text: #   Copyright (c) 2019 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 paddle
  import paddle.fluid as fluid
  import contextlib
  import math
  import sys
  import numpy
  import unittest
  import os
  import copy
  import numpy as np
  
  paddle.enable_static()
  
  
  def resnet_cifar10(input, depth=32):
      def conv_bn_layer(input,
                        ch_out,
                        filter_size,
                        stride,
                        padding,
                        act='relu',
                        bias_attr=False):
          tmp = fluid.layers.conv2d(
              input=input,
              filter_size=filter_size,
              num_filters=ch_out,
              stride=stride,
              padding=padding,
              act=None,
              bias_attr=bias_attr)
          return fluid.layers.batch_norm(input=tmp, act=act)
  
      def shortcut(input, ch_in, ch_out, stride):
          if ch_in != ch_out:
              return conv_bn_layer(input, ch_out, 1, stride, 0, None)
          else:
              return input
  
      def basicblock(input, ch_in, ch_out, stride):
          tmp = conv_bn_layer(input, ch_out, 3, stride, 1)
          tmp = conv_bn_layer(tmp, ch_out, 3, 1, 1, act=None, bias_attr=True)
          short = shortcut(input, ch_in, ch_out, stride)
          return fluid.layers.elementwise_add(x=tmp, y=short, act='relu')
  
      def layer_warp(block_func, input, ch_in, ch_out, count, stride):
          tmp = block_func(input, ch_in, ch_out, stride)
          for i in range(1, count):
              tmp = block_func(tmp, ch_out, ch_out, 1)
          return tmp
  
      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, 16, n, 1)
      res2 = layer_warp(basicblock, res1, 16, 32, n, 2)
      res3 = layer_warp(basicblock, res2, 32, 64, n, 2)
      pool = fluid.layers.pool2d(
          input=res3, pool_size=8, pool_type='avg', pool_stride=1)
      return pool
  
  
  def vgg16_bn_drop(input):
      def conv_block(input, num_filter, groups, dropouts):
          return fluid.nets.img_conv_group(
              input=input,
              pool_size=2,
              pool_stride=2,
              conv_num_filter=[num_filter] * groups,
              conv_filter_size=3,
              conv_act='relu',
              conv_with_batchnorm=True,
              conv_batchnorm_drop_rate=dropouts,
              pool_type='max')
  
      conv1 = conv_block(input, 64, 2, [0.3, 0])
      conv2 = conv_block(conv1, 128, 2, [0.4, 0])
      conv3 = conv_block(conv2, 256, 3, [0.4, 0.4, 0])
      conv4 = conv_block(conv3, 512, 3, [0.4, 0.4, 0])
      conv5 = conv_block(conv4, 512, 3, [0.4, 0.4, 0])
  
      drop = fluid.layers.dropout(x=conv5, dropout_prob=0.5)
      fc1 = fluid.layers.fc(input=drop, size=4096, act=None)
      bn = fluid.layers.batch_norm(input=fc1, act='relu')
      drop2 = fluid.layers.dropout(x=bn, dropout_prob=0.5)
      fc2 = fluid.layers.fc(input=drop2, size=4096, act=None)
      return fc2
  
  
  def train(net_type, use_cuda, save_dirname, is_local):
      classdim = 10
      data_shape = [3, 32, 32]
  
      train_program = fluid.Program()
      startup_prog = fluid.Program()
      train_program.random_seed = 123
      startup_prog.random_seed = 456
      with fluid.program_guard(train_program, startup_prog):
          images = fluid.layers.data(
              name='pixel', shape=data_shape, dtype='float32')
          label = fluid.layers.data(name='label', shape=[1], dtype='int64')
  
          if net_type == "vgg":
              print("train vgg net")
              net = vgg16_bn_drop(images)
          elif net_type == "resnet":
              print("train resnet")
              net = resnet_cifar10(images, 32)
          else:
              raise ValueError("%s network is not supported" % net_type)
  
          logits = fluid.layers.fc(input=net, size=classdim, act="softmax")
          cost, predict = fluid.layers.softmax_with_cross_entropy(
              logits, label, return_softmax=True)
          avg_cost = fluid.layers.mean(cost)
          acc = fluid.layers.accuracy(input=predict, label=label)
  
          # Test program
          test_program = train_program.clone(for_test=True)
  
          optimizer = fluid.optimizer.Lamb(learning_rate=0.001)
  
          amp_lists = fluid.contrib.mixed_precision.AutoMixedPrecisionLists(
              custom_black_varnames={"loss", "conv2d_0.w_0"})
          mp_optimizer = fluid.contrib.mixed_precision.decorate(
              optimizer=optimizer,
              amp_lists=amp_lists,
              init_loss_scaling=8.0,
              use_dynamic_loss_scaling=True)
  
          mp_optimizer.minimize(avg_cost)
          loss_scaling = mp_optimizer.get_loss_scaling()
          scaled_loss = mp_optimizer.get_scaled_loss()
  
      BATCH_SIZE = 128
      PASS_NUM = 1
  
      # no shuffle for unit test
      train_reader = paddle.batch(
          paddle.dataset.cifar.train10(), batch_size=BATCH_SIZE)
  
      test_reader = paddle.batch(
          paddle.dataset.cifar.test10(), batch_size=BATCH_SIZE)
  
      place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
      exe = fluid.Executor(place)
      feeder = fluid.DataFeeder(place=place, feed_list=[images, label])
  
      def train_loop(main_program):
          exe.run(startup_prog)
          loss = 0.0
          for pass_id in range(PASS_NUM):
              for batch_id, data in enumerate(train_reader()):
                  np_scaled_loss, loss = exe.run(
                      main_program,
                      feed=feeder.feed(data),
                      fetch_list=[scaled_loss, avg_cost])
                  print(
                      'PassID {0:1}, BatchID {1:04}, train loss {2:2.4}, scaled train closs {3:2.4}'.
                      format(pass_id, batch_id + 1,
                             float(loss), float(np_scaled_loss)))
                  if (batch_id % 10) == 0:
                      acc_list = []
                      avg_loss_list = []
                      for tid, test_data in enumerate(test_reader()):
                          loss_t, acc_t = exe.run(program=test_program,
                                                  feed=feeder.feed(test_data),
                                                  fetch_list=[avg_cost, acc])
                          if math.isnan(float(loss_t)):
                              sys.exit("got NaN loss, training failed.")
                          acc_list.append(float(acc_t))
                          avg_loss_list.append(float(loss_t))
                          break  # Use 1 segment for speeding up CI
  
                      acc_value = numpy.array(acc_list).mean()
                      avg_loss_value = numpy.array(avg_loss_list).mean()
  
                      print(
                          'PassID {0:1}, BatchID {1:04}, test loss {2:2.2}, acc {3:2.2}'.
                          format(pass_id, batch_id + 1,
                                 float(avg_loss_value), float(acc_value)))
  
                      if acc_value > 0.08:  # Low threshold for speeding up CI
                          fluid.io.save_inference_model(
                              save_dirname, ["pixel"], [predict],
                              exe,
                              main_program=train_program)
                          return
  
      if is_local:
          train_loop(train_program)
      else:
          port = os.getenv("PADDLE_PSERVER_PORT", "6174")
          pserver_ips = os.getenv("PADDLE_PSERVER_IPS")  # ip,ip...
          eplist = []
          for ip in pserver_ips.split(","):
              eplist.append(':'.join([ip, port]))
          pserver_endpoints = ",".join(eplist)  # ip:port,ip:port...
          trainers = int(os.getenv("PADDLE_TRAINERS"))
          current_endpoint = os.getenv("POD_IP") + ":" + port
          trainer_id = int(os.getenv("PADDLE_TRAINER_ID"))
          training_role = os.getenv("PADDLE_TRAINING_ROLE", "TRAINER")
          t = fluid.DistributeTranspiler()
          t.transpile(trainer_id, pservers=pserver_endpoints, trainers=trainers)
          if training_role == "PSERVER":
              pserver_prog = t.get_pserver_program(current_endpoint)
              pserver_startup = t.get_startup_program(current_endpoint,
                                                      pserver_prog)
              exe.run(pserver_startup)
              exe.run(pserver_prog)
          elif training_role == "TRAINER":
              train_loop(t.get_trainer_program())
  
