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bugfix aspect ratio sampling in transforms.RandomErasing #3344

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Merged
merged 9 commits into from
Feb 9, 2021
Merged

bugfix aspect ratio sampling in transforms.RandomErasing #3344

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2f28380
aspect ratio must be a sampling from log scale.
Feb 3, 2021
b3c3498
add random erasing unittest code
Feb 3, 2021
78f129f
Increased threshold for difference in sampling rate
Feb 3, 2021
97fddf0
Merge branch 'master' of https://github.com/pytorch/vision
Feb 4, 2021
6478124
Merge branch 'master' into master
wbaek Feb 4, 2021
ec52ff4
Merge branch 'master' into master
datumbox Feb 4, 2021
f7c88f4
move outside of the loop
Feb 9, 2021
561b7b0
log_ratio move outside of the loop. RandomResizedCrop also
Feb 9, 2021
8a8dba4
Merge branch 'master' into master
wbaek Feb 9, 2021
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19 changes: 19 additions & 0 deletions test/test_transforms.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1955,6 +1955,25 @@ def test_autoaugment(self):
img = transform(img)
transform.__repr__()

def test_random_erasing(self):
img = torch.ones(3, 128, 128)

t = transforms.RandomErasing(scale=(0.1, 0.1), ratio=(1. / 3., 3. / 1.))
y, x, h, w, v = t.get_params(img, t.scale, t.ratio, [t.value, ])
aspect_ratio = h / w
self.assertTrue(aspect_ratio > 1. / 3. and aspect_ratio < 3. / 1.)

aspect_ratios = []
random.seed(42)
trial = 1000
for _ in range(trial):
y, x, h, w, v = t.get_params(img, t.scale, t.ratio, [t.value, ])
aspect_ratios.append(h / w)

count_bigger_then_ones = len([1 for aspect_ratio in aspect_ratios if aspect_ratio > 1])
count_smaller_then_ones = len([1 for aspect_ratio in aspect_ratios if aspect_ratio < 1])
self.assertAlmostEqual(count_bigger_then_ones / trial, count_smaller_then_ones / trial, 1)


if __name__ == '__main__':
unittest.main()
7 changes: 5 additions & 2 deletions torchvision/transforms/transforms.py
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Original file line number Diff line number Diff line change
Expand Up @@ -828,9 +828,9 @@ def get_params(
width, height = F._get_image_size(img)
area = height * width

log_ratio = torch.log(torch.tensor(ratio))
for _ in range(10):
target_area = area * torch.empty(1).uniform_(scale[0], scale[1]).item()
log_ratio = torch.log(torch.tensor(ratio))
aspect_ratio = torch.exp(
torch.empty(1).uniform_(log_ratio[0], log_ratio[1])
).item()
Expand Down Expand Up @@ -1576,9 +1576,12 @@ def get_params(
img_c, img_h, img_w = img.shape[-3], img.shape[-2], img.shape[-1]
area = img_h * img_w

log_ratio = torch.log(torch.tensor(ratio))
for _ in range(10):
erase_area = area * torch.empty(1).uniform_(scale[0], scale[1]).item()
aspect_ratio = torch.empty(1).uniform_(ratio[0], ratio[1]).item()
aspect_ratio = torch.exp(
torch.empty(1).uniform_(log_ratio[0], log_ratio[1])
).item()

h = int(round(math.sqrt(erase_area * aspect_ratio)))
w = int(round(math.sqrt(erase_area / aspect_ratio)))
Expand Down