text: # Copyright 2020 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.
  # ============================================================================
  """ test Activations """
  import numpy as np
  
  import mindspore.nn as nn
  from mindspore import Tensor
  from mindspore.common.api import _executor
  from ..ut_filter import non_graph_engine
  
  
  class SoftmaxNet(nn.Cell):
      def __init__(self, dim):
          super(SoftmaxNet, self).__init__()
          self.softmax = nn.Softmax(dim)
  
      def construct(self, x):
          return self.softmax(x)
  
  
  @non_graph_engine
  def test_compile():
      net = SoftmaxNet(0)
      input_tensor = Tensor(np.array([[1.2, 2.1], [2.2, 3.2]], dtype=np.float32))
      net(input_tensor)
  
  
  @non_graph_engine
  def test_compile_axis():
      net = SoftmaxNet(-1)
      prob = 355
      input_data = np.random.randn(4, 16, 1, 1).astype(np.float32) * prob
      input_tensor = Tensor(input_data)
      net(input_tensor)
  
  
  class LogSoftmaxNet(nn.Cell):
      def __init__(self, dim):
          super(LogSoftmaxNet, self).__init__()
          self.logsoftmax = nn.LogSoftmax(dim)
  
      def construct(self, x):
          return self.logsoftmax(x)
  
  
  @non_graph_engine
  def test_compile_logsoftmax():
      net = LogSoftmaxNet(0)
      input_tensor = Tensor(np.array([[1.2, 2.1], [2.2, 3.2]]))
      net(input_tensor)
  
  
  class Net1(nn.Cell):
      def __init__(self):
          super(Net1, self).__init__()
          self.relu = nn.ReLU()
  
      def construct(self, x):
          return self.relu(x)
  
  
  def test_compile_relu():
      net = Net1()
      input_data = Tensor(np.array([[1.2, 2.1], [2.2, 3.2]], dtype=np.float32))
      _executor.compile(net, input_data)
  
  
  class Net_gelu(nn.Cell):
      def __init__(self):
          super(Net_gelu, self).__init__()
          self.gelu = nn.GELU()
  
      def construct(self, x):
          return self.gelu(x)
  
  
  def test_compile_gelu():
      net = Net_gelu()
      input_data = Tensor(np.array([[1.2, 2.1], [2.2, 3.2]], dtype=np.float32))
      _executor.compile(net, input_data)
  
  
  class NetLeakyReLU(nn.Cell):
      def __init__(self, alpha):
          super(NetLeakyReLU, self).__init__()
          self.leaky_relu = nn.LeakyReLU(alpha)
  
      def construct(self, x):
          return self.leaky_relu(x)
  
  
  def test_compile_leaky_relu():
      net = NetLeakyReLU(alpha=0.1)
      input_data = Tensor(np.array([[1.6, 0, 0.6], [6, 0, -6]], dtype=np.float32))
      _executor.compile(net, input_data)
  