Is it possible to release vgg network pretrained on GCC? about gcc-sfcn HOT 6 CLOSED

class VGG(nn.Module):
    def __init__(self, pretrained=True):
        super(VGG, self).__init__()
        vgg = models.vgg16()
        if pretrained:
            vgg.load_state_dict(torch.load(model_path))
        features = list(vgg.features.children())
        self.features4 = nn.Sequential(*features[0:23])

        self.de_pred = nn.Sequential(
            Conv2d(512, 128, 1, same_padding=True, NL='relu'),
            Conv2d(128, 1, 1, same_padding=True, NL='relu')
        )

    def forward(self, x):
        x = self.features4(x)       
        x = self.de_pred(x)
        x = F.upsample(x,scale_factor=8)
        return x

class VGG_decoder(nn.Module):
    def __init__(self, pretrained=True):
        super(VGG_decoder, self).__init__()
        vgg = models.vgg16()
        if pretrained:
            vgg.load_state_dict(torch.load(model_path))
        features = list(vgg.features.children())
        self.features4 = nn.Sequential(*features[0:23])

        self.de_pred = nn.Sequential(
            Conv2d( 512, 128, 3, same_padding=True, NL='relu'),
            nn.ConvTranspose2d(128,64,4,stride=2,padding=1,output_padding=0,bias=True),
            nn.ReLU(),
            nn.ConvTranspose2d(64,32,4,stride=2,padding=1,output_padding=0,bias=True),
            nn.ReLU(),
            nn.ConvTranspose2d(32,16,4,stride=2,padding=1,output_padding=0,bias=True),
            nn.ReLU(),
            Conv2d(16, 1, 1, same_padding=True, NL='relu')
        )

    def forward(self, x):
        x = self.features4(x)       
        x = self.de_pred(x)
        return x