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/metrics.py
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| a | b/metrics.py | ||
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| 1 | import numpy as np |
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| 2 | |||
| 3 | |||
| 4 | class Metric: |
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| 5 | def __init__(self): |
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| 6 | pass |
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| 7 | |||
| 8 | def __call__(self, outputs, target, loss): |
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| 9 | raise NotImplementedError |
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| 10 | |||
| 11 | def reset(self): |
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| 12 | raise NotImplementedError |
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| 13 | |||
| 14 | def value(self): |
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| 15 | raise NotImplementedError |
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| 16 | |||
| 17 | def name(self): |
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| 18 | raise NotImplementedError |
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| 19 | |||
| 20 | |||
| 21 | class AccumulatedAccuracyMetric(Metric): |
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| 22 | """ |
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| 23 | Works with classification model |
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| 24 | """ |
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| 25 | |||
| 26 | def __init__(self): |
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| 27 | self.correct = 0 |
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| 28 | self.total = 0 |
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| 29 | |||
| 30 | def __call__(self, outputs, target, loss): |
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| 31 | pred = outputs[0].data.max(1, keepdim=True)[1] |
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| 32 | self.correct += pred.eq(target[0].data.view_as(pred)).cpu().sum() |
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| 33 | self.total += target[0].size(0) |
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| 34 | return self.value() |
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| 35 | |||
| 36 | def reset(self): |
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| 37 | self.correct = 0 |
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| 38 | self.total = 0 |
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| 39 | |||
| 40 | def value(self): |
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| 41 | return 100 * float(self.correct) / self.total |
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| 42 | |||
| 43 | def name(self): |
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| 44 | return 'Accuracy' |
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| 45 | |||
| 46 | |||
| 47 | class AverageNonzeroTripletsMetric(Metric): |
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| 48 | ''' |
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| 49 | Counts average number of nonzero triplets found in minibatches |
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| 50 | ''' |
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| 51 | |||
| 52 | def __init__(self): |
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| 53 | self.values = [] |
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| 54 | |||
| 55 | def __call__(self, outputs, target, loss): |
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| 56 | self.values.append(loss[1]) |
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| 57 | return self.value() |
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| 58 | |||
| 59 | def reset(self): |
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| 60 | self.values = [] |
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| 61 | |||
| 62 | def value(self): |
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| 63 | return np.mean(self.values) |
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| 64 | |||
| 65 | def name(self): |
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| 66 | return 'Average nonzero triplets' |