code: # Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
  #
  # 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.
  
  from __future__ import division
  from __future__ import print_function
  
  import unittest
  import os
  import six
  import numpy as np
  import shutil
  import copy
  
  import paddle
  from paddle import fluid
  
  from paddle.incubate.hapi.model import Model, Input
  from paddle.incubate.hapi.loss import CrossEntropy, SoftmaxWithCrossEntropy
  
  
  def stable_softmax(x):
      """Compute the softmax of vector x in a numerically stable way."""
      # clip to shiftx, otherwise, when calc loss with
      # log(exp(shiftx)), may get log(0)=INF
      shiftx = (x - np.max(x)).clip(-64.)
      exps = np.exp(shiftx)
      return exps / np.sum(exps)
  
  
  def randomize_probability(batch_size, class_num, dtype='float32'):
      prob = np.random.uniform(
          0.1, 1.0, size=(batch_size, class_num)).astype(dtype)
      prob_sum = prob.sum(axis=1)
      for i in six.moves.xrange(len(prob)):
          prob[i] /= prob_sum[i]
      return prob
  
  
  def numpy_ce(x, label):
      return np.asmatrix(
          [[-np.log(x[i][label[i][0]])] for i in range(x.shape[0])],
          dtype="float32").mean()
  
  
  class TestLoss(unittest.TestCase):
      def test_cross_entropy(self):
          class_num = 100
          batch_size = 128
          inputs = [randomize_probability(128, class_num) for _ in range(2)]
  
          labels = [
              np.random.randint(
                  0, class_num, (batch_size, 1), dtype="int64") for _ in range(2)
          ]
  
          gt_out = [numpy_ce(inputs[i], labels[i]) for i in range(2)]
  
          fluid.enable_dygraph()
          cross_entropy = CrossEntropy()
          out = cross_entropy(
              [fluid.dygraph.to_variable(x) for x in inputs],
              [fluid.dygraph.to_variable(label) for label in labels])
          out = [o.numpy() for o in out]
  
          for o, g in zip(out, gt_out):
              np.testing.assert_allclose(o, g, atol=1e-5)
  
      def test_soft_cross_entronpy(self):
          class_num = 100
          batch_size = 128
  
          inputs = [randomize_probability(128, class_num) for _ in range(2)]
  
          labels = [
              np.random.randint(
                  0, class_num, (batch_size, 1), dtype="int64") for _ in range(2)
          ]
  
          fluid.enable_dygraph()
          softmax_cross_entropy = SoftmaxWithCrossEntropy()
  
          softmax_cross_entropy(
              [fluid.dygraph.to_variable(x) for x in inputs],
              [fluid.dygraph.to_variable(label) for label in labels])
  
          softmax_cross_entropy = SoftmaxWithCrossEntropy(average=False)
  
          inputs = [randomize_probability(128, class_num)]
  
          labels = [
              np.random.randint(
                  0, class_num, (batch_size, 1), dtype="int64")
          ]
  
          softmax_cross_entropy([fluid.dygraph.to_variable(x) for x in inputs],
                                fluid.dygraph.to_variable(labels[0]))
  
  
  if __name__ == '__main__':
      unittest.main()
  
apis: numpy.random.uniform numpy.sum numpy.exp numpy.max numpy.log numpy.testing.assert_allclose numpy.random.randint