Comments (18)
Hi,
the "cov53.pt" in the code was actually for testing, which I mistakenly uploaded it. Really sorry about that. Code has been corrected, please contact us if you have more questions.
Thanks for correcting~
from hrank.
I have another problem, when I test the accuracy of pretrained resnet-50 on imagenet you provided, but the accuracy is just 0.0899, it's too low?
from hrank.
I have another problem, when I test the accuracy of pretrained resnet-50 on imagenet you provided, but the accuracy is just 0.0899, it's too low?
Make sure you are using our data processing code and your imagenet is fine. Everything goes smooth in our testing.
from hrank.
Thanks for your quick reply!
I run your code just change the pretrained model path and dataset path, my imagenet testset was downloaded from official website named 'ILSVRC2012_img_val.tar'(size: 6.7GB) , is anything wrong?
from hrank.
Thanks for your quick reply!
I run your code just change the pretrained model path and dataset path, my imagenet testset was downloaded from official website named 'ILSVRC2012_img_val.tar'(size: 6.7GB) , is anything wrong?
I don't think this is our problem. Many readers adopt our pre-trained model and nothing goes wrong in our communication. You need to check your usage.
from hrank.
I have found my mistake: when I untar my validation dataset from official website(6.3GB) , just a single folder, under the folder just many images, it's different from train dataset(there are many subfolders), can you tell me I am right?
老兄啊,你可以直接说中文. 一堆语法错误看不懂你在说什么.
from hrank.
哈哈^-^
就是说我在官网下载的测试数据集解压过后只有一个文件夹,下面全是图片,代码读取图片是用ImageFolder读的,所以得把测试数据集按照标签分一下类,放到对应的类下面去。。。
from hrank.
正常是同一个类别的数据放在一个文件里. 你需要分类下.
from hrank.
好的,知道了,谢谢
from hrank.
好的,知道了,谢谢
不客气,有问题再留言~ 你也可以参考这个代码 https://github.com/lmbxmu/HRankPlus
from hrank.
我刚刚测试了一下,发现精度还是不对,你能把你的测试集文件目录给我看看嘛?
from hrank.
图片无法上传. 请联系你身边的人. 这是常识性问题,不属于我们代码bug范围.
from hrank.
好的,已经解决了,主要之前没做过分类的实验。。。麻烦你了!
from hrank.
好的,已经解决了,主要之前没做过分类的实验。。。麻烦你了!
不客气~ 任何关于我们代码和论文的问题,欢迎随时提问交流
from hrank.
Hi~
你好,在main.py中第272行的for循环代码中,m.layer_mask将指定层的通道剪枝即w置为0,然后你加载了权重文件
问题一:在此处加载权重是否会覆盖之前已经剪枝为0的参数?
这个问题我后来想通了,m.layer_mask中其实生成了一个self.mask,在后面train的时候才会根据这个mask来进行剪枝。
问题二:在train代码中,为什么把m.grad_mask放在outputs=net(inputs)之后呢?这样在训练第一个batch的时候不就不是基于剪枝的权重(虽然这样不是不可以。。。)
from hrank.
Hi~
你好,在main.py中第272行的for循环代码中,m.layer_mask将指定层的通道剪枝即w置为0,然后你加载了权重文件
问题一:在此处加载权重是否会覆盖之前已经剪枝为0的参数?
这个问题我后来想通了,m.layer_mask中其实生成了一个self.mask,在后面train的时候才会根据这个mask来进行剪枝。
问题二:在train代码中,为什么把m.grad_mask放在outputs=net(inputs)之后呢?这样在训练第一个batch的时候不就不是基于剪枝的权重(虽然这样不是不可以。。。)
想通的、可以的。 都没有留言的必要
from hrank.
你好,我复现了一下ResNet-50在ImageNet上的剪枝实验,但是精度并没有达到文中那么高,大概低了4个点,我每层剪完后训练15个epoch,请问是不是要增加训练的epoch呢?
from hrank.
你好,我复现了一下ResNet-50在ImageNet上的剪枝实验,但是精度并没有达到文中那么高,大概低了4个点,我每层剪完后训练15个epoch,请问是不是要增加训练的epoch呢?
大哥,你是怎么跑的能跑成这样. 麻烦移驾 https://github.com/lmbxmu/HRankPlus 看参数配置. 还有我实在不明白为什么微信留言下消失了一下又跑这里来???
from hrank.
Related Issues (20)
- 关于rank的疑问 HOT 2
- Download ResNet-50 on ImageNet HOT 2
- 代码疑问 HOT 2
- The meaning of the "rank" of feature maps. HOT 3
- 关于剪枝后模型的问题 HOT 1
- 关于compute_rate、秩的计算顺序的问题 HOT 8
- 关于Vgg16中compress_rate的问题 HOT 5
- 预训练模型下载链接
- Pruning other algorithmic models
- 论文中的公式问题 HOT 1
- rank_generation.py 中关于 参数 --gpu的疑问?
- 模型问题
- 学习率设置
- Size of the feature maps at later layers
- Automatically resume training from the highest test acc epoch may cause data leak.
- 有关计算flops和params的问题
- How to determine the per-layer filter pruning rate for a given model HOT 1
- Question about code
- 关于论文图2的问题 HOT 1
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from hrank.