def testNoBatchNormScaleByDefault(self):
        height, width = 299, 299
        num_classes = 1000
        inputs = tf.placeholder(tf.float32, (1, height, width, 3))
        with tf.contrib.slim.arg_scope(inception.inception_v4_arg_scope()):
            inception.inception_v4(inputs, num_classes, is_training=False)

        self.assertEqual(tf.global_variables('.*/BatchNorm/gamma:0$'), [])
    def testBatchNormScale(self):
        height, width = 299, 299
        num_classes = 1000
        inputs = tf.placeholder(tf.float32, (1, height, width, 3))
        with tf.contrib.slim.arg_scope(
                inception.inception_v4_arg_scope(batch_norm_scale=True)):
            inception.inception_v4(inputs, num_classes, is_training=False)

        gamma_names = set(
            v.op.name for v in tf.global_variables('.*/BatchNorm/gamma:0$'))
        self.assertGreater(len(gamma_names), 0)
        for v in tf.global_variables('.*/BatchNorm/moving_mean:0$'):
            self.assertIn(v.op.name[:-len('moving_mean')] + 'gamma',
                          gamma_names)
 def testVariablesSetDevice(self):
     batch_size = 5
     height, width = 299, 299
     num_classes = 1000
     inputs = tf.random_uniform((batch_size, height, width, 3))
     # Force all Variables to reside on the device.
     with tf.variable_scope('on_cpu'), tf.device('/cpu:0'):
         inception.inception_v4(inputs, num_classes)
     with tf.variable_scope('on_gpu'), tf.device('/gpu:0'):
         inception.inception_v4(inputs, num_classes)
     for v in tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES,
                                scope='on_cpu'):
         self.assertDeviceEqual(v.device, '/cpu:0')
     for v in tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES,
                                scope='on_gpu'):
         self.assertDeviceEqual(v.device, '/gpu:0')
 def testTrainEvalWithReuse(self):
     train_batch_size = 5
     eval_batch_size = 2
     height, width = 150, 150
     num_classes = 1000
     with self.test_session() as sess:
         train_inputs = tf.random_uniform(
             (train_batch_size, height, width, 3))
         inception.inception_v4(train_inputs, num_classes)
         eval_inputs = tf.random_uniform(
             (eval_batch_size, height, width, 3))
         logits, _ = inception.inception_v4(eval_inputs,
                                            num_classes,
                                            is_training=False,
                                            reuse=True)
         predictions = tf.argmax(logits, 1)
         sess.run(tf.global_variables_initializer())
         output = sess.run(predictions)
         self.assertEqual(output.shape, (eval_batch_size, ))
 def testBuildPreLogitsNetwork(self):
     batch_size = 5
     height, width = 299, 299
     num_classes = None
     inputs = tf.random_uniform((batch_size, height, width, 3))
     net, end_points = inception.inception_v4(inputs, num_classes)
     self.assertTrue(net.op.name.startswith('InceptionV4/Logits/AvgPool'))
     self.assertListEqual(net.get_shape().as_list(),
                          [batch_size, 1, 1, 1536])
     self.assertFalse('Logits' in end_points)
     self.assertFalse('Predictions' in end_points)
 def testBuildWithoutAuxLogits(self):
     batch_size = 5
     height, width = 299, 299
     num_classes = 1000
     inputs = tf.random_uniform((batch_size, height, width, 3))
     logits, endpoints = inception.inception_v4(inputs,
                                                num_classes,
                                                create_aux_logits=False)
     self.assertFalse('AuxLogits' in endpoints)
     self.assertTrue(logits.op.name.startswith('InceptionV4/Logits'))
     self.assertListEqual(logits.get_shape().as_list(),
                          [batch_size, num_classes])
 def testGlobalPool(self):
     batch_size = 1
     height, width = 350, 400
     num_classes = 1000
     inputs = tf.random_uniform((batch_size, height, width, 3))
     logits, end_points = inception.inception_v4(inputs, num_classes)
     self.assertTrue(logits.op.name.startswith('InceptionV4/Logits'))
     self.assertListEqual(logits.get_shape().as_list(),
                          [batch_size, num_classes])
     pre_pool = end_points['Mixed_7d']
     self.assertListEqual(pre_pool.get_shape().as_list(),
                          [batch_size, 9, 11, 1536])
 def testEvaluation(self):
     batch_size = 2
     height, width = 299, 299
     num_classes = 1000
     with self.test_session() as sess:
         eval_inputs = tf.random_uniform((batch_size, height, width, 3))
         logits, _ = inception.inception_v4(eval_inputs,
                                            num_classes,
                                            is_training=False)
         predictions = tf.argmax(logits, 1)
         sess.run(tf.global_variables_initializer())
