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- import numpy as np
- import pandas as pd
-
-
- results = {
- 'results-imagenet.csv': [
- 'results-imagenet-real.csv',
- 'results-imagenetv2-matched-frequency.csv',
- 'results-sketch.csv'
- ],
- 'results-imagenet-a-clean.csv': [
- 'results-imagenet-a.csv',
- ],
- 'results-imagenet-r-clean.csv': [
- 'results-imagenet-r.csv',
- ],
- }
-
-
- def diff(base_df, test_csv):
- base_models = base_df['model'].values
- test_df = pd.read_csv(test_csv)
- test_models = test_df['model'].values
-
- rank_diff = np.zeros_like(test_models, dtype='object')
- top1_diff = np.zeros_like(test_models, dtype='object')
- top5_diff = np.zeros_like(test_models, dtype='object')
-
- for rank, model in enumerate(test_models):
- if model in base_models:
- base_rank = int(np.where(base_models == model)[0])
- top1_d = test_df['top1'][rank] - base_df['top1'][base_rank]
- top5_d = test_df['top5'][rank] - base_df['top5'][base_rank]
-
- # rank_diff
- if rank == base_rank:
- rank_diff[rank] = f'0'
- elif rank > base_rank:
- rank_diff[rank] = f'-{rank - base_rank}'
- else:
- rank_diff[rank] = f'+{base_rank - rank}'
-
- # top1_diff
- if top1_d >= .0:
- top1_diff[rank] = f'+{top1_d:.3f}'
- else:
- top1_diff[rank] = f'-{abs(top1_d):.3f}'
-
- # top5_diff
- if top5_d >= .0:
- top5_diff[rank] = f'+{top5_d:.3f}'
- else:
- top5_diff[rank] = f'-{abs(top5_d):.3f}'
-
- else:
- rank_diff[rank] = ''
- top1_diff[rank] = ''
- top5_diff[rank] = ''
-
- test_df['top1_diff'] = top1_diff
- test_df['top5_diff'] = top5_diff
- test_df['rank_diff'] = rank_diff
-
- test_df['param_count'] = test_df['param_count'].map('{:,.2f}'.format)
- test_df.sort_values(['top1', 'top5', 'model'], ascending=[False, False, True], inplace=True)
- test_df.to_csv(test_csv, index=False, float_format='%.3f')
-
-
- for base_results, test_results in results.items():
- base_df = pd.read_csv(base_results)
- base_df.sort_values(['top1', 'top5', 'model'], ascending=[False, False, True], inplace=True)
- for test_csv in test_results:
- diff(base_df, test_csv)
- base_df['param_count'] = base_df['param_count'].map('{:,.2f}'.format)
- base_df.to_csv(base_results, index=False, float_format='%.3f')
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