kl_divergence:介于normal mmse之间,推荐20~100张 quantized_method:支持channel,layer,layer每层权重只有一套量化参数,channel,每个权重的每个通道有一套参数,channel比layer精度要高。 optimization_level:模型优化等级。默认值为3,3打开所有优化选项,2或1关闭部分对精度影响的优化选项,0不开启优化。 target_platform:...
quantized_method, default is channel, layer weight only only 1 set. load origin model 例如:load_pytorch float_dtype, gen un scale version optimization_level 0,1,2,3 3 takes more time. target_platform :rk3588 custom_string: a label of model. remove_weight : size can be done.but need ...
import numpy as np import cv2 from rknn.api import RKNN from blazefacenumpy import BlazeFace ONNX_MODEL = 'best.onnx' # onnx 模型的路径 ONNX_MODEL = 'rppg_new.onnx' # onnx 模型的路径 ONNX_MODEL = 'blazeface128.onnx' # onnx 模型的路径 # ONNX_MODEL = 'v3.onnx' # onnx 模型...
quantized_method='channel', # optimization_level=2, compress_weight=False, # 压缩模型的权值,可以减小rknn模型的大小。默认值为False。 single_core_mode=True, # model_pruning=False, # 修剪模型以减小模型大小,默认值为False。 target_platform='rk3588' ) 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. ...
# quantized_method 量化方法 channel, layer可选; layer:每层的 weight 只有一套量化参数; channel:每层的 weight 的每个通道都有一套量化参数。默认使用channel # target_platform 可以用来配置不同的芯片, 目前支持 rk3566、rk3568、rk3588、rv1103、rv1106, 该参数的值大小写不敏感。
配置文件格式如下: %YAML 1.2 --- # hybrid_quantization_action can be delete, add or modify, only one of these can be set at a hybrid quantization hybrid_quantization_action: delete '@attach_concat_1/out0_0:out0': dtype: asymmetric_quantized method: layer max_value: - 10.568130493164062 ...
The configuration file format is as follows: %YAML 1.2 --- # hybrid_quantization_action can be delete, add or modify, only one of these can be set at a hybrid quantization hybrid_quantization_action: delete '@attach_concat_1/out0_0:out0': dtype: asymmetric_quantized method: layer max_...
RKNPU2_SITE_METHOD:定义了源码下载的方式,该处指定为本地获取(local); RKNPU2_LICENSE:设置许可证类型为ROCKCHIP; RKNPU2_LICENSE_FILES:设置许可证文件LICENSE。 1.2.2 变量 RKNPU2_ARCH =$(callqstrip,$(BR2_PACKAGE_RKNPU2_ARCH)) $(call qstrip, ...)是一个用于去掉前后空格的函数,因此RKNPU2_AR...
rknn.config(channel_mean_value='0 0 0 1', reorder_channel='0 1 2', quantized_dtype='asymmetric_quantized-u8')复制代码但是转换完成后,结果依旧不正确。菜鸟已经不知路在何方,求大神指教该怎么调试。这里附上模型地址,求教。。。https://pan.baidu.com/s/13k_uSwMMMll4TKCyueT03A 提取码:6ytn...
(quantized_algorithm='normal',quantized_method='channel',target_platform='rk3588',optimization_level=3)print('done')# Load ONNX modelprint('--> Loading model')ret=rknn.load_onnx(model=ONNX_MODEL,inputs=['/emb/Gather_output_0','input_state','scale_ratio'],input_size_list=[ [batch_...