nn.py

# 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 print_function

from six.moves import reduce

from .. import core
from ..layers import utils
from . import layers
from ..framework import Variable, in_dygraph_mode, OpProtoHolder, Parameter
from ..param_attr import ParamAttr
from ..initializer import Normal, Constant, NumpyArrayInitializer
import numpy as np

__all__ = [
    'Conv2D', 'Conv3D', 'Pool2D', 'FC', 'BatchNorm', 'Embedding', 'GRUUnit',
    'LayerNorm', 'NCE', 'PRelu', 'BilinearTensorProduct', 'Conv2DTranspose',
    'Conv3DTranspose', 'GroupNorm', 'SpectralNorm', 'TreeConv'
]


class Conv2D(layers.Layer):
    """
    The convolution2D layer calculates the output based on the input, filter
    and strides, paddings, dilations, groups parameters. Input and
    Output are in NCHW format, where N is batch size, C is the number of
    channels, H is the height of the feature, and W is the width of the feature.
    Filter is in MCHW format, where M is the number of output image channels,
    C is the number of input image channels, H is the height of the filter,
    and W is the width of the filter. If the groups is greater than 1,
    C will equal the number of input image channels divided by the groups.
    Please refer to UFLDL's `convolution
    <http://ufldl.stanford.edu/tutorial/supervised/FeatureExtractionUsingConvolution/>`_
    for more detials.
    If bias attribution and activation type are provided, bias is added to the
    output of the convolution, and the corresponding activation function is
    applied to the final result.

    For each input :math:`X`, the equation is:

    .. math::

        Out = \sigma (W \\ast X + b)

    Where:

    * :math:`X`: Input value, a tensor with NCHW format.
    * :math:`W`: Filter value, a tensor with MCHW format.
    * :math:`\\ast`: Convolution operation.
    * :math:`b`: Bias value, a 2-D tensor with shape [M, 1].
    * :math:`\\sigma`: Activation function.
    * :math:`Out`: Output value, the shape of :math:`Out` and :math:`X` may be different.

    Example:

        - Input:

          Input shape: :math:`(N, C_{in}, H_{in}, W_{in})`

          Filter shape: :math:`(C_{out}, C_{in}, H_f, W_f)`

        - Output:

          Output shape: :math:`(N, C_{out}, H_{out}, W_{out})`

        Where

        .. math::

            H_{out}&= \\frac{(H_{in} + 2 * paddings[0] - (dilations[0] * (H_f - 1) + 1))}{strides[0]} + 1 \\\\
            W_{out}&= \\frac{(W_{in} + 2 * paddings[1] - (dilations[1] * (W_f - 1) + 1))}{strides[1]} + 1

    Args:
        name_scope(str) : The name for this class.
        num_filters(int): The number of filter. It is as same as the output
            image channel.
        filter_size (int|tuple|None): The filter size. If filter_size is a tuple,
            it must contain two integers, (filter_size_H, filter_size_W).
            Otherwise, the filter will be a square.
        stride (int|tuple): The stride size. If stride is a tuple, it must
            contain two integers, (stride_H, stride_W). Otherwise, the
            stride_H = stride_W = stride. Default: stride = 1.
        padding (int|tuple): The padding size. If padding is a tuple, it must
            contain two integers, (padding_H, padding_W). Otherwise, the
            padding_H = padding_W = padding. Default: padding = 0.
        dilation (int|tuple): The dilation size. If dilation is a tuple, it must
            contain two integers, (dilation_H, dilation_W). Otherwise, the
            dilation_H = dilation_W = dilation. Default: dilation = 1.
        groups (int): The groups number of the Conv2d Layer. According to grouped
            convolution in Alex Krizhevsky's Deep CNN paper: when group=2,
            the first half of the filters is only connected to the first half
            of the input channels, while the second half of the filters is only
            connected to the second half of the input channels. Default: groups=1.
        param_attr (ParamAttr|None): The parameter attribute for learnable parameters/weights
            of conv2d. If it is set to None or one attribute of ParamAttr, conv2d
            will create ParamAttr as param_attr. If the Initializer of the param_attr
            is not set, the parameter is initialized with :math:`Normal(0.0, std)`,
            and the :math:`std` is :math:`(\\frac{2.0 }{filter\_elem\_num})^{0.5}`. Default: None.
        bias_attr (ParamAttr|bool|None): The parameter attribute for the bias of conv2d.
            If it is set to False, no bias will be added to the output units.
            If it is set to None or one attribute of ParamAttr, conv2d
            will create ParamAttr as bias_attr. If the Initializer of the bias_attr
            is not set, the bias is initialized zero. Default: None.
        use_cudnn (bool): Use cudnn kernel or not, it is valid only when the cudnn
            library is installed. Default: True
        act (str): Activation type, if it is set to None, activation is not appended.
            Default: None

    Raises:
        ValueError: If the shapes of input, filter_size, stride, padding and
                    groups mismatch.

    Examples:
        .. code-block:: python
          
          with fluid.dygraph.guard():
             conv2d = Conv2D( "conv2d", 2, 3)
             data = to_variable( data )
             conv = conv2d( data )
          from paddle.fluid.dygraph.base import to_variable
          import paddle.fluid as fluid
          from paddle.fluid.dygraph import Conv2D
          import numpy as np

          data = np.random.uniform( -1, 1, [10, 3, 32, 32] ).astype('float32')
          with fluid.dygraph.guard():
              conv2d = Conv2D( "conv2d", 2, 3)
              data = to_variable( data )
              conv = conv2d( data )

    """

    def __init__(self,
                 name_scope,
                 num_filters,
                 filter_size,
                 stride=1,
                 padding=0,
                 dilation=1,
                 groups=None,
                 param_attr=None,
                 bias_attr=None,
                 use_cudnn=True,
                 act=None,
                 dtype='float32'):
        assert param_attr is not False, "param_attr should not be False here."
        super(Conv2D, self).__init__(name_scope, dtype)
        self._groups = groups
        self._stride = utils.convert_to_list(stride, 2, 'stride')
        self._padding = utils.convert_to_list(padding, 2, 'padding')
        self._dilation = utils.convert_to_list(dilation, 2, 'dilation')
        self._act = act
        if not isinstance(use_cudnn, bool):
            raise ValueError("use_cudnn should be True or False")
        self._use_cudnn = use_cudnn
        self._filter_size = filter_size
        self._num_filters = num_filters
        self._param_attr = param_attr
        self._bias_attr = bias_attr
        self._dtype = dtype
        # if (self._num_channels == self._groups and
        #         num_filters % self._num_channels == 0 and not self._use_cudnn):
        #     self._l_type = 'depthwise_conv2d'
        # else:
        # TODO(jiabin): recover the usage of depthwise_conv2d when it's
        #  kernel fixed https://github.com/PaddlePaddle/Paddle/issues/17275
        self._l_type = 'conv2d'

    def _build_once(self, input):
        self._num_channels = input.shape[1]
        if self._groups is None:
            num_filter_channels = self._num_channels
        else:
            if self._num_channels % self._groups != 0:
                raise ValueError("num_channels must be divisible by groups.")
            num_filter_channels = self._num_channels // self._groups
        filter_size = utils.convert_to_list(self._filter_size, 2, 'filter_size')
        filter_shape = [self._num_filters, int(num_filter_channels)
                        ] + filter_size

        def _get_default_param_initializer():
            filter_elem_num = filter_size[0] * filter_size[
                1] * self._num_channels
            std = (2.0 / filter_elem_num)**0.5
            return Normal(0.0, std, 0)

        self._filter_param = self.create_parameter(
            attr=self._param_attr,
            shape=filter_shape,
            dtype=self._dtype,
            default_initializer=_get_default_param_initializer())

        if self._use_cudnn:
            self.create_variable(
                name="kCUDNNFwdAlgoCache",
                persistable=True,
                type=core.VarDesc.VarType.RAW)
            self.create_variable(
                name="kCUDNNBwdDataAlgoCache",
                persistable=True,
                type=core.VarDesc.VarType.RAW)
            self.create_variable(
                name="kCUDNNBwdFilterAlgoCache",
                persistable=True,
                type=core.VarDesc.VarType.RAW)

        self._bias_param = self.create_parameter(
            attr=self._bias_attr,
            shape=[self._num_filters],
            dtype=self._dtype,
            is_bias=True)

    def forward(self, input):
        pre_bias = self._helper.create_variable_for_type_inference(
            dtype=self._dtype)

        self._helper.append_op(
            type=self._l_type,
            inputs={
                'Input': input,
                'Filter': self._filter_param,
            },
            outputs={"Output": pre_bias},
            attrs={
                'strides': self._stride,
                'paddings': self._padding,
                'dilations': self._dilation,
                'groups': self._groups if self._groups else 1,
                'use_cudnn': self._use_cudnn,
                'use_mkldnn': False,
            })

        if self._bias_param is not None:
            pre_act = self._helper.create_variable_for_type_inference(
                dtype=self._dtype)
            self._helper.append_op(
                type='elementwise_add',
                inputs={'X': [pre_bias],
                        'Y': [self._bias_param]},
                outputs={'Out': [pre_act]},
                attrs={'axis': 1})
        else:
            pre_act = pre_bias

