Backport compatibility with TensorRT version 10 from yolov8 (#12984)

Add compatibility with TensorRT version 10.

Based on the is_trt10 code in yolov8.

models/common.py

@@ -527,18 +527,34 @@ class DetectMultiBackend(nn.Module):
             output_names = []
             fp16 = False  # default updated below
             dynamic = False
-            for i in range(model.num_bindings):
-                name = model.get_binding_name(i)
-                dtype = trt.nptype(model.get_binding_dtype(i))
-                if model.binding_is_input(i):
-                    if -1 in tuple(model.get_binding_shape(i)):  # dynamic
-                        dynamic = True
-                        context.set_binding_shape(i, tuple(model.get_profile_shape(0, i)[2]))
-                    if dtype == np.float16:
-                        fp16 = True
-                else:  # output
-                    output_names.append(name)
-                shape = tuple(context.get_binding_shape(i))
+            is_trt10 = not hasattr(model, "num_bindings")
+            num = range(model.num_io_tensors) if is_trt10 else range(model.num_bindings)
+            for i in num:
+                if is_trt10:
+                    name = model.get_tensor_name(i)
+                    dtype = trt.nptype(model.get_tensor_dtype(name))
+                    is_input = model.get_tensor_mode(name) == trt.TensorIOMode.INPUT
+                    if is_input:
+                        if -1 in tuple(model.get_tensor_shape(name)):  # dynamic
+                            dynamic = True
+                            context.set_input_shape(name, tuple(model.get_profile_shape(name, 0)[2]))
+                        if dtype == np.float16:
+                            fp16 = True
+                    else:  # output
+                        output_names.append(name)
+                    shape = tuple(context.get_tensor_shape(name))
+                else:
+                    name = model.get_binding_name(i)
+                    dtype = trt.nptype(model.get_binding_dtype(i))
+                    if model.binding_is_input(i):
+                        if -1 in tuple(model.get_binding_shape(i)):  # dynamic
+                            dynamic = True
+                            context.set_binding_shape(i, tuple(model.get_profile_shape(0, i)[2]))
+                        if dtype == np.float16:
+                            fp16 = True
+                    else:  # output
+                        output_names.append(name)
+                    shape = tuple(context.get_binding_shape(i))
                 im = torch.from_numpy(np.empty(shape, dtype=dtype)).to(device)
                 bindings[name] = Binding(name, dtype, shape, im, int(im.data_ptr()))
             binding_addrs = OrderedDict((n, d.ptr) for n, d in bindings.items())