Add backbone registry

src/batdetect2/config.py

@@ -12,7 +12,7 @@ from batdetect2.evaluate.config import (
     get_default_eval_config,
 )
 from batdetect2.inference.config import InferenceConfig
-from batdetect2.models.config import BackboneConfig
+from batdetect2.models.backbones import BackboneConfig
 from batdetect2.postprocess.config import PostprocessConfig
 from batdetect2.preprocess.config import PreprocessingConfig
 from batdetect2.targets.config import TargetConfig

src/batdetect2/models/__init__.py

@@ -30,7 +30,13 @@ from typing import List
 
 import torch
 
-from batdetect2.models.backbones import Backbone, build_backbone
+from batdetect2.models.backbones import (
+    UNetBackbone,
+    UNetBackboneConfig,
+    BackboneConfig,
+    build_backbone,
+    load_backbone_config,
+)
 from batdetect2.models.blocks import (
     ConvConfig,
     FreqCoordConvDownConfig,
@@ -43,7 +49,6 @@ from batdetect2.models.bottleneck import (
     BottleneckConfig,
     build_bottleneck,
 )
-from batdetect2.models.config import BackboneConfig, load_backbone_config
 from batdetect2.models.decoder import (
     DEFAULT_DECODER_CONFIG,
     DecoderConfig,
@@ -66,7 +71,7 @@ from batdetect2.typing import (
 
 __all__ = [
     "BBoxHead",
-    "Backbone",
+    "UNetBackbone",
     "BackboneConfig",
     "Bottleneck",
     "BottleneckConfig",
@@ -128,7 +133,7 @@ def build_model(
     from batdetect2.preprocess import build_preprocessor
     from batdetect2.targets import build_targets
 
-    config = config or BackboneConfig()
+    config = config or UNetBackboneConfig()
     targets = targets or build_targets()
     preprocessor = preprocessor or build_preprocessor()
     postprocessor = postprocessor or build_postprocessor(

src/batdetect2/models/backbones.py

@@ -18,16 +18,20 @@ automatic padding to handle input sizes not perfectly divisible by the
 network's total downsampling factor.
 """
 
-from typing import Tuple
+
+from typing import Annotated, Literal, Tuple
 
 import torch
 import torch.nn.functional as F
-from torch import nn
+from pydantic import Field
+
+from batdetect2.core.configs import BaseConfig, load_config
+from batdetect2.core.registries import Registry
+from soundevent import data
+from batdetect2.models.bottleneck import BottleneckConfig, DEFAULT_BOTTLENECK_CONFIG, build_bottleneck
+from batdetect2.models.decoder import DecoderConfig, DEFAULT_DECODER_CONFIG, build_decoder
+from batdetect2.models.encoder import EncoderConfig, DEFAULT_ENCODER_CONFIG, build_encoder
 
-from batdetect2.models.bottleneck import build_bottleneck
-from batdetect2.models.config import BackboneConfig
-from batdetect2.models.decoder import build_decoder
-from batdetect2.models.encoder import build_encoder
 from batdetect2.typing.models import (
     BackboneModel,
     BottleneckProtocol,
@@ -35,13 +39,38 @@ from batdetect2.typing.models import (
     EncoderProtocol,
 )
 
+
+class UNetBackboneConfig(BaseConfig):
+    name: Literal["UNetBackbone"] = "UNetBackbone"
+    input_height: int = 128
+    in_channels: int = 1
+    encoder: EncoderConfig = DEFAULT_ENCODER_CONFIG
+    bottleneck: BottleneckConfig = DEFAULT_BOTTLENECK_CONFIG
+    decoder: DecoderConfig = DEFAULT_DECODER_CONFIG
+    out_channels: int = 32
+
+BackboneConfig = Annotated[
+    UNetBackboneConfig,
+    Field(discriminator="name")
+]
+
+def load_backbone_config(
+    path: data.PathLike,
+    field: str | None = None,
+) -> BackboneConfig:
+    return load_config(path, schema=BackboneConfig, field=field)
+
+backbone_registry: Registry[BackboneModel, []] = Registry("backbone")
+
 __all__ = [
-    "Backbone",
+    "UNetBackbone",
+    "BackboneConfig",
+    "load_backbone_config",
     "build_backbone",
 ]
 
 
-class Backbone(BackboneModel):
+class UNetBackbone(BackboneModel):
     """Encoder-Decoder Backbone Network Implementation.
 
     Combines an Encoder, Bottleneck, and Decoder module sequentially, using
@@ -149,66 +178,46 @@ class Backbone(BackboneModel):
         return x
 
 
-def build_backbone(config: BackboneConfig) -> BackboneModel:
+    @backbone_registry.register(UNetBackboneConfig)
-    """Factory function to build a Backbone from configuration.
+    @staticmethod
-
+    def from_config(config: UNetBackboneConfig) -> BackboneModel:
-    Constructs the `Encoder`, `Bottleneck`, and `Decoder` components based on
+        encoder = build_encoder(
-    the provided `BackboneConfig`, validates their compatibility, and assembles
+            in_channels=config.in_channels,
-    them into a `Backbone` instance.
+            input_height=config.input_height,
-
+            config=config.encoder,
-    Parameters
-    ----------
-    config : BackboneConfig
-        The configuration object detailing the backbone architecture, including
-        input dimensions and configurations for encoder, bottleneck, and
-        decoder.
-
-    Returns
-    -------
-    BackboneModel
-        An initialized `Backbone` module ready for use.
-
-    Raises
-    ------
-    ValueError
-        If sub-component configurations are incompatible
-        (e.g., channel mismatches, decoder output height doesn't match backbone
-        input height).
-    NotImplementedError
-        If an unknown block type is specified in sub-configs.
-    """
-    encoder = build_encoder(
-        in_channels=config.in_channels,
-        input_height=config.input_height,
-        config=config.encoder,
-    )
-
-    bottleneck = build_bottleneck(
-        input_height=encoder.output_height,
-        in_channels=encoder.out_channels,
-        config=config.bottleneck,
-    )
-
-    decoder = build_decoder(
-        in_channels=bottleneck.out_channels,
-        input_height=encoder.output_height,
-        config=config.decoder,
-    )
-
-    if decoder.output_height != config.input_height:
-        raise ValueError(
-            "Invalid configuration: Decoder output height "
-            f"({decoder.output_height}) must match the Backbone input height "
-            f"({config.input_height}). Check encoder/decoder layer "
-            "configurations and input/bottleneck heights."
         )
 
-    return Backbone(
+        bottleneck = build_bottleneck(
-        input_height=config.input_height,
+            input_height=encoder.output_height,
-        encoder=encoder,
+            in_channels=encoder.out_channels,
-        decoder=decoder,
+            config=config.bottleneck,
-        bottleneck=bottleneck,
+        )
-    )
+
+        decoder = build_decoder(
+            in_channels=bottleneck.out_channels,
+            input_height=encoder.output_height,
+            config=config.decoder,
+        )
+
+        if decoder.output_height != config.input_height:
+            raise ValueError(
+                "Invalid configuration: Decoder output height "
+                f"({decoder.output_height}) must match the Backbone input height "
+                f"({config.input_height}). Check encoder/decoder layer "
+                "configurations and input/bottleneck heights."
+            )
+
+        return UNetBackbone(
+            input_height=config.input_height,
+            encoder=encoder,
+            decoder=decoder,
+            bottleneck=bottleneck,
+        )
+
+
+def build_backbone(config: BackboneConfig | None = None) -> BackboneModel:
+    config = config or UNetBackboneConfig()
+    return backbone_registry.build(config)
 
 
 def _pad_adjust(

src/batdetect2/models/config.py

@@ -1,96 +0,0 @@
-from soundevent import data
-
-from batdetect2.core.configs import BaseConfig, load_config
-from batdetect2.models.bottleneck import (
-    DEFAULT_BOTTLENECK_CONFIG,
-    BottleneckConfig,
-)
-from batdetect2.models.decoder import (
-    DEFAULT_DECODER_CONFIG,
-    DecoderConfig,
-)
-from batdetect2.models.encoder import (
-    DEFAULT_ENCODER_CONFIG,
-    EncoderConfig,
-)
-
-__all__ = [
-    "BackboneConfig",
-    "load_backbone_config",
-]
-
-
-class BackboneConfig(BaseConfig):
-    """Configuration for the Encoder-Decoder Backbone network.
-
-    Aggregates configurations for the encoder, bottleneck, and decoder
-    components, along with defining the input and final output dimensions
-    for the complete backbone.
-
-    Attributes
-    ----------
-    input_height : int, default=128
-        Expected height (frequency bins) of the input spectrograms to the
-        backbone. Must be positive.
-    in_channels : int, default=1
-        Expected number of channels in the input spectrograms (e.g., 1 for
-        mono). Must be positive.
-    encoder : EncoderConfig, optional
-        Configuration for the encoder. If None or omitted,
-        the default encoder configuration (`DEFAULT_ENCODER_CONFIG` from the
-        encoder module) will be used.
-    bottleneck : BottleneckConfig, optional
-        Configuration for the bottleneck layer connecting encoder and decoder.
-        If None or omitted, the default bottleneck configuration will be used.
-    decoder : DecoderConfig, optional
-        Configuration for the decoder. If None or omitted,
-        the default decoder configuration (`DEFAULT_DECODER_CONFIG` from the
-        decoder module) will be used.
-    out_channels : int, default=32
-        Desired number of channels in the final feature map output by the
-        backbone. Must be positive.
-    """
-
-    input_height: int = 128
-    in_channels: int = 1
-    encoder: EncoderConfig = DEFAULT_ENCODER_CONFIG
-    bottleneck: BottleneckConfig = DEFAULT_BOTTLENECK_CONFIG
-    decoder: DecoderConfig = DEFAULT_DECODER_CONFIG
-    out_channels: int = 32
-
-
-def load_backbone_config(
-    path: data.PathLike,
-    field: str | None = None,
-) -> BackboneConfig:
-    """Load the backbone configuration from a file.
-
-    Reads a configuration file (YAML) and validates it against the
-    `BackboneConfig` schema, potentially extracting data from a nested field.
-
-    Parameters
-    ----------
-    path : PathLike
-        Path to the configuration file.
-    field : str, optional
-        Dot-separated path to a nested section within the file containing the
-        backbone configuration (e.g., "model.backbone"). If None, the entire
-        file content is used.
-
-    Returns
-    -------
-    BackboneConfig
-        The loaded and validated backbone configuration object.
-
-    Raises
-    ------
-    FileNotFoundError
-        If the config file path does not exist.
-    yaml.YAMLError
-        If the file content is not valid YAML.
-    pydantic.ValidationError
-        If the loaded config data does not conform to `BackboneConfig`.
-    KeyError, TypeError
-        If `field` specifies an invalid path.
-    """
-    return load_config(path, schema=BackboneConfig, field=field)

src/batdetect2/models/detectors.py

@@ -17,7 +17,11 @@ the `batdetect2.preprocess` and `batdetect2.postprocess` packages, respectively.
 import torch
 from loguru import logger
 
-from batdetect2.models.backbones import BackboneConfig, build_backbone
+from batdetect2.models.backbones import (
+    BackboneConfig,
+    UNetBackboneConfig,
+    build_backbone,
+)
 from batdetect2.models.heads import BBoxHead, ClassifierHead
 from batdetect2.typing.models import BackboneModel, DetectionModel, ModelOutput
 
@@ -152,7 +156,7 @@ def build_detector(
         construction of the backbone or detector components (e.g., incompatible
         configurations, invalid parameters).
     """
-    config = config or BackboneConfig()
+    config = config or UNetBackboneConfig()
 
     logger.opt(lazy=True).debug(
         "Building model with config: \n{}",

tests/test_models/test_detectors.py

@@ -2,7 +2,7 @@ import numpy as np
 import pytest
 import torch
 
-from batdetect2.models.config import BackboneConfig
+from batdetect2.models.backbones import BackboneConfig
 from batdetect2.models.detectors import Detector, build_detector
 from batdetect2.models.heads import BBoxHead, ClassifierHead
 from batdetect2.typing.models import ModelOutput