diff --git a/python/tvm/relay/frontend/keras.py b/python/tvm/relay/frontend/keras.py
index 16192617fe13..d963a5d160bf 100644
--- a/python/tvm/relay/frontend/keras.py
+++ b/python/tvm/relay/frontend/keras.py
@@ -767,10 +767,8 @@ def _convert_upsample(
         params["scale_h"] = h
     elif upsample_type == "UpSampling2D":
         h, w = keras_layer.size
-        if h != w:
-            raise tvm.error.OpAttributeInvalid("Height must equal width for operator Upsample.")
         params["scale_h"] = h
-        params["scale_w"] = h
+        params["scale_w"] = w
 
         if hasattr(keras_layer, "interpolation"):
             interpolation = keras_layer.interpolation
diff --git a/tests/python/frontend/keras/test_forward.py b/tests/python/frontend/keras/test_forward.py
index debd50b37a2e..45935f87f4f4 100644
--- a/tests/python/frontend/keras/test_forward.py
+++ b/tests/python/frontend/keras/test_forward.py
@@ -389,6 +389,11 @@ def test_forward_upsample(self, keras_mod, interpolation="nearest"):
         x = keras_mod.layers.UpSampling2D(size=(3, 3), interpolation=interpolation)(data)
         keras_model = keras_mod.models.Model(data, x)
         verify_keras_frontend(keras_model)
+        # Height and width are not equal for the attribute size
+        data = keras_mod.layers.Input(shape=(2, 1, 3))
+        x = keras_mod.layers.UpSampling2D(size=(1, 2), interpolation=interpolation)(data)
+        keras_model = keras_mod.models.Model(data, x)
+        verify_keras_frontend(keras_model)
 
     def test_forward_reshape(self, keras_mod):
         """test_forward_reshape"""