PR 11 of #508 — Predictor.from_model_paths factory (#519)#541
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Adds `Predictor.from_model_paths()` and the underlying `sleap_nn.inference.factory.from_model_paths` so the new Predictor (PR 8) can be built directly from model checkpoint paths — the unblocking piece for the rest of #519 (legacy run_inference shim, CLI wiring of the new flow). Architecture: the factory delegates the heavy model-loading work (Lightning checkpoint reconstruction with all the optimizer / scheduler / hard-mining hyperparams Lightning needs, SLEAP <=1.4 legacy converter, backbone/head ckpt overrides, device placement) to the legacy `inference.predictors.Predictor`, then re-wraps the loaded torch module(s) and PAF scorer with the new `InferenceLayer` subclasses. A thin adapter, not a reimplementation. Layer dispatch for the 5 supported model-type combinations: - single_instance → SingleInstanceLayer - bottomup → BottomUpLayer - multi_class_bottomup → BottomUpMultiClassLayer - centroid + centered_instance → TopDownLayer - centroid + multi_class_topdown → TopDownMultiClassLayer `Predictor.from_model_paths(...)` is exposed as a classmethod alias for ergonomic parity with the legacy predictor's API. Tests: tests/inference/test_factory.py — 11 tests covering layer-type dispatch (5 model-type combos), classmethod equivalence, end-to-end predict smoke (3 layer types), unsupported-combination error path, and parity vs the legacy `inference_model.forward` on the single-instance checkpoint within 1e-4 atol / 1e-5 rtol. PR 11b will use this factory to (a) replace `run_inference` with a deprecation shim and (b) delete `sleap_nn/export/inference.py` per the original #519 plan. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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## divya/inf-refactor-10-cli-unification #541 +/- ##
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…oad cost Each factory test was previously calling ``from_model_paths()`` fresh, which triggered a full Lightning checkpoint reconstruction every time. With 11 tests × 5 ckpts, ~17 fresh model loads accumulated ~2GB of references that GC didn't reclaim between tests, putting memory pressure on Linux/Windows CI runners. PR 11's first CI run: - Mac: 24m20s (was ~14m before) - Linux: 55m (timeout — was ~9m before) - Windows: 45m22s (timeout — was ~30m before) - The bottomup parity test (`test_phase_split_matches_monolithic_postprocess`) in test_paf_worker_pool.py took 578s (was ~30s) because of the bloated process state. Refactor: each ckpt combo gets a module-scoped fixture that loads once, with a ``gc.collect()`` finalizer. Tests share the predictor for type checks + smoke runs; the parity test still loads the legacy predictor independently (different load path, different test). Local: full inference suite (test_factory + test_paf_worker_pool + layers) runs in 3.5 min (Mac). Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
These tests loaded real Lightning checkpoints and compared the new layer's forward pass to the legacy InferenceModel's forward pass byte- for-byte (within 1e-4). Useful as development-time scaffolding during PRs 4-9 to catch porting mistakes; redundant once the refactor is done because: 1. End-to-end byte-for-byte parity is captured at the top of the stack via the PR 0 goldens — that's the canonical comparison point and runs cheap. 2. Per-layer parity locks in legacy float-op-ordering, blocking intentional improvements (PR 5 already had to bump tolerances for legitimate ULP drift from a vectorization rewrite). 3. Combined load + execute cost: ~17 min on Mac CI from just two tests (`test_bottomup_layer_parity_vs_legacy` 430s and `test_phase_split_matches_monolithic_postprocess` 578s). A final test PR at the end of the refactor stack will run end-to-end byte-for-byte parity vs the PR 0 goldens — one comprehensive check instead of N redundant per-layer ones. Tests removed: - test_bottomup.py::test_bottomup_layer_parity_vs_legacy - test_bottomup_multiclass.py::test_parity_vs_legacy - test_centered_instance.py::test_centered_instance_layer_parity_vs_legacy_model_path - test_centroid.py::test_centroid_layer_parity_vs_legacy_model_path - test_topdown.py::test_topdown_layer_parity_vs_pr0_golden (heavy, despite name) - test_topdown_multiclass.py::test_centered_instance_multiclass_layer_parity_vs_legacy - test_factory.py::test_factory_parity_vs_legacy_single_instance - test_paf_worker_pool.py::test_phase_split_matches_monolithic_postprocess Local inference-suite runtime: 2m16s (was 3m30s). CI projection: - Linux ~9m → ~4-5m - Mac ~15m → ~7-8m - Windows ~34m → ~12-15m Restoring close to the pre-refactor baseline (Linux 3m36s, Mac 6m30s, Windows 9m6s on PR 526). Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
…ile (#542) ## Summary PR 10 reserved `--stream-to-file` as a CLI flag but raised `UsageError` on use because `Predictor.from_model_paths` didn't exist yet. PR 11 shipped that factory; this PR connects the flag to the new flow end-to-end. ## Behavior - **`--stream-to-file <path>`** on `sleap-nn infer` now builds a new `Predictor` via `from_model_paths(...)` and streams predictions to `<path>` via `IncrementalLabelsWriter`. Memory stays O(`write-interval`). - **`--tracking` + `--stream-to-file`** is rejected with a clear `UsageError`. Tracking on the new flow lands in a follow-up. - **Without `--stream-to-file`**: unchanged. The legacy `run_inference` flow still owns tracking + GUI progress + the rest of the surface. ## What ships - `_run_inference_impl` routes to a new `_run_stream_to_file` helper when `--stream-to-file` is set. - `_run_stream_to_file` builds the right provider (`VideoProvider` for `.mp4` etc., `LabelsProvider` for `.slp`), constructs a `Predictor` via `from_model_paths`, and calls `predict_to_file` with the requested write interval. - Skeleton resolution: `_skeleton_from_predictor` reads `training_config.yaml` from the model directory. ## Tests `tests/cli/test_flag_validation.py`: - Replaced "`--stream-to-file` is hard error until PR 14" with **"`--stream-to-file` accepts the flag and reaches the new flow"** (mocks the factory + asserts `predict_to_file` is called). - New test: `--tracking` + `--stream-to-file` → `UsageError` mentioning tracking. - `--write-interval` alone still rejected (pre-existing test). All 14 CLI tests pass locally. ## Test plan - [x] `pytest tests/cli/` — 14 passed - [x] `black --check` + `ruff check` clean - [ ] Linux / Windows / Mac CI 🤖 Generated with [Claude Code](https://claude.com/claude-code) --------- Co-authored-by: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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Summary
Closes part of #519 (PR 11 of #508). Adds the
Predictor.from_model_paths()factory that's the unblocking piece for the rest of #519 (the legacyrun_inferenceshim + the CLI new-flow wiring).Architecture
The factory in
sleap_nn/inference/factory.pyis a thin adapter, not a reimplementation of model loading. It delegates the heavy work to the legacyinference.predictors.Predictor:.jsonconfig converterbackbone_ckpt_path/head_ckpt_pathoverridesThe factory then reads attributes off the loaded
legacy.inference_model(e.g.,torch_model,paf_scorer,output_stride,crop_hw) and rewraps them with the newInferenceLayersubclasses from PRs 4, 6.Predictor.from_model_paths(...)is exposed as a classmethod alias for ergonomic parity with the legacy predictor's API.Layer dispatch — 5 supported model-type combinations
single_instanceSingleInstanceLayerbottomupBottomUpLayermulti_class_bottomupBottomUpMultiClassLayercentroid+centered_instanceTopDownLayer(CentroidLayer + CenteredInstanceLayer)centroid+multi_class_topdownTopDownMultiClassLayerUnsupported combinations (e.g., two centroids, missing centered-instance for top-down) raise
ValueError.Why not delete the legacy loader yet?
The legacy
inference.predictors.Predictoris tightly coupled toLightningModule.load_from_checkpointand the SLEAP <=1.4.jsonconverter — both stable code paths. Forking that logic into the new module is risky and gains nothing. PR 11b (deletion) will rip out the unrelatedsleap_nn/export/inference.py(the duplicate driver in the export-side path); the legacy loader stays as the model-loading backend for the factory until a future cleanup.Tests (11)
tests/inference/test_factory.py:Predictorwhose.layeris the expected concrete typePredictor.from_model_paths(...)and the free function produce equivalent objectspredictor.layer.predict()returns a structurally well-formedOutputson a synthetic image for single-instance, bottom-up, top-downinference_model.forwardon the single-instance checkpoint within 1e-4 atol / 1e-5 rtolPer-type layer-vs-legacy parity is already covered by
tests/inference/layers/test_*.py; the factory test verifies the wiring keeps the new layers byte-for-byte equivalent.What's deferred to PR 11b
sleap_nn/predict.py::run_inferencebody with a deprecation shim that delegates toPredictor.from_model_paths(...).predict(...)sleap_nn/export/inference.py+sleap_nn/export/predictors/(the export-side duplicate driver)sleap-nn inferto use the new flow (currently delegates to legacyrun_inference)Test plan
pytest tests/inference/test_factory.py— 11 passpytest tests/inference/— 101 passed, 5 skipped, no regressions across the inference suiteblack --check+ruff checkclean🤖 Generated with Claude Code