Merge pull request #1116 from Trusted-AI/development_setup

Update setup.py

Author: beat-buesser

.github/workflows/ci-style-checks.yml

@@ -38,7 +38,7 @@ jobs:
       - name: Install Dependencies
         run: |
           python -m pip install --upgrade pip setuptools wheel
-          pip install -q pylint==2.7.4 pycodestyle==2.7.0 black==20.8b1
+          pip install -q pylint==2.7.4 mypy==0.812 pycodestyle==2.7.0 black==20.8b1
           pip install -q -r requirements.txt
           pip list
       - name: pycodestyle

art/evaluations/security_curve/security_curve.py

@@ -23,7 +23,6 @@
 from typing import List, Optional, Tuple, TYPE_CHECKING, Union
 
 import numpy as np
-from matplotlib import pyplot as plt
 
 from art.evaluations.evaluation import Evaluation
 from art.attacks.evasion.projected_gradient_descent.projected_gradient_descent import ProjectedGradientDescent
@@ -162,6 +161,8 @@ def plot(self) -> None:
         Plot the Security Curve of adversarial accuracy as function opf attack budget `eps` together with the accuracy
         on benign samples.
         """
+        from matplotlib import pyplot as plt
+
         plt.plot(self.eps_list, self.accuracy_adv_list, label="adversarial", marker="o")
         plt.plot([self.eps_list[0], self.eps_list[-1]], [self.accuracy, self.accuracy], linestyle="--", label="benign")
         plt.legend()

setup.py

@@ -9,18 +9,10 @@
 install_requires = [
     "numpy>=1.18.0",
     "scipy>=1.4.1",
-    "matplotlib",
     "scikit-learn>=0.22.2,<0.24.3",
     "six",
     "setuptools",
-    "Pillow",
     "tqdm",
-    "statsmodels",
-    "pydub",
-    "resampy",
-    "ffmpeg-python",
-    "cma",
-    "mypy",
     "numba~=0.53.1"
 ]
 
@@ -30,7 +22,7 @@
     "sphinx-autodoc-annotation",
     "sphinx-autodoc-typehints",
     "matplotlib",
-    "numpy",
+    "numpy>=1.18.0",
     "scipy>=1.4.1",
     "six>=1.13.0",
     "scikit-learn>=0.22.2,<0.24.3",
@@ -73,9 +65,13 @@ def get_version(rel_path):
         "lightgbm": ["lightgbm"],
         "mxnet": ["mxnet"],
         "tensorflow": ["tensorflow", "tensorflow_addons", "h5py"],
-        "pytorch": ["torch", "torchvision", "torchaudio"],
+        "tensorflow_image": ["tensorflow", "tensorflow_addons", "h5py", "Pillow", "ffmpeg-python", "opencv-python"],
+        "tensorflow_audio": ["tensorflow", "tensorflow_addons", "h5py", "pydub", "resampy", "librosa"],
+        "pytorch": ["torch", "torchvision"],
+        "pytorch_image": ["torch", "torchvision", "kornia", "Pillow", "ffmpeg-python", "opencv-python"],
+        "pytorch_audio": ["torch", "torchvision", "torchaudio", "pydub", "resampy", "librosa"],
         "xgboost": ["xgboost"],
-        "lingvo_asr": ["tensorflow-gpu==2.1.0", "lingvo==0.6.4"],
+        "lingvo_asr": ["tensorflow-gpu==2.1.0", "lingvo==0.6.4", "pydub", "resampy", "librosa"],
         "all": [
             "mxnet",
             "catboost",
@@ -88,6 +84,15 @@ def get_version(rel_path):
             "xgboost",
             "pandas",
             "kornia",
+            "matplotlib",
+            "Pillow",
+            "statsmodels",
+            "pydub",
+            "resampy",
+            "ffmpeg-python",
+            "cma",
+            "librosa",
+            "opencv-python",
         ],
     },
     classifiers=[