wip

rastervision_core/rastervision/core/box.py

@@ -1,6 +1,6 @@
 from typing import (TYPE_CHECKING, Callable, Dict, List, Literal, Optional,
                     Sequence, Tuple, Union)
-from pydantic import PositiveInt as PosInt, conint
+from pydantic import NonNegativeInt as NonNegInt, PositiveInt as PosInt
 import math
 import random
 
@@ -11,8 +11,6 @@
 
 from rastervision.pipeline.utils import repr_with_args
 
-NonNegInt = conint(ge=0)
-
 if TYPE_CHECKING:
     from shapely.geometry import MultiPolygon
 

rastervision_core/rastervision/core/data/class_config.py

@@ -1,7 +1,7 @@
-from typing import List, Optional, Tuple, Union
+from typing import List, Optional, Self, Tuple, Union
 
 from rastervision.pipeline.config import (Config, register_config, ConfigError,
-                                          Field, validator)
+                                          Field, model_validator)
 from rastervision.core.data.utils import color_to_triple, normalize_color
 
 DEFAULT_NULL_CLASS_NAME = 'null'
@@ -32,42 +32,43 @@ class ClassConfig(Config):
         'Config is part of a SemanticSegmentationConfig, a null class will be '
         'added automatically.')
 
-    @validator('colors', always=True)
-    def validate_colors(cls, v: Optional[List[Union[str, Tuple]]],
-                        values: dict) -> Optional[List[Union[str, Tuple]]]:
+    @model_validator(mode='after')
+    def validate_colors(self) -> Self:
         """Compare length w/ names. Also auto-generate if not specified."""
-        class_names = values['names']
-        class_colors = v
-        if class_colors is None:
-            class_colors = [color_to_triple() for _ in class_names]
-        elif len(class_names) != len(class_colors):
-            raise ConfigError(f'len(class_names) ({len(class_names)}) != '
-                              f'len(class_colors) ({len(class_colors)})\n'
-                              f'class_names: {class_names}\n'
-                              f'class_colors: {class_colors}')
-        return class_colors
-
-    @validator('null_class', always=True)
-    def validate_null_class(cls, v: Optional[str],
-                            values: dict) -> Optional[str]:
+        names = self.names
+        colors = self.colors
+        if colors is None:
+            self.colors = [color_to_triple() for _ in names]
+        elif len(names) != len(colors):
+            raise ConfigError(f'len(class_names) ({len(names)}) != '
+                              f'len(class_colors) ({len(colors)})\n'
+                              f'class_names: {names}\n'
+                              f'class_colors: {colors}')
+        return self
+
+    @model_validator(mode='after')
+    def validate_null_class(self) -> Self:
         """Check if in names. If 'null' in names, use it as null class."""
-        names = values['names']
-        if v is None:
+        names = self.names
+        null_class = self.null_class
+        if null_class is None:
             if DEFAULT_NULL_CLASS_NAME in names:
-                v = DEFAULT_NULL_CLASS_NAME
+                self.null_class = DEFAULT_NULL_CLASS_NAME
         else:
-            if v not in names:
+            if null_class not in names:
                 raise ConfigError(
-                    f'The null_class, "{v}", must be in list of class names.')
+                    f'The null_class, "{null_class}", must be in list of '
+                    'class names.')
 
             # edge case
             default_null_class_in_names = (DEFAULT_NULL_CLASS_NAME in names)
-            null_class_neq_default = (v != DEFAULT_NULL_CLASS_NAME)
+            null_class_neq_default = (null_class != DEFAULT_NULL_CLASS_NAME)
             if default_null_class_in_names and null_class_neq_default:
                 raise ConfigError(
                     f'"{DEFAULT_NULL_CLASS_NAME}" is in names but the '
-                    f'specified null_class is something else ("{v}").')
-        return v
+                    'specified null_class is something else '
+                    f'("{null_class}").')
+        return self
 
     def get_class_id(self, name: str) -> int:
         return self.names.index(name)

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

@@ -1,10 +1,10 @@
-from typing import Optional
+from typing import Optional, Self
 
 from rastervision.core.data.vector_source import (VectorSourceConfig)
 from rastervision.core.data.label_source import (LabelSourceConfig,
                                                  ChipClassificationLabelSource)
 from rastervision.pipeline.config import (ConfigError, register_config, Field,
-                                          validator, root_validator)
+                                          field_validator, model_validator)
 from rastervision.core.data.vector_transformer import (
     ClassInferenceTransformerConfig, BufferTransformerConfig)
 
@@ -62,7 +62,8 @@ class ChipClassificationLabelSourceConfig(LabelSourceConfig):
         description='If True, labels will not be populated automatically '
         'during initialization of the label source.')
 
