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$ ./scripts/run_local.sh nets/resnet_at_cifar10_run.py \ --learner=channel \ --cp_uniform_preserve_ratio=0.5 \ --cp_prune_option=uniform \ --resnet_size=56 4. 模型转换 步骤3之后会在models产生ckpt文件,需要通过进行模型转化,最终会生成model_original.pb,model_transformed.pb,同时也会生成...
另外生成这些被剪掉通道的feature map的卷积核也可以删掉(对应Figure2中第二列的6个长条矩形块中的2个黑色虚线框的矩形块)。 因此最重要的就是如何判断B中的哪些通道要剪掉,具体是分两步来迭代进行的:In one step, we figure out the most representative channels, and prune redundant ones, b...
论文地址:https://arxiv.org/abs/1608.08710 路人实现代码:https://github.com/tyui592/Pruning_filters_for_efficient_convnets https://github.com/slothkong/DNN-Pruning 主要思想 这篇文章主要讲了对filters的裁剪,裁剪方法是计算L1范数,然后裁剪掉较少的,多少取决于加速比。 实现效果 VGG-16 34%加速 ResNe...
代码地址:https://github.com/yihui-he/channel-pruning 这是一篇ICCV2017的文章,关于用通道剪枝(channel pruning)来做模型加速,通道减枝是模型压缩和加速领域的一个重要分支。 文章的核心内容是对训练好的模型进行通道剪枝(channel pruning),而通道减枝是通过迭代两步操作进行的:第一步是channel selection,这一步是...
Following similar setting as Filter pruning [31], we keep 70% channels for sensitive residual blocks (res5 and blocks close to the position where spatial size change, e.g. res3a,res3d). As for other blocks, we keep 30% channels. With multi-branch enhancement, we prune branch2a more ...
内容提示: Sparsity Meets Robustness: Channel Pruning for the Feynman-KacFormalism Principled Robust Deep Neural NetsThu Dinh ∗Department of MathematicsUniversity of California, Irvinethud2@uci.eduBao Wang ∗Department of MathematicsUniversity of California, Los Angeleswangbaonj@gmail.comAndrea L. ...
With this objective in mind, we leverage the relaxed augmented Lagrangian based algorithms to prune the weights of adversarially trained DNNs, at both structured and unstructured levels. Using a Feynman-Kac formalism principled robust and sparse DNNs, we can at least double the channel sparsity of ...
[L]} by Eq.(4) ; 8 Prune {κlCl, l ∈ [L]} channels by CSS-based pruning in Algorithm 2 ; 9 Compute the channel regrowing factor by a decay scheduler function ; 10 Perform importance sampling-based channel regrowing in Algorithm 3 ; Suppose κl denotes the channel sparsity...
By removing constraints from existing pruners, we improve ImageNet accuracy for post-training pruned models by 2.1 points on average -- benefiting DenseNet (+16.9), EfficientNetV2 (+7.9), and ResNet (+6.2). Furthermore, by reordering channels, UPSCALE improves inference speeds by up to 2x ...