def test(args):
transform = transforms.Compose(
[transforms.Resize((args.crop_height,args.crop_width)),
transforms.ToTensor(),
# transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
])
dataset_dirs = utils.get_testdata_link(args.dataset_dir)
# a_test_data = dsets.ImageFolder(dataset_dirs['testA'], transform=transform)
# b_test_data = dsets.ImageFolder(dataset_dirs['testB'], transform=transform)
a_test_data = ListDataSet('/media/l/新加卷1/city/data/river/train_256_9w.lst', transform=transform)
b_test_data = ListDataSet('/media/l/新加卷/city/jinan_z3.lst', transform=transform)
a_test_loader = torch.utils.data.DataLoader(a_test_data, batch_size=args.batch_size, shuffle=True, num_workers=4)
b_test_loader = torch.utils.data.DataLoader(b_test_data, batch_size=args.batch_size, shuffle=True, num_workers=4)
Gab = define_Gen(input_nc=3, output_nc=3, ngf=args.ngf, netG='resnet_9blocks', norm=args.norm,
use_dropout= not args.no_dropout, gpu_ids=args.gpu_ids)
Gba = define_Gen(input_nc=3, output_nc=3, ngf=args.ngf, netG='resnet_9blocks', norm=args.norm,
use_dropout= not args.no_dropout, gpu_ids=args.gpu_ids)
utils.print_networks([Gab, Gba], ['Gab', 'Gba'])
try:
ckpt = utils.load_checkpoint('/media/l/新加卷/city/project/cycleGAN-PyTorch/checkpoints/horse2zebra/latest.ckpt')
Gab.load_state_dict(ckpt['Gab'])
Gba.load_state_dict(ckpt['Gba'])
except:
print(' [*] No checkpoint!')
res = []
for i in range(3):
""" run """
a_real_test = Variable(iter(a_test_loader).next()[0], requires_grad=True)
b_real_test = Variable(iter(b_test_loader).next()[0], requires_grad=True)
a_real_test, b_real_test = utils.cuda([a_real_test, b_real_test])
Gab.eval()
Gba.eval()
with torch.no_grad():
a_fake_test = Gab(b_real_test)
b_fake_test = Gba(a_real_test)
a_recon_test = Gab(b_fake_test)
b_recon_test = Gba(a_fake_test)
res.append(a_real_test)
res.append(b_fake_test)
res.append(b_real_test)
res.append(a_fake_test)
pic = (torch.cat(res,
dim=0).data + 1) / 2.0
if not os.path.isdir(args.results_dir):
os.makedirs(args.results_dir)
torchvision.utils.save_image(pic, args.results_dir + '/sample.png', nrow=2)
def test(args):
transform = transforms.Compose([
transforms.Resize((args.crop_height, args.crop_width)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
])
dataset_dirs = utils.get_testdata_link(args.dataset_dir)
a_test_data = dsets.ImageFolder(dataset_dirs['testA'], transform=transform)
b_test_data = dsets.ImageFolder(dataset_dirs['testB'], transform=transform)
a_test_loader = DataLoader(a_test_data,
batch_size=args.batch_size,
shuffle=True,
num_workers=4)
b_test_loader = DataLoader(b_test_data,
batch_size=args.batch_size,
shuffle=True,
num_workers=4)
Gab = define_Gen(input_nc=3,
output_nc=3,
netG=args.gen_net,
gpu_ids=args.gpu_ids)
Gba = define_Gen(input_nc=3,
output_nc=3,
netG=args.gen_net,
gpu_ids=args.gpu_ids)
ckpt = utils.load_checkpoint('%s/latest.ckpt' % (args.checkpoint_dir))
Gab.load_state_dict(ckpt['Gab'])
Gba.load_state_dict(ckpt['Gba'])
a_real_test = iter(a_test_loader).next()[0]
b_real_test = iter(b_test_loader).next()[0]
a_real_test, b_real_test = utils.cuda([a_real_test, b_real_test])
Gab.eval()
Gba.eval()
with torch.no_grad():
a_fake_test = Gab(b_real_test)
b_fake_test = Gba(a_real_test)
a_recon_test = Gab(b_fake_test)
b_recon_test = Gba(a_fake_test)
pic = (torch.cat([
a_real_test, b_fake_test, a_recon_test, b_real_test, a_fake_test,
b_recon_test
],
dim=0).data + 1) / 2.0
if not os.path.isdir(args.results_dir):
os.makedirs(args.results_dir)
torchvision.utils.save_image(pic, args.results_dir + '/sample.jpg', nrow=3)
def test(args, epoch):
transform = transforms.Compose(
[transforms.Resize((args.crop_height, args.crop_width)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])])
dataset_dirs = utils.get_testdata_link(args.dataset_dir)
a_test_data = dsets.ImageFolder(dataset_dirs['testA'], transform=transform)
b_test_data = dsets.ImageFolder(dataset_dirs['testB'], transform=transform)
a_test_loader = torch.utils.data.DataLoader(a_test_data, batch_size=args.batch_size, shuffle=True, num_workers=4)
b_test_loader = torch.utils.data.DataLoader(b_test_data, batch_size=args.batch_size, shuffle=True, num_workers=4)
Gab = define_Gen(input_nc=3, output_nc=3, ngf=args.ngf, netG=args.gen_net, norm=args.norm,
use_dropout= args.use_dropout, gpu_ids=args.gpu_ids, self_attn=args.self_attn, spectral = args.spectral)
Gba = define_Gen(input_nc=3, output_nc=3, ngf=args.ngf, netG=args.gen_net, norm=args.norm,
use_dropout= args.use_dropout, gpu_ids=args.gpu_ids, self_attn=args.self_attn, spectral = args.spectral)
utils.print_networks([Gab,Gba], ['Gab','Gba'])
ckpt = utils.load_checkpoint('%s/latest.ckpt' % (args.checkpoint_path))
Gab.load_state_dict(ckpt['Gab'])
Gba.load_state_dict(ckpt['Gba'])
""" run """
a_real_test = Variable(iter(a_test_loader).next()[0], requires_grad=True)
b_real_test = Variable(iter(b_test_loader).next()[0], requires_grad=True)
a_real_test, b_real_test = utils.cuda([a_real_test, b_real_test])
Gab.eval()
Gba.eval()
with torch.no_grad():
a_fake_test = Gab(b_real_test)
b_fake_test = Gba(a_real_test)
a_recon_test = Gab(b_fake_test)
b_recon_test = Gba(a_fake_test)
# Calculate ssim loss
gray = kornia.color.RgbToGrayscale()
m = kornia.losses.SSIM(11, 'mean')
ba_ssim = m(gray((a_real_test + 1) / 2.0), gray((b_fake_test + 1) / 2.0))
ab_ssim = m(gray((b_real_test + 1) / 2.0), gray((a_fake_test + 1) / 2.0))
pic = (torch.cat([a_real_test, b_fake_test, a_recon_test, b_real_test, a_fake_test, b_recon_test], dim=0).data + 1) / 2.0
if not os.path.isdir(args.results_path):
os.makedirs(args.results_path)
torchvision.utils.save_image(pic, args.results_path+'/sample_' + str(epoch) + '_' + str(1 - 2*round(ba_ssim.item(), 4)) + '_' + str(1 - 2*round(ab_ssim.item(), 4)) + '.jpg', nrow=args.batch_size)
def test(args):
transform = transforms.Compose(
[transforms.Resize((args.crop_height, args.crop_width)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])])
dataset_dirs = utils.get_testdata_link(args.dataset_dir)
a_test_data = dsets.ImageFolder(dataset_dirs['testA'], transform=transform)
b_test_data = dsets.ImageFolder(dataset_dirs['testB'], transform=transform)
a_test_loader = torch.utils.data.DataLoader(a_test_data, batch_size=args.batch_size, shuffle=True, num_workers=4)
b_test_loader = torch.utils.data.DataLoader(b_test_data, batch_size=args.batch_size, shuffle=True, num_workers=4)
Gab = define_Gen(input_nc=3, output_nc=3, ngf=args.ngf, netG='resnet_9blocks', norm=args.norm,
use_dropout=not args.no_dropout, gpu_ids=args.gpu_ids)
Gba = define_Gen(input_nc=3, output_nc=3, ngf=args.ngf, netG='resnet_9blocks', norm=args.norm,
use_dropout=not args.no_dropout, gpu_ids=args.gpu_ids)
utils.print_networks([Gab, Gba], ['Gab', 'Gba'])
try:
# ckpt = utils.load_checkpoint('%s/latest.ckpt' % (args.checkpoint_dir))
ckpt = utils.load_checkpoint('%s' % args.checkpoint)
Gab.load_state_dict(ckpt['Gab'])
Gba.load_state_dict(ckpt['Gba'])
except:
print(' [*] No checkpoint!')
""" run """
a_real_test = Variable(iter(a_test_loader).next()[0], requires_grad=True)
b_real_test = Variable(iter(b_test_loader).next()[0], requires_grad=True)
a_real_test, b_real_test = utils.cuda([a_real_test, b_real_test])
Gab.eval()
Gba.eval()
with torch.no_grad():
a_fake_test = Gab(b_real_test)
b_fake_test = Gba(a_real_test)
a_recon_test = Gab(b_fake_test)
b_recon_test = Gba(a_fake_test)
pic = (torch.cat([a_real_test, b_fake_test, a_recon_test, b_real_test, a_fake_test, b_recon_test],
dim=0).data + 1) / 2.0
if not os.path.isdir(args.results_dir):
os.makedirs(args.results_dir)
torchvision.utils.save_image(pic, args.results_dir + '/sample.jpg', nrow=3)
def test(args):
utils.cuda_devices([args.gpu_id])
transform = transforms.Compose(
[transforms.Resize((args.img_height,args.img_width)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])])
dataset_dirs = utils.get_testdata_link(args.dataset_dir)
a_test_data = dsets.ImageFolder(dataset_dirs['testA'], transform=transform)
b_test_data = dsets.ImageFolder(dataset_dirs['testB'], transform=transform)
a_test_loader = torch.utils.data.DataLoader(a_test_data, batch_size=args.batch_size, shuffle=False, num_workers=4)
b_test_loader = torch.utils.data.DataLoader(b_test_data, batch_size=args.batch_size, shuffle=False, num_workers=4)
Gab = model.Generator()
Gba = model.Generator()
utils.cuda([Gab, Gba])
try:
ckpt = utils.load_checkpoint('%s/latest.ckpt' % (args.checkpoint_dir))
Gab.load_state_dict(ckpt['Gab'])
Gba.load_state_dict(ckpt['Gba'])
except:
print(' [*] No checkpoint!')
