feat: persist target configs in checkpoints

src/batdetect2/api_v2.py

@@ -610,17 +610,15 @@ class BatDetect2API:
         from batdetect2.postprocess import build_postprocessor
         from batdetect2.preprocess import build_preprocessor
         from batdetect2.targets import (
-            build_default_target_config,
             build_roi_mapping,
             build_targets,
             check_target_compatibility,
         )
-        from batdetect2.train import (
-            TrainingConfig,
-            load_model_from_checkpoint,
-        )
+        from batdetect2.train import TrainingConfig, load_model_from_checkpoint
 
-        model, model_config = load_model_from_checkpoint(path)
+        model, configs = load_model_from_checkpoint(path)
+
+        model_config = configs.model
 
         audio_config = audio_config or AudioConfig(
             samplerate=model_config.samplerate,
@@ -630,9 +628,7 @@ class BatDetect2API:
         inference_config = inference_config or InferenceConfig()
         outputs_config = outputs_config or OutputsConfig()
         logging_config = logging_config or AppLoggingConfig()
-        targets_config = targets_config or build_default_target_config(
-            class_names=model.class_names
-        )
+        targets_config = targets_config or configs.targets
 
         targets = build_targets(config=targets_config)
         roi_mapper = build_roi_mapping(config=targets_config.roi)

src/batdetect2/models/__init__.py

@@ -211,7 +211,7 @@ class Model(torch.nn.Module):
 
 
 def build_model(
-    config: ModelConfig | None = None,
+    config: ModelConfig | dict | None = None,
     class_names: list[str] | None = None,
     dimension_names: list[str] | None = None,
     preprocessor: PreprocessorProtocol | None = None,
@@ -257,6 +257,9 @@ def build_model(
 
     config = config or ModelConfig()
 
+    if isinstance(config, dict):
+        config = ModelConfig.model_validate(config)
+
     if class_names is None:
         raise ValueError("class_names must be provided when building a model.")
 

src/batdetect2/train/lightning.py

@@ -1,15 +1,21 @@
+from dataclasses import dataclass
+
 import lightning as L
 from soundevent.data import PathLike
 
 from batdetect2.models import Model, ModelConfig, build_model
 from batdetect2.models.types import ModelOutput
+from batdetect2.targets import TargetConfig
 from batdetect2.train.config import TrainingConfig
 from batdetect2.train.losses import build_loss
 from batdetect2.train.optimizers import build_optimizer
 from batdetect2.train.schedulers import build_scheduler
 from batdetect2.train.types import LossProtocol, TrainExample
 
-__all__ = ["TrainingModule"]
+__all__ = [
+    "TrainingModule",
+    "load_model_from_checkpoint",
+]
 
 
 class TrainingModule(L.LightningModule):
@@ -19,6 +25,7 @@ class TrainingModule(L.LightningModule):
     def __init__(
         self,
         model_config: dict | None = None,
+        targets_config: dict | None = None,
         class_names: list[str] | None = None,
         dimension_names: list[str] | None = None,
         train_config: dict | None = None,
@@ -29,9 +36,11 @@ class TrainingModule(L.LightningModule):
 
         self.save_hyperparameters(ignore=["model", "loss"], logger=False)
 
-        self.model_config = ModelConfig.model_validate(model_config or {})
+        self.model_config: dict = model_config or {}
+        self.targets_config: dict = targets_config or {}
         self.class_names = list(class_names or [])
         self.dimension_names = list(dimension_names or [])
+
         self.train_config = TrainingConfig.model_validate(train_config or {})
 
         if loss is None:
@@ -113,9 +122,16 @@ class TrainingModule(L.LightningModule):
         }
 
 
+@dataclass
+class StoredConfig:
+    model: ModelConfig
+    targets: TargetConfig
+    train: TrainingConfig
+
+
 def load_model_from_checkpoint(
     path: PathLike,
-) -> tuple[Model, ModelConfig]:
+) -> tuple[Model, StoredConfig]:
     """Load a model and its configuration from a Lightning checkpoint.
 
     Parameters
@@ -131,11 +147,19 @@ def load_model_from_checkpoint(
         describes its architecture, preprocessing, and postprocessing.
     """
     module = TrainingModule.load_from_checkpoint(path)  # type: ignore
-    return module.model, module.model_config
+    training_config = TrainingConfig.model_validate(module.train_config)
+    model_config = ModelConfig.model_validate(module.model_config)
+    targets_config = TargetConfig.model_validate(module.targets_config)
+    return module.model, StoredConfig(
+        model=model_config,
+        targets=targets_config,
+        train=training_config,
+    )
 
 
 def build_training_module(
     model_config: ModelConfig | None = None,
+    targets_config: TargetConfig | dict | None = None,
     class_names: list[str] | None = None,
     dimension_names: list[str] | None = None,
     train_config: TrainingConfig | None = None,
@@ -147,10 +171,16 @@ def build_training_module(
     if train_config is None:
         train_config = TrainingConfig()
 
+    if targets_config is None:
+        targets_config = TargetConfig()
+
+    targets_config = TargetConfig.model_validate(targets_config)
+
     return TrainingModule(
         model_config=model_config.model_dump(mode="json"),
+        targets_config=targets_config.model_dump(mode="json"),
+        train_config=train_config.model_dump(mode="json"),
         class_names=class_names,
         dimension_names=dimension_names,
-        train_config=train_config.model_dump(mode="json"),
         model=model,
     )

src/batdetect2/train/train.py

@@ -81,7 +81,10 @@ def run_train(
                 "model."
             )
 
-    targets = targets or build_targets(config=targets_config)
+    if targets is None:
+        targets = build_targets(config=targets_config)
+    else:
+        targets_config = TargetConfig.model_validate(targets.get_config())
 
     roi_mapper = roi_mapper or build_roi_mapping(config=targets_config.roi)
 
@@ -132,6 +135,7 @@ def run_train(
 
     module = build_training_module(
         model_config=model_config,
+        targets_config=targets_config,
         class_names=targets.class_names,
         dimension_names=roi_mapper.dimension_names,
         train_config=train_config,

tests/conftest.py

@@ -472,6 +472,7 @@ def tiny_checkpoint_path(
     tmp_path: Path,
 ) -> Path:
     module = build_training_module(
+        targets_config=sample_targets.get_config(),
         class_names=sample_targets.class_names,
         dimension_names=sample_roi_mapper.dimension_names,
     )

tests/test_api_v2/test_api_v2.py

@@ -225,6 +225,7 @@ def test_user_can_load_checkpoint_and_finetune(
     )
     module = build_training_module(
         model_config=api.model_config,
+        targets_config=example_targets_config,
         class_names=api.targets.class_names,
         dimension_names=api.roi_mapper.dimension_names,
     )
@@ -273,6 +274,7 @@ def test_checkpoint_with_same_targets_config_keeps_heads_unchanged(
     )
     module = build_training_module(
         model_config=source_api.model_config,
+        targets_config=example_targets_config,
         class_names=source_api.targets.class_names,
         dimension_names=source_api.roi_mapper.dimension_names,
     )

tests/test_train/test_lightning.py

@@ -73,7 +73,7 @@ def test_can_save_checkpoint(
     torch.testing.assert_close(output1, output2, rtol=0, atol=0)
 
 
-def test_load_model_from_checkpoint_returns_model_and_config(
+def test_load_model_from_checkpoint_returns_model_and_configs(
     tmp_path: Path,
 ):
     input_model_config = ModelConfig(samplerate=192_000)
@@ -95,12 +95,18 @@ def test_load_model_from_checkpoint_returns_model_and_config(
     trainer.strategy.connect(module)
     trainer.save_checkpoint(path)
 
-    model, loaded_model_config = load_model_from_checkpoint(path)
+    model, loaded_configs = load_model_from_checkpoint(path)
 
     assert model is not None
-    assert loaded_model_config.model_dump(
+    assert loaded_configs.model.model_dump(
         mode="json"
     ) == expected_model_config.model_dump(mode="json")
+    assert loaded_configs.targets.model_dump(
+        mode="json"
+    ) == targets_config.model_dump(mode="json")
+    assert loaded_configs.train.model_dump(
+        mode="json"
+    ) == train_config.model_dump(mode="json")
     assert model.class_names == targets.class_names
     assert model.dimension_names == roi_mapper.dimension_names
 
