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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.config import Config
from tools.engine.trainer import 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/ultra_long_26_35_list.txt',
'../ltb/ultra_long_36_55_list.txt',
'../ltb/ultra_long_56_list.txt',
],
]
cfg = cfg.cfg
cfg['Eval']['dataset']['name'] = 'SimpleDataSet'
file_csv = open(
cfg['Global']['output_dir'] + '/' +
cfg['Global']['output_dir'].split('/')[-1] +
'_result1_1_test_all_long_simple_bi_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']['label_file_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()
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