execute_async_v3(stream_handle=stream.handle) # 异步执行推理 for out in outputs: cuda.memcpy_dtoh_async(out['host'],out['device'],stream) # 将输出数据从设备内存复制到主机内存 stream.synchronize() # 同步CUDA流 return [out['host']
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的优化策略正是基于GPU的这一特点,更倾向于优化大规模...
context.set_tensor_address(name,ptr) Several Python packages allow you to allocate memory on the GPU, including, but not limited to, the official CUDA Python bindings, PyTorch, cuPy, and Numba. After populating the input buffer, you can call TensorRT’sexecute_async_v3method to start inference...
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...
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 compl...
execute_async_v3(stream_handle=stream.handle) # Transfer prediction output from the GPU. for output in out_mem: output_mem = out_mem[output] if output_mem is None: # Must have been allocated using OutputAllocator.reallocate. assert output in output_allocators assert output_allocators[output]...
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_v2(bindings=[int(d_input), int(d_output)], stream_handle=stream.handle) # Transfer predictions back from the GPU. cuda.memcpy_dtoh_async(h_output, d_output, stream) # Synchronize the stream stream.synchronize() ...
# Parse model fileifnot os.path.exists(onnx_file_path):print("ONNX file {} not found, please run yolov3_to_onnx.py first to generate it.".format(onnx_file_path))exit(0)print("Loading ONNX file from path {}...".format(onnx_file_path))withopen(onnx_file_path,"rb")asmodel...
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 ...