def test_mobilenet_scaling(self, model_id, filter_size_scale): """Test for creation of a MobileNet classifier.""" mobilenet_params = { ('MobileNetV1', 1.0): 3228864, ('MobileNetV1', 0.75): 1832976, ('MobileNetV2', 1.0): 2257984, ('MobileNetV2', 0.75): 1382064, ('MobileNetV3Large', 1.0): 4226432, ('MobileNetV3Large', 0.75): 2731616, ('MobileNetV3Small', 1.0): 1529968, ('MobileNetV3Small', 0.75): 1026552, ('MobileNetV3EdgeTPU', 1.0): 2849312, ('MobileNetV3EdgeTPU', 0.75): 1737288, ('MobileNetMultiAVG', 1.0): 3700576, ('MobileNetMultiAVG', 0.75): 2345864, ('MobileNetMultiMAX', 1.0): 3170720, ('MobileNetMultiMAX', 0.75): 2041976, } input_size = 224 network = mobilenet.MobileNet(model_id=model_id, filter_size_scale=filter_size_scale) self.assertEqual(network.count_params(), mobilenet_params[(model_id, filter_size_scale)]) inputs = tf.keras.Input(shape=(input_size, input_size, 3), batch_size=1) _ = network(inputs)
def test_serialize_deserialize(self, model_id): # Create a network object that sets all of its config options. kwargs = dict(model_id=model_id, filter_size_scale=1.0, stochastic_depth_drop_rate=None, use_sync_bn=False, kernel_initializer='VarianceScaling', kernel_regularizer=None, bias_regularizer=None, norm_momentum=0.99, norm_epsilon=0.001, output_stride=None, min_depth=8, divisible_by=8, regularize_depthwise=False, finegrain_classification_mode=True) network = mobilenet.MobileNet(**kwargs) expected_config = dict(kwargs) self.assertEqual(network.get_config(), expected_config) # Create another network object from the first object's config. new_network = mobilenet.MobileNet.from_config(network.get_config()) # Validate that the config can be forced to JSON. _ = new_network.to_json() # If the serialization was successful, the new config should match the old. self.assertAllEqual(network.get_config(), new_network.get_config())
def test_mobilenet_creation(self, model_id, input_size): """Test creation of MobileNet family models.""" tf.keras.backend.set_image_data_format('channels_last') mobilenet_layers = { # The stride (relative to input) and number of filters # of first few layers for filter_size_scale = 0.75 'MobileNetV1': [(1, 24), (1, 48), (2, 96), (2, 96)], 'MobileNetV2': [(1, 24), (1, 16), (2, 24), (2, 24)], 'MobileNetV3Small': [(1, 16), (2, 16), (3, 24), (3, 24)], 'MobileNetV3Large': [(1, 16), (1, 16), (2, 24), (2, 24)], 'MobileNetV3EdgeTPU': [(1, 24), (1, 16), (2, 24), (2, 24)], 'MobileNetMultiMAX': [(1, 24), (2, 24), (3, 48), (3, 48)], 'MobileNetMultiAVG': [(1, 24), (2, 24), (2, 24), (3, 48)], } network = mobilenet.MobileNet(model_id=model_id, filter_size_scale=0.75) inputs = tf.keras.Input(shape=(input_size, input_size, 3), batch_size=1) endpoints = network(inputs) for idx, (stride, num_filter) in enumerate(mobilenet_layers[model_id]): self.assertAllEqual( [1, input_size / 2 ** stride, input_size / 2 ** stride, num_filter], endpoints[idx+1].shape.as_list())
def test_mobilenet_output_stride(self, model_id, output_stride): """Test for creation of a MobileNet with different output strides.""" tf.keras.backend.set_image_data_format('channels_last') mobilenet_layers = { # The number of filters of the layers outputs been collected # for filter_size_scale = 1.0. 'MobileNetV1': 1024, 'MobileNetV2': 320, 'MobileNetV3Small': 96, 'MobileNetV3Large': 160, 'MobileNetV3EdgeTPU': 192, 'MobileNetMultiMAX': 160, 'MobileNetMultiAVG': 192, 'MobileNetMultiAVGSeg': 448, 'MobileNetMultiMAXSeg': 448, 'MobileNetV3SmallReducedFilters': 48, } network = mobilenet.MobileNet(model_id=model_id, filter_size_scale=1.0, output_stride=output_stride) level = int(math.log2(output_stride)) input_size = 224 inputs = tf.keras.Input(shape=(input_size, input_size, 3), batch_size=1) endpoints = network(inputs) num_filter = mobilenet_layers[model_id] self.assertAllEqual([ 1, input_size / output_stride, input_size / output_stride, num_filter ], endpoints[str(level)].shape.as_list())
def test_mobilenet_creation(self, model_id, input_size): """Test creation of MobileNet family models.""" tf.keras.backend.set_image_data_format('channels_last') mobilenet_layers = { # The number of filters of layers having outputs been collected # for filter_size_scale = 1.0 'MobileNetV1': [128, 256, 512, 1024], 'MobileNetV2': [24, 32, 96, 320], 'MobileNetV3Small': [16, 24, 48, 96], 'MobileNetV3Large': [24, 40, 112, 160], 'MobileNetV3EdgeTPU': [32, 48, 96, 192], 'MobileNetMultiMAX': [32, 64, 128, 160], 'MobileNetMultiAVG': [32, 64, 160, 192], 'MobileNetMultiAVGSeg': [32, 64, 160, 96], 'MobileNetMultiMAXSeg': [32, 64, 128, 96], 'MobileNetV3SmallReducedFilters': [16, 24, 48, 48], } network = mobilenet.MobileNet(model_id=model_id, filter_size_scale=1.0) inputs = tf.keras.Input(shape=(input_size, input_size, 3), batch_size=1) endpoints = network(inputs) for idx, num_filter in enumerate(mobilenet_layers[model_id]): self.assertAllEqual([ 1, input_size / 2**(idx + 2), input_size / 2**(idx + 2), num_filter ], endpoints[str(idx + 2)].shape.as_list())
def test_mobilenet_intermediate_layers(self, model_id, input_size): tf.keras.backend.set_image_data_format('channels_last') # Tests the mobilenet intermediate depthwise layers. mobilenet_depthwise_layers = { # The number of filters of depthwise layers having outputs been # collected for filter_size_scale = 1.0. Only tests the mobilenet # model with inverted bottleneck block using depthwise which excludes # MobileNetV1. 'MobileNetV1': [], 'MobileNetV2': [144, 192, 576, 960], 'MobileNetV3Small': [16, 88, 144, 576], 'MobileNetV3Large': [72, 120, 672, 960], 'MobileNetV3EdgeTPU': [None, None, 384, 1280], 'MobileNetMultiMAX': [96, 128, 384, 640], 'MobileNetMultiAVG': [64, 192, 640, 768], } network = mobilenet.MobileNet(model_id=model_id, filter_size_scale=1.0, output_intermediate_endpoints=True) inputs = tf.keras.Input(shape=(input_size, input_size, 3), batch_size=1) endpoints = network(inputs) for idx, num_filter in enumerate(mobilenet_depthwise_layers[model_id]): # Not using depthwise conv in this layer. if num_filter is None: continue self.assertAllEqual([ 1, input_size / 2**(idx + 2), input_size / 2**(idx + 2), num_filter ], endpoints[str(idx + 2) + '/depthwise'].shape.as_list())
def test_input_specs(self, input_dim, model_id): """Test different input feature dimensions.""" tf.keras.backend.set_image_data_format('channels_last') input_specs = tf.keras.layers.InputSpec(shape=[None, None, None, input_dim]) network = mobilenet.MobileNet(model_id=model_id, input_specs=input_specs) inputs = tf.keras.Input(shape=(128, 128, input_dim), batch_size=1) _ = network(inputs)
def test_mobilenet_v3_edgetpu_scaling(self, filter_size_scale): mobilenet_v3_edgetpu_params = {1.0: 2849312, 0.75: 1737288} input_size = 224 network = mobilenet.MobileNet(model_id='MobileNetV3EdgeTPU', filter_size_scale=filter_size_scale) self.assertEqual(network.count_params(), mobilenet_v3_edgetpu_params[filter_size_scale]) inputs = tf.keras.Input(shape=(input_size, input_size, 3), batch_size=1) _ = network(inputs)
def test_mobilenet_v3_small_scaling(self, filter_size_scale): mobilenet_v3_small_params = {1.0: 1529968, 0.75: 1026552} input_size = 224 network = mobilenet.MobileNet(model_id='MobileNetV3Small', filter_size_scale=filter_size_scale) self.assertEqual(network.count_params(), mobilenet_v3_small_params[filter_size_scale]) inputs = tf.keras.Input(shape=(input_size, input_size, 3), batch_size=1) _ = network(inputs)
def test_mobilenet_v3_large_scaling(self, filter_size_scale): mobilenet_v3_large_params = {1.0: 4226432, 0.75: 2731616} input_size = 224 network = mobilenet.MobileNet(model_id='MobileNetV3Large', filter_size_scale=filter_size_scale) self.assertEqual(network.count_params(), mobilenet_v3_large_params[filter_size_scale]) inputs = tf.keras.Input(shape=(input_size, input_size, 3), batch_size=1) _ = network(inputs)
def test_mobilenet_v2_scaling(self, filter_size_scale): mobilenet_v2_params = {1.0: 2257984, 0.75: 1382064} input_size = 224 network = mobilenet.MobileNet(model_id='MobileNetV2', filter_size_scale=filter_size_scale) self.assertEqual(network.count_params(), mobilenet_v2_params[filter_size_scale]) inputs = tf.keras.Input(shape=(input_size, input_size, 3), batch_size=1) _ = network(inputs)
def test_mobilenet_v2_creation(self, input_size): """Test creation of EfficientNet family models.""" tf.keras.backend.set_image_data_format('channels_last') network = mobilenet.MobileNet(model_id='MobileNetV2', filter_size_scale=1.0) inputs = tf.keras.Input(shape=(input_size, input_size, 3), batch_size=1) endpoints = network(inputs) self.assertAllEqual([1, input_size / 2**1, input_size / 2**1, 32], endpoints[1].shape.as_list()) self.assertAllEqual([1, input_size / 2**1, input_size / 2**1, 16], endpoints[2].shape.as_list()) self.assertAllEqual([1, input_size / 2**2, input_size / 2**2, 24], endpoints[3].shape.as_list()) self.assertAllEqual([1, input_size / 2**2, input_size / 2**2, 24], endpoints[4].shape.as_list())
def test_network_creation_with_mobilenet(self, input_size, min_level, max_level, use_separable_conv): """Test creation of FPN with mobilenet backbone.""" tf.keras.backend.set_image_data_format('channels_last') inputs = tf.keras.Input(shape=(input_size, input_size, 3), batch_size=1) backbone = mobilenet.MobileNet(model_id='MobileNetV2') network = fpn.FPN(input_specs=backbone.output_specs, min_level=min_level, max_level=max_level, use_separable_conv=use_separable_conv) endpoints = backbone(inputs) feats = network(endpoints) for level in range(min_level, max_level + 1): self.assertIn(str(level), feats) self.assertAllEqual( [1, input_size // 2**level, input_size // 2**level, 256], feats[str(level)].shape.as_list())