This is my save model code:
import cv2
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchfile
import numpy as np

class VGG16(nn.Module):
    """
    Main Class
    """

    def __init__(self):
        """
        Constructor
        """
        super(VGG16, self).__init__()
        self.block_size = [2, 2, 3, 3, 3]
        self.conv_1_1 = nn.Conv2d(3, 64, 3, stride=1, padding=1)
        self.conv_1_2 = nn.Conv2d(64, 64, 3, stride=1, padding=1)
        self.conv_2_1 = nn.Conv2d(64, 128, 3, stride=1, padding=1)
        self.conv_2_2 = nn.Conv2d(128, 128, 3, stride=1, padding=1)
        self.conv_3_1 = nn.Conv2d(128, 256, 3, stride=1, padding=1)
        self.conv_3_2 = nn.Conv2d(256, 256, 3, stride=1, padding=1)
        self.conv_3_3 = nn.Conv2d(256, 256, 3, stride=1, padding=1)
        self.conv_4_1 = nn.Conv2d(256, 512, 3, stride=1, padding=1)
        self.conv_4_2 = nn.Conv2d(512, 512, 3, stride=1, padding=1)
        self.conv_4_3 = nn.Conv2d(512, 512, 3, stride=1, padding=1)
        self.conv_5_1 = nn.Conv2d(512, 512, 3, stride=1, padding=1)
        self.conv_5_2 = nn.Conv2d(512, 512, 3, stride=1, padding=1)
        self.conv_5_3 = nn.Conv2d(512, 512, 3, stride=1, padding=1)
        self.fc6 = nn.Linear(512 * 7 * 7, 4096)
        self.fc7 = nn.Linear(4096, 4096)
        self.fc8 = nn.Linear(4096, 2622)


    def forward(self, x):
        """ Pytorch forward

        Args:
            x: input image (224x224)

        Returns: class logits

        """
        x = F.relu(self.conv_1_1(x))
        x = F.relu(self.conv_1_2(x))
        x = F.max_pool2d(x, 2, 2)
        x = F.relu(self.conv_2_1(x))
        x = F.relu(self.conv_2_2(x))
        x = F.max_pool2d(x, 2, 2)
        x = F.relu(self.conv_3_1(x))
        x = F.relu(self.conv_3_2(x))
        x = F.relu(self.conv_3_3(x))
        x = F.max_pool2d(x, 2, 2)
        x = F.relu(self.conv_4_1(x))
        x = F.relu(self.conv_4_2(x))
        x = F.relu(self.conv_4_3(x))
        x = F.max_pool2d(x, 2, 2)
        x = F.relu(self.conv_5_1(x))
        x = F.relu(self.conv_5_2(x))
        x = F.relu(self.conv_5_3(x))
        x = F.max_pool2d(x, 2, 2)
        x = x.view(x.size(0), -1)
        x = F.relu(self.fc6(x))
        x = F.dropout(x, 0.5, self.training)
        x = F.relu(self.fc7(x))
        x = F.dropout(x, 0.5, self.training)
        return self.fc8(x)


if __name__ == "__main__":
    model = VGG16().double()
    torch.save(model.state_dict(), 'vggFaceModel.pth')

This is my load model code:
import torch
from torch.autograd import Variable
import os
from PIL import Image
import torchvision.transforms as transforms
import torchvision.models as models
from vggModel import vgg16
from vggModel3 import VGG16
#model = torch.load('model.pth')
#model = torch.load('vgg16-397923af.pth')
#model1 = models.vgg16(pretrained=False)
#model1.load_state_dict(torch.load('vgg16-397923af.pth'))
#preModel = VGG16
preModel = vgg16
#ass = torch.load('vggFaceModel.pth')
preModel.load_state_dict(torch.load('vggFaceModel.pth'))

The wrong text is: load_state_dict() missing 1 required positional argument: ‘state_dict’