def test_mobilenet_v2(self, quantization_type):
saved_model_dir = model_coverage.get_filepath(
"keras_applications/mobilenet_v2_tf2")
converter = _lite.TFLiteConverterV2.from_saved_model(saved_model_dir)
img_array = keras.applications.inception_v3.preprocess_input(
model_coverage.get_image(224))
self._test_quantization_goldens(quantization_type,
converter,
input_data=[img_array],
golden_name="mobilenet_v2_%s" %
quantization_type.value)
def test_inception_v3(self, quantization_type):
keras_model = keras.models.load_model(
model_coverage.get_filepath("keras_applications/inception_v3.h5"))
keras_model.inputs[0].set_shape([1, 299, 299, 3])
converter = _lite.TFLiteConverterV2.from_keras_model(keras_model)
img_array = keras.applications.inception_v3.preprocess_input(
model_coverage.get_image(299))
self._test_quantization_goldens(quantization_type,
converter,
input_data=[img_array],
golden_name="inception_v3_%s" %
quantization_type.value)
def test_mobilenet_v1(self, quantization_type):
frozengraph_file = model_coverage.get_filepath(
"mobilenet/mobilenet_v1_1.0_224_frozen.pb")
converter = _lite.TFLiteConverter.from_frozen_graph(
frozengraph_file,
input_arrays=["input"],
output_arrays=["MobilenetV1/Predictions/Reshape_1"],
input_shapes={"input": (1, 224, 224, 3)})
img_array = keras.applications.inception_v3.preprocess_input(
model_coverage.get_image(224))
self._test_quantization_goldens(quantization_type,
converter,
input_data=[img_array],
golden_name="mobilenet_v1_%s" %
quantization_type.value)