A sample of relabel dataset has shape of [2,5,15,15] Could you expla

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about relabel dataset about relabel_imagenet HOT 3 CLOSED

naver-ai commented on July 26, 2024

about relabel dataset

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Comments (3)

hellbell commented on July 26, 2024

Hi, are you looking at label_map?

relabel_imagenet/utils/relabel_functions.py

Lines 15 to 27 in 6cd84df

 def get_labelmaps(label_maps_topk, num_batches): 

 label_maps_topk_sizes = label_maps_topk[0].size() 

 label_maps = torch.zeros([num_batches, 1000, label_maps_topk_sizes[2], 

 label_maps_topk_sizes[3]]) 

 for _label_map, _label_topk in zip(label_maps, label_maps_topk): 

 _label_map = _label_map.scatter_( 

 0, 

 _label_topk[1][:, :, :].long(), 

 _label_topk[0][:, :, :] 

 ) 

 label_maps = label_maps.cuda() 

 return label_maps

For the first dim of label_map,
label_map[0] is a tensor for the prediction scores, and label_map[1] is for the class indices as you saying.
The second dim indicates the top-k index because we only store the top-k label maps for storage efficiency.

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SeungyounShin commented on July 26, 2024

This is a sample image from the Imagenet and Relabel dataset. The dominant pixel value of label map is 769, and I would like to know the class name of the number. Is there any way of converting this?

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SanghyukChun commented on July 26, 2024

@SeungyounShin
You can use this JSON data.
https://gist.github.com/yrevar/942d3a0ac09ec9e5eb3a
769 is 'rule, ruler'

from relabel_imagenet.

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	def get_labelmaps(label_maps_topk, num_batches):
	label_maps_topk_sizes = label_maps_topk[0].size()

	label_maps = torch.zeros([num_batches, 1000, label_maps_topk_sizes[2],
	label_maps_topk_sizes[3]])
	for _label_map, _label_topk in zip(label_maps, label_maps_topk):
	_label_map = _label_map.scatter_(
	0,
	_label_topk[1][:, :, :].long(),
	_label_topk[0][:, :, :]
	)
	label_maps = label_maps.cuda()
	return label_maps

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