0d7620c3bc
Also add swish activation to keras.activations (used by EfficientNets). PiperOrigin-RevId: 286614744 Change-Id: Ieba8b1f47735bdbb31c4efc84f45f763b9daa9a4
142 lines
5.1 KiB
Python
142 lines
5.1 KiB
Python
# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
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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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"""Integration tests for Keras applications."""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from absl.testing import parameterized
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from tensorflow.python.keras import backend
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from tensorflow.python.keras.applications import densenet
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from tensorflow.python.keras.applications import efficientnet
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from tensorflow.python.keras.applications import inception_resnet_v2
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from tensorflow.python.keras.applications import inception_v3
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from tensorflow.python.keras.applications import mobilenet
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from tensorflow.python.keras.applications import mobilenet_v2
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from tensorflow.python.keras.applications import nasnet
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from tensorflow.python.keras.applications import resnet
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from tensorflow.python.keras.applications import resnet_v2
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from tensorflow.python.keras.applications import vgg16
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from tensorflow.python.keras.applications import vgg19
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from tensorflow.python.keras.applications import xception
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from tensorflow.python.platform import test
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MODEL_LIST_NO_NASNET = [
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(resnet.ResNet50, 2048),
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(resnet.ResNet101, 2048),
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(resnet.ResNet152, 2048),
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(resnet_v2.ResNet50V2, 2048),
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(resnet_v2.ResNet101V2, 2048),
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(resnet_v2.ResNet152V2, 2048),
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(vgg16.VGG16, 512),
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(vgg19.VGG19, 512),
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(xception.Xception, 2048),
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(inception_v3.InceptionV3, 2048),
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(inception_resnet_v2.InceptionResNetV2, 1536),
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(mobilenet.MobileNet, 1024),
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(mobilenet_v2.MobileNetV2, 1280),
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(densenet.DenseNet121, 1024),
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(densenet.DenseNet169, 1664),
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(densenet.DenseNet201, 1920),
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(efficientnet.EfficientNetB0, 1280),
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(efficientnet.EfficientNetB1, 1280),
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(efficientnet.EfficientNetB2, 1408),
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(efficientnet.EfficientNetB3, 1536),
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(efficientnet.EfficientNetB4, 1792),
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(efficientnet.EfficientNetB5, 2048),
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(efficientnet.EfficientNetB6, 2304),
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(efficientnet.EfficientNetB7, 2560),
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]
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NASNET_LIST = [
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(nasnet.NASNetMobile, 1056),
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(nasnet.NASNetLarge, 4032),
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]
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MODEL_LIST = MODEL_LIST_NO_NASNET + NASNET_LIST
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class ApplicationsTest(test.TestCase, parameterized.TestCase):
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def assertShapeEqual(self, shape1, shape2):
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if len(shape1) != len(shape2):
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raise AssertionError(
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'Shapes are different rank: %s vs %s' % (shape1, shape2))
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for v1, v2 in zip(shape1, shape2):
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if v1 != v2:
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raise AssertionError('Shapes differ: %s vs %s' % (shape1, shape2))
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@parameterized.parameters(*MODEL_LIST)
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def test_application_base(self, app, _):
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# Can be instantiated with default arguments
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model = app(weights=None)
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# Can be serialized and deserialized
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config = model.get_config()
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reconstructed_model = model.__class__.from_config(config)
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self.assertEqual(len(model.weights), len(reconstructed_model.weights))
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backend.clear_session()
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@parameterized.parameters(*MODEL_LIST)
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def test_application_notop(self, app, last_dim):
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if 'NASNet' in app.__name__:
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only_check_last_dim = True
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else:
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only_check_last_dim = False
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output_shape = _get_output_shape(
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lambda: app(weights=None, include_top=False))
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if only_check_last_dim:
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self.assertEqual(output_shape[-1], last_dim)
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else:
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self.assertShapeEqual(output_shape, (None, None, None, last_dim))
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backend.clear_session()
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@parameterized.parameters(MODEL_LIST)
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def test_application_pooling(self, app, last_dim):
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output_shape = _get_output_shape(
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lambda: app(weights=None, include_top=False, pooling='avg'))
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self.assertShapeEqual(output_shape, (None, last_dim))
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@parameterized.parameters(*MODEL_LIST_NO_NASNET)
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def test_application_variable_input_channels(self, app, last_dim):
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if backend.image_data_format() == 'channels_first':
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input_shape = (1, None, None)
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else:
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input_shape = (None, None, 1)
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output_shape = _get_output_shape(
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lambda: app(weights=None, include_top=False, input_shape=input_shape))
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self.assertShapeEqual(output_shape, (None, None, None, last_dim))
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backend.clear_session()
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if backend.image_data_format() == 'channels_first':
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input_shape = (4, None, None)
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else:
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input_shape = (None, None, 4)
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output_shape = _get_output_shape(
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lambda: app(weights=None, include_top=False, input_shape=input_shape))
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self.assertShapeEqual(output_shape, (None, None, None, last_dim))
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backend.clear_session()
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def _get_output_shape(model_fn):
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model = model_fn()
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return model.output_shape
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if __name__ == '__main__':
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test.main()
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