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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 FPN.""" | |
| # Import libraries | |
| from absl.testing import parameterized | |
| import tensorflow as tf, tf_keras | |
| from official.vision.modeling.backbones import mobilenet | |
| from official.vision.modeling.backbones import resnet | |
| from official.vision.modeling.decoders import fpn | |
| class FPNTest(parameterized.TestCase, tf.test.TestCase): | |
| def test_network_creation(self, input_size, min_level, max_level, | |
| use_separable_conv, use_keras_layer, fusion_type): | |
| """Test creation of FPN.""" | |
| tf_keras.backend.set_image_data_format('channels_last') | |
| inputs = tf_keras.Input(shape=(input_size, input_size, 3), batch_size=1) | |
| backbone = resnet.ResNet(model_id=50) | |
| network = fpn.FPN( | |
| input_specs=backbone.output_specs, | |
| min_level=min_level, | |
| max_level=max_level, | |
| fusion_type=fusion_type, | |
| use_separable_conv=use_separable_conv, | |
| use_keras_layer=use_keras_layer) | |
| endpoints = backbone(inputs) | |
| feats = network(endpoints) | |
| for level in range(min_level, max_level + 1): | |
| self.assertIn(str(level), feats) | |
| self.assertAllEqual( | |
| [1, input_size // 2**level, input_size // 2**level, 256], | |
| feats[str(level)].shape.as_list()) | |
| def test_network_creation_with_mobilenet(self, input_size, min_level, | |
| max_level, use_separable_conv, | |
| use_keras_layer): | |
| """Test creation of FPN with mobilenet backbone.""" | |
| tf_keras.backend.set_image_data_format('channels_last') | |
| inputs = tf_keras.Input(shape=(input_size, input_size, 3), batch_size=1) | |
| backbone = mobilenet.MobileNet(model_id='MobileNetV2') | |
| network = fpn.FPN( | |
| input_specs=backbone.output_specs, | |
| min_level=min_level, | |
| max_level=max_level, | |
| use_separable_conv=use_separable_conv, | |
| use_keras_layer=use_keras_layer) | |
| endpoints = backbone(inputs) | |
| feats = network(endpoints) | |
| for level in range(min_level, max_level + 1): | |
| self.assertIn(str(level), feats) | |
| self.assertAllEqual( | |
| [1, input_size // 2**level, input_size // 2**level, 256], | |
| feats[str(level)].shape.as_list()) | |
| def test_serialize_deserialize(self): | |
| # Create a network object that sets all of its config options. | |
| kwargs = dict( | |
| input_specs=resnet.ResNet(model_id=50).output_specs, | |
| min_level=3, | |
| max_level=7, | |
| num_filters=256, | |
| fusion_type='sum', | |
| use_separable_conv=False, | |
| use_keras_layer=False, | |
| use_sync_bn=False, | |
| activation='relu', | |
| norm_momentum=0.99, | |
| norm_epsilon=0.001, | |
| kernel_initializer='VarianceScaling', | |
| kernel_regularizer=None, | |
| bias_regularizer=None, | |
| ) | |
| network = fpn.FPN(**kwargs) | |
| expected_config = dict(kwargs) | |
| self.assertEqual(network.get_config(), expected_config) | |
| # Create another network object from the first object's config. | |
| new_network = fpn.FPN.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() | |