeated_s.shape[0]) - repeated_s = repeated_s.repeat(1, in_data.shape[1], 1) - in_data = torch.cat((in_data, repeated_s), dim=2) - - output, _ = self.LSTM(in_data) - output = output[:, :, :self.hidden_size] + output[:, :, self.hidden_size:] - output = self.out(output.reshape(-1, output.shape[2])) - decoder_outputs = output.reshape(in_data.shape[0], in_data.shape[1], -1) - return decoder_outputs - - -class CaMN(PoseGenerator): - def __init__(self, args): - super().__init__(args) - self.audio_fusion_dim = self.audio_f+self.speaker_f+self.emotion_f+self.word_f - self.facial_fusion_dim = self.audio_fusion_dim + self.facial_f - self.audio_fusion = nn.Sequential( - nn.Linear(self.audio_fusion_dim, self.hidden_size//2), - nn.LeakyReLU(True), - nn.Linear(self.hidden_size//2, self.audio_f), - nn.LeakyReLU(True), - ) - - self.facial_fusion = nn.Sequential( - nn.Linear(self.facial_fusion_dim, self.hidden_size//2), - nn.LeakyReLU(True), - nn.Linear(self.hidden_size//2, self.facial_f), - nn.LeakyReLU(True), - ) - - def forward(self, pre_seq, in_audio=None, in_facial=None, in_text=None, in_id=None, in_emo=None): - if self.do_flatten_parameters: - self.LSTM.flatten_parameters() - - decoder_hidden = decoder_hidden_hands = None - text_feat_seq = audio_feat_seq = speaker_feat_seq = emo_feat_seq = face_feat_seq = None - in_data = None - - if self.speaker_embedding: - speaker_feat_seq = self.speaker_embedding(in_id).squeeze(2) - in_data = torch.cat((in_data, speaker_feat_seq), 2) if in_data is not None else speaker_feat_seq - - if self.emotion_embedding: - emo_feat_seq = self.emotion_embedding(in_emo).squeeze(2) - in_data = torch.cat((in_data, emo_feat_seq), 2) - - if in_text is not None: - text_feat_seq, _ = self.text_encoder(in_text) - in_data = torch.cat((in_data, text_feat_seq), 2) if in_data is not None else text_feat_seq - - if in_audio is not None: - audio_feat_seq = self.audio_encoder(in_audio) - if in_text is not None: - if (audio_feat_seq.shape[1] != text_feat_seq.shape[1]): - min_gap = text_feat_seq.shape[1] - audio_feat_seq.shape[1] - audio_feat_seq = torch.cat((audio_feat_seq, audio_feat_seq[:,-min_gap:, :]),1) - audio_fusion_seq = self.audio_fusion(torch.cat((audio_feat_seq, emo_feat_seq, speaker_feat_seq, text_feat_seq), dim=2).reshape(-1, self.audio_fusion_dim)) - audio_feat_seq = audio_fusion_seq.reshape(*audio_feat_seq.shape) - in_data = torch.cat((in_data, audio_feat_seq), 2) if in_data is not None else audio_feat_seq - - if self.facial_f is not 0: - face_feat_seq = self.facial_encoder(in_facial.permute([0, 2, 1])) - face_feat_seq = face_feat_seq.permute([0, 2, 1]) - if (audio_feat_seq.shape[1] != face_feat_seq.shape[1]): - min_gap_2 = face_feat_seq.shape[1] - audio_feat_seq.shape[1] - if min_gap_2 > 0: - face_feat_seq = face_feat_seq[:,:audio_feat_seq.shape[1], :] - else: - face_feat_seq = torch.cat((face_feat_seq, face_feat_seq[:,-min_gap_2:, :]),1) - - face_fusion_seq = self.facial_fusion(torch.cat((face_feat_seq, audio_feat_seq, emo_feat_seq, speaker_feat_seq, text_feat_seq), dim=2).reshape(-1, self.facial_fusion_dim)) - face_feat_seq = face_fusion_seq.reshape(*face_feat_seq.shape) - in_data = torch.cat((in_data, face_feat_seq), 2) if in_data is not None else face_feat_seq - - - in_data = torch.cat((pre_seq, in_data), dim=2) - output, _ = self.LSTM(in_data) - output = output[:, :, :self.hidden_size] + output[:, :, self.hidden_size:] - output = self.out(output.reshape(-1, output.shape[2])) - decoder_outputs = output.reshape(in_data.shape[0], in_data.shape[1], -1) - - in_data = torch.cat((in_data, decoder_outputs), dim=2) - output_hands, _ = self.LSTM_hands(in_data) - output_hands = output_hands[:, :, :self.hidden_size] + output_hands[:, :, self.hidden_size:] - output_hands = self.out_hands(output_hands.reshape(-1, output_hands.shape[2])) - decoder_outputs_hands = output_hands.reshape(in_data.shape[0], in_data.shape[1], -1) - - decoder_outputs_final = torch.zeros((in_data.shape[0], in_data.shape[1], 333)).to(in_data.device) - decoder_outputs_final[:, :, 0:150] = decoder_outputs[:, :, 0:150] - decoder_outputs_final[:, :, 150:333] = decoder_outputs_hands[:, :, 0:183] - return { - "rec_pose": decoder_outputs_final, - } - - -class ConvDiscriminator(nn.Module): - def __init__(self, args): - super().__init__() - self.input_size = args.pose_dims - - self.hidden_size = 64 - self.pre_conv = nn.Sequential( - nn.Conv1d(self.input_size, 16, 3), - nn.BatchNorm1d(16), - nn.LeakyReLU(True), - nn.Conv1d(16, 8, 3), - nn.BatchNorm1d(8), - nn.LeakyReLU(True), - nn.Conv1d(8, 8, 3), - ) - - self.LSTM = nn.LSTM(8, hidden_size=self.hidden_size, num_layers=4, bidirectional=True, - dropout=0.3, batch_first=True) - self.out = nn.Linear(self.hidden_size, 1) - self.out2 = nn.Linear(34-6, 1) - - self.do_flatten_parameters = False - if torch.cuda.device_count() > 1: - self.do_flatten_parameters = True - - def forward(self, poses): - if self.do_flatten_parameters: - self.LSTM.flatten_parameters() - poses = poses.transpose(1, 2) - feat = self.pre_conv(poses) - feat = feat.transpose(1, 2) - output, _ = self.LSTM(feat) - output = output[:, :, :self.hidden_size] + output[:, :, self.hidden_size:] - batch_size = poses.shape[0] - output = output.contiguous().view(-1, output.shape[2]) - output = self.out(output) # apply linear to every output - output = output.view(batch_size, -1) - output = self.out2(output) - output = torch.sigmoid(output) - return output \ No newline at end of file diff --git a/models/camn_audio/__init__.py b/models/camn_audio/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..2b51ff3f92641f7b0b2742ee491d4a3aa5bc2dc3 --- /dev/null +++ b/models/camn_audio/__init__.py @@ -0,0 +1,8 @@ +from .configuration_camn_audio import CamnAudioConfig +from .modeling_camn_audio import CamnAudioModel, CamnAudioPreTrainedModel + +__all__ = [ + "CamnAudioConfig", + "CamnAudioModel", + "CamnAudioPreTrainedModel", +] diff --git a/models/camn_audio/__pycache__/__init__.cpython-39.pyc b/models/camn_audio/__pycache__/__init__.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..681fc73c01f9ed482272a2cf79369c42c7c57243 Binary files /dev/null and b/models/camn_audio/__pycache__/__init__.cpython-39.pyc differ diff --git a/models/camn_audio/__pycache__/configuration_camn_audio.cpython-39.pyc b/models/camn_audio/__pycache__/configuration_camn_audio.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..a6f358aea68a1966370d075bace7c047298d2ba6 Binary files /dev/null and b/models/camn_audio/__pycache__/configuration_camn_audio.cpython-39.pyc differ diff --git a/models/camn_audio/__pycache__/modeling_camn_audio.cpython-39.pyc b/models/camn_audio/__pycache__/modeling_camn_audio.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..1cc5929eadb26ebcee18b054486ff8f3bb5ba3dd Binary files /dev/null and b/models/camn_audio/__pycache__/modeling_camn_audio.cpython-39.pyc differ diff --git a/models/camn_audio/configuration_camn_audio.py b/models/camn_audio/configuration_camn_audio.py new file mode 100644 index 0000000000000000000000000000000000000000..7bd3dfe1777d85e95b53ad33878473f0182ba754 --- /dev/null +++ b/models/camn_audio/configuration_camn_audio.py @@ -0,0 +1,12 @@ +from transformers import PretrainedConfig +from omegaconf import OmegaConf + +class CamnAudioConfig(PretrainedConfig): + model_type = "camn_audio" + + def __init__(self, config_obj=None, **kwargs): + if config_obj is not None: + cfg_dict = OmegaConf.to_container(config_obj, resolve=True) + kwargs.update(cfg_dict) + + super().__init__(**kwargs) diff --git a/models/camn_audio/modeling_camn_audio.py b/models/camn_audio/modeling_camn_audio.py new file mode 100644 index 0000000000000000000000000000000000000000..ce6ad68d824bda822d00cf7e3e3cde7d7ecdb25c --- /dev/null +++ b/models/camn_audio/modeling_camn_audio.py @@ -0,0 +1,281 @@ +""" +motion_in and motion_out are all (bs, t, c), not (bs, t, j, c//j) +input: + audio: (bs, audio_t) + speaker_id: (bs, 1) + seed_frames: int + seed_motion: (bs, t, j*6) # rot6d +output: + motion: (bs, t, j*6) # rot6d + motion_axis_angle: (bs, t, j*3) # axis-angle +""" +import torch +import torch.nn as nn +import torch.nn.functional as F +from transformers import PreTrainedModel +from .configuration_camn_audio import CamnAudioConfig + +# ------------------ utils ---------------------- # +MASK_DICT = { + "local_upper": [ + False, False, False, True, False, False, True, False, False, True, + False, False, True, True, True, True, True, True, True, True, + True, True, False, False, False, True, True, True, True, True, + True, True, True, True, True, True, True, True, True, True, + True, True, True, True, True, True, True, True, True, True, + True, True, True, True, True + ], + "local_full": [False] + [True]*54 +} + +def _copysign(a, b): + signs_differ = (a < 0) != (b < 0) + return torch.where(signs_differ, -a, a) + +def _sqrt_positive_part(x): + ret = torch.zeros_like(x) + positive_mask = x > 0 + ret[positive_mask] = torch.sqrt(x[positive_mask]) + return ret + +def matrix_to_quaternion(matrix): + if matrix.size(-1) != 3 or matrix.size(-2) != 3: + raise ValueError(f"Invalid rotation matrix shape {matrix.shape}.") + m00 = matrix[..., 0, 0] + m11 = matrix[..., 1, 1] + m22 = matrix[..., 2, 2] + o0 = 0.5 * _sqrt_positive_part(1 + m00 + m11 + m22) + x = 0.5 * _sqrt_positive_part(1 + m00 - m11 - m22) + y = 0.5 * _sqrt_positive_part(1 - m00 + m11 - m22) + z = 0.5 * _sqrt_positive_part(1 - m00 - m11 + m22) + o1 = _copysign(x, matrix[..., 2, 1] - matrix[..., 1, 2]) + o2 = _copysign(y, matrix[..., 0, 2] - matrix[..., 2, 0]) + o3 = _copysign(z, matrix[..., 1, 0] - matrix[..., 0, 1]) + return torch.stack((o0, o1, o2, o3), -1) + +def quaternion_to_axis_angle(quaternions): + norms = torch.norm(quaternions[..., 1:], p=2, dim=-1, keepdim=True) + half_angles = torch.atan2(norms, quaternions[..., :1]) + angles = 2 * half_angles + eps = 1e-6 + small_angles = angles.abs() < eps + sin_half_angles_over_angles = torch.empty_like(angles) + sin_half_angles_over_angles[~small_angles] = ( + torch.sin(half_angles[~small_angles]) / angles[~small_angles] + ) + sin_half_angles_over_angles[small_angles] = ( + 0.5 - (angles[small_angles] * angles[small_angles]) / 48 + ) + return quaternions[..., 1:] / sin_half_angles_over_angles + +def matrix_to_axis_angle(matrix): + return quaternion_to_axis_angle(matrix_to_quaternion(matrix)) + +def rotation_6d_to_matrix(d6: torch.Tensor) -> torch.Tensor: + a1, a2 = d6[..., :3], d6[..., 3:] + b1 = F.normalize(a1, dim=-1) + b2 = a2 - (b1 * a2).sum(-1, keepdim=True) * b1 + b2 = F.normalize(b2, dim=-1) + b3 = torch.cross(b1, b2, dim=-1) + return torch.stack((b1, b2, b3), dim=-2) + +def rotation_6d_to_axis_angle(rot6d): + return matrix_to_axis_angle(rotation_6d_to_matrix(rot6d)) + +def recover_from_mask_ts(selected_motion: torch.Tensor, mask: list[bool]) -> torch.Tensor: + device = selected_motion.device + dtype = selected_motion.dtype + mask_arr = torch.tensor(mask, dtype=torch.bool, device=device) + j = len(mask_arr) + sum_mask = mask_arr.sum().item() + c_channels = selected_motion.shape[-1] // sum_mask + new_shape = selected_motion.shape[:-1] + (sum_mask, c_channels) + selected_motion = selected_motion.reshape(new_shape) + out_shape = list(selected_motion.shape[:-2]) + [j, c_channels] + recovered = torch.zeros(out_shape, dtype=dtype, device=device) + recovered[..., mask_arr, :] = selected_motion + final_shape = list(recovered.shape[:-2]) + [j * c_channels] + recovered = recovered.reshape(final_shape) + return recovered + + +# ------------------ network ---------------------- # +class BasicBlock(nn.Module): + """Basic 1D residual block.""" + def __init__(self, inplanes, planes, ker_size, stride=1, first_dilation=None, norm_layer=nn.BatchNorm1d, act_layer=nn.LeakyReLU): + super().__init__() + self.conv1 = nn.Conv1d(inplanes, planes, kernel_size=ker_size, stride=stride, + padding=first_dilation, dilation=1, bias=True) + self.bn1 = norm_layer(planes) + self.act1 = act_layer(inplace=True) + self.conv2 = nn.Conv1d(planes, planes, kernel_size=ker_size, padding=ker_size//2, bias=True) + self.bn2 = norm_layer(planes) + self.act2 = act_layer(inplace=True) + self.downsample = None + if stride != 1 or inplanes != planes: + self.downsample = nn.Sequential( + nn.Conv1d(inplanes, planes, stride=stride, kernel_size=ker_size, padding=first_dilation, bias=True), + norm_layer(planes) + ) + + def forward(self, x): + shortcut = x + x = self.conv1(x) + x = self.bn1(x) + x = self.act1(x) + x = self.conv2(x) + x = self.bn2(x) + if self.downsample is not None: + shortcut = self.downsample(shortcut) + x += shortcut + x = self.act2(x) + return x + + +class WavEncoder(nn.Module): + """Waveform encoder that uses stacked residual blocks.""" + def __init__(self, out_dim): + super().__init__() + self.feat_extractor = nn.Sequential( + BasicBlock(1, 32, 15, 5, first_dilation=1600), + BasicBlock(32,32,15,6,first_dilation=0), + BasicBlock(32,32,15,1,first_dilation=7), + BasicBlock(32,64,15,6,first_dilation=0), + BasicBlock(64,64,15,1,first_dilation=7), + BasicBlock(64,128,15,6,first_dilation=0), + ) + + def forward(self, wav_data): + wav_data = wav_data.unsqueeze(1) + out = self.feat_extractor(wav_data) + return out.transpose(1, 2) + + +class MLP(nn.Module): + """A simple MLP for projection.""" + def __init__(self, in_dim, middle_dim, out_dim): + super().__init__() + self.fc1 = nn.Linear(in_dim, middle_dim) + self.fc2 = nn.Linear(middle_dim, out_dim) + self.act = nn.LeakyReLU(0.1, True) + + def forward(self, x): + x = self.fc1(x) + x = self.act(x) + x = self.fc2(x) + return x + + +class Empty(nn.Module): + """Empty module that returns input as is.""" + def forward(self, x): + return x + + +class CamnAudioPreTrainedModel(PreTrainedModel): + config_class = CamnAudioConfig + base_model_prefix = "camn_audio" + + def _init_weights(self, module): + pass + + +class CamnAudioModel(CamnAudioPreTrainedModel): + """CaMNAudio model for audio-driven motion generation. + + This model assumes that the config (CamnAudioConfig) can be initialized from a dict-like object + or OmegaConf directly by passing them as kwargs. For example: + + from omegaconf import OmegaConf + cfg = OmegaConf.load("configs/camn_audio.yaml") + config = CamnAudioConfig(config_obj=cfg.model) + + This way all attributes from cfg.model become config attributes without having to manually map each one. + """ + def __init__(self, config: CamnAudioConfig): + super().__init__(config) + self.pose_rep = config.pose_rep + self.cfg = config + self.audio_encoder = WavEncoder(self.cfg.audio_f) + self.speaker_embedding = nn.Embedding(self.cfg.speaker_dims, self.cfg.speaker_f) if self.cfg.speaker_f > 0 else None + self.motion_encoder = Empty() + self.joint_mask = MASK_DICT[config.joint_mask] + + input_dim_body = self.cfg.pose_dims+1+self.cfg.speaker_f+self.cfg.audio_f + self.body_motion_decoder = nn.LSTM( + input_dim_body, hidden_size=self.cfg.hidden_size, + num_layers=self.cfg.n_layer, batch_first=True, + bidirectional=True, dropout=self.cfg.dropout_prob + ) + self.body_out = MLP(self.cfg.hidden_size, self.cfg.hidden_size, self.cfg.body_dims) + + input_dim_hands = input_dim_body + self.cfg.body_dims + self.hands_motion_decoder = nn.LSTM( + input_dim_hands, hidden_size=self.cfg.hidden_size, + num_layers=self.cfg.n_layer, batch_first=True, + bidirectional=True, dropout=self.cfg.dropout_prob + ) + self.hands_out = MLP(self.cfg.hidden_size, self.cfg.hidden_size, self.cfg.hands_dims) + + if self.pose_rep == "bvh": + self.bvh_dims = self.cfg.body_dims + self.cfg.hands_dims + + def recombine(self, body_out, hands_out): + bs, t, _ = body_out.shape + if self.pose_rep == "bvh": + recombine = torch.zeros((bs, t, self.bvh_dims), device=body_out.device) + recombine[:, :, :self.cfg.body_dims] = body_out + recombine[:, :, self.cfg.body_dims:] = hands_out + elif self.pose_rep == "smplx": + body_out = body_out.reshape(bs, t, self.cfg.body_dims//6, 6) + hands_out = hands_out.reshape(bs, t, self.cfg.hands_dims//6, 6) + recombine = torch.zeros((bs, t, self.cfg.pose_dims//6, 6), device=body_out.device) + recombine[:, :, :self.cfg.body_dims//6] = body_out + recombine[:, :, self.cfg.body_dims//6:] = hands_out + return recombine + + def forward(self, audio, speaker_id, seed_frames=4, seed_motion=None, return_axis_angle=True): + audio_feat = self.audio_encoder(audio) + bs, t, _ = audio_feat.shape + + if self.speaker_embedding is not None: + speaker_feat = self.speaker_embedding(speaker_id) + speaker_feat = speaker_feat.repeat(1, t, 1) + else: + speaker_feat = torch.zeros(bs, t, 0, device=audio.device) + + if seed_motion is None: + seed_motion = torch.zeros(bs, t, self.cfg.pose_dims+1, device=audio.device) + seed_motion[:, :seed_frames, -1] = 1 + else: + _, t_m, _ = seed_motion.shape + seed_motion_pad = torch.zeros(bs, t_m, self.cfg.pose_dims+1, device=audio.device) + seed_motion_pad[:, :seed_frames, :-1] = seed_motion[:, :seed_frames] + seed_motion_pad[:, :seed_frames, -1] = 1 + seed_motion = seed_motion_pad + if t_m != t: + diff_length = t_m - t + if diff_length > 0: + seed_motion = seed_motion[:, :t, :] + else: + seed_motion = torch.cat((seed_motion, seed_motion[:, -diff_length:, :]), 1) + + in_fea = torch.cat((audio_feat, speaker_feat, seed_motion), dim=2) + body_out, _ = self.body_motion_decoder(in_fea) + body_out = body_out[:, :, :self.cfg.hidden_size] + body_out[:, :, self.cfg.hidden_size:] + body_out = self.body_out(body_out) + + in_fea_hands = torch.cat((in_fea, body_out), dim=2) + hands_out, _ = self.hands_motion_decoder(in_fea_hands) + hands_out = hands_out[:, :, :self.cfg.hidden_size] + hands_out[:, :, self.cfg.hidden_size:] + hands_out = self.hands_out(hands_out) + + recombine = self.recombine(body_out, hands_out) + motion_axis_angle = None + if return_axis_angle: + motion_axis_angle = rotation_6d_to_axis_angle(recombine.reshape(-1, self.cfg.pose_dims//6, 6)).reshape(bs, t, -1) + motion_axis_angle = recover_from_mask_ts(motion_axis_angle, self.joint_mask) + return { + "motion": recombine, + "motion_axis_angle": motion_axis_angle, + } \ No newline at end of file diff --git a/models/disco_audio/__init__.py b/models/disco_audio/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..50bc9a94d9782c54f1b9f1185e1fa8992db57278 --- /dev/null +++ b/models/disco_audio/__init__.py @@ -0,0 +1,8 @@ +from .configuration_disco_audio import DiscoAudioConfig +from .modeling_disco_audio import DiscoAudioModel, DiscoAudioPreTrainedModel + +__all__ = [ + "DiscoAudioConfig", + "DiscoAudioModel", + "DiscoAudioPreTrainedModel", +] \ No newline at end of file diff --git a/models/disco_audio/__pycache__/__init__.cpython-39.pyc b/models/disco_audio/__pycache__/__init__.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..4c5af62cacbb5d1230a4facab25aed52c2718fe4 Binary files /dev/null and b/models/disco_audio/__pycache__/__init__.cpython-39.pyc differ diff --git a/models/disco_audio/__pycache__/configuration_disco_audio.cpython-39.pyc b/models/disco_audio/__pycache__/configuration_disco_audio.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..7a0f49dbfe172551236b445dbe367aea37cd91dd Binary files /dev/null and b/models/disco_audio/__pycache__/configuration_disco_audio.cpython-39.pyc differ diff --git a/models/disco_audio/__pycache__/modeling_disco_audio.cpython-39.pyc b/models/disco_audio/__pycache__/modeling_disco_audio.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..25be4e97d026f116d95d129e4cbcbf00c62e79c6 Binary files /dev/null and b/models/disco_audio/__pycache__/modeling_disco_audio.cpython-39.pyc differ diff --git a/models/disco_audio/configuration_disco_audio.py b/models/disco_audio/configuration_disco_audio.py new file mode 100644 index 0000000000000000000000000000000000000000..b6175b6e904a6f6fd61ca50020ebd04daf1a32f5 --- /dev/null +++ b/models/disco_audio/configuration_disco_audio.py @@ -0,0 +1,12 @@ +from transformers import PretrainedConfig +from omegaconf import OmegaConf + +class DiscoAudioConfig(PretrainedConfig): + model_type = "disco_audio" + + def __init__(self, config_obj=None, **kwargs): + if config_obj is not None: + cfg_dict = OmegaConf.to_container(config_obj, resolve=True) + kwargs.update(cfg_dict) + + super().__init__(**kwargs) diff --git a/models/disco_audio/modeling_disco_audio.py b/models/disco_audio/modeling_disco_audio.py new file mode 100644 index 0000000000000000000000000000000000000000..a3a8807153fd71b73f3e4a17ada58e16a82b7876 --- /dev/null +++ b/models/disco_audio/modeling_disco_audio.py @@ -0,0 +1,267 @@ +""" +motion_in and motion_out are all (bs, t, c), not (bs, t, j, c//j) +input: + audio: (bs, audio_t) + speaker_id: (bs, 1) + seed_frames: int + seed_motion: (bs, t, j*6) # rot6d +output: + motion: (bs, t, j*6) # rot6d + motion_axis_angle: (bs, t, j*3) # axis-angle +""" +import torch +import torch.nn as nn +import torch.nn.functional as F +from transformers import PreTrainedModel +from .configuration_disco_audio import DiscoAudioConfig + +# ------------------ utils ---------------------- # +MASK_DICT = { + "local_upper": [ + False, False, False, True, False, False, True, False, False, True, + False, False, True, True, True, True, True, True, True, True, + True, True, False, False, False, True, True, True, True, True, + True, True, True, True, True, True, True, True, True, True, + True, True, True, True, True, True, True, True, True, True, + True, True, True, True, True + ], + "local_full": [False] + [True]*54 +} + +def _copysign(a, b): + signs_differ = (a < 0) != (b < 0) + return torch.where(signs_differ, -a, a) + +def _sqrt_positive_part(x): + ret = torch.zeros_like(x) + positive_mask = x > 0 + ret[positive_mask] = torch.sqrt(x[positive_mask]) + return ret + +def matrix_to_quaternion(matrix): + if matrix.size(-1) != 3 or matrix.size(-2) != 3: + raise ValueError(f"Invalid rotation matrix shape {matrix.shape}.") + m00 = matrix[..., 0, 0] + m11 = matrix[..., 1, 1] + m22 = matrix[..., 2, 2] + o0 = 0.5 * _sqrt_positive_part(1 + m00 + m11 + m22) + x = 0.5 * _sqrt_positive_part(1 + m00 - m11 - m22) + y = 0.5 * _sqrt_positive_part(1 - m00 + m11 - m22) + z = 0.5 * _sqrt_positive_part(1 - m00 - m11 + m22) + o1 = _copysign(x, matrix[..., 2, 1] - matrix[..., 1, 2]) + o2 = _copysign(y, matrix[..., 0, 2] - matrix[..., 2, 0]) + o3 = _copysign(z, matrix[..., 1, 0] - matrix[..., 0, 1]) + return torch.stack((o0, o1, o2, o3), -1) + +def quaternion_to_axis_angle(quaternions): + norms = torch.norm(quaternions[..., 1:], p=2, dim=-1, keepdim=True) + half_angles = torch.atan2(norms, quaternions[..., :1]) + angles = 2 * half_angles + eps = 1e-6 + small_angles = angles.abs() < eps + sin_half_angles_over_angles = torch.empty_like(angles) + sin_half_angles_over_angles[~small_angles] = ( + torch.sin(half_angles[~small_angles]) / angles[~small_angles] + ) + sin_half_angles_over_angles[small_angles] = ( + 0.5 - (angles[small_angles] * angles[small_angles]) / 48 + ) + return quaternions[..., 1:] / sin_half_angles_over_angles + +def matrix_to_axis_angle(matrix): + return quaternion_to_axis_angle(matrix_to_quaternion(matrix)) + +def rotation_6d_to_matrix(d6: torch.Tensor) -> torch.Tensor: + a1, a2 = d6[..., :3], d6[..., 3:] + b1 = F.normalize(a1, dim=-1) + b2 = a2 - (b1 * a2).sum(-1, keepdim=True) * b1 + b2 = F.normalize(b2, dim=-1) + b3 = torch.cross(b1, b2, dim=-1) + return torch.stack((b1, b2, b3), dim=-2) + +def rotation_6d_to_axis_angle(rot6d): + return matrix_to_axis_angle(rotation_6d_to_matrix(rot6d)) + +def recover_from_mask_ts(selected_motion: torch.Tensor, mask: list[bool]) -> torch.Tensor: + device = selected_motion.device + dtype = selected_motion.dtype + mask_arr = torch.tensor(mask, dtype=torch.bool, device=device) + j = len(mask_arr) + sum_mask = mask_arr.sum().item() + c_channels = selected_motion.shape[-1] // sum_mask + new_shape = selected_motion.shape[:-1] + (sum_mask, c_channels) + selected_motion = selected_motion.reshape(new_shape) + out_shape = list(selected_motion.shape[:-2]) + [j, c_channels] + recovered = torch.zeros(out_shape, dtype=dtype, device=device) + recovered[..., mask_arr, :] = selected_motion + final_shape = list(recovered.shape[:-2]) + [j * c_channels] + recovered = recovered.reshape(final_shape) + return recovered + + +# ------------------ network ---------------------- # +class BasicBlock(nn.Module): + """Basic 1D residual block.""" + def __init__(self, inplanes, planes, ker_size, stride=1, first_dilation=None, norm_layer=nn.BatchNorm1d, act_layer=nn.LeakyReLU): + super().__init__() + self.conv1 = nn.Conv1d(inplanes, planes, kernel_size=ker_size, stride=stride, + padding=first_dilation, dilation=1, bias=True) + self.bn1 = norm_layer(planes) + self.act1 = act_layer(inplace=True) + self.conv2 = nn.Conv1d(planes, planes, kernel_size=ker_size, padding=ker_size//2, bias=True) + self.bn2 = norm_layer(planes) + self.act2 = act_layer(inplace=True) + self.downsample = None + if stride != 1 or inplanes != planes: + self.downsample = nn.Sequential( + nn.Conv1d(inplanes, planes, stride=stride, kernel_size=ker_size, padding=first_dilation, bias=True), + norm_layer(planes) + ) + + def forward(self, x): + shortcut = x + x = self.conv1(x) + x = self.bn1(x) + x = self.act1(x) + x = self.conv2(x) + x = self.bn2(x) + if self.downsample is not None: + shortcut = self.downsample(shortcut) + x += shortcut + x = self.act2(x) + return x + + +class WavEncoder(nn.Module): + """Waveform encoder that uses stacked residual blocks.""" + def __init__(self, out_dim): + super().__init__() + self.feat_extractor = nn.Sequential( + BasicBlock(1, 32, 15, 5, first_dilation=1600), + BasicBlock(32,32,15,6,first_dilation=0), + BasicBlock(32,32,15,1,first_dilation=7), + BasicBlock(32,64,15,6,first_dilation=0), + BasicBlock(64,64,15,1,first_dilation=7), + BasicBlock(64,128,15,6,first_dilation=0), + ) + + def forward(self, wav_data): + wav_data = wav_data.unsqueeze(1) + out = self.feat_extractor(wav_data) + return out.transpose(1, 2) + + +class MLP(nn.Module): + """A simple MLP for projection.""" + def __init__(self, in_dim, middle_dim, out_dim): + super().__init__() + self.fc1 = nn.Linear(in_dim, middle_dim) + self.fc2 = nn.Linear(middle_dim, out_dim) + self.act = nn.LeakyReLU(0.1, True) + + def forward(self, x): + x = self.fc1(x) + x = self.act(x) + x = self.fc2(x) + return x + + +class Empty(nn.Module): + """Empty module that returns input as is.""" + def forward(self, x): + return x + + +class DiscoAudioPreTrainedModel(PreTrainedModel): + config_class = DiscoAudioConfig + base_model_prefix = "camn_audio" + + def _init_weights(self, module): + pass + + +class DiscoAudioModel(DiscoAudioPreTrainedModel): + """DiscoAudio model for audio-driven motion generation. + + This model assumes that the config (DiscoAudioConfig) can be initialized from a dict-like object + or OmegaConf directly by passing them as kwargs. For example: + + from omegaconf import OmegaConf + cfg = OmegaConf.load("configs/camn_audio.yaml") + config = DiscoAudioConfig(config_obj=cfg.model) + + This way all attributes from cfg.model become config attributes without having to manually map each one. + """ + def __init__(self, config: DiscoAudioConfig): + super().__init__(config) + self.pose_rep = config.pose_rep + self.cfg = config + self.audio_encoder = WavEncoder(self.cfg.audio_f) + self.speaker_embedding = nn.Embedding(self.cfg.speaker_dims, self.cfg.speaker_f) if self.cfg.speaker_f > 0 else None + self.motion_encoder = Empty() + self.joint_mask = MASK_DICT[config.joint_mask] + + self.audio_encoder_c1 = MLP(self.cfg.audio_f, self.cfg.hidden_size, self.cfg.audio_f) + self.audio_encoder_c2 = MLP(self.cfg.audio_f, self.cfg.hidden_size, self.cfg.audio_f) + self.audio_encoder_r = MLP(self.cfg.audio_f, self.cfg.hidden_size, self.cfg.audio_f) + + self.selector = MLP(self.cfg.audio_f, self.cfg.hidden_size, 2) + self.softmax = nn.Softmax(dim=2) + + input_dim_body = self.cfg.pose_dims+1+self.cfg.speaker_f+self.cfg.audio_f*2 + self.body_motion_decoder = nn.LSTM( + input_dim_body, hidden_size=self.cfg.hidden_size, + num_layers=self.cfg.n_layer, batch_first=True, + bidirectional=True, dropout=self.cfg.dropout_prob + ) + self.body_out = MLP(self.cfg.hidden_size, self.cfg.hidden_size, self.cfg.pose_dims) + + + def forward(self, audio, speaker_id, seed_frames=4, seed_motion=None, return_axis_angle=True): + audio_feat = self.audio_encoder(audio) + bs, t, _ = audio_feat.shape + + if self.speaker_embedding is not None: + speaker_feat = self.speaker_embedding(speaker_id) + speaker_feat = speaker_feat.repeat(1, t, 1) + else: + speaker_feat = torch.zeros(bs, t, 0, device=audio.device) + + if seed_motion is None: + seed_motion = torch.zeros(bs, t, self.cfg.pose_dims+1, device=audio.device) + seed_motion[:, :seed_frames, -1] = 1 + else: + _, t_m, _ = seed_motion.shape + seed_motion_pad = torch.zeros(bs, t_m, self.cfg.pose_dims+1, device=audio.device) + seed_motion_pad[:, :seed_frames, :-1] = seed_motion[:, :seed_frames] + seed_motion_pad[:, :seed_frames, -1] = 1 + seed_motion = seed_motion_pad + if t_m != t: + diff_length = t_m - t + if diff_length > 0: + seed_motion = seed_motion[:, :t, :] + else: + seed_motion = torch.cat((seed_motion, seed_motion[:, -diff_length:, :]), 1) + + audio_feat_c1 = self.audio_encoder_c1(audio_feat) + audio_feat_c2 = self.audio_encoder_c2(audio_feat) + audio_feat_r = self.audio_encoder_r(audio_feat) + weight_c = self.softmax(self.selector(audio_feat)) + audio_feat_c = weight_c[:, :, 0:1] * audio_feat_c1 + weight_c[:, :, 1:2] * audio_feat_c2 + audio_feat = torch.cat((audio_feat_c, audio_feat_r), dim=2) + + in_fea = torch.cat((audio_feat, speaker_feat, seed_motion), dim=2) + body_out, _ = self.body_motion_decoder(in_fea) + body_out = body_out[:, :, :self.cfg.hidden_size] + body_out[:, :, self.cfg.hidden_size:] + recombine = self.body_out(body_out) + + motion_axis_angle = None + if return_axis_angle: + motion_axis_angle = rotation_6d_to_axis_angle(recombine.reshape(-1, self.cfg.pose_dims//6, 6)).reshape(bs, t, -1) + motion_axis_angle = recover_from_mask_ts(motion_axis_angle, self.joint_mask) + return { + "motion": recombine, + "motion_axis_angle": motion_axis_angle, + "audio_fea_c": audio_feat_c, + "audio_fea_r": audio_feat_r, + } \ No newline at end of file diff --git a/models/emage.py b/models/emage.py deleted file mode 100644 index 1849cc9af7ba7875b7a686ad091a3c87331739bf..0000000000000000000000000000000000000000 --- a/models/emage.py +++ /dev/null @@ -1,241 +0,0 @@ -import copy -import math -import pickle -import numpy as np -import torch -import torch.nn as nn -from .utils.layer import BasicBlock -from .motion_encoder import * - - -class WavEncoder(nn.Module): - def __init__(self, out_dim, audio_in=1): - super().__init__() - self.out_dim = out_dim - self.feat_extractor = nn.Sequential( - BasicBlock(audio_in, out_dim//4, 15, 5, first_dilation=1600, downsample=True), - BasicBlock(out_dim//4, out_dim//4, 15, 6, first_dilation=0, downsample=True), - BasicBlock(out_dim//4, out_dim//4, 15, 1, first_dilation=7, ), - BasicBlock(out_dim//4, out_dim//2, 15, 6, first_dilation=0, downsample=True), - BasicBlock(out_dim//2, out_dim//2, 15, 1, first_dilation=7), - BasicBlock(out_dim//2, out_dim, 15, 3, first_dilation=0,downsample=True), - ) - def forward(self, wav_data): - if wav_data.dim() == 2: - wav_data = wav_data.unsqueeze(1) - else: - wav_data = wav_data.transpose(1, 2) - out = self.feat_extractor(wav_data) - return out.transpose(1, 2) - - -class MLP(nn.Module): - def __init__(self, in_dim, hidden_size, out_dim): - super().__init__() - self.mlp = nn.Sequential( - nn.Linear(in_dim, hidden_size), - nn.LeakyReLU(0.2, True), - nn.Linear(hidden_size, out_dim) - ) - def forward(self, inputs): - out = self.mlp(inputs) - return out - - -class PeriodicPositionalEncoding(nn.Module): - def __init__(self, d_model, dropout=0.1, period=15, max_seq_len=60): - super(PeriodicPositionalEncoding, self).__init__() - self.dropout = nn.Dropout(p=dropout) - pe = torch.zeros(period, d_model) - position = torch.arange(0, period, dtype=torch.float).unsqueeze(1) - div_term = torch.exp(torch.arange(0, d_model, 2).float() * (-math.log(10000.0) / d_model)) - pe[:, 0::2] = torch.sin(position * div_term) - pe[:, 1::2] = torch.cos(position * div_term) - pe = pe.unsqueeze(0) # (1, period, d_model) - repeat_num = (max_seq_len//period) + 1 - pe = pe.repeat(1, repeat_num, 1) # (1, repeat_num, period, d_model) - self.register_buffer('pe', pe) - def forward(self, x): - # print(self.pe.shape, x.shape) - x = x + self.pe[:, :x.size(1), :] - return self.dropout(x) - - -class MAGE_Transformer(nn.Module): - def __init__(self, args): - super(MAGE_Transformer, self).__init__() - self.args = args - with open(f"{args.data_path}weights/vocab.pkl", 'rb') as f: - self.lang_model = pickle.load(f) - pre_trained_embedding = self.lang_model.word_embedding_weights - self.text_pre_encoder_face = nn.Embedding.from_pretrained(torch.FloatTensor(pre_trained_embedding),freeze=args.t_fix_pre) - self.text_encoder_face = nn.Linear(300, args.audio_f) - self.text_encoder_face = nn.Linear(300, args.audio_f) - self.text_pre_encoder_body = nn.Embedding.from_pretrained(torch.FloatTensor(pre_trained_embedding),freeze=args.t_fix_pre) - self.text_encoder_body = nn.Linear(300, args.audio_f) - self.text_encoder_body = nn.Linear(300, args.audio_f) - - self.audio_pre_encoder_face = WavEncoder(args.audio_f, audio_in=2) - self.audio_pre_encoder_body = WavEncoder(args.audio_f, audio_in=2) - - self.at_attn_face = nn.Linear(args.audio_f*2, args.audio_f*2) - self.at_attn_body = nn.Linear(args.audio_f*2, args.audio_f*2) - - args_top = copy.deepcopy(self.args) - args_top.vae_layer = 3 - args_top.vae_length = args.motion_f - args_top.vae_test_dim = args.pose_dims+3+4 - self.motion_encoder = VQEncoderV6(args_top) # masked motion to latent bs t 333 to bs t 256 - - # face decoder - self.feature2face = nn.Linear(args.audio_f*2, args.hidden_size) - self.face2latent = nn.Linear(args.hidden_size, args.vae_codebook_size) - self.transformer_de_layer = nn.TransformerDecoderLayer( - d_model=self.args.hidden_size, - nhead=4, - dim_feedforward=self.args.hidden_size*2, - batch_first=True - ) - self.face_decoder = nn.TransformerDecoder(self.transformer_de_layer, num_layers=4) - self.position_embeddings = PeriodicPositionalEncoding(self.args.hidden_size, period=self.args.pose_length, max_seq_len=self.args.pose_length) - - # motion decoder - self.transformer_en_layer = nn.TransformerEncoderLayer( - d_model=self.args.hidden_size, - nhead=4, - dim_feedforward=self.args.hidden_size*2, - batch_first=True - ) - self.motion_self_encoder = nn.TransformerEncoder(self.transformer_en_layer, num_layers=1) - self.audio_feature2motion = nn.Linear(args.audio_f, args.hidden_size) - self.feature2motion = nn.Linear(args.motion_f, args.hidden_size) - - self.bodyhints_face = MLP(args.motion_f, args.hidden_size, args.motion_f) - self.bodyhints_body = MLP(args.motion_f, args.hidden_size, args.motion_f) - self.motion2latent_upper = MLP(args.hidden_size, args.hidden_size, self.args.hidden_size) - self.motion2latent_hands = MLP(args.hidden_size, args.hidden_size, self.args.hidden_size) - self.motion2latent_lower = MLP(args.hidden_size, args.hidden_size, self.args.hidden_size) - self.wordhints_decoder = nn.TransformerDecoder(self.transformer_de_layer, num_layers=8) - - self.upper_decoder = nn.TransformerDecoder(self.transformer_de_layer, num_layers=1) - self.hands_decoder = nn.TransformerDecoder(self.transformer_de_layer, num_layers=1) - self.lower_decoder = nn.TransformerDecoder(self.transformer_de_layer, num_layers=1) - - self.face_classifier = MLP(self.args.vae_codebook_size, args.hidden_size, self.args.vae_codebook_size) - self.upper_classifier = MLP(self.args.vae_codebook_size, args.hidden_size, self.args.vae_codebook_size) - self.hands_classifier = MLP(self.args.vae_codebook_size, args.hidden_size, self.args.vae_codebook_size) - self.lower_classifier = MLP(self.args.vae_codebook_size, args.hidden_size, self.args.vae_codebook_size) - - self.mask_embeddings = nn.Parameter(torch.zeros(1, 1, self.args.pose_dims+3+4)) - self.motion_down_upper = nn.Linear(args.hidden_size, self.args.vae_codebook_size) - self.motion_down_hands = nn.Linear(args.hidden_size, self.args.vae_codebook_size) - self.motion_down_lower = nn.Linear(args.hidden_size, self.args.vae_codebook_size) - self.motion_down_upper = nn.Linear(args.hidden_size, self.args.vae_codebook_size) - self.motion_down_hands = nn.Linear(args.hidden_size, self.args.vae_codebook_size) - self.motion_down_lower = nn.Linear(args.hidden_size, self.args.vae_codebook_size) - self._reset_parameters() - - self.spearker_encoder_body = nn.Embedding(25, args.hidden_size) - self.spearker_encoder_face = nn.Embedding(25, args.hidden_size) - - def _reset_parameters(self): - nn.init.normal_(self.mask_embeddings, 0, self.args.hidden_size ** -0.5) - - def forward(self, in_audio=None, in_word=None, mask=None, is_test=None, in_motion=None, use_attentions=True, use_word=True, in_id = None): - in_word_face = self.text_pre_encoder_face(in_word) - in_word_face = self.text_encoder_face(in_word_face) - in_word_body = self.text_pre_encoder_body(in_word) - in_word_body = self.text_encoder_body(in_word_body) - bs, t, c = in_word_face.shape - in_audio_face = self.audio_pre_encoder_face(in_audio) - in_audio_body = self.audio_pre_encoder_body(in_audio) - if in_audio_face.shape[1] != in_motion.shape[1]: - diff_length = in_motion.shape[1]- in_audio_face.shape[1] - if diff_length < 0: - in_audio_face = in_audio_face[:, :diff_length, :] - in_audio_body = in_audio_body[:, :diff_length, :] - else: - in_audio_face = torch.cat((in_audio_face, in_audio_face[:,-diff_length:]),1) - in_audio_body = torch.cat((in_audio_body, in_audio_body[:,-diff_length:]),1) - - if use_attentions: - alpha_at_face = torch.cat([in_word_face, in_audio_face], dim=-1).reshape(bs, t, c*2) - alpha_at_face = self.at_attn_face(alpha_at_face).reshape(bs, t, c, 2) - alpha_at_face = alpha_at_face.softmax(dim=-1) - fusion_face = in_word_face * alpha_at_face[:,:,:,1] + in_audio_face * alpha_at_face[:,:,:,0] - alpha_at_body = torch.cat([in_word_body, in_audio_body], dim=-1).reshape(bs, t, c*2) - alpha_at_body = self.at_attn_body(alpha_at_body).reshape(bs, t, c, 2) - alpha_at_body = alpha_at_body.softmax(dim=-1) - fusion_body = in_word_body * alpha_at_body[:,:,:,1] + in_audio_body * alpha_at_body[:,:,:,0] - else: - fusion_face = in_word_face + in_audio_face - fusion_body = in_word_body + in_audio_body - - masked_embeddings = self.mask_embeddings.expand_as(in_motion) - masked_motion = torch.where(mask == 1, masked_embeddings, in_motion) # bs, t, 256 - body_hint = self.motion_encoder(masked_motion) # bs t 256 - speaker_embedding_face = self.spearker_encoder_face(in_id).squeeze(2) - speaker_embedding_body = self.spearker_encoder_body(in_id).squeeze(2) - - # decode face - use_body_hints = True - if use_body_hints: - body_hint_face = self.bodyhints_face(body_hint) - fusion_face = torch.cat([fusion_face, body_hint_face], dim=2) - a2g_face = self.feature2face(fusion_face) - face_embeddings = speaker_embedding_face - face_embeddings = self.position_embeddings(face_embeddings) - decoded_face = self.face_decoder(tgt=face_embeddings, memory=a2g_face) - face_latent = self.face2latent(decoded_face) - cls_face = self.face_classifier(face_latent) - - # motion spatial encoder - body_hint_body = self.bodyhints_body(body_hint) - motion_embeddings = self.feature2motion(body_hint_body) - motion_embeddings = speaker_embedding_body + motion_embeddings - motion_embeddings = self.position_embeddings(motion_embeddings) - - # bi-directional self-attention - motion_refined_embeddings = self.motion_self_encoder(motion_embeddings) - - # audio to gesture cross-modal attention - if use_word: - a2g_motion = self.audio_feature2motion(fusion_body) - motion_refined_embeddings_in = motion_refined_embeddings + speaker_embedding_body - motion_refined_embeddings_in = self.position_embeddings(motion_refined_embeddings) - word_hints = self.wordhints_decoder(tgt=motion_refined_embeddings_in, memory=a2g_motion) - motion_refined_embeddings = motion_refined_embeddings + word_hints - - # feedforward - upper_latent = self.motion2latent_upper(motion_refined_embeddings) - hands_latent = self.motion2latent_hands(motion_refined_embeddings) - lower_latent = self.motion2latent_lower(motion_refined_embeddings) - - upper_latent_in = upper_latent + speaker_embedding_body - upper_latent_in = self.position_embeddings(upper_latent_in) - hands_latent_in = hands_latent + speaker_embedding_body - hands_latent_in = self.position_embeddings(hands_latent_in) - lower_latent_in = lower_latent + speaker_embedding_body - lower_latent_in = self.position_embeddings(lower_latent_in) - - # transformer decoder - motion_upper = self.upper_decoder(tgt=upper_latent_in, memory=hands_latent+lower_latent) - motion_hands = self.hands_decoder(tgt=hands_latent_in, memory=upper_latent+lower_latent) - motion_lower = self.lower_decoder(tgt=lower_latent_in, memory=upper_latent+hands_latent) - upper_latent = self.motion_down_upper(motion_upper+upper_latent) - hands_latent = self.motion_down_hands(motion_hands+hands_latent) - lower_latent = self.motion_down_lower(motion_lower+lower_latent) - cls_lower = self.lower_classifier(lower_latent) - cls_upper = self.upper_classifier(upper_latent) - cls_hands = self.hands_classifier(hands_latent) - - return { - "rec_face":face_latent, - "rec_upper":upper_latent, - "rec_lower":lower_latent, - "rec_hands":hands_latent, - "cls_face":cls_face, - "cls_upper":cls_upper, - "cls_lower":cls_lower, - "cls_hands":cls_hands, - } \ No newline at end of file diff --git a/models/emage_audio.py b/models/emage_audio.py deleted file mode 100644 index 06d43a28284c4b4d339c38efeb44c4a131e3e62d..0000000000000000000000000000000000000000 --- a/models/emage_audio.py +++ /dev/null @@ -1,243 +0,0 @@ -import copy -import math -import pickle -import numpy as np -import torch -import torch.nn as nn -from .utils.layer import BasicBlock -from .motion_encoder import * - - -class WavEncoder(nn.Module): - def __init__(self, out_dim, audio_in=1): - super().__init__() - self.out_dim = out_dim - self.feat_extractor = nn.Sequential( - BasicBlock(audio_in, out_dim//4, 15, 5, first_dilation=1600, downsample=True), - BasicBlock(out_dim//4, out_dim//4, 15, 6, first_dilation=0, downsample=True), - BasicBlock(out_dim//4, out_dim//4, 15, 1, first_dilation=7, ), - BasicBlock(out_dim//4, out_dim//2, 15, 6, first_dilation=0, downsample=True), - BasicBlock(out_dim//2, out_dim//2, 15, 1, first_dilation=7), - BasicBlock(out_dim//2, out_dim, 15, 3, first_dilation=0,downsample=True), - ) - def forward(self, wav_data): - # print(wav_data.shape) - if wav_data.dim() == 2: - wav_data = wav_data.unsqueeze(1) - else: - wav_data = wav_data.transpose(1, 2) - out = self.feat_extractor(wav_data) - return out.transpose(1, 2) - - -class MLP(nn.Module): - def __init__(self, in_dim, hidden_size, out_dim): - super().__init__() - self.mlp = nn.Sequential( - nn.Linear(in_dim, hidden_size), - nn.LeakyReLU(0.2, True), - nn.Linear(hidden_size, out_dim) - ) - def forward(self, inputs): - out = self.mlp(inputs) - return out - - -class PeriodicPositionalEncoding(nn.Module): - def __init__(self, d_model, dropout=0.1, period=15, max_seq_len=60): - super(PeriodicPositionalEncoding, self).__init__() - self.dropout = nn.Dropout(p=dropout) - pe = torch.zeros(period, d_model) - position = torch.arange(0, period, dtype=torch.float).unsqueeze(1) - div_term = torch.exp(torch.arange(0, d_model, 2).float() * (-math.log(10000.0) / d_model)) - pe[:, 0::2] = torch.sin(position * div_term) - pe[:, 1::2] = torch.cos(position * div_term) - pe = pe.unsqueeze(0) # (1, period, d_model) - repeat_num = (max_seq_len//period) + 1 - pe = pe.repeat(1, repeat_num, 1) # (1, repeat_num, period, d_model) - self.register_buffer('pe', pe) - def forward(self, x): - # print(self.pe.shape, x.shape) - x = x + self.pe[:, :x.size(1), :] - return self.dropout(x) - - -class MAGE_Transformer(nn.Module): - def __init__(self, args): - super(MAGE_Transformer, self).__init__() - self.args = args - # with open(f"{args.data_path}weights/vocab.pkl", 'rb') as f: - # self.lang_model = pickle.load(f) - # pre_trained_embedding = self.lang_model.word_embedding_weights - # self.text_pre_encoder_face = nn.Embedding.from_pretrained(torch.FloatTensor(pre_trained_embedding),freeze=args.t_fix_pre) - # self.text_encoder_face = nn.Linear(300, args.audio_f) - # self.text_encoder_face = nn.Linear(300, args.audio_f) - # self.text_pre_encoder_body = nn.Embedding.from_pretrained(torch.FloatTensor(pre_trained_embedding),freeze=args.t_fix_pre) - # self.text_encoder_body = nn.Linear(300, args.audio_f) - # self.text_encoder_body = nn.Linear(300, args.audio_f) - - self.audio_pre_encoder_face = WavEncoder(args.audio_f, audio_in=1) - self.audio_pre_encoder_body = WavEncoder(args.audio_f, audio_in=1) - - # self.at_attn_face = nn.Linear(args.audio_f*2, args.audio_f*2) - # self.at_attn_body = nn.Linear(args.audio_f*2, args.audio_f*2) - - args_top = copy.deepcopy(self.args) - args_top.vae_layer = 3 - args_top.vae_length = args.motion_f - args_top.vae_test_dim = args.pose_dims+3+4 - self.motion_encoder = VQEncoderV6(args_top) # masked motion to latent bs t 333 to bs t 256 - - # face decoder - self.feature2face = nn.Linear(args.audio_f*2, args.hidden_size) - self.face2latent = nn.Linear(args.hidden_size, args.vae_codebook_size) - self.transformer_de_layer = nn.TransformerDecoderLayer( - d_model=self.args.hidden_size, - nhead=4, - dim_feedforward=self.args.hidden_size*2, - batch_first=True - ) - self.face_decoder = nn.TransformerDecoder(self.transformer_de_layer, num_layers=4) - self.position_embeddings = PeriodicPositionalEncoding(self.args.hidden_size, period=self.args.pose_length, max_seq_len=self.args.pose_length) - - # motion decoder - self.transformer_en_layer = nn.TransformerEncoderLayer( - d_model=self.args.hidden_size, - nhead=4, - dim_feedforward=self.args.hidden_size*2, - batch_first=True - ) - self.motion_self_encoder = nn.TransformerEncoder(self.transformer_en_layer, num_layers=1) - self.audio_feature2motion = nn.Linear(args.audio_f, args.hidden_size) - self.feature2motion = nn.Linear(args.motion_f, args.hidden_size) - - self.bodyhints_face = MLP(args.motion_f, args.hidden_size, args.motion_f) - self.bodyhints_body = MLP(args.motion_f, args.hidden_size, args.motion_f) - self.motion2latent_upper = MLP(args.hidden_size, args.hidden_size, self.args.hidden_size) - self.motion2latent_hands = MLP(args.hidden_size, args.hidden_size, self.args.hidden_size) - self.motion2latent_lower = MLP(args.hidden_size, args.hidden_size, self.args.hidden_size) - self.wordhints_decoder = nn.TransformerDecoder(self.transformer_de_layer, num_layers=8) - - self.upper_decoder = nn.TransformerDecoder(self.transformer_de_layer, num_layers=1) - self.hands_decoder = nn.TransformerDecoder(self.transformer_de_layer, num_layers=1) - self.lower_decoder = nn.TransformerDecoder(self.transformer_de_layer, num_layers=1) - - self.face_classifier = MLP(self.args.vae_codebook_size, args.hidden_size, self.args.vae_codebook_size) - self.upper_classifier = MLP(self.args.vae_codebook_size, args.hidden_size, self.args.vae_codebook_size) - self.hands_classifier = MLP(self.args.vae_codebook_size, args.hidden_size, self.args.vae_codebook_size) - self.lower_classifier = MLP(self.args.vae_codebook_size, args.hidden_size, self.args.vae_codebook_size) - - self.mask_embeddings = nn.Parameter(torch.zeros(1, 1, self.args.pose_dims+3+4)) - self.motion_down_upper = nn.Linear(args.hidden_size, self.args.vae_codebook_size) - self.motion_down_hands = nn.Linear(args.hidden_size, self.args.vae_codebook_size) - self.motion_down_lower = nn.Linear(args.hidden_size, self.args.vae_codebook_size) - self.motion_down_upper = nn.Linear(args.hidden_size, self.args.vae_codebook_size) - self.motion_down_hands = nn.Linear(args.hidden_size, self.args.vae_codebook_size) - self.motion_down_lower = nn.Linear(args.hidden_size, self.args.vae_codebook_size) - self._reset_parameters() - - self.spearker_encoder_body = nn.Embedding(25, args.hidden_size) - self.spearker_encoder_face = nn.Embedding(25, args.hidden_size) - - def _reset_parameters(self): - nn.init.normal_(self.mask_embeddings, 0, self.args.hidden_size ** -0.5) - - def forward(self, in_audio=None, in_word=None, mask=None, is_test=None, in_motion=None, use_attentions=True, use_word=True, in_id = None): - # in_word_face = self.text_pre_encoder_face(in_word) - # in_word_face = self.text_encoder_face(in_word_face) - # in_word_body = self.text_pre_encoder_body(in_word) - # in_word_body = self.text_encoder_body(in_word_body) - # bs, t, c = in_word_face.shape - in_audio_face = self.audio_pre_encoder_face(in_audio) - in_audio_body = self.audio_pre_encoder_body(in_audio) - bs, t, c = in_audio_body.shape - # if in_audio_face.shape[1] != in_motion.shape[1]: - # diff_length = in_motion.shape[1]- in_audio_face.shape[1] - # if diff_length < 0: - # in_audio_face = in_audio_face[:, :diff_length, :] - # in_audio_body = in_audio_body[:, :diff_length, :] - # else: - # in_audio_face = torch.cat((in_audio_face, in_audio_face[:,-diff_length:]),1) - # in_audio_body = torch.cat((in_audio_body, in_audio_body[:,-diff_length:]),1) - - # if use_attentions: - # alpha_at_face = torch.cat([in_word_face, in_audio_face], dim=-1).reshape(bs, t, c*2) - # alpha_at_face = self.at_attn_face(alpha_at_face).reshape(bs, t, c, 2) - # alpha_at_face = alpha_at_face.softmax(dim=-1) - # fusion_face = in_word_face * alpha_at_face[:,:,:,1] + in_audio_face * alpha_at_face[:,:,:,0] - # alpha_at_body = torch.cat([in_word_body, in_audio_body], dim=-1).reshape(bs, t, c*2) - # alpha_at_body = self.at_attn_body(alpha_at_body).reshape(bs, t, c, 2) - # alpha_at_body = alpha_at_body.softmax(dim=-1) - # fusion_body = in_word_body * alpha_at_body[:,:,:,1] + in_audio_body * alpha_at_body[:,:,:,0] - # else: - fusion_face = in_audio_face - fusion_body = in_audio_body - - masked_embeddings = self.mask_embeddings.expand_as(in_motion) - masked_motion = torch.where(mask == 1, masked_embeddings, in_motion) # bs, t, 256 - body_hint = self.motion_encoder(masked_motion) # bs t 256 - speaker_embedding_face = self.spearker_encoder_face(in_id).squeeze(2) - speaker_embedding_body = self.spearker_encoder_body(in_id).squeeze(2) - - # decode face - use_body_hints = True - if use_body_hints: - body_hint_face = self.bodyhints_face(body_hint) - fusion_face = torch.cat([fusion_face, body_hint_face], dim=2) - a2g_face = self.feature2face(fusion_face) - face_embeddings = speaker_embedding_face - face_embeddings = self.position_embeddings(face_embeddings) - decoded_face = self.face_decoder(tgt=face_embeddings, memory=a2g_face) - face_latent = self.face2latent(decoded_face) - cls_face = self.face_classifier(face_latent) - - # motion spatial encoder - body_hint_body = self.bodyhints_body(body_hint) - motion_embeddings = self.feature2motion(body_hint_body) - motion_embeddings = speaker_embedding_body + motion_embeddings - motion_embeddings = self.position_embeddings(motion_embeddings) - - # bi-directional self-attention - motion_refined_embeddings = self.motion_self_encoder(motion_embeddings) - - # audio to gesture cross-modal attention - if use_word: - a2g_motion = self.audio_feature2motion(fusion_body) - motion_refined_embeddings_in = motion_refined_embeddings + speaker_embedding_body - motion_refined_embeddings_in = self.position_embeddings(motion_refined_embeddings) - word_hints = self.wordhints_decoder(tgt=motion_refined_embeddings_in, memory=a2g_motion) - motion_refined_embeddings = motion_refined_embeddings + word_hints - - # feedforward - upper_latent = self.motion2latent_upper(motion_refined_embeddings) - hands_latent = self.motion2latent_hands(motion_refined_embeddings) - lower_latent = self.motion2latent_lower(motion_refined_embeddings) - - upper_latent_in = upper_latent + speaker_embedding_body - upper_latent_in = self.position_embeddings(upper_latent_in) - hands_latent_in = hands_latent + speaker_embedding_body - hands_latent_in = self.position_embeddings(hands_latent_in) - lower_latent_in = lower_latent + speaker_embedding_body - lower_latent_in = self.position_embeddings(lower_latent_in) - - # transformer decoder - motion_upper = self.upper_decoder(tgt=upper_latent_in, memory=hands_latent+lower_latent) - motion_hands = self.hands_decoder(tgt=hands_latent_in, memory=upper_latent+lower_latent) - motion_lower = self.lower_decoder(tgt=lower_latent_in, memory=upper_latent+hands_latent) - upper_latent = self.motion_down_upper(motion_upper+upper_latent) - hands_latent = self.motion_down_hands(motion_hands+hands_latent) - lower_latent = self.motion_down_lower(motion_lower+lower_latent) - cls_lower = self.lower_classifier(lower_latent) - cls_upper = self.upper_classifier(upper_latent) - cls_hands = self.hands_classifier(hands_latent) - - return { - "rec_face":face_latent, - "rec_upper":upper_latent, - "rec_lower":lower_latent, - "rec_hands":hands_latent, - "cls_face":cls_face, - "cls_upper":cls_upper, - "cls_lower":cls_lower, - "cls_hands":cls_hands, - } \ No newline at end of file diff --git a/models/emage_audio/__init__.py b/models/emage_audio/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..6866a8e452f2de1485ceccc0e3634e1321eff258 --- /dev/null +++ b/models/emage_audio/__init__.py @@ -0,0 +1,12 @@ +from .configuration_emage_audio import EmageAudioConfig, EmageVQVAEConvConfig, EmageVAEConvConfig +from .modeling_emage_audio import EmageAudioModel, EmageVQVAEConv, EmageVQModel, EmageVAEConv + +__all__ = [ + "EmageAudioConfig", + "EmageAudioModel", + "EmageVQVAEConvConfig", + "EmageVQVAEConv", + "EmageVQModel", + "EmageVAEConvConfig", + "EmageVAEConv", +] diff --git a/models/emage_audio/__pycache__/__init__.cpython-39.pyc b/models/emage_audio/__pycache__/__init__.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..138d0328d1e044898a5e5c7ae28570ffc68d08bb Binary files /dev/null and b/models/emage_audio/__pycache__/__init__.cpython-39.pyc differ diff --git a/models/emage_audio/__pycache__/configuration_emage_audio.cpython-39.pyc b/models/emage_audio/__pycache__/configuration_emage_audio.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..7d58fa2294cf61ae613aff0787497a904f4c35e1 Binary files /dev/null and b/models/emage_audio/__pycache__/configuration_emage_audio.cpython-39.pyc differ diff --git a/models/emage_audio/__pycache__/modeling_emage_audio.cpython-39.pyc b/models/emage_audio/__pycache__/modeling_emage_audio.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..eebc7c92bf8f62e7b0119fc734e96a80d83662b6 Binary files /dev/null and b/models/emage_audio/__pycache__/modeling_emage_audio.cpython-39.pyc differ diff --git a/models/emage_audio/__pycache__/processing_emage_audio.cpython-39.pyc b/models/emage_audio/__pycache__/processing_emage_audio.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..a6459b313a28ffa4a13b3185c88bfec1c4305b32 Binary files /dev/null and b/models/emage_audio/__pycache__/processing_emage_audio.cpython-39.pyc differ diff --git a/models/emage_audio/configuration_emage_audio.py b/models/emage_audio/configuration_emage_audio.py new file mode 100644 index 0000000000000000000000000000000000000000..68efb69c1a086bb6ab04dbdba5ee91431f2aa205 --- /dev/null +++ b/models/emage_audio/configuration_emage_audio.py @@ -0,0 +1,32 @@ +from transformers import PretrainedConfig +from omegaconf import OmegaConf + +class EmageAudioConfig(PretrainedConfig): + model_type = "emage_audio" + + def __init__(self, config_obj=None, **kwargs): + if config_obj is not None: + cfg_dict = OmegaConf.to_container(config_obj, resolve=True) + kwargs.update(cfg_dict) + + super().__init__(**kwargs) + +class EmageVQVAEConvConfig(PretrainedConfig): + model_type = "emage_vqvaeconv" + + def __init__(self, config_obj=None, **kwargs): + if config_obj is not None: + cfg_dict = OmegaConf.to_container(config_obj, resolve=True) + kwargs.update(cfg_dict) + + super().__init__(**kwargs) + +class EmageVAEConvConfig(PretrainedConfig): + model_type = "emage_vaeconv" + + def __init__(self, config_obj=None, **kwargs): + if config_obj is not None: + cfg_dict = OmegaConf.to_container(config_obj, resolve=True) + kwargs.update(cfg_dict) + + super().__init__(**kwargs) diff --git a/models/emage_audio/modeling_emage_audio.py b/models/emage_audio/modeling_emage_audio.py new file mode 100644 index 0000000000000000000000000000000000000000..7ef09e106228220358aaa73f4fa668129da7049a --- /dev/null +++ b/models/emage_audio/modeling_emage_audio.py @@ -0,0 +1,494 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +import math +import copy +from transformers import PreTrainedModel +from .configuration_emage_audio import EmageAudioConfig, EmageVQVAEConvConfig, EmageVAEConvConfig +from .processing_emage_audio import Quantizer, VQEncoderV5, VQDecoderV5, WavEncoder, MLP, PeriodicPositionalEncoding, VQEncoderV6, recover_from_mask_ts, rotation_6d_to_axis_angle, velocity2position, axis_angle_to_rotation_6d, rotation_6d_to_matrix, matrix_to_axis_angle, axis_angle_to_matrix, matrix_to_rotation_6d + + +def inverse_selection_tensor(filtered_t, selection_array, n): + selection_array = torch.from_numpy(selection_array).cuda() + original_shape_t = torch.zeros((n, 165)).cuda() + selected_indices = torch.where(selection_array == 1)[0] + for i in range(n): + original_shape_t[i, selected_indices] = filtered_t[i] + return original_shape_t + +class EmageVAEConv(PreTrainedModel): + config_class = EmageVAEConvConfig + base_model_prefix = "emage_vaeconv" + def __init__(self, config): + super().__init__(config) + self.encoder = VQEncoderV5(config) + self.decoder = VQDecoderV5(config) + + def forward(self, inputs): + pre_latent = self.encoder(inputs) + rec_pose = self.decoder(pre_latent) + return { + "rec_pose": rec_pose + } + +class EmageVQVAEConv(PreTrainedModel): + config_class = EmageVQVAEConvConfig + base_model_prefix = "emage_vqvaeconv" + def __init__(self, config): + super().__init__(config) + self.encoder = VQEncoderV5(config) + self.quantizer = Quantizer(config.vae_codebook_size, config.vae_length, config.vae_quantizer_lambda) + self.decoder = VQDecoderV5(config) + def forward(self, inputs): + pre_latent = self.encoder(inputs) + embedding_loss, vq_latent, _, perplexity = self.quantizer(pre_latent) + rec_pose = self.decoder(vq_latent) + return {"poses_feat":vq_latent,"embedding_loss":embedding_loss,"perplexity":perplexity,"rec_pose": rec_pose} + def map2index(self, inputs): + pre_latent = self.encoder(inputs) + index = self.quantizer.map2index(pre_latent) + return index + def map2latent(self, inputs): + pre_latent = self.encoder(inputs) + index = self.quantizer.map2index(pre_latent) + z_q = self.quantizer.get_codebook_entry(index) + return z_q + def decode(self, index): + z_q = self.quantizer.get_codebook_entry(index) + rec_pose = self.decoder(z_q) + return rec_pose + def decode_from_latent(self, latent): + # print(latent.shape) + z_flattened = latent.contiguous().view(-1, self.quantizer.e_dim) + + d = torch.sum(z_flattened**2, dim=1, keepdim=True) + torch.sum(self.quantizer.embedding.weight**2, dim=1) - 2*torch.matmul(z_flattened, self.quantizer.embedding.weight.t()) + min_encoding_indices = torch.argmin(d, dim=1) + # print(min_encoding_indices.shape) + indices = min_encoding_indices.view(latent.shape[0], latent.shape[1]) + z_q = self.quantizer.get_codebook_entry(indices) + rec_pose = self.decoder(z_q) + return rec_pose + +class EmageVQModel(nn.Module): + def __init__(self, face_model, upper_model, hands_model, lower_model, global_model): + super().__init__() + self.joint_mask_upper = [ + False, False, False, True, False, False, True, False, False, True, + False, False, True, True, True, True, True, True, True, True, + True, True, False, False, False, False, False, False, False, False, + False, False, False, False, False, False, False, False, False, False, + False, False, False, False, False, False, False, False, False, False, + False, False, False, False, False + ] + self.joint_mask_lower = [ + True, True, True, False, True, True, False, True, True, False, + True, True, False, False, False, False, False, False, False, False, + False, False, False, False, False, False, False, False, False, False, + False, False, False, False, False, False, False, False, False, False, + False, False, False, False, False, False, False, False, False, False, + False, False, False, False, False + ] + self.vq_model_face = face_model + self.vq_model_upper = upper_model + self.vq_model_hands = hands_model + self.vq_model_lower = lower_model + self.global_motion = global_model + + def spilt_inputs(self, smplx_body_rot6d, expression, tar_contact=None, tar_trans=None): + bs, t, j6 = smplx_body_rot6d.shape + smplx_body_rot6d = smplx_body_rot6d.reshape(bs, t, j6//6, 6) + jaw_rot6d = smplx_body_rot6d[:, :, 22:23, :].reshape(bs, t, 6) + face = torch.cat([jaw_rot6d, expression], dim=2) + upper_rot6d = smplx_body_rot6d[:, :,self.joint_mask_upper, :].reshape(bs, t, 78) + hands_rot6d = smplx_body_rot6d[:, :,25:55, :].reshape(bs, t, 180) + lower_rot6d = smplx_body_rot6d[:, :,self.joint_mask_lower, :].reshape(bs, t, 54) + tar_contact = torch.zeros(bs, t, 4, device=smplx_body_rot6d.device) if tar_contact is None else tar_contact + tar_trans = torch.zeros(bs, t, 3, device=smplx_body_rot6d.device) if tar_trans is None else tar_trans + lower = torch.cat([lower_rot6d, tar_trans, tar_contact], dim=2) + return dict(face=face, upper=upper_rot6d, hands=hands_rot6d, lower=lower) + + def map2index(self, smplx_body_rot6d, expression, tar_contact=None, tar_trans=None): + inputs = self.spilt_inputs(smplx_body_rot6d, expression, tar_contact=tar_contact, tar_trans=tar_trans) + face_index = self.vq_model_face.map2index(inputs["face"]) + upper_index = self.vq_model_upper.map2index(inputs["upper"]) + hands_index = self.vq_model_hands.map2index(inputs["hands"]) + lower_index = self.vq_model_lower.map2index(inputs["lower"]) + return dict(face=face_index, upper=upper_index, hands=hands_index, lower=lower_index) + + def map2latent(self, smplx_body_rot6d, expression, tar_contact=None, tar_trans=None): + inputs = self.spilt_inputs(smplx_body_rot6d, expression,tar_contact=tar_contact, tar_trans=tar_trans) + face_latent = self.vq_model_face.map2latent(inputs["face"]) + upper_latent = self.vq_model_upper.map2latent(inputs["upper"]) + hands_latent = self.vq_model_hands.map2latent(inputs["hands"]) + lower_latent = self.vq_model_lower.map2latent(inputs["lower"]) + return dict(face=face_latent, upper=upper_latent, hands=hands_latent, lower=lower_latent) + + def decode(self, face_index=None, upper_index=None, hands_index=None, lower_index=None, + face_latent=None, upper_latent=None, hands_latent=None, lower_latent=None, + get_global_motion=False, ref_trans=None): + + for input_tensor in [face_index, upper_index, hands_index, lower_index, face_latent, upper_latent, hands_latent, lower_latent]: + if input_tensor is not None: + bs, t = input_tensor.shape[:2] + break + + if face_index is not None: + face_mix = self.vq_model_face.decode(face_index) # bs, t, 106 + face_jaw_6d, expression = face_mix[:, :, :6], face_mix[:, :, 6:] + face_jaw = rotation_6d_to_axis_angle(face_jaw_6d) + elif face_latent is not None: + face_mix = self.vq_model_face.decode_from_latent(face_latent) + face_jaw_6d, expression = face_mix[:, :, :6], face_mix[:, :, 6:] + face_jaw = rotation_6d_to_axis_angle(face_jaw_6d) + else: + face_jaw = torch.zeros(bs, t, 3, device=self.vq_model_face.device) + expression = torch.zeros(bs, t, 100, device=self.vq_model_face.device) + + if upper_index is not None: + # print(upper_index) + upper_6d = self.vq_model_upper.decode(upper_index) # bs, t, 78 + upper = rotation_6d_to_axis_angle(upper_6d.reshape(bs, t, -1, 6)).reshape(bs, t, -1) + elif upper_latent is not None: + upper_6d = self.vq_model_upper.decode_from_latent(upper_latent) + upper = rotation_6d_to_axis_angle(upper_6d.reshape(bs, t, -1, 6)).reshape(bs, t, -1) + else: + upper = torch.zeros(bs, t, 39, device=self.vq_model_upper.device) + + if hands_index is not None: + hands_6d = self.vq_model_hands.decode(hands_index) + hands = rotation_6d_to_axis_angle(hands_6d.reshape(bs, t, -1, 6)).reshape(bs, t, -1) + elif hands_latent is not None: + hands_6d = self.vq_model_hands.decode_from_latent(hands_latent) + hands = rotation_6d_to_axis_angle(hands_6d.reshape(bs, t, -1, 6)).reshape(bs, t, -1) + else: + hands = torch.zeros(bs, t, 90, device=self.vq_model_hands.device) + + if lower_index is not None: + lower_mix = self.vq_model_lower.decode(lower_index) + lower_6d, transfoot = lower_mix[:, :, :-7], lower_mix[:, :, -7:] + lower = rotation_6d_to_axis_angle(lower_6d.reshape(bs, t, -1, 6)).reshape(bs, t, -1) + elif lower_latent is not None: + lower_mix = self.vq_model_lower.decode_from_latent(lower_latent) + lower_6d, transfoot = lower_mix[:, :, :-7], lower_mix[:, :, -7:] + lower = rotation_6d_to_axis_angle(lower_6d.reshape(bs, t, -1, 6)).reshape(bs, t, -1) + else: + lower = torch.zeros(bs, t, 27, device=self.vq_model_lower.device) + transfoot = torch.zeros(bs, t, 7, device=self.vq_model_lower.device) + lower_6d = axis_angle_to_rotation_6d(lower.reshape(bs, t, -1, 3)).reshape(bs, t, -1) + lower_mix = torch.cat([lower_6d, transfoot], dim=-1) + + upper2all = recover_from_mask_ts(upper, self.joint_mask_upper) + hands2all = recover_from_mask_ts(hands, [False]*25+[True]*30) + lower2all = recover_from_mask_ts(lower, self.joint_mask_lower) + + all_motion_axis_angle = upper2all + hands2all + lower2all + all_motion_axis_angle[:, :, 22*3:22*3+3] = face_jaw + all_motion_rot6d = axis_angle_to_rotation_6d(all_motion_axis_angle.reshape(bs, t, 55, 3)).reshape(bs, t, 55*6) + + all_motion4inference = torch.cat([all_motion_rot6d, transfoot], dim=2) # 330 + 3 + 4 + + global_motion = None + if get_global_motion: + global_motion = self.get_global_motion(lower_mix, ref_trans) + return dict(expression=expression, all_motion4inference=all_motion4inference, motion_axis_angle=all_motion_axis_angle, trans=global_motion) + + def get_global_motion(self, lower_body, ref_trans): + global_motion = self.global_motion(lower_body) + rec_trans_v_s = global_motion["rec_pose"][:, :, 54:57] + if len(ref_trans.shape) == 2: + ref_trans = ref_trans.unsqueeze(0).repeat(rec_trans_v_s.shape[0], 1, 1) + + rec_x_trans = velocity2position(rec_trans_v_s[:, :, 0:1], 1/30, ref_trans[:, 0, 0:1]) + rec_z_trans = velocity2position(rec_trans_v_s[:, :, 2:3], 1/30, ref_trans[:, 0, 2:3]) + rec_y_trans = rec_trans_v_s[:,:,1:2] + global_motion = torch.cat([rec_x_trans, rec_y_trans, rec_z_trans], dim=-1) + return global_motion + + +class EmageAudioModel(PreTrainedModel): + config_class = EmageAudioConfig + base_model_prefix = "emage_audio" + def __init__(self, config: EmageAudioConfig): + super().__init__(config) + self.cfg = config + # audio encoder + self.audio_encoder_face = WavEncoder(self.cfg.audio_f) + self.audio_encoder_body = WavEncoder(self.cfg.audio_f) + #speaker id + self.speaker_embedding_body = nn.Embedding(self.cfg.speaker_dims, self.cfg.hidden_size) + self.speaker_embedding_face = nn.Embedding(self.cfg.speaker_dims, self.cfg.hidden_size) + # mask embedding + self.mask_embedding = nn.Parameter(torch.zeros(1,1,self.cfg.pose_dims+3+4)) + nn.init.normal_(self.mask_embedding, 0, self.cfg.hidden_size**-0.5) + # nn.init.normal_(self.speaker_embedding_body.weight, 0, self.cfg.hidden_size/2**-0.5) + # nn.init.normal_(self.speaker_embedding_face.weight, 0, self.cfg.hidden_size*2**-0.5) + + # motion pre encoder + args_top = copy.deepcopy(self.cfg) + args_top.vae_layer = 3 + args_top.vae_length = self.cfg.motion_f + args_top.vae_test_dim = self.cfg.pose_dims+3+4 + self.motion_encoder = VQEncoderV6(args_top) + self.bodyhints_face = MLP(self.cfg.motion_f, self.cfg.hidden_size, self.cfg.motion_f) + self.bodyhints_body = MLP(self.cfg.motion_f, self.cfg.hidden_size, self.cfg.motion_f) + # motion encoder + self.audio_body_motion_proj = nn.Linear(self.cfg.audio_f, self.cfg.hidden_size) + self.moton_proj = nn.Linear(self.cfg.motion_f, self.cfg.hidden_size) + self.position_embeddings = PeriodicPositionalEncoding(self.cfg.hidden_size, period=self.cfg.pose_length, max_seq_len=self.cfg.pose_length) + self.transformer_en_layer = nn.TransformerEncoderLayer(d_model=self.cfg.hidden_size,nhead=4,dim_feedforward=self.cfg.hidden_size*2) + self.motion_self_encoder = nn.TransformerEncoder(self.transformer_en_layer, num_layers=1) + # coss attn + self.audio_motion_cross_attn_layer = nn.TransformerDecoderLayer(d_model=self.cfg.hidden_size,nhead=4,dim_feedforward=self.cfg.hidden_size*2) + self.audio_motion_cross_attn = nn.TransformerDecoder(self.audio_motion_cross_attn_layer, num_layers=8) + # feed forward + self.motion2latent_upper = MLP(self.cfg.hidden_size, self.cfg.hidden_size, self.cfg.hidden_size) + self.motion2latent_hands = MLP(self.cfg.hidden_size, self.cfg.hidden_size, self.cfg.hidden_size) + self.motion2latent_lower = MLP(self.cfg.hidden_size, self.cfg.hidden_size, self.cfg.hidden_size) + # refine + self.body_motion_decoder_upper = nn.TransformerDecoder(self.audio_motion_cross_attn_layer, num_layers=1) + self.body_motion_decoder_hands = nn.TransformerDecoder(self.audio_motion_cross_attn_layer, num_layers=1) + self.body_motion_decoder_lower = nn.TransformerDecoder(self.audio_motion_cross_attn_layer, num_layers=1) + # deocder + self.motion_out_proj_upper = nn.Linear(self.cfg.hidden_size, self.cfg.vae_codebook_size) + self.motion_out_proj_hands = nn.Linear(self.cfg.hidden_size, self.cfg.vae_codebook_size) + self.motion_out_proj_lower = nn.Linear(self.cfg.hidden_size, self.cfg.vae_codebook_size) + self.motion_cls_upper = MLP(self.cfg.vae_codebook_size, self.cfg.hidden_size, self.cfg.vae_codebook_size) + self.motion_cls_hands = MLP(self.cfg.vae_codebook_size, self.cfg.hidden_size, self.cfg.vae_codebook_size) + self.motion_cls_lower = MLP(self.cfg.vae_codebook_size, self.cfg.hidden_size, self.cfg.vae_codebook_size) + + # face decoder + self.audio_face_motion_proj = nn.Linear(self.cfg.audio_f+self.cfg.motion_f, self.cfg.hidden_size) + self.face_motion_decoder = nn.TransformerDecoder(self.audio_motion_cross_attn_layer, num_layers=4) + self.face_out_proj = nn.Linear(self.cfg.hidden_size, self.cfg.vae_codebook_size) + self.face_cls = MLP(self.cfg.vae_codebook_size, self.cfg.hidden_size, self.cfg.vae_codebook_size) + + def forward(self, audio, speaker_id, masked_motion, mask, use_audio=True): + audio2face_fea = self.audio_encoder_face(audio) + audio2body_fea = self.audio_encoder_body(audio) + bs, t, _ = audio2face_fea.shape + + speaker_motion_fea_proj = self.speaker_embedding_body(speaker_id).repeat(1, t, 1) + speaker_face_fea_proj = self.speaker_embedding_face(speaker_id).repeat(1, t, 1) + + # mask motion + masked_embeddings = self.mask_embedding.expand_as(masked_motion) + masked_motion = torch.where(mask==1, masked_embeddings, masked_motion) + + # motion token (spatial hints) + body_hint = self.motion_encoder(masked_motion) + body_hint_body = self.bodyhints_body(body_hint) + body_hint_face = self.bodyhints_face(body_hint) + + audio2face_fea_proj = self.audio_face_motion_proj(torch.cat([audio2face_fea, body_hint_face], dim=2)) + # audio2face_fea_proj = self.position_embeddings(audio2face_fea_proj) + # audio2face_fea_proj = speaker_face_fea_proj + audio2face_fea_proj + face_proj = self.position_embeddings(speaker_face_fea_proj) + decode_face = self.face_motion_decoder(tgt=face_proj.permute(1,0,2), memory=audio2face_fea_proj.permute(1,0,2)).permute(1,0,2) + face_latent = self.face_out_proj(decode_face) + classify_face = self.face_cls(face_latent) + + # motion self attn (temporal) + masked_motion_proj = self.moton_proj(body_hint_body) + masked_motion_proj = self.position_embeddings(masked_motion_proj) + masked_motion_proj = speaker_motion_fea_proj + masked_motion_proj + motion_fea = self.motion_self_encoder(masked_motion_proj.permute(1,0,2)).permute(1,0,2) + + # audio_cross_attn + + audio2body_fea_proj = self.audio_body_motion_proj(audio2body_fea) + # audio2body_fea_proj = self.position_embeddings(audio2body_fea_proj) + # audio2body_fea_proj = speaker_motion_fea_proj + audio2body_fea_proj + motion_fea = motion_fea + speaker_motion_fea_proj + motion_fea = self.position_embeddings(motion_fea) + audio2body_fea_cross = self.audio_motion_cross_attn(tgt=motion_fea.permute(1,0,2), memory=audio2body_fea_proj.permute(1,0,2)).permute(1,0,2) + if not use_audio: + audio2body_fea_cross = audio2body_fea_cross * 0. + motion_fea = motion_fea + audio2body_fea_cross + + # mlp + upper_latent = self.motion2latent_upper(motion_fea) + hands_latent = self.motion2latent_hands(motion_fea) + lower_latent = self.motion2latent_lower(motion_fea) + + # refine + motion_upper_refine = self.body_motion_decoder_upper(tgt=upper_latent.permute(1,0,2)+speaker_motion_fea_proj.permute(1,0,2), memory=(hands_latent+lower_latent).permute(1,0,2)).permute(1,0,2) + motion_hands_refine = self.body_motion_decoder_hands(tgt=hands_latent.permute(1,0,2)+speaker_motion_fea_proj.permute(1,0,2), memory=(upper_latent+lower_latent).permute(1,0,2)).permute(1,0,2) + motion_lower_refine = self.body_motion_decoder_lower(tgt=lower_latent.permute(1,0,2)+speaker_motion_fea_proj.permute(1,0,2), memory=(upper_latent+hands_latent).permute(1,0,2)).permute(1,0,2) + upper_latent = self.motion_out_proj_upper(upper_latent + motion_upper_refine) + hands_latent = self.motion_out_proj_hands(hands_latent + motion_hands_refine) + lower_latent = self.motion_out_proj_lower(lower_latent + motion_lower_refine) + + # decode body + classify_upper = self.motion_cls_upper(upper_latent) + classify_hands = self.motion_cls_hands(hands_latent) + classify_lower = self.motion_cls_lower(lower_latent) + + return { + "rec_face": face_latent, + "rec_upper": upper_latent, + "rec_hands": hands_latent, + "rec_lower": lower_latent, + "cls_face": classify_face, + "cls_upper": classify_upper, + "cls_hands": classify_hands, + "cls_lower": classify_lower, + } + + def inference(self, audio, speaker_id, vq_model, masked_motion=None, mask=None): + # generate default mask and masked motion if not provided + length = audio.shape[1] * 30 // 16000 + bs = audio.shape[0] + + fake_axis_angle = torch.zeros(bs, length, 55, 3).to(audio.device) + fake_motion = axis_angle_to_rotation_6d(fake_axis_angle).reshape(bs, length, -1) + fake_foot_and_trans = torch.zeros(bs, length, 7).to(audio.device) + fake_motion = torch.cat([fake_motion, fake_foot_and_trans], dim=-1) + if masked_motion is not None: + fake_motion[:, :masked_motion.shape[1]] = masked_motion + masked_motion = fake_motion + + fake_mask = torch.ones_like(masked_motion) + if mask is not None: + fake_mask[:, :mask.shape[1]] = mask + mask = fake_mask + + # print(length, masked_motion.shape, mask.shape) + + # Autoregressive inference + bs, total_len, c = masked_motion.shape + window = self.cfg.pose_length + pre_frames = self.cfg.seed_frames + rounds = (total_len - pre_frames) // (window - pre_frames) + remain = (total_len - pre_frames) % (window - pre_frames) + + rec_all_face = [] + rec_all_lower = [] + rec_all_upper = [] + rec_all_hands = [] + cls_all_face = [] + cls_all_lower = [] + cls_all_upper = [] + cls_all_hands = [] + + last_motion = masked_motion[:, :pre_frames, :] + for i in range(rounds): + start_idx = i*(window - pre_frames) + end_idx = start_idx + window + + window_mask = mask[:, start_idx:end_idx, :].clone() + window_motion = masked_motion[:, start_idx:end_idx, :].clone() + window_motion[:, :pre_frames, :] = torch.where( + (window_mask[:, :pre_frames, :] == 0), + masked_motion[:, start_idx:start_idx+pre_frames, :], + last_motion, + ) + window_mask[:, :pre_frames, :] = 0 + + audio_slice_len = (end_idx - start_idx)*(16000//30) + audio_slice = audio[:, start_idx*(16000//30) : start_idx*(16000//30)+audio_slice_len] + # print(i, audio_slice.shape, speaker_id.shape, window_motion.shape, window_mask.shape) + net_out_val = self.forward(audio_slice, speaker_id, masked_motion=window_motion, mask=window_mask, use_audio=True) + + _, cls_face = torch.max(F.log_softmax(net_out_val["cls_face"], dim=2), dim=2) + _, cls_upper = torch.max(F.log_softmax(net_out_val["cls_upper"], dim=2), dim=2) + _, cls_hands = torch.max(F.log_softmax(net_out_val["cls_hands"], dim=2), dim=2) + _, cls_lower = torch.max(F.log_softmax(net_out_val["cls_lower"], dim=2), dim=2) + + face_latent = net_out_val["rec_face"] if self.cfg.lf > 0 and self.cfg.cf == 0 else None + upper_latent = net_out_val["rec_upper"] if self.cfg.lu > 0 and self.cfg.cu == 0 else None + hands_latent = net_out_val["rec_hands"] if self.cfg.lh > 0 and self.cfg.ch == 0 else None + lower_latent = net_out_val["rec_lower"] if self.cfg.ll > 0 and self.cfg.cl == 0 else None + face_index = cls_face if self.cfg.cf > 0 else None + upper_index = cls_upper if self.cfg.cu > 0 else None + hands_index = cls_hands if self.cfg.ch > 0 else None + lower_index = cls_lower if self.cfg.cl > 0 else None + + decode_dict = vq_model.decode( + face_latent=face_latent, upper_latent=upper_latent, lower_latent=lower_latent, hands_latent=hands_latent, + face_index=face_index, upper_index=upper_index, lower_index=lower_index, hands_index=hands_index,) + + # decode_dict = vq_model.decode(face_latent=net_out_val["rec_face"], upper_index=net_out_val["cls_upper"], hands_index=net_out_val["cls_hands"], lower_index=net_out_val["cls_lower"]) + + last_motion = decode_dict["all_motion4inference"][:, -pre_frames:, :] + rec_all_face.append(net_out_val["rec_face"][:, :-pre_frames, :]) + rec_all_upper.append(net_out_val["rec_upper"][:, :-pre_frames, :]) + rec_all_hands.append(net_out_val["rec_hands"][:, :-pre_frames, :]) + rec_all_lower.append(net_out_val["rec_lower"][:, :-pre_frames, :]) + cls_all_face.append(net_out_val["cls_face"][:, :-pre_frames]) + cls_all_upper.append(net_out_val["cls_upper"][:, :-pre_frames]) + cls_all_hands.append(net_out_val["cls_hands"][:, :-pre_frames]) + cls_all_lower.append(net_out_val["cls_lower"][:, :-pre_frames]) + + if remain > pre_frames: + final_start = rounds*(window - pre_frames) + final_end = final_start + pre_frames + remain + + final_mask = mask[:, final_start:final_end, :].clone() + final_motion = masked_motion[:, final_start:final_end, :].clone() + final_motion[:, :pre_frames, :] = torch.where( + (final_mask[:, :pre_frames, :] == 0), + masked_motion[:, final_start:final_start+pre_frames, :], + last_motion, + ) + final_mask[:, :pre_frames, :] = 0 + + audio_slice_len = (final_end - final_start)*(16000//30) + audio_slice = audio[:, final_start*(16000//30) : final_start*(16000//30)+audio_slice_len] + net_out_val = self.forward(audio_slice, speaker_id, masked_motion=final_motion, mask=final_mask, use_audio=True) + + _, cls_face = torch.max(F.log_softmax(net_out_val["cls_face"], dim=2), dim=2) + _, cls_upper = torch.max(F.log_softmax(net_out_val["cls_upper"], dim=2), dim=2) + _, cls_hands = torch.max(F.log_softmax(net_out_val["cls_hands"], dim=2), dim=2) + _, cls_lower = torch.max(F.log_softmax(net_out_val["cls_lower"], dim=2), dim=2) + + face_latent = net_out_val["rec_face"] if self.cfg.lf > 0 and self.cfg.cf == 0 else None + upper_latent = net_out_val["rec_upper"] if self.cfg.lu > 0 and self.cfg.cu == 0 else None + hands_latent = net_out_val["rec_hands"] if self.cfg.lh > 0 and self.cfg.ch == 0 else None + lower_latent = net_out_val["rec_lower"] if self.cfg.ll > 0 and self.cfg.cl == 0 else None + face_index = cls_face if self.cfg.cf > 0 else None + upper_index = cls_upper if self.cfg.cu > 0 else None + hands_index = cls_hands if self.cfg.ch > 0 else None + lower_index = cls_lower if self.cfg.cl > 0 else None + + decode_dict = vq_model.decode( + face_latent=face_latent, upper_latent=upper_latent, lower_latent=lower_latent, hands_latent=hands_latent, + face_index=face_index, upper_index=upper_index, lower_index=lower_index, hands_index=hands_index,) + + rec_all_face.append(net_out_val["rec_face"]) + rec_all_upper.append(net_out_val["rec_upper"]) + rec_all_hands.append(net_out_val["rec_hands"]) + rec_all_lower.append(net_out_val["rec_lower"]) + cls_all_face.append(net_out_val["cls_face"]) + cls_all_upper.append(net_out_val["cls_upper"]) + cls_all_hands.append(net_out_val["cls_hands"]) + cls_all_lower.append(net_out_val["cls_lower"]) + + rec_all_face = torch.cat(rec_all_face, dim=1) + rec_all_upper = torch.cat(rec_all_upper, dim=1) + rec_all_hands = torch.cat(rec_all_hands, dim=1) + rec_all_lower = torch.cat(rec_all_lower, dim=1) + cls_all_face = torch.cat(cls_all_face, dim=1) + cls_all_upper = torch.cat(cls_all_upper, dim=1) + cls_all_hands = torch.cat(cls_all_hands, dim=1) + cls_all_lower = torch.cat(cls_all_lower, dim=1) + + return { + "rec_face": rec_all_face, + "rec_upper": rec_all_upper, + "rec_hands": rec_all_hands, + "rec_lower": rec_all_lower, + "cls_face": cls_all_face, + "cls_upper": cls_all_upper, + "cls_hands": cls_all_hands, + "cls_lower": cls_all_lower, + } + + + + + + + + + + diff --git a/models/emage_audio/processing_emage_audio.py b/models/emage_audio/processing_emage_audio.py new file mode 100644 index 0000000000000000000000000000000000000000..4599ac6eeeff383095494cd2481a6ab35df71607 --- /dev/null +++ b/models/emage_audio/processing_emage_audio.py @@ -0,0 +1,343 @@ +import math +import torch +import torch.nn as nn +import torch.nn.functional as F + +def _copysign(a, b): + signs_differ = (a < 0) != (b < 0) + return torch.where(signs_differ, -a, a) + +def _sqrt_positive_part(x): + ret = torch.zeros_like(x) + positive_mask = x > 0 + ret[positive_mask] = torch.sqrt(x[positive_mask]) + return ret + +def matrix_to_quaternion(matrix): + if matrix.size(-1) != 3 or matrix.size(-2) != 3: + raise ValueError + m00 = matrix[..., 0, 0] + m11 = matrix[..., 1, 1] + m22 = matrix[..., 2, 2] + o0 = 0.5 * _sqrt_positive_part(1 + m00 + m11 + m22) + x = 0.5 * _sqrt_positive_part(1 + m00 - m11 - m22) + y = 0.5 * _sqrt_positive_part(1 - m00 + m11 - m22) + z = 0.5 * _sqrt_positive_part(1 - m00 - m11 + m22) + o1 = _copysign(x, matrix[..., 2, 1] - matrix[..., 1, 2]) + o2 = _copysign(y, matrix[..., 0, 2] - matrix[..., 2, 0]) + o3 = _copysign(z, matrix[..., 1, 0] - matrix[..., 0, 1]) + return torch.stack((o0, o1, o2, o3), -1) + +def quaternion_to_axis_angle(quaternions): + norms = torch.norm(quaternions[..., 1:], p=2, dim=-1, keepdim=True) + half_angles = torch.atan2(norms, quaternions[..., :1]) + angles = 2 * half_angles + eps = 1e-6 + small_angles = angles.abs() < eps + sin_half_angles_over_angles = torch.empty_like(angles) + sin_half_angles_over_angles[~small_angles] = ( + torch.sin(half_angles[~small_angles]) / angles[~small_angles] + ) + sin_half_angles_over_angles[small_angles] = ( + 0.5 - (angles[small_angles]*angles[small_angles])/48 + ) + return quaternions[..., 1:] / sin_half_angles_over_angles + +def matrix_to_axis_angle(matrix): + return quaternion_to_axis_angle(matrix_to_quaternion(matrix)) + +def rotation_6d_to_matrix(d6: torch.Tensor) -> torch.Tensor: + a1, a2 = d6[..., :3], d6[..., 3:] + b1 = F.normalize(a1, dim=-1) + b2 = a2 - (b1 * a2).sum(-1, keepdim=True) * b1 + b2 = F.normalize(b2, dim=-1) + b3 = torch.cross(b1, b2, dim=-1) + return torch.stack((b1, b2, b3), dim=-2) + +def rotation_6d_to_axis_angle(rot6d): + return matrix_to_axis_angle(rotation_6d_to_matrix(rot6d)) + +def matrix_to_rotation_6d(matrix: torch.Tensor) -> torch.Tensor: + return matrix[..., :2, :].clone().reshape(*matrix.size()[:-2], 6) + +def axis_angle_to_quaternion(axis_angle): + angles = torch.norm(axis_angle, p=2, dim=-1, keepdim=True) + half_angles = 0.5 * angles + eps = 1e-6 + small_angles = angles.abs() < eps + sin_half_angles_over_angles = torch.empty_like(angles) + sin_half_angles_over_angles[~small_angles] = ( + torch.sin(half_angles[~small_angles]) / angles[~small_angles] + ) + sin_half_angles_over_angles[small_angles] = ( + 0.5 - (angles[small_angles] * angles[small_angles]) / 48 + ) + quaternions = torch.cat( + [torch.cos(half_angles), axis_angle * sin_half_angles_over_angles], dim=-1 + ) + return quaternions + +def quaternion_to_matrix(quaternions): + r, i, j, k = torch.unbind(quaternions, -1) + two_s = 2.0 / (quaternions * quaternions).sum(-1) + + o = torch.stack( + ( + 1 - two_s * (j * j + k * k), + two_s * (i * j - k * r), + two_s * (i * k + j * r), + two_s * (i * j + k * r), + 1 - two_s * (i * i + k * k), + two_s * (j * k - i * r), + two_s * (i * k - j * r), + two_s * (j * k + i * r), + 1 - two_s * (i * i + j * j), + ), + -1, + ) + return o.reshape(quaternions.shape[:-1] + (3, 3)) + +def axis_angle_to_matrix(axis_angle): + return quaternion_to_matrix(axis_angle_to_quaternion(axis_angle)) + +def axis_angle_to_rotation_6d(axis_angle): + return matrix_to_rotation_6d(axis_angle_to_matrix(axis_angle)) + + +def velocity2position(data_seq, dt, init_pos): + res_trans = [] + for i in range(data_seq.shape[1]): + if i == 0: + res_trans.append(init_pos.unsqueeze(1)) + else: + res = data_seq[:, i-1:i] * dt + res_trans[-1] + res_trans.append(res) + return torch.cat(res_trans, dim=1) + + +def recover_from_mask_ts(selected_motion: torch.Tensor, mask: list) -> torch.Tensor: + device = selected_motion.device + dtype = selected_motion.dtype + mask_arr = torch.tensor(mask, dtype=torch.bool, device=device) + j = len(mask_arr) + sum_mask = mask_arr.sum().item() + c_channels = selected_motion.shape[-1] // sum_mask + new_shape = selected_motion.shape[:-1] + (sum_mask, c_channels) + selected_motion = selected_motion.reshape(new_shape) + out_shape = list(selected_motion.shape[:-2]) + [j, c_channels] + recovered = torch.zeros(out_shape, dtype=dtype, device=device) + recovered[..., mask_arr, :] = selected_motion + final_shape = list(recovered.shape[:-2]) + [j*c_channels] + recovered = recovered.reshape(final_shape) + return recovered + + +class Quantizer(nn.Module): + def __init__(self, n_e, e_dim, beta): + super().__init__() + self.e_dim = e_dim + self.n_e = n_e + self.beta = beta + self.embedding = nn.Embedding(self.n_e, self.e_dim) + self.embedding.weight.data.uniform_(-1.0/self.n_e, 1.0/self.n_e) + + def forward(self, z): + assert z.shape[-1] == self.e_dim + z_flattened = z.contiguous().view(-1, self.e_dim) + d = torch.sum(z_flattened**2, dim=1, keepdim=True) + \ + torch.sum(self.embedding.weight**2, dim=1) - 2*torch.matmul(z_flattened, self.embedding.weight.t()) + min_encoding_indices = torch.argmin(d, dim=1) + z_q = self.embedding(min_encoding_indices).view(z.shape) + loss = torch.mean((z_q - z.detach())**2) + self.beta*torch.mean((z_q.detach() - z)**2) + z_q = z + (z_q - z).detach() + min_encodings = F.one_hot(min_encoding_indices, self.n_e).type(z.dtype) + e_mean = torch.mean(min_encodings, dim=0) + perplexity = torch.exp(-torch.sum(e_mean*torch.log(e_mean+1e-10))) + return loss, z_q, min_encoding_indices, perplexity + + def map2index(self, z): + assert z.shape[-1] == self.e_dim + z_flattened = z.contiguous().view(-1, self.e_dim) + d = torch.sum(z_flattened**2, dim=1, keepdim=True) + \ + torch.sum(self.embedding.weight**2, dim=1) - 2*torch.matmul(z_flattened, self.embedding.weight.t()) + min_encoding_indices = torch.argmin(d, dim=1) + return min_encoding_indices.reshape(z.shape[0], -1) + + def get_codebook_entry(self, indices): + index_flattened = indices.view(-1) + z_q = self.embedding(index_flattened) + z_q = z_q.view(indices.shape+(self.e_dim,)).contiguous() + return z_q + +def init_weight(m): + if isinstance(m, nn.Conv1d) or isinstance(m, nn.Linear) or isinstance(m, nn.ConvTranspose1d): + nn.init.xavier_normal_(m.weight) + if m.bias is not None: + nn.init.constant_(m.bias, 0) + +class ResBlock(nn.Module): + def __init__(self, channel): + super().__init__() + self.model = nn.Sequential( + nn.Conv1d(channel, channel, 3, 1, 1), + nn.LeakyReLU(0.2, True), + nn.Conv1d(channel, channel, 3, 1, 1), + ) + def forward(self, x): + return self.model(x)+x + +class VQEncoderV5(nn.Module): + def __init__(self, args): + super().__init__() + n_down = args.vae_layer + channels = [args.vae_length]*(n_down) + input_size = args.vae_test_dim + layers = [ + nn.Conv1d(input_size, channels[0], 3, 1, 1), + nn.LeakyReLU(0.2, True), + ResBlock(channels[0]), + ] + for i in range(1, n_down): + layers += [ + nn.Conv1d(channels[i-1], channels[i], 3, 1, 1), + nn.LeakyReLU(0.2, True), + ResBlock(channels[i]), + ] + self.main = nn.Sequential(*layers) + self.main.apply(init_weight) + def forward(self, inputs): + inputs = inputs.permute(0,2,1) + outputs = self.main(inputs).permute(0,2,1) + return outputs + +class VQEncoderV6(nn.Module): + def __init__(self, args): + super().__init__() + n_down = args.vae_layer + channels = [args.vae_length]*(n_down) + input_size = args.vae_test_dim + layers = [ + nn.Conv1d(input_size, channels[0], 3, 1, 1), + nn.LeakyReLU(0.2, True), + ResBlock(channels[0]), + ] + for i in range(1, n_down): + layers += [ + nn.Conv1d(channels[i-1], channels[i], 3, 1, 1), + nn.LeakyReLU(0.2, True), + ResBlock(channels[i]), + ] + self.main = nn.Sequential(*layers) + self.main.apply(init_weight) + def forward(self, inputs): + inputs = inputs.permute(0,2,1) + outputs = self.main(inputs).permute(0,2,1) + return outputs + +class VQDecoderV5(nn.Module): + def __init__(self, args): + super().__init__() + n_up = args.vae_layer + channels = [args.vae_length]*(n_up)+[args.vae_test_dim] + input_size = args.vae_length + n_resblk = 2 + if input_size == channels[0]: + layers = [] + else: + layers = [nn.Conv1d(input_size, channels[0], 3, 1, 1)] + for i in range(n_resblk): + layers += [ResBlock(channels[0])] + for i in range(n_up): + layers += [ + nn.Conv1d(channels[i], channels[i+1], 3, 1, 1), + nn.LeakyReLU(0.2, True) + ] + layers += [nn.Conv1d(channels[-1], channels[-1], 3, 1, 1)] + self.main = nn.Sequential(*layers) + self.main.apply(init_weight) + def forward(self, inputs): + inputs = inputs.permute(0,2,1) + outputs = self.main(inputs).permute(0,2,1) + return outputs + +class BasicBlock(nn.Module): + """ based on timm: https://github.com/rwightman/pytorch-image-models """ + def __init__(self, inplanes, planes, ker_size, stride=1, downsample=None, dilation=1, first_dilation=None, act_layer=nn.LeakyReLU, norm_layer=nn.BatchNorm1d): + super(BasicBlock, self).__init__() + self.conv1 = nn.Conv1d( + inplanes, planes, kernel_size=ker_size, stride=stride, padding=first_dilation, + dilation=dilation, bias=True) + self.bn1 = norm_layer(planes) + self.act1 = act_layer(inplace=True) + self.conv2 = nn.Conv1d( + planes, planes, kernel_size=ker_size, padding=ker_size//2, dilation=dilation, bias=True) + self.bn2 = norm_layer(planes) + self.act2 = act_layer(inplace=True) + if downsample is not None: + self.downsample = nn.Sequential( + nn.Conv1d(inplanes, planes, stride=stride, kernel_size=ker_size, padding=first_dilation, dilation=dilation, bias=True), + norm_layer(planes), + ) + else: self.downsample=None + + def forward(self, x): + shortcut = x + x = self.conv1(x) + x = self.bn1(x) + x = self.act1(x) + x = self.conv2(x) + x = self.bn2(x) + if self.downsample is not None: + shortcut = self.downsample(shortcut) + x += shortcut + x = self.act2(x) + return x + +class WavEncoder(nn.Module): + def __init__(self, out_dim, audio_in=1): + super().__init__() + self.out_dim = out_dim + self.feat_extractor = nn.Sequential( + BasicBlock(audio_in, out_dim//4, 15, 5, first_dilation=1600, downsample=True), + BasicBlock(out_dim//4, out_dim//4, 15, 6, first_dilation=0, downsample=True), + BasicBlock(out_dim//4, out_dim//4, 15, 1, first_dilation=7, ), + BasicBlock(out_dim//4, out_dim//2, 15, 6, first_dilation=0, downsample=True), + BasicBlock(out_dim//2, out_dim//2, 15, 1, first_dilation=7), + BasicBlock(out_dim//2, out_dim, 15, 3, first_dilation=0,downsample=True), + ) + def forward(self, wav_data): + if wav_data.dim() == 2: + wav_data = wav_data.unsqueeze(1) + else: + wav_data = wav_data.transpose(1, 2) + out = self.feat_extractor(wav_data) + return out.transpose(1, 2) + +class MLP(nn.Module): + def __init__(self, in_dim, middle_dim, out_dim): + super().__init__() + self.fc1 = nn.Linear(in_dim, middle_dim) + self.fc2 = nn.Linear(middle_dim, out_dim) + self.act = nn.LeakyReLU(0.1, True) + def forward(self, x): + x = self.fc1(x) + x = self.act(x) + x = self.fc2(x) + return x + +class PeriodicPositionalEncoding(nn.Module): + def __init__(self, d_model, dropout=0.1, period=15, max_seq_len=60): + super().__init__() + self.dropout = nn.Dropout(p=dropout) + pe = torch.zeros(period, d_model) + position = torch.arange(0, period, dtype=torch.float).unsqueeze(1) + div_term = torch.exp(torch.arange(0, d_model, 2).float()*(-math.log(10000.0)/d_model)) + pe[:,0::2] = torch.sin(position*div_term) + pe[:,1::2] = torch.cos(position*div_term) + pe = pe.unsqueeze(0) + repeat_num = (max_seq_len//period)+1 + pe = pe.repeat(1, repeat_num, 1) + self.register_buffer('pe', pe) + def forward(self, x): + x = x + self.pe[:, :x.size(1), :] + return self.dropout(x) \ No newline at end of file diff --git a/models/motion_encoder.py b/models/motion_encoder.py deleted file mode 100644 index afa8513ea66ea0446230d796aff277ed142b8801..0000000000000000000000000000000000000000 --- a/models/motion_encoder.py +++ /dev/null @@ -1,789 +0,0 @@ -import random -import math -import numpy as np -import torch -import torch.nn as nn -import torch.nn.functional as F -import smplx - -# ----------- 1 full conv-based encoder------------- # -""" -from tm2t -TM2T: Stochastical and Tokenized Modeling for the Reciprocal Generation of 3D Human Motions and Texts -https://github.com/EricGuo5513/TM2T -""" -from .quantizer import * -from .utils.layer import ResBlock, init_weight - -class SCFormer(nn.Module): - def __init__(self, args): - super(VQEncoderV3, self).__init__() - - - n_down = args.vae_layer - channels = [args.vae_length] - for i in range(n_down-1): - channels.append(args.vae_length) - - input_size = args.vae_test_dim - assert len(channels) == n_down - layers = [ - nn.Conv1d(input_size, channels[0], 4, 2, 1), - nn.LeakyReLU(0.2, inplace=True), - ResBlock(channels[0]), - ] - - for i in range(1, n_down): - layers += [ - nn.Conv1d(channels[i-1], channels[i], 4, 2, 1), - nn.LeakyReLU(0.2, inplace=True), - ResBlock(channels[i]), - ] - self.main = nn.Sequential(*layers) - # self.out_net = nn.Linear(output_size, output_size) - self.main.apply(init_weight) - # self.out_net.apply(init_weight) - def forward(self, inputs): # bs t n - ''' - face 51 or 106 - hand 30*(15) - upper body - lower body - global 1*3 - max length around 180 --> 450 - ''' - bs, t, n = inputs.shape - inputs = inputs.reshape(bs*t, n) - inputs = self.spatial_transformer_encoder(inputs) # bs*t c - cs = inputs.shape[1] - inputs = inputs.reshape(bs, t, cs).permute(0, 2, 1).reshape(bs*cs, t) - inputs = self.temporal_cnn_encoder(inputs) # bs*c t - ct = inputs.shape[1] - outputs = inputs.reshape(bs, cs, ct).permute(0, 2, 1) # bs ct cs - return outputs - -class VQEncoderV3(nn.Module): - def __init__(self, args): - super(VQEncoderV3, self).__init__() - n_down = args.vae_layer - channels = [args.vae_length] - for i in range(n_down-1): - channels.append(args.vae_length) - - input_size = args.vae_test_dim - assert len(channels) == n_down - layers = [ - nn.Conv1d(input_size, channels[0], 4, 2, 1), - nn.LeakyReLU(0.2, inplace=True), - ResBlock(channels[0]), - ] - - for i in range(1, n_down): - layers += [ - nn.Conv1d(channels[i-1], channels[i], 4, 2, 1), - nn.LeakyReLU(0.2, inplace=True), - ResBlock(channels[i]), - ] - self.main = nn.Sequential(*layers) - # self.out_net = nn.Linear(output_size, output_size) - self.main.apply(init_weight) - # self.out_net.apply(init_weight) - def forward(self, inputs): - inputs = inputs.permute(0, 2, 1) - outputs = self.main(inputs).permute(0, 2, 1) - return outputs - -class VQEncoderV6(nn.Module): - def __init__(self, args): - super(VQEncoderV6, self).__init__() - n_down = args.vae_layer - channels = [args.vae_length] - for i in range(n_down-1): - channels.append(args.vae_length) - - input_size = args.vae_test_dim - assert len(channels) == n_down - layers = [ - nn.Conv1d(input_size, channels[0], 3, 1, 1), - nn.LeakyReLU(0.2, inplace=True), - ResBlock(channels[0]), - ] - - for i in range(1, n_down): - layers += [ - nn.Conv1d(channels[i-1], channels[i], 3, 1, 1), - nn.LeakyReLU(0.2, inplace=True), - ResBlock(channels[i]), - ] - self.main = nn.Sequential(*layers) - # self.out_net = nn.Linear(output_size, output_size) - self.main.apply(init_weight) - # self.out_net.apply(init_weight) - def forward(self, inputs): - inputs = inputs.permute(0, 2, 1) - outputs = self.main(inputs).permute(0, 2, 1) - return outputs - -class VQEncoderV4(nn.Module): - def __init__(self, args): - super(VQEncoderV4, self).__init__() - n_down = args.vae_layer - channels = [args.vae_length] - for i in range(n_down-1): - channels.append(args.vae_length) - - input_size = args.vae_test_dim - assert len(channels) == n_down - layers = [ - nn.Conv1d(input_size, channels[0], 4, 2, 1), - nn.LeakyReLU(0.2, inplace=True), - ResBlock(channels[0]), - ] - - for i in range(1, n_down): - layers += [ - nn.Conv1d(channels[i-1], channels[i], 3, 1, 1), - nn.LeakyReLU(0.2, inplace=True), - ResBlock(channels[i]), - ] - self.main = nn.Sequential(*layers) - # self.out_net = nn.Linear(output_size, output_size) - self.main.apply(init_weight) - # self.out_net.apply(init_weight) - def forward(self, inputs): - inputs = inputs.permute(0, 2, 1) - outputs = self.main(inputs).permute(0, 2, 1) - # print(outputs.shape) - return outputs - -class VQEncoderV5(nn.Module): - def __init__(self, args): - super(VQEncoderV5, self).__init__() - n_down = args.vae_layer - channels = [args.vae_length] - for i in range(n_down-1): - channels.append(args.vae_length) - - input_size = args.vae_test_dim - assert len(channels) == n_down - layers = [ - nn.Conv1d(input_size, channels[0], 3, 1, 1), - nn.LeakyReLU(0.2, inplace=True), - ResBlock(channels[0]), - ] - - for i in range(1, n_down): - layers += [ - nn.Conv1d(channels[i-1], channels[i], 3, 1, 1), - nn.LeakyReLU(0.2, inplace=True), - ResBlock(channels[i]), - ] - self.main = nn.Sequential(*layers) - # self.out_net = nn.Linear(output_size, output_size) - self.main.apply(init_weight) - # self.out_net.apply(init_weight) - def forward(self, inputs): - inputs = inputs.permute(0, 2, 1) - outputs = self.main(inputs).permute(0, 2, 1) - # print(outputs.shape) - return outputs - -class VQDecoderV4(nn.Module): - def __init__(self, args): - super(VQDecoderV4, self).__init__() - n_up = args.vae_layer - channels = [] - for i in range(n_up-1): - channels.append(args.vae_length) - channels.append(args.vae_length) - channels.append(args.vae_test_dim) - input_size = args.vae_length - n_resblk = 2 - assert len(channels) == n_up + 1 - if input_size == channels[0]: - layers = [] - else: - layers = [nn.Conv1d(input_size, channels[0], kernel_size=3, stride=1, padding=1)] - - for i in range(n_resblk): - layers += [ResBlock(channels[0])] - # channels = channels - for i in range(n_up): - up_factor = 2 if i < n_up - 1 else 1 - layers += [ - nn.Upsample(scale_factor=up_factor, mode='nearest'), - nn.Conv1d(channels[i], channels[i+1], kernel_size=3, stride=1, padding=1), - nn.LeakyReLU(0.2, inplace=True) - ] - layers += [nn.Conv1d(channels[-1], channels[-1], kernel_size=3, stride=1, padding=1)] - self.main = nn.Sequential(*layers) - self.main.apply(init_weight) - - def forward(self, inputs): - inputs = inputs.permute(0, 2, 1) - outputs = self.main(inputs).permute(0, 2, 1) - return outputs - -class VQDecoderV5(nn.Module): - def __init__(self, args): - super(VQDecoderV5, self).__init__() - n_up = args.vae_layer - channels = [] - for i in range(n_up-1): - channels.append(args.vae_length) - channels.append(args.vae_length) - channels.append(args.vae_test_dim) - input_size = args.vae_length - n_resblk = 2 - assert len(channels) == n_up + 1 - if input_size == channels[0]: - layers = [] - else: - layers = [nn.Conv1d(input_size, channels[0], kernel_size=3, stride=1, padding=1)] - - for i in range(n_resblk): - layers += [ResBlock(channels[0])] - # channels = channels - for i in range(n_up): - up_factor = 2 if i < n_up - 1 else 1 - layers += [ - #nn.Upsample(scale_factor=up_factor, mode='nearest'), - nn.Conv1d(channels[i], channels[i+1], kernel_size=3, stride=1, padding=1), - nn.LeakyReLU(0.2, inplace=True) - ] - layers += [nn.Conv1d(channels[-1], channels[-1], kernel_size=3, stride=1, padding=1)] - self.main = nn.Sequential(*layers) - self.main.apply(init_weight) - - def forward(self, inputs): - inputs = inputs.permute(0, 2, 1) - outputs = self.main(inputs).permute(0, 2, 1) - return outputs - -class VQDecoderV7(nn.Module): - def __init__(self, args): - super(VQDecoderV7, self).__init__() - n_up = args.vae_layer - channels = [] - for i in range(n_up-1): - channels.append(args.vae_length) - channels.append(args.vae_length) - channels.append(args.vae_test_dim+4) - input_size = args.vae_length - n_resblk = 2 - assert len(channels) == n_up + 1 - if input_size == channels[0]: - layers = [] - else: - layers = [nn.Conv1d(input_size, channels[0], kernel_size=3, stride=1, padding=1)] - - for i in range(n_resblk): - layers += [ResBlock(channels[0])] - # channels = channels - for i in range(n_up): - up_factor = 2 if i < n_up - 1 else 1 - layers += [ - #nn.Upsample(scale_factor=up_factor, mode='nearest'), - nn.Conv1d(channels[i], channels[i+1], kernel_size=3, stride=1, padding=1), - nn.LeakyReLU(0.2, inplace=True) - ] - layers += [nn.Conv1d(channels[-1], channels[-1], kernel_size=3, stride=1, padding=1)] - self.main = nn.Sequential(*layers) - self.main.apply(init_weight) - - def forward(self, inputs): - inputs = inputs.permute(0, 2, 1) - outputs = self.main(inputs).permute(0, 2, 1) - return outputs - -class VQDecoderV3(nn.Module): - def __init__(self, args): - super(VQDecoderV3, self).__init__() - n_up = args.vae_layer - channels = [] - for i in range(n_up-1): - channels.append(args.vae_length) - channels.append(args.vae_length) - channels.append(args.vae_test_dim) - input_size = args.vae_length - n_resblk = 2 - assert len(channels) == n_up + 1 - if input_size == channels[0]: - layers = [] - else: - layers = [nn.Conv1d(input_size, channels[0], kernel_size=3, stride=1, padding=1)] - - for i in range(n_resblk): - layers += [ResBlock(channels[0])] - # channels = channels - for i in range(n_up): - layers += [ - nn.Upsample(scale_factor=2, mode='nearest'), - nn.Conv1d(channels[i], channels[i+1], kernel_size=3, stride=1, padding=1), - nn.LeakyReLU(0.2, inplace=True) - ] - layers += [nn.Conv1d(channels[-1], channels[-1], kernel_size=3, stride=1, padding=1)] - self.main = nn.Sequential(*layers) - self.main.apply(init_weight) - - def forward(self, inputs): - inputs = inputs.permute(0, 2, 1) - outputs = self.main(inputs).permute(0, 2, 1) - return outputs - -class VQDecoderV6(nn.Module): - def __init__(self, args): - super(VQDecoderV6, self).__init__() - n_up = args.vae_layer - channels = [] - for i in range(n_up-1): - channels.append(args.vae_length) - channels.append(args.vae_length) - channels.append(args.vae_test_dim) - input_size = args.vae_length * 2 - n_resblk = 2 - assert len(channels) == n_up + 1 - if input_size == channels[0]: - layers = [] - else: - layers = [nn.Conv1d(input_size, channels[0], kernel_size=3, stride=1, padding=1)] - - for i in range(n_resblk): - layers += [ResBlock(channels[0])] - # channels = channels - for i in range(n_up): - layers += [ - # nn.Upsample(scale_factor=2, mode='nearest'), - nn.Conv1d(channels[i], channels[i+1], kernel_size=3, stride=1, padding=1), - nn.LeakyReLU(0.2, inplace=True) - ] - layers += [nn.Conv1d(channels[-1], channels[-1], kernel_size=3, stride=1, padding=1)] - self.main = nn.Sequential(*layers) - self.main.apply(init_weight) - - def forward(self, inputs): - inputs = inputs.permute(0, 2, 1) - outputs = self.main(inputs).permute(0, 2, 1) - return outputs - - -# -----------2 conv+mlp based fix-length input ae ------------- # -from .utils.layer import reparameterize, ConvNormRelu, BasicBlock -""" -from Trimodal, -encoder: - bs, n, c_in --conv--> bs, n/k, c_out_0 --mlp--> bs, c_out_1, only support fixed length -decoder: - bs, c_out_1 --mlp--> bs, n/k*c_out_0 --> bs, n/k, c_out_0 --deconv--> bs, n, c_in -""" -class PoseEncoderConv(nn.Module): - def __init__(self, length, dim, feature_length=32): - super().__init__() - self.base = feature_length - self.net = nn.Sequential( - ConvNormRelu(dim, self.base, batchnorm=True), #32 - ConvNormRelu(self.base, self.base*2, batchnorm=True), #30 - ConvNormRelu(self.base*2, self.base*2, True, batchnorm=True), #14 - nn.Conv1d(self.base*2, self.base, 3) - ) - self.out_net = nn.Sequential( - nn.Linear(12*self.base, self.base*4), # for 34 frames - nn.BatchNorm1d(self.base*4), - nn.LeakyReLU(True), - nn.Linear(self.base*4, self.base*2), - nn.BatchNorm1d(self.base*2), - nn.LeakyReLU(True), - nn.Linear(self.base*2, self.base), - ) - self.fc_mu = nn.Linear(self.base, self.base) - self.fc_logvar = nn.Linear(self.base, self.base) - - def forward(self, poses, variational_encoding=None): - poses = poses.transpose(1, 2) # to (bs, dim, seq) - out = self.net(poses) - out = out.flatten(1) - out = self.out_net(out) - mu = self.fc_mu(out) - logvar = self.fc_logvar(out) - if variational_encoding: - z = reparameterize(mu, logvar) - else: - z = mu - return z, mu, logvar - - -class PoseDecoderFC(nn.Module): - def __init__(self, gen_length, pose_dim, use_pre_poses=False): - super().__init__() - self.gen_length = gen_length - self.pose_dim = pose_dim - self.use_pre_poses = use_pre_poses - - in_size = 32 - if use_pre_poses: - self.pre_pose_net = nn.Sequential( - nn.Linear(pose_dim * 4, 32), - nn.BatchNorm1d(32), - nn.ReLU(), - nn.Linear(32, 32), - ) - in_size += 32 - - self.net = nn.Sequential( - nn.Linear(in_size, 128), - nn.BatchNorm1d(128), - nn.ReLU(), - nn.Linear(128, 128), - nn.BatchNorm1d(128), - nn.ReLU(), - nn.Linear(128, 256), - nn.BatchNorm1d(256), - nn.ReLU(), - nn.Linear(256, 512), - nn.BatchNorm1d(512), - nn.ReLU(), - nn.Linear(512, gen_length * pose_dim), - ) - - def forward(self, latent_code, pre_poses=None): - if self.use_pre_poses: - pre_pose_feat = self.pre_pose_net(pre_poses.reshape(pre_poses.shape[0], -1)) - feat = torch.cat((pre_pose_feat, latent_code), dim=1) - else: - feat = latent_code - output = self.net(feat) - output = output.view(-1, self.gen_length, self.pose_dim) - return output - - -class PoseDecoderConv(nn.Module): - def __init__(self, length, dim, use_pre_poses=False, feature_length=32): - super().__init__() - self.use_pre_poses = use_pre_poses - self.feat_size = feature_length - - if use_pre_poses: - self.pre_pose_net = nn.Sequential( - nn.Linear(dim * 4, 32), - nn.BatchNorm1d(32), - nn.ReLU(), - nn.Linear(32, 32), - ) - self.feat_size += 32 - - if length == 64: - self.pre_net = nn.Sequential( - nn.Linear(self.feat_size, self.feat_size), - nn.BatchNorm1d(self.feat_size), - nn.LeakyReLU(True), - nn.Linear(self.feat_size, self.feat_size//8*64), - ) - elif length == 34: - self.pre_net = nn.Sequential( - nn.Linear(self.feat_size, self.feat_size*2), - nn.BatchNorm1d(self.feat_size*2), - nn.LeakyReLU(True), - nn.Linear(self.feat_size*2, self.feat_size//8*34), - ) - elif length == 32: - self.pre_net = nn.Sequential( - nn.Linear(self.feat_size, self.feat_size*2), - nn.BatchNorm1d(self.feat_size*2), - nn.LeakyReLU(True), - nn.Linear(self.feat_size*2, self.feat_size//8*32), - ) - else: - assert False - self.decoder_size = self.feat_size//8 - self.net = nn.Sequential( - nn.ConvTranspose1d(self.decoder_size, self.feat_size, 3), - nn.BatchNorm1d(self.feat_size), - nn.LeakyReLU(0.2, True), - - nn.ConvTranspose1d(self.feat_size, self.feat_size, 3), - nn.BatchNorm1d(self.feat_size), - nn.LeakyReLU(0.2, True), - nn.Conv1d(self.feat_size, self.feat_size*2, 3), - nn.Conv1d(self.feat_size*2, dim, 3), - ) - - def forward(self, feat, pre_poses=None): - if self.use_pre_poses: - pre_pose_feat = self.pre_pose_net(pre_poses.reshape(pre_poses.shape[0], -1)) - feat = torch.cat((pre_pose_feat, feat), dim=1) - #print(feat.shape) - out = self.pre_net(feat) - #print(out.shape) - out = out.view(feat.shape[0], self.decoder_size, -1) - #print(out.shape) - out = self.net(out) - out = out.transpose(1, 2) - return out - -''' -Our CaMN Modification -''' -class PoseEncoderConvResNet(nn.Module): - def __init__(self, length, dim, feature_length=32): - super().__init__() - self.base = feature_length - self.conv1=BasicBlock(dim, self.base, reduce_first = 1, downsample = False, first_dilation=1) #34 - self.conv2=BasicBlock(self.base, self.base*2, downsample = False, first_dilation=1,) #34 - self.conv3=BasicBlock(self.base*2, self.base*2, first_dilation=1, downsample = True, stride=2)#17 - self.conv4=BasicBlock(self.base*2, self.base, first_dilation=1, downsample = False) - - self.out_net = nn.Sequential( - # nn.Linear(864, 256), # for 64 frames - nn.Linear(17*self.base, self.base*4), # for 34 frames - nn.BatchNorm1d(self.base*4), - nn.LeakyReLU(True), - nn.Linear(self.base*4, self.base*2), - nn.BatchNorm1d(self.base*2), - nn.LeakyReLU(True), - nn.Linear(self.base*2, self.base), - ) - - self.fc_mu = nn.Linear(self.base, self.base) - self.fc_logvar = nn.Linear(self.base, self.base) - - def forward(self, poses, variational_encoding=None): - poses = poses.transpose(1, 2) # to (bs, dim, seq) - out1 = self.conv1(poses) - out2 = self.conv2(out1) - out3 = self.conv3(out2) - out = self.conv4(out3) - out = out.flatten(1) - out = self.out_net(out) - mu = self.fc_mu(out) - logvar = self.fc_logvar(out) - if variational_encoding: - z = reparameterize(mu, logvar) - else: - z = mu - return z, mu, logvar - - -# -----------3 lstm ------------- # -''' -bs, n, c_int --> bs, n, c_out or bs, 1 (hidden), c_out -''' -class AELSTM(nn.Module): - def __init__(self, args): - super().__init__() - self.motion_emb = nn.Linear(args.vae_test_dim, args.vae_length) - self.lstm = nn.LSTM(args.vae_length, hidden_size=args.vae_length, num_layers=4, batch_first=True, - bidirectional=True, dropout=0.3) - self.out = nn.Sequential( - nn.Linear(args.vae_length, args.vae_length//2), - nn.LeakyReLU(0.2, True), - nn.Linear(args.vae_length//2, args.vae_test_dim) - ) - self.hidden_size = args.vae_length - - def forward(self, inputs): - poses = self.motion_emb(inputs) - out, _ = self.lstm(poses) - out = out[:, :, :self.hidden_size] + out[:, :, self.hidden_size:] - out_poses = self.out(out) - return { - "poses_feat":out, - "rec_pose": out_poses, - } - -class PoseDecoderLSTM(nn.Module): - """ - input bs*n*64 - """ - def __init__(self,pose_dim, feature_length): - super().__init__() - self.pose_dim = pose_dim - self.base = feature_length - self.hidden_size = 256 - self.lstm_d = nn.LSTM(self.base, hidden_size=self.hidden_size, num_layers=4, batch_first=True, - bidirectional=True, dropout=0.3) - self.out_d = nn.Sequential( - nn.Linear(self.hidden_size, self.hidden_size // 2), - nn.LeakyReLU(True), - nn.Linear(self.hidden_size // 2, self.pose_dim) - ) - - def forward(self, latent_code): - output, _ = self.lstm_d(latent_code) - output = output[:, :, :self.hidden_size] + output[:, :, self.hidden_size:] # sum bidirectional outputs - #print("outd:", output.shape) - output = self.out_d(output.reshape(-1, output.shape[2])) - output = output.view(latent_code.shape[0], latent_code.shape[1], -1) - #print("resotuput:", output.shape) - return output - -# ---------------4 transformer --------------- # -class PositionalEncoding(nn.Module): - def __init__(self, d_model, dropout=0.1, max_len=5000): - super(PositionalEncoding, self).__init__() - self.dropout = nn.Dropout(p=dropout) - pe = torch.zeros(max_len, d_model) - position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1) - div_term = torch.exp(torch.arange(0, d_model, 2).float() * (-np.log(10000.0) / d_model)) - pe[:, 0::2] = torch.sin(position * div_term) - pe[:, 1::2] = torch.cos(position * div_term) - pe = pe.unsqueeze(0)#.transpose(0, 1) - - self.register_buffer('pe', pe) - - def forward(self, x): - #print(self.pe.shape, x.shape) - x = x + self.pe[:, :x.shape[1]] - return self.dropout(x) - -class Encoder_TRANSFORMER(nn.Module): - def __init__(self, args): - super().__init__() - self.skelEmbedding = nn.Linear(args.vae_test_dim, args.vae_length) - self.sequence_pos_encoder = PositionalEncoding(args.vae_length, 0.3) - seqTransEncoderLayer = nn.TransformerEncoderLayer(d_model=args.vae_length, - nhead=4, - dim_feedforward=1025, - dropout=0.3, - activation="gelu", - batch_first=True - ) - self.seqTransEncoder = nn.TransformerEncoder(seqTransEncoderLayer, - num_layers=4) - def _generate_square_subsequent_mask(self, sz): - mask = (torch.triu(torch.ones(sz, sz)) == 1).transpose(0, 1) - mask = mask.float().masked_fill(mask == 0, float('-inf')).masked_fill(mask == 1, float(0.0)) - return mask - - def forward(self, inputs): - x = self.skelEmbedding(inputs) #bs * n * 128 - #print(x.shape) - xseq = self.sequence_pos_encoder(x) - device = xseq.device - #mask = self._generate_square_subsequent_mask(xseq.size(1)).to(device) - final = self.seqTransEncoder(xseq) - #print(final.shape) - mu = final[:, 0:1, :] - logvar = final[:, 1:2, :] - return final, mu, logvar - -class Decoder_TRANSFORMER(nn.Module): - def __init__(self, args): - super().__init__() - self.vae_test_len = args.vae_test_len - self.vae_length = args.vae_length - self.sequence_pos_encoder = PositionalEncoding(args.vae_length, 0.3) - seqTransDecoderLayer = nn.TransformerDecoderLayer(d_model=args.vae_length, - nhead=4, - dim_feedforward=1024, - dropout=0.3, - activation="gelu", - batch_first=True) - self.seqTransDecoder = nn.TransformerDecoder(seqTransDecoderLayer, - num_layers=4) - self.finallayer = nn.Linear(args.vae_length, args.vae_test_dim) - - def forward(self, inputs): - timequeries = torch.zeros(inputs.shape[0], self.vae_test_len, self.vae_length, device=inputs.device) - timequeries = self.sequence_pos_encoder(timequeries) - output = self.seqTransDecoder(tgt=timequeries, memory=inputs) - output = self.finallayer(output) - return output - -# --------- 5 skcnn --------------- # -''' -from NeMF, -NeMF: Neural Motion Fields for Kinematic Animation -''' -from .utils.skeleton import ResidualBlock, SkeletonResidual, residual_ratio, SkeletonConv, SkeletonPool, find_neighbor, build_edge_topology -class LocalEncoder(nn.Module): - def __init__(self, args, topology): - super(LocalEncoder, self).__init__() - args.channel_base = 6 - args.activation = "tanh" - args.use_residual_blocks=True - args.z_dim=1024 - args.temporal_scale=8 - args.kernel_size=4 - args.num_layers=args.vae_layer - args.skeleton_dist=2 - args.extra_conv=0 - # check how to reflect in 1d - args.padding_mode="constant" - args.skeleton_pool="mean" - args.upsampling="linear" - - - self.topologies = [topology] - self.channel_base = [args.channel_base] - - self.channel_list = [] - self.edge_num = [len(topology)] - self.pooling_list = [] - self.layers = nn.ModuleList() - self.args = args - # self.convs = [] - - kernel_size = args.kernel_size - kernel_even = False if kernel_size % 2 else True - padding = (kernel_size - 1) // 2 - bias = True - self.grow = args.vae_grow - for i in range(args.num_layers): - self.channel_base.append(self.channel_base[-1]*self.grow[i]) - - for i in range(args.num_layers): - seq = [] - neighbour_list = find_neighbor(self.topologies[i], args.skeleton_dist) - in_channels = self.channel_base[i] * self.edge_num[i] - out_channels = self.channel_base[i + 1] * self.edge_num[i] - if i == 0: - self.channel_list.append(in_channels) - self.channel_list.append(out_channels) - last_pool = True if i == args.num_layers - 1 else False - - # (T, J, D) => (T, J', D) - pool = SkeletonPool(edges=self.topologies[i], pooling_mode=args.skeleton_pool, - channels_per_edge=out_channels // len(neighbour_list), last_pool=last_pool) - - if args.use_residual_blocks: - # (T, J, D) => (T/2, J', 2D) - seq.append(SkeletonResidual(self.topologies[i], neighbour_list, joint_num=self.edge_num[i], in_channels=in_channels, out_channels=out_channels, - kernel_size=kernel_size, stride=2, padding=padding, padding_mode=args.padding_mode, bias=bias, - extra_conv=args.extra_conv, pooling_mode=args.skeleton_pool, activation=args.activation, last_pool=last_pool)) - else: - for _ in range(args.extra_conv): - # (T, J, D) => (T, J, D) - seq.append(SkeletonConv(neighbour_list, in_channels=in_channels, out_channels=in_channels, - joint_num=self.edge_num[i], kernel_size=kernel_size - 1 if kernel_even else kernel_size, - stride=1, - padding=padding, padding_mode=args.padding_mode, bias=bias)) - seq.append(nn.PReLU() if args.activation == 'relu' else nn.Tanh()) - # (T, J, D) => (T/2, J, 2D) - seq.append(SkeletonConv(neighbour_list, in_channels=in_channels, out_channels=out_channels, - joint_num=self.edge_num[i], kernel_size=kernel_size, stride=2, - padding=padding, padding_mode=args.padding_mode, bias=bias, add_offset=False, - in_offset_channel=3 * self.channel_base[i] // self.channel_base[0])) - # self.convs.append(seq[-1]) - - seq.append(pool) - seq.append(nn.PReLU() if args.activation == 'relu' else nn.Tanh()) - self.layers.append(nn.Sequential(*seq)) - - self.topologies.append(pool.new_edges) - self.pooling_list.append(pool.pooling_list) - self.edge_num.append(len(self.topologies[-1])) - - # in_features = self.channel_base[-1] * len(self.pooling_list[-1]) - # in_features *= int(args.temporal_scale / 2) - # self.reduce = nn.Linear(in_features, args.z_dim) - # self.mu = nn.Linear(in_features, args.z_dim) - # self.logvar = nn.Linear(in_features, args.z_dim) - - def forward(self, input): - #bs, n, c = input.shape[0], input.shape[1], input.shape[2] - output = input.permute(0, 2, 1)#input.reshape(bs, n, -1, 6) - for layer in self.layers: - output = layer(output) - #output = output.view(output.shape[0], -1) - output = output.permute(0, 2, 1) - return output \ No newline at end of file diff --git a/models/motion_representation.py b/models/motion_representation.py deleted file mode 100644 index b5d93b49931d45ae0b8bf5013c76b08445e13eff..0000000000000000000000000000000000000000 --- a/models/motion_representation.py +++ /dev/null @@ -1,431 +0,0 @@ -import random -import math -import numpy as np -import torch -import torch.nn as nn -import torch.nn.functional as F -import smplx -import copy -from .motion_encoder import * - -# ----------- AE, VAE ------------- # -class VAEConvZero(nn.Module): - def __init__(self, args): - super(VAEConvZero, self).__init__() - self.encoder = VQEncoderV5(args) - # self.quantizer = Quantizer(args.vae_codebook_size, args.vae_length, args.vae_quantizer_lambda) - self.decoder = VQDecoderV5(args) - - def forward(self, inputs): - pre_latent = self.encoder(inputs) - # print(pre_latent.shape) - # embedding_loss, vq_latent, _, perplexity = self.quantizer(pre_latent) - rec_pose = self.decoder(pre_latent) - return { - # "poses_feat":vq_latent, - # "embedding_loss":embedding_loss, - # "perplexity":perplexity, - "rec_pose": rec_pose - } - -class VAEConv(nn.Module): - def __init__(self, args): - super(VAEConv, self).