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Hi, 我注意到再dorefa的quantizer中 micronet/micronet/compression/quantization/wqaq/dorefa/quantize.py:43 关于activation部分的量化操作，在量化前先乘了0.1进行了数据缩放 output = torch.clamp(input * 0.1, 0, 1) # 特征A截断前先进行缩放（* 0.1），以减小截断误差 但是在量化/反量化操作之后，并没有再乘10回到原来的数据范围。这个问题怎么理解
output = torch.clamp(input * 0.1, 0, 1) # 特征A截断前先进行缩放（* 0.1），以减小截断误差
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Hi, 我注意到再dorefa的quantizer中
micronet/micronet/compression/quantization/wqaq/dorefa/quantize.py:43
关于activation部分的量化操作，在量化前先乘了0.1进行了数据缩放
output = torch.clamp(input * 0.1, 0, 1) # 特征A截断前先进行缩放（* 0.1），以减小截断误差但是在量化/反量化操作之后，并没有再乘10回到原来的数据范围。这个问题怎么理解
The text was updated successfully, but these errors were encountered: