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# Copyright 2021 Huawei Technologies Co., Ltd
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# ============================================================================
import torch.nn as nn
import torch.utils.model_zoo as model_zoo


model_urls = {
    'vgg11': 'https://download.pytorch.org/models/vgg11-bbd30ac9.pth',
    'vgg13': 'https://download.pytorch.org/models/vgg13-c768596a.pth',
    'vgg16': 'https://download.pytorch.org/models/vgg16-397923af.pth',
    'vgg19': 'https://download.pytorch.org/models/vgg19-dcbb9e9d.pth',
    'vgg11_bn': 'https://download.pytorch.org/models/vgg11_bn-6002323d.pth',
    'vgg13_bn': 'https://download.pytorch.org/models/vgg13_bn-abd245e5.pth',
    'vgg16_bn': 'https://download.pytorch.org/models/vgg16_bn-6c64b313.pth',
    'vgg19_bn': 'https://download.pytorch.org/models/vgg19_bn-c79401a0.pth',
}


class VGG(nn.Module):

    def __init__(self, features, num_classes=1000, init_weights=True):
        super(VGG, self).__init__()
        self.features = features
        self.classifier = nn.Sequential(
            nn.Linear(512 * 7 * 7, 4096),
            nn.ReLU(True),
            nn.Dropout(),
            nn.Linear(4096, 4096),
            nn.ReLU(True),
            nn.Dropout(),
            nn.Linear(4096, num_classes),
        )
        if init_weights:
            self._initialize_weights()

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

    def _initialize_weights(self):
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
                if m.bias is not None:
                    nn.init.constant_(m.bias, 0)
            elif isinstance(m, nn.BatchNorm2d):
                nn.init.constant_(m.weight, 1)
                nn.init.constant_(m.bias, 0)
            elif isinstance(m, nn.Linear):
                nn.init.normal_(m.weight, 0, 0.01)
                nn.init.constant_(m.bias, 0)


def make_layers(cfg, batch_norm=False):
    layers = []
    in_channels = 3
    for v in cfg:
        if v == 'M':
            layers += [nn.MaxPool2d(kernel_size=2, stride=2)]
        else:
            conv2d = nn.Conv2d(in_channels, v, kernel_size=3, padding=1)
            if batch_norm:
                layers += [conv2d, nn.BatchNorm2d(v), nn.ReLU(inplace=True)]
            else:
                layers += [conv2d, nn.ReLU(inplace=True)]
            in_channels = v
    return nn.Sequential(*layers)


cfg = {
    'A': [64, 'M', 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],
    'B': [64, 64, 'M', 128, 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],
    'D': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'M', 512, 512, 512, 'M', 512, 512, 512, 'M'],
    'E': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 256, 'M', 512, 512, 512, 512, 'M', 512, 512, 512, 512, 'M'],
}

class VGG16(nn.Module):

    def __init__(self, pretrain=True):
        super().__init__()
        net = VGG(make_layers(cfg['D']), init_weights=False)
        #if pretrain:
        #    net.load_state_dict(model_zoo.load_url(model_urls['vgg16']))

        self.stage1 = nn.Sequential(*[net.features[layer] for layer in range(0, 5)])
        self.stage2 = nn.Sequential(*[net.features[layer] for layer in range(5, 10)])
        self.stage3 = nn.Sequential(*[net.features[layer] for layer in range(10, 17)])
        self.stage4 = nn.Sequential(*[net.features[layer] for layer in range(17, 24)])
        self.stage5 = nn.Sequential(*[net.features[layer] for layer in range(24, 31)])

    def forward(self, x):
        C1 = self.stage1(x)
        C2 = self.stage2(C1)
        C3 = self.stage3(C2)
        C4 = self.stage4(C3)
        C5 = self.stage5(C4)
        return C1, C2, C3, C4, C5


if __name__ == '__main__':
    import torch
    input = torch.randn((4, 3, 512, 512))
    net = VGG16()
    C1, C2, C3, C4, C5 = net(input)
    print(C1.size())
    print(C2.size())
    print(C3.size())
    print(C4.size())
    print(C5.size())