Merge pull request #91 from neuro-ml/develop

Fixes

.github/workflows/release.yml

@@ -12,11 +12,11 @@ jobs:
     runs-on: ubuntu-latest
 
     steps:
-      - uses: actions/checkout@v3
-      - name: Set up Python 3.9
-        uses: actions/setup-python@v4
+      - uses: actions/checkout@v4
+      - name: Set up Python
+        uses: actions/setup-python@v5
         with:
-          python-version: 3.9
+          python-version: '3.10'
 
       - id: get_version
         name: Get the release version
@@ -30,7 +30,8 @@ jobs:
           echo $MATCH
           if [ "$GITHUB_BASE_REF" = "master" ] && [ "$MATCH" != "" ]; then echo "Version $VERSION already present" && exit 1; fi
           if [ "$VERSION" != "$RELEASE" ]; then echo "$VERSION vs $RELEASE" && exit 1; fi
-          python setup.py sdist
+          pip install build
+          python -m build --sdist
 
       - name: Publish to PyPi
         uses: pypa/gh-action-pypi-publish@master

.github/workflows/tests.yml

@@ -10,25 +10,19 @@ jobs:
     runs-on: ubuntu-20.04
     strategy:
       matrix:
-        python-version: [ '3.6', '3.7', '3.8', '3.9', '3.10', '3.11' ]
+        python-version: [ '3.7', '3.8', '3.9', '3.10', '3.11', '3.12', '3.13' ]
 
     steps:
-      - uses: actions/checkout@v3
+      - uses: actions/checkout@v4
       - name: Set up Python ${{ matrix.python-version }}
-        uses: actions/setup-python@v4
+        uses: actions/setup-python@v5
         with:
           python-version: ${{ matrix.python-version }}
 
-      - name: Check the version
-        if: "! github.event.pull_request.head.repo.fork "
-        run: |
-          VERSION=$(python -c "from pathlib import Path; import runpy; folder, = {d.parent for d in Path().resolve().glob('*/__init__.py') if d.parent.is_dir() and (d.parent / '__version__.py').exists()}; print(runpy.run_path(folder / '__version__.py')['__version__'])")
-          MATCH=$(pip index versions deep-pipe | grep "Available versions:" | grep $VERSION) || echo
-          echo $MATCH
-          if [ "$GITHUB_BASE_REF" = "master" ] && [ "$MATCH" != "" ]; then exit 1; fi
       - name: Build the package
         run: |
-          python setup.py sdist
+          pip install build
+          python -m build --sdist
 
       - name: Install
         run: |
@@ -52,17 +46,16 @@ jobs:
           sed -i -e "s|$(echo $MODULE_PARENT/ | tr "/" .)||g" reports/coverage-${{ matrix.python-version }}.xml
 
       - name: Upload artifacts
-        uses: actions/upload-artifact@v3
+        uses: actions/upload-artifact@v4
         with:
           name: reports-${{ matrix.python-version }}
-          path: reports/*-${{ matrix.python-version }}.xml
+          path: reports/*.xml
         if: ${{ always() }}
 
       - name: Upload coverage results
-        uses: codecov/codecov-action@v3
-        env:
-          CODECOV_TOKEN: ${{ secrets.CODECOV_TOKEN }}
+        uses: codecov/codecov-action@v4
         with:
           fail_ci_if_error: true
-          files: reports/coverage-${{ matrix.python-version }}.xml
+          token: ${{ secrets.CODECOV_TOKEN }}
+          files: reports/coverage-*.xml
           verbose: true

.github/workflows/version.yml

@@ -0,0 +1,25 @@
+name: Check Version
+
+on: [ pull_request ]
+
+env:
+  MODULE_NAME: deep-pipe
+
+jobs:
+  check:
+    runs-on: ubuntu-20.04
+
+    steps:
+      - uses: actions/checkout@v4
+      - name: Set up Python ${{ matrix.python-version }}
+        uses: actions/setup-python@v5
+        with:
+          python-version: '3.10'
+
+      - name: Check the version
+        if: "! github.event.pull_request.head.repo.fork "
+        run: |
+          VERSION=$(python -c "from pathlib import Path; import runpy; folder, = {d.parent for d in Path().resolve().glob('*/__init__.py') if d.parent.is_dir() and (d.parent / '__version__.py').exists()}; print(runpy.run_path(folder / '__version__.py')['__version__'])")
+          MATCH=$(pip index versions $MODULE_NAME | grep "Available versions:" | grep $VERSION) || echo
+          echo $MATCH
+          if [ "$GITHUB_BASE_REF" = "master" ] && [ "$MATCH" != "" ]; then exit 1; fi

LICENSE

@@ -1,6 +1,6 @@
 MIT License
 
-Copyright (c) 2017-2020 NeuroML Group
+Copyright (c) 2017-2024 NeuroML Group
 
 Permission is hereby granted, free of charge, to any person obtaining a copy
 of this software and associated documentation files (the "Software"), to deal

dpipe/__version__.py

@@ -1 +1 @@
-__version__ = '0.4.1'
+__version__ = '0.4.2'

dpipe/checks.py

@@ -1,6 +1,11 @@
 from functools import wraps
 from typing import Callable
-import numpy as np
+
+
+try:
+    from numpy.lib.array_utils import normalize_axis_tuple
+except ImportError:
+    from numpy.core.numeric import normalize_axis_tuple
 
 from .itertools import extract
 
@@ -12,7 +17,7 @@ def join(values):
 def check_shape_along_axis(*arrays, axis):
     sizes = []
     for array in arrays:
-        sizes.append(tuple(extract(array.shape, np.core.numeric.normalize_axis_tuple(axis, array.ndim))))
+        sizes.append(tuple(extract(array.shape, normalize_axis_tuple(axis, array.ndim))))
 
     if any(x != sizes[0] for x in sizes):
         raise ValueError(f'Arrays of equal size along axis {axis} are required: {join(sizes)}')

dpipe/im/axes.py

@@ -2,6 +2,12 @@
 
 import numpy as np
 
+
+try:
+    from numpy.lib.array_utils import normalize_axis_tuple
+except ImportError:
+    from numpy.core.numeric import normalize_axis_tuple
+
 from ..itertools import lmap
 from ..checks import join
 
@@ -46,7 +52,7 @@ def axis_from_dim(axis: Union[AxesLike, None], dim: int) -> tuple:
     if min(axis) < left or max(axis) > right:
         raise ValueError(f'For dim={dim} axis must be within ({left}, {right}): but provided {axis}.')
 
-    return np.core.numeric.normalize_axis_tuple(axis, dim, 'axis')
+    return normalize_axis_tuple(axis, dim, 'axis')
 
 
 def check_axes(axes) -> tuple:

dpipe/torch/functional.py

@@ -1,11 +1,11 @@
 import warnings
 from typing import Union, Callable
 
-import numpy as np
 import torch
 from torch.nn import functional
 
-from dpipe.im.axes import AxesLike
+from ..im.axes import AxesLike, normalize_axis_tuple
+
 
 __all__ = [
     'focal_loss_with_logits', 'linear_focal_loss_with_logits', 'weighted_cross_entropy_with_logits',
@@ -177,11 +177,12 @@ def tversky_loss(pred: torch.Tensor, target: torch.Tensor, alpha=0.5, epsilon=1e
     sum_dims = list(range(1, target.dim()))
     beta = 1 - alpha
 
-    intersection = pred*target
-    fps, fns = pred*(1-target), (1-pred)*target
+    intersection = pred * target
+    fps, fns = pred * (1 - target), (1 - pred) * target
 
     numerator = torch.sum(intersection, dim=sum_dims)
-    denumenator = torch.sum(intersection, dim=sum_dims) + alpha*torch.sum(fps, dim=sum_dims) + beta*torch.sum(fns, dim=sum_dims)
+    denumenator = torch.sum(intersection, dim=sum_dims) + alpha * torch.sum(fps, dim=sum_dims) + beta * torch.sum(fns,
+                                                                                                                  dim=sum_dims)
     tversky = numerator / (denumenator + epsilon)
     loss = 1 - tversky
 
