Backport compatibility with TensorRT version 10 from yolov8

export.py

@@ -346,6 +346,7 @@ def export_engine(model, im, file, half, dynamic, simplify, workspace=4, verbose
     onnx = file.with_suffix(".onnx")
 
     LOGGER.info(f"\n{prefix} starting export with TensorRT {trt.__version__}...")
+    is_trt10 = int(trt.__version__.split(".")[0]) >= 10  # is TensorRT >= 10
     assert onnx.exists(), f"failed to export ONNX file: {onnx}"
     f = file.with_suffix(".engine")  # TensorRT engine file
     logger = trt.Logger(trt.Logger.INFO)
@@ -354,9 +355,10 @@ def export_engine(model, im, file, half, dynamic, simplify, workspace=4, verbose
 
     builder = trt.Builder(logger)
     config = builder.create_builder_config()
-    config.max_workspace_size = workspace * 1 << 30
-    # config.set_memory_pool_limit(trt.MemoryPoolType.WORKSPACE, workspace << 30)  # fix TRT 8.4 deprecation notice
-
+    if is_trt10:
+        config.set_memory_pool_limit(trt.MemoryPoolType.WORKSPACE, workspace << 30)
+    else:  # TensorRT versions 7, 8
+        config.max_workspace_size = workspace * 1 << 30
     flag = 1 << int(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH)
     network = builder.create_network(flag)
     parser = trt.OnnxParser(network, logger)
@@ -381,8 +383,10 @@ def export_engine(model, im, file, half, dynamic, simplify, workspace=4, verbose
     LOGGER.info(f"{prefix} building FP{16 if builder.platform_has_fast_fp16 and half else 32} engine as {f}")
     if builder.platform_has_fast_fp16 and half:
         config.set_flag(trt.BuilderFlag.FP16)
-    with builder.build_engine(network, config) as engine, open(f, "wb") as t:
-        t.write(engine.serialize())
+
+    build = builder.build_serialized_network if is_trt10 else builder.build_engine
+    with build(network, config) as engine, open(f, "wb") as t:
+        t.write(engine if is_trt10 else engine.serialize())
     return f, None
 
 

models/common.py

@@ -527,18 +527,34 @@ def __init__(self, weights="yolov5s.pt", device=torch.device("cpu"), dnn=False,
             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())