feat: add VKITTI far-depth post-training and separate Stage 1 training

[Haruko386]

Summary

After Stage 2 training (including FFT refinement) finishes, optionally run 6000 additional steps from its final U-Net checkpoint to restore supervision for known VKITTI far depths. Stage 2 keeps this feature disabled; only the post-training config enables it. Finite [80, 655.35] m values complete the target before VAE encoding, while evaluation masks and normalization quantiles stay unchanged. Post-training uses reconstruction MSE/L1 plus separately averaged far latent MSE and pixel L1 losses with weight 0.5, and starts fresh optimizer, learning-rate and iteration state.

• Add a dedicated post-training section in README, a 6000-step post-training config, coverage audit script, far-supervision metrics, and --init_checkpoint for starting from the completed Stage 2 weights.
• Remove the bundled Stage 1 entry point, trainer, configs, script, external encoder and adapter utility. Direct Stage 1 users to ApDepth_Stage1; this repository retains Stage 2 training, inference and evaluation.
• Correct the default training config path and honor BASE_DATA_DIR / BASE_CKPT_DIR. Make training errors exit unsuccessfully and document initialization, resume and inference checkpoint usage.

Related Issue

No linked issue.

Type of Change

• Bug fix
• New feature
• Documentation update
• Refactor
• CI / build change
• Other

Test Results

• Verified the resolved Stage 2 config disables far supervision and the post-training config enables it for 6000 steps while retaining the original sampling and batch settings.
• ruff check . --select E9,F63,F7,F82 and git diff --check: passed.
• Parsed all 79 remaining Python files and all repository YAML files; checked repository files against the 100 MB limit: passed.
• CPU checks for mixed valid/far/unknown masks, far-only loss gradients on both sides of the reconstruction/FFT boundary, and Stage 2 trainer/config loading: passed.
• Verified --init_checkpoint accepts a valid unet/diffusion_pytorch_model.safetensors, loads exact weights without torch.load, preserves fresh optimizer/iteration state, and rejects missing or .bin-only checkpoints before configuration/model loading.
• Verified train.py --help, mutually exclusive checkpoint arguments, and the audit CLI.

Full GPU training and KITTI/NYU benchmark evaluation were not run: project weights and datasets are not available in this workspace. This PR does not claim a measured accuracy improvement.

Summary by CodeRabbit

• New Features

  • Added optional VKITTI far-depth supervision with configurable weighting and training metrics.
  • Added checkpoint initialization for training, separate from full training-state resumption.
  • Added an audit tool for reviewing far-depth coverage and saturation.

• Documentation

  • Updated setup and training guidance for Stage 2, including dataset configuration, fine-tuning, monitoring, and inference checkpoint requirements.
  • Documented external Stage 1 setup and required pretrained checkpoints.

• Changes

  • Removed the in-repository Stage 1 training scripts, configurations, and bundled encoder implementation.

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⚙️ Run configuration

Configuration used: Organization UI

Review profile: CHILL

Plan: Advanced

Run ID: cfe2b367-a754-491c-92aa-6e509bd35479

📥 Commits

Reviewing files that changed from the base of the PR and between 2310835 and eb085f1.

⛔ Files ignored due to path filters (1)

• doc/main.jpg is excluded by !**/*.jpg

📒 Files selected for processing (4)

• README.md
• config/train_apdepth.yaml
• config/train_sky_finetune.yaml
• train.py

📝 Walkthrough

Walkthrough

Stage 2 training now supports VKITTI far-depth supervision, checkpoint initialization, and dedicated fine-tuning. Stage 1 code and bundled DINOv2 components were removed. Documentation and CLI defaults were updated for the revised workflow.

Changes

Stage 2 training

Layer / File(s)	Summary
Remove Stage 1 and bundled encoder components apdepth_train_s1.py, config/*s1*, src/trainer/*s1*, external_encoder/dinov2/*, src/util/build_mlp.py	Stage 1 scripts, configurations, trainer registration, DINOv2 modules, and feature-builder utilities were removed.
Add far-depth data preparation and auditing src/dataset/*, src/util/far_supervision.py, script/audit_far_supervision.py, config/train_apdepth.yaml	Datasets now expose known far-depth masks. Utilities prepare far-depth targets and masked losses. The audit CLI reports VKITTI coverage and saves diagnostics.
Integrate far-depth loss and fine-tuning src/trainer/apdepth_trainer.py, config/train_sky_finetune.yaml	The trainer validates far-depth settings, adds far-depth losses and metrics, and supports the new 6000-step fine-tuning configuration.
Update Stage 2 workflow and documentation train.py, README.md	The CLI supports checkpoint initialization separately from resume. Documentation describes external Stage 1 setup, Stage 2 training modes, far-depth fine-tuning, and complete-pipeline evaluation.

