docs/source/reference/api.md

@@ -15,9 +15,6 @@ Defined in `batdetect2.api_v2`.
 - `BatDetect2API.from_config(model_config=..., targets_config=..., ...)`
   - build a full model stack from config objects.
 
-Both constructors accept `compile_model=True` to compile the detector after the
-API is built.
-
 ## Common tasks
 
 - Load a checkpoint and run prediction on one file.
@@ -25,8 +22,6 @@ API is built.
 - Save predictions in one of the supported output formats.
 - Evaluate a model on labelled data.
 - Fine-tune an existing checkpoint on new targets.
-- Compile the detector explicitly with `BatDetect2API.compile()` when you want
-  to opt into PyTorch runtime compilation from Python.
 
 ## Generated reference
 

docs/source/reference/configs/inference/inference-config.md

@@ -7,8 +7,6 @@ Defined in `batdetect2.inference.config`.
 
 ## Top-level fields
 
-- `compile_model`
-  - compile the detector before batch prediction. This is off by default.
 - `loader`
   - data-loader settings for inference.
 - `clipping`
@@ -36,19 +34,8 @@ Override `InferenceConfig` when:
 
 - long recordings need different clipping behavior,
 - you want to tune batch size for your hardware,
-- you want to opt into runtime model compilation for repeated predictions,
 - you need reproducible prediction settings across runs.
 
-## Runtime compilation
-
-Set `compile_model: true` to compile the detector before batch inference. This
-can help when you run repeated predictions with stable input shapes. For a
-single short run, the compile step can cost more time than it saves.
-
-In Python, you can also compile explicitly with `BatDetect2API.compile()` or by
-passing `compile_model=True` to `BatDetect2API.from_checkpoint(...)` or
-`BatDetect2API.from_config(...)`.
-
 ## Related pages
 
 - Tune inference clipping:

docs/source/reference/configs/training/training-config.md

@@ -7,8 +7,6 @@ Defined in `batdetect2.train.config`.
 
 ## Top-level fields
 
-- `compile_model`
-  - compile the detector before training starts. This is off by default.
 - `train_loader`
   - training data loading and clipping settings.
 - `val_loader`
@@ -35,18 +33,10 @@ Use `TrainingConfig` when you want to change things like:
 - batch size,
 - augmentation,
 - optimiser and scheduler settings,
-- runtime options such as model compilation,
 - number of epochs,
 - validation frequency,
 - checkpoint behaviour.
 
-## Runtime options
-
-Use `compile_model: true` to call `torch.compile` on the detector used during
-training. This can help on longer runs with stable tensor shapes, but it may be
-slower for short CPU-only experiments because PyTorch has to compile the graph
-before it can reuse it.
-
 Example files live under `example_data/configs/`, including
 `example_data/configs/training.yaml`.
 

justfile

@@ -136,7 +136,7 @@ clean: clean-build clean-pyc clean-test clean-docs
 
 # Train on example data.
 example-train OPTIONS="":
-    uv run batdetect2 -v train \
+    uv run batdetect2 train \
         --val-dataset example_data/dataset.yaml \
         --base-dir . \
         --targets example_data/targets.yaml \

pyproject.toml

@@ -25,8 +25,8 @@ dependencies = [
   "soundevent[audio,geometry,plot]>=2.10.0",
   "soundfile>=0.12.1",
   "tensorboard>=2.16.2",
-  "torch>=2.0.0",
+  "torch>=1.13.1",
-  "torchaudio>=2.0.0",
+  "torchaudio>=1.13.1",
   "xarray>=2024.0.0",
 ]
 requires-python = ">=3.10,<3.14"

src/batdetect2/api_v2.py

@@ -1,4 +1,5 @@
 from __future__ import annotations
+from lightning.pytorch.loggers import Logger
 
 from pathlib import Path
 from typing import TYPE_CHECKING, Literal
@@ -8,7 +9,6 @@ if TYPE_CHECKING:
 
     import numpy as np
     import torch
-    from lightning.pytorch.loggers import Logger
     from soundevent import data
 
     from batdetect2.audio import AudioConfig, AudioLoader
@@ -153,19 +153,6 @@ class BatDetect2API:
 
         self.model.eval()
 
-    def compile(self) -> "BatDetect2API":
-        """Compile the detector path used by inference.
-
-        Returns
-        -------
-        BatDetect2API
-            This API instance with the detector compiled.
-        """
-        from batdetect2.models import compile_model
-
-        compile_model(self.model)
-        return self
-
     def load_annotations(
         self,
         path: data.PathLike,
@@ -241,9 +228,6 @@ class BatDetect2API:
             Training logger config override.
         logging_callbacks : Sequence[LoggingCallback[TrainLoggingContext]], optional
             Extra logging callbacks to run during training setup.
-        train_logger : Logger | None, optional
-            Pre-built Lightning logger to use for training. If omitted, one is
-            built from ``logger_config``.
 
         Returns
         -------
@@ -998,7 +982,6 @@ class BatDetect2API:
         inference_config: InferenceConfig | None = None,
         outputs_config: OutputsConfig | None = None,
         logging_config: AppLoggingConfig | None = None,
-        compile_model: bool = False,
     ) -> "BatDetect2API":
         """Build an API instance from config objects.
 
@@ -1024,8 +1007,6 @@ class BatDetect2API:
             Output config. If omitted, the default outputs config is used.
         logging_config : AppLoggingConfig | None, optional
             Logging config. If omitted, the default logging config is used.
-        compile_model : bool, optional
-            If ``True``, compile the detector path after building the API.
 
         Returns
         -------
@@ -1108,7 +1089,7 @@ class BatDetect2API:
             ),
         )
 
-        api = cls(
+        return cls(
             model_config=model_config,
             audio_config=audio_config,
             train_config=train_config,
@@ -1127,11 +1108,6 @@ class BatDetect2API:
             output_transform=output_transform,
         )
 
-        if compile_model:
-            api.compile()
-
-        return api
-
     @classmethod
     def from_checkpoint(
         cls,
@@ -1142,7 +1118,6 @@ class BatDetect2API:
         inference_config: InferenceConfig | None = None,
         outputs_config: OutputsConfig | None = None,
         logging_config: AppLoggingConfig | None = None,
-        compile_model: bool = False,
     ) -> "BatDetect2API":
         """Build an API instance from a saved checkpoint.
 
@@ -1163,8 +1138,6 @@ class BatDetect2API:
             Output config override.
         logging_config : AppLoggingConfig | None, optional
             Logging config override.
-        compile_model : bool, optional
-            If ``True``, compile the detector path after building the API.
 
         Returns
         -------
@@ -1248,7 +1221,7 @@ class BatDetect2API:
             transform=output_transform,
         )
 
-        api = cls(
+        return cls(
             model_config=model_config,
             audio_config=audio_config,
             train_config=train_config,
@@ -1267,11 +1240,6 @@ class BatDetect2API:
             output_transform=output_transform,
         )
 
-        if compile_model:
-            api.compile()
-
-        return api
-
     def _set_trainable_parameters(
         self,
         trainable: Literal["all", "heads", "classifier_head", "size_head"],

src/batdetect2/inference/batch.py

@@ -10,7 +10,6 @@ from batdetect2.inference.clips import get_clips_from_files
 from batdetect2.inference.config import InferenceConfig
 from batdetect2.inference.dataset import build_inference_loader
 from batdetect2.inference.lightning import InferenceModule
-from batdetect2.models import compile_model
 from batdetect2.models.types import ModelProtocol
 from batdetect2.outputs import (
     OutputsConfig,
@@ -72,9 +71,6 @@ def run_batch_inference(
         batch_size=batch_size,
     )
 
-    if inference_config.compile_model:
-        compile_model(model)
-
     module = InferenceModule(
         model,
         output_transform=output_transform,

src/batdetect2/inference/config.py

@@ -15,7 +15,6 @@ class ClipingConfig(BaseConfig):
 
 
 class InferenceConfig(BaseConfig):
-    compile_model: bool = False
     loader: InferenceLoaderConfig = Field(
         default_factory=InferenceLoaderConfig
     )

src/batdetect2/models/__init__.py

@@ -100,7 +100,6 @@ __all__ = [
     "ModelConfig",
     "build_model",
     "build_model_with_new_targets",
-    "compile_model",
 ]
 
 
@@ -113,6 +112,9 @@ class ModelConfig(BaseConfig):
 
     Attributes
     ----------
+    compile : bool
+        If ``True``, compile the model before training.  Defaults to
+        ``False``.
     samplerate : int
         Expected input audio sample rate in Hz.  Audio must be resampled
         to this rate before being passed to the model.  Defaults to
@@ -130,6 +132,7 @@ class ModelConfig(BaseConfig):
         ``PostprocessConfig()``.
     """
 
