changing the onnxwrapper script for gpu issue (open-mmlab#532)

* changing the onnxwrapper script

* gpu_issue

* Update wrapper.py

* Update wrapper.py

* Update runtime.txt

* Update runtime.txt

* Update wrapper.py

mmdeploy/backend/onnxruntime/wrapper.py

@@ -50,18 +50,17 @@ def __init__(self,
  logger.warning(f'The library of onnxruntime custom ops does \
  not exist: {ort_custom_op_path}')
  device_id = parse_device_id(device)
- is_cuda_available = ort.get_device() == 'GPU'
- providers = [('CUDAExecutionProvider', {'device_id': device_id})] \
- if is_cuda_available else ['CPUExecutionProvider']
+ providers = ['CPUExecutionProvider'] \
+  if device == 'cpu' else \
+ [('CUDAExecutionProvider', {'device_id': device_id})]
  sess = ort.InferenceSession(
  onnx_file, session_options, providers=providers)
  if output_names is None:
  output_names = [_.name for _ in sess.get_outputs()]
  self.sess = sess
  self.io_binding = sess.io_binding()
  self.device_id = device_id
- self.is_cuda_available = is_cuda_available
- self.device_type = 'cuda' if is_cuda_available else 'cpu'
+ self.device_type = 'cpu' if device == 'cpu' else 'cuda'
  super().__init__(output_names)
 
  def forward(self, inputs: Dict[str,
@@ -77,7 +76,7 @@ def forward(self, inputs: Dict[str,
  for name, input_tensor in inputs.items():
  # set io binding for inputs/outputs
  input_tensor = input_tensor.contiguous()
- if not self.is_cuda_available:
+ if self.device_type == 'cpu':
  input_tensor = input_tensor.cpu()
  # Avoid unnecessary data transfer between host and device
  element_type = input_tensor.new_zeros(

requirements/runtime.txt

@@ -2,5 +2,6 @@ h5py
 matplotlib
 numpy
 onnx>=1.8.0
+protobuf==3.20.0
 six
 terminaltables