Adding vai_q_pytorch APIs to Float Scripts - 2.0 English

Vitis AI User Guide (UG1414)

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2.0 English
If, before quantization, there is a trained float model and some Python scripts to evaluate accuracy/mAP of the model, the Quantizer API replaces the float module with a quantized module. The normal evaluate function encourages quantized module forwarding. Quantize calibration determines quantization steps of tensors in evaluation process if flag quant_mode is set to "calib". After calibration, evaluate the quantized model by setting quant_mode to "test".
  1. Import the vai_q_pytorch module.
    from pytorch_nndct.apis import torch_quantizer, dump_xmodel
  2. Generate a quantizer with quantization needed input and get the converted model.
    input = torch.randn([batch_size, 3, 224, 224])
    quantizer = torch_quantizer(quant_mode, model, (input))
    quant_model = quantizer.quant_model
  3. Forward a neural network with the converted model.
    acc1_gen, acc5_gen, loss_gen = evaluate(quant_model, val_loader, loss_fn)
  4. Output the quantization result and deploy the model.
    if quant_mode == 'calib':
    if deploy: