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flow, mask, merged = self.flownet(torch.cat((imgs, gt), 1), scale=scale, training=training) loss_l1 = (merged[2] - gt).abs().mean() loss_smooth = self.sobel(flow[2], flow[2]*0).mean() # loss_vgg = self.vgg(merged[2], gt) if training: self.optimG.zero_grad() loss_G = loss_cons + loss_smooth * 0.1 loss_G.backward() self.optimG.step() else: flow_teacher = flow[2] return merged[2], { 'mask': mask, 'flow': flow[2][:, :2], 'loss_l1': loss_l1, 'loss_cons': loss_cons, 'loss_smooth': loss_smooth, } 想问您使用的是几个loss?是”loss_l1+loss_cons+loss_smooth“三个loss吗?还是仅仅loss_cons + loss_smooth * 0.1? 还想问下作者,使用HDv3复现插多帧模型的时候,训练并不成功,模型的psnr值为2.多,是什么原因呢?
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Hi,我用的是 loss_l1+loss_cons*0.01+loss_vgg(可选);psnr这么低的情况下,是不是有可能数据喂错了 你可以只用 l1 loss跑通再试别的
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flow, mask, merged = self.flownet(torch.cat((imgs, gt), 1), scale=scale, training=training)
loss_l1 = (merged[2] - gt).abs().mean()
loss_smooth = self.sobel(flow[2], flow[2]*0).mean()
# loss_vgg = self.vgg(merged[2], gt)
if training:
self.optimG.zero_grad()
loss_G = loss_cons + loss_smooth * 0.1
loss_G.backward()
self.optimG.step()
else:
flow_teacher = flow[2]
return merged[2], {
'mask': mask,
'flow': flow[2][:, :2],
'loss_l1': loss_l1,
'loss_cons': loss_cons,
'loss_smooth': loss_smooth,
}
想问您使用的是几个loss?是”loss_l1+loss_cons+loss_smooth“三个loss吗?还是仅仅loss_cons + loss_smooth * 0.1?
还想问下作者,使用HDv3复现插多帧模型的时候,训练并不成功,模型的psnr值为2.多,是什么原因呢?
The text was updated successfully, but these errors were encountered: