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metrics.py
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metrics.py
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import numpy as np
class Metric:
def __init__(self):
pass
def __call__(self, outputs, target, loss):
raise NotImplementedError
def reset(self):
raise NotImplementedError
def value(self):
raise NotImplementedError
def name(self):
raise NotImplementedError
class AccumulatedAccuracyMetric(Metric):
"""
Works with classification model
"""
def __init__(self):
self.correct = 0
self.total = 0
def __call__(self, outputs, target, loss):
pred = outputs[0].data.max(1, keepdim=True)[1]
self.correct += pred.eq(target[0].data.view_as(pred)).cpu().sum()
self.total += target[0].size(0)
return self.value()
def reset(self):
self.correct = 0
self.total = 0
def value(self):
return 100 * float(self.correct) / self.total
def name(self):
return 'Accuracy'
class AverageNonzeroTripletsMetric(Metric):
'''
Counts average number of nonzero triplets found in minibatches
'''
def __init__(self):
self.values = []
def __call__(self, outputs, target, loss):
self.values.append(loss[1])
return self.value()
def reset(self):
self.values = []
def value(self):
return np.mean(self.values)
def name(self):
return 'Average nonzero triplets'