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. <!-- unpinned: BEVFusion-only container --> The shared TAO PyTorch 7.0 RC image
does not package mmdet3d and fails 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

Action	Spec Key	Source	Files	List?
dataset_convert	root_dir	id		No
evaluate	dataset.test_dataset	train_datasets	ann_file: results/{dataset_convert_job_id}/kitti_person_infos_val.pkl	No
inference	dataset.root_dir	train_datasets		No
inference	dataset.test_dataset	train_datasets	ann_file: results/{dataset_convert_job_id}/kitti_person_infos_val.pkl	No
train	dataset.train_dataset	train_datasets	ann_file: results/{dataset_convert_job_id}/kitti_person_infos_train.pkl	No
train	dataset.val_dataset	train_datasets	ann_file: results/{dataset_convert_job_id}/kitti_person_infos_val.pkl	No
train	dataset.test_dataset	train_datasets	ann_file: results/{dataset_convert_job_id}/kitti_person_infos_val.pkl	No

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.

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):

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

train (mandatory data sources):

{
    "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):

{
    "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):

{
    "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.

Spec Key	Description	Default
train.num_gpus	Number of GPUs per node	1
train.gpu_ids	GPU device indices	[0]
train.num_nodes	Number of nodes	1

• 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):

Variable	Purpose
WORLD_SIZE	Number of nodes
NODE_RANK	This node's rank
MASTER_ADDR	Rank-0 node IP
MASTER_PORT	Rank-0 port (default 29500)
NUM_GPU_PER_NODE	GPUs per node

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 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': The shared TAO PyTorch
7.0 RC image does not include the BEVFusion mmdet3d dependency. Use
nvcr.io/nvidia/tao/tao-toolkit:5.5.0-pyt; <!-- unpinned: BEVFusion-only container --> 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. 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:

Action	Spec Field	Inference Function	Meaning
dataset_convert	results_dir	output_dir	current job results directory
evaluate	encryption_key	key	encryption key
evaluate	evaluate.checkpoint	parent_model	model file inferred from the parent job results folder
evaluate	results_dir	output_dir	current job results directory
inference	encryption_key	key	encryption key
inference	inference.checkpoint	parent_model	model file inferred from the parent job results folder
inference	results_dir	output_dir	current job results directory
train	encryption_key	key	encryption key
train	results_dir	output_dir	current job results directory
train	train.pretrained_checkpoint	ptm_if_no_resume_model	PTM when no resume checkpoint exists
train	train.resume_training_checkpoint_path	resume_model	model file inferred from the current job results folder

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.