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config/config_spec.yaml

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+qconfig_dict:
+  w_observer: minmax  #default: minmax. option: minmax percentile
+  a_observer: mse     #default: mse. option: minmax percentile mse kl
+  w_qscheme:
+    bit: 8       #default: 8.   option: 4 8 mix 
+    per_channel: true  #default: false. option: true false
+  a_qscheme:
+    bit: 8       # only support 8 now   
+    per_channel: false  # only support false now 
+  output_layer:
+    names: [ ]   # assign quant method for specific layers
+    a_observer: kl # final output use kl quant take effect probably
+    bit: 8
+quantize:
+  quantize_type: naive_ptq  # default: naive_ptq. option: naive_ptq advanced_ptq
+  calib_steps: 16 
+  backend: Octans  #default: Octans.   represent AX520   
+  QuantizationOptimizationPass:
+    LayerwiseEqualizationPass: true
+    LearnedStepSizePass: false 
+    BiasCorrectionPass: true    
+    AdaroundPass: false
+  deploy:
+    NPUDeploy: true   # enable generate axmodel
+    export: true      # enable generate quant model
+    output_bin_name: deploy.axmodel  #rename output axmodel. type: string. required: false. default: compiled.axmodel.
+    output_path: output   #axmodel and log output directory. type: string. required: true.       
+model: #required: true
+  path: deploy.onnx    #input model file path. type: string. required: true.
+data: #required: true
+  path: filelist.txt   #quantize calibration dataset,only support images list now. 
+  batch_size: 16
+  num_workers: 16  
+  input_shape:   # onnx model input shape
+  - 1
+  - 3
+  - 640
+  - 640
+  mean: 
+  - 0
+  std: 
+  - 255
+  task: 'det'  
+  post:
+    yolo_type: 'yolov5'  
+    anchors: ''
+    class_names: {
+            '0': 'person',
+            '1': 'bicycle',
+            '2': 'car',
+            '3': 'motorcycle',
+            '4': 'airplane',
+            '5': 'bus',
+            '6': 'train',
+            '7': 'truck',
+            '8': 'boat',
+            '9': 'traffic light',
+            '10': 'fire hydrant',
+            '11': 'stop sign',
+            '12': 'parking meter',
+            '13': 'bench',
+            '14': 'bird',
+            '15': 'cat',
+            '16': 'dog',
+            '17': 'horse',
+            '18': 'sheep',
+            '19': 'cow',
+            '20': 'elephant',
+            '21': 'bear',
+            '22': 'zebra',
+            '23': 'giraffe',
+            '24': 'backpack',
+            '25': 'umbrella',
+            '26': 'handbag',
+            '27': 'tie',
+            '28': 'suitcase',
+            '29': 'frisbee',
+            '30': 'skis',
+            '31': 'snowboard',
+            '32': 'sports ball',
+            '33': 'kite',
+            '34': 'baseball bat',
+            '35': 'baseball glove',
+            '36': 'skateboard',
+            '37': 'surfboard',
+            '38': 'tennis racket',
+            '39': 'bottle',
+            '40': 'wine glass',
+            '41': 'cup',
+            '42': 'fork',
+            '43': 'knife',
+            '44': 'spoon',
+            '45': 'bowl',
+            '46': 'banana',
+            '47': 'apple',
+            '48': 'sandwich',
+            '49': 'orange',
+            '50': 'broccoli',
+            '51': 'carrot',
+            '52': 'hot dog',
+            '53': 'pizza',
+            '54': 'donut',
+            '55': 'cake',
+            '56': 'chair',
+            '57': 'couch',
+            '58': 'potted plant',
+            '59': 'bed',
+            '60': 'dining table',
+            '61': 'toilet',
+            '62': 'tv',
+            '63': 'laptop',
+            '64': 'mouse',
+            '65': 'remote',
+            '66': 'keyboard',
+            '67': 'cell phone',
+            '68': 'microwave',
+            '69': 'oven',
+            '70': 'toaster',
+            '71': 'sink',
+            '72': 'refrigerator',
+            '73': 'book',
+            '74': 'clock',
+            '75': 'vase',
+            '76': 'scissors',
+            '77': 'teddy bear',
+            '78': 'hair drier',
+            '79': 'toothbrush',
+        }
+  executing_device: 'cuda'
+  UseDVPResize: false
+input_processors: #required: false
+- tensor_name: images #input tensor name in origin model. "DEFAULT" means processor for all input tensors. type: string. required: true.
+  tensor_format: RGB #input tensor format in origin model. type: enum. required: false. default: AutoColorSpace. option: AutoColorSpace, BGR, RGB, GRAY.
+  tensor_layout: NCHW #input tensor layout in origin model. type: enum. required: false. only support NCHW now.
+  src_format: YUV420SP #input format in runtime. type: enum. required: false. default: AutoColorSpace. option: AutoColorSpace, GRAY, BGR, RGB, YUYV422, UYVY422, YUV420SP, YVU420SP.
+  src_layout: NCHW #input layout in runtime; type: enum. required: false. default: NCHW. option: NHWC, NCHW.
+  src_dtype: U8 #input data type in runtime. type: enum. required: false. default: U8. option: U8, S8, U16, S16, U32, S32, FP16, FP32.
+  csc_mode: NoCSC #color space mode. type: enum. required: false. default: NoCSC. option: NoCSC, Matrix, FullRange, LimitedRange. 
+  csc_mat: [] #color space conversion matrix, 12 elements array that represents a 3x4 matrix. type: float array. required: false. default: [].
+output_processors:  #required: false
+- dst_type:  #output data type in runtime. type: enum. required: false. default: S8. option: S8, FP32.
+  tensor_name:   #output tensor name in origin model. required: false.
+- dst_type:  #output data type in runtime. required: false.
+  tensor_name:   #output tensor name in origin model. required: false.
+- dst_type:  #output data type in runtime. required: false.
+  tensor_name:  #output tensor name in origin model. required: false.
+process: #required: false
+  seed: 1005 
+evaluate:  #required: false
+  path: 'coco/val.txt'
+  analysis_graph_method: false
+  analysis_layer_method: false
+  analysis_metric: cosine
+  analysis_statistical_method: false
+  evaluate_fake_quant_float: false
+  evaluate_fake_quant_int: false
+  evaluate_float: false
+Xsnn: #required: false
+  UseMemPoolOptim: true
+  UseDVPResize: false
+  UseXsnnEval: false     
+  path: coco/val.txt
+  YUVHeight: ''
+  YUVWidth: ''
+
+
+
+
+

