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pytorch-image-models/results/generate_csv_results.py

58 lines
2.3 KiB

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