def init_weights()

in models/swin_transformer_3d.py [0:0]

• 29 lines of code
• 11 McCabe index (conditional complexity)

    def init_weights(self, pretrained=None):
        """Initialize the weights in backbone.
        Args:
            pretrained (str, optional): Path to pre-trained weights.
                Defaults to None.
        """

        def _init_weights(m):
            if isinstance(m, nn.Linear):
                trunc_normal_(m.weight, std=0.02)
                if isinstance(m, nn.Linear) and m.bias is not None:
                    nn.init.constant_(m.bias, 0)
            elif isinstance(m, nn.LayerNorm):
                nn.init.constant_(m.bias, 0)
                nn.init.constant_(m.weight, 1.0)

        if pretrained:
            self.pretrained = pretrained
        if isinstance(self.pretrained, str) or isinstance(self.pretrained, list):
            self.apply(_init_weights)
            logging.info(f"load model from: {self.pretrained}")

            if self.pretrained2d:
                # Inflate 2D model into 3D model.
                logging.info(f"Inflating with {self.pretrained_model_key}")
                self.inflate_weights(logging)
            elif self.pretrained3d:
                logging.info(f"Loading 3D model with {self.pretrained_model_key}")
                self.load_and_interpolate_3d_weights(logging)
            else:
                raise ValueError(
                    "Use VISSL loading for this. This code "
                    "is only for Swin inflation."
                )
        elif self.pretrained is None:
            self.apply(_init_weights)
        else:
            raise TypeError("pretrained must be a str or None")