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train.py
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train.py
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from __future__ import print_function
import argparse
import os
from math import log10
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader
import torch.backends.cudnn as cudnn
from networks import define_G, define_D, GANLoss, get_scheduler, update_learning_rate
from data import get_training_set, get_test_set
# Training settings
parser = argparse.ArgumentParser(description='pix2pix-pytorch-implementation')
parser.add_argument('--dataset', required=True, help='facades')
parser.add_argument('--batch_size', type=int, default=1, help='training batch size')
parser.add_argument('--test_batch_size', type=int, default=1, help='testing batch size')
parser.add_argument('--direction', type=str, default='b2a', help='a2b or b2a')
parser.add_argument('--input_nc', type=int, default=3, help='input image channels')
parser.add_argument('--output_nc', type=int, default=3, help='output image channels')
parser.add_argument('--ngf', type=int, default=64, help='generator filters in first conv layer')
parser.add_argument('--ndf', type=int, default=64, help='discriminator filters in first conv layer')
parser.add_argument('--epoch_count', type=int, default=1, help='the starting epoch count')
parser.add_argument('--niter', type=int, default=100, help='# of iter at starting learning rate')
parser.add_argument('--niter_decay', type=int, default=100, help='# of iter to linearly decay learning rate to zero')
parser.add_argument('--lr', type=float, default=0.0002, help='initial learning rate for adam')
parser.add_argument('--lr_policy', type=str, default='lambda', help='learning rate policy: lambda|step|plateau|cosine')
parser.add_argument('--lr_decay_iters', type=int, default=50, help='multiply by a gamma every lr_decay_iters iterations')
parser.add_argument('--beta1', type=float, default=0.5, help='beta1 for adam. default=0.5')
parser.add_argument('--cuda', action='store_true', help='use cuda?')
parser.add_argument('--threads', type=int, default=4, help='number of threads for data loader to use')
parser.add_argument('--seed', type=int, default=123, help='random seed to use. Default=123')
parser.add_argument('--lamb', type=int, default=10, help='weight on L1 term in objective')
opt = parser.parse_args()
print(opt)
if opt.cuda and not torch.cuda.is_available():
raise Exception("No GPU found, please run without --cuda")
cudnn.benchmark = True
torch.manual_seed(opt.seed)
if opt.cuda:
torch.cuda.manual_seed(opt.seed)
print('===> Loading datasets')
root_path = "dataset/"
train_set = get_training_set(root_path + opt.dataset, opt.direction)
test_set = get_test_set(root_path + opt.dataset, opt.direction)
training_data_loader = DataLoader(dataset=train_set, num_workers=opt.threads, batch_size=opt.batch_size, shuffle=True)
testing_data_loader = DataLoader(dataset=test_set, num_workers=opt.threads, batch_size=opt.test_batch_size, shuffle=False)
device = torch.device("cuda:0" if opt.cuda else "cpu")
print('===> Building models')
net_g = define_G(opt.input_nc, opt.output_nc, opt.ngf, 'batch', False, 'normal', 0.02, gpu_id=device)
net_d = define_D(opt.input_nc + opt.output_nc, opt.ndf, 'basic', gpu_id=device)
criterionGAN = GANLoss().to(device)
criterionL1 = nn.L1Loss().to(device)
criterionMSE = nn.MSELoss().to(device)
# setup optimizer
optimizer_g = optim.Adam(net_g.parameters(), lr=opt.lr, betas=(opt.beta1, 0.999))
optimizer_d = optim.Adam(net_d.parameters(), lr=opt.lr, betas=(opt.beta1, 0.999))
net_g_scheduler = get_scheduler(optimizer_g, opt)
net_d_scheduler = get_scheduler(optimizer_d, opt)
for epoch in range(opt.epoch_count, opt.niter + opt.niter_decay + 1):
# train
for iteration, batch in enumerate(training_data_loader, 1):
# forward
real_a, real_b = batch[0].to(device), batch[1].to(device)
fake_b = net_g(real_a)
######################
# (1) Update D network
######################
optimizer_d.zero_grad()
# train with fake
fake_ab = torch.cat((real_a, fake_b), 1)
pred_fake = net_d.forward(fake_ab.detach())
loss_d_fake = criterionGAN(pred_fake, False)
# train with real
real_ab = torch.cat((real_a, real_b), 1)
pred_real = net_d.forward(real_ab)
loss_d_real = criterionGAN(pred_real, True)
# Combined D loss
loss_d = (loss_d_fake + loss_d_real) * 0.5
loss_d.backward()
optimizer_d.step()
######################
# (2) Update G network
######################
optimizer_g.zero_grad()
# First, G(A) should fake the discriminator
fake_ab = torch.cat((real_a, fake_b), 1)
pred_fake = net_d.forward(fake_ab)
loss_g_gan = criterionGAN(pred_fake, True)
# Second, G(A) = B
loss_g_l1 = criterionL1(fake_b, real_b) * opt.lamb
loss_g = loss_g_gan + loss_g_l1
loss_g.backward()
optimizer_g.step()
print("===> Epoch[{}]({}/{}): Loss_D: {:.4f} Loss_G: {:.4f}".format(
epoch, iteration, len(training_data_loader), loss_d.item(), loss_g.item()))
update_learning_rate(net_g_scheduler, optimizer_g)
update_learning_rate(net_d_scheduler, optimizer_d)
# test
avg_psnr = 0
for batch in testing_data_loader:
input, target = batch[0].to(device), batch[1].to(device)
prediction = net_g(input)
mse = criterionMSE(prediction, target)
psnr = 10 * log10(1 / mse.item())
avg_psnr += psnr
print("===> Avg. PSNR: {:.4f} dB".format(avg_psnr / len(testing_data_loader)))
#checkpoint
if epoch % 50 == 0:
if not os.path.exists("checkpoint"):
os.mkdir("checkpoint")
if not os.path.exists(os.path.join("checkpoint", opt.dataset)):
os.mkdir(os.path.join("checkpoint", opt.dataset))
net_g_model_out_path = "checkpoint/{}/netG_model_epoch_{}.pth".format(opt.dataset, epoch)
net_d_model_out_path = "checkpoint/{}/netD_model_epoch_{}.pth".format(opt.dataset, epoch)
torch.save(net_g, net_g_model_out_path)
torch.save(net_d, net_d_model_out_path)
print("Checkpoint saved to {}".format("checkpoint" + opt.dataset))