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71 lines
2.7 KiB
Python
71 lines
2.7 KiB
Python
# LICENSE HEADER MANAGED BY add-license-header
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#
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# Copyright 2018 Kornia Team
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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import pytest
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import torch
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import kornia
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import kornia.augmentation as K
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from testing.augmentation.utils import reproducibility_test
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class TestSequential:
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@pytest.mark.parametrize("random_apply_weights", [None, [0.8, 0.9]])
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def test_exception(self, random_apply_weights, device, dtype):
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inp = torch.randn(1, 3, 30, 30, device=device, dtype=dtype)
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with pytest.raises(Exception): # AssertError and NotImplementedError
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K.ImageSequential(
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K.ColorJiggle(0.1, 0.1, 0.1, 0.1, p=1.0), random_apply_weights=random_apply_weights
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).inverse(inp)
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@pytest.mark.parametrize("same_on_batch", [True, False, None])
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@pytest.mark.parametrize("keepdim", [True, False, None])
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@pytest.mark.parametrize("random_apply", [1, (2, 2), (1, 2), (2,), 20, True, False])
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def test_construction(self, same_on_batch, keepdim, random_apply):
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aug = K.ImageSequential(
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K.ColorJiggle(0.1, 0.1, 0.1, 0.1, p=1.0),
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K.RandomAffine(360, p=1.0),
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K.RandomMixUpV2(p=1.0),
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same_on_batch=same_on_batch,
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keepdim=keepdim,
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random_apply=random_apply,
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)
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aug.same_on_batch = True
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aug.keepdim = True
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for m in aug.children():
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assert m.same_on_batch is True, m.same_on_batch
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assert m.keepdim is True, m.keepdim
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@pytest.mark.parametrize("random_apply", [1, (2, 2), (1, 2), (2,), 10, True, False])
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def test_forward(self, random_apply, device, dtype):
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inp = torch.randn(1, 3, 30, 30, device=device, dtype=dtype)
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aug = K.ImageSequential(
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K.ColorJiggle(0.1, 0.1, 0.1, 0.1, p=1.0),
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kornia.filters.MedianBlur((3, 3)),
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K.ColorJiggle(0.1, 0.1, 0.1, 0.1, p=1.0),
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K.ImageSequential(K.ColorJiggle(0.1, 0.1, 0.1, 0.1, p=1.0)),
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K.ImageSequential(K.RandomAffine(360, p=1.0)),
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K.RandomAffine(360, p=1.0),
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K.RandomMixUpV2(p=1.0),
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random_apply=random_apply,
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)
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out = aug(inp)
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assert out.shape == inp.shape
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aug.inverse(inp)
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reproducibility_test(inp, aug)
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