[Solved] Using summary to View network parameters Error: RuntimeError: Input type (torch.cuda.FloatTensor)

Use summary to view network parameters

If you need to view the specific parameters of the network, use the use summary

from torchsummary import summary
summary(model, (3, 448, 448))

Show results

        Layer (type)               Output Shape         Param #
================================================================
            Conv2d-1         [-1, 64, 224, 224]           9,408
       BatchNorm2d-2         [-1, 64, 224, 224]             128
              ReLU-3         [-1, 64, 224, 224]               0
         MaxPool2d-4         [-1, 64, 112, 112]               0
            Conv2d-5         [-1, 64, 112, 112]           4,096
       BatchNorm2d-6         [-1, 64, 112, 112]             128
              ReLU-7         [-1, 64, 112, 112]               0
            Conv2d-8         [-1, 64, 112, 112]          36,864
       BatchNorm2d-9         [-1, 64, 112, 112]             128
             ReLU-10         [-1, 64, 112, 112]               0
           Conv2d-11        [-1, 256, 112, 112]          16,384
      BatchNorm2d-12        [-1, 256, 112, 112]             512
           Conv2d-13        [-1, 256, 112, 112]          16,384
      BatchNorm2d-14        [-1, 256, 112, 112]             512
             ReLU-15        [-1, 256, 112, 112]               0
       Bottleneck-16        [-1, 256, 112, 112]               0
           Conv2d-17         [-1, 64, 112, 112]          16,384
      BatchNorm2d-18         [-1, 64, 112, 112]             128
             ReLU-19         [-1, 64, 112, 112]               0
           Conv2d-20         [-1, 64, 112, 112]          36,864
      BatchNorm2d-21         [-1, 64, 112, 112]             128
             ReLU-22         [-1, 64, 112, 112]               0
           Conv2d-23        [-1, 256, 112, 112]          16,384
      BatchNorm2d-24        [-1, 256, 112, 112]             512
             ReLU-25        [-1, 256, 112, 112]               0
       Bottleneck-26        [-1, 256, 112, 112]               0
           Conv2d-27         [-1, 64, 112, 112]          16,384
      BatchNorm2d-28         [-1, 64, 112, 112]             128
             ReLU-29         [-1, 64, 112, 112]               0
           Conv2d-30         [-1, 64, 112, 112]          36,864
      BatchNorm2d-31         [-1, 64, 112, 112]             128
             ReLU-32         [-1, 64, 112, 112]               0
           Conv2d-33        [-1, 256, 112, 112]          16,384
      BatchNorm2d-34        [-1, 256, 112, 112]             512
             ReLU-35        [-1, 256, 112, 112]               0
       Bottleneck-36        [-1, 256, 112, 112]               0
           Conv2d-37        [-1, 128, 112, 112]          32,768
      BatchNorm2d-38        [-1, 128, 112, 112]             256
             ReLU-39        [-1, 128, 112, 112]               0
           Conv2d-40          [-1, 128, 56, 56]         147,456
      BatchNorm2d-41          [-1, 128, 56, 56]             256
             ReLU-42          [-1, 128, 56, 56]               0
           Conv2d-43          [-1, 512, 56, 56]          65,536
      BatchNorm2d-44          [-1, 512, 56, 56]           1,024
           Conv2d-45          [-1, 512, 56, 56]         131,072
      BatchNorm2d-46          [-1, 512, 56, 56]           1,024
             ReLU-47          [-1, 512, 56, 56]               0
       Bottleneck-48          [-1, 512, 56, 56]               0
           Conv2d-49          [-1, 128, 56, 56]          65,536
      BatchNorm2d-50          [-1, 128, 56, 56]             256
             ReLU-51          [-1, 128, 56, 56]               0
           Conv2d-52          [-1, 128, 56, 56]         147,456
      BatchNorm2d-53          [-1, 128, 56, 56]             256
             ReLU-54          [-1, 128, 56, 56]               0
           Conv2d-55          [-1, 512, 56, 56]          65,536
      BatchNorm2d-56          [-1, 512, 56, 56]           1,024
             ReLU-57          [-1, 512, 56, 56]               0
       Bottleneck-58          [-1, 512, 56, 56]               0
           Conv2d-59          [-1, 128, 56, 56]          65,536
