At present, only the model but no trained. There may be some bug in the code, and some details may be different from the original paper, if you are interested in this, welcome to discuss.
Add: CutUp,MixUp,RandomErasing,SyncBatchNorm for DDP train
Inference:
paper:https://arxiv.org/pdf/2108.05895.pdf
https://github.com/xiaolai-sqlai/mobilenetv3
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Great job! However, I think there are probably two tiny issues in you code.
The first one is in bridge.py(line 24 & line 53). I think there are some differences in the following two lines of code
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x = x.reshape(b, c, h*w).transpose(1,2).unsqueeze(1)
x = x.contiguous().view(b, h * w, c).unsqueeze(1)
May be the first line is correct?
The second one is in config.py.Accroding to the original paper, in page 13,
Figure 7. Visualization of cross attention on the two-way bridge: MobileāFormer and MobileāFormer. Mobile-Former-294M is used,which includes 6 tokens (each corresponds to a column) and 11 Mobile-Former blocks (block 2ā12) across 4 stages. Each block has two attention heads that are visualized in two rows. Attention in MobileāFormer (left half) is normalized over pixels, showing the focused region per token. Attention in MobileāFormer (right half) is normalized over tokens showing the contribution per token at each pixel.
But in config.py, there are some stages with only one head.
I'm not sure whether the above is correct. Looking forward to your reply!
class Mobile(nn.Module): def init(self, ks, inp, hid, out, se, stride, dim, reduction=4, k=2):
hi, call you tell me, the k value why equal to 2, What is it used for
Great jobļ¼ However, i try the training on imagent and it does not converge. I also try another implement https://github.com/slwang9353/MobileFormer and it does not converge either. Does anyone successfully reproduce the results in the paper?
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