def main():
args = parse_args()
set_affinity(args.local_rank)
set_random_seed(args.seed, by_rank=True)
cfg = Config(args.config)
# If args.single_gpu is set to True,
# we will disable distributed data parallel
if not args.single_gpu:
cfg.local_rank = args.local_rank
init_dist(cfg.local_rank)
# Override the number of data loading workers if necessary
if args.num_workers is not None:
cfg.data.num_workers = args.num_workers
# Create log directory for storing training results.
cfg.date_uid, cfg.logdir = init_logging(args.config, args.logdir)
make_logging_dir(cfg.logdir)
# Initialize cudnn.
init_cudnn(cfg.cudnn.deterministic, cfg.cudnn.benchmark)
# Initialize data loaders and models.
train_data_loader, val_data_loader = get_train_and_val_dataloader(cfg)
net_G, net_D, opt_G, opt_D, sch_G, sch_D = \
get_model_optimizer_and_scheduler(cfg, seed=args.seed)
trainer = get_trainer(cfg, net_G, net_D,
opt_G, opt_D,
sch_G, sch_D,
train_data_loader, val_data_loader)
current_epoch, current_iteration = trainer.load_checkpoint(
cfg, args.checkpoint,resume=args.resume)
# Start training.
for epoch in range(current_epoch, cfg.max_epoch):
print('Epoch {} ...'.format(epoch))
if not args.single_gpu:
train_data_loader.sampler.set_epoch(current_epoch)
trainer.start_of_epoch(current_epoch)
for it, data in enumerate(train_data_loader):
data = trainer.start_of_iteration(data, current_iteration)
for _ in range(cfg.trainer.dis_step):
trainer.dis_update(data)
for _ in range(cfg.trainer.gen_step):
trainer.gen_update(data)
current_iteration += 1
trainer.end_of_iteration(data, current_epoch, current_iteration)
if current_iteration >= cfg.max_iter:
print('Done with training!!!')
return
current_epoch += 1
trainer.end_of_epoch(data, current_epoch, current_iteration)
print('Done with training!!!')
return
def main():
args = parse_args()
set_affinity(args.local_rank)
set_random_seed(args.seed, by_rank=True)
cfg = Config(args.config)
if not hasattr(cfg, 'inference_args'):
cfg.inference_args = None
# If args.single_gpu is set to True,
# we will disable distributed data parallel.
if not args.single_gpu:
cfg.local_rank = args.local_rank
init_dist(cfg.local_rank)
# Override the number of data loading workers if necessary
if args.num_workers is not None:
cfg.data.num_workers = args.num_workers
# Create log directory for storing training results.
cfg.date_uid, cfg.logdir = init_logging(args.config, args.logdir)
# Initialize cudnn.
init_cudnn(cfg.cudnn.deterministic, cfg.cudnn.benchmark)
# Initialize data loaders and models.
test_data_loader = get_test_dataloader(cfg)
net_G, net_D, opt_G, opt_D, sch_G, sch_D = \
get_model_optimizer_and_scheduler(cfg, seed=args.seed)
trainer = get_trainer(cfg, net_G, net_D,
opt_G, opt_D,
sch_G, sch_D,
None, test_data_loader)
# if args.checkpoint == '':
# # Download pretrained weights.
# pretrained_weight_url = cfg.pretrained_weight
# if pretrained_weight_url == '':
# print('google link to the pretrained weight is not specified.')
# raise
# default_checkpoint_path = args.config.replace('.yaml', '.pt')
# args.checkpoint = get_checkpoint(
# default_checkpoint_path, pretrained_weight_url)
# print('Checkpoint downloaded to', args.checkpoint)
# Load checkpoint.
trainer.load_checkpoint(cfg, args.checkpoint)
# Do inference.
trainer.current_epoch = -1
trainer.current_iteration = -1
trainer.test(test_data_loader, args.output_dir, cfg.inference_args)
def main():
args = parse_args()
set_affinity(args.local_rank)
set_random_seed(args.seed, by_rank=True)
cfg = Config(args.config)
# If args.single_gpu is set to True,
# we will disable distributed data parallel
if not args.single_gpu:
cfg.local_rank = args.local_rank
init_dist(cfg.local_rank)
# Override the number of data loading workers if necessary
if args.num_workers is not None:
cfg.data.num_workers = args.num_workers
# Create log directory for storing training results.
cfg.date_uid, cfg.logdir = init_logging(args.config, args.logdir)
make_logging_dir(cfg.logdir)
# Initialize cudnn.
init_cudnn(cfg.cudnn.deterministic, cfg.cudnn.benchmark)
# Initialize data loaders and models.
train_data_loader, val_data_loader = get_train_and_val_dataloader(cfg)
net_G, net_D, opt_G, opt_D, sch_G, sch_D = \
get_model_optimizer_and_scheduler(cfg, seed=args.seed)
trainer = get_trainer(cfg, net_G, net_D,
opt_G, opt_D,
sch_G, sch_D,
train_data_loader, val_data_loader)
# Start evaluation.
checkpoints = \
sorted(glob.glob('{}/*.pt'.format(args.checkpoint_logdir)))
for checkpoint in checkpoints:
current_epoch, current_iteration = \
trainer.load_checkpoint(cfg, checkpoint, resume=True)
trainer.current_epoch = current_epoch
trainer.current_iteration = current_iteration
trainer.write_metrics()
print('Done with evaluation!!!')
return
def _init_single_image_model(self, load_weights=True):
r"""Load single image model, if any."""
if self.single_image_model is None and \
hasattr(self.gen_cfg, 'single_image_model'):
print('Using single image model...')
single_image_cfg = Config(self.gen_cfg.single_image_model.config)
# Init model.
net_G, net_D, opt_G, opt_D, sch_G, sch_D = \
get_model_optimizer_and_scheduler(single_image_cfg)
# Init trainer and load checkpoint.
trainer = get_trainer(single_image_cfg, net_G, net_D, opt_G, opt_D,
sch_G, sch_D, None, None)
if load_weights:
print('Loading single image model checkpoint')
single_image_ckpt = self.gen_cfg.single_image_model.checkpoint
trainer.load_checkpoint(single_image_cfg, single_image_ckpt)
print('Loaded single image model checkpoint')
self.single_image_model = net_G.module
self.single_image_model_z = None
# encoder
x_en2 = self.layer1(x)
x_en2 = torch.cat([x_en2, x_d02], dim=1)
x_en4 = self.layer2(x_en2)
x_en4 = torch.cat([x_en4, x_d04], dim=1)
x_en8 = self.layer3(x_en4)
x_en8 = torch.cat([x_en8, x_d08], dim=1)
x_en16 = self.layer4(x_en8)
x_en16 = torch.cat([x_en16, x_d16], dim=1)
# decoder
x_de8 = self.layer5(x_en16, x_en8)
# x_de8 = torch.cat([x_de8, x_en8], dim=1)
x_de4 = self.layer6(x_de8, x_en4)
# x_de4 = torch.cat([x_de4, x_en4], dim=1)
x_de2 = self.layer7(x_de4, x_en2)
# x_de2 = torch.cat([x_de2, x_en2], dim=1)
out = self.layer8(x_de2, xi_yj)
out = self.outlayer(out)
return out
if __name__ == "__main__":
from imaginaire.config import Config
cfg = Config("/configs/projects/cagan/LipMPV/base_dis2_gen1.yaml")
gen = Generator(cfg.gen, cfg.data)
batch = torch.randn((8, 9, 256, 192))
y = gen(batch)
print(y.shape)
"""
batch_size = images.size(0)
features = self.model(images)
# outputs = self.classifier(features.view(batch_size, -1))
# return outputs, features, images
return features
class Discriminator(nn.Module):
def __init__(self, gen_cfg, data_cfg):
super(Discriminator, self).__init__()
self.dis = ResDiscriminator(image_channels=6)
self.sigmoid = nn.Sigmoid()
def forward(self, x):
out = self.dis(x)
out = self.sigmoid(out)
return out
if __name__ == "__main__":
from imaginaire.config import Config
cfg = Config(
"D:/workspace/develop/imaginaire/configs/projects/cagan/LipMPV/base.yaml"
)
dis = Discriminator(cfg.dis, cfg.data)
batch = torch.randn((8, 6, 256, 192))
features, images = dis(batch)
print(features.shape)
def main():
r""" Build lmdb for training/testing.
Usage:
python scripts/build_lmdb.py \
--config configs/data_image.yaml \
--data_root /mnt/bigdata01/datasets/test_image \
--output_root /mnt/bigdata01/datasets/test_image/lmdb_0/ \
--overwrite
"""
args = parse_args()
cfg = Config(args.config)
# Check if output file already exists.
if os.path.exists(args.output_root):
if args.overwrite:
print('Deleting existing output LMDB.')
shutil.rmtree(args.output_root)
else:
print('Output root LMDB already exists. Use --overwrite. ' +
'Exiting...')
return
all_filenames, extensions = \
create_metadata(data_root=args.data_root,
cfg=cfg,
paired=args.paired,
input_list=args.input_list)
required_data_types = cfg.data.data_types
# Build LMDB.
os.makedirs(args.output_root)
for data_type in required_data_types:
data_size = 0
print('Data type:', data_type)
filepaths, keys = [], []
print('>> Building file list.')
# Get appropriate list of files.
if args.paired:
filenames = all_filenames
else:
filenames = all_filenames[data_type]
for sequence in tqdm(filenames):
for filename in copy.deepcopy(filenames[sequence]):
filepath = construct_file_path(args.data_root, data_type,
sequence, filename,
extensions[data_type])
key = '%s/%s' % (sequence, filename)
filesize = check_and_add(filepath,
key,
filepaths,
keys,
remove_missing=args.remove_missing)
# Remove file from list, if missing.
if filesize == -1 and args.paired and args.remove_missing:
print('Removing %s from list' % (filename))
filenames[sequence].remove(filename)
data_size += filesize
# Remove empty sequences.
if args.paired and args.remove_missing:
for sequence in copy.deepcopy(all_filenames):
if not all_filenames[sequence]:
all_filenames.pop(sequence)
# Allocate size.
data_size = max(int((1 + args.metadata_factor) * data_size), 1e9)
print('Reserved size: %s, %dGB' % (data_type, data_size // 1e9))
# Write LMDB to file.
output_filepath = os.path.join(args.output_root, data_type)
build_lmdb(filepaths, keys, output_filepath, data_size, args.large)
# Output list of all filenames.
if args.output_root:
with open(args.output_root + '/all_filenames.json', 'w') as fout:
json.dump(all_filenames, fout, indent=4)
# Output metadata.
with open(args.output_root + '/metadata.json', 'w') as fout:
json.dump(extensions, fout, indent=4)
else:
return all_filenames, extensions
def main():
args = parse_args()
cfg = Config(args.config)
# Check if output file already exists.
if os.path.exists(args.output_root):
if args.overwrite:
print('Deleting existing output LMDB.')
shutil.rmtree(args.output_root)
else:
print('Output root LMDB already exists. Use --overwrite. ' +
'Exiting...')
return
# all_filenames: dictionary
# "images_content" -> "class01": ["image01.jpg",...], "class02": ["image01.jpg",...]
# "images_style" -> "class01": ["image01.jpg",...], "class02": ["image01.jpg",...]
all_filenames, extensions = \
create_metadata(data_root=args.data_root,
cfg=cfg,
paired=args.paired,
input_list=args.input_list)
required_data_types = cfg.data.data_types
# Build LMDB.
os.makedirs(args.output_root)
for data_type in required_data_types: # required_data_types = ['images_content', 'images_style']
data_size = 0
print('Data type:', data_type)
filepaths, keys = [], []
print('>> Building file list.')
# Get appropriate list of files.
if args.paired:
filenames = all_filenames
else:
filenames = all_filenames[data_type]
for sequence in tqdm(filenames): # each class
# append key and filepath to keys and filepaths in each class
for filename in copy.deepcopy(filenames[sequence]):
filepath = construct_file_path(args.data_root, data_type,
sequence, filename,
extensions[data_type])
# key = '%s/%s' % (sequence, filename)
key = os.path.join(sequence, filename)
filesize = check_and_add(filepath,
key,
filepaths,
keys,
remove_missing=args.remove_missing)
# Remove file from list, if missing.
if filesize == -1 and args.paired and args.remove_missing:
print('Removing %s from list' % (filename))
filenames[sequence].remove(filename)
data_size += filesize
# Remove empty sequences.
if args.paired and args.remove_missing:
for sequence in copy.deepcopy(all_filenames):
if not all_filenames[sequence]:
all_filenames.pop(sequence)
# Allocate size.
data_size = max(int((1 + args.metadata_factor) * data_size), 1e9)
print('Reserved size: %s, %dGB' % (data_type, data_size // 1e9))
# Write LMDB to file.
output_filepath = os.path.join(args.output_root, data_type)
build_lmdb(filepaths, keys, output_filepath, data_size, args.large)
# Output list of all filenames.
if args.output_root:
with open(os.path.join(args.output_root, 'all_filenames.json'),
'w') as fout:
json.dump(all_filenames, fout, indent=4)
# Output metadata.
with open(os.path.join(args.output_root, 'metadata.json'),
'w') as fout:
json.dump(extensions, fout, indent=4)
else:
return all_filenames, extensions