+    compile: bool = False
     samplerate: int = Field(default=TARGET_SAMPLERATE_HZ, gt=0)
     architecture: BackboneConfig = Field(default_factory=UNetBackboneConfig)
     preprocess: PreprocessingConfig = Field(
@@ -320,15 +323,3 @@ def build_model_with_new_targets(
         dimension_names=roi_mapper.dimension_names,
         config=model.get_config(),
     )
-
-
-def compile_model(model: ModelProtocol) -> ModelProtocol:
-    """Compile the detector path used by training and inference."""
-    if not isinstance(model.detector, torch.nn.Module):
-        raise TypeError("Detector must be a torch.nn.Module to compile.")
-
-    if getattr(model.detector, "_compiled_call_impl", None) is not None:
-        return model
-
-    model.detector.compile()
-    return model

src/batdetect2/models/blocks.py

@@ -70,16 +70,11 @@ __all__ = [
     "FreqCoordConvUpBlock",
     "StandardConvUpBlock",
     "SelfAttention",
-    "EfficientSelfAttention",
-    "VerticalMean",
     "ConvConfig",
-    "EfficientSelfAttentionConfig",
     "FreqCoordConvDownConfig",
     "StandardConvDownConfig",
     "FreqCoordConvUpConfig",
     "StandardConvUpConfig",
-    "VerticalConvConfig",
-    "VerticalMeanConfig",
     "LayerConfig",
     "build_layer",
 ]
@@ -150,8 +145,8 @@ class SelfAttentionConfig(BaseConfig):
     attention_channels : int
         Dimensionality of the query, key, and value projections.
     temperature : float
-        Divisor applied together with ``attention_channels`` when scaling
+        Scaling factor applied to the weighted values before the final
-        dot-product attention logits. Defaults to ``1``.
+        linear projection. Defaults to ``1``.
     """
 
     name: Literal["SelfAttention"] = "SelfAttention"
@@ -311,126 +306,6 @@ class SelfAttention(Block):
         )
 
 
-class EfficientSelfAttentionConfig(BaseConfig):
-    """Configuration for an ``EfficientSelfAttention`` block.
-
-    Attributes
-    ----------
-    name : str
-        Discriminator field; always ``"EfficientSelfAttention"``.
-    attention_channels : int
-        Dimensionality of the query, key, and value projections.
-    temperature : float
-        Divisor applied together with ``attention_channels`` when scaling
-        dot-product attention logits. Defaults to ``1``.
-    """
-
-    name: Literal["EfficientSelfAttention"] = "EfficientSelfAttention"
-    attention_channels: int
-    temperature: float = 1.0
-
-
-class EfficientSelfAttention(Block):
-    """An optimized self-attention block operating along the time axis.
-
-    Applies a scaled dot-product self-attention mechanism across the time
-    steps of an input feature map. This version uses a fused QKV linear
-    projection and PyTorch's native scaled dot-product attention (SDPA)
-    for optimal memory usage and execution speed.
-
-    Parameters
-    ----------
-    in_channels : int
-        Number of input channels (features per time step).
-    attention_channels : int
-        Dimensionality of the query, key, and value projections.
-    temperature : float, default=1.0
-        Divisor applied together with ``attention_channels`` when scaling
-        the dot-product scores before softmax.
-
-    Attributes
-    ----------
-    qkv_proj : nn.Linear
-        Fused linear projection for queries, keys, and values.
-    pro_fun : nn.Linear
-        Final linear projection applied to the attended values.
-    temperature : float
-        Scaling divisor used when computing attention scores.
-    att_dim : int
-        Dimensionality of the attention space (``attention_channels``).
-    """
-
-    def __init__(
-        self,
-        in_channels: int,
-        attention_channels: int,
-        temperature: float = 1.0,
-    ):
-        super().__init__()
-        self.in_channels = in_channels
-        self.out_channels = in_channels
-        self.temperature = temperature
-        self.att_dim = attention_channels
-        self.output_channels = in_channels
-
-        self.qkv_proj = nn.Linear(in_channels, 3 * attention_channels)
-        self.pro_fun = nn.Linear(attention_channels, in_channels)
-
-        self.scale_factor = 1.0 / (self.temperature * self.att_dim)
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        """Apply self-attention along the time dimension."""
-        x = x.squeeze(2).permute(0, 2, 1)
-
-        # Single projection pass
-        qkv = self.qkv_proj(x)
-
-        # Split along the last dimension into Q, K, V
-        query, key, value = torch.chunk(qkv, 3, dim=-1)
-
-        att = F.scaled_dot_product_attention(
-            query,
-            key,
-            value,
-            attn_mask=None,
-            dropout_p=0.0,
-            is_causal=False,
-            scale=self.scale_factor,
-        )
-
-        op = self.pro_fun(att)
-
-        return op.permute(0, 2, 1).unsqueeze(2)
-
-    def compute_attention_weights(self, x: torch.Tensor) -> torch.Tensor:
-        """Return the softmax attention weight matrix.
-
-        Useful for visualising which time steps attend to which others.
-        """
-        x = x.squeeze(2).permute(0, 2, 1)
-
-        qkv = self.qkv_proj(x)
-        query, key, _ = torch.chunk(qkv, 3, dim=-1)
-
-        kk_qq = torch.bmm(key, query.permute(0, 2, 1)) * self.scale_factor
-        att_weights = F.softmax(kk_qq, dim=1)
-
-        return att_weights
-
-    @block_registry.register(EfficientSelfAttentionConfig)
-    @staticmethod
-    def from_config(
-        config: EfficientSelfAttentionConfig,
-        input_channels: int,
-        input_height: int,
-    ) -> "EfficientSelfAttention":
-        return EfficientSelfAttention(
-            in_channels=input_channels,
-            attention_channels=config.attention_channels,
-            temperature=config.temperature,
-        )
-
-
 class ConvConfig(BaseConfig):
     """Configuration for a basic ConvBlock."""
 
@@ -585,10 +460,6 @@ class VerticalConv(Block):
         """
         return F.relu_(self.bn(self.conv(x)))
 
-    def get_output_height(self, input_height: int) -> int:
-        """Return the collapsed output height."""
-        return 1
-
     @block_registry.register(VerticalConvConfig)
     @staticmethod
     def from_config(
@@ -603,94 +474,6 @@ class VerticalConv(Block):
         )
 
 
-class VerticalMeanConfig(BaseConfig):
-    """Configuration for a ``VerticalMean`` block.
-
-    Attributes
-    ----------
-    name : str
-        Discriminator field; always ``"VerticalMean"``.
-    """
-
-    name: Literal["VerticalMean"] = "VerticalMean"
-    """Discriminator field indicating the block type."""
-
-    channels: int
-    """Number of output channels."""
-
-
-class VerticalMean(Block):
-    """Mean pooling block operating along the height dimension.
-
-    Applies a 2D mean pooling operation, followed by a 2D convolution,
-    followed by a batch normalization and ReLU activation.
-
-    Sequence: Mean Pool -> Conv -> BN -> ReLU.
-
-    Parameters
-    ----------
-    in_channels : int
-        Number of channels in the input tensor.
-    out_channels : int
-        Number of output channels after the mean pooling.
-    input_height : int
-        The height (H dimension) of the input tensor. The convolutional kernel
-        will be sized `(1, 1)`.
-    """
-
-    def __init__(
-        self,
-        in_channels: int,
-        out_channels: int,
-        input_height: int,
-    ):
-        super().__init__()
-        self.in_channels = in_channels
-        self.out_channels = out_channels
-        self.input_height = input_height
-        self.conv = nn.Conv2d(
-            in_channels,
-            out_channels,
-            kernel_size=(1, 1),
-            padding=0,
-        )
-        self.batch_norm = nn.BatchNorm2d(out_channels)
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        """Apply avg pooling -> Conv -> BN -> ReLU.
-
-        Parameters
-        ----------
-        x : torch.Tensor
-            Input tensor, shape `(B, C_in, H, W)`.
-
-        Returns
-        -------
-        torch.Tensor
-            Output tensor, shape `(B, C_out, 1, W)`.
-        """
-        x = x.mean(dim=2, keepdim=True)
-        x = self.conv(x)
-        return F.relu(self.batch_norm(x), inplace=True)
-
-    def get_output_height(self, input_height: int) -> int:
-        """Return the collapsed output height."""
-        return 1
-
-    @block_registry.register(VerticalMeanConfig)
-    @staticmethod
-    def from_config(
-        config: VerticalMeanConfig,
-        input_channels: int,
-        input_height: int,
-    ):
-        return VerticalMean(
-            in_channels=input_channels,
-            out_channels=config.channels,
-            input_height=input_height,
-        )
-
-
 class FreqCoordConvDownConfig(BaseConfig):
     """Configuration for a FreqCoordConvDownBlock."""
 
