megengine.functional.metric 源代码

# -*- coding: utf-8 -*-
from typing import Iterable, Union

import numpy as np

from ..tensor import Tensor
from .elemwise import abs, maximum, minimum
from .math import topk as _topk
from .tensor import broadcast_to, transpose

__all__ = [

[文档]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. Args: logits: model predictions of shape `[batch_size, num_classes]`, representing the probability (likelyhood) of each class. target: ground-truth labels, 1d tensor of int32. topk: specifies the topk values, could be an int or tuple of ints. Default: 1 Returns: tensor(s) of classification accuracy between 0.0 and 1.0. """ 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