fixes variable names

Signed-off-by: Wenqi Li <wenqil@nvidia.com>

Author: wyli

monai/transforms/croppad/functional.py

@@ -28,7 +28,7 @@
 __all__ = ["pad_func", "crop_func"]
 
 
-def pad_func(img_t, to_pad_, mode_, kwargs_, transform_info):
+def pad_func(img_t, to_pad_, mode, kwargs, transform_info):
     extra_info = {"padded": to_pad_}
     img_size = img_t.peek_pending_shape() if isinstance(img_t, MetaTensor) else img_t.shape[1:]
     _affine = (
@@ -55,25 +55,25 @@ def pad_func(img_t, to_pad_, mode_, kwargs_, transform_info):
                 extra_info=extra_info,
                 transform_info=transform_info,
             )
-        if mode_ in {"linear_ramp", "maximum", "mean", "median", "minimum", "symmetric", "empty"}:
-            out = monai.transforms.Pad._np_pad(img_t, pad_width=to_pad_, mode=mode_, **kwargs_)
+        if mode in {"linear_ramp", "maximum", "mean", "median", "minimum", "symmetric", "empty"}:
+            out = monai.transforms.Pad._np_pad(img_t, pad_width=to_pad_, mode=mode, **kwargs)
         else:
-            mode_ = convert_pad_mode(dst=img_t, mode=mode_).value
+            mode = convert_pad_mode(dst=img_t, mode=mode).value
             try:
                 _pad = (
                     monai.transforms.Pad._pt_pad
-                    if mode_ in {"reflect", "replicate"}
+                    if mode in {"reflect", "replicate"}
                     and img_t.dtype not in {torch.int16, torch.int64, torch.bool, torch.uint8}
                     else monai.transforms.Pad._np_pad
                 )
-                out = _pad(img_t, pad_width=to_pad_, mode=mode_, **kwargs_)
+                out = _pad(img_t, pad_width=to_pad_, mode=mode, **kwargs)
             except (ValueError, TypeError, RuntimeError) as err:
                 if isinstance(err, NotImplementedError) or any(
                     k in str(err) for k in ("supported", "unexpected keyword", "implemented")
                 ):
-                    out = monai.transforms.Pad._np_pad(img_t, pad_width=to_pad_, mode=mode_, **kwargs_)
+                    out = monai.transforms.Pad._np_pad(img_t, pad_width=to_pad_, mode=mode, **kwargs)
                 else:
-                    raise ValueError(f"{img_t.shape} {to_pad_} {mode_} {kwargs_} {img_t.dtype} {img_t.device}") from err
+                    raise ValueError(f"{img_t.shape} {to_pad_} {mode} {kwargs} {img_t.dtype} {img_t.device}") from err
     else:
         out = img_t
     if get_track_meta():

monai/transforms/spatial/functional.py

@@ -184,7 +184,7 @@ def orientation(data_array, original_affine, spatial_ornt, transform_info):
     if get_track_meta():
         new_affine = to_affine_nd(len(spatial_shape), original_affine) @ affine_x
         new_affine = to_affine_nd(original_affine, new_affine)
-        new_affine, *_ = convert_data_type(new_affine, torch.Tensor, dtype=torch.float32, device=data_array.device)
+        new_affine, *_ = convert_data_type(new_affine, torch.Tensor, dtype=torch.float64, device=data_array.device)
         data_array.affine = new_affine
     return TraceableTransform.track_transform(data_array, extra_info=extra_info, transform_info=transform_info)
 
@@ -418,8 +418,8 @@ def update_meta(img, spatial_size, new_spatial_size, axes, k):
     return TraceableTransform.track_transform(out, extra_info=extra_info, transform_info=transform_info)
 
 
-def affine_func(img, affine, grid, resampler, sp_size, _mode, _padding_mode, do_resampling, image_only, transform_info):
-    extra_info = {"affine": affine, "mode": _mode, "padding_mode": _padding_mode, "do_resampling": do_resampling}
+def affine_func(img, affine, grid, resampler, sp_size, mode, padding_mode, do_resampling, image_only, transform_info):
+    extra_info = {"affine": affine, "mode": mode, "padding_mode": padding_mode, "do_resampling": do_resampling}
     img_size = img.peek_pending_shape() if isinstance(img, MetaTensor) else img.shape[1:]
     if transform_info.get(TraceKeys.LAZY_EVALUATION):
         if not get_track_meta():
@@ -436,7 +436,7 @@ def affine_func(img, affine, grid, resampler, sp_size, _mode, _padding_mode, do_
         )
         return img if image_only else (img, affine)
     if do_resampling:
-        out = resampler(img=img, grid=grid, mode=_mode, padding_mode=_padding_mode)
+        out = resampler(img=img, grid=grid, mode=mode, padding_mode=padding_mode)
     else:
         out = convert_data_type(img, dtype=torch.float32, device=resampler.device)[0]