74 lines
2.6 KiB
Python
74 lines
2.6 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 that film_efficientnet can detect an image of a cat."""
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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_efficientnet_encoder
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from skimage import data
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import tensorflow as tf
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class FilmEfficientnetTest(tf.test.TestCase, parameterized.TestCase):
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def _helper(self, include_film, model_variant):
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if model_variant == 'b0':
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size = 224
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fe = film_efficientnet_encoder.EfficientNetB0
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elif model_variant == 'b1':
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size = 240
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fe = film_efficientnet_encoder.EfficientNetB1
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elif model_variant == 'b2':
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size = 260
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fe = film_efficientnet_encoder.EfficientNetB2
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elif model_variant == 'b3':
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size = 300
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fe = film_efficientnet_encoder.EfficientNetB3
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elif model_variant == 'b4':
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size = 380
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fe = film_efficientnet_encoder.EfficientNetB4
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elif model_variant == 'b5':
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size = 456
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fe = film_efficientnet_encoder.EfficientNetB5
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elif model_variant == 'b6':
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size = 528
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fe = film_efficientnet_encoder.EfficientNetB6
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elif model_variant == 'b7':
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size = 600
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fe = film_efficientnet_encoder.EfficientNetB7
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else:
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raise ValueError(f'Unknown variant: {model_variant}')
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fe = fe(include_top=True, weights='imagenet', include_film=include_film)
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image = np.expand_dims(data.chelsea(), axis=0)
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image = tf.image.resize(image, (size, size))
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context = np.random.randn(1, 512)
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if include_film:
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eff_output = fe(
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(film_efficientnet_encoder.preprocess_input(image), context),
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training=False)
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else:
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eff_output = fe(
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film_efficientnet_encoder.preprocess_input(image), training=False)
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film_preds = film_efficientnet_encoder.decode_predictions(
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eff_output.numpy(), top=10)
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self.assertIn('tabby', [f[1] for f in film_preds[0]])
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@parameterized.parameters([True, False])
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def test_keras_equivalence_b3(self, include_film):
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self._helper(include_film, 'b3')
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if __name__ == '__main__':
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tf.test.main()
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