Comments (2)
Yeah, in that case, for VGG style network, there is nothing you can do except reduce the prune ratio. For ResNet and DenseNet style network, you can use mask-implementation but it does not reduce the actual network size.
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OK.Get it.
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Related Issues (20)
- 为什么我裁剪完成后,模型运行占用的显存比没裁剪的还要高? HOT 1
- 预测单张照片时,准确率只有0.0几 HOT 2
- Minor Bugs caused by old version
- Sparse Confusion HOT 1
- channel_selection layer intraining process HOT 2
- 剪枝后保存的权重文件和newmodel加载不上 HOT 3
- RuntimeError: Given groups=1, weight of size [15, 14, 1, 1], expected input[64, 16, 32, 32] to have 14 channels, but got 16 channels instead HOT 1
- If the remaining channel for a layer is zero, it reports zero division error HOT 2
- 原论文中的memory是指 HOT 6
- TypeError: item() takes no arguments (1 given) HOT 1
- mnn加载剪枝模型错误 HOT 1
- 经稀疏训练剪枝后模型变小,但是refine微调后模型又变大了
- 关于L1 regular HOT 1
- m.weight.grad.data.add_的问题
- 问题咨询:剪枝后通道数为0 HOT 7
- RuntimeError: CUDA error: device-side assert triggered HOT 1
- About other visions
- 题外话:模型压缩如何入门?对于自己的网络架构该如何着手去写剪枝代码?
- ResNet和DenseNet这种每一层的输入会作为后续多个层的输入,且其BN层是在卷积层之前,在这种情况下,稀疏化是在层的输入末端得到的,一个层选择性接受所有通道的子集去做下一步的卷积运算。
- 对HRNet剪枝,第一个bn的grad是None的问题
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