3DFauna_demo / video3d /model_ddp.py
kyleleey
remove unused parts
288ffdd
raw
history blame
180 kB
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.parallel import DistributedDataParallel as DDP
import torch.nn.init as init
import torchvision.models as models
import nvdiffrast.torch as dr
import numpy as np
import matplotlib.pyplot as plt
import os
import os.path as osp
import pickle
from video3d.render.regularizer import get_edge_length, normal_consistency, laplace_regularizer_const
from . import networks
from .renderer import *
from .utils import misc, meters, flow_viz, arap, custom_loss
from .dataloaders import get_sequence_loader, get_image_loader
from .dataloaders_ddp import get_sequence_loader_ddp, get_image_loader_ddp
from .cub_dataloaders import get_cub_loader
from .cub_dataloaders_ddp import get_cub_loader_ddp
from .utils.skinning_v4 import estimate_bones, skinning
import lpips
from einops import rearrange, repeat
# import clip
import torchvision.transforms.functional as tvf
from . import discriminator_architecture
from .geometry.dmtet import DMTetGeometry
from .geometry.dlmesh import DLMesh
from .triplane_texture.triplane_predictor import TriPlaneTex
from .render import renderutils as ru
from .render import material
from .render import mlptexture
from .render import util
from .render import mesh
from .render import light
from .render import render
EPS = 1e-7
def get_optimizer(model, lr=0.0001, betas=(0.9, 0.999), weight_decay=0):
return torch.optim.Adam(
filter(lambda p: p.requires_grad, model.parameters()),
lr=lr, betas=betas, weight_decay=weight_decay)
def set_requires_grad(model, requires_grad):
if model is not None:
for param in model.parameters():
param.requires_grad = requires_grad
def forward_to_matrix(vec_forward, up=[0,1,0]):
up = torch.FloatTensor(up).to(vec_forward.device)
# vec_forward = nn.functional.normalize(vec_forward, p=2, dim=-1) # x right, y up, z forward
vec_right = up.expand_as(vec_forward).cross(vec_forward, dim=-1)
vec_right = nn.functional.normalize(vec_right, p=2, dim=-1)
vec_up = vec_forward.cross(vec_right, dim=-1)
vec_up = nn.functional.normalize(vec_up, p=2, dim=-1)
rot_mat = torch.stack([vec_right, vec_up, vec_forward], -2)
return rot_mat
def sample_pose_hypothesis_from_quad_prediction(poses_raw, total_iter, batch_size, num_frames, pose_xflip_recon=False, input_image_xflip_flag=None, rot_temp_scalar=1., num_hypos=4, naive_probs_iter=2000, best_pose_start_iter=6000, random_sample=True, temp_clip_low = 1., temp_clip_high=100.):
rots_pred = poses_raw[..., :num_hypos*4].view(-1, num_hypos, 4)
rots_logits = rots_pred[..., 0] # Nx4
# temp = 1 / np.clip(total_iter / 1000 / rot_temp_scalar, 1., 100.)
temp = 1 / np.clip(total_iter / 1000 / rot_temp_scalar, temp_clip_low, temp_clip_high)
rots_probs = torch.nn.functional.softmax(-rots_logits / temp, dim=1) # N x K
# naive_probs = torch.FloatTensor([10] + [1] * (num_hypos - 1)).to(rots_logits.device)
naive_probs = torch.ones(num_hypos).to(rots_logits.device)
naive_probs = naive_probs / naive_probs.sum()
naive_probs_weight = np.clip(1 - (total_iter - naive_probs_iter) / 2000, 0, 1)
rots_probs = naive_probs.view(1, num_hypos) * naive_probs_weight + rots_probs * (1 - naive_probs_weight)
rots_pred = rots_pred[..., 1:4]
trans_pred = poses_raw[..., -3:]
best_rot_idx = torch.argmax(rots_probs, dim=1) # N
#print("best_rot_idx", best_rot_idx)
#print("best_of_best", torch.argmax(rots_probs))
#print("similar 7", torch.zeros_like(best_rot_idx) + 7)
#print("similar 2", torch.zeros_like(best_rot_idx) + torch.argmax(rots_probs))
if random_sample:
# rand_rot_idx = torch.randint(0, 4, (batch_size * num_frames,), device=poses_raw.device) # N
rand_rot_idx = torch.randperm(batch_size * num_frames, device=poses_raw.device) % num_hypos # N
# rand_rot_idx = torch.randperm(batch_size, device=poses_raw.device)[:,None].repeat(1, num_frames).view(-1) % 4 # N
best_flag = (torch.randperm(batch_size * num_frames, device=poses_raw.device) / (batch_size * num_frames) < np.clip((total_iter - best_pose_start_iter)/2000, 0, 0.8)).long()
rand_flag = 1 - best_flag
# best_flag = torch.zeros_like(best_rot_idx)
rot_idx = best_rot_idx * best_flag + rand_rot_idx * (1 - best_flag)
else:
rand_flag = torch.zeros_like(best_rot_idx)
#rot_idx = torch.full_like(torch.argmax(rots_probs, dim=1), torch.argmax(rots_probs), device=poses_raw.device)
rot_idx = best_rot_idx
rot_pred = torch.gather(rots_pred, 1, rot_idx[:, None, None].expand(-1, 1, 3))[:, 0] # Nx3
pose_raw = torch.cat([rot_pred, trans_pred], -1)
rot_prob = torch.gather(rots_probs, 1, rot_idx[:, None].expand(-1, 1))[:, 0] # N
rot_logit = torch.gather(rots_logits, 1, rot_idx[:, None].expand(-1, 1))[:, 0] # N
if pose_xflip_recon:
raise NotImplementedError
#up = torch.FloatTensor([0, 1, 0]).to(pose_raw.device)
rot_mat = forward_to_matrix(pose_raw[:, :3], up=[0, 1, 0])
pose = torch.cat([rot_mat.view(batch_size * num_frames, -1), pose_raw[:, 3:]], -1)
return pose_raw, pose, rot_idx, rot_prob, rot_logit, rots_probs, rand_flag
def get_joints_20_bones(bones, aux):
# the bones shape is [1, 1, 20, 2, 3]
body_bones_to_joints = aux['bones_to_joints']
body_bones = bones[:, :, :len(body_bones_to_joints), :, :]
body_joints = torch.empty(bones.shape[0], bones.shape[1], len(body_bones_to_joints) + 1, 3)
for i, (a, b) in enumerate(body_bones_to_joints):
body_joints[:, :, a, :] = body_bones[:, :, i, 0, :]
body_joints[:, :, b, :] = body_bones[:, :, i, 1, :]
leg_aux = aux['legs']
all_leg_joints = []
for i in range(len(leg_aux)):
leg_bones = bones[:, :, 8+i*3:11+i*3, :, :]
leg_joints = torch.empty(bones.shape[0], bones.shape[1], len(leg_aux[i]['leg_bones_to_joints']), 3)
for j in range(len(leg_aux[i]['leg_bones_to_joints'])-1):
leg_joint_idx_a = leg_aux[i]['leg_bones_to_joints'][j][0]
leg_joint_idx_b = leg_aux[i]['leg_bones_to_joints'][j][1]
leg_joints[:, :, leg_joint_idx_a, :] = leg_bones[:, :, j, 0, :]
leg_joints[:, :, leg_joint_idx_b, :] = leg_bones[:, :, j, 1, :]
all_leg_joints.append(leg_joints)
all_joints = [body_joints] + all_leg_joints
all_joints = torch.cat(all_joints, dim=2)
return all_joints
def get_20_bones_joints(joints, aux):
# the joints shape is [1, 1, 21, 3]
body_bones_to_joints = aux['bones_to_joints']
body_bones = []
for a,b in body_bones_to_joints:
body_bones += [torch.stack([joints[:, :, a, :], joints[:, :, b, :]], dim=2)]
body_bones = torch.stack(body_bones, dim=2) # [1, 1, 8, 2, 3]
legs_bones = []
legs_aux = aux['legs']
for i in range(len(legs_aux)):
leg_aux = legs_aux[i]
leg_bones = []
leg_bones_to_joints = leg_aux['leg_bones_to_joints']
for j in range(len(leg_bones_to_joints)-1):
leg_bones += [torch.stack([joints[:, :, 9+i*3+leg_bones_to_joints[j][0], :], joints[:, :, 9+i*3+leg_bones_to_joints[j][1], :]], dim=2)]
# the last bone is attached to the body
leg_bones += [torch.stack([
body_bones[:, :, leg_aux['body_bone_idx'], 1, :], joints[:, :, 9+i*3+leg_bones_to_joints[-1][1], :]
], dim=2)]
leg_bones = torch.stack(leg_bones, dim=2)
legs_bones.append(leg_bones)
bones = torch.cat([body_bones] + legs_bones, dim=2)
return bones
class FixedDirectionLight(torch.nn.Module):
def __init__(self, direction, amb, diff):
super(FixedDirectionLight, self).__init__()
self.light_dir = direction
self.amb = amb
self.diff = diff
self.is_hacking = not (isinstance(self.amb, float)
or isinstance(self.amb, int))
def forward(self, feat):
batch_size = feat.shape[0]
if self.is_hacking:
return torch.concat([self.light_dir, self.amb, self.diff], -1)
else:
return torch.concat([self.light_dir, torch.FloatTensor([self.amb, self.diff]).to(self.light_dir.device)], -1).expand(batch_size, -1)
def shade(self, feat, kd, normal):
light_params = self.forward(feat)
light_dir = light_params[..., :3][:, None, None, :]
int_amb = light_params[..., 3:4][:, None, None, :]
int_diff = light_params[..., 4:5][:, None, None, :]
shading = (int_amb + int_diff *
torch.clamp(util.dot(light_dir, normal), min=0.0))
shaded = shading * kd
return shaded, shading
class SmoothLoss(nn.Module):
def __init__(self, dim=0, smooth_type=None, loss_type="l2"):
super(SmoothLoss, self).__init__()
self.dim = dim
supported_smooth_types = ['mid_frame', 'dislocation', 'avg']
assert smooth_type in supported_smooth_types, f"supported smooth type: {supported_smooth_types}"
self.smooth_type = smooth_type
supported_loss_types = ['l2', 'mse', 'l1']
assert loss_type in supported_loss_types, f"supported loss type: {supported_loss_types}"
self.loss_type = loss_type
if self.loss_type in ['l2', 'mse']:
self.loss_fn = torch.nn.MSELoss(reduction='mean')
elif self.loss_type in ['l1']:
self.loss_fn = torch.nn.L1Loss()
else:
raise NotImplementedError
def mid_frame_smooth(self, inputs):
nframe = inputs.shape[self.dim]
mid_num = (nframe-1) // 2
# from IPython import embed; embed();
mid_frame = torch.index_select(inputs, self.dim, torch.tensor([mid_num], device=inputs.device))
repeat_num = self.get_repeat_num(inputs)
smooth = mid_frame.repeat(repeat_num)
loss = self.loss_fn(inputs, smooth)
# print(loss)
return loss
def dislocation_smooth(self, inputs):
# from IPython import embed; embed()
nframe = inputs.shape[self.dim]
t = torch.index_select(inputs, self.dim, torch.arange(0, nframe-1).to(inputs.device))
t_1 = torch.index_select(inputs, self.dim, torch.arange(1, nframe).to(inputs.device))
loss = self.loss_fn(t, t_1)
return loss
def avg_smooth(self, inputs):
# nframe = inputs.shape[self.dim]
# from IPython import embed; embed()
avg = inputs.mean(dim=self.dim, keepdim=True)
repeat_num = self.get_repeat_num(inputs)
smooth = avg.repeat(repeat_num)
loss = self.loss_fn(inputs, smooth)
return loss
def get_repeat_num(self, inputs):
repeat_num = [1] * inputs.dim()
repeat_num[self.dim] = inputs.shape[self.dim]
return repeat_num
def forward(self, inputs):
print(f"smooth_type: {self.smooth_type}")
if self.smooth_type is None:
return 0.
elif self.smooth_type == 'mid_frame':
return self.mid_frame_smooth(inputs)
elif self.smooth_type == 'dislocation':
return self.dislocation_smooth(inputs)
elif self.smooth_type == 'avg':
return self.avg_smooth(inputs)
else:
raise NotImplementedError()
class PriorPredictor(nn.Module):
def __init__(self, cfgs):
super().__init__()
#add nnParameters
dmtet_grid = cfgs.get('dmtet_grid', 64)
grid_scale = cfgs.get('grid_scale', 5)
prior_sdf_mode = cfgs.get('prior_sdf_mode', 'mlp')
num_layers_shape = cfgs.get('num_layers_shape', 5)
hidden_size = cfgs.get('hidden_size', 64)
embedder_freq_shape = cfgs.get('embedder_freq_shape', 8)
embed_concat_pts = cfgs.get('embed_concat_pts', True)
init_sdf = cfgs.get('init_sdf', None)
jitter_grid = cfgs.get('jitter_grid', 0.)
perturb_sdf_iter = cfgs.get('perturb_sdf_iter', 10000)
sym_prior_shape = cfgs.get('sym_prior_shape', False)
train_data_dir = cfgs.get("train_data_dir", None)
if isinstance(train_data_dir, str):
num_of_classes = 1
elif isinstance(train_data_dir, dict):
self.category_id_map = {}
num_of_classes = len(train_data_dir)
for i, (k, _) in enumerate(train_data_dir.items()):
self.category_id_map[k] = i
dim_of_classes = cfgs.get('dim_of_classes', 256) if num_of_classes > 1 else 0
condition_choice = cfgs.get('prior_condition_choice', 'concat')
self.netShape = DMTetGeometry(dmtet_grid, grid_scale, prior_sdf_mode, num_layers=num_layers_shape, hidden_size=hidden_size, embedder_freq=embedder_freq_shape, embed_concat_pts=embed_concat_pts, init_sdf=init_sdf, jitter_grid=jitter_grid, perturb_sdf_iter=perturb_sdf_iter, sym_prior_shape=sym_prior_shape,
dim_of_classes=dim_of_classes, condition_choice=condition_choice)
mlp_hidden_size = cfgs.get('hidden_size', 64)
tet_bbox = self.netShape.getAABB()
self.render_dino_mode = cfgs.get('render_dino_mode', None)
num_layers_dino = cfgs.get("num_layers_dino", 5)
dino_feature_recon_dim = cfgs.get('dino_feature_recon_dim', 64)
sym_dino = cfgs.get("sym_dino", False)
dino_min = torch.zeros(dino_feature_recon_dim) + cfgs.get('dino_min', 0.)
dino_max = torch.zeros(dino_feature_recon_dim) + cfgs.get('dino_max', 1.)
min_max = torch.stack((dino_min, dino_max), dim=0)
if self.render_dino_mode is None:
pass
elif self.render_dino_mode == 'feature_mlpnv':
#MLPTexture3D predict the dino for each single point.
self.netDINO = mlptexture.MLPTexture3D(tet_bbox, channels=dino_feature_recon_dim, internal_dims=mlp_hidden_size, hidden=num_layers_dino-1, feat_dim=0, min_max=min_max, bsdf=None, perturb_normal=False, symmetrize=sym_dino)
elif self.render_dino_mode == 'feature_mlp':
embedder_scaler = 2 * np.pi / grid_scale * 0.9 # originally (-0.5*s, 0.5*s) rescale to (-pi, pi) * 0.9
embed_concat_pts = cfgs.get('embed_concat_pts', True)
self.netDINO = networks.MLPTextureSimple(
3, # x, y, z coordinates
dino_feature_recon_dim,
num_layers_dino,
nf=mlp_hidden_size,
dropout=0,
activation="sigmoid",
min_max=min_max,
n_harmonic_functions=cfgs.get('embedder_freq_dino', 8),
omega0=embedder_scaler,
extra_dim=dim_of_classes,
embed_concat_pts=embed_concat_pts,
perturb_normal=False,
symmetrize=sym_dino
)
elif self.render_dino_mode == 'cluster':
num_layers_dino = cfgs.get("num_layers_dino", 5)
dino_cluster_dim = cfgs.get('dino_cluster_dim', 64)
self.netDINO = mlptexture.MLPTexture3D(tet_bbox, channels=dino_cluster_dim, internal_dims=mlp_hidden_size, hidden=num_layers_dino-1, feat_dim=0, min_max=None, bsdf=None, perturb_normal=False, symmetrize=sym_dino)
else:
raise NotImplementedError
self.classes_vectors = None
if num_of_classes > 1:
self.classes_vectors = torch.nn.Parameter(torch.nn.init.uniform_(torch.empty(num_of_classes, dim_of_classes), a=-0.05, b=0.05))
def forward(self, category_name=None, perturb_sdf=False, total_iter=None, is_training=True, class_embedding=None):
class_vector = None
if category_name is not None:
# print(category_name)
if class_embedding is not None:
class_vector = class_embedding[0] # [128]
return_classes_vectors = class_vector
else:
class_vector = self.classes_vectors[self.category_id_map[category_name]]
return_classes_vectors = self.classes_vectors
prior_shape = self.netShape.getMesh(perturb_sdf=perturb_sdf, total_iter=total_iter, jitter_grid=is_training, class_vector=class_vector)
# print(prior_shape.v_pos.shape)
# return prior_shape, self.netDINO, self.classes_vectors
return prior_shape, self.netDINO, return_classes_vectors
class InstancePredictor(nn.Module):
def __init__(self, cfgs, tet_bbox=None):
super().__init__()
self.cfgs = cfgs
self.grid_scale = cfgs.get('grid_scale', 5)
self.enable_encoder = cfgs.get('enable_encoder', False)
if self.enable_encoder:
encoder_latent_dim = cfgs.get('latent_dim', 256)
encoder_pretrained = cfgs.get('encoder_pretrained', False)
encoder_frozen = cfgs.get('encoder_frozen', False)
encoder_arch = cfgs.get('encoder_arch', 'simple')
in_image_size = cfgs.get('in_image_size', 256)
self.dino_feature_input = cfgs.get('dino_feature_input', False)
dino_feature_dim = cfgs.get('dino_feature_dim', 64)
if encoder_arch == 'simple':
if self.dino_feature_input:
self.netEncoder = networks.EncoderWithDINO(cin_rgb=3, cin_dino=dino_feature_dim, cout=encoder_latent_dim, in_size=in_image_size, zdim=None, nf=64, activation=None)
else:
self.netEncoder = networks.Encoder(cin=3, cout=encoder_latent_dim, in_size=in_image_size, zdim=None, nf=64, activation=None)
elif encoder_arch == 'vgg':
self.netEncoder = networks.VGGEncoder(cout=encoder_latent_dim, pretrained=encoder_pretrained)
elif encoder_arch == 'resnet':
self.netEncoder = networks.ResnetEncoder(cout=encoder_latent_dim, pretrained=encoder_pretrained)
elif encoder_arch == 'vit':
which_vit = cfgs.get('which_vit', 'dino_vits8')
vit_final_layer_type = cfgs.get('vit_final_layer_type', 'conv')
root_dir = cfgs.get('root_dir', '/root')
self.netEncoder = networks.ViTEncoder(cout=encoder_latent_dim, which_vit=which_vit, pretrained=encoder_pretrained, frozen=encoder_frozen, in_size=in_image_size, final_layer_type=vit_final_layer_type, root=root_dir)
else:
raise NotImplementedError
else:
encoder_latent_dim = 0
mlp_hidden_size = cfgs.get('hidden_size', 64)
bsdf = cfgs.get("bsdf", 'diffuse')
num_layers_tex = cfgs.get("num_layers_tex", 5)
feat_dim = cfgs.get("latent_dim", 64) if self.enable_encoder else 0
perturb_normal = cfgs.get("perturb_normal", False)
sym_texture = cfgs.get("sym_texture", False)
kd_min = torch.FloatTensor(cfgs.get('kd_min', [0., 0., 0., 0.]))
kd_max = torch.FloatTensor(cfgs.get('kd_max', [1., 1., 1., 1.]))
ks_min = torch.FloatTensor(cfgs.get('ks_min', [0., 0., 0.]))
ks_max = torch.FloatTensor(cfgs.get('ks_max', [0., 0., 0.]))
nrm_min = torch.FloatTensor(cfgs.get('nrm_min', [-1., -1., 0.]))
nrm_max = torch.FloatTensor(cfgs.get('nrm_max', [1., 1., 1.]))
