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import os
import os.path as osp
from copy import deepcopy
from collections import OrderedDict
import glob
from datetime import datetime
import random
import copy
import imageio
import torch
# import clip
import torchvision.transforms.functional as tvf
import video3d.utils.meters as meters
import video3d.utils.misc as misc
# from video3d.dataloaders import get_image_loader
from video3d.dataloaders_ddp import get_sequence_loader_ddp, get_sequence_loader_quadrupeds, get_test_loader_quadrupeds
from . import discriminator_architecture
def sample_frames(batch, num_sample_frames, iteration, stride=1):
## window slicing sampling
images, masks, flows, bboxs, bg_image, seq_idx, frame_idx = batch
num_seqs, total_num_frames = images.shape[:2]
# start_frame_idx = iteration % (total_num_frames - num_sample_frames +1)
## forward and backward
num_windows = total_num_frames - num_sample_frames +1
start_frame_idx = (iteration * stride) % (2*num_windows)
## x' = (2n-1)/2 - |(2n-1)/2 - x| : 0,1,2,3,4,5 -> 0,1,2,2,1,0
mid_val = (2*num_windows -1) /2
start_frame_idx = int(mid_val - abs(mid_val -start_frame_idx))
new_batch = images[:, start_frame_idx:start_frame_idx+num_sample_frames], \
masks[:, start_frame_idx:start_frame_idx+num_sample_frames], \
flows[:, start_frame_idx:start_frame_idx+num_sample_frames-1], \
bboxs[:, start_frame_idx:start_frame_idx+num_sample_frames], \
bg_image, \
seq_idx, \
frame_idx[:, start_frame_idx:start_frame_idx+num_sample_frames]
return new_batch
def indefinite_generator(loader):
while True:
for x in loader:
yield x
def indefinite_generator_from_list(loaders):
while True:
random_idx = random.randint(0, len(loaders)-1)
for x in loaders[random_idx]:
yield x
break
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)
class Fewshot_Trainer:
def __init__(self, cfgs, model):
# only now supports one gpu
self.cfgs = cfgs
# here should be the one gpu ddp setting
self.rank = cfgs.get('rank', 0)
self.world_size = cfgs.get('world_size', 1)
self.use_ddp = cfgs.get('use_ddp', True)
self.device = cfgs.get('device', 'cpu')
self.num_epochs = cfgs.get('num_epochs', 1)
self.lr = cfgs.get('few_shot_lr', 1e-4)
self.dataset = 'image'
self.metrics_trace = meters.MetricsTrace()
self.make_metrics = lambda m=None: meters.StandardMetrics(m)
self.archive_code = cfgs.get('archive_code', True)
self.batch_size = cfgs.get('batch_size', 64)
self.in_image_size = cfgs.get('in_image_size', 256)
self.out_image_size = cfgs.get('out_image_size', 256)
self.num_workers = cfgs.get('num_workers', 4)
self.checkpoint_dir = cfgs.get('checkpoint_dir', 'results')
misc.xmkdir(self.checkpoint_dir)
self.few_shot_resume = cfgs.get('few_shot_resume', False)
self.save_checkpoint_freq = cfgs.get('save_checkpoint_freq', 1)
self.keep_num_checkpoint = cfgs.get('keep_num_checkpoint', 2) # -1 for keeping all checkpoints
self.few_shot_data_dir = cfgs.get('few_shot_data_dir', None)
assert self.few_shot_data_dir is not None
# in case we add more data source
if isinstance(self.few_shot_data_dir, list):
self.few_shot_data_dir_more = self.few_shot_data_dir[1:]
self.few_shot_data_dir = self.few_shot_data_dir[0]
else:
self.few_shot_data_dir_more = None
assert "data_resize_update" in self.few_shot_data_dir # TODO: a hack way to make sure not using wrong data, needs to remove
self.few_shot_categories, self.few_shot_categories_paths = self.parse_few_shot_categories(self.few_shot_data_dir, self.few_shot_data_dir_more)
# if we need test categories, we pop it from self.few_shot_categories and self.few_shot_categories_path
# the test category needs to be category from few-shot, and we're using bs=1 on them, no need for back views enhancement (for now, use back view images, but don't duplicate them)
self.test_category_num = cfgs.get('few_shot_test_category_num', 0)
self.test_category_names = cfgs.get('few_shot_test_category_names', None)
if self.test_category_num > 0:
# if we have valid test_category names, then use them, the number doesn't need to be equal
if self.test_category_names is not None:
test_cats = self.test_category_names
else:
test_cats = list(self.few_shot_categories_paths.keys())[-(self.test_category_num):]
test_categories_paths = {}
for test_cat in test_cats:
test_categories_paths.update({test_cat: self.few_shot_categories_paths[test_cat]})
assert test_cat in self.few_shot_categories
self.few_shot_categories.remove(test_cat)
self.few_shot_categories_paths.pop(test_cat)
self.test_categories_paths = test_categories_paths
else:
self.test_categories_paths = None
# also load the original 7 categories
self.original_train_data_path = cfgs.get('train_data_dir', None)
self.original_val_data_path = cfgs.get('val_data_dir', None)
self.original_categories = []
self.original_categories_paths = self.original_train_data_path
for k, v in self.original_train_data_path.items():
self.original_categories.append(k)
self.categories = self.original_categories + self.few_shot_categories
self.categories_paths = self.original_train_data_path.copy()
self.categories_paths.update(self.few_shot_categories_paths)
print(f'Using {len(self.categories)} cateogires: ', self.categories)
# initialize new things
# self.original_classes_num = cfgs.get('few_shot_original_classes_num', 7)
self.original_classes_num = len(self.original_categories)
self.new_classes_num = len(self.categories) - self.original_classes_num
self.combine_dataset = cfgs.get('combine_dataset', False)
assert self.combine_dataset, "we should use combine dataset, it's up to date"
if self.combine_dataset:
self.train_loader, self.val_loader, self.test_loader = self.get_data_loaders_quadrupeds(self.cfgs, self.batch_size, self.num_workers, self.in_image_size, self.out_image_size)
else:
self.train_loader_few_shot, self.val_loader_few_shot = self.get_data_loaders_few_shot(self.cfgs, self.batch_size, self.num_workers, self.in_image_size, self.out_image_size)
self.train_loader_original, self.val_loader_original = self.get_data_loaders_original(self.cfgs, self.batch_size, self.num_workers, self.in_image_size, self.out_image_size)
self.train_loader = self.train_loader_original + self.train_loader_few_shot
if self.val_loader_few_shot is not None and self.val_loader_original is not None:
self.val_loader = self.val_loader_original + self.val_loader_few_shot
self.num_iterations = cfgs.get('num_iterations', 0)
if self.num_iterations != 0:
self.use_total_iterations = True
else:
self.use_total_iterations = False
if self.use_total_iterations:
# reset the epoch related cfgs
dataloader_length = max([len(loader) for loader in self.train_loader]) * len(self.train_loader)
print("Total length of data loader is: ", dataloader_length)
total_epoch = int(self.num_iterations / dataloader_length) + 1
print(f'run for {total_epoch} epochs')
print('is_main_process()?', misc.is_main_process())
for k, v in cfgs.items():
if 'epoch' in k:
# if isinstance(v, list):
# new_v = [int(total_epoch * x / 120) + 1 for x in v]
# cfgs[k] = new_v
# elif isinstance(v, int):
# new_v = int(total_epoch * v / 120) + 1
# cfgs[k] = new_v
# a better transformation
if isinstance(v, int):
# use the floor int
new_v = int(total_epoch * v / 120)
cfgs[k] = new_v
elif isinstance(v, list):
if v[0] == v[1]:
# if the values in v are the same, then we use both the floor value
new_v = [int(total_epoch * x / 120) for x in v]
else:
# if the values are not the same, make the first using floor value and others using ceil value
new_v = [int(total_epoch * x / 120) + 1 for x in v]
new_v[0] = new_v[0] - 1
cfgs[k] = new_v
else:
continue
self.num_epochs = total_epoch
self.cub_start_epoch = cfgs.get('cub_start_epoch', 0)
self.cfgs = cfgs
# the model is with nothing now
self.model = model(cfgs)
self.metrics_trace = meters.MetricsTrace()
self.make_metrics = lambda m=None: meters.StandardMetrics(m)
self.use_logger = True
self.log_freq_images = cfgs.get('log_freq_images', 1000)
self.log_train_images = cfgs.get('log_train_images', False)
self.log_freq_losses = cfgs.get('log_freq_losses', 100)
self.save_result_freq = cfgs.get('save_result_freq', None)
self.train_result_dir = osp.join(self.checkpoint_dir, 'results')
self.fix_viz_batch = cfgs.get('fix_viz_batch', False)
self.visualize_validation = cfgs.get('visualize_validation', False)
# self.visualize_validation = False
self.iteration_save = cfgs.get('few_shot_iteration_save', False)
self.iteration_save_freq = cfgs.get('few_shot_iteration_save_freq', 2000)
self.enable_memory_bank = cfgs.get('enable_memory_bank', False)
if self.enable_memory_bank:
self.memory_bank_dim = 128
self.memory_bank_size = cfgs.get('memory_bank_size', 60)
self.memory_bank_topk = cfgs.get('memory_bank_topk', 10)
# assert self.memory_bank_topk < self.memory_bank_size
assert self.memory_bank_topk <= self.memory_bank_size
self.memory_retrieve = cfgs.get('memory_retrieve', 'cos-linear')
self.memory_bank_init = cfgs.get('memory_bank_init', 'random')
if self.memory_bank_init == 'copy':
# use trained 7 embeddings to initialize
num_piece = self.memory_bank_size // self.original_classes_num
num_left = self.memory_bank_size - num_piece * self.original_classes_num
tmp_1 = torch.empty_like(self.model.netPrior.classes_vectors)
tmp_1 = tmp_1.copy_(self.model.netPrior.classes_vectors)
tmp_1 = tmp_1.unsqueeze(0).repeat(num_piece, 1, 1)
tmp_1 = tmp_1.reshape(tmp_1.shape[0] * tmp_1.shape[1], tmp_1.shape[-1])
if num_left > 0:
tmp_2 = torch.empty_like(self.model.netPrior.classes_vectors)
tmp_2 = tmp_2.copy_(self.model.netPrior.classes_vectors)
tmp_2 = tmp_2[:num_left]
tmp = torch.cat([tmp_1, tmp_2], dim=0)
else:
tmp = tmp_1
self.memory_bank = torch.nn.Parameter(tmp, requires_grad=True)
elif self.memory_bank_init == 'random':
self.memory_bank = torch.nn.Parameter(torch.nn.init.uniform_(torch.empty(self.memory_bank_size, self.memory_bank_dim), a=-0.05, b=0.05), requires_grad=True)
else:
raise NotImplementedError
self.memory_encoder = cfgs.get('memory_encoder', 'DINO') # if DINO then just use the network encoder
if self.memory_encoder == 'CLIP':
self.clip_model, _ = clip.load('ViT-B/32', self.device)
self.clip_model = self.clip_model.eval().requires_grad_(False)
self.clip_mean = [0.48145466, 0.4578275, 0.40821073]
self.clip_std = [0.26862954, 0.26130258, 0.27577711]
self.clip_reso = 224
self.memory_bank_keys_dim = 512
elif self.memory_encoder == 'DINO':
self.memory_bank_keys_dim = 384
else:
raise NotImplementedError
memory_bank_keys = torch.nn.init.uniform_(torch.empty(self.memory_bank_size, self.memory_bank_keys_dim), a=-0.05, b=0.05)
self.memory_bank_keys = torch.nn.Parameter(memory_bank_keys, requires_grad=True)
else:
print("no memory bank, just use image embedding, this is only for one experiment!")
