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microsoft / ProDA

Prototypical Pseudo Label Denoising and Target Structure Learning for Domain Adaptive Semantic Segmentation (CVPR 2021)
https://arxiv.org/abs/2101.10979
MIT License
286 stars 44 forks source link

About the flip Image #44

Closed JunXieFront closed 2 years ago

JunXieFront commented 2 years ago 
if opt.flip:
    flip_out = model.BaseNet_DP(fliplr(images_val))
    flip_out['out'] = F.interpolate(sm(flip_out['out']), size=images_val.size()[2:], mode='bilinear', align_corners=True)
    out['out'] = F.interpolate(sm(out['out']), size=images_val.size()[2:], mode='bilinear', align_corners=True)
    out['out'] = (out['out'] + fliplr(flip_out['out'])) / 2

Dear author，thanks for sharing the code with us. I don't the reason why we should flip the image and compute flip_out here, If it can help increasing the performance, why not using it in generating soft label at first time?

QChhh123 commented 1 year ago

Hi, can you tell me the reason? Thank you very much.