def network_initialization(self, in_channels, config, D): self.conv1 = ME.MinkowskiConvolution( in_channels, config.proposal_feat_size, kernel_size=1, dimension=3) self.bn1 = ME.MinkowskiInstanceNorm(config.proposal_feat_size) self.conv2 = ME.MinkowskiConvolution( config.proposal_feat_size, config.proposal_feat_size, kernel_size=1, dimension=3) self.bn2 = ME.MinkowskiInstanceNorm(config.proposal_feat_size) self.final_class_logits = ME.MinkowskiConvolution( config.proposal_feat_size, self.out_channels * 2, kernel_size=1, dimension=3, has_bias=True) self.final_bbox = ME.MinkowskiConvolution( config.proposal_feat_size, self.out_channels * 6, kernel_size=1, dimension=3, has_bias=True) self.elu = ME.MinkowskiELU() self.softmax = ME.MinkowskiSoftmax() if self.is_rotation_bbox: self.final_rotation = ME.MinkowskiConvolution( config.proposal_feat_size, self.out_channels * self.rotation_criterion.NUM_OUTPUT, kernel_size=1, dimension=3, has_bias=True)