Comments (1)
mjw123bs
commented on June 16, 2024
The above is only for the resnet and densenet models. After the model is compressed, only the input size of Conv2d is changed, but the size of BatchNorm2d is not changed, and the size does not correspond, so an error is reported, as shown in the figure. But I saw that you wrote If the next layer is the channel selection layer, then the current batch normalization layer won't be pruned. in the pruning files of the two models, so please see how to solve this problem.
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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
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