Fix typos and grammar errors (#7065)

* fix typos throughout the code base

* fix grammar

* revert formatting changes to gallery

* revert 'an uXX'

* remove 'number of the best'

torchvision/transforms/functional_tensor.py

@@ -272,7 +272,7 @@ def _rgb2hsv(img: Tensor) -> Tensor:
     #   + S channel has division by `maxc`, which is zero only if `maxc = minc`
     #   + H channel has division by `(maxc - minc)`.
     #
-    # Instead of overwriting NaN afterwards, we just prevent it from occuring so
+    # Instead of overwriting NaN afterwards, we just prevent it from occurring, so
     # we don't need to deal with it in case we save the NaN in a buffer in
     # backprop, if it is ever supported, but it doesn't hurt to do so.
     eqc = maxc == minc
@@ -416,7 +416,7 @@ def pad(
     out_dtype = img.dtype
     need_cast = False
     if (padding_mode != "constant") and img.dtype not in (torch.float32, torch.float64):
-        # Here we temporary cast input tensor to float
+        # Here we temporarily cast input tensor to float
         # until pytorch issue is resolved :
         # https://github.com/pytorch/pytorch/issues/40763
         need_cast = True

torchvision/transforms/transforms.py

@@ -304,7 +304,7 @@ class Resize(torch.nn.Module):
             the resized image: if the longer edge of the image is greater
             than ``max_size`` after being resized according to ``size``, then
             the image is resized again so that the longer edge is equal to
-            ``max_size``. As a result, ``size`` might be overruled, i.e the
+            ``max_size``. As a result, ``size`` might be overruled, i.e. the
             smaller edge may be shorter than ``size``. This is only supported
             if ``size`` is an int (or a sequence of length 1 in torchscript
             mode).
@@ -1160,7 +1160,7 @@ class ColorJitter(torch.nn.Module):
             or the given [min, max]. Should be non negative numbers.
         contrast (float or tuple of float (min, max)): How much to jitter contrast.
             contrast_factor is chosen uniformly from [max(0, 1 - contrast), 1 + contrast]
-            or the given [min, max]. Should be non negative numbers.
+            or the given [min, max]. Should be non-negative numbers.
         saturation (float or tuple of float (min, max)): How much to jitter saturation.
             saturation_factor is chosen uniformly from [max(0, 1 - saturation), 1 + saturation]
             or the given [min, max]. Should be non negative numbers.
@@ -1381,10 +1381,10 @@ class RandomAffine(torch.nn.Module):
         scale (tuple, optional): scaling factor interval, e.g (a, b), then scale is
             randomly sampled from the range a <= scale <= b. Will keep original scale by default.
         shear (sequence or number, optional): Range of degrees to select from.
-            If shear is a number, a shear parallel to the x axis in the range (-shear, +shear)
-            will be applied. Else if shear is a sequence of 2 values a shear parallel to the x axis in the
+            If shear is a number, a shear parallel to the x-axis in the range (-shear, +shear)
+            will be applied. Else if shear is a sequence of 2 values a shear parallel to the x-axis in the
             range (shear[0], shear[1]) will be applied. Else if shear is a sequence of 4 values,
-            a x-axis shear in (shear[0], shear[1]) and y-axis shear in (shear[2], shear[3]) will be applied.
+            an x-axis shear in (shear[0], shear[1]) and y-axis shear in (shear[2], shear[3]) will be applied.
             Will not apply shear by default.
         interpolation (InterpolationMode): Desired interpolation enum defined by
             :class:`torchvision.transforms.InterpolationMode`. Default is ``InterpolationMode.NEAREST``.
@@ -1602,7 +1602,7 @@ class RandomGrayscale(torch.nn.Module):
 
 
 class RandomErasing(torch.nn.Module):
-    """Randomly selects a rectangle region in an torch Tensor image and erases its pixels.
+    """Randomly selects a rectangle region in a torch.Tensor image and erases its pixels.
     This transform does not support PIL Image.
     'Random Erasing Data Augmentation' by Zhong et al. See https://arxiv.org/abs/1708.04896
 
@@ -1938,7 +1938,7 @@ class RandomAdjustSharpness(torch.nn.Module):
 
     Args:
         sharpness_factor (float):  How much to adjust the sharpness. Can be
-            any non negative number. 0 gives a blurred image, 1 gives the
+            any non-negative number. 0 gives a blurred image, 1 gives the
             original image while 2 increases the sharpness by a factor of 2.
         p (float): probability of the image being sharpened. Default value is 0.5
     """

torchvision/utils.py

@@ -145,7 +145,7 @@ def save_image(
     if not torch.jit.is_scripting() and not torch.jit.is_tracing():
         _log_api_usage_once(save_image)
     grid = make_grid(tensor, **kwargs)
-    # Add 0.5 after unnormalizing to [0, 255] to round to nearest integer
+    # Add 0.5 after unnormalizing to [0, 255] to round to the nearest integer
     ndarr = grid.mul(255).add_(0.5).clamp_(0, 255).permute(1, 2, 0).to("cpu", torch.uint8).numpy()
     im = Image.fromarray(ndarr)
     im.save(fp, format=format)
@@ -565,7 +565,7 @@ def _log_api_usage_once(obj: Any) -> None:
 def _make_ntuple(x: Any, n: int) -> Tuple[Any, ...]:
     """
     Make n-tuple from input x. If x is an iterable, then we just convert it to tuple.
-    Otherwise we will make a tuple of length n, all with value of x.
+    Otherwise, we will make a tuple of length n, all with value of x.
     reference: https://github.com/pytorch/pytorch/blob/master/torch/nn/modules/utils.py#L8
 
     Args: