def rknn_convert(input_model, output_model, dataset_file, target_platform):
# Create RKNN object
rknn = RKNN()
print('--> config model')
rknn.config(channel_mean_value='127.5 127.5 127.5 127.5', reorder_channel='0 1 2', batch_size=1, target_platform=target_platform)
# Load onnx model
print('--> Loading model')
ret = rknn.load_onnx(model=input_model)
if ret != 0:
print('Load failed!')
exit(ret)
# Build model
print('--> Building model')
ret = rknn.build(do_quantization=True, dataset=dataset_file, pre_compile=True)
if ret != 0:
print('Build failed!')
exit(ret)
# Export rknn model
print('--> Export RKNN model')
ret = rknn.export_rknn(output_model)
if ret != 0:
print('Export .rknn failed!')
exit(ret)
# Release RKNN object
rknn.release()
def convert_to_rknn():
from rknn.api import RKNN
# Create RKNN object
rknn = RKNN(verbose=True)
# pre-process config
print('--> config model')
rknn.config(channel_mean_value='127.5 127.5 127.5 128',
reorder_channel='0 1 2')
print('done')
# Load onnx model
print('--> Loading model')
ret = rknn.load_onnx(model='lprnet.onnx')
if ret != 0:
print('Load model failed!')
exit(ret)
print('done')
# Build model
print('--> Building model')
ret = rknn.build(do_quantization=False, pre_compile=True, dataset='./data/dataset.txt')
if ret != 0:
print('Build model failed!')
exit(ret)
print('done')
# Export rknn model
print('--> Export RKNN model')
ret = rknn.export_rknn('./lprnet.rknn')
if ret != 0:
print('Export model failed!')
exit(ret)
print('done')
if __name__ == '__main__':
add_perm = False # 如果设置成True,则将模型输入layout修改成NHWC
# Create RKNN object
rknn = RKNN(verbose=True)
# pre-process config
print('--> config model')
rknn.config(batch_size=1, mean_values=[[0, 0, 0]], std_values=[[255, 255, 255]], reorder_channel='0 1 2', target_platform=[platform],
force_builtin_perm=add_perm, output_optimize=1)
print('done')
# Load tensorflow model
print('--> Loading model')
ret = rknn.load_onnx(model=ONNX_MODEL)
if ret != 0:
print('Load resnet50v2 failed!')
exit(ret)
print('done')
# Build model
print('--> Building model')
ret = rknn.build(do_quantization=True, dataset='./dataset.txt')
if ret != 0:
print('Build resnet50 failed!')
exit(ret)
print('done')
# rknn.export_rknn_precompile_model(RKNN_MODEL)
rknn.export_rknn(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
