Comments (8)
另外对于pruning后的精度对于vgg模型的结果也非常的奇怪,而且在vgg会在很早就出现pruning掉整层的情况,finetune后较原来的baseline会有很大的提升,我认为这都是一开始training的时候的问题,希望您可以为我解答,谢谢。
ps: 训练的硬件参数为K80/一块卡/CUDA Version: 10.1
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能给出你的commands吗?就是你跑实验的命令, 我们在README里给出的命令都是我们自己测试过的https://github.com/Eric-mingjie/network-slimming#train-with-sparsity
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注意我们使用的是torch v0.3.1.
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我的command用的和您是一样的。但是我使用的torch 1.0.1.post2,并将类似Variable的语法改成了with torch.no_grad()。但是版本的不同会导致训练结果存在很大差异吗?
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另外在实验中也出现了在vgg某些prune percent较低的时候,pruning后的精度exactly等于pruning之前,这也是因为版本的原因吗?
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另外想问您一下您那里有没有训练好的ImageNet数据集上的全连接层也有BN的vgg11和16模型,可以share一下吗?
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我的command用的和您是一样的。但是我使用的torch 1.0.1.post2,并将类似Variable的语法改成了with torch.no_grad()。但是版本的不同会导致训练结果存在很大差异吗?
不太清楚,可以试下。
另外想问您一下您那里有没有训练好的ImageNet数据集上的全连接层也有BN的vgg11和16模型,可以share一下吗?
抱歉,我们没有train这些模型。
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好的,感谢您的回复
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Related Issues (20)
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