Make transforms optional

monai/data/dataset.py

@@ -669,7 +669,7 @@ class CacheDataset(Dataset):
     def __init__(
         self,
         data: Sequence,
-        transform: Union[Sequence[Callable], Callable],
+        transform: Optional[Union[Sequence[Callable], Callable]] = None,
         cache_num: int = sys.maxsize,
         cache_rate: float = 1.0,
         num_workers: Optional[int] = 1,
@@ -856,7 +856,7 @@ class SmartCacheDataset(Randomizable, CacheDataset):
     Args:
         data: input data to load and transform to generate dataset for model.
         transform: transforms to execute operations on input data.
-        replace_rate: percentage of the cached items to be replaced in every epoch.
+        replace_rate: percentage of the cached items to be replaced in every epoch (default to 0.1).
         cache_num: number of items to be cached. Default is `sys.maxsize`.
             will take the minimum of (cache_num, data_length x cache_rate, data_length).
         cache_rate: percentage of cached data in total, default is 1.0 (cache all).
@@ -883,8 +883,8 @@ class SmartCacheDataset(Randomizable, CacheDataset):
     def __init__(
         self,
         data: Sequence,
-        transform: Union[Sequence[Callable], Callable],
-        replace_rate: float,
+        transform: Optional[Union[Sequence[Callable], Callable]] = None,
+        replace_rate: float = 0.1,
         cache_num: int = sys.maxsize,
         cache_rate: float = 1.0,
         num_init_workers: Optional[int] = 1,

tests/test_cachedataset.py

@@ -55,6 +55,12 @@ def test_shape(self, transform, expected_shape):
             data4 = dataset[-1]
             self.assertEqual(len(data3), 1)
 
+            if transform is None:
+                # Check without providing transfrom
+                dataset2 = CacheDataset(data=test_data, cache_rate=0.5, as_contiguous=True)
+                for k in ["image", "label", "extra"]:
+                    self.assertEqual(dataset[0][k], dataset2[0][k])
+
         if transform is None:
             self.assertEqual(data1["image"], os.path.join(tempdir, "image1.nii.gz"))
             self.assertEqual(data2["label"], os.path.join(tempdir, "label2.nii.gz"))

tests/test_smartcachedataset.py

@@ -56,6 +56,17 @@ def test_shape(self, replace_rate, num_replace_workers, transform):
                 num_init_workers=4,
                 num_replace_workers=num_replace_workers,
             )
+            if transform is None:
+                # Check without providing transfrom
+                dataset2 = SmartCacheDataset(
+                    data=test_data,
+                    replace_rate=replace_rate,
+                    cache_num=16,
+                    num_init_workers=4,
+                    num_replace_workers=num_replace_workers,
+                )
+                for k in ["image", "label", "extra"]:
+                    self.assertEqual(dataset[0][k], dataset2[0][k])
 
             self.assertEqual(len(dataset._cache), dataset.cache_num)
             for i in range(dataset.cache_num):

tests/test_wsireader.py

@@ -84,7 +84,9 @@
 
 TEST_CASE_RGB_1 = [np.ones((3, 100, 100), dtype=np.uint8)]  # CHW
 
-TEST_CASE_ERROR_GRAY = [np.ones((16, 16, 2), dtype=np.uint8)]  # wrong color channel
+TEST_CASE_ERROR_0C = [np.ones((16, 16), dtype=np.uint8)]  # no color channel
+TEST_CASE_ERROR_1C = [np.ones((16, 16, 1), dtype=np.uint8)]  # one color channel
+TEST_CASE_ERROR_2C = [np.ones((16, 16, 2), dtype=np.uint8)]  # two color channels
 TEST_CASE_ERROR_3D = [np.ones((16, 16, 16, 3), dtype=np.uint8)]  # 3D + color
 
 
@@ -106,20 +108,6 @@ def save_rgba_tiff(array: np.ndarray, filename: str, mode: str):
     return filename
 
