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metrics.py
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metrics.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Feb 17 14:15:59 2021
@author: hossein
here we can find different types of metrics
that are define for person-attribute detection.
this is Hossein Bodaghies thesis
"""
import torch
'''
first defined tp tn fn fp and then manipulate them to
calculate precision recall and accuracy and f1
precision = tp/(tp+fp)
recall = tp/(tp+fn)
accuracy = tp+tf/(tp+tn+fp+fn)
f1 = 2*(precision+recall)/(precision+recall)
'''
def tensor_metrics(target,predict):
'''
Parameters
----------
target : torch-tensor
(N,C) the value of each element should be zero or one.
N is batch size and C is number of classes.
C must be more than one (not recommended for boolians).
predict : torch-tensor
(N,C) the value of element should be zero or one.
N is batch size and C is number of classes.
C must be more than one (not recommended for boolians).
Returns
-------
list
[precision,
recall,
accuracy,
f1,
precision_total,
recall_total,
accuracy_total,
f1_total].
'''
eps = 1e-6
true_positive = torch.zeros((predict.size()[1]))
true_negative = torch.zeros((predict.size()[1]))
false_positive = torch.zeros((predict.size()[1]))
false_negative = torch.zeros((predict.size()[1]))
true_positive_total = 0
true_negative_total = 0
false_positive_total = 0
false_negative_total = 0
for i in range(len(predict)):
for j in range(predict.size()[1]):
if predict[i,j] == target[i,j] and target[i,j] == 1:
true_positive[j] += 1
true_positive_total += 1
elif predict[i,j] == target[i,j] and target[i,j] == 0:
true_negative[j] += 1
true_negative_total += 1
elif predict[i,j] != target[i,j] and target[i,j] == 1:
false_negative[j] += 1
false_negative_total += 1
elif predict[i,j] != target[i,j] and target[i,j] == 0:
false_positive[j] += 1
false_positive_total += 1
precision = torch.zeros((predict.size()[1]))
recall = torch.zeros((predict.size()[1]))
accuracy = torch.zeros((predict.size()[1]))
f1 = torch.zeros((predict.size()[1]))
mean_accuracy = torch.zeros((predict.size()[1]))
precision_total = true_positive_total/(true_positive_total+false_positive_total+eps)
recall_total = true_positive_total/(true_positive_total+false_negative_total+eps)
accuracy_total = (true_positive_total+true_negative_total)/(true_positive_total+false_negative_total+true_negative_total+false_positive_total+eps)
f1_total = 2*(precision_total*recall_total)/(precision_total+recall_total+eps)
for j in range(predict.size()[1]):
precision[j] = true_positive[j]/(true_positive[j]+false_positive[j]+eps)
recall[j] = true_positive[j]/(true_positive[j]+false_negative[j]+eps)
accuracy[j] = (true_positive[j]+true_negative[j])/(true_positive[j]+false_negative[j]+true_negative[j]+false_positive[j]+eps)
f1[j] = 2*(precision[j]*recall[j])/(precision[j]+recall[j]+eps)
mean_accuracy[j] = true_positive[j]/(true_positive[j]+false_negative[j]+eps) + true_negative[j]/(true_negative[j]+false_positive[j]+eps)
mean_accuracy = mean_accuracy/2
mean_accuracy_total = torch.mean(mean_accuracy).item()
return [precision,
recall,
accuracy,
f1,
mean_accuracy,
precision_total,
recall_total,
accuracy_total,
f1_total,
mean_accuracy_total]
def boolian_metrics(target,predict):
eps = 1e-6
true_positive_total = 0
true_negative_total = 0
false_positive_total = 0
false_negative_total = 0
for i in range(len(predict)):
if predict[i] == target[i] and target[i] == 1:
true_positive_total += 1
elif predict[i] == target[i] and target[i] == 0:
true_negative_total += 1
elif predict[i] != target[i] and target[i] == 1:
false_negative_total += 1
elif predict[i] != target[i] and target[i] == 0:
false_positive_total += 1
precision_total = true_positive_total/(true_positive_total+false_positive_total+eps)
recall_total = true_positive_total/(true_positive_total+false_negative_total+eps)
accuracy_total = (true_positive_total+true_negative_total)/(true_positive_total+false_negative_total+true_negative_total+false_positive_total+eps)
f1_total = 2*(precision_total*recall_total)/(precision_total+recall_total+eps)
return [precision_total,
recall_total,
accuracy_total,
f1_total]
def tensor_metrics_detailes(target,predict):
'''
Parameters
----------
target : torch-tensor
(N,C) the value of each element should be zero or one.
N is batch size and C is number of classes.
C must be more than one (not recommended for boolians).
predict : torch-tensor
(N,C) the value of element should be zero or one.
