We tested the Python code as a standalone(no C++), reading a picture from a file. This method proved as working as expected. When we integrated the Python code from C++ using boost python we are crashing while calling pycuda.driver.memcpy_htod_async with this printed : #assertiongridAncho...
Traceback (most recent call last): line 126, in <listcomp> [cuda.memcpy_htod_async(inp.device, inp.host, stream) for inp in inputs] pycuda._driver.LogicError: cuMemcpyHtoDAsync failed: invalid argument 解决: def get_img_np_nchw(filename): image = cv2.imread(filename) image_cv ...
defdo_inference(context, bindings, inputs, outputs, stream, batch_size=1):# Transfer data from CPU to the GPU.[cuda.memcpy_htod_async(inp.device, inp.host, stream)forinpininputs]# Run inference.context.execute_async(batch_size=batch_size, bind...
data_loader) # 假设images是[N, C, H, W]格式的numpy数组 cuda.memcpy_htod_async(bindings[0], images.astype(np.float32).ravel(), np.prod(images.shape) * 4) return cuda.get_cuda_runtime_version() != 0 构建TensorRT引擎: 使用TensorRT的Builder类配置量化参数,并设置INT8量化校准器。 调用Bu...
d_output=cuda.mem_alloc(h_output.nbytes)#创建cuda流stream =cuda.Stream()#创建context并进行推理with engine.create_execution_context() as context:#Transfer input data to the GPU.cuda.memcpy_htod_async(d_input, h_input, stream)#Run inference.context.execute_async_v2(bindings=[int(d_input),...
pycuda._driver.LogicError: cuMemcpyHtoDAsync failed: invalid argument 1. 2. 3. 4. 解决: def get_img_np_nchw(filename): image = cv2.imread(filename) image_cv = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) image_cv = cv2.resize(image_cv, (1920, 1080)) ...
d_output = cuda.mem_alloc(h_output.nbytes) #创建cuda流 stream = cuda.Stream() #创建context并进行推理 with engine.create_execution_context() as context: # Transfer input data to the GPU. cuda.memcpy_htod_async(d_input, h_input, stream) ...
[cuda.memcpy_htod_async(inp.device, inp.host, stream) for inp in yolo_inputs] # 线程同步 stream.synchronize() start_t = time.time() # 执行模型推理 context.execute_async_v2(bindings=yolo_bindings, stream_handle=stream.handle) stream.synchronize() ...
start = time.time()# Transfer input data to the GPU.cuda.memcpy_htod_async(cuda_inputs[0], host_inputs[0], stream)# Run inference.context.execute_async(batch_size=self.batch_size, bindings=bindings, stream_handle=stream.handle)# Transfer predictions back from the GPU.cuda.memcpy_dtoh_...
Height of the output imagewidth: Width of the output imageOutput:The list of output images"""load_images_to_buffer(pics_1, h_input_1)withengine.create_execution_context()ascontext:# Transfer input data to the GPU.cuda.memcpy_htod_async(d_input_1, h_input_1, stream)# Run infer...