112 lines
4.0 KiB
Python
112 lines
4.0 KiB
Python
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import logging
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import numpy as np
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import paddle.fluid as fluid
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__all__ = ['eval_rec_run', 'test_rec_benchmark']
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import logging
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FORMAT = '%(asctime)s-%(levelname)s: %(message)s'
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logging.basicConfig(level=logging.INFO, format=FORMAT)
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logger = logging.getLogger(__name__)
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from ppocr.utils.character import cal_predicts_accuracy
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from ppocr.utils.character import convert_rec_label_to_lod
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from ppocr.utils.character import convert_rec_attention_infer_res
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from ppocr.utils.utility import create_module
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import json
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from copy import deepcopy
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import cv2
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from ppocr.data.reader_main import reader_main
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def eval_rec_run(exe, config, eval_info_dict, mode):
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"""
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Run evaluation program, return program outputs.
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"""
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char_ops = config['Global']['char_ops']
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total_loss = 0
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total_sample_num = 0
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total_acc_num = 0
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total_batch_num = 0
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if mode == "test":
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is_remove_duplicate = False
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else:
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is_remove_duplicate = True
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for data in eval_info_dict['reader']():
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img_num = len(data)
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img_list = []
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label_list = []
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for ino in range(img_num):
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img_list.append(data[ino][0])
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label_list.append(data[ino][1])
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img_list = np.concatenate(img_list, axis=0)
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outs = exe.run(eval_info_dict['program'], \
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feed={'image': img_list}, \
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fetch_list=eval_info_dict['fetch_varname_list'], \
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return_numpy=False)
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preds = np.array(outs[0])
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if preds.shape[1] != 1:
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preds, preds_lod = convert_rec_attention_infer_res(preds)
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else:
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preds_lod = outs[0].lod()[0]
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labels, labels_lod = convert_rec_label_to_lod(label_list)
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acc, acc_num, sample_num = cal_predicts_accuracy(
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char_ops, preds, preds_lod, labels, labels_lod, is_remove_duplicate)
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total_acc_num += acc_num
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total_sample_num += sample_num
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total_batch_num += 1
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avg_acc = total_acc_num * 1.0 / total_sample_num
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metrics = {'avg_acc': avg_acc, "total_acc_num": total_acc_num, \
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"total_sample_num": total_sample_num}
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return metrics
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def test_rec_benchmark(exe, config, eval_info_dict):
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" 评估lmdb 数据"
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eval_data_list = ['IIIT5k_3000', 'SVT', 'IC03_860', 'IC03_867', \
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'IC13_857', 'IC13_1015', 'IC15_1811', 'IC15_2077', 'SVTP', 'CUTE80']
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eval_data_dir = config['TestReader']['lmdb_sets_dir']
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total_evaluation_data_number = 0
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total_correct_number = 0
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eval_data_acc_info = {}
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for eval_data in eval_data_list:
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config['EvalReader']['lmdb_sets_dir'] = \
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eval_data_dir + "/" + eval_data
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eval_reader = reader_main(config=config, mode="eval")
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eval_info_dict['reader'] = eval_reader
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metrics = eval_rec_run(exe, config, eval_info_dict, "eval")
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total_evaluation_data_number += metrics['total_sample_num']
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total_correct_number += metrics['total_acc_num']
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eval_data_acc_info[eval_data] = metrics
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avg_acc = total_correct_number * 1.0 / total_evaluation_data_number
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logger.info('-' * 50)
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strs = ""
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for eval_data in eval_data_list:
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eval_acc = eval_data_acc_info[eval_data]['avg_acc']
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strs += "\n {}, accuracy:{:.6f}".format(eval_data, eval_acc)
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strs += "\n average, accuracy:{:.6f}".format(avg_acc)
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logger.info(strs)
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logger.info('-' * 50)
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