122 lines
3.9 KiB
Python
122 lines
3.9 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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import argparse
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import os.path as osp
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from pathlib import Path
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import mmcv
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import mmengine
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import torch
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from mmengine import DictAction
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from mmpretrain.datasets import build_dataset
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from mmpretrain.structures import DataSample
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from mmpretrain.visualization import UniversalVisualizer
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def parse_args():
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parser = argparse.ArgumentParser(
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description='MMCls evaluate prediction success/fail')
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parser.add_argument('config', help='test config file path')
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parser.add_argument('result', help='test result json/pkl file')
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parser.add_argument(
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'--out-dir', required=True, help='dir to store output files')
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parser.add_argument(
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'--topk',
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default=20,
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type=int,
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help='Number of images to select for success/fail')
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parser.add_argument(
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'--rescale-factor',
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'-r',
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type=float,
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help='image rescale factor, which is useful if the output is too '
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'large or too small.')
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parser.add_argument(
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'--cfg-options',
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nargs='+',
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action=DictAction,
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help='override some settings in the used config, the key-value pair '
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'in xxx=yyy format will be merged into config file. If the value to '
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'be overwritten is a list, it should be like key="[a,b]" or key=a,b '
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'It also allows nested list/tuple values, e.g. key="[(a,b),(c,d)]" '
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'Note that the quotation marks are necessary and that no white space '
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'is allowed.')
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args = parser.parse_args()
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return args
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def save_imgs(result_dir, folder_name, results, dataset, rescale_factor=None):
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full_dir = osp.join(result_dir, folder_name)
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vis = UniversalVisualizer()
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vis.dataset_meta = {'classes': dataset.CLASSES}
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# save imgs
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dump_infos = []
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for data_sample in results:
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data_info = dataset.get_data_info(data_sample.sample_idx)
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if 'img' in data_info:
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img = data_info['img']
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name = str(data_sample.sample_idx)
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elif 'img_path' in data_info:
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img = mmcv.imread(data_info['img_path'], channel_order='rgb')
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name = Path(data_info['img_path']).name
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else:
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raise ValueError('Cannot load images from the dataset infos.')
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if rescale_factor is not None:
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img = mmcv.imrescale(img, rescale_factor)
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vis.visualize_cls(
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img, data_sample, out_file=osp.join(full_dir, name + '.png'))
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dump = dict()
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for k, v in data_sample.items():
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if isinstance(v, torch.Tensor):
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dump[k] = v.tolist()
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else:
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dump[k] = v
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dump_infos.append(dump)
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mmengine.dump(dump_infos, osp.join(full_dir, folder_name + '.json'))
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def main():
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args = parse_args()
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cfg = mmengine.Config.fromfile(args.config)
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if args.cfg_options is not None:
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cfg.merge_from_dict(args.cfg_options)
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# build the dataloader
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cfg.test_dataloader.dataset.pipeline = []
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dataset = build_dataset(cfg.test_dataloader.dataset)
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results = list()
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for result in mmengine.load(args.result):
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data_sample = DataSample()
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data_sample.set_metainfo({'sample_idx': result['sample_idx']})
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data_sample.set_gt_label(result['gt_label'])
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data_sample.set_pred_label(result['pred_label'])
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data_sample.set_pred_score(result['pred_score'])
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results.append(data_sample)
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# sort result
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results = sorted(results, key=lambda x: torch.max(x.pred_score))
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success = list()
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fail = list()
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for data_sample in results:
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if (data_sample.pred_label == data_sample.gt_label).all():
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success.append(data_sample)
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else:
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fail.append(data_sample)
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success = success[:args.topk]
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fail = fail[:args.topk]
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save_imgs(args.out_dir, 'success', success, dataset, args.rescale_factor)
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save_imgs(args.out_dir, 'fail', fail, dataset, args.rescale_factor)
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if __name__ == '__main__':
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main()
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