import torch
from torch import nn, optim
from torch.nn import functional as F
class ModelWithTemperature(nn.Module):
"""
A thin decorator, which wraps a model with temperature scaling
model (nn.Module):
A classification neural network
NB: Output of the neural network should be the classification logits,
NOT the softmax (or log softmax)!
"""
def __init__(self, model):
super(ModelWithTemperature, self).__init__()
self.model = model
self.temperature = nn.Parameter(torch.ones(1) * 1.5)
def forward(self, input):
logits = self.model(input)
return self.temperature_scale(logits)
def temperature_scale(self, logits):
"""
Perform temperature scaling on logits
"""
# Expand temperature to match the size of logits
temperature = self.temperature.unsqueeze(1).expand(logits.size(0), logits.size(1))
return logits / temperature
# This function probably should live outside of this class, but whatever
def set_temperature(self, valid_loader):
"""
Tune the tempearature of the model (using the validation set).
We're going to set it to optimize NLL.
valid_loader (DataLoader): validation set loader
"""
self.cuda()
nll_criterion = nn.CrossEntropyLoss().cuda()
ece_criterion = _ECELoss().cuda()
# First: collect all the logits and labels for the validation set
logits_list = []
labels_list = []
with torch.no_grad():
for input, label in valid_loader:
input = input.cuda()
logits = self.model(input)
logits_list.append(logits)
labels_list.append(label)
logits = torch.cat(logits_list).cuda()
labels = torch.cat(labels_list).cuda()
# Calculate NLL and ECE before temperature scaling
before_temperature_nll = nll_criterion(logits, labels).item()
before_temperature_ece = ece_criterion(logits, labels).item()
print('Before temperature - NLL: %.3f, ECE: %.3f' % (before_temperature_nll, before_temperature_ece))
# Next: optimize the temperature w.r.t. NLL
optimizer = optim.LBFGS([self.temperature], lr=0.01, max_iter=50)
def eval():
loss = nll_criterion(self.temperature_scale(logits), labels)
loss.backward()
return loss
optimizer.step(eval)
# Calculate NLL and ECE after temperature scaling
after_temperature_nll = nll_criterion(self.temperature_scale(logits), labels).item()
after_temperature_ece = ece_criterion(self.temperature_scale(logits), labels).item()
print('Optimal temperature: %.3f' % self.temperature.item())
print('After temperature - NLL: %.3f, ECE: %.3f' % (after_temperature_nll, after_temperature_ece))
return self
class _ECELoss(nn.Module):
"""
Calculates the Expected Calibration Error of a model.
(This isn't necessary for temperature scaling, just a cool metric).
The input to this loss is the logits of a model, NOT the softmax scores.
This divides the confidence outputs into equally-sized interval bins.
In each bin, we compute the confidence gap:
bin_gap = | avg_confidence_in_bin - accuracy_in_bin |
We then return a weighted average of the gaps, based on the number
of samples in each bin
See: Naeini, Mahdi Pakdaman, Gregory F. Cooper, and Milos Hauskrecht.
"Obtaining Well Calibrated Probabilities Using Bayesian Binning." AAAI.
2015.
"""
def __init__(self, n_bins=15):
"""
n_bins (int): number of confidence interval bins
"""
super(_ECELoss, self).__init__()
bin_boundaries = torch.linspace(0, 1, n_bins + 1)
self.bin_lowers = bin_boundaries[:-1]
self.bin_uppers = bin_boundaries[1:]
def forward(self, logits, labels):
softmaxes = F.softmax(logits, dim=1)
confidences, predictions = torch.max(softmaxes, 1)
accuracies = predictions.eq(labels)
ece = torch.zeros(1, device=logits.device)
for bin_lower, bin_upper in zip(self.bin_lowers, self.bin_uppers):
# Calculated |confidence - accuracy| in each bin
in_bin = confidences.gt(bin_lower.item()) * confidences.le(bin_upper.item())
prop_in_bin = in_bin.float().mean()
if prop_in_bin.item() > 0:
accuracy_in_bin = accuracies[in_bin].float().mean()
avg_confidence_in_bin = confidences[in_bin].mean()
ece += torch.abs(avg_confidence_in_bin - accuracy_in_bin) * prop_in_bin
return ece
