def cntk_emit(original_framework, architecture_name, architecture_path,
weight_path, test_input_path):
from mmdnn.conversion.cntk.cntk_emitter import CntkEmitter
# IR to code
converted_file = TestModels.tmpdir + original_framework + '_cntk_' + architecture_name + "_converted"
converted_file = converted_file.replace('.', '_')
emitter = CntkEmitter((architecture_path, weight_path))
emitter.run(converted_file + '.py', None, 'test')
del emitter
del CntkEmitter
model_converted = imp.load_source('CntkModel', converted_file +
'.py').KitModel(weight_path)
if 'rnn' not in architecture_name:
func = TestKit.preprocess_func[original_framework][
architecture_name]
img = func(test_input_path(architecture_name))
input_data = img
else:
sentence = np.load(test_input_path(architecture_name))
from keras.utils import to_categorical
input_data = to_categorical(sentence, 30000)[0]
predict = model_converted.eval(
{model_converted.arguments[0]: [input_data]})
converted_predict = np.squeeze(predict)
del model_converted
del sys.modules['CntkModel']
os.remove(converted_file + '.py')
return converted_predict
def CntkEmit(original_framework, architecture_name, architecture_path,
weight_path, image_path):
print("Testing {} from {} to CNTK.".format(architecture_name,
original_framework))
# IR to code
emitter = CntkEmitter((architecture_path, weight_path))
emitter.run("converted_model.py", None, 'test')
del emitter
# import converted model
import converted_model
reload_module(converted_model)
model_converted = converted_model.KitModel(TestModels.tmpdir +
architecture_name +
"_converted.npy")
func = TestKit.preprocess_func[original_framework][architecture_name]
img = func(image_path)
predict = model_converted.eval({model_converted.arguments[0]: [img]})
converted_predict = np.squeeze(predict)
del model_converted
del converted_model
os.remove("converted_model.py")
return converted_predict
def CntkEmit(original_framework, architecture_name, architecture_path, weight_path, image_path):
# IR to code
converted_file = original_framework + '_cntk_' + architecture_name + "_converted"
converted_file = converted_file.replace('.', '_')
emitter = CntkEmitter((architecture_path, weight_path))
emitter.run(converted_file + '.py', None, 'test')
del emitter
model_converted = __import__(converted_file).KitModel(weight_path)
func = TestKit.preprocess_func[original_framework][architecture_name]
img = func(image_path)
predict = model_converted.eval({model_converted.arguments[0]:[img]})
converted_predict = np.squeeze(predict)
del model_converted
del sys.modules[converted_file]
os.remove(converted_file + '.py')
return converted_predict
def CntkEmit(original_framework, architecture_name, architecture_path, weight_path, image_path):
# IR to code
converted_file = original_framework + '_cntk_' + architecture_name + "_converted"
converted_file = converted_file.replace('.', '_')
emitter = CntkEmitter((architecture_path, weight_path))
emitter.run(converted_file + '.py', None, 'test')
del emitter
model_converted = __import__(converted_file).KitModel(weight_path)
func = TestKit.preprocess_func[original_framework][architecture_name]
img = func(image_path)
predict = model_converted.eval({model_converted.arguments[0]:[img]})
converted_predict = np.squeeze(predict)
del model_converted
del sys.modules[converted_file]
os.remove(converted_file + '.py')
return converted_predict