Depth Net Mono
Monocular depth estimation using Metric Depth Anything v2 or Relative Depth Anything architectures. Predicts per-pixel depth from single RGB images.
Pretrained checkpoint loading varies by model variant and use case — see the Pretrained checkpoint loading — use case matrix in references/parameters.md.
The mono and stereo skills both invoke the unified TAO depth_net CLI inside the container; the mono/stereo family is selected via model.model_type (see references/parameters.md).
For TAO Deploy TensorRT actions (gen_trt_engine, TensorRT evaluate, and TensorRT inference), read references/tao-deploy-depth-anything-v2.md first. The deploy spec template lives in this skill’s references/spec_template_deploy.yaml.
PyT actions packaged by this model skill: train, evaluate, inference, export, and quantize. The PyT depth_net entrypoint does not accept a PyT-side gen_trt_engine action in the current TAO image. The gen_trt_engine action metadata must run with the TAO Deploy container, and the deploy workflow remains the deploy-specific entrypoint.
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.
Workflow
Prerequisites — data accessibility
Your dataset (RGB images + GT depth files) must be reachable from inside the container:
- SDK runner: place files at the S3 paths the runner resolves (the
S3_TRAIN / S3_EVAL placeholders shown in Typical Spec Overrides). The runner handles S3 → container-path mounting transparently.
- 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.
Step 1 — Annotation file
Per-line annotation file referenced by data_sources[*].data_file:
| Columns |
Format |
Use |
| 1 |
<image> |
Mono inference (no GT) |
| 2 |
<image> <gt_depth> |
Mono with GT |
Do not pass stereo annotation rows such as `
` directly to mono train/evaluate/inference. If only a stereo depth
dataset is available, derive a mono annotation file by keeping the left image
and GT depth columns, then mount or stage the image/depth archive at the same
container paths referenced by that derived annotation file.
If you already have one, point to it. Otherwise generate via `depth_net convert`:
```
depth_net convert -e
```
`convert_spec.yaml` template:
```yaml
results_dir:
data_root: <directory whose immediate children are scene/sample folders that contain your image+depth files; convert walks data_root recursively but expects per-scene subdirectories at one level below>
image_dir_pattern: [<substring matching left/RGB image paths>]
depth_dir_pattern: []
image_extension: '' # optional .endswith filter, e.g. '.jpg'
depth_extension: '' # optional, swapped during depth derivation, e.g. '.png'
split_ratio: 0.0 # 0.0/1.0 = test-only; 0.8 = 80/20 train+val
```
`convert` walks `data_root` recursively, selects paths whose path-string contains *all* substrings in `image_dir_pattern` (AND-filter), then derives the depth path by replacing `image_dir_pattern[0]` with `depth_dir_pattern[0]` and `image_extension` with `depth_extension`. Inspect your dataset's directory layout and identify the substring distinguishing RGB images from depth files (e.g. `rgb_` vs `sync_depth_`).
`data_root` must point at the parent that contains the per-scene subdirectories (e.g. for NYU eval, use `/data/nyu_v2/eval/test`, not `/data/nyu_v2/eval/test/bathroom` — the latter limits the walk to a single scene). Always include the leading dot in `image_extension` / `depth_extension` (e.g. `'.jpg'` not `'jpg'`); the substring swap is form-sensitive and a mismatch silently corrupts derived paths.
### Step 2 — Pair `model_type` and `dataset_name` based on your data
Default — generic class for each task:
| Data category | `model_type` | `dataset_name` |
|---|---|---|
| Disparity-encoded data (pixels) | `RelativeDepthAnything` | `RelativeMonoDataset` |
| Metric depth (meters) | `MetricDepthAnything` | `MetricMonoDataset` |
| Mono inference (no GT, any image) | matches train choice | `RelativeMonoDataset` or `MetricMonoDataset` |
Dataset-specific class — switch when the data needs preprocessing the generic class does not perform:
| Special case | `model_type` | `dataset_name` | What the class adds |
|---|---|---|---|
| NYU `sync_depth_*.png` (raw uint16 millimetres) — relative | `RelativeDepthAnything` | `NYUDV2Relative` | mm→m unit conversion + Eigen evaluation crop |
| NYU `sync_depth_*.png` (raw uint16 millimetres) — metric | `MetricDepthAnything` | `NYUDV2` | same |
Using a generic class on data that requires unit conversion (e.g. raw NYU uint16 PNGs) results in an empty valid mask and silent `train_loss = NaN`. Match the class to your data's encoding.
For relative mono data (`RelativeMonoDataset` or `NYUDV2Relative`), leave `dataset.min_depth` and `dataset.max_depth` unset or set both to `null`. Non-null metric depth ranges are passed into the relative dataset constructor and fail with `BaseRelativeMonoDataset.__init__() got an unexpected keyword argument 'min_depth'`.
### Step 3 — Write spec yaml from Typical Spec Overrides
Copy the action block from **Typical Spec Overrides** (`references/spec-overrides.md`). Replace:
- `model.model_type` from Step 2
- `dataset.<...>.data_sources[*].dataset_name` from Step 2
- `data_sources[*].data_file` with the path from Step 1 (S3 path under SDK runner, host path for direct docker)
- For metric finetune: additionally apply the **Metric Variant Finetuning Recipe** in `references/finetuning-recipes.md`.
For mono training set `train.precision: fp32` (recommended) or `bf16` (Ampere SM80+, alternative).
### Step 4 — Run
Create writable home/cache directories inside the mounted output path before using
`--user`. Some TAO containers do not have an `/etc/passwd` entry for the host UID,
and PyTorch / matplotlib need writable cache paths when running as that UID.
```bash
mkdir -p /home \
/.cache/matplotlib \
/.cache/torchinductor \
/.cache/xdg
```
```
docker run --gpus 'device=0' --shm-size 16G --ipc=host \
--user "$(id -u):$(id -g)" \
-e USER="$(id -un)" \
-e LOGNAME="$(id -un)" \
-e HOME=/home \
-e MPLCONFIGDIR=/.cache/matplotlib \
-e TORCHINDUCTOR_CACHE_DIR=/.cache/torchinductor \
-e XDG_CACHE_HOME=/.cache/xdg \
-v ::ro \
-v : \
\
depth_net -e
```
Without `--user "$(id -u):$(id -g)"` the container writes outputs as `nobody:nogroup`, blocking host-side cleanup and retry.
### Step 5 — Verify
- Container exit code 0
- `status.json` `kpi` block populated
- For `train`: inspect per-step `train_loss` directly — the entrypoint reports `Execution status: PASS` even when `train_loss = NaN` (see the Metric Variant Finetuning Recipe → Sanity-run PASS criteria in `references/finetuning-recipes.md`)
- For `evaluate` / `inference`: artifacts under `results_dir`
For TAO Deploy TensorRT actions (`gen_trt_engine`, TensorRT `evaluate`, and TensorRT `inference`), read `references/tao-deploy-depth-anything-v2.md` first. Deploy spec templates live in this skill's `references/` folder with the `spec_template_deploy_*.yaml` prefix.
## Training Requirements
- **Valid `dataset_name` values for mono `data_sources`** (case-insensitive): `ThreeDVLM`, `FSD`, `NvCLIP`, `IssacStereo`, `Crestereo`, `Middlebury`, `NYUDV2`, `NYUDV2Relative`, `RelativeMonoDataset`, `MetricMonoDataset`. `NYUDV2` carries metric depth GT (meters) — pair with `MetricDepthAnything`; `NYUDV2Relative` is the same data with relative-depth conventions — pair with `RelativeDepthAnything`.
- **Monitoring metric:** val/d1, val/loss
- For AutoML sanity runs on the packaged relative-depth smoke data, use `val/d1` as the primary monitor. `val/loss` can be emitted as `NaN` even when the trainer exits successfully and writes a usable checkpoint, so it is not a reliable AutoML objective unless the run's status metrics show a finite value.
### 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 |
| quantize | dataset.train_dataset.data_sources | train_datasets | data_file: annotations.txt + dataset_name | Yes |
| quantize | dataset.val_dataset.data_sources | eval_dataset | data_file: annotations.txt + dataset_name | Yes |
| quantize | dataset.quant_calibration_dataset.images_dir | train_datasets | images.tar.gz | No |
| 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** — construct data source paths from the Per-Action Dataset Requirements table above and include them in `spec_overrides`. Each `data_sources` entry is a dict with **two mandatory fields**: `data_file` and `dataset_name`. See `references/spec-overrides.md` for the full per-action override blocks (`train`, `evaluate`, `export`, `inference`, `quantize`), the `S3_TRAIN` / `S3_EVAL` placeholders, the relative-variant precision recommendation, and the `quantize` known-issue note.
## Eval Dataset
Optional. Val dataset configured via `dataset.val_dataset.data_sources` (each entry needs `data_file` and `dataset_name`).
## Important Parameters
See `references/parameters.md` for the full parameter glossary (model, train, dataset, export, and inference keys with options, defaults, and sources) and the **Pretrained checkpoint loading — use case matrix**.
## Finetuning Recipes
See `references/finetuning-recipes.md` for:
- **Relative Variant Finetuning Recipe** — finetune from a TAO-trained `RelativeDepthAnything` checkpoint (lr `5e-6`, `LambdaLR`, sanity-vs-convergent guidance, deploy LSQ alignment note).
- **Metric Variant Finetuning Recipe** — checkpoint compatibility, required overrides, the dataset normalization block (`normalize_depth`/`min_depth`/`max_depth`) required in train AND export specs, trainer-enforced defaults, precision, the 1-epoch sanity-run override, and the Sanity-run PASS criteria with the NaN-mitigation order.
## 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] |
| `train.num_nodes` | Number of nodes | 1 |
| `train.distributed_strategy` | `ddp` or `fsdp` | `ddp` |
- `ddp` with activation checkpointing: `find_unused_parameters=False`
- `ddp` without: `find_unused_parameters=True`
- `fsdp` forces precision to FP16
**Multi-node env vars** (set by orchestrator): `WORLD_SIZE`, `NODE_RANK`, `MASTER_ADDR`, `MASTER_PORT`, `NUM_GPU_PER_NODE`.
## Export / TRT Defaults
- TRT data types: FP32, BF16 (Ampere SM80+). FP16 is not supported for the ViT-L mono backbone.
- Fresh-install TRT precision: `fp32`. BF16 is supported on Ampere SM80+ hardware, but keep smoke tests on FP32 unless the user explicitly requests BF16.
## Hardware
Minimum 1 GPU(s), recommended 2 GPU(s). 24GB+ VRAM per GPU. ViT-Large encoder is memory intensive. Use `fp32` (recommended) or `bf16` (Ampere SM80+, alternative) for training. Activation checkpointing is available for larger inputs.
## Error Patterns
See `references/troubleshooting.md` for the full error-pattern catalog (depth range mismatch, relative dataset rejecting `min_depth`, missing pretrained weights, `encoder` key location, `dataset_name` not in struct, `depth_net_mono` not found, metric variant hyperparameter sourcing, and export ONNX overwrite).
## Spec Param / Parent Model Inference
See `references/spec-param-inference.md` for the model-specific inference mappings (the TAO Core `depth_net_mono.config.json` action table), checkpoint-file naming under `/train/`, the `dn_model_latest.pth` policy, the parent-`gen_trt_engine` rationale, and the `parent_model` / `parent_job_id` resolution rules.
## Deployment
- [tao-deploy-depth-anything-v2](references/tao-deploy-depth-anything-v2.md)
license: Apache-2.0
compatibility: Requires docker + nvidia-container-toolkit.
metadata: {"version" => "0.1.0", "author" => "NVIDIA Corporation"}
allowed-tools: Read Bash
tags: monoculardepthestimation