text: # Copyright (c) 2018 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 print_function
  
  import os
  import contextlib
  import unittest
  import numpy as np
  import six
  import pickle
  
  import paddle
  import paddle.fluid as fluid
  import paddle.fluid.dygraph as dygraph
  from paddle.fluid import core
  from paddle.fluid.optimizer import SGDOptimizer
  from paddle.fluid.dygraph.nn import Conv2D, Pool2D, FC
  from paddle.fluid.dygraph.base import to_variable
  
  from test_dist_base import runtime_main, TestParallelDyGraphRunnerBase
  
  
  class SimpleImgConvPool(fluid.dygraph.Layer):
      def __init__(self,
                   name_scope,
                   num_channels,
                   num_filters,
                   filter_size,
                   pool_size,
                   pool_stride,
                   pool_padding=0,
                   pool_type='max',
                   global_pooling=False,
                   conv_stride=1,
                   conv_padding=0,
                   conv_dilation=1,
                   conv_groups=1,
                   act=None,
                   use_cudnn=False,
                   param_attr=None,
                   bias_attr=None):
          super(SimpleImgConvPool, self).__init__(name_scope)
  
          self._conv2d = Conv2D(
              self.full_name(),
              num_channels=num_channels,
              num_filters=num_filters,
              filter_size=filter_size,
              stride=conv_stride,
              padding=conv_padding,
              dilation=conv_dilation,
              groups=conv_groups,
              param_attr=None,
              bias_attr=None,
              use_cudnn=use_cudnn)
  
          self._pool2d = Pool2D(
              self.full_name(),
              pool_size=pool_size,
              pool_type=pool_type,
              pool_stride=pool_stride,
              pool_padding=pool_padding,
              global_pooling=global_pooling,
              use_cudnn=use_cudnn)
  
      def forward(self, inputs):
          x = self._conv2d(inputs)
          x = self._pool2d(x)
          return x
  
  
  class MNIST(fluid.dygraph.Layer):
      def __init__(self, name_scope):
          super(MNIST, self).__init__(name_scope)
  
          self._simple_img_conv_pool_1 = SimpleImgConvPool(
              self.full_name(), 1, 20, 5, 2, 2, act="relu")
  
          self._simple_img_conv_pool_2 = SimpleImgConvPool(
              self.full_name(), 20, 50, 5, 2, 2, act="relu")
  
          pool_2_shape = 50 * 4 * 4
          SIZE = 10
          scale = (2.0 / (pool_2_shape**2 * SIZE))**0.5
          self._fc = FC(self.full_name(),
                        10,
                        param_attr=fluid.param_attr.ParamAttr(
                            initializer=fluid.initializer.NormalInitializer(
                                loc=0.0, scale=scale)),
                        act="softmax")
  
      def forward(self, inputs, label):
          x = self._simple_img_conv_pool_1(inputs)
          x = self._simple_img_conv_pool_2(x)
          cost = self._fc(x)
          loss = fluid.layers.cross_entropy(cost, label)
          avg_loss = fluid.layers.mean(loss)
          return avg_loss
  
  
  class TestMnist(TestParallelDyGraphRunnerBase):
      def get_model(self):
          model = MNIST("mnist")
          train_reader = paddle.batch(
              paddle.dataset.mnist.train(), batch_size=2, drop_last=True)
          opt = fluid.optimizer.SGD(learning_rate=1e-3)
          return model, train_reader, opt
  
      def run_one_loop(self, model, opt, data):
          batch_size = len(data)
          dy_x_data = np.array([x[0].reshape(1, 28, 28)
                                for x in data]).astype('float32')
          y_data = np.array(
              [x[1] for x in data]).astype('int64').reshape(batch_size, 1)
          img = to_variable(dy_x_data)
          label = to_variable(y_data)
          label.stop_gradient = True
  
          avg_loss = model(img, label)
  
          return avg_loss
  
  
  if __name__ == "__main__":
      runtime_main(TestMnist)
  