tao-train-centerpose - SKILL.md Agent Skill

name: tao-train-centerpose description: CenterPose for keypoint / pose estimation. Detects object centers and regresses keypoint locations for 6-DoF object pose estimation. Use when training, evaluating, exporting, or running inference for a TAO CenterPose model. Trigger phrases include "train CenterPose", "6-DoF object pose", "keypoint estimation", "object pose regression". license: Apache-2.0 compatibility: Requires docker + nvidia-container-toolkit. metadata: version: "0.1.0" author: NVIDIA Corporation allowed-tools: Read Bash tags:

pose
estimation

CenterPose

CenterPose for keypoint / pose estimation. Detects object centers and regresses keypoint locations. Used for 6-DoF object pose estimation.

Set model.backbone.pretrained_backbone_path.

For TAO Deploy TensorRT actions (gen_trt_engine, TensorRT evaluate, and TensorRT inference), use the deploy spec templates packaged 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. 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: centerpose
Formats: default
Monitoring metric: val_3DIoU

Per-Action Dataset Requirements

Action	Spec Key	Source	Files	List?
evaluate	dataset.test_data	eval_dataset	test.tar.gz	No
gen_trt_engine	gen_trt_engine.tensorrt.calibration.cal_image_dir	calibration_dataset	train.tar.gz	Yes
inference	dataset.inference_data	inference_dataset	val.tar.gz	No
train	dataset.train_data	train_datasets	train.tar.gz	No
train	dataset.val_data	eval_dataset	val.tar.gz	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.

TRAIN_DIR = "/path/to/extracted/train"
VAL_DIR = "/path/to/extracted/val"
TEST_DIR = "/path/to/extracted/test"
INFER_DIR = VAL_DIR
CAL_IMAGE_DIRS = ["/path/to/extracted/train/<sequence_or_image_dir>"]

train (mandatory data sources):

{
    "train.num_epochs": 30,
    "train.checkpoint_interval": 10,
    "train.validation_interval": 10,
    "train.num_gpus": 1,
    "dataset.category": "bike",
    "dataset.batch_size": 4,
    "dataset.train_data": TRAIN_DIR,
    "dataset.val_data": VAL_DIR,
}

evaluate (mandatory data sources):

{
    "dataset.category": "bike",
    "dataset.test_data": TEST_DIR,
}

inference (mandatory data sources):

{
    "dataset.category": "bike",
    "dataset.inference_data": INFER_DIR,
}

gen_trt_engine (mandatory data sources):

{
    "gen_trt_engine.tensorrt.calibration.cal_image_dir": CAL_IMAGE_DIRS,
}

Eval Dataset

Optional. Val and test datasets are provided as separate tarballs.

Important Parameters

dataset.num_classes: Number of object categories. Default 1.
dataset.num_joints: Number of keypoints per object. Fixed at 8 (bbox keypoints). Valid range: exactly 8.
dataset.input_res: Input resolution. Fixed at 512. Output resolution fixed at 128.
dataset.category: Object category name. Default "cereal_box".
model.backbone.model_type: Default fan_small. Backbone options limited in schema.
train.optim.lr: Learning rate. Default 6e-5. MultiStep scheduler with lr_steps=[90, 120], lr_decay=0.1.
train.loss_config: Rich loss config with toggles: mse_loss, obj_scale, obj_scale_uncertainty, hps_uncertainty, reg_bbox, hm_hp. Weights: wh_weight=0.1, off_weight=1, hp_weight=1.
inference.use_pnp: Use PnP for 6-DoF pose. Default True. Requires camera intrinsics (focal_length_x/y, principle_point_x/y).
export.input_width: Export input size. Fixed at 512x512. opset_version=16.

Multi-GPU / Multi-Node

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

Spec Key	Description	Default
`train.num_gpus`	Number of GPUs	1
`train.gpu_ids`	GPU device indices	[0]

Strategy: auto (Lightning picks the best strategy automatically)
No explicit num_nodes or distributed_strategy config — single-node only
No sync_batchnorm

Export / TRT Defaults

Export input: 512x512 (fixed), opset 16
TRT data types: FP32, FP16, INT8
TRT opt_batch_size: 4, max_batch_size: 8

Hardware

Minimum 1 GPU(s), recommended 2 GPU(s). 16GB+ VRAM per GPU. CenterPose is moderately memory-intensive depending on input resolution and number of keypoints.

Error Patterns

num_joints mismatch: Ensure dataset.num_joints matches the keypoint count in your annotations.

Extract S3 tarballs for local Docker: The starter-kit S3 data is packaged as train.tar.gz, val.tar.gz, and test.tar.gz, but the CenterPose TAO actions consume extracted folders. Extract each archive and set dataset.train_data, dataset.val_data, dataset.test_data, and dataset.inference_data to the extracted split directories.

Checkpoint handoff: CenterPose training writes concrete checkpoints such as model_epoch_000_step_00008.pth and a centerpose_model_latest.pth symlink. Use the SDK/model checkpoint resolver or the exact epoch/step checkpoint for evaluate, inference, export, and resume. Use the symlink only when the user explicitly asks for latest.

TAO Deploy postprocessor compatibility: Use the deploy image resolved from versions.yaml or the selected platform. A successful gen_trt_engine run does not prove deploy evaluate or inference works; inspect those action exit codes and logs separately, especially for CenterPose postprocessor errors such as TypeError: only 0-dimensional arrays can be converted to Python scalars.

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 centerpose.config.json:

Action	Spec Field	Inference Function	Meaning
evaluate	`encryption_key`	`key`	encryption key
evaluate	`evaluate.checkpoint`	`parent_model`	model file inferred from the parent job results folder
evaluate	`evaluate.trt_engine`	`parent_model`	model file inferred from the parent job results folder
evaluate	`results_dir`	`output_dir`	current job results directory
export	`encryption_key`	`key`	encryption key
export	`export.checkpoint`	`parent_model`	model file inferred from the parent job results folder
export	`export.onnx_file`	`create_onnx_file`	output ONNX path
export	`results_dir`	`output_dir`	current job results directory
gen_trt_engine	`encryption_key`	`key`	encryption key
gen_trt_engine	`gen_trt_engine.onnx_file`	`parent_model`	model file inferred from the parent job results folder
gen_trt_engine	`gen_trt_engine.tensorrt.calibration.cal_cache_file`	`create_cal_cache`	calibration cache path
gen_trt_engine	`gen_trt_engine.trt_engine`	`create_engine_file`	output TensorRT engine path
gen_trt_engine	`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	`inference.trt_engine`	`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	`model.backbone.pretrained_backbone_path`	`ptm_if_no_resume_model`	PTM when no resume checkpoint exists
train	`results_dir`	`output_dir`	current job results directory
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.

Deployment

tao-deploy-centerpose