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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
49 lines
1.4 KiB
Markdown
49 lines
1.4 KiB
Markdown
# FastDeploy 量化配置文件说明
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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.
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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
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## 实例解读
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```
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#全局信息
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Global:
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model_dir: ./yolov7-tiny.onnx #输入模型路径
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format: 'onnx' #输入模型格式，选项为 onnx 或者 paddle
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model_filename: model.pdmodel #paddle模型的模型文件名
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params_filename: model.pdiparams #paddle模型的参数文件名
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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
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arch: YOLOv7 #模型系列
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#量化蒸馏训练中的蒸馏参数设置
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Distillation:
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alpha: 1.0
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loss: soft_label
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#量化蒸馏训练中的量化参数设置
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Quantization:
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onnx_format: true
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activation_quantize_type: 'moving_average_abs_max'
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quantize_op_types:
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- conv2d
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- depthwise_conv2d
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#离线量化参数配置
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PTQ:
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calibration_method: 'avg' #Calibraion算法，可选为 avg, abs_max, hist, KL, mse
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skip_tensor_list: None #不进行离线量化的tensor
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#训练参数
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TrainConfig:
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train_iter: 3000
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learning_rate:
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type: CosineAnnealingDecay
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learning_rate: 0.00003
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T_max: 8000
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optimizer_builder:
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optimizer:
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type: SGD
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weight_decay: 0.00004
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```
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