removed from transform functions

Author: nkaenzig

monai/transforms/utility/array.py

@@ -66,7 +66,6 @@
     optional_import,
 )
 from monai.utils.enums import TransformBackends
-from monai.utils.misc import is_module_ver_at_least
 from monai.utils.type_conversion import convert_to_dst_type, get_dtype_string, get_equivalent_dtype
 
 PILImageImage, has_pil = optional_import("PIL.Image", name="Image")
@@ -939,19 +938,10 @@ def __call__(
             data = img[[*select_labels]]
         else:
             where: Callable = np.where if isinstance(img, np.ndarray) else torch.where  # type: ignore
-            if isinstance(img, np.ndarray) or is_module_ver_at_least(torch, (1, 8, 0)):
-                data = where(in1d(img, select_labels), True, False).reshape(img.shape)
-            # pre pytorch 1.8.0, need to use 1/0 instead of True/False
-            else:
-                data = where(
-                    in1d(img, select_labels), torch.tensor(1, device=img.device), torch.tensor(0, device=img.device)
-                ).reshape(img.shape)
+            data = where(in1d(img, select_labels), True, False).reshape(img.shape)
 
         if merge_channels or self.merge_channels:
-            if isinstance(img, np.ndarray) or is_module_ver_at_least(torch, (1, 8, 0)):
-                return data.any(0)[None]
-            # pre pytorch 1.8.0 compatibility
-            return data.to(torch.uint8).any(0)[None].to(bool)  # type: ignore
+            return data.any(0)[None]
 
         return data
 

monai/transforms/utils_pytorch_numpy_unification.py

@@ -18,7 +18,6 @@
 import torch
 
 from monai.config.type_definitions import NdarrayOrTensor, NdarrayTensor
-from monai.utils.misc import is_module_ver_at_least
 from monai.utils.type_conversion import convert_data_type, convert_to_dst_type
 
 __all__ = [
@@ -215,10 +214,9 @@ def floor_divide(a: NdarrayOrTensor, b) -> NdarrayOrTensor:
         Element-wise floor division between two arrays/tensors.
     """
     if isinstance(a, torch.Tensor):
-        if is_module_ver_at_least(torch, (1, 8, 0)):
-            return torch.div(a, b, rounding_mode="floor")
         return torch.floor_divide(a, b)
-    return np.floor_divide(a, b)
+    else:
+        return np.floor_divide(a, b)
 
 
 def unravel_index(idx, shape) -> NdarrayOrTensor: