def main(argv):
(opts, args) = parser.parse_args(argv)
torch.manual_seed(opts.seed)
torch.cuda.manual_seed(opts.seed)
if not os.path.exists(opts.output_folder):
os.makedirs(opts.output_folder)
# Load experiment setting
config = get_config(opts.config)
style_dim = config['gen']['style_dim']
opts.num_style = 1 if opts.style != '' else opts.num_style
# Setup model and data loader
trainer = MUNIT_Trainer(config)
state_dict = torch.load(opts.checkpoint)
trainer.gen_a.load_state_dict(state_dict['a'])
trainer.gen_b.load_state_dict(state_dict['b'])
trainer.cuda()
trainer.eval()
encode = trainer.gen_a.encode if opts.a2b else trainer.gen_b.encode # encode function
style_encode = trainer.gen_b.encode if opts.a2b else trainer.gen_a.encode # encode function
decode = trainer.gen_b.decode if opts.a2b else trainer.gen_a.decode # decode function
transform = transforms.Compose([
transforms.Resize(config['new_size']),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
image = Variable(transform(Image.open(
opts.input).convert('RGB')).unsqueeze(0).cuda(),
volatile=True)
style_image = Variable(transform(Image.open(
opts.style).convert('RGB')).unsqueeze(0).cuda(),
volatile=True) if opts.style != '' else None
# Start testing
style_rand = Variable(torch.randn(opts.num_style, style_dim, 1, 1).cuda(),
volatile=True)
content, _ = encode(image)
if opts.style != '':
_, style = style_encode(style_image)
else:
style = style_rand
for j in range(opts.num_style):
s = style[j].unsqueeze(0)
outputs = decode(content, s)
outputs = (outputs + 1) / 2.
path = os.path.join(opts.output_folder, 'output{:03d}.jpg'.format(j))
vutils.save_image(outputs.data, path, padding=0, normalize=True)
if not opts.output_only:
# also save input images
vutils.save_image(image.data,
os.path.join(opts.output_folder, 'input.jpg'),
padding=0,
normalize=True)
def main(argv):
(opts, args) = parser.parse_args(argv)
torch.manual_seed(opts.seed)
torch.cuda.manual_seed(opts.seed)
if not os.path.exists(opts.output_folder):
os.makedirs(opts.output_folder)
# Load experiment setting
config = get_config(opts.config)
input_dim = config['new_size'] if opts.a2b else config['input_dim_b']
style_dim = config['gen']['style_dim']
# Setup model and data loader
data_loader = get_data_loader_folder(opts.input_folder,
1,
False,
input_dim == 1,
crop=False)
trainer = MUNIT_Trainer(config)
state_dict = torch.load(opts.checkpoint)
trainer.gen_a.load_state_dict(state_dict['a'])
trainer.gen_b.load_state_dict(state_dict['b'])
trainer.cuda()
trainer.eval()
encode = trainer.gen_a.encode if opts.a2b else trainer.gen_b.encode # encode function
decode = trainer.gen_b.decode if opts.a2b else trainer.gen_a.decode # decode function
# Start testing
style_fixed = Variable(torch.randn(opts.num_style, style_dim, 1, 1).cuda(),
volatile=True)
for i, images in enumerate(data_loader):
images = Variable(images.cuda(), volatile=True)
content, _ = encode(images)
style = style_fixed if opts.synchronized else Variable(
torch.randn(opts.num_style, style_dim, 1, 1).cuda(), volatile=True)
for j in range(opts.num_style):
s = style[j].unsqueeze(0)
outputs = decode(content, s)
outputs = (outputs + 1) / 2.
path = os.path.join(opts.output_folder,
'input{:03d}_output{:03d}.jpg'.format(i, j))
vutils.save_image(outputs.data, path, padding=0, normalize=True)
if not opts.output_only:
# also save input images
vutils.save_image(images.data,
os.path.join(opts.output_folder,
'input{:03d}.jpg'.format(i)),
padding=0,
normalize=True)
type=str,
default='MUNIT',
help="MUNIT|UNIT")
opts = parser.parse_args()
cudnn.benchmark = True
# Load experiment setting
config = get_config(opts.config)
max_iter = config['max_iter']
display_size = config['display_size']
config['vgg_model_path'] = opts.output_path
# Setup model and data loader
if opts.trainer == 'MUNIT':
trainer = MUNIT_Trainer(config)
elif opts.trainer == 'UNIT':
trainer = UNIT_Trainer(config)
else:
sys.exit("Only support MUNIT|UNIT")
trainer.cuda()
train_loader_a, train_loader_b, test_loader_a, test_loader_b = get_all_data_loaders(
config)
train_display_images_a = torch.stack(
[train_loader_a.dataset[i] for i in range(display_size)]).cuda()
train_display_images_b = torch.stack(
[train_loader_b.dataset[i] for i in range(display_size)]).cuda()
test_display_images_a = torch.stack(
[test_loader_a.dataset[i] for i in range(display_size)]).cuda()
test_display_images_b = torch.stack(
[test_loader_b.dataset[i] for i in range(display_size)]).cuda()
def main(argv):
(opts, args) = parser.parse_args(argv)
cudnn.benchmark = True
model_name = os.path.splitext(os.path.basename(opts.config))[0]
# Load experiment setting
config = get_config(opts.config)
max_iter = config['max_iter']
display_size = config['display_size']
# Setup model and data loader
trainer = MUNIT_Trainer(config)
trainer.cuda()
train_loader_a, train_loader_b, test_loader_a, test_loader_b = get_all_data_loaders(
config)
test_display_images_a = Variable(torch.stack(
[test_loader_a.dataset[i] for i in range(display_size)]).cuda(),
volatile=True)
test_display_images_b = Variable(torch.stack(
[test_loader_b.dataset[i] for i in range(display_size)]).cuda(),
volatile=True)
train_display_images_a = Variable(torch.stack(
[train_loader_a.dataset[i] for i in range(display_size)]).cuda(),
volatile=True)
train_display_images_b = Variable(torch.stack(
[train_loader_b.dataset[i] for i in range(display_size)]).cuda(),
volatile=True)
# Setup logger and output folders
train_writer = tensorboard.SummaryWriter(os.path.join(
opts.log, model_name))
output_directory = os.path.join(opts.outputs, model_name)
checkpoint_directory, image_directory = prepare_sub_folder(
output_directory)
shutil.copy(opts.config, os.path.join(
output_directory, 'config.yaml')) # copy config file to output folder
# Start training
iterations = trainer.resume(checkpoint_directory) if opts.resume else 0
while True:
for it, (images_a,
images_b) in enumerate(izip(train_loader_a, train_loader_b)):
trainer.update_learning_rate()
images_a, images_b = Variable(images_a.cuda()), Variable(
images_b.cuda())
# Main training code
trainer.dis_update(images_a, images_b, config)
trainer.gen_update(images_a, images_b, config)
# Dump training stats in log file
if (iterations + 1) % config['log_iter'] == 0:
print("Iteration: %08d/%08d" % (iterations + 1, max_iter))
write_loss(iterations, trainer, train_writer)
# Write images
if (iterations + 1) % config['image_save_iter'] == 0:
# Test set images
image_outputs = trainer.sample(test_display_images_a,
test_display_images_b)
write_images(
image_outputs, display_size,
'%s/gen_test%08d.jpg' % (image_directory, iterations + 1))
# Train set images
image_outputs = trainer.sample(train_display_images_a,
train_display_images_b)
write_images(
image_outputs, display_size,
'%s/gen_train%08d.jpg' % (image_directory, iterations + 1))
# HTML
write_html(output_directory + "/index.html", iterations + 1,
config['image_save_iter'], 'images')
if (iterations + 1) % config['image_save_iter'] == 0:
image_outputs = trainer.sample(test_display_images_a,
test_display_images_b)
write_images(image_outputs, display_size,
'%s/gen.jpg' % image_directory)
# Save network weights
if (iterations + 1) % config['snapshot_save_iter'] == 0:
trainer.save(checkpoint_directory, iterations)
iterations += 1
if iterations >= max_iter:
return
default='MUNIT',
help="MUNIT|UNIT")
opts = parser.parse_args()
device = torch.device('cuda:{}'.format(int(
opts.gpu_ids))) if opts.gpu_ids != '-1' else torch.device('cpu')
cudnn.benchmark = True
# Load experiment setting
config = get_config(opts.config)
max_iter = config['max_iter']
display_size = config['display_size']
config['vgg_model_path'] = opts.output_path
# Setup model and data loader
if opts.trainer == 'MUNIT':
trainer = MUNIT_Trainer(config, device)
elif opts.trainer == 'UNIT':
trainer = UNIT_Trainer(config)
else:
sys.exit("Only support MUNIT|UNIT")
trainer.to(device)
train_loader_a, train_loader_b, test_loader_a, test_loader_b = get_all_data_loaders(
config)
train_display_images_a = torch.stack(
[train_loader_a.dataset[i] for i in range(display_size)]).to(device)
train_display_images_b = torch.stack(
[train_loader_b.dataset[i] for i in range(display_size)]).to(device)
test_display_images_a = torch.stack(
[test_loader_a.dataset[i] for i in range(display_size)]).to(device)
test_display_images_b = torch.stack(
[test_loader_b.dataset[i] for i in range(display_size)]).to(device)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--config',
type=str,
default='configs/edges2handbags_folder.yaml',
help='Path to the config file.')
parser.add_argument('--output_path',
type=str,
default='.',
help="outputs path")
#resume option => [, default='730000']
parser.add_argument("--resume", default='150000', action="store_true")
parser.add_argument('--trainer',
type=str,
default='MUNIT',
help="MUNIT|UNIT")
opts = parser.parse_args()
cudnn.benchmark = True
# Load experiment setting
config = get_config(opts.config)
max_iter = config['max_iter']
display_size = config['display_size']
config['vgg_model_path'] = opts.output_path
# Setup model and data loader
if opts.trainer == 'MUNIT':
trainer = MUNIT_Trainer(config)
elif opts.trainer == 'UNIT':
trainer = UNIT_Trainer(config)
else:
sys.exit("Only support MUNIT|UNIT")
trainer.cuda()
train_loader_a, train_loader_b, test_loader_a, test_loader_b = get_all_data_loaders(
config)
train_display_images_a = torch.stack(
[train_loader_a.dataset[i] for i in range(display_size)]).cuda()
train_display_images_b = torch.stack(
[train_loader_b.dataset[i] for i in range(display_size)]).cuda()
test_display_images_a = torch.stack(
[test_loader_a.dataset[i] for i in range(display_size)]).cuda()
test_display_images_b = torch.stack(
[test_loader_b.dataset[i] for i in range(display_size)]).cuda()
# Setup logger and output folders
model_name = os.path.splitext(os.path.basename(opts.config))[0]
train_writer = tensorboardX.SummaryWriter(
os.path.join(opts.output_path + "/logs", model_name))
output_directory = os.path.join(opts.output_path + "/outputs", model_name)
checkpoint_directory, image_directory = prepare_sub_folder(
output_directory)
shutil.copy(opts.config, os.path.join(
output_directory, 'config.yaml')) # copy config file to output folder
# Start training
iterations = trainer.resume(checkpoint_directory,
hyperparameters=config) if opts.resume else 0
while True:
for it, (images_a,
images_b) in enumerate(zip(train_loader_a, train_loader_b)):
trainer.update_learning_rate()
images_a, images_b = images_a.cuda().detach(), images_b.cuda(
).detach()
with Timer("Elapsed time in update: %f"):
# Main training code
trainer.dis_update(images_a, images_b, config)
trainer.gen_update(images_a, images_b, config)
torch.cuda.synchronize()
# Dump training stats in log file
if (iterations + 1) % config['log_iter'] == 0:
print("Iteration: %08d/%08d" % (iterations + 1, max_iter))
write_loss(iterations, trainer, train_writer)
# Write images
if (iterations + 1) % config['image_save_iter'] == 0:
with torch.no_grad():
test_image_outputs = trainer.sample(
test_display_images_a, test_display_images_b)
train_image_outputs = trainer.sample(
train_display_images_a, train_display_images_b)
write_2images(test_image_outputs, display_size,
image_directory, 'test_%08d' % (iterations + 1))
write_2images(train_image_outputs, display_size,
image_directory, 'train_%08d' % (iterations + 1))
# HTML
write_html(output_directory + "/index.html", iterations + 1,
config['image_save_iter'], 'images')
if (iterations + 1) % config['image_display_iter'] == 0:
with torch.no_grad():
image_outputs = trainer.sample(train_display_images_a,
train_display_images_b)
write_2images(image_outputs, display_size, image_directory,
'train_current')
# Save network weights
if (iterations + 1) % config['snapshot_save_iter'] == 0:
trainer.save(checkpoint_directory, iterations)
iterations += 1
if iterations >= max_iter:
sys.exit('Finish training')
if o in config:
print("Overwriting {:20} {:30} -> {:}".format(
