Tao Train Pointpillars

PointPillars

PointPillars for 3D object detection from LiDAR point clouds. Encodes point clouds into a pseudo-image via pillar-based representation, then applies 2D detection. Used in autonomous driving / robotics.

Typically trained from scratch. Provide train.resume_training_checkpoint_path to resume.

For TAO Deploy TensorRT actions (gen_trt_engine, TensorRT evaluate, and TensorRT inference), read references/tao-deploy-pointpillars.md first. Deploy spec templates live in this skill's references/ folder with the spec_template_deploy_*.yaml prefix.

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: pointpillars
• Formats: default
• Monitoring metric: loss

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.data_path": f"{S3_TRAIN}",
    "dataset.data_info_path": f"{S3_TRAIN}//results/{dataset_convert_job_id}/data_info/",
}

evaluate (mandatory data sources):

{
    "dataset.data_path": f"{S3_TRAIN}",
    "dataset.data_info_path": f"{S3_TRAIN}//results/{dataset_convert_job_id}/data_info/",
}

export (mandatory data sources):

{
    "dataset.data_path": f"{S3_TRAIN}",
    "dataset.data_info_path": f"{S3_TRAIN}//results/{dataset_convert_job_id}/data_info/",
}

inference (mandatory data sources):

{
    "dataset.data_path": f"{S3_TRAIN}",
    "dataset.data_info_path": f"{S3_TRAIN}//results/{dataset_convert_job_id}/data_info/",
}

prune (mandatory data sources):

{
    "dataset.data_path": f"{S3_TRAIN}",
    "dataset.data_info_path": f"{S3_TRAIN}//results/{dataset_convert_job_id}/data_info/",
}

retrain (mandatory data sources):

{
    "dataset.data_path": f"{S3_TRAIN}",
    "dataset.data_info_path": f"{S3_TRAIN}//results/{dataset_convert_job_id}/data_info/",
}

Eval Dataset

Optional. Validation data (val.tar.gz) is separate from training. Used for mAP evaluation.

Important Parameters

• train.num_epochs: Default 80 (much higher than other TAO models). PointPillars needs more epochs for convergence on 3D detection.
• train.lr: Learning rate. Default 0.003 (adam_onecycle scheduler).
• dataset.class_names: List of 3D object classes. Default 7 classes (KITTI-style). Modify to match your dataset.
• dataset.data_path: Path to point cloud data directory.
• dataset.data_info_path: Path to data info files from dataset_convert step.
• dataset.point_cloud_range: Spatial extent of the point cloud to consider. Must match your sensor configuration.
• model.dense_head.anchor_generator_config: Anchor configurations per class. Must be tuned for your object sizes and the point cloud range.

Multi-GPU / Multi-Node

Launch method: torchrun (LIGHTNING_EXCLUDED_NETWORK). Uses PyTorch native DistributedDataParallel (NOT Lightning Trainer).

• CUDA_VISIBLE_DEVICES is explicitly set from TAO_VISIBLE_DEVICES
• Uses nn.parallel.DistributedDataParallel directly (not Lightning strategy)
• NODE_RANK is copied to RANK if RANK is unset

Multi-node env vars (set by orchestrator):

Hardware

Minimum 1 GPU(s), recommended 4 GPU(s). 16GB+ (V100 or A100) VRAM per GPU. PointPillars is relatively efficient for 3D detection. The main bottleneck is data I/O for large point cloud datasets.

Error Patterns

dataset_convert required: Training will fail if data_info_path is not populated from a prior dataset_convert job. Always run convert first.

Point cloud range mismatch: If point_cloud_range does not match the actual sensor data extent, detections will be poor or empty.

Epoch numbering: PointPillars checkpoint epoch numbers may be offset by 1 from status.json reported epochs.

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 pointpillars.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.

Deployment

• tao-deploy-pointpillars — PointPillars deploy workflow for TensorRT engine generation, TensorRT evaluation, and TensorRT inference using TAO Deploy.