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import numpy as np
from sklearn import metrics
from collections import defaultdict
import torch
import torch.nn as nn
def get_accracy(output, label):
_, prediction = torch.max(output, 1) # argmax
correct = (prediction == label).sum().item()
accuracy = correct / prediction.size(0)
return accuracy
def get_prediction(output, label):
prob = nn.functional.softmax(output, dim=1)[:, 1]
prob = prob.view(prob.size(0), 1)
label = label.view(label.size(0), 1)
#print(prob.size(), label.size())
datas = torch.cat((prob, label.float()), dim=1)
return datas
def calculate_metrics_for_train(label, output):
if output.size(1) == 2:
prob = torch.softmax(output, dim=1)[:, 1]
else:
prob = output
# Accuracy
_, prediction = torch.max(output, 1)
correct = (prediction == label).sum().item()
accuracy = correct / prediction.size(0)
# Average Precision
y_true = label.cpu().detach().numpy()
y_pred = prob.cpu().detach().numpy()
ap = metrics.average_precision_score(y_true, y_pred)
# AUC and EER
try:
fpr, tpr, thresholds = metrics.roc_curve(label.squeeze().cpu().numpy(),
prob.squeeze().cpu().numpy(),
pos_label=1)
except:
# for the case when we only have one sample
return None, None, accuracy, ap
if np.isnan(fpr[0]) or np.isnan(tpr[0]):
# for the case when all the samples within a batch is fake/real
auc, eer = None, None
else:
auc = metrics.auc(fpr, tpr)
fnr = 1 - tpr
eer = fpr[np.nanargmin(np.absolute((fnr - fpr)))]
return auc, eer, accuracy, ap
# ------------ compute average metrics of batches---------------------
class Metrics_batch():
def __init__(self):
self.tprs = []
self.mean_fpr = np.linspace(0, 1, 100)
self.aucs = []
self.eers = []
self.aps = []
self.correct = 0
self.total = 0
self.losses = []
def update(self, label, output):
acc = self._update_acc(label, output)
if output.size(1) == 2:
prob = torch.softmax(output, dim=1)[:, 1]
else:
prob = output
#label = 1-label
#prob = torch.softmax(output, dim=1)[:, 1]
auc, eer = self._update_auc(label, prob)
ap = self._update_ap(label, prob)
return acc, auc, eer, ap
def _update_auc(self, lab, prob):
fpr, tpr, thresholds = metrics.roc_curve(lab.squeeze().cpu().numpy(),
prob.squeeze().cpu().numpy(),
pos_label=1)
if np.isnan(fpr[0]) or np.isnan(tpr[0]):
return -1, -1
auc = metrics.auc(fpr, tpr)
interp_tpr = np.interp(self.mean_fpr, fpr, tpr)
interp_tpr[0] = 0.0
self.tprs.append(interp_tpr)
self.aucs.append(auc)
# return auc
# EER
fnr = 1 - tpr
eer = fpr[np.nanargmin(np.absolute((fnr - fpr)))]
self.eers.append(eer)
return auc, eer
def _update_acc(self, lab, output):
_, prediction = torch.max(output, 1) # argmax
correct = (prediction == lab).sum().item()
accuracy = correct / prediction.size(0)
# self.accs.append(accuracy)
self.correct = self.correct+correct
self.total = self.total+lab.size(0)
return accuracy
def _update_ap(self, label, prob):
y_true = label.cpu().detach().numpy()
y_pred = prob.cpu().detach().numpy()
ap = metrics.average_precision_score(y_true,y_pred)
self.aps.append(ap)
return np.mean(ap)
def get_mean_metrics(self):
mean_acc, std_acc = self.correct/self.total, 0
mean_auc, std_auc = self._mean_auc()
mean_err, std_err = np.mean(self.eers), np.std(self.eers)
mean_ap, std_ap = np.mean(self.aps), np.std(self.aps)
return {'acc':mean_acc, 'auc':mean_auc, 'eer':mean_err, 'ap':mean_ap}
def _mean_auc(self):
mean_tpr = np.mean(self.tprs, axis=0)
mean_tpr[-1] = 1.0
mean_auc = metrics.auc(self.mean_fpr, mean_tpr)
std_auc = np.std(self.aucs)
return mean_auc, std_auc
def clear(self):
self.tprs.clear()
self.aucs.clear()
# self.accs.clear()
self.correct=0
self.total=0
self.eers.clear()
self.aps.clear()
self.losses.clear()
# ------------ compute average metrics of all data ---------------------
class Metrics_all():
def __init__(self):
self.probs = []
self.labels = []
self.correct = 0
self.total = 0
def store(self, label, output):
prob = torch.softmax(output, dim=1)[:, 1]
_, prediction = torch.max(output, 1) # argmax
correct = (prediction == label).sum().item()
self.correct += correct
self.total += label.size(0)
self.labels.append(label.squeeze().cpu().numpy())
self.probs.append(prob.squeeze().cpu().numpy())
def get_metrics(self):
y_pred = np.concatenate(self.probs)
y_true = np.concatenate(self.labels)
# auc
fpr, tpr, thresholds = metrics.roc_curve(y_true,y_pred,pos_label=1)
auc = metrics.auc(fpr, tpr)
# eer
fnr = 1 - tpr
eer = fpr[np.nanargmin(np.absolute((fnr - fpr)))]
# ap
ap = metrics.average_precision_score(y_true,y_pred)
# acc
acc = self.correct / self.total
return {'acc':acc, 'auc':auc, 'eer':eer, 'ap':ap}
def clear(self):
self.probs.clear()
self.labels.clear()
self.correct = 0
self.total = 0
# only used to record a series of scalar value
class Recorder:
def __init__(self):
self.sum = 0
self.num = 0
def update(self, item, num=1):
if item is not None:
self.sum += item * num
self.num += num
def average(self):
if self.num == 0:
return None
return self.sum/self.num
def clear(self):
self.sum = 0
self.num = 0
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