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View Code? Open in Web Editor NEWACFNet: Attentional Class Feature Network for Semantic Segmentation.(ICCV2019)
ACFNet: Attentional Class Feature Network for Semantic Segmentation.(ICCV2019)
Line 59&60 in loss/criterion.py
pred = preds[0] + preds[1]
loss4 = lovasz_softmax(F.softmax(pred, dim=1), target, ignore=self.ignore_index)
这里的 pred
没有做upsample, 然后直接给了lovasz_softmax, 而 target
是原图的size大小,这样会造成 pred 与 target 的size 不匹配。
hi,good work! I only have two 2080ti cards. Is it enough to run the code ?
你好,请问你是用几张gpu跑出的效果啊,
# multiply & normalize ->(B, N, C')
energy = torch.bmm(proj_query, proj_key)
energy_new = torch.max(energy, -1, keepdim=True)[0].expand_as(energy) - energy
I think the normalization implemented in acfnet.py is different from the equation (2) in the original paper.
And I wonder why you implement this way.
As mentioned in title. I didn't see this operation in paper. Could you show me some guidance?
Lines 134 to 137 in ceba02e
Hi,
Thanks for your contribution! I wanna know what's the final mIoU of your training code? Is it the same as the paper?
Meanwhile, I notice that the learning rate you set is 1e-2 while it is 1e-3 in the original paper. Have you tried 1e-3?
Thank you again! All the best!
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