Misc. documentation improvements

docs/conf.py

@@ -200,6 +200,10 @@ def setup(app: 'Sphinx') -> None:
         'https://pytorch.org/docs/stable/',
         'https://pytorch.org/docs/stable/objects.inv',
     ),
+    'onnx': (
+        'https://onnx.ai/onnx/',
+        'https://onnx.ai/onnx/objects.inv',
+    ),
 }
 
 #########################

rastervision_core/rastervision/core/data/crs_transformer/rasterio_crs_transformer.py

@@ -1,4 +1,4 @@
-from typing import Any, Optional
+from typing import Any, Optional, Union
 from pyproj import Transformer
 
 import numpy as np
@@ -99,10 +99,16 @@ def _pixel_to_map(self, pixel_point):
         return map_point
 
     @classmethod
-    def from_dataset(cls,
-                     dataset,
-                     map_crs: Optional[str] = 'epsg:4326',
-                     **kwargs) -> 'RasterioCRSTransformer':
+    def from_dataset(
+            cls, dataset: Any, map_crs: Optional[str] = 'epsg:4326', **kwargs
+    ) -> Union[IdentityCRSTransformer, 'RasterioCRSTransformer']:
+        """Build from rasterio dataset.
+
+        Args:
+            dataset (Any): Rasterio dataset.
+            map_crs (Optional[str]): Target map CRS. Defaults to 'epsg:4326'.
+            **kwargs: Extra args for :meth:`.__init__`.
+        """
         transform = dataset.transform
         image_crs = None if dataset.crs is None else dataset.crs.wkt
         map_crs = image_crs if map_crs is None else map_crs
@@ -118,7 +124,14 @@ def from_dataset(cls,
         return cls(transform, image_crs, map_crs, **kwargs)
 
     @classmethod
-    def from_uri(cls, uri: str, map_crs: Optional[str] = 'epsg:4326',
-                 **kwargs) -> 'RasterioCRSTransformer':
+    def from_uri(cls, uri: str, map_crs: Optional[str] = 'epsg:4326', **kwargs
+                 ) -> Union[IdentityCRSTransformer, 'RasterioCRSTransformer']:
+        """Build from raster URI.
+
+        Args:
+            uri (Any): Raster URI.
+            map_crs (Optional[str]): Target map CRS. Defaults to 'epsg:4326'.
+            **kwargs: Extra args for :meth:`.__init__`.
+        """
         with rio.open(uri) as ds:
             return cls.from_dataset(ds, map_crs=map_crs, **kwargs)

rastervision_core/rastervision/core/data/label/labels.py

@@ -1,9 +1,10 @@
 """Defines the abstract Labels class."""
 
-from typing import TYPE_CHECKING, Any, Iterable
+from typing import TYPE_CHECKING, Any, Iterable, List
 from abc import (ABC, abstractclassmethod, abstractmethod)
 
 if TYPE_CHECKING:
+    from shapely.geometry import Polygon
     from rastervision.core.box import Box
 
 
@@ -15,24 +16,25 @@ class Labels(ABC):
     """
 
     @abstractmethod
-    def __add__(self, other):
+    def __add__(self, other: 'Labels'):
         """Add labels to these labels.
 
         Returns a concatenation of this and the other labels.
         """
         pass
 
     @abstractmethod
-    def filter_by_aoi(self, aoi_polygons):
-        """Returns a copy of these labels filtered by a given set of AOI polygons
+    def filter_by_aoi(self, aoi_polygons: List['Polygon']) -> 'Labels':
+        """Return a copy of these labels filtered by given AOI polygons.
 
         Args:
-          aoi_polygons - A list of AOI polygons to filter by, in pixel coordinates.
+          aoi_polygons: List of AOI polygons to filter by, in pixel
+            coordinates.
         """
         pass
 
     @abstractmethod
-    def __eq__(self, other):
+    def __eq__(self, other: 'Labels'):
         pass
 
     @abstractmethod

rastervision_core/rastervision/core/data/label/object_detection_labels.py

@@ -4,7 +4,7 @@
 
 from rastervision.core.box import Box
 from rastervision.core.data.label.labels import Labels
-from rastervision.core.data.label.tfod_utils.np_box_list import BoxList
+from rastervision.core.data.label.tfod_utils.np_box_list import NpBoxList
 from rastervision.core.data.label.tfod_utils.np_box_list_ops import (
     prune_non_overlapping_boxes, clip_to_window, concatenate,
     non_max_suppression)
@@ -33,7 +33,7 @@ def __init__(self,
             class_ids: int numpy array of size n with class ids
             scores: float numpy array of size n
         """
-        self.boxlist = BoxList(npboxes)
+        self.boxlist = NpBoxList(npboxes)
         # This field name actually needs to be 'classes' to be able to use
         # certain utility functions in the TF Object Detection API.
         self.boxlist.add_field('classes', class_ids)
@@ -103,7 +103,7 @@ def make_empty(cls) -> 'ObjectDetectionLabels':
         return cls(npboxes, class_ids, scores)
 
     @staticmethod
-    def from_boxlist(boxlist: BoxList):
+    def from_boxlist(boxlist: NpBoxList):
         """Make ObjectDetectionLabels from BoxList object."""
         scores = (boxlist.get_field('scores')
                   if boxlist.has_field('scores') else None)
@@ -166,7 +166,7 @@ def __len__(self) -> int:
     def __str__(self) -> str:
         return str(self.boxlist.get())
 
-    def to_boxlist(self) -> BoxList:
+    def to_boxlist(self) -> NpBoxList:
         return self.boxlist
 
     def to_dict(self, round_boxes: bool = True) -> dict:
@@ -245,7 +245,7 @@ def get_overlapping(labels: 'ObjectDetectionLabels',
             clip: If True, clip label boxes to the window.
         """
         window_npbox = window.npbox_format()
-        window_boxlist = BoxList(np.expand_dims(window_npbox, axis=0))
+        window_boxlist = NpBoxList(np.expand_dims(window_npbox, axis=0))
         boxlist = prune_non_overlapping_boxes(
             labels.boxlist, window_boxlist, minoverlap=ioa_thresh)
         if clip:

rastervision_core/rastervision/core/data/label/semantic_segmentation_labels.py

@@ -11,6 +11,7 @@
 from rastervision.core.data.label.utils import discard_prediction_edges
 
 if TYPE_CHECKING:
+    from shapely.geometry import Polygon
     from rastervision.core.data import (ClassConfig, CRSTransformer,
                                         VectorOutputConfig)
 
@@ -66,21 +67,34 @@ def get_label_arr(self, window: Box,
         pass
 
     def get_windows(self, **kwargs) -> List[Box]:
-        """Generate sliding windows over the local extent. The keyword args
-        are passed to Box.get_windows() and can therefore be used to control
-        the specifications of the windows.
+        """Generate sliding windows over the local extent.
+
+        The keyword args are passed to :meth:`.Box.get_windows` and can
+        therefore be used to control the specifications of the windows.
 
         If the keyword args do not contain size, a list of length 1,
         containing the full extent is returned.
+
+        Args:
+            **kwargs: Extra args for :meth:`.Box.get_windows`.
         """
         size: Optional[int] = kwargs.pop('size', None)
         if size is None:
             return [self.extent]
         return self.extent.get_windows(size, size, **kwargs)
 
-    def filter_by_aoi(self, aoi_polygons: list, null_class_id: int,
+    def filter_by_aoi(self, aoi_polygons: List['Polygon'], null_class_id: int,
                       **kwargs) -> 'SemanticSegmentationLabels':
-        """Keep only the values that lie inside the AOI."""
+        """Keep only the values that lie inside the AOI.
+
+        Args:
+            aoi_polygons (List[Polygon]): AOI polygons to filter by, in pixel
+                coordinates.
+            null_class_id (int): Class ID to assign to pixels falling outside
+                the AOI polygons.
+            **kwargs: Extra args for
+                :meth:`.SemanticSegmentationLabels.get_windows`.
+        """
         if not aoi_polygons:
             return self
         for window in self.get_windows(**kwargs):
@@ -95,7 +109,7 @@ def mask_fill(self, window: Box, mask: np.ndarray,
         """
         pass
 
-    def _filter_window_by_aoi(self, window: Box, aoi_polygons: list,
+    def _filter_window_by_aoi(self, window: Box, aoi_polygons: List['Polygon'],
                               null_class_id: int) -> None:
         window_geom = window.to_shapely()
         label_arr = self[window]

rastervision_core/rastervision/core/data/label/tfod_utils/np_box_list.py

@@ -19,7 +19,7 @@
 import numpy as np
 
 
-class BoxList(object):
+class NpBoxList(object):
     """A list of bounding boxes as a [y_min, x_min, y_max, x_max] numpy array.
 
     It is assumed that all bounding boxes within a given list correspond to a