diff --git a/test/test_transforms.py b/test/test_transforms.py
index 25d61fafeb4..b92b73d21c7 100644
--- a/test/test_transforms.py
+++ b/test/test_transforms.py
@@ -1955,6 +1955,25 @@ def test_autoaugment(self):
                     img = transform(img)
                 transform.__repr__()
 
+    def test_random_erasing(self):
+        img = torch.ones(3, 128, 128)
+
+        t = transforms.RandomErasing(scale=(0.1, 0.1), ratio=(1. / 3., 3. / 1.))
+        y, x, h, w, v = t.get_params(img, t.scale, t.ratio, [t.value, ])
+        aspect_ratio = h / w
+        self.assertTrue(aspect_ratio > 1. / 3. and aspect_ratio < 3. / 1.)
+
+        aspect_ratios = []
+        random.seed(42)
+        trial = 1000
+        for _ in range(trial):
+            y, x, h, w, v = t.get_params(img, t.scale, t.ratio, [t.value, ])
+            aspect_ratios.append(h / w)
+
+        count_bigger_then_ones = len([1 for aspect_ratio in aspect_ratios if aspect_ratio > 1])
+        count_smaller_then_ones = len([1 for aspect_ratio in aspect_ratios if aspect_ratio < 1])
+        self.assertAlmostEqual(count_bigger_then_ones / trial, count_smaller_then_ones / trial, 1)
+
 
 if __name__ == '__main__':
     unittest.main()
diff --git a/torchvision/transforms/transforms.py b/torchvision/transforms/transforms.py
index 30911978558..916956e29fd 100644
--- a/torchvision/transforms/transforms.py
+++ b/torchvision/transforms/transforms.py
@@ -828,9 +828,9 @@ def get_params(
         width, height = F._get_image_size(img)
         area = height * width
 
+        log_ratio = torch.log(torch.tensor(ratio))
         for _ in range(10):
             target_area = area * torch.empty(1).uniform_(scale[0], scale[1]).item()
-            log_ratio = torch.log(torch.tensor(ratio))
             aspect_ratio = torch.exp(
                 torch.empty(1).uniform_(log_ratio[0], log_ratio[1])
             ).item()
@@ -1576,9 +1576,12 @@ def get_params(
         img_c, img_h, img_w = img.shape[-3], img.shape[-2], img.shape[-1]
         area = img_h * img_w
 
+        log_ratio = torch.log(torch.tensor(ratio))
         for _ in range(10):
             erase_area = area * torch.empty(1).uniform_(scale[0], scale[1]).item()
-            aspect_ratio = torch.empty(1).uniform_(ratio[0], ratio[1]).item()
+            aspect_ratio = torch.exp(
+                torch.empty(1).uniform_(log_ratio[0], log_ratio[1])
+            ).item()
 
             h = int(round(math.sqrt(erase_area * aspect_ratio)))
             w = int(round(math.sqrt(erase_area / aspect_ratio)))