Merge branch 'master' of https://github.com/VainF/Torch-Pruning

README.md

@@ -21,10 +21,10 @@
 Torch-Pruning (TP) is a framework for structural pruning with the following features:
 
 *  **General-purpose Pruning Toolkit:** TP enables structural pruning for a wide range of deep neural networks. Different from [torch.nn.utils.prune](https://pytorch.org/tutorials/intermediate/pruning_tutorial.html) that zeroizes parameters via masking, Torch-Pruning deploys an algorithm called ⚡ **[DepGraph](https://openaccess.thecvf.com/content/CVPR2023/html/Fang_DepGraph_Towards_Any_Structural_Pruning_CVPR_2023_paper.html)** to group and remove coupled parameters. 
-* **Examples**: Pruning off-the-shelf models from Huggingface, Timm, Torchvision, including [Large Language Models (LLMs)](https://github.com/VainF/Torch-Pruning/tree/master/examples/LLMs), [Segment Anything Model (SAM)](https://github.com/czg1225/SlimSAM), [Diffusion Models](https://github.com/VainF/Diff-Pruning), [Vision Transformers](https://github.com/VainF/Isomorphic-Pruning), [ConvNext](https://github.com/VainF/Isomorphic-Pruning), [Yolov7](examples/yolov7/), [yolov8](examples/yolov8/),  [Swin Transformers](examples/transformers#swin-transformers-from-hf-transformers), [BERT](examples/transformers#bert-from-hf-transformers), FasterRCNN, SSD, ResNe(X)t, DenseNet, RegNet, DeepLab, etc. As detailed list can be found in 🎨 [Examples](examples).
+* **Examples**: Pruning off-the-shelf models from Huggingface, Timm, Torchvision, including [Large Language Models (LLMs)](https://github.com/VainF/Torch-Pruning/tree/master/examples/LLMs), [Segment Anything Model (SAM)](https://github.com/czg1225/SlimSAM), [Diffusion Models](https://github.com/VainF/Diff-Pruning), [Vision Transformers](https://github.com/VainF/Isomorphic-Pruning), [ConvNext](https://github.com/VainF/Isomorphic-Pruning), [Yolov7](examples/yolov7/), [yolov8](examples/yolov8/),  [Swin Transformers](examples/transformers#swin-transformers-from-hf-transformers), [BERT](examples/transformers#bert-from-hf-transformers), FasterRCNN, SSD, ResNe(X)t, DenseNet, RegNet, DeepLab, etc. A detailed list can be found in 🎨 [Examples](examples).
 
 
-For more technical details, please refer to our CVPR'23 paper. You can also find the [code](reproduce) to reproduce paper results.
+For more technical details, please refer to our CVPR'23 paper.
 > [**DepGraph: Towards Any Structural Pruning**](https://openaccess.thecvf.com/content/CVPR2023/html/Fang_DepGraph_Towards_Any_Structural_Pruning_CVPR_2023_paper.html)   
 > *[Gongfan Fang](https://fangggf.github.io/), [Xinyin Ma](https://horseee.github.io/), [Mingli Song](https://person.zju.edu.cn/en/msong), [Michael Bi Mi](https://dblp.org/pid/317/0937.html), [Xinchao Wang](https://sites.google.com/site/sitexinchaowang/)*    
 > *[Learning and Vision Lab](http://lv-nus.org/), National University of Singapore*
@@ -82,7 +82,7 @@ Here we provide a quick start for Torch-Pruning. More explained details can be f
 
 ### Why Torch-Pruning?
 
-In structural pruning, the removal of a single parameter may affect multiple layers. For example, pruning an output dimension of a linear layer will require the removal of the corresponding input dimension in the following linear layer as shown in (a). This dependency between layers makes it challenging to prune complicated networks manually. Torch-Pruning addresses this issue by introducing a graph-based algorithm called ``DependencyGraph`` to automatically identify dependencies and collect groups for pruning.
+In structural pruning, the removal of a single parameter may affect multiple layers. For example, pruning an output dimension of a linear layer will require the removal of the corresponding input dimension in the following linear layer as shown in (a). This dependency between layers makes it challenging to prune complicated networks manually. Torch-Pruning addresses this issue by introducing a graph-based algorithm called **[DepGraph](https://openaccess.thecvf.com/content/CVPR2023/html/Fang_DepGraph_Towards_Any_Structural_Pruning_CVPR_2023_paper.html)** to automatically identify dependencies and collect groups for pruning.
 
 <div align="center">
 <img src="assets/dep.png" width="100%">