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
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
