def KerasParse(architecture_name, image_path): # get original model prediction result original_predict = keras_extractor.inference(architecture_name, image_path) # download model model_filename = keras_extractor.download(architecture_name, TestModels.cachedir) # original to IR parser = Keras2Parser(model_filename) parser.gen_IR() parser.save_to_proto(TestModels.tmpdir + architecture_name + "_converted.pb") parser.save_weights(TestModels.tmpdir + architecture_name + "_converted.npy") del parser return original_predict
def KerasParse(architecture_name, image_path): from mmdnn.conversion.examples.keras.extractor import keras_extractor from mmdnn.conversion.keras.keras2_parser import Keras2Parser # get original model prediction result original_predict = keras_extractor.inference(architecture_name, TestModels.cachedir, image_path) # download model model_filename = keras_extractor.download(architecture_name, TestModels.cachedir) del keras_extractor # original to IR IR_file = TestModels.tmpdir + 'keras_' + architecture_name + "_converted" parser = Keras2Parser(model_filename) parser.run(IR_file) del parser del Keras2Parser return original_predict