Why doesn’t the in-place operation on leaf variables in PyTorch optimizers cause an error?
When I want to do in-place operation on leaf variable, I get an error:
How can I convert images to 1-bit tensor and use them for To reduce RAM and GPU usage and training in PyTorch?
I created a code using the PyTorch library, and I am training with .png images that are normally 24-32 bits. To reduce RAM and GPU usage, I converted the images to 1-bit (keeping their size fixed at 512×512). However, there was no change in the training time. I have included the code; please review it and provide feedback.
Download location of Pytorch
RuntimeError: PytorchStreamReader failed reading zip archive: failed finding central directory
Why am I getting “RuntimeError: Trying to backward through the graph a second time”?
My code:
Why am I getting “RuntimeError: Trying to backward through the graph a second time”?
My code:
Why am I getting “RuntimeError: Trying to backward through the graph a second time”?
My code:
How does multidimensional input to a nn.Linear layer work?
When sending a mulditimensional tensor to a nn.Linear layer, how does it work in practice? Does it just process the input vector by vector, or does it actually perform matrix multiplication over the whole input at once?
Torch backward PowBackward0 causes nan gradient where it shouldn’t
I have a pytorch tensor with NaN inside, when I calculate the loss function using a simple MSE Loss the gradient becomes NaN even if I mask out the NaN values.
Torch backward PowBackward0 causes nan gradient where it shouldn’t
I have a pytorch tensor with NaN inside, when I calculate the loss function using a simple MSE Loss the gradient becomes NaN even if I mask out the NaN values.
Torch backward PowBackward0 causes nan gradient where it shouldn’t
I have a pytorch tensor with NaN inside, when I calculate the loss function using a simple MSE Loss the gradient becomes NaN even if I mask out the NaN values.