| 2026/03/15 22:48:11 - bevformer - INFO - |
| ------------------------------------------------------------ |
| System environment: |
| sys.platform: darwin |
| Python: 3.10.0 (default, Oct 17 2021, 11:56:26) [Clang 13.0.0 ] |
| CUDA available: False |
| numpy_random_seed: 321 |
| GCC: Apple clang version 15.0.0 (clang-1500.3.9.4) |
| PyTorch: 2.10.0 |
| PyTorch compiling details: PyTorch built with: |
| - GCC 4.2 |
| - C++ Version: 201703 |
| - clang 15.0.0 |
| - OpenMP 201811 |
| - LAPACK is enabled (usually provided by MKL) |
| - NNPACK is enabled |
| - CPU capability usage: DEFAULT |
| - Build settings: BLAS_INFO=accelerate, BUILD_TYPE=Release, COMMIT_SHA=449b1768410104d3ed79d3bcfe4ba1d65c7f22c0, CXX_COMPILER=/usr/bin/c++, CXX_FLAGS= -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -DNDEBUG -DUSE_KINETO -DLIBKINETO_NOCUPTI -DLIBKINETO_NOROCTRACER -DLIBKINETO_NOXPUPTI=ON -DUSE_PYTORCH_QNNPACK -DAT_BUILD_ARM_VEC256_WITH_SLEEF -DUSE_XNNPACK -DUSE_PYTORCH_METAL_EXPORT -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DUSE_COREML_DELEGATE -O2 -fPIC -DC10_NODEPRECATED -Wall -Wextra -Werror=return-type -Werror=non-virtual-dtor -Werror=braced-scalar-init -Werror=range-loop-construct -Werror=bool-operation -Wnarrowing -Wno-missing-field-initializers -Wno-unknown-pragmas -Wno-unused-parameter -Wno-strict-overflow -Wno-strict-aliasing -Wvla-extension -Wsuggest-override -Wnewline-eof -Winconsistent-missing-override -Winconsistent-missing-destructor-override -Wno-pass-failed -Wno-error=old-style-cast -Wconstant-conversion -Qunused-arguments -faligned-new -fno-math-errno -fno-trapping-math -Werror=format -DUSE_MPS -Wno-missing-braces, LAPACK_INFO=accelerate, TORCH_VERSION=2.10.0, USE_CUDA=OFF, USE_CUDNN=OFF, USE_CUSPARSELT=OFF, USE_EIGEN_FOR_BLAS=ON, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_GLOO=ON, USE_MKL=OFF, USE_MKLDNN=OFF, USE_MPI=OFF, USE_NCCL=OFF, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, USE_ROCM_KERNEL_ASSERT=OFF, USE_XCCL=OFF, USE_XPU=OFF, |
|
|
| TorchVision: 0.25.0 |
| OpenCV: 4.13.0 |
| MMEngine: 0.8.5 |
|
|
| Runtime environment: |
| dist_cfg: {'backend': 'nccl'} |
| seed: 321 |
| Distributed launcher: none |
| Distributed training: False |
| GPU number: 1 |
| ------------------------------------------------------------ |
|
|
| 2026/03/15 22:48:11 - bevformer - INFO - Set random seed to 321, deterministic: False |
| 2026/03/15 22:48:11 - bevformer - INFO - Building model |
| 2026/03/15 22:48:12 - bevformer - INFO - Model architecture: |
| BEVFormerDetector( |
| (data_preprocessor): BaseDataPreprocessor() |
| (pts_bbox_head): BEVFormerHead( |
| (loss_cls): FocalLoss() |
| (loss_bbox): L1Loss() |
| (loss_iou): GIoULoss() |
| (transformer): PerceptionTransformer( |
| (encoder): BEVFormerEncoder( |
| (layers): ModuleList( |
| (0-2): 3 x BEVFormerLayer( |
| (attentions): ModuleList( |
| (0): TemporalSelfAttention( |
| (sampling_offsets): Linear(in_features=512, out_features=128, bias=True) |
| (attention_weights): Linear(in_features=512, out_features=64, bias=True) |
| (value_proj): Linear(in_features=256, out_features=256, bias=True) |
| (output_proj): Linear(in_features=256, out_features=256, bias=True) |
| (dropout): Dropout(p=0.1, inplace=False) |
| ) |
| (1): SpatialCrossAttention( |
| (deformable_attention): MSDeformableAttention3D( |
| (sampling_offsets): Linear(in_features=256, out_features=128, bias=True) |
| (attention_weights): Linear(in_features=256, out_features=64, bias=True) |
| (value_proj): Linear(in_features=256, out_features=256, bias=True) |
| ) |
| (output_proj): Linear(in_features=256, out_features=256, bias=True) |
| (dropout): Dropout(p=0.1, inplace=False) |
| ) |
| ) |
| (ffns): ModuleList( |
| (0): FFN( |
| (layers): Sequential( |
| (0): Sequential( |
| (0): Linear(in_features=256, out_features=512, bias=True) |
| (1): ReLU(inplace=True) |
| (2): Dropout(p=0.1, inplace=False) |
| ) |
| (1): Linear(in_features=512, out_features=256, bias=True) |
| (2): Dropout(p=0.1, inplace=False) |
| ) |
| (dropout_layer): Identity() |
| (gamma2): Identity() |
| ) |
| ) |
| (norms): ModuleList( |
| (0-2): 3 x LayerNorm((256,), eps=1e-05, elementwise_affine=True) |
| ) |
| ) |
| ) |
| ) |
| (decoder): DetectionTransformerDecoder( |
| (layers): ModuleList( |
| (0-5): 6 x DetrTransformerDecoderLayer( |
| (attentions): ModuleList( |
| (0): MultiheadAttention( |
| (q_proj): Linear(in_features=256, out_features=256, bias=True) |
| (k_proj): Linear(in_features=256, out_features=256, bias=True) |
| (v_proj): Linear(in_features=256, out_features=256, bias=True) |
| (output_proj): Linear(in_features=256, out_features=256, bias=True) |
| (dropout): Dropout(p=0.1, inplace=False) |
| ) |
| (1): CustomMSDeformableAttention( |
| (sampling_offsets): Linear(in_features=256, out_features=64, bias=True) |
| (attention_weights): Linear(in_features=256, out_features=32, bias=True) |
| (value_proj): Linear(in_features=256, out_features=256, bias=True) |
| (output_proj): Linear(in_features=256, out_features=256, bias=True) |
| (dropout): Dropout(p=0.1, inplace=False) |
| ) |
| ) |
| (ffns): ModuleList( |
| (0): FFN( |
| (layers): Sequential( |
| (0): Sequential( |
| (0): Linear(in_features=256, out_features=512, bias=True) |
| (1): ReLU(inplace=True) |
| (2): Dropout(p=0.1, inplace=False) |
| ) |
| (1): Linear(in_features=512, out_features=256, bias=True) |
| (2): Dropout(p=0.1, inplace=False) |
| ) |
| (dropout_layer): Identity() |
| (gamma2): Identity() |
| ) |
| ) |
| (norms): ModuleList( |
| (0-2): 3 x LayerNorm((256,), eps=1e-05, elementwise_affine=True) |
| ) |
| ) |
| ) |
| ) |
| (reference_points): Linear(in_features=256, out_features=3, bias=True) |
| (can_bus_mlp): Sequential( |
| (0): Linear(in_features=18, out_features=128, bias=True) |
| (1): ReLU(inplace=True) |
| (2): Linear(in_features=128, out_features=256, bias=True) |
| (3): ReLU(inplace=True) |
| (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True) |
| ) |
| ) |
| (cls_branches): ModuleList( |
| (0-5): 6 x Sequential( |
| (0): Linear(in_features=256, out_features=256, bias=True) |
| (1): LayerNorm((256,), eps=1e-05, elementwise_affine=True) |
| (2): ReLU(inplace=True) |
| (3): Linear(in_features=256, out_features=10, bias=True) |
| ) |
| ) |
| (reg_branches): ModuleList( |
| (0-5): 6 x Sequential( |
| (0): Linear(in_features=256, out_features=256, bias=True) |
| (1): ReLU() |
| (2): Linear(in_features=256, out_features=256, bias=True) |
| (3): ReLU() |
| (4): Linear(in_features=256, out_features=10, bias=True) |
| ) |
| ) |
| (bev_embedding): Embedding(2500, 256) |
| (object_query_embedding): Embedding(900, 512) |
| (positional_encoding): LearnedPositionalEncoding(num_feats=128, row_num_embed=50, col_num_embed=50) |
| ) |
| (img_backbone): ResNet( |
| (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False) |
| (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| (relu): ReLU(inplace=True) |
| (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False) |
| (layer1): ResLayer( |
| (0): Bottleneck( |
| (conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) |
| (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) |
| (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) |
| (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| (relu): ReLU(inplace=True) |
| (downsample): Sequential( |
| (0): Conv2d(64, 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) |
| ) |
| ) |
| init_cfg={'type': 'Constant', 'val': 0, 'override': {'name': 'norm3'}} |
| (1): Bottleneck( |
| (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) |
| (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) |
| (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) |
| (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| (relu): ReLU(inplace=True) |
| ) |
| init_cfg={'type': 'Constant', 'val': 0, 'override': {'name': 'norm3'}} |
| (2): Bottleneck( |
| (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) |
| (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) |
| (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) |
| (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| (relu): ReLU(inplace=True) |
| ) |
| init_cfg={'type': 'Constant', 'val': 0, 'override': {'name': 'norm3'}} |
| ) |
| (layer2): ResLayer( |
| (0): Bottleneck( |
| (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) |
| (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False) |
| (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False) |
| (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| (relu): ReLU(inplace=True) |
| (downsample): Sequential( |
| (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False) |
| (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| ) |
| ) |
| init_cfg={'type': 'Constant', 'val': 0, 'override': {'name': 'norm3'}} |
| (1): Bottleneck( |
| (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) |
| (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) |
| (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False) |
| (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| (relu): ReLU(inplace=True) |
| ) |
| init_cfg={'type': 'Constant', 'val': 0, 'override': {'name': 'norm3'}} |
| (2): Bottleneck( |
| (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) |
| (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) |
| (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False) |
| (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| (relu): ReLU(inplace=True) |
| ) |
| init_cfg={'type': 'Constant', 'val': 0, 'override': {'name': 'norm3'}} |
| (3): Bottleneck( |
| (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) |
| (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) |
| (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False) |
| (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| (relu): ReLU(inplace=True) |
| ) |
| init_cfg={'type': 'Constant', 'val': 0, 'override': {'name': 'norm3'}} |
| ) |
| (layer3): ResLayer( |
| (0): Bottleneck( |
| (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) |
| (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False) |
| (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False) |
| (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| (relu): ReLU(inplace=True) |
| (downsample): Sequential( |
| (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False) |
| (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| ) |
| ) |
| init_cfg={'type': 'Constant', 'val': 0, 'override': {'name': 'norm3'}} |
| (1): Bottleneck( |
| (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) |
| (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) |
| (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False) |
| (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| (relu): ReLU(inplace=True) |
| ) |
| init_cfg={'type': 'Constant', 'val': 0, 'override': {'name': 'norm3'}} |
| (2): Bottleneck( |
| (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) |
| (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) |
| (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False) |
| (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| (relu): ReLU(inplace=True) |
| ) |
| init_cfg={'type': 'Constant', 'val': 0, 'override': {'name': 'norm3'}} |
| (3): Bottleneck( |
| (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) |
| (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) |
| (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False) |
| (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| (relu): ReLU(inplace=True) |
| ) |
| init_cfg={'type': 'Constant', 'val': 0, 'override': {'name': 'norm3'}} |
| (4): Bottleneck( |
| (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) |
| (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) |
| (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False) |
| (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| (relu): ReLU(inplace=True) |
| ) |
| init_cfg={'type': 'Constant', 'val': 0, 'override': {'name': 'norm3'}} |
| (5): Bottleneck( |
| (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) |
| (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) |
| (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False) |
