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avuc_loss.py
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avuc_loss.py
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# Copyright (C) 2024 Intel Corporation
#
# BSD-3-Clause License
#
# Redistribution and use in source and binary forms, with or without modification,
# are permitted provided that the following conditions are met:
# 1. Redistributions of source code must retain the above copyright notice,
# this list of conditions and the following disclaimer.
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
# 3. Neither the name of the copyright holder nor the names of its contributors
# may be used to endorse or promote products derived from this software
# without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO,
# THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS
# BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY,
# OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT
# OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
# OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY,
# WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE
# OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE,
# EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
#
# AvULoss -> compute accuracy versus uncertainty calibration loss
# AUAvULoss -> compute accuracy versus uncertainty calibration loss
# without uncertainty threshold
# accuracy_versus_uncertainty -> compute AvU metric
# eval_AvU -> get AvU scores at differemt uncertainty thresholds
# predictive_entropy -> compute predictive uncertainty of the model
# mutual_information -> compute model uncertainty of the model
#
# @authors: Ranganath Krishnan
#
# ===============================================================================================
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import torch.nn.functional as F
import torch
from torch import nn
import numpy as np
from sklearn.metrics import auc
class AvULoss(nn.Module):
"""
Calculates Accuracy vs Uncertainty Loss of a model.
The input to this loss is logits from Monte_carlo sampling of the model, true labels,
and the type of uncertainty to be used [0: predictive uncertainty (default);
1: model uncertainty]
Reference:
[1]: Ranganath Krishnan, Omesh Tickoo. Improving model calibration with
accuracy versus uncertainty optimization. Advances in Neural Information
Processing Systems 33 (NeurIPS) 2020.
https://arxiv.org/abs/2012.07923
"""
def __init__(self, beta=1):
super(AvULoss, self).__init__()
self.beta = beta
self.eps = 1e-10
def entropy(self, prob):
return -1 * torch.sum(prob * torch.log(prob + self.eps), dim=-1)
def expected_entropy(self, mc_preds):
return torch.mean(self.entropy(mc_preds), dim=0)
def predictive_uncertainty(self, mc_preds):
"""
Compute the entropy of the mean of the predictive distribution
obtained from Monte Carlo sampling.
"""
return self.entropy(torch.mean(mc_preds, dim=0))
def model_uncertainty(self, mc_preds):
"""
Compute the difference between the entropy of the mean of the
predictive distribution and the mean of the entropy.
"""
return self.entropy(torch.mean(
mc_preds, dim=0)) - self.expected_entropy(mc_preds)
def accuracy_vs_uncertainty(self, prediction, true_label, uncertainty,
optimal_threshold):
# number of samples accurate and certain
n_ac = torch.zeros(1, device=true_label.device)
# number of samples inaccurate and certain
n_ic = torch.zeros(1, device=true_label.device)
# number of samples accurate and uncertain
n_au = torch.zeros(1, device=true_label.device)
# number of samples inaccurate and uncertain
n_iu = torch.zeros(1, device=true_label.device)
avu = torch.ones(1, device=true_label.device)
avu.requires_grad_(True)
for i in range(len(true_label)):
if ((true_label[i].item() == prediction[i].item())
and uncertainty[i].item() <= optimal_threshold):
""" accurate and certain """
n_ac += 1
elif ((true_label[i].item() == prediction[i].item())
and uncertainty[i].item() > optimal_threshold):
""" accurate and uncertain """
n_au += 1
elif ((true_label[i].item() != prediction[i].item())
and uncertainty[i].item() <= optimal_threshold):
""" inaccurate and certain """
n_ic += 1
elif ((true_label[i].item() != prediction[i].item())
and uncertainty[i].item() > optimal_threshold):
""" inaccurate and uncertain """
n_iu += 1
print('n_ac: ', n_ac, ' ; n_au: ', n_au, ' ; n_ic: ', n_ic, ' ;n_iu: ',
n_iu)
avu = (n_ac + n_iu) / (n_ac + n_au + n_ic + n_iu)
return avu
def forward(self, logits, labels, optimal_uncertainty_threshold, type=0):
probs = F.softmax(logits, dim=1)
confidences, predictions = torch.max(probs, 1)
if type == 0:
unc = self.entropy(probs)
else:
unc = self.model_uncertainty(probs)
unc_th = torch.tensor(optimal_uncertainty_threshold,
device=logits.device)
n_ac = torch.zeros(
1, device=logits.device) # number of samples accurate and certain
n_ic = torch.zeros(
1,
device=logits.device) # number of samples inaccurate and certain
n_au = torch.zeros(
1,
device=logits.device) # number of samples accurate and uncertain
n_iu = torch.zeros(
1,
device=logits.device) # number of samples inaccurate and uncertain
avu = torch.ones(1, device=logits.device)
avu_loss = torch.zeros(1, device=logits.device)
for i in range(len(labels)):
if ((labels[i].item() == predictions[i].item())
and unc[i].item() <= unc_th.item()):
""" accurate and certain """
n_ac += confidences[i] * (1 - torch.tanh(unc[i]))
elif ((labels[i].item() == predictions[i].item())
and unc[i].item() > unc_th.item()):
""" accurate and uncertain """
n_au += confidences[i] * torch.tanh(unc[i])
elif ((labels[i].item() != predictions[i].item())
and unc[i].item() <= unc_th.item()):
""" inaccurate and certain """
n_ic += (1 - confidences[i]) * (1 - torch.tanh(unc[i]))
elif ((labels[i].item() != predictions[i].item())
and unc[i].item() > unc_th.item()):
""" inaccurate and uncertain """
n_iu += (1 - confidences[i]) * torch.tanh(unc[i])
avu = (n_ac + n_iu) / (n_ac + n_au + n_ic + n_iu + self.eps)
#print('AvU metric: ', self.accuracy_vs_uncertainty(predictions, labels, uncertainty, optimal_threshold))
avu_loss = -1 * self.beta * torch.log(avu + self.eps)
return avu_loss
class AUAvULoss(nn.Module):
"""
Calculates Accuracy vs Uncertainty Loss of a model without the need for optimal
uncertainty threshold, but compute intensive.
The input to this loss is logits from Monte_carlo sampling of the model, true labels,
and the type of uncertainty to be used [0: predictive uncertainty (default);
1: model uncertainty]
Reference:
[1]: Ranganath Krishnan, Omesh Tickoo. Improving model calibration with
accuracy versus uncertainty optimization. Advances in Neural Information
Processing Systems 33 (NeurIPS) 2020.
https://arxiv.org/abs/2012.07923
"""
def __init__(self, beta=1):
super(AUAvULoss, self).__init__()
self.beta = beta
self.eps = 1e-10
def entropy(self, prob):
return -1 * torch.sum(prob * torch.log(prob + self.eps), dim=-1)
def expected_entropy(self, mc_preds):
return torch.mean(self.entropy(mc_preds), dim=0)
def predictive_uncertainty(self, mc_preds):
"""
Compute the entropy of the mean of the predictive distribution
obtained from Monte Carlo sampling.
"""
return self.entropy(torch.mean(mc_preds, dim=0))
def model_uncertainty(self, mc_preds):
"""
Compute the difference between the entropy of the mean of the
predictive distribution and the mean of the entropy.
"""
return self.entropy(torch.mean(
mc_preds, dim=0)) - self.expected_entropy(mc_preds)
def auc_avu(self, logits, labels, unc):
""" returns AvU at various uncertainty thresholds"""
th_list = np.linspace(0, 1, 21)
umin = torch.min(unc)
umax = torch.max(unc)
avu_list = []
unc_list = []
probs = F.softmax(logits, dim=1)
confidences, predictions = torch.max(probs, 1)
auc_avu = torch.ones(1, device=labels.device)
auc_avu.requires_grad_(True)
for t in th_list:
unc_th = umin + (torch.tensor(t) * (umax - umin))
n_ac = torch.zeros(
1,
device=labels.device) # number of samples accurate and certain
n_ic = torch.zeros(1, device=labels.device
) # number of samples inaccurate and certain
n_au = torch.zeros(1, device=labels.device
) # number of samples accurate and uncertain
n_iu = torch.zeros(1, device=labels.device
) # number of samples inaccurate and uncertain
for i in range(len(labels)):
if ((labels[i].item() == predictions[i].item())
and unc[i].item() <= unc_th.item()):
""" accurate and certain """
n_ac += confidences[i] * (1 - torch.tanh(unc[i]))
elif ((labels[i].item() == predictions[i].item())
and unc[i].item() > unc_th.item()):
""" accurate and uncertain """
n_au += confidences[i] * torch.tanh(unc[i])
elif ((labels[i].item() != predictions[i].item())
and unc[i].item() <= unc_th.item()):
""" inaccurate and certain """
n_ic += (1 - confidences[i]) * (1 - torch.tanh(unc[i]))
elif ((labels[i].item() != predictions[i].item())
and unc[i].item() > unc_th.item()):
""" inaccurate and uncertain """
n_iu += (1 - confidences[i]) * torch.tanh(unc[i])
AvU = (n_ac + n_iu) / (n_ac + n_au + n_ic + n_iu + 1e-10)
avu_list.append(AvU.data.cpu().numpy())
unc_list.append(unc_th)
auc_avu = auc(th_list, avu_list)
return auc_avu
def accuracy_vs_uncertainty(self, prediction, true_label, uncertainty,
optimal_threshold):
n_ac = torch.zeros(
1,
device=true_label.device) # number of samples accurate and certain
n_ic = torch.zeros(1, device=true_label.device
) # number of samples inaccurate and certain
n_au = torch.zeros(1, device=true_label.device
) # number of samples accurate and uncertain
n_iu = torch.zeros(1, device=true_label.device
) # number of samples inaccurate and uncertain
avu = torch.ones(1, device=true_label.device)
avu.requires_grad_(True)
for i in range(len(true_label)):
if ((true_label[i].item() == prediction[i].item())
and uncertainty[i].item() <= optimal_threshold):
""" accurate and certain """
n_ac += 1
elif ((true_label[i].item() == prediction[i].item())
and uncertainty[i].item() > optimal_threshold):
""" accurate and uncertain """
n_au += 1
elif ((true_label[i].item() != prediction[i].item())
and uncertainty[i].item() <= optimal_threshold):
""" inaccurate and certain """
n_ic += 1
elif ((true_label[i].item() != prediction[i].item())
and uncertainty[i].item() > optimal_threshold):
""" inaccurate and uncertain """
n_iu += 1
print('n_ac: ', n_ac, ' ; n_au: ', n_au, ' ; n_ic: ', n_ic, ' ;n_iu: ',
n_iu)
avu = (n_ac + n_iu) / (n_ac + n_au + n_ic + n_iu)
return avu
def forward(self, logits, labels, type=0):
probs = F.softmax(logits, dim=1)
confidences, predictions = torch.max(probs, 1)
if type == 0:
unc = self.entropy(probs)
else:
unc = self.model_uncertainty(probs)
th_list = np.linspace(0, 1, 21)
umin = torch.min(unc)
umax = torch.max(unc)
avu_list = []
unc_list = []
auc_avu = torch.ones(1, device=labels.device)
auc_avu.requires_grad_(True)
for t in th_list:
unc_th = umin + (torch.tensor(t, device=labels.device) *
(umax - umin))
n_ac = torch.zeros(
1,
device=labels.device) # number of samples accurate and certain
n_ic = torch.zeros(1, device=labels.device
) # number of samples inaccurate and certain
n_au = torch.zeros(1, device=labels.device
) # number of samples accurate and uncertain
n_iu = torch.zeros(1, device=labels.device
) # number of samples inaccurate and uncertain
for i in range(len(labels)):
if ((labels[i].item() == predictions[i].item())
and unc[i].item() <= unc_th.item()):
""" accurate and certain """
n_ac += confidences[i] * (1 - torch.tanh(unc[i]))
elif ((labels[i].item() == predictions[i].item())
and unc[i].item() > unc_th.item()):
""" accurate and uncertain """
n_au += confidences[i] * torch.tanh(unc[i])
elif ((labels[i].item() != predictions[i].item())
and unc[i].item() <= unc_th.item()):
""" inaccurate and certain """
n_ic += (1 - confidences[i]) * (1 - torch.tanh(unc[i]))
elif ((labels[i].item() != predictions[i].item())
and unc[i].item() > unc_th.item()):
""" inaccurate and uncertain """
n_iu += (1 - confidences[i]) * torch.tanh(unc[i])
AvU = (n_ac + n_iu) / (n_ac + n_au + n_ic + n_iu + self.eps)
avu_list.append(AvU)
unc_list.append(unc_th)
auc_avu = auc(th_list, avu_list)
avu_loss = -1 * self.beta * torch.log(auc_avu + self.eps)
return avu_loss, auc_avu
def entropy(prob):
return -1 * np.sum(prob * np.log(prob + 1e-15), axis=-1)
def predictive_entropy(mc_preds):
"""
Compute the entropy of the mean of the predictive distribution
obtained from Monte Carlo sampling during prediction phase.
"""
return entropy(np.mean(mc_preds, axis=0))
def mutual_information(mc_preds):
"""
Compute the difference between the entropy of the mean of the
predictive distribution and the mean of the entropy.
"""
MI = entropy(np.mean(mc_preds, axis=0)) - np.mean(entropy(mc_preds),
axis=0)
return MI
def eval_avu(pred_label, true_label, uncertainty):
""" returns AvU at various uncertainty thresholds"""
t_list = np.linspace(0, 1, 21)
umin = np.amin(uncertainty, axis=0)
umax = np.amax(uncertainty, axis=0)
avu_list = []
unc_list = []
for t in t_list:
u_th = umin + (t * (umax - umin))
n_ac = 0
n_ic = 0
n_au = 0
n_iu = 0
for i in range(len(true_label)):
if ((true_label[i] == pred_label[i]) and uncertainty[i] <= u_th):
n_ac += 1
elif ((true_label[i] == pred_label[i]) and uncertainty[i] > u_th):
n_au += 1
elif ((true_label[i] != pred_label[i]) and uncertainty[i] <= u_th):
n_ic += 1
elif ((true_label[i] != pred_label[i]) and uncertainty[i] > u_th):
n_iu += 1
AvU = (n_ac + n_iu) / (n_ac + n_au + n_ic + n_iu + 1e-15)
avu_list.append(AvU)
unc_list.append(u_th)
return np.asarray(avu_list), np.asarray(unc_list)
def accuracy_vs_uncertainty(pred_label, true_label, uncertainty,
optimal_threshold):
n_ac = 0
n_ic = 0
n_au = 0
n_iu = 0
for i in range(len(true_label)):
if ((true_label[i] == pred_label[i])
and uncertainty[i] <= optimal_threshold):
n_ac += 1
elif ((true_label[i] == pred_label[i])
and uncertainty[i] > optimal_threshold):
n_au += 1
elif ((true_label[i] != pred_label[i])
and uncertainty[i] <= optimal_threshold):
n_ic += 1
elif ((true_label[i] != pred_label[i])
and uncertainty[i] > optimal_threshold):
n_iu += 1
AvU = (n_ac + n_iu) / (n_ac + n_au + n_ic + n_iu)
return AvU