def quanlization_darknet_model(model_path,
weight_path,
dataset_txt,
is_quantization=True,
pre_compile=False):
# 创建RKNN执行对象
rknn = RKNN(verbose=True, verbose_file='verbose.log')
rknn.config(channel_mean_value='0 0 0 255',
reorder_channel='0 1 2',
batch_size=4)
flag = rknn.load_darknet(model=model_path, weight=weight_path)
# 后面不用修改
if flag == 0:
print('load_darknet success')
else:
print('load_darknet failure')
print('done')
# 创建解析pb模型
# do_quantization=False指定不进行量化
# 量化会减小模型的体积和提升运算速度，但是会有精度的丢失
print('--> Building model')
print(os.getcwd())
# flag = rknn.build(do_quantization=False)
if is_quantization == True:
flag = rknn.build(do_quantization=True,
dataset=dataset_txt,
pre_compile=pre_compile)
print('do quantization ')
# 导出保存rknn模型文件
save_rknn_path = model_dir + '/' + model_path.split('/')[-1].split(
'.')[0] + '_quan.rknn'
else:
flag = rknn.build(do_quantization=False)
print('not do quantization')
# 导出保存rknn模型文件
save_rknn_path = model_dir + '/' + model_path.split('/')[-1].split(
'.')[0] + '.rknn'
if flag == 0:
print('build success')
else:
print('build failure')
print('done')
flag = rknn.export_rknn(save_rknn_path)
if flag == 0:
print('export success')
else:
print('export failure')
print('done')
print(save_rknn_path)
# Release RKNN Context
rknn.release()
print('save_rknn_path:', save_rknn_path)
def keras_to_rknn(cfg_path, h5_path, darknet_path, rknn_path, dataset_path,
flag):
# ------------------------------------------------------#
# h5 -> darknet, 保存成.weights
# ------------------------------------------------------#
keras_loader = KerasParser(cfg_path, h5_path, darknet_path)
for block in keras_loader.block_gen:
if 'convolutional' in block['type']:
keras_loader.conv(block)
keras_loader.close()
# ------------------------------------------------------#
# darknet -> rknn， 保存成.rknn
# ------------------------------------------------------#
# Create RKNN object
rknn = RKNN()
# Load tensorflow model
print('--> Loading model')
rknn.load_darknet(model=cfg_path, weight=darknet_path)
print('done')
rknn.config(channel_mean_value='0 0 0 255',
reorder_channel='0 1 2',
batch_size=1)
# Build model
print('--> Building model')
if flag:
rknn.build(do_quantization=True,
dataset=dataset_path,
pre_compile=True)
else:
rknn.build(do_quantization=False, pre_compile=True)
print('done')
# export model
print('--> Export model')
rknn.export_rknn(rknn_path)
print('done')
from settings import yolov3_weights, yolov3_model_cfg, rknn_model, \
pre_compile
# Create RKNN object
total_timer = timer()
rknn = RKNN()
# pre-process config
print('--> config model')
rknn.config(channel_mean_value='103.94 116.78 123.68 58.82',
reorder_channel='0 1 2')
print('done')
# Load tensorflow model
print('--> Loading model')
ret = rknn.load_darknet(model=yolov3_model_cfg, weight=yolov3_weights)
if ret != 0:
raise Exception('Load darknet yolov3 failed!')
print('done')
# Build model
print('--> Building model')
build_timer = timer()
# ret = rknn.build(do_quantization=True, dataset='./dataset.txt')
# do_quantization:是否对模型进行量化,值为 True 或 False。
ret = rknn.build(do_quantization=False, pre_compile=pre_compile)
if ret != 0:
raise Exception('Build yolov3 failed!')
print('done, time: %.2fs' % (timer() - build_timer))
# Export rknn model
if __name__ == "__main__":
VIDEO_PATH = sys.argv[1]
# Create RKNN object
rknn = RKNN(verbose=True)
# pre-process config
print('--> config model')
rknn.config(reorder_channel='0 1 2', channel_mean_value='0 0 0 255')
print('done')
if NEED_BUILD_MODEL:
# Load pytorch model
print('--> Loading model {}'.format(MODEL))
ret = rknn.load_darknet(model='/home/labasus/DataDisk/luqiao/deep_sort_yolov3/yolov3.cfg', weight=MODEL)
# ret = rknn.load_onnx(model=MODEL)
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')
if ret != 0:
print('Build onnx failed!')
exit(ret)
print('done')
# Export rknn model
print('--> Export RKNN model {}'.format(RKNN_MODEL_PATH))
RKNN_MODEL_PATH = './yolov3_416.rknn'
im_file = './dog_bike_car_416x416.jpg'
DATASET = './dataset.txt'
# Download yolov3.weight
download_yolov3_weight(WEIGHT_PATH)
# Create RKNN object
rknn = RKNN()
NEED_BUILD_MODEL = True
if NEED_BUILD_MODEL:
# Load caffe model
print('--> Loading model')
ret = rknn.load_darknet(model=MODEL_PATH, weight=WEIGHT_PATH)
if ret != 0:
print('load caffe model failed!')
exit(ret)
print('done')
rknn.config(reorder_channel='0 1 2', channel_mean_value='0 0 0 255')
# Build model
print('--> Building model')
ret = rknn.build(do_quantization=True, dataset='./dataset.txt')
if ret != 0:
print('build model failed.')
exit(ret)
print('done')
from matplotlib import pyplot as plt
import re
import math
import random
from rknn.api import RKNN
if __name__ == '__main__':
# Create RKNN object
rknn = RKNN()
# Load tensorflow model
print('--> Loading model')
rknn.load_darknet(model='./yolov3.cfg', weight="./yolov3.weights")
#rknn.load_darknet(model='./yolov3-tiny.cfg', weight="./yolov3-tiny.weights")
print('done')
rknn.config(channel_mean_value='0 0 0 255',
reorder_channel='0 1 2',
batch_size=1)
# Build model
print('--> Building model')
#rknn.build(do_quantization=True, dataset='./dataset.txt')
rknn.build(do_quantization=True, dataset='./dataset_608x608.txt')
print('done')
#rknn.export_rknn('./yolov3_tiny.rknn')
#from matplotlib import pyplot as plt
import re
import math
import random
from rknn.api import RKNN
if __name__ == '__main__':
# Create RKNN object
rknn = RKNN()
# Load tensorflow model
print('--> Loading model')
#rknn.load_darknet(model='./yolov3_416x416.cfg', weight="./yolov3.weights")
rknn.load_darknet(model='../cfg/yolov3_416x416.cfg', weight="../logs/cc.weights")
print('done')
rknn.config(channel_mean_value='0 0 0 255', reorder_channel='0 1 2', batch_size=1)
# Build model
print('--> Building model')
rknn.build(do_quantization=True, dataset='./dataset.txt', pre_compile=True)
print('done')
#rknn.export_rknn('./yolov3_416x416.rknn')
rknn.export_rknn("./new.rknn")
exit(0)
