def forward()

in depth_upsampling/models/mspf/densenet.py [0:0]

• 13 lines of code
• 6 McCabe index (conditional complexity)

    def forward(self, x):
        _, _, h, w = x.shape
        features = [x]
        skip_feat = {"x1": x}
        for k, v in self.base_model._modules.items():
            # ignore classification head
            if 'fc' in k or 'avgpool' in k:
                continue
            feature = v(features[-1])
            features.append(feature)
            if any(x in k for x in self.skip_feature_names):
                _, _, fh, fw = feature.shape
                skip_feat[f"x{int(h/fh)}"] = feature
        return skip_feat