mlp_min = torch.cat((kd_min[0:3], ks_min, nrm_min), dim=0)
mlp_max = torch.cat((kd_max[0:3], ks_max, nrm_max), dim=0)
min_max = torch.stack((mlp_min, mlp_max), dim=0)
out_chn = 9
# TODO: if the tet verts are deforming, we need to recompute tet_bbox
texture_mode = cfgs.get("texture_mode", 'mlp')
if texture_mode == 'mlpnv':
self.netTexture = mlptexture.MLPTexture3D(tet_bbox, channels=out_chn, internal_dims=mlp_hidden_size, hidden=num_layers_tex-1, feat_dim=feat_dim, min_max=min_max, bsdf=bsdf, perturb_normal=perturb_normal, symmetrize=sym_texture)
elif texture_mode == 'mlp':
embedder_scaler = 2 * np.pi / self.grid_scale * 0.9 # originally (-0.5*s, 0.5*s) rescale to (-pi, pi) * 0.9
embed_concat_pts = cfgs.get('embed_concat_pts', True)
self.texture_way = cfgs.get('texture_way', None)
if self.texture_way is None:
texture_act = cfgs.get('texture_act', 'relu')
texture_bias = cfgs.get('texture_bias', False)
self.netTexture = networks.MLPTextureSimple(
3, # x, y, z coordinates
out_chn,
num_layers_tex,
nf=mlp_hidden_size,
dropout=0,
activation="sigmoid",
min_max=min_max,
n_harmonic_functions=cfgs.get('embedder_freq_tex', 10),
omega0=embedder_scaler,
extra_dim=feat_dim,
embed_concat_pts=embed_concat_pts,
perturb_normal=perturb_normal,
symmetrize=sym_texture,
texture_act=texture_act,
linear_bias=texture_bias
)
else:
self.netTexture = networks.MLPTextureTriplane(
3, # x, y, z coordinates
out_chn,
num_layers_tex,
nf=mlp_hidden_size,
dropout=0,
activation="sigmoid",
min_max=min_max,
n_harmonic_functions=cfgs.get('embedder_freq_tex', 10),
omega0=embedder_scaler,
extra_dim=feat_dim,
embed_concat_pts=embed_concat_pts,
perturb_normal=perturb_normal,
symmetrize=sym_texture,
texture_act='relu',
linear_bias=False,
cam_pos_z_offset=cfgs.get('cam_pos_z_offset', 10.),
grid_scale=self.grid_scale
)
# if 'lift' in self.texture_way:
# # GET3D use global feature to get a tri-plane
# self.netTexture = TriPlaneTex(
# w_dim=512,
# img_channels=out_chn,
# tri_plane_resolution=256,
# device=cfgs.get('device', 'cpu'),
# mlp_latent_channel=32,
# n_implicit_layer=1,
# feat_dim=256,
# n_mapping_layer=8,
# sym_texture=sym_texture,
# grid_scale=self.grid_scale,
# min_max=min_max,
# perturb_normal=perturb_normal
# )
# # # project the local feature map into a grid
# # self.netTexture = networks.LiftTexture(
# # 3, # x, y, z coordinates
# # out_chn,
# # num_layers_tex,
# # nf=mlp_hidden_size,
# # dropout=0,
# # activation="sigmoid",
# # min_max=min_max,
# # n_harmonic_functions=cfgs.get('embedder_freq_tex', 10),
# # omega0=embedder_scaler,
# # extra_dim=feat_dim,
# # embed_concat_pts=embed_concat_pts,
# # perturb_normal=perturb_normal,
# # symmetrize=sym_texture,
# # texture_way=self.texture_way,
# # cam_pos_z_offset=cfgs.get('cam_pos_z_offset', 10.),
# # grid_scale=self.grid_scale,
# # local_feat_dim=cfgs.get("lift_local_feat_dim", 128),
# # grid_size=cfgs.get("lift_grid_size", 32),
# # optim_latent=cfgs.get("lift_optim_latent", False)
# # )
# else:
# # a texture mlp with local feature map from patch_out
# self.netTexture = networks.MLPTextureLocal(
# 3, # x, y, z coordinates
# out_chn,
# num_layers_tex,
# nf=mlp_hidden_size,
# dropout=0,
# activation="sigmoid",
# min_max=min_max,
# n_harmonic_functions=cfgs.get('embedder_freq_tex', 10),
# omega0=embedder_scaler,
# extra_dim=feat_dim,
# embed_concat_pts=embed_concat_pts,
# perturb_normal=perturb_normal,
# symmetrize=sym_texture,
# texture_way=self.texture_way,
# larger_tex_dim=cfgs.get('larger_tex_dim', False),
# cam_pos_z_offset=cfgs.get('cam_pos_z_offset', 10.),
# grid_scale=self.grid_scale
# )
self.rot_rep = cfgs.get('rot_rep', 'euler_angle')
self.enable_pose = cfgs.get('enable_pose', False)
if self.enable_pose:
cam_pos_z_offset = cfgs.get('cam_pos_z_offset', 10.)
fov = cfgs.get('crop_fov_approx', 25)
half_range = np.tan(fov /2 /180 * np.pi) * cam_pos_z_offset # 2.22
self.max_trans_xy_range = half_range * cfgs.get('max_trans_xy_range_ratio', 1.)
self.max_trans_z_range = half_range * cfgs.get('max_trans_z_range_ratio', 1.)
self.lookat_init = cfgs.get('lookat_init', None)
self.lookat_zeroy = cfgs.get('lookat_zeroy', False)
self.rot_temp_scalar = cfgs.get('rot_temp_scalar', 1.)
self.naive_probs_iter = cfgs.get('naive_probs_iter', 2000)
self.best_pose_start_iter = cfgs.get('best_pose_start_iter', 6000)
if self.rot_rep == 'euler_angle':
pose_cout = 6
elif self.rot_rep == 'quaternion':
pose_cout = 7
elif self.rot_rep == 'lookat':
pose_cout = 6
elif self.rot_rep == 'quadlookat':
self.num_pose_hypos = 4
pose_cout = (3 + 1) * self.num_pose_hypos + 3 # 4 forward vectors for 4 quadrants, 4 quadrant classification logits, 3 for translation
self.orthant_signs = torch.FloatTensor([[1,1,1], [-1,1,1], [-1,1,-1], [1,1,-1]])
elif self.rot_rep == 'octlookat':
self.num_pose_hypos = 8
pose_cout = (3 + 1) * self.num_pose_hypos + 3 # 4 forward vectors for 8 octants, 8 octant classification logits, 3 for translation
self.orthant_signs = torch.stack(torch.meshgrid([torch.arange(1, -2, -2)] *3), -1).view(-1, 3) # 8x3
else:
raise NotImplementedError
self.pose_arch = cfgs.get('pose_arch', 'mlp')
if self.pose_arch == 'mlp':
num_layers_pose = cfgs.get('num_layers_pose', 5)
self.netPose = networks.MLP(
encoder_latent_dim,
pose_cout,
num_layers_pose,
nf=mlp_hidden_size,
dropout=0,
activation=None
)
elif self.pose_arch == 'encoder':
if self.dino_feature_input:
dino_feature_dim = cfgs.get('dino_feature_dim', 64)
self.netPose = networks.EncoderWithDINO(cin_rgb=3, cin_dino=dino_feature_dim, cout=pose_cout, in_size=in_image_size, zdim=None, nf=64, activation=None)
else:
self.netPose = networks.Encoder(cin=3, cout=pose_cout, in_size=in_image_size, zdim=None, nf=64, activation=None)
elif self.pose_arch in ['encoder_dino_patch_out', 'encoder_dino_patch_key']:
if which_vit == 'dino_vits8':
dino_feat_dim = 384
elif which_vit == 'dinov2_vits14':
dino_feat_dim = 384
elif which_vit == 'dino_vitb8':
dino_feat_dim = 768
self.netPose = networks.Encoder32(cin=dino_feat_dim, cout=pose_cout, nf=256, activation=None)
elif self.pose_arch == 'vit':
encoder_pretrained = cfgs.get('encoder_pretrained', False)
encoder_frozen = cfgs.get('encoder_frozen', False)
which_vit = cfgs.get('which_vit', 'dino_vits8')
vit_final_layer_type = cfgs.get('vit_final_layer_type', 'conv')
root_dir = cfgs.get('root_dir', '/root')
self.netPose = networks.ViTEncoder(cout=encoder_latent_dim, which_vit=which_vit, pretrained=encoder_pretrained, frozen=encoder_frozen, in_size=in_image_size, final_layer_type=vit_final_layer_type, root=root_dir)
else:
raise NotImplementedError
self.enable_deform = cfgs.get('enable_deform', False)
if self.enable_deform:
embedder_scaler = 2 * np.pi / self.grid_scale * 0.9 # originally (-0.5*s, 0.5*s) rescale to (-pi, pi) * 0.9
embed_concat_pts = cfgs.get('embed_concat_pts', True)
num_layers_deform = cfgs.get('num_layers_deform', 5)
self.deform_epochs = np.arange(*cfgs.get('deform_epochs', [0, 0]))
sym_deform = cfgs.get("sym_deform", False)
self.netDeform = networks.MLPWithPositionalEncoding(
3, # x, y, z coordinates
3, # dx, dy, dz deformation
num_layers_deform,
nf=mlp_hidden_size,
dropout=0,
activation=None,
n_harmonic_functions=cfgs.get('embedder_freq_deform', 10),
omega0=embedder_scaler,
extra_dim=encoder_latent_dim,
embed_concat_pts=embed_concat_pts,
symmetrize=sym_deform
)
# self.avg_deform = cfgs.get('avg_deform', False)
# print(f'********avg_deform: {self.avg_deform}********')
self.enable_articulation = cfgs.get('enable_articulation', False)
if self.enable_articulation:
self.num_body_bones = cfgs.get('num_body_bones', 4)
self.articulation_multiplier = cfgs.get('articulation_multiplier', 1)
self.static_root_bones = cfgs.get('static_root_bones', False)
self.skinning_temperature = cfgs.get('skinning_temperature', 1)
self.articulation_epochs = np.arange(*cfgs.get('articulation_epochs', [0, 0]))
self.num_legs = cfgs.get('num_legs', 0)
self.num_leg_bones = cfgs.get('num_leg_bones', 0)
self.body_bones_type = cfgs.get('body_bones_type', 'z_minmax')
self.perturb_articulation_epochs = np.arange(*cfgs.get('perturb_articulation_epochs', [0, 0]))
self.num_bones = self.num_body_bones + self.num_legs * self.num_leg_bones
self.constrain_legs = cfgs.get('constrain_legs', False)
self.attach_legs_to_body_epochs = np.arange(*cfgs.get('attach_legs_to_body_epochs', [0, 0]))
self.max_arti_angle = cfgs.get('max_arti_angle', 60)
num_layers_arti = cfgs.get('num_layers_arti', 5)
which_vit = cfgs.get('which_vit', 'dino_vits8')
if which_vit == 'dino_vits8':
dino_feat_dim = 384
elif which_vit == 'dino_vitb8':
dino_feat_dim = 768
self.articulation_arch = cfgs.get('articulation_arch', 'mlp')
self.articulation_feature_mode = cfgs.get('articulation_feature_mode', 'sample')
embedder_freq_arti = cfgs.get('embedder_freq_arti', 8)
if self.articulation_feature_mode == 'global':
feat_dim = encoder_latent_dim
elif self.articulation_feature_mode == 'sample':
feat_dim = dino_feat_dim
elif self.articulation_feature_mode == 'sample+global':
feat_dim = encoder_latent_dim + dino_feat_dim
if self.articulation_feature_mode == 'attention':
arti_feat_attn_zdim = cfgs.get('arti_feat_attn_zdim', 128)
pos_dim = 1 + 2 + 3*2
self.netFeatureAttn = networks.FeatureAttention(which_vit, pos_dim, embedder_freq_arti, arti_feat_attn_zdim, img_size=in_image_size)
embedder_scaler = np.pi * 0.9 # originally (-1, 1) rescale to (-pi, pi) * 0.9
enable_articulation_idadd = cfgs.get('enable_articulation_idadd', False)
self.netArticulation = networks.ArticulationNetwork(self.articulation_arch, feat_dim, 1+2+3*2, num_layers_arti, mlp_hidden_size, n_harmonic_functions=embedder_freq_arti, omega0=embedder_scaler,
enable_articulation_idadd=enable_articulation_idadd)
self.kinematic_tree_epoch = -1
self.enable_lighting = cfgs.get('enable_lighting', False)
if self.enable_lighting:
num_layers_light = cfgs.get('num_layers_light', 5)
amb_diff_min = torch.FloatTensor(cfgs.get('amb_diff_min', [0., 0.]))
amb_diff_max = torch.FloatTensor(cfgs.get('amb_diff_max', [1., 1.]))
intensity_min_max = torch.stack((amb_diff_min, amb_diff_max), dim=0)
self.netLight = light.DirectionalLight(encoder_latent_dim, num_layers_light, mlp_hidden_size, intensity_min_max=intensity_min_max)
self.cam_pos_z_offset = cfgs.get('cam_pos_z_offset', 10.)
self.crop_fov_approx = cfgs.get("crop_fov_approx", 25)
self.temp_clip_low = cfgs.get('temp_clip_low', 1.)
self.temp_clip_high = cfgs.get('temp_clip_high', 100.)
# if the articulation and deformation is set as iterations, then use iteration to decide, not epoch
self.iter_articulation_start = cfgs.get('iter_articulation_start', None)
self.iter_deformation_start = cfgs.get('iter_deformation_start', None)
self.iter_nozeroy_start = cfgs.get('iter_nozeroy_start', None)
self.iter_attach_leg_to_body_start = cfgs.get('iter_attach_leg_to_body_start', None)
def forward_encoder(self, images, dino_features=None):
images_in = images.view(-1, *images.shape[2:]) * 2 - 1 # rescale to (-1, 1)
patch_out = patch_key = None
if self.dino_feature_input and self.cfgs.get('encoder_arch', 'simple') != 'vit':
dino_features_in = dino_features.view(-1, *dino_features.shape[2:]) * 2 - 1 # rescale to (-1, 1)
feat_out = self.netEncoder(images_in, dino_features_in) # Shape: (B, latent_dim)
elif self.cfgs.get('encoder_arch', 'simple') == 'vit':
feat_out, feat_key, patch_out, patch_key = self.netEncoder(images_in, return_patches=True)
else:
feat_out = self.netEncoder(images_in) # Shape: (B, latent_dim)
return feat_out, feat_key, patch_out, patch_key
def forward_pose(self, images, feat, patch_out, patch_key, dino_features):
if self.pose_arch == 'mlp':
pose = self.netPose(feat)
elif self.pose_arch == 'encoder':
images_in = images.view(-1, *images.shape[2:]) * 2 - 1 # rescale to (-1, 1)
if self.dino_feature_input:
dino_features_in = dino_features.view(-1, *dino_features.shape[2:]) * 2 - 1 # rescale to (-1, 1)
pose = self.netPose(images_in, dino_features_in) # Shape: (B, latent_dim)
else:
pose = self.netPose(images_in) # Shape: (B, latent_dim)
elif self.pose_arch == 'vit':
images_in = images.view(-1, *images.shape[2:]) * 2 - 1 # rescale to (-1, 1)
pose = self.netPose(images_in)
elif self.pose_arch == 'encoder_dino_patch_out':
pose = self.netPose(patch_out) # Shape: (B, latent_dim)
elif self.pose_arch == 'encoder_dino_patch_key':
pose = self.netPose(patch_key) # Shape: (B, latent_dim)
else:
raise NotImplementedError
trans_pred = pose[...,-3:].tanh() * torch.FloatTensor([self.max_trans_xy_range, self.max_trans_xy_range, self.max_trans_z_range]).to(pose.device)
if self.rot_rep == 'euler_angle':
multiplier = 1.
if self.gradually_expand_yaw:
# multiplier += (min(iteration, 20000) // 500) * 0.25
multiplier *= 1.2 ** (min(iteration, 20000) // 500) # 1.125^40 = 111.200
rot_pred = torch.cat([pose[...,:1], pose[...,1:2]*multiplier, pose[...,2:3]], -1).tanh()
rot_pred = rot_pred * torch.FloatTensor([self.max_rot_x_range, self.max_rot_y_range, self.max_rot_z_range]).to(pose.device) /180 * np.pi
elif self.rot_rep == 'quaternion':
quat_init = torch.FloatTensor([0.01,0,0,0]).to(pose.device)
rot_pred = pose[...,:4] + quat_init
rot_pred = nn.functional.normalize(rot_pred, p=2, dim=-1)
# rot_pred = torch.cat([rot_pred[...,:1].abs(), rot_pred[...,1:]], -1) # make real part non-negative
rot_pred = rot_pred * rot_pred[...,:1].sign() # make real part non-negative
elif self.rot_rep == 'lookat':
vec_forward_raw = pose[...,:3]
if self.lookat_init is not None:
vec_forward_raw = vec_forward_raw + torch.FloatTensor(self.lookat_init).to(pose.device)
if self.lookat_zeroy:
vec_forward_raw = vec_forward_raw * torch.FloatTensor([1,0,1]).to(pose.device)
vec_forward_raw = nn.functional.normalize(vec_forward_raw, p=2, dim=-1) # x right, y up, z forward
rot_pred = vec_forward_raw
elif self.rot_rep in ['quadlookat', 'octlookat']:
rots_pred = pose[..., :self.num_pose_hypos*4].view(-1, self.num_pose_hypos, 4) # (B, T, K, 4)
rots_logits = rots_pred[..., :1]
vec_forward_raw = rots_pred[..., 1:4]
xs, ys, zs = vec_forward_raw.unbind(-1)
margin = 0.
xs = nn.functional.softplus(xs, beta=np.log(2)/(0.5+margin)) - margin # initialize to 0.5
if self.rot_rep == 'octlookat':
ys = nn.functional.softplus(ys, beta=np.log(2)/(0.5+margin)) - margin # initialize to 0.5
if self.lookat_zeroy:
ys = ys * 0
zs = nn.functional.softplus(zs, beta=2*np.log(2)) # initialize to 0.5
vec_forward_raw = torch.stack([xs, ys, zs], -1)
vec_forward_raw = vec_forward_raw * self.orthant_signs.to(pose.device)
vec_forward_raw = nn.functional.normalize(vec_forward_raw, p=2, dim=-1) # x right, y up, z forward
rot_pred = torch.cat([rots_logits, vec_forward_raw], -1).view(-1, self.num_pose_hypos*4)
else:
raise NotImplementedError
pose = torch.cat([rot_pred, trans_pred], -1)
return pose
def forward_deformation(self, shape, feat=None, batch_size=None, num_frames=None):
original_verts = shape.v_pos
num_verts = original_verts.shape[1]
if feat is not None:
deform_feat = feat[:, None, :].repeat(1, num_verts, 1) # Shape: (B, num_verts, latent_dim)
original_verts = original_verts.repeat(len(feat),1,1)
deformation = self.netDeform(original_verts, deform_feat) * 0.1 # Shape: (B, num_verts, 3)
# if self.avg_deform:
# assert batch_size is not None and num_frames is not None
# assert deformation.shape[0] == batch_size * num_frames
# deformation = deformation.view(batch_size, num_frames, *deformation.shape[1:])
# deformation = deformation.mean(dim=1, keepdim=True)
# deformation = deformation.repeat(1,num_frames,*[1]*(deformation.dim()-2))
# deformation = deformation.view(batch_size*num_frames, *deformation.shape[2:])
shape = shape.deform(deformation)
return shape, deformation
def forward_articulation(self, shape, feat, patch_feat, mvp, w2c, batch_size, num_frames, epoch, category, total_iter=None):
"""
Forward propagation of articulation. For each bone, the network takes: 1) the 3D location of the bone; 2) the feature of the patch which
the bone is projected to; and 3) an encoding of the bone's index to predict the bone's rotation (represented by an Euler angle).
Args:
shape: a Mesh object, whose v_pos has batch size BxF or 1.
feat: the feature of the patches. Shape: (BxF, feat_dim, num_patches_per_axis, num_patches_per_axis)
mvp: the model-view-projection matrix. Shape: (BxF, 4, 4)
Returns:
shape: a Mesh object, whose v_pos has batch size BxF (collapsed).
articulation_angles: the predicted bone rotations. Shape: (B, F, num_bones, 3)
aux: a dictionary containing auxiliary information.
"""
verts = shape.v_pos
if len(verts) == 1:
verts = verts[None]
else:
verts = verts.view(batch_size, num_frames, *verts.shape[1:])
if self.kinematic_tree_epoch != epoch:
# if (epoch == self.articulation_epochs[0]) and (self.kinematic_tree_epoch != epoch):
# if (epoch in [self.articulation_epochs[0], self.articulation_epochs[0]+2, self.articulation_epochs[0]+4]) and (self.kinematic_tree_epoch != epoch):
if total_iter is not None and self.iter_attach_leg_to_body_start is not None:
attach_legs_to_body = total_iter > self.iter_attach_leg_to_body_start
else:
attach_legs_to_body = epoch in self.attach_legs_to_body_epochs
# bone_y_thresh = None if category is None or not category == "giraffe" else 0.1
bone_y_thresh = self.cfgs.get('bone_y_thresh', None)
# trivial set here
body_bone_idx_preset_cfg = self.cfgs.get('body_bone_idx_preset', [0, 0, 0, 0])
if isinstance(body_bone_idx_preset_cfg, list):
body_bone_idx_preset = body_bone_idx_preset_cfg
elif isinstance(body_bone_idx_preset_cfg, dict):
iter_point = list(body_bone_idx_preset_cfg.keys())[1]
if total_iter <= iter_point:
body_bone_idx_preset = body_bone_idx_preset_cfg[0] # the first is start from 0 iter
else:
body_bone_idx_preset = body_bone_idx_preset_cfg[iter_point]
else:
raise NotImplementedError
bones, self.kinematic_tree, self.bone_aux = estimate_bones(verts.detach(), self.num_body_bones, n_legs=self.num_legs, n_leg_bones=self.num_leg_bones, body_bones_type=self.body_bones_type, compute_kinematic_chain=True, attach_legs_to_body=attach_legs_to_body, bone_y_threshold=bone_y_thresh, body_bone_idx_preset=body_bone_idx_preset)
# self.kinematic_tree_epoch = epoch
else:
bones = estimate_bones(verts.detach(), self.num_body_bones, n_legs=self.num_legs, n_leg_bones=self.num_leg_bones, body_bones_type=self.body_bones_type, compute_kinematic_chain=False, aux=self.bone_aux)
bones_pos = bones # Shape: (B, F, K, 2, 3)
if batch_size > bones_pos.shape[0] or num_frames > bones_pos.shape[1]:
assert bones_pos.shape[0] == 1 and bones_pos.shape[1] == 1, "If there is a mismatch, then there must be only one canonical mesh."
