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| # Copyright 2023 The TensorFlow Authors. All Rights Reserved. | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| """Tests for RevNet.""" | |
| # Import libraries | |
| from absl.testing import parameterized | |
| import tensorflow as tf, tf_keras | |
| from official.vision.modeling.backbones import revnet | |
| class RevNetTest(parameterized.TestCase, tf.test.TestCase): | |
| def test_network_creation(self, input_size, model_id, | |
| endpoint_filter_scale): | |
| """Test creation of RevNet family models.""" | |
| tf_keras.backend.set_image_data_format('channels_last') | |
| network = revnet.RevNet(model_id=model_id) | |
| inputs = tf_keras.Input(shape=(input_size, input_size, 3), batch_size=1) | |
| endpoints = network(inputs) | |
| network.summary() | |
| self.assertAllEqual( | |
| [1, input_size / 2**2, input_size / 2**2, 128 * endpoint_filter_scale], | |
| endpoints['2'].shape.as_list()) | |
| self.assertAllEqual( | |
| [1, input_size / 2**3, input_size / 2**3, 256 * endpoint_filter_scale], | |
| endpoints['3'].shape.as_list()) | |
| self.assertAllEqual( | |
| [1, input_size / 2**4, input_size / 2**4, 512 * endpoint_filter_scale], | |
| endpoints['4'].shape.as_list()) | |
| self.assertAllEqual( | |
| [1, input_size / 2**5, input_size / 2**5, 832 * endpoint_filter_scale], | |
| endpoints['5'].shape.as_list()) | |
| def test_input_specs(self, input_dim): | |
| """Test different input feature dimensions.""" | |
| tf_keras.backend.set_image_data_format('channels_last') | |
| input_specs = tf_keras.layers.InputSpec(shape=[None, None, None, input_dim]) | |
| network = revnet.RevNet(model_id=56, input_specs=input_specs) | |
| inputs = tf_keras.Input(shape=(128, 128, input_dim), batch_size=1) | |
| _ = network(inputs) | |
| def test_serialize_deserialize(self): | |
| # Create a network object that sets all of its config options. | |
| kwargs = dict( | |
| model_id=56, | |
| activation='relu', | |
| use_sync_bn=False, | |
| norm_momentum=0.99, | |
| norm_epsilon=0.001, | |
| kernel_initializer='VarianceScaling', | |
| kernel_regularizer=None, | |
| ) | |
| network = revnet.RevNet(**kwargs) | |
| expected_config = dict(kwargs) | |
| self.assertEqual(network.get_config(), expected_config) | |
| # Create another network object from the first object's config. | |
| new_network = revnet.RevNet.from_config(network.get_config()) | |
| # Validate that the config can be forced to JSON. | |
| _ = new_network.to_json() | |
| # If the serialization was successful, the new config should match the old. | |
| self.assertAllEqual(network.get_config(), new_network.get_config()) | |
| if __name__ == '__main__': | |
| tf.test.main() | |