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)
