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Add epsilon for tolerance level to fix flaky test random_resize_crop #15098

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May 31, 2019
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Add epsilon for tolerance level to fix flaky test random_resize_crop #15098

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5 changes: 3 additions & 2 deletions tests/python/unittest/test_image.py
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Original file line number Diff line number Diff line change
Expand Up @@ -356,17 +356,18 @@ def test_det_augmenters(self):
pass

@with_seed()
@unittest.skip('Flaky test. Skipping until a solution can be found. Tracked by https://github.com/apache/incubator-mxnet/issues/14718')
def test_random_size_crop(self):
# test aspect ratio within bounds
width = np.random.randint(100, 500)
height = np.random.randint(100, 500)
src = np.random.rand(height, width, 3) * 255.
ratio = (0.75, 1)
epsilon = 0.05
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how do we decide epsilon? is 0.05 too large here?

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Thanks @stu1130 for the discussion offline. The randomness is caused by sqrt of area / or * ratio. Same epsilon is used in pytorch test as well. Approve this change thereby.

out, (x0, y0, new_w, new_h) = mx.image.random_size_crop(mx.nd.array(src), size=(width, height), area=0.08, ratio=ratio)
_, pts = mx.image.center_crop(mx.nd.array(src), size=(width, height))
if (x0, y0, new_w, new_h) != pts:
assert ratio[0] <= float(new_w)/new_h <= ratio[1]
assert ratio[0] - epsilon <= float(new_w)/new_h <= ratio[1] + epsilon, \
'ration of new width and height out of the bound{}/{}={}'.format(new_w, new_h, float(new_w)/new_h)


if __name__ == '__main__':
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