Hi, I’m getting this error when I try to use a resnet net:
Traceback (most recent call last):
  File "test.py", line 58, in <module>
    _, concat_logits, _, _, _ = net(img)
  File "/home/julio/anaconda3/envs/logo2k/lib/python3.7/site-packages/torch/nn/modules/module.py", line 722, in _call_impl
    result = self.forward(*input, **kwargs)
  File "/home/julio/anaconda3/envs/logo2k/lib/python3.7/site-packages/torch/nn/parallel/data_parallel.py", line 153, in forward
    return self.module(*inputs[0], **kwargs[0])
  File "/home/julio/anaconda3/envs/logo2k/lib/python3.7/site-packages/torch/nn/modules/module.py", line 722, in _call_impl
    result = self.forward(*input, **kwargs)
  File "/home/julio/wdir/code/Logo-2k-plus-Dataset/Drna-master/core/model_resnet.py", line 84, in forward
    resnet_out, rpn_feature, feature = self.pretrained_model(x)
  File "/home/julio/anaconda3/envs/logo2k/lib/python3.7/site-packages/torch/nn/modules/module.py", line 722, in _call_impl
    result = self.forward(*input, **kwargs)
  File "/home/julio/wdir/code/Logo-2k-plus-Dataset/Drna-master/core/resnet.py", line 154, in forward
    x = self.fc(x)
  File "/home/julio/anaconda3/envs/logo2k/lib/python3.7/site-packages/torch/nn/modules/module.py", line 722, in _call_impl
    result = self.forward(*input, **kwargs)
  File "/home/julio/anaconda3/envs/logo2k/lib/python3.7/site-packages/torch/nn/modules/linear.py", line 91, in forward
    return F.linear(input, self.weight, self.bias)
  File "/home/julio/anaconda3/envs/logo2k/lib/python3.7/site-packages/torch/nn/functional.py", line 1674, in linear
    ret = torch.addmm(bias, input, weight.t())
RuntimeError: mat1 dim 1 must match mat2 dim 0

And here is my code:
class ResNet(nn.Module):
    def __init__(self, block, layers, num_classes=1000):
        self.inplanes = 64
        super(ResNet, self).__init__()
        self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
                               bias=False)
        self.bn1 = nn.BatchNorm2d(64)
        self.relu = nn.ReLU(inplace=True)
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        self.layer1 = self._make_layer(block, 64, layers[0])
        self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
        self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
        self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
        self.avgpool = nn.AvgPool2d(7)
        self.fc = nn.Linear(512 * block.expansion, num_classes)

        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
                m.weight.data.normal_(0, math.sqrt(2. / n))
            elif isinstance(m, nn.BatchNorm2d):
                m.weight.data.fill_(1)
                m.bias.data.zero_()

    def _make_layer(self, block, planes, blocks, stride=1):
        downsample = None
        if stride != 1 or self.inplanes != planes * block.expansion:
            downsample = nn.Sequential(
                nn.Conv2d(self.inplanes, planes * block.expansion,
                          kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(planes * block.expansion),
            )

        layers = []
        layers.append(block(self.inplanes, planes, stride, downsample))
        self.inplanes = planes * block.expansion
        for i in range(1, blocks):
            layers.append(block(self.inplanes, planes))

        return nn.Sequential(*layers)

    def forward(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)
        x = self.maxpool(x)

        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)
        feature1 = x
        x = self.avgpool(x)
        x = x.view(x.size(0), -1)
        x = nn.Dropout(p=0.5)(x)
        feature2 = x
        x = self.fc(x)

        return x, feature1, feature2

The error is raised when I run “_, concat_logits, _, _, _ = net(img)” so I investigated which was the shape of img. It is: “Torch.size([2, 3, 448, 448])”. In addition I see that the error originally comes from the file “resnet.py” in: " x = self.fc(x)" so I also investigated which was the shape of “x”. It is: “Torch.size([2, 2048])”. I think that might help you.
Where am I commiting a mistake? I saw other similar posts but I couldn’t find my error. Sorry for the inconvenience.