add test for MultiMacenkoNormalizer torch

tests/test_torch.py

@@ -57,6 +57,40 @@ def test_macenko_torch():
     # assess whether the normalized images are identical across backends
     np.testing.assert_almost_equal(result_numpy.flatten(), result_torch.flatten(), decimal=2, verbose=True)
 
+def test_multitarget_macenko_torch():
+    size = 1024
+    curr_file_path = os.path.dirname(os.path.realpath(__file__))
+    target = cv2.resize(cv2.cvtColor(cv2.imread(os.path.join(curr_file_path, "../data/target.png")), cv2.COLOR_BGR2RGB), (size, size))
+    to_transform = cv2.resize(cv2.cvtColor(cv2.imread(os.path.join(curr_file_path, "../data/source.png")), cv2.COLOR_BGR2RGB), (size, size))
+
+    # setup preprocessing and preprocess image to be normalized
+    T = transforms.Compose([
+        transforms.ToTensor(),
+        transforms.Lambda(lambda x: x * 255)
+    ])
+    target = T(target)
+    t_to_transform = T(to_transform)
+
+    # initialize normalizers for each backend and fit to target image
+    single_normalizer = torchstain.normalizers.MacenkoNormalizer(backend="torch")
+    single_normalizer.fit(target)
+
+    multi_normalizer = torchstain.normalizers.MultiMacenkoNormalizer(backend="torch", norm_mode="avg-post")
+    multi_normalizer.fit([target, target, target])
+
+
+    # transform
+    result_single, _, _ = single_normalizer.normalize(I=t_to_transform, stains=True)
+    result_multi, _, _ = multi_normalizer.normalize(I=t_to_transform, stains=True)
+
+    # convert to numpy and set dtype
+    result_single = result_single.numpy().astype("float32") / 255.
+    result_multi = result_multi.numpy().astype("float32") / 255.
+
+    # assess whether the normalized images are identical across backends
+    np.testing.assert_almost_equal(result_single.flatten(), result_multi.flatten(), decimal=2, verbose=True)
+
+
 def test_reinhard_torch():
     size = 1024
     curr_file_path = os.path.dirname(os.path.realpath(__file__))