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- # LICENSE HEADER MANAGED BY add-license-header
- #
- # Copyright 2018 Kornia Team
- #
- # Licensed under the Apache License, Version 2.0 (the "License");
- # you may not use this file except in compliance with the License.
- # You may obtain a copy of the License at
- #
- # http://www.apache.org/licenses/LICENSE-2.0
- #
- # Unless required by applicable law or agreed to in writing, software
- # distributed under the License is distributed on an "AS IS" BASIS,
- # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- # See the License for the specific language governing permissions and
- # limitations under the License.
- #
- from typing import List, Tuple
- from kornia.core import Tensor
- def accuracy(pred: Tensor, target: Tensor, topk: Tuple[int, ...] = (1,)) -> List[Tensor]:
- """Compute the accuracy over the k top predictions for the specified values of k.
- Args:
- pred: the input tensor with the logits to evaluate.
- target: the tensor containing the ground truth.
- topk: the expected topk ranking.
- Example:
- >>> logits = torch.tensor([[0, 1, 0]])
- >>> target = torch.tensor([[1]])
- >>> accuracy(logits, target)
- [tensor(100.)]
- """
- maxk = min(max(topk), pred.size()[1])
- batch_size = target.size(0)
- _, pred = pred.topk(maxk, 1, True, True)
- pred = pred.t()
- correct = pred.eq(target.reshape(1, -1).expand_as(pred))
- return [correct[: min(k, maxk)].reshape(-1).float().sum(0) * 100.0 / batch_size for k in topk]
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