tao-train-pose-classification

Pose Classification

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).

Pose classification using ST-GCN (Spatial Temporal Graph Convolutional Network). Classifies skeleton sequences into action categories from pose keypoint data.

Typically trained from scratch on skeleton data.

The packaged PyTorch Pose Classification CLI supports dataset_convert, train, evaluate, export, and inference. dataset_convert is conditional: run it only when the input is raw DeepStream BodyPose JSON. If the dataset is already converted to TAO-ready .npy / .pkl files, start directly with train on those files and mark dataset conversion as not run: preconverted dataset provided in validation reports. This model does not expose deploy, prune, quantize, or standalone retrain actions. Resume/retrain behavior uses pose_classification train -e ... with train.resume_training_checkpoint_path populated.

Quick Start (docker run)

Docker-native launch — no TAO SDK and no Python on the host. Use the local Docker/platform skill instead when it gives a stricter environment-specific command (non-root UID mapping, cache redirects, remote daemons).

TAO_PYT_IMAGE_DEFAULT=nvcr.io/nvidia/tao/tao-toolkit:7.2.0-pyt  # versions-key: images.tao_toolkit.pyt
TAO_PYT_IMAGE="${TAO_PYT_IMAGE:-$TAO_PYT_IMAGE_DEFAULT}"
RUN_ROOT="${RUN_ROOT:-$PWD}"
DOCKER_COMMON=(
  --rm --gpus all --shm-size=8g
  --ulimit memlock=-1
  --ulimit stack=67108864
  -v "$RUN_ROOT/data:/data:ro"
  -v "$RUN_ROOT/specs:/specs:ro"
  -v "$RUN_ROOT/results:/results"
)

Dataset convert:

docker run "${DOCKER_COMMON[@]}" "$TAO_PYT_IMAGE" \
  pose_classification dataset_convert -e /specs/dataset_convert.yaml

Train:

docker run "${DOCKER_COMMON[@]}" "$TAO_PYT_IMAGE" \
  pose_classification train -e /specs/train.yaml

Evaluate:

docker run "${DOCKER_COMMON[@]}" "$TAO_PYT_IMAGE" \
  pose_classification evaluate -e /specs/evaluate.yaml

Inference:

docker run "${DOCKER_COMMON[@]}" "$TAO_PYT_IMAGE" \
  pose_classification inference -e /specs/inference.yaml

Export:

docker run "${DOCKER_COMMON[@]}" "$TAO_PYT_IMAGE" \
  pose_classification export -e /specs/export.yaml

Every action takes its spec with -e; results_dir is set in the spec or overridden on the command line. Mount any pretrained-weights directory the spec references, and keep every in-container path consistent across actions.

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: pose_classification
• Formats: default
• AutoML training metric: val_loss, with direction=minimize
• Standalone evaluation metric: accuracy, with direction=maximize. Use val_loss to rank train-stage AutoML recommendations and use accuracy only to verify that the selected checkpoint loads and evaluates successfully.

Per-Action Dataset Requirements

Typical Spec Overrides

Data source overrides are mandatory for every action being run — the agent MUST construct data source paths from the Per-Action Dataset Requirements table above and include them in spec_overrides. Do not run dataset_convert when the supplied dataset is already converted to .npy / .pkl files.

S3_TRAIN = "s3://bucket/data/purpose_built_models_pose_classification_train/nvidia"
CHECKPOINT = "/results/{train_job_id}/results_dir/model_epoch_000_step_00007.pth"

dataset_convert (optional; raw DeepStream BodyPose JSON only):

{
    "dataset_convert.data": "s3://bucket/data/<deepstream-bodypose-output>.json",
}

train (mandatory data sources):

{
    "train.num_epochs": 30,
    "train.checkpoint_interval": 10,
    "train.validation_interval": 10,
    "train.num_gpus": 1,
    "wandb.enable": False,
    "dataset.num_classes": 6,
    "dataset.label_map": {
        "class_0": 0,
        "class_1": 1,
        "class_2": 2,
        "class_3": 3,
        "class_4": 4,
        "class_5": 5,
    },
    "model.graph_layout": "nvidia",
    "dataset.train_dataset.data_path": f"{S3_TRAIN}/train_data.npy",
    "dataset.train_dataset.label_path": f"{S3_TRAIN}/train_label.pkl",
    "dataset.val_dataset.data_path": f"{S3_TRAIN}/val_data.npy",
    "dataset.val_dataset.label_path": f"{S3_TRAIN}/val_label.pkl",
}

resume train (mandatory checkpoint):

{
    "train.num_epochs": 31,
    "train.resume_training_checkpoint_path": CHECKPOINT,
    "dataset.train_dataset.data_path": f"{S3_TRAIN}/train_data.npy",
    "dataset.train_dataset.label_path": f"{S3_TRAIN}/train_label.pkl",
    "dataset.val_dataset.data_path": f"{S3_TRAIN}/val_data.npy",
    "dataset.val_dataset.label_path": f"{S3_TRAIN}/val_label.pkl",
}

evaluate (mandatory data sources):

{
    "evaluate.test_dataset.data_path": f"{S3_TRAIN}/val_data.npy",
    "evaluate.test_dataset.label_path": f"{S3_TRAIN}/val_label.pkl",
    "evaluate.checkpoint": CHECKPOINT,
}

export (mandatory checkpoint and output):

{
    "export.checkpoint": CHECKPOINT,
    "export.onnx_file": "/results/{export_job_id}/results_dir/pose_classification.onnx",
}

inference (mandatory data sources):

{
    "inference.test_dataset.data_path": f"{S3_TRAIN}/test_data.npy",
    "inference.checkpoint": CHECKPOINT,
    "inference.output_file": "/results/pose_classification_inference.txt",
}

Dataset Convert

Dataset conversion is optional for Pose Classification. Run pose_classification dataset_convert only when the user supplies raw DeepStream BodyPose JSON. For the common S3 validation dataset, the data is already converted to train_data.npy, train_label.pkl, val_data.npy, val_label.pkl, test_data.npy, and test_label.pkl; use those files directly for train/evaluate/inference/export flows and do not synthesize fake BodyPose JSON.

Eval Dataset

Optional. Validation data is provided alongside training as val_data.npy / val_label.pkl. TAO training emits val_loss as the TensorBoard validation scalar for this model; use val_loss with minimize direction for AutoML selection unless a custom evaluation hook supplies a different metric. The standalone evaluate action emits accuracy; compare that value with the selected checkpoint's evaluation result, but do not substitute it for the train-stage val_loss ranking KPI.

Important Parameters

• dataset.num_classes: Number of pose action classes. Default 6.
• model.graph_layout: Skeleton graph layout. Options: nvidia, openpose. Determines joint connectivity.
• model.graph_strategy: Graph partitioning strategy for GCN.
• train.optim.lr: Learning rate. Default 0.1 (SGD). Higher than vision models due to graph convolution properties.
• model.dropout: Dropout rate for regularization.

Multi-GPU / Multi-Node

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

• Strategy: auto (Lightning picks best strategy automatically)
• No explicit num_nodes or distributed_strategy config — single-node only
• Lightweight model, single GPU typically sufficient

Hardware

Minimum 1 GPU(s), recommended 1 GPU(s). 8GB+ VRAM per GPU. Pose classification is very lightweight — skeleton data is small. Single GPU is sufficient.

Error Patterns

Graph layout mismatch: Ensure model.graph_layout matches the skeleton format in your .npy data files.

Label shape mismatch: train_label.pkl class indices must be in range [0, num_classes).

Missing label map: The training dataloader expects dataset.label_map to be a dictionary. If the dataset only supplies numeric class IDs, set a synthetic contiguous map such as class_0: 0 through class_5: 5 for the six-class NVIDIA sample data.

Checkpoint handoff: After AutoML/train, use the checkpoint resolver to select the intended saved .pth checkpoint under the parent result folder, such as model_epoch_000_step_00007.pth, and pass that exact file as evaluate.checkpoint, export.checkpoint, inference.checkpoint, or train.resume_training_checkpoint_path. pc_model_latest.pth is a latest-checkpoint symlink; use it only when the user explicitly asks for latest rather than a specific/best checkpoint. Keep the same dataset.num_classes, dataset.label_map, and model.graph_layout overrides for downstream actions.

Dataset conversion source: dataset_convert expects the raw JSON output from the DeepStream BodyPose app. The common NVIDIA sample S3 folder is already converted to train_data.npy, train_label.pkl, val_data.npy, val_label.pkl, test_data.npy, and test_label.pkl; skip conversion and start from the converted files when those are present.

Action-specific dataset paths: The evaluate and inference templates also contain the training dataset.train_dataset and dataset.val_dataset blocks. For evaluate, populate evaluate.test_dataset.data_path and evaluate.test_dataset.label_path. For inference, populate inference.test_dataset.data_path and set inference.output_file; do not stop after replacing the first data_path or label_path in the file.

Output files: Export needs an explicit export.onnx_file path. Inference must set inference.output_file to a writable file path; the packaged template default is an empty string, and the current PyTorch inference code opens that value directly.

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 pose_classification.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.