256 lines
8.7 KiB
Python
256 lines
8.7 KiB
Python
# Copyright 2019 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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"""Tests for imagenet_utils."""
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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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import numpy as np
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from tensorflow.python import keras
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from tensorflow.python.keras import keras_parameterized
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from tensorflow.python.keras.applications import imagenet_utils as utils
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from tensorflow.python.platform import test
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class TestImageNetUtils(keras_parameterized.TestCase):
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def test_preprocess_input(self):
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# Test invalid mode check
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x = np.random.uniform(0, 255, (10, 10, 3))
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with self.assertRaises(ValueError):
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utils.preprocess_input(x, mode='some_unknown_mode')
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# Test image batch with float and int image input
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x = np.random.uniform(0, 255, (2, 10, 10, 3))
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xint = x.astype('int32')
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self.assertEqual(utils.preprocess_input(x).shape, x.shape)
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self.assertEqual(utils.preprocess_input(xint).shape, xint.shape)
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out1 = utils.preprocess_input(x, 'channels_last')
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out1int = utils.preprocess_input(xint, 'channels_last')
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out2 = utils.preprocess_input(
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np.transpose(x, (0, 3, 1, 2)), 'channels_first')
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out2int = utils.preprocess_input(
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np.transpose(xint, (0, 3, 1, 2)), 'channels_first')
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self.assertAllClose(out1, out2.transpose(0, 2, 3, 1))
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self.assertAllClose(out1int, out2int.transpose(0, 2, 3, 1))
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# Test single image
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x = np.random.uniform(0, 255, (10, 10, 3))
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xint = x.astype('int32')
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self.assertEqual(utils.preprocess_input(x).shape, x.shape)
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self.assertEqual(utils.preprocess_input(xint).shape, xint.shape)
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out1 = utils.preprocess_input(x, 'channels_last')
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out1int = utils.preprocess_input(xint, 'channels_last')
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out2 = utils.preprocess_input(np.transpose(x, (2, 0, 1)), 'channels_first')
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out2int = utils.preprocess_input(
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np.transpose(xint, (2, 0, 1)), 'channels_first')
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self.assertAllClose(out1, out2.transpose(1, 2, 0))
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self.assertAllClose(out1int, out2int.transpose(1, 2, 0))
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# Test that writing over the input data works predictably
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for mode in ['torch', 'tf']:
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x = np.random.uniform(0, 255, (2, 10, 10, 3))
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xint = x.astype('int')
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x2 = utils.preprocess_input(x, mode=mode)
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xint2 = utils.preprocess_input(xint)
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self.assertAllClose(x, x2)
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self.assertNotEqual(xint.astype('float').max(), xint2.max())
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# Caffe mode works differently from the others
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x = np.random.uniform(0, 255, (2, 10, 10, 3))
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xint = x.astype('int')
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x2 = utils.preprocess_input(x, data_format='channels_last', mode='caffe')
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xint2 = utils.preprocess_input(xint)
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self.assertAllClose(x, x2[..., ::-1])
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self.assertNotEqual(xint.astype('float').max(), xint2.max())
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def test_preprocess_input_symbolic(self):
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# Test image batch
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x = np.random.uniform(0, 255, (2, 10, 10, 3))
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inputs = keras.layers.Input(shape=x.shape[1:])
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outputs = keras.layers.Lambda(
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utils.preprocess_input, output_shape=x.shape[1:])(
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inputs)
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model = keras.Model(inputs, outputs)
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self.assertEqual(model.predict(x).shape, x.shape)
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outputs1 = keras.layers.Lambda(
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lambda x: utils.preprocess_input(x, 'channels_last'),
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output_shape=x.shape[1:])(
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inputs)
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model1 = keras.Model(inputs, outputs1)
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out1 = model1.predict(x)
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x2 = np.transpose(x, (0, 3, 1, 2))
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inputs2 = keras.layers.Input(shape=x2.shape[1:])
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outputs2 = keras.layers.Lambda(
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lambda x: utils.preprocess_input(x, 'channels_first'),
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output_shape=x2.shape[1:])(
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inputs2)
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model2 = keras.Model(inputs2, outputs2)
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out2 = model2.predict(x2)
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self.assertAllClose(out1, out2.transpose(0, 2, 3, 1))
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# Test single image
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x = np.random.uniform(0, 255, (10, 10, 3))
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inputs = keras.layers.Input(shape=x.shape)
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outputs = keras.layers.Lambda(
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utils.preprocess_input, output_shape=x.shape)(
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inputs)
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model = keras.Model(inputs, outputs)
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self.assertEqual(model.predict(x[np.newaxis])[0].shape, x.shape)
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outputs1 = keras.layers.Lambda(
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lambda x: utils.preprocess_input(x, 'channels_last'),
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output_shape=x.shape)(
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inputs)
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model1 = keras.Model(inputs, outputs1)
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out1 = model1.predict(x[np.newaxis])[0]
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x2 = np.transpose(x, (2, 0, 1))
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inputs2 = keras.layers.Input(shape=x2.shape)
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outputs2 = keras.layers.Lambda(
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lambda x: utils.preprocess_input(x, 'channels_first'),
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output_shape=x2.shape)(
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inputs2)
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model2 = keras.Model(inputs2, outputs2)
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out2 = model2.predict(x2[np.newaxis])[0]
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self.assertAllClose(out1, out2.transpose(1, 2, 0))
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@parameterized.named_parameters([
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{'testcase_name': 'channels_last_format',
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'data_format': 'channels_last'},
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{'testcase_name': 'channels_first_format',
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'data_format': 'channels_first'},
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])
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def test_obtain_input_shape(self, data_format):
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# input_shape and default_size are not identical.
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with self.assertRaises(ValueError):
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utils.obtain_input_shape(
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input_shape=(224, 224, 3),
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default_size=299,
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min_size=139,
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data_format='channels_last',
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require_flatten=True,
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weights='imagenet')
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# Test invalid use cases
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shape = (139, 139)
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if data_format == 'channels_last':
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input_shape = shape + (99,)
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else:
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input_shape = (99,) + shape
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# input_shape is smaller than min_size.
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shape = (100, 100)
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if data_format == 'channels_last':
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input_shape = shape + (3,)
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else:
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input_shape = (3,) + shape
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with self.assertRaises(ValueError):
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utils.obtain_input_shape(
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input_shape=input_shape,
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default_size=None,
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min_size=139,
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data_format=data_format,
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require_flatten=False)
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# shape is 1D.
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shape = (100,)
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if data_format == 'channels_last':
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input_shape = shape + (3,)
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else:
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input_shape = (3,) + shape
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with self.assertRaises(ValueError):
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utils.obtain_input_shape(
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input_shape=input_shape,
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default_size=None,
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min_size=139,
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data_format=data_format,
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require_flatten=False)
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# the number of channels is 5 not 3.
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shape = (100, 100)
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if data_format == 'channels_last':
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input_shape = shape + (5,)
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else:
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input_shape = (5,) + shape
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with self.assertRaises(ValueError):
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utils.obtain_input_shape(
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input_shape=input_shape,
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default_size=None,
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min_size=139,
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data_format=data_format,
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require_flatten=False)
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# require_flatten=True with dynamic input shape.
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with self.assertRaises(ValueError):
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utils.obtain_input_shape(
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input_shape=None,
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default_size=None,
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min_size=139,
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data_format='channels_first',
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require_flatten=True)
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# test include top
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self.assertEqual(utils.obtain_input_shape(
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input_shape=(3, 200, 200),
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default_size=None,
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min_size=139,
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data_format='channels_first',
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require_flatten=True), (3, 200, 200))
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self.assertEqual(utils.obtain_input_shape(
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input_shape=None,
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default_size=None,
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min_size=139,
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data_format='channels_last',
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require_flatten=False), (None, None, 3))
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self.assertEqual(utils.obtain_input_shape(
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input_shape=None,
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default_size=None,
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min_size=139,
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data_format='channels_first',
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require_flatten=False), (3, None, None))
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self.assertEqual(utils.obtain_input_shape(
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input_shape=None,
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default_size=None,
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min_size=139,
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data_format='channels_last',
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require_flatten=False), (None, None, 3))
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self.assertEqual(utils.obtain_input_shape(
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input_shape=(150, 150, 3),
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default_size=None,
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min_size=139,
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data_format='channels_last',
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require_flatten=False), (150, 150, 3))
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self.assertEqual(utils.obtain_input_shape(
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input_shape=(3, None, None),
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default_size=None,
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min_size=139,
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data_format='channels_first',
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require_flatten=False), (3, None, None))
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if __name__ == '__main__':
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test.main()
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