@@ -1168,16 +951,13 @@ LayerConfig = Annotated[
     | FreqCoordConvUpConfig
     | StandardConvUpConfig
     | SelfAttentionConfig
-    | EfficientSelfAttentionConfig
-    | VerticalConvConfig
-    | VerticalMeanConfig
     | LayerGroupConfig,
     Field(discriminator="name"),
 ]
 """Type alias for the discriminated union of block configuration models."""
 
 
-class LayerGroup(Block):
+class LayerGroup(nn.Module):
     """Sequential chain of blocks that acts as a single composite block.
 
     Wraps multiple ``Block`` instances in an ``nn.Sequential`` container,

src/batdetect2/models/bottleneck.py

@@ -21,18 +21,14 @@ This module provides:
 from typing import Annotated, List
 
 import torch
-from pydantic import Field, model_validator
+from pydantic import Field
 from torch import nn
 
 from batdetect2.core.configs import BaseConfig
 from batdetect2.models.blocks import (
     Block,
-    EfficientSelfAttentionConfig,
     SelfAttentionConfig,
     VerticalConv,
-    VerticalConvConfig,
-    VerticalMean,
-    VerticalMeanConfig,
     build_layer,
 )
 from batdetect2.models.types import BottleneckProtocol
@@ -98,7 +94,6 @@ class Bottleneck(Block):
         in_channels: int,
         out_channels: int,
         bottleneck_channels: int | None = None,
-        frequency_aggregator: Block | None = None,
         layers: List[torch.nn.Module] | None = None,
     ) -> None:
         """Initialise the Bottleneck layer.
@@ -130,14 +125,11 @@ class Bottleneck(Block):
         )
         self.layers = nn.ModuleList(layers or [])
 
-        if frequency_aggregator is None:
+        self.conv_vert = VerticalConv(
-            frequency_aggregator = VerticalConv(
+            in_channels=in_channels,
-                in_channels=in_channels,
+            out_channels=self.bottleneck_channels,
-                out_channels=self.bottleneck_channels,
+            input_height=input_height,
-                input_height=input_height,
+        )
-            )
-
-        self.conv_vert = frequency_aggregator
 
     def forward(self, x: torch.Tensor) -> torch.Tensor:
         """Process the encoder's bottleneck features.
@@ -168,19 +160,12 @@ class Bottleneck(Block):
 
 
 BottleneckLayerConfig = Annotated[
-    SelfAttentionConfig | EfficientSelfAttentionConfig,
+    SelfAttentionConfig,
     Field(discriminator="name"),
 ]
 """Type alias for the discriminated union of block configs usable in the Bottleneck."""
 
 
-FrequencyAggregationLayerConfig = Annotated[
-    (VerticalConvConfig | VerticalMeanConfig),
-    Field(discriminator="name"),
-]
-"""Type alias for the discriminated union of block configs usable in the FrequencyAggregation."""
-
-
 class BottleneckConfig(BaseConfig):
     """Configuration for the bottleneck component.
 
@@ -197,79 +182,17 @@ class BottleneckConfig(BaseConfig):
     """
 
     channels: int
-    frequency_aggregation: FrequencyAggregationLayerConfig | None = None
     layers: List[BottleneckLayerConfig] = Field(default_factory=list)
 
-    @model_validator(mode="after")
-    def set_default_frequency_aggregation(self) -> "BottleneckConfig":
-        """Default frequency aggregation to the bottleneck channel count."""
-        if self.frequency_aggregation is None:
-            self.frequency_aggregation = VerticalConvConfig(
-                channels=self.channels
-            )
-
-        return self
-
 
 DEFAULT_BOTTLENECK_CONFIG: BottleneckConfig = BottleneckConfig(
     channels=256,
-    frequency_aggregation=VerticalConvConfig(channels=256),
     layers=[
         SelfAttentionConfig(attention_channels=256),
     ],
 )
 
 
-def build_frequency_aggregation(
-    input_height: int,
-    in_channels: int,
-    config: FrequencyAggregationLayerConfig,
-) -> Block:
-    """Build a block for aggregating frequency information.
-
-    Parameters
-    ----------
-    input_height : int
-        Height (number of frequency bins) of the input tensor from the
-        encoder. Must be positive.
-    in_channels : int
-        Number of channels in the input tensor from the encoder. Must be
-        positive.
-    config : FrequencyAggregationLayerConfig, optional
-        Configuration specifying the output channel count and any
-        additional layers. Uses ``VerticalConvConfig`` if ``None``.
-
-    Returns
-    -------
-    Block
-        An initialised ``VerticalConv`` module.
-
-    Raises
-    ------
-    AssertionError
-        If any configured layer changes the height of the feature map
-        (bottleneck layers must preserve height so that it can be restored
-        by repetition).
-    """
-    if config.name == "VerticalConv":
-        return VerticalConv(
-            in_channels=in_channels,
-            out_channels=config.channels,
-            input_height=input_height,
-        )
-
-    if config.name == "VerticalMean":
-        return VerticalMean(
-            in_channels=in_channels,
-            out_channels=config.channels,
-            input_height=input_height,
-        )
-
-    raise NotImplementedError(
-        f"Unknown frequency aggregation layer: {config.name}"
-    )
-
-
 def build_bottleneck(
     input_height: int,
     in_channels: int,
@@ -308,26 +231,13 @@ def build_bottleneck(
         by repetition).
     """
     config = config or DEFAULT_BOTTLENECK_CONFIG
-    frequency_aggregation = config.frequency_aggregation
-    if frequency_aggregation is None:
-        raise ValueError("frequency_aggregation must be configured.")
 
-    frequency_aggregator = build_frequency_aggregation(
+    current_channels = in_channels
-        input_height=input_height,
+    current_height = input_height
-        in_channels=in_channels,
-        config=frequency_aggregation,
-    )
-
-    current_channels = frequency_aggregator.out_channels
-    current_height = frequency_aggregator.get_output_height(input_height)
-    assert current_height == 1, (
-        "Bottleneck frequency aggregation should collapse spectrogram height"
-    )
 
     layers = []
 
     for layer_config in config.layers:
-        previous_height = current_height
         layer = build_layer(
             input_height=current_height,
             in_channels=current_channels,
@@ -335,7 +245,7 @@ def build_bottleneck(
         )
         current_height = layer.get_output_height(current_height)
         current_channels = layer.out_channels
-        assert current_height == previous_height, (
+        assert current_height == input_height, (
             "Bottleneck layers should not change the spectrogram height"
         )
         layers.append(layer)
@@ -343,7 +253,6 @@ def build_bottleneck(
     return Bottleneck(
         input_height=input_height,
         in_channels=in_channels,
-        out_channels=current_channels,
+        out_channels=config.channels,
-        frequency_aggregator=frequency_aggregator,
         layers=layers,
     )

src/batdetect2/outputs/formats/raw.py

@@ -1,6 +1,4 @@
-import json
 from collections import defaultdict
-from multiprocessing import Pool
 from pathlib import Path
 from typing import List, Literal, Sequence
 from uuid import UUID, uuid4
@@ -10,7 +8,6 @@ import xarray as xr
 from loguru import logger
 from soundevent import data
 from soundevent.geometry import compute_bounds
-from tqdm import tqdm
 
 from batdetect2.core import BaseConfig
 from batdetect2.outputs.formats.base import (
@@ -28,8 +25,6 @@ class RawOutputConfig(BaseConfig):
     include_class_scores: bool = True
     include_features: bool = True
     include_geometry: bool = True
-    n_jobs: int = 1
-    show_progress: bool = False
 
 
 class RawFormatter(OutputFormatterProtocol[ClipDetections]):
@@ -40,19 +35,12 @@ class RawFormatter(OutputFormatterProtocol[ClipDetections]):
         include_features: bool = True,
         include_geometry: bool = True,
         parse_full_geometry: bool = False,
-        n_jobs: int = 1,
-        show_progress: bool = False,
     ):
         self.targets = targets
         self.include_class_scores = include_class_scores
         self.include_features = include_features
         self.include_geometry = include_geometry
         self.parse_full_geometry = parse_full_geometry
-        self.n_jobs = n_jobs
-        self.show_progress = show_progress
-
-        if n_jobs < 1:
-            raise ValueError("n_jobs must be >= 1")
 
     def format(
         self,
@@ -80,40 +68,15 @@ class RawFormatter(OutputFormatterProtocol[ClipDetections]):
     def load(self, path: data.PathLike) -> List[ClipDetections]:
         path = Path(path)
         files = list(path.glob("*.nc"))
+        predictions: List[ClipDetections] = []
 
-        if self.n_jobs == 1:
+        for filepath in files:
-            return self._load_sequential(files)
+            logger.debug(f"Loading clip predictions {filepath}")
+            clip_data = xr.load_dataset(filepath)
+            prediction = self.pred_from_xr(clip_data)
+            predictions.append(prediction)
 
-        return self._load_parallel(files)
+        return predictions
-
-    def _load_sequential(
-        self, files: Sequence[data.PathLike]
-    ) -> List[ClipDetections]:
-
-        iterable = files
-        if self.show_progress:
-            iterable = tqdm(files, total=len(files))
-
-        return [self.load_single_file(filepath) for filepath in iterable]
-
-    def _load_parallel(
-        self, files: Sequence[data.PathLike]
-    ) -> List[ClipDetections]:
-        with Pool(self.n_jobs) as pool:
-            if not self.show_progress:
-                return pool.map(self.load_single_file, files)
-
-            return list(
-                tqdm(
-                    pool.imap(self.load_single_file, files),
-                    total=len(files),
-                )
-            )
-
-    def load_single_file(self, filepath: data.PathLike) -> ClipDetections:
-        logger.debug(f"Loading clip predictions {filepath}")
-        clip_data = xr.load_dataset(filepath)
-        return self.pred_from_xr(clip_data)
 
     def pred_to_xr(
         self,
@@ -177,7 +140,7 @@ class RawFormatter(OutputFormatterProtocol[ClipDetections]):
             "clip_id": str(clip.uuid),
         }
 
-        if self.include_class_scores and values["class_scores"]:
+        if self.include_class_scores:
             class_scores = np.stack(values["class_scores"], axis=0)
             data_vars["class_scores"] = (
                 ["detection", "classes"],
@@ -185,7 +148,7 @@ class RawFormatter(OutputFormatterProtocol[ClipDetections]):
             )
             coords["classes"] = ("classes", self.targets.class_names)
 
-        if self.include_features and values["features"]:
+        if self.include_features:
             features = np.stack(values["features"], axis=0)
             data_vars["features"] = (["detection", "feature"], features)
             coords["feature"] = ("feature", np.arange(num_features))
@@ -204,81 +167,59 @@ class RawFormatter(OutputFormatterProtocol[ClipDetections]):
     def pred_from_xr(self, dataset: xr.Dataset) -> ClipDetections:
         clip_data = dataset
 
-        recording = data.Recording.model_validate(
+        recording = data.Recording.model_validate_json(
-            json.loads(clip_data.attrs["recording"])
+            clip_data.attrs["recording"]
         )
 
         clip_id = clip_data.clip_id.item()
-        clip = data.Clip.model_construct(
+        clip = data.Clip(
             recording=recording,
             uuid=UUID(clip_id),
-            start_time=float(clip_data.clip_start),
+            start_time=clip_data.clip_start,
-            end_time=float(clip_data.clip_end),
+            end_time=clip_data.clip_end,
         )
 
         sound_events = []
 
-        num_detections = len(clip_data.coords["detection"])
+        for detection in clip_data.coords["detection"]:
+            detection_data = clip_data.sel(detection=detection)
+            score = detection_data.score.item()
 
-        scores = clip_data.score.data
+            if "geometry" in clip_data and self.parse_full_geometry:
-        start_times = clip_data.start_time.data
+                geometry = data.geometry_validate(
-        end_times = clip_data.end_time.data
+                    detection_data.geometry.item()
-        low_freqs = clip_data.low_freq.data
+                )
-        high_freqs = clip_data.high_freq.data
-
-        top_class_scores = clip_data.top_class_score.data
-        top_class = clip_data.top_class.data
-
-        num_classes = len(self.targets.class_names)
-        class_map = dict(
-            zip(
-                self.targets.class_names,
-                range(num_classes),
-                strict=True,
-            )
-        )
-
-        geometries = None
-        if self.parse_full_geometry and "geometry" in clip_data:
-            geometries = clip_data.geometry.data
-
-        class_scores = None
-        if "class_scores" in clip_data:
-            class_scores = clip_data.class_scores.data
-
-        features = None
-        if "features" in clip_data:
-            features = clip_data.features.data
-
-        for index in range(num_detections):
-            score = scores[index]
-
-            if geometries is not None:
-                geometry = data.geometry_validate(geometries[index])
             else:
-                start_time = start_times[index]
+                start_time = detection_data.start_time.item()
-                end_time = end_times[index]
+                end_time = detection_data.end_time.item()
-                low_freq = low_freqs[index]
+                low_freq = detection_data.low_freq.item()
-                high_freq = high_freqs[index]
+                high_freq = detection_data.high_freq.item()
                 geometry = data.BoundingBox.model_construct(
                     coordinates=[start_time, low_freq, end_time, high_freq]
                 )
 
-            if class_scores is not None:
+            if "class_scores" in detection_data:
-                class_score = class_scores[index]
+                class_scores = detection_data.class_scores.data
             else:
-                class_score = np.zeros(num_classes)
+                class_scores = np.zeros(len(self.targets.class_names))
-                class_index = class_map[top_class[index]]
+                class_index = self.targets.class_names.index(
-                class_score[class_index] = top_class_scores[index]
+                    detection_data.top_class.item()
+                )
+                class_scores[class_index] = (
+                    detection_data.top_class_score.item()
+                )
 
-            feats = features[index] if features is not None else np.zeros(0)
+            if "features" in detection_data:
+                features = detection_data.features.data
+            else:
+                features = np.zeros(0)
 
             sound_events.append(
                 Detection(
                     geometry=geometry,
                     detection_score=score,
-                    class_scores=class_score,
+                    class_scores=class_scores,
-                    features=feats,
+                    features=features,
                 )
             )
 
@@ -295,6 +236,4 @@ class RawFormatter(OutputFormatterProtocol[ClipDetections]):
             include_class_scores=config.include_class_scores,
             include_features=config.include_features,
             include_geometry=config.include_geometry,
-            n_jobs=config.n_jobs,
-            show_progress=config.show_progress,
         )

src/batdetect2/train/config.py

@@ -1,3 +1,5 @@
+from typing import Literal
+
 from pydantic import Field
 
 from batdetect2.core.configs import BaseConfig
@@ -39,7 +41,7 @@ class PLTrainerConfig(BaseConfig):
 
 
 class TrainingConfig(BaseConfig):
-    compile_model: bool = False
+    precision: Literal["medium", "high"] | None = None
     train_loader: TrainLoaderConfig = Field(default_factory=TrainLoaderConfig)
     val_loader: ValLoaderConfig = Field(default_factory=ValLoaderConfig)
     optimizer: OptimizerConfig = Field(default_factory=AdamOptimizerConfig)

src/batdetect2/train/train.py

@@ -2,6 +2,7 @@ from collections.abc import Sequence
 from pathlib import Path
 from typing import Optional
 
+import torch
 from lightning import Trainer, seed_everything
 from lightning.pytorch.loggers import Logger
 from loguru import logger
@@ -15,7 +16,7 @@ from batdetect2.logging import (
     LoggingCallback,
     build_logger,
 )
-from batdetect2.models import ModelConfig, build_model, compile_model
+from batdetect2.models import ModelConfig, build_model
 from batdetect2.models.types import ModelProtocol
 from batdetect2.preprocess import PreprocessorProtocol, build_preprocessor
 from batdetect2.targets import (
@@ -207,9 +208,16 @@ def run_train(
         run_name=run_name,
     )
 
-    if train_config.compile_model:
+    if model_config.compile:
-        logger.info("Compiling detector...")
+        logger.info("Compiling model...")
-        compile_model(module.model)
+        module.compile()
+
+    if train_config.precision is not None:
+        logger.info(
+            "Setting precision float precision to {}",
+            train_config.precision,
+        )
+        torch.set_float32_matmul_precision(train_config.precision)
 
     logger.info("Starting main training loop...")
     trainer.fit(

tests/conftest.py

@@ -1,7 +1,6 @@
 import uuid
-from dataclasses import dataclass
 from pathlib import Path
-from typing import Any, Callable, List, Optional, cast
+from typing import Callable, List, Optional
 from uuid import uuid4
 
 import lightning as L
@@ -16,7 +15,6 @@ from batdetect2.audio.clips import build_clipper
 from batdetect2.audio.types import AudioLoader, ClipperProtocol
 from batdetect2.data import DatasetConfig, load_dataset
 from batdetect2.data.annotations.batdetect2 import BatDetect2FilesAnnotations
-from batdetect2.models.types import ModelProtocol
 from batdetect2.preprocess import build_preprocessor
 from batdetect2.preprocess.types import PreprocessorProtocol
 from batdetect2.targets import (
@@ -33,12 +31,6 @@ from batdetect2.train.lightning import build_training_module
 from batdetect2.train.types import ClipLabeller
 
