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- # -*- coding: utf-8 -*-
- # MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
- #
- # Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
- #
- # Unless required by applicable law or agreed to in writing,
- # software distributed under the License is distributed on an
- # "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- from typing import Iterable, Union
-
- import numpy as np
-
- from ..tensor import Tensor
- from .math import topk as _topk
- from .tensor import broadcast_to, transpose
-
-
- def topk_accuracy(
- logits: Tensor, target: Tensor, topk: Union[int, Iterable[int]] = 1
- ) -> Union[Tensor, Iterable[Tensor]]:
- r"""
- Calculates the classification accuracy given predicted logits and ground-truth labels.
-
- :param logits: model predictions of shape `[batch_size, num_classes]`,
- representing the probability (likelyhood) of each class.
- :param target: ground-truth labels, 1d tensor of int32.
- :param topk: specifies the topk values, could be an int or tuple of ints. Default: 1
- :return: tensor(s) of classification accuracy between 0.0 and 1.0.
-
- Examples:
-
- .. testcode::
-
- import numpy as np
- from megengine import tensor
- import megengine.functional as F
-
- logits = tensor(np.arange(80, dtype=np.int32).reshape(8,10))
- target = tensor(np.arange(8, dtype=np.int32))
- top1, top5 = F.metric.topk_accuracy(logits, target, (1, 5))
- print(top1.numpy(), top5.numpy())
-
- Outputs:
-
- .. testoutput::
-
- 0.0 0.375
- """
- if isinstance(topk, int):
- topk = (topk,)
- _, pred = _topk(logits, k=max(topk), descending=True)
- accs = []
- for k in topk:
- correct = pred[:, :k].detach() == broadcast_to(
- transpose(target, (0, "x")), (target.shape[0], k)
- )
- accs.append(correct.astype(np.float32).sum() / target.shape[0])
- if len(topk) == 1: # type: ignore[arg-type]
- accs = accs[0]
- return accs
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