Depth Net Fast Stereo

Real-time stereo depth estimation using FastFoundationStereo (FFS) — the bp2 commercial distilled variant of FoundationStereo. Predicts disparity maps from rectified stereo image pairs with per-layer pruned widths for real-time inference.

The mono / stereo / fast-stereo skills share the unified TAO depth_net CLI; FFS is selected via model.model_type: FastFoundationStereo. FFS differs from FoundationStereo only in pruned per-layer widths and a serialized forward path; everything else (entrypoint, action verbs, dataset classes, deploy chain) is identical to depth-net-stereo.

For TAO Deploy TensorRT actions (gen_trt_engine, TensorRT evaluate, TensorRT inference), read references/tao-deploy-fast-foundation-stereo.md first. The deploy spec template lives at references/spec_template_deploy.yaml.

When to Use

Use this skill to train, evaluate, export, or run inference for a TAO FastFoundationStereo model. Two supported use cases:

FFS raw-deploy and bp2-finetune flows require a pre-trained bp2 commercial checkpoint (model_best_bp2_serialize.pth). The default PyT image does not guarantee that this file is present on disk, so treat the checkpoint path as a required user/registry artifact. If no bp2 checkpoint is available, scratch training is still usable for workflow validation, but the resulting metrics are not representative of the bp2 model.

1. Raw deploy — use the bp2 ckpt as-is. Skip train; run inference / evaluate / export / gen_trt_engine directly with the bp2 file as the action's checkpoint.
2. Finetune on user data — set train.pretrained_model_path to the bp2 file, train on user data, then verify + deploy on the resulting ckpt. The full 7-action sequence (train → evaluate pyt → inference pyt → export → gen_trt_engine → inference deploy → evaluate deploy) is supported.

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.

FFS shares the depth_net_stereo schema but its bp2 architecture widths are fixed invariants. For default AutoML, search only train.optim.lr and train.optim.lr_decay unless the user explicitly requests a wider search. Do not include FFS architecture fields such as model.volume_dim, model.hidden_dims, or other bp2 width settings in the default search space. 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.

Workflow

Prerequisites — data accessibility

Your dataset (left + right images + GT disparity for train / evaluate, left + right only for inference) must be reachable from inside the container:

• SDK runner: place files at the S3 paths the runner resolves (S3_TRAIN / S3_EVAL placeholders shown in spec overrides).
• Direct docker run (e.g. local testing): mount the host dataset root read-only at the same in-container path:

docker run ... -v <host_data_root>:<host_data_root>:ro <container> ...

The same accessibility requirement applies to the <output_dir> written by all actions, and to the bp2 checkpoint path.

Step 1 — Annotation file

Per-line annotation file referenced by data_sources[*].data_file. Schema is identical to depth-net-stereo:

Columns	Format	Use
2	<left> <right>	Stereo inference (no GT)
3	<left> <right> <disparity>	Stereo with GT
4	<left> <right> <disparity> <occlusion_mask>	Stereo with GT and occlusion mask

Generate via depth_net convert if needed; see the depth-net-stereo skill for convert_spec.yaml template.

Step 2 — Pair model_type and dataset_name based on your data

Use model_type: FastFoundationStereo for FFS. The dataset_name choice mirrors the stereo skill — pick the dataset-specific class when your layout matches a registered one, otherwise GenericDataset.

Data category	model_type	dataset_name
Middlebury	FastFoundationStereo	Middlebury
KITTI	FastFoundationStereo	Kitti
ETH3D	FastFoundationStereo	Eth3d
FSD synthetic	FastFoundationStereo	FSD
IsaacReal synthetic	FastFoundationStereo	IsaacRealDataset
Crestereo synthetic	FastFoundationStereo	Crestereo
Other / non-canonical	FastFoundationStereo	GenericDataset

For inference with 2-column annotations (left + right, no GT), use dataset_name: GenericDataset regardless of layout.

Step 3 — Set the bp2 distilled width overrides

FFS requires 15 model-section width override fields whose values match the bp2 commercial checkpoint exactly. Omitting any field falls back to TAO defaults that do not match the bp2 ckpt and produce shape-mismatch errors at forward time. See references/setup-and-run.md for the full copy-as-is model: block and notes. The spec templates at references/spec_template_*.yaml carry this block as the canonical source.