""" run """
a_real_test = Variable(iter(a_test_loader).next()[0], requires_grad=True)
b_real_test = Variable(iter(b_test_loader).next()[0], requires_grad=True)
a_real_test, b_real_test = utils.cuda([a_real_test, b_real_test])
Gab.eval()
Gba.eval()
with torch.no_grad():
a_fake_test = Gab(b_real_test)
b_fake_test = Gba(a_real_test)
a_recon_test = Gab(b_fake_test)
b_recon_test = Gba(a_fake_test)
pic = (torch.cat([a_real_test, b_fake_test, a_recon_test, b_real_test, a_fake_test, b_recon_test], dim=0).data + 1) / 2.0
if not os.path.isdir(args.results_dir):
os.makedirs(args.results_dir)
torchvision.utils.save_image(pic, args.results_dir+'/sample.jpg', nrow=3)
def test(args):
transform = transforms.Compose([
transforms.Resize((args.crop_height, args.crop_width)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
])
dataset_dirs = utils.get_testdata_link(args.dataset_dir)
a_test_data = datasets.ImageFolder(dataset_dirs['testA'],
transform=transform)
b_test_data = datasets.ImageFolder(dataset_dirs['testB'],
transform=transform)
a_test_loader = torch.utils.data.DataLoader(a_test_data,
batch_size=args.batch_size,
shuffle=True,
num_workers=4)
b_test_loader = torch.utils.data.DataLoader(b_test_data,
batch_size=args.batch_size,
shuffle=True,
num_workers=4)
G_AtoB = define_Gen(input_nc=3,
output_nc=3,
ngf=args.ngf,
norm=args.norm,
use_dropout=not args.no_dropout,
gpu_ids=args.gpu_ids)
G_BtoA = define_Gen(input_nc=3,
output_nc=3,
ngf=args.ngf,
norm=args.norm,
use_dropout=not args.no_dropout,
gpu_ids=args.gpu_ids)
utils.print_networks([G_AtoB, G_BtoA], ['G_AtoB', 'G_BtoA'])
try:
ckpt = utils.load_checkpoint('%s/latest.ckpt' % (args.checkpoint_dir))
G_AtoB.load_state_dict(ckpt['G_AtoB'])
G_BtoA.load_state_dict(ckpt['G_BtoA'])
except:
print(' [*] No checkpoint! ')
for i, (a_real_test,
b_real_test) in enumerate(zip(a_test_loader, b_test_loader)):
a_real_test = Variable(a_real_test[0], requires_grad=True)
b_real_test = Variable(b_real_test[0], requires_grad=True)
a_real_test, b_real_test = utils.cuda([a_real_test, b_real_test])
G_AtoB.eval()
G_BtoA.eval()
with torch.no_grad():
a_fake_test = G_BtoA(b_real_test)
b_fake_test = G_AtoB(a_real_test)
a_recon_test = G_BtoA(b_fake_test)
b_recon_test = G_AtoB(a_fake_test)
pic = (torch.cat([
a_real_test, b_fake_test, a_recon_test, b_real_test, a_fake_test,
b_recon_test
],
dim=0).data + 1) / 2.0
if not os.path.isdir(args.results_dir):
os.makedirs(args.results_dir)
torchvision.utils.save_image(pic,
args.results_dir + '/sample_' + str(i) +
'.jpg',
nrow=3)
checkpoint_dir = './checkpoints/sketch2pokemon'
checkpoint_name = 'latest'
results_dir = './results'
batch_size = 1
ngf = 64
norm = 'instance'
no_dropout = False
gpu_ids = [0]
transform = transforms.Compose([
transforms.Resize((crop_height, crop_width)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
])
dataset_dirs = utils.get_testdata_link(dataset_dir)
a_test_data = dsets.ImageFolder(dataset_dirs['testA'], transform=transform)
b_test_data = dsets.ImageFolder(dataset_dirs['testB'], transform=transform)
a_test_loader = torch.utils.data.DataLoader(
a_test_data, batch_size=batch_size, shuffle=False,
num_workers=4) # set shuffle to false for testing
b_test_loader = torch.utils.data.DataLoader(b_test_data,
batch_size=batch_size,
shuffle=False,
num_workers=4)
Gab = define_Gen(input_nc=3,
output_nc=3,
ngf=ngf,
def test(args):
transform = transforms.Compose([
transforms.Resize((args.crop_height, args.crop_width)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
])
dataset_dirs = utils.get_testdata_link(args.dataset_dir)
a_test_data = dsets.ImageFolder(dataset_dirs['testA'], transform=transform)
b_test_data = dsets.ImageFolder(dataset_dirs['testB'], transform=transform)
a_test_loader = torch.utils.data.DataLoader(a_test_data,
batch_size=args.batch_size,
num_workers=4)
b_test_loader = torch.utils.data.DataLoader(b_test_data,
batch_size=args.batch_size,
num_workers=4)
Gab = Generator(input_nc=3,
output_nc=3,
ngf=args.ngf,
netG='resnet_9blocks',
norm=args.norm,
use_dropout=not args.no_dropout,
gpu_ids=args.gpu_ids)
Gba = Generator(input_nc=3,
output_nc=3,
ngf=args.ngf,
netG='resnet_9blocks',
norm=args.norm,
use_dropout=not args.no_dropout,
gpu_ids=args.gpu_ids)
utils.print_networks([Gab, Gba], ['Gab', 'Gba'])
try:
ckpt = utils.load_checkpoint('%s/latest.ckpt' % (args.checkpoint_dir))
Gab.load_state_dict(ckpt['Gab'])
Gba.load_state_dict(ckpt['Gba'])
except:
print(' [*] No checkpoint!')
""" run """
a_real_test = Variable(iter(a_test_loader).next()[0], requires_grad=True)
b_real_test = Variable(iter(b_test_loader).next()[0], requires_grad=True)
a_real_test, b_real_test = utils.cuda([a_real_test, b_real_test])
Gab.eval()
Gba.eval()
with torch.no_grad():
a_fake_test = Gab(b_real_test)
b_fake_test = Gba(a_real_test)
a_recon_test = Gab(b_fake_test)
b_recon_test = Gba(a_fake_test)
pic = (torch.cat([
a_real_test, b_fake_test, a_recon_test, b_real_test, a_fake_test,
b_recon_test
],
dim=0).data + 1) / 2.0
if not os.path.isdir(args.results_dir):
os.makedirs(args.results_dir)
torchvision.utils.save_image(pic, args.results_dir + '/sample.jpg', nrow=3)
#create output dirs if they don't exist
if not os.path.exists(args.results_dir + '/inputA'):
os.makedirs(args.results_dir + '/inputA')
if not os.path.exists(args.results_dir + '/outputA'):
os.makedirs(args.results_dir + '/outputA')
if not os.path.exists(args.results_dir + './outputB'):
os.makedirs(args.results_dir + '/outputB')
for i, (a_real_test,
b_real_test) in enumerate(zip(a_test_loader, b_test_loader)):
#set model input
a_real_test = Variable(a_real_test[0], requires_grad=True)
b_real_test = Variable(b_real_test[0], requires_grad=True)
a_real_test, b_real_test = utils.cuda([a_real_test, b_real_test])
Gab.eval()
Gba.eval()
with torch.no_grad():
a_fake_test = Gab(b_real_test)
b_fake_test = Gba(a_real_test)
a_recon_test = Gab(b_fake_test)
b_recon_test = Gba(a_fake_test)
a_fake_test = (a_fake_test + 1) / 2.0
b_real_test = (b_real_test + 1) / 2.0
a_real_test = (a_real_test + 1) / 2.0
b_fake_test = (b_fake_test + 1) / 2.0
# Save image files
torchvision.utils.save_image(
b_real_test, args.results_dir + '/inputA/%04d.png' % (i + 1))
torchvision.utils.save_image(
a_fake_test, args.results_dir + '/outputA/%04d.png' % (i + 1))
print("\n\nCreated Output Directories\n\n")
def train(self, args):
# Test input
transform_test = transforms.Compose([
transforms.Resize((args.crop_height, args.crop_width)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
])
dataset_dirs = utils.get_testdata_link(args.dataset_dir)
a_test_data = dsets.ImageFolder(dataset_dirs['testA'],
transform=transform_test)
b_test_data = dsets.ImageFolder(dataset_dirs['testB'],
transform=transform_test)
a_test_loader = torch.utils.data.DataLoader(a_test_data,
batch_size=args.batch_size,
shuffle=True,
num_workers=4)
b_test_loader = torch.utils.data.DataLoader(b_test_data,
batch_size=args.batch_size,
shuffle=True,
num_workers=4)
# For transforming the input image
transform = transforms.Compose([
transforms.RandomHorizontalFlip(),
transforms.Resize((args.load_height, args.load_width)),
transforms.RandomCrop((args.crop_height, args.crop_width)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
])
dataset_dirs = utils.get_traindata_link(args.dataset_dir)
# Pytorch dataloader
a_loader = torch.utils.data.DataLoader(dsets.ImageFolder(
dataset_dirs['trainA'], transform=transform),
batch_size=args.batch_size,
shuffle=True,
num_workers=4)
b_loader = torch.utils.data.DataLoader(dsets.ImageFolder(
dataset_dirs['trainB'], transform=transform),
batch_size=args.batch_size,
shuffle=True,
num_workers=4)
a_fake_sample = utils.Sample_from_Pool()
b_fake_sample = utils.Sample_from_Pool()
for epoch in range(self.start_epoch, args.epochs):
lr = self.g_optimizer.param_groups[0]['lr']
print('learning rate = %.7f' % lr)
for i, (a_real, b_real) in enumerate(zip(a_loader, b_loader)):
# step
step = epoch * min(len(a_loader), len(b_loader)) + i + 1
# Generator Computations
##################################################
set_grad([self.Da, self.Db], False)
self.g_optimizer.zero_grad()
a_real = Variable(a_real[0])
b_real = Variable(b_real[0])
a_real, b_real = utils.cuda([a_real, b_real])
# Forward pass through generators
##################################################
a_fake = self.Gab(b_real)
b_fake = self.Gba(a_real)
a_recon = self.Gab(b_fake)
b_recon = self.Gba(a_fake)
a_idt = self.Gab(a_real)
b_idt = self.Gba(b_real)
# Identity losses
###################################################
