code: # coding=utf-8
  # 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 slim.nets.vgg."""
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  from tf_slim.nets import vgg
  from tf_slim.ops import variables as variables_lib
  # pylint:disable=g-direct-tensorflow-import
  from tensorflow.python.framework import ops
  from tensorflow.python.ops import math_ops
  from tensorflow.python.ops import random_ops
  from tensorflow.python.ops import variable_scope
  from tensorflow.python.ops import variables
  from tensorflow.python.platform import test
  # pylint:enable=g-direct-tensorflow-import
  
  
  class VGGATest(test.TestCase):
  
    def testBuild(self):
      batch_size = 5
      height, width = 224, 224
      num_classes = 1000
      with self.cached_session():
        inputs = random_ops.random_uniform((batch_size, height, width, 3))
        logits, _ = vgg.vgg_a(inputs, num_classes)
        self.assertEquals(logits.op.name, 'vgg_a/fc8/squeezed')
        self.assertListEqual(logits.get_shape().as_list(),
                             [batch_size, num_classes])
  
    def testFullyConvolutional(self):
      batch_size = 1
      height, width = 256, 256
      num_classes = 1000
      with self.cached_session():
        inputs = random_ops.random_uniform((batch_size, height, width, 3))
        logits, _ = vgg.vgg_a(inputs, num_classes, spatial_squeeze=False)
        self.assertEquals(logits.op.name, 'vgg_a/fc8/BiasAdd')
        self.assertListEqual(logits.get_shape().as_list(),
                             [batch_size, 2, 2, num_classes])
  
    def testEndPoints(self):
      batch_size = 5
      height, width = 224, 224
      num_classes = 1000
      for is_training in [True, False]:
        with ops.Graph().as_default():
          inputs = random_ops.random_uniform((batch_size, height, width, 3))
          _, end_points = vgg.vgg_a(inputs, num_classes, is_training=is_training)
          expected_names = [
              'vgg_a/conv1/conv1_1', 'vgg_a/pool1', 'vgg_a/conv2/conv2_1',
              'vgg_a/pool2', 'vgg_a/conv3/conv3_1', 'vgg_a/conv3/conv3_2',
              'vgg_a/pool3', 'vgg_a/conv4/conv4_1', 'vgg_a/conv4/conv4_2',
              'vgg_a/pool4', 'vgg_a/conv5/conv5_1', 'vgg_a/conv5/conv5_2',
              'vgg_a/pool5', 'vgg_a/fc6', 'vgg_a/fc7', 'vgg_a/fc8'
          ]
          self.assertSetEqual(set(end_points.keys()), set(expected_names))
  
    def testModelVariables(self):
      batch_size = 5
      height, width = 224, 224
      num_classes = 1000
      with self.cached_session():
        inputs = random_ops.random_uniform((batch_size, height, width, 3))
        vgg.vgg_a(inputs, num_classes)
        expected_names = [
            'vgg_a/conv1/conv1_1/weights',
            'vgg_a/conv1/conv1_1/biases',
            'vgg_a/conv2/conv2_1/weights',
            'vgg_a/conv2/conv2_1/biases',
            'vgg_a/conv3/conv3_1/weights',
            'vgg_a/conv3/conv3_1/biases',
            'vgg_a/conv3/conv3_2/weights',
            'vgg_a/conv3/conv3_2/biases',
            'vgg_a/conv4/conv4_1/weights',
            'vgg_a/conv4/conv4_1/biases',
            'vgg_a/conv4/conv4_2/weights',
            'vgg_a/conv4/conv4_2/biases',
            'vgg_a/conv5/conv5_1/weights',
            'vgg_a/conv5/conv5_1/biases',
            'vgg_a/conv5/conv5_2/weights',
            'vgg_a/conv5/conv5_2/biases',
            'vgg_a/fc6/weights',
            'vgg_a/fc6/biases',
            'vgg_a/fc7/weights',
            'vgg_a/fc7/biases',
            'vgg_a/fc8/weights',
            'vgg_a/fc8/biases',
        ]
        model_variables = [v.op.name for v in variables_lib.get_model_variables()]
        self.assertSetEqual(set(model_variables), set(expected_names))
  
    def testEvaluation(self):
      batch_size = 2
      height, width = 224, 224
      num_classes = 1000
      with self.cached_session():
        eval_inputs = random_ops.random_uniform((batch_size, height, width, 3))
        logits, _ = vgg.vgg_a(eval_inputs, is_training=False)
        self.assertListEqual(logits.get_shape().as_list(),
                             [batch_size, num_classes])
        predictions = math_ops.argmax(logits, 1)
        self.assertListEqual(predictions.get_shape().as_list(), [batch_size])
  
    def testTrainEvalWithReuse(self):
      train_batch_size = 2
      eval_batch_size = 1
      train_height, train_width = 224, 224
      eval_height, eval_width = 256, 256
      num_classes = 1000
      with self.cached_session():
        train_inputs = random_ops.random_uniform(
            (train_batch_size, train_height, train_width, 3))
        logits, _ = vgg.vgg_a(train_inputs)
        self.assertListEqual(logits.get_shape().as_list(),
                             [train_batch_size, num_classes])
        variable_scope.get_variable_scope().reuse_variables()
        eval_inputs = random_ops.random_uniform(
            (eval_batch_size, eval_height, eval_width, 3))
        logits, _ = vgg.vgg_a(
            eval_inputs, is_training=False, spatial_squeeze=False)
        self.assertListEqual(logits.get_shape().as_list(),
                             [eval_batch_size, 2, 2, num_classes])
        logits = math_ops.reduce_mean(logits, [1, 2])
        predictions = math_ops.argmax(logits, 1)
        self.assertEquals(predictions.get_shape().as_list(), [eval_batch_size])
  
    def testForward(self):
      batch_size = 1
      height, width = 224, 224
      with self.cached_session() as sess:
        inputs = random_ops.random_uniform((batch_size, height, width, 3))
        logits, _ = vgg.vgg_a(inputs)
        sess.run(variables.global_variables_initializer())
        output = sess.run(logits)
        self.assertTrue(output.any())
  
  
  class VGG16Test(test.TestCase):
  
    def testBuild(self):
      batch_size = 5
      height, width = 224, 224
      num_classes = 1000
      with self.cached_session():
        inputs = random_ops.random_uniform((batch_size, height, width, 3))
        logits, _ = vgg.vgg_16(inputs, num_classes)
        self.assertEquals(logits.op.name, 'vgg_16/fc8/squeezed')
        self.assertListEqual(logits.get_shape().as_list(),
                             [batch_size, num_classes])
  
    def testFullyConvolutional(self):
      batch_size = 1
      height, width = 256, 256
      num_classes = 1000
      with self.cached_session():
        inputs = random_ops.random_uniform((batch_size, height, width, 3))
        logits, _ = vgg.vgg_16(inputs, num_classes, spatial_squeeze=False)
        self.assertEquals(logits.op.name, 'vgg_16/fc8/BiasAdd')
        self.assertListEqual(logits.get_shape().as_list(),
                             [batch_size, 2, 2, num_classes])
  
    def testEndPoints(self):
      batch_size = 5
      height, width = 224, 224
      num_classes = 1000
      for is_training in [True, False]:
        with ops.Graph().as_default():
          inputs = random_ops.random_uniform((batch_size, height, width, 3))
          _, end_points = vgg.vgg_16(inputs, num_classes, is_training=is_training)
          expected_names = [
              'vgg_16/conv1/conv1_1', 'vgg_16/conv1/conv1_2', 'vgg_16/pool1',
              'vgg_16/conv2/conv2_1', 'vgg_16/conv2/conv2_2', 'vgg_16/pool2',
              'vgg_16/conv3/conv3_1', 'vgg_16/conv3/conv3_2',
              'vgg_16/conv3/conv3_3', 'vgg_16/pool3', 'vgg_16/conv4/conv4_1',
              'vgg_16/conv4/conv4_2', 'vgg_16/conv4/conv4_3', 'vgg_16/pool4',
              'vgg_16/conv5/conv5_1', 'vgg_16/conv5/conv5_2',
              'vgg_16/conv5/conv5_3', 'vgg_16/pool5', 'vgg_16/fc6', 'vgg_16/fc7',
              'vgg_16/fc8'
          ]
          self.assertSetEqual(set(end_points.keys()), set(expected_names))
  
    def testModelVariables(self):
      batch_size = 5
      height, width = 224, 224
      num_classes = 1000
      with self.cached_session():
        inputs = random_ops.random_uniform((batch_size, height, width, 3))
        vgg.vgg_16(inputs, num_classes)
        expected_names = [
            'vgg_16/conv1/conv1_1/weights',
            'vgg_16/conv1/conv1_1/biases',
            'vgg_16/conv1/conv1_2/weights',
            'vgg_16/conv1/conv1_2/biases',
            'vgg_16/conv2/conv2_1/weights',
            'vgg_16/conv2/conv2_1/biases',
            'vgg_16/conv2/conv2_2/weights',
            'vgg_16/conv2/conv2_2/biases',
            'vgg_16/conv3/conv3_1/weights',
            'vgg_16/conv3/conv3_1/biases',
            'vgg_16/conv3/conv3_2/weights',
            'vgg_16/conv3/conv3_2/biases',
            'vgg_16/conv3/conv3_3/weights',
            'vgg_16/conv3/conv3_3/biases',
            'vgg_16/conv4/conv4_1/weights',
            'vgg_16/conv4/conv4_1/biases',
            'vgg_16/conv4/conv4_2/weights',
            'vgg_16/conv4/conv4_2/biases',
            'vgg_16/conv4/conv4_3/weights',
            'vgg_16/conv4/conv4_3/biases',
            'vgg_16/conv5/conv5_1/weights',
            'vgg_16/conv5/conv5_1/biases',
            'vgg_16/conv5/conv5_2/weights',
            'vgg_16/conv5/conv5_2/biases',
            'vgg_16/conv5/conv5_3/weights',
            'vgg_16/conv5/conv5_3/biases',
            'vgg_16/fc6/weights',
            'vgg_16/fc6/biases',
            'vgg_16/fc7/weights',
            'vgg_16/fc7/biases',
            'vgg_16/fc8/weights',
            'vgg_16/fc8/biases',
        ]
        model_variables = [v.op.name for v in variables_lib.get_model_variables()]
        self.assertSetEqual(set(model_variables), set(expected_names))
  
    def testEvaluation(self):
      batch_size = 2
      height, width = 224, 224
      num_classes = 1000
      with self.cached_session():
        eval_inputs = random_ops.random_uniform((batch_size, height, width, 3))
        logits, _ = vgg.vgg_16(eval_inputs, is_training=False)
        self.assertListEqual(logits.get_shape().as_list(),
                             [batch_size, num_classes])
        predictions = math_ops.argmax(logits, 1)
        self.assertListEqual(predictions.get_shape().as_list(), [batch_size])
  
    def testTrainEvalWithReuse(self):
      train_batch_size = 2
      eval_batch_size = 1
      train_height, train_width = 224, 224
      eval_height, eval_width = 256, 256
      num_classes = 1000
      with self.cached_session():
        train_inputs = random_ops.random_uniform(
            (train_batch_size, train_height, train_width, 3))
        logits, _ = vgg.vgg_16(train_inputs)
        self.assertListEqual(logits.get_shape().as_list(),
                             [train_batch_size, num_classes])
        variable_scope.get_variable_scope().reuse_variables()
        eval_inputs = random_ops.random_uniform(
            (eval_batch_size, eval_height, eval_width, 3))
        logits, _ = vgg.vgg_16(
            eval_inputs, is_training=False, spatial_squeeze=False)
        self.assertListEqual(logits.get_shape().as_list(),
                             [eval_batch_size, 2, 2, num_classes])
        logits = math_ops.reduce_mean(logits, [1, 2])
        predictions = math_ops.argmax(logits, 1)
        self.assertEquals(predictions.get_shape().as_list(), [eval_batch_size])
  
