I’m trying to train SegNet in pytorch but I don’t know how to map the prediction results to the label, I use the CityScapes Segmentation Dataset. The label contains 16 classes.
class SegNet(nn.Module):
    """Segnet network."""

    def __init__(self, input_nbr, label_nbr):
        """Init fields."""
        super(SegNet, self).__init__()

        self.input_nbr = input_nbr

        batchNorm_momentum = 0.1

        self.conv11 = nn.Conv2d(input_nbr, 64, kernel_size=3, padding=1)
        self.bn11 = nn.BatchNorm2d(64, momentum=batchNorm_momentum)
        self.conv12 = nn.Conv2d(64, 64, kernel_size=3, padding=1)
        self.bn12 = nn.BatchNorm2d(64, momentum=batchNorm_momentum)

        self.conv21 = nn.Conv2d(64, 128, kernel_size=3, padding=1)
        self.bn21 = nn.BatchNorm2d(128, momentum=batchNorm_momentum)
        self.conv22 = nn.Conv2d(128, 128, kernel_size=3, padding=1)
        self.bn22 = nn.BatchNorm2d(128, momentum=batchNorm_momentum)

        self.conv31 = nn.Conv2d(128, 256, kernel_size=3, padding=1)
        self.bn31 = nn.BatchNorm2d(256, momentum=batchNorm_momentum)
        self.conv32 = nn.Conv2d(256, 256, kernel_size=3, padding=1)
        self.bn32 = nn.BatchNorm2d(256, momentum=batchNorm_momentum)
        self.conv33 = nn.Conv2d(256, 256, kernel_size=3, padding=1)
        self.bn33 = nn.BatchNorm2d(256, momentum=batchNorm_momentum)

        self.conv41 = nn.Conv2d(256, 512, kernel_size=3, padding=1)
        self.bn41 = nn.BatchNorm2d(512, momentum=batchNorm_momentum)
        self.conv42 = nn.Conv2d(512, 512, kernel_size=3, padding=1)
        self.bn42 = nn.BatchNorm2d(512, momentum=batchNorm_momentum)
        self.conv43 = nn.Conv2d(512, 512, kernel_size=3, padding=1)
        self.bn43 = nn.BatchNorm2d(512, momentum=batchNorm_momentum)

        self.conv51 = nn.Conv2d(512, 512, kernel_size=3, padding=1)
        self.bn51 = nn.BatchNorm2d(512, momentum=batchNorm_momentum)
        self.conv52 = nn.Conv2d(512, 512, kernel_size=3, padding=1)
        self.bn52 = nn.BatchNorm2d(512, momentum=batchNorm_momentum)
        self.conv53 = nn.Conv2d(512, 512, kernel_size=3, padding=1)
        self.bn53 = nn.BatchNorm2d(512, momentum=batchNorm_momentum)

        self.conv53d = nn.Conv2d(512, 512, kernel_size=3, padding=1)
        self.bn53d = nn.BatchNorm2d(512, momentum=batchNorm_momentum)
        self.conv52d = nn.Conv2d(512, 512, kernel_size=3, padding=1)
        self.bn52d = nn.BatchNorm2d(512, momentum=batchNorm_momentum)
        self.conv51d = nn.Conv2d(512, 512, kernel_size=3, padding=1)
        self.bn51d = nn.BatchNorm2d(512, momentum=batchNorm_momentum)

        self.conv43d = nn.Conv2d(512, 512, kernel_size=3, padding=1)
        self.bn43d = nn.BatchNorm2d(512, momentum=batchNorm_momentum)
        self.conv42d = nn.Conv2d(512, 512, kernel_size=3, padding=1)
        self.bn42d = nn.BatchNorm2d(512, momentum=batchNorm_momentum)
        self.conv41d = nn.Conv2d(512, 256, kernel_size=3, padding=1)
        self.bn41d = nn.BatchNorm2d(256, momentum=batchNorm_momentum)

        self.conv33d = nn.Conv2d(256, 256, kernel_size=3, padding=1)
        self.bn33d = nn.BatchNorm2d(256, momentum=batchNorm_momentum)
        self.conv32d = nn.Conv2d(256, 256, kernel_size=3, padding=1)
        self.bn32d = nn.BatchNorm2d(256, momentum=batchNorm_momentum)
        self.conv31d = nn.Conv2d(256,  128, kernel_size=3, padding=1)
        self.bn31d = nn.BatchNorm2d(128, momentum=batchNorm_momentum)

        self.conv22d = nn.Conv2d(128, 128, kernel_size=3, padding=1)
        self.bn22d = nn.BatchNorm2d(128, momentum=batchNorm_momentum)
        self.conv21d = nn.Conv2d(128, 64, kernel_size=3, padding=1)
        self.bn21d = nn.BatchNorm2d(64, momentum=batchNorm_momentum)

        self.conv12d = nn.Conv2d(64, 64, kernel_size=3, padding=1)
        self.bn12d = nn.BatchNorm2d(64, momentum=batchNorm_momentum)
        self.conv11d = nn.Conv2d(64, label_nbr, kernel_size=3, padding=1)

    def forward(self, x):
        """Forward method."""
        # Stage 1
        x11 = F.relu(self.bn11(self.conv11(x)))
        x12 = F.relu(self.bn12(self.conv12(x11)))
        x1p, id1 = F.max_pool2d(
            x12, kernel_size=2, stride=2, return_indices=True)
        size1 = x12.size()