| (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| (relu): ReLU(inplace=True) |
| ) |
| init_cfg={'type': 'Constant', 'val': 0, 'override': {'name': 'norm3'}} |
| ) |
| (layer4): ResLayer( |
| (0): Bottleneck( |
| (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False) |
| (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False) |
| (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False) |
| (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| (relu): ReLU(inplace=True) |
| (downsample): Sequential( |
| (0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False) |
| (1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| ) |
| ) |
| init_cfg={'type': 'Constant', 'val': 0, 'override': {'name': 'norm3'}} |
| (1): Bottleneck( |
| (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False) |
| (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) |
| (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False) |
| (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| (relu): ReLU(inplace=True) |
| ) |
| init_cfg={'type': 'Constant', 'val': 0, 'override': {'name': 'norm3'}} |
| (2): Bottleneck( |
| (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False) |
| (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) |
| (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False) |
| (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| (relu): ReLU(inplace=True) |
| ) |
| init_cfg={'type': 'Constant', 'val': 0, 'override': {'name': 'norm3'}} |
| ) |
| ) |
| init_cfg=[{'type': 'Kaiming', 'layer': 'Conv2d'}, {'type': 'Constant', 'val': 1, 'layer': ['_BatchNorm', 'GroupNorm']}] |
| (img_neck): FPN( |
| (lateral_convs): ModuleList( |
| (0): ConvModule( |
| (conv): Conv2d(2048, 256, kernel_size=(1, 1), stride=(1, 1)) |
| ) |
| ) |
| (fpn_convs): ModuleList( |
| (0): ConvModule( |
| (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) |
| ) |
| ) |
| ) |
| init_cfg={'type': 'Xavier', 'layer': 'Conv2d', 'distribution': 'uniform'} |
| (grid_mask): GridMask() |
| ) |
| 2026/03/15 22:48:12 - bevformer - INFO - Wrapping model |
| 2026/03/15 22:48:12 - bevformer - INFO - Distributed training is not used, all SyncBatchNorm (SyncBN) layers in the model will be automatically reverted to BatchNormXd layers if they are used. |
| 2026/03/15 22:48:12 - bevformer - INFO - Hooks will be executed in the following order: |
| before_run: |
| (VERY_HIGH ) RuntimeInfoHook |
| (BELOW_NORMAL) LoggerHook |
| (VERY_LOW ) CheckpointHookV2 |
| (VERY_LOW ) CheckpointUploader |
| (VERY_LOW ) CheckpointResumer |
| -------------------- |
| before_train: |
| (VERY_HIGH ) RuntimeInfoHook |
| (NORMAL ) IterTimerHook |
| (VERY_LOW ) CheckpointHookV2 |
| (VERY_LOW ) CheckpointUploader |
| (VERY_LOW ) CheckpointResumer |
| -------------------- |
| before_train_epoch: |
| (VERY_HIGH ) RuntimeInfoHook |
| (NORMAL ) IterTimerHook |
| (NORMAL ) DistSamplerSeedHook |
| -------------------- |
| before_train_iter: |
| (VERY_HIGH ) RuntimeInfoHook |
| (NORMAL ) IterTimerHook |
| -------------------- |
| after_train_iter: |
| (VERY_HIGH ) RuntimeInfoHook |
| (NORMAL ) IterTimerHook |
| (BELOW_NORMAL) LoggerHook |
| (LOW ) ParamSchedulerHook |
| (VERY_LOW ) CheckpointHookV2 |
| (VERY_LOW ) CheckpointUploader |
| -------------------- |
| after_train_epoch: |
| (NORMAL ) IterTimerHook |
| (LOW ) ParamSchedulerHook |
| (VERY_LOW ) CheckpointHookV2 |
| (VERY_LOW ) CheckpointUploader |
| -------------------- |
| before_val: |
| (VERY_HIGH ) RuntimeInfoHook |
| (VERY_LOW ) CheckpointHookV2 |
| (VERY_LOW ) CheckpointResumer |
| -------------------- |
| before_val_epoch: |
| (NORMAL ) IterTimerHook |
| -------------------- |
| before_val_iter: |
| (NORMAL ) IterTimerHook |
| -------------------- |
| after_val_iter: |
| (NORMAL ) IterTimerHook |
| (BELOW_NORMAL) LoggerHook |
| -------------------- |
| after_val_epoch: |
| (VERY_HIGH ) RuntimeInfoHook |
| (NORMAL ) IterTimerHook |
| (BELOW_NORMAL) LoggerHook |
| (LOW ) ParamSchedulerHook |
| (VERY_LOW ) CheckpointHookV2 |
| (VERY_LOW ) CheckpointUploader |
| -------------------- |
| after_val: |
| (VERY_HIGH ) RuntimeInfoHook |
| -------------------- |
| after_train: |
| (VERY_HIGH ) RuntimeInfoHook |
| (VERY_LOW ) CheckpointHookV2 |
| (VERY_LOW ) CheckpointUploader |
| -------------------- |
| before_test: |
| (VERY_HIGH ) RuntimeInfoHook |
| -------------------- |
| before_test_epoch: |
| (NORMAL ) IterTimerHook |
| -------------------- |
| before_test_iter: |
| (NORMAL ) IterTimerHook |
| -------------------- |
| after_test_iter: |
| (NORMAL ) IterTimerHook |
| (BELOW_NORMAL) LoggerHook |
| -------------------- |
| after_test_epoch: |
| (VERY_HIGH ) RuntimeInfoHook |
| (NORMAL ) IterTimerHook |
| (BELOW_NORMAL) LoggerHook |
| -------------------- |
| after_test: |
| (VERY_HIGH ) RuntimeInfoHook |
| -------------------- |
| after_run: |
| (BELOW_NORMAL) LoggerHook |
| -------------------- |
| 2026/03/15 22:48:12 - bevformer - INFO - Config: |
| _dim_ = 256 |
| _ffn_dim_ = 512 |
| _num_levels_ = 1 |
| _pos_dim_ = 128 |
| auto_scale_lr = dict(base_batch_size=16, enable=False) |
| bev_h_ = 50 |
| bev_w_ = 50 |
| by_epoch = False |
| class_names = [ |
| 'car', |
| 'truck', |
| 'construction_vehicle', |
| 'bus', |
| 'trailer', |
| 'barrier', |
| 'motorcycle', |
| 'bicycle', |
| 'pedestrian', |
| 'traffic_cone', |
| ] |
| custom_hooks = [ |
| dict( |
| by_epoch=False, |
| clean_local=False, |
| interval=1, |
| repo_id='5421Project', |
| type='CheckpointUploader'), |
| dict(repo_id='5421Project', resume_type='last', type='CheckpointResumer'), |
| ] |
| data = dict( |
| nonshuffler_sampler=dict(type='DistributedSampler'), |
| samples_per_gpu=1, |
| shuffler_sampler=dict(type='DistributedGroupSampler'), |
| test=dict( |
| ann_file='data/nuscenes/v1.0-mini/nuscenes_infos_temporal_val.pkl', |
| bev_size=( |
| 50, |
| 50, |
| ), |
| classes=[ |
| 'car', |
| 'truck', |
| 'construction_vehicle', |
| 'bus', |
| 'trailer', |
| 'barrier', |
| 'motorcycle', |
| 'bicycle', |
| 'pedestrian', |
| 'traffic_cone', |
| ], |
| data_root='data/nuscenes/v1.0-mini/', |
| frame=[ |
| -3, |
| -2, |
| -1, |
| ], |