Priority: ➖ Normal

Estimated code review effort: 4 (Complex) | ~45 minutes

Change: Bug fix

Sequence Diagram(s)

sequenceDiagram
  participant VKITTI
  participant ApDepthTrainer
  participant FarSupervision
  VKITTI->>ApDepthTrainer: provide depth and known_far_mask
  ApDepthTrainer->>FarSupervision: prepare far-depth targets and masks
  FarSupervision-->>ApDepthTrainer: return targets and support masks
  ApDepthTrainer->>ApDepthTrainer: compute weighted far-depth loss and metrics

Loading

Merge Risk: 🔵 Low · up to 23108

Stage 2 training gains far-depth supervision and a new option to start from an existing checkpoint. The default configuration is internally consistent and should train without setup errors. The remaining consideration is that checkpoint files other than .safetensors are loaded with a format that executes embedded code, so operators should only initialize from checkpoints they trust; this matches how the existing resume option already behaves.

🚥 Pre-merge checks | ✅ 4 | ❌ 1

❌ Failed checks (1 warning)

Check name	Status	Explanation	Resolution
Docstring Coverage	⚠️ Warning	Docstring coverage is 13.33% which is insufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff. Analyzed 15 functions across 6 files. (3 skipped: …	Write docstrings for the functions missing them to satisfy the coverage threshold.

✅ Passed checks (4 passed)

Check name	Status	Explanation
Linked Issues check	✅ Passed	Check skipped because no linked issues were found for this pull request.
Out of Scope Changes check	✅ Passed	Check skipped because no linked issues were found for this pull request.
Title check	✅ Passed	The title clearly summarizes the two main changes: VKITTI far-depth post-training and separation of Stage 1 training.
Description check	✅ Passed	The description includes all required sections, explains the implementation and scope, identifies that no issue is linked, classifies the change, and reports completed and unavailable tests.

Full details: Docstring Coverage

Explanation

Docstring coverage is 13.33% which is insufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff. Analyzed 15 functions across 6 files. (3 skipped: 3 unsupported.)

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Actionable comments posted: 1

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Inline comments:
In `@train.py`:
- Line 369: Update the --init_checkpoint initialization path before
trainer.load_checkpoint to accept only .safetensors files and reject .bin
checkpoints with a clear validation error. Remove .bin as a supported format
from the documented external-checkpoint workflow while preserving loading of
valid safetensors checkpoints.

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ℹ️ Review info

⚙️ Run configuration

Configuration used: Organization UI

Review profile: CHILL

Plan: Advanced

Run ID: 82e7d486-ab27-47a3-b06a-0041d9fff3e6

📥 Commits

Reviewing files that changed from the base of the PR and between 41538cf and 2310835.

📒 Files selected for processing (28)

• README.md
• apdepth_train_s1.py
• config/apdepth_train_s1.yaml
• config/dataset/dataset_apdepth_train_s1.yaml
• config/train_apdepth.yaml
• config/train_sky_finetune.yaml
• external_encoder/__init__.py
• external_encoder/dinov2/__init__.py
• external_encoder/dinov2/dinov2.py
• external_encoder/dinov2/dinov2_layers/__init__.py
• external_encoder/dinov2/dinov2_layers/attention.py
• external_encoder/dinov2/dinov2_layers/block.py
• external_encoder/dinov2/dinov2_layers/drop_path.py
• external_encoder/dinov2/dinov2_layers/layer_scale.py
• external_encoder/dinov2/dinov2_layers/mlp.py
• external_encoder/dinov2/dinov2_layers/patch_embed.py
• external_encoder/dinov2/dinov2_layers/swiglu_ffn.py
• external_encoder/dinov2/util/transform.py
• script/apdepth_train_s1.sh
• script/audit_far_supervision.py
• src/dataset/base_depth_dataset.py
• src/dataset/vkitti_dataset.py
• src/trainer/__init__.py
• src/trainer/apdepth_trainer.py
• src/trainer/apdepth_trainer_s1.py
• src/util/build_mlp.py
• src/util/far_supervision.py
• train.py

💤 Files with no reviewable changes (19)

• external_encoder/dinov2/dinov2_layers/mlp.py
• apdepth_train_s1.py
• src/trainer/apdepth_trainer_s1.py
• external_encoder/init.py
• external_encoder/dinov2/dinov2_layers/layer_scale.py
• external_encoder/dinov2/dinov2_layers/block.py
• external_encoder/dinov2/init.py
• config/apdepth_train_s1.yaml
• src/trainer/init.py
• external_encoder/dinov2/dinov2_layers/swiglu_ffn.py
• src/util/build_mlp.py
• external_encoder/dinov2/dinov2_layers/init.py
• external_encoder/dinov2/dinov2_layers/patch_embed.py
• external_encoder/dinov2/util/transform.py
• external_encoder/dinov2/dinov2_layers/attention.py
• script/apdepth_train_s1.sh
• external_encoder/dinov2/dinov2_layers/drop_path.py
• config/dataset/dataset_apdepth_train_s1.yaml
• external_encoder/dinov2/dinov2.py

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