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| import csv | |
| import os | |
| import sys | |
| __dir__ = os.path.dirname(os.path.abspath(__file__)) | |
| sys.path.append(__dir__) | |
| sys.path.insert(0, os.path.abspath(os.path.join(__dir__, '..'))) | |
| import numpy as np | |
| from tools.data import build_dataloader | |
| from tools.engine import Config, Trainer | |
| from tools.utility import ArgsParser | |
| def parse_args(): | |
| parser = ArgsParser() | |
| args = parser.parse_args() | |
| return args | |
| def main(): | |
| FLAGS = parse_args() | |
| cfg = Config(FLAGS.config) | |
| FLAGS = vars(FLAGS) | |
| opt = FLAGS.pop('opt') | |
| cfg.merge_dict(FLAGS) | |
| cfg.merge_dict(opt) | |
| cfg.cfg['Global']['use_amp'] = False | |
| if cfg.cfg['Global']['output_dir'][-1] == '/': | |
| cfg.cfg['Global']['output_dir'] = cfg.cfg['Global']['output_dir'][:-1] | |
| cfg.cfg['Global']['max_text_length'] = 200 | |
| cfg.cfg['Architecture']['Decoder']['max_len'] = 200 | |
| cfg.cfg['Metric']['name'] = 'RecMetricLong' | |
| if cfg.cfg['Global']['pretrained_model'] is None: | |
| cfg.cfg['Global'][ | |
| 'pretrained_model'] = cfg.cfg['Global']['output_dir'] + '/best.pth' | |
| trainer = Trainer(cfg, mode='eval') | |
| best_model_dict = trainer.status.get('metrics', {}) | |
| trainer.logger.info('metric in ckpt ***************') | |
| for k, v in best_model_dict.items(): | |
| trainer.logger.info('{}:{}'.format(k, v)) | |
| data_dirs_list = [ | |
| ['../ltb/long_lmdb'], | |
| ] | |
| cfg = cfg.cfg | |
| file_csv = open( | |
| cfg['Global']['output_dir'] + '/' + | |
| cfg['Global']['output_dir'].split('/')[-1] + | |
| '_result1_1_test_all_long_final_ultra_bs1.csv', 'w') | |
| csv_w = csv.writer(file_csv) | |
| for data_dirs in data_dirs_list: | |
| acc_each = [] | |
| acc_each_num = [] | |
| acc_each_dis = [] | |
| each_long = {} | |
| for datadir in data_dirs: | |
| config_each = cfg.copy() | |
| config_each['Eval']['dataset']['data_dir_list'] = [datadir] | |
| valid_dataloader = build_dataloader(config_each, 'Eval', | |
| trainer.logger) | |
| trainer.logger.info( | |
| f'{datadir} valid dataloader has {len(valid_dataloader)} iters' | |
| ) | |
| trainer.valid_dataloader = valid_dataloader | |
| metric = trainer.eval() | |
| acc_each.append(metric['acc'] * 100) | |
| acc_each_dis.append(metric['norm_edit_dis']) | |
| acc_each_num.append(metric['all_num']) | |
| trainer.logger.info('metric eval ***************') | |
| for k, v in metric.items(): | |
| trainer.logger.info('{}:{}'.format(k, v)) | |
| if 'each' in k: | |
| csv_w.writerow([k] + v[26:]) | |
| each_long[k] = each_long.get(k, []) + [np.array(v[26:])] | |
| avg1 = np.array(acc_each) * np.array(acc_each_num) / sum(acc_each_num) | |
| csv_w.writerow(acc_each + [avg1.sum().tolist()] + | |
| [sum(acc_each) / len(acc_each)]) | |
| print(acc_each + [avg1.sum().tolist()] + | |
| [sum(acc_each) / len(acc_each)]) | |
| avg1 = np.array(acc_each_dis) * np.array(acc_each_num) / sum( | |
| acc_each_num) | |
| csv_w.writerow(acc_each_dis + [avg1.sum().tolist()] + | |
| [sum(acc_each_dis) / len(acc_each)]) | |
| sum_all = np.array(each_long['each_len_num']).sum(0) | |
| for k, v in each_long.items(): | |
| if k != 'each_len_num': | |
| v_sum_weight = (np.array(v) * | |
| np.array(each_long['each_len_num'])).sum(0) | |
| sum_all_pad = np.where(sum_all == 0, 1., sum_all) | |
| v_all = v_sum_weight / sum_all_pad | |
| v_all = np.where(sum_all == 0, 0., v_all) | |
| csv_w.writerow([k] + v_all.tolist()) | |
| v_26_40 = (v_all[:10] * sum_all[:10]) / sum_all[:10].sum() | |
| csv_w.writerow([k + '26_35'] + [v_26_40.sum().tolist()] + | |
| [sum_all[:10].sum().tolist()]) | |
| v_41_55 = (v_all[10:30] * | |
| sum_all[10:30]) / sum_all[10:30].sum() | |
| csv_w.writerow([k + '36_55'] + [v_41_55.sum().tolist()] + | |
| [sum_all[10:30].sum().tolist()]) | |
| v_56_70 = (v_all[30:] * sum_all[30:]) / sum_all[30:].sum() | |
| csv_w.writerow([k + '56'] + [v_56_70.sum().tolist()] + | |
| [sum_all[30:].sum().tolist()]) | |
| else: | |
| csv_w.writerow([k] + sum_all.tolist()) | |
| file_csv.close() | |
| if __name__ == '__main__': | |
| main() | |