Tao Train Sparse4d

Sparse4D

Sparse4D for multi-camera temporal 3D object detection and tracking. Uses sparse queries with deformable attention across camera views and time for end-to-end 3D perception. Includes instance bank for temporal tracking.

Requires pretrained ResNet-101 backbone. Set train.pretrained_model_path.

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: sparse4d
• Formats: ovpkl
• Monitoring metric: val_mAP

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"
S3_EVAL = "s3://bucket/data/eval"

train (mandatory data sources):

{
    "train.num_epochs": 30,
    "train.checkpoint_interval": 10,
    "train.validation_interval": 10,
    "train.num_gpus": 1,
    "dataset.sequences.split_num": 90,
    "train_dataset.sequences_split_num": 90,
    "dataset.data_root": {"spec": f"{S3_TRAIN}/aicity.split)"},
    "model.head.instance_bank.anchor": f"{S3_TRAIN}//results/{dataset_convert_job_id}/anchor_init.npy",
    "dataset.train_dataset.ann_file": {"spec": f"{S3_TRAIN}/aicity.split)"},
    "dataset.val_dataset.ann_file": {"spec": f"{S3_EVAL}/aicity.split)"},
    "dataset.test_dataset.ann_file": {"spec": f"{S3_EVAL}/aicity.split)"},
}

evaluate (mandatory data sources):

{
    "dataset.data_root": {"spec": f"{S3_EVAL}/aicity.split)"},
    "model.head.instance_bank.anchor": f"{S3_TRAIN}//results/{dataset_convert_job_id}/anchor_init.npy",
    "dataset.train_dataset.ann_file": {"spec": f"{S3_TRAIN}/aicity.split)"},
    "dataset.val_dataset.ann_file": {"spec": f"{S3_EVAL}/aicity.split)"},
    "dataset.test_dataset.ann_file": {"spec": f"{S3_EVAL}/aicity.split)"},
}

export (mandatory data sources):

{
    "model.head.instance_bank.anchor": f"{S3_TRAIN}//results/{dataset_convert_job_id}/anchor_init.npy",
}

inference (mandatory data sources):

{
    "dataset.data_root": {"spec": f"{S3_EVAL}/aicity.split)"},
    "model.head.instance_bank.anchor": f"{S3_TRAIN}//results/{dataset_convert_job_id}/anchor_init.npy",
    "dataset.train_dataset.ann_file": {"spec": f"{S3_TRAIN}/aicity.split)"},
    "dataset.val_dataset.ann_file": {"spec": f"{S3_EVAL}/aicity.split)"},
    "dataset.test_dataset.ann_file": {"spec": f"{S3_EVAL}/aicity.split)"},
}

quantize (mandatory data sources):

{
    "dataset.data_root": {"spec": f"{S3_TRAIN}/aicity.split)"},
    "model.head.instance_bank.anchor": f"{S3_TRAIN}//results/{dataset_convert_job_id}/anchor_init.npy",
    "dataset.train_dataset.ann_file": {"spec": f"{S3_TRAIN}/aicity.split)"},
    "dataset.val_dataset.ann_file": {"spec": f"{S3_EVAL}/aicity.split)"},
    "dataset.test_dataset.ann_file": {"spec": f"{S3_EVAL}/aicity.split)"},
    "dataset.quant_calibration_dataset.images_dir": f"{S3_TRAIN}",
}

Eval Dataset

Optional. Val/test splits configured via dataset ann_file paths.

Important Parameters

• model.backbone: Backbone. Default resnet_101.
• model.neck.out_channels: FPN output channels. Default 256. num_outs=4.
• model.input_shape: Input image shape [W, H]. Default [1408, 512].
• model.head.num_output: Number of detection output queries. Default 300.
• model.head.num_decoder: Number of decoder layers. Default 6.
• model.head.temporal: Enable temporal reasoning. Default True.
• model.head.instance_bank.num_anchor: Instance bank anchors. Default 900.
• model.head.instance_bank.num_temp_instances: Temporal instance count. Default 600.
• model.depth_branch.loss_weight: Depth supervision loss weight. Default 0.2.
• dataset.batch_size: Per-GPU batch size. Default 2.
• dataset.num_frames: Sequence length. Default 200.
• dataset.classes: Detection classes. Default [person, gr1_t2, agility_digit, nova_carter]. num_ids=70 for tracking.
• train.optim.lr: Learning rate. Default 5e-5. img_backbone lr_mult=0.2.
• train.lr_scheduler: Cosine scheduler with linear warmup (500 iters, ratio 0.333).
• train.grad_clip.max_norm: Gradient clipping. Default 25.
• train.precision: Options: bf16, fp16, fp32. Default bf16.
• evaluate.metrics: Eval metrics. Default ["detection"]. Optional tracking evaluation.
• evaluate.tracking.enabled: Enable tracking evaluation. tracking_threshold=0.2.

Multi-GPU / Multi-Node

Launch method: Lightning-managed (single python process, Lightning spawns workers).

• Multi-GPU strategy: ddp_find_unused_parameters_true (no fsdp support)
• sync_batchnorm is always enabled (True)
• Iterations per epoch computed as: num_frames * num_bev_groups / (num_nodes * num_gpus * batch_size)
• Scaling: When increasing GPUs, effective batch size grows and iterations-per-epoch shrinks proportionally

Multi-node env vars (set by orchestrator): WORLD_SIZE, NODE_RANK, MASTER_ADDR, MASTER_PORT, NUM_GPU_PER_NODE.

Hardware

Minimum 2 GPU(s), recommended 8 GPU(s). 40GB+ (A100 recommended) VRAM per GPU. Multi-camera temporal model is memory intensive. bf16 required for practical training. Multi-GPU strongly recommended. Instance bank requires substantial memory for temporal reasoning.

Error Patterns

dataset_convert required: Must run dataset_convert first to produce annotation pickles and anchor_init.npy.

Missing anchor file: Set model.head.instance_bank.anchor to the anchor_init.npy path from dataset_convert results.

Temporal OOM: Reduce dataset.num_frames or dataset.batch_size if running out of memory during temporal training.

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 sparse4d.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.