Files
FastDeploy/tools/quantization/configs/detection/ppyoloe_l_quant.yaml
yunyaoXYY b0663209f6 Add Examples to deploy quantized models (#342)
* Add PaddleOCR Support

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* Add PaddleOCRv3 Support

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* Update README.md

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* Add PaddleOCRv3 Support

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* Fix Rec diff

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* remove useless parameters

* Add utils of sorting det boxes

* Fix code naming convention

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* Fix bug in the Classify process

* Imporve OCR Readme

* Fix diff in Cls model

* Update Model Download Link in Readme

* Fix diff in PPOCRv2

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* Add Model Quantization Demo

* Fix Model Quantization Readme

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* Add the function to do PTQ quantization

* Improve quant tools readme

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* 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

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* 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

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* Improve quantize YOLO readme

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* Fix bug, change Fronted to ModelFormat

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* Add examples to deploy quantized paddleclas models

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* Improve quantization tools readme

* Improve quantization readme

* Improve PaddleClas quantized model deployment  readme

* Add PPYOLOE-l quantized deployment examples

* Improve quantization tools readme
2022-10-14 13:35:45 +08:00

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YAML

Global:
model_dir: ./ppyoloe_crn_l_300e_coco
format: paddle
model_filename: model.pdmodel
params_filename: model.pdiparams
image_path: ./COCO_val_320
arch: PPYOLOE
input_list: ['image','scale_factor']
preprocess: ppdet_image_preprocess
Distillation:
alpha: 1.0
loss: soft_label
Quantization:
onnx_format: true
use_pact: 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: 5000
learning_rate:
type: CosineAnnealingDecay
learning_rate: 0.00003
T_max: 6000
optimizer_builder:
optimizer:
type: SGD
weight_decay: 4.0e-05