        # Stage 2
        x21 = F.relu(self.bn21(self.conv21(x1p)))
        x22 = F.relu(self.bn22(self.conv22(x21)))
        x2p, id2 = F.max_pool2d(
            x22, kernel_size=2, stride=2, return_indices=True)
        size2 = x22.size()
        # Stage 3
        x31 = F.relu(self.bn31(self.conv31(x2p)))
        x32 = F.relu(self.bn32(self.conv32(x31)))
        x33 = F.relu(self.bn33(self.conv33(x32)))
        x3p, id3 = F.max_pool2d(
            x33, kernel_size=2, stride=2, return_indices=True)
        size3 = x33.size()

        # Stage 4
        x41 = F.relu(self.bn41(self.conv41(x3p)))
        x42 = F.relu(self.bn42(self.conv42(x41)))
        x43 = F.relu(self.bn43(self.conv43(x42)))
        x4p, id4 = F.max_pool2d(
            x43, kernel_size=2, stride=2, return_indices=True)
        size4 = x43.size()

        # Stage 5
        x51 = F.relu(self.bn51(self.conv51(x4p)))
        x52 = F.relu(self.bn52(self.conv52(x51)))
        x53 = F.relu(self.bn53(self.conv53(x52)))
        x5p, id5 = F.max_pool2d(
            x53, kernel_size=2, stride=2, return_indices=True)
        size5 = x53.size()

        # Stage 5d
        x5d = F.max_unpool2d(x5p, id5, kernel_size=2,
                             stride=2, output_size=size5)
        x53d = F.relu(self.bn53d(self.conv53d(x5d)))
        x52d = F.relu(self.bn52d(self.conv52d(x53d)))
        x51d = F.relu(self.bn51d(self.conv51d(x52d)))

        # Stage 4d
        x4d = F.max_unpool2d(x51d, id4, kernel_size=2,
                             stride=2, output_size=size4)
        x43d = F.relu(self.bn43d(self.conv43d(x4d)))
        x42d = F.relu(self.bn42d(self.conv42d(x43d)))
        x41d = F.relu(self.bn41d(self.conv41d(x42d)))

        # Stage 3d
        x3d = F.max_unpool2d(x41d, id3, kernel_size=2,
                             stride=2, output_size=size3)
        x33d = F.relu(self.bn33d(self.conv33d(x3d)))
        x32d = F.relu(self.bn32d(self.conv32d(x33d)))
        x31d = F.relu(self.bn31d(self.conv31d(x32d)))

        # Stage 2d
        x2d = F.max_unpool2d(x31d, id2, kernel_size=2,
                             stride=2, output_size=size2)
        x22d = F.relu(self.bn22d(self.conv22d(x2d)))
        x21d = F.relu(self.bn21d(self.conv21d(x22d)))

        # Stage 1d
        x1d = F.max_unpool2d(x21d, id1, kernel_size=2,
                             stride=2, output_size=size1)
        x12d = F.relu(self.bn12d(self.conv12d(x1d)))
        x11d = self.conv11d(x12d)

        return x11d

The input image is RGB .png image :  [torch.DoubleTensor of size BatchSize x 3 x 240 x 320]
The label image is 8-bit .png image with different intensity  :  [torch.DoubleTensor of size BatchSize x 240 x 320]
criterion = nn.MSELoss()
The output dimension has to be same as the input dimension in order to caculate the Loss.
input_nbr=3
label_nbr=1
After the tranning, I use the weight to predict the segmentation. The prediction result dimension is [torch.cuda.FloatTensor of size BatchSize x1x240x320]
The values are here.
Variable containing:
( 0 , 0 ,.,.) = 
1.00000e-02 *
  3.0703  2.9676  3.0414  ...   2.9636  3.0638  3.1489
  2.9122  2.8096  2.9196  ...   2.9567  3.0647  3.1829
  2.8284  2.8920  3.1619  ...   3.1253  2.9853  3.2484
           ...             ⋱             ...          
  3.0241  2.9300  2.7415  ...   2.9179  2.9869  3.1802
  2.9750  2.7484  2.9876  ...   2.9093  2.8946  3.0839
  3.2451  3.1227  3.4103  ...   3.1746  3.2903  3.4108

How to map these values to the labels？