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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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