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train.py
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train.py
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# Copyright (C) 2018 Elvis Yu-Jing Lin <elvisyjlin@gmail.com>
#
# This work is licensed under the MIT License. To view a copy of this license,
# visit https://opensource.org/licenses/MIT.
"""Main entry point for training AttGAN network."""
import argparse
import datetime
import json
import os
from os.path import join
import torch.utils.data as data
import torch
import torchvision.utils as vutils
from attgan import AttGAN
from data import check_attribute_conflict
from helpers import Progressbar, add_scalar_dict
from tensorboardX import SummaryWriter
attrs_default = [
'Bald', 'Bangs', 'Black_Hair', 'Blond_Hair', 'Brown_Hair', 'Bushy_Eyebrows',
'Eyeglasses', 'Male', 'Mouth_Slightly_Open', 'Mustache', 'No_Beard', 'Pale_Skin', 'Young'
]
def parse(args=None):
parser = argparse.ArgumentParser()
parser.add_argument('--attrs', dest='attrs', default=attrs_default, nargs='+', help='attributes to learn')
parser.add_argument('--data', dest='data', type=str, choices=['CelebA', 'CelebA-HQ'], default='CelebA')
parser.add_argument('--data_path', dest='data_path', type=str, default='data/img_align_celeba')
parser.add_argument('--attr_path', dest='attr_path', type=str, default='data/list_attr_celeba.txt')
parser.add_argument('--image_list_path', dest='image_list_path', type=str, default='data/image_list.txt')
parser.add_argument('--img_size', dest='img_size', type=int, default=128)
parser.add_argument('--shortcut_layers', dest='shortcut_layers', type=int, default=1)
parser.add_argument('--inject_layers', dest='inject_layers', type=int, default=0)
parser.add_argument('--enc_dim', dest='enc_dim', type=int, default=64)
parser.add_argument('--dec_dim', dest='dec_dim', type=int, default=64)
parser.add_argument('--dis_dim', dest='dis_dim', type=int, default=64)
parser.add_argument('--dis_fc_dim', dest='dis_fc_dim', type=int, default=1024)
parser.add_argument('--enc_layers', dest='enc_layers', type=int, default=5)
parser.add_argument('--dec_layers', dest='dec_layers', type=int, default=5)
parser.add_argument('--dis_layers', dest='dis_layers', type=int, default=5)
parser.add_argument('--enc_norm', dest='enc_norm', type=str, default='batchnorm')
parser.add_argument('--dec_norm', dest='dec_norm', type=str, default='batchnorm')
parser.add_argument('--dis_norm', dest='dis_norm', type=str, default='instancenorm')
parser.add_argument('--dis_fc_norm', dest='dis_fc_norm', type=str, default='none')
parser.add_argument('--enc_acti', dest='enc_acti', type=str, default='lrelu')
parser.add_argument('--dec_acti', dest='dec_acti', type=str, default='relu')
parser.add_argument('--dis_acti', dest='dis_acti', type=str, default='lrelu')
parser.add_argument('--dis_fc_acti', dest='dis_fc_acti', type=str, default='relu')
parser.add_argument('--lambda_1', dest='lambda_1', type=float, default=100.0)
parser.add_argument('--lambda_2', dest='lambda_2', type=float, default=10.0)
parser.add_argument('--lambda_3', dest='lambda_3', type=float, default=1.0)
parser.add_argument('--lambda_gp', dest='lambda_gp', type=float, default=10.0)
parser.add_argument('--mode', dest='mode', default='wgan', choices=['wgan', 'lsgan', 'dcgan'])
parser.add_argument('--epochs', dest='epochs', type=int, default=200, help='# of epochs')
parser.add_argument('--batch_size', dest='batch_size', type=int, default=32)
parser.add_argument('--num_workers', dest='num_workers', type=int, default=0)
parser.add_argument('--lr', dest='lr', type=float, default=0.0002, help='learning rate')
parser.add_argument('--beta1', dest='beta1', type=float, default=0.5)
parser.add_argument('--beta2', dest='beta2', type=float, default=0.999)
parser.add_argument('--n_d', dest='n_d', type=int, default=5, help='# of d updates per g update')
parser.add_argument('--b_distribution', dest='b_distribution', default='none', choices=['none', 'uniform', 'truncated_normal'])
parser.add_argument('--thres_int', dest='thres_int', type=float, default=0.5)
parser.add_argument('--test_int', dest='test_int', type=float, default=1.0)
parser.add_argument('--n_samples', dest='n_samples', type=int, default=16, help='# of sample images')
parser.add_argument('--save_interval', dest='save_interval', type=int, default=1000)
parser.add_argument('--sample_interval', dest='sample_interval', type=int, default=1000)
parser.add_argument('--gpu', dest='gpu', action='store_true')
parser.add_argument('--multi_gpu', dest='multi_gpu', action='store_true')
parser.add_argument('--experiment_name', dest='experiment_name', default=datetime.datetime.now().strftime("%I:%M%p on %B %d, %Y"))
return parser.parse_args(args)
args = parse()
print(args)
args.lr_base = args.lr
args.n_attrs = len(args.attrs)
args.betas = (args.beta1, args.beta2)
os.makedirs(join('output', args.experiment_name), exist_ok=True)
os.makedirs(join('output', args.experiment_name, 'checkpoint'), exist_ok=True)
os.makedirs(join('output', args.experiment_name, 'sample_training'), exist_ok=True)
with open(join('output', args.experiment_name, 'setting.txt'), 'w') as f:
f.write(json.dumps(vars(args), indent=4, separators=(',', ':')))
if args.data == 'CelebA':
from data import CelebA
train_dataset = CelebA(args.data_path, args.attr_path, args.img_size, 'train', args.attrs)
valid_dataset = CelebA(args.data_path, args.attr_path, args.img_size, 'valid', args.attrs)
if args.data == 'CelebA-HQ':
from data import CelebA_HQ
train_dataset = CelebA_HQ(args.data_path, args.attr_path, args.image_list_path, args.img_size, 'train', args.attrs)
valid_dataset = CelebA_HQ(args.data_path, args.attr_path, args.image_list_path, args.img_size, 'valid', args.attrs)
train_dataloader = data.DataLoader(
train_dataset, batch_size=args.batch_size, num_workers=args.num_workers,
shuffle=True, drop_last=True
)
valid_dataloader = data.DataLoader(
valid_dataset, batch_size=args.n_samples, num_workers=args.num_workers,
shuffle=False, drop_last=False
)
print('Training images:', len(train_dataset), '/', 'Validating images:', len(valid_dataset))
attgan = AttGAN(args)
progressbar = Progressbar()
writer = SummaryWriter(join('output', args.experiment_name, 'summary'))
fixed_img_a, fixed_att_a = next(iter(valid_dataloader))
fixed_img_a = fixed_img_a.cuda() if args.gpu else fixed_img_a
fixed_att_a = fixed_att_a.cuda() if args.gpu else fixed_att_a
fixed_att_a = fixed_att_a.type(torch.float)
sample_att_b_list = [fixed_att_a]
for i in range(args.n_attrs):
tmp = fixed_att_a.clone()
tmp[:, i] = 1 - tmp[:, i]
tmp = check_attribute_conflict(tmp, args.attrs[i], args.attrs)
sample_att_b_list.append(tmp)
it = 0
it_per_epoch = len(train_dataset) // args.batch_size
for epoch in range(args.epochs):
# train with base lr in the first 100 epochs
# and half the lr in the last 100 epochs
lr = args.lr_base / (10 ** (epoch // 100))
attgan.set_lr(lr)
writer.add_scalar('LR/learning_rate', lr, it+1)
for img_a, att_a in progressbar(train_dataloader):
attgan.train()
img_a = img_a.cuda() if args.gpu else img_a
att_a = att_a.cuda() if args.gpu else att_a
idx = torch.randperm(len(att_a))
att_b = att_a[idx].contiguous()
att_a = att_a.type(torch.float)
att_b = att_b.type(torch.float)
att_a_ = (att_a * 2 - 1) * args.thres_int
if args.b_distribution == 'none':
att_b_ = (att_b * 2 - 1) * args.thres_int
if args.b_distribution == 'uniform':
att_b_ = (att_b * 2 - 1) * \
torch.rand_like(att_b) * \
(2 * args.thres_int)
if args.b_distribution == 'truncated_normal':
att_b_ = (att_b * 2 - 1) * \
(torch.fmod(torch.randn_like(att_b), 2) + 2) / 4.0 * \
(2 * args.thres_int)
if (it+1) % (args.n_d+1) != 0:
errD = attgan.trainD(img_a, att_a, att_a_, att_b, att_b_)
add_scalar_dict(writer, errD, it+1, 'D')
else:
errG = attgan.trainG(img_a, att_a, att_a_, att_b, att_b_)
add_scalar_dict(writer, errG, it+1, 'G')
progressbar.say(epoch=epoch, iter=it+1, d_loss=errD['d_loss'], g_loss=errG['g_loss'])
if (it+1) % args.save_interval == 0:
# To save storage space, I only checkpoint the weights of G.
# If you'd like to keep weights of G, D, optim_G, optim_D,
# please use save() instead of saveG().
attgan.saveG(os.path.join(
'output', args.experiment_name, 'checkpoint', 'weights.{:d}.pth'.format(epoch)
))
# attgan.save(os.path.join(
# 'output', args.experiment_name, 'checkpoint', 'weights.{:d}.pth'.format(epoch)
# ))
if (it+1) % args.sample_interval == 0:
attgan.eval()
with torch.no_grad():
samples = [fixed_img_a]
for i, att_b in enumerate(sample_att_b_list):
att_b_ = (att_b * 2 - 1) * args.thres_int
if i > 0:
att_b_[..., i - 1] = att_b_[..., i - 1] * args.test_int / args.thres_int
samples.append(attgan.G(fixed_img_a, att_b_))
samples = torch.cat(samples, dim=3)
writer.add_image('sample', vutils.make_grid(samples, nrow=1, normalize=True, value_range=(-1., 1.)), it+1)
vutils.save_image(samples, os.path.join(
'output', args.experiment_name, 'sample_training',
'Epoch_({:d})_({:d}of{:d}).jpg'.format(epoch, it%it_per_epoch+1, it_per_epoch)
), nrow=1, normalize=True, value_range=(-1., 1.))
it += 1