125 lines
4.1 KiB
Python
125 lines
4.1 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 ClsDataSample
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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('--out-dir', 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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for result in results:
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data_sample = ClsDataSample()\
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.set_gt_label(result['gt_label'])\
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.set_pred_label(result['pred_label'])\
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.set_pred_score(result['pred_scores'])
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data_info = dataset.get_data_info(result['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(result['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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for k, v in result.items():
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if isinstance(v, torch.Tensor):
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result[k] = v.tolist()
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mmengine.dump(results, osp.join(full_dir, folder_name + '.json'))
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def main():
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args = parse_args()
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# load test results
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outputs = mmengine.load(args.result)
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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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outputs_list = list()
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for i in range(len(outputs)):
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output = dict()
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output['sample_idx'] = outputs[i]['sample_idx']
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output['gt_label'] = outputs[i]['gt_label']['label']
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output['pred_score'] = float(
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torch.max(outputs[i]['pred_label']['score']).item())
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output['pred_scores'] = outputs[i]['pred_label']['score']
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output['pred_label'] = outputs[i]['pred_label']['label']
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outputs_list.append(output)
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# sort result
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outputs_list = sorted(outputs_list, key=lambda x: x['pred_score'])
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success = list()
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fail = list()
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for output in outputs_list:
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if output['pred_label'] == output['gt_label']:
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success.append(output)
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else:
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fail.append(output)
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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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