Thanks for your reply! Hopefully this provides enough information.
The input and target:
train_loader = torch.utils.data.DataLoader(train_data, batch_size=1, shuffle=True, **kwargs)
for data, target in train_loader: break
print(data.shape)
print(target.shape)

Print result:
torch.Size([1, 3, 539, 456])
torch.Size([1, 539, 456, 3])

The output shape of each layer (I used the following code line):

summary(model, (3, 539, 456))):

[/root/data/models/pytorch/vgg16_from_caffe.pth] Checking md5 (aa75b158f4181e7f6230029eb96c1b13)
Train:   0%|                                            | 0/250 [00:00<?, ?it/s]----------------------------------------------------------------
        Layer (type)               Output Shape         Param #
================================================================
            Conv2d-1         [-1, 64, 745, 478]           1,792
              ReLU-2         [-1, 64, 745, 478]               0
            Conv2d-3         [-1, 64, 745, 478]          36,928
              ReLU-4         [-1, 64, 745, 478]               0
         MaxPool2d-5         [-1, 64, 373, 239]               0
            Conv2d-6        [-1, 128, 373, 239]          73,856
              ReLU-7        [-1, 128, 373, 239]               0
            Conv2d-8        [-1, 128, 373, 239]         147,584
              ReLU-9        [-1, 128, 373, 239]               0
        MaxPool2d-10        [-1, 128, 187, 120]               0
           Conv2d-11        [-1, 256, 187, 120]         295,168
             ReLU-12        [-1, 256, 187, 120]               0
           Conv2d-13        [-1, 256, 187, 120]         590,080
             ReLU-14        [-1, 256, 187, 120]               0
           Conv2d-15        [-1, 256, 187, 120]         590,080
             ReLU-16        [-1, 256, 187, 120]               0
        MaxPool2d-17          [-1, 256, 94, 60]               0
           Conv2d-18          [-1, 512, 94, 60]       1,180,160
             ReLU-19          [-1, 512, 94, 60]               0
           Conv2d-20          [-1, 512, 94, 60]       2,359,808
             ReLU-21          [-1, 512, 94, 60]               0
           Conv2d-22          [-1, 512, 94, 60]       2,359,808
             ReLU-23          [-1, 512, 94, 60]               0
        MaxPool2d-24          [-1, 512, 47, 30]               0
           Conv2d-25          [-1, 512, 47, 30]       2,359,808
             ReLU-26          [-1, 512, 47, 30]               0
           Conv2d-27          [-1, 512, 47, 30]       2,359,808
             ReLU-28          [-1, 512, 47, 30]               0
           Conv2d-29          [-1, 512, 47, 30]       2,359,808
             ReLU-30          [-1, 512, 47, 30]               0
        MaxPool2d-31          [-1, 512, 24, 15]               0
           Conv2d-32          [-1, 4096, 18, 9]     102,764,544
             ReLU-33          [-1, 4096, 18, 9]               0
        Dropout2d-34          [-1, 4096, 18, 9]               0
           Conv2d-35          [-1, 4096, 18, 9]      16,781,312
             ReLU-36          [-1, 4096, 18, 9]               0
        Dropout2d-37          [-1, 4096, 18, 9]               0
           Conv2d-38             [-1, 8, 18, 9]          32,776
  ConvTranspose2d-39            [-1, 8, 38, 20]           1,024
           Conv2d-40            [-1, 8, 47, 30]           4,104
  ConvTranspose2d-41            [-1, 8, 78, 42]           1,024
           Conv2d-42            [-1, 8, 94, 60]           2,056
  ConvTranspose2d-43          [-1, 8, 632, 344]          16,384
================================================================
Total params: 134,317,912
Trainable params: 134,317,912
Non-trainable params: 0
----------------------------------------------------------------
Input size (MB): 1.75
Forward/backward pass size (MB): 1599.66
Params size (MB): 512.38
Estimated Total Size (MB): 2113.80
----------------------------------------------------------------