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.cpu().numpy() -> .numpy() (#1594)

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* .cpu().numpy() -> .numpy()

* restore ops_torch

* restore test_speed_v_torch
This commit is contained in:
Yixiang Gao
2023-08-21 11:53:29 -05:00
committed by GitHub
parent 35bf21276f
commit 8d6662a741
18 changed files with 84 additions and 198 deletions
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6
examples/mnist_gan.py
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@@ -59,7 +59,7 @@ def train_discriminator(optimizer, data_real, data_fake):
loss_real.backward()
loss_fake.backward()
optimizer.step()
return (loss_real + loss_fake).cpu().numpy()
return (loss_real + loss_fake).numpy()
def train_generator(optimizer, data_fake):
real_labels = make_labels(batch_size, 1)
@@ -68,7 +68,7 @@ def train_generator(optimizer, data_fake):
loss = (output * real_labels).mean()
loss.backward()
optimizer.step()
return loss.cpu().numpy()
return loss.numpy()
if __name__ == "__main__":
# data for training and validation
@@ -100,7 +100,7 @@ if __name__ == "__main__":
data_fake = generator.forward(noise)
loss_g += train_generator(optim_g, data_fake)
if (epoch + 1) % sample_interval == 0:
fake_images = generator.forward(ds_noise).detach().cpu().numpy()
fake_images = generator.forward(ds_noise).detach().numpy()
fake_images = (fake_images.reshape(-1, 1, 28, 28) + 1) / 2 # 0 - 1 range.
save_image(make_grid(torch.tensor(fake_images)), output_dir / f"image_{epoch+1}.jpg")
t.set_description(f"Generator loss: {loss_g/n_steps}, Discriminator loss: {loss_d/n_steps}")