  
  def infer(use_cuda, save_dirname=None):
      if save_dirname is None:
          return
  
      place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
      exe = fluid.Executor(place)
  
      inference_scope = fluid.core.Scope()
      with fluid.scope_guard(inference_scope):
          # Use fluid.io.load_inference_model to obtain the inference program desc,
          # the feed_target_names (the names of variables that will be fed
          # data using feed operators), and the fetch_targets (variables that
          # we want to obtain data from using fetch operators).
          [inference_program, feed_target_names,
           fetch_targets] = fluid.io.load_inference_model(save_dirname, exe)
  
          # The input's dimension of conv should be 4-D or 5-D.
          # Use normilized image pixels as input data, which should be in the range [0, 1.0].
          batch_size = 1
          tensor_img = numpy.random.rand(batch_size, 3, 32, 32).astype("float32")
  
          # Construct feed as a dictionary of {feed_target_name: feed_target_data}
          # and results will contain a list of data corresponding to fetch_targets.
          results = exe.run(inference_program,
                            feed={feed_target_names[0]: tensor_img},
                            fetch_list=fetch_targets)
  
          print("infer results: ", results[0])
  
          fluid.io.save_inference_model(save_dirname, feed_target_names,
                                        fetch_targets, exe, inference_program)
  
  
  def main(net_type, use_cuda, is_local=True):
      if use_cuda and not fluid.core.is_compiled_with_cuda():
          return
  
      # Directory for saving the trained model
      save_dirname = "image_classification_" + net_type + ".inference.model"
  
      train(net_type, use_cuda, save_dirname, is_local)
      #infer(use_cuda, save_dirname)
  
  
  class TestImageClassification(unittest.TestCase):
      def test_amp_lists(self):
          white_list = copy.copy(
              fluid.contrib.mixed_precision.fp16_lists.white_list)
          black_list = copy.copy(
              fluid.contrib.mixed_precision.fp16_lists.black_list)
          gray_list = copy.copy(
              fluid.contrib.mixed_precision.fp16_lists.gray_list)
  
          amp_lists = fluid.contrib.mixed_precision.AutoMixedPrecisionLists()
          self.assertEqual(amp_lists.white_list, white_list)
          self.assertEqual(amp_lists.black_list, black_list)
          self.assertEqual(amp_lists.gray_list, gray_list)
  
      def test_amp_lists_1(self):
          white_list = copy.copy(
              fluid.contrib.mixed_precision.fp16_lists.white_list)
          black_list = copy.copy(
              fluid.contrib.mixed_precision.fp16_lists.black_list)
          gray_list = copy.copy(
              fluid.contrib.mixed_precision.fp16_lists.gray_list)
  
          # 1. w={'exp}, b=None
          white_list.add('exp')
          black_list.remove('exp')
  
          amp_lists = fluid.contrib.mixed_precision.AutoMixedPrecisionLists(
              {'exp'})
          self.assertEqual(amp_lists.white_list, white_list)
          self.assertEqual(amp_lists.black_list, black_list)
          self.assertEqual(amp_lists.gray_list, gray_list)
  
      def test_amp_lists_2(self):
          white_list = copy.copy(
              fluid.contrib.mixed_precision.fp16_lists.white_list)
          black_list = copy.copy(
              fluid.contrib.mixed_precision.fp16_lists.black_list)
          gray_list = copy.copy(
              fluid.contrib.mixed_precision.fp16_lists.gray_list)
  
