PyTorchObjectDetection#

class PyTorchObjectDetection[source]#

Bases: PyTorchLearnerBackend

__init__(pipeline_cfg: RVPipelineConfig, learner_cfg: LearnerConfig, tmp_dir: str)#
Parameters

Methods

__init__(pipeline_cfg, learner_cfg, tmp_dir)

get_sample_writer()

Returns a SampleWriter for this Backend.

load_model()

Load the model in preparation for one or more prediction calls.

predict_scene(scene, chip_sz[, stride])

Return predictions for an entire scene using the model.

train([source_bundle_uri])

Train a model.

__init__(pipeline_cfg: RVPipelineConfig, learner_cfg: LearnerConfig, tmp_dir: str)#
Parameters
get_sample_writer()[source]#

Returns a SampleWriter for this Backend.

load_model()#

Load the model in preparation for one or more prediction calls.

predict_scene(scene: Scene, chip_sz: int, stride: Optional[int] = None) ObjectDetectionLabels[source]#

Return predictions for an entire scene using the model.

Parameters
Returns

Labels object containing predictions

Return type

ObjectDetectionLabels

train(source_bundle_uri=None)#

Train a model.

This should download chips created by the SampleWriter, train the model, and then saving it to disk.