unet error with (1,3,256,256) input

I’m try to input a tensor with the size of (1, 3, 256, 256), after many conv2d and maxpool, the centre feature size is (1, 256, 8, 8), and the covn4 is (1, 128, 24, 24), after up_layer, the feature size is (1, 128, 12, 12), and after up is (1,64,24,24), but the conv3 is (1, 64, 57, 57), and if this size goes with cropping, it will be (1,64,23,23), which is different from (1,64,24,24), and makes an error.

[flyking1994]

def forward(self, inputs1, inputs2):
    outputs2 = self.up(inputs2)
    offset1 = outputs2.size()[2] - inputs1.size()[2]
    offset2 = inputs1.size()[2] - outputs2.size()[2]
    padding = 2 * [offset1//2, -(offset2//2)]  
    outputs1 = F.pad(inputs1, padding)
    return self.conv(torch.cat([outputs1, outputs2], 1))

I changed the calculation method of padding. It seems that this problem has been solved.

[svanbodegraven]

Thanks @flyking1994, +1 here for Cityscapes datasets gtFine_trainvaltest.zip and leftImg8bit_trainvaltest.zip, cropped and resized to (224, 224). Will try a run later with the original size of (2048, 1024)