openpose_caffe_to_rknn.py
from rknn.api import RKNN import cv2 import time import numpy as np if __name__ == '__main__': # Create RKNN object rknn = RKNN() # pre-process config print('--> config model') # 配置模型输入,用于NPU对数据输入的预处理 # channel_mean_value='0 0 0 255',那么模型推理时,将会对RGB数据做如下转换 # (R - 0)/255, (G - 0)/255, (B - 0)/255。推理时,RKNN模型会自动做均值和归一化处理 # reorder_channel=’0 1 2’用于指定是否调整RBG顺序,设置成0 1 2即按输入的RGB顺序不做调整 # reorder_channel=’2 1 0’表示交换0和2通道,如果输入是RGB,将会被调整为BGR。如果是BGR将会被 # 调整为BGR rknn.config(channel_mean_value='0 0 0 255', reorder_channel='2 1 0') print('done') # Load tensorflow model print('--> Loading model') ret = rknn.load_caffe(model='E:\usb_test\example\yolov3\openpose_keras_18key\pose\coco\pose_deploy_linevec.prototxt', proto='caffe', blobs='E:\usb_test\example\yolov3\openpose_keras_18key\pose\coco\pose_iter_440000.caffemodel') if ret != 0: print('Load model failed!') exit(ret) print('done') # Build model print('--> Building model') ret = rknn.build(do_quantization=True, dataset='./dataset.txt') #ret = rknn.build(do_quantization=False) if ret != 0: print('Build model failed!') exit(ret) print('done') # Export rknn model print('--> Export RKNN model') ret = rknn.export_rknn('./coco_quantization_368_654.rknn') if ret != 0: print('Export model failed!') exit(ret) print('done') rknn.release()
需要注意事项:由于rknn的模型是静态的,所以,在模型量化的时候,输入尺寸被固定,根据自己的图片更改caffe模型,如下:
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