tao-train-action-recognition

Action Recognition

Action recognition from video sequences. Supports RGB, optical flow, and joint (multi-stream) input types for classifying temporal actions in video clips.

Set model.pretrained_model_path for pretrained backbone weights.

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: action_recognition
• Formats: default
• Monitoring metric: val_acc

Per-Action Dataset Requirements

Action	Spec Key	Source	Files	List?
evaluate	evaluate.test_dataset_dir	train_datasets	test.tar.gz	No
inference	inference.inference_dataset_dir	train_datasets	test/smile.tar.gz	No
train	dataset.train_dataset_dir	train_datasets	train.tar.gz	No
train	dataset.val_dataset_dir	train_datasets	test.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.

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.label_map": {
        "catch": 0,
        "smile": 1
    },
    "dataset.batch_size": 2,
    "dataset.train_dataset_dir": f"{S3_TRAIN}/train.tar.gz",
    "dataset.val_dataset_dir": f"{S3_TRAIN}/test.tar.gz",
}

evaluate (mandatory data sources):

{
    "evaluate.test_dataset_dir": f"{S3_TRAIN}/test.tar.gz",
}

inference (mandatory data sources):

{
    "inference.inference_dataset_dir": f"{S3_TRAIN}/test/smile.tar.gz",
}

Eval Dataset

Optional. Test dataset is provided as test.tar.gz separate from training.

Important Parameters

• model.model_type: Input type: rgb, of (optical flow), or joint (multi-stream).
• model.backbone: Default resnet_18. Used as the spatial feature extractor.
• dataset.label_map: Dictionary mapping class names to indices.
• model.rgb_seq_length: Number of frames per clip for RGB input.
• model.of_seq_length: Number of frames for optical flow input.
• train.optim.lr: Learning rate. Default 5e-4.

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 best strategy automatically)
• No explicit num_nodes or distributed_strategy config — single-node oriented

Hardware

Minimum 1 GPU(s), recommended 2 GPU(s). 16GB+ VRAM per GPU. Memory depends on sequence length and input resolution. batch_size=2 is conservative for video data.

Error Patterns

Sequence length mismatch: Ensure video clips have enough frames for the configured rgb_seq_length or of_seq_length.

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 action_recognition.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	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
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	model.of_pretrained_model_path	ptm_if_no_resume_model	PTM when no resume checkpoint exists
train	model.rgb_pretrained_model_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.