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.
  
  import contextlib
  import unittest
  import numpy as np
  import six
  
  import paddle
  import paddle.fluid as fluid
  from paddle.fluid import core
  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 test_imperative_base import new_program_scope
  
  batch_size = 8
  train_parameters = {
      "input_size": [3, 224, 224],
      "input_mean": [0.485, 0.456, 0.406],
      "input_std": [0.229, 0.224, 0.225],
      "learning_strategy": {
          "name": "piecewise_decay",
          "batch_size": batch_size,
          "epochs": [30, 60, 90],
          "steps": [0.1, 0.01, 0.001, 0.0001]
      },
      "batch_size": batch_size,
      "lr": 0.1,
      "total_images": 6149,
  }
  
  
  def optimizer_setting(params):
      ls = params["learning_strategy"]
      if ls["name"] == "piecewise_decay":
          if "total_images" not in params:
              total_images = 6149
          else:
              total_images = params["total_images"]
          # TODO(Yancey1989): using lr decay if it is ready.
          #batch_size = ls["batch_size"]
          #step = int(total_images / batch_size + 1)
  
          #bd = [step * e for e in ls["epochs"]]
          #base_lr = params["lr"]
          #lr = [base_lr * (0.1**i) for i in range(len(bd) + 1)]
          optimizer = fluid.optimizer.SGD(learning_rate=0.01)
  
      return optimizer
  
  
  class ConvBNLayer(fluid.dygraph.Layer):
      def __init__(self,
                   name_scope,
                   num_filters,
                   filter_size,
                   stride=1,
                   groups=1,
                   act=None):
          super(ConvBNLayer, self).__init__(name_scope)
  
          self._conv = Conv2D(
              self.full_name(),
              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 SqueezeExcitation(fluid.dygraph.Layer):
      def __init__(self, name_scope, num_channels, reduction_ratio):
  
          super(SqueezeExcitation, self).__init__(name_scope)
          self._pool = Pool2D(
              self.full_name(), pool_size=0, pool_type='avg', global_pooling=True)
          self._squeeze = FC(
              self.full_name(),
              size=num_channels // reduction_ratio,
              param_attr=fluid.ParamAttr(
                  initializer=fluid.initializer.Constant(value=0.05)),
              act='relu')
          self._excitation = FC(
              self.full_name(),
              size=num_channels,
              param_attr=fluid.ParamAttr(
                  initializer=fluid.initializer.Constant(value=0.05)),
              act='sigmoid')
  
      def forward(self, input):
          y = self._pool(input)
          y = self._squeeze(y)
          y = self._excitation(y)
          y = fluid.layers.elementwise_mul(x=input, y=y, axis=0)
          return y
  
  
  class BottleneckBlock(fluid.dygraph.Layer):
      def __init__(self,
                   name_scope,
                   num_channels,
                   num_filters,
                   stride,
                   cardinality,
                   reduction_ratio,
                   shortcut=True):
          super(BottleneckBlock, self).__init__(name_scope)
  
          self.conv0 = ConvBNLayer(
              self.full_name(), num_filters=num_filters, filter_size=1)
          self.conv1 = ConvBNLayer(
              self.full_name(),
              num_filters=num_filters,
              filter_size=3,
              stride=stride,
              groups=cardinality)
          self.conv2 = ConvBNLayer(
              self.full_name(),
              num_filters=num_filters * 4,
              filter_size=1,
              act='relu')
  
          self.scale = SqueezeExcitation(
              self.full_name(),
              num_channels=num_filters * 4,
              reduction_ratio=reduction_ratio)
  
          if not shortcut:
              self.short = ConvBNLayer(
                  self.full_name(),
                  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)
          scale = self.scale(conv2)
  
          if self.shortcut:
              short = inputs
          else:
              short = self.short(inputs)
  
          y = fluid.layers.elementwise_add(x=short, y=scale)
  
          layer_helper = LayerHelper(self.full_name(), act='relu')
          y = layer_helper.append_activation(y)
          return y
  