__init__() - self.encoder = VQEncoderV3(args) - self.decoder = VQDecoderV3(args) - self.fc_mu = nn.Linear(args.vae_length, args.vae_length) - self.fc_logvar = nn.Linear(args.vae_length, args.vae_length) - self.variational = args.variational - - def forward(self, inputs): - pre_latent = self.encoder(inputs) - mu, logvar = None, None - if self.variational: - mu = self.fc_mu(pre_latent) - logvar = self.fc_logvar(pre_latent) - pre_latent = reparameterize(mu, logvar) - rec_pose = self.decoder(pre_latent) - return { - "poses_feat":pre_latent, - "rec_pose": rec_pose, - "pose_mu": mu, - "pose_logvar": logvar, - } - - def map2latent(self, inputs): - pre_latent = self.encoder(inputs) - if self.variational: - mu = self.fc_mu(pre_latent) - logvar = self.fc_logvar(pre_latent) - pre_latent = reparameterize(mu, logvar) - return pre_latent - - def decode(self, pre_latent): - rec_pose = self.decoder(pre_latent) - return rec_pose - -class VAESKConv(VAEConv): - def __init__(self, args): - super(VAESKConv, self).__init__(args) - smpl_fname = args.data_path_1+'smplx_models/smplx/SMPLX_NEUTRAL_2020.npz' - smpl_data = np.load(smpl_fname, encoding='latin1') - parents = smpl_data['kintree_table'][0].astype(np.int32) - edges = build_edge_topology(parents) - self.encoder = LocalEncoder(args, edges) - self.decoder = VQDecoderV3(args) - -class VAEConvMLP(VAEConv): - def __init__(self, args): - super(VAEConvMLP, self).__init__(args) - self.encoder = PoseEncoderConv(args.vae_test_len, args.vae_test_dim, feature_length=args.vae_length) - self.decoder = PoseDecoderConv(args.vae_test_len, args.vae_test_dim, feature_length=args.vae_length) - -class VAELSTM(VAEConv): - def __init__(self, args): - super(VAELSTM, self).__init__(args) - pose_dim = args.vae_test_dim - feature_length = args.vae_length - self.encoder = PoseEncoderLSTM_Resnet(pose_dim, feature_length=feature_length) - self.decoder = PoseDecoderLSTM(pose_dim, feature_length=feature_length) - -class VAETransformer(VAEConv): - def __init__(self, args): - super(VAETransformer, self).__init__(args) - self.encoder = Encoder_TRANSFORMER(args) - self.decoder = Decoder_TRANSFORMER(args) - -# ----------- VQVAE --------------- # -class VQVAEConv(nn.Module): - def __init__(self, args): - super(VQVAEConv, self).__init__() - self.encoder = VQEncoderV3(args) - self.quantizer = Quantizer(args.vae_codebook_size, args.vae_length, args.vae_quantizer_lambda) - self.decoder = VQDecoderV3(args) - - def forward(self, inputs): - pre_latent = self.encoder(inputs) - # print(pre_latent.shape) - embedding_loss, vq_latent, _, perplexity = self.quantizer(pre_latent) - rec_pose = self.decoder(vq_latent) - return { - "poses_feat":vq_latent, - "embedding_loss":embedding_loss, - "perplexity":perplexity, - "rec_pose": rec_pose - } - - def map2index(self, inputs): - pre_latent = self.encoder(inputs) - index = self.quantizer.map2index(pre_latent) - return index - - def map2latent(self, inputs): - pre_latent = self.encoder(inputs) - index = self.quantizer.map2index(pre_latent) - z_q = self.quantizer.get_codebook_entry(index) - return z_q - - def decode(self, index): - z_q = self.quantizer.get_codebook_entry(index) - rec_pose = self.decoder(z_q) - return rec_pose - -class VQVAESKConv(VQVAEConv): - def __init__(self, args): - super(VQVAESKConv, self).__init__(args) - smpl_fname = args.data_path_1+'smplx_models/smplx/SMPLX_NEUTRAL_2020.npz' - smpl_data = np.load(smpl_fname, encoding='latin1') - parents = smpl_data['kintree_table'][0].astype(np.int32) - edges = build_edge_topology(parents) - self.encoder = LocalEncoder(args, edges) - - -class VQVAEConvStride(nn.Module): - def __init__(self, args): - super(VQVAEConvStride, self).__init__() - self.encoder = VQEncoderV4(args) - self.quantizer = Quantizer(args.vae_codebook_size, args.vae_length, args.vae_quantizer_lambda) - self.decoder = VQDecoderV4(args) - - def forward(self, inputs): - pre_latent = self.encoder(inputs) - # print(pre_latent.shape) - embedding_loss, vq_latent, _, perplexity = self.quantizer(pre_latent) - rec_pose = self.decoder(vq_latent) - return { - "poses_feat":vq_latent, - "embedding_loss":embedding_loss, - "perplexity":perplexity, - "rec_pose": rec_pose - } - - def map2index(self, inputs): - pre_latent = self.encoder(inputs) - index = self.quantizer.map2index(pre_latent) - return index - - def map2latent(self, inputs): - pre_latent = self.encoder(inputs) - index = self.quantizer.map2index(pre_latent) - z_q = self.quantizer.get_codebook_entry(index) - return z_q - - def decode(self, index): - z_q = self.quantizer.get_codebook_entry(index) - rec_pose = self.decoder(z_q) - return rec_pose - -class VQVAEConvZero(nn.Module): - def __init__(self, args): - super(VQVAEConvZero, self).__init__() - self.encoder = VQEncoderV5(args) - self.quantizer = Quantizer(args.vae_codebook_size, args.vae_length, args.vae_quantizer_lambda) - self.decoder = VQDecoderV5(args) - - def forward(self, inputs): - pre_latent = self.encoder(inputs) - # print(pre_latent.shape) - embedding_loss, vq_latent, _, perplexity = self.quantizer(pre_latent) - rec_pose = self.decoder(vq_latent) - return { - "poses_feat":vq_latent, - "embedding_loss":embedding_loss, - "perplexity":perplexity, - "rec_pose": rec_pose - } - - def map2index(self, inputs): - pre_latent = self.encoder(inputs) - index = self.quantizer.map2index(pre_latent) - return index - - def map2latent(self, inputs): - pre_latent = self.encoder(inputs) - index = self.quantizer.map2index(pre_latent) - z_q = self.quantizer.get_codebook_entry(index) - return z_q - - def decode(self, index): - z_q = self.quantizer.get_codebook_entry(index) - rec_pose = self.decoder(z_q) - return rec_pose - - -class VAEConvZero(nn.Module): - def __init__(self, args): - super(VAEConvZero, self).__init__() - self.encoder = VQEncoderV5(args) - # self.quantizer = Quantizer(args.vae_codebook_size, args.vae_length, args.vae_quantizer_lambda) - self.decoder = VQDecoderV5(args) - - def forward(self, inputs): - pre_latent = self.encoder(inputs) - # print(pre_latent.shape) - # embedding_loss, vq_latent, _, perplexity = self.quantizer(pre_latent) - rec_pose = self.decoder(pre_latent) - return { - # "poses_feat":vq_latent, - # "embedding_loss":embedding_loss, - # "perplexity":perplexity, - "rec_pose": rec_pose - } - - # def map2index(self, inputs): - # pre_latent = self.encoder(inputs) - # index = self.quantizer.map2index(pre_latent) - # return index - - # def map2latent(self, inputs): - # pre_latent = self.encoder(inputs) - # index = self.quantizer.map2index(pre_latent) - # z_q = self.quantizer.get_codebook_entry(index) - # return z_q - - # def decode(self, index): - # z_q = self.quantizer.get_codebook_entry(index) - # rec_pose = self.decoder(z_q) - # return rec_pose - - -class VQVAEConvZero3(nn.Module): - def __init__(self, args): - super(VQVAEConvZero3, self).__init__() - self.encoder = VQEncoderV5(args) - self.quantizer = Quantizer(args.vae_codebook_size, args.vae_length, args.vae_quantizer_lambda) - self.decoder = VQDecoderV5(args) - - def forward(self, inputs): - pre_latent = self.encoder(inputs) - # print(pre_latent.shape) - embedding_loss, vq_latent, _, perplexity = self.quantizer(pre_latent) - rec_pose = self.decoder(vq_latent) - return { - "poses_feat":vq_latent, - "embedding_loss":embedding_loss, - "perplexity":perplexity, - "rec_pose": rec_pose - } - - def map2index(self, inputs): - pre_latent = self.encoder(inputs) - index = self.quantizer.map2index(pre_latent) - return index - - def map2latent(self, inputs): - pre_latent = self.encoder(inputs) - index = self.quantizer.map2index(pre_latent) - z_q = self.quantizer.get_codebook_entry(index) - return z_q - - def decode(self, index): - z_q = self.quantizer.get_codebook_entry(index) - rec_pose = self.decoder(z_q) - return rec_pose - -class VQVAEConvZero2(nn.Module): - def __init__(self, args): - super(VQVAEConvZero2, self).__init__() - self.encoder = VQEncoderV5(args) - self.quantizer = Quantizer(args.vae_codebook_size, args.vae_length, args.vae_quantizer_lambda) - self.decoder = VQDecoderV7(args) - - def forward(self, inputs): - pre_latent = self.encoder(inputs) - # print(pre_latent.shape) - embedding_loss, vq_latent, _, perplexity = self.quantizer(pre_latent) - rec_pose = self.decoder(vq_latent) - return { - "poses_feat":vq_latent, - "embedding_loss":embedding_loss, - "perplexity":perplexity, - "rec_pose": rec_pose - } - - def map2index(self, inputs): - pre_latent = self.encoder(inputs) - index = self.quantizer.map2index(pre_latent) - return index - - def map2latent(self, inputs): - pre_latent = self.encoder(inputs) - index = self.quantizer.map2index(pre_latent) - z_q = self.quantizer.get_codebook_entry(index) - return z_q - - def decode(self, index): - z_q = self.quantizer.get_codebook_entry(index) - rec_pose = self.decoder(z_q) - return rec_pose - -class VQVAE2(nn.Module): - def __init__(self, args): - super(VQVAE2, self).__init__() - # Bottom-level encoder and decoder - args_bottom = copy.deepcopy(args) - args_bottom.vae_layer = 2 - self.bottom_encoder = VQEncoderV6(args_bottom) - self.bottom_quantizer = Quantizer(args.vae_codebook_size, args.vae_length, args.vae_quantizer_lambda) - args_bottom.vae_test_dim = args.vae_test_dim - self.bottom_decoder = VQDecoderV6(args_bottom) - - # Top-level encoder and decoder - args_top = copy.deepcopy(args) - args_top.vae_layer = 3 - args_top.vae_test_dim = args.vae_length - self.top_encoder = VQEncoderV3(args_top) # Adjust according to the top level's design - self.quantize_conv_t = nn.Conv1d(args.vae_length+args.vae_length, args.vae_length, 1) - self.top_quantizer = Quantizer(args.vae_codebook_size, args.vae_length, args.vae_quantizer_lambda) - # self.upsample_t_up = nn.Upsample(scale_factor=2, mode='nearest') - layers = [ - nn.Upsample(scale_factor=2, mode='nearest'), - nn.Conv1d(args.vae_length, args.vae_length, kernel_size=3, stride=1, padding=1), - nn.LeakyReLU(0.2, inplace=True), - nn.Upsample(scale_factor=2, mode='nearest'), - nn.Conv1d(args.vae_length, args.vae_length, kernel_size=3, stride=1, padding=1), - nn.LeakyReLU(0.2, inplace=True), - nn.Upsample(scale_factor=2, mode='nearest'), - nn.Conv1d(args.vae_length, args.vae_length, kernel_size=3, stride=1, padding=1), - nn.LeakyReLU(0.2, inplace=True) - ] - self.upsample_t= nn.Sequential(*layers) - self.top_decoder = VQDecoderV3(args_top) # Adjust to handle top level features appropriately - - def forward(self, inputs): - # Bottom-level processing - enc_b = self.bottom_encoder(inputs) - enc_t = self.top_encoder(enc_b) - #print(enc_b.shape, enc_t.shape) - top_embedding_loss, quant_t, _, top_perplexity = self.top_quantizer(enc_t) - #print(quant_t.shape) - dec_t = self.top_decoder(quant_t) - #print(dec_t.shape) - enc_b = torch.cat([dec_t, enc_b], dim=2).permute(0,2,1) - #print(enc_b.shape) - quant_b = self.quantize_conv_t(enc_b).permute(0,2,1) - #print("5",quant_b.shape) - bottom_embedding_loss, quant_b, _, bottom_perplexity = self.bottom_quantizer(quant_b) - #print("6",quant_b.shape) - upsample_t = self.upsample_t(quant_t.permute(0,2,1)).permute(0,2,1) - #print("7",upsample_t.shape) - quant = torch.cat([upsample_t, quant_b], 2) - rec_pose = self.bottom_decoder(quant) - # print(quant_t.shape, quant_b.shape, rec_pose.shape) - return { - "poses_feat_top": quant_t, - "pose_feat_bottom": quant_b, - "embedding_loss":top_embedding_loss+bottom_embedding_loss, - #"perplexity":perplexity, - "rec_pose": rec_pose - } - - def map2index(self, inputs): - enc_b = self.bottom_encoder(inputs) - enc_t = self.top_encoder(enc_b) - - _, quant_t, _, _ = self.top_quantizer(enc_t) - top_index = self.top_quantizer.map2index(enc_t) - dec_t = self.top_decoder(quant_t) - - enc_b = torch.cat([dec_t, enc_b], dim=2).permute(0,2,1) - #print(enc_b.shape) - quant_b = self.quantize_conv_t(enc_b).permute(0,2,1) - # quant_b = self.quantize_conv_t(enc_b) - bottom_index = self.bottom_quantizer.map2index(quant_b) - return top_index, bottom_index - - def get_top_laent(self, top_index): - z_q_top = self.top_quantizer.get_codebook_entry(top_index) - return z_q_top - - def map2latent(self, inputs): - enc_b = self.bottom_encoder(inputs) - enc_t = self.top_encoder(enc_b) - - _, quant_t, _, _ = self.top_quantizer(enc_t) - top_index = self.top_quantizer.map2index(enc_t) - dec_t = self.top_decoder(quant_t) - - enc_b = torch.cat([dec_t, enc_b], dim=2).permute(0,2,1) - #print(enc_b.shape) - quant_b = self.quantize_conv_t(enc_b).permute(0,2,1) - # quant_b = self.quantize_conv_t(enc_b) - bottom_index = self.bottom_quantizer.map2index(quant_b) - z_q_top = self.top_quantizer.get_codebook_entry(top_index) - z_q_bottom = self.bottom_quantizer.get_codebook_entry(bottom_index) - return z_q_top, z_q_bottom - - def map2latent_top(self, inputs): - enc_b = self.bottom_encoder(inputs) - enc_t = self.top_encoder(enc_b) - top_index = self.top_quantizer.map2index(enc_t) - z_q_top = self.top_quantizer.get_codebook_entry(top_index) - return z_q_top - - def decode(self, top_index, bottom_index): - quant_t = self.top_quantizer.get_codebook_entry(top_index) - quant_b = self.bottom_quantizer.get_codebook_entry(bottom_index) - upsample_t = self.upsample_t(quant_t.permute(0,2,1)).permute(0,2,1) - #print("7",upsample_t.shape) - quant = torch.cat([upsample_t, quant_b], 2) - rec_pose = self.bottom_decoder(quant) - return rec_pose \ No newline at end of file diff --git a/models/quantizer.py b/models/quantizer.py deleted file mode 100644 index 896c973ea25513e27feccc564d85d5dd361a4dc5..0000000000000000000000000000000000000000 --- a/models/quantizer.py +++ /dev/null @@ -1,159 +0,0 @@ -import torch -import torch.nn as nn -import torch.nn.functional as F - - -class Quantizer(nn.Module): - def __init__(self, n_e, e_dim, beta): - super(Quantizer, self).__init__() - - self.e_dim = e_dim - self.n_e = n_e - self.beta = beta - - self.embedding = nn.Embedding(self.n_e, self.e_dim) - self.embedding.weight.data.uniform_(-1.0 / self.n_e, 1.0 / self.n_e) - - def forward(self, z): - """ - Inputs the output of the encoder network z and maps it to a discrete - one-hot vectort that is the index of the closest embedding vector e_j - z (continuous) -> z_q (discrete) - :param z (B, seq_len, channel): - :return z_q: - """ - assert z.shape[-1] == self.e_dim - z_flattened = z.contiguous().view(-1, self.e_dim) - - # B x V - d = torch.sum(z_flattened ** 2, dim=1, keepdim=True) + \ - torch.sum(self.embedding.weight**2, dim=1) - 2 * \ - torch.matmul(z_flattened, self.embedding.weight.t()) - # B x 1 - min_encoding_indices = torch.argmin(d, dim=1) - z_q = self.embedding(min_encoding_indices).view(z.shape) - - # compute loss for embedding - loss = torch.mean((z_q - z.detach())**2) + self.beta * \ - torch.mean((z_q.detach() - z)**2) - - # preserve gradients - z_q = z + (z_q - z).detach() - - min_encodings = F.one_hot(min_encoding_indices, self.n_e).type(z.dtype) - e_mean = torch.mean(min_encodings, dim=0) - perplexity = torch.exp(-torch.sum(e_mean*torch.log(e_mean + 1e-10))) - return loss, z_q, min_encoding_indices, perplexity - - def map2index(self, z): - """ - Inputs the output of the encoder network z and maps it to a discrete - one-hot vectort that is the index of the closest embedding vector e_j - z (continuous) -> z_q (discrete) - :param z (B, seq_len, channel): - :return z_q: - """ - assert z.shape[-1] == self.e_dim - #print(z.shape) - z_flattened = z.contiguous().view(-1, self.e_dim) - #print(z_flattened.shape) - - # B x V - d = torch.sum(z_flattened ** 2, dim=1, keepdim=True) + \ - torch.sum(self.embedding.weight**2, dim=1) - 2 * \ - torch.matmul(z_flattened, self.embedding.weight.t()) - # B x 1 - min_encoding_indices = torch.argmin(d, dim=1) - return min_encoding_indices.reshape(z.shape[0], -1) - - def get_codebook_entry(self, indices): - """ - - :param indices(B, seq_len): - :return z_q(B, seq_len, e_dim): - """ - index_flattened = indices.view(-1) - z_q = self.embedding(index_flattened) - z_q = z_q.view(indices.shape + (self.e_dim, )).contiguous() - return z_q - - -class EmbeddingEMA(nn.Module): - def __init__(self, num_tokens, codebook_dim, decay=0.99, eps=1e-5): - super(EmbeddingEMA, self).__init__() - self.decay = decay - self.eps = eps - weight = torch.randn(num_tokens, codebook_dim) - self.weight = nn.Parameter(weight, requires_grad=False) - self.cluster_size = nn.Parameter(torch.zeros(num_tokens), requires_grad=False) - self.embed_avg = nn.Parameter(weight.clone(), requires_grad=False) - self.update = True - - def forward(self, embed_id): - return F.embedding(embed_id, self.weight) - - def cluster_size_ema_update(self, new_cluster_size): - self.cluster_size.data.mul_(self.decay).add_(new_cluster_size, alpha=1 - self.decay) - - def embed_avg_ema_update(self, new_emb_avg): - self.embed_avg.data.mul_(self.decay).add(new_emb_avg, alpha=1 - self.decay) - - def weight_update(self, num_tokens): - n = self.cluster_size.sum() - smoothed_cluster_size = ( - (self.cluster_size + self.eps) / (n + num_tokens*self.eps) * n - ) - embed_normalized = self.embed_avg / smoothed_cluster_size.unsqueeze(1) - self.weight.data.copy_(embed_normalized) - - -class EMAVectorQuantizer(nn.Module): - def __init__(self, n_embed, embedding_dim, beta, decay=0.99, eps=1e-5): - super(EMAVectorQuantizer, self).__init__() - self.codebook_dim = embedding_dim - self.num_tokens = n_embed - self.beta = beta - self.embedding = EmbeddingEMA(self.num_tokens, self.codebook_dim, decay, eps) - - def forward(self, z): - z_flattened = z.view(-1, self.codebook_dim) - - d = torch.sum(z_flattened ** 2, dim=1, keepdim=True) + \ - torch.sum(self.embedding.weight ** 2, dim=1) - 2 * \ - torch.matmul(z_flattened, self.embedding.weight.t()) - - min_encoding_indices = torch.argmin(d, dim=1) - z_q = self.embedding(min_encoding_indices).view(z.shape) - - min_encodings = F.one_hot(min_encoding_indices, self.num_tokens).type(z.dtype) - e_mean = torch.mean(min_encodings, dim=0) - perplexity = torch.exp(-torch.sum(e_mean * torch.log(e_mean + 1e-10))) - - if self.training and self.embedding.update: - encoding_sum = min_encodings.sum(0) - embed_sum = min_encodings.transpose(0, 1)@z_flattened - - self.embedding.cluster_size_ema_update(encoding_sum) - self.embedding.embed_avg_ema_update(embed_sum) - self.embedding.weight_update(self.num_tokens) - - loss = self.beta * F.mse_loss(z_q.detach(), z) - - z_q = z + (z_q - z).detach() - return loss, z_q, min_encoding_indices, perplexity - - -# class GumbelQuantizer(nn.Module): -# def __init__(self, num_hiddens, embedding_dim, n_embed, straight_through=True, -# kl_weight=5e-4, temp_init=1.0): -# super(GumbelQuantizer, self).__init__() -# -# self.embedding_dim = embedding_dim -# self.n_embed = n_embed -# -# self.straight_through = straight_through -# self.temperature = temp_init -# self.kl_weight = kl_weight -# -# self.proj = nn.Linear(num_hiddens, n_embed) -# self.embed = 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a/models/utils/audio_utils.py +++ /dev/null @@ -1,148 +0,0 @@ -import numpy as np -import torch as t -import models.utils.dist_adapter as dist -import soundfile -import librosa -from models.utils.dist_utils import print_once - -class DefaultSTFTValues: - def __init__(self, hps): - self.sr = hps.sr - self.n_fft = 2048 - self.hop_length = 256 - self.window_size = 6 * self.hop_length - -class STFTValues: - def __init__(self, hps, n_fft, hop_length, window_size): - self.sr = hps.sr - self.n_fft = n_fft - self.hop_length = hop_length - self.window_size = window_size - -def calculate_bandwidth(dataset, hps, duration=600): - hps = DefaultSTFTValues(hps) - n_samples = int(dataset.sr * duration) - l1, total, total_sq, n_seen, idx = 0.0, 0.0, 0.0, 0.0, dist.get_rank() - spec_norm_total, spec_nelem = 0.0, 0.0 - while n_seen < n_samples: - x = dataset[idx] - if isinstance(x, (tuple, list)): - x, y = x - samples = x.astype(np.float64) - stft = librosa.core.stft(np.mean(samples, axis=1), hps.n_fft, hop_length=hps.hop_length, win_length=hps.window_size) - spec = np.absolute(stft) - spec_norm_total += np.linalg.norm(spec) - spec_nelem += 1 - n_seen += int(np.prod(samples.shape)) - l1 += np.sum(np.abs(samples)) - total += np.sum(samples) - total_sq += np.sum(samples ** 2) - idx += max(16, dist.get_world_size()) - - if dist.is_available(): - from jukebox.utils.dist_utils import allreduce - n_seen = allreduce(n_seen) - total = allreduce(total) - total_sq = allreduce(total_sq) - l1 = allreduce(l1) - spec_nelem = allreduce(spec_nelem) - spec_norm_total = allreduce(spec_norm_total) - - mean = total / n_seen - bandwidth = dict(l2 = total_sq / n_seen - mean ** 2, - l1 = l1 / n_seen, - spec = spec_norm_total / spec_nelem) - print_once(bandwidth) - return bandwidth - -def audio_preprocess(x, hps): - # Extra layer in case we want to experiment with different preprocessing - # For two channel, blend randomly into mono (standard is .5 left, .5 right) - - # x: NTC - # x = x.float() - # if x.shape[-1]==2: - # if hps.aug_blend: - # mix=t.rand((x.shape[0],1), device=x.device) #np.random.rand() - # else: - # mix = 0.5 - # x=(mix*x[:,:,0]+(1-mix)*x[:,:,1]) - # elif x.shape[-1]==1: - # x=x[:,:,0] - # else: - # assert False, f'Expected channels {hps.channels}. Got unknown {x.shape[-1]} channels' - - # # x: NT -> NTC - # x = x.unsqueeze(2) - return x - -def audio_postprocess(x, hps): - return x - -def stft(sig, hps): - return t.stft(sig, hps.n_fft, hps.hop_length, win_length=hps.window_size, window=t.hann_window(hps.window_size, device=sig.device)) - -def spec(x, hps): - return t.norm(stft(x, hps), p=2, dim=-1) - -def norm(x): - return (x.view(x.shape[0], -1) ** 2).sum(dim=-1).sqrt() - -def squeeze(x): - if len(x.shape) == 3: - assert x.shape[-1] in [1,2] - x = t.mean(x, -1) - if len(x.shape) != 2: - raise ValueError(f'Unknown input shape {x.shape}') - return x - -def spectral_loss(x_in, x_out, hps): - hps = DefaultSTFTValues(hps) - spec_in = spec(squeeze(x_in.float()), hps) - spec_out = spec(squeeze(x_out.float()), hps) - return norm(spec_in - spec_out) - -def multispectral_loss(x_in, x_out, hps): - losses = [] - assert len(hps.multispec_loss_n_fft) == len(hps.multispec_loss_hop_length) == len(hps.multispec_loss_window_size) - args = [hps.multispec_loss_n_fft, - hps.multispec_loss_hop_length, - hps.multispec_loss_window_size] - for n_fft, hop_length, window_size in zip(*args): - hps = STFTValues(hps, n_fft, hop_length, window_size) - spec_in = spec(squeeze(x_in.float()), hps) - spec_out = spec(squeeze(x_out.float()), hps) - losses.append(norm(spec_in - spec_out)) - return sum(losses) / len(losses) - -def spectral_convergence(x_in, x_out, hps, epsilon=2e-3): - hps = DefaultSTFTValues(hps) - spec_in = spec(squeeze(x_in.float()), hps) - spec_out = spec(squeeze(x_out.float()), hps) - - gt_norm = norm(spec_in) - residual_norm = norm(spec_in - spec_out) - mask = (gt_norm > epsilon).float() - return (residual_norm * mask) / t.clamp(gt_norm, min=epsilon) - -def log_magnitude_loss(x_in, x_out, hps, epsilon=1e-4): - hps = DefaultSTFTValues(hps) - spec_in = t.log(spec(squeeze(x_in.float()), hps) + epsilon) - spec_out = t.log(spec(squeeze(x_out.float()), hps) + epsilon) - return t.mean(t.abs(spec_in - spec_out)) - -def load_audio(file, sr, offset, duration, mono=False): - # Librosa loads more filetypes than soundfile - x, _ = librosa.load(file, sr=sr, mono=mono, offset=offset/sr, duration=duration/sr) - if len(x.shape) == 1: - x = x.reshape((1, -1)) - return x - - -def save_wav(fname, aud, sr): - # clip before saving? - aud = t.clamp(aud, -1, 1).cpu().numpy() - for i in list(range(aud.shape[0])): - soundfile.write(f'{fname}/item_{i}.wav', aud[i], samplerate=sr, format='wav') - - diff --git a/models/utils/build_vocab.py b/models/utils/build_vocab.py deleted file mode 100644 index fef722da80a10583927941af1cb811341e721862..0000000000000000000000000000000000000000 --- a/models/utils/build_vocab.py +++ /dev/null @@ -1,145 +0,0 @@ -import numpy as np -import glob -import os -import pickle -import lmdb -import pyarrow -import fasttext -from loguru import logger -from scipy import linalg - - -class Vocab: - PAD_token = 0 - SOS_token = 1 - EOS_token = 2 - UNK_token = 3 - - def __init__(self, name, insert_default_tokens=True): - self.name = name - self.trimmed = False - self.word_embedding_weights = None - self.reset_dictionary(insert_default_tokens) - - def reset_dictionary(self, insert_default_tokens=True): - self.word2index = {} - self.word2count = {} - if insert_default_tokens: - self.index2word = {self.PAD_token: "", self.SOS_token: "", - self.EOS_token: "", self.UNK_token: ""} - else: - self.index2word = {self.UNK_token: ""} - self.n_words = len(self.index2word) # count default tokens - - def index_word(self, word): - if word not in self.word2index: - self.word2index[word] = self.n_words - self.word2count[word] = 1 - self.index2word[self.n_words] = word - self.n_words += 1 - else: - self.word2count[word] += 1 - - def add_vocab(self, other_vocab): - for word, _ in other_vocab.word2count.items(): - self.index_word(word) - - # remove words below a certain count threshold - def trim(self, min_count): - if self.trimmed: - return - self.trimmed = True - - keep_words = [] - - for k, v in self.word2count.items(): - if v >= min_count: - keep_words.append(k) - - print(' word trimming, kept %s / %s = %.4f' % ( - len(keep_words), len(self.word2index), len(keep_words) / len(self.word2index) - )) - - # reinitialize dictionary - self.reset_dictionary() - for word in keep_words: - self.index_word(word) - - def get_word_index(self, word): - if word in self.word2index: - return self.word2index[word] - else: - return self.UNK_token - - def load_word_vectors(self, pretrained_path, embedding_dim=300): - print(" loading word vectors from '{}'...".format(pretrained_path)) - - # initialize embeddings to random values for special words - init_sd = 1 / np.sqrt(embedding_dim) - weights = np.random.normal(0, scale=init_sd, size=[self.n_words, embedding_dim]) - weights = weights.astype(np.float32) - - # read word vectors - word_model = fasttext.load_model(pretrained_path) - for word, id in self.word2index.items(): - vec = word_model.get_word_vector(word) - weights[id] = vec - self.word_embedding_weights = weights - -def build_vocab(name, data_path, cache_path, word_vec_path=None, feat_dim=None): - print(' building a language model...') - lang_model = Vocab(name) - print(' indexing words from {}'.format(data_path)) - index_words_from_textgrid(lang_model, data_path) - - if word_vec_path is not None: - lang_model.load_word_vectors(word_vec_path, feat_dim) - else: - print(' loaded from {}'.format(cache_path)) - with open(cache_path, 'rb') as f: - lang_model = pickle.load(f) - if word_vec_path is None: - lang_model.word_embedding_weights = None - elif lang_model.word_embedding_weights.shape[0] != lang_model.n_words: - logging.warning(' failed to load word embedding weights. check this') - assert False - - with open(cache_path, 'wb') as f: - pickle.dump(lang_model, f) - - return lang_model - -def index_words(lang_model, data_path): - #index words form text - with open(data_path, "r") as f: - for line in f.readlines(): - line = line.replace(",", " ") - line = line.replace(".", " ") - line = line.replace("?", " ") - line = line.replace("!", " ") - for word in line.split(): - lang_model.index_word(word) - print(' indexed %d words' % lang_model.n_words) - -def index_words_from_textgrid(lang_model, data_path): - import textgrid as tg - trainvaltest=os.listdir(data_path) - for loadtype in trainvaltest: - if "." in loadtype: continue #ignore .ipynb_checkpoints - texts = os.listdir(data_path+loadtype+"/text/") - for textfile in texts: - tgrid = tg.TextGrid.fromFile(data_path+loadtype+"/text/"+textfile) - for word in tgrid[0]: - word_n, word_s, word_e = word.mark, word.minTime, word.maxTime - word_n = word_n.replace(",", " ") - word_n = word_n.replace(".", " ") - word_n = word_n.replace("?", " ") - word_n = word_n.replace("!", " ") - #print(word_n) - lang_model.index_word(word_n) - print(' indexed %d words' % lang_model.n_words) - -if __name__ == "__main__": - #11195 for all, 5793 for 4 speakers - build_vocab("beat_english_15_141", "/home/ma-user/work/datasets/beat_cache/beat_english_15_141/", "/home/ma-user/work/datasets/beat_cache/beat_english_15_141/vocab.pkl", "/home/ma-user/work/datasets/cc.en.300.bin", 300) - \ No newline at end of file diff --git a/models/utils/fk.py b/models/utils/fk.py deleted file mode 100644 index d6ae32341c1ccab559772e053fecf6ded608dc1f..0000000000000000000000000000000000000000 --- a/models/utils/fk.py +++ /dev/null @@ -1,149 +0,0 @@ -"""Based on Daniel Holden code from: - A Deep Learning Framework for Character Motion Synthesis and Editing - (http://www.ipab.inf.ed.ac.uk/cgvu/motionsynthesis.pdf) -""" - -import os - -import numpy as np -import torch -import torch.nn as nn -from .rotations import euler_angles_to_matrix, quaternion_to_matrix, rotation_6d_to_matrix - - -class ForwardKinematicsLayer(nn.Module): - """ Forward Kinematics Layer Class """ - - def __init__(self, args=None, parents=None, positions=None, device=None): - super().__init__() - self.b_idxs = None - if device is None: - self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') - else: - self.device = device - if parents is None and positions is None: - # Load SMPL skeleton (their joint order is different from the one we use for bvh export) - smpl_fname = os.path.join(args.smpl.smpl_body_model, args.data.gender, 'model.npz') - smpl_data = np.load(smpl_fname, encoding='latin1') - self.parents = torch.from_numpy(smpl_data['kintree_table'][0].astype(np.int32)).to(self.device) - self.parents = self.parents.long() - self.positions = torch.from_numpy(smpl_data['J'].astype(np.float32)).to(self.device) - self.positions[1:] -= self.positions[self.parents[1:]] - else: - self.parents = torch.from_numpy(parents).to(self.device) - self.parents = self.parents.long() - self.positions = torch.from_numpy(positions).to(self.device) - self.positions = self.positions.float() - self.positions[0] = 0 - - def rotate(self, t0s, t1s): - return torch.matmul(t0s, t1s) - - def identity_rotation(self, rotations): - diagonal = torch.diag(torch.tensor([1.0, 1.0, 1.0, 1.0])).to(self.device) - diagonal = torch.reshape( - diagonal, torch.Size([1] * len(rotations.shape[:2]) + [4, 4])) - ts = diagonal.repeat(rotations.shape[:2] + torch.Size([1, 1])) - return ts - - def make_fast_rotation_matrices(self, positions, rotations): - if len(rotations.shape) == 4 and rotations.shape[-2:] == torch.Size([3, 3]): - rot_matrices = rotations - elif rotations.shape[-1] == 3: - rot_matrices = euler_angles_to_matrix(rotations, convention='XYZ') - elif rotations.shape[-1] == 4: - rot_matrices = quaternion_to_matrix(rotations) - elif rotations.shape[-1] == 6: - rot_matrices = rotation_6d_to_matrix(rotations) - else: - raise NotImplementedError(f'Unimplemented rotation representation in FK layer, shape of {rotations.shape}') - - rot_matrices = torch.cat([rot_matrices, positions[..., None]], dim=-1) - zeros = torch.zeros(rot_matrices.shape[:-2] + torch.Size([1, 3])).to(self.device) - ones = torch.ones(rot_matrices.shape[:-2] + torch.Size([1, 1])).to(self.device) - zerosones = torch.cat([zeros, ones], dim=-1) - rot_matrices = torch.cat([rot_matrices, zerosones], dim=-2) - return rot_matrices - - def rotate_global(self, parents, positions, rotations): - locals = self.make_fast_rotation_matrices(positions, rotations) - globals = self.identity_rotation(rotations) - - globals = torch.cat([locals[:, 0:1], globals[:, 1:]], dim=1) - b_size = positions.shape[0] - if self.b_idxs is None: - self.b_idxs = torch.LongTensor(np.arange(b_size)).to(self.device) - elif self.b_idxs.shape[-1] != b_size: - self.b_idxs = torch.LongTensor(np.arange(b_size)).to(self.device) - - for i in range(1, positions.shape[1]): - globals[:, i] = self.rotate( - globals[self.b_idxs, parents[i]], locals[:, i]) - - return globals - - def get_tpose_joints(self, offsets, parents): - num_joints = len(parents) - joints = [offsets[:, 0]] - for j in range(1, len(parents)): - joints.append(joints[parents[j]] + offsets[:, j]) - - return torch.stack(joints, dim=1) - - def canonical_to_local(self, canonical_xform, global_orient=None): - """ - Args: - canonical_xform: (B, J, 3, 3) - global_orient: (B, 3, 3) - - Returns: - local_xform: (B, J, 3, 3) - """ - local_xform = torch.zeros_like(canonical_xform) - - if global_orient is None: - global_xform = canonical_xform - else: - global_xform = torch.matmul(global_orient.unsqueeze(1), canonical_xform) - for i in range(global_xform.shape[1]): - if i == 0: - local_xform[:, i] = global_xform[:, i] - else: - local_xform[:, i] = torch.bmm(torch.linalg.inv(global_xform[:, self.parents[i]]), global_xform[:, i]) - - return local_xform - - def global_to_local(self, global_xform): - """ - Args: - global_xform: (B, J, 3, 3) - - Returns: - local_xform: (B, J, 3, 3) - """ - local_xform = torch.zeros_like(global_xform) - - for i in range(global_xform.shape[1]): - if i == 0: - local_xform[:, i] = global_xform[:, i] - else: - local_xform[:, i] = torch.bmm(torch.linalg.inv(global_xform[:, self.parents[i]]), global_xform[:, i]) - - return local_xform - - def forward(self, rotations, positions=None): - """ - Args: - rotations (B, J, D) - - Returns: - The global position of each joint after FK (B, J, 3) - """ - # Get the full transform with rotations for skinning - b_size = rotations.shape[0] - if positions is None: - positions = self.positions.repeat(b_size, 1, 1) - transforms = self.rotate_global(self.parents, positions, rotations) - coordinates = transforms[:, :, :3, 3] / transforms[:, :, 3:, 3] - - return coordinates, transforms diff --git a/models/utils/layer.py b/models/utils/layer.py deleted file mode 100644 index 86f8013512086280656ee10225952642abe7b11e..0000000000000000000000000000000000000000 --- a/models/utils/layer.py +++ /dev/null @@ -1,217 +0,0 @@ -import random -import math -import numpy as np -import torch -import torch.nn as nn -from torch.nn.utils import weight_norm -import torch.nn.functional as F - -from .build_vocab import Vocab - -class Chomp1d(nn.Module): - def __init__(self, chomp_size): - super(Chomp1d, self).__init__() - self.chomp_size = chomp_size - - def forward(self, x): - return x[:, :, :-self.chomp_size].contiguous() - - -class TemporalBlock(nn.Module): - def __init__(self, n_inputs, n_outputs, kernel_size, stride, dilation, padding, dropout=0.2): - super(TemporalBlock, self).__init__() - self.conv1 = weight_norm(nn.Conv1d(n_inputs, n_outputs, kernel_size, - stride=stride, padding=padding, dilation=dilation)) - self.chomp1 = Chomp1d(padding) - self.relu1 = nn.ReLU() - self.dropout1 = nn.Dropout(dropout) - - self.conv2 = weight_norm(nn.Conv1d(n_outputs, n_outputs, kernel_size, - stride=stride, padding=padding, dilation=dilation)) - self.chomp2 = Chomp1d(padding) - self.relu2 = nn.ReLU() - self.dropout2 = nn.Dropout(dropout) - - self.net = nn.Sequential(self.conv1, self.chomp1, self.relu1, self.dropout1, - self.conv2, self.chomp2, self.relu2, self.dropout2) - self.downsample = nn.Conv1d(n_inputs, n_outputs, 1) if n_inputs != n_outputs else None - self.relu = nn.ReLU() - self.init_weights() - - def init_weights(self): - self.conv1.weight.data.normal_(0, 0.01) - self.conv2.weight.data.normal_(0, 0.01) - if self.downsample is not None: - self.downsample.weight.data.normal_(0, 0.01) - - def forward(self, x): - out = self.net(x) - res = x if self.downsample is None else self.downsample(x) - return self.relu(out + res) - - -class TemporalConvNet(nn.Module): - def __init__(self, num_inputs, num_channels, kernel_size=2, dropout=0.2): - super(TemporalConvNet, self).__init__() - layers = [] - num_levels = len(num_channels) - for i in range(num_levels): - dilation_size = 2 ** i - in_channels = num_inputs if i == 0 else num_channels[i-1] - out_channels = num_channels[i] - layers += [TemporalBlock(in_channels, out_channels, kernel_size, stride=1, dilation=dilation_size, - padding=(kernel_size-1) * dilation_size, dropout=dropout)] - - self.network = nn.Sequential(*layers) - - def forward(self, x): - return self.network(x) - - -class TextEncoderTCN(nn.Module): - """ based on https://github.com/locuslab/TCN/blob/master/TCN/word_cnn/model.py """ - def __init__(self, args, n_words=11195, embed_size=300, pre_trained_embedding=None, - kernel_size=2, dropout=0.3, emb_dropout=0.1, word_cache=False): - super(TextEncoderTCN, self).__init__() -# if word_cache: -# self.embedding = None -# else: -# if pre_trained_embedding is not None: # use pre-trained embedding (fasttext) -# #print(pre_trained_embedding.shape) -# assert pre_trained_embedding.shape[0] == n_words -# assert pre_trained_embedding.shape[1] == embed_size -# self.embedding = nn.Embedding.from_pretrained(torch.FloatTensor(pre_trained_embedding), -# freeze=args.freeze_wordembed) -# else: -# self.embedding = nn.Embedding(n_words, embed_size) - - num_channels = [args.hidden_size] #* args.n_layer - self.tcn = TemporalConvNet(embed_size, num_channels, kernel_size, dropout=dropout) - self.decoder = nn.Linear(num_channels[-1], args.word_f) - self.drop = nn.Dropout(emb_dropout) - #self.emb_dropout = emb_dropout - self.init_weights() - - def init_weights(self): - self.decoder.bias.data.fill_(0) - self.decoder.weight.data.normal_(0, 0.01) - - def forward(self, input): - #print(input.shape) -# if self.embedding is None: -# emb = self.drop(input) -# else: -# emb = self.drop(self.embedding(input)) - y = self.tcn(input.transpose(1, 2)).transpose(1, 2) - y = self.decoder(y) - return y, torch.max(y, dim=1)[0] - - - - - - - - - -def reparameterize(mu, logvar): - std = torch.exp(0.5 * logvar) - eps = torch.randn_like(std) - return mu + eps * std - -def ConvNormRelu(in_channels, out_channels, downsample=False, padding=0, batchnorm=True): - if not downsample: - k = 3 - s = 1 - else: - k = 4 - s = 2 - conv_block = nn.Conv1d(in_channels, out_channels, kernel_size=k, stride=s, padding=padding) - norm_block = nn.BatchNorm1d(out_channels) - if batchnorm: - net = nn.Sequential( - conv_block, - norm_block, - nn.LeakyReLU(0.2, True) - ) - else: - net = nn.Sequential( - conv_block, - nn.LeakyReLU(0.2, True) - ) - return net - -class BasicBlock(nn.Module): - """ based on timm: https://github.com/rwightman/pytorch-image-models """ - def __init__(self, inplanes, planes, ker_size, stride=1, downsample=None, cardinality=1, base_width=64, - reduce_first=1, dilation=1, first_dilation=None, act_layer=nn.LeakyReLU, norm_layer=nn.BatchNorm1d, attn_layer=None, aa_layer=None, drop_block=None, drop_path=None): - super(BasicBlock, self).__init__() - - self.conv1 = nn.Conv1d( - inplanes, planes, kernel_size=ker_size, stride=stride, padding=first_dilation, - dilation=dilation, bias=True) - self.bn1 = norm_layer(planes) - self.act1 = act_layer(inplace=True) - self.conv2 = nn.Conv1d( - planes, planes, kernel_size=ker_size, padding=ker_size//2, dilation=dilation, bias=True) - self.bn2 = norm_layer(planes) - self.act2 = act_layer(inplace=True) - if downsample is not None: - self.downsample = nn.Sequential( - nn.Conv1d(inplanes, planes, stride=stride, kernel_size=ker_size, padding=first_dilation, dilation=dilation, bias=True), - norm_layer(planes), - ) - else: self.downsample=None - self.stride = stride - self.dilation = dilation - self.drop_block = drop_block - self.drop_path = drop_path - - def zero_init_last_bn(self): - nn.init.zeros_(self.bn2.weight) - - def forward(self, x): - shortcut = x - x = self.conv1(x) - x = self.bn1(x) - x = self.act1(x) - x = self.conv2(x) - x = self.bn2(x) - if self.downsample is not None: - shortcut = self.downsample(shortcut) - x += shortcut - x = self.act2(x) - return x - -def init_weight(m): - if isinstance(m, nn.Conv1d) or isinstance(m, nn.Linear) or isinstance(m, nn.ConvTranspose1d): - nn.init.xavier_normal_(m.weight) - # m.bias.data.fill_(0.01) - if m.bias is not None: - nn.init.constant_(m.bias, 0) - -def init_weight_skcnn(m): - if isinstance(m, nn.Conv1d) or isinstance(m, nn.Linear) or isinstance(m, nn.ConvTranspose1d): - nn.init.kaiming_uniform_(m.weight, a=math.sqrt(5)) - # m.bias.data.fill_(0.01) - if m.bias is not None: - #nn.init.constant_(m.bias, 0) - fan_in, _ = nn.init._calculate_fan_in_and_fan_out(m.weight) - bound = 1 / math.sqrt(fan_in) - nn.init.uniform_(m.bias, -bound, bound) - -class ResBlock(nn.Module): - def __init__(self, channel): - super(ResBlock, self).__init__() - self.model = nn.Sequential( - nn.Conv1d(channel, channel, kernel_size=3, stride=1, padding=1), - nn.LeakyReLU(0.2, inplace=True), - nn.Conv1d(channel, channel, kernel_size=3, stride=1, padding=1), - ) - - def forward(self, x): - residual = x - out = self.model(x) - out += residual - return out - \ No newline at end of file diff --git a/models/utils/rotations.py b/models/utils/rotations.py deleted file mode 100644 index 55729b2724c9c34234bddb63a826aa1f9a4321b9..0000000000000000000000000000000000000000 --- a/models/utils/rotations.py +++ /dev/null @@ -1,587 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. -# -# This source code is licensed under the BSD-style license found in the -# LICENSE file in the root directory of this source tree. - -from typing import Optional, Union - -import torch -import torch.nn.functional as F - -Device = Union[str, torch.device] - - -""" -The transformation matrices returned from the functions in this file assume -the points on which the transformation will be applied are column vectors. -i.e. the R matrix is structured as - - R = [ - [Rxx, Rxy, Rxz], - [Ryx, Ryy, Ryz], - [Rzx, Rzy, Rzz], - ] # (3, 3) - -This matrix can be applied to column vectors by post multiplication -by the points e.g. - - points = [[0], [1], [2]] # (3 x 1) xyz coordinates of a point - transformed_points = R * points - -To apply the same matrix to points which are row vectors, the R matrix -can be transposed and pre multiplied by the points: - -e.g. - points = [[0, 1, 2]] # (1 x 3) xyz coordinates of a point - transformed_points = points * R.transpose(1, 0) -""" - - -def quaternion_to_matrix(quaternions: torch.Tensor) -> torch.Tensor: - """ - Convert rotations given as quaternions to rotation matrices. - - Args: - quaternions: quaternions with real part first, - as tensor of shape (..., 4). - - Returns: - Rotation matrices as tensor of shape (..., 3, 3). - """ - r, i, j, k = torch.unbind(quaternions, -1) - two_s = 2.0 / (quaternions * quaternions).sum(-1) - - o = torch.stack( - ( - 1 - two_s * (j * j + k * k), - two_s * (i * j - k * r), - two_s * (i * k + j * r), - two_s * (i * j + k * r), - 1 - two_s * (i * i + k * k), - two_s * (j * k - i * r), - two_s * (i * k - j * r), - two_s * (j * k + i * r), - 1 - two_s * (i * i + j * j), - ), - -1, - ) - return o.reshape(quaternions.shape[:-1] + (3, 3)) - - -def _copysign(a: torch.Tensor, b: torch.Tensor) -> torch.Tensor: - """ - Return a tensor where each element has the absolute value taken from the, - corresponding element of a, with sign taken from the corresponding - element of b. This is like the standard copysign floating-point operation, - but is not careful about negative 0 and NaN. - - Args: - a: source tensor. - b: tensor whose signs will be used, of the same shape as a. - - Returns: - Tensor of the same shape as a with the signs of b. - """ - signs_differ = (a < 0) != (b < 0) - return torch.where(signs_differ, -a, a) - - -def _sqrt_positive_part(x: torch.Tensor) -> torch.Tensor: - """ - Returns torch.sqrt(torch.max(0, x)) - but with a zero subgradient where x is 0. - """ - ret = torch.zeros_like(x) - positive_mask = x > 0 - ret[positive_mask] = torch.sqrt(x[positive_mask]) - return ret - - -def matrix_to_quaternion(matrix: torch.Tensor) -> torch.Tensor: - """ - Convert rotations given as rotation matrices to quaternions. - - Args: - matrix: Rotation matrices as tensor of shape (..., 3, 3). - - Returns: - quaternions with real part first, as tensor of shape (..., 4). - """ - if matrix.size(-1) != 3 or matrix.size(-2) != 3: - raise ValueError(f"Invalid rotation matrix shape {matrix.shape}.") - - batch_dim = matrix.shape[:-2] - m00, m01, m02, m10, m11, m12, m20, m21, m22 = torch.unbind( - matrix.reshape(batch_dim + (9,)), dim=-1 - ) - - q_abs = _sqrt_positive_part( - torch.stack( - [ - 1.0 + m00 + m11 + m22, - 1.0 + m00 - m11 - m22, - 1.0 - m00 + m11 - m22, - 1.0 - m00 - m11 + m22, - ], - dim=-1, - ) - ) - - # we produce the desired quaternion multiplied by each of r, i, j, k - quat_by_rijk = torch.stack( - [ - torch.stack([q_abs[..., 0] ** 2, m21 - m12, m02 - m20, m10 - m01], dim=-1), - torch.stack([m21 - m12, q_abs[..., 1] ** 2, m10 + m01, m02 + m20], dim=-1), - torch.stack([m02 - m20, m10 + m01, q_abs[..., 2] ** 2, m12 + m21], dim=-1), - torch.stack([m10 - m01, m20 + m02, m21 + m12, q_abs[..., 3] ** 2], dim=-1), - ], - dim=-2, - ) - - # We floor here at 0.1 but the exact level is not important; if q_abs is small, - # the candidate won't be picked. - flr = torch.tensor(0.1).to(dtype=q_abs.dtype, device=q_abs.device) - quat_candidates = quat_by_rijk / (2.0 * q_abs[..., None].max(flr)) - - # if not for numerical problems, quat_candidates[i] should be same (up to a sign), - # forall i; we pick the best-conditioned one (with the largest denominator) - - return quat_candidates[ - F.one_hot(q_abs.argmax(dim=-1), num_classes=4) > 0.5, : # pyre-ignore[16] - ].reshape(batch_dim + (4,)) - - -def _axis_angle_rotation(axis: str, angle: torch.Tensor) -> torch.Tensor: - """ - Return the rotation matrices for one of the rotations about an axis - of which Euler angles describe, for each value of the angle given. - - Args: - axis: Axis label "X" or "Y or "Z". - angle: any shape tensor of Euler angles in radians - - Returns: - Rotation matrices as tensor of shape (..., 3, 3). - """ - - cos = torch.cos(angle) - sin = torch.sin(angle) - one = torch.ones_like(angle) - zero = torch.zeros_like(angle) - - if axis == "X": - R_flat = (one, zero, zero, zero, cos, -sin, zero, sin, cos) - elif axis == "Y": - R_flat = (cos, zero, sin, zero, one, zero, -sin, zero, cos) - elif axis == "Z": - R_flat = (cos, -sin, zero, sin, cos, zero, zero, zero, one) - else: - raise ValueError("letter must be either X, Y or Z.") - - return torch.stack(R_flat, -1).reshape(angle.shape + (3, 3)) - - -def euler_angles_to_matrix(euler_angles: torch.Tensor, convention: str) -> torch.Tensor: - """ - Convert rotations given as Euler angles in radians to rotation matrices. - - Args: - euler_angles: Euler angles in radians as tensor of shape (..., 3). - convention: Convention string of three uppercase letters from - {"X", "Y", and "Z"}. - - Returns: - Rotation matrices as tensor of shape (..., 3, 3). - """ - if euler_angles.dim() == 0 or euler_angles.shape[-1] != 3: - raise ValueError("Invalid input euler angles.") - if len(convention) != 3: - raise ValueError("Convention must have 3 letters.") - if convention[1] in (convention[0], convention[2]): - raise ValueError(f"Invalid convention {convention}.") - for letter in convention: - if letter not in ("X", "Y", "Z"): - raise ValueError(f"Invalid letter {letter} in convention string.") - matrices = [ - _axis_angle_rotation(c, e) - for c, e in zip(convention, torch.unbind(euler_angles, -1)) - ] - # return functools.reduce(torch.matmul, matrices) - return torch.matmul(torch.matmul(matrices[0], matrices[1]), matrices[2]) - - -def _angle_from_tan( - axis: str, other_axis: str, data, horizontal: bool, tait_bryan: bool -) -> torch.Tensor: - """ - Extract the first or third Euler angle from the two members of - the matrix which are positive constant times its sine and cosine. - - Args: - axis: Axis label "X" or "Y or "Z" for the angle we are finding. - other_axis: Axis label "X" or "Y or "Z" for the middle axis in the - convention. - data: Rotation matrices as tensor of shape (..., 3, 3). - horizontal: Whether we are looking for the angle for the third axis, - which means the relevant entries are in the same row of the - rotation matrix. If not, they are in the same column. - tait_bryan: Whether the first and third axes in the convention differ. - - Returns: - Euler Angles in radians for each matrix in data as a tensor - of shape (...). - """ - - i1, i2 = {"X": (2, 1), "Y": (0, 2), "Z": (1, 0)}[axis] - if horizontal: - i2, i1 = i1, i2 - even = (axis + other_axis) in ["XY", "YZ", "ZX"] - if horizontal == even: - return torch.atan2(data[..., i1], data[..., i2]) - if tait_bryan: - return torch.atan2(-data[..., i2], data[..., i1]) - return torch.atan2(data[..., i2], -data[..., i1]) - - -def _index_from_letter(letter: str) -> int: - if letter == "X": - return 0 - if letter == "Y": - return 1 - if letter == "Z": - return 2 - raise ValueError("letter must be either X, Y or Z.") - - -def matrix_to_euler_angles(matrix: torch.Tensor, convention: str) -> torch.Tensor: - """ - Convert rotations given as rotation matrices to Euler angles in radians. - - Args: - matrix: Rotation matrices as tensor of shape (..., 3, 3). - convention: Convention string of three uppercase letters. - - Returns: - Euler angles in radians as tensor of shape (..., 3). - """ - if len(convention) != 3: - raise ValueError("Convention must have 3 letters.") - if convention[1] in (convention[0], convention[2]): - raise ValueError(f"Invalid convention {convention}.") - for letter in convention: - if letter not in ("X", "Y", "Z"): - raise ValueError(f"Invalid letter {letter} in convention string.") - if matrix.size(-1) != 3 or matrix.size(-2) != 3: - raise ValueError(f"Invalid rotation matrix shape {matrix.shape}.") - i0 = _index_from_letter(convention[0]) - i2 = _index_from_letter(convention[2]) - tait_bryan = i0 != i2 - if tait_bryan: - central_angle = torch.asin( - matrix[..., i0, i2] * (-1.0 if i0 - i2 in [-1, 2] else 1.0) - ) - else: - central_angle = torch.acos(matrix[..., i0, i0]) - - o = ( - _angle_from_tan( - convention[0], convention[1], matrix[..., i2], False, tait_bryan - ), - central_angle, - _angle_from_tan( - convention[2], convention[1], matrix[..., i0, :], True, tait_bryan - ), - ) - return torch.stack(o, -1) - - -def random_quaternions( - n: int, dtype: Optional[torch.dtype] = None, device: Optional[Device] = None -) -> torch.Tensor: - """ - Generate random quaternions representing rotations, - i.e. versors with nonnegative real part. - - Args: - n: Number of quaternions in a batch to return. - dtype: Type to return. - device: Desired device of returned tensor. Default: - uses the current device for the default tensor type. - - Returns: - Quaternions as tensor of shape (N, 4). - """ - if isinstance(device, str): - device = torch.device(device) - o = torch.randn((n, 4), dtype=dtype, device=device) - s = (o * o).sum(1) - o = o / _copysign(torch.sqrt(s), o[:, 0])[:, None] - return o - - -def random_rotations( - n: int, dtype: Optional[torch.dtype] = None, device: Optional[Device] = None -) -> torch.Tensor: - """ - Generate random rotations as 3x3 rotation matrices. - - Args: - n: Number of rotation matrices in a batch to return. - dtype: Type to return. - device: Device of returned tensor. Default: if None, - uses the current device for the default tensor type. - - Returns: - Rotation matrices as tensor of shape (n, 3, 3). - """ - quaternions = random_quaternions(n, dtype=dtype, device=device) - return quaternion_to_matrix(quaternions) - - -def random_rotation( - dtype: Optional[torch.dtype] = None, device: Optional[Device] = None -) -> torch.Tensor: - """ - Generate a single random 3x3 rotation matrix. - - Args: - dtype: Type to return - device: Device of returned tensor. Default: if None, - uses the current device for the default tensor type - - Returns: - Rotation matrix as tensor of shape (3, 3). - """ - return random_rotations(1, dtype, device)[0] - - -def standardize_quaternion(quaternions: torch.Tensor) -> torch.Tensor: - """ - Convert a unit quaternion to a standard form: one in which the real - part is non negative. - - Args: - quaternions: Quaternions with real part first, - as tensor of shape (..., 4). - - Returns: - Standardized quaternions as tensor of shape (..., 4). - """ - return torch.where(quaternions[..., 0:1] < 0, -quaternions, quaternions) - - -def quaternion_raw_multiply(a: torch.Tensor, b: torch.Tensor) -> torch.Tensor: - """ - Multiply two quaternions. - Usual torch rules for broadcasting apply. - - Args: - a: Quaternions as tensor of shape (..., 4), real part first. - b: Quaternions as tensor of shape (..., 4), real part first. - - Returns: - The product of a and b, a tensor of quaternions shape (..., 4). - """ - aw, ax, ay, az = torch.unbind(a, -1) - bw, bx, by, bz = torch.unbind(b, -1) - ow = aw * bw - ax * bx - ay * by - az * bz - ox = aw * bx + ax * bw + ay * bz - az * by - oy = aw * by - ax * bz + ay * bw + az * bx - oz = aw * bz + ax * by - ay * bx + az * bw - return torch.stack((ow, ox, oy, oz), -1) - - -def quaternion_multiply(a: torch.Tensor, b: torch.Tensor) -> torch.Tensor: - """ - Multiply two quaternions representing rotations, returning the quaternion - representing their composition, i.e. the versor with nonnegative real part. - Usual torch rules for broadcasting apply. - - Args: - a: Quaternions as tensor of shape (..., 4), real part first. - b: Quaternions as tensor of shape (..., 4), real part first. - - Returns: - The product of a and b, a tensor of quaternions of shape (..., 4). - """ - ab = quaternion_raw_multiply(a, b) - return standardize_quaternion(ab) - - -def quaternion_invert(quaternion: torch.Tensor) -> torch.Tensor: - """ - Given a quaternion representing rotation, get the quaternion representing - its inverse. - - Args: - quaternion: Quaternions as tensor of shape (..., 4), with real part - first, which must be versors (unit quaternions). - - Returns: - The inverse, a tensor of quaternions of shape (..., 4). - """ - - scaling = torch.tensor([1, -1, -1, -1], device=quaternion.device) - return quaternion * scaling - - -def quaternion_apply(quaternion: torch.Tensor, point: torch.Tensor) -> torch.Tensor: - """ - Apply the rotation given by a quaternion to a 3D point. - Usual torch rules for broadcasting apply. - - Args: - quaternion: Tensor of quaternions, real part first, of shape (..., 4). - point: Tensor of 3D points of shape (..., 3). - - Returns: - Tensor of rotated points of shape (..., 3). - """ - if point.size(-1) != 3: - raise ValueError(f"Points are not in 3D, {point.shape}.") - real_parts = point.new_zeros(point.shape[:-1] + (1,)) - point_as_quaternion = torch.cat((real_parts, point), -1) - out = quaternion_raw_multiply( - quaternion_raw_multiply(quaternion, point_as_quaternion), - quaternion_invert(quaternion), - ) - return out[..., 1:] - - -def axis_angle_to_matrix(axis_angle: torch.Tensor) -> torch.Tensor: - """ - Convert rotations given as axis/angle to rotation matrices. - - Args: - axis_angle: Rotations given as a vector in axis angle form, - as a tensor of shape (..., 3), where the magnitude is - the angle turned anticlockwise in radians around the - vector's direction. - - Returns: - Rotation matrices as tensor of shape (..., 3, 3). - """ - return quaternion_to_matrix(axis_angle_to_quaternion(axis_angle)) - - -def matrix_to_axis_angle(matrix: torch.Tensor) -> torch.Tensor: - """ - Convert rotations given as rotation matrices to axis/angle. - - Args: - matrix: Rotation matrices as tensor of shape (..., 3, 3). - - Returns: - Rotations given as a vector in axis angle form, as a tensor - of shape (..., 3), where the magnitude is the angle - turned anticlockwise in radians around the vector's - direction. - """ - return quaternion_to_axis_angle(matrix_to_quaternion(matrix)) - - -def axis_angle_to_quaternion(axis_angle: torch.Tensor) -> torch.Tensor: - """ - Convert rotations given as axis/angle to quaternions. - - Args: - axis_angle: Rotations given as a vector in axis angle form, - as a tensor of shape (..., 3), where the magnitude is - the angle turned anticlockwise in radians around the - vector's direction. - - Returns: - quaternions with real part first, as tensor of shape (..., 4). - """ - angles = torch.norm(axis_angle, p=2, dim=-1, keepdim=True) - half_angles = angles * 0.5 - eps = 1e-6 - small_angles = angles.abs() < eps - sin_half_angles_over_angles = torch.empty_like(angles) - sin_half_angles_over_angles[~small_angles] = ( - torch.sin(half_angles[~small_angles]) / angles[~small_angles] - ) - # for x small, sin(x/2) is about x/2 - (x/2)^3/6 - # so sin(x/2)/x is about 1/2 - (x*x)/48 - sin_half_angles_over_angles[small_angles] = ( - 0.5 - (angles[small_angles] * angles[small_angles]) / 48 - ) - quaternions = torch.cat( - [torch.cos(half_angles), axis_angle * sin_half_angles_over_angles], dim=-1 - ) - return quaternions - - -def quaternion_to_axis_angle(quaternions: torch.Tensor) -> torch.Tensor: - """ - Convert rotations given as quaternions to axis/angle. - - Args: - quaternions: quaternions with real part first, - as tensor of shape (..., 4). - - Returns: - Rotations given as a vector in axis angle form, as a tensor - of shape (..., 3), where the magnitude is the angle - turned anticlockwise in radians around the vector's - direction. - """ - norms = torch.norm(quaternions[..., 1:], p=2, dim=-1, keepdim=True) - half_angles = torch.atan2(norms, quaternions[..., :1]) - angles = 2 * half_angles - eps = 1e-6 - small_angles = angles.abs() < eps - sin_half_angles_over_angles = torch.empty_like(angles) - sin_half_angles_over_angles[~small_angles] = ( - torch.sin(half_angles[~small_angles]) / angles[~small_angles] - ) - # for x small, sin(x/2) is about x/2 - (x/2)^3/6 - # so sin(x/2)/x is about 1/2 - (x*x)/48 - sin_half_angles_over_angles[small_angles] = ( - 0.5 - (angles[small_angles] * angles[small_angles]) / 48 - ) - return quaternions[..., 1:] / sin_half_angles_over_angles - - -def rotation_6d_to_matrix(d6: torch.Tensor) -> torch.Tensor: - """ - Converts 6D rotation representation by Zhou et al. [1] to rotation matrix - using Gram--Schmidt orthogonalization per Section B of [1]. - Args: - d6: 6D rotation representation, of size (*, 6) - - Returns: - batch of rotation matrices of size (*, 3, 3) - - [1] Zhou, Y., Barnes, C., Lu, J., Yang, J., & Li, H. - On the Continuity of Rotation Representations in Neural Networks. - IEEE Conference on Computer Vision and Pattern Recognition, 2019. - Retrieved from http://arxiv.org/abs/1812.07035 - """ - - a1, a2 = d6[..., :3], d6[..., 3:] - b1 = F.normalize(a1, dim=-1) - b2 = a2 - (b1 * a2).sum(-1, keepdim=True) * b1 - b2 = F.normalize(b2, dim=-1) - b3 = torch.cross(b1, b2, dim=-1) - return torch.stack((b1, b2, b3), dim=-2) - - -def matrix_to_rotation_6d(matrix: torch.Tensor) -> torch.Tensor: - """ - Converts rotation matrices to 6D rotation representation by Zhou et al. [1] - by dropping the last row. Note that 6D representation is not unique. - Args: - matrix: batch of rotation matrices of size (*, 3, 3) - - Returns: - 6D rotation representation, of size (*, 6) - - [1] Zhou, Y., Barnes, C., Lu, J., Yang, J., & Li, H. - On the Continuity of Rotation Representations in Neural Networks. - IEEE Conference on Computer Vision and Pattern Recognition, 2019. - Retrieved from http://arxiv.org/abs/1812.07035 - """ - batch_dim = matrix.size()[:-2] - return matrix[..., :2, :].clone().reshape(batch_dim + (6,)) diff --git a/models/utils/skeleton.py b/models/utils/skeleton.py deleted file mode 100644 index 123656b7516aec1b424f9f87d384837eb820ccc9..0000000000000000000000000000000000000000 --- a/models/utils/skeleton.py +++ /dev/null @@ -1,636 +0,0 @@ -import math - -import numpy as np -import torch -import torch.nn as nn -import torch.nn.functional as F - - -class SkeletonConv(nn.Module): - def __init__(self, neighbour_list, in_channels, out_channels, kernel_size, joint_num, stride=1, padding=0, - bias=True, padding_mode='zeros', add_offset=False, in_offset_channel=0): - self.in_channels_per_joint = in_channels // joint_num - self.out_channels_per_joint = out_channels // joint_num - if in_channels % joint_num != 0 or out_channels % joint_num != 0: - raise Exception('BAD') - super(SkeletonConv, self).__init__() - - if padding_mode == 'zeros': - padding_mode = 'constant' - if padding_mode == 'reflection': - padding_mode = 'reflect' - - self.expanded_neighbour_list = [] - self.expanded_neighbour_list_offset = [] - self.neighbour_list = neighbour_list - self.add_offset = add_offset - self.joint_num = joint_num - - self.stride = stride - self.dilation = 1 - self.groups = 1 - self.padding = padding - self.padding_mode = padding_mode - self._padding_repeated_twice = (padding, padding) - - for neighbour in neighbour_list: - expanded = [] - for k in neighbour: - for i in range(self.in_channels_per_joint): - expanded.append(k * self.in_channels_per_joint + i) - self.expanded_neighbour_list.append(expanded) - - if self.add_offset: - self.offset_enc = SkeletonLinear(neighbour_list, in_offset_channel * len(neighbour_list), out_channels) - - for neighbour in neighbour_list: - expanded = [] - for k in neighbour: - for i in range(add_offset): - expanded.append(k * in_offset_channel + i) - self.expanded_neighbour_list_offset.append(expanded) - - self.weight = torch.zeros(out_channels, in_channels, kernel_size) - if bias: - self.bias = torch.zeros(out_channels) - else: - self.register_parameter('bias', None) - - self.mask = torch.zeros_like(self.weight) - for i, neighbour in enumerate(self.expanded_neighbour_list): - self.mask[self.out_channels_per_joint * i: self.out_channels_per_joint * (i + 1), neighbour, ...] = 1 - self.mask = nn.Parameter(self.mask, requires_grad=False) - - self.description = 'SkeletonConv(in_channels_per_armature={}, out_channels_per_armature={}, kernel_size={}, ' \ - 'joint_num={}, stride={}, padding={}, bias={})'.format( - in_channels // joint_num, out_channels // joint_num, kernel_size, joint_num, stride, padding, bias - ) - - self.reset_parameters() - - def reset_parameters(self): - for i, neighbour in enumerate(self.expanded_neighbour_list): - """ Use temporary variable to avoid assign to copy of slice, which might lead to unexpected result """ - tmp = torch.zeros_like(self.weight[self.out_channels_per_joint * i: self.out_channels_per_joint * (i + 1), - neighbour, ...]) - nn.init.kaiming_uniform_(tmp, a=math.sqrt(5)) - self.weight[self.out_channels_per_joint * i: self.out_channels_per_joint * (i + 1), - neighbour, ...] = tmp - if self.bias is not None: - fan_in, _ = nn.init._calculate_fan_in_and_fan_out( - self.weight[self.out_channels_per_joint * i: self.out_channels_per_joint * (i + 1), neighbour, ...]) - bound = 1 / math.sqrt(fan_in) - tmp = torch.zeros_like( - self.bias[self.out_channels_per_joint * i: self.out_channels_per_joint * (i + 1)]) - nn.init.uniform_(tmp, -bound, bound) - self.bias[self.out_channels_per_joint * i: self.out_channels_per_joint * (i + 1)] = tmp - - self.weight = nn.Parameter(self.weight) - if self.bias is not None: - self.bias = nn.Parameter(self.bias) - - def set_offset(self, offset): - if not self.add_offset: - raise Exception('Wrong Combination of Parameters') - self.offset = offset.reshape(offset.shape[0], -1) - - def forward(self, input): - # print('SkeletonConv') - weight_masked = self.weight * self.mask - #print(f'input: {input.size()}') - res = F.conv1d(F.pad(input, self._padding_repeated_twice, mode=self.padding_mode), - weight_masked, self.bias, self.stride, - 0, self.dilation, self.groups) - - if self.add_offset: - offset_res = self.offset_enc(self.offset) - offset_res = offset_res.reshape(offset_res.shape + (1, )) - res += offset_res / 100 - #print(f'res: {res.size()}') - return res - - -class SkeletonLinear(nn.Module): - def __init__(self, neighbour_list, in_channels, out_channels, extra_dim1=False): - super(SkeletonLinear, self).__init__() - self.neighbour_list = neighbour_list - self.in_channels = in_channels - self.out_channels = out_channels - self.in_channels_per_joint = in_channels // len(neighbour_list) - self.out_channels_per_joint = out_channels // len(neighbour_list) - self.extra_dim1 = extra_dim1 - self.expanded_neighbour_list = [] - - for neighbour in neighbour_list: - expanded = [] - for k in neighbour: - for i in range(self.in_channels_per_joint): - expanded.append(k * self.in_channels_per_joint + i) - self.expanded_neighbour_list.append(expanded) - - self.weight = torch.zeros(out_channels, in_channels) - self.mask = torch.zeros(out_channels, in_channels) - self.bias = nn.Parameter(torch.Tensor(out_channels)) - - self.reset_parameters() - - def reset_parameters(self): - for i, neighbour in enumerate(self.expanded_neighbour_list): - tmp = torch.zeros_like( - self.weight[i*self.out_channels_per_joint: (i + 1)*self.out_channels_per_joint, neighbour] - ) - self.mask[i*self.out_channels_per_joint: (i + 1)*self.out_channels_per_joint, neighbour] = 1 - nn.init.kaiming_uniform_(tmp, a=math.sqrt(5)) - self.weight[i*self.out_channels_per_joint: (i + 1)*self.out_channels_per_joint, neighbour] = tmp - - fan_in, _ = nn.init._calculate_fan_in_and_fan_out(self.weight) - bound = 1 / math.sqrt(fan_in) - nn.init.uniform_(self.bias, -bound, bound) - - self.weight = nn.Parameter(self.weight) - self.mask = nn.Parameter(self.mask, requires_grad=False) - - def forward(self, input): - input = input.reshape(input.shape[0], -1) - weight_masked = self.weight * self.mask - res = F.linear(input, weight_masked, self.bias) - if self.extra_dim1: - res = res.reshape(res.shape + (1,)) - return res - - -class SkeletonPool(nn.Module): - def __init__(self, edges, pooling_mode, channels_per_edge, last_pool=False): - super(SkeletonPool, self).__init__() - - if pooling_mode != 'mean': - raise Exception('Unimplemented pooling mode in matrix_implementation') - - self.channels_per_edge = channels_per_edge - self.pooling_mode = pooling_mode - self.edge_num = len(edges) - # self.edge_num = len(edges) + 1 - self.seq_list = [] - self.pooling_list = [] - self.new_edges = [] - degree = [0] * 100 # each element represents the degree of the corresponding joint - - for edge in edges: - degree[edge[0]] += 1 - degree[edge[1]] += 1 - - # seq_list contains multiple sub-lists where each sub-list is an edge chain from the joint whose degree > 2 to the end effectors or joints whose degree > 2. - def find_seq(j, seq): - nonlocal self, degree, edges - - if degree[j] > 2 and j != 0: - self.seq_list.append(seq) - seq = [] - - if degree[j] == 1: - self.seq_list.append(seq) - return - - for idx, edge in enumerate(edges): - if edge[0] == j: - find_seq(edge[1], seq + [idx]) - - find_seq(0, []) - # print(f'self.seq_list: {self.seq_list}') - - for seq in self.seq_list: - if last_pool: - self.pooling_list.append(seq) - continue - if len(seq) % 2 == 1: - self.pooling_list.append([seq[0]]) - self.new_edges.append(edges[seq[0]]) - seq = seq[1:] - for i in range(0, len(seq), 2): - self.pooling_list.append([seq[i], seq[i + 1]]) - self.new_edges.append([edges[seq[i]][0], edges[seq[i + 1]][1]]) - # print(f'self.pooling_list: {self.pooling_list}') - # print(f'self.new_egdes: {self.new_edges}') - - # add global position - # self.pooling_list.append([self.edge_num - 1]) - - self.description = 'SkeletonPool(in_edge_num={}, out_edge_num={})'.format( - len(edges), len(self.pooling_list) - ) - - self.weight = torch.zeros(len(self.pooling_list) * channels_per_edge, self.edge_num * channels_per_edge) - - for i, pair in enumerate(self.pooling_list): - for j in pair: - for c in range(channels_per_edge): - self.weight[i * channels_per_edge + c, j * channels_per_edge + c] = 1.0 / len(pair) - - self.weight = nn.Parameter(self.weight, requires_grad=False) - - def forward(self, input: torch.Tensor): - # print('SkeletonPool') - # print(f'input: {input.size()}') - # print(f'self.weight: {self.weight.size()}') - return torch.matmul(self.weight, input) - - -class SkeletonUnpool(nn.Module): - def __init__(self, pooling_list, channels_per_edge): - super(SkeletonUnpool, self).__init__() - self.pooling_list = pooling_list - self.input_edge_num = len(pooling_list) - self.output_edge_num = 0 - self.channels_per_edge = channels_per_edge - for t in self.pooling_list: - self.output_edge_num += len(t) - - self.description = 'SkeletonUnpool(in_edge_num={}, out_edge_num={})'.format( - self.input_edge_num, self.output_edge_num, - ) - - self.weight = torch.zeros(self.output_edge_num * channels_per_edge, self.input_edge_num * channels_per_edge) - - for i, pair in enumerate(self.pooling_list): - for j in pair: - for c in range(channels_per_edge): - self.weight[j * channels_per_edge + c, i * channels_per_edge + c] = 1 - - self.weight = nn.Parameter(self.weight) - self.weight.requires_grad_(False) - - def forward(self, input: torch.Tensor): - # print('SkeletonUnpool') - # print(f'input: {input.size()}') - # print(f'self.weight: {self.weight.size()}') - return torch.matmul(self.weight, input) - - -""" -Helper functions for skeleton operation -""" - - -def dfs(x, fa, vis, dist): - vis[x] = 1 - for y in range(len(fa)): - if (fa[y] == x or fa[x] == y) and vis[y] == 0: - dist[y] = dist[x] + 1 - dfs(y, fa, vis, dist) - - -""" -def find_neighbor_joint(fa, threshold): - neighbor_list = [[]] - for x in range(1, len(fa)): - vis = [0 for _ in range(len(fa))] - dist = [0 for _ in range(len(fa))] - dist[0] = 10000 - dfs(x, fa, vis, dist) - neighbor = [] - for j in range(1, len(fa)): - if dist[j] <= threshold: - neighbor.append(j) - neighbor_list.append(neighbor) - - neighbor = [0] - for i, x in enumerate(neighbor_list): - if i == 0: continue - if 1 in x: - neighbor.append(i) - neighbor_list[i] = [0] + neighbor_list[i] - neighbor_list[0] = neighbor - return neighbor_list - - -def build_edge_topology(topology, offset): - # get all edges (pa, child, offset) - edges = [] - joint_num = len(topology) - for i in range(1, joint_num): - edges.append((topology[i], i, offset[i])) - return edges -""" - - -def build_edge_topology(topology): - # get all edges (pa, child) - edges = [] - joint_num = len(topology) - edges.append((0, joint_num)) # add an edge between the root joint and a virtual joint - for i in range(1, joint_num): - edges.append((topology[i], i)) - return edges - - -def build_joint_topology(edges, origin_names): - parent = [] - offset = [] - names = [] - edge2joint = [] - joint_from_edge = [] # -1 means virtual joint - joint_cnt = 0 - out_degree = [0] * (len(edges) + 10) - for edge in edges: - out_degree[edge[0]] += 1 - - # add root joint - joint_from_edge.append(-1) - parent.append(0) - offset.append(np.array([0, 0, 0])) - names.append(origin_names[0]) - joint_cnt += 1 - - def make_topology(edge_idx, pa): - nonlocal edges, parent, offset, names, edge2joint, joint_from_edge, joint_cnt - edge = edges[edge_idx] - if out_degree[edge[0]] > 1: - parent.append(pa) - offset.append(np.array([0, 0, 0])) - names.append(origin_names[edge[1]] + '_virtual') - edge2joint.append(-1) - pa = joint_cnt - joint_cnt += 1 - - parent.append(pa) - offset.append(edge[2]) - names.append(origin_names[edge[1]]) - edge2joint.append(edge_idx) - pa = joint_cnt - joint_cnt += 1 - - for idx, e in enumerate(edges): - if e[0] == edge[1]: - make_topology(idx, pa) - - for idx, e in enumerate(edges): - if e[0] == 0: - make_topology(idx, 0) - - return parent, offset, names, edge2joint - - -def calc_edge_mat(edges): - edge_num = len(edges) - # edge_mat[i][j] = distance between edge(i) and edge(j) - edge_mat = [[100000] * edge_num for _ in range(edge_num)] - for i in range(edge_num): - edge_mat[i][i] = 0 - - # initialize edge_mat with direct neighbor - for i, a in enumerate(edges): - for j, b in enumerate(edges): - link = 0 - for x in range(2): - for y in range(2): - if a[x] == b[y]: - link = 1 - if link: - edge_mat[i][j] = 1 - - # calculate all the pairs distance - for k in range(edge_num): - for i in range(edge_num): - for j in range(edge_num): - edge_mat[i][j] = min(edge_mat[i][j], edge_mat[i][k] + edge_mat[k][j]) - return edge_mat - - -def find_neighbor(edges, d): - """ - Args: - edges: The list contains N elements, each element represents (parent, child). - d: Distance between edges (the distance of the same edge is 0 and the distance of adjacent edges is 1). - - Returns: - The list contains N elements, each element is a list of edge indices whose distance <= d. - """ - edge_mat = calc_edge_mat(edges) - neighbor_list = [] - edge_num = len(edge_mat) - for i in range(edge_num): - neighbor = [] - for j in range(edge_num): - if edge_mat[i][j] <= d: - neighbor.append(j) - neighbor_list.append(neighbor) - - # # add neighbor for global part - # global_part_neighbor = neighbor_list[0].copy() - # """ - # Line #373 is buggy. Thanks @crissallan!! - # See issue #30 (https://github.com/DeepMotionEditing/deep-motion-editing/issues/30) - # However, fixing this bug will make it unable to load the pretrained model and - # affect the reproducibility of quantitative error reported in the paper. - # It is not a fatal bug so we didn't touch it and we are looking for possible solutions. - # """ - # for i in global_part_neighbor: - # neighbor_list[i].append(edge_num) - # neighbor_list.append(global_part_neighbor) - - return neighbor_list - - -def calc_node_depth(topology): - def dfs(node, topology): - if topology[node] < 0: - return 0 - return 1 + dfs(topology[node], topology) - depth = [] - for i in range(len(topology)): - depth.append(dfs(i, topology)) - - return depth - - -def residual_ratio(k): - return 1 / (k + 1) - - -class Affine(nn.Module): - def __init__(self, num_parameters, scale=True, bias=True, scale_init=1.0): - super(Affine, self).__init__() - if scale: - self.scale = nn.Parameter(torch.ones(num_parameters) * scale_init) - else: - self.register_parameter('scale', None) - - if bias: - self.bias = nn.Parameter(torch.zeros(num_parameters)) - else: - self.register_parameter('bias', None) - - def forward(self, input): - output = input - if self.scale is not None: - scale = self.scale.unsqueeze(0) - while scale.dim() < input.dim(): - scale = scale.unsqueeze(2) - output = output.mul(scale) - - if self.bias is not None: - bias = self.bias.unsqueeze(0) - while bias.dim() < input.dim(): - bias = bias.unsqueeze(2) - output += bias - - return output - - -class BatchStatistics(nn.Module): - def __init__(self, affine=-1): - super(BatchStatistics, self).__init__() - self.affine = nn.Sequential() if affine == -1 else Affine(affine) - self.loss = 0 - - def clear_loss(self): - self.loss = 0 - - def compute_loss(self, input): - input_flat = input.view(input.size(1), input.numel() // input.size(1)) - mu = input_flat.mean(1) - logvar = (input_flat.pow(2).mean(1) - mu.pow(2)).sqrt().log() - - self.loss = mu.pow(2).mean() + logvar.pow(2).mean() - - def forward(self, input): - self.compute_loss(input) - return self.affine(input) - - -class ResidualBlock(nn.Module): - def __init__(self, in_channels, out_channels, kernel_size, stride, padding, residual_ratio, activation, batch_statistics=False, last_layer=False): - super(ResidualBlock, self).__init__() - - self.residual_ratio = residual_ratio - self.shortcut_ratio = 1 - residual_ratio - - residual = [] - residual.append(nn.Conv1d(in_channels, out_channels, kernel_size, stride, padding)) - if batch_statistics: - residual.append(BatchStatistics(out_channels)) - if not last_layer: - residual.append(nn.PReLU() if activation == 'relu' else nn.Tanh()) - self.residual = nn.Sequential(*residual) - - self.shortcut = nn.Sequential( - nn.AvgPool1d(kernel_size=2) if stride == 2 else nn.Sequential(), - nn.Conv1d(in_channels, out_channels, kernel_size=1, stride=1, padding=0), - BatchStatistics(out_channels) if (in_channels != out_channels and batch_statistics is True) else nn.Sequential() - ) - - def forward(self, input): - return self.residual(input).mul(self.residual_ratio) + self.shortcut(input).mul(self.shortcut_ratio) - - -class ResidualBlockTranspose(nn.Module): - def __init__(self, in_channels, out_channels, kernel_size, stride, padding, residual_ratio, activation): - super(ResidualBlockTranspose, self).__init__() - - self.residual_ratio = residual_ratio - self.shortcut_ratio = 1 - residual_ratio - - self.residual = nn.Sequential( - nn.ConvTranspose1d(in_channels, out_channels, kernel_size, stride, padding), - nn.PReLU() if activation == 'relu' else nn.Tanh() - ) - - self.shortcut = nn.Sequential( - nn.Upsample(scale_factor=2, mode='linear', align_corners=False) if stride == 2 else nn.Sequential(), - nn.Conv1d(in_channels, out_channels, kernel_size=1, stride=1, padding=0) - ) - - def forward(self, input): - return self.residual(input).mul(self.residual_ratio) + self.shortcut(input).mul(self.shortcut_ratio) - - -class SkeletonResidual(nn.Module): - def __init__(self, topology, neighbour_list, joint_num, in_channels, out_channels, kernel_size, stride, padding, padding_mode, bias, extra_conv, pooling_mode, activation, last_pool): - super(SkeletonResidual, self).__init__() - - kernel_even = False if kernel_size % 2 else True - - seq = [] - for _ in range(extra_conv): - # (T, J, D) => (T, J, D) - seq.append(SkeletonConv(neighbour_list, in_channels=in_channels, out_channels=in_channels, - joint_num=joint_num, kernel_size=kernel_size - 1 if kernel_even else kernel_size, - stride=1, - padding=padding, padding_mode=padding_mode, bias=bias)) - seq.append(nn.PReLU() if activation == 'relu' else nn.Tanh()) - # (T, J, D) => (T/2, J, 2D) - seq.append(SkeletonConv(neighbour_list, in_channels=in_channels, out_channels=out_channels, - joint_num=joint_num, kernel_size=kernel_size, stride=stride, - padding=padding, padding_mode=padding_mode, bias=bias, add_offset=False)) - seq.append(nn.GroupNorm(10, out_channels)) # FIXME: REMEMBER TO CHANGE BACK !!! - self.residual = nn.Sequential(*seq) - - # (T, J, D) => (T/2, J, 2D) - self.shortcut = SkeletonConv(neighbour_list, in_channels=in_channels, out_channels=out_channels, - joint_num=joint_num, kernel_size=1, stride=stride, padding=0, - bias=True, add_offset=False) - - seq = [] - # (T/2, J, 2D) => (T/2, J', 2D) - pool = SkeletonPool(edges=topology, pooling_mode=pooling_mode, - channels_per_edge=out_channels // len(neighbour_list), last_pool=last_pool) - if len(pool.pooling_list) != pool.edge_num: - seq.append(pool) - seq.append(nn.PReLU() if activation == 'relu' else nn.Tanh()) - self.common = nn.Sequential(*seq) - - def forward(self, input): - output = self.residual(input) + self.shortcut(input) - - return self.common(output) - - -class SkeletonResidualTranspose(nn.Module): - def __init__(self, neighbour_list, joint_num, in_channels, out_channels, kernel_size, padding, padding_mode, bias, extra_conv, pooling_list, upsampling, activation, last_layer): - super(SkeletonResidualTranspose, self).__init__() - - kernel_even = False if kernel_size % 2 else True - - seq = [] - # (T, J, D) => (2T, J, D) - if upsampling is not None: - seq.append(nn.Upsample(scale_factor=2, mode=upsampling, align_corners=False)) - # (2T, J, D) => (2T, J', D) - unpool = SkeletonUnpool(pooling_list, in_channels // len(neighbour_list)) - if unpool.input_edge_num != unpool.output_edge_num: - seq.append(unpool) - self.common = nn.Sequential(*seq) - - seq = [] - for _ in range(extra_conv): - # (2T, J', D) => (2T, J', D) - seq.append(SkeletonConv(neighbour_list, in_channels=in_channels, out_channels=in_channels, - joint_num=joint_num, kernel_size=kernel_size - 1 if kernel_even else kernel_size, - stride=1, - padding=padding, padding_mode=padding_mode, bias=bias)) - seq.append(nn.PReLU() if activation == 'relu' else nn.Tanh()) - # (2T, J', D) => (2T, J', D/2) - seq.append(SkeletonConv(neighbour_list, in_channels=in_channels, out_channels=out_channels, - joint_num=joint_num, kernel_size=kernel_size - 1 if kernel_even else kernel_size, - stride=1, - padding=padding, padding_mode=padding_mode, bias=bias, add_offset=False)) - self.residual = nn.Sequential(*seq) - - # (2T, J', D) => (2T, J', D/2) - self.shortcut = SkeletonConv(neighbour_list, in_channels=in_channels, out_channels=out_channels, - joint_num=joint_num, kernel_size=1, stride=1, padding=0, - bias=True, add_offset=False) - - if activation == 'relu': - self.activation = nn.PReLU() if not last_layer else None - else: - self.activation = nn.Tanh() if not last_layer else None - - def forward(self, input): - output = self.common(input) - output = self.residual(output) + self.shortcut(output) - - if self.activation is not None: - return self.activation(output) - else: - return output \ No newline at end of file diff --git a/models/utils/wav2vec.py b/models/utils/wav2vec.py deleted file mode 100644 index ca23fe1d5a03834986885ed776cbf83c29e391ea..0000000000000000000000000000000000000000 --- a/models/utils/wav2vec.py +++ /dev/null @@ -1,150 +0,0 @@ -import torch -import torch.nn as nn -import torch.nn.functional as F -import numpy as np -import copy -import math -from transformers import Wav2Vec2Model,Wav2Vec2Config -from transformers.modeling_outputs import BaseModelOutput -from typing import Optional, Tuple -_CONFIG_FOR_DOC = "Wav2Vec2Config" - -# the implementation of Wav2Vec2Model is borrowed from https://huggingface.co/transformers/_modules/transformers/models/wav2vec2/modeling_wav2vec2.html#Wav2Vec2Model -# initialize our encoder with the pre-trained wav2vec 2.0 weights. -def _compute_mask_indices( - shape: Tuple[int, int], - mask_prob: float, - mask_length: int, - attention_mask: Optional[torch.Tensor] = None, - min_masks: int = 0, -) -> np.ndarray: - bsz, all_sz = shape - mask = np.full((bsz, all_sz), False) - - all_num_mask = int( - mask_prob * all_sz / float(mask_length) - + np.random.rand() - ) - all_num_mask = max(min_masks, all_num_mask) - mask_idcs = [] - padding_mask = attention_mask.ne(1) if attention_mask is not None else None - for i in range(bsz): - if padding_mask is not None: - sz = all_sz - padding_mask[i].long().sum().item() - num_mask = int( - mask_prob * sz / float(mask_length) - + np.random.rand() - ) - num_mask = max(min_masks, num_mask) - else: - sz = all_sz - num_mask = all_num_mask - - lengths = np.full(num_mask, mask_length) - - if sum(lengths) == 0: - lengths[0] = min(mask_length, sz - 1) - - min_len = min(lengths) - if sz - min_len <= num_mask: - min_len = sz - num_mask - 1 - - mask_idc = np.random.choice(sz - min_len, num_mask, replace=False) - mask_idc = np.asarray([mask_idc[j] + offset for j in range(len(mask_idc)) for offset in range(lengths[j])]) - mask_idcs.append(np.unique(mask_idc[mask_idc < sz])) - - min_len = min([len(m) for m in mask_idcs]) - for i, mask_idc in enumerate(mask_idcs): - if len(mask_idc) > min_len: - mask_idc = np.random.choice(mask_idc, min_len, replace=False) - mask[i, mask_idc] = True - return mask - -# linear interpolation layer -def linear_interpolation(features, input_fps, output_fps, output_len=None): - features = features.transpose(1, 2) - seq_len = features.shape[2] / float(input_fps) - if output_len is None: - output_len = int(seq_len * output_fps) - output_features = F.interpolate(features,size=output_len,align_corners=True,mode='linear') - return output_features.transpose(1, 2) - -class Wav2Vec2Model(Wav2Vec2Model): - def __init__(self, config): - super().__init__(config) - self.args = config - self.args.audio_fps = 15 #args.audio_fps - #input_values 16K hz, 49fps, 20ms overlap, 25ms recepion field - def forward( - self, - input_values, - dataset="beat", - attention_mask=None, - output_attentions=None, - output_hidden_states=None, - return_dict=None, - frame_num=None - ): - #print(input_values.shape) - self.config.output_attentions = True - output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions - output_hidden_states = ( - output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states - ) - return_dict = return_dict if return_dict is not None else self.config.use_return_dict - - hidden_states = self.feature_extractor(input_values) - hidden_states = hidden_states.transpose(1, 2) - #print(hidden_states.shape) - if dataset == "beat": - hidden_states = linear_interpolation(hidden_states, 49, self.args.audio_fps, output_len=frame_num) - #print(hidden_states.shape) - if attention_mask is not None: - output_lengths = self._get_feat_extract_output_lengths(attention_mask.sum(-1)) - attention_mask = torch.zeros( - hidden_states.shape[:2], dtype=hidden_states.dtype, device=hidden_states.device - ) - attention_mask[ - (torch.arange(attention_mask.shape[0], device=hidden_states.device), output_lengths - 1) - ] = 1 - attention_mask = attention_mask.flip([-1]).cumsum(-1).flip([-1]).bool() - - hidden_states = self.feature_projection(hidden_states)[0] - #print(hidden_states.shape) - if self.config.apply_spec_augment and self.training: - batch_size, sequence_length, hidden_size = hidden_states.size() - if self.config.mask_time_prob > 0: - mask_time_indices = _compute_mask_indices( - (batch_size, sequence_length), - self.config.mask_time_prob, - self.config.mask_time_length, - attention_mask=attention_mask, - min_masks=2, - ) - hidden_states[torch.from_numpy(mask_time_indices)] = self.masked_spec_embed.to(hidden_states.dtype) - if self.config.mask_feature_prob > 0: - mask_feature_indices = _compute_mask_indices( - (batch_size, hidden_size), - self.config.mask_feature_prob, - self.config.mask_feature_length, - ) - mask_feature_indices = torch.from_numpy(mask_feature_indices).to(hidden_states.device) - hidden_states[mask_feature_indices[:, None].expand(-1, sequence_length, -1)] = 0 - encoder_outputs = self.encoder( - hidden_states, - attention_mask=attention_mask, - output_attentions=output_attentions, - output_hidden_states=output_hidden_states, - return_dict=return_dict, - ) - hidden_states = encoder_outputs[0] - #print(encoder_outputs.shape) - if not return_dict: - return (hidden_states,) + encoder_outputs[1:] - - return hidden_states -# BaseModelOutput( -# last_hidden_state=hidden_states, -# hidden_states=encoder_outputs.hidden_states, -# attentions=encoder_outputs.attentions, -# ) \ No newline at end of file diff --git a/optimizers/__init__.py b/optimizers/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/optimizers/__pycache__/__init__.cpython-310.pyc b/optimizers/__pycache__/__init__.cpython-310.pyc deleted file mode 100644 index 257820bcf39400dcaebd8e1ad434eebafd3fbfeb..0000000000000000000000000000000000000000 Binary files a/optimizers/__pycache__/__init__.cpython-310.pyc and /dev/null differ diff --git a/optimizers/__pycache__/__init__.cpython-38.pyc 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angular difference between two rotation matrices in radian form [0, pi]. - """ - m1 = m1.reshape(-1, 3, 3) - m2 = m2.reshape(-1, 3, 3) - batch = m1.shape[0] - m = torch.bmm(m1, m2.transpose(1, 2)) # batch*3*3 - - cos = (m[:, 0, 0] + m[:, 1, 1] + m[:, 2, 2] - 1) / 2 - cos = torch.clamp(cos, min=-1 + 1E-6, max=1-1E-6) - - theta = torch.acos(cos) - - return theta - - def __call__(self, m1, m2, reduction='mean'): - loss = self.compute_geodesic_distance(m1, m2) - - if reduction == 'mean': - return loss.mean() - elif reduction == 'none': - return loss - else: - raise RuntimeError(f'unsupported reduction: {reduction}') - - -class BCE_Loss(nn.Module): - def __init__(self, args=None): - super(BCE_Loss, self).__init__() - - def forward(self, fake_outputs, real_target): - final_loss = F.cross_entropy(fake_outputs, real_target, reduce="mean") - return final_loss - -class weight_Loss(nn.Module): - def __init__(self, args=None): - super(weight_Loss, self).__init__() - def forward(self, weight_f): - weight_loss_div = torch.mean(weight_f[:, :, 0]*weight_f[:, :, 1]) - weight_loss_gap = torch.mean(-torch.log(torch.max(weight_f[:, :, 0], dim=1)[0] - torch.min(weight_f[:, :, 0], dim=1)[0])) - return weight_loss_div, weight_loss_gap - - -class HuberLoss(nn.Module): - def __init__(self, beta=0.1, reduction="mean"): - super(HuberLoss, self).__init__() - self.beta = beta - self.reduction = reduction - - def forward(self, outputs, targets): - final_loss = F.smooth_l1_loss(outputs / self.beta, targets / self.beta, reduction=self.reduction) * self.beta - return final_loss - - -class KLDLoss(nn.Module): - def __init__(self, beta=0.1): - super(KLDLoss, self).__init__() - self.beta = beta - - def forward(self, outputs, targets): - final_loss = F.smooth_l1_loss((outputs / self.beta, targets / self.beta) * self.beta) - return final_loss - - -class REGLoss(nn.Module): - def __init__(self, beta=0.1): - super(REGLoss, self).__init__() - self.beta = beta - - def forward(self, outputs, targets): - final_loss = F.smooth_l1_loss((outputs / self.beta, targets / self.beta) * self.beta) - return final_loss - - -class L2Loss(nn.Module): - def __init__(self): - super(L2Loss, self).__init__() - - def forward(self, outputs, targets): - final_loss = F.l2_loss(outputs, targets) - return final_loss - -LOSS_FUNC_LUT = { - "bce_loss": BCE_Loss, - "l2_loss": L2Loss, - "huber_loss": HuberLoss, - "kl_loss": KLDLoss, - "id_loss": REGLoss, - "GeodesicLoss": GeodesicLoss, - "weight_Loss": weight_Loss, - } - - -def get_loss_func(loss_name, **kwargs): - loss_func_class = LOSS_FUNC_LUT.get(loss_name) - loss_func = loss_func_class(**kwargs) - return loss_func - - diff --git a/optimizers/optim_factory.py b/optimizers/optim_factory.py deleted file mode 100644 index 3016448c5dcfe0dea27f6fc305ab7a5b83063f7f..0000000000000000000000000000000000000000 --- a/optimizers/optim_factory.py +++ /dev/null @@ -1,176 +0,0 @@ -""" Optimizer Factory w/ Custom Weight Decay -Hacked together by / Copyright 2020 Ross Wightman -""" -from typing import Optional - -import torch -import torch.nn as nn -import torch.optim as optim - -from .timm.adafactor import Adafactor -from .timm.adahessian import Adahessian -from .timm.adamp import AdamP -from .timm.lookahead import Lookahead -from .timm.nadam import Nadam -from .timm.novograd import NovoGrad -from .timm.nvnovograd import NvNovoGrad -from .timm.radam import RAdam -from .timm.rmsprop_tf import RMSpropTF -from .timm.sgdp import SGDP -from .timm.adabelief import AdaBelief - -try: - from apex.optimizers import FusedNovoGrad, FusedAdam, FusedLAMB, FusedSGD - has_apex = True -except ImportError: - has_apex = False - - -def add_weight_decay(model, weight_decay=1e-5, skip_list=()): - decay = [] - no_decay = [] - for name, param in model.named_parameters(): - if not param.requires_grad: - continue # frozen weights - if len(param.shape) == 1 or name.endswith(".bias") or name in skip_list: - no_decay.append(param) - else: - decay.append(param) - return [ - {"params": no_decay, "weight_decay": 0.}, - {"params": decay, "weight_decay": weight_decay}] - - -def optimizer_kwargs(args, lr_weight): - """ args/argparse to kwargs helper - Convert optimizer args in argparse args or args like object to keyword args for updated create fn. - """ - kwargs = dict( - optimizer_name=args.opt, - learning_rate=args.lr_base*args.batch_size/128*lr_weight, - weight_decay=args.weight_decay, - momentum=args.momentum) - if getattr(args, "opt_eps", None) is not None: - kwargs["eps"] = args.opt_eps - if getattr(args, "opt_betas", None) is not None: - kwargs["betas"] = args.opt_betas - if getattr(args, "opt_args", None) is not None: - kwargs.update(args.opt_args) - return kwargs - - -def create_optimizer(args, model, filter_bias_and_bn=True, lr_weight=1): - """ Legacy optimizer factory for backwards compatibility. - NOTE: Use create_optimizer_v2 for new code. - """ - return create_optimizer_v2( - model, - **optimizer_kwargs(args, lr_weight), - filter_bias_and_bn=filter_bias_and_bn, - ) - - -def create_optimizer_v2( - model: nn.Module, - optimizer_name: str = "sgd", - learning_rate: Optional[float] = None, - weight_decay: float = 0., - momentum: float = 0.9, - filter_bias_and_bn: bool = True, - **kwargs): - """ Create an optimizer. - - TODO currently the model is passed in and all parameters are selected for optimization. - For more general use an interface that allows selection of parameters to optimize and lr groups, one of: - * a filter fn interface that further breaks params into groups in a weight_decay compatible fashion - * expose the parameters interface and leave it up to caller - - Args: - model (nn.Module): model containing parameters to optimize - optimizer_name: name of optimizer to create - learning_rate: initial learning rate - weight_decay: weight decay to apply in optimizer - momentum: momentum for momentum based optimizers (others may use betas via kwargs) - filter_bias_and_bn: filter out bias, bn and other 1d params from weight decay - **kwargs: extra optimizer specific kwargs to pass through - - Returns: - Optimizer - """ - opt_lower = optimizer_name.lower() - if weight_decay and filter_bias_and_bn: - skip = {} - if hasattr(model, "no_weight_decay"): - skip = model.no_weight_decay() - parameters = add_weight_decay(model, weight_decay, skip) - weight_decay = 0. - else: - parameters = model.parameters() - if "fused" in opt_lower: - assert has_apex and torch.cuda.is_available(), "APEX and CUDA required for fused optimizers" - - opt_args = dict(lr=learning_rate, weight_decay=weight_decay, **kwargs) - opt_split = opt_lower.split("_") - opt_lower = opt_split[-1] - if opt_lower == "sgd" or opt_lower == "nesterov": - opt_args.pop("eps", None) - optimizer = optim.SGD(parameters, momentum=momentum, nesterov=True, **opt_args) - elif opt_lower == "momentum": - opt_args.pop("eps", None) - optimizer = optim.SGD(parameters, momentum=momentum, nesterov=False, **opt_args) - - elif opt_lower == "adam": - optimizer = optim.Adam(parameters, **opt_args) - elif opt_lower == "adabelief": - optimizer = AdaBelief(parameters, rectify=False, **opt_args) - elif opt_lower == "adamw": - optimizer = optim.AdamW(parameters, lr=learning_rate, weight_decay=weight_decay) - - elif opt_lower == "nadam": - optimizer = Nadam(parameters, **opt_args) - elif opt_lower == "radam": - optimizer = RAdam(parameters, **opt_args) - elif opt_lower == "adamp": - optimizer = AdamP(parameters, wd_ratio=0.01, nesterov=True, **opt_args) - elif opt_lower == "sgdp": - optimizer = SGDP(parameters, momentum=momentum, nesterov=True, **opt_args) - elif opt_lower == "adadelta": - optimizer = optim.Adadelta(parameters, **opt_args) - elif opt_lower == "adafactor": - if not learning_rate: - opt_args["lr"] = None - optimizer = Adafactor(parameters, **opt_args) - elif opt_lower == "adahessian": - optimizer = Adahessian(parameters, **opt_args) - elif opt_lower == "rmsprop": - optimizer = optim.RMSprop(parameters, alpha=0.9, momentum=momentum, **opt_args) - elif opt_lower == "rmsproptf": - optimizer = RMSpropTF(parameters, alpha=0.9, momentum=momentum, **opt_args) - elif opt_lower == "novograd": - optimizer = NovoGrad(parameters, **opt_args) - elif opt_lower == "nvnovograd": - optimizer = NvNovoGrad(parameters, **opt_args) - elif opt_lower == "fusedsgd": - opt_args.pop("eps", None) - optimizer = FusedSGD(parameters, momentum=momentum, nesterov=True, **opt_args) - elif opt_lower == "fusedmomentum": - opt_args.pop("eps", None) - optimizer = FusedSGD(parameters, momentum=momentum, nesterov=False, **opt_args) - elif opt_lower == "fusedadam": - optimizer = FusedAdam(parameters, adam_w_mode=False, **opt_args) - elif opt_lower == "fusedadamw": - optimizer = FusedAdam(parameters, adam_w_mode=True, **opt_args) - elif opt_lower == "fusedlamb": - optimizer = FusedLAMB(parameters, **opt_args) - elif opt_lower == "fusednovograd": - opt_args.setdefault("betas", (0.95, 0.98)) - optimizer = FusedNovoGrad(parameters, **opt_args) - else: - assert False and "Invalid optimizer" - raise ValueError - - if len(opt_split) > 1: - if opt_split[0] == "lookahead": - optimizer = Lookahead(optimizer) - - return optimizer diff --git a/optimizers/scheduler_factory.py b/optimizers/scheduler_factory.py deleted file mode 100644 index 8cff09723d2c2128317220bb2d316ddf7b035b76..0000000000000000000000000000000000000000 --- a/optimizers/scheduler_factory.py +++ /dev/null @@ -1,103 +0,0 @@ -""" Scheduler Factory -Hacked together by / Copyright 2020 Ross Wightman -""" -from .timm.cosine_lr import CosineLRScheduler -from .timm.tanh_lr import TanhLRScheduler -from .timm.step_lr import StepLRScheduler -from .timm.plateau_lr import PlateauLRScheduler -import torch - -def create_scheduler(args, optimizer, **kwargs): - num_epochs = args.epochs - - if getattr(args, 'lr_noise', None) is not None: - lr_noise = getattr(args, 'lr_noise') - if isinstance(lr_noise, (list, tuple)): - noise_range = [n * num_epochs for n in lr_noise] - if len(noise_range) == 1: - noise_range = noise_range[0] - else: - noise_range = lr_noise * num_epochs - else: - noise_range = None - - lr_scheduler = None - if args.lr_policy == 'cosine': - lr_scheduler = CosineLRScheduler( - optimizer, - t_initial=num_epochs, - t_mul=getattr(args, 'lr_cycle_mul', 1.), - lr_min=args.lr_min, - decay_rate=args.decay_rate, - warmup_lr_init=args.warmup_lr, - warmup_t=args.warmup_epochs, - cycle_limit=getattr(args, 'lr_cycle_limit', 1), - t_in_epochs=True, - noise_range_t=noise_range, - noise_pct=getattr(args, 'lr_noise_pct', 0.67), - noise_std=getattr(args, 'lr_noise_std', 1.), - noise_seed=getattr(args, 'seed', 42), - ) - num_epochs = lr_scheduler.get_cycle_length() + args.COOLDOWN_EPOCHS - elif args.lr_policy == 'tanh': - lr_scheduler = TanhLRScheduler( - optimizer, - t_initial=num_epochs, - t_mul=getattr(args, 'lr_cycle_mul', 1.), - lr_min=args.min_lr, - warmup_lr_init=args.warmup_lr, - warmup_t=args.warmup_epochs, - cycle_limit=getattr(args, 'lr_cycle_limit', 1), - t_in_epochs=True, - noise_range_t=noise_range, - noise_pct=getattr(args, 'lr_noise_pct', 0.67), - noise_std=getattr(args, 'lr_noise_std', 1.), - noise_seed=getattr(args, 'seed', 42), - ) - num_epochs = lr_scheduler.get_cycle_length() + args.COOLDOWN_EPOCHS - elif args.lr_policy == 'step': - lr_scheduler = StepLRScheduler( - optimizer, - decay_t=args.decay_epochs - getattr(kwargs, 'init_epoch', 0), # for D - decay_rate=args.decay_rate, - warmup_lr_init=args.warmup_lr, - warmup_t=args.warmup_epochs, - noise_range_t=noise_range, - noise_pct=getattr(args, 'lr_noise_pct', 0.67), - noise_std=getattr(args, 'lr_noise_std', 1.), - noise_seed=getattr(args, 'seed', 42), - ) - elif args.lr_policy == 'plateau': - mode = 'min' if 'loss' in getattr(args, 'eval_metric', '') else 'max' - lr_scheduler = PlateauLRScheduler( - optimizer, - decay_rate=args.decay_rate, - patience_t=args.patience_epochs, - lr_min=args.min_lr, - mode=mode, - warmup_lr_init=args.warmup_lr, - warmup_t=args.warmup_epochs, - cooldown_t=0, - noise_range_t=noise_range, - noise_pct=getattr(args, 'lr_noise_pct', 0.67), - noise_std=getattr(args, 'lr_noise_std', 1.), - noise_seed=getattr(args, 'seed', 42), - ) - elif args.lr_policy == "onecyclelr": - lr_scheduler = torch.optim.lr_scheduler.OneCycleLR( - optimizer, - max_lr=args.LR, - total_steps=kwargs["total_steps"], - pct_start=args.PCT_START, - div_factor=args.DIV_FACTOR_ONECOS, - final_div_factor=args.FIN_DACTOR_ONCCOS, - ) - elif args.lr_policy == "cosinerestart": - lr_scheduler = torch.optim.lr_scheduler.CosineAnnealingWarmRestarts( - optimizer, - T_0 = kwargs["total_steps"], - T_mult=2, - eta_min = 1e-6, - last_epoch=-1, - ) - return lr_scheduler \ No newline at end of file diff --git a/optimizers/timm/__init__.py b/optimizers/timm/__init__.py deleted file mode 100644 index 612eaaebb068a160663278c01db7c544a67907f3..0000000000000000000000000000000000000000 --- a/optimizers/timm/__init__.py +++ /dev/null @@ -1,16 +0,0 @@ -from .adamp import AdamP -from .adamw import AdamW -from .adafactor import Adafactor -from .adahessian import Adahessian -from .lookahead import Lookahead -from .nadam import Nadam -from .novograd import NovoGrad -from .nvnovograd import NvNovoGrad -from .radam import RAdam -from .rmsprop_tf import RMSpropTF -from .sgdp import SGDP -from .adabelief import AdaBelief -from .cosine_lr import CosineLRScheduler -from .plateau_lr import PlateauLRScheduler -from .step_lr import StepLRScheduler -from .tanh_lr import TanhLRScheduler \ No newline at end of file diff --git a/optimizers/timm/__pycache__/__init__.cpython-310.pyc b/optimizers/timm/__pycache__/__init__.cpython-310.pyc deleted file mode 100644 index 1f5b186623a6a0f5c7ec4d10dd9651f0ef54a7de..0000000000000000000000000000000000000000 Binary files a/optimizers/timm/__pycache__/__init__.cpython-310.pyc and /dev/null differ diff --git a/optimizers/timm/__pycache__/__init__.cpython-38.pyc b/optimizers/timm/__pycache__/__init__.cpython-38.pyc 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Modified from Adam in PyTorch - - Arguments: - params (iterable): iterable of parameters to optimize or dicts defining - parameter groups - lr (float, optional): learning rate (default: 1e-3) - betas (Tuple[float, float], optional): coefficients used for computing - running averages of gradient and its square (default: (0.9, 0.999)) - eps (float, optional): term added to the denominator to improve - numerical stability (default: 1e-16) - weight_decay (float, optional): weight decay (L2 penalty) (default: 0) - amsgrad (boolean, optional): whether to use the AMSGrad variant of this - algorithm from the paper `On the Convergence of Adam and Beyond`_ - (default: False) - weight_decouple (boolean, optional): ( default: True) If set as True, then - the optimizer uses decoupled weight decay as in AdamW - fixed_decay (boolean, optional): (default: False) This is used when weight_decouple - is set as True. - When fixed_decay == True, the weight decay is performed as - $W_{new} = W_{old} - W_{old} \times decay$. - When fixed_decay == False, the weight decay is performed as - $W_{new} = W_{old} - W_{old} \times decay \times lr$. Note that in this case, the - weight decay ratio decreases with learning rate (lr). - rectify (boolean, optional): (default: True) If set as True, then perform the rectified - update similar to RAdam - degenerated_to_sgd (boolean, optional) (default:True) If set as True, then perform SGD update - when variance of gradient is high - reference: AdaBelief Optimizer, adapting stepsizes by the belief in observed gradients, NeurIPS 2020 - - For a complete table of recommended hyperparameters, see https://github.com/juntang-zhuang/Adabelief-Optimizer' - For example train/args for EfficientNet see these gists - - link to train_scipt: https://gist.github.com/juntang-zhuang/0a501dd51c02278d952cf159bc233037 - - link to args.yaml: https://gist.github.com/juntang-zhuang/517ce3c27022b908bb93f78e4f786dc3 - """ - - def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-16, - weight_decay=0, amsgrad=False, weight_decouple=True, fixed_decay=False, rectify=True, - degenerated_to_sgd=True): - - if not 0.0 <= lr: - raise ValueError("Invalid learning rate: {}".format(lr)) - if not 0.0 <= eps: - raise ValueError("Invalid epsilon value: {}".format(eps)) - if not 0.0 <= betas[0] < 1.0: - raise ValueError("Invalid beta parameter at index 0: {}".format(betas[0])) - if not 0.0 <= betas[1] < 1.0: - raise ValueError("Invalid beta parameter at index 1: {}".format(betas[1])) - - self.degenerated_to_sgd = degenerated_to_sgd - if isinstance(params, (list, tuple)) and len(params) > 0 and isinstance(params[0], dict): - for param in params: - if 'betas' in param and (param['betas'][0] != betas[0] or param['betas'][1] != betas[1]): - param['buffer'] = [[None, None, None] for _ in range(10)] - - defaults = dict(lr=lr, betas=betas, eps=eps, - weight_decay=weight_decay, amsgrad=amsgrad, buffer=[[None, None, None] for _ in range(10)]) - super(AdaBelief, self).__init__(params, defaults) - - self.degenerated_to_sgd = degenerated_to_sgd - self.weight_decouple = weight_decouple - self.rectify = rectify - self.fixed_decay = fixed_decay - - def __setstate__(self, state): - super(AdaBelief, self).__setstate__(state) - for group in self.param_groups: - group.setdefault('amsgrad', False) - - def reset(self): - for group in self.param_groups: - for p in group['params']: - state = self.state[p] - amsgrad = group['amsgrad'] - - # State initialization - state['step'] = 0 - # Exponential moving average of gradient values - state['exp_avg'] = torch.zeros_like(p.data) - - # Exponential moving average of squared gradient values - state['exp_avg_var'] = torch.zeros_like(p.data) - if amsgrad: - # Maintains max of all exp. moving avg. of sq. grad. values - state['max_exp_avg_var'] = torch.zeros_like(p.data) - - def step(self, closure=None): - """Performs a single optimization step. - Arguments: - closure (callable, optional): A closure that reevaluates the model - and returns the loss. - """ - loss = None - if closure is not None: - loss = closure() - - for group in self.param_groups: - for p in group['params']: - if p.grad is None: - continue - - # cast data type - half_precision = False - if p.data.dtype == torch.float16: - half_precision = True - p.data = p.data.float() - p.grad = p.grad.float() - - grad = p.grad.data - if grad.is_sparse: - raise RuntimeError( - 'AdaBelief does not support sparse gradients, please consider SparseAdam instead') - amsgrad = group['amsgrad'] - - state = self.state[p] - - beta1, beta2 = group['betas'] - - # State initialization - if len(state) == 0: - state['step'] = 0 - # Exponential moving average of gradient values - state['exp_avg'] = torch.zeros_like(p.data) - # Exponential moving average of squared gradient values - state['exp_avg_var'] = torch.zeros_like(p.data) - if amsgrad: - # Maintains max of all exp. moving avg. of sq. grad. values - state['max_exp_avg_var'] = torch.zeros_like(p.data) - - # perform weight decay, check if decoupled weight decay - if self.weight_decouple: - if not self.fixed_decay: - p.data.mul_(1.0 - group['lr'] * group['weight_decay']) - else: - p.data.mul_(1.0 - group['weight_decay']) - else: - if group['weight_decay'] != 0: - grad.add_(p.data, alpha=group['weight_decay']) - - # get current state variable - exp_avg, exp_avg_var = state['exp_avg'], state['exp_avg_var'] - - state['step'] += 1 - bias_correction1 = 1 - beta1 ** state['step'] - bias_correction2 = 1 - beta2 ** state['step'] - - # Update first and second moment running average - exp_avg.mul_(beta1).add_(grad, alpha=1 - beta1) - grad_residual = grad - exp_avg - exp_avg_var.mul_(beta2).addcmul_( grad_residual, grad_residual, value=1 - beta2) - - if amsgrad: - max_exp_avg_var = state['max_exp_avg_var'] - # Maintains the maximum of all 2nd moment running avg. till now - torch.max(max_exp_avg_var, exp_avg_var.add_(group['eps']), out=max_exp_avg_var) - - # Use the max. for normalizing running avg. of gradient - denom = (max_exp_avg_var.sqrt() / math.sqrt(bias_correction2)).add_(group['eps']) - else: - denom = (exp_avg_var.add_(group['eps']).sqrt() / math.sqrt(bias_correction2)).add_(group['eps']) - - # update - if not self.rectify: - # Default update - step_size = group['lr'] / bias_correction1 - p.data.addcdiv_( exp_avg, denom, value=-step_size) - - else: # Rectified update, forked from RAdam - buffered = group['buffer'][int(state['step'] % 10)] - if state['step'] == buffered[0]: - N_sma, step_size = buffered[1], buffered[2] - else: - buffered[0] = state['step'] - beta2_t = beta2 ** state['step'] - N_sma_max = 2 / (1 - beta2) - 1 - N_sma = N_sma_max - 2 * state['step'] * beta2_t / (1 - beta2_t) - buffered[1] = N_sma - - # more conservative since it's an approximated value - if N_sma >= 5: - step_size = math.sqrt( - (1 - beta2_t) * (N_sma - 4) / (N_sma_max - 4) * (N_sma - 2) / N_sma * N_sma_max / ( - N_sma_max - 2)) / (1 - beta1 ** state['step']) - elif self.degenerated_to_sgd: - step_size = 1.0 / (1 - beta1 ** state['step']) - else: - step_size = -1 - buffered[2] = step_size - - if N_sma >= 5: - denom = exp_avg_var.sqrt().add_(group['eps']) - p.data.addcdiv_(exp_avg, denom, value=-step_size * group['lr']) - elif step_size > 0: - p.data.add_( exp_avg, alpha=-step_size * group['lr']) - - if half_precision: - p.data = p.data.half() - p.grad = p.grad.half() - - return loss diff --git a/optimizers/timm/adafactor.py b/optimizers/timm/adafactor.py deleted file mode 100644 index 088ce3acd82e2be1b393afafa05f48435e538a1a..0000000000000000000000000000000000000000 --- a/optimizers/timm/adafactor.py +++ /dev/null @@ -1,174 +0,0 @@ -""" Adafactor Optimizer - -Lifted from https://github.com/pytorch/fairseq/blob/master/fairseq/optim/adafactor.py - -Original header/copyright below. - -""" -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. -import torch -import math - - -class Adafactor(torch.optim.Optimizer): - """Implements Adafactor algorithm. - This implementation is based on: `Adafactor: Adaptive Learning Rates with Sublinear Memory Cost` - (see https://arxiv.org/abs/1804.04235) - - Note that this optimizer internally adjusts the learning rate depending on the - *scale_parameter*, *relative_step* and *warmup_init* options. - - To use a manual (external) learning rate schedule you should set `scale_parameter=False` and - `relative_step=False`. - - Arguments: - params (iterable): iterable of parameters to optimize or dicts defining parameter groups - lr (float, optional): external learning rate (default: None) - eps (tuple[float, float]): regularization constants for square gradient - and parameter scale respectively (default: (1e-30, 1e-3)) - clip_threshold (float): threshold of root mean square of final gradient update (default: 1.0) - decay_rate (float): coefficient used to compute running averages of square gradient (default: -0.8) - beta1 (float): coefficient used for computing running averages of gradient (default: None) - weight_decay (float, optional): weight decay (L2 penalty) (default: 0) - scale_parameter (bool): if True, learning rate is scaled by root mean square of parameter (default: True) - relative_step (bool): if True, time-dependent learning rate is computed - instead of external learning rate (default: True) - warmup_init (bool): time-dependent learning rate computation depends on - whether warm-up initialization is being used (default: False) - """ - - def __init__(self, params, lr=None, eps=1e-30, eps_scale=1e-3, clip_threshold=1.0, - decay_rate=-0.8, betas=None, weight_decay=0.0, scale_parameter=True, warmup_init=False): - relative_step = lr is None - if warmup_init and not relative_step: - raise ValueError('warmup_init requires relative_step=True') - - beta1 = None if betas is None else betas[0] # make it compat with standard betas arg - defaults = dict(lr=lr, eps=eps, eps_scale=eps_scale, clip_threshold=clip_threshold, decay_rate=decay_rate, - beta1=beta1, weight_decay=weight_decay, scale_parameter=scale_parameter, - relative_step=relative_step, warmup_init=warmup_init) - super(Adafactor, self).__init__(params, defaults) - - @staticmethod - def _get_lr(param_group, param_state): - if param_group['relative_step']: - min_step = 1e-6 * param_state['step'] if param_group['warmup_init'] else 1e-2 - lr_t = min(min_step, 1.0 / math.sqrt(param_state['step'])) - param_scale = 1.0 - if param_group['scale_parameter']: - param_scale = max(param_group['eps_scale'], param_state['RMS']) - param_group['lr'] = lr_t * param_scale - return param_group['lr'] - - @staticmethod - def _get_options(param_group, param_shape): - factored = len(param_shape) >= 2 - use_first_moment = param_group['beta1'] is not None - return factored, use_first_moment - - @staticmethod - def _rms(tensor): - return tensor.norm(2) / (tensor.numel() ** 0.5) - - def _approx_sq_grad(self, exp_avg_sq_row, exp_avg_sq_col): - r_factor = (exp_avg_sq_row / exp_avg_sq_row.mean(dim=-1, keepdim=True)).rsqrt_().unsqueeze(-1) - c_factor = exp_avg_sq_col.unsqueeze(-2).rsqrt() - return torch.mul(r_factor, c_factor) - - def step(self, closure=None): - """Performs a single optimization step. - Arguments: - closure (callable, optional): A closure that reevaluates the model and returns the loss. - """ - loss = None - if closure is not None: - loss = closure() - - for group in self.param_groups: - for p in group['params']: - if p.grad is None: - continue - grad = p.grad.data - if grad.dtype in {torch.float16, torch.bfloat16}: - grad = grad.float() - if grad.is_sparse: - raise RuntimeError('Adafactor does not support sparse gradients.') - - state = self.state[p] - grad_shape = grad.shape - - factored, use_first_moment = self._get_options(group, grad_shape) - # State Initialization - if len(state) == 0: - state['step'] = 0 - - if use_first_moment: - # Exponential moving average of gradient values - state['exp_avg'] = torch.zeros_like(grad) - if factored: - state['exp_avg_sq_row'] = torch.zeros(grad_shape[:-1]).to(grad) - state['exp_avg_sq_col'] = torch.zeros(grad_shape[:-2] + grad_shape[-1:]).to(grad) - else: - state['exp_avg_sq'] = torch.zeros_like(grad) - - state['RMS'] = 0 - else: - if use_first_moment: - state['exp_avg'] = state['exp_avg'].to(grad) - if factored: - state['exp_avg_sq_row'] = state['exp_avg_sq_row'].to(grad) - state['exp_avg_sq_col'] = state['exp_avg_sq_col'].to(grad) - else: - state['exp_avg_sq'] = state['exp_avg_sq'].to(grad) - - p_data_fp32 = p.data - if p.data.dtype in {torch.float16, torch.bfloat16}: - p_data_fp32 = p_data_fp32.float() - - state['step'] += 1 - state['RMS'] = self._rms(p_data_fp32) - lr_t = self._get_lr(group, state) - - beta2t = 1.0 - math.pow(state['step'], group['decay_rate']) - update = grad ** 2 + group['eps'] - if factored: - exp_avg_sq_row = state['exp_avg_sq_row'] - exp_avg_sq_col = state['exp_avg_sq_col'] - - exp_avg_sq_row.mul_(beta2t).add_(1.0 - beta2t, update.mean(dim=-1)) - exp_avg_sq_col.mul_(beta2t).add_(1.0 - beta2t, update.mean(dim=-2)) - #exp_avg_sq_row.mul_(beta2t).add_(update.mean(dim=-1), alpha=1.0 - beta2t) # pytorch 1.6+ - #exp_avg_sq_col.mul_(beta2t).add_(update.mean(dim=-2), alpha=1.0 - beta2t) - - # Approximation of exponential moving average of square of gradient - update = self._approx_sq_grad(exp_avg_sq_row, exp_avg_sq_col) - update.mul_(grad) - else: - exp_avg_sq = state['exp_avg_sq'] - - exp_avg_sq.mul_(beta2t).add_(1.0 - beta2t, update) - #exp_avg_sq.mul_(beta2t).add_(update, alpha=1.0 - beta2t) # pytorch 1.6+ - update = exp_avg_sq.rsqrt().mul_(grad) - - update.div_((self._rms(update) / group['clip_threshold']).clamp_(min=1.0)) - update.mul_(lr_t) - - if use_first_moment: - exp_avg = state['exp_avg'] - exp_avg.mul_(group["beta1"]).add_(1 - group["beta1"], update) - #exp_avg.mul_(group['beta1']).add_(update, alpha=1 - group['beta1']) # pytorch 1.6+ - update = exp_avg - - if group['weight_decay'] != 0: - p_data_fp32.add_(-group["weight_decay"] * lr_t, p_data_fp32) - #p_data_fp32.add_(p_data_fp32, alpha=-group['weight_decay'] * lr_t) # pytorch 1.6+ - - p_data_fp32.add_(-update) - - if p.data.dtype in {torch.float16, torch.bfloat16}: - p.data.copy_(p_data_fp32) - - return loss \ No newline at end of file diff --git a/optimizers/timm/adahessian.py b/optimizers/timm/adahessian.py deleted file mode 100644 index 985c67ca686a65f61f5c5b1a7db3e5bba815a19b..0000000000000000000000000000000000000000 --- a/optimizers/timm/adahessian.py +++ /dev/null @@ -1,156 +0,0 @@ -""" AdaHessian Optimizer - -Lifted from https://github.com/davda54/ada-hessian/blob/master/ada_hessian.py -Originally licensed MIT, Copyright 2020, David Samuel -""" -import torch - - -class Adahessian(torch.optim.Optimizer): - """ - Implements the AdaHessian algorithm from "ADAHESSIAN: An Adaptive Second OrderOptimizer for Machine Learning" - - Arguments: - params (iterable): iterable of parameters to optimize or dicts defining parameter groups - lr (float, optional): learning rate (default: 0.1) - betas ((float, float), optional): coefficients used for computing running averages of gradient and the - squared hessian trace (default: (0.9, 0.999)) - eps (float, optional): term added to the denominator to improve numerical stability (default: 1e-8) - weight_decay (float, optional): weight decay (L2 penalty) (default: 0.0) - hessian_power (float, optional): exponent of the hessian trace (default: 1.0) - update_each (int, optional): compute the hessian trace approximation only after *this* number of steps - (to save time) (default: 1) - n_samples (int, optional): how many times to sample `z` for the approximation of the hessian trace (default: 1) - """ - - def __init__(self, params, lr=0.1, betas=(0.9, 0.999), eps=1e-8, weight_decay=0.0, - hessian_power=1.0, update_each=1, n_samples=1, avg_conv_kernel=False): - if not 0.0 <= lr: - raise ValueError(f"Invalid learning rate: {lr}") - if not 0.0 <= eps: - raise ValueError(f"Invalid epsilon value: {eps}") - if not 0.0 <= betas[0] < 1.0: - raise ValueError(f"Invalid beta parameter at index 0: {betas[0]}") - if not 0.0 <= betas[1] < 1.0: - raise ValueError(f"Invalid beta parameter at index 1: {betas[1]}") - if not 0.0 <= hessian_power <= 1.0: - raise ValueError(f"Invalid Hessian power value: {hessian_power}") - - self.n_samples = n_samples - self.update_each = update_each - self.avg_conv_kernel = avg_conv_kernel - - # use a separate generator that deterministically generates the same `z`s across all GPUs in case of distributed training - self.seed = 2147483647 - self.generator = torch.Generator().manual_seed(self.seed) - - defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay, hessian_power=hessian_power) - super(Adahessian, self).__init__(params, defaults) - - for p in self.get_params(): - p.hess = 0.0 - self.state[p]["hessian step"] = 0 - - @property - def is_second_order(self): - return True - - def get_params(self): - """ - Gets all parameters in all param_groups with gradients - """ - - return (p for group in self.param_groups for p in group['params'] if p.requires_grad) - - def zero_hessian(self): - """ - Zeros out the accumalated hessian traces. - """ - - for p in self.get_params(): - if not isinstance(p.hess, float) and self.state[p]["hessian step"] % self.update_each == 0: - p.hess.zero_() - - @torch.no_grad() - def set_hessian(self): - """ - Computes the Hutchinson approximation of the hessian trace and accumulates it for each trainable parameter. - """ - - params = [] - for p in filter(lambda p: p.grad is not None, self.get_params()): - if self.state[p]["hessian step"] % self.update_each == 0: # compute the trace only each `update_each` step - params.append(p) - self.state[p]["hessian step"] += 1 - - if len(params) == 0: - return - - if self.generator.device != params[0].device: # hackish way of casting the generator to the right device - self.generator = torch.Generator(params[0].device).manual_seed(self.seed) - - grads = [p.grad for p in params] - - for i in range(self.n_samples): - # Rademacher distribution {-1.0, 1.0} - zs = [torch.randint(0, 2, p.size(), generator=self.generator, device=p.device) * 2.0 - 1.0 for p in params] - h_zs = torch.autograd.grad( - grads, params, grad_outputs=zs, only_inputs=True, retain_graph=i < self.n_samples - 1) - for h_z, z, p in zip(h_zs, zs, params): - p.hess += h_z * z / self.n_samples # approximate the expected values of z*(H@z) - - @torch.no_grad() - def step(self, closure=None): - """ - Performs a single optimization step. - Arguments: - closure (callable, optional) -- a closure that reevaluates the model and returns the loss (default: None) - """ - - loss = None - if closure is not None: - loss = closure() - - self.zero_hessian() - self.set_hessian() - - for group in self.param_groups: - for p in group['params']: - if p.grad is None or p.hess is None: - continue - - if self.avg_conv_kernel and p.dim() == 4: - p.hess = torch.abs(p.hess).mean(dim=[2, 3], keepdim=True).expand_as(p.hess).clone() - - # Perform correct stepweight decay as in AdamW - p.mul_(1 - group['lr'] * group['weight_decay']) - - state = self.state[p] - - # State initialization - if len(state) == 1: - state['step'] = 0 - # Exponential moving average of gradient values - state['exp_avg'] = torch.zeros_like(p) - # Exponential moving average of Hessian diagonal square values - state['exp_hessian_diag_sq'] = torch.zeros_like(p) - - exp_avg, exp_hessian_diag_sq = state['exp_avg'], state['exp_hessian_diag_sq'] - beta1, beta2 = group['betas'] - state['step'] += 1 - - # Decay the first and second moment running average coefficient - exp_avg.mul_(beta1).add_(p.grad, alpha=1 - beta1) - exp_hessian_diag_sq.mul_(beta2).addcmul_(p.hess, p.hess, value=1 - beta2) - - bias_correction1 = 1 - beta1 ** state['step'] - bias_correction2 = 1 - beta2 ** state['step'] - - k = group['hessian_power'] - denom = (exp_hessian_diag_sq / bias_correction2).pow_(k / 2).add_(group['eps']) - - # make update - step_size = group['lr'] / bias_correction1 - p.addcdiv_(exp_avg, denom, value=-step_size) - - return loss diff --git a/optimizers/timm/adamp.py b/optimizers/timm/adamp.py deleted file mode 100644 index 468c3e865e0ceb6fb2bf22f9388237a783314f07..0000000000000000000000000000000000000000 --- a/optimizers/timm/adamp.py +++ /dev/null @@ -1,107 +0,0 @@ -""" -AdamP Optimizer Implementation copied from https://github.com/clovaai/AdamP/blob/master/adamp/adamp.py - -Paper: `Slowing Down the Weight Norm Increase in Momentum-based Optimizers` - https://arxiv.org/abs/2006.08217 -Code: https://github.com/clovaai/AdamP - -Copyright (c) 2020-present NAVER Corp. -MIT license -""" - -import torch -import torch.nn as nn -from torch.optim.optimizer import Optimizer, required -import math - -class AdamP(Optimizer): - def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8, - weight_decay=0, delta=0.1, wd_ratio=0.1, nesterov=False): - defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay, - delta=delta, wd_ratio=wd_ratio, nesterov=nesterov) - super(AdamP, self).__init__(params, defaults) - - def _channel_view(self, x): - return x.view(x.size(0), -1) - - def _layer_view(self, x): - return x.view(1, -1) - - def _cosine_similarity(self, x, y, eps, view_func): - x = view_func(x) - y = view_func(y) - - x_norm = x.norm(dim=1).add_(eps) - y_norm = y.norm(dim=1).add_(eps) - dot = (x * y).sum(dim=1) - - return dot.abs() / x_norm / y_norm - - def _projection(self, p, grad, perturb, delta, wd_ratio, eps): - wd = 1 - expand_size = [-1] + [1] * (len(p.shape) - 1) - for view_func in [self._channel_view, self._layer_view]: - - cosine_sim = self._cosine_similarity(grad, p.data, eps, view_func) - - if cosine_sim.max() < delta / math.sqrt(view_func(p.data).size(1)): - p_n = p.data / view_func(p.data).norm(dim=1).view(expand_size).add_(eps) - perturb -= p_n * view_func(p_n * perturb).sum(dim=1).view(expand_size) - wd = wd_ratio - - return perturb, wd - - return perturb, wd - - def step(self, closure=None): - loss = None - if closure is not None: - loss = closure() - - for group in self.param_groups: - for p in group['params']: - if p.grad is None: - continue - - grad = p.grad.data - beta1, beta2 = group['betas'] - nesterov = group['nesterov'] - - state = self.state[p] - - # State initialization - if len(state) == 0: - state['step'] = 0 - state['exp_avg'] = torch.zeros_like(p.data) - state['exp_avg_sq'] = torch.zeros_like(p.data) - - # Adam - exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq'] - - state['step'] += 1 - bias_correction1 = 1 - beta1 ** state['step'] - bias_correction2 = 1 - beta2 ** state['step'] - - exp_avg.mul_(beta1).add_(1 - beta1, grad) - exp_avg_sq.mul_(beta2).addcmul_(1 - beta2, grad, grad) - - denom = (exp_avg_sq.sqrt() / math.sqrt(bias_correction2)).add_(group['eps']) - step_size = group['lr'] / bias_correction1 - - if nesterov: - perturb = (beta1 * exp_avg + (1 - beta1) * grad) / denom - else: - perturb = exp_avg / denom - - # Projection - wd_ratio = 1 - if len(p.shape) > 1: - perturb, wd_ratio = self._projection(p, grad, perturb, group['delta'], group['wd_ratio'], group['eps']) - - # Weight decay - if group['weight_decay'] > 0: - p.data.mul_(1 - group['lr'] * group['weight_decay'] * wd_ratio) - - # Step - p.data.add_(-step_size, perturb) - - return loss diff --git a/optimizers/timm/adamw.py b/optimizers/timm/adamw.py deleted file mode 100644 index 66f9a959de586356a29ace2f9c57d3fee8d1057a..0000000000000000000000000000000000000000 --- a/optimizers/timm/adamw.py +++ /dev/null @@ -1,117 +0,0 @@ -""" AdamW Optimizer -Impl copied from PyTorch master -""" -import math -import torch -from torch.optim.optimizer import Optimizer - - -class AdamW(Optimizer): - r"""Implements AdamW algorithm. - - The original Adam algorithm was proposed in `Adam: A Method for Stochastic Optimization`_. - The AdamW variant was proposed in `Decoupled Weight Decay Regularization`_. - - Arguments: - params (iterable): iterable of parameters to optimize or dicts defining - parameter groups - lr (float, optional): learning rate (default: 1e-3) - betas (Tuple[float, float], optional): coefficients used for computing - running averages of gradient and its square (default: (0.9, 0.999)) - eps (float, optional): term added to the denominator to improve - numerical stability (default: 1e-8) - weight_decay (float, optional): weight decay coefficient (default: 1e-2) - amsgrad (boolean, optional): whether to use the AMSGrad variant of this - algorithm from the paper `On the Convergence of Adam and Beyond`_ - (default: False) - - .. _Adam\: A Method for Stochastic Optimization: - https://arxiv.org/abs/1412.6980 - .. _Decoupled Weight Decay Regularization: - https://arxiv.org/abs/1711.05101 - .. _On the Convergence of Adam and Beyond: - https://openreview.net/forum?id=ryQu7f-RZ - """ - - def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8, - weight_decay=1e-2, amsgrad=False): - if not 0.0 <= lr: - raise ValueError("Invalid learning rate: {}".format(lr)) - if not 0.0 <= eps: - raise ValueError("Invalid epsilon value: {}".format(eps)) - if not 0.0 <= betas[0] < 1.0: - raise ValueError("Invalid beta parameter at index 0: {}".format(betas[0])) - if not 0.0 <= betas[1] < 1.0: - raise ValueError("Invalid beta parameter at index 1: {}".format(betas[1])) - defaults = dict(lr=lr, betas=betas, eps=eps, - weight_decay=weight_decay, amsgrad=amsgrad) - super(AdamW, self).__init__(params, defaults) - - def __setstate__(self, state): - super(AdamW, self).__setstate__(state) - for group in self.param_groups: - group.setdefault('amsgrad', False) - - def step(self, closure=None): - """Performs a single optimization step. - - Arguments: - closure (callable, optional): A closure that reevaluates the model - and returns the loss. - """ - loss = None - if closure is not None: - loss = closure() - - for group in self.param_groups: - for p in group['params']: - if p.grad is None: - continue - - # Perform stepweight decay - p.data.mul_(1 - group['lr'] * group['weight_decay']) - - # Perform optimization step - grad = p.grad.data - if grad.is_sparse: - raise RuntimeError('Adam does not support sparse gradients, please consider SparseAdam instead') - amsgrad = group['amsgrad'] - - state = self.state[p] - - # State initialization - if len(state) == 0: - state['step'] = 0 - # Exponential moving average of gradient values - state['exp_avg'] = torch.zeros_like(p.data) - # Exponential moving average of squared gradient values - state['exp_avg_sq'] = torch.zeros_like(p.data) - if amsgrad: - # Maintains max of all exp. moving avg. of sq. grad. values - state['max_exp_avg_sq'] = torch.zeros_like(p.data) - - exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq'] - if amsgrad: - max_exp_avg_sq = state['max_exp_avg_sq'] - beta1, beta2 = group['betas'] - - state['step'] += 1 - bias_correction1 = 1 - beta1 ** state['step'] - bias_correction2 = 1 - beta2 ** state['step'] - - # Decay the first and second moment running average coefficient - exp_avg.mul_(beta1).add_(1 - beta1, grad) - exp_avg_sq.mul_(beta2).addcmul_(1 - beta2, grad, grad) - if amsgrad: - # Maintains the maximum of all 2nd moment running avg. till now - torch.max(max_exp_avg_sq, exp_avg_sq, out=max_exp_avg_sq) - # Use the max. for normalizing running avg. of gradient - denom = (max_exp_avg_sq.sqrt() / math.sqrt(bias_correction2)).add_(group['eps']) - else: - denom = (exp_avg_sq.sqrt() / math.sqrt(bias_correction2)).add_(group['eps']) - - step_size = group['lr'] / bias_correction1 - - p.data.addcdiv_(-step_size, exp_avg, denom) - - return loss diff --git a/optimizers/timm/cosine_lr.py b/optimizers/timm/cosine_lr.py deleted file mode 100644 index 1532f092b5cc8c0af5125967cfb84b32ce03ca4a..0000000000000000000000000000000000000000 --- a/optimizers/timm/cosine_lr.py +++ /dev/null @@ -1,116 +0,0 @@ -""" Cosine Scheduler - -Cosine LR schedule with warmup, cycle/restarts, noise. - -Hacked together by / Copyright 2020 Ross Wightman -""" -import logging -import math -import numpy as np -import torch - -from .scheduler import Scheduler - - -_logger = logging.getLogger(__name__) - - -class CosineLRScheduler(Scheduler): - """ - Cosine decay with restarts. - This is described in the paper https://arxiv.org/abs/1608.03983. - - Inspiration from - https://github.com/allenai/allennlp/blob/master/allennlp/training/learning_rate_schedulers/cosine.py - """ - - def __init__(self, - optimizer: torch.optim.Optimizer, - t_initial: int, - t_mul: float = 1., - lr_min: float = 0., - decay_rate: float = 1., - warmup_t=0, - warmup_lr_init=0, - warmup_prefix=False, - cycle_limit=0, - t_in_epochs=True, - noise_range_t=None, - noise_pct=0.67, - noise_std=1.0, - noise_seed=42, - initialize=True) -> None: - super().__init__( - optimizer, param_group_field="lr", - noise_range_t=noise_range_t, noise_pct=noise_pct, noise_std=noise_std, noise_seed=noise_seed, - initialize=initialize) - - assert t_initial > 0 - assert lr_min >= 0 - if t_initial == 1 and t_mul == 1 and decay_rate == 1: - _logger.warning("Cosine annealing scheduler will have no effect on the learning " - "rate since t_initial = t_mul = eta_mul = 1.") - self.t_initial = t_initial - self.t_mul = t_mul - self.lr_min = lr_min - self.decay_rate = decay_rate - self.cycle_limit = cycle_limit - self.warmup_t = warmup_t - self.warmup_lr_init = warmup_lr_init - self.warmup_prefix = warmup_prefix - self.t_in_epochs = t_in_epochs - if self.warmup_t: - self.warmup_steps = [(v - warmup_lr_init) / self.warmup_t for v in self.base_values] - super().update_groups(self.warmup_lr_init) - else: - self.warmup_steps = [1 for _ in self.base_values] - - def _get_lr(self, t): - if t < self.warmup_t: - lrs = [self.warmup_lr_init + t * s for s in self.warmup_steps] - else: - if self.warmup_prefix: - t = t - self.warmup_t - - if self.t_mul != 1: - i = math.floor(math.log(1 - t / self.t_initial * (1 - self.t_mul), self.t_mul)) - t_i = self.t_mul ** i * self.t_initial - t_curr = t - (1 - self.t_mul ** i) / (1 - self.t_mul) * self.t_initial - else: - i = t // self.t_initial - t_i = self.t_initial - t_curr = t - (self.t_initial * i) - - gamma = self.decay_rate ** i - lr_min = self.lr_min * gamma - lr_max_values = [v * gamma for v in self.base_values] - - if self.cycle_limit == 0 or (self.cycle_limit > 0 and i < self.cycle_limit): - lrs = [ - lr_min + 0.5 * (lr_max - lr_min) * (1 + math.cos(math.pi * t_curr / t_i)) for lr_max in lr_max_values - ] - else: - lrs = [self.lr_min for _ in self.base_values] - - return lrs - - def get_epoch_values(self, epoch: int): - if self.t_in_epochs: - return self._get_lr(epoch) - else: - return None - - def get_update_values(self, num_updates: int): - if not self.t_in_epochs: - return self._get_lr(num_updates) - else: - return None - - def get_cycle_length(self, cycles=0): - if not cycles: - cycles = self.cycle_limit - cycles = max(1, cycles) - if self.t_mul == 1.0: - return self.t_initial * cycles - else: - return int(math.floor(-self.t_initial * (self.t_mul ** cycles - 1) / (1 - self.t_mul))) diff --git a/optimizers/timm/lookahead.py b/optimizers/timm/lookahead.py deleted file mode 100644 index 6b5b7f38ec8cb6594e3986b66223fa2881daeca3..0000000000000000000000000000000000000000 --- a/optimizers/timm/lookahead.py +++ /dev/null @@ -1,92 +0,0 @@ -""" Lookahead Optimizer Wrapper. -Implementation modified from: https://github.com/alphadl/lookahead.pytorch -Paper: `Lookahead Optimizer: k steps forward, 1 step back` - https://arxiv.org/abs/1907.08610 - -Hacked together by / Copyright 2020 Ross Wightman -""" -import torch -from torch.optim.optimizer import Optimizer -from collections import defaultdict - - -class Lookahead(Optimizer): - def __init__(self, base_optimizer, alpha=0.5, k=6): - if not 0.0 <= alpha <= 1.0: - raise ValueError(f'Invalid slow update rate: {alpha}') - if not 1 <= k: - raise ValueError(f'Invalid lookahead steps: {k}') - defaults = dict(lookahead_alpha=alpha, lookahead_k=k, lookahead_step=0) - self.base_optimizer = base_optimizer - self.param_groups = self.base_optimizer.param_groups - self.defaults = base_optimizer.defaults - self.defaults.update(defaults) - self.state = defaultdict(dict) - # manually add our defaults to the param groups - for name, default in defaults.items(): - for group in self.param_groups: - group.setdefault(name, default) - - def update_slow(self, group): - for fast_p in group["params"]: - if fast_p.grad is None: - continue - param_state = self.state[fast_p] - if 'slow_buffer' not in param_state: - param_state['slow_buffer'] = torch.empty_like(fast_p.data) - param_state['slow_buffer'].copy_(fast_p.data) - slow = param_state['slow_buffer'] - slow.add_(group['lookahead_alpha'], fast_p.data - slow) - fast_p.data.copy_(slow) - - def sync_lookahead(self): - for group in self.param_groups: - self.update_slow(group) - - def step(self, closure=None): - #assert id(self.param_groups) == id(self.base_optimizer.param_groups) - loss = self.base_optimizer.step(closure) - for group in self.param_groups: - group['lookahead_step'] += 1 - if group['lookahead_step'] % group['lookahead_k'] == 0: - self.update_slow(group) - return loss - - def state_dict(self): - fast_state_dict = self.base_optimizer.state_dict() - slow_state = { - (id(k) if isinstance(k, torch.Tensor) else k): v - for k, v in self.state.items() - } - fast_state = fast_state_dict['state'] - param_groups = fast_state_dict['param_groups'] - return { - 'state': fast_state, - 'slow_state': slow_state, - 'param_groups': param_groups, - } - - def load_state_dict(self, state_dict): - fast_state_dict = { - 'state': state_dict['state'], - 'param_groups': state_dict['param_groups'], - } - self.base_optimizer.load_state_dict(fast_state_dict) - - # We want to restore the slow state, but share param_groups reference - # with base_optimizer. This is a bit redundant but least code - slow_state_new = False - if 'slow_state' not in state_dict: - print('Loading state_dict from optimizer without Lookahead applied.') - state_dict['slow_state'] = defaultdict(dict) - slow_state_new = True - slow_state_dict = { - 'state': state_dict['slow_state'], - 'param_groups': state_dict['param_groups'], # this is pointless but saves code - } - super(Lookahead, self).load_state_dict(slow_state_dict) - self.param_groups = self.base_optimizer.param_groups # make both ref same container - if slow_state_new: - # reapply defaults to catch missing lookahead specific ones - for name, default in self.defaults.items(): - for group in self.param_groups: - group.setdefault(name, default) diff --git a/optimizers/timm/nadam.py b/optimizers/timm/nadam.py deleted file mode 100644 index d994d1b83485c9b068de73f5f3cf2efb1e5bec39..0000000000000000000000000000000000000000 --- a/optimizers/timm/nadam.py +++ /dev/null @@ -1,88 +0,0 @@ -import torch -from torch.optim import Optimizer - - -class Nadam(Optimizer): - """Implements Nadam algorithm (a variant of Adam based on Nesterov momentum). - - It has been proposed in `Incorporating Nesterov Momentum into Adam`__. - - Arguments: - params (iterable): iterable of parameters to optimize or dicts defining - parameter groups - lr (float, optional): learning rate (default: 2e-3) - betas (Tuple[float, float], optional): coefficients used for computing - running averages of gradient and its square - eps (float, optional): term added to the denominator to improve - numerical stability (default: 1e-8) - weight_decay (float, optional): weight decay (L2 penalty) (default: 0) - schedule_decay (float, optional): momentum schedule decay (default: 4e-3) - - __ http://cs229.stanford.edu/proj2015/054_report.pdf - __ http://www.cs.toronto.edu/~fritz/absps/momentum.pdf - - Originally taken from: https://github.com/pytorch/pytorch/pull/1408 - NOTE: Has potential issues but does work well on some problems. - """ - - def __init__(self, params, lr=2e-3, betas=(0.9, 0.999), eps=1e-8, - weight_decay=0, schedule_decay=4e-3): - defaults = dict(lr=lr, betas=betas, eps=eps, - weight_decay=weight_decay, schedule_decay=schedule_decay) - super(Nadam, self).__init__(params, defaults) - - def step(self, closure=None): - """Performs a single optimization step. - - Arguments: - closure (callable, optional): A closure that reevaluates the model - and returns the loss. - """ - loss = None - if closure is not None: - loss = closure() - - for group in self.param_groups: - for p in group['params']: - if p.grad is None: - continue - grad = p.grad.data - state = self.state[p] - - # State initialization - if len(state) == 0: - state['step'] = 0 - state['m_schedule'] = 1. - state['exp_avg'] = grad.new().resize_as_(grad).zero_() - state['exp_avg_sq'] = grad.new().resize_as_(grad).zero_() - - # Warming momentum schedule - m_schedule = state['m_schedule'] - schedule_decay = group['schedule_decay'] - exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq'] - beta1, beta2 = group['betas'] - eps = group['eps'] - state['step'] += 1 - t = state['step'] - - if group['weight_decay'] != 0: - grad = grad.add(group['weight_decay'], p.data) - - momentum_cache_t = beta1 * \ - (1. - 0.5 * (0.96 ** (t * schedule_decay))) - momentum_cache_t_1 = beta1 * \ - (1. - 0.5 * (0.96 ** ((t + 1) * schedule_decay))) - m_schedule_new = m_schedule * momentum_cache_t - m_schedule_next = m_schedule * momentum_cache_t * momentum_cache_t_1 - state['m_schedule'] = m_schedule_new - - # Decay the first and second moment running average coefficient - exp_avg.mul_(beta1).add_(1. - beta1, grad) - exp_avg_sq.mul_(beta2).addcmul_(1. - beta2, grad, grad) - exp_avg_sq_prime = exp_avg_sq / (1. - beta2 ** t) - denom = exp_avg_sq_prime.sqrt_().add_(eps) - - p.data.addcdiv_(-group['lr'] * (1. - momentum_cache_t) / (1. - m_schedule_new), grad, denom) - p.data.addcdiv_(-group['lr'] * momentum_cache_t_1 / (1. - m_schedule_next), exp_avg, denom) - - return loss diff --git a/optimizers/timm/novograd.py b/optimizers/timm/novograd.py deleted file mode 100644 index 4137c6aa9406360d29f5f7234ebbdef294404d0e..0000000000000000000000000000000000000000 --- a/optimizers/timm/novograd.py +++ /dev/null @@ -1,77 +0,0 @@ -"""NovoGrad Optimizer. -Original impl by Masashi Kimura (Convergence Lab): https://github.com/convergence-lab/novograd -Paper: `Stochastic Gradient Methods with Layer-wise Adaptive Moments for Training of Deep Networks` - - https://arxiv.org/abs/1905.11286 -""" - -import torch -from torch.optim.optimizer import Optimizer -import math - - -class NovoGrad(Optimizer): - def __init__(self, params, grad_averaging=False, lr=0.1, betas=(0.95, 0.98), eps=1e-8, weight_decay=0): - defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay) - super(NovoGrad, self).__init__(params, defaults) - self._lr = lr - self._beta1 = betas[0] - self._beta2 = betas[1] - self._eps = eps - self._wd = weight_decay - self._grad_averaging = grad_averaging - - self._momentum_initialized = False - - def step(self, closure=None): - loss = None - if closure is not None: - loss = closure() - - if not self._momentum_initialized: - for group in self.param_groups: - for p in group['params']: - if p.grad is None: - continue - state = self.state[p] - grad = p.grad.data - if grad.is_sparse: - raise RuntimeError('NovoGrad does not support sparse gradients') - - v = torch.norm(grad)**2 - m = grad/(torch.sqrt(v) + self._eps) + self._wd * p.data - state['step'] = 0 - state['v'] = v - state['m'] = m - state['grad_ema'] = None - self._momentum_initialized = True - - for group in self.param_groups: - for p in group['params']: - if p.grad is None: - continue - state = self.state[p] - state['step'] += 1 - - step, v, m = state['step'], state['v'], state['m'] - grad_ema = state['grad_ema'] - - grad = p.grad.data - g2 = torch.norm(grad)**2 - grad_ema = g2 if grad_ema is None else grad_ema * \ - self._beta2 + g2 * (1. - self._beta2) - grad *= 1.0 / (torch.sqrt(grad_ema) + self._eps) - - if self._grad_averaging: - grad *= (1. - self._beta1) - - g2 = torch.norm(grad)**2 - v = self._beta2*v + (1. - self._beta2)*g2 - m = self._beta1*m + (grad / (torch.sqrt(v) + self._eps) + self._wd * p.data) - bias_correction1 = 1 - self._beta1 ** step - bias_correction2 = 1 - self._beta2 ** step - step_size = group['lr'] * math.sqrt(bias_correction2) / bias_correction1 - - state['v'], state['m'] = v, m - state['grad_ema'] = grad_ema - p.data.add_(-step_size, m) - return loss diff --git a/optimizers/timm/nvnovograd.py b/optimizers/timm/nvnovograd.py deleted file mode 100644 index 323312d2fc36d028124f7a7ec604d248e71503cd..0000000000000000000000000000000000000000 --- a/optimizers/timm/nvnovograd.py +++ /dev/null @@ -1,118 +0,0 @@ -""" Nvidia NovoGrad Optimizer. -Original impl by Nvidia from Jasper example: - - https://github.com/NVIDIA/DeepLearningExamples/blob/master/PyTorch/SpeechRecognition/Jasper -Paper: `Stochastic Gradient Methods with Layer-wise Adaptive Moments for Training of Deep Networks` - - https://arxiv.org/abs/1905.11286 -""" - -import torch -from torch.optim.optimizer import Optimizer -import math - - -class NvNovoGrad(Optimizer): - """ - Implements Novograd algorithm. - - Args: - params (iterable): iterable of parameters to optimize or dicts defining - parameter groups - lr (float, optional): learning rate (default: 1e-3) - betas (Tuple[float, float], optional): coefficients used for computing - running averages of gradient and its square (default: (0.95, 0.98)) - eps (float, optional): term added to the denominator to improve - numerical stability (default: 1e-8) - weight_decay (float, optional): weight decay (L2 penalty) (default: 0) - grad_averaging: gradient averaging - amsgrad (boolean, optional): whether to use the AMSGrad variant of this - algorithm from the paper `On the Convergence of Adam and Beyond`_ - (default: False) - """ - - def __init__(self, params, lr=1e-3, betas=(0.95, 0.98), eps=1e-8, - weight_decay=0, grad_averaging=False, amsgrad=False): - if not 0.0 <= lr: - raise ValueError("Invalid learning rate: {}".format(lr)) - if not 0.0 <= eps: - raise ValueError("Invalid epsilon value: {}".format(eps)) - if not 0.0 <= betas[0] < 1.0: - raise ValueError("Invalid beta parameter at index 0: {}".format(betas[0])) - if not 0.0 <= betas[1] < 1.0: - raise ValueError("Invalid beta parameter at index 1: {}".format(betas[1])) - defaults = dict(lr=lr, betas=betas, eps=eps, - weight_decay=weight_decay, - grad_averaging=grad_averaging, - amsgrad=amsgrad) - - super(NvNovoGrad, self).__init__(params, defaults) - - def __setstate__(self, state): - super(NvNovoGrad, self).__setstate__(state) - for group in self.param_groups: - group.setdefault('amsgrad', False) - - def step(self, closure=None): - """Performs a single optimization step. - - Arguments: - closure (callable, optional): A closure that reevaluates the model - and returns the loss. - """ - loss = None - if closure is not None: - loss = closure() - - for group in self.param_groups: - for p in group['params']: - if p.grad is None: - continue - grad = p.grad.data - if grad.is_sparse: - raise RuntimeError('Sparse gradients are not supported.') - amsgrad = group['amsgrad'] - - state = self.state[p] - - # State initialization - if len(state) == 0: - state['step'] = 0 - # Exponential moving average of gradient values - state['exp_avg'] = torch.zeros_like(p.data) - # Exponential moving average of squared gradient values - state['exp_avg_sq'] = torch.zeros([]).to(state['exp_avg'].device) - if amsgrad: - # Maintains max of all exp. moving avg. of sq. grad. values - state['max_exp_avg_sq'] = torch.zeros([]).to(state['exp_avg'].device) - - exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq'] - if amsgrad: - max_exp_avg_sq = state['max_exp_avg_sq'] - beta1, beta2 = group['betas'] - - state['step'] += 1 - - norm = torch.sum(torch.pow(grad, 2)) - - if exp_avg_sq == 0: - exp_avg_sq.copy_(norm) - else: - exp_avg_sq.mul_(beta2).add_(1 - beta2, norm) - - if amsgrad: - # Maintains the maximum of all 2nd moment running avg. till now - torch.max(max_exp_avg_sq, exp_avg_sq, out=max_exp_avg_sq) - # Use the max. for normalizing running avg. of gradient - denom = max_exp_avg_sq.sqrt().add_(group['eps']) - else: - denom = exp_avg_sq.sqrt().add_(group['eps']) - - grad.div_(denom) - if group['weight_decay'] != 0: - grad.add_(group['weight_decay'], p.data) - if group['grad_averaging']: - grad.mul_(1 - beta1) - exp_avg.mul_(beta1).add_(grad) - - p.data.add_(-group['lr'], exp_avg) - - return loss diff --git a/optimizers/timm/plateau_lr.py b/optimizers/timm/plateau_lr.py deleted file mode 100644 index 4f2cacb65a1bf23d10aa6fd296f74579571043cf..0000000000000000000000000000000000000000 --- a/optimizers/timm/plateau_lr.py +++ /dev/null @@ -1,113 +0,0 @@ -""" Plateau Scheduler - -Adapts PyTorch plateau scheduler and allows application of noise, warmup. - -Hacked together by / Copyright 2020 Ross Wightman -""" -import torch - -from .scheduler import Scheduler - - -class PlateauLRScheduler(Scheduler): - """Decay the LR by a factor every time the validation loss plateaus.""" - - def __init__(self, - optimizer, - decay_rate=0.1, - patience_t=10, - verbose=True, - threshold=1e-4, - cooldown_t=0, - warmup_t=0, - warmup_lr_init=0, - lr_min=0, - mode='max', - noise_range_t=None, - noise_type='normal', - noise_pct=0.67, - noise_std=1.0, - noise_seed=None, - initialize=True, - ): - super().__init__(optimizer, 'lr', initialize=initialize) - - self.lr_scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau( - self.optimizer, - patience=patience_t, - factor=decay_rate, - verbose=verbose, - threshold=threshold, - cooldown=cooldown_t, - mode=mode, - min_lr=lr_min - ) - - self.noise_range = noise_range_t - self.noise_pct = noise_pct - self.noise_type = noise_type - self.noise_std = noise_std - self.noise_seed = noise_seed if noise_seed is not None else 42 - self.warmup_t = warmup_t - self.warmup_lr_init = warmup_lr_init - if self.warmup_t: - self.warmup_steps = [(v - warmup_lr_init) / self.warmup_t for v in self.base_values] - super().update_groups(self.warmup_lr_init) - else: - self.warmup_steps = [1 for _ in self.base_values] - self.restore_lr = None - - def state_dict(self): - return { - 'best': self.lr_scheduler.best, - 'last_epoch': self.lr_scheduler.last_epoch, - } - - def load_state_dict(self, state_dict): - self.lr_scheduler.best = state_dict['best'] - if 'last_epoch' in state_dict: - self.lr_scheduler.last_epoch = state_dict['last_epoch'] - - # override the base class step fn completely - def step(self, epoch, metric=None): - if epoch <= self.warmup_t: - lrs = [self.warmup_lr_init + epoch * s for s in self.warmup_steps] - super().update_groups(lrs) - else: - if self.restore_lr is not None: - # restore actual LR from before our last noise perturbation before stepping base - for i, param_group in enumerate(self.optimizer.param_groups): - param_group['lr'] = self.restore_lr[i] - self.restore_lr = None - - self.lr_scheduler.step(metric, epoch) # step the base scheduler - - if self.noise_range is not None: - if isinstance(self.noise_range, (list, tuple)): - apply_noise = self.noise_range[0] <= epoch < self.noise_range[1] - else: - apply_noise = epoch >= self.noise_range - if apply_noise: - self._apply_noise(epoch) - - def _apply_noise(self, epoch): - g = torch.Generator() - g.manual_seed(self.noise_seed + epoch) - if self.noise_type == 'normal': - while True: - # resample if noise out of percent limit, brute force but shouldn't spin much - noise = torch.randn(1, generator=g).item() - if abs(noise) < self.noise_pct: - break - else: - noise = 2 * (torch.rand(1, generator=g).item() - 0.5) * self.noise_pct - - # apply the noise on top of previous LR, cache the old value so we can restore for normal - # stepping of base scheduler - restore_lr = [] - for i, param_group in enumerate(self.optimizer.param_groups): - old_lr = float(param_group['lr']) - restore_lr.append(old_lr) - new_lr = old_lr + old_lr * noise - param_group['lr'] = new_lr - self.restore_lr = restore_lr diff --git a/optimizers/timm/radam.py b/optimizers/timm/radam.py deleted file mode 100644 index 9987a334460286b1a6c8ec6d57ee023596a74219..0000000000000000000000000000000000000000 --- a/optimizers/timm/radam.py +++ /dev/null @@ -1,152 +0,0 @@ -"""RAdam Optimizer. -Implementation lifted from: https://github.com/LiyuanLucasLiu/RAdam -Paper: `On the Variance of the Adaptive Learning Rate and Beyond` - https://arxiv.org/abs/1908.03265 -""" -import math -import torch -from torch.optim.optimizer import Optimizer, required - - -class RAdam(Optimizer): - - def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8, weight_decay=0): - defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay) - self.buffer = [[None, None, None] for ind in range(10)] - super(RAdam, self).__init__(params, defaults) - - def __setstate__(self, state): - super(RAdam, self).__setstate__(state) - - def step(self, closure=None): - - loss = None - if closure is not None: - loss = closure() - - for group in self.param_groups: - - for p in group['params']: - if p.grad is None: - continue - grad = p.grad.data.float() - if grad.is_sparse: - raise RuntimeError('RAdam does not support sparse gradients') - - p_data_fp32 = p.data.float() - - state = self.state[p] - - if len(state) == 0: - state['step'] = 0 - state['exp_avg'] = torch.zeros_like(p_data_fp32) - state['exp_avg_sq'] = torch.zeros_like(p_data_fp32) - else: - state['exp_avg'] = state['exp_avg'].type_as(p_data_fp32) - state['exp_avg_sq'] = state['exp_avg_sq'].type_as(p_data_fp32) - - exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq'] - beta1, beta2 = group['betas'] - - exp_avg_sq.mul_(beta2).addcmul_(1 - beta2, grad, grad) - exp_avg.mul_(beta1).add_(1 - beta1, grad) - - state['step'] += 1 - buffered = self.buffer[int(state['step'] % 10)] - if state['step'] == buffered[0]: - N_sma, step_size = buffered[1], buffered[2] - else: - buffered[0] = state['step'] - beta2_t = beta2 ** state['step'] - N_sma_max = 2 / (1 - beta2) - 1 - N_sma = N_sma_max - 2 * state['step'] * beta2_t / (1 - beta2_t) - buffered[1] = N_sma - - # more conservative since it's an approximated value - if N_sma >= 5: - step_size = group['lr'] * math.sqrt( - (1 - beta2_t) * (N_sma - 4) / (N_sma_max - 4) * (N_sma - 2) / N_sma * N_sma_max / ( - N_sma_max - 2)) / (1 - beta1 ** state['step']) - else: - step_size = group['lr'] / (1 - beta1 ** state['step']) - buffered[2] = step_size - - if group['weight_decay'] != 0: - p_data_fp32.add_(-group['weight_decay'] * group['lr'], p_data_fp32) - - # more conservative since it's an approximated value - if N_sma >= 5: - denom = exp_avg_sq.sqrt().add_(group['eps']) - p_data_fp32.addcdiv_(-step_size, exp_avg, denom) - else: - p_data_fp32.add_(-step_size, exp_avg) - - p.data.copy_(p_data_fp32) - - return loss - - -class PlainRAdam(Optimizer): - - def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8, weight_decay=0): - defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay) - - super(PlainRAdam, self).__init__(params, defaults) - - def __setstate__(self, state): - super(PlainRAdam, self).__setstate__(state) - - def step(self, closure=None): - - loss = None - if closure is not None: - loss = closure() - - for group in self.param_groups: - - for p in group['params']: - if p.grad is None: - continue - grad = p.grad.data.float() - if grad.is_sparse: - raise RuntimeError('RAdam does not support sparse gradients') - - p_data_fp32 = p.data.float() - - state = self.state[p] - - if len(state) == 0: - state['step'] = 0 - state['exp_avg'] = torch.zeros_like(p_data_fp32) - state['exp_avg_sq'] = torch.zeros_like(p_data_fp32) - else: - state['exp_avg'] = state['exp_avg'].type_as(p_data_fp32) - state['exp_avg_sq'] = state['exp_avg_sq'].type_as(p_data_fp32) - - exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq'] - beta1, beta2 = group['betas'] - - exp_avg_sq.mul_(beta2).addcmul_(1 - beta2, grad, grad) - exp_avg.mul_(beta1).add_(1 - beta1, grad) - - state['step'] += 1 - beta2_t = beta2 ** state['step'] - N_sma_max = 2 / (1 - beta2) - 1 - N_sma = N_sma_max - 2 * state['step'] * beta2_t / (1 - beta2_t) - - if group['weight_decay'] != 0: - p_data_fp32.add_(-group['weight_decay'] * group['lr'], p_data_fp32) - - # more conservative since it's an approximated value - if N_sma >= 5: - step_size = group['lr'] * math.sqrt( - (1 - beta2_t) * (N_sma - 4) / (N_sma_max - 4) * (N_sma - 2) / N_sma * N_sma_max / ( - N_sma_max - 2)) / (1 - beta1 ** state['step']) - denom = exp_avg_sq.sqrt().add_(group['eps']) - p_data_fp32.addcdiv_(-step_size, exp_avg, denom) - else: - step_size = group['lr'] / (1 - beta1 ** state['step']) - p_data_fp32.add_(-step_size, exp_avg) - - p.data.copy_(p_data_fp32) - - return loss diff --git a/optimizers/timm/rmsprop_tf.py b/optimizers/timm/rmsprop_tf.py deleted file mode 100644 index 5115555cd26040e3af297a6e79e7bd5e4d202623..0000000000000000000000000000000000000000 --- a/optimizers/timm/rmsprop_tf.py +++ /dev/null @@ -1,136 +0,0 @@ -""" RMSProp modified to behave like Tensorflow impl - -Originally cut & paste from PyTorch RMSProp -https://github.com/pytorch/pytorch/blob/063946d2b3f3f1e953a2a3b54e0b34f1393de295/torch/optim/rmsprop.py -Licensed under BSD-Clause 3 (ish), https://github.com/pytorch/pytorch/blob/master/LICENSE - -Modifications Copyright 2020 Ross Wightman -""" - -import torch -from torch.optim import Optimizer - - -class RMSpropTF(Optimizer): - """Implements RMSprop algorithm (TensorFlow style epsilon) - - NOTE: This is a direct cut-and-paste of PyTorch RMSprop with eps applied before sqrt - and a few other modifications to closer match Tensorflow for matching hyper-params. - - Noteworthy changes include: - 1. Epsilon applied inside square-root - 2. square_avg initialized to ones - 3. LR scaling of update accumulated in momentum buffer - - Proposed by G. Hinton in his - `course `_. - - The centered version first appears in `Generating Sequences - With Recurrent Neural Networks `_. - - Arguments: - params (iterable): iterable of parameters to optimize or dicts defining - parameter groups - lr (float, optional): learning rate (default: 1e-2) - momentum (float, optional): momentum factor (default: 0) - alpha (float, optional): smoothing (decay) constant (default: 0.9) - eps (float, optional): term added to the denominator to improve - numerical stability (default: 1e-10) - centered (bool, optional) : if ``True``, compute the centered RMSProp, - the gradient is normalized by an estimation of its variance - weight_decay (float, optional): weight decay (L2 penalty) (default: 0) - decoupled_decay (bool, optional): decoupled weight decay as per https://arxiv.org/abs/1711.05101 - lr_in_momentum (bool, optional): learning rate scaling is included in the momentum buffer - update as per defaults in Tensorflow - - """ - - def __init__(self, params, lr=1e-2, alpha=0.9, eps=1e-10, weight_decay=0, momentum=0., centered=False, - decoupled_decay=False, lr_in_momentum=True): - if not 0.0 <= lr: - raise ValueError("Invalid learning rate: {}".format(lr)) - if not 0.0 <= eps: - raise ValueError("Invalid epsilon value: {}".format(eps)) - if not 0.0 <= momentum: - raise ValueError("Invalid momentum value: {}".format(momentum)) - if not 0.0 <= weight_decay: - raise ValueError("Invalid weight_decay value: {}".format(weight_decay)) - if not 0.0 <= alpha: - raise ValueError("Invalid alpha value: {}".format(alpha)) - - defaults = dict(lr=lr, momentum=momentum, alpha=alpha, eps=eps, centered=centered, weight_decay=weight_decay, - decoupled_decay=decoupled_decay, lr_in_momentum=lr_in_momentum) - super(RMSpropTF, self).__init__(params, defaults) - - def __setstate__(self, state): - super(RMSpropTF, self).__setstate__(state) - for group in self.param_groups: - group.setdefault('momentum', 0) - group.setdefault('centered', False) - - def step(self, closure=None): - """Performs a single optimization step. - - Arguments: - closure (callable, optional): A closure that reevaluates the model - and returns the loss. - """ - loss = None - if closure is not None: - loss = closure() - - for group in self.param_groups: - for p in group['params']: - if p.grad is None: - continue - grad = p.grad.data - if grad.is_sparse: - raise RuntimeError('RMSprop does not support sparse gradients') - state = self.state[p] - - # State initialization - if len(state) == 0: - state['step'] = 0 - state['square_avg'] = torch.ones_like(p.data) # PyTorch inits to zero - if group['momentum'] > 0: - state['momentum_buffer'] = torch.zeros_like(p.data) - if group['centered']: - state['grad_avg'] = torch.zeros_like(p.data) - - square_avg = state['square_avg'] - one_minus_alpha = 1. - group['alpha'] - - state['step'] += 1 - - if group['weight_decay'] != 0: - if 'decoupled_decay' in group and group['decoupled_decay']: - p.data.add_(-group['weight_decay'], p.data) - else: - grad = grad.add(group['weight_decay'], p.data) - - # Tensorflow order of ops for updating squared avg - square_avg.add_(one_minus_alpha, grad.pow(2) - square_avg) - # square_avg.mul_(alpha).addcmul_(1 - alpha, grad, grad) # PyTorch original - - if group['centered']: - grad_avg = state['grad_avg'] - grad_avg.add_(one_minus_alpha, grad - grad_avg) - # grad_avg.mul_(alpha).add_(1 - alpha, grad) # PyTorch original - avg = square_avg.addcmul(-1, grad_avg, grad_avg).add(group['eps']).sqrt_() # eps moved in sqrt - else: - avg = square_avg.add(group['eps']).sqrt_() # eps moved in sqrt - - if group['momentum'] > 0: - buf = state['momentum_buffer'] - # Tensorflow accumulates the LR scaling in the momentum buffer - if 'lr_in_momentum' in group and group['lr_in_momentum']: - buf.mul_(group['momentum']).addcdiv_(group['lr'], grad, avg) - p.data.add_(-buf) - else: - # PyTorch scales the param update by LR - buf.mul_(group['momentum']).addcdiv_(grad, avg) - p.data.add_(-group['lr'], buf) - else: - p.data.addcdiv_(-group['lr'], grad, avg) - - return loss diff --git a/optimizers/timm/scheduler.py b/optimizers/timm/scheduler.py deleted file mode 100644 index 21d51509c87a0783c6b61986c574a3ed5366e165..0000000000000000000000000000000000000000 --- a/optimizers/timm/scheduler.py +++ /dev/null @@ -1,105 +0,0 @@ -from typing import Dict, Any - -import torch - - -class Scheduler: - """ Parameter Scheduler Base Class - A scheduler base class that can be used to schedule any optimizer parameter groups. - - Unlike the builtin PyTorch schedulers, this is intended to be consistently called - * At the END of each epoch, before incrementing the epoch count, to calculate next epoch's value - * At the END of each optimizer update, after incrementing the update count, to calculate next update's value - - The schedulers built on this should try to remain as stateless as possible (for simplicity). - - This family of schedulers is attempting to avoid the confusion of the meaning of 'last_epoch' - and -1 values for special behaviour. All epoch and update counts must be tracked in the training - code and explicitly passed in to the schedulers on the corresponding step or step_update call. - - Based on ideas from: - * https://github.com/pytorch/fairseq/tree/master/fairseq/optim/lr_scheduler - * https://github.com/allenai/allennlp/tree/master/allennlp/training/learning_rate_schedulers - """ - - def __init__(self, - optimizer: torch.optim.Optimizer, - param_group_field: str, - noise_range_t=None, - noise_type='normal', - noise_pct=0.67, - noise_std=1.0, - noise_seed=None, - initialize: bool = True) -> None: - self.optimizer = optimizer - self.param_group_field = param_group_field - self._initial_param_group_field = f"initial_{param_group_field}" - if initialize: - for i, group in enumerate(self.optimizer.param_groups): - if param_group_field not in group: - raise KeyError(f"{param_group_field} missing from param_groups[{i}]") - group.setdefault(self._initial_param_group_field, group[param_group_field]) - else: - for i, group in enumerate(self.optimizer.param_groups): - if self._initial_param_group_field not in group: - raise KeyError(f"{self._initial_param_group_field} missing from param_groups[{i}]") - self.base_values = [group[self._initial_param_group_field] for group in self.optimizer.param_groups] - self.metric = None # any point to having this for all? - self.noise_range_t = noise_range_t - self.noise_pct = noise_pct - self.noise_type = noise_type - self.noise_std = noise_std - self.noise_seed = noise_seed if noise_seed is not None else 42 - self.update_groups(self.base_values) - - def state_dict(self) -> Dict[str, Any]: - return {key: value for key, value in self.__dict__.items() if key != 'optimizer'} - - def load_state_dict(self, state_dict: Dict[str, Any]) -> None: - self.__dict__.update(state_dict) - - def get_epoch_values(self, epoch: int): - return None - - def get_update_values(self, num_updates: int): - return None - - def step(self, epoch: int, metric: float = None) -> None: - self.metric = metric - values = self.get_epoch_values(epoch) - if values is not None: - values = self._add_noise(values, epoch) - self.update_groups(values) - - def step_update(self, num_updates: int, metric: float = None): - self.metric = metric - values = self.get_update_values(num_updates) - if values is not None: - values = self._add_noise(values, num_updates) - self.update_groups(values) - - def update_groups(self, values): - if not isinstance(values, (list, tuple)): - values = [values] * len(self.optimizer.param_groups) - for param_group, value in zip(self.optimizer.param_groups, values): - param_group[self.param_group_field] = value - - def _add_noise(self, lrs, t): - if self.noise_range_t is not None: - if isinstance(self.noise_range_t, (list, tuple)): - apply_noise = self.noise_range_t[0] <= t < self.noise_range_t[1] - else: - apply_noise = t >= self.noise_range_t - if apply_noise: - g = torch.Generator() - g.manual_seed(self.noise_seed + t) - if self.noise_type == 'normal': - while True: - # resample if noise out of percent limit, brute force but shouldn't spin much - noise = torch.randn(1, generator=g).item() - if abs(noise) < self.noise_pct: - break - else: - noise = 2 * (torch.rand(1, generator=g).item() - 0.5) * self.noise_pct - lrs = [v + v * noise for v in lrs] - return lrs diff --git a/optimizers/timm/sgdp.py b/optimizers/timm/sgdp.py deleted file mode 100644 index f4a94aa332d7030a70e888342eb6cc4623d69836..0000000000000000000000000000000000000000 --- a/optimizers/timm/sgdp.py +++ /dev/null @@ -1,96 +0,0 @@ -""" -SGDP Optimizer Implementation copied from https://github.com/clovaai/AdamP/blob/master/adamp/sgdp.py - -Paper: `Slowing Down the Weight Norm Increase in Momentum-based Optimizers` - https://arxiv.org/abs/2006.08217 -Code: https://github.com/clovaai/AdamP - -Copyright (c) 2020-present NAVER Corp. -MIT license -""" - -import torch -import torch.nn as nn -from torch.optim.optimizer import Optimizer, required -import math - -class SGDP(Optimizer): - def __init__(self, params, lr=required, momentum=0, dampening=0, - weight_decay=0, nesterov=False, eps=1e-8, delta=0.1, wd_ratio=0.1): - defaults = dict(lr=lr, momentum=momentum, dampening=dampening, weight_decay=weight_decay, - nesterov=nesterov, eps=eps, delta=delta, wd_ratio=wd_ratio) - super(SGDP, self).__init__(params, defaults) - - def _channel_view(self, x): - return x.view(x.size(0), -1) - - def _layer_view(self, x): - return x.view(1, -1) - - def _cosine_similarity(self, x, y, eps, view_func): - x = view_func(x) - y = view_func(y) - - x_norm = x.norm(dim=1).add_(eps) - y_norm = y.norm(dim=1).add_(eps) - dot = (x * y).sum(dim=1) - - return dot.abs() / x_norm / y_norm - - def _projection(self, p, grad, perturb, delta, wd_ratio, eps): - wd = 1 - expand_size = [-1] + [1] * (len(p.shape) - 1) - for view_func in [self._channel_view, self._layer_view]: - - cosine_sim = self._cosine_similarity(grad, p.data, eps, view_func) - - if cosine_sim.max() < delta / math.sqrt(view_func(p.data).size(1)): - p_n = p.data / view_func(p.data).norm(dim=1).view(expand_size).add_(eps) - perturb -= p_n * view_func(p_n * perturb).sum(dim=1).view(expand_size) - wd = wd_ratio - - return perturb, wd - - return perturb, wd - - def step(self, closure=None): - loss = None - if closure is not None: - loss = closure() - - for group in self.param_groups: - weight_decay = group['weight_decay'] - momentum = group['momentum'] - dampening = group['dampening'] - nesterov = group['nesterov'] - - for p in group['params']: - if p.grad is None: - continue - grad = p.grad.data - state = self.state[p] - - # State initialization - if len(state) == 0: - state['momentum'] = torch.zeros_like(p.data) - - # SGD - buf = state['momentum'] - buf.mul_(momentum).add_(1 - dampening, grad) - if nesterov: - d_p = grad + momentum * buf - else: - d_p = buf - - # Projection - wd_ratio = 1 - if len(p.shape) > 1: - d_p, wd_ratio = self._projection(p, grad, d_p, group['delta'], group['wd_ratio'], group['eps']) - - # Weight decay - if weight_decay != 0: - p.data.mul_(1 - group['lr'] * group['weight_decay'] * wd_ratio / (1-momentum)) - - # Step - p.data.add_(-group['lr'], d_p) - - return loss diff --git a/optimizers/timm/step_lr.py b/optimizers/timm/step_lr.py deleted file mode 100644 index f797e1a8cf35999531dd5f1ccbbe09a9d0cf30a9..0000000000000000000000000000000000000000 --- a/optimizers/timm/step_lr.py +++ /dev/null @@ -1,63 +0,0 @@ -""" Step Scheduler - -Basic step LR schedule with warmup, noise. - -Hacked together by / Copyright 2020 Ross Wightman -""" -import math -import torch - -from .scheduler import Scheduler - - -class StepLRScheduler(Scheduler): - """ - """ - - def __init__(self, - optimizer: torch.optim.Optimizer, - decay_t: float, - decay_rate: float = 1., - warmup_t=0, - warmup_lr_init=0, - t_in_epochs=True, - noise_range_t=None, - noise_pct=0.67, - noise_std=1.0, - noise_seed=42, - initialize=True, - ) -> None: - super().__init__( - optimizer, param_group_field="lr", - noise_range_t=noise_range_t, noise_pct=noise_pct, noise_std=noise_std, noise_seed=noise_seed, - initialize=initialize) - - self.decay_t = decay_t - self.decay_rate = decay_rate - self.warmup_t = warmup_t - self.warmup_lr_init = warmup_lr_init - self.t_in_epochs = t_in_epochs - if self.warmup_t: - self.warmup_steps = [(v - warmup_lr_init) / self.warmup_t for v in self.base_values] - super().update_groups(self.warmup_lr_init) - else: - self.warmup_steps = [1 for _ in self.base_values] - - def _get_lr(self, t): - if t < self.warmup_t: - lrs = [self.warmup_lr_init + t * s for s in self.warmup_steps] - else: - lrs = [v * (self.decay_rate ** (t // self.decay_t)) for v in self.base_values] - return lrs - - def get_epoch_values(self, epoch: int): - if self.t_in_epochs: - return self._get_lr(epoch) - else: - return None - - def get_update_values(self, num_updates: int): - if not self.t_in_epochs: - return self._get_lr(num_updates) - else: - return None diff --git a/optimizers/timm/tanh_lr.py b/optimizers/timm/tanh_lr.py deleted file mode 100644 index 8cc338bb1df7a564d9207b32ab0f59cdf1ef4c59..0000000000000000000000000000000000000000 --- a/optimizers/timm/tanh_lr.py +++ /dev/null @@ -1,120 +0,0 @@ -""" TanH Scheduler - -TanH schedule with warmup, cycle/restarts, noise. - -Hacked together by / Copyright 2020 Ross Wightman -""" -import logging -import math -import numpy as np -import torch - -from .scheduler import Scheduler - - -_logger = logging.getLogger(__name__) - - -class TanhLRScheduler(Scheduler): - """ - Hyberbolic-Tangent decay with restarts. - This is described in the paper https://arxiv.org/abs/1806.01593 - """ - - def __init__(self, - optimizer: torch.optim.Optimizer, - t_initial: int, - lb: float = -6., - ub: float = 4., - t_mul: float = 1., - lr_min: float = 0., - decay_rate: float = 1., - warmup_t=0, - warmup_lr_init=0, - warmup_prefix=False, - cycle_limit=0, - t_in_epochs=True, - noise_range_t=None, - noise_pct=0.67, - noise_std=1.0, - noise_seed=42, - initialize=True) -> None: - super().__init__( - optimizer, param_group_field="lr", - noise_range_t=noise_range_t, noise_pct=noise_pct, noise_std=noise_std, noise_seed=noise_seed, - initialize=initialize) - - assert t_initial > 0 - assert lr_min >= 0 - assert lb < ub - assert cycle_limit >= 0 - assert warmup_t >= 0 - assert warmup_lr_init >= 0 - self.lb = lb - self.ub = ub - self.t_initial = t_initial - self.t_mul = t_mul - self.lr_min = lr_min - self.decay_rate = decay_rate - self.cycle_limit = cycle_limit - self.warmup_t = warmup_t - self.warmup_lr_init = warmup_lr_init - self.warmup_prefix = warmup_prefix - self.t_in_epochs = t_in_epochs - if self.warmup_t: - t_v = self.base_values if self.warmup_prefix else self._get_lr(self.warmup_t) - self.warmup_steps = [(v - warmup_lr_init) / self.warmup_t for v in t_v] - super().update_groups(self.warmup_lr_init) - else: - self.warmup_steps = [1 for _ in self.base_values] - - def _get_lr(self, t): - if t < self.warmup_t: - lrs = [self.warmup_lr_init + t * s for s in self.warmup_steps] - else: - if self.warmup_prefix: - t = t - self.warmup_t - - if self.t_mul != 1: - i = math.floor(math.log(1 - t / self.t_initial * (1 - self.t_mul), self.t_mul)) - t_i = self.t_mul ** i * self.t_initial - t_curr = t - (1 - self.t_mul ** i) / (1 - self.t_mul) * self.t_initial - else: - i = t // self.t_initial - t_i = self.t_initial - t_curr = t - (self.t_initial * i) - - if self.cycle_limit == 0 or (self.cycle_limit > 0 and i < self.cycle_limit): - gamma = self.decay_rate ** i - lr_min = self.lr_min * gamma - lr_max_values = [v * gamma for v in self.base_values] - - tr = t_curr / t_i - lrs = [ - lr_min + 0.5 * (lr_max - lr_min) * (1 - math.tanh(self.lb * (1. - tr) + self.ub * tr)) - for lr_max in lr_max_values - ] - else: - lrs = [self.lr_min * (self.decay_rate ** self.cycle_limit) for _ in self.base_values] - return lrs - - def get_epoch_values(self, epoch: int): - if self.t_in_epochs: - return self._get_lr(epoch) - else: - return None - - def get_update_values(self, num_updates: int): - if not self.t_in_epochs: - return self._get_lr(num_updates) - else: - return None - - def get_cycle_length(self, cycles=0): - if not cycles: - cycles = self.cycle_limit - cycles = max(1, cycles) - if self.t_mul == 1.0: - return self.t_initial * cycles - else: - return int(math.floor(-self.t_initial * (self.t_mul ** cycles - 1) / (1 - self.t_mul))) diff --git a/outputs/audio2pose/custom/hf/999/gt_2_scott_0_3_3.npz b/outputs/audio2pose/custom/hf/999/gt_2_scott_0_3_3.npz deleted file mode 100644 index 62b0df13d933e4f482832a1f6293f1f529ac883a..0000000000000000000000000000000000000000 --- a/outputs/audio2pose/custom/hf/999/gt_2_scott_0_3_3.npz +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:8c22b5851a4a73eb4b5ecc8c3cc0430a344bf226b58488aa442a0d3b55d41d95 -size 71632 diff --git a/outputs/audio2pose/custom/hf/999/res_2_scott_0_3_3.mp4 b/outputs/audio2pose/custom/hf/999/res_2_scott_0_3_3.mp4 deleted file mode 100644 index 89fb48c981c2b468b1522f862343b257162ce866..0000000000000000000000000000000000000000 Binary files a/outputs/audio2pose/custom/hf/999/res_2_scott_0_3_3.mp4 and /dev/null differ diff --git a/outputs/audio2pose/custom/hf/999/res_2_scott_0_3_3.npz b/outputs/audio2pose/custom/hf/999/res_2_scott_0_3_3.npz deleted file mode 100644 index f07491391d338fdd97b45c93d61a71b58b7b4ac6..0000000000000000000000000000000000000000 --- a/outputs/audio2pose/custom/hf/999/res_2_scott_0_3_3.npz +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:77fd86b392889c8da1d292f23326217639190933ac605cce49b2f1d41d5a81dd -size 71632 diff --git a/outputs/audio2pose/custom/hf/tmp.wav b/outputs/audio2pose/custom/hf/tmp.wav deleted file mode 100644 index a2bd689d57c4e35395570cb379c5dc98d364326b..0000000000000000000000000000000000000000 Binary files a/outputs/audio2pose/custom/hf/tmp.wav and /dev/null differ diff --git a/pre-requirements.txt b/pre-requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..ba1cf2dec43cf1bd737527402e561cfb15db9b8c --- /dev/null +++ b/pre-requirements.txt @@ -0,0 +1,4 @@ +numpy==1.23 +torch==2.0.0 +torchvision +torchaudio \ No newline at end of file diff --git a/pyrender/.coveragerc b/pyrender/.coveragerc deleted file mode 100644 index ee31cded3509cbd991a33dd27e2525b93a1a6558..0000000000000000000000000000000000000000 --- a/pyrender/.coveragerc +++ /dev/null @@ -1,5 +0,0 @@ -[report] -exclude_lines = - def __repr__ - def __str__ - @abc.abstractmethod diff --git a/pyrender/.flake8 b/pyrender/.flake8 deleted file mode 100644 index fec4bcfc3ba774b53a866d839ea15bae6ebdb4a6..0000000000000000000000000000000000000000 --- a/pyrender/.flake8 +++ /dev/null @@ -1,8 +0,0 @@ -[flake8] -ignore = E231,W504,F405,F403 -max-line-length = 79 -select = B,C,E,F,W,T4,B9 -exclude = - docs/source/conf.py, - __pycache__, - examples/* diff --git a/pyrender/.gitignore b/pyrender/.gitignore deleted file mode 100644 index ae59dec631f71a23d4255aaf9c0274a699f4ba25..0000000000000000000000000000000000000000 --- a/pyrender/.gitignore +++ /dev/null @@ -1,106 +0,0 @@ -# Byte-compiled / optimized / DLL files -__pycache__/ -*.py[cod] -*$py.class - -docs/**/generated/** - -# C extensions -*.so - -# Distribution / packaging -.Python -build/ -develop-eggs/ -dist/ -downloads/ -eggs/ -.eggs/ -lib/ -lib64/ -parts/ -sdist/ -var/ -wheels/ -*.egg-info/ -.installed.cfg -*.egg -MANIFEST - -# PyInstaller -# Usually these files are written by a python script from a template -# before PyInstaller builds the exe, so as to inject date/other infos into it. -*.manifest -*.spec - -# Installer logs -pip-log.txt -pip-delete-this-directory.txt - -# Unit test / coverage reports -htmlcov/ -.tox/ -.coverage -.coverage.* -.cache -nosetests.xml -coverage.xml -*.cover -.hypothesis/ -.pytest_cache/ - -# Translations -*.mo -*.pot - -# Django stuff: -*.log -local_settings.py -db.sqlite3 - -# Flask stuff: -instance/ -.webassets-cache - -# Scrapy stuff: -.scrapy - -# Sphinx documentation -docs/_build/ - -# PyBuilder -target/ - -# Jupyter Notebook -.ipynb_checkpoints - -# pyenv -.python-version - -# celery beat schedule file -celerybeat-schedule - -# SageMath parsed files -*.sage.py - -# Environments -.env -.venv -env/ -venv/ -ENV/ -env.bak/ -venv.bak/ - -# Spyder project settings -.spyderproject -.spyproject - -# Rope project settings -.ropeproject - -# mkdocs documentation -/site - -# mypy -.mypy_cache/ diff --git a/pyrender/.pre-commit-config.yaml b/pyrender/.pre-commit-config.yaml deleted file mode 100644 index 1817eb39bf409aff80c7d2cc79a3bc3856c70dbd..0000000000000000000000000000000000000000 --- a/pyrender/.pre-commit-config.yaml +++ /dev/null @@ -1,6 +0,0 @@ -repos: -- repo: https://gitlab.com/pycqa/flake8 - rev: 3.7.1 - hooks: - - id: flake8 - exclude: ^setup.py diff --git a/pyrender/.travis.yml b/pyrender/.travis.yml deleted file mode 100644 index 1ad289ae1513eaf8fda74f8d5ab7840be3ef56cb..0000000000000000000000000000000000000000 --- a/pyrender/.travis.yml +++ /dev/null @@ -1,43 +0,0 @@ -language: python -sudo: required -dist: xenial - -python: -- '3.6' -- '3.7' - -before_install: - # Pre-install osmesa - - sudo apt update - - sudo wget https://github.com/mmatl/travis_debs/raw/master/xenial/mesa_18.3.3-0.deb - - sudo dpkg -i ./mesa_18.3.3-0.deb || true - - sudo apt install -f - - git clone https://github.com/mmatl/pyopengl.git - - cd pyopengl - - pip install . - - cd .. - -install: - - pip install . - # - pip install -q pytest pytest-cov coveralls - - pip install pytest pytest-cov coveralls - - pip install ./pyopengl - -script: - - PYOPENGL_PLATFORM=osmesa pytest --cov=pyrender tests - -after_success: -- coveralls || true - -deploy: - provider: pypi - skip_existing: true - user: mmatl - on: - tags: true - branch: master - password: - secure: 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 - distributions: sdist bdist_wheel -notifications: - email: false diff --git a/pyrender/LICENSE b/pyrender/LICENSE deleted file mode 100644 index 4276f7d204e4d85104246df637e0e36adbef14a7..0000000000000000000000000000000000000000 --- a/pyrender/LICENSE +++ /dev/null @@ -1,21 +0,0 @@ -MIT License - -Copyright (c) 2019 Matthew Matl - -Permission is hereby granted, free of charge, to any person obtaining a copy -of this software and associated documentation files (the "Software"), to deal -in the Software without restriction, including without limitation the rights -to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -copies of the Software, and to permit persons to whom the Software is -furnished to do so, subject to the following conditions: - -The above copyright notice and this permission notice shall be included in all -copies or substantial portions of the Software. - -THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -SOFTWARE. diff --git a/pyrender/MANIFEST.in b/pyrender/MANIFEST.in deleted file mode 100644 index 097bcca3b4fccdc39ddd63c10f710ad524898e95..0000000000000000000000000000000000000000 --- a/pyrender/MANIFEST.in +++ /dev/null @@ -1,5 +0,0 @@ -# Include the license -include LICENSE -include README.rst -include pyrender/fonts/* -include pyrender/shaders/* diff --git a/pyrender/README.md b/pyrender/README.md deleted file mode 100644 index ae88ed1c5e78f247e38291ed83cf4c81230bf976..0000000000000000000000000000000000000000 --- a/pyrender/README.md +++ /dev/null @@ -1,92 +0,0 @@ -# Pyrender - -[![Build Status](https://travis-ci.org/mmatl/pyrender.svg?branch=master)](https://travis-ci.org/mmatl/pyrender) -[![Documentation Status](https://readthedocs.org/projects/pyrender/badge/?version=latest)](https://pyrender.readthedocs.io/en/latest/?badge=latest) -[![Coverage Status](https://coveralls.io/repos/github/mmatl/pyrender/badge.svg?branch=master)](https://coveralls.io/github/mmatl/pyrender?branch=master) -[![PyPI version](https://badge.fury.io/py/pyrender.svg)](https://badge.fury.io/py/pyrender) -[![Downloads](https://pepy.tech/badge/pyrender)](https://pepy.tech/project/pyrender) - -Pyrender is a pure Python (2.7, 3.4, 3.5, 3.6) library for physically-based -rendering and visualization. -It is designed to meet the [glTF 2.0 specification from Khronos](https://www.khronos.org/gltf/). - -Pyrender is lightweight, easy to install, and simple to use. -It comes packaged with both an intuitive scene viewer and a headache-free -offscreen renderer with support for GPU-accelerated rendering on headless -servers, which makes it perfect for machine learning applications. - -Extensive documentation, including a quickstart guide, is provided [here](https://pyrender.readthedocs.io/en/latest/). - -For a minimal working example of GPU-accelerated offscreen rendering using EGL, -check out the [EGL Google CoLab Notebook](https://colab.research.google.com/drive/1pcndwqeY8vker3bLKQNJKr3B-7-SYenE?usp=sharing). - - -

- GIF of Viewer - Damaged Helmet -

- -## Installation -You can install pyrender directly from pip. - -```bash -pip install pyrender -``` - -## Features - -Despite being lightweight, pyrender has lots of features, including: - -* Simple interoperation with the amazing [trimesh](https://github.com/mikedh/trimesh) project, -which enables out-of-the-box support for dozens of mesh types, including OBJ, -STL, DAE, OFF, PLY, and GLB. -* An easy-to-use scene viewer with support for animation, showing face and vertex -normals, toggling lighting conditions, and saving images and GIFs. -* An offscreen rendering module that supports OSMesa and EGL backends. -* Shadow mapping for directional and spot lights. -* Metallic-roughness materials for physically-based rendering, including several -types of texture and normal mapping. -* Transparency. -* Depth and color image generation. - -## Sample Usage - -For sample usage, check out the [quickstart -guide](https://pyrender.readthedocs.io/en/latest/examples/index.html) or one of -the Google CoLab Notebooks: - -* [EGL Google CoLab Notebook](https://colab.research.google.com/drive/1pcndwqeY8vker3bLKQNJKr3B-7-SYenE?usp=sharing) - -## Viewer Keyboard and Mouse Controls - -When using the viewer, the basic controls for moving about the scene are as follows: - -* To rotate the camera about the center of the scene, hold the left mouse button and drag the cursor. -* To rotate the camera about its viewing axis, hold `CTRL` left mouse button and drag the cursor. -* To pan the camera, do one of the following: - * Hold `SHIFT`, then hold the left mouse button and drag the cursor. - * Hold the middle mouse button and drag the cursor. -* To zoom the camera in or out, do one of the following: - * Scroll the mouse wheel. - * Hold the right mouse button and drag the cursor. - -The available keyboard commands are as follows: - -* `a`: Toggles rotational animation mode. -* `c`: Toggles backface culling. -* `f`: Toggles fullscreen mode. -* `h`: Toggles shadow rendering. -* `i`: Toggles axis display mode (no axes, world axis, mesh axes, all axes). -* `l`: Toggles lighting mode (scene lighting, Raymond lighting, or direct lighting). -* `m`: Toggles face normal visualization. -* `n`: Toggles vertex normal visualization. -* `o`: Toggles orthographic camera mode. -* `q`: Quits the viewer. -* `r`: Starts recording a GIF, and pressing again stops recording and opens a file dialog. -* `s`: Opens a file dialog to save the current view as an image. -* `w`: Toggles wireframe mode (scene default, flip wireframes, all wireframe, or all solid). -* `z`: Resets the camera to the default view. - -As a note, displaying shadows significantly slows down rendering, so if you're -experiencing low framerates, just kill shadows or reduce the number of lights in -your scene. diff --git a/pyrender/docs/Makefile b/pyrender/docs/Makefile deleted file mode 100644 index b1064a04362a0c4372fae351f99ed3bd9f82ff92..0000000000000000000000000000000000000000 --- a/pyrender/docs/Makefile +++ /dev/null @@ -1,23 +0,0 @@ -# Minimal makefile for Sphinx documentation -# - -# You can set these variables from the command line. -SPHINXOPTS = -SPHINXBUILD = sphinx-build -SOURCEDIR = source -BUILDDIR = build - -# Put it first so that "make" without argument is like "make help". -help: - @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) - -.PHONY: help Makefile - -clean: - @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) - rm -rf ./source/generated/* - -# Catch-all target: route all unknown targets to Sphinx using the new -# "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). -%: Makefile - @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) diff --git a/pyrender/docs/make.bat b/pyrender/docs/make.bat deleted file mode 100644 index 4d9eb83d9f9309029f4b14ff09024658bb0f5563..0000000000000000000000000000000000000000 --- a/pyrender/docs/make.bat +++ /dev/null @@ -1,35 +0,0 @@ -@ECHO OFF - -pushd %~dp0 - -REM Command file for Sphinx documentation - -if "%SPHINXBUILD%" == "" ( - set SPHINXBUILD=sphinx-build -) -set SOURCEDIR=source -set BUILDDIR=build - -if "%1" == "" goto help - -%SPHINXBUILD% >NUL 2>NUL -if errorlevel 9009 ( - echo. - echo.The 'sphinx-build' command was not found. Make sure you have Sphinx - echo.installed, then set the SPHINXBUILD environment variable to point - echo.to the full path of the 'sphinx-build' executable. Alternatively you - echo.may add the Sphinx directory to PATH. - echo. - echo.If you don't have Sphinx installed, grab it from - echo.http://sphinx-doc.org/ - exit /b 1 -) - -%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% -goto end - -:help -%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% - -:end -popd diff --git a/pyrender/docs/source/api/index.rst b/pyrender/docs/source/api/index.rst deleted file mode 100644 index b6e473149d8f132f176e242c93406fdb84ce0b04..0000000000000000000000000000000000000000 --- a/pyrender/docs/source/api/index.rst +++ /dev/null @@ -1,59 +0,0 @@ -Pyrender API Documentation -========================== - -Constants ---------- -.. automodapi:: pyrender.constants - :no-inheritance-diagram: - :no-main-docstr: - :no-heading: - -Cameras -------- -.. automodapi:: pyrender.camera - :no-inheritance-diagram: - :no-main-docstr: - :no-heading: - -Lighting --------- -.. automodapi:: pyrender.light - :no-inheritance-diagram: - :no-main-docstr: - :no-heading: - -Objects -------- -.. automodapi:: pyrender - :no-inheritance-diagram: - :no-main-docstr: - :no-heading: - :skip: Camera, DirectionalLight, Light, OffscreenRenderer, Node - :skip: OrthographicCamera, PerspectiveCamera, PointLight, RenderFlags - :skip: Renderer, Scene, SpotLight, TextAlign, Viewer, GLTF - -Scenes ------- -.. automodapi:: pyrender - :no-inheritance-diagram: - :no-main-docstr: - :no-heading: - :skip: Camera, DirectionalLight, Light, OffscreenRenderer - :skip: OrthographicCamera, PerspectiveCamera, PointLight, RenderFlags - :skip: Renderer, SpotLight, TextAlign, Viewer, Sampler, Texture, Material - :skip: MetallicRoughnessMaterial, Primitive, Mesh, GLTF - -On-Screen Viewer ----------------- -.. automodapi:: pyrender.viewer - :no-inheritance-diagram: - :no-inherited-members: - :no-main-docstr: - :no-heading: - -Off-Screen Rendering --------------------- -.. automodapi:: pyrender.offscreen - :no-inheritance-diagram: - :no-main-docstr: - :no-heading: diff --git a/pyrender/docs/source/conf.py b/pyrender/docs/source/conf.py deleted file mode 100644 index 6bf194c375e7e789b334a838953adfeaf2eb59b6..0000000000000000000000000000000000000000 --- a/pyrender/docs/source/conf.py +++ /dev/null @@ -1,352 +0,0 @@ -# -*- coding: utf-8 -*- -# -# core documentation build configuration file, created by -# sphinx-quickstart on Sun Oct 16 14:33:48 2016. -# -# This file is execfile()d with the current directory set to its -# containing dir. -# -# Note that not all possible configuration values are present in this -# autogenerated file. -# -# All configuration values have a default; values that are commented out -# serve to show the default. - -import sys -import os -from pyrender import __version__ -from sphinx.domains.python import PythonDomain - -# If extensions (or modules to document with autodoc) are in another directory, -# add these directories to sys.path here. If the directory is relative to the -# documentation root, use os.path.abspath to make it absolute, like shown here. -sys.path.insert(0, os.path.abspath('../../')) - -# -- General configuration ------------------------------------------------ - -# If your documentation needs a minimal Sphinx version, state it here. -#needs_sphinx = '1.0' - -# Add any Sphinx extension module names here, as strings. They can be -# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom -# ones. -extensions = [ - 'sphinx.ext.autodoc', - 'sphinx.ext.autosummary', - 'sphinx.ext.coverage', - 'sphinx.ext.githubpages', - 'sphinx.ext.intersphinx', - 'sphinx.ext.napoleon', - 'sphinx.ext.viewcode', - 'sphinx_automodapi.automodapi', - 'sphinx_automodapi.smart_resolver' -] -numpydoc_class_members_toctree = False -automodapi_toctreedirnm = 'generated' -automodsumm_inherited_members = True - -# Add any paths that contain templates here, relative to this directory. -templates_path = ['_templates'] - -# The suffix(es) of source filenames. -# You can specify multiple suffix as a list of string: -# source_suffix = ['.rst', '.md'] -source_suffix = '.rst' - -# The encoding of source files. -#source_encoding = 'utf-8-sig' - -# The master toctree document. -master_doc = 'index' - -# General information about the project. -project = u'pyrender' -copyright = u'2018, Matthew Matl' -author = u'Matthew Matl' - -# The version info for the project you're documenting, acts as replacement for -# |version| and |release|, also used in various other places throughout the -# built documents. -# -# The short X.Y version. -version = __version__ -# The full version, including alpha/beta/rc tags. -release = __version__ - -# The language for content autogenerated by Sphinx. Refer to documentation -# for a list of supported languages. -# -# This is also used if you do content translation via gettext catalogs. -# Usually you set "language" from the command line for these cases. -language = None - -# There are two options for replacing |today|: either, you set today to some -# non-false value, then it is used: -#today = '' -# Else, today_fmt is used as the format for a strftime call. -#today_fmt = '%B %d, %Y' - -# List of patterns, relative to source directory, that match files and -# directories to ignore when looking for source files. -exclude_patterns = [] - -# The reST default role (used for this markup: `text`) to use for all -# documents. -#default_role = None - -# If true, '()' will be appended to :func: etc. cross-reference text. -#add_function_parentheses = True - -# If true, the current module name will be prepended to all description -# unit titles (such as .. function::). -#add_module_names = True - -# If true, sectionauthor and moduleauthor directives will be shown in the -# output. They are ignored by default. -#show_authors = False - -# The name of the Pygments (syntax highlighting) style to use. -pygments_style = 'sphinx' - -# A list of ignored prefixes for module index sorting. -#modindex_common_prefix = [] - -# If true, keep warnings as "system message" paragraphs in the built documents. -#keep_warnings = False - -# If true, `todo` and `todoList` produce output, else they produce nothing. -todo_include_todos = False - - -# -- Options for HTML output ---------------------------------------------- - -# The theme to use for HTML and HTML Help pages. See the documentation for -# a list of builtin themes. -import sphinx_rtd_theme -html_theme = 'sphinx_rtd_theme' -html_theme_path = [sphinx_rtd_theme.get_html_theme_path()] - -# Theme options are theme-specific and customize the look and feel of a theme -# further. For a list of options available for each theme, see the -# documentation. -#html_theme_options = {} - -# Add any paths that contain custom themes here, relative to this directory. -#html_theme_path = [] - -# The name for this set of Sphinx documents. If None, it defaults to -# " v documentation". -#html_title = None - -# A shorter title for the navigation bar. Default is the same as html_title. -#html_short_title = None - -# The name of an image file (relative to this directory) to place at the top -# of the sidebar. -#html_logo = None - -# The name of an image file (relative to this directory) to use as a favicon of -# the docs. This file should be a Windows icon file (.ico) being 16x16 or 32x32 -# pixels large. -#html_favicon = None - -# Add any paths that contain custom static files (such as style sheets) here, -# relative to this directory. They are copied after the builtin static files, -# so a file named "default.css" will overwrite the builtin "default.css". -html_static_path = ['_static'] - -# Add any extra paths that contain custom files (such as robots.txt or -# .htaccess) here, relative to this directory. These files are copied -# directly to the root of the documentation. -#html_extra_path = [] - -# If not '', a 'Last updated on:' timestamp is inserted at every page bottom, -# using the given strftime format. -#html_last_updated_fmt = '%b %d, %Y' - -# If true, SmartyPants will be used to convert quotes and dashes to -# typographically correct entities. -#html_use_smartypants = True - -# Custom sidebar templates, maps document names to template names. -#html_sidebars = {} - -# Additional templates that should be rendered to pages, maps page names to -# template names. -#html_additional_pages = {} - -# If false, no module index is generated. -#html_domain_indices = True - -# If false, no index is generated. -#html_use_index = True - -# If true, the index is split into individual pages for each letter. -#html_split_index = False - -# If true, links to the reST sources are added to the pages. -#html_show_sourcelink = True - -# If true, "Created using Sphinx" is shown in the HTML footer. Default is True. -#html_show_sphinx = True - -# If true, "(C) Copyright ..." is shown in the HTML footer. Default is True. -#html_show_copyright = True - -# If true, an OpenSearch description file will be output, and all pages will -# contain a tag referring to it. The value of this option must be the -# base URL from which the finished HTML is served. -#html_use_opensearch = '' - -# This is the file name suffix for HTML files (e.g. ".xhtml"). -#html_file_suffix = None - -# Language to be used for generating the HTML full-text search index. -# Sphinx supports the following languages: -# 'da', 'de', 'en', 'es', 'fi', 'fr', 'hu', 'it', 'ja' -# 'nl', 'no', 'pt', 'ro', 'ru', 'sv', 'tr' -#html_search_language = 'en' - -# A dictionary with options for the search language support, empty by default. -# Now only 'ja' uses this config value -#html_search_options = {'type': 'default'} - -# The name of a javascript file (relative to the configuration directory) that -# implements a search results scorer. If empty, the default will be used. -#html_search_scorer = 'scorer.js' - -# Output file base name for HTML help builder. -htmlhelp_basename = 'coredoc' - -# -- Options for LaTeX output --------------------------------------------- - -latex_elements = { -# The paper size ('letterpaper' or 'a4paper'). -#'papersize': 'letterpaper', - -# The font size ('10pt', '11pt' or '12pt'). -#'pointsize': '10pt', - -# Additional stuff for the LaTeX preamble. -#'preamble': '', - -# Latex figure (float) alignment -#'figure_align': 'htbp', -} - -# Grouping the document tree into LaTeX files. List of tuples -# (source start file, target name, title, -# author, documentclass [howto, manual, or own class]). -latex_documents = [ - (master_doc, 'pyrender.tex', u'pyrender Documentation', - u'Matthew Matl', 'manual'), -] - -# The name of an image file (relative to this directory) to place at the top of -# the title page. -#latex_logo = None - -# For "manual" documents, if this is true, then toplevel headings are parts, -# not chapters. -#latex_use_parts = False - -# If true, show page references after internal links. -#latex_show_pagerefs = False - -# If true, show URL addresses after external links. -#latex_show_urls = False - -# Documents to append as an appendix to all manuals. -#latex_appendices = [] - -# If false, no module index is generated. -#latex_domain_indices = True - - -# -- Options for manual page output --------------------------------------- - -# One entry per manual page. List of tuples -# (source start file, name, description, authors, manual section). -man_pages = [ - (master_doc, 'pyrender', u'pyrender Documentation', - [author], 1) -] - -# If true, show URL addresses after external links. -#man_show_urls = False - - -# -- Options for Texinfo output ------------------------------------------- - -# Grouping the document tree into Texinfo files. List of tuples -# (source start file, target name, title, author, -# dir menu entry, description, category) -texinfo_documents = [ - (master_doc, 'pyrender', u'pyrender Documentation', - author, 'pyrender', 'One line description of project.', - 'Miscellaneous'), -] - -# Documents to append as an appendix to all manuals. -#texinfo_appendices = [] - -# If false, no module index is generated. -#texinfo_domain_indices = True - -# How to display URL addresses: 'footnote', 'no', or 'inline'. -#texinfo_show_urls = 'footnote' - -# If true, do not generate a @detailmenu in the "Top" node's menu. -#texinfo_no_detailmenu = False - -intersphinx_mapping = { - 'python' : ('https://docs.python.org/', None), - 'pyrender' : ('https://pyrender.readthedocs.io/en/latest/', None), -} - -# Autosummary fix -autosummary_generate = True - -# Try to suppress multiple-definition warnings by always taking the shorter -# path when two or more paths have the same base module - -class MyPythonDomain(PythonDomain): - - def find_obj(self, env, modname, classname, name, type, searchmode=0): - """Ensures an object always resolves to the desired module - if defined there.""" - orig_matches = PythonDomain.find_obj( - self, env, modname, classname, name, type, searchmode - ) - - if len(orig_matches) <= 1: - return orig_matches - - # If multiple matches, try to take the shortest if all the modules are - # the same - first_match_name_sp = orig_matches[0][0].split('.') - base_name = first_match_name_sp[0] - min_len = len(first_match_name_sp) - best_match = orig_matches[0] - - for match in orig_matches[1:]: - match_name = match[0] - match_name_sp = match_name.split('.') - match_base = match_name_sp[0] - - # If we have mismatched bases, return them all to trigger warnings - if match_base != base_name: - return orig_matches - - # Otherwise, check and see if it's shorter - if len(match_name_sp) < min_len: - min_len = len(match_name_sp) - best_match = match - - return (best_match,) - - -def setup(sphinx): - """Use MyPythonDomain in place of PythonDomain""" - sphinx.override_domain(MyPythonDomain) - diff --git a/pyrender/docs/source/examples/cameras.rst b/pyrender/docs/source/examples/cameras.rst deleted file mode 100644 index 39186b75b16584d11fd1606b92291c104e0452bd..0000000000000000000000000000000000000000 --- a/pyrender/docs/source/examples/cameras.rst +++ /dev/null @@ -1,26 +0,0 @@ -.. _camera_guide: - -Creating Cameras -================ - -Pyrender supports three camera types -- :class:`.PerspectiveCamera` and -:class:`.IntrinsicsCamera` types, -which render scenes as a human would see them, and -:class:`.OrthographicCamera` types, which preserve distances between points. - -Creating cameras is easy -- just specify their basic attributes: - ->>> pc = pyrender.PerspectiveCamera(yfov=np.pi / 3.0, aspectRatio=1.414) ->>> oc = pyrender.OrthographicCamera(xmag=1.0, ymag=1.0) - -For more information, see the Khronos group's documentation here_: - -.. _here: https://github.com/KhronosGroup/glTF/tree/master/specification/2.0#projection-matrices - -When you add cameras to the scene, make sure that you're using OpenGL camera -coordinates to specify their pose. See the illustration below for details. -Basically, the camera z-axis points away from the scene, the x-axis points -right in image space, and the y-axis points up in image space. - -.. image:: /_static/camera_coords.png - diff --git a/pyrender/docs/source/examples/index.rst b/pyrender/docs/source/examples/index.rst deleted file mode 100644 index 4be536cd62c1cca112228f4e114e783be77a0ab8..0000000000000000000000000000000000000000 --- a/pyrender/docs/source/examples/index.rst +++ /dev/null @@ -1,20 +0,0 @@ -.. _guide: - -User Guide -========== - -This section contains guides on how to use Pyrender to quickly visualize -your 3D data, including a quickstart guide and more detailed descriptions -of each part of the rendering pipeline. - - -.. toctree:: - :maxdepth: 2 - - quickstart.rst - models.rst - lighting.rst - cameras.rst - scenes.rst - offscreen.rst - viewer.rst diff --git a/pyrender/docs/source/examples/lighting.rst b/pyrender/docs/source/examples/lighting.rst deleted file mode 100644 index f89bee7d15027a0f52711622b053b49cc6e1b410..0000000000000000000000000000000000000000 --- a/pyrender/docs/source/examples/lighting.rst +++ /dev/null @@ -1,21 +0,0 @@ -.. _lighting_guide: - -Creating Lights -=============== - -Pyrender supports three types of punctual light: - -- :class:`.PointLight`: Point-based light sources, such as light bulbs. -- :class:`.SpotLight`: A conical light source, like a flashlight. -- :class:`.DirectionalLight`: A general light that does not attenuate with - distance. - -Creating lights is easy -- just specify their basic attributes: - ->>> pl = pyrender.PointLight(color=[1.0, 1.0, 1.0], intensity=2.0) ->>> sl = pyrender.SpotLight(color=[1.0, 1.0, 1.0], intensity=2.0, -... innerConeAngle=0.05, outerConeAngle=0.5) ->>> dl = pyrender.DirectionalLight(color=[1.0, 1.0, 1.0], intensity=2.0) - -For more information about how these lighting models are implemented, -see their class documentation. diff --git a/pyrender/docs/source/examples/models.rst b/pyrender/docs/source/examples/models.rst deleted file mode 100644 index 84e71c4ff41a8d2e0eb2dc48434caedb757ff954..0000000000000000000000000000000000000000 --- a/pyrender/docs/source/examples/models.rst +++ /dev/null @@ -1,143 +0,0 @@ -.. _model_guide: - -Loading and Configuring Models -============================== -The first step to any rendering application is loading your models. -Pyrender implements the GLTF 2.0 specification, which means that all -models are composed of a hierarchy of objects. - -At the top level, we have a :class:`.Mesh`. The :class:`.Mesh` is -basically a wrapper of any number of :class:`.Primitive` types, -which actually represent geometry that can be drawn to the screen. - -Primitives are composed of a variety of parameters, including -vertex positions, vertex normals, color and texture information, -and triangle indices if smooth rendering is desired. -They can implement point clouds, triangular meshes, or lines -depending on how you configure their data and set their -:attr:`.Primitive.mode` parameter. - -Although you can create primitives yourself if you want to, -it's probably easier to just use the utility functions provided -in the :class:`.Mesh` class. - -Creating Triangular Meshes --------------------------- - -Simple Construction -~~~~~~~~~~~~~~~~~~~ -Pyrender allows you to create a :class:`.Mesh` containing a -triangular mesh model directly from a :class:`~trimesh.base.Trimesh` object -using the :meth:`.Mesh.from_trimesh` static method. - ->>> import trimesh ->>> import pyrender ->>> import numpy as np ->>> tm = trimesh.load('examples/models/fuze.obj') ->>> m = pyrender.Mesh.from_trimesh(tm) ->>> m.primitives -[] - -You can also create a single :class:`.Mesh` from a list of -:class:`~trimesh.base.Trimesh` objects: - ->>> tms = [trimesh.creation.icosahedron(), trimesh.creation.cylinder()] ->>> m = pyrender.Mesh.from_trimesh(tms) -[, - ] - -Vertex Smoothing -~~~~~~~~~~~~~~~~ - -The :meth:`.Mesh.from_trimesh` method has a few additional optional parameters. -If you want to render the mesh without interpolating face normals, which can -be useful for meshes that are supposed to be angular (e.g. a cube), you -can specify ``smooth=False``. - ->>> m = pyrender.Mesh.from_trimesh(tm, smooth=False) - -Per-Face or Per-Vertex Coloration -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -If you have an untextured trimesh, you can color it in with per-face or -per-vertex colors: - ->>> tm.visual.vertex_colors = np.random.uniform(size=tm.vertices.shape) ->>> tm.visual.face_colors = np.random.uniform(size=tm.faces.shape) ->>> m = pyrender.Mesh.from_trimesh(tm) - -Instancing -~~~~~~~~~~ - -If you want to render many copies of the same mesh at different poses, -you can statically create a vast array of them in an efficient manner. -Simply specify the ``poses`` parameter to be a list of ``N`` 4x4 homogenous -transformation matrics that position the meshes relative to their common -base frame: - ->>> tfs = np.tile(np.eye(4), (3,1,1)) ->>> tfs[1,:3,3] = [0.1, 0.0, 0.0] ->>> tfs[2,:3,3] = [0.2, 0.0, 0.0] ->>> tfs -array([[[1. , 0. , 0. , 0. ], - [0. , 1. , 0. , 0. ], - [0. , 0. , 1. , 0. ], - [0. , 0. , 0. , 1. ]], - [[1. , 0. , 0. , 0.1], - [0. , 1. , 0. , 0. ], - [0. , 0. , 1. , 0. ], - [0. , 0. , 0. , 1. ]], - [[1. , 0. , 0. , 0.2], - [0. , 1. , 0. , 0. ], - [0. , 0. , 1. , 0. ], - [0. , 0. , 0. , 1. ]]]) - ->>> m = pyrender.Mesh.from_trimesh(tm, poses=tfs) - -Custom Materials -~~~~~~~~~~~~~~~~ - -You can also specify a custom material for any triangular mesh you create -in the ``material`` parameter of :meth:`.Mesh.from_trimesh`. -The main material supported by Pyrender is the -:class:`.MetallicRoughnessMaterial`. -The metallic-roughness model supports rendering highly-realistic objects across -a wide gamut of materials. - -For more information, see the documentation of the -:class:`.MetallicRoughnessMaterial` constructor or look at the Khronos_ -documentation for more information. - -.. _Khronos: https://github.com/KhronosGroup/glTF/tree/master/specification/2.0#materials - -Creating Point Clouds ---------------------- - -Point Sprites -~~~~~~~~~~~~~ -Pyrender also allows you to create a :class:`.Mesh` containing a -point cloud directly from :class:`numpy.ndarray` instances -using the :meth:`.Mesh.from_points` static method. - -Simply provide a list of points and optional per-point colors and normals. - ->>> pts = tm.vertices.copy() ->>> colors = np.random.uniform(size=pts.shape) ->>> m = pyrender.Mesh.from_points(pts, colors=colors) - -Point clouds created in this way will be rendered as square point sprites. - -.. image:: /_static/points.png - -Point Spheres -~~~~~~~~~~~~~ -If you have a monochromatic point cloud and would like to render it with -spheres, you can render it by instancing a spherical trimesh: - ->>> sm = trimesh.creation.uv_sphere(radius=0.1) ->>> sm.visual.vertex_colors = [1.0, 0.0, 0.0] ->>> tfs = np.tile(np.eye(4), (len(pts), 1, 1)) ->>> tfs[:,:3,3] = pts ->>> m = pyrender.Mesh.from_trimesh(sm, poses=tfs) - -.. image:: /_static/points2.png diff --git a/pyrender/docs/source/examples/offscreen.rst b/pyrender/docs/source/examples/offscreen.rst deleted file mode 100644 index 291532b6e0c0e512df35a97e3c826cc83015aeca..0000000000000000000000000000000000000000 --- a/pyrender/docs/source/examples/offscreen.rst +++ /dev/null @@ -1,87 +0,0 @@ -.. _offscreen_guide: - -Offscreen Rendering -=================== - -.. note:: - If you're using a headless server, you'll need to use either EGL (for - GPU-accelerated rendering) or OSMesa (for CPU-only software rendering). - If you're using OSMesa, be sure that you've installed it properly. See - :ref:`osmesa` for details. - -Choosing a Backend ------------------- - -Once you have a scene set up with its geometry, cameras, and lights, -you can render it using the :class:`.OffscreenRenderer`. Pyrender supports -three backends for offscreen rendering: - -- Pyglet, the same engine that runs the viewer. This requires an active - display manager, so you can't run it on a headless server. This is the - default option. -- OSMesa, a software renderer. -- EGL, which allows for GPU-accelerated rendering without a display manager. - -If you want to use OSMesa or EGL, you need to set the ``PYOPENGL_PLATFORM`` -environment variable before importing pyrender or any other OpenGL library. -You can do this at the command line: - -.. code-block:: bash - - PYOPENGL_PLATFORM=osmesa python render.py - -or at the top of your Python script: - -.. code-block:: bash - - # Top of main python script - import os - os.environ['PYOPENGL_PLATFORM'] = 'egl' - -The handle for EGL is ``egl``, and the handle for OSMesa is ``osmesa``. - -Running the Renderer --------------------- - -Once you've set your environment variable appropriately, create your scene and -then configure the :class:`.OffscreenRenderer` object with a window width, -a window height, and a size for point-cloud points: - ->>> r = pyrender.OffscreenRenderer(viewport_width=640, -... viewport_height=480, -... point_size=1.0) - -Then, just call the :meth:`.OffscreenRenderer.render` function: - ->>> color, depth = r.render(scene) - -.. image:: /_static/scene.png - -This will return a ``(w,h,3)`` channel floating-point color image and -a ``(w,h)`` floating-point depth image rendered from the scene's main camera. - -You can customize the rendering process by using flag options from -:class:`.RenderFlags` and bitwise or-ing them together. For example, -the following code renders a color image with an alpha channel -and enables shadow mapping for all directional lights: - ->>> flags = RenderFlags.RGBA | RenderFlags.SHADOWS_DIRECTIONAL ->>> color, depth = r.render(scene, flags=flags) - -Once you're done with the offscreen renderer, you need to close it before you -can run a different renderer or open the viewer for the same scene: - ->>> r.delete() - -Google CoLab Examples ---------------------- - -For a minimal working example of offscreen rendering using OSMesa, -see the `OSMesa Google CoLab notebook`_. - -.. _OSMesa Google CoLab notebook: https://colab.research.google.com/drive/1Z71mHIc-Sqval92nK290vAsHZRUkCjUx - -For a minimal working example of offscreen rendering using EGL, -see the `EGL Google CoLab notebook`_. - -.. _EGL Google CoLab notebook: https://colab.research.google.com/drive/1rTLHk0qxh4dn8KNe-mCnN8HAWdd2_BEh diff --git a/pyrender/docs/source/examples/quickstart.rst b/pyrender/docs/source/examples/quickstart.rst deleted file mode 100644 index ac556419e5206c2ccd4bc985feb1a8c7347310af..0000000000000000000000000000000000000000 --- a/pyrender/docs/source/examples/quickstart.rst +++ /dev/null @@ -1,71 +0,0 @@ -.. _quickstart_guide: - -Quickstart -========== - - -Minimal Example for 3D Viewer ------------------------------ -Here is a minimal example of loading and viewing a triangular mesh model -in pyrender. - ->>> import trimesh ->>> import pyrender ->>> fuze_trimesh = trimesh.load('examples/models/fuze.obj') ->>> mesh = pyrender.Mesh.from_trimesh(fuze_trimesh) ->>> scene = pyrender.Scene() ->>> scene.add(mesh) ->>> pyrender.Viewer(scene, use_raymond_lighting=True) - -.. image:: /_static/fuze.png - - -Minimal Example for Offscreen Rendering ---------------------------------------- -.. note:: - If you're using a headless server, make sure that you followed the guide - for installing OSMesa. See :ref:`osmesa`. - -Here is a minimal example of rendering a mesh model offscreen in pyrender. -The only additional necessities are that you need to add lighting and a camera. - ->>> import numpy as np ->>> import trimesh ->>> import pyrender ->>> import matplotlib.pyplot as plt - ->>> fuze_trimesh = trimesh.load('examples/models/fuze.obj') ->>> mesh = pyrender.Mesh.from_trimesh(fuze_trimesh) ->>> scene = pyrender.Scene() ->>> scene.add(mesh) ->>> camera = pyrender.PerspectiveCamera(yfov=np.pi / 3.0, aspectRatio=1.0) ->>> s = np.sqrt(2)/2 ->>> camera_pose = np.array([ -... [0.0, -s, s, 0.3], -... [1.0, 0.0, 0.0, 0.0], -... [0.0, s, s, 0.35], -... [0.0, 0.0, 0.0, 1.0], -... ]) ->>> scene.add(camera, pose=camera_pose) ->>> light = pyrender.SpotLight(color=np.ones(3), intensity=3.0, -... innerConeAngle=np.pi/16.0, -... outerConeAngle=np.pi/6.0) ->>> scene.add(light, pose=camera_pose) ->>> r = pyrender.OffscreenRenderer(400, 400) ->>> color, depth = r.render(scene) ->>> plt.figure() ->>> plt.subplot(1,2,1) ->>> plt.axis('off') ->>> plt.imshow(color) ->>> plt.subplot(1,2,2) ->>> plt.axis('off') ->>> plt.imshow(depth, cmap=plt.cm.gray_r) ->>> plt.show() - -.. image:: /_static/minexcolor.png - :width: 45% - :align: left -.. image:: /_static/minexdepth.png - :width: 45% - :align: right - diff --git a/pyrender/docs/source/examples/scenes.rst b/pyrender/docs/source/examples/scenes.rst deleted file mode 100644 index 94c243f8b860b9669ac26105fd2b9906054f4568..0000000000000000000000000000000000000000 --- a/pyrender/docs/source/examples/scenes.rst +++ /dev/null @@ -1,78 +0,0 @@ -.. _scene_guide: - -Creating Scenes -=============== - -Before you render anything, you need to put all of your lights, cameras, -and meshes into a scene. The :class:`.Scene` object keeps track of the relative -poses of these primitives by inserting them into :class:`.Node` objects and -keeping them in a directed acyclic graph. - -Adding Objects --------------- - -To create a :class:`.Scene`, simply call the constructor. You can optionally -specify an ambient light color and a background color: - ->>> scene = pyrender.Scene(ambient_light=[0.02, 0.02, 0.02], -... bg_color=[1.0, 1.0, 1.0]) - -You can add objects to a scene by first creating a :class:`.Node` object -and adding the object and its pose to the :class:`.Node`. Poses are specified -as 4x4 homogenous transformation matrices that are stored in the node's -:attr:`.Node.matrix` attribute. Note that the :class:`.Node` -constructor requires you to specify whether you're adding a mesh, light, -or camera. - ->>> mesh = pyrender.Mesh.from_trimesh(tm) ->>> light = pyrender.PointLight(color=[1.0, 1.0, 1.0], intensity=2.0) ->>> cam = pyrender.PerspectiveCamera(yfov=np.pi / 3.0, aspectRatio=1.414) ->>> nm = pyrender.Node(mesh=mesh, matrix=np.eye(4)) ->>> nl = pyrender.Node(light=light, matrix=np.eye(4)) ->>> nc = pyrender.Node(camera=cam, matrix=np.eye(4)) ->>> scene.add_node(nm) ->>> scene.add_node(nl) ->>> scene.add_node(nc) - -You can also add objects directly to a scene with the :meth:`.Scene.add` function, -which takes care of creating a :class:`.Node` for you. - ->>> scene.add(mesh, pose=np.eye(4)) ->>> scene.add(light, pose=np.eye(4)) ->>> scene.add(cam, pose=np.eye(4)) - -Nodes can be hierarchical, in which case the node's :attr:`.Node.matrix` -specifies that node's pose relative to its parent frame. You can add nodes to -a scene hierarchically by specifying a parent node in your calls to -:meth:`.Scene.add` or :meth:`.Scene.add_node`: - ->>> scene.add_node(nl, parent_node=nc) ->>> scene.add(cam, parent_node=nm) - -If you add multiple cameras to a scene, you can specify which one to render from -by setting the :attr:`.Scene.main_camera_node` attribute. - -Updating Objects ----------------- - -You can update the poses of existing nodes with the :meth:`.Scene.set_pose` -function. Simply call it with a :class:`.Node` that is already in the scene -and the new pose of that node with respect to its parent as a 4x4 homogenous -transformation matrix: - ->>> scene.set_pose(nl, pose=np.eye(4)) - -If you want to get the local pose of a node, you can just access its -:attr:`.Node.matrix` attribute. However, if you want to the get -the pose of a node *with respect to the world frame*, you can call the -:meth:`.Scene.get_pose` method. - ->>> tf = scene.get_pose(nl) - -Removing Objects ----------------- - -Finally, you can remove a :class:`.Node` and all of its children from the -scene with the :meth:`.Scene.remove_node` function: - ->>> scene.remove_node(nl) diff --git a/pyrender/docs/source/examples/viewer.rst b/pyrender/docs/source/examples/viewer.rst deleted file mode 100644 index 00a7973b46ec7da33b51b65581af6f25c1b1652f..0000000000000000000000000000000000000000 --- a/pyrender/docs/source/examples/viewer.rst +++ /dev/null @@ -1,61 +0,0 @@ -.. _viewer_guide: - -Live Scene Viewer -================= - -Standard Usage --------------- -In addition to the offscreen renderer, Pyrender comes with a live scene viewer. -In its standard invocation, calling the :class:`.Viewer`'s constructor will -immediately pop a viewing window that you can navigate around in. - ->>> pyrender.Viewer(scene) - -By default, the viewer uses your scene's lighting. If you'd like to start with -some additional lighting that moves around with the camera, you can specify that -with: - ->>> pyrender.Viewer(scene, use_raymond_lighting=True) - -For a full list of the many options that the :class:`.Viewer` supports, check out its -documentation. - -.. image:: /_static/rotation.gif - -Running the Viewer in a Separate Thread ---------------------------------------- -If you'd like to animate your models, you'll want to run the viewer in a -separate thread so that you can update the scene while the viewer is running. -To do this, first pop the viewer in a separate thread by calling its constructor -with the ``run_in_thread`` option set: - ->>> v = pyrender.Viewer(scene, run_in_thread=True) - -Then, you can manipulate the :class:`.Scene` while the viewer is running to -animate things. However, be careful to acquire the viewer's -:attr:`.Viewer.render_lock` before editing the scene to prevent data corruption: - ->>> i = 0 ->>> while True: -... pose = np.eye(4) -... pose[:3,3] = [i, 0, 0] -... v.render_lock.acquire() -... scene.set_pose(mesh_node, pose) -... v.render_lock.release() -... i += 0.01 - -.. image:: /_static/scissors.gif - -You can wait on the viewer to be closed manually: - ->>> while v.is_active: -... pass - -Or you can close it from the main thread forcibly. -Make sure to still loop and block for the viewer to actually exit before using -the scene object again. - ->>> v.close_external() ->>> while v.is_active: -... pass - diff --git a/pyrender/docs/source/index.rst b/pyrender/docs/source/index.rst deleted file mode 100644 index baf189ede6bb3435cad5b8795e1937ef1a3c2c56..0000000000000000000000000000000000000000 --- a/pyrender/docs/source/index.rst +++ /dev/null @@ -1,41 +0,0 @@ -.. core documentation master file, created by - sphinx-quickstart on Sun Oct 16 14:33:48 2016. - You can adapt this file completely to your liking, but it should at least - contain the root `toctree` directive. - -Pyrender Documentation -======================== -Pyrender is a pure Python (2.7, 3.4, 3.5, 3.6) library for physically-based -rendering and visualization. -It is designed to meet the glTF 2.0 specification_ from Khronos - -.. _specification: https://www.khronos.org/gltf/ - -Pyrender is lightweight, easy to install, and simple to use. -It comes packaged with both an intuitive scene viewer and a headache-free -offscreen renderer with support for GPU-accelerated rendering on headless -servers, which makes it perfect for machine learning applications. -Check out the :ref:`guide` for a full tutorial, or fork me on -Github_. - -.. _Github: https://github.com/mmatl/pyrender - -.. image:: _static/rotation.gif - -.. image:: _static/damaged_helmet.png - -.. toctree:: - :maxdepth: 2 - - install/index.rst - examples/index.rst - api/index.rst - - -Indices and tables -================== - -* :ref:`genindex` -* :ref:`modindex` -* :ref:`search` - diff --git a/pyrender/docs/source/install/index.rst b/pyrender/docs/source/install/index.rst deleted file mode 100644 index c785f202d877f8bbaf286c21eddca1925973f75e..0000000000000000000000000000000000000000 --- a/pyrender/docs/source/install/index.rst +++ /dev/null @@ -1,172 +0,0 @@ -Installation Guide -================== - -Python Installation -------------------- - -This package is available via ``pip``. - -.. code-block:: bash - - pip install pyrender - -If you're on MacOS, you'll need -to pre-install my fork of ``pyglet``, as the version on PyPI hasn't yet included -my change that enables OpenGL contexts on MacOS. - -.. code-block:: bash - - git clone https://github.com/mmatl/pyglet.git - cd pyglet - pip install . - -.. _osmesa: - -Getting Pyrender Working with OSMesa ------------------------------------- -If you want to render scenes offscreen but don't want to have to -install a display manager or deal with the pains of trying to get -OpenGL to work over SSH, you have two options. - -The first (and preferred) option is using EGL, which enables you to perform -GPU-accelerated rendering on headless servers. -However, you'll need EGL 1.5 to get modern OpenGL contexts. -This comes packaged with NVIDIA's current drivers, but if you are having issues -getting EGL to work with your hardware, you can try using OSMesa, -a software-based offscreen renderer that is included with any Mesa -install. - -If you want to use OSMesa with pyrender, you'll have to perform two additional -installation steps: - -- :ref:`installmesa` -- :ref:`installpyopengl` - -Then, read the offscreen rendering tutorial. See :ref:`offscreen_guide`. - -.. _installmesa: - -Installing OSMesa -***************** - -As a first step, you'll need to rebuild and re-install Mesa with support -for fast offscreen rendering and OpenGL 3+ contexts. -I'd recommend installing from source, but you can also try my ``.deb`` -for Ubuntu 16.04 and up. - -Installing from a Debian Package -******************************** - -If you're running Ubuntu 16.04 or newer, you should be able to install the -required version of Mesa from my ``.deb`` file. - -.. code-block:: bash - - sudo apt update - sudo wget https://github.com/mmatl/travis_debs/raw/master/xenial/mesa_18.3.3-0.deb - sudo dpkg -i ./mesa_18.3.3-0.deb || true - sudo apt install -f - -If this doesn't work, try building from source. - -Building From Source -******************** - -First, install build dependencies via `apt` or your system's package manager. - -.. code-block:: bash - - sudo apt-get install llvm-6.0 freeglut3 freeglut3-dev - -Then, download the current release of Mesa from here_. -Unpack the source and go to the source folder: - -.. _here: https://archive.mesa3d.org/mesa-18.3.3.tar.gz - -.. code-block:: bash - - tar xfv mesa-18.3.3.tar.gz - cd mesa-18.3.3 - -Replace ``PREFIX`` with the path you want to install Mesa at. -If you're not worried about overwriting your default Mesa install, -a good place is at ``/usr/local``. - -Now, configure the installation by running the following command: - -.. code-block:: bash - - ./configure --prefix=PREFIX \ - --enable-opengl --disable-gles1 --disable-gles2 \ - --disable-va --disable-xvmc --disable-vdpau \ - --enable-shared-glapi \ - --disable-texture-float \ - --enable-gallium-llvm --enable-llvm-shared-libs \ - --with-gallium-drivers=swrast,swr \ - --disable-dri --with-dri-drivers= \ - --disable-egl --with-egl-platforms= --disable-gbm \ - --disable-glx \ - --disable-osmesa --enable-gallium-osmesa \ - ac_cv_path_LLVM_CONFIG=llvm-config-6.0 - -Finally, build and install Mesa. - -.. code-block:: bash - - make -j8 - make install - -Finally, if you didn't install Mesa in the system path, -add the following lines to your ``~/.bashrc`` file after -changing ``MESA_HOME`` to your mesa installation path (i.e. what you used as -``PREFIX`` during the configure command). - -.. code-block:: bash - - MESA_HOME=/path/to/your/mesa/installation - export LIBRARY_PATH=$LIBRARY_PATH:$MESA_HOME/lib - export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$MESA_HOME/lib - export C_INCLUDE_PATH=$C_INCLUDE_PATH:$MESA_HOME/include/ - export CPLUS_INCLUDE_PATH=$CPLUS_INCLUDE_PATH:$MESA_HOME/include/ - -.. _installpyopengl: - -Installing a Compatible Fork of PyOpenGL -**************************************** - -Next, install and use my fork of ``PyOpenGL``. -This fork enables getting modern OpenGL contexts with OSMesa. -My patch has been included in ``PyOpenGL``, but it has not yet been released -on PyPI. - -.. code-block:: bash - - git clone https://github.com/mmatl/pyopengl.git - pip install ./pyopengl - - -Building Documentation ----------------------- - -The online documentation for ``pyrender`` is automatically built by Read The Docs. -Building ``pyrender``'s documentation locally requires a few extra dependencies -- -specifically, `sphinx`_ and a few plugins. - -.. _sphinx: http://www.sphinx-doc.org/en/master/ - -To install the dependencies required, simply change directories into the `pyrender` source and run - -.. code-block:: bash - - $ pip install .[docs] - -Then, go to the ``docs`` directory and run ``make`` with the appropriate target. -For example, - -.. code-block:: bash - - $ cd docs/ - $ make html - -will generate a set of web pages. Any documentation files -generated in this manner can be found in ``docs/build``. diff --git a/pyrender/examples/duck.py b/pyrender/examples/duck.py deleted file mode 100644 index 9a94bad5bfb30493f7364f2e52cbb4badbccb2c7..0000000000000000000000000000000000000000 --- a/pyrender/examples/duck.py +++ /dev/null @@ -1,13 +0,0 @@ -from pyrender import Mesh, Scene, Viewer -from io import BytesIO -import numpy as np -import trimesh -import requests - -duck_source = "https://github.com/KhronosGroup/glTF-Sample-Models/raw/master/2.0/Duck/glTF-Binary/Duck.glb" - -duck = trimesh.load(BytesIO(requests.get(duck_source).content), file_type='glb') -duckmesh = Mesh.from_trimesh(list(duck.geometry.values())[0]) -scene = Scene(ambient_light=np.array([1.0, 1.0, 1.0, 1.0])) -scene.add(duckmesh) -Viewer(scene) diff --git a/pyrender/examples/example.py b/pyrender/examples/example.py deleted file mode 100644 index 599a4850a5899cdeb1a76db1c5cf1c91c263cd41..0000000000000000000000000000000000000000 --- a/pyrender/examples/example.py +++ /dev/null @@ -1,157 +0,0 @@ -"""Examples of using pyrender for viewing and offscreen rendering. -""" -import pyglet -pyglet.options['shadow_window'] = False -import os -import numpy as np -import trimesh - -from pyrender import PerspectiveCamera,\ - DirectionalLight, SpotLight, PointLight,\ - MetallicRoughnessMaterial,\ - Primitive, Mesh, Node, Scene,\ - Viewer, OffscreenRenderer, RenderFlags - -#============================================================================== -# Mesh creation -#============================================================================== - -#------------------------------------------------------------------------------ -# Creating textured meshes from trimeshes -#------------------------------------------------------------------------------ - -# Fuze trimesh -fuze_trimesh = trimesh.load('./models/fuze.obj') -fuze_mesh = Mesh.from_trimesh(fuze_trimesh) - -# Drill trimesh -drill_trimesh = trimesh.load('./models/drill.obj') -drill_mesh = Mesh.from_trimesh(drill_trimesh) -drill_pose = np.eye(4) -drill_pose[0,3] = 0.1 -drill_pose[2,3] = -np.min(drill_trimesh.vertices[:,2]) - -# Wood trimesh -wood_trimesh = trimesh.load('./models/wood.obj') -wood_mesh = Mesh.from_trimesh(wood_trimesh) - -# Water bottle trimesh -bottle_gltf = trimesh.load('./models/WaterBottle.glb') -bottle_trimesh = bottle_gltf.geometry[list(bottle_gltf.geometry.keys())[0]] -bottle_mesh = Mesh.from_trimesh(bottle_trimesh) -bottle_pose = np.array([ - [1.0, 0.0, 0.0, 0.1], - [0.0, 0.0, -1.0, -0.16], - [0.0, 1.0, 0.0, 0.13], - [0.0, 0.0, 0.0, 1.0], -]) - -#------------------------------------------------------------------------------ -# Creating meshes with per-vertex colors -#------------------------------------------------------------------------------ -boxv_trimesh = trimesh.creation.box(extents=0.1*np.ones(3)) -boxv_vertex_colors = np.random.uniform(size=(boxv_trimesh.vertices.shape)) -boxv_trimesh.visual.vertex_colors = boxv_vertex_colors -boxv_mesh = Mesh.from_trimesh(boxv_trimesh, smooth=False) - -#------------------------------------------------------------------------------ -# Creating meshes with per-face colors -#------------------------------------------------------------------------------ -boxf_trimesh = trimesh.creation.box(extents=0.1*np.ones(3)) -boxf_face_colors = np.random.uniform(size=boxf_trimesh.faces.shape) -boxf_trimesh.visual.face_colors = boxf_face_colors -boxf_mesh = Mesh.from_trimesh(boxf_trimesh, smooth=False) - -#------------------------------------------------------------------------------ -# Creating meshes from point clouds -#------------------------------------------------------------------------------ -points = trimesh.creation.icosphere(radius=0.05).vertices -point_colors = np.random.uniform(size=points.shape) -points_mesh = Mesh.from_points(points, colors=point_colors) - -#============================================================================== -# Light creation -#============================================================================== - -direc_l = DirectionalLight(color=np.ones(3), intensity=1.0) -spot_l = SpotLight(color=np.ones(3), intensity=10.0, - innerConeAngle=np.pi/16, outerConeAngle=np.pi/6) -point_l = PointLight(color=np.ones(3), intensity=10.0) - -#============================================================================== -# Camera creation -#============================================================================== - -cam = PerspectiveCamera(yfov=(np.pi / 3.0)) -cam_pose = np.array([ - [0.0, -np.sqrt(2)/2, np.sqrt(2)/2, 0.5], - [1.0, 0.0, 0.0, 0.0], - [0.0, np.sqrt(2)/2, np.sqrt(2)/2, 0.4], - [0.0, 0.0, 0.0, 1.0] -]) - -#============================================================================== -# Scene creation -#============================================================================== - -scene = Scene(ambient_light=np.array([0.02, 0.02, 0.02, 1.0])) - -#============================================================================== -# Adding objects to the scene -#============================================================================== - -#------------------------------------------------------------------------------ -# By manually creating nodes -#------------------------------------------------------------------------------ -fuze_node = Node(mesh=fuze_mesh, translation=np.array([0.1, 0.15, -np.min(fuze_trimesh.vertices[:,2])])) -scene.add_node(fuze_node) -boxv_node = Node(mesh=boxv_mesh, translation=np.array([-0.1, 0.10, 0.05])) -scene.add_node(boxv_node) -boxf_node = Node(mesh=boxf_mesh, translation=np.array([-0.1, -0.10, 0.05])) -scene.add_node(boxf_node) - -#------------------------------------------------------------------------------ -# By using the add() utility function -#------------------------------------------------------------------------------ -drill_node = scene.add(drill_mesh, pose=drill_pose) -bottle_node = scene.add(bottle_mesh, pose=bottle_pose) -wood_node = scene.add(wood_mesh) -direc_l_node = scene.add(direc_l, pose=cam_pose) -spot_l_node = scene.add(spot_l, pose=cam_pose) - -#============================================================================== -# Using the viewer with a default camera -#============================================================================== - -v = Viewer(scene, shadows=True) - -#============================================================================== -# Using the viewer with a pre-specified camera -#============================================================================== -cam_node = scene.add(cam, pose=cam_pose) -v = Viewer(scene, central_node=drill_node) - -#============================================================================== -# Rendering offscreen from that camera -#============================================================================== - -r = OffscreenRenderer(viewport_width=640*2, viewport_height=480*2) -color, depth = r.render(scene) - -import matplotlib.pyplot as plt -plt.figure() -plt.imshow(color) -plt.show() - -#============================================================================== -# Segmask rendering -#============================================================================== - -nm = {node: 20*(i + 1) for i, node in enumerate(scene.mesh_nodes)} -seg = r.render(scene, RenderFlags.SEG, nm)[0] -plt.figure() -plt.imshow(seg) -plt.show() - -r.delete() - diff --git a/pyrender/pyrender/__init__.py b/pyrender/pyrender/__init__.py deleted file mode 100644 index ee3709846823b7c4b71b22da0e24d63d805528a8..0000000000000000000000000000000000000000 --- a/pyrender/pyrender/__init__.py +++ /dev/null @@ -1,24 +0,0 @@ -from .camera import (Camera, PerspectiveCamera, OrthographicCamera, - IntrinsicsCamera) -from .light import Light, PointLight, DirectionalLight, SpotLight -from .sampler import Sampler -from .texture import Texture -from .material import Material, MetallicRoughnessMaterial -from .primitive import Primitive -from .mesh import Mesh -from .node import Node -from .scene import Scene -from .renderer import Renderer -from .viewer import Viewer -from .offscreen import OffscreenRenderer -from .version import __version__ -from .constants import RenderFlags, TextAlign, GLTF - -__all__ = [ - 'Camera', 'PerspectiveCamera', 'OrthographicCamera', 'IntrinsicsCamera', - 'Light', 'PointLight', 'DirectionalLight', 'SpotLight', - 'Sampler', 'Texture', 'Material', 'MetallicRoughnessMaterial', - 'Primitive', 'Mesh', 'Node', 'Scene', 'Renderer', 'Viewer', - 'OffscreenRenderer', '__version__', 'RenderFlags', 'TextAlign', - 'GLTF' -] diff --git a/pyrender/pyrender/camera.py b/pyrender/pyrender/camera.py deleted file mode 100644 index e019358039033c3a372c990ebad3151258c3651d..0000000000000000000000000000000000000000 --- a/pyrender/pyrender/camera.py +++ /dev/null @@ -1,437 +0,0 @@ -"""Virtual cameras compliant with the glTF 2.0 specification as described at -https://github.com/KhronosGroup/glTF/tree/master/specification/2.0#reference-camera - -Author: Matthew Matl -""" -import abc -import numpy as np -import six -import sys - -from .constants import DEFAULT_Z_NEAR, DEFAULT_Z_FAR - - -@six.add_metaclass(abc.ABCMeta) -class Camera(object): - """Abstract base class for all cameras. - - Note - ---- - Camera poses are specified in the OpenGL format, - where the z axis points away from the view direction and the - x and y axes point to the right and up in the image plane, respectively. - - Parameters - ---------- - znear : float - The floating-point distance to the near clipping plane. - zfar : float - The floating-point distance to the far clipping plane. - ``zfar`` must be greater than ``znear``. - name : str, optional - The user-defined name of this object. - """ - - def __init__(self, - znear=DEFAULT_Z_NEAR, - zfar=DEFAULT_Z_FAR, - name=None): - self.name = name - self.znear = znear - self.zfar = zfar - - @property - def name(self): - """str : The user-defined name of this object. - """ - return self._name - - @name.setter - def name(self, value): - if value is not None: - value = str(value) - self._name = value - - @property - def znear(self): - """float : The distance to the near clipping plane. - """ - return self._znear - - @znear.setter - def znear(self, value): - value = float(value) - if value < 0: - raise ValueError('z-near must be >= 0.0') - self._znear = value - - @property - def zfar(self): - """float : The distance to the far clipping plane. - """ - return self._zfar - - @zfar.setter - def zfar(self, value): - value = float(value) - if value <= 0 or value <= self.znear: - raise ValueError('zfar must be >0 and >znear') - self._zfar = value - - @abc.abstractmethod - def get_projection_matrix(self, width=None, height=None): - """Return the OpenGL projection matrix for this camera. - - Parameters - ---------- - width : int - Width of the current viewport, in pixels. - height : int - Height of the current viewport, in pixels. - """ - pass - - -class PerspectiveCamera(Camera): - - """A perspective camera for perspective projection. - - Parameters - ---------- - yfov : float - The floating-point vertical field of view in radians. - znear : float - The floating-point distance to the near clipping plane. - If not specified, defaults to 0.05. - zfar : float, optional - The floating-point distance to the far clipping plane. - ``zfar`` must be greater than ``znear``. - If None, the camera uses an infinite projection matrix. - aspectRatio : float, optional - The floating-point aspect ratio of the field of view. - If not specified, the camera uses the viewport's aspect ratio. - name : str, optional - The user-defined name of this object. - """ - - def __init__(self, - yfov, - znear=DEFAULT_Z_NEAR, - zfar=None, - aspectRatio=None, - name=None): - super(PerspectiveCamera, self).__init__( - znear=znear, - zfar=zfar, - name=name, - ) - - self.yfov = yfov - self.aspectRatio = aspectRatio - - @property - def yfov(self): - """float : The vertical field of view in radians. - """ - return self._yfov - - @yfov.setter - def yfov(self, value): - value = float(value) - if value <= 0.0: - raise ValueError('Field of view must be positive') - self._yfov = value - - @property - def zfar(self): - """float : The distance to the far clipping plane. - """ - return self._zfar - - @zfar.setter - def zfar(self, value): - if value is not None: - value = float(value) - if value <= 0 or value <= self.znear: - raise ValueError('zfar must be >0 and >znear') - self._zfar = value - - @property - def aspectRatio(self): - """float : The ratio of the width to the height of the field of view. - """ - return self._aspectRatio - - @aspectRatio.setter - def aspectRatio(self, value): - if value is not None: - value = float(value) - if value <= 0.0: - raise ValueError('Aspect ratio must be positive') - self._aspectRatio = value - - def get_projection_matrix(self, width=None, height=None): - """Return the OpenGL projection matrix for this camera. - - Parameters - ---------- - width : int - Width of the current viewport, in pixels. - height : int - Height of the current viewport, in pixels. - """ - aspect_ratio = self.aspectRatio - if aspect_ratio is None: - if width is None or height is None: - raise ValueError('Aspect ratio of camera must be defined') - aspect_ratio = float(width) / float(height) - - a = aspect_ratio - t = np.tan(self.yfov / 2.0) - n = self.znear - f = self.zfar - - P = np.zeros((4,4)) - P[0][0] = 1.0 / (a * t) - P[1][1] = 1.0 / t - P[3][2] = -1.0 - - if f is None: - P[2][2] = -1.0 - P[2][3] = -2.0 * n - else: - P[2][2] = (f + n) / (n - f) - P[2][3] = (2 * f * n) / (n - f) - - return P - - -class OrthographicCamera(Camera): - """An orthographic camera for orthographic projection. - - Parameters - ---------- - xmag : float - The floating-point horizontal magnification of the view. - ymag : float - The floating-point vertical magnification of the view. - znear : float - The floating-point distance to the near clipping plane. - If not specified, defaults to 0.05. - zfar : float - The floating-point distance to the far clipping plane. - ``zfar`` must be greater than ``znear``. - If not specified, defaults to 100.0. - name : str, optional - The user-defined name of this object. - """ - - def __init__(self, - xmag, - ymag, - znear=DEFAULT_Z_NEAR, - zfar=DEFAULT_Z_FAR, - name=None): - super(OrthographicCamera, self).__init__( - znear=znear, - zfar=zfar, - name=name, - ) - - self.xmag = xmag - self.ymag = ymag - - @property - def xmag(self): - """float : The horizontal magnification of the view. - """ - return self._xmag - - @xmag.setter - def xmag(self, value): - value = float(value) - if value <= 0.0: - raise ValueError('X magnification must be positive') - self._xmag = value - - @property - def ymag(self): - """float : The vertical magnification of the view. - """ - return self._ymag - - @ymag.setter - def ymag(self, value): - value = float(value) - if value <= 0.0: - raise ValueError('Y magnification must be positive') - self._ymag = value - - @property - def znear(self): - """float : The distance to the near clipping plane. - """ - return self._znear - - @znear.setter - def znear(self, value): - value = float(value) - if value <= 0: - raise ValueError('z-near must be > 0.0') - self._znear = value - - def get_projection_matrix(self, width=None, height=None): - """Return the OpenGL projection matrix for this camera. - - Parameters - ---------- - width : int - Width of the current viewport, in pixels. - Unused in this function. - height : int - Height of the current viewport, in pixels. - Unused in this function. - """ - xmag = self.xmag - ymag = self.ymag - - # If screen width/height defined, rescale xmag - if width is not None and height is not None: - xmag = width / height * ymag - - n = self.znear - f = self.zfar - P = np.zeros((4,4)) - P[0][0] = 1.0 / xmag - P[1][1] = 1.0 / ymag - P[2][2] = 2.0 / (n - f) - P[2][3] = (f + n) / (n - f) - P[3][3] = 1.0 - return P - - -class IntrinsicsCamera(Camera): - """A perspective camera with custom intrinsics. - - Parameters - ---------- - fx : float - X-axis focal length in pixels. - fy : float - Y-axis focal length in pixels. - cx : float - X-axis optical center in pixels. - cy : float - Y-axis optical center in pixels. - znear : float - The floating-point distance to the near clipping plane. - If not specified, defaults to 0.05. - zfar : float - The floating-point distance to the far clipping plane. - ``zfar`` must be greater than ``znear``. - If not specified, defaults to 100.0. - name : str, optional - The user-defined name of this object. - """ - - def __init__(self, - fx, - fy, - cx, - cy, - znear=DEFAULT_Z_NEAR, - zfar=DEFAULT_Z_FAR, - name=None): - super(IntrinsicsCamera, self).