87 lines
4.1 KiB
Python
87 lines
4.1 KiB
Python
import os
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from glob import glob
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import numpy as np
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from .config import Config
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config = Config()
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eval_txts = sorted(glob('e_results/*_eval.txt'))
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print('eval_txts:', [_.split(os.sep)[-1] for _ in eval_txts])
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score_panel = {}
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sep = '&'
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metrics = ['sm', 'wfm', 'hce'] # we used HCE for DIS and wFm for others.
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if 'DIS5K' not in config.task:
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metrics.remove('hce')
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for metric in metrics:
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print('Metric:', metric)
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current_line_nums = []
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for idx_et, eval_txt in enumerate(eval_txts):
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with open(eval_txt, 'r') as f:
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lines = [l for l in f.readlines()[3:] if '.' in l]
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current_line_nums.append(len(lines))
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for idx_et, eval_txt in enumerate(eval_txts):
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with open(eval_txt, 'r') as f:
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lines = [l for l in f.readlines()[3:] if '.' in l]
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for idx_line, line in enumerate(lines[:min(current_line_nums)]): # Consist line numbers by the minimal result file.
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properties = line.strip().strip(sep).split(sep)
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dataset = properties[0].strip()
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ckpt = properties[1].strip()
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if int(ckpt.split('--epoch_')[-1].strip()) < 0:
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continue
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targe_idx = {
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'sm': [5, 2, 2, 5, 2],
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'wfm': [3, 3, 8, 3, 8],
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'hce': [7, -1, -1, 7, -1]
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}[metric][['DIS5K', 'COD', 'HRSOD', 'General', 'Matting'].index(config.task)]
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if metric != 'hce':
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score_sm = float(properties[targe_idx].strip())
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else:
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score_sm = int(properties[targe_idx].strip().strip('.'))
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if idx_et == 0:
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score_panel[ckpt] = []
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score_panel[ckpt].append(score_sm)
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metrics_min = ['hce', 'mae']
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max_or_min = min if metric in metrics_min else max
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score_max = max_or_min(score_panel.values(), key=lambda x: np.sum(x))
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good_models = []
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for k, v in score_panel.items():
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if (np.sum(v) <= np.sum(score_max)) if metric in metrics_min else (np.sum(v) >= np.sum(score_max)):
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print(k, v)
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good_models.append(k)
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# Write
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with open(eval_txt, 'r') as f:
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lines = f.readlines()
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info4good_models = lines[:3]
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metric_names = [m.strip() for m in lines[1].strip().strip('&').split('&')[2:]]
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testset_mean_values = {metric_name: [] for metric_name in metric_names}
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for good_model in good_models:
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for idx_et, eval_txt in enumerate(eval_txts):
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with open(eval_txt, 'r') as f:
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lines = f.readlines()
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for line in lines:
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if set([good_model]) & set([_.strip() for _ in line.split(sep)]):
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info4good_models.append(line)
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metric_scores = [float(m.strip()) for m in line.strip().strip('&').split('&')[2:]]
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for idx_score, metric_score in enumerate(metric_scores):
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testset_mean_values[metric_names[idx_score]].append(metric_score)
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if 'DIS5K' in config.task:
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testset_mean_values_lst = ['{:<4}'.format(int(np.mean(v_lst[:-1]).round())) if name == 'HCE' else '{:.3f}'.format(np.mean(v_lst[:-1])).lstrip('0') for name, v_lst in testset_mean_values.items()] # [:-1] to remove DIS-VD
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sample_line_for_placing_mean_values = info4good_models[-2]
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numbers_placed_well = sample_line_for_placing_mean_values.replace(sample_line_for_placing_mean_values.split('&')[1].strip(), 'DIS-TEs').strip().split('&')[3:]
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for idx_number, (number_placed_well, testset_mean_value) in enumerate(zip(numbers_placed_well, testset_mean_values_lst)):
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numbers_placed_well[idx_number] = number_placed_well.replace(number_placed_well.strip(), testset_mean_value)
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testset_mean_line = '&'.join(sample_line_for_placing_mean_values.replace(sample_line_for_placing_mean_values.split('&')[1].strip(), 'DIS-TEs').split('&')[:3] + numbers_placed_well) + '\n'
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info4good_models.append(testset_mean_line)
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info4good_models.append(lines[-1])
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info = ''.join(info4good_models)
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print(info)
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with open(os.path.join('e_results', 'eval-{}_best_on_{}.txt'.format(config.task, metric)), 'w') as f:
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f.write(info + '\n')
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