-
Notifications
You must be signed in to change notification settings - Fork 450
/
metrics.py
162 lines (138 loc) · 5.76 KB
/
metrics.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
## Created by: Hang Zhang
## ECE Department, Rutgers University
## Email: zhang.hang@rutgers.edu
## Copyright (c) 2017
##
## This source code is licensed under the MIT-style license found in the
## LICENSE file in the root directory of this source tree
##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
import threading
import numpy as np
import torch
__all__ = ['accuracy', 'get_pixacc_miou',
'SegmentationMetric', 'batch_intersection_union', 'batch_pix_accuracy',
'pixel_accuracy', 'intersection_and_union']
def accuracy(output, target, topk=(1,)):
"""Computes the accuracy over the k top predictions for the specified values of k"""
with torch.no_grad():
maxk = max(topk)
batch_size = target.size(0)
_, pred = output.topk(maxk, 1, True, True)
pred = pred.t()
correct = pred.eq(target.view(1, -1).expand_as(pred))
res = []
for k in topk:
correct_k = correct[:k].view(-1).float().sum(0, keepdim=True)
res.append(correct_k.mul_(100.0 / batch_size))
return res
def get_pixacc_miou(total_correct, total_label, total_inter, total_union):
pixAcc = 1.0 * total_correct / (np.spacing(1) + total_label)
IoU = 1.0 * total_inter / (np.spacing(1) + total_union)
mIoU = IoU.mean()
return pixAcc, mIoU
class SegmentationMetric(object):
"""Computes pixAcc and mIoU metric scroes
"""
def __init__(self, nclass):
self.nclass = nclass
self.lock = threading.Lock()
self.reset()
def update(self, labels, preds):
def evaluate_worker(self, label, pred):
correct, labeled = batch_pix_accuracy(
pred, label)
inter, union = batch_intersection_union(
pred, label, self.nclass)
with self.lock:
self.total_correct += correct
self.total_label += labeled
self.total_inter += inter
self.total_union += union
return
if isinstance(preds, torch.Tensor):
evaluate_worker(self, labels, preds)
elif isinstance(preds, (list, tuple)):
threads = [threading.Thread(target=evaluate_worker,
args=(self, label, pred),
)
for (label, pred) in zip(labels, preds)]
for thread in threads:
thread.start()
for thread in threads:
thread.join()
else:
raise NotImplemented
def get_all(self):
return self.total_correct, self.total_label, self.total_inter, self.total_union
def get(self):
return get_pixacc_miou(self.total_correct, self.total_label, self.total_inter, self.total_union)
def reset(self):
self.total_inter = 0
self.total_union = 0
self.total_correct = 0
self.total_label = 0
return
def batch_pix_accuracy(output, target):
"""Batch Pixel Accuracy
Args:
predict: input 4D tensor
target: label 3D tensor
"""
_, predict = torch.max(output, 1)
predict = predict.cpu().numpy().astype('int64') + 1
target = target.cpu().numpy().astype('int64') + 1
pixel_labeled = np.sum(target > 0)
pixel_correct = np.sum((predict == target)*(target > 0))
assert pixel_correct <= pixel_labeled, \
"Correct area should be smaller than Labeled"
return pixel_correct, pixel_labeled
def batch_intersection_union(output, target, nclass):
"""Batch Intersection of Union
Args:
predict: input 4D tensor
target: label 3D tensor
nclass: number of categories (int)
"""
_, predict = torch.max(output, 1)
mini = 1
maxi = nclass
nbins = nclass
predict = predict.cpu().numpy().astype('int64') + 1
target = target.cpu().numpy().astype('int64') + 1
predict = predict * (target > 0).astype(predict.dtype)
intersection = predict * (predict == target)
# areas of intersection and union
area_inter, _ = np.histogram(intersection, bins=nbins, range=(mini, maxi))
area_pred, _ = np.histogram(predict, bins=nbins, range=(mini, maxi))
area_lab, _ = np.histogram(target, bins=nbins, range=(mini, maxi))
area_union = area_pred + area_lab - area_inter
assert (area_inter <= area_union).all(), \
"Intersection area should be smaller than Union area"
return area_inter, area_union
# ref https://github.com/CSAILVision/sceneparsing/blob/master/evaluationCode/utils_eval.py
def pixel_accuracy(im_pred, im_lab):
im_pred = np.asarray(im_pred)
im_lab = np.asarray(im_lab)
# Remove classes from unlabeled pixels in gt image.
# We should not penalize detections in unlabeled portions of the image.
pixel_labeled = np.sum(im_lab > 0)
pixel_correct = np.sum((im_pred == im_lab) * (im_lab > 0))
#pixel_accuracy = 1.0 * pixel_correct / pixel_labeled
return pixel_correct, pixel_labeled
def intersection_and_union(im_pred, im_lab, num_class):
im_pred = np.asarray(im_pred)
im_lab = np.asarray(im_lab)
# Remove classes from unlabeled pixels in gt image.
im_pred = im_pred * (im_lab > 0)
# Compute area intersection:
intersection = im_pred * (im_pred == im_lab)
area_inter, _ = np.histogram(intersection, bins=num_class-1,
range=(1, num_class - 1))
# Compute area union:
area_pred, _ = np.histogram(im_pred, bins=num_class-1,
range=(1, num_class - 1))
area_lab, _ = np.histogram(im_lab, bins=num_class-1,
range=(1, num_class - 1))
area_union = area_pred + area_lab - area_inter
return area_inter, area_union