packaging1

Author: MMathisLab

AROS/main.py main.pyAROS/main.py renamed to main.py

@@ -1,13 +1,13 @@
 
-!pip install -r requirements.txt
+import aros
 import argparse
 import torch
 import torch.nn as nn
-from evaluate import *
-from utils import *
+from aros.evaluate import *
+from aros.utils import *
 from tqdm.notebook import tqdm
-from data_loader import *
-from stability_loss_function import *
+from aros.data_loader import *
+from aros.stability_loss_function import *
 
 def main():
     parser = argparse.ArgumentParser(description="Hyperparameters for the script")

pyproject.toml

@@ -0,0 +1,3 @@
+[build-system]
+requires = ["setuptools>=42", "wheel"]
+build-backend = "setuptools.build_meta"

requirements.txt

@@ -1,4 +1,9 @@
 geotorch
+torch
 torchdiffeq
-git+https://github.com/RobustBench/robustbench.git
-timm==1.0.9
+timm==1.0.9
+robustbench
+numpy
+scikit-learn
+scipy
+tqdm

setup.cfg

@@ -6,36 +6,18 @@ author_email = [email protected]
 description = AROS: Adversarially Robust Out-of-Distribution Detection through Stability
 long_description = file: README.md
 long_description_content_type = text/markdown
-license_files = LICENSE.md
-license_file_type = text/markdown
 url = https://github.com/AdaptiveMotorControlLab/AROS
-project_urls =
-    Bug Tracker = https://github.com/AdaptiveMotorControlLab/AROS/issues
-classifiers =
-    Development Status :: 4 - Beta
-    Environment :: GPU :: NVIDIA CUDA
-    Intended Audience :: Science/Research
-    Operating System :: OS Independent
-    Programming Language :: Python :: 3
-    Topic :: Scientific/Engineering :: Artificial Intelligence
-    License :: OSI Approved :: Apache Software License
 
 [options]
 packages = find:
 include_package_data = True
 python_requires = >=3.10
-install_requires =
-    geotorch
-    torchdiffeq
-    git+https://github.com/RobustBench/robustbench.git
+install_requires = file: requirements.txt
 
 [options.extras_require]
 dev =
     pylint
     toml
     yapf
     black
-    pytest
-
-[bdist_wheel]
-universal=0
+    pytest

tests/test_dataloaders.py

@@ -0,0 +1,69 @@
+import pytest
+import torch
+from torch.utils.data import DataLoader, Subset
+from torchvision.datasets import CIFAR10, CIFAR100
+from torchvision.transforms import ToTensor
+from aros import (
+    LabelChangedDataset,
+    get_subsampled_subset,
+    get_loaders,
+)
+
+# Set up transformations and datasets for tests
+transform_tensor = ToTensor()
+
+@pytest.fixture
+def cifar10_datasets():
+    trainset = CIFAR10(root='./data', train=True, download=True, transform=transform_tensor)
+    testset = CIFAR10(root='./data', train=False, download=True, transform=transform_tensor)
+    return trainset, testset
+
+@pytest.fixture
+def cifar100_datasets():
+    trainset = CIFAR100(root='./data', train=True, download=True, transform=transform_tensor)
+    testset = CIFAR100(root='./data', train=False, download=True, transform=transform_tensor)
+    return trainset, testset
+
+def test_label_changed_dataset(cifar10_datasets):
+    _, testset = cifar10_datasets
+    new_label = 99
+    relabeled_dataset = LabelChangedDataset(testset, new_label)
+    
+    assert len(relabeled_dataset) == len(testset), "Relabeled dataset should match the original dataset length"
+    
+    for img, label in relabeled_dataset:
+        assert label == new_label, "All labels should be changed to the new label"
+
+def test_get_subsampled_subset(cifar10_datasets):
+    trainset, _ = cifar10_datasets
+    subset_ratio = 0.1
+    subset = get_subsampled_subset(trainset, subset_ratio=subset_ratio)
+    
+    expected_size = int(len(trainset) * subset_ratio)
+    assert len(subset) == expected_size, f"Subset size should be {expected_size}"
+
+def test_get_loaders_cifar10(cifar10_datasets):
+    train_loader, test_loader, test_set, test_loader_vs_other = get_loaders('cifar10')
+    
+    assert isinstance(train_loader, DataLoader)
+    assert isinstance(test_loader, DataLoader)
+    assert isinstance(test_loader_vs_other, DataLoader)
+
+    for images, labels in test_loader:
+        assert images.shape[0] == 16, "Test loader batch size should be 16"
+        break
+
+def test_get_loaders_cifar100(cifar100_datasets):
+    train_loader, test_loader, test_set, test_loader_vs_other = get_loaders('cifar100')
+    
+    assert isinstance(train_loader, DataLoader)
+    assert isinstance(test_loader, DataLoader)
+    assert isinstance(test_loader_vs_other, DataLoader)
+
+    for images, labels in test_loader:
+        assert images.shape[0] == 16, "Test loader batch size should be 16"
+        break
+
+def test_get_loaders_invalid_dataset():
+    with pytest.raises(ValueError, match="Dataset 'invalid_dataset' is not supported."):
+        get_loaders('invalid_dataset')