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标题: caffe人形检测模型转换rknn之后精度降低 [打印本页]

作者: zxs 时间: 2019-11-4 16:12
标题: 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。附上推理和转换模型代码。
# -*- coding: utf-8 -*-

"""
detection head of caffe model by cv2.dnn
"""

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

def caffe2rknn():
# 创建RKNN执行对象
rknn = RKNN(verbose=False)
rknn.config(batch_size=20,
            channel_mean_value='127.5 127.5 127.5 127.5',
            reorder_channel='2 1 0',
            epochs=10,
            quantized_dtype='asymmetric_quantized-u8'
            )
print('--> Loading Caffe model')
rknn.load_caffe(model='./ASSD-person.prototxt',
                  proto='caffe',
                  blobs='./ASSD-person.caffemodel')
print('done')

# 创建解析pb模型
print('--> Building model')
ret = rknn.build(do_quantization=False)
print('done')
# 导出保存rknn模型文件
rknn.export_rknn('./Person_ASSD_noquantization-caffe.rknn')

# Release RKNN Context
rknn.release()

def detection_by_cv2(img):
img = cv2.imread(img)
img = cv2.resize(img, (1159, 655))
output_blob_name = ["conv10_3", "conv13_3", "conv15_3", "conv16_3"]
blob = dnn.blobFromImage(img, 1 / 127.5, (1159, 655), (127.5, 127.5, 127.5))
print("Caffe-Input:", blob.shape, blob.dtype)
net = dnn.readNetFromCaffe('./ASSD-person.prototxt',
                           './ASSD-person.caffemodel')
net.setInput(blob)
outputs = net.forward(output_blob_name)
outputs = [i.ravel() for i in outputs]
print("Caffe-Output:", len(outputs), outputs[1].shape)
return outputs

def rknn_detection(image):
rknn = RKNN()
print('--> Load RKNN Model')
ret = rknn.load_rknn('./Person_ASSD_noquantization-caffe.rknn')
if ret != 0:
      print('load rknn model failed')
      exit(ret)
else:
      print('load rknn model done')
print('---->begin init runtime')
ret = rknn.init_runtime(target=None)
if ret == 0:
      print('init run time success')
else:
      print('init runtime failed')

print('Class Asssd successfully')
# Set inputs
img = cv2.imread(image)
img = cv2.resize(img, (1159, 655))
img = img.astype(np.float32)
input_image = img[np.newaxis, :, :, :]
# Inference
print('--> Running rknn model')
# outputs
outputs = rknn.inference(inputs=[input_image], data_type='float32')
print('rknn-outputs-->', len(outputs), outputs[0].shape)
rknn.release()
return outputs

def check_caffe_rknn_output(img):
"""4outputs"""
outputs_caffe = detection_by_cv2(img)
outputs_rknn = rknn_detection(img)
for ca_val, rk_val in zip(outputs_caffe, outputs_rknn):
      print('========================================================')
      print('caffe_model--->', ca_val)
      print('rknn_model---->', rk_val)
      print('rk - ca --->', ca_val - rk_val)
      print('Max: %0.3f.' % np.max(np.abs(ca_val - rk_val)))
      print('Sum: %0.3f ' % np.sum((ca_val - rk_val) ** 2))

if __name__ == "__main__":
img = './a.jpg'
check_caffe_rknn_output(img)

作者: zxs 时间: 2019-11-4 16:31
http://t.rock-chips.com/forum.ph ... tid=1030&extra=
转到这个帖子，附上模型文件

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