qat.py

from pytorch_quantization import nn as quant_nn
from pytorch_quantization import quant_modules
from pytorch_quantization import calib
from tqdm import tqdm


def collect_stats(model, data_loader, num_batches, device):
    
    # Enable calibrators
    for name, module in model.named_modules():
        if isinstance(module, quant_nn.TensorQuantizer):
            if module._calibrator is not None:
                module.disable_quant()
                module.enable_calib()
            else:
                module.disable()
    
    for i, (image, _) in tqdm(enumerate(data_loader), total=num_batches):
        model(image.to(device))
        if i >= num_batches:
            break
    
    # Disable calibrators
    for name, module in model.named_modules():
        if isinstance(module, quant_nn.TensorQuantizer):
            if module._calibrator is not None:
                module.enable_quant()
                module.disable_calib()
            else:
                module.enable()


def compute_amax(model, device, **kwargs):
    # Load calib result
    for name, module in model.named_modules():
        if isinstance(module, quant_nn.TensorQuantizer):
            if module._calibrator is not None:
                if isinstance(module._calibrator, calib.MaxCalibrator):
                    module.load_calib_amax()
                else:
                    module.load_calib_amax(**kwargs)
    
    model.to(device)