Extra Cutout Operation in Preprocessing

This extra cutout operation is (deterministically for every image) done in the repository and not mentioned in the updated paper using RandAugment. Is the performance reported in the paper consistent with this implementation or the one specified in the paper?

https://github.com/google-research/uda/blob/6aabffa896458fb7806eba9d94fbfeca77f3b72e/image/preprocess.py#L274

More context:

for image in ori_images:
   chosen_policy = aug_policies[np.random.choice(
       len(aug_policies))]
   aug_image = augmentation_transforms.apply_policy(
       chosen_policy, image)
   aug_image = augmentation_transforms.cutout_numpy(aug_image)

[qizhex]

I think we might have a different understanding of “The default augmentations for all methods include flips, pad-and-crop and Cutout”. Do you mean that Cutout, flip & pad-and-crop are in the search space of RandAugment? It actually means that they apply Cutout, flip, pad-and-crop in addition to two random operations.

This is similar to what has been done in AutoAugment:

      epoch_policy = self.good_policies[np.random.choice(
          len(self.good_policies))]
      final_img = augmentation_transforms.apply_policy(
          epoch_policy, data)
      final_img = augmentation_transforms.random_flip(
          augmentation_transforms.zero_pad_and_crop(final_img, 4))
      # Apply cutout
      final_img = augmentation_transforms.cutout_numpy(final_img)

So this is what has been done in AutoAugment: two operations->pad_and_crop->flip->cutout.

[qizhex]

Yes. The order in this codebase should be two random ops -> cutout -> flip -> crop. The flip and crop is applied during training in data.py. We changed the order since we use random ops and cutout by numpy and PIL instead of building them into the TF computational graph. Flip and crop are applied during training so that the model sees more diverse images.