Fixes several typos (#8712)

Hope this helps a bit 👍 @ericspod Thanks as always for reviewing!

---------

Signed-off-by: benediktjohannes <benedikt.johannes.hofer@gmail.com>
Co-authored-by: YunLiu <55491388+KumoLiu@users.noreply.github.com>
Co-authored-by: Eric Kerfoot <17726042+ericspod@users.noreply.github.com>

Author: 3 people

monai/data/image_writer.py

@@ -405,7 +405,7 @@ def set_data_array(
                 ``None`` indicates data without any channel dimension.
             squeeze_end_dims: if ``True``, any trailing singleton dimensions will be removed.
             kwargs: keyword arguments passed to ``self.convert_to_channel_last``,
-                currently support ``spatial_ndim`` and ``contiguous``, defauting to ``3`` and ``False`` respectively.
+                currently support ``spatial_ndim`` and ``contiguous``, defaulting to ``3`` and ``False`` respectively.
         """
         n_chns = data_array.shape[channel_dim] if channel_dim is not None else 0
         self.data_obj = self.convert_to_channel_last(
@@ -424,7 +424,7 @@ def set_data_array(
 
     def set_metadata(self, meta_dict: Mapping | None = None, resample: bool = True, **options):
         """
-        Resample ``self.dataobj`` if needed.  This method assumes ``self.data_obj`` is a 'channel-last' ndarray.
+        Resample ``self.data_obj`` if needed.  This method assumes ``self.data_obj`` is a 'channel-last' ndarray.
 
         Args:
             meta_dict: a metadata dictionary for affine, original affine and spatial shape information.
@@ -575,7 +575,7 @@ def set_data_array(
                 ``None`` indicates data without any channel dimension.
             squeeze_end_dims: if ``True``, any trailing singleton dimensions will be removed.
             kwargs: keyword arguments passed to ``self.convert_to_channel_last``,
-                currently support ``spatial_ndim``, defauting to ``3``.
+                currently support ``spatial_ndim``, defaulting to ``3``.
         """
         self.data_obj = self.convert_to_channel_last(
             data=data_array,
@@ -586,7 +586,7 @@ def set_data_array(
 
     def set_metadata(self, meta_dict: Mapping | None, resample: bool = True, **options):
         """
-        Resample ``self.dataobj`` if needed.  This method assumes ``self.data_obj`` is a 'channel-last' ndarray.
+        Resample ``self.data_obj`` if needed.  This method assumes ``self.data_obj`` is a 'channel-last' ndarray.
 
         Args:
             meta_dict: a metadata dictionary for affine, original affine and spatial shape information.
@@ -726,7 +726,7 @@ def set_data_array(
             squeeze_end_dims: if ``True``, any trailing singleton dimensions will be removed.
             contiguous: if ``True``, the data array will be converted to a contiguous array. Default is ``False``.
             kwargs: keyword arguments passed to ``self.convert_to_channel_last``,
-                currently support ``spatial_ndim``, defauting to ``2``.
+                currently support ``spatial_ndim``, defaulting to ``2``.
         """
         self.data_obj = self.convert_to_channel_last(
             data=data_array,
@@ -738,7 +738,7 @@ def set_data_array(
 
     def set_metadata(self, meta_dict: Mapping | None = None, resample: bool = True, **options):
         """
-        Resample ``self.dataobj`` if needed.  This method assumes ``self.data_obj`` is a 'channel-last' ndarray.
+        Resample ``self.data_obj`` if needed.  This method assumes ``self.data_obj`` is a 'channel-last' ndarray.
 
         Args:
             meta_dict: a metadata dictionary for affine, original affine and spatial shape information.

tests/data/test_cachedataset.py

@@ -57,7 +57,7 @@ def test_shape(self, transform, expected_shape):
             self.assertEqual(len(data3), 1)
 
             if transform is None:
-                # Check without providing transfrom
+                # Check without providing transform
                 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])

tests/data/test_smartcachedataset.py

@@ -61,7 +61,7 @@ def test_shape(self, replace_rate, num_replace_workers, transform):
                 num_replace_workers=num_replace_workers,
             )
             if transform is None:
-                # Check without providing transfrom
+                # Check without providing transform
                 dataset2 = SmartCacheDataset(
                     data=test_data,
                     replace_rate=replace_rate,