Adding vai_q_pytorch APIs to Float Scripts - 3.5 English

Vitis AI User Guide (UG1414)

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3.5 English
Suppose there is a pre-trained float model and Python scripts for evaluating its accuracy/mAP before quantization. In that case, the Quantizer API substitutes the floating-point module with a quantized module. For standard evaluation, the evaluate function promotes the forwarding of the quantized module. By configuring the quant_mode flag to caliber, you can determine the quantization steps of tensors during the evaluation process for post-training quantization. After calibration is complete, 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: