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 nn.Dense """
  import numpy as np
  import pytest
  
  import mindspore.context as context
  import mindspore.nn as nn
  from mindspore import Tensor
  from mindspore.ops import operations as P
  from mindspore.common.api import _executor
  from ..ut_filter import non_graph_engine
  
  
  def test_dense_none():
      with pytest.raises(TypeError):
          nn.Dense(3, 2, None, None)
  
  
  @non_graph_engine
  def test_dense_str_activation():
      dense = nn.Dense(1, 1, activation='relu')
      assert isinstance(dense.activation, nn.ReLU)
  
      input_data = Tensor(np.random.randint(0, 255, [1, 1]).astype(np.float32))
      dense(input_data)
  
  
  @non_graph_engine
  def test_dense_nn_activation_():
      dense = nn.Dense(1, 1, activation=nn.ReLU())
      assert isinstance(dense.activation, nn.ReLU)
  
      input_data = Tensor(np.random.randint(0, 255, [1, 1]).astype(np.float32))
      dense(input_data)
  
  
  @non_graph_engine
  def test_dense_ops_activation_():
      dense = nn.Dense(1, 1, activation=P.ReLU())
      assert isinstance(dense.activation, P.ReLU)
  
      input_data = Tensor(np.random.randint(0, 255, [1, 1]).astype(np.float32))
      dense(input_data)
  
  
  def test_dense_weight_error():
      dim_error = Tensor(np.array([[[0.1], [0.3], [0.6]], [[0.4], [0.5], [0.2]]]))
      with pytest.raises(ValueError):
          nn.Dense(3, 2, dim_error)
  
      shape_error = Tensor(np.array([[0.1, 0.3, 0.6], [0.4, 0.5, 0.2]]))
      with pytest.raises(ValueError):
          nn.Dense(2, 2, shape_error)
      with pytest.raises(ValueError):
          nn.Dense(3, 3, shape_error)
  
  
  def test_dense_bias_error():
      dim_error = Tensor(np.array([[0.5, 0.3]]))
      with pytest.raises(ValueError):
          nn.Dense(3, 2, bias_init=dim_error)
  
      shape_error = Tensor(np.array([0.5, 0.3, 0.4]))
      with pytest.raises(ValueError):
          nn.Dense(3, 2, bias_init=shape_error)
  
  
  def test_dense_channels_error():
      with pytest.raises(ValueError):
          nn.Dense(3, 0)
  
      with pytest.raises(ValueError):
          nn.Dense(-1, 2)
  
  
  class Net(nn.Cell):
      """ Net definition """
  
      def __init__(self,
                   input_channels,
                   output_channels,
                   weight='normal',
                   bias='zeros',
                   has_bias=True,
                   activation=None):
          super(Net, self).__init__()
          self.dense = nn.Dense(input_channels,
                                output_channels,
                                weight,
                                bias,
                                has_bias,
                                activation=activation)
  
      def construct(self, input_x):
          return self.dense(input_x)
  
  
  def test_compile():
      """ test_compile """
      # has bias
      weight = Tensor(np.random.randint(0, 255, [8, 64]).astype(np.float32))
      bias = Tensor(np.random.randint(0, 255, [8]).astype(np.float32))
      net = Net(64, 8, weight=weight, bias=bias)
      input_data = Tensor(np.random.randint(0, 255, [128, 64]).astype(np.float32))
      _executor.compile(net, input_data)
  
      # training
      net_train = Net(64, 8, weight=weight, bias=bias)
      net_train.set_train()
      _executor.compile(net_train, input_data)
  
  
  def test_compile_2():
      """ test_compile_2 """
      # no bias
      weight = Tensor(np.random.randint(0, 255, [8, 64]).astype(np.float32))
      net = Net(64, 8, weight=weight, has_bias=False)
      input_data = Tensor(np.random.randint(0, 255, [128, 64]).astype(np.float32))
      _executor.compile(net, input_data)
  
      # training
      net_train = Net(64, 8, weight=weight, has_bias=False)
      net_train.set_train()
      _executor.compile(net_train, input_data)
  
  
  def test_compile_3():
      """ test_compile_3 """
      # test for Graph mode
      # has bias
      context.set_context(mode=context.GRAPH_MODE)
      net = Net(128, 10)
      input_data = Tensor(np.random.randint(0, 255, [128, 128]).astype(np.float32))
      _executor.compile(net, input_data)
  
      # training
      net_train = Net(128, 10)
      net_train.set_train()
      _executor.compile(net_train, input_data)
  
  
  def test_compile_4():
      """ test_compile_4 """
      # test for Graph mode
      # no bias
      context.set_context(mode=context.GRAPH_MODE)
      net = Net(128, 10, has_bias=False)
      input_data = Tensor(np.random.randint(0, 255, [128, 128]).astype(np.float32))
      _executor.compile(net, input_data)
  
      # training
      net_train = Net(128, 10, has_bias=False)
      net_train.set_train()
      _executor.compile(net_train, input_data)
  