Merge branch 'train' of github.com:macaodha/batdetect2 into train

batdetect2/postprocess/remapping.py

@@ -84,7 +84,7 @@ def features_to_xarray(
     freqs = np.linspace(min_freq, max_freq, height, endpoint=False)
 
     return xr.DataArray(
-        data=features.detach().numpy(),
+        data=features.detach().cpu().numpy(),
         dims=[
             Dimensions.feature.value,
             Dimensions.frequency.value,
@@ -157,7 +157,7 @@ def detection_to_xarray(
     freqs = np.linspace(min_freq, max_freq, height, endpoint=False)
 
     return xr.DataArray(
-        data=detection.squeeze(dim=0).detach().numpy(),
+        data=detection.squeeze(dim=0).detach().cpu().numpy(),
         dims=[
             Dimensions.frequency.value,
             Dimensions.time.value,
@@ -233,7 +233,7 @@ def classification_to_xarray(
     freqs = np.linspace(min_freq, max_freq, height, endpoint=False)
 
     return xr.DataArray(
-        data=classes.detach().numpy(),
+        data=classes.detach().cpu().numpy(),
         dims=[
             "category",
             Dimensions.frequency.value,
@@ -302,7 +302,7 @@ def sizes_to_xarray(
     freqs = np.linspace(min_freq, max_freq, height, endpoint=False)
 
     return xr.DataArray(
-        data=sizes.detach().numpy(),
+        data=sizes.detach().cpu().numpy(),
         dims=[
             "dimension",
             Dimensions.frequency.value,

batdetect2/train/dataset.py

@@ -9,6 +9,7 @@ from torch.utils.data import Dataset
 
 from batdetect2.train.augmentations import Augmentation
 from batdetect2.train.types import ClipperProtocol, TrainExample
+from batdetect2.utils.tensors import adjust_width
 
 __all__ = [
     "LabeledDataset",
@@ -87,6 +88,26 @@ class LabeledDataset(Dataset):
         )
 
 
+def collate_fn(batch: List[TrainExample]):
+    width = 512
+
+    return TrainExample(
+        spec=torch.stack([adjust_width(x.spec, width) for x in batch]),
+        detection_heatmap=torch.stack(
+            [adjust_width(x.detection_heatmap, width) for x in batch]
+        ),
+        class_heatmap=torch.stack(
+            [adjust_width(x.class_heatmap, width) for x in batch]
+        ),
+        size_heatmap=torch.stack(
+            [adjust_width(x.size_heatmap, width) for x in batch]
+        ),
+        idx=torch.stack([x.idx for x in batch]),
+        start_time=torch.stack([x.start_time for x in batch]),
+        end_time=torch.stack([x.end_time for x in batch]),
+    )
+
+
 def list_preprocessed_files(
     directory: data.PathLike, extension: str = ".nc"
 ) -> List[Path]:

batdetect2/train/preprocess.py

@@ -276,8 +276,11 @@ def preprocess_single_annotation(
             labeller=labeller,
         )
     except Exception as error:
-        raise RuntimeError(
+        logger.error(
-            f"Failed to process annotation: {clip_annotation.uuid}"
+            "Failed to process annotation: {uuid}. Error {error}",
-        ) from error
+            uuid=clip_annotation.uuid,
+            error=error,
+        )
+        return
 
     _save_xr_dataset_to_file(sample, path)

batdetect2/train/train.py

@@ -17,7 +17,11 @@ from batdetect2.train.augmentations import (
 )
 from batdetect2.train.clips import build_clipper
 from batdetect2.train.config import TrainingConfig
-from batdetect2.train.dataset import LabeledDataset, RandomExampleSource
+from batdetect2.train.dataset import (
+    LabeledDataset,
+    RandomExampleSource,
+    collate_fn,
+)
 from batdetect2.train.lightning import TrainingModule
 from batdetect2.train.losses import build_loss
 
@@ -88,6 +92,7 @@ def train(
         batch_size=config.batch_size,
         shuffle=True,
         num_workers=train_workers,
+        collate_fn=collate_fn,
     )
 
     val_dataloader = None
@@ -101,6 +106,7 @@ def train(
             batch_size=config.batch_size,
             shuffle=False,
             num_workers=val_workers,
+            collate_fn=collate_fn,
         )
 
     trainer.fit(