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* 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
FastDeploy 量化配置文件说明
FastDeploy 量化配置文件中，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，可以按需求自行修改相关配置文件
实例解读
# 全局配置
Global:
model_dir: ./yolov5s.onnx #输入模型的路径
format: 'onnx' #输入模型的格式, paddle模型请选择'paddle'
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
image_path: ./COCO_val_320 #离线量化或者量化蒸馏训练使用的数据集路径
arch: YOLOv5 #模型结构
input_list: ['x2paddle_images'] #待量化的模型的输入名字
preprocess: yolo_image_preprocess #模型量化时,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
#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
更多详细配置方法
FastDeploy一键量化功能由PaddeSlim助力, 更详细的量化配置方法请参考: 自动化压缩超参详细教程