rknn.eval_perf(inputs=[img])模型性能不一致问题

import numpy as np
import cv2
from rknn.api import RKNN

def show_outputs(outputs):
    output = outputs[0][0]
    output_sorted = sorted(output, reverse=True)
    top5_str = 'mobilenet_v1\n-----TOP 5-----\n'
    for i in range(5):
        value = output_sorted
        index = np.where(output == value)
        for j in range(len(index)):
            if (i + j) >= 5:
                break
            if value > 0:
                topi = '{}: {}\n'.format(index[j], value)
            else:
                topi = '-1: 0.0\n'
            top5_str+= topi
    print(top5_str)

def show_perfs(perfs):
    perfs = 'perfs:{}\n'.format(outputs)
    print(perfs)

if __name__ == '__main__':

    #Create RKNN object
    importtime
    rknn = RKNN()
   
    #pre-process config
    T1=time.time()
    print('--> config model')
    rknn.config(channel_mean_value='103.94 116.78 123.68 58.82', reorder_channel='0 1 2')
    T2=time.time()
    print("config model time:%.2fms"%((T2-T1)*1000))
    print('done')

    #Load tensorflow model
    print('--> Loading model')
    ret = rknn.load_tflite(model='./mobilenet_v1.tflite')
    if ret != 0:
        print('Load mobilenet_v1 failed!')
        exit(ret)
    T3 = time.time()
    print("Loading model time:%.2fms"%((T3-T2)*1000))
    print('done')

    #Build model
    print('--> Building model')
    ret = rknn.build(do_quantization=True, dataset='./dataset.txt')
    if ret != 0:
        print('Build mobilenet_v1 failed!')
        exit(ret)
    T4 = time.time()
    print("Building model time:%.2fms"%((T4-T3)*1000))
    print('done')

    #Export rknn model
    print('--> Export RKNN model')
    ret = rknn.export_rknn('./mobilenet_v1.rknn')
    if ret != 0:
        print('Export mobilenet_v1.rknn failed!')
        exit(ret)
    T5=time.time()
    print("Export RKNN model time:%.2fms"%((T5-T4)*1000))
    print('done')

    #Set inputs
    img= cv2.imread('./dog_224x224.jpg')
    img = cv2.cvtColor(img,cv2.COLOR_BGR2RGB)
    T6 = time.time()
    print("Set inputs time:%.2fms"%((T6-T5)*1000))

    #init runtime environment
    print('--> Init runtime environment')
    ret = rknn.init_runtime()
    if ret != 0:
        print('Init runtime environment failed')
        exit(ret)
    T7 = time.time()
    print("Init runtime environment time:%.2fms"%((T7-T6)*1000))
    print('done')

    #Inference
    print('--> Running model')
    outputs = rknn.inference(inputs=[img])
    show_outputs(outputs)
    T8 = time.time()
    print("Inference time:%.2fms"%((T8-T7)*1000))
    print('done')

    #perf
    print('--> Begin evaluate modelperformance')
    perf_results = rknn.eval_perf(inputs=[img])
    print('done')
    T9=time.time()
    print("perf time:%.2fms"%((T9-T8)*1000))
    rknn.release()