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Selector Operator¤

Select specific fields or elements from data.

See Also¤

datarax.operators.selector_operator ¤

SelectorOperator - Random selection from multiple operators.

This operator wraps multiple OperatorModules and randomly selects ONE to apply per batch element.

Key Features:

  • Wraps multiple OperatorModules with random selection
  • Configurable weights for weighted random selection (defaults to uniform)
  • Uses jax.lax.switch for JIT-compatible dynamic selection
  • Always stochastic (always makes a random choice)
  • Full JAX compatibility (JIT, vmap)

Examples:

Basic usage:

config = SelectorOperatorConfig(
    operators=[op1, op2, op3],
    weights=[0.5, 0.3, 0.2]
)
op = SelectorOperator(config, rngs=rngs)

logger module-attribute ¤

logger = getLogger(__name__)

SelectorOperatorConfig dataclass ¤

SelectorOperatorConfig(cacheable: bool = False, batch_stats_fn: Callable | Module | None = None, precomputed_stats: dict[str, Any] | None = None, stochastic: bool = False, stream_name: str | None = None, batch_strategy: str = 'vmap', *, operators: list[OperatorModule], weights: list[float] | None = None)

Bases: OperatorConfig

Configuration for SelectorOperator.

Extends OperatorConfig with operators list and optional weights.

Attributes:

Name Type Description
operators list[OperatorModule]

List of operators to select from (minimum 1)
weights list[float] | None

Optional weights for random selection (defaults to uniform) Will be normalized to sum to 1.0

Note:

- stochastic is always True (always makes random choice)
- stream_name defaults to "augment" for random selection

operators class-attribute instance-attribute ¤

operators: list[OperatorModule] = field(kw_only=True)

weights class-attribute instance-attribute ¤

weights: list[float] | None = field(default=None, kw_only=True)

normalized_weights class-attribute instance-attribute ¤

normalized_weights: Array = field(init=False, repr=False)

cacheable class-attribute instance-attribute ¤

cacheable: bool = False

batch_stats_fn class-attribute instance-attribute ¤

batch_stats_fn: Callable | Module | None = None

precomputed_stats class-attribute instance-attribute ¤

precomputed_stats: dict[str, Any] | None = None

stochastic class-attribute instance-attribute ¤

stochastic: bool = False

stream_name class-attribute instance-attribute ¤

stream_name: str | None = None

batch_strategy class-attribute instance-attribute ¤

batch_strategy: str = 'vmap'

SelectorOperator ¤

SelectorOperator(config: SelectorOperatorConfig, *, rngs: Rngs | None = None)

Bases: OperatorModule

Wrapper operator that randomly selects ONE operator to apply.

Wraps multiple OperatorModules and uses weighted random selection to choose which one to apply per batch element.

Uses jax.lax.switch for JIT-compatible operator selection with the unified operator interface.

Examples:

op1 = BrightnessOperator(brightness_config, rngs=nnx.Rngs(0))  # Different transforms
op2 = NoiseOperator(noise_config, rngs=nnx.Rngs(0))
op3 = RotationOperator(rotation_config, rngs=nnx.Rngs(0))
selector_config = SelectorOperatorConfig(  # 50% brightness, 30% noise, 20% rotation
    operators=[op1, op2, op3],
    weights=[0.5, 0.3, 0.2]
)
selector = SelectorOperator(selector_config, rngs=nnx.Rngs(0))
result_batch = selector(batch)  # Each element gets one randomly selected operator

Parameters:

Name Type Description Default
config SelectorOperatorConfig

SelectorOperatorConfig with operators list and optional weights

 required
rngs Rngs | None

Random number generators (required for random selection)

 None

config instance-attribute ¤

config: SelectorOperatorConfig = config

operators instance-attribute ¤

operators = List(operators)

weights instance-attribute ¤

weights = static(tuple((float(w)) for w in (tolist())))

rngs instance-attribute ¤

rngs = rngs

name instance-attribute ¤

name = static(name)

stochastic instance-attribute ¤

stochastic = static(stochastic)

stream_name instance-attribute ¤

stream_name = static(stream_name)

get_output_structure ¤

get_output_structure(sample_data: PyTree, sample_state: PyTree) -> tuple[PyTree, PyTree]

Declare output structure using first operator.

SelectorOperator's apply() requires random_params which isn't available during jax.eval_shape tracing. Since all child operators should produce compatible output structures, we use the first operator's structure.

Parameters:

Name Type Description Default
sample_data PyTree

Single element data (not batched)

 required
sample_state PyTree

Single element state (not batched)

 required

Returns:

Type Description
tuple[PyTree, PyTree]

Tuple of (output_data_structure, output_state_structure) with 0 leaves.

generate_random_params ¤

generate_random_params(rng: Array, data_shapes: PyTree) -> dict[str, Any]

Generate random operator selection indices for each batch element.

Creates integer indices determining which operator to apply per element, plus delegates to all child operators for their random params.

Parameters:

Name Type Description Default
rng Array

JAX random key

 required
data_shapes PyTree

PyTree with same structure as batch.data, containing shapes Examples: {"image": (batch_size, H, W, C)}

 required

Returns:

Type Description
dict[str, Any]

Dict with:

  • "selected_indices": Array of operator indices per batch element
  • "child_params": Dict mapping operator index to its random params

apply ¤

apply(data: PyTree, state: PyTree, metadata: dict[str, Any] | None, random_params: Any = None, stats: dict[str, Any] | None = None) -> tuple[PyTree, PyTree, dict[str, Any] | None]

Apply the randomly selected operator to the data.

Uses jax.lax.switch for JIT-compatible operator selection based on the pre-generated random index.

Parameters:

Name Type Description Default
data PyTree

Element data PyTree (no batch dimension)

 required
state PyTree

Element state PyTree

 required
metadata dict[str, Any] | None

Element metadata

 required
random_params Any

Dict with "selected_indices" (int) and "child_params"

 None
stats dict[str, Any] | None

Optional statistics

 None

Returns:

Type Description
tuple[PyTree, PyTree, dict[str, Any] | None]

Tuple of (transformed_data, state, metadata) from selected operator

get_operation_stats ¤

get_operation_stats() -> dict[str, int]

Get operation statistics.

Note: This method converts JAX arrays to Python ints for introspection. It is intended for use outside of JIT-compiled functions.

Returns:

Type Description
dict[str, int]

Dictionary with 'applied_count' and 'skipped_count'

reset_operation_stats ¤

reset_operation_stats() -> None

Reset operation statistics to zero.

Note: Creates new JAX arrays to reset the counters.

compute_statistics ¤

compute_statistics(data: Any) -> dict[str, Any] | None

Compute statistics from data using batch_stats_fn.

If batch_stats_fn is not configured, returns None. Computed statistics are cached in _computed_stats.

Parameters:

Name Type Description Default
data Any

Input data to compute statistics from

 required

Returns:

Type Description
dict[str, Any] | None

Dictionary of statistics, or None if no batch_stats_fn configured

get_statistics ¤

get_statistics() -> dict[str, Any] | None

Get current statistics.

Returns precomputed_stats if configured (unless reset was called), otherwise returns cached computed statistics, or None if no statistics available.

Returns:

Type Description
dict[str, Any] | None

Dictionary of statistics, or None if no statistics available

set_statistics ¤

set_statistics(stats: dict[str, Any]) -> None

Manually set statistics.

This overwrites any previously computed statistics and clears reset flag.

Parameters:

Name Type Description Default
stats dict[str, Any]

Dictionary of statistics to set

 required

reset_statistics ¤

reset_statistics() -> None

Reset all statistics to None.

This clears both computed statistics and marks that precomputed_stats should be ignored (via internal flag). After reset, get_statistics() will return None until new statistics are set or computed.

reset_cache ¤

reset_cache() -> None

Clear the cache.

Only has effect if cacheable=True in config.

copy ¤

copy(*, config: DataraxModuleConfig | None = None, rngs: Rngs | None = None, name: str | None = None) -> DataraxModule

Create a copy of this module with optional config/parameter changes.

This allows creating a new module instance with modified configuration while preserving other attributes. Useful for hyperparameter tuning.

Parameters:

Name Type Description Default
config DataraxModuleConfig | None

New config (if None, uses current config)

 None
rngs Rngs | None

New RNG state (if None, uses current rngs)

 None
name str | None

New name (if None, uses current name)

 None

Returns:

Type Description
DataraxModule

New module instance with updated parameters

Examples:

Change configuration¤

new_config = DataraxModuleConfig(cacheable=True) new_module = module.copy(config=new_config)

Change name only¤

renamed = module.copy(name="new_name")

Note

Subclasses can override this method to provide more fine-grained control over copying, such as allowing individual config field updates without requiring dataclass replace().

get_state ¤

get_state() -> dict[str, Any]

Get module state for checkpointing.

This method implements the Checkpointable protocol using NNX state management. It extracts all state variables from the module and converts them to a serializable format.

Returns:

Type Description
dict[str, Any]

A dictionary containing the internal state of the component.

set_state ¤

set_state(state: dict[str, Any]) -> None

Restore module state from a checkpoint.

This method implements the Checkpointable protocol using NNX state management. It restores the module state from a serialized format. Restoration is strict: checkpoint structure must match module state.

Parameters:

Name Type Description Default
state dict[str, Any]

A dictionary containing the internal state to restore.

 required

Raises:

Type Description
TypeError

If state is not a dictionary.
ValueError

If checkpoint structure does not match module state.

clone ¤

clone() -> DataraxModule

Create a new instance with the same state as this module.

Uses NNX's clone function for proper deep cloning of all state.

Returns:

Type Description
DataraxModule

A new module instance with the same state.

requires_rng_streams ¤

requires_rng_streams() -> list[str] | None

Get the list of RNG streams required by this module.

Returns:

Type Description
list[str] | None

A list of required RNG stream names, or None if no RNG streams
list[str] | None

are required.

ensure_rng_streams ¤

ensure_rng_streams(stream_names: list[str]) -> None

Ensure that the required RNG streams are available.

Parameters:

Name Type Description Default
stream_names list[str]

A list of available RNG stream names.

 required

Raises:

Type Description
ValueError

If a required RNG stream is not available.

apply_batch ¤

apply_batch(batch: Batch, stats: dict[str, Any] | None = None) -> Batch

Process entire batch with vmap and optional RNG generation.

This method implements the batch processing logic for both stochastic and deterministic modes. It uses static branching on self.stochastic for JIT compilation efficiency.

The implementation delegates to _vmap_apply() for the shared computational core, then wraps the result in a Batch object.

Parameters:

Name Type Description Default
batch Batch

Input batch (Batch[Element] structure)

 required
stats dict[str, Any] | None

Optional statistics (if None, uses get_statistics())

 None

Returns:

Type Description
Batch

Transformed batch with same structure
Note

This method is concrete (not abstract). Subclasses typically don't override it, but can if they need custom batch processing logic.

output_spec ¤

output_spec(input_spec: PyTree) -> PyTree

Return the operator's output spec given an input spec.

Most operators (normalization, additive noise, simple element-wise transforms) do not change shape; the default returns input_spec unchanged. Shape-changing operators (Resize, Crop, Reshape) MUST override this method.

Parameters:

Name Type Description Default
input_spec PyTree

PyTree of jax.ShapeDtypeStruct describing the input element (matching the upstream DataSourceModule.element_spec() or another operator's output_spec).

 required

Returns:

Type Description
PyTree

PyTree of jax.ShapeDtypeStruct describing the operator's output.
PyTree

By default, equal to input_spec.