Comments (2)
cardwing
commented on June 12, 2024
In my case, replacing mask = weight_copy.abs().gt(thre).float().cuda() with mask = weight_copy.abs().gt(thre.cuda()).float().cuda() works.
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ningscapr
commented on June 12, 2024
Thanks @cardwing, it works!
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Related Issues (20)
- 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
- Regarding the calculation of FLOPs after model compression HOT 1
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