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class Spacial_IRNN(nn.Module): def __init__(self, in_channels, alpha=1.0): super(Spacial_IRNN, self).__init__() ... self.left_weight.weight = nn.Parameter(torch.tensor([[[[alpha]]]] * in_channels)) ... according to your paper,alpha is a parameter of gate map G to measure the importance...

Hi, First, thanks for publishing your code. It is easy to understand your code. However, I think that there are some unnecessary network models in the net.py. Probably, they are...

你好,感谢您共享研究。请问该模型有shadow removal的模块吗?可否进行阴影去除呢?

1. nets.py: return irnn()(input, self.up_weight.weight....) -> return irnn().forward(input, self.up_weight.weight....) 2. irnn.py: @cupy.util.memorize(...) -> @cupy.memorize(...)

你好,请问论文中与其他方法对比时,有否改变他们的backbone为MobileNet V2来跟你的保持一致,还是用他们原本的?还有能否上传一下你方法得到的检测图,谢谢!

Hello, I can not find the ShadowNet3, can you tell me? Thank you!

Could you please share your pre-train model ? We want to compare our method with yours