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
  # regarding copyright ownership.  The ASF licenses this file
  # to you under the Apache License, Version 2.0 (the
  # "License"); you may not use this file except in compliance
  # with the License.  You may obtain a copy of the License at
  #
  #   http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing,
  # software distributed under the License is distributed on an
  # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
  # KIND, either express or implied.  See the License for the
  # specific language governing permissions and limitations
  # under the License.
  """
  Compile CoreML Models
  =====================
  **Author**: `Joshua Z. Zhang <https://zhreshold.github.io/>`_, \
              `Kazutaka Morita <https://github.com/kazum>`_
  
  This article is an introductory tutorial to deploy CoreML models with Relay.
  
  For us to begin with, coremltools module is required to be installed.
  
  A quick solution is to install via pip
  
  .. code-block:: bash
  
      pip install -U coremltools --user
  
  or please refer to official site
  https://github.com/apple/coremltools
  """
  import tvm
  import tvm.relay as relay
  from tvm.contrib.download import download_testdata
  import coremltools as cm
  import numpy as np
  from PIL import Image
  
  ######################################################################
  # Load pretrained CoreML model
  # ----------------------------
  # We will download and load a pretrained mobilenet classification network
  # provided by apple in this example
  model_url = 'https://docs-assets.developer.apple.com/coreml/models/MobileNet.mlmodel'
  model_file = 'mobilenet.mlmodel'
  model_path = download_testdata(model_url, model_file, module='coreml')
  # Now you have mobilenet.mlmodel on disk
  mlmodel = cm.models.MLModel(model_path)
  
  ######################################################################
  # Load a test image
  # ------------------
  # A single cat dominates the examples!
  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))
  x = np.transpose(img, (2, 0, 1))[np.newaxis, :]
  
  ######################################################################
  # Compile the model on Relay
  # ---------------------------
  # We should be familiar with the process right now.
  target = 'llvm'
  shape_dict = {'image': x.shape}
  
  # Parse CoreML model and convert into Relay computation graph
  func, params = relay.frontend.from_coreml(mlmodel, shape_dict)
  
  with relay.build_config(opt_level=3):
      graph, lib, params = relay.build(func, target, params=params)
  
  ######################################################################
  # Execute on TVM
  # -------------------
  # The process is no different from other example
  from tvm.contrib import graph_runtime
  ctx = tvm.cpu(0)
  dtype = 'float32'
  m = graph_runtime.create(graph, lib, ctx)
  # set inputs
  m.set_input('image', tvm.nd.array(x.astype(dtype)))
  m.set_input(**params)
  # execute
  m.run()
  # get outputs
  tvm_output = m.get_output(0)
  top1 = np.argmax(tvm_output.asnumpy()[0])
  
  #####################################################################
  # Look up synset name
  # -------------------
  # Look up prediction top 1 index in 1000 class synset.
  synset_url = ''.join(['https://gist.githubusercontent.com/zhreshold/',
                        '4d0b62f3d01426887599d4f7ede23ee5/raw/',
                        '596b27d23537e5a1b5751d2b0481ef172f58b539/',
                        'imagenet1000_clsid_to_human.txt'])
  synset_name = 'imagenet1000_clsid_to_human.txt'
  synset_path = download_testdata(synset_url, synset_name, module='data')
  with open(synset_path) as f:
      synset = eval(f.read())
  print('Top-1 id', top1, 'class name', synset[top1])
  