ResNet50-Pytorch-Face-Recognition/VGG.py at master · KaihuaTang/ResNet50-Pytorch-Face-Recognition · GitHub

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import torch.nn as nn
import math
import torch.utils.model_zoo as model_zoo

def vgg16(pretrained = False, **kwargs):
    model = VGG(**kwargs)
    if pretrained:
        model.load_state_dict(torch.load(model.modelPath))
    return model

class VGG(nn.Module):
    def __init__(self, number_classes = 2000, model_path="model.pkl"):
        super(VGG, self).__init__()
        self.conv11 = nn.Conv2d(3,64,kernel_size=3,stride=1,padding=1)
        self.conv12 = nn.Conv2d(64,64,kernel_size=3,stride=1,padding=1)

        self.conv21 = nn.Conv2d(64,128,kernel_size=3,stride=1,padding=1)
        self.conv22 = nn.Conv2d(128,128,kernel_size=3,stride=1,padding=1)

        self.conv31 = nn.Conv2d(128,256,kernel_size=3,stride=1,padding=1)
        self.conv32 = nn.Conv2d(256,256,kernel_size=3,stride=1,padding=1)
        self.conv33 = nn.Conv2d(256,256,kernel_size=3,stride=1,padding=1)

        self.conv41 = nn.Conv2d(256,512,kernel_size=3,stride=1,padding=1)
        self.conv42 = nn.Conv2d(512,512,kernel_size=3,stride=1,padding=1)
        self.conv43 = nn.Conv2d(512,512,kernel_size=3,stride=1,padding=1)

        self.conv51 = nn.Conv2d(512,512,kernel_size=3,stride=1,padding=1)
        self.conv52 = nn.Conv2d(512,512,kernel_size=3,stride=1,padding=1)
        self.conv53 = nn.Conv2d(512,512,kernel_size=3,stride=1,padding=1)

        self.relu = nn.ReLU(inplace = True)
        self.maxpool = nn.MaxPool2d(2, stride = 2)
        self.fc1 = nn.Linear(512*7*7, 1024)
        self.fc2 = nn.Linear(1024, number_classes)

        self.init_param()

    def init_param(self):
        # The following is initialization
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
                m.weight.data.normal_(0, math.sqrt(2./n))
                m.bias.data.zero_()
            elif isinstance(m, nn.BatchNorm2d):
                m.weight.data.fill_(1)
                m.bias.data.zero_()
            elif isinstance(m, nn.Linear):
                n = m.weight.shape[0] * m.weight.shape[1]
                m.weight.data.normal_(0, math.sqrt(2./n))
                m.bias.data.zero_()

    def forward(self, x):
        x = self.conv11(x)
        x = self.relu(x)
        x = self.conv12(x)
        x = self.relu(x)
        x = self.maxpool(x)

        x = self.conv21(x)
        x = self.relu(x)
        x = self.conv22(x)
        x = self.relu(x)
        x = self.maxpool(x)

        x = self.conv31(x)
        x = self.relu(x)
        x = self.conv32(x)
        x = self.relu(x)
        x = self.conv33(x)
        x = self.relu(x)
        x = self.maxpool(x)

        x = self.conv41(x)
        x = self.relu(x)
        x = self.conv42(x)
        x = self.relu(x)
        x = self.conv43(x)
        x = self.relu(x)
        x = self.maxpool(x)

        x = self.conv51(x)
        x = self.relu(x)
        x = self.conv52(x)
        x = self.relu(x)
        x = self.conv53(x)
        x = self.relu(x)
        x = self.maxpool(x)

        x = x.view(x.size(0), -1)
        x = self.fc1(x)
        x = self.relu(x)
        x = self.fc2(x)
        x = self.relu(x)

        return x