File size: 2,175 Bytes
09349b9 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 |
#!/usr/bin/env python
# coding: utf-8
import datetime
from rknn.api import RKNN
from sys import exit
import argparse
ONNX_MODEL="model.onnx"
detailed_performance_log = True
# 添加命令行参数解析
parser = argparse.ArgumentParser(description='将ONNX模型转换为RKNN模型')
parser.add_argument('--quantize', '-q', action='store_true',
help='是否对模型进行量化')
parser.add_argument('--dataset', '-d',
default="/home/zt/rk3588-nn/rknn_model_zoo/datasets/COCO/coco_subset_20.txt",
help='量化校准数据集的路径')
args = parser.parse_args()
DATASET=args.dataset # 使用命令行参数指定数据集路径
QUANTIZE=args.quantize
# 根据是否量化决定输出文件名
if args.quantize:
RKNN_MODEL=ONNX_MODEL.replace(".onnx","_int8.rknn")
else:
RKNN_MODEL=ONNX_MODEL.replace(".onnx",".rknn")
timedate_iso = datetime.datetime.now().isoformat()
rknn = RKNN(verbose=True)
rknn.config(
# mean_values=[x * 255 for x in [0.485, 0.456, 0.406]],
# std_values=[x * 255 for x in [0.229, 0.224, 0.225]],
quantized_dtype='w8a8',
quantized_algorithm='kl_divergence',
quantized_method='channel',
quantized_hybrid_level=0,
target_platform='rk3588',
quant_img_RGB2BGR = False,
float_dtype='float16',
optimization_level=3,
custom_string=f"converted by: qq: 232004040, email: 2302004040@qq.com at {timedate_iso}",
remove_weight=False,
compress_weight=False,
inputs_yuv_fmt=None,
single_core_mode=False,
dynamic_input=None,
model_pruning=False,
op_target=None,
quantize_weight=False,
remove_reshape=False,
sparse_infer=False,
enable_flash_attention=False,
)
ret = rknn.load_onnx(model=ONNX_MODEL, inputs=["/Transpose_output_0"], input_size_list=[[1,3,448,448]])
ret = rknn.build(do_quantization=QUANTIZE, dataset=DATASET, rknn_batch_size=None)
ret = rknn.export_rknn(RKNN_MODEL)
# ret = rknn.init_runtime(target='rk3588',device_id='cbb956772bf5dac9',core_mask=RKNN.NPU_CORE_0,perf_debug=detailed_performance_log)
# rknn.eval_perf()
ret = rknn.accuracy_analysis(inputs=['img.npy'], target='rk3588')
|