add tensor kernels for normalize and erase

torchvision/transforms/functional.py

@@ -338,30 +338,9 @@ def normalize(tensor: Tensor, mean: List[float], std: List[float], inplace: bool
     if not torch.jit.is_scripting() and not torch.jit.is_tracing():
         _log_api_usage_once(normalize)
     if not isinstance(tensor, torch.Tensor):
-        raise TypeError(f"Input tensor should be a torch tensor. Got {type(tensor)}.")
+        raise TypeError(f"img should be Tensor Image. Got {type(tensor)}")
 
-    if not tensor.is_floating_point():
-        raise TypeError(f"Input tensor should be a float tensor. Got {tensor.dtype}.")
-
-    if tensor.ndim < 3:
-        raise ValueError(
-            f"Expected tensor to be a tensor image of size (..., C, H, W). Got tensor.size() = {tensor.size()}"
-        )
-
-    if not inplace:
-        tensor = tensor.clone()
-
-    dtype = tensor.dtype
-    mean = torch.as_tensor(mean, dtype=dtype, device=tensor.device)
-    std = torch.as_tensor(std, dtype=dtype, device=tensor.device)
-    if (std == 0).any():
-        raise ValueError(f"std evaluated to zero after conversion to {dtype}, leading to division by zero.")
-    if mean.ndim == 1:
-        mean = mean.view(-1, 1, 1)
-    if std.ndim == 1:
-        std = std.view(-1, 1, 1)
-    tensor.sub_(mean).div_(std)
-    return tensor
+    return F_t.normalize(tensor, mean=mean, std=std, inplace=inplace)
 
 
 def resize(
@@ -1281,11 +1260,7 @@ def erase(img: Tensor, i: int, j: int, h: int, w: int, v: Tensor, inplace: bool
     if not isinstance(img, torch.Tensor):
         raise TypeError(f"img should be Tensor Image. Got {type(img)}")
 
-    if not inplace:
-        img = img.clone()
-
-    img[..., i : i + h, j : j + w] = v
-    return img
+    return F_t.erase(img, i, j, h, w, v, inplace=inplace)
 
 
 def gaussian_blur(img: Tensor, kernel_size: List[int], sigma: Optional[List[float]] = None) -> Tensor:

torchvision/transforms/functional_tensor.py

@@ -918,3 +918,40 @@ def equalize(img: Tensor) -> Tensor:
         return _equalize_single_image(img)
 
     return torch.stack([_equalize_single_image(x) for x in img])
+
+
+def normalize(tensor: Tensor, mean: List[float], std: List[float], inplace: bool = False) -> Tensor:
+    _assert_image_tensor(tensor)
+
+    if not tensor.is_floating_point():
+        raise TypeError(f"Input tensor should be a float tensor. Got {tensor.dtype}.")
+
+    if tensor.ndim < 3:
+        raise ValueError(
+            f"Expected tensor to be a tensor image of size (..., C, H, W). Got tensor.size() = {tensor.size()}"
+        )
+
+    if not inplace:
+        tensor = tensor.clone()
+
+    dtype = tensor.dtype
+    mean = torch.as_tensor(mean, dtype=dtype, device=tensor.device)
+    std = torch.as_tensor(std, dtype=dtype, device=tensor.device)
+    if (std == 0).any():
+        raise ValueError(f"std evaluated to zero after conversion to {dtype}, leading to division by zero.")
+    if mean.ndim == 1:
+        mean = mean.view(-1, 1, 1)
+    if std.ndim == 1:
+        std = std.view(-1, 1, 1)
+    tensor.sub_(mean).div_(std)
+    return tensor
+
+
+def erase(img: Tensor, i: int, j: int, h: int, w: int, v: Tensor, inplace: bool = False) -> Tensor:
+    _assert_image_tensor(img)
+
+    if not inplace:
+        img = img.clone()
+
+    img[..., i : i + h, j : j + w] = v
+    return img