.normalize(imagenet_stats) will silently add channels

After a lot of debugging, I realized that calling .normalize(imagenet_stats) on a 1-channel image dataset will silently turn the images into 3-channel. I understand that the imagenet_stats are 3-channel, but I seems like throwing an error would be a better user experience.

Would an error like that make sense in this case? - I’m happy to try to add it and file a PR.

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I disagree. ImageNet has 3-channel images so if you are normalizing with those, you a kind of implying that you want 3-channel images. If not, you should normalize with custom channel stats.

Also, most pretrained models take in 3-channel images, so it is helpful to convert them directly to 3-channel images. Otherwise, you will have to adjust the first layer of your pretrained model.

I agree with almost everything you wrote, but not your conclusion. Normalizing a 1-channel image with 3-channel stats shouldn’t just work, you should be warned or an error should be thrown. If you are normalizing a 1-channel image with imagenet-stats, you are probably making an error.

Anyway, I’ll leave it to the group - thanks for the reply!

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I agree, you should know when you are doing something wrong and without an error (or at least a warning) people may not even realize their image is converted to 3-channel.

Problem is that a channel dimension of 1 is compatible with a channel dimension of 3 in the normalizing stats because that’s a broadcasting rule. So it’s normal you don’t get an error + get automatic conversion to 3 channels.

Maybe at the time you call normalize, we could try to load one batch and return a warning if the stats have 3 channels but the images only one.

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I also read the image normalization provides almost no improvements so you can avoid it. What is the benefit of using normalization if you use BatchNorms?

Calling normalize() without specifying any parameters also seems to add extra channels silently. Is this expected behavior? I have a Tensor of shape (1, 24, 24) and calling normalize() converted it to (4, 24, 24). Unfortunately I spent the whole day trying to debug where I’d gone wrong with my code. :sweat_smile:

This confused me as well, FWIW.