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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
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
#全局信息
Global:
model_dir: ./yolov7-tiny.onnx #输入模型路径
format: 'onnx' #输入模型格式，选项为 onnx 或者 paddle
model_filename: model.pdmodel #paddle模型的模型文件名
params_filename: model.pdiparams #paddle模型的参数文件名
image_path: ./COCO_val_320 #PTQ所有的Calibration数据集或者量化训练所用的训练集
arch: YOLOv7 #模型系列
#量化蒸馏训练中的蒸馏参数设置
Distillation:
alpha: 1.0
loss: soft_label
#量化蒸馏训练中的量化参数设置
Quantization:
onnx_format: true
activation_quantize_type: 'moving_average_abs_max'
quantize_op_types:
- conv2d
- depthwise_conv2d
#离线量化参数配置
PTQ:
calibration_method: 'avg' #Calibraion算法，可选为 avg, abs_max, hist, KL, mse
skip_tensor_list: None #不进行离线量化的tensor
#训练参数
TrainConfig:
train_iter: 3000
learning_rate:
type: CosineAnnealingDecay
learning_rate: 0.00003
T_max: 8000
optimizer_builder:
optimizer:
type: SGD
weight_decay: 0.00004