Runtime error:The size of tensor a (160) must match the size of tensor b (192) at non-singleton dimension 1

Short cut
I am trying to modify the Binary Neural network but my network gets deeper, and I have a vanishing gradient problem. I tried to put batch normalization, and also use ReLU function but no luck, I used also dirac initialization but also no luck. So, I decided to use the Residual Block instead but the problem persist saying
The size of tensor a (160) must match the size of tensor b (192) at non-singleton dimension 1
I tried a different way, I even printed every tensor and the tensor change through the network but I was not able to catch the error

class BinConv2d(nn.Module):
    def __init__(self, input_channels, output_channels,
            kernel_size=-1, stride=-1, padding=-1, dropout=0):
        super(BinConv2d, self).__init__()
        self.layer_type = 'BinConv2d'
        self.kernel_size = kernel_size
        self.stride = stride
        self.padding = padding
        self.dropout_ratio = dropout = nn.BatchNorm2d(input_channels, eps=1e-4, momentum=0.1, affine=True) =
        if dropout!=0:
            self.dropout = nn.Dropout(dropout)
        self.conv = nn.Conv2d(input_channels, output_channels,
                kernel_size=kernel_size, stride=stride, padding=padding)
        self.relu = nn.ReLU(inplace=True)

    def forward(self, x):
        res = x
        x =
        x, mean = BinActive(x)
        if self.dropout_ratio!=0:
            x = self.dropout(x)
        x = self.conv(x)
        x += res
        x = self.relu(x)
        return x

class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.xnor = nn.Sequential(
                nn.Conv2d(3, 192, kernel_size=5, stride=1, padding=2),
                nn.BatchNorm2d(192, eps=1e-4, momentum=0.1, affine=False),
                BinConv2d(192, 160, kernel_size=1, stride=1, padding=0),
                BinConv2d(160,  96, kernel_size=1, stride=1, padding=0),
                nn.MaxPool2d(kernel_size=3, stride=2, padding=1),

                BinConv2d( 96, 192, kernel_size=5, stride=1, padding=2, dropout=0.5),
                BinConv2d(192, 192, kernel_size=1, stride=1, padding=0),
                BinConv2d(192, 192, kernel_size=1, stride=1, padding=0),
                nn.AvgPool2d(kernel_size=3, stride=2, padding=1),

                BinConv2d(192, 192, kernel_size=3, stride=1, padding=1, dropout=0.5),
                BinConv2d(192, 192, kernel_size=1, stride=1, padding=0),
                nn.BatchNorm2d(192, eps=1e-4, momentum=0.1, affine=False),
                nn.Conv2d(192,  10, kernel_size=1, stride=1, padding=0),
                nn.AvgPool2d(kernel_size=8, stride=1, padding=0),

    def forward(self, x):

        for m in self.modules():
            if isinstance(m, nn.BatchNorm2d) or isinstance(m, nn.BatchNorm1d):
                if hasattr(m.weight, 'data'):

        x = self.xnor(x)
        x = x.view(x.size(0), 10)

        return x


I guess the error is raised on this line of code:

x += res

Could you print the shapes of x and res and make sure they are equal?
In the torchvision implementation an additional downsample module is used to create the expected shapes, if necessary.

PS: Don’t use the .data attribute, as it might yield unwanted side effects.

1 Like

I tried to do every possibe to solve this issue but I could not, I am not really If I am applying the downsample the right way

res shape torch.Size([2, 160, 32, 32])
x in binActive torch.Size([2, 160, 32, 32])
x after the conv torch.Size([2, 96, 32, 32])

The size of tensor a (96) must match the size of tensor b (160) at non-singleton dimension 1


Since you have a mismatch in the channel dimension, you could use an additional conv layer (with a 1x1 kernel) to either increase the channels from 96 to 160 in x_after_conv or decrease the channels to 96 for res.