text: # Licensed to the Apache Software Foundation (ASF) under one
  # or more contributor license agreements.  See the NOTICE file
  # distributed with this work for additional information
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  #
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  # KIND, either express or implied.  See the License for the
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  # under the License.
  """
  Compile ONNX Models
  ===================
  **Author**: `Joshua Z. Zhang <https://zhreshold.github.io/>`_
  
  This article is an introductory tutorial to deploy ONNX models with Relay.
  
  For us to begin with, ONNX package must be installed.
  
  A quick solution is to install protobuf compiler, and
  
  .. code-block:: bash
  
      pip install onnx --user
  
  or please refer to offical site.
  https://github.com/onnx/onnx
  """
  import onnx
  import numpy as np
  import tvm
  from tvm import te
  import tvm.relay as relay
  from tvm.contrib.download import download_testdata
  
  ######################################################################
  # 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
  model_url = ''.join(['https://gist.github.com/zhreshold/',
                       'bcda4716699ac97ea44f791c24310193/raw/',
                       '93672b029103648953c4e5ad3ac3aadf346a4cdc/',
                       'super_resolution_0.2.onnx'])
  model_path = download_testdata(model_url, 'super_resolution.onnx', module='onnx')
  # now you have super_resolution.onnx on disk
  onnx_model = onnx.load(model_path)
  
  ######################################################################
  # Load a test image
  # ---------------------------------------------
  # A single cat dominates the examples!
  from PIL import Image
  img_url = 'https://github.com/dmlc/mxnet.js/blob/master/data/cat.png?raw=true'
  img_path = download_testdata(img_url, 'cat.png', module='data')
  img = Image.open(img_path).resize((224, 224))
  img_ycbcr = img.convert("YCbCr")  # convert to YCbCr
  img_y, img_cb, img_cr = img_ycbcr.split()
  x = np.array(img_y)[np.newaxis, np.newaxis, :, :]
  
  ######################################################################
  # Compile the model with relay
  # ---------------------------------------------
  target = 'llvm'
  
  input_name = '1'
  shape_dict = {input_name: x.shape}
  mod, params = relay.frontend.from_onnx(onnx_model, shape_dict)
  
  with tvm.transform.PassContext(opt_level=1):
      intrp = relay.build_module.create_executor('graph', mod, tvm.cpu(0), target)
  
  ######################################################################
  # Execute on TVM
  # ---------------------------------------------
  dtype = 'float32'
  tvm_output = intrp.evaluate()(tvm.nd.array(x.astype(dtype)), **params).asnumpy()
  
  ######################################################################
  # Display results
  # ---------------------------------------------
  # We put input and output image neck to neck
  from matplotlib import pyplot as plt
  out_y = Image.fromarray(np.uint8((tvm_output[0, 0]).clip(0, 255)), mode='L')
  out_cb = img_cb.resize(out_y.size, Image.BICUBIC)
  out_cr = img_cr.resize(out_y.size, Image.BICUBIC)
  result = Image.merge('YCbCr', [out_y, out_cb, out_cr]).convert('RGB')
  canvas = np.full((672, 672*2, 3), 255)
  canvas[0:224, 0:224, :] = np.asarray(img)
  canvas[:, 672:, :] = np.asarray(result)
  plt.imshow(canvas.astype(np.uint8))
  plt.show()
  