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model_pdv.log
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INFO PDV(
(vfe): MeanVFE()
(backbone_3d): VoxelBackBone8x(
(conv_input): SparseSequential(
(0): SubMConv3d()
(1): BatchNorm1d(16, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(2): ReLU()
)
(conv1): SparseSequential(
(0): SparseSequential(
(0): SubMConv3d()
(1): BatchNorm1d(16, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(2): ReLU()
)
)
(conv2): SparseSequential(
(0): SparseSequential(
(0): SparseConv3d()
(1): BatchNorm1d(32, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(2): ReLU()
)
(1): SparseSequential(
(0): SubMConv3d()
(1): BatchNorm1d(32, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(2): ReLU()
)
(2): SparseSequential(
(0): SubMConv3d()
(1): BatchNorm1d(32, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(2): ReLU()
)
)
(conv3): SparseSequential(
(0): SparseSequential(
(0): SparseConv3d()
(1): BatchNorm1d(64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(2): ReLU()
)
(1): SparseSequential(
(0): SubMConv3d()
(1): BatchNorm1d(64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(2): ReLU()
)
(2): SparseSequential(
(0): SubMConv3d()
(1): BatchNorm1d(64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(2): ReLU()
)
)
(conv4): SparseSequential(
(0): SparseSequential(
(0): SparseConv3d()
(1): BatchNorm1d(64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(2): ReLU()
)
(1): SparseSequential(
(0): SubMConv3d()
(1): BatchNorm1d(64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(2): ReLU()
)
(2): SparseSequential(
(0): SubMConv3d()
(1): BatchNorm1d(64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(2): ReLU()
)
)
(conv_out): SparseSequential(
(0): SparseConv3d()
(1): BatchNorm1d(128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(2): ReLU()
)
)
(map_to_bev_module): HeightCompression()
(pfe): None
(backbone_2d): BaseBEVBackbone(
(blocks): ModuleList(
(0): Sequential(
(0): ZeroPad2d(padding=(1, 1, 1, 1), value=0.0)
(1): Conv2d(256, 128, kernel_size=(3, 3), stride=(1, 1), bias=False)
(2): BatchNorm2d(128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(3): ReLU()
(4): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(5): BatchNorm2d(128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(6): ReLU()
(7): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(8): BatchNorm2d(128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(9): ReLU()
(10): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(11): BatchNorm2d(128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(12): ReLU()
(13): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(14): BatchNorm2d(128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(15): ReLU()
(16): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(17): BatchNorm2d(128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(18): ReLU()
)
(1): Sequential(
(0): ZeroPad2d(padding=(1, 1, 1, 1), value=0.0)
(1): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), bias=False)
(2): BatchNorm2d(256, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(3): ReLU()
(4): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(5): BatchNorm2d(256, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(6): ReLU()
(7): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(8): BatchNorm2d(256, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(9): ReLU()
(10): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(11): BatchNorm2d(256, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(12): ReLU()
(13): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(14): BatchNorm2d(256, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(15): ReLU()
(16): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(17): BatchNorm2d(256, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(18): ReLU()
)
)
(deblocks): ModuleList(
(0): Sequential(
(0): ConvTranspose2d(128, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(256, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(2): ReLU()
)
(1): Sequential(
(0): ConvTranspose2d(256, 256, kernel_size=(2, 2), stride=(2, 2), bias=False)
(1): BatchNorm2d(256, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(2): ReLU()
)
)
)
(dense_head): AnchorHeadSingle(
(cls_loss_func): SigmoidFocalClassificationLoss()
(reg_loss_func): WeightedSmoothL1Loss()
(dir_loss_func): WeightedCrossEntropyLoss()
(conv_cls): Conv2d(512, 18, kernel_size=(1, 1), stride=(1, 1))
(conv_box): Conv2d(512, 42, kernel_size=(1, 1), stride=(1, 1))
(conv_dir_cls): Conv2d(512, 12, kernel_size=(1, 1), stride=(1, 1))
)
(point_head): None
(roi_head): PDVHead(
(proposal_target_layer): ProposalTargetLayer()
(reg_loss_func): WeightedSmoothL1Loss()
(roi_grid_pool_layers): ModuleList(
(0): StackSAModuleMSGAttention(
(groupers): ModuleList(
(0): QueryAndGroup()
(1): QueryAndGroup()
)
(mlps): ModuleList(
(0): Sequential(
(0): Conv2d(68, 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()
)
(1): Sequential(
(0): Conv2d(68, 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()
)
)
)
(1): StackSAModuleMSGAttention(
(groupers): ModuleList(
(0): QueryAndGroup()
(1): QueryAndGroup()
)
(mlps): ModuleList(
(0): Sequential(
(0): Conv2d(68, 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()
)
(1): Sequential(
(0): Conv2d(68, 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()
)
)
)
)
(attention_head): TransformerEncoder(
(pos_encoder): FeedForwardPositionalEncoding(
(ffn): Sequential(
(0): Conv1d(4, 64, kernel_size=(1,), stride=(1,))
(1): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
(3): Conv1d(64, 128, kernel_size=(1,), stride=(1,))
)
)
(transformer_encoder): TransformerEncoder(
(layers): ModuleList(
(0): TransformerEncoderLayer(
(self_attn): MultiheadAttention(
(out_proj): _LinearWithBias(in_features=128, out_features=128, bias=True)
)
(linear1): Linear(in_features=128, out_features=128, bias=True)
(dropout): Dropout(p=0.1, inplace=False)
(linear2): Linear(in_features=128, out_features=128, bias=True)
(norm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True)
(norm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True)
(dropout1): Dropout(p=0.1, inplace=False)
(dropout2): Dropout(p=0.1, inplace=False)
)
)
)
)
(shared_fc_layer): Sequential(
(0): Conv1d(27648, 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): Dropout(p=0.3, inplace=False)
(4): Conv1d(256, 256, kernel_size=(1,), stride=(1,), bias=False)
(5): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(6): ReLU()
)
(reg_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): Dropout(p=0.3, inplace=False)
(4): Conv1d(256, 256, kernel_size=(1,), stride=(1,), bias=False)
(5): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(6): ReLU()
(7): Conv1d(256, 7, kernel_size=(1,), stride=(1,))
)
(cls_layers): Sequential(
(0): Conv1d(260, 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): Dropout(p=0.3, inplace=False)
(4): Conv1d(256, 256, kernel_size=(1,), stride=(1,), bias=False)
(5): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(6): ReLU()
(7): Conv1d(256, 1, kernel_size=(1,), stride=(1,))
)
)
)