diff --git a/python/tvm/relay/frontend/mxnet.py b/python/tvm/relay/frontend/mxnet.py
index 4edf0b80de4c..be2f1105d960 100644
--- a/python/tvm/relay/frontend/mxnet.py
+++ b/python/tvm/relay/frontend/mxnet.py
@@ -1073,6 +1073,20 @@ def _mx_one_hot(inputs, attrs):
     return _op.one_hot(indices, on_value, off_value, depth, -1, dtype)
 
 
+def _mx_depth_to_space(inputs, attrs):
+    assert len(inputs) == 1
+    new_attrs = {}
+    new_attrs["block_size"] = attrs.get_int("block_size")
+    return _op.nn.depth_to_space(*inputs, **new_attrs)
+
+
+def _mx_space_to_depth(inputs, attrs):
+    assert len(inputs) == 1
+    new_attrs = {}
+    new_attrs["block_size"] = attrs.get_int("block_size")
+    return _op.nn.space_to_depth(*inputs, **new_attrs)
+
+
 def _mx_contrib_fifo_buffer(inputs, attrs):
     new_attrs = {}
     new_attrs['axis'] = attrs.get_int('axis')
@@ -1854,6 +1868,8 @@ def _get_bias_requantize_scale(_inputs, _data_scale, _kernel_scale):
     "make_loss"     : _mx_make_loss,
     "_contrib_div_sqrt_dim": _mx_contrib_div_sqrt_dim,
     "one_hot"           : _mx_one_hot,
+    "depth_to_space"    : _mx_depth_to_space,
+    "space_to_depth"    : _mx_space_to_depth,
     # vision
     "_contrib_BilinearResize2D" : _mx_resize,
     "_contrib_MultiBoxPrior" : _mx_multibox_prior,
diff --git a/tests/python/frontend/mxnet/test_forward.py b/tests/python/frontend/mxnet/test_forward.py
index 4a9848e03b5e..10edff9031fe 100644
--- a/tests/python/frontend/mxnet/test_forward.py
+++ b/tests/python/frontend/mxnet/test_forward.py
@@ -995,6 +995,38 @@ def _verify_swap_axis(in_shape, out_shape, dim1, dim2):
     # _verify_swap_axis((4, 5), (5, 4), 0, 0)
 
 
+def test_forward_depth_to_space():
+    def verify(shape, blocksize=2):
+        x = np.random.uniform(size=shape).astype("float32")
+        ref_res = mx.nd.depth_to_space(mx.nd.array(x), blocksize)
+        mx_sym = mx.sym.depth_to_space(mx.sym.var("x"), blocksize)
+        shape_dict = {"x": x.shape, }
+        mod, _ = relay.frontend.from_mxnet(mx_sym, shape_dict)
+        for target, ctx in ctx_list():
+            for kind in ["graph", "debug"]:
+                intrp = relay.create_executor(kind, mod=mod, ctx=ctx, target=target)
+                op_res = intrp.evaluate()(x)
+                tvm.testing.assert_allclose(op_res.asnumpy(), ref_res.asnumpy(), rtol=1e-3, atol=1e-5)
+
+    verify((1, 18, 3, 3), 3)
+
+
+def test_forward_space_to_depth():
+    def verify(shape, blocksize=2):
+        x = np.random.uniform(size=shape).astype("float32")
+        ref_res = mx.nd.space_to_depth(mx.nd.array(x), blocksize)
+        mx_sym = mx.sym.space_to_depth(mx.sym.var("x"), blocksize)
+        shape_dict = {"x": x.shape, }
+        mod, _ = relay.frontend.from_mxnet(mx_sym, shape_dict)
+        for target, ctx in ctx_list():
+            for kind in ["graph", "debug"]:
+                intrp = relay.create_executor(kind, mod=mod, ctx=ctx, target=target)
+                op_res = intrp.evaluate()(x)
+                tvm.testing.assert_allclose(op_res.asnumpy(), ref_res.asnumpy(), rtol=1e-3, atol=1e-5)
+
+    verify((1, 1, 9, 9), 3)
+
+
 if __name__ == '__main__':
     test_forward_mlp()
     test_forward_vgg()
@@ -1047,6 +1079,8 @@ def _verify_swap_axis(in_shape, out_shape, dim1, dim2):
     test_forward_instance_norm()
     test_forward_layer_norm()
     test_forward_one_hot()
+    test_forward_depth_to_space()
+    test_forward_space_to_depth()
     test_forward_convolution()
     test_forward_deconvolution()
     test_forward_cond()