def caffe2rknn(caffe_proto,caffe_weight,rknn_model):
print("start export")
rknn=RKNN(verbose=True)
ret=rknn.load_caffe(model=caffe_proto,
proto="caffe",
blobs=caffe_weight)
rknn.config(channel_mean_value='127.5 127.5 127.5 128.0',
reorder_channel='2 1 0',
#reorder_channel='0 1 2',
#need_horizontal_merge=True
)
ret = rknn.build(do_quantization=False)
#ret = rknn.build(do_quantization=True)
ret=rknn.export_rknn(export_path=rknn_model)
print("export finished")
exit(-1)
# Create RKNN object
rknn = RKNN(verbose=False)
# Set model config
print('--> Config model')
rknn.config(mean_values=[[103.94, 116.78, 123.68]],
std_values=[[1, 1, 1]],
reorder_channel='2 1 0')
print('done')
# Load caffe model
print('--> Loading model')
ret = rknn.load_caffe(
model='./deploy_rm_detection_output.prototxt',
proto='caffe',
blobs='./VGG_VOC0712_SSD_300x300_iter_120000.caffemodel')
if ret != 0:
print('Load model failed! Ret = {}'.format(ret))
exit(ret)
print('done')
# 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')
# Export RKNN model
if __name__ == '__main__':
# Create RKNN object
rknn = RKNN()
# pre-process config
print('--> config model')
rknn.config(mean_values=[[0, 0, 0]],
std_values=[[1, 1, 1]],
reorder_channel='2 1 0')
print('done')
# Load tensorflow model
print('--> Loading model')
ret = rknn.load_caffe(model='./deploy.prototxt',
proto='caffe',
blobs='./solver_iter_45.caffemodel')
if ret != 0:
print('Load interp_test failed! Ret = {}'.format(ret))
exit(ret)
print('done')
# Build model
print('--> Building model')
ret = rknn.build(do_quantization=True, dataset='./dataset.txt')
if ret != 0:
print('Build interp_test failed!')
exit(ret)
print('done')
# Export rknn model
if __name__ == '__main__':
# Create RKNN object
rknn = RKNN()
# pre-process config
print('--> config model')
rknn.config(channel_mean_value='127.5 127.5 127.5 128',
reorder_channel='2 1 0',
quantized_dtype='dynamic_fixed_point-8')
print('done')
# Load tensorflow model
print('--> Loading model')
ret = rknn.load_caffe(model='./ONet.prototxt',
proto='caffe',
blobs='./ONet.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_onet.txt')
if ret != 0:
print('Build model failed!')
exit(ret)
print('done')
# Export rknn model
if __name__ == '__main__':
# Create RKNN object
rknn = RKNN()
# pre-process config
print('--> config model')
rknn.config(channel_mean_value='103.94 116.78 123.68 58.82',
reorder_channel='2 1 0')
print('done')
# Load tensorflow model
print('--> Loading model')
ret = rknn.load_caffe(model='./mobilenet_v2.prototxt',
proto='caffe',
blobs='./mobilenet_v2.caffemodel')
if ret != 0:
print('Load mobilenet_v2 failed! Ret = {}'.format(ret))
exit(ret)
print('done')
# Build model
print('--> Building model')
ret = rknn.build(do_quantization=True, dataset='./dataset.txt')
if ret != 0:
print('Build mobilenet_v2 failed!')
exit(ret)
print('done')
# Export rknn model
def convert_model(model_path, out_path, pre_compile):
if os.path.isfile(model_path):
yaml_config_file = model_path
model_path = os.path.dirname(yaml_config_file)
else:
yaml_config_file = os.path.join(model_path, 'model_config.yml')
if not os.path.exists(yaml_config_file):
print('model config % not exist!' % yaml_config_file)
exit(-1)
model_configs = parse_model_config(yaml_config_file)
exported_rknn_model_path_list = []
for model_name in model_configs['models']:
model = model_configs['models'][model_name]
rknn = RKNN()
rknn.config(**model['configs'])
print('--> Loading model...')
if model['platform'] == 'tensorflow':
model_file_path = os.path.join(model_path,
model['model_file_path'])
input_size_list = []
for input_size_str in model['subgraphs']['input-size-list']:
input_size = list(map(int, input_size_str.split(',')))
input_size_list.append(input_size)
pass
rknn.load_tensorflow(tf_pb=model_file_path,
inputs=model['subgraphs']['inputs'],
outputs=model['subgraphs']['outputs'],
input_size_list=input_size_list)
elif model['platform'] == 'tflite':
model_file_path = os.path.join(model_path,
model['model_file_path'])
rknn.load_tflite(model=model_file_path)
elif model['platform'] == 'caffe':
prototxt_file_path = os.path.join(model_path,
model['prototxt_file_path'])
caffemodel_file_path = os.path.join(model_path,
model['caffemodel_file_path'])
rknn.load_caffe(model=prototxt_file_path,
proto='caffe',
blobs=caffemodel_file_path)
elif model['platform'] == 'onnx':
model_file_path = os.path.join(model_path,
model['model_file_path'])
rknn.load_onnx(model=model_file_path)
else:
print("platform %s not support!" % (model['platform']))
print('done')
if model['quantize']:
dataset_path = os.path.join(model_path, model['dataset'])
else:
dataset_path = './dataset'
print('--> Build RKNN model...')
rknn.build(do_quantization=model['quantize'],
dataset=dataset_path,
pre_compile=pre_compile)
print('done')
export_rknn_model_path = "%s.rknn" % (os.path.join(
out_path, model_name))
print('--> Export RKNN model to: {}'.format(export_rknn_model_path))
rknn.export_rknn(export_path=export_rknn_model_path)
exported_rknn_model_path_list.append(export_rknn_model_path)
print('done')
return exported_rknn_model_path_list
from rknn.api import RKNN
# create RKNN object
rknn = RKNN(verbose=True)
# Load caffe model
print('--> Loading model')
ret = rknn.load_caffe(model='./model/new_model/openpose.prototxt',
proto='caffe',
blobs='./model/new_model/openpose.caffemodel')
# print error message
if ret !=0:
print('Load failed!')
exit(ret)
print('done')
# Build model
print('--> Building model')
ret = rknn.build(do_quantization=False)
# print error message
if ret !=0:
print('Build failed!')
exit(ret)
print('done')
# Export rknn model
print('--> Export RKNN model')
ret = rknn.export_rknn('./openpose_caffe.rknn')
# print error message
from rknn.api import RKNN
if __name__ == '__main__':
rknn = RKNN()
#rknn.config(channel_mean_value='0 0 0 1', reorder_channel='0 1 2',quantized_dtype='dynamic_fixed_point-8')
rknn.config(channel_mean_value='0 0 0 1',
reorder_channel='0 1 2',
quantized_dtype='dynamic_fixed_point-16',
batch_size=2)
print('--> Loading model')
rknn.load_caffe(model='./enet_deploy_final.prototxt',
proto='caffe',
blobs='./cityscapes_weights.caffemodel')
print('done')
print('--> Building model')
rknn.build(do_quantization=True, dataset='./dateset.txt')
#rknn.build(do_quantization=False)
print('done')
# 导出保存rknn模型文件
rknn.export_rknn('./cityscapes.rknn')
# Release RKNN Context
rknn.release()
