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import torch |
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class VadAccuracy(object): |
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def __init__(self, threshold: float = 0.5) -> None: |
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self.threshold = threshold |
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self.correct_count = 0. |
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self.total_count = 0. |
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def __call__(self, |
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predictions: torch.Tensor, |
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gold_labels: torch.Tensor, |
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): |
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""" |
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:param predictions: torch.Tensor, shape: [b, t, 1]. vad prob, after sigmoid activation. |
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:param gold_labels: torch.Tensor, shape: [b, t, 1]. |
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:return: |
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""" |
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predictions = (predictions > self.threshold).float() |
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correct = predictions.eq(gold_labels).float() |
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self.correct_count += correct.sum() |
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self.total_count += gold_labels.numel() |
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def get_metric(self, reset: bool = False): |
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""" |
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Returns |
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------- |
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The accumulated accuracy. |
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""" |
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if self.total_count > 1e-12: |
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accuracy = float(self.correct_count) / float(self.total_count) |
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else: |
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accuracy = 0.0 |
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if reset: |
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self.reset() |
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return {'accuracy': accuracy} |
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def reset(self): |
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self.correct_count = 0.0 |
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self.total_count = 0.0 |
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def main(): |
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inputs = torch.zeros(size=(1, 198, 1), dtype=torch.float32) |
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targets = torch.zeros(size=(1, 198, 1), dtype=torch.float32) |
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metric_fn = VadAccuracy() |
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metric_fn.__call__(inputs, targets) |
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metrics = metric_fn.get_metric() |
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print(metrics) |
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return |
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if __name__ == "__main__": |
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main() |
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