__init__( - znear=znear, - zfar=zfar, - name=name, - ) - - self.fx = fx - self.fy = fy - self.cx = cx - self.cy = cy - - @property - def fx(self): - """float : X-axis focal length in meters. - """ - return self._fx - - @fx.setter - def fx(self, value): - self._fx = float(value) - - @property - def fy(self): - """float : Y-axis focal length in meters. - """ - return self._fy - - @fy.setter - def fy(self, value): - self._fy = float(value) - - @property - def cx(self): - """float : X-axis optical center in pixels. - """ - return self._cx - - @cx.setter - def cx(self, value): - self._cx = float(value) - - @property - def cy(self): - """float : Y-axis optical center in pixels. - """ - return self._cy - - @cy.setter - def cy(self, value): - self._cy = float(value) - - def get_projection_matrix(self, width, height): - """Return the OpenGL projection matrix for this camera. - - Parameters - ---------- - width : int - Width of the current viewport, in pixels. - height : int - Height of the current viewport, in pixels. - """ - width = float(width) - height = float(height) - - cx, cy = self.cx, self.cy - fx, fy = self.fx, self.fy - if sys.platform == 'darwin': - cx = self.cx * 2.0 - cy = self.cy * 2.0 - fx = self.fx * 2.0 - fy = self.fy * 2.0 - - P = np.zeros((4,4)) - P[0][0] = 2.0 * fx / width - P[1][1] = 2.0 * fy / height - P[0][2] = 1.0 - 2.0 * cx / width - P[1][2] = 2.0 * cy / height - 1.0 - P[3][2] = -1.0 - - n = self.znear - f = self.zfar - if f is None: - P[2][2] = -1.0 - P[2][3] = -2.0 * n - else: - P[2][2] = (f + n) / (n - f) - P[2][3] = (2 * f * n) / (n - f) - - return P - - -__all__ = ['Camera', 'PerspectiveCamera', 'OrthographicCamera', - 'IntrinsicsCamera'] diff --git a/pyrender/pyrender/constants.py b/pyrender/pyrender/constants.py deleted file mode 100644 index 8a5785b6fdb21910a174252c5af2f05b40ece4a5..0000000000000000000000000000000000000000 --- a/pyrender/pyrender/constants.py +++ /dev/null @@ -1,149 +0,0 @@ -DEFAULT_Z_NEAR = 0.05 # Near clipping plane, in meters -DEFAULT_Z_FAR = 100.0 # Far clipping plane, in meters -DEFAULT_SCENE_SCALE = 2.0 # Default scene scale -MAX_N_LIGHTS = 4 # Maximum number of lights of each type allowed -TARGET_OPEN_GL_MAJOR = 4 # Target OpenGL Major Version -TARGET_OPEN_GL_MINOR = 1 # Target OpenGL Minor Version -MIN_OPEN_GL_MAJOR = 3 # Minimum OpenGL Major Version -MIN_OPEN_GL_MINOR = 3 # Minimum OpenGL Minor Version -FLOAT_SZ = 4 # Byte size of GL float32 -UINT_SZ = 4 # Byte size of GL uint32 -SHADOW_TEX_SZ = 2048 # Width and Height of Shadow Textures -TEXT_PADDING = 20 # Width of padding for rendering text (px) - - -# Flags for render type -class RenderFlags(object): - """Flags for rendering in the scene. - - Combine them with the bitwise or. For example, - - >>> flags = OFFSCREEN | SHADOWS_DIRECTIONAL | VERTEX_NORMALS - - would result in an offscreen render with directional shadows and - vertex normals enabled. - """ - NONE = 0 - """Normal PBR Render.""" - DEPTH_ONLY = 1 - """Only render the depth buffer.""" - OFFSCREEN = 2 - """Render offscreen and return the depth and (optionally) color buffers.""" - FLIP_WIREFRAME = 4 - """Invert the status of wireframe rendering for each mesh.""" - ALL_WIREFRAME = 8 - """Render all meshes as wireframes.""" - ALL_SOLID = 16 - """Render all meshes as solids.""" - SHADOWS_DIRECTIONAL = 32 - """Render shadows for directional lights.""" - SHADOWS_POINT = 64 - """Render shadows for point lights.""" - SHADOWS_SPOT = 128 - """Render shadows for spot lights.""" - SHADOWS_ALL = 32 | 64 | 128 - """Render shadows for all lights.""" - VERTEX_NORMALS = 256 - """Render vertex normals.""" - FACE_NORMALS = 512 - """Render face normals.""" - SKIP_CULL_FACES = 1024 - """Do not cull back faces.""" - RGBA = 2048 - """Render the color buffer with the alpha channel enabled.""" - FLAT = 4096 - """Render the color buffer flat, with no lighting computations.""" - SEG = 8192 - - -class TextAlign: - """Text alignment options for captions. - - Only use one at a time. - """ - CENTER = 0 - """Center the text by width and height.""" - CENTER_LEFT = 1 - """Center the text by height and left-align it.""" - CENTER_RIGHT = 2 - """Center the text by height and right-align it.""" - BOTTOM_LEFT = 3 - """Put the text in the bottom-left corner.""" - BOTTOM_RIGHT = 4 - """Put the text in the bottom-right corner.""" - BOTTOM_CENTER = 5 - """Center the text by width and fix it to the bottom.""" - TOP_LEFT = 6 - """Put the text in the top-left corner.""" - TOP_RIGHT = 7 - """Put the text in the top-right corner.""" - TOP_CENTER = 8 - """Center the text by width and fix it to the top.""" - - -class GLTF(object): - """Options for GL objects.""" - NEAREST = 9728 - """Nearest neighbor interpolation.""" - LINEAR = 9729 - """Linear interpolation.""" - NEAREST_MIPMAP_NEAREST = 9984 - """Nearest mipmapping.""" - LINEAR_MIPMAP_NEAREST = 9985 - """Linear mipmapping.""" - NEAREST_MIPMAP_LINEAR = 9986 - """Nearest mipmapping.""" - LINEAR_MIPMAP_LINEAR = 9987 - """Linear mipmapping.""" - CLAMP_TO_EDGE = 33071 - """Clamp to the edge of the texture.""" - MIRRORED_REPEAT = 33648 - """Mirror the texture.""" - REPEAT = 10497 - """Repeat the texture.""" - POINTS = 0 - """Render as points.""" - LINES = 1 - """Render as lines.""" - LINE_LOOP = 2 - """Render as a line loop.""" - LINE_STRIP = 3 - """Render as a line strip.""" - TRIANGLES = 4 - """Render as triangles.""" - TRIANGLE_STRIP = 5 - """Render as a triangle strip.""" - TRIANGLE_FAN = 6 - """Render as a triangle fan.""" - - -class BufFlags(object): - POSITION = 0 - NORMAL = 1 - TANGENT = 2 - TEXCOORD_0 = 4 - TEXCOORD_1 = 8 - COLOR_0 = 16 - JOINTS_0 = 32 - WEIGHTS_0 = 64 - - -class TexFlags(object): - NONE = 0 - NORMAL = 1 - OCCLUSION = 2 - EMISSIVE = 4 - BASE_COLOR = 8 - METALLIC_ROUGHNESS = 16 - DIFFUSE = 32 - SPECULAR_GLOSSINESS = 64 - - -class ProgramFlags: - NONE = 0 - USE_MATERIAL = 1 - VERTEX_NORMALS = 2 - FACE_NORMALS = 4 - - -__all__ = ['RenderFlags', 'TextAlign', 'GLTF'] diff --git a/pyrender/pyrender/font.py b/pyrender/pyrender/font.py deleted file mode 100644 index 5ac530d7b949f50314a0d9cf5d744bedcace0571..0000000000000000000000000000000000000000 --- a/pyrender/pyrender/font.py +++ /dev/null @@ -1,272 +0,0 @@ -"""Font texture loader and processor. - -Author: Matthew Matl -""" -import freetype -import numpy as np -import os - -import OpenGL -from OpenGL.GL import * - -from .constants import TextAlign, FLOAT_SZ -from .texture import Texture -from .sampler import Sampler - - -class FontCache(object): - """A cache for fonts. - """ - - def __init__(self, font_dir=None): - self._font_cache = {} - self.font_dir = font_dir - if self.font_dir is None: - base_dir, _ = os.path.split(os.path.realpath(__file__)) - self.font_dir = os.path.join(base_dir, 'fonts') - - def get_font(self, font_name, font_pt): - # If it's a file, load it directly, else, try to load from font dir. - if os.path.isfile(font_name): - font_filename = font_name - _, font_name = os.path.split(font_name) - font_name, _ = os.path.split(font_name) - else: - font_filename = os.path.join(self.font_dir, font_name) + '.ttf' - - cid = OpenGL.contextdata.getContext() - key = (cid, font_name, int(font_pt)) - - if key not in self._font_cache: - self._font_cache[key] = Font(font_filename, font_pt) - return self._font_cache[key] - - def clear(self): - for key in self._font_cache: - self._font_cache[key].delete() - self._font_cache = {} - - -class Character(object): - """A single character, with its texture and attributes. - """ - - def __init__(self, texture, size, bearing, advance): - self.texture = texture - self.size = size - self.bearing = bearing - self.advance = advance - - -class Font(object): - """A font object. - - Parameters - ---------- - font_file : str - The file to load the font from. - font_pt : int - The height of the font in pixels. - """ - - def __init__(self, font_file, font_pt=40): - self.font_file = font_file - self.font_pt = int(font_pt) - self._face = freetype.Face(font_file) - self._face.set_pixel_sizes(0, font_pt) - self._character_map = {} - - for i in range(0, 128): - - # Generate texture - face = self._face - face.load_char(chr(i)) - buf = face.glyph.bitmap.buffer - src = (np.array(buf) / 255.0).astype(np.float32) - src = src.reshape((face.glyph.bitmap.rows, - face.glyph.bitmap.width)) - tex = Texture( - sampler=Sampler( - magFilter=GL_LINEAR, - minFilter=GL_LINEAR, - wrapS=GL_CLAMP_TO_EDGE, - wrapT=GL_CLAMP_TO_EDGE - ), - source=src, - source_channels='R', - ) - character = Character( - texture=tex, - size=np.array([face.glyph.bitmap.width, - face.glyph.bitmap.rows]), - bearing=np.array([face.glyph.bitmap_left, - face.glyph.bitmap_top]), - advance=face.glyph.advance.x - ) - self._character_map[chr(i)] = character - - self._vbo = None - self._vao = None - - @property - def font_file(self): - """str : The file the font was loaded from. - """ - return self._font_file - - @font_file.setter - def font_file(self, value): - self._font_file = value - - @property - def font_pt(self): - """int : The height of the font in pixels. - """ - return self._font_pt - - @font_pt.setter - def font_pt(self, value): - self._font_pt = int(value) - - def _add_to_context(self): - - self._vao = glGenVertexArrays(1) - glBindVertexArray(self._vao) - self._vbo = glGenBuffers(1) - glBindBuffer(GL_ARRAY_BUFFER, self._vbo) - glBufferData(GL_ARRAY_BUFFER, FLOAT_SZ * 6 * 4, None, GL_DYNAMIC_DRAW) - glEnableVertexAttribArray(0) - glVertexAttribPointer( - 0, 4, GL_FLOAT, GL_FALSE, 4 * FLOAT_SZ, ctypes.c_void_p(0) - ) - glBindVertexArray(0) - - glPixelStorei(GL_UNPACK_ALIGNMENT, 1) - for c in self._character_map: - ch = self._character_map[c] - if not ch.texture._in_context(): - ch.texture._add_to_context() - - def _remove_from_context(self): - for c in self._character_map: - ch = self._character_map[c] - ch.texture.delete() - if self._vao is not None: - glDeleteVertexArrays(1, [self._vao]) - glDeleteBuffers(1, [self._vbo]) - self._vao = None - self._vbo = None - - def _in_context(self): - return self._vao is not None - - def _bind(self): - glBindVertexArray(self._vao) - - def _unbind(self): - glBindVertexArray(0) - - def delete(self): - self._unbind() - self._remove_from_context() - - def render_string(self, text, x, y, scale=1.0, - align=TextAlign.BOTTOM_LEFT): - """Render a string to the current view buffer. - - Note - ---- - Assumes correct shader program already bound w/ uniforms set. - - Parameters - ---------- - text : str - The text to render. - x : int - Horizontal pixel location of text. - y : int - Vertical pixel location of text. - scale : int - Scaling factor for text. - align : int - One of the TextAlign options which specifies where the ``x`` - and ``y`` parameters lie on the text. For example, - :attr:`.TextAlign.BOTTOM_LEFT` means that ``x`` and ``y`` indicate - the position of the bottom-left corner of the textbox. - """ - glActiveTexture(GL_TEXTURE0) - glEnable(GL_BLEND) - glBlendFunc(GL_SRC_ALPHA, GL_ONE_MINUS_SRC_ALPHA) - glDisable(GL_DEPTH_TEST) - glPolygonMode(GL_FRONT_AND_BACK, GL_FILL) - self._bind() - - # Determine width and height of text relative to x, y - width = 0.0 - height = 0.0 - for c in text: - ch = self._character_map[c] - height = max(height, ch.bearing[1] * scale) - width += (ch.advance >> 6) * scale - - # Determine offsets based on alignments - xoff = 0 - yoff = 0 - if align == TextAlign.BOTTOM_RIGHT: - xoff = -width - elif align == TextAlign.BOTTOM_CENTER: - xoff = -width / 2.0 - elif align == TextAlign.TOP_LEFT: - yoff = -height - elif align == TextAlign.TOP_RIGHT: - yoff = -height - xoff = -width - elif align == TextAlign.TOP_CENTER: - yoff = -height - xoff = -width / 2.0 - elif align == TextAlign.CENTER: - xoff = -width / 2.0 - yoff = -height / 2.0 - elif align == TextAlign.CENTER_LEFT: - yoff = -height / 2.0 - elif align == TextAlign.CENTER_RIGHT: - xoff = -width - yoff = -height / 2.0 - - x += xoff - y += yoff - - ch = None - for c in text: - ch = self._character_map[c] - xpos = x + ch.bearing[0] * scale - ypos = y - (ch.size[1] - ch.bearing[1]) * scale - w = ch.size[0] * scale - h = ch.size[1] * scale - - vertices = np.array([ - [xpos, ypos, 0.0, 0.0], - [xpos + w, ypos, 1.0, 0.0], - [xpos + w, ypos + h, 1.0, 1.0], - [xpos + w, ypos + h, 1.0, 1.0], - [xpos, ypos + h, 0.0, 1.0], - [xpos, ypos, 0.0, 0.0], - ], dtype=np.float32) - - ch.texture._bind() - - glBindBuffer(GL_ARRAY_BUFFER, self._vbo) - glBufferData( - GL_ARRAY_BUFFER, FLOAT_SZ * 6 * 4, vertices, GL_DYNAMIC_DRAW - ) - # TODO MAKE THIS MORE EFFICIENT, lgBufferSubData is broken - # glBufferSubData( - # GL_ARRAY_BUFFER, 0, 6 * 4 * FLOAT_SZ, - # np.ascontiguousarray(vertices.flatten) - # ) - glDrawArrays(GL_TRIANGLES, 0, 6) - x += (ch.advance >> 6) * scale - - self._unbind() - if ch: - ch.texture._unbind() diff --git a/pyrender/pyrender/fonts/OpenSans-Bold.ttf b/pyrender/pyrender/fonts/OpenSans-Bold.ttf deleted file mode 100644 index fd79d43bea0293ac1b20e8aca1142627983d2c07..0000000000000000000000000000000000000000 Binary files a/pyrender/pyrender/fonts/OpenSans-Bold.ttf and /dev/null differ diff --git a/pyrender/pyrender/fonts/OpenSans-BoldItalic.ttf b/pyrender/pyrender/fonts/OpenSans-BoldItalic.ttf deleted file mode 100644 index 9bc800958a421d937fc392e00beaef4eea76dc71..0000000000000000000000000000000000000000 Binary files a/pyrender/pyrender/fonts/OpenSans-BoldItalic.ttf and /dev/null differ diff --git a/pyrender/pyrender/fonts/OpenSans-ExtraBold.ttf b/pyrender/pyrender/fonts/OpenSans-ExtraBold.ttf deleted file mode 100644 index 21f6f84a0799946fc4ae02c52b27e61c3762c745..0000000000000000000000000000000000000000 Binary files a/pyrender/pyrender/fonts/OpenSans-ExtraBold.ttf and /dev/null differ diff --git a/pyrender/pyrender/fonts/OpenSans-ExtraBoldItalic.ttf b/pyrender/pyrender/fonts/OpenSans-ExtraBoldItalic.ttf deleted file mode 100644 index 31cb688340eff462dddf47efbb4dfef66cb7fbed..0000000000000000000000000000000000000000 Binary files a/pyrender/pyrender/fonts/OpenSans-ExtraBoldItalic.ttf and /dev/null differ diff --git a/pyrender/pyrender/fonts/OpenSans-Italic.ttf b/pyrender/pyrender/fonts/OpenSans-Italic.ttf deleted file mode 100644 index c90da48ff3b8ad6167236d70c48df4d7b5de3bbb..0000000000000000000000000000000000000000 Binary files a/pyrender/pyrender/fonts/OpenSans-Italic.ttf and /dev/null differ diff --git a/pyrender/pyrender/fonts/OpenSans-Light.ttf b/pyrender/pyrender/fonts/OpenSans-Light.ttf deleted file mode 100644 index 0d381897da20345fa63112f19042561f44ee3aa0..0000000000000000000000000000000000000000 Binary files a/pyrender/pyrender/fonts/OpenSans-Light.ttf and /dev/null differ diff --git a/pyrender/pyrender/fonts/OpenSans-LightItalic.ttf b/pyrender/pyrender/fonts/OpenSans-LightItalic.ttf deleted file mode 100644 index 68299c4bc6b5b7adfff2c9aee4aed7c1547100ef..0000000000000000000000000000000000000000 Binary files a/pyrender/pyrender/fonts/OpenSans-LightItalic.ttf and /dev/null differ diff --git a/pyrender/pyrender/fonts/OpenSans-Regular.ttf b/pyrender/pyrender/fonts/OpenSans-Regular.ttf deleted file mode 100644 index db433349b7047f72f40072630c1bc110620bf09e..0000000000000000000000000000000000000000 Binary files a/pyrender/pyrender/fonts/OpenSans-Regular.ttf and /dev/null differ diff --git a/pyrender/pyrender/fonts/OpenSans-Semibold.ttf b/pyrender/pyrender/fonts/OpenSans-Semibold.ttf deleted file mode 100644 index 1a7679e3949fb045f152f456bc4adad31e8b9f55..0000000000000000000000000000000000000000 Binary files a/pyrender/pyrender/fonts/OpenSans-Semibold.ttf and /dev/null differ diff --git a/pyrender/pyrender/fonts/OpenSans-SemiboldItalic.ttf b/pyrender/pyrender/fonts/OpenSans-SemiboldItalic.ttf deleted file mode 100644 index 59b6d16b065f6baa6f70ddbd4322a4f44bb9636a..0000000000000000000000000000000000000000 Binary files a/pyrender/pyrender/fonts/OpenSans-SemiboldItalic.ttf and /dev/null differ diff --git a/pyrender/pyrender/light.py b/pyrender/pyrender/light.py deleted file mode 100644 index 333d9e4e553a245c259251a89b69cb46b73b1278..0000000000000000000000000000000000000000 --- a/pyrender/pyrender/light.py +++ /dev/null @@ -1,385 +0,0 @@ -"""Punctual light sources as defined by the glTF 2.0 KHR extension at -https://github.com/KhronosGroup/glTF/tree/master/extensions/2.0/Khronos/KHR_lights_punctual - -Author: Matthew Matl -""" -import abc -import numpy as np -import six - -from OpenGL.GL import * - -from .utils import format_color_vector -from .texture import Texture -from .constants import SHADOW_TEX_SZ -from .camera import OrthographicCamera, PerspectiveCamera - - - -@six.add_metaclass(abc.ABCMeta) -class Light(object): - """Base class for all light objects. - - Parameters - ---------- - color : (3,) float - RGB value for the light's color in linear space. - intensity : float - Brightness of light. The units that this is defined in depend on the - type of light. Point and spot lights use luminous intensity in candela - (lm/sr), while directional lights use illuminance in lux (lm/m2). - name : str, optional - Name of the light. - """ - def __init__(self, - color=None, - intensity=None, - name=None): - - if color is None: - color = np.ones(3) - if intensity is None: - intensity = 1.0 - - self.name = name - self.color = color - self.intensity = intensity - self._shadow_camera = None - self._shadow_texture = None - - @property - def name(self): - """str : The user-defined name of this object. - """ - return self._name - - @name.setter - def name(self, value): - if value is not None: - value = str(value) - self._name = value - - @property - def color(self): - """(3,) float : The light's color. - """ - return self._color - - @color.setter - def color(self, value): - self._color = format_color_vector(value, 3) - - @property - def intensity(self): - """float : The light's intensity in candela or lux. - """ - return self._intensity - - @intensity.setter - def intensity(self, value): - self._intensity = float(value) - - @property - def shadow_texture(self): - """:class:`.Texture` : A texture used to hold shadow maps for this light. - """ - return self._shadow_texture - - @shadow_texture.setter - def shadow_texture(self, value): - if self._shadow_texture is not None: - if self._shadow_texture._in_context(): - self._shadow_texture.delete() - self._shadow_texture = value - - @abc.abstractmethod - def _generate_shadow_texture(self, size=None): - """Generate a shadow texture for this light. - - Parameters - ---------- - size : int, optional - Size of texture map. Must be a positive power of two. - """ - pass - - @abc.abstractmethod - def _get_shadow_camera(self, scene_scale): - """Generate and return a shadow mapping camera for this light. - - Parameters - ---------- - scene_scale : float - Length of scene's bounding box diagonal. - - Returns - ------- - camera : :class:`.Camera` - The camera used to render shadowmaps for this light. - """ - pass - - -class DirectionalLight(Light): - """Directional lights are light sources that act as though they are - infinitely far away and emit light in the direction of the local -z axis. - This light type inherits the orientation of the node that it belongs to; - position and scale are ignored except for their effect on the inherited - node orientation. Because it is at an infinite distance, the light is - not attenuated. Its intensity is defined in lumens per metre squared, - or lux (lm/m2). - - Parameters - ---------- - color : (3,) float, optional - RGB value for the light's color in linear space. Defaults to white - (i.e. [1.0, 1.0, 1.0]). - intensity : float, optional - Brightness of light, in lux (lm/m^2). Defaults to 1.0 - name : str, optional - Name of the light. - """ - - def __init__(self, - color=None, - intensity=None, - name=None): - super(DirectionalLight, self).__init__( - color=color, - intensity=intensity, - name=name, - ) - - def _generate_shadow_texture(self, size=None): - """Generate a shadow texture for this light. - - Parameters - ---------- - size : int, optional - Size of texture map. Must be a positive power of two. - """ - if size is None: - size = SHADOW_TEX_SZ - self.shadow_texture = Texture(width=size, height=size, - source_channels='D', data_format=GL_FLOAT) - - def _get_shadow_camera(self, scene_scale): - """Generate and return a shadow mapping camera for this light. - - Parameters - ---------- - scene_scale : float - Length of scene's bounding box diagonal. - - Returns - ------- - camera : :class:`.Camera` - The camera used to render shadowmaps for this light. - """ - return OrthographicCamera( - znear=0.01 * scene_scale, - zfar=10 * scene_scale, - xmag=scene_scale, - ymag=scene_scale - ) - - -class PointLight(Light): - """Point lights emit light in all directions from their position in space; - rotation and scale are ignored except for their effect on the inherited - node position. The brightness of the light attenuates in a physically - correct manner as distance increases from the light's position (i.e. - brightness goes like the inverse square of the distance). Point light - intensity is defined in candela, which is lumens per square radian (lm/sr). - - Parameters - ---------- - color : (3,) float - RGB value for the light's color in linear space. - intensity : float - Brightness of light in candela (lm/sr). - range : float - Cutoff distance at which light's intensity may be considered to - have reached zero. If None, the range is assumed to be infinite. - name : str, optional - Name of the light. - """ - - def __init__(self, - color=None, - intensity=None, - range=None, - name=None): - super(PointLight, self).__init__( - color=color, - intensity=intensity, - name=name, - ) - self.range = range - - @property - def range(self): - """float : The cutoff distance for the light. - """ - return self._range - - @range.setter - def range(self, value): - if value is not None: - value = float(value) - if value <= 0: - raise ValueError('Range must be > 0') - self._range = value - self._range = value - - def _generate_shadow_texture(self, size=None): - """Generate a shadow texture for this light. - - Parameters - ---------- - size : int, optional - Size of texture map. Must be a positive power of two. - """ - raise NotImplementedError('Shadows not implemented for point lights') - - def _get_shadow_camera(self, scene_scale): - """Generate and return a shadow mapping camera for this light. - - Parameters - ---------- - scene_scale : float - Length of scene's bounding box diagonal. - - Returns - ------- - camera : :class:`.Camera` - The camera used to render shadowmaps for this light. - """ - raise NotImplementedError('Shadows not implemented for point lights') - - -class SpotLight(Light): - """Spot lights emit light in a cone in the direction of the local -z axis. - The angle and falloff of the cone is defined using two numbers, the - ``innerConeAngle`` and ``outerConeAngle``. - As with point lights, the brightness - also attenuates in a physically correct manner as distance increases from - the light's position (i.e. brightness goes like the inverse square of the - distance). Spot light intensity refers to the brightness inside the - ``innerConeAngle`` (and at the location of the light) and is defined in - candela, which is lumens per square radian (lm/sr). A spot light's position - and orientation are inherited from its node transform. Inherited scale does - not affect cone shape, and is ignored except for its effect on position - and orientation. - - Parameters - ---------- - color : (3,) float - RGB value for the light's color in linear space. - intensity : float - Brightness of light in candela (lm/sr). - range : float - Cutoff distance at which light's intensity may be considered to - have reached zero. If None, the range is assumed to be infinite. - innerConeAngle : float - Angle, in radians, from centre of spotlight where falloff begins. - Must be greater than or equal to ``0`` and less - than ``outerConeAngle``. Defaults to ``0``. - outerConeAngle : float - Angle, in radians, from centre of spotlight where falloff ends. - Must be greater than ``innerConeAngle`` and less than or equal to - ``PI / 2.0``. Defaults to ``PI / 4.0``. - name : str, optional - Name of the light. - """ - - def __init__(self, - color=None, - intensity=None, - range=None, - innerConeAngle=0.0, - outerConeAngle=(np.pi / 4.0), - name=None): - super(SpotLight, self).__init__( - name=name, - color=color, - intensity=intensity, - ) - self.outerConeAngle = outerConeAngle - self.innerConeAngle = innerConeAngle - self.range = range - - @property - def innerConeAngle(self): - """float : The inner cone angle in radians. - """ - return self._innerConeAngle - - @innerConeAngle.setter - def innerConeAngle(self, value): - if value < 0.0 or value > self.outerConeAngle: - raise ValueError('Invalid value for inner cone angle') - self._innerConeAngle = float(value) - - @property - def outerConeAngle(self): - """float : The outer cone angle in radians. - """ - return self._outerConeAngle - - @outerConeAngle.setter - def outerConeAngle(self, value): - if value < 0.0 or value > np.pi / 2.0 + 1e-9: - raise ValueError('Invalid value for outer cone angle') - self._outerConeAngle = float(value) - - @property - def range(self): - """float : The cutoff distance for the light. - """ - return self._range - - @range.setter - def range(self, value): - if value is not None: - value = float(value) - if value <= 0: - raise ValueError('Range must be > 0') - self._range = value - self._range = value - - def _generate_shadow_texture(self, size=None): - """Generate a shadow texture for this light. - - Parameters - ---------- - size : int, optional - Size of texture map. Must be a positive power of two. - """ - if size is None: - size = SHADOW_TEX_SZ - self.shadow_texture = Texture(width=size, height=size, - source_channels='D', data_format=GL_FLOAT) - - def _get_shadow_camera(self, scene_scale): - """Generate and return a shadow mapping camera for this light. - - Parameters - ---------- - scene_scale : float - Length of scene's bounding box diagonal. - - Returns - ------- - camera : :class:`.Camera` - The camera used to render shadowmaps for this light. - """ - return PerspectiveCamera( - znear=0.01 * scene_scale, - zfar=10 * scene_scale, - yfov=np.clip(2 * self.outerConeAngle + np.pi / 16.0, 0.0, np.pi), - aspectRatio=1.0 - ) - - -__all__ = ['Light', 'DirectionalLight', 'SpotLight', 'PointLight'] diff --git a/pyrender/pyrender/material.py b/pyrender/pyrender/material.py deleted file mode 100644 index 3ce9c2d184ed213c84b015e36bea558cd1efc6b7..0000000000000000000000000000000000000000 --- a/pyrender/pyrender/material.py +++ /dev/null @@ -1,707 +0,0 @@ -"""Material properties, conforming to the glTF 2.0 standards as specified in -https://github.com/KhronosGroup/glTF/tree/master/specification/2.0#reference-material -and -https://github.com/KhronosGroup/glTF/tree/master/extensions/2.0/Khronos/KHR_materials_pbrSpecularGlossiness - -Author: Matthew Matl -""" -import abc -import numpy as np -import six - -from .constants import TexFlags -from .utils import format_color_vector, format_texture_source -from .texture import Texture - - -@six.add_metaclass(abc.ABCMeta) -class Material(object): - """Base for standard glTF 2.0 materials. - - Parameters - ---------- - name : str, optional - The user-defined name of this object. - normalTexture : (n,n,3) float or :class:`Texture`, optional - A tangent space normal map. The texture contains RGB components in - linear space. Each texel represents the XYZ components of a normal - vector in tangent space. Red [0 to 255] maps to X [-1 to 1]. Green - [0 to 255] maps to Y [-1 to 1]. Blue [128 to 255] maps to Z - [1/255 to 1]. The normal vectors use OpenGL conventions where +X is - right and +Y is up. +Z points toward the viewer. - occlusionTexture : (n,n,1) float or :class:`Texture`, optional - The occlusion map texture. The occlusion values are sampled from the R - channel. Higher values indicate areas that should receive full indirect - lighting and lower values indicate no indirect lighting. These values - are linear. If other channels are present (GBA), they are ignored for - occlusion calculations. - emissiveTexture : (n,n,3) float or :class:`Texture`, optional - The emissive map controls the color and intensity of the light being - emitted by the material. This texture contains RGB components in sRGB - color space. If a fourth component (A) is present, it is ignored. - emissiveFactor : (3,) float, optional - The RGB components of the emissive color of the material. These values - are linear. If an emissiveTexture is specified, this value is - multiplied with the texel values. - alphaMode : str, optional - The material's alpha rendering mode enumeration specifying the - interpretation of the alpha value of the main factor and texture. - Allowed Values: - - - `"OPAQUE"` The alpha value is ignored and the rendered output is - fully opaque. - - `"MASK"` The rendered output is either fully opaque or fully - transparent depending on the alpha value and the specified alpha - cutoff value. - - `"BLEND"` The alpha value is used to composite the source and - destination areas. The rendered output is combined with the - background using the normal painting operation (i.e. the Porter - and Duff over operator). - - alphaCutoff : float, optional - Specifies the cutoff threshold when in MASK mode. If the alpha value is - greater than or equal to this value then it is rendered as fully - opaque, otherwise, it is rendered as fully transparent. - A value greater than 1.0 will render the entire material as fully - transparent. This value is ignored for other modes. - doubleSided : bool, optional - Specifies whether the material is double sided. When this value is - false, back-face culling is enabled. When this value is true, - back-face culling is disabled and double sided lighting is enabled. - smooth : bool, optional - If True, the material is rendered smoothly by using only one normal - per vertex and face indexing. - wireframe : bool, optional - If True, the material is rendered in wireframe mode. - """ - - def __init__(self, - name=None, - normalTexture=None, - occlusionTexture=None, - emissiveTexture=None, - emissiveFactor=None, - alphaMode=None, - alphaCutoff=None, - doubleSided=False, - smooth=True, - wireframe=False): - - # Set defaults - if alphaMode is None: - alphaMode = 'OPAQUE' - - if alphaCutoff is None: - alphaCutoff = 0.5 - - if emissiveFactor is None: - emissiveFactor = np.zeros(3).astype(np.float32) - - self.name = name - self.normalTexture = normalTexture - self.occlusionTexture = occlusionTexture - self.emissiveTexture = emissiveTexture - self.emissiveFactor = emissiveFactor - self.alphaMode = alphaMode - self.alphaCutoff = alphaCutoff - self.doubleSided = doubleSided - self.smooth = smooth - self.wireframe = wireframe - - self._tex_flags = None - - @property - def name(self): - """str : The user-defined name of this object. - """ - return self._name - - @name.setter - def name(self, value): - if value is not None: - value = str(value) - self._name = value - - @property - def normalTexture(self): - """(n,n,3) float or :class:`Texture` : The tangent-space normal map. - """ - return self._normalTexture - - @normalTexture.setter - def normalTexture(self, value): - # TODO TMP - self._normalTexture = self._format_texture(value, 'RGB') - self._tex_flags = None - - @property - def occlusionTexture(self): - """(n,n,1) float or :class:`Texture` : The ambient occlusion map. - """ - return self._occlusionTexture - - @occlusionTexture.setter - def occlusionTexture(self, value): - self._occlusionTexture = self._format_texture(value, 'R') - self._tex_flags = None - - @property - def emissiveTexture(self): - """(n,n,3) float or :class:`Texture` : The emission map. - """ - return self._emissiveTexture - - @emissiveTexture.setter - def emissiveTexture(self, value): - self._emissiveTexture = self._format_texture(value, 'RGB') - self._tex_flags = None - - @property - def emissiveFactor(self): - """(3,) float : Base multiplier for emission colors. - """ - return self._emissiveFactor - - @emissiveFactor.setter - def emissiveFactor(self, value): - if value is None: - value = np.zeros(3) - self._emissiveFactor = format_color_vector(value, 3) - - @property - def alphaMode(self): - """str : The mode for blending. - """ - return self._alphaMode - - @alphaMode.setter - def alphaMode(self, value): - if value not in set(['OPAQUE', 'MASK', 'BLEND']): - raise ValueError('Invalid alpha mode {}'.format(value)) - self._alphaMode = value - - @property - def alphaCutoff(self): - """float : The cutoff threshold in MASK mode. - """ - return self._alphaCutoff - - @alphaCutoff.setter - def alphaCutoff(self, value): - if value < 0 or value > 1: - raise ValueError('Alpha cutoff must be in range [0,1]') - self._alphaCutoff = float(value) - - @property - def doubleSided(self): - """bool : Whether the material is double-sided. - """ - return self._doubleSided - - @doubleSided.setter - def doubleSided(self, value): - if not isinstance(value, bool): - raise TypeError('Double sided must be a boolean value') - self._doubleSided = value - - @property - def smooth(self): - """bool : Whether to render the mesh smoothly by - interpolating vertex normals. - """ - return self._smooth - - @smooth.setter - def smooth(self, value): - if not isinstance(value, bool): - raise TypeError('Double sided must be a boolean value') - self._smooth = value - - @property - def wireframe(self): - """bool : Whether to render the mesh in wireframe mode. - """ - return self._wireframe - - @wireframe.setter - def wireframe(self, value): - if not isinstance(value, bool): - raise TypeError('Wireframe must be a boolean value') - self._wireframe = value - - @property - def is_transparent(self): - """bool : If True, the object is partially transparent. - """ - return self._compute_transparency() - - @property - def tex_flags(self): - """int : Texture availability flags. - """ - if self._tex_flags is None: - self._tex_flags = self._compute_tex_flags() - return self._tex_flags - - @property - def textures(self): - """list of :class:`Texture` : The textures associated with this - material. - """ - return self._compute_textures() - - def _compute_transparency(self): - return False - - def _compute_tex_flags(self): - tex_flags = TexFlags.NONE - if self.normalTexture is not None: - tex_flags |= TexFlags.NORMAL - if self.occlusionTexture is not None: - tex_flags |= TexFlags.OCCLUSION - if self.emissiveTexture is not None: - tex_flags |= TexFlags.EMISSIVE - return tex_flags - - def _compute_textures(self): - all_textures = [ - self.normalTexture, self.occlusionTexture, self.emissiveTexture - ] - textures = set([t for t in all_textures if t is not None]) - return textures - - def _format_texture(self, texture, target_channels='RGB'): - """Format a texture as a float32 np array. - """ - if isinstance(texture, Texture) or texture is None: - return texture - else: - source = format_texture_source(texture, target_channels) - return Texture(source=source, source_channels=target_channels) - - -class MetallicRoughnessMaterial(Material): - """A material based on the metallic-roughness material model from - Physically-Based Rendering (PBR) methodology. - - Parameters - ---------- - name : str, optional - The user-defined name of this object. - normalTexture : (n,n,3) float or :class:`Texture`, optional - A tangent space normal map. The texture contains RGB components in - linear space. Each texel represents the XYZ components of a normal - vector in tangent space. Red [0 to 255] maps to X [-1 to 1]. Green - [0 to 255] maps to Y [-1 to 1]. Blue [128 to 255] maps to Z - [1/255 to 1]. The normal vectors use OpenGL conventions where +X is - right and +Y is up. +Z points toward the viewer. - occlusionTexture : (n,n,1) float or :class:`Texture`, optional - The occlusion map texture. The occlusion values are sampled from the R - channel. Higher values indicate areas that should receive full indirect - lighting and lower values indicate no indirect lighting. These values - are linear. If other channels are present (GBA), they are ignored for - occlusion calculations. - emissiveTexture : (n,n,3) float or :class:`Texture`, optional - The emissive map controls the color and intensity of the light being - emitted by the material. This texture contains RGB components in sRGB - color space. If a fourth component (A) is present, it is ignored. - emissiveFactor : (3,) float, optional - The RGB components of the emissive color of the material. These values - are linear. If an emissiveTexture is specified, this value is - multiplied with the texel values. - alphaMode : str, optional - The material's alpha rendering mode enumeration specifying the - interpretation of the alpha value of the main factor and texture. - Allowed Values: - - - `"OPAQUE"` The alpha value is ignored and the rendered output is - fully opaque. - - `"MASK"` The rendered output is either fully opaque or fully - transparent depending on the alpha value and the specified alpha - cutoff value. - - `"BLEND"` The alpha value is used to composite the source and - destination areas. The rendered output is combined with the - background using the normal painting operation (i.e. the Porter - and Duff over operator). - - alphaCutoff : float, optional - Specifies the cutoff threshold when in MASK mode. If the alpha value is - greater than or equal to this value then it is rendered as fully - opaque, otherwise, it is rendered as fully transparent. - A value greater than 1.0 will render the entire material as fully - transparent. This value is ignored for other modes. - doubleSided : bool, optional - Specifies whether the material is double sided. When this value is - false, back-face culling is enabled. When this value is true, - back-face culling is disabled and double sided lighting is enabled. - smooth : bool, optional - If True, the material is rendered smoothly by using only one normal - per vertex and face indexing. - wireframe : bool, optional - If True, the material is rendered in wireframe mode. - baseColorFactor : (4,) float, optional - The RGBA components of the base color of the material. The fourth - component (A) is the alpha coverage of the material. The alphaMode - property specifies how alpha is interpreted. These values are linear. - If a baseColorTexture is specified, this value is multiplied with the - texel values. - baseColorTexture : (n,n,4) float or :class:`Texture`, optional - The base color texture. This texture contains RGB(A) components in sRGB - color space. The first three components (RGB) specify the base color of - the material. If the fourth component (A) is present, it represents the - alpha coverage of the material. Otherwise, an alpha of 1.0 is assumed. - The alphaMode property specifies how alpha is interpreted. - The stored texels must not be premultiplied. - metallicFactor : float - The metalness of the material. A value of 1.0 means the material is a - metal. A value of 0.0 means the material is a dielectric. Values in - between are for blending between metals and dielectrics such as dirty - metallic surfaces. This value is linear. If a metallicRoughnessTexture - is specified, this value is multiplied with the metallic texel values. - roughnessFactor : float - The roughness of the material. A value of 1.0 means the material is - completely rough. A value of 0.0 means the material is completely - smooth. This value is linear. If a metallicRoughnessTexture is - specified, this value is multiplied with the roughness texel values. - metallicRoughnessTexture : (n,n,2) float or :class:`Texture`, optional - The metallic-roughness texture. The metalness values are sampled from - the B channel. The roughness values are sampled from the G channel. - These values are linear. If other channels are present (R or A), they - are ignored for metallic-roughness calculations. - """ - - def __init__(self, - name=None, - normalTexture=None, - occlusionTexture=None, - emissiveTexture=None, - emissiveFactor=None, - alphaMode=None, - alphaCutoff=None, - doubleSided=False, - smooth=True, - wireframe=False, - baseColorFactor=None, - baseColorTexture=None, - metallicFactor=1.0, - roughnessFactor=1.0, - metallicRoughnessTexture=None): - super(MetallicRoughnessMaterial, self).__init__( - name=name, - normalTexture=normalTexture, - occlusionTexture=occlusionTexture, - emissiveTexture=emissiveTexture, - emissiveFactor=emissiveFactor, - alphaMode=alphaMode, - alphaCutoff=alphaCutoff, - doubleSided=doubleSided, - smooth=smooth, - wireframe=wireframe - ) - - # Set defaults - if baseColorFactor is None: - baseColorFactor = np.ones(4).astype(np.float32) - - self.baseColorFactor = baseColorFactor - self.baseColorTexture = baseColorTexture - self.metallicFactor = metallicFactor - self.roughnessFactor = roughnessFactor - self.metallicRoughnessTexture = metallicRoughnessTexture - - @property - def baseColorFactor(self): - """(4,) float or :class:`Texture` : The RGBA base color multiplier. - """ - return self._baseColorFactor - - @baseColorFactor.setter - def baseColorFactor(self, value): - if value is None: - value = np.ones(4) - self._baseColorFactor = format_color_vector(value, 4) - - @property - def baseColorTexture(self): - """(n,n,4) float or :class:`Texture` : The diffuse texture. - """ - return self._baseColorTexture - - @baseColorTexture.setter - def baseColorTexture(self, value): - self._baseColorTexture = self._format_texture(value, 'RGBA') - self._tex_flags = None - - @property - def metallicFactor(self): - """float : The metalness of the material. - """ - return self._metallicFactor - - @metallicFactor.setter - def metallicFactor(self, value): - if value is None: - value = 1.0 - if value < 0 or value > 1: - raise ValueError('Metallic factor must be in range [0,1]') - self._metallicFactor = float(value) - - @property - def roughnessFactor(self): - """float : The roughness of the material. - """ - return self.RoughnessFactor - - @roughnessFactor.setter - def roughnessFactor(self, value): - if value is None: - value = 1.0 - if value < 0 or value > 1: - raise ValueError('Roughness factor must be in range [0,1]') - self.RoughnessFactor = float(value) - - @property - def metallicRoughnessTexture(self): - """(n,n,2) float or :class:`Texture` : The metallic-roughness texture. - """ - return self._metallicRoughnessTexture - - @metallicRoughnessTexture.setter - def metallicRoughnessTexture(self, value): - self._metallicRoughnessTexture = self._format_texture(value, 'GB') - self._tex_flags = None - - def _compute_tex_flags(self): - tex_flags = super(MetallicRoughnessMaterial, self)._compute_tex_flags() - if self.baseColorTexture is not None: - tex_flags |= TexFlags.BASE_COLOR - if self.metallicRoughnessTexture is not None: - tex_flags |= TexFlags.METALLIC_ROUGHNESS - return tex_flags - - def _compute_transparency(self): - if self.alphaMode == 'OPAQUE': - return False - cutoff = self.alphaCutoff - if self.alphaMode == 'BLEND': - cutoff = 1.0 - if self.baseColorFactor[3] < cutoff: - return True - if (self.baseColorTexture is not None and - self.baseColorTexture.is_transparent(cutoff)): - return True - return False - - def _compute_textures(self): - textures = super(MetallicRoughnessMaterial, self)._compute_textures() - all_textures = [self.baseColorTexture, self.metallicRoughnessTexture] - all_textures = {t for t in all_textures if t is not None} - textures |= all_textures - return textures - - -class SpecularGlossinessMaterial(Material): - """A material based on the specular-glossiness material model from - Physically-Based Rendering (PBR) methodology. - - Parameters - ---------- - name : str, optional - The user-defined name of this object. - normalTexture : (n,n,3) float or :class:`Texture`, optional - A tangent space normal map. The texture contains RGB components in - linear space. Each texel represents the XYZ components of a normal - vector in tangent space. Red [0 to 255] maps to X [-1 to 1]. Green - [0 to 255] maps to Y [-1 to 1]. Blue [128 to 255] maps to Z - [1/255 to 1]. The normal vectors use OpenGL conventions where +X is - right and +Y is up. +Z points toward the viewer. - occlusionTexture : (n,n,1) float or :class:`Texture`, optional - The occlusion map texture. The occlusion values are sampled from the R - channel. Higher values indicate areas that should receive full indirect - lighting and lower values indicate no indirect lighting. These values - are linear. If other channels are present (GBA), they are ignored for - occlusion calculations. - emissiveTexture : (n,n,3) float or :class:`Texture`, optional - The emissive map controls the color and intensity of the light being - emitted by the material. This texture contains RGB components in sRGB - color space. If a fourth component (A) is present, it is ignored. - emissiveFactor : (3,) float, optional - The RGB components of the emissive color of the material. These values - are linear. If an emissiveTexture is specified, this value is - multiplied with the texel values. - alphaMode : str, optional - The material's alpha rendering mode enumeration specifying the - interpretation of the alpha value of the main factor and texture. - Allowed Values: - - - `"OPAQUE"` The alpha value is ignored and the rendered output is - fully opaque. - - `"MASK"` The rendered output is either fully opaque or fully - transparent depending on the alpha value and the specified alpha - cutoff value. - - `"BLEND"` The alpha value is used to composite the source and - destination areas. The rendered output is combined with the - background using the normal painting operation (i.e. the Porter - and Duff over operator). - - alphaCutoff : float, optional - Specifies the cutoff threshold when in MASK mode. If the alpha value is - greater than or equal to this value then it is rendered as fully - opaque, otherwise, it is rendered as fully transparent. - A value greater than 1.0 will render the entire material as fully - transparent. This value is ignored for other modes. - doubleSided : bool, optional - Specifies whether the material is double sided. When this value is - false, back-face culling is enabled. When this value is true, - back-face culling is disabled and double sided lighting is enabled. - smooth : bool, optional - If True, the material is rendered smoothly by using only one normal - per vertex and face indexing. - wireframe : bool, optional - If True, the material is rendered in wireframe mode. - diffuseFactor : (4,) float - The RGBA components of the reflected diffuse color of the material. - Metals have a diffuse value of [0.0, 0.0, 0.0]. The fourth component - (A) is the opacity of the material. The values are linear. - diffuseTexture : (n,n,4) float or :class:`Texture`, optional - The diffuse texture. This texture contains RGB(A) components of the - reflected diffuse color of the material in sRGB color space. If the - fourth component (A) is present, it represents the alpha coverage of - the material. Otherwise, an alpha of 1.0 is assumed. - The alphaMode property specifies how alpha is interpreted. - The stored texels must not be premultiplied. - specularFactor : (3,) float - The specular RGB color of the material. This value is linear. - glossinessFactor : float - The glossiness or smoothness of the material. A value of 1.0 means the - material has full glossiness or is perfectly smooth. A value of 0.0 - means the material has no glossiness or is perfectly rough. This value - is linear. - specularGlossinessTexture : (n,n,4) or :class:`Texture`, optional - The specular-glossiness texture is a RGBA texture, containing the - specular color (RGB) in sRGB space and the glossiness value (A) in - linear space. - """ - - def __init__(self, - name=None, - normalTexture=None, - occlusionTexture=None, - emissiveTexture=None, - emissiveFactor=None, - alphaMode=None, - alphaCutoff=None, - doubleSided=False, - smooth=True, - wireframe=False, - diffuseFactor=None, - diffuseTexture=None, - specularFactor=None, - glossinessFactor=1.0, - specularGlossinessTexture=None): - super(SpecularGlossinessMaterial, self).__init__( - name=name, - normalTexture=normalTexture, - occlusionTexture=occlusionTexture, - emissiveTexture=emissiveTexture, - emissiveFactor=emissiveFactor, - alphaMode=alphaMode, - alphaCutoff=alphaCutoff, - doubleSided=doubleSided, - smooth=smooth, - wireframe=wireframe - ) - - # Set defaults - if diffuseFactor is None: - diffuseFactor = np.ones(4).astype(np.float32) - if specularFactor is None: - specularFactor = np.ones(3).astype(np.float32) - - self.diffuseFactor = diffuseFactor - self.diffuseTexture = diffuseTexture - self.specularFactor = specularFactor - self.glossinessFactor = glossinessFactor - self.specularGlossinessTexture = specularGlossinessTexture - - @property - def diffuseFactor(self): - """(4,) float : The diffuse base color. - """ - return self._diffuseFactor - - @diffuseFactor.setter - def diffuseFactor(self, value): - self._diffuseFactor = format_color_vector(value, 4) - - @property - def diffuseTexture(self): - """(n,n,4) float or :class:`Texture` : The diffuse map. - """ - return self._diffuseTexture - - @diffuseTexture.setter - def diffuseTexture(self, value): - self._diffuseTexture = self._format_texture(value, 'RGBA') - self._tex_flags = None - - @property - def specularFactor(self): - """(3,) float : The specular color of the material. - """ - return self._specularFactor - - @specularFactor.setter - def specularFactor(self, value): - self._specularFactor = format_color_vector(value, 3) - - @property - def glossinessFactor(self): - """float : The glossiness of the material. - """ - return self.glossinessFactor - - @glossinessFactor.setter - def glossinessFactor(self, value): - if value < 0 or value > 1: - raise ValueError('glossiness factor must be in range [0,1]') - self._glossinessFactor = float(value) - - @property - def specularGlossinessTexture(self): - """(n,n,4) or :class:`Texture` : The specular-glossiness texture. - """ - return self._specularGlossinessTexture - - @specularGlossinessTexture.setter - def specularGlossinessTexture(self, value): - self._specularGlossinessTexture = self._format_texture(value, 'GB') - self._tex_flags = None - - def _compute_tex_flags(self): - flags = super(SpecularGlossinessMaterial, self)._compute_tex_flags() - if self.diffuseTexture is not None: - flags |= TexFlags.DIFFUSE - if self.specularGlossinessTexture is not None: - flags |= TexFlags.SPECULAR_GLOSSINESS - return flags - - def _compute_transparency(self): - if self.alphaMode == 'OPAQUE': - return False - cutoff = self.alphaCutoff - if self.alphaMode == 'BLEND': - cutoff = 1.0 - if self.diffuseFactor[3] < cutoff: - return True - if (self.diffuseTexture is not None and - self.diffuseTexture.is_transparent(cutoff)): - return True - return False - - def _compute_textures(self): - textures = super(SpecularGlossinessMaterial, self)._compute_textures() - all_textures = [self.diffuseTexture, self.specularGlossinessTexture] - all_textures = {t for t in all_textures if t is not None} - textures |= all_textures - return textures diff --git a/pyrender/pyrender/mesh.py b/pyrender/pyrender/mesh.py deleted file mode 100644 index 36833ea3dfa6c095a18fc745ff34cf106e83c95d..0000000000000000000000000000000000000000 --- a/pyrender/pyrender/mesh.py +++ /dev/null @@ -1,328 +0,0 @@ -"""Meshes, conforming to the glTF 2.0 standards as specified in -https://github.com/KhronosGroup/glTF/tree/master/specification/2.0#reference-mesh - -Author: Matthew Matl -""" -import copy - -import numpy as np -import trimesh - -from .primitive import Primitive -from .constants import GLTF -from .material import MetallicRoughnessMaterial - - -class Mesh(object): - """A set of primitives to be rendered. - - Parameters - ---------- - name : str - The user-defined name of this object. - primitives : list of :class:`Primitive` - The primitives associated with this mesh. - weights : (k,) float - Array of weights to be applied to the Morph Targets. - is_visible : bool - If False, the mesh will not be rendered. - """ - - def __init__(self, primitives, name=None, weights=None, is_visible=True): - self.primitives = primitives - self.name = name - self.weights = weights - self.is_visible = is_visible - - self._bounds = None - - @property - def name(self): - """str : The user-defined name of this object. - """ - return self._name - - @name.setter - def name(self, value): - if value is not None: - value = str(value) - self._name = value - - @property - def primitives(self): - """list of :class:`Primitive` : The primitives associated - with this mesh. - """ - return self._primitives - - @primitives.setter - def primitives(self, value): - self._primitives = value - - @property - def weights(self): - """(k,) float : Weights to be applied to morph targets. - """ - return self._weights - - @weights.setter - def weights(self, value): - self._weights = value - - @property - def is_visible(self): - """bool : Whether the mesh is visible. - """ - return self._is_visible - - @is_visible.setter - def is_visible(self, value): - self._is_visible = value - - @property - def bounds(self): - """(2,3) float : The axis-aligned bounds of the mesh. - """ - if self._bounds is None: - bounds = np.array([[np.infty, np.infty, np.infty], - [-np.infty, -np.infty, -np.infty]]) - for p in self.primitives: - bounds[0] = np.minimum(bounds[0], p.bounds[0]) - bounds[1] = np.maximum(bounds[1], p.bounds[1]) - self._bounds = bounds - return self._bounds - - @property - def centroid(self): - """(3,) float : The centroid of the mesh's axis-aligned bounding box - (AABB). - """ - return np.mean(self.bounds, axis=0) - - @property - def extents(self): - """(3,) float : The lengths of the axes of the mesh's AABB. - """ - return np.diff(self.bounds, axis=0).reshape(-1) - - @property - def scale(self): - """(3,) float : The length of the diagonal of the mesh's AABB. - """ - return np.linalg.norm(self.extents) - - @property - def is_transparent(self): - """bool : If True, the mesh is partially-transparent. - """ - for p in self.primitives: - if p.is_transparent: - return True - return False - - @staticmethod - def from_points(points, colors=None, normals=None, - is_visible=True, poses=None): - """Create a Mesh from a set of points. - - Parameters - ---------- - points : (n,3) float - The point positions. - colors : (n,3) or (n,4) float, optional - RGB or RGBA colors for each point. - normals : (n,3) float, optionals - The normal vectors for each point. - is_visible : bool - If False, the points will not be rendered. - poses : (x,4,4) - Array of 4x4 transformation matrices for instancing this object. - - Returns - ------- - mesh : :class:`Mesh` - The created mesh. - """ - primitive = Primitive( - positions=points, - normals=normals, - color_0=colors, - mode=GLTF.POINTS, - poses=poses - ) - mesh = Mesh(primitives=[primitive], is_visible=is_visible) - return mesh - - @staticmethod - def from_trimesh(mesh, material=None, is_visible=True, - poses=None, wireframe=False, smooth=True): - """Create a Mesh from a :class:`~trimesh.base.Trimesh`. - - Parameters - ---------- - mesh : :class:`~trimesh.base.Trimesh` or list of them - A triangular mesh or a list of meshes. - material : :class:`Material` - The material of the object. Overrides any mesh material. - If not specified and the mesh has no material, a default material - will be used. - is_visible : bool - If False, the mesh will not be rendered. - poses : (n,4,4) float - Array of 4x4 transformation matrices for instancing this object. - wireframe : bool - If `True`, the mesh will be rendered as a wireframe object - smooth : bool - If `True`, the mesh will be rendered with interpolated vertex - normals. Otherwise, the mesh edges will stay sharp. - - Returns - ------- - mesh : :class:`Mesh` - The created mesh. - """ - - if isinstance(mesh, (list, tuple, set, np.ndarray)): - meshes = list(mesh) - elif isinstance(mesh, trimesh.Trimesh): - meshes = [mesh] - else: - raise TypeError('Expected a Trimesh or a list, got a {}' - .format(type(mesh))) - - primitives = [] - for m in meshes: - positions = None - normals = None - indices = None - - # Compute positions, normals, and indices - if smooth: - positions = m.vertices.copy() - normals = m.vertex_normals.copy() - indices = m.faces.copy() - else: - positions = m.vertices[m.faces].reshape((3 * len(m.faces), 3)) - normals = np.repeat(m.face_normals, 3, axis=0) - - # Compute colors, texture coords, and material properties - color_0, texcoord_0, primitive_material = Mesh._get_trimesh_props(m, smooth=smooth, material=material) - - # Override if material is given. - if material is not None: - #primitive_material = copy.copy(material) - primitive_material = copy.deepcopy(material) # TODO - - if primitive_material is None: - # Replace material with default if needed - primitive_material = MetallicRoughnessMaterial( - alphaMode='BLEND', - baseColorFactor=[0.3, 0.3, 0.3, 1.0], - metallicFactor=0.2, - roughnessFactor=0.8 - ) - - primitive_material.wireframe = wireframe - - # Create the primitive - primitives.append(Primitive( - positions=positions, - normals=normals, - texcoord_0=texcoord_0, - color_0=color_0, - indices=indices, - material=primitive_material, - mode=GLTF.TRIANGLES, - poses=poses - )) - - return Mesh(primitives=primitives, is_visible=is_visible) - - @staticmethod - def _get_trimesh_props(mesh, smooth=False, material=None): - """Gets the vertex colors, texture coordinates, and material properties - from a :class:`~trimesh.base.Trimesh`. - """ - colors = None - texcoords = None - - # If the trimesh visual is undefined, return none for both - if not mesh.visual.defined: - return colors, texcoords, material - - # Process vertex colors - if material is None: - if mesh.visual.kind == 'vertex': - vc = mesh.visual.vertex_colors.copy() - if smooth: - colors = vc - else: - colors = vc[mesh.faces].reshape( - (3 * len(mesh.faces), vc.shape[1]) - ) - material = MetallicRoughnessMaterial( - alphaMode='BLEND', - baseColorFactor=[1.0, 1.0, 1.0, 1.0], - metallicFactor=0.2, - roughnessFactor=0.8 - ) - # Process face colors - elif mesh.visual.kind == 'face': - if smooth: - raise ValueError('Cannot use face colors with a smooth mesh') - else: - colors = np.repeat(mesh.visual.face_colors, 3, axis=0) - - material = MetallicRoughnessMaterial( - alphaMode='BLEND', - baseColorFactor=[1.0, 1.0, 1.0, 1.0], - metallicFactor=0.2, - roughnessFactor=0.8 - ) - - # Process texture colors - if mesh.visual.kind == 'texture': - # Configure UV coordinates - if mesh.visual.uv is not None and len(mesh.visual.uv) != 0: - uv = mesh.visual.uv.copy() - if smooth: - texcoords = uv - else: - texcoords = uv[mesh.faces].reshape( - (3 * len(mesh.faces), uv.shape[1]) - ) - - if material is None: - # Configure mesh material - mat = mesh.visual.material - - if isinstance(mat, trimesh.visual.texture.PBRMaterial): - material = MetallicRoughnessMaterial( - normalTexture=mat.normalTexture, - occlusionTexture=mat.occlusionTexture, - emissiveTexture=mat.emissiveTexture, - emissiveFactor=mat.emissiveFactor, - alphaMode='BLEND', - baseColorFactor=mat.baseColorFactor, - baseColorTexture=mat.baseColorTexture, - metallicFactor=mat.metallicFactor, - roughnessFactor=mat.roughnessFactor, - metallicRoughnessTexture=mat.metallicRoughnessTexture, - doubleSided=mat.doubleSided, - alphaCutoff=mat.alphaCutoff - ) - elif isinstance(mat, trimesh.visual.texture.SimpleMaterial): - glossiness = mat.kwargs.get('Ns', 1.0) - if isinstance(glossiness, list): - glossiness = float(glossiness[0]) - roughness = (2 / (glossiness + 2)) ** (1.0 / 4.0) - material = MetallicRoughnessMaterial( - alphaMode='BLEND', - roughnessFactor=roughness, - baseColorFactor=mat.diffuse, - baseColorTexture=mat.image, - ) - elif isinstance(mat, MetallicRoughnessMaterial): - material = mat - - return colors, texcoords, material diff --git a/pyrender/pyrender/node.py b/pyrender/pyrender/node.py deleted file mode 100644 index 1f37f7856cc732a37dc58253022a7c331489493e..0000000000000000000000000000000000000000 --- a/pyrender/pyrender/node.py +++ /dev/null @@ -1,263 +0,0 @@ -"""Nodes, conforming to the glTF 2.0 standards as specified in -https://github.com/KhronosGroup/glTF/tree/master/specification/2.0#reference-node - -Author: Matthew Matl -""" -import numpy as np - -import trimesh.transformations as transformations - -from .camera import Camera -from .mesh import Mesh -from .light import Light - - -class Node(object): - """A node in the node hierarchy. - - Parameters - ---------- - name : str, optional - The user-defined name of this object. - camera : :class:`Camera`, optional - The camera in this node. - children : list of :class:`Node` - The children of this node. - skin : int, optional - The index of the skin referenced by this node. - matrix : (4,4) float, optional - A floating-point 4x4 transformation matrix. - mesh : :class:`Mesh`, optional - The mesh in this node. - rotation : (4,) float, optional - The node's unit quaternion in the order (x, y, z, w), where - w is the scalar. - scale : (3,) float, optional - The node's non-uniform scale, given as the scaling factors along the x, - y, and z axes. - translation : (3,) float, optional - The node's translation along the x, y, and z axes. - weights : (n,) float - The weights of the instantiated Morph Target. Number of elements must - match number of Morph Targets of used mesh. - light : :class:`Light`, optional - The light in this node. - """ - - def __init__(self, - name=None, - camera=None, - children=None, - skin=None, - matrix=None, - mesh=None, - rotation=None, - scale=None, - translation=None, - weights=None, - light=None): - # Set defaults - if children is None: - children = [] - - self._matrix = None - self._scale = None - self._rotation = None - self._translation = None - if matrix is None: - if rotation is None: - rotation = np.array([0.0, 0.0, 0.0, 1.0]) - if translation is None: - translation = np.zeros(3) - if scale is None: - scale = np.ones(3) - self.rotation = rotation - self.translation = translation - self.scale = scale - else: - self.matrix = matrix - - self.name = name - self.camera = camera - self.children = children - self.skin = skin - self.mesh = mesh - self.weights = weights - self.light = light - - @property - def name(self): - """str : The user-defined name of this object. - """ - return self._name - - @name.setter - def name(self, value): - if value is not None: - value = str(value) - self._name = value - - @property - def camera(self): - """:class:`Camera` : The camera in this node. - """ - return self._camera - - @camera.setter - def camera(self, value): - if value is not None and not isinstance(value, Camera): - raise TypeError('Value must be a camera') - self._camera = value - - @property - def children(self): - """list of :class:`Node` : The children of this node. - """ - return self._children - - @children.setter - def children(self, value): - self._children = value - - @property - def skin(self): - """int : The skin index for this node. - """ - return self._skin - - @skin.setter - def skin(self, value): - self._skin = value - - @property - def mesh(self): - """:class:`Mesh` : The mesh in this node. - """ - return self._mesh - - @mesh.setter - def mesh(self, value): - if value is not None and not isinstance(value, Mesh): - raise TypeError('Value must be a mesh') - self._mesh = value - - @property - def light(self): - """:class:`Light` : The light in this node. - """ - return self._light - - @light.setter - def light(self, value): - if value is not None and not isinstance(value, Light): - raise TypeError('Value must be a light') - self._light = value - - @property - def rotation(self): - """(4,) float : The xyzw quaternion for this node. - """ - return self._rotation - - @rotation.setter - def rotation(self, value): - value = np.asanyarray(value) - if value.shape != (4,): - raise ValueError('Quaternion must be a (4,) vector') - if np.abs(np.linalg.norm(value) - 1.0) > 1e-3: - raise ValueError('Quaternion must have norm == 1.0') - self._rotation = value - self._matrix = None - - @property - def translation(self): - """(3,) float : The translation for this node. - """ - return self._translation - - @translation.setter - def translation(self, value): - value = np.asanyarray(value) - if value.shape != (3,): - raise ValueError('Translation must be a (3,) vector') - self._translation = value - self._matrix = None - - @property - def scale(self): - """(3,) float : The scale for this node. - """ - return self._scale - - @scale.setter - def scale(self, value): - value = np.asanyarray(value) - if value.shape != (3,): - raise ValueError('Scale must be a (3,) vector') - self._scale = value - self._matrix = None - - @property - def matrix(self): - """(4,4) float : The homogenous transform matrix for this node. - - Note that this matrix's elements are not settable, - it's just a copy of the internal matrix. You can set the whole - matrix, but not an individual element. - """ - if self._matrix is None: - self._matrix = self._m_from_tqs( - self.translation, self.rotation, self.scale - ) - return self._matrix.copy() - - @matrix.setter - def matrix(self, value): - value = np.asanyarray(value) - if value.shape != (4,4): - raise ValueError('Matrix must be a 4x4 numpy ndarray') - if not np.allclose(value[3,:], np.array([0.0, 0.0, 0.0, 1.0])): - raise ValueError('Bottom row of matrix must be [0,0,0,1]') - self.rotation = Node._q_from_m(value) - self.scale = Node._s_from_m(value) - self.translation = Node._t_from_m(value) - self._matrix = value - - @staticmethod - def _t_from_m(m): - return m[:3,3] - - @staticmethod - def _r_from_m(m): - U = m[:3,:3] - norms = np.linalg.norm(U.T, axis=1) - return U / norms - - @staticmethod - def _q_from_m(m): - M = np.eye(4) - M[:3,:3] = Node._r_from_m(m) - q_wxyz = transformations.quaternion_from_matrix(M) - return np.roll(q_wxyz, -1) - - @staticmethod - def _s_from_m(m): - return np.linalg.norm(m[:3,:3].T, axis=1) - - @staticmethod - def _r_from_q(q): - q_wxyz = np.roll(q, 1) - return transformations.quaternion_matrix(q_wxyz)[:3,:3] - - @staticmethod - def _m_from_tqs(t, q, s): - S = np.eye(4) - S[:3,:3] = np.diag(s) - - R = np.eye(4) - R[:3,:3] = Node._r_from_q(q) - - T = np.eye(4) - T[:3,3] = t - - return T.dot(R.dot(S)) diff --git a/pyrender/pyrender/offscreen.py b/pyrender/pyrender/offscreen.py deleted file mode 100644 index 340142983006cdc6f51b6d114e9b2b294aa4a919..0000000000000000000000000000000000000000 --- a/pyrender/pyrender/offscreen.py +++ /dev/null @@ -1,160 +0,0 @@ -"""Wrapper for offscreen rendering. - -Author: Matthew Matl -""" -import os - -from .renderer import Renderer -from .constants import RenderFlags - - -class OffscreenRenderer(object): - """A wrapper for offscreen rendering. - - Parameters - ---------- - viewport_width : int - The width of the main viewport, in pixels. - viewport_height : int - The height of the main viewport, in pixels. - point_size : float - The size of screen-space points in pixels. - """ - - def __init__(self, viewport_width, viewport_height, point_size=1.0): - self.viewport_width = viewport_width - self.viewport_height = viewport_height - self.point_size = point_size - - self._platform = None - self._renderer = None - self._create() - - @property - def viewport_width(self): - """int : The width of the main viewport, in pixels. - """ - return self._viewport_width - - @viewport_width.setter - def viewport_width(self, value): - self._viewport_width = int(value) - - @property - def viewport_height(self): - """int : The height of the main viewport, in pixels. - """ - return self._viewport_height - - @viewport_height.setter - def viewport_height(self, value): - self._viewport_height = int(value) - - @property - def point_size(self): - """float : The pixel size of points in point clouds. - """ - return self._point_size - - @point_size.setter - def point_size(self, value): - self._point_size = float(value) - - def render(self, scene, flags=RenderFlags.NONE, seg_node_map=None): - """Render a scene with the given set of flags. - - Parameters - ---------- - scene : :class:`Scene` - A scene to render. - flags : int - A bitwise or of one or more flags from :class:`.RenderFlags`. - seg_node_map : dict - A map from :class:`.Node` objects to (3,) colors for each. - If specified along with flags set to :attr:`.RenderFlags.SEG`, - the color image will be a segmentation image. - - Returns - ------- - color_im : (h, w, 3) uint8 or (h, w, 4) uint8 - The color buffer in RGB format, or in RGBA format if - :attr:`.RenderFlags.RGBA` is set. - Not returned if flags includes :attr:`.RenderFlags.DEPTH_ONLY`. - depth_im : (h, w) float32 - The depth buffer in linear units. - """ - self._platform.make_current() - # If platform does not support dynamically-resizing framebuffers, - # destroy it and restart it - if (self._platform.viewport_height != self.viewport_height or - self._platform.viewport_width != self.viewport_width): - if not self._platform.supports_framebuffers(): - self.delete() - self._create() - - self._platform.make_current() - self._renderer.viewport_width = self.viewport_width - self._renderer.viewport_height = self.viewport_height - self._renderer.point_size = self.point_size - - if self._platform.supports_framebuffers(): - flags |= RenderFlags.OFFSCREEN - retval = self._renderer.render(scene, flags, seg_node_map) - else: - self._renderer.render(scene, flags, seg_node_map) - depth = self._renderer.read_depth_buf() - if flags & RenderFlags.DEPTH_ONLY: - retval = depth - else: - color = self._renderer.read_color_buf() - retval = color, depth - - # Make the platform not current - self._platform.make_uncurrent() - return retval - - def delete(self): - """Free all OpenGL resources. - """ - self._platform.make_current() - self._renderer.delete() - self._platform.delete_context() - del self._renderer - del self._platform - self._renderer = None - self._platform = None - import gc - gc.collect() - - def _create(self): - if 'PYOPENGL_PLATFORM' not in os.environ: - from pyrender.platforms.pyglet_platform import PygletPlatform - self._platform = PygletPlatform(self.viewport_width, - self.viewport_height) - elif os.environ['PYOPENGL_PLATFORM'] == 'egl': - from pyrender.platforms import egl - device_id = int(os.environ.get('EGL_DEVICE_ID', '0')) - egl_device = egl.get_device_by_index(device_id) - self._platform = egl.EGLPlatform(self.viewport_width, - self.viewport_height, - device=egl_device) - elif os.environ['PYOPENGL_PLATFORM'] == 'osmesa': - from pyrender.platforms.osmesa import OSMesaPlatform - self._platform = OSMesaPlatform(self.viewport_width, - self.viewport_height) - else: - raise ValueError('Unsupported PyOpenGL platform: {}'.format( - os.environ['PYOPENGL_PLATFORM'] - )) - self._platform.init_context() - self._platform.make_current() - self._renderer = Renderer(self.viewport_width, self.viewport_height) - - def __del__(self): - try: - self.delete() - except Exception: - pass - - -__all__ = ['OffscreenRenderer'] diff --git a/pyrender/pyrender/platforms/__init__.py b/pyrender/pyrender/platforms/__init__.py deleted file mode 100644 index 7837fd5fdeccab5e48c85e41d20b238ea7396599..0000000000000000000000000000000000000000 --- a/pyrender/pyrender/platforms/__init__.py +++ /dev/null @@ -1,6 +0,0 @@ -"""Platforms for generating offscreen OpenGL contexts for rendering. - -Author: Matthew Matl -""" - -from .base import Platform diff --git a/pyrender/pyrender/platforms/base.py b/pyrender/pyrender/platforms/base.py deleted file mode 100644 index c9ecda906145e239737901809aa59db8d3e231c6..0000000000000000000000000000000000000000 --- a/pyrender/pyrender/platforms/base.py +++ /dev/null @@ -1,76 +0,0 @@ -import abc - -import six - - -@six.add_metaclass(abc.ABCMeta) -class Platform(object): - """Base class for all OpenGL platforms. - - Parameters - ---------- - viewport_width : int - The width of the main viewport, in pixels. - viewport_height : int - The height of the main viewport, in pixels - """ - - def __init__(self, viewport_width, viewport_height): - self.viewport_width = viewport_width - self.viewport_height = viewport_height - - @property - def viewport_width(self): - """int : The width of the main viewport, in pixels. - """ - return self._viewport_width - - @viewport_width.setter - def viewport_width(self, value): - self._viewport_width = value - - @property - def viewport_height(self): - """int : The height of the main viewport, in pixels. - """ - return self._viewport_height - - @viewport_height.setter - def viewport_height(self, value): - self._viewport_height = value - - @abc.abstractmethod - def init_context(self): - """Create an OpenGL context. - """ - pass - - @abc.abstractmethod - def make_current(self): - """Make the OpenGL context current. - """ - pass - - @abc.abstractmethod - def make_uncurrent(self): - """Make the OpenGL context uncurrent. - """ - pass - - @abc.abstractmethod - def delete_context(self): - """Delete the OpenGL context. - """ - pass - - @abc.abstractmethod - def supports_framebuffers(self): - """Returns True if the method supports framebuffer rendering. - """ - pass - - def __del__(self): - try: - self.delete_context() - except Exception: - pass diff --git a/pyrender/pyrender/platforms/egl.py b/pyrender/pyrender/platforms/egl.py deleted file mode 100644 index ae2478d29c9a538c53ad83fa31f8e2277cd897c8..0000000000000000000000000000000000000000 --- a/pyrender/pyrender/platforms/egl.py +++ /dev/null @@ -1,219 +0,0 @@ -import ctypes -import os - -import OpenGL.platform - -from .base import Platform - -EGL_PLATFORM_DEVICE_EXT = 0x313F -EGL_DRM_DEVICE_FILE_EXT = 0x3233 - - -def _ensure_egl_loaded(): - plugin = OpenGL.platform.PlatformPlugin.by_name('egl') - if plugin is None: - raise RuntimeError("EGL platform plugin is not available.") - - plugin_class = plugin.load() - plugin.loaded = True - # create instance of this platform implementation - plugin = plugin_class() - - plugin.install(vars(OpenGL.platform)) - - -_ensure_egl_loaded() -from OpenGL import EGL as egl - - -def _get_egl_func(func_name, res_type, *arg_types): - address = egl.eglGetProcAddress(func_name) - if address is None: - return None - - proto = ctypes.CFUNCTYPE(res_type) - proto.argtypes = arg_types - func = proto(address) - return func - - -def _get_egl_struct(struct_name): - from OpenGL._opaque import opaque_pointer_cls - return opaque_pointer_cls(struct_name) - - -# These are not defined in PyOpenGL by default. -_EGLDeviceEXT = _get_egl_struct('EGLDeviceEXT') -_eglGetPlatformDisplayEXT = _get_egl_func('eglGetPlatformDisplayEXT', egl.EGLDisplay) -_eglQueryDevicesEXT = _get_egl_func('eglQueryDevicesEXT', egl.EGLBoolean) -_eglQueryDeviceStringEXT = _get_egl_func('eglQueryDeviceStringEXT', ctypes.c_char_p) - - -def query_devices(): - if _eglQueryDevicesEXT is None: - raise RuntimeError("EGL query extension is not loaded or is not supported.") - - num_devices = egl.EGLint() - success = _eglQueryDevicesEXT(0, None, ctypes.pointer(num_devices)) - if not success or num_devices.value < 1: - return [] - - devices = (_EGLDeviceEXT * num_devices.value)() # array of size num_devices - success = _eglQueryDevicesEXT(num_devices.value, devices, ctypes.pointer(num_devices)) - if not success or num_devices.value < 1: - return [] - - return [EGLDevice(devices[i]) for i in range(num_devices.value)] - - -def get_default_device(): - # Fall back to not using query extension. - if _eglQueryDevicesEXT is None: - return EGLDevice(None) - - return query_devices()[0] - - -def get_device_by_index(device_id): - if _eglQueryDevicesEXT is None and device_id == 0: - return get_default_device() - - devices = query_devices() - if device_id >= len(devices): - raise ValueError('Invalid device ID ({})'.format(device_id, len(devices))) - return devices[device_id] - - -class EGLDevice: - - def __init__(self, display=None): - self._display = display - - def get_display(self): - if self._display is None: - return egl.eglGetDisplay(egl.EGL_DEFAULT_DISPLAY) - - return _eglGetPlatformDisplayEXT(EGL_PLATFORM_DEVICE_EXT, self._display, None) - - @property - def name(self): - if self._display is None: - return 'default' - - name = _eglQueryDeviceStringEXT(self._display, EGL_DRM_DEVICE_FILE_EXT) - if name is None: - return None - - return name.decode('ascii') - - def __repr__(self): - return "".format(self.name) - - -class EGLPlatform(Platform): - """Renders using EGL. - """ - - def __init__(self, viewport_width, viewport_height, device: EGLDevice = None): - super(EGLPlatform, self).__init__(viewport_width, viewport_height) - if device is None: - device = get_default_device() - - self._egl_device = device - self._egl_display = None - self._egl_context = None - - def init_context(self): - _ensure_egl_loaded() - - from OpenGL.EGL import ( - EGL_SURFACE_TYPE, EGL_PBUFFER_BIT, EGL_BLUE_SIZE, - EGL_RED_SIZE, EGL_GREEN_SIZE, EGL_DEPTH_SIZE, - EGL_COLOR_BUFFER_TYPE, EGL_RGB_BUFFER, - EGL_RENDERABLE_TYPE, EGL_OPENGL_BIT, EGL_CONFORMANT, - EGL_NONE, EGL_DEFAULT_DISPLAY, EGL_NO_CONTEXT, - EGL_OPENGL_API, EGL_CONTEXT_MAJOR_VERSION, - EGL_CONTEXT_MINOR_VERSION, - EGL_CONTEXT_OPENGL_PROFILE_MASK, - EGL_CONTEXT_OPENGL_CORE_PROFILE_BIT, - eglGetDisplay, eglInitialize, eglChooseConfig, - eglBindAPI, eglCreateContext, EGLConfig - ) - from OpenGL import arrays - - config_attributes = arrays.GLintArray.asArray([ - EGL_SURFACE_TYPE, EGL_PBUFFER_BIT, - EGL_BLUE_SIZE, 8, - EGL_RED_SIZE, 8, - EGL_GREEN_SIZE, 8, - EGL_DEPTH_SIZE, 24, - EGL_COLOR_BUFFER_TYPE, EGL_RGB_BUFFER, - EGL_RENDERABLE_TYPE, EGL_OPENGL_BIT, - EGL_CONFORMANT, EGL_OPENGL_BIT, - EGL_NONE - ]) - context_attributes = arrays.GLintArray.asArray([ - EGL_CONTEXT_MAJOR_VERSION, 4, - EGL_CONTEXT_MINOR_VERSION, 1, - EGL_CONTEXT_OPENGL_PROFILE_MASK, - EGL_CONTEXT_OPENGL_CORE_PROFILE_BIT, - EGL_NONE - ]) - major, minor = ctypes.c_long(), ctypes.c_long() - num_configs = ctypes.c_long() - configs = (EGLConfig * 1)() - - # Cache DISPLAY if necessary and get an off-screen EGL display - orig_dpy = None - if 'DISPLAY' in os.environ: - orig_dpy = os.environ['DISPLAY'] - del os.environ['DISPLAY'] - - self._egl_display = self._egl_device.get_display() - if orig_dpy is not None: - os.environ['DISPLAY'] = orig_dpy - - # Initialize EGL - assert eglInitialize(self._egl_display, major, minor) - assert eglChooseConfig( - self._egl_display, config_attributes, configs, 1, num_configs - ) - - # Bind EGL to the OpenGL API - assert eglBindAPI(EGL_OPENGL_API) - - # Create an EGL context - self._egl_context = eglCreateContext( - self._egl_display, configs[0], - EGL_NO_CONTEXT, context_attributes - ) - - # Make it current - self.make_current() - - def make_current(self): - from OpenGL.EGL import eglMakeCurrent, EGL_NO_SURFACE - assert eglMakeCurrent( - self._egl_display, EGL_NO_SURFACE, EGL_NO_SURFACE, - self._egl_context - ) - - def make_uncurrent(self): - """Make the OpenGL context uncurrent. - """ - pass - - def delete_context(self): - from OpenGL.EGL import eglDestroyContext, eglTerminate - if self._egl_display is not None: - if self._egl_context is not None: - eglDestroyContext(self._egl_display, self._egl_context) - self._egl_context = None - eglTerminate(self._egl_display) - self._egl_display = None - - def supports_framebuffers(self): - return True - - -__all__ = ['EGLPlatform'] diff --git a/pyrender/pyrender/platforms/osmesa.py b/pyrender/pyrender/platforms/osmesa.py deleted file mode 100644 index deaa5ff44031a107883913ae9a18fc425d650f3d..0000000000000000000000000000000000000000 --- a/pyrender/pyrender/platforms/osmesa.py +++ /dev/null @@ -1,59 +0,0 @@ -from .base import Platform - - -__all__ = ['OSMesaPlatform'] - - -class OSMesaPlatform(Platform): - """Renders into a software buffer using OSMesa. Requires special versions - of OSMesa to be installed, plus PyOpenGL upgrade. - """ - - def __init__(self, viewport_width, viewport_height): - super(OSMesaPlatform, self).__init__(viewport_width, viewport_height) - self._context = None - self._buffer = None - - def init_context(self): - from OpenGL import arrays - from OpenGL.osmesa import ( - OSMesaCreateContextAttribs, OSMESA_FORMAT, - OSMESA_RGBA, OSMESA_PROFILE, OSMESA_CORE_PROFILE, - OSMESA_CONTEXT_MAJOR_VERSION, OSMESA_CONTEXT_MINOR_VERSION, - OSMESA_DEPTH_BITS - ) - - attrs = arrays.GLintArray.asArray([ - OSMESA_FORMAT, OSMESA_RGBA, - OSMESA_DEPTH_BITS, 24, - OSMESA_PROFILE, OSMESA_CORE_PROFILE, - OSMESA_CONTEXT_MAJOR_VERSION, 3, - OSMESA_CONTEXT_MINOR_VERSION, 3, - 0 - ]) - self._context = OSMesaCreateContextAttribs(attrs, None) - self._buffer = arrays.GLubyteArray.zeros( - (self.viewport_height, self.viewport_width, 4) - ) - - def make_current(self): - from OpenGL import GL as gl - from OpenGL.osmesa import OSMesaMakeCurrent - assert(OSMesaMakeCurrent( - self._context, self._buffer, gl.GL_UNSIGNED_BYTE, - self.viewport_width, self.viewport_height - )) - - def make_uncurrent(self): - """Make the OpenGL context uncurrent. - """ - pass - - def delete_context(self): - from OpenGL.osmesa import OSMesaDestroyContext - OSMesaDestroyContext(self._context) - self._context = None - self._buffer = None - - def supports_framebuffers(self): - return False diff --git a/pyrender/pyrender/platforms/pyglet_platform.py b/pyrender/pyrender/platforms/pyglet_platform.py deleted file mode 100644 index a70cf7b659bc85a92f6c9c8ebcc360662a068507..0000000000000000000000000000000000000000 --- a/pyrender/pyrender/platforms/pyglet_platform.py +++ /dev/null @@ -1,90 +0,0 @@ -from pyrender.constants import (TARGET_OPEN_GL_MAJOR, TARGET_OPEN_GL_MINOR, - MIN_OPEN_GL_MAJOR, MIN_OPEN_GL_MINOR) -from .base import Platform - -import OpenGL - - -__all__ = ['PygletPlatform'] - - -class PygletPlatform(Platform): - """Renders on-screen using a 1x1 hidden Pyglet window for getting - an OpenGL context. - """ - - def __init__(self, viewport_width, viewport_height): - super(PygletPlatform, self).__init__(viewport_width, viewport_height) - self._window = None - - def init_context(self): - import pyglet - pyglet.options['shadow_window'] = False - - try: - pyglet.lib.x11.xlib.XInitThreads() - except Exception: - pass - - self._window = None - confs = [pyglet.gl.Config(sample_buffers=1, samples=4, - depth_size=24, - double_buffer=True, - major_version=TARGET_OPEN_GL_MAJOR, - minor_version=TARGET_OPEN_GL_MINOR), - pyglet.gl.Config(depth_size=24, - double_buffer=True, - major_version=TARGET_OPEN_GL_MAJOR, - minor_version=TARGET_OPEN_GL_MINOR), - pyglet.gl.Config(sample_buffers=1, samples=4, - depth_size=24, - double_buffer=True, - major_version=MIN_OPEN_GL_MAJOR, - minor_version=MIN_OPEN_GL_MINOR), - pyglet.gl.Config(depth_size=24, - double_buffer=True, - major_version=MIN_OPEN_GL_MAJOR, - minor_version=MIN_OPEN_GL_MINOR)] - for conf in confs: - try: - self._window = pyglet.window.Window(config=conf, visible=False, - resizable=False, - width=1, height=1) - break - except pyglet.window.NoSuchConfigException as e: - pass - - if not self._window: - raise ValueError( - 'Failed to initialize Pyglet window with an OpenGL >= 3+ ' - 'context. If you\'re logged in via SSH, ensure that you\'re ' - 'running your script with vglrun (i.e. VirtualGL). The ' - 'internal error message was "{}"'.format(e) - ) - - def make_current(self): - if self._window: - self._window.switch_to() - - def make_uncurrent(self): - try: - import pyglet - pyglet.gl.xlib.glx.glXMakeContextCurrent(self._window.context.x_display, 0, 0, None) - except Exception: - pass - - def delete_context(self): - if self._window is not None: - self.make_current() - cid = OpenGL.contextdata.getContext() - try: - self._window.context.destroy() - self._window.close() - except Exception: - pass - self._window = None - OpenGL.contextdata.cleanupContext(cid) - del cid - - def supports_framebuffers(self): - return True diff --git a/pyrender/pyrender/primitive.py b/pyrender/pyrender/primitive.py deleted file mode 100644 index 7f83f46f532b126a4573e715dd03d079fef755ca..0000000000000000000000000000000000000000 --- a/pyrender/pyrender/primitive.py +++ /dev/null @@ -1,489 +0,0 @@ -"""Primitives, conforming to the glTF 2.0 standards as specified in -https://github.com/KhronosGroup/glTF/tree/master/specification/2.0#reference-primitive - -Author: Matthew Matl -""" -import numpy as np - -from OpenGL.GL import * - -from .material import Material, MetallicRoughnessMaterial -from .constants import FLOAT_SZ, UINT_SZ, BufFlags, GLTF -from .utils import format_color_array - - -class Primitive(object): - """A primitive object which can be rendered. - - Parameters - ---------- - positions : (n, 3) float - XYZ vertex positions. - normals : (n, 3) float - Normalized XYZ vertex normals. - tangents : (n, 4) float - XYZW vertex tangents where the w component is a sign value - (either +1 or -1) indicating the handedness of the tangent basis. - texcoord_0 : (n, 2) float - The first set of UV texture coordinates. - texcoord_1 : (n, 2) float - The second set of UV texture coordinates. - color_0 : (n, 4) float - RGBA vertex colors. - joints_0 : (n, 4) float - Joint information. - weights_0 : (n, 4) float - Weight information for morphing. - indices : (m, 3) int - Face indices for triangle meshes or fans. - material : :class:`Material` - The material to apply to this primitive when rendering. - mode : int - The type of primitives to render, one of the following: - - - ``0``: POINTS - - ``1``: LINES - - ``2``: LINE_LOOP - - ``3``: LINE_STRIP - - ``4``: TRIANGLES - - ``5``: TRIANGLES_STRIP - - ``6``: TRIANGLES_FAN - targets : (k,) int - Morph target indices. - poses : (x,4,4), float - Array of 4x4 transformation matrices for instancing this object. - """ - - def __init__(self, - positions, - normals=None, - tangents=None, - texcoord_0=None, - texcoord_1=None, - color_0=None, - joints_0=None, - weights_0=None, - indices=None, - material=None, - mode=None, - targets=None, - poses=None): - - if mode is None: - mode = GLTF.TRIANGLES - - self.positions = positions - self.normals = normals - self.tangents = tangents - self.texcoord_0 = texcoord_0 - self.texcoord_1 = texcoord_1 - self.color_0 = color_0 - self.joints_0 = joints_0 - self.weights_0 = weights_0 - self.indices = indices - self.material = material - self.mode = mode - self.targets = targets - self.poses = poses - - self._bounds = None - self._vaid = None - self._buffers = [] - self._is_transparent = None - self._buf_flags = None - - @property - def positions(self): - """(n,3) float : XYZ vertex positions. - """ - return self._positions - - @positions.setter - def positions(self, value): - value = np.asanyarray(value, dtype=np.float32) - self._positions = np.ascontiguousarray(value) - self._bounds = None - - @property - def normals(self): - """(n,3) float : Normalized XYZ vertex normals. - """ - return self._normals - - @normals.setter - def normals(self, value): - if value is not None: - value = np.asanyarray(value, dtype=np.float32) - value = np.ascontiguousarray(value) - if value.shape != self.positions.shape: - raise ValueError('Incorrect normals shape') - self._normals = value - - @property - def tangents(self): - """(n,4) float : XYZW vertex tangents. - """ - return self._tangents - - @tangents.setter - def tangents(self, value): - if value is not None: - value = np.asanyarray(value, dtype=np.float32) - value = np.ascontiguousarray(value) - if value.shape != (self.positions.shape[0], 4): - raise ValueError('Incorrect tangent shape') - self._tangents = value - - @property - def texcoord_0(self): - """(n,2) float : The first set of UV texture coordinates. - """ - return self._texcoord_0 - - @texcoord_0.setter - def texcoord_0(self, value): - if value is not None: - value = np.asanyarray(value, dtype=np.float32) - value = np.ascontiguousarray(value) - if (value.ndim != 2 or value.shape[0] != self.positions.shape[0] or - value.shape[1] < 2): - raise ValueError('Incorrect texture coordinate shape') - if value.shape[1] > 2: - value = value[:,:2] - self._texcoord_0 = value - - @property - def texcoord_1(self): - """(n,2) float : The second set of UV texture coordinates. - """ - return self._texcoord_1 - - @texcoord_1.setter - def texcoord_1(self, value): - if value is not None: - value = np.asanyarray(value, dtype=np.float32) - value = np.ascontiguousarray(value) - if (value.ndim != 2 or value.shape[0] != self.positions.shape[0] or - value.shape[1] != 2): - raise ValueError('Incorrect texture coordinate shape') - self._texcoord_1 = value - - @property - def color_0(self): - """(n,4) float : RGBA vertex colors. - """ - return self._color_0 - - @color_0.setter - def color_0(self, value): - if value is not None: - value = np.ascontiguousarray( - format_color_array(value, shape=(len(self.positions), 4)) - ) - self._is_transparent = None - self._color_0 = value - - @property - def joints_0(self): - """(n,4) float : Joint information. - """ - return self._joints_0 - - @joints_0.setter - def joints_0(self, value): - self._joints_0 = value - - @property - def weights_0(self): - """(n,4) float : Weight information for morphing. - """ - return self._weights_0 - - @weights_0.setter - def weights_0(self, value): - self._weights_0 = value - - @property - def indices(self): - """(m,3) int : Face indices for triangle meshes or fans. - """ - return self._indices - - @indices.setter - def indices(self, value): - if value is not None: - value = np.asanyarray(value, dtype=np.float32) - value = np.ascontiguousarray(value) - self._indices = value - - @property - def material(self): - """:class:`Material` : The material for this primitive. - """ - return self._material - - @material.setter - def material(self, value): - # Create default material - if value is None: - value = MetallicRoughnessMaterial() - else: - if not isinstance(value, Material): - raise TypeError('Object material must be of type Material') - self._material = value - - @property - def mode(self): - """int : The type of primitive to render. - """ - return self._mode - - @mode.setter - def mode(self, value): - value = int(value) - if value < GLTF.POINTS or value > GLTF.TRIANGLE_FAN: - raise ValueError('Invalid mode') - self._mode = value - - @property - def targets(self): - """(k,) int : Morph target indices. - """ - return self._targets - - @targets.setter - def targets(self, value): - self._targets = value - - @property - def poses(self): - """(x,4,4) float : Homogenous transforms for instancing this primitive. - """ - return self._poses - - @poses.setter - def poses(self, value): - if value is not None: - value = np.asanyarray(value, dtype=np.float32) - value = np.ascontiguousarray(value) - if value.ndim == 2: - value = value[np.newaxis,:,:] - if value.shape[1] != 4 or value.shape[2] != 4: - raise ValueError('Pose matrices must be of shape (n,4,4), ' - 'got {}'.format(value.shape)) - self._poses = value - self._bounds = None - - @property - def bounds(self): - if self._bounds is None: - self._bounds = self._compute_bounds() - return self._bounds - - @property - def centroid(self): - """(3,) float : The centroid of the primitive's AABB. - """ - return np.mean(self.bounds, axis=0) - - @property - def extents(self): - """(3,) float : The lengths of the axes of the primitive's AABB. - """ - return np.diff(self.bounds, axis=0).reshape(-1) - - @property - def scale(self): - """(3,) float : The length of the diagonal of the primitive's AABB. - """ - return np.linalg.norm(self.extents) - - @property - def buf_flags(self): - """int : The flags for the render buffer. - """ - if self._buf_flags is None: - self._buf_flags = self._compute_buf_flags() - return self._buf_flags - - def delete(self): - self._unbind() - self._remove_from_context() - - @property - def is_transparent(self): - """bool : If True, the mesh is partially-transparent. - """ - return self._compute_transparency() - - def _add_to_context(self): - if self._vaid is not None: - raise ValueError('Mesh is already bound to a context') - - # Generate and bind VAO - self._vaid = glGenVertexArrays(1) - glBindVertexArray(self._vaid) - - ####################################################################### - # Fill vertex buffer - ####################################################################### - - # Generate and bind vertex buffer - vertexbuffer = glGenBuffers(1) - self._buffers.append(vertexbuffer) - glBindBuffer(GL_ARRAY_BUFFER, vertexbuffer) - - # positions - vertex_data = self.positions - attr_sizes = [3] - - # Normals - if self.normals is not None: - vertex_data = np.hstack((vertex_data, self.normals)) - attr_sizes.append(3) - - # Tangents - if self.tangents is not None: - vertex_data = np.hstack((vertex_data, self.tangents)) - attr_sizes.append(4) - - # Texture Coordinates - if self.texcoord_0 is not None: - vertex_data = np.hstack((vertex_data, self.texcoord_0)) - attr_sizes.append(2) - if self.texcoord_1 is not None: - vertex_data = np.hstack((vertex_data, self.texcoord_1)) - attr_sizes.append(2) - - # Color - if self.color_0 is not None: - vertex_data = np.hstack((vertex_data, self.color_0)) - attr_sizes.append(4) - - # TODO JOINTS AND WEIGHTS - # PASS - - # Copy data to buffer - vertex_data = np.ascontiguousarray( - vertex_data.flatten().astype(np.float32) - ) - glBufferData( - GL_ARRAY_BUFFER, FLOAT_SZ * len(vertex_data), - vertex_data, GL_STATIC_DRAW - ) - total_sz = sum(attr_sizes) - offset = 0 - for i, sz in enumerate(attr_sizes): - glVertexAttribPointer( - i, sz, GL_FLOAT, GL_FALSE, FLOAT_SZ * total_sz, - ctypes.c_void_p(FLOAT_SZ * offset) - ) - glEnableVertexAttribArray(i) - offset += sz - - ####################################################################### - # Fill model matrix buffer - ####################################################################### - - if self.poses is not None: - pose_data = np.ascontiguousarray( - np.transpose(self.poses, [0,2,1]).flatten().astype(np.float32) - ) - else: - pose_data = np.ascontiguousarray( - np.eye(4).flatten().astype(np.float32) - ) - - modelbuffer = glGenBuffers(1) - self._buffers.append(modelbuffer) - glBindBuffer(GL_ARRAY_BUFFER, modelbuffer) - glBufferData( - GL_ARRAY_BUFFER, FLOAT_SZ * len(pose_data), - pose_data, GL_STATIC_DRAW - ) - - for i in range(0, 4): - idx = i + len(attr_sizes) - glEnableVertexAttribArray(idx) - glVertexAttribPointer( - idx, 4, GL_FLOAT, GL_FALSE, FLOAT_SZ * 4 * 4, - ctypes.c_void_p(4 * FLOAT_SZ * i) - ) - glVertexAttribDivisor(idx, 1) - - ####################################################################### - # Fill element buffer - ####################################################################### - if self.indices is not None: - elementbuffer = glGenBuffers(1) - self._buffers.append(elementbuffer) - glBindBuffer(GL_ELEMENT_ARRAY_BUFFER, elementbuffer) - glBufferData(GL_ELEMENT_ARRAY_BUFFER, UINT_SZ * self.indices.size, - self.indices.flatten().astype(np.uint32), - GL_STATIC_DRAW) - - glBindVertexArray(0) - - def _remove_from_context(self): - if self._vaid is not None: - glDeleteVertexArrays(1, [self._vaid]) - glDeleteBuffers(len(self._buffers), self._buffers) - self._vaid = None - self._buffers = [] - - def _in_context(self): - return self._vaid is not None - - def _bind(self): - if self._vaid is None: - raise ValueError('Cannot bind a Mesh that has not been added ' - 'to a context') - glBindVertexArray(self._vaid) - - def _unbind(self): - glBindVertexArray(0) - - def _compute_bounds(self): - """Compute the bounds of this object. - """ - # Compute bounds of this object - bounds = np.array([np.min(self.positions, axis=0), - np.max(self.positions, axis=0)]) - - # If instanced, compute translations for approximate bounds - if self.poses is not None: - bounds += np.array([np.min(self.poses[:,:3,3], axis=0), - np.max(self.poses[:,:3,3], axis=0)]) - return bounds - - def _compute_transparency(self): - """Compute whether or not this object is transparent. - """ - if self.material.is_transparent: - return True - if self._is_transparent is None: - self._is_transparent = False - if self.color_0 is not None: - if np.any(self._color_0[:,3] != 1.0): - self._is_transparent = True - return self._is_transparent - - def _compute_buf_flags(self): - buf_flags = BufFlags.POSITION - - if self.normals is not None: - buf_flags |= BufFlags.NORMAL - if self.tangents is not None: - buf_flags |= BufFlags.TANGENT - if self.texcoord_0 is not None: - buf_flags |= BufFlags.TEXCOORD_0 - if self.texcoord_1 is not None: - buf_flags |= BufFlags.TEXCOORD_1 - if self.color_0 is not None: - buf_flags |= BufFlags.COLOR_0 - if self.joints_0 is not None: - buf_flags |= BufFlags.JOINTS_0 - if self.weights_0 is not None: - buf_flags |= BufFlags.WEIGHTS_0 - - return buf_flags diff --git a/pyrender/pyrender/renderer.py b/pyrender/pyrender/renderer.py deleted file mode 100644 index 5ae14c5cdb1785226a52ae6b71b08f01de069962..0000000000000000000000000000000000000000 --- a/pyrender/pyrender/renderer.py +++ /dev/null @@ -1,1339 +0,0 @@ -"""PBR renderer for Python. - -Author: Matthew Matl -""" -import sys - -import numpy as np -import PIL - -from .constants import (RenderFlags, TextAlign, GLTF, BufFlags, TexFlags, - ProgramFlags, DEFAULT_Z_FAR, DEFAULT_Z_NEAR, - SHADOW_TEX_SZ, MAX_N_LIGHTS) -from .shader_program import ShaderProgramCache -from .material import MetallicRoughnessMaterial, SpecularGlossinessMaterial -from .light import PointLight, SpotLight, DirectionalLight -from .font import FontCache -from .utils import format_color_vector - -from OpenGL.GL import * - - -class Renderer(object): - """Class for handling all rendering operations on a scene. - - Note - ---- - This renderer relies on the existence of an OpenGL context and - does not create one on its own. - - Parameters - ---------- - viewport_width : int - Width of the viewport in pixels. - viewport_height : int - Width of the viewport height in pixels. - point_size : float, optional - Size of points in pixels. Defaults to 1.0. - """ - - def __init__(self, viewport_width, viewport_height, point_size=1.0): - self.dpscale = 1 - # Scaling needed on retina displays - if sys.platform == 'darwin': - self.dpscale = 2 - - self.viewport_width = viewport_width - self.viewport_height = viewport_height - self.point_size = point_size - - # Optional framebuffer for offscreen renders - self._main_fb = None - self._main_cb = None - self._main_db = None - self._main_fb_ms = None - self._main_cb_ms = None - self._main_db_ms = None - self._main_fb_dims = (None, None) - self._shadow_fb = None - self._latest_znear = DEFAULT_Z_NEAR - self._latest_zfar = DEFAULT_Z_FAR - - # Shader Program Cache - self._program_cache = ShaderProgramCache() - self._font_cache = FontCache() - self._meshes = set() - self._mesh_textures = set() - self._shadow_textures = set() - self._texture_alloc_idx = 0 - - @property - def viewport_width(self): - """int : The width of the main viewport, in pixels. - """ - return self._viewport_width - - @viewport_width.setter - def viewport_width(self, value): - self._viewport_width = self.dpscale * value - - @property - def viewport_height(self): - """int : The height of the main viewport, in pixels. - """ - return self._viewport_height - - @viewport_height.setter - def viewport_height(self, value): - self._viewport_height = self.dpscale * value - - @property - def point_size(self): - """float : The size of screen-space points, in pixels. - """ - return self._point_size - - @point_size.setter - def point_size(self, value): - self._point_size = float(value) - - def render(self, scene, flags, seg_node_map=None): - """Render a scene with the given set of flags. - - Parameters - ---------- - scene : :class:`Scene` - A scene to render. - flags : int - A specification from :class:`.RenderFlags`. - seg_node_map : dict - A map from :class:`.Node` objects to (3,) colors for each. - If specified along with flags set to :attr:`.RenderFlags.SEG`, - the color image will be a segmentation image. - - Returns - ------- - color_im : (h, w, 3) uint8 or (h, w, 4) uint8 - If :attr:`RenderFlags.OFFSCREEN` is set, the color buffer. This is - normally an RGB buffer, but if :attr:`.RenderFlags.RGBA` is set, - the buffer will be a full RGBA buffer. - depth_im : (h, w) float32 - If :attr:`RenderFlags.OFFSCREEN` is set, the depth buffer - in linear units. - """ - # Update context with meshes and textures - self._update_context(scene, flags) - - # Render necessary shadow maps - if not bool(flags & RenderFlags.DEPTH_ONLY or flags & RenderFlags.SEG): - for ln in scene.light_nodes: - take_pass = False - if (isinstance(ln.light, DirectionalLight) and - bool(flags & RenderFlags.SHADOWS_DIRECTIONAL)): - take_pass = True - elif (isinstance(ln.light, SpotLight) and - bool(flags & RenderFlags.SHADOWS_SPOT)): - take_pass = True - elif (isinstance(ln.light, PointLight) and - bool(flags & RenderFlags.SHADOWS_POINT)): - take_pass = True - if take_pass: - self._shadow_mapping_pass(scene, ln, flags) - - # Make forward pass - retval = self._forward_pass(scene, flags, seg_node_map=seg_node_map) - - # If necessary, make normals pass - if flags & (RenderFlags.VERTEX_NORMALS | RenderFlags.FACE_NORMALS): - self._normals_pass(scene, flags) - - # Update camera settings for retrieving depth buffers - self._latest_znear = scene.main_camera_node.camera.znear - self._latest_zfar = scene.main_camera_node.camera.zfar - - return retval - - def render_text(self, text, x, y, font_name='OpenSans-Regular', - font_pt=40, color=None, scale=1.0, - align=TextAlign.BOTTOM_LEFT): - """Render text into the current viewport. - - Note - ---- - This cannot be done into an offscreen buffer. - - Parameters - ---------- - text : str - The text to render. - x : int - Horizontal pixel location of text. - y : int - Vertical pixel location of text. - font_name : str - Name of font, from the ``pyrender/fonts`` folder, or - a path to a ``.ttf`` file. - font_pt : int - Height of the text, in font points. - color : (4,) float - The color of the text. Default is black. - scale : int - Scaling factor for text. - align : int - One of the :class:`TextAlign` options which specifies where the - ``x`` and ``y`` parameters lie on the text. For example, - :attr:`TextAlign.BOTTOM_LEFT` means that ``x`` and ``y`` indicate - the position of the bottom-left corner of the textbox. - """ - x *= self.dpscale - y *= self.dpscale - font_pt *= self.dpscale - - if color is None: - color = np.array([0.0, 0.0, 0.0, 1.0]) - else: - color = format_color_vector(color, 4) - - # Set up viewport for render - self._configure_forward_pass_viewport(0) - - # Load font - font = self._font_cache.get_font(font_name, font_pt) - if not font._in_context(): - font._add_to_context() - - # Load program - program = self._get_text_program() - program._bind() - - # Set uniforms - p = np.eye(4) - p[0,0] = 2.0 / self.viewport_width - p[0,3] = -1.0 - p[1,1] = 2.0 / self.viewport_height - p[1,3] = -1.0 - program.set_uniform('projection', p) - program.set_uniform('text_color', color) - - # Draw text - font.render_string(text, x, y, scale, align) - - def read_color_buf(self): - """Read and return the current viewport's color buffer. - - Alpha cannot be computed for an on-screen buffer. - - Returns - ------- - color_im : (h, w, 3) uint8 - The color buffer in RGB byte format. - """ - # Extract color image from frame buffer - width, height = self.viewport_width, self.viewport_height - glBindFramebuffer(GL_READ_FRAMEBUFFER, 0) - glReadBuffer(GL_FRONT) - color_buf = glReadPixels(0, 0, width, height, GL_RGB, GL_UNSIGNED_BYTE) - - # Re-format them into numpy arrays - color_im = np.frombuffer(color_buf, dtype=np.uint8) - color_im = color_im.reshape((height, width, 3)) - color_im = np.flip(color_im, axis=0) - - # Resize for macos if needed - if sys.platform == 'darwin': - color_im = self._resize_image(color_im, True) - - return color_im - - def read_depth_buf(self): - """Read and return the current viewport's color buffer. - - Returns - ------- - depth_im : (h, w) float32 - The depth buffer in linear units. - """ - width, height = self.viewport_width, self.viewport_height - glBindFramebuffer(GL_READ_FRAMEBUFFER, 0) - glReadBuffer(GL_FRONT) - depth_buf = glReadPixels( - 0, 0, width, height, GL_DEPTH_COMPONENT, GL_FLOAT - ) - - depth_im = np.frombuffer(depth_buf, dtype=np.float32) - depth_im = depth_im.reshape((height, width)) - depth_im = np.flip(depth_im, axis=0) - - inf_inds = (depth_im == 1.0) - depth_im = 2.0 * depth_im - 1.0 - z_near, z_far = self._latest_znear, self._latest_zfar - noninf = np.logical_not(inf_inds) - if z_far is None: - depth_im[noninf] = 2 * z_near / (1.0 - depth_im[noninf]) - else: - depth_im[noninf] = ((2.0 * z_near * z_far) / - (z_far + z_near - depth_im[noninf] * - (z_far - z_near))) - depth_im[inf_inds] = 0.0 - - # Resize for macos if needed - if sys.platform == 'darwin': - depth_im = self._resize_image(depth_im) - - return depth_im - - def delete(self): - """Free all allocated OpenGL resources. - """ - # Free shaders - self._program_cache.clear() - - # Free fonts - self._font_cache.clear() - - # Free meshes - for mesh in self._meshes: - for p in mesh.primitives: - p.delete() - - # Free textures - for mesh_texture in self._mesh_textures: - mesh_texture.delete() - - for shadow_texture in self._shadow_textures: - shadow_texture.delete() - - self._meshes = set() - self._mesh_textures = set() - self._shadow_textures = set() - self._texture_alloc_idx = 0 - - self._delete_main_framebuffer() - self._delete_shadow_framebuffer() - - def __del__(self): - try: - self.delete() - except Exception: - pass - - ########################################################################### - # Rendering passes - ########################################################################### - - def _forward_pass(self, scene, flags, seg_node_map=None): - # Set up viewport for render - self._configure_forward_pass_viewport(flags) - - # Clear it - if bool(flags & RenderFlags.SEG): - glClearColor(0.0, 0.0, 0.0, 1.0) - if seg_node_map is None: - seg_node_map = {} - else: - glClearColor(*scene.bg_color) - - glClear(GL_COLOR_BUFFER_BIT | GL_DEPTH_BUFFER_BIT) - - if not bool(flags & RenderFlags.SEG): - glEnable(GL_MULTISAMPLE) - else: - glDisable(GL_MULTISAMPLE) - - # Set up camera matrices - V, P = self._get_camera_matrices(scene) - - program = None - # Now, render each object in sorted order - for node in self._sorted_mesh_nodes(scene): - mesh = node.mesh - - # Skip the mesh if it's not visible - if not mesh.is_visible: - continue - - # If SEG, set color - if bool(flags & RenderFlags.SEG): - if node not in seg_node_map: - continue - color = seg_node_map[node] - if not isinstance(color, (list, tuple, np.ndarray)): - color = np.repeat(color, 3) - else: - color = np.asanyarray(color) - color = color / 255.0 - - for primitive in mesh.primitives: - - # First, get and bind the appropriate program - program = self._get_primitive_program( - primitive, flags, ProgramFlags.USE_MATERIAL - ) - program._bind() - - # Set the camera uniforms - program.set_uniform('V', V) - program.set_uniform('P', P) - program.set_uniform( - 'cam_pos', scene.get_pose(scene.main_camera_node)[:3,3] - ) - if bool(flags & RenderFlags.SEG): - program.set_uniform('color', color) - - # Next, bind the lighting - if not (flags & RenderFlags.DEPTH_ONLY or flags & RenderFlags.FLAT or - flags & RenderFlags.SEG): - self._bind_lighting(scene, program, node, flags) - - # Finally, bind and draw the primitive - self._bind_and_draw_primitive( - primitive=primitive, - pose=scene.get_pose(node), - program=program, - flags=flags - ) - self._reset_active_textures() - - # Unbind the shader and flush the output - if program is not None: - program._unbind() - glFlush() - - # If doing offscreen render, copy result from framebuffer and return - if flags & RenderFlags.OFFSCREEN: - return self._read_main_framebuffer(scene, flags) - else: - return - - def _shadow_mapping_pass(self, scene, light_node, flags): - light = light_node.light - - # Set up viewport for render - self._configure_shadow_mapping_viewport(light, flags) - - # Set up camera matrices - V, P = self._get_light_cam_matrices(scene, light_node, flags) - - # Now, render each object in sorted order - for node in self._sorted_mesh_nodes(scene): - mesh = node.mesh - - # Skip the mesh if it's not visible - if not mesh.is_visible: - continue - - for primitive in mesh.primitives: - - # First, get and bind the appropriate program - program = self._get_primitive_program( - primitive, flags, ProgramFlags.NONE - ) - program._bind() - - # Set the camera uniforms - program.set_uniform('V', V) - program.set_uniform('P', P) - program.set_uniform( - 'cam_pos', scene.get_pose(scene.main_camera_node)[:3,3] - ) - - # Finally, bind and draw the primitive - self._bind_and_draw_primitive( - primitive=primitive, - pose=scene.get_pose(node), - program=program, - flags=RenderFlags.DEPTH_ONLY - ) - self._reset_active_textures() - - # Unbind the shader and flush the output - if program is not None: - program._unbind() - glFlush() - - def _normals_pass(self, scene, flags): - # Set up viewport for render - self._configure_forward_pass_viewport(flags) - program = None - - # Set up camera matrices - V, P = self._get_camera_matrices(scene) - - # Now, render each object in sorted order - for node in self._sorted_mesh_nodes(scene): - mesh = node.mesh - - # Skip the mesh if it's not visible - if not mesh.is_visible: - continue - - for primitive in mesh.primitives: - - # Skip objects that don't have normals - if not primitive.buf_flags & BufFlags.NORMAL: - continue - - # First, get and bind the appropriate program - pf = ProgramFlags.NONE - if flags & RenderFlags.VERTEX_NORMALS: - pf = pf | ProgramFlags.VERTEX_NORMALS - if flags & RenderFlags.FACE_NORMALS: - pf = pf | ProgramFlags.FACE_NORMALS - program = self._get_primitive_program(primitive, flags, pf) - program._bind() - - # Set the camera uniforms - program.set_uniform('V', V) - program.set_uniform('P', P) - program.set_uniform('normal_magnitude', 0.05 * primitive.scale) - program.set_uniform( - 'normal_color', np.array([0.1, 0.1, 1.0, 1.0]) - ) - - # Finally, bind and draw the primitive - self._bind_and_draw_primitive( - primitive=primitive, - pose=scene.get_pose(node), - program=program, - flags=RenderFlags.DEPTH_ONLY - ) - self._reset_active_textures() - - # Unbind the shader and flush the output - if program is not None: - program._unbind() - glFlush() - - ########################################################################### - # Handlers for binding uniforms and drawing primitives - ########################################################################### - - def _bind_and_draw_primitive(self, primitive, pose, program, flags): - # Set model pose matrix - program.set_uniform('M', pose) - - # Bind mesh buffers - primitive._bind() - - # Bind mesh material - if not (flags & RenderFlags.DEPTH_ONLY or flags & RenderFlags.SEG): - material = primitive.material - - # Bind textures - tf = material.tex_flags - if tf & TexFlags.NORMAL: - self._bind_texture(material.normalTexture, - 'material.normal_texture', program) - if tf & TexFlags.OCCLUSION: - self._bind_texture(material.occlusionTexture, - 'material.occlusion_texture', program) - if tf & TexFlags.EMISSIVE: - self._bind_texture(material.emissiveTexture, - 'material.emissive_texture', program) - if tf & TexFlags.BASE_COLOR: - self._bind_texture(material.baseColorTexture, - 'material.base_color_texture', program) - if tf & TexFlags.METALLIC_ROUGHNESS: - self._bind_texture(material.metallicRoughnessTexture, - 'material.metallic_roughness_texture', - program) - if tf & TexFlags.DIFFUSE: - self._bind_texture(material.diffuseTexture, - 'material.diffuse_texture', program) - if tf & TexFlags.SPECULAR_GLOSSINESS: - self._bind_texture(material.specularGlossinessTexture, - 'material.specular_glossiness_texture', - program) - - # Bind other uniforms - b = 'material.{}' - program.set_uniform(b.format('emissive_factor'), - material.emissiveFactor) - if isinstance(material, MetallicRoughnessMaterial): - program.set_uniform(b.format('base_color_factor'), - material.baseColorFactor) - program.set_uniform(b.format('metallic_factor'), - material.metallicFactor) - program.set_uniform(b.format('roughness_factor'), - material.roughnessFactor) - elif isinstance(material, SpecularGlossinessMaterial): - program.set_uniform(b.format('diffuse_factor'), - material.diffuseFactor) - program.set_uniform(b.format('specular_factor'), - material.specularFactor) - program.set_uniform(b.format('glossiness_factor'), - material.glossinessFactor) - - # Set blending options - if material.alphaMode == 'BLEND': - glEnable(GL_BLEND) - glBlendFunc(GL_SRC_ALPHA, GL_ONE_MINUS_SRC_ALPHA) - else: - glEnable(GL_BLEND) - glBlendFunc(GL_ONE, GL_ZERO) - - # Set wireframe mode - wf = material.wireframe - if flags & RenderFlags.FLIP_WIREFRAME: - wf = not wf - if (flags & RenderFlags.ALL_WIREFRAME) or wf: - glPolygonMode(GL_FRONT_AND_BACK, GL_LINE) - else: - glPolygonMode(GL_FRONT_AND_BACK, GL_FILL) - - # Set culling mode - if material.doubleSided or flags & RenderFlags.SKIP_CULL_FACES: - glDisable(GL_CULL_FACE) - else: - glEnable(GL_CULL_FACE) - glCullFace(GL_BACK) - else: - glEnable(GL_CULL_FACE) - glEnable(GL_BLEND) - glCullFace(GL_BACK) - glBlendFunc(GL_ONE, GL_ZERO) - glPolygonMode(GL_FRONT_AND_BACK, GL_FILL) - - # Set point size if needed - glDisable(GL_PROGRAM_POINT_SIZE) - if primitive.mode == GLTF.POINTS: - glEnable(GL_PROGRAM_POINT_SIZE) - glPointSize(self.point_size) - - # Render mesh - n_instances = 1 - if primitive.poses is not None: - n_instances = len(primitive.poses) - - if primitive.indices is not None: - glDrawElementsInstanced( - primitive.mode, primitive.indices.size, GL_UNSIGNED_INT, - ctypes.c_void_p(0), n_instances - ) - else: - glDrawArraysInstanced( - primitive.mode, 0, len(primitive.positions), n_instances - ) - - # Unbind mesh buffers - primitive._unbind() - - def _bind_lighting(self, scene, program, node, flags): - """Bind all lighting uniform values for a scene. - """ - max_n_lights = self._compute_max_n_lights(flags) - - n_d = min(len(scene.directional_light_nodes), max_n_lights[0]) - n_s = min(len(scene.spot_light_nodes), max_n_lights[1]) - n_p = min(len(scene.point_light_nodes), max_n_lights[2]) - program.set_uniform('ambient_light', scene.ambient_light) - program.set_uniform('n_directional_lights', n_d) - program.set_uniform('n_spot_lights', n_s) - program.set_uniform('n_point_lights', n_p) - plc = 0 - slc = 0 - dlc = 0 - - light_nodes = scene.light_nodes - if (len(scene.directional_light_nodes) > max_n_lights[0] or - len(scene.spot_light_nodes) > max_n_lights[1] or - len(scene.point_light_nodes) > max_n_lights[2]): - light_nodes = self._sorted_nodes_by_distance( - scene, scene.light_nodes, node - ) - - for n in light_nodes: - light = n.light - pose = scene.get_pose(n) - position = pose[:3,3] - direction = -pose[:3,2] - - if isinstance(light, PointLight): - if plc == max_n_lights[2]: - continue - b = 'point_lights[{}].'.format(plc) - plc += 1 - shadow = bool(flags & RenderFlags.SHADOWS_POINT) - program.set_uniform(b + 'position', position) - elif isinstance(light, SpotLight): - if slc == max_n_lights[1]: - continue - b = 'spot_lights[{}].'.format(slc) - slc += 1 - shadow = bool(flags & RenderFlags.SHADOWS_SPOT) - las = 1.0 / max(0.001, np.cos(light.innerConeAngle) - - np.cos(light.outerConeAngle)) - lao = -np.cos(light.outerConeAngle) * las - program.set_uniform(b + 'direction', direction) - program.set_uniform(b + 'position', position) - program.set_uniform(b + 'light_angle_scale', las) - program.set_uniform(b + 'light_angle_offset', lao) - else: - if dlc == max_n_lights[0]: - continue - b = 'directional_lights[{}].'