@@ -190,7 +191,7 @@ def tversky_loss(pred: torch.Tensor, target: torch.Tensor, alpha=0.5, epsilon=1e
     return loss
 
 
-def focal_tversky_loss(pred: torch.Tensor, target: torch.Tensor, gamma=4/3, alpha=0.5, epsilon=1e-7):
+def focal_tversky_loss(pred: torch.Tensor, target: torch.Tensor, gamma=4 / 3, alpha=0.5, epsilon=1e-7):
     """
     References
     ----------
@@ -200,7 +201,7 @@ def focal_tversky_loss(pred: torch.Tensor, target: torch.Tensor, gamma=4/3, alph
         warnings.warn("Gamma is <=1, to focus on less accurate predictions choose gamma > 1.")
     tl = tversky_loss(pred, target, alpha, epsilon, reduce=None)
 
-    return torch.pow(tl, 1/gamma).mean()
+    return torch.pow(tl, 1 / gamma).mean()
 
 
 def loss_with_logits(criterion: Callable, logit: torch.Tensor, target: torch.Tensor, **kwargs):
@@ -243,8 +244,8 @@ def moveaxis(x: torch.Tensor, source: AxesLike, destination: AxesLike):
     Move axes of a torch.Tensor to new positions.
     Other axes remain in their original order.
     """
-    source = np.core.numeric.normalize_axis_tuple(source, x.ndim, 'source')
-    destination = np.core.numeric.normalize_axis_tuple(destination, x.ndim, 'destination')
+    source = normalize_axis_tuple(source, x.ndim, 'source')
+    destination = normalize_axis_tuple(destination, x.ndim, 'destination')
     if len(source) != len(destination):
         raise ValueError('`source` and `destination` arguments must have '
                          'the same number of elements')
@@ -260,7 +261,7 @@ def softmax(x: torch.Tensor, axis: AxesLike):
     """
     A multidimensional version of softmax.
     """
-    source = np.core.numeric.normalize_axis_tuple(axis, x.ndim, 'axis')
+    source = normalize_axis_tuple(axis, x.ndim, 'axis')
     dim = len(source)
     destination = range(-dim, 0)
 

dpipe/torch/model.py

@@ -46,7 +46,7 @@ def optimizer_step(
 
     if scaler is not None:
         # autocast is not recommended during backward
-        with torch.cuda.amp.autocast(False):
+        with torch.autocast("cuda", enabled=False):
             scaler.scale(loss).backward()
 
             if not accumulate:
@@ -134,7 +134,7 @@ def train_step(
     inputs = sequence_to_var(*inputs, device=architecture)
     inputs, targets = inputs[:n_inputs], inputs[n_inputs:]
 
-    with torch.cuda.amp.autocast(scaler is not None or torch.is_autocast_enabled()):
+    with torch.autocast("cuda", enabled=scaler is not None or torch.is_autocast_enabled()):
         loss = criterion(architecture(*inputs), *targets)
 
     if loss_key is not None:
@@ -177,7 +177,7 @@ def inference_step(*inputs: np.ndarray, architecture: Module, activation: Callab
     amp = amp or torch.is_autocast_enabled()
     in_dtype = in_dtype or (torch.float16 if amp else None)
     with _inference_ctx_manager():
-        with torch.cuda.amp.autocast(amp, cache_enabled=amp_cache_enabled):
+        with torch.autocast("cuda", enabled=amp, cache_enabled=amp_cache_enabled):
             return to_np(
                 activation(architecture(*sequence_to_var(*inputs, device=architecture, dtype=in_dtype))),
                 dtype=out_dtype,
@@ -204,7 +204,7 @@ def multi_inference_step(*inputs: np.ndarray, architecture: Module,
     amp = amp or torch.is_autocast_enabled()
     in_dtype = in_dtype or (torch.float16 if amp else None)
     with _inference_ctx_manager():
-        with torch.cuda.amp.autocast(amp, cache_enabled=amp_cache_enabled):
+        with torch.autocast("cuda", enabled=amp, cache_enabled=amp_cache_enabled):
             results = architecture(*sequence_to_var(*inputs, device=architecture, dtype=in_dtype))
             if callable(activations):
                 activations = [activations] * len(results)

