version bump

README.md

@@ -8,10 +8,10 @@
 A set of adversarial attacks implemented in PyTorch. _For internal use._
 
 ```shell
-# install from github source
+# Install from github source
 python -m pip install git+https://github.com/daisylab-bit/torchattack
 
-# install from gitee mirror
+# Install from gitee mirror
 python -m pip install git+https://gitee.com/daisylab-bit/torchattack
 ```
 
@@ -24,7 +24,7 @@ from torchvision.transforms import transforms
 from torchattack import FGSM, MIFGSM
 
 # Load a model
-model = resnet50(weights="DEFAULT")
+model = resnet50(weights='DEFAULT')
 
 # Define transforms (you are responsible for normalizing the data if needed)
 transform = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
@@ -36,29 +36,43 @@ attack = FGSM(model, transform, eps=0.03)
 attack = MIFGSM(model, transform, eps=0.03, steps=10, decay=1.0)
 ```
 
+Check out [`torchattack.utils.run_attack`](src/torchattack/utils.py) for a simple example.
+
 ## Attacks
 
 Gradient-based attacks:
 
-| Name       | Paper                                                                                                                      | `torchattack` class    |
-| :--------- | :------------------------------------------------------------------------------------------------------------------------- | :--------------------- |
-| FGSM       | [Explaining and Harnessing Adversarial Examples](https://arxiv.org/abs/1412.6572)                                          | `torchattack.FGSM`     |
-| PGD        | [Towards Deep Learning Models Resistant to Adversarial Attacks](https://arxiv.org/abs/1706.06083)                          | `torchattack.PGD`      |
-| PGD (L2)   | [Towards Deep Learning Models Resistant to Adversarial Attacks](https://arxiv.org/abs/1706.06083)                          | `torchattack.PGDL2`    |
-| MI-FGSM    | [Boosting Adversarial Attacks with Momentum](https://arxiv.org/abs/1710.06081)                                             | `torchattack.MIFGSM`   |
-| DI-FGSM    | [Improving Transferability of Adversarial Examples with Input Diversity](https://arxiv.org/abs/1803.06978)                 | `torchattack.DIFGSM`   |
-| TI-FGSM    | [Evading Defenses to Transferable Adversarial Examples by Translation-Invariant Attacks](https://arxiv.org/abs/1904.02884) | `torchattack.TIFGSM`   |
-| NI-FGSM    | [Nesterov Accelerated Gradient and Scale Invariance for Adversarial Attacks](https://arxiv.org/abs/1908.06281)             | `torchattack.NIFGSM`   |
-| SI-NI-FGSM | [Nesterov Accelerated Gradient and Scale Invariance for Adversarial Attacks](https://arxiv.org/abs/1908.06281)             | `torchattack.SINIFGSM` |
-| VMI-FGSM   | [Enhancing the Transferability of Adversarial Attacks through Variance Tuning](https://arxiv.org/abs/2103.15571)           | `torchattack.VMIFGSM`  |
-| VNI-FGSM   | [Enhancing the Transferability of Adversarial Attacks through Variance Tuning](https://arxiv.org/abs/2103.15571)           | `torchattack.VNIFGSM`  |
+|            Name            | Paper                                                                                                                      | `torchattack` class    |
+| :------------------------: | :------------------------------------------------------------------------------------------------------------------------- | :--------------------- |
+|    FGSM ($\ell_\infty$)    | [Explaining and Harnessing Adversarial Examples](https://arxiv.org/abs/1412.6572)                                          | `torchattack.FGSM`     |
+|    PGD ($\ell_\infty$)     | [Towards Deep Learning Models Resistant to Adversarial Attacks](https://arxiv.org/abs/1706.06083)                          | `torchattack.PGD`      |
