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_lenet_core_after_exception"""
  import numpy as np
  import pytest
  
  import mindspore.nn as nn
  from mindspore.common.api import _cell_graph_executor
  from mindspore.common.tensor import Tensor
  from mindspore.ops import operations as P
  from ....train_step_wrap import train_step_with_loss_warp
  
  
  class LeNet5(nn.Cell):
      """LeNet5 definition"""
  
      def __init__(self):
          super(LeNet5, self).__init__()
          self.conv1 = nn.Conv2d(3, 6, 5, pad_mode="valid")
          self.conv2 = nn.Conv2d(6, 16, 5, pad_mode="valid")
          self.fc1 = nn.Dense(16 * 5 * 5, 120)
          self.fc2 = nn.Dense(120, 84)
          self.fc3 = nn.Dense(84, 3)
          self.relu = nn.ReLU()
          self.max_pool2d = nn.MaxPool2d(kernel_size=2)
          self.flatten = P.Flatten()
  
      def construct(self, x):
          x = self.max_pool2d(self.relu(self.conv1(x)))
          x = self.max_pool2d(self.relu(self.conv2(x)))
          x = self.flatten(x)
          x = self.relu(self.fc1(x))
          x = self.relu(self.fc2(x))
          x = self.fc3(x)
          return x
  
  
  def test_lenet5_exception():
      in1 = np.ones([1, 1, 32, 32]).astype(np.float32) * 0.01
      in2 = np.zeros([1, 10]).astype(np.float32)
      predict = Tensor(in1)
      label = Tensor(in2)
      net = train_step_with_loss_warp(LeNet5())
      with pytest.raises(RuntimeError) as info:
          _cell_graph_executor.compile(net, predict, label)
      assert "'C_in' of input 'x' shape divide by parameter 'group' should be " in str(info.value)
  