text: # Copyright 2021 Huawei Technologies Co., Ltd
  #
  # 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.
  # ============================================================================
  """Dice"""
  import numpy as np
  from mindspore._checkparam import Validator as validator
  from .metric import Metric, rearrange_inputs
  
  
  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 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:
          >>> 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 = 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()
  
      def clear(self):
          """Clears the internal evaluation result."""
          self._dice_coeff_sum = 0
          self._samples_num = 0
  
      @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 should have different the dimension.
          """
          if len(inputs) != 2:
              raise ValueError('Dice need 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('y_pred and y should have same the dimension, 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
  
      def eval(self):
          r"""
          Computes the Dice.
  
          Returns:
              Float, the computed result.
  
          Raises:
              RuntimeError: If the total samples num is 0.
          """
          if self._samples_num == 0:
              raise RuntimeError('Total samples num must not be 0.')
  
          return self._dice_coeff_sum / float(self._samples_num)
  