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import torch |
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def compute_f1_score(preds, gts, ignores=[]): |
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"""Compute the F1-score of prediction. |
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Args: |
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preds (Tensor): The predicted probability NxC map |
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with N and C being the sample number and class |
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number respectively. |
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gts (Tensor): The ground truth vector of size N. |
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ignores (list): The index set of classes that are ignored when |
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reporting results. |
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Note: all samples are participated in computing. |
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Returns: |
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The numpy list of f1-scores of valid classes. |
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""" |
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C = preds.size(1) |
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classes = torch.LongTensor(sorted(set(range(C)) - set(ignores))) |
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hist = torch.bincount( |
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gts * C + preds.argmax(1), minlength=C**2).view(C, C).float() |
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diag = torch.diag(hist) |
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recalls = diag / hist.sum(1).clamp(min=1) |
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precisions = diag / hist.sum(0).clamp(min=1) |
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f1 = 2 * recalls * precisions / (recalls + precisions).clamp(min=1e-8) |
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return f1[classes].cpu().numpy() |
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