 
-@dataclass
-class DetectorCompileRecorder:
-    compile_count: int = 0
-    call_count: int = 0
-
-
 @pytest.fixture
 def example_data_dir() -> Path:
     pkg_dir = Path(__file__).parent.parent
@@ -164,15 +156,12 @@ def generate_whistle(tmp_path: Path):
 
         offset = int((time - duration / 2) * samplerate)
         t = np.linspace(-duration / 2, duration / 2, frames, endpoint=False)
-        pulse = np.asarray(
+        data = signal.gausspulse(
-            signal.gausspulse(
+            t,
-                t,
+            fc=frequency,
-                fc=frequency,
+            bw=2 / (frequency * whistle_duration),
-                bw=2 / (frequency * whistle_duration),
-            ),
-            dtype=np.float64,
         )
-        wave = (np.roll(pulse, offset) * np.iinfo(np.int16).max).astype(
+        wave = (np.roll(data, offset) * np.iinfo(np.int16).max).astype(
             np.int16
         )
         sf.write(str(path), wave, samplerate, subtype="PCM_16")
@@ -374,30 +363,6 @@ def sample_audio_loader() -> AudioLoader:
     return build_audio_loader()
 
 
-@pytest.fixture
-def record_detector_compilation(
-    monkeypatch: pytest.MonkeyPatch,
-) -> Callable[[ModelProtocol], DetectorCompileRecorder]:
-    def factory(model: ModelProtocol) -> DetectorCompileRecorder:
-        recorder = DetectorCompileRecorder()
-        detector = cast(Any, model.detector)
-        original_call_impl = detector._call_impl
-
-        def compile_detector() -> None:
-            recorder.compile_count += 1
-
-            def compiled_call(*args, **kwargs):
-                recorder.call_count += 1
-                return original_call_impl(*args, **kwargs)
-
-            detector._compiled_call_impl = compiled_call
-
-        monkeypatch.setattr(detector, "compile", compile_detector)
-        return recorder
-
-    return factory
-
-
 @pytest.fixture
 def bat_tag() -> data.Tag:
     return data.Tag(key="class", value="bat")

tests/test_api_v2/test_api_v2.py

@@ -153,61 +153,6 @@ def test_process_spectrogram_rejects_batched_input(
         api_v2.process_spectrogram(spec)
 
 
-def test_user_can_compile_api_detector(
-    api_v2: BatDetect2API,
-    example_audio_files: list[Path],
-    record_detector_compilation,
-) -> None:
-    recorder = record_detector_compilation(api_v2.model)
-    audio = api_v2.load_audio(example_audio_files[0])
-    spec = api_v2.generate_spectrogram(audio)
-
-    api_v2.compile()
-    api_v2.compile()
-    api_v2.process_spectrogram(spec)
-
-    assert recorder.compile_count == 1
-    assert recorder.call_count == 1
-
-
-def test_api_from_config_compiles_detector_when_requested(
-    monkeypatch: pytest.MonkeyPatch,
-) -> None:
-    compiled_models = []
-
-    def compile_model(model):
-        compiled_models.append(model)
-        return model
-
-    monkeypatch.setattr("batdetect2.models.compile_model", compile_model)
-
-    api = BatDetect2API.from_config(
-        compile_model=True,
-    )
-
-    assert compiled_models == [api.model]
-
-
-def test_api_from_checkpoint_compiles_detector_when_requested(
-    tiny_checkpoint_path: Path,
-    monkeypatch: pytest.MonkeyPatch,
-) -> None:
-    compiled_models = []
-
-    def compile_model(model):
-        compiled_models.append(model)
-        return model
-
-    monkeypatch.setattr("batdetect2.models.compile_model", compile_model)
-
-    api = BatDetect2API.from_checkpoint(
-        tiny_checkpoint_path,
-        compile_model=True,
-    )
-
-    assert compiled_models == [api.model]
-
-
 def test_user_can_read_top_class_and_other_class_scores(
     api_v2: BatDetect2API,
     example_audio_files: list[Path],

tests/test_data/test_predictions/test_raw.py

@@ -61,102 +61,3 @@ def test_roundtrip(
         ).all()
         assert (recovered_prediction.features == detection.features).all()
         assert recovered_prediction.geometry == detection.geometry
-
-
-def test_roundtrip_recovers_recording_metadata(
-    sample_formatter,
-    create_recording,
-    create_clip,
-    sample_targets: TargetProtocol,
-    tmp_path: Path,
-):
-    recording = create_recording(
-        tags=[data.Tag(key="source", value="test-recorder")],
-        duration=2,
-        samplerate=384_000,
-        time_expansion=10,
-    )
-    clip = create_clip(recording=recording, start_time=0.25, end_time=0.75)
-    detection = Detection(
-        geometry=data.BoundingBox(
-            coordinates=[0.3, 45_000, 0.4, 70_000],
-        ),
-        detection_score=0.5,
-        class_scores=np.ones(len(sample_targets.class_names)),
-        features=np.ones(32),
-    )
-    prediction = ClipDetections(clip=clip, detections=[detection])
-
-    path = tmp_path / "predictions"
-
-    sample_formatter.save(predictions=[prediction], path=path)
-    recovered = sample_formatter.load(path=path)
-
-    assert len(recovered) == 1
-    assert recovered[0].clip.recording.model_dump(mode="json") == (
-        recording.model_dump(mode="json")
-    )
-
-
-def test_roundtrip_empty_detections(
-    sample_formatter,
-    clip: data.Clip,
-    tmp_path: Path,
-):
-    prediction = ClipDetections(clip=clip, detections=[])
-
-    path = tmp_path / "predictions"
-
-    sample_formatter.save(predictions=[prediction], path=path)
-    recovered = sample_formatter.load(path=path)
-
-    assert len(recovered) == 1
-    assert recovered[0].detections == []
-    assert recovered[0].clip.uuid == prediction.clip.uuid
-    assert recovered[0].clip.start_time == prediction.clip.start_time
-    assert recovered[0].clip.end_time == prediction.clip.end_time
-
-
-def test_roundtrip_loads_with_multiprocessing(
-    clip: data.Clip,
-    sample_targets: TargetProtocol,
-    tmp_path: Path,
-):
-    save_formatter = build_output_formatter(
-        config=RawOutputConfig(),
-        targets=sample_targets,
-    )
-    load_formatter = build_output_formatter(
-        config=RawOutputConfig(n_jobs=2),
-        targets=sample_targets,
-    )
-    predictions = [
-        ClipDetections(
-            clip=data.Clip(
-                recording=clip.recording,
-                start_time=index,
-                end_time=index + 0.5,
-            ),
-            detections=[
-                Detection(
-                    geometry=data.BoundingBox(
-                        coordinates=[index, 45_000, index + 0.1, 70_000],
-                    ),
-                    detection_score=0.5,
-                    class_scores=np.ones(len(sample_targets.class_names)),
-                    features=np.ones(32),
-                )
-            ],
-        )
-        for index in range(2)
-    ]
-
-    path = tmp_path / "predictions"
-
-    save_formatter.save(predictions=predictions, path=path)
-    recovered = load_formatter.load(path=path)
-
-    assert len(recovered) == len(predictions)
-    assert {item.clip.uuid for item in recovered} == {
-        item.clip.uuid for item in predictions
-    }

tests/test_inference/test_batch.py

@@ -3,8 +3,6 @@ from pathlib import Path
 import pytest
 from soundevent import data
 
-from batdetect2.api_v2 import BatDetect2API
-from batdetect2.inference import InferenceConfig
 from batdetect2.inference.batch import run_batch_inference
 from batdetect2.targets import build_roi_mapping, build_targets
 from batdetect2.train import load_model_from_checkpoint
@@ -55,54 +53,3 @@ def test_run_batch_inference_matches_single_clip_inference(
         strict=True,
     ):
         assert_clip_detections_equal(batched, single)
-
-
-def test_run_batch_inference_compiles_detector_when_config_requests_compile(
-    example_annotations: list[data.ClipAnnotation],
-    record_detector_compilation,
-) -> None:
-    api = BatDetect2API.from_config()
-    recorder = record_detector_compilation(api.model)
-
-    predictions = run_batch_inference(
-        api.model,
-        [example_annotations[0].clip],
-        targets=api.targets,
-        roi_mapper=api.roi_mapper,
-        audio_loader=api.audio_loader,
-        preprocessor=api.preprocessor,
-        output_transform=api.output_transform,
-        inference_config=InferenceConfig(compile_model=True),
-        batch_size=1,
-        num_workers=0,
-    )
-
-    assert predictions
-    assert recorder.compile_count == 1
-    assert recorder.call_count > 0
-
-
-def test_run_batch_inference_does_not_recompile_compiled_detector(
-    example_annotations: list[data.ClipAnnotation],
-    record_detector_compilation,
-) -> None:
-    api = BatDetect2API.from_config()
-    recorder = record_detector_compilation(api.model)
-    api.compile()
-
-    predictions = run_batch_inference(
-        api.model,
-        [example_annotations[0].clip],
-        targets=api.targets,
-        roi_mapper=api.roi_mapper,
-        audio_loader=api.audio_loader,
-        preprocessor=api.preprocessor,
-        output_transform=api.output_transform,
-        inference_config=InferenceConfig(compile_model=True),
-        batch_size=1,
-        num_workers=0,
-    )
-
-    assert predictions
-    assert recorder.compile_count == 1
-    assert recorder.call_count > 0

tests/test_model.py

@@ -10,8 +10,6 @@ from hypothesis import strategies as st
 
 from batdetect2 import api
 from batdetect2.detector import parameters
-from batdetect2.models.backbones import UNetBackboneConfig
-from batdetect2.train import load_model_from_checkpoint
 
