# 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. """ Baidu's open-source Lexical Analysis tool for Chinese, including: 1. Word Segmentation, 2. Part-of-Speech Tagging 3. Named Entity Recognition """ from __future__ import absolute_import from __future__ import division from __future__ import print_function import os import time import argparse import numpy as np import multiprocessing import sys from collections import namedtuple import paddle.fluid as fluid import creator import utils sys.path.append("..") from models.representation.ernie import ErnieConfig from models.model_check import check_cuda from models.model_check import check_version def evaluate(exe, test_program, test_pyreader, test_ret): """ Evaluation Function """ test_ret["chunk_evaluator"].reset() total_loss = [] start_time = time.time() for data in test_pyreader(): loss, nums_infer, nums_label, nums_correct = exe.run( test_program, fetch_list=[ test_ret["avg_cost"], test_ret["num_infer_chunks"], test_ret["num_label_chunks"], test_ret["num_correct_chunks"], ], feed=data[0] ) total_loss.append(loss) test_ret["chunk_evaluator"].update(nums_infer, nums_label, nums_correct) precision, recall, f1 = test_ret["chunk_evaluator"].eval() end_time = time.time() print("\t[test] loss: %.5f, P: %.5f, R: %.5f, F1: %.5f, elapsed time: %.3f s" % (np.mean(total_loss), precision, recall, f1, end_time - start_time)) def do_train(args): """ Main Function """ ernie_config = ErnieConfig(args.ernie_config_path) ernie_config.print_config() if args.use_cuda: place = fluid.CUDAPlace(int(os.getenv('FLAGS_selected_gpus', '0'))) dev_count = 1 else: dev_count = min(multiprocessing.cpu_count(), args.cpu_num) if (dev_count < args.cpu_num): print("WARNING: The total CPU NUM in this machine is %d, which is less than cpu_num parameter you set. " "Change the cpu_num from %d to %d"%(dev_count, args.cpu_num, dev_count)) os.environ['CPU_NUM'] = str(dev_count) place = fluid.CPUPlace() exe = fluid.Executor(place) startup_prog = fluid.Program() if args.random_seed is not None: startup_prog.random_seed = args.random_seed train_program = fluid.Program() with fluid.program_guard(train_program, startup_prog): with fluid.unique_name.guard(): # user defined model based on ernie embeddings train_ret = creator.create_ernie_model(args, ernie_config) # ernie pyreader train_pyreader = creator.create_pyreader(args, file_name=args.train_data, feed_list=train_ret['feed_list'], model="ernie", place=place) test_program = train_program.clone(for_test=True) test_pyreader = creator.create_pyreader(args, file_name=args.test_data, feed_list=train_ret['feed_list'], model="ernie", place=place) optimizer = fluid.optimizer.Adam(learning_rate=args.base_learning_rate) fluid.clip.set_gradient_clip(clip=fluid.clip.GradientClipByGlobalNorm(clip_norm=1.0)) optimizer.minimize(train_ret["avg_cost"]) lower_mem, upper_mem, unit = fluid.contrib.memory_usage( program=train_program, batch_size=args.batch_size) print("Theoretical memory usage in training: %.3f - %.3f %s" % (lower_mem, upper_mem, unit)) print("Device count: %d" % dev_count) exe.run(startup_prog) # load checkpoints if args.init_checkpoint and args.init_pretraining_params: print("WARNING: args 'init_checkpoint' and 'init_pretraining_params' " "both are set! Only arg 'init_checkpoint' is made valid.") if args.init_checkpoint: utils.init_checkpoint(exe, args.init_checkpoint, startup_prog) elif args.init_pretraining_params: utils.init_pretraining_params(exe, args.init_pretraining_params, startup_prog) if dev_count>1 and not args.use_cuda: device = "GPU" if args.use_cuda else "CPU" print("%d %s are used to train model"%(dev_count, device)) # multi cpu/gpu config exec_strategy = fluid.ExecutionStrategy() build_strategy = fluid.BuildStrategy() compiled_prog = fluid.compiler.CompiledProgram(train_program).with_data_parallel( loss_name=train_ret['avg_cost'].name, build_strategy=build_strategy, exec_strategy=exec_strategy) else: compiled_prog = fluid.compiler.CompiledProgram(train_program) # start training steps = 0 for epoch_id in range(args.epoch): for data in train_pyreader(): steps += 1 if steps % args.print_steps == 0: fetch_list = [ train_ret["avg_cost"], train_ret["precision"], train_ret["recall"], train_ret["f1_score"], ] else: fetch_list = [] start_time = time.time() outputs = exe.run(program=compiled_prog, feed=data[0], fetch_list=fetch_list) end_time = time.time() if steps % args.print_steps == 0: loss, precision, recall, f1_score = [np.mean(x) for x in outputs] print("[train] batch_id = %d, loss = %.5f, P: %.5f, R: %.5f, F1: %.5f, elapsed time %.5f, " "pyreader queue_size: %d " % (steps, loss, precision, recall, f1_score, end_time - start_time, train_pyreader.queue.size())) if steps % args.save_steps == 0: save_path = os.path.join(args.model_save_dir, "step_" + str(steps)) print("\tsaving model as %s" % (save_path)) fluid.io.save_persistables(exe, save_path, train_program) if steps % args.validation_steps == 0: evaluate(exe, test_program, test_pyreader, train_ret) save_path = os.path.join(args.model_save_dir, "step_" + str(steps)) fluid.io.save_persistables(exe, save_path, train_program) def do_eval(args): # init executor if args.use_cuda: place = fluid.CUDAPlace(int(os.getenv('FLAGS_selected_gpus', '0'))) else: place = fluid.CPUPlace() ernie_config = ErnieConfig(args.ernie_config_path) ernie_config.print_config() test_program = fluid.Program() with fluid.program_guard(test_program, fluid.default_startup_program()): with fluid.unique_name.guard(): test_ret = creator.create_ernie_model(args, ernie_config) test_program = test_program.clone(for_test=True) pyreader = creator.create_pyreader(args, file_name=args.test_data, feed_list=test_ret['feed_list'], model="ernie", place=place, mode='test',) print('program startup') exe = fluid.Executor(place) exe.run(fluid.default_startup_program()) print('program loading') # load model if not args.init_checkpoint: raise ValueError("args 'init_checkpoint' should be set if only doing test or infer!") utils.init_checkpoint(exe, args.init_checkpoint, test_program) evaluate(exe, test_program, pyreader, test_ret) def do_infer(args): # init executor if args.use_cuda: place = fluid.CUDAPlace(int(os.getenv('FLAGS_selected_gpus', '0'))) else: place = fluid.CPUPlace() # define network and reader ernie_config = ErnieConfig(args.ernie_config_path) ernie_config.print_config() infer_program = fluid.Program() with fluid.program_guard(infer_program, fluid.default_startup_program()): with fluid.unique_name.guard(): infer_ret = creator.create_ernie_model(args, ernie_config) infer_program = infer_program.clone(for_test=True) print(args.test_data) pyreader, reader = creator.create_pyreader(args, file_name=args.test_data, feed_list=infer_ret['feed_list'], model="ernie", place=place, return_reader=True, mode='test') exe = fluid.Executor(place) exe.run(fluid.default_startup_program()) # load model if not args.init_checkpoint: raise ValueError("args 'init_checkpoint' should be set if only doing test or infer!") utils.init_checkpoint(exe, args.init_checkpoint, infer_program) # create dict id2word_dict = dict([(str(word_id), word) for word, word_id in reader.vocab.items()]) id2label_dict = dict([(str(label_id), label) for label, label_id in reader.label_map.items()]) Dataset = namedtuple("Dataset", ["id2word_dict", "id2label_dict"]) dataset = Dataset(id2word_dict, id2label_dict) # make prediction for data in pyreader(): (words, crf_decode) = exe.run(infer_program, fetch_list=[infer_ret["words"], infer_ret["crf_decode"]], feed=data[0], return_numpy=False) # User should notice that words had been clipped if long than args.max_seq_len results = utils.parse_result(words, crf_decode, dataset) for sent, tags in results: result_list = ['(%s, %s)' % (ch, tag) for ch, tag in zip(sent, tags)] print(''.join(result_list)) if __name__ == "__main__": parser = argparse.ArgumentParser(__doc__) utils.load_yaml(parser, './conf/ernie_args.yaml') args = parser.parse_args() check_cuda(args.use_cuda) check_version() utils.print_arguments(args) if args.mode == 'train': do_train(args) elif args.mode == 'eval': do_eval(args) elif args.mode == 'infer': do_infer(args) else: print("Usage: %s --mode train|eval|infer " % sys.argv[0])