.github/workflows/tests.yml → .github/workflows/tests_full.yml

@@ -3,10 +3,10 @@ name: tests
 on:
   push:
     branches:
-      - '*'
+      - main
   pull_request:
     branches:
-      - '*'
+      - main
 
 jobs:
   build:
@@ -36,8 +36,7 @@ jobs:
     runs-on: ${{ matrix.os }}
     strategy:
       matrix:
-        # os: [ windows-2019, ubuntu-18.04, macos-11 ]
-        os: [ubuntu-18.04, ubuntu-20.04]
+        os: [ windows-2019, ubuntu-18.04, macos-11 ]
         python-version: [ 3.7, 3.8, 3.9 ]
         tf-version: [2.7.0, 2.8.0, 2.9.0]
 
@@ -69,10 +68,14 @@ jobs:
     runs-on: ${{ matrix.os }}
     strategy:
       matrix:
-        # os: [ windows-2019, ubuntu-18.04, macos-11 ]
-        os: [ubuntu-18.04, ubuntu-20.04]
-        python-version: [ 3.7, 3.8, 3.9 ]
+        os: [ windows-2019, ubuntu-18.04, macos-11 ]
+        python-version: [ 3.6, 3.7, 3.8, 3.9 ]
         pytorch-version: [1.8.0, 1.9.0, 1.10.0, 1.11.0, 1.12.0]
+        exclude:
+          - python-version: 3.6
+            pytorch-version: 1.11.0
+          - python-version: 3.6
+            pytorch-version: 1.12.0
 
     steps:
       - uses: actions/checkout@v1

.github/workflows/tests_quick.yml

@@ -0,0 +1,86 @@
+name: tests
+
+on:
+  push:
+    branches-ignore:
+      - main
+  pull_request:
+    branches-ignore:
+      - main
+
+jobs:
+  build:
+    runs-on: ubuntu-18.04
+    steps:
+      - uses: actions/checkout@v1
+      - name: Set up Python 3.6
+        uses: actions/setup-python@v2
+        with:
+          python-version: 3.6
+
+      - name: Install dependencies
+        run: pip install wheel setuptools
+
+      - name: Build wheel
+        run: python setup.py bdist_wheel
+
+      - name: Upload Python wheel
+        uses: actions/upload-artifact@v2
+        with:
+          name: Python wheel
+          path: ${{github.workspace}}/dist/torchstain-*.whl
+          if-no-files-found: error
+
+  test-tf:
+    needs: build
+    runs-on: ubuntu-18.04
+
+    steps:
+      - uses: actions/checkout@v1
+      - name: Set up Python 3.8
+        uses: actions/setup-python@v2
+        with:
+          python-version: 3.8
+
+      - name: Download artifact
+        uses: actions/download-artifact@master
+        with:
+          name: "Python wheel"
+
+      - name: Install dependencies
+        run: |
+          pip install tensorflow protobuf==3.20.* opencv-python-headless scikit-image
+          pip install pytest
+
+      - name: Install wheel
+        run: pip install --find-links=${{github.workspace}} torchstain
+
+      - name: Run tests
+        run: pytest -vs tests/test_tf.py
+
+  test-torch:
+    needs: build
+    runs-on: ubuntu-18.04
+
+    steps:
+      - uses: actions/checkout@v1
+      - name: Set up Python 3.8
+        uses: actions/setup-python@v2
+        with:
+          python-version: 3.8
+
+      - name: Download artifact
+        uses: actions/download-artifact@master
+        with:
+          name: "Python wheel"
+
+      - name: Install dependencies
+        run: |
+          pip install torch torchvision opencv-python-headless scikit-image
+          pip install pytest
+
+      - name: Install wheel
+        run: pip install --find-links=${{github.workspace}} torchstain
+
+      - name: Run tests
+        run: pytest -vs tests/test_torch.py

README.md

@@ -3,6 +3,9 @@
 [![License](https://img.shields.io/badge/License-MIT-green.svg)](https://opensource.org/licenses/MIT)
 [![tests](https://github.com/EIDOSLAB/torchstain/workflows/tests/badge.svg)](https://github.com/EIDOSLAB/torchstain/actions)
 [![Pip Downloads](https://img.shields.io/pypi/dm/torchstain?label=pip%20downloads&logo=python)](https://pypi.org/project/torchstain/)
+[![DOI](https://zenodo.org/badge/323590093.svg)](https://zenodo.org/badge/latestdoi/323590093)
+
+
 
 GPU-accelerated stain normalization tools for histopathological images. Compatible with PyTorch, TensorFlow, and Numpy.
 Normalization algorithms currently implemented:
@@ -69,3 +72,23 @@ Results with 10 runs per size on a Intel(R) Core(TM) i5-8365U CPU @ 1.60GHz
 ## Reference
 
 - [1] Macenko, Marc, et al. "A method for normalizing histology slides for quantitative analysis." 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro. IEEE, 2009.
+
+
+## Citing
+
+If you find this software useful for your research, please cite it as: 
+
+
+```bibtex
+@software{barbano2022torchstain,
+  author       = {Carlo Alberto Barbano and
+                  André Pedersen},
+  title        = {EIDOSLAB/torchstain: v1.2.0-rc1},
+  month        = aug,
+  year         = 2022,
+  publisher    = {Zenodo},
+  version      = {v1.2.0-rc1},
+  doi          = {10.5281/zenodo.6976410},
+  url          = {https://doi.org/10.5281/zenodo.6976410}
+}
+```

setup.py

@@ -7,7 +7,7 @@
 setup(
     name='torchstain',
     version='1.2.0',
-    description='Pytorch stain normalization utils',
+    description='Stain normalization tools for histological analysis and computational pathology',
     long_description=README,
     long_description_content_type='text/markdown',
     url='https://github.com/EIDOSlab/torchstain',

torchstain/tf/utils/percentile.py

@@ -6,7 +6,6 @@ def percentile(t: tf.Tensor, q: float) -> Union[int, float]:
     Return the ``q``-th percentile of the flattened input tensor's data.
 
     CAUTION:
-     * Needs PyTorch >= 1.1.0, as ``torch.kthvalue()`` is used.
      * Values are not interpolated, which corresponds to
        ``numpy.percentile(..., interpolation="nearest")``.