self.memory_encoder = cfgs.get('memory_encoder', 'DINO') # if DINO then just use the network encoder
if self.memory_encoder == 'CLIP':
self.clip_model, _ = clip.load('ViT-B/32', self.device)
self.clip_model = self.clip_model.eval().requires_grad_(False)
self.clip_mean = [0.48145466, 0.4578275, 0.40821073]
self.clip_std = [0.26862954, 0.26130258, 0.27577711]
self.clip_reso = 224
self.memory_bank_keys_dim = 512
elif self.memory_encoder == 'DINO':
self.memory_bank_keys_dim = 384
else:
raise NotImplementedError
self.prepare_model()
def parse_few_shot_categories(self, data_dir, data_dir_more=None):
# parse the categories data_dir
few_shot_category_num = self.cfgs.get('few_shot_category_num', -1)
assert few_shot_category_num != 0
categories = sorted(os.listdir(data_dir))
cnt = 0
category_names = []
category_names_paths = {}
for category in categories:
if osp.isdir(osp.join(self.few_shot_data_dir, category, 'train')):
category_path = osp.join(self.few_shot_data_dir, category, 'train')
category_names.append(category)
category_names_paths.update({category: category_path})
cnt += 1
if few_shot_category_num > 0 and cnt >= few_shot_category_num:
break
# more data
if data_dir_more is not None:
for data_dir_one in data_dir_more:
new_categories = os.listdir(data_dir_one)
for new_category in new_categories:
'''
if this category is not used before, add a new item
if there is this category before, add the paths to original paths,
if its a str, make it a list
if its already a list, append it
'''
if new_category not in category_names:
#TODO: a hacky way here, if in new data there is category used in 7-cat, we just make it a new one
if new_category in list(self.cfgs.get('train_data_dir', None).keys()):
new_category = '_' + new_category
category_names.append(new_category)
category_names_paths.update({
new_category: osp.join(data_dir_one, new_category, 'train')
})
else:
old_category_path = category_names_paths[new_category]
if isinstance(old_category_path, str):
category_names_paths[new_category] = [
old_category_path,
osp.join(data_dir_one, new_category, 'train')
]
elif isinstance(old_category_path, list):
old_category_path = old_category_path + [osp.join(data_dir_one, new_category, 'train')]
category_names_paths[new_category] = old_category_path
else:
raise NotImplementedError
# category_names = sorted(category_names)
return category_names, category_names_paths
def prepare_model(self):
# here we prepare the model weights at outside
# 1. load the pretrain weight
# 2. initialize anything new, like new class vectors
# 3. initialize new optimizer for chosen parameters
assert self.original_classes_num == len(self.model.netPrior.category_id_map)
# load pretrain
# if not assigned few_shot_checkpoint_name, then skip this part
if self.cfgs.get('few_shot_checkpoint_name', None) is not None:
original_checkpoint_path = osp.join(self.checkpoint_dir, self.cfgs.get('few_shot_checkpoint_name', 'checkpoint060.pth'))
assert osp.exists(original_checkpoint_path)
print(f"Loading pre-trained checkpoint from {original_checkpoint_path}")
cp = torch.load(original_checkpoint_path, map_location=self.device)
# if using local-texture network in fine-tuning, the texture in previous pre-train ckpt is global
# here we use a hack way, we just get rid of original texture ckpt
if (self.cfgs.get('texture_way', None) 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.model.netInstance.netTexture.state_dict()
#add the new weights to the new model weights
for k, v in texture_weights.items():
# for the overlapping part in netTexture, we also use them
# if ('netTexture.' + k) in cp['netInstance'].keys():
# new_netInstance_weights['netTexture.' + k] = cp['netInstance']['netTexture.' + k]
# else:
# new_netInstance_weights['netTexture.' + k] = v
new_netInstance_weights['netTexture.' + k] = v
_ = cp.pop("netInstance")
cp.update({"netInstance": new_netInstance_weights})
self.model.netInstance.load_state_dict(cp["netInstance"], strict=False) # For Deform
# self.model.netInstance.load_state_dict(cp["netInstance"])
self.model.netPrior.load_state_dict(cp["netPrior"])
self.original_total_iter = cp["total_iter"]
else:
print("not load any pre-train weight, the iter will start from 0, make sure you set all the needed parameters")
self.original_total_iter = 0
if not self.cfgs.get('disable_fewshot', False):
for i, category in enumerate(self.few_shot_categories):
category_id = self.original_classes_num + i
self.model.netPrior.category_id_map.update({category: category_id})
few_shot_class_vector_init = self.cfgs.get('few_shot_class_vector_init', 'random')
if few_shot_class_vector_init == 'random':
tmp = torch.nn.init.uniform_(torch.empty(self.new_classes_num, self.model.netPrior.classes_vectors.shape[-1]), a=-0.05, b=0.05)
tmp = tmp.to(self.model.netPrior.classes_vectors.device)
self.model.netPrior.classes_vectors = torch.nn.Parameter(torch.cat([self.model.netPrior.classes_vectors, tmp], dim=0))
elif few_shot_class_vector_init == 'copy':
num_7_cat_piece = self.new_classes_num // self.original_classes_num if self.new_classes_num > self.original_classes_num else 0
num_left = self.new_classes_num - num_7_cat_piece * self.original_classes_num
if num_7_cat_piece > 0:
tmp_1 = torch.empty_like(self.model.netPrior.classes_vectors)
tmp_1 = tmp_1.copy_(self.model.netPrior.classes_vectors)
tmp_1 = tmp_1.unsqueeze(0).repeat(num_7_cat_piece, 1, 1)
tmp_1 = tmp_1.reshape(tmp_1.shape[0] * tmp_1.shape[1], tmp_1.shape[-1])
else:
tmp_1 = None
if num_left > 0:
tmp_2 = torch.empty_like(self.model.netPrior.classes_vectors)
tmp_2 = tmp_2.copy_(self.model.netPrior.classes_vectors)
tmp_2 = tmp_2[:num_left]
else:
tmp_2 = None
if tmp_1 != None and tmp_2 != None:
tmp = torch.cat([tmp_1, tmp_2], dim=0)
elif tmp_1 == None and tmp_2 != None:
tmp = tmp_2
elif tmp_2 == None and tmp_1 != None:
tmp = tmp_1
else:
raise NotImplementedError
tmp = tmp.to(self.model.netPrior.classes_vectors.device)
self.model.netPrior.classes_vectors = torch.nn.Parameter(torch.cat([self.model.netPrior.classes_vectors, tmp], dim=0))
else:
raise NotImplementedError
else:
print("disable few shot, not increasing embedding vectors")
# initialize new optimizer
optimize_rule = self.cfgs.get('few_shot_optimize', 'all')
if optimize_rule == 'all':
optimize_list = [
{'name': 'net_Prior', 'params': list(self.model.netPrior.parameters()), 'lr': self.lr * 10.},
{'name': 'net_Instance', 'params': list(self.model.netInstance.parameters()), 'lr': self.lr * 1.},
]
elif optimize_rule == 'only-emb':
optimize_list = [
{'name': 'class_embeddings', 'params': list([self.model.netPrior.classes_vectors]), 'lr': self.lr * 10.}
]
elif optimize_rule == 'emb-instance':
optimize_list = [
{'name': 'class_embeddings', 'params': list([self.model.netPrior.classes_vectors]), 'lr': self.lr * 10.},
{'name': 'net_Instance', 'params': list(self.model.netInstance.parameters()), 'lr': self.lr * 1.},
]
elif optimize_rule == 'custom':
optimize_list = [
{'name': 'net_Prior', 'params': list(self.model.netPrior.parameters()), 'lr': self.lr * 10.},
{'name': 'netEncoder', 'params': list(self.model.netInstance.netEncoder.parameters()), 'lr': self.lr * 1.},
{'name': 'netTexture', 'params': list(self.model.netInstance.netTexture.parameters()), 'lr': self.lr * 1.},
{'name': 'netPose', 'params': list(self.model.netInstance.netPose.parameters()), 'lr': self.lr * 0.01},
{'name': 'netArticulation', 'params': list(self.model.netInstance.netArticulation.parameters()), 'lr': self.lr * 1.},
{'name': 'netLight', 'params': list(self.model.netInstance.netLight.parameters()), 'lr': self.lr * 1.}
]
elif optimize_rule == 'custom-deform':
optimize_list = [
{'name': 'net_Prior', 'params': list(self.model.netPrior.parameters()), 'lr': self.lr * 10.},
{'name': 'netEncoder', 'params': list(self.model.netInstance.netEncoder.parameters()), 'lr': self.lr * 1.},
{'name': 'netTexture', 'params': list(self.model.netInstance.netTexture.parameters()), 'lr': self.lr * 1.},
{'name': 'netPose', 'params': list(self.model.netInstance.netPose.parameters()), 'lr': self.lr * 0.01},
{'name': 'netArticulation', 'params': list(self.model.netInstance.netArticulation.parameters()), 'lr': self.lr * 1.},
{'name': 'netLight', 'params': list(self.model.netInstance.netLight.parameters()), 'lr': self.lr * 1.},
{'name': 'netDeform', 'params': list(self.model.netInstance.netDeform.parameters()), 'lr': self.lr * 1.}
]
elif optimize_rule == 'texture':
optimize_list = [
{'name': 'netTexture', 'params': list(self.model.netInstance.netTexture.parameters()), 'lr': self.lr * 1.}
]
elif optimize_rule == 'texture-light':
optimize_list = [
{'name': 'netTexture', 'params': list(self.model.netInstance.netTexture.parameters()), 'lr': self.lr * 1.},
{'name': 'netLight', 'params': list(self.model.netInstance.netLight.parameters()), 'lr': self.lr * 1.}
]
elif optimize_rule == 'exp':
optimize_list = [
{'name': 'net_Prior', 'params': list(self.model.netPrior.parameters()), 'lr': self.lr * 10.},
{'name': 'netEncoder', 'params': list(self.model.netInstance.netEncoder.parameters()), 'lr': self.lr * 1.},
{'name': 'netTexture', 'params': list(self.model.netInstance.netTexture.parameters()), 'lr': self.lr * 1.},
{'name': 'netPose', 'params': list(self.model.netInstance.netPose.parameters()), 'lr': self.lr * 1.},
{'name': 'netArticulation', 'params': list(self.model.netInstance.netArticulation.parameters()), 'lr': self.lr * 1.},
{'name': 'netLight', 'params': list(self.model.netInstance.netLight.parameters()), 'lr': self.lr * 1.},
{'name': 'netDeform', 'params': list(self.model.netInstance.netDeform.parameters()), 'lr': self.lr * 1.}
]
else:
raise NotImplementedError
if self.enable_memory_bank and optimize_rule != 'texture':
optimize_bank_components = self.cfgs.get('few_shot_optimize_bank', 'all')
if optimize_bank_components == 'value':
optimize_list += [
{'name': 'memory_bank', 'params': list([self.memory_bank]), 'lr': self.lr * 10.}
]
elif optimize_bank_components == 'key':
optimize_list += [
{'name': 'memory_bank_keys', 'params': list([self.memory_bank_keys]), 'lr': self.lr * 10.}
]
else:
optimize_list += [
{'name': 'memory_bank', 'params': list([self.memory_bank]), 'lr': self.lr * 10.},
{'name': 'memory_bank_keys', 'params': list([self.memory_bank_keys]), 'lr': self.lr * 10.}
]
if self.model.enable_vsd:
optimize_list += [
{'name': 'lora', 'params': list(self.model.stable_diffusion.parameters()), 'lr': self.lr}
]
# self.optimizerFewShot = torch.optim.Adam(
# [
# # {'name': 'class_embeddings', 'params': list([self.model.netPrior.classes_vectors]), 'lr': self.lr * 1.},
# {'name': 'net_Prior', 'params': list(self.model.netPrior.parameters()), 'lr': self.lr * 10.},
# {'name': 'net_Instance', 'params': list(self.model.netInstance.parameters()), 'lr': self.lr * 1.},
# # {'name': 'net_articulation', 'params': list(self.model.netInstance.netArticulation.parameters()), 'lr': self.lr * 10.}
# ], betas=(0.9, 0.99), eps=1e-15
# )
self.optimizerFewShot = torch.optim.Adam(optimize_list, betas=(0.9, 0.99), eps=1e-15)
# if self.cfgs.get('texture_way', None) is not None and self.cfgs.get('gan_tex', False):
if self.cfgs.get('gan_tex', False):
self.optimizerDiscTex = torch.optim.Adam(filter(lambda p: p.requires_grad, self.model.discriminator_texture.parameters()), lr=self.lr, betas=(0.9, 0.99), eps=1e-15)
def load_checkpoint(self, optim=True, checkpoint_name=None):
# use to load the checkpoint of model and optimizer in the finetuning
"""Search the specified/latest checkpoint in checkpoint_dir and load the model and optimizer."""