.format(dlc) - dlc += 1 - shadow = bool(flags & RenderFlags.SHADOWS_DIRECTIONAL) - program.set_uniform(b + 'direction', direction) - - program.set_uniform(b + 'color', light.color) - program.set_uniform(b + 'intensity', light.intensity) - # if light.range is not None: - # program.set_uniform(b + 'range', light.range) - # else: - # program.set_uniform(b + 'range', 0) - - if shadow: - self._bind_texture(light.shadow_texture, - b + 'shadow_map', program) - if not isinstance(light, PointLight): - V, P = self._get_light_cam_matrices(scene, n, flags) - program.set_uniform(b + 'light_matrix', P.dot(V)) - else: - raise NotImplementedError( - 'Point light shadows not implemented' - ) - - def _sorted_mesh_nodes(self, scene): - cam_loc = scene.get_pose(scene.main_camera_node)[:3,3] - solid_nodes = [] - trans_nodes = [] - for node in scene.mesh_nodes: - mesh = node.mesh - if mesh.is_transparent: - trans_nodes.append(node) - else: - solid_nodes.append(node) - - # TODO BETTER SORTING METHOD - trans_nodes.sort( - key=lambda n: -np.linalg.norm(scene.get_pose(n)[:3,3] - cam_loc) - ) - solid_nodes.sort( - key=lambda n: -np.linalg.norm(scene.get_pose(n)[:3,3] - cam_loc) - ) - - return solid_nodes + trans_nodes - - def _sorted_nodes_by_distance(self, scene, nodes, compare_node): - nodes = list(nodes) - compare_posn = scene.get_pose(compare_node)[:3,3] - nodes.sort(key=lambda n: np.linalg.norm( - scene.get_pose(n)[:3,3] - compare_posn) - ) - return nodes - - ########################################################################### - # Context Management - ########################################################################### - - def _update_context(self, scene, flags): - - # Update meshes - scene_meshes = scene.meshes - - # Add new meshes to context - for mesh in scene_meshes - self._meshes: - for p in mesh.primitives: - p._add_to_context() - - # Remove old meshes from context - for mesh in self._meshes - scene_meshes: - for p in mesh.primitives: - p.delete() - - self._meshes = scene_meshes.copy() - - # Update mesh textures - mesh_textures = set() - for m in scene_meshes: - for p in m.primitives: - mesh_textures |= p.material.textures - - # Add new textures to context - for texture in mesh_textures - self._mesh_textures: - texture._add_to_context() - - # Remove old textures from context - for texture in self._mesh_textures - mesh_textures: - texture.delete() - - self._mesh_textures = mesh_textures.copy() - - shadow_textures = set() - for l in scene.lights: - # Create if needed - active = False - if (isinstance(l, DirectionalLight) and - flags & RenderFlags.SHADOWS_DIRECTIONAL): - active = True - elif (isinstance(l, PointLight) and - flags & RenderFlags.SHADOWS_POINT): - active = True - elif isinstance(l, SpotLight) and flags & RenderFlags.SHADOWS_SPOT: - active = True - - if active and l.shadow_texture is None: - l._generate_shadow_texture() - if l.shadow_texture is not None: - shadow_textures.add(l.shadow_texture) - - # Add new textures to context - for texture in shadow_textures - self._shadow_textures: - texture._add_to_context() - - # Remove old textures from context - for texture in self._shadow_textures - shadow_textures: - texture.delete() - - self._shadow_textures = shadow_textures.copy() - - ########################################################################### - # Texture Management - ########################################################################### - - def _bind_texture(self, texture, uniform_name, program): - """Bind a texture to an active texture unit and return - the texture unit index that was used. - """ - tex_id = self._get_next_active_texture() - glActiveTexture(GL_TEXTURE0 + tex_id) - texture._bind() - program.set_uniform(uniform_name, tex_id) - - def _get_next_active_texture(self): - val = self._texture_alloc_idx - self._texture_alloc_idx += 1 - return val - - def _reset_active_textures(self): - self._texture_alloc_idx = 0 - - ########################################################################### - # Camera Matrix Management - ########################################################################### - - def _get_camera_matrices(self, scene): - main_camera_node = scene.main_camera_node - if main_camera_node is None: - raise ValueError('Cannot render scene without a camera') - P = main_camera_node.camera.get_projection_matrix( - width=self.viewport_width, height=self.viewport_height - ) - pose = scene.get_pose(main_camera_node) - V = np.linalg.inv(pose) # V maps from world to camera - return V, P - - def _get_light_cam_matrices(self, scene, light_node, flags): - light = light_node.light - pose = scene.get_pose(light_node).copy() - s = scene.scale - camera = light._get_shadow_camera(s) - P = camera.get_projection_matrix() - if isinstance(light, DirectionalLight): - direction = -pose[:3,2] - c = scene.centroid - loc = c - direction * s - pose[:3,3] = loc - V = np.linalg.inv(pose) # V maps from world to camera - return V, P - - ########################################################################### - # Shader Program Management - ########################################################################### - - def _get_text_program(self): - program = self._program_cache.get_program( - vertex_shader='text.vert', - fragment_shader='text.frag' - ) - - if not program._in_context(): - program._add_to_context() - - return program - - def _compute_max_n_lights(self, flags): - max_n_lights = [MAX_N_LIGHTS, MAX_N_LIGHTS, MAX_N_LIGHTS] - n_tex_units = glGetIntegerv(GL_MAX_TEXTURE_IMAGE_UNITS) - - # Reserved texture units: 6 - # Normal Map - # Occlusion Map - # Emissive Map - # Base Color or Diffuse Map - # MR or SG Map - # Environment cubemap - - n_reserved_textures = 6 - n_available_textures = n_tex_units - n_reserved_textures - - # Distribute textures evenly among lights with shadows, with - # a preference for directional lights - n_shadow_types = 0 - if flags & RenderFlags.SHADOWS_DIRECTIONAL: - n_shadow_types += 1 - if flags & RenderFlags.SHADOWS_SPOT: - n_shadow_types += 1 - if flags & RenderFlags.SHADOWS_POINT: - n_shadow_types += 1 - - if n_shadow_types > 0: - tex_per_light = n_available_textures // n_shadow_types - - if flags & RenderFlags.SHADOWS_DIRECTIONAL: - max_n_lights[0] = ( - tex_per_light + - (n_available_textures - tex_per_light * n_shadow_types) - ) - if flags & RenderFlags.SHADOWS_SPOT: - max_n_lights[1] = tex_per_light - if flags & RenderFlags.SHADOWS_POINT: - max_n_lights[2] = tex_per_light - - return max_n_lights - - def _get_primitive_program(self, primitive, flags, program_flags): - vertex_shader = None - fragment_shader = None - geometry_shader = None - defines = {} - - if (bool(program_flags & ProgramFlags.USE_MATERIAL) and - not flags & RenderFlags.DEPTH_ONLY and - not flags & RenderFlags.FLAT and - not flags & RenderFlags.SEG): - vertex_shader = 'mesh.vert' - fragment_shader = 'mesh.frag' - elif bool(program_flags & (ProgramFlags.VERTEX_NORMALS | - ProgramFlags.FACE_NORMALS)): - vertex_shader = 'vertex_normals.vert' - if primitive.mode == GLTF.POINTS: - geometry_shader = 'vertex_normals_pc.geom' - else: - geometry_shader = 'vertex_normals.geom' - fragment_shader = 'vertex_normals.frag' - elif flags & RenderFlags.FLAT: - vertex_shader = 'flat.vert' - fragment_shader = 'flat.frag' - elif flags & RenderFlags.SEG: - vertex_shader = 'segmentation.vert' - fragment_shader = 'segmentation.frag' - else: - vertex_shader = 'mesh_depth.vert' - fragment_shader = 'mesh_depth.frag' - - # Set up vertex buffer DEFINES - bf = primitive.buf_flags - buf_idx = 1 - if bf & BufFlags.NORMAL: - defines['NORMAL_LOC'] = buf_idx - buf_idx += 1 - if bf & BufFlags.TANGENT: - defines['TANGENT_LOC'] = buf_idx - buf_idx += 1 - if bf & BufFlags.TEXCOORD_0: - defines['TEXCOORD_0_LOC'] = buf_idx - buf_idx += 1 - if bf & BufFlags.TEXCOORD_1: - defines['TEXCOORD_1_LOC'] = buf_idx - buf_idx += 1 - if bf & BufFlags.COLOR_0: - defines['COLOR_0_LOC'] = buf_idx - buf_idx += 1 - if bf & BufFlags.JOINTS_0: - defines['JOINTS_0_LOC'] = buf_idx - buf_idx += 1 - if bf & BufFlags.WEIGHTS_0: - defines['WEIGHTS_0_LOC'] = buf_idx - buf_idx += 1 - defines['INST_M_LOC'] = buf_idx - - # Set up shadow mapping defines - if flags & RenderFlags.SHADOWS_DIRECTIONAL: - defines['DIRECTIONAL_LIGHT_SHADOWS'] = 1 - if flags & RenderFlags.SHADOWS_SPOT: - defines['SPOT_LIGHT_SHADOWS'] = 1 - if flags & RenderFlags.SHADOWS_POINT: - defines['POINT_LIGHT_SHADOWS'] = 1 - max_n_lights = self._compute_max_n_lights(flags) - defines['MAX_DIRECTIONAL_LIGHTS'] = max_n_lights[0] - defines['MAX_SPOT_LIGHTS'] = max_n_lights[1] - defines['MAX_POINT_LIGHTS'] = max_n_lights[2] - - # Set up vertex normal defines - if program_flags & ProgramFlags.VERTEX_NORMALS: - defines['VERTEX_NORMALS'] = 1 - if program_flags & ProgramFlags.FACE_NORMALS: - defines['FACE_NORMALS'] = 1 - - # Set up material texture defines - if bool(program_flags & ProgramFlags.USE_MATERIAL): - tf = primitive.material.tex_flags - if tf & TexFlags.NORMAL: - defines['HAS_NORMAL_TEX'] = 1 - if tf & TexFlags.OCCLUSION: - defines['HAS_OCCLUSION_TEX'] = 1 - if tf & TexFlags.EMISSIVE: - defines['HAS_EMISSIVE_TEX'] = 1 - if tf & TexFlags.BASE_COLOR: - defines['HAS_BASE_COLOR_TEX'] = 1 - if tf & TexFlags.METALLIC_ROUGHNESS: - defines['HAS_METALLIC_ROUGHNESS_TEX'] = 1 - if tf & TexFlags.DIFFUSE: - defines['HAS_DIFFUSE_TEX'] = 1 - if tf & TexFlags.SPECULAR_GLOSSINESS: - defines['HAS_SPECULAR_GLOSSINESS_TEX'] = 1 - if isinstance(primitive.material, MetallicRoughnessMaterial): - defines['USE_METALLIC_MATERIAL'] = 1 - elif isinstance(primitive.material, SpecularGlossinessMaterial): - defines['USE_GLOSSY_MATERIAL'] = 1 - - program = self._program_cache.get_program( - vertex_shader=vertex_shader, - fragment_shader=fragment_shader, - geometry_shader=geometry_shader, - defines=defines - ) - - if not program._in_context(): - program._add_to_context() - - return program - - ########################################################################### - # Viewport Management - ########################################################################### - - def _configure_forward_pass_viewport(self, flags): - - # If using offscreen render, bind main framebuffer - if flags & RenderFlags.OFFSCREEN: - self._configure_main_framebuffer() - glBindFramebuffer(GL_DRAW_FRAMEBUFFER, self._main_fb_ms) - else: - glBindFramebuffer(GL_DRAW_FRAMEBUFFER, 0) - - glViewport(0, 0, self.viewport_width, self.viewport_height) - glEnable(GL_DEPTH_TEST) - glDepthMask(GL_TRUE) - glDepthFunc(GL_LESS) - glDepthRange(0.0, 1.0) - - def _configure_shadow_mapping_viewport(self, light, flags): - self._configure_shadow_framebuffer() - glBindFramebuffer(GL_FRAMEBUFFER, self._shadow_fb) - light.shadow_texture._bind() - light.shadow_texture._bind_as_depth_attachment() - glActiveTexture(GL_TEXTURE0) - light.shadow_texture._bind() - glDrawBuffer(GL_NONE) - glReadBuffer(GL_NONE) - - glClear(GL_DEPTH_BUFFER_BIT) - glViewport(0, 0, SHADOW_TEX_SZ, SHADOW_TEX_SZ) - glEnable(GL_DEPTH_TEST) - glDepthMask(GL_TRUE) - glDepthFunc(GL_LESS) - glDepthRange(0.0, 1.0) - glDisable(GL_CULL_FACE) - glDisable(GL_BLEND) - - ########################################################################### - # Framebuffer Management - ########################################################################### - - def _configure_shadow_framebuffer(self): - if self._shadow_fb is None: - self._shadow_fb = glGenFramebuffers(1) - - def _delete_shadow_framebuffer(self): - if self._shadow_fb is not None: - glDeleteFramebuffers(1, [self._shadow_fb]) - - def _configure_main_framebuffer(self): - # If mismatch with prior framebuffer, delete it - if (self._main_fb is not None and - self.viewport_width != self._main_fb_dims[0] or - self.viewport_height != self._main_fb_dims[1]): - self._delete_main_framebuffer() - - # If framebuffer doesn't exist, create it - if self._main_fb is None: - # Generate standard buffer - self._main_cb, self._main_db = glGenRenderbuffers(2) - - glBindRenderbuffer(GL_RENDERBUFFER, self._main_cb) - glRenderbufferStorage( - GL_RENDERBUFFER, GL_RGBA, - self.viewport_width, self.viewport_height - ) - - glBindRenderbuffer(GL_RENDERBUFFER, self._main_db) - glRenderbufferStorage( - GL_RENDERBUFFER, GL_DEPTH_COMPONENT24, - self.viewport_width, self.viewport_height - ) - - self._main_fb = glGenFramebuffers(1) - glBindFramebuffer(GL_DRAW_FRAMEBUFFER, self._main_fb) - glFramebufferRenderbuffer( - GL_DRAW_FRAMEBUFFER, GL_COLOR_ATTACHMENT0, - GL_RENDERBUFFER, self._main_cb - ) - glFramebufferRenderbuffer( - GL_DRAW_FRAMEBUFFER, GL_DEPTH_ATTACHMENT, - GL_RENDERBUFFER, self._main_db - ) - - # Generate multisample buffer - self._main_cb_ms, self._main_db_ms = glGenRenderbuffers(2) - glBindRenderbuffer(GL_RENDERBUFFER, self._main_cb_ms) - # glRenderbufferStorageMultisample( - # GL_RENDERBUFFER, 4, GL_RGBA, - # self.viewport_width, self.viewport_height - # ) - # glBindRenderbuffer(GL_RENDERBUFFER, self._main_db_ms) - # glRenderbufferStorageMultisample( - # GL_RENDERBUFFER, 4, GL_DEPTH_COMPONENT24, - # self.viewport_width, self.viewport_height - # ) - # 增加这一行 - num_samples = min(glGetIntegerv(GL_MAX_SAMPLES), 4) # No more than GL_MAX_SAMPLES - - # 其实就是把 4 替换成 num_samples，其余不变 - glRenderbufferStorageMultisample(GL_RENDERBUFFER, num_samples, GL_RGBA, self.viewport_width, self.viewport_height) - - glBindRenderbuffer(GL_RENDERBUFFER, self._main_db_ms) # 这行不变 - - # 这一行也是将 4 替换成 num_samples - glRenderbufferStorageMultisample(GL_RENDERBUFFER, num_samples, GL_DEPTH_COMPONENT24, self.viewport_width, self.viewport_height) - - self._main_fb_ms = glGenFramebuffers(1) - glBindFramebuffer(GL_DRAW_FRAMEBUFFER, self._main_fb_ms) - glFramebufferRenderbuffer( - GL_DRAW_FRAMEBUFFER, GL_COLOR_ATTACHMENT0, - GL_RENDERBUFFER, self._main_cb_ms - ) - glFramebufferRenderbuffer( - GL_DRAW_FRAMEBUFFER, GL_DEPTH_ATTACHMENT, - GL_RENDERBUFFER, self._main_db_ms - ) - - self._main_fb_dims = (self.viewport_width, self.viewport_height) - - def _delete_main_framebuffer(self): - if self._main_fb is not None: - glDeleteFramebuffers(2, [self._main_fb, self._main_fb_ms]) - if self._main_cb is not None: - glDeleteRenderbuffers(2, [self._main_cb, self._main_cb_ms]) - if self._main_db is not None: - glDeleteRenderbuffers(2, [self._main_db, self._main_db_ms]) - - self._main_fb = None - self._main_cb = None - self._main_db = None - self._main_fb_ms = None - self._main_cb_ms = None - self._main_db_ms = None - self._main_fb_dims = (None, None) - - def _read_main_framebuffer(self, scene, flags): - width, height = self._main_fb_dims[0], self._main_fb_dims[1] - - # Bind framebuffer and blit buffers - glBindFramebuffer(GL_READ_FRAMEBUFFER, self._main_fb_ms) - glBindFramebuffer(GL_DRAW_FRAMEBUFFER, self._main_fb) - glBlitFramebuffer( - 0, 0, width, height, 0, 0, width, height, - GL_COLOR_BUFFER_BIT, GL_LINEAR - ) - glBlitFramebuffer( - 0, 0, width, height, 0, 0, width, height, - GL_DEPTH_BUFFER_BIT, GL_NEAREST - ) - glBindFramebuffer(GL_READ_FRAMEBUFFER, self._main_fb) - - # Read depth - depth_buf = glReadPixels( - 0, 0, width, height, GL_DEPTH_COMPONENT, GL_FLOAT - ) - depth_im = np.frombuffer(depth_buf, dtype=np.float32) - depth_im = depth_im.reshape((height, width)) - depth_im = np.flip(depth_im, axis=0) - inf_inds = (depth_im == 1.0) - depth_im = 2.0 * depth_im - 1.0 - z_near = scene.main_camera_node.camera.znear - z_far = scene.main_camera_node.camera.zfar - noninf = np.logical_not(inf_inds) - if z_far is None: - depth_im[noninf] = 2 * z_near / (1.0 - depth_im[noninf]) - else: - depth_im[noninf] = ((2.0 * z_near * z_far) / - (z_far + z_near - depth_im[noninf] * - (z_far - z_near))) - depth_im[inf_inds] = 0.0 - - # Resize for macos if needed - if sys.platform == 'darwin': - depth_im = self._resize_image(depth_im) - - if flags & RenderFlags.DEPTH_ONLY: - return depth_im - - # Read color - if flags & RenderFlags.RGBA: - color_buf = glReadPixels( - 0, 0, width, height, GL_RGBA, GL_UNSIGNED_BYTE - ) - color_im = np.frombuffer(color_buf, dtype=np.uint8) - color_im = color_im.reshape((height, width, 4)) - else: - color_buf = glReadPixels( - 0, 0, width, height, GL_RGB, GL_UNSIGNED_BYTE - ) - color_im = np.frombuffer(color_buf, dtype=np.uint8) - color_im = color_im.reshape((height, width, 3)) - color_im = np.flip(color_im, axis=0) - - # Resize for macos if needed - if sys.platform == 'darwin': - color_im = self._resize_image(color_im, True) - - return color_im, depth_im - - def _resize_image(self, value, antialias=False): - """If needed, rescale the render for MacOS.""" - img = PIL.Image.fromarray(value) - resample = PIL.Image.NEAREST - if antialias: - resample = PIL.Image.BILINEAR - size = (self.viewport_width // self.dpscale, - self.viewport_height // self.dpscale) - img = img.resize(size, resample=resample) - return np.array(img) - - ########################################################################### - # Shadowmap Debugging - ########################################################################### - - def _forward_pass_no_reset(self, scene, flags): - # Set up camera matrices - V, P = self._get_camera_matrices(scene) - - # Now, render each object in sorted order - for node in self._sorted_mesh_nodes(scene): - mesh = node.mesh - - # Skip the mesh if it's not visible - if not mesh.is_visible: - continue - - for primitive in mesh.primitives: - - # First, get and bind the appropriate program - program = self._get_primitive_program( - primitive, flags, ProgramFlags.USE_MATERIAL - ) - program._bind() - - # Set the camera uniforms - program.set_uniform('V', V) - program.set_uniform('P', P) - program.set_uniform( - 'cam_pos', scene.get_pose(scene.main_camera_node)[:3,3] - ) - - # Next, bind the lighting - if not flags & RenderFlags.DEPTH_ONLY and not flags & RenderFlags.FLAT: - self._bind_lighting(scene, program, node, flags) - - # Finally, bind and draw the primitive - self._bind_and_draw_primitive( - primitive=primitive, - pose=scene.get_pose(node), - program=program, - flags=flags - ) - self._reset_active_textures() - - # Unbind the shader and flush the output - if program is not None: - program._unbind() - glFlush() - - def _render_light_shadowmaps(self, scene, light_nodes, flags, tile=False): - glBindFramebuffer(GL_DRAW_FRAMEBUFFER, 0) - glClearColor(*scene.bg_color) - glClear(GL_COLOR_BUFFER_BIT | GL_DEPTH_BUFFER_BIT) - glEnable(GL_DEPTH_TEST) - glDepthMask(GL_TRUE) - glDepthFunc(GL_LESS) - glDepthRange(0.0, 1.0) - - w = self.viewport_width - h = self.viewport_height - - num_nodes = len(light_nodes) - viewport_dims = { - (0, 2): [0, h // 2, w // 2, h], - (1, 2): [w // 2, h // 2, w, h], - (0, 3): [0, h // 2, w // 2, h], - (1, 3): [w // 2, h // 2, w, h], - (2, 3): [0, 0, w // 2, h // 2], - (0, 4): [0, h // 2, w // 2, h], - (1, 4): [w // 2, h // 2, w, h], - (2, 4): [0, 0, w // 2, h // 2], - (3, 4): [w // 2, 0, w, h // 2] - } - - if tile: - for i, ln in enumerate(light_nodes): - light = ln.light - - if light.shadow_texture is None: - raise ValueError('Light does not have a shadow texture') - - glViewport(*viewport_dims[(i, num_nodes + 1)]) - - program = self._get_debug_quad_program() - program._bind() - self._bind_texture(light.shadow_texture, 'depthMap', program) - self._render_debug_quad() - self._reset_active_textures() - glFlush() - i += 1 - glViewport(*viewport_dims[(i, num_nodes + 1)]) - self._forward_pass_no_reset(scene, flags) - else: - for i, ln in enumerate(light_nodes): - light = ln.light - - if light.shadow_texture is None: - raise ValueError('Light does not have a shadow texture') - - glViewport(0, 0, self.viewport_width, self.viewport_height) - - program = self._get_debug_quad_program() - program._bind() - self._bind_texture(light.shadow_texture, 'depthMap', program) - self._render_debug_quad() - self._reset_active_textures() - glFlush() - return - - def _get_debug_quad_program(self): - program = self._program_cache.get_program( - vertex_shader='debug_quad.vert', - fragment_shader='debug_quad.frag' - ) - if not program._in_context(): - program._add_to_context() - return program - - def _render_debug_quad(self): - x = glGenVertexArrays(1) - glBindVertexArray(x) - glDrawArrays(GL_TRIANGLES, 0, 6) - glBindVertexArray(0) - glDeleteVertexArrays(1, [x]) diff --git a/pyrender/pyrender/sampler.py b/pyrender/pyrender/sampler.py deleted file mode 100644 index e4784d068f808a40a56c8e748d83175f7f4e6233..0000000000000000000000000000000000000000 --- a/pyrender/pyrender/sampler.py +++ /dev/null @@ -1,102 +0,0 @@ -"""Samplers, conforming to the glTF 2.0 standards as specified in -https://github.com/KhronosGroup/glTF/tree/master/specification/2.0#reference-sampler - -Author: Matthew Matl -""" -from .constants import GLTF - - -class Sampler(object): - """Texture sampler properties for filtering and wrapping modes. - - Parameters - ---------- - name : str, optional - The user-defined name of this object. - magFilter : int, optional - Magnification filter. Valid values: - - :attr:`.GLTF.NEAREST` - - :attr:`.GLTF.LINEAR` - minFilter : int, optional - Minification filter. Valid values: - - :attr:`.GLTF.NEAREST` - - :attr:`.GLTF.LINEAR` - - :attr:`.GLTF.NEAREST_MIPMAP_NEAREST` - - :attr:`.GLTF.LINEAR_MIPMAP_NEAREST` - - :attr:`.GLTF.NEAREST_MIPMAP_LINEAR` - - :attr:`.GLTF.LINEAR_MIPMAP_LINEAR` - wrapS : int, optional - S (U) wrapping mode. Valid values: - - :attr:`.GLTF.CLAMP_TO_EDGE` - - :attr:`.GLTF.MIRRORED_REPEAT` - - :attr:`.GLTF.REPEAT` - wrapT : int, optional - T (V) wrapping mode. Valid values: - - :attr:`.GLTF.CLAMP_TO_EDGE` - - :attr:`.GLTF.MIRRORED_REPEAT` - - :attr:`.GLTF.REPEAT` - """ - - def __init__(self, - name=None, - magFilter=None, - minFilter=None, - wrapS=GLTF.REPEAT, - wrapT=GLTF.REPEAT): - self.name = name - self.magFilter = magFilter - self.minFilter = minFilter - self.wrapS = wrapS - self.wrapT = wrapT - - @property - def name(self): - """str : The user-defined name of this object. - """ - return self._name - - @name.setter - def name(self, value): - if value is not None: - value = str(value) - self._name = value - - @property - def magFilter(self): - """int : Magnification filter type. - """ - return self._magFilter - - @magFilter.setter - def magFilter(self, value): - self._magFilter = value - - @property - def minFilter(self): - """int : Minification filter type. - """ - return self._minFilter - - @minFilter.setter - def minFilter(self, value): - self._minFilter = value - - @property - def wrapS(self): - """int : S (U) wrapping mode. - """ - return self._wrapS - - @wrapS.setter - def wrapS(self, value): - self._wrapS = value - - @property - def wrapT(self): - """int : T (V) wrapping mode. - """ - return self._wrapT - - @wrapT.setter - def wrapT(self, value): - self._wrapT = value diff --git a/pyrender/pyrender/scene.py b/pyrender/pyrender/scene.py deleted file mode 100644 index 2fe057ec66f52f2dd9c1363aacf72a7c6cec4e6c..0000000000000000000000000000000000000000 --- a/pyrender/pyrender/scene.py +++ /dev/null @@ -1,585 +0,0 @@ -"""Scenes, conforming to the glTF 2.0 standards as specified in -https://github.com/KhronosGroup/glTF/tree/master/specification/2.0#reference-scene - -Author: Matthew Matl -""" -import numpy as np -import networkx as nx -import trimesh - -from .mesh import Mesh -from .camera import Camera -from .light import Light, PointLight, DirectionalLight, SpotLight -from .node import Node -from .utils import format_color_vector - - -class Scene(object): - """A hierarchical scene graph. - - Parameters - ---------- - nodes : list of :class:`Node` - The set of all nodes in the scene. - bg_color : (4,) float, optional - Background color of scene. - ambient_light : (3,) float, optional - Color of ambient light. Defaults to no ambient light. - name : str, optional - The user-defined name of this object. - """ - - def __init__(self, - nodes=None, - bg_color=None, - ambient_light=None, - name=None): - - if bg_color is None: - bg_color = np.ones(4) - else: - bg_color = format_color_vector(bg_color, 4) - - if ambient_light is None: - ambient_light = np.zeros(3) - - if nodes is None: - nodes = set() - self._nodes = set() # Will be added at the end of this function - - self.bg_color = bg_color - self.ambient_light = ambient_light - self.name = name - - self._name_to_nodes = {} - self._obj_to_nodes = {} - self._obj_name_to_nodes = {} - self._mesh_nodes = set() - self._point_light_nodes = set() - self._spot_light_nodes = set() - self._directional_light_nodes = set() - self._camera_nodes = set() - self._main_camera_node = None - self._bounds = None - - # Transform tree - self._digraph = nx.DiGraph() - self._digraph.add_node('world') - self._path_cache = {} - - # Find root nodes and add them - if len(nodes) > 0: - node_parent_map = {n: None for n in nodes} - for node in nodes: - for child in node.children: - if node_parent_map[child] is not None: - raise ValueError('Nodes may not have more than ' - 'one parent') - node_parent_map[child] = node - for node in node_parent_map: - if node_parent_map[node] is None: - self.add_node(node) - - @property - def name(self): - """str : The user-defined name of this object. - """ - return self._name - - @name.setter - def name(self, value): - if value is not None: - value = str(value) - self._name = value - - @property - def nodes(self): - """set of :class:`Node` : Set of nodes in the scene. - """ - return self._nodes - - @property - def bg_color(self): - """(3,) float : The scene background color. - """ - return self._bg_color - - @bg_color.setter - def bg_color(self, value): - if value is None: - value = np.ones(4) - else: - value = format_color_vector(value, 4) - self._bg_color = value - - @property - def ambient_light(self): - """(3,) float : The ambient light in the scene. - """ - return self._ambient_light - - @ambient_light.setter - def ambient_light(self, value): - if value is None: - value = np.zeros(3) - else: - value = format_color_vector(value, 3) - self._ambient_light = value - - @property - def meshes(self): - """set of :class:`Mesh` : The meshes in the scene. - """ - return set([n.mesh for n in self.mesh_nodes]) - - @property - def mesh_nodes(self): - """set of :class:`Node` : The nodes containing meshes. - """ - return self._mesh_nodes - - @property - def lights(self): - """set of :class:`Light` : The lights in the scene. - """ - return self.point_lights | self.spot_lights | self.directional_lights - - @property - def light_nodes(self): - """set of :class:`Node` : The nodes containing lights. - """ - return (self.point_light_nodes | self.spot_light_nodes | - self.directional_light_nodes) - - @property - def point_lights(self): - """set of :class:`PointLight` : The point lights in the scene. - """ - return set([n.light for n in self.point_light_nodes]) - - @property - def point_light_nodes(self): - """set of :class:`Node` : The nodes containing point lights. - """ - return self._point_light_nodes - - @property - def spot_lights(self): - """set of :class:`SpotLight` : The spot lights in the scene. - """ - return set([n.light for n in self.spot_light_nodes]) - - @property - def spot_light_nodes(self): - """set of :class:`Node` : The nodes containing spot lights. - """ - return self._spot_light_nodes - - @property - def directional_lights(self): - """set of :class:`DirectionalLight` : The directional lights in - the scene. - """ - return set([n.light for n in self.directional_light_nodes]) - - @property - def directional_light_nodes(self): - """set of :class:`Node` : The nodes containing directional lights. - """ - return self._directional_light_nodes - - @property - def cameras(self): - """set of :class:`Camera` : The cameras in the scene. - """ - return set([n.camera for n in self.camera_nodes]) - - @property - def camera_nodes(self): - """set of :class:`Node` : The nodes containing cameras in the scene. - """ - return self._camera_nodes - - @property - def main_camera_node(self): - """set of :class:`Node` : The node containing the main camera in the - scene. - """ - return self._main_camera_node - - @main_camera_node.setter - def main_camera_node(self, value): - if value not in self.nodes: - raise ValueError('New main camera node must already be in scene') - self._main_camera_node = value - - @property - def bounds(self): - """(2,3) float : The axis-aligned bounds of the scene. - """ - if self._bounds is None: - # Compute corners - corners = [] - for mesh_node in self.mesh_nodes: - mesh = mesh_node.mesh - pose = self.get_pose(mesh_node) - corners_local = trimesh.bounds.corners(mesh.bounds) - corners_world = pose[:3,:3].dot(corners_local.T).T + pose[:3,3] - corners.append(corners_world) - if len(corners) == 0: - self._bounds = np.zeros((2,3)) - else: - corners = np.vstack(corners) - self._bounds = np.array([np.min(corners, axis=0), - np.max(corners, axis=0)]) - return self._bounds - - @property - def centroid(self): - """(3,) float : The centroid of the scene's axis-aligned bounding box - (AABB). - """ - return np.mean(self.bounds, axis=0) - - @property - def extents(self): - """(3,) float : The lengths of the axes of the scene's AABB. - """ - return np.diff(self.bounds, axis=0).reshape(-1) - - @property - def scale(self): - """(3,) float : The length of the diagonal of the scene's AABB. - """ - return np.linalg.norm(self.extents) - - def add(self, obj, name=None, pose=None, - parent_node=None, parent_name=None): - """Add an object (mesh, light, or camera) to the scene. - - Parameters - ---------- - obj : :class:`Mesh`, :class:`Light`, or :class:`Camera` - The object to add to the scene. - name : str - A name for the new node to be created. - pose : (4,4) float - The local pose of this node relative to its parent node. - parent_node : :class:`Node` - The parent of this Node. If None, the new node is a root node. - parent_name : str - The name of the parent node, can be specified instead of - `parent_node`. - - Returns - ------- - node : :class:`Node` - The newly-created and inserted node. - """ - if isinstance(obj, Mesh): - node = Node(name=name, matrix=pose, mesh=obj) - elif isinstance(obj, Light): - node = Node(name=name, matrix=pose, light=obj) - elif isinstance(obj, Camera): - node = Node(name=name, matrix=pose, camera=obj) - else: - raise TypeError('Unrecognized object type') - - if parent_node is None and parent_name is not None: - parent_nodes = self.get_nodes(name=parent_name) - if len(parent_nodes) == 0: - raise ValueError('No parent node with name {} found' - .format(parent_name)) - elif len(parent_nodes) > 1: - raise ValueError('More than one parent node with name {} found' - .format(parent_name)) - parent_node = list(parent_nodes)[0] - - self.add_node(node, parent_node=parent_node) - - return node - - def get_nodes(self, node=None, name=None, obj=None, obj_name=None): - """Search for existing nodes. Only nodes matching all specified - parameters is returned, or None if no such node exists. - - Parameters - ---------- - node : :class:`Node`, optional - If present, returns this node if it is in the scene. - name : str - A name for the Node. - obj : :class:`Mesh`, :class:`Light`, or :class:`Camera` - An object that is attached to the node. - obj_name : str - The name of an object that is attached to the node. - - Returns - ------- - nodes : set of :class:`.Node` - The nodes that match all query terms. - """ - if node is not None: - if node in self.nodes: - return set([node]) - else: - return set() - nodes = set(self.nodes) - if name is not None: - matches = set() - if name in self._name_to_nodes: - matches = self._name_to_nodes[name] - nodes = nodes & matches - if obj is not None: - matches = set() - if obj in self._obj_to_nodes: - matches = self._obj_to_nodes[obj] - nodes = nodes & matches - if obj_name is not None: - matches = set() - if obj_name in self._obj_name_to_nodes: - matches = self._obj_name_to_nodes[obj_name] - nodes = nodes & matches - - return nodes - - def add_node(self, node, parent_node=None): - """Add a Node to the scene. - - Parameters - ---------- - node : :class:`Node` - The node to be added. - parent_node : :class:`Node` - The parent of this Node. If None, the new node is a root node. - """ - if node in self.nodes: - raise ValueError('Node already in scene') - self.nodes.add(node) - - # Add node to sets - if node.name is not None: - if node.name not in self._name_to_nodes: - self._name_to_nodes[node.name] = set() - self._name_to_nodes[node.name].add(node) - for obj in [node.mesh, node.camera, node.light]: - if obj is not None: - if obj not in self._obj_to_nodes: - self._obj_to_nodes[obj] = set() - self._obj_to_nodes[obj].add(node) - if obj.name is not None: - if obj.name not in self._obj_name_to_nodes: - self._obj_name_to_nodes[obj.name] = set() - self._obj_name_to_nodes[obj.name].add(node) - if node.mesh is not None: - self._mesh_nodes.add(node) - if node.light is not None: - if isinstance(node.light, PointLight): - self._point_light_nodes.add(node) - if isinstance(node.light, SpotLight): - self._spot_light_nodes.add(node) - if isinstance(node.light, DirectionalLight): - self._directional_light_nodes.add(node) - if node.camera is not None: - self._camera_nodes.add(node) - if self._main_camera_node is None: - self._main_camera_node = node - - if parent_node is None: - parent_node = 'world' - elif parent_node not in self.nodes: - raise ValueError('Parent node must already be in scene') - elif node not in parent_node.children: - parent_node.children.append(node) - - # Create node in graph - self._digraph.add_node(node) - self._digraph.add_edge(node, parent_node) - - # Iterate over children - for child in node.children: - self.add_node(child, node) - - self._path_cache = {} - self._bounds = None - - def has_node(self, node): - """Check if a node is already in the scene. - - Parameters - ---------- - node : :class:`Node` - The node to be checked. - - Returns - ------- - has_node : bool - True if the node is already in the scene and false otherwise. - """ - return node in self.nodes - - def remove_node(self, node): - """Remove a node and all its children from the scene. - - Parameters - ---------- - node : :class:`Node` - The node to be removed. - """ - # Disconnect self from parent who is staying in the graph - parent = list(self._digraph.neighbors(node))[0] - self._remove_node(node) - if isinstance(parent, Node): - parent.children.remove(node) - self._path_cache = {} - self._bounds = None - - def get_pose(self, node): - """Get the world-frame pose of a node in the scene. - - Parameters - ---------- - node : :class:`Node` - The node to find the pose of. - - Returns - ------- - pose : (4,4) float - The transform matrix for this node. - """ - if node not in self.nodes: - raise ValueError('Node must already be in scene') - if node in self._path_cache: - path = self._path_cache[node] - else: - # Get path from from_frame to to_frame - path = nx.shortest_path(self._digraph, node, 'world') - self._path_cache[node] = path - - # Traverse from from_node to to_node - pose = np.eye(4) - for n in path[:-1]: - pose = np.dot(n.matrix, pose) - - return pose - - def set_pose(self, node, pose): - """Set the local-frame pose of a node in the scene. - - Parameters - ---------- - node : :class:`Node` - The node to set the pose of. - pose : (4,4) float - The pose to set the node to. - """ - if node not in self.nodes: - raise ValueError('Node must already be in scene') - node._matrix = pose - if node.mesh is not None: - self._bounds = None - - def clear(self): - """Clear out all nodes to form an empty scene. - """ - self._nodes = set() - - self._name_to_nodes = {} - self._obj_to_nodes = {} - self._obj_name_to_nodes = {} - self._mesh_nodes = set() - self._point_light_nodes = set() - self._spot_light_nodes = set() - self._directional_light_nodes = set() - self._camera_nodes = set() - self._main_camera_node = None - self._bounds = None - - # Transform tree - self._digraph = nx.DiGraph() - self._digraph.add_node('world') - self._path_cache = {} - - def _remove_node(self, node): - """Remove a node and all its children from the scene. - - Parameters - ---------- - node : :class:`Node` - The node to be removed. - """ - - # Remove self from nodes - self.nodes.remove(node) - - # Remove children - for child in node.children: - self._remove_node(child) - - # Remove self from the graph - self._digraph.remove_node(node) - - # Remove from maps - if node.name in self._name_to_nodes: - self._name_to_nodes[node.name].remove(node) - if len(self._name_to_nodes[node.name]) == 0: - self._name_to_nodes.pop(node.name) - for obj in [node.mesh, node.camera, node.light]: - if obj is None: - continue - self._obj_to_nodes[obj].remove(node) - if len(self._obj_to_nodes[obj]) == 0: - self._obj_to_nodes.pop(obj) - if obj.name is not None: - self._obj_name_to_nodes[obj.name].remove(node) - if len(self._obj_name_to_nodes[obj.name]) == 0: - self._obj_name_to_nodes.pop(obj.name) - if node.mesh is not None: - self._mesh_nodes.remove(node) - if node.light is not None: - if isinstance(node.light, PointLight): - self._point_light_nodes.remove(node) - if isinstance(node.light, SpotLight): - self._spot_light_nodes.remove(node) - if isinstance(node.light, DirectionalLight): - self._directional_light_nodes.remove(node) - if node.camera is not None: - self._camera_nodes.remove(node) - if self._main_camera_node == node: - if len(self._camera_nodes) > 0: - self._main_camera_node = next(iter(self._camera_nodes)) - else: - self._main_camera_node = None - - @staticmethod - def from_trimesh_scene(trimesh_scene, - bg_color=None, ambient_light=None): - """Create a :class:`.Scene` from a :class:`trimesh.scene.scene.Scene`. - - Parameters - ---------- - trimesh_scene : :class:`trimesh.scene.scene.Scene` - Scene with :class:~`trimesh.base.Trimesh` objects. - bg_color : (4,) float - Background color for the created scene. - ambient_light : (3,) float or None - Ambient light in the scene. - - Returns - ------- - scene_pr : :class:`Scene` - A scene containing the same geometry as the trimesh scene. - """ - # convert trimesh geometries to pyrender geometries - geometries = {name: Mesh.from_trimesh(geom) - for name, geom in trimesh_scene.geometry.items()} - - # create the pyrender scene object - scene_pr = Scene(bg_color=bg_color, ambient_light=ambient_light) - - # add every node with geometry to the pyrender scene - for node in trimesh_scene.graph.nodes_geometry: - pose, geom_name = trimesh_scene.graph[node] - scene_pr.add(geometries[geom_name], pose=pose) - - return scene_pr diff --git a/pyrender/pyrender/shader_program.py b/pyrender/pyrender/shader_program.py deleted file mode 100644 index c1803f280c98033abe0769771a9ad8ecfec942e3..0000000000000000000000000000000000000000 --- a/pyrender/pyrender/shader_program.py +++ /dev/null @@ -1,283 +0,0 @@ -"""OpenGL shader program wrapper. -""" -import numpy as np -import os -import re - -import OpenGL -from OpenGL.GL import * -from OpenGL.GL import shaders as gl_shader_utils - - -class ShaderProgramCache(object): - """A cache for shader programs. - """ - - def __init__(self, shader_dir=None): - self._program_cache = {} - self.shader_dir = shader_dir - if self.shader_dir is None: - base_dir, _ = os.path.split(os.path.realpath(__file__)) - self.shader_dir = os.path.join(base_dir, 'shaders') - - def get_program(self, vertex_shader, fragment_shader, - geometry_shader=None, defines=None): - """Get a program via a list of shader files to include in the program. - - Parameters - ---------- - vertex_shader : str - The vertex shader filename. - fragment_shader : str - The fragment shader filename. - geometry_shader : str - The geometry shader filename. - defines : dict - Defines and their values for the shader. - - Returns - ------- - program : :class:`.ShaderProgram` - The program. - """ - shader_names = [] - if defines is None: - defines = {} - shader_filenames = [ - x for x in [vertex_shader, fragment_shader, geometry_shader] - if x is not None - ] - for fn in shader_filenames: - if fn is None: - continue - _, name = os.path.split(fn) - shader_names.append(name) - cid = OpenGL.contextdata.getContext() - key = tuple([cid] + sorted( - [(s,1) for s in shader_names] + [(d, defines[d]) for d in defines] - )) - - if key not in self._program_cache: - shader_filenames = [ - os.path.join(self.shader_dir, fn) for fn in shader_filenames - ] - if len(shader_filenames) == 2: - shader_filenames.append(None) - vs, fs, gs = shader_filenames - self._program_cache[key] = ShaderProgram( - vertex_shader=vs, fragment_shader=fs, - geometry_shader=gs, defines=defines - ) - return self._program_cache[key] - - def clear(self): - for key in self._program_cache: - self._program_cache[key].delete() - self._program_cache = {} - - -class ShaderProgram(object): - """A thin wrapper about OpenGL shader programs that supports easy creation, - binding, and uniform-setting. - - Parameters - ---------- - vertex_shader : str - The vertex shader filename. - fragment_shader : str - The fragment shader filename. - geometry_shader : str - The geometry shader filename. - defines : dict - Defines and their values for the shader. - """ - - def __init__(self, vertex_shader, fragment_shader, - geometry_shader=None, defines=None): - - self.vertex_shader = vertex_shader - self.fragment_shader = fragment_shader - self.geometry_shader = geometry_shader - - self.defines = defines - if self.defines is None: - self.defines = {} - - self._program_id = None - self._vao_id = None # PYOPENGL BUG - - # DEBUG - # self._unif_map = {} - - def _add_to_context(self): - if self._program_id is not None: - raise ValueError('Shader program already in context') - shader_ids = [] - - # Load vert shader - shader_ids.append(gl_shader_utils.compileShader( - self._load(self.vertex_shader), GL_VERTEX_SHADER) - ) - # Load frag shader - shader_ids.append(gl_shader_utils.compileShader( - self._load(self.fragment_shader), GL_FRAGMENT_SHADER) - ) - # Load geometry shader - if self.geometry_shader is not None: - shader_ids.append(gl_shader_utils.compileShader( - self._load(self.geometry_shader), GL_GEOMETRY_SHADER) - ) - - # Bind empty VAO PYOPENGL BUG - if self._vao_id is None: - self._vao_id = glGenVertexArrays(1) - glBindVertexArray(self._vao_id) - - # Compile program - self._program_id = gl_shader_utils.compileProgram(*shader_ids) - - # Unbind empty VAO PYOPENGL BUG - glBindVertexArray(0) - - def _in_context(self): - return self._program_id is not None - - def _remove_from_context(self): - if self._program_id is not None: - glDeleteProgram(self._program_id) - glDeleteVertexArrays(1, [self._vao_id]) - self._program_id = None - self._vao_id = None - - def _load(self, shader_filename): - path, _ = os.path.split(shader_filename) - - with open(shader_filename) as f: - text = f.read() - - def ifdef(matchobj): - if matchobj.group(1) in self.defines: - return '#if 1' - else: - return '#if 0' - - def ifndef(matchobj): - if matchobj.group(1) in self.defines: - return '#if 0' - else: - return '#if 1' - - ifdef_regex = re.compile( - '#ifdef\\s+([a-zA-Z_][a-zA-Z_0-9]*)\\s*$', re.MULTILINE - ) - ifndef_regex = re.compile( - '#ifndef\\s+([a-zA-Z_][a-zA-Z_0-9]*)\\s*$', re.MULTILINE - ) - text = re.sub(ifdef_regex, ifdef, text) - text = re.sub(ifndef_regex, ifndef, text) - - for define in self.defines: - value = str(self.defines[define]) - text = text.replace(define, value) - - return text - - def _bind(self): - """Bind this shader program to the current OpenGL context. - """ - if self._program_id is None: - raise ValueError('Cannot bind program that is not in context') - # glBindVertexArray(self._vao_id) - glUseProgram(self._program_id) - - def _unbind(self): - """Unbind this shader program from the current OpenGL context. - """ - glUseProgram(0) - - def delete(self): - """Delete this shader program from the current OpenGL context. - """ - self._remove_from_context() - - def set_uniform(self, name, value, unsigned=False): - """Set a uniform value in the current shader program. - - Parameters - ---------- - name : str - Name of the uniform to set. - value : int, float, or ndarray - Value to set the uniform to. - unsigned : bool - If True, ints will be treated as unsigned values. - """ - try: - # DEBUG - # self._unif_map[name] = 1, (1,) - loc = glGetUniformLocation(self._program_id, name) - - if loc == -1: - raise ValueError('Invalid shader variable: {}'.format(name)) - - if isinstance(value, np.ndarray): - # DEBUG - # self._unif_map[name] = value.size, value.shape - if value.ndim == 1: - if (np.issubdtype(value.dtype, np.unsignedinteger) or - unsigned): - dtype = 'u' - value = value.astype(np.uint32) - elif np.issubdtype(value.dtype, np.integer): - dtype = 'i' - value = value.astype(np.int32) - else: - dtype = 'f' - value = value.astype(np.float32) - self._FUNC_MAP[(value.shape[0], dtype)](loc, 1, value) - else: - self._FUNC_MAP[(value.shape[0], value.shape[1])]( - loc, 1, GL_TRUE, value - ) - - # Call correct uniform function - elif isinstance(value, float): - glUniform1f(loc, value) - elif isinstance(value, int): - if unsigned: - glUniform1ui(loc, value) - else: - glUniform1i(loc, value) - elif isinstance(value, bool): - if unsigned: - glUniform1ui(loc, int(value)) - else: - glUniform1i(loc, int(value)) - else: - raise ValueError('Invalid data type') - except Exception: - pass - - _FUNC_MAP = { - (1,'u'): glUniform1uiv, - (2,'u'): glUniform2uiv, - (3,'u'): glUniform3uiv, - (4,'u'): glUniform4uiv, - (1,'i'): glUniform1iv, - (2,'i'): glUniform2iv, - (3,'i'): glUniform3iv, - (4,'i'): glUniform4iv, - (1,'f'): glUniform1fv, - (2,'f'): glUniform2fv, - (3,'f'): glUniform3fv, - (4,'f'): glUniform4fv, - (2,2): glUniformMatrix2fv, - (2,3): glUniformMatrix2x3fv, - (2,4): glUniformMatrix2x4fv, - (3,2): glUniformMatrix3x2fv, - (3,3): glUniformMatrix3fv, - (3,4): glUniformMatrix3x4fv, - (4,2): glUniformMatrix4x2fv, - (4,3): glUniformMatrix4x3fv, - (4,4): glUniformMatrix4fv, - } diff --git a/pyrender/pyrender/shaders/debug_quad.frag b/pyrender/pyrender/shaders/debug_quad.frag deleted file mode 100644 index 4647bb50dfa1e4510e2d4afb37959c7f57532eca..0000000000000000000000000000000000000000 --- a/pyrender/pyrender/shaders/debug_quad.frag +++ /dev/null @@ -1,23 +0,0 @@ -#version 330 core -out vec4 FragColor; - -in vec2 TexCoords; - -uniform sampler2D depthMap; -//uniform float near_plane; -//uniform float far_plane; -// -//// required when using a perspective projection matrix -//float LinearizeDepth(float depth) -//{ -// float z = depth * 2.0 - 1.0; // Back to NDC -// return (2.0 * near_plane * far_plane) / (far_plane + near_plane - z * (far_plane - near_plane)); -//} - -void main() -{ - float depthValue = texture(depthMap, TexCoords).r; - // FragColor = vec4(vec3(LinearizeDepth(depthValue) / far_plane), 1.0); // perspective - FragColor = vec4(vec3(depthValue), 1.0); // orthographic - //FragColor = vec4(1.0, 1.0, 0.0, 1.0); -} diff --git a/pyrender/pyrender/shaders/debug_quad.vert b/pyrender/pyrender/shaders/debug_quad.vert deleted file mode 100644 index d2f2fcb7626f6c22e0d52bf4d6c91251cbdb9f52..0000000000000000000000000000000000000000 --- a/pyrender/pyrender/shaders/debug_quad.vert +++ /dev/null @@ -1,25 +0,0 @@ -#version 330 core -//layout (location = 0) in vec3 aPos; -//layout (location = 1) in vec2 aTexCoords; -// -//out vec2 TexCoords; -// -//void main() -//{ -// TexCoords = aTexCoords; -// gl_Position = vec4(aPos, 1.0); -//} -// -// -//layout(location = 0) out vec2 uv; - -out vec2 TexCoords; - -void main() -{ - float x = float(((uint(gl_VertexID) + 2u) / 3u)%2u); - float y = float(((uint(gl_VertexID) + 1u) / 3u)%2u); - - gl_Position = vec4(-1.0f + x*2.0f, -1.0f+y*2.0f, 0.0f, 1.0f); - TexCoords = vec2(x, y); -} diff --git a/pyrender/pyrender/shaders/flat.frag b/pyrender/pyrender/shaders/flat.frag deleted file mode 100644 index 7ec01c6d095ec5dacc693accd3ad507ced61a79a..0000000000000000000000000000000000000000 --- a/pyrender/pyrender/shaders/flat.frag +++ /dev/null @@ -1,126 +0,0 @@ -#version 330 core -/////////////////////////////////////////////////////////////////////////////// -// Structs -/////////////////////////////////////////////////////////////////////////////// - -struct Material { - vec3 emissive_factor; - -#ifdef USE_METALLIC_MATERIAL - vec4 base_color_factor; - float metallic_factor; - float roughness_factor; -#endif - -#ifdef USE_GLOSSY_MATERIAL - vec4 diffuse_factor; - vec3 specular_factor; - float glossiness_factor; -#endif - -#ifdef HAS_NORMAL_TEX - sampler2D normal_texture; -#endif -#ifdef HAS_OCCLUSION_TEX - sampler2D occlusion_texture; -#endif -#ifdef HAS_EMISSIVE_TEX - sampler2D emissive_texture; -#endif -#ifdef HAS_BASE_COLOR_TEX - sampler2D base_color_texture; -#endif -#ifdef HAS_METALLIC_ROUGHNESS_TEX - sampler2D metallic_roughness_texture; -#endif -#ifdef HAS_DIFFUSE_TEX - sampler2D diffuse_texture; -#endif -#ifdef HAS_SPECULAR_GLOSSINESS_TEX - sampler2D specular_glossiness; -#endif -}; - -/////////////////////////////////////////////////////////////////////////////// -// Uniforms -/////////////////////////////////////////////////////////////////////////////// -uniform Material material; -uniform vec3 cam_pos; - -#ifdef USE_IBL -uniform samplerCube diffuse_env; -uniform samplerCube specular_env; -#endif - -/////////////////////////////////////////////////////////////////////////////// -// Inputs -/////////////////////////////////////////////////////////////////////////////// - -in vec3 frag_position; -#ifdef NORMAL_LOC -in vec3 frag_normal; -#endif -#ifdef HAS_NORMAL_TEX -#ifdef TANGENT_LOC -#ifdef NORMAL_LOC -in mat3 tbn; -#endif -#endif -#endif -#ifdef TEXCOORD_0_LOC -in vec2 uv_0; -#endif -#ifdef TEXCOORD_1_LOC -in vec2 uv_1; -#endif -#ifdef COLOR_0_LOC -in vec4 color_multiplier; -#endif - -/////////////////////////////////////////////////////////////////////////////// -// OUTPUTS -/////////////////////////////////////////////////////////////////////////////// - -out vec4 frag_color; - -/////////////////////////////////////////////////////////////////////////////// -// Constants -/////////////////////////////////////////////////////////////////////////////// -const float PI = 3.141592653589793; -const float min_roughness = 0.04; - -/////////////////////////////////////////////////////////////////////////////// -// Utility Functions -/////////////////////////////////////////////////////////////////////////////// -vec4 srgb_to_linear(vec4 srgb) -{ -#ifndef SRGB_CORRECTED - // Fast Approximation - //vec3 linOut = pow(srgbIn.xyz,vec3(2.2)); - // - vec3 b_less = step(vec3(0.04045),srgb.xyz); - vec3 lin_out = mix( srgb.xyz/vec3(12.92), pow((srgb.xyz+vec3(0.055))/vec3(1.055),vec3(2.4)), b_less ); - return vec4(lin_out, srgb.w); -#else - return srgb; -#endif -} - -/////////////////////////////////////////////////////////////////////////////// -// MAIN -/////////////////////////////////////////////////////////////////////////////// -void main() -{ - - // Compute albedo - vec4 base_color = material.base_color_factor; -#ifdef HAS_BASE_COLOR_TEX - base_color = base_color * texture(material.base_color_texture, uv_0); -#endif - -#ifdef COLOR_0_LOC - base_color *= color_multiplier; -#endif - - frag_color = clamp(base_color, 0.0, 1.0); -} diff --git a/pyrender/pyrender/shaders/flat.vert b/pyrender/pyrender/shaders/flat.vert deleted file mode 100644 index cfd241c3544718a261f961c3aa3c03aa13c97761..0000000000000000000000000000000000000000 --- a/pyrender/pyrender/shaders/flat.vert +++ /dev/null @@ -1,86 +0,0 @@ -#version 330 core - -// Vertex Attributes -layout(location = 0) in vec3 position; -#ifdef NORMAL_LOC -layout(location = NORMAL_LOC) in vec3 normal; -#endif -#ifdef TANGENT_LOC -layout(location = TANGENT_LOC) in vec4 tangent; -#endif -#ifdef TEXCOORD_0_LOC -layout(location = TEXCOORD_0_LOC) in vec2 texcoord_0; -#endif -#ifdef TEXCOORD_1_LOC -layout(location = TEXCOORD_1_LOC) in vec2 texcoord_1; -#endif -#ifdef COLOR_0_LOC -layout(location = COLOR_0_LOC) in vec4 color_0; -#endif -#ifdef JOINTS_0_LOC -layout(location = JOINTS_0_LOC) in vec4 joints_0; -#endif -#ifdef WEIGHTS_0_LOC -layout(location = WEIGHTS_0_LOC) in vec4 weights_0; -#endif -layout(location = INST_M_LOC) in mat4 inst_m; - -// Uniforms -uniform mat4 M; -uniform mat4 V; -uniform mat4 P; - -// Outputs -out vec3 frag_position; -#ifdef NORMAL_LOC -out vec3 frag_normal; -#endif -#ifdef HAS_NORMAL_TEX -#ifdef TANGENT_LOC -#ifdef NORMAL_LOC -out mat3 tbn; -#endif -#endif -#endif -#ifdef TEXCOORD_0_LOC -out vec2 uv_0; -#endif -#ifdef TEXCOORD_1_LOC -out vec2 uv_1; -#endif -#ifdef COLOR_0_LOC -out vec4 color_multiplier; -#endif - - -void main() -{ - gl_Position = P * V * M * inst_m * vec4(position, 1); - frag_position = vec3(M * inst_m * vec4(position, 1.0)); - - mat4 N = transpose(inverse(M * inst_m)); - -#ifdef NORMAL_LOC - frag_normal = normalize(vec3(N * vec4(normal, 0.0))); -#endif - -#ifdef HAS_NORMAL_TEX -#ifdef TANGENT_LOC -#ifdef NORMAL_LOC - vec3 normal_w = normalize(vec3(N * vec4(normal, 0.0))); - vec3 tangent_w = normalize(vec3(N * vec4(tangent.xyz, 0.0))); - vec3 bitangent_w = cross(normal_w, tangent_w) * tangent.w; - tbn = mat3(tangent_w, bitangent_w, normal_w); -#endif -#endif -#endif -#ifdef TEXCOORD_0_LOC - uv_0 = texcoord_0; -#endif -#ifdef TEXCOORD_1_LOC - uv_1 = texcoord_1; -#endif -#ifdef COLOR_0_LOC - color_multiplier = color_0; -#endif -} diff --git a/pyrender/pyrender/shaders/mesh.frag b/pyrender/pyrender/shaders/mesh.frag deleted file mode 100644 index 43187621b4388b18badf4e562a7ad300e59b029d..0000000000000000000000000000000000000000 --- a/pyrender/pyrender/shaders/mesh.frag +++ /dev/null @@ -1,456 +0,0 @@ -#version 330 core -/////////////////////////////////////////////////////////////////////////////// -// Structs -/////////////////////////////////////////////////////////////////////////////// - -struct SpotLight { - vec3 color; - float intensity; - float range; - vec3 position; - vec3 direction; - float light_angle_scale; - float light_angle_offset; - - #ifdef SPOT_LIGHT_SHADOWS - sampler2D shadow_map; - mat4 light_matrix; - #endif -}; - -struct DirectionalLight { - vec3 color; - float intensity; - vec3 direction; - - #ifdef DIRECTIONAL_LIGHT_SHADOWS - sampler2D shadow_map; - mat4 light_matrix; - #endif -}; - -struct PointLight { - vec3 color; - float intensity; - float range; - vec3 position; - - #ifdef POINT_LIGHT_SHADOWS - samplerCube shadow_map; - #endif -}; - -struct Material { - vec3 emissive_factor; - -#ifdef USE_METALLIC_MATERIAL - vec4 base_color_factor; - float metallic_factor; - float roughness_factor; -#endif - -#ifdef USE_GLOSSY_MATERIAL - vec4 diffuse_factor; - vec3 specular_factor; - float glossiness_factor; -#endif - -#ifdef HAS_NORMAL_TEX - sampler2D normal_texture; -#endif -#ifdef HAS_OCCLUSION_TEX - sampler2D occlusion_texture; -#endif -#ifdef HAS_EMISSIVE_TEX - sampler2D emissive_texture; -#endif -#ifdef HAS_BASE_COLOR_TEX - sampler2D base_color_texture; -#endif -#ifdef HAS_METALLIC_ROUGHNESS_TEX - sampler2D metallic_roughness_texture; -#endif -#ifdef HAS_DIFFUSE_TEX - sampler2D diffuse_texture; -#endif -#ifdef HAS_SPECULAR_GLOSSINESS_TEX - sampler2D specular_glossiness; -#endif -}; - -struct PBRInfo { - float nl; - float nv; - float nh; - float lh; - float vh; - float roughness; - float metallic; - vec3 f0; - vec3 c_diff; -}; - -/////////////////////////////////////////////////////////////////////////////// -// Uniforms -/////////////////////////////////////////////////////////////////////////////// -uniform Material material; -uniform PointLight point_lights[MAX_POINT_LIGHTS]; -uniform int n_point_lights; -uniform DirectionalLight directional_lights[MAX_DIRECTIONAL_LIGHTS]; -uniform int n_directional_lights; -uniform SpotLight spot_lights[MAX_SPOT_LIGHTS]; -uniform int n_spot_lights; -uniform vec3 cam_pos; -uniform vec3 ambient_light; - -#ifdef USE_IBL -uniform samplerCube diffuse_env; -uniform samplerCube specular_env; -#endif - -/////////////////////////////////////////////////////////////////////////////// -// Inputs -/////////////////////////////////////////////////////////////////////////////// - -in vec3 frag_position; -#ifdef NORMAL_LOC -in vec3 frag_normal; -#endif -#ifdef HAS_NORMAL_TEX -#ifdef TANGENT_LOC -#ifdef NORMAL_LOC -in mat3 tbn; -#endif -#endif -#endif -#ifdef TEXCOORD_0_LOC -in vec2 uv_0; -#endif -#ifdef TEXCOORD_1_LOC -in vec2 uv_1; -#endif -#ifdef COLOR_0_LOC -in vec4 color_multiplier; -#endif - -/////////////////////////////////////////////////////////////////////////////// -// OUTPUTS -/////////////////////////////////////////////////////////////////////////////// - -out vec4 frag_color; - -/////////////////////////////////////////////////////////////////////////////// -// Constants -/////////////////////////////////////////////////////////////////////////////// -const float PI = 3.141592653589793; -const float min_roughness = 0.04; - -/////////////////////////////////////////////////////////////////////////////// -// Utility Functions -/////////////////////////////////////////////////////////////////////////////// -vec4 srgb_to_linear(vec4 srgb) -{ -#ifndef SRGB_CORRECTED - // Fast Approximation - //vec3 linOut = pow(srgbIn.xyz,vec3(2.2)); - // - vec3 b_less = step(vec3(0.04045),srgb.xyz); - vec3 lin_out = mix( srgb.xyz/vec3(12.92), pow((srgb.xyz+vec3(0.055))/vec3(1.055),vec3(2.4)), b_less ); - return vec4(lin_out, srgb.w); -#else - return srgb; -#endif -} - -// Normal computation -vec3 get_normal() -{ -#ifdef HAS_NORMAL_TEX - -#ifndef HAS_TANGENTS - vec3 pos_dx = dFdx(frag_position); - vec3 pos_dy = dFdy(frag_position); - vec3 tex_dx = dFdx(vec3(uv_0, 0.0)); - vec3 tex_dy = dFdy(vec3(uv_0, 0.0)); - vec3 t = (tex_dy.t * pos_dx - tex_dx.t * pos_dy) / (tex_dx.s * tex_dy.t - tex_dy.s * tex_dx.t); - -#ifdef NORMAL_LOC - vec3 ng = normalize(frag_normal); -#else - vec3 = cross(pos_dx, pos_dy); -#endif - - t = normalize(t - ng * dot(ng, t)); - vec3 b = normalize(cross(ng, t)); - mat3 tbn_n = mat3(t, b, ng); - -#else - - mat3 tbn_n = tbn; - -#endif - - vec3 n = texture(material.normal_texture, uv_0).rgb; - n = normalize(tbn_n * ((2.0 * n - 1.0) * vec3(1.0, 1.0, 1.0))); - return n; // TODO NORMAL MAPPING - -#else - -#ifdef NORMAL_LOC - return frag_normal; -#else - return normalize(cam_pos - frag_position); -#endif - -#endif -} - -// Fresnel -vec3 specular_reflection(PBRInfo info) -{ - vec3 res = info.f0 + (1.0 - info.f0) * pow(clamp(1.0 - info.vh, 0.0, 1.0), 5.0); - return res; -} - -// Smith -float geometric_occlusion(PBRInfo info) -{ - float r = info.roughness + 1.0; - float k = r * r / 8.0; - float g1 = info.nv / (info.nv * (1.0 - k) + k); - float g2 = info.nl / (info.nl * (1.0 - k) + k); - //float k = info.roughness * sqrt(2.0 / PI); - //float g1 = info.lh / (info.lh * (1.0 - k) + k); - //float g2 = info.nh / (info.nh * (1.0 - k) + k); - return g1 * g2; -} - -float microfacet_distribution(PBRInfo info) -{ - float a = info.roughness * info.roughness; - float a2 = a * a; - float nh2 = info.nh * info.nh; - - float denom = (nh2 * (a2 - 1.0) + 1.0); - return a2 / (PI * denom * denom); -} - -vec3 compute_brdf(vec3 n, vec3 v, vec3 l, - float roughness, float metalness, - vec3 f0, vec3 c_diff, vec3 albedo, - vec3 radiance) -{ - vec3 h = normalize(l+v); - float nl = clamp(dot(n, l), 0.001, 1.0); - float nv = clamp(abs(dot(n, v)), 0.001, 1.0); - float nh = clamp(dot(n, h), 0.0, 1.0); - float lh = clamp(dot(l, h), 0.0, 1.0); - float vh = clamp(dot(v, h), 0.0, 1.0); - - PBRInfo info = PBRInfo(nl, nv, nh, lh, vh, roughness, metalness, f0, c_diff); - - // Compute PBR terms - vec3 F = specular_reflection(info); - float G = geometric_occlusion(info); - float D = microfacet_distribution(info); - - // Compute BRDF - vec3 diffuse_contrib = (1.0 - F) * c_diff / PI; - vec3 spec_contrib = F * G * D / (4.0 * nl * nv + 0.001); - - vec3 color = nl * radiance * (diffuse_contrib + spec_contrib); - return color; -} - -float texture2DCompare(sampler2D depths, vec2 uv, float compare) { - return compare > texture(depths, uv.xy).r ? 1.0 : 0.0; -} - -float texture2DShadowLerp(sampler2D depths, vec2 size, vec2 uv, float compare) { - vec2 texelSize = vec2(1.0)/size; - vec2 f = fract(uv*size+0.5); - vec2 centroidUV = floor(uv*size+0.5)/size; - - float lb = texture2DCompare(depths, centroidUV+texelSize*vec2(0.0, 0.0), compare); - float lt = texture2DCompare(depths, centroidUV+texelSize*vec2(0.0, 1.0), compare); - float rb = texture2DCompare(depths, centroidUV+texelSize*vec2(1.0, 0.0), compare); - float rt = texture2DCompare(depths, centroidUV+texelSize*vec2(1.0, 1.0), compare); - float a = mix(lb, lt, f.y); - float b = mix(rb, rt, f.y); - float c = mix(a, b, f.x); - return c; -} - -float PCF(sampler2D depths, vec2 size, vec2 uv, float compare){ - float result = 0.0; - for(int x=-1; x<=1; x++){ - for(int y=-1; y<=1; y++){ - vec2 off = vec2(x,y)/size; - result += texture2DShadowLerp(depths, size, uv+off, compare); - } - } - return result/9.0; -} - -float shadow_calc(mat4 light_matrix, sampler2D shadow_map, float nl) -{ - // Compute light texture UV coords - vec4 proj_coords = vec4(light_matrix * vec4(frag_position.xyz, 1.0)); - vec3 light_coords = proj_coords.xyz / proj_coords.w; - light_coords = light_coords * 0.5 + 0.5; - float current_depth = light_coords.z; - float bias = max(0.001 * (1.0 - nl), 0.0001) / proj_coords.w; - float compare = (current_depth - bias); - float shadow = PCF(shadow_map, textureSize(shadow_map, 0), light_coords.xy, compare); - if (light_coords.z > 1.0) { - shadow = 0.0; - } - return shadow; -} - -/////////////////////////////////////////////////////////////////////////////// -// MAIN -/////////////////////////////////////////////////////////////////////////////// -void main() -{ - - vec4 color = vec4(vec3(0.0), 1.0); -/////////////////////////////////////////////////////////////////////////////// -// Handle Metallic Materials -/////////////////////////////////////////////////////////////////////////////// -#ifdef USE_METALLIC_MATERIAL - - // Compute metallic/roughness factors - float roughness = material.roughness_factor; - float metallic = material.metallic_factor; -#ifdef HAS_METALLIC_ROUGHNESS_TEX - vec2 mr = texture(material.metallic_roughness_texture, uv_0).rg; - roughness = roughness * mr.r; - metallic = metallic * mr.g; -#endif - roughness = clamp(roughness, min_roughness, 1.0); - metallic = clamp(metallic, 0.0, 1.0); - // In convention, material roughness is perceputal roughness ^ 2 - float alpha_roughness = roughness * roughness; - - // Compute albedo - vec4 base_color = material.base_color_factor; -#ifdef HAS_BASE_COLOR_TEX - base_color = base_color * srgb_to_linear(texture(material.base_color_texture, uv_0)); -#endif - - // Compute specular and diffuse colors - vec3 dialectric_spec = vec3(min_roughness); - vec3 c_diff = mix(vec3(0.0), base_color.rgb * (1 - min_roughness), 1.0 - metallic); - vec3 f0 = mix(dialectric_spec, base_color.rgb, metallic); - - // Compute normal - vec3 n = normalize(get_normal()); - - // Loop over lights - for (int i = 0; i < n_directional_lights; i++) { - vec3 direction = directional_lights[i].direction; - vec3 v = normalize(cam_pos - frag_position); // Vector towards camera - vec3 l = normalize(-1.0 * direction); // Vector towards light - - // Compute attenuation and radiance - float attenuation = directional_lights[i].intensity; - vec3 radiance = attenuation * directional_lights[i].color; - - // Compute outbound color - vec3 res = compute_brdf(n, v, l, roughness, metallic, - f0, c_diff, base_color.rgb, radiance); - - // Compute shadow -#ifdef DIRECTIONAL_LIGHT_SHADOWS - float nl = clamp(dot(n,l), 0.0, 1.0); - float shadow = shadow_calc( - directional_lights[i].light_matrix, - directional_lights[i].shadow_map, - nl - ); - res = res * (1.0 - shadow); -#endif - color.xyz += res; - } - - for (int i = 0; i < n_point_lights; i++) { - vec3 position = point_lights[i].position; - vec3 v = normalize(cam_pos - frag_position); // Vector towards camera - vec3 l = normalize(position - frag_position); // Vector towards light - - // Compute attenuation and radiance - float dist = length(position - frag_position); - float attenuation = point_lights[i].intensity / (dist * dist); - vec3 radiance = attenuation * point_lights[i].color; - - // Compute outbound color - vec3 res = compute_brdf(n, v, l, roughness, metallic, - f0, c_diff, base_color.rgb, radiance); - color.xyz += res; - } - for (int i = 0; i < n_spot_lights; i++) { - vec3 position = spot_lights[i].position; - vec3 v = normalize(cam_pos - frag_position); // Vector towards camera - vec3 l = normalize(position - frag_position); // Vector towards light - - // Compute attenuation and radiance - vec3 direction = spot_lights[i].direction; - float las = spot_lights[i].light_angle_scale; - float lao = spot_lights[i].light_angle_offset; - float dist = length(position - frag_position); - float cd = clamp(dot(direction, -l), 0.0, 1.0); - float attenuation = clamp(cd * las + lao, 0.0, 1.0); - attenuation = attenuation * attenuation * spot_lights[i].intensity; - attenuation = attenuation / (dist * dist); - vec3 radiance = attenuation * spot_lights[i].color; - - // Compute outbound color - vec3 res = compute_brdf(n, v, l, roughness, metallic, - f0, c_diff, base_color.rgb, radiance); -#ifdef SPOT_LIGHT_SHADOWS - float nl = clamp(dot(n,l), 0.0, 1.0); - float shadow = shadow_calc( - spot_lights[i].light_matrix, - spot_lights[i].shadow_map, - nl - ); - res = res * (1.0 - shadow); -#endif - color.xyz += res; - } - color.xyz += base_color.xyz * ambient_light; - - // Calculate lighting from environment -#ifdef USE_IBL - // TODO -#endif - - // Apply occlusion -#ifdef HAS_OCCLUSION_TEX - float ao = texture(material.occlusion_texture, uv_0).r; - color.xyz *= ao; -#endif - - // Apply emissive map - vec3 emissive = material.emissive_factor; -#ifdef HAS_EMISSIVE_TEX - emissive *= srgb_to_linear(texture(material.emissive_texture, uv_0)).rgb; -#endif - color.xyz += emissive * material.emissive_factor; - -#ifdef COLOR_0_LOC - color *= color_multiplier; -#endif - - frag_color = clamp(vec4(pow(color.xyz, vec3(1.0/2.2)), color.a * base_color.a), 0.0, 1.0); - -#else - // TODO GLOSSY MATERIAL BRDF -#endif - -/////////////////////////////////////////////////////////////////////////////// -// Handle Glossy Materials -/////////////////////////////////////////////////////////////////////////////// - -} diff --git a/pyrender/pyrender/shaders/mesh.vert b/pyrender/pyrender/shaders/mesh.vert deleted file mode 100644 index cfd241c3544718a261f961c3aa3c03aa13c97761..0000000000000000000000000000000000000000 --- a/pyrender/pyrender/shaders/mesh.vert +++ /dev/null @@ -1,86 +0,0 @@ -#version 330 core - -// Vertex Attributes -layout(location = 0) in vec3 position; -#ifdef NORMAL_LOC -layout(location = NORMAL_LOC) in vec3 normal; -#endif -#ifdef TANGENT_LOC -layout(location = TANGENT_LOC) in vec4 tangent; -#endif -#ifdef TEXCOORD_0_LOC -layout(location = TEXCOORD_0_LOC) in vec2 texcoord_0; -#endif -#ifdef TEXCOORD_1_LOC -layout(location = TEXCOORD_1_LOC) in vec2 texcoord_1; -#endif -#ifdef COLOR_0_LOC -layout(location = COLOR_0_LOC) in vec4 color_0; -#endif -#ifdef JOINTS_0_LOC -layout(location = JOINTS_0_LOC) in vec4 joints_0; -#endif -#ifdef WEIGHTS_0_LOC -layout(location = WEIGHTS_0_LOC) in vec4 weights_0; -#endif -layout(location = INST_M_LOC) in mat4 inst_m; - -// Uniforms -uniform mat4 M; -uniform mat4 V; -uniform mat4 P; - -// Outputs -out vec3 frag_position; -#ifdef NORMAL_LOC -out vec3 frag_normal; -#endif -#ifdef HAS_NORMAL_TEX -#ifdef TANGENT_LOC -#ifdef NORMAL_LOC -out mat3 tbn; -#endif -#endif -#endif -#ifdef TEXCOORD_0_LOC -out vec2 uv_0; -#endif -#ifdef TEXCOORD_1_LOC -out vec2 uv_1; -#endif -#ifdef COLOR_0_LOC -out vec4 color_multiplier; -#endif - - -void main() -{ - gl_Position = P * V * M * inst_m * vec4(position, 1); - frag_position = vec3(M * inst_m * vec4(position, 1.0)); - - mat4 N = transpose(inverse(M * inst_m)); - -#ifdef NORMAL_LOC - frag_normal = normalize(vec3(N * vec4(normal, 0.0))); -#endif - -#ifdef HAS_NORMAL_TEX -#ifdef TANGENT_LOC -#ifdef NORMAL_LOC - vec3 normal_w = normalize(vec3(N * vec4(normal, 0.0))); - vec3 tangent_w = normalize(vec3(N * vec4(tangent.xyz, 0.0))); - vec3 bitangent_w = cross(normal_w, tangent_w) * tangent.w; - tbn = mat3(tangent_w, bitangent_w, normal_w); -#endif -#endif -#endif -#ifdef TEXCOORD_0_LOC - uv_0 = texcoord_0; -#endif -#ifdef TEXCOORD_1_LOC - uv_1 = texcoord_1; -#endif -#ifdef COLOR_0_LOC - color_multiplier = color_0; -#endif -} diff --git a/pyrender/pyrender/shaders/mesh_depth.frag b/pyrender/pyrender/shaders/mesh_depth.frag deleted file mode 100644 index d8b1fac6091cfa457ba835ae0758e955f06d8754..0000000000000000000000000000000000000000 --- a/pyrender/pyrender/shaders/mesh_depth.frag +++ /dev/null @@ -1,8 +0,0 @@ -#version 330 core - -out vec4 frag_color; - -void main() -{ - frag_color = vec4(1.0); -} diff --git a/pyrender/pyrender/shaders/mesh_depth.vert b/pyrender/pyrender/shaders/mesh_depth.vert deleted file mode 100644 index e534c058fb3e7b0efbec090513d55982db68ccaf..0000000000000000000000000000000000000000 --- a/pyrender/pyrender/shaders/mesh_depth.vert +++ /dev/null @@ -1,13 +0,0 @@ -#version 330 core -layout(location = 0) in vec3 position; -layout(location = INST_M_LOC) in mat4 inst_m; - -uniform mat4 P; -uniform mat4 V; -uniform mat4 M; - -void main() -{ - mat4 light_matrix = P * V; - gl_Position = light_matrix * M * inst_m * vec4(position, 1.0); -} diff --git a/pyrender/pyrender/shaders/segmentation.frag b/pyrender/pyrender/shaders/segmentation.frag deleted file mode 100644 index 40deb92cbdef3ec9fd952632624cd5f4b5ce0c84..0000000000000000000000000000000000000000 --- a/pyrender/pyrender/shaders/segmentation.frag +++ /dev/null @@ -1,13 +0,0 @@ -#version 330 core - -uniform vec3 color; -out vec4 frag_color; - -/////////////////////////////////////////////////////////////////////////////// -// MAIN -/////////////////////////////////////////////////////////////////////////////// -void main() -{ - frag_color = vec4(color, 1.0); - //frag_color = vec4(1.0, 0.5, 0.5, 1.0); -} diff --git a/pyrender/pyrender/shaders/segmentation.vert b/pyrender/pyrender/shaders/segmentation.vert deleted file mode 100644 index 503382599dae3c9415845f35b99d6678cfc7f716..0000000000000000000000000000000000000000 --- a/pyrender/pyrender/shaders/segmentation.vert +++ /dev/null @@ -1,14 +0,0 @@ -#version 330 core -layout(location = 0) in vec3 position; -layout(location = INST_M_LOC) in mat4 inst_m; - -uniform mat4 P; -uniform mat4 V; -uniform mat4 M; - -void main() -{ - mat4 light_matrix = P * V; - gl_Position = light_matrix * M * inst_m * vec4(position, 1.0); -} - diff --git a/pyrender/pyrender/shaders/text.frag b/pyrender/pyrender/shaders/text.frag deleted file mode 100644 index 486c97dc94ed5e9083ae348bc1e85c5cb26c44dc..0000000000000000000000000000000000000000 --- a/pyrender/pyrender/shaders/text.frag +++ /dev/null @@ -1,12 +0,0 @@ -#version 330 core -in vec2 uv; -out vec4 color; - -uniform sampler2D text; -uniform vec4 text_color; - -void main() -{ - vec4 sampled = vec4(1.0, 1.0, 1.0, texture(text, uv).r); - color = text_color * sampled; -} diff --git a/pyrender/pyrender/shaders/text.vert b/pyrender/pyrender/shaders/text.vert deleted file mode 100644 index 005bc439b3d63522df99e5db2088953eb8defcf4..0000000000000000000000000000000000000000 --- a/pyrender/pyrender/shaders/text.vert +++ /dev/null @@ -1,12 +0,0 @@ -#version 330 core -layout (location = 0) in vec4 vertex; - -out vec2 uv; - -uniform mat4 projection; - -void main() -{ - gl_Position = projection * vec4(vertex.xy, 0.0, 1.0); - uv = vertex.zw; -} diff --git a/pyrender/pyrender/shaders/vertex_normals.frag b/pyrender/pyrender/shaders/vertex_normals.frag deleted file mode 100644 index edf5beb7f283dd67e1710bff922555539966cee4..0000000000000000000000000000000000000000 --- a/pyrender/pyrender/shaders/vertex_normals.frag +++ /dev/null @@ -1,10 +0,0 @@ -#version 330 core - -out vec4 frag_color; - -uniform vec4 normal_color; - -void main() -{ - frag_color = normal_color; -} diff --git a/pyrender/pyrender/shaders/vertex_normals.geom b/pyrender/pyrender/shaders/vertex_normals.geom deleted file mode 100644 index 57f0b0e645e72d41116f5767d66fc37d01ed2714..0000000000000000000000000000000000000000 --- a/pyrender/pyrender/shaders/vertex_normals.geom +++ /dev/null @@ -1,74 +0,0 @@ -#version 330 core - -layout (triangles) in; - -#ifdef FACE_NORMALS - -#ifdef VERTEX_NORMALS - layout (line_strip, max_vertices = 8) out; -#else - layout (line_strip, max_vertices = 2) out; -#endif - -#else - - layout (line_strip, max_vertices = 6) out; - -#endif - -in VS_OUT { - vec3 position; - vec3 normal; - mat4 mvp; -} gs_in[]; - -uniform float normal_magnitude; - -void GenerateVertNormal(int index) -{ - - vec4 p0 = gs_in[index].mvp * vec4(gs_in[index].position, 1.0); - vec4 p1 = gs_in[index].mvp * vec4(normal_magnitude * normalize(gs_in[index].normal) + gs_in[index].position, 1.0); - gl_Position = p0; - EmitVertex(); - gl_Position = p1; - EmitVertex(); - EndPrimitive(); -} - -void GenerateFaceNormal() -{ - vec3 p0 = gs_in[0].position.xyz; - vec3 p1 = gs_in[1].position.xyz; - vec3 p2 = gs_in[2].position.xyz; - - vec3 v0 = p0 - p1; - vec3 v1 = p2 - p1; - - vec3 N = normalize(cross(v1, v0)); - vec3 P = (p0 + p1 + p2) / 3.0; - - vec4 np0 = gs_in[0].mvp * vec4(P, 1.0); - vec4 np1 = gs_in[0].mvp * vec4(normal_magnitude * N + P, 1.0); - - gl_Position = np0; - EmitVertex(); - gl_Position = np1; - EmitVertex(); - EndPrimitive(); -} - -void main() -{ - -#ifdef FACE_NORMALS - GenerateFaceNormal(); -#endif - -#ifdef VERTEX_NORMALS - GenerateVertNormal(0); - GenerateVertNormal(1); - GenerateVertNormal(2); -#endif - -} diff --git a/pyrender/pyrender/shaders/vertex_normals.vert b/pyrender/pyrender/shaders/vertex_normals.vert deleted file mode 100644 index be22eed2a0e904bcaf1ac5a4721558e574cddc62..0000000000000000000000000000000000000000 --- a/pyrender/pyrender/shaders/vertex_normals.vert +++ /dev/null @@ -1,27 +0,0 @@ -#version 330 core - -// Inputs -layout(location = 0) in vec3 position; -layout(location = NORMAL_LOC) in vec3 normal; -layout(location = INST_M_LOC) in mat4 inst_m; - -// Output data -out VS_OUT { - vec3 position; - vec3 normal; - mat4 mvp; -} vs_out; - -// Uniform data -uniform mat4 M; -uniform mat4 V; -uniform mat4 P; - -// Render loop -void main() { - vs_out.mvp = P * V * M * inst_m; - vs_out.position = position; - vs_out.normal = normal; - - gl_Position = vec4(position, 1.0); -} diff --git a/pyrender/pyrender/shaders/vertex_normals_pc.geom b/pyrender/pyrender/shaders/vertex_normals_pc.geom deleted file mode 100644 index 4ea4e7b8542703f64b8d28fd187e425137861fe4..0000000000000000000000000000000000000000 --- a/pyrender/pyrender/shaders/vertex_normals_pc.geom +++ /dev/null @@ -1,29 +0,0 @@ -#version 330 core - -layout (points) in; - -layout (line_strip, max_vertices = 2) out; - -in VS_OUT { - vec3 position; - vec3 normal; - mat4 mvp; -} gs_in[]; - -uniform float normal_magnitude; - -void GenerateVertNormal(int index) -{ - vec4 p0 = gs_in[index].mvp * vec4(gs_in[index].position, 1.0); - vec4 p1 = gs_in[index].mvp * vec4(normal_magnitude * normalize(gs_in[index].normal) + gs_in[index].position, 1.0); - gl_Position = p0; - EmitVertex(); - gl_Position = p1; - EmitVertex(); - EndPrimitive(); -} - -void main() -{ - GenerateVertNormal(0); -} diff --git a/pyrender/pyrender/texture.py b/pyrender/pyrender/texture.py deleted file mode 100644 index 477759729d7b995a4f276e81d649617d045a066e..0000000000000000000000000000000000000000 --- a/pyrender/pyrender/texture.py +++ /dev/null @@ -1,259 +0,0 @@ -"""Textures, conforming to the glTF 2.0 standards as specified in -https://github.com/KhronosGroup/glTF/tree/master/specification/2.0#reference-texture - -Author: Matthew Matl -""" -import numpy as np - -from OpenGL.GL import * - -from .utils import format_texture_source -from .sampler import Sampler - - -class Texture(object): - """A texture and its sampler. - - Parameters - ---------- - name : str, optional - The user-defined name of this object. - sampler : :class:`Sampler` - The sampler used by this texture. - source : (h,w,c) uint8 or (h,w,c) float or :class:`PIL.Image.Image` - The image used by this texture. If None, the texture is created - empty and width and height must be specified. - source_channels : str - Either `D`, `R`, `RG`, `GB`, `RGB`, or `RGBA`. Indicates the - channels to extract from `source`. Any missing channels will be filled - with `1.0`. - width : int, optional - For empty textures, the width of the texture buffer. - height : int, optional - For empty textures, the height of the texture buffer. - tex_type : int - Either GL_TEXTURE_2D or GL_TEXTURE_CUBE. - data_format : int - For now, just GL_FLOAT. - """ - - def __init__(self, - name=None, - sampler=None, - source=None, - source_channels=None, - width=None, - height=None, - tex_type=GL_TEXTURE_2D, - data_format=GL_UNSIGNED_BYTE): - self.source_channels = source_channels - self.name = name - self.sampler = sampler - self.source = source - self.width = width - self.height = height - self.tex_type = tex_type - self.data_format = data_format - - self._texid = None - self._is_transparent = False - - @property - def name(self): - """str : The user-defined name of this object. - """ - return self._name - - @name.setter - def name(self, value): - if value is not None: - value = str(value) - self._name = value - - @property - def sampler(self): - """:class:`Sampler` : The sampler used by this texture. - """ - return self._sampler - - @sampler.setter - def sampler(self, value): - if value is None: - value = Sampler() - self._sampler = value - - @property - def source(self): - """(h,w,c) uint8 or float or :class:`PIL.Image.Image` : The image - used in this texture. - """ - return self._source - - @source.setter - def source(self, value): - if value is None: - self._source = None - else: - self._source = format_texture_source(value, self.source_channels) - self._is_transparent = False - - @property - def source_channels(self): - """str : The channels that were extracted from the original source. - """ - return self._source_channels - - @source_channels.setter - def source_channels(self, value): - self._source_channels = value - - @property - def width(self): - """int : The width of the texture buffer. - """ - return self._width - - @width.setter - def width(self, value): - self._width = value - - @property - def height(self): - """int : The height of the texture buffer. - """ - return self._height - - @height.setter - def height(self, value): - self._height = value - - @property - def tex_type(self): - """int : The type of the texture. - """ - return self._tex_type - - @tex_type.setter - def tex_type(self, value): - self._tex_type = value - - @property - def data_format(self): - """int : The format of the texture data. - """ - return self._data_format - - @data_format.setter - def data_format(self, value): - self._data_format = value - - def is_transparent(self, cutoff=1.0): - """bool : If True, the texture is partially transparent. - """ - if self._is_transparent is None: - self._is_transparent = False - if self.source_channels == 'RGBA' and self.source is not None: - if np.any(self.source[:,:,3] < cutoff): - self._is_transparent = True - return self._is_transparent - - def delete(self): - """Remove this texture from the OpenGL context. - """ - self._unbind() - self._remove_from_context() - - ################## - # OpenGL code - ################## - def _add_to_context(self): - if self._texid is not None: - raise ValueError('Texture already loaded into OpenGL context') - - fmt = GL_DEPTH_COMPONENT - if self.source_channels == 'R': - fmt = GL_RED - elif self.source_channels == 'RG' or self.source_channels == 'GB': - fmt = GL_RG - elif self.source_channels == 'RGB': - fmt = GL_RGB - elif self.source_channels == 'RGBA': - fmt = GL_RGBA - - # Generate the OpenGL texture - self._texid = glGenTextures(1) - glBindTexture(self.tex_type, self._texid) - - # Flip data for OpenGL buffer - data = None - width = self.width - height = self.height - if self.source is not None: - data = np.ascontiguousarray(np.flip(self.source, axis=0).flatten()) - width = self.source.shape[1] - height = self.source.shape[0] - - # Bind texture and generate mipmaps - glTexImage2D( - self.tex_type, 0, fmt, width, height, 0, fmt, - self.data_format, data - ) - if self.source is not None: - glGenerateMipmap(self.tex_type) - - if self.sampler.magFilter is not None: - glTexParameteri( - self.tex_type, GL_TEXTURE_MAG_FILTER, self.sampler.magFilter - ) - else: - if self.source is not None: - glTexParameteri(self.tex_type, GL_TEXTURE_MAG_FILTER, GL_LINEAR) - else: - glTexParameteri(self.tex_type, GL_TEXTURE_MAG_FILTER, GL_NEAREST) - if self.sampler.minFilter is not None: - glTexParameteri( - self.tex_type, GL_TEXTURE_MIN_FILTER, self.sampler.minFilter - ) - else: - if self.source is not None: - glTexParameteri(self.tex_type, GL_TEXTURE_MIN_FILTER, GL_LINEAR_MIPMAP_LINEAR) - else: - glTexParameteri(self.tex_type, GL_TEXTURE_MIN_FILTER, GL_NEAREST) - - glTexParameteri(self.tex_type, GL_TEXTURE_WRAP_S, self.sampler.wrapS) - glTexParameteri(self.tex_type, GL_TEXTURE_WRAP_T, self.sampler.wrapT) - border_color = 255 * np.ones(4).astype(np.uint8) - if self.data_format == GL_FLOAT: - border_color = np.ones(4).astype(np.float32) - glTexParameterfv( - self.tex_type, GL_TEXTURE_BORDER_COLOR, - border_color - ) - - # Unbind texture - glBindTexture(self.tex_type, 0) - - def _remove_from_context(self): - if self._texid is not None: - # TODO OPENGL BUG? - # glDeleteTextures(1, [self._texid]) - glDeleteTextures([self._texid]) - self._texid = None - - def _in_context(self): - return self._texid is not None - - def _bind(self): - # TODO HANDLE INDEXING INTO OTHER UV's - glBindTexture(self.tex_type, self._texid) - - def _unbind(self): - glBindTexture(self.tex_type, 0) - - def _bind_as_depth_attachment(self): - glFramebufferTexture2D(GL_FRAMEBUFFER, GL_DEPTH_ATTACHMENT, - self.tex_type, self._texid, 0) - - def _bind_as_color_attachment(self): - glFramebufferTexture2D(GL_FRAMEBUFFER, GL_COLOR_ATTACHMENT0, - self.tex_type, self._texid, 0) diff --git a/pyrender/pyrender/trackball.py b/pyrender/pyrender/trackball.py deleted file mode 100644 index 3e57a0e82d3f07b80754f575c28a0e05cb73fc50..0000000000000000000000000000000000000000 --- a/pyrender/pyrender/trackball.py +++ /dev/null @@ -1,216 +0,0 @@ -"""Trackball class for 3D manipulation of viewpoints. -""" -import numpy as np - -import trimesh.transformations as transformations - - -class Trackball(object): - """A trackball class for creating camera transforms from mouse movements. - """ - STATE_ROTATE = 0 - STATE_PAN = 1 - STATE_ROLL = 2 - STATE_ZOOM = 3 - - def __init__(self, pose, size, scale, - target=np.array([0.0, 0.0, 0.0])): - """Initialize a trackball with an initial camera-to-world pose - and the given parameters. - - Parameters - ---------- - pose : [4,4] - An initial camera-to-world pose for the trackball. - - size : (float, float) - The width and height of the camera image in pixels. - - scale : float - The diagonal of the scene's bounding box -- - used for ensuring translation motions are sufficiently - fast for differently-sized scenes. - - target : (3,) float - The center of the scene in world coordinates. - The trackball will revolve around this point. - """ - self._size = np.array(size) - self._scale = float(scale) - - self._pose = pose - self._n_pose = pose - - self._target = target - self._n_target = target - - self._state = Trackball.STATE_ROTATE - - @property - def pose(self): - """autolab_core.RigidTransform : The current camera-to-world pose. - """ - return self._n_pose - - def set_state(self, state): - """Set the state of the trackball in order to change the effect of - dragging motions. - - Parameters - ---------- - state : int - One of Trackball.STATE_ROTATE, Trackball.STATE_PAN, - Trackball.STATE_ROLL, and Trackball.STATE_ZOOM. - """ - self._state = state - - def resize(self, size): - """Resize the window. - - Parameters - ---------- - size : (float, float) - The new width and height of the camera image in pixels. - """ - self._size = np.array(size) - - def down(self, point): - """Record an initial mouse press at a given point. - - Parameters - ---------- - point : (2,) int - The x and y pixel coordinates of the mouse press. - """ - self._pdown = np.array(point, dtype=np.float32) - self._pose = self._n_pose - self._target = self._n_target - - def drag(self, point): - """Update the tracball during a drag. - - Parameters - ---------- - point : (2,) int - The current x and y pixel coordinates of the mouse during a drag. - This will compute a movement for the trackball with the relative - motion between this point and the one marked by down(). - """ - point = np.array(point, dtype=np.float32) - dx, dy = point - self._pdown - mindim = 0.3 * np.min(self._size) - - target = self._target - x_axis = self._pose[:3,0].flatten() - y_axis = self._pose[:3,1].flatten() - z_axis = self._pose[:3,2].flatten() - eye = self._pose[:3,3].flatten() - - # Interpret drag as a rotation - if self._state == Trackball.STATE_ROTATE: - x_angle = -dx / mindim - x_rot_mat = transformations.rotation_matrix( - x_angle, y_axis, target - ) - - y_angle = dy / mindim - y_rot_mat = transformations.rotation_matrix( - y_angle, x_axis, target - ) - - self._n_pose = y_rot_mat.dot(x_rot_mat.dot(self._pose)) - - # Interpret drag as a roll about the camera axis - elif self._state == Trackball.STATE_ROLL: - center = self._size / 2.0 - v_init = self._pdown - center - v_curr = point - center - v_init = v_init / np.linalg.norm(v_init) - v_curr = v_curr / np.linalg.norm(v_curr) - - theta = (-np.arctan2(v_curr[1], v_curr[0]) + - np.arctan2(v_init[1], v_init[0])) - - rot_mat = transformations.rotation_matrix(theta, z_axis, target) - - self._n_pose = rot_mat.dot(self._pose) - - # Interpret drag as a camera pan in view plane - elif self._state == Trackball.STATE_PAN: - dx = -dx / (5.0 * mindim) * self._scale - dy = -dy / (5.0 * mindim) * self._scale - - translation = dx * x_axis + dy * y_axis - self._n_target = self._target + translation - t_tf = np.eye(4) - t_tf[:3,3] = translation - self._n_pose = t_tf.dot(self._pose) - - # Interpret drag as a zoom motion - elif self._state == Trackball.STATE_ZOOM: - radius = np.linalg.norm(eye - target) - ratio = 0.0 - if dy > 0: - ratio = np.exp(abs(dy) / (0.5 * self._size[1])) - 1.0 - elif dy < 0: - ratio = 1.0 - np.exp(dy / (0.5 * (self._size[1]))) - translation = -np.sign(dy) * ratio * radius * z_axis - t_tf = np.eye(4) - t_tf[:3,3] = translation - self._n_pose = t_tf.dot(self._pose) - - def scroll(self, clicks): - """Zoom using a mouse scroll wheel motion. - - Parameters - ---------- - clicks : int - The number of clicks. Positive numbers indicate forward wheel - movement. - """ - target = self._target - ratio = 0.90 - - mult = 1.0 - if clicks > 0: - mult = ratio**clicks - elif clicks < 0: - mult = (1.0 / ratio)**abs(clicks) - - z_axis = self._n_pose[:3,2].flatten() - eye = self._n_pose[:3,3].flatten() - radius = np.linalg.norm(eye - target) - translation = (mult * radius - radius) * z_axis - t_tf = np.eye(4) - t_tf[:3,3] = translation - self._n_pose = t_tf.dot(self._n_pose) - - z_axis = self._pose[:3,2].flatten() - eye = self._pose[:3,3].flatten() - radius = np.linalg.norm(eye - target) - translation = (mult * radius - radius) * z_axis - t_tf = np.eye(4) - t_tf[:3,3] = translation - self._pose = t_tf.dot(self._pose) - - def rotate(self, azimuth, axis=None): - """Rotate the trackball about the "Up" axis by azimuth radians. - - Parameters - ---------- - azimuth : float - The number of radians to rotate. - """ - target = self._target - - y_axis = self._n_pose[:3,1].flatten() - if axis is not None: - y_axis = axis - x_rot_mat = transformations.rotation_matrix(azimuth, y_axis, target) - self._n_pose = x_rot_mat.dot(self._n_pose) - - y_axis = self._pose[:3,1].flatten() - if axis is not None: - y_axis = axis - x_rot_mat = transformations.rotation_matrix(azimuth, y_axis, target) - self._pose = x_rot_mat.dot(self._pose) diff --git a/pyrender/pyrender/utils.py b/pyrender/pyrender/utils.py deleted file mode 100644 index 48a11faf991606ad7fb0691582f0bc6f06101a45..0000000000000000000000000000000000000000 --- a/pyrender/pyrender/utils.py +++ /dev/null @@ -1,115 +0,0 @@ -import numpy as np -from PIL import Image - - -def format_color_vector(value, length): - """Format a color vector. - """ - if isinstance(value, int): - value = value / 255.0 - if isinstance(value, float): - value = np.repeat(value, length) - if isinstance(value, list) or isinstance(value, tuple): - value = np.array(value) - if isinstance(value, np.ndarray): - value = value.squeeze() - if np.issubdtype(value.dtype, np.integer): - value = (value / 255.0).astype(np.float32) - if value.ndim != 1: - raise ValueError('Format vector takes only 1-D vectors') - if length > value.shape[0]: - value = np.hstack((value, np.ones(length - value.shape[0]))) - elif length < value.shape[0]: - value = value[:length] - else: - raise ValueError('Invalid vector data type') - - return value.squeeze().astype(np.float32) - - -def format_color_array(value, shape): - """Format an array of colors. - """ - # Convert uint8 to floating - value = np.asanyarray(value) - if np.issubdtype(value.dtype, np.integer): - value = (value / 255.0).astype(np.float32) - - # Match up shapes - if value.ndim == 1: - value = np.tile(value, (shape[0],1)) - if value.shape[1] < shape[1]: - nc = shape[1] - value.shape[1] - value = np.column_stack((value, np.ones((value.shape[0], nc)))) - elif value.shape[1] > shape[1]: - value = value[:,:shape[1]] - return value.astype(np.float32) - - -def format_texture_source(texture, target_channels='RGB'): - """Format a texture as a float32 np array. - """ - - # Pass through None - if texture is None: - return None - - # Convert PIL images into numpy arrays - if isinstance(texture, Image.Image): - if texture.mode == 'P' and target_channels in ('RGB', 'RGBA'): - texture = np.array(texture.convert(target_channels)) - else: - texture = np.array(texture) - - # Format numpy arrays - if isinstance(texture, np.ndarray): - if np.issubdtype(texture.dtype, np.floating): - texture = np.array(texture * 255.0, dtype=np.uint8) - elif np.issubdtype(texture.dtype, np.integer): - texture = texture.astype(np.uint8) - else: - raise TypeError('Invalid type {} for texture'.format( - type(texture) - )) - - # Format array by picking out correct texture channels or padding - if texture.ndim == 2: - texture = texture[:,:,np.newaxis] - if target_channels == 'R': - texture = texture[:,:,0] - texture = texture.squeeze() - elif target_channels == 'RG': - if texture.shape[2] == 1: - texture = np.repeat(texture, 2, axis=2) - else: - texture = texture[:,:,(0,1)] - elif target_channels == 'GB': - if texture.shape[2] == 1: - texture = np.repeat(texture, 2, axis=2) - elif texture.shape[2] > 2: - texture = texture[:,:,(1,2)] - elif target_channels == 'RGB': - if texture.shape[2] == 1: - texture = np.repeat(texture, 3, axis=2) - elif texture.shape[2] == 2: - raise ValueError('Cannot reformat 2-channel texture into RGB') - else: - texture = texture[:,:,(0,1,2)] - elif target_channels == 'RGBA': - if texture.shape[2] == 1: - texture = np.repeat(texture, 4, axis=2) - texture[:,:,3] = 255 - elif texture.shape[2] == 2: - raise ValueError('Cannot reformat 2-channel texture into RGBA') - elif texture.shape[2] == 3: - tx = np.empty((texture.shape[0], texture.shape[1], 4), dtype=np.uint8) - tx[:,:,:3] = texture - tx[:,:,3] = 255 - texture = tx - else: - raise ValueError('Invalid texture channel specification: {}' - .format(target_channels)) - else: - raise TypeError('Invalid type {} for texture'.format(type(texture))) - - return texture diff --git a/pyrender/pyrender/version.py b/pyrender/pyrender/version.py deleted file mode 100644 index a33fc87f61f528780e3319a5160769cc84512b1b..0000000000000000000000000000000000000000 --- a/pyrender/pyrender/version.py +++ /dev/null @@ -1 +0,0 @@ -__version__ = '0.1.45' diff --git a/pyrender/pyrender/viewer.py b/pyrender/pyrender/viewer.py deleted file mode 100644 index d2326c38205c6eaddb4f567e3b088329187af258..0000000000000000000000000000000000000000 --- a/pyrender/pyrender/viewer.py +++ /dev/null @@ -1,1160 +0,0 @@ -"""A pyglet-based interactive 3D scene viewer. -""" -import copy -import os -import sys -from threading import Thread, RLock -import time - -import imageio -import numpy as np -import OpenGL -import trimesh - -try: - from Tkinter import Tk, tkFileDialog as filedialog -except Exception: - try: - from tkinter import Tk, filedialog as filedialog - except Exception: - pass - -from .constants import (TARGET_OPEN_GL_MAJOR, TARGET_OPEN_GL_MINOR, - MIN_OPEN_GL_MAJOR, MIN_OPEN_GL_MINOR, - TEXT_PADDING, DEFAULT_SCENE_SCALE, - DEFAULT_Z_FAR, DEFAULT_Z_NEAR, RenderFlags, TextAlign) -from .light import DirectionalLight -from .node import Node -from .camera import PerspectiveCamera, OrthographicCamera, IntrinsicsCamera -from .trackball import Trackball -from .renderer import Renderer -from .mesh import Mesh - -import pyglet -from pyglet import clock -pyglet.options['shadow_window'] = False - - -class Viewer(pyglet.window.Window): - """An interactive viewer for 3D scenes. - - The viewer's camera is separate from the scene's, but will take on - the parameters of the scene's main view camera and start in the same pose. - If the scene does not have a camera, a suitable default will be provided. - - Parameters - ---------- - scene : :class:`Scene` - The scene to visualize. - viewport_size : (2,) int - The width and height of the initial viewing window. - render_flags : dict - A set of flags for rendering the scene. Described in the note below. - viewer_flags : dict - A set of flags for controlling the viewer's behavior. - Described in the note below. - registered_keys : dict - A map from ASCII key characters to tuples containing: - - - A function to be called whenever the key is pressed, - whose first argument will be the viewer itself. - - (Optionally) A list of additional positional arguments - to be passed to the function. - - (Optionally) A dict of keyword arguments to be passed - to the function. - - kwargs : dict - Any keyword arguments left over will be interpreted as belonging to - either the :attr:`.Viewer.render_flags` or :attr:`.Viewer.viewer_flags` - dictionaries. Those flag sets will be updated appropriately. - - Note - ---- - The basic commands for moving about the scene are given as follows: - - - **Rotating about the scene**: Hold the left mouse button and - drag the cursor. - - **Rotating about the view axis**: Hold ``CTRL`` and the left mouse - button and drag the cursor. - - **Panning**: - - - Hold SHIFT, then hold the left mouse button and drag the cursor, or - - Hold the middle mouse button and drag the cursor. - - - **Zooming**: - - - Scroll the mouse wheel, or - - Hold the right mouse button and drag the cursor. - - Other keyboard commands are as follows: - - - ``a``: Toggles rotational animation mode. - - ``c``: Toggles backface culling. - - ``f``: Toggles fullscreen mode. - - ``h``: Toggles shadow rendering. - - ``i``: Toggles axis display mode - (no axes, world axis, mesh axes, all axes). - - ``l``: Toggles lighting mode - (scene lighting, Raymond lighting, or direct lighting). - - ``m``: Toggles face normal visualization. - - ``n``: Toggles vertex normal visualization. - - ``o``: Toggles orthographic mode. - - ``q``: Quits the viewer. - - ``r``: Starts recording a GIF, and pressing again stops recording - and opens a file dialog. - - ``s``: Opens a file dialog to save the current view as an image. - - ``w``: Toggles wireframe mode - (scene default, flip wireframes, all wireframe, or all solid). - - ``z``: Resets the camera to the initial view. - - Note - ---- - The valid keys for ``render_flags`` are as follows: - - - ``flip_wireframe``: `bool`, If `True`, all objects will have their - wireframe modes flipped from what their material indicates. - Defaults to `False`. - - ``all_wireframe``: `bool`, If `True`, all objects will be rendered - in wireframe mode. Defaults to `False`. - - ``all_solid``: `bool`, If `True`, all objects will be rendered in - solid mode. Defaults to `False`. - - ``shadows``: `bool`, If `True`, shadows will be rendered. - Defaults to `False`. - - ``vertex_normals``: `bool`, If `True`, vertex normals will be - rendered as blue lines. Defaults to `False`. - - ``face_normals``: `bool`, If `True`, face normals will be rendered as - blue lines. Defaults to `False`. - - ``cull_faces``: `bool`, If `True`, backfaces will be culled. - Defaults to `True`. - - ``point_size`` : float, The point size in pixels. Defaults to 1px. - - Note - ---- - The valid keys for ``viewer_flags`` are as follows: - - - ``rotate``: `bool`, If `True`, the scene's camera will rotate - about an axis. Defaults to `False`. - - ``rotate_rate``: `float`, The rate of rotation in radians per second. - Defaults to `PI / 3.0`. - - ``rotate_axis``: `(3,) float`, The axis in world coordinates to rotate - about. Defaults to ``[0,0,1]``. - - ``view_center``: `(3,) float`, The position to rotate the scene about. - Defaults to the scene's centroid. - - ``use_raymond_lighting``: `bool`, If `True`, an additional set of three - directional lights that move with the camera will be added to the scene. - Defaults to `False`. - - ``use_direct_lighting``: `bool`, If `True`, an additional directional - light that moves with the camera and points out of it will be added to - the scene. Defaults to `False`. - - ``lighting_intensity``: `float`, The overall intensity of the - viewer's additional lights (when they're in use). Defaults to 3.0. - - ``use_perspective_cam``: `bool`, If `True`, a perspective camera will - be used. Otherwise, an orthographic camera is used. Defaults to `True`. - - ``save_directory``: `str`, A directory to open the file dialogs in. - Defaults to `None`. - - ``window_title``: `str`, A title for the viewer's application window. - Defaults to `"Scene Viewer"`. - - ``refresh_rate``: `float`, A refresh rate for rendering, in Hertz. - Defaults to `30.0`. - - ``fullscreen``: `bool`, Whether to make viewer fullscreen. - Defaults to `False`. - - ``show_world_axis``: `bool`, Whether to show the world axis. - Defaults to `False`. - - ``show_mesh_axes``: `bool`, Whether to show the individual mesh axes. - Defaults to `False`. - - ``caption``: `list of dict`, Text caption(s) to display on the viewer. - Defaults to `None`. - - Note - ---- - Animation can be accomplished by running the viewer with ``run_in_thread`` - enabled. Then, just run a loop in your main thread, updating the scene as - needed. Before updating the scene, be sure to acquire the - :attr:`.Viewer.render_lock`, and release it when your update is done. - """ - - def __init__(self, scene, viewport_size=None, - render_flags=None, viewer_flags=None, - registered_keys=None, run_in_thread=False, - auto_start=True, - **kwargs): - - ####################################################################### - # Save attributes and flags - ####################################################################### - if viewport_size is None: - viewport_size = (640, 480) - self._scene = scene - self._viewport_size = viewport_size - self._render_lock = RLock() - self._is_active = False - self._should_close = False - self._run_in_thread = run_in_thread - self._auto_start = auto_start - - self._default_render_flags = { - 'flip_wireframe': False, - 'all_wireframe': False, - 'all_solid': False, - 'shadows': False, - 'vertex_normals': False, - 'face_normals': False, - 'cull_faces': True, - 'point_size': 1.0, - } - self._default_viewer_flags = { - 'mouse_pressed': False, - 'rotate': False, - 'rotate_rate': np.pi / 3.0, - 'rotate_axis': np.array([0.0, 0.0, 1.0]), - 'view_center': None, - 'record': False, - 'use_raymond_lighting': False, - 'use_direct_lighting': False, - 'lighting_intensity': 3.0, - 'use_perspective_cam': True, - 'save_directory': None, - 'window_title': 'Scene Viewer', - 'refresh_rate': 30.0, - 'fullscreen': False, - 'show_world_axis': False, - 'show_mesh_axes': False, - 'caption': None - } - self._render_flags = self._default_render_flags.copy() - self._viewer_flags = self._default_viewer_flags.copy() - self._viewer_flags['rotate_axis'] = ( - self._default_viewer_flags['rotate_axis'].copy() - ) - - if render_flags is not None: - self._render_flags.update(render_flags) - if viewer_flags is not None: - self._viewer_flags.update(viewer_flags) - - for key in kwargs: - if key in self.render_flags: - self._render_flags[key] = kwargs[key] - elif key in self.viewer_flags: - self._viewer_flags[key] = kwargs[key] - - # TODO MAC OS BUG FOR SHADOWS - if sys.platform == 'darwin': - self._render_flags['shadows'] = False - - self._registered_keys = {} - if registered_keys is not None: - self._registered_keys = { - ord(k.lower()): registered_keys[k] for k in registered_keys - } - - ####################################################################### - # Save internal settings - ####################################################################### - - # Set up caption stuff - self._message_text = None - self._ticks_till_fade = 2.0 / 3.0 * self.viewer_flags['refresh_rate'] - self._message_opac = 1.0 + self._ticks_till_fade - - # Set up raymond lights and direct lights - self._raymond_lights = self._create_raymond_lights() - self._direct_light = self._create_direct_light() - - # Set up axes - self._axes = {} - self._axis_mesh = Mesh.from_trimesh( - trimesh.creation.axis(origin_size=0.1, axis_radius=0.05, - axis_length=1.0), smooth=False) - if self.viewer_flags['show_world_axis']: - self._set_axes(world=self.viewer_flags['show_world_axis'], - mesh=self.viewer_flags['show_mesh_axes']) - - ####################################################################### - # Set up camera node - ####################################################################### - self._camera_node = None - self._prior_main_camera_node = None - self._default_camera_pose = None - self._default_persp_cam = None - self._default_orth_cam = None - self._trackball = None - self._saved_frames = [] - - # Extract main camera from scene and set up our mirrored copy - znear = None - zfar = None - if scene.main_camera_node is not None: - n = scene.main_camera_node - camera = copy.copy(n.camera) - if isinstance(camera, (PerspectiveCamera, IntrinsicsCamera)): - self._default_persp_cam = camera - znear = camera.znear - zfar = camera.zfar - elif isinstance(camera, OrthographicCamera): - self._default_orth_cam = camera - znear = camera.znear - zfar = camera.zfar - self._default_camera_pose = scene.get_pose(scene.main_camera_node) - self._prior_main_camera_node = n - - # Set defaults as needed - if zfar is None: - zfar = max(scene.scale * 10.0, DEFAULT_Z_FAR) - if znear is None or znear == 0: - if scene.scale == 0: - znear = DEFAULT_Z_NEAR - else: - znear = min(scene.scale / 10.0, DEFAULT_Z_NEAR) - - if self._default_persp_cam is None: - self._default_persp_cam = PerspectiveCamera( - yfov=np.pi / 3.0, znear=znear, zfar=zfar - ) - if self._default_orth_cam is None: - xmag = ymag = scene.scale - if scene.scale == 0: - xmag = ymag = 1.0 - self._default_orth_cam = OrthographicCamera( - xmag=xmag, ymag=ymag, - znear=znear, - zfar=zfar - ) - if self._default_camera_pose is None: - self._default_camera_pose = self._compute_initial_camera_pose() - - # Pick camera - if self.viewer_flags['use_perspective_cam']: - camera = self._default_persp_cam - else: - camera = self._default_orth_cam - - self._camera_node = Node( - matrix=self._default_camera_pose, camera=camera - ) - scene.add_node(self._camera_node) - scene.main_camera_node = self._camera_node - self._reset_view() - - ####################################################################### - # Initialize OpenGL context and renderer - ####################################################################### - self._renderer = Renderer( - self._viewport_size[0], self._viewport_size[1], - self.render_flags['point_size'] - ) - self._is_active = True - - if self.run_in_thread: - self._thread = Thread(target=self._init_and_start_app) - self._thread.start() - else: - if auto_start: - self._init_and_start_app() - - def start(self): - self._init_and_start_app() - - @property - def scene(self): - """:class:`.Scene` : The scene being visualized. - """ - return self._scene - - @property - def viewport_size(self): - """(2,) int : The width and height of the viewing window. - """ - return self._viewport_size - - @property - def render_lock(self): - """:class:`threading.RLock` : If acquired, prevents the viewer from - rendering until released. - - Run :meth:`.Viewer.render_lock.acquire` before making updates to - the scene in a different thread, and run - :meth:`.Viewer.render_lock.release` once you're done to let the viewer - continue. - """ - return self._render_lock - - @property - def is_active(self): - """bool : `True` if the viewer is active, or `False` if it has - been closed. - """ - return self._is_active - - @property - def run_in_thread(self): - """bool : Whether the viewer was run in a separate thread. - """ - return self._run_in_thread - - @property - def render_flags(self): - """dict : Flags for controlling the renderer's behavior. - - - ``flip_wireframe``: `bool`, If `True`, all objects will have their - wireframe modes flipped from what their material indicates. - Defaults to `False`. - - ``all_wireframe``: `bool`, If `True`, all objects will be rendered - in wireframe mode. Defaults to `False`. - - ``all_solid``: `bool`, If `True`, all objects will be rendered in - solid mode. Defaults to `False`. - - ``shadows``: `bool`, If `True`, shadows will be rendered. - Defaults to `False`. - - ``vertex_normals``: `bool`, If `True`, vertex normals will be - rendered as blue lines. Defaults to `False`. - - ``face_normals``: `bool`, If `True`, face normals will be rendered as - blue lines. Defaults to `False`. - - ``cull_faces``: `bool`, If `True`, backfaces will be culled. - Defaults to `True`. - - ``point_size`` : float, The point size in pixels. Defaults to 1px. - - """ - return self._render_flags - - @render_flags.setter - def render_flags(self, value): - self._render_flags = value - - @property - def viewer_flags(self): - """dict : Flags for controlling the viewer's behavior. - - The valid keys for ``viewer_flags`` are as follows: - - - ``rotate``: `bool`, If `True`, the scene's camera will rotate - about an axis. Defaults to `False`. - - ``rotate_rate``: `float`, The rate of rotation in radians per second. - Defaults to `PI / 3.0`. - - ``rotate_axis``: `(3,) float`, The axis in world coordinates to - rotate about. Defaults to ``[0,0,1]``. - - ``view_center``: `(3,) float`, The position to rotate the scene - about. Defaults to the scene's centroid. - - ``use_raymond_lighting``: `bool`, If `True`, an additional set of - three directional lights that move with the camera will be added to - the scene. Defaults to `False`. - - ``use_direct_lighting``: `bool`, If `True`, an additional directional - light that moves with the camera and points out of it will be - added to the scene. Defaults to `False`. - - ``lighting_intensity``: `float`, The overall intensity of the - viewer's additional lights (when they're in use). Defaults to 3.0. - - ``use_perspective_cam``: `bool`, If `True`, a perspective camera will - be used. Otherwise, an orthographic camera is used. Defaults to - `True`. - - ``save_directory``: `str`, A directory to open the file dialogs in. - Defaults to `None`. - - ``window_title``: `str`, A title for the viewer's application window. - Defaults to `"Scene Viewer"`. - - ``refresh_rate``: `float`, A refresh rate for rendering, in Hertz. - Defaults to `30.0`. - - ``fullscreen``: `bool`, Whether to make viewer fullscreen. - Defaults to `False`. - - ``show_world_axis``: `bool`, Whether to show the world axis. - Defaults to `False`. - - ``show_mesh_axes``: `bool`, Whether to show the individual mesh axes. - Defaults to `False`. - - ``caption``: `list of dict`, Text caption(s) to display on - the viewer. Defaults to `None`. - - """ - return self._viewer_flags - - @viewer_flags.setter - def viewer_flags(self, value): - self._viewer_flags = value - - @property - def registered_keys(self): - """dict : Map from ASCII key character to a handler function. - - This is a map from ASCII key characters to tuples containing: - - - A function to be called whenever the key is pressed, - whose first argument will be the viewer itself. - - (Optionally) A list of additional positional arguments - to be passed to the function. - - (Optionally) A dict of keyword arguments to be passed - to the function. - - """ - return self._registered_keys - - @registered_keys.setter - def registered_keys(self, value): - self._registered_keys = value - - def close_external(self): - """Close the viewer from another thread. - - This function will wait for the actual close, so you immediately - manipulate the scene afterwards. - """ - self._should_close = True - while self.is_active: - time.sleep(1.0 / self.viewer_flags['refresh_rate']) - - def save_gif(self, filename=None): - """Save the stored GIF frames to a file. - - To use this asynchronously, run the viewer with the ``record`` - flag and the ``run_in_thread`` flags set. - Kill the viewer after your desired time with - :meth:`.Viewer.close_external`, and then call :meth:`.Viewer.save_gif`. - - Parameters - ---------- - filename : str - The file to save the GIF to. If not specified, - a file dialog will be opened to ask the user where - to save the GIF file. - """ - if filename is None: - filename = self._get_save_filename(['gif', 'all']) - if filename is not None: - self.viewer_flags['save_directory'] = os.path.dirname(filename) - imageio.mimwrite(filename, self._saved_frames, - fps=self.viewer_flags['refresh_rate'], - palettesize=128, subrectangles=True) - self._saved_frames = [] - - def on_close(self): - """Exit the event loop when the window is closed. - """ - # Remove our camera and restore the prior one - if self._camera_node is not None: - self.scene.remove_node(self._camera_node) - if self._prior_main_camera_node is not None: - self.scene.main_camera_node = self._prior_main_camera_node - - # Delete any lighting nodes that we've attached - if self.viewer_flags['use_raymond_lighting']: - for n in self._raymond_lights: - if self.scene.has_node(n): - self.scene.remove_node(n) - if self.viewer_flags['use_direct_lighting']: - if self.scene.has_node(self._direct_light): - self.scene.remove_node(self._direct_light) - - # Delete any axis nodes that we've attached - self._remove_axes() - - # Delete renderer - if self._renderer is not None: - self._renderer.delete() - self._renderer = None - - # Force clean-up of OpenGL context data - try: - OpenGL.contextdata.cleanupContext() - self.close() - except Exception: - pass - finally: - self._is_active = False - super(Viewer, self).on_close() - pyglet.app.exit() - - def on_draw(self): - """Redraw the scene into the viewing window. - """ - if self._renderer is None: - return - - if self.run_in_thread or not self._auto_start: - self.render_lock.acquire() - - # Make OpenGL context current - self.switch_to() - - # Render the scene - self.clear() - self._render() - - if self._message_text is not None: - self._renderer.render_text( - self._message_text, - self.viewport_size[0] - TEXT_PADDING, - TEXT_PADDING, - font_pt=20, - color=np.array([0.1, 0.7, 0.2, - np.clip(self._message_opac, 0.0, 1.0)]), - align=TextAlign.BOTTOM_RIGHT - ) - - if self.viewer_flags['caption'] is not None: - for caption in self.viewer_flags['caption']: - xpos, ypos = self._location_to_x_y(caption['location']) - self._renderer.render_text( - caption['text'], - xpos, - ypos, - font_name=caption['font_name'], - font_pt=caption['font_pt'], - color=caption['color'], - scale=caption['scale'], - align=caption['location'] - ) - - if self.run_in_thread or not self._auto_start: - self.render_lock.release() - - def on_resize(self, width, height): - """Resize the camera and trackball when the window is resized. - """ - if self._renderer is None: - return - - self._viewport_size = (width, height) - self._trackball.resize(self._viewport_size) - self._renderer.viewport_width = self._viewport_size[0] - self._renderer.viewport_height = self._viewport_size[1] - self.on_draw() - - def on_mouse_press(self, x, y, buttons, modifiers): - """Record an initial mouse press. - """ - self._trackball.set_state(Trackball.STATE_ROTATE) - if (buttons == pyglet.window.mouse.LEFT): - ctrl = (modifiers & pyglet.window.key.MOD_CTRL) - shift = (modifiers & pyglet.window.key.MOD_SHIFT) - if (ctrl and shift): - self._trackball.set_state(Trackball.STATE_ZOOM) - elif ctrl: - self._trackball.set_state(Trackball.STATE_ROLL) - elif shift: - self._trackball.set_state(Trackball.STATE_PAN) - elif (buttons == pyglet.window.mouse.MIDDLE): - self._trackball.set_state(Trackball.STATE_PAN) - elif (buttons == pyglet.window.mouse.RIGHT): - self._trackball.set_state(Trackball.STATE_ZOOM) - - self._trackball.down(np.array([x, y])) - - # Stop animating while using the mouse - self.viewer_flags['mouse_pressed'] = True - - def on_mouse_drag(self, x, y, dx, dy, buttons, modifiers): - """Record a mouse drag. - """ - self._trackball.drag(np.array([x, y])) - - def on_mouse_release(self, x, y, button, modifiers): - """Record a mouse release. - """ - self.viewer_flags['mouse_pressed'] = False - - def on_mouse_scroll(self, x, y, dx, dy): - """Record a mouse scroll. - """ - if self.viewer_flags['use_perspective_cam']: - self._trackball.scroll(dy) - else: - spfc = 0.95 - spbc = 1.0 / 0.95 - sf = 1.0 - if dy > 0: - sf = spfc * dy - elif dy < 0: - sf = - spbc * dy - - c = self._camera_node.camera - xmag = max(c.xmag * sf, 1e-8) - ymag = max(c.ymag * sf, 1e-8 * c.ymag / c.xmag) - c.xmag = xmag - c.ymag = ymag - - def on_key_press(self, symbol, modifiers): - """Record a key press. - """ - # First, check for registered key callbacks - if symbol in self.registered_keys: - tup = self.registered_keys[symbol] - callback = None - args = [] - kwargs = {} - if not isinstance(tup, (list, tuple, np.ndarray)): - callback = tup - else: - callback = tup[0] - if len(tup) == 2: - args = tup[1] - if len(tup) == 3: - kwargs = tup[2] - callback(self, *args, **kwargs) - return - - # Otherwise, use default key functions - - # A causes the frame to rotate - self._message_text = None - if symbol == pyglet.window.key.A: - self.viewer_flags['rotate'] = not self.viewer_flags['rotate'] - if self.viewer_flags['rotate']: - self._message_text = 'Rotation On' - else: - self._message_text = 'Rotation Off' - - # C toggles backface culling - elif symbol == pyglet.window.key.C: - self.render_flags['cull_faces'] = ( - not self.render_flags['cull_faces'] - ) - if self.render_flags['cull_faces']: - self._message_text = 'Cull Faces On' - else: - self._message_text = 'Cull Faces Off' - - # F toggles face normals - elif symbol == pyglet.window.key.F: - self.viewer_flags['fullscreen'] = ( - not self.viewer_flags['fullscreen'] - ) - self.set_fullscreen(self.viewer_flags['fullscreen']) - self.activate() - if self.viewer_flags['fullscreen']: - self._message_text = 'Fullscreen On' - else: - self._message_text = 'Fullscreen Off' - - # S toggles shadows - elif symbol == pyglet.window.key.H and sys.platform != 'darwin': - self.render_flags['shadows'] = not self.render_flags['shadows'] - if self.render_flags['shadows']: - self._message_text = 'Shadows On' - else: - self._message_text = 'Shadows Off' - - elif symbol == pyglet.window.key.I: - if (self.viewer_flags['show_world_axis'] and not - self.viewer_flags['show_mesh_axes']): - self.viewer_flags['show_world_axis'] = False - self.viewer_flags['show_mesh_axes'] = True - self._set_axes(False, True) - self._message_text = 'Mesh Axes On' - elif (not self.viewer_flags['show_world_axis'] and - self.viewer_flags['show_mesh_axes']): - self.viewer_flags['show_world_axis'] = True - self.viewer_flags['show_mesh_axes'] = True - self._set_axes(True, True) - self._message_text = 'All Axes On' - elif (self.viewer_flags['show_world_axis'] and - self.viewer_flags['show_mesh_axes']): - self.viewer_flags['show_world_axis'] = False - self.viewer_flags['show_mesh_axes'] = False - self._set_axes(False, False) - self._message_text = 'All Axes Off' - else: - self.viewer_flags['show_world_axis'] = True - self.viewer_flags['show_mesh_axes'] = False - self._set_axes(True, False) - self._message_text = 'World Axis On' - - # L toggles the lighting mode - elif symbol == pyglet.window.key.L: - if self.viewer_flags['use_raymond_lighting']: - self.viewer_flags['use_raymond_lighting'] = False - self.viewer_flags['use_direct_lighting'] = True - self._message_text = 'Direct Lighting' - elif self.viewer_flags['use_direct_lighting']: - self.viewer_flags['use_raymond_lighting'] = False - self.viewer_flags['use_direct_lighting'] = False - self._message_text = 'Default Lighting' - else: - self.viewer_flags['use_raymond_lighting'] = True - self.viewer_flags['use_direct_lighting'] = False - self._message_text = 'Raymond Lighting' - - # M toggles face normals - elif symbol == pyglet.window.key.M: - self.render_flags['face_normals'] = ( - not self.render_flags['face_normals'] - ) - if self.render_flags['face_normals']: - self._message_text = 'Face Normals On' - else: - self._message_text = 'Face Normals Off' - - # N toggles vertex normals - elif symbol == pyglet.window.key.N: - self.render_flags['vertex_normals'] = ( - not self.render_flags['vertex_normals'] - ) - if self.render_flags['vertex_normals']: - self._message_text = 'Vert Normals On' - else: - self._message_text = 'Vert Normals Off' - - # O toggles orthographic camera mode - elif symbol == pyglet.window.key.O: - self.viewer_flags['use_perspective_cam'] = ( - not self.viewer_flags['use_perspective_cam'] - ) - if self.viewer_flags['use_perspective_cam']: - camera = self._default_persp_cam - self._message_text = 'Perspective View' - else: - camera = self._default_orth_cam - self._message_text = 'Orthographic View' - - cam_pose = self._camera_node.matrix.copy() - cam_node = Node(matrix=cam_pose, camera=camera) - self.scene.remove_node(self._camera_node) - self.scene.add_node(cam_node) - self.scene.main_camera_node = cam_node - self._camera_node = cam_node - - # Q quits the viewer - elif symbol == pyglet.window.key.Q: - self.on_close() - - # R starts recording frames - elif symbol == pyglet.window.key.R: - if self.viewer_flags['record']: - self.save_gif() - self.set_caption(self.viewer_flags['window_title']) - else: - self.set_caption( - '{} (RECORDING)'.format(self.viewer_flags['window_title']) - ) - self.viewer_flags['record'] = not self.viewer_flags['record'] - - # S saves the current frame as an image - elif symbol == pyglet.window.key.S: - self._save_image() - - # W toggles through wireframe modes - elif symbol == pyglet.window.key.W: - if self.render_flags['flip_wireframe']: - self.render_flags['flip_wireframe'] = False - self.render_flags['all_wireframe'] = True - self.render_flags['all_solid'] = False - self._message_text = 'All Wireframe' - elif self.render_flags['all_wireframe']: - self.render_flags['flip_wireframe'] = False - self.render_flags['all_wireframe'] = False - self.render_flags['all_solid'] = True - self._message_text = 'All Solid' - elif self.render_flags['all_solid']: - self.render_flags['flip_wireframe'] = False - self.render_flags['all_wireframe'] = False - self.render_flags['all_solid'] = False - self._message_text = 'Default Wireframe' - else: - self.render_flags['flip_wireframe'] = True - self.render_flags['all_wireframe'] = False - self.render_flags['all_solid'] = False - self._message_text = 'Flip Wireframe' - - # Z resets the camera viewpoint - elif symbol == pyglet.window.key.Z: - self._reset_view() - - if self._message_text is not None: - self._message_opac = 1.0 + self._ticks_till_fade - - @staticmethod - def _time_event(dt, self): - """The timer callback. - """ - # Don't run old dead events after we've already closed - if not self._is_active: - return - - if self.viewer_flags['record']: - self._record() - if (self.viewer_flags['rotate'] and not - self.viewer_flags['mouse_pressed']): - self._rotate() - - # Manage message opacity - if self._message_text is not None: - if self._message_opac > 1.0: - self._message_opac -= 1.0 - else: - self._message_opac *= 0.90 - if self._message_opac < 0.05: - self._message_opac = 1.0 + self._ticks_till_fade - self._message_text = None - - if self._should_close: - self.on_close() - else: - self.on_draw() - - def _reset_view(self): - """Reset the view to a good initial state. - - The view is initially along the positive x-axis at a - sufficient distance from the scene. - """ - scale = self.scene.scale - if scale == 0.0: - scale = DEFAULT_SCENE_SCALE - centroid = self.scene.centroid - - if self.viewer_flags['view_center'] is not None: - centroid = self.viewer_flags['view_center'] - - self._camera_node.matrix = self._default_camera_pose - self._trackball = Trackball( - self._default_camera_pose, self.viewport_size, scale, centroid - ) - - def _get_save_filename(self, file_exts): - file_types = { - 'png': ('png files', '*.png'), - 'jpg': ('jpeg files', '*.jpg'), - 'gif': ('gif files', '*.gif'), - 'all': ('all files', '*'), - } - filetypes = [file_types[x] for x in file_exts] - try: - root = Tk() - save_dir = self.viewer_flags['save_directory'] - if save_dir is None: - save_dir = os.getcwd() - filename = filedialog.asksaveasfilename( - initialdir=save_dir, title='Select file save location', - filetypes=filetypes - ) - except Exception: - return None - - root.destroy() - if filename == (): - return None - return filename - - def _save_image(self): - filename = self._get_save_filename(['png', 'jpg', 'gif', 'all']) - if filename is not None: - self.viewer_flags['save_directory'] = os.path.dirname(filename) - imageio.imwrite(filename, self._renderer.read_color_buf()) - - def _record(self): - """Save another frame for the GIF. - """ - data = self._renderer.read_color_buf() - if not np.all(data == 0.0): - self._saved_frames.append(data) - - def _rotate(self): - """Animate the scene by rotating the camera. - """ - az = (self.viewer_flags['rotate_rate'] / - self.viewer_flags['refresh_rate']) - self._trackball.rotate(az, self.viewer_flags['rotate_axis']) - - def _render(self): - """Render the scene into the framebuffer and flip. - """ - scene = self.scene - self._camera_node.matrix = self._trackball.pose.copy() - - # Set lighting - vli = self.viewer_flags['lighting_intensity'] - if self.viewer_flags['use_raymond_lighting']: - for n in self._raymond_lights: - n.light.intensity = vli / 3.0 - if not self.scene.has_node(n): - scene.add_node(n, parent_node=self._camera_node) - else: - self._direct_light.light.intensity = vli - for n in self._raymond_lights: - if self.scene.has_node(n): - self.scene.remove_node(n) - - if self.viewer_flags['use_direct_lighting']: - if not self.scene.has_node(self._direct_light): - scene.add_node( - self._direct_light, parent_node=self._camera_node - ) - elif self.scene.has_node(self._direct_light): - self.scene.remove_node(self._direct_light) - - flags = RenderFlags.NONE - if self.render_flags['flip_wireframe']: - flags |= RenderFlags.FLIP_WIREFRAME - elif self.render_flags['all_wireframe']: - flags |= RenderFlags.ALL_WIREFRAME - elif self.render_flags['all_solid']: - flags |= RenderFlags.ALL_SOLID - - if self.render_flags['shadows']: - flags |= RenderFlags.SHADOWS_DIRECTIONAL | RenderFlags.SHADOWS_SPOT - if self.render_flags['vertex_normals']: - flags |= RenderFlags.VERTEX_NORMALS - if self.render_flags['face_normals']: - flags |= RenderFlags.FACE_NORMALS - if not self.render_flags['cull_faces']: - flags |= RenderFlags.SKIP_CULL_FACES - - self._renderer.render(self.scene, flags) - - def _init_and_start_app(self): - # Try multiple configs starting with target OpenGL version - # and multisampling and removing these options if exception - # Note: multisampling not available on all hardware - from pyglet.gl import Config - confs = [Config(sample_buffers=1, samples=4, - depth_size=24, - double_buffer=True, - major_version=TARGET_OPEN_GL_MAJOR, - minor_version=TARGET_OPEN_GL_MINOR), - Config(depth_size=24, - double_buffer=True, - major_version=TARGET_OPEN_GL_MAJOR, - minor_version=TARGET_OPEN_GL_MINOR), - Config(sample_buffers=1, samples=4, - depth_size=24, - double_buffer=True, - major_version=MIN_OPEN_GL_MAJOR, - minor_version=MIN_OPEN_GL_MINOR), - Config(depth_size=24, - double_buffer=True, - major_version=MIN_OPEN_GL_MAJOR, - minor_version=MIN_OPEN_GL_MINOR)] - for conf in confs: - try: - super(Viewer, self).