@@ -135,17 +141,40 @@ def test_checkpoint_stores_train_config_hyperparameters(tmp_path: Path):
     trainer.strategy.connect(module)
     trainer.save_checkpoint(path)
 
-    recovered = TrainingModule.load_from_checkpoint(path)
+    _, recovered_configs = load_model_from_checkpoint(path)
     assert not DeepDiff(
-        recovered.model_config.model_dump(mode="json"),
+        recovered_configs.model.model_dump(mode="json"),
         expected_model_config.model_dump(mode="json"),
     )
     assert not DeepDiff(
-        recovered.train_config.model_dump(mode="json"),
+        recovered_configs.train.model_dump(mode="json"),
         train_config.model_dump(mode="json"),
     )
 
 
+def test_load_model_from_checkpoint_includes_targets_config(tmp_path: Path):
+    targets_config = TargetConfig()
+    targets = build_targets(targets_config)
+    roi_mapper = build_roi_mapping(targets_config.roi)
+    module = build_training_module(
+        model_config=ModelConfig(),
+        targets_config=targets_config,
+        class_names=targets.class_names,
+        dimension_names=roi_mapper.dimension_names,
+        train_config=TrainingConfig(),
+    )
+    trainer = L.Trainer()
+    path = tmp_path / "example.ckpt"
+    trainer.strategy.connect(module)
+    trainer.save_checkpoint(path)
+
+    _, loaded_configs = load_model_from_checkpoint(path)
+
+    assert loaded_configs.targets.model_dump(
+        mode="json"
+    ) == targets_config.model_dump(mode="json")
+
+
 def test_configure_optimizers_uses_train_config_values(tmp_path: Path):
     model_config = ModelConfig()
     expected_model_config = ModelConfig.model_validate(
@@ -179,14 +208,16 @@ def test_configure_optimizers_uses_train_config_values(tmp_path: Path):
     trainer.strategy.connect(module)
     trainer.save_checkpoint(path)
 
-    recovered = TrainingModule.load_from_checkpoint(path)
-    assert recovered.model_config.model_dump(
+    _, recovered_configs = load_model_from_checkpoint(path)
+    assert recovered_configs.model.model_dump(
         mode="json"
     ) == expected_model_config.model_dump(mode="json")
-    assert recovered.train_config.model_dump(
+    assert recovered_configs.train.model_dump(
         mode="json"
     ) == train_config.model_dump(mode="json")
 
+    recovered = TrainingModule.load_from_checkpoint(path)
+
     loaded_optimization_config = recovered.configure_optimizers()
     loaded_optimizer = loaded_optimization_config["optimizer"]
     loaded_scheduler = loaded_optimization_config["lr_scheduler"]["scheduler"]
@@ -201,12 +232,28 @@ def test_api_from_checkpoint_reconstructs_model_config(tmp_path: Path):
     trainer.strategy.connect(module)
     trainer.save_checkpoint(path)
 
+    _, stored_configs = load_model_from_checkpoint(path)
     api = BatDetect2API.from_checkpoint(path)
 
     assert api.model_config.model_dump(
         mode="json"
-    ) == module.model_config.model_dump(mode="json")
-    assert api.audio_config.samplerate == module.model_config.samplerate
+    ) == stored_configs.model.model_dump(mode="json")
+    assert api.audio_config.samplerate == stored_configs.model.samplerate
+
+
+def test_api_from_checkpoint_reconstructs_targets_from_checkpoint(
+    tmp_path: Path,
+) -> None:
+    targets_config = TargetConfig()
+    module = build_default_module(target_config=targets_config)
+    trainer = L.Trainer()
+    path = tmp_path / "example.ckpt"
+    trainer.strategy.connect(module)
+    trainer.save_checkpoint(path)
+
+    api = BatDetect2API.from_checkpoint(path)
+
+    assert api.targets.get_config() == targets_config.model_dump(mode="json")
 
 
 @pytest.mark.slow