Tao Train Bevfusion

BEVFusion

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.

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 still requires schemas/train.schema.json and references/spec_template_train.yaml to exist and parse. Use the packaged train 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. Treat phrases like "turn off AutoML", "disable AutoML", "no HPO", or "plain training" as automl_policy: off for this run only; otherwise default to auto. When automl_policy: auto, 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.

S3_TRAIN = "s3://bucket/data/train"

train (mandatory data sources):

{
    "train.num_epochs": 30,
    "train.checkpoint_interval": 10,
    "train.validation_interval": 10,
    "train.num_gpus": 1,
    "dataset.train_dataset": {"ann_file": f"{S3_TRAIN}/results/{dataset_convert_job_id}/kitti_person_infos_train.pkl"},
    "dataset.val_dataset": {"ann_file": f"{S3_TRAIN}/results/{dataset_convert_job_id}/kitti_person_infos_val.pkl"},
    "dataset.test_dataset": {"ann_file": f"{S3_TRAIN}/results/{dataset_convert_job_id}/kitti_person_infos_val.pkl"},
}

evaluate (mandatory data sources):

{
    "dataset.test_dataset": {"ann_file": f"{S3_TRAIN}/results/{dataset_convert_job_id}/kitti_person_infos_val.pkl"},
}

inference (mandatory data sources):

{
    "dataset.root_dir": f"{S3_TRAIN}",
    "dataset.test_dataset": {"ann_file": f"{S3_TRAIN}/results/{dataset_convert_job_id}/kitti_person_infos_val.pkl"},
}

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

Minimum 2 GPU(s), recommended 4 GPU(s). 24GB+ (A100 recommended) VRAM per GPU. BEVFusion is memory-intensive due to multi-sensor fusion. A100 GPUs strongly recommended. Multi-GPU training expected.

Error Patterns

dataset_convert required: Run dataset_convert before training to produce info pickle files.

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.

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.