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 absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import functools
  import numpy as np
  import time
  import os
  
  import cProfile, pstats, StringIO
  
  import paddle
  import paddle.fluid as fluid
  import paddle.fluid.core as core
  import paddle.fluid.profiler as profiler
  from recordio_converter import imagenet_train, imagenet_test
  
  
  def conv_bn_layer(input, ch_out, filter_size, stride, padding, act='relu'):
      conv1 = fluid.layers.conv2d(
          input=input,
          filter_size=filter_size,
          num_filters=ch_out,
          stride=stride,
          padding=padding,
          act=None,
          bias_attr=False)
      return fluid.layers.batch_norm(input=conv1, act=act)
  
  
  def shortcut(input, ch_out, stride):
      ch_in = input.shape[1]  # if args.data_format == 'NCHW' else input.shape[-1]
      if ch_in != ch_out:
          return conv_bn_layer(input, ch_out, 1, stride, 0, None)
      else:
          return input
  
  
  def basicblock(input, ch_out, stride):
      short = shortcut(input, ch_out, stride)
      conv1 = conv_bn_layer(input, ch_out, 3, stride, 1)
      conv2 = conv_bn_layer(conv1, ch_out, 3, 1, 1, act=None)
      return fluid.layers.elementwise_add(x=short, y=conv2, act='relu')
  
  
  def bottleneck(input, ch_out, stride):
      short = shortcut(input, ch_out * 4, stride)
      conv1 = conv_bn_layer(input, ch_out, 1, stride, 0)
      conv2 = conv_bn_layer(conv1, ch_out, 3, 1, 1)
      conv3 = conv_bn_layer(conv2, ch_out * 4, 1, 1, 0, act=None)
      return fluid.layers.elementwise_add(x=short, y=conv3, act='relu')
  
  
  def layer_warp(block_func, input, ch_out, count, stride):
      res_out = block_func(input, ch_out, stride)
      for i in range(1, count):
          res_out = block_func(res_out, ch_out, 1)
      return res_out
  
  
  def resnet_imagenet(input, class_dim, depth=50, data_format='NCHW'):
  
      cfg = {
          18: ([2, 2, 2, 1], basicblock),
          34: ([3, 4, 6, 3], basicblock),
          50: ([3, 4, 6, 3], bottleneck),
          101: ([3, 4, 23, 3], bottleneck),
          152: ([3, 8, 36, 3], bottleneck)
      }
      stages, block_func = cfg[depth]
      conv1 = conv_bn_layer(input, ch_out=64, filter_size=7, stride=2, padding=3)
      pool1 = fluid.layers.pool2d(
          input=conv1, pool_type='avg', pool_size=3, pool_stride=2)
      res1 = layer_warp(block_func, pool1, 64, stages[0], 1)
      res2 = layer_warp(block_func, res1, 128, stages[1], 2)
      res3 = layer_warp(block_func, res2, 256, stages[2], 2)
      res4 = layer_warp(block_func, res3, 512, stages[3], 2)
      pool2 = fluid.layers.pool2d(
          input=res4,
          pool_size=7,
          pool_type='avg',
          pool_stride=1,
          global_pooling=True)
      out = fluid.layers.fc(input=pool2, size=class_dim, act='softmax')
      return out
  
  
  def resnet_cifar10(input, class_dim, depth=32, data_format='NCHW'):
      assert (depth - 2) % 6 == 0
  
      n = (depth - 2) // 6
  
      conv1 = conv_bn_layer(
          input=input, ch_out=16, filter_size=3, stride=1, padding=1)
      res1 = layer_warp(basicblock, conv1, 16, n, 1)
      res2 = layer_warp(basicblock, res1, 32, n, 2)
      res3 = layer_warp(basicblock, res2, 64, n, 2)
      pool = fluid.layers.pool2d(
          input=res3, pool_size=8, pool_type='avg', pool_stride=1)
      out = fluid.layers.fc(input=pool, size=class_dim, act='softmax')
      return out
  
  
  def get_model(args):
      model = resnet_cifar10
      if args.data_set == "cifar10":
          class_dim = 10
          if args.data_format == 'NCHW':
              dshape = [3, 32, 32]
          else:
              dshape = [32, 32, 3]
          model = resnet_cifar10
          train_reader = paddle.dataset.cifar.train10()
          test_reader = paddle.dataset.cifar.test10()
      elif args.data_set == "flowers":
          class_dim = 102
          if args.data_format == 'NCHW':
              dshape = [3, 224, 224]
          else:
              dshape = [224, 224, 3]
          model = resnet_imagenet
          train_reader = paddle.dataset.flowers.train()
          test_reader = paddle.dataset.flowers.test()
      elif args.data_set == "imagenet":
          class_dim = 1000
          if args.data_format == 'NCHW':
              dshape = [3, 224, 224]
          else:
              dshape = [224, 224, 3]
          model = resnet_imagenet
          if not args.data_path:
              raise Exception(
                  "Must specify --data_path when training with imagenet")
          train_reader = imagenet_train(args.data_path)
          test_reader = imagenet_test(args.data_path)
  
      if args.use_reader_op:
          filelist = [
              os.path.join(args.data_path, f) for f in os.listdir(args.data_path)
          ]
          data_file = fluid.layers.open_files(
              filenames=filelist,
              shapes=[[-1] + dshape, (-1, 1)],
              lod_levels=[0, 0],
              dtypes=["float32", "int64"],
              thread_num=args.gpus,
              pass_num=args.pass_num)
          data_file = fluid.layers.double_buffer(
              fluid.layers.batch(
                  data_file, batch_size=args.batch_size))
          input, label = fluid.layers.read_file(data_file)
      else:
          input = fluid.layers.data(name='data', shape=dshape, dtype='float32')
          label = fluid.layers.data(name='label', shape=[1], dtype='int64')
  
      if args.device == 'CPU' and args.cpus > 1:
          places = fluid.layers.get_places(args.cpus)
          pd = fluid.layers.ParallelDo(places)
          with pd.do():
              predict = model(pd.read_input(input), class_dim)
              label = pd.read_input(label)
              cost = fluid.layers.cross_entropy(input=predict, label=label)
              avg_cost = fluid.layers.mean(x=cost)
              batch_acc = fluid.layers.accuracy(input=predict, label=label)
  
              pd.write_output(avg_cost)
              pd.write_output(batch_acc)
  
          avg_cost, batch_acc = pd()
          avg_cost = fluid.layers.mean(avg_cost)
          batch_acc = fluid.layers.mean(batch_acc)
      else:
          predict = model(input, class_dim)
          cost = fluid.layers.cross_entropy(input=predict, label=label)
          avg_cost = fluid.layers.mean(x=cost)
          batch_acc = fluid.layers.accuracy(input=predict, label=label)
  
      inference_program = fluid.default_main_program().clone()
      with fluid.program_guard(inference_program):
          inference_program = fluid.io.get_inference_program(
              target_vars=[batch_acc])
  
      optimizer = fluid.optimizer.Momentum(learning_rate=0.01, momentum=0.9)
  
      batched_train_reader = paddle.batch(
          paddle.reader.shuffle(
              train_reader, buf_size=5120),
          batch_size=args.batch_size * args.gpus)
      batched_test_reader = paddle.batch(train_reader, batch_size=args.batch_size)
  
      return avg_cost, inference_program, optimizer, batched_train_reader, batched_test_reader, batch_acc
  