o, config[k], getattr(opts, o)))
config[o] = getattr(opts, o)
comet_exp.log_asset(opts.config)
max_iter = config["max_iter"]
display_size = config["display_size"]
config["vgg_model_path"] = opts.output_path
comet_exp.log_parameters(config)
print("Using model", opts.trainer)
# Setup model and data loader
if opts.trainer == "MUNIT":
trainer = MUNIT_Trainer(config, comet_exp)
elif opts.trainer == "UNIT":
trainer = UNIT_Trainer(config)
elif opts.trainer == "DoubleMUNIT":
trainer = DoubleMUNIT_Trainer(config, comet_exp)
else:
sys.exit("Only support MUNIT|UNIT|DOubleMUNIT")
trainer.cuda()
train_loader_a, train_loader_b, test_loader_a, test_loader_b = get_all_data_loaders(
config)
train_display_images_a = torch.stack(
[train_loader_a.dataset[i] for i in range(display_size)]).cuda()
train_display_images_b = torch.stack(
[train_loader_b.dataset[i] for i in range(display_size)]).cuda()
test_display_images_a = torch.stack(
[test_loader_a.dataset[i] for i in range(display_size)]).cuda()
help="outputs path")
parser.add_argument('--load', type=int, default=400)
parser.add_argument('--snapshot_dir', type=str, default='.')
opts = parser.parse_args()
cudnn.benchmark = True
# Load experiment setting.
config = get_config(opts.config)
display_size = config['display_size']
config['vgg_model_path'] = opts.output_path
# Setup model and data loader.
if config['trainer'] == 'MUNIT':
trainer = MUNIT_Trainer(config,
resume_epoch=opts.load,
snapshot_dir=opts.snapshot_dir)
elif config['trainer'] == 'UNIT':
trainer = UNIT_Trainer(config,
resume_epoch=opts.load,
snapshot_dir=opts.snapshot_dir)
else:
sys.exit("Only support MUNIT|UNIT.")
os.exit()
trainer.cuda()
dataset_letters = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I']
samples = list()
dataset_probs = list()
augmentation = list()
type=str,
default='munit_semantic_loss',
help='name of the experiment')
opts = parser.parse_args()
cudnn.benchmark = True
# Load experiment setting
config = get_config(opts.config)
max_iter = config['max_iter']
display_size = config['display_size']
config['data_root'] = opts.data_root
# Setup model and data loader
if opts.trainer == 'MUNIT':
trainer = MUNIT_Trainer(config, opts)
else:
sys.exit("Only support MUNIT")
trainer.cuda()
train_loader_a, train_loader_b, test_loader_a, test_loader_b = get_all_data_loaders(
config)
data_masks = [train_loader_a.dataset[i] for i in range(config['display_size'])]
train_display_images_a = torch.stack([dm[0] for dm in data_masks]).cuda()
train_display_target_a = torch.stack([dm[1] for dm in data_masks]).cuda()
data_masks = [train_loader_b.dataset[i] for i in range(config['display_size'])]
train_display_images_b = torch.stack([dm[0] for dm in data_masks]).cuda()
train_display_target_b = torch.stack([dm[1] for dm in data_masks]).cuda()
def main():
from utils import get_all_data_loaders, prepare_sub_folder, write_html, write_loss, get_config, write_2images, Timer
import argparse
from torch.autograd import Variable
from trainer import MUNIT_Trainer, UNIT_Trainer
import torch.backends.cudnn as cudnn
import torch
# try:
# from itertools import izip as zip
# except ImportError: # will be 3.x series
# pass
import os
import sys
import tensorboardX
import shutil
os.environ["CUDA_VISIBLE_DEVICES"] = str(0)
parser = argparse.ArgumentParser()
parser.add_argument('--config',
type=str,
default='configs/edges2handbags_folder.yaml',
help='Path to the config file.')