__init__(config=conf, resizable=True, - width=self._viewport_size[0], - height=self._viewport_size[1]) - break - except pyglet.window.NoSuchConfigException: - pass - - if not self.context: - raise ValueError('Unable to initialize an OpenGL 3+ context') - clock.schedule_interval( - Viewer._time_event, 1.0 / self.viewer_flags['refresh_rate'], self - ) - self.switch_to() - self.set_caption(self.viewer_flags['window_title']) - pyglet.app.run() - - def _compute_initial_camera_pose(self): - centroid = self.scene.centroid - if self.viewer_flags['view_center'] is not None: - centroid = self.viewer_flags['view_center'] - scale = self.scene.scale - if scale == 0.0: - scale = DEFAULT_SCENE_SCALE - - s2 = 1.0 / np.sqrt(2.0) - cp = np.eye(4) - cp[:3,:3] = np.array([ - [0.0, -s2, s2], - [1.0, 0.0, 0.0], - [0.0, s2, s2] - ]) - hfov = np.pi / 6.0 - dist = scale / (2.0 * np.tan(hfov)) - cp[:3,3] = dist * np.array([1.0, 0.0, 1.0]) + centroid - - return cp - - def _create_raymond_lights(self): - thetas = np.pi * np.array([1.0 / 6.0, 1.0 / 6.0, 1.0 / 6.0]) - phis = np.pi * np.array([0.0, 2.0 / 3.0, 4.0 / 3.0]) - - nodes = [] - - for phi, theta in zip(phis, thetas): - xp = np.sin(theta) * np.cos(phi) - yp = np.sin(theta) * np.sin(phi) - zp = np.cos(theta) - - z = np.array([xp, yp, zp]) - z = z / np.linalg.norm(z) - x = np.array([-z[1], z[0], 0.0]) - if np.linalg.norm(x) == 0: - x = np.array([1.0, 0.0, 0.0]) - x = x / np.linalg.norm(x) - y = np.cross(z, x) - - matrix = np.eye(4) - matrix[:3,:3] = np.c_[x,y,z] - nodes.append(Node( - light=DirectionalLight(color=np.ones(3), intensity=1.0), - matrix=matrix - )) - - return nodes - - def _create_direct_light(self): - light = DirectionalLight(color=np.ones(3), intensity=1.0) - n = Node(light=light, matrix=np.eye(4)) - return n - - def _set_axes(self, world, mesh): - scale = self.scene.scale - if world: - if 'scene' not in self._axes: - n = Node(mesh=self._axis_mesh, scale=np.ones(3) * scale * 0.3) - self.scene.add_node(n) - self._axes['scene'] = n - else: - if 'scene' in self._axes: - self.scene.remove_node(self._axes['scene']) - self._axes.pop('scene') - - if mesh: - old_nodes = [] - existing_axes = set([self._axes[k] for k in self._axes]) - for node in self.scene.mesh_nodes: - if node not in existing_axes: - old_nodes.append(node) - - for node in old_nodes: - if node in self._axes: - continue - n = Node( - mesh=self._axis_mesh, - scale=np.ones(3) * node.mesh.scale * 0.5 - ) - self.scene.add_node(n, parent_node=node) - self._axes[node] = n - else: - to_remove = set() - for main_node in self._axes: - if main_node in self.scene.mesh_nodes: - self.scene.remove_node(self._axes[main_node]) - to_remove.add(main_node) - for main_node in to_remove: - self._axes.pop(main_node) - - def _remove_axes(self): - for main_node in self._axes: - axis_node = self._axes[main_node] - self.scene.remove_node(axis_node) - self._axes = {} - - def _location_to_x_y(self, location): - if location == TextAlign.CENTER: - return (self.viewport_size[0] / 2.0, self.viewport_size[1] / 2.0) - elif location == TextAlign.CENTER_LEFT: - return (TEXT_PADDING, self.viewport_size[1] / 2.0) - elif location == TextAlign.CENTER_RIGHT: - return (self.viewport_size[0] - TEXT_PADDING, - self.viewport_size[1] / 2.0) - elif location == TextAlign.BOTTOM_LEFT: - return (TEXT_PADDING, TEXT_PADDING) - elif location == TextAlign.BOTTOM_RIGHT: - return (self.viewport_size[0] - TEXT_PADDING, TEXT_PADDING) - elif location == TextAlign.BOTTOM_CENTER: - return (self.viewport_size[0] / 2.0, TEXT_PADDING) - elif location == TextAlign.TOP_LEFT: - return (TEXT_PADDING, self.viewport_size[1] - TEXT_PADDING) - elif location == TextAlign.TOP_RIGHT: - return (self.viewport_size[0] - TEXT_PADDING, - self.viewport_size[1] - TEXT_PADDING) - elif location == TextAlign.TOP_CENTER: - return (self.viewport_size[0] / 2.0, - self.viewport_size[1] - TEXT_PADDING) - - -__all__ = ['Viewer'] diff --git a/pyrender/requirements.txt b/pyrender/requirements.txt deleted file mode 100644 index 8c40b74256f0dc6697754bb8609f69a39d51beba..0000000000000000000000000000000000000000 --- a/pyrender/requirements.txt +++ /dev/null @@ -1,14 +0,0 @@ -freetype-py -imageio -networkx -numpy -Pillow -pyglet==1.4.0a1 -PyOpenGL -PyOpenGL_accelerate -six -trimesh -sphinx -sphinx_rtd_theme -sphinx-automodapi - diff --git a/pyrender/setup.py b/pyrender/setup.py deleted file mode 100644 index c3b5ba0da2b0f17b759e5556597981096a80bda8..0000000000000000000000000000000000000000 --- a/pyrender/setup.py +++ /dev/null @@ -1,76 +0,0 @@ -""" -Setup of pyrender Python codebase. - -Author: Matthew Matl -""" -import sys -from setuptools import setup - -# load __version__ -exec(open('pyrender/version.py').read()) - -def get_imageio_dep(): - if sys.version[0] == "2": - return 'imageio<=2.6.1' - return 'imageio' - -requirements = [ - 'freetype-py', # For font loading - get_imageio_dep(), # For Image I/O - 'networkx', # For the scene graph - 'numpy', # Numpy - 'Pillow', # For Trimesh texture conversions - 'pyglet>=1.4.10', # For the pyglet viewer - 'PyOpenGL~=3.1.0', # For OpenGL -# 'PyOpenGL_accelerate~=3.1.0', # For OpenGL - 'scipy', # Because of trimesh missing dep - 'six', # For Python 2/3 interop - 'trimesh', # For meshes -] - -dev_requirements = [ - 'flake8', # Code formatting checker - 'pre-commit', # Pre-commit hooks - 'pytest', # Code testing - 'pytest-cov', # Coverage testing - 'tox', # Automatic virtualenv testing -] - -docs_requirements = [ - 'sphinx', # General doc library - 'sphinx_rtd_theme', # RTD theme for sphinx - 'sphinx-automodapi' # For generating nice tables -] - - -setup( - name = 'pyrender', - version=__version__, - description='Easy-to-use Python renderer for 3D visualization', - long_description='A simple implementation of Physically-Based Rendering ' - '(PBR) in Python. Compliant with the glTF 2.0 standard.', - author='Matthew Matl', - author_email='matthewcmatl@gmail.com', - license='MIT License', - url = 'https://github.com/mmatl/pyrender', - classifiers = [ - 'Development Status :: 4 - Beta', - 'License :: OSI Approved :: MIT License', - 'Operating System :: POSIX :: Linux', - 'Operating System :: MacOS :: MacOS X', - 'Programming Language :: Python :: 2.7', - 'Programming Language :: Python :: 3.5', - 'Programming Language :: Python :: 3.6', - 'Natural Language :: English', - 'Topic :: Scientific/Engineering' - ], - keywords = 'rendering graphics opengl 3d visualization pbr gltf', - packages = ['pyrender', 'pyrender.platforms'], - setup_requires = requirements, - install_requires = requirements, - extras_require={ - 'dev': dev_requirements, - 'docs': docs_requirements, - }, - include_package_data=True -) diff --git a/pyrender/tests/__init__.py b/pyrender/tests/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/pyrender/tests/conftest.py b/pyrender/tests/conftest.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/pyrender/tests/pytest.ini b/pyrender/tests/pytest.ini deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/pyrender/tests/unit/__init__.py b/pyrender/tests/unit/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/pyrender/tests/unit/test_cameras.py b/pyrender/tests/unit/test_cameras.py deleted file mode 100644 index 7544ad8f8e3ee55236fd2e32dbc12065153cbe5b..0000000000000000000000000000000000000000 --- a/pyrender/tests/unit/test_cameras.py +++ /dev/null @@ -1,164 +0,0 @@ -import numpy as np -import pytest - -from pyrender import PerspectiveCamera, OrthographicCamera - - -def test_perspective_camera(): - - # Set up constants - znear = 0.05 - zfar = 100 - yfov = np.pi / 3.0 - width = 1000.0 - height = 500.0 - aspectRatio = 640.0 / 480.0 - - # Test basics - with pytest.raises(TypeError): - p = PerspectiveCamera() - - p = PerspectiveCamera(yfov=yfov) - assert p.yfov == yfov - assert p.znear == 0.05 - assert p.zfar is None - assert p.aspectRatio is None - p.name = 'asdf' - p.name = None - - with pytest.raises(ValueError): - p.yfov = 0.0 - - with pytest.raises(ValueError): - p.yfov = -1.0 - - with pytest.raises(ValueError): - p.znear = -1.0 - - p.znear = 0.0 - p.znear = 0.05 - p.zfar = 100.0 - assert p.zfar == 100.0 - - with pytest.raises(ValueError): - p.zfar = 0.03 - - with pytest.raises(ValueError): - p.zfar = 0.05 - - p.aspectRatio = 10.0 - assert p.aspectRatio == 10.0 - - with pytest.raises(ValueError): - p.aspectRatio = 0.0 - - with pytest.raises(ValueError): - p.aspectRatio = -1.0 - - # Test matrix getting/setting - - # NF - p.znear = 0.05 - p.zfar = 100 - p.aspectRatio = None - - with pytest.raises(ValueError): - p.get_projection_matrix() - - assert np.allclose( - p.get_projection_matrix(width, height), - np.array([ - [1.0 / (width / height * np.tan(yfov / 2.0)), 0.0, 0.0, 0.0], - [0.0, 1.0 / np.tan(yfov / 2.0), 0.0, 0.0], - [0.0, 0.0, (zfar + znear) / (znear - zfar), - (2 * zfar * znear) / (znear - zfar)], - [0.0, 0.0, -1.0, 0.0] - ]) - ) - - # NFA - p.aspectRatio = aspectRatio - assert np.allclose( - p.get_projection_matrix(width, height), - np.array([ - [1.0 / (aspectRatio * np.tan(yfov / 2.0)), 0.0, 0.0, 0.0], - [0.0, 1.0 / np.tan(yfov / 2.0), 0.0, 0.0], - [0.0, 0.0, (zfar + znear) / (znear - zfar), - (2 * zfar * znear) / (znear - zfar)], - [0.0, 0.0, -1.0, 0.0] - ]) - ) - assert np.allclose( - p.get_projection_matrix(), p.get_projection_matrix(width, height) - ) - - # N - p.zfar = None - p.aspectRatio = None - assert np.allclose( - p.get_projection_matrix(width, height), - np.array([ - [1.0 / (width / height * np.tan(yfov / 2.0)), 0.0, 0.0, 0.0], - [0.0, 1.0 / np.tan(yfov / 2.0), 0.0, 0.0], - [0.0, 0.0, -1.0, -2.0 * znear], - [0.0, 0.0, -1.0, 0.0] - ]) - ) - - -def test_orthographic_camera(): - xm = 1.0 - ym = 2.0 - n = 0.05 - f = 100.0 - - with pytest.raises(TypeError): - c = OrthographicCamera() - - c = OrthographicCamera(xmag=xm, ymag=ym) - - assert c.xmag == xm - assert c.ymag == ym - assert c.znear == 0.05 - assert c.zfar == 100.0 - assert c.name is None - - with pytest.raises(TypeError): - c.ymag = None - - with pytest.raises(ValueError): - c.ymag = 0.0 - - with pytest.raises(ValueError): - c.ymag = -1.0 - - with pytest.raises(TypeError): - c.xmag = None - - with pytest.raises(ValueError): - c.xmag = 0.0 - - with pytest.raises(ValueError): - c.xmag = -1.0 - - with pytest.raises(TypeError): - c.znear = None - - with pytest.raises(ValueError): - c.znear = 0.0 - - with pytest.raises(ValueError): - c.znear = -1.0 - - with pytest.raises(ValueError): - c.zfar = 0.01 - - assert np.allclose( - c.get_projection_matrix(), - np.array([ - [1.0 / xm, 0, 0, 0], - [0, 1.0 / ym, 0, 0], - [0, 0, 2.0 / (n - f), (f + n) / (n - f)], - [0, 0, 0, 1.0] - ]) - ) diff --git a/pyrender/tests/unit/test_egl.py b/pyrender/tests/unit/test_egl.py deleted file mode 100644 index e2f4bef39e33c2794e6837b5a1bb127d8d4dba06..0000000000000000000000000000000000000000 --- a/pyrender/tests/unit/test_egl.py +++ /dev/null @@ -1,16 +0,0 @@ -# from pyrender.platforms import egl - - -def tmp_test_default_device(): - egl.get_default_device() - - -def tmp_test_query_device(): - devices = egl.query_devices() - assert len(devices) > 0 - - -def tmp_test_init_context(): - device = egl.query_devices()[0] - platform = egl.EGLPlatform(128, 128, device=device) - platform.init_context() diff --git a/pyrender/tests/unit/test_lights.py b/pyrender/tests/unit/test_lights.py deleted file mode 100644 index ffde856b21e8cce9532f0308fcd1c7eb2d1eba90..0000000000000000000000000000000000000000 --- a/pyrender/tests/unit/test_lights.py +++ /dev/null @@ -1,104 +0,0 @@ -import numpy as np -import pytest - -from pyrender import (DirectionalLight, SpotLight, PointLight, Texture, - PerspectiveCamera, OrthographicCamera) -from pyrender.constants import SHADOW_TEX_SZ - - -def test_directional_light(): - - d = DirectionalLight() - assert d.name is None - assert np.all(d.color == 1.0) - assert d.intensity == 1.0 - - d.name = 'direc' - with pytest.raises(ValueError): - d.color = None - with pytest.raises(TypeError): - d.intensity = None - - d = DirectionalLight(color=[0.0, 0.0, 0.0]) - assert np.all(d.color == 0.0) - - d._generate_shadow_texture() - st = d.shadow_texture - assert isinstance(st, Texture) - assert st.width == st.height == SHADOW_TEX_SZ - - sc = d._get_shadow_camera(scene_scale=5.0) - assert isinstance(sc, OrthographicCamera) - assert sc.xmag == sc.ymag == 5.0 - assert sc.znear == 0.01 * 5.0 - assert sc.zfar == 10 * 5.0 - - -def test_spot_light(): - - s = SpotLight() - assert s.name is None - assert np.all(s.color == 1.0) - assert s.intensity == 1.0 - assert s.innerConeAngle == 0.0 - assert s.outerConeAngle == np.pi / 4.0 - assert s.range is None - - with pytest.raises(ValueError): - s.range = -1.0 - - with pytest.raises(ValueError): - s.range = 0.0 - - with pytest.raises(ValueError): - s.innerConeAngle = -1.0 - - with pytest.raises(ValueError): - s.innerConeAngle = np.pi / 3.0 - - with pytest.raises(ValueError): - s.outerConeAngle = -1.0 - - with pytest.raises(ValueError): - s.outerConeAngle = np.pi - - s.range = 5.0 - s.outerConeAngle = np.pi / 2 - 0.05 - s.innerConeAngle = np.pi / 3 - s.innerConeAngle = 0.0 - s.outerConeAngle = np.pi / 4.0 - - s._generate_shadow_texture() - st = s.shadow_texture - assert isinstance(st, Texture) - assert st.width == st.height == SHADOW_TEX_SZ - - sc = s._get_shadow_camera(scene_scale=5.0) - assert isinstance(sc, PerspectiveCamera) - assert sc.znear == 0.01 * 5.0 - assert sc.zfar == 10 * 5.0 - assert sc.aspectRatio == 1.0 - assert np.allclose(sc.yfov, np.pi / 16.0 * 9.0) # Plus pi / 16 - - -def test_point_light(): - - s = PointLight() - assert s.name is None - assert np.all(s.color == 1.0) - assert s.intensity == 1.0 - assert s.range is None - - with pytest.raises(ValueError): - s.range = -1.0 - - with pytest.raises(ValueError): - s.range = 0.0 - - s.range = 5.0 - - with pytest.raises(NotImplementedError): - s._generate_shadow_texture() - - with pytest.raises(NotImplementedError): - s._get_shadow_camera(scene_scale=5.0) diff --git a/pyrender/tests/unit/test_meshes.py b/pyrender/tests/unit/test_meshes.py deleted file mode 100644 index 7070b01171c97069fa013c6eba8eee217017f08e..0000000000000000000000000000000000000000 --- a/pyrender/tests/unit/test_meshes.py +++ /dev/null @@ -1,133 +0,0 @@ -import numpy as np -import pytest -import trimesh - -from pyrender import (Mesh, Primitive) - - -def test_meshes(): - - with pytest.raises(TypeError): - x = Mesh() - with pytest.raises(TypeError): - x = Primitive() - with pytest.raises(ValueError): - x = Primitive([], mode=10) - - # Basics - x = Mesh([]) - assert x.name is None - assert x.is_visible - assert x.weights is None - - x.name = 'str' - - # From Trimesh - x = Mesh.from_trimesh(trimesh.creation.box()) - assert isinstance(x, Mesh) - assert len(x.primitives) == 1 - assert x.is_visible - assert np.allclose(x.bounds, np.array([ - [-0.5, -0.5, -0.5], - [0.5, 0.5, 0.5] - ])) - assert np.allclose(x.centroid, np.zeros(3)) - assert np.allclose(x.extents, np.ones(3)) - assert np.allclose(x.scale, np.sqrt(3)) - assert not x.is_transparent - - # Test some primitive functions - x = x.primitives[0] - with pytest.raises(ValueError): - x.normals = np.zeros(10) - with pytest.raises(ValueError): - x.tangents = np.zeros(10) - with pytest.raises(ValueError): - x.texcoord_0 = np.zeros(10) - with pytest.raises(ValueError): - x.texcoord_1 = np.zeros(10) - with pytest.raises(TypeError): - x.material = np.zeros(10) - assert x.targets is None - assert np.allclose(x.bounds, np.array([ - [-0.5, -0.5, -0.5], - [0.5, 0.5, 0.5] - ])) - assert np.allclose(x.centroid, np.zeros(3)) - assert np.allclose(x.extents, np.ones(3)) - assert np.allclose(x.scale, np.sqrt(3)) - x.material.baseColorFactor = np.array([0.0, 0.0, 0.0, 0.0]) - assert x.is_transparent - - # From two trimeshes - x = Mesh.from_trimesh([trimesh.creation.box(), - trimesh.creation.cylinder(radius=0.1, height=2.0)], - smooth=False) - assert isinstance(x, Mesh) - assert len(x.primitives) == 2 - assert x.is_visible - assert np.allclose(x.bounds, np.array([ - [-0.5, -0.5, -1.0], - [0.5, 0.5, 1.0] - ])) - assert np.allclose(x.centroid, np.zeros(3)) - assert np.allclose(x.extents, [1.0, 1.0, 2.0]) - assert np.allclose(x.scale, np.sqrt(6)) - assert not x.is_transparent - - # From bad data - with pytest.raises(TypeError): - x = Mesh.from_trimesh(None) - - # With instancing - poses = np.tile(np.eye(4), (5,1,1)) - poses[:,0,3] = np.array([0,1,2,3,4]) - x = Mesh.from_trimesh(trimesh.creation.box(), poses=poses) - assert np.allclose(x.bounds, np.array([ - [-0.5, -0.5, -0.5], - [4.5, 0.5, 0.5] - ])) - poses = np.eye(4) - x = Mesh.from_trimesh(trimesh.creation.box(), poses=poses) - poses = np.eye(3) - with pytest.raises(ValueError): - x = Mesh.from_trimesh(trimesh.creation.box(), poses=poses) - - # From textured meshes - fm = trimesh.load('tests/data/fuze.obj') - x = Mesh.from_trimesh(fm) - assert isinstance(x, Mesh) - assert len(x.primitives) == 1 - assert x.is_visible - assert not x.is_transparent - assert x.primitives[0].material.baseColorTexture is not None - - x = Mesh.from_trimesh(fm, smooth=False) - fm.visual = fm.visual.to_color() - fm.visual.face_colors = np.array([1.0, 0.0, 0.0, 1.0]) - x = Mesh.from_trimesh(fm, smooth=False) - with pytest.raises(ValueError): - x = Mesh.from_trimesh(fm, smooth=True) - - fm.visual.vertex_colors = np.array([1.0, 0.0, 0.0, 0.5]) - x = Mesh.from_trimesh(fm, smooth=False) - x = Mesh.from_trimesh(fm, smooth=True) - assert x.primitives[0].color_0 is not None - assert x.is_transparent - - bm = trimesh.load('tests/data/WaterBottle.glb').dump()[0] - x = Mesh.from_trimesh(bm) - assert x.primitives[0].material.baseColorTexture is not None - assert x.primitives[0].material.emissiveTexture is not None - assert x.primitives[0].material.metallicRoughnessTexture is not None - - # From point cloud - x = Mesh.from_points(fm.vertices) - -# def test_duck(): -# bm = trimesh.load('tests/data/Duck.glb').dump()[0] -# x = Mesh.from_trimesh(bm) -# assert x.primitives[0].material.baseColorTexture is not None -# pixel = x.primitives[0].material.baseColorTexture.source[100, 100] -# yellowish = np.array([1.0, 0.7411765, 0.0, 1.0]) -# assert np.allclose(pixel, yellowish) diff --git a/pyrender/tests/unit/test_nodes.py b/pyrender/tests/unit/test_nodes.py deleted file mode 100644 index 9857c8221b7f6fb8530699bdf5593f8f0b74e152..0000000000000000000000000000000000000000 --- a/pyrender/tests/unit/test_nodes.py +++ /dev/null @@ -1,124 +0,0 @@ -import numpy as np -import pytest -from trimesh import transformations - -from pyrender import (DirectionalLight, PerspectiveCamera, Mesh, Node) - - -def test_nodes(): - - x = Node() - assert x.name is None - assert x.camera is None - assert x.children == [] - assert x.skin is None - assert np.allclose(x.matrix, np.eye(4)) - assert x.mesh is None - assert np.allclose(x.rotation, [0,0,0,1]) - assert np.allclose(x.scale, np.ones(3)) - assert np.allclose(x.translation, np.zeros(3)) - assert x.weights is None - assert x.light is None - - x.name = 'node' - - # Test node light/camera/mesh tests - c = PerspectiveCamera(yfov=2.0) - m = Mesh([]) - d = DirectionalLight() - x.camera = c - assert x.camera == c - with pytest.raises(TypeError): - x.camera = m - x.camera = d - x.camera = None - x.mesh = m - assert x.mesh == m - with pytest.raises(TypeError): - x.mesh = c - x.mesh = d - x.light = d - assert x.light == d - with pytest.raises(TypeError): - x.light = m - x.light = c - - # Test transformations getters/setters/etc... - # Set up test values - x = np.array([1.0, 0.0, 0.0]) - y = np.array([0.0, 1.0, 0.0]) - t = np.array([1.0, 2.0, 3.0]) - s = np.array([0.5, 2.0, 1.0]) - - Mx = transformations.rotation_matrix(np.pi / 2.0, x) - qx = np.roll(transformations.quaternion_about_axis(np.pi / 2.0, x), -1) - Mxt = Mx.copy() - Mxt[:3,3] = t - S = np.eye(4) - S[:3,:3] = np.diag(s) - Mxts = Mxt.dot(S) - - My = transformations.rotation_matrix(np.pi / 2.0, y) - qy = np.roll(transformations.quaternion_about_axis(np.pi / 2.0, y), -1) - Myt = My.copy() - Myt[:3,3] = t - - x = Node(matrix=Mx) - assert np.allclose(x.matrix, Mx) - assert np.allclose(x.rotation, qx) - assert np.allclose(x.translation, np.zeros(3)) - assert np.allclose(x.scale, np.ones(3)) - - x.matrix = My - assert np.allclose(x.matrix, My) - assert np.allclose(x.rotation, qy) - assert np.allclose(x.translation, np.zeros(3)) - assert np.allclose(x.scale, np.ones(3)) - x.translation = t - assert np.allclose(x.matrix, Myt) - assert np.allclose(x.rotation, qy) - x.rotation = qx - assert np.allclose(x.matrix, Mxt) - x.scale = s - assert np.allclose(x.matrix, Mxts) - - x = Node(matrix=Mxt) - assert np.allclose(x.matrix, Mxt) - assert np.allclose(x.rotation, qx) - assert np.allclose(x.translation, t) - assert np.allclose(x.scale, np.ones(3)) - - x = Node(matrix=Mxts) - assert np.allclose(x.matrix, Mxts) - assert np.allclose(x.rotation, qx) - assert np.allclose(x.translation, t) - assert np.allclose(x.scale, s) - - # Individual element getters - x.scale[0] = 0 - assert np.allclose(x.scale[0], 0) - - x.translation[0] = 0 - assert np.allclose(x.translation[0], 0) - - x.matrix = np.eye(4) - x.matrix[0,0] = 500 - assert x.matrix[0,0] == 1.0 - - # Failures - with pytest.raises(ValueError): - x.matrix = 5 * np.eye(4) - with pytest.raises(ValueError): - x.matrix = np.eye(5) - with pytest.raises(ValueError): - x.matrix = np.eye(4).dot([5,1,1,1]) - with pytest.raises(ValueError): - x.rotation = np.array([1,2]) - with pytest.raises(ValueError): - x.rotation = np.array([1,2,3]) - with pytest.raises(ValueError): - x.rotation = np.array([1,2,3,4]) - with pytest.raises(ValueError): - x.translation = np.array([1,2,3,4]) - with pytest.raises(ValueError): - x.scale = np.array([1,2,3,4]) diff --git a/pyrender/tests/unit/test_offscreen.py b/pyrender/tests/unit/test_offscreen.py deleted file mode 100644 index 88983b0ff4e2ab6f5ef252c51f2ac669c3a0e0ca..0000000000000000000000000000000000000000 --- a/pyrender/tests/unit/test_offscreen.py +++ /dev/null @@ -1,92 +0,0 @@ -import numpy as np -import trimesh - -from pyrender import (OffscreenRenderer, PerspectiveCamera, DirectionalLight, - SpotLight, Mesh, Node, Scene) - - -def test_offscreen_renderer(tmpdir): - - # Fuze trimesh - fuze_trimesh = trimesh.load('examples/models/fuze.obj') - fuze_mesh = Mesh.from_trimesh(fuze_trimesh) - - # Drill trimesh - drill_trimesh = trimesh.load('examples/models/drill.obj') - drill_mesh = Mesh.from_trimesh(drill_trimesh) - drill_pose = np.eye(4) - drill_pose[0,3] = 0.1 - drill_pose[2,3] = -np.min(drill_trimesh.vertices[:,2]) - - # Wood trimesh - wood_trimesh = trimesh.load('examples/models/wood.obj') - wood_mesh = Mesh.from_trimesh(wood_trimesh) - - # Water bottle trimesh - bottle_gltf = trimesh.load('examples/models/WaterBottle.glb') - bottle_trimesh = bottle_gltf.geometry[list(bottle_gltf.geometry.keys())[0]] - bottle_mesh = Mesh.from_trimesh(bottle_trimesh) - bottle_pose = np.array([ - [1.0, 0.0, 0.0, 0.1], - [0.0, 0.0, -1.0, -0.16], - [0.0, 1.0, 0.0, 0.13], - [0.0, 0.0, 0.0, 1.0], - ]) - - boxv_trimesh = trimesh.creation.box(extents=0.1 * np.ones(3)) - boxv_vertex_colors = np.random.uniform(size=(boxv_trimesh.vertices.shape)) - boxv_trimesh.visual.vertex_colors = boxv_vertex_colors - boxv_mesh = Mesh.from_trimesh(boxv_trimesh, smooth=False) - boxf_trimesh = trimesh.creation.box(extents=0.1 * np.ones(3)) - boxf_face_colors = np.random.uniform(size=boxf_trimesh.faces.shape) - boxf_trimesh.visual.face_colors = boxf_face_colors - # Instanced - poses = np.tile(np.eye(4), (2,1,1)) - poses[0,:3,3] = np.array([-0.1, -0.10, 0.05]) - poses[1,:3,3] = np.array([-0.15, -0.10, 0.05]) - boxf_mesh = Mesh.from_trimesh(boxf_trimesh, poses=poses, smooth=False) - - points = trimesh.creation.icosphere(radius=0.05).vertices - point_colors = np.random.uniform(size=points.shape) - points_mesh = Mesh.from_points(points, colors=point_colors) - - direc_l = DirectionalLight(color=np.ones(3), intensity=1.0) - spot_l = SpotLight(color=np.ones(3), intensity=10.0, - innerConeAngle=np.pi / 16, outerConeAngle=np.pi / 6) - - cam = PerspectiveCamera(yfov=(np.pi / 3.0)) - cam_pose = np.array([ - [0.0, -np.sqrt(2) / 2, np.sqrt(2) / 2, 0.5], - [1.0, 0.0, 0.0, 0.0], - [0.0, np.sqrt(2) / 2, np.sqrt(2) / 2, 0.4], - [0.0, 0.0, 0.0, 1.0] - ]) - - scene = Scene(ambient_light=np.array([0.02, 0.02, 0.02])) - - fuze_node = Node(mesh=fuze_mesh, translation=np.array([ - 0.1, 0.15, -np.min(fuze_trimesh.vertices[:,2]) - ])) - scene.add_node(fuze_node) - boxv_node = Node(mesh=boxv_mesh, translation=np.array([-0.1, 0.10, 0.05])) - scene.add_node(boxv_node) - boxf_node = Node(mesh=boxf_mesh) - scene.add_node(boxf_node) - - _ = scene.add(drill_mesh, pose=drill_pose) - _ = scene.add(bottle_mesh, pose=bottle_pose) - _ = scene.add(wood_mesh) - _ = scene.add(direc_l, pose=cam_pose) - _ = scene.add(spot_l, pose=cam_pose) - _ = scene.add(points_mesh) - - _ = scene.add(cam, pose=cam_pose) - - r = OffscreenRenderer(viewport_width=640, viewport_height=480) - color, depth = r.render(scene) - - assert color.shape == (480, 640, 3) - assert depth.shape == (480, 640) - assert np.max(depth.data) > 0.05 - assert np.count_nonzero(depth.data) > (0.2 * depth.size) - r.delete() diff --git a/pyrender/tests/unit/test_scenes.py b/pyrender/tests/unit/test_scenes.py deleted file mode 100644 index d85dd714cb5d842ea12dee4140adfd7db55c9c01..0000000000000000000000000000000000000000 --- a/pyrender/tests/unit/test_scenes.py +++ /dev/null @@ -1,235 +0,0 @@ -import numpy as np -import pytest -import trimesh - -from pyrender import (Mesh, PerspectiveCamera, DirectionalLight, - SpotLight, PointLight, Scene, Node, OrthographicCamera) - - -def test_scenes(): - - # Basics - s = Scene() - assert np.allclose(s.bg_color, np.ones(4)) - assert np.allclose(s.ambient_light, np.zeros(3)) - assert len(s.nodes) == 0 - assert s.name is None - s.name = 'asdf' - s.bg_color = None - s.ambient_light = None - assert np.allclose(s.bg_color, np.ones(4)) - assert np.allclose(s.ambient_light, np.zeros(3)) - - assert s.nodes == set() - assert s.cameras == set() - assert s.lights == set() - assert s.point_lights == set() - assert s.spot_lights == set() - assert s.directional_lights == set() - assert s.meshes == set() - assert s.camera_nodes == set() - assert s.light_nodes == set() - assert s.point_light_nodes == set() - assert s.spot_light_nodes == set() - assert s.directional_light_nodes == set() - assert s.mesh_nodes == set() - assert s.main_camera_node is None - assert np.all(s.bounds == 0) - assert np.all(s.centroid == 0) - assert np.all(s.extents == 0) - assert np.all(s.scale == 0) - - # From trimesh scene - tms = trimesh.load('tests/data/WaterBottle.glb') - s = Scene.from_trimesh_scene(tms) - assert len(s.meshes) == 1 - assert len(s.mesh_nodes) == 1 - - # Test bg color formatting - s = Scene(bg_color=[0, 1.0, 0]) - assert np.allclose(s.bg_color, np.array([0.0, 1.0, 0.0, 1.0])) - - # Test constructor for nodes - n1 = Node() - n2 = Node() - n3 = Node() - nodes = [n1, n2, n3] - s = Scene(nodes=nodes) - n1.children.append(n2) - s = Scene(nodes=nodes) - n3.children.append(n2) - with pytest.raises(ValueError): - s = Scene(nodes=nodes) - n3.children = [] - n2.children.append(n3) - n3.children.append(n2) - with pytest.raises(ValueError): - s = Scene(nodes=nodes) - - # Test node accessors - n1 = Node() - n2 = Node() - n3 = Node() - nodes = [n1, n2] - s = Scene(nodes=nodes) - assert s.has_node(n1) - assert s.has_node(n2) - assert not s.has_node(n3) - - # Test node poses - for n in nodes: - assert np.allclose(s.get_pose(n), np.eye(4)) - with pytest.raises(ValueError): - s.get_pose(n3) - with pytest.raises(ValueError): - s.set_pose(n3, np.eye(4)) - tf = np.eye(4) - tf[:3,3] = np.ones(3) - s.set_pose(n1, tf) - assert np.allclose(s.get_pose(n1), tf) - assert np.allclose(s.get_pose(n2), np.eye(4)) - - nodes = [n1, n2, n3] - tf2 = np.eye(4) - tf2[:3,:3] = np.diag([-1,-1,1]) - n1.children.append(n2) - n1.matrix = tf - n2.matrix = tf2 - s = Scene(nodes=nodes) - assert np.allclose(s.get_pose(n1), tf) - assert np.allclose(s.get_pose(n2), tf.dot(tf2)) - assert np.allclose(s.get_pose(n3), np.eye(4)) - - n1 = Node() - n2 = Node() - n3 = Node() - n1.children.append(n2) - s = Scene() - s.add_node(n1) - with pytest.raises(ValueError): - s.add_node(n2) - s.set_pose(n1, tf) - assert np.allclose(s.get_pose(n1), tf) - assert np.allclose(s.get_pose(n2), tf) - s.set_pose(n2, tf2) - assert np.allclose(s.get_pose(n2), tf.dot(tf2)) - - # Test node removal - n1 = Node() - n2 = Node() - n3 = Node() - n1.children.append(n2) - n2.children.append(n3) - s = Scene(nodes=[n1, n2, n3]) - s.remove_node(n2) - assert len(s.nodes) == 1 - assert n1 in s.nodes - assert len(n1.children) == 0 - assert len(n2.children) == 1 - s.add_node(n2, parent_node=n1) - assert len(n1.children) == 1 - n1.matrix = tf - n3.matrix = tf2 - assert np.allclose(s.get_pose(n3), tf.dot(tf2)) - - # Now test ADD function - s = Scene() - m = Mesh([], name='m') - cp = PerspectiveCamera(yfov=2.0) - co = OrthographicCamera(xmag=1.0, ymag=1.0) - dl = DirectionalLight() - pl = PointLight() - sl = SpotLight() - - n1 = s.add(m, name='mn') - assert n1.mesh == m - assert len(s.nodes) == 1 - assert len(s.mesh_nodes) == 1 - assert n1 in s.mesh_nodes - assert len(s.meshes) == 1 - assert m in s.meshes - assert len(s.get_nodes(node=n2)) == 0 - n2 = s.add(m, pose=tf) - assert len(s.nodes) == len(s.mesh_nodes) == 2 - assert len(s.meshes) == 1 - assert len(s.get_nodes(node=n1)) == 1 - assert len(s.get_nodes(node=n1, name='mn')) == 1 - assert len(s.get_nodes(name='mn')) == 1 - assert len(s.get_nodes(obj=m)) == 2 - assert len(s.get_nodes(obj=m, obj_name='m')) == 2 - assert len(s.get_nodes(obj=co)) == 0 - nsl = s.add(sl, name='sln') - npl = s.add(pl, parent_name='sln') - assert nsl.children[0] == npl - ndl = s.add(dl, parent_node=npl) - assert npl.children[0] == ndl - nco = s.add(co) - ncp = s.add(cp) - - assert len(s.light_nodes) == len(s.lights) == 3 - assert len(s.point_light_nodes) == len(s.point_lights) == 1 - assert npl in s.point_light_nodes - assert len(s.spot_light_nodes) == len(s.spot_lights) == 1 - assert nsl in s.spot_light_nodes - assert len(s.directional_light_nodes) == len(s.directional_lights) == 1 - assert ndl in s.directional_light_nodes - assert len(s.cameras) == len(s.camera_nodes) == 2 - assert s.main_camera_node == nco - s.main_camera_node = ncp - s.remove_node(ncp) - assert len(s.cameras) == len(s.camera_nodes) == 1 - assert s.main_camera_node == nco - s.remove_node(n2) - assert len(s.meshes) == 1 - s.remove_node(n1) - assert len(s.meshes) == 0 - s.remove_node(nsl) - assert len(s.lights) == 0 - s.remove_node(nco) - assert s.main_camera_node is None - - s.add_node(n1) - s.clear() - assert len(s.nodes) == 0 - - # Trigger final errors - with pytest.raises(ValueError): - s.main_camera_node = None - with pytest.raises(ValueError): - s.main_camera_node = ncp - with pytest.raises(ValueError): - s.add(m, parent_node=n1) - with pytest.raises(ValueError): - s.add(m, name='asdf') - s.add(m, name='asdf') - s.add(m, parent_name='asdf') - with pytest.raises(ValueError): - s.add(m, parent_name='asfd') - with pytest.raises(TypeError): - s.add(None) - - s.clear() - # Test bounds - m1 = Mesh.from_trimesh(trimesh.creation.box()) - m2 = Mesh.from_trimesh(trimesh.creation.box()) - m3 = Mesh.from_trimesh(trimesh.creation.box()) - n1 = Node(mesh=m1) - n2 = Node(mesh=m2, translation=[1.0, 0.0, 0.0]) - n3 = Node(mesh=m3, translation=[0.5, 0.0, 1.0]) - s.add_node(n1) - s.add_node(n2) - s.add_node(n3) - assert np.allclose(s.bounds, [[-0.5, -0.5, -0.5], [1.5, 0.5, 1.5]]) - s.clear() - s.add_node(n1) - s.add_node(n2, parent_node=n1) - s.add_node(n3, parent_node=n2) - assert np.allclose(s.bounds, [[-0.5, -0.5, -0.5], [2.0, 0.5, 1.5]]) - tf = np.eye(4) - tf[:3,3] = np.ones(3) - s.set_pose(n3, tf) - assert np.allclose(s.bounds, [[-0.5, -0.5, -0.5], [2.5, 1.5, 1.5]]) - s.remove_node(n2) - assert np.allclose(s.bounds, [[-0.5, -0.5, -0.5], [0.5, 0.5, 0.5]]) - s.clear() - assert np.allclose(s.bounds, 0.0) diff --git a/requirements.txt b/requirements.txt index 924c282b799630240602eb639d8fb25d0505d74c..ca829be454e6e5d015105ac1ff2b0234f186aea4 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,39 +1,39 @@ +-f https://dl.fbaipublicfiles.com/pytorch3d/packaging/wheels/py39_cu118_pyt200/download.html +pytorch3d +scikit-image==0.21.0 +scikit-learn==1.3.2 +scipy==1.11.4 +smplx==0.1.28 +tqdm +opencv-contrib-python==4.8.1.78 +opencv-python==4.8.1.78 +easydict +timm +wget +av==11.0.0 +ffmpeg-python +imageio-ffmpeg==0.4.9 +omegaconf==2.2.3 ffmpeg ConfigArgParse==1.7 -fasttext==0.9.2 -h5py==3.10.0 imageio==2.31.4 ipython==8.12.3 joblib==1.3.2 librosa==0.10.1 -lmdb==1.4.1 -loguru==0.7.2 matplotlib==3.7.3 moviepy==1.0.3 -gradio -fasttext-wheel -opencv_contrib_python==4.8.1.78 -opencv_python==4.8.1.78 +gradio==4.44.1 pandas==1.5.3 peakutils==1.3.4 ptflops==0.7.1.2 python_igraph==0.11.3 pyvirtualdisplay==3.0 PyYAML==6.0.1 -replicate==0.15.4 -scikit_learn==1.3.2 -scipy soundfile==0.12.1 -termcolor==2.4.0 -textgrid==1.5 -torch==2.1.0 -torchvision tqdm==4.66.1 transformers==4.35.2 trimesh==3.23.5 wandb==0.16.0 -pyglet<2 +pyglet smplx -tensorboard -pyrender -pyarrow \ No newline at end of file +pyrender \ No newline at end of file diff --git a/setup.sh b/setup.sh new file mode 100644 index 0000000000000000000000000000000000000000..e34a22b903053aaa6d9c66ba17c84630c7e8e4e1 --- /dev/null +++ b/setup.sh @@ -0,0 +1,21 @@ +#!/usr/bin/env bash +set -e + +# Update package lists +sudo apt-get update + +# Install system dependencies +sudo apt-get install -y python3.9 python3.9-venv python3.9-dev bzip2 tmux git git-lfs libglu1-mesa-dev + +# Create and activate virtual environment +python3.9 -m venv /content/py39 +source /content/py39/bin/activate + +# Upgrade pip +pip install --upgrade pip==24.0 + +# Install Python dependencies +pip install -r ./pre-requirements.txt +pip install -r ./requirements.txt +git lfs install +git clone https://huggingface.co/H-Liu1997/emage_evaltools \ No newline at end of file diff --git a/test.py b/test.py deleted file mode 100644 index dda6f4be674b353b49a59de12bb20882dbeba756..0000000000000000000000000000000000000000 --- a/test.py +++ /dev/null @@ -1,223 +0,0 @@ -import os -import signal -import time -import csv -import sys -import warnings -import random -import torch -import torch.nn as nn -import torch.nn.functional as F -import torch.distributed as dist -from torch.nn.parallel import DistributedDataParallel as DDP -import torch.multiprocessing as mp -import numpy as np -import time -import pprint -from loguru import logger -import smplx -from torch.utils.tensorboard import SummaryWriter -import wandb -import matplotlib.pyplot as plt -from utils import config, logger_tools, other_tools, metric -from dataloaders import data_tools -from dataloaders.build_vocab import Vocab -from optimizers.optim_factory import create_optimizer -from optimizers.scheduler_factory import create_scheduler -from optimizers.loss_factory import get_loss_func - - -class BaseTrainer(object): - def __init__(self, args): - self.args = args - self.rank = dist.get_rank() - self.checkpoint_path = args.out_path + "custom/" + args.name + args.notes + "/" #wandb.run.dir #args.cache_path+args.out_path+"/"+args.name - if self.rank==0: - if self.args.stat == "ts": - self.writer = SummaryWriter(log_dir=args.out_path + "custom/" + args.name + args.notes + "/") - else: - wandb.init(project=args.project, entity="liu1997", dir=args.out_path, name=args.name[12:] + args.notes) - wandb.config.update(args) - self.writer = None - #self.test_demo = args.data_path + args.test_data_path + "bvh_full/" - # self.train_data = __import__(f"dataloaders.{args.dataset}", fromlist=["something"]).CustomDataset(args, "train") - # self.train_loader = torch.utils.data.DataLoader( - # self.train_data, - # batch_size=args.batch_size, - # shuffle=False if args.ddp else True, - # num_workers=args.loader_workers, - # drop_last=True, - # sampler=torch.utils.data.distributed.DistributedSampler(self.train_data) if args.ddp else None, - # ) - # self.train_length = len(self.train_loader) - # logger.info(f"Init train dataloader success") - - # self.val_data = __import__(f"dataloaders.{args.dataset}", fromlist=["something"]).CustomDataset(args, "val") - # self.val_loader = torch.utils.data.DataLoader( - # self.val_data, - # batch_size=args.batch_size, - # shuffle=False, - # num_workers=args.loader_workers, - # drop_last=False, - # sampler=torch.utils.data.distributed.DistributedSampler(self.val_data) if args.ddp else None, - # ) - # logger.info(f"Init val dataloader success") - if self.rank == 0: - self.test_data = __import__(f"dataloaders.{args.dataset}", fromlist=["something"]).CustomDataset(args, "test") - self.test_loader = torch.utils.data.DataLoader( - self.test_data, - batch_size=1, - shuffle=False, - num_workers=args.loader_workers, - drop_last=False, - ) - logger.info(f"Init test dataloader success") - model_module = __import__(f"models.{args.model}", fromlist=["something"]) - - if args.ddp: - self.model = getattr(model_module, args.g_name)(args).to(self.rank) - process_group = torch.distributed.new_group() - self.model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(self.model, process_group) - self.model = DDP(self.model, device_ids=[self.rank], output_device=self.rank, - broadcast_buffers=False, find_unused_parameters=False) - else: - self.model = torch.nn.DataParallel(getattr(model_module, args.g_name)(args), args.gpus).cuda() - - if self.rank == 0: - logger.info(self.model) - logger.info(f"init {args.g_name} success") - if args.stat == "wandb": - wandb.watch(self.model) - - # if args.d_name is not None: - # if args.ddp: - # self.d_model = getattr(model_module, args.d_name)(args).to(self.rank) - # self.d_model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(self.d_model, process_group) - # self.d_model = DDP(self.d_model, device_ids=[self.rank], output_device=self.rank, - # broadcast_buffers=False, find_unused_parameters=False) - # else: - # self.d_model = torch.nn.DataParallel(getattr(model_module, args.d_name)(args), args.gpus).cuda() - # if self.rank == 0: - # logger.info(self.d_model) - # logger.info(f"init {args.d_name} success") - # if args.stat == "wandb": - # wandb.watch(self.d_model) - # self.opt_d = create_optimizer(args, self.d_model, lr_weight=args.d_lr_weight) - # self.opt_d_s = create_scheduler(args, self.opt_d) - - if args.e_name is not None: - """ - bugs on DDP training using eval_model, using additional eval_copy for evaluation - """ - eval_model_module = __import__(f"models.{args.eval_model}", fromlist=["something"]) - # eval copy is for single card evaluation - if self.args.ddp: - self.eval_model = getattr(eval_model_module, args.e_name)(args).to(self.rank) - self.eval_copy = getattr(eval_model_module, args.e_name)(args).to(self.rank) - else: - self.eval_model = getattr(eval_model_module, args.e_name)(args) - self.eval_copy = getattr(eval_model_module, args.e_name)(args).to(self.rank) - - #if self.rank == 0: - other_tools.load_checkpoints(self.eval_copy, args.data_path+args.e_path, args.e_name) - other_tools.load_checkpoints(self.eval_model, args.data_path+args.e_path, args.e_name) - if self.args.ddp: - self.eval_model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(self.eval_model, process_group) - self.eval_model = DDP(self.eval_model, device_ids=[self.rank], output_device=self.rank, - broadcast_buffers=False, find_unused_parameters=False) - self.eval_model.eval() - self.eval_copy.eval() - if self.rank == 0: - logger.info(self.eval_model) - logger.info(f"init {args.e_name} success") - if args.stat == "wandb": - wandb.watch(self.eval_model) - self.smplx = smplx.create( - self.args.data_path_1+"smplx_models/", - model_type='smplx', - gender='NEUTRAL_2020', - use_face_contour=False, - num_betas=300, - num_expression_coeffs=100, - ext='npz', - use_pca=False, - ).to(self.rank).eval() - self.alignmenter = metric.alignment(0.3, 7, self.train_data.avg_vel, upper_body=[3,6,9,12,13,14,15,16,17,18,19,20,21]) if self.rank == 0 else None - self.align_mask = 60 - self.l1_calculator = metric.L1div() if self.rank == 0 else None - - def train_recording(self, epoch, its, t_data, t_train, mem_cost, lr_g, lr_d=None): - pstr = "[%03d][%03d/%03d] "%(epoch, its, self.train_length) - for name, states in self.tracker.loss_meters.items(): - metric = states['train'] - if metric.count > 0: - pstr += "{}: {:.3f}\t".format(name, metric.avg) - self.writer.add_scalar(f"train/{name}", metric.avg, epoch*self.train_length+its) if self.args.stat == "ts" else wandb.log({name: metric.avg}, step=epoch*self.train_length+its) - pstr += "glr: {:.1e}\t".format(lr_g) - self.writer.add_scalar("lr/glr", lr_g, epoch*self.train_length+its) if self.args.stat == "ts" else wandb.log({'glr': lr_g}, step=epoch*self.train_length+its) - if lr_d is not None: - pstr += "dlr: {:.1e}\t".format(lr_d) - self.writer.add_scalar("lr/dlr", lr_d, epoch*self.train_length+its) if self.args.stat == "ts" else wandb.log({'dlr': lr_d}, step=epoch*self.train_length+its) - pstr += "dtime: %04d\t"%(t_data*1000) - pstr += "ntime: %04d\t"%(t_train*1000) - pstr += "mem: {:.2f} ".format(mem_cost*len(self.args.gpus)) - logger.info(pstr) - - def val_recording(self, epoch): - pstr_curr = "Curr info >>>> " - pstr_best = "Best info >>>> " - for name, states in self.tracker.loss_meters.items(): - metric = states['val'] - if metric.count > 0: - pstr_curr += "{}: {:.3f} \t".format(name, metric.avg) - if epoch != 0: - if self.args.stat == "ts": - self.writer.add_scalars(f"val/{name}", {name+"_val":metric.avg, name+"_train":states['train'].avg}, epoch*self.train_length) - else: - wandb.log({name+"_val": metric.avg, name+"_train":states['train'].avg}, step=epoch*self.train_length) - new_best_train, new_best_val = self.tracker.update_and_plot(name, epoch, self.checkpoint_path+f"{name}_{self.args.name+self.args.notes}.png") - if new_best_val: - other_tools.save_checkpoints(os.path.join(self.checkpoint_path, f"{name}.bin"), self.model, opt=None, epoch=None, lrs=None) - for k, v in self.tracker.values.items(): - metric = v['val']['best'] - if self.tracker.loss_meters[k]['val'].count > 0: - pstr_best += "{}: {:.3f}({:03d})\t".format(k, metric['value'], metric['epoch']) - logger.info(pstr_curr) - logger.info(pstr_best) - - def test_recording(self, dict_name, value, epoch): - self.tracker.update_meter(dict_name, "test", value) - _ = self.tracker.update_values(dict_name, 'test', epoch) - -@logger.catch -def main_worker(rank, world_size, args): - #os.environ['TRANSFORMERS_CACHE'] = args.data_path_1 + "hub/" - if not sys.warnoptions: - warnings.simplefilter("ignore") - dist.init_process_group(backend="nccl", rank=rank, world_size=world_size) - - logger_tools.set_args_and_logger(args, rank) - other_tools.set_random_seed(args) - other_tools.print_exp_info(args) - - # return one intance of trainer - trainer = __import__(f"{args.trainer}_trainer", fromlist=["something"]).CustomTrainer(args) if args.trainer != "base" else BaseTrainer(args) - other_tools.load_checkpoints(trainer.model, args.test_ckpt, args.g_name) - trainer.test(999) - - - -if __name__ == "__main__": - os.environ["MASTER_ADDR"]='127.0.0.1' - os.environ["MASTER_PORT"]='8675' - #os.environ["TORCH_DISTRIBUTED_DEBUG"] = "DETAIL" - args = config.parse_args() - if args.ddp: - mp.set_start_method("spawn", force=True) - mp.spawn( - main_worker, - args=(len(args.gpus), args,), - nprocs=len(args.gpus), - ) - else: - main_worker(0, 1, args) \ No newline at end of file diff --git a/test_camn_audio.py b/test_camn_audio.py new file mode 100644 index 0000000000000000000000000000000000000000..edaa52355720d2a122023713d775a5402ba2710c --- /dev/null +++ b/test_camn_audio.py @@ -0,0 +1,68 @@ +import os +import argparse +import torch +from torchvision.io import write_video +import librosa +import time +import numpy as np +from tqdm import tqdm +from emage_utils.motion_io import beat_format_save +from emage_utils import fast_render +from models.camn_audio import CamnAudioModel + + +def inference(model, audio_path, device, save_folder, sr, pose_fps, seed_frames): + audio, _ = librosa.load(audio_path, sr=sr) + audio = torch.from_numpy(audio).to(device).unsqueeze(0) + speaker_id = torch.zeros(1,1).long().to(device) + with torch.no_grad(): + motion_pred = model(audio, speaker_id, seed_frames=seed_frames, seed_motion=None)["motion_axis_angle"] + t = motion_pred.shape[1] + motion_pred = motion_pred.cpu().numpy().reshape(t, -1) + beat_format_save(os.path.join(save_folder, f"{os.path.splitext(os.path.basename(audio_path))[0]}_output.npz"), + motion_pred, upsample=30//pose_fps) + return t + + +def visualize_one(save_folder, audio_path, nopytorch3d=False): + npz_path = os.path.join(save_folder, f"{os.path.splitext(os.path.basename(audio_path))[0]}_output.npz") + motion_dict = np.load(npz_path, allow_pickle=True) + if not nopytorch3d: + from emage_utils.npz2pose import render2d + v2d_body = render2d(motion_dict, (720, 480), face_only=False, remove_global=True) + write_video(npz_path.replace(".npz", "_2dbody.mp4"), v2d_body.permute(0, 2, 3, 1), fps=30) + fast_render.add_audio_to_video(npz_path.replace(".npz", "_2dbody.mp4"), audio_path, npz_path.replace(".npz", "_2dbody_audio.mp4")) + fast_render.render_one_sequence_no_gt(npz_path, os.path.dirname(npz_path), audio_path, model_folder="./emage_evaltools/smplx_models/") + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("--audio_folder", type=str, default="./examples/audio") + parser.add_argument("--save_folder", type=str, default="./examples/motion") + parser.add_argument("--visualization", action="store_true") + parser.add_argument("--nopytorch3d", action="store_true") + args = parser.parse_args() + + os.makedirs(args.save_folder, exist_ok=True) + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + + model = CamnAudioModel.from_pretrained("H-Liu1997/camn_audio").to(device) + model.eval() + + audio_files = [os.path.join(args.audio_folder, f) for f in os.listdir(args.audio_folder) if f.endswith(".wav")] + sr, pose_fps, seed_frames = model.cfg.audio_sr, model.cfg.pose_fps, model.cfg.seed_frames + all_t = 0 + start_time = time.time() + for audio_path in tqdm(audio_files, desc="Inference"): + all_t += inference(model, audio_path, device, args.save_folder, sr, pose_fps, seed_frames) + print(f"generate total {all_t/pose_fps:.2f} seconds motion in {time.time()-start_time:.2f} seconds, saved in {args.save_folder}") + + start_time = time.time() + if args.visualization: + for audio_path in tqdm(audio_files, desc="Visualize"): + visualize_one(args.save_folder, audio_path, args.nopytorch3d) + print(f"render total {all_t/pose_fps:.2f} seconds motion in {time.time()-start_time:.2f} seconds, saved in {args.save_folder}") + + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/test_demo.py b/test_demo.py deleted file mode 100644 index 6ad0b83c01947ccba896e8dd4d20a5b36860a060..0000000000000000000000000000000000000000 --- a/test_demo.py +++ /dev/null @@ -1,581 +0,0 @@ -import os -import signal -import time -import csv -import sys -import warnings -import random -import torch -import torch.nn as nn -import torch.nn.functional as F -import torch.distributed as dist -from torch.nn.parallel import DistributedDataParallel as DDP -import torch.multiprocessing as mp -import numpy as np -import time -import pprint -from loguru import logger -import smplx -from torch.utils.tensorboard import SummaryWriter -import wandb -import matplotlib.pyplot as plt -from utils import config, logger_tools, other_tools, metric, data_transfer -from dataloaders import data_tools -from dataloaders.build_vocab import Vocab -from optimizers.optim_factory import create_optimizer -from optimizers.scheduler_factory import create_scheduler -from optimizers.loss_factory import get_loss_func -from dataloaders.data_tools import joints_list -from utils import rotation_conversions as rc - -class BaseTrainer(object): - def __init__(self, args): - self.args = args - self.rank = dist.get_rank() - self.checkpoint_path = args.out_path + "custom/" + args.name + args.notes + "/" #wandb.run.dir #args.cache_path+args.out_path+"/"+args.name - if self.rank == 0: - self.test_data = __import__(f"dataloaders.{args.dataset}", fromlist=["something"]).CustomDataset(args, "test") - self.test_loader = torch.utils.data.DataLoader( - self.test_data, - batch_size=1, - shuffle=False, - num_workers=args.loader_workers, - drop_last=False, - ) - logger.info(f"Init test dataloader success") - model_module = __import__(f"models.{args.model}", fromlist=["something"]) - - if args.ddp: - self.model = getattr(model_module, args.g_name)(args).to(self.rank) - process_group = torch.distributed.new_group() - self.model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(self.model, process_group) - self.model = DDP(self.model, device_ids=[self.rank], output_device=self.rank, - broadcast_buffers=False, find_unused_parameters=False) - else: - self.model = torch.nn.DataParallel(getattr(model_module, args.g_name)(args), args.gpus).cuda() - - if self.rank == 0: - logger.info(self.model) - logger.info(f"init {args.g_name} success") - - self.smplx = smplx.create( - self.args.data_path_1+"smplx_models/", - model_type='smplx', - gender='NEUTRAL_2020', - use_face_contour=False, - num_betas=300, - num_expression_coeffs=100, - ext='npz', - use_pca=False, - ).to(self.rank).eval() - - self.args = args - self.joints = self.test_data.joints - self.ori_joint_list = joints_list[self.args.ori_joints] - self.tar_joint_list_face = joints_list["beat_smplx_face"] - self.tar_joint_list_upper = joints_list["beat_smplx_upper"] - self.tar_joint_list_hands = joints_list["beat_smplx_hands"] - self.tar_joint_list_lower = joints_list["beat_smplx_lower"] - - self.joint_mask_face = np.zeros(len(list(self.ori_joint_list.keys()))*3) - self.joints = 55 - for joint_name in self.tar_joint_list_face: - self.joint_mask_face[self.ori_joint_list[joint_name][1] - self.ori_joint_list[joint_name][0]:self.ori_joint_list[joint_name][1]] = 1 - self.joint_mask_upper = np.zeros(len(list(self.ori_joint_list.keys()))*3) - for joint_name in self.tar_joint_list_upper: - self.joint_mask_upper[self.ori_joint_list[joint_name][1] - self.ori_joint_list[joint_name][0]:self.ori_joint_list[joint_name][1]] = 1 - self.joint_mask_hands = np.zeros(len(list(self.ori_joint_list.keys()))*3) - for joint_name in self.tar_joint_list_hands: - self.joint_mask_hands[self.ori_joint_list[joint_name][1] - self.ori_joint_list[joint_name][0]:self.ori_joint_list[joint_name][1]] = 1 - self.joint_mask_lower = np.zeros(len(list(self.ori_joint_list.keys()))*3) - for joint_name in self.tar_joint_list_lower: - self.joint_mask_lower[self.ori_joint_list[joint_name][1] - self.ori_joint_list[joint_name][0]:self.ori_joint_list[joint_name][1]] = 1 - - self.tracker = other_tools.EpochTracker(["fid", "l1div", "bc", "rec", "trans", "vel", "transv", 'dis', 'gen', 'acc', 'transa', 'exp', 'lvd', 'mse', "cls", "rec_face", "latent", "cls_full", "cls_self", "cls_word", "latent_word","latent_self"], [False,True,True, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False,False,False,False]) - - vq_model_module = __import__(f"models.motion_representation", fromlist=["something"]) - self.args.vae_layer = 2 - self.args.vae_length = 256 - self.args.vae_test_dim = 106 - self.vq_model_face = getattr(vq_model_module, "VQVAEConvZero")(self.args).to(self.rank) - # print(self.vq_model_face) - other_tools.load_checkpoints(self.vq_model_face, self.args.data_path_1 + "pretrained_vq/last_790_face_v2.bin", args.e_name) - self.args.vae_test_dim = 78 - self.vq_model_upper = getattr(vq_model_module, "VQVAEConvZero")(self.args).to(self.rank) - other_tools.load_checkpoints(self.vq_model_upper, self.args.data_path_1 + "pretrained_vq/upper_vertex_1layer_710.bin", args.e_name) - self.args.vae_test_dim = 180 - self.vq_model_hands = getattr(vq_model_module, "VQVAEConvZero")(self.args).to(self.rank) - other_tools.load_checkpoints(self.vq_model_hands, self.args.data_path_1 + "pretrained_vq/hands_vertex_1layer_710.bin", args.e_name) - self.args.vae_test_dim = 61 - self.args.vae_layer = 4 - self.vq_model_lower = getattr(vq_model_module, "VQVAEConvZero")(self.args).to(self.rank) - other_tools.load_checkpoints(self.vq_model_lower, self.args.data_path_1 + "pretrained_vq/lower_foot_600.bin", args.e_name) - self.args.vae_test_dim = 61 - self.args.vae_layer = 4 - self.global_motion = getattr(vq_model_module, "VAEConvZero")(self.args).to(self.rank) - other_tools.load_checkpoints(self.global_motion, self.args.data_path_1 + "pretrained_vq/last_1700_foot.bin", args.e_name) - self.args.vae_test_dim = 330 - self.args.vae_layer = 4 - self.args.vae_length = 240 - - self.vq_model_face.eval() - self.vq_model_upper.eval() - self.vq_model_hands.eval() - self.vq_model_lower.eval() - self.global_motion.eval() - - self.cls_loss = nn.NLLLoss().to(self.rank) - self.reclatent_loss = nn.MSELoss().to(self.rank) - self.vel_loss = torch.nn.L1Loss(reduction='mean').to(self.rank) - self.rec_loss = get_loss_func("GeodesicLoss").to(self.rank) - self.log_softmax = nn.LogSoftmax(dim=2).to(self.rank) - - - def inverse_selection(self, filtered_t, selection_array, n): - original_shape_t = np.zeros((n, selection_array.size)) - selected_indices = np.where(selection_array == 1)[0] - for i in range(n): - original_shape_t[i, selected_indices] = filtered_t[i] - return original_shape_t - - def inverse_selection_tensor(self, filtered_t, selection_array, n): - selection_array = torch.from_numpy(selection_array).cuda() - original_shape_t = torch.zeros((n, 165)).cuda() - selected_indices = torch.where(selection_array == 1)[0] - for i in range(n): - original_shape_t[i, selected_indices] = filtered_t[i] - return original_shape_t - - def _load_data(self, dict_data): - tar_pose_raw = dict_data["pose"] - tar_pose = tar_pose_raw[:, :, :165].to(self.rank) - tar_contact = tar_pose_raw[:, :, 165:169].to(self.rank) - tar_trans = dict_data["trans"].to(self.rank) - tar_exps = dict_data["facial"].to(self.rank) - in_audio = dict_data["audio"].to(self.rank) - in_word = dict_data["word"].to(self.rank) - tar_beta = dict_data["beta"].to(self.rank) - tar_id = dict_data["id"].to(self.rank).long() - bs, n, j = tar_pose.shape[0], tar_pose.shape[1], self.joints - - tar_pose_jaw = tar_pose[:, :, 66:69] - tar_pose_jaw = rc.axis_angle_to_matrix(tar_pose_jaw.reshape(bs, n, 1, 3)) - tar_pose_jaw = rc.matrix_to_rotation_6d(tar_pose_jaw).reshape(bs, n, 1*6) - tar_pose_face = torch.cat([tar_pose_jaw, tar_exps], dim=2) - - tar_pose_hands = tar_pose[:, :, 25*3:55*3] - tar_pose_hands = rc.axis_angle_to_matrix(tar_pose_hands.reshape(bs, n, 30, 3)) - tar_pose_hands = rc.matrix_to_rotation_6d(tar_pose_hands).reshape(bs, n, 30*6) - - tar_pose_upper = tar_pose[:, :, self.joint_mask_upper.astype(bool)] - tar_pose_upper = rc.axis_angle_to_matrix(tar_pose_upper.reshape(bs, n, 13, 3)) - tar_pose_upper = rc.matrix_to_rotation_6d(tar_pose_upper).reshape(bs, n, 13*6) - - tar_pose_leg = tar_pose[:, :, self.joint_mask_lower.astype(bool)] - tar_pose_leg = rc.axis_angle_to_matrix(tar_pose_leg.reshape(bs, n, 9, 3)) - tar_pose_leg = rc.matrix_to_rotation_6d(tar_pose_leg).reshape(bs, n, 9*6) - tar_pose_lower = torch.cat([tar_pose_leg, tar_trans, tar_contact], dim=2) - - # tar_pose = rc.axis_angle_to_matrix(tar_pose.reshape(bs, n, j, 3)) - # tar_pose = rc.matrix_to_rotation_6d(tar_pose).reshape(bs, n, j*6) - tar4dis = torch.cat([tar_pose_jaw, tar_pose_upper, tar_pose_hands, tar_pose_leg], dim=2) - - tar_index_value_face_top = self.vq_model_face.map2index(tar_pose_face) # bs*n/4 - tar_index_value_upper_top = self.vq_model_upper.map2index(tar_pose_upper) # bs*n/4 - tar_index_value_hands_top = self.vq_model_hands.map2index(tar_pose_hands) # bs*n/4 - tar_index_value_lower_top = self.vq_model_lower.map2index(tar_pose_lower) # bs*n/4 - - latent_face_top = self.vq_model_face.map2latent(tar_pose_face) # bs*n/4 - latent_upper_top = self.vq_model_upper.map2latent(tar_pose_upper) # bs*n/4 - latent_hands_top = self.vq_model_hands.map2latent(tar_pose_hands) # bs*n/4 - latent_lower_top = self.vq_model_lower.map2latent(tar_pose_lower) # bs*n/4 - - latent_in = torch.cat([latent_upper_top, latent_hands_top, latent_lower_top], dim=2) - - index_in = torch.stack([tar_index_value_upper_top, tar_index_value_hands_top, tar_index_value_lower_top], dim=-1).long() - - tar_pose_6d = rc.axis_angle_to_matrix(tar_pose.reshape(bs, n, 55, 3)) - tar_pose_6d = rc.matrix_to_rotation_6d(tar_pose_6d).reshape(bs, n, 55*6) - latent_all = torch.cat([tar_pose_6d, tar_trans, tar_contact], dim=-1) - # print(tar_index_value_upper_top.shape, index_in.shape) - return { - "tar_pose_jaw": tar_pose_jaw, - "tar_pose_face": tar_pose_face, - "tar_pose_upper": tar_pose_upper, - "tar_pose_lower": tar_pose_lower, - "tar_pose_hands": tar_pose_hands, - 'tar_pose_leg': tar_pose_leg, - "in_audio": in_audio, - "in_word": in_word, - "tar_trans": tar_trans, - "tar_exps": tar_exps, - "tar_beta": tar_beta, - "tar_pose": tar_pose, - "tar4dis": tar4dis, - "tar_index_value_face_top": tar_index_value_face_top, - "tar_index_value_upper_top": tar_index_value_upper_top, - "tar_index_value_hands_top": tar_index_value_hands_top, - "tar_index_value_lower_top": tar_index_value_lower_top, - "latent_face_top": latent_face_top, - "latent_upper_top": latent_upper_top, - "latent_hands_top": latent_hands_top, - "latent_lower_top": latent_lower_top, - "latent_in": latent_in, - "index_in": index_in, - "tar_id": tar_id, - "latent_all": latent_all, - "tar_pose_6d": tar_pose_6d, - "tar_contact": tar_contact, - } - - def _g_test(self, loaded_data): - mode = 'test' - bs, n, j = loaded_data["tar_pose"].shape[0], loaded_data["tar_pose"].shape[1], self.joints - tar_pose = loaded_data["tar_pose"] - tar_beta = loaded_data["tar_beta"] - in_word = loaded_data["in_word"] - tar_exps = loaded_data["tar_exps"] - tar_contact = loaded_data["tar_contact"] - in_audio = loaded_data["in_audio"] - tar_trans = loaded_data["tar_trans"] - - remain = n%8 - if remain != 0: - tar_pose = tar_pose[:, :-remain, :] - tar_beta = tar_beta[:, :-remain, :] - tar_trans = tar_trans[:, :-remain, :] - in_word = in_word[:, :-remain] - tar_exps = tar_exps[:, :-remain, :] - tar_contact = tar_contact[:, :-remain, :] - n = n - remain - - tar_pose_jaw = tar_pose[:, :, 66:69] - tar_pose_jaw = rc.axis_angle_to_matrix(tar_pose_jaw.reshape(bs, n, 1, 3)) - tar_pose_jaw = rc.matrix_to_rotation_6d(tar_pose_jaw).reshape(bs, n, 1*6) - tar_pose_face = torch.cat([tar_pose_jaw, tar_exps], dim=2) - - tar_pose_hands = tar_pose[:, :, 25*3:55*3] - tar_pose_hands = rc.axis_angle_to_matrix(tar_pose_hands.reshape(bs, n, 30, 3)) - tar_pose_hands = rc.matrix_to_rotation_6d(tar_pose_hands).reshape(bs, n, 30*6) - - tar_pose_upper = tar_pose[:, :, self.joint_mask_upper.astype(bool)] - tar_pose_upper = rc.axis_angle_to_matrix(tar_pose_upper.reshape(bs, n, 13, 3)) - tar_pose_upper = rc.matrix_to_rotation_6d(tar_pose_upper).reshape(bs, n, 13*6) - - tar_pose_leg = tar_pose[:, :, self.joint_mask_lower.astype(bool)] - tar_pose_leg = rc.axis_angle_to_matrix(tar_pose_leg.reshape(bs, n, 9, 3)) - tar_pose_leg = rc.matrix_to_rotation_6d(tar_pose_leg).reshape(bs, n, 9*6) - tar_pose_lower = torch.cat([tar_pose_leg, tar_trans, tar_contact], dim=2) - - tar_pose_6d = rc.axis_angle_to_matrix(tar_pose.reshape(bs, n, 55, 3)) - tar_pose_6d = rc.matrix_to_rotation_6d(tar_pose_6d).reshape(bs, n, 55*6) - latent_all = torch.cat([tar_pose_6d, tar_trans, tar_contact], dim=-1) - - rec_index_all_face = [] - rec_index_all_upper = [] - rec_index_all_lower = [] - rec_index_all_hands = [] - - roundt = (n - self.args.pre_frames) // (self.args.pose_length - self.args.pre_frames) - remain = (n - self.args.pre_frames) % (self.args.pose_length - self.args.pre_frames) - round_l = self.args.pose_length - self.args.pre_frames - - for i in range(0, roundt): - in_word_tmp = in_word[:, i*(round_l):(i+1)*(round_l)+self.args.pre_frames] - # audio fps is 16000 and pose fps is 30 - in_audio_tmp = in_audio[:, i*(16000//30*round_l):(i+1)*(16000//30*round_l)+16000//30*self.args.pre_frames] - in_id_tmp = loaded_data['tar_id'][:, i*(round_l):(i+1)*(round_l)+self.args.pre_frames] - mask_val = torch.ones(bs, self.args.pose_length, self.args.pose_dims+3+4).float().cuda() - mask_val[:, :self.args.pre_frames, :] = 0.0 - if i == 0: - latent_all_tmp = latent_all[:, i*(round_l):(i+1)*(round_l)+self.args.pre_frames, :] - else: - latent_all_tmp = latent_all[:, i*(round_l):(i+1)*(round_l)+self.args.pre_frames, :] - # print(latent_all_tmp.shape, latent_last.shape) - latent_all_tmp[:, :self.args.pre_frames, :] = latent_last[:, -self.args.pre_frames:, :] - - net_out_val = self.model( - in_audio = in_audio_tmp, - in_word=in_word_tmp, - mask=mask_val, - in_motion = latent_all_tmp, - in_id = in_id_tmp, - use_attentions=True,) - - if self.args.cu != 0: - rec_index_upper = self.log_softmax(net_out_val["cls_upper"]).reshape(-1, self.args.vae_codebook_size) - _, rec_index_upper = torch.max(rec_index_upper.reshape(-1, self.args.pose_length, self.args.vae_codebook_size), dim=2) - #rec_upper = self.vq_model_upper.decode(rec_index_upper) - else: - _, rec_index_upper, _, _ = self.vq_model_upper.quantizer(net_out_val["rec_upper"]) - #rec_upper = self.vq_model_upper.decoder(rec_index_upper) - if self.args.cl != 0: - rec_index_lower = self.log_softmax(net_out_val["cls_lower"]).reshape(-1, self.args.vae_codebook_size) - _, rec_index_lower = torch.max(rec_index_lower.reshape(-1, self.args.pose_length, self.args.vae_codebook_size), dim=2) - #rec_lower = self.vq_model_lower.decode(rec_index_lower) - else: - _, rec_index_lower, _, _ = self.vq_model_lower.quantizer(net_out_val["rec_lower"]) - #rec_lower = self.vq_model_lower.decoder(rec_index_lower) - if self.args.ch != 0: - rec_index_hands = self.log_softmax(net_out_val["cls_hands"]).reshape(-1, self.args.vae_codebook_size) - _, rec_index_hands = torch.max(rec_index_hands.reshape(-1, self.args.pose_length, self.args.vae_codebook_size), dim=2) - #rec_hands = self.vq_model_hands.decode(rec_index_hands) - else: - _, rec_index_hands, _, _ = self.vq_model_hands.quantizer(net_out_val["rec_hands"]) - #rec_hands = self.vq_model_hands.decoder(rec_index_hands) - if self.args.cf != 0: - rec_index_face = self.log_softmax(net_out_val["cls_face"]).reshape(-1, self.args.vae_codebook_size) - _, rec_index_face = torch.max(rec_index_face.reshape(-1, self.args.pose_length, self.args.vae_codebook_size), dim=2) - #rec_face = self.vq_model_face.decoder(rec_index_face) - else: - _, rec_index_face, _, _ = self.vq_model_face.quantizer(net_out_val["rec_face"]) - #rec_face = self.vq_model_face.decoder(rec_index_face) - - if i == 0: - rec_index_all_face.append(rec_index_face) - rec_index_all_upper.append(rec_index_upper) - rec_index_all_lower.append(rec_index_lower) - rec_index_all_hands.append(rec_index_hands) - else: - rec_index_all_face.append(rec_index_face[:, self.args.pre_frames:]) - rec_index_all_upper.append(rec_index_upper[:, self.args.pre_frames:]) - rec_index_all_lower.append(rec_index_lower[:, self.args.pre_frames:]) - rec_index_all_hands.append(rec_index_hands[:, self.args.pre_frames:]) - - if self.args.cu != 0: - rec_upper_last = self.vq_model_upper.decode(rec_index_upper) - else: - rec_upper_last = self.vq_model_upper.decoder(rec_index_upper) - if self.args.cl != 0: - rec_lower_last = self.vq_model_lower.decode(rec_index_lower) - else: - rec_lower_last = self.vq_model_lower.decoder(rec_index_lower) - if self.args.ch != 0: - rec_hands_last = self.vq_model_hands.decode(rec_index_hands) - else: - rec_hands_last = self.vq_model_hands.decoder(rec_index_hands) - # if self.args.cf != 0: - # rec_face_last = self.vq_model_face.decode(rec_index_face) - # else: - # rec_face_last = self.vq_model_face.decoder(rec_index_face) - - rec_pose_legs = rec_lower_last[:, :, :54] - bs, n = rec_pose_legs.shape[0], rec_pose_legs.shape[1] - rec_pose_upper = rec_upper_last.reshape(bs, n, 13, 6) - rec_pose_upper = rc.rotation_6d_to_matrix(rec_pose_upper)# - rec_pose_upper = rc.matrix_to_axis_angle(rec_pose_upper).reshape(bs*n, 13*3) - rec_pose_upper_recover = self.inverse_selection_tensor(rec_pose_upper, self.joint_mask_upper, bs*n) - rec_pose_lower = rec_pose_legs.reshape(bs, n, 9, 6) - rec_pose_lower = rc.rotation_6d_to_matrix(rec_pose_lower) - rec_pose_lower = rc.matrix_to_axis_angle(rec_pose_lower).reshape(bs*n, 9*3) - rec_pose_lower_recover = self.inverse_selection_tensor(rec_pose_lower, self.joint_mask_lower, bs*n) - rec_pose_hands = rec_hands_last.reshape(bs, n, 30, 6) - rec_pose_hands = rc.rotation_6d_to_matrix(rec_pose_hands) - rec_pose_hands = rc.matrix_to_axis_angle(rec_pose_hands).reshape(bs*n, 30*3) - rec_pose_hands_recover = self.inverse_selection_tensor(rec_pose_hands, self.joint_mask_hands, bs*n) - rec_pose = rec_pose_upper_recover + rec_pose_lower_recover + rec_pose_hands_recover - rec_pose = rc.axis_angle_to_matrix(rec_pose.reshape(bs, n, j, 3)) - rec_pose = rc.matrix_to_rotation_6d(rec_pose).reshape(bs, n, j*6) - rec_trans_v_s = rec_lower_last[:, :, 54:57] - rec_x_trans = other_tools.velocity2position(rec_trans_v_s[:, :, 0:1], 1/self.args.pose_fps, tar_trans[:, 0, 0:1]) - rec_z_trans = other_tools.velocity2position(rec_trans_v_s[:, :, 2:3], 1/self.args.pose_fps, tar_trans[:, 0, 2:3]) - rec_y_trans = rec_trans_v_s[:,:,1:2] - rec_trans = torch.cat([rec_x_trans, rec_y_trans, rec_z_trans], dim=-1) - latent_last = torch.cat([rec_pose, rec_trans, rec_lower_last[:, :, 57:61]], dim=-1) - - rec_index_face = torch.cat(rec_index_all_face, dim=1) - rec_index_upper = torch.cat(rec_index_all_upper, dim=1) - rec_index_lower = torch.cat(rec_index_all_lower, dim=1) - rec_index_hands = torch.cat(rec_index_all_hands, dim=1) - if self.args.cu != 0: - rec_upper = self.vq_model_upper.decode(rec_index_upper) - else: - rec_upper = self.vq_model_upper.decoder(rec_index_upper) - if self.args.cl != 0: - rec_lower = self.vq_model_lower.decode(rec_index_lower) - else: - rec_lower = self.vq_model_lower.decoder(rec_index_lower) - if self.args.ch != 0: - rec_hands = self.vq_model_hands.decode(rec_index_hands) - else: - rec_hands = self.vq_model_hands.decoder(rec_index_hands) - if self.args.cf != 0: - rec_face = self.vq_model_face.decode(rec_index_face) - else: - rec_face = self.vq_model_face.decoder(rec_index_face) - - rec_exps = rec_face[:, :, 6:] - rec_pose_jaw = rec_face[:, :, :6] - rec_pose_legs = rec_lower[:, :, :54] - bs, n = rec_pose_jaw.shape[0], rec_pose_jaw.shape[1] - rec_pose_upper = rec_upper.reshape(bs, n, 13, 6) - rec_pose_upper = rc.rotation_6d_to_matrix(rec_pose_upper)# - rec_pose_upper = rc.matrix_to_axis_angle(rec_pose_upper).reshape(bs*n, 13*3) - rec_pose_upper_recover = self.inverse_selection_tensor(rec_pose_upper, self.joint_mask_upper, bs*n) - rec_pose_lower = rec_pose_legs.reshape(bs, n, 9, 6) - rec_pose_lower = rc.rotation_6d_to_matrix(rec_pose_lower) - rec_lower2global = rc.matrix_to_rotation_6d(rec_pose_lower.clone()).reshape(bs, n, 9*6) - rec_pose_lower = rc.matrix_to_axis_angle(rec_pose_lower).reshape(bs*n, 9*3) - rec_pose_lower_recover = self.inverse_selection_tensor(rec_pose_lower, self.joint_mask_lower, bs*n) - rec_pose_hands = rec_hands.reshape(bs, n, 30, 6) - rec_pose_hands = rc.rotation_6d_to_matrix(rec_pose_hands) - rec_pose_hands = rc.matrix_to_axis_angle(rec_pose_hands).reshape(bs*n, 30*3) - rec_pose_hands_recover = self.inverse_selection_tensor(rec_pose_hands, self.joint_mask_hands, bs*n) - rec_pose_jaw = rec_pose_jaw.reshape(bs*n, 6) - rec_pose_jaw = rc.rotation_6d_to_matrix(rec_pose_jaw) - rec_pose_jaw = rc.matrix_to_axis_angle(rec_pose_jaw).reshape(bs*n, 1*3) - rec_pose = rec_pose_upper_recover + rec_pose_lower_recover + rec_pose_hands_recover - rec_pose[:, 66:69] = rec_pose_jaw - - to_global = rec_lower - to_global[:, :, 54:57] = 0.0 - to_global[:, :, :54] = rec_lower2global - rec_global = self.global_motion(to_global) - - rec_trans_v_s = rec_global["rec_pose"][:, :, 54:57] - rec_x_trans = other_tools.velocity2position(rec_trans_v_s[:, :, 0:1], 1/self.args.pose_fps, tar_trans[:, 0, 0:1]) - rec_z_trans = other_tools.velocity2position(rec_trans_v_s[:, :, 2:3], 1/self.args.pose_fps, tar_trans[:, 0, 2:3]) - rec_y_trans = rec_trans_v_s[:,:,1:2] - rec_trans = torch.cat([rec_x_trans, rec_y_trans, rec_z_trans], dim=-1) - tar_pose = tar_pose[:, :n, :] - tar_exps = tar_exps[:, :n, :] - tar_trans = tar_trans[:, :n, :] - tar_beta = tar_beta[:, :n, :] - - rec_pose = rc.axis_angle_to_matrix(rec_pose.reshape(bs*n, j, 3)) - rec_pose = rc.matrix_to_rotation_6d(rec_pose).reshape(bs, n, j*6) - tar_pose = rc.axis_angle_to_matrix(tar_pose.reshape(bs*n, j, 3)) - tar_pose = rc.matrix_to_rotation_6d(tar_pose).reshape(bs, n, j*6) - - return { - 'rec_pose': rec_pose, - 'rec_trans': rec_trans, - 'tar_pose': tar_pose, - 'tar_exps': tar_exps, - 'tar_beta': tar_beta, - 'tar_trans': tar_trans, - 'rec_exps': rec_exps, - } - - - def test_demo(self, epoch): - ''' - input audio and text, output motion - do not calculate loss and metric - save video - ''' - results_save_path = self.checkpoint_path + f"/{epoch}/" - if os.path.exists(results_save_path): - return 0 - os.makedirs(results_save_path) - start_time = time.time() - total_length = 0 - test_seq_list = self.test_data.selected_file - align = 0 - latent_out = [] - latent_ori = [] - l2_all = 0 - lvel = 0 - self.model.eval() - self.smplx.eval() - # self.eval_copy.eval() - with torch.no_grad(): - for its, batch_data in enumerate(self.test_loader): - loaded_data = self._load_data(batch_data) - net_out = self._g_test(loaded_data) - tar_pose = net_out['tar_pose'] - rec_pose = net_out['rec_pose'] - tar_exps = net_out['tar_exps'] - tar_beta = net_out['tar_beta'] - rec_trans = net_out['rec_trans'] - tar_trans = net_out['tar_trans'] - rec_exps = net_out['rec_exps'] - # print(rec_pose.shape, tar_pose.shape) - bs, n, j = tar_pose.shape[0], tar_pose.shape[1], self.joints - - # interpolate to 30fps - if (30/self.args.pose_fps) != 1: - assert 30%self.args.pose_fps == 0 - n *= int(30/self.args.pose_fps) - tar_pose = torch.nn.functional.interpolate(tar_pose.permute(0, 2, 1), scale_factor=30/self.args.pose_fps, mode='linear').permute(0,2,1) - rec_pose = torch.nn.functional.interpolate(rec_pose.permute(0, 2, 1), scale_factor=30/self.args.pose_fps, mode='linear').permute(0,2,1) - - # print(rec_pose.shape, tar_pose.shape) - rec_pose = rc.rotation_6d_to_matrix(rec_pose.reshape(bs*n, j, 6)) - rec_pose = rc.matrix_to_axis_angle(rec_pose).reshape(bs*n, j*3) - - tar_pose = rc.rotation_6d_to_matrix(tar_pose.reshape(bs*n, j, 6)) - tar_pose = rc.matrix_to_axis_angle(tar_pose).reshape(bs*n, j*3) - - tar_pose_np = tar_pose.detach().cpu().numpy() - rec_pose_np = rec_pose.detach().cpu().numpy() - rec_trans_np = rec_trans.detach().cpu().numpy().reshape(bs*n, 3) - rec_exp_np = rec_exps.detach().cpu().numpy().reshape(bs*n, 100) - tar_exp_np = tar_exps.detach().cpu().numpy().reshape(bs*n, 100) - tar_trans_np = tar_trans.detach().cpu().numpy().reshape(bs*n, 3) - - gt_npz = np.load(self.args.data_path+self.args.pose_rep +"/"+test_seq_list.iloc[its]['id']+".npz", allow_pickle=True) - np.savez(results_save_path+"gt_"+test_seq_list.iloc[its]['id']+'.npz', - betas=gt_npz["betas"], - poses=tar_pose_np, - expressions=tar_exp_np, - trans=tar_trans_np, - model='smplx2020', - gender='neutral', - mocap_frame_rate = 30 , - ) - np.savez(results_save_path+"res_"+test_seq_list.iloc[its]['id']+'.npz', - betas=gt_npz["betas"], - poses=rec_pose_np, - expressions=rec_exp_np, - trans=rec_trans_np, - model='smplx2020', - gender='neutral', - mocap_frame_rate = 30, - ) - total_length += n - # other_tools.render_one_sequence( - # results_save_path+"res_"+test_seq_list.iloc[its]['id']+'.npz', - # results_save_path+"gt_"+test_seq_list.iloc[its]['id']+'.npz', - # results_save_path, - # self.args.data_path+"wave16k/"+test_seq_list.iloc[its]['id']+".wav", - # self.args.data_path_1+"smplx_models/", - # use_matplotlib = False, - # args = self.args, - # ) - end_time = time.time() - start_time - logger.info(f"total inference time: {int(end_time)} s for {int(total_length/self.args.pose_fps)} s motion") - -@logger.catch -def main_worker(rank, world_size, args): - #os.environ['TRANSFORMERS_CACHE'] = args.data_path_1 + "hub/" - if not sys.warnoptions: - warnings.simplefilter("ignore") - dist.init_process_group(backend="nccl", rank=rank, world_size=world_size) - - logger_tools.set_args_and_logger(args, rank) - other_tools.set_random_seed(args) - other_tools.print_exp_info(args) - - # return one intance of trainer - other_tools.write_wav_names_to_csv(args.data_path, args.data_path+"test.csv") - trainer = BaseTrainer(args) - other_tools.load_checkpoints(trainer.model, args.test_ckpt, args.g_name) - trainer.test_demo(999) - - - -if __name__ == "__main__": - os.environ["MASTER_ADDR"]='127.0.0.1' - os.environ["MASTER_PORT"]='8675' - #os.environ["TORCH_DISTRIBUTED_DEBUG"] = "DETAIL" - args = config.parse_args() - if args.ddp: - mp.set_start_method("spawn", force=True) - mp.spawn( - main_worker, - args=(len(args.gpus), args,), - nprocs=len(args.gpus), - ) - else: - main_worker(0, 1, args) \ No newline at end of file diff --git a/test_disco_audio.py b/test_disco_audio.py new file mode 100644 index 0000000000000000000000000000000000000000..b434f5f557a3db7a6ae59dda0b72e93ff1336668 --- /dev/null +++ b/test_disco_audio.py @@ -0,0 +1,68 @@ +import os +import argparse +import torch +from torchvision.io import write_video +import librosa +import time +import numpy as np +from tqdm import tqdm +from emage_utils.motion_io import beat_format_save +from emage_utils import fast_render +from models.disco_audio import DiscoAudioModel + + +def inference(model, audio_path, device, save_folder, sr, pose_fps, seed_frames): + audio, _ = librosa.load(audio_path, sr=sr) + audio = torch.from_numpy(audio).to(device).unsqueeze(0) + speaker_id = torch.zeros(1,1).long().to(device) + with torch.no_grad(): + motion_pred = model(audio, speaker_id, seed_frames=seed_frames, seed_motion=None)["motion_axis_angle"] + t = motion_pred.shape[1] + motion_pred = motion_pred.cpu().numpy().reshape(t, -1) + beat_format_save(os.path.join(save_folder, f"{os.path.splitext(os.path.basename(audio_path))[0]}_output.npz"), + motion_pred, upsample=30//pose_fps) + return t + + +def visualize_one(save_folder, audio_path, nopytorch3d=False): + npz_path = os.path.join(save_folder, f"{os.path.splitext(os.path.basename(audio_path))[0]}_output.npz") + motion_dict = np.load(npz_path, allow_pickle=True) + if not nopytorch3d: + from emage_utils.npz2pose import render2d + v2d_body = render2d(motion_dict, (720, 480), face_only=False, remove_global=True) + write_video(npz_path.replace(".npz", "_2dbody.mp4"), v2d_body.permute(0, 2, 3, 1), fps=30) + fast_render.add_audio_to_video(npz_path.replace(".npz", "_2dbody.mp4"), audio_path, npz_path.replace(".npz", "_2dbody_audio.mp4")) + fast_render.render_one_sequence_no_gt(npz_path, os.path.dirname(npz_path), audio_path, model_folder="./emage_evaltools/smplx_models/") + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("--audio_folder", type=str, default="./examples/audio") + parser.add_argument("--save_folder", type=str, default="./examples/motion") + parser.add_argument("--visualization", action="store_true") + parser.add_argument("--nopytorch3d", action="store_true") + args = parser.parse_args() + + os.makedirs(args.save_folder, exist_ok=True) + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + + model = DiscoAudioModel.from_pretrained("H-Liu1997/disco_audio").to(device) + model.eval() + + audio_files = [os.path.join(args.audio_folder, f) for f in os.listdir(args.audio_folder) if f.endswith(".wav")] + sr, pose_fps, seed_frames = model.cfg.audio_sr, model.cfg.pose_fps, model.cfg.seed_frames + all_t = 0 + start_time = time.time() + for audio_path in tqdm(audio_files, desc="Inference"): + all_t += inference(model, audio_path, device, args.save_folder, sr, pose_fps, seed_frames) + print(f"generate total {all_t/pose_fps:.2f} seconds motion in {time.time()-start_time:.2f} seconds, saved in {args.save_folder}") + + start_time = time.time() + if args.visualization: + for audio_path in tqdm(audio_files, desc="Visualize"): + visualize_one(args.save_folder, audio_path, args.nopytorch3d) + print(f"render total {all_t/pose_fps:.2f} seconds motion in {time.time()-start_time:.2f} seconds, saved in {args.save_folder}") + + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/test_emage_audio.py b/test_emage_audio.py new file mode 100644 index 0000000000000000000000000000000000000000..bcab7119ecdddca3dadd7f098e997adb36471c93 --- /dev/null +++ b/test_emage_audio.py @@ -0,0 +1,107 @@ +import os +import argparse +import torch +import torch.nn.functional as F +from torchvision.io import write_video + +import librosa +import time +import numpy as np +from tqdm import tqdm +from emage_utils.motion_io import beat_format_save +from emage_utils import fast_render +from models.emage_audio import EmageAudioModel, EmageVQVAEConv, EmageVAEConv, EmageVQModel + + +def inference(model, motion_vq, audio_path, device, save_folder, sr, pose_fps,): + audio, _ = librosa.load(audio_path, sr=sr) + audio = torch.from_numpy(audio).to(device).unsqueeze(0) + speaker_id = torch.zeros(1,1).long().to(device) + with torch.no_grad(): + # motion seed + # motion_path = audio_path.replace("audio", "motion").replace(".wav", ".npz") + # motion_data = np.load(motion_path, allow_pickle=True) + # poses = torch.from_numpy(motion_data["poses"]).unsqueeze(0).to(device).float() + # foot_contact = torch.from_numpy(np.load(motion_path.replace("smplxflame_30", "footcontact").replace(".npz", ".npy"))).unsqueeze(0).to(device).float() + # trans = torch.from_numpy(motion_data["trans"]).unsqueeze(0).to(device).float() + # bs, t, _ = poses.shape + # poses_6d = rc.axis_angle_to_rotation_6d(poses.reshape(bs, t, -1, 3)).reshape(bs, t, -1) + # masked_motion = torch.cat([poses_6d, trans, foot_contact], dim=-1) # bs t 337 + trans = torch.zeros(1, 1, 3).to(device) + + latent_dict = model.inference(audio, speaker_id, motion_vq, masked_motion=None, mask=None) + + face_latent = latent_dict["rec_face"] if model.cfg.lf > 0 and model.cfg.cf == 0 else None + upper_latent = latent_dict["rec_upper"] if model.cfg.lu > 0 and model.cfg.cu == 0 else None + hands_latent = latent_dict["rec_hands"] if model.cfg.lh > 0 and model.cfg.ch == 0 else None + lower_latent = latent_dict["rec_lower"] if model.cfg.ll > 0 and model.cfg.cl == 0 else None + + face_index = torch.max(F.log_softmax(latent_dict["cls_face"], dim=2), dim=2)[1] if model.cfg.cf > 0 else None + upper_index = torch.max(F.log_softmax(latent_dict["cls_upper"], dim=2), dim=2)[1] if model.cfg.cu > 0 else None + hands_index = torch.max(F.log_softmax(latent_dict["cls_hands"], dim=2), dim=2)[1] if model.cfg.ch > 0 else None + lower_index = torch.max(F.log_softmax(latent_dict["cls_lower"], dim=2), dim=2)[1] if model.cfg.cl > 0 else None + + all_pred = motion_vq.decode( + face_latent=face_latent, upper_latent=upper_latent, lower_latent=lower_latent, hands_latent=hands_latent, + face_index=face_index, upper_index=upper_index, lower_index=lower_index, hands_index=hands_index, + get_global_motion=True, ref_trans=trans[:,0]) + + motion_pred = all_pred["motion_axis_angle"] + t = motion_pred.shape[1] + motion_pred = motion_pred.cpu().numpy().reshape(t, -1) + face_pred = all_pred["expression"].cpu().numpy().reshape(t, -1) + trans_pred = all_pred["trans"].cpu().numpy().reshape(t, -1) + beat_format_save(os.path.join(save_folder, f"{os.path.splitext(os.path.basename(audio_path))[0]}_output.npz"), + motion_pred, upsample=30//pose_fps, expressions=face_pred, trans=trans_pred) + return t + +def visualize_one(save_folder, audio_path, nopytorch3d=False): + npz_path = os.path.join(save_folder, f"{os.path.splitext(os.path.basename(audio_path))[0]}_output.npz") + motion_dict = np.load(npz_path, allow_pickle=True) + if not nopytorch3d: + from emage_utils.npz2pose import render2d + v2d_face = render2d(motion_dict, (512, 512), face_only=True, remove_global=True) + write_video(npz_path.replace(".npz", "_2dface.mp4"), v2d_face.permute(0, 2, 3, 1), fps=30) + fast_render.add_audio_to_video(npz_path.replace(".npz", "_2dface.mp4"), audio_path, npz_path.replace(".npz", "_2dface_audio.mp4")) + v2d_body = render2d(motion_dict, (720, 480), face_only=False, remove_global=True) + write_video(npz_path.replace(".npz", "_2dbody.mp4"), v2d_body.permute(0, 2, 3, 1), fps=30) + fast_render.add_audio_to_video(npz_path.replace(".npz", "_2dbody.mp4"), audio_path, npz_path.replace(".npz", "_2dbody_audio.mp4")) + fast_render.render_one_sequence_with_face(npz_path, os.path.dirname(npz_path), audio_path, model_folder="./emage_evaltools/smplx_models/") + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("--audio_folder", type=str, default="./examples/audio") + parser.add_argument("--save_folder", type=str, default="./examples/motion") + parser.add_argument("--visualization", action="store_true") + parser.add_argument("--nopytorch3d", action="store_true") + args = parser.parse_args() + + os.makedirs(args.save_folder, exist_ok=True) + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + + face_motion_vq = EmageVQVAEConv.from_pretrained("H-Liu1997/emage_audio", subfolder="emage_vq/face").to(device) + upper_motion_vq = EmageVQVAEConv.from_pretrained("H-Liu1997/emage_audio", subfolder="emage_vq/upper").to(device) + lower_motion_vq = EmageVQVAEConv.from_pretrained("H-Liu1997/emage_audio", subfolder="emage_vq/lower").to(device) + hands_motion_vq = EmageVQVAEConv.from_pretrained("H-Liu1997/emage_audio", subfolder="emage_vq/hands").to(device) + global_motion_ae = EmageVAEConv.from_pretrained("H-Liu1997/emage_audio", subfolder="emage_vq/global").to(device) + motion_vq = EmageVQModel( + face_model=face_motion_vq, upper_model=upper_motion_vq, + lower_model=lower_motion_vq, hands_model=hands_motion_vq, + global_model=global_motion_ae).to(device) + motion_vq.eval() + + model = EmageAudioModel.from_pretrained("H-Liu1997/emage_audio").to(device) + model.eval() + + audio_files = [os.path.join(args.audio_folder, f) for f in os.listdir(args.audio_folder) if f.endswith(".wav")] + sr, pose_fps = model.cfg.audio_sr, model.cfg.pose_fps + all_t = 0 + start_time = time.time() + + for audio_path in tqdm(audio_files, desc="Inference"): + all_t += inference(model, motion_vq, audio_path, device, args.save_folder, sr, pose_fps) + if args.visualization: + visualize_one(args.save_folder, audio_path, args.nopytorch3d) + print(f"generate total {all_t/pose_fps:.2f} seconds motion in {time.time()-start_time:.2f} seconds") +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/train.py b/train.py deleted file mode 100644 index 3a47475400185fc5042ccd3e1ce0f66fe2a1cae5..0000000000000000000000000000000000000000 --- a/train.py +++ /dev/null @@ -1,307 +0,0 @@ -import os -import signal -import time -import csv -import sys -import warnings -import random -import torch -import torch.nn as nn -import torch.nn.functional as F -import torch.distributed as dist -from torch.nn.parallel import DistributedDataParallel as DDP -import torch.multiprocessing as mp -import numpy as np -import time -import pprint -from loguru import logger -import smplx -from torch.utils.tensorboard import SummaryWriter -import wandb -import matplotlib.pyplot as plt -from utils import config, logger_tools, other_tools, metric -from dataloaders import data_tools -from dataloaders.build_vocab import Vocab -from optimizers.optim_factory import create_optimizer -from optimizers.scheduler_factory import create_scheduler -from optimizers.loss_factory import get_loss_func - - -class BaseTrainer(object): - def __init__(self, args): - self.args = args - self.rank = dist.get_rank() - self.checkpoint_path = args.out_path + "custom/" + args.name + args.notes + "/" #wandb.run.dir #args.cache_path+args.out_path+"/"+args.name - if self.rank==0: - if self.args.stat == "ts": - self.writer = SummaryWriter(log_dir=args.out_path + "custom/" + args.name + args.notes + "/") - else: - wandb.init(project=args.project, entity="liu1997", dir=args.out_path, name=args.name[12:] + args.notes) - wandb.config.update(args) - self.writer = None - #self.test_demo = args.data_path + args.test_data_path + "bvh_full/" - self.train_data = __import__(f"dataloaders.{args.dataset}", fromlist=["something"]).CustomDataset(args, "train") - self.train_loader = torch.utils.data.DataLoader( - self.train_data, - batch_size=args.batch_size, - shuffle=False if args.ddp else True, - num_workers=args.loader_workers, - drop_last=True, - sampler=torch.utils.data.distributed.DistributedSampler(self.train_data) if args.ddp else None, - ) - self.train_length = len(self.train_loader) - logger.info(f"Init train dataloader success") - - self.val_data = __import__(f"dataloaders.{args.dataset}", fromlist=["something"]).CustomDataset(args, "val") - self.val_loader = torch.utils.data.DataLoader( - self.val_data, - batch_size=args.batch_size, - shuffle=False, - num_workers=args.loader_workers, - drop_last=False, - sampler=torch.utils.data.distributed.DistributedSampler(self.val_data) if args.ddp else None, - ) - logger.info(f"Init val dataloader success") - if self.rank == 0: - self.test_data = __import__(f"dataloaders.{args.dataset}", fromlist=["something"]).CustomDataset(args, "test") - self.test_loader = torch.utils.data.DataLoader( - self.test_data, - batch_size=1, - shuffle=False, - num_workers=args.loader_workers, - drop_last=False, - ) - logger.info(f"Init test dataloader success") - model_module = __import__(f"models.{args.model}", fromlist=["something"]) - - if args.ddp: - self.model = getattr(model_module, args.g_name)(args).to(self.rank) - process_group = torch.distributed.new_group() - self.model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(self.model, process_group) - self.model = DDP(self.model, device_ids=[self.rank], output_device=self.rank, - broadcast_buffers=False, find_unused_parameters=False) - else: - self.model = torch.nn.DataParallel(getattr(model_module, args.g_name)(args), args.gpus).cuda() - - if self.rank == 0: - logger.info(self.model) - logger.info(f"init {args.g_name} success") - if args.stat == "wandb": - wandb.watch(self.model) - - if args.d_name is not None: - if args.ddp: - self.d_model = getattr(model_module, args.d_name)(args).to(self.rank) - self.d_model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(self.d_model, process_group) - self.d_model = DDP(self.d_model, device_ids=[self.rank], output_device=self.rank, - broadcast_buffers=False, find_unused_parameters=False) - else: - self.d_model = torch.nn.DataParallel(getattr(model_module, args.d_name)(args), args.gpus).cuda() - if self.rank == 0: - logger.info(self.d_model) - logger.info(f"init {args.d_name} success") - if args.stat == "wandb": - wandb.watch(self.d_model) - self.opt_d = create_optimizer(args, self.d_model, lr_weight=args.d_lr_weight) - self.opt_d_s = create_scheduler(args, self.opt_d) - - if args.e_name is not None: - """ - bugs on DDP training using eval_model, using additional eval_copy for evaluation - """ - eval_model_module = __import__(f"models.{args.eval_model}", fromlist=["something"]) - # eval copy is for single card evaluation - if self.args.ddp: - self.eval_model = getattr(eval_model_module, args.e_name)(args).to(self.rank) - self.eval_copy = getattr(eval_model_module, args.e_name)(args).to(self.rank) - else: - self.eval_model = getattr(eval_model_module, args.e_name)(args) - self.eval_copy = getattr(eval_model_module, args.e_name)(args).to(self.rank) - - #if self.rank == 0: - other_tools.load_checkpoints(self.eval_copy, args.data_path+args.e_path, args.e_name) - other_tools.load_checkpoints(self.eval_model, args.data_path+args.e_path, args.e_name) - if self.args.ddp: - self.eval_model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(self.eval_model, process_group) - self.eval_model = DDP(self.eval_model, device_ids=[self.rank], output_device=self.rank, - broadcast_buffers=False, find_unused_parameters=False) - self.eval_model.eval() - self.eval_copy.eval() - if self.rank == 0: - logger.info(self.eval_model) - logger.info(f"init {args.e_name} success") - if args.stat == "wandb": - wandb.watch(self.eval_model) - self.opt = create_optimizer(args, self.model) - self.opt_s = create_scheduler(args, self.opt) - self.smplx = smplx.create( - self.args.data_path_1+"smplx_models/", - model_type='smplx', - gender='NEUTRAL_2020', - use_face_contour=False, - num_betas=300, - num_expression_coeffs=100, - ext='npz', - use_pca=False, - ).to(self.rank).eval() - self.alignmenter = metric.alignment(0.3, 7, self.train_data.avg_vel, upper_body=[3,6,9,12,13,14,15,16,17,18,19,20,21]) if self.rank == 0 else None - self.align_mask = 60 - self.l1_calculator = metric.L1div() if self.rank == 0 else None - - - def inverse_selection(self, filtered_t, selection_array, n): - original_shape_t = np.zeros((n, selection_array.size)) - selected_indices = np.where(selection_array == 1)[0] - for i in range(n): - original_shape_t[i, selected_indices] = filtered_t[i] - return original_shape_t - - # def inverse_selection_6d(self, filtered_t, selection_array, n): - # original_shape_t = np.zeros((n, selection_array.size)) - # selected_indices = np.where(selection_array == 1)[0] - # new_selected_indices = np.zeros((n, selected_indices.size*2)) - # new_selected_indices[:, ::2] = selected_indices - # new_selected_indices[:, 1::2] = selected_indices - # selected_indices = new_selected_indices.astype(np.bool) - # for i in range(n): - # original_shape_t[i, selected_indices] = filtered_t[i] - # return original_shape_t - - def inverse_selection_tensor(self, filtered_t, selection_array, n): - selection_array = torch.from_numpy(selection_array).cuda() - selected_indices = torch.where(selection_array == 1)[0] - if len(filtered_t.shape) == 2: - original_shape_t = torch.zeros((n, 165)).cuda() - for i in range(n): - original_shape_t[i, selected_indices] = filtered_t[i] - elif len(filtered_t.shape) == 3: - bs, n, _ = filtered_t.shape - original_shape_t = torch.zeros((bs, n, 165), device='cuda') - expanded_indices = selected_indices.unsqueeze(0).unsqueeze(0).expand(bs, n, -1) - original_shape_t.scatter_(2, expanded_indices, filtered_t) - return original_shape_t - - def inverse_selection_tensor_6d(self, filtered_t, selection_array, n): - new_selected_array = np.zeros((330)) - new_selected_array[::2] = selection_array - new_selected_array[1::2] = selection_array - selection_array = new_selected_array - selection_array = torch.from_numpy(selection_array).cuda() - selected_indices = torch.where(selection_array == 1)[0] - if len(filtered_t.shape) == 2: - original_shape_t = torch.zeros((n, 330)).cuda() - for i in range(n): - original_shape_t[i, selected_indices] = filtered_t[i] - elif len(filtered_t.shape) == 3: - bs, n, _ = filtered_t.shape - original_shape_t = torch.zeros((bs, n, 330), device='cuda') - expanded_indices = selected_indices.unsqueeze(0).unsqueeze(0).expand(bs, n, -1) - original_shape_t.scatter_(2, expanded_indices, filtered_t) - return original_shape_t - - def train_recording(self, epoch, its, t_data, t_train, mem_cost, lr_g, lr_d=None): - pstr = "[%03d][%03d/%03d] "%(epoch, its, self.train_length) - for name, states in self.tracker.loss_meters.items(): - metric = states['train'] - if metric.count > 0: - pstr += "{}: {:.3f}\t".format(name, metric.avg) - self.writer.add_scalar(f"train/{name}", metric.avg, epoch*self.train_length+its) if self.args.stat == "ts" else wandb.log({name: metric.avg}, step=epoch*self.train_length+its) - pstr += "glr: {:.1e}\t".format(lr_g) - self.writer.add_scalar("lr/glr", lr_g, epoch*self.train_length+its) if self.args.stat == "ts" else wandb.log({'glr': lr_g}, step=epoch*self.train_length+its) - if lr_d is not None: - pstr += "dlr: {:.1e}\t".format(lr_d) - self.writer.add_scalar("lr/dlr", lr_d, epoch*self.train_length+its) if self.args.stat == "ts" else wandb.log({'dlr': lr_d}, step=epoch*self.train_length+its) - pstr += "dtime: %04d\t"%(t_data*1000) - pstr += "ntime: %04d\t"%(t_train*1000) - pstr += "mem: {:.2f} ".format(mem_cost*len(self.args.gpus)) - logger.info(pstr) - - def val_recording(self, epoch): - pstr_curr = "Curr info >>>> " - pstr_best = "Best info >>>> " - for name, states in self.tracker.loss_meters.items(): - metric = states['val'] - if metric.count > 0: - pstr_curr += "{}: {:.3f} \t".format(name, metric.avg) - if epoch != 0: - if self.args.stat == "ts": - self.writer.add_scalars(f"val/{name}", {name+"_val":metric.avg, name+"_train":states['train'].avg}, epoch*self.train_length) - else: - wandb.log({name+"_val": metric.avg, name+"_train":states['train'].avg}, step=epoch*self.train_length) - new_best_train, new_best_val = self.tracker.update_and_plot(name, epoch, self.checkpoint_path+f"{name}_{self.args.name+self.args.notes}.png") - if new_best_val: - other_tools.save_checkpoints(os.path.join(self.checkpoint_path, f"{name}.bin"), self.model, opt=None, epoch=None, lrs=None) - for k, v in self.tracker.values.items(): - metric = v['val']['best'] - if self.tracker.loss_meters[k]['val'].count > 0: - pstr_best += "{}: {:.3f}({:03d})\t".format(k, metric['value'], metric['epoch']) - logger.info(pstr_curr) - logger.info(pstr_best) - - def test_recording(self, dict_name, value, epoch): - self.tracker.update_meter(dict_name, "test", value) - _ = self.tracker.update_values(dict_name, 'test', epoch) - -@logger.catch -def main_worker(rank, world_size, args): - #os.environ['TRANSFORMERS_CACHE'] = args.data_path_1 + "hub/" - if not sys.warnoptions: - warnings.simplefilter("ignore") - dist.init_process_group(backend="nccl", rank=rank, world_size=world_size) - - logger_tools.set_args_and_logger(args, rank) - other_tools.set_random_seed(args) - other_tools.print_exp_info(args) - - # return one intance of trainer - trainer = __import__(f"{args.trainer}_trainer", fromlist=["something"]).CustomTrainer(args) if args.trainer != "base" else BaseTrainer(args) - logger.info("Training from scratch ...") - start_time = time.time() - for epoch in range(args.epochs+1): - if args.ddp: trainer.val_loader.sampler.set_epoch(epoch) - trainer.val(epoch) - # if (epoch) % args.test_period == 1: trainer.val(epoch) - epoch_time = time.time()-start_time - if trainer.rank == 0: logger.info("Time info >>>> elapsed: %.2f mins\t"%(epoch_time/60)+"remain: %.2f mins"%((args.epochs/(epoch+1e-7)-1)*epoch_time/60)) - if epoch != args.epochs: - if args.ddp: trainer.train_loader.sampler.set_epoch(epoch) - trainer.tracker.reset() - trainer.train(epoch) - if args.debug: - other_tools.save_checkpoints(os.path.join(trainer.checkpoint_path, f"last_{epoch}.bin"), trainer.model, opt=None, epoch=None, lrs=None) - other_tools.load_checkpoints(trainer.model, os.path.join(trainer.checkpoint_path, f"last_{epoch}.bin"), args.g_name) - #other_tools.load_checkpoints(trainer.model, "/home/s24273/datasets/hub/pretrained_vq/last_140.bin", args.g_name) - trainer.test(epoch) - if (epoch) % args.test_period == 0 and epoch !=0: - if rank == 0: - other_tools.save_checkpoints(os.path.join(trainer.checkpoint_path, f"last_{epoch}.bin"), trainer.model, opt=None, epoch=None, lrs=None) - trainer.test(epoch) - - if rank == 0: - for k, v in trainer.tracker.values.items(): - if trainer.tracker.loss_meters[k]['val'].count > 0: - other_tools.load_checkpoints(trainer.model, os.path.join(trainer.checkpoint_path, f"{k}.bin"), args.g_name) - logger.info(f"inference on ckpt {k}_val_{v['val']['best']['epoch']}:") - trainer.test(v['val']['best']['epoch']) - other_tools.record_trial(args, trainer.tracker) - wandb.log({"fid_test": trainer.tracker["fid"]["test"]["best"]}) - if args.stat == "ts": - trainer.writer.close() - else: - wandb.finish() - - -if __name__ == "__main__": - os.environ["MASTER_ADDR"]='127.0.0.1' - os.environ["MASTER_PORT"]='8675' - #os.environ["TORCH_DISTRIBUTED_DEBUG"] = "DETAIL" - args = config.parse_args() - if args.ddp: - mp.set_start_method("spawn", force=True) - mp.spawn( - main_worker, - args=(len(args.gpus), args,), - nprocs=len(args.gpus), - ) - else: - main_worker(0, 1, args) \ No newline at end of file diff --git a/train_camn_audio.py b/train_camn_audio.py new file mode 100644 index 0000000000000000000000000000000000000000..bc53f98c4e274478c011184a5cebb12a3a52b23e --- /dev/null +++ b/train_camn_audio.py @@ -0,0 +1,507 @@ +import os +import shutil +import argparse +import random +import numpy as np +from datetime import datetime +from tqdm import tqdm +import importlib +import copy +import librosa +from pathlib import Path +import json +import time +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch.utils.data import DataLoader +from torch.nn.parallel import DistributedDataParallel as DDP +import wandb + +from diffusers.optimization import get_scheduler +from omegaconf import OmegaConf + +from emage_evaltools.mertic import FGD, BC, L1div +from emage_utils.motion_io import beat_format_load, beat_format_save, MASK_DICT, recover_from_mask, recover_from_mask_ts +import emage_utils.rotation_conversions as rc +from emage_utils import fast_render +from emage_utils.motion_rep_transfer import get_motion_rep_numpy + + +# --------------------------------- loss here --------------------------------- # +class GeodesicLoss(nn.Module): + def __init__(self): + super(GeodesicLoss, self).__init__() + def compute_geodesic_distance(self, m1, m2): + m1 = m1.reshape(-1, 3, 3) + m2 = m2.reshape(-1, 3, 3) + m = torch.bmm(m1, m2.transpose(1, 2)) + cos = (m[:, 0, 0] + m[:, 1, 1] + m[:, 2, 2] - 1) / 2 + cos = torch.clamp(cos, min=-1 + 1E-6, max=1-1E-6) + theta = torch.acos(cos) + return theta + def __call__(self, m1, m2, reduction='mean'): + loss = self.compute_geodesic_distance(m1, m2) + if reduction == 'mean': + return loss.mean() + elif reduction == 'none': + return loss + else: + raise RuntimeError + +GeodesicLossFn = GeodesicLoss() + + +# --------------------------------- train,val,test fn here --------------------------------- # +def inference_fn(cfg, model, device, test_path, save_path): + actual_model = model.module if isinstance(model, torch.nn.parallel.DistributedDataParallel) else model + actual_model.eval() + torch.set_grad_enabled(False) + test_list = [] + for data_meta_path in test_path: + test_list.extend(json.load(open(data_meta_path, "r"))) + test_list = [item for item in test_list if item.get("mode") == "test"] + seen_ids = set() + test_list = [item for item in test_list if not (item["video_id"] in seen_ids or seen_ids.add(item["video_id"]))] + + save_list = [] + start_time = time.time() + total_length = 0 + for test_file in tqdm(test_list, desc="Testing"): + audio, _ = librosa.load(test_file["audio_path"], sr=cfg.audio_sr) + audio = torch.from_numpy(audio).to(device).unsqueeze(0) + speaker_id = torch.zeros(1,1).to(device).long() + motion_pred = actual_model(audio, speaker_id, seed_frames=4, seed_motion=None)["motion_axis_angle"] + t = motion_pred.shape[1] + motion_pred = motion_pred.cpu().numpy().reshape(t, -1) + beat_format_save(os.path.join(save_path, f"{test_file['video_id']}_output.npz"), motion_pred, upsample=30//cfg.pose_fps) + save_list.append( + { + "audio_path": test_file["audio_path"], + "motion_path": os.path.join(save_path, f"{test_file['video_id']}_output.npz"), + "video_id": test_file["video_id"], + } + ) + total_length+=t + time_cost = time.time() - start_time + print(f"\n cost {time_cost:.2f} seconds to generate {total_length / cfg.pose_fps:.2f} seconds of motion") + return test_list, save_list + + +def train_val_fn(cfg, batch, model, device, mode="train", optimizer=None, lr_scheduler=None, fgd_evaluator=None): + model.train() if mode == "train" else model.eval() + torch.set_grad_enabled(mode == "train") + joint_mask = MASK_DICT[cfg.model.joint_mask] + if mode == "train": + optimizer.zero_grad() + motion_gt = batch["motion"].to(device) + audio = batch["audio"].to(device) + bs, t, jc = motion_gt.shape + j = jc // 3 + speaker_id = torch.zeros(bs,1).to(device).long() + motion_gt = rc.axis_angle_to_rotation_6d(motion_gt.reshape(bs,t,j,3)).reshape(bs, t, j*6) + motion_pred = model(audio, speaker_id, seed_frames=4, seed_motion=motion_gt, return_axis_angle=False)["motion"] + motion_pred = rc.rotation_6d_to_matrix(motion_pred.reshape(bs,t,j,6)) + motion_gt = rc.rotation_6d_to_matrix(motion_gt.reshape(bs,t,j,6)) + loss = GeodesicLossFn(motion_pred, motion_gt) + loss_dict = {"loss": loss} + all_loss = sum(loss_dict.values()) + loss_dict["all_loss"] = all_loss + + if mode == "train": + if cfg.solver.max_grad_norm > 0: + torch.nn.utils.clip_grad_norm_(model.parameters(), cfg.solver.max_grad_norm) + all_loss.backward() + optimizer.step() + lr_scheduler.step() + + if mode == "val": + motion_pred = rc.matrix_to_rotation_6d(motion_pred).reshape(bs, t, j*6) + motion_gt = rc.matrix_to_rotation_6d(motion_gt).reshape(bs, t, j*6) + padded_pred = recover_from_mask_ts(motion_pred, joint_mask) + padded_gt = recover_from_mask_ts(motion_gt, joint_mask) + fgd_evaluator.update(padded_pred, padded_gt) + return loss_dict + + +# --------------------------------- main train loop here --------------------------------- # +def main(cfg): + seed_everything(cfg.seed) + os.environ["WANDB_API_KEY"] = cfg.wandb_key + local_rank = int(os.environ["LOCAL_RANK"]) if "LOCAL_RANK" in os.environ else 0 + torch.cuda.set_device(local_rank) + device = torch.device("cuda", local_rank) + torch.distributed.init_process_group(backend="nccl") + log_dir = os.path.join(cfg.output_dir, cfg.exp_name) + experiment_ckpt_dir = os.path.join(log_dir, "checkpoints") + os.makedirs(experiment_ckpt_dir, exist_ok=True) + + if local_rank == 0 and cfg.validation.wandb: + wandb.init( + project=cfg.wandb_project, + name=cfg.exp_name, + entity=cfg.wandb_entity, + dir=log_dir, + config=OmegaConf.to_container(cfg) + ) + + # init + if cfg.test: + from models.camn_audio import CamnAudioModel + model = CamnAudioModel.from_pretrained("H-Liu1997/camn_audio").to(device) + else: + model = init_hf_class(cfg.model.name_pyfile, cfg.model.class_name, cfg.model).to(device) + model = DDP(model, device_ids=[local_rank], output_device=local_rank, find_unused_parameters=True) + + # optimizer + optimizer_cls = torch.optim.Adam + optimizer = optimizer_cls( + filter(lambda p: p.requires_grad, model.parameters()), + lr=cfg.solver.learning_rate, + betas=(cfg.solver.adam_beta1, cfg.solver.adam_beta2), + weight_decay=cfg.solver.adam_weight_decay, + eps=cfg.solver.adam_epsilon + ) + lr_scheduler = get_scheduler( + cfg.solver.lr_scheduler, + optimizer=optimizer, + num_warmup_steps=cfg.solver.lr_warmup_steps * cfg.solver.gradient_accumulation_steps, + num_training_steps=cfg.solver.max_train_steps * cfg.solver.gradient_accumulation_steps + ) + + # dataset + train_dataset = init_class(cfg.data.name_pyfile, cfg.data.class_name, cfg, split='train') + test_dataset = init_class(cfg.data.name_pyfile, cfg.data.class_name, cfg, split='test') + train_sampler = torch.utils.data.distributed.DistributedSampler(train_dataset) + test_sampler = torch.utils.data.distributed.DistributedSampler(test_dataset) + train_loader = DataLoader(train_dataset, batch_size=cfg.data.train_bs, sampler=train_sampler, drop_last=True, num_workers=8) + test_loader = DataLoader(test_dataset, batch_size=cfg.data.train_bs, sampler=test_sampler, drop_last=False, num_workers=8) + + # resume + if cfg.resume_from_checkpoint: + checkpoint = torch.load(cfg.resume_from_checkpoint, map_location="cpu") + model.load_state_dict(checkpoint["model_state_dict"]) + optimizer.load_state_dict(checkpoint["optimizer_state_dict"]) + lr_scheduler.load_state_dict(checkpoint["lr_scheduler_state_dict"]) + iteration = checkpoint["iteration"] + else: + iteration = 0 + if cfg.test: + iteration = 0 + + max_epochs = (cfg.solver.max_train_steps // len(train_loader)) + (1 if cfg.solver.max_train_steps % len(train_loader) != 0 else 0) + start_epoch = iteration // len(train_loader) + start_step_in_epoch = iteration % len(train_loader) + fgd_evaluator = FGD(download_path="./emage_evaltools/") + bc_evaluator = BC(download_path="./emage_evaltools/", sigma=0.3, order=7) + l1div_evaluator= L1div() + loss_meters = {} + loss_meters_val = {} + best_fgd_val = np.inf + best_fgd_iteration_val= 0 + best_fgd_test = np.inf + best_fgd_iteration_test = 0 + + # train loop + data_start = time.time() + for epoch in range(start_epoch, max_epochs): + train_sampler.set_epoch(epoch) + pbar = tqdm(train_loader, leave=True) + for i, batch in enumerate(pbar): + # for correct resume, if the dataset is very large. since we fixed the seed, we can skip the data + if i < start_step_in_epoch: + iteration += 1 + continue + + # test + if iteration % cfg.validation.test_steps == 0 and local_rank == 0: + test_save_path = os.path.join(log_dir, f"test_{iteration}") + os.makedirs(test_save_path, exist_ok=True) + with torch.no_grad(): + test_list, save_list = inference_fn(cfg.model, model, device, cfg.data.test_meta_paths, test_save_path) + if cfg.validation.evaluation: + metrics = evaluation_fn([True]*55, test_list, save_list, fgd_evaluator, bc_evaluator, l1div_evaluator, device) + if cfg.validation.visualization: visualization_fn(save_list, test_save_path, test_list, only_check_one=True) + if cfg.validation.evaluation: best_fgd_test, best_fgd_iteration_test = log_test(model, metrics, iteration, best_fgd_test, best_fgd_iteration_test, cfg, local_rank, experiment_ckpt_dir, test_save_path) + if cfg.test: return 0 + + # validation + if iteration % cfg.validation.validation_steps == 0: + loss_meters = {} + loss_meters_val = {} + fgd_evaluator.reset() + pbar_val = tqdm(test_loader, leave=True) + + data_start_val = time.time() + for j, batch in enumerate(pbar_val): + data_time_val = time.time() - data_start_val + with torch.no_grad(): + val_loss_dict = train_val_fn(cfg, batch, model, device, mode="val", fgd_evaluator=fgd_evaluator) + net_time_val = time.time() - data_start_val + val_loss_dict["fgd"] = fgd_evaluator.compute() if j == len(test_loader) - 1 else 0 + log_train_val(cfg, val_loss_dict, local_rank, loss_meters_val, pbar_val, epoch, max_epochs, iteration, net_time_val, data_time_val, optimizer, "Val ") + data_start_val = time.time() + if cfg.debug and j > 1: break + + if local_rank == 0: + best_fgd_val, best_fgd_iteration_val = save_last_and_best_ckpt( + model, optimizer, lr_scheduler, iteration, experiment_ckpt_dir, best_fgd_val, best_fgd_iteration_val, val_loss_dict["fgd"], lower_is_better=True, mertic_name="fgd") + + # train + data_time = time.time() - data_start + loss_dict = train_val_fn(cfg, batch, model, device, mode="train", optimizer=optimizer, lr_scheduler=lr_scheduler) + net_time = time.time() - data_start - data_time + log_train_val(cfg, loss_dict, local_rank, loss_meters, pbar, epoch, max_epochs, iteration, net_time, data_time, optimizer, "Train") + data_start = time.time() + + iteration += 1 + start_step_in_epoch = 0 + epoch += 1 + + if local_rank == 0 and cfg.validation.wandb: + wandb.finish() + torch.distributed.destroy_process_group() + + +# --------------------------------- utils fn here --------------------------------- # +def evaluation_fn(joint_mask, gt_list, pred_list, fgd_evaluator, bc_evaluator, l1_evaluator, device): + fgd_evaluator.reset() + bc_evaluator.reset() + l1_evaluator.reset() + # lvd_evaluator.reset() + # mse_evaluator.reset() + + for test_file in tqdm(gt_list, desc="Evaluation"): + # only load selective joints + pred_file = [item for item in pred_list if item["video_id"] == test_file["video_id"]][0] + if not pred_file: + print(f"Missing prediction for {test_file['video_id']}") + continue + # print(test_file["motion_path"], pred_file["motion_path"]) + gt_dict = beat_format_load(test_file["motion_path"], joint_mask) + pred_dict = beat_format_load(pred_file["motion_path"], joint_mask) + + motion_gt = gt_dict["poses"] + motion_pred = pred_dict["poses"] + # expressions_gt = gt_dict["expressions"] + # expressions_pred = pred_dict["expressions"] + betas = gt_dict["betas"] + # motion_gt = recover_from_mask(motion_gt, joint_mask) # t1*165 + # motion_pred = recover_from_mask(motion_pred, joint_mask) # t2*165 + + t = min(motion_gt.shape[0], motion_pred.shape[0]) + motion_gt = motion_gt[:t] + motion_pred = motion_pred[:t] + # expressions_gt = expressions_gt[:t] + # expressions_pred = expressions_pred[:t] + + # bc and l1 require position representation + motion_position_pred = get_motion_rep_numpy(motion_pred, device=device, betas=betas)["position"] # t*55*3 + motion_position_pred = motion_position_pred.reshape(t, -1) + # ignore the start and end 2s, this may for beat dataset only + audio_beat = bc_evaluator.load_audio(test_file["audio_path"], t_start=2 * 16000, t_end=int((t-60)/30*16000)) + motion_beat = bc_evaluator.load_motion(motion_position_pred, t_start=60, t_end=t-60, pose_fps=30, without_file=True) + bc_evaluator.compute(audio_beat, motion_beat, length=t-120, pose_fps=30) + # audio_beat = bc_evaluator.load_audio(test_file["audio_path"], t_start=0 * 16000, t_end=int((t-0)/30*16000)) + # motion_beat = bc_evaluator.load_motion(motion_position_pred, t_start=0, t_end=t-0, pose_fps=30, without_file=True) + # bc_evaluator.compute(audio_beat, motion_beat, length=t-0, pose_fps=30) + + l1_evaluator.compute(motion_position_pred) + + # face_position_pred = get_motion_rep_numpy(motion_pred, device=device, expressions=expressions_pred, expression_only=True, betas=betas)["vertices"] # t -1 + # face_position_gt = get_motion_rep_numpy(motion_gt, device=device, expressions=expressions_gt, expression_only=True, betas=betas)["vertices"] + # lvd_evaluator.compute(face_position_pred, face_position_gt) + # mse_evaluator.compute(face_position_pred, face_position_gt) + + # fgd requires rotation 6d representaiton + motion_gt = torch.from_numpy(motion_gt).to(device).unsqueeze(0) + motion_pred = torch.from_numpy(motion_pred).to(device).unsqueeze(0) + motion_gt = rc.axis_angle_to_rotation_6d(motion_gt.reshape(1, t, 55, 3)).reshape(1, t, 55*6) + motion_pred = rc.axis_angle_to_rotation_6d(motion_pred.reshape(1, t, 55, 3)).reshape(1, t, 55*6) + fgd_evaluator.update(motion_pred.float(), motion_gt.float()) + + metrics = {} + metrics["fgd"] = fgd_evaluator.compute() + metrics["bc"] = bc_evaluator.avg() + metrics["l1"] = l1_evaluator.avg() + # metrics["lvd"] = lvd_evaluator.avg() + # metrics["mse"] = mse_evaluator.avg() + return metrics + +def visualization_fn(pred_list, save_path, gt_list=None, only_check_one=True): + if gt_list is None: # single visualization + for i in range(len(pred_list)): + fast_render.render_one_sequence( + pred_list[i]["motion_path"], + save_path, + pred_list[i]["audio_path"], + model_folder="./evaluation/smplx_models/", + ) + if only_check_one: break + else: # paired visualization, pad the translation + for i in range(len(pred_list)): + npz_pred = np.load(pred_list[i]["motion_path"], allow_pickle=True) + gt_file = [item for item in gt_list if item["video_id"] == pred_list[i]["video_id"]][0] + if not gt_file: + print(f"Missing prediction for {pred_list[i]['video_id']}") + continue + npz_gt = np.load(gt_file["motion_path"], allow_pickle=True) + t = npz_gt["poses"].shape[0] + np.savez( + os.path.join(save_path, f"{pred_list[i]['video_id']}_transpad.npz"), + betas=npz_pred['betas'][:t], + poses=npz_pred['poses'][:t], + expressions=npz_pred['expressions'][:t], + trans=npz_pred["trans"][:t], + model='smplx2020', + gender='neutral', + mocap_frame_rate=30, + ) + fast_render.render_one_sequence( + os.path.join(save_path, f"{pred_list[i]['video_id']}_transpad.npz"), + gt_file["motion_path"], + save_path, + pred_list[i]["audio_path"], + model_folder="./evaluation/smplx_models/", + ) + if only_check_one: break + +def log_test(model, metrics, iteration, best_mertics, best_iteration, cfg, local_rank, experiment_ckpt_dir, video_save_path=None): + if local_rank == 0: + print(f"\n Test Results at iteration {iteration}:") + for key, value in metrics.items(): + print(f" {key}: {value:.10f}") + if cfg.validation.wandb: + for key, value in metrics.items(): + wandb.log({f"test/{key}": value}, step=iteration) + if cfg.validation.wandb and cfg.validation.visualization: + videos_to_log = [] + for filename in os.listdir(video_save_path): + if filename.endswith(".mp4"): + videos_to_log.append(wandb.Video(os.path.join(video_save_path, filename))) + if videos_to_log: + wandb.log({"test/videos": videos_to_log}, step=iteration) + if metrics["fgd"] < best_mertics: + best_mertics = metrics["fgd"] + best_iteration = iteration + model.module.save_pretrained(os.path.join(experiment_ckpt_dir, "test_best")) + # print(metrics, best_mertics, best_iteration) + message = f"Current Test FGD: {metrics['fgd']:.4f} (Best: {best_mertics:.4f} at iteration {best_iteration})" + log_metric_with_box(message) + return best_mertics, best_iteration + +def log_metric_with_box(message): + box_width = len(message) + 2 + border = "-" * box_width + print(f"\n{border}") + print(f"|{message}|") + print(f"{border}\n") + +def log_train_val(cfg, loss_dict, local_rank, loss_meters, pbar, epoch, max_epochs, iteration, net_time, data_time, optimizer, ptype="Train"): + new_loss_dict = {} + for k, v in loss_dict.items(): + if "fgd" in k: continue + v_cpu = torch.as_tensor(v).float().cpu().item() + if k not in loss_meters: + loss_meters[k] = {"sum":0,"count":0} + loss_meters[k]["sum"] += v_cpu + loss_meters[k]["count"] += 1 + new_loss_dict[k] = v_cpu + mem_used = torch.cuda.memory_reserved() / 1E9 + lr = optimizer.param_groups[0]["lr"] + loss_str = " ".join([f"{k}: {new_loss_dict[k]:.4f}({loss_meters[k]['sum']/loss_meters[k]['count']:.4f})" for k in new_loss_dict]) + desc = f"{ptype}: Epoch[{epoch}/{max_epochs}] Iter[{iteration}] {loss_str} lr: {lr:.2E} data_time: {data_time:.3f} net_time: {net_time:.3f} mem: {mem_used:.2f}GB" + pbar.set_description(desc) + pbar.bar_format = "{desc} {n_fmt}/{total_fmt} [{elapsed}<{remaining}, {rate_fmt}]" + if cfg.validation.wandb and local_rank == 0: + for k, v in new_loss_dict.items(): + wandb.log({f"loss/{ptype}/{k}": v}, step=iteration) + +def save_last_and_best_ckpt(model, optimizer, lr_scheduler, iteration, save_dir, previous_best, best_iteration, current, lower_is_better=True, mertic_name="fgd"): + checkpoint = { + "model_state_dict": model.state_dict(), + "optimizer_state_dict": optimizer.state_dict(), + "lr_scheduler_state_dict": lr_scheduler.state_dict(), + "iteration": iteration, + } + torch.save(checkpoint, os.path.join(save_dir, "last.bin")) + model.module.save_pretrained(os.path.join(save_dir, "last")) + if (lower_is_better and current < previous_best) or (not lower_is_better and current > previous_best): + previous_best = current + best_iteration = iteration + shutil.copy(os.path.join(save_dir, "last.bin"), os.path.join(save_dir, "best.bin")) + model.module.save_pretrained(os.path.join(save_dir, "best")) + message = f"Current interation {iteration} {mertic_name}: {current:.4f} (Best: {previous_best:.4f} at iteration {best_iteration})" + log_metric_with_box(message) + return previous_best, best_iteration + +def init_hf_class(module_name, class_name, config, **kwargs): + module = importlib.import_module(module_name) + model_class = getattr(module, class_name) + config_class = model_class.config_class + config = config_class(config_obj=config) + instance = model_class(config, **kwargs) + return instance + +def init_class(module_name, class_name, config, **kwargs): + module = importlib.import_module(module_name) + model_class = getattr(module, class_name) + instance = model_class(config, **kwargs) + return instance + +def seed_everything(seed): + os.environ['PYTHONHASHSEED'] = str(seed) + random.seed(seed) + np.random.seed(seed) + torch.manual_seed(seed) + torch.cuda.manual_seed_all(seed) + torch.cuda.manual_seed(seed) + torch.backends.cudnn.deterministic = True + torch.backends.cudnn.benchmark = True + torch.backends.cudnn.enabled = True + +def init_env(): + parser = argparse.ArgumentParser() + parser.add_argument("--config", type=str, default="./configs/train/stage2.yaml") + parser.add_argument("--debug", action="store_true") + parser.add_argument("--wandb", action="store_true") + parser.add_argument("--visualization", action="store_true") + parser.add_argument("--evaluation", action="store_true") + parser.add_argument("--test", action="store_true") + parser.add_argument('overrides', nargs=argparse.REMAINDER) + args = parser.parse_args() + config = OmegaConf.load(args.config) + config.exp_name = os.path.splitext(os.path.basename(args.config))[0] + + if args.overrides: config = OmegaConf.merge(config, OmegaConf.from_dotlist(args.overrides)) + if args.debug: + config.wandb_project = "debug" + config.exp_name = "debug" + config.solver.max_train_steps = 4 + else: + run_time = datetime.now().strftime("%Y%m%d-%H%M") + config.exp_name = config.exp_name + "_" + run_time + if args.wandb: + config.validation.wandb = True + if args.visualization: + config.validation.visualization = True + if args.evaluation: + config.validation.evaluation = True + if args.test: + config.test = True + save_dir = os.path.join(config.output_dir, config.exp_name) + os.makedirs(save_dir, exist_ok=True) + sanity_check_dir = os.path.join(save_dir, 'sanity_check') + os.makedirs(sanity_check_dir, exist_ok=True) + with open(os.path.join(sanity_check_dir, f'{config.exp_name}.yaml'), 'w') as f: + OmegaConf.save(config, f) + current_dir = Path.cwd() + for py_file in current_dir.rglob('*.py'): + dest_path = Path(sanity_check_dir) / py_file.relative_to(current_dir) + dest_path.parent.mkdir(parents=True, exist_ok=True) + shutil.copy(py_file, dest_path) + return config + +if __name__ == "__main__": + config = init_env() + main(config) \ No newline at end of file diff --git a/train_disco_audio.py b/train_disco_audio.py new file mode 100644 index 0000000000000000000000000000000000000000..dff3b555251f7750e25f8defc10a3e1483edd505 --- /dev/null +++ b/train_disco_audio.py @@ -0,0 +1,561 @@ +import os +import shutil +import argparse +import random +import numpy as np +from datetime import datetime +from tqdm import tqdm +import importlib +import copy +import librosa +from pathlib import Path +import json +import time +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch.utils.data import DataLoader, WeightedRandomSampler +from torch.nn.parallel import DistributedDataParallel as DDP +import wandb + +from diffusers.optimization import get_scheduler +from omegaconf import OmegaConf + +from emage_evaltools.mertic import FGD, BC, L1div +from emage_utils.motion_io import beat_format_load, beat_format_save, MASK_DICT, recover_from_mask, recover_from_mask_ts +import emage_utils.rotation_conversions as rc +from emage_utils import fast_render +from emage_utils.motion_rep_transfer import get_motion_rep_numpy + + +# --------------------------------- loss here --------------------------------- # +class GeodesicLoss(nn.Module): + def __init__(self): + super(GeodesicLoss, self).