diff --git a/include/tvm/relay/attrs/vision.h b/include/tvm/relay/attrs/vision.h
index 0bbad3f0b0e3f..0133bcb1b4d85 100644
--- a/include/tvm/relay/attrs/vision.h
+++ b/include/tvm/relay/attrs/vision.h
@@ -53,9 +53,12 @@ struct NMSAttrs : public tvm::AttrsNode<NMSAttrs>{
         .describe("Suppress all detections regardless of class_id.");
       TVM_ATTR_FIELD(topk).set_default(-1)
         .describe("Keep maximum top k detections before nms, -1 for no limit.");
+  }
+};
+
 /*! \brief Attributes used in yolo reorg operators */
 struct YoloReorgAttrs : public tvm::AttrsNode<YoloReorgAttrs> {
-  IndexExpr stride;
+  Integer stride;
 
   TVM_DECLARE_ATTRS(YoloReorgAttrs, "relay.attrs.YoloReorgAttrs") {
     TVM_ATTR_FIELD(stride)
diff --git a/python/tvm/relay/op/vision/__init__.py b/python/tvm/relay/op/vision/__init__.py
index 14c49a37e8f62..8dd876275e6a7 100644
--- a/python/tvm/relay/op/vision/__init__.py
+++ b/python/tvm/relay/op/vision/__init__.py
@@ -5,3 +5,4 @@
 from .multibox import *
 from .nms import *
 from .yolo import *
+from ._yolo import *
diff --git a/python/tvm/relay/op/vision/_yolo.py b/python/tvm/relay/op/vision/_yolo.py
new file mode 100644
index 0000000000000..749ebfa26dd09
--- /dev/null
+++ b/python/tvm/relay/op/vision/_yolo.py
@@ -0,0 +1,9 @@
+#pylint: disable=invalid-name, unused-argument
+"""Backend compiler related feature registration"""
+from __future__ import absolute_import
+from ..op import  register_schedule, register_pattern
+from ..op import schedule_injective, OpPattern
+
+# reorg
+register_pattern("vision.yolo_reorg", OpPattern.INJECTIVE)
+register_schedule("vision.yolo_reorg", schedule_injective)
diff --git a/tests/python/relay/test_op_level5.py b/tests/python/relay/test_op_level5.py
index c86f20be9e609..33d525029b310 100644
--- a/tests/python/relay/test_op_level5.py
+++ b/tests/python/relay/test_op_level5.py
@@ -1,7 +1,10 @@
 """ Support level5 operator test cases.
 """
+import numpy as np
 import tvm
 from tvm import relay
+from tvm.relay.testing import ctx_list
+import topi.testing
 
 def test_resize_infer_type():
     n, c, h, w = tvm.var("n"), tvm.var("c"), tvm.var("h"), tvm.var("w")
@@ -70,22 +73,46 @@ def test_nms():
     zz = relay.ir_pass.infer_type(z)
     assert zz.checked_type == relay.ty.TensorType(
         (n, num_anchors, 6), "float32")
-def test_yolo_reorg():
+
+
+def test_yolo_reorg_infer_shape():
+    def verify_yolo_reorg(shape, stride, out_shape):
+        x = relay.var("x", relay.TensorType(shape, "float32"))
+        z = relay.vision.yolo_reorg(x, stride=stride)
+        zz = relay.ir_pass.infer_type(z)
+        assert "stride=" in z.astext()
+        assert zz.checked_type == relay.ty.TensorType(out_shape, "float32")
+
     n, c, h, w = tvm.var("n"), tvm.var("c"), tvm.var("h"), tvm.var("w")
-    x = relay.var("x", relay.TensorType((n, c, 20, 20), "float32"))
-    z = relay.vision.yolo_reorg(x, stride=10)
-    zz = relay.ir_pass.infer_type(z)
-    assert "stride=10" in z.astext()
-    assert zz.checked_type == relay.ty.TensorType((n, c*10*10, 2, 2), "float32")
+    verify_yolo_reorg((n, c, 20, 20), 10, (n, c*10*10, 2, 2))
+    verify_yolo_reorg((n, c, h, w), 2, (n, c*2*2, h/2, w/2))
 
-    x = relay.var("x", relay.TensorType((n, c, h, w), "float32"))
-    z = relay.vision.yolo_reorg(x, stride=2)
-    assert "stride=2" in z.astext()
-    zz = relay.ir_pass.infer_type(z)
-    assert zz.checked_type == relay.ty.TensorType((n, c*2*2, h/2, w/2), "float32")
+def test_yolo_reorg():
+    def verify_yolo_reorg(shape, stride):
+        x_data = np.random.uniform(low=-1, high=1, size=shape).astype("float32")
+        ref_res = topi.testing.reorg_python(x_data, stride)
+
+        x = relay.var("x", relay.TensorType(shape, "float32"))
+        z = relay.vision.yolo_reorg(x, stride=stride)
+        zz = relay.ir_pass.infer_type(z)
+        assert "stride=" in z.astext()
+        assert zz.checked_type == relay.ty.TensorType(ref_res.shape, "float32")
+
+        func = relay.Function([x], z)
+
+        for target, ctx in ctx_list():
+            for kind in ["graph", "debug"]:
+                intrp = relay.create_executor(kind, ctx=ctx, target=target)
+                op_res = intrp.evaluate(func)(x_data)
+                tvm.testing.assert_allclose(op_res.asnumpy(), ref_res, rtol=1e-5)
+
+    n, c, h, w = tvm.var("n"), tvm.var("c"), tvm.var("h"), tvm.var("w")
+    verify_yolo_reorg((1, 100, 20, 20), 10)
+    verify_yolo_reorg((1, 4, 6, 6), 2)
 
 if __name__ == "__main__":
     test_resize_infer_type()
     test_multibox_prior()
     test_nms()
+    test_yolo_reorg_infer_shape()
     test_yolo_reorg()