bones_pos = bones_pos.repeat(batch_size, num_frames, 1, 1, 1)
num_bones = bones_pos.shape[2]
bones_pos = bones_pos.view(batch_size*num_frames, num_bones, 2, 3) # NxKx2x3
bones_mid_pos = bones_pos.mean(2) # NxKx3
bones_idx = torch.arange(num_bones).to(bones_pos.device)
bones_mid_pos_world4 = torch.cat([bones_mid_pos, torch.ones_like(bones_mid_pos[..., :1])], -1) # NxKx4
bones_mid_pos_clip4 = bones_mid_pos_world4 @ mvp.transpose(-1, -2)
bones_mid_pos_uv = bones_mid_pos_clip4[..., :2] / bones_mid_pos_clip4[..., 3:4]
bones_mid_pos_uv = bones_mid_pos_uv.detach()
bones_pos_world4 = torch.cat([bones_pos, torch.ones_like(bones_pos[..., :1])], -1) # NxKx2x4
bones_pos_cam4 = bones_pos_world4 @ w2c[:,None].transpose(-1, -2)
bones_pos_cam3 = bones_pos_cam4[..., :3] / bones_pos_cam4[..., 3:4]
bones_pos_cam3 = bones_pos_cam3 + torch.FloatTensor([0, 0, self.cam_pos_z_offset]).to(bones_pos_cam3.device).view(1, 1, 1, 3)
bones_pos_in = bones_pos_cam3.view(batch_size*num_frames, num_bones, 2*3) / self.grid_scale * 2 # (-1, 1), NxKx(2*3)
bones_idx_in = ((bones_idx[None, :, None] + 0.5) / num_bones * 2 - 1).repeat(batch_size * num_frames, 1, 1) # (-1, 1)
bones_pos_in = torch.cat([bones_mid_pos_uv, bones_pos_in, bones_idx_in], -1).detach()
if self.articulation_feature_mode == 'global':
bones_patch_features = feat[:, None].repeat(1, num_bones, 1) # (BxF, K, feat_dim)
elif self.articulation_feature_mode == 'sample':
bones_patch_features = F.grid_sample(patch_feat, bones_mid_pos_uv.view(batch_size * num_frames, 1, -1, 2), mode='bilinear').squeeze(dim=-2).permute(0, 2, 1) # (BxF, K, feat_dim)
elif self.articulation_feature_mode == 'sample+global':
bones_patch_features = F.grid_sample(patch_feat, bones_mid_pos_uv.view(batch_size * num_frames, 1, -1, 2), mode='bilinear').squeeze(dim=-2).permute(0, 2, 1) # (BxF, K, feat_dim)
bones_patch_features = torch.cat([feat[:, None].repeat(1, num_bones, 1), bones_patch_features], -1)
elif self.articulation_feature_mode == 'attention':
bones_patch_features = self.netFeatureAttn(bones_pos_in, patch_feat)
else:
raise NotImplementedError
articulation_angles = self.netArticulation(bones_patch_features, bones_pos_in).view(batch_size, num_frames, num_bones, 3) * self.articulation_multiplier
if self.static_root_bones:
root_bones = [self.num_body_bones // 2 - 1, self.num_body_bones - 1]
tmp_mask = torch.ones_like(articulation_angles)
tmp_mask[:, :, root_bones] = 0
articulation_angles = articulation_angles * tmp_mask
articulation_angles = articulation_angles.tanh()
if self.cfgs.get('iter_leg_rotation_start', -1) > 0:
if total_iter <= self.cfgs.get('iter_leg_rotation_start', -1):
self.constrain_legs = True
else:
self.constrain_legs = False
if self.constrain_legs:
leg_bones_posx = [self.num_body_bones + i for i in range(self.num_leg_bones * self.num_legs // 2)]
leg_bones_negx = [self.num_body_bones + self.num_leg_bones * self.num_legs // 2 + i for i in range(self.num_leg_bones * self.num_legs // 2)]
tmp_mask = torch.zeros_like(articulation_angles)
tmp_mask[:, :, leg_bones_posx + leg_bones_negx, 2] = 1
articulation_angles = tmp_mask * (articulation_angles * 0.3) + (1 - tmp_mask) * articulation_angles # no twist
tmp_mask = torch.zeros_like(articulation_angles)
tmp_mask[:, :, leg_bones_posx + leg_bones_negx, 1] = 1
articulation_angles = tmp_mask * (articulation_angles * 0.3) + (1 - tmp_mask) * articulation_angles # (-0.4, 0.4), limit side bending
# new regularizations, for bottom 2 bones of each leg, they can only rotate around x-axis,
# and for the toppest bone of legs, restrict its angles in a smaller range
if (self.cfgs.get('iter_leg_rotation_start', -1) > 0) and (total_iter > self.cfgs.get('iter_leg_rotation_start', -1)):
if self.cfgs.get('forbid_leg_rotate', False):
if self.cfgs.get('small_leg_angle', False):
# regularize the rotation angle of first leg bones
leg_bones_top = [8, 11, 14, 17]
# leg_bones_top = [10, 13, 16, 19]
tmp_mask = torch.zeros_like(articulation_angles)
tmp_mask[:, :, leg_bones_top, 1] = 1
tmp_mask[:, :, leg_bones_top, 2] = 1
articulation_angles = tmp_mask * (articulation_angles * 0.05) + (1 - tmp_mask) * articulation_angles
leg_bones_bottom = [9, 10, 12, 13, 15, 16, 18, 19]
# leg_bones_bottom = [8, 9, 11, 12, 14, 15, 17, 18]
tmp_mask = torch.ones_like(articulation_angles)
tmp_mask[:, :, leg_bones_bottom, 1] = 0
tmp_mask[:, :, leg_bones_bottom, 2] = 0
# tmp_mask[:, :, leg_bones_bottom, 0] = 0.3
articulation_angles = tmp_mask * articulation_angles
if epoch in self.perturb_articulation_epochs:
articulation_angles = articulation_angles + torch.randn_like(articulation_angles) * 0.1
articulation_angles = articulation_angles * self.max_arti_angle / 180 * np.pi
# check if regularize the leg-connecting body bones z-rotation first
# then check if regularize all the body bones z-rotation
# regularize z-rotation using 0.1 in pi-space
body_rotate_mult = self.cfgs.get('reg_body_rotate_mult', 0.1)
body_rotate_mult = body_rotate_mult * 180 * 1.0 / (self.max_arti_angle * np.pi) # the max angle = mult*original_max_angle
body_rotate_reg_mode = self.cfgs.get('body_rotate_reg_mode', 'nothing')
if body_rotate_reg_mode == 'leg-connect':
body_bones_mask = [2, 3, 4, 5]
tmp_body_mask = torch.zeros_like(articulation_angles)
tmp_body_mask[:, :, body_bones_mask, 2] = 1
articulation_angles = tmp_body_mask * (articulation_angles * body_rotate_mult) + (1 - tmp_body_mask) * articulation_angles
elif body_rotate_reg_mode == 'all-bones':
body_bones_mask = [0, 1, 2, 3, 4, 5, 6, 7]
tmp_body_mask = torch.zeros_like(articulation_angles)
tmp_body_mask[:, :, body_bones_mask, 2] = 1
articulation_angles = tmp_body_mask * (articulation_angles * body_rotate_mult) + (1 - tmp_body_mask) * articulation_angles
elif body_rotate_reg_mode == 'nothing':
articulation_angles = articulation_angles * 1.
else:
raise NotImplementedError
verts_articulated, aux = skinning(verts, bones, self.kinematic_tree, articulation_angles,
output_posed_bones=True, temperature=self.skinning_temperature)
verts_articulated = verts_articulated.view(batch_size*num_frames, *verts_articulated.shape[2:])
v_tex = shape.v_tex
if len(v_tex) != len(verts_articulated):
v_tex = v_tex.repeat(len(verts_articulated), 1, 1)
shape = mesh.make_mesh(
verts_articulated,
shape.t_pos_idx,
v_tex,
shape.t_tex_idx,
shape.material)
return shape, articulation_angles, aux
def get_camera_extrinsics_from_pose(self, pose, znear=0.1, zfar=1000., crop_fov_approx=None, offset_extra=None):
if crop_fov_approx is None:
crop_fov_approx = self.crop_fov_approx
N = len(pose)
if offset_extra is not None:
cam_pos_offset = torch.FloatTensor([0, 0, -self.cam_pos_z_offset - offset_extra]).to(pose.device)
else:
cam_pos_offset = torch.FloatTensor([0, 0, -self.cam_pos_z_offset]).to(pose.device)
pose_R = pose[:, :9].view(N, 3, 3).transpose(2, 1)
pose_T = pose[:, -3:] + cam_pos_offset[None, None, :]
pose_T = pose_T.view(N, 3, 1)
pose_RT = torch.cat([pose_R, pose_T], axis=2) # Nx3x4
w2c = torch.cat([pose_RT, torch.FloatTensor([0, 0, 0, 1]).repeat(N, 1, 1).to(pose.device)], axis=1) # Nx4x4
# We assume the images are perfect square.
if isinstance(crop_fov_approx, float) or isinstance(crop_fov_approx, int):
proj = util.perspective(crop_fov_approx / 180 * np.pi, 1, znear, zfar)[None].to(pose.device)
elif isinstance(crop_fov_approx, torch.Tensor):
proj = util.batched_perspective(crop_fov_approx / 180 * np.pi, 1, znear, zfar).to(pose.device)
else:
raise ValueError('crop_fov_approx must be float or torch.Tensor')
mvp = torch.matmul(proj, w2c)
campos = -torch.matmul(pose_R.transpose(2, 1), pose_T).view(N, 3)
return mvp, w2c, campos
def forward(self, category=None, images=None, prior_shape=None, epoch=None, dino_features=None, dino_clusters=None, total_iter=None, is_training=True):
batch_size, num_frames = images.shape[:2]
if self.enable_encoder:
feat_out, feat_key, patch_out, patch_key = self.forward_encoder(images, dino_features)
else:
feat_out = feat_key = patch_out = patch_key = None
shape = prior_shape
texture = self.netTexture
multi_hypothesis_aux = {}
if self.iter_nozeroy_start is not None and total_iter >= self.iter_nozeroy_start:
self.lookat_zeroy = False
if self.enable_pose:
poses_raw = self.forward_pose(images, feat_out, patch_out, patch_key, dino_features)
pose_raw, pose, rot_idx, rot_prob, rot_logit, rots_probs, rand_pose_flag = sample_pose_hypothesis_from_quad_prediction(poses_raw, total_iter, batch_size, num_frames, rot_temp_scalar=self.rot_temp_scalar, num_hypos=self.num_pose_hypos, naive_probs_iter=self.naive_probs_iter, best_pose_start_iter=self.best_pose_start_iter, random_sample=is_training, temp_clip_low=self.temp_clip_low, temp_clip_high=self.temp_clip_high)
multi_hypothesis_aux['rot_idx'] = rot_idx
multi_hypothesis_aux['rot_prob'] = rot_prob
multi_hypothesis_aux['rot_logit'] = rot_logit
multi_hypothesis_aux['rots_probs'] = rots_probs
multi_hypothesis_aux['rand_pose_flag'] = rand_pose_flag
else:
raise NotImplementedError
mvp, w2c, campos = self.get_camera_extrinsics_from_pose(pose)
deformation = None
if self.iter_deformation_start is not None:
if self.enable_deform and total_iter >= self.iter_deformation_start:
shape, deformation = self.forward_deformation(shape, feat_key, batch_size, num_frames)
else:
if self.enable_deform and epoch in self.deform_epochs:
shape, deformation = self.forward_deformation(shape, feat_key, batch_size, num_frames)
arti_params, articulation_aux = None, {}
if self.iter_articulation_start is not None:
if self.enable_articulation and total_iter >= self.iter_articulation_start:
shape, arti_params, articulation_aux = self.forward_articulation(shape, feat_key, patch_key, mvp, w2c, batch_size, num_frames, epoch, category, total_iter=total_iter)
else:
if self.enable_articulation and epoch in self.articulation_epochs:
shape, arti_params, articulation_aux = self.forward_articulation(shape, feat_key, patch_key, mvp, w2c, batch_size, num_frames, epoch, category, total_iter=None)
if self.enable_lighting:
light = self.netLight
else:
light = None
aux = articulation_aux
aux.update(multi_hypothesis_aux)
# if using texture_way to control a local texture, output patch_out
if self.texture_way is None:
return shape, pose_raw, pose, mvp, w2c, campos, texture, feat_out, patch_key, deformation, arti_params, light, aux
else:
return shape, pose_raw, pose, mvp, w2c, campos, texture, feat_out, patch_key, deformation, arti_params, light, aux, patch_out
class Unsup3DDDP:
def __init__(self, cfgs):
self.cfgs = cfgs
self.device = cfgs.get('device', 'cpu')
self.in_image_size = cfgs.get('in_image_size', 128)
self.out_image_size = cfgs.get('out_image_size', 128)
self.num_epochs = cfgs.get('num_epochs', 10)
self.lr = cfgs.get('lr', 1e-4)
self.use_scheduler = cfgs.get('use_scheduler', False)
if self.use_scheduler:
scheduler_milestone = cfgs.get('scheduler_milestone', [1,2,3,4,5])
scheduler_gamma = cfgs.get('scheduler_gamma', 0.5)
self.make_scheduler = lambda optim: torch.optim.lr_scheduler.MultiStepLR(optim, milestones=scheduler_milestone, gamma=scheduler_gamma)
self.cam_pos_z_offset = cfgs.get('cam_pos_z_offset', 10.)
self.full_size_h = cfgs.get('full_size_h', 1080)
self.full_size_w = cfgs.get('full_size_w', 1920)
# self.fov_w = cfgs.get('fov_w', 60)
# self.fov_h = np.arctan(np.tan(self.fov_w /2 /180*np.pi) / self.full_size_w * self.full_size_h) *2 /np.pi*180 # 36
self.crop_fov_approx = cfgs.get("crop_fov_approx", 25)
self.mesh_regularization_mode = cfgs.get('mesh_regularization_mode', 'seq')
self.enable_prior = cfgs.get('enable_prior', False)
if self.enable_prior:
self.netPrior = PriorPredictor(self.cfgs) #DOR - add label
self.prior_lr = cfgs.get('prior_lr', self.lr)
self.prior_weight_decay = cfgs.get('prior_weight_decay', 0.)
self.prior_only_epochs = cfgs.get('prior_only_epochs', 0)
self.netInstance = InstancePredictor(self.cfgs, tet_bbox=self.netPrior.netShape.getAABB())
self.perturb_sdf = cfgs.get('perturb_sdf', False)
self.blur_mask = cfgs.get('blur_mask', False)
self.blur_mask_iter = cfgs.get('blur_mask_iter', 1)
self.seqshape_epochs = np.arange(*cfgs.get('seqshape_epochs', [0, self.num_epochs]))
self.avg_texture_epochs = np.arange(*cfgs.get('avg_texture_epochs', [0, 0]))
self.swap_texture_epochs = np.arange(*cfgs.get('swap_texture_epochs', [0, 0]))
self.swap_priorshape_epochs = np.arange(*cfgs.get('swap_priorshape_epochs', [0, 0]))
self.avg_seqshape_epochs = np.arange(*cfgs.get('avg_seqshape_epochs', [0, 0]))
self.swap_seqshape_epochs = np.arange(*cfgs.get('swap_seqshape_epochs', [0, 0]))
self.pose_epochs = np.arange(*cfgs.get('pose_epochs', [0, 0]))
self.pose_iters = cfgs.get('pose_iters', 0)
self.deform_type = cfgs.get('deform_type', None)
self.mesh_reg_decay_epoch = cfgs.get('mesh_reg_decay_epoch', 0)
self.sdf_reg_decay_start_iter = cfgs.get('sdf_reg_decay_start_iter', 0)
self.mesh_reg_decay_rate = cfgs.get('mesh_reg_decay_rate', 1)
self.texture_epochs = np.arange(*cfgs.get('texture_epochs', [0, self.num_epochs]))
self.zflip_epochs = np.arange(*cfgs.get('zflip_epochs', [0, self.num_epochs]))
self.lookat_zflip_loss_epochs = np.arange(*cfgs.get('lookat_zflip_loss_epochs', [0, self.num_epochs]))
self.lookat_zflip_no_other_losses = cfgs.get('lookat_zflip_no_other_losses', False)
self.flow_loss_epochs = np.arange(*cfgs.get('flow_loss_epochs', [0, self.num_epochs]))
self.sdf_inflate_reg_loss_epochs = np.arange(*cfgs.get('sdf_inflate_reg_loss_epochs', [0, self.num_epochs]))
self.arti_reg_loss_epochs = np.arange(*cfgs.get('arti_reg_loss_epochs', [0, self.num_epochs]))
self.background_mode = cfgs.get('background_mode', 'background')
self.shape_prior_type = cfgs.get('shape_prior_type', 'deform')
self.backward_prior = cfgs.get('backward_prior', True)
self.resume_prior_optim = cfgs.get('resume_prior_optim', True)
self.dmtet_grid_smaller_epoch = cfgs.get('dmtet_grid_smaller_epoch', 0)
self.dmtet_grid_smaller = cfgs.get('dmtet_grid_smaller', 128)
self.dmtet_grid = cfgs.get('dmtet_grid', 256)
self.pose_xflip_recon_epochs = np.arange(*cfgs.get('pose_xflip_recon_epochs', [0, 0]))
self.rot_rand_quad_epochs = np.arange(*cfgs.get('rot_rand_quad_epochs', [0, 0]))
self.rot_all_quad_epochs = np.arange(*cfgs.get('rot_all_quad_epochs', [0, 0]))
self.calc_dino_features = cfgs.get('calc_dino_features', False)
# self.smooth_type = cfgs.get('smooth_type', 'None')
# print(f"****smooth_type: {self.smooth_type}****")
## smooth losses
# smooth articulation
self.arti_smooth_type = cfgs.get('arti_smooth_type', None)
self.arti_smooth_loss_type = cfgs.get('arti_smooth_loss_type', None)
self.arti_smooth_loss_weight = cfgs.get('arti_smooth_loss_weight', 0.)
self.using_arti_smooth_loss = self.arti_smooth_type and self.arti_smooth_loss_type and self.arti_smooth_loss_weight > 0.
if self.using_arti_smooth_loss:
self.arti_smooth_loss_fn = SmoothLoss(dim=1, smooth_type=self.arti_smooth_type, loss_type=self.arti_smooth_loss_type)
else:
self.arti_smooth_loss_fn = None
# smooth deformation
self.deform_smooth_type = cfgs.get('deform_smooth_type', None)
self.deform_smooth_loss_type = cfgs.get('deform_smooth_loss_type', None)
self.deform_smooth_loss_weight = cfgs.get('deform_smooth_loss_weight', 0.)
self.using_deform_smooth_loss = self.deform_smooth_type and self.deform_smooth_loss_type and self.deform_smooth_loss_weight > 0.
if self.using_deform_smooth_loss:
self.deform_smooth_loss_fn = SmoothLoss(dim=1, smooth_type=self.deform_smooth_type, loss_type=self.deform_smooth_loss_type)
else:
self.deform_smooth_loss_fn = None
# smooth camera pose
self.campos_smooth_type = cfgs.get('campos_smooth_type', None)
self.campos_smooth_loss_type = cfgs.get('campos_smooth_loss_type', None)
self.campos_smooth_loss_weight = cfgs.get('campos_smooth_loss_weight', 0.)
self.using_campos_smooth_loss = self.campos_smooth_type and self.campos_smooth_loss_type and self.campos_smooth_loss_weight > 0.
if self.using_campos_smooth_loss:
self.campos_smooth_loss_fn = SmoothLoss(dim=1, smooth_type=self.campos_smooth_type, loss_type=self.campos_smooth_loss_type)
else:
self.campos_smooth_loss_fn = None
# smooth articulation velocity
self.artivel_smooth_type = cfgs.get('artivel_smooth_type', None)
self.artivel_smooth_loss_type = cfgs.get('artivel_smooth_loss_type', None)
self.artivel_smooth_loss_weight = cfgs.get('artivel_smooth_loss_weight', 0.)
self.using_artivel_smooth_loss = self.artivel_smooth_type and self.artivel_smooth_loss_type and self.artivel_smooth_loss_weight > 0.
if self.using_artivel_smooth_loss:
self.artivel_smooth_loss_fn = SmoothLoss(dim=1, smooth_type=self.artivel_smooth_type, loss_type=self.artivel_smooth_loss_type)
else:
self.artivel_smooth_loss_fn = None
# smooth bone
self.bone_smooth_type = cfgs.get('bone_smooth_type', None)
self.bone_smooth_loss_type = cfgs.get('bone_smooth_loss_type', None)
self.bone_smooth_loss_weight = cfgs.get('bone_smooth_loss_weight', 0.)
self.using_bone_smooth_loss = self.bone_smooth_type and self.bone_smooth_loss_type and self.bone_smooth_loss_weight > 0.
if self.using_bone_smooth_loss:
self.bone_smooth_loss_fn = SmoothLoss(dim=1, smooth_type=self.bone_smooth_type, loss_type=self.bone_smooth_loss_type)
else:
self.bone_smooth_loss_fn = None
# smooth bone velocity
self.bonevel_smooth_type = cfgs.get('bonevel_smooth_type', None)
self.bonevel_smooth_loss_type = cfgs.get('bonevel_smooth_loss_type', None)
self.bonevel_smooth_loss_weight = cfgs.get('bonevel_smooth_loss_weight', 0.)
self.using_bonevel_smooth_loss = self.bonevel_smooth_type and self.bonevel_smooth_loss_type and self.bonevel_smooth_loss_weight > 0.
if self.using_bonevel_smooth_loss:
self.bonevel_smooth_loss_fn = SmoothLoss(dim=1, smooth_type=self.bonevel_smooth_type, loss_type=self.bonevel_smooth_loss_type)
else:
self.bonevel_smooth_loss_fn = None
## perceptual loss
if cfgs.get('perceptual_loss_weight', 0.) > 0:
self.perceptual_loss_use_lin = cfgs.get('perceptual_loss_use_lin', True)
self.perceptual_loss = lpips.LPIPS(net='vgg', lpips=self.perceptual_loss_use_lin)
# self.glctx = dr.RasterizeGLContext()
self.glctx = dr.RasterizeCudaContext()
self.render_flow = self.cfgs.get('flow_loss_weight', 0.) > 0.
self.extra_renders = cfgs.get('extra_renders', [])
self.renderer_spp = cfgs.get('renderer_spp', 1)
self.dino_feature_recon_dim = cfgs.get('dino_feature_recon_dim', 64)
self.total_loss = 0.