if checkpoint_name is not None:
checkpoint_path = osp.join(self.checkpoint_dir, checkpoint_name)
else:
checkpoints = sorted(glob.glob(osp.join(self.checkpoint_dir, '*.pth')))
if len(checkpoints) == 0:
return 0, 0
checkpoint_path = checkpoints[-1]
self.checkpoint_name = osp.basename(checkpoint_path)
print(f"Loading checkpoint from {checkpoint_path}")
cp = torch.load(checkpoint_path, map_location=self.device)
self.model.load_model_state(cp) # the cp has netPrior and netInstance as keys
if optim:
try:
self.optimizerFewShot.load_state_dict(cp['optimizerFewShot'])
except:
print('you should be using the local texture so dont need to load the previous optimizer')
if self.enable_memory_bank:
self.memory_bank_keys = cp['memory_bank_keys']
self.memory_bank = cp['memory_bank']
self.metrics_trace = cp['metrics_trace']
epoch = cp['epoch']
total_iter = cp['total_iter']
return epoch, total_iter
def save_checkpoint(self, epoch, total_iter=0, optim=True, use_iter=False):
"""Save model, optimizer, and metrics state to a checkpoint in checkpoint_dir for the specified epoch."""
misc.xmkdir(self.checkpoint_dir)
if use_iter:
checkpoint_path = osp.join(self.checkpoint_dir, f'iter{total_iter:07}.pth')
prefix = 'iter*.pth'
else:
checkpoint_path = osp.join(self.checkpoint_dir, f'checkpoint{epoch:03}.pth')
prefix = 'checkpoint*.pth'
state_dict = self.model.get_model_state()
if optim:
optimizer_state = {'optimizerFewShot': self.optimizerFewShot.state_dict()}
state_dict = {**state_dict, **optimizer_state}
state_dict['metrics_trace'] = self.metrics_trace
state_dict['epoch'] = epoch
state_dict['total_iter'] = total_iter
if self.enable_memory_bank:
state_dict['memory_bank_keys'] = self.memory_bank_keys
state_dict['memory_bank'] = self.memory_bank
print(f"Saving checkpoint to {checkpoint_path}")
torch.save(state_dict, checkpoint_path)
if self.keep_num_checkpoint > 0:
self.clean_checkpoint(self.checkpoint_dir, keep_num=self.keep_num_checkpoint, prefix=prefix)
def clean_checkpoint(self, checkpoint_dir, keep_num=2, prefix='checkpoint*.pth'):
if keep_num > 0:
names = list(sorted(
glob.glob(os.path.join(checkpoint_dir, prefix))
))
if len(names) > keep_num:
for name in names[:-keep_num]:
print(f"Deleting obslete checkpoint file {name}")
os.remove(name)
def get_data_loaders_few_shot(self, cfgs, batch_size, num_workers, in_image_size, out_image_size):
# support the train_data_loaders, and also an identical val_data_loader?
train_loader = val_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)
data_loader_mode = cfgs.get('data_loader_mode', 'n_frame')
num_sample_frames = cfgs.get('num_sample_frames', 2)
shuffle_train_seqs = cfgs.get('shuffle_train_seqs', 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)
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,
num_sample_frames=num_sample_frames,
load_background=load_background,
rgb_suffix=rgb_suffix,
load_dino_feature=load_dino_feature,
dino_feature_dim=dino_feature_dim,
flow_bool=0,
**kwargs)
print(f"Loading training data...")
train_loader = get_loader_ddp(data_dir=[self.original_classes_num, self.few_shot_categories_paths], rank=self.rank, world_size=self.world_size, use_few_shot=True, shuffle=False, color_jitter=color_jitter_train, random_flip=random_flip_train)
return train_loader, val_loader
def get_data_loaders_original(self, cfgs, batch_size, num_workers, in_image_size, out_image_size):
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)
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=0,
**kwargs)
# just the train now
train_data_dir = self.original_categories_paths
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...")
# the train_data_dir is a dict and will go into the original dataset type
train_loader = get_loader_ddp(data_dir=train_data_dir, rank=self.rank, world_size=self.world_size, is_validation=False, use_few_shot=False, random_sample=random_sample_train_frames, shuffle=shuffle_train_seqs, dense_sample=True, color_jitter=color_jitter_train, random_flip=random_flip_train)
return train_loader, val_loader
def get_data_loaders_quadrupeds(self, cfgs, batch_size, num_workers, in_image_size, out_image_size):
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)
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)
enhance_back_view = cfgs.get('enhance_back_view', False)
enhance_back_view_path = cfgs.get('enhance_back_view_path', None)
override_categories = cfgs.get('override_categories', None)
disable_fewshot = cfgs.get('disable_fewshot', False)
dataset_split_num = cfgs.get('dataset_split_num', -1)
get_loader_ddp = lambda **kwargs: get_sequence_loader_quadrupeds(
mode=data_loader_mode,
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=0,
enhance_back_view=enhance_back_view,
enhance_back_view_path=enhance_back_view_path,
override_categories=override_categories,
disable_fewshot=disable_fewshot,
dataset_split_num=dataset_split_num,
**kwargs)
# just the train now
print(f"Loading training data...")
