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

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

python

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

train (mandatory data sources):

python

{
    "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):

python

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

inference (mandatory data sources):

python

{
    "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

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 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

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

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.

tao-train-bevfusion

NPX Install

Tags

SKILL.md Content

BEVFusion

Dataclass Schemas

Train Action Policy

Training Requirements

Per-Action Dataset Requirements

Typical Spec Overrides

Eval Dataset

Important Parameters

Multi-GPU / Multi-Node

Hardware

Error Patterns

Spec Param / Parent Model Inference