warning to truncate affine

monai/transforms/spatial/array.py

@@ -499,6 +499,13 @@ def __call__(
             warnings.warn("`data_array` is not of type MetaTensor, assuming affine to be identity.")
             # default to identity
             input_affine = np.eye(sr + 1, dtype=np.float64)
+        input_affine_shape = input_affine.shape[0]
+        if input_affine_shape != sr + 1:
+            warnings.warn(
+                f"Expected `affine_matrix` of size ({sr+1},{sr+1}) for {sr}D input got "
+                f"({input_affine_shape},{input_affine_shape}). "
+                f"The affine matrix will be truncated for {sr}D input."
+            )
         affine_ = to_affine_nd(sr, convert_data_type(input_affine, np.ndarray)[0])
 
         out_d = self.pixdim[:sr].copy()

monai/transforms/spatial/functional.py

@@ -101,6 +101,12 @@ def spatial_resample(
     spatial_rank = min(len(img.shape) - 1, src_affine.shape[0] - 1, 3)
     if (not isinstance(spatial_size, int) or spatial_size != -1) and spatial_size is not None:
         spatial_rank = min(len(ensure_tuple(spatial_size)), 3)  # infer spatial rank based on spatial_size
+    if src_affine.shape[0] != spatial_rank + 1:
+        warnings.warn(
+            f"Expected `affine_matrix` of size ({spatial_rank+1},{spatial_rank+1}) "
+            f"for {spatial_rank}D input got ({src_affine.shape[0]},{src_affine.shape[0]}). "
+            f"The affine matrix will be truncated for {spatial_rank}D input."
+        )
     src_affine = to_affine_nd(spatial_rank, src_affine).to(torch.float64)
     dst_affine = to_affine_nd(spatial_rank, dst_affine) if dst_affine is not None else src_affine
     dst_affine = convert_to_dst_type(dst_affine, src_affine)[0]