Multi-scale testing #804
Multi-scale testing #804
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hi @fallingdust |
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hi @zimenglan-sysu-512 ,
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This looks very reasonable to me.
One drawback is that I think it will be very slow, as it will compute a lot of mask predictions, which will be filtered afterwards. But as a first implementation this is great!
One thing I'd do differently is that I would not use a global config in the code, but let's not be blocked by changing this.
Thanks a lot!
| import torch | ||
| import torchvision.transforms as TT | ||
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| from maskrcnn_benchmark.config import cfg |
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using the config globally in the library is something I've tried to avoid, but let's merge this as is and maybe modify this later on
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Since you disabled the bbox filtering after box head, you will have 1000 boxes fed into the mask head. How could you fit 1000 masks into GPU memory? |
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I guess you test on faster rcnn, but the config file is mask rcnn. |
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@fallingdust @shikunyu8 @fmassa |
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Hi, @youngwanLEE |
* Implement multi-scale testing(bbox aug) like Detectron. * Add comment. * Fix missing cfg after merge.
Implemented bbox augmentations(h-flip, multi-scale, multi-scale h-flip) in test time, just like Detectron.
There's a little hack in the data loading process, if you have better solution, please tell me.