rknn.eval_perf(inputs=[img])模型性能不一致问题 - Toybrick

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()