MegEngine/imperative/python/megengine/functional/metric.py

# -*- 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__ = [
    "topk_accuracy",
]


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