While completing the first c++ tutorial with recent libtorch, I hit the resize warning described here : DCGAN C++ warning after PyTorch update · Issue #819 · pytorch/examples · GitHub
One of the comments mentions reshaping with fake_labels.size() (instead of batch_size), which I promptly tried. However, the training results seem underwhelming compared to the expected results posted in the guide (trained 60 epochs). Here are some beginner questions related to this.
- It’s unclear to me how the batch size differs from fake_labels.size(). Are we talking only portability / code robustness here, or would they ever differ?
- Assuming I got it right and I can simply think of tensors’ data as multi-dimentional arrays (to start), how does one reshape their array dimensions without data loss?
- Can I investigate the loaded dataset schema? This tutorial uses MNIST (characters), but the memory layout / loaded data is a black-box, which makes it really difficult to understand how the batching works. Can you print / reflect / debug dataset schemas?
Any links to other documentation appreciated, thank you!
The tut : Using the PyTorch C++ Frontend — PyTorch Tutorials 2.0.0+cu117 documentation
My reshape changes :
// Train discriminator with real images.
discriminator->zero_grad();
torch::Tensor real_images = batch.data.to(device);
torch::Tensor real_labels = torch::empty(batch.data.size(0), device)
.uniform_(0.8, 1.0);
torch::Tensor real_output = discriminator->forward(real_images)
.reshape({ batch_size });
torch::Tensor d_loss_real
= torch::binary_cross_entropy(real_output, real_labels);
d_loss_real.backward();
// Train discriminator with fake images.
torch::Tensor noise = torch::randn(
{ batch.data.size(0), k_noise_size, 1, 1 }, device);
torch::Tensor fake_images = generator->forward(noise);
torch::Tensor fake_labels
= torch::zeros(batch.data.size(0), device);
torch::Tensor fake_output
= discriminator->forward(fake_images.detach())
.reshape(fake_labels.sizes());
torch::Tensor d_loss_fake
= torch::binary_cross_entropy(fake_output, fake_labels);
d_loss_fake.backward();
torch::Tensor d_loss = d_loss_real + d_loss_fake;
d_optimizer.step();
// Train generator.
generator->zero_grad();
fake_labels.fill_(1);
fake_output = discriminator->forward(fake_images)
.reshape(fake_labels.sizes());