      BatchNorm2d-60          [-1, 128, 56, 56]             256
             ReLU-61          [-1, 128, 56, 56]               0
           Conv2d-62          [-1, 128, 56, 56]         147,456
      BatchNorm2d-63          [-1, 128, 56, 56]             256
             ReLU-64          [-1, 128, 56, 56]               0
           Conv2d-65          [-1, 512, 56, 56]          65,536
      BatchNorm2d-66          [-1, 512, 56, 56]           1,024
             ReLU-67          [-1, 512, 56, 56]               0
       Bottleneck-68          [-1, 512, 56, 56]               0
           Conv2d-69          [-1, 128, 56, 56]          65,536
      BatchNorm2d-70          [-1, 128, 56, 56]             256
             ReLU-71          [-1, 128, 56, 56]               0
           Conv2d-72          [-1, 128, 56, 56]         147,456
      BatchNorm2d-73          [-1, 128, 56, 56]             256
             ReLU-74          [-1, 128, 56, 56]               0
           Conv2d-75          [-1, 512, 56, 56]          65,536
      BatchNorm2d-76          [-1, 512, 56, 56]           1,024
             ReLU-77          [-1, 512, 56, 56]               0
       Bottleneck-78          [-1, 512, 56, 56]               0
           Conv2d-79          [-1, 256, 56, 56]         131,072
      BatchNorm2d-80          [-1, 256, 56, 56]             512
             ReLU-81          [-1, 256, 56, 56]               0
           Conv2d-82          [-1, 256, 28, 28]         589,824
      BatchNorm2d-83          [-1, 256, 28, 28]             512
             ReLU-84          [-1, 256, 28, 28]               0
           Conv2d-85         [-1, 1024, 28, 28]         262,144
      BatchNorm2d-86         [-1, 1024, 28, 28]           2,048
           Conv2d-87         [-1, 1024, 28, 28]         524,288
      BatchNorm2d-88         [-1, 1024, 28, 28]           2,048
             ReLU-89         [-1, 1024, 28, 28]               0
       Bottleneck-90         [-1, 1024, 28, 28]               0
           Conv2d-91          [-1, 256, 28, 28]         262,144
      BatchNorm2d-92          [-1, 256, 28, 28]             512
             ReLU-93          [-1, 256, 28, 28]               0
           Conv2d-94          [-1, 256, 28, 28]         589,824
      BatchNorm2d-95          [-1, 256, 28, 28]             512
             ReLU-96          [-1, 256, 28, 28]               0
           Conv2d-97         [-1, 1024, 28, 28]         262,144
      BatchNorm2d-98         [-1, 1024, 28, 28]           2,048
             ReLU-99         [-1, 1024, 28, 28]               0
      Bottleneck-100         [-1, 1024, 28, 28]               0
          Conv2d-101          [-1, 256, 28, 28]         262,144
     BatchNorm2d-102          [-1, 256, 28, 28]             512
            ReLU-103          [-1, 256, 28, 28]               0
          Conv2d-104          [-1, 256, 28, 28]         589,824
     BatchNorm2d-105          [-1, 256, 28, 28]             512
            ReLU-106          [-1, 256, 28, 28]               0
          Conv2d-107         [-1, 1024, 28, 28]         262,144
     BatchNorm2d-108         [-1, 1024, 28, 28]           2,048
            ReLU-109         [-1, 1024, 28, 28]               0
      Bottleneck-110         [-1, 1024, 28, 28]               0
          Conv2d-111          [-1, 256, 28, 28]         262,144
     BatchNorm2d-112          [-1, 256, 28, 28]             512
            ReLU-113          [-1, 256, 28, 28]               0
          Conv2d-114          [-1, 256, 28, 28]         589,824
     BatchNorm2d-115          [-1, 256, 28, 28]             512
            ReLU-116          [-1, 256, 28, 28]               0
          Conv2d-117         [-1, 1024, 28, 28]         262,144
     BatchNorm2d-118         [-1, 1024, 28, 28]           2,048
            ReLU-119         [-1, 1024, 28, 28]               0
      Bottleneck-120         [-1, 1024, 28, 28]               0
          Conv2d-121          [-1, 256, 28, 28]         262,144
     BatchNorm2d-122          [-1, 256, 28, 28]             512
            ReLU-123          [-1, 256, 28, 28]               0
          Conv2d-124          [-1, 256, 28, 28]         589,824
     BatchNorm2d-125          [-1, 256, 28, 28]             512
