Make sure api_v2 loads fast

src/batdetect2/api_v2.py

@@ -1,68 +1,46 @@
+from __future__ import annotations
+
 from pathlib import Path
-from typing import Literal, Sequence, cast
+from typing import TYPE_CHECKING, Literal
 
 import numpy as np
-import torch
 from soundevent import data
-from soundevent.audio.files import get_audio_files
 
-from batdetect2.audio import AudioConfig, AudioLoader, build_audio_loader
+if TYPE_CHECKING:
-from batdetect2.config import BatDetect2Config
+    from collections.abc import Sequence
-from batdetect2.data import Dataset, load_dataset_from_config
+
-from batdetect2.evaluate import (
+    import torch
-    DEFAULT_EVAL_DIR,
+
-    EvaluationConfig,
+    from batdetect2.audio import AudioConfig, AudioLoader
-    EvaluatorProtocol,
+    from batdetect2.config import BatDetect2Config
-    build_evaluator,
+    from batdetect2.data import Dataset
-    run_evaluate,
+    from batdetect2.evaluate import EvaluationConfig, EvaluatorProtocol
-    save_evaluation_results,
+    from batdetect2.inference import InferenceConfig
-)
+    from batdetect2.logging import AppLoggingConfig, LoggerConfig
-from batdetect2.inference import (
+    from batdetect2.models import Model, ModelConfig
-    InferenceConfig,
+    from batdetect2.outputs import (
-    process_file_list,
-    run_batch_inference,
-)
-from batdetect2.logging import (
-    DEFAULT_LOGS_DIR,
-    AppLoggingConfig,
-    LoggerConfig,
-)
-from batdetect2.models import (
-    Model,
-    ModelConfig,
-    build_model,
-    build_model_with_new_targets,
-)
-from batdetect2.models.detectors import Detector
-from batdetect2.outputs import (
         OutputFormatConfig,
         OutputFormatterProtocol,
         OutputsConfig,
         OutputTransformProtocol,
-    build_output_formatter,
+    )
-    build_output_transform,
+    from batdetect2.postprocess import (
-    get_output_formatter,
-)
-from batdetect2.postprocess import (
         ClipDetections,
         Detection,
         PostprocessorProtocol,
-    build_postprocessor,
+    )
-)
+    from batdetect2.preprocess import PreprocessorProtocol
-from batdetect2.preprocess import PreprocessorProtocol, build_preprocessor
+    from batdetect2.targets import (
-from batdetect2.targets import (
         ROIMapperProtocol,
         TargetConfig,
         TargetProtocol,
-    build_roi_mapping,
+    )
-    build_targets,
+    from batdetect2.train import TrainingConfig
-)
+
-from batdetect2.train import (
+
-    DEFAULT_CHECKPOINT_DIR,
+DEFAULT_CHECKPOINT_DIR: Path = Path("outputs") / "checkpoints"
-    TrainingConfig,
+DEFAULT_LOGS_DIR: Path = Path("outputs") / "logs"
-    load_model_from_checkpoint,
+DEFAULT_EVAL_DIR: Path = Path("outputs") / "evaluations"
-    run_train,
-)
 
 
 class BatDetect2API:
@@ -109,6 +87,8 @@ class BatDetect2API:
         path: data.PathLike,
         base_dir: data.PathLike | None = None,
     ) -> Dataset:
+        from batdetect2.data import load_dataset_from_config
+
         return load_dataset_from_config(path, base_dir=base_dir)
 
     def train(
@@ -128,6 +108,8 @@ class BatDetect2API:
         train_config: TrainingConfig | None = None,
         logger_config: LoggerConfig | None = None,
     ):
+        from batdetect2.train import run_train
+
         run_train(
             train_annotations=train_annotations,
             val_annotations=val_annotations,
@@ -172,6 +154,7 @@ class BatDetect2API:
         logger_config: LoggerConfig | None = None,
     ) -> "BatDetect2API":
         """Fine-tune the model with trainable-parameter selection."""
+        from batdetect2.train import run_train
 
         self._set_trainable_parameters(trainable)
 
@@ -211,6 +194,8 @@ class BatDetect2API:
         outputs_config: OutputsConfig | None = None,
         logger_config: LoggerConfig | None = None,
     ) -> tuple[dict[str, float], list[ClipDetections]]:
+        from batdetect2.evaluate import run_evaluate
+
         return run_evaluate(
             self.model,
             test_annotations,
@@ -235,6 +220,8 @@ class BatDetect2API:
         predictions: Sequence[ClipDetections],
         output_dir: data.PathLike | None = None,
     ):
+        from batdetect2.evaluate import save_evaluation_results
+
         clip_evals = self.evaluator.evaluate(
             annotations,
             predictions,
@@ -307,6 +294,8 @@ class BatDetect2API:
         self,
         audio: np.ndarray,
     ) -> torch.Tensor:
+        import torch
+
         tensor = torch.tensor(audio).unsqueeze(0)
         return self.preprocessor(tensor)
 
