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58 lines
2.3 KiB
58 lines
2.3 KiB
import numpy as np
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import pandas as pd
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results = {
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'results-imagenet.csv': pd.read_csv('results-imagenet.csv'),
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'results-imagenetv2-matched-frequency.csv': pd.read_csv('results-imagenetv2-matched-frequency.csv'),
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'results-sketch.csv': pd.read_csv('results-sketch.csv'),
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'results-imagenet-a.csv': pd.read_csv('results-imagenet-a.csv'),
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'results-imagenet-r.csv': pd.read_csv('results-imagenet-r.csv'),
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'results-imagenet-real.csv': pd.read_csv('results-imagenet-real.csv'),
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}
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def diff(csv_file):
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base_models = results['results-imagenet.csv']['model'].values
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csv_models = results[csv_file]['model'].values
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rank_diff = np.zeros_like(csv_models, dtype='object')
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top1_diff = np.zeros_like(csv_models, dtype='object')
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top5_diff = np.zeros_like(csv_models, dtype='object')
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for rank, model in enumerate(csv_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 = results[csv_file]['top1'][rank]-results['results-imagenet.csv']['top1'][base_rank]
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top5_d = results[csv_file]['top5'][rank]-results['results-imagenet.csv']['top5'][base_rank]
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# rank_diff
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if rank == base_rank: rank_diff[rank] = f'='
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elif rank > base_rank: rank_diff[rank] = f'-{rank-base_rank}'
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else: rank_diff[rank] = f'+{base_rank-rank}'
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# top1_diff
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if top1_d >= .0: top1_diff[rank] = f'+{top1_d:.3f}'
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else : top1_diff[rank] = f'-{abs(top1_d):.3f}'
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# top5_diff
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if top5_d >= .0: top5_diff[rank] = f'+{top5_d:.3f}'
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else : top5_diff[rank] = f'-{abs(top5_d):.3f}'
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else:
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rank_diff[rank] = 'X'
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top1_diff[rank] = 'X'
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top5_diff[rank] = 'X'
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results[csv_file]['rank_diff'] = rank_diff
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results[csv_file]['top1_diff'] = top1_diff
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results[csv_file]['top5_diff'] = top5_diff
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results[csv_file]['param_count'] = results[csv_file]['param_count'].map('{:,.2f}'.format)
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results[csv_file].to_csv(csv_file, index=False, float_format='%.3f')
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for csv_file in results:
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if csv_file != 'results-imagenet.csv':
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diff(csv_file)
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