execute_async_v3(cuda_stream) if not noerror: raise ValueError("ERROR: inference failed.") # 获取TensorRT推理结果 print(tensors["output"]) 这里需要说明的一点是,序列化后的引擎文件可以保存为 .plan 文件,方便后续的重复使用,由于引擎文件在构建时涉及对硬件的优化,因此需要保证构建时与运行时的环境的...
cuda.memcpy_htod(dInput, hInput)# 执行推理 context.execute_async_v3(0)# 复制数据从device到host cuda.memcpy_dtoh(houtput, doutput)print(houtput) TensorRT的性能提升效果受多种因素影响,包括模型的复杂性、规模以及使用的GPU型号。 GPU因其硬件架构的优势,特别适合处理并行和密集型计算任务。TensorRT的优化...
Next, start inference: context.execute_async_v3(buffers, stream_ptr) It is common to enqueue asynchronous transfers (cudaMemcpyAsync()) before and after the kernels to move data from the GPU if it is not already there. To determine when inference (and asynchronous transfers) are complete, use...
After populating the input buffer, you can call TensorRT's execute_async_v3 method to start inference asynchronously using a CUDA stream. First, create the CUDA stream. If you already have a CUDA stream, you can use a pointer to the existing stream. For example, for PyTorch CUDA streams...
context->enqueueV3() or context->executeV3() APIs to enqueue the jobs and then synchronize on the stream to wait until the GPU completes the jobs. If you only look at the CPU activities, it may appear that the system is doing nothing for a while in the ...
import cv2 # Initialize camera and face recognition engine cap = cv2.VideoCapture(0) context = face_recognition_engine.create_execution_context() while True: ret, frame = cap.read() if not ret: break # Prepare input and output buffers # ... # Run inference context.execute_async(batch_size...
context.execute_async(batch_size=batch_size,bindings=bindings, stream_handle=stream.handle) # 将结果从 GPU写回到host端 [cuda.memcpy_dtoh_async(out.host, out.device, stream) for out in outputs] # 同步stream stream.synchronize() # 返回host端的输出结果 ...
context.execute_async_v2(bindings=yolo_bindings, stream_handle=stream.handle) stream.synchronize() end_t = time.time() # Transfer predictions back from the GPU.从GPU传回的传输预测。[cuda.memcpy_dtoh_async(out.host, out.device, stream) for ...
将处理好的图片从CPU内存中复制到GPU显存cuda.memcpy_htod_async(self.cuda_inputs[0],self.host_inputs[0],self.stream)# 开始执行推理任务self.context.execute_async(batch_size=1,bindings=self.bindings,stream_handle=self.stream.handle)# 将推理结果输出从GPU显存复制到CPU内存cuda.memcpy_dtoh_async(...
. 9.59.3.6 enqueueV3() . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.59.3.7 execute() . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.59.3.8 execute...