完成配置文件的编写后,我们在终端输入下面代码即可开始转换: python -m rknn.api.rknn_convert -t rk3588 -i ./model_config.yml -o ./output_path 至此,rknn模型转换完成。 附件:*附件:02_Rockchip_RKNPU_User_Guide_RKNN_SDK_V2.3.0_CN.pdf
多个输入之间空格间隔,只用于量化阶段,实际推理阶段如果模型的输入为BGR,则实际输入的图片的格式也应该为BGR。 quantized_dtype:量化类型,目前只支持asymmetric_quantized-8quantized_algorithm:计算每层量化参数采用的量化算法,支持的算法有normal,mmse 及kl_divergence。默认为normal normal:速度快,推荐数据20~100张 mmse:...
因此板端推理的前处理中要剔除相关操作 rknn.config( # see:ultralytics/yolo/data/utils.py mean_values=[[0, 0, 0]], std_values=[[255, 255, 255]], quantized_algorithm='normal', quantized_method='channel', # optimization_level=2, compress_weight=False, # 压缩模型的权值,可以减小rknn模型的...
模型转换,int8量化,设置rknn_batch_size = 2报错, config,设置如下: rknn.config( mean_values=mean, std_values=std, target_platform=PLATEFORM, quantized_algorithm="mmse", optimization_level=3, )
config mean_values 参数零点 [[128,128,128]] or None == [[0,0,0]] std_values 用来作为量化标准: 模型参数先减去mean_values然后再除以这个ratio.色值量化。 quant_img_RGB2BGR, only used for Caffe, or set to False quantized_dtype 量化目标;asymmetric_quantized-8 quantized_algorithm,把原始参数打...
rknn.config(mean_values=[[0, 0, 0]], std_values=[[255, 255, 255]], quant_img_RGB2BGR=True, quantized_algorithm='normal', quantized_method='channel', target_platform='rk3566') 1. 2. 3. 4. 5. 6. 7. 8. 模型加载 # RKNN-Toolkit2 目前支持 Caffe、TensorFlow、TensorFlow Lite、ONN...
config: target_platform: RV1109 quantized_dtype: asymmetric_affine-u8 quantized_algorithm: normal optimization_level: 3 mean_values: [[0, 0, 0]] std_values: [[255, 255, 255]] mmse_epoch: 3 do_sparse_network: True output_optimize: 0 batch_size: 100 quantize_input_node: False merge_de...
rknn.config(mean_values=[[0, 0, 0]], std_values=[[128, 128, 128]], target_platform='rv1103', quantized_algorithm="normal") Load the model rknn.load_onnx(model = model_path) Build the model rknn.build(do_quantization=do_quant, dataset=DATASET_PATH) Initialize runtime environment. ...
('--> Config model')rknn.config(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_...
1.1Config.in 我们首先从Config.in文件入手,该文件实际上就是定义make menuconfig支持的配置选项; config BR2_PACKAGE_RKNPU2 bool "rknpu2" help "rknpu runtime lib and server" config BR2_PACKAGE_RKNPU2_ARCH string depends on BR2_ARCH = "arm" || BR2_ARCH = "aarch64" ...