tests: fix torch preprocessing tests

.github/workflows/check.yml

@@ -31,7 +31,7 @@ jobs:
       run: |
         # https://github.com/luispedro/mahotas/issues/144
         pip install mahotas==1.4.13
-        pip install -e .
+        pip install .[torch]
     - name: List installed packages
       run: |
         pip freeze

CHANGELOG

@@ -1,5 +1,6 @@
 0.23.1
  - enh: support passing custom default arguments to get_class_method_info
+ - tests: fix torch preprocessing tests
 0.23.0
  - feat: implement segmentation using PyTorch models
  - fix: always compute image_bg if it is not in the input file

src/dcnum/segm/segm_torch/torch_preproc.py

@@ -4,8 +4,8 @@
 
 
 def preprocess_images(images: np.ndarray,
-                      norm_mean: float,
-                      norm_std: float,
+                      norm_mean: float | None,
+                      norm_std: float | None,
                       image_shape: Tuple[int, int] = None,
                       ):
     """Transform image data to something torch models expect
@@ -24,10 +24,11 @@ def preprocess_images(images: np.ndarray,
         2D image, it will be reshaped to a 3D image with a batch_size of 1.
     norm_mean:
         Mean value used for standard score data normalization, i.e.
-        `normalized = `(images / 255 - norm_mean) / norm_std`
+        `normalized = `(images / 255 - norm_mean) / norm_std`; Set
+        to None to disable normalization.
     norm_std:
-        Standard deviation used for standard score data normalization
-        (see above)
+        Standard deviation used for standard score data normalization;
+        Set to None to disable normalization (see above).
     image_shape
         Image shape for which the model was created (height, width).
         If the image shape does not match the input image shape, then
@@ -102,8 +103,12 @@ def preprocess_images(images: np.ndarray,
         # Replace img_norm
         img_proc = img_pad
 
-    # normalize images
-    img_norm = (img_proc.astype(np.float32) / 255 - norm_mean) / norm_std
+    if norm_mean is None or norm_std is None:
+        # convert to float32
+        img_norm = img_proc.astype(np.float32)
+    else:
+        # normalize images
+        img_norm = (img_proc.astype(np.float32) / 255 - norm_mean) / norm_std
 
     # Add a "channels" axis for the ML models.
     return img_norm[:, np.newaxis, :, :]

tests/test_segm_torch_preproc.py

@@ -11,8 +11,8 @@ def test_reshape_crop_both():
     image = np.arange(64, dtype=np.float32).reshape(8, 8)
     out = torch_preproc.preprocess_images(images=image,
                                           image_shape=(6, 6),
-                                          norm_std=1,
-                                          norm_mean=1,
+                                          norm_std=None,
+                                          norm_mean=None,
                                           )
     assert out.shape == (1, 1, 6, 6)
     imout = out[0, 0, :, :]
@@ -23,8 +23,8 @@ def test_reshape_crop_height():
     image = np.arange(64, dtype=np.float32).reshape(8, 8)
     out = torch_preproc.preprocess_images(images=image,
                                           image_shape=(6, 8),
-                                          norm_std=1,
-                                          norm_mean=1,
+                                          norm_std=None,
+                                          norm_mean=None,
                                           )
     assert out.shape == (1, 1, 6, 8)
     imout = out[0, 0, :, :]
@@ -35,8 +35,8 @@ def test_reshape_crop_width():
     image = np.arange(64, dtype=np.float32).reshape(8, 8)
     out = torch_preproc.preprocess_images(images=image,
                                           image_shape=(8, 6),
-                                          norm_std=1,
-                                          norm_mean=1,
+                                          norm_std=None,
+                                          norm_mean=None,
                                           )
     assert out.shape == (1, 1, 8, 6)
     imout = out[0, 0, :, :]
@@ -47,8 +47,8 @@ def test_reshape_pad_both():
     image = np.arange(64, dtype=np.float32).reshape(8, 8)
     out = torch_preproc.preprocess_images(images=image,
                                           image_shape=(10, 10),
-                                          norm_std=1,
-                                          norm_mean=1,
+                                          norm_std=None,
+                                          norm_mean=None,
                                           )
     assert out.shape == (1, 1, 10, 10)
     imout = out[0, 0, :, :]
@@ -64,8 +64,8 @@ def test_reshape_pad_height():
     image = np.arange(64, dtype=np.float32).reshape(8, 8)
     out = torch_preproc.preprocess_images(images=image,
                                           image_shape=(10, 8),
-                                          norm_std=1,
-                                          norm_mean=1,
+                                          norm_std=None,
+                                          norm_mean=None,
                                           )
     assert out.shape == (1, 1, 10, 8)
     imout = out[0, 0, :, :]
@@ -79,8 +79,8 @@ def test_reshape_pad_width_crop_height():
     image = np.arange(64, dtype=np.float32).reshape(8, 8)
     out = torch_preproc.preprocess_images(images=image,
                                           image_shape=(6, 10),
-                                          norm_std=1,
-                                          norm_mean=1,
+                                          norm_std=None,
+                                          norm_mean=None,
                                           )
     assert out.shape == (1, 1, 6, 10)
     imout = out[0, 0, :, :]