conv size of up-conv2x2

[JohnTargaryen]

Hi, the paper says that "Every step in the expansive path consists of an upsampling of the feature map followed by a 2x2 convolution ('up-convolution') that halves the number of feature channels"

1. In your code, however, I notice that you use a conv2d of size3x3, would you explain the reason to that?

   class up_conv(nn.Module):
   def __init__(self,ch_in,ch_out):
       super(up_conv,self).__init__()
       self.up = nn.Sequential(
           nn.Upsample(scale_factor=2),
           nn.Conv2d(ch_in,ch_out,kernel_size=3,stride=1,padding=1,bias=True),
           nn.BatchNorm2d(ch_out),
           nn.ReLU(inplace=True)
       )
   
   def forward(self,x):
       x = self.up(x)
       return x

2. And also, let's say I want to stick to the paper and use 2x2 conv, how would I choose the padding and stride to make output size the same as the input size?

Thanks for sharing your work.

[LeeJunHyun]

Hi, @JohnTargaryen. Thank you for your interest in my repo. I just had wanted to compare a series of U-Net. So I adapted a same convolution filter size to models. If you want, please modify the convolution filter size. You can calculate the padding and the stride size by referring here. Thanks.

[JohnTargaryen]

Hi, @JohnTargaryen. Thank you for your interest in my repo. I just had wanted to compare a series of U-Net. So I adapted a same convolution filter size to models. If you want, please modify the convolution filter size. You can calculate the padding and the stride size by referring here. Thanks.

Thx, I'll look into that