Step 4 — Write spec yaml from spec overrides

Copy the action block from references/spec-overrides.md. Replace:

• model.model_type: FastFoundationStereo (already set)
• dataset.<...>.data_sources[*].dataset_name from Step 2
• dataset.<...>.data_sources[*].data_file with the path from Step 1
• For raw deploy use cases (no train): set <action>.checkpoint to the bp2 file path
• For finetune use cases: set train.pretrained_model_path to the bp2 file path

For chained train → next-action checkpoint path resolution and shape-consistency notes, see references/setup-and-run.md. SDK-runner deploys resolve handoff automatically via parent_job_id — see references/parent-model-inference.md.

Step 5 — Run

Create writable home/cache directories inside the mounted output path before using --user, then launch docker run ... depth_net <action> -e <spec.yaml>. See references/setup-and-run.md for the full mkdir + docker run command, the --user rationale, and the local bind-mount __pycache__ tip.

Step 6 — Verify

Check container exit code 0 and a populated status.json kpi block. For train inspect per-step train_loss directly (the entrypoint reports Execution status: PASS even when loss is NaN); for evaluate rely on epe / bp1 / bp2 / bp3 / d1 / rmse; for inference check artifacts under results_dir. The pyt-vs-deploy KPI namespace difference and the expected deploy drift are detailed in references/setup-and-run.md.

7-action deploy flow

train (optional)            → finetuned ckpt
evaluate (pyt)              → PyT eager EPE / bp on val GT
inference (pyt)             → PyT eager disparity samples (visual sanity)
export                      → static fp32 ONNX (recommended at 480×736 or 320×736)
gen_trt_engine             → fp16 TRT engine on static ONNX path
inference (deploy)         → TRT disparity samples
evaluate (deploy)          → TRT EPE / bp drift vs PyT eager fp32

Skip train for raw-bp2 deploy. The remaining 6 actions (or the 4 deploy-only verbs starting from export) cover both use cases.

Training Requirements

• Valid dataset_name values for stereo data_sources (case-insensitive): FSD, IsaacRealDataset, Crestereo, Middlebury, Eth3d, Kitti, GenericDataset
• Monitoring metric: val/loss

Per-Action Dataset Requirements

Action	Spec Key	Source	Files	List?
evaluate	dataset.test_dataset.data_sources	eval_dataset	data_file: annotations.txt + dataset_name	Yes
inference	dataset.infer_dataset.data_sources	inference_dataset	data_file: annotations.txt + dataset_name	Yes
train	dataset.train_dataset.data_sources	train_datasets	data_file: annotations.txt + dataset_name	Yes
train	dataset.val_dataset.data_sources	eval_dataset	data_file: annotations.txt + dataset_name	Yes

Typical Spec Overrides

Data source overrides are mandatory for every action. Each data_sources entry is a dict with two mandatory fields: data_file and dataset_name. The model.* width fields are also mandatory — see Step 3. See references/spec-overrides.md for the FFS_MODEL_BLOCK and per-action (train / evaluate / inference / export) Python override dicts.

Eval Dataset

Optional. Val dataset configured via dataset.val_dataset.data_sources (each entry needs data_file and dataset_name).

Important Parameters

Key knobs include model.model_type (FastFoundationStereo), model.encoder (vitl), model.max_disparity (set 192 explicitly — schema default 416 causes severe drift), model.mixed_precision (false), model.gwc_feature_normalize (true), model.volume_dim (28), model.valid_iters (8), and per-split batch_size / workers / crop_size / data_sources. Full parameter reference, evaluation metrics, multi-GPU / multi-node spec keys, export / TRT defaults, the export use-case matrix, and hardware guidance are in references/important-parameters.md.

Error Patterns

For shape mismatch, gwc_feature_normalize schema errors, max_disparity drift, negative disparity, depth_net_stereo: not found, the pyt-evaluate crop_size asymmetry, the Failed to import SAM3 warning, and the dynamic-engine stride-incompatible silent failure, see references/error-patterns.md.

Spec Param / Parent Model Inference

Model-specific inference mappings (per-action spec field → inference function) for train / evaluate / inference / export / gen_trt_engine, plus parent_job_id / parent_model resolution and raw-bp2 explicit-checkpoint handling, are in references/parent-model-inference.md. Generated runners should read that section and apply the mappings with SDK helpers before create_job().

Deployment

• tao-deploy-fast-foundation-stereo