使用OpenVINO的AsyncInferQueue類提升AI推理程序的吞吐量

本文將介紹基於OpenVINO的異步推理隊列類 AyncInferQueue，啓動多個(＞2)推理請求(infer request)，在硬件投入不變的情況下，進一步提升 AI 推理程序的吞吐量(Throughput)。

OpenVINO運行時(Runtime)用推理請求(infer request)來抽象在指定計算設備上運行已編譯模型(Compiled_Model)。從編寫程序的角度看，推理請求是一個類，封裝了支持推理請求以同步或異步方式運行的屬性和方法

OpenVINO運行時(Runtime)提供 AsyncInferQueue 類來抽象並管理異步推理請求池，其常用方法和屬性有：

• init(self, compiled_model, jobs = 0)：創建AsyncInferQueue對象
• set_callback(func_name)：爲推理請求池中所有的推理請求設置統一的回調函數
• start_async(inputs, userdata = None)：異步啓動推理請求
• wait_all()：等待所有的推理請求執行完畢

基於 AsyncInferQueue 類 YOLOv5 模型的異步推理範例程序: yolov5_async_infer_queue.py

...
def preprocess(frame):
    # Preprocess the frame
    letterbox_im, _, _= letterbox(frame, auto=False) # preprocess frame by letterbox
    im = letterbox_im.transpose((2, 0, 1))[::-1]  # HWC to CHW, BGR to RGB
    im = np.float32(im) / 255.0    # 0 - 255 to 0.0 - 1.0
    blob = im[None]  # expand for batch dim
    return blob, letterbox_im.shape[:-1], frame.shape[:-1]
def postprocess(ireq: InferRequest, user_data: tuple):
    result = ireq.results[ireq.model_outputs[0]]
    dets = non_max_suppression(torch.tensor(result))[0].numpy()
    bboxes, scores, class_ids= dets[:,:4], dets[:,4], dets[:,5]
    # rescale the coordinates
    bboxes = scale_coords(user_data[1], bboxes, user_data[2]).astype(int)
    print(user_data[0],"\t"+f"{ireq.latency:.3f}"+"\t", class_ids)
    return 
# Step1：Initialize OpenVINO Runtime Core
core = Core()
# Step2:  Build compiled model
device = device = ['GPU.0', 'GPU.1', 'CPU', 'AUTO', 'AUTO:GPU,-CPU'][0]
cfgs = {}
cfgs['PERFORMANCE_HINT'] = ['THROUGHPUT', 'LATENCY', 'CUMULATIVE_THROUGHPUT'][0]
net = core.compile_model("yolov5s.xml",device,cfgs)
output_node = net.outputs[0]
b,n,input_h,input_w = net.inputs[0].shape
# Step3:  Initialize InferQueue
ireqs = AsyncInferQueue(net)
print('Number of infer requests in InferQueue:', len(ireqs))
# Step3.1: Set unified callback on all InferRequests from queue's pool
ireqs.set_callback(postprocess)
# Step4:  Read the images
image_folder = "./data/images/"
image_files= os.listdir(image_folder)
print(image_files)
frames = []
for image_file in image_files:
    frame = cv2.imread(os.path.join(image_folder, image_file))
    frames.append(frame)
# 4.1 Warm up
for id, _ in enumerate(ireqs):
    # Preprocess the frame
    start = perf_counter()
    blob, letterbox_shape, frame_shape = preprocess(frames[id % 4])
    end = perf_counter()
    print(f"Preprocess {id}: {(end-start):.4f}.")
    # Run asynchronous inference using the next available InferRequest from the pool
    ireqs.start_async({0:blob},(id, letterbox_shape, frame_shape))
ireqs.wait_all()
# Step5:  Benchmark the Async Infer
start = perf_counter()
in_fly = set()
latencies = []
niter = 16
for i in range(niter):
    # Preprocess the frame
    blob, letterbox_shape, frame_shape = preprocess(frames[i % 4]) 
    idle_id = ireqs.get_idle_request_id()
    if idle_id in in_fly:
        latencies.append(ireqs[idle_id].latency)
    else:
        in_fly.add(idle_id)
    # Run asynchronous inference using the next available InferRequest from the pool 
    ireqs.start_async({0:blob},(i, letterbox_shape, frame_shape) )
ireqs.wait_all()

在蝰蛇峽谷NUC上運行結果：

結論：使用 OpenVINO™ Runtime 的 AsyncInferQueue 類，可以極大提升 AI 推理程序的吞出量。