new_contents: # 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 PyTorch Models
  ======================
  **Author**: `Alex Wong <https://github.com/alexwong/>`_
  
  This article is an introductory tutorial to deploy PyTorch models with Relay.
  
  For us to begin with, PyTorch should be installed.
  TorchVision is also required since we will be using it as our model zoo.
  
  A quick solution is to install via pip
  
  .. code-block:: bash
  
      pip install torch==1.4.0
      pip install torchvision==0.5.0
  
  or please refer to official site
  https://pytorch.org/get-started/locally/
  
  PyTorch versions should be backwards compatible but should be used
  with the proper TorchVision version.
  
  Currently, TVM supports PyTorch 1.4, 1.3, and 1.2. Other versions may
  be unstable.
  """
  
  # tvm, relay
  import tvm
  from tvm import relay
  
  # numpy, packaging
  import numpy as np
  from packaging import version
  from tvm.contrib.download import download_testdata
  
  # PyTorch imports
  import torch
  import torchvision
  
  ######################################################################
  # Load a pretrained PyTorch model
  # -------------------------------
  model_name = 'resnet18'
  model = getattr(torchvision.models, model_name)(pretrained=True)
  model = model.eval()
  
  # We grab the TorchScripted model via tracing
  input_shape = [1, 3, 224, 224]
  input_data = torch.randn(input_shape)
  scripted_model = torch.jit.trace(model, input_data).eval()
  
  ######################################################################
  # Load a test image
  # -----------------
  # Classic cat example!
  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))
  
  # Preprocess the image and convert to tensor
  from torchvision import transforms
  my_preprocess = transforms.Compose([
      transforms.Resize(256),
      transforms.CenterCrop(224),
      transforms.ToTensor(),
      transforms.Normalize(mean=[0.485, 0.456, 0.406],
                           std=[0.229, 0.224, 0.225])
  ])
  img = my_preprocess(img)
  img = np.expand_dims(img, 0)
  
  ######################################################################
  # Import the graph to Relay
  # -------------------------
  # Convert PyTorch graph to Relay graph.
  shape_dict = {'img': img.shape}
  mod, params = relay.frontend.from_pytorch(scripted_model,
                                            shape_dict)
  
  ######################################################################
  # Relay Build
  # -----------
  # Compile the graph to llvm target with given input specification.
  target = 'llvm'
  target_host = 'llvm'
  ctx = tvm.cpu(0)
  with relay.build_config(opt_level=3):
      graph, lib, params = relay.build(mod,
                                       target=target,
                                       target_host=target_host,
                                       params=params)
  
  ######################################################################
  # Execute the portable graph on TVM
  # ---------------------------------
  # Now we can try deploying the compiled model on target.
  from tvm.contrib import graph_runtime
  dtype = 'float32'
  m = graph_runtime.create(graph, lib, ctx)
  # Set inputs
  m.set_input('img', tvm.nd.array(img.astype(dtype)))
  m.set_input(**params)
  # Execute
  m.run()
  # Get outputs
  tvm_output = m.get_output(0, tvm.nd.empty(((1, 1000)), 'float32'))
  
  #####################################################################
  # Look up synset name
  # -------------------
  # Look up prediction top 1 index in 1000 class synset.
  synset_url = ''.join(['https://raw.githubusercontent.com/Cadene/',
                        'pretrained-models.pytorch/master/data/',
                        'imagenet_synsets.txt'])
  synset_name = 'imagenet_synsets.txt'
  synset_path = download_testdata(synset_url, synset_name, module='data')
  with open(synset_path) as f:
      synsets = f.readlines()
  
  synsets = [x.strip() for x in synsets]
  splits = [line.split(' ') for line in synsets]
  key_to_classname = {spl[0]:' '.join(spl[1:]) for spl in splits}
  
  class_url = ''.join(['https://raw.githubusercontent.com/Cadene/',
                        'pretrained-models.pytorch/master/data/',
                        'imagenet_classes.txt'])
  class_name = 'imagenet_classes.txt'
  class_path = download_testdata(class_url, class_name, module='data')
  with open(class_path) as f:
      class_id_to_key = f.readlines()
  
  class_id_to_key = [x.strip() for x in class_id_to_key]
  
  # Get top-1 result for TVM
  top1_tvm = np.argmax(tvm_output.asnumpy()[0])
  tvm_class_key = class_id_to_key[top1_tvm]
  
  # Convert input to PyTorch variable and get PyTorch result for comparison
  with torch.no_grad():
      torch_img = torch.from_numpy(img)
      output = model(torch_img)
  
      # Get top-1 result for PyTorch
      top1_torch = np.argmax(output.numpy())
      torch_class_key = class_id_to_key[top1_torch]
  
  print('Relay top-1 id: {}, class name: {}'.format(top1_tvm, key_to_classname[tvm_class_key]))
  print('Torch top-1 id: {}, class name: {}'.format(top1_torch, key_to_classname[torch_class_key]))
subject: Add a tutorial for PyTorch (#4936)