Source code for

from typing import Dict, List, Set

from rastervision.pipeline.config import (Config, register_config, ConfigError,
from rastervision.pipeline.utils import split_into_groups
from import SceneConfig
from import ClassConfig

def dataset_config_upgrader(cfg_dict: dict, version: int) -> dict:
    if version < 1:
            # removed in version 1
            del cfg_dict['img_channels']
        except KeyError:
    return cfg_dict

[docs]@register_config('dataset', upgrader=dataset_config_upgrader) class DatasetConfig(Config): """Configure train, validation, and test splits for a dataset.""" class_config: ClassConfig train_scenes: List[SceneConfig] validation_scenes: List[SceneConfig] test_scenes: List[SceneConfig] = [] scene_groups: Dict[str, Set[str]] = Field( {}, description='Groupings of scenes. Should be a dict of the form: ' '{<group-name>: Set(scene_id_1, scene_id_2, ...)}. Three groups are ' 'added by default: "train_scenes", "validation_scenes", and ' '"test_scenes"')
[docs] def update(self, pipeline=None): super().update() self.class_config.update(pipeline=pipeline) for s in self.train_scenes: s.update(pipeline=pipeline) for s in self.validation_scenes: s.update(pipeline=pipeline) if self.test_scenes is not None: for s in self.test_scenes: s.update(pipeline=pipeline) # add default scene groups self.scene_groups['train_scenes'] = { for s in self.train_scenes} self.scene_groups['test_scenes'] = { for s in self.test_scenes} self.scene_groups['validation_scenes'] = { for s in self.validation_scenes }
[docs] def validate_config(self): ids = [ for s in self.train_scenes] if len(set(ids)) != len(ids): raise ConfigError('All training scene ids must be unique.') ids = [ for s in self.validation_scenes + self.test_scenes] if len(set(ids)) != len(ids): raise ConfigError( 'All validation and test scene ids must be unique.') all_ids = { for s in self.all_scenes} for group_name, group_ids in self.scene_groups.items(): unknown_ids = group_ids.difference(all_ids) if len(unknown_ids) > 0: raise ConfigError( f'IDs {unknown_ids} in scene group ' f'"{group_name}" do not match any scene in the dataset.')
[docs] def get_split_config(self, split_ind, num_splits): new_cfg = self.copy() groups = split_into_groups(self.train_scenes, num_splits) new_cfg.train_scenes = groups[ split_ind] if split_ind < len(groups) else [] groups = split_into_groups(self.validation_scenes, num_splits) new_cfg.validation_scenes = groups[ split_ind] if split_ind < len(groups) else [] if self.test_scenes: groups = split_into_groups(self.test_scenes, num_splits) new_cfg.test_scenes = groups[ split_ind] if split_ind < len(groups) else [] return new_cfg
@property def all_scenes(self) -> List[SceneConfig]: return self.train_scenes + self.validation_scenes + self.test_scenes def __repr__(self): num_train = len(self.train_scenes) num_val = len(self.validation_scenes) num_test = len(self.test_scenes) out = (f'DatasetConfig(train_scenes=<{num_train} scenes>, ' f'validation_scenes=<{num_val} scenes>, ' f'test_scenes=<{num_test} scenes>)') return out