self.all_scores = torch.Tensor()
self.checkpoint_dir = cfgs.get('checkpoint_dir', 'results')
# iter
self.iter_arti_reg_loss_start = cfgs.get('iter_arti_reg_loss_start', None)
# mask distribution
self.enable_mask_distribution = cfgs.get('enable_mask_distribution', False)
self.enable_mask_distribution = False
self.random_mask_law = cfgs.get('random_mask_law', 'batch_swap_noy') # batch_swap, batch_swap_noy, # random_azimuth # random_all
self.mask_distribution_path = cfgs.get('mask_distribution_path', None)
self.enable_clip = cfgs.get('enable_clip', False)
self.enable_clip = False
self.enable_disc = cfgs.get('enable_disc', False)
self.enable_disc = False
self.few_shot_gan_tex = False
self.few_shot_clip_tex = False
self.enable_sds = cfgs.get('enable_sds', False)
self.enable_vsd = cfgs.get('enable_vsd', False)
self.enable_sds = False
self.enable_vsd = False
@staticmethod
def get_data_loaders(cfgs, dataset, in_image_size=256, out_image_size=256, batch_size=64, num_workers=4, run_train=False, run_test=False, train_data_dir=None, val_data_dir=None, test_data_dir=None, flow_bool=False):
train_loader = val_loader = test_loader = None
color_jitter_train = cfgs.get('color_jitter_train', None)
color_jitter_val = cfgs.get('color_jitter_val', None)
random_flip_train = cfgs.get('random_flip_train', False)
## video dataset
if dataset == 'video':
data_loader_mode = cfgs.get('data_loader_mode', 'n_frame')
skip_beginning = cfgs.get('skip_beginning', 4)
skip_end = cfgs.get('skip_end', 4)
num_sample_frames = cfgs.get('num_sample_frames', 2)
min_seq_len = cfgs.get('min_seq_len', 10)
max_seq_len = cfgs.get('max_seq_len', 10)
debug_seq = cfgs.get('debug_seq', False)
random_sample_train_frames = cfgs.get('random_sample_train_frames', False)
shuffle_train_seqs = cfgs.get('shuffle_train_seqs', False)
random_sample_val_frames = cfgs.get('random_sample_val_frames', False)
load_background = cfgs.get('background_mode', 'none') == 'background'
rgb_suffix = cfgs.get('rgb_suffix', '.png')
load_dino_feature = cfgs.get('load_dino_feature', False)
load_dino_cluster = cfgs.get('load_dino_cluster', False)
dino_feature_dim = cfgs.get('dino_feature_dim', 64)
get_loader = lambda **kwargs: get_sequence_loader(
mode=data_loader_mode,
batch_size=batch_size,
num_workers=num_workers,
in_image_size=in_image_size,
out_image_size=out_image_size,
debug_seq=debug_seq,
skip_beginning=skip_beginning,
skip_end=skip_end,
num_sample_frames=num_sample_frames,
min_seq_len=min_seq_len,
max_seq_len=max_seq_len,
load_background=load_background,
rgb_suffix=rgb_suffix,
load_dino_feature=load_dino_feature,
load_dino_cluster=load_dino_cluster,
dino_feature_dim=dino_feature_dim,
flow_bool=flow_bool,
**kwargs)
if run_train:
assert osp.isdir(train_data_dir), f"Training data directory does not exist: {train_data_dir}"
print(f"Loading training data from {train_data_dir}")
train_loader = get_loader(data_dir=train_data_dir, is_validation=False, random_sample=random_sample_train_frames, shuffle=shuffle_train_seqs, dense_sample=True, color_jitter=color_jitter_train, random_flip=random_flip_train)
if val_data_dir is not None:
assert osp.isdir(val_data_dir), f"Validation data directory does not exist: {val_data_dir}"
print(f"Loading validation data from {val_data_dir}")
val_loader = get_loader(data_dir=val_data_dir, is_validation=True, random_sample=random_sample_val_frames, shuffle=False, dense_sample=False, color_jitter=color_jitter_val, random_flip=False)
if run_test:
assert osp.isdir(test_data_dir), f"Testing data directory does not exist: {test_data_dir}"
print(f"Loading testing data from {test_data_dir}")
test_loader = get_loader(data_dir=test_data_dir, is_validation=True, dense_sample=False, color_jitter=None, random_flip=False)
## CUB dataset
elif dataset == 'cub':
get_loader = lambda **kwargs: get_cub_loader(
batch_size=batch_size,
num_workers=num_workers,
image_size=in_image_size,
**kwargs)
if run_train:
assert osp.isdir(train_data_dir), f"Training data directory does not exist: {train_data_dir}"
print(f"Loading training data from {train_data_dir}")
train_loader = get_loader(data_dir=train_data_dir, split='train', is_validation=False)
val_loader = get_loader(data_dir=val_data_dir, split='val', is_validation=True)
if run_test:
assert osp.isdir(test_data_dir), f"Testing data directory does not exist: {test_data_dir}"
print(f"Loading testing data from {test_data_dir}")
test_loader = get_loader(data_dir=test_data_dir, split='test', is_validation=True)
## other datasets
else:
get_loader = lambda **kwargs: get_image_loader(
batch_size=batch_size,
num_workers=num_workers,
image_size=in_image_size,
**kwargs)
if run_train:
assert osp.isdir(train_data_dir), f"Training data directory does not exist: {train_data_dir}"
print(f"Loading training data from {train_data_dir}")
train_loader = get_loader(data_dir=train_data_dir, is_validation=False, color_jitter=color_jitter_train)
if val_data_dir is not None:
assert osp.isdir(val_data_dir), f"Validation data directory does not exist: {val_data_dir}"
print(f"Loading validation data from {val_data_dir}")
val_loader = get_loader(data_dir=val_data_dir, is_validation=True, color_jitter=color_jitter_val)
if run_test:
assert osp.isdir(test_data_dir), f"Testing data directory does not exist: {test_data_dir}"
print(f"Loading testing data from {test_data_dir}")
test_loader = get_loader(data_dir=test_data_dir, is_validation=True, color_jitter=None)
return train_loader, val_loader, test_loader
@staticmethod
def get_data_loaders_ddp(cfgs, dataset, rank, world_size, in_image_size=256, out_image_size=256, batch_size=64, num_workers=4, run_train=False, run_test=False, train_data_dir=None, val_data_dir=None, test_data_dir=None, flow_bool=False):
train_loader = val_loader = test_loader = None
color_jitter_train = cfgs.get('color_jitter_train', None)
color_jitter_val = cfgs.get('color_jitter_val', None)
random_flip_train = cfgs.get('random_flip_train', False)
## video dataset
if dataset == 'video':
data_loader_mode = cfgs.get('data_loader_mode', 'n_frame')
skip_beginning = cfgs.get('skip_beginning', 4)
skip_end = cfgs.get('skip_end', 4)
num_sample_frames = cfgs.get('num_sample_frames', 2)
min_seq_len = cfgs.get('min_seq_len', 10)
max_seq_len = cfgs.get('max_seq_len', 10)
debug_seq = cfgs.get('debug_seq', False)
random_sample_train_frames = cfgs.get('random_sample_train_frames', False)
shuffle_train_seqs = cfgs.get('shuffle_train_seqs', False)
random_sample_val_frames = cfgs.get('random_sample_val_frames', False)
load_background = cfgs.get('background_mode', 'none') == 'background'
rgb_suffix = cfgs.get('rgb_suffix', '.png')
load_dino_feature = cfgs.get('load_dino_feature', False)
load_dino_cluster = cfgs.get('load_dino_cluster', False)
dino_feature_dim = cfgs.get('dino_feature_dim', 64)
get_loader_ddp = lambda **kwargs: get_sequence_loader_ddp(
mode=data_loader_mode,
batch_size=batch_size,
num_workers=num_workers,
in_image_size=in_image_size,
out_image_size=out_image_size,
debug_seq=debug_seq,
skip_beginning=skip_beginning,
skip_end=skip_end,
num_sample_frames=num_sample_frames,
min_seq_len=min_seq_len,
max_seq_len=max_seq_len,
load_background=load_background,
rgb_suffix=rgb_suffix,
load_dino_feature=load_dino_feature,
load_dino_cluster=load_dino_cluster,
dino_feature_dim=dino_feature_dim,
flow_bool=flow_bool,
**kwargs)
get_loader = lambda **kwargs: get_sequence_loader(
mode=data_loader_mode,
batch_size=batch_size,
num_workers=num_workers,
in_image_size=in_image_size,
out_image_size=out_image_size,
debug_seq=debug_seq,
skip_beginning=skip_beginning,
skip_end=skip_end,
num_sample_frames=num_sample_frames,
min_seq_len=min_seq_len,
max_seq_len=max_seq_len,
load_background=load_background,
rgb_suffix=rgb_suffix,
load_dino_feature=load_dino_feature,
load_dino_cluster=load_dino_cluster,
dino_feature_dim=dino_feature_dim,
**kwargs)
if run_train:
if isinstance(train_data_dir, dict):
for data_path in train_data_dir.values():
assert osp.isdir(data_path), f"Training data directory does not exist: {data_path}"
elif isinstance(train_data_dir, str):
assert osp.isdir(train_data_dir), f"Training data directory does not exist: {train_data_dir}"
else:
raise ValueError("train_data_dir must be a string or a dict of strings")
print(f"Loading training data...")
train_loader = get_loader_ddp(data_dir=train_data_dir, rank=rank, world_size=world_size, is_validation=False, random_sample=random_sample_train_frames, shuffle=shuffle_train_seqs, dense_sample=True, color_jitter=color_jitter_train, random_flip=random_flip_train)
if val_data_dir is not None:
if isinstance(val_data_dir, dict):
for data_path in val_data_dir.values():
assert osp.isdir(data_path), f"Training data directory does not exist: {data_path}"
elif isinstance(val_data_dir, str):
assert osp.isdir(val_data_dir), f"Training data directory does not exist: {val_data_dir}"
else:
raise ValueError("train_data_dir must be a string or a dict of strings")
print(f"Loading validation data...")
# No need for data parallel for the validation data loader.
val_loader = get_loader(data_dir=val_data_dir, is_validation=True, random_sample=random_sample_val_frames, shuffle=False, dense_sample=False, color_jitter=color_jitter_val, random_flip=False)
if run_test:
assert osp.isdir(test_data_dir), f"Testing data directory does not exist: {test_data_dir}"
print(f"Loading testing data from {test_data_dir}")
test_loader = get_loader_ddp(data_dir=test_data_dir, rank=rank, world_size=world_size, is_validation=True, dense_sample=False, color_jitter=None, random_flip=False)
## CUB dataset
elif dataset == 'cub':
get_loader = lambda **kwargs: get_cub_loader_ddp(
batch_size=batch_size,
num_workers=num_workers,
image_size=in_image_size,
**kwargs)
if run_train:
assert osp.isdir(train_data_dir), f"Training data directory does not exist: {train_data_dir}"
print(f"Loading training data from {train_data_dir}")
train_loader = get_loader(data_dir=train_data_dir, rank=rank, world_size=world_size, split='train', is_validation=False)
val_loader = get_loader(data_dir=val_data_dir, rank=rank, world_size=world_size, split='val', is_validation=True)
if run_test:
assert osp.isdir(test_data_dir), f"Testing data directory does not exist: {test_data_dir}"
print(f"Loading testing data from {test_data_dir}")
test_loader = get_loader(data_dir=test_data_dir, rank=rank, world_size=world_size, split='test', is_validation=True)
## other datasets
else:
get_loader = lambda **kwargs: get_image_loader_ddp(
batch_size=batch_size,
num_workers=num_workers,
image_size=in_image_size,
**kwargs)
if run_train:
assert osp.isdir(train_data_dir), f"Training data directory does not exist: {train_data_dir}"
print(f"Loading training data from {train_data_dir}")
train_loader = get_loader(data_dir=train_data_dir, rank=rank, world_size=world_size, is_validation=False, color_jitter=color_jitter_train)
if val_data_dir is not None:
assert osp.isdir(val_data_dir), f"Validation data directory does not exist: {val_data_dir}"
print(f"Loading validation data from {val_data_dir}")
val_loader = get_loader(data_dir=val_data_dir, rank=rank, world_size=world_size, is_validation=True, color_jitter=color_jitter_val)
if run_test:
assert osp.isdir(test_data_dir), f"Testing data directory does not exist: {test_data_dir}"
print(f"Loading testing data from {test_data_dir}")
test_loader = get_loader(data_dir=test_data_dir, rank=rank, world_size=world_size, is_validation=True, color_jitter=None)
return train_loader, val_loader, test_loader
def load_model_state(self, cp):
# TODO: very hacky: if using local texture, which is also usually finetuned from global texture
# we need to check if needs some handcrafted load in netInstance
if (self.netInstance.texture_way is not None) or (self.cfgs.get('texture_act', 'relu') != 'relu'):
new_netInstance_weights = {k: v for k, v in cp['netInstance'].items() if 'netTexture' not in k}
#find the new texture weights
texture_weights = self.netInstance.netTexture.state_dict()
#add the new weights to the new model weights
for k, v in texture_weights.items():
new_netInstance_weights['netTexture.' + k] = v
self.netInstance.load_state_dict(new_netInstance_weights)
else:
self.netInstance.load_state_dict(cp["netInstance"])
if self.enable_disc and "net_mask_disc" in cp:
self.mask_disc.load_state_dict(cp["net_mask_disc"])
if self.enable_prior:
self.netPrior.load_state_dict(cp["netPrior"])
def load_optimizer_state(self, cp):
# TODO: also very hacky here, as the load_model_state above
if self.netInstance.texture_way is not None:
opt_state_dict = self.optimizerInstance.state_dict()
param_ids = [id(p) for p in self.netInstance.netTexture.parameters()]
new_opt_state_dict = {}
new_opt_state_dict['state'] = {k: v for k, v in opt_state_dict['state'].items() if k not in param_ids}
new_param_groups = []
for param_group in opt_state_dict['param_groups']:
new_param_group = {k: v for k, v in param_group.items() if k != 'params'}
new_param_group['params'] = [p_id for p_id in param_group['params'] if p_id not in param_ids]
new_param_groups.append(new_param_group)
new_opt_state_dict['param_groups'] = new_param_groups
self.optimizerInstance.load_state_dict(new_opt_state_dict)
else:
self.optimizerInstance.load_state_dict(cp["optimizerInstance"])
# add parameters into optimizerInstance here
# if self.enable_disc:
# print('add mask discriminator parameters to Instance optimizer')
# self.optimizerInstance.add_param_group({'params': self.mask_disc.parameters()})
if self.use_scheduler:
if 'schedulerInstance' in cp:
self.schedulerInstance.load_state_dict(cp["schedulerInstance"])
if self.enable_disc and "optimizerDiscriminator" in cp:
self.optimizerDiscriminator.load_state_dict(cp["optimizerDiscriminator"])
if self.enable_prior and self.resume_prior_optim:
self.optimizerPrior.load_state_dict(cp["optimizerPrior"])
if self.use_scheduler:
if 'schedulerPrior' in cp:
self.schedulerPrior.load_state_dict(cp["schedulerPrior"])
def get_model_state(self):
state = {"netInstance": self.netInstance.state_dict()}
if self.enable_disc:
state["net_mask_disc"] = self.mask_disc.state_dict()
if self.enable_prior:
state["netPrior"] = self.netPrior.state_dict()
return state
def get_optimizer_state(self):
state = {"optimizerInstance": self.optimizerInstance.state_dict()}
if self.enable_disc:
state['optimizerDiscriminator'] = self.optimizerDiscriminator.state_dict()
if self.use_scheduler:
state["schedulerInstance"] = self.schedulerInstance.state_dict()
if self.enable_prior:
state["optimizerPrior"] = self.optimizerPrior.state_dict()
if self.use_scheduler:
state["schedulerPrior"] = self.schedulerPrior.state_dict()
return state
def to(self, device):
self.device = device
self.netInstance.to(device)
if self.enable_prior:
self.netPrior.to(device)
for v in vars(self.netPrior.netShape):
attr = getattr(self.netPrior.netShape,v)
if type(attr) == torch.Tensor:
setattr(self.netPrior.netShape, v, attr.to(device))
if hasattr(self, 'perceptual_loss'):
self.perceptual_loss.to(device)
def ddp(self, rank, world_size):
self.rank = rank
self.world_size = world_size
if self.world_size > 1:
self.netInstance_ddp = DDP(
self.netInstance, device_ids=[rank],
find_unused_parameters=True)
self.netInstance_ddp._set_static_graph()
self.netInstance = self.netInstance_ddp.module
if self.enable_prior:
self.netPrior_ddp = DDP(
self.netPrior, device_ids=[rank],
find_unused_parameters=True)
self.netPrior_ddp._set_static_graph()
self.netPrior = self.netPrior_ddp.module
if hasattr(self, 'perceptual_loss'):
self.perceptual_loss_ddp = DDP(
self.perceptual_loss, device_ids=[rank],
find_unused_parameters=True)
self.perceptual_loss = self.perceptual_loss_ddp.module
else:
print('actually no DDP for model')
def set_train(self):
if self.world_size > 1:
self.netInstance_ddp.train()
if self.enable_prior:
self.netPrior_ddp.train()
else:
self.netInstance.train()
if self.enable_disc:
self.mask_disc.train()
if self.enable_prior:
self.netPrior.train()
def set_eval(self):
if self.world_size > 1:
self.netInstance_ddp.eval()
if self.enable_prior:
self.netPrior_ddp.eval()
else:
self.netInstance.eval()
if self.enable_disc:
self.mask_disc.eval()
if self.enable_prior:
self.netPrior.eval()
def reset_optimizers(self):
print("Resetting optimizers...")
self.optimizerInstance = get_optimizer(self.netInstance, self.lr)
if self.enable_disc:
self.optimizerDiscriminator = get_optimizer(self.mask_disc, self.lr)
if self.use_scheduler:
self.schedulerInstance = self.make_scheduler(self.optimizerInstance)
if self.enable_prior:
self.optimizerPrior = get_optimizer(self.netPrior, lr=self.prior_lr, weight_decay=self.prior_weight_decay)
if self.use_scheduler:
self.schedulerPrior = self.make_scheduler(self.optimizerPrior)
def reset_only_disc_optimizer(self):
if self.enable_disc:
self.optimizerDiscriminator = get_optimizer(self.mask_disc, self.lr)
def backward(self):
self.optimizerInstance.zero_grad()
if self.backward_prior:
self.optimizerPrior.zero_grad()
# self.total_loss = self.add_unused()
self.total_loss.backward()
self.optimizerInstance.step()
if self.backward_prior:
self.optimizerPrior.step()
self.total_loss = 0.
