adjust openvino2024 new api

mmdeploy/backend/openvino/wrapper.py

@@ -36,20 +36,20 @@ def __init__(self,
                  output_names: Optional[Sequence[str]] = None,
                  **kwargs):
 
-        from openvino.inference_engine import IECore
+        from openvino.runtime import Core as IECore
         self.ie = IECore()
         bin_path = osp.splitext(ir_model_file)[0] + '.bin'
-        self.net = self.ie.read_network(ir_model_file, bin_path)
-        for input in self.net.input_info.values():
-            batch_size = input.input_data.shape[0]
-            dims = len(input.input_data.shape)
+        self.net = self.ie.read_model(model=ir_model_file, weights=bin_path)
+        for input in self.net.inputs:
+            batch_size = input.get_partial_shape()[0]
+            dims = len(input.get_partial_shape())
             # if input is a image, it has (B,C,H,W) channels,
             # need batch_size==1
             assert not dims == 4 or batch_size == 1, \
                 'Only batch 1 is supported.'
         self.device = 'cpu'
-        self.sess = self.ie.load_network(
-            network=self.net, device_name=self.device.upper(), num_requests=1)
+        self.sess = self.ie.compile_model(
+            model=self.net, device_name=self.device.upper())
 
         # TODO: Check if output_names can be read
         if output_names is None:
@@ -84,7 +84,8 @@ def __reshape(self, inputs: Dict[str, torch.Tensor]):
         input_shapes = {name: data.shape for name, data in inputs.items()}
         reshape_needed = False
         for input_name, input_shape in input_shapes.items():
-            blob_shape = self.net.input_info[input_name].input_data.shape
+            input_node = next(input for input in self.net.inputs if input.get_any_name() == input_name)
+            blob_shape = input_node.get_partial_shape()
             if not np.array_equal(input_shape, blob_shape):
                 reshape_needed = True
                 break
@@ -147,5 +148,8 @@ def __openvino_execute(
         Returns:
             Dict[str, numpy.ndarray]: The output name and tensor pairs.
         """
-        outputs = self.sess.infer(inputs)
+        infer_request = self.sess.create_infer_request()
+        infer_request.infer(inputs)
+        outputs = {output.get_any_name(): infer_request.get_tensor(output).data 
+                for output in self.net.outputs}
         return outputs