41 lines
1.3 KiB
Python
41 lines
1.3 KiB
Python
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import torch
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def th_accuracy(pad_outputs, pad_targets, ignore_label):
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"""Calculate accuracy.
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Args:
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pad_outputs (Tensor): Prediction tensors (B * Lmax, D).
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pad_targets (LongTensor): Target label tensors (B, Lmax, D).
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ignore_label (int): Ignore label id.
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Returns:
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float: Accuracy value (0.0 - 1.0).
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"""
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pad_pred = pad_outputs.view(
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pad_targets.size(0), pad_targets.size(1), pad_outputs.size(1)
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).argmax(2)
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mask = pad_targets != ignore_label
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numerator = torch.sum(pad_pred.masked_select(mask) == pad_targets.masked_select(mask))
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denominator = torch.sum(mask)
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return float(numerator) / float(denominator)
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def compute_accuracy(pad_outputs, pad_targets, ignore_label):
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"""Calculate accuracy.
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Args:
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pad_outputs (LongTensor): Prediction tensors (B, Lmax).
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pad_targets (LongTensor): Target label tensors (B, Lmax).
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ignore_label (int): Ignore label id.
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Returns:
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float: Accuracy value (0.0 - 1.0).
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"""
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mask = pad_targets != ignore_label
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numerator = torch.sum(pad_outputs.masked_select(mask) == pad_targets.masked_select(mask))
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denominator = torch.sum(mask)
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return numerator.float() / denominator.float() # (FIX:MZY):return torch.Tensor type
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