from typing import Union, Tuple, List, Optional, Mapping
from attribench.masking.image import ImageMasker
from attribench._model_factory import ModelFactory
from ..._worker import Worker, WorkerConfig
from ._irof_worker import IrofWorker
from ..deletion._deletion import Deletion
from attribench.data import AttributionsDataset
[docs]class Irof(Deletion):
"""Compute the IROF metric for a given :class:`~attribench.data.AttributionsDataset` and model
using multiple processes.
IROF starts segmenting the input image using SLIC. Then, it iteratively
masks out the top (Most Relevant First, or MoRF) or bottom (Least Relevant
First, or LeRF) segments and computes the confidence of the model on the
masked samples. The relevance of a segment is computed as the average
relevance of the features in the segment.
This results in a curve of confidence vs. number of segments masked. The
area under (or equivalently over) this curve is the IROF metric.
`start`, `stop`, and `num_steps` are used to determine the range of segments
to mask. The range is determined by `start` and `stop` as a percentage of
the total number of segments. `num_steps` is the number of steps to take
between `start` and `stop`.
The IROF metric is computed for each masker in `maskers` and for each
activation function in `activation_fns`. The number of processes is
determined by the number of devices. If `devices` is None, then all
available devices are used. Samples are distributed evenly across the
processes.
"""
def __init__(
self,
model_factory: ModelFactory,
attributions_dataset: AttributionsDataset,
batch_size: int,
maskers: Mapping[str, ImageMasker],
activation_fns: Union[List[str], str],
mode: str = "morf",
start: float = 0.0,
stop: float = 1.0,
num_steps: int = 100,
address="localhost",
port="12355",
devices: Optional[Tuple] = None,
):
"""
Parameters
----------
model_factory : ModelFactory
ModelFactory instance or callable that returns a model.
Used to create a model for each subprocess.
attributions_dataset : AttributionsDataset
Dataset containing the samples and attributions to compute
IROF on.
batch_size : int
The batch size to use when computing the metric.
maskers : Dict[str, Masker]
Dictionary of maskers to use for computing the metric.
activation_fns : Union[List[str], str]
List of activation functions to use for computing the metric.
If a single string is given, it is converted to a single-element
list.
mode : str, optional
Mode to use for computing the metric. Either "morf" or "lerf".
Default: "morf"
start : float, optional
Relative start of the range of features to mask. Must be between 0 and 1.
Default: 0.0
stop : float, optional
Relative end of the range of features to mask. Must be between 0 and 1.
Default: 1.0
num_steps : int, optional
Number of steps to use for the range of features to mask.
Default: 100
address : str, optional
Address to use for the multiprocessing connection.
Default: "localhost"
port : str, optional
Port to use for the multiprocessing connection.
Default: "12355"
devices : Optional[Tuple], optional
Devices to use. If None, then all available devices are used.
Default: None
"""
super().__init__(
model_factory,
attributions_dataset,
batch_size,
maskers,
activation_fns,
mode,
start,
stop,
num_steps,
address,
port,
devices,
)
self.maskers = maskers
def _create_worker(self, worker_config: WorkerConfig) -> Worker:
return IrofWorker(
worker_config,
self.model_factory,
self.dataset,
self.batch_size,
self.maskers,
self.activation_fns,
self.mode,
self._start,
self.stop,
self.num_steps,
)