Switch to pyproject.toml for specifying dependencies

codecov.yml

@@ -2,6 +2,7 @@ ignore:
   - "examples"
   - "tests"
   - "tools"
+  - "setup.py"
 
 #codecov:
 #  notify:

examples/torch/common/models/classification/mobilenet_v3_tv_092.py

@@ -301,7 +301,7 @@ def _mobilenet_v3_model(
     last_channel: int,
     pretrained: bool,
     progress: bool,
-    **kwargs: Any
+    **kwargs: Any,
 ):
     model = MobileNetV3(inverted_residual_setting, last_channel, **kwargs)
     if pretrained:

nncf/experimental/torch/nas/bootstrapNAS/training/lr_scheduler.py

@@ -147,7 +147,7 @@ def __init__(
         base_lr: float,
         num_epochs: float,
         warmup_epochs: float = 0,
-        warmup_lr: float = 3.4e-4
+        warmup_lr: float = 3.4e-4,
     ):
         super().__init__(optimizer, num_steps_in_epoch)
         self._base_lr = base_lr

pyproject.toml

@@ -0,0 +1,93 @@
+[project]
+name = "nncf"
+authors = [
+    { name = "Intel Corporation", email = "[email protected]" },
+]
+description = "Neural Networks Compression Framework"
+readme = "README.md"
+requires-python = ">=3.7"
+keywords = [
+    "compression",
+    "quantization",
+    "sparsity",
+    "mixed-precision-training",
+    "quantization-aware-training",
+    "hawq",
+    "classification",
+    "pruning",
+    "object-detection",
+    "semantic-segmentation",
+    "nas",
+    "nlp",
+    "bert",
+    "transformers",
+    "mmdetection"
+]
+license = { text = "Apache-2.0" }
+classifiers = [
+    "Programming Language :: Python :: 3",
+    "License :: OSI Approved :: Apache Software License",
+    "Operating System :: OS Independent",
+    "Intended Audience :: Developers",
+    "Intended Audience :: Science/Research",
+    "Intended Audience :: Information Technology",
+    "Topic :: Scientific/Engineering :: Artificial Intelligence",
+    "Topic :: Software Development :: Libraries :: Application Frameworks",
+    "Topic :: Software Development :: Libraries :: Python Modules"
+]
+dependencies = [
+    "ninja>=1.10.0.post2, <1.11",
+    "texttable>=1.6.3",
+    "scipy>=1.3.2, <1.11",
+    "networkx>=2.6, <=2.8.2", # see ticket 94048 or https://github.com/networkx/networkx/issues/5962
+    "numpy>=1.19.1, <1.25",
+    # The recent pyparsing major version update seems to break
+    # integration with networkx - the graphs parsed from current .dot
+    # reference files no longer match against the graphs produced in tests.
+    # Using 2.x versions of pyparsing seems to fix the issue.
+    # Ticket: 69520
+    "packaging>=20.0",
+    "pyparsing<3.0",
+    "pymoo @ git+https://github.com/anyoptimization/pymoo.git@695cb26923903f872c7256a9013609769f3cc2bd",
+    "jsonschema>=3.2.0",
+    "pydot>=1.4.1",
+    "jstyleson>=0.0.2",
+    "tqdm>=4.54.1",
+    "natsort>=7.1.0",
+    "pandas>=1.1.5,<2.1",
+    "scikit-learn>=0.24.0",
+    "openvino-telemetry>=2023.1.0",
+    "psutil"
+]
+dynamic = ["version"]
+
+[project.urls]
+homepage = "https://openvinotoolkit.github.io/nncf/"
+repository = "https://github.com/openvinotoolkit/nncf"
+
+[project.optional-dependencies]
+torch = ["torch>=1.13.0,<2.1;python_version < '3.11'"]
+tf = ["tensorflow~=2.12.0", "tensorflow-metadata<=1.13.0"]
+onnx = ["onnx~=1.13.1", "onnxruntime~=1.14.1;python_version < '3.11'"]
+openvino = ["openvino==2023.0.1"]
+dev = [
+    "black==23.3.0",
+    "isort==5.12.0",
+    "kaleido>=0.2.1",
+    "matplotlib>=3.3.4, <3.6",
+    "pillow>=9.0.0",
+    "plotly-express>=0.4.1",
+    "pre-commit==3.2.2"
+]
+tests = ["pytest"]
+docs = []
+tensorflow = ["nncf[tf]"]
+tensorflow2 = ["nncf[tf]"]
+pytorch = ["nncf[torch]"]
+all = ["nncf[torch,tf,openvino,onnx]"]
+
+[tool.setuptools.packages.find]
+where = ["nncf"]
+
+[build-system]
+requires = ["setuptools>=59.5"]

