text: # Licensed to the Apache Software Foundation (ASF) under one
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  # distributed with this work for additional information
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  # 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.
  # pylint: disable=import-self, invalid-name, unused-argument
  """Test torch vision fasterrcnn and maskrcnn models"""
  import numpy as np
  import torch
  import torchvision
  import cv2
  
  import tvm
  
  import tvm.testing
  from tvm import relay
  from tvm.runtime.vm import VirtualMachine
  from tvm.relay.frontend.pytorch_utils import rewrite_nms_to_batched_nms
  from tvm.contrib.download import download
  
  
  in_size = 300
  
  
  def process_image(img):
      img = cv2.imread(img).astype("float32")
      img = cv2.resize(img, (in_size, in_size))
      img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
      img = torch.from_numpy(img / 255.0).permute(2, 0, 1).float()
      img = torch.unsqueeze(img, axis=0)
  
      return img
  
  
  def do_trace(model, inp, in_size=in_size):
      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])
  
  
  def generate_jit_model(index):
      model_funcs = [
          torchvision.models.detection.fasterrcnn_resnet50_fpn,
          torchvision.models.detection.maskrcnn_resnet50_fpn,
      ]
  
      model_func = model_funcs[index]
      model = TraceWrapper(model_func(pretrained=True, rpn_pre_nms_top_n_test=200))
  
      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)
          script_out = script_module(inp)
  
          assert len(out[0]) > 0 and len(script_out[0]) > 0
          return script_module
  
  
  def test_detection_models():
      img = "test_street_small.jpg"
      img_url = (
          "https://raw.githubusercontent.com/dmlc/web-data/"
          "master/gluoncv/detection/street_small.jpg"
      )
      download(img_url, img)
  
      input_shape = (1, 3, in_size, in_size)
  
      input_name = "input0"
      shape_list = [(input_name, input_shape)]
  
      scripted_model = generate_jit_model(1)
      mod, params = relay.frontend.from_pytorch(scripted_model, shape_list)
  
      data = process_image(img)
      data_np = data.detach().numpy()
  
      with torch.no_grad():
          pt_res = scripted_model(data)
  
      def compile_and_run_vm(mod, params, data_np, target):
          with tvm.transform.PassContext(opt_level=3):
              vm_exec = relay.vm.compile(mod, target=target, params=params)
  
          ctx = tvm.context(target, 0)
          vm = VirtualMachine(vm_exec, ctx)
          vm.set_input("main", **{input_name: data_np})
          return vm.run()
  
      for target in ["cuda", "llvm"]:
          tvm_res = compile_and_run_vm(mod, params, data_np, target)
  
          # Bounding boxes
          tvm.testing.assert_allclose(
              pt_res[0].cpu().numpy(), tvm_res[0].asnumpy(), rtol=1e-5, atol=1e-5
          )
          # Scores
          tvm.testing.assert_allclose(
              pt_res[1].cpu().numpy(), tvm_res[1].asnumpy(), rtol=1e-5, atol=1e-5
          )
          # Class ids
          np.testing.assert_equal(pt_res[2].cpu().numpy(), tvm_res[2].asnumpy())
  
          score_threshold = 0.9
          print("Num boxes:", pt_res[0].cpu().numpy().shape[0])
          print("Num valid boxes:", np.sum(pt_res[1].cpu().numpy() >= score_threshold))
  
      before = mod["main"]
      mod = rewrite_nms_to_batched_nms(mod)
      after = mod["main"]
      assert not tvm.ir.structural_equal(after, before)
  
      tvm_res_after_rewrite = compile_and_run_vm(mod, params, data_np, "llvm")
  
      # Results should be equivalent after rewriting
      for res1, res2 in zip(tvm_res, tvm_res_after_rewrite):
          tvm.testing.assert_allclose(res1.asnumpy(), res2.asnumpy())
  