diff --git a/test/test_transforms.py b/test/test_transforms.py
index 7346e2c5094..b8045703267 100644
--- a/test/test_transforms.py
+++ b/test/test_transforms.py
@@ -219,6 +219,11 @@ def test_resize(self):
         width = random.randint(24, 32) * 2
         osize = random.randint(5, 12) * 2
 
+        # TODO: Check output size check for bug-fix, improve this later
+        t = transforms.Resize(osize)
+        self.assertTrue(isinstance(t.size, int))
+        self.assertEqual(t.size, osize)
+
         img = torch.ones(3, height, width)
         result = transforms.Compose([
             transforms.ToPILImage(),
diff --git a/test/test_transforms_tensor.py b/test/test_transforms_tensor.py
index 51c9cd0280b..1fc0ab61ec4 100644
--- a/test/test_transforms_tensor.py
+++ b/test/test_transforms_tensor.py
@@ -280,6 +280,17 @@ def test_ten_crop(self):
         )
 
     def test_resize(self):
+
+        # TODO: Minimal check for bug-fix, improve this later
+        x = torch.rand(3, 32, 46)
+        t = T.Resize(size=38)
+        y = t(x)
+        # If size is an int, smaller edge of the image will be matched to this number.
+        # i.e, if height > width, then image will be rescaled to (size * height / width, size).
+        self.assertTrue(isinstance(y, torch.Tensor))
+        self.assertEqual(y.shape[1], 38)
+        self.assertEqual(y.shape[2], int(38 * 46 / 32))
+
         tensor, _ = self._create_data(height=34, width=36, device=self.device)
         batch_tensors = torch.randint(0, 255, size=(4, 3, 44, 56), dtype=torch.uint8, device=self.device)
         script_fn = torch.jit.script(F.resize)
diff --git a/torchvision/transforms/transforms.py b/torchvision/transforms/transforms.py
index 6491ed38c20..bd1479b7cf0 100644
--- a/torchvision/transforms/transforms.py
+++ b/torchvision/transforms/transforms.py
@@ -249,7 +249,11 @@ class Resize(torch.nn.Module):
 
     def __init__(self, size, interpolation=Image.BILINEAR):
         super().__init__()
-        self.size = _setup_size(size, error_msg="If size is a sequence, it should have 2 values")
+        if not isinstance(size, (int, Sequence)):
+            raise TypeError("Size should be int or sequence. Got {}".format(type(size)))
+        if isinstance(size, Sequence) and len(size) not in (1, 2):
+            raise ValueError("If size is a sequence, it should have 1 or 2 values")
+        self.size = size
         self.interpolation = interpolation
 
     def forward(self, img):