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Included review availability: Your plan provides up to 8 included reviews per hour; 7 remain after this review. 📝 WalkthroughWalkthroughThe documentation adds a custom DINO augmentation tutorial to the navigation. The tutorial demonstrates configurable policies, multi-view execution, policy serialization, and seeded diagnostic reproduction with sampled parameters. ChangesDINO augmentation tutorial
Priority: ⬇️ Low Estimated code review effort: 2 (Simple) | ~10 minutes Change: Other Merge Risk: ⚪ Minimal · up to The tutorial’s seeded diagnostic and policy-serialization examples are in place, with no current actionable merge risk identified. 🚥 Pre-merge checks | ✅ 4 | ❌ 1❌ Failed checks (1 warning)
✅ Passed checks (4 passed)
✨ Finishing Touches🧪 Generate unit tests (beta)
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Document role-specific global and local view policies with the current MultiViewTransform contract. Show independent repeated local sampling and policy serialization.
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In `@docs/source/tutorials_source/package/tutorial_custom_dino_augmentations.py`:
- Line 196: Add a separate diagnostic example near the transform invocation that
calls each A.Compose policy via run_with_trace(image=..., invocation_seed=...),
exposing the applied operations for inspection and reproducibility. Leave
MultiViewTransform and its existing views = transform(image) training path
unchanged.
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docs/source/index.rstdocs/source/tutorials_source/package/tutorial_custom_dino_augmentations.py
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Trace each view role with an explicit invocation seed and reproduce a diagnostic tensor from its serialized policy. Include the required plotting extra and render tutorial explanations as prose.
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Yes, @gabrielfruet, this is ready for review now. I added the missing seeded diagnostics and verified the complete tutorial, policy reproduction, and Lightly dataset integration. The focused tests and docs build pass. The GitHub Actions runs still need maintainer approval. |
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Closes #2047
Description
Adds a Sphinx Gallery tutorial for customizing DINO crop and photometric policies with optional AlbumentationsX. It uses Lightly's current
MultiViewTransformto produce two global and six local tensors, then shows dataset integration, policy serialization, and seeded tracing of sampled transforms. A restored policy and the same invocation seed reproduce the diagnostic view exactly.AlbumentationsX stays outside Lightly's runtime dependencies. The installation instructions include the Matplotlib extra used by the tutorial and document Python 3.10+, the user-selected PyTorch build, and AlbumentationsX's AGPL-3.0-only license.
Tests
Executed the complete tutorial with
albumentationsx==2.4.3; verified tensor and sampled-parameter reproduction for all three restored policies, fresh training samples, and an eight-viewLightlyDatasetbatch. The focused transform tests passed (3 tests), along with full-tree Ruff checks, all hooks on the PR files, and Sphinx documentation and tutorial execution.Repository-wide pre-commit still reports the existing codespell finding in unchanged
lightly/loss/frossl_loss.py:90(fro). The docs build also has existing cross-reference warnings outside this tutorial.Documentation
.rstfiles).The generated tutorial and PDF preview were inspected visually. OpenCV and torchvision can differ pixel-wise; the tutorial recommends evaluating policy changes on the intended downstream task.
Adds a standalone Sphinx Gallery tutorial for customizing DINO global and local views with optional AlbumentationsX. The tutorial uses
MultiViewTransform, supports configurable crop and photometric policies, serializes policies withA.to_dict(), and reproduces diagnostic views withrun_with_trace(). Adds the tutorial to the documentation index without changing runtime dependencies or existing documentation.