val_image_num = cfgs.get('few_shot_val_image_num', 5)
# the train_data_dir is a dict and will go into the original dataset type
train_loader = get_loader_ddp(original_data_dirs=self.original_categories_paths, few_shot_data_dirs=self.few_shot_categories_paths, original_num=self.original_classes_num, few_shot_num=self.new_classes_num, rank=self.rank, world_size=self.world_size, batch_size=batch_size, is_validation=False, val_image_num=val_image_num, shuffle=shuffle_train_seqs, dense_sample=True, color_jitter=color_jitter_train, random_flip=random_flip_train)
val_loader = get_loader_ddp(original_data_dirs=self.original_val_data_path, few_shot_data_dirs=self.few_shot_categories_paths, original_num=self.original_classes_num, few_shot_num=self.new_classes_num, rank=self.rank, world_size=self.world_size, batch_size=1, is_validation=True, val_image_num=val_image_num, shuffle=False, dense_sample=True, color_jitter=color_jitter_val, random_flip=False)
if self.test_categories_paths is not None:
get_test_loader_ddp = lambda **kwargs: get_test_loader_quadrupeds(
mode=data_loader_mode,
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=0,
enhance_back_view=enhance_back_view,
enhance_back_view_path=enhance_back_view_path,
**kwargs)
print(f"Loading testing data...")
test_loader = get_test_loader_ddp(test_data_dirs=self.test_categories_paths, rank=self.rank, world_size=self.world_size, batch_size=1, is_validation=True, shuffle=False, dense_sample=True, color_jitter=color_jitter_val, random_flip=False)
else:
test_loader = None
return train_loader, val_loader, test_loader
def forward_frozen_ViT(self, images):
# this part use the frozen pre-train ViT
x = images
with torch.no_grad():
b, c, h, w = x.shape
self.model.netInstance.netEncoder._feats = []
self.model.netInstance.netEncoder._register_hooks([11], 'key')
#self._register_hooks([11], 'token')
x = self.model.netInstance.netEncoder.ViT.prepare_tokens(x)
#x = self.ViT.prepare_tokens_with_masks(x)
for blk in self.model.netInstance.netEncoder.ViT.blocks:
x = blk(x)
out = self.model.netInstance.netEncoder.ViT.norm(x)
self.model.netInstance.netEncoder._unregister_hooks()
ph, pw = h // self.model.netInstance.netEncoder.patch_size, w // self.model.netInstance.netEncoder.patch_size
patch_out = out[:, 1:] # first is class token
patch_out = patch_out.reshape(b, ph, pw, self.model.netInstance.netEncoder.vit_feat_dim).permute(0, 3, 1, 2)
patch_key = self.model.netInstance.netEncoder._feats[0][:,:,1:] # B, num_heads, num_patches, dim
patch_key = patch_key.permute(0, 1, 3, 2).reshape(b, self.model.netInstance.netEncoder.vit_feat_dim, ph, pw)
global_feat = out[:, 0]
return global_feat
def forward_fix_embeddings(self, batch):
images = batch[0]
images = images.to(self.device)
batch_size, num_frames, _, h0, w0 = images.shape
images = images.reshape(batch_size*num_frames, *images.shape[2:]) # 0~1
if self.memory_encoder == 'DINO':
images_in = images * 2 - 1 # rescale to (-1, 1)
batch_features = self.forward_frozen_ViT(images_in)
elif self.memory_encoder == 'CLIP':
images_in = torch.nn.functional.interpolate(images, (self.clip_reso, self.clip_reso), mode='bilinear')
images_in = tvf.normalize(images_in, self.clip_mean, self.clip_std)
batch_features = self.clip_model.encode_image(images_in).float()
else:
raise NotImplementedError
return batch_features
def retrieve_memory_bank(self, batch_features, batch):
batch_size = batch_features.shape[0]
if self.memory_retrieve == 'cos-linear':
query = torch.nn.functional.normalize(batch_features.unsqueeze(1), dim=-1) # [B, 1, d_k]
key = torch.nn.functional.normalize(self.memory_bank_keys, dim=-1) # [size, d_k]
key = key.transpose(1, 0).unsqueeze(0).repeat(batch_size, 1, 1).to(query.device) # [B, d_k, size]
cos_dist = torch.bmm(query, key).squeeze(1) # [B, size], larger the more similar
rank_idx = torch.sort(cos_dist, dim=-1, descending=True)[1][:, :self.memory_bank_topk] # [B, k]
value = self.memory_bank.unsqueeze(0).repeat(batch_size, 1, 1).to(query.device) # [B, size, d_v]
out = torch.gather(value, dim=1, index=rank_idx[..., None].repeat(1, 1, self.memory_bank_dim)) # [B, k, d_v]
weights = torch.gather(cos_dist, dim=-1, index=rank_idx) # [B, k]
weights = torch.nn.functional.normalize(weights, p=1.0, dim=-1).unsqueeze(-1).repeat(1, 1, self.memory_bank_dim) # [B, k, d_v] weights have been normalized
out = weights * out
out = torch.sum(out, dim=1)
else:
raise NotImplementedError
batch_mean_out = torch.mean(out, dim=0)
weight_aux = {
'weights': weights[:, :, 0], # [B, k], weights from large to small
'pick_idx': rank_idx, # [B, k]
}
return batch_mean_out, out, weight_aux
def discriminator_texture_step(self):
image_iv = self.model.record_image_iv
image_rv = self.model.record_image_rv
image_gt = self.model.record_image_gt
self.model.record_image_iv = None
self.model.record_image_rv = None
self.model.record_image_gt = None
image_iv = image_iv.requires_grad_(True)
image_rv = image_rv.requires_grad_(True)
image_gt = image_gt.requires_grad_(True)
self.optimizerDiscTex.zero_grad()
disc_loss_gt = 0.0
disc_loss_iv = 0.0
disc_loss_rv = 0.0
grad_penalty = 0.0
# for the gt image, it can only be in real or not
if 'gt' in self.model.few_shot_gan_tex_real:
d_gt = self.model.discriminator_texture(image_gt)
disc_loss_gt += discriminator_architecture.bce_loss_target(d_gt, 1)
if image_gt.requires_grad:
grad_penalty_gt = 10. * discriminator_architecture.compute_grad2(d_gt, image_gt)
disc_loss_gt += grad_penalty_gt
grad_penalty += grad_penalty_gt
# for the input view image, it can be in real or fake
if 'iv' in self.model.few_shot_gan_tex_real:
d_iv = self.model.discriminator_texture(image_iv)
disc_loss_iv += discriminator_architecture.bce_loss_target(d_iv, 1)
if image_iv.requires_grad:
grad_penalty_iv = 10. * discriminator_architecture.compute_grad2(d_iv, image_iv)
disc_loss_iv += grad_penalty_iv
grad_penalty += grad_penalty_iv
elif 'iv' in self.model.few_shot_gan_tex_fake:
d_iv = self.model.discriminator_texture(image_iv)
disc_loss_iv += discriminator_architecture.bce_loss_target(d_iv, 0)
# for the random view image, it can only be in fake
if 'rv' in self.model.few_shot_gan_tex_fake:
d_rv = self.model.discriminator_texture(image_rv)
disc_loss_rv += discriminator_architecture.bce_loss_target(d_rv, 0)
all_loss = disc_loss_iv + disc_loss_rv + disc_loss_gt
all_loss = all_loss * self.cfgs.get('gan_tex_loss_discriminator_weight', 0.1)
self.discriminator_texture_loss = all_loss
self.discriminator_texture_loss.backward()
self.optimizerDiscTex.step()
self.discriminator_texture_loss = 0.