a_idt_loss = self.L1(a_idt,
a_real) * args.lamda * args.idt_coef
b_idt_loss = self.L1(b_idt,
b_real) * args.lamda * args.idt_coef
# Adversarial losses
###################################################
a_fake_dis = self.Da(a_fake)
b_fake_dis = self.Db(b_fake)
real_label = utils.cuda(Variable(torch.ones(
a_fake_dis.size())))
a_gen_loss = self.MSE(a_fake_dis, real_label)
b_gen_loss = self.MSE(b_fake_dis, real_label)
# Cycle consistency losses
###################################################
a_cycle_loss = self.L1(a_recon, a_real) * args.lamda
b_cycle_loss = self.L1(b_recon, b_real) * args.lamda
# Total generators losses
###################################################
gen_loss = a_gen_loss + b_gen_loss + a_cycle_loss + b_cycle_loss + a_idt_loss + b_idt_loss
# Update generators
###################################################
gen_loss.backward()
self.g_optimizer.step()
# Discriminator Computations
#################################################
set_grad([self.Da, self.Db], True)
self.d_optimizer.zero_grad()
# Sample from history of generated images
#################################################
a_fake = Variable(
torch.Tensor(
a_fake_sample([a_fake.cpu().data.numpy()])[0]))
b_fake = Variable(
torch.Tensor(
b_fake_sample([b_fake.cpu().data.numpy()])[0]))
a_fake, b_fake = utils.cuda([a_fake, b_fake])
# Forward pass through discriminators
#################################################
a_real_dis = self.Da(a_real)
a_fake_dis = self.Da(a_fake)
b_real_dis = self.Db(b_real)
b_fake_dis = self.Db(b_fake)
real_label = utils.cuda(Variable(torch.ones(
a_real_dis.size())))
fake_label = utils.cuda(
Variable(torch.zeros(a_fake_dis.size())))
# Discriminator losses
##################################################
a_dis_real_loss = self.MSE(a_real_dis, real_label)
a_dis_fake_loss = self.MSE(a_fake_dis, fake_label)
b_dis_real_loss = self.MSE(b_real_dis, real_label)
b_dis_fake_loss = self.MSE(b_fake_dis, fake_label)
# Total discriminators losses
a_dis_loss = (a_dis_real_loss + a_dis_fake_loss) * 0.5
b_dis_loss = (b_dis_real_loss + b_dis_fake_loss) * 0.5
# Update discriminators
##################################################
a_dis_loss.backward()
b_dis_loss.backward()
self.d_optimizer.step()
print(
"Epoch: (%3d) (%5d/%5d) | Gen Loss:%.2e | Dis Loss:%.2e" %
(epoch, i + 1, min(len(a_loader), len(b_loader)), gen_loss,
a_dis_loss + b_dis_loss))
# Override the latest checkpoint
#######################################################
utils.save_checkpoint(
{
'epoch': epoch + 1,
'Da': self.Da.state_dict(),
'Db': self.Db.state_dict(),
'Gab': self.Gab.state_dict(),
'Gba': self.Gba.state_dict(),
'd_optimizer': self.d_optimizer.state_dict(),
'g_optimizer': self.g_optimizer.state_dict()
}, '%s/latest.ckpt' % (args.checkpoint_dir))
# Save image current :
#######################################################################
""" run """
a_real_test = Variable(iter(a_test_loader).next()[0],
requires_grad=True)
b_real_test = Variable(iter(b_test_loader).next()[0],
requires_grad=True)
a_real_test, b_real_test = utils.cuda([a_real_test, b_real_test])
self.Gab.eval()
self.Gba.eval()
with torch.no_grad():
a_fake_test = self.Gab(b_real_test)
b_fake_test = self.Gba(a_real_test)
a_recon_test = self.Gab(b_fake_test)
b_recon_test = self.Gba(a_fake_test)
pic = (torch.cat([
a_real_test, b_fake_test, a_recon_test, b_real_test,
a_fake_test, b_recon_test
],
dim=0).data + 1) / 2.0
if not os.path.isdir(args.results_dir):
os.makedirs(args.results_dir)
torchvision.utils.save_image(pic,
args.results_dir +
'/sample_{}.jpg'.format(epoch),
nrow=3)
self.Gab.train()
self.Gba.train()
# Update learning rates
########################
self.g_lr_scheduler.step()
self.d_lr_scheduler.step()
def train(self, args):
# For transforming the input image
transform = transforms.Compose([
transforms.RandomHorizontalFlip(),
transforms.Resize((args.load_height, args.load_width)),
transforms.RandomCrop((args.crop_height, args.crop_width)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
])
test_transform = transforms.Compose([
transforms.Resize((args.test_crop_height, args.test_crop_width)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
])
dataset_dirs = utils.get_traindata_link(args.dataset_dir)
testset_dirs = utils.get_testdata_link(args.dataset_dir)
# Pytorch dataloader
a_loader = torch.utils.data.DataLoader(dsets.ImageFolder(
dataset_dirs['trainA'], transform=transform),
batch_size=args.batch_size,
shuffle=True,
num_workers=4)
b_loader = torch.utils.data.DataLoader(dsets.ImageFolder(
dataset_dirs['trainB'], transform=transform),
batch_size=args.batch_size,
shuffle=True,
num_workers=4)
a_test_loader = torch.utils.data.DataLoader(dsets.ImageFolder(
testset_dirs['testA'], transform=test_transform),
batch_size=1,
shuffle=False,
num_workers=4)
b_test_loader = torch.utils.data.DataLoader(dsets.ImageFolder(
testset_dirs['testB'], transform=test_transform),
batch_size=1,
shuffle=False,
num_workers=4)
a_fake_sample = utils.Sample_from_Pool()
b_fake_sample = utils.Sample_from_Pool()
for epoch in range(self.start_epoch, args.epochs):
if epoch >= 1:
print('generating test result...')
self.save_sample_image(args.test_length, a_test_loader,
b_test_loader, args.results_dir, epoch)
lr = self.g_optimizer.param_groups[0]['lr']
print('learning rate = %.7f' % lr)
running_Gen_loss = 0
running_Dis_loss = 0
##################################################
# BEGIN TRAINING FOR ONE EPOCH
##################################################
for i, (a_real, b_real) in enumerate(zip(a_loader, b_loader)):
# step
step = epoch * min(len(a_loader), len(b_loader)) + i + 1
##################################################
# Part 1: Generator Computations
##################################################
set_grad([self.Da, self.Db], False)
self.g_optimizer.zero_grad()
a_real = Variable(a_real[0])
b_real = Variable(b_real[0])
a_real, b_real = utils.cuda([a_real, b_real])
# Forward pass through generators
##################################################
a_fake = self.Gab(b_real)
b_fake = self.Gba(a_real)
a_recon = self.Gab(b_fake)
b_recon = self.Gba(a_fake)
a_idt = self.Gab(a_real)
b_idt = self.Gba(b_real)
# Identity losses
###################################################
a_idt_loss = self.identity_criteron(
a_idt, a_real) * args.lamda * args.idt_coef
b_idt_loss = self.identity_criteron(
b_idt, b_real) * args.lamda * args.idt_coef
# a_idt_loss = 0
# b_idt_loss = 0
# Adversarial losses
###################################################
a_fake_dis = self.Da(a_fake)
b_fake_dis = self.Db(b_fake)
real_label = utils.cuda(Variable(torch.ones(
a_fake_dis.size())))
a_gen_loss = self.adversarial_criteron(a_fake_dis, real_label)
b_gen_loss = self.adversarial_criteron(b_fake_dis, real_label)
# Cycle consistency losses
###################################################
a_cycle_loss = self.cycle_consistency_criteron(
a_recon, a_real) * args.lamda
b_cycle_loss = self.cycle_consistency_criteron(
b_recon, b_real) * args.lamda
# Total generators losses
###################################################
gen_loss = a_gen_loss + b_gen_loss + a_cycle_loss + b_cycle_loss + a_idt_loss + b_idt_loss
# Update generators
###################################################
gen_loss.backward()
self.g_optimizer.step()
##################################################
# Part 2: Discriminator Computations
#################################################
set_grad([self.Da, self.Db], True)
self.d_optimizer.zero_grad()
# Sample from history of generated images
#################################################
a_fake = Variable(
torch.Tensor(
a_fake_sample([a_fake.cpu().data.numpy()])[0]))
b_fake = Variable(
torch.Tensor(
b_fake_sample([b_fake.cpu().data.numpy()])[0]))
a_fake, b_fake = utils.cuda([a_fake, b_fake])
# Forward pass through discriminators
#################################################
a_real_dis = self.Da(a_real)
a_fake_dis = self.Da(a_fake)
b_real_dis = self.Db(b_real)
b_fake_dis = self.Db(b_fake)
real_label = utils.cuda(Variable(torch.ones(
a_real_dis.size())))
fake_label = utils.cuda(
Variable(torch.zeros(a_fake_dis.size())))
# Discriminator losses
##################################################
a_dis_real_loss = self.adversarial_criteron(
a_real_dis, real_label)
a_dis_fake_loss = self.adversarial_criteron(
a_fake_dis, fake_label)
b_dis_real_loss = self.adversarial_criteron(
b_real_dis, real_label)
b_dis_fake_loss = self.adversarial_criteron(
b_fake_dis, fake_label)
# Total discriminators losses
a_dis_loss = (a_dis_real_loss + a_dis_fake_loss) * 0.5
b_dis_loss = (b_dis_real_loss + b_dis_fake_loss) * 0.5
# Update discriminators
##################################################
a_dis_loss.backward()
b_dis_loss.backward()
self.d_optimizer.step()
print(
"Epoch: (%3d) (%5d/%5d) | Gen Loss:%.2e | Dis Loss:%.2e" %
(epoch, i + 1, min(len(a_loader), len(b_loader)), gen_loss,
a_dis_loss + b_dis_loss))
running_Gen_loss += gen_loss
running_Dis_loss += (a_dis_loss + b_dis_loss)
##################################################
# END TRAINING FOR ONE EPOCH
##################################################
self.writer.add_scalar(
'Gen Loss',
running_Gen_loss / min(len(a_loader), len(b_loader)), epoch)
self.writer.add_scalar(
'Dis Loss',
running_Dis_loss / min(len(a_loader), len(b_loader)), epoch)
self.writer.add_scalar('Gen_LR',
self.g_lr_scheduler.get_lr()[0], epoch)
self.writer.add_scalar('Dis_LR',
self.d_lr_scheduler.get_lr()[0], epoch)
# Override the latest checkpoint
#######################################################
utils.save_checkpoint(
{
'epoch': epoch + 1,
'Da': self.Da.state_dict(),
'Db': self.Db.state_dict(),
'Gab': self.Gab.state_dict(),
'Gba': self.Gba.state_dict(),
'd_optimizer': self.d_optimizer.state_dict(),
'g_optimizer': self.g_optimizer.state_dict()
}, '%s/latest.ckpt' % (args.checkpoint_dir))
# Update learning rates
########################
self.g_lr_scheduler.step()
self.d_lr_scheduler.step()
self.writer.close()
def gen_samples(args, epoch):
transform = transforms.Compose([
transforms.Resize((args.crop_height, args.crop_width)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
])
if args.specific_samples:
dataset_dirs = utils.get_sampledata_link(args.dataset_dir)
a_test_data = dsets.ImageFolder(dataset_dirs['sampleA'],
transform=transform)
b_test_data = dsets.ImageFolder(dataset_dirs['sampleB'],
transform=transform)
else:
dataset_dirs = utils.get_testdata_link(args.dataset_dir)
a_test_data = dsets.ImageFolder(dataset_dirs['testA'],
transform=transform)
b_test_data = dsets.ImageFolder(dataset_dirs['testB'],
transform=transform)
a_test_loader = torch.utils.data.DataLoader(a_test_data,
batch_size=args.batch_size,
shuffle=False,
num_workers=4)
b_test_loader = torch.utils.data.DataLoader(b_test_data,
batch_size=args.batch_size,
shuffle=False,
num_workers=4)
Gab = define_Gen(input_nc=3,
output_nc=3,
ngf=args.ngf,
netG=args.gen_net,
norm=args.norm,
use_dropout=args.use_dropout,
gpu_ids=args.gpu_ids,
self_attn=args.self_attn,
spectral=args.spectral)
Gba = define_Gen(input_nc=3,
output_nc=3,
ngf=args.ngf,
netG=args.gen_net,
norm=args.norm,
use_dropout=args.use_dropout,
gpu_ids=args.gpu_ids,
self_attn=args.self_attn,
spectral=args.spectral)
utils.print_networks([Gab, Gba], ['Gab', 'Gba'])
ckpt = utils.load_checkpoint('%s/latest.ckpt' % (args.checkpoint_path))
Gab.load_state_dict(ckpt['Gab'])
Gba.load_state_dict(ckpt['Gba'])
ab_ssims = []
ba_ssims = []
a_names = []
b_names = []
""" run """
for i, (a_real_test,
b_real_test) in enumerate(zip(a_test_loader, b_test_loader)):
a_fnames = a_test_loader.dataset.samples[i * 16:i * 16 + 16]
b_fnames = b_test_loader.dataset.samples[i * 16:i * 16 + 16]
a_real_test = Variable(a_real_test[0], requires_grad=True)
b_real_test = Variable(b_real_test[0], requires_grad=True)
a_real_test, b_real_test = utils.cuda([a_real_test, b_real_test])
gray = kornia.color.RgbToGrayscale()
m = kornia.losses.SSIM(11, 'mean')
Gab.eval()
Gba.eval()
with torch.no_grad():
a_fake_test = Gab(b_real_test)
b_fake_test = Gba(a_real_test)
a_recon_test = Gab(b_fake_test)
b_recon_test = Gba(a_fake_test)
# Calculate ssim loss
b = a_real_test.size(0)
for j in range(min(args.batch_size, b)):
a_real = a_real_test[j].unsqueeze(0)
