63 lines
1.9 KiB
Python
63 lines
1.9 KiB
Python
import torch.nn as nn
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def accuracy(pred, target, topk=1):
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"""Calculate accuracy according to the prediction and target
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Args:
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pred (torch.Tensor): The model prediction.
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target (torch.Tensor): The target of each prediction
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topk (int | tuple[int], optional): If the predictions in ``topk``
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matches the target, the predictions will be regarded as
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correct ones. Defaults to 1.
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Returns:
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float | tuple[float]: If the input ``topk`` is a single integer,
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the function will return a single float as accuracy. If
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``topk`` is a tuple containing multiple integers, the
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function will return a tuple containing accuracies of
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each ``topk`` number.
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"""
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assert isinstance(topk, (int, tuple))
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if isinstance(topk, int):
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topk = (topk, )
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return_single = True
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else:
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return_single = False
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maxk = max(topk)
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_, pred_label = pred.topk(maxk, dim=1)
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pred_label = pred_label.t()
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correct = pred_label.eq(target.view(1, -1).expand_as(pred_label))
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res = []
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for k in topk:
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correct_k = correct[:k].view(-1).float().sum(0, keepdim=True)
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res.append(correct_k.mul_(100.0 / pred.size(0)))
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return res[0] if return_single else res
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class Accuracy(nn.Module):
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def __init__(self, topk=(1, )):
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"""Module to calculate the accuracy
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Args:
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topk (tuple, optional): The criterion used to calculate the
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accuracy. Defaults to (1,).
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"""
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super().__init__()
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self.topk = topk
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def forward(self, pred, target):
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"""Forward function to calculate accuracy
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Args:
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pred (torch.Tensor): Prediction of models.
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target (torch.Tensor): Target for each prediction.
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Returns:
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tuple[float]: The accuracies under different topk criterions.
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"""
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return accuracy(pred, target, self.topk)
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