Skip to content

Change reusing of Variables in DCGAN #150

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
merged 1 commit into from
Jun 6, 2017
Merged

Change reusing of Variables in DCGAN #150

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
31 changes: 16 additions & 15 deletions dcgan/main.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -198,9 +198,6 @@ def forward(self, input):
input, label = input.cuda(), label.cuda()
noise, fixed_noise = noise.cuda(), fixed_noise.cuda()

input = Variable(input)
label = Variable(label)
noise = Variable(noise)
fixed_noise = Variable(fixed_noise)

# setup optimizer
Expand All @@ -216,21 +213,25 @@ def forward(self, input):
netD.zero_grad()
real_cpu, _ = data
batch_size = real_cpu.size(0)
input.data.resize_(real_cpu.size()).copy_(real_cpu)
label.data.resize_(batch_size).fill_(real_label)

output = netD(input)
errD_real = criterion(output, label)
if opt.cuda:
real_cpu = real_cpu.cuda()
input.resize_as_(real_cpu).copy_(real_cpu)
label.resize_(batch_size).fill_(real_label)
inputv = Variable(input)
labelv = Variable(label)

output = netD(inputv)
errD_real = criterion(output, labelv)
errD_real.backward()
D_x = output.data.mean()

# train with fake
noise.data.resize_(batch_size, nz, 1, 1)
noise.data.normal_(0, 1)
fake = netG(noise)
label.data.fill_(fake_label)
noise.resize_(batch_size, nz, 1, 1).normal_(0, 1)
noisev = Variable(noise)
fake = netG(noisev)
labelv = Variable(label.fill_(fake_label))
output = netD(fake.detach())
errD_fake = criterion(output, label)
errD_fake = criterion(output, labelv)
errD_fake.backward()
D_G_z1 = output.data.mean()
errD = errD_real + errD_fake
Expand All @@ -240,9 +241,9 @@ def forward(self, input):
# (2) Update G network: maximize log(D(G(z)))
###########################
netG.zero_grad()
label.data.fill_(real_label) # fake labels are real for generator cost
labelv = Variable(label.fill_(real_label)) # fake labels are real for generator cost
output = netD(fake)
errG = criterion(output, label)
errG = criterion(output, labelv)
errG.backward()
D_G_z2 = output.data.mean()
optimizerG.step()
Expand Down