parser.add_argument('--output_path',
type=str,
default='.',
help="outputs path")
parser.add_argument("--resume", action="store_true")
parser.add_argument('--trainer',
type=str,
default='MUNIT',
help="MUNIT|UNIT")
opts = parser.parse_args()
cudnn.benchmark = True
'''
Note: https://www.pytorchtutorial.com/when-should-we-set-cudnn-benchmark-to-true/
大部分情况下，设置这个 flag 可以让内置的 cuDNN 的 auto-tuner 自动寻找最适合当前配置的高效算法，来达到优化运行效率的问题
1. 如果网络的输入数据维度或类型上变化不大，设置 torch.backends.cudnn.benchmark = true 可以增加运行效率；
2. 如果网络的输入数据在每次 iteration 都变化的话，会导致 cnDNN 每次都会去寻找一遍最优配置，这样反而会降低运行效率。
'''
# Load experiment setting
config = get_config(opts.config)
max_iter = config['max_iter']
display_size = config['display_size']
config['vgg_model_path'] = opts.output_path
# Setup model and data loader
if opts.trainer == 'MUNIT':
trainer = MUNIT_Trainer(config)
elif opts.trainer == 'UNIT':
trainer = UNIT_Trainer(config)
else:
sys.exit("Only support MUNIT|UNIT")
trainer.cuda()
train_loader_a, train_loader_b, test_loader_a, test_loader_b = get_all_data_loaders(
config)
train_display_images_a = torch.stack(
[train_loader_a.dataset[i] for i in range(display_size)]).cuda()
train_display_images_b = torch.stack(
[train_loader_b.dataset[i] for i in range(display_size)]).cuda()
test_display_images_a = torch.stack(
[test_loader_a.dataset[i] for i in range(display_size)]).cuda()
test_display_images_b = torch.stack(
[test_loader_b.dataset[i] for i in range(display_size)]).cuda()
# Setup logger and output folders
model_name = os.path.splitext(os.path.basename(opts.config))[0]
train_writer = tensorboardX.SummaryWriter(
os.path.join(opts.output_path + "/logs", model_name))
output_directory = os.path.join(opts.output_path + "/outputs", model_name)
checkpoint_directory, image_directory = prepare_sub_folder(
output_directory)
shutil.copy(opts.config, os.path.join(
output_directory, 'config.yaml')) # copy config file to output folder
# Start training
iterations = trainer.resume(checkpoint_directory,
hyperparameters=config) if opts.resume else 0
while True:
for it, (images_a,
images_b) in enumerate(zip(train_loader_a, train_loader_b)):
trainer.update_learning_rate()
images_a, images_b = images_a.cuda().detach(), images_b.cuda(
).detach()
with Timer("Elapsed time in update: %f"):
# Main training code
trainer.dis_update(images_a, images_b, config)
trainer.gen_update(images_a, images_b, config)
torch.cuda.synchronize()
# Dump training stats in log file
if (iterations + 1) % config['log_iter'] == 0:
print("Iteration: %08d/%08d" % (iterations + 1, max_iter))
write_loss(iterations, trainer, train_writer)
# Write images
if (iterations + 1) % config['image_save_iter'] == 0:
with torch.no_grad():
test_image_outputs = trainer.sample(
test_display_images_a, test_display_images_b)
train_image_outputs = trainer.sample(
train_display_images_a, train_display_images_b)
write_2images(test_image_outputs, display_size,
image_directory, 'test_%08d' % (iterations + 1))
write_2images(train_image_outputs, display_size,
image_directory, 'train_%08d' % (iterations + 1))
# HTML
write_html(output_directory + "/index.html", iterations + 1,
config['image_save_iter'], 'images')
if (iterations + 1) % config['image_display_iter'] == 0:
with torch.no_grad():
image_outputs = trainer.sample(train_display_images_a,
train_display_images_b)
write_2images(image_outputs, display_size, image_directory,
'train_current')
# Save network weights
if (iterations + 1) % config['snapshot_save_iter'] == 0:
trainer.save(checkpoint_directory, iterations)
iterations += 1
if iterations >= max_iter:
sys.exit('Finish training')
def setup(opts):
generator_checkpoint_path = opts['generator_checkpoint']
# generator_checkpoint_path = './checkpoints/ffhq2ladiescrop.pt'
# Load experiment settings
config = {
