# Create RKNN object
rknn = RKNN()
# Set model config
print('--> Config model')
rknn.config(mean_values=[[127.5, 127.5, 127.5]],
std_values=[[127.5, 127.5, 127.5]],
reorder_channel='0 1 2',
batch_size=16)
print('done')
# Hybrid quantization step2
print('--> hybrid_quantization_step2')
ret = rknn.hybrid_quantization_step2(
model_input='./ssd_mobilenet_v2.json',
data_input='./ssd_mobilenet_v2.data',
model_quantization_cfg='./ssd_mobilenet_v2.quantization.cfg',
dataset='./dataset.txt')
if ret != 0:
print('hybrid_quantization_step2 failed!')
exit(ret)
print('done')
# Export RKNN model
print('--> Export RKNN model')
ret = rknn.export_rknn('./ssd_mobilenet_v2.rknn')
if ret != 0:
print('Export RKNN model failed!')
exit(ret)
print('done')
from rknn.api import RKNN
if __name__ == '__main__':
# Create RKNN object
rknn = RKNN()
# Set model config
print('--> config model')
rknn.config(channel_mean_value='123.675 116.28 103.53 58.395', reorder_channel='0 1 2')
print('done')
# Hybrid quantization step2
print('--> hybrid_quantization_step2')
ret = rknn.hybrid_quantization_step2(model_input='./mnasnet0_5.json',
data_input='./mnasnet0_5.data',
model_quantization_cfg='./mnasnet0_5.quantization.cfg',
dataset='./dataset.txt')
if ret != 0:
print('hybrid_quantization_step2 failed!')
exit(ret)
print('done')
# Export RKNN model
print('--> Export RKNN model')
ret = rknn.export_rknn('./mnasnet0_5.rknn')
if ret != 0:
print('Export RKNN model failed!')
exit(ret)
print('done')
rknn.release()
