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# A Consistent and Efficient Evaluation Strategy for Attribution Methods
# https://arxiv.org/abs/2202.00449
# Taken from https://raw.githubusercontent.com/tleemann/road_evaluation/main/imputations.py
# MIT License
# Copyright (c) 2022 Tobias Leemann
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
# Implementations of our imputation models.
import torch
import numpy as np
from scipy.sparse import lil_matrix, csc_matrix
from scipy.sparse.linalg import spsolve
from typing import List, Callable
from pytorch_grad_cam.metrics.perturbation_confidence import PerturbationConfidenceMetric, \
AveragerAcrossThresholds, \
RemoveMostRelevantFirst, \
RemoveLeastRelevantFirst
# The weights of the surrounding pixels
neighbors_weights = [((1, 1), 1 / 12),
((0, 1), 1 / 6),
((-1, 1), 1 / 12),
((1, -1), 1 / 12),
((0, -1), 1 / 6),
((-1, -1), 1 / 12),
((1, 0), 1 / 6),
((-1, 0), 1 / 6)]
class NoisyLinearImputer:
def __init__(self,
noise: float = 0.01,
weighting: List[float] = neighbors_weights):
"""
Noisy linear imputation.
noise: magnitude of noise to add (absolute, set to 0 for no noise)
weighting: Weights of the neighboring pixels in the computation.
List of tuples of (offset, weight)
"""
self.noise = noise
self.weighting = neighbors_weights
@staticmethod
def add_offset_to_indices(indices, offset, mask_shape):
""" Add the corresponding offset to the indices.
Return new indices plus a valid bit-vector. """
cord1 = indices % mask_shape[1]
cord0 = indices // mask_shape[1]
cord0 += offset[0]
cord1 += offset[1]
valid = ((cord0 < 0) | (cord1 < 0) |
(cord0 >= mask_shape[0]) |
(cord1 >= mask_shape[1]))
return ~valid, indices + offset[0] * mask_shape[1] + offset[1]
@staticmethod
def setup_sparse_system(mask, img, neighbors_weights):
""" Vectorized version to set up the equation system.
mask: (H, W)-tensor of missing pixels.
Image: (H, W, C)-tensor of all values.
Return (N,N)-System matrix, (N,C)-Right hand side for each of the C channels.
"""
maskflt = mask.flatten()
imgflat = img.reshape((img.shape[0], -1))
# Indices that are imputed in the flattened mask:
indices = np.argwhere(maskflt == 0).flatten()
coords_to_vidx = np.zeros(len(maskflt), dtype=int)
coords_to_vidx[indices] = np.arange(len(indices))
numEquations = len(indices)
# System matrix:
A = lil_matrix((numEquations, numEquations))
b = np.zeros((numEquations, img.shape[0]))
# Sum of weights assigned:
sum_neighbors = np.ones(numEquations)
for n in neighbors_weights:
offset, weight = n[0], n[1]
# Take out outliers
valid, new_coords = NoisyLinearImputer.add_offset_to_indices(
indices, offset, mask.shape)
valid_coords = new_coords[valid]
valid_ids = np.argwhere(valid == 1).flatten()
# Add values to the right hand-side
has_values_coords = valid_coords[maskflt[valid_coords] > 0.5]
has_values_ids = valid_ids[maskflt[valid_coords] > 0.5]
b[has_values_ids, :] -= weight * imgflat[:, has_values_coords].T
# Add weights to the system (left hand side)
# Find coordinates in the system.
has_no_values = valid_coords[maskflt[valid_coords] < 0.5]
variable_ids = coords_to_vidx[has_no_values]
has_no_values_ids = valid_ids[maskflt[valid_coords] < 0.5]
A[has_no_values_ids, variable_ids] = weight
# Reduce weight for invalid
sum_neighbors[np.argwhere(valid == 0).flatten()] = \
sum_neighbors[np.argwhere(valid == 0).flatten()] - weight
A[np.arange(numEquations), np.arange(numEquations)] = -sum_neighbors
return A, b
def __call__(self, img: torch.Tensor, mask: torch.Tensor):
""" Our linear inputation scheme. """
"""
This is the function to do the linear infilling
img: original image (C,H,W)-tensor;
mask: mask; (H,W)-tensor
"""
imgflt = img.reshape(img.shape[0], -1)
maskflt = mask.reshape(-1)
# Indices that need to be imputed.
indices_linear = np.argwhere(maskflt == 0).flatten()
# Set up sparse equation system, solve system.
A, b = NoisyLinearImputer.setup_sparse_system(
mask.numpy(), img.numpy(), neighbors_weights)
res = torch.tensor(spsolve(csc_matrix(A), b), dtype=torch.float)
# Fill the values with the solution of the system.
img_infill = imgflt.clone()
img_infill[:, indices_linear] = res.t() + self.noise * \
torch.randn_like(res.t())
return img_infill.reshape_as(img)
class ROADMostRelevantFirst(PerturbationConfidenceMetric):
def __init__(self, percentile=80):
super(ROADMostRelevantFirst, self).__init__(
RemoveMostRelevantFirst(percentile, NoisyLinearImputer()))
class ROADLeastRelevantFirst(PerturbationConfidenceMetric):
def __init__(self, percentile=20):
super(ROADLeastRelevantFirst, self).__init__(
RemoveLeastRelevantFirst(percentile, NoisyLinearImputer()))
class ROADMostRelevantFirstAverage(AveragerAcrossThresholds):
def __init__(self, percentiles=[10, 20, 30, 40, 50, 60, 70, 80, 90]):
super(ROADMostRelevantFirstAverage, self).__init__(
ROADMostRelevantFirst, percentiles)
class ROADLeastRelevantFirstAverage(AveragerAcrossThresholds):
def __init__(self, percentiles=[10, 20, 30, 40, 50, 60, 70, 80, 90]):
super(ROADLeastRelevantFirstAverage, self).__init__(
ROADLeastRelevantFirst, percentiles)
class ROADCombined:
def __init__(self, percentiles=[10, 20, 30, 40, 50, 60, 70, 80, 90]):
self.percentiles = percentiles
self.morf_averager = ROADMostRelevantFirstAverage(percentiles)
self.lerf_averager = ROADLeastRelevantFirstAverage(percentiles)
def __call__(self,
input_tensor: torch.Tensor,
cams: np.ndarray,
targets: List[Callable],
model: torch.nn.Module):
scores_lerf = self.lerf_averager(input_tensor, cams, targets, model)
scores_morf = self.morf_averager(input_tensor, cams, targets, model)
return (scores_lerf - scores_morf) / 2
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