 
-def save_gray_tiff(array: np.ndarray, filename: str):
-    """
-    Save numpy array into a TIFF file
-
-    Args:
-        array: numpy ndarray with any shape
-        filename: the filename to be used for the tiff file.
-    """
-    img_gray = array
-    imwrite(filename, img_gray, shape=img_gray.shape, photometric="minisblack")
-
-    return filename
-
-
 @skipUnless(has_cucim or has_osl or has_tiff, "Requires cucim, openslide, or tifffile!")
 def setUpModule():  # noqa: N802
     hash_type = testing_data_config("images", FILE_KEY, "hash_type")
@@ -187,13 +175,15 @@ def test_read_rgba(self, img_expected):
             self.assertIsNone(assert_array_equal(image["RGB"], img_expected))
             self.assertIsNone(assert_array_equal(image["RGBA"], img_expected))
 
-        @parameterized.expand([TEST_CASE_ERROR_GRAY, TEST_CASE_ERROR_3D])
+        @parameterized.expand([TEST_CASE_ERROR_0C, TEST_CASE_ERROR_1C, TEST_CASE_ERROR_2C, TEST_CASE_ERROR_3D])
         @skipUnless(has_tiff, "Requires tifffile.")
         def test_read_malformats(self, img_expected):
+            if self.backend == "cucim" and (len(img_expected.shape) < 3 or img_expected.shape[2] == 1):
+                # Until cuCIM addresses https://github.com/rapidsai/cucim/issues/230
+                return
             reader = WSIReader(self.backend)
-            file_path = save_gray_tiff(
-                img_expected, os.path.join(os.path.dirname(__file__), "testing_data", "temp_tiff_image_gray.tiff")
-            )
+            file_path = os.path.join(os.path.dirname(__file__), "testing_data", "temp_tiff_image_gray.tiff")
+            imwrite(file_path, img_expected, shape=img_expected.shape)
             with self.assertRaises((RuntimeError, ValueError, openslide.OpenSlideError if has_osl else ValueError)):
                 with reader.read(file_path) as img_obj:
                     reader.get_data(img_obj)

tests/test_wsireader_new.py

@@ -72,7 +72,9 @@
 
 TEST_CASE_RGB_1 = [np.ones((3, 100, 100), dtype=np.uint8)]  # CHW
 
-TEST_CASE_ERROR_GRAY = [np.ones((16, 16, 2), dtype=np.uint8)]  # wrong color channel
+TEST_CASE_ERROR_0C = [np.ones((16, 16), dtype=np.uint8)]  # no color channel
+TEST_CASE_ERROR_1C = [np.ones((16, 16, 1), dtype=np.uint8)]  # one color channel
+TEST_CASE_ERROR_2C = [np.ones((16, 16, 2), dtype=np.uint8)]  # two color channels
 TEST_CASE_ERROR_3D = [np.ones((16, 16, 16, 3), dtype=np.uint8)]  # 3D + color
 
 
@@ -103,7 +105,7 @@ def save_gray_tiff(array: np.ndarray, filename: str):
         filename: the filename to be used for the tiff file.
     """
     img_gray = array
-    imwrite(filename, img_gray, shape=img_gray.shape, photometric="minisblack")
+    imwrite(filename, img_gray, shape=img_gray.shape)
 
     return filename
 
@@ -180,13 +182,15 @@ def test_read_rgba(self, img_expected):
             self.assertIsNone(assert_array_equal(image["RGB"], img_expected))
             self.assertIsNone(assert_array_equal(image["RGBA"], img_expected))
 
-        @parameterized.expand([TEST_CASE_ERROR_GRAY, TEST_CASE_ERROR_3D])
+        @parameterized.expand([TEST_CASE_ERROR_0C, TEST_CASE_ERROR_1C, TEST_CASE_ERROR_2C, TEST_CASE_ERROR_3D])
         @skipUnless(has_tiff, "Requires tifffile.")
         def test_read_malformats(self, img_expected):
+            if self.backend == "cucim" and (len(img_expected.shape) < 3 or img_expected.shape[2] == 1):
+                # Until cuCIM addresses https://github.com/rapidsai/cucim/issues/230
+                return
             reader = WSIReader(self.backend)
-            file_path = save_gray_tiff(
-                img_expected, os.path.join(os.path.dirname(__file__), "testing_data", "temp_tiff_image_gray.tiff")
-            )
+            file_path = os.path.join(os.path.dirname(__file__), "testing_data", "temp_tiff_image_gray.tiff")
+            imwrite(file_path, img_expected, shape=img_expected.shape)
             with self.assertRaises((RuntimeError, ValueError, openslide.OpenSlideError if has_osl else ValueError)):
                 with reader.read(file_path) as img_obj:
                     reader.get_data(img_obj)