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Copy pathIA-SSDnetwork.txt
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executable file
·200 lines (199 loc) · 9.04 KB
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IASSD(
(vfe): None
(backbone_3d): IASSD_Backbone(
(SA_modules): ModuleList(
(0): PointnetSAModuleMSG_WithSampling(
(groupers): ModuleList(
(0): QueryAndGroup()
(1): QueryAndGroup()
)
(mlps): ModuleList(
(0): Sequential(
(0): Conv2d(4, 16, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU()
(3): Conv2d(16, 16, kernel_size=(1, 1), stride=(1, 1), bias=False)
(4): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(5): ReLU()
(6): Conv2d(16, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)
(7): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(8): ReLU()
)
(1): Sequential(
(0): Conv2d(4, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU()
(3): Conv2d(32, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)
(4): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(5): ReLU()
(6): Conv2d(32, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
(7): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(8): ReLU()
)
)
(aggregation_layer): Sequential(
(0): Conv1d(96, 64, kernel_size=(1,), stride=(1,), bias=False)
(1): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU()
)
)
(1): PointnetSAModuleMSG_WithSampling(
(groupers): ModuleList(
(0): QueryAndGroup()
(1): QueryAndGroup()
)
(mlps): ModuleList(
(0): Sequential(
(0): Conv2d(67, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU()
(3): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
(4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(5): ReLU()
(6): Conv2d(64, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(7): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(8): ReLU()
)
(1): Sequential(
(0): Conv2d(67, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU()
(3): Conv2d(64, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
(4): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(5): ReLU()
(6): Conv2d(96, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(7): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(8): ReLU()
)
)
(aggregation_layer): Sequential(
(0): Conv1d(256, 128, kernel_size=(1,), stride=(1,), bias=False)
(1): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU()
)
(confidence_layers): Sequential(
(0): Conv1d(128, 128, kernel_size=(1,), stride=(1,), bias=False)
(1): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU()
(3): Conv1d(128, 3, kernel_size=(1,), stride=(1,))
)
)
(2): PointnetSAModuleMSG_WithSampling(
(groupers): ModuleList(
(0): QueryAndGroup()
(1): QueryAndGroup()
)
(mlps): ModuleList(
(0): Sequential(
(0): Conv2d(131, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU()
(3): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(4): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(5): ReLU()
(6): Conv2d(128, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(7): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(8): ReLU()
)
(1): Sequential(
(0): Conv2d(131, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU()
(3): Conv2d(128, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(4): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(5): ReLU()
(6): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(7): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(8): ReLU()
)
)
(aggregation_layer): Sequential(
(0): Conv1d(512, 256, kernel_size=(1,), stride=(1,), bias=False)
(1): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU()
)
(confidence_layers): Sequential(
(0): Conv1d(256, 256, kernel_size=(1,), stride=(1,), bias=False)
(1): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU()
(3): Conv1d(256, 3, kernel_size=(1,), stride=(1,))
)
)
(3): PointnetSAModuleMSG_WithSampling(
(groupers): ModuleList()
(mlps): ModuleList()
)
(4): Vote_layer(
(mlp_modules): Sequential(
(0): Conv1d(256, 128, kernel_size=(1,), stride=(1,), bias=False)
(1): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU()
)
(ctr_reg): Conv1d(128, 3, kernel_size=(1,), stride=(1,))
)
(5): PointnetSAModuleMSG_WithSampling(
(groupers): ModuleList(
(0): QueryAndGroup()
(1): QueryAndGroup()
)
(mlps): ModuleList(
(0): Sequential(
(0): Conv2d(259, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU()
(3): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(4): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(5): ReLU()
(6): Conv2d(256, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(7): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(8): ReLU()
)
(1): Sequential(
(0): Conv2d(259, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU()
(3): Conv2d(256, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(4): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(5): ReLU()
(6): Conv2d(512, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(7): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(8): ReLU()
)
)
(aggregation_layer): Sequential(
(0): Conv1d(1536, 512, kernel_size=(1,), stride=(1,), bias=False)
(1): BatchNorm1d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU()
)
)
)
)
(map_to_bev_module): None
(pfe): None
(backbone_2d): None
(dense_head): None
(point_head): IASSD_Head(
(cls_loss_func): WeightedClassificationLoss()
(reg_loss_func): WeightedSmoothL1Loss()
(ins_loss_func): WeightedClassificationLoss()
(cls_center_layers): Sequential(
(0): Linear(in_features=512, out_features=256, bias=False)
(1): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU()
(3): Linear(in_features=256, out_features=256, bias=False)
(4): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(5): ReLU()
(6): Linear(in_features=256, out_features=3, bias=True)
)
(box_center_layers): Sequential(
(0): Linear(in_features=512, out_features=256, bias=False)
(1): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU()
(3): Linear(in_features=256, out_features=256, bias=False)
(4): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(5): ReLU()
(6): Linear(in_features=256, out_features=30, bias=True)
)
)
(roi_head): None
)