Source code for rastervision.core.backend.backend

from typing import TYPE_CHECKING, Optional
from abc import ABC, abstractmethod
from contextlib import AbstractContextManager

    from rastervision.core.data_sample import DataSample
    from import DatasetConfig, Labels, Scene
    from rastervision.core.rv_pipeline import ChipOptions, PredictOptions

[docs]class SampleWriter(AbstractContextManager): """Writes DataSamples in a streaming fashion. This is a context manager used for creating training and validation chips, and should be subclassed for each Backend. """
[docs] @abstractmethod def write_sample(self, sample: 'DataSample'): """Writes a single sample."""
[docs]class Backend(ABC): """Abstraction around core ML functionality used by an RVPipeline. This should be subclassed to enable use of a third party ML library with an RVPipeline. There is a one-to-many relationship from RVPipeline to Backend. """
[docs] @abstractmethod def get_sample_writer(self): """Returns a SampleWriter for this Backend."""
[docs] @abstractmethod def train(self): """Train a model. This should download chips created by the SampleWriter, train the model, and then saving it to disk. """
[docs] @abstractmethod def load_model(self, uri: Optional[str] = None): """Load the model in preparation for one or more prediction calls. Args: uri: Optional URI to load the model from. """
[docs] @abstractmethod def predict_scene(self, scene: 'Scene', predict_options: 'PredictOptions') -> 'Labels': """Return predictions for an entire scene using the model. Args: scene: Scene to run inference on. predict_options: Prediction options. Return: Labels object containing predictions """
[docs] @abstractmethod def chip_dataset(self, dataset: 'DatasetConfig', chip_options: 'ChipOptions') -> None: """Create and write chips for scenes in a :class:`.DatasetConfig`. Args: scenes: Scenes to chip. """