text: # 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
  
  import os
  import sys
  import six
  import time
  import unittest
  import multiprocessing
  import numpy as np
  
  import paddle.fluid as fluid
  from paddle.io import Dataset, BatchSampler, DataLoader
  from paddle.fluid.dygraph.nn import Linear
  from paddle.fluid.dygraph.base import to_variable
  
  from test_multiprocess_dataloader_iterable_dataset_static import RandomDataset, RandomBatchedDataset, prepare_places
  from test_multiprocess_dataloader_iterable_dataset_static import EPOCH_NUM, BATCH_SIZE, IMAGE_SIZE, SAMPLE_NUM, CLASS_NUM
  
  
  class SimpleFCNet(fluid.dygraph.Layer):
      def __init__(self):
          super(SimpleFCNet, self).__init__()
  
          param_attr = fluid.ParamAttr(initializer=fluid.initializer.Constant(
              value=0.8))
          bias_attr = fluid.ParamAttr(initializer=fluid.initializer.Constant(
              value=0.5))
          self._fcs = []
          in_channel = IMAGE_SIZE
          for hidden_size in [10, 20, 30]:
              self._fcs.append(
                  Linear(
                      in_channel,
                      hidden_size,
                      act='tanh',
                      param_attr=param_attr,
                      bias_attr=bias_attr))
              in_channel = hidden_size
          self._fcs.append(
              Linear(
                  in_channel,
                  CLASS_NUM,
                  act='softmax',
                  param_attr=param_attr,
                  bias_attr=bias_attr))
  
      def forward(self, image):
          out = image
          for fc in self._fcs:
              out = fc(out)
          return out
  
  
  class TestDygraphDataLoader(unittest.TestCase):
      def run_main(self, num_workers, places):
          fluid.default_startup_program().random_seed = 1
          fluid.default_main_program().random_seed = 1
          with fluid.dygraph.guard(places[0]):
              fc_net = SimpleFCNet()
              optimizer = fluid.optimizer.Adam(parameter_list=fc_net.parameters())
  
              dataset = RandomDataset(SAMPLE_NUM, CLASS_NUM)
              dataloader = DataLoader(
                  dataset,
                  num_workers=num_workers,
                  batch_size=BATCH_SIZE,
                  drop_last=True)
  
              step_list = []
              loss_list = []
              start_t = time.time()
              for _ in six.moves.range(EPOCH_NUM):
                  step = 0
                  for image, label in dataloader():
                      out = fc_net(image)
                      loss = fluid.layers.cross_entropy(out, label)
                      avg_loss = fluid.layers.reduce_mean(loss)
                      avg_loss.backward()
                      optimizer.minimize(avg_loss)
                      fc_net.clear_gradients()
  
                      loss_list.append(np.mean(avg_loss.numpy()))
                      step += 1
                  step_list.append(step)
  
          end_t = time.time()
          ret = {
              "time": end_t - start_t,
              "step": step_list,
              "loss": np.array(loss_list)
          }
          print("time cost", ret['time'], 'step_list', ret['step'])
          return ret
  
      def test_main(self):
          # dynamic graph do not run with_data_parallel
          for p in prepare_places(False):
              results = []
              for num_workers in [0, 2]:
                  print(self.__class__.__name__, p, num_workers)
                  sys.stdout.flush()
                  ret = self.run_main(num_workers=num_workers, places=p)
                  results.append(ret)
              assert results[0]['loss'].shape[0] * 2 == results[1]['loss'].shape[
                  0]
  
  
  class TestDygraphDataLoaderWithBatchedDataset(TestDygraphDataLoader):
      def run_main(self, num_workers, places):
          fluid.default_startup_program().random_seed = 1
          fluid.default_main_program().random_seed = 1
          with fluid.dygraph.guard(places[0]):
              fc_net = SimpleFCNet()
              optimizer = fluid.optimizer.Adam(parameter_list=fc_net.parameters())
  
              dataset = RandomBatchedDataset(SAMPLE_NUM, CLASS_NUM)
              dataloader = DataLoader(
                  dataset,
                  num_workers=num_workers,
                  batch_size=None,
                  drop_last=True)
  
              step_list = []
              loss_list = []
              start_t = time.time()
              for _ in six.moves.range(EPOCH_NUM):
                  step = 0
                  for image, label in dataloader():
                      out = fc_net(image)
                      loss = fluid.layers.cross_entropy(out, label)
                      avg_loss = fluid.layers.reduce_mean(loss)
                      avg_loss.backward()
                      optimizer.minimize(avg_loss)
                      fc_net.clear_gradients()
  
                      loss_list.append(np.mean(avg_loss.numpy()))
                      step += 1
                  step_list.append(step)
  
          end_t = time.time()
          ret = {
              "time": end_t - start_t,
              "step": step_list,
              "loss": np.array(loss_list)
          }
          print("time cost", ret['time'], 'step_list', ret['step'])
          return ret
  
  
  if __name__ == '__main__':
      unittest.main()
  