Misc. minor fixes

.vscode/settings.json

@@ -1,3 +1,6 @@
 {
-    "python.dataScience.jupyterServerURI": "http://localhost:8888/?token=8a748bee8b02072f3212cf087d8497af6bb0d1598de505ca"
-}
+    "python.dataScience.jupyterServerURI": "http://localhost:8888/?token=8a748bee8b02072f3212cf087d8497af6bb0d1598de505ca",
+    "[python]": {
+        "editor.defaultFormatter": "eeyore.yapf"
+    }
+}

rastervision_core/rastervision/core/analyzer/analyzer.py

@@ -13,4 +13,3 @@ class Analyzer(ABC):
     @abstractmethod
     def process(self, scenes: List[Scene], tmp_dir: str):
         """Process scenes and save result."""
-        pass

rastervision_core/rastervision/core/backend/backend.py

@@ -17,7 +17,6 @@ class SampleWriter(AbstractContextManager):
     @abstractmethod
     def write_sample(self, sample: 'DataSample'):
         """Writes a single sample."""
-        pass
 
 
 class Backend(ABC):
@@ -30,7 +29,6 @@ class Backend(ABC):
     @abstractmethod
     def get_sample_writer(self):
         """Returns a SampleWriter for this Backend."""
-        pass
 
     @abstractmethod
     def train(self):
@@ -39,7 +37,6 @@ def train(self):
         This should download chips created by the SampleWriter, train the model, and
         then saving it to disk.
         """
-        pass
 
     @abstractmethod
     def load_model(self, uri: Optional[str] = None):
@@ -48,7 +45,6 @@ def load_model(self, uri: Optional[str] = None):
         Args:
             uri: Optional URI to load the model from.
         """
-        pass
 
     @abstractmethod
     def predict_scene(self, scene: 'Scene', chip_sz: int,
@@ -61,4 +57,3 @@ def predict_scene(self, scene: 'Scene', chip_sz: int,
         Return:
             Labels object containing predictions
         """
-        pass

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

@@ -167,7 +167,6 @@ def _map_to_pixel(self, point: Tuple[float, float]) -> Tuple[int, int]:
         Returns:
             Tuple[int, int]: (x, y) tuple in pixel coordinates.
         """
-        pass
 
     @abstractmethod
     def _pixel_to_map(self, point: Tuple[float, float]) -> Tuple[float, float]:
@@ -180,4 +179,3 @@ def _pixel_to_map(self, point: Tuple[float, float]) -> Tuple[float, float]:
         Returns:
             Tuple[float, float]: (x, y) tuple in map coordinates (eg. lon/lat).
         """
-        pass

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

@@ -21,7 +21,6 @@ def __add__(self, other: 'Labels'):
 
         Returns a concatenation of this and the other labels.
         """
-        pass
 
     @abstractmethod
     def filter_by_aoi(self, aoi_polygons: List['Polygon']) -> 'Labels':
@@ -31,7 +30,6 @@ def filter_by_aoi(self, aoi_polygons: List['Polygon']) -> 'Labels':
           aoi_polygons: List of AOI polygons to filter by, in pixel
             coordinates.
         """
-        pass
 
     @abstractmethod
     def __eq__(self, other: 'Labels'):
@@ -49,7 +47,6 @@ def make_empty(cls) -> 'Labels':
             Labels: An object of the Label subclass on which this method is
             called.
         """
-        pass
 
     @classmethod
     def from_predictions(cls, windows: Iterable['Box'],
@@ -80,4 +77,3 @@ def from_predictions(cls, windows: Iterable['Box'],
     @abstractmethod
     def save(self, uri: str) -> None:
         """Save to file."""
-        pass

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

@@ -35,7 +35,6 @@ def __init__(self, extent: Box, num_classes: int, dtype: np.dtype):
     def __add__(self, other: 'SemanticSegmentationLabels'
                 ) -> 'SemanticSegmentationLabels':
         """Merge self with other labels."""
-        pass
 
     def __setitem__(self, window: Box, values: np.ndarray) -> None:
         """Set labels for the given window."""
@@ -44,17 +43,14 @@ def __setitem__(self, window: Box, values: np.ndarray) -> None:
     @abstractmethod
     def __delitem__(self, window: Box) -> None:
         """Delete labels for the given window."""
-        pass
 
     @abstractmethod
     def __getitem__(self, window: Box) -> np.ndarray:
         """Get labels for the given window."""
-        pass
 
     @abstractmethod
     def add_window(self, window: Box, values: np.ndarray) -> List[Box]:
         """Set labels for the given window."""
-        pass
 
