update readme

README.md

@@ -12,14 +12,14 @@
   <a href="https://pytorch.org/"><img src="https://img.shields.io/badge/PyTorch-1.8 %20%7C%201.12 %20%7C%202.0-673ab7.svg" alt="Tested PyTorch Versions"></a>
   <a href="https://opensource.org/licenses/MIT"><img src="https://img.shields.io/badge/License-MIT-4caf50.svg" alt="License"></a>
   <a href="https://pepy.tech/project/Torch-Pruning"><img src="https://pepy.tech/badge/Torch-Pruning?color=2196f3" alt="Downloads"></a>
-  <a href="https://github.com/VainF/Torch-Pruning/releases/latest"><img src="https://img.shields.io/badge/Latest%20Version-1.1.9-3f51b5.svg" alt="Latest Version"></a>
+  <a href="https://github.com/VainF/Torch-Pruning/releases/latest"><img src="https://img.shields.io/badge/Latest%20Version-1.2.0-3f51b5.svg" alt="Latest Version"></a>
   <a href="https://colab.research.google.com/drive/1TRvELQDNj9PwM-EERWbF3IQOyxZeDepp?usp=sharing">
   <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
 </a>
   <a href="https://arxiv.org/abs/2301.12900" target="_blank"><img src="https://img.shields.io/badge/arXiv-2301.12900-009688.svg" alt="arXiv"></a>
 </p>
 
-Torch-Pruning (TP) is a library for structural pruning that enables the following features:
+Torch-Pruning (TP) is a library for structural pruning with the following features:
 
 * **General-purpose Pruning Toolkit:** TP enables structural pruning for a wide range of deep neural networks, including *[Large Language Models (LLMs)](https://github.com/horseee/LLM-Pruner), [Diffusion Models](https://github.com/VainF/Diff-Pruning), [Yolov7](examples/yolov7/), [yolov8](examples/yolov8/), [ViT](examples/torchvision_models/), FasterRCNN, SSD, ResNe(X)t, ConvNext, DenseNet, ConvNext, RegNet, DeepLab, etc*. Different from [torch.nn.utils.prune](https://pytorch.org/tutorials/intermediate/pruning_tutorial.html) that zeroizes parameters through masking, Torch-Pruning deploys a (non-deep) graph algorithm called **DepGraph** to remove parameters physically. Currently, TP is able to prune approximately **81/85=95.3%** of the models from Torchvision 0.13.1. Try this [Colab Demo](https://colab.research.google.com/drive/1TRvELQDNj9PwM-EERWbF3IQOyxZeDepp?usp=sharing) for a quick start.
 * **[Performance Benchmark](benchmarks)**: Reproduce the our results in the DepGraph paper.
@@ -41,7 +41,7 @@ Please do not hesitate to open a [discussion](https://github.com/VainF/Torch-Pru
 
 ### **Features:**
 - [x] Structural pruning for CNNs, Transformers, Detectors, Language Models and Diffusion Models. Please refer to the [examples](examples).
-- [x] High-level pruners: [MagnitudePruner](https://arxiv.org/abs/1608.08710), [BNScalePruner](https://arxiv.org/abs/1708.06519), [GroupNormPruner](https://arxiv.org/abs/2301.12900), RandomPruner, etc.
+- [x] High-level pruners: [MagnitudePruner](https://arxiv.org/abs/1608.08710), [BNScalePruner](https://arxiv.org/abs/1708.06519), [GroupNormPruner](https://arxiv.org/abs/2301.12900), [GrowingRegPruner](https://arxiv.org/abs/2012.09243), RandomPruner, etc.
 - [x] Importance Criteria: L-p Norm, Taylor, Random, BNScaling, etc.
 - [x] Dependency Graph for automatic structrual pruning
 - [x] Supported modules: Linear, (Transposed) Conv, Normalization, PReLU, Embedding, MultiheadAttention, nn.Parameters and [customized modules](tests/test_customized_layer.py).
@@ -53,7 +53,7 @@ Please do not hesitate to open a [discussion](https://github.com/VainF/Torch-Pru
 - [ ] A strong baseline with bags of tricks from existing methods.
 - [ ] A benchmark for [Torchvision](https://pytorch.org/vision/stable/models.html) compatibility (**81/85=95.3%**, :heavy_check_mark:) and [timm](https://github.com/huggingface/pytorch-image-models) compatibility.
 - [ ] Pruning from Scratch / at Initialization.
-- [ ] More high-level pruners like [FisherPruner](https://arxiv.org/abs/2108.00708), [GrowingReg](https://arxiv.org/abs/2012.09243), etc.
+- [ ] More high-level pruners like [FisherPruner](https://arxiv.org/abs/2108.00708), etc.
 - [ ] More Transformers like Vision Transformers (:heavy_check_mark:), Swin Transformers, PoolFormers.
 - [ ] Block/Layer/Depth Pruning
 - [ ] Pruning benchmarks for CIFAR, ImageNet and COCO.
@@ -146,7 +146,7 @@ for group in DG.get_all_groups(ignored_layers=[model.conv1], root_module_types=[
 
 ### 2. High-level Pruners
 
-Leveraging the DependencyGraph, we developed several high-level pruners in this repository to facilitate effortless pruning. By specifying the desired channel sparsity, the pruner will scan all prunable groups, prune the entire model, and fine-tune it using your own training code. For detailed information on this process, please refer to [this tutorial](https://github.com/VainF/Torch-Pruning/blob/master/tutorials/1%20-%20Customize%20Your%20Own%20Pruners.ipynb), which shows how to implement a [slimming](https://arxiv.org/abs/1708.06519) pruner from scratch. Additionally, a more practical example is available in [benchmarks/main.py](benchmarks/main.py). 
+Leveraging the DependencyGraph, we developed several high-level pruners in this repository to facilitate effortless pruning. By specifying the desired channel sparsity, the pruner will scan all prunable groups, prune the entire model, and fine-tune it using your own training code. For detailed information on this process, please refer to [this tutorial](https://github.com/VainF/Torch-Pruning/blob/master/examples/notebook/1%20-%20Customize%20Your%20Own%20Pruners.ipynb), which shows how to implement a [slimming](https://arxiv.org/abs/1708.06519) pruner from scratch. Additionally, a more practical example is available in [benchmarks/main.py](benchmarks/main.py). 
 
 ```python
 import torch