def testModelHasExpectedNumberOfParameters(self):
     batch_size = 5
     height, width = 224, 224
     inputs = tf.random_uniform((batch_size, height, width, 3))
     with slim.arg_scope([slim.conv2d, slim.separable_conv2d],
                         normalizer_fn=slim.batch_norm):
         mobilenet_v1.mobilenet_v1_base(inputs)
         total_params, _ = slim.model_analyzer.analyze_vars(
             slim.get_model_variables())
         self.assertAlmostEqual(3217920, total_params)
    def testBuildAndCheckAllEndPointsApproximateFaceNet(self):
        batch_size = 5
        height, width = 128, 128

        inputs = tf.random_uniform((batch_size, height, width, 3))
        with slim.arg_scope([slim.conv2d, slim.separable_conv2d],
                            normalizer_fn=slim.batch_norm):
            _, end_points = mobilenet_v1.mobilenet_v1_base(
                inputs,
                final_endpoint='Conv2d_13_pointwise',
                depth_multiplier=0.75)
        # For the Conv2d_0 layer FaceNet has depth=16
        endpoints_shapes = {
            'Conv2d_0': [batch_size, 64, 64, 24],
            'Conv2d_1_depthwise': [batch_size, 64, 64, 24],
            'Conv2d_1_pointwise': [batch_size, 64, 64, 48],
            'Conv2d_2_depthwise': [batch_size, 32, 32, 48],
            'Conv2d_2_pointwise': [batch_size, 32, 32, 96],
            'Conv2d_3_depthwise': [batch_size, 32, 32, 96],
            'Conv2d_3_pointwise': [batch_size, 32, 32, 96],
            'Conv2d_4_depthwise': [batch_size, 16, 16, 96],
            'Conv2d_4_pointwise': [batch_size, 16, 16, 192],
            'Conv2d_5_depthwise': [batch_size, 16, 16, 192],
            'Conv2d_5_pointwise': [batch_size, 16, 16, 192],
            'Conv2d_6_depthwise': [batch_size, 8, 8, 192],
            'Conv2d_6_pointwise': [batch_size, 8, 8, 384],
            'Conv2d_7_depthwise': [batch_size, 8, 8, 384],
            'Conv2d_7_pointwise': [batch_size, 8, 8, 384],
            'Conv2d_8_depthwise': [batch_size, 8, 8, 384],
            'Conv2d_8_pointwise': [batch_size, 8, 8, 384],
            'Conv2d_9_depthwise': [batch_size, 8, 8, 384],
            'Conv2d_9_pointwise': [batch_size, 8, 8, 384],
            'Conv2d_10_depthwise': [batch_size, 8, 8, 384],
            'Conv2d_10_pointwise': [batch_size, 8, 8, 384],
            'Conv2d_11_depthwise': [batch_size, 8, 8, 384],
            'Conv2d_11_pointwise': [batch_size, 8, 8, 384],
            'Conv2d_12_depthwise': [batch_size, 4, 4, 384],
            'Conv2d_12_pointwise': [batch_size, 4, 4, 768],
            'Conv2d_13_depthwise': [batch_size, 4, 4, 768],
            'Conv2d_13_pointwise': [batch_size, 4, 4, 768]
        }
        self.assertItemsEqual(endpoints_shapes.keys(), end_points.keys())
        for endpoint_name, expected_shape in endpoints_shapes.items():
            self.assertTrue(endpoint_name in end_points)
            self.assertListEqual(
                end_points[endpoint_name].get_shape().as_list(),
                expected_shape)
    def testOutputStride8BuildAndCheckAllEndPointsUptoConv2d_13(self):
        batch_size = 5
        height, width = 224, 224
        output_stride = 8

        inputs = tf.random_uniform((batch_size, height, width, 3))
        with slim.arg_scope([slim.conv2d, slim.separable_conv2d],
                            normalizer_fn=slim.batch_norm):
            _, end_points = mobilenet_v1.mobilenet_v1_base(
                inputs,
                output_stride=output_stride,
                final_endpoint='Conv2d_13_pointwise')
        endpoints_shapes = {
            'Conv2d_0': [batch_size, 112, 112, 32],
            'Conv2d_1_depthwise': [batch_size, 112, 112, 32],
            'Conv2d_1_pointwise': [batch_size, 112, 112, 64],
            'Conv2d_2_depthwise': [batch_size, 56, 56, 64],
            'Conv2d_2_pointwise': [batch_size, 56, 56, 128],
            'Conv2d_3_depthwise': [batch_size, 56, 56, 128],
            'Conv2d_3_pointwise': [batch_size, 56, 56, 128],
            'Conv2d_4_depthwise': [batch_size, 28, 28, 128],
            'Conv2d_4_pointwise': [batch_size, 28, 28, 256],
            'Conv2d_5_depthwise': [batch_size, 28, 28, 256],
            'Conv2d_5_pointwise': [batch_size, 28, 28, 256],
            'Conv2d_6_depthwise': [batch_size, 28, 28, 256],
            'Conv2d_6_pointwise': [batch_size, 28, 28, 512],
            'Conv2d_7_depthwise': [batch_size, 28, 28, 512],
            'Conv2d_7_pointwise': [batch_size, 28, 28, 512],
            'Conv2d_8_depthwise': [batch_size, 28, 28, 512],
            'Conv2d_8_pointwise': [batch_size, 28, 28, 512],
            'Conv2d_9_depthwise': [batch_size, 28, 28, 512],
            'Conv2d_9_pointwise': [batch_size, 28, 28, 512],
            'Conv2d_10_depthwise': [batch_size, 28, 28, 512],
            'Conv2d_10_pointwise': [batch_size, 28, 28, 512],
            'Conv2d_11_depthwise': [batch_size, 28, 28, 512],
            'Conv2d_11_pointwise': [batch_size, 28, 28, 512],
            'Conv2d_12_depthwise': [batch_size, 28, 28, 512],
            'Conv2d_12_pointwise': [batch_size, 28, 28, 1024],
            'Conv2d_13_depthwise': [batch_size, 28, 28, 1024],
            'Conv2d_13_pointwise': [batch_size, 28, 28, 1024]
        }
        self.assertItemsEqual(endpoints_shapes.keys(), end_points.keys())
        for endpoint_name, expected_shape in endpoints_shapes.items():
            self.assertTrue(endpoint_name in end_points)
            self.assertListEqual(
                end_points[endpoint_name].get_shape().as_list(),
                expected_shape)
    def testBuildBaseNetwork(self):
        batch_size = 5
        height, width = 224, 224

