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layer_name simulator_error runtime_error
entire single entire single_sim
cos euc cos euc cos euc cos euc
-----------------------------------------------------------------------------------------------------------------------
[Input] images 1.00000 | 0.0 1.00000 | 0.0 1.00000 | 404.33 1.00000 | 404.33
[exDataConvert] images_int8 1.00000 | 0.1896 1.00000 | 0.1896
[Conv] 131
[Relu] 89 0.99978 | 3.5098 0.99978 | 3.5098 0.99978 | 3.5098 1.00000 | 0.0
[Conv] 134
[Relu] 92 0.99868 | 4.9987 0.99868 | 4.9987 0.99868 | 4.9987 1.00000 | 0.0
[Conv] 137
[Relu] 95 0.99526 | 9.9630 0.99656 | 8.6235 0.99526 | 9.9630 1.00000 | 0.0
[Conv] 140
[Relu] 98 0.99744 | 16.443 0.99969 | 5.5234 0.99744 | 16.443 1.00000 | 0.0
[MaxPool] 99 0.99756 | 8.3143 0.99994 | 1.2698 0.99756 | 8.3143 1.00000 | 0.0
[Conv] 143
[Relu] 102 0.99548 | 10.145 0.99983 | 2.0447 0.99548 | 10.145 1.00000 | 0.0
[Conv] 146
[Relu] 105 0.99713 | 9.8246 0.99987 | 2.1592 0.99713 | 9.8251 1.00000 | 0.2776
[MaxPool] 106 0.99711 | 5.4939 0.99995 | 0.7312 0.99712 | 5.4886 1.00000 | 0.0
[Conv] 149
[Relu] 109 0.99741 | 4.4895 0.99988 | 0.9680 0.99743 | 4.4667 1.00000 | 0.0
[Conv] 152
[Relu] 112 0.99241 | 2.7811 0.99974 | 0.5176 0.99240 | 2.7846 1.00000 | 0.0220
[MaxPool] 113 0.99367 | 1.7026 0.99992 | 0.1912 0.99380 | 1.6843 1.00000 | 0.0
[Conv] 155
[Relu] 116 0.99738 | 1.7186 0.99986 | 0.3834 0.99746 | 1.6965 1.00000 | 0.0374
[Conv] 158
[Relu] 119 0.99299 | 4.1908 0.99943 | 1.1924 0.99366 | 3.9831 1.00000 | 0.0
[Conv] 161
[Relu] 122 0.99960 | 0.3034 0.99997 | 0.0780 0.99963 | 0.2924 1.00000 | 0.0
[Conv] 164 0.99473 | 1.5335 0.99968 | 0.4397 0.99376 | 1.6592 1.00000 | 0.0
[Conv] GlobalAveragePool_26_2conv0 0.99647 | 0.8139 0.99995 | 0.1021 0.99544 | 0.9055 1.00000 | 0.0
[Conv] 125 0.99680 | 0.2554 0.99999 | 0.0173 0.99576 | 0.2870 1.00000 | 0.0
[Reshape] output_2_int8 0.99680 | 0.2554 0.99999 | 0.0124
[exDataConvert] output_2 0.99680 | 0.2554 0.99999 | 0.0124 0.99576 | 0.2870 1.00000 | 0.0
[MaxPool] 127 0.99575 | 3.0204 0.99987 | 0.5405 0.99618 | 2.8607 1.00000 | 0.0
[Conv] 128 0.99839 | 15.575 0.99934 | 9.9484 0.99846 | 15.255 1.00000 | 0.0
[Transpose] output_1-rs 0.99839 | 15.575 0.99937 | 9.7783 0.99846 | 15.255 1.00000 | 0.0
[Reshape] output_1_int8 0.99839 | 15.575 0.99937 | 9.7783
[exDataConvert] output_1 0.99839 | 15.575 0.99937 | 9.7783 0.99846 | 15.255 1.00000 | 0.0
I The error analysis results save to: snapshot/error_analysis.txt
这是我的量化分析代码
from rknn.api import RKNN
import cv2
mean_values=[[255*0.588]]
std_values=[[255*0.193]]
if __name__ == '__main__':
# 第一步:创建RKNN对象
rknn = RKNN(verbose=True)
# 第二步:配置RKNN对象参数
rknn.config(
mean_values=[[255*0.588,255*0.588,255*0.588]],
std_values=[[255*0.193,255*0.193,255*0.193]],
target_platform='rv1106',
quantized_algorithm='normal',
quantized_dtype='w8a8',
optimization_level=0,
# 其余参数保持默认即可
)
# 第三步:调用load_pytorch接口导入pt模型
rknn.load_onnx(model='./utils/plate_recognition_color.onnx',input_size_list=[[1, 3, 48, 168]])
# 第四步:调用build接口构建RKNN模型
rknn.build(
do_quantization=True, # 表示开启rknn模型量化
dataset='./utils/dataset.txt', # 量化所用到的数据集
)
# 导出rknn模型
rknn.export_rknn(export_path='./resnet.rknn')
img = './data/9-沪GCLE7G.jpg'
img = cv2.imread(img)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
img = cv2.resize(img, (168, 48), interpolation=cv2.INTER_CUBIC)
img = img.transpose(2, 0, 1)
img = img.reshape(1, 3, 48, 168)
# 第五步:使用accuracy_analysis 接口进行模型量化精度分析
rknn.accuracy_analysis(
inputs=[img], # 表示进行推理的图像
output_dir='snapshot', # 表示精度分析的输出目录
target='rv1106', # 默认为None,表示运行在模拟器上
device_id='10.1.1.144:5555', # 设备的编号
)
# 最后一步:释放RKNN对象
rknn.release()
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