import cv2import numpy as npfrom ais_bench.infer.interface import InferSessionfrom ais_bench.infer.common.utils import logger_print

def infer_api_static():    device_id = 0    model_path = '/home/w30067200/paddle_test/PaddleOCR/inference/om/inference.om'    image_path = '/home/w30067200/paddle_test/PaddleOCR/test/general_ocr_002.png'    image = cv2.imread(image_path)
    # create session of om model for inference    session = InferSession(device_id, model_path)
    # create new numpy data according inputs info    shape0 = session.get_inputs()[0].shape    print(shape0)    height, width = shape0[2], shape0[3]    resized_image = cv2.resize(image, (width, height))    image_array = np.array(resized_image).astype(np.float32)
    feeds = [image_array]
    # execute inference, inputs is ndarray list and outputs is ndarray list    outputs = session.infer(feeds, mode='static')    print(outputs[0].shape)
    np.set_printoptions(threshold=np.inf)    print(outputs1[0])
    # cv2.imwrite('output.png', outputs1[0])    logger_print("outputs: %s" % outputs)
    # free model resource and device context of session    session0.free_resource()

infer_api_static()

import cv2import numpy as npfrom ais_bench.infer.interface import InferSessionfrom ais_bench.infer.common.utils import logger_print

def infer_api_static():    device_id = 0    model_path0 = './det.om'    model_path = './rec.om'    image_path = './general_ocr_002.png'    image = cv2.imread(image_path)
    # create session of om model for inference    session0 = InferSession(device_id, model_path0)    session = InferSession(device_id, model_path)
    # create new numpy data according inputs info    shape0 = session0.get_inputs()[0].shape    shape1 = session.get_inputs()[0].shape    print("shape0:",shape0)    print("shape1:",shape1)    height, width = shape0[2], shape0[3]    resized_image = cv2.resize(image, (width, height))    image_array = np.array(resized_image).astype(np.float32)
    feeds = [image_array]
    # execute inference, inputs is ndarray list and outputs is ndarray list    outputs = session0.infer(feeds, mode='static')    print("outputs[0].shape:",outputs[0].shape)    # det.om推理结果输出的shape [1, 1, 640, 480] 转化为下一个rec.om推理模型输入的shape[1, 3, 48, 320]    height, width = shape1[2], shape1[3]    arr = np.repeat(outputs[0], 3, axis=1)    arr = arr.transpose(0, 2, 3, 1)  # [1, 224, 224, 3]
    resized = cv2.resize(arr[0], (width, height), interpolation=cv2.INTER_LINEAR)

    resized = resized[np.newaxis, ...].transpose(0, 3, 1, 2)  # [1, 3, 48, 680]
    '''    resized_image1 = cv2.resize(arr, (width, height))    image_array1 = np.array(resized_image1).astype(np.float32)    '''    feeds = [resized]    print("feeds[0].shape",feeds[0].shape)    # execute inference, inputs is ndarray list and outputs is ndarray list    outputs1 = session.infer(feeds, mode='static')    np.set_printoptions(threshold=np.inf)    # print(outputs1[0].shape)
    # cv2.imwrite('output.png', outputs1[0])    # logger_print("outputs1: %s" % outputs1)
    # free model resource and device context of session    session0.free_resource()

infer_api_static()

import onnximport numpy as npfrom ais_bench.infer.interface import InferSession


def infer_api_dymshape():    model_path0 = './model_dest_linux_x86_64.om'    model_path1 = './mode_loop_input2_i_cond_linux_x86_64.om'
    # Load the model    device_id = 1    session0 = InferSession(device_id, model_path0)    session1 = InferSession(device_id, model_path1)
    # 假设 batch 大小为 4    batch = 4
    # 生成全 1 的 input1    input1 = np.ones((batch, 16, 32), dtype=np.float32)
    # 生成全 2 的 input2    input2 = np.full((batch, 16, 32), 2, dtype=np.float32)
    # print("input1 的形状:", input1.shape)    # print("input2 的形状:", input2.shape)
    input = [input1, input2]    # 执行model_dest_linux_x86_64.om推理    output0 = session0.infer(input, mode='dymshape')    input3 = output0[0]    cnts = output0[1]
    for i in range(cnts):        inputs = {"x.13": input3, "input2": input2}        # 执行mode_loop_input2_i_cond_linux_x86_64.om 推理        input3 = session1.infer(inputs, mode='dymshape')    print("input3", input3)
    # free model resource and device context of session    session0.free_resource()    session1.free_resource()
infer_api_dymshape()