'image_save_iter': 10000,
'image_display_iter': 100,
'display_size': 16,
'snapshot_save_iter': 10000,
'log_iter': 100,
'max_iter': 1000000,
'batch_size': 1,
'weight_decay': 0.0001,
'beta1': 0.5,
'beta2': 0.999,
'init': 'kaiming',
'lr': 0.0001,
'lr_policy': 'step',
'step_size': 100000,
'gamma': 0.5,
'gan_w': 1,
'recon_x_w': 10,
'recon_s_w': 1,
'recon_c_w': 1,
'recon_x_cyc_w': 10,
'vgg_w': 0,
'gen': {
'dim': 64,
'mlp_dim': 256,
'style_dim': 8,
'activ': 'relu',
'n_downsample': 2,
'n_res': 4,
'pad_type': 'reflect'
},
'dis': {
'dim': 64,
'norm': 'none',
'activ': 'lrelu',
'n_layer': 4,
'gan_type': 'lsgan',
'num_scales': 3,
'pad_type': 'reflect'
},
'input_dim_a': 3,
'input_dim_b': 3,
'num_workers': 8,
'new_size': 1024,
'crop_image_height': 400,
'crop_image_width': 400,
'data_root': './datasets/ffhq2ladies/'
}
# Setup model and data loader
trainer = MUNIT_Trainer(config)
state_dict = torch.load(generator_checkpoint_path)
trainer.gen_a.load_state_dict(state_dict['a'])
trainer.gen_b.load_state_dict(state_dict['b'])
return {'model': trainer, 'config': config}
def main(argv):
(opts, args) = parser.parse_args(argv)
cudnn.benchmark = True
# Load experiment setting
config = get_config(opts.config)
max_iter = config['max_iter']
# Setup logger and output folders
output_subfolders = prepare_logging_folders(config['output_root'],
config['experiment_name'])
logger = create_logger(
os.path.join(output_subfolders['logs'], 'train_log.log'))
shutil.copy(opts.config,
os.path.join(
output_subfolders['logs'],
'config.yaml')) # copy config file to output folder
tb_logger = tensorboard_logger.Logger(output_subfolders['logs'])
logger.info('============ Initialized logger ============')
logger.info('Config File: {}'.format(opts.config))
# Setup model and data loader
trainer = MUNIT_Trainer(config, opts)
trainer.cuda()
loaders = get_all_data_loaders(config)
val_display_images = next(iter(loaders['val']))
logger.info('Test images: {}'.format(val_display_images['A_paths']))
# Start training
iterations = trainer.resume(opts.model_path,
hyperparameters=config) if opts.resume else 0
while True:
for it, images in enumerate(loaders['train']):
trainer.update_learning_rate()
images_a = images['A']
images_b = images['B']
images_a, images_b = Variable(images_a.cuda()), Variable(
images_b.cuda())
# Main training code
trainer.dis_update(images_a, images_b, config)
trainer.gen_update(images_a, images_b, config)
# Dump training stats in log file
if (iterations + 1) % config['log_iter'] == 0:
for tag, value in trainer.loss.items():
tb_logger.scalar_summary(tag, value, iterations)
val_output_imgs = trainer.sample(
Variable(val_display_images['A'].cuda()),
Variable(val_display_images['B'].cuda()))
tb_imgs = []
for imgs in val_output_imgs.values():
tb_imgs.append(torch.cat(torch.unbind(imgs, 0), dim=2))
tb_logger.image_summary(list(val_output_imgs.keys()), tb_imgs,
iterations)
if (iterations + 1) % config['print_iter'] == 0:
logger.info(
"Iteration: {:08}/{:08} Discriminator Loss: {:.4f} Generator Loss: {:.4f}"
.format(iterations + 1, max_iter, trainer.loss['D/total'],
trainer.loss['G/total']))
# Write images
# if (iterations + 1) % config['image_save_iter'] == 0:
# val_output_imgs = trainer.sample(
# Variable(val_display_images['A'].cuda()),
# Variable(val_display_images['B'].cuda()))
#
# for key, imgs in val_output_imgs.items():
# key = key.replace('/', '_')
# write_images(imgs, config['display_size'], '{}/{}_{:08}.jpg'.format(output_subfolders['images'], key, iterations+1))
#
# logger.info('Saved images to: {}'.format(output_subfolders['images']))
# Save network weights
if (iterations + 1) % config['snapshot_save_iter'] == 0:
trainer.save(output_subfolders['models'], iterations)
iterations += 1
if iterations >= max_iter:
return
