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# Copyright 2016 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 pooling layers."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
from tensorflow.python import keras
from tensorflow.python.eager import context
from tensorflow.python.framework import test_util as tf_test_util
from tensorflow.python.keras import testing_utils
from tensorflow.python.platform import test
from tensorflow.python.training import rmsprop
class GlobalPoolingTest(test.TestCase):
@tf_test_util.run_in_graph_and_eager_modes
def test_globalpooling_1d(self):
testing_utils.layer_test(keras.layers.pooling.GlobalMaxPooling1D,
input_shape=(3, 4, 5))
testing_utils.layer_test(keras.layers.pooling.GlobalMaxPooling1D,
kwargs={'data_format': 'channels_first'},
input_shape=(3, 4, 5))
testing_utils.layer_test(
keras.layers.pooling.GlobalAveragePooling1D, input_shape=(3, 4, 5))
testing_utils.layer_test(keras.layers.pooling.GlobalAveragePooling1D,
kwargs={'data_format': 'channels_first'},
input_shape=(3, 4, 5))
@tf_test_util.run_in_graph_and_eager_modes
def test_globalpooling_1d_masking_support(self):
model = keras.Sequential()
model.add(keras.layers.Masking(mask_value=0., input_shape=(3, 4)))
model.add(keras.layers.GlobalAveragePooling1D())
model.compile(loss='mae', optimizer=rmsprop.RMSPropOptimizer(0.001))
model_input = np.random.random((2, 3, 4))
model_input[0, 1:, :] = 0
output = model.predict(model_input)
self.assertAllClose(output[0], model_input[0, 0, :])
@tf_test_util.run_in_graph_and_eager_modes
def test_globalpooling_2d(self):
testing_utils.layer_test(
keras.layers.pooling.GlobalMaxPooling2D,
kwargs={'data_format': 'channels_first'},
input_shape=(3, 4, 5, 6))
testing_utils.layer_test(
keras.layers.pooling.GlobalMaxPooling2D,
kwargs={'data_format': 'channels_last'},
input_shape=(3, 5, 6, 4))
testing_utils.layer_test(
keras.layers.pooling.GlobalAveragePooling2D,
kwargs={'data_format': 'channels_first'},
input_shape=(3, 4, 5, 6))
testing_utils.layer_test(
keras.layers.pooling.GlobalAveragePooling2D,
kwargs={'data_format': 'channels_last'},
input_shape=(3, 5, 6, 4))
@tf_test_util.run_in_graph_and_eager_modes
def test_globalpooling_3d(self):
testing_utils.layer_test(
keras.layers.pooling.GlobalMaxPooling3D,
kwargs={'data_format': 'channels_first'},
input_shape=(3, 4, 3, 4, 3))
testing_utils.layer_test(
keras.layers.pooling.GlobalMaxPooling3D,
kwargs={'data_format': 'channels_last'},
input_shape=(3, 4, 3, 4, 3))
testing_utils.layer_test(
keras.layers.pooling.GlobalAveragePooling3D,
kwargs={'data_format': 'channels_first'},
input_shape=(3, 4, 3, 4, 3))
testing_utils.layer_test(
keras.layers.pooling.GlobalAveragePooling3D,
kwargs={'data_format': 'channels_last'},
input_shape=(3, 4, 3, 4, 3))
class Pooling2DTest(test.TestCase):
@tf_test_util.run_in_graph_and_eager_modes
def test_maxpooling_2d(self):
pool_size = (3, 3)
for strides in [(1, 1), (2, 2)]:
testing_utils.layer_test(
keras.layers.MaxPooling2D,
kwargs={
'strides': strides,
'padding': 'valid',
'pool_size': pool_size
},
input_shape=(3, 5, 6, 4))
@tf_test_util.run_in_graph_and_eager_modes
def test_averagepooling_2d(self):
testing_utils.layer_test(
keras.layers.AveragePooling2D,
kwargs={'strides': (2, 2),
'padding': 'same',
'pool_size': (2, 2)},
input_shape=(3, 5, 6, 4))
testing_utils.layer_test(
keras.layers.AveragePooling2D,
kwargs={'strides': (2, 2),
'padding': 'valid',
'pool_size': (3, 3)},
input_shape=(3, 5, 6, 4))
# This part of the test can only run on GPU but doesn't appear
# to be properly assigned to a GPU when running in eager mode.
if not context.executing_eagerly():
# Only runs on GPU with CUDA, channels_first is not supported on CPU.
# TODO(b/62340061): Support channels_first on CPU.
if test.is_gpu_available(cuda_only=True):
testing_utils.layer_test(
keras.layers.AveragePooling2D,
kwargs={
'strides': (1, 1),
'padding': 'valid',
'pool_size': (2, 2),
'data_format': 'channels_first'
},
input_shape=(3, 4, 5, 6))
class Pooling3DTest(test.TestCase):
@tf_test_util.run_in_graph_and_eager_modes
def test_maxpooling_3d(self):
pool_size = (3, 3, 3)
testing_utils.layer_test(
keras.layers.MaxPooling3D,
kwargs={'strides': 2,
'padding': 'valid',
'pool_size': pool_size},
input_shape=(3, 11, 12, 10, 4))
testing_utils.layer_test(
keras.layers.MaxPooling3D,
kwargs={
'strides': 3,
'padding': 'valid',
'data_format': 'channels_first',
'pool_size': pool_size
},
input_shape=(3, 4, 11, 12, 10))
@tf_test_util.run_in_graph_and_eager_modes
def test_averagepooling_3d(self):
pool_size = (3, 3, 3)
testing_utils.layer_test(
keras.layers.AveragePooling3D,
kwargs={'strides': 2,
'padding': 'valid',
'pool_size': pool_size},
input_shape=(3, 11, 12, 10, 4))
testing_utils.layer_test(
keras.layers.AveragePooling3D,
kwargs={
'strides': 3,
'padding': 'valid',
'data_format': 'channels_first',
'pool_size': pool_size
},
input_shape=(3, 4, 11, 12, 10))
class Pooling1DTest(test.TestCase):
@tf_test_util.run_in_graph_and_eager_modes
def test_maxpooling_1d(self):
for padding in ['valid', 'same']:
for stride in [1, 2]:
testing_utils.layer_test(
keras.layers.MaxPooling1D,
kwargs={'strides': stride,
'padding': padding},
input_shape=(3, 5, 4))
testing_utils.layer_test(
keras.layers.MaxPooling1D,
kwargs={'data_format': 'channels_first'},
input_shape=(3, 2, 6))
@tf_test_util.run_in_graph_and_eager_modes
def test_averagepooling_1d(self):
for padding in ['valid', 'same']:
for stride in [1, 2]:
testing_utils.layer_test(
keras.layers.AveragePooling1D,
kwargs={'strides': stride,
'padding': padding},
input_shape=(3, 5, 4))
testing_utils.layer_test(
keras.layers.AveragePooling1D,
kwargs={'data_format': 'channels_first'},
input_shape=(3, 2, 6))
if __name__ == '__main__':
test.main()
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