def OnnxEmit(original_framework, architecture_name, architecture_path, weight_path, image_path): from mmdnn.conversion.onnx.onnx_emitter import OnnxEmitter original_framework = checkfrozen(original_framework) # IR to code converted_file = original_framework + '_onnx_' + architecture_name + "_converted" converted_file = converted_file.replace('.', '_') emitter = OnnxEmitter(architecture_path, weight_path) emitter.run(converted_file + '.py', None, 'test') del emitter del OnnxEmitter # import converted model from onnx_tf.backend import prepare model_converted = __import__(converted_file).KitModel(weight_path) tf_rep = prepare(model_converted) func = TestKit.preprocess_func[original_framework][architecture_name] img = func(image_path) input_data = np.expand_dims(img, 0) predict = tf_rep.run(input_data)[0] del prepare del model_converted del tf_rep os.remove(converted_file + '.py') return predict
def OnnxEmit(original_framework, architecture_name, architecture_path, weight_path, image_path): try: from mmdnn.conversion.onnx.onnx_emitter import OnnxEmitter original_framework = checkfrozen(original_framework) # IR to code converted_file = original_framework + '_onnx_' + architecture_name + "_converted" converted_file = converted_file.replace('.', '_') emitter = OnnxEmitter(architecture_path, weight_path) emitter.run(converted_file + '.py', converted_file + '.npy', 'test') del emitter del OnnxEmitter # import converted model from onnx_tf.backend import prepare model_converted = imp.load_source( 'OnnxModel', converted_file + '.py').KitModel(converted_file + '.npy') tf_rep = prepare(model_converted) func = TestKit.preprocess_func[original_framework][ architecture_name] img = func(image_path) input_data = np.expand_dims(img, 0) predict = tf_rep.run(input_data)[0] return predict except ImportError: print( 'Please install Onnx! Or Onnx is not supported in your platform.', file=sys.stderr) except: raise ValueError finally: del prepare del model_converted del tf_rep del sys.modules['OnnxModel'] os.remove(converted_file + '.py') os.remove(converted_file + '.npy')
def onnx_emit(original_framework, architecture_name, architecture_path, weight_path, test_input_path): from mmdnn.conversion.onnx.onnx_emitter import OnnxEmitter # IR to code converted_file = TestModels.tmpdir + original_framework + '_onnx_' + architecture_name + "_converted" converted_file = converted_file.replace('.', '_') emitter = OnnxEmitter(architecture_path, weight_path) emitter.run(converted_file + '.py', converted_file + '.npy', 'test') del emitter del OnnxEmitter # import converted model from onnx_tf.backend import prepare model_converted = imp.load_source( 'OnnxModel', converted_file + '.py').KitModel(converted_file + '.npy') tf_rep = prepare(model_converted) original_framework = checkfrozen(original_framework) func = TestKit.preprocess_func[original_framework][architecture_name] img = func(test_input_path) input_data = np.expand_dims(img, 0) predict = tf_rep.run(input_data)[0] del prepare del model_converted del tf_rep del sys.modules['OnnxModel'] os.remove(converted_file + '.py') os.remove(converted_file + '.npy') return predict