 
 @settings(deadline=None, max_examples=5)
@@ -74,16 +72,3 @@ def test_can_import_model_without_pickle_on_test_data(
             model=model_with_pickle,
         )
         assert predictions_without_pickle == predictions_with_pickle
-
-
-def test_bundled_checkpoint_loads_with_current_config_schema() -> None:
-    """Bundled checkpoints remain loadable after config schema changes."""
-    model, configs = load_model_from_checkpoint()
-
-    assert model.class_names
-    assert isinstance(configs.model.architecture, UNetBackboneConfig)
-    frequency_aggregation = (
-        configs.model.architecture.bottleneck.frequency_aggregation
-    )
-    assert frequency_aggregation is not None
-    assert frequency_aggregation.name

tests/test_models/test_blocks.py

@@ -4,8 +4,6 @@ import torch
 from batdetect2.models.blocks import (
     ConvBlock,
     ConvConfig,
-    EfficientSelfAttention,
-    EfficientSelfAttentionConfig,
     FreqCoordConvDownBlock,
     FreqCoordConvDownConfig,
     FreqCoordConvUpBlock,
@@ -20,8 +18,6 @@ from batdetect2.models.blocks import (
     StandardConvUpConfig,
     VerticalConv,
     VerticalConvConfig,
-    VerticalMean,
-    VerticalMeanConfig,
     build_layer,
 )
 
@@ -103,20 +99,6 @@ def test_vertical_conv_forward_shape(dummy_input):
     assert block.out_channels == out_channels
 
 
-def test_vertical_mean_forward_shape(dummy_input):
-    """Test that VerticalMean collapses the height dimension to 1."""
-    in_channels = dummy_input.size(1)
-    input_height = dummy_input.size(2)
-    out_channels = 32
-
-    block = VerticalMean(in_channels, out_channels, input_height)
-    output = block(dummy_input)
-
-    assert output.shape == (2, out_channels, 1, 32)
-    assert block.out_channels == out_channels
-    assert block.get_output_height(input_height) == 1
-
-
 def test_self_attention_forward_shape(dummy_bottleneck_input):
     """Test that SelfAttention maintains the exact shape."""
     in_channels = dummy_bottleneck_input.size(1)
@@ -131,20 +113,6 @@ def test_self_attention_forward_shape(dummy_bottleneck_input):
     assert block.out_channels == in_channels
 
 
-def test_efficient_self_attention_forward_shape(dummy_bottleneck_input):
-    """Test that EfficientSelfAttention maintains the exact shape."""
-    in_channels = dummy_bottleneck_input.size(1)
-    attention_channels = 32
-
-    block = EfficientSelfAttention(
-        in_channels=in_channels, attention_channels=attention_channels
-    )
-    output = block(dummy_bottleneck_input)
-
-    assert output.shape == dummy_bottleneck_input.shape
-    assert block.out_channels == in_channels
-
-
 def test_self_attention_weights(dummy_bottleneck_input):
     """Test that attention weights sum to 1 over the time sequence."""
     in_channels = dummy_bottleneck_input.size(1)
@@ -163,51 +131,6 @@ def test_self_attention_weights(dummy_bottleneck_input):
     assert torch.allclose(sum_weights, torch.ones_like(sum_weights), atol=1e-5)
 
 
-def test_efficient_self_attention_matches_self_attention_with_copied_weights(
-    dummy_bottleneck_input,
-):
-    """Temporarily compare efficient and original attention outputs."""
-    in_channels = dummy_bottleneck_input.size(1)
-    attention_channels = 32
-
-    block = SelfAttention(
-        in_channels=in_channels,
-        attention_channels=attention_channels,
-    )
-    efficient_block = EfficientSelfAttention(
-        in_channels=in_channels,
-        attention_channels=attention_channels,
-    )
-
-    # Match the fused QKV projection to the original separate projections.
-    with torch.no_grad():
-        efficient_block.qkv_proj.weight[:attention_channels].copy_(
-            block.query_fun.weight
-        )
-        efficient_block.qkv_proj.bias[:attention_channels].copy_(
-            block.query_fun.bias
-        )
-        efficient_block.qkv_proj.weight[
-            attention_channels : 2 * attention_channels
-        ].copy_(block.key_fun.weight)
-        efficient_block.qkv_proj.bias[
-            attention_channels : 2 * attention_channels
-        ].copy_(block.key_fun.bias)
-        efficient_block.qkv_proj.weight[2 * attention_channels :].copy_(
-            block.value_fun.weight
-        )
-        efficient_block.qkv_proj.bias[2 * attention_channels :].copy_(
-            block.value_fun.bias
-        )
-        efficient_block.pro_fun.weight.copy_(block.pro_fun.weight)
-        efficient_block.pro_fun.bias.copy_(block.pro_fun.bias)
-
-    output = block(dummy_bottleneck_input)
-    efficient_output = efficient_block(dummy_bottleneck_input)
-
-    torch.testing.assert_close(output, efficient_output)
-
-
 @pytest.mark.parametrize(
     "layer_config, expected_type",
     [
@@ -217,12 +140,7 @@ def test_efficient_self_attention_matches_self_attention_with_copied_weights(
         (FreqCoordConvDownConfig(out_channels=32), FreqCoordConvDownBlock),
         (FreqCoordConvUpConfig(out_channels=32), FreqCoordConvUpBlock),
         (SelfAttentionConfig(attention_channels=32), SelfAttention),
-        (
-            EfficientSelfAttentionConfig(attention_channels=32),
-            EfficientSelfAttention,
-        ),
         (VerticalConvConfig(channels=32), VerticalConv),
-        (VerticalMeanConfig(channels=32), VerticalMean),
     ],
 )
 def test_build_layer_factory(layer_config, expected_type):

tests/test_models/test_bottleneck.py

@@ -1,55 +0,0 @@
-import torch
-
-from batdetect2.models.blocks import (
-    SelfAttention,
-    SelfAttentionConfig,
-    VerticalMeanConfig,
-)
-from batdetect2.models.bottleneck import (
-    Bottleneck,
-    BottleneckConfig,
-    build_bottleneck,
-)
-
-
-def test_bottleneck_layers_use_frequency_aggregation_channels() -> None:
-    """Layers after frequency aggregation are built for aggregated channels."""
-    config = BottleneckConfig(
-        channels=128,
-        frequency_aggregation=VerticalMeanConfig(channels=128),
-        layers=[SelfAttentionConfig(attention_channels=32)],
-    )
-
-    bottleneck = build_bottleneck(
-        input_height=8,
-        in_channels=64,
-        config=config,
-    )
-
-    assert isinstance(bottleneck, Bottleneck)
-    attention = bottleneck.layers[0]
-    assert isinstance(attention, SelfAttention)
-    assert attention.in_channels == 128
-
-    output = bottleneck(torch.randn(2, 64, 8, 10))
-
-    assert output.shape == (2, 128, 8, 10)
-
-
-def test_bottleneck_default_frequency_aggregation_matches_channels() -> None:
-    """Minimal configs keep advertised and actual output channels in sync."""
-    config = BottleneckConfig(channels=128, layers=[])
-
-    bottleneck = build_bottleneck(
-        input_height=8,
-        in_channels=64,
-        config=config,
-    )
-
-    assert isinstance(bottleneck, Bottleneck)
-    assert bottleneck.out_channels == 128
-    assert bottleneck.conv_vert.out_channels == 128
-
-    output = bottleneck(torch.randn(2, 64, 8, 10))
-
-    assert output.shape == (2, bottleneck.out_channels, 8, 10)

tests/test_train/test_lightning.py

@@ -49,16 +49,6 @@ def build_default_module(
     )
 
