Revert "assume that integer images are [0, 255] in equalize (pytorch#…

…6859)"

This reverts commit 436ff9a.

torchvision/prototype/transforms/functional/_color.py

@@ -387,14 +387,18 @@ def equalize_image_tensor(image: torch.Tensor) -> torch.Tensor:
     if image.numel() == 0:
         return image
 
+    # 1. The algorithm below can easily be extended to support arbitrary integer dtypes. However, the histogram that
+    #    would be needed to computed will have at least `torch.iinfo(dtype).max + 1` values. That is perfectly fine for
+    #    `torch.int8`, `torch.uint8`, and `torch.int16`, at least questionable for `torch.int32` and completely
+    #    unfeasible for `torch.int64`.
+    # 2. Floating point inputs need to be binned for this algorithm. Apart from converting them to an integer dtype, we
+    #    could also use PyTorch's builtin histogram functionality. However, that has its own set of issues: in addition
+    #    to being slow in general, PyTorch's implementation also doesn't support batches. In total, that makes it slower
+    #    and more complicated to implement than a simple conversion and a fast histogram implementation for integers.
+    # Since we need to convert in most cases anyway and out of the acceptable dtypes mentioned in 1. `torch.uint8` is
+    # by far the most common, we choose it as base.
     output_dtype = image.dtype
-    if image.is_floating_point():
-        # Floating point inputs need to be binned for this algorithm. Apart from converting them to an integer dtype, we
-        # could also use PyTorch's builtin histogram functionality. However, that has its own set of issues: in addition
-        # to being slow in general, PyTorch's implementation also doesn't support batches. In total, that makes it
-        # slower and more complicated to implement than a simple conversion and a fast histogram implementation for
-        # integers.
-        image = convert_dtype_image_tensor(image, torch.uint8)
+    image = convert_dtype_image_tensor(image, torch.uint8)
 
     # The histogram is computed by using the flattened image as index. For example, a pixel value of 127 in the image
     # corresponds to adding 1 to index 127 in the histogram.