diff --git a/topi/python/topi/generic/nn.py b/topi/python/topi/generic/nn.py
index d606213a5270..6f641e99f7dd 100644
--- a/topi/python/topi/generic/nn.py
+++ b/topi/python/topi/generic/nn.py
@@ -177,7 +177,6 @@ def schedule_global_pool(outs):
     """
     return _default_schedule(outs, False)
 
-
 @tvm.target.generic_func
 def schedule_binarize_pack(outs):
     """Schedule for binarize_pack
diff --git a/topi/python/topi/nn/__init__.py b/topi/python/topi/nn/__init__.py
index 3cdf3122e78e..918f399f503c 100644
--- a/topi/python/topi/nn/__init__.py
+++ b/topi/python/topi/nn/__init__.py
@@ -14,3 +14,4 @@
 from .softmax import *
 from .conv2d_transpose import *
 from .bnn import *
+from .upsampling import *
diff --git a/topi/python/topi/nn/upsampling.py b/topi/python/topi/nn/upsampling.py
new file mode 100644
index 000000000000..e1234741e286
--- /dev/null
+++ b/topi/python/topi/nn/upsampling.py
@@ -0,0 +1,28 @@
+"""TVM operator upsampling compute."""
+from __future__ import absolute_import
+import tvm
+
+
+def upsampling(data, scale):
+    """Perform nearest neighbor upsampling on the data.
+       Bilinear upsampling is not supported.
+
+    Parameters
+    ----------
+    data : tvm.Tensor
+        4-D with shape [batch, channel, in_height, in_width]
+
+    scale: int
+        upsampling scaling factor
+
+    Returns
+    -------
+    output : tvm.Tensor
+        4-D with shape [batch, channel, in_height*scale, in_width*scale]
+    """
+    batch, channel, height, width = data.shape
+    out_height = height * scale
+    out_width = width * scale
+
+    return tvm.compute((batch, channel, out_height, out_width), \
+                        lambda n, c, h, w: data[n, c, h/scale, w/scale])
diff --git a/topi/python/topi/testing/__init__.py b/topi/python/topi/testing/__init__.py
index 3a43a04437a1..6a1b361e3097 100644
--- a/topi/python/topi/testing/__init__.py
+++ b/topi/python/topi/testing/__init__.py
@@ -10,3 +10,4 @@
 from .depthwise_conv2d_python import depthwise_conv2d_python_nchw, depthwise_conv2d_python_nhwc
 from .dilate_python import dilate_python
 from .softmax_python import softmax_python, log_softmax_python
+from .upsampling_python import upsampling_python
diff --git a/topi/python/topi/testing/upsampling_python.py b/topi/python/topi/testing/upsampling_python.py
new file mode 100644
index 000000000000..328c7a5a0bc1
--- /dev/null
+++ b/topi/python/topi/testing/upsampling_python.py
@@ -0,0 +1,15 @@
+# pylint: disable=invalid-name, line-too-long, unused-variable, too-many-locals
+"""Upsampling in python"""
+import numpy as np
+
+def upsample_nearest(arr, scale):
+    return arr.repeat(scale, axis=0).repeat(scale, axis=1)
+
+def upsampling_python(data, scale):
+    ishape = data.shape
+    oshape = (ishape[0], ishape[1], ishape[2]*scale, ishape[3]*scale)
+    output_np = np.zeros(oshape, dtype=data.dtype)
+    for b in range(oshape[0]):
+        for c in range(oshape[1]):
+            output_np[b, c, :, :] = upsample_nearest(data[b, c, :, :], scale)
+    return output_np
diff --git a/topi/tests/python/test_topi_upsampling.py b/topi/tests/python/test_topi_upsampling.py
new file mode 100644
index 000000000000..08b8f987694d
--- /dev/null
+++ b/topi/tests/python/test_topi_upsampling.py
@@ -0,0 +1,39 @@
+"""Test code for upsampling"""
+import numpy as np
+import tvm
+import topi
+import math
+
+def verify_upsampling(batch, in_channel, in_height, in_width, scale):
+    A = tvm.placeholder((batch, in_channel, in_height, in_width), name='A')
+    B = topi.nn.upsampling(A, scale)
+    out_shape = (batch, in_channel, in_height*scale, in_width*scale)
+    dtype = A.dtype
+
+    a_np = np.random.uniform(size=(batch, in_channel, in_height, in_width)).astype(dtype)
+    b_np = topi.testing.upsampling_python(a_np, scale)
+
+    def check_device(device):
+        if not tvm.module.enabled(device):
+            print("Skip because %s is not enabled" % device)
+            return
+        print("Running on target: %s" % device)
+        with tvm.target.create(device):
+            s = topi.generic.schedule_injective(B)
+        ctx = tvm.context(device, 0)
+        a = tvm.nd.array(a_np, ctx)
+        b = tvm.nd.array(np.zeros(out_shape, dtype=dtype), ctx)
+        f = tvm.build(s, [A, B], device)
+        f(a, b)
+
+        np.testing.assert_allclose(b.asnumpy(), b_np, rtol=1e-5)
+
+    for device in ['llvm', 'cuda']:
+        check_device(device)
+
+def test_upsampling():
+    verify_upsampling(8, 16, 32, 32, 2)
+    verify_upsampling(12, 32, 64, 64, 3)
+
+if __name__ == "__main__":
+    test_upsampling()