        inputs = tf.random_uniform((batch_size, height, width, 3))
        net, end_points = mobilenet_v1.mobilenet_v1_base(inputs)
        self.assertTrue(net.op.name.startswith('MobilenetV1/Conv2d_13'))
        self.assertListEqual(net.get_shape().as_list(),
                             [batch_size, 7, 7, 1024])
        expected_endpoints = [
            'Conv2d_0', 'Conv2d_1_depthwise', 'Conv2d_1_pointwise',
            'Conv2d_2_depthwise', 'Conv2d_2_pointwise', 'Conv2d_3_depthwise',
            'Conv2d_3_pointwise', 'Conv2d_4_depthwise', 'Conv2d_4_pointwise',
            'Conv2d_5_depthwise', 'Conv2d_5_pointwise', 'Conv2d_6_depthwise',
            'Conv2d_6_pointwise', 'Conv2d_7_depthwise', 'Conv2d_7_pointwise',
            'Conv2d_8_depthwise', 'Conv2d_8_pointwise', 'Conv2d_9_depthwise',
            'Conv2d_9_pointwise', 'Conv2d_10_depthwise', 'Conv2d_10_pointwise',
            'Conv2d_11_depthwise', 'Conv2d_11_pointwise',
            'Conv2d_12_depthwise', 'Conv2d_12_pointwise',
            'Conv2d_13_depthwise', 'Conv2d_13_pointwise'
        ]
        self.assertItemsEqual(end_points.keys(), expected_endpoints)
    def testBuildCustomNetworkUsingConvDefs(self):
        batch_size = 5
        height, width = 224, 224
        conv_defs = [
            mobilenet_v1.Conv(kernel=[3, 3], stride=2, depth=32),
            mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=1, depth=64),
            mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=2, depth=128),
            mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=1, depth=512)
        ]

        inputs = tf.random_uniform((batch_size, height, width, 3))
        net, end_points = mobilenet_v1.mobilenet_v1_base(
            inputs, final_endpoint='Conv2d_3_pointwise', conv_defs=conv_defs)
        self.assertTrue(net.op.name.startswith('MobilenetV1/Conv2d_3'))
        self.assertListEqual(net.get_shape().as_list(),
                             [batch_size, 56, 56, 512])
        expected_endpoints = [
            'Conv2d_0', 'Conv2d_1_depthwise', 'Conv2d_1_pointwise',
            'Conv2d_2_depthwise', 'Conv2d_2_pointwise', 'Conv2d_3_depthwise',
            'Conv2d_3_pointwise'
        ]
        self.assertItemsEqual(end_points.keys(), expected_endpoints)
 def testBuildOnlyUptoFinalEndpoint(self):
     batch_size = 5
     height, width = 224, 224
     endpoints = [
         'Conv2d_0', 'Conv2d_1_depthwise', 'Conv2d_1_pointwise',
         'Conv2d_2_depthwise', 'Conv2d_2_pointwise', 'Conv2d_3_depthwise',
         'Conv2d_3_pointwise', 'Conv2d_4_depthwise', 'Conv2d_4_pointwise',
         'Conv2d_5_depthwise', 'Conv2d_5_pointwise', 'Conv2d_6_depthwise',
         'Conv2d_6_pointwise', 'Conv2d_7_depthwise', 'Conv2d_7_pointwise',
         'Conv2d_8_depthwise', 'Conv2d_8_pointwise', 'Conv2d_9_depthwise',
         'Conv2d_9_pointwise', 'Conv2d_10_depthwise', 'Conv2d_10_pointwise',
         'Conv2d_11_depthwise', 'Conv2d_11_pointwise',
         'Conv2d_12_depthwise', 'Conv2d_12_pointwise',
         'Conv2d_13_depthwise', 'Conv2d_13_pointwise'
     ]
     for index, endpoint in enumerate(endpoints):
         with tf.Graph().as_default():
             inputs = tf.random_uniform((batch_size, height, width, 3))
             out_tensor, end_points = mobilenet_v1.mobilenet_v1_base(
                 inputs, final_endpoint=endpoint)
             self.assertTrue(
                 out_tensor.op.name.startswith('MobilenetV1/' + endpoint))
             self.assertItemsEqual(endpoints[:index + 1], end_points)