RuntimeError: The size of tensor a (11) must match the size of tensor b (10) at non-singleton dimension 3

[QingdaChen]

This is my code :

class Accurate_Modle((torch.nn.Module)):
def init(self,data_kind,padding):
super(Accurate_Modle, self).init()
self.con6 = torch.nn.Conv2d(2*112,384, kernel_size=1, stride=1, padding=0)
self.con7 = torch.nn.Conv2d(384, 384, kernel_size=1, stride=1, padding=0)
self.con8 = torch.nn.Conv2d(384, 384, kernel_size=1, stride=1, padding=0)
self.con9 = torch.nn.Conv2d(384, 1, kernel_size=1, stride=1, padding=0)
if data_kind=='mb':

        # torch.nn.init.constant_(self.con5.bias, 0)
        self.conv1 = torch.nn.Sequential(OctConv2d('first', in_channels=1, out_channels=112, kernel_size=3),
                                   OctReLU(),
                                   OctConv2d('regular', in_channels=112, out_channels=112, kernel_size=3),
                                   OctReLU(),
                                   OctConv2d('regular', in_channels=112, out_channels=112, kernel_size=3),
                                   OctReLU(),
                                   OctConv2d('regular', in_channels=112, out_channels=112, kernel_size=3),
                                   OctReLU(),
                                   OctConv2d('last', in_channels=112, out_channels=112, kernel_size=3),
                                   nn.ReLU(),
                                         )
        self.full = torch.nn.Sequential(
                                      self.con6,
                                      torch.nn.ReLU(),
                                      self.con7,
                                      torch.nn.ReLU(),
                                      self.con8,
                                      torch.nn.ReLU(),
                                      self.con9,
                                      torch.nn.Sigmoid()
                                )
    # elif data_kind=='kitt':
    #    #...



def forward(self,x0,x1,flag):
   if flag=='train':
       y0 = self.conv1(x0)  # left_patch
       y1 = self.conv1(x1)
       y3 = torch.cat((y0, y1), 1)
       # print(y3.shape)
       y = self.full(y3)

[QingdaChen]

File "OctConv-master\octconv.py", line 100, in forward
high_group_out = high_group_hh + low_group_upsampled
RuntimeError: The size of tensor a (11) must match the size of tensor b (10) at non-singleton dimension 3

[savourylie]

This is due to that fact that the code isn't able handle the valid padding as it isn't made very clear in the original paper. I'm fixing this up and will push it up shortly.

[xyxxmb]

@savourylie Has you solved the question yet? If solved, could you update the new code?