pytorch-image-models

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