N is batch size and C is number of classes.
C must be more than one (not recommended for boolians).
Returns
-------
list
[precision,
recall,
accuracy,
f1,
precision_total,
recall_total,
accuracy_total,
f1_total].
'''
eps = 1e-6
true_positive = torch.zeros((predict.size()[1]))
true_negative = torch.zeros((predict.size()[1]))
false_positive = torch.zeros((predict.size()[1]))
false_negative = torch.zeros((predict.size()[1]))
real_positive = torch.zeros((predict.size()[1]))
real_negative = torch.zeros((predict.size()[1]))
true_positive_total = 0
true_negative_total = 0
false_positive_total = 0
false_negative_total = 0
for i in range(len(predict)):
for j in range(predict.size()[1]):
if predict[i,j] == target[i,j] and target[i,j] == 1:
real_positive[j] += 1
true_positive[j] += 1
true_positive_total += 1
elif predict[i,j] == target[i,j] and target[i,j] == 0:
real_negative[j] += 1
true_negative[j] += 1
true_negative_total += 1
elif predict[i,j] != target[i,j] and target[i,j] == 1:
real_positive[j] += 1
false_negative[j] += 1
false_negative_total += 1
elif predict[i,j] != target[i,j] and target[i,j] == 0:
real_negative[j] += 1
false_positive[j] += 1
false_positive_total += 1
precision = torch.zeros((predict.size()[1]))
recall = torch.zeros((predict.size()[1]))
accuracy = torch.zeros((predict.size()[1]))
f1 = torch.zeros((predict.size()[1]))
precision_total = true_positive_total/(true_positive_total+false_positive_total+eps)
recall_total = true_positive_total/(true_positive_total+false_negative_total+eps)
accuracy_total = (true_positive_total+true_negative_total)/(true_positive_total+false_negative_total+true_negative_total+false_positive_total+eps)
f1_total = 2*(precision_total*recall_total)/(precision_total+recall_total+eps)
for j in range(predict.size()[1]):
precision[j] = true_positive[j]/(true_positive[j]+false_negative[j]+eps)
recall[j] = true_positive[j]/(true_positive[j]+false_negative[j]+eps)
accuracy[j] = (true_positive[j]+true_negative[j])/(true_positive[j]+false_negative[j]+true_negative[j]+false_positive[j]+eps)
f1[j] = 2*(precision[j]*recall[j])/(precision[j]+recall[j]+eps)
return [precision,
recall,
accuracy,
f1,
precision_total,
recall_total,
accuracy_total,
f1_total,
real_positive,
true_positive,
false_positive,
real_negative,
true_negative,
false_negative]
def category_metrics(target, predict):
t_p = 0
t = target.argmax(dim=1)
p = predict.argmax(dim=1)
for i in range(len(target)):
if t[i] == p[i]:
t_p += 1
acc = t_p / len(target)
return acc
class instance_metrics(torch.nn.Module):
def __init__(self, weights=None):
super(instance_metrics, self).__init__()
self.weights = weights
self.eps=1e-6
def forward(self, y_true, y_pred):
result = {}
true_positives = torch.sum( (y_true * y_pred))
predicted_positives = torch.sum(y_pred)
possible_positives = torch.sum(y_true)
precision = true_positives / (predicted_positives + self.eps)
recall = true_positives / (possible_positives + self.eps)
F1 = 2 * ( (precision * recall) / (precision + recall) )
result.update({'precision':precision, 'recall':recall, 'F1':F1})
return result
def IOU(y_pred, y_true):
intersection = torch.sum(torch.abs(y_true * y_pred), axis=-1)
union = torch.sum(y_true,-1) + torch.sum(y_pred,-1) - intersection
IOU = intersection / union
return IOU
# distances: 'euclidean' , 'cosin_similarity'
# takes two dictionary that should containe ['id']
def eval_map(query, gallery, model, device, distance='euclidean', num_accept=20, thr_needed=False, thr=0.5):
"""
Parameters
----------
query: TYPE dict
DESCRIPTION.
gallery_path : TYPE dict
DESCRIPTION. are two dictionaries that should containe 'id' and 'img_names' as key
model : TYPE
DESCRIPTION. takes feature extraction part of any model
device : TYPE
DESCRIPTION.
distance : TYPE, optional
DESCRIPTION. The default is 'euclidean'. 'euclidean' , 'cosin_similarity'
num_accept : TYPE, optional
DESCRIPTION. The default is 20.
thr_needed : TYPE, optional
DESCRIPTION. The default is False. in some cases insted of sorting in n number
of best choices we put a threshold and except only greater than threshold
thr : TYPE, optional
DESCRIPTION. The default is 0.5.
Returns
-------
"""
model.eval()
with torch.no_grad():
for idx, data in enumerate():
pass
return