            ReLU-126          [-1, 256, 28, 28]               0
          Conv2d-127         [-1, 1024, 28, 28]         262,144
     BatchNorm2d-128         [-1, 1024, 28, 28]           2,048
            ReLU-129         [-1, 1024, 28, 28]               0
      Bottleneck-130         [-1, 1024, 28, 28]               0
          Conv2d-131          [-1, 256, 28, 28]         262,144
     BatchNorm2d-132          [-1, 256, 28, 28]             512
            ReLU-133          [-1, 256, 28, 28]               0
          Conv2d-134          [-1, 256, 28, 28]         589,824
     BatchNorm2d-135          [-1, 256, 28, 28]             512
            ReLU-136          [-1, 256, 28, 28]               0
          Conv2d-137         [-1, 1024, 28, 28]         262,144
     BatchNorm2d-138         [-1, 1024, 28, 28]           2,048
            ReLU-139         [-1, 1024, 28, 28]               0
      Bottleneck-140         [-1, 1024, 28, 28]               0
          Conv2d-141          [-1, 512, 28, 28]         524,288
     BatchNorm2d-142          [-1, 512, 28, 28]           1,024
            ReLU-143          [-1, 512, 28, 28]               0
          Conv2d-144          [-1, 512, 14, 14]       2,359,296
     BatchNorm2d-145          [-1, 512, 14, 14]           1,024
            ReLU-146          [-1, 512, 14, 14]               0
          Conv2d-147         [-1, 2048, 14, 14]       1,048,576
     BatchNorm2d-148         [-1, 2048, 14, 14]           4,096
          Conv2d-149         [-1, 2048, 14, 14]       2,097,152
     BatchNorm2d-150         [-1, 2048, 14, 14]           4,096
            ReLU-151         [-1, 2048, 14, 14]               0
      Bottleneck-152         [-1, 2048, 14, 14]               0
          Conv2d-153          [-1, 512, 14, 14]       1,048,576
     BatchNorm2d-154          [-1, 512, 14, 14]           1,024
            ReLU-155          [-1, 512, 14, 14]               0
          Conv2d-156          [-1, 512, 14, 14]       2,359,296
     BatchNorm2d-157          [-1, 512, 14, 14]           1,024
            ReLU-158          [-1, 512, 14, 14]               0
          Conv2d-159         [-1, 2048, 14, 14]       1,048,576
     BatchNorm2d-160         [-1, 2048, 14, 14]           4,096
            ReLU-161         [-1, 2048, 14, 14]               0
      Bottleneck-162         [-1, 2048, 14, 14]               0
          Conv2d-163          [-1, 512, 14, 14]       1,048,576
     BatchNorm2d-164          [-1, 512, 14, 14]           1,024
            ReLU-165          [-1, 512, 14, 14]               0
          Conv2d-166          [-1, 512, 14, 14]       2,359,296
     BatchNorm2d-167          [-1, 512, 14, 14]           1,024
            ReLU-168          [-1, 512, 14, 14]               0
          Conv2d-169         [-1, 2048, 14, 14]       1,048,576
     BatchNorm2d-170         [-1, 2048, 14, 14]           4,096
            ReLU-171         [-1, 2048, 14, 14]               0
      Bottleneck-172         [-1, 2048, 14, 14]               0
          Conv2d-173          [-1, 256, 14, 14]         524,288
     BatchNorm2d-174          [-1, 256, 14, 14]             512
          Conv2d-175          [-1, 256, 14, 14]         589,824
     BatchNorm2d-176          [-1, 256, 14, 14]             512
          Conv2d-177          [-1, 256, 14, 14]          65,536
     BatchNorm2d-178          [-1, 256, 14, 14]             512
          Conv2d-179          [-1, 256, 14, 14]         524,288
     BatchNorm2d-180          [-1, 256, 14, 14]             512
detnet_bottleneck-181          [-1, 256, 14, 14]               0
          Conv2d-182          [-1, 256, 14, 14]          65,536
     BatchNorm2d-183          [-1, 256, 14, 14]             512
          Conv2d-184          [-1, 256, 14, 14]         589,824
     BatchNorm2d-185          [-1, 256, 14, 14]             512
     BatchNorm2d-197           [-1, 30, 14, 14]              60
================================================================

Error reported:

RuntimeError: Input type (torch.cuda.FloatTensor) and weight type (torch.FloatTensor) should be the same

Run the model in the graphics card:

from torchsummary import summary
summary(net.cuda(), (3, 448, 448))

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