[Enhancement] Update analyze_results.py for dev-1.x

[okotaku]

Motivation

Update analyze_results.py for mmcls v1.0.

Results

mim test mmcls configs/resnet/resnet18_8xb32_in1k.py --checkpoint https://download.openmmlab.com/mmclassification/v0/resnet/resnet18_8xb32_in1k_20210831-fbbb1da6.pth --dump result.pkl
mim run mmcls analyze_results configs/resnet/resnet18_8xb32_in1k.py result.pkl --out-dir show

Checklist

Before PR:

• Pre-commit or other linting tools are used to fix the potential lint issues.
• Bug fixes are fully covered by unit tests, the case that causes the bug should be added in the unit tests.
• The modification is covered by complete unit tests. If not, please add more unit test to ensure the correctness.
• The documentation has been modified accordingly, like docstring or example tutorials.

After PR:

• If the modification has potential influence on downstream or other related projects, this PR should be tested with those projects, like MMDet or MMSeg.
• CLA has been signed and all committers have signed the CLA in this PR.

[mzr1996]

Some images are too small to visualize, better to provide --rescale-factor option to rescale images before visualizing them. Please refers to https://github.com/open-mmlab/mmclassification/blob/ae37d7fd276d983eba3acc2676a2f6e2d720bd69/tools/visualizations/browse_dataset.py#L64-L69

[mzr1996]

Not all datasets have the img_path key. For example, the CIFAR dataset directly loads all images during initialization and only has img key.
Better to use sample_idx to get the data info from the dataset directly.

Suggested change

	output['img_path'] = outputs[i]['img_path']
	output['filename'] = Path(outputs[i]['img_path']).name
	output['gt_label'] = int(outputs[i]['gt_label']['label'][0])
	output['pred_score'] = float(
	torch.max(outputs[i]['pred_label']['score']).item())
	output['pred_scores'] = outputs[i]['pred_label']['score'].tolist()
	output['pred_label'] = int(outputs[i]['pred_label']['label'][0])
	output['sample_idx'] = outputs[i]['sample_idx']
	output['gt_label'] = outputs[i]['gt_label']['label']
	output['pred_score'] = float(
	torch.max(outputs[i]['pred_label']['score']).item())
	output['pred_scores'] = outputs[i]['pred_label']['score']
	output['pred_label'] = outputs[i]['pred_label']['label']

And in the save_imgs:

    for result in results:
        data_sample = ClsDataSample()\
            .set_gt_label(result['gt_label'])\
            .set_pred_label(result['pred_label'])\
            .set_pred_score(result['pred_scores'])
        data_info = dataset.get_data_info(result['sample_idx'])
        if 'img' in data_info:
            img = data_info['img']
            name = str(result['sample_idx'])
        elif 'img_path' in data_info:
            img = mmcv.imread(data_info['img_path'], channel_order='rgb')
            name = Path(data_info['img_path']).name
        else:
            raise ValueError('Cannot load images from the dataset infos.')
        if rescale_factor is not None:
            img = mmcv.imrescale(img, rescale_factor)
        vis.add_datasample(name, img, data_sample)

        for k, v in result.items():
            if isinstance(v, torch.Tensor):
                result[k] = v.tolist()