File size: 3,353 Bytes
62a2f1c |
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 |
import numpy as np
from sklearn.metrics import confusion_matrix
class _StreamMetrics(object):
def __init__(self):
""" Overridden by subclasses """
raise NotImplementedError()
def update(self, gt, pred):
""" Overridden by subclasses """
raise NotImplementedError()
def get_results(self):
""" Overridden by subclasses """
raise NotImplementedError()
def to_str(self, metrics):
""" Overridden by subclasses """
raise NotImplementedError()
def reset(self):
""" Overridden by subclasses """
raise NotImplementedError()
class StreamSegMetrics(_StreamMetrics):
"""
Stream Metrics for Semantic Segmentation Task
"""
def __init__(self, n_classes):
self.n_classes = n_classes
self.confusion_matrix = np.zeros((n_classes, n_classes))
def update(self, label_trues, label_preds):
for lt, lp in zip(label_trues, label_preds):
self.confusion_matrix += self._fast_hist( lt.flatten(), lp.flatten() )
@staticmethod
def to_str(results):
string = "\n"
for k, v in results.items():
if k!="Class IoU":
string += "%s: %f\n"%(k, v)
#string+='Class IoU:\n'
#for k, v in results['Class IoU'].items():
# string += "\tclass %d: %f\n"%(k, v)
return string
def _fast_hist(self, label_true, label_pred):
mask = (label_true >= 0) & (label_true < self.n_classes)
hist = np.bincount(
self.n_classes * label_true[mask].astype(int) + label_pred[mask],
minlength=self.n_classes ** 2,
).reshape(self.n_classes, self.n_classes)
return hist
def get_results(self):
"""Returns accuracy score evaluation result.
- overall accuracy
- mean accuracy
- mean IU
- fwavacc
"""
hist = self.confusion_matrix
acc = np.diag(hist).sum() / hist.sum()
acc_cls = np.diag(hist) / hist.sum(axis=1)
acc_cls = np.nanmean(acc_cls)
iu = np.diag(hist) / (hist.sum(axis=1) + hist.sum(axis=0) - np.diag(hist))
mean_iu = np.nanmean(iu)
freq = hist.sum(axis=1) / hist.sum()
fwavacc = (freq[freq > 0] * iu[freq > 0]).sum()
cls_iu = dict(zip(range(self.n_classes), iu))
return {
"Overall Acc": acc,
"Mean Acc": acc_cls,
"FreqW Acc": fwavacc,
"Mean IoU": mean_iu,
"Class IoU": cls_iu,
}
def reset(self):
self.confusion_matrix = np.zeros((self.n_classes, self.n_classes))
class AverageMeter(object):
"""Computes average values"""
def __init__(self):
self.book = dict()
def reset_all(self):
self.book.clear()
def reset(self, id):
item = self.book.get(id, None)
if item is not None:
item[0] = 0
item[1] = 0
def update(self, id, val):
record = self.book.get(id, None)
if record is None:
self.book[id] = [val, 1]
else:
record[0]+=val
record[1]+=1
def get_results(self, id):
record = self.book.get(id, None)
assert record is not None
return record[0] / record[1]
|