@@ -316,6 +305,8 @@ class BatDetect2API:
         batch_size: int | None = None,
         detection_threshold: float | None = None,
     ) -> ClipDetections:
+        from batdetect2.postprocess import ClipDetections
+
         recording = data.Recording.from_file(audio_file, compute_hash=False)
 
         predictions = self.process_files(
@@ -382,6 +373,8 @@ class BatDetect2API:
         audio_dir: data.PathLike,
         detection_threshold: float | None = None,
     ) -> list[ClipDetections]:
+        from soundevent.audio.files import get_audio_files
+
         files = list(get_audio_files(audio_dir))
         return self.process_files(
             files,
@@ -398,6 +391,8 @@ class BatDetect2API:
         output_config: OutputsConfig | None = None,
         detection_threshold: float | None = None,
     ) -> list[ClipDetections]:
+        from batdetect2.inference import process_file_list
+
         return process_file_list(
             self.model,
             audio_files,
@@ -424,6 +419,8 @@ class BatDetect2API:
         output_config: OutputsConfig | None = None,
         detection_threshold: float | None = None,
     ) -> list[ClipDetections]:
+        from batdetect2.inference import run_batch_inference
+
         return run_batch_inference(
             self.model,
             clips,
@@ -448,6 +445,8 @@ class BatDetect2API:
         format: str | None = None,
         config: OutputFormatConfig | None = None,
     ):
+        from batdetect2.outputs import get_output_formatter
+
         formatter = self.formatter
 
         if format is not None or config is not None:
@@ -467,6 +466,8 @@ class BatDetect2API:
         format: str | None = None,
         config: OutputFormatConfig | None = None,
     ) -> list[object]:
+        from batdetect2.outputs import get_output_formatter
+
         formatter = self.formatter
 
         if format is not None or config is not None:
@@ -484,6 +485,17 @@ class BatDetect2API:
         cls,
         config: BatDetect2Config,
     ) -> "BatDetect2API":
+        from batdetect2.audio import build_audio_loader
+        from batdetect2.evaluate import build_evaluator
+        from batdetect2.models import build_model
+        from batdetect2.outputs import (
+            build_output_formatter,
+            build_output_transform,
+        )
+        from batdetect2.postprocess import build_postprocessor
+        from batdetect2.preprocess import build_preprocessor
+        from batdetect2.targets import build_roi_mapping, build_targets
+
         targets = build_targets(config=config.model.targets)
         roi_mapper = build_roi_mapping(config=config.model.targets.roi)
 
@@ -563,6 +575,21 @@ class BatDetect2API:
         outputs_config: OutputsConfig | None = None,
         logging_config: AppLoggingConfig | None = None,
     ) -> "BatDetect2API":
+        from batdetect2.audio import AudioConfig, build_audio_loader
+        from batdetect2.evaluate import EvaluationConfig, build_evaluator
+        from batdetect2.inference import InferenceConfig
+        from batdetect2.logging import AppLoggingConfig
+        from batdetect2.models import build_model_with_new_targets
+        from batdetect2.outputs import (
+            OutputsConfig,
+            build_output_formatter,
+            build_output_transform,
+        )
+        from batdetect2.postprocess import build_postprocessor
+        from batdetect2.preprocess import build_preprocessor
+        from batdetect2.targets import build_roi_mapping, build_targets
+        from batdetect2.train import TrainingConfig, load_model_from_checkpoint
+
         model, model_config = load_model_from_checkpoint(path)
 
         audio_config = audio_config or AudioConfig(
@@ -645,7 +672,7 @@ class BatDetect2API:
         self,
         trainable: Literal["all", "heads", "classifier_head", "bbox_head"],
     ) -> None:
-        detector = cast(Detector, self.model.detector)
+        detector = self.model.detector
 
         for parameter in detector.parameters():
             parameter.requires_grad = False

tests/test_api_v2/test_api_v2.py

@@ -8,12 +8,10 @@ import torch
 from soundevent.geometry import compute_bounds
 
 from batdetect2.api_v2 import BatDetect2API
-from batdetect2.audio import AudioConfig
 from batdetect2.config import BatDetect2Config
-from batdetect2.inference import InferenceConfig
 from batdetect2.models.detectors import Detector
 from batdetect2.models.heads import ClassifierHead
-from batdetect2.train import TrainingConfig, load_model_from_checkpoint
+from batdetect2.train import load_model_from_checkpoint
 from batdetect2.train.lightning import build_training_module
 
 
@@ -452,51 +450,3 @@ def test_detection_threshold_override_changes_spectrogram_results(
     )
 
     assert len(strict_detections) <= len(default_detections)
-
-
-def test_per_call_overrides_are_ephemeral(monkeypatch) -> None:
-    """User story: call-level overrides do not mutate resolved defaults."""
-
-    api = BatDetect2API.from_config(BatDetect2Config())
-
-    override_inference = InferenceConfig.model_validate(
-        {"loader": {"batch_size": 7}}
-    )
-    override_audio = AudioConfig.model_validate({"samplerate": 384000})
-    override_train = TrainingConfig.model_validate(
-        {"trainer": {"max_epochs": 2}}
-    )
-
-    captured_process: dict[str, object] = {}
-    captured_train: dict[str, object] = {}
-
-    def fake_process_file_list(*args, **kwargs):
-        captured_process["inference_config"] = kwargs["inference_config"]
-        captured_process["audio_config"] = kwargs["audio_config"]
-        return []
-
-    def fake_run_train(*args, **kwargs):
-        captured_train["train_config"] = kwargs["train_config"]
-        captured_train["audio_config"] = kwargs["audio_config"]
-        captured_train["model_config"] = kwargs["model_config"]
-        return None
-
-    monkeypatch.setattr(
-        "batdetect2.api_v2.process_file_list", fake_process_file_list
-    )
-    monkeypatch.setattr("batdetect2.api_v2.run_train", fake_run_train)
-
-    api.process_files(
-        [], inference_config=override_inference, audio_config=override_audio
-    )
-    api.train([], train_config=override_train, audio_config=override_audio)
-
-    assert captured_process["inference_config"] is override_inference
-    assert captured_process["audio_config"] is override_audio
-    assert captured_train["train_config"] is override_train
-    assert captured_train["audio_config"] is override_audio
-    assert captured_train["model_config"] is api.model_config
-
-    assert api.inference_config.loader.batch_size != 7
-    assert api.audio_config.samplerate != 384000
-    assert api.train_config.trainer.max_epochs != 2