######### Train generator #########
generator.zero_grad()
generator_content_loss = content_criterion(high_res_fake,
high_res_real)
mean_generator_content_loss += generator_content_loss.data[0]
generator_content_loss.backward()
optim_generator.step()
######### Status and display #########
sys.stdout.write(
'\r[%d/%d][%d/%d] Generator_MSE_Loss: %.4f' %
(epoch, 2, i, len(dataloader), generator_content_loss.data[0]))
visualizer.show(low_res,
high_res_real.cpu().data,
high_res_fake.cpu().data)
sys.stdout.write('\r[%d/%d][%d/%d] Generator_MSE_Loss: %.4f\n' %
(epoch, 2, i, len(dataloader),
mean_generator_content_loss / len(dataloader)))
log_value('generator_mse_loss',
mean_generator_content_loss / len(dataloader), epoch)
# Do checkpointing
torch.save(generator.state_dict(), '%s/generator_pretrain.pth' % opt.out)
# SRGAN training
optim_generator = optim.Adam(generator.parameters(), lr=opt.generatorLR * 0.1)
optim_discriminator = optim.Adam(discriminator.parameters(),
lr=opt.discriminatorLR * 0.1)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--dataset', type=str, default='cifar100', help='cifar10 | cifar100 | folder')
parser.add_argument('--dataroot', type=str, default='./data', help='path to dataset')
parser.add_argument('--workers', type=int, default=2, help='number of data loading workers')
parser.add_argument('--batchSize', type=int, default=16, help='input batch size')
parser.add_argument('--imageSize', type=int, default=15, help='the low resolution image size')
parser.add_argument('--upSampling', type=int, default=2, help='low to high resolution scaling factor')
parser.add_argument('--nEpochs', type=int, default=100, help='number of epochs to train for')
parser.add_argument('--nPreEpochs', type=int, default=2, help='number of epochs to pre-train Generator')
parser.add_argument('--generatorLR', type=float, default=0.0001, help='learning rate for generator')
parser.add_argument('--discriminatorLR', type=float, default=0.0001, help='learning rate for discriminator')
parser.add_argument('--cuda', action='store_true', help='enables cuda')
parser.add_argument('--nGPU', type=int, default=1, help='number of GPUs to use')
parser.add_argument('--generatorWeights', type=str, default='', help="path to generator weights (to continue training)")
parser.add_argument('--discriminatorWeights', type=str, default='', help="path to discriminator weights (to continue training)")
parser.add_argument('--out', type=str, default='checkpoints', help='folder to output model checkpoints')
opt = parser.parse_args()
print(opt)
try:
os.makedirs(opt.out)
except OSError:
pass
if torch.cuda.is_available() and not opt.cuda:
print("WARNING: You have a CUDA device, so you should probably run with --cuda")
transform = transforms.Compose([transforms.RandomCrop(opt.imageSize*opt.upSampling),
transforms.ToTensor()])
normalize = transforms.Normalize(mean = [0.485, 0.456, 0.406],
std = [0.229, 0.224, 0.225])
scale = transforms.Compose([transforms.ToPILImage(),
transforms.Scale(opt.imageSize),
transforms.ToTensor(),
transforms.Normalize(mean = [0.485, 0.456, 0.406],
std = [0.229, 0.224, 0.225])
])
if opt.dataset == 'folder':
# folder dataset
dataset = datasets.ImageFolder(root=opt.dataroot, transform=transform)
elif opt.dataset == 'cifar10':
dataset = datasets.CIFAR10(root=opt.dataroot, train=True, download=True, transform=transform)
elif opt.dataset == 'cifar100':
dataset = datasets.CIFAR100(root=opt.dataroot, train=True, download=True, transform=transform)
assert dataset
dataloader = torch.utils.data.DataLoader(dataset, batch_size=opt.batchSize,
shuffle=True, num_workers=int(opt.workers))
generator = Generator(16, opt.upSampling)
if opt.generatorWeights != '':
generator.load_state_dict(torch.load(opt.generatorWeights))
print(generator)
discriminator = Discriminator()
if opt.discriminatorWeights != '':
discriminator.load_state_dict(torch.load(opt.discriminatorWeights))
print(discriminator)
# For the content loss
feature_extractor = FeatureExtractor(torchvision.models.vgg19(pretrained=True))
print(feature_extractor)
content_criterion = nn.MSELoss()
adversarial_criterion = nn.BCELoss()
ones_const = Variable(torch.ones(opt.batchSize, 1))
# if gpu is to be used
if opt.cuda:
generator.cuda()
discriminator.cuda()
feature_extractor.cuda()
content_criterion.cuda()
adversarial_criterion.cuda()
ones_const = ones_const.cuda()
optim_generator = optim.Adam(generator.parameters(), lr=opt.generatorLR)
optim_discriminator = optim.Adam(discriminator.parameters(), lr=opt.discriminatorLR)
configure('logs/' + opt.dataset + '-' + str(opt.batchSize) + '-' + str(opt.generatorLR) + '-' + str(opt.discriminatorLR), flush_secs=5)
visualizer = Visualizer(image_size=opt.imageSize*opt.upSampling)
low_res = torch.FloatTensor(opt.batchSize, 3, opt.imageSize, opt.imageSize)
# Pre-train generator using raw MSE loss
print('Generator pre-training')
for epoch in range(opt.nPreEpochs):
mean_generator_content_loss = 0.0
for i, data in enumerate(dataloader):
# Generate data
high_res_real, _ = data
# Downsample images to low resolution
if len(high_res_real) < opt.batchSize: # skip final batch , len = batchsize if not last batch else len < batchsize
continue
for j in range(opt.batchSize):
low_res[j] = scale(high_res_real[j])
high_res_real[j] = normalize(high_res_real[j])
# Generate real and fake inputs
if opt.cuda:
high_res_real = Variable(high_res_real.cuda())
high_res_fake = generator(Variable(low_res).cuda())
else:
high_res_real = Variable(high_res_real)
high_res_fake = generator(Variable(low_res))
######### Train generator #########
generator.zero_grad()
generator_content_loss = content_criterion(high_res_fake, high_res_real)
mean_generator_content_loss += generator_content_loss.data
generator_content_loss.backward()
optim_generator.step()
######### Status and display #########
sys.stdout.write('\r[%d/%d][%d/%d] Generator_MSE_Loss: %.4f' % (epoch, opt.nPreEpochs, i, len(dataloader), generator_content_loss.data))
visualizer.show(low_res, high_res_real.cpu().data, high_res_fake.cpu().data)
sys.stdout.write('\r[%d/%d][%d/%d] Generator_MSE_Loss: %.4f\n' % (epoch, 2, i, len(dataloader), mean_generator_content_loss/len(dataloader)))
log_value('generator_mse_loss', mean_generator_content_loss/len(dataloader), epoch)
# Do checkpointing every epoch
# torch.save(generator.state_dict(), '%s/generator_pretrain_%s.pth' %(opt.out,str(epoch)))
# Do checkpointing
torch.save(generator.state_dict(), '%s/generator_pretrain.pth' % opt.out)
# SRGAN training
optim_generator = optim.Adam(generator.parameters(), lr=opt.generatorLR*0.1)
optim_discriminator = optim.Adam(discriminator.parameters(), lr=opt.discriminatorLR*0.1)
print('SRGAN training')
for epoch in range(opt.nEpochs):
mean_generator_content_loss = 0.0
mean_generator_adversarial_loss = 0.0
mean_generator_total_loss = 0.0
mean_discriminator_loss = 0.0
for i, data in enumerate(dataloader):
# Generate data
high_res_real, _ = data
# Downsample images to low resolution
if len(high_res_real) < opt.batchSize: # skip final batch , len = batchsize if not last batch else len < batchsize
continue
for j in range(opt.batchSize):
low_res[j] = scale(high_res_real[j])
high_res_real[j] = normalize(high_res_real[j])
# Generate real and fake inputs
if opt.cuda:
high_res_real = Variable(high_res_real.cuda())
high_res_fake = generator(Variable(low_res).cuda())
target_real = Variable(torch.rand(opt.batchSize,1)*0.5 + 0.7).cuda()
# size: opt.batchSize*1, and element is in 0.7~1.2
target_fake = Variable(torch.rand(opt.batchSize,1)*0.3).cuda()
# size: opt.batchSize*1, and element is in 0~0.3
else:
high_res_real = Variable(high_res_real)
high_res_fake = generator(Variable(low_res))
target_real = Variable(torch.rand(opt.batchSize,1)*0.5 + 0.7)
target_fake = Variable(torch.rand(opt.batchSize,1)*0.3)
######### Train discriminator #########
discriminator.zero_grad()
discriminator_loss = adversarial_criterion(discriminator(high_res_real), target_real) + \
adversarial_criterion(discriminator(Variable(high_res_fake.data)), target_fake)
mean_discriminator_loss += discriminator_loss.data
discriminator_loss.backward()
optim_discriminator.step()
######### Train generator #########
generator.zero_grad()
real_features = Variable(feature_extractor(high_res_real).data)
fake_features = feature_extractor(high_res_fake)
# for content loss, we use total images' pixel-wise MSE loss and 0.006* VggLoss, which VggLoss is actual
# MSE loss of some layers result(feature) in VggNet
generator_content_loss = content_criterion(high_res_fake, high_res_real) + 0.006*content_criterion(fake_features, real_features)
mean_generator_content_loss += generator_content_loss.data
generator_adversarial_loss = adversarial_criterion(discriminator(high_res_fake), ones_const)
mean_generator_adversarial_loss += generator_adversarial_loss.data
generator_total_loss = generator_content_loss + 1e-3*generator_adversarial_loss
mean_generator_total_loss += generator_total_loss.data
generator_total_loss.backward()
optim_generator.step()
######### Status and display #########
sys.stdout.write('\r[%d/%d][%d/%d] Discriminator_Loss: %.4f Generator_Loss (Content/Advers/Total): %.4f/%.4f/%.4f' % (epoch, opt.nEpochs, i, len(dataloader),
discriminator_loss.data, generator_content_loss.data, generator_adversarial_loss.data, generator_total_loss.data))
visualizer.show(low_res, high_res_real.cpu().data, high_res_fake.cpu().data)
sys.stdout.write('\r[%d/%d][%d/%d] Discriminator_Loss: %.4f Generator_Loss (Content/Advers/Total): %.4f/%.4f/%.4f\n' % (epoch, opt.nEpochs, i, len(dataloader),
mean_discriminator_loss/len(dataloader), mean_generator_content_loss/len(dataloader),
mean_generator_adversarial_loss/len(dataloader), mean_generator_total_loss/len(dataloader)))
log_value('generator_content_loss', mean_generator_content_loss/len(dataloader), epoch)
log_value('generator_adversarial_loss', mean_generator_adversarial_loss/len(dataloader), epoch)
log_value('generator_total_loss', mean_generator_total_loss/len(dataloader), epoch)
log_value('discriminator_loss', mean_discriminator_loss/len(dataloader), epoch)
# Do checkpointing every epoch
torch.save(generator.state_dict(), '%s/generator_final.pth' % opt.out)
torch.save(discriminator.state_dict(), '%s/discriminator_final.pth' % opt.out)
# Avoid closing
print("train is over, and here can kill off threading after you watch the control log...")
while True:
pass
