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Added <if __name__ == '__main__'> to avoid RuntimeError #201

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2 changes: 1 addition & 1 deletion .gitignore
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Original file line number Diff line number Diff line change
Expand Up @@ -3,7 +3,7 @@
*.h5
*.hdf5
.DS_Store

.idea
data/*/
implementations/*/data
implementations/*/images
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185 changes: 93 additions & 92 deletions implementations/dualgan/dualgan.py
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Expand Up @@ -152,109 +152,110 @@ def sample_images(batches_done):
# Training
# ----------

batches_done = 0
prev_time = time.time()
for epoch in range(opt.n_epochs):
for i, batch in enumerate(dataloader):
if __name__ == "__main__":
batches_done = 0
prev_time = time.time()
for epoch in range(opt.n_epochs):
for i, batch in enumerate(dataloader):

# Configure input
imgs_A = Variable(batch["A"].type(FloatTensor))
imgs_B = Variable(batch["B"].type(FloatTensor))
# Configure input
imgs_A = Variable(batch["A"].type(FloatTensor))
imgs_B = Variable(batch["B"].type(FloatTensor))

# ----------------------
# Train Discriminators
# ----------------------
# ----------------------
# Train Discriminators
# ----------------------

optimizer_D_A.zero_grad()
optimizer_D_B.zero_grad()
optimizer_D_A.zero_grad()
optimizer_D_B.zero_grad()

# Generate a batch of images
fake_A = G_BA(imgs_B).detach()
fake_B = G_AB(imgs_A).detach()
# Generate a batch of images
fake_A = G_BA(imgs_B).detach()
fake_B = G_AB(imgs_A).detach()

# ----------
# Domain A
# ----------
# ----------
# Domain A
# ----------

# Compute gradient penalty for improved wasserstein training
gp_A = compute_gradient_penalty(D_A, imgs_A.data, fake_A.data)
# Adversarial loss
D_A_loss = -torch.mean(D_A(imgs_A)) + torch.mean(D_A(fake_A)) + lambda_gp * gp_A

# ----------
# Domain B
# ----------

# Compute gradient penalty for improved wasserstein training
gp_B = compute_gradient_penalty(D_B, imgs_B.data, fake_B.data)
# Adversarial loss
D_B_loss = -torch.mean(D_B(imgs_B)) + torch.mean(D_B(fake_B)) + lambda_gp * gp_B

# Total loss
D_loss = D_A_loss + D_B_loss

D_loss.backward()
optimizer_D_A.step()
optimizer_D_B.step()

if i % opt.n_critic == 0:

# ------------------
# Train Generators
# ------------------

optimizer_G.zero_grad()

# Translate images to opposite domain
fake_A = G_BA(imgs_B)
fake_B = G_AB(imgs_A)
# Compute gradient penalty for improved wasserstein training
gp_A = compute_gradient_penalty(D_A, imgs_A.data, fake_A.data)
# Adversarial loss
D_A_loss = -torch.mean(D_A(imgs_A)) + torch.mean(D_A(fake_A)) + lambda_gp * gp_A

# Reconstruct images
recov_A = G_BA(fake_B)
recov_B = G_AB(fake_A)
# ----------
# Domain B
# ----------

# Compute gradient penalty for improved wasserstein training
gp_B = compute_gradient_penalty(D_B, imgs_B.data, fake_B.data)
# Adversarial loss
G_adv = -torch.mean(D_A(fake_A)) - torch.mean(D_B(fake_B))
# Cycle loss
G_cycle = cycle_loss(recov_A, imgs_A) + cycle_loss(recov_B, imgs_B)
D_B_loss = -torch.mean(D_B(imgs_B)) + torch.mean(D_B(fake_B)) + lambda_gp * gp_B

# Total loss
G_loss = lambda_adv * G_adv + lambda_cycle * G_cycle

G_loss.backward()
optimizer_G.step()

# --------------
# Log Progress
# --------------

# Determine approximate time left
batches_left = opt.n_epochs * len(dataloader) - batches_done
time_left = datetime.timedelta(seconds=batches_left * (time.time() - prev_time) / opt.n_critic)
prev_time = time.time()

sys.stdout.write(
"\r[Epoch %d/%d] [Batch %d/%d] [D loss: %f] [G loss: %f, cycle: %f] ETA: %s"
% (
epoch,
opt.n_epochs,
i,
len(dataloader),
D_loss.item(),
G_adv.data.item(),
G_cycle.item(),
time_left,
D_loss = D_A_loss + D_B_loss

D_loss.backward()
optimizer_D_A.step()
optimizer_D_B.step()

if i % opt.n_critic == 0:

# ------------------
# Train Generators
# ------------------

optimizer_G.zero_grad()

# Translate images to opposite domain
fake_A = G_BA(imgs_B)
fake_B = G_AB(imgs_A)

# Reconstruct images
recov_A = G_BA(fake_B)
recov_B = G_AB(fake_A)

# Adversarial loss
G_adv = -torch.mean(D_A(fake_A)) - torch.mean(D_B(fake_B))
# Cycle loss
G_cycle = cycle_loss(recov_A, imgs_A) + cycle_loss(recov_B, imgs_B)
# Total loss
G_loss = lambda_adv * G_adv + lambda_cycle * G_cycle

G_loss.backward()
optimizer_G.step()

# --------------
# Log Progress
# --------------

# Determine approximate time left
batches_left = opt.n_epochs * len(dataloader) - batches_done
time_left = datetime.timedelta(seconds=batches_left * (time.time() - prev_time) / opt.n_critic)
prev_time = time.time()

sys.stdout.write(
"\r[Epoch %d/%d] [Batch %d/%d] [D loss: %f] [G loss: %f, cycle: %f] ETA: %s"
% (
epoch,
opt.n_epochs,
i,
len(dataloader),
D_loss.item(),
G_adv.data.item(),
G_cycle.item(),
time_left,
)
)
)

# Check sample interval => save sample if there
if batches_done % opt.sample_interval == 0:
sample_images(batches_done)
# Check sample interval => save sample if there
if batches_done % opt.sample_interval == 0:
sample_images(batches_done)

batches_done += 1
batches_done += 1

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))
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))