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  # 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 Object Detection Models
  =======================================
  This article is an introductory tutorial to deploy PyTorch object
  detection models with Relay VM.
  
  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 and 1.3. Other versions may
  be unstable.
  """
  
  import tvm
  from tvm import relay
  from tvm import relay
  from tvm.runtime.vm import VirtualMachine
  from tvm.contrib.download import download
  
  import numpy as np
  import cv2
  
  # PyTorch imports
  import torch
  import torchvision
  
  ######################################################################
  # Load pre-trained maskrcnn from torchvision and do tracing
  # ---------------------------------------------------------
  in_size = 300
  
  input_shape = (1, 3, in_size, in_size)
  
  
  def do_trace(model, inp):
      model_trace = torch.jit.trace(model, inp)
      model_trace.eval()
      return model_trace
  
  
  def dict_to_tuple(out_dict):
      if "masks" in out_dict.keys():
          return out_dict["boxes"], out_dict["scores"], out_dict["labels"], out_dict["masks"]
      return out_dict["boxes"], out_dict["scores"], out_dict["labels"]
  
  
  class TraceWrapper(torch.nn.Module):
      def __init__(self, model):
          super().__init__()
          self.model = model
  
      def forward(self, inp):
          out = self.model(inp)
          return dict_to_tuple(out[0])
  
  
  model_func = torchvision.models.detection.maskrcnn_resnet50_fpn
  model = TraceWrapper(model_func(pretrained=True))
  
  model.eval()
  inp = torch.Tensor(np.random.uniform(0.0, 250.0, size=(1, 3, in_size, in_size)))
  
  with torch.no_grad():
      out = model(inp)
      script_module = do_trace(model, inp)
  
  ######################################################################
  # Download a test image and pre-process
  # -------------------------------------
  img_path = "test_street_small.jpg"
  img_url = (
      "https://raw.githubusercontent.com/dmlc/web-data/" "master/gluoncv/detection/street_small.jpg"
  )
  download(img_url, img_path)
  
  img = cv2.imread(img_path).astype("float32")
  img = cv2.resize(img, (in_size, in_size))
  img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
  img = np.transpose(img / 255.0, [2, 0, 1])
  img = np.expand_dims(img, axis=0)
  
  ######################################################################
  # Import the graph to Relay
  # -------------------------
  input_name = "input0"
  shape_list = [(input_name, input_shape)]
  mod, params = relay.frontend.from_pytorch(script_module, shape_list)
  
  ######################################################################
  # Compile with Relay VM
  # ---------------------
  # Note: Currently only CPU target is supported. For x86 target, it is
  # highly recommended to build TVM with Intel MKL and Intel OpenMP to get
  # best performance, due to the existence of large dense operator in
  # torchvision rcnn models.
  
  # Add "-libs=mkl" to get best performance on x86 target.
  # For x86 machine supports AVX512, the complete target is
  # "llvm -mcpu=skylake-avx512 -libs=mkl"
  target = "llvm"
  
  with tvm.transform.PassContext(opt_level=3, disabled_pass=["FoldScaleAxis"]):
      vm_exec = relay.vm.compile(mod, target=target, params=params)
  
  ######################################################################
  # Inference with Relay VM
  # -----------------------
  ctx = tvm.cpu()
  vm = VirtualMachine(vm_exec, ctx)
  vm.set_input("main", **{input_name: img})
  tvm_res = vm.run()
  
  ######################################################################
  # Get boxes with score larger than 0.9
  # ------------------------------------
  score_threshold = 0.9
  boxes = tvm_res[0].asnumpy().tolist()
  valid_boxes = []
  for i, score in enumerate(tvm_res[1].asnumpy().tolist()):
      if score > score_threshold:
          valid_boxes.append(boxes[i])
      else:
          break
  
  print("Get {} valid boxes".format(len(valid_boxes)))
  
subject: Add PT OD tutorial (#6500)