config/convert.sh

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+python app/ppq-xs/Helium/myes.py --config config/config_spec.yaml 

config/readme.txt

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+
+模型转换说明：
+
+##1、环境准备（镜像版或源码版）：
+*docker镜像版安装：w4a8quant-1.7.6.tar.gz
+*源码版安装：ppq-xs-1.7.6   （pip -r requirements.txt）
+
+##2、模型从.pt转.onnx
+yolov5系类参考脚本：
+https://github.com/ultralytics/yolov5/blob/master/export.py
+
+yolov8/yolov11系类参考脚本：
+https://github.com/ultralytics/ultralytics/blob/main/tests/test_exports.py
+
+
+##3、模型从.onnx转换到.axmodel
+模型配置文件（通用）：
+config/config_spec.yaml
+
+#模型及量化数据准备：
+准备量化图片（建议使用训练的图片数据1~100张）,将图片路径保存为文件列表filelist.txt
+修改配置文件如下：
+data: 
+  path: filelist.txt   #quantize calibration dataset,only support images list now. 
+
+#准备onnx模型：
+修改配置文件模型路径：
+model: 
+  path: deploy.onnx    #input model file path. type: string. required: true.
+
+修改配置文件模型信息，如下：
+  input_shape:   # onnx model input shape
+  - 1
+  - 3
+  - 640
+  - 640
+  mean: 
+  - 0
+  std: 
+  - 255
+
+修改生成axmodel文件名，如下：
+deploy:
+	NPUDeploy: true   # enable generate axmodel
+	export: true      # enable generate quant model
+	output_bin_name: deploy.axmodel  #rename output axmodel. type: string. required: false. default: compiled.axmodel.
+
+#config修改完成后，转换命令如下：
+python app/ppq-xs/Helium/myes.py --config config/config_spec.yaml 
+
+## 板端运行sample, 目前提供yolov5、yolov8两个模型参考示例
+samples/demo_yolov5
+samples/demo_yolov8
+
+该示例可以直接运行并绘制出目标框，对用户自己的模型，需修改如下地方：
+#修改模型位置,替换该目录下的模型：
+/data/model/deploy.axmodel
+
+#修改模型检测类别数：
+文件位置：src/detech.h，修改如下：
+/***************************************************************************************************
+* 宏定义
+***************************************************************************************************/
+#define DET_LABEL_NUM (80)  // 对应类别数
+
+#修改模型输入shape：
+文件位置：xpu_infer.cpp，修改如下：
+static int gXpuTestNetWidth  = 640; // 网络宽度
+static int gXpuTestNetHeight = 640; // 网络高度
+
+#修改完成后，板端加载ko驱动
+运行目录下run.sh即可
+
+
+
+