"""
Goal Function for Targeted classification with bonus score
------------------------------------------------------------
"""
import numpy as np
import torch
from textattack.goal_function_results import TargetedBonusGoalFunctionResult
from textattack.goal_functions import GoalFunction
[docs]class TargetedBonus(GoalFunction):
"""A modified targeted attack on classification models which awards a bonus
score of 1 if the class with the highest predicted probability is exactly
equal to the target_class."""
def __init__(self, *args, target_class=0, **kwargs):
super().__init__(*args, **kwargs)
self.target_class = target_class
def _process_model_outputs(self, inputs, scores):
"""Processes and validates a list of model outputs.
This is a task-dependent operation. For example, classification
outputs need to have a softmax applied.
"""
# Automatically cast a list or ndarray of predictions to a tensor.
if isinstance(scores, list) or isinstance(scores, np.ndarray):
scores = torch.tensor(scores)
# Ensure the returned value is now a tensor.
if not isinstance(scores, torch.Tensor):
raise TypeError(
"Must have list, np.ndarray, or torch.Tensor of "
f"scores. Got type {type(scores)}"
)
# Validation check on model score dimensions
if scores.ndim == 1:
# Unsqueeze prediction, if it's been squeezed by the model.
if len(inputs) == 1:
scores = scores.unsqueeze(dim=0)
else:
raise ValueError(
f"Model return score of shape {scores.shape} for {len(inputs)} inputs."
)
elif scores.ndim != 2:
# If model somehow returns too may dimensions, throw an error.
raise ValueError(
f"Model return score of shape {scores.shape} for {len(inputs)} inputs."
)
elif scores.shape[0] != len(inputs):
# If model returns an incorrect number of scores, throw an error.
raise ValueError(
f"Model return score of shape {scores.shape} for {len(inputs)} inputs."
)
elif not ((scores.sum(dim=1) - 1).abs() < 1e-6).all():
# Values in each row should sum up to 1. The model should return a
# set of numbers corresponding to probabilities, which should add
# up to 1. Since they are `torch.float` values, allow a small
# error in the summation.
scores = torch.nn.functional.softmax(scores, dim=1)
if not ((scores.sum(dim=1) - 1).abs() < 1e-6).all():
raise ValueError("Model scores do not add up to 1.")
return scores.cpu()
def _is_goal_complete(self, model_output, _):
return self._get_score(model_output, None) >= 1
def _get_score(self, model_output, _):
if np.argmax(model_output) == self.target_class:
return model_output[self.target_class] + 1
return model_output[self.target_class]
def _goal_function_result_type(self):
"""Returns the class of this goal function's results."""
return TargetedBonusGoalFunctionResult