Comments (9)
- 因为在我的实验中, query不做region划分, 只有第一行对应的是query原图与positive的原图及region的相似度
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- 我不清楚你的query和gallery具体如何划分, 你的query和gallery是否会有类别重叠? 如果你每张query最多只有4个positive, 那么pos_num最大为4, neg_num的话不一定是10, 可以就性能和GPU memory来做具体限制.
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另外,如果你在reID上做实验的话,其实我建议在一个reID的codebase上加上这个region的loss. 因为这个code针对的是街景图,无论是图像预处理,还是training scheme都跟reID上最适合的不一样. netvlad也不一定在reID上能收敛的好.
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好的,感谢。我做局部特征的检索,我试验下直接用区域-区域之间是否可以通过这种无监督方式来做。netVLAD刚好也可以用提局部特征。
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还想请教下,sync_gather的两种模式,内存和显存占用有大概统计下极限吗? True的时候11G显存超,False的时候128GB内存超。query+gallery大概5w多张样本。
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超内存和超显存的代码位置应该不一样. 为false的时候哪句话超的内存?
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True: dist.all_gather(all_features, features)显存超
False:bc_features.data.copy_(torch.cat(features))内存超
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bc_features = torch.cat(features).cuda(gpu)
for k in range(world_size):
bc_features.data.copy_(torch.cat(features))
dist.broadcast(bt_features,k)
True显存超可能没法解决,False内存超这里有点疑问?
bc_features = torch.cat(features).cuda(gpu)这里定义在gpu上面,为啥copy_的时候是内存在涨?
copy_是不是应该移到循环外面?
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这里features太多的时候确实可能超,应该是代码上有缺陷.
有人做了修改(#6) ,可以参考一下
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Related Issues (20)
- About negative samples HOT 3
- how the training set is set up HOT 1
- Question on tab.3 of the paper HOT 2
- Evalution protocol
- Performances on Oxford and Paris HOT 6
- models for torch.hub
- .mat files HOT 3
- missing keys in state_dict HOT 1
- pitts 250k top-1 88.2% HOT 1
- About Normalize in get_transformer_train and get_transformer_test HOT 1
- reproduction problem HOT 9
- reproduction problem
- Extract descriptor of single image using models trained on custom datasets
- How to visualize the feature map like Fig.5 in paper?
- Reproducing SARE results
- Test on RParis and ROxford dataset
- about modify d=25 meters HOT 2
- Reproduce results on Tokyo247 dataset HOT 2
- authors dont reply, found a better repo
- Is the pretrained model also MIT? HOT 2
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