Merge branch 'main' into dev

Author: LarsKue

bayesflow/datasets/offline_dataset.py

@@ -2,6 +2,7 @@
 import numpy as np
 
 from bayesflow.adapters import Adapter
+from bayesflow.utils import logging
 
 
 class OfflineDataset(keras.utils.PyDataset):
@@ -11,12 +12,20 @@ class OfflineDataset(keras.utils.PyDataset):
     See the `DiskDataset` class for handling large datasets that are split into multiple smaller files.
     """
 
-    def __init__(self, data: dict[str, np.ndarray], batch_size: int, adapter: Adapter | None, **kwargs):
+    def __init__(
+        self, data: dict[str, np.ndarray], batch_size: int, adapter: Adapter | None, num_samples: int = None, **kwargs
+    ):
         super().__init__(**kwargs)
         self.batch_size = batch_size
         self.data = data
         self.adapter = adapter
-        self.num_samples = next(iter(data.values())).shape[0]
+
+        if num_samples is None:
+            self.num_samples = self._get_num_samples_from_data(data)
+            logging.debug(f"Automatically determined {self.num_samples} samples in data.")
+        else:
+            self.num_samples = num_samples
+
         self.indices = np.arange(self.num_samples, dtype="int64")
 
         self.shuffle()
@@ -29,7 +38,10 @@ def __getitem__(self, item: int) -> dict[str, np.ndarray]:
         item = slice(item * self.batch_size, (item + 1) * self.batch_size)
         item = self.indices[item]
 
-        batch = {key: np.take(value, item, axis=0) for key, value in self.data.items()}
+        batch = {
+            key: np.take(value, item, axis=0) if isinstance(value, np.ndarray) else value
+            for key, value in self.data.items()
+        }
 
         if self.adapter is not None:
             batch = self.adapter(batch)
@@ -46,3 +58,13 @@ def on_epoch_end(self) -> None:
     def shuffle(self) -> None:
         """Shuffle the dataset in-place."""
         np.random.shuffle(self.indices)
+
+    @staticmethod
+    def _get_num_samples_from_data(data: dict) -> int:
+        for key, value in data.items():
+            if hasattr(value, "shape"):
+                ndim = len(value.shape)
+                if ndim > 1:
+                    return value.shape[0]
+
+        raise ValueError("Could not determine number of samples from data. Please pass it manually.")