Source code for mindspore.nn.metrics.dice

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"""Dice"""
import numpy as np
from mindspore._checkparam import Validator as validator
from .metric import Metric, rearrange_inputs


[docs]class Dice(Metric): r""" The Dice coefficient is a set similarity metric. It is used to calculate the similarity between two samples. The value of the Dice coefficient is 1 when the segmentation result is the best and is 0 when the segmentation result is the worst. The Dice coefficient indicates the ratio of the area between two objects to the total area. The function is shown as follows: .. math:: dice = \frac{2 * (pred \bigcap true)}{pred \bigcup true} Args: smooth (float): A term added to the denominator to improve numerical stability. Should be greater than 0. Default: 1e-5. 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, 0.1], [0.9, 0.6]])) >>> y = Tensor(np.array([[0, 1], [1, 0], [0, 1]])) >>> metric = nn.Dice(smooth=1e-5) >>> metric.clear() >>> metric.update(x, y) >>> dice = metric.eval() >>> print(dice) 0.20467791371802546 """ def __init__(self, smooth=1e-5): super(Dice, self).__init__() self.smooth = validator.check_positive_float(smooth, "smooth") self._dice_coeff_sum = 0 self._samples_num = 0 self.clear()
[docs] def clear(self): """Clears the internal evaluation result.""" self._dice_coeff_sum = 0 self._samples_num = 0
[docs] @rearrange_inputs def update(self, *inputs): """ Updates the internal evaluation result :math:`y_pred` and :math:`y`. Args: inputs: Input `y_pred` and `y`. `y_pred` and `y` are Tensor, list or numpy.ndarray. `y_pred` is the predicted value, `y` is the true value. The shape of `y_pred` and `y` are both :math:`(N, ...)`. Raises: ValueError: If the number of the inputs is not 2. RuntimeError: If y_pred and y do not have the same shape. """ if len(inputs) != 2: raise ValueError('The Dice needs 2 inputs (y_pred, y), but got {}'.format(len(inputs))) y_pred = self._convert_data(inputs[0]) y = self._convert_data(inputs[1]) self._samples_num += y.shape[0] if y_pred.shape != y.shape: raise RuntimeError('The y_pred and y should have the same shape, but the shape of y_pred is {}, ' 'the shape of y is {}.'.format(y_pred.shape, y.shape)) intersection = np.dot(y_pred.flatten(), y.flatten()) unionset = np.dot(y_pred.flatten(), y_pred.flatten()) + np.dot(y.flatten(), y.flatten()) single_dice_coeff = 2 * float(intersection) / float(unionset + self.smooth) self._dice_coeff_sum += single_dice_coeff
[docs] def eval(self): r""" Computes the Dice. Returns: Float, the computed result. Raises: RuntimeError: If the total number of samples is 0. """ if self._samples_num == 0: raise RuntimeError('The total number of samples can not be 0.') return self._dice_coeff_sum / float(self._samples_num)