When using PyTorch’s torch.fft.rfft function, I observed that specifying an output tensor using the out parameter is slower than letting PyTorch manage the output internally. Here is a simple benchmark:

When using PyTorch’s torch.fft.rfft function, I observed that specifying an output tensor using the out parameter is slower than letting PyTorch manage the output internally. Here is a simple benchmark:

When using PyTorch’s torch.fft.rfft function, I observed that specifying an output tensor using the out parameter is slower than letting PyTorch manage the output internally. Here is a simple benchmark:

When using PyTorch’s torch.fft.rfft function, I observed that specifying an output tensor using the out parameter is slower than letting PyTorch manage the output internally. Here is a simple benchmark:

When using PyTorch’s torch.fft.rfft function, I observed that specifying an output tensor using the out parameter is slower than letting PyTorch manage the output internally. Here is a simple benchmark:

When using PyTorch’s torch.fft.rfft function, I observed that specifying an output tensor using the out parameter is slower than letting PyTorch manage the output internally. Here is a simple benchmark:

im trying to code an upsacaling gan.
I am using a random cnn for the discriminator but for the generator i found out about unet and try to give it a go.

im trying to code an upsacaling gan.
I am using a random cnn for the discriminator but for the generator i found out about unet and try to give it a go.

im trying to code an upsacaling gan.
I am using a random cnn for the discriminator but for the generator i found out about unet and try to give it a go.

When I want to do in-place operation on leaf variable, I get an error: