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61 lines
2.0 KiB
Python
61 lines
2.0 KiB
Python
# Adapted from score written by wkentaro
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# https://github.com/wkentaro/pytorch-fcn/blob/master/torchfcn/utils.py
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import numpy as np
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class runningScore(object):
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def __init__(self, n_classes):
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self.n_classes = n_classes
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self.confusion_matrix = np.zeros((n_classes, n_classes))
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def _fast_hist(self, label_true, label_pred, n_class):
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mask = (label_true >= 0) & (label_true < n_class)
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if np.sum((label_pred[mask] < 0)) > 0:
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print(label_pred[label_pred < 0])
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hist = np.bincount(
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n_class * label_true[mask].astype(int) + label_pred[mask],
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minlength=n_class**2,
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).reshape(n_class, n_class)
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return hist
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def update(self, label_trues, label_preds):
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# print label_trues.dtype, label_preds.dtype
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for lt, lp in zip(label_trues, label_preds):
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try:
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self.confusion_matrix += self._fast_hist(
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lt.flatten(), lp.flatten(), self.n_classes
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)
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except:
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pass
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def get_scores(self):
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"""Returns accuracy score evaluation result.
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- overall accuracy
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- mean accuracy
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- mean IU
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- fwavacc
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"""
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hist = self.confusion_matrix
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acc = np.diag(hist).sum() / (hist.sum() + 0.0001)
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acc_cls = np.diag(hist) / (hist.sum(axis=1) + 0.0001)
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acc_cls = np.nanmean(acc_cls)
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iu = np.diag(hist) / (
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hist.sum(axis=1) + hist.sum(axis=0) - np.diag(hist) + 0.0001
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)
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mean_iu = np.nanmean(iu)
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freq = hist.sum(axis=1) / (hist.sum() + 0.0001)
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fwavacc = (freq[freq > 0] * iu[freq > 0]).sum()
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cls_iu = dict(zip(range(self.n_classes), iu))
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return {
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"Overall Acc": acc,
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"Mean Acc": acc_cls,
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"FreqW Acc": fwavacc,
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"Mean IoU": mean_iu,
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}, cls_iu
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def reset(self):
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self.confusion_matrix = np.zeros((self.n_classes, self.n_classes))
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