          # 2. w={'tanh'}, b=None
          white_list.add('tanh')
          gray_list.remove('tanh')
  
          amp_lists = fluid.contrib.mixed_precision.AutoMixedPrecisionLists(
              {'tanh'})
          self.assertEqual(amp_lists.white_list, white_list)
          self.assertEqual(amp_lists.black_list, black_list)
          self.assertEqual(amp_lists.gray_list, gray_list)
  
      def test_amp_lists_3(self):
          white_list = copy.copy(
              fluid.contrib.mixed_precision.fp16_lists.white_list)
          black_list = copy.copy(
              fluid.contrib.mixed_precision.fp16_lists.black_list)
          gray_list = copy.copy(
              fluid.contrib.mixed_precision.fp16_lists.gray_list)
  
          # 3. w={'lstm'}, b=None
          white_list.add('lstm')
  
          amp_lists = fluid.contrib.mixed_precision.AutoMixedPrecisionLists(
              {'lstm'})
          self.assertEqual(amp_lists.white_list, white_list)
          self.assertEqual(amp_lists.black_list, black_list)
          self.assertEqual(amp_lists.gray_list, gray_list)
  
      def test_amp_lists_4(self):
          white_list = copy.copy(
              fluid.contrib.mixed_precision.fp16_lists.white_list)
          black_list = copy.copy(
              fluid.contrib.mixed_precision.fp16_lists.black_list)
          gray_list = copy.copy(
              fluid.contrib.mixed_precision.fp16_lists.gray_list)
  
          # 4. w=None, b={'conv2d'}
          white_list.remove('conv2d')
          black_list.add('conv2d')
  
          amp_lists = fluid.contrib.mixed_precision.AutoMixedPrecisionLists(
              custom_black_list={'conv2d'})
          self.assertEqual(amp_lists.white_list, white_list)
          self.assertEqual(amp_lists.black_list, black_list)
          self.assertEqual(amp_lists.gray_list, gray_list)
  
      def test_amp_lists_5(self):
          white_list = copy.copy(
              fluid.contrib.mixed_precision.fp16_lists.white_list)
          black_list = copy.copy(
              fluid.contrib.mixed_precision.fp16_lists.black_list)
          gray_list = copy.copy(
              fluid.contrib.mixed_precision.fp16_lists.gray_list)
  
          # 5. w=None, b={'tanh'}
          black_list.add('tanh')
          gray_list.remove('tanh')
  
          amp_lists = fluid.contrib.mixed_precision.AutoMixedPrecisionLists(
              custom_black_list={'tanh'})
          self.assertEqual(amp_lists.white_list, white_list)
          self.assertEqual(amp_lists.black_list, black_list)
          self.assertEqual(amp_lists.gray_list, gray_list)
  
      def test_amp_lists_6(self):
          white_list = copy.copy(
              fluid.contrib.mixed_precision.fp16_lists.white_list)
          black_list = copy.copy(
              fluid.contrib.mixed_precision.fp16_lists.black_list)
          gray_list = copy.copy(
              fluid.contrib.mixed_precision.fp16_lists.gray_list)
  
          # 6. w=None, b={'lstm'}
          black_list.add('lstm')
  
          amp_lists = fluid.contrib.mixed_precision.AutoMixedPrecisionLists(
              custom_black_list={'lstm'})
          self.assertEqual(amp_lists.white_list, white_list)
          self.assertEqual(amp_lists.black_list, black_list)
          self.assertEqual(amp_lists.gray_list, gray_list)
  
      def test_amp_lists_7(self):
          # 7. w={'lstm'} b={'lstm'}
          # raise ValueError
          self.assertRaises(ValueError,
                            fluid.contrib.mixed_precision.AutoMixedPrecisionLists,
                            {'lstm'}, {'lstm'})
  
      def test_vgg_cuda(self):
          with self.scope_prog_guard():
              main('vgg', use_cuda=True)
  
      def test_resnet_cuda(self):
          with self.scope_prog_guard():
              main('resnet', use_cuda=True)
  
      @contextlib.contextmanager
      def scope_prog_guard(self):
          prog = fluid.Program()
          startup_prog = fluid.Program()
          scope = fluid.core.Scope()
          with fluid.scope_guard(scope):
              with fluid.program_guard(prog, startup_prog):
                  yield
  
  
  if __name__ == '__main__':
      unittest.main()
  