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 sys
  
  import paddle.fluid as fluid
  
  try:
      from paddle.fluid.contrib.trainer import *
      from paddle.fluid.contrib.inferencer import *
  except ImportError:
      print(
          "In the fluid 1.0, the trainer and inferencer are moving to paddle.fluid.contrib",
          file=sys.stderr)
      from paddle.fluid.trainer import *
      from paddle.fluid.inferencer import *
  import paddle.fluid.core as core
  import paddle
  import numpy
  import math
  import sys
  import os
  
  BATCH_SIZE = 64
  
  
  def inference_program():
      img = fluid.layers.data(name='img', shape=[1, 28, 28], dtype='float32')
  
      conv_pool_1 = fluid.nets.simple_img_conv_pool(
          input=img,
          filter_size=5,
          num_filters=20,
          pool_size=2,
          pool_stride=2,
          act="relu")
      conv_pool_1 = fluid.layers.batch_norm(conv_pool_1)
      conv_pool_2 = fluid.nets.simple_img_conv_pool(
          input=conv_pool_1,
          filter_size=5,
          num_filters=50,
          pool_size=2,
          pool_stride=2,
          act="relu")
      prediction = fluid.layers.fc(input=conv_pool_2, size=10, act='softmax')
      return prediction
  
  
  def train_program():
      label = fluid.layers.data(name='label', shape=[1], dtype='int64')
  
      predict = inference_program()
      cost = fluid.layers.cross_entropy(input=predict, label=label)
      avg_cost = fluid.layers.mean(cost)
      acc = fluid.layers.accuracy(input=predict, label=label)
      return [avg_cost, acc]
  
  
  def optimizer_func():
      return fluid.optimizer.Adam(learning_rate=0.001)
  
  
  def train(use_cuda, train_program, parallel, params_dirname):
      place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
  
      trainer = Trainer(
          train_func=train_program,
          place=place,
          optimizer_func=optimizer_func,
          parallel=parallel)
  
      def event_handler(event):
          if isinstance(event, EndEpochEvent):
              test_reader = paddle.batch(
                  paddle.dataset.mnist.test(), batch_size=BATCH_SIZE)
              avg_cost, acc = trainer.test(
                  reader=test_reader, feed_order=['img', 'label'])
  
              print("avg_cost: %s" % avg_cost)
              print("acc     : %s" % acc)
  
              if acc > 0.2:  # Smaller value to increase CI speed
                  trainer.save_params(params_dirname)
              else:
                  print('BatchID {0}, Test Loss {1:0.2}, Acc {2:0.2}'.format(
                      event.epoch + 1, avg_cost, acc))
                  if math.isnan(avg_cost):
                      sys.exit("got NaN loss, training failed.")
          elif isinstance(event, EndStepEvent):
              print(
                  ("Step {0}, Epoch {1} Metrics {2}".format(
                      event.step, event.epoch,
                      list(map(numpy.array, event.metrics)))))
  
      train_reader = paddle.batch(
          paddle.reader.shuffle(
              paddle.dataset.mnist.train(), buf_size=500),
          batch_size=BATCH_SIZE)
  
      trainer.train(
          num_epochs=1,
          event_handler=event_handler,
          reader=train_reader,
          feed_order=['img', 'label'])
  
  
  def infer(use_cuda, inference_program, parallel, params_dirname=None):
      place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
  
      inferencer = Inferencer(
          infer_func=inference_program,
          param_path=params_dirname,
          place=place,
          parallel=parallel)
  
      batch_size = 1
      tensor_img = numpy.random.uniform(-1.0, 1.0,
                                        [batch_size, 1, 28, 28]).astype("float32")
  
      results = inferencer.infer({'img': tensor_img})
  
      print("infer results: ", results[0])
  
  
  def main(use_cuda, parallel):
      params_dirname = "recognize_digits_conv.inference.model"
  
      # call train() with is_local argument to run distributed train
      os.environ['CPU_NUM'] = str(4)
      train(
          use_cuda=use_cuda,
          train_program=train_program,
          params_dirname=params_dirname,
          parallel=parallel)
  
      # FIXME(zcd): in the inference stage, the number of
      # input data is one, it is not appropriate to use parallel.
      if parallel and use_cuda:
          return
      os.environ['CPU_NUM'] = str(1)
      infer(
          use_cuda=use_cuda,
          inference_program=inference_program,
          params_dirname=params_dirname,
          parallel=parallel)
  
  
  if __name__ == '__main__':
      for use_cuda in (False, True):
          for parallel in (False, True):
              if use_cuda and not core.is_compiled_with_cuda():
                  continue
              main(use_cuda=use_cuda, parallel=parallel)
  