"manually" creating a channels-last tensor

vision
1

Howdy! I am relatively fresh to channels last memory format. I’ve spent time reading around, and I don’t think my question has been answered yet.

I am doing some object detection: (a batch of) grayscale image(s) in, to a (batch of) tensor(s) of shape (prediction dim, H, W) out, where H and W specify the “prediction grid” shape, and prediction dim has bounding box and class information.

With results from a batch of images, I naturally have to do a lot of indexing along the prediction grid (i.e. I want to know the result at H = 12, W = 15). Right now, I index the result tensor like this: res[:, 12, 15]. However, I think this is probably slower than if I had the prediction dim last, like res[12,15,:]. So in a sense, I want channels last.

I get the result tensor from a conv net. Right now, if setting memory_format to channels_last also set the shape of the tensor, I could then gladly index the result in the “channels last” format like above. However, channels_last simply sets the stride.

Is it possible to manually set the shape as I desire (e.g. a permute of the input image into channels last) and override the designation of the memory format to channels last? If I do a permute and then to(channels_last), the stride changes, and I don’t quite get what I want.

Thanks!

2

I don’t know what exactly you want to do with the result tensor but wouldn’t a simple permute(...).contiguous() and indexing work? The channels_last memory format is used by some layers, such as conv layers, for performance reason but in case you are only concerned about indexing specific values a “manual” permutation could work.

3

Ah OK, thank you. I suppose I don’t quite understand the semantics of how Pytorch handles channels_last memory format. You’re right, the permute(...).contiguous() works. Thanks!