b_fake = b_fake_test[j].unsqueeze(0)
a_recon = a_recon_test[j].unsqueeze(0)
b_real = b_real_test[j].unsqueeze(0)
a_fake = a_fake_test[j].unsqueeze(0)
b_recon = b_recon_test[j].unsqueeze(0)
ba_ssim = m(gray((a_real + 1) / 2.0), gray((b_fake + 1) / 2.0))
ab_ssim = m(gray((b_real + 1) / 2.0), gray((a_fake + 1) / 2.0))
ab_ssims.append(ab_ssim.item())
ba_ssims.append(ba_ssim.item())
pic = (torch.cat([
a_real_test, b_fake_test, a_recon_test, b_real_test,
a_fake_test, b_recon_test
],
dim=0).data + 1) / 2.0
path = args.results_path + '/b_fake/'
image_path = path + a_fnames[j][0].split('/')[-1]
if not os.path.isdir(path):
os.makedirs(path)
torchvision.utils.save_image((b_fake.data + 1) / 2.0,
image_path)
a_names.append(a_fnames[j][0].split('/')[-1])
path = args.results_path + '/a_recon/'
image_path = path + a_fnames[j][0].split('/')[-1]
if not os.path.isdir(path):
os.makedirs(path)
torchvision.utils.save_image((a_recon.data + 1) / 2.0,
image_path)
path = args.results_path + '/a_fake/'
image_path = path + b_fnames[j][0].split('/')[-1]
if not os.path.isdir(path):
os.makedirs(path)
torchvision.utils.save_image((a_fake.data + 1) / 2.0,
image_path)
b_names.append(b_fnames[j][0].split('/')[-1])
path = args.results_path + '/b_recon/'
image_path = path + b_fnames[j][0].split('/')[-1]
if not os.path.isdir(path):
os.makedirs(path)
torchvision.utils.save_image((b_recon.data + 1) / 2.0,
image_path)
df1 = pd.DataFrame(list(zip(a_names, ba_ssims)),
columns=['Name', 'SSIM_A_to_B'])
df2 = pd.DataFrame(list(zip(b_names, ab_ssims)),
columns=['Name', 'SSIM_B_to_A'])
df1.to_csv(args.results_path + '/b_fake/' + 'SSIM_A_to_B.csv')
df2.to_csv(args.results_path + '/a_fake/' + 'SSIM_B_to_A.csv')
def test(args):
transform = transforms.Compose([
transforms.Resize((args.crop_height, args.crop_width)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
])
dataset_dirs = utils.get_testdata_link(args.dataset_dir)
a_test_data = dsets.ImageFolder(dataset_dirs['testA'], transform=transform)
b_test_data = dsets.ImageFolder(dataset_dirs['testB'], transform=transform)
a_test_loader = torch.utils.data.DataLoader(a_test_data,
batch_size=args.batch_size,
shuffle=True,
num_workers=4)
b_test_loader = torch.utils.data.DataLoader(b_test_data,
batch_size=args.batch_size,
shuffle=True,
num_workers=4)
Gab = define_Gen(input_nc=3,
output_nc=3,
ngf=args.ngf,
netG=args.gen_net,
norm=args.norm,
use_dropout=not args.no_dropout,
gpu_ids=args.gpu_ids)
Gba = define_Gen(input_nc=3,
output_nc=3,
ngf=args.ngf,
netG=args.gen_net,
norm=args.norm,
use_dropout=not args.no_dropout,
gpu_ids=args.gpu_ids)
utils.print_networks([Gab, Gba], ['Gab', 'Gba'])
try:
ckpt = utils.load_checkpoint('%s/latest.ckpt' % (args.checkpoint_dir))
if len(args.gpu_ids) > 0:
print(args.gpu_ids)
Gab.load_state_dict(ckpt['Gab'])
Gba.load_state_dict(ckpt['Gba'])
else:
new_state_dict = {}
for k, v in ckpt['Gab'].items():
name = k.replace('module.', '')
new_state_dict[name] = v
Gab.load_state_dict(new_state_dict)
new_state_dict = {}
for k, v in ckpt['Gba'].items():
name = k.replace('module.', '')
new_state_dict[name] = v
Gba.load_state_dict(new_state_dict)
except:
print(' [*] No checkpoint!')
""" run """
a_it = iter(a_test_loader)
b_it = iter(b_test_loader)
for i in range(len(a_test_loader)):
try:
a_real_test = Variable(next(a_it)[0], requires_grad=True)
except:
a_it = iter(a_test_loader)
try:
b_real_test = Variable(next(b_it)[0], requires_grad=True)
except:
b_it = iter(b_test_loader)
print(a_real_test)
return
a_real_test, b_real_test = utils.cuda([a_real_test, b_real_test])
print(a_real_test.shape)
Gab.eval()
Gba.eval()
with torch.no_grad():
a_fake_test = Gab(b_real_test)
b_fake_test = Gba(a_real_test)
a_recon_test = Gab(b_fake_test)
b_recon_test = Gba(a_fake_test)
pic = (torch.cat([
a_real_test, b_fake_test, a_recon_test, b_real_test, a_fake_test,
b_recon_test
],
dim=0).data + 1) / 2.0
if not os.path.isdir(args.results_dir):
os.makedirs(args.results_dir)
path = str.format("{}/sample-{}.jpg", args.results_dir, i)
torchvision.utils.save_image(pic, path, nrow=3)
