Is it possible to release vgg network pretrained on GCC?

[xialeiliu]

I would like to do a quick experiment with vgg network, I would appreciate if you could help on it.

[gjy3035]

vgg network? you mean sfcn based on vgg or pure vgg? we have conducted two baselines with vgg backbone. unfortunately, the trained models are saved in my server. when i return lab, i can share the pretrained weights and the model definitions with you.

[xialeiliu]

Pure vgg, I want to compare training from ImageNet-pretrained and GCC-pretrained VGG-16. Thanks a lot! By the way, have you observed that GCC-pretrained network is better than ImageNet-pretrained? I guess you compare these two in your paper.

[Elin24]

@xialeiliu I am a co-author of this paper. The code you want is as follows

VGG :

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

VGG decoder:

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

@gjy3035 will provide the pretrained vgg model parameters later, if you need.

[gjy3035]

@xialeiliu in our paper, we have compared the results using different installation. due to other stuff, our paper can not be polished. we will upload the paper in 5 days.

[xialeiliu]

That's exactly what I want to see, very interesting results. Thanks for posting it here!

[gjy3035]

We have upload the download link for VGG and VGG_decoder, which are trained under the C^3 Framework (the performance is better this repo). https://mailnwpueducn-my.sharepoint.com/:u:/g/personal/gjy3035_mail_nwpu_edu_cn/EVdYCMGL5WRKp-QRSXs6KlsBGinp0XA3KR4No9cC0OuBcw?e=nehYUH

https://mailnwpueducn-my.sharepoint.com/:u:/g/personal/gjy3035_mail_nwpu_edu_cn/EZNmWR2Si25Auz3yGhrjXYUBgcGVY84yCslFS_pHYTqAtw?e=zYtXb0

Thanks for your attention!