Optical Inspection

Optical inspection for defect detection using Siamese networks. Compares image pairs to detect manufacturing defects, anomalies, or quality issues.

Set train.pretrained_model_path for pretrained Siamese weights.

For TAO Deploy TensorRT actions (gen_trt_engine, TensorRT evaluate, and TensorRT inference), read references/tao-deploy-optical-inspection.md first. The parent PyT container does not expose optical_inspection gen_trt_engine; TensorRT engine generation is deploy-only. Deploy spec templates live 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: optical_inspection
• Formats: default
• Monitoring metric: val_acc

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"
S3_INFERENCE = "s3://bucket/data/inference"

train (mandatory data sources):

{
    "train.num_epochs": 30,
    "train.checkpoint_interval": 10,
    "train.validation_interval": 10,
    "train.num_gpus": 1,
    "dataset.batch_size": 8,
    "dataset.train_dataset.images_dir": f"{S3_TRAIN}/images.tar.gz",
    "dataset.train_dataset.csv_path": f"{S3_TRAIN}/dataset.csv",
    "dataset.validation_dataset.images_dir": f"{S3_EVAL}/images.tar.gz",
    "dataset.validation_dataset.csv_path": f"{S3_EVAL}/dataset.csv",
    "dataset.test_dataset.images_dir": f"{S3_EVAL}/images.tar.gz",
    "dataset.test_dataset.csv_path": f"{S3_EVAL}/dataset.csv",
}

evaluate (mandatory data sources):

{
    "evaluate.checkpoint": "<selected train/AutoML checkpoint>",
    "dataset.test_dataset.images_dir": f"{S3_EVAL}/images.tar.gz",
    "dataset.test_dataset.csv_path": f"{S3_EVAL}/dataset.csv",
}

Use the workflow’s checkpoint resolver for downstream actions instead of guessing a filename. For Optical Inspection smoke runs, AutoML may produce model_epoch_000_step_00006.pth; resume can then produce model_epoch_001_step_00012.pth. Best-checkpoint actions should use the AutoML best child job’s selected checkpoint, epoch-specific actions should pass the exact epoch/step checkpoint requested, and only explicit “latest” requests should resolve to the latest checkpoint.

export:

{
    "export.checkpoint": "<selected train/AutoML checkpoint>",
    "export.onnx_file": "/results/optical_inspection.onnx",
    "export.input_width": 128,
    "export.input_height": 512,
    "export.batch_size": 1,
}

inference (mandatory data sources):

{
    "inference.checkpoint": "<selected train/AutoML checkpoint>",
    "dataset.infer_dataset.images_dir": f"{S3_INFERENCE}/images.tar.gz",
    "dataset.infer_dataset.csv_path": f"{S3_INFERENCE}/dataset.csv",
}

Dataset Convert

Dataset conversion is optional for Optical Inspection. If the dataset is already in TAO-ready Optical Inspection format, start directly from the images.tar.gz plus dataset.csv splits and run train, evaluate, inference, and downstream checkpoint/export/deploy actions on that converted data.

The PyT container exposes optical_inspection dataset_convert, but this model skill does not package a dataset_convert action/template. The converter expects the raw Factory PCB layout (root_dataset_dir, train/val/all PCB directories, golden_csv_dir, project_name, and bot_top). The S3 validation bucket currently contains preconverted Optical Inspection images.tar.gz plus dataset.csv splits, not the raw PCB/golden CSV source. Do not synthesize a fake PCB dataset. In model validation reports, mark dataset conversion as not run: preconverted dataset provided rather than failed or blocked when only converted data is available.

When using the preconverted S3 validation tarballs locally, verify the extracted directory before writing specs. The tarballs may unpack an images/ wrapper directory; point dataset.*.images_dir at the inner directory that contains golden/ and the board/image folders referenced by dataset.csv, for example .../<split>/images/images, not the outer wrapper.

Eval Dataset

Optional. Eval dataset uses same format (images + CSV).

Important Parameters

• model.model_type: Siamese variant. Options include Siamese, Siamese_3.
• model.model_backbone: Default custom.
• model.embedding_vectors: Number of embedding dimensions. Default 5.
• train.optim.lr: Learning rate. Default 5e-4.
• dataset.batch_size: Training batch size. Must be greater than 1; use 2 or higher for minimal smoke runs.
• dataset.num_input: Number of input images per comparison.
• dataset.input_map: Mapping of input channels / image pairs.

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 Siamese network, single GPU typically sufficient

Hardware

Minimum 1 GPU(s), recommended 1 GPU(s). 8GB+ VRAM per GPU. Siamese networks for inspection are lightweight. Single GPU sufficient.

Error Patterns

CSV format error: Ensure dataset.csv has the correct column format for image pair paths and labels.

Extracted image root mismatch: If train, evaluate, or inference cannot find paths from dataset.csv, inspect the extracted images.tar.gz tree. The TAO-ready root must contain golden/ plus the board folders referenced in the CSV. For validation S3 tarballs this can be one level below the extraction target, such as images/images.

Training batch size assertion: The Optical Inspection dataloader rejects dataset.batch_size: 1 for train. Keep the template default of 8 for normal runs, or set dataset.batch_size: 2 for minimal AutoML smoke validation.

PyTorch checkpoint load failure on downstream actions: For checkpoints produced by the same trusted TAO train/AutoML workflow, set TORCH_FORCE_NO_WEIGHTS_ONLY_LOAD=1 for evaluate, inference, export, and resume jobs if the current PyTorch default blocks loading the full checkpoint. Do not use this env var for untrusted checkpoints.

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 optical_inspection.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.

Deployment

• tao-deploy-optical-inspection