__init__() + def compute_geodesic_distance(self, m1, m2): + m1 = m1.reshape(-1, 3, 3) + m2 = m2.reshape(-1, 3, 3) + m = torch.bmm(m1, m2.transpose(1, 2)) + cos = (m[:, 0, 0] + m[:, 1, 1] + m[:, 2, 2] - 1) / 2 + cos = torch.clamp(cos, min=-1 + 1E-6, max=1-1E-6) + theta = torch.acos(cos) + return theta + def __call__(self, m1, m2, reduction='mean'): + loss = self.compute_geodesic_distance(m1, m2) + if reduction == 'mean': + return loss.mean() + elif reduction == 'none': + return loss + else: + raise RuntimeError + +GeodesicLossFn = GeodesicLoss() + +def contrastive_loss(features, labels, margin=1.0): + # features: [bs, n, c] + # labels: [bs, 1] + # first, reduce features along time (or sequence) dimension + feats = features.mean(dim=1) # [bs, c] + lbs = labels.squeeze(-1) # [bs] + + # compute pairwise distances + dist = torch.cdist(feats, feats, p=2) # [bs, bs] + pos_mask = (lbs.unsqueeze(0) == lbs.unsqueeze(1)).float() # [bs, bs] + + # positive pairs: distance should be small + pos_loss = pos_mask * dist + + # negative pairs: distance should be large + # margin-based loss + neg_loss = (1.0 - pos_mask) * F.relu(margin - dist) + + return pos_loss.mean() + neg_loss.mean() + + + +def get_weighted_sampler(dataset): + # Collect labels + labels = [] + for item in dataset.data_list: + labels.append(item["content_label"]) + labels = np.array(labels) + class_counts = np.bincount(labels) + weights = 1.0 / class_counts[labels] + sampler = WeightedRandomSampler( + weights=weights, + num_samples=len(weights), # Usually same as dataset size + replacement=True # Typically True for weighted sampling + ) + return sampler + + +# --------------------------------- train,val,test fn here --------------------------------- # +def inference_fn(cfg, model, device, test_path, save_path): + actual_model = model.module if isinstance(model, torch.nn.parallel.DistributedDataParallel) else model + actual_model.eval() + torch.set_grad_enabled(False) + test_list = [] + for data_meta_path in test_path: + test_list.extend(json.load(open(data_meta_path, "r"))) + test_list = [item for item in test_list if item.get("mode") == "test"] + seen_ids = set() + test_list = [item for item in test_list if not (item["video_id"] in seen_ids or seen_ids.add(item["video_id"]))] + + save_list = [] + start_time = time.time() + total_length = 0 + for test_file in tqdm(test_list, desc="Testing"): + audio, _ = librosa.load(test_file["audio_path"], sr=cfg.audio_sr) + audio = torch.from_numpy(audio).to(device).unsqueeze(0) + speaker_id = torch.zeros(1,1).to(device).long() + motion_pred = actual_model(audio, speaker_id, seed_frames=4, seed_motion=None)["motion_axis_angle"] + t = motion_pred.shape[1] + motion_pred = motion_pred.cpu().numpy().reshape(t, -1) + beat_format_save(os.path.join(save_path, f"{test_file['video_id']}_output.npz"), motion_pred, upsample=30//cfg.pose_fps) + save_list.append( + { + "audio_path": test_file["audio_path"], + "motion_path": os.path.join(save_path, f"{test_file['video_id']}_output.npz"), + "video_id": test_file["video_id"], + } + ) + total_length+=t + time_cost = time.time() - start_time + print(f"\n cost {time_cost:.2f} seconds to generate {total_length / cfg.pose_fps:.2f} seconds of motion") + return test_list, save_list + + +def train_val_fn(cfg, batch, model, device, mode="train", optimizer=None, lr_scheduler=None, fgd_evaluator=None): + model.train() if mode == "train" else model.eval() + torch.set_grad_enabled(mode == "train") + joint_mask = MASK_DICT[cfg.model.joint_mask] + if mode == "train": + optimizer.zero_grad() + motion_gt = batch["motion"].to(device) + audio = batch["audio"].to(device) + rhythm = batch["rhythm_label"].to(device) + content = batch["content_label"].to(device) + + bs, t, jc = motion_gt.shape + j = jc // 3 + speaker_id = torch.zeros(bs,1).to(device).long() + motion_gt = rc.axis_angle_to_rotation_6d(motion_gt.reshape(bs,t,j,3)).reshape(bs, t, j*6) + all_pred = model(audio, speaker_id, seed_frames=4, seed_motion=motion_gt, return_axis_angle=False) + + motion_pred = all_pred["motion"] + motion_pred = rc.rotation_6d_to_matrix(motion_pred.reshape(bs,t,j,6)) + motion_gt = rc.rotation_6d_to_matrix(motion_gt.reshape(bs,t,j,6)) + loss = GeodesicLossFn(motion_pred, motion_gt) + loss_dict = {"loss": loss} + + # feature disentanglement loss + rhythm_fea = all_pred["audio_fea_r"] + content_fea = all_pred["audio_fea_c"] + # if two features are the same rhythm class, the distance should be small, other wise large + rhythm_fea = F.normalize(rhythm_fea, dim=1) + content_fea = F.normalize(content_fea, dim=1) + rhythm_disentangle_loss = contrastive_loss(rhythm_fea, rhythm) + content_disentangle_loss = contrastive_loss(content_fea, content) + loss_dict["rhythm"] = rhythm_disentangle_loss + loss_dict["content"] = content_disentangle_loss + + all_loss = sum(loss_dict.values()) + loss_dict["all_loss"] = all_loss + + if mode == "train": + if cfg.solver.max_grad_norm > 0: + torch.nn.utils.clip_grad_norm_(model.parameters(), cfg.solver.max_grad_norm) + all_loss.backward() + optimizer.step() + lr_scheduler.step() + + if mode == "val": + motion_pred = rc.matrix_to_rotation_6d(motion_pred).reshape(bs, t, j*6) + motion_gt = rc.matrix_to_rotation_6d(motion_gt).reshape(bs, t, j*6) + padded_pred = recover_from_mask_ts(motion_pred, joint_mask) + padded_gt = recover_from_mask_ts(motion_gt, joint_mask) + fgd_evaluator.update(padded_pred, padded_gt) + return loss_dict + + +# --------------------------------- main train loop here --------------------------------- # +def main(cfg): + seed_everything(cfg.seed) + os.environ["WANDB_API_KEY"] = cfg.wandb_key + local_rank = int(os.environ["LOCAL_RANK"]) if "LOCAL_RANK" in os.environ else 0 + torch.cuda.set_device(local_rank) + device = torch.device("cuda", local_rank) + torch.distributed.init_process_group(backend="nccl") + log_dir = os.path.join(cfg.output_dir, cfg.exp_name) + experiment_ckpt_dir = os.path.join(log_dir, "checkpoints") + os.makedirs(experiment_ckpt_dir, exist_ok=True) + + if local_rank == 0 and cfg.validation.wandb: + wandb.init( + project=cfg.wandb_project, + name=cfg.exp_name, + entity=cfg.wandb_entity, + dir=log_dir, + config=OmegaConf.to_container(cfg) + ) + + # init + if cfg.test: + from models.disco_audio import DiscoAudioModel + model = DiscoAudioModel.from_pretrained("/content/outputs/disco_audio/checkpoints/last").to(device) + else: + model = init_hf_class(cfg.model.name_pyfile, cfg.model.class_name, cfg.model).to(device) + model = DDP(model, device_ids=[local_rank], output_device=local_rank, find_unused_parameters=True) + + # optimizer + optimizer_cls = torch.optim.Adam + optimizer = optimizer_cls( + filter(lambda p: p.requires_grad, model.parameters()), + lr=cfg.solver.learning_rate, + betas=(cfg.solver.adam_beta1, cfg.solver.adam_beta2), + weight_decay=cfg.solver.adam_weight_decay, + eps=cfg.solver.adam_epsilon + ) + lr_scheduler = get_scheduler( + cfg.solver.lr_scheduler, + optimizer=optimizer, + num_warmup_steps=cfg.solver.lr_warmup_steps * cfg.solver.gradient_accumulation_steps, + num_training_steps=cfg.solver.max_train_steps * cfg.solver.gradient_accumulation_steps + ) + + # dataset + train_dataset = init_class(cfg.data.name_pyfile, cfg.data.class_name, cfg, split='train') + test_dataset = init_class(cfg.data.name_pyfile, cfg.data.class_name, cfg, split='test') + train_sampler = get_weighted_sampler(train_dataset) + test_sampler = torch.utils.data.distributed.DistributedSampler(test_dataset) + train_loader = DataLoader(train_dataset, batch_size=cfg.data.train_bs, sampler=train_sampler, drop_last=True, num_workers=8) + test_loader = DataLoader(test_dataset, batch_size=cfg.data.train_bs, sampler=test_sampler, drop_last=False, num_workers=8) + + # resume + if cfg.resume_from_checkpoint: + checkpoint = torch.load(cfg.resume_from_checkpoint, map_location="cpu") + model.load_state_dict(checkpoint["model_state_dict"]) + optimizer.load_state_dict(checkpoint["optimizer_state_dict"]) + lr_scheduler.load_state_dict(checkpoint["lr_scheduler_state_dict"]) + iteration = checkpoint["iteration"] + else: + iteration = 0 + if cfg.test: + iteration = 0 + + max_epochs = (cfg.solver.max_train_steps // len(train_loader)) + (1 if cfg.solver.max_train_steps % len(train_loader) != 0 else 0) + start_epoch = iteration // len(train_loader) + start_step_in_epoch = iteration % len(train_loader) + fgd_evaluator = FGD(download_path="./emage_evaltools/") + bc_evaluator = BC(download_path="./emage_evaltools/", sigma=0.3, order=7) + l1div_evaluator= L1div() + loss_meters = {} + loss_meters_val = {} + best_fgd_val = np.inf + best_fgd_iteration_val= 0 + best_fgd_test = np.inf + best_fgd_iteration_test = 0 + + # train loop + data_start = time.time() + for epoch in range(start_epoch, max_epochs): + # train_sampler.set_epoch(epoch) + pbar = tqdm(train_loader, leave=True) + for i, batch in enumerate(pbar): + # for correct resume, if the dataset is very large. since we fixed the seed, we can skip the data + if i < start_step_in_epoch: + iteration += 1 + continue + + # test + if iteration % cfg.validation.test_steps == 0 and local_rank == 0: + test_save_path = os.path.join(log_dir, f"test_{iteration}") + os.makedirs(test_save_path, exist_ok=True) + with torch.no_grad(): + test_list, save_list = inference_fn(cfg.model, model, device, cfg.data.test_meta_paths, test_save_path) + if cfg.validation.evaluation: + metrics = evaluation_fn([True]*55, test_list, save_list, fgd_evaluator, bc_evaluator, l1div_evaluator, device) + if cfg.validation.visualization: visualization_fn(save_list, test_save_path, test_list, only_check_one=True) + if cfg.validation.evaluation: best_fgd_test, best_fgd_iteration_test = log_test(model, metrics, iteration, best_fgd_test, best_fgd_iteration_test, cfg, local_rank, experiment_ckpt_dir, test_save_path) + if cfg.test: return 0 + + # validation + if iteration % cfg.validation.validation_steps == 0: + loss_meters = {} + loss_meters_val = {} + fgd_evaluator.reset() + pbar_val = tqdm(test_loader, leave=True) + + data_start_val = time.time() + for j, batch in enumerate(pbar_val): + data_time_val = time.time() - data_start_val + with torch.no_grad(): + val_loss_dict = train_val_fn(cfg, batch, model, device, mode="val", fgd_evaluator=fgd_evaluator) + net_time_val = time.time() - data_start_val + val_loss_dict["fgd"] = fgd_evaluator.compute() if j == len(test_loader) - 1 else 0 + log_train_val(cfg, val_loss_dict, local_rank, loss_meters_val, pbar_val, epoch, max_epochs, iteration, net_time_val, data_time_val, optimizer, "Val ") + data_start_val = time.time() + if cfg.debug and j > 1: break + + if local_rank == 0: + best_fgd_val, best_fgd_iteration_val = save_last_and_best_ckpt( + model, optimizer, lr_scheduler, iteration, experiment_ckpt_dir, best_fgd_val, best_fgd_iteration_val, val_loss_dict["fgd"], lower_is_better=True, mertic_name="fgd") + + # train + data_time = time.time() - data_start + loss_dict = train_val_fn(cfg, batch, model, device, mode="train", optimizer=optimizer, lr_scheduler=lr_scheduler) + net_time = time.time() - data_start - data_time + log_train_val(cfg, loss_dict, local_rank, loss_meters, pbar, epoch, max_epochs, iteration, net_time, data_time, optimizer, "Train") + data_start = time.time() + + iteration += 1 + start_step_in_epoch = 0 + epoch += 1 + + if local_rank == 0 and cfg.validation.wandb: + wandb.finish() + torch.distributed.destroy_process_group() + + +# --------------------------------- utils fn here --------------------------------- # +def evaluation_fn(joint_mask, gt_list, pred_list, fgd_evaluator, bc_evaluator, l1_evaluator, device): + fgd_evaluator.reset() + bc_evaluator.reset() + l1_evaluator.reset() + # lvd_evaluator.reset() + # mse_evaluator.reset() + + for test_file in tqdm(gt_list, desc="Evaluation"): + # only load selective joints + pred_file = [item for item in pred_list if item["video_id"] == test_file["video_id"]][0] + if not pred_file: + print(f"Missing prediction for {test_file['video_id']}") + continue + # print(test_file["motion_path"], pred_file["motion_path"]) + gt_dict = beat_format_load(test_file["motion_path"], joint_mask) + pred_dict = beat_format_load(pred_file["motion_path"], joint_mask) + + motion_gt = gt_dict["poses"] + motion_pred = pred_dict["poses"] + # expressions_gt = gt_dict["expressions"] + # expressions_pred = pred_dict["expressions"] + betas = gt_dict["betas"] + # motion_gt = recover_from_mask(motion_gt, joint_mask) # t1*165 + # motion_pred = recover_from_mask(motion_pred, joint_mask) # t2*165 + + t = min(motion_gt.shape[0], motion_pred.shape[0]) + motion_gt = motion_gt[:t] + motion_pred = motion_pred[:t] + # expressions_gt = expressions_gt[:t] + # expressions_pred = expressions_pred[:t] + + # bc and l1 require position representation + motion_position_pred = get_motion_rep_numpy(motion_pred, device=device, betas=betas)["position"] # t*55*3 + motion_position_pred = motion_position_pred.reshape(t, -1) + # ignore the start and end 2s, this may for beat dataset only + audio_beat = bc_evaluator.load_audio(test_file["audio_path"], t_start=2 * 16000, t_end=int((t-60)/30*16000)) + motion_beat = bc_evaluator.load_motion(motion_position_pred, t_start=60, t_end=t-60, pose_fps=30, without_file=True) + bc_evaluator.compute(audio_beat, motion_beat, length=t-120, pose_fps=30) + # audio_beat = bc_evaluator.load_audio(test_file["audio_path"], t_start=0 * 16000, t_end=int((t-0)/30*16000)) + # motion_beat = bc_evaluator.load_motion(motion_position_pred, t_start=0, t_end=t-0, pose_fps=30, without_file=True) + # bc_evaluator.compute(audio_beat, motion_beat, length=t-0, pose_fps=30) + + l1_evaluator.compute(motion_position_pred) + + # face_position_pred = get_motion_rep_numpy(motion_pred, device=device, expressions=expressions_pred, expression_only=True, betas=betas)["vertices"] # t -1 + # face_position_gt = get_motion_rep_numpy(motion_gt, device=device, expressions=expressions_gt, expression_only=True, betas=betas)["vertices"] + # lvd_evaluator.compute(face_position_pred, face_position_gt) + # mse_evaluator.compute(face_position_pred, face_position_gt) + + # fgd requires rotation 6d representaiton + motion_gt = torch.from_numpy(motion_gt).to(device).unsqueeze(0) + motion_pred = torch.from_numpy(motion_pred).to(device).unsqueeze(0) + motion_gt = rc.axis_angle_to_rotation_6d(motion_gt.reshape(1, t, 55, 3)).reshape(1, t, 55*6) + motion_pred = rc.axis_angle_to_rotation_6d(motion_pred.reshape(1, t, 55, 3)).reshape(1, t, 55*6) + fgd_evaluator.update(motion_pred.float(), motion_gt.float()) + + metrics = {} + metrics["fgd"] = fgd_evaluator.compute() + metrics["bc"] = bc_evaluator.avg() + metrics["l1"] = l1_evaluator.avg() + # metrics["lvd"] = lvd_evaluator.avg() + # metrics["mse"] = mse_evaluator.avg() + return metrics + +def visualization_fn(pred_list, save_path, gt_list=None, only_check_one=True): + if gt_list is None: # single visualization + for i in range(len(pred_list)): + fast_render.render_one_sequence( + pred_list[i]["motion_path"], + save_path, + pred_list[i]["audio_path"], + model_folder="./evaluation/smplx_models/", + ) + if only_check_one: break + else: # paired visualization, pad the translation + for i in range(len(pred_list)): + npz_pred = np.load(pred_list[i]["motion_path"], allow_pickle=True) + gt_file = [item for item in gt_list if item["video_id"] == pred_list[i]["video_id"]][0] + if not gt_file: + print(f"Missing prediction for {pred_list[i]['video_id']}") + continue + npz_gt = np.load(gt_file["motion_path"], allow_pickle=True) + t = npz_gt["poses"].shape[0] + np.savez( + os.path.join(save_path, f"{pred_list[i]['video_id']}_transpad.npz"), + betas=npz_pred['betas'][:t], + poses=npz_pred['poses'][:t], + expressions=npz_pred['expressions'][:t], + trans=npz_pred["trans"][:t], + model='smplx2020', + gender='neutral', + mocap_frame_rate=30, + ) + fast_render.render_one_sequence( + os.path.join(save_path, f"{pred_list[i]['video_id']}_transpad.npz"), + gt_file["motion_path"], + save_path, + pred_list[i]["audio_path"], + model_folder="./evaluation/smplx_models/", + ) + if only_check_one: break + +def log_test(model, metrics, iteration, best_mertics, best_iteration, cfg, local_rank, experiment_ckpt_dir, video_save_path=None): + if local_rank == 0: + print(f"\n Test Results at iteration {iteration}:") + for key, value in metrics.items(): + print(f" {key}: {value:.10f}") + if cfg.validation.wandb: + for key, value in metrics.items(): + wandb.log({f"test/{key}": value}, step=iteration) + if cfg.validation.wandb and cfg.validation.visualization: + videos_to_log = [] + for filename in os.listdir(video_save_path): + if filename.endswith(".mp4"): + videos_to_log.append(wandb.Video(os.path.join(video_save_path, filename))) + if videos_to_log: + wandb.log({"test/videos": videos_to_log}, step=iteration) + if metrics["fgd"] < best_mertics: + best_mertics = metrics["fgd"] + best_iteration = iteration + model.module.save_pretrained(os.path.join(experiment_ckpt_dir, "test_best")) + # print(metrics, best_mertics, best_iteration) + message = f"Current Test FGD: {metrics['fgd']:.4f} (Best: {best_mertics:.4f} at iteration {best_iteration})" + log_metric_with_box(message) + return best_mertics, best_iteration + +def log_metric_with_box(message): + box_width = len(message) + 2 + border = "-" * box_width + print(f"\n{border}") + print(f"|{message}|") + print(f"{border}\n") + +def log_train_val(cfg, loss_dict, local_rank, loss_meters, pbar, epoch, max_epochs, iteration, net_time, data_time, optimizer, ptype="Train"): + new_loss_dict = {} + for k, v in loss_dict.items(): + if "fgd" in k: continue + v_cpu = torch.as_tensor(v).float().cpu().item() + if k not in loss_meters: + loss_meters[k] = {"sum":0,"count":0} + loss_meters[k]["sum"] += v_cpu + loss_meters[k]["count"] += 1 + new_loss_dict[k] = v_cpu + mem_used = torch.cuda.memory_reserved() / 1E9 + lr = optimizer.param_groups[0]["lr"] + loss_str = " ".join([f"{k}: {new_loss_dict[k]:.4f}({loss_meters[k]['sum']/loss_meters[k]['count']:.4f})" for k in new_loss_dict]) + desc = f"{ptype}: Epoch[{epoch}/{max_epochs}] Iter[{iteration}] {loss_str} lr: {lr:.2E} data_time: {data_time:.3f} net_time: {net_time:.3f} mem: {mem_used:.2f}GB" + pbar.set_description(desc) + pbar.bar_format = "{desc} {n_fmt}/{total_fmt} [{elapsed}<{remaining}, {rate_fmt}]" + if cfg.validation.wandb and local_rank == 0: + for k, v in new_loss_dict.items(): + wandb.log({f"loss/{ptype}/{k}": v}, step=iteration) + +def save_last_and_best_ckpt(model, optimizer, lr_scheduler, iteration, save_dir, previous_best, best_iteration, current, lower_is_better=True, mertic_name="fgd"): + checkpoint = { + "model_state_dict": model.state_dict(), + "optimizer_state_dict": optimizer.state_dict(), + "lr_scheduler_state_dict": lr_scheduler.state_dict(), + "iteration": iteration, + } + torch.save(checkpoint, os.path.join(save_dir, "last.bin")) + model.module.save_pretrained(os.path.join(save_dir, "last")) + if (lower_is_better and current < previous_best) or (not lower_is_better and current > previous_best): + previous_best = current + best_iteration = iteration + shutil.copy(os.path.join(save_dir, "last.bin"), os.path.join(save_dir, "best.bin")) + model.module.save_pretrained(os.path.join(save_dir, "best")) + message = f"Current interation {iteration} {mertic_name}: {current:.4f} (Best: {previous_best:.4f} at iteration {best_iteration})" + log_metric_with_box(message) + return previous_best, best_iteration + +def init_hf_class(module_name, class_name, config, **kwargs): + module = importlib.import_module(module_name) + model_class = getattr(module, class_name) + config_class = model_class.config_class + config = config_class(config_obj=config) + instance = model_class(config, **kwargs) + return instance + +def init_class(module_name, class_name, config, **kwargs): + module = importlib.import_module(module_name) + model_class = getattr(module, class_name) + instance = model_class(config, **kwargs) + return instance + +def seed_everything(seed): + os.environ['PYTHONHASHSEED'] = str(seed) + random.seed(seed) + np.random.seed(seed) + torch.manual_seed(seed) + torch.cuda.manual_seed_all(seed) + torch.cuda.manual_seed(seed) + torch.backends.cudnn.deterministic = True + torch.backends.cudnn.benchmark = True + torch.backends.cudnn.enabled = True + +def init_env(): + parser = argparse.ArgumentParser() + parser.add_argument("--config", type=str, default="./configs/train/stage2.yaml") + parser.add_argument("--debug", action="store_true") + parser.add_argument("--wandb", action="store_true") + parser.add_argument("--visualization", action="store_true") + parser.add_argument("--evaluation", action="store_true") + parser.add_argument("--test", action="store_true") + parser.add_argument('overrides', nargs=argparse.REMAINDER) + args = parser.parse_args() + config = OmegaConf.load(args.config) + config.exp_name = os.path.splitext(os.path.basename(args.config))[0] + + if args.overrides: config = OmegaConf.merge(config, OmegaConf.from_dotlist(args.overrides)) + if args.debug: + config.wandb_project = "debug" + config.exp_name = "debug" + config.solver.max_train_steps = 4 + else: + run_time = datetime.now().strftime("%Y%m%d-%H%M") + config.exp_name = config.exp_name + "_" + run_time + if args.wandb: + config.validation.wandb = True + if args.visualization: + config.validation.visualization = True + if args.evaluation: + config.validation.evaluation = True + if args.test: + config.test = True + save_dir = os.path.join(config.output_dir, config.exp_name) + os.makedirs(save_dir, exist_ok=True) + sanity_check_dir = os.path.join(save_dir, 'sanity_check') + os.makedirs(sanity_check_dir, exist_ok=True) + with open(os.path.join(sanity_check_dir, f'{config.exp_name}.yaml'), 'w') as f: + OmegaConf.save(config, f) + current_dir = Path.cwd() + for py_file in current_dir.rglob('*.py'): + dest_path = Path(sanity_check_dir) / py_file.relative_to(current_dir) + dest_path.parent.mkdir(parents=True, exist_ok=True) + shutil.copy(py_file, dest_path) + return config + +if __name__ == "__main__": + config = init_env() + main(config) \ No newline at end of file diff --git a/train_emage_audio.py b/train_emage_audio.py new file mode 100644 index 0000000000000000000000000000000000000000..0f3e891d349571144faac6aee1da7968897ac867 --- /dev/null +++ b/train_emage_audio.py @@ -0,0 +1,611 @@ +import os +import shutil +import argparse +import random +import numpy as np +from datetime import datetime +from tqdm import tqdm +import importlib +import copy +import librosa +from pathlib import Path +import json +import time +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch.utils.data import DataLoader +from torch.nn.parallel import DistributedDataParallel as DDP +import wandb + +from diffusers.optimization import get_scheduler +from omegaconf import OmegaConf + +from emage_evaltools.mertic import FGD, BC, L1div, LVDFace, MSEFace +from emage_utils.motion_io import beat_format_load, beat_format_save, MASK_DICT, recover_from_mask +import emage_utils.rotation_conversions as rc +from emage_utils import fast_render +from emage_utils.motion_rep_transfer import get_motion_rep_numpy +from models.emage_audio import EmageVQVAEConv, EmageVAEConv, EmageVQModel, EmageAudioModel + + +# --------------------------------- train,val,test fn here --------------------------------- # +def inference_fn(cfg, model, device, test_path, save_path, **kwargs): + motion_vq = kwargs["motion_vq"] + actual_model = model.module if isinstance(model, torch.nn.parallel.DistributedDataParallel) else model + actual_model.eval() + test_list = [] + for data_meta_path in test_path: + test_list.extend(json.load(open(data_meta_path, "r"))) + test_list = [item for item in test_list if item.get("mode") == "test"] + seen_ids = set() + test_list = [item for item in test_list if not (item["video_id"] in seen_ids or seen_ids.add(item["video_id"]))] + + save_list = [] + start_time = time.time() + total_length = 0 + for test_file in tqdm(test_list, desc="Testing"): + audio, _ = librosa.load(test_file["audio_path"], sr=cfg.audio_sr) + audio = torch.from_numpy(audio).to(device).unsqueeze(0) + speaker_id = torch.zeros(1,1).to(device).long() + + # motion seed + motion_data = np.load(test_file["motion_path"], allow_pickle=True) + poses = torch.from_numpy(motion_data["poses"]).unsqueeze(0).to(device).float() + foot_contact = torch.from_numpy(np.load(test_file["motion_path"].replace("smplxflame_30", "footcontact").replace(".npz", ".npy"))).unsqueeze(0).to(device).float() + trans = torch.from_numpy(motion_data["trans"]).unsqueeze(0).to(device).float() + expression = torch.from_numpy(motion_data["expressions"]).unsqueeze(0).to(device).float() + bs, t, _ = poses.shape + poses_6d = rc.axis_angle_to_rotation_6d(poses.reshape(bs, t, -1, 3)).reshape(bs, t, -1) + masked_motion = torch.cat([poses_6d, trans, foot_contact], dim=-1) # bs t 337 + + # reconstrcution check + # latent_dict = motion_vq.map2latent(poses_6d, expression, tar_contact=foot_contact, tar_trans=trans) + # face_latent = latent_dict["face"] + # upper_latent = latent_dict["upper"] + # lower_latent = latent_dict["lower"] + # hands_latent = latent_dict["hands"] + # face_index, upper_index, lower_index, hands_index = None, None, None, None + latent_dict = actual_model.inference(audio, speaker_id, motion_vq, masked_motion=masked_motion) + face_latent = latent_dict["rec_face"] if cfg.lf > 0 and cfg.cf == 0 else None + upper_latent = latent_dict["rec_upper"] if cfg.lu > 0 and cfg.cu == 0 else None + hands_latent = latent_dict["rec_hands"] if cfg.lh > 0 and cfg.ch == 0 else None + lower_latent = latent_dict["rec_lower"] if cfg.ll > 0 and cfg.cl == 0 else None + # print(latent_dict["rec_face"].shape,latent_dict["cls_upper"].shape) + face_index = torch.max(F.log_softmax(latent_dict["cls_face"], dim=2), dim=2)[1] if cfg.cf > 0 else None + upper_index = torch.max(F.log_softmax(latent_dict["cls_upper"], dim=2), dim=2)[1] if cfg.cu > 0 else None + hands_index = torch.max(F.log_softmax(latent_dict["cls_hands"], dim=2), dim=2)[1] if cfg.ch > 0 else None + lower_index = torch.max(F.log_softmax(latent_dict["cls_lower"], dim=2), dim=2)[1] if cfg.cl > 0 else None + + motion_all = motion_vq.decode( + face_latent=face_latent, upper_latent=upper_latent, lower_latent=lower_latent, hands_latent=hands_latent, + face_index=face_index, upper_index=upper_index, lower_index=lower_index, hands_index=hands_index, + get_global_motion=True, ref_trans=trans[:,0]) + + motion_pred = motion_all["motion_axis_angle"] + t = motion_pred.shape[1] + motion_pred = motion_pred.cpu().numpy().reshape(t, -1) + expression_pred = motion_all["expression"].cpu().numpy().reshape(t, -1) + trans_pred = motion_all["trans"].cpu().numpy().reshape(t, -1) + # print(motion_pred.shape, expression_pred.shape, trans_pred.shape) + beat_format_save(os.path.join(save_path, f"{test_file['video_id']}_output.npz"), motion_pred, upsample=30//cfg.pose_fps, expressions=expression_pred, trans=trans_pred) + save_list.append( + { + "audio_path": test_file["audio_path"], + "motion_path": os.path.join(save_path, f"{test_file['video_id']}_output.npz"), + "video_id": test_file["video_id"], + } + ) + total_length+=t + time_cost = time.time() - start_time + print(f"\n cost {time_cost:.2f} seconds to generate {total_length / cfg.pose_fps:.2f} seconds of motion") + return test_list, save_list + +def get_mask(mask, ratio): + pass + +def get_rec_loss(motion_pred, motion_gt, lu, ll, lh, lf): + rec_loss_upper = lu * F.mse_loss(motion_pred["rec_upper"], motion_gt["upper"]) + rec_loss_lower = ll * F.mse_loss(motion_pred["rec_lower"], motion_gt["lower"]) + rec_loss_hands = lh * F.mse_loss(motion_pred["rec_hands"], motion_gt["hands"]) + rec_loss_face = lf * F.mse_loss(motion_pred["rec_face"], motion_gt["face"]) + return rec_loss_upper+rec_loss_lower+rec_loss_hands+rec_loss_face + +def get_cls_loss(motion_pred, motion_gt, cu, cl, ch, cf, ClsFn): + ClsFn = ClsFn.to(motion_pred["cls_upper"].device) + pred_upper = F.log_softmax(motion_pred["cls_upper"], dim=2) + pred_lower = F.log_softmax(motion_pred["cls_lower"], dim=2) + pred_hands = F.log_softmax(motion_pred["cls_hands"], dim=2) + pred_face = F.log_softmax(motion_pred["cls_face"], dim=2) + pred_upper = pred_upper.permute(0, 2, 1) + pred_lower = pred_lower.permute(0, 2, 1) + pred_hands = pred_hands.permute(0, 2, 1) + pred_face = pred_face.permute(0, 2, 1) + cls_loss_upper = cu * ClsFn(pred_upper, motion_gt["upper"]) + cls_loss_lower = cl * ClsFn(pred_lower, motion_gt["lower"]) + cls_loss_hands = ch * ClsFn(pred_hands, motion_gt["hands"]) + cls_loss_face = cf * ClsFn(pred_face, motion_gt["face"]) + return cls_loss_upper+cls_loss_lower+cls_loss_hands+cls_loss_face + +def train_val_fn(cfg, batch, model, device, mode="train", **kwargs): + if mode == "train": + model.train() + kwargs["optimizer"].zero_grad() + else: + model.eval() + + motion_vq = kwargs["motion_vq"] + motion_gt = batch["motion"].to(device) + audio = batch["audio"].to(device) + expressions_gt = batch["expressions"].to(device) + trans = batch["trans"].to(device) + foot_contact = batch["foot_contact"].to(device) + + bs, t, jc = motion_gt.shape + j = jc // 3 + speaker_id = torch.zeros(bs,1).to(device).long() + motion_gt = rc.axis_angle_to_rotation_6d(motion_gt.reshape(bs,t,j,3)).reshape(bs, t, j*6) + + latent_index_dict = motion_vq.map2index(motion_gt, expressions_gt, tar_contact = foot_contact, tar_trans = trans) + latent_dict = motion_vq.map2latent(motion_gt, expressions_gt, tar_contact = foot_contact, tar_trans = trans) + masked_motion = torch.cat([motion_gt, trans, foot_contact], dim=-1) + # forward use audio + mask = torch.ones_like(masked_motion).to(device) + mask[:, :cfg.model.seed_frames] = 0 + + motion_pred = model(audio, speaker_id, masked_motion=masked_motion, mask=mask, use_audio=True) + loss_dict = { + "rec_seed": get_rec_loss(motion_pred, latent_dict, cfg.model.lu, cfg.model.ll, cfg.model.lh, cfg.model.lf), + "cls_seed": get_cls_loss(motion_pred, latent_index_dict, cfg.model.cu, cfg.model.cl, cfg.model.ch, cfg.model.cf, kwargs["ClsFn"]), + } + + # forward use randon mask and audio + mask_ratio = (kwargs["iteration"]/135*400) * 0.95 + 0.05 + mask = torch.rand(bs, t, cfg.model.pose_dims+3+4) < mask_ratio + mask = mask.float().to(device) + motion_pred_random_audio = model(audio, speaker_id, masked_motion=masked_motion, mask=mask, use_audio=True) + loss_dict["rec_audio"] = get_rec_loss(motion_pred_random_audio, latent_dict, cfg.model.lu, cfg.model.ll, cfg.model.lh, cfg.model.lf) + loss_dict["cls_audio"] = get_cls_loss(motion_pred_random_audio, latent_index_dict, cfg.model.cu, cfg.model.cl, cfg.model.ch, cfg.model.cf, kwargs["ClsFn"]) + + # forward use random mask + motion_pred_random_mask = model(audio, speaker_id, masked_motion=masked_motion, mask=mask, use_audio=False) + loss_dict["rec_mask"] = get_rec_loss(motion_pred_random_mask, latent_dict, cfg.model.lu, cfg.model.ll, cfg.model.lh, cfg.model.lf) + loss_dict["cls_mask"] = get_cls_loss(motion_pred_random_mask, latent_index_dict, cfg.model.cu, cfg.model.cl, cfg.model.ch, cfg.model.cf, kwargs["ClsFn"]) + + all_loss = sum(loss_dict.values()) + loss_dict["all"] = all_loss + + if mode == "train": + if cfg.solver.max_grad_norm > 0: + torch.nn.utils.clip_grad_norm_(model.parameters(), cfg.solver.max_grad_norm) + all_loss.backward() + kwargs["optimizer"].step() + kwargs["lr_scheduler"].step() + + if mode == "val": + _, cls_face = torch.max(F.log_softmax(motion_pred["cls_face"], dim=2), dim=2) + _, cls_upper = torch.max(F.log_softmax(motion_pred["cls_upper"], dim=2), dim=2) + _, cls_hands = torch.max(F.log_softmax(motion_pred["cls_hands"], dim=2), dim=2) + _, cls_lower = torch.max(F.log_softmax(motion_pred["cls_lower"], dim=2), dim=2) + face_latent = motion_pred["rec_face"] if cfg.model.lf > 0 and cfg.model.cf == 0 else None + upper_latent = motion_pred["rec_upper"] if cfg.model.lu > 0 and cfg.model.cu == 0 else None + hands_latent = motion_pred["rec_hands"] if cfg.model.lh > 0 and cfg.model.ch == 0 else None + lower_latent = motion_pred["rec_lower"] if cfg.model.ll > 0 and cfg.model.cl == 0 else None + face_index = cls_face if cfg.model.cf > 0 else None + upper_index = cls_upper if cfg.model.cu > 0 else None + hands_index = cls_hands if cfg.model.ch > 0 else None + lower_index = cls_lower if cfg.model.cl > 0 else None + decode_dict = motion_vq.decode( + face_latent=face_latent, upper_latent=upper_latent, lower_latent=lower_latent, hands_latent=hands_latent, + face_index=face_index, upper_index=upper_index, lower_index=lower_index, hands_index=hands_index,) + motion_pred_rot6d = decode_dict["all_motion4inference"][:, :, :-7] + # cache feature for evaluation + kwargs["fgd_evaluator"].update(motion_pred_rot6d, motion_gt) + return loss_dict + + +# --------------------------------- main train loop here --------------------------------- # +def main(cfg): + seed_everything(cfg.seed) + os.environ["WANDB_API_KEY"] = cfg.wandb_key + local_rank = int(os.environ["LOCAL_RANK"]) if "LOCAL_RANK" in os.environ else 0 + torch.cuda.set_device(local_rank) + device = torch.device("cuda", local_rank) + torch.distributed.init_process_group(backend="nccl") + log_dir = os.path.join(cfg.output_dir, cfg.exp_name) + experiment_ckpt_dir = os.path.join(log_dir, "checkpoints") + os.makedirs(experiment_ckpt_dir, exist_ok=True) + + if local_rank == 0 and cfg.validation.wandb: + run_time = datetime.now().strftime("%Y%m%d-%H%M") + wandb.init( + project=cfg.wandb_project, + name=f"{cfg.exp_name}_{run_time}", + entity=cfg.wandb_entity, + dir=log_dir, + config=OmegaConf.to_container(cfg) + ) + + # init + face_motion_vq = EmageVQVAEConv.from_pretrained("H-Liu1997/emage_audio", subfolder="emage_vq/face").to(device) + upper_motion_vq = EmageVQVAEConv.from_pretrained("H-Liu1997/emage_audio", subfolder="emage_vq/upper").to(device) + lower_motion_vq = EmageVQVAEConv.from_pretrained("H-Liu1997/emage_audio", subfolder="emage_vq/lower").to(device) + hands_motion_vq = EmageVQVAEConv.from_pretrained("H-Liu1997/emage_audio", subfolder="emage_vq/hands").to(device) + global_motion_ae = EmageVAEConv.from_pretrained("H-Liu1997/emage_audio", subfolder="emage_vq/global").to(device) + motion_vq = EmageVQModel( + face_model=face_motion_vq, upper_model=upper_motion_vq, + lower_model=lower_motion_vq, hands_model=hands_motion_vq, + global_model=global_motion_ae).to(device) + for param in motion_vq.parameters(): + param.requires_grad = False + motion_vq.eval() + + if cfg.test: + model = EmageAudioModel.from_pretrained("/content/drive/MyDrive/weights/emage3/best").to(device) + else: + model = init_hf_class(cfg.model.name_pyfile, cfg.model.class_name, cfg.model).to(device) + + model = nn.SyncBatchNorm.convert_sync_batchnorm(model) + for name, param in model.named_parameters(): + param.requires_grad = True + model = DDP(model, device_ids=[local_rank], output_device=local_rank, find_unused_parameters=True, broadcast_buffers=False) + + # optimizer + optimizer_cls = torch.optim.Adam + optimizer = optimizer_cls( + filter(lambda p: p.requires_grad, model.parameters()), + lr=cfg.solver.learning_rate, + betas=(cfg.solver.adam_beta1, cfg.solver.adam_beta2), + weight_decay=cfg.solver.adam_weight_decay, + eps=cfg.solver.adam_epsilon + ) + lr_scheduler = get_scheduler( + cfg.solver.lr_scheduler, + optimizer=optimizer, + num_warmup_steps=cfg.solver.lr_warmup_steps * cfg.solver.gradient_accumulation_steps, + num_training_steps=cfg.solver.max_train_steps * cfg.solver.gradient_accumulation_steps + ) + + # loss + ClsFn = nn.NLLLoss() + + # dataset + train_dataset = init_class(cfg.data.name_pyfile, cfg.data.class_name, cfg, split='train') + test_dataset = init_class(cfg.data.name_pyfile, cfg.data.class_name, cfg, split='test') + train_sampler = torch.utils.data.distributed.DistributedSampler(train_dataset) + test_sampler = torch.utils.data.distributed.DistributedSampler(test_dataset) + train_loader = DataLoader(train_dataset, batch_size=cfg.data.train_bs, sampler=train_sampler, drop_last=True, num_workers=8) + test_loader = DataLoader(test_dataset, batch_size=cfg.data.train_bs, sampler=test_sampler, drop_last=False, num_workers=8) + + # resume + if cfg.resume_from_checkpoint: + checkpoint = torch.load(cfg.resume_from_checkpoint, map_location="cpu") + model.load_state_dict(checkpoint["model_state_dict"]) + optimizer.load_state_dict(checkpoint["optimizer_state_dict"]) + lr_scheduler.load_state_dict(checkpoint["lr_scheduler_state_dict"]) + iteration = checkpoint["iteration"] + else: + iteration = 0 + if cfg.test: + iteration = 0 + + max_epochs = (cfg.solver.max_train_steps // len(train_loader)) + (1 if cfg.solver.max_train_steps % len(train_loader) != 0 else 0) + start_epoch = iteration // len(train_loader) + start_step_in_epoch = iteration % len(train_loader) + fgd_evaluator = FGD(download_path="./emage_evaltools/") + bc_evaluator = BC(download_path="./emage_evaltools/", sigma=0.3, order=7) + l1div_evaluator= L1div() + lvd_evaluator = LVDFace() + mse_evaluator = MSEFace() + loss_meters = {} + loss_meters_val = {} + best_fgd_val = np.inf + best_fgd_iteration_val= 0 + best_fgd_test = np.inf + best_fgd_iteration_test = 0 + + # train loop + epoch = start_epoch + while iteration < cfg.solver.max_train_steps: + train_sampler.set_epoch(epoch) + data_start = time.time() + pbar = tqdm(train_loader, leave=True) + for i, batch in enumerate(pbar): + # for correct resume, if the dataset is very large. since we fixed the seed, we can skip the data + if i < start_step_in_epoch: + iteration += 1 + continue + + # test + if iteration % cfg.validation.test_steps == 0 and local_rank == 0: + test_save_path = os.path.join(log_dir, f"test_{iteration}") + os.makedirs(test_save_path, exist_ok=True) + with torch.no_grad(): + test_list, save_list = inference_fn(cfg.model, model, device, cfg.data.test_meta_paths, test_save_path, motion_vq=motion_vq) + if cfg.validation.evaluation: + metrics = evaluation_fn([True]*55, test_list, save_list, fgd_evaluator, bc_evaluator, l1div_evaluator, device, lvd_evaluator, mse_evaluator) + if cfg.validation.visualization: visualization_fn(save_list, test_save_path, test_list, only_check_one=True) + if cfg.validation.evaluation: best_fgd_test, best_fgd_iteration_test = log_test(model, metrics, iteration, best_fgd_test, best_fgd_iteration_test, cfg, local_rank, experiment_ckpt_dir, test_save_path) + if cfg.test: return 0 + + # validation + if iteration % cfg.validation.validation_steps == 0: + loss_meters = {} + loss_meters_val = {} + fgd_evaluator.reset() + pbar_val = tqdm(test_loader, leave=True) + + data_start_val = time.time() + for j, batch in enumerate(pbar_val): + data_time_val = time.time() - data_start_val + with torch.no_grad(): + val_loss_dict = train_val_fn(cfg, batch, model, device, mode="val", fgd_evaluator=fgd_evaluator, motion_vq=motion_vq, ClsFn=ClsFn, iteration=iteration) + net_time_val = time.time() - data_start_val + val_loss_dict["fgd"] = fgd_evaluator.compute() if j == len(test_loader) - 1 else 0 + log_train_val(cfg, val_loss_dict, local_rank, loss_meters_val, pbar_val, epoch, max_epochs, iteration, net_time_val, data_time_val, optimizer, "Val ") + data_start_val = time.time() + if cfg.debug and j > 1: break + + if local_rank == 0: + best_fgd_val, best_fgd_iteration_val = save_last_and_best_ckpt( + model, optimizer, lr_scheduler, iteration, experiment_ckpt_dir, best_fgd_val, best_fgd_iteration_val, val_loss_dict["fgd"], lower_is_better=True, mertic_name="fgd") + + # train + data_time = time.time() - data_start + loss_dict = train_val_fn(cfg, batch, model, device, mode="train", motion_vq=motion_vq, optimizer=optimizer, lr_scheduler=lr_scheduler, ClsFn=ClsFn, iteration=iteration) + net_time = time.time() - data_start - data_time + log_train_val(cfg, loss_dict, local_rank, loss_meters, pbar, epoch, max_epochs, iteration, net_time, data_time, optimizer, "Train") + data_start = time.time() + + iteration += 1 + + start_step_in_epoch = 0 + epoch += 1 + + if local_rank == 0 and cfg.validation.wandb: + wandb.finish() + torch.distributed.destroy_process_group() + + +# --------------------------------- utils fn here --------------------------------- # +def evaluation_fn(joint_mask, gt_list, pred_list, fgd_evaluator, bc_evaluator, l1_evaluator, device, lvd_evaluator, mse_evaluator): + fgd_evaluator.reset() + bc_evaluator.reset() + l1_evaluator.reset() + lvd_evaluator.reset() + mse_evaluator.reset() + + for test_file in tqdm(gt_list, desc="Evaluation"): + # only load selective joints + pred_file = [item for item in pred_list if item["video_id"] == test_file["video_id"]][0] + if not pred_file: + print(f"Missing prediction for {test_file['video_id']}") + continue + # print(test_file["motion_path"], pred_file["motion_path"]) + gt_dict = beat_format_load(test_file["motion_path"], joint_mask) + pred_dict = beat_format_load(pred_file["motion_path"], joint_mask) + + motion_gt = gt_dict["poses"] + motion_pred = pred_dict["poses"] + expressions_gt = gt_dict["expressions"] + expressions_pred = pred_dict["expressions"] + betas = gt_dict["betas"] + # motion_gt = recover_from_mask(motion_gt, joint_mask) # t1*165 + # motion_pred = recover_from_mask(motion_pred, joint_mask) # t2*165 + + t = min(motion_gt.shape[0], motion_pred.shape[0]) + motion_gt = motion_gt[:t] + motion_pred = motion_pred[:t] + expressions_gt = expressions_gt[:t] + expressions_pred = expressions_pred[:t] + + # bc and l1 require position representation + motion_position_pred = get_motion_rep_numpy(motion_pred, device=device, betas=betas)["position"] # t*55*3 + motion_position_pred = motion_position_pred.reshape(t, -1) + # ignore the start and end 2s, this may for beat dataset only + audio_beat = bc_evaluator.load_audio(test_file["audio_path"], t_start=2 * 16000, t_end=int((t-60)/30*16000)) + motion_beat = bc_evaluator.load_motion(motion_position_pred, t_start=60, t_end=t-60, pose_fps=30, without_file=True) + bc_evaluator.compute(audio_beat, motion_beat, length=t-120, pose_fps=30) + # audio_beat = bc_evaluator.load_audio(test_file["audio_path"], t_start=0 * 16000, t_end=int((t-0)/30*16000)) + # motion_beat = bc_evaluator.load_motion(motion_position_pred, t_start=0, t_end=t-0, pose_fps=30, without_file=True) + # bc_evaluator.compute(audio_beat, motion_beat, length=t-0, pose_fps=30) + + l1_evaluator.compute(motion_position_pred) + + face_position_pred = get_motion_rep_numpy(motion_pred, device=device, expressions=expressions_pred, expression_only=True, betas=betas)["vertices"] # t -1 + face_position_gt = get_motion_rep_numpy(motion_gt, device=device, expressions=expressions_gt, expression_only=True, betas=betas)["vertices"] + lvd_evaluator.compute(face_position_pred, face_position_gt) + mse_evaluator.compute(face_position_pred, face_position_gt) + + # fgd requires rotation 6d representaiton + motion_gt = torch.from_numpy(motion_gt).to(device).unsqueeze(0) + motion_pred = torch.from_numpy(motion_pred).to(device).unsqueeze(0) + motion_gt = rc.axis_angle_to_rotation_6d(motion_gt.reshape(1, t, 55, 3)).reshape(1, t, 55*6) + motion_pred = rc.axis_angle_to_rotation_6d(motion_pred.reshape(1, t, 55, 3)).reshape(1, t, 55*6) + fgd_evaluator.update(motion_pred.float(), motion_gt.float()) + + metrics = {} + metrics["fgd"] = fgd_evaluator.compute() + metrics["bc"] = bc_evaluator.avg() + metrics["l1"] = l1_evaluator.avg() + metrics["lvd"] = lvd_evaluator.avg() + metrics["mse"] = mse_evaluator.avg() + return metrics + +def visualization_fn(pred_list, save_path, gt_list=None, only_check_one=True): + if gt_list is None: # single visualization + for i in range(len(pred_list)): + fast_render.render_one_sequence( + pred_list[i]["motion_path"], + save_path, + pred_list[i]["audio_path"], + model_folder="./evaluation/smplx_models/", + ) + if only_check_one: break + else: # paired visualization, pad the translation + for i in range(len(pred_list)): + npz_pred = np.load(pred_list[i]["motion_path"], allow_pickle=True) + gt_file = [item for item in gt_list if item["video_id"] == pred_list[i]["video_id"]][0] + if not gt_file: + print(f"Missing prediction for {pred_list[i]['video_id']}") + continue + npz_gt = np.load(gt_file["motion_path"], allow_pickle=True) + t = npz_gt["poses"].shape[0] + np.savez( + os.path.join(save_path, f"{pred_list[i]['video_id']}_transpad.npz"), + betas=npz_pred['betas'][:t], + poses=npz_pred['poses'][:t], + expressions=npz_pred['expressions'][:t], + trans=npz_pred["trans"][:t], + model='smplx2020', + gender='neutral', + mocap_frame_rate=30, + ) + fast_render.render_one_sequence( + os.path.join(save_path, f"{pred_list[i]['video_id']}_transpad.npz"), + gt_file["motion_path"], + save_path, + pred_list[i]["audio_path"], + model_folder="./evaluation/smplx_models/", + ) + if only_check_one: break + +def log_test(model, metrics, iteration, best_mertics, best_iteration, cfg, local_rank, experiment_ckpt_dir, video_save_path=None): + if local_rank == 0: + print(f"\n Test Results at iteration {iteration}:") + for key, value in metrics.items(): + print(f" {key}: {value:.10f}") + if cfg.validation.wandb: + for key, value in metrics.items(): + wandb.log({f"test/{key}": value}, step=iteration) + if cfg.validation.wandb and cfg.validation.visualization: + videos_to_log = [] + for filename in os.listdir(video_save_path): + if filename.endswith(".mp4"): + videos_to_log.append(wandb.Video(os.path.join(video_save_path, filename))) + if videos_to_log: + wandb.log({"test/videos": videos_to_log}, step=iteration) + if metrics["fgd"] < best_mertics: + best_mertics = metrics["fgd"] + best_iteration = iteration + model.module.save_pretrained(os.path.join(experiment_ckpt_dir, "test_best")) + # print(metrics, best_mertics, best_iteration) + message = f"Current Test FGD: {metrics['fgd']:.4f} (Best: {best_mertics:.4f} at iteration {best_iteration})" + log_metric_with_box(message) + return best_mertics, best_iteration + +def log_metric_with_box(message): + box_width = len(message) + 2 + border = "-" * box_width + print(f"\n{border}") + print(f"|{message}|") + print(f"{border}\n") + +def log_train_val(cfg, loss_dict, local_rank, loss_meters, pbar, epoch, max_epochs, iteration, net_time, data_time, optimizer, ptype="Train"): + new_loss_dict = {} + for k, v in loss_dict.items(): + if "fgd" in k: continue + v_cpu = torch.as_tensor(v).float().cpu().item() + if k not in loss_meters: + loss_meters[k] = {"sum":0,"count":0} + loss_meters[k]["sum"] += v_cpu + loss_meters[k]["count"] += 1 + new_loss_dict[k] = v_cpu + mem_used = torch.cuda.memory_reserved() / 1E9 + lr = optimizer.param_groups[0]["lr"] + loss_str = " ".join([f"{k}: {new_loss_dict[k]:.4f}({loss_meters[k]['sum']/loss_meters[k]['count']:.4f})" for k in new_loss_dict]) + desc = f"{ptype}: Epoch[{epoch}/{max_epochs}] Iter[{iteration}] {loss_str} lr: {lr:.2E} data_time: {data_time:.3f} net_time: {net_time:.3f} mem: {mem_used:.2f}GB" + pbar.set_description(desc) + pbar.bar_format = "{desc} {n_fmt}/{total_fmt} [{elapsed}<{remaining}, {rate_fmt}]" + if cfg.validation.wandb and local_rank == 0: + for k, v in new_loss_dict.items(): + wandb.log({f"loss/{ptype}/{k}": v}, step=iteration) + +def save_last_and_best_ckpt(model, optimizer, lr_scheduler, iteration, save_dir, previous_best, best_iteration, current, lower_is_better=True, mertic_name="fgd"): + checkpoint = { + "model_state_dict": model.state_dict(), + "optimizer_state_dict": optimizer.state_dict(), + "lr_scheduler_state_dict": lr_scheduler.state_dict(), + "iteration": iteration, + } + torch.save(checkpoint, os.path.join(save_dir, "last.bin")) + model.module.save_pretrained(os.path.join(save_dir, "last")) + if (lower_is_better and current < previous_best) or (not lower_is_better and current > previous_best): + previous_best = current + best_iteration = iteration + shutil.copy(os.path.join(save_dir, "last.bin"), os.path.join(save_dir, "best.bin")) + model.module.save_pretrained(os.path.join(save_dir, "best")) + message = f"Current interation {iteration} {mertic_name}: {current:.4f} (Best: {previous_best:.4f} at iteration {best_iteration})" + log_metric_with_box(message) + return previous_best, best_iteration + +def init_hf_class(module_name, class_name, config, **kwargs): + module = importlib.import_module(module_name) + model_class = getattr(module, class_name) + config_class = model_class.config_class + config = config_class(config_obj=config) + instance = model_class(config, **kwargs) + return instance + +def init_class(module_name, class_name, config, **kwargs): + module = importlib.import_module(module_name) + model_class = getattr(module, class_name) + instance = model_class(config, **kwargs) + return instance + +def seed_everything(seed): + os.environ['PYTHONHASHSEED'] = str(seed) + random.seed(seed) + np.random.seed(seed) + torch.manual_seed(seed) + torch.cuda.manual_seed_all(seed) + torch.cuda.manual_seed(seed) + torch.backends.cudnn.deterministic = True + torch.backends.cudnn.benchmark = True + torch.backends.cudnn.enabled = True + +def init_env(): + parser = argparse.ArgumentParser() + parser.add_argument("--config", type=str, default="./configs/train/stage2.yaml") + parser.add_argument("--debug", action="store_true") + parser.add_argument("--wandb", action="store_true") + parser.add_argument("--visualization", action="store_true") + parser.add_argument("--evaluation", action="store_true") + parser.add_argument("--test", action="store_true") + parser.add_argument('overrides', nargs=argparse.REMAINDER) + args = parser.parse_args() + config = OmegaConf.load(args.config) + config.exp_name = os.path.splitext(os.path.basename(args.config))[0] + + if args.overrides: config = OmegaConf.merge(config, OmegaConf.from_dotlist(args.overrides)) + if args.debug: + config.wandb_project = "debug" + config.exp_name = "debug" + config.solver.max_train_steps = 4 + else: + run_time = datetime.now().strftime("%Y%m%d-%H%M") + config.exp_name = config.exp_name + "_" + run_time + if args.wandb: + config.validation.wandb = True + if args.visualization: + config.validation.visualization = True + if args.evaluation: + config.validation.evaluation = True + if args.test: + config.test = True + save_dir = os.path.join(config.output_dir, config.exp_name) + os.makedirs(save_dir, exist_ok=True) + sanity_check_dir = os.path.join(save_dir, 'sanity_check') + os.makedirs(sanity_check_dir, exist_ok=True) + with open(os.path.join(sanity_check_dir, f'{config.exp_name}.yaml'), 'w') as f: + OmegaConf.save(config, f) + current_dir = Path.cwd() + for py_file in current_dir.rglob('*.py'): + dest_path = Path(sanity_check_dir) / py_file.relative_to(current_dir) + dest_path.parent.mkdir(parents=True, exist_ok=True) + shutil.copy(py_file, dest_path) + return config + +if __name__ == "__main__": + config = init_env() + main(config) \ No newline at end of file diff --git a/utils/.ipynb_checkpoints/config-checkpoint.py b/utils/.ipynb_checkpoints/config-checkpoint.py deleted file mode 100644 index 7ac207d7270c15532fa55b9184af3d64ceae9fc6..0000000000000000000000000000000000000000 --- a/utils/.ipynb_checkpoints/config-checkpoint.py +++ /dev/null @@ -1,275 +0,0 @@ -import configargparse -import time -import json -import yaml -import os - - -def str2bool(v): - """ from https://stackoverflow.com/a/43357954/1361529 """ - if isinstance(v, bool): - return v - if v.lower() in ('yes', 'true', 't', 'y', '1'): - return True - elif v.lower() in ('no', 'false', 'f', 'n', '0'): - return False - else: - raise configargparse.ArgumentTypeError('Boolean value expected.') - - -def parse_args(): - """ - requirement for config - 1. command > yaml > default - 2. avoid re-definition - 3. lowercase letters is better - 4. hierarchical is not necessary - """ - parser = configargparse.ArgParser() - parser.add("-c", "--config", default='./configs/emage_test_hf.yaml', is_config_file=True) - parser.add("--project", default="audio2pose", type=str) # wandb project name - parser.add("--stat", default="ts", type=str) - parser.add("--csv_name", default="a2g_0", type=str) # local device id - parser.add("--notes", default="", type=str) - parser.add("--trainer", default="camn", type=str) - - parser.add("--l", default=4, type=int) - # ------------- path and save name ---------------- # - parser.add("--is_train", default=True, type=str2bool) - parser.add("--debug", default=False, type=str2bool) - # different between environments - parser.add("--root_path", default="/home/ma-user/work/") - parser.add("--cache_path", default="/outputs/audio2pose/", type=str) - parser.add("--out_path", default="/outputs/audio2pose/", type=str) - parser.add("--data_path", default="/outputs/audio2pose/", type=str) - parser.add("--train_data_path", default="/datasets/trinity/train/", type=str) - parser.add("--val_data_path", default="/datasets/trinity/val/", type=str) - parser.add("--test_data_path", default="/datasets/trinity/test/", type=str) - parser.add("--mean_pose_path", default="/datasets/trinity/train/", type=str) - parser.add("--std_pose_path", default="/datasets/trinity/train/", type=str) - # for pretrian weights - parser.add("--data_path_1", default="../../datasets/checkpoints/", type=str) - # ------------------- evaluation ----------------------- # - parser.add("--test_ckpt", default="/datasets/beat_cache/beat_4english_15_141/last.bin") - parser.add("--eval_model", default="vae", type=str) - parser.add("--e_name", default=None, type=str) #HalfEmbeddingNet - parser.add("--e_path", default="/datasets/beat/generated_data/self_vae_128.bin") - parser.add("--variational", default=False, type=str2bool) - parser.add("--vae_length", default=256, type=int) - parser.add("--vae_test_dim", default=141, type=int) - parser.add("--vae_test_len", default=34, type=int) - parser.add("--vae_test_stride", default=10, type=int) - #parser.add("--vae_pose_length", default=34, type=int) - parser.add("--test_period", default=20, type=int) - parser.add("--vae_codebook_size", default=1024, type=int) - parser.add("--vae_quantizer_lambda", default=1., type=float) - - parser.add("--vae_layer", default=2, type=int) - parser.add("--vae_grow", default=[1,1,2,1], type=int, nargs="*") - parser.add("--lf", default=0., type=float) - parser.add("--ll", default=0., type=float) - parser.add("--lu", default=0., type=float) - parser.add("--lh", default=0., type=float) - parser.add("--cf", default=0., type=float) - parser.add("--cl", default=0., type=float) - parser.add("--cu", default=0., type=float) - parser.add("--ch", default=0., type=float) - - - # --------------- data ---------------------------- # - parser.add("--additional_data", default=False, type=str2bool) - parser.add("--train_trans", default=True, type=str2bool) - parser.add("--dataset", default="beat", type=str) - parser.add("--rot6d", default=True, type=str2bool) - parser.add("--ori_joints", default="spine_neck_141", type=str) - parser.add("--tar_joints", default="spine_neck_141", type=str) - parser.add("--training_speakers", default=[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30], type=int, nargs="*") - #parser.add("--pose_version", default="spine_neck_141", type=str) - parser.add("--new_cache", default=True, type=str2bool) - parser.add("--beat_align", default=True, type=str2bool) - parser.add("--cache_only", default=False, type=str2bool) - parser.add("--word_cache", default=False, type=str2bool) - parser.add("--use_aug", default=False, type=str2bool) - parser.add("--disable_filtering", default=False, type=str2bool) - parser.add("--clean_first_seconds", default=0, type=int) - parser.add("--clean_final_seconds", default=0, type=int) - - parser.add("--audio_rep", default=None, type=str) - parser.add("--audio_sr", default=16000, type=int) - parser.add("--word_rep", default=None, type=str) - parser.add("--emo_rep", default=None, type=str) - parser.add("--sem_rep", default=None, type=str) - parser.add("--facial_rep", default=None, type=str) - parser.add("--pose_rep", default="bvhrot", type=str) - parser.add("--id_rep", default="onehot", type=str) - parser.add("--speaker_id", default="onehot", type=str) - - parser.add("--a_pre_encoder", default=None, type=str) - parser.add("--a_encoder", default=None, type=str) - parser.add("--a_fix_pre", default=False, type=str2bool) - parser.add("--t_pre_encoder", default=None, type=str) - parser.add("--t_encoder", default=None, type=str) - parser.add("--t_fix_pre", default=False, type=str2bool) - parser.add("--m_pre_encoder", default=None, type=str) - parser.add("--m_encoder", default=None, type=str) - parser.add("--m_fix_pre", default=False, type=str2bool) - parser.add("--f_pre_encoder", default=None, type=str) - parser.add("--f_encoder", default=None, type=str) - parser.add("--f_fix_pre", default=False, type=str2bool) - parser.add("--m_decoder", default=None, type=str) - parser.add("--decode_fusion", default=None, type=str) - parser.add("--atmr", default=0.0, type=float) - parser.add("--ttmr", default=0., type=float) - parser.add("--mtmr", default=0., type=float) - parser.add("--ftmr", default=0., type=float) - parser.add("--asmr", default=0., type=float) - parser.add("--tsmr", default=0., type=float) - parser.add("--msmr", default=0., type=float) - parser.add("--fsmr", default=0., type=float) -# parser.add("--m_encoder", default=None, type=str) -# parser.add("--m_pre_fix", default=None, type=str) -# parser.add("--id_rep", default=None, type=str) - - parser.add("--freeze_wordembed", default=True, type=str2bool) - parser.add("--audio_fps", default=16000, type=int) - parser.add("--facial_fps", default=15, type=int) - parser.add("--pose_fps", default=15, type=int) - - parser.add("--audio_dims", default=1, type=int) - parser.add("--facial_dims", default=39, type=int) - parser.add("--pose_dims", default=123, type=int) - parser.add("--word_index_num", default=5793, type=int) - parser.add("--word_dims", default=300, type=int) - parser.add("--speaker_dims", default=4, type=int) - parser.add("--emotion_dims", default=8, type=int) - - parser.add("--audio_norm", default=False, type=str2bool) - parser.add("--facial_norm", default=False, type=str2bool) - parser.add("--pose_norm", default=False, type=str2bool) - - parser.add("--pose_length", default=34, type=int) - parser.add("--pre_frames", default=4, type=int) - parser.add("--stride", default=10, type=int) - parser.add("--pre_type", default="zero", type=str) - - parser.add("--audio_f", default=0, type=int) - parser.add("--motion_f", default=0, type=int) - parser.add("--facial_f", default=0, type=int) - parser.add("--speaker_f", default=0, type=int) - parser.add("--word_f", default=0, type=int) - parser.add("--emotion_f", default=0, type=int) - parser.add("--aud_prob", default=1.0, type=float) - parser.add("--pos_prob", default=1.0, type=float) - parser.add("--txt_prob", default=1.0, type=float) - parser.add("--fac_prob", default=1.0, type=float) - parser.add("--multi_length_training", default=[1.0], type=float, nargs="*") - # --------------- model ---------------------------- # - parser.add("--pretrain", default=False, type=str2bool) - parser.add("--model", default="camn", type=str) - parser.add("--g_name", default="CaMN", type=str) - parser.add("--d_name", default=None, type=str) #ConvDiscriminator - parser.add("--dropout_prob", default=0.3, type=float) - parser.add("--n_layer", default=4, type=int) - parser.add("--hidden_size", default=300, type=int) - #parser.add("--period", default=34, type=int) - parser.add("--test_length", default=34, type=int) - # Self-designed "Multi-Stage", "Seprate", or "Original" - parser.add("--finger_net", default="original", type=str) - parser.add("--pos_encoding_type", default="sin", type=str) - parser.add("--queue_size", default=1024, type=int) - - # --------------- training ------------------------- # - parser.add("--epochs", default=120, type=int) - parser.add("--epoch_stage", default=0, type=int) - parser.add("--grad_norm", default=0, type=float) - parser.add("--no_adv_epoch", default=999, type=int) - parser.add("--batch_size", default=128, type=int) - parser.add("--opt", default="adam", type=str) - parser.add("--lr_base", default=0.00025, type=float) - parser.add("--opt_betas", default=[0.5, 0.999], type=float, nargs="*") - parser.add("--weight_decay", default=0., type=float) - # for warmup and cosine - parser.add("--lr_min", default=1e-7, type=float) - parser.add("--warmup_lr", default=5e-4, type=float) - parser.add("--warmup_epochs", default=0, type=int) - parser.add("--decay_epochs", default=9999, type=int) - parser.add("--decay_rate", default=0.1, type=float) - parser.add("--lr_policy", default="step", type=str) - # for sgd - parser.add("--momentum", default=0.8, type=float) - parser.add("--nesterov", default=True, type=str2bool) - parser.add("--amsgrad", default=False, type=str2bool) - parser.add("--d_lr_weight", default=0.2, type=float) - parser.add("--rec_weight", default=500, type=float) - parser.add("--adv_weight", default=20.0, type=float) - parser.add("--fid_weight", default=0.0, type=float) - parser.add("--vel_weight", default=0.0, type=float) - parser.add("--acc_weight", default=0.0, type=float) - parser.add("--kld_weight", default=0.0, type=float) - parser.add("--kld_aud_weight", default=0.0, type=float) - parser.add("--kld_fac_weight", default=0.0, type=float) - parser.add("--ali_weight", default=0.0, type=float) - parser.add("--ita_weight", default=0.0, type=float) - parser.add("--iwa_weight", default=0.0, type=float) - parser.add("--wei_weight", default=0.0, type=float) - parser.add("--gap_weight", default=0.0, type=float) - parser.add("--atcont", default=0.0, type=float) - parser.add("--fusion_mode", default="sum", type=str) - - parser.add("--div_reg_weight", default=0.0, type=float) - parser.add("--rec_aud_weight", default=0.0, type=float) - parser.add("--rec_ver_weight", default=0.0, type=float) - parser.add("--rec_pos_weight", default=0.0, type=float) - parser.add("--rec_fac_weight", default=0.0, type=float) - parser.add("--rec_txt_weight", default=0.0, type=float) -# parser.add("--gan_noise_size", default=0, type=int) - # --------------- ha2g -------------------------- # - parser.add("--n_pre_poses", default=4, type=int) - parser.add("--n_poses", default=34, type=int) - parser.add("--input_context", default="both", type=str) - parser.add("--loss_contrastive_pos_weight", default=0.2, type=float) - parser.add("--loss_contrastive_neg_weight", default=0.005, type=float) - parser.add("--loss_physical_weight", default=0.0, type=float) - parser.add("--loss_reg_weight", default=0.05, type=float) - parser.add("--loss_regression_weight", default=70.0, type=float) - parser.add("--loss_gan_weight", default=5.0, type=float) - parser.add("--loss_kld_weight", default=0.1, type=float) - parser.add("--z_type", default="speaker", type=str) - # --------------- device -------------------------- # - parser.add("--random_seed", default=2021, type=int) - parser.add("--deterministic", default=True, type=str2bool) - parser.add("--benchmark", default=True, type=str2bool) - parser.add("--cudnn_enabled", default=True, type=str2bool) - # mix precision - parser.add("--apex", default=False, type=str2bool) - parser.add("--gpus", default=[0], type=int, nargs="*") - parser.add("--loader_workers", default=0, type=int) - parser.add("--ddp", default=False, type=str2bool) - parser.add("--sparse", default=1, type=int) - #parser.add("--world_size") - parser.add("--render_video_fps", default=30, type=int) - parser.add("--render_video_width", default=1920, type=int) - parser.add("--render_video_height", default=720, type=int) - cpu_cores = os.cpu_count() if os.cpu_count() is not None else 1 - default_concurrent = max(1, cpu_cores // 2) - parser.add("--render_concurrent_num", default=default_concurrent, type=int) - parser.add("--render_tmp_img_filetype", default="bmp", type=str) - - # logging - parser.add("--log_period", default=10, type=int) - - - args = parser.parse_args() - idc = 0 - for i, char in enumerate(args.config): - if char == "/": idc = i - args.name = args.config[idc+1:-5] - - is_train = args.is_train - - if is_train: - time_local = time.localtime() - name_expend = "%02d%02d_%02d%02d%02d_"%(time_local[1], time_local[2],time_local[3], time_local[4], time_local[5]) - args.name = name_expend + args.name - - return args \ No newline at end of file diff --git a/utils/.ipynb_checkpoints/fast_render-checkpoint.py b/utils/.ipynb_checkpoints/fast_render-checkpoint.py deleted file mode 100644 index 78311cbcc99ac414c7724e1081615825ad20843a..0000000000000000000000000000000000000000 --- a/utils/.ipynb_checkpoints/fast_render-checkpoint.py +++ /dev/null @@ -1,266 +0,0 @@ -import os -import time -import numpy as np -import pyrender -import trimesh -import queue -import imageio -import threading -import multiprocessing -import utils.media -import glob - -def deg_to_rad(degrees): - return degrees * np.pi / 180 - -def create_pose_camera(angle_deg): - angle_rad = deg_to_rad(angle_deg) - return np.array([ - [1.0, 0.0, 0.0, 0.0], - [0.0, np.cos(angle_rad), -np.sin(angle_rad), 1.0], - [0.0, np.sin(angle_rad), np.cos(angle_rad), 5.0], - [0.0, 0.0, 0.0, 1.0] - ]) - -def create_pose_light(angle_deg): - angle_rad = deg_to_rad(angle_deg) - return np.array([ - [1.0, 0.0, 0.0, 0.0], - [0.0, np.cos(angle_rad), -np.sin(angle_rad), 0.0], - [0.0, np.sin(angle_rad), np.cos(angle_rad), 3.0], - [0.0, 0.0, 0.0, 1.0] - ]) - -def create_scene_with_mesh(vertices, faces, uniform_color, pose_camera, pose_light): - trimesh_mesh = trimesh.Trimesh(vertices=vertices, faces=faces, vertex_colors=uniform_color) - mesh = pyrender.Mesh.from_trimesh(trimesh_mesh, smooth=True) - scene = pyrender.Scene() - scene.add(mesh) - camera = pyrender.OrthographicCamera(xmag=1.0, ymag=1.0) - scene.add(camera, pose=pose_camera) - light = pyrender.DirectionalLight(color=[1.0, 1.0, 1.0], intensity=4.0) - scene.add(light, pose=pose_light) - return scene - -def do_render_one_frame(renderer, frame_idx, vertices, vertices1, faces): - if frame_idx % 100 == 0: - print('processed', frame_idx, 'frames') - - uniform_color = [220, 220, 220, 255] - pose_camera = create_pose_camera(angle_deg=-2) - pose_light = create_pose_light(angle_deg=-30) - - figs = [] - for vtx in [vertices, vertices1]: - # print(vtx.shape) - scene = create_scene_with_mesh(vtx, faces, uniform_color, pose_camera, pose_light) - fig, _ = renderer.render(scene) - figs.append(fig) - - return figs[0], figs[1] - -def do_render_one_frame_no_gt(renderer, frame_idx, vertices, faces): - if frame_idx % 100 == 0: - print('processed', frame_idx, 'frames') - - uniform_color = [220, 220, 220, 255] - pose_camera = create_pose_camera(angle_deg=-2) - pose_light = create_pose_light(angle_deg=-30) - - figs = [] - # for vtx in [vertices]: - # print(vtx.shape) - # print(vertices.shape) - scene = create_scene_with_mesh(vertices, faces, uniform_color, pose_camera, pose_light) - fig, _ = renderer.render(scene) - figs.append(fig) - - return figs[0] - -def write_images_from_queue(fig_queue, output_dir, img_filetype): - while True: - e = fig_queue.get() - if e is None: - break - fid, fig1, fig2 = e - filename = os.path.join(output_dir, f"frame_{fid}.{img_filetype}") - merged_fig = np.hstack((fig1, fig2)) - try: - imageio.imwrite(filename, merged_fig) - except Exception as ex: - print(f"Error writing image {filename}: {ex}") - raise ex - -def write_images_from_queue_no_gt(fig_queue, output_dir, img_filetype): - while True: - e = fig_queue.get() - if e is None: - break - fid, fig1, fig2 = e - filename = os.path.join(output_dir, f"frame_{fid}.{img_filetype}") - merged_fig = fig1 #np.hstack((fig1)) - try: - imageio.imwrite(filename, merged_fig) - except Exception as ex: - print(f"Error writing image {filename}: {ex}") - raise ex - - -def render_frames_and_enqueue(fids, frame_vertex_pairs, faces, render_width, render_height, fig_queue): - fig_resolution = (render_width // 2, render_height) - renderer = pyrender.OffscreenRenderer(*fig_resolution) - - for idx, fid in enumerate(fids): - fig1, fig2 = do_render_one_frame(renderer, fid, frame_vertex_pairs[idx][0], frame_vertex_pairs[idx][1], faces) - fig_queue.put((fid, fig1, fig2)) - - renderer.delete() - -def render_frames_and_enqueue_no_gt(fids, frame_vertex_pairs, faces, render_width, render_height, fig_queue): - fig_resolution = (render_width // 2, render_height) - renderer = pyrender.OffscreenRenderer(*fig_resolution) - - for idx, fid in enumerate(fids): - fig1 = do_render_one_frame_no_gt(renderer, fid, frame_vertex_pairs[idx][0], faces) - fig_queue.put((fid, fig1)) - - renderer.delete() - -def sub_process_process_frame(subprocess_index, render_video_width, render_video_height, render_tmp_img_filetype, fids, frame_vertex_pairs, faces, output_dir): - begin_ts = time.time() - print(f"subprocess_index={subprocess_index} begin_ts={begin_ts}") - - fig_queue = queue.Queue() - render_frames_and_enqueue(fids, frame_vertex_pairs, faces, render_video_width, render_video_height, fig_queue) - fig_queue.put(None) - render_end_ts = time.time() - - image_writer_thread = threading.Thread(target=write_images_from_queue, args=(fig_queue, output_dir, render_tmp_img_filetype)) - image_writer_thread.start() - image_writer_thread.join() - - write_end_ts = time.time() - print( - f"subprocess_index={subprocess_index} " - f"render={render_end_ts - begin_ts:.2f} " - f"all={write_end_ts - begin_ts:.2f} " - f"begin_ts={begin_ts:.2f} " - f"render_end_ts={render_end_ts:.2f} " - f"write_end_ts={write_end_ts:.2f}" - ) - -def sub_process_process_frame_no_gt(subprocess_index, render_video_width, render_video_height, render_tmp_img_filetype, fids, frame_vertex_pairs, faces, output_dir): - begin_ts = time.time() - print(f"subprocess_index={subprocess_index} begin_ts={begin_ts}") - - fig_queue = queue.Queue() - render_frames_and_enqueue(fids, frame_vertex_pairs, faces, render_video_width, render_video_height, fig_queue) - fig_queue.put(None) - render_end_ts = time.time() - - image_writer_thread = threading.Thread(target=write_images_from_queue_no_gt, args=(fig_queue, output_dir, render_tmp_img_filetype)) - image_writer_thread.start() - image_writer_thread.join() - - write_end_ts = time.time() - print( - f"subprocess_index={subprocess_index} " - f"render={render_end_ts - begin_ts:.2f} " - f"all={write_end_ts - begin_ts:.2f} " - f"begin_ts={begin_ts:.2f} " - f"render_end_ts={render_end_ts:.2f} " - f"write_end_ts={write_end_ts:.2f}" - ) - -def distribute_frames(frames, render_video_fps, render_concurent_nums, vertices_all, vertices1_all): - sample_interval = max(1, int(30 // render_video_fps)) - subproc_frame_ids = [[] for _ in range(render_concurent_nums)] - subproc_vertices = [[] for _ in range(render_concurent_nums)] - sampled_frame_id = 0 - - for i in range(frames): - if i % sample_interval != 0: - continue - subprocess_index = sampled_frame_id % render_concurent_nums - subproc_frame_ids[subprocess_index].append(sampled_frame_id) - subproc_vertices[subprocess_index].append((vertices_all[i], vertices1_all[i])) - sampled_frame_id += 1 - - return subproc_frame_ids, subproc_vertices - -def distribute_frames_no_gt(frames, render_video_fps, render_concurent_nums, vertices_all): - sample_interval = max(1, int(30 // render_video_fps)) - subproc_frame_ids = [[] for _ in range(render_concurent_nums)] - subproc_vertices = [[] for _ in range(render_concurent_nums)] - sampled_frame_id = 0 - - for i in range(frames): - if i % sample_interval != 0: - continue - subprocess_index = sampled_frame_id % render_concurent_nums - subproc_frame_ids[subprocess_index].append(sampled_frame_id) - subproc_vertices[subprocess_index].append((vertices_all[i], vertices_all[i])) - sampled_frame_id += 1 - - return subproc_frame_ids, subproc_vertices - -def generate_silent_videos(render_video_fps, - render_video_width, - render_video_height, - render_concurent_nums, - render_tmp_img_filetype, - frames, - vertices_all, - vertices1_all, - faces, - output_dir): - - subproc_frame_ids, subproc_vertices = distribute_frames(frames, render_video_fps, render_concurent_nums, vertices_all, vertices1_all) - - print(f"generate_silent_videos concurrentNum={render_concurent_nums} time={time.time()}") - with multiprocessing.Pool(render_concurent_nums) as pool: - pool.starmap( - sub_process_process_frame, - [ - (subprocess_index, render_video_width, render_video_height, render_tmp_img_filetype, subproc_frame_ids[subprocess_index], subproc_vertices[subprocess_index], faces, output_dir) - for subprocess_index in range(render_concurent_nums) - ] - ) - - output_file = os.path.join(output_dir, "silence_video.mp4") - utils.media.convert_img_to_mp4(os.path.join(output_dir, f"frame_%d.{render_tmp_img_filetype}"), output_file, render_video_fps) - filenames = glob.glob(os.path.join(output_dir, f"*.{render_tmp_img_filetype}")) - for filename in filenames: - os.remove(filename) - - return output_file - -def generate_silent_videos_no_gt(render_video_fps, - render_video_width, - render_video_height, - render_concurent_nums, - render_tmp_img_filetype, - frames, - vertices_all, - faces, - output_dir): - - subproc_frame_ids, subproc_vertices = distribute_frames_no_gt(frames, render_video_fps, render_concurent_nums, vertices_all) - - print(f"generate_silent_videos concurrentNum={render_concurent_nums} time={time.time()}") - with multiprocessing.Pool(render_concurent_nums) as pool: - pool.starmap( - sub_process_process_frame_no_gt, - [ - (subprocess_index, render_video_width, render_video_height, render_tmp_img_filetype, subproc_frame_ids[subprocess_index], subproc_vertices[subprocess_index], faces, output_dir) - for subprocess_index in range(render_concurent_nums) - ] - ) - - output_file = os.path.join(output_dir, "silence_video.mp4") - utils.media.convert_img_to_mp4(os.path.join(output_dir, f"frame_%d.{render_tmp_img_filetype}"), output_file, render_video_fps) - filenames = glob.glob(os.path.join(output_dir, f"*.{render_tmp_img_filetype}")) - for filename in filenames: - os.remove(filename) - - return output_file \ No newline at end of file diff --git a/utils/.ipynb_checkpoints/media-checkpoint.py b/utils/.ipynb_checkpoints/media-checkpoint.py deleted file mode 100644 index 2bd21e079a9e48f97f1511bd289d39f4aeccc40e..0000000000000000000000000000000000000000 --- a/utils/.ipynb_checkpoints/media-checkpoint.py +++ /dev/null @@ -1,39 +0,0 @@ -import numpy as np -import subprocess - -def add_audio_to_video(silent_video_path, audio_path, output_video_path): - command = [ - 'ffmpeg', - '-y', - '-i', silent_video_path, - '-i', audio_path, - '-map', '0:v', - '-map', '1:a', - '-c:v', 'copy', - '-shortest', - output_video_path - ] - - try: - subprocess.run(command, check=True) - print(f"Video with audio generated successfully: {output_video_path}") - except subprocess.CalledProcessError as e: - print(f"Error occurred: {e}") - - -def convert_img_to_mp4(input_pattern, output_file, framerate=30): - command = [ - 'ffmpeg', - '-framerate', str(framerate), - '-i', input_pattern, - '-c:v', 'libx264', - '-pix_fmt', 'yuv420p', - output_file, - '-y' - ] - - try: - subprocess.run(command, check=True) - print(f"Video conversion successful. Output file: {output_file}") - except subprocess.CalledProcessError as e: - print(f"Error during video conversion: {e}") diff --git a/utils/.ipynb_checkpoints/other_tools_hf-checkpoint.py b/utils/.ipynb_checkpoints/other_tools_hf-checkpoint.py deleted file mode 100644 index a5b9f943120954e5745f1beca4e37ff461f663bb..0000000000000000000000000000000000000000 --- a/utils/.ipynb_checkpoints/other_tools_hf-checkpoint.py +++ /dev/null @@ -1,975 +0,0 @@ -import os -import numpy as np -import random -import torch -import shutil -import csv -import pprint -import pandas as pd -from loguru import logger -from collections import OrderedDict -import matplotlib.pyplot as plt -import pickle -import time -import hashlib -from scipy.spatial.transform import Rotation as R -from scipy.spatial.transform import Slerp -import cv2 -import utils.media -import utils.fast_render - -def write_wav_names_to_csv(folder_path, csv_path): - """ - Traverse a folder and write the base names of all .wav files to a CSV file. - - :param folder_path: Path to the folder to traverse. - :param csv_path: Path to the CSV file to write. - """ - # Open the CSV file for writing - with open(csv_path, mode='w', newline='') as file: - writer = csv.writer(file) - # Write the header - writer.writerow(['id', 'type']) - - # Walk through the folder - for root, dirs, files in os.walk(folder_path): - for file in files: - # Check if the file ends with .wav - if file.endswith('.wav'): - # Extract the base name without the extension - base_name = os.path.splitext(file)[0] - # Write the base name and type to the CSV - writer.writerow([base_name, 'test']) - -def resize_motion_sequence_tensor(sequence, target_frames): - """ - Resize a batch of 8-frame motion sequences to a specified number of frames using interpolation. - - :param sequence: A (bs, 8, 165) tensor representing a batch of 8-frame motion sequences - :param target_frames: An integer representing the desired number of frames in the output sequences - :return: A (bs, target_frames, 165) tensor representing the resized motion sequences - """ - bs, _, _ = sequence.shape - - # Create a time vector for the original and target sequences - original_time = torch.linspace(0, 1, 8, device=sequence.device).view(1, -1, 1) - target_time = torch.linspace(0, 1, target_frames, device=sequence.device).view(1, -1, 1) - - # Permute the dimensions to (bs, 165, 8) for interpolation - sequence = sequence.permute(0, 2, 1) - - # Interpolate each joint's motion to the target number of frames - resized_sequence = torch.nn.functional.interpolate(sequence, size=target_frames, mode='linear', align_corners=True) - - # Permute the dimensions back to (bs, target_frames, 165) - resized_sequence = resized_sequence.permute(0, 2, 1) - - return resized_sequence - -def adjust_speed_according_to_ratio_tensor(chunks): - """ - Adjust the playback speed within a batch of 32-frame chunks according to random intervals. - - :param chunks: A (bs, 32, 165) tensor representing a batch of motion chunks - :return: A (bs, 32, 165) tensor representing the motion chunks after speed adjustment - """ - bs, _, _ = chunks.shape - - # Step 1: Divide the chunk into 4 equal intervals of 8 frames - equal_intervals = torch.chunk(chunks, 4, dim=1) - - # Step 2: Randomly sample 3 points within the chunk to determine new intervals - success = 0 - all_success = [] - #sample_points = torch.sort(torch.randint(1, 32, (bs, 3), device=chunks.device), dim=1).values - # new_intervals_boundaries = torch.cat([torch.zeros((bs, 1), device=chunks.device, dtype=torch.long), sample_points, 32*torch.ones((bs, 1), device=chunks.device, dtype=torch.long)], dim=1) - while success != 1: - sample_points = sorted(random.sample(range(1, 32), 3)) - new_intervals_boundaries = [0] + sample_points + [32] - new_intervals = [chunks[0][new_intervals_boundaries[i]:new_intervals_boundaries[i+1]] for i in range(4)] - speed_ratios = [8 / len(new_interval) for new_interval in new_intervals] - # if any of the speed ratios is greater than 3 or less than 0.33, resample - if all([0.33 <= speed_ratio <= 3 for speed_ratio in speed_ratios]): - success += 1 - all_success.append(new_intervals_boundaries) - new_intervals_boundaries = torch.from_numpy(np.array(all_success)) - # print(new_intervals_boundaries) - all_shapes = new_intervals_boundaries[:, 1:] - new_intervals_boundaries[:, :-1] - # Step 4: Adjust the speed of each new interval - adjusted_intervals = [] - # print(equal_intervals[0].shape) - for i in range(4): - adjusted_interval = resize_motion_sequence_tensor(equal_intervals[i], all_shapes[0, i]) - adjusted_intervals.append(adjusted_interval) - - # Step 5: Concatenate the adjusted intervals - adjusted_chunk = torch.cat(adjusted_intervals, dim=1) - - return adjusted_chunk - -def compute_exact_iou(bbox1, bbox2): - x1 = max(bbox1[0], bbox2[0]) - y1 = max(bbox1[1], bbox2[1]) - x2 = min(bbox1[0] + bbox1[2], bbox2[0] + bbox2[2]) - y2 = min(bbox1[1] + bbox1[3], bbox2[1] + bbox2[3]) - - intersection_area = max(0, x2 - x1) * max(0, y2 - y1) - bbox1_area = bbox1[2] * bbox1[3] - bbox2_area = bbox2[2] * bbox2[3] - union_area = bbox1_area + bbox2_area - intersection_area - - if union_area == 0: - return 0 - - return intersection_area / union_area - -def compute_iou(mask1, mask2): - # Compute the intersection - intersection = np.logical_and(mask1, mask2).sum() - - # Compute the union - union = np.logical_or(mask1, mask2).sum() - - # Compute the IoU - iou = intersection / union - - return iou - -def blankblending(all_frames, x, n): - return all_frames[x:x+n+1] - -def synthesize_intermediate_frames_FILM(frame1, frame2, t, name, save_path): - import replicate - from urllib.request import urlretrieve - import os - cv2.imwrite(save_path[:-9]+name+"_frame1.png", frame1) - cv2.imwrite(save_path[:-9]+name+"_frame2.png", frame2) - os.environ["REPLICATE_API_TOKEN"] = "r8_He1rkPk9GAxNQ3LpOohK8sYw1SUfMYV3Fxk9b" - output = replicate.run( - "google-research/frame-interpolation:4f88a16a13673a8b589c18866e540556170a5bcb2ccdc12de556e800e9456d3d", - input={ - "frame1": open(save_path[:-9]+name+"_frame1.png", "rb"), - "frame2": open(save_path[:-9]+name+"_frame2.png", "rb"), - "times_to_interpolate": t, - } - ) - print(output) - urlretrieve(output, save_path[:-9]+name+"_inter.mp4") - return load_video_as_numpy_array(save_path[:-9]+name+"_inter.mp4") - -def load_video_as_numpy_array(video_path): - cap = cv2.VideoCapture(video_path) - - # Using list comprehension to read frames and store in a list - frames = [frame for ret, frame in iter(lambda: cap.read(), (False, None)) if ret] - - cap.release() - - return np.array(frames) - -def synthesize_intermediate_frames_bidirectional(all_frames, x, n): - frame1 = all_frames[x] - frame2 = all_frames[x + n] - - # Convert the frames to grayscale - gray1 = cv2.cvtColor(frame1, cv2.COLOR_BGR2GRAY) - gray2 = cv2.cvtColor(frame2, cv2.COLOR_BGR2GRAY) - - # Calculate the forward and backward optical flow - forward_flow = cv2.calcOpticalFlowFarneback(gray1, gray2, None, 0.5, 3, 15, 3, 5, 1.2, 0) - backward_flow = cv2.calcOpticalFlowFarneback(gray2, gray1, None, 0.5, 3, 15, 3, 5, 1.2, 0) - - synthesized_frames = [] - for i in range(1, n): # For each intermediate frame between x and x + n - alpha = i / n # Interpolation factor - - # Compute the intermediate forward and backward flow - intermediate_forward_flow = forward_flow * alpha - intermediate_backward_flow = backward_flow * (1 - alpha) - - # Warp the frames based on the intermediate flow - h, w = frame1.shape[:2] - flow_map = np.column_stack((np.repeat(np.arange(h), w), np.tile(np.arange(w), h))) - forward_displacement = flow_map + intermediate_forward_flow.reshape(-1, 2) - backward_displacement = flow_map - intermediate_backward_flow.reshape(-1, 2) - - # Use cv2.remap for efficient warping - remap_x_forward, remap_y_forward = np.clip(forward_displacement[:, 1], 0, w - 1), np.clip(forward_displacement[:, 0], 0, h - 1) - remap_x_backward, remap_y_backward = np.clip(backward_displacement[:, 1], 0, w - 1), np.clip(backward_displacement[:, 0], 0, h - 1) - - warped_forward = cv2.remap(frame1, remap_x_forward.reshape(h, w).astype(np.float32), remap_y_forward.reshape(h, w).astype(np.float32), interpolation=cv2.INTER_LINEAR) - warped_backward = cv2.remap(frame2, remap_x_backward.reshape(h, w).astype(np.float32), remap_y_backward.reshape(h, w).astype(np.float32), interpolation=cv2.INTER_LINEAR) - - # Blend the warped frames to generate the intermediate frame - intermediate_frame = cv2.addWeighted(warped_forward, 1 - alpha, warped_backward, alpha, 0) - synthesized_frames.append(intermediate_frame) - - return synthesized_frames # Return n-2 synthesized intermediate frames - - -def linear_interpolate_frames(all_frames, x, n): - frame1 = all_frames[x] - frame2 = all_frames[x + n] - - synthesized_frames = [] - for i in range(1, n): # For each intermediate frame between x and x + n - alpha = i / (n) # Correct interpolation factor - inter_frame = cv2.addWeighted(frame1, 1 - alpha, frame2, alpha, 0) - synthesized_frames.append(inter_frame) - return synthesized_frames[:-1] - -def warp_frame(src_frame, flow): - h, w = flow.shape[:2] - flow_map = np.column_stack((np.repeat(np.arange(h), w), np.tile(np.arange(w), h))) - displacement = flow_map + flow.reshape(-1, 2) - - # Extract x and y coordinates of the displacement - x_coords = np.clip(displacement[:, 1], 0, w - 1).reshape(h, w).astype(np.float32) - y_coords = np.clip(displacement[:, 0], 0, h - 1).reshape(h, w).astype(np.float32) - - # Use cv2.remap for efficient warping - warped_frame = cv2.remap(src_frame, x_coords, y_coords, interpolation=cv2.INTER_LINEAR) - - return warped_frame - -def synthesize_intermediate_frames(all_frames, x, n): - # Calculate Optical Flow between the first and last frame - frame1 = cv2.cvtColor(all_frames[x], cv2.COLOR_BGR2GRAY) - frame2 = cv2.cvtColor(all_frames[x + n], cv2.COLOR_BGR2GRAY) - flow = cv2.calcOpticalFlowFarneback(frame1, frame2, None, 0.5, 3, 15, 3, 5, 1.2, 0) - - synthesized_frames = [] - for i in range(1, n): # For each intermediate frame - alpha = i / (n) # Interpolation factor - intermediate_flow = flow * alpha # Interpolate the flow - intermediate_frame = warp_frame(all_frames[x], intermediate_flow) # Warp the first frame - synthesized_frames.append(intermediate_frame) - - return synthesized_frames - - -def map2color(s): - m = hashlib.md5() - m.update(s.encode('utf-8')) - color_code = m.hexdigest()[:6] - return '#' + color_code - -def euclidean_distance(a, b): - return np.sqrt(np.sum((a - b)**2)) - -def adjust_array(x, k): - len_x = len(x) - len_k = len(k) - - # If x is shorter than k, pad with zeros - if len_x < len_k: - return np.pad(x, (0, len_k - len_x), 'constant') - - # If x is longer than k, truncate x - elif len_x > len_k: - return x[:len_k] - - # If both are of same length - else: - return x - -def onset_to_frame(onset_times, audio_length, fps): - # Calculate total number of frames for the given audio length - total_frames = int(audio_length * fps) - - # Create an array of zeros of shape (total_frames,) - frame_array = np.zeros(total_frames, dtype=np.int32) - - # For each onset time, calculate the frame number and set it to 1 - for onset in onset_times: - frame_num = int(onset * fps) - # Check if the frame number is within the array bounds - if 0 <= frame_num < total_frames: - frame_array[frame_num] = 1 - - return frame_array - -# def np_slerp(q1, q2, t): -# dot_product = np.sum(q1 * q2, axis=-1) -# q2_flip = np.where(dot_product[:, None] < 0, -q2, q2) # Flip quaternions where dot_product is negative -# dot_product = np.abs(dot_product) - -# angle = np.arccos(np.clip(dot_product, -1, 1)) -# sin_angle = np.sin(angle) - -# t1 = np.sin((1.0 - t) * angle) / sin_angle -# t2 = np.sin(t * angle) / sin_angle - -# return t1 * q1 + t2 * q2_flip - - -def smooth_rotvec_animations(animation1, animation2, blend_frames): - """ - Smoothly transition between two animation clips using SLERP. - - Parameters: - - animation1: The first animation clip, a numpy array of shape [n, k]. - - animation2: The second animation clip, a numpy array of shape [n, k]. - - blend_frames: Number of frames over which to blend the two animations. - - Returns: - - A smoothly blended animation clip of shape [2n, k]. - """ - - # Ensure blend_frames doesn't exceed the length of either animation - n1, k1 = animation1.shape - n2, k2 = animation2.shape - animation1 = animation1.reshape(n1, k1//3, 3) - animation2 = animation2.reshape(n2, k2//3, 3) - blend_frames = min(blend_frames, len(animation1), len(animation2)) - all_int = [] - for i in range(k1//3): - # Convert rotation vectors to quaternion for the overlapping part - q = R.from_rotvec(np.concatenate([animation1[0:1, i], animation2[-2:-1, i]], axis=0))#.as_quat() - # q2 = R.from_rotvec()#.as_quat() - times = [0, blend_frames * 2 - 1] - slerp = Slerp(times, q) - interpolated = slerp(np.arange(blend_frames * 2)) - interpolated_rotvecs = interpolated.as_rotvec() - all_int.append(interpolated_rotvecs) - interpolated_rotvecs = np.concatenate(all_int, axis=1) - # result = np.vstack((animation1[:-blend_frames], interpolated_rotvecs, animation2[blend_frames:])) - result = interpolated_rotvecs.reshape(2*n1, k1) - return result - -def smooth_animations(animation1, animation2, blend_frames): - """ - Smoothly transition between two animation clips using linear interpolation. - - Parameters: - - animation1: The first animation clip, a numpy array of shape [n, k]. - - animation2: The second animation clip, a numpy array of shape [n, k]. - - blend_frames: Number of frames over which to blend the two animations. - - Returns: - - A smoothly blended animation clip of shape [2n, k]. - """ - - # Ensure blend_frames doesn't exceed the length of either animation - blend_frames = min(blend_frames, len(animation1), len(animation2)) - - # Extract overlapping sections - overlap_a1 = animation1[-blend_frames:-blend_frames+1, :] - overlap_a2 = animation2[blend_frames-1:blend_frames, :] - - # Create blend weights for linear interpolation - alpha = np.linspace(0, 1, 2 * blend_frames).reshape(-1, 1) - - # Linearly interpolate between overlapping sections - blended_overlap = overlap_a1 * (1 - alpha) + overlap_a2 * alpha - - # Extend the animations to form the result with 2n frames - if blend_frames == len(animation1) and blend_frames == len(animation2): - result = blended_overlap - else: - before_blend = animation1[:-blend_frames] - after_blend = animation2[blend_frames:] - result = np.vstack((before_blend, blended_overlap, after_blend)) - return result - -def interpolate_sequence(quaternions): - bs, n, j, _ = quaternions.shape - new_n = 2 * n - new_quaternions = torch.zeros((bs, new_n, j, 4), device=quaternions.device, dtype=quaternions.dtype) - - for i in range(n): - q1 = quaternions[:, i, :, :] - new_quaternions[:, 2*i, :, :] = q1 - - if i < n - 1: - q2 = quaternions[:, i + 1, :, :] - new_quaternions[:, 2*i + 1, :, :] = slerp(q1, q2, 0.5) - else: - # For the last point, duplicate the value - new_quaternions[:, 2*i + 1, :, :] = q1 - - return new_quaternions - -def quaternion_multiply(q1, q2): - w1, x1, y1, z1 = q1 - w2, x2, y2, z2 = q2 - w = w1 * w2 - x1 * x2 - y1 * y2 - z1 * z2 - x = w1 * x2 + x1 * w2 + y1 * z2 - z1 * y2 - y = w1 * y2 + y1 * w2 + z1 * x2 - x1 * z2 - z = w1 * z2 + z1 * w2 + x1 * y2 - y1 * x2 - return w, x, y, z - -def quaternion_conjugate(q): - w, x, y, z = q - return (w, -x, -y, -z) - -def slerp(q1, q2, t): - dot = torch.sum(q1 * q2, dim=-1, keepdim=True) - - flip = (dot < 0).float() - q2 = (1 - flip * 2) * q2 - dot = dot * (1 - flip * 2) - - DOT_THRESHOLD = 0.9995 - mask = (dot > DOT_THRESHOLD).float() - - theta_0 = torch.acos(dot) - theta = theta_0 * t - - q3 = q2 - q1 * dot - q3 = q3 / torch.norm(q3, dim=-1, keepdim=True) - - interpolated = (torch.cos(theta) * q1 + torch.sin(theta) * q3) - - return mask * (q1 + t * (q2 - q1)) + (1 - mask) * interpolated - -def estimate_linear_velocity(data_seq, dt): - ''' - Given some batched data sequences of T timesteps in the shape (B, T, ...), estimates - the velocity for the middle T-2 steps using a second order central difference scheme. - The first and last frames are with forward and backward first-order - differences, respectively - - h : step size - ''' - # first steps is forward diff (t+1 - t) / dt - init_vel = (data_seq[:, 1:2] - data_seq[:, :1]) / dt - # middle steps are second order (t+1 - t-1) / 2dt - middle_vel = (data_seq[:, 2:] - data_seq[:, 0:-2]) / (2 * dt) - # last step is backward diff (t - t-1) / dt - final_vel = (data_seq[:, -1:] - data_seq[:, -2:-1]) / dt - - vel_seq = torch.cat([init_vel, middle_vel, final_vel], dim=1) - return vel_seq - -def velocity2position(data_seq, dt, init_pos): - res_trans = [] - for i in range(data_seq.shape[1]): - if i == 0: - res_trans.append(init_pos.unsqueeze(1)) - else: - res = data_seq[:, i-1:i] * dt + res_trans[-1] - res_trans.append(res) - return torch.cat(res_trans, dim=1) - -def estimate_angular_velocity(rot_seq, dt): - ''' - Given a batch of sequences of T rotation matrices, estimates angular velocity at T-2 steps. - Input sequence should be of shape (B, T, ..., 3, 3) - ''' - # see https://en.wikipedia.org/wiki/Angular_velocity#Calculation_from_the_orientation_matrix - dRdt = estimate_linear_velocity(rot_seq, dt) - R = rot_seq - RT = R.transpose(-1, -2) - # compute skew-symmetric angular velocity tensor - w_mat = torch.matmul(dRdt, RT) - # pull out angular velocity vector by averaging symmetric entries - w_x = (-w_mat[..., 1, 2] + w_mat[..., 2, 1]) / 2.0 - w_y = (w_mat[..., 0, 2] - w_mat[..., 2, 0]) / 2.0 - w_z = (-w_mat[..., 0, 1] + w_mat[..., 1, 0]) / 2.0 - w = torch.stack([w_x, w_y, w_z], axis=-1) - return w - -def image_from_bytes(image_bytes): - import matplotlib.image as mpimg - from io import BytesIO - return mpimg.imread(BytesIO(image_bytes), format='PNG') - -def process_frame(i, vertices_all, vertices1_all, faces, output_dir, filenames): - import matplotlib - matplotlib.use('Agg') - import matplotlib.pyplot as plt - import trimesh - import pyrender - - def deg_to_rad(degrees): - return degrees * np.pi / 180 - - uniform_color = [220, 220, 220, 255] - resolution = (1000, 1000) - figsize = (10, 10) - - fig, axs = plt.subplots( - nrows=1, - ncols=2, - figsize=(figsize[0] * 2, figsize[1] * 1) - ) - axs = axs.flatten() - - vertices = vertices_all[i] - vertices1 = vertices1_all[i] - filename = f"{output_dir}frame_{i}.png" - filenames.append(filename) - if i%100 == 0: - print('processed', i, 'frames') - #time_s = time.time() - #print(vertices.shape) - angle_rad = deg_to_rad(-2) - pose_camera = np.array([ - [1.0, 0.0, 0.0, 0.0], - [0.0, np.cos(angle_rad), -np.sin(angle_rad), 1.0], - [0.0, np.sin(angle_rad), np.cos(angle_rad), 5.0], - [0.0, 0.0, 0.0, 1.0] - ]) - angle_rad = deg_to_rad(-30) - pose_light = np.array([ - [1.0, 0.0, 0.0, 0.0], - [0.0, np.cos(angle_rad), -np.sin(angle_rad), 0.0], - [0.0, np.sin(angle_rad), np.cos(angle_rad), 3.0], - [0.0, 0.0, 0.0, 1.0] - ]) - - for vtx_idx, vtx in enumerate([vertices, vertices1]): - trimesh_mesh = trimesh.Trimesh( - vertices=vtx, - faces=faces, - vertex_colors=uniform_color - ) - mesh = pyrender.Mesh.from_trimesh( - trimesh_mesh, smooth=True - ) - scene = pyrender.Scene() - scene.add(mesh) - camera = pyrender.OrthographicCamera(xmag=1.0, ymag=1.0) - scene.add(camera, pose=pose_camera) - light = pyrender.DirectionalLight(color=[1.0, 1.0, 1.0], intensity=4.0) - scene.add(light, pose=pose_light) - renderer = pyrender.OffscreenRenderer(*resolution) - color, _ = renderer.render(scene) - axs[vtx_idx].imshow(color) - axs[vtx_idx].axis('off') - renderer.delete() - - plt.savefig(filename, bbox_inches='tight') - plt.close(fig) - -def generate_images(frames, vertices_all, vertices1_all, faces, output_dir, filenames): - import multiprocessing - # import trimesh - num_cores = multiprocessing.cpu_count() - 1 # This will get the number of cores on your machine. - # mesh = trimesh.Trimesh(vertices_all[0], faces) - # scene = mesh.scene() - # fov = scene.camera.fov.copy() - # fov[0] = 80.0 - # fov[1] = 60.0 - # camera_params = { - # 'fov': fov, - # 'resolution': scene.camera.resolution, - # 'focal': scene.camera.focal, - # 'z_near': scene.camera.z_near, - # "z_far": scene.camera.z_far, - # 'transform': scene.graph[scene.camera.name][0] - # } - # mesh1 = trimesh.Trimesh(vertices1_all[0], faces) - # scene1 = mesh1.scene() - # camera_params1 = { - # 'fov': fov, - # 'resolution': scene1.camera.resolution, - # 'focal': scene1.camera.focal, - # 'z_near': scene1.camera.z_near, - # "z_far": scene1.camera.z_far, - # 'transform': scene1.graph[scene1.camera.name][0] - # } - # Use a Pool to manage the processes - # print(num_cores) - # for i in range(frames): - # process_frame(i, vertices_all, vertices1_all, faces, output_dir, use_matplotlib, filenames, camera_params, camera_params1) - for i in range(frames): - process_frame(i*3, vertices_all, vertices1_all, faces, output_dir, filenames) - - # progress = multiprocessing.Value('i', 0) - # lock = multiprocessing.Lock() - # with multiprocessing.Pool(num_cores) as pool: - # # pool.starmap(process_frame, [(i, vertices_all, vertices1_all, faces, output_dir, use_matplotlib, filenames, camera_params, camera_params1) for i in range(frames)]) - # pool.starmap( - # process_frame, - # [ - # (i, vertices_all, vertices1_all, faces, output_dir, filenames) - # for i in range(frames) - # ] - # ) - - # progress = multiprocessing.Value('i', 0) - # lock = multiprocessing.Lock() - # with multiprocessing.Pool(num_cores) as pool: - # # pool.starmap(process_frame, [(i, vertices_all, vertices1_all, faces, output_dir, use_matplotlib, filenames, camera_params, camera_params1) for i in range(frames)]) - # pool.starmap( - # process_frame, - # [ - # (i, vertices_all, vertices1_all, faces, output_dir, filenames) - # for i in range(frames) - # ] - # ) - -def render_one_sequence( - res_npz_path, - gt_npz_path, - output_dir, - audio_path, - model_folder="/data/datasets/smplx_models/", - model_type='smplx', - gender='NEUTRAL_2020', - ext='npz', - num_betas=300, - num_expression_coeffs=100, - use_face_contour=False, - use_matplotlib=False, - args=None): - import smplx - import matplotlib.pyplot as plt - import imageio - from tqdm import tqdm - import os - import numpy as np - import torch - import moviepy.editor as mp - import librosa - - model = smplx.create(model_folder, model_type=model_type, - gender=gender, use_face_contour=use_face_contour, - num_betas=num_betas, - num_expression_coeffs=num_expression_coeffs, - ext=ext, use_pca=False).cuda() - - #data_npz = np.load(f"{output_dir}{res_npz_path}.npz") - data_np_body = np.load(res_npz_path, allow_pickle=True) - gt_np_body = np.load(gt_npz_path, allow_pickle=True) - - if not os.path.exists(output_dir): os.makedirs(output_dir) - # if not use_matplotlib: - # import trimesh - #import pyrender - from pyvirtualdisplay import Display - #''' - #display = Display(visible=0, size=(1000, 1000)) - #display.start() - faces = np.load(f"{model_folder}/smplx/SMPLX_NEUTRAL_2020.npz", allow_pickle=True)["f"] - seconds = 1 - #data_npz["jaw_pose"].shape[0] - n = data_np_body["poses"].shape[0] - beta = torch.from_numpy(data_np_body["betas"]).to(torch.float32).unsqueeze(0).cuda() - beta = beta.repeat(n, 1) - expression = torch.from_numpy(data_np_body["expressions"][:n]).to(torch.float32).cuda() - jaw_pose = torch.from_numpy(data_np_body["poses"][:n, 66:69]).to(torch.float32).cuda() - pose = torch.from_numpy(data_np_body["poses"][:n]).to(torch.float32).cuda() - transl = torch.from_numpy(data_np_body["trans"][:n]).to(torch.float32).cuda() - # print(beta.shape, expression.shape, jaw_pose.shape, pose.shape, transl.shape, pose[:,:3].shape) - output = model(betas=beta, transl=transl, expression=expression, jaw_pose=jaw_pose, - global_orient=pose[:,:3], body_pose=pose[:,3:21*3+3], left_hand_pose=pose[:,25*3:40*3], right_hand_pose=pose[:,40*3:55*3], - leye_pose=pose[:, 69:72], - reye_pose=pose[:, 72:75], - return_verts=True) - vertices_all = output["vertices"].cpu().detach().numpy() - - beta1 = torch.from_numpy(gt_np_body["betas"]).to(torch.float32).unsqueeze(0).cuda() - expression1 = torch.from_numpy(gt_np_body["expressions"][:n]).to(torch.float32).cuda() - jaw_pose1 = torch.from_numpy(gt_np_body["poses"][:n,66:69]).to(torch.float32).cuda() - pose1 = torch.from_numpy(gt_np_body["poses"][:n]).to(torch.float32).cuda() - transl1 = torch.from_numpy(gt_np_body["trans"][:n]).to(torch.float32).cuda() - output1 = model(betas=beta1, transl=transl1, expression=expression1, jaw_pose=jaw_pose1, global_orient=pose1[:,:3], body_pose=pose1[:,3:21*3+3], left_hand_pose=pose1[:,25*3:40*3], right_hand_pose=pose1[:,40*3:55*3], - leye_pose=pose1[:, 69:72], - reye_pose=pose1[:, 72:75],return_verts=True) - vertices1_all = output1["vertices"].cpu().detach().numpy() - if args.debug: - seconds = 1 - else: - seconds = vertices_all.shape[0]//30 - silent_video_file_path = utils.fast_render.generate_silent_videos(args.render_video_fps, - args.render_video_width, - args.render_video_height, - args.render_concurrent_num, - args.render_tmp_img_filetype, - int(seconds*args.render_video_fps), - vertices_all, - vertices1_all, - faces, - output_dir) - base_filename_without_ext = os.path.splitext(os.path.basename(res_npz_path))[0] - final_clip = os.path.join(output_dir, f"{base_filename_without_ext}.mp4") - utils.media.add_audio_to_video(silent_video_file_path, audio_path, final_clip) - os.remove(silent_video_file_path) - return final_clip - -def render_one_sequence_no_gt( - res_npz_path, - output_dir, - audio_path, - model_folder="/data/datasets/smplx_models/", - model_type='smplx', - gender='NEUTRAL_2020', - ext='npz', - num_betas=300, - num_expression_coeffs=100, - use_face_contour=False, - use_matplotlib=False, - args=None): - import smplx - import matplotlib.pyplot as plt - import imageio - from tqdm import tqdm - import os - import numpy as np - import torch - import moviepy.editor as mp - import librosa - - model = smplx.create(model_folder, model_type=model_type, - gender=gender, use_face_contour=use_face_contour, - num_betas=num_betas, - num_expression_coeffs=num_expression_coeffs, - ext=ext, use_pca=False).cuda() - - #data_npz = np.load(f"{output_dir}{res_npz_path}.npz") - data_np_body = np.load(res_npz_path, allow_pickle=True) - # gt_np_body = np.load(gt_npz_path, allow_pickle=True) - - if not os.path.exists(output_dir): os.makedirs(output_dir) - # if not use_matplotlib: - # import trimesh - #import pyrender - from pyvirtualdisplay import Display - #''' - #display = Display(visible=0, size=(1000, 1000)) - #display.start() - faces = np.load(f"{model_folder}/smplx/SMPLX_NEUTRAL_2020.npz", allow_pickle=True)["f"] - seconds = 1 - #data_npz["jaw_pose"].shape[0] - n = data_np_body["poses"].shape[0] - beta = torch.from_numpy(data_np_body["betas"]).to(torch.float32).unsqueeze(0).cuda() - beta = beta.repeat(n, 1) - expression = torch.from_numpy(data_np_body["expressions"][:n]).to(torch.float32).cuda() - jaw_pose = torch.from_numpy(data_np_body["poses"][:n, 66:69]).to(torch.float32).cuda() - pose = torch.from_numpy(data_np_body["poses"][:n]).to(torch.float32).cuda() - transl = torch.from_numpy(data_np_body["trans"][:n]).to(torch.float32).cuda() - # print(beta.shape, expression.shape, jaw_pose.shape, pose.shape, transl.shape, pose[:,:3].shape) - output = model(betas=beta, transl=transl, expression=expression, jaw_pose=jaw_pose, - global_orient=pose[:,:3], body_pose=pose[:,3:21*3+3], left_hand_pose=pose[:,25*3:40*3], right_hand_pose=pose[:,40*3:55*3], - leye_pose=pose[:, 69:72], - reye_pose=pose[:, 72:75], - return_verts=True) - vertices_all = output["vertices"].cpu().detach().numpy() - - # beta1 = torch.from_numpy(gt_np_body["betas"]).to(torch.float32).unsqueeze(0).cuda() - # expression1 = torch.from_numpy(gt_np_body["expressions"][:n]).to(torch.float32).cuda() - # jaw_pose1 = torch.from_numpy(gt_np_body["poses"][:n,66:69]).to(torch.float32).cuda() - # pose1 = torch.from_numpy(gt_np_body["poses"][:n]).to(torch.float32).cuda() - # transl1 = torch.from_numpy(gt_np_body["trans"][:n]).to(torch.float32).cuda() - # output1 = model(betas=beta1, transl=transl1, expression=expression1, jaw_pose=jaw_pose1, global_orient=pose1[:,:3], body_pose=pose1[:,3:21*3+3], left_hand_pose=pose1[:,25*3:40*3], right_hand_pose=pose1[:,40*3:55*3], - # leye_pose=pose1[:, 69:72], - # reye_pose=pose1[:, 72:75],return_verts=True) - # vertices1_all = output1["vertices"].cpu().detach().numpy() - if args.debug: - seconds = 1 - else: - seconds = vertices_all.shape[0]//30 - silent_video_file_path = utils.fast_render.generate_silent_videos_no_gt(args.render_video_fps, - args.render_video_width, - args.render_video_height, - args.render_concurrent_num, - args.render_tmp_img_filetype, - int(seconds*args.render_video_fps), - vertices_all, - faces, - output_dir) - base_filename_without_ext = os.path.splitext(os.path.basename(res_npz_path))[0] - final_clip = os.path.join(output_dir, f"{base_filename_without_ext}.mp4") - utils.media.add_audio_to_video(silent_video_file_path, audio_path, final_clip) - os.remove(silent_video_file_path) - return final_clip - -def print_exp_info(args): - logger.info(pprint.pformat(vars(args))) - logger.info(f"# ------------ {args.name} ----------- #") - logger.info("PyTorch version: {}".format(torch.__version__)) - logger.info("CUDA version: {}".format(torch.version.cuda)) - logger.info("{} GPUs".format(torch.cuda.device_count())) - logger.info(f"Random Seed: {args.random_seed}") - -def args2csv(args, get_head=False, list4print=[]): - for k, v in args.items(): - if isinstance(args[k], dict): - args2csv(args[k], get_head, list4print) - else: list4print.append(k) if get_head else list4print.append(v) - return list4print - -class EpochTracker: - def __init__(self, metric_names, metric_directions): - assert len(metric_names) == len(metric_directions), "Metric names and directions should have the same length" - - - self.metric_names = metric_names - self.states = ['train', 'val', 'test'] - self.types = ['last', 'best'] - - - self.values = {name: {state: {type_: {'value': np.inf if not is_higher_better else -np.inf, 'epoch': 0} - for type_ in self.types} - for state in self.states} - for name, is_higher_better in zip(metric_names, metric_directions)} - - self.loss_meters = {name: {state: AverageMeter(f"{name}_{state}") - for state in self.states} - for name in metric_names} - - - self.is_higher_better = {name: direction for name, direction in zip(metric_names, metric_directions)} - self.train_history = {name: [] for name in metric_names} - self.val_history = {name: [] for name in metric_names} - - - def update_meter(self, name, state, value): - self.loss_meters[name][state].update(value) - - - def update_values(self, name, state, epoch): - value_avg = self.loss_meters[name][state].avg - new_best = False - - - if ((value_avg < self.values[name][state]['best']['value'] and not self.is_higher_better[name]) or - (value_avg > self.values[name][state]['best']['value'] and self.is_higher_better[name])): - self.values[name][state]['best']['value'] = value_avg - self.values[name][state]['best']['epoch'] = epoch - new_best = True - self.values[name][state]['last']['value'] = value_avg - self.values[name][state]['last']['epoch'] = epoch - return new_best - - - def get(self, name, state, type_): - return self.values[name][state][type_] - - - def reset(self): - for name in self.metric_names: - for state in self.states: - self.loss_meters[name][state].reset() - - - def flatten_values(self): - flat_dict = {} - for name in self.metric_names: - for state in self.states: - for type_ in self.types: - value_key = f"{name}_{state}_{type_}" - epoch_key = f"{name}_{state}_{type_}_epoch" - flat_dict[value_key] = self.values[name][state][type_]['value'] - flat_dict[epoch_key] = self.values[name][state][type_]['epoch'] - return flat_dict - - def update_and_plot(self, name, epoch, save_path): - new_best_train = self.update_values(name, 'train', epoch) - new_best_val = self.update_values(name, 'val', epoch) - - - self.train_history[name].append(self.loss_meters[name]['train'].avg) - self.val_history[name].append(self.loss_meters[name]['val'].avg) - - - train_values = self.train_history[name] - val_values = self.val_history[name] - epochs = list(range(1, len(train_values) + 1)) - - - plt.figure(figsize=(10, 6)) - plt.plot(epochs, train_values, label='Train') - plt.plot(epochs, val_values, label='Val') - plt.title(f'Train vs Val {name} over epochs') - plt.xlabel('Epochs') - plt.ylabel(name) - plt.legend() - plt.savefig(save_path) - plt.close() - - - return new_best_train, new_best_val - -def record_trial(args, tracker): - """ - 1. record notes, score, env_name, experments_path, - """ - csv_path = args.out_path + "custom/" +args.csv_name+".csv" - all_print_dict = vars(args) - all_print_dict.update(tracker.flatten_values()) - if not os.path.exists(csv_path): - pd.DataFrame([all_print_dict]).to_csv(csv_path, index=False) - else: - df_existing = pd.read_csv(csv_path) - df_new = pd.DataFrame([all_print_dict]) - df_aligned = df_existing.append(df_new).fillna("") - df_aligned.to_csv(csv_path, index=False) - -def set_random_seed(args): - os.environ['PYTHONHASHSEED'] = str(args.random_seed) - random.seed(args.random_seed) - np.random.seed(args.random_seed) - torch.manual_seed(args.random_seed) - torch.cuda.manual_seed_all(args.random_seed) - torch.cuda.manual_seed(args.random_seed) - torch.backends.cudnn.deterministic = args.deterministic #args.CUDNN_DETERMINISTIC - torch.backends.cudnn.benchmark = args.benchmark - torch.backends.cudnn.enabled = args.cudnn_enabled - -def save_checkpoints(save_path, model, opt=None, epoch=None, lrs=None): - if lrs is not None: - states = { 'model_state': model.state_dict(), - 'epoch': epoch + 1, - 'opt_state': opt.state_dict(), - 'lrs':lrs.state_dict(),} - elif opt is not None: - states = { 'model_state': model.state_dict(), - 'epoch': epoch + 1, - 'opt_state': opt.state_dict(),} - else: - states = { 'model_state': model.state_dict(),} - torch.save(states, save_path) - -def load_checkpoints(model, save_path, load_name='model'): - states = torch.load(save_path) - new_weights = OrderedDict() - flag=False - for k, v in states['model_state'].items(): - #print(k) - if "module" not in k: - break - else: - new_weights[k[7:]]=v - flag=True - if flag: - try: - model.load_state_dict(new_weights) - except: - #print(states['model_state']) - model.load_state_dict(states['model_state']) - else: - model.load_state_dict(states['model_state']) - logger.info(f"load self-pretrained checkpoints for {load_name}") - -def model_complexity(model, args): - from ptflops import get_model_complexity_info - flops, params = get_model_complexity_info(model, (args.T_GLOBAL._DIM, args.TRAIN.CROP, args.TRAIN), - as_strings=False, print_per_layer_stat=False) - logging.info('{:<30} {:<8} BFlops'.format('Computational complexity: ', flops / 1e9)) - logging.info('{:<30} {:<8} MParams'.format('Number of parameters: ', params / 1e6)) - -class AverageMeter(object): - """Computes and stores the average and current value""" - def __init__(self, name, fmt=':f'): - self.name = name - self.fmt = fmt - self.reset() - - def reset(self): - self.val = 0 - self.avg = 0 - self.sum = 0 - self.count = 0 - - def update(self, val, n=1): - self.val = val - self.sum += val * n - self.count += n - self.avg = self.sum / self.count - - def __str__(self): - fmtstr = '{name} {val' + self.fmt + '} ({avg' + self.fmt + '})' - return fmtstr.format(**self.