caffe人形检测模型转换rknn之后精度降低

caffe模型转成rknn未量化模型后，推理出来的结果出现了精度下降的问题。希望能够得到回复，谢谢。图片中为推理的误差，和图片结果差异，附上推理和转换模型代码。模型放在了附件

1. # -*- coding: utf-8 -*-
   
2. 
   
3. """
   
4. detection head of caffe model by cv2.dnn
   
5. """
   
6. 
   
7. import numpy as np
   
8. import cv2
   
9. from cv2 import dnn
   
10. from rknn.api import RKNN
    
11. 
    
12. 
    
13. def caffe2rknn():
    
14.     # 创建RKNN执行对象
    
15.     rknn = RKNN(verbose=False)
    
16.     rknn.config(batch_size=20,
    
17.                 channel_mean_value='127.5 127.5 127.5 127.5',
    
18.                 reorder_channel='2 1 0',
    
19.                 epochs=10,
    
20.                 quantized_dtype='asymmetric_quantized-u8'
    
21.                 )
    
22.     print('--> Loading Caffe model')
    
23.     rknn.load_caffe(model='./ASSD-person.prototxt',
    
24.                     proto='caffe',
    
25.                     blobs='./ASSD-person.caffemodel')
    
26.     print('done')
    
27. 
    
28.     # 创建解析pb模型
    
29.     print('--> Building model')
    
30.     ret = rknn.build(do_quantization=False)
    
31.     print('done')
    
32.     # 导出保存rknn模型文件
    
33.     rknn.export_rknn('./Person_ASSD_noquantization-caffe.rknn')
    
34. 
    
35.     # Release RKNN Context
    
36.     rknn.release()
    
37. 
    
38. 
    
39. def detection_by_cv2(img):
    
40.     img = cv2.imread(img)
    
41.     img = cv2.resize(img, (1159, 655))
    
42.     output_blob_name = ["conv10_3", "conv13_3", "conv15_3", "conv16_3"]
    
43.     blob = dnn.blobFromImage(img, 1 / 127.5, (1159, 655), (127.5, 127.5, 127.5))
    
44.     print("Caffe-Input:", blob.shape, blob.dtype)
    
45.     net = dnn.readNetFromCaffe('./ASSD-person.prototxt',
    
46.                                './ASSD-person.caffemodel')
    
47.     net.setInput(blob)
    
48.     outputs = net.forward(output_blob_name)
    
49.     outputs = [i.ravel() for i in outputs]
    
50.     print("Caffe-Output:", len(outputs), outputs[1].shape)
    
51.     return outputs
    
52. 
    
53. 
    
54. def rknn_detection(image):
    
55.     rknn = RKNN()
    
56.     print('--> Load RKNN Model')
    
57.     ret = rknn.load_rknn('./Person_ASSD_noquantization-caffe.rknn')
    
58.     if ret != 0:
    
59.         print('load rknn model failed')
    
60.         exit(ret)
    
61.     else:
    
62.         print('load rknn model done')
    
63.     print('---->begin init runtime')
    
64.     ret = rknn.init_runtime(target=None)
    
65.     if ret == 0:
    
66.         print('init run time success')
    
67.     else:
    
68.         print('init runtime failed')
    
69. 
    
70.     print('Class Asssd successfully')
    
71.     # Set inputs
    
72.     img = cv2.imread(image)
    
73.     img = cv2.resize(img, (1159, 655))
    
74.     img = img.astype(np.float32)
    
75.     input_image = img[np.newaxis, :, :, :]
    
76.     # Inference
    
77.     print('--> Running rknn model')
    
78.     # outputs
    
79.     outputs = rknn.inference(inputs=[input_image], data_type='float32')
    
80.     print('rknn-outputs-->', len(outputs), outputs[0].shape)
    
81.     rknn.release()
    
82.     return outputs
    
83. 
    
84. 
    
85. def check_caffe_rknn_output(img):
    
86.     """4outputs"""
    
87.     outputs_caffe = detection_by_cv2(img)
    
88.     outputs_rknn = rknn_detection(img)
    
89.     for ca_val, rk_val in zip(outputs_caffe, outputs_rknn):
    
90.         print('========================================================')
    
91.         print('caffe_model--->', ca_val)
    
92.         print('rknn_model---->', rk_val)
    
93.         print('rk - ca --->', ca_val - rk_val)
    
94.         print('Max: %0.3f.' % np.max(np.abs(ca_val - rk_val)))
    
95.         print('Sum: %0.3f ' % np.sum((ca_val - rk_val) ** 2))
    
96. 
    
97. 
    
98. if __name__ == "__main__":
    
99.     img = './a.jpg'
    
100.     check_caffe_rknn_output(img)
     

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你好，请问精度差异解决了吗