Comments (6)
表6是figure 6吗, figure 6 里没有说memory. https://openaccess.thecvf.com/content_ICCV_2017/papers/Liu_Learning_Efficient_Convolutional_ICCV_2017_paper.pdf
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你好,为什么我把图片大小改为224*224后,剪枝时会报错,不过可以运行一半,文件pruned都能保存,但是最后还是报错,说维度不匹配,可以帮帮我吗
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memory不与原文的数字吻合可能是因为不同的ml框架,原论文的数字是基于torch, 而本论文是pytorch实现。
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这个repo没有imagenet的network slimming实现,可参考https://github.com/Eric-mingjie/rethinking-network-pruning/tree/master/imagenet/network-slimming。
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你好,我试了ImageNet里面的network—sliming的方法,微调后为什么又返回原来的尺寸了,还有ImageNet里面的vgg比这里的vgg网络大的多,但是为什么它训练出来的准确率反而低了呢,并且它的输入尺寸还是224×224的。还有能告诉我这里的prune代码和ImageNet里面224×224的prune哪里不同,我想换成224×224的,但一直报错,您可以帮帮我吗
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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
- 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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