setup.py

@@ -20,16 +20,19 @@
 # +-------------------------------------+---------------------------------------------+
 # | python setup.py install             | pip install .                               |
 # | python setup.py develop             | pip install -e .                            |
-# | python setup.py develop --*arg*     | pip install --install-option="*arg*" -e  .  |
+# | python setup.py develop --*arg*     | pip install --install-option="--*arg*" -e  .| <-- removed since pip 23.x
 # | python setup.py sdist               | python -m build -s                          | <-- using the "build" package
 # | python setup.py bdist_wheel         | python -m build -w                          | <-- pypi.org/project/build/
 # | python setup.py bdist_wheel --*arg* | python -m build -w -C--global-option=--*arg*|
 # +-------------------------------------+---------------------------------------------+
 #
-# PyPA in general recommends to move away from setup.py and use pyproject.toml
-# instead. This doesn't fit us as we currently want to do custom stuff during
-# installation such as setting version based on the commit SHA for repo-based
-# installs.
+# The majority of the usual setup.py-related metadata is now in the pyproject.toml,
+# but the PSF/PyPA/whoever still can't tackle the simple problem of allowing to specify custom, dynamic versions
+# via pyproject.toml only.
+# Obscure PR discussions such as https://github.com/pypa/setuptools/pull/3885
+# reveal that setup.py can actually still stay, and shall be called even considering the pyproject.toml, so
+# it is currently to be used solely for setting version based on the commit SHA
+# for repo-based installs.
 
 
 import codecs
@@ -40,28 +43,9 @@
 import sys
 import sysconfig
 
-import setuptools
-from pkg_resources import parse_version
-from setuptools import find_packages
 from setuptools import setup
 
 here = os.path.abspath(os.path.dirname(__file__))
-BKC_SETUPTOOLS_VERSION = "59.5.0"
-
-setuptools_version = parse_version(setuptools.__version__).base_version
-if setuptools_version < "43.0.0":
-    raise RuntimeError(
-        "To properly install NNCF, please install setuptools>=43.0.0, "
-        f"while current setuptools version is {setuptools.__version__}. "
-        f"Recommended version is {BKC_SETUPTOOLS_VERSION}."
-    )
-
-python_version = sys.version_info
-if python_version < (3, 7, 0):
-    print("Only Python >= 3.7.0 is supported")
-    sys.exit(0)
-
-version_string = "{}{}".format(sys.version_info[0], sys.version_info[1])
 
 is_installing_editable = "develop" in sys.argv
 is_building_release = not is_installing_editable and "--release" in sys.argv
@@ -100,114 +84,7 @@ def find_version(*file_paths):
     return version_value
 
