models/boxnet.py [57:83]:
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        self.pnet = ProposalModule(num_class, num_heading_bin, num_size_cluster,
            mean_size_arr, num_proposal, sampling)

    def forward(self, inputs):
        """ Forward pass of the network

        Args:
            inputs: dict
                {point_clouds}

                point_clouds: Variable(torch.cuda.FloatTensor)
                    (B, N, 3 + input_channels) tensor
                    Point cloud to run predicts on
                    Each point in the point-cloud MUST
                    be formated as (x, y, z, features...)
        Returns:
            end_points: dict
        """
        end_points = {}
        batch_size = inputs['point_clouds'].shape[0]

        end_points = self.backbone_net(inputs['point_clouds'], end_points)
        xyz = end_points['fp2_xyz']
        features = end_points['fp2_features']
        end_points['seed_inds'] = end_points['fp2_inds']
        end_points['seed_xyz'] = xyz
        end_points['seed_features'] = features
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models/votenet.py [66:94]:
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        self.pnet = ProposalModule(num_class, num_heading_bin, num_size_cluster,
            mean_size_arr, num_proposal, sampling)

    def forward(self, inputs):
        """ Forward pass of the network

        Args:
            inputs: dict
                {point_clouds}

                point_clouds: Variable(torch.cuda.FloatTensor)
                    (B, N, 3 + input_channels) tensor
                    Point cloud to run predicts on
                    Each point in the point-cloud MUST
                    be formated as (x, y, z, features...)
        Returns:
            end_points: dict
        """
        end_points = {}
        batch_size = inputs['point_clouds'].shape[0]

        end_points = self.backbone_net(inputs['point_clouds'], end_points)
                
        # --------- HOUGH VOTING ---------
        xyz = end_points['fp2_xyz']
        features = end_points['fp2_features']
        end_points['seed_inds'] = end_points['fp2_inds']
        end_points['seed_xyz'] = xyz
        end_points['seed_features'] = features
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