dpipe/torch/utils.py

@@ -38,7 +38,7 @@ def load_model_state(module: nn.Module, path: PathLike, modify_state_fn: Callabl
         For example, it could help you to transfer weights from similar but not completely equal architecture.
     strict: bool
     """
-    state_to_load = torch.load(path, map_location=get_device(module))
+    state_to_load = torch.load(path, map_location=get_device(module), weights_only=True)
     if modify_state_fn is not None:
         current_state = module.state_dict()
         state_to_load = modify_state_fn(current_state, state_to_load)

pyproject.toml

@@ -0,0 +1,41 @@
+[project]
+name = 'deep_pipe'
+dynamic = ['version', 'dependencies']
+description = 'A collection of tools for deep learning experiments'
+readme = 'README.md'
+requires-python = '>=3.7'
+license = { file = 'LICENSE' }
+keywords = []
+classifiers = [
+    'Development Status :: 4 - Beta',
+    'License :: OSI Approved :: MIT License',
+    'Programming Language :: Python :: 3',
+    'Programming Language :: Python :: 3.7',
+    'Programming Language :: Python :: 3.8',
+    'Programming Language :: Python :: 3.9',
+    'Programming Language :: Python :: 3.10',
+    'Programming Language :: Python :: 3.11',
+    'Programming Language :: Python :: 3.12',
+    'Programming Language :: Python :: 3.13',
+    'Programming Language :: Python :: 3 :: Only',
+]
+
+[project.urls]
+'Homepage' = 'https://github.com/neuro-ml/deep_pipe'
+'Issues' = 'https://github.com/neuro-ml/deep_pipe/issues'
+'Source' = 'https://github.com/neuro-ml/deep_pipe'
+
+[project.scripts]
+dpipe-run = 'dpipe.layout.scripts:run'
+dpipe-build = 'dpipe.layout.scripts:build'
+
+[build-system]
+requires = ['setuptools>=43.0.0', 'wheel']
+build-backend = 'setuptools.build_meta'
+
+[tool.setuptools.packages.find]
+include = ['dpipe']
+
+[tool.setuptools.dynamic]
+version = { attr = 'dpipe.__version__.__version__' }
+dependencies = { file = 'requirements.txt' }

requirements.txt

@@ -5,7 +5,6 @@ pandas
 scikit-learn>=0.19
 tqdm
 pdp==0.3.*
-tensorboard-easy
 lazycon>=0.1.0,<1.0.0
 loky>=3.0.0,<4.0.0
 torch

setup.py

@@ -2,14 +2,20 @@
 
 from setuptools import setup, find_packages
 
-classifiers = '''Development Status :: 4 - Beta
-Programming Language :: Python :: 3.6
-Programming Language :: Python :: 3.7
-Programming Language :: Python :: 3.8
-Programming Language :: Python :: 3.9
-Programming Language :: Python :: 3.10
-Programming Language :: Python :: 3.11
-Programming Language :: Python :: 3.12'''
+
+classifiers = [
+    'Development Status :: 4 - Beta',
+    'License :: OSI Approved :: MIT License',
+    'Programming Language :: Python :: 3',
+    'Programming Language :: Python :: 3.7',
+    'Programming Language :: Python :: 3.8',
+    'Programming Language :: Python :: 3.9',
+    'Programming Language :: Python :: 3.10',
+    'Programming Language :: Python :: 3.11',
+    'Programming Language :: Python :: 3.12',
+    'Programming Language :: Python :: 3.13',
+    'Programming Language :: Python :: 3 :: Only',
+]
 
 with open('README.md', encoding='utf-8') as file:
     long_description = file.read()
@@ -35,9 +41,9 @@
     url='https://github.com/neuro-ml/deep_pipe',
     download_url='https://github.com/neuro-ml/deep_pipe/v%s.tar.gz' % __version__,
     keywords=[],
-    classifiers=classifiers.splitlines(),
+    classifiers=classifiers,
     install_requires=requirements,
-    python_requires='>=3.6',
+    python_requires='>=3.7',
     entry_points={
         'console_scripts': [
             'dpipe-run = dpipe.layout.scripts:run',