# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License" # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from six import text_type as _text_type import argparse import sys def arg_parser(): parser = argparse.ArgumentParser() parser.add_argument("--model", "-m", type=_text_type, default=None, help="define model file path for tensorflow or onnx") parser.add_argument("--prototxt", "-p", type=_text_type, default=None, help="prototxt file of caffe model") parser.add_argument("--weight", "-w", type=_text_type, default=None, help="weight file of caffe model") parser.add_argument("--save_dir", "-s", type=_text_type, default=None, help="path to save translated model") parser.add_argument( "--framework", "-f", type=_text_type, default=None, help="define which deeplearning framework(tensorflow/caffe/onnx)") parser.add_argument( "--caffe_proto", "-c", type=_text_type, default=None, help="optional: the .py file compiled by caffe proto file of caffe model" ) parser.add_argument("--version", "-v", action="store_true", default=False, help="get version of x2paddle") parser.add_argument( "--without_data_format_optimization", "-wo", action="store_true", default=False, help="tf model conversion without data format optimization") parser.add_argument("--define_input_shape", "-d", action="store_true", default=False, help="define input shape for tf model") return parser def tf2paddle(model_path, save_dir, without_data_format_optimization=False, define_input_shape=False): # check tensorflow installation and version try: import os os.environ["TF_CPP_MIN_LOG_LEVEL"] = '3' import tensorflow as tf version = tf.__version__ if version >= '2.0.0' or version < '1.0.0': print( "1.0.0<=tensorflow<2.0.0 is required, and v1.14.0 is recommended" ) return except: print("Tensorflow is not installed, use \"pip install tensorflow\".") return from x2paddle.decoder.tf_decoder import TFDecoder from x2paddle.op_mapper.tf_op_mapper import TFOpMapper from x2paddle.op_mapper.tf_op_mapper_nhwc import TFOpMapperNHWC from x2paddle.optimizer.tf_optimizer import TFOptimizer print("Now translating model from tensorflow to paddle.") model = TFDecoder(model_path, define_input_shape=define_input_shape) if not without_data_format_optimization: mapper = TFOpMapper(model) optimizer = TFOptimizer(mapper) # neccesary optimization optimizer.delete_redundance_code() # optimizer below is experimental optimizer.merge_activation() optimizer.merge_bias() else: mapper = TFOpMapperNHWC(model) optimizer = TFOptimizer(mapper) optimizer.delete_redundance_code() optimizer.strip_graph() optimizer.merge_activation() optimizer.merge_bias() optimizer.remove_transpose() mapper.save_inference_model(save_dir) def caffe2paddle(proto, weight, save_dir, caffe_proto): from x2paddle.decoder.caffe_decoder import CaffeDecoder from x2paddle.op_mapper.caffe_op_mapper import CaffeOpMapper from x2paddle.optimizer.caffe_optimizer import CaffeOptimizer print("Now translating model from caffe to paddle.") model = CaffeDecoder(proto, weight, caffe_proto) mapper = CaffeOpMapper(model) optimizer = CaffeOptimizer(mapper) optimizer.merge_bn_scale() optimizer.merge_op_activation() mapper.save_inference_model(save_dir) def onnx2paddle(model_path, save_dir): # check onnx installation and version try: import onnx version = onnx.version.version if version != '1.5.0': print("onnx==1.5.0 is required") return except: print("onnx is not installed, use \"pip install onnx==1.5.0\".") return print("Now translating model from onnx to paddle.") from x2paddle.decoder.onnx_decoder import ONNXDecoder model = ONNXDecoder(model_path) from x2paddle.op_mapper.onnx_op_mapper import ONNXOpMapper mapper = ONNXOpMapper(model) from x2paddle.optimizer.onnx_optimizer import ONNXOptimizer optimizer = ONNXOptimizer(mapper) optimizer.delete_redundance_code() mapper.save_inference_model(save_dir) def main(): if len(sys.argv) < 2: print("Use \"x2paddle -h\" to print the help information") print("For more information, please follow our github repo below:)") print("\nGithub: https://github.com/PaddlePaddle/X2Paddle.git\n") return parser = arg_parser() args = parser.parse_args() if args.version: import x2paddle print("x2paddle-{} with python>=3.5, paddlepaddle>=1.5.0\n".format( x2paddle.__version__)) return try: import paddle v0, v1, v2 = paddle.__version__.split('.') if int(v0) != 1 or int(v1) < 5: print("paddlepaddle>=1.5.0 is required") return except: print("paddlepaddle not installed, use \"pip install paddlepaddle\"") assert args.framework is not None, "--framework is not defined(support tensorflow/caffe/onnx)" assert args.save_dir is not None, "--save_dir is not defined" if args.framework == "tensorflow": assert args.model is not None, "--model should be defined while translating tensorflow model" without_data_format_optimization = False define_input_shape = False if args.without_data_format_optimization: without_data_format_optimization = True if args.define_input_shape: define_input_shape = True tf2paddle(args.model, args.save_dir, without_data_format_optimization, define_input_shape) elif args.framework == "caffe": assert args.prototxt is not None and args.weight is not None, "--prototxt and --weight should be defined while translating caffe model" caffe2paddle(args.prototxt, args.weight, args.save_dir, args.caffe_proto) elif args.framework == "onnx": assert args.model is not None, "--model should be defined while translating onnx model" onnx2paddle(args.model, args.save_dir) else: raise Exception("--framework only support tensorflow/caffe/onnx now") if __name__ == "__main__": main()