Adapter.reshape()

bayesflow/adapters/adapter.py

@@ -21,6 +21,7 @@
     NumpyTransform,
     OneHot,
     Rename,
+    Reshape,
     SerializableCustomTransform,
     Squeeze,
     Sqrt,
@@ -746,6 +747,25 @@ def rename(self, from_key: str, to_key: str):
         self.transforms.append(Rename(from_key, to_key))
         return self
 
+    def reshape(self, keys: str | Sequence[str], *, to: int | Sequence[int]):
+        """Append a :py:class:`~transforms.Reshape` transform to the adapter.
+
+        Parameters
+        ----------
+        keys : str or Sequence of str
+            Variables that should be reshaped
+        to : int or tuple of int
+            Target shape of the variables
+        """
+        from .transforms import Reshape
+
+        if isinstance(keys, str):
+            keys = [keys]
+
+        transform = MapTransform({key: Reshape(shape=to) for key in keys})
+        self.transforms.append(transform)
+        return self
+
     def scale(self, keys: str | Sequence[str], by: float | np.ndarray):
         from .transforms import Scale
 

bayesflow/adapters/transforms/__init__.py

@@ -15,6 +15,7 @@
 from .numpy_transform import NumpyTransform
 from .one_hot import OneHot
 from .rename import Rename
+from .reshape import Reshape
 from .scale import Scale
 from .serializable_custom_transform import SerializableCustomTransform
 from .shift import Shift

bayesflow/adapters/transforms/reshape.py

@@ -0,0 +1,28 @@
+import numpy as np
+
+from collections.abc import Sequence
+from bayesflow.utils.serialization import serializable, serialize
+
+from .elementwise_transform import ElementwiseTransform
+
+
+@serializable("bayesflow.adapters")
+class Reshape(ElementwiseTransform):
+
+    def __init__(self, shape: int | Sequence[int]):
+        super().__init__()
+
+        if isinstance(shape, Sequence):
+            shape = tuple(shape)
+        self.shape = shape
+
+    def forward(self, data: np.ndarray, **kwargs) -> np.ndarray:
+        return np.reshape(data, self.shape)
+
+
+    def inverse(self, data: np.ndarray, **kwargs) -> np.ndarray:
+        return np.reshape(data, self.shape)
+
+
+    def get_config(self) -> dict:
+        return {"shape": self.shape}