class VGG16_model(nn.Module):
    def __init__(self, numClasses=7):
        super(VGG16_model, self).__init__()
        self.vgg16 = nn.Sequential(
            nn.Conv2d(3, 64, 3),
            nn.ReLU(inplace=True),
            nn.Conv2d(64, 64, 3),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=2, stride=2),
            nn.Conv2d(64, 128, 3),
            nn.ReLU(inplace=True),
            nn.Conv2d(128, 128, 3),
            nn.MaxPool2d(kernel_size=2, stride=2),
            nn.ReLU(inplace=True),
            nn.Conv2d(128, 256, 3),
            nn.ReLU(inplace=True),
            nn.Conv2d(256, 256, 3),
            nn.ReLU(inplace=True),
            nn.Conv2d(256, 256, 3),
            nn.MaxPool2d(kernel_size=2, stride=2),
            nn.ReLU(inplace=True),
            nn.Conv2d(256, 512, 3),
            nn.ReLU(inplace=True),
            nn.Conv2d(512, 512, 3),
            nn.ReLU(inplace=True),
            nn.Conv2d(512, 512, 3),
            nn.MaxPool2d(kernel_size=2, stride=2),
            nn.ReLU(inplace=True),
            nn.Conv2d(512, 512, 3),
            nn.ReLU(inplace=True),
            nn.Conv2d(512, 512, 3),
            nn.ReLU(inplace=True),
            nn.Conv2d(512, 512, 3),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=2, stride=2)
        )
        self.avgpool = nn.AdaptiveAvgPool2d((7, 7))
        self.classifier = nn.Sequential(
            nn.Linear(512 * 7 * 7, 4096),
            nn.ReLU(inplace=True),
            nn.Dropout(),
            nn.Linear(4096, 4096),
            nn.ReLU(inplace=True),
            nn.Dropout(),
            nn.Linear(4096, numClasses)
        )

    def forward(self, x):
        x = self.vgg16(x)
        x = self.avgpool(x)
        x = x.view(x.size(0), -1)
        x = self.classifier(x)
        return x