     @abstractmethod
     def get_label_arr(self, window: Box,
@@ -64,7 +60,6 @@ def get_label_arr(self, window: Box,
         Note: The returned array is not guaranteed to be the same size as the
         input window.
         """
-        pass
 
     def get_windows(self, **kwargs) -> List[Box]:
         """Generate sliding windows over the local extent.
@@ -109,7 +104,6 @@ def mask_fill(self, window: Box, mask: np.ndarray,
         """Given a window and a binary mask, set all the pixels in the window
         for which the mask is ON to the fill_value.
         """
-        pass
 
     def _filter_window_by_aoi(self, window: Box, aoi_polygons: List['Polygon'],
                               null_class_id: int) -> None:

rastervision_core/rastervision/core/data/label_source/label_source.py

@@ -1,5 +1,5 @@
 from typing import TYPE_CHECKING, Any, Optional
-from abc import ABC, abstractmethod, abstractproperty
+from abc import ABC, abstractmethod
 
 from rastervision.core.box import Box
 from rastervision.core.data.utils import parse_array_slices_2d
@@ -28,21 +28,21 @@ def get_labels(self, window: Optional['Box'] = None) -> 'Labels':
             Labels overlapping with window. If window is None, returns all
             labels.
         """
-        pass
 
     @property
     def extent(self) -> 'Box':
         """Extent of the ``LabelSource``."""
         return self.bbox.extent
 
-    @abstractproperty
+    @property
+    @abstractmethod
     def bbox(self) -> 'Box':
         """Bounding box applied to the source."""
-        pass
 
-    @abstractproperty
+    @property
+    @abstractmethod
     def crs_transformer(self) -> 'CRSTransformer':
-        pass
+        """Associated :class:`.CRSTransformer`."""
 
     @abstractmethod
     def set_bbox(self, extent: 'Box') -> None:
@@ -53,7 +53,6 @@ def set_bbox(self, extent: 'Box') -> None:
         Args:
             extent (Box): User-specified extent in pixel coordinates.
         """
-        pass
 
     def __getitem__(self, key: Any) -> Any:
         if isinstance(key, Box):

rastervision_core/rastervision/core/data/label_store/label_store.py

@@ -1,5 +1,5 @@
 from typing import TYPE_CHECKING, Optional
-from abc import ABC, abstractmethod, abstractproperty
+from abc import ABC, abstractmethod
 
 if TYPE_CHECKING:
     from rastervision.core.box import Box
@@ -17,20 +17,20 @@ def save(self, labels):
            labels - Labels to be saved, the type of which will be dependent on the type
                     of pipeline.
         """
-        pass
 
     @abstractmethod
     def get_labels(self):
         """Loads Labels from this label store."""
-        pass
 
-    @abstractproperty
+    @property
+    @abstractmethod
     def bbox(self) -> Optional['Box']:
-        pass
+        """Bounding box applied to the source."""
 
-    @abstractproperty
+    @property
+    @abstractmethod
     def crs_transformer(self) -> 'CRSTransformer':
-        pass
+        """Associated :class:`.CRSTransformer`."""
 
     @abstractmethod
     def set_bbox(self, extent: 'Box') -> None:
@@ -41,4 +41,3 @@ def set_bbox(self, extent: 'Box') -> None:
         Args:
             extent (Box): User-specified extent in pixel coordinates.
         """
-        pass

rastervision_core/rastervision/core/data/label_store/semantic_segmentation_label_store.py

@@ -250,7 +250,7 @@ def save(self,
             self.write_smooth_raster_output(out_profile, scores_path,
                                             hits_path, labels)
 
-        if self.vector_outputs is not None:
+        if self.vector_outputs:
             vector_output_dir = get_local_path(self.vector_output_uri,
                                                self.tmp_dir)
             self.write_vector_outputs(labels, vector_output_dir)

rastervision_core/rastervision/core/data/raster_source/raster_source.py

@@ -1,5 +1,5 @@
 from typing import TYPE_CHECKING, Any, List, Optional, Tuple
-from abc import ABC, abstractmethod, abstractproperty
+from abc import ABC, abstractmethod
 
 import numpy as np
 from skimage.transform import resize
@@ -65,10 +65,10 @@ def shape(self) -> Tuple[int, int, int]:
         H, W = self.bbox.size
         return H, W, self.num_channels
 
-    @abstractproperty
+    @property
+    @abstractmethod
     def dtype(self) -> 'np.dtype':
         """``numpy.dtype`` of the chips read from this source."""
-        pass
 
     @property
     def bbox(self) -> 'Box':
@@ -80,10 +80,10 @@ def extent(self) -> 'Box':
         """Extent of the ``RasterSource``."""
         return self.bbox.extent
 
-    @abstractproperty
+    @property
+    @abstractmethod
     def crs_transformer(self) -> 'CRSTransformer':
         """Associated :class:`.CRSTransformer`."""
-        pass
 
     def set_bbox(self, bbox: 'Box') -> None:
         """Set self.bbox to the given value.
@@ -109,7 +109,6 @@ def _get_chip(self,
         Returns:
             [height, width, channels] numpy array
         """
-        pass
 
     def __getitem__(self, key: Any) -> 'np.ndarray':
         if isinstance(key, Box):

rastervision_core/rastervision/core/data/raster_transformer/raster_transformer.py

@@ -23,4 +23,3 @@ def transform(self, chip: 'np.ndarray',
         Returns:
             (np.ndarray): Array of shape (..., H, W, C)
         """
-        pass

rastervision_core/rastervision/core/data/vector_source/vector_source.py

@@ -104,7 +104,6 @@ def get_geoms(self,
     @abstractmethod
     def _get_geojson(self) -> dict:
         """Return raw GeoJSON."""
-        pass
 
     def get_dataframe(self,
                       window: Optional[Box] = None,

rastervision_core/rastervision/core/data/vector_transformer/vector_transformer.py

@@ -38,4 +38,3 @@ def transform(self,
         Returns:
             dict: Transformed GeoJSON.
         """
-        pass

rastervision_core/rastervision/core/evaluation/classification_evaluation.py

@@ -151,4 +151,3 @@ def compute(self, ground_truth_labels, prediction_labels):
             ground_truth_labels: Ground Truth labels to evaluate against.
             prediction_labels: The predicted labels to evaluate.
         """
-        pass

rastervision_core/rastervision/core/evaluation/classification_evaluator_config.py

@@ -5,5 +5,3 @@
 @register_config('classification_evaluator')
 class ClassificationEvaluatorConfig(EvaluatorConfig):
     """Configure a :class:`.ClassificationEvaluator`."""
-
-    pass

rastervision_core/rastervision/core/evaluation/evaluation_item.py

@@ -10,7 +10,6 @@ def merge(self, other):
         This is used to average metrics over scenes. Merges by taking a
         weighted average (by gt_count) of the metrics.
         """
-        pass
 
     @abstractmethod
     def to_json(self) -> dict:

rastervision_core/rastervision/core/evaluation/evaluator.py

@@ -15,7 +15,6 @@ def process(self, scenes: Iterable['Scene']) -> None:
         Args:
             scenes (Iterable[Scene]): Scenes to evaluate.
         """
-        pass
 
     @abstractmethod
     def evaluate_scene(self, scene: 'Scene') -> Any:
@@ -30,7 +29,6 @@ def evaluate_scene(self, scene: 'Scene') -> Any:
         Returns:
             ClassificationEvaluation: The evaluation.
         """
-        pass
 
     @abstractmethod
     def evaluate_predictions(self, ground_truth: 'Labels',
@@ -44,4 +42,3 @@ def evaluate_predictions(self, ground_truth: 'Labels',
         Returns:
             Any: The evaluation.
         """
-        pass

rastervision_core/rastervision/core/rv_pipeline/semantic_segmentation.py

@@ -2,6 +2,7 @@
 import logging
 
 import numpy as np
+from numpy.random import random_sample
 
 from rastervision.core.box import Box
 from rastervision.core.rv_pipeline.rv_pipeline import RVPipeline
@@ -80,7 +81,7 @@ def should_use_window(window: Box) -> bool:
                     window, co.target_count_threshold, co.target_class_ids)
             if is_positive:
                 return True
-            keep_negative = np.random.sample() < co.negative_survival_prob
+            keep_negative = random_sample() < co.negative_survival_prob
             return keep_negative
 
     if co.window_method == SemanticSegmentationWindowMethod.sliding:

rastervision_pipeline/rastervision/pipeline/config.py

@@ -20,7 +20,6 @@
 
 class ConfigError(ValueError):
     """Exception raised for invalid configuration."""
-    pass
 
 
 class Config(BaseModel):
@@ -50,23 +49,20 @@ def update(self, *args, **kwargs):
         example, setting default values as a function of the values of other
         fields. The arguments to this method will vary depending on the type of Config.
         """
-        pass
 
     def build(self):
         """Build an instance of the corresponding type of object using this config.
 
         For example, BackendConfig will build a Backend object. The arguments to this
         method will vary depending on the type of Config.
         """
-        pass
 
     def validate_config(self):
         """Validate fields that should be checked after update is called.
 
         This is to complement the builtin validation that Pydantic performs at the time
         of object construction.
         """
-        pass
 
     def revalidate(self):
         """Re-validate an instantiated Config.