text: #copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
  #
  #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 paddle
  import paddle.fluid as fluid
  from paddle.fluid.param_attr import ParamAttr
  import math
  
  
  def get_para_bias_attr(l2_decay, k, name):
      regularizer = fluid.regularizer.L2Decay(l2_decay)
      stdv = 1.0 / math.sqrt(k * 1.0)
      initializer = fluid.initializer.Uniform(-stdv, stdv)
      para_attr = fluid.ParamAttr(
          regularizer=regularizer, initializer=initializer, name=name + "_w_attr")
      bias_attr = fluid.ParamAttr(
          regularizer=regularizer, initializer=initializer, name=name + "_b_attr")
      return [para_attr, bias_attr]
  
  
  def conv_bn_layer(input,
                    num_filters,
                    filter_size,
                    stride=1,
                    groups=1,
                    act=None,
                    name=None):
      conv = fluid.layers.conv2d(
          input=input,
          num_filters=num_filters,
          filter_size=filter_size,
          stride=stride,
          padding=(filter_size - 1) // 2,
          groups=groups,
          act=None,
          param_attr=ParamAttr(name=name + "_weights"),
          bias_attr=False,
          name=name + '.conv2d')
  
      bn_name = "bn_" + name
      return fluid.layers.batch_norm(
          input=conv,
          act=act,
          name=bn_name + '.output',
          param_attr=ParamAttr(name=bn_name + '_scale'),
          bias_attr=ParamAttr(bn_name + '_offset'),
          moving_mean_name=bn_name + '_mean',
          moving_variance_name=bn_name + '_variance')
  
  
  def deconv_bn_layer(input,
                      num_filters,
                      filter_size=4,
                      stride=2,
                      act='relu',
                      name=None):
      deconv = fluid.layers.conv2d_transpose(
          input=input,
          num_filters=num_filters,
          filter_size=filter_size,
          stride=stride,
          padding=1,
          act=None,
          param_attr=ParamAttr(name=name + "_weights"),
          bias_attr=False,
          name=name + '.deconv2d')
      bn_name = "bn_" + name
      return fluid.layers.batch_norm(
          input=deconv,
          act=act,
          name=bn_name + '.output',
          param_attr=ParamAttr(name=bn_name + '_scale'),
          bias_attr=ParamAttr(bn_name + '_offset'),
          moving_mean_name=bn_name + '_mean',
          moving_variance_name=bn_name + '_variance')
  
  
  def create_tmp_var(program, name, dtype, shape, lod_level=0):
      return program.current_block().create_var(
          name=name, dtype=dtype, shape=shape, lod_level=lod_level)
  