def scheduler_step(self):
if self.use_scheduler:
self.schedulerInstance.step()
if self.enable_prior:
self.schedulerPrior.step()
def zflip_pose(self, pose):
if self.rot_rep == 'lookat':
vec_forward = pose[:,:,6:9]
vec_forward = vec_forward * torch.FloatTensor([1,1,-1]).view(1,1,3).to(vec_forward.device)
up = torch.FloatTensor([0,1,0]).to(pose.device).view(1,1,3)
vec_right = up.expand_as(vec_forward).cross(vec_forward, dim=-1)
vec_right = nn.functional.normalize(vec_right, p=2, dim=-1)
vec_up = vec_forward.cross(vec_right, dim=-1)
vec_up = nn.functional.normalize(vec_up, p=2, dim=-1)
rot_mat = torch.stack([vec_right, vec_up, vec_forward], 2)
rot_pred = rot_mat.reshape(*pose.shape[:-1], -1)
pose_zflip = torch.cat([rot_pred, pose[:,:,9:]], -1)
else:
raise NotImplementedError
return pose_zflip
def render(self, shape, texture, mvp, w2c, campos, resolution, background='none', im_features=None, light=None, prior_shape=None, render_flow=False, dino_pred=None, class_vector=None, render_mode='diffuse', two_sided_shading=True, num_frames=None, spp=1, bg_image=None, im_features_map=None):
h, w = resolution
N = len(mvp)
if bg_image is None:
if background in ['none', 'black']:
bg_image = torch.zeros((N, h, w, 3), device=mvp.device)
elif background == 'white':
bg_image = torch.ones((N, h, w, 3), device=mvp.device)
elif background == 'checkerboard':
bg_image = torch.FloatTensor(util.checkerboard((h, w), 8), device=self.device).repeat(N, 1, 1, 1) # NxHxWxC
elif background == 'random':
bg_image = torch.rand((N, h, w, 3), device=mvp.device) # NxHxWxC
elif background == 'random-pure':
random_values = torch.rand(N)
bg_image = random_values[..., None, None, None].repeat(1, h, w, 3).to(self.device)
else:
raise NotImplementedError
#insider render_mesh -> render_layer -> shade DOR
frame_rendered = render.render_mesh(
self.glctx,
shape,
mtx_in=mvp,
w2c=w2c,
view_pos=campos,
material=texture,
lgt=light,
resolution=resolution,
spp=spp,
msaa=True,
background=bg_image,
bsdf=render_mode,
feat=im_features,
prior_mesh=prior_shape,
two_sided_shading=two_sided_shading,
render_flow=render_flow,
dino_pred=dino_pred,
class_vector=class_vector,
num_frames=num_frames,
im_features_map=im_features_map)
shaded = frame_rendered['shaded'].permute(0, 3, 1, 2)
image_pred = shaded[:, :3, :, :]
mask_pred = shaded[:, 3, :, :]
albedo = frame_rendered['kd'].permute(0, 3, 1, 2)[:, :3, :, :]
if 'shading' in frame_rendered:
shading = frame_rendered['shading'].permute(0, 3, 1, 2)[:, :1, :, :]
else:
shading = None
if render_flow:
flow_pred = frame_rendered['flow']
flow_pred = flow_pred.permute(0, 3, 1, 2)[:, :2, :, :]
else:
flow_pred = None
if dino_pred is not None:
dino_feat_im_pred = frame_rendered['dino_feat_im_pred']
dino_feat_im_pred = dino_feat_im_pred.permute(0, 3, 1, 2)[:, :-1]
else:
dino_feat_im_pred = None
return image_pred, mask_pred, flow_pred, dino_feat_im_pred, albedo, shading
def compute_reconstruction_losses(self, image_pred, image_gt, mask_pred, mask_gt, mask_dt, mask_valid, flow_pred, flow_gt, dino_feat_im_gt, dino_feat_im_pred, background_mode='none', reduce=False):
losses = {}
batch_size, num_frames, _, h, w = image_pred.shape # BxFxCxHxW
# image_loss = (image_pred - image_gt) ** 2
image_loss = (image_pred - image_gt).abs()
## silhouette loss
mask_pred_valid = mask_pred * mask_valid
# mask_pred_valid = mask_pred
# losses["silhouette_loss"] = ((mask_pred - mask_gt) ** 2).mean()
# mask_loss_mask = (image_loss.mean(2).detach() > 0.05).float()
mask_loss = (mask_pred_valid - mask_gt) ** 2
# mask_loss = nn.functional.mse_loss(mask_pred, mask_gt)
# num_mask_pixels = mask_loss_mask.reshape(batch_size*num_frames, -1).sum(1).clamp(min=1)
# losses["silhouette_loss"] = (mask_loss.reshape(batch_size*num_frames, -1).sum(1) / num_mask_pixels).mean()
losses['silhouette_loss'] = mask_loss.view(batch_size, num_frames, -1).mean(2)
losses['silhouette_dt_loss'] = (mask_pred * mask_dt[:,:,1]).view(batch_size, num_frames, -1).mean(2)
losses['silhouette_inv_dt_loss'] = ((1-mask_pred) * mask_dt[:,:,0]).view(batch_size, num_frames, -1).mean(2)
mask_pred_binary = (mask_pred_valid > 0.).float().detach()
mask_both_binary = (mask_pred_binary * mask_gt).view(batch_size*num_frames, 1, *mask_pred.shape[2:])
mask_both_binary = (nn.functional.avg_pool2d(mask_both_binary, 3, stride=1, padding=1).view(batch_size, num_frames, *mask_pred.shape[2:]) > 0.99).float().detach() # erode by 1 pixel
## reconstruction loss
# image_loss_mask = (mask_pred*mask_gt).unsqueeze(2).expand_as(image_gt)
# image_loss = image_loss * image_loss_mask
# num_mask_pixels = image_loss_mask.reshape(batch_size*num_frames, -1).sum(1).clamp(min=1)
# losses["rgb_loss"] = (image_loss.reshape(batch_size*num_frames, -1).sum(1) / num_mask_pixels).mean()
if background_mode in ['background', 'input']:
pass
else:
image_loss = image_loss * mask_both_binary.unsqueeze(2)
losses['rgb_loss'] = image_loss.reshape(batch_size, num_frames, -1).mean(2)
if self.cfgs.get('perceptual_loss_weight', 0.) > 0:
if background_mode in ['background', 'input']:
perc_image_pred = image_pred
perc_image_gt = image_gt
else:
perc_image_pred = image_pred * mask_pred_binary.unsqueeze(2) + 0.5 * (1-mask_pred_binary.unsqueeze(2))
perc_image_gt = image_gt * mask_pred_binary.unsqueeze(2) + 0.5 * (1-mask_pred_binary.unsqueeze(2))
losses['perceptual_loss'] = self.perceptual_loss(perc_image_pred.view(-1, *image_pred.shape[2:]) *2-1, perc_image_gt.view(-1, *image_gt.shape[2:]) *2-1).view(batch_size, num_frames)
## flow loss - between first and second frame
if flow_pred is not None:
flow_loss = (flow_pred - flow_gt).abs()
flow_loss_mask = mask_both_binary[:,:-1].unsqueeze(2).expand_as(flow_gt).detach()
## ignore frames where GT flow is too large (likely inaccurate)
large_flow = (flow_gt.abs() > 0.5).float() * flow_loss_mask
large_flow = (large_flow.view(batch_size, num_frames-1, -1).sum(2) > 0).float()
self.large_flow = large_flow
flow_loss = flow_loss * flow_loss_mask * (1 - large_flow[:,:,None,None,None])
num_mask_pixels = flow_loss_mask.reshape(batch_size, num_frames-1, -1).sum(2).clamp(min=1)
losses['flow_loss'] = (flow_loss.reshape(batch_size, num_frames-1, -1).sum(2) / num_mask_pixels)
# losses["flow_loss"] = flow_loss.mean()
if dino_feat_im_pred is not None and dino_feat_im_gt is not None:
dino_feat_loss = (dino_feat_im_pred - dino_feat_im_gt) ** 2
dino_feat_loss = dino_feat_loss * mask_both_binary.unsqueeze(2)
losses['dino_feat_im_loss'] = dino_feat_loss.reshape(batch_size, num_frames, -1).mean(2)
if reduce:
for k, v in losses.item():
losses[k] = v.mean()
return losses
def compute_pose_xflip_reg_loss(self, input_image, dino_feat_im, pose_raw, input_image_xflip_flag=None):
image_xflip = input_image.flip(4)
if dino_feat_im is not None:
dino_feat_im_xflip = dino_feat_im.flip(4)
else:
dino_feat_im_xflip = None
if self.world_size > 1:
netInst = self.netInstance_ddp
else:
netInst = self.netInstance
# feat_xflip, _ = self.netInstance_ddp.forward_encoder(image_xflip, dino_feat_im_xflip)
feat_xflip, _ = netInst.forward_encoder(image_xflip, dino_feat_im_xflip)
batch_size, num_frames = input_image.shape[:2]
# pose_xflip_raw = self.netInstance_ddp.forward_pose(image_xflip, feat_xflip, dino_feat_im_xflip)
pose_xflip_raw = netInst.forward_pose(image_xflip, feat_xflip, dino_feat_im_xflip)
if input_image_xflip_flag is not None:
pose_xflip_raw_xflip = pose_xflip_raw * torch.FloatTensor([-1,1,1,-1,1,1]).to(pose_raw.device) # forward x, trans x
pose_xflip_raw = pose_xflip_raw * (1 - input_image_xflip_flag.view(batch_size * num_frames, 1)) + pose_xflip_raw_xflip * input_image_xflip_flag.view(batch_size * num_frames, 1)
# rot_rep = self.netInstance_ddp.rot_rep
rot_rep = netInst.rot_rep
if rot_rep == 'euler_angle' or rot_rep == 'soft_calss':
pose_xflip_xflip = pose_xflip * torch.FloatTensor([1,-1,-1,-1,1,1]).to(pose_xflip.device) # rot y+z, trans x
pose_xflip_reg_loss = ((pose_xflip_xflip - pose) ** 2.).mean()
elif rot_rep == 'quaternion':
rot_euler = pytorch3d.transforms.matrix_to_euler_angles(pytorch3d.transforms.quaternion_to_matrix(pose[...,:4]), convention='XYZ')
pose_euler = torch.cat([rot_euler, pose[...,4:]], -1)
rot_xflip_euler = pytorch3d.transforms.matrix_to_euler_angles(pytorch3d.transforms.quaternion_to_matrix(pose_xflip[...,:4]), convention='XYZ')
pose_xflip_euler = torch.cat([rot_xflip_euler, pose_xflip[...,4:]], -1)
pose_xflip_euler_xflip = pose_xflip_euler * torch.FloatTensor([1,-1,-1,-1,1,1]).to(pose_xflip.device) # rot y+z, trans x
pose_xflip_reg_loss = ((pose_xflip_euler_xflip - pose_euler) ** 2.).mean()
elif rot_rep == 'lookat':
pose_xflip_raw_xflip = pose_xflip_raw * torch.FloatTensor([-1,1,1,-1,1,1]).to(pose_raw.device) # forward x, trans x
pose_xflip_reg_loss = ((pose_xflip_raw_xflip - pose_raw)[...,0] ** 2.) # compute x only
# if epoch >= self.nolookat_zflip_loss_epochs and self.lookat_zflip_no_other_losses:
# pose_xflip_reg_loss = pose_xflip_reg_loss.mean(1) * is_pose_1_better
pose_xflip_reg_loss = pose_xflip_reg_loss.mean()
return pose_xflip_reg_loss, pose_xflip_raw
def compute_edge_length_reg_loss(self, mesh, prior_mesh):
prior_edge_lengths = get_edge_length(prior_mesh.v_pos, prior_mesh.t_pos_idx)
max_length = prior_edge_lengths.max().detach() *1.1
edge_lengths = get_edge_length(mesh.v_pos, mesh.t_pos_idx)
mesh_edge_length_loss = ((edge_lengths - max_length).clamp(min=0)**2).mean()
return mesh_edge_length_loss, edge_lengths
def compute_regularizers(self, mesh, prior_mesh, input_image, dino_feat_im, pose_raw, input_image_xflip_flag=None, arti_params=None, deformation=None, mid_img_idx=0, posed_bones=None, class_vector=None):
losses = {}
aux = {}
if self.enable_prior:
losses.update(self.netPrior.netShape.get_sdf_reg_loss(class_vector=class_vector))
if self.cfgs.get('pose_xflip_reg_loss_weight', 0.) > 0:
losses["pose_xflip_reg_loss"], aux['pose_xflip_raw'] = self.compute_pose_xflip_reg_loss(input_image, dino_feat_im, pose_raw, input_image_xflip_flag)
if self.using_campos_smooth_loss:
# from IPython import embed; embed()
pose_raw_ = pose_raw.view(self.bs, self.nf, *pose_raw.shape[1:])
losses['campos_smooth_loss'] = self.campos_smooth_loss_fn(pose_raw_)
b, f = input_image.shape[:2]
if b >= 2:
vec_forward = pose_raw[..., :3]
losses['pose_entropy_loss'] = (vec_forward[:b//2] * vec_forward[b//2:(b//2)*2]).sum(-1).mean()
else:
losses['pose_entropy_loss'] = 0.
losses['mesh_normal_consistency_loss'] = normal_consistency(mesh.v_pos, mesh.t_pos_idx)
losses['mesh_laplacian_consistency_loss'] = laplace_regularizer_const(mesh.v_pos, mesh.t_pos_idx)
losses['mesh_edge_length_loss'], aux['edge_lengths'] = self.compute_edge_length_reg_loss(mesh, prior_mesh)
if arti_params is not None:
#losses['arti_reg_loss'] = (arti_params ** 2).mean()
losses['arti_reg_loss'] = (arti_params ** 2).mean() #TODO dor Rart
if arti_params is not None and self.using_arti_smooth_loss:
arti_smooth_loss = self.arti_smooth_loss_fn(arti_params)
losses['arti_smooth_loss'] = arti_smooth_loss
# if arti_params is not None and self.cfgs.get('arti_smooth_loss_weight', 0.) > 0:
# if self.smooth_type == 'loss' and mid_img_idx > 0:
# # print("+++++++++++++++++add smooth to *articulation* loss")
# # from IPython import embed; embed()
# arti_smooth_loss = (
# ((arti_params[:,mid_img_idx,:,:] - arti_params[:,0:mid_img_idx,:,:])**2)
# + ((arti_params[:,mid_img_idx,:,:] - arti_params[:,mid_img_idx+1:2*mid_img_idx+1,:,:])**2)
# ).mean()
# losses['arti_smooth_loss'] = arti_smooth_loss
if arti_params is not None and self.using_artivel_smooth_loss:
# from IPython import embed; embed()
_, nf, _, _= arti_params.shape
arti_vel = arti_params[:,1:nf,:,:] - arti_params[:,:(nf-1),:,:]
artivel_smooth_loss = self.artivel_smooth_loss_fn(arti_vel)
losses['artivel_smooth_loss'] = artivel_smooth_loss
if deformation is not None:
#losses['deformation_reg_loss'] = (deformation ** 2).mean()
losses['deformation_reg_loss'] = (deformation ** 2).mean() #TODO dor - Rdef
d1 = deformation[:, mesh.t_pos_idx[0, :, 0], :]
d2 = deformation[:, mesh.t_pos_idx[0, :, 1], :]
d3 = deformation[:, mesh.t_pos_idx[0, :, 2], :]
num_samples = 5000
sample_idx1 = torch.randperm(d1.shape[1])[:num_samples].to(self.device)
sample_idx2 = torch.randperm(d1.shape[1])[:num_samples].to(self.device)
sample_idx3 = torch.randperm(d1.shape[1])[:num_samples].to(self.device)
dist1 = ((d1[:, sample_idx1, :] - d2[:, sample_idx1, :]) ** 2).mean()
dist2 = ((d2[:, sample_idx2, :] - d3[:, sample_idx2, :]) ** 2).mean()
dist3 = ((d3[:, sample_idx3, :] - d1[:, sample_idx3, :]) ** 2).mean()
losses['smooth_deformation_loss'] = dist1 + dist2 + dist3
if deformation is not None and self.using_deform_smooth_loss:
deformation_ = deformation.view(self.bs, self.nf, *deformation.shape[1:])
losses['deform_smooth_loss'] = self.deform_smooth_loss_fn(deformation_)
# if deformation is not None and self.cfgs.get('deformation_smooth_loss_weight', 0.) > 0:
# if self.smooth_type == 'loss' and mid_img_idx > 0:
# # print("+++++++++++++++++add smooth to *deformation* loss")
# deformation = deformation.view(self.bs, self.nf, *deformation.shape[1:])
# deformation_smooth_loss = (
# ((deformation[:, mid_img_idx,:,:] - deformation[:, 0:mid_img_idx,:,:]) ** 2)
# + ((deformation[:, mid_img_idx,:,:] - deformation[:, mid_img_idx+1:2*mid_img_idx+1,:,:]) ** 2)
# ).mean()
# losses['deformation_smooth_loss'] = deformation_smooth_loss
# # deformation = deformation.view(self.bs * self.nf, *deformation.shape[2:])
# # losses['deformation_reg_loss'] = deformation.abs().mean()
## posed bones.
if posed_bones is not None and self.using_bone_smooth_loss:
bone_smooth_loss = self.bone_smooth_loss_fn(posed_bones)
losses['bone_smooth_loss'] = bone_smooth_loss
if posed_bones is not None and self.using_bonevel_smooth_loss:
_, nf, _, _, _= posed_bones.shape
bone_vel = posed_bones[:,1:nf,...] - posed_bones[:,:(nf-1),...]
bonevel_smooth_loss = self.bonevel_smooth_loss_fn(bone_vel)
losses['bonevel_smooth_loss'] = bonevel_smooth_loss
return losses, aux
def parse_dict_definition(self, dict_config, total_iter):
'''
The dict_config is a diction-based configuration with ascending order
The key: value is the NUM_ITERATION_WEIGHT_BEGIN: WEIGHT
For example,
{0: 0.1, 1000: 0.2, 10000: 0.3}
means at beginning, the weight is 0.1, from 1k iterations, weight is 0.2, and after 10k, weight is 0.3
'''
length = len(dict_config)
all_iters = list(dict_config.keys())
all_weights = list(dict_config.values())
weight = all_weights[-1]
for i in range(length-1):
# this works for dict having at least two items, otherwise you don't need dict to set config
iter_num = all_iters[i]
iter_num_next = all_iters[i+1]
if iter_num <= total_iter and total_iter < iter_num_next:
weight = all_weights[i]
break
return weight
def compute_clip_loss(self, random_image_pred, image_pred, category):
# image preprocess for CLIP
random_image = torch.nn.functional.interpolate(random_image_pred, (self.clip_reso, self.clip_reso), mode='bilinear')
image_pred = torch.nn.functional.interpolate(image_pred.squeeze(1), (self.clip_reso, self.clip_reso), mode='bilinear')
random_image = tvf.normalize(random_image, self.clip_mean, self.clip_std)
image_pred = tvf.normalize(image_pred, self.clip_mean, self.clip_std)
feat_img_1 = self.clip_model.encode_image(random_image)
feat_img_2 = self.clip_model.encode_image(image_pred)
clip_all_loss = torch.nn.functional.cosine_similarity(feat_img_1, feat_img_2)
clip_all_loss = 1 - clip_all_loss.mean()
# feat_img_1 = torch.mean(feat_img_1, dim=0)
# feat_img_2 = torch.mean(feat_img_2, dim=0)
# clip_all_loss = torch.nn.functional.cosine_similarity(feat_img_1, feat_img_2, dim=0)
# clip_all_loss = 1 - clip_all_loss
if self.enable_clip_text:
text_feature = self.clip_text_feature[category].repeat(feat_img_1.shape[0], 1)
text_loss_1 = torch.nn.functional.cosine_similarity(feat_img_1, text_feature).mean()
text_loss_2 = torch.nn.functional.cosine_similarity(feat_img_2, text_feature).mean()
# text_feature = self.clip_text_feature[category][0]
# text_loss_1 = torch.nn.functional.cosine_similarity(feat_img_1, text_feature, dim=0)
# text_loss_2 = torch.nn.functional.cosine_similarity(feat_img_2, text_feature, dim=0)
clip_all_loss = clip_all_loss + (1 - text_loss_1) + (1 - text_loss_2)
return {'clip_all_loss': clip_all_loss}
def generate_patch_crop(self, images, masks, patch_size=128, patch_num_per_mask=1):
b, _, H, W = masks.shape
patches = []
for i in range(masks.shape[0]):
mask = masks[i]
# mask: [1, H, W]
nonzero_indices = torch.nonzero(mask > 0, as_tuple=False) # [K', 3]
valid_mask = (nonzero_indices[:, 1] > patch_size // 2) & (nonzero_indices[:, 1] < (H - 1 - patch_size // 2)) & (nonzero_indices[:, 2] > patch_size // 2) & (nonzero_indices[:, 2] < (W - 1 - patch_size // 2))
valid_idx = nonzero_indices[valid_mask]
patch_idx = valid_idx[torch.randperm(valid_idx.shape[0])[:patch_num_per_mask]] # [K, 3]
if patch_idx.shape[0] < patch_num_per_mask:
patches_this_img = torch.zeros(patch_num_per_mask, 3, self.few_shot_gan_tex_patch, self.few_shot_gan_tex_patch).to(self.device)
else:
patches_this_img = []
for idx in range(patch_idx.shape[0]):
_, y, x = patch_idx[idx]
y_start = max(0, y - patch_size // 2)
y_end = min(H, y_start + patch_size)
x_start = max(0, x - patch_size // 2)
x_end = min(W, x_start + patch_size)
patch_content = images[i, :, y_start:y_end, x_start:x_end]
patch = F.interpolate(patch_content.unsqueeze(0), size=self.few_shot_gan_tex_patch, mode='bilinear') # [1, 3, ps, ps]
patches_this_img.append(patch)
patches_this_img = torch.cat(patches_this_img, dim=0) # [K, 3, ps, ps]
patches.append(patches_this_img)
patches = torch.concat(patches, dim=0) # [B*K, 3, ps, ps]
return patches
def compute_gan_tex_loss(self, category, image_gt, mask_gt, iv_image_pred, iv_mask_pred, w2c_pred, campos_pred, shape, prior_shape, texture, dino_pred, im_features, light, class_vector, num_frames, im_features_map, bins=360):
'''
This part is used to do gan training on texture, this is meant to only be used in fine-tuning, with local texture network
Ideally this loss only contributes to the Texture
'''
delta_angle = 2 * np.pi / bins
b = len(shape)
rand_degree = torch.randint(120, [b])
rand_degree = rand_degree + 120
# rand_degree = torch.ones(b) * 180 # we want to see the reversed side
delta_angle = delta_angle * rand_degree
delta_rot_matrix = []
for i in range(b):
angle = delta_angle[i].item()
angle_matrix = torch.FloatTensor([
[np.cos(angle), 0, np.sin(angle), 0],
[0, 1, 0, 0],
[-np.sin(angle), 0, np.cos(angle), 0],
[0, 0, 0, 1],
]).to(self.device)
delta_rot_matrix.append(angle_matrix)
delta_rot_matrix = torch.stack(delta_rot_matrix, dim=0)
proj = util.perspective(self.crop_fov_approx / 180 * np.pi, 1, n=0.1, f=1000.0).repeat(b, 1, 1).to(self.device)
original_mvp = torch.bmm(proj, w2c_pred)
# original_campos = -w2c_pred[:, :3, 3]
original_campos = campos_pred
mvp = torch.matmul(original_mvp, delta_rot_matrix)
campos = torch.matmul(delta_rot_matrix[:,:3,:3].transpose(2,1), original_campos[:,:,None])[:,:,0]
w2c = w2c_pred
resolution = (self.few_shot_gan_tex_reso, self.few_shot_gan_tex_reso)
# only train the texture
safe_detach = lambda x: x.detach() if x is not None else None
mesh = safe_detach(shape)
im_features = safe_detach(im_features)
im_features_map = safe_detach(im_features_map)
class_vector = safe_detach(class_vector)
set_requires_grad(texture, True)
set_requires_grad(dino_pred, False)
set_requires_grad(light, False)
background_for_reverse = 'none'
# background_for_reverse = 'random-pure'
image_pred, mask_pred, _, _, _, _ = self.render(
mesh,
texture,
mvp,
w2c,
campos,
resolution,
