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yunyaoXYY a231c9e7f3 [Quantization] Update quantized model deployment examples and update readme. (#377 )

* Add PaddleOCR Support

* Add PaddleOCR Support

* Add PaddleOCRv3 Support

* Add PaddleOCRv3 Support

* Update README.md

* Update README.md

* Update README.md

* Update README.md

* Add PaddleOCRv3 Support

* Add PaddleOCRv3 Supports

* Add PaddleOCRv3 Suport

* Fix Rec diff

* Remove useless functions

* Remove useless comments

* Add PaddleOCRv2 Support

* Add PaddleOCRv3 & PaddleOCRv2 Support

* remove useless parameters

* Add utils of sorting det boxes

* Fix code naming convention

* Fix code naming convention

* Fix code naming convention

* Fix bug in the Classify process

* Imporve OCR Readme

* Fix diff in Cls model

* Update Model Download Link in Readme

* Fix diff in PPOCRv2

* Improve OCR readme

* Imporve OCR readme

* Improve OCR readme

* Improve OCR readme

* Imporve OCR readme

* Improve OCR readme

* Fix conflict

* Add readme for OCRResult

* Improve OCR readme

* Add OCRResult readme

* Improve OCR readme

* Improve OCR readme

* Add Model Quantization Demo

* Fix Model Quantization Readme

* Fix Model Quantization Readme

* Add the function to do PTQ quantization

* Improve quant tools readme

* Improve quant tool readme

* Improve quant tool readme

* Add PaddleInference-GPU for OCR Rec model

* Add QAT method to fastdeploy-quantization tool

* Remove examples/slim for now

* Move configs folder

* Add Quantization Support for Classification Model

* Imporve ways of importing preprocess

* Upload YOLO Benchmark on readme

* Upload YOLO Benchmark on readme

* Upload YOLO Benchmark on readme

* Improve Quantization configs and readme

* Add support for multi-inputs model

* Add backends and params file for YOLOv7

* Add quantized model deployment support for YOLO series

* Fix YOLOv5 quantize readme

* Fix YOLO quantize readme

* Fix YOLO quantize readme

* Improve quantize YOLO readme

* Improve quantize YOLO readme

* Improve quantize YOLO readme

* Improve quantize YOLO readme

* Improve quantize YOLO readme

* Fix bug, change Fronted to ModelFormat

* Change Fronted to ModelFormat

* Add examples to deploy quantized paddleclas models

* Fix readme

* Add quantize Readme

* Add quantize Readme

* Add quantize Readme

* Modify readme of quantization tools

* Modify readme of quantization tools

* Improve quantization tools readme

* Improve quantization readme

* Improve PaddleClas quantized model deployment  readme

* Add PPYOLOE-l quantized deployment examples

* Improve quantization tools readme

* Improve Quantize Readme

* Fix conflicts

* Fix conflicts

* improve readme

* Improve quantization tools and readme

* Improve quantization tools and readme

* Add quantized deployment examples for PaddleSeg model

* Fix cpp readme

* Fix memory leak of reader_wrapper function

* Fix model file name in PaddleClas quantization examples

* Update Runtime and E2E benchmark

* Update Runtime and E2E benchmark

* Rename quantization tools to auto compression tools

* Remove PPYOLOE data when deployed on MKLDNN

* Fix readme

* Support PPYOLOE with OR without NMS and update readme

* Update Readme

* Update configs and readme

* Update configs and readme

* Add Paddle-TensorRT backend in quantized model deploy examples

* Support PPYOLOE+ series

2022-11-02 20:29:29 +08:00

classification

[Quantization] Update quantized model deployment examples and update readme. (#377 )

2022-11-02 20:29:29 +08:00

detection

[Quantization] Update quantized model deployment examples and update readme. (#377 )

2022-11-02 20:29:29 +08:00

segmentation

[Quantization] Update quantized model deployment examples and update readme. (#377 )

2022-11-02 20:29:29 +08:00

README.md

[Quantization] Update quantized model deployment examples and update readme. (#377 )

2022-11-02 20:29:29 +08:00

README.md

FastDeploy 一键自动化压缩配置文件说明

FastDeploy 一键自动化压缩配置文件中，包含了全局配置，量化蒸馏训练配置，离线量化配置和训练配置. 用户除了直接使用FastDeploy提供在本目录的配置文件外，可以按照以下示例,自行修改相关配置文件, 来尝试压缩自己的模型.

实例解读

# 全局配置
Global:
  model_dir: ./ppyoloe_plus_crn_s_80e_coco    #输入模型的路径, 用户若需量化自己的模型，替换此处即可
  format: paddle                              #输入模型的格式, paddle模型请选择'paddle', onnx模型选择'onnx'
  model_filename: model.pdmodel               #量化后转为paddle格式模型的模型名字
  params_filename: model.pdiparams            #量化后转为paddle格式模型的参数名字
  qat_image_path: ./COCO_train_320            #量化蒸馏训练使用的数据集,此例为少量无标签数据, 选自COCO2017训练集中的前320张图片, 做少量数据训练
  ptq_image_path: ./COCO_val_320              #离线训练使用的Carlibration数据集, 选自COCO2017验证集中的前320张图片.
  input_list: ['image','scale_factor']        #待量化的模型的输入名字
  qat_preprocess: ppyoloe_plus_withNMS_image_preprocess #模型量化蒸馏训练时,对数据做的预处理函数, 用户可以在 ../fdquant/dataset.py 中修改或自行编写新的预处理函数, 来支自定义模型的量化
  ptq_preprocess: ppyoloe_plus_withNMS_image_preprocess #模型离线量化时,对数据做的预处理函数, 用户可以在 ../fdquant/dataset.py 中修改或自行编写新的预处理函数, 来支自定义模型的量化
  qat_batch_size: 4                           #量化蒸馏训练时的batch_size, 若为onnx格式的模型,此处只能为1


#量化蒸馏训练配置
Distillation:
  alpha: 1.0                                  #蒸馏loss所占权重
  loss: soft_label                            #蒸馏loss算法

Quantization:
  onnx_format: true                           #是否采用ONNX量化标准格式, 要在FastDeploy上部署, 必须选true
  use_pact: true                              #量化训练是否使用PACT方法
  activation_quantize_type: 'moving_average_abs_max'     #激活量化方式
  quantize_op_types:                          #需要进行量化的OP
  - conv2d
  - depthwise_conv2d

#离线量化配置
PTQ:
  calibration_method: 'avg'                   #离线量化的激活校准算法, 可选: avg, abs_max, hist, KL, mse, emd
  skip_tensor_list: None                      #用户可指定跳过某些conv层,不进行量化

#训练参数配置
TrainConfig:
  train_iter: 3000
  learning_rate: 0.00001
  optimizer_builder:
    optimizer:
      type: SGD
    weight_decay: 4.0e-05
  target_metric: 0.365

README.md Unescape Escape

FastDeploy 一键自动化压缩配置文件说明

实例解读

更多详细配置方法

README.md