fix input format conversion

README.md

@@ -24,8 +24,8 @@ https://torchmetrics.readthedocs.io/en/stable/classification/calibration_error.h
 
 ### Inputs
 *List all input arguments in the format below*
-- **input_field** *(tensor or numpy array, float32): predictions (after softmax). They must have a shape (N,C,...) if multiclass, or (N,...) if binary.*
-- **references** *(tensor or numpy array, int64): reference for each prediction, with a shape (N,...).*
+- **predictions** *(float32): predictions (after softmax). They must have a shape (N,C,...) if multiclass, or (N,...) if binary.*
+- **references** *(int64): reference for each prediction, with a shape (N,...).*
 
 ### Output Values
 

ece.py

@@ -16,7 +16,7 @@ from typing import Dict
 
 import evaluate
 import datasets
-from torch import from_numpy, amax
+from torch import Tensor, LongTensor, amax
 from torchmetrics.functional.classification.calibration_error import (
     binary_calibration_error,
     multiclass_calibration_error,
@@ -106,10 +106,8 @@ class ECE(evaluate.Metric):
         be used as "num_classes".
         """
         # Convert the input
-        if isinstance(predictions, ndarray):
-            predictions = from_numpy(predictions)
-        if isinstance(references, ndarray):
-            references = from_numpy(references)
+        predictions = Tensor(predictions)
+        references = LongTensor(references)
 
         max_label = amax(references, list(range(references.dim())))
         if max_label > 1 and "num_classes" not in kwargs: