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[Fix] flip_ratio in RandomAffine pipeline is inverted #799

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merged 5 commits into from
Mar 24, 2022
Merged

[Fix] flip_ratio in RandomAffine pipeline is inverted #799

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224e100
fix flip_ratio bug
wdmwhh Mar 17, 2022
f8cbb69
add some notes to modification
wangruohui Mar 19, 2022
457b1ff
modify center, add unittests
wangruohui Mar 19, 2022
dff7dab
clean test codes
wangruohui Mar 23, 2022
cd1328d
clean test codes
wangruohui Mar 23, 2022
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8 changes: 6 additions & 2 deletions mmedit/datasets/pipelines/augmentation.py
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Original file line number Diff line number Diff line change
Expand Up @@ -447,7 +447,11 @@ def _get_params(degrees, translate, scale_ranges, shears, flip_ratio,
else:
shear = 0.0

flip = (np.random.rand(2) < flip_ratio).astype(np.int32) * 2 - 1
# Because `flip` is used as a multiplier in line 479 and 480,
# so -1 stands for flip and 1 stands for no flip. Thus `flip`
# should be an 'inverse' flag as the result of the comparison.
# See https://github.com/open-mmlab/mmediting/pull/799 for more detail
flip = (np.random.rand(2) > flip_ratio).astype(np.int32) * 2 - 1

return angle, translations, scale, shear, flip

Expand Down Expand Up @@ -521,7 +525,7 @@ def __call__(self, results):
params = self._get_params(self.degrees, self.translate, self.scale,
self.shear, self.flip_ratio, (h, w))

center = (w * 0.5 + 0.5, h * 0.5 + 0.5)
center = (w * 0.5 - 0.5, h * 0.5 - 0.5)
M = self._get_inverse_affine_matrix(center, *params)
M = np.array(M).reshape((2, 3))

Expand Down
21 changes: 21 additions & 0 deletions tests/test_data/test_pipelines/test_augmentation.py
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Original file line number Diff line number Diff line change
Expand Up @@ -265,7 +265,28 @@ def test_random_affine(self):

target_keys = ['fg', 'alpha']

# Test identical transformation
alpha = np.random.rand(4, 4).astype(np.float32)
fg = np.random.rand(4, 4).astype(np.float32)
results = dict(alpha=alpha, fg=fg)
random_affine = RandomAffine(['fg', 'alpha'],
degrees=0, flip_ratio=0.0)
random_affine_results = random_affine(results)
assert np.allclose(alpha, random_affine_results['alpha'])
assert np.allclose(fg, random_affine_results['fg'])

# Test flip in both direction
alpha = np.random.rand(4, 4).astype(np.float32)
fg = np.random.rand(4, 4).astype(np.float32)
results = dict(alpha=alpha, fg=fg)
random_affine = RandomAffine(['fg', 'alpha'],
degrees=0, flip_ratio=1.0)
random_affine_results = random_affine(results)
assert np.allclose(alpha[::-1, ::-1], random_affine_results['alpha'])
assert np.allclose(fg[::-1, ::-1], random_affine_results['fg'])

# test random affine with different valid setting combinations
# only shape are tested
alpha = np.random.rand(240, 320).astype(np.float32)
fg = np.random.rand(240, 320).astype(np.float32)
results = dict(alpha=alpha, fg=fg)
Expand Down