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.
  
  import numpy as np
  
  import paddle
  import paddle.fluid as fluid
  from paddle.fluid.layer_helper import LayerHelper
  from paddle.fluid.dygraph.nn import Conv2D, Pool2D, BatchNorm, FC
  from paddle.fluid.dygraph.base import to_variable
  
  from paddle.fluid import framework
  
  import math
  import sys
  
  batch_size = 32
  epoch = 120
  IMAGENET1000 = 1281167
  base_lr = 0.1
  momentum_rate = 0.9
  l2_decay = 1e-4
  
  
  def optimizer_setting():
  
      total_images = IMAGENET1000
  
      step = int(math.ceil(float(total_images) / batch_size))
  
      epochs = [30, 60, 90]
      bd = [step * e for e in epochs]
  
      lr = []
      lr = [base_lr * (0.1**i) for i in range(len(bd) + 1)]
      optimizer = fluid.optimizer.Momentum(
          learning_rate=fluid.layers.piecewise_decay(
              boundaries=bd, values=lr),
          momentum=momentum_rate,
          regularization=fluid.regularizer.L2Decay(l2_decay))
  
      return optimizer
  
  
  class ConvBNLayer(fluid.dygraph.Layer):
      def __init__(self,
                   name_scope,
                   num_channels,
                   num_filters,
                   filter_size,
                   stride=1,
                   groups=1,
                   act=None):
          super(ConvBNLayer, self).__init__(name_scope)
  
          self._conv = Conv2D(
              self.full_name(),
              num_channels=num_channels,
              num_filters=num_filters,
              filter_size=filter_size,
              stride=stride,
              padding=(filter_size - 1) // 2,
              groups=groups,
              act=None,
              bias_attr=None)
  
          self._batch_norm = BatchNorm(self.full_name(), num_filters, act=act)
  
      def forward(self, inputs):
          y = self._conv(inputs)
          y = self._batch_norm(y)
  
          return y
  
  
  class BottleneckBlock(fluid.dygraph.Layer):
      def __init__(self,
                   name_scope,
                   num_channels,
                   num_filters,
                   stride,
                   shortcut=True):
          super(BottleneckBlock, self).__init__(name_scope)
  
          self.conv0 = ConvBNLayer(
              self.full_name(),
              num_channels=num_channels,
              num_filters=num_filters,
              filter_size=1,
              act='relu')
          self.conv1 = ConvBNLayer(
              self.full_name(),
              num_channels=num_filters,
              num_filters=num_filters,
              filter_size=3,
              stride=stride,
              act='relu')
          self.conv2 = ConvBNLayer(
              self.full_name(),
              num_channels=num_filters,
              num_filters=num_filters * 4,
              filter_size=1,
              act=None)
  
          if not shortcut:
              self.short = ConvBNLayer(
                  self.full_name(),
                  num_channels=num_channels,
                  num_filters=num_filters * 4,
                  filter_size=1,
                  stride=stride)
  
          self.shortcut = shortcut
  
          self._num_channels_out = num_filters * 4
  
      def forward(self, inputs):
          y = self.conv0(inputs)
          conv1 = self.conv1(y)
          conv2 = self.conv2(conv1)
  
          if self.shortcut:
              short = inputs
          else:
              short = self.short(inputs)
  
          y = fluid.layers.elementwise_add(x=short, y=conv2)
  
          layer_helper = LayerHelper(self.full_name(), act='relu')
          return layer_helper.append_activation(y)
  
  
  class ResNet(fluid.dygraph.Layer):
      def __init__(self, name_scope, layers=50, class_dim=102):
          super(ResNet, self).__init__(name_scope)
  
          self.layers = layers
          supported_layers = [50, 101, 152]
          assert layers in supported_layers, \
              "supported layers are {} but input layer is {}".format(supported_layers, layers)
  
          if layers == 50:
              depth = [3, 4, 6, 3]
          elif layers == 101:
              depth = [3, 4, 23, 3]
          elif layers == 152:
              depth = [3, 8, 36, 3]
          num_filters = [64, 128, 256, 512]
  
          self.conv = ConvBNLayer(
              self.full_name(),
              num_channels=3,
              num_filters=64,
              filter_size=7,
              stride=2,
              act='relu')
          self.pool2d_max = Pool2D(
              self.full_name(),
              pool_size=3,
              pool_stride=2,
              pool_padding=1,
              pool_type='max')
  
          self.bottleneck_block_list = []
          num_channels = 64
          for block in range(len(depth)):
              shortcut = False
              for i in range(depth[block]):
                  bottleneck_block = self.add_sublayer(
                      'bb_%d_%d' % (block, i),
                      BottleneckBlock(
                          self.full_name(),
                          num_channels=num_channels,
                          num_filters=num_filters[block],
                          stride=2 if i == 0 and block != 0 else 1,
                          shortcut=shortcut))
                  num_channels = bottleneck_block._num_channels_out
                  self.bottleneck_block_list.append(bottleneck_block)
                  shortcut = True
  
          self.pool2d_avg = Pool2D(
              self.full_name(), pool_size=7, pool_type='avg', global_pooling=True)
  
          import math
          stdv = 1.0 / math.sqrt(2048 * 1.0)
  
          self.out = FC(self.full_name(),
                        size=class_dim,
                        act='softmax',
                        param_attr=fluid.param_attr.ParamAttr(
                            initializer=fluid.initializer.Uniform(-stdv, stdv)))
  
      def forward(self, inputs):
          y = self.conv(inputs)
          y = self.pool2d_max(y)
          for bottleneck_block in self.bottleneck_block_list:
              y = bottleneck_block(y)
          y = self.pool2d_avg(y)
          y = self.out(y)
          return y
  
  
  def eval(model, data):
  
      model.eval()
      total_loss = 0.0
      total_acc1 = 0.0
      total_acc5 = 0.0
      total_sample = 0
      for batch_id, data in enumerate(data()):
          dy_x_data = np.array(
              [x[0].reshape(3, 224, 224) for x in data]).astype('float32')
          if len(np.array([x[1] for x in data]).astype('int64')) != batch_size:
              continue
          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
  
          out = model(img)
          #loss = fluid.layers.cross_entropy(input=out, label=label)
          #avg_loss = fluid.layers.mean(x=loss)
  
          acc_top1 = fluid.layers.accuracy(input=out, label=label, k=1)
          acc_top5 = fluid.layers.accuracy(input=out, label=label, k=5)
  
          #dy_out = avg_loss.numpy()
  
          #total_loss += dy_out
          total_acc1 += acc_top1.numpy()
          total_acc5 += acc_top5.numpy()
          total_sample += 1
  
          # print("epoch id: %d, batch step: %d, loss: %f" % (eop, batch_id, dy_out))
          if batch_id % 10 == 0:
              print("test | batch step %d, loss %0.3f acc1 %0.3f acc5 %0.3f" % \
                    ( batch_id, total_loss / total_sample, \
                     total_acc1 / total_sample, total_acc5 / total_sample))
      print("final eval loss %0.3f acc1 %0.3f acc5 %0.3f" % \
            (total_loss / total_sample, \
             total_acc1 / total_sample, total_acc5 / total_sample))
  
  
  def train_resnet():
      with fluid.dygraph.guard():
          resnet = ResNet("resnet")
          optimizer = optimizer_setting()
          train_reader = paddle.batch(
              paddle.dataset.flowers.train(use_xmap=False), batch_size=batch_size)
  
          test_reader = paddle.batch(
              paddle.dataset.flowers.test(use_xmap=False), batch_size=batch_size)
  
          #file_name = './model/epoch_0.npz'
          #model_data = np.load( file_name )
  
          for eop in range(epoch):
  
              resnet.train()
              total_loss = 0.0
              total_acc1 = 0.0
              total_acc5 = 0.0
              total_sample = 0
  
              #dict_state = resnet.state_dict()
  
              #resnet.load_dict( model_data )
  
              print("load finished")
  
              for batch_id, data in enumerate(train_reader()):
                  dy_x_data = np.array(
                      [x[0].reshape(3, 224, 224) for x in data]).astype('float32')
                  if len(np.array([x[1]
                                   for x in data]).astype('int64')) != batch_size:
                      continue
                  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
  
                  out = resnet(img)
                  loss = fluid.layers.cross_entropy(input=out, label=label)
                  avg_loss = fluid.layers.mean(x=loss)
  
                  acc_top1 = fluid.layers.accuracy(input=out, label=label, k=1)
                  acc_top5 = fluid.layers.accuracy(input=out, label=label, k=5)
  
                  dy_out = avg_loss.numpy()
                  avg_loss.backward()
  
                  optimizer.minimize(avg_loss)
                  resnet.clear_gradients()
  
                  framework._dygraph_tracer_._clear_ops()
  
                  total_loss += dy_out
                  total_acc1 += acc_top1.numpy()
                  total_acc5 += acc_top5.numpy()
                  total_sample += 1
  
                  #print("epoch id: %d, batch step: %d, loss: %f" % (eop, batch_id, dy_out))
                  if batch_id % 10 == 0:
                      print( "epoch %d | batch step %d, loss %0.3f acc1 %0.3f acc5 %0.3f" % \
                             ( eop, batch_id, total_loss / total_sample, \
                               total_acc1 / total_sample, total_acc5 / total_sample))
  
              print("epoch %d | batch step %d, loss %0.3f acc1 %0.3f acc5 %0.3f" % \
                    (eop, batch_id, total_loss / total_sample, \
                     total_acc1 / total_sample, total_acc5 / total_sample))
              resnet.eval()
              eval(resnet, test_reader)
  
  
  if __name__ == '__main__':
      train_resnet()
  