41 lines
1.4 KiB
Python
41 lines
1.4 KiB
Python
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# Copyright 2022 Google LLC
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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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"""Tests for film_conditioning_layer."""
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from absl.testing import parameterized
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import numpy as np
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from robotics_transformer.film_efficientnet import film_conditioning_layer
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import tensorflow as tf
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class FilmConditioningLayerTest(tf.test.TestCase, parameterized.TestCase):
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@parameterized.parameters([2, 4])
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def test_film_conditioning_rank_two_and_four(self, conv_rank):
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batch = 2
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num_channels = 3
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if conv_rank == 2:
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conv_layer = np.random.randn(batch, num_channels)
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elif conv_rank == 4:
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conv_layer = np.random.randn(batch, 1, 1, num_channels)
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else:
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raise ValueError(f'Unexpected conv rank: {conv_rank}')
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context = np.random.rand(batch, num_channels)
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film_layer = film_conditioning_layer.FilmConditioning(num_channels)
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out = film_layer(conv_layer, context)
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tf.debugging.assert_rank(out, conv_rank)
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if __name__ == '__main__':
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tf.test.main()
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