Fix upsampling issues (maybe) (#38, #32)

unet/unet_parts.py

@@ -61,10 +61,18 @@ def __init__(self, in_ch, out_ch, bilinear=True):
 
     def forward(self, x1, x2):
         x1 = self.up(x1)
-        diffX = x1.size()[2] - x2.size()[2]
-        diffY = x1.size()[3] - x2.size()[3]
-        x2 = F.pad(x2, (diffX // 2, int(diffX / 2),
-                        diffY // 2, int(diffY / 2)))
+
+        # input is CHW
+        diffY = x2.size()[2] - x1.size()[2]
+        diffX = x2.size()[3] - x1.size()[3]
+
+        x1 = F.pad(x1, (diffX // 2, diffX - diffX//2,
+                        diffY // 2, diffY - diffY//2))
+
+        # for padding issues, see 
+        # https://github.com/HaiyongJiang/U-Net-Pytorch-Unstructured-Buggy/commit/0e854509c2cea854e247a9c615f175f76fbb2e3a
+        # https://github.com/xiaopeng-liao/Pytorch-UNet/commit/8ebac70e633bac59fc22bb5195e513d5832fb3bd
+
         x = torch.cat([x2, x1], dim=1)
         x = self.conv(x)
         return x