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import os |
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import pandas as pd |
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from saicinpainting.evaluation.data import PrecomputedInpaintingResultsDataset |
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from saicinpainting.evaluation.evaluator import InpaintingEvaluator, lpips_fid100_f1 |
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from saicinpainting.evaluation.losses.base_loss import SegmentationAwareSSIM, \ |
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SegmentationClassStats, SSIMScore, LPIPSScore, FIDScore, SegmentationAwareLPIPS, SegmentationAwareFID |
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from saicinpainting.evaluation.utils import load_yaml |
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def main(args): |
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config = load_yaml(args.config) |
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dataset = PrecomputedInpaintingResultsDataset(args.datadir, args.predictdir, **config.dataset_kwargs) |
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metrics = { |
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'ssim': SSIMScore(), |
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'lpips': LPIPSScore(), |
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'fid': FIDScore() |
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} |
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enable_segm = config.get('segmentation', dict(enable=False)).get('enable', False) |
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if enable_segm: |
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weights_path = os.path.expandvars(config.segmentation.weights_path) |
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metrics.update(dict( |
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segm_stats=SegmentationClassStats(weights_path=weights_path), |
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segm_ssim=SegmentationAwareSSIM(weights_path=weights_path), |
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segm_lpips=SegmentationAwareLPIPS(weights_path=weights_path), |
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segm_fid=SegmentationAwareFID(weights_path=weights_path) |
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)) |
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evaluator = InpaintingEvaluator(dataset, scores=metrics, |
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integral_title='lpips_fid100_f1', integral_func=lpips_fid100_f1, |
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**config.evaluator_kwargs) |
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os.makedirs(os.path.dirname(args.outpath), exist_ok=True) |
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results = evaluator.evaluate() |
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results = pd.DataFrame(results).stack(1).unstack(0) |
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results.dropna(axis=1, how='all', inplace=True) |
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results.to_csv(args.outpath, sep='\t', float_format='%.4f') |
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if enable_segm: |
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only_short_results = results[[c for c in results.columns if not c[0].startswith('segm_')]].dropna(axis=1, how='all') |
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only_short_results.to_csv(args.outpath + '_short', sep='\t', float_format='%.4f') |
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print(only_short_results) |
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segm_metrics_results = results[['segm_ssim', 'segm_lpips', 'segm_fid']].dropna(axis=1, how='all').transpose().unstack(0).reorder_levels([1, 0], axis=1) |
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segm_metrics_results.drop(['mean', 'std'], axis=0, inplace=True) |
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segm_stats_results = results['segm_stats'].dropna(axis=1, how='all').transpose() |
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segm_stats_results.index = pd.MultiIndex.from_tuples(n.split('/') for n in segm_stats_results.index) |
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segm_stats_results = segm_stats_results.unstack(0).reorder_levels([1, 0], axis=1) |
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segm_stats_results.sort_index(axis=1, inplace=True) |
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segm_stats_results.dropna(axis=0, how='all', inplace=True) |
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segm_results = pd.concat([segm_metrics_results, segm_stats_results], axis=1, sort=True) |
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segm_results.sort_values(('mask_freq', 'total'), ascending=False, inplace=True) |
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segm_results.to_csv(args.outpath + '_segm', sep='\t', float_format='%.4f') |
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else: |
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print(results) |
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if __name__ == '__main__': |
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import argparse |
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aparser = argparse.ArgumentParser() |
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aparser.add_argument('config', type=str, help='Path to evaluation config') |
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aparser.add_argument('datadir', type=str, |
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help='Path to folder with images and masks (output of gen_mask_dataset.py)') |
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aparser.add_argument('predictdir', type=str, |
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help='Path to folder with predicts (e.g. predict_hifill_baseline.py)') |
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aparser.add_argument('outpath', type=str, help='Where to put results') |
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main(aparser.parse_args()) |
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