Speed optimizations

.github/workflows/build.yaml

@@ -6,7 +6,7 @@ on:
 jobs:
   build_wheels:
     name: Build release
-    runs-on: ubuntu-18.04
+    runs-on: ubuntu-20.04
 
     steps:
       - uses: actions/checkout@v3
@@ -27,7 +27,7 @@ jobs:
 
   upload_pypi:
     needs: build_wheels
-    runs-on: ubuntu-18.04
+    runs-on: ubuntu-20.04
 
     if: github.event_name == 'push' && startsWith(github.ref, 'refs/tags/v')
 

.github/workflows/tests_full.yml

@@ -10,7 +10,7 @@ on:
 
 jobs:
   build:
-    runs-on: ubuntu-18.04
+    runs-on: ubuntu-20.04
 
     if: startsWith(github.ref, 'refs/tags/v') != true
 
@@ -39,7 +39,7 @@ jobs:
     runs-on: ${{ matrix.os }}
     strategy:
       matrix:
-        os: [ windows-2019, ubuntu-18.04, macos-11 ]
+        os: [ windows-2019, ubuntu-20.04, macos-11 ]
         python-version: [ 3.7, 3.8, 3.9 ]
         tf-version: [2.7.0, 2.8.0, 2.9.0]
 
@@ -71,14 +71,16 @@ jobs:
     runs-on: ${{ matrix.os }}
     strategy:
       matrix:
-        os: [ windows-2019, ubuntu-18.04, macos-11 ]
+        os: [ windows-2019, ubuntu-20.04, 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, 1.13.0]
         exclude:
           - python-version: 3.6
             pytorch-version: 1.11.0
           - python-version: 3.6
             pytorch-version: 1.12.0
+          - python-version: 3.6
+            pytorch-version: 1.13.0
 
     steps:
       - uses: actions/checkout@v1

.github/workflows/tests_quick.yml

@@ -10,7 +10,7 @@ on:
 
 jobs:
   build:
-    runs-on: ubuntu-18.04
+    runs-on: ubuntu-20.04
     steps:
       - uses: actions/checkout@v1
       - name: Set up Python 3.6
@@ -33,7 +33,7 @@ jobs:
 
   test-tf:
     needs: build
-    runs-on: ubuntu-18.04
+    runs-on: ubuntu-20.04
 
     steps:
       - uses: actions/checkout@v1

setup.py

@@ -6,7 +6,7 @@
 
 setup(
     name='torchstain',
-    version='1.2.0',
+    version='1.3.0',
     description='Stain normalization tools for histological analysis and computational pathology',
     long_description=README,
     long_description_content_type='text/markdown',

tests/test_color_conv.py

@@ -4,12 +4,16 @@
 import cv2
 import os
 
-def test_rgb_to_lab():
+def test_rgb_lab():
     size = 1024
     curr_file_path = os.path.dirname(os.path.realpath(__file__))
     img = cv2.resize(cv2.cvtColor(cv2.imread(os.path.join(curr_file_path, "../data/source.png")), cv2.COLOR_BGR2RGB), (size, size))
+
+    # rgb2lab expects data to be float32 in range [0, 1]
+    img = img / 255
 
+    # convert from RGB to LAB and back again to RGB
     reconstructed_img = lab2rgb(rgb2lab(img))
-    val = np.mean(np.abs(reconstructed_img - img))
-    print("MAE:", val)
-    assert val < 0.1
+
+    # assess if the reconstructed image is similar to the original image
+    np.testing.assert_almost_equal(np.mean(np.abs(reconstructed_img - img)), 0.0, decimal=4, verbose=True)

tests/test_tf.py

@@ -3,8 +3,6 @@
 import torchstain
 import torchstain.tf
 import tensorflow as tf
-import time
-from skimage.metrics import structural_similarity as ssim
 import numpy as np
 
 def test_cov():
@@ -44,11 +42,11 @@ def test_macenko_tf():
     result_tf, _, _ = tf_normalizer.normalize(I=t_to_transform, stains=True)
 
     # convert to numpy and set dtype
-    result_numpy = result_numpy.astype("float32")
-    result_tf = result_tf.numpy().astype("float32")
+    result_numpy = result_numpy.astype("float32") / 255.
+    result_tf = result_tf.numpy().astype("float32") / 255.
 
     # assess whether the normalized images are identical across backends
-    np.testing.assert_almost_equal(ssim(result_numpy.flatten(), result_tf.flatten()), 1.0, decimal=4, verbose=True)
+    np.testing.assert_almost_equal(result_numpy.flatten(), result_tf.flatten(), decimal=2, verbose=True)
 
 def test_reinhard_tf():
     size = 1024
@@ -72,8 +70,8 @@ def test_reinhard_tf():
     result_tf = tf_normalizer.normalize(I=t_to_transform)
 
     # convert to numpy and set dtype
-    result_numpy = result_numpy.astype("float32")
-    result_tf = result_tf.numpy().astype("float32")
+    result_numpy = result_numpy.astype("float32") / 255.
+    result_tf = result_tf.numpy().astype("float32") / 255.
 
