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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.
- # ============================================================================
- """post process for 310 inference"""
- import os
- import numpy as np
- from src.model_utils.config import config
- from src.util import CTCLabelConverter
-
-
- def calcul_acc(labels, preds):
- return sum(1 for x, y in zip(labels, preds) if x == y) / len(labels)
-
-
- def get_result(result_path, label_path):
- """Get result."""
- converter = CTCLabelConverter(config.CHARACTER)
- files = os.listdir(result_path)
- preds = []
- labels = []
- label_dict = {}
- with open(label_path, 'r') as f:
- lines = f.readlines()
- for line in lines:
- label_dict[line.split(',')[0]] = line.split(',')[1].replace('\n', '')
- for file in files:
- file_name = file.split('.')[0]
- label = label_dict[file_name]
- labels.append(label)
- new_result_path = os.path.join(result_path, file)
- output = np.fromfile(new_result_path, dtype=np.float32)
- output = np.reshape(output, (config.FINAL_FEATURE_WIDTH, config.NUM_CLASS))
- model_predict = np.squeeze(output)
- preds_size = np.array([model_predict.shape[0]] * 1)
- preds_index = np.argmax(model_predict, axis=1)
- preds_str = converter.decode(preds_index, preds_size)
- preds.append(preds_str[0])
- acc = calcul_acc(labels, preds)
- print("Total data: {}, accuracy: {}".format(len(labels), acc))
-
-
- if __name__ == '__main__':
- get_result(config.result_path, config.label_path)
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