LR Scheduler takes num of total batches

src/batdetect2/evaluate/match.py

@@ -1,6 +1,6 @@
 from collections.abc import Callable, Iterable, Mapping
 from dataclasses import dataclass, field
-from typing import List, Literal, Optional, Tuple
+from typing import List, Literal, Optional, Protocol, Tuple
 
 import numpy as np
 from soundevent import data
@@ -23,6 +23,16 @@ MatchingGeometry = Literal["bbox", "interval", "timestamp"]
 """The geometry representation to use for matching."""
 
 
+class AffinityFunction(Protocol):
+    def __call__(
+        self,
+        geometry1: data.Geometry,
+        geometry2: data.Geometry,
+        time_buffer: float = 0.01,
+        freq_buffer: float = 1000,
+    ) -> float: ...
+
+
 class MatchConfig(BaseConfig):
     """Configuration for matching geometries.
 
@@ -74,6 +84,65 @@ _geometry_cast_functions: Mapping[
 }
 
 
+def _timestamp_affinity(
+    geometry1: data.Geometry,
+    geometry2: data.Geometry,
+    time_buffer: float = 0.01,
+    freq_buffer: float = 1000,
+) -> float:
+    assert isinstance(geometry1, data.TimeStamp)
+    assert isinstance(geometry2, data.TimeStamp)
+
+    start_time1 = geometry1.coordinates
+    start_time2 = geometry2.coordinates
+
+    a = min(start_time1, start_time2)
+    b = max(start_time1, start_time2)
+
+    if b - a >= 2 * time_buffer:
+        return 0
+
+    intersection = a - b + 2 * time_buffer
+    union = b - a + 2 * time_buffer
+    return intersection / union
+
+
+def _interval_affinity(
+    geometry1: data.Geometry,
+    geometry2: data.Geometry,
+    time_buffer: float = 0.01,
+    freq_buffer: float = 1000,
+) -> float:
+    assert isinstance(geometry1, data.TimeInterval)
+    assert isinstance(geometry2, data.TimeInterval)
+
+    start_time1, end_time1 = geometry1.coordinates
+    start_time2, end_time2 = geometry1.coordinates
+
+    start_time1 -= time_buffer
+    start_time2 -= time_buffer
+    end_time1 += time_buffer
+    end_time2 += time_buffer
+
+    intersection = max(
+        0, min(end_time1, end_time2) - max(start_time1, start_time2)
+    )
+    union = (
+        (end_time1 - start_time1) + (end_time2 - start_time2) - intersection
+    )
+
+    if union == 0:
+        return 0
+
+    return intersection / union
+
+
+_affinity_functions: Mapping[MatchingGeometry, AffinityFunction] = {
+    "timestamp": _timestamp_affinity,
+    "interval": _interval_affinity,
+}
+
+
 def match_geometries(
     source: List[data.Geometry],
     target: List[data.Geometry],
@@ -81,6 +150,10 @@ def match_geometries(
     scores: Optional[List[float]] = None,
 ) -> Iterable[Tuple[Optional[int], Optional[int], float]]:
     geometry_cast = _geometry_cast_functions[config.geometry]
+    affinity_function = _affinity_functions.get(
+        config.geometry,
+        compute_affinity,
+    )
 
     if config.strategy == "optimal":
         return optimal_match(
@@ -98,6 +171,7 @@ def match_geometries(
             time_buffer=config.time_buffer,
             freq_buffer=config.frequency_buffer,
             affinity_threshold=config.affinity_threshold,
+            affinity_function=affinity_function,
             scores=scores,
         )
 
@@ -111,6 +185,7 @@ def greedy_match(
     target: List[data.Geometry],
     scores: Optional[List[float]] = None,
     affinity_threshold: float = 0.5,
+    affinity_function: AffinityFunction = compute_affinity,
     time_buffer: float = 0.001,
     freq_buffer: float = 1000,
 ) -> Iterable[Tuple[Optional[int], Optional[int], float]]:
@@ -168,7 +243,7 @@ def greedy_match(
 
         affinities = np.array(
             [
-                compute_affinity(
+                affinity_function(
                     source_geometry,
                     target_geometry,
                     time_buffer=time_buffer,

src/batdetect2/train/train.py

@@ -14,7 +14,7 @@ from batdetect2.evaluate.metrics import (
     ClassificationMeanAveragePrecision,
     DetectionAveragePrecision,
 )
-from batdetect2.models import build_model
+from batdetect2.models import Model, build_model
 from batdetect2.train.augmentations import (
     RandomExampleSource,
     build_augmentations,
@@ -55,17 +55,13 @@ def train(
 ):
     config = config or FullTrainingConfig()
 
-    if model_path is not None:
-        logger.debug("Loading model from: {path}", path=model_path)
-        module = TrainingModule.load_from_checkpoint(model_path)  # type: ignore
-    else:
-        module = build_training_module(config)
+    model = build_model(config=config)
 
-    trainer = build_trainer(config, targets=module.model.targets)
+    trainer = build_trainer(config, targets=model.targets)
 
     train_dataloader = build_train_loader(
         train_examples,
-        preprocessor=module.model.preprocessor,
+        preprocessor=model.preprocessor,
         config=config.train,
         num_workers=train_workers,
     )
@@ -73,7 +69,7 @@ def train(
     val_dataloader = (
         build_val_loader(
             val_examples,
-            preprocessor=module.model.preprocessor,
+            preprocessor=model.preprocessor,
             config=config.train,
             num_workers=val_workers,
         )
@@ -81,6 +77,16 @@ def train(
         else None
     )
 
+    if model_path is not None:
+        logger.debug("Loading model from: {path}", path=model_path)
+        module = TrainingModule.load_from_checkpoint(model_path)  # type: ignore
+    else:
+        module = build_training_module(
+            model,
+            config,
+            batches_per_epoch=len(train_dataloader),
+        )
+
     logger.info("Starting main training loop...")
     trainer.fit(
         module,
@@ -90,14 +96,17 @@ def train(
     logger.info("Training complete.")
 
 
-def build_training_module(config: FullTrainingConfig) -> TrainingModule:
-    model = build_model(config=config)
+def build_training_module(
+    model: Model,
+    config: FullTrainingConfig,
+    batches_per_epoch: int,
+) -> TrainingModule:
     loss = build_loss(config=config.train.loss)
     return TrainingModule(
         model=model,
         loss=loss,
         learning_rate=config.train.learning_rate,
-        t_max=config.train.t_max,
+        t_max=config.train.t_max * batches_per_epoch,
     )