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52 lines
2.6 KiB
Markdown
52 lines
2.6 KiB
Markdown
# Quantization Config File on FastDeploy
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The FastDeploy quantization configuration file contains global configuration, quantization distillation training configuration, post-training quantization configuration and training configuration.
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In addition to using the configuration files provided by FastDeploy directly in this directory, users can modify the relevant configuration files according to their needs
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## Demo
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```
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# Global config
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Global:
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model_dir: ./yolov5s.onnx #Path to input model
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format: 'onnx' #Input model format, please select 'paddle' for paddle model
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model_filename: model.pdmodel #Quantized model name in Paddle format
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params_filename: model.pdiparams #Parameter name for quantized model name in Paddle format
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image_path: ./COCO_val_320 #Data set paths for post-training quantization or quantized distillation
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arch: YOLOv5 #Model Architecture
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input_list: ['x2paddle_images'] #Input name of the model to be quantified
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preprocess: yolo_image_preprocess #The preprocessing functions for the data when quantizing the model. Developers can modify or write a new one in . /fdquant/dataset.py
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#uantization distillation training configuration
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Distillation:
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alpha: 1.0 # Distillation loss weight
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loss: soft_label #Distillation loss algorithm
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Quantization:
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onnx_format: true #Whether to use ONNX quantization standard format or not, must be true to deploy on FastDeploye
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use_pact: true #Whether to use the PACT method for training
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activation_quantize_type: 'moving_average_abs_max' #Activate quantization methods
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quantize_op_types: #OPs that need to be quantized
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- conv2d
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- depthwise_conv2d
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#Post-Training Quantization
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PTQ:
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calibration_method: 'avg' #Activate calibration algorithm of post-training quantization , Options: avg, abs_max, hist, KL, mse, emd
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skip_tensor_list: None #Developers can skip some conv layers‘ quantization
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#Traning
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TrainConfig:
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train_iter: 3000
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learning_rate: 0.00001
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optimizer_builder:
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optimizer:
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type: SGD
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weight_decay: 4.0e-05
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target_metric: 0.365
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```
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## More details
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FastDeploy one-click quantization tool is powered by PaddeSlim, please refer to [Automated Compression of Hyperparameter Tutorial](https://github.com/PaddlePaddle/PaddleSlim/blob/develop/example/auto_compression/hyperparameter_tutorial.md) for more details.
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