+|       PGD ($\ell_2$)       | [Towards Deep Learning Models Resistant to Adversarial Attacks](https://arxiv.org/abs/1706.06083)                          | `torchattack.PGDL2`    |
+|  MI-FGSM ($\ell_\infty$)   | [Boosting Adversarial Attacks with Momentum](https://arxiv.org/abs/1710.06081)                                             | `torchattack.MIFGSM`   |
+|  DI-FGSM ($\ell_\infty$)   | [Improving Transferability of Adversarial Examples with Input Diversity](https://arxiv.org/abs/1803.06978)                 | `torchattack.DIFGSM`   |
+|  TI-FGSM ($\ell_\infty$)   | [Evading Defenses to Transferable Adversarial Examples by Translation-Invariant Attacks](https://arxiv.org/abs/1904.02884) | `torchattack.TIFGSM`   |
+|  NI-FGSM ($\ell_\infty$)   | [Nesterov Accelerated Gradient and Scale Invariance for Adversarial Attacks](https://arxiv.org/abs/1908.06281)             | `torchattack.NIFGSM`   |
+| SI-NI-FGSM ($\ell_\infty$) | [Nesterov Accelerated Gradient and Scale Invariance for Adversarial Attacks](https://arxiv.org/abs/1908.06281)             | `torchattack.SINIFGSM` |
+|  VMI-FGSM ($\ell_\infty$)  | [Enhancing the Transferability of Adversarial Attacks through Variance Tuning](https://arxiv.org/abs/2103.15571)           | `torchattack.VMIFGSM`  |
+|  VNI-FGSM ($\ell_\infty$)  | [Enhancing the Transferability of Adversarial Attacks through Variance Tuning](https://arxiv.org/abs/2103.15571)           | `torchattack.VNIFGSM`  |
+|   Admix ($\ell_\infty$)    | [Admix: Enhancing the Transferability of Adversarial Attacks](https://arxiv.org/abs/2102.00436)                            | `torchattack.Admix`    |
 
 Others:
 
-| Name       | Paper                                                                                                                      | `torchattack` class    |
-| :--------- | :------------------------------------------------------------------------------------------------------------------------- | :--------------------- |
-| DeepFool   | [DeepFool: A Simple and Accurate Method to Fool Deep Neural Networks](https://arxiv.org/abs/1511.04599)                    | `torchattack.DeepFool` |
-| GeoDA      | [GeoDA: A Geometric Framework for Black-box Adversarial Attacks](https://arxiv.org/abs/2003.06468)                         | `torchattack.GeoDA`    |
+|                Name                | Paper                                                                                                   | `torchattack` class    |
+| :--------------------------------: | :------------------------------------------------------------------------------------------------------ | :--------------------- |
+|        DeepFool ($\ell_2$)         | [DeepFool: A Simple and Accurate Method to Fool Deep Neural Networks](https://arxiv.org/abs/1511.04599) | `torchattack.DeepFool` |
+| GeoDA ($\ell_\infty$ and $\ell_2$) | [GeoDA: A Geometric Framework for Black-box Adversarial Attacks](https://arxiv.org/abs/2003.06468)      | `torchattack.GeoDA`    |
+
+## Development
+
+```shell
+# Create a virtual environment
+python -m venv .venv
+source .venv/bin/activate
+
+# Install deps with dev extras
+python -m pip install -e '.[dev]'
+```
 
 ## License
 

pyproject.toml

@@ -1,9 +1,9 @@
 [project]
 name = "torchattack"
-version = "0.2.1"
+version = "0.2.2"
 description = "A set of adversarial attacks implemented in PyTorch"
 authors = [{ name = "spencerwooo", email = "spencer.woo@outlook.com" }]
-requires-python = ">=3.10"
+requires-python = ">=3.10,<3.12"
 readme = "README.md"
 license = { text = "MIT" }
 dependencies = [
@@ -14,6 +14,9 @@ dependencies = [
     "rich>=13.3.5",
 ]
 
+[project.optional-dependencies]
+dev = ["mypy"]
+
 [build-system]
 requires = ["setuptools"]
 build-backend = "setuptools.build_meta"