  
  class SeResNeXt(fluid.dygraph.Layer):
      def __init__(self, name_scope, layers=50, class_dim=102):
          super(SeResNeXt, 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:
              cardinality = 32
              reduction_ratio = 16
              depth = [3, 4, 6, 3]
              num_filters = [128, 256, 512, 1024]
              self.conv0 = ConvBNLayer(
                  self.full_name(),
                  num_filters=64,
                  filter_size=7,
                  stride=2,
                  act='relu')
              self.pool = Pool2D(
                  self.full_name(),
                  pool_size=3,
                  pool_stride=2,
                  pool_padding=1,
                  pool_type='max')
          elif layers == 101:
              cardinality = 32
              reduction_ratio = 16
              depth = [3, 4, 23, 3]
              num_filters = [128, 256, 512, 1024]
              self.conv0 = ConvBNLayer(
                  self.full_name(),
                  num_filters=3,
                  filter_size=7,
                  stride=2,
                  act='relu')
              self.pool = Pool2D(
                  self.full_name(),
                  pool_size=3,
                  pool_stride=2,
                  pool_padding=1,
                  pool_type='max')
          elif layers == 152:
              cardinality = 64
              reduction_ratio = 16
              depth = [3, 8, 36, 3]
              num_filters = [128, 256, 512, 1024]
              self.conv0 = ConvBNLayer(
                  self.full_name(),
                  num_filters=3,
                  filter_size=7,
                  stride=2,
                  act='relu')
              self.conv1 = ConvBNLayer(
                  self.full_name(),
                  num_filters=3,
                  filter_size=7,
                  stride=2,
                  act='relu')
              self.conv2 = ConvBNLayer(
                  self.full_name(),
                  num_filters=3,
                  filter_size=7,
                  stride=2,
                  act='relu')
              self.pool = 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,
                          cardinality=cardinality,
                          reduction_ratio=reduction_ratio,
                          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):
          if self.layers == 50 or self.layers == 101:
              y = self.conv0(inputs)
              y = self.pool(y)
          elif self.layers == 152:
              y = self.conv0(inputs)
              y = self.conv1(inputs)
              y = self.conv2(inputs)
              y = self.pool(y)
  
          for bottleneck_block in self.bottleneck_block_list:
              y = bottleneck_block(y)
          y = self.pool2d_avg(y)
          y = fluid.layers.dropout(y, dropout_prob=0.2)
          y = self.out(y)
          return y
  
  
  class TestImperativeResneXt(unittest.TestCase):
      def reader_decorator(self, reader):
          def _reader_imple():
              for item in reader():
                  doc = np.array(item[0]).reshape(3, 224, 224)
                  label = np.array(item[1]).astype('int64').reshape(1)
                  yield doc, label
  
          return _reader_imple
  
      def test_se_resnext_float32(self):
          seed = 90
  
          batch_size = train_parameters["batch_size"]
          batch_num = 1
          epoch_num = 1
          with fluid.dygraph.guard():
              fluid.default_startup_program().random_seed = seed
              fluid.default_main_program().random_seed = seed
  
              se_resnext = SeResNeXt("se_resnext")
              optimizer = optimizer_setting(train_parameters)
              np.random.seed(seed)
              import random
              random.seed = seed
  
              batch_py_reader = fluid.io.PyReader(capacity=1)
              batch_py_reader.decorate_sample_list_generator(
                  paddle.batch(
                      self.reader_decorator(
                          paddle.dataset.flowers.train(use_xmap=False)),
                      batch_size=batch_size,
                      drop_last=True),
                  places=fluid.CPUPlace())
  
              dy_param_init_value = {}
              for param in se_resnext.parameters():
                  dy_param_init_value[param.name] = param.numpy()
              for epoch_id in range(epoch_num):
                  for batch_id, data in enumerate(batch_py_reader()):
  
                      if batch_id >= batch_num and batch_num != -1:
                          break
  
                      img = data[0]
                      label = data[1]
                      label.stop_gradient = True
                      label.stop_gradient = True
  
                      out = se_resnext(img)
                      loss = fluid.layers.cross_entropy(input=out, label=label)
                      avg_loss = fluid.layers.mean(x=loss)
  
                      dy_out = avg_loss.numpy()
  
                      if batch_id == 0:
                          for param in se_resnext.parameters():
                              if param.name not in dy_param_init_value:
                                  dy_param_init_value[param.name] = param.numpy()
                      avg_loss.backward()
  
                      #dy_grad_value = {}
                      #for param in se_resnext.parameters():
                      #    if param.trainable:
                      #        np_array = np.array(param._grad_ivar().value()
                      #                            .get_tensor())
                      #        dy_grad_value[param.name + core.grad_var_suffix()] = np_array
  
                      optimizer.minimize(avg_loss)
                      se_resnext.clear_gradients()
  
                      dy_param_value = {}
                      for param in se_resnext.parameters():
                          dy_param_value[param.name] = param.numpy()
  
          with new_program_scope():
              fluid.default_startup_program().random_seed = seed
              fluid.default_main_program().random_seed = seed
  
              exe = fluid.Executor(fluid.CPUPlace(
              ) if not core.is_compiled_with_cuda() else fluid.CUDAPlace(0))
  
              se_resnext = SeResNeXt("se_resnext")
              optimizer = optimizer_setting(train_parameters)
  
              np.random.seed(seed)
              import random
              random.seed = seed
              train_reader = paddle.batch(
                  paddle.dataset.flowers.train(use_xmap=False),
                  batch_size=batch_size,
                  drop_last=True)
  
              img = fluid.layers.data(
                  name='pixel', shape=[3, 224, 224], dtype='float32')
              label = fluid.layers.data(name='label', shape=[1], dtype='int64')
              out = se_resnext(img)
              loss = fluid.layers.cross_entropy(input=out, label=label)
              avg_loss = fluid.layers.mean(x=loss)
              optimizer.minimize(avg_loss)
  
              # initialize params and fetch them
              static_param_init_value = {}
              static_param_name_list = []
              static_grad_name_list = []
              for param in se_resnext.parameters():
                  static_param_name_list.append(param.name)
              for param in se_resnext.parameters():
                  if param.trainable:
                      static_grad_name_list.append(param.name +
                                                   core.grad_var_suffix())
  
              out = exe.run(fluid.default_startup_program(),
                            fetch_list=static_param_name_list)
  
              for i in range(len(static_param_name_list)):
                  static_param_init_value[static_param_name_list[i]] = out[i]
              for epoch_id in range(epoch_num):
                  for batch_id, data in enumerate(train_reader()):
                      if batch_id >= batch_num and batch_num != -1:
                          break
  
                      static_x_data = np.array(
                          [x[0].reshape(3, 224, 224)
                           for x in data]).astype('float32')
                      y_data = np.array(
                          [x[1] for x in data]).astype('int64').reshape(
                              [batch_size, 1])
  
                      fetch_list = [avg_loss.name]
                      fetch_list.extend(static_param_name_list)
                      fetch_list.extend(static_grad_name_list)
                      out = exe.run(
                          fluid.default_main_program(),
                          feed={"pixel": static_x_data,
                                "label": y_data},
                          fetch_list=fetch_list)
  
                      static_param_value = {}
                      static_grad_value = {}
                      static_out = out[0]
                      param_start_pos = 1
                      grad_start_pos = len(
                          static_param_name_list) + param_start_pos
                      for i in range(
                              param_start_pos,
                              len(static_param_name_list) + param_start_pos):
                          static_param_value[static_param_name_list[
                              i - param_start_pos]] = out[i]
                      for i in range(grad_start_pos,
                                     len(static_grad_name_list) + grad_start_pos):
                          static_grad_value[static_grad_name_list[
                              i - grad_start_pos]] = out[i]
          self.assertTrue(np.allclose(static_out, dy_out))
  
          self.assertEqual(len(dy_param_init_value), len(static_param_init_value))
  
          for key, value in six.iteritems(static_param_init_value):
              self.assertTrue(np.allclose(value, dy_param_init_value[key]))
              self.assertTrue(np.isfinite(value.all()))
              self.assertFalse(np.isnan(value.any()))
          # FIXME(Yancey1989): np.array(_ivar.value().get_tensor()) leads to memory lake
          #self.assertEqual(len(dy_grad_value), len(static_grad_value))
          #for key, value in six.iteritems(static_grad_value):
          #    self.assertTrue(np.allclose(value, dy_grad_value[key]))
          #    self.assertTrue(np.isfinite(value.all()))
          #    self.assertFalse(np.isnan(value.any()))
  
          self.assertEqual(len(dy_param_value), len(static_param_value))
          for key, value in six.iteritems(static_param_value):
              self.assertTrue(np.allclose(value, dy_param_value[key]))
              self.assertTrue(np.isfinite(value.all()))
              self.assertFalse(np.isnan(value.any()))
  
  
  if __name__ == '__main__':
      unittest.main()
  