| modality=dict( |
| use_camera=True, |
| use_external=False, |
| use_lidar=False, |
| use_map=False, |
| use_radar=False), |
| pipeline=[ |
| dict(to_float32=True, type='LoadMultiViewImageFromFiles'), |
| dict( |
| mean=[ |
| 123.675, |
| 116.28, |
| 103.53, |
| ], |
| std=[ |
| 58.395, |
| 57.12, |
| 57.375, |
| ], |
| to_rgb=True, |
| type='NormalizeMultiviewImage'), |
| dict( |
| flip=False, |
| img_scale=( |
| 800, |
| 450, |
| ), |
| pts_scale_ratio=[ |
| 1.0, |
| ], |
| transforms=[ |
| dict( |
| scales=[ |
| 0.5, |
| ], type='RandomScaleImageMultiViewImage'), |
| dict(size_divisor=32, type='PadMultiViewImage'), |
| dict( |
| class_names=[ |
| 'car', |
| 'truck', |
| 'construction_vehicle', |
| 'bus', |
| 'trailer', |
| 'barrier', |
| 'motorcycle', |
| 'bicycle', |
| 'pedestrian', |
| 'traffic_cone', |
| ], |
| type='CustomDefaultFormatBundle3D'), |
| dict(keys=[ |
| 'img', |
| ], type='CustomCollect3D'), |
| ], |
| type='MultiScaleFlipAug3D'), |
| ], |
| test_mode=True, |
| type='CustomNuScenesDataset'), |
| train=dict( |
| ann_file='data/nuscenes/v1.0-mini/nuscenes_infos_temporal_train.pkl', |
| bev_size=( |
| 50, |
| 50, |
| ), |
| box_type_3d='LiDAR', |
| classes=[ |
| 'car', |
| 'truck', |
| 'construction_vehicle', |
| 'bus', |
| 'trailer', |
| 'barrier', |
| 'motorcycle', |
| 'bicycle', |
| 'pedestrian', |
| 'traffic_cone', |
| ], |
| data_root='data/nuscenes/v1.0-mini/', |
| modality=dict( |
| use_camera=True, |
| use_external=False, |
| use_lidar=False, |
| use_map=False, |
| use_radar=False), |
| pipeline=[ |
| dict(to_float32=True, type='LoadMultiViewImageFromFiles'), |
| dict( |
| type='LoadAnnotations3D', |
| with_bbox_3d=True, |
| with_label_3d=True), |
| dict( |
| point_cloud_range=[ |
| -51.2, |
| -51.2, |
| -5.0, |
| 51.2, |
| 51.2, |
| 3.0, |
| ], |
| type='ObjectRangeFilter'), |
| dict( |
| classes=[ |
| 'car', |
| 'truck', |
| 'construction_vehicle', |
| 'bus', |
| 'trailer', |
| 'barrier', |
| 'motorcycle', |
| 'bicycle', |
| 'pedestrian', |
| 'traffic_cone', |
| ], |
| type='ObjectNameFilter'), |
| dict(type='PhotoMetricDistortionMultiViewImage'), |
| dict( |
| mean=[ |
| 123.675, |
| 116.28, |
| 103.53, |
| ], |
| std=[ |
| 58.395, |
| 57.12, |
| 57.375, |
| ], |
| to_rgb=True, |
| type='NormalizeMultiviewImage'), |
| dict(scales=[ |
| 0.5, |
| ], type='RandomScaleImageMultiViewImage'), |
| dict(size_divisor=32, type='PadMultiViewImage'), |
| dict( |
| class_names=[ |
| 'car', |
| 'truck', |
| 'construction_vehicle', |
| 'bus', |
| 'trailer', |
| 'barrier', |
| 'motorcycle', |
| 'bicycle', |
| 'pedestrian', |
| 'traffic_cone', |
| ], |
| type='CustomDefaultFormatBundle3D'), |
| dict( |
| keys=[ |
| 'gt_bboxes_3d', |
| 'gt_labels_3d', |
| 'img', |
| ], |
| type='CustomCollect3D'), |
| dict(type='TypeConverter'), |
| ], |
| queue_length=4, |
| test_mode=False, |
| type='CustomNuScenesDataset', |
| use_valid_flag=True), |
| val=dict( |
| ann_file='data/nuscenes/v1.0-mini/nuscenes_infos_temporal_val.pkl', |
| bev_size=( |
| 50, |
| 50, |
| ), |
| classes=[ |
| 'car', |
| 'truck', |
| 'construction_vehicle', |
| 'bus', |
| 'trailer', |
| 'barrier', |
| 'motorcycle', |
| 'bicycle', |
| 'pedestrian', |
| 'traffic_cone', |
| ], |
| data_root='data/nuscenes/v1.0-mini/', |
| frame=(), |
| frames=[ |
| -3, |
| -2, |
| -1, |
| ], |
| modality=dict( |
| use_camera=True, |
| use_external=False, |
| use_lidar=False, |
| use_map=False, |
| use_radar=False), |
| pipeline=[ |
| dict(to_float32=True, type='LoadMultiViewImageFromFiles'), |
| dict( |
| mean=[ |
| 123.675, |
| 116.28, |
| 103.53, |
| ], |
| std=[ |
| 58.395, |
| 57.12, |
| 57.375, |
| ], |
| to_rgb=True, |
| type='NormalizeMultiviewImage'), |
| dict( |
| flip=False, |
| img_scale=( |
| 800, |
| 450, |
| ), |
| pts_scale_ratio=[ |
| 1.0, |
| ], |
| transforms=[ |
| dict( |
| scales=[ |
| 0.5, |
| ], type='RandomScaleImageMultiViewImage'), |
| dict(size_divisor=32, type='PadMultiViewImage'), |
| dict( |
| class_names=[ |
| 'car', |
| 'truck', |
| 'construction_vehicle', |
| 'bus', |
| 'trailer', |
| 'barrier', |
| 'motorcycle', |
| 'bicycle', |
| 'pedestrian', |
| 'traffic_cone', |
| ], |
| type='CustomDefaultFormatBundle3D'), |
| dict(keys=[ |
| 'img', |
| ], type='CustomCollect3D'), |
| ], |
| type='MultiScaleFlipAug3D'), |
| ], |
| samples_per_gpu=1, |
| test_mode=True, |
| type='CustomNuScenesDataset'), |
| workers_per_gpu=4) |
| data_root = 'data/nuscenes/v1.0-mini/' |
| dataset_type = 'CustomNuScenesDataset' |
| decoder = dict( |
| num_layers=6, |
| return_intermediate=True, |
| transformerlayers=dict( |
| attn_cfgs=[ |
| dict( |
| dropout=0.1, |
| embed_dims=256, |
| num_heads=8, |
| type='MultiheadAttention'), |
| dict( |
| embed_dims=256, |
| num_levels=1, |
| type='CustomMSDeformableAttention'), |
| ], |
| ffn_cfgs=dict( |
| feedforward_channels=512, ffn_drop=0.1, num_fcs=2, type='FFN'), |
| operation_order=( |
| 'self_attn', |
| 'norm', |
| 'cross_attn', |
| 'norm', |
| 'ffn', |
| 'norm', |
| ), |
| type='DetrTransformerDecoderLayer'), |
| type='DetectionTransformerDecoder') |
| default_hooks = dict( |
| checkpoint=dict( |
| by_epoch=False, |
| interval=1, |
| max_keep_ckpts=1, |
| save_best=[ |
| 'loss', |
| 'mAP', |
| 'NDS', |
| ], |
| type='CheckpointHookV2'), |
| logger=dict( |
| interval=1, |
| interval_exp_name=1000, |
| log_metric_by_epoch=False, |
| type='LoggerHook'), |
| param_scheduler=dict(type='ParamSchedulerHook'), |
| runtime_info=dict(type='RuntimeInfoHook'), |
| sampler_seed=dict(type='DistSamplerSeedHook'), |
| timer=dict(type='IterTimerHook')) |
| encoder = dict( |
| num_layers=3, |
| num_points_in_pillar=8, |
| pc_range=[ |
| -51.2, |
| -51.2, |
| -5.0, |
| 51.2, |
| 51.2, |
| 3.0, |
| ], |
| return_intermediate=False, |
| transformerlayers=dict( |
| attn_cfgs=[ |
| dict(embed_dims=256, num_levels=1, type='TemporalSelfAttention'), |
| dict( |
| deformable_attention=dict( |
| embed_dims=256, |
| num_levels=1, |
| num_points=8, |
| type='MSDeformableAttention3D'), |
| embed_dims=256, |
| pc_range=[ |
| -51.2, |
| -51.2, |
| -5.0, |
| 51.2, |
| 51.2, |
| 3.0, |
| ], |
| type='SpatialCrossAttention'), |