 
-def build_fast_train_config() -> TrainingConfig:
-    train_config = TrainingConfig()
-    train_config.trainer.limit_train_batches = 1
-    train_config.trainer.limit_val_batches = 1
-    train_config.trainer.log_every_n_steps = 1
-    train_config.train_loader.batch_size = 1
-    train_config.train_loader.augmentations.enabled = False
-    return train_config
-
-
 def test_can_initialize_default_module():
     module = build_default_module()
     assert isinstance(module, L.LightningModule)
@@ -281,7 +271,19 @@ def test_train_smoke_produces_loadable_checkpoint(
     sample_audio_loader: AudioLoader,
 ):
     # Given
-    train_config = build_fast_train_config()
+    train_config = TrainingConfig.model_validate(
+        {
+            "trainer": {
+                "limit_train_batches": 1,
+                "limit_val_batches": 1,
+                "log_every_n_steps": 1,
+            },
+            "train_loader": {
+                "batch_size": 1,
+                "augmentations": {"enabled": False},
+            },
+        }
+    )
 
     # When
     run_train(
@@ -308,47 +310,6 @@ def test_train_smoke_produces_loadable_checkpoint(
     assert outputs is not None
 
 
-@pytest.mark.slow
-def test_run_train_compiles_detector_when_train_config_requests_compile(
-    tmp_path: Path,
-    example_annotations: list[data.ClipAnnotation],
-    record_detector_compilation,
-) -> None:
-    targets_config = TargetConfig()
-    targets = build_targets(targets_config)
-    roi_mapper = build_roi_mapping(targets_config.roi)
-    model = build_model(
-        ModelConfig(),
-        class_names=targets.class_names,
-        dimension_names=roi_mapper.dimension_names,
-    )
-    train_config = build_fast_train_config()
-    train_config.compile_model = True
-    recorder = record_detector_compilation(model)
-
-    module = run_train(
-        train_annotations=example_annotations[:1],
-        val_annotations=example_annotations[:1],
-        model=model,
-        targets=targets,
-        roi_mapper=roi_mapper,
-        targets_config=targets_config,
-        train_config=train_config,
-        num_epochs=1,
-        train_workers=0,
-        val_workers=0,
-        checkpoint_dir=tmp_path / "checkpoints",
-        log_dir=tmp_path / "logs",
-        seed=0,
-    )
-
-    assert (
-        getattr(module.model.detector, "_compiled_call_impl", None) is not None
-    )
-    assert recorder.compile_count == 1
-    assert recorder.call_count > 0
-
-
 def test_build_training_module_uses_provided_model() -> None:
     targets = build_targets(TargetConfig())
     roi_mapper = build_roi_mapping(TargetConfig().roi)

uv.lock

@@ -453,7 +453,7 @@ wheels = [
 
 [[package]]
 name = "batdetect2"
-version = "2.0.0b2"
+version = "2.0.0b1"
 source = { editable = "." }
 dependencies = [
     { name = "click" },
@@ -557,8 +557,8 @@ requires-dist = [
     { name = "soundevent", extras = ["audio", "geometry", "plot"], specifier = ">=2.10.0" },
     { name = "soundfile", specifier = ">=0.12.1" },
     { name = "tensorboard", specifier = ">=2.16.2" },
-    { name = "torch", specifier = ">=2.0.0" },
+    { name = "torch", specifier = ">=1.13.1" },
-    { name = "torchaudio", specifier = ">=2.0.0" },
+    { name = "torchaudio", specifier = ">=1.13.1" },
     { name = "xarray", specifier = ">=2024.0.0" },
 ]
 
@@ -626,10 +626,10 @@ name = "bitsandbytes"
 version = "0.49.2"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" },
+    { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "(python_full_version < '3.11' and sys_platform == 'darwin') or (python_full_version < '3.11' and sys_platform == 'linux') or (python_full_version < '3.11' and sys_platform == 'win32')" },
-    { name = "numpy", version = "2.4.4", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
+    { name = "numpy", version = "2.4.4", source = { registry = "https://pypi.org/simple" }, marker = "(python_full_version >= '3.11' and sys_platform == 'darwin') or (python_full_version >= '3.11' and sys_platform == 'linux') or (python_full_version >= '3.11' and sys_platform == 'win32')" },
-    { name = "packaging" },
+    { name = "packaging", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" },
-    { name = "torch" },
+    { name = "torch", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" },
 ]
 wheels = [
     { url = "https://files.pythonhosted.org/packages/d8/7d/f1fe0992334b18cd8494f89aeec1dcc674635584fcd9f115784fea3a1d05/bitsandbytes-0.49.2-py3-none-macosx_14_0_arm64.whl", hash = "sha256:87be5975edeac5396d699ecbc39dfc47cf2c026daaf2d5852a94368611a6823f", size = 131940, upload-time = "2026-02-16T21:26:04.572Z" },
@@ -1070,7 +1070,7 @@ resolution-markers = [
     "python_full_version < '3.11' and sys_platform != 'darwin' and sys_platform != 'linux' and sys_platform != 'win32'",
 ]
 dependencies = [
-    { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" } },
+    { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/66/54/eb9bfc647b19f2009dd5c7f5ec51c4e6ca831725f1aea7a993034f483147/contourpy-1.3.2.tar.gz", hash = "sha256:b6945942715a034c671b7fc54f9588126b0b8bf23db2696e3ca8328f3ff0ab54", size = 13466130, upload-time = "2025-04-15T17:47:53.79Z" }
 wheels = [
@@ -1151,7 +1151,7 @@ resolution-markers = [
     "python_full_version == '3.11.*' and sys_platform != 'darwin' and sys_platform != 'linux' and sys_platform != 'win32'",
 ]
 dependencies = [
-    { name = "numpy", version = "2.4.4", source = { registry = "https://pypi.org/simple" } },
+    { name = "numpy", version = "2.4.4", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/58/01/1253e6698a07380cd31a736d248a3f2a50a7c88779a1813da27503cadc2a/contourpy-1.3.3.tar.gz", hash = "sha256:083e12155b210502d0bca491432bb04d56dc3432f95a979b429f2848c3dbe880", size = 13466174, upload-time = "2025-07-26T12:03:12.549Z" }
 wheels = [
@@ -1345,7 +1345,7 @@ name = "cuda-bindings"
 version = "13.2.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "cuda-pathfinder" },
+    { name = "cuda-pathfinder", marker = "(platform_machine != 'ARM64' and sys_platform == 'win32') or sys_platform == 'linux'" },
 ]
 wheels = [
     { url = "https://files.pythonhosted.org/packages/1a/fe/7351d7e586a8b4c9f89731bfe4cf0148223e8f9903ff09571f78b3fb0682/cuda_bindings-13.2.0-cp310-cp310-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:08b395f79cb89ce0cd8effff07c4a1e20101b873c256a1aeb286e8fd7bd0f556", size = 5744254, upload-time = "2026-03-11T00:12:29.798Z" },
@@ -1376,37 +1376,37 @@ wheels = [
 
 [package.optional-dependencies]
 cublas = [
-    { name = "nvidia-cublas" },
+    { name = "nvidia-cublas", marker = "(platform_machine != 'ARM64' and sys_platform == 'win32') or sys_platform == 'linux'" },
 ]
 cudart = [
-    { name = "nvidia-cuda-runtime" },
+    { name = "nvidia-cuda-runtime", marker = "(platform_machine != 'ARM64' and sys_platform == 'win32') or sys_platform == 'linux'" },
 ]
 cufft = [
-    { name = "nvidia-cufft" },
+    { name = "nvidia-cufft", marker = "(platform_machine != 'ARM64' and sys_platform == 'win32') or sys_platform == 'linux'" },
 ]
 cufile = [
-    { name = "nvidia-cufile" },
+    { name = "nvidia-cufile", marker = "sys_platform == 'linux'" },
 ]
 cupti = [
-    { name = "nvidia-cuda-cupti" },
+    { name = "nvidia-cuda-cupti", marker = "(platform_machine != 'ARM64' and sys_platform == 'win32') or sys_platform == 'linux'" },
 ]
 curand = [
-    { name = "nvidia-curand" },
+    { name = "nvidia-curand", marker = "(platform_machine != 'ARM64' and sys_platform == 'win32') or sys_platform == 'linux'" },
 ]
 cusolver = [
-    { name = "nvidia-cusolver" },
+    { name = "nvidia-cusolver", marker = "(platform_machine != 'ARM64' and sys_platform == 'win32') or sys_platform == 'linux'" },
 ]
 cusparse = [
-    { name = "nvidia-cusparse" },
+    { name = "nvidia-cusparse", marker = "(platform_machine != 'ARM64' and sys_platform == 'win32') or sys_platform == 'linux'" },
 ]
 nvjitlink = [
-    { name = "nvidia-nvjitlink" },
+    { name = "nvidia-nvjitlink", marker = "(platform_machine != 'ARM64' and sys_platform == 'win32') or sys_platform == 'linux'" },
 ]
 nvrtc = [
-    { name = "nvidia-cuda-nvrtc" },
+    { name = "nvidia-cuda-nvrtc", marker = "(platform_machine != 'ARM64' and sys_platform == 'win32') or sys_platform == 'linux'" },
 ]
 nvtx = [
-    { name = "nvidia-nvtx" },
+    { name = "nvidia-nvtx", marker = "(platform_machine != 'ARM64' and sys_platform == 'win32') or sys_platform == 'linux'" },
 ]
 
 [[package]]
@@ -1875,7 +1875,7 @@ name = "exceptiongroup"
 version = "1.3.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "typing-extensions" },
+    { name = "typing-extensions", marker = "python_full_version < '3.11'" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/50/79/66800aadf48771f6b62f7eb014e352e5d06856655206165d775e675a02c9/exceptiongroup-1.3.1.tar.gz", hash = "sha256:8b412432c6055b0b7d14c310000ae93352ed6754f70fa8f7c34141f91c4e3219", size = 30371, upload-time = "2025-11-21T23:01:54.787Z" }
 wheels = [
@@ -2417,7 +2417,7 @@ name = "gunicorn"
 version = "25.3.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "packaging" },
+    { name = "packaging", marker = "platform_machine != 'ARM64' or sys_platform != 'win32'" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/c4/f4/e78fa054248fab913e2eab0332c6c2cb07421fca1ce56d8fe43b6aef57a4/gunicorn-25.3.0.tar.gz", hash = "sha256:f74e1b2f9f76f6cd1ca01198968bd2dd65830edc24b6e8e4d78de8320e2fe889", size = 634883, upload-time = "2026-03-27T00:00:26.092Z" }
 wheels = [
@@ -2638,17 +2638,17 @@ resolution-markers = [
     "python_full_version < '3.11' and sys_platform != 'darwin' and sys_platform != 'linux' and sys_platform != 'win32'",
 ]
 dependencies = [
-    { name = "colorama", marker = "sys_platform == 'win32'" },
+    { name = "colorama", marker = "python_full_version < '3.11' and sys_platform == 'win32'" },
-    { name = "decorator" },
+    { name = "decorator", marker = "python_full_version < '3.11'" },
-    { name = "exceptiongroup" },
+    { name = "exceptiongroup", marker = "python_full_version < '3.11'" },
-    { name = "jedi" },
+    { name = "jedi", marker = "python_full_version < '3.11'" },
-    { name = "matplotlib-inline" },
+    { name = "matplotlib-inline", marker = "python_full_version < '3.11'" },
-    { name = "pexpect", marker = "sys_platform != 'emscripten' and sys_platform != 'win32'" },
+    { name = "pexpect", marker = "python_full_version < '3.11' and sys_platform != 'emscripten' and sys_platform != 'win32'" },
-    { name = "prompt-toolkit" },
+    { name = "prompt-toolkit", marker = "python_full_version < '3.11'" },
-    { name = "pygments" },
+    { name = "pygments", marker = "python_full_version < '3.11'" },
-    { name = "stack-data" },
+    { name = "stack-data", marker = "python_full_version < '3.11'" },
-    { name = "traitlets" },
+    { name = "traitlets", marker = "python_full_version < '3.11'" },
-    { name = "typing-extensions" },
+    { name = "typing-extensions", marker = "python_full_version < '3.11'" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/40/18/f8598d287006885e7136451fdea0755af4ebcbfe342836f24deefaed1164/ipython-8.39.0.tar.gz", hash = "sha256:4110ae96012c379b8b6db898a07e186c40a2a1ef5d57a7fa83166047d9da7624", size = 5513971, upload-time = "2026-03-27T10:02:13.94Z" }
 wheels = [
@@ -2674,18 +2674,18 @@ resolution-markers = [
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 ]
 dependencies = [
-    { name = "colorama", marker = "sys_platform == 'win32'" },
+    { name = "colorama", marker = "python_full_version >= '3.11' and sys_platform == 'win32'" },
-    { name = "decorator" },
+    { name = "decorator", marker = "python_full_version >= '3.11'" },
-    { name = "ipython-pygments-lexers" },
+    { name = "ipython-pygments-lexers", marker = "python_full_version >= '3.11'" },
-    { name = "jedi" },
+    { name = "jedi", marker = "python_full_version >= '3.11'" },
-    { name = "matplotlib-inline" },
+    { name = "matplotlib-inline", marker = "python_full_version >= '3.11'" },
-    { name = "pexpect", marker = "sys_platform != 'emscripten' and sys_platform != 'win32'" },
+    { name = "pexpect", marker = "python_full_version >= '3.11' and sys_platform != 'emscripten' and sys_platform != 'win32'" },
-    { name = "prompt-toolkit" },
+    { name = "prompt-toolkit", marker = "python_full_version >= '3.11'" },
-    { name = "psutil" },
+    { name = "psutil", marker = "python_full_version >= '3.11'" },
-    { name = "pygments" },
+    { name = "pygments", marker = "python_full_version >= '3.11'" },
-    { name = "stack-data" },
+    { name = "stack-data", marker = "python_full_version >= '3.11'" },
-    { name = "traitlets" },
+    { name = "traitlets", marker = "python_full_version >= '3.11'" },
-    { name = "typing-extensions", marker = "python_full_version < '3.12'" },
+    { name = "typing-extensions", marker = "python_full_version == '3.11.*'" },
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 sdist = { url = "https://files.pythonhosted.org/packages/cd/c4/87cda5842cf5c31837c06ddb588e11c3c35d8ece89b7a0108c06b8c9b00a/ipython-9.13.0.tar.gz", hash = "sha256:7e834b6afc99f020e3f05966ced34792f40267d64cb1ea9043886dab0dde5967", size = 4430549, upload-time = "2026-04-24T12:24:55.221Z" }
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@@ -2697,7 +2697,7 @@ name = "ipython-pygments-lexers"
 version = "1.1.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "pygments" },
+    { name = "pygments", marker = "python_full_version >= '3.11'" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/ef/4c/5dd1d8af08107f88c7f741ead7a40854b8ac24ddf9ae850afbcf698aa552/ipython_pygments_lexers-1.1.1.tar.gz", hash = "sha256:09c0138009e56b6854f9535736f4171d855c8c08a563a0dcd8022f78355c7e81", size = 8393, upload-time = "2025-01-17T11:24:34.505Z" }
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@@ -3460,7 +3460,7 @@ resolution-markers = [
     "python_full_version < '3.11' and sys_platform != 'darwin' and sys_platform != 'linux' and sys_platform != 'win32'",
 ]
 dependencies = [
-    { name = "mdurl" },
+    { name = "mdurl", marker = "python_full_version < '3.11'" },
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 sdist = { url = "https://files.pythonhosted.org/packages/38/71/3b932df36c1a044d397a1f92d1cf91ee0a503d91e470cbd670aa66b07ed0/markdown-it-py-3.0.0.tar.gz", hash = "sha256:e3f60a94fa066dc52ec76661e37c851cb232d92f9886b15cb560aaada2df8feb", size = 74596, upload-time = "2023-06-03T06:41:14.443Z" }
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 ]
 dependencies = [
-    { name = "mdurl" },
+    { name = "mdurl", marker = "python_full_version >= '3.11'" },
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@@ -3961,12 +3961,12 @@ resolution-markers = [
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 ]
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-    { name = "docutils", version = "0.21.2", source = { registry = "https://pypi.org/simple" } },
+    { name = "docutils", version = "0.21.2", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" },
-    { name = "jinja2" },
+    { name = "jinja2", marker = "python_full_version < '3.11'" },
-    { name = "markdown-it-py", version = "3.0.0", source = { registry = "https://pypi.org/simple" } },
+    { name = "markdown-it-py", version = "3.0.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" },
-    { name = "mdit-py-plugins" },
+    { name = "mdit-py-plugins", marker = "python_full_version < '3.11'" },
-    { name = "pyyaml" },
+    { name = "pyyaml", marker = "python_full_version < '3.11'" },
-    { name = "sphinx", version = "8.1.3", source = { registry = "https://pypi.org/simple" } },
+    { name = "sphinx", version = "8.1.3", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" },
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@@ -3992,12 +3992,12 @@ resolution-markers = [
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 ]
 dependencies = [
-    { name = "docutils", version = "0.22.4", source = { registry = "https://pypi.org/simple" } },
+    { name = "docutils", version = "0.22.4", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
-    { name = "jinja2" },
+    { name = "jinja2", marker = "python_full_version >= '3.11'" },
-    { name = "markdown-it-py", version = "4.2.0", source = { registry = "https://pypi.org/simple" } },
+    { name = "markdown-it-py", version = "4.2.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
-    { name = "mdit-py-plugins" },
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