# 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. import paddle.fluid as fluid from paddle.fluid.proto import framework_pb2 from x2paddle.core.util import * import inspect import os def export_paddle_param(param, param_name, dir): dtype_map = { "int16": [framework_pb2.VarType.INT16, 'h'], "int32": [framework_pb2.VarType.INT32, 'i'], "int64": [framework_pb2.VarType.INT64, 'q'], "float16": [framework_pb2.VarType.FP16, 'e'], "float32": [framework_pb2.VarType.FP32, 'f'], "float64": [framework_pb2.VarType.FP64, 'd'], "bool": [framework_pb2.VarType.BOOL, None] } shape = param.shape if len(shape) == 0: assert param.size == 1, "Unexpected situation happend!" shape = [1] assert str(param.dtype) in dtype_map, "Unknown dtype of params." fp = open(os.path.join(dir, param_name), 'wb') numpy.array([0], dtype='int32').tofile(fp) numpy.array([0], dtype='int64').tofile(fp) numpy.array([0], dtype='int32').tofile(fp) tensor_desc = framework_pb2.VarType.TensorDesc() tensor_desc.data_type = dtype_map[str(param.dtype)][0] tensor_desc.dims.extend(shape) desc_size = tensor_desc.ByteSize() numpy.array([desc_size], dtype='int32').tofile(fp) fp.write(tensor_desc.SerializeToString()) param.tofile(fp) fp.close() # This func will copy to generate code file def run_net(param_dir="./"): import os inputs, outputs = x2paddle_net() for i, out in enumerate(outputs): if isinstance(out, list): for out_part in out: outputs.append(out_part) del outputs[i] exe = fluid.Executor(fluid.CPUPlace()) exe.run(fluid.default_startup_program()) def if_exist(var): b = os.path.exists(os.path.join(param_dir, var.name)) return b fluid.io.load_vars(exe, param_dir, fluid.default_main_program(), predicate=if_exist) class OpMapper(object): def __init__(self): self.paddle_codes = "" self.tab = " " self.net_code = list() self.weights = dict() self.inputs = list() self.outputs = list() def op_checker(self): unsupported_ops = set() for node_name in self.graph.topo_sort: node = self.graph.get_node(node_name) op = node.layer_type if not hasattr(self, op): unsupported_ops.add(op) if len(unsupported_ops) == 0: return True else: print("There are {} ops not supported yet, list as below".format( len(unsupported_ops))) for op in unsupported_ops: print(op) return False def add_codes(self, codes, indent=0): if isinstance(codes, list): for code in codes: self.paddle_codes += (self.tab * indent + code.strip('\n') + '\n') elif isinstance(codes, str): self.paddle_codes += (self.tab * indent + codes.strip('\n') + '\n') else: raise Exception("Unknown type of codes") def add_heads(self): self.add_codes("from paddle.fluid.initializer import Constant") self.add_codes("from paddle.fluid.param_attr import ParamAttr") self.add_codes("import paddle.fluid as fluid") self.add_codes("") def save_inference_model(self, save_dir, params_merge): self.save_python_model(save_dir) import sys import paddle.fluid as fluid py_code_dir = os.path.join(save_dir, "model_with_code") sys.path.append(py_code_dir) import model try: inputs, outputs = model.x2paddle_net() for i, out in enumerate(outputs): if isinstance(out, list): for out_part in out: outputs.append(out_part) del outputs[i] input_names = [input.name for input in inputs] exe = fluid.Executor(fluid.CPUPlace()) exe.run(fluid.default_startup_program()) def if_exist(var): b = os.path.exists( os.path.join(os.path.join(py_code_dir, var.name))) return b fluid.io.load_vars(exe, py_code_dir, fluid.default_main_program(), predicate=if_exist) if params_merge: fluid.io.save_inference_model(dirname=os.path.join( save_dir, "inference_model"), feeded_var_names=input_names, target_vars=outputs, executor=exe, params_filename="__params__") else: fluid.io.save_inference_model(dirname=os.path.join( save_dir, "inference_model"), feeded_var_names=input_names, target_vars=outputs, executor=exe, params_filename=None) except: raise Exception( "Paddle code was saved in {}/model.py, but seems there's wrong exist, please check model.py manually." .format(py_code_dir)) def save_python_model(self, save_dir): if not os.path.exists(save_dir): os.makedirs(save_dir) py_code_dir = os.path.join(save_dir, "model_with_code") if not os.path.exists(py_code_dir): os.makedirs(py_code_dir) for name, param in self.weights.items(): export_paddle_param(param, name, py_code_dir) self.add_heads() if hasattr(self, "used_custom_layers"): for _, layer_code in self.used_custom_layers.items(): self.add_codes(layer_code, 0) self.add_codes("", 0) self.add_codes("\ndef x2paddle_net():", 0) for i in range(len(self.graph.topo_sort)): node_name = self.graph.topo_sort[i] node = self.graph.get_node(node_name) if node is None: continue if len(node.fluid_code.layers) == 0: continue self.add_codes(node.fluid_code.gen_codes(), 1) self.add_codes("", 0) input_str = "[" for name in self.graph.input_nodes: input_str += (name + ", ") input_str = input_str.strip(", ") + "]" output_str = "[" for name in self.graph.output_nodes: output_str += (name + ", ") output_str = output_str.strip(", ") + "]" return_code = "return {}, {}".format(input_str, output_str) self.add_codes(return_code, 1) self.add_codes("", 0) self.add_codes(inspect.getsourcelines(run_net)[0]) fp = open(os.path.join(py_code_dir, "model.py"), 'w') fp.write(self.paddle_codes) fp.close()