Document SLAT training downloads, dataset paths, and upload requirements

README.md

@@ -63,6 +63,153 @@ Archive inventories and SHA-256 checksums are in `bundles/<dataset>/index.json`.
 The extent archives exclude diagnostic subsets, raw downloads, model weights, and model-specific feature caches.
 FoundationPose currently has raw archives only.
 
+
+## SLAT shape: upload and download specification
+
+The repository contains prepared extent datasets and separate SLAT-shape targets.
+Combined 32³ SLAT finetuning requires both artifact types for both training datasets.
+The 6.84 GB SLAT upload contains latent targets and metadata.
+It excludes RGB observations, depth maps, masks, and frozen image features.
+
+The sizes and coverage below describe data snapshot `0e4623fb0c8d9bad7cbe1dfb161a993ac165b6aa`.
+GB means decimal gigabytes.
+
+| Bundle | Contents | Training archives and metadata, GB | All archives, GB |
+| --- | --- | ---: | ---: |
+| `bundles/3dfront` | Calibrated observations, extent geometry, and occupancy targets | 234.67 | 293.70 |
+| `bundles/sage` | Calibrated observations, extent geometry, and occupancy targets | 460.59 | 510.88 |
+| `precomputed_slat/3dfront` | 32³ object latents and available scene latents | 5.57 | 5.57 |
+| `precomputed_slat/sage` | 32³ object latents and recorded failures | 1.27 | 1.27 |
+| Total | Both datasets and both artifact types | 702.10 | 811.42 |
+
+Training requires each extent bundle's `metadata.tar` and `train-*.tar` archives.
+Evaluation also requires its `test-*.tar` archives.
+The current download command restores every archive in a bundle, including available test archives.
+It has no split filter.
+The four commands below therefore download about 811.42 GB.
+
+### Download and restore
+
+Use the updated PartProbe3D checkout with latent generation in `preprocess/slat/`.
+Install its preprocessing dependencies and `huggingface-hub`.
+Authenticate with a Hugging Face account that can access this dataset repository.
+Run these commands from the repository root.
+
+1. Restore the prepared 3D-FRONT extent dataset.
+
+   ```bash
+   python -m preprocess.huggingface download \
+     --repo cubec/PartProbe3D-preprocessed --bundle-root bundles \
+     --dataset 3dfront --revision 0e4623fb0c8d9bad7cbe1dfb161a993ac165b6aa --cleanup
+   ```
+
+2. Restore the prepared SAGE-10k extent dataset.
+
+   ```bash
+   python -m preprocess.huggingface download \
+     --repo cubec/PartProbe3D-preprocessed --bundle-root bundles \
+     --dataset sage --revision 0e4623fb0c8d9bad7cbe1dfb161a993ac165b6aa --cleanup
+   ```
+
+3. Restore the 3D-FRONT shape targets.
+
+   ```bash
+   python -m preprocess.huggingface download \
+     --repo cubec/PartProbe3D-preprocessed --bundle-root precomputed_slat \
+     --dataset 3dfront --revision 0e4623fb0c8d9bad7cbe1dfb161a993ac165b6aa --cleanup
+   ```
+
+4. Restore the SAGE-10k shape targets.
+
+   ```bash
+   python -m preprocess.huggingface download \
+     --repo cubec/PartProbe3D-preprocessed --bundle-root precomputed_slat \
+     --dataset sage --revision 0e4623fb0c8d9bad7cbe1dfb161a993ac165b6aa --cleanup
+   ```
+
+| Dataset | Prepared observations and extent targets | SLAT targets |
+| --- | --- | --- |
+| 3D-FRONT | `datasets/3dfront/extent/point_cloud/` | `datasets/3dfront/slat/32/targets/` |
+| SAGE-10k | `datasets/sage/extent/point_cloud/` | `datasets/sage/slat/32/targets/` |
+
+The updated downloader maps historical SLAT archive paths under `outputs/` to these dataset locations.
+It preserves target bytes and verifies every archive's size and SHA-256 checksum.
+It refuses to replace different existing files.
+`--cleanup` retains one temporary archive at a time and removes it after successful restoration.
+Temporary archives reside under `datasets/<dataset>/archives/huggingface/`.
+Extracted files and future feature caches need additional disk space.
+
+Preserve the original extent manifests without edits.
+The SLAT manifests bind their source datasets through manifest checksums.
+Raw dataset archives, rendering again, and SS feature caches are unnecessary for this workflow.
+
+### Complete preparation before training
+
+The published targets cover 39,930 3D-FRONT training views and 17,456 SAGE-10k training views.
+Three SAGE-10k views retain one failed object target each.
+These failures block strict combined training until object encoding succeeds.
+The snapshot contains scene latents for 6,720 3D-FRONT views and no SAGE-10k scenes.
+The current object loss does not require completed scene encoding.
+
+1. Obtain the configured Pixal3D, DINOv3, and NAF weights separately.
+2. Resolve the three failed SAGE-10k object targets.
+3. Prepare frozen conditioning features and immutable object targets.
+4. Start finetuning after feature preparation succeeds.
+
+The TRELLIS.2 shape encoder and paired decoder are also required when regenerating shape targets.
+Image features and immutable training snapshots are not included in the uploaded target bundles.
+The commands below do not automatically schedule scene encoding or training during feature preparation.
+
+```bash
+python -m preprocess.slat.prepare \
+  --config preprocess/slat/configs/front3d_sage_32.json \
+  --stage features --devices cuda:1 cuda:2
+python -m slat_multiobj_shape.train \
+  --config slat_multiobj_shape/configs/front3d_sage_32.json
+```
+
+Feature preparation writes `object_targets/` and `object_features/` under each dataset's `slat/32/` directory.
+It retains clean GT depth for fresh augmentation during training.
+Testing uses MoGe-3 depth and calibration.
+Training results reside under `outputs/`, and model checkpoints reside under `checkpoints/`.
+
+### Upload contract
+
+`bundles/<dataset>/index.json` describes prepared extent archives.
+`precomputed_slat/<dataset>/index.json` describes SLAT target archives.
+Each inventory records archive sizes, SHA-256 checksums, and the restore destination.
+SLAT inventories also record source manifests, codec provenance, coverage, and encoding failures.
+
+An inventory's `complete=true` means packaging covers the snapshot files.
+The uploader publishes a complete inventory after every archive is present with matching size.
+It also compares remote SHA-256 checksums when Hub file metadata provides them.
+`source_complete` separately records whether source preprocessing finished.
+An upload can therefore finish while source object or scene encoding remains incomplete.
+The upload preserves failure records and pending scene statuses.
+
+Package shape targets with this command:
+
+```bash
+python -m preprocess.huggingface pack \
+  --artifact-kind slat-shape-targets \
+  --dataset 3dfront=datasets/3dfront/slat/32/targets \
+  --dataset sage=datasets/sage/slat/32/targets \
+  --output outputs/huggingface/slat_targets_publish
+```
+
+Publish those archives to an existing dataset repository with this command:
+
+```bash
+python -m preprocess.huggingface upload \
+  --repo cubec/PartProbe3D-preprocessed \
+  --bundle-root precomputed_slat --dataset 3dfront --dataset sage \
+  --output outputs/huggingface/slat_targets_publish
+```
+
+The target upload excludes worker jobs, raw assets, model weights, and frozen image feature caches.
+Use a fresh packaging directory when source manifests change.
+Publish matching extent and target snapshots before documenting a new reproducible data revision.
+
 ## Restore on another server
 
 Use the PartProbe3D checkout containing `preprocess/huggingface.py` and install its
@@ -158,21 +305,3 @@ View: `003c09039641a2b2efa1`.
 ![Prompt masks](previews/ycbv/test-overlay.png)
 
 [Posed prompted GT geometry (GLB)](previews/ycbv/test-geometry.glb)
-
-## Precomputed SLAT-shape targets
-
-The [SLAT target snapshots](precomputed_slat/README.md) contain available 32³ shape latents for both training datasets.
-The frozen TRELLIS.2 shape encoder produced these latents.
-
-| Dataset | Training views | Encoded objects | Archive GiB |
-| --- | ---: | ---: | ---: |
-| 3D-FRONT | 39,930 | 240,952 | 5.19 |
-| SAGE-10k | 17,456 | 397,203 | 1.18 |
-
-Three SAGE-10k objects retain encoding failures.
-Scene encoding remains partial.
-Frozen DINOv3 and NAF feature caches still require preparation.
-The snapshots preserve the recorded completion states.
-
-The [SLAT instructions](precomputed_slat/README.md#restore-the-caches) describe restoration with `--bundle-root precomputed_slat`.
-The extent archives remain under `bundles/`.

precomputed_slat/README.md

@@ -22,26 +22,152 @@ The inventories include archive SHA-256 checksums and encoder checkpoint provena
 
 [3D-FRONT inventory](3dfront/index.json) · [SAGE-10k inventory](sage/index.json)
 
-## Restore the caches
 
-Use the PartProbe3D checkout that supports `--bundle-root` and `--artifact-kind slat-shape-targets`.
-Run these commands from the repository root:
+## SLAT shape: upload and download specification
+
+The repository contains prepared extent datasets and separate SLAT-shape targets.
+Combined 32³ SLAT finetuning requires both artifact types for both training datasets.
+The 6.84 GB SLAT upload contains latent targets and metadata.
+It excludes RGB observations, depth maps, masks, and frozen image features.
+
+The sizes and coverage below describe data snapshot `0e4623fb0c8d9bad7cbe1dfb161a993ac165b6aa`.
+GB means decimal gigabytes.
+
+| Bundle | Contents | Training archives and metadata, GB | All archives, GB |
+| --- | --- | ---: | ---: |
+| `bundles/3dfront` | Calibrated observations, extent geometry, and occupancy targets | 234.67 | 293.70 |
+| `bundles/sage` | Calibrated observations, extent geometry, and occupancy targets | 460.59 | 510.88 |
+| `precomputed_slat/3dfront` | 32³ object latents and available scene latents | 5.57 | 5.57 |
+| `precomputed_slat/sage` | 32³ object latents and recorded failures | 1.27 | 1.27 |
+| Total | Both datasets and both artifact types | 702.10 | 811.42 |
+
+Training requires each extent bundle's `metadata.tar` and `train-*.tar` archives.
+Evaluation also requires its `test-*.tar` archives.
+The current download command restores every archive in a bundle, including available test archives.
+It has no split filter.
+The four commands below therefore download about 811.42 GB.
+
+### Download and restore
+
+Use the updated PartProbe3D checkout with latent generation in `preprocess/slat/`.
+Install its preprocessing dependencies and `huggingface-hub`.
+Authenticate with a Hugging Face account that can access this dataset repository.
+Run these commands from the repository root.
+
+1. Restore the prepared 3D-FRONT extent dataset.
+
+   ```bash
+   python -m preprocess.huggingface download \
+     --repo cubec/PartProbe3D-preprocessed --bundle-root bundles \
+     --dataset 3dfront --revision 0e4623fb0c8d9bad7cbe1dfb161a993ac165b6aa --cleanup
+   ```
+
+2. Restore the prepared SAGE-10k extent dataset.
+
+   ```bash
+   python -m preprocess.huggingface download \
+     --repo cubec/PartProbe3D-preprocessed --bundle-root bundles \
+     --dataset sage --revision 0e4623fb0c8d9bad7cbe1dfb161a993ac165b6aa --cleanup
+   ```
+
+3. Restore the 3D-FRONT shape targets.
+
+   ```bash
+   python -m preprocess.huggingface download \
+     --repo cubec/PartProbe3D-preprocessed --bundle-root precomputed_slat \
+     --dataset 3dfront --revision 0e4623fb0c8d9bad7cbe1dfb161a993ac165b6aa --cleanup
+   ```
+
+4. Restore the SAGE-10k shape targets.
+
+   ```bash
+   python -m preprocess.huggingface download \
+     --repo cubec/PartProbe3D-preprocessed --bundle-root precomputed_slat \
+     --dataset sage --revision 0e4623fb0c8d9bad7cbe1dfb161a993ac165b6aa --cleanup
+   ```
+
+| Dataset | Prepared observations and extent targets | SLAT targets |
+| --- | --- | --- |
+| 3D-FRONT | `datasets/3dfront/extent/point_cloud/` | `datasets/3dfront/slat/32/targets/` |
+| SAGE-10k | `datasets/sage/extent/point_cloud/` | `datasets/sage/slat/32/targets/` |
+
+The updated downloader maps historical SLAT archive paths under `outputs/` to these dataset locations.
+It preserves target bytes and verifies every archive's size and SHA-256 checksum.
+It refuses to replace different existing files.
+`--cleanup` retains one temporary archive at a time and removes it after successful restoration.
+Temporary archives reside under `datasets/<dataset>/archives/huggingface/`.
+Extracted files and future feature caches need additional disk space.
+
+Preserve the original extent manifests without edits.
+The SLAT manifests bind their source datasets through manifest checksums.
+Raw dataset archives, rendering again, and SS feature caches are unnecessary for this workflow.
+
+### Complete preparation before training
+
+The published targets cover 39,930 3D-FRONT training views and 17,456 SAGE-10k training views.
+Three SAGE-10k views retain one failed object target each.
+These failures block strict combined training until object encoding succeeds.
+The snapshot contains scene latents for 6,720 3D-FRONT views and no SAGE-10k scenes.
+The current object loss does not require completed scene encoding.
+
+1. Obtain the configured Pixal3D, DINOv3, and NAF weights separately.
+2. Resolve the three failed SAGE-10k object targets.
+3. Prepare frozen conditioning features and immutable object targets.
+4. Start finetuning after feature preparation succeeds.
+
+The TRELLIS.2 shape encoder and paired decoder are also required when regenerating shape targets.
+Image features and immutable training snapshots are not included in the uploaded target bundles.
+The commands below do not automatically schedule scene encoding or training during feature preparation.
 
 ```bash
-python -m preprocess.huggingface download \
-  --repo cubec/PartProbe3D-preprocessed --bundle-root precomputed_slat \
-  --dataset 3dfront --cleanup
-python -m preprocess.huggingface download \
-  --repo cubec/PartProbe3D-preprocessed --bundle-root precomputed_slat \
-  --dataset sage --cleanup
+python -m preprocess.slat.prepare \
+  --config preprocess/slat/configs/front3d_sage_32.json \
+  --stage features --devices cuda:1 cuda:2
+python -m slat_multiobj_shape.train \
+  --config slat_multiobj_shape/configs/front3d_sage_32.json
 ```
 
-Targets restore to `outputs/slat_multiobj_shape/front3d_32/targets/` and `outputs/slat_multiobj_shape/sage_32/targets/`.
-Restoration verifies every archive checksum and rejects replacement of different existing files.
-Add `--revision <commit>` to select a fixed snapshot.
+Feature preparation writes `object_targets/` and `object_features/` under each dataset's `slat/32/` directory.
+It retains clean GT depth for fresh augmentation during training.
+Testing uses MoGe-3 depth and calibration.
+Training results reside under `outputs/`, and model checkpoints reside under `checkpoints/`.
 
-The corresponding extent datasets reside in `bundles/3dfront/` and `bundles/sage/`.
-Target loading requires those datasets and their matching manifests.
+### Upload contract
+
+`bundles/<dataset>/index.json` describes prepared extent archives.
+`precomputed_slat/<dataset>/index.json` describes SLAT target archives.
+Each inventory records archive sizes, SHA-256 checksums, and the restore destination.
+SLAT inventories also record source manifests, codec provenance, coverage, and encoding failures.
+
+An inventory's `complete=true` means packaging covers the snapshot files.
+The uploader publishes a complete inventory after every archive is present with matching size.
+It also compares remote SHA-256 checksums when Hub file metadata provides them.
+`source_complete` separately records whether source preprocessing finished.
+An upload can therefore finish while source object or scene encoding remains incomplete.
+The upload preserves failure records and pending scene statuses.
+
+Package shape targets with this command:
+
+```bash
+python -m preprocess.huggingface pack \
+  --artifact-kind slat-shape-targets \
+  --dataset 3dfront=datasets/3dfront/slat/32/targets \
+  --dataset sage=datasets/sage/slat/32/targets \
+  --output outputs/huggingface/slat_targets_publish
+```
+
+Publish those archives to an existing dataset repository with this command:
+
+```bash
+python -m preprocess.huggingface upload \
+  --repo cubec/PartProbe3D-preprocessed \
+  --bundle-root precomputed_slat --dataset 3dfront --dataset sage \
+  --output outputs/huggingface/slat_targets_publish
+```
+
+The target upload excludes worker jobs, raw assets, model weights, and frozen image feature caches.
+Use a fresh packaging directory when source manifests change.
+Publish matching extent and target snapshots before documenting a new reproducible data revision.
 
 ## Format and provenance