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+"""
+Compiling ONNX Models with NNVM
+================================
+**Author**: `Joshua Z. Zhang <https://zhreshold.github.io/>`_
+
+This article is an introductory tutorial to deploy ONNX models with NNVM.
+
+For us to begin with, onnx module is required to be installed.
+
+A quick solution is to install protobuf compiler, and
+```bash
+pip install onnx --user
+```
+or please refer to offical site.
+https://github.com/onnx/onnx
+"""
+import nnvm
+import tvm
+import onnx
+import numpy as np
+
+######################################################################
+# Load pretrained ONNX model
+# ---------------------------------------------
+# The example super resolution model used here is exactly the same model in onnx tutorial
+# http://pytorch.org/tutorials/advanced/super_resolution_with_caffe2.html
+# we skip the pytorch model construction part, and download the saved onnx model
+import urllib2
+model_url = ''.join(['https://gist.github.com/zhreshold/',
+                     'bcda4716699ac97ea44f791c24310193/raw/',
+                     '41b443bf2b6cf795892d98edd28bacecd8eb0d8d/',
+                     'super_resolution.onnx'])
+with open('super_resolution.onnx', 'w') as f:
+    f.write(urllib2.urlopen(model_url).read())
+# now you have super_resolution.onnx on disk
+onnx_graph = onnx.load('super_resolution.onnx')
+# we can load the graph as NNVM compatible model
+sym, params = nnvm.frontend.from_onnx(onnx_graph)
+
+######################################################################
+# Load a test image
+# ---------------------------------------------
+# A single cat dominates the examples!
+import Image
+img_url = 'https://github.com/dmlc/mxnet.js/blob/master/data/cat.png?raw=true'
+with open('cat.jpg', 'w') as f:
+    f.write(urllib2.urlopen(img_url).read())
+img = Image.open('cat.jpg').convert("L")  # convert to greyscale
+x = np.array(img.resize((224, 224)))[np.newaxis, np.newaxis, :, :]
+
+######################################################################
+# Compile the model on NNVM
+# ---------------------------------------------
+# We should be familiar with the process right now.
+import nnvm.compiler
+target = 'cuda'
+shape_dict = {'input_0': x.shape}
+graph, lib, params = nnvm.compiler.build(sym, target, shape_dict, params=params)
+
+######################################################################
+# Execute on TVM
+# ---------------------------------------------
+# The process is no different from other example
+from tvm.contrib import graph_runtime
+ctx = tvm.gpu(0)
+dtype = 'float32'
+m = graph_runtime.create(graph, lib, ctx)
+# set inputs
+m.set_input('input_0', tvm.nd.array(x.astype(dtype)))
+m.set_input(**params)
+# execute
+m.run()
+# get outputs
+output_shape = (1, 1, 672, 672)
+tvm_output = m.get_output(0, tvm.nd.empty(output_shape, dtype)).asnumpy()
+out_img = tvm_output.reshape((672, 672))
+
+######################################################################
+# Display results
+# ---------------------------------------------
+# We put input and output image neck to neck
+from matplotlib import pyplot as plt
+canvas = np.full((672, 672*2), 255)
+canvas[0:224, 0:224] = x[0, 0, :, :]
+canvas[:, 672:] = out_img
+plt.imshow(canvas, cmap='gray')
+plt.show()