return {
'discriminator_loss': all_loss.detach(),
'discriminator_loss_iv': disc_loss_iv.detach(),
'discriminator_loss_rv': disc_loss_rv.detach(),
'discriminator_loss_gt': disc_loss_gt.detach(),
'discriminator_loss_grad': grad_penalty.detach()
}
def train(self):
"""Perform training."""
# archive code and configs
if self.archive_code:
misc.archive_code(osp.join(self.checkpoint_dir, 'archived_code.zip'), filetypes=['.py'])
misc.dump_yaml(osp.join(self.checkpoint_dir, 'configs.yml'), self.cfgs)
# initialize
start_epoch = 0
self.total_iter = 0
self.total_iter = self.original_total_iter
self.metrics_trace.reset()
self.model.to(self.device)
if self.model.enable_disc:
self.model.reset_only_disc_optimizer()
if self.few_shot_resume:
resume_model_name = self.cfgs.get('few_shot_resume_name', None)
start_epoch, self.total_iter = self.load_checkpoint(optim=True, checkpoint_name=resume_model_name)
self.model.ddp(self.rank, self.world_size)
# use tensorboard
if self.use_logger:
from torch.utils.tensorboard import SummaryWriter
self.logger = SummaryWriter(osp.join(self.checkpoint_dir, 'logs', datetime.now().strftime("%Y%m%d-%H%M%S")), flush_secs=10)
# self.viz_data_iterator = indefinite_generator_from_list(self.val_loader) if self.visualize_validation else indefinite_generator_from_list(self.train_loader)
self.viz_data_iterator = indefinite_generator(self.val_loader[0]) if self.visualize_validation else indefinite_generator(self.train_loader[0])
if self.fix_viz_batch:
self.viz_batch = next(self.viz_data_iterator)
if self.test_loader is not None:
self.viz_test_data_iterator = indefinite_generator(self.test_loader[0]) if self.visualize_validation else indefinite_generator(self.train_loader[0])
# run_epochs
epoch = 0
for epoch in range(start_epoch, self.num_epochs):
metrics = self.run_epoch(epoch)
if self.combine_dataset:
self.train_loader[0].dataset._shuffle_all()
self.metrics_trace.append("train", metrics)
if (epoch+1) % self.save_checkpoint_freq == 0:
self.save_checkpoint(epoch+1, total_iter=self.total_iter, optim=True)
# if self.cfgs.get('pyplot_metrics', True):
# self.metrics_trace.plot(pdf_path=osp.join(self.checkpoint_dir, 'metrics.pdf'))
self.metrics_trace.save(osp.join(self.checkpoint_dir, 'metrics.json'))
print(f"Training completed for all {epoch+1} epochs.")
def run_epoch(self, epoch):
"""Run one training epoch."""
metrics = self.make_metrics()
self.model.set_train()
max_loader_len = max([len(loader) for loader in self.train_loader])
train_generators = [indefinite_generator(loader) for loader in self.train_loader]
iteration = 0
while iteration < max_loader_len * len(self.train_loader):
for generator in train_generators:
batch = next(generator)
self.total_iter += 1
num_seqs, num_frames = batch[0].shape[:2]
total_im_num = num_seqs * num_frames
if self.enable_memory_bank:
batch_features = self.forward_fix_embeddings(batch)
batch_embedding, embeddings, weights = self.retrieve_memory_bank(batch_features, batch)
bank_embedding_model_input = [batch_embedding, embeddings, weights]
else:
# bank_embedding_model_input = None
batch_features = self.forward_fix_embeddings(batch)
weights = {
"weights": torch.rand(1,10).to(batch_features.device),
"pick_idx": torch.randint(low=0, high=60, size=(1, 10)).to(batch_features.device)
}
bank_embedding_model_input = [batch_features[0], batch_features, weights]
m = self.model.forward(batch, epoch=epoch, iter=iteration, total_iter=self.total_iter, which_data=self.dataset, is_training=True, bank_embedding=bank_embedding_model_input)
# self.model.backward()
self.optimizerFewShot.zero_grad()
self.model.total_loss.backward()
self.optimizerFewShot.step()
self.model.total_loss = 0.
# if self.cfgs.get('texture_way', None) is not None and self.cfgs.get('gan_tex', False):
if self.model.few_shot_gan_tex:
# the discriminator for local texture
disc_ret = self.discriminator_texture_step()
m.update(disc_ret)
if self.model.enable_disc and (self.model.mask_discriminator_iter[0] < self.total_iter) and (self.model.mask_discriminator_iter[1] > self.total_iter):
# the discriminator training
discriminator_loss_dict, grad_loss = self.model.discriminator_step()
m.update(
{
'mask_disc_loss_discriminator': discriminator_loss_dict['discriminator_loss'] - grad_loss,
'mask_disc_loss_discriminator_grad': grad_loss,
'mask_disc_loss_discriminator_rv': discriminator_loss_dict['discriminator_loss_rv'],
'mask_disc_loss_discriminator_iv': discriminator_loss_dict['discriminator_loss_iv'],
'mask_disc_loss_discriminator_gt': discriminator_loss_dict['discriminator_loss_gt']
}
)
self.logger.add_histogram('train_'+'discriminator_logits/random_view', discriminator_loss_dict['d_rv'], self.total_iter)
if discriminator_loss_dict['d_iv'] is not None:
self.logger.add_histogram('train_'+'discriminator_logits/input_view', discriminator_loss_dict['d_iv'], self.total_iter)
if discriminator_loss_dict['d_gt'] is not None:
self.logger.add_histogram('train_'+'discriminator_logits/gt_view', discriminator_loss_dict['d_gt'], self.total_iter)
metrics.update(m, total_im_num)
if self.rank == 0:
print(f"T{epoch:04}/{iteration:05}/{metrics}")
if self.iteration_save and self.total_iter % self.iteration_save_freq == 0:
self.save_checkpoint(epoch+1, total_iter=self.total_iter, optim=True, use_iter=True)
# ## reset optimizers
# if self.cfgs.get('opt_reset_every_iter', 0) > 0 and self.total_iter < self.cfgs.get('opt_reset_end_iter', 0):
# if self.total_iter % self.cfgs.get('opt_reset_every_iter', 0) == 0:
# self.model.reset_optimizers()
if misc.is_main_process() and self.use_logger:
if self.rank == 0 and self.total_iter % self.log_freq_losses == 0:
for name, loss in m.items():
label = f'cub_loss_train/{name[4:]}' if 'cub' in name else f'loss_train/{name}'
self.logger.add_scalar(label, loss, self.total_iter)
if self.rank == 0 and self.save_result_freq is not None and self.total_iter % self.save_result_freq == 0:
with torch.no_grad():
m = self.model.forward(batch, epoch=epoch, iter=iteration, total_iter=self.total_iter, save_results=False, save_dir=self.train_result_dir, which_data=self.dataset, is_training=False, bank_embedding=bank_embedding_model_input)
torch.cuda.empty_cache()
if self.total_iter % self.log_freq_images == 0:
with torch.no_grad():
if self.rank == 0 and self.log_train_images:
m = self.model.forward(batch, epoch=epoch, iter=iteration, viz_logger=self.logger, total_iter=self.total_iter, which_data=self.dataset, logger_prefix='train_', is_training=False, bank_embedding=bank_embedding_model_input)
if self.fix_viz_batch:
print(f'fix_viz_batch:{self.fix_viz_batch}')
batch_val = self.viz_batch
else:
batch_val = next(self.viz_data_iterator)
if self.visualize_validation:
import time
vis_start = time.time()
# batch = next(self.viz_data_iterator)
# try:
# batch = next(self.viz_data_iterator)
# except: # iterator exhausted
# self.reset_viz_data_iterator()
# batch = next(self.viz_data_iterator)
if self.enable_memory_bank:
batch_features_val = self.forward_fix_embeddings(batch_val)
batch_embedding_val, embeddings_val, weights_val = self.retrieve_memory_bank(batch_features_val, batch_val)
bank_embedding_model_input_val = [batch_embedding_val, embeddings_val, weights_val]
else:
# bank_embedding_model_input_val = None
batch_features_val = self.forward_fix_embeddings(batch_val)
weights_val = {
"weights": torch.rand(1,10).to(batch_features_val.device),
"pick_idx": torch.randint(low=0, high=60, size=(1, 10)).to(batch_features_val.device)
}
bank_embedding_model_input_val = [batch_features_val[0], batch_features_val, weights_val]
if self.total_iter % self.save_result_freq == 0:
m = self.model.forward(batch_val, epoch=epoch, iter=iteration, viz_logger=self.logger, total_iter=self.total_iter, save_results=False, save_dir=self.train_result_dir, which_data=self.dataset, logger_prefix='val_', is_training=False, bank_embedding=bank_embedding_model_input_val)
torch.cuda.empty_cache()
vis_end = time.time()
print(f"vis time: {vis_end - vis_start}")
if self.test_loader is not None:
# unseen category test visualization
batch_test = next(self.viz_test_data_iterator)
if self.enable_memory_bank:
batch_features_test = self.forward_fix_embeddings(batch_test)
batch_embedding_test, embeddings_test, weights_test = self.retrieve_memory_bank(batch_features_test, batch_test)
bank_embedding_model_input_test = [batch_embedding_test, embeddings_test, weights_test]
else:
# bank_embedding_model_input_test = None
batch_features_test = self.forward_fix_embeddings(batch_test)
weights_test = {
"weights": torch.rand(1,10).to(batch_features_test.device),
"pick_idx": torch.randint(low=0, high=60, size=(1, 10)).to(batch_features_test.device)
}
bank_embedding_model_input_test = [batch_features_test[0], batch_features_test, weights_test]
m_test = self.model.forward(batch_test, epoch=epoch, iter=iteration, viz_logger=self.logger, total_iter=self.total_iter, which_data=self.dataset, logger_prefix='test_', is_training=False, bank_embedding=bank_embedding_model_input_test)
vis_test_end = time.time()
print(f"vis test time: {vis_test_end - vis_end}")
for name, loss in m_test.items():
if self.rank == 0:
self.logger.add_scalar(f'loss_test/{name}', loss, self.total_iter)
for name, loss in m.items():
if self.rank == 0:
self.logger.add_scalar(f'loss_val/{name}', loss, self.total_iter)
torch.cuda.empty_cache()
iteration += 1
self.model.scheduler_step()
return metrics
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