     # assess whether the normalized images are identical across backends
-    np.testing.assert_almost_equal(ssim(result_numpy.flatten(), result_tf.flatten()), 1.0, decimal=4, verbose=True)
+    np.testing.assert_almost_equal(result_numpy.flatten(), result_tf.flatten(), decimal=2, verbose=True)

tests/test_torch.py

@@ -4,10 +4,9 @@
 import torchstain.torch
 import torch
 import torchvision
-import time
 import numpy as np
 from torchvision import transforms
-from skimage.metrics import structural_similarity as ssim
+
 
 def setup_function(fn):
     print("torch version:", torch.__version__, "torchvision version:", torchvision.__version__)
@@ -52,11 +51,11 @@ def test_macenko_torch():
     result_torch, _, _ = torch_normalizer.normalize(I=t_to_transform, stains=True)
 
     # convert to numpy and set dtype
-    result_numpy = result_numpy.astype("float32")
-    result_torch = result_torch.numpy().astype("float32")
+    result_numpy = result_numpy.astype("float32") / 255.
+    result_torch = result_torch.numpy().astype("float32") / 255.
 
     # assess whether the normalized images are identical across backends
-    np.testing.assert_almost_equal(ssim(result_numpy.flatten(), result_torch.flatten()), 1.0, decimal=4, verbose=True)
+    np.testing.assert_almost_equal(result_numpy.flatten(), result_torch.flatten(), decimal=2, verbose=True)
 
 def test_reinhard_torch():
     size = 1024
@@ -83,8 +82,9 @@ def test_reinhard_torch():
     result_torch = torch_normalizer.normalize(I=t_to_transform)
 
     # convert to numpy and set dtype
-    result_numpy = result_numpy.astype("float32")
-    result_torch = result_torch.numpy().astype("float32")
+    result_numpy = result_numpy.astype("float32") / 255.
+    result_torch = result_torch.numpy().astype("float32") / 255.
 
+
     # assess whether the normalized images are identical across backends
-    np.testing.assert_almost_equal(ssim(result_numpy.flatten(), result_torch.flatten()), 1.0, decimal=4, verbose=True)
+    np.testing.assert_almost_equal(result_numpy.flatten(), result_torch.flatten(), decimal=2, verbose=True)

torchstain/__init__.py

@@ -1,3 +1,3 @@
-__version__ = '1.2.0'
+__version__ = '1.3.0'
 
 from torchstain.base import normalizers

torchstain/numpy/utils/lab2rgb.py

@@ -7,32 +7,39 @@
 Implementation is based on:
 https://github.com/scikit-image/scikit-image/blob/00177e14097237ef20ed3141ed454bc81b308f82/skimage/color/colorconv.py#L704
 """
-def lab2rgb(lab):
-    lab = lab.astype("float32")
+def lab2rgb(lab: np.ndarray) -> np.ndarray:
+    """
+    Convert an array of LAB values to RGB values.
+
+    Args:
+        lab (np.ndarray): An array of shape (..., 3) containing LAB values.
+
+    Returns:
+        np.ndarray: An array of shape (..., 3) containing RGB values.
+    """
     # first rescale back from OpenCV format
     lab[..., 0] /= 2.55
-    lab[..., 1] -= 128
-    lab[..., 2] -= 128
+    lab[..., 1:] -= 128
 
     # convert LAB -> XYZ color domain
-    L, a, b = lab[..., 0], lab[..., 1], lab[..., 2]
-    y = (L + 16.) / 116.
-    x = (a / 500.) + y
-    z = y - (b / 200.)
+    y = (lab[..., 0] + 16.) / 116.
+    x = (lab[..., 1] / 500.) + y
+    z = y - (lab[..., 2] / 200.)
 
-    out = np.stack([x, y, z], axis=-1)
+    xyz = np.stack([x, y, z], axis=-1)
 
-    mask = out > 0.2068966
-    out[mask] = np.power(out[mask], 3.)
-    out[~mask] = (out[~mask] - 16.0 / 116.) / 7.787
+    mask = xyz > 0.2068966
+    xyz[mask] = np.power(xyz[mask], 3.)
+    xyz[~mask] = (xyz[~mask] - 16.0 / 116.) / 7.787
 
     # rescale to the reference white (illuminant)
-    out *= np.array((0.95047, 1., 1.08883), dtype=out.dtype)
-    
+    xyz *= np.array((0.95047, 1., 1.08883), dtype=xyz.dtype)
+
     # convert XYZ -> RGB color domain
-    arr = out.copy()
-    arr = np.dot(arr, _xyz2rgb.T)
-    mask = arr > 0.0031308
-    arr[mask] = 1.055 * np.power(arr[mask], 1 / 2.4) - 0.055
-    arr[~mask] *= 12.92
-    return np.clip(arr, 0, 1)
+    rgb = np.matmul(xyz, _xyz2rgb.T)
+
+    mask = rgb > 0.0031308
+    rgb[mask] = 1.055 * np.power(rgb[mask], 1 / 2.4) - 0.055
+    rgb[~mask] *= 12.92
+
+    return np.clip(rgb, 0, 1)

torchstain/torch/normalizers/macenko.py

@@ -14,7 +14,9 @@ def __init__(self):
                                    [0.7201, 0.8012],
                                    [0.4062, 0.5581]])
         self.maxCRef = torch.tensor([1.9705, 1.0308])
-        self.deprecated_torch = torch.__version__ < (1,9,0)
+
+        # Avoid using deprecated torch.lstsq (since 1.9.0)
+        self.updated_lstsq = hasattr(torch.linalg, 'lstsq')
 
     def __convert_rgb2od(self, I, Io, beta):
         I = I.permute(1, 2, 0)
@@ -50,7 +52,7 @@ def __find_concentration(self, OD, HE):
         Y = OD.T
 
         # determine concentrations of the individual stains
-        if self.deprecated_torch:
+        if not self.updated_lstsq:
             return torch.lstsq(Y, HE)[0][:2]
 
         return torch.linalg.lstsq(HE, Y)[0]