Add Quantization Function. (#256)

* 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
This commit is contained in:
yunyaoXYY
2022-10-08 15:45:28 +08:00
committed by GitHub
parent 8d47637541
commit 1efc0fa6b0
13 changed files with 736 additions and 0 deletions

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Global:
model_dir: ./yolov7.onnx
format: 'onnx'
model_filename: model.pdmodel
params_filename: model.pdiparams
image_path: ./COCO_val_320
arch: YOLOv7
input_list: ['x2paddle_images']
Distillation:
alpha: 1.0
loss: soft_label
Quantization:
onnx_format: true
activation_quantize_type: 'moving_average_abs_max'
quantize_op_types:
- conv2d
- depthwise_conv2d
PTQ:
calibration_method: 'avg' # option: avg, abs_max, hist, KL, mse
skip_tensor_list: None
TrainConfig:
train_iter: 3000
learning_rate:
type: CosineAnnealingDecay
learning_rate: 0.00003
T_max: 8000
optimizer_builder:
optimizer:
type: SGD
weight_decay: 0.00004