__dict__) \ No newline at end of file diff --git a/utils/__pycache__/config.cpython-310.pyc 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a/utils/config.py +++ /dev/null @@ -1,275 +0,0 @@ -import configargparse -import time -import json -import yaml -import os - - -def str2bool(v): - """ from https://stackoverflow.com/a/43357954/1361529 """ - if isinstance(v, bool): - return v - if v.lower() in ('yes', 'true', 't', 'y', '1'): - return True - elif v.lower() in ('no', 'false', 'f', 'n', '0'): - return False - else: - raise configargparse.ArgumentTypeError('Boolean value expected.') - - -def parse_args(): - """ - requirement for config - 1. command > yaml > default - 2. avoid re-definition - 3. lowercase letters is better - 4. hierarchical is not necessary - """ - parser = configargparse.ArgParser() - parser.add("-c", "--config", default='./configs/emage_test_hf.yaml', is_config_file=True) - parser.add("--project", default="audio2pose", type=str) # wandb project name - parser.add("--stat", default="ts", type=str) - parser.add("--csv_name", default="a2g_0", type=str) # local device id - parser.add("--notes", default="", type=str) - parser.add("--trainer", default="camn", type=str) - - parser.add("--l", default=4, type=int) - # ------------- path and save name ---------------- # - parser.add("--is_train", default=True, type=str2bool) - parser.add("--debug", default=False, type=str2bool) - # different between environments - parser.add("--root_path", default="/home/ma-user/work/") - parser.add("--cache_path", default="/outputs/audio2pose/", type=str) - parser.add("--out_path", default="/outputs/audio2pose/", type=str) - parser.add("--data_path", default="/outputs/audio2pose/", type=str) - parser.add("--train_data_path", default="/datasets/trinity/train/", type=str) - parser.add("--val_data_path", default="/datasets/trinity/val/", type=str) - parser.add("--test_data_path", default="/datasets/trinity/test/", type=str) - parser.add("--mean_pose_path", default="/datasets/trinity/train/", type=str) - parser.add("--std_pose_path", default="/datasets/trinity/train/", type=str) - # for pretrian weights - parser.add("--data_path_1", default="../../datasets/checkpoints/", type=str) - # ------------------- evaluation ----------------------- # - parser.add("--test_ckpt", default="/datasets/beat_cache/beat_4english_15_141/last.bin") - parser.add("--eval_model", default="vae", type=str) - parser.add("--e_name", default=None, type=str) #HalfEmbeddingNet - parser.add("--e_path", default="/datasets/beat/generated_data/self_vae_128.bin") - parser.add("--variational", default=False, type=str2bool) - parser.add("--vae_length", default=256, type=int) - parser.add("--vae_test_dim", default=141, type=int) - parser.add("--vae_test_len", default=34, type=int) - parser.add("--vae_test_stride", default=10, type=int) - #parser.add("--vae_pose_length", default=34, type=int) - parser.add("--test_period", default=20, type=int) - parser.add("--vae_codebook_size", default=1024, type=int) - parser.add("--vae_quantizer_lambda", default=1., type=float) - - parser.add("--vae_layer", default=2, type=int) - parser.add("--vae_grow", default=[1,1,2,1], type=int, nargs="*") - parser.add("--lf", default=0., type=float) - parser.add("--ll", default=0., type=float) - parser.add("--lu", default=0., type=float) - parser.add("--lh", default=0., type=float) - parser.add("--cf", default=0., type=float) - parser.add("--cl", default=0., type=float) - parser.add("--cu", default=0., type=float) - parser.add("--ch", default=0., type=float) - - - # --------------- data ---------------------------- # - parser.add("--additional_data", default=False, type=str2bool) - parser.add("--train_trans", default=True, type=str2bool) - parser.add("--dataset", default="beat", type=str) - parser.add("--rot6d", default=True, type=str2bool) - parser.add("--ori_joints", default="spine_neck_141", type=str) - parser.add("--tar_joints", default="spine_neck_141", type=str) - parser.add("--training_speakers", default=[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30], type=int, nargs="*") - #parser.add("--pose_version", default="spine_neck_141", type=str) - parser.add("--new_cache", default=True, type=str2bool) - parser.add("--beat_align", default=True, type=str2bool) - parser.add("--cache_only", default=False, type=str2bool) - parser.add("--word_cache", default=False, type=str2bool) - parser.add("--use_aug", default=False, type=str2bool) - parser.add("--disable_filtering", default=False, type=str2bool) - parser.add("--clean_first_seconds", default=0, type=int) - parser.add("--clean_final_seconds", default=0, type=int) - - parser.add("--audio_rep", default=None, type=str) - parser.add("--audio_sr", default=16000, type=int) - parser.add("--word_rep", default=None, type=str) - parser.add("--emo_rep", default=None, type=str) - parser.add("--sem_rep", default=None, type=str) - parser.add("--facial_rep", default=None, type=str) - parser.add("--pose_rep", default="bvhrot", type=str) - parser.add("--id_rep", default="onehot", type=str) - parser.add("--speaker_id", default="onehot", type=str) - - parser.add("--a_pre_encoder", default=None, type=str) - parser.add("--a_encoder", default=None, type=str) - parser.add("--a_fix_pre", default=False, type=str2bool) - parser.add("--t_pre_encoder", default=None, type=str) - parser.add("--t_encoder", default=None, type=str) - parser.add("--t_fix_pre", default=False, type=str2bool) - parser.add("--m_pre_encoder", default=None, type=str) - parser.add("--m_encoder", default=None, type=str) - parser.add("--m_fix_pre", default=False, type=str2bool) - parser.add("--f_pre_encoder", default=None, type=str) - parser.add("--f_encoder", default=None, type=str) - parser.add("--f_fix_pre", default=False, type=str2bool) - parser.add("--m_decoder", default=None, type=str) - parser.add("--decode_fusion", default=None, type=str) - parser.add("--atmr", default=0.0, type=float) - parser.add("--ttmr", default=0., type=float) - parser.add("--mtmr", default=0., type=float) - parser.add("--ftmr", default=0., type=float) - parser.add("--asmr", default=0., type=float) - parser.add("--tsmr", default=0., type=float) - parser.add("--msmr", default=0., type=float) - parser.add("--fsmr", default=0., type=float) -# parser.add("--m_encoder", default=None, type=str) -# parser.add("--m_pre_fix", default=None, type=str) -# parser.add("--id_rep", default=None, type=str) - - parser.add("--freeze_wordembed", default=True, type=str2bool) - parser.add("--audio_fps", default=16000, type=int) - parser.add("--facial_fps", default=15, type=int) - parser.add("--pose_fps", default=15, type=int) - - parser.add("--audio_dims", default=1, type=int) - parser.add("--facial_dims", default=39, type=int) - parser.add("--pose_dims", default=123, type=int) - parser.add("--word_index_num", default=5793, type=int) - parser.add("--word_dims", default=300, type=int) - parser.add("--speaker_dims", default=4, type=int) - parser.add("--emotion_dims", default=8, type=int) - - parser.add("--audio_norm", default=False, type=str2bool) - parser.add("--facial_norm", default=False, type=str2bool) - parser.add("--pose_norm", default=False, type=str2bool) - - parser.add("--pose_length", default=34, type=int) - parser.add("--pre_frames", default=4, type=int) - parser.add("--stride", default=10, type=int) - parser.add("--pre_type", default="zero", type=str) - - parser.add("--audio_f", default=0, type=int) - parser.add("--motion_f", default=0, type=int) - parser.add("--facial_f", default=0, type=int) - parser.add("--speaker_f", default=0, type=int) - parser.add("--word_f", default=0, type=int) - parser.add("--emotion_f", default=0, type=int) - parser.add("--aud_prob", default=1.0, type=float) - parser.add("--pos_prob", default=1.0, type=float) - parser.add("--txt_prob", default=1.0, type=float) - parser.add("--fac_prob", default=1.0, type=float) - parser.add("--multi_length_training", default=[1.0], type=float, nargs="*") - # --------------- model ---------------------------- # - parser.add("--pretrain", default=False, type=str2bool) - parser.add("--model", default="camn", type=str) - parser.add("--g_name", default="CaMN", type=str) - parser.add("--d_name", default=None, type=str) #ConvDiscriminator - parser.add("--dropout_prob", default=0.3, type=float) - parser.add("--n_layer", default=4, type=int) - parser.add("--hidden_size", default=300, type=int) - #parser.add("--period", default=34, type=int) - parser.add("--test_length", default=34, type=int) - # Self-designed "Multi-Stage", "Seprate", or "Original" - parser.add("--finger_net", default="original", type=str) - parser.add("--pos_encoding_type", default="sin", type=str) - parser.add("--queue_size", default=1024, type=int) - - # --------------- training ------------------------- # - parser.add("--epochs", default=120, type=int) - parser.add("--epoch_stage", default=0, type=int) - parser.add("--grad_norm", default=0, type=float) - parser.add("--no_adv_epoch", default=999, type=int) - parser.add("--batch_size", default=128, type=int) - parser.add("--opt", default="adam", type=str) - parser.add("--lr_base", default=0.00025, type=float) - parser.add("--opt_betas", default=[0.5, 0.999], type=float, nargs="*") - parser.add("--weight_decay", default=0., type=float) - # for warmup and cosine - parser.add("--lr_min", default=1e-7, type=float) - parser.add("--warmup_lr", default=5e-4, type=float) - parser.add("--warmup_epochs", default=0, type=int) - parser.add("--decay_epochs", default=9999, type=int) - parser.add("--decay_rate", default=0.1, type=float) - parser.add("--lr_policy", default="step", type=str) - # for sgd - parser.add("--momentum", default=0.8, type=float) - parser.add("--nesterov", default=True, type=str2bool) - parser.add("--amsgrad", default=False, type=str2bool) - parser.add("--d_lr_weight", default=0.2, type=float) - parser.add("--rec_weight", default=500, type=float) - parser.add("--adv_weight", default=20.0, type=float) - parser.add("--fid_weight", default=0.0, type=float) - parser.add("--vel_weight", default=0.0, type=float) - parser.add("--acc_weight", default=0.0, type=float) - parser.add("--kld_weight", default=0.0, type=float) - parser.add("--kld_aud_weight", default=0.0, type=float) - parser.add("--kld_fac_weight", default=0.0, type=float) - parser.add("--ali_weight", default=0.0, type=float) - parser.add("--ita_weight", default=0.0, type=float) - parser.add("--iwa_weight", default=0.0, type=float) - parser.add("--wei_weight", default=0.0, type=float) - parser.add("--gap_weight", default=0.0, type=float) - parser.add("--atcont", default=0.0, type=float) - parser.add("--fusion_mode", default="sum", type=str) - - parser.add("--div_reg_weight", default=0.0, type=float) - parser.add("--rec_aud_weight", default=0.0, type=float) - parser.add("--rec_ver_weight", default=0.0, type=float) - parser.add("--rec_pos_weight", default=0.0, type=float) - parser.add("--rec_fac_weight", default=0.0, type=float) - parser.add("--rec_txt_weight", default=0.0, type=float) -# parser.add("--gan_noise_size", default=0, type=int) - # --------------- ha2g -------------------------- # - parser.add("--n_pre_poses", default=4, type=int) - parser.add("--n_poses", default=34, type=int) - parser.add("--input_context", default="both", type=str) - parser.add("--loss_contrastive_pos_weight", default=0.2, type=float) - parser.add("--loss_contrastive_neg_weight", default=0.005, type=float) - parser.add("--loss_physical_weight", default=0.0, type=float) - parser.add("--loss_reg_weight", default=0.05, type=float) - parser.add("--loss_regression_weight", default=70.0, type=float) - parser.add("--loss_gan_weight", default=5.0, type=float) - parser.add("--loss_kld_weight", default=0.1, type=float) - parser.add("--z_type", default="speaker", type=str) - # --------------- device -------------------------- # - parser.add("--random_seed", default=2021, type=int) - parser.add("--deterministic", default=True, type=str2bool) - parser.add("--benchmark", default=True, type=str2bool) - parser.add("--cudnn_enabled", default=True, type=str2bool) - # mix precision - parser.add("--apex", default=False, type=str2bool) - parser.add("--gpus", default=[0], type=int, nargs="*") - parser.add("--loader_workers", default=0, type=int) - parser.add("--ddp", default=False, type=str2bool) - parser.add("--sparse", default=1, type=int) - #parser.add("--world_size") - parser.add("--render_video_fps", default=30, type=int) - parser.add("--render_video_width", default=1920, type=int) - parser.add("--render_video_height", default=720, type=int) - cpu_cores = os.cpu_count() if os.cpu_count() is not None else 1 - default_concurrent = max(1, cpu_cores // 2) - parser.add("--render_concurrent_num", default=default_concurrent, type=int) - parser.add("--render_tmp_img_filetype", default="bmp", type=str) - - # logging - parser.add("--log_period", default=10, type=int) - - - args = parser.parse_args() - idc = 0 - for i, char in enumerate(args.config): - if char == "/": idc = i - args.name = args.config[idc+1:-5] - - is_train = args.is_train - - if is_train: - time_local = time.localtime() - name_expend = "%02d%02d_%02d%02d%02d_"%(time_local[1], time_local[2],time_local[3], time_local[4], time_local[5]) - args.name = name_expend + args.name - - return args \ No newline at end of file diff --git a/utils/data_transfer.py b/utils/data_transfer.py deleted file mode 100644 index 025110cac44f42d581c4414bd3d0c6c7b21f33e0..0000000000000000000000000000000000000000 --- a/utils/data_transfer.py +++ /dev/null @@ -1,202 +0,0 @@ -import os -import logging -import random -import h5py -import numpy as np -import pickle -import math -import numbers -import torch -import torch.nn as nn -import torch.nn.functional as F -from torch.optim.lr_scheduler import StepLR -from torch.distributions import Normal - - -def _index_from_letter(letter: str) -> int: - if letter == "X": - return 0 - if letter == "Y": - return 1 - if letter == "Z": - return 2 - raise ValueError("letter must be either X, Y or Z.") - - -def _angle_from_tan( - axis: str, other_axis: str, data, horizontal: bool, tait_bryan: bool -) -> torch.Tensor: - """ - Extract the first or third Euler angle from the two members of - the matrix which are positive constant times its sine and cosine. - - Args: - axis: Axis label "X" or "Y or "Z" for the angle we are finding. - other_axis: Axis label "X" or "Y or "Z" for the middle axis in the - convention. - data: Rotation matrices as tensor of shape (..., 3, 3). - horizontal: Whether we are looking for the angle for the third axis, - which means the relevant entries are in the same row of the - rotation matrix. If not, they are in the same column. - tait_bryan: Whether the first and third axes in the convention differ. - - Returns: - Euler Angles in radians for each matrix in data as a tensor - of shape (...). - """ - - i1, i2 = {"X": (2, 1), "Y": (0, 2), "Z": (1, 0)}[axis] - if horizontal: - i2, i1 = i1, i2 - even = (axis + other_axis) in ["XY", "YZ", "ZX"] - if horizontal == even: - return torch.atan2(data[..., i1], data[..., i2]) - if tait_bryan: - return torch.atan2(-data[..., i2], data[..., i1]) - return torch.atan2(data[..., i2], -data[..., i1]) - - -def _axis_angle_rotation(axis: str, angle: torch.Tensor) -> torch.Tensor: - """ - Return the rotation matrices for one of the rotations about an axis - of which Euler angles describe, for each value of the angle given. - - Args: - axis: Axis label "X" or "Y or "Z". - angle: any shape tensor of Euler angles in radians - - Returns: - Rotation matrices as tensor of shape (..., 3, 3). - """ - - cos = torch.cos(angle) - sin = torch.sin(angle) - one = torch.ones_like(angle) - zero = torch.zeros_like(angle) - - if axis == "X": - R_flat = (one, zero, zero, zero, cos, -sin, zero, sin, cos) - elif axis == "Y": - R_flat = (cos, zero, sin, zero, one, zero, -sin, zero, cos) - elif axis == "Z": - R_flat = (cos, -sin, zero, sin, cos, zero, zero, zero, one) - else: - raise ValueError("letter must be either X, Y or Z.") - - return torch.stack(R_flat, -1).reshape(angle.shape + (3, 3)) - -def euler_angles_to_matrix(euler_angles: torch.Tensor, convention: str) -> torch.Tensor: - """ - Convert rotations given as Euler angles in radians to rotation matrices. - - Args: - euler_angles: Euler angles in radians as tensor of shape (..., 3). - convention: Convention string of three uppercase letters from - {"X", "Y", and "Z"}. - - Returns: - Rotation matrices as tensor of shape (..., 3, 3). - """ - if euler_angles.dim() == 0 or euler_angles.shape[-1] != 3: - raise ValueError("Invalid input euler angles.") - if len(convention) != 3: - raise ValueError("Convention must have 3 letters.") - if convention[1] in (convention[0], convention[2]): - raise ValueError(f"Invalid convention {convention}.") - for letter in convention: - if letter not in ("X", "Y", "Z"): - raise ValueError(f"Invalid letter {letter} in convention string.") - matrices = [ - _axis_angle_rotation(c, e) - for c, e in zip(convention, torch.unbind(euler_angles, -1)) - ] - # return functools.reduce(torch.matmul, matrices) - return torch.matmul(torch.matmul(matrices[0], matrices[1]), matrices[2]) - - -def matrix_to_rotation_6d(matrix: torch.Tensor) -> torch.Tensor: - """ - Converts rotation matrices to 6D rotation representation by Zhou et al. [1] - by dropping the last row. Note that 6D representation is not unique. - Args: - matrix: batch of rotation matrices of size (*, 3, 3) - Returns: - 6D rotation representation, of size (*, 6) - [1] Zhou, Y., Barnes, C., Lu, J., Yang, J., & Li, H. - On the Continuity of Rotation Representations in Neural Networks. - IEEE Conference on Computer Vision and Pattern Recognition, 2019. - Retrieved from http://arxiv.org/abs/1812.07035 - """ - return matrix[..., :2, :].clone().reshape(*matrix.size()[:-2], 6) - - -def rotation_6d_to_matrix(d6: torch.Tensor) -> torch.Tensor: - """ - Args: - d6: 6D rotation representation, of size (*, 6) - Returns: - batch of rotation matrices of size (*, 3, 3) - """ - a1, a2 = d6[..., :3], d6[..., 3:] - b1 = F.normalize(a1, dim=-1) - b2 = a2 - (b1 * a2).sum(-1, keepdim=True) * b1 - b2 = F.normalize(b2, dim=-1) - b3 = torch.cross(b1, b2, dim=-1) - return torch.stack((b1, b2, b3), dim=-2) - - -def matrix_to_euler_angles(matrix: torch.Tensor, convention: str) -> torch.Tensor: - """ - Convert rotations given as rotation matrices to Euler angles in radians. - - Args: - matrix: Rotation matrices as tensor of shape (..., 3, 3). - convention: Convention string of three uppercase letters. - - Returns: - Euler angles in radians as tensor of shape (..., 3). - """ - if len(convention) != 3: - raise ValueError("Convention must have 3 letters.") - if convention[1] in (convention[0], convention[2]): - raise ValueError(f"Invalid convention {convention}.") - for letter in convention: - if letter not in ("X", "Y", "Z"): - raise ValueError(f"Invalid letter {letter} in convention string.") - if matrix.size(-1) != 3 or matrix.size(-2) != 3: - raise ValueError(f"Invalid rotation matrix shape {matrix.shape}.") - i0 = _index_from_letter(convention[0]) - i2 = _index_from_letter(convention[2]) - tait_bryan = i0 != i2 - if tait_bryan: - central_angle = torch.asin( - matrix[..., i0, i2] * (-1.0 if i0 - i2 in [-1, 2] else 1.0) - ) - else: - central_angle = torch.acos(matrix[..., i0, i0]) - - o = ( - _angle_from_tan( - convention[0], convention[1], matrix[..., i2], False, tait_bryan - ), - central_angle, - _angle_from_tan( - convention[2], convention[1], matrix[..., i0, :], True, tait_bryan - ), - ) - return torch.stack(o, -1) - - -def so3_relative_angle(m1, m2): - m1 = m1.reshape(-1, 3, 3) - m2 = m2.reshape(-1, 3, 3) - #print(m2.shape) - m = torch.bmm(m1, m2.transpose(1, 2)) # batch*3*3 - #print(m.shape) - cos = (m[:, 0, 0] + m[:, 1, 1] + m[:, 2, 2] - 1) / 2 - #print(cos.shape) - cos = torch.clamp(cos, min=-1 + 1E-6, max=1-1E-6) - #print(cos.shape) - theta = torch.acos(cos) - #print(theta.shape) - return torch.mean(theta) diff --git a/utils/fast_render.py b/utils/fast_render.py deleted file mode 100644 index 78311cbcc99ac414c7724e1081615825ad20843a..0000000000000000000000000000000000000000 --- a/utils/fast_render.py +++ /dev/null @@ -1,266 +0,0 @@ -import os -import time -import numpy as np -import pyrender -import trimesh -import queue -import imageio -import threading -import multiprocessing -import utils.media -import glob - -def deg_to_rad(degrees): - return degrees * np.pi / 180 - -def create_pose_camera(angle_deg): - angle_rad = deg_to_rad(angle_deg) - return np.array([ - [1.0, 0.0, 0.0, 0.0], - [0.0, np.cos(angle_rad), -np.sin(angle_rad), 1.0], - [0.0, np.sin(angle_rad), np.cos(angle_rad), 5.0], - [0.0, 0.0, 0.0, 1.0] - ]) - -def create_pose_light(angle_deg): - angle_rad = deg_to_rad(angle_deg) - return np.array([ - [1.0, 0.0, 0.0, 0.0], - [0.0, np.cos(angle_rad), -np.sin(angle_rad), 0.0], - [0.0, np.sin(angle_rad), np.cos(angle_rad), 3.0], - [0.0, 0.0, 0.0, 1.0] - ]) - -def create_scene_with_mesh(vertices, faces, uniform_color, pose_camera, pose_light): - trimesh_mesh = trimesh.Trimesh(vertices=vertices, faces=faces, vertex_colors=uniform_color) - mesh = pyrender.Mesh.from_trimesh(trimesh_mesh, smooth=True) - scene = pyrender.Scene() - scene.add(mesh) - camera = pyrender.OrthographicCamera(xmag=1.0, ymag=1.0) - scene.add(camera, pose=pose_camera) - light = pyrender.DirectionalLight(color=[1.0, 1.0, 1.0], intensity=4.0) - scene.add(light, pose=pose_light) - return scene - -def do_render_one_frame(renderer, frame_idx, vertices, vertices1, faces): - if frame_idx % 100 == 0: - print('processed', frame_idx, 'frames') - - uniform_color = [220, 220, 220, 255] - pose_camera = create_pose_camera(angle_deg=-2) - pose_light = create_pose_light(angle_deg=-30) - - figs = [] - for vtx in [vertices, vertices1]: - # print(vtx.shape) - scene = create_scene_with_mesh(vtx, faces, uniform_color, pose_camera, pose_light) - fig, _ = renderer.render(scene) - figs.append(fig) - - return figs[0], figs[1] - -def do_render_one_frame_no_gt(renderer, frame_idx, vertices, faces): - if frame_idx % 100 == 0: - print('processed', frame_idx, 'frames') - - uniform_color = [220, 220, 220, 255] - pose_camera = create_pose_camera(angle_deg=-2) - pose_light = create_pose_light(angle_deg=-30) - - figs = [] - # for vtx in [vertices]: - # print(vtx.shape) - # print(vertices.shape) - scene = create_scene_with_mesh(vertices, faces, uniform_color, pose_camera, pose_light) - fig, _ = renderer.render(scene) - figs.append(fig) - - return figs[0] - -def write_images_from_queue(fig_queue, output_dir, img_filetype): - while True: - e = fig_queue.get() - if e is None: - break - fid, fig1, fig2 = e - filename = os.path.join(output_dir, f"frame_{fid}.{img_filetype}") - merged_fig = np.hstack((fig1, fig2)) - try: - imageio.imwrite(filename, merged_fig) - except Exception as ex: - print(f"Error writing image {filename}: {ex}") - raise ex - -def write_images_from_queue_no_gt(fig_queue, output_dir, img_filetype): - while True: - e = fig_queue.get() - if e is None: - break - fid, fig1, fig2 = e - filename = os.path.join(output_dir, f"frame_{fid}.{img_filetype}") - merged_fig = fig1 #np.hstack((fig1)) - try: - imageio.imwrite(filename, merged_fig) - except Exception as ex: - print(f"Error writing image {filename}: {ex}") - raise ex - - -def render_frames_and_enqueue(fids, frame_vertex_pairs, faces, render_width, render_height, fig_queue): - fig_resolution = (render_width // 2, render_height) - renderer = pyrender.OffscreenRenderer(*fig_resolution) - - for idx, fid in enumerate(fids): - fig1, fig2 = do_render_one_frame(renderer, fid, frame_vertex_pairs[idx][0], frame_vertex_pairs[idx][1], faces) - fig_queue.put((fid, fig1, fig2)) - - renderer.delete() - -def render_frames_and_enqueue_no_gt(fids, frame_vertex_pairs, faces, render_width, render_height, fig_queue): - fig_resolution = (render_width // 2, render_height) - renderer = pyrender.OffscreenRenderer(*fig_resolution) - - for idx, fid in enumerate(fids): - fig1 = do_render_one_frame_no_gt(renderer, fid, frame_vertex_pairs[idx][0], faces) - fig_queue.put((fid, fig1)) - - renderer.delete() - -def sub_process_process_frame(subprocess_index, render_video_width, render_video_height, render_tmp_img_filetype, fids, frame_vertex_pairs, faces, output_dir): - begin_ts = time.time() - print(f"subprocess_index={subprocess_index} begin_ts={begin_ts}") - - fig_queue = queue.Queue() - render_frames_and_enqueue(fids, frame_vertex_pairs, faces, render_video_width, render_video_height, fig_queue) - fig_queue.put(None) - render_end_ts = time.time() - - image_writer_thread = threading.Thread(target=write_images_from_queue, args=(fig_queue, output_dir, render_tmp_img_filetype)) - image_writer_thread.start() - image_writer_thread.join() - - write_end_ts = time.time() - print( - f"subprocess_index={subprocess_index} " - f"render={render_end_ts - begin_ts:.2f} " - f"all={write_end_ts - begin_ts:.2f} " - f"begin_ts={begin_ts:.2f} " - f"render_end_ts={render_end_ts:.2f} " - f"write_end_ts={write_end_ts:.2f}" - ) - -def sub_process_process_frame_no_gt(subprocess_index, render_video_width, render_video_height, render_tmp_img_filetype, fids, frame_vertex_pairs, faces, output_dir): - begin_ts = time.time() - print(f"subprocess_index={subprocess_index} begin_ts={begin_ts}") - - fig_queue = queue.Queue() - render_frames_and_enqueue(fids, frame_vertex_pairs, faces, render_video_width, render_video_height, fig_queue) - fig_queue.put(None) - render_end_ts = time.time() - - image_writer_thread = threading.Thread(target=write_images_from_queue_no_gt, args=(fig_queue, output_dir, render_tmp_img_filetype)) - image_writer_thread.start() - image_writer_thread.join() - - write_end_ts = time.time() - print( - f"subprocess_index={subprocess_index} " - f"render={render_end_ts - begin_ts:.2f} " - f"all={write_end_ts - begin_ts:.2f} " - f"begin_ts={begin_ts:.2f} " - f"render_end_ts={render_end_ts:.2f} " - f"write_end_ts={write_end_ts:.2f}" - ) - -def distribute_frames(frames, render_video_fps, render_concurent_nums, vertices_all, vertices1_all): - sample_interval = max(1, int(30 // render_video_fps)) - subproc_frame_ids = [[] for _ in range(render_concurent_nums)] - subproc_vertices = [[] for _ in range(render_concurent_nums)] - sampled_frame_id = 0 - - for i in range(frames): - if i % sample_interval != 0: - continue - subprocess_index = sampled_frame_id % render_concurent_nums - subproc_frame_ids[subprocess_index].append(sampled_frame_id) - subproc_vertices[subprocess_index].append((vertices_all[i], vertices1_all[i])) - sampled_frame_id += 1 - - return subproc_frame_ids, subproc_vertices - -def distribute_frames_no_gt(frames, render_video_fps, render_concurent_nums, vertices_all): - sample_interval = max(1, int(30 // render_video_fps)) - subproc_frame_ids = [[] for _ in range(render_concurent_nums)] - subproc_vertices = [[] for _ in range(render_concurent_nums)] - sampled_frame_id = 0 - - for i in range(frames): - if i % sample_interval != 0: - continue - subprocess_index = sampled_frame_id % render_concurent_nums - subproc_frame_ids[subprocess_index].append(sampled_frame_id) - subproc_vertices[subprocess_index].append((vertices_all[i], vertices_all[i])) - sampled_frame_id += 1 - - return subproc_frame_ids, subproc_vertices - -def generate_silent_videos(render_video_fps, - render_video_width, - render_video_height, - render_concurent_nums, - render_tmp_img_filetype, - frames, - vertices_all, - vertices1_all, - faces, - output_dir): - - subproc_frame_ids, subproc_vertices = distribute_frames(frames, render_video_fps, render_concurent_nums, vertices_all, vertices1_all) - - print(f"generate_silent_videos concurrentNum={render_concurent_nums} time={time.time()}") - with multiprocessing.Pool(render_concurent_nums) as pool: - pool.starmap( - sub_process_process_frame, - [ - (subprocess_index, render_video_width, render_video_height, render_tmp_img_filetype, subproc_frame_ids[subprocess_index], subproc_vertices[subprocess_index], faces, output_dir) - for subprocess_index in range(render_concurent_nums) - ] - ) - - output_file = os.path.join(output_dir, "silence_video.mp4") - utils.media.convert_img_to_mp4(os.path.join(output_dir, f"frame_%d.{render_tmp_img_filetype}"), output_file, render_video_fps) - filenames = glob.glob(os.path.join(output_dir, f"*.{render_tmp_img_filetype}")) - for filename in filenames: - os.remove(filename) - - return output_file - -def generate_silent_videos_no_gt(render_video_fps, - render_video_width, - render_video_height, - render_concurent_nums, - render_tmp_img_filetype, - frames, - vertices_all, - faces, - output_dir): - - subproc_frame_ids, subproc_vertices = distribute_frames_no_gt(frames, render_video_fps, render_concurent_nums, vertices_all) - - print(f"generate_silent_videos concurrentNum={render_concurent_nums} time={time.time()}") - with multiprocessing.Pool(render_concurent_nums) as pool: - pool.starmap( - sub_process_process_frame_no_gt, - [ - (subprocess_index, render_video_width, render_video_height, render_tmp_img_filetype, subproc_frame_ids[subprocess_index], subproc_vertices[subprocess_index], faces, output_dir) - for subprocess_index in range(render_concurent_nums) - ] - ) - - output_file = os.path.join(output_dir, "silence_video.mp4") - utils.media.convert_img_to_mp4(os.path.join(output_dir, f"frame_%d.{render_tmp_img_filetype}"), output_file, render_video_fps) - filenames = glob.glob(os.path.join(output_dir, f"*.{render_tmp_img_filetype}")) - for filename in filenames: - os.remove(filename) - - return output_file \ No newline at end of file diff --git a/utils/logger_tools.py b/utils/logger_tools.py deleted file mode 100644 index 5eaf6c06274589018ba3cc21e2d4214e76611d5d..0000000000000000000000000000000000000000 --- a/utils/logger_tools.py +++ /dev/null @@ -1,59 +0,0 @@ -import os -import inspect -import sys -import yaml -#import wandb -from loguru import logger - -def setup_logger(save_dir, distributed_rank=0, filename="log.txt", mode="a"): - """setup logger for training and testing. - Args: - save_dir(str): location to save log file - distributed_rank(int): device rank when multi-gpu environment - filename (string): log save name. - mode(str): log file write mode, `append` or `override`. default is `a`. - - Return: - logger instance. - """ - loguru_format = ( - "{time: MM-DD HH:mm:ss} | " - #"{level: <8} | " - #"{name}:{line} - {message}" - "{message}" - ) - - logger.remove() - save_file = os.path.join(save_dir, filename) - if mode == "o" and os.path.exists(save_file): - os.remove(save_file) - # only keep logger in rank0 process - if distributed_rank == 0: - logger.add( - sys.stderr, - format=loguru_format, - level="INFO", - enqueue=True, - ) - logger.add(save_file, - format=loguru_format, - ) - - -def set_args_and_logger(args, rank): - """ - set logger file and print args - """ - args_name_dir = args.out_path + "custom/" + args.name + args.notes + "/" - if rank == 0: - if not os.path.exists(args_name_dir): os.makedirs(args_name_dir) - args_name = args_name_dir + "/" + args.name +".yaml" - if os.path.exists(args_name): - s_add = 10 - logger.warning(f"Already exist args, add {s_add} to ran_seed to continue training") - args.random_seed += s_add - else: - with open(args_name, "w+") as f: - yaml.dump(args.__dict__, f, default_flow_style=True) - #json.dump(args.__dict__, f) - setup_logger(args_name_dir, rank, filename=f"{args.name}.txt") \ No newline at end of file diff --git a/utils/media.py b/utils/media.py deleted file mode 100644 index 2bd21e079a9e48f97f1511bd289d39f4aeccc40e..0000000000000000000000000000000000000000 --- a/utils/media.py +++ /dev/null @@ -1,39 +0,0 @@ -import numpy as np -import subprocess - -def add_audio_to_video(silent_video_path, audio_path, output_video_path): - command = [ - 'ffmpeg', - '-y', - '-i', silent_video_path, - '-i', audio_path, - '-map', '0:v', - '-map', '1:a', - '-c:v', 'copy', - '-shortest', - output_video_path - ] - - try: - subprocess.run(command, check=True) - print(f"Video with audio generated successfully: {output_video_path}") - except subprocess.CalledProcessError as e: - print(f"Error occurred: {e}") - - -def convert_img_to_mp4(input_pattern, output_file, framerate=30): - command = [ - 'ffmpeg', - '-framerate', str(framerate), - '-i', input_pattern, - '-c:v', 'libx264', - '-pix_fmt', 'yuv420p', - output_file, - '-y' - ] - - try: - subprocess.run(command, check=True) - print(f"Video conversion successful. Output file: {output_file}") - except subprocess.CalledProcessError as e: - print(f"Error during video conversion: {e}") diff --git a/utils/metric.py b/utils/metric.py deleted file mode 100644 index 53930062137b7ee82adb21ce226f572be77176e5..0000000000000000000000000000000000000000 --- a/utils/metric.py +++ /dev/null @@ -1,242 +0,0 @@ -import librosa -import glob -import os -import numpy as np -import matplotlib.pyplot as plt -import librosa.display -from matplotlib.pyplot import figure -import math -from scipy.signal import argrelextrema - - -class L1div(object): - def __init__(self): - self.counter = 0 - self.sum = 0 - def run(self, results): - self.counter += results.shape[0] - mean = np.mean(results, 0) - for i in range(results.shape[0]): - results[i, :] = abs(results[i, :] - mean) - sum_l1 = np.sum(results) - self.sum += sum_l1 - def avg(self): - return self.sum/self.counter - def reset(self): - self.counter = 0 - self.sum = 0 - - -class SRGR(object): - def __init__(self, threshold=0.1, joints=47): - self.threshold = threshold - self.pose_dimes = joints - self.counter = 0 - self.sum = 0 - - def run(self, results, targets, semantic): - results = results.reshape(-1, self.pose_dimes, 3) - targets = targets.reshape(-1, self.pose_dimes, 3) - semantic = semantic.reshape(-1) - diff = np.sum(abs(results-targets),2) - success = np.where(diffself.threshold) - #print(vel.shape) - #t_end = 80 - #vel[::2, :] -= 0.000001 - #print(vel[t_start:t_end, i], vel[t_start:t_end, i].shape) - beat_vel = argrelextrema(vel[t_start:t_end, i], np.less, order=self.order) # n*47 - #print(beat_vel, t_start, t_end) - beat_vel_list = [] - for j in beat_vel[0]: - if j in vel_mask[0]: - beat_vel_list.append(j) - beat_vel = np.array(beat_vel_list) - beat_vel_all.append(beat_vel) - #print(beat_vel_all) - return beat_vel_all #beat_right_arm, beat_right_shoulder, beat_right_wrist, beat_left_arm, beat_left_shoulder, beat_left_wrist - - - def load_data(self, wave, pose, t_start, t_end, pose_fps): - onset_raw, onset_bt, onset_bt_rms = self.load_audio(wave, t_start, t_end) - beat_right_arm, beat_right_shoulder, beat_right_wrist, beat_left_arm, beat_left_shoulder, beat_left_wrist = self.load_pose(pose, t_start, t_end, pose_fps) - return onset_raw, onset_bt, onset_bt_rms, beat_right_arm, beat_right_shoulder, beat_right_wrist, beat_left_arm, beat_left_shoulder, beat_left_wrist - - def eval_random_pose(self, wave, pose, t_start, t_end, pose_fps, num_random=60): - onset_raw, onset_bt, onset_bt_rms = self.load_audio(wave, t_start, t_end) - dur = t_end - t_start - for i in range(num_random): - beat_right_arm, beat_right_shoulder, beat_right_wrist, beat_left_arm, beat_left_shoulder, beat_left_wrist = self.load_pose(pose, i, i+dur, pose_fps) - dis_all_b2a= self.calculate_align(onset_raw, onset_bt, onset_bt_rms, beat_right_arm, beat_right_shoulder, beat_right_wrist, beat_left_arm, beat_left_shoulder, beat_left_wrist) - print(f"{i}s: ",dis_all_b2a) - - - @staticmethod - def plot_onsets(audio, sr, onset_times_1, onset_times_2): - import librosa - import librosa.display - import matplotlib.pyplot as plt - # Plot audio waveform - fig, axarr = plt.subplots(2, 1, figsize=(10, 10), sharex=True) - - # Plot audio waveform in both subplots - librosa.display.waveshow(audio, sr=sr, alpha=0.7, ax=axarr[0]) - librosa.display.waveshow(audio, sr=sr, alpha=0.7, ax=axarr[1]) - - # Plot onsets from first method on the first subplot - for onset in onset_times_1: - axarr[0].axvline(onset, color='r', linestyle='--', alpha=0.9, label='Onset Method 1') - axarr[0].legend() - axarr[0].set(title='Onset Method 1', xlabel='', ylabel='Amplitude') - - # Plot onsets from second method on the second subplot - for onset in onset_times_2: - axarr[1].axvline(onset, color='b', linestyle='-', alpha=0.7, label='Onset Method 2') - axarr[1].legend() - axarr[1].set(title='Onset Method 2', xlabel='Time (s)', ylabel='Amplitude') - - - # Add legend (eliminate duplicate labels) - handles, labels = plt.gca().get_legend_handles_labels() - by_label = dict(zip(labels, handles)) - plt.legend(by_label.values(), by_label.keys()) - - # Show plot - plt.title("Audio waveform with Onsets") - plt.savefig("./onset.png", dpi=500) - - def audio_beat_vis(self, onset_raw, onset_bt, onset_bt_rms): - figure(figsize=(24, 6), dpi=80) - fig, ax = plt.subplots(nrows=4, sharex=True) - librosa.display.specshow(librosa.amplitude_to_db(self.S, ref=np.max), - y_axis='log', x_axis='time', ax=ax[0]) - ax[0].label_outer() - ax[1].plot(self.times, self.oenv, label='Onset strength') - ax[1].vlines(librosa.frames_to_time(onset_raw), 0, self.oenv.max(), label='Raw onsets', color='r') - ax[1].legend() - ax[1].label_outer() - - ax[2].plot(self.times, self.oenv, label='Onset strength') - ax[2].vlines(librosa.frames_to_time(onset_bt), 0, self.oenv.max(), label='Backtracked', color='r') - ax[2].legend() - ax[2].label_outer() - - ax[3].plot(self.times, self.rms[0], label='RMS') - ax[3].vlines(librosa.frames_to_time(onset_bt_rms), 0, self.oenv.max(), label='Backtracked (RMS)', color='r') - ax[3].legend() - fig.savefig("./onset.png", dpi=500) - - @staticmethod - def motion_frames2time(vel, offset, pose_fps): - time_vel = vel/pose_fps + offset - return time_vel - - @staticmethod - def GAHR(a, b, sigma): - dis_all_a2b = 0 - dis_all_b2a = 0 - for b_each in b: - l2_min = np.inf - for a_each in a: - l2_dis = abs(a_each - b_each) - if l2_dis < l2_min: - l2_min = l2_dis - dis_all_b2a += math.exp(-(l2_min**2)/(2*sigma**2)) - dis_all_b2a /= len(b) - return dis_all_b2a - - @staticmethod - def fix_directed_GAHR(a, b, sigma): - a = alignment.motion_frames2time(a, 0, 30) - b = alignment.motion_frames2time(b, 0, 30) - t = len(a)/30 - a = [0] + a + [t] - b = [0] + b + [t] - dis_a2b = alignment.GAHR(a, b, sigma) - return dis_a2b - - def calculate_align(self, onset_bt_rms, beat_vel, pose_fps=30): - audio_bt = onset_bt_rms - avg_dis_all_b2a_list = [] - for its, beat_vel_each in enumerate(beat_vel): - if its not in self.upper_body: - continue - #print(beat_vel_each) - #print(audio_bt.shape, beat_vel_each.shape) - pose_bt = self.motion_frames2time(beat_vel_each, 0, pose_fps) - #print(pose_bt) - avg_dis_all_b2a_list.append(self.GAHR(pose_bt, audio_bt, self.sigma)) - # avg_dis_all_b2a = max(avg_dis_all_b2a_list) - avg_dis_all_b2a = sum(avg_dis_all_b2a_list)/len(avg_dis_all_b2a_list) #max(avg_dis_all_b2a_list) - #print(avg_dis_all_b2a, sum(avg_dis_all_b2a_list)/47) - return avg_dis_all_b2a \ No newline at end of file diff --git a/utils/other_tools.py b/utils/other_tools.py deleted file mode 100644 index 64dc17ffd10a8cb32c7a0bd1d141bc724c753b03..0000000000000000000000000000000000000000 --- a/utils/other_tools.py +++ /dev/null @@ -1,891 +0,0 @@ -import os -import numpy as np -import random -import torch -import shutil -import csv -import pprint -import pandas as pd -from loguru import logger -from collections import OrderedDict -import matplotlib.pyplot as plt -import pickle -import time -import hashlib -from scipy.spatial.transform import Rotation as R -from scipy.spatial.transform import Slerp -import cv2 -import utils.media -import utils.fast_render - -def write_wav_names_to_csv(folder_path, csv_path): - """ - Traverse a folder and write the base names of all .wav files to a CSV file. - - :param folder_path: Path to the folder to traverse. - :param csv_path: Path to the CSV file to write. - """ - # Open the CSV file for writing - with open(csv_path, mode='w', newline='') as file: - writer = csv.writer(file) - # Write the header - writer.writerow(['id', 'type']) - - # Walk through the folder - for root, dirs, files in os.walk(folder_path): - for file in files: - # Check if the file ends with .wav - if file.endswith('.wav'): - # Extract the base name without the extension - base_name = os.path.splitext(file)[0] - # Write the base name and type to the CSV - writer.writerow([base_name, 'test']) - -def resize_motion_sequence_tensor(sequence, target_frames): - """ - Resize a batch of 8-frame motion sequences to a specified number of frames using interpolation. - - :param sequence: A (bs, 8, 165) tensor representing a batch of 8-frame motion sequences - :param target_frames: An integer representing the desired number of frames in the output sequences - :return: A (bs, target_frames, 165) tensor representing the resized motion sequences - """ - bs, _, _ = sequence.shape - - # Create a time vector for the original and target sequences - original_time = torch.linspace(0, 1, 8, device=sequence.device).view(1, -1, 1) - target_time = torch.linspace(0, 1, target_frames, device=sequence.device).view(1, -1, 1) - - # Permute the dimensions to (bs, 165, 8) for interpolation - sequence = sequence.permute(0, 2, 1) - - # Interpolate each joint's motion to the target number of frames - resized_sequence = torch.nn.functional.interpolate(sequence, size=target_frames, mode='linear', align_corners=True) - - # Permute the dimensions back to (bs, target_frames, 165) - resized_sequence = resized_sequence.permute(0, 2, 1) - - return resized_sequence - -def adjust_speed_according_to_ratio_tensor(chunks): - """ - Adjust the playback speed within a batch of 32-frame chunks according to random intervals. - - :param chunks: A (bs, 32, 165) tensor representing a batch of motion chunks - :return: A (bs, 32, 165) tensor representing the motion chunks after speed adjustment - """ - bs, _, _ = chunks.shape - - # Step 1: Divide the chunk into 4 equal intervals of 8 frames - equal_intervals = torch.chunk(chunks, 4, dim=1) - - # Step 2: Randomly sample 3 points within the chunk to determine new intervals - success = 0 - all_success = [] - #sample_points = torch.sort(torch.randint(1, 32, (bs, 3), device=chunks.device), dim=1).values - # new_intervals_boundaries = torch.cat([torch.zeros((bs, 1), device=chunks.device, dtype=torch.long), sample_points, 32*torch.ones((bs, 1), device=chunks.device, dtype=torch.long)], dim=1) - while success != 1: - sample_points = sorted(random.sample(range(1, 32), 3)) - new_intervals_boundaries = [0] + sample_points + [32] - new_intervals = [chunks[0][new_intervals_boundaries[i]:new_intervals_boundaries[i+1]] for i in range(4)] - speed_ratios = [8 / len(new_interval) for new_interval in new_intervals] - # if any of the speed ratios is greater than 3 or less than 0.33, resample - if all([0.33 <= speed_ratio <= 3 for speed_ratio in speed_ratios]): - success += 1 - all_success.append(new_intervals_boundaries) - new_intervals_boundaries = torch.from_numpy(np.array(all_success)) - # print(new_intervals_boundaries) - all_shapes = new_intervals_boundaries[:, 1:] - new_intervals_boundaries[:, :-1] - # Step 4: Adjust the speed of each new interval - adjusted_intervals = [] - # print(equal_intervals[0].shape) - for i in range(4): - adjusted_interval = resize_motion_sequence_tensor(equal_intervals[i], all_shapes[0, i]) - adjusted_intervals.append(adjusted_interval) - - # Step 5: Concatenate the adjusted intervals - adjusted_chunk = torch.cat(adjusted_intervals, dim=1) - - return adjusted_chunk - -def compute_exact_iou(bbox1, bbox2): - x1 = max(bbox1[0], bbox2[0]) - y1 = max(bbox1[1], bbox2[1]) - x2 = min(bbox1[0] + bbox1[2], bbox2[0] + bbox2[2]) - y2 = min(bbox1[1] + bbox1[3], bbox2[1] + bbox2[3]) - - intersection_area = max(0, x2 - x1) * max(0, y2 - y1) - bbox1_area = bbox1[2] * bbox1[3] - bbox2_area = bbox2[2] * bbox2[3] - union_area = bbox1_area + bbox2_area - intersection_area - - if union_area == 0: - return 0 - - return intersection_area / union_area - -def compute_iou(mask1, mask2): - # Compute the intersection - intersection = np.logical_and(mask1, mask2).sum() - - # Compute the union - union = np.logical_or(mask1, mask2).sum() - - # Compute the IoU - iou = intersection / union - - return iou - -def blankblending(all_frames, x, n): - return all_frames[x:x+n+1] - -def synthesize_intermediate_frames_FILM(frame1, frame2, t, name, save_path): - import replicate - from urllib.request import urlretrieve - import os - cv2.imwrite(save_path[:-9]+name+"_frame1.png", frame1) - cv2.imwrite(save_path[:-9]+name+"_frame2.png", frame2) - os.environ["REPLICATE_API_TOKEN"] = "r8_He1rkPk9GAxNQ3LpOohK8sYw1SUfMYV3Fxk9b" - output = replicate.run( - "google-research/frame-interpolation:4f88a16a13673a8b589c18866e540556170a5bcb2ccdc12de556e800e9456d3d", - input={ - "frame1": open(save_path[:-9]+name+"_frame1.png", "rb"), - "frame2": open(save_path[:-9]+name+"_frame2.png", "rb"), - "times_to_interpolate": t, - } - ) - print(output) - urlretrieve(output, save_path[:-9]+name+"_inter.mp4") - return load_video_as_numpy_array(save_path[:-9]+name+"_inter.mp4") - -def load_video_as_numpy_array(video_path): - cap = cv2.VideoCapture(video_path) - - # Using list comprehension to read frames and store in a list - frames = [frame for ret, frame in iter(lambda: cap.read(), (False, None)) if ret] - - cap.release() - - return np.array(frames) - -def synthesize_intermediate_frames_bidirectional(all_frames, x, n): - frame1 = all_frames[x] - frame2 = all_frames[x + n] - - # Convert the frames to grayscale - gray1 = cv2.cvtColor(frame1, cv2.COLOR_BGR2GRAY) - gray2 = cv2.cvtColor(frame2, cv2.COLOR_BGR2GRAY) - - # Calculate the forward and backward optical flow - forward_flow = cv2.calcOpticalFlowFarneback(gray1, gray2, None, 0.5, 3, 15, 3, 5, 1.2, 0) - backward_flow = cv2.calcOpticalFlowFarneback(gray2, gray1, None, 0.5, 3, 15, 3, 5, 1.2, 0) - - synthesized_frames = [] - for i in range(1, n): # For each intermediate frame between x and x + n - alpha = i / n # Interpolation factor - - # Compute the intermediate forward and backward flow - intermediate_forward_flow = forward_flow * alpha - intermediate_backward_flow = backward_flow * (1 - alpha) - - # Warp the frames based on the intermediate flow - h, w = frame1.shape[:2] - flow_map = np.column_stack((np.repeat(np.arange(h), w), np.tile(np.arange(w), h))) - forward_displacement = flow_map + intermediate_forward_flow.reshape(-1, 2) - backward_displacement = flow_map - intermediate_backward_flow.reshape(-1, 2) - - # Use cv2.remap for efficient warping - remap_x_forward, remap_y_forward = np.clip(forward_displacement[:, 1], 0, w - 1), np.clip(forward_displacement[:, 0], 0, h - 1) - remap_x_backward, remap_y_backward = np.clip(backward_displacement[:, 1], 0, w - 1), np.clip(backward_displacement[:, 0], 0, h - 1) - - warped_forward = cv2.remap(frame1, remap_x_forward.reshape(h, w).astype(np.float32), remap_y_forward.reshape(h, w).astype(np.float32), interpolation=cv2.INTER_LINEAR) - warped_backward = cv2.remap(frame2, remap_x_backward.reshape(h, w).astype(np.float32), remap_y_backward.reshape(h, w).astype(np.float32), interpolation=cv2.INTER_LINEAR) - - # Blend the warped frames to generate the intermediate frame - intermediate_frame = cv2.addWeighted(warped_forward, 1 - alpha, warped_backward, alpha, 0) - synthesized_frames.append(intermediate_frame) - - return synthesized_frames # Return n-2 synthesized intermediate frames - - -def linear_interpolate_frames(all_frames, x, n): - frame1 = all_frames[x] - frame2 = all_frames[x + n] - - synthesized_frames = [] - for i in range(1, n): # For each intermediate frame between x and x + n - alpha = i / (n) # Correct interpolation factor - inter_frame = cv2.addWeighted(frame1, 1 - alpha, frame2, alpha, 0) - synthesized_frames.append(inter_frame) - return synthesized_frames[:-1] - -def warp_frame(src_frame, flow): - h, w = flow.shape[:2] - flow_map = np.column_stack((np.repeat(np.arange(h), w), np.tile(np.arange(w), h))) - displacement = flow_map + flow.reshape(-1, 2) - - # Extract x and y coordinates of the displacement - x_coords = np.clip(displacement[:, 1], 0, w - 1).reshape(h, w).astype(np.float32) - y_coords = np.clip(displacement[:, 0], 0, h - 1).reshape(h, w).astype(np.float32) - - # Use cv2.remap for efficient warping - warped_frame = cv2.remap(src_frame, x_coords, y_coords, interpolation=cv2.INTER_LINEAR) - - return warped_frame - -def synthesize_intermediate_frames(all_frames, x, n): - # Calculate Optical Flow between the first and last frame - frame1 = cv2.cvtColor(all_frames[x], cv2.COLOR_BGR2GRAY) - frame2 = cv2.cvtColor(all_frames[x + n], cv2.COLOR_BGR2GRAY) - flow = cv2.calcOpticalFlowFarneback(frame1, frame2, None, 0.5, 3, 15, 3, 5, 1.2, 0) - - synthesized_frames = [] - for i in range(1, n): # For each intermediate frame - alpha = i / (n) # Interpolation factor - intermediate_flow = flow * alpha # Interpolate the flow - intermediate_frame = warp_frame(all_frames[x], intermediate_flow) # Warp the first frame - synthesized_frames.append(intermediate_frame) - - return synthesized_frames - - -def map2color(s): - m = hashlib.md5() - m.update(s.encode('utf-8')) - color_code = m.hexdigest()[:6] - return '#' + color_code - -def euclidean_distance(a, b): - return np.sqrt(np.sum((a - b)**2)) - -def adjust_array(x, k): - len_x = len(x) - len_k = len(k) - - # If x is shorter than k, pad with zeros - if len_x < len_k: - return np.pad(x, (0, len_k - len_x), 'constant') - - # If x is longer than k, truncate x - elif len_x > len_k: - return x[:len_k] - - # If both are of same length - else: - return x - -def onset_to_frame(onset_times, audio_length, fps): - # Calculate total number of frames for the given audio length - total_frames = int(audio_length * fps) - - # Create an array of zeros of shape (total_frames,) - frame_array = np.zeros(total_frames, dtype=np.int32) - - # For each onset time, calculate the frame number and set it to 1 - for onset in onset_times: - frame_num = int(onset * fps) - # Check if the frame number is within the array bounds - if 0 <= frame_num < total_frames: - frame_array[frame_num] = 1 - - return frame_array - -# def np_slerp(q1, q2, t): -# dot_product = np.sum(q1 * q2, axis=-1) -# q2_flip = np.where(dot_product[:, None] < 0, -q2, q2) # Flip quaternions where dot_product is negative -# dot_product = np.abs(dot_product) - -# angle = np.arccos(np.clip(dot_product, -1, 1)) -# sin_angle = np.sin(angle) - -# t1 = np.sin((1.0 - t) * angle) / sin_angle -# t2 = np.sin(t * angle) / sin_angle - -# return t1 * q1 + t2 * q2_flip - - -def smooth_rotvec_animations(animation1, animation2, blend_frames): - """ - Smoothly transition between two animation clips using SLERP. - - Parameters: - - animation1: The first animation clip, a numpy array of shape [n, k]. - - animation2: The second animation clip, a numpy array of shape [n, k]. - - blend_frames: Number of frames over which to blend the two animations. - - Returns: - - A smoothly blended animation clip of shape [2n, k]. - """ - - # Ensure blend_frames doesn't exceed the length of either animation - n1, k1 = animation1.shape - n2, k2 = animation2.shape - animation1 = animation1.reshape(n1, k1//3, 3) - animation2 = animation2.reshape(n2, k2//3, 3) - blend_frames = min(blend_frames, len(animation1), len(animation2)) - all_int = [] - for i in range(k1//3): - # Convert rotation vectors to quaternion for the overlapping part - q = R.from_rotvec(np.concatenate([animation1[0:1, i], animation2[-2:-1, i]], axis=0))#.as_quat() - # q2 = R.from_rotvec()#.as_quat() - times = [0, blend_frames * 2 - 1] - slerp = Slerp(times, q) - interpolated = slerp(np.arange(blend_frames * 2)) - interpolated_rotvecs = interpolated.as_rotvec() - all_int.append(interpolated_rotvecs) - interpolated_rotvecs = np.concatenate(all_int, axis=1) - # result = np.vstack((animation1[:-blend_frames], interpolated_rotvecs, animation2[blend_frames:])) - result = interpolated_rotvecs.reshape(2*n1, k1) - return result - -def smooth_animations(animation1, animation2, blend_frames): - """ - Smoothly transition between two animation clips using linear interpolation. - - Parameters: - - animation1: The first animation clip, a numpy array of shape [n, k]. - - animation2: The second animation clip, a numpy array of shape [n, k]. - - blend_frames: Number of frames over which to blend the two animations. - - Returns: - - A smoothly blended animation clip of shape [2n, k]. - """ - - # Ensure blend_frames doesn't exceed the length of either animation - blend_frames = min(blend_frames, len(animation1), len(animation2)) - - # Extract overlapping sections - overlap_a1 = animation1[-blend_frames:-blend_frames+1, :] - overlap_a2 = animation2[blend_frames-1:blend_frames, :] - - # Create blend weights for linear interpolation - alpha = np.linspace(0, 1, 2 * blend_frames).reshape(-1, 1) - - # Linearly interpolate between overlapping sections - blended_overlap = overlap_a1 * (1 - alpha) + overlap_a2 * alpha - - # Extend the animations to form the result with 2n frames - if blend_frames == len(animation1) and blend_frames == len(animation2): - result = blended_overlap - else: - before_blend = animation1[:-blend_frames] - after_blend = animation2[blend_frames:] - result = np.vstack((before_blend, blended_overlap, after_blend)) - return result - -def interpolate_sequence(quaternions): - bs, n, j, _ = quaternions.shape - new_n = 2 * n - new_quaternions = torch.zeros((bs, new_n, j, 4), device=quaternions.device, dtype=quaternions.dtype) - - for i in range(n): - q1 = quaternions[:, i, :, :] - new_quaternions[:, 2*i, :, :] = q1 - - if i < n - 1: - q2 = quaternions[:, i + 1, :, :] - new_quaternions[:, 2*i + 1, :, :] = slerp(q1, q2, 0.5) - else: - # For the last point, duplicate the value - new_quaternions[:, 2*i + 1, :, :] = q1 - - return new_quaternions - -def quaternion_multiply(q1, q2): - w1, x1, y1, z1 = q1 - w2, x2, y2, z2 = q2 - w = w1 * w2 - x1 * x2 - y1 * y2 - z1 * z2 - x = w1 * x2 + x1 * w2 + y1 * z2 - z1 * y2 - y = w1 * y2 + y1 * w2 + z1 * x2 - x1 * z2 - z = w1 * z2 + z1 * w2 + x1 * y2 - y1 * x2 - return w, x, y, z - -def quaternion_conjugate(q): - w, x, y, z = q - return (w, -x, -y, -z) - -def slerp(q1, q2, t): - dot = torch.sum(q1 * q2, dim=-1, keepdim=True) - - flip = (dot < 0).float() - q2 = (1 - flip * 2) * q2 - dot = dot * (1 - flip * 2) - - DOT_THRESHOLD = 0.9995 - mask = (dot > DOT_THRESHOLD).float() - - theta_0 = torch.acos(dot) - theta = theta_0 * t - - q3 = q2 - q1 * dot - q3 = q3 / torch.norm(q3, dim=-1, keepdim=True) - - interpolated = (torch.cos(theta) * q1 + torch.sin(theta) * q3) - - return mask * (q1 + t * (q2 - q1)) + (1 - mask) * interpolated - -def estimate_linear_velocity(data_seq, dt): - ''' - Given some batched data sequences of T timesteps in the shape (B, T, ...), estimates - the velocity for the middle T-2 steps using a second order central difference scheme. - The first and last frames are with forward and backward first-order - differences, respectively - - h : step size - ''' - # first steps is forward diff (t+1 - t) / dt - init_vel = (data_seq[:, 1:2] - data_seq[:, :1]) / dt - # middle steps are second order (t+1 - t-1) / 2dt - middle_vel = (data_seq[:, 2:] - data_seq[:, 0:-2]) / (2 * dt) - # last step is backward diff (t - t-1) / dt - final_vel = (data_seq[:, -1:] - data_seq[:, -2:-1]) / dt - - vel_seq = torch.cat([init_vel, middle_vel, final_vel], dim=1) - return vel_seq - -def velocity2position(data_seq, dt, init_pos): - res_trans = [] - for i in range(data_seq.shape[1]): - if i == 0: - res_trans.append(init_pos.unsqueeze(1)) - else: - res = data_seq[:, i-1:i] * dt + res_trans[-1] - res_trans.append(res) - return torch.cat(res_trans, dim=1) - -def estimate_angular_velocity(rot_seq, dt): - ''' - Given a batch of sequences of T rotation matrices, estimates angular velocity at T-2 steps. - Input sequence should be of shape (B, T, ..., 3, 3) - ''' - # see https://en.wikipedia.org/wiki/Angular_velocity#Calculation_from_the_orientation_matrix - dRdt = estimate_linear_velocity(rot_seq, dt) - R = rot_seq - RT = R.transpose(-1, -2) - # compute skew-symmetric angular velocity tensor - w_mat = torch.matmul(dRdt, RT) - # pull out angular velocity vector by averaging symmetric entries - w_x = (-w_mat[..., 1, 2] + w_mat[..., 2, 1]) / 2.0 - w_y = (w_mat[..., 0, 2] - w_mat[..., 2, 0]) / 2.0 - w_z = (-w_mat[..., 0, 1] + w_mat[..., 1, 0]) / 2.0 - w = torch.stack([w_x, w_y, w_z], axis=-1) - return w - -def image_from_bytes(image_bytes): - import matplotlib.image as mpimg - from io import BytesIO - return mpimg.imread(BytesIO(image_bytes), format='PNG') - -def process_frame(i, vertices_all, vertices1_all, faces, output_dir, filenames): - import matplotlib - matplotlib.use('Agg') - import matplotlib.pyplot as plt - import trimesh - import pyrender - - def deg_to_rad(degrees): - return degrees * np.pi / 180 - - uniform_color = [220, 220, 220, 255] - resolution = (1000, 1000) - figsize = (10, 10) - - fig, axs = plt.subplots( - nrows=1, - ncols=2, - figsize=(figsize[0] * 2, figsize[1] * 1) - ) - axs = axs.flatten() - - vertices = vertices_all[i] - vertices1 = vertices1_all[i] - filename = f"{output_dir}frame_{i}.png" - filenames.append(filename) - if i%100 == 0: - print('processed', i, 'frames') - #time_s = time.time() - #print(vertices.shape) - angle_rad = deg_to_rad(-2) - pose_camera = np.array([ - [1.0, 0.0, 0.0, 0.0], - [0.0, np.cos(angle_rad), -np.sin(angle_rad), 1.0], - [0.0, np.sin(angle_rad), np.cos(angle_rad), 5.0], - [0.0, 0.0, 0.0, 1.0] - ]) - angle_rad = deg_to_rad(-30) - pose_light = np.array([ - [1.0, 0.0, 0.0, 0.0], - [0.0, np.cos(angle_rad), -np.sin(angle_rad), 0.0], - [0.0, np.sin(angle_rad), np.cos(angle_rad), 3.0], - [0.0, 0.0, 0.0, 1.0] - ]) - - for vtx_idx, vtx in enumerate([vertices, vertices1]): - trimesh_mesh = trimesh.Trimesh( - vertices=vtx, - faces=faces, - vertex_colors=uniform_color - ) - mesh = pyrender.Mesh.from_trimesh( - trimesh_mesh, smooth=True - ) - scene = pyrender.Scene() - scene.add(mesh) - camera = pyrender.OrthographicCamera(xmag=1.0, ymag=1.0) - scene.add(camera, pose=pose_camera) - light = pyrender.DirectionalLight(color=[1.0, 1.0, 1.0], intensity=4.0) - scene.add(light, pose=pose_light) - renderer = pyrender.OffscreenRenderer(*resolution) - color, _ = renderer.render(scene) - axs[vtx_idx].imshow(color) - axs[vtx_idx].axis('off') - renderer.delete() - - plt.savefig(filename, bbox_inches='tight') - plt.close(fig) - -def generate_images(frames, vertices_all, vertices1_all, faces, output_dir, filenames): - import multiprocessing - # import trimesh - num_cores = multiprocessing.cpu_count() - 1 # This will get the number of cores on your machine. - # mesh = trimesh.Trimesh(vertices_all[0], faces) - # scene = mesh.scene() - # fov = scene.camera.fov.copy() - # fov[0] = 80.0 - # fov[1] = 60.0 - # camera_params = { - # 'fov': fov, - # 'resolution': scene.camera.resolution, - # 'focal': scene.camera.focal, - # 'z_near': scene.camera.z_near, - # "z_far": scene.camera.z_far, - # 'transform': scene.graph[scene.camera.name][0] - # } - # mesh1 = trimesh.Trimesh(vertices1_all[0], faces) - # scene1 = mesh1.scene() - # camera_params1 = { - # 'fov': fov, - # 'resolution': scene1.camera.resolution, - # 'focal': scene1.camera.focal, - # 'z_near': scene1.camera.z_near, - # "z_far": scene1.camera.z_far, - # 'transform': scene1.graph[scene1.camera.name][0] - # } - # Use a Pool to manage the processes - # print(num_cores) - # for i in range(frames): - # process_frame(i, vertices_all, vertices1_all, faces, output_dir, use_matplotlib, filenames, camera_params, camera_params1) - for i in range(frames): - process_frame(i, vertices_all, vertices1_all, faces, output_dir, filenames) - - # progress = multiprocessing.Value('i', 0) - # lock = multiprocessing.Lock() - # with multiprocessing.Pool(num_cores) as pool: - # # pool.starmap(process_frame, [(i, vertices_all, vertices1_all, faces, output_dir, use_matplotlib, filenames, camera_params, camera_params1) for i in range(frames)]) - # pool.starmap( - # process_frame, - # [ - # (i, vertices_all, vertices1_all, faces, output_dir, filenames) - # for i in range(frames) - # ] - # ) - - # progress = multiprocessing.Value('i', 0) - # lock = multiprocessing.Lock() - # with multiprocessing.Pool(num_cores) as pool: - # # pool.starmap(process_frame, [(i, vertices_all, vertices1_all, faces, output_dir, use_matplotlib, filenames, camera_params, camera_params1) for i in range(frames)]) - # pool.starmap( - # process_frame, - # [ - # (i, vertices_all, vertices1_all, faces, output_dir, filenames) - # for i in range(frames) - # ] - # ) - -def render_one_sequence( - res_npz_path, - gt_npz_path, - output_dir, - audio_path, - model_folder="/data/datasets/smplx_models/", - model_type='smplx', - gender='NEUTRAL_2020', - ext='npz', - num_betas=300, - num_expression_coeffs=100, - use_face_contour=False, - use_matplotlib=False, - args=None): - import smplx - import matplotlib.pyplot as plt - import imageio - from tqdm import tqdm - import os - import numpy as np - import torch - import moviepy.editor as mp - import librosa - - model = smplx.create(model_folder, model_type=model_type, - gender=gender, use_face_contour=use_face_contour, - num_betas=num_betas, - num_expression_coeffs=num_expression_coeffs, - ext=ext, use_pca=False).cuda() - - #data_npz = np.load(f"{output_dir}{res_npz_path}.npz") - data_np_body = np.load(res_npz_path, allow_pickle=True) - gt_np_body = np.load(gt_npz_path, allow_pickle=True) - - if not os.path.exists(output_dir): os.makedirs(output_dir) - filenames = [] - # if not use_matplotlib: - # import trimesh - #import pyrender - #!!! I only have Windows, the following lines of comments are feasible, but have not been tested on other platforms. - #from pyvirtualdisplay import Display - #display = Display(visible=0, size=(500, 500)) - #display.start() - faces = np.load(f"{model_folder}/smplx/SMPLX_NEUTRAL_2020.npz", allow_pickle=True)["f"] - seconds = 1 - #data_npz["jaw_pose"].shape[0] - n = data_np_body["poses"].shape[0] - beta = torch.from_numpy(data_np_body["betas"]).to(torch.float32).unsqueeze(0).cuda() - beta = beta.repeat(n, 1) - expression = torch.from_numpy(data_np_body["expressions"][:n]).to(torch.float32).cuda() - jaw_pose = torch.from_numpy(data_np_body["poses"][:n, 66:69]).to(torch.float32).cuda() - pose = torch.from_numpy(data_np_body["poses"][:n]).to(torch.float32).cuda() - transl = torch.from_numpy(data_np_body["trans"][:n]).to(torch.float32).cuda() - # print(beta.shape, expression.shape, jaw_pose.shape, pose.shape, transl.shape, pose[:,:3].shape) - output = model(betas=beta, transl=transl, expression=expression, jaw_pose=jaw_pose, - global_orient=pose[:,:3], body_pose=pose[:,3:21*3+3], left_hand_pose=pose[:,25*3:40*3], right_hand_pose=pose[:,40*3:55*3], - leye_pose=pose[:, 69:72], - reye_pose=pose[:, 72:75], - return_verts=True) - vertices_all = output["vertices"].cpu().detach().numpy() - - beta1 = torch.from_numpy(gt_np_body["betas"]).to(torch.float32).unsqueeze(0).cuda() - expression1 = torch.from_numpy(gt_np_body["expressions"][:n]).to(torch.float32).cuda() - jaw_pose1 = torch.from_numpy(gt_np_body["poses"][:n,66:69]).to(torch.float32).cuda() - pose1 = torch.from_numpy(gt_np_body["poses"][:n]).to(torch.float32).cuda() - transl1 = torch.from_numpy(gt_np_body["trans"][:n]).to(torch.float32).cuda() - output1 = model(betas=beta1, transl=transl1, expression=expression1, jaw_pose=jaw_pose1, global_orient=pose1[:,:3], body_pose=pose1[:,3:21*3+3], left_hand_pose=pose1[:,25*3:40*3], right_hand_pose=pose1[:,40*3:55*3], - leye_pose=pose1[:, 69:72], - reye_pose=pose1[:, 72:75],return_verts=True) - vertices1_all = output1["vertices"].cpu().detach().numpy() - if args.debug: - seconds = 1 - else: - seconds = vertices_all.shape[0]//30 - - silent_video_file_path = utils.fast_render.generate_silent_videos(args.render_video_fps, - args.render_video_width, - args.render_video_height, - args.render_concurrent_num, - args.render_tmp_img_filetype, - int(seconds*args.render_video_fps), - vertices_all, - vertices1_all, - faces, - output_dir) - - #final_clip = f"{output_dir}{res_npz_path.split('/')[-1][4:-4]}.mp4" - base_filename_without_ext = os.path.splitext(os.path.basename(res_npz_path))[0] - final_clip = os.path.join(output_dir, f"{base_filename_without_ext}.mp4") - utils.media.add_audio_to_video(silent_video_file_path, audio_path, final_clip) - os.remove(silent_video_file_path) - -def print_exp_info(args): - logger.info(pprint.pformat(vars(args))) - logger.info(f"# ------------ {args.name} ----------- #") - logger.info("PyTorch version: {}".format(torch.__version__)) - logger.info("CUDA version: {}".format(torch.version.cuda)) - logger.info("{} GPUs".format(torch.cuda.device_count())) - logger.info(f"Random Seed: {args.random_seed}") - -def args2csv(args, get_head=False, list4print=[]): - for k, v in args.items(): - if isinstance(args[k], dict): - args2csv(args[k], get_head, list4print) - else: list4print.append(k) if get_head else list4print.append(v) - return list4print - -class EpochTracker: - def __init__(self, metric_names, metric_directions): - assert len(metric_names) == len(metric_directions), "Metric names and directions should have the same length" - - - self.metric_names = metric_names - self.states = ['train', 'val', 'test'] - self.types = ['last', 'best'] - - - self.values = {name: {state: {type_: {'value': np.inf if not is_higher_better else -np.inf, 'epoch': 0} - for type_ in self.types} - for state in self.states} - for name, is_higher_better in zip(metric_names, metric_directions)} - - self.loss_meters = {name: {state: AverageMeter(f"{name}_{state}") - for state in self.states} - for name in metric_names} - - - self.is_higher_better = {name: direction for name, direction in zip(metric_names, metric_directions)} - self.train_history = {name: [] for name in metric_names} - self.val_history = {name: [] for name in metric_names} - - - def update_meter(self, name, state, value): - self.loss_meters[name][state].update(value) - - - def update_values(self, name, state, epoch): - value_avg = self.loss_meters[name][state].avg - new_best = False - - - if ((value_avg < self.values[name][state]['best']['value'] and not self.is_higher_better[name]) or - (value_avg > self.values[name][state]['best']['value'] and self.is_higher_better[name])): - self.values[name][state]['best']['value'] = value_avg - self.values[name][state]['best']['epoch'] = epoch - new_best = True - self.values[name][state]['last']['value'] = value_avg - self.values[name][state]['last']['epoch'] = epoch - return new_best - - - def get(self, name, state, type_): - return self.values[name][state][type_] - - - def reset(self): - for name in self.metric_names: - for state in self.states: - self.loss_meters[name][state].reset() - - - def flatten_values(self): - flat_dict = {} - for name in self.metric_names: - for state in self.states: - for type_ in self.types: - value_key = f"{name}_{state}_{type_}" - epoch_key = f"{name}_{state}_{type_}_epoch" - flat_dict[value_key] = self.values[name][state][type_]['value'] - flat_dict[epoch_key] = self.values[name][state][type_]['epoch'] - return flat_dict - - def update_and_plot(self, name, epoch, save_path): - new_best_train = self.update_values(name, 'train', epoch) - new_best_val = self.update_values(name, 'val', epoch) - - - self.train_history[name].append(self.loss_meters[name]['train'].avg) - self.val_history[name].append(self.loss_meters[name]['val'].avg) - - - train_values = self.train_history[name] - val_values = self.val_history[name] - epochs = list(range(1, len(train_values) + 1)) - - - plt.figure(figsize=(10, 6)) - plt.plot(epochs, train_values, label='Train') - plt.plot(epochs, val_values, label='Val') - plt.title(f'Train vs Val {name} over epochs') - plt.xlabel('Epochs') - plt.ylabel(name) - plt.legend() - plt.savefig(save_path) - plt.close() - - - return new_best_train, new_best_val - -def record_trial(args, tracker): - """ - 1. record notes, score, env_name, experments_path, - """ - csv_path = args.out_path + "custom/" +args.csv_name+".csv" - all_print_dict = vars(args) - all_print_dict.update(tracker.flatten_values()) - if not os.path.exists(csv_path): - pd.DataFrame([all_print_dict]).to_csv(csv_path, index=False) - else: - df_existing = pd.read_csv(csv_path) - df_new = pd.DataFrame([all_print_dict]) - df_aligned = df_existing.append(df_new).fillna("") - df_aligned.to_csv(csv_path, index=False) - -def set_random_seed(args): - os.environ['PYTHONHASHSEED'] = str(args.random_seed) - random.seed(args.random_seed) - np.random.seed(args.random_seed) - torch.manual_seed(args.random_seed) - torch.cuda.manual_seed_all(args.random_seed) - torch.cuda.manual_seed(args.random_seed) - torch.backends.cudnn.deterministic = args.deterministic #args.CUDNN_DETERMINISTIC - torch.backends.cudnn.benchmark = args.benchmark - torch.backends.cudnn.enabled = args.cudnn_enabled - -def save_checkpoints(save_path, model, opt=None, epoch=None, lrs=None): - if lrs is not None: - states = { 'model_state': model.state_dict(), - 'epoch': epoch + 1, - 'opt_state': opt.state_dict(), - 'lrs':lrs.state_dict(),} - elif opt is not None: - states = { 'model_state': model.state_dict(), - 'epoch': epoch + 1, - 'opt_state': opt.state_dict(),} - else: - states = { 'model_state': model.state_dict(),} - torch.save(states, save_path) - -def load_checkpoints(model, save_path, load_name='model'): - states = torch.load(save_path) - new_weights = OrderedDict() - flag=False - for k, v in states['model_state'].items(): - #print(k) - if "module" not in k: - break - else: - new_weights[k[7:]]=v - flag=True - if flag: - try: - model.load_state_dict(new_weights) - except: - #print(states['model_state']) - model.load_state_dict(states['model_state']) - else: - model.load_state_dict(states['model_state']) - logger.info(f"load self-pretrained checkpoints for {load_name}") - -def model_complexity(model, args): - from ptflops import get_model_complexity_info - flops, params = get_model_complexity_info(model, (args.T_GLOBAL._DIM, args.TRAIN.CROP, args.TRAIN), - as_strings=False, print_per_layer_stat=False) - logging.info('{:<30} {:<8} BFlops'.format('Computational complexity: ', flops / 1e9)) - logging.info('{:<30} {:<8} MParams'.format('Number of parameters: ', params / 1e6)) - -class AverageMeter(object): - """Computes and stores the average and current value""" - def __init__(self, name, fmt=':f'): - self.name = name - self.fmt = fmt - self.reset() - - def reset(self): - self.val = 0 - self.avg = 0 - self.sum = 0 - self.count = 0 - - def update(self, val, n=1): - self.val = val - self.sum += val * n - self.count += n - self.avg = self.sum / self.count - - def __str__(self): - fmtstr = '{name} {val' + self.fmt + '} ({avg' + self.fmt + '})' - return fmtstr.format(**self.__dict__) diff --git a/utils/other_tools_hf.py b/utils/other_tools_hf.py deleted file mode 100644 index 03e005ca6d226a408ca95e8689d651da4c446543..0000000000000000000000000000000000000000 --- a/utils/other_tools_hf.py +++ /dev/null @@ -1,1072 +0,0 @@ -import os -import numpy as np -import random -import torch -import shutil -import csv -import pprint -import pandas as pd -from loguru import logger -from collections import OrderedDict -import matplotlib.pyplot as plt -import pickle -import time -import hashlib -from scipy.spatial.transform import Rotation as R -from scipy.spatial.transform import Slerp -import cv2 -import utils.media -import utils.fast_render - -def write_wav_names_to_csv(folder_path, csv_path): - """ - Traverse a folder and write the base names of all .wav files to a CSV file. - - :param folder_path: Path to the folder to traverse. - :param csv_path: Path to the CSV file to write. - """ - # Open the CSV file for writing - with open(csv_path, mode='w', newline='') as file: - writer = csv.writer(file) - # Write the header - writer.writerow(['id', 'type']) - - # Walk through the folder - for root, dirs, files in os.walk(folder_path): - for file in files: - # Check if the file ends with .wav - if file.endswith('.wav'): - # Extract the base name without the extension - base_name = os.path.splitext(file)[0] - # Write the base name and type to the CSV - writer.writerow([base_name, 'test']) - -def resize_motion_sequence_tensor(sequence, target_frames): - """ - Resize a batch of 8-frame motion sequences to a specified number of frames using interpolation. - - :param sequence: A (bs, 8, 165) tensor representing a batch of 8-frame motion sequences - :param target_frames: An integer representing the desired number of frames in the output sequences - :return: A (bs, target_frames, 165) tensor representing the resized motion sequences - """ - bs, _, _ = sequence.shape - - # Create a time vector for the original and target sequences - original_time = torch.linspace(0, 1, 8, device=sequence.device).view(1, -1, 1) - target_time = torch.linspace(0, 1, target_frames, device=sequence.device).view(1, -1, 1) - - # Permute the dimensions to (bs, 165, 8) for interpolation - sequence = sequence.permute(0, 2, 1) - - # Interpolate each joint's motion to the target number of frames - resized_sequence = torch.nn.functional.interpolate(sequence, size=target_frames, mode='linear', align_corners=True) - - # Permute the dimensions back to (bs, target_frames, 165) - resized_sequence = resized_sequence.permute(0, 2, 1) - - return resized_sequence - -def adjust_speed_according_to_ratio_tensor(chunks): - """ - Adjust the playback speed within a batch of 32-frame chunks according to random intervals. - - :param chunks: A (bs, 32, 165) tensor representing a batch of motion chunks - :return: A (bs, 32, 165) tensor representing the motion chunks after speed adjustment - """ - bs, _, _ = chunks.shape - - # Step 1: Divide the chunk into 4 equal intervals of 8 frames - equal_intervals = torch.chunk(chunks, 4, dim=1) - - # Step 2: Randomly sample 3 points within the chunk to determine new intervals - success = 0 - all_success = [] - #sample_points = torch.sort(torch.randint(1, 32, (bs, 3), device=chunks.device), dim=1).values - # new_intervals_boundaries = torch.cat([torch.zeros((bs, 1), device=chunks.device, dtype=torch.long), sample_points, 32*torch.ones((bs, 1), device=chunks.device, dtype=torch.long)], dim=1) - while success != 1: - sample_points = sorted(random.sample(range(1, 32), 3)) - new_intervals_boundaries = [0] + sample_points + [32] - new_intervals = [chunks[0][new_intervals_boundaries[i]:new_intervals_boundaries[i+1]] for i in range(4)] - speed_ratios = [8 / len(new_interval) for new_interval in new_intervals] - # if any of the speed ratios is greater than 3 or less than 0.33, resample - if all([0.33 <= speed_ratio <= 3 for speed_ratio in speed_ratios]): - success += 1 - all_success.append(new_intervals_boundaries) - new_intervals_boundaries = torch.from_numpy(np.array(all_success)) - # print(new_intervals_boundaries) - all_shapes = new_intervals_boundaries[:, 1:] - new_intervals_boundaries[:, :-1] - # Step 4: Adjust the speed of each new interval - adjusted_intervals = [] - # print(equal_intervals[0].shape) - for i in range(4): - adjusted_interval = resize_motion_sequence_tensor(equal_intervals[i], all_shapes[0, i]) - adjusted_intervals.append(adjusted_interval) - - # Step 5: Concatenate the adjusted intervals - adjusted_chunk = torch.cat(adjusted_intervals, dim=1) - - return adjusted_chunk - -def compute_exact_iou(bbox1, bbox2): - x1 = max(bbox1[0], bbox2[0]) - y1 = max(bbox1[1], bbox2[1]) - x2 = min(bbox1[0] + bbox1[2], bbox2[0] + bbox2[2]) - y2 = min(bbox1[1] + bbox1[3], bbox2[1] + bbox2[3]) - - intersection_area = max(0, x2 - x1) * max(0, y2 - y1) - bbox1_area = bbox1[2] * bbox1[3] - bbox2_area = bbox2[2] * bbox2[3] - union_area = bbox1_area + bbox2_area - intersection_area - - if union_area == 0: - return 0 - - return intersection_area / union_area - -def compute_iou(mask1, mask2): - # Compute the intersection - intersection = np.logical_and(mask1, mask2).sum() - - # Compute the union - union = np.logical_or(mask1, mask2).sum() - - # Compute the IoU - iou = intersection / union - - return iou - -def blankblending(all_frames, x, n): - return all_frames[x:x+n+1] - -def synthesize_intermediate_frames_FILM(frame1, frame2, t, name, save_path): - import replicate - from urllib.request import urlretrieve - import os - cv2.imwrite(save_path[:-9]+name+"_frame1.png", frame1) - cv2.imwrite(save_path[:-9]+name+"_frame2.png", frame2) - os.environ["REPLICATE_API_TOKEN"] = "r8_He1rkPk9GAxNQ3LpOohK8sYw1SUfMYV3Fxk9b" - output = replicate.run( - "google-research/frame-interpolation:4f88a16a13673a8b589c18866e540556170a5bcb2ccdc12de556e800e9456d3d", - input={ - "frame1": open(save_path[:-9]+name+"_frame1.png", "rb"), - "frame2": open(save_path[:-9]+name+"_frame2.png", "rb"), - "times_to_interpolate": t, - } - ) - print(output) - urlretrieve(output, save_path[:-9]+name+"_inter.mp4") - return load_video_as_numpy_array(save_path[:-9]+name+"_inter.mp4") - -def load_video_as_numpy_array(video_path): - cap = cv2.VideoCapture(video_path) - - # Using list comprehension to read frames and store in a list - frames = [frame for ret, frame in iter(lambda: cap.read(), (False, None)) if ret] - - cap.release() - - return np.array(frames) - -def synthesize_intermediate_frames_bidirectional(all_frames, x, n): - frame1 = all_frames[x] - frame2 = all_frames[x + n] - - # Convert the frames to grayscale - gray1 = cv2.cvtColor(frame1, cv2.COLOR_BGR2GRAY) - gray2 = cv2.cvtColor(frame2, cv2.COLOR_BGR2GRAY) - - # Calculate the forward and backward optical flow - forward_flow = cv2.calcOpticalFlowFarneback(gray1, gray2, None, 0.5, 3, 15, 3, 5, 1.2, 0) - backward_flow = cv2.calcOpticalFlowFarneback(gray2, gray1, None, 0.5, 3, 15, 3, 5, 1.2, 0) - - synthesized_frames = [] - for i in range(1, n): # For each intermediate frame between x and x + n - alpha = i / n # Interpolation factor - - # Compute the intermediate forward and backward flow - intermediate_forward_flow = forward_flow * alpha - intermediate_backward_flow = backward_flow * (1 - alpha) - - # Warp the frames based on the intermediate flow - h, w = frame1.shape[:2] - flow_map = np.column_stack((np.repeat(np.arange(h), w), np.tile(np.arange(w), h))) - forward_displacement = flow_map + intermediate_forward_flow.reshape(-1, 2) - backward_displacement = flow_map - intermediate_backward_flow.reshape(-1, 2) - - # Use cv2.remap for efficient warping - remap_x_forward, remap_y_forward = np.clip(forward_displacement[:, 1], 0, w - 1), np.clip(forward_displacement[:, 0], 0, h - 1) - remap_x_backward, remap_y_backward = np.clip(backward_displacement[:, 1], 0, w - 1), np.clip(backward_displacement[:, 0], 0, h - 1) - - warped_forward = cv2.remap(frame1, remap_x_forward.reshape(h, w).astype(np.float32), remap_y_forward.reshape(h, w).astype(np.float32), interpolation=cv2.INTER_LINEAR) - warped_backward = cv2.remap(frame2, remap_x_backward.reshape(h, w).astype(np.float32), remap_y_backward.reshape(h, w).astype(np.float32), interpolation=cv2.INTER_LINEAR) - - # Blend the warped frames to generate the intermediate frame - intermediate_frame = cv2.addWeighted(warped_forward, 1 - alpha, warped_backward, alpha, 0) - synthesized_frames.append(intermediate_frame) - - return synthesized_frames # Return n-2 synthesized intermediate frames - - -def linear_interpolate_frames(all_frames, x, n): - frame1 = all_frames[x] - frame2 = all_frames[x + n] - - synthesized_frames = [] - for i in range(1, n): # For each intermediate frame between x and x + n - alpha = i / (n) # Correct interpolation factor - inter_frame = cv2.addWeighted(frame1, 1 - alpha, frame2, alpha, 0) - synthesized_frames.append(inter_frame) - return synthesized_frames[:-1] - -def warp_frame(src_frame, flow): - h, w = flow.shape[:2] - flow_map = np.column_stack((np.repeat(np.arange(h), w), np.tile(np.arange(w), h))) - displacement = flow_map + flow.reshape(-1, 2) - - # Extract x and y coordinates of the displacement - x_coords = np.clip(displacement[:, 1], 0, w - 1).reshape(h, w).astype(np.float32) - y_coords = np.clip(displacement[:, 0], 0, h - 1).reshape(h, w).astype(np.float32) - - # Use cv2.remap for efficient warping - warped_frame = cv2.remap(src_frame, x_coords, y_coords, interpolation=cv2.INTER_LINEAR) - - return warped_frame - -def synthesize_intermediate_frames(all_frames, x, n): - # Calculate Optical Flow between the first and last frame - frame1 = cv2.cvtColor(all_frames[x], cv2.COLOR_BGR2GRAY) - frame2 = cv2.cvtColor(all_frames[x + n], cv2.COLOR_BGR2GRAY) - flow = cv2.calcOpticalFlowFarneback(frame1, frame2, None, 0.5, 3, 15, 3, 5, 1.2, 0) - - synthesized_frames = [] - for i in range(1, n): # For each intermediate frame - alpha = i / (n) # Interpolation factor - intermediate_flow = flow * alpha # Interpolate the flow - intermediate_frame = warp_frame(all_frames[x], intermediate_flow) # Warp the first frame - synthesized_frames.append(intermediate_frame) - - return synthesized_frames - - -def map2color(s): - m = hashlib.md5() - m.update(s.encode('utf-8')) - color_code = m.hexdigest()[:6] - return '#' + color_code - -def euclidean_distance(a, b): - return np.sqrt(np.sum((a - b)**2)) - -def adjust_array(x, k): - len_x = len(x) - len_k = len(k) - - # If x is shorter than k, pad with zeros - if len_x < len_k: - return np.pad(x, (0, len_k - len_x), 'constant') - - # If x is longer than k, truncate x - elif len_x > len_k: - return x[:len_k] - - # If both are of same length - else: - return x - -def onset_to_frame(onset_times, audio_length, fps): - # Calculate total number of frames for the given audio length - total_frames = int(audio_length * fps) - - # Create an array of zeros of shape (total_frames,) - frame_array = np.zeros(total_frames, dtype=np.int32) - - # For each onset time, calculate the frame number and set it to 1 - for onset in onset_times: - frame_num = int(onset * fps) - # Check if the frame number is within the array bounds - if 0 <= frame_num < total_frames: - frame_array[frame_num] = 1 - - return frame_array - -# def np_slerp(q1, q2, t): -# dot_product = np.sum(q1 * q2, axis=-1) -# q2_flip = np.where(dot_product[:, None] < 0, -q2, q2) # Flip quaternions where dot_product is negative -# dot_product = np.abs(dot_product) - -# angle = np.arccos(np.clip(dot_product, -1, 1)) -# sin_angle = np.sin(angle) - -# t1 = np.sin((1.0 - t) * angle) / sin_angle -# t2 = np.sin(t * angle) / sin_angle - -# return t1 * q1 + t2 * q2_flip - - -def smooth_rotvec_animations(animation1, animation2, blend_frames): - """ - Smoothly transition between two animation clips using SLERP. - - Parameters: - - animation1: The first animation clip, a numpy array of shape [n, k]. - - animation2: The second animation clip, a numpy array of shape [n, k]. - - blend_frames: Number of frames over which to blend the two animations. - - Returns: - - A smoothly blended animation clip of shape [2n, k]. - """ - - # Ensure blend_frames doesn't exceed the length of either animation - n1, k1 = animation1.shape - n2, k2 = animation2.shape - animation1 = animation1.reshape(n1, k1//3, 3) - animation2 = animation2.reshape(n2, k2//3, 3) - blend_frames = min(blend_frames, len(animation1), len(animation2)) - all_int = [] - for i in range(k1//3): - # Convert rotation vectors to quaternion for the overlapping part - q = R.from_rotvec(np.concatenate([animation1[0:1, i], animation2[-2:-1, i]], axis=0))#.as_quat() - # q2 = R.from_rotvec()#.as_quat() - times = [0, blend_frames * 2 - 1] - slerp = Slerp(times, q) - interpolated = slerp(np.arange(blend_frames * 2)) - interpolated_rotvecs = interpolated.as_rotvec() - all_int.append(interpolated_rotvecs) - interpolated_rotvecs = np.concatenate(all_int, axis=1) - # result = np.vstack((animation1[:-blend_frames], interpolated_rotvecs, animation2[blend_frames:])) - result = interpolated_rotvecs.reshape(2*n1, k1) - return result - -def smooth_animations(animation1, animation2, blend_frames): - """ - Smoothly transition between two animation clips using linear interpolation. - - Parameters: - - animation1: The first animation clip, a numpy array of shape [n, k]. - - animation2: The second animation clip, a numpy array of shape [n, k]. - - blend_frames: Number of frames over which to blend the two animations. - - Returns: - - A smoothly blended animation clip of shape [2n, k]. - """ - - # Ensure blend_frames doesn't exceed the length of either animation - blend_frames = min(blend_frames, len(animation1), len(animation2)) - - # Extract overlapping sections - overlap_a1 = animation1[-blend_frames:-blend_frames+1, :] - overlap_a2 = animation2[blend_frames-1:blend_frames, :] - - # Create blend weights for linear interpolation - alpha = np.linspace(0, 1, 2 * blend_frames).reshape(-1, 1) - - # Linearly interpolate between overlapping sections - blended_overlap = overlap_a1 * (1 - alpha) + overlap_a2 * alpha - - # Extend the animations to form the result with 2n frames - if blend_frames == len(animation1) and blend_frames == len(animation2): - result = blended_overlap - else: - before_blend = animation1[:-blend_frames] - after_blend = animation2[blend_frames:] - result = np.vstack((before_blend, blended_overlap, after_blend)) - return result - -def interpolate_sequence(quaternions): - bs, n, j, _ = quaternions.shape - new_n = 2 * n - new_quaternions = torch.zeros((bs, new_n, j, 4), device=quaternions.device, dtype=quaternions.dtype) - - for i in range(n): - q1 = quaternions[:, i, :, :] - new_quaternions[:, 2*i, :, :] = q1 - - if i < n - 1: - q2 = quaternions[:, i + 1, :, :] - new_quaternions[:, 2*i + 1, :, :] = slerp(q1, q2, 0.5) - else: - # For the last point, duplicate the value - new_quaternions[:, 2*i + 1, :, :] = q1 - - return new_quaternions - -def quaternion_multiply(q1, q2): - w1, x1, y1, z1 = q1 - w2, x2, y2, z2 = q2 - w = w1 * w2 - x1 * x2 - y1 * y2 - z1 * z2 - x = w1 * x2 + x1 * w2 + y1 * z2 - z1 * y2 - y = w1 * y2 + y1 * w2 + z1 * x2 - x1 * z2 - z = w1 * z2 + z1 * w2 + x1 * y2 - y1 * x2 - return w, x, y, z - -def quaternion_conjugate(q): - w, x, y, z = q - return (w, -x, -y, -z) - -def slerp(q1, q2, t): - dot = torch.sum(q1 * q2, dim=-1, keepdim=True) - - flip = (dot < 0).float() - q2 = (1 - flip * 2) * q2 - dot = dot * (1 - flip * 2) - - DOT_THRESHOLD = 0.9995 - mask = (dot > DOT_THRESHOLD).float() - - theta_0 = torch.acos(dot) - theta = theta_0 * t - - q3 = q2 - q1 * dot - q3 = q3 / torch.norm(q3, dim=-1, keepdim=True) - - interpolated = (torch.cos(theta) * q1 + torch.sin(theta) * q3) - - return mask * (q1 + t * (q2 - q1)) + (1 - mask) * interpolated - -def estimate_linear_velocity(data_seq, dt): - ''' - Given some batched data sequences of T timesteps in the shape (B, T, ...), estimates - the velocity for the middle T-2 steps using a second order central difference scheme. - The first and last frames are with forward and backward first-order - differences, respectively - - h : step size - ''' - # first steps is forward diff (t+1 - t) / dt - init_vel = (data_seq[:, 1:2] - data_seq[:, :1]) / dt - # middle steps are second order (t+1 - t-1) / 2dt - middle_vel = (data_seq[:, 2:] - data_seq[:, 0:-2]) / (2 * dt) - # last step is backward diff (t - t-1) / dt - final_vel = (data_seq[:, -1:] - data_seq[:, -2:-1]) / dt - - vel_seq = torch.cat([init_vel, middle_vel, final_vel], dim=1) - return vel_seq - -def velocity2position(data_seq, dt, init_pos): - res_trans = [] - for i in range(data_seq.shape[1]): - if i == 0: - res_trans.append(init_pos.unsqueeze(1)) - else: - res = data_seq[:, i-1:i] * dt + res_trans[-1] - res_trans.append(res) - return torch.cat(res_trans, dim=1) - -def estimate_angular_velocity(rot_seq, dt): - ''' - Given a batch of sequences of T rotation matrices, estimates angular velocity at T-2 steps. - Input sequence should be of shape (B, T, ..., 3, 3) - ''' - # see https://en.wikipedia.org/wiki/Angular_velocity#Calculation_from_the_orientation_matrix - dRdt = estimate_linear_velocity(rot_seq, dt) - R = rot_seq - RT = R.transpose(-1, -2) - # compute skew-symmetric angular velocity tensor - w_mat = torch.matmul(dRdt, RT) - # pull out angular velocity vector by averaging symmetric entries - w_x = (-w_mat[..., 1, 2] + w_mat[..., 2, 1]) / 2.0 - w_y = (w_mat[..., 0, 2] - w_mat[..., 2, 0]) / 2.0 - w_z = (-w_mat[..., 0, 1] + w_mat[..., 1, 0]) / 2.0 - w = torch.stack([w_x, w_y, w_z], axis=-1) - return w - -def image_from_bytes(image_bytes): - import matplotlib.image as mpimg - from io import BytesIO - return mpimg.imread(BytesIO(image_bytes), format='PNG') - -def process_frame(i, vertices_all, vertices1_all, faces, output_dir, filenames): - import matplotlib - matplotlib.use('Agg') - import matplotlib.pyplot as plt - import trimesh - import pyrender - - def deg_to_rad(degrees): - return degrees * np.pi / 180 - - uniform_color = [220, 220, 220, 255] - resolution = (1000, 1000) - figsize = (10, 10) - - fig, axs = plt.subplots( - nrows=1, - ncols=2, - figsize=(figsize[0] * 2, figsize[1] * 1) - ) - axs = axs.flatten() - - vertices = vertices_all[i] - vertices1 = vertices1_all[i] - filename = f"{output_dir}frame_{i}.png" - filenames.append(filename) - if i%100 == 0: - print('processed', i, 'frames') - #time_s = time.time() - #print(vertices.shape) - angle_rad = deg_to_rad(-2) - pose_camera = np.array([ - [1.0, 0.0, 0.0, 0.0], - [0.0, np.cos(angle_rad), -np.sin(angle_rad), 1.0], - [0.0, np.sin(angle_rad), np.cos(angle_rad), 5.0], - [0.0, 0.0, 0.0, 1.0] - ]) - angle_rad = deg_to_rad(-30) - pose_light = np.array([ - [1.0, 0.0, 0.0, 0.0], - [0.0, np.cos(angle_rad), -np.sin(angle_rad), 0.0], - [0.0, np.sin(angle_rad), np.cos(angle_rad), 3.0], - [0.0, 0.0, 0.0, 1.0] - ]) - - for vtx_idx, vtx in enumerate([vertices, vertices1]): - trimesh_mesh = trimesh.Trimesh( - vertices=vtx, - faces=faces, - vertex_colors=uniform_color - ) - mesh = pyrender.Mesh.from_trimesh( - trimesh_mesh, smooth=True - ) - scene = pyrender.Scene() - scene.add(mesh) - camera = pyrender.OrthographicCamera(xmag=1.0, ymag=1.0) - scene.add(camera, pose=pose_camera) - light = pyrender.DirectionalLight(color=[1.0, 1.0, 1.0], intensity=4.0) - scene.add(light, pose=pose_light) - renderer = pyrender.OffscreenRenderer(*resolution) - color, _ = renderer.render(scene) - axs[vtx_idx].imshow(color) - axs[vtx_idx].axis('off') - renderer.delete() - - plt.savefig(filename, bbox_inches='tight') - plt.close(fig) - -def generate_images(frames, vertices_all, vertices1_all, faces, output_dir, filenames): - import multiprocessing - # import trimesh - num_cores = multiprocessing.cpu_count() - 1 # This will get the number of cores on your machine. - # mesh = trimesh.Trimesh(vertices_all[0], faces) - # scene = mesh.scene() - # fov = scene.camera.fov.copy() - # fov[0] = 80.0 - # fov[1] = 60.0 - # camera_params = { - # 'fov': fov, - # 'resolution': scene.camera.resolution, - # 'focal': scene.camera.focal, - # 'z_near': scene.camera.z_near, - # "z_far": scene.camera.z_far, - # 'transform': scene.graph[scene.camera.name][0] - # } - # mesh1 = trimesh.Trimesh(vertices1_all[0], faces) - # scene1 = mesh1.scene() - # camera_params1 = { - # 'fov': fov, - # 'resolution': scene1.camera.resolution, - # 'focal': scene1.camera.focal, - # 'z_near': scene1.camera.z_near, - # "z_far": scene1.camera.z_far, - # 'transform': scene1.graph[scene1.camera.name][0] - # } - # Use a Pool to manage the processes - # print(num_cores) - # for i in range(frames): - # process_frame(i, vertices_all, vertices1_all, faces, output_dir, use_matplotlib, filenames, camera_params, camera_params1) - for i in range(frames): - process_frame(i*3, vertices_all, vertices1_all, faces, output_dir, filenames) - - # progress = multiprocessing.Value('i', 0) - # lock = multiprocessing.Lock() - # with multiprocessing.Pool(num_cores) as pool: - # # pool.starmap(process_frame, [(i, vertices_all, vertices1_all, faces, output_dir, use_matplotlib, filenames, camera_params, camera_params1) for i in range(frames)]) - # pool.starmap( - # process_frame, - # [ - # (i, vertices_all, vertices1_all, faces, output_dir, filenames) - # for i in range(frames) - # ] - # ) - - # progress = multiprocessing.Value('i', 0) - # lock = multiprocessing.Lock() - # with multiprocessing.Pool(num_cores) as pool: - # # pool.starmap(process_frame, [(i, vertices_all, vertices1_all, faces, output_dir, use_matplotlib, filenames, camera_params, camera_params1) for i in range(frames)]) - # pool.starmap( - # process_frame, - # [ - # (i, vertices_all, vertices1_all, faces, output_dir, filenames) - # for i in range(frames) - # ] - # ) - -def render_one_sequence_with_face( - res_npz_path, - gt_npz_path, - output_dir, - audio_path, - model_folder="/data/datasets/smplx_models/", - model_type='smplx', - gender='NEUTRAL_2020', - ext='npz', - num_betas=300, - num_expression_coeffs=100, - use_face_contour=False, - use_matplotlib=False, - args=None): - import smplx - import matplotlib.pyplot as plt - import imageio - from tqdm import tqdm - import os - import numpy as np - import torch - import moviepy.editor as mp - import librosa - - model = smplx.create(model_folder, model_type=model_type, - gender=gender, use_face_contour=use_face_contour, - num_betas=num_betas, - num_expression_coeffs=num_expression_coeffs, - ext=ext, use_pca=False).cuda() - - #data_npz = np.load(f"{output_dir}{res_npz_path}.npz") - data_np_body = np.load(res_npz_path, allow_pickle=True) - gt_np_body = np.load(gt_npz_path, allow_pickle=True) - - if not os.path.exists(output_dir): os.makedirs(output_dir) - # if not use_matplotlib: - # import trimesh - #import pyrender - from pyvirtualdisplay import Display - #''' - #display = Display(visible=0, size=(1000, 1000)) - #display.start() - faces = np.load(f"{model_folder}/smplx/SMPLX_NEUTRAL_2020.npz", allow_pickle=True)["f"] - seconds = 1 - #data_npz["jaw_pose"].shape[0] - n = data_np_body["poses"].shape[0] - beta = torch.from_numpy(data_np_body["betas"]).to(torch.float32).unsqueeze(0).cuda() - beta = beta.repeat(n, 1) - expression = torch.from_numpy(data_np_body["expressions"][:n]).to(torch.float32).cuda() - jaw_pose = torch.from_numpy(data_np_body["poses"][:n, 66:69]).to(torch.float32).cuda() - pose = torch.from_numpy(data_np_body["poses"][:n]).to(torch.float32).cuda() - transl = torch.from_numpy(data_np_body["trans"][:n]).to(torch.float32).cuda() - # print(beta.shape, expression.shape, jaw_pose.shape, pose.shape, transl.shape, pose[:,:3].shape) - output = model(betas=beta, transl=transl, expression=expression, jaw_pose=jaw_pose, - global_orient=pose[:,:3], body_pose=pose[:,3:21*3+3], left_hand_pose=pose[:,25*3:40*3], right_hand_pose=pose[:,40*3:55*3], - leye_pose=pose[:, 69:72], - reye_pose=pose[:, 72:75], - return_verts=True) - vertices_all = output["vertices"].cpu().detach().numpy() - - beta1 = torch.from_numpy(data_np_body["betas"]).to(torch.float32).unsqueeze(0).cuda() - beta1 = beta1.repeat(n, 1) - expression1 = torch.from_numpy(data_np_body["expressions"][:n]).to(torch.float32).cuda() - zero_pose = np.zeros_like(data_np_body["poses"]) - jaw_pose1 = torch.from_numpy(zero_pose[:n,66:69]).to(torch.float32).cuda() - pose1 = torch.from_numpy(zero_pose[:n]).to(torch.float32).cuda() - zero_trans = np.zeros_like(data_np_body["trans"]) - transl1 = torch.from_numpy(zero_trans[:n]).to(torch.float32).cuda() - output1 = model(betas=beta1, transl=transl1, expression=expression1, jaw_pose=jaw_pose1, - global_orient=pose1[:,:3], body_pose=pose1[:,3:21*3+3], left_hand_pose=pose1[:,25*3:40*3], right_hand_pose=pose1[:,40*3:55*3], - leye_pose=pose1[:, 69:72], - reye_pose=pose1[:, 72:75], - return_verts=True) - vertices1_all = output1["vertices"].cpu().detach().numpy()*8 - trans_down = np.zeros_like(vertices1_all) - trans_down[:, :, 1] = 1.55 - vertices1_all = vertices1_all - trans_down - if args.debug: - seconds = 1 - else: - seconds = vertices_all.shape[0]//30 - silent_video_file_path = utils.fast_render.generate_silent_videos(args.render_video_fps, - args.render_video_width, - args.render_video_height, - args.render_concurrent_num, - args.render_tmp_img_filetype, - int(seconds*args.render_video_fps), - vertices1_all, - vertices_all, - faces, - output_dir) - base_filename_without_ext = os.path.splitext(os.path.basename(res_npz_path))[0] - final_clip = os.path.join(output_dir, f"{base_filename_without_ext}.mp4") - utils.media.add_audio_to_video(silent_video_file_path, audio_path, final_clip) - os.remove(silent_video_file_path) - return final_clip - -def render_one_sequence( - res_npz_path, - gt_npz_path, - output_dir, - audio_path, - model_folder="/data/datasets/smplx_models/", - model_type='smplx', - gender='NEUTRAL_2020', - ext='npz', - num_betas=300, - num_expression_coeffs=100, - use_face_contour=False, - use_matplotlib=False, - args=None): - import smplx - import matplotlib.pyplot as plt - import imageio - from tqdm import tqdm - import os - import numpy as np - import torch - import moviepy.editor as mp - import librosa - - model = smplx.create(model_folder, model_type=model_type, - gender=gender, use_face_contour=use_face_contour, - num_betas=num_betas, - num_expression_coeffs=num_expression_coeffs, - ext=ext, use_pca=False).cuda() - - #data_npz = np.load(f"{output_dir}{res_npz_path}.npz") - data_np_body = np.load(res_npz_path, allow_pickle=True) - gt_np_body = np.load(gt_npz_path, allow_pickle=True) - - if not os.path.exists(output_dir): os.makedirs(output_dir) - # if not use_matplotlib: - # import trimesh - #import pyrender - from pyvirtualdisplay import Display - #''' - #display = Display(visible=0, size=(1000, 1000)) - #display.start() - faces = np.load(f"{model_folder}/smplx/SMPLX_NEUTRAL_2020.npz", allow_pickle=True)["f"] - seconds = 1 - #data_npz["jaw_pose"].shape[0] - n = data_np_body["poses"].shape[0] - beta = torch.from_numpy(data_np_body["betas"]).to(torch.float32).unsqueeze(0).cuda() - beta = beta.repeat(n, 1) - expression = torch.from_numpy(data_np_body["expressions"][:n]).to(torch.float32).cuda() - jaw_pose = torch.from_numpy(data_np_body["poses"][:n, 66:69]).to(torch.float32).cuda() - pose = torch.from_numpy(data_np_body["poses"][:n]).to(torch.float32).cuda() - transl = torch.from_numpy(data_np_body["trans"][:n]).to(torch.float32).cuda() - # print(beta.shape, expression.shape, jaw_pose.shape, pose.shape, transl.shape, pose[:,:3].shape) - output = model(betas=beta, transl=transl, expression=expression, jaw_pose=jaw_pose, - global_orient=pose[:,:3], body_pose=pose[:,3:21*3+3], left_hand_pose=pose[:,25*3:40*3], right_hand_pose=pose[:,40*3:55*3], - leye_pose=pose[:, 69:72], - reye_pose=pose[:, 72:75], - return_verts=True) - vertices_all = output["vertices"].cpu().detach().numpy() - - beta1 = torch.from_numpy(gt_np_body["betas"]).to(torch.float32).unsqueeze(0).cuda() - expression1 = torch.from_numpy(gt_np_body["expressions"][:n]).to(torch.float32).cuda() - jaw_pose1 = torch.from_numpy(gt_np_body["poses"][:n,66:69]).to(torch.float32).cuda() - pose1 = torch.from_numpy(gt_np_body["poses"][:n]).to(torch.float32).cuda() - transl1 = torch.from_numpy(gt_np_body["trans"][:n]).to(torch.float32).cuda() - output1 = model(betas=beta1, transl=transl1, expression=expression1, jaw_pose=jaw_pose1, global_orient=pose1[:,:3], body_pose=pose1[:,3:21*3+3], left_hand_pose=pose1[:,25*3:40*3], right_hand_pose=pose1[:,40*3:55*3], - leye_pose=pose1[:, 69:72], - reye_pose=pose1[:, 72:75],return_verts=True) - vertices1_all = output1["vertices"].cpu().detach().numpy() - if args.debug: - seconds = 1 - else: - seconds = vertices_all.shape[0]//30 - silent_video_file_path = utils.fast_render.generate_silent_videos(args.render_video_fps, - args.render_video_width, - args.render_video_height, - args.render_concurrent_num, - args.render_tmp_img_filetype, - int(seconds*args.render_video_fps), - vertices_all, - vertices1_all, - faces, - output_dir) - base_filename_without_ext = os.path.splitext(os.path.basename(res_npz_path))[0] - final_clip = os.path.join(output_dir, f"{base_filename_without_ext}.mp4") - utils.media.add_audio_to_video(silent_video_file_path, audio_path, final_clip) - os.remove(silent_video_file_path) - return final_clip - -def render_one_sequence_no_gt( - res_npz_path, - output_dir, - audio_path, - model_folder="/data/datasets/smplx_models/", - model_type='smplx', - gender='NEUTRAL_2020', - ext='npz', - num_betas=300, - num_expression_coeffs=100, - use_face_contour=False, - use_matplotlib=False, - args=None): - import smplx - import matplotlib.pyplot as plt - import imageio - from tqdm import tqdm - import os - import numpy as np - import torch - import moviepy.editor as mp - import librosa - - model = smplx.create(model_folder, model_type=model_type, - gender=gender, use_face_contour=use_face_contour, - num_betas=num_betas, - num_expression_coeffs=num_expression_coeffs, - ext=ext, use_pca=False).cuda() - - #data_npz = np.load(f"{output_dir}{res_npz_path}.npz") - data_np_body = np.load(res_npz_path, allow_pickle=True) - # gt_np_body = np.load(gt_npz_path, allow_pickle=True) - - if not os.path.exists(output_dir): os.makedirs(output_dir) - # if not use_matplotlib: - # import trimesh - #import pyrender - from pyvirtualdisplay import Display - #''' - #display = Display(visible=0, size=(1000, 1000)) - #display.start() - faces = np.load(f"{model_folder}/smplx/SMPLX_NEUTRAL_2020.npz", allow_pickle=True)["f"] - seconds = 1 - #data_npz["jaw_pose"].shape[0] - n = data_np_body["poses"].shape[0] - beta = torch.from_numpy(data_np_body["betas"]).to(torch.float32).unsqueeze(0).cuda() - beta = beta.repeat(n, 1) - expression = torch.from_numpy(data_np_body["expressions"][:n]).to(torch.float32).cuda() - jaw_pose = torch.from_numpy(data_np_body["poses"][:n, 66:69]).to(torch.float32).cuda() - pose = torch.from_numpy(data_np_body["poses"][:n]).to(torch.float32).cuda() - transl = torch.from_numpy(data_np_body["trans"][:n]).to(torch.float32).cuda() - # print(beta.shape, expression.shape, jaw_pose.shape, pose.shape, transl.shape, pose[:,:3].shape) - output = model(betas=beta, transl=transl, expression=expression, jaw_pose=jaw_pose, - global_orient=pose[:,:3], body_pose=pose[:,3:21*3+3], left_hand_pose=pose[:,25*3:40*3], right_hand_pose=pose[:,40*3:55*3], - leye_pose=pose[:, 69:72], - reye_pose=pose[:, 72:75], - return_verts=True) - vertices_all = output["vertices"].cpu().detach().numpy() - - # beta1 = torch.from_numpy(gt_np_body["betas"]).to(torch.float32).unsqueeze(0).cuda() - # expression1 = torch.from_numpy(gt_np_body["expressions"][:n]).to(torch.float32).cuda() - # jaw_pose1 = torch.from_numpy(gt_np_body["poses"][:n,66:69]).to(torch.float32).cuda() - # pose1 = torch.from_numpy(gt_np_body["poses"][:n]).to(torch.float32).cuda() - # transl1 = torch.from_numpy(gt_np_body["trans"][:n]).to(torch.float32).cuda() - # output1 = model(betas=beta1, transl=transl1, expression=expression1, jaw_pose=jaw_pose1, global_orient=pose1[:,:3], body_pose=pose1[:,3:21*3+3], left_hand_pose=pose1[:,25*3:40*3], right_hand_pose=pose1[:,40*3:55*3], - # leye_pose=pose1[:, 69:72], - # reye_pose=pose1[:, 72:75],return_verts=True) - # vertices1_all = output1["vertices"].cpu().detach().numpy() - if args.debug: - seconds = 1 - else: - seconds = vertices_all.shape[0]//30 - silent_video_file_path = utils.fast_render.generate_silent_videos_no_gt(args.render_video_fps, - args.render_video_width, - args.render_video_height, - args.render_concurrent_num, - args.render_tmp_img_filetype, - int(seconds*args.render_video_fps), - vertices_all, - faces, - output_dir) - base_filename_without_ext = os.path.splitext(os.path.basename(res_npz_path))[0] - final_clip = os.path.join(output_dir, f"{base_filename_without_ext}.mp4") - utils.media.add_audio_to_video(silent_video_file_path, audio_path, final_clip) - os.remove(silent_video_file_path) - return final_clip - -def print_exp_info(args): - logger.info(pprint.pformat(vars(args))) - logger.info(f"# ------------ {args.name} ----------- #") - logger.info("PyTorch version: {}".format(torch.__version__)) - logger.info("CUDA version: {}".format(torch.version.cuda)) - logger.info("{} GPUs".format(torch.cuda.device_count())) - logger.info(f"Random Seed: {args.random_seed}") - -def args2csv(args, get_head=False, list4print=[]): - for k, v in args.items(): - if isinstance(args[k], dict): - args2csv(args[k], get_head, list4print) - else: list4print.append(k) if get_head else list4print.append(v) - return list4print - -class EpochTracker: - def __init__(self, metric_names, metric_directions): - assert len(metric_names) == len(metric_directions), "Metric names and directions should have the same length" - - - self.metric_names = metric_names - self.states = ['train', 'val', 'test'] - self.types = ['last', 'best'] - - - self.values = {name: {state: {type_: {'value': np.inf if not is_higher_better else -np.inf, 'epoch': 0} - for type_ in self.types} - for state in self.states} - for name, is_higher_better in zip(metric_names, metric_directions)} - - self.loss_meters = {name: {state: AverageMeter(f"{name}_{state}") - for state in self.states} - for name in metric_names} - - - self.is_higher_better = {name: direction for name, direction in zip(metric_names, metric_directions)} - self.train_history = {name: [] for name in metric_names} - self.val_history = {name: [] for name in metric_names} - - - def update_meter(self, name, state, value): - self.loss_meters[name][state].update(value) - - - def update_values(self, name, state, epoch): - value_avg = self.loss_meters[name][state].avg - new_best = False - - - if ((value_avg < self.values[name][state]['best']['value'] and not self.is_higher_better[name]) or - (value_avg > self.values[name][state]['best']['value'] and self.is_higher_better[name])): - self.values[name][state]['best']['value'] = value_avg - self.values[name][state]['best']['epoch'] = epoch - new_best = True - self.values[name][state]['last']['value'] = value_avg - self.values[name][state]['last']['epoch'] = epoch - return new_best - - - def get(self, name, state, type_): - return self.values[name][state][type_] - - - def reset(self): - for name in self.metric_names: - for state in self.states: - self.loss_meters[name][state].reset() - - - def flatten_values(self): - flat_dict = {} - for name in self.metric_names: - for state in self.states: - for type_ in self.types: - value_key = f"{name}_{state}_{type_}" - epoch_key = f"{name}_{state}_{type_}_epoch" - flat_dict[value_key] = self.values[name][state][type_]['value'] - flat_dict[epoch_key] = self.values[name][state][type_]['epoch'] - return flat_dict - - def update_and_plot(self, name, epoch, save_path): - new_best_train = self.update_values(name, 'train', epoch) - new_best_val = self.update_values(name, 'val', epoch) - - - self.train_history[name].append(self.loss_meters[name]['train'].avg) - self.val_history[name].append(self.loss_meters[name]['val'].avg) - - - train_values = self.train_history[name] - val_values = self.val_history[name] - epochs = list(range(1, len(train_values) + 1)) - - - plt.figure(figsize=(10, 6)) - plt.plot(epochs, train_values, label='Train') - plt.plot(epochs, val_values, label='Val') - plt.title(f'Train vs Val {name} over epochs') - plt.xlabel('Epochs') - plt.ylabel(name) - plt.legend() - plt.savefig(save_path) - plt.close() - - - return new_best_train, new_best_val - -def record_trial(args, tracker): - """ - 1. record notes, score, env_name, experments_path, - """ - csv_path = args.out_path + "custom/" +args.csv_name+".csv" - all_print_dict = vars(args) - all_print_dict.update(tracker.flatten_values()) - if not os.path.exists(csv_path): - pd.DataFrame([all_print_dict]).to_csv(csv_path, index=False) - else: - df_existing = pd.read_csv(csv_path) - df_new = pd.DataFrame([all_print_dict]) - df_aligned = df_existing.append(df_new).fillna("") - df_aligned.to_csv(csv_path, index=False) - -def set_random_seed(args): - os.environ['PYTHONHASHSEED'] = str(args.random_seed) - random.seed(args.random_seed) - np.random.seed(args.random_seed) - torch.manual_seed(args.random_seed) - torch.cuda.manual_seed_all(args.random_seed) - torch.cuda.manual_seed(args.random_seed) - torch.backends.cudnn.deterministic = args.deterministic #args.CUDNN_DETERMINISTIC - torch.backends.cudnn.benchmark = args.benchmark - torch.backends.cudnn.enabled = args.cudnn_enabled - -def save_checkpoints(save_path, model, opt=None, epoch=None, lrs=None): - if lrs is not None: - states = { 'model_state': model.state_dict(), - 'epoch': epoch + 1, - 'opt_state': opt.state_dict(), - 'lrs':lrs.state_dict(),} - elif opt is not None: - states = { 'model_state': model.state_dict(), - 'epoch': epoch + 1, - 'opt_state': opt.state_dict(),} - else: - states = { 'model_state': model.state_dict(),} - torch.save(states, save_path) - -def load_checkpoints(model, save_path, load_name='model'): - states = torch.load(save_path) - new_weights = OrderedDict() - flag=False - for k, v in states['model_state'].items(): - #print(k) - if "module" not in k: - break - else: - new_weights[k[7:]]=v - flag=True - if flag: - try: - model.load_state_dict(new_weights) - except: - #print(states['model_state']) - model.load_state_dict(states['model_state']) - else: - model.load_state_dict(states['model_state']) - logger.info(f"load self-pretrained checkpoints for {load_name}") - -def model_complexity(model, args): - from ptflops import get_model_complexity_info - flops, params = get_model_complexity_info(model, (args.T_GLOBAL._DIM, args.TRAIN.CROP, args.TRAIN), - as_strings=False, print_per_layer_stat=False) - logging.info('{:<30} {:<8} BFlops'.format('Computational complexity: ', flops / 1e9)) - logging.info('{:<30} {:<8} MParams'.format('Number of parameters: ', params / 1e6)) - -class AverageMeter(object): - """Computes and stores the average and current value""" - def __init__(self, name, fmt=':f'): - self.name = name - self.fmt = fmt - self.reset() - - def reset(self): - self.val = 0 - self.avg = 0 - self.sum = 0 - self.count = 0 - - def update(self, val, n=1): - self.val = val - self.sum += val * n - self.count += n - self.avg = self.sum / self.count - - def __str__(self): - fmtstr = '{name} {val' + self.fmt + '} ({avg' + self.fmt + '})' - return fmtstr.format(**self.__dict__) \ No newline at end of file diff --git a/utils/rotation_conversions.py b/utils/rotation_conversions.py deleted file mode 100644 index f2bfaa1b2247622bff35d3f9b15e8eb84064aa53..0000000000000000000000000000000000000000 --- a/utils/rotation_conversions.py +++ /dev/null @@ -1,550 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved. - -import functools -from typing import Optional - -import torch -import torch.nn.functional as F - - -""" -The transformation matrices returned from the functions in this file assume -the points on which the transformation will be applied are column vectors. -i.e. the R matrix is structured as - - R = [ - [Rxx, Rxy, Rxz], - [Ryx, Ryy, Ryz], - [Rzx, Rzy, Rzz], - ] # (3, 3) - -This matrix can be applied to column vectors by post multiplication -by the points e.g. - - points = [[0], [1], [2]] # (3 x 1) xyz coordinates of a point - transformed_points = R * points - -To apply the same matrix to points which are row vectors, the R matrix -can be transposed and pre multiplied by the points: - -e.g. - points = [[0, 1, 2]] # (1 x 3) xyz coordinates of a point - transformed_points = points * R.transpose(1, 0) -""" - - -def quaternion_to_matrix(quaternions): - """ - Convert rotations given as quaternions to rotation matrices. - - Args: - quaternions: quaternions with real part first, - as tensor of shape (..., 4). - - Returns: - Rotation matrices as tensor of shape (..., 3, 3). - """ - r, i, j, k = torch.unbind(quaternions, -1) - two_s = 2.0 / (quaternions * quaternions).sum(-1) - - o = torch.stack( - ( - 1 - two_s * (j * j + k * k), - two_s * (i * j - k * r), - two_s * (i * k + j * r), - two_s * (i * j + k * r), - 1 - two_s * (i * i + k * k), - two_s * (j * k - i * r), - two_s * (i * k - j * r), - two_s * (j * k + i * r), - 1 - two_s * (i * i + j * j), - ), - -1, - ) - return o.reshape(quaternions.shape[:-1] + (3, 3)) - - -def _copysign(a, b): - """ - Return a tensor where each element has the absolute value taken from the, - corresponding element of a, with sign taken from the corresponding - element of b. This is like the standard copysign floating-point operation, - but is not careful about negative 0 and NaN. - - Args: - a: source tensor. - b: tensor whose signs will be used, of the same shape as a. - - Returns: - Tensor of the same shape as a with the signs of b. - """ - signs_differ = (a < 0) != (b < 0) - return torch.where(signs_differ, -a, a) - - -def _sqrt_positive_part(x): - """ - Returns torch.sqrt(torch.max(0, x)) - but with a zero subgradient where x is 0. - """ - ret = torch.zeros_like(x) - positive_mask = x > 0 - ret[positive_mask] = torch.sqrt(x[positive_mask]) - return ret - - -def matrix_to_quaternion(matrix): - """ - Convert rotations given as rotation matrices to quaternions. - - Args: - matrix: Rotation matrices as tensor of shape (..., 3, 3). - - Returns: - quaternions with real part first, as tensor of shape (..., 4). - """ - if matrix.size(-1) != 3 or matrix.size(-2) != 3: - raise ValueError(f"Invalid rotation matrix shape f{matrix.shape}.") - m00 = matrix[..., 0, 0] - m11 = matrix[..., 1, 1] - m22 = matrix[..., 2, 2] - o0 = 0.5 * _sqrt_positive_part(1 + m00 + m11 + m22) - x = 0.5 * _sqrt_positive_part(1 + m00 - m11 - m22) - y = 0.5 * _sqrt_positive_part(1 - m00 + m11 - m22) - z = 0.5 * _sqrt_positive_part(1 - m00 - m11 + m22) - o1 = _copysign(x, matrix[..., 2, 1] - matrix[..., 1, 2]) - o2 = _copysign(y, matrix[..., 0, 2] - matrix[..., 2, 0]) - o3 = _copysign(z, matrix[..., 1, 0] - matrix[..., 0, 1]) - return torch.stack((o0, o1, o2, o3), -1) - - -def _axis_angle_rotation(axis: str, angle): - """ - Return the rotation matrices for one of the rotations about an axis - of which Euler angles describe, for each value of the angle given. - - Args: - axis: Axis label "X" or "Y or "Z". - angle: any shape tensor of Euler angles in radians - - Returns: - Rotation matrices as tensor of shape (..., 3, 3). - """ - - cos = torch.cos(angle) - sin = torch.sin(angle) - one = torch.ones_like(angle) - zero = torch.zeros_like(angle) - - if axis == "X": - R_flat = (one, zero, zero, zero, cos, -sin, zero, sin, cos) - if axis == "Y": - R_flat = (cos, zero, sin, zero, one, zero, -sin, zero, cos) - if axis == "Z": - R_flat = (cos, -sin, zero, sin, cos, zero, zero, zero, one) - - return torch.stack(R_flat, -1).reshape(angle.shape + (3, 3)) - - -def euler_angles_to_matrix(euler_angles, convention: str): - """ - Convert rotations given as Euler angles in radians to rotation matrices. - - Args: - euler_angles: Euler angles in radians as tensor of shape (..., 3). - convention: Convention string of three uppercase letters from - {"X", "Y", and "Z"}. - - Returns: - Rotation matrices as tensor of shape (..., 3, 3). - """ - if euler_angles.dim() == 0 or euler_angles.shape[-1] != 3: - raise ValueError("Invalid input euler angles.") - if len(convention) != 3: - raise ValueError("Convention must have 3 letters.") - if convention[1] in (convention[0], convention[2]): - raise ValueError(f"Invalid convention {convention}.") - for letter in convention: - if letter not in ("X", "Y", "Z"): - raise ValueError(f"Invalid letter {letter} in convention string.") - matrices = map(_axis_angle_rotation, convention, torch.unbind(euler_angles, -1)) - return functools.reduce(torch.matmul, matrices) - - -def _angle_from_tan( - axis: str, other_axis: str, data, horizontal: bool, tait_bryan: bool -): - """ - Extract the first or third Euler angle from the two members of - the matrix which are positive constant times its sine and cosine. - - Args: - axis: Axis label "X" or "Y or "Z" for the angle we are finding. - other_axis: Axis label "X" or "Y or "Z" for the middle axis in the - convention. - data: Rotation matrices as tensor of shape (..., 3, 3). - horizontal: Whether we are looking for the angle for the third axis, - which means the relevant entries are in the same row of the - rotation matrix. If not, they are in the same column. - tait_bryan: Whether the first and third axes in the convention differ. - - Returns: - Euler Angles in radians for each matrix in data as a tensor - of shape (...). - """ - - i1, i2 = {"X": (2, 1), "Y": (0, 2), "Z": (1, 0)}[axis] - if horizontal: - i2, i1 = i1, i2 - even = (axis + other_axis) in ["XY", "YZ", "ZX"] - if horizontal == even: - return torch.atan2(data[..., i1], data[..., i2]) - if tait_bryan: - return torch.atan2(-data[..., i2], data[..., i1]) - return torch.atan2(data[..., i2], -data[..., i1]) - - -def _index_from_letter(letter: str): - if letter == "X": - return 0 - if letter == "Y": - return 1 - if letter == "Z": - return 2 - - -def matrix_to_euler_angles(matrix, convention: str): - """ - Convert rotations given as rotation matrices to Euler angles in radians. - - Args: - matrix: Rotation matrices as tensor of shape (..., 3, 3). - convention: Convention string of three uppercase letters. - - Returns: - Euler angles in radians as tensor of shape (..., 3). - """ - if len(convention) != 3: - raise ValueError("Convention must have 3 letters.") - if convention[1] in (convention[0], convention[2]): - raise ValueError(f"Invalid convention {convention}.") - for letter in convention: - if letter not in ("X", "Y", "Z"): - raise ValueError(f"Invalid letter {letter} in convention string.") - if matrix.size(-1) != 3 or matrix.size(-2) != 3: - raise ValueError(f"Invalid rotation matrix shape f{matrix.shape}.") - i0 = _index_from_letter(convention[0]) - i2 = _index_from_letter(convention[2]) - tait_bryan = i0 != i2 - if tait_bryan: - central_angle = torch.asin( - matrix[..., i0, i2] * (-1.0 if i0 - i2 in [-1, 2] else 1.0) - ) - else: - central_angle = torch.acos(matrix[..., i0, i0]) - - o = ( - _angle_from_tan( - convention[0], convention[1], matrix[..., i2], False, tait_bryan - ), - central_angle, - _angle_from_tan( - convention[2], convention[1], matrix[..., i0, :], True, tait_bryan - ), - ) - return torch.stack(o, -1) - - -def random_quaternions( - n: int, dtype: Optional[torch.dtype] = None, device=None, requires_grad=False -): - """ - Generate random quaternions representing rotations, - i.e. versors with nonnegative real part. - - Args: - n: Number of quaternions in a batch to return. - dtype: Type to return. - device: Desired device of returned tensor. Default: - uses the current device for the default tensor type. - requires_grad: Whether the resulting tensor should have the gradient - flag set. - - Returns: - Quaternions as tensor of shape (N, 4). - """ - o = torch.randn((n, 4), dtype=dtype, device=device, requires_grad=requires_grad) - s = (o * o).sum(1) - o = o / _copysign(torch.sqrt(s), o[:, 0])[:, None] - return o - - -def random_rotations( - n: int, dtype: Optional[torch.dtype] = None, device=None, requires_grad=False -): - """ - Generate random rotations as 3x3 rotation matrices. - - Args: - n: Number of rotation matrices in a batch to return. - dtype: Type to return. - device: Device of returned tensor. Default: if None, - uses the current device for the default tensor type. - requires_grad: Whether the resulting tensor should have the gradient - flag set. - - Returns: - Rotation matrices as tensor of shape (n, 3, 3). - """ - quaternions = random_quaternions( - n, dtype=dtype, device=device, requires_grad=requires_grad - ) - return quaternion_to_matrix(quaternions) - - -def random_rotation( - dtype: Optional[torch.dtype] = None, device=None, requires_grad=False -): - """ - Generate a single random 3x3 rotation matrix. - - Args: - dtype: Type to return - device: Device of returned tensor. Default: if None, - uses the current device for the default tensor type - requires_grad: Whether the resulting tensor should have the gradient - flag set - - Returns: - Rotation matrix as tensor of shape (3, 3). - """ - return random_rotations(1, dtype, device, requires_grad)[0] - - -def standardize_quaternion(quaternions): - """ - Convert a unit quaternion to a standard form: one in which the real - part is non negative. - - Args: - quaternions: Quaternions with real part first, - as tensor of shape (..., 4). - - Returns: - Standardized quaternions as tensor of shape (..., 4). - """ - return torch.where(quaternions[..., 0:1] < 0, -quaternions, quaternions) - - -def quaternion_raw_multiply(a, b): - """ - Multiply two quaternions. - Usual torch rules for broadcasting apply. - - Args: - a: Quaternions as tensor of shape (..., 4), real part first. - b: Quaternions as tensor of shape (..., 4), real part first. - - Returns: - The product of a and b, a tensor of quaternions shape (..., 4). - """ - aw, ax, ay, az = torch.unbind(a, -1) - bw, bx, by, bz = torch.unbind(b, -1) - ow = aw * bw - ax * bx - ay * by - az * bz - ox = aw * bx + ax * bw + ay * bz - az * by - oy = aw * by - ax * bz + ay * bw + az * bx - oz = aw * bz + ax * by - ay * bx + az * bw - return torch.stack((ow, ox, oy, oz), -1) - - -def quaternion_multiply(a, b): - """ - Multiply two quaternions representing rotations, returning the quaternion - representing their composition, i.e. the versor with nonnegative real part. - Usual torch rules for broadcasting apply. - - Args: - a: Quaternions as tensor of shape (..., 4), real part first. - b: Quaternions as tensor of shape (..., 4), real part first. - - Returns: - The product of a and b, a tensor of quaternions of shape (..., 4). - """ - ab = quaternion_raw_multiply(a, b) - return standardize_quaternion(ab) - - -def quaternion_invert(quaternion): - """ - Given a quaternion representing rotation, get the quaternion representing - its inverse. - - Args: - quaternion: Quaternions as tensor of shape (..., 4), with real part - first, which must be versors (unit quaternions). - - Returns: - The inverse, a tensor of quaternions of shape (..., 4). - """ - - return quaternion * quaternion.new_tensor([1, -1, -1, -1]) - - -def quaternion_apply(quaternion, point): - """ - Apply the rotation given by a quaternion to a 3D point. - Usual torch rules for broadcasting apply. - - Args: - quaternion: Tensor of quaternions, real part first, of shape (..., 4). - point: Tensor of 3D points of shape (..., 3). - - Returns: - Tensor of rotated points of shape (..., 3). - """ - if point.size(-1) != 3: - raise ValueError(f"Points are not in 3D, f{point.shape}.") - real_parts = point.new_zeros(point.shape[:-1] + (1,)) - point_as_quaternion = torch.cat((real_parts, point), -1) - out = quaternion_raw_multiply( - quaternion_raw_multiply(quaternion, point_as_quaternion), - quaternion_invert(quaternion), - ) - return out[..., 1:] - - -def axis_angle_to_matrix(axis_angle): - """ - Convert rotations given as axis/angle to rotation matrices. - - Args: - axis_angle: Rotations given as a vector in axis angle form, - as a tensor of shape (..., 3), where the magnitude is - the angle turned anticlockwise in radians around the - vector's direction. - - Returns: - Rotation matrices as tensor of shape (..., 3, 3). - """ - return quaternion_to_matrix(axis_angle_to_quaternion(axis_angle)) - - -def matrix_to_axis_angle(matrix): - """ - Convert rotations given as rotation matrices to axis/angle. - - Args: - matrix: Rotation matrices as tensor of shape (..., 3, 3). - - Returns: - Rotations given as a vector in axis angle form, as a tensor - of shape (..., 3), where the magnitude is the angle - turned anticlockwise in radians around the vector's - direction. - """ - return quaternion_to_axis_angle(matrix_to_quaternion(matrix)) - - -def axis_angle_to_quaternion(axis_angle): - """ - Convert rotations given as axis/angle to quaternions. - - Args: - axis_angle: Rotations given as a vector in axis angle form, - as a tensor of shape (..., 3), where the magnitude is - the angle turned anticlockwise in radians around the - vector's direction. - - Returns: - quaternions with real part first, as tensor of shape (..., 4). - """ - angles = torch.norm(axis_angle, p=2, dim=-1, keepdim=True) - half_angles = 0.5 * angles - eps = 1e-6 - small_angles = angles.abs() < eps - sin_half_angles_over_angles = torch.empty_like(angles) - sin_half_angles_over_angles[~small_angles] = ( - torch.sin(half_angles[~small_angles]) / angles[~small_angles] - ) - # for x small, sin(x/2) is about x/2 - (x/2)^3/6 - # so sin(x/2)/x is about 1/2 - (x*x)/48 - sin_half_angles_over_angles[small_angles] = ( - 0.5 - (angles[small_angles] * angles[small_angles]) / 48 - ) - quaternions = torch.cat( - [torch.cos(half_angles), axis_angle * sin_half_angles_over_angles], dim=-1 - ) - return quaternions - - -def quaternion_to_axis_angle(quaternions): - """ - Convert rotations given as quaternions to axis/angle. - - Args: - quaternions: quaternions with real part first, - as tensor of shape (..., 4). - - Returns: - Rotations given as a vector in axis angle form, as a tensor - of shape (..., 3), where the magnitude is the angle - turned anticlockwise in radians around the vector's - direction. - """ - norms = torch.norm(quaternions[..., 1:], p=2, dim=-1, keepdim=True) - half_angles = torch.atan2(norms, quaternions[..., :1]) - angles = 2 * half_angles - eps = 1e-6 - small_angles = angles.abs() < eps - sin_half_angles_over_angles = torch.empty_like(angles) - sin_half_angles_over_angles[~small_angles] = ( - torch.sin(half_angles[~small_angles]) / angles[~small_angles] - ) - # for x small, sin(x/2) is about x/2 - (x/2)^3/6 - # so sin(x/2)/x is about 1/2 - (x*x)/48 - sin_half_angles_over_angles[small_angles] = ( - 0.5 - (angles[small_angles] * angles[small_angles]) / 48 - ) - return quaternions[..., 1:] / sin_half_angles_over_angles - - -def rotation_6d_to_matrix(d6: torch.Tensor) -> torch.Tensor: - """ - Converts 6D rotation representation by Zhou et al. [1] to rotation matrix - using Gram--Schmidt orthogonalisation per Section B of [1]. - Args: - d6: 6D rotation representation, of size (*, 6) - - Returns: - batch of rotation matrices of size (*, 3, 3) - - [1] Zhou, Y., Barnes, C., Lu, J., Yang, J., & Li, H. - On the Continuity of Rotation Representations in Neural Networks. - IEEE Conference on Computer Vision and Pattern Recognition, 2019. - Retrieved from http://arxiv.org/abs/1812.07035 - """ - - a1, a2 = d6[..., :3], d6[..., 3:] - b1 = F.normalize(a1, dim=-1) - b2 = a2 - (b1 * a2).sum(-1, keepdim=True) * b1 - b2 = F.normalize(b2, dim=-1) - b3 = torch.cross(b1, b2, dim=-1) - return torch.stack((b1, b2, b3), dim=-2) - - -def matrix_to_rotation_6d(matrix: torch.Tensor) -> torch.Tensor: - """ - Converts rotation matrices to 6D rotation representation by Zhou et al. [1] - by dropping the last row. Note that 6D representation is not unique. - Args: - matrix: batch of rotation matrices of size (*, 3, 3) - - Returns: - 6D rotation representation, of size (*, 6) - - [1] Zhou, Y., Barnes, C., Lu, J., Yang, J., & Li, H. - On the Continuity of Rotation Representations in Neural Networks. - IEEE Conference on Computer Vision and Pattern Recognition, 2019. - Retrieved from http://arxiv.org/abs/1812.07035 - """ - return matrix[..., :2, :].clone().reshape(*matrix.size()[:-2], 6)