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# Copyright 2018 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 basic ops used in eager mode RevNet."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
from tensorflow.contrib.eager.python.examples.revnet import ops
tfe = tf.contrib.eager
class OpsTest(tf.test.TestCase):
def test_downsample(self):
"""Test `possible_down_sample` function with mock object."""
batch_size = 100
# NHWC format
x = tf.random_normal(shape=[batch_size, 32, 32, 3])
# HW doesn't change but number of features increased
y = ops.downsample(x, filters=5, strides=(1, 1), axis=3)
self.assertEqual(y.shape, [batch_size, 32, 32, 5])
# Feature map doesn't change but HW reduced
y = ops.downsample(x, filters=3, strides=(2, 2), axis=3)
self.assertEqual(y.shape, [batch_size, 16, 16, 3])
# Number of feature increased and HW reduced
y = ops.downsample(x, filters=5, strides=(2, 2), axis=3)
self.assertEqual(y.shape, [batch_size, 16, 16, 5])
# Test gradient flow
x = tf.random_normal(shape=[batch_size, 32, 32, 3])
with tfe.GradientTape() as tape:
tape.watch(x)
y = ops.downsample(x, filters=3, strides=(1, 1))
self.assertEqual(y.shape, x.shape)
dy = tf.random_normal(shape=[batch_size, 3, 32, 32])
grad, = tape.gradient(y, [x], output_gradients=[dy])
self.assertEqual(grad.shape, x.shape)
# Default NCHW format
if tf.test.is_gpu_available():
x = tf.random_normal(shape=[batch_size, 3, 32, 32])
# HW doesn't change but feature map reduced
y = ops.downsample(x, filters=5, strides=(1, 1))
self.assertEqual(y.shape, [batch_size, 5, 32, 32])
# Feature map doesn't change but HW reduced
y = ops.downsample(x, filters=3, strides=(2, 2))
self.assertEqual(y.shape, [batch_size, 3, 16, 16])
# Both feature map and HW reduced
y = ops.downsample(x, filters=5, strides=(2, 2))
self.assertEqual(y.shape, [batch_size, 5, 16, 16])
# Test gradient flow
x = tf.random_normal(shape=[batch_size, 3, 32, 32])
with tfe.GradientTape() as tape:
tape.watch(x)
y = ops.downsample(x, filters=3, strides=(1, 1))
self.assertEqual(y.shape, x.shape)
dy = tf.random_normal(shape=[batch_size, 3, 32, 32])
grad, = tape.gradient(y, [x], output_gradients=[dy])
self.assertEqual(grad.shape, x.shape)
if __name__ == '__main__':
tf.enable_eager_execution()
tf.test.main()
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