 
-INSTALL_REQUIRES = [
-    "jsonschema>=3.2.0",
-    "jstyleson>=0.0.2",
-    "natsort>=7.1.0",
-    "networkx>=2.6, <=2.8.2",  # see ticket 94048 or https://github.com/networkx/networkx/issues/5962
-    "ninja>=1.10.0.post2, <1.11",
-    "numpy>=1.19.1, <1.25",
-    "openvino-telemetry>=2023.1.1",
-    "packaging>=20.0",
-    "pandas>=1.1.5,<2.1",
-    "psutil",
-    "pydot>=1.4.1",
-    "pymoo @ git+https://github.com/anyoptimization/pymoo.git@695cb26923903f872c7256a9013609769f3cc2bd",
-    # The recent pyparsing major version update seems to break
-    # integration with networkx - the graphs parsed from current .dot
-    # reference files no longer match against the graphs produced in tests.
-    # Using 2.x versions of pyparsing seems to fix the issue.
-    # Ticket: 69520
-    "pyparsing<3.0",
-    "scikit-learn>=0.24.0",
-    "scipy>=1.3.2, <1.11",
-    "texttable>=1.6.3",
-    "tqdm>=4.54.1",
-]
-
-
-TF_EXTRAS = [
-    "tensorflow~=2.12.0",
-    # This is required for support of TF 2.8.4 which needs protobuf<=3.19.6
-    "tensorflow-metadata<=1.13.0",
-]
-
-TORCH_EXTRAS = [
-    "torch>=1.13.0,<2.1;python_version < '3.11'",
-]
-
-ONNX_EXTRAS = ["onnx~=1.13.1", "onnxruntime~=1.14.1;python_version < '3.11'"]
-
-OPENVINO_EXTRAS = ["openvino==2023.0.1"]
-
-
-EXTRAS_REQUIRE = {
-    "dev": [
-        "black==23.3.0",
-        "isort==5.12.0",
-        "kaleido>=0.2.1",
-        "matplotlib>=3.3.4, <3.6",
-        "pillow>=9.0.0",
-        "plotly-express>=0.4.1",
-        "pre-commit==3.2.2",
-    ],
-    "tests": ["pytest"],
-    "docs": [],
-    "tf": TF_EXTRAS,
-    "tensorflow": TF_EXTRAS,
-    "tensorflow2": TF_EXTRAS,
-    "torch": TORCH_EXTRAS,
-    "pytorch": TORCH_EXTRAS,
-    "onnx": ONNX_EXTRAS,
-    "openvino": OPENVINO_EXTRAS,
-    "all": [
-        TF_EXTRAS,
-        TORCH_EXTRAS,
-        ONNX_EXTRAS,
-        OPENVINO_EXTRAS,
-    ],
-}
-
-with open("{}/README.md".format(here), "r", encoding="utf8") as fh:
-    long_description = fh.read()
-
-setup(
-    name="nncf",
-    version=find_version(os.path.join(here, "nncf/version.py")),
-    author="Intel",
-    author_email="[email protected]",
-    description="Neural Networks Compression Framework",
-    long_description=long_description,
-    long_description_content_type="text/markdown",
-    url="https://github.com/openvinotoolkit/nncf",
-    license="Apache-2.0",
-    packages=find_packages(exclude=["tests", "tests.*", "examples", "examples.*", "tools", "tools.*"]),
-    classifiers=[
-        "Programming Language :: Python :: 3",
-        "License :: OSI Approved :: Apache Software License",
-        "Operating System :: OS Independent",
-    ],
-    install_requires=INSTALL_REQUIRES,
-    extras_require=EXTRAS_REQUIRE,
-    keywords=[
-        "compression",
-        "quantization",
-        "sparsity",
-        "mixed-precision-training",
-        "quantization-aware-training",
-        "hawq",
-        "classification",
-        "pruning",
-        "object-detection",
-        "semantic-segmentation",
-        "nas",
-        "nlp",
-        "bert",
-        "transformers",
-        "mmdetection",
-    ],
-    include_package_data=True,
-)
+setup(version=find_version(os.path.join(here, "nncf/version.py")))
 
 path_to_ninja = glob.glob(str(sysconfig.get_paths()["purelib"] + "/ninja*/ninja/data/bin/"))
 if path_to_ninja:

tests/experimental/tensorflow/test_models/resnet.py

@@ -60,7 +60,7 @@ def __init__(
         activation="relu",
         gating_activation="sigmoid",
         round_down_protect=True,
-        **kwargs
+        **kwargs,
     ):
         """Initializes a squeeze and excitation layer.
 
@@ -291,7 +291,7 @@ def __init__(
         norm_momentum=0.99,
         norm_epsilon=0.001,
         bn_trainable=True,
-        **kwargs
+        **kwargs,
     ):
         """Initializes a residual block with BN after convolutions.
 
@@ -490,7 +490,7 @@ def __init__(
         norm_momentum=0.99,
         norm_epsilon=0.001,
         bn_trainable=True,
-        **kwargs
+        **kwargs,
     ):
         """Initializes a standard bottleneck block with BN after convolutions.
 
@@ -798,7 +798,7 @@ def __init__(
         kernel_regularizer: Optional[tf.keras.regularizers.Regularizer] = None,
         bias_regularizer: Optional[tf.keras.regularizers.Regularizer] = None,
         bn_trainable: bool = True,
-        **kwargs
+        **kwargs,
     ):
         """Initializes a ResNet model.
 
@@ -1066,7 +1066,7 @@ def __init__(
         norm_momentum: float = 0.99,
         norm_epsilon: float = 0.001,
         skip_logits_layer: bool = False,
-        **kwargs
+        **kwargs,
     ):
         """Classification initialization function.