background=background_for_reverse,
im_features=im_features,
light=light,
prior_shape=prior_shape,
render_flow=False,
dino_pred=dino_pred,
spp=self.renderer_spp,
class_vector=class_vector,
render_mode='diffuse',
two_sided_shading=False,
num_frames=num_frames,
im_features_map={"original_mvp": original_mvp, "im_features_map": im_features_map} if im_features_map is not None else None # in other views we need to pass the original mvp
)
mask_pred = mask_pred.unsqueeze(1)
if self.few_shot_gan_tex_reso != self.out_image_size:
image_pred = torch.nn.functional.interpolate(image_pred, (self.out_image_size, self.out_image_size), mode='bilinear')
mask_pred = torch.nn.functional.interpolate(mask_pred, (self.out_image_size, self.out_image_size), mode='bilinear')
# image_pred = image_pred.clamp(0, 1)
# mask_pred = mask_pred.clamp(0, 1) # [B, 1, H, W]
if background_for_reverse == 'random':
# as we set a random background for rendering, we also need another random background for input view
# for background, we use the same as random view: a small resolution then upsample
random_bg = torch.rand(self.bs, self.nf, 3, self.few_shot_gan_tex_reso, self.few_shot_gan_tex_reso).to(self.device)
random_bg = torch.nn.functional.interpolate(random_bg.squeeze(1), (self.out_image_size, self.out_image_size), mode='bilinear').unsqueeze(1)
iv_mask_pred = iv_mask_pred.unsqueeze(2).repeat(1, 1, 3, 1, 1)
iv_image_pred = iv_image_pred * iv_mask_pred + random_bg * (1. - iv_mask_pred)
iv_image_pred = iv_image_pred.squeeze(1)
random_bg_gt = torch.rand(self.bs, self.nf, 3, self.few_shot_gan_tex_reso, self.few_shot_gan_tex_reso).to(self.device)
random_bg_gt = torch.nn.functional.interpolate(random_bg_gt.squeeze(1), (self.out_image_size, self.out_image_size), mode='bilinear').unsqueeze(1)
mask_gt = mask_gt.unsqueeze(2).repeat(1, 1, 3, 1, 1)
image_gt = image_gt * mask_gt + random_bg_gt * (1. - mask_gt)
image_gt = image_gt.squeeze(1)
elif background_for_reverse == 'random-pure':
# the background is random but with one color
random_values = torch.rand(b)
random_bg = random_values[..., None, None, None, None].repeat(1, 1, 3, self.few_shot_gan_tex_reso, self.few_shot_gan_tex_reso).to(self.device)
random_bg = torch.nn.functional.interpolate(random_bg.squeeze(1), (self.out_image_size, self.out_image_size), mode='bilinear').unsqueeze(1)
iv_mask_pred = iv_mask_pred.unsqueeze(2).repeat(1, 1, 3, 1, 1)
iv_image_pred = iv_image_pred * iv_mask_pred + random_bg * (1. - iv_mask_pred)
iv_image_pred = iv_image_pred.squeeze(1)
random_values_gt = torch.rand(b)
random_bg_gt = random_values_gt[..., None, None, None, None].repeat(1, 1, 3, self.few_shot_gan_tex_reso, self.few_shot_gan_tex_reso).to(self.device)
random_bg_gt = torch.nn.functional.interpolate(random_bg_gt.squeeze(1), (self.out_image_size, self.out_image_size), mode='bilinear').unsqueeze(1)
mask_gt = mask_gt.unsqueeze(2).repeat(1, 1, 3, 1, 1)
image_gt = image_gt * mask_gt + random_bg_gt * (1. - mask_gt)
image_gt = image_gt.squeeze(1)
elif background_for_reverse == 'none':
iv_image_pred = iv_image_pred.squeeze(1)
iv_mask_pred = iv_mask_pred.unsqueeze(2).repeat(1, 1, 3, 1, 1)
# image_gt = image_gt * mask_gt + random_bg_gt * (1. - mask_gt)
mask_gt = mask_gt.unsqueeze(2).repeat(1, 1, 3, 1, 1)
image_gt = image_gt * mask_gt
image_gt = image_gt.squeeze(1)
else:
raise NotImplementedError
# image_gt = torch.nn.functional.interpolate(image_gt, (32, 32), mode='bilinear')
# image_gt = torch.nn.functional.interpolate(image_gt, (256, 256), mode='bilinear')
# we need to let discriminator think this reverse view is Real sample
if self.cfgs.get('few_shot_gan_tex_patch', 0) > 0:
patch_size = torch.randint(self.few_shot_gan_tex_patch, self.few_shot_gan_tex_patch_max, (1,)).item()
# random view
image_pred = self.generate_patch_crop(image_pred, mask_pred, patch_size, self.few_shot_gan_tex_patch_num)
# input view
iv_image_pred = self.generate_patch_crop(iv_image_pred, iv_mask_pred.squeeze(1)[:, 0:1, :, :], patch_size, self.few_shot_gan_tex_patch_num)
# gt view
image_gt = self.generate_patch_crop(image_gt, mask_gt.squeeze(1)[:, 0:1, :, :], patch_size, self.few_shot_gan_tex_patch_num)
return_loss = {}
if self.few_shot_gan_tex:
# here we compute the fake sample as real loss
gan_tex_loss = 0.0
if 'rv' in self.few_shot_gan_tex_fake:
d_rv = self.discriminator_texture(image_pred)
gan_tex_loss_rv = discriminator_architecture.bce_loss_target(d_rv, 1)
gan_tex_loss += gan_tex_loss_rv
if 'iv' in self.few_shot_gan_tex_fake:
d_iv = self.discriminator_texture(iv_image_pred)
gan_tex_loss_iv = discriminator_architecture.bce_loss_target(d_iv, 1)
gan_tex_loss += gan_tex_loss_iv
return_loss['gan_tex_loss'] = gan_tex_loss
if self.few_shot_clip_tex:
clip_tex_loss_rv_iv = self.compute_clip_loss(image_pred, iv_image_pred.unsqueeze(1), category='none')
clip_tex_loss_rv_gt = self.compute_clip_loss(image_pred, image_gt.unsqueeze(1), category='none')
clip_tex_loss = clip_tex_loss_rv_iv['clip_all_loss'] + clip_tex_loss_rv_gt['clip_all_loss']
return_loss['clip_tex_loss'] = clip_tex_loss
return_aux = {
'gan_tex_render_image': image_pred.clone().clamp(0, 1),
'gan_tex_inpview_image': iv_image_pred.clone().clamp(0, 1),
'gan_tex_gt_image': image_gt.clone().clamp(0, 1)
}
with torch.no_grad():
# self.record_image_iv = iv_image_pred.clone().clamp(0, 1)
# self.record_image_rv = image_pred.clone().clamp(0, 1)
# self.record_image_gt = image_gt.clone().clamp(0, 1)
self.record_image_iv = iv_image_pred.clone()
self.record_image_rv = image_pred.clone()
self.record_image_gt = image_gt.clone()
return return_loss, return_aux
def compute_mask_distribution_loss(self, category, w2c_pred, shape, prior_shape, texture, dino_pred, im_features, light, class_vector, num_frames, im_features_map, bins=360):
delta_angle = 2 * np.pi / bins
b = len(shape)
if self.random_mask_law == 'batch_swap':
# shuffle in predicted poses
rand_degree_1 = torch.randperm(int(w2c_pred.shape[0] // 2))
rand_degree_2 = torch.randperm(w2c_pred.shape[0] - int(w2c_pred.shape[0] // 2)) + int(w2c_pred.shape[0] // 2)
rand_degree = torch.cat([rand_degree_2, rand_degree_1], dim=0).long().to(w2c_pred.device)
w2c = w2c_pred[rand_degree]
proj = util.perspective(self.crop_fov_approx / 180 * np.pi, 1, n=0.1, f=1000.0).repeat(b, 1, 1).to(self.device)
mvp = torch.bmm(proj, w2c)
campos = -w2c[:, :3, 3]
elif self.random_mask_law == 'batch_swap_noy':
# shuffle in predicted poses
rand_degree_1 = torch.randperm(int(w2c_pred.shape[0] // 2))
rand_degree_2 = torch.randperm(w2c_pred.shape[0] - int(w2c_pred.shape[0] // 2)) + int(w2c_pred.shape[0] // 2)
rand_degree = torch.cat([rand_degree_2, rand_degree_1], dim=0).long().to(w2c_pred.device)
w2c = w2c_pred[rand_degree]
# we don't random swap the y-translation in discriminator loss
w2c[:, 1, 3] = w2c_pred[:, 1, 3]
proj = util.perspective(self.crop_fov_approx / 180 * np.pi, 1, n=0.1, f=1000.0).repeat(b, 1, 1).to(self.device)
mvp = torch.bmm(proj, w2c)
campos = -w2c[:, :3, 3]
elif self.random_mask_law == 'random_azimuth':
# the render rotation matrix is different
rand_degree = torch.randint(bins, [b])
delta_angle = delta_angle * rand_degree
delta_rot_matrix = []
for i in range(b):
angle = delta_angle[i].item()
angle_matrix = torch.FloatTensor([
[np.cos(angle), 0, np.sin(angle), 0],
[0, 1, 0, 0],
[-np.sin(angle), 0, np.cos(angle), 0],
[0, 0, 0, 1],
]).to(self.device)
delta_rot_matrix.append(angle_matrix)
delta_rot_matrix = torch.stack(delta_rot_matrix, dim=0)
w2c = torch.FloatTensor(np.diag([1., 1., 1., 1]))
w2c[:3, 3] = torch.FloatTensor([0, 0, -self.cam_pos_z_offset *1.4])
w2c = w2c.repeat(b, 1, 1).to(self.device)
# use the predicted transition
w2c_pred = w2c_pred.detach()
w2c[:, :3, 3] = w2c_pred[:b][:, :3, 3]
proj = util.perspective(self.crop_fov_approx / 180 * np.pi, 1, n=0.1, f=1000.0).repeat(b, 1, 1).to(self.device)
mvp = torch.bmm(proj, w2c)
campos = -w2c[:, :3, 3]
mvp = torch.matmul(mvp, delta_rot_matrix)
campos = torch.matmul(delta_rot_matrix[:,:3,:3].transpose(2,1), campos[:,:,None])[:,:,0]
elif self.random_mask_law == 'random_all':
# the render rotation matrix is different, and actually the translation are just pre-set
rand_degree = torch.randint(bins, [b])
delta_angle = delta_angle * rand_degree
delta_rot_matrix = []
for i in range(b):
angle = delta_angle[i].item()
angle_matrix = torch.FloatTensor([
[np.cos(angle), 0, np.sin(angle), 0],
[0, 1, 0, 0],
[-np.sin(angle), 0, np.cos(angle), 0],
[0, 0, 0, 1],
]).to(self.device)
delta_rot_matrix.append(angle_matrix)
delta_rot_matrix = torch.stack(delta_rot_matrix, dim=0)
w2c = torch.FloatTensor(np.diag([1., 1., 1., 1]))
w2c[:3, 3] = torch.FloatTensor([0, 0, -self.cam_pos_z_offset *1.4])
w2c = w2c.repeat(b, 1, 1).to(self.device)
proj = util.perspective(self.crop_fov_approx / 180 * np.pi, 1, n=0.1, f=1000.0).repeat(b, 1, 1).to(self.device)
mvp = torch.bmm(proj, w2c)
campos = -w2c[:, :3, 3]
mvp = torch.matmul(mvp, delta_rot_matrix)
campos = torch.matmul(delta_rot_matrix[:,:3,:3].transpose(2,1), campos[:,:,None])[:,:,0]
else:
raise NotImplementedError
resolution = (self.out_image_size, self.out_image_size)
# render the articulated shape
mesh = shape
if self.enable_clip:
resolution = (self.clip_render_size, self.clip_render_size)
set_requires_grad(texture, False)
image_pred, mask_pred, _, _, _, _ = self.render(
mesh,
texture,
mvp,
w2c,
campos,
resolution,
background='none',
im_features=im_features,
light=light,
prior_shape=prior_shape,
render_flow=False,
dino_pred=dino_pred,
spp=self.renderer_spp,
class_vector=class_vector,
render_mode='diffuse',
two_sided_shading=False,
num_frames=num_frames,
im_features_map=im_features_map
)
if resolution[0] != self.out_image_size:
image_pred = torch.nn.functional.interpolate(image_pred, (self.out_image_size, self.out_image_size), mode='bilinear')
mask_pred = torch.nn.functional.interpolate(mask_pred.unsqueeze(1), (self.out_image_size, self.out_image_size), mode='bilinear').squeeze(1)
else:
_, mask_pred, _, _, _, _ = self.render(
mesh,
None,
mvp,
w2c,
campos,
resolution,
background='none',
im_features=None,
light=None,
prior_shape=prior_shape,
render_flow=False,
dino_pred=None,
class_vector=class_vector,
render_mode='diffuse',
two_sided_shading=False,
num_frames=num_frames,
im_features_map=None
)
image_pred = None
# TODO: disable mask distribution and isolate mask discriminator loss
# mask_distribution = self.class_mask_distribution[category]
# mask_distribution = torch.Tensor(mask_distribution).to(self.device).unsqueeze(0).repeat(b, 1, 1)
mask_distribution = torch.Tensor(self.class_mask_distribution["zebra"]).to(self.device).unsqueeze(0).repeat(b, 1, 1)
if self.mask_distribution_average:
# if use mask_distribution_average, then first average across batch then compute the loss
mask_pred = mask_pred.mean(dim=0).unsqueeze(0).repeat(b, 1, 1)
mask_pred = mask_pred.clamp(0,1)
mask_distribution = mask_distribution.clamp(0,1)
distribution_loss = torch.nn.functional.binary_cross_entropy(mask_pred, mask_distribution)
out_loss = {'mask_distribution_loss': 0 * distribution_loss}
out_aux = {
'mask_random_pred': mask_pred.unsqueeze(1),
'mask_distribution': mask_distribution.unsqueeze(1),
'rand_degree': rand_degree
}
if self.enable_clip:
out_aux.update({'random_render_image': image_pred})
return out_loss, out_aux
def use_line_correct_valid_mask(self, mask_valid, p1, p2, mvp, mask_gt):
line = torch.cat([p1.unsqueeze(-2), p2.unsqueeze(-2)], dim=-2) # [B, 2, 3]
line_world4 = torch.cat([line, torch.ones_like(line[..., :1])], -1)
line_clip4 = line_world4 @ mvp.transpose(-1, -2)
line_uv = line_clip4[..., :2] / line_clip4[..., 3:4]
line_uv = line_uv.detach()
b, _, n_uv = line_uv.shape
line_uv = line_uv * torch.Tensor([mask_valid.shape[-2] // 2, mask_valid.shape[-1] // 2]).to(line_uv.device).unsqueeze(0).unsqueeze(-1).repeat(b, 1, n_uv)
line_uv = line_uv + torch.Tensor([mask_valid.shape[-2] // 2, mask_valid.shape[-1] // 2]).to(line_uv.device).unsqueeze(0).unsqueeze(-1).repeat(b, 1, n_uv)
from pdb import set_trace; set_trace()
line_slope = (line_uv[:, 0, 1] - line_uv[:, 1, 1]) / (line_uv[:, 0, 0] - line_uv[:, 1, 0])
uv = np.mgrid[0:mask_valid.shape[-2], 0:mask_valid.shape[-1]].astype(np.int32)
uv = torch.from_numpy(np.flip(uv, axis=0).copy()).float().unsqueeze(0).repeat(b, 1, 1, 1) # [B, 2, 256, 256]
tmp_u = uv[:, 0, ...][mask_gt[:, 0, ...].bool()]
tmp_v = uv[:, 1, ...][mask_gt[:, 0, ...].bool()]
return mask_valid
def discriminator_step(self):
mask_gt = self.record_mask_gt
mask_pred = self.record_mask_iv
mask_random_pred = self.record_mask_rv
self.optimizerDiscriminator.zero_grad()
# the random view mask are False
d_random_pred = self.mask_disc(mask_random_pred)
disc_loss = discriminator_architecture.bce_loss_target(d_random_pred, 0) # in gen loss, train it to be real
grad_loss = 0.0
count = 1
discriminator_loss_rv = disc_loss.detach()
discriminator_loss_gt = 0.0
discriminator_loss_iv = 0.
d_gt = None
d_iv = None
if self.disc_gt:
mask_gt.requires_grad_()
d_gt = self.mask_disc(mask_gt)
if d_gt.requires_grad is False:
# in the test case
disc_gt_loss = discriminator_architecture.bce_loss_target(d_gt, 1)
else:
grad_penalty = self.disc_reg_mul * discriminator_architecture.compute_grad2(d_gt, mask_gt)
disc_gt_loss = discriminator_architecture.bce_loss_target(d_gt, 1) + grad_penalty
grad_loss += grad_penalty
disc_loss = disc_loss + disc_gt_loss
discriminator_loss_gt = disc_gt_loss
count = count + 1
if self.disc_iv:
mask_pred.requires_grad_()
d_iv = self.mask_disc(mask_pred)
if self.disc_iv_label == 'Real':
if d_iv.requires_grad is False:
# in the test case
disc_iv_loss = discriminator_architecture.bce_loss_target(d_iv, 1)
else:
grad_penalty = self.disc_reg_mul * discriminator_architecture.compute_grad2(d_iv, mask_pred)
disc_iv_loss = discriminator_architecture.bce_loss_target(d_iv, 1) + grad_penalty
grad_loss += grad_penalty
else:
disc_iv_loss = discriminator_architecture.bce_loss_target(d_iv, 0)
disc_loss = disc_loss + disc_iv_loss
count = count + 1
discriminator_loss_iv = disc_iv_loss
disc_loss = disc_loss / count
grad_loss = grad_loss / count
self.discriminator_loss = disc_loss * self.discriminator_loss_weight
self.discriminator_loss.backward()
self.optimizerDiscriminator.step()
self.discriminator_loss = 0.
return {
'discriminator_loss': disc_loss,
'discriminator_loss_rv': discriminator_loss_rv,
'discriminator_loss_iv': discriminator_loss_iv,
'discriminator_loss_gt': discriminator_loss_gt,
'd_rv': d_random_pred,
'd_iv': d_iv if d_iv is not None else None,
'd_gt': d_gt if d_gt is not None else None,
}, grad_loss
def compute_mask_disc_loss_gen(self, mask_gt, mask_pred, mask_random_pred, category_name=None, condition_feat=None):
# mask_gt[mask_gt < 1.] = 0.
# mask_pred[mask_pred > 0.] = 1.
# mask_random_pred[mask_random_pred > 0.] = 1.
if not self.mask_disc_feat_condition:
try:
class_idx = list(self.netPrior.category_id_map.keys()).index(category_name)
except:
class_idx = 100
num_classes = len(list(self.netPrior.category_id_map.keys()))
class_idx = torch.LongTensor([class_idx])
# class_one_hot = torch.nn.functional.one_hot(class_idx, num_classes=7).unsqueeze(-1).unsqueeze(-1).to(mask_gt.device) # [1, 7, 1, 1]
class_one_hot = torch.nn.functional.one_hot(class_idx, num_classes=num_classes).unsqueeze(-1).unsqueeze(-1).to(mask_gt.device)
class_one_hot = class_one_hot.repeat(mask_gt.shape[0], 1, mask_gt.shape[-2], mask_gt.shape[-1])
# TODO: a hack try here
class_one_hot = class_one_hot[:, :(self.mask_disc.in_dim-1), :, :]
else:
class_one_hot = condition_feat.detach()
class_one_hot = class_one_hot.reshape(1, -1, 1, 1).repeat(mask_gt.shape[0], 1, mask_gt.shape[-2], mask_gt.shape[-1])
# concat
mask_gt = torch.cat([mask_gt, class_one_hot], dim=1)
mask_pred = torch.cat([mask_pred, class_one_hot], dim=1)
mask_random_pred = torch.cat([mask_random_pred, class_one_hot], dim=1)
# mask shape are all [B,1,256,256]
# the random view mask are False
d_random_pred = self.mask_disc(mask_random_pred)
disc_loss = discriminator_architecture.bce_loss_target(d_random_pred, 1) # in gen loss, train it to be real
count = 1
disc_loss_rv = disc_loss.detach()
disc_loss_iv = 0.0
if self.disc_iv:
if self.disc_iv_label != 'Real': # consider the input view also fake
d_iv = self.mask_disc(mask_pred)
disc_iv_loss = discriminator_architecture.bce_loss_target(d_iv, 1) # so now we need to train them to be real
disc_loss = disc_loss + disc_iv_loss
count = count + 1
disc_loss_iv = disc_iv_loss.detach()
disc_loss = disc_loss / count
# record the masks for discriminator training
self.record_mask_gt = mask_gt.clone().detach()
self.record_mask_iv = mask_pred.clone().detach()
self.record_mask_rv = mask_random_pred.clone().detach()
return {
'mask_disc_loss': disc_loss,
'mask_disc_loss_rv': disc_loss_rv,
'mask_disc_loss_iv': disc_loss_iv,
}
def forward(self, batch, epoch, iter, is_train=True, viz_logger=None, total_iter=None, save_results=False, save_dir=None, which_data='', logger_prefix='', is_training=True, bank_embedding=None):
batch = [x.to(self.device) if x is not None and isinstance(x, torch.Tensor) else x for x in batch]
input_image, mask_gt, mask_dt, mask_valid, flow_gt, bbox, bg_image, dino_feat_im, dino_cluster_im, seq_idx, frame_idx, category_name = batch
# if save_results:
# save_for_pkl = {
# "image": input_image.cpu(),
# "mask_gt": mask_gt.cpu(),
# "mask_dt": mask_dt.cpu(),
# "mask_valid": mask_valid.cpu(),
# "flow_gt": None,
# "bbox": bbox.cpu(),
# "bg_image": bg_image.cpu(),
# "dino_feat_im": dino_feat_im.cpu(),
# "dino_cluster_im": dino_cluster_im.cpu(),
# "seq_idx": seq_idx.cpu(),
# "frame_idx": frame_idx.cpu(),
# "category_name": category_name
# }
batch_size, num_frames, _, h0, w0 = input_image.shape # BxFxCxHxW
self.bs = batch_size
self.nf = num_frames
mid_img_idx = int((input_image.shape[1]-1)//2)
# print(f"mid_img_idx: {mid_img_idx}")
h = w = self.out_image_size
def collapseF(x):
return None if x is None else x.view(batch_size * num_frames, *x.shape[2:])
def expandF(x):
return None if x is None else x.view(batch_size, num_frames, *x.shape[1:])
if flow_gt.dim() == 2: # dummy tensor for not loading flow
flow_gt = None
if dino_cluster_im.dim() == 2: # dummy tensor for not loading dino clusters
dino_cluster_im = None
dino_cluster_im_gt = None
else:
dino_cluster_im_gt = expandF(torch.nn.functional.interpolate(collapseF(dino_cluster_im), size=[h, w], mode="nearest"))
seq_idx = seq_idx.squeeze(1)
# seq_idx = seq_idx * 0 # single sequnce model
frame_id, crop_x0, crop_y0, crop_w, crop_h, full_w, full_h, sharpness, label = bbox.unbind(2) # BxFx7
bbox = torch.stack([crop_x0, crop_y0, crop_w, crop_h], 2)
mask_gt = (mask_gt[:, :, 0, :, :] > 0.9).float() # BxFxHxW
mask_dt = mask_dt / self.in_image_size
if which_data != 'video':
flow_gt = None
aux_viz = {}
## GT
image_gt = input_image
if self.out_image_size != self.in_image_size:
image_gt = expandF(torch.nn.functional.interpolate(collapseF(image_gt), size=[h, w], mode='bilinear'))
if flow_gt is not None:
flow_gt = torch.nn.functional.interpolate(flow_gt.view(batch_size*(num_frames-1), 2, h0, w0), size=[h, w], mode="bilinear").view(batch_size, num_frames-1, 2, h, w)
self.train_pose_only = False
if epoch in self.pose_epochs:
if (total_iter // self.pose_iters) % 2 == 0:
self.train_pose_only = True
## flip input and pose
if epoch in self.pose_xflip_recon_epochs:
input_image_xflip = input_image.flip(-1)
input_image_xflip_flag = torch.randint(0, 2, (batch_size, num_frames), device=input_image.device)
input_image = input_image * (1 - input_image_xflip_flag[:,:,None,None,None]) + input_image_xflip * input_image_xflip_flag[:,:,None,None,None]
else:
input_image_xflip_flag = None
## 1st pose hypothesis with original predictions
# ==============================================================================================
# Predict prior mesh.
# ==============================================================================================
if self.enable_prior:
if self.world_size > 1:
if epoch < self.dmtet_grid_smaller_epoch:
if self.netPrior_ddp.module.netShape.grid_res != self.dmtet_grid_smaller:
self.netPrior_ddp.module.netShape.load_tets(self.dmtet_grid_smaller)
else:
if self.netPrior_ddp.module.netShape.grid_res != self.dmtet_grid:
self.netPrior_ddp.module.netShape.load_tets(self.dmtet_grid)
else:
if epoch < self.dmtet_grid_smaller_epoch:
if self.netPrior.netShape.grid_res != self.dmtet_grid_smaller:
self.netPrior.netShape.load_tets(self.dmtet_grid_smaller)
else:
if self.netPrior.netShape.grid_res != self.dmtet_grid:
self.netPrior.netShape.load_tets(self.dmtet_grid)
perturb_sdf = self.perturb_sdf if is_train else False
# DINO prior category specific - DOR
if self.world_size > 1:
prior_shape, dino_pred, classes_vectors = self.netPrior_ddp(category_name=category_name[0], perturb_sdf=perturb_sdf, total_iter=total_iter, is_training=is_training, class_embedding=bank_embedding)
else:
prior_shape, dino_pred, classes_vectors = self.netPrior(category_name=category_name[0], perturb_sdf=perturb_sdf, total_iter=total_iter, is_training=is_training, class_embedding=bank_embedding)
else:
prior_shape = None
raise NotImplementedError
if self.world_size > 1:
shape, pose_raw, pose, mvp, w2c, campos, texture, im_features, dino_feat_im_calc, deformation, arti_params, light, forward_aux = self.netInstance_ddp(category_name, input_image, prior_shape, epoch, dino_feat_im, dino_cluster_im, total_iter, is_training=is_training) # frame dim collapsed N=(B*F)
else:
Instance_out = self.netInstance(category_name, input_image, prior_shape, epoch, dino_feat_im, dino_cluster_im, total_iter, is_training=is_training) # frame dim collapsed N=(B*F)
# if no patch_out as output from netInstance, then set im_features_map as None in following part
if len(Instance_out) == 13:
shape, pose_raw, pose, mvp, w2c, campos, texture, im_features, dino_feat_im_calc, deformation, arti_params, light, forward_aux = Instance_out
im_features_map = None
else:
shape, pose_raw, pose, mvp, w2c, campos, texture, im_features, dino_feat_im_calc, deformation, arti_params, light, forward_aux, im_features_map = Instance_out
# if save_results:
# save_for_pkl.update(
# {
# "pose_raw": pose_raw.cpu(),
# "pose": pose.cpu(),
# "mvp": mvp.cpu(),
# "w2c": w2c.cpu(),
# "campos": campos.cpu(),
# "campos_z_offset": self.netInstance.cam_pos_z_offset
# }
# )
if self.calc_dino_features == True:
# get the shape parameters of the tensor
batch_size, height, width, channels = dino_feat_im_calc.shape #3 X 384 X 32 X 32
# reshape the tensor to have 2 dimensions, with the last dimension being preserved
dino_feat_im = dino_feat_im_calc.reshape(batch_size , height, -1)
# normalize the tensor using L2 normalization
norm = torch.norm(dino_feat_im, dim=-1, keepdim=True)
dino_feat_im = dino_feat_im / norm
# reshape the tensor back to the original shape with an additional singleton dimension along the first dimension
dino_feat_im = dino_feat_im.reshape(batch_size, height, width, channels)
dino_feat_im = dino_feat_im.unsqueeze(1)
if dino_feat_im.dim() == 2: # dummy tensor for not loading dino features
dino_feat_im = None
dino_feat_im_gt = None
else:
dino_feat_im_gt = expandF(torch.nn.functional.interpolate(collapseF(dino_feat_im), size=[h, w], mode="bilinear"))[:, :, :self.dino_feature_recon_dim]
rot_logit = forward_aux['rot_logit']
rot_idx = forward_aux['rot_idx']
rot_prob = forward_aux['rot_prob']
if self.using_bonevel_smooth_loss:
posed_bones = forward_aux['posed_bones']
else:
posed_bones = None
aux_viz.update(forward_aux)
if self.train_pose_only:
safe_detach = lambda x: x.detach() if x is not None else None
prior_shape = safe_detach(prior_shape)
shape = safe_detach(shape)
im_features = safe_detach(im_features)
arti_params = safe_detach(arti_params)
deformation = safe_detach(deformation)
set_requires_grad(texture, False)
set_requires_grad(light, False)
set_requires_grad(dino_pred, False)
else:
set_requires_grad(texture, True)
set_requires_grad(light, True)
set_requires_grad(dino_pred, True)
render_flow = self.render_flow and num_frames > 1 #false
# from IPython import embed; embed()
# if num_frames > 1 and self.smooth_type == 'rend':
# print("rendererr smoothness !!!!")
# image_pred, mask_pred, flow_pred, dino_feat_im_pred, albedo, shading = self.render(shape, texture, mvp, w2c, campos, (h, w), background=self.background_mode, im_features=im_features[torch.randperm(im_features.size(0))], light=light, prior_shape=prior_shape, render_flow=render_flow, dino_pred=dino_pred, num_frames=num_frames, spp=self.renderer_spp) #the real rendering process
# else:
# print("regular render")
#print("a cecond before rendering .... need to get the correct label and the correct vector")
#print("label", label)
#print("classes_vectors", classes_vectors)
#print("im_features", im_features.shape)
class_vector = None
if classes_vectors is not None:
if len(classes_vectors.shape) == 1:
class_vector = classes_vectors
else:
class_vector = classes_vectors[self.netPrior.category_id_map[category_name[0]], :]
image_pred, mask_pred, flow_pred, dino_feat_im_pred, albedo, shading = self.render(shape, texture, mvp, w2c, campos, (h, w), background=self.background_mode, im_features=im_features, light=light, prior_shape=prior_shape, render_flow=render_flow, dino_pred=dino_pred, class_vector=class_vector[None, :].expand(batch_size * num_frames, -1), num_frames=num_frames, spp=self.renderer_spp, im_features_map=im_features_map) #the real rendering process
image_pred, mask_pred, flow_pred, dino_feat_im_pred = map(expandF, (image_pred, mask_pred, flow_pred, dino_feat_im_pred))
if flow_pred is not None:
flow_pred = flow_pred[:, :-1] # Bx(F-1)x2xHxW
if self.blur_mask:
sigma = max(0.5, 3 * (1 - total_iter / self.blur_mask_iter))
if sigma > 0.5:
mask_gt = util.blur_image(mask_gt, kernel_size=9, sigma=sigma, mode='gaussian')
# mask_pred = util.blur_image(mask_pred, kernel_size=7, mode='average')
# back_line_p1 = forward_aux['posed_bones'][:, :, 3, -1].squeeze(1) # [8, 3]
# back_line_p2 = forward_aux['posed_bones'][:, :, 7, -1].squeeze(1)
# mask_valid = self.use_line_correct_valid_mask(mask_valid, back_line_p1, back_line_p2, mvp, mask_gt)
losses = self.compute_reconstruction_losses(image_pred, image_gt, mask_pred, mask_gt, mask_dt, mask_valid, flow_pred, flow_gt, dino_feat_im_gt, dino_feat_im_pred, background_mode=self.background_mode, reduce=False)
## TODO: assume flow loss is not used
logit_loss_target = torch.zeros_like(expandF(rot_logit))
final_losses = {}
for name, loss in losses.items():
if name == 'flow_loss':
continue
loss_weight_logit = self.cfgs.get(f"{name}_weight", 0.)
if isinstance(loss_weight_logit, dict):
loss_weight_logit = self.parse_dict_definition(loss_weight_logit, total_iter)
# from IPython import embed; embed()
# print("-"*10)
# print(f"{name}_weight: {loss_weight_logit}.")
# print(f"logit_loss_target.shape: {logit_loss_target.shape}.")
# print(f"loss.shape: {loss.shape}.")
# if (name in ['flow_loss'] and epoch not in self.flow_loss_epochs) or (name in ['rgb_loss', 'perceptual_loss'] and epoch not in self.texture_epochs):
# if name in ['flow_loss', 'rgb_loss', 'perceptual_loss']:
# loss_weight_logit = 0.
if name in ['sdf_bce_reg_loss', 'sdf_gradient_reg_loss', 'sdf_inflate_reg_loss']:
if total_iter >= self.sdf_reg_decay_start_iter:
decay_rate = max(0, 1 - (total_iter-self.sdf_reg_decay_start_iter) / 10000)
loss_weight_logit = max(loss_weight_logit * decay_rate, self.cfgs.get(f"{name}_min_weight", 0.))
if name in ['dino_feat_im_loss']:
dino_feat_im_loss_multipler = self.cfgs.get("logit_loss_dino_feat_im_loss_multiplier", 1.)
if isinstance(dino_feat_im_loss_multipler, dict):
dino_feat_im_loss_multipler = self.parse_dict_definition(dino_feat_im_loss_multipler, total_iter)
loss_weight_logit = loss_weight_logit * dino_feat_im_loss_multipler
# loss_weight_logit = loss_weight_logit * self.cfgs.get("logit_loss_dino_feat_im_loss_multiplier", 1.)
if loss_weight_logit > 0:
logit_loss_target += loss * loss_weight_logit
if self.netInstance.rot_rep in ['quadlookat', 'octlookat']:
loss = loss * rot_prob.detach().view(batch_size, num_frames)[:, :loss.shape[1]] *self.netInstance.num_pose_hypos
if name == 'flow_loss' and num_frames > 1:
ri = rot_idx.view(batch_size, num_frames)
same_rot_idx = (ri[:, 1:] == ri[:, :-1]).float()
loss = loss * same_rot_idx
final_losses[name] = loss.mean()
final_losses['logit_loss'] = ((expandF(rot_logit) - logit_loss_target.detach())**2.).mean()
## mask distribution loss
mask_distribution_aux = None
if self.enable_mask_distribution:
if total_iter % self.mask_distribution_loss_freq == 0:
mask_distribution_loss, mask_distribution_aux = self.compute_mask_distribution_loss(category_name[0], w2c, shape, prior_shape, texture, dino_pred, im_features, light, class_vector[None, :].expand(batch_size * num_frames, -1), num_frames, im_features_map)
final_losses.update(mask_distribution_loss)
# this also follows the iteration frequency
if self.enable_clip:
random_render_image = mask_distribution_aux["random_render_image"]
clip_all_loss = self.compute_clip_loss(random_render_image, image_pred, category_name[0]) # a dict
final_losses.update(clip_all_loss)
# implement the mask discriminator
if self.enable_disc and (self.mask_discriminator_iter[0] < total_iter) and (self.mask_discriminator_iter[1] > total_iter):
disc_loss = self.compute_mask_disc_loss_gen(mask_gt, mask_pred, mask_distribution_aux['mask_random_pred'], category_name=category_name[0], condition_feat=class_vector)
final_losses.update(disc_loss)
# implement the gan training for local texture in fine-tuning
gan_tex_aux = None
if (self.few_shot_gan_tex and viz_logger is None) or (self.few_shot_gan_tex and viz_logger is not None and logger_prefix == 'train_'):
gan_tex_loss, gan_tex_aux = self.compute_gan_tex_loss(category_name[0], image_gt, mask_gt, image_pred, mask_pred, w2c, campos, shape, prior_shape, texture, dino_pred, im_features, light, class_vector[None, :].expand(batch_size * num_frames, -1), num_frames, im_features_map)
final_losses.update(gan_tex_loss)
# implement the memory bank related loss
if bank_embedding is not None:
batch_embedding = bank_embedding[0] # [d]
embeddings = bank_embedding[1] # [B, d]
bank_mean_dist = torch.nn.functional.mse_loss(embeddings, batch_embedding.unsqueeze(0).repeat(batch_size, 1))
final_losses.update({'bank_mean_dist_loss': bank_mean_dist})
## regularizers
regularizers, aux = self.compute_regularizers(shape, prior_shape, input_image, dino_feat_im, pose_raw, input_image_xflip_flag, arti_params, deformation, mid_img_idx, posed_bones=posed_bones, class_vector=class_vector.detach() if class_vector is not None else None)
final_losses.update(regularizers)
aux_viz.update(aux)
total_loss = 0
for name, loss in final_losses.items():
loss_weight = self.cfgs.get(f"{name}_weight", 0.)
if isinstance(loss_weight, dict):
loss_weight = self.parse_dict_definition(loss_weight, total_iter)
if loss_weight <= 0:
continue
if self.train_pose_only:
if name not in ['silhouette_loss', 'silhouette_dt_loss', 'silhouette_inv_dt_loss', 'flow_loss', 'pose_xflip_reg_loss', 'lookat_zflip_loss', 'dino_feat_im_loss']:
continue
if epoch not in self.flow_loss_epochs:
if name in ['flow_loss']:
continue
if epoch not in self.texture_epochs:
if name in ['rgb_loss', 'perceptual_loss']:
continue
if epoch not in self.lookat_zflip_loss_epochs:
if name in ['lookat_zflip_loss']:
continue
if name in ['mesh_laplacian_smoothing_loss', 'mesh_normal_consistency_loss']:
if total_iter < self.cfgs.get('mesh_reg_start_iter', 0):
continue
if epoch >= self.mesh_reg_decay_epoch:
decay_rate = self.mesh_reg_decay_rate ** (epoch - self.mesh_reg_decay_epoch)
loss_weight = max(loss_weight * decay_rate, self.cfgs.get(f"{name}_min_weight", 0.))
if epoch not in self.sdf_inflate_reg_loss_epochs:
if name in ['sdf_inflate_reg_loss']:
continue
if self.iter_arti_reg_loss_start is not None:
if total_iter <= self.iter_arti_reg_loss_start:
if name in ['arti_reg_loss']:
continue
else:
if epoch not in self.arti_reg_loss_epochs:
if name in ['arti_reg_loss']:
continue
if name in ['sdf_bce_reg_loss', 'sdf_gradient_reg_loss', 'sdf_inflate_reg_loss']:
if total_iter >= self.sdf_reg_decay_start_iter:
decay_rate = max(0, 1 - (total_iter-self.sdf_reg_decay_start_iter) / 10000)
loss_weight = max(loss_weight * decay_rate, self.cfgs.get(f"{name}_min_weight", 0.))
total_loss += loss * loss_weight
self.total_loss += total_loss # reset to 0 in backward step
if torch.isnan(self.total_loss):
print("NaN in loss...")
import ipdb; ipdb.set_trace()
final_losses['logit_loss_target'] = logit_loss_target.mean()
metrics = {'loss': total_loss, **final_losses}
## log visuals
if viz_logger is not None:
b0 = max(min(batch_size, 16//num_frames), 1)
viz_logger.add_image(logger_prefix+'image/image_gt', misc.image_grid(image_gt.detach().cpu()[:b0,:].reshape(-1,*input_image.shape[2:]).clamp(0,1)), total_iter)
viz_logger.add_image(logger_prefix+'image/image_pred', misc.image_grid(image_pred.detach().cpu()[:b0,:].reshape(-1,*image_pred.shape[2:]).clamp(0,1)), total_iter)
# viz_logger.add_image(logger_prefix+'image/flow_loss_mask', misc.image_grid(flow_loss_mask[:b0,:,:1].reshape(-1,1,*flow_loss_mask.shape[3:]).repeat(1,3,1,1).clamp(0,1)), total_iter)
viz_logger.add_image(logger_prefix+'image/mask_gt', misc.image_grid(mask_gt.detach().cpu()[:b0,:].reshape(-1,*mask_gt.shape[2:]).unsqueeze(1).repeat(1,3,1,1).clamp(0,1)), total_iter)
viz_logger.add_image(logger_prefix+'image/mask_pred', misc.image_grid(mask_pred.detach().cpu()[:b0,:].reshape(-1,*mask_pred.shape[2:]).unsqueeze(1).repeat(1,3,1,1).clamp(0,1)), total_iter)
if self.render_flow and flow_gt is not None:
# if False:
flow_gt = flow_gt.detach().cpu()
flow_gt_viz = torch.cat([flow_gt[:b0], torch.zeros_like(flow_gt[:b0,:,:1])], 2) + 0.5 # -0.5~1.5
flow_gt_viz = torch.nn.functional.pad(flow_gt_viz, pad=[0, 0, 0, 0, 0, 0, 0, 1])
# ## draw marker on large flow frames
# large_flow_marker_mask = torch.zeros_like(flow_gt_viz)
# large_flow_marker_mask[:,:,:,:8,:8] = 1.
# large_flow = torch.cat([self.large_flow, self.large_flow[:,:1] *0.], 1).detach().cpu()[:b0]
# large_flow_marker_mask = large_flow_marker_mask * large_flow[:,:,None,None,None]
# red = torch.FloatTensor([1,0,0])[None,None,:,None,None]
# flow_gt_viz = large_flow_marker_mask * red + (1-large_flow_marker_mask) * flow_gt_viz
viz_logger.add_image(logger_prefix+'image/flow_gt', misc.image_grid(flow_gt_viz.reshape(-1,*flow_gt_viz.shape[2:])), total_iter)
if self.render_flow and flow_pred is not None:
# if False
flow_pred = flow_pred.detach().cpu()
flow_pred_viz = torch.cat([flow_pred[:b0], torch.zeros_like(flow_pred[:b0,:,:1])], 2) + 0.5 # -0.5~1.5
flow_pred_viz = torch.nn.functional.pad(flow_pred_viz, pad=[0, 0, 0, 0, 0, 0, 0, 1])
viz_logger.add_image(logger_prefix+'image/flow_pred', misc.image_grid(flow_pred_viz.reshape(-1,*flow_pred_viz.shape[2:])), total_iter)
if sds_random_images is not None:
viz_logger.add_image(
logger_prefix + 'image/sds_image',
self.vis_sds_image(sds_random_images, sds_aux),
total_iter)
viz_logger.add_image(
logger_prefix + 'image/sds_grad',
self.vis_sds_grads(sds_aux), total_iter)
if mask_distribution_aux is not None:
degree_text = mask_distribution_aux['rand_degree']
mask_random_pred = mask_distribution_aux['mask_random_pred'].detach().cpu().clamp(0, 1)
mask_distribution_data = mask_distribution_aux['mask_distribution'].detach().cpu().clamp(0, 1)
mask_random_pred_image = [misc.add_text_to_image(img, str(text.item())) for img, text in zip(mask_random_pred, degree_text)]
mask_random_pred_image = misc.image_grid(mask_random_pred_image)
mask_distribution_image = misc.image_grid(mask_distribution_data)
viz_logger.add_image(
logger_prefix + 'image/mask_random_pred',
mask_random_pred_image,
total_iter)
viz_logger.add_image(
logger_prefix + 'image/mask_distribution',
mask_distribution_image,
total_iter)
if gan_tex_aux is not None:
gan_tex_render_image = gan_tex_aux['gan_tex_render_image'].detach().cpu().clamp(0, 1)
gan_tex_render_image = misc.image_grid(gan_tex_render_image)
viz_logger.add_image(
logger_prefix + 'image/gan_tex_render_image',
gan_tex_render_image,
total_iter)
gan_tex_render_image_iv = gan_tex_aux['gan_tex_inpview_image'].detach().cpu().clamp(0, 1)
gan_tex_render_image_iv = misc.image_grid(gan_tex_render_image_iv)
viz_logger.add_image(
logger_prefix + 'image/gan_tex_inpview_image',
gan_tex_render_image_iv,
total_iter)
gan_tex_render_image_gt = gan_tex_aux['gan_tex_gt_image'].detach().cpu().clamp(0, 1)
gan_tex_render_image_gt = misc.image_grid(gan_tex_render_image_gt)
viz_logger.add_image(
logger_prefix + 'image/gan_tex_gt_image',
gan_tex_render_image_gt,
total_iter)
# if self.render_flow and flow_gt is not None and flow_pred is not None:
# flow_gt = flow_gt.detach().cpu()
# # flow_gt_viz = torch.cat([flow_gt[:b0], torch.zeros_like(flow_gt[:b0,:,:1])], 2) + 0.5 # -0.5~1.5
# # flow_gt_viz = torch.nn.functional.pad(flow_gt_viz, pad=[0, 0, 0, 0, 0, 0, 0, 1])
# # ## draw marker on large flow frames
# # large_flow_marker_mask = torch.zeros_like(flow_gt_viz)
# # large_flow_marker_mask[:,:,:,:8,:8] = 1.
# # large_flow = torch.cat([self.large_flow, self.large_flow[:,:1] *0.], 1).detach().cpu()[:b0]
# # large_flow_marker_mask = large_flow_marker_mask * large_flow[:,:,None,None,None]
# # red = torch.FloatTensor([1,0,0])[None,None,:,None,None]
# # flow_gt_viz = large_flow_marker_mask * red + (1-large_flow_marker_mask) * flow_gt_viz
# # viz_logger.add_image(logger_prefix+'image/flow_gt', misc.image_grid(flow_gt_viz.reshape(-1,*flow_gt_viz.shape[2:])), total_iter)
# flow_pred = flow_pred.detach().cpu()
# # flow_pred_viz = torch.cat([flow_pred[:b0], torch.zeros_like(flow_pred[:b0,:,:1])], 2) + 0.5 # -0.5~1.5
# # flow_pred_viz = torch.nn.functional.pad(flow_pred_viz, pad=[0, 0, 0, 0, 0, 0, 0, 1])
# flow_gt_pred = torch.cat([flow_gt, flow_pred], dim=-1)
# flow_gt_pred = flow_gt_pred.permute(0,1,3,4,2).detach().cpu().reshape(flow_gt_pred.shape[0]*flow_gt_pred.shape[1],*flow_gt_pred.shape[2:])
# flow_gt_pred = flow_viz.flow_batch_to_images(flow_gt_pred)
# # flow_gt_pred = torch.tensor(flow_gt_pred).permute(0,3,1,2)
# # viz_logger.add_image(logger_prefix+'image/flow_gt_pred', misc.image_grid(flow_gt_pred.reshape(-1,*flow_gt_pred.shape[2:])), total_iter)
# viz_logger.add_image(logger_prefix+'image/flow_gt_pred', misc.image_grid(flow_gt_pred), total_iter)
if light is not None:
param_names = ['dir_x', 'dir_y', 'dir_z', 'int_ambient', 'int_diffuse']
for name, param in zip(param_names, light.light_params.unbind(-1)):
viz_logger.add_histogram(logger_prefix+'light/'+name, param, total_iter)
viz_logger.add_image(
logger_prefix + f'image/albedo',
misc.image_grid(expandF(albedo)[:b0, ...].view(-1, *albedo.shape[1:])),
total_iter)
viz_logger.add_image(
logger_prefix + f'image/shading',
misc.image_grid(expandF(shading)[:b0, ...].view(-1, *shading.shape[1:]).repeat(1, 3, 1, 1) /2.),
total_iter)
viz_logger.add_histogram(logger_prefix+'sdf', self.netPrior.netShape.get_sdf(perturb_sdf=False, class_vector=class_vector), total_iter)
viz_logger.add_histogram(logger_prefix+'coordinates', shape.v_pos, total_iter)
if arti_params is not None:
viz_logger.add_histogram(logger_prefix+'arti_params', arti_params, total_iter)
viz_logger.add_histogram(logger_prefix+'edge_lengths', aux_viz['edge_lengths'], total_iter)
if deformation is not None:
viz_logger.add_histogram(logger_prefix+'deformation', deformation, total_iter)
rot_rep = self.netInstance.rot_rep
if rot_rep == 'euler_angle' or rot_rep == 'soft_calss':
for i, name in enumerate(['rot_x', 'rot_y', 'rot_z', 'trans_x', 'trans_y', 'trans_z']):
viz_logger.add_histogram(logger_prefix+'pose/'+name, pose[...,i], total_iter)
elif rot_rep == 'quaternion':
for i, name in enumerate(['qt_0', 'qt_1', 'qt_2', 'qt_3', 'trans_x', 'trans_y', 'trans_z']):
viz_logger.add_histogram(logger_prefix+'pose/'+name, pose[...,i], total_iter)
rot_euler = pytorch3d.transforms.matrix_to_euler_angles(pytorch3d.transforms.quaternion_to_matrix(pose.detach().cpu()[...,:4]), convention='XYZ')
for i, name in enumerate(['rot_x', 'rot_y', 'rot_z']):
viz_logger.add_histogram(logger_prefix+'pose/'+name, rot_euler[...,i], total_iter)
elif rot_rep in ['lookat', 'quadlookat', 'octlookat']:
for i, name in enumerate(['fwd_x', 'fwd_y', 'fwd_z']):
viz_logger.add_histogram(logger_prefix+'pose/'+name, pose_raw[...,i], total_iter)
for i, name in enumerate(['trans_x', 'trans_y', 'trans_z']):
viz_logger.add_histogram(logger_prefix+'pose/'+name, pose_raw[...,-3+i], total_iter)
if rot_rep in ['quadlookat', 'octlookat']:
for i, rp in enumerate(forward_aux['rots_probs'].unbind(-1)):
viz_logger.add_histogram(logger_prefix+'pose/rot_prob_%d'%i, rp, total_iter)
if bank_embedding is not None:
weights_for_emb = bank_embedding[2]['weights'] # [B, k]
for i, weight_for_emb in enumerate(weights_for_emb.unbind(-1)):
viz_logger.add_histogram(logger_prefix+'bank_embedding/emb_weight_%d'%i, weight_for_emb, total_iter)
indices_for_emb = bank_embedding[2]['pick_idx'] # [B, k]
for i, idx_for_emb in enumerate(indices_for_emb.unbind(-1)):
viz_logger.add_histogram(logger_prefix+'bank_embedding/emb_idx_%d'%i, idx_for_emb, total_iter)
if 'pose_xflip_raw' in aux_viz:
pose_xflip_raw = aux_viz['pose_xflip_raw']
if rot_rep == 'euler_angle' or rot_rep == 'soft_calss':
for i, name in enumerate(['rot_x', 'rot_y', 'rot_z', 'trans_x', 'trans_y', 'trans_z']):
viz_logger.add_histogram(logger_prefix+'pose_xflip/'+name, pose_xflip[...,i], total_iter)
elif rot_rep == 'quaternion':
for i, name in enumerate(['qt_0', 'qt_1', 'qt_2', 'qt_3', 'trans_x', 'trans_y', 'trans_z']):
viz_logger.add_histogram(logger_prefix+'pose_xflip/'+name, pose_xflip[...,i], total_iter)
rot_euler = pytorch3d.transforms.matrix_to_euler_angles(pytorch3d.transforms.quaternion_to_matrix(pose_xflip.detach().cpu()[...,:4]), convention='XYZ')
for i, name in enumerate(['rot_x', 'rot_y', 'rot_z']):
viz_logger.add_histogram(logger_prefix+'pose_xflip/'+name, rot_euler[...,i], total_iter)
elif rot_rep in ['lookat', 'quadlookat', 'octlookat']:
for i, name in enumerate(['fwd_x', 'fwd_y', 'fwd_z']):
viz_logger.add_histogram(logger_prefix+'pose_xflip/'+name, pose_xflip_raw[...,i], total_iter)
for i, name in enumerate(['trans_x', 'trans_y', 'trans_z']):
viz_logger.add_histogram(logger_prefix+'pose_xflip/'+name, pose_xflip_raw[...,-3+i], total_iter)
if dino_feat_im_gt is not None:
dino_feat_im_gt_first3 = dino_feat_im_gt[:,:,:3]
viz_logger.add_image(logger_prefix+'image/dino_feat_im_gt', misc.image_grid(dino_feat_im_gt_first3.detach().cpu()[:b0,:].reshape(-1,*dino_feat_im_gt_first3.shape[2:]).clamp(0,1)), total_iter)
if dino_cluster_im_gt is not None:
viz_logger.add_image(logger_prefix+'image/dino_cluster_im_gt', misc.image_grid(dino_cluster_im_gt.detach().cpu()[:b0,:].reshape(-1,*dino_cluster_im_gt.shape[2:]).clamp(0,1)), total_iter)
if dino_feat_im_pred is not None:
dino_feat_im_pred_first3 = dino_feat_im_pred[:,:,:3]
viz_logger.add_image(logger_prefix+'image/dino_feat_im_pred', misc.image_grid(dino_feat_im_pred_first3.detach().cpu()[:b0,:].reshape(-1,*dino_feat_im_pred_first3.shape[2:]).clamp(0,1)), total_iter)
for which_shape, modes in self.extra_renders.items():
# This is wrong
# if which_shape == "prior":
# shape_to_render = prior_shape.extend(im_features.shape[0])
# needed_im_features = None
if which_shape == "instance":
shape_to_render = shape
needed_im_features = im_features
else:
raise NotImplementedError
for mode in modes:
if mode in ['gray']:
gray_light = FixedDirectionLight(direction=torch.FloatTensor([0, 0, 1]).to(self.device), amb=0.2, diff=0.7)
_, render_mask, _, _, _, rendered = self.render(shape_to_render, texture, mvp, w2c, campos, (h, w), background=self.background_mode, im_features=needed_im_features, prior_shape=prior_shape, render_mode='diffuse', light=gray_light, render_flow=False, dino_pred=None, im_features_map=im_features_map) #renderer for visualization only!!!
if self.background_mode == 'white':
# we want to render shading here, which is always black background, so modify here
render_mask = render_mask.unsqueeze(1)
rendered[render_mask == 0] = 1
rendered = rendered.repeat(1, 3, 1, 1)
else:
rendered, _, _, _, _, _ = self.render(shape_to_render, texture, mvp, w2c, campos, (h, w), background=self.background_mode, im_features=needed_im_features, prior_shape=prior_shape, render_mode=mode, render_flow=False, dino_pred=None, im_features_map=im_features_map) #renderer for visualization only!!!
if 'kd' in mode:
rendered = util.rgb_to_srgb(rendered)
rendered = rendered.detach().cpu()
rendered_wo_bones = rendered
if 'posed_bones' in aux_viz:
rendered_bone_image = self.render_bones(mvp, aux_viz['posed_bones'], (h, w))
rendered_bone_image_mask = (rendered_bone_image < 1).any(1, keepdim=True).float()
# viz_logger.add_image(logger_prefix+'image/articulation_bones', misc.image_grid(self.render_bones(mvp, aux_viz['posed_bones'])), total_iter)
rendered = rendered_bone_image_mask*0.8 * rendered_bone_image + (1-rendered_bone_image_mask*0.8) * rendered
if rot_rep in ['quadlookat', 'octlookat']:
rand_pose_flag = forward_aux['rand_pose_flag'].detach().cpu()
rand_pose_marker_mask = torch.zeros_like(rendered)
rand_pose_marker_mask[:,:,:16,:16] = 1.
rand_pose_marker_mask = rand_pose_marker_mask * rand_pose_flag[:,None,None,None]
red = torch.FloatTensor([1,0,0])[None,:,None,None]
rendered = rand_pose_marker_mask * red + (1-rand_pose_marker_mask) * rendered
viz_logger.add_image(
logger_prefix + f'image/{which_shape}_{mode}',
misc.image_grid(expandF(rendered)[:b0, ...].view(-1, *rendered.shape[1:])),
total_iter)
if rendered_wo_bones is not None:
viz_logger.add_image(
logger_prefix + f'image/{which_shape}_{mode}_raw',
misc.image_grid(expandF(rendered_wo_bones)[:b0, ...].view(-1, *rendered_wo_bones.shape[1:])),
total_iter)
if mode in ['gray']:
viz_logger.add_video(
logger_prefix + f'animation/{which_shape}_{mode}',
self.render_rotation_frames(shape_to_render, texture, gray_light, (h, w), background=self.background_mode, im_features=needed_im_features, prior_shape=prior_shape, num_frames=15, render_mode='diffuse', b=1, im_features_map=im_features_map, original_mvp=mvp, original_w2c=w2c, original_campos=campos, render_gray=True).detach().cpu().unsqueeze(0),
total_iter,
fps=2)
else:
viz_logger.add_video(
logger_prefix + f'animation/{which_shape}_{mode}',
self.render_rotation_frames(shape_to_render, texture, light, (h, w), background=self.background_mode, im_features=needed_im_features, prior_shape=prior_shape, num_frames=15, render_mode=mode, b=1, im_features_map=im_features_map, original_mvp=mvp, original_w2c=w2c, original_campos=campos).detach().cpu().unsqueeze(0),
total_iter,
fps=2)
viz_logger.add_video(
logger_prefix+'animation/prior_image_rotation',
self.render_rotation_frames(prior_shape, texture, light, (h, w), background=self.background_mode, im_features=im_features, num_frames=15, b=1, text=category_name[0], im_features_map=im_features_map, original_mvp=mvp).detach().cpu().unsqueeze(0).clamp(0,1),
total_iter,
fps=2)
viz_logger.add_video(
logger_prefix+'animation/prior_normal_rotation',
self.render_rotation_frames(prior_shape, texture, light, (h, w), background=self.background_mode, im_features=im_features, num_frames=15, render_mode='geo_normal', b=1, text=category_name[0], im_features_map=im_features_map, original_mvp=mvp).detach().cpu().unsqueeze(0),
total_iter,
fps=2)
if save_results and self.rank == 0:
b0 = self.cfgs.get('num_saved_from_each_batch', batch_size*num_frames)
# from IPython import embed; embed()
fnames = [f'{total_iter:07d}_{fid:010d}' for fid in collapseF(frame_id.int())][:b0]
# pkl_str = osp.join(save_dir, f'{total_iter:07d}_animal_data.pkl')
os.makedirs(save_dir, exist_ok=True)
# with open(pkl_str, 'wb') as fpkl:
# pickle.dump(save_for_pkl, fpkl)
# fpkl.close()
misc.save_images(save_dir, collapseF(image_gt)[:b0].clamp(0,1).detach().cpu().numpy(), suffix='image_gt', fnames=fnames)
misc.save_images(save_dir, collapseF(image_pred)[:b0].clamp(0,1).detach().cpu().numpy(), suffix='image_pred', fnames=fnames)
misc.save_images(save_dir, collapseF(mask_gt)[:b0].unsqueeze(1).repeat(1,3,1,1).clamp(0,1).detach().cpu().numpy(), suffix='mask_gt', fnames=fnames)
misc.save_images(save_dir, collapseF(mask_pred)[:b0].unsqueeze(1).repeat(1,3,1,1).clamp(0,1).detach().cpu().numpy(), suffix='mask_pred', fnames=fnames)
# tmp_shape = shape.first_n(b0).clone()
# tmp_shape.material = texture
# feat = im_features[:b0] if im_features is not None else None
# misc.save_obj(save_dir, tmp_shape, save_material=False, feat=feat, suffix="mesh", fnames=fnames) # Save the first mesh.
if self.render_flow and flow_gt is not None:
flow_gt_viz = torch.cat([flow_gt, torch.zeros_like(flow_gt[:,:,:1])], 2) + 0.5 # -0.5~1.5
flow_gt_viz = flow_gt_viz.view(-1, *flow_gt_viz.shape[2:])
misc.save_images(save_dir, flow_gt_viz[:b0].clamp(0,1).detach().cpu().numpy(), suffix='flow_gt', fnames=fnames)
if flow_pred is not None:
flow_pred_viz = torch.cat([flow_pred, torch.zeros_like(flow_pred[:,:,:1])], 2) + 0.5 # -0.5~1.5
flow_pred_viz = flow_pred_viz.view(-1, *flow_pred_viz.shape[2:])
misc.save_images(save_dir, flow_pred_viz[:b0].clamp(0,1).detach().cpu().numpy(), suffix='flow_pred', fnames=fnames)
misc.save_txt(save_dir, pose[:b0].detach().cpu().numpy(), suffix='pose', fnames=fnames)
return metrics
def save_scores(self, path):
header = 'mask_mse, \
mask_iou, \
image_mse, \
flow_mse'
mean = self.all_scores.mean(0)
std = self.all_scores.std(0)
header = header + '\nMean: ' + ',\t'.join(['%.8f'%x for x in mean])
header = header + '\nStd: ' + ',\t'.join(['%.8f'%x for x in std])
misc.save_scores(path, self.all_scores, header=header)
print(header)
def render_rotation_frames(self, mesh, texture, light, resolution, background='none', im_features=None, prior_shape=None, num_frames=36, render_mode='diffuse', b=None, text=None, im_features_map=None, original_mvp=None, original_w2c=None, original_campos=None, render_gray=False):
frames = []
if b is None:
b = len(mesh)
else:
mesh = mesh.first_n(b)
feat = im_features[:b] if im_features is not None else None
im_features_map = im_features_map[:b] if im_features_map is not None else None
original_mvp = original_mvp[:b] if original_mvp is not None else None # [b, 4, 4]
if im_features_map is not None:
im_features_map = {'im_features_map': im_features_map, 'original_mvp':original_mvp}
delta_angle = np.pi / num_frames * 2
delta_rot_matrix = torch.FloatTensor([
[np.cos(delta_angle), 0, np.sin(delta_angle), 0],
[0, 1, 0, 0],
[-np.sin(delta_angle), 0, np.cos(delta_angle), 0],
[0, 0, 0, 1],
]).to(self.device).repeat(b, 1, 1)
w2c = torch.FloatTensor(np.diag([1., 1., 1., 1]))
w2c[:3, 3] = torch.FloatTensor([0, 0, -self.cam_pos_z_offset *1.1])
w2c = w2c.repeat(b, 1, 1).to(self.device)
proj = util.perspective(self.crop_fov_approx / 180 * np.pi, 1, n=0.1, f=1000.0).repeat(b, 1, 1).to(self.device)
mvp = torch.bmm(proj, w2c)
campos = -w2c[:, :3, 3]
if original_w2c is not None and original_campos is not None and original_mvp is not None:
w2c = original_w2c[:b]
campos = original_campos[:b]
mvp = original_mvp[:b]
def rotate_pose(mvp, campos):
mvp = torch.matmul(mvp, delta_rot_matrix)
campos = torch.matmul(delta_rot_matrix[:,:3,:3].transpose(2,1), campos[:,:,None])[:,:,0]
return mvp, campos
for _ in range(num_frames):
if render_gray:
_, render_mask, _, _, _, image_pred = self.render(mesh, texture, mvp, w2c, campos, resolution, background=background, im_features=feat, light=light, prior_shape=prior_shape, render_flow=False, dino_pred=None, render_mode=render_mode, two_sided_shading=False, im_features_map=im_features_map)
if self.background_mode == 'white':
# we want to render shading here, which is always black background, so modify here
render_mask = render_mask.unsqueeze(1)
image_pred[render_mask == 0] = 1
image_pred = image_pred.repeat(1, 3, 1, 1)
else:
image_pred, _, _, _, _, _ = self.render(mesh, texture, mvp, w2c, campos, resolution, background=background, im_features=feat, light=light, prior_shape=prior_shape, render_flow=False, dino_pred=None, render_mode=render_mode, two_sided_shading=False, im_features_map=im_features_map) #for rotation frames only!
image_pred = image_pred.clamp(0, 1)
frames += [misc.image_grid(image_pred)]
mvp, campos = rotate_pose(mvp, campos)
if text is not None:
frames = [torch.Tensor(misc.add_text_to_image(f, text)).permute(2, 0, 1) for f in frames]
return torch.stack(frames, dim=0) # Shape: (T, C, H, W)
def render_bones(self, mvp, bones_pred, size=(256, 256)):
bone_world4 = torch.concat([bones_pred, torch.ones_like(bones_pred[..., :1]).to(bones_pred.device)], dim=-1)
b, f, num_bones = bone_world4.shape[:3]
bones_clip4 = (bone_world4.view(b, f, num_bones*2, 1, 4) @ mvp.transpose(-1, -2).reshape(b, f, 1, 4, 4)).view(b, f, num_bones, 2, 4)
bones_uv = bones_clip4[..., :2] / bones_clip4[..., 3:4] # b, f, num_bones, 2, 2
dpi = 32
fx, fy = size[1] // dpi, size[0] // dpi
rendered = []
for b_idx in range(b):
for f_idx in range(f):
frame_bones_uv = bones_uv[b_idx, f_idx].cpu().numpy()
fig = plt.figure(figsize=(fx, fy), dpi=dpi, frameon=False)
ax = plt.Axes(fig, [0., 0., 1., 1.])
ax.set_axis_off()
for bone in frame_bones_uv:
ax.plot(bone[:, 0], bone[:, 1], marker='o', linewidth=8, markersize=20)
ax.set_xlim(-1, 1)
ax.set_ylim(-1, 1)
ax.invert_yaxis()
# Convert to image
fig.add_axes(ax)
fig.canvas.draw_idle()
image = np.frombuffer(fig.canvas.tostring_rgb(), dtype=np.uint8)
w, h = fig.canvas.get_width_height()
image.resize(h, w, 3)
rendered += [image / 255.]
return torch.from_numpy(np.stack(rendered, 0).transpose(0, 3, 1, 2))
def render_deformation_frames(self, mesh, texture, batch_size, num_frames, resolution, background='none', im_features=None, render_mode='diffuse', b=None):
# frames = []
# if b is None:
# b = batch_size
# im_features = im_features[]
# mesh = mesh.first_n(num_frames * b)
# for i in range(b):
# tmp_mesh = mesh.get_m_to_n(i*num_frames:(i+1)*num_frames)
pass
def vis_sds_image(self, sds_image, sds_aux):
sds_image = sds_image.detach().cpu().clamp(0, 1)
sds_image = [misc.add_text_to_image(img, text) for img, text in zip(sds_image, sds_aux['dirs'])]
return misc.image_grid(sds_image)
def vis_sds_grads(self, sds_aux):
grads = sds_aux['sd_aux']['grad']
grads = grads.detach().cpu()
# compute norm
grads_norm = grads.norm(dim=1, keepdim=True)
# interpolate to 4x size
grads_norm = F.interpolate(grads_norm, scale_factor=4, mode='nearest')
# add time step and weight
t = sds_aux['sd_aux']['t']
w = sds_aux['sd_aux']['w']
# max norm for each sample over dim (1, 2, 3)
n = grads_norm.view(grads_norm.shape[0], -1).max(dim=1)[0]
texts = [f"t: {t_} w: {w_:.2f} n: {n_:.2e}" for t_, w_ , n_ in zip(t, w, n)]
return misc.image_grid_multi_channel(grads_norm, texts=texts, font_scale=0.5)