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#!/usr/bin/env python3
import warnings
from typing import Callable, Tuple
import torch
from torch import Tensor
def _divide_and_aggregate_metrics(
inputs: Tuple[Tensor, ...],
n_perturb_samples: int,
metric_func: Callable,
agg_func: Callable = torch.add,
max_examples_per_batch: int = None,
) -> Tensor:
r"""
This function is used to slice large number of samples `n_perturb_samples` per
input example into smaller pieces, computing the metrics for each small piece and
aggregating the results across all `n_perturb_samples` per example. The function
returns overall aggregated metric per sample. The size of each slice is determined
by the `max_examples_per_batch` input parameter.
Args:
inputs (tuple): The original inputs formatted in a tuple that are passed to
the metrics function and that are used to compute the
attributions for.
n_perturb_samples (int): The number of samples per example that are used for
perturbation purposes for example.
metric_func (callable): This function takes the number of samples per
input batch and returns an overall metric for each example.
agg_func (callable, optional): This function is used to aggregate the
metrics across multiple sub-batches and that are
generated by `metric_func`.
max_examples_per_batch (int, optional): The maximum number of allowed examples
per batch.
Returns:
metric (tensor): A metric score estimated by `metric_func` per
input example.
"""
bsz = inputs[0].size(0)
if max_examples_per_batch is not None and (
max_examples_per_batch // bsz < 1
or max_examples_per_batch // bsz > n_perturb_samples
):
warnings.warn(
(
"`max_examples_per_batch` must be at least equal to the"
" input batch size and at most to "
"`input batch size` * `n_perturb_samples`."
"`max_examples_per_batch` is: {} and the input batch size is: {}."
"This is necessary because we require that each sub-batch that is used "
"to compute the metrics, contains at least an instance of "
"the original example and doesn't exceed the number of "
"expanded n_perturb_samples."
).format(max_examples_per_batch, bsz)
)
max_inps_per_batch = (
n_perturb_samples
if max_examples_per_batch is None
else min(max(max_examples_per_batch // bsz, 1), n_perturb_samples)
)
current_n_steps = max_inps_per_batch
metrics_sum = metric_func(max_inps_per_batch)
while current_n_steps < n_perturb_samples:
current_n_steps += max_inps_per_batch
metric = metric_func(
max_inps_per_batch
if current_n_steps <= n_perturb_samples
else max_inps_per_batch - (current_n_steps - n_perturb_samples)
)
current_n_steps = min(current_n_steps, n_perturb_samples)
metrics_sum = agg_func(metrics_sum, metric)
return metrics_sum
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