SemanticSegmentation

class SemanticSegmentation

Bases: RVPipeline

Attributes

commands	Built-in mutable sequence.
gpu_commands	Built-in mutable sequence.
split_commands	Built-in mutable sequence.

__init__(config: RVPipelineConfig, tmp_dir: str)

Constructor

Parameters:

• config (RVPipelineConfig) – the configuration of this pipeline

• tmp_dir (str) – the root any temporary directories created by running this pipeline

Methods

__init__(config, tmp_dir)	Constructor
analyze()	Run each analyzer over training scenes.
build_backend([uri])
bundle()	Save a model bundle with whatever is needed to make predictions.
chip([split_ind, num_splits])	Save training and validation chips.
eval()	Evaluate predictions against ground truth.
get_train_labels(window, scene)	Return the training labels in a window for a scene.
get_train_windows(scene)	Return the training windows for a Scene.
post_process_batch(windows, chips, labels)	Post-process a batch of predictions.
post_process_predictions(labels, scene)	Post-process all labels at end of prediction.
post_process_sample(sample)	Post-process sample in pipeline-specific way.
predict([split_ind, num_splits])	Make predictions over each validation and test scene.
predict_scene(scene)
test_cpu([split_ind, num_splits])	A command to test the ability to run split jobs on CPU.
test_gpu()	A command to test the ability to run on GPU.
train()	Train a model and save it.

__init__(config: RVPipelineConfig, tmp_dir: str)

Constructor

Parameters:

• config (RVPipelineConfig) – the configuration of this pipeline

• tmp_dir (str) – the root any temporary directories created by running this pipeline

analyze()

Run each analyzer over training scenes.

build_backend(uri: str | None = None) → None

Parameters:

uri (str | None) –

Return type:

None

bundle()

Save a model bundle with whatever is needed to make predictions.

The model bundle is a zip file and it is used by the Predictor and predict CLI subcommand.

chip(split_ind: int = 0, num_splits: int = 1)

Save training and validation chips.

Parameters:

• split_ind (int) –

• num_splits (int) –

eval()

Evaluate predictions against ground truth.

get_train_labels(window: Box, scene: Scene) → Labels

Return the training labels in a window for a scene.

Returns:

Labels that lie within window

Parameters:

• window (Box) –

• scene (Scene) –

Return type:

Labels

get_train_windows(scene: Scene) → list[rastervision.core.box.Box]

Return the training windows for a Scene.

Each training window represents the spatial extent of a training chip to generate.

Parameters:

scene (Scene) – Scene to generate windows for

Return type:

list[rastervision.core.box.Box]

post_process_batch(windows: list[rastervision.core.box.Box], chips: ndarray, labels: Labels) → Labels

Post-process a batch of predictions.

Parameters:

• windows (list[rastervision.core.box.Box]) –

• chips (ndarray) –

• labels (Labels) –

Return type:

Labels

post_process_predictions(labels: Labels, scene: Scene) → Labels

Post-process all labels at end of prediction.

Parameters:

• labels (Labels) –

• scene (Scene) –

Return type:

Labels

post_process_sample(sample: DataSample) → DataSample

Post-process sample in pipeline-specific way.

This should be called before writing a sample during chipping.

Parameters:

sample (DataSample) –

Return type:

DataSample

predict(split_ind=0, num_splits=1)

Make predictions over each validation and test scene.

This uses a sliding window.

predict_scene(scene: Scene) → Labels

Parameters:

scene (Scene) –

Return type:

Labels

test_cpu(split_ind: int = 0, num_splits: int = 1)

A command to test the ability to run split jobs on CPU.

Parameters:

• split_ind (int) –

• num_splits (int) –

test_gpu()

A command to test the ability to run on GPU.

train()

Train a model and save it.

property commands

Built-in mutable sequence.

If no argument is given, the constructor creates a new empty list. The argument must be an iterable if specified.

property gpu_commands

Built-in mutable sequence.

If no argument is given, the constructor creates a new empty list. The argument must be an iterable if specified.

property split_commands

Built-in mutable sequence.

If no argument is given, the constructor creates a new empty list. The argument must be an iterable if specified.