    def testForward(self):
      batch_size = 1
      height, width = 224, 224
      with self.cached_session() as sess:
        inputs = random_ops.random_uniform((batch_size, height, width, 3))
        logits, _ = vgg.vgg_16(inputs)
        sess.run(variables.global_variables_initializer())
        output = sess.run(logits)
        self.assertTrue(output.any())
  
  
  class VGG19Test(test.TestCase):
  
    def testBuild(self):
      batch_size = 5
      height, width = 224, 224
      num_classes = 1000
      with self.cached_session():
        inputs = random_ops.random_uniform((batch_size, height, width, 3))
        logits, _ = vgg.vgg_19(inputs, num_classes)
        self.assertEquals(logits.op.name, 'vgg_19/fc8/squeezed')
        self.assertListEqual(logits.get_shape().as_list(),
                             [batch_size, num_classes])
  
    def testFullyConvolutional(self):
      batch_size = 1
      height, width = 256, 256
      num_classes = 1000
      with self.cached_session():
        inputs = random_ops.random_uniform((batch_size, height, width, 3))
        logits, _ = vgg.vgg_19(inputs, num_classes, spatial_squeeze=False)
        self.assertEquals(logits.op.name, 'vgg_19/fc8/BiasAdd')
        self.assertListEqual(logits.get_shape().as_list(),
                             [batch_size, 2, 2, num_classes])
  
    def testEndPoints(self):
      batch_size = 5
      height, width = 224, 224
      num_classes = 1000
      for is_training in [True, False]:
        with ops.Graph().as_default():
          inputs = random_ops.random_uniform((batch_size, height, width, 3))
          _, end_points = vgg.vgg_19(inputs, num_classes, is_training=is_training)
          expected_names = [
              'vgg_19/conv1/conv1_1', 'vgg_19/conv1/conv1_2', 'vgg_19/pool1',
              'vgg_19/conv2/conv2_1', 'vgg_19/conv2/conv2_2', 'vgg_19/pool2',
              'vgg_19/conv3/conv3_1', 'vgg_19/conv3/conv3_2',
              'vgg_19/conv3/conv3_3', 'vgg_19/conv3/conv3_4', 'vgg_19/pool3',
              'vgg_19/conv4/conv4_1', 'vgg_19/conv4/conv4_2',
              'vgg_19/conv4/conv4_3', 'vgg_19/conv4/conv4_4', 'vgg_19/pool4',
              'vgg_19/conv5/conv5_1', 'vgg_19/conv5/conv5_2',
              'vgg_19/conv5/conv5_3', 'vgg_19/conv5/conv5_4', 'vgg_19/pool5',
              'vgg_19/fc6', 'vgg_19/fc7', 'vgg_19/fc8'
          ]
          self.assertSetEqual(set(end_points.keys()), set(expected_names))
  
    def testModelVariables(self):
      batch_size = 5
      height, width = 224, 224
      num_classes = 1000
      with self.cached_session():
        inputs = random_ops.random_uniform((batch_size, height, width, 3))
        vgg.vgg_19(inputs, num_classes)
        expected_names = [
            'vgg_19/conv1/conv1_1/weights',
            'vgg_19/conv1/conv1_1/biases',
            'vgg_19/conv1/conv1_2/weights',
            'vgg_19/conv1/conv1_2/biases',
            'vgg_19/conv2/conv2_1/weights',
            'vgg_19/conv2/conv2_1/biases',
            'vgg_19/conv2/conv2_2/weights',
            'vgg_19/conv2/conv2_2/biases',
            'vgg_19/conv3/conv3_1/weights',
            'vgg_19/conv3/conv3_1/biases',
            'vgg_19/conv3/conv3_2/weights',
            'vgg_19/conv3/conv3_2/biases',
            'vgg_19/conv3/conv3_3/weights',
            'vgg_19/conv3/conv3_3/biases',
            'vgg_19/conv3/conv3_4/weights',
            'vgg_19/conv3/conv3_4/biases',
            'vgg_19/conv4/conv4_1/weights',
            'vgg_19/conv4/conv4_1/biases',
            'vgg_19/conv4/conv4_2/weights',
            'vgg_19/conv4/conv4_2/biases',
            'vgg_19/conv4/conv4_3/weights',
            'vgg_19/conv4/conv4_3/biases',
            'vgg_19/conv4/conv4_4/weights',
            'vgg_19/conv4/conv4_4/biases',
            'vgg_19/conv5/conv5_1/weights',
            'vgg_19/conv5/conv5_1/biases',
            'vgg_19/conv5/conv5_2/weights',
            'vgg_19/conv5/conv5_2/biases',
            'vgg_19/conv5/conv5_3/weights',
            'vgg_19/conv5/conv5_3/biases',
            'vgg_19/conv5/conv5_4/weights',
            'vgg_19/conv5/conv5_4/biases',
            'vgg_19/fc6/weights',
            'vgg_19/fc6/biases',
            'vgg_19/fc7/weights',
            'vgg_19/fc7/biases',
            'vgg_19/fc8/weights',
            'vgg_19/fc8/biases',
        ]
        model_variables = [v.op.name for v in variables_lib.get_model_variables()]
        self.assertSetEqual(set(model_variables), set(expected_names))
  
    def testEvaluation(self):
      batch_size = 2
      height, width = 224, 224
      num_classes = 1000
      with self.cached_session():
        eval_inputs = random_ops.random_uniform((batch_size, height, width, 3))
        logits, _ = vgg.vgg_19(eval_inputs, is_training=False)
        self.assertListEqual(logits.get_shape().as_list(),
                             [batch_size, num_classes])
        predictions = math_ops.argmax(logits, 1)
        self.assertListEqual(predictions.get_shape().as_list(), [batch_size])
  
    def testTrainEvalWithReuse(self):
      train_batch_size = 2
      eval_batch_size = 1
      train_height, train_width = 224, 224
      eval_height, eval_width = 256, 256
      num_classes = 1000
      with self.cached_session():
        train_inputs = random_ops.random_uniform(
            (train_batch_size, train_height, train_width, 3))
        logits, _ = vgg.vgg_19(train_inputs)
        self.assertListEqual(logits.get_shape().as_list(),
                             [train_batch_size, num_classes])
        variable_scope.get_variable_scope().reuse_variables()
        eval_inputs = random_ops.random_uniform(
            (eval_batch_size, eval_height, eval_width, 3))
        logits, _ = vgg.vgg_19(
            eval_inputs, is_training=False, spatial_squeeze=False)
        self.assertListEqual(logits.get_shape().as_list(),
                             [eval_batch_size, 2, 2, num_classes])
        logits = math_ops.reduce_mean(logits, [1, 2])
        predictions = math_ops.argmax(logits, 1)
        self.assertEquals(predictions.get_shape().as_list(), [eval_batch_size])
  
    def testForward(self):
      batch_size = 1
      height, width = 224, 224
      with self.cached_session() as sess:
        inputs = random_ops.random_uniform((batch_size, height, width, 3))
        logits, _ = vgg.vgg_19(inputs)
        sess.run(variables.global_variables_initializer())
        output = sess.run(logits)
        self.assertTrue(output.any())
  
  
  if __name__ == '__main__':
    test.main()
  
apis: tensorflow.python.framework.ops.Graph tensorflow.python.ops.variables.global_variables_initializer tensorflow.python.platform.test.main tensorflow.python.ops.math_ops.reduce_mean tensorflow.python.ops.math_ops.argmax tensorflow.python.ops.random_ops.random_uniform tensorflow.python.ops.variable_scope.get_variable_scope