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main.py
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main.py
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"""
StarGAN v2
Copyright (c) 2020-present NAVER Corp.
This work is licensed under the Creative Commons Attribution-NonCommercial
4.0 International License. To view a copy of this license, visit
http://creativecommons.org/licenses/by-nc/4.0/ or send a letter to
Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.
"""
import os
import argparse
import csv
from munch import Munch
from torch.backends import cudnn
import torch
from core.data_loader import get_train_loader
from core.data_loader import get_test_loader
from core.solver import Solver
def str2bool(v):
return v.lower() in ('true')
def subdirs(dname):
return [d for d in os.listdir(dname)
if os.path.isdir(os.path.join(dname, d))]
def main(args):
print(args)
cudnn.benchmark = True
torch.manual_seed(args.seed)
solver = Solver(args)
#create csv file
with open(args.loss_csv_path, 'wb') as csvfile:
filewriter = csv.writer(csvfile, delimiter=',',quotechar='|', quoting=csv.QUOTE_MINIMAL)
with open(args.loss_csv_path, 'a') as file:
writer = csv.writer(file)
writer.writerow(["epoch", "d_loss_z_trg", "d_loss_x_ref", "g_loss_z_trg", "g_loss_x_ref"])
if args.mode == 'train':
assert len(subdirs(args.train_img_dir)) == args.num_domains
assert len(subdirs(args.val_img_dir)) == args.num_domains
loaders = Munch(src=get_train_loader(root=args.train_img_dir,
which='source',
img_size=args.img_size,
batch_size=args.batch_size,
prob=args.randcrop_prob,
num_workers=args.num_workers),
src_skt=get_train_loader(root=args.train_sketch_img_dir,
which='source',
img_size=args.img_size,
batch_size=args.batch_size,
prob=args.randcrop_prob,
num_workers=args.num_workers),
ref=get_train_loader(root=args.train_img_dir,
which='reference',
img_size=args.img_size,
batch_size=args.batch_size,
prob=args.randcrop_prob,
num_workers=args.num_workers),
val=get_test_loader(root=args.val_img_dir,
img_size=args.img_size,
batch_size=args.val_batch_size,
shuffle=True,
num_workers=args.num_workers))
solver.train(loaders)
elif args.mode == 'sample':
assert len(subdirs(args.src_dir)) == args.num_domains
assert len(subdirs(args.ref_dir)) == args.num_domains
loaders = Munch(src=get_test_loader(root=args.src_dir,
img_size=args.img_size,
batch_size=args.val_batch_size,
shuffle=True,
num_workers=args.num_workers),
ref=get_test_loader(root=args.ref_dir,
img_size=args.img_size,
batch_size=args.val_batch_size,
shuffle=True,
num_workers=args.num_workers))
solver.sample(loaders)
elif args.mode == 'eval':
solver.evaluate()
elif args.mode == 'align':
from core.wing import align_faces
align_faces(args, args.inp_dir, args.out_dir)
else:
raise NotImplementedError
if __name__ == '__main__':
parser = argparse.ArgumentParser()
# model arguments
parser.add_argument('--img_size', type=int, default=256,
help='Image resolution')
parser.add_argument('--num_domains', type=int, default=2,
help='Number of domains')
parser.add_argument('--latent_dim', type=int, default=16,
help='Latent vector dimension')
parser.add_argument('--hidden_dim', type=int, default=512,
help='Hidden dimension of mapping network')
parser.add_argument('--style_dim', type=int, default=64,
help='Style code dimension')
# weight for objective functions
parser.add_argument('--lambda_reg', type=float, default=1,
help='Weight for R1 regularization')
parser.add_argument('--lambda_cyc', type=float, default=1,
help='Weight for cyclic consistency loss')
parser.add_argument('--lambda_sty', type=float, default=1,
help='Weight for style reconstruction loss')
parser.add_argument('--lambda_ds', type=float, default=1,
help='Weight for diversity sensitive loss')
parser.add_argument('--ds_iter', type=int, default=100000,
help='Number of iterations to optimize diversity sensitive loss')
parser.add_argument('--w_hpf', type=float, default=1,
help='weight for high-pass filtering')
# training arguments
parser.add_argument('--randcrop_prob', type=float, default=0.5,
help='Probabilty of using random-resized cropping')
parser.add_argument('--total_iters', type=int, default=100000,
help='Number of total iterations')
parser.add_argument('--resume_iter', type=int, default=0,
help='Iterations to resume training/testing')
parser.add_argument('--batch_size', type=int, default=8,
help='Batch size for training')
parser.add_argument('--val_batch_size', type=int, default=4,
help='Batch size for validation')
parser.add_argument('--lr', type=float, default=1e-4,
help='Learning rate for D, E and G')
parser.add_argument('--f_lr', type=float, default=1e-6,
help='Learning rate for F')
parser.add_argument('--beta1', type=float, default=0.0,
help='Decay rate for 1st moment of Adam')
parser.add_argument('--beta2', type=float, default=0.99,
help='Decay rate for 2nd moment of Adam')
parser.add_argument('--weight_decay', type=float, default=1e-4,
help='Weight decay for optimizer')
parser.add_argument('--num_outs_per_domain', type=int, default=2,
help='Number of generated images per domain during sampling')
# misc
parser.add_argument('--mode', type=str, required=True,
choices=['train', 'sample', 'eval', 'align'],
help='This argument is used in solver')
parser.add_argument('--num_workers', type=int, default=0,
help='Number of workers used in DataLoader')
parser.add_argument('--seed', type=int, default=777,
help='Seed for random number generator')
# directory for training
parser.add_argument('--train_img_dir', type=str, default='data/celeba_hq/train',
help='Directory containing training images')
parser.add_argument('--train_sketch_img_dir', type=str, default='data/celeba_hq/train',
help='Directory containing training sketch images')
parser.add_argument('--val_img_dir', type=str, default='data/celeba_hq/val',
help='Directory containing validation images')
parser.add_argument('--sample_dir', type=str, default='expr/samples',
help='Directory for saving generated images')
parser.add_argument('--checkpoint_dir', type=str, default='expr/checkpoints',
help='Directory for saving network checkpoints')
# directory for calculating metrics
parser.add_argument('--eval_dir', type=str, default='expr/eval',
help='Directory for saving metrics, i.e., FID and LPIPS')
# directory for testing
parser.add_argument('--result_dir', type=str, default='expr/results',
help='Directory for saving generated images and videos')
parser.add_argument('--src_dir', type=str, default='assets/representative/celeba_hq/src',
help='Directory containing input source images')
parser.add_argument('--ref_dir', type=str, default='assets/representative/celeba_hq/ref',
help='Directory containing input reference images')
parser.add_argument('--inp_dir', type=str, default='assets/representative/custom/female',
help='input directory when aligning faces')
parser.add_argument('--out_dir', type=str, default='assets/representative/celeba_hq/src/female',
help='output directory when aligning faces')
# face alignment
parser.add_argument('--wing_path', type=str, default='expr/checkpoints/wing.ckpt')
parser.add_argument('--lm_path', type=str, default='expr/checkpoints/celeba_lm_mean.npz')
# step size
parser.add_argument('--print_every', type=int, default=10)
parser.add_argument('--sample_every', type=int, default=500)
parser.add_argument('--save_every', type=int, default=500)
parser.add_argument('--eval_every', type=int, default=2000)
# csv file
parser.add_argument('--loss_csv_path', type=str, default='expr/loss_value.csv')
args = parser.parse_args()
main(args)