# Source code for mindspore.nn.metrics.topk

# Copyright 2020-2021 Huawei Technologies Co., Ltd
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"""Topk."""
from __future__ import absolute_import

import numpy as np

from .metric import Metric, rearrange_inputs, _check_onehot_data

[文档]class TopKCategoricalAccuracy(Metric): """ Calculates the top-k categorical accuracy. Args: k (int): Specifies the top-k categorical accuracy to compute. Raises: TypeError: If `k` is not int. ValueError: If `k` is less than 1. Supported Platforms: ``Ascend`` ``GPU`` ``CPU`` Examples: >>> import mindspore >>> import numpy as np >>> from mindspore import nn, Tensor >>> >>> x = Tensor(np.array([[0.2, 0.5, 0.3, 0.6, 0.2], [0.1, 0.35, 0.5, 0.2, 0.], ... [0.9, 0.6, 0.2, 0.01, 0.3]]), mindspore.float32) >>> y = Tensor(np.array([2, 0, 1]), mindspore.float32) >>> topk = nn.TopKCategoricalAccuracy(3) >>> topk.clear() >>> topk.update(x, y) >>> output = topk.eval() >>> print(output) 0.6666666666666666 """ def __init__(self, k): super(TopKCategoricalAccuracy, self).__init__() if not isinstance(k, int): raise TypeError("For 'TopKCategoricalAccuracy', the type of " "the argument 'k' should be int, but got 'k' type: {}.".format(type(k))) if k < 1: raise ValueError("For 'TopKCategoricalAccuracy', " "the argument 'k' must be at least 1, but got 'k' value: {}.".format(k)) self.k = k self.clear()
[文档] def clear(self): """Clear the internal evaluation result.""" self._correct_num = 0 self._samples_num = 0
[文档] @rearrange_inputs def update(self, *inputs): """ Updates the internal evaluation result `y_pred` and `y`. Args: inputs: Input `y_pred` and `y`. `y_pred` and `y` are Tensor, list or numpy.ndarray. `y_pred` is in most cases (not strictly) a list of floating numbers in range :math:`[0, 1]` and the shape is :math:`(N, C)`, where :math:`N` is the number of cases and :math:`C` is the number of categories. `y` contains values of integers. The shape is :math:`(N, C)` if one-hot encoding is used. Shape can also be :math:`(N,)` if category index is used. Note: The method `update` must receive input of the form :math:`(y_{pred}, y)`. If some samples have the same accuracy, the first sample will be chosen. """ if len(inputs) != 2: raise ValueError("For 'TopKCategoricalAccuracy.update', " "it needs 2 inputs (predicted value, true value), " "but got 'inputs' size: {}.".format(len(inputs))) y_pred = self._convert_data(inputs[0]) y = self._convert_data(inputs[1]) if y_pred.ndim == y.ndim and _check_onehot_data(y): y = y.argmax(axis=1) indices = np.argsort(-y_pred, axis=1)[:, :self.k] repeated_y = y.reshape(-1, 1).repeat(self.k, axis=1) correct = np.equal(indices, repeated_y).sum(axis=1) self._correct_num += correct.sum() self._samples_num += repeated_y.shape[0]
[文档] def eval(self): """ Computes the top-k categorical accuracy. Returns: numpy.float64, computed result. """ if self._samples_num == 0: raise RuntimeError("The 'TopKCategoricalAccuracy' " "can not be calculated, because the number of samples is 0, " "please check whether your inputs (predicted value, true value) are empty, " "or has called update method before calling eval method.") return self._correct_num / self._samples_num
[文档]class Top1CategoricalAccuracy(TopKCategoricalAccuracy): """ Calculates the top-1 categorical accuracy. This class is a specialized class for TopKCategoricalAccuracy. Refer to :class:`TopKCategoricalAccuracy` for more details. Supported Platforms: ``Ascend`` ``GPU`` ``CPU`` Examples: >>> import numpy as np >>> from mindspore import nn, Tensor >>> >>> x = Tensor(np.array([[0.2, 0.5, 0.3, 0.6, 0.2], [0.1, 0.35, 0.5, 0.2, 0.], ... [0.9, 0.6, 0.2, 0.01, 0.3]]), mindspore.float32) >>> y = Tensor(np.array([2, 0, 1]), mindspore.float32) >>> topk = nn.Top1CategoricalAccuracy() >>> topk.clear() >>> topk.update(x, y) >>> output = topk.eval() >>> print(output) 0.0 """ def __init__(self): super(Top1CategoricalAccuracy, self).__init__(1)
[文档]class Top5CategoricalAccuracy(TopKCategoricalAccuracy): """ Calculates the top-5 categorical accuracy. This class is a specialized class for TopKCategoricalAccuracy. Refer to :class:`TopKCategoricalAccuracy` for more details. Supported Platforms: ``Ascend`` ``GPU`` ``CPU`` Examples: >>> import numpy as np >>> from mindspore import nn, Tensor >>> >>> x = Tensor(np.array([[0.2, 0.5, 0.3, 0.6, 0.2], [0.1, 0.35, 0.5, 0.2, 0.], ... [0.9, 0.6, 0.2, 0.01, 0.3]]), mindspore.float32) >>> y = Tensor(np.array([2, 0, 1]), mindspore.float32) >>> topk = nn.Top5CategoricalAccuracy() >>> topk.clear() >>> topk.update(x, y) >>> output = topk.eval() >>> print(output) 1.0 """ def __init__(self): super(Top5CategoricalAccuracy, self).__init__(5)