Fix typos (#2389)

requirements/min-reqs.old

@@ -26,6 +26,7 @@ h5py==3.6.0
 laspy==2.0.0
 opencv-python==4.5.4.58
 pycocotools==2.0.7
+pyarrow==15.0.0  # Remove when we upgrade min version of pandas to `pandas[parquet]>=2`
 pyvista==0.34.2
 scikit-image==0.19.0
 scipy==1.7.2

tests/data/digital_typhoon/data.py

@@ -78,7 +78,7 @@
         }
     )
 
-    # Save the DataFrame to correspoding typhoon id as metadata
+    # Save the DataFrame to corresponding typhoon id as metadata
     df.to_csv(os.path.join(root, 'metadata', f'{typhoon_id}.csv'), index=False)
 
     all_dfs.append(df)

torchgeo/datasets/biomassters.py

@@ -40,7 +40,7 @@ class BioMassters(NonGeoDataset):
     * Sentinel 1 and Sentinel 2 data for each location
     * Sentinel 1 available for every month
     * Sentinel 2 available for almost every month
-      (not available for every month due to ESA aquisition halt over the region
+      (not available for every month due to ESA acquisition halt over the region
       during particular periods)
 
     If you use this dataset in your research, please cite the following paper:

torchgeo/datasets/cms_mangrove_canopy.py

@@ -24,7 +24,7 @@ class CMSGlobalMangroveCanopy(RasterDataset):
     consists of a single band map at 30m resolution of either aboveground biomass (agb),
     basal area weighted height (hba95), or maximum canopy height (hmax95).
 
-    The dataset needs to be manually dowloaded from the above link, where you can make
+    The dataset needs to be manually downloaded from the above link, where you can make
     an account and subsequently download the dataset.
 
     .. versionadded:: 0.3

torchgeo/datasets/esri2020.py

@@ -41,7 +41,7 @@ class Esri2020(RasterDataset):
     9. Snow/Ice
     10. Clouds
 
-    A more detailed explanation of the invidual classes can be found
+    A more detailed explanation of the individual classes can be found
     `here <https://www.arcgis.com/home/item.html?id=fc92d38533d440078f17678ebc20e8e2>`_.
 
     If you use this dataset please cite the following paper:

torchgeo/datasets/forestdamage.py

@@ -206,7 +206,7 @@ def _load_target(
         """Load the target mask for a single image.
 
         Args:
-            bboxes: list of bbox coordinats [xmin, ymin, xmax, ymax]
+            bboxes: list of bbox coordinates [xmin, ymin, xmax, ymax]
             labels_list: list of class labels
 
         Returns:

torchgeo/datasets/geo.py

@@ -352,7 +352,7 @@ class RasterDataset(GeoDataset):
     #: Minimum timestamp if not in filename
     mint: float = 0
 
-    #: Maximum timestmap if not in filename
+    #: Maximum timestamp if not in filename
     maxt: float = sys.maxsize
 
     #: True if the dataset only contains model inputs (such as images). False if the

torchgeo/datasets/mapinwild.py

@@ -160,7 +160,7 @@ def __init__(
             ):
                 self._merge_parts(mode)
 
-        # Masks will be loaded seperately in the :meth:`__getitem__`
+        # Masks will be loaded separately in the :meth:`__getitem__`
         if 'mask' in self.modality:
             self.modality.remove('mask')
 

torchgeo/datasets/seasonet.py

@@ -39,7 +39,7 @@ class SeasoNet(NonGeoDataset):
 
     Dataset format:
 
-    * images are 16-bit GeoTiffs, split into seperate files based on resolution
+    * images are 16-bit GeoTiffs, split into separate files based on resolution
     * images include 12 spectral bands with 10, 20 and 60 m per pixel resolutions
     * masks are single-channel 8-bit GeoTiffs
 

torchgeo/datasets/skippd.py

@@ -91,7 +91,7 @@ def __init__(
         Args:
             root: root directory where dataset can be found
             split: one of "trainval", or "test"
-            task: one fo "nowcast", or "forecast"
+            task: one of "nowcast", or "forecast"
             transforms: a function/transform that takes an input sample
                 and returns a transformed version
             download: if True, download dataset and store it in the root directory

torchgeo/datasets/ssl4eo_benchmark.py

@@ -320,7 +320,7 @@ def _load_mask(self, path: Path) -> Tensor:
         Args:
             path: path to mask
 
-        Retuns:
+        Returns:
             mask
         """
         with rasterio.open(path) as src:

torchgeo/datasets/utils.py

@@ -536,7 +536,7 @@ def draw_semantic_segmentation_masks(
         colors: list of RGB int tuples, or color strings e.g. red, #FF00FF
 
     Returns:
-        a version of ``image`` overlayed with the colors given by ``mask`` and
+        a version of ``image`` overlaid with the colors given by ``mask`` and
             ``colors``
     """
     classes = torch.from_numpy(np.arange(len(colors) if colors else 0, dtype=np.uint8))

torchgeo/datasets/vhr10.py

@@ -195,7 +195,7 @@ def __init__(
 
         Args:
             root: root directory where dataset can be found
-            split: one of "postive" or "negative"
+            split: one of "positive" or "negative"
             transforms: a function/transform that takes input sample and its target as
                 entry and returns a transformed version
             download: if True, download dataset and store it in the root directory

torchgeo/models/dofa.py

@@ -428,7 +428,7 @@ def dofa_small_patch16_224(*args: Any, **kwargs: Any) -> DOFA:
 
     Args:
         *args: Additional arguments to pass to :class:`DOFA`.
-        **kwargs: Additional keywork arguments to pass to :class:`DOFA`.
+        **kwargs: Additional keyword arguments to pass to :class:`DOFA`.
 
     Returns:
         A DOFA small 16 model.
@@ -452,7 +452,7 @@ def dofa_base_patch16_224(
     Args:
         weights: Pre-trained model weights to use.
         *args: Additional arguments to pass to :class:`DOFA`.
-        **kwargs: Additional keywork arguments to pass to :class:`DOFA`.
+        **kwargs: Additional keyword arguments to pass to :class:`DOFA`.
 
     Returns:
         A DOFA base 16 model.
@@ -490,7 +490,7 @@ def dofa_large_patch16_224(
     Args:
         weights: Pre-trained model weights to use.
         *args: Additional arguments to pass to :class:`DOFA`.
-        **kwargs: Additional keywork arguments to pass to :class:`DOFA`.
+        **kwargs: Additional keyword arguments to pass to :class:`DOFA`.
 
     Returns:
         A DOFA large 16 model.
@@ -525,7 +525,7 @@ def dofa_huge_patch16_224(*args: Any, **kwargs: Any) -> DOFA:
 
     Args:
         *args: Additional arguments to pass to :class:`DOFA`.
-        **kwargs: Additional keywork arguments to pass to :class:`DOFA`.
+        **kwargs: Additional keyword arguments to pass to :class:`DOFA`.
 
     Returns:
         A DOFA huge 16 model.

torchgeo/models/rcf.py

@@ -78,7 +78,7 @@ def __init__(
 
         # We register the weight and bias tensors as "buffers". This does two things:
         # makes them behave correctly when we call .to(...) on the module, and makes
-        # them explicitely _not_ Parameters of the model (which might get updated) if
+        # them explicitly _not_ Parameters of the model (which might get updated) if
         # a user tries to train with this model.
         self.register_buffer(
             'weights',

torchgeo/models/resnet.py

@@ -760,7 +760,7 @@ def resnet18(
     Args:
         weights: Pre-trained model weights to use.
         *args: Additional arguments to pass to :func:`timm.create_model`
-        **kwargs: Additional keywork arguments to pass to :func:`timm.create_model`
+        **kwargs: Additional keyword arguments to pass to :func:`timm.create_model`
 
     Returns:
         A ResNet-18 model.
@@ -795,7 +795,7 @@ def resnet50(
     Args:
         weights: Pre-trained model weights to use.
         *args: Additional arguments to pass to :func:`timm.create_model`.
-        **kwargs: Additional keywork arguments to pass to :func:`timm.create_model`.
+        **kwargs: Additional keyword arguments to pass to :func:`timm.create_model`.
 
     Returns:
         A ResNet-50 model.
@@ -829,7 +829,7 @@ def resnet152(
     Args:
         weights: Pre-trained model weights to use.
         *args: Additional arguments to pass to :func:`timm.create_model`.
-        **kwargs: Additional keywork arguments to pass to :func:`timm.create_model`.
+        **kwargs: Additional keyword arguments to pass to :func:`timm.create_model`.
 
     Returns:
         A ResNet-152 model.

torchgeo/models/swin.py

@@ -266,7 +266,7 @@ def swin_v2_t(
         weights: Pre-trained model weights to use.
         *args: Additional arguments to
             pass to :class:`torchvision.models.swin_transformer.SwinTransformer`.
-        **kwargs: Additional keywork arguments to
+        **kwargs: Additional keyword arguments to
             pass to :class:`torchvision.models.swin_transformer.SwinTransformer`.
 
     Returns:
@@ -305,7 +305,7 @@ def swin_v2_b(
         weights: Pre-trained model weights to use.
         *args: Additional arguments to
             pass to :class:`torchvision.models.swin_transformer.SwinTransformer`.
-        **kwargs: Additional keywork arguments to
+        **kwargs: Additional keyword arguments to
             pass to :class:`torchvision.models.swin_transformer.SwinTransformer`.
 
     Returns:

torchgeo/models/vit.py

@@ -235,7 +235,7 @@ def vit_small_patch16_224(
     Args:
         weights: Pre-trained model weights to use.
         *args: Additional arguments to pass to :func:`timm.create_model`.
-        **kwargs: Additional keywork arguments to pass to :func:`timm.create_model`.
+        **kwargs: Additional keyword arguments to pass to :func:`timm.create_model`.
 
     Returns:
         A ViT small 16 model.

torchgeo/trainers/byol.py

@@ -66,7 +66,7 @@ def __init__(self, image_size: tuple[int, int] = (256, 256)) -> None:
         )
 
     def forward(self, x: Tensor) -> Tensor:
-        """Applys SimCLR augmentations to the input tensor.
+        """Applies SimCLR augmentations to the input tensor.
 
         Args:
             x: a batch of imagery
@@ -137,7 +137,7 @@ def __init__(
 
         Args:
             model: model to encode
-            projection_size: size of the ouput layer of the projector MLP
+            projection_size: size of the output layer of the projector MLP
             hidden_size: size of hidden layer of the projector MLP
             layer: layer from model to project
         """