         output = sess.run(predictions)
         self.assertEqual(output.shape, (batch_size, ))
 def testUnknownBatchSize(self):
     batch_size = 1
     height, width = 299, 299
     num_classes = 1000
     with self.test_session() as sess:
         inputs = tf.placeholder(tf.float32, (None, height, width, 3))
         logits, _ = inception.inception_v4(inputs, num_classes)
         self.assertTrue(logits.op.name.startswith('InceptionV4/Logits'))
         self.assertListEqual(logits.get_shape().as_list(),
                              [None, num_classes])
         images = tf.random_uniform((batch_size, height, width, 3))
         sess.run(tf.global_variables_initializer())
         output = sess.run(logits, {inputs: images.eval()})
         self.assertEqual(output.shape, (batch_size, num_classes))
 def testAllEndPointsShapes(self):
     batch_size = 5
     height, width = 299, 299
     num_classes = 1000
     inputs = tf.random_uniform((batch_size, height, width, 3))
     _, end_points = inception.inception_v4(inputs, num_classes)
     endpoints_shapes = {
         'Conv2d_1a_3x3': [batch_size, 149, 149, 32],
         'Conv2d_2a_3x3': [batch_size, 147, 147, 32],
         'Conv2d_2b_3x3': [batch_size, 147, 147, 64],
         'Mixed_3a': [batch_size, 73, 73, 160],
         'Mixed_4a': [batch_size, 71, 71, 192],
         'Mixed_5a': [batch_size, 35, 35, 384],
         # 4 x Inception-A blocks
         'Mixed_5b': [batch_size, 35, 35, 384],
         'Mixed_5c': [batch_size, 35, 35, 384],
         'Mixed_5d': [batch_size, 35, 35, 384],
         'Mixed_5e': [batch_size, 35, 35, 384],
         # Reduction-A block
         'Mixed_6a': [batch_size, 17, 17, 1024],
         # 7 x Inception-B blocks
         'Mixed_6b': [batch_size, 17, 17, 1024],
         'Mixed_6c': [batch_size, 17, 17, 1024],
         'Mixed_6d': [batch_size, 17, 17, 1024],
         'Mixed_6e': [batch_size, 17, 17, 1024],
         'Mixed_6f': [batch_size, 17, 17, 1024],
         'Mixed_6g': [batch_size, 17, 17, 1024],
         'Mixed_6h': [batch_size, 17, 17, 1024],
         # Reduction-A block
         'Mixed_7a': [batch_size, 8, 8, 1536],
         # 3 x Inception-C blocks
         'Mixed_7b': [batch_size, 8, 8, 1536],
         'Mixed_7c': [batch_size, 8, 8, 1536],
         'Mixed_7d': [batch_size, 8, 8, 1536],
         # Logits and predictions
         'AuxLogits': [batch_size, num_classes],
         'global_pool': [batch_size, 1, 1, 1536],
         'PreLogitsFlatten': [batch_size, 1536],
         'Logits': [batch_size, num_classes],
         'Predictions': [batch_size, num_classes]
     }
     self.assertItemsEqual(list(endpoints_shapes.keys()),
                           list(end_points.keys()))
     for endpoint_name in endpoints_shapes:
         expected_shape = endpoints_shapes[endpoint_name]
         self.assertTrue(endpoint_name in end_points)
         self.assertListEqual(
             end_points[endpoint_name].get_shape().as_list(),
             expected_shape)
 def testBuildLogits(self):
     batch_size = 5
     height, width = 299, 299
     num_classes = 1000
     inputs = tf.random_uniform((batch_size, height, width, 3))
     logits, end_points = inception.inception_v4(inputs, num_classes)
     auxlogits = end_points['AuxLogits']
     predictions = end_points['Predictions']
     self.assertTrue(auxlogits.op.name.startswith('InceptionV4/AuxLogits'))
     self.assertListEqual(auxlogits.get_shape().as_list(),
                          [batch_size, num_classes])
     self.assertTrue(logits.op.name.startswith('InceptionV4/Logits'))
     self.assertListEqual(logits.get_shape().as_list(),
                          [batch_size, num_classes])
     self.assertTrue(
         predictions.op.name.startswith('InceptionV4/Logits/Predictions'))
     self.assertListEqual(predictions.get_shape().as_list(),
                          [batch_size, num_classes])
 def testGlobalPoolUnknownImageShape(self):
     batch_size = 1
     height, width = 350, 400
     num_classes = 1000
     with self.test_session() as sess:
         inputs = tf.placeholder(tf.float32, (batch_size, None, None, 3))
         logits, end_points = inception.inception_v4(
             inputs, num_classes, create_aux_logits=False)
         self.assertTrue(logits.op.name.startswith('InceptionV4/Logits'))
         self.assertListEqual(logits.get_shape().as_list(),
                              [batch_size, num_classes])
         pre_pool = end_points['Mixed_7d']
         images = tf.random_uniform((batch_size, height, width, 3))
         sess.run(tf.global_variables_initializer())
         logits_out, pre_pool_out = sess.run([logits, pre_pool],
                                             {inputs: images.eval()})
         self.assertTupleEqual(logits_out.shape, (batch_size, num_classes))
         self.assertTupleEqual(pre_pool_out.shape,
                               (batch_size, 9, 11, 1536))