-    @validator('vector_source')
+    @field_validator('vector_source')
+    @classmethod
     def ensure_required_transformers(
             cls, v: VectorSourceConfig) -> VectorSourceConfig:
         """Add class-inference and buffer transformers if absent."""
@@ -84,14 +85,14 @@ def ensure_required_transformers(
 
         return v
 
-    @root_validator(skip_on_failure=True)
-    def ensure_bg_class_id_if_inferring(cls, values: dict) -> dict:
-        infer_cells = values.get('infer_cells')
-        has_bg_class_id = values.get('background_class_id') is not None
+    @model_validator(mode='after')
+    def ensure_bg_class_id_if_inferring(self) -> Self:
+        infer_cells = self.infer_cells
+        has_bg_class_id = self.background_class_id is not None
         if infer_cells and not has_bg_class_id:
             raise ConfigError(
                 'background_class_id is required if infer_cells=True.')
-        return values
+        return self
 
     def build(self, class_config, crs_transformer, bbox=None,
               tmp_dir=None) -> ChipClassificationLabelSource:

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

@@ -3,7 +3,7 @@
 from rastervision.core.data.vector_source import VectorSourceConfig
 from rastervision.core.data.vector_transformer import (
     ClassInferenceTransformerConfig, BufferTransformerConfig)
-from rastervision.pipeline.config import register_config, validator
+from rastervision.pipeline.config import register_config, field_validator
 
 
 @register_config('object_detection_label_source')
@@ -12,7 +12,8 @@ class ObjectDetectionLabelSourceConfig(LabelSourceConfig):
 
     vector_source: VectorSourceConfig
 
-    @validator('vector_source')
+    @field_validator('vector_source')
+    @classmethod
     def ensure_required_transformers(
             cls, v: VectorSourceConfig) -> VectorSourceConfig:
         """Add class-inference and buffer transformers if absent."""

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

@@ -1,6 +1,6 @@
 from typing import TYPE_CHECKING, Optional, Sequence, Self, Tuple
-from pydantic import conint
 
+from pydantic import NonNegativeInt as NonNegInt
 import numpy as np
 from pystac import Item
 
@@ -18,29 +18,28 @@ class MultiRasterSource(RasterSource):
 
     def __init__(self,
                  raster_sources: Sequence[RasterSource],
-                 primary_source_idx: conint(ge=0) = 0,
+                 primary_source_idx: NonNegInt = 0,
                  force_same_dtype: bool = False,
-                 channel_order: Optional[Sequence[conint(ge=0)]] = None,
+                 channel_order: Sequence[NonNegInt] | None = None,
                  raster_transformers: Sequence = [],
-                 bbox: Optional[Box] = None):
+                 bbox: Box | None = None):
         """Constructor.
 
         Args:
-            raster_sources (Sequence[RasterSource]): Sequence of RasterSources.
+            raster_sources: Sequence of RasterSources.
             primary_source_idx (0 <= int < len(raster_sources)): Index of the
                 raster source whose CRS, dtype, and other attributes will
                 override those of the other raster sources.
-            force_same_dtype (bool): If true, force all sub-chips to have the
+            force_same_dtype: If true, force all sub-chips to have the
                 same dtype as the primary_source_idx-th sub-chip. No careful
                 conversion is done, just a quick cast. Use with caution.
-            channel_order (Sequence[conint(ge=0)], optional): Channel ordering
-                that will be used by .get_chip(). Defaults to None.
-            raster_transformers (Sequence, optional): Sequence of transformers.
-                Defaults to [].
-            bbox (Optional[Box], optional): User-specified crop of the extent.
-                If given, the primary raster source's bbox is set to this.
-                If None, the full extent available in the source file of the
-                primary raster source is used.
+            channel_order: Channel ordering that will be used by
+                :meth:`MultiRasterSource.get_chip()`. Defaults to ``None``.
+            raster_transformers: List of transformers. Defaults to ``[]``.
+            bbox: User-specified crop of the extent. If specified, the primary
+                raster source's bbox is set to this. If ``None``, the full
+                extent available in the source file of the primary raster
+                source is used.
         """
         num_channels_raw = sum(rs.num_channels for rs in raster_sources)
         if not channel_order:
@@ -78,7 +77,7 @@ def from_stac(
             cls,
             item: Item,
             assets: list[str] | None,
-            primary_source_idx: conint(ge=0) = 0,
+            primary_source_idx: NonNegInt = 0,
             raster_transformers: list['RasterTransformer'] = [],
             force_same_dtype: bool = False,
             channel_order: Sequence[int] | None = None,

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

@@ -1,7 +1,10 @@
-from typing import Optional
-from pydantic import conint, conlist
+from typing import List, Optional, Self
 
-from rastervision.pipeline.config import (Field, register_config, validator)
+from typing_extensions import Annotated
+from pydantic import NonNegativeInt as NonNegInt
+
+from rastervision.pipeline.config import (Field, register_config,
+                                          model_validator)
 from rastervision.core.box import Box
 from rastervision.core.data.raster_source import (RasterSourceConfig,
                                                   MultiRasterSource)
@@ -25,10 +28,10 @@ class MultiRasterSourceConfig(RasterSourceConfig):
     Or :class:`.TemporalMultiRasterSource`, if ``temporal=True``.
     """
 
-    raster_sources: conlist(
-        RasterSourceConfig, min_items=1) = Field(
+    raster_sources: Annotated[List[
+        RasterSourceConfig], Field(min_length=1)] = Field(
             ..., description='List of RasterSourceConfig to combine.')
-    primary_source_idx: conint(ge=0) = Field(
+    primary_source_idx: NonNegInt = Field(
         0,
         description=
         'Index of the raster source whose CRS, dtype, and other attributes '
@@ -42,21 +45,21 @@ class MultiRasterSourceConfig(RasterSourceConfig):
         description='Stack images from sub raster sources into a time-series '
         'of shape (T, H, W, C) instead of concatenating bands.')
 
