Comments (4)
Jzz24
commented on September 26, 2024
�改一下bit配置测试一下就知道啦
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louvinci
commented on September 26, 2024
�改一下bit配置测试一下就知道啦
感谢回复，我这就准备试一下。在Image net上量化的准确度，位宽为4或者其他位数时，BN层的参数是不是仍然是32位的啊。谢谢！
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Jzz24
commented on September 26, 2024
�改一下bit配置测试一下就知道啦
感谢回复，我这就准备试一下。在Image net上量化的准确度，位宽为4或者其他位数时，BN层的参数是不是仍然是32位的啊。谢谢！
是的，这里对bn没做处理，仍然是fp32，可以参考一下google whitepaper文章bn的处理
from pytorch_quantization.
louvinci
commented on September 26, 2024
�改一下bit配置测试一下就知道啦
感谢回复，我这就准备试一下。在Image net上量化的准确度，位宽为4或者其他位数时，BN层的参数是不是仍然是32位的啊。谢谢！是的，这里对bn没做处理，仍然是fp32，可以参考一下google whitepaper文章bn的处理
好的。非常感谢分享。确实BN层的处理可以有很多手法，但是BN层使用浮点数或者INT32对计算量的影响并不大。
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Related Issues (8)
- 关于量化精度的请教 HOT 5
- 量化后的模型大小 HOT 1
- Not same with the Paper HOT 3
- 支持转换成onnx吗？ HOT 1
- 大佬，想请教一些量化方面的问题，可以留个联系方式吗？^.^ HOT 1
- 你好，请问一下文中的梯度量化在哪里实现的呀，在代码中没有看到 HOT 1
- yolov5的4bit量化 HOT 1
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