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import numpy as np |
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class Meter(object): |
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def __init__(self, name, val, avg): |
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self.name = name |
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self.val = val |
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self.avg = avg |
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def __repr__(self): |
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return "{name}: {val:.6f} ({avg:.6f})".format( |
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name=self.name, val=self.val, avg=self.avg |
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) |
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def __format__(self, *tuples, **kwargs): |
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return self.__repr__() |
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class AverageMeter(object): |
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"""Computes and stores the average and current value""" |
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def __init__(self): |
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self.reset() |
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def reset(self): |
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self.val = {} |
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self.sum = {} |
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self.count = {} |
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def update(self, batch=1, **kwargs): |
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val = {} |
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for k in kwargs: |
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val[k] = kwargs[k] / float(batch) |
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self.val.update(val) |
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for k in kwargs: |
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if k not in self.sum: |
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self.sum[k] = 0 |
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self.count[k] = 0 |
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self.sum[k] += kwargs[k] |
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self.count[k] += batch |
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def __repr__(self): |
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s = '' |
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for k in self.sum: |
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s += self.format_str(k) |
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return s |
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def format_str(self, attr): |
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return "{name}: {val:.6f} ({avg:.6f}) ".format( |
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name=attr, |
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val=float(self.val[attr]), |
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avg=float(self.sum[attr]) / self.count[attr]) |
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def __getattr__(self, attr): |
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if attr in self.__dict__: |
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return super(AverageMeter, self).__getattr__(attr) |
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if attr not in self.sum: |
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print("invalid key '{}'".format(attr)) |
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return Meter(attr, 0, 0) |
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return Meter(attr, self.val[attr], self.avg(attr)) |
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def avg(self, attr): |
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return float(self.sum[attr]) / self.count[attr] |
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class IouMeter(object): |
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def __init__(self, thrs, sz): |
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self.sz = sz |
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self.iou = np.zeros((sz, len(thrs)), dtype=np.float32) |
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self.thrs = thrs |
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self.reset() |
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def reset(self): |
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self.iou.fill(0.) |
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self.n = 0 |
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def add(self, output, target): |
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if self.n >= len(self.iou): |
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return |
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target, output = target.squeeze(), output.squeeze() |
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for i, thr in enumerate(self.thrs): |
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pred = output > thr |
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mask_sum = (pred == 1).astype(np.uint8) + (target > 0).astype(np.uint8) |
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intxn = np.sum(mask_sum == 2) |
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union = np.sum(mask_sum > 0) |
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if union > 0: |
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self.iou[self.n, i] = intxn / union |
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elif union == 0 and intxn == 0: |
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self.iou[self.n, i] = 1 |
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self.n += 1 |
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def value(self, s): |
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nb = max(int(np.sum(self.iou > 0)), 1) |
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iou = self.iou[:nb] |
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def is_number(s): |
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try: |
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float(s) |
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return True |
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except ValueError: |
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return False |
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if s == 'mean': |
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res = np.mean(iou, axis=0) |
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elif s == 'median': |
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res = np.median(iou, axis=0) |
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elif is_number(s): |
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res = np.sum(iou > float(s), axis=0) / float(nb) |
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return res |
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if __name__ == '__main__': |
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avg = AverageMeter() |
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avg.update(time=1.1, accuracy=.99) |
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avg.update(time=1.0, accuracy=.90) |
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print(avg) |
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print(avg.time) |
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print(avg.time.avg) |
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print(avg.time.val) |
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print(avg.SS) |
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