| ], |
| ffn_cfgs=dict( |
| feedforward_channels=512, ffn_drop=0.1, num_fcs=2, type='FFN'), |
| operation_order=( |
| 'self_attn', |
| 'norm', |
| 'cross_attn', |
| 'norm', |
| 'ffn', |
| 'norm', |
| ), |
| type='BEVFormerLayer'), |
| type='BEVFormerEncoder') |
| env_cfg = dict(dist_cfg=dict(backend='nccl')) |
| experiment_name = 'debug' |
| file_client_args = dict(backend='disk') |
| frames = [ |
| -3, |
| -2, |
| -1, |
| ] |
| gpu_ids = range(0, 1) |
| img_norm_cfg = dict( |
| mean=[ |
| 123.675, |
| 116.28, |
| 103.53, |
| ], |
| std=[ |
| 58.395, |
| 57.12, |
| 57.375, |
| ], |
| to_rgb=True) |
| input_modality = dict( |
| use_camera=True, |
| use_external=False, |
| use_lidar=False, |
| use_map=False, |
| use_radar=False) |
| interval = 1 |
| launcher = 'none' |
| load_from = None |
| log_interval = 1 |
| log_processor = dict(window_size=20) |
| lr_config = dict( |
| min_lr_ratio=0.001, |
| policy='CosineAnnealing', |
| warmup='linear', |
| warmup_iters=500, |
| warmup_ratio=0.3333333333333333) |
| max_epochs = 5 |
| max_iters = 2 |
| model = dict( |
| img_backbone=dict( |
| depth=50, |
| frozen_stages=1, |
| norm_cfg=dict(requires_grad=False, type='BN'), |
| norm_eval=True, |
| num_stages=4, |
| out_indices=(3, ), |
| style='pytorch', |
| type='ResNet'), |
| img_neck=dict( |
| add_extra_convs='on_output', |
| in_channels=[ |
| 2048, |
| ], |
| num_outs=1, |
| out_channels=256, |
| relu_before_extra_convs=True, |
| start_level=0, |
| type='FPN'), |
| pretrained=dict(img='torchvision://resnet50'), |
| pts_bbox_head=dict( |
| as_two_stage=False, |
| bbox_coder=dict( |
| max_num=300, |
| num_classes=10, |
| pc_range=[ |
| -51.2, |
| -51.2, |
| -5.0, |
| 51.2, |
| 51.2, |
| 3.0, |
| ], |
| post_center_range=[ |
| -61.2, |
| -61.2, |
| -10.0, |
| 61.2, |
| 61.2, |
| 10.0, |
| ], |
| type='NMSFreeCoder', |
| voxel_size=[ |
| 0.2, |
| 0.2, |
| 8, |
| ]), |
| bev_h=50, |
| bev_w=50, |
| in_channels=256, |
| loss_bbox=dict(loss_weight=0.5, type='L1Loss'), |
| loss_cls=dict( |
| alpha=0.25, |
| gamma=2.0, |
| loss_weight=2.0, |
| type='FocalLoss', |
| use_sigmoid=True), |
| loss_iou=dict(loss_weight=0.25, type='GIoULoss'), |
| num_classes=10, |
| num_query=900, |
| positional_encoding=dict( |
| col_num_embed=50, |
| num_feats=128, |
| row_num_embed=50, |
| type='LearnedPositionalEncoding'), |
| sync_cls_avg_factor=True, |
| transformer=dict( |
| decoder=dict( |
| num_layers=6, |
| return_intermediate=True, |
| transformerlayers=dict( |
| attn_cfgs=[ |
| dict( |
| dropout=0.1, |
| embed_dims=256, |
| num_heads=8, |
| type='MultiheadAttention'), |
| dict( |
| embed_dims=256, |
| num_levels=1, |
| type='CustomMSDeformableAttention'), |
| ], |
| ffn_cfgs=dict( |
| feedforward_channels=512, |
| ffn_drop=0.1, |
| num_fcs=2, |
| type='FFN'), |
| operation_order=( |
| 'self_attn', |
| 'norm', |
| 'cross_attn', |
| 'norm', |
| 'ffn', |
| 'norm', |
| ), |
| type='DetrTransformerDecoderLayer'), |
| type='DetectionTransformerDecoder'), |
| embed_dims=256, |
| encoder=dict( |
| num_layers=3, |
| num_points_in_pillar=8, |
| pc_range=[ |
| -51.2, |
| -51.2, |
| -5.0, |
| 51.2, |
| 51.2, |
| 3.0, |
| ], |
| return_intermediate=False, |
| transformerlayers=dict( |
| attn_cfgs=[ |
| dict( |
| embed_dims=256, |
| num_levels=1, |
| type='TemporalSelfAttention'), |
| dict( |
| deformable_attention=dict( |
| embed_dims=256, |
| num_levels=1, |
| num_points=8, |
| type='MSDeformableAttention3D'), |
| embed_dims=256, |
| pc_range=[ |
| -51.2, |
| -51.2, |
| -5.0, |
| 51.2, |
| 51.2, |
| 3.0, |
| ], |
| type='SpatialCrossAttention'), |
| ], |
| ffn_cfgs=dict( |
| feedforward_channels=512, |
| ffn_drop=0.1, |
| num_fcs=2, |
| type='FFN'), |
| operation_order=( |
| 'self_attn', |
| 'norm', |
| 'cross_attn', |
| 'norm', |
| 'ffn', |
| 'norm', |
| ), |
| type='BEVFormerLayer'), |
| type='BEVFormerEncoder'), |
| num_cams=6, |
| num_feature_levels=1, |
| rotate_prev_bev=True, |
| type='PerceptionTransformer', |
| use_can_bus=True, |
| use_shift=True), |
| type='BEVFormerHead', |
| with_box_refine=True), |
| train_cfg=dict( |
| pts=dict( |
| assigner=dict( |
| cls_cost=dict(type='FocalCost', weight=2.0), |
| iou_cost=dict(type='SmoothL1Cost', weight=0.25), |
| pc_range=[ |
| -51.2, |
| -51.2, |
| -5.0, |
| 51.2, |
| 51.2, |
| 3.0, |
| ], |
| reg_cost=dict(type='BBox3DL1Cost', weight=0.25), |
| type='HungarianAssigner3D'), |
| grid_size=[ |
| 512, |
| 512, |
| 1, |
| ], |
| out_size_factor=4, |
| point_cloud_range=[ |
| -51.2, |
| -51.2, |
| -5.0, |
| 51.2, |
| 51.2, |
| 3.0, |
| ], |
| voxel_size=[ |
| 0.2, |
| 0.2, |
| 8, |
| ])), |
| type='BEVFormerDetector', |
| use_grid_mask=True, |
| video_test_mode=True) |
| optim_wrapper = dict( |
| optimizer=dict(lr=0.0001, type='AdamW', weight_decay=0.01), |
| type='OptimWrapper') |
| optimizer = dict(lr=0.0001, type='AdamW', weight_decay=0.01) |
| param_scheduler = dict( |
| milestones=[ |
| 1, |
| 2, |
| ], type='MultiStepLR') |
| point_cloud_range = [ |
| -51.2, |
| -51.2, |
| -5.0, |
| 51.2, |
| 51.2, |
| 3.0, |
| ] |
| pts_bbox_head = dict( |
| as_two_stage=False, |
| bbox_coder=dict( |
| max_num=300, |
| num_classes=10, |
| pc_range=[ |
| -51.2, |
| -51.2, |
| -5.0, |
| 51.2, |
| 51.2, |
| 3.0, |
| ], |
| post_center_range=[ |
| -61.2, |
| -61.2, |
| -10.0, |
| 61.2, |
| 61.2, |
| 10.0, |
| ], |
| type='NMSFreeCoder', |
| voxel_size=[ |
| 0.2, |
| 0.2, |
| 8, |
| ]), |
| bev_h=50, |
| bev_w=50, |
| in_channels=256, |
| loss_bbox=dict(loss_weight=0.5, type='L1Loss'), |
| loss_cls=dict( |
| alpha=0.25, |
| gamma=2.0, |
| loss_weight=2.0, |
| type='FocalLoss', |
| use_sigmoid=True), |
| loss_iou=dict(loss_weight=0.25, type='GIoULoss'), |
| num_classes=10, |
| num_query=900, |
| positional_encoding=dict( |
| col_num_embed=50, |
| num_feats=128, |
| row_num_embed=50, |
| type='LearnedPositionalEncoding'), |
| sync_cls_avg_factor=True, |
| transformer=dict( |
| decoder=dict( |
| num_layers=6, |
| return_intermediate=True, |
| transformerlayers=dict( |
| attn_cfgs=[ |
| dict( |
| dropout=0.1, |
| embed_dims=256, |
| num_heads=8, |
| type='MultiheadAttention'), |
| dict( |
| embed_dims=256, |
