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model.txt
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model.txt
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RPN(
(backbone_net): Pointnet2MSG(
(SA_modules): ModuleList(
(0): PointnetSAModuleMSG(
(groupers): ModuleList(
(0): QueryAndGroup()
(1): QueryAndGroup()
)
(mlps): ModuleList(
(0): SharedMLP(
(layer0): Conv2d(
(conv): Conv2d(3, 16, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(
(bn): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(activation): ReLU(inplace=True)
)
(layer1): Conv2d(
(conv): Conv2d(16, 16, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(
(bn): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(activation): ReLU(inplace=True)
)
(layer2): Conv2d(
(conv): Conv2d(16, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(
(bn): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(activation): ReLU(inplace=True)
)
)
(1): SharedMLP(
(layer0): Conv2d(
(conv): Conv2d(3, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(
(bn): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(activation): ReLU(inplace=True)
)
(layer1): Conv2d(
(conv): Conv2d(32, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(
(bn): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(activation): ReLU(inplace=True)
)
(layer2): Conv2d(
(conv): Conv2d(32, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(
(bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(activation): ReLU(inplace=True)
)
)
)
)
(1): PointnetSAModuleMSG(
(groupers): ModuleList(
(0): QueryAndGroup()
(1): QueryAndGroup()
)
(mlps): ModuleList(
(0): SharedMLP(
(layer0): Conv2d(
(conv): Conv2d(99, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(
(bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(activation): ReLU(inplace=True)
)
(layer1): Conv2d(
(conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(
(bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(activation): ReLU(inplace=True)
)
(layer2): Conv2d(
(conv): Conv2d(64, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(
(bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(activation): ReLU(inplace=True)
)
)
(1): SharedMLP(
(layer0): Conv2d(
(conv): Conv2d(99, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(
(bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(activation): ReLU(inplace=True)
)
(layer1): Conv2d(
(conv): Conv2d(64, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(
(bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(activation): ReLU(inplace=True)
)
(layer2): Conv2d(
(conv): Conv2d(96, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(
(bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(activation): ReLU(inplace=True)
)
)
)
)
(2): PointnetSAModuleMSG(
(groupers): ModuleList(
(0): QueryAndGroup()
(1): QueryAndGroup()
)
(mlps): ModuleList(
(0): SharedMLP(
(layer0): Conv2d(
(conv): Conv2d(259, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(
(bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(activation): ReLU(inplace=True)
)
(layer1): Conv2d(
(conv): Conv2d(128, 196, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(
(bn): BatchNorm2d(196, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(activation): ReLU(inplace=True)
)
(layer2): Conv2d(
(conv): Conv2d(196, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(
(bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(activation): ReLU(inplace=True)
)
)
(1): SharedMLP(
(layer0): Conv2d(
(conv): Conv2d(259, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(
(bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(activation): ReLU(inplace=True)
)
(layer1): Conv2d(
(conv): Conv2d(128, 196, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(
(bn): BatchNorm2d(196, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(activation): ReLU(inplace=True)
)
(layer2): Conv2d(
(conv): Conv2d(196, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(
(bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(activation): ReLU(inplace=True)
)
)
)
)
(3): PointnetSAModuleMSG(
(groupers): ModuleList(
(0): QueryAndGroup()
(1): QueryAndGroup()
)
(mlps): ModuleList(
(0): SharedMLP(
(layer0): Conv2d(
(conv): Conv2d(515, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(
(bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(activation): ReLU(inplace=True)
)
(layer1): Conv2d(
(conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(
(bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(activation): ReLU(inplace=True)
)
(layer2): Conv2d(
(conv): Conv2d(256, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(
(bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(activation): ReLU(inplace=True)
)
)
(1): SharedMLP(
(layer0): Conv2d(
(conv): Conv2d(515, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(
(bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(activation): ReLU(inplace=True)
)
(layer1): Conv2d(
(conv): Conv2d(256, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(
(bn): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(activation): ReLU(inplace=True)
)
(layer2): Conv2d(
(conv): Conv2d(384, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(
(bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(activation): ReLU(inplace=True)
)
)
)
)
)
(FP_modules): ModuleList(
(0): PointnetFPModule(
(mlp): SharedMLP(
(layer0): Conv2d(
(conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(
(bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(activation): ReLU(inplace=True)
)
(layer1): Conv2d(
(conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(
(bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(activation): ReLU(inplace=True)
)
)
)
(1): PointnetFPModule(
(mlp): SharedMLP(
(layer0): Conv2d(
(conv): Conv2d(608, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(
(bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(activation): ReLU(inplace=True)
)
(layer1): Conv2d(
(conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(
(bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(activation): ReLU(inplace=True)
)
)
)
(2): PointnetFPModule(
(mlp): SharedMLP(
(layer0): Conv2d(
(conv): Conv2d(768, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(
(bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(activation): ReLU(inplace=True)
)
(layer1): Conv2d(
(conv): Conv2d(512, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(
(bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(activation): ReLU(inplace=True)
)
)
)
(3): PointnetFPModule(
(mlp): SharedMLP(
(layer0): Conv2d(
(conv): Conv2d(1536, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(
(bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(activation): ReLU(inplace=True)
)
(layer1): Conv2d(
(conv): Conv2d(512, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(
(bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(activation): ReLU(inplace=True)
)
)
)
)
)
(rpn_cls_layer): Sequential(
(0): Conv1d(
(conv): Conv1d(128, 128, kernel_size=(1,), stride=(1,), bias=False)
(bn): BatchNorm1d(
(bn): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(activation): ReLU(inplace=True)
)
(1): Dropout(p=0.5, inplace=False)
(2): Conv1d(
(conv): Conv1d(128, 1, kernel_size=(1,), stride=(1,))
)
)
(rpn_reg_layer): Sequential(
(0): Conv1d(
(conv): Conv1d(128, 128, kernel_size=(1,), stride=(1,), bias=False)
(bn): BatchNorm1d(
(bn): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(activation): ReLU(inplace=True)
)
(1): Dropout(p=0.5, inplace=False)
(2): Conv1d(
(conv): Conv1d(128, 76, kernel_size=(1,), stride=(1,))
)
)
(rpn_cls_loss_func): SigmoidFocalClassificationLoss()
(proposal_layer): ProposalLayer()
)
RCNNNet(
(SA_modules): ModuleList(
(0): PointnetSAModule(
(groupers): ModuleList(
(0): QueryAndGroup()
)
(mlps): ModuleList(
(0): SharedMLP(
(layer0): Conv2d(
(conv): Conv2d(131, 128, kernel_size=(1, 1), stride=(1, 1))
(activation): ReLU(inplace=True)
)
(layer1): Conv2d(
(conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))
(activation): ReLU(inplace=True)
)
(layer2): Conv2d(
(conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))
(activation): ReLU(inplace=True)
)
)
)
)
(1): PointnetSAModule(
(groupers): ModuleList(
(0): QueryAndGroup()
)
(mlps): ModuleList(
(0): SharedMLP(
(layer0): Conv2d(
(conv): Conv2d(131, 128, kernel_size=(1, 1), stride=(1, 1))
(activation): ReLU(inplace=True)
)
(layer1): Conv2d(
(conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))
(activation): ReLU(inplace=True)
)
(layer2): Conv2d(
(conv): Conv2d(128, 256, kernel_size=(1, 1), stride=(1, 1))
(activation): ReLU(inplace=True)
)
)
)
)
(2): PointnetSAModule(
(groupers): ModuleList(
(0): GroupAll()
)
(mlps): ModuleList(
(0): SharedMLP(
(layer0): Conv2d(
(conv): Conv2d(259, 256, kernel_size=(1, 1), stride=(1, 1))
(activation): ReLU(inplace=True)
)
(layer1): Conv2d(
(conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1))
(activation): ReLU(inplace=True)
)
(layer2): Conv2d(
(conv): Conv2d(256, 512, kernel_size=(1, 1), stride=(1, 1))
(activation): ReLU(inplace=True)
)
)
)
)
)
(xyz_up_layer): SharedMLP(
(layer0): Conv2d(
(conv): Conv2d(5, 128, kernel_size=(1, 1), stride=(1, 1))
(activation): ReLU(inplace=True)
)
(layer1): Conv2d(
(conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))
(activation): ReLU(inplace=True)
)
)
(merge_down_layer): SharedMLP(
(layer0): Conv2d(
(conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1))
(activation): ReLU(inplace=True)
)
)
(cls_layer): Sequential(
(0): Conv1d(
(conv): Conv1d(512, 256, kernel_size=(1,), stride=(1,))
(activation): ReLU(inplace=True)
)
(1): Dropout(p=0.0, inplace=False)
(2): Conv1d(
(conv): Conv1d(256, 256, kernel_size=(1,), stride=(1,))
(activation): ReLU(inplace=True)
)
(3): Conv1d(
(conv): Conv1d(256, 1, kernel_size=(1,), stride=(1,))
)
)
(reg_layer): Sequential(
(0): Conv1d(
(conv): Conv1d(512, 256, kernel_size=(1,), stride=(1,))
(activation): ReLU(inplace=True)
)
(1): Dropout(p=0.0, inplace=False)
(2): Conv1d(
(conv): Conv1d(256, 256, kernel_size=(1,), stride=(1,))
(activation): ReLU(inplace=True)
)
(3): Conv1d(
(conv): Conv1d(256, 46, kernel_size=(1,), stride=(1,))
)
)
(proposal_target_layer): ProposalTargetLayer()
)