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- # Copyright 2021 Huawei Technologies Co., Ltd
- #
- # 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.
- # ============================================================================
-
- '''
- postprocess script.
- '''
-
- import os
- import argparse
- import numpy as np
- from mindspore import Tensor
- from src.finetune_eval_config import bert_net_cfg
- from src.assessment_method import Accuracy, F1, MCC, Spearman_Correlation
- from run_ner import eval_result_print
-
- parser = argparse.ArgumentParser(description="postprocess")
- parser.add_argument("--batch_size", type=int, default=1, help="Eval batch size, default is 1")
- parser.add_argument("--label_dir", type=str, default="", help="label data dir")
- parser.add_argument("--assessment_method", type=str, default="BF1", choices=["BF1", "clue_benchmark", "MF1"],
- help="assessment_method include: [BF1, clue_benchmark, MF1], default is BF1")
- parser.add_argument("--result_dir", type=str, default="./result_Files", help="infer result Files")
- parser.add_argument("--use_crf", type=str, default="false", choices=["true", "false"],
- help="Use crf, default is false")
-
- args, _ = parser.parse_known_args()
-
- if __name__ == "__main__":
- num_class = 41
- assessment_method = args.assessment_method.lower()
- use_crf = args.use_crf
-
- if assessment_method == "accuracy":
- callback = Accuracy()
- elif assessment_method == "bf1":
- callback = F1((use_crf.lower() == "true"), num_class)
- elif assessment_method == "mf1":
- callback = F1((use_crf.lower() == "true"), num_labels=num_class, mode="MultiLabel")
- elif assessment_method == "mcc":
- callback = MCC()
- elif assessment_method == "spearman_correlation":
- callback = Spearman_Correlation()
- else:
- raise ValueError("Assessment method not supported, support: [accuracy, f1, mcc, spearman_correlation]")
-
- file_name = os.listdir(args.label_dir)
- for f in file_name:
- if use_crf.lower() == "true":
- logits = ()
- for j in range(bert_net_cfg.seq_length):
- f_name = f.split('.')[0] + '_' + str(j) + '.bin'
- data_tmp = np.fromfile(os.path.join(args.result_dir, f_name), np.int32)
- data_tmp = data_tmp.reshape(args.batch_size, num_class + 2)
- logits += ((Tensor(data_tmp),),)
- f_name = f.split('.')[0] + '_' + str(bert_net_cfg.seq_length) + '.bin'
- data_tmp = np.fromfile(os.path.join(args.result_dir, f_name), np.int32).tolist()
- data_tmp = Tensor(data_tmp)
- logits = (logits, data_tmp)
- else:
- f_name = os.path.join(args.result_dir, f.split('.')[0] + '_0.bin')
- logits = np.fromfile(f_name, np.float32).reshape(bert_net_cfg.seq_length * args.batch_size, num_class)
- logits = Tensor(logits)
- label_ids = np.fromfile(os.path.join(args.label_dir, f), np.int32)
- label_ids = Tensor(label_ids.reshape(args.batch_size, bert_net_cfg.seq_length))
- callback.update(logits, label_ids)
-
- print("==============================================================")
- eval_result_print(assessment_method, callback)
- print("==============================================================")
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