Quantize already pretrained english recognition model

[176gauravsharma]

I already have a pre trained english recognition model having model files names inference.pdmodel,inference.pdiparams,inference.pdiparams.info. Now i want to quantize them without do training,how to achieve this. Im using python 3.8

[GreatV]

To quantize your already pretrained English recognition model (inference.pdmodel, inference.pdiparams, inference.pdiparams.info) using PaddlePaddle/PaddleOCR and Python 3.8 without additional training, here’s how you should proceed:

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1. Install PaddleSlim

First, install the PaddleSlim package, which provides quantization utilities:

pip install paddleslim==2.3.2

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2. Understand Quantization Methods

• Post-Training Quantization (PTQ): This method can quantize a model using a calibration dataset without additional training. However, most PaddleOCR quantization tools are oriented to Quantization-Aware Training (QAT), which may require at least a few epochs of fine-tuning.
• If you want true “no training, no data” static quantization, PaddleOCR’s toolchain currently doesn’t directly support fully automatic post-training quantization for OCR models as part of their official scripts. Limited post-training quantization is possible if you have a calibration set.

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3. Recommended Workflow (with minimal or no training)

A. Prepare a Quantization Config File

1. Copy an existing recognition config file, e.g., configs/rec/rec_mv3_none_bilstm_ctc.yml.
2. Modify it according to your model and set quantization parameters.
   • Make sure to set the model path to your pretrained model in Global.pretrained_model.
   • Adjust dataset/calibration entries if needed.

B. Quantization Process Example

Navigate to the PaddleOCR project root. Then:

python deploy/slim/quantization/quant.py \
  -c configs/rec/rec_mv3_none_bilstm_ctc.yml \
  -o Global.pretrained_model='./path_to_your_model_directory/' \
     Global.save_model_dir='./output/quant_model/'

Note:

• If you want no further training, set epochs: 0 or slim.quant_epochs: 0 in the config. However, some level of calibration data is typically required even for PTQ.

C. Export the Quantized Inference Model

After quantization calibration (or minimal training if required):

python deploy/slim/quantization/export_model.py \
  -c configs/rec/rec_mv3_none_bilstm_ctc.yml \
  -o Global.checkpoints=./output/quant_model/best_accuracy \
     Global.save_inference_dir=./output/quant_inference_model

This will generate a quantized inference model you can deploy just like the original.

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4. Alternative: PaddleLite's Post-Training Quantization

If you need fully post-training, no-training quantization for fast inference on mobile, consider using PaddleLite’s opt tool with post-training quantization. Convert the model for mobile deployment with INT8 support.

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5. Summary of Steps

1. Install PaddleSlim.
2. Copy and modify a recognition .yml config suitable for your model.
3. Run quant.py, specifying your model and output directory.
4. Export the quantized inference model.
5. (For full PTQ) Use PaddleLite’s opt tool to convert and deploy.

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Tips & References

• Official PaddleSlim Quantization API: PaddleSlim Quantization Docs
• Typical PaddleOCR quantization assumes access to a few calibration images, even if not retraining.
• For fully automated post-training quantization tools, consider PaddleLite or consult: PaddleLite Post-Training Quantization

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In summary:
You can quantize your pretrained model with minimal or no additional training using PaddleSlim’s quantization scripts, by configuring your model as the initial checkpoint, and running the quantization and export scripts with the relevant config and model paths. For fully automated quantization (no data, no training), use PaddleLite's opt tool for PTQ.

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Response generated by 🤖 feifei-bot | gpt-4.1

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[176gauravsharma]

how calibration data maintained and provide to this quant.py file