Tao Train Bevfusion

BEVFusion

Standalone install? If this session was not initialized by the TAO skill bank plugin, run the tao-setup skill first (host preflight, credentials, cross-skill discovery).

BEVFusion for multi-sensor 3D object detection. Fuses LiDAR point clouds and camera images in bird's-eye-view (BEV) space. Used in autonomous driving for robust 3D perception.

Set pretrained backbone paths for Swin image backbone.

BEVFusion requires the BEVFusion-specific TAO container nvcr.io/nvidia/tao/tao-toolkit:5.5.0-pyt. Shared TAO PyTorch 7.x images do not package mmdet3d and fail before any BEVFusion action can parse its spec. The model-skill action is named dataset_convert, but the 5.5 container CLI subtask is bevfusion convert -e <spec>.

Dataclass Schemas

Generated TAO Core schemas are packaged in schemas/<action>.schema.json, with schemas/manifest.json listing available actions. Each generated schema also emits references/spec_template_<action>.yaml from the schema top-level default field. AutoML enablement is declared at the model layer in references/skill_info.yaml via automl_enabled. Runnable AutoML for an action requires schemas/<action>.schema.json and references/spec_template_<action>.yaml to exist and parse. Use the packaged selected-action schema for automl_default_parameters, automl_disabled_parameters, defaults, min/max bounds, enums, option weights, math conditions, dependencies, and popular parameters. Do not expect ~/tao-core at runtime; maintainers regenerate schemas/templates before packaging the skill bank.

Train Action Policy

This model is AutoML-enabled at the model layer. Before handling any train-stage request, read references/skill_info.yaml and resolve the run override from either an explicit automl_policy value or the user's workflow request. Use automl_policy: on by default and only expose on / off in new launch prompts. Treat phrases like "turn off AutoML", "disable AutoML", "no HPO", or "plain training" as automl_policy: off for this run only. When automl_policy: on, automl_enabled: true, and both schemas/train.schema.json and references/spec_template_train.yaml are packaged, route the train action through tao-skill-bank:tao-run-automl by default with this model's skill_dir. Preserve workflow/application overrides for datasets, specs, output directories, GPU/platform settings, parent checkpoints, and automl_policy. Use direct model training only when automl_policy: off or the packaged train schema/template is missing; in the missing-schema case, report that AutoML is enabled but not runnable for this model until schemas are generated.

Non-train actions such as evaluate, inference, export, and deploy flows stay in this model skill. The per-run automl_policy override does not change model metadata.

Training Requirements

• Dataset type: bevfusion
• Formats: default
• Monitoring metric: AP11

Per-Action Dataset Requirements

Typical Spec Overrides

Data source overrides are mandatory for every action — the agent MUST construct data source paths from the Per-Action Dataset Requirements table above and include them in spec_overrides.

pythonCopy code
DATA_ROOT = "/path/to/kitti_root"
CONVERTED = DATA_ROOT  # BEVFusion 5.5 writes info pickles into root_dir.
DATA_PREFIX = {"pts": "training/velodyne_reduced", "img": "training/image_2"}

dataset_convert (mandatory data sources):

pythonCopy code
{
    "root_dir": DATA_ROOT,
    "results_dir": DATA_ROOT,
    "mode": "training",
}

train (mandatory data sources):

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{
    "train.num_epochs": 30,
    "train.checkpoint_interval": 10,
    "train.validation_interval": 10,
    "train.num_gpus": 1,
    "dataset.root_dir": DATA_ROOT,
    "dataset.train_dataset": {"ann_file": f"{CONVERTED}/kitti_person_infos_train.pkl", "data_prefix": DATA_PREFIX},
    "dataset.val_dataset": {"ann_file": f"{CONVERTED}/kitti_person_infos_val.pkl", "data_prefix": DATA_PREFIX},
    "dataset.test_dataset": {"ann_file": f"{CONVERTED}/kitti_person_infos_val.pkl", "data_prefix": DATA_PREFIX},
}

evaluate (mandatory data sources):

pythonCopy code
{
    "dataset.root_dir": DATA_ROOT,
    "dataset.test_dataset": {"ann_file": f"{CONVERTED}/kitti_person_infos_val.pkl", "data_prefix": DATA_PREFIX},
}

inference (mandatory data sources):

pythonCopy code
{
    "dataset.root_dir": DATA_ROOT,
    "dataset.test_dataset": {"ann_file": f"{CONVERTED}/kitti_person_infos_val.pkl", "data_prefix": DATA_PREFIX},
}

Eval Dataset

Optional. Val dataset split is configured via ann_file in dataset config.

Important Parameters

• dataset.classes: List of detection classes. Default ["person"]. Must match the annotation categories.
• dataset.type: Dataset type. Options: KittiPersonDataset, TAO3DSyntheticDataset, TAO3DDataset.
• dataset.root_dir: Root directory of the KITTI-style dataset.
• dataset.box_type_3d: 3D box coordinate frame. Options: lidar, camera. Default lidar.
• train.optimizer.lr: Learning rate. Default 2e-4 (AdamW). Use AmpOptimWrapper for mixed precision via optimizer.wrapper_type.
• input_modality: Dict controlling sensor modalities. Keys: use_lidar (True), use_camera (True), use_radar (False), use_map (False).
• model.img_backbone: Image backbone. Default mmdet.SwinTransformer (Swin-Tiny). embed_dims=96, depths=[2,2,6,2].
• model.view_transform.type: View transform for BEV projection. Options: DepthLSSTransform, LSSTransform. Default DepthLSSTransform.
• model.point_cloud_range: Spatial extent of LiDAR. Default [0,-40,-3,70.4,40,1].
• model.voxel_size: Voxel dimensions. Default [0.05, 0.05, 0.1].
• dataset.train_dataset.batch_size: Per-GPU batch size. Default 4.

Multi-GPU / Multi-Node

Launch method: torchrun (LIGHTNING_EXCLUDED_NETWORK). The entrypoint runs torchrun --nnodes=N --nproc-per-node=M train.py, NOT plain python.

• CUDA_VISIBLE_DEVICES is explicitly set from TAO_VISIBLE_DEVICES
• BEVFusion uses mmdet3d-based distributed training, not Lightning DDP
• NODE_RANK is copied to RANK if RANK is unset

Multi-node env vars (set by orchestrator):

Hardware

One GPU is supported for minimal smoke/AutoML validation with a small dataset and batch size. Use 2+ GPUs for ordinary training and 4 GPUs when practical. 24GB+ (A100 recommended) VRAM per GPU. BEVFusion is memory-intensive due to multi-sensor fusion.

Error Patterns

dataset_convert required: Run the model-skill dataset_convert action (bevfusion convert -e <spec> in the BEVFusion 5.5 container) before training to produce kitti_person_infos_train.pkl, kitti_person_infos_val.pkl, and training/velodyne_reduced. For direct local-docker 5.5 runs, set results_dir to the same mounted path as root_dir; the converter writes the info pickles there and later expects them under root_dir while reducing point clouds.

KITTI directory names: The BEVFusion 5.5 converter writes reduced point clouds under training/velodyne_reduced and expects camera images under training/image_2. Do not use the stale training/lidar_reduced or training/images/ defaults when chaining dataset_convert into train/evaluate or inference.

BEVFusion 5.5 config surface: Use the 5.5 dataclass keys in packaged templates. Remove newer top-level/action keys such as model_name, wandb.group, wandb.run_id, train.checkpoint_interval_unit, evaluate.trt_engine, evaluate.batch_size, inference.trt_engine, and inference.batch_size. For train, evaluate, and inference specs, keep the non-running action stubs (train, evaluate, and inference) present with empty checkpoint strings where needed; the 5.5 runners materialize the full experiment config before running the selected action. Use YAML null, not an empty string, for train.pretrained_checkpoint and train.resume_training_checkpoint_path when no checkpoint is intended.

ModuleNotFoundError: No module named 'mmdet3d': Shared TAO PyTorch 7.x images do not include the BEVFusion mmdet3d dependency. Use nvcr.io/nvidia/tao/tao-toolkit:5.5.0-pyt; it contains mmdet3d and exposes the BEVFusion convert, train, evaluate, and inference subtasks.

Post-evaluation SIGSEGV in BEVFusion 5.5: Some local-docker runs can write checkpoints or prediction files and still finish with TAO Execution status: FAIL after Signal 11 (SIGSEGV) in cuMemRetainAllocationHandle. Do not mark the action successful from the Docker exit code alone; inspect the TAO log or status.json. On CUDA 12+ hosts, retain the TAO 5.5 dependency stack and apply the BEVFusion rotated-IoU CPU fallback and runner cleanup from tao-pytorch; do not switch the action to a shared 7.x image. The fallback is the default; BEVFUSION_ROTATE_IOU_BACKEND=gpu is an explicit opt-in to the legacy Numba CUDA evaluator. If a checkpoint was produced before this failure, use only the exact intended checkpoint such as epoch_1.pth for downstream diagnostics and do not treat last_checkpoint as a best checkpoint unless the action explicitly requests the latest checkpoint.

Missing modality data: Ensure both camera images and LiDAR point clouds are present if using multi-modal fusion.

Epoch numbering: BEVFusion checkpoint epoch numbers may not follow standard zero-padded format.

Checkpoint handoff: Use the SDK/model checkpoint resolver for parent-model selection. For direct local-docker chaining, inspect the train results and pass the exact intended checkpoint path such as epoch_1.pth; use latest.pth only when the user explicitly asks for latest. Resume/retrain must set train.resume: true and train.resume_training_checkpoint_path to the exact checkpoint being resumed.

Spec Param / Parent Model Inference

Model-specific inference mappings belong in this MD file, not in config.json. Generated runners should read this section and apply the mappings with SDK helpers before create_job(). This mirrors the old microservices infer_params.py flow.

Inference mappings from TAO Core bevfusion.config.json:

For parent_model or parent_model_folder, pass the upstream train/export/AutoML child job id as parent_job_id. The SDK lists the parent result folder, filters checkpoint artifacts, and returns the selected model file or folder. Do not add these mappings back to config.json and do not patch generated runner scripts to guess checkpoint paths.