-    @validator('primary_source_idx')
-    def validate_primary_source_idx(cls, v: int, values: dict):
-        raster_sources = values.get('raster_sources', [])
-        if not (0 <= v < len(raster_sources)):
+    @model_validator(mode='after')
+    def validate_primary_source_idx(self) -> Self:
+        primary_source_idx = self.primary_source_idx
+        raster_sources = self.raster_sources
+        if not (0 <= primary_source_idx < len(raster_sources)):
             raise IndexError('primary_source_idx must be in range '
                              '[0, len(raster_sources)].')
-        return v
+        return self
 
-    @validator('temporal')
-    def validate_temporal(cls, v: int, values: dict):
-        channel_order = values.get('channel_order')
-        if v and channel_order is not None:
+    @model_validator(mode='after')
+    def validate_temporal(self) -> Self:
+        if self.temporal and self.channel_order is not None:
             raise ValueError(
                 'Setting channel_order is not allowed if temporal=True.')
-        return v
+        return self
 
     def build(self,
               tmp_dir: Optional[str] = None,

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

@@ -5,7 +5,7 @@
 from rastervision.core.data.vector_transformer import (
     ClassInferenceTransformerConfig, BufferTransformerConfig)
 from rastervision.pipeline.config import (register_config, Config, Field,
-                                          validator)
+                                          field_validator)
 
 if TYPE_CHECKING:
     from rastervision.core.box import Box
@@ -35,7 +35,8 @@ class RasterizedSourceConfig(Config):
     vector_source: VectorSourceConfig
     rasterizer_config: RasterizerConfig
 
-    @validator('vector_source')
+    @field_validator('vector_source')
+    @classmethod
     def ensure_required_transformers(
             cls, v: VectorSourceConfig) -> VectorSourceConfig:
         """Add class-inference and buffer transformers if absent."""

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

@@ -1,6 +1,6 @@
 from typing import Any, Optional, Sequence, Tuple
-from pydantic import conint
 
+from pydantic import NonNegativeInt as NonNegInt
 import numpy as np
 
 from rastervision.core.box import Box
@@ -14,7 +14,7 @@ class TemporalMultiRasterSource(MultiRasterSource):
 
     def __init__(self,
                  raster_sources: Sequence[RasterSource],
-                 primary_source_idx: conint(ge=0) = 0,
+                 primary_source_idx: NonNegInt = 0,
                  force_same_dtype: bool = False,
                  raster_transformers: Sequence = [],
                  bbox: Optional[Box] = None):

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

@@ -1,4 +1,4 @@
-from typing import TYPE_CHECKING, Dict, Optional
+from typing import TYPE_CHECKING
 
 from rastervision.pipeline.config import register_config, Field
 from rastervision.core.data.vector_transformer import (
@@ -12,13 +12,13 @@
 class ClassInferenceTransformerConfig(VectorTransformerConfig):
     """Configure a :class:`.ClassInferenceTransformer`."""
 
-    default_class_id: Optional[int] = Field(
+    default_class_id: int | None = Field(
         None,
         description='The default ``class_id`` to use if class cannot be '
         'inferred using other mechanisms. If a feature has an inferred '
         '``class_id`` of ``None``, then it will be deleted. '
         'Defaults to ``None``.')
-    class_id_to_filter: Optional[Dict[int, list]] = Field(
+    class_id_to_filter: dict[int, list] | None = Field(
         None,
         description='Map from ``class_id`` to JSON filter used to infer '
         'missing class IDs. Each key should be a class ID, and its value '
@@ -28,15 +28,15 @@ class ClassInferenceTransformerConfig(VectorTransformerConfig):
         'is that described by '
         'https://docs.mapbox.com/mapbox-gl-js/style-spec/other/#other-filter. '
         'Defaults to ``None``.')
-    class_name_mapping: dict[str, str] = Field(
+    class_name_mapping: dict[str, str] | None = Field(
         None,
         description='``old_name --> new_name`` mapping for values in the '
         '``class_name`` or ``label`` property of the GeoJSON features. The '
         '``new_name`` must be a valid class name in the ``ClassConfig``. This '
         'can also be used to merge multiple classes into one e.g.: '
         '``dict(car="vehicle", truck="vehicle")``. Defaults to None.')
 
-    def build(self, class_config: Optional['ClassConfig'] = None
+    def build(self, class_config: 'ClassConfig | None' = None
               ) -> ClassInferenceTransformer:
         return ClassInferenceTransformer(
             self.default_class_id,