def forward()

in models/networks.py [0:0]

• 16 lines of code
• 1 McCabe index (conditional complexity)

    def forward(self, x, visual_feat):
        audio_conv1feature = self.audionet_convlayer1(x)
        audio_conv2feature = self.audionet_convlayer2(audio_conv1feature)
        audio_conv3feature = self.audionet_convlayer3(audio_conv2feature)
        audio_conv4feature = self.audionet_convlayer4(audio_conv3feature)
        audio_conv5feature = self.audionet_convlayer5(audio_conv4feature)

        visual_feat = self.conv1x1(visual_feat)
        visual_feat = visual_feat.view(visual_feat.shape[0], -1, 1, 1) #flatten visual feature
        visual_feat = visual_feat.repeat(1, 1, audio_conv5feature.shape[-2], audio_conv5feature.shape[-1]) #tile visual feature
        
        audioVisual_feature = torch.cat((visual_feat, audio_conv5feature), dim=1)
        
        audio_upconv1feature = self.audionet_upconvlayer1(audioVisual_feature)
        audio_upconv2feature = self.audionet_upconvlayer2(torch.cat((audio_upconv1feature, audio_conv4feature), dim=1))
        audio_upconv3feature = self.audionet_upconvlayer3(torch.cat((audio_upconv2feature, audio_conv3feature), dim=1))
        audio_upconv4feature = self.audionet_upconvlayer4(torch.cat((audio_upconv3feature, audio_conv2feature), dim=1))
        mask_prediction = self.audionet_upconvlayer5(torch.cat((audio_upconv4feature, audio_conv1feature), dim=1)) * 2 - 1
        return mask_prediction