def testModelHasExpectedNumberOfParameters(self):
     batch_size = 5
     height, width = 224, 224
     inputs = tf.random_uniform((batch_size, height, width, 3))
     with slim.arg_scope(inception.inception_v1_arg_scope()):
         inception.inception_v1_base(inputs)
     total_params, _ = slim.model_analyzer.analyze_vars(
         slim.get_model_variables())
     self.assertAlmostEqual(5607184, total_params)
    def testBuildAndCheckAllEndPointsUptoMixed5c(self):
        batch_size = 5
        height, width = 224, 224

        inputs = tf.random_uniform((batch_size, height, width, 3))
        _, end_points = inception.inception_v1_base(inputs,
                                                    final_endpoint='Mixed_5c')
        endpoints_shapes = {
            'Conv2d_1a_7x7': [5, 112, 112, 64],
            'MaxPool_2a_3x3': [5, 56, 56, 64],
            'Conv2d_2b_1x1': [5, 56, 56, 64],
            'Conv2d_2c_3x3': [5, 56, 56, 192],
            'MaxPool_3a_3x3': [5, 28, 28, 192],
            'Mixed_3b': [5, 28, 28, 256],
            'Mixed_3c': [5, 28, 28, 480],
            'MaxPool_4a_3x3': [5, 14, 14, 480],
            'Mixed_4b': [5, 14, 14, 512],
            'Mixed_4c': [5, 14, 14, 512],
            'Mixed_4d': [5, 14, 14, 512],
            'Mixed_4e': [5, 14, 14, 528],
            'Mixed_4f': [5, 14, 14, 832],
            'MaxPool_5a_2x2': [5, 7, 7, 832],
            'Mixed_5b': [5, 7, 7, 832],
            'Mixed_5c': [5, 7, 7, 1024]
        }

        self.assertItemsEqual(endpoints_shapes.keys(), 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 testHalfSizeImages(self):
        batch_size = 5
        height, width = 112, 112

        inputs = tf.random_uniform((batch_size, height, width, 3))
        mixed_5c, _ = inception.inception_v1_base(inputs)
        self.assertTrue(mixed_5c.op.name.startswith('InceptionV1/Mixed_5c'))
        self.assertListEqual(mixed_5c.get_shape().as_list(),
                             [batch_size, 4, 4, 1024])
    def testBuildBaseNetwork(self):
        batch_size = 5
        height, width = 224, 224

        inputs = tf.random_uniform((batch_size, height, width, 3))
        mixed_6c, end_points = inception.inception_v1_base(inputs)
        self.assertTrue(mixed_6c.op.name.startswith('InceptionV1/Mixed_5c'))
        self.assertListEqual(mixed_6c.get_shape().as_list(),
                             [batch_size, 7, 7, 1024])
        expected_endpoints = [
            'Conv2d_1a_7x7', 'MaxPool_2a_3x3', 'Conv2d_2b_1x1',
            'Conv2d_2c_3x3', 'MaxPool_3a_3x3', 'Mixed_3b', 'Mixed_3c',
            'MaxPool_4a_3x3', 'Mixed_4b', 'Mixed_4c', 'Mixed_4d', 'Mixed_4e',
            'Mixed_4f', 'MaxPool_5a_2x2', 'Mixed_5b', 'Mixed_5c'
        ]
        self.assertItemsEqual(end_points.keys(), expected_endpoints)
 def testBuildOnlyUptoFinalEndpoint(self):
     batch_size = 5
     height, width = 224, 224
     endpoints = [
         'Conv2d_1a_7x7', 'MaxPool_2a_3x3', 'Conv2d_2b_1x1',
         'Conv2d_2c_3x3', 'MaxPool_3a_3x3', 'Mixed_3b', 'Mixed_3c',
         'MaxPool_4a_3x3', 'Mixed_4b', 'Mixed_4c', 'Mixed_4d', 'Mixed_4e',
         'Mixed_4f', 'MaxPool_5a_2x2', 'Mixed_5b', 'Mixed_5c'
     ]
     for index, endpoint in enumerate(endpoints):
         with tf.Graph().as_default():
             inputs = tf.random_uniform((batch_size, height, width, 3))
             out_tensor, end_points = inception.inception_v1_base(
                 inputs, final_endpoint=endpoint)
             self.assertTrue(
                 out_tensor.op.name.startswith('InceptionV1/' + endpoint))
             self.assertItemsEqual(endpoints[:index + 1], end_points.keys())