diff --git a/torchvision/transforms/transforms.py b/torchvision/transforms/transforms.py
index b995101c3c7..efd54bf6c54 100644
--- a/torchvision/transforms/transforms.py
+++ b/torchvision/transforms/transforms.py
@@ -3,7 +3,7 @@
 import random
 import warnings
 from collections.abc import Sequence
-from typing import Tuple, List, Optional
+from typing import Tuple, List, Optional, Union
 
 import torch
 from PIL import Image
@@ -1018,8 +1018,9 @@ def __init__(self, brightness=0, contrast=0, saturation=0, hue=0):
         self.hue = self._check_input(hue, 'hue', center=0, bound=(-0.5, 0.5),
                                      clip_first_on_zero=False)
 
+    @staticmethod
     @torch.jit.unused
-    def _check_input(self, value, name, center=1, bound=(0, float('inf')), clip_first_on_zero=True):
+    def _check_input(value, name, center=1, bound=(0, float('inf')), clip_first_on_zero=True):
         if isinstance(value, numbers.Number):
             if value < 0:
                 raise ValueError("If {} is a single number, it must be non negative.".format(name))
@@ -1040,7 +1041,8 @@ def _check_input(self, value, name, center=1, bound=(0, float('inf')), clip_firs
 
     @staticmethod
     @torch.jit.unused
-    def get_params(brightness, contrast, saturation, hue):
+    def get_params(brightness: Union[float, tuple], contrast: Union[float, tuple],
+                   saturation: Union[float, tuple], hue: Union[float, tuple]):
         """Get a randomized transform to be applied on image.
 
         Arguments are same as that of __init__.
@@ -1052,18 +1054,26 @@ def get_params(brightness, contrast, saturation, hue):
         transforms = []
 
         if brightness is not None:
+            if isinstance(brightness, float):
+                ColorJitter._check_input(brightness, 'brightness')
             brightness_factor = random.uniform(brightness[0], brightness[1])
             transforms.append(Lambda(lambda img: F.adjust_brightness(img, brightness_factor)))
 
         if contrast is not None:
+            if isinstance(contrast, float):
+                ColorJitter._check_input(contrast, 'contrast')
             contrast_factor = random.uniform(contrast[0], contrast[1])
             transforms.append(Lambda(lambda img: F.adjust_contrast(img, contrast_factor)))
 
         if saturation is not None:
+            if isinstance(saturation, float):
+                ColorJitter._check_input(saturation, 'saturation')
             saturation_factor = random.uniform(saturation[0], saturation[1])
             transforms.append(Lambda(lambda img: F.adjust_saturation(img, saturation_factor)))
 
         if hue is not None:
+            if isinstance(hue, float):
+                ColorJitter._check_input(hue, 'hue')
             hue_factor = random.uniform(hue[0], hue[1])
             transforms.append(Lambda(lambda img: F.adjust_hue(img, hue_factor)))