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train_base.py
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import os
import argparse
import itertools
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision.transforms as transforms
from torch.utils.data import DataLoader
from PIL import Image
from tensorboardX import SummaryWriter
from torchvision.utils import make_grid
from tqdm import tqdm
from models import GeneratorResNet, PatchDiscriminator, weights_init_normal
from utils import ReplayBuffer, LambdaLR, set_requires_grad
from datasets import ImageDataset
parser = argparse.ArgumentParser()
parser.add_argument('--batchSize', type=int, default=1)
parser.add_argument('--G_lr', type=float, default=2e-4)
parser.add_argument('--D_lr', type=float, default=2e-4)
parser.add_argument('--idt_weight', type=float, default=5.0)
parser.add_argument('--cyc_weight', type=float, default=10.0)
parser.add_argument('--resize_size', type=int, default=288)
parser.add_argument('--crop_size', type=int, default=256)
parser.add_argument('--workers', type=int, default=16)
parser.add_argument('--epochs', type=int, default=200)
parser.add_argument('--decay_epoch', type=int, default=100)
parser.add_argument('--sample_freq', type=int, default=100)
parser.add_argument('--save_freq', type=int, default=10)
parser.add_argument('--task', type=str, default='day2dusk')
parser.add_argument('--dataset_dir', type=str, default='./data/')
parser.add_argument('--save_dir', type=str, default='./pretrained_models/')
parser.add_argument('--log_dir', type=str, default='./logs/')
parser.add_argument('--gpu', type=str, default='0')
args = parser.parse_args()
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
if __name__ == '__main__':
netG_A2B = GeneratorResNet()
netG_B2A = GeneratorResNet()
netD_A = PatchDiscriminator()
netD_B = PatchDiscriminator()
netG_A2B.apply(weights_init_normal)
netG_B2A.apply(weights_init_normal)
netD_A.apply(weights_init_normal)
netD_B.apply(weights_init_normal)
netG_A2B.cuda()
netG_B2A.cuda()
netD_A.cuda()
netD_B.cuda()
criterion_GAN = nn.MSELoss()
criterion_identity = nn.L1Loss()
criterion_cycle = nn.L1Loss()
optimizer_G = torch.optim.Adam(itertools.chain(netG_A2B.parameters(), netG_B2A.parameters()), lr=args.G_lr, betas=(0.5, 0.999))
optimizer_D = torch.optim.Adam(itertools.chain(netD_A.parameters(), netD_B.parameters()), lr=args.D_lr, betas=(0.5, 0.999))
lr_scheduler_G = torch.optim.lr_scheduler.LambdaLR(optimizer_G, lr_lambda=LambdaLR(args.epochs, 0, args.decay_epoch).step)
lr_scheduler_D = torch.optim.lr_scheduler.LambdaLR(optimizer_D, lr_lambda=LambdaLR(args.epochs, 0, args.decay_epoch).step)
fake_A_buffer = ReplayBuffer()
fake_B_buffer = ReplayBuffer()
transforms_ = [ transforms.Resize(args.resize_size, Image.BICUBIC),
transforms.RandomCrop(args.crop_size),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize((0.5,0.5,0.5), (0.5,0.5,0.5))]
dataloader = DataLoader(ImageDataset(os.path.join(args.dataset_dir, args.task), transforms_=transforms_, unaligned=True),
batch_size=args.batchSize, shuffle=True, drop_last=True, num_workers=args.workers)
writer = SummaryWriter(log_dir=os.path.join(args.log_dir, args.task))
os.makedirs(os.path.join(args.save_dir, args.task), exist_ok=True)
step = 0
for epoch in tqdm(range(args.epochs)):
for i, batch in enumerate(dataloader):
real_A = batch['A'].cuda()
real_B = batch['B'].cuda()
''' G update '''
set_requires_grad([netD_A, netD_B], False)
# forward
same_B = netG_A2B(real_B)
same_A = netG_B2A(real_A)
fake_B = netG_A2B(real_A)
fake_A = netG_B2A(real_B)
recovered_A = netG_B2A(fake_B)
recovered_B = netG_A2B(fake_A)
pred_fake_B = netD_B(fake_B)
pred_fake_A = netD_A(fake_A)
# GAN loss
loss_GAN = criterion_GAN(pred_fake_B, torch.ones_like(pred_fake_B)) + \
criterion_GAN(pred_fake_A, torch.ones_like(pred_fake_A))
# Identity loss
loss_identity = criterion_identity(same_B, real_B) + \
criterion_identity(same_A, real_A)
# Cycle loss
loss_cycle = criterion_cycle(recovered_A, real_A) + \
criterion_cycle(recovered_B, real_B)
# Total loss
loss_G = loss_GAN + loss_identity * args.idt_weight + loss_cycle * args.cyc_weight
# backward
optimizer_G.zero_grad()
loss_G.backward()
optimizer_G.step()
# save result
if step % args.sample_freq == 0:
with torch.no_grad():
show_img = torch.cat([
torch.cat([real_A, fake_B, recovered_A], dim=3),
torch.cat([real_B, fake_A, recovered_B], dim=3),
], dim=2).clamp(-1, 1)[[0], :, :, :]
show_img = make_grid(show_img, normalize=True, scale_each=True)
writer.add_image('image', show_img, step)
''' D update '''
set_requires_grad([netD_A, netD_B], True)
# forward
fake_A = fake_A_buffer.push_and_pop(fake_A)
pred_fake_A = netD_A(fake_A.detach())
pred_real_A = netD_A(real_A)
fake_B = fake_B_buffer.push_and_pop(fake_B)
pred_fake_B = netD_B(fake_B.detach())
pred_real_B = netD_B(real_B)
loss_D_A = (criterion_GAN(pred_real_A, torch.ones_like(pred_real_A)) + \
criterion_GAN(pred_fake_A, torch.zeros_like(pred_fake_A))) * 0.5
loss_D_B = (criterion_GAN(pred_real_B, torch.ones_like(pred_real_B)) + \
criterion_GAN(pred_fake_B, torch.zeros_like(pred_fake_B))) * 0.5
# backward
optimizer_D.zero_grad()
loss_D_A.backward()
loss_D_B.backward()
optimizer_D.step()
writer.add_scalar('loss_G', loss_G.item(), step)
writer.add_scalar('loss_G_GAN', loss_GAN.item(), step)
writer.add_scalar('loss_G_identity', loss_identity.item(), step)
writer.add_scalar('loss_G_cycle', loss_cycle.item(), step)
writer.add_scalar('loss_D', (loss_D_A + loss_D_B).item(), step)
writer.add_scalar('loss_D_A', loss_D_A.item(), step)
writer.add_scalar('loss_D_B', loss_D_B.item(), step)
step += 1
lr_scheduler_G.step()
lr_scheduler_D.step()
if (epoch + 1) % args.save_freq == 0:
torch.save(netG_A2B.state_dict(), os.path.join(args.save_dir, args.task, 'G_A2B.pth'))
torch.save(netG_B2A.state_dict(), os.path.join(args.save_dir, args.task, 'G_B2A.pth'))
torch.save(netD_A.state_dict(), os.path.join(args.save_dir, args.task, 'D_A.pth'))
torch.save(netD_B.state_dict(), os.path.join(args.save_dir, args.task, 'D_B.pth'))