timestamp = now.strftime('%Y_%m_%d_%H_%M_%S')
output_dir = '../output/%s_%s_%s' % \
(cfg.DATASET_NAME, cfg.CONFIG_NAME, timestamp)
num_gpu = len(cfg.GPU_ID.split(','))
if cfg.TRAIN.FLAG:
image_transform = transforms.Compose([
transforms.RandomCrop(cfg.IMSIZE),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
dataset = TextDataset(cfg.DATA_DIR,
'train',
imsize=cfg.IMSIZE,
transform=image_transform)
assert dataset
dataloader = torch.utils.data.DataLoader(
dataset,
batch_size=cfg.TRAIN.BATCH_SIZE * num_gpu,
drop_last=True,
shuffle=True,
num_workers=int(cfg.WORKERS))
algo = GANTrainer(output_dir)
algo.train(dataloader, cfg.STAGE)
else:
datapath = '%s/test/val_captions_custom.t7' % (cfg.DATA_DIR)
algo = GANTrainer(output_dir)
algo.sample(datapath, cfg.STAGE)
copyfile("model.py", output_dir + "/" + "model.py")
copyfile("miscc/utils.py", output_dir + "/" + "utils.py")
copyfile("miscc/datasets.py", output_dir + "/" + "datasets.py")
copyfile(args.cfg_file, output_dir + "/" + "cfg_file.yml")
imsize=64
img_transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
dataset = TextDataset(cfg.DATA_DIR, split="train", imsize=imsize, transform=img_transform)
assert dataset
dataloader = torch.utils.data.DataLoader(
dataset, batch_size=cfg.TRAIN.BATCH_SIZE,
drop_last=True, shuffle=True, num_workers=int(cfg.WORKERS))
algo = GANTrainer(output_dir)
algo.train(dataloader)
else:
imsize=64
datapath= '%s/test/' % (cfg.DATA_DIR)
img_transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
dataset = TextDataset(cfg.DATA_DIR, split="test", imsize=imsize, transform=img_transform)
assert dataset
dataloader = torch.utils.data.DataLoader(
dataset, batch_size=cfg.TRAIN.BATCH_SIZE,
drop_last=True, shuffle=True, num_workers=int(cfg.WORKERS))
algo = GANTrainer(output_dir)
algo.sample(dataloader, num_samples=25, draw_bbox=True)
if exc.errno == errno.EEXIST and os.path.isdir(path):
pass
else:
raise
copyfile(sys.argv[0], output_dir + "/" + sys.argv[0])
copyfile("trainer.py", output_dir + "/" + "trainer.py")
copyfile("model.py", output_dir + "/" + "model.py")
copyfile("miscc/utils.py", output_dir + "/" + "utils.py")
copyfile("miscc/datasets.py", output_dir + "/" + "datasets.py")
copyfile(args.cfg_file, output_dir + "/" + "cfg_file.yml")
imsize=64
img_transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
dataset = TextDataset(cfg.DATA_DIR, split="train", imsize=imsize, transform=img_transform,
crop=True)
assert dataset
dataloader = torch.utils.data.DataLoader(
dataset, batch_size=cfg.TRAIN.BATCH_SIZE,
drop_last=True, shuffle=True, num_workers=int(cfg.WORKERS))
algo = GANTrainer(output_dir)
algo.train(dataloader)
else:
datapath = os.path.join(cfg.DATA_DIR, "test")
algo = GANTrainer(output_dir)
algo.sample(datapath, num_samples=25, draw_bbox=True)
elif cfg.STAGE == 2:
resize = 268
imsize = 256
img_transform = transforms.Compose([
transforms.Resize((resize, resize)),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
dataset = TextDataset(cfg.DATA_DIR,
cfg.IMG_DIR,
split="train",
imsize=imsize,
transform=img_transform,
crop=True,
stage=cfg.STAGE)
assert dataset
dataloader = torch.utils.data.DataLoader(
dataset,
batch_size=cfg.TRAIN.BATCH_SIZE,
drop_last=True,
shuffle=True,
num_workers=int(cfg.WORKERS))
algo = GANTrainer(output_dir)
algo.train(dataloader, cfg.STAGE)
else:
datapath = '%s/test/' % (cfg.DATA_DIR)
algo = GANTrainer(output_dir)
algo.sample(datapath, num_samples=25, stage=cfg.STAGE, draw_bbox=True)
random.seed(args.manualSeed)
torch.manual_seed(args.manualSeed)
if cfg.CUDA:
torch.cuda.manual_seed_all(args.manualSeed)
now = datetime.datetime.now(dateutil.tz.tzlocal())
timestamp = now.strftime('%Y_%m_%d_%H_%M_%S')
output_dir = '../output/%s_%s_%s' % \
(cfg.DATASET_NAME, cfg.CONFIG_NAME, timestamp)
num_gpu = len(cfg.GPU_ID.split(','))
if cfg.TRAIN.FLAG:
image_transform = transforms.Compose([
transforms.RandomCrop(cfg.IMSIZE),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
dataset = TextDataset(cfg.DATA_DIR, 'train',
imsize=cfg.IMSIZE,
transform=image_transform)
assert dataset
dataloader = torch.utils.data.DataLoader(
dataset, batch_size=cfg.TRAIN.BATCH_SIZE * num_gpu,
drop_last=True, shuffle=True, num_workers=int(cfg.WORKERS))
algo = GANTrainer(output_dir)
algo.train(dataloader, cfg.STAGE)
else:
datapath= '%s/test/val_captions.t7' % (cfg.DATA_DIR)
algo = GANTrainer(output_dir)
algo.sample(datapath, cfg.STAGE)
def main(gpu_id, data_dir, manual_seed, cuda, train_flag, image_size,
batch_size, workers, stage, dataset_name, config_name, max_epoch,
snapshot_interval, net_g, net_d, z_dim, generator_lr,
discriminator_lr, lr_decay_epoch, coef_kl, stage1_g, embedding_type,
condition_dim, df_dim, gf_dim, res_num, text_dim, regularizer):
if manual_seed is None:
manual_seed = random.randint(1, 10000)
random.seed(manual_seed)
torch.manual_seed(manual_seed)
if cuda:
torch.cuda.manual_seed_all(manual_seed)
now = datetime.datetime.now(dateutil.tz.tzlocal())
timestamp = now.strftime('%Y_%m_%d_%H_%M_%S')
output_dir = '../output/%s_%s_%s' % (dataset_name, config_name, timestamp)
num_gpu = len(gpu_id.split(','))
if train_flag:
image_transform = transforms.Compose([
transforms.RandomCrop(image_size),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
dataset = TextDataset(data_dir,
'train',
imsize=image_size,
transform=image_transform)
assert dataset
dataloader = torch.utils.data.DataLoader(dataset,
batch_size=batch_size *
num_gpu,
drop_last=True,
shuffle=True,
num_workers=int(workers))
algo = GANTrainer(output_dir, max_epoch, snapshot_interval, gpu_id,
batch_size, train_flag, net_g, net_d, cuda, stage1_g,
z_dim, generator_lr, discriminator_lr,
lr_decay_epoch, coef_kl, regularizer)
algo.train(dataloader, stage, text_dim, gf_dim, condition_dim, z_dim,
df_dim, res_num)
elif dataset_name == 'birds' and train_flag is False:
image_transform = transforms.Compose([
transforms.RandomCrop(image_size),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
dataset = TextDataset(data_dir,
'train',
imsize=image_size,
transform=image_transform)
assert dataset
dataloader = torch.utils.data.DataLoader(dataset,
batch_size=batch_size *
num_gpu,
drop_last=True,
shuffle=True,
num_workers=int(workers))
algo = GANTrainer(output_dir, max_epoch, snapshot_interval, gpu_id,
batch_size, train_flag, net_g, net_d, cuda, stage1_g,
z_dim, generator_lr, discriminator_lr,
lr_decay_epoch, coef_kl, regularizer)
algo.birds_eval(dataloader, stage)
else:
datapath = '%s/test/val_captions.t7' % (data_dir)
algo = GANTrainer(output_dir, max_epoch, snapshot_interval, gpu_id,
batch_size, train_flag, net_g, net_d, cuda, stage1_g,
z_dim, generator_lr, discriminator_lr,
lr_decay_epoch, coef_kl, regularizer)
algo.sample(datapath, stage)
# Initialize the main class which includes the training and evaluation
algo = GANTrainer(output_dir, cfg_path=args.cfg_file)
if cfg.TRAIN.FLAG:
algo.train(dataloader)
elif args.eval:
date_str = datetime.datetime.now().strftime('%b-%d-%I%M%p-%G')
if args.FID_eval:
'''Do FID evaluations.'''
f = open(os.path.join(output_dir, 'all_FID_eval.txt'), 'a')
for net_G_name in net_G_names:
cfg.NET_G = net_G_name
algo.sample(dataloader, eval_name='eval',
eval_num=args.eval_num)
fid_score_now = \
fid_scores(output_dir, cfg, sample_num=args.sample_num,
gen_images_path=args.gen_paths, loop=True)
f.write('%s, %s, %.4f\n' % (date_str, net_G_name, fid_score_now))
f.close()
# Save the best FID score model
with open(os.path.join(output_dir, 'all_FID_eval.txt'), 'r') as f:
all_lines = f.readlines()
score_array =\
np.asarry([float(line.strip('\n').split(', ')[-1]) \
for line in all_lines])
if fid_score_now == score_array.min():
copyfile("model.py", output_dir + "/" + "model.py")
copyfile("miscc/utils.py", output_dir + "/" + "utils.py")
copyfile("miscc/datasets.py", output_dir + "/" + "datasets.py")
copyfile(args.cfg_file, output_dir + "/" + "cfg_file.yml")
if cfg.STAGE == 1:
resize = 76
imsize=64
elif cfg.STAGE == 2:
resize = 268
imsize = 256
img_transform = transforms.Compose([
transforms.Resize((resize, resize)),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
dataset = TextDataset(cfg.DATA_DIR, cfg.IMG_DIR, split="train", imsize=imsize, transform=img_transform,
crop=True, stage=cfg.STAGE)
assert dataset
dataloader = torch.utils.data.DataLoader(
dataset, batch_size=cfg.TRAIN.BATCH_SIZE,
drop_last=True, shuffle=True, num_workers=int(cfg.WORKERS))
algo = GANTrainer(output_dir)
algo.train(dataloader, cfg.STAGE)
else:
#https://github.com/taoxugit/AttnGAN/blob/0d000e652b407e976cb88fab299e8566f3de8a37/code/main.py#L146
datapath= '%s/test/' % (cfg.DATA_DIR)
algo = GANTrainer(output_dir)
algo.sample(datapath, num_samples=5, stage=cfg.STAGE, draw_bbox=False)