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import csv |
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import os |
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import sys |
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import numpy as np |
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__dir__ = os.path.dirname(os.path.abspath(__file__)) |
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sys.path.append(__dir__) |
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sys.path.insert(0, os.path.abspath(os.path.join(__dir__, '..'))) |
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from tools.data import build_dataloader |
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from tools.engine.config import Config |
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from tools.engine.trainer import Trainer |
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from tools.utility import ArgsParser |
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def parse_args(): |
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parser = ArgsParser() |
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args = parser.parse_args() |
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return args |
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def main(): |
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FLAGS = parse_args() |
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cfg = Config(FLAGS.config) |
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FLAGS = vars(FLAGS) |
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opt = FLAGS.pop('opt') |
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cfg.merge_dict(FLAGS) |
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cfg.merge_dict(opt) |
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msr = False |
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if 'RatioDataSet' in cfg.cfg['Eval']['dataset']['name']: |
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msr = True |
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if cfg.cfg['Global']['output_dir'][-1] == '/': |
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cfg.cfg['Global']['output_dir'] = cfg.cfg['Global']['output_dir'][:-1] |
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if cfg.cfg['Global']['pretrained_model'] is None: |
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cfg.cfg['Global'][ |
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'pretrained_model'] = cfg.cfg['Global']['output_dir'] + '/best.pth' |
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cfg.cfg['Global']['use_amp'] = False |
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cfg.cfg['PostProcess']['with_ratio'] = True |
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cfg.cfg['Metric']['with_ratio'] = True |
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cfg.cfg['Metric']['max_len'] = 25 |
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cfg.cfg['Metric']['max_ratio'] = 12 |
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cfg.cfg['Eval']['dataset']['transforms'][-1]['KeepKeys'][ |
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'keep_keys'].append('real_ratio') |
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trainer = Trainer(cfg, mode='eval') |
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best_model_dict = trainer.status.get('metrics', {}) |
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trainer.logger.info('metric in ckpt ***************') |
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for k, v in best_model_dict.items(): |
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trainer.logger.info('{}:{}'.format(k, v)) |
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data_dirs_list = [[ |
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'../benchmark_bctr/benchmark_bctr_test/scene_test', |
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'../benchmark_bctr/benchmark_bctr_test/web_test', |
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'../benchmark_bctr/benchmark_bctr_test/document_test', |
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'../benchmark_bctr/benchmark_bctr_test/handwriting_test' |
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]] |
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cfg = cfg.cfg |
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file_csv = open( |
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cfg['Global']['output_dir'] + '/' + |
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cfg['Global']['output_dir'].split('/')[-1] + |
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'_eval_all_ch_length_ratio.csv', 'w') |
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csv_w = csv.writer(file_csv) |
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for data_dirs in data_dirs_list: |
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acc_each = [] |
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acc_each_real = [] |
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acc_each_ingore_space = [] |
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acc_each_ignore_space_symbol = [] |
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acc_each_lower_ignore_space_symbol = [] |
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acc_each_num = [] |
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acc_each_dis = [] |
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each_len = {} |
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each_ratio = {} |
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for datadir in data_dirs: |
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config_each = cfg.copy() |
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if msr: |
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config_each['Eval']['dataset']['data_dir_list'] = [datadir] |
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else: |
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config_each['Eval']['dataset']['data_dir'] = datadir |
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valid_dataloader = build_dataloader(config_each, 'Eval', |
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trainer.logger) |
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trainer.logger.info( |
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f'{datadir} valid dataloader has {len(valid_dataloader)} iters' |
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) |
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trainer.valid_dataloader = valid_dataloader |
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metric = trainer.eval() |
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acc_each.append(metric['acc'] * 100) |
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acc_each_real.append(metric['acc_real'] * 100) |
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acc_each_ingore_space.append(metric['acc_ignore_space'] * 100) |
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acc_each_ignore_space_symbol.append( |
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metric['acc_ignore_space_symbol'] * 100) |
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acc_each_lower_ignore_space_symbol.append( |
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metric['acc_lower_ignore_space_symbol'] * 100) |
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acc_each_dis.append(metric['norm_edit_dis']) |
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acc_each_num.append(metric['num_samples']) |
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trainer.logger.info('metric eval ***************') |
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csv_w.writerow([datadir]) |
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for k, v in metric.items(): |
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trainer.logger.info('{}:{}'.format(k, v)) |
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if 'each' in k: |
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csv_w.writerow([k] + v) |
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if 'each_len' in k: |
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each_len[k] = each_len.get(k, []) + [np.array(v)] |
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if 'each_ratio' in k: |
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each_ratio[k] = each_ratio.get(k, []) + [np.array(v)] |
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data_name = [ |
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data_n[:-1].split('/')[-1] |
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if data_n[-1] == '/' else data_n.split('/')[-1] |
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for data_n in data_dirs |
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] |
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csv_w.writerow(['-'] + data_name + ['arithmetic_avg'] + |
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['weighted_avg']) |
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csv_w.writerow([''] + acc_each_num) |
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avg1 = np.array(acc_each) * np.array(acc_each_num) / sum(acc_each_num) |
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csv_w.writerow(['acc'] + acc_each + [sum(acc_each) / len(acc_each)] + |
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[avg1.sum().tolist()]) |
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print(acc_each + [sum(acc_each) / len(acc_each)] + |
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[avg1.sum().tolist()]) |
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avg1 = np.array(acc_each_dis) * np.array(acc_each_num) / sum( |
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acc_each_num) |
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csv_w.writerow(['norm_edit_dis'] + acc_each_dis + |
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[sum(acc_each_dis) / len(acc_each)] + |
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[avg1.sum().tolist()]) |
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avg1 = np.array(acc_each_real) * np.array(acc_each_num) / sum( |
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acc_each_num) |
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csv_w.writerow(['acc_real'] + acc_each_real + |
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[sum(acc_each_real) / len(acc_each_real)] + |
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[avg1.sum().tolist()]) |
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avg1 = np.array(acc_each_ingore_space) * np.array(acc_each_num) / sum( |
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acc_each_num) |
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csv_w.writerow( |
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['acc_ignore_space'] + acc_each_ingore_space + |
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[sum(acc_each_ingore_space) / len(acc_each_ingore_space)] + |
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[avg1.sum().tolist()]) |
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avg1 = np.array(acc_each_ignore_space_symbol) * np.array( |
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acc_each_num) / sum(acc_each_num) |
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csv_w.writerow(['acc_ignore_space_symbol'] + |
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acc_each_ignore_space_symbol + [ |
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sum(acc_each_ignore_space_symbol) / |
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len(acc_each_ignore_space_symbol) |
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] + [avg1.sum().tolist()]) |
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avg1 = np.array(acc_each_lower_ignore_space_symbol) * np.array( |
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acc_each_num) / sum(acc_each_num) |
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csv_w.writerow(['acc_lower_ignore_space_symbol'] + |
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acc_each_lower_ignore_space_symbol + [ |
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sum(acc_each_lower_ignore_space_symbol) / |
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len(acc_each_lower_ignore_space_symbol) |
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] + [avg1.sum().tolist()]) |
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sum_all = np.array(each_len['each_len_num']).sum(0) |
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for k, v in each_len.items(): |
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if k != 'each_len_num': |
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v_sum_weight = (np.array(v) * |
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np.array(each_len['each_len_num'])).sum(0) |
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sum_all_pad = np.where(sum_all == 0, 1., sum_all) |
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v_all = v_sum_weight / sum_all_pad |
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v_all = np.where(sum_all == 0, 0., v_all) |
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csv_w.writerow([k] + v_all.tolist()) |
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else: |
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csv_w.writerow([k] + sum_all.tolist()) |
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sum_all = np.array(each_ratio['each_ratio_num']).sum(0) |
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for k, v in each_ratio.items(): |
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if k != 'each_ratio_num': |
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v_sum_weight = (np.array(v) * |
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np.array(each_ratio['each_ratio_num'])).sum(0) |
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sum_all_pad = np.where(sum_all == 0, 1., sum_all) |
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v_all = v_sum_weight / sum_all_pad |
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v_all = np.where(sum_all == 0, 0., v_all) |
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csv_w.writerow([k] + v_all.tolist()) |
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else: |
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csv_w.writerow([k] + sum_all.tolist()) |
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file_csv.close() |
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if __name__ == '__main__': |
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main() |
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