def get_GAN_AB_model(folder_model, model_name, device):
n_residual_blocks = 9 # this should be the same values used in training the G_AB model
G_AB = GeneratorResNet(input_shape=(3,0), num_residual_blocks = n_residual_blocks)
G_AB.load_state_dict(torch.load(folder_model + model_name, map_location=device ), )
if cuda:
G_AB = G_AB.to(device)
return G_AB
def get_generator_model():
generator = GeneratorResNet(img_shape=img_shape,
res_blocks=residual_blocks,
c_dim=c_dim)
generator.load_state_dict(
torch.load(PATH_G, map_location=torch.device('cpu')))
generator.eval()
return generator
opt = parser.parse_args()
SCALE_FACTOR = opt.scale_factor
MODEL_NAME = opt.model_name
hr_shape = (opt.hr_height, opt.hr_width)
results = {'Test': {'psnr': [], 'ssim': []}}
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
generator = GeneratorResNet()
generator = nn.DataParallel(generator, device_ids=[0, 1, 2])
generator.to(device)
# generator.load_state_dict(torch.load("saved_models/generator_%d_%d.pth" % (4,99)))
generator.load_state_dict(torch.load("saved_models/" + MODEL_NAME))
generator.eval()
test_dataloader = DataLoader(
TestImageDataset("../My_dataset/single_channel_100000/%s" %
opt.test_dataset_name,
hr_shape=hr_shape,
scale_factor=opt.scale_factor), # change
batch_size=1,
shuffle=False,
num_workers=opt.n_cpu,
)
test_bar = tqdm(test_dataloader, desc='[testing datasets]')
test_out_path = 'testing_results/SRF_' + str(SCALE_FACTOR) + '/'
if cuda:
generator = generator.cuda()
discriminator = discriminator.cuda()
criterion_cycle.cuda()
if opt.is_print:
print_network(generator, 'Generator')
print_network(discriminator, 'Discriminator')
# Tensor type
Tensor = torch.cuda.FloatTensor if cuda else torch.FloatTensor
if opt.epoch != 0:
# Load pre-trained models
generator.load_state_dict(torch.load("saved_models/generator_%d.pth" % opt.epoch))
discriminator.load_state_dict(torch.load("saved_models/discriminator_%d.pth" % opt.epoch))
else:
generator.apply(weights_init_normal)
discriminator.apply(weights_init_normal)
# Optimizers
optimizer_G = torch.optim.Adam(generator.parameters(), lr=opt.lr, betas=(opt.b1, opt.b2))
optimizer_D = torch.optim.Adam(discriminator.parameters(), lr=opt.lr, betas=(opt.b1, opt.b2))
'''
lr_scheduler_G = torch.optim.lr_scheduler.LambdaLR(optimizer_G,
lr_lambda=LambdaLR(opt.n_epochs, opt.epoch, opt.decay_epoch).step)
lr_scheduler_D = torch.optim.lr_scheduler.LambdaLR(optimizer_D,
lr_lambda=LambdaLR(opt.n_epochs, opt.epoch, opt.decay_epoch).step)
'''
# Configure transforms
parser = argparse.ArgumentParser()
parser.add_argument('--check_point', type=str, default='saved_models/G_AB_10.pth',
help='check point from which load trained model')
parser.add_argument('--batch_size', type=int, default=1, help='size of the batches')
parser.add_argument('--A_file', type=str, default='test.png', help='path of the data')
parser.add_argument('--img_height', type=int, default=256, help='size of image height')
parser.add_argument('--img_width', type=int, default=256, help='size of image width')
parser.add_argument('--gpu_id', type=int, default=-1, help='GPU id')
opt = parser.parse_args()
cuda = opt.gpu_id > -1
# # Load pretrained model G_AB
G_AB = GeneratorResNet()
if cuda:
G_AB = G_AB.cuda()
G_AB.load_state_dict(torch.load(opt.check_point))
Tensor = torch.cuda.FloatTensor if cuda else torch.Tensor
# Image transformations
transforms_ = [transforms.Resize((opt.img_height, opt.img_width)),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))]
img_transformer = transforms.Compose(transforms_)
# Test data
img = img_transformer(Image.open(opt.A_file).convert("RGB"))
real_A = Variable(img.reshape(1, 3, opt.img_height, opt.img_width).type(Tensor))
img_sample = G_AB(real_A)
def main():
cuda = torch.cuda.is_available()
input_shape = (opt.channels, opt.img_height, opt.img_width)
# Initialize generator and discriminator
G_AB = GeneratorResNet(input_shape, opt.n_residual_blocks)
G_BA = GeneratorResNet(input_shape, opt.n_residual_blocks)
D_A = Discriminator(input_shape)
D_B = Discriminator(input_shape)
if cuda:
G_AB = G_AB.cuda()
G_BA = G_BA.cuda()
D_A = D_A.cuda()
D_B = D_B.cuda()
criterion_GAN.cuda()
criterion_cycle.cuda()
criterion_identity.cuda()
if opt.epoch != 0:
# Load pretrained models
G_AB.load_state_dict(
torch.load("saved_models/%s/G_AB_%d.pth" %
(opt.dataset_name, opt.epoch)))
G_BA.load_state_dict(
torch.load("saved_models/%s/G_BA_%d.pth" %
(opt.dataset_name, opt.epoch)))
D_A.load_state_dict(
torch.load("saved_models/%s/D_A_%d.pth" %
(opt.dataset_name, opt.epoch)))
D_B.load_state_dict(
torch.load("saved_models/%s/D_B_%d.pth" %
(opt.dataset_name, opt.epoch)))
else:
# Initialize weights
G_AB.apply(weights_init_normal)
G_BA.apply(weights_init_normal)
D_A.apply(weights_init_normal)
D_B.apply(weights_init_normal)
# Optimizers
optimizer_G = torch.optim.Adam(itertools.chain(G_AB.parameters(),
G_BA.parameters()),
lr=opt.lr,
betas=(opt.b1, opt.b2))
optimizer_D_A = torch.optim.Adam(D_A.parameters(),
lr=opt.lr,
betas=(opt.b1, opt.b2))
optimizer_D_B = torch.optim.Adam(D_B.parameters(),
lr=opt.lr,
betas=(opt.b1, opt.b2))
# Learning rate update schedulers
lr_scheduler_G = torch.optim.lr_scheduler.LambdaLR(
optimizer_G,
lr_lambda=LambdaLR(opt.n_epochs, opt.epoch, opt.decay_epoch).step)
lr_scheduler_D_A = torch.optim.lr_scheduler.LambdaLR(
optimizer_D_A,
lr_lambda=LambdaLR(opt.n_epochs, opt.epoch, opt.decay_epoch).step)
lr_scheduler_D_B = torch.optim.lr_scheduler.LambdaLR(
optimizer_D_B,
lr_lambda=LambdaLR(opt.n_epochs, opt.epoch, opt.decay_epoch).step)
Tensor = torch.cuda.FloatTensor if cuda else torch.Tensor
# Buffers of previously generated samples
fake_A_buffer = ReplayBuffer()
fake_B_buffer = ReplayBuffer()
# Image transformations
transforms_ = [
transforms.Resize(int(opt.img_height * 1.12), Image.BICUBIC),
transforms.RandomCrop((opt.img_height, opt.img_width)),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
]
# Training data loader
dataloader = DataLoader(
ImageDataset("../../data/%s" % opt.dataset_name,
transforms_=transforms_,
unaligned=True),
batch_size=opt.batch_size,
shuffle=True,
num_workers=opt.n_cpu,
)
# Test data loader
val_dataloader = DataLoader(
ImageDataset("../../data/%s" % opt.dataset_name,
transforms_=transforms_,
unaligned=True,
mode="test"),
batch_size=5,
shuffle=True,
num_workers=1,
)
def sample_images(batches_done):
"""Saves a generated sample from the test set"""
imgs = next(iter(val_dataloader))
G_AB.eval()
G_BA.eval()
real_A = Variable(imgs["A"].type(Tensor))
fake_B = G_AB(real_A)
real_B = Variable(imgs["B"].type(Tensor))
fake_A = G_BA(real_B)
# Arange images along x-axis
real_A = make_grid(real_A, nrow=5, normalize=True)
real_B = make_grid(real_B, nrow=5, normalize=True)
fake_A = make_grid(fake_A, nrow=5, normalize=True)
fake_B = make_grid(fake_B, nrow=5, normalize=True)
# Arange images along y-axis
image_grid = torch.cat((real_A, fake_B, real_B, fake_A), 1)
save_image(image_grid,
"images/%s/%s.png" % (opt.dataset_name, batches_done),
normalize=False)
# ----------
# Training
# ----------
prev_time = time.time()
for epoch in range(opt.epoch, opt.n_epochs):
for i, batch in enumerate(dataloader):
# Set model input
real_A = Variable(batch["A"].type(Tensor))
real_B = Variable(batch["B"].type(Tensor))
# Adversarial ground truths
valid = Variable(Tensor(
np.ones((real_A.size(0), *D_A.output_shape))),
requires_grad=False)
fake = Variable(Tensor(
np.zeros((real_A.size(0), *D_A.output_shape))),
requires_grad=False)
# ------------------
# Train Generators
# ------------------
G_AB.train()
G_BA.train()
optimizer_G.zero_grad()
# Identity loss
loss_id_A = criterion_identity(G_BA(real_A), real_A)
loss_id_B = criterion_identity(G_AB(real_B), real_B)
loss_identity = (loss_id_A + loss_id_B) / 2
# GAN loss
fake_B = G_AB(real_A)
loss_GAN_AB = criterion_GAN(D_B(fake_B), valid)
fake_A = G_BA(real_B)
loss_GAN_BA = criterion_GAN(D_A(fake_A), valid)
loss_GAN = (loss_GAN_AB + loss_GAN_BA) / 2
# Cycle loss
recov_A = G_BA(fake_B)
loss_cycle_A = criterion_cycle(recov_A, real_A)
recov_B = G_AB(fake_A)
loss_cycle_B = criterion_cycle(recov_B, real_B)
loss_cycle = (loss_cycle_A + loss_cycle_B) / 2
# Total loss
loss_G = loss_GAN + opt.lambda_cyc * loss_cycle + opt.lambda_id * loss_identity
loss_G.backward()
optimizer_G.step()
# -----------------------
# Train Discriminator A
# -----------------------
optimizer_D_A.zero_grad()
# Real loss
loss_real = criterion_GAN(D_A(real_A), valid)
# Fake loss (on batch of previously generated samples)
fake_A_ = fake_A_buffer.push_and_pop(fake_A)
loss_fake = criterion_GAN(D_A(fake_A_.detach()), fake)
# Total loss
loss_D_A = (loss_real + loss_fake) / 2
loss_D_A.backward()
optimizer_D_A.step()
# -----------------------
# Train Discriminator B
# -----------------------
optimizer_D_B.zero_grad()
# Real loss
loss_real = criterion_GAN(D_B(real_B), valid)
# Fake loss (on batch of previously generated samples)
fake_B_ = fake_B_buffer.push_and_pop(fake_B)
loss_fake = criterion_GAN(D_B(fake_B_.detach()), fake)
# Total loss
loss_D_B = (loss_real + loss_fake) / 2
loss_D_B.backward()
optimizer_D_B.step()
loss_D = (loss_D_A + loss_D_B) / 2
# --------------
# Log Progress
# --------------
# Determine approximate time left
batches_done = epoch * len(dataloader) + i
batches_left = opt.n_epochs * len(dataloader) - batches_done
time_left = datetime.timedelta(seconds=batches_left *
(time.time() - prev_time))
prev_time = time.time()
# Print log
sys.stdout.write(
"\r[Epoch %d/%d] [Batch %d/%d] [D loss: %f] [G loss: %f, adv: %f, cycle: %f, identity: %f] ETA: %s"
% (
epoch,
opt.n_epochs,
i,
len(dataloader),
loss_D.item(),
loss_G.item(),
loss_GAN.item(),
loss_cycle.item(),
loss_identity.item(),
time_left,
))
# If at sample interval save image
if batches_done % opt.sample_interval == 0:
sample_images(batches_done)
# Update learning rates
lr_scheduler_G.step()
lr_scheduler_D_A.step()
lr_scheduler_D_B.step()
if opt.checkpoint_interval != -1 and epoch % opt.checkpoint_interval == 0:
# Save model checkpoints
torch.save(
G_AB.state_dict(),
"saved_models/%s/G_AB_%d.pth" % (opt.dataset_name, epoch))
torch.save(
G_BA.state_dict(),
"saved_models/%s/G_BA_%d.pth" % (opt.dataset_name, epoch))
torch.save(
D_A.state_dict(),
"saved_models/%s/D_A_%d.pth" % (opt.dataset_name, epoch))
torch.save(
D_B.state_dict(),
"saved_models/%s/D_B_%d.pth" % (opt.dataset_name, epoch))
criterion_cycle = torch.nn.L1Loss()
criterion_identity = torch.nn.L1Loss()
if cuda:
G_AB = G_AB.cuda()
G_BA = G_BA.cuda()
D_A = D_A.cuda()
D_B = D_B.cuda()
criterion_GAN.cuda()
criterion_cycle.cuda()
criterion_identity.cuda()
if opt.epoch != 0:
# Load pretrained models
G_AB.load_state_dict(
torch.load('saved_models/%s/G_AB_%d.pth' %
(opt.model_name, opt.epoch)))
G_BA.load_state_dict(
torch.load('saved_models/%s/G_BA_%d.pth' %
(opt.model_name, opt.epoch)))
D_A.load_state_dict(
torch.load('saved_models/%s/D_A_%d.pth' % (opt.model_name, opt.epoch)))
D_B.load_state_dict(
torch.load('saved_models/%s/D_B_%d.pth' % (opt.model_name, opt.epoch)))
else:
# Initialize weights
G_AB.apply(weights_init_normal)
G_BA.apply(weights_init_normal)
D_A.apply(weights_init_normal)
D_B.apply(weights_init_normal)
def _define_generator(self, path_to_model):
gen = GeneratorResNet()
gen.load_state_dict(torch.load(path_to_model))
return gen
D_A = Discriminator(input_shape)
D_B = Discriminator(input_shape)
if cuda:
G_AB = G_AB.cuda()
G_BA = G_BA.cuda()
D_A = D_A.cuda()
D_B = D_B.cuda()
criterion_GAN.cuda()
criterion_cycle.cuda()
criterion_identity.cuda()
if opt.epoch != 0:
# Load pretrained models
G_AB.load_state_dict(
torch.load("saved_models/%s/G_AB_%d.pth" %
(opt.dataset_name, opt.epoch)))
G_BA.load_state_dict(
torch.load("saved_models/%s/G_BA_%d.pth" %
(opt.dataset_name, opt.epoch)))
D_A.load_state_dict(
torch.load("saved_models/%s/D_A_%d.pth" %
(opt.dataset_name, opt.epoch)))
D_B.load_state_dict(
torch.load("saved_models/%s/D_B_%d.pth" %
(opt.dataset_name, opt.epoch)))
else:
# Initialize weights
G_AB.apply(weights_init_normal)
G_BA.apply(weights_init_normal)
D_A.apply(weights_init_normal)