在modelzoo中看到了DeepLabV3+的onnx模型，希望能得到yaml文件中的训练参数，排布方式、归一化参数等，以完成Deeplab的模型转化
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请问是XJ3的OE包吗？我们在J5的OE中有提供deeplabv3+的yaml，需要替换校准数据集和onnx路径：
# Copyright (c) 2020 Horizon Robotics.All Rights Reserved.## The material in this file is confidential and contains trade secrets# of Horizon Robotics Inc. This is proprietary information owned by# Horizon Robotics Inc. No part of this work may be disclosed,# reproduced, copied, transmitted, or used in any way for any purpose,# without the express written permission of Horizon Robotics Inc.# 模型转化相关的参数# ------------------------------------# model conversion related parametersmodel_parameters: # onnx 浮点模型文件 # ----------------------------------------------------------- # floating-point ONNX model file onnx_model: "../../../01_common/model_zoo/mapper/segmentation/deeplabv3plus_efficientnetb0/deeplabv3plus_efficientnetb0.onnx" # 适用BPU架构 # -------------------------------- # the applicable BPU architecture march: "bernoulli2" # 指定模型转换过程中是否输出各层的中间结果，如果为True，则输出所有层的中间输出结果， # -------------------------------------------------------------------------------------- # specifies whether or not to dump the intermediate results of all layers in conversion # if set to True, then the intermediate results of all layers shall be dumped layer_out_dump: False # 模型转换输出的结果的存放目录 # ----------------------------------------------------------- # the directory in which model conversion results are stored working_dir: 'model_output' # 模型转换输出的用于上板执行的模型文件的名称前缀 # ----------------------------------------------------------------------------------------- # model conversion generated name prefix of those model files used for dev board execution output_model_file_prefix: 'deeplabv3plus_efficientnetb0_1024x2048_nv12' remove_node_type: "Reshape; Cast"# 模型输入相关参数, 若输入多个节点, 则应使用';'进行分隔, 使用默认缺省设置则写None# --------------------------------------------------------------------------# model input related parameters,# please use ";" to seperate when inputting multiple nodes,# please use None for default settinginput_parameters: # (选填) 模型输入的节点名称, 此名称应与模型文件中的名称一致, 否则会报错, 不填则会使用模型文件中的节点名称 # -------------------------------------------------------------------------------------------------------- # (Optional) node name of model input, # it shall be the same as the name of model file, otherwise an error will be reported, # the node name of model file will be used when left blank input_name: "" # 网络实际执行时，输入给网络的数据格式，包括 nv12/rgb/bgr/yuv444/gray/featuremap, # ----------------------------------------------------------------------------------------- # the data formats to be passed into neural network when actually performing neural network # available options: nv12/rgb/bgr/yuv444/gray/featuremap, input_type_rt: 'nv12' # 网络实际执行时输入的数据排布, 可选值为 NHWC/NCHW # 若input_type_rt配置为nv12，则此处参数不需要配置 # ------------------------------------------------------------------ # the data layout formats to be passed into neural network when actually performing neural network, available options: NHWC/NCHW # If input_type_rt is configured as nv12, then this parameter does not need to be configured #input_layout_rt: '' # 网络训练时输入的数据格式，可选的值为rgb/bgr/gray/featuremap/yuv444 # -------------------------------------------------------------------- # the data formats in network training # available options: rgb/bgr/gray/featuremap/yuv444 input_type_train: 'yuv444' # 网络训练时输入的数据排布, 可选值为 NHWC/NCHW # ------------------------------------------------------------------ # the data layout in network training, available options: NHWC/NCHW input_layout_train: 'NCHW' # (选填) 模型网络的输入大小, 以'x'分隔, 不填则会使用模型文件中的网络输入大小，否则会覆盖模型文件中输入大小 # ------------------------------------------------------------------------------------------- # (Optional)the input size of model network, seperated by 'x' # note that the network input size of model file will be used if left blank # otherwise it will overwrite the input size of model file input_shape: '1x3x1024x2048' # 网络输入的预处理方法，主要有以下几种： # no_preprocess 不做任何操作 # data_mean 减去通道均值mean_value # data_scale 对图像像素乘以data_scale系数 # data_mean_and_scale 减去通道均值后再乘以scale系数 # ------------------------------------------------------------------------------------------- # preprocessing methods of network input, available options: # 'no_preprocess' indicates that no preprocess will be made # 'data_mean' indicates that to minus the channel mean, i.e. mean_value # 'data_scale' indicates that image pixels to multiply data_scale ratio # 'data_mean_and_scale' indicates that to multiply scale ratio after channel mean is minused norm_type: 'data_mean_and_scale' # 图像减去的均值, 如果是通道均值，value之间必须用空格分隔 # -------------------------------------------------------------------------- # the mean value minused by image # note that values must be seperated by space if channel mean value is used mean_value: 128 # 图像预处理缩放比例，如果是通道缩放比例，value之间必须用空格分隔 # --------------------------------------------------------------------------- # scale value of image preprocess # note that values must be seperated by space if channel scale value is used scale_value: 0.0078125# 模型量化相关参数# -----------------------------# model calibration parameterscalibration_parameters: # 模型量化的参考图像的存放目录，图片格式支持Jpeg、Bmp等格式，输入的图片 # 应该是使用的典型场景，一般是从测试集中选择20~100张图片，另外输入 # 的图片要覆盖典型场景，不要是偏僻场景，如过曝光、饱和、模糊、纯黑、纯白等图片 # 若有多个输入节点, 则应使用';'进行分隔 # ------------------------------------------------------------------------------------------------- # the directory where reference images of model quantization are stored # image formats include JPEG, BMP etc. # should be classic application scenarios, usually 20~100 images are picked out from test datasets # in addition, note that input images should cover typical scenarios # and try to avoid those overexposed, oversaturated, vague, # pure blank or pure white images # use ';' to seperate when there are multiple input nodes cal_data_dir: './calibration_data_yuv_f32' # 校准数据二进制文件的数据存储类型，可选值为：float32, uint8 # calibration data binary file save type, available options: float32, uint8 cal_data_type: 'float32' # 如果输入的图片文件尺寸和模型训练的尺寸不一致时，并且preprocess_on为true， # 则将采用默认预处理方法(skimage resize)， # 将输入图片缩放或者裁减到指定尺寸，否则，需要用户提前把图片处理为训练时的尺寸 # --------------------------------------------------------------------------------- # In case the size of input image file is different from that of in model training # and that preprocess_on is set to True, # shall the default preprocess method(skimage resize) be used # i.e., to resize or crop input image into specified size # otherwise user must keep image size as that of in training in advance # preprocess_on: False # 模型量化的算法类型，支持default、mix、kl、max、load，通常采用default即可满足要求 # 如不符合预期可先尝试修改为mix 仍不符合预期再尝试kl或max # 当使用QAT导出模型时，此参数则应设置为load # 相关参数的技术原理及说明请您参考用户手册中的PTQ原理及步骤中参数组详细介绍部分 # ---------------------------------------------------------------------------------- # The algorithm type of model quantization, support default, mix, kl, max, load, usually use default can meet the requirements. # If it does not meet the expectation, you can try to change it to mix first. If there is still no expectation, try kl or max again. # When using QAT to export the model, this parameter should be set to load. # For more details of the parameters, please refer to the parameter details in PTQ Principle And Steps section of the user manual. calibration_type: 'max' # 该参数为'max'校准方法的参数，用以调整'max'校准的截取点。此参数仅在calibration_type为'max'时有效。 # 该参数取值范围：0.0 ~ 1.0。常用配置选项有：0.99999/0.99995/0.99990/0.99950/0.99900。 # ------------------------------------------------------------------------------------------------ # this is the parameter of the 'max' calibration method and it is used for adjusting the intercept point of the 'max' calibration. # this parameter will only become valid when the calibration_type is specified as 'max'. # RANGE: 0.0 - 1.0. Typical options includes: 0.99999/0.99995/0.99990/0.99950/0.99900. max_percentile: 0.99999# 编译器相关参数# ----------------------------# compiler related parameterscompiler_parameters: # 编译策略，支持bandwidth和latency两种优化模式; # bandwidth以优化ddr的访问带宽为目标； # latency以优化推理时间为目标 # ------------------------------------------------------------------------------------------- # compilation strategy, there are 2 available optimization modes: 'bandwidth' and 'lantency' # the 'bandwidth' mode aims to optimize ddr access bandwidth # while the 'lantency' mode aims to optimize inference duration compile_mode: 'latency' # 设置debug为True将打开编译器的debug模式，能够输出性能仿真的相关信息，如帧率、DDR带宽占用等 # ----------------------------------------------------------------------------------- # the compiler's debug mode will be enabled by setting to True # this will dump performance simulation related information # such as: frame rate, DDR bandwidth usage etc. debug: False # 编译模型指定核数，不指定默认编译单核模型, 若编译双核模型，将下边注释打开即可 # ------------------------------------------------------------------------------------- # specifies number of cores to be used in model compilation # as default, single core is used as this value left blank # please delete the "# " below to enable dual-core mode when compiling dual-core model # core_num: 2 # 优化等级可选范围为O0~O3 # O0不做任何优化, 编译速度最快，优化程度最低, # O1-O3随着优化等级提高，预期编译后的模型的执行速度会更快，但是所需编译时间也会变长。 # 推荐用O2做最快验证 # ---------------------------------------------------------------------------------------------------------- # optimization level ranges between O0~O3 # O0 indicates that no optimization will be made # the faster the compilation, the lower optimization level will be # O1-O3: as optimization levels increase gradually, model execution, after compilation, shall become faster # while compilation will be prolonged # it is recommended to use O2 for fastest verification optimize_level: 'O3'
好的，那我去J5的OE包看一下，谢谢