[add] A PairRandomCrop for both input and target.#221
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Time1ess wants to merge 5 commits intopytorch:masterfrom
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[add] A PairRandomCrop for both input and target.#221Time1ess wants to merge 5 commits intopytorch:masterfrom
Time1ess wants to merge 5 commits intopytorch:masterfrom
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When we crop images in Semantic Segmentation and other related work, we want to crop both input image and its target image, however, RandomCrop generates a new random positon each time being called, PairRandomCrop instead, will use the same position twice.
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Hi, |
Contributor
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Hi, this functionality can now be achieved in your own dataset using the new functional interface in from torchvision.transforms import crop, RandomCrop
class MyDataset(Dataset):
def __init__(self, crop_size):
super(Dataset, self).__init__()
self.crop_size = crop_size
def __getitem__(self, idx):
img = self.get_img(idx)
target = self.get_target(idx)
i, j, h, w = RandomCrop.get_params(img, self.crop_size)
img = crop(img, i, j, h, w)
target = crop(img, i, j, h, w)
return img, target |
rajveerb
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Nov 30, 2023
* adding code for data generation * fix imports in run_expansion * more fixes, and added readme * More fixes
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RandomCrop does not support crop both image and target at same position, each time we call RandomCrop it will generate a new position which is not what we want.
This PairRandomCrop will remember the crop position of the image and use this same position to crop the target(vice versa).
It follows this idea:
A dict is used to remember the crop position of the image, key is the os.getpid() and value is the position.
So each time we crop a image, PairRandomCrop will generate a new position, when it comes to the target, it will pop and reuse the position.