| num_levels=1, |
| type='CustomMSDeformableAttention'), |
| ], |
| ffn_cfgs=dict( |
| feedforward_channels=512, |
| ffn_drop=0.1, |
| num_fcs=2, |
| type='FFN'), |
| operation_order=( |
| 'self_attn', |
| 'norm', |
| 'cross_attn', |
| 'norm', |
| 'ffn', |
| 'norm', |
| ), |
| type='DetrTransformerDecoderLayer'), |
| type='DetectionTransformerDecoder'), |
| embed_dims=256, |
| encoder=dict( |
| num_layers=3, |
| num_points_in_pillar=8, |
| pc_range=[ |
| -51.2, |
| -51.2, |
| -5.0, |
| 51.2, |
| 51.2, |
| 3.0, |
| ], |
| return_intermediate=False, |
| transformerlayers=dict( |
| attn_cfgs=[ |
| dict( |
| embed_dims=256, |
| num_levels=1, |
| type='TemporalSelfAttention'), |
| dict( |
| deformable_attention=dict( |
| embed_dims=256, |
| num_levels=1, |
| num_points=8, |
| type='MSDeformableAttention3D'), |
| embed_dims=256, |
| pc_range=[ |
| -51.2, |
| -51.2, |
| -5.0, |
| 51.2, |
| 51.2, |
| 3.0, |
| ], |
| type='SpatialCrossAttention'), |
| ], |
| ffn_cfgs=dict( |
| feedforward_channels=512, |
| ffn_drop=0.1, |
| num_fcs=2, |
| type='FFN'), |
| operation_order=( |
| 'self_attn', |
| 'norm', |
| 'cross_attn', |
| 'norm', |
| 'ffn', |
| 'norm', |
| ), |
| type='BEVFormerLayer'), |
| type='BEVFormerEncoder'), |
| num_cams=6, |
| num_feature_levels=1, |
| rotate_prev_bev=True, |
| type='PerceptionTransformer', |
| use_can_bus=True, |
| use_shift=True), |
| type='BEVFormerHead', |
| with_box_refine=True) |
| queue_length = 4 |
| resume = False |
| scales = [ |
| 0.5, |
| ] |
| test_cfg = dict(max_iters=1) |
| test_dataloader = dict( |
| batch_size=1, |
| collate_fn=dict(type='test_collate'), |
| dataset=dict( |
| ann_file='data/nuscenes/v1.0-mini/nuscenes_infos_temporal_val.pkl', |
| bev_size=( |
| 50, |
| 50, |
| ), |
| classes=[ |
| 'car', |
| 'truck', |
| 'construction_vehicle', |
| 'bus', |
| 'trailer', |
| 'barrier', |
| 'motorcycle', |
| 'bicycle', |
| 'pedestrian', |
| 'traffic_cone', |
| ], |
| data_root='data/nuscenes/v1.0-mini/', |
| frame=[ |
| -3, |
| -2, |
| -1, |
| ], |
| modality=dict( |
| use_camera=True, |
| use_external=False, |
| use_lidar=False, |
| use_map=False, |
| use_radar=False), |
| pipeline=[ |
| dict(to_float32=True, type='LoadMultiViewImageFromFiles'), |
| dict( |
| mean=[ |
| 123.675, |
| 116.28, |
| 103.53, |
| ], |
| std=[ |
| 58.395, |
| 57.12, |
| 57.375, |
| ], |
| to_rgb=True, |
| type='NormalizeMultiviewImage'), |
| dict( |
| flip=False, |
| img_scale=( |
| 800, |
| 450, |
| ), |
| pts_scale_ratio=[ |
| 1.0, |
| ], |
| transforms=[ |
| dict( |
| scales=[ |
| 0.5, |
| ], type='RandomScaleImageMultiViewImage'), |
| dict(size_divisor=32, type='PadMultiViewImage'), |
| dict( |
| class_names=[ |
| 'car', |
| 'truck', |
| 'construction_vehicle', |
| 'bus', |
| 'trailer', |
| 'barrier', |
| 'motorcycle', |
| 'bicycle', |
| 'pedestrian', |
| 'traffic_cone', |
| ], |
| type='CustomDefaultFormatBundle3D'), |
| dict(keys=[ |
| 'img', |
| ], type='CustomCollect3D'), |
| ], |
| type='MultiScaleFlipAug3D'), |
| ], |
| test_mode=True, |
| type='CustomNuScenesDataset'), |
| num_workers=0, |
| sampler=dict(shuffle=True, type='DefaultSampler')) |
| test_evaluator = dict(metrics=[ |
| dict( |
| ann_file='data/nuscenes/v1.0-mini/nuscenes_infos_temporal_val.pkl', |
| data_root='data/nuscenes/v1.0-mini/', |
| type='src.NuScenesMetric', |
| version='v1.0-mini'), |
| ]) |
| test_max_iters = 1 |
| test_pipeline = [ |
| dict(to_float32=True, type='LoadMultiViewImageFromFiles'), |
| dict( |
| mean=[ |
| 123.675, |
| 116.28, |
| 103.53, |
| ], |
| std=[ |
| 58.395, |
| 57.12, |
| 57.375, |
| ], |
| to_rgb=True, |
| type='NormalizeMultiviewImage'), |
| dict( |
| flip=False, |
| img_scale=( |
| 800, |
| 450, |
| ), |
| pts_scale_ratio=[ |
| 1.0, |
| ], |
| transforms=[ |
| dict(scales=[ |
| 0.5, |
| ], type='RandomScaleImageMultiViewImage'), |
| dict(size_divisor=32, type='PadMultiViewImage'), |
| dict( |
| class_names=[ |
| 'car', |
| 'truck', |
| 'construction_vehicle', |
| 'bus', |
| 'trailer', |
| 'barrier', |
| 'motorcycle', |
| 'bicycle', |
| 'pedestrian', |
| 'traffic_cone', |
| ], |
| type='CustomDefaultFormatBundle3D'), |
| dict(keys=[ |
| 'img', |
| ], type='CustomCollect3D'), |
| ], |
| type='MultiScaleFlipAug3D'), |
| ] |
| train_cfg = dict(by_epoch=False, max_epochs=5, max_iters=2, val_interval=1) |
| train_dataloader = dict( |
| batch_size=1, |
| collate_fn=dict(type='train_collate'), |
| dataset=dict( |
| ann_file='data/nuscenes/v1.0-mini/nuscenes_infos_temporal_train.pkl', |
| bev_size=( |
| 50, |
| 50, |
| ), |
| box_type_3d='LiDAR', |
| classes=[ |
| 'car', |
| 'truck', |
| 'construction_vehicle', |
| 'bus', |
| 'trailer', |
| 'barrier', |
| 'motorcycle', |
| 'bicycle', |
| 'pedestrian', |
| 'traffic_cone', |
| ], |
| data_root='data/nuscenes/v1.0-mini/', |
| modality=dict( |
| use_camera=True, |
| use_external=False, |
| use_lidar=False, |
| use_map=False, |
| use_radar=False), |
| pipeline=[ |
| dict(to_float32=True, type='LoadMultiViewImageFromFiles'), |
| dict( |
| type='LoadAnnotations3D', |
| with_bbox_3d=True, |
| with_label_3d=True), |
| dict( |
| point_cloud_range=[ |
| -51.2, |
| -51.2, |
| -5.0, |
| 51.2, |
| 51.2, |
| 3.0, |
| ], |
| type='ObjectRangeFilter'), |
| dict( |
| classes=[ |
| 'car', |
| 'truck', |
| 'construction_vehicle', |
| 'bus', |
| 'trailer', |
| 'barrier', |
| 'motorcycle', |
| 'bicycle', |
| 'pedestrian', |
| 'traffic_cone', |
| ], |
| type='ObjectNameFilter'), |
| dict(type='PhotoMetricDistortionMultiViewImage'), |
| dict( |
| mean=[ |
| 123.675, |
| 116.28, |
| 103.53, |
| ], |
| std=[ |
| 58.395, |
| 57.12, |
| 57.375, |
| ], |
| to_rgb=True, |
| type='NormalizeMultiviewImage'), |
| dict(scales=[ |
| 0.5, |
| ], type='RandomScaleImageMultiViewImage'), |
| dict(size_divisor=32, type='PadMultiViewImage'), |
| dict( |
| class_names=[ |
| 'car', |
| 'truck', |
| 'construction_vehicle', |
| 'bus', |
| 'trailer', |
| 'barrier', |
| 'motorcycle', |
| 'bicycle', |
| 'pedestrian', |
| 'traffic_cone', |
| ], |
| type='CustomDefaultFormatBundle3D'), |
| dict( |
| keys=[ |
| 'gt_bboxes_3d', |
| 'gt_labels_3d', |
| 'img', |
| ], |
| type='CustomCollect3D'), |
| dict(type='TypeConverter'), |
| ], |
| queue_length=4, |
| test_mode=False, |
| type='CustomNuScenesDataset', |
| use_valid_flag=True), |
| num_workers=0, |
| sampler=dict(shuffle=True, type='DefaultSampler')) |
| train_pipeline = [ |
| dict(to_float32=True, type='LoadMultiViewImageFromFiles'), |
| dict(type='LoadAnnotations3D', with_bbox_3d=True, with_label_3d=True), |
| dict( |
| point_cloud_range=[ |
| -51.2, |
| -51.2, |
| -5.0, |
| 51.2, |
| 51.2, |
| 3.0, |
| ], |
| type='ObjectRangeFilter'), |
| dict( |
| classes=[ |
| 'car', |
| 'truck', |
| 'construction_vehicle', |
| 'bus', |
| 'trailer', |
| 'barrier', |
| 'motorcycle', |
| 'bicycle', |
| 'pedestrian', |
| 'traffic_cone', |
| ], |
| type='ObjectNameFilter'), |
| dict(type='PhotoMetricDistortionMultiViewImage'), |
| dict( |
| mean=[ |
| 123.675, |
| 116.28, |
| 103.53, |
| ], |
| std=[ |
| 58.395, |
| 57.12, |
| 57.375, |
| ], |
| to_rgb=True, |
| type='NormalizeMultiviewImage'), |
| dict(scales=[ |
| 0.5, |
| ], type='RandomScaleImageMultiViewImage'), |
| dict(size_divisor=32, type='PadMultiViewImage'), |
| dict( |
| class_names=[ |
| 'car', |
| 'truck', |
| 'construction_vehicle', |
| 'bus', |
| 'trailer', |
| 'barrier', |
| 'motorcycle', |
| 'bicycle', |
| 'pedestrian', |
| 'traffic_cone', |
| ], |
| type='CustomDefaultFormatBundle3D'), |
| dict( |
| keys=[ |
| 'gt_bboxes_3d', |
| 'gt_labels_3d', |
| 'img', |
| ], type='CustomCollect3D'), |
| dict(type='TypeConverter'), |
| ] |
| transformer = dict( |
| decoder=dict( |
| num_layers=6, |
| return_intermediate=True, |
| transformerlayers=dict( |
| attn_cfgs=[ |
| dict( |
| dropout=0.1, |
| embed_dims=256, |
| num_heads=8, |
| type='MultiheadAttention'), |
| dict( |
| embed_dims=256, |
| num_levels=1, |
| type='CustomMSDeformableAttention'), |
| ], |
| ffn_cfgs=dict( |
| feedforward_channels=512, ffn_drop=0.1, num_fcs=2, type='FFN'), |
| operation_order=( |
| 'self_attn', |
| 'norm', |
| 'cross_attn', |
| 'norm', |
| 'ffn', |
| 'norm', |
| ), |
| type='DetrTransformerDecoderLayer'), |
| type='DetectionTransformerDecoder'), |
| embed_dims=256, |
| encoder=dict( |
| num_layers=3, |
| num_points_in_pillar=8, |
| pc_range=[ |
| -51.2, |
| -51.2, |
| -5.0, |
| 51.2, |
| 51.2, |
| 3.0, |
| ], |
| return_intermediate=False, |
| transformerlayers=dict( |
| attn_cfgs=[ |
| dict( |
| embed_dims=256, num_levels=1, |
| type='TemporalSelfAttention'), |
| dict( |
| deformable_attention=dict( |
| embed_dims=256, |
| num_levels=1, |
| num_points=8, |
| type='MSDeformableAttention3D'), |
| embed_dims=256, |
| pc_range=[ |
| -51.2, |
| -51.2, |
| -5.0, |
| 51.2, |
| 51.2, |
| 3.0, |
| ], |
| type='SpatialCrossAttention'), |
| ], |
| ffn_cfgs=dict( |
| feedforward_channels=512, ffn_drop=0.1, num_fcs=2, type='FFN'), |
| operation_order=( |
| 'self_attn', |
| 'norm', |
| 'cross_attn', |
| 'norm', |
| 'ffn', |
| 'norm', |
| ), |
| type='BEVFormerLayer'), |
| type='BEVFormerEncoder'), |
| num_cams=6, |
| num_feature_levels=1, |
| rotate_prev_bev=True, |
| type='PerceptionTransformer', |
| use_can_bus=True, |
| use_shift=True) |
| val_cfg = dict(max_iters=1) |
| val_dataloader = dict( |
| batch_size=1, |
| collate_fn=dict(type='test_collate'), |
| dataset=dict( |
| ann_file='data/nuscenes/v1.0-mini/nuscenes_infos_temporal_val.pkl', |
| bev_size=( |
| 50, |
| 50, |
| ), |
| classes=[ |
| 'car', |
| 'truck', |
| 'construction_vehicle', |
| 'bus', |
| 'trailer', |
| 'barrier', |
| 'motorcycle', |
| 'bicycle', |
| 'pedestrian', |
| 'traffic_cone', |
| ], |
| data_root='data/nuscenes/v1.0-mini/', |
| frame=(), |
| frames=[ |
| -3, |
| -2, |
| -1, |
| ], |
| modality=dict( |
| use_camera=True, |
| use_external=False, |
| use_lidar=False, |
| use_map=False, |
| use_radar=False), |
| pipeline=[ |
| dict(to_float32=True, type='LoadMultiViewImageFromFiles'), |
| dict( |
| mean=[ |
| 123.675, |
| 116.28, |
| 103.53, |
| ], |
| std=[ |
| 58.395, |
| 57.12, |
| 57.375, |
| ], |
| to_rgb=True, |
| type='NormalizeMultiviewImage'), |
| dict( |
| flip=False, |
| img_scale=( |
| 800, |
| 450, |
| ), |
| pts_scale_ratio=[ |
| 1.0, |
| ], |
| transforms=[ |
| dict( |
| scales=[ |
| 0.5, |
| ], type='RandomScaleImageMultiViewImage'), |
| dict(size_divisor=32, type='PadMultiViewImage'), |
| dict( |
| class_names=[ |
| 'car', |
| 'truck', |
| 'construction_vehicle', |
| 'bus', |
| 'trailer', |
| 'barrier', |
| 'motorcycle', |
| 'bicycle', |
| 'pedestrian', |
| 'traffic_cone', |
| ], |
| type='CustomDefaultFormatBundle3D'), |
| dict(keys=[ |
| 'img', |
| ], type='CustomCollect3D'), |
| ], |
| type='MultiScaleFlipAug3D'), |
| ], |
| samples_per_gpu=1, |
| test_mode=True, |
| type='CustomNuScenesDataset'), |
| num_workers=0, |
| sampler=dict(shuffle=True, type='DefaultSampler')) |
| val_evaluator = dict(metrics=[ |
| dict( |
| ann_file='data/nuscenes/v1.0-mini/nuscenes_infos_temporal_val.pkl', |
| classes=[ |
| 'car', |
| 'truck', |
| 'construction_vehicle', |
| 'bus', |
| 'trailer', |
| 'barrier', |
| 'motorcycle', |
| 'bicycle', |
| 'pedestrian', |
| 'traffic_cone', |
| ], |
| data_root='data/nuscenes/v1.0-mini/', |
| jsonfile_prefix='results', |
| modality=dict( |
| use_camera=True, |
| use_external=False, |
| use_lidar=False, |
| use_map=False, |
| use_radar=False), |
| plot_every_run=True, |
| plot_examples=1, |
| type='src.NuScenesMetric', |
| version='v1.0-mini'), |
| ]) |
| val_interval = 1 |
| val_max_iters = 1 |
| version = 'v1.0-mini' |
| visualizer = dict( |
| type='Visualizer', |
| vis_backends=[ |
| dict(type='LocalVisBackend'), |
| dict(type='TensorboardVisBackend'), |
| ]) |
| voxel_size = [ |
| 0.2, |
| 0.2, |
| 8, |
| ] |
| work_dir = 'experiment' |
|
|
| 2026/03/15 22:48:13 - bevformer - INFO - See full config in 'experiment/debug/bevformer_tiny_test.py'. |
| 2026/03/15 22:48:15 - bevformer - INFO - Checkpoints will be saved to 'experiment/debug' after every 1 steps. |
| 2026/03/15 22:48:15 - bevformer - INFO - Initialize best checkpoints by train phase. |
| 2026/03/15 22:48:15 - bevformer - INFO - Set best path for 'loss' None. |
| 2026/03/15 22:48:15 - bevformer - INFO - Set best path for 'mAP' None. |
| 2026/03/15 22:48:15 - bevformer - INFO - Set best path for 'NDS' None. |
| 2026/03/15 22:48:15 - bevformer - INFO - The best checkpoints will be saved to 'experiment/debug' based on ['loss', 'mAP', 'NDS'] with rules ['less', 'greater', 'greater'] after every 1 steps. |
| 2026/03/15 22:48:15 - bevformer - INFO - Keep maximum 1 checkpoints in local. |
| 2026/03/15 22:48:15 - bevformer - INFO - Checkpoints will be pushed to repo 'https://huggingface.co/5421Project/debug' after every 1 steps. |
| 2026/03/15 22:48:39 - bevformer - INFO - Epoch(train) [1][ 1/323] lr: 1.0000e-04 eta: 0:00:24 time: 24.1245 data_time: 1.0970 loss: 58.3901 loss_cls: 2.3029 loss_bbox: 7.7319 d0.loss_cls: 2.2785 d0.loss_bbox: 7.3663 d1.loss_cls: 2.2856 d1.loss_bbox: 7.4108 d2.loss_cls: 2.2401 d2.loss_bbox: 7.3151 d3.loss_cls: 2.2908 d3.loss_bbox: 7.3724 d4.loss_cls: 2.3867 d4.loss_bbox: 7.4091 |
| 2026/03/15 22:48:39 - bevformer - INFO - Saving checkpoint at 1 iterations |
| 2026/03/15 22:48:40 - bevformer - INFO - Saving best checkpoints... |
| 2026/03/15 22:48:40 - bevformer - INFO - Set 'best_score_loss' to +/-inf as it is not in message hub. |
| 2026/03/15 22:48:40 - bevformer - INFO - [loss]: Best score: inf, current score: 58.39009094238281 |
| 2026/03/15 22:48:41 - bevformer - INFO - The best checkpoint with 58.3901 loss at 1 iter is saved to 'best_loss_iter_1.pth'. |
| 2026/03/15 22:48:41 - bevformer - INFO - Resaving checkpoint at 1 iter... |
| 2026/03/15 22:48:42 - bevformer - INFO - Pushing checkpoint at 1 steps... |
| 2026/03/15 22:48:52 - bevformer - INFO - Pushed last checkpoint 'experiment/debug/iter_1.pth' to repo |
| 2026/03/15 22:48:56 - bevformer - INFO - Pushed best checkpoint 'best_loss_iter_1.pth' of [loss]... |
| 2026/03/15 22:48:59 - bevformer - INFO - Epoch(val) [0][ 1/81] eta: 0:02:55 time: 2.1911 data_time: 0.6630 |
| 2026/03/15 22:49:10 - bevformer - INFO - Epoch(val) [0][81/81] NDS: 0.0185 mAP: 0.0002 data_time: 0.6630 time: 2.1911 |
| 2026/03/15 22:49:10 - bevformer - INFO - Save best checkpoints after val epoch. |
| 2026/03/15 22:49:10 - bevformer - INFO - Saving best checkpoints... |
| 2026/03/15 22:49:10 - bevformer - INFO - Set 'best_score_mAP' to +/-inf as it is not in message hub. |
| 2026/03/15 22:49:10 - bevformer - INFO - [mAP]: Best score: -inf, current score: 0.00023853881585173065 |
| 2026/03/15 22:49:10 - bevformer - INFO - The best checkpoint with 0.0002 mAP at 1 iter is saved to 'best_mAP_iter_1.pth'. |
| 2026/03/15 22:49:10 - bevformer - INFO - Set 'best_score_NDS' to +/-inf as it is not in message hub. |
| 2026/03/15 22:49:10 - bevformer - INFO - [NDS]: Best score: -inf, current score: 0.018478272642075605 |
| 2026/03/15 22:49:11 - bevformer - INFO - The best checkpoint with 0.0185 NDS at 1 iter is saved to 'best_NDS_iter_1.pth'. |
| 2026/03/15 22:49:11 - bevformer - INFO - Resaving checkpoint at 1 iter... |
| 2026/03/15 22:49:14 - bevformer - INFO - Pushed best checkpoint 'best_mAP_iter_1.pth' of [mAP]... |
| 2026/03/15 22:49:16 - bevformer - INFO - Pushed best checkpoint 'best_NDS_iter_1.pth' of [NDS]... |
| 2026/03/15 22:49:40 - bevformer - INFO - Epoch(train) [1][ 2/323] lr: 1.0000e-04 eta: 0:00:00 time: 32.7943 data_time: 10.6893 loss: 54.7058 loss_cls: 2.2897 loss_bbox: 7.1520 d0.loss_cls: 2.2585 d0.loss_bbox: 6.7714 d1.loss_cls: 2.2267 d1.loss_bbox: 6.7523 d2.loss_cls: 2.1501 d2.loss_bbox: 6.7822 d3.loss_cls: 2.2182 d3.loss_bbox: 6.8959 d4.loss_cls: 2.2799 d4.loss_bbox: 6.9290 |
| 2026/03/15 22:49:40 - bevformer - INFO - Saving checkpoint at 2 iterations |
| 2026/03/15 22:49:42 - bevformer - INFO - Saving best checkpoints... |
| 2026/03/15 22:49:42 - bevformer - INFO - Got best score ['loss'] from message hub |
| 2026/03/15 22:49:42 - bevformer - INFO - [loss]: Best score: 58.39009094238281, current score: 51.02156066894531 |
| 2026/03/15 22:49:42 - bevformer - INFO - The previous best checkpoint 'experiment/debug/best_loss_iter_1.pth' is removed |
| 2026/03/15 22:49:42 - bevformer - INFO - The best checkpoint with 51.0216 loss at 2 iter is saved to 'best_loss_iter_2.pth'. |
| 2026/03/15 22:49:42 - bevformer - INFO - Resaving checkpoint at 2 iter... |
| 2026/03/15 22:49:44 - bevformer - INFO - Pushing checkpoint at 2 steps... |
| 2026/03/15 22:49:47 - bevformer - INFO - Pushed last checkpoint 'experiment/debug/iter_2.pth' to repo |
| 2026/03/15 22:49:48 - bevformer - INFO - Removed 'iter_1.pth' from repo |
| 2026/03/15 22:49:53 - bevformer - INFO - Pushed best checkpoint 'best_loss_iter_2.pth' of [loss]... |
| 2026/03/15 22:49:55 - bevformer - INFO - Removed 'best_loss_iter_1.pth' from repo |
| 2026/03/15 22:50:00 - bevformer - INFO - Epoch(val) [0][ 1/81] eta: 0:03:12 time: 2.3014 data_time: 0.7300 |
| 2026/03/15 22:50:11 - bevformer - INFO - Epoch(val) [0][81/81] NDS: 0.0178 mAP: 0.0002 data_time: 0.7300 time: 2.3014 |
| 2026/03/15 22:50:11 - bevformer - INFO - Save best checkpoints after val epoch. |
| 2026/03/15 22:50:11 - bevformer - INFO - Saving best checkpoints... |
| 2026/03/15 22:50:11 - bevformer - INFO - Got best score ['mAP'] from message hub |
| 2026/03/15 22:50:11 - bevformer - INFO - [mAP]: Best score: 0.00023853881585173065, current score: 0.0002481977075675005 |
| 2026/03/15 22:50:11 - bevformer - INFO - The previous best checkpoint 'experiment/debug/best_mAP_iter_1.pth' is removed |
| 2026/03/15 22:50:12 - bevformer - INFO - The best checkpoint with 0.0002 mAP at 2 iter is saved to 'best_mAP_iter_2.pth'. |
| 2026/03/15 22:50:12 - bevformer - INFO - Got best score ['NDS'] from message hub |
| 2026/03/15 22:50:12 - bevformer - INFO - [NDS]: Best score: 0.018478272642075605, current score: 0.017786070826831646 |
| 2026/03/15 22:50:12 - bevformer - INFO - Resaving checkpoint at 2 iter... |
| 2026/03/15 22:50:15 - bevformer - INFO - Pushed best checkpoint 'best_mAP_iter_2.pth' of [mAP]... |
| 2026/03/15 22:50:16 - bevformer - INFO - Removed 'best_mAP_iter_1.pth' from repo |
| 2026/03/15 22:50:17 - bevformer - INFO - Pushing visualizing data and safetensors to repo after training... |
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