Class autoencoder_vgg3

script/feature/model.py:113–155

vgg encoder

        return None, x

class autoencoder_vgg3(nn.Module): # psnr: 37.77
    ''' vgg encoder '''
    def __init__(self):
        super(autoencoder_vgg3, self).__init__()
        self.encoder = models.vgg19(pretrained=True).features
        # receptive field not equal? so maybe it does not work very well
        self.decoder = nn.Sequential(
            nn.ConvTranspose2d(512, 512, 2, stride=2), # (b, 512, 14, 14)
            nn.ReLU(True),
            nn.ConvTranspose2d(512, 256, 4, stride=4), # (b, 256, 56, 56)
            nn.ReLU(True),
            nn.ConvTranspose2d(256, 64, 4, stride=4), # (b, 64, 224, 224)
            nn.ReLU(True),
            nn.Conv2d(64, 3, 3, stride=1, padding=1),
            nn.Tanh() # MSELoss
            # nn.Sigmoid() # BCELoss
        )

    def forward(self, x):
        feat = []
        feat_out = []
        for i in range(len(self.encoder)):
            # print("layer {} encoder layer: {}".format(i, self.encoder[i]))
            x = self.encoder[i](x)
            if i == 35: # ReLU-36
                feat.append(x)
            elif i == 17: # ReLU-17
                feat.append(x)
            elif i == 3: # ReLU-4
                feat.append(x)      
        for i in range(len(self.decoder)):
            # print("layer {} decoder layer: {}".format(i, self.decoder[i]))
            x = self.decoder[i](x)
            if i == 1:
                x = x + feat[2]
                feat_out.append(x)
            elif i == 3:
                x = x + feat[1]
                feat_out.append(x)
            elif i == 5:
                x = x + feat[0]
                feat_out.append(x)
        return feat_out, x

class autoencoder_vgg4(nn.Module): # 35.54 PSNR 36.05 BCELoss (120x120)
    ''' vgg encoder with bilinear upsampling'''