Comments (3)
tanluren
commented on June 15, 2024
2
稀疏训练中只是对bn层的gamma进行L1正则化，达到压缩效果，但是beta没有进行压缩，它经过激活函数后会输入到下一层卷积，并且对下一层卷积的输出有影响，如果剪掉了某个通道，就会导致由beta引起的激活偏移值，并且参与剪枝的层和通道越多，产生的偏差越大，直接影响了模型的输出，所以需要将偏移值传递到下一层的bn running mean或者conv bias中。这个问题在shortcut中更加复杂，需要进行连续的传递。
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tanluren
commented on June 15, 2024
看稀疏情况的，有时0.01比0.1剪枝后的精度更高，它可能对应了更合理的结构，去掉了冗余的甚至是不利于精度的权重，可以下载我分享的cfg和权重剪剪试下。你也可以观察下剪枝后的cfg，哪些位置被剪的多，哪些剪得少。
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MiniBullLab
commented on June 15, 2024
在SlimYOLOv3中提到“在本项目中还对激活偏移值添加了处理，降低剪枝时的精度损失”，这个怎么理解
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