Created
November 19, 2018 23:48
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model = dict() | |
optimizer = dict() | |
criterion = dict() | |
model['gen'] = UncertaintyNet(ndense=12, nconvs=8, growth_rate=8, scale=scale).cuda() | |
optimizer['gen'] = optim.Adam(model['gen'].parameters(), lr=lr, weight_decay=weight_decay) | |
criterion['gen'] = nn.L1Loss().cuda() | |
model['disc'] = Discriminator().cuda() | |
optimizer['disc'] = optim.Adam(model['disc'].parameters(), lr=lr, weight_decay=weight_decay) | |
criterion['disc'] = nn.BCELoss().cuda() | |
def gan_train(model, weightdir, epochs, dataloader, optimizer, criterion, scale, log_step=10): | |
lr_size = map(lambda x: x / scale, IMAGE_SIZE) | |
updown = transforms.Compose([transforms.ToPILImage(), | |
transforms.Resize(size=lr_size, interpolation=Image.BICUBIC), | |
transforms.Grayscale(), | |
transforms.ToTensor()]) | |
with tqdm(total=epochs, leave=False, dynamic_ncols=True, disable=True) as pbar: | |
for epoch in range(1, epochs + 1): | |
g_loss_avg = 0. | |
d_loss_avg = 0. | |
gd_loss_avg = 0. | |
for step, highres in enumerate(dataloader): | |
lowres = torch.FloatTensor(highres.size()[0], 1, lr_size[0], lr_size[1]) | |
for j in range(highres.size()[0]): | |
lowres[j] = updown(highres[j]) | |
highres = Variable(highres).cuda() | |
lowres = Variable(lowres).cuda() | |
# --- Train Discriminator Real --- | |
for p in model['disc'].parameters(): | |
p.requires_grad = True | |
model['disc'].zero_grad() | |
model['gen'].eval() | |
model['disc'].train() | |
real_targets = Variable(torch.ones(highres.size()[0]).cuda()) | |
real_output = model['disc'](highres) | |
real_loss = criterion['disc'](real_output.squeeze(), real_targets.squeeze()) | |
# --- Train Discriminator for Fake --- | |
gen_outputs = model['gen'](lowres) | |
gen_outputs = gen_outputs.detach() | |
fake_targets = Variable(torch.zeros(highres.size()[0]).cuda()) | |
fake_output = model['disc'](gen_outputs) # detach for speed concers | |
fake_loss = criterion['disc'](fake_output.squeeze(), fake_targets.squeeze()) | |
disc_loss = (real_loss + fake_loss) | |
disc_loss.backward(disc_loss) | |
d_loss_avg += disc_loss.data.cpu().numpy() * highres.size()[0] | |
optimizer['disc'].step() | |
# --- Train Generator --- | |
# --- Do not update Discriminator --- | |
for p in model['disc'].parameters(): | |
p.requires_grad = False | |
model['gen'].zero_grad() | |
model['gen'].train() | |
model['disc'].eval() | |
alpha = 10 | |
gen_outputs = model['gen'](lowres) | |
loss = criterion['gen'](gen_outputs, highres) | |
real_targets = Variable(torch.ones(highres.size()[0], 1).cuda()) | |
output = model['disc'](gen_outputs) | |
gen_loss = criterion['disc'](output, real_targets) | |
gd_loss_avg += gen_loss.data.cpu().numpy() * highres.size()[0] | |
gen_loss = gen_loss + alpha * loss | |
gen_loss.backward() | |
optimizer['gen'].step() | |
g_loss_avg += gen_loss.data.cpu().numpy() * highres.size()[0] | |
pbar.update() |
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