xception_test.py

# 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.
# ==============================================================================
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import tensorflow as tf

import xception

slim = tf.contrib.slim


class XceptionTest(tf.test.TestCase):

  def testBuild(self):
    batch_size = 5
    height, width = 299, 299
    num_classes = 1001
    with self.test_session():
      inputs = tf.random_uniform((batch_size, height, width, 3))
      logits, end_points = xception.xception(inputs, num_classes)

      #Entry Flow
      self.assertEquals(end_points['Xception/block1_res_conv'].get_shape().as_list(), [5, 74, 74 ,128])
      self.assertEquals(end_points['Xception/block2_res_conv'].get_shape().as_list(), [5, 37, 37, 256])
      self.assertEquals(end_points['Xception/block3_res_conv'].get_shape().as_list(), [5, 19, 19, 728])

      #Mid Flow
      self.assertEquals(end_points['Xception/block5_dws_conv3'].get_shape().as_list(), [5, 19, 19, 728])
      self.assertEquals(end_points['Xception/block6_dws_conv3'].get_shape().as_list(), [5, 19, 19, 728])
      self.assertEquals(end_points['Xception/block7_dws_conv3'].get_shape().as_list(), [5, 19, 19, 728])
      self.assertEquals(end_points['Xception/block8_dws_conv3'].get_shape().as_list(), [5, 19, 19, 728])
      self.assertEquals(end_points['Xception/block9_dws_conv3'].get_shape().as_list(), [5, 19, 19, 728])
      self.assertEquals(end_points['Xception/block10_dws_conv3'].get_shape().as_list(), [5, 19, 19, 728])
      self.assertEquals(end_points['Xception/block11_dws_conv3'].get_shape().as_list(), [5, 19, 19, 728])
      self.assertEquals(end_points['Xception/block12_dws_conv3'].get_shape().as_list(), [5, 19, 19, 728])
      self.assertEquals(end_points['Xception/block12_res_conv'].get_shape().as_list(), [5, 10, 10, 1024])

      #Exit Flow
      self.assertEquals(end_points['Xception/block14_dws_conv1'].get_shape().as_list(), [5, 10, 10, 1536])
      self.assertEquals(end_points['Xception/block14_dws_conv2'].get_shape().as_list(), [5, 10, 10, 2048])
      self.assertEquals(end_points['Xception/block15_avg_pool'].get_shape().as_list(), [5, 1, 1, 2048])
      self.assertEquals(end_points['Xception/block15_conv1'].get_shape().as_list(), [5, 1, 1, 2048])
      self.assertEquals(end_points['Xception/block15_conv2'].get_shape().as_list(), [5, 1, 1, 1001])

      #Check outputs
      self.assertListEqual(logits.get_shape().as_list(), [batch_size, num_classes])
      self.assertListEqual(end_points['Predictions'].get_shape().as_list(), [batch_size, num_classes])


  def testEvaluation(self):
    batch_size = 1
    height, width = 299, 299
    num_classes = 1001
    with self.test_session():
      eval_inputs = tf.random_uniform((batch_size, height, width, 3))
      logits, _ = xception.xception(eval_inputs, is_training=False)
      self.assertListEqual(logits.get_shape().as_list(),
                           [batch_size, num_classes])
      predictions = tf.argmax(logits, 1)
      self.assertListEqual(predictions.get_shape().as_list(), [batch_size])


  def testForward(self):
    batch_size = 1
    height, width = 299, 299
    with self.test_session() as sess:
      inputs = tf.random_uniform((batch_size, height, width, 3))
      logits, _ = xception.xception(inputs)
      sess.run(tf.global_variables_initializer())
      output = sess.run(logits)
      self.assertTrue(output.any())

if __name__ == '__main__':
  tf.test.main()