ultralytics 8.1.46 add TensorRT 10 support (ultralytics#9516)

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: 九是否随意的称呼 <1069679911@qq.com>

tests/test_cuda.py

@@ -19,6 +19,13 @@ def test_checks():
     assert torch.cuda.is_available() == CUDA_IS_AVAILABLE
     assert torch.cuda.device_count() == CUDA_DEVICE_COUNT
 
+@pytest.mark.slow
+@pytest.mark.skipif(not CUDA_IS_AVAILABLE, reason="CUDA is not available")
+def test_export_engine():
+    """Test exporting the YOLO model to NVIDIA TensorRT format."""
+    f = YOLO(MODEL).export(format="engine", device=0)
+    YOLO(f)(BUS, device=0)
+
 
 @pytest.mark.skipif(not CUDA_IS_AVAILABLE, reason="CUDA is not available")
 def test_train():

ultralytics/__init__.py

@@ -1,6 +1,6 @@
 # Ultralytics YOLO 🚀, AGPL-3.0 license
 
-__version__ = "8.1.45"
+__version__ = "8.1.46"
 
 from ultralytics.data.explorer.explorer import Explorer
 from ultralytics.models import RTDETR, SAM, YOLO, YOLOWorld

ultralytics/engine/exporter.py

@@ -658,6 +658,7 @@ def export_coreml(self, prefix=colorstr("CoreML:")):
     def export_engine(self, prefix=colorstr("TensorRT:")):
         """YOLOv8 TensorRT export https://developer.nvidia.com/tensorrt."""
         assert self.im.device.type != "cpu", "export running on CPU but must be on GPU, i.e. use 'device=0'"
+        self.args.simplify = True
         f_onnx, _ = self.export_onnx()  # run before trt import https://github.com/ultralytics/ultralytics/issues/7016
 
         try:
@@ -666,12 +667,10 @@ def export_engine(self, prefix=colorstr("TensorRT:")):
             if LINUX:
                 check_requirements("nvidia-tensorrt", cmds="-U --index-url https://pypi.ngc.nvidia.com")
             import tensorrt as trt  # noqa
-
         check_version(trt.__version__, "7.0.0", hard=True)  # require tensorrt>=7.0.0
 
-        self.args.simplify = True
-
         LOGGER.info(f"\n{prefix} starting export with TensorRT {trt.__version__}...")
+        is_trt10 = int(trt.__version__.split(".")[0]) >= 10  # is TensorRT >= 10
         assert Path(f_onnx).exists(), f"failed to export ONNX file: {f_onnx}"
         f = self.file.with_suffix(".engine")  # TensorRT engine file
         logger = trt.Logger(trt.Logger.INFO)
@@ -680,7 +679,11 @@ def export_engine(self, prefix=colorstr("TensorRT:")):
 
         builder = trt.Builder(logger)
         config = builder.create_builder_config()
-        config.max_workspace_size = int(self.args.workspace * (1 << 30))
+        workspace = int(self.args.workspace * (1 << 30))
+        if is_trt10:
+            config.set_memory_pool_limit(trt.MemoryPoolType.WORKSPACE, workspace)
+        else:  # TensorRT versions 7, 8
+            config.max_workspace_size = workspace
         flag = 1 << int(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH)
         network = builder.create_network(flag)
         parser = trt.OnnxParser(network, logger)
@@ -699,27 +702,31 @@ def export_engine(self, prefix=colorstr("TensorRT:")):
             if shape[0] <= 1:
                 LOGGER.warning(f"{prefix} WARNING ⚠️ 'dynamic=True' model requires max batch size, i.e. 'batch=16'")
             profile = builder.create_optimization_profile()
+            min_shape = (1, shape[1], 32, 32)  # minimum input shape
+            opt_shape = (max(1, shape[0] // 2), *shape[1:])  # optimal input shape
+            max_shape = (*shape[:2], *(max(1, self.args.workspace) * d for d in shape[2:]))  # max input shape
             for inp in inputs:
-                profile.set_shape(inp.name, (1, *shape[1:]), (max(1, shape[0] // 2), *shape[1:]), shape)
+                profile.set_shape(inp.name, min_shape, opt_shape, max_shape)
             config.add_optimization_profile(profile)
 
-        LOGGER.info(
-            f"{prefix} building FP{16 if builder.platform_has_fast_fp16 and self.args.half else 32} engine as {f}"
-        )
-        if builder.platform_has_fast_fp16 and self.args.half:
+        half = builder.platform_has_fast_fp16 and self.args.half
+        LOGGER.info(f"{prefix} building FP{16 if half else 32} engine as {f}")
+        if half:
             config.set_flag(trt.BuilderFlag.FP16)
 
+        # Free CUDA memory
         del self.model
         torch.cuda.empty_cache()
 
         # Write file
-        with builder.build_engine(network, config) as engine, open(f, "wb") as t:
+        build = builder.build_serialized_network if is_trt10 else builder.build_engine
+        with build(network, config) as engine, open(f, "wb") as t:
             # Metadata
             meta = json.dumps(self.metadata)
             t.write(len(meta).to_bytes(4, byteorder="little", signed=True))
             t.write(meta.encode())
             # Model
-            t.write(engine.serialize())
+            t.write(engine if is_trt10 else engine.serialize())
 
         return f, None
 

ultralytics/nn/autobackend.py

@@ -234,23 +234,47 @@ def __init__(
                 meta_len = int.from_bytes(f.read(4), byteorder="little")  # read metadata length
                 metadata = json.loads(f.read(meta_len).decode("utf-8"))  # read metadata
                 model = runtime.deserialize_cuda_engine(f.read())  # read engine
-            context = model.create_execution_context()
+
+            # Model context
+            try:
+                context = model.create_execution_context()
+            except Exception as e:  # model is None
+                LOGGER.error(f"ERROR: TensorRT model exported with a different version than {trt.__version__}\n")
+                raise e
+
             bindings = OrderedDict()
             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 = True
+                            context.set_input_shape(name, tuple(model.get_tensor_profile_shape(name, 0)[1]))
+                            if dtype == np.float16:
+                                fp16 = True
+                    else:
+                        output_names.append(name)
+                    shape = tuple(context.get_tensor_shape(name))
+                else:  # TensorRT < 10.0
+                    name = model.get_binding_name(i)
+                    dtype = trt.nptype(model.get_binding_dtype(i))
+                    is_input = model.binding_is_input(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)[1]))
+                        if dtype == np.float16:
+                            fp16 = True
+                    else:
+                        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())
@@ -467,13 +491,20 @@ def callback(request, userdata):
 
         # TensorRT
         elif self.engine:
-            if self.dynamic and im.shape != self.bindings["images"].shape:
-                i = self.model.get_binding_index("images")
-                self.context.set_binding_shape(i, im.shape)  # reshape if dynamic
-                self.bindings["images"] = self.bindings["images"]._replace(shape=im.shape)
-                for name in self.output_names:
-                    i = self.model.get_binding_index(name)
-                    self.bindings[name].data.resize_(tuple(self.context.get_binding_shape(i)))
+            if self.dynamic or im.shape != self.bindings["images"].shape:
+                if self.is_trt10:
+                    self.context.set_input_shape("images", im.shape)
+                    self.bindings["images"] = self.bindings["images"]._replace(shape=im.shape)
+                    for name in self.output_names:
+                        self.bindings[name].data.resize_(tuple(self.context.get_tensor_shape(name)))
+                else:
+                    i = self.model.get_binding_index("images")
+                    self.context.set_binding_shape(i, im.shape)
+                    self.bindings["images"] = self.bindings["images"]._replace(shape=im.shape)
+                    for name in self.output_names:
+                        i = self.model.get_binding_index(name)
+                        self.bindings[name].data.resize_(tuple(self.context.get_binding_shape(i)))
+
             s = self.bindings["images"].shape
             assert im.shape == s, f"input size {im.shape} {'>' if self.dynamic else 'not equal to'} max model size {s}"
             self.binding_addrs["images"] = int(im.data_ptr())