Default to normal anchor

src/batdetect2/models/blocks.py

@@ -225,7 +225,7 @@ class ConvBlock(nn.Module):
             kernel_size=kernel_size,
             padding=pad_size,
         )
-        self.conv_bn = nn.BatchNorm2d(out_channels)
+        self.batch_norm = nn.BatchNorm2d(out_channels)
 
     def forward(self, x: torch.Tensor) -> torch.Tensor:
         """Apply Conv -> BN -> ReLU.
@@ -240,7 +240,7 @@ class ConvBlock(nn.Module):
         torch.Tensor
             Output tensor, shape `(B, C_out, H, W)`.
         """
-        return F.relu_(self.conv_bn(self.conv(x)))
+        return F.relu_(self.batch_norm(self.conv(x)))
 
 
 class VerticalConv(nn.Module):
@@ -364,7 +364,7 @@ class FreqCoordConvDownBlock(nn.Module):
             padding=pad_size,
             stride=1,
         )
-        self.conv_bn = nn.BatchNorm2d(out_channels)
+        self.batch_norm = nn.BatchNorm2d(out_channels)
 
     def forward(self, x: torch.Tensor) -> torch.Tensor:
         """Apply CoordF -> Conv -> MaxPool -> BN -> ReLU.
@@ -383,7 +383,7 @@ class FreqCoordConvDownBlock(nn.Module):
         freq_info = self.coords.repeat(x.shape[0], 1, 1, x.shape[3])
         x = torch.cat((x, freq_info), 1)
         x = F.max_pool2d(self.conv(x), 2, 2)
-        x = F.relu(self.conv_bn(x), inplace=True)
+        x = F.relu(self.batch_norm(x), inplace=True)
         return x
 
 
@@ -438,7 +438,7 @@ class StandardConvDownBlock(nn.Module):
             padding=pad_size,
             stride=1,
         )
-        self.conv_bn = nn.BatchNorm2d(out_channels)
+        self.batch_norm = nn.BatchNorm2d(out_channels)
 
     def forward(self, x):
         """Apply Conv -> MaxPool -> BN -> ReLU.
@@ -454,7 +454,7 @@ class StandardConvDownBlock(nn.Module):
             Output tensor, shape `(B, C_out, H/2, W/2)`.
         """
         x = F.max_pool2d(self.conv(x), 2, 2)
-        return F.relu(self.conv_bn(x), inplace=True)
+        return F.relu(self.batch_norm(x), inplace=True)
 
 
 class FreqCoordConvUpConfig(BaseConfig):
@@ -534,7 +534,7 @@ class FreqCoordConvUpBlock(nn.Module):
             kernel_size=kernel_size,
             padding=pad_size,
         )
-        self.conv_bn = nn.BatchNorm2d(out_channels)
+        self.batch_norm = nn.BatchNorm2d(out_channels)
 
     def forward(self, x: torch.Tensor) -> torch.Tensor:
         """Apply Interpolate -> Concat Coords -> Conv -> BN -> ReLU.
@@ -562,7 +562,7 @@ class FreqCoordConvUpBlock(nn.Module):
         freq_info = self.coords.repeat(op.shape[0], 1, 1, op.shape[3])
         op = torch.cat((op, freq_info), 1)
         op = self.conv(op)
-        op = F.relu(self.conv_bn(op), inplace=True)
+        op = F.relu(self.batch_norm(op), inplace=True)
         return op
 
 
@@ -625,7 +625,7 @@ class StandardConvUpBlock(nn.Module):
             kernel_size=kernel_size,
             padding=pad_size,
         )
-        self.conv_bn = nn.BatchNorm2d(out_channels)
+        self.batch_norm = nn.BatchNorm2d(out_channels)
 
     def forward(self, x: torch.Tensor) -> torch.Tensor:
         """Apply Interpolate -> Conv -> BN -> ReLU.
@@ -650,7 +650,7 @@ class StandardConvUpBlock(nn.Module):
             align_corners=False,
         )
         op = self.conv(op)
-        op = F.relu(self.conv_bn(op), inplace=True)
+        op = F.relu(self.batch_norm(op), inplace=True)
         return op
 
 

src/batdetect2/postprocess/__init__.py

@@ -51,7 +51,7 @@ __all__ = [
 DEFAULT_DETECTION_THRESHOLD = 0.01
 
 
-TOP_K_PER_SEC = 200
+TOP_K_PER_SEC = 100
 
 
 class PostprocessConfig(BaseConfig):
@@ -206,11 +206,13 @@ class Postprocessor(torch.nn.Module, PostprocessorProtocol):
         if clips is None:
             return detections
 
+        width = output.detection_probs.shape[-1]
+        duration = width / self.samplerate
         return [
             map_detection_to_clip(
                 detection,
                 start_time=clip.start_time,
-                end_time=clip.end_time,
+                end_time=clip.start_time + duration,
                 min_freq=self.min_freq,
                 max_freq=self.max_freq,
             )
@@ -220,9 +222,9 @@ class Postprocessor(torch.nn.Module, PostprocessorProtocol):
 
 def get_raw_predictions(
     output: ModelOutput,
-    clips: List[data.Clip],
     targets: TargetProtocol,
     postprocessor: PostprocessorProtocol,
+    clips: Optional[List[data.Clip]] = None,
 ) -> List[List[RawPrediction]]:
     """Extract intermediate RawPrediction objects for a batch.
 
@@ -259,9 +261,9 @@ def get_sound_event_predictions(
 ) -> List[List[BatDetect2Prediction]]:
     raw_predictions = get_raw_predictions(
         output,
-        clips,
         targets=targets,
         postprocessor=postprocessor,
+        clips=clips,
     )
     return [
         [
@@ -308,9 +310,9 @@ def get_predictions(
     """
     raw_predictions = get_raw_predictions(
         output,
-        clips,
         targets=targets,
         postprocessor=postprocessor,
+        clips=clips,
     )
     return [
         convert_raw_predictions_to_clip_prediction(

src/batdetect2/targets/classes.py

@@ -14,7 +14,7 @@ from batdetect2.data.conditions import (
     SoundEventConditionConfig,
     build_sound_event_condition,
 )
-from batdetect2.targets.rois import AnchorBBoxMapperConfig, ROIMapperConfig
+from batdetect2.targets.rois import ROIMapperConfig
 from batdetect2.typing.targets import SoundEventDecoder, SoundEventEncoder
 
 __all__ = [
@@ -140,7 +140,6 @@ DEFAULT_CLASSES = [
     TargetClassConfig(
         name="rhihip",
         tags=[data.Tag(key="class", value="Rhinolophus hipposideros")],
-        roi=AnchorBBoxMapperConfig(anchor="top-left"),
     ),
     TargetClassConfig(
         name="nyclei",
@@ -149,7 +148,6 @@ DEFAULT_CLASSES = [
     TargetClassConfig(
         name="rhifer",
         tags=[data.Tag(key="class", value="Rhinolophus ferrumequinum")],
-        roi=AnchorBBoxMapperConfig(anchor="top-left"),
     ),
     TargetClassConfig(
         name="pleaur",

src/batdetect2/train/config.py

@@ -52,7 +52,7 @@ class ValLoaderConfig(BaseConfig):
     num_workers: int = 0
 
     clipping_strategy: ClipConfig = Field(
-        default_factory=lambda: RandomClipConfig()
+        default_factory=lambda: PaddedClipConfig()
     )
 
 

src/batdetect2/train/labels.py

@@ -14,7 +14,8 @@ from loguru import logger
 from soundevent import data
 
 from batdetect2.configs import BaseConfig, load_config
-from batdetect2.targets import iterate_encoded_sound_events
+from batdetect2.preprocess import MAX_FREQ, MIN_FREQ
+from batdetect2.targets import build_targets, iterate_encoded_sound_events
 from batdetect2.typing import (
     ClipLabeller,
     Heatmaps,
@@ -45,9 +46,9 @@ class LabelConfig(BaseConfig):
 
 
 def build_clip_labeler(
-    targets: TargetProtocol,
-    min_freq: float,
-    max_freq: float,
+    targets: Optional[TargetProtocol] = None,
+    min_freq: float = MIN_FREQ,
+    max_freq: float = MAX_FREQ,
     config: Optional[LabelConfig] = None,
 ) -> ClipLabeller:
     """Construct the final clip labelling function."""
@@ -56,6 +57,10 @@ def build_clip_labeler(
         "Building clip labeler with config: \n{}",
         lambda: config.to_yaml_string(),
     )
+
+    if targets is None:
+        targets = build_targets()
+
     return partial(
         generate_heatmaps,
         targets=targets,

src/batdetect2/train/train.py

@@ -226,9 +226,9 @@ def build_trainer(
 
 def build_train_loader(
     clip_annotations: Sequence[data.ClipAnnotation],
-    audio_loader: AudioLoader,
-    labeller: ClipLabeller,
-    preprocessor: PreprocessorProtocol,
+    audio_loader: Optional[AudioLoader] = None,
+    labeller: Optional[ClipLabeller] = None,
+    preprocessor: Optional[PreprocessorProtocol] = None,
     config: Optional[TrainLoaderConfig] = None,
     num_workers: Optional[int] = None,
 ) -> DataLoader:
@@ -260,9 +260,9 @@ def build_train_loader(
 
 def build_val_loader(
     clip_annotations: Sequence[data.ClipAnnotation],
-    audio_loader: AudioLoader,
-    labeller: ClipLabeller,
-    preprocessor: PreprocessorProtocol,
+    audio_loader: Optional[AudioLoader] = None,
+    labeller: Optional[ClipLabeller] = None,
+    preprocessor: Optional[PreprocessorProtocol] = None,
     config: Optional[ValLoaderConfig] = None,
     num_workers: Optional[int] = None,
 ):
@@ -293,9 +293,9 @@ def build_val_loader(
 
 def build_train_dataset(
     clip_annotations: Sequence[data.ClipAnnotation],
-    audio_loader: AudioLoader,
-    labeller: ClipLabeller,
-    preprocessor: PreprocessorProtocol,
+    audio_loader: Optional[AudioLoader] = None,
+    labeller: Optional[ClipLabeller] = None,
+    preprocessor: Optional[PreprocessorProtocol] = None,
     config: Optional[TrainLoaderConfig] = None,
 ) -> TrainingDataset:
     logger.info("Building training dataset...")
@@ -303,6 +303,18 @@ def build_train_dataset(
 
     clipper = build_clipper(config=config.clipping_strategy)
 
+    if audio_loader is None:
+        audio_loader = build_audio_loader()
+
+    if preprocessor is None:
+        preprocessor = build_preprocessor()
+
+    if labeller is None:
+        labeller = build_clip_labeler(
+            min_freq=preprocessor.min_freq,
+            max_freq=preprocessor.max_freq,
+        )
+
     random_example_source = RandomAudioSource(
         clip_annotations,
         audio_loader=audio_loader,
@@ -332,14 +344,26 @@ def build_train_dataset(
 
 def build_val_dataset(
     clip_annotations: Sequence[data.ClipAnnotation],
-    audio_loader: AudioLoader,
-    labeller: ClipLabeller,
-    preprocessor: PreprocessorProtocol,
+    audio_loader: Optional[AudioLoader] = None,
+    labeller: Optional[ClipLabeller] = None,
+    preprocessor: Optional[PreprocessorProtocol] = None,
     config: Optional[ValLoaderConfig] = None,
 ) -> ValidationDataset:
     logger.info("Building validation dataset...")
     config = config or ValLoaderConfig()
 
+    if audio_loader is None:
+        audio_loader = build_audio_loader()
+
+    if preprocessor is None:
+        preprocessor = build_preprocessor()
+
+    if labeller is None:
+        labeller = build_clip_labeler(
+            min_freq=preprocessor.min_freq,
+            max_freq=preprocessor.max_freq,
+        )
+
     clipper = build_clipper(config.clipping_strategy)
     return ValidationDataset(
         clip_annotations,