support ArrayLike data in to_xarray

This commit makes dianna swallow more kinds of data items without users having to mangle them manually themselves. As long as your data is numpy ArrayLike it will go. The only blocker for this was the direct use of the .ndim attribute on data, which assumes it is already a numpy array rather than something that can trivially be converted to a numpy array. A PIL.Image is an example of such an ArrayLike type. We use this in explainable_embedding to feed items into the OpenAI CLIP model.

dianna/utils/misc.py

@@ -1,6 +1,8 @@
 import inspect
 import warnings
 from pathlib import Path
+import numpy as np
+import numpy.typing
 
 
 def get_function(model_or_function, preprocess_function=None):
@@ -49,7 +51,7 @@ def get_kwargs_applicable_to_function(function, kwargs):
     }
 
 
-def to_xarray(data, axis_labels, required_labels=None):
+def to_xarray(data: numpy.typing.ArrayLike, axis_labels, required_labels=None):
     """Converts numpy data and axes labels to an xarray object."""
     if isinstance(axis_labels, dict):
         # key = axis index, value = label
@@ -59,10 +61,10 @@ def to_xarray(data, axis_labels, required_labels=None):
         indices = list(axis_labels.keys())
         for index in indices:
             if index < 0:
-                axis_labels[data.ndim + index] = axis_labels.pop(index)
+                axis_labels[np.ndim(data) + index] = axis_labels.pop(index)
         labels = [
             axis_labels[index] if index in axis_labels else f'dim_{index}'
-            for index in range(data.ndim)
+            for index in range(np.ndim(data))
         ]
     else:
         labels = list(axis_labels)

setup.cfg

@@ -39,7 +39,7 @@ install_requires =
     ipython
     lime
     matplotlib
-    numpy
+    numpy>=1.20
     onnx==1.14.1
     onnx_tf
     onnxruntime