        # Currently, we don't support inplace in dygraph mode
        return self._helper.append_activation(pre_act, act=self._act)


class Conv3D(layers.Layer):
    """
    **Convlution3D Layer**

    The convolution3D layer calculates the output based on the input, filter
    and strides, paddings, dilations, groups parameters. Input(Input) and
    Output(Output) are in NCDHW format. Where N is batch size C is the number of
    channels, D is the depth of the feature, H is the height of the feature,
    and W is the width of the feature. Convlution3D is similar with Convlution2D
    but adds one dimension(depth). If bias attribution and activation type are
    provided, bias is added to the output of the convolution, and the
    corresponding activation function is applied to the final result.

    For each input :math:`X`, the equation is:

    .. math::

        Out = \sigma (W \\ast X + b)

    In the above equation:

    * :math:`X`: Input value, a tensor with NCDHW format.
    * :math:`W`: Filter value, a tensor with MCDHW format.
    * :math:`\\ast`: Convolution operation.
    * :math:`b`: Bias value, a 2-D tensor with shape [M, 1].
    * :math:`\\sigma`: Activation function.
    * :math:`Out`: Output value, the shape of :math:`Out` and :math:`X` may be different.

    Example:

        - Input:

          Input shape: :math:`(N, C_{in}, D_{in}, H_{in}, W_{in})`

          Filter shape: :math:`(C_{out}, C_{in}, D_f, H_f, W_f)`

        - Output:
          Output shape: :math:`(N, C_{out}, D_{out}, H_{out}, W_{out})`

        Where

        .. math::

            D_{out}&= \\frac{(D_{in} + 2 * paddings[0] - (dilations[0] * (D_f - 1) + 1))}{strides[0]} + 1 \\\\
            H_{out}&= \\frac{(H_{in} + 2 * paddings[1] - (dilations[1] * (H_f - 1) + 1))}{strides[1]} + 1 \\\\
            W_{out}&= \\frac{(W_{in} + 2 * paddings[2] - (dilations[2] * (W_f - 1) + 1))}{strides[2]} + 1

    Args:
        filter_size (int|tuple|None): The filter size. If filter_size is a tuple,
            it must contain three integers, (filter_size_D, filter_size_H, filter_size_W).
            Otherwise, the filter will be a square.
        stride (int|tuple): The stride size. If stride is a tuple, it must
            contain three integers, (stride_D, stride_H, stride_W). Otherwise, the
            stride_D = stride_H = stride_W = stride. Default: stride = 1.
        padding (int|tuple): The padding size. If padding is a tuple, it must
            contain three integers, (padding_D, padding_H, padding_W). Otherwise, the
            padding_D = padding_H = padding_W = padding. Default: padding = 0.
        dilation (int|tuple): The dilation size. If dilation is a tuple, it must
            contain three integers, (dilation_D, dilation_H, dilation_W). Otherwise, the
            dilation_D = dilation_H = dilation_W = dilation. Default: dilation = 1.
        groups (int): The groups number of the Conv3d Layer. According to grouped
            convolution in Alex Krizhevsky's Deep CNN paper: when group=2,
            the first half of the filters is only connected to the first half
            of the input channels, while the second half of the filters is only
            connected to the second half of the input channels. Default: groups=1
        param_attr (ParamAttr|None): The parameter attribute for learnable parameters/weights
            of conv3d. If it is set to None or one attribute of ParamAttr, conv3d
            will create ParamAttr as param_attr. If it is set to None, the parameter
            is initialized with :math:`Normal(0.0, std)`, and the :math:`std` is
            :math:`(\\frac{2.0 }{filter\_elem\_num})^{0.5}`. Default: None.
        bias_attr (ParamAttr|bool|None): The parameter attribute for the bias of conv3d.
            If it is set to False, no bias will be added to the output units.
            If it is set to None or one attribute of ParamAttr, conv3d
            will create ParamAttr as bias_attr. If the Initializer of the bias_attr
            is not set, the bias is initialized zero. Default: None.
        use_cudnn (bool): Use cudnn kernel or not, it is valid only when the cudnn
            library is installed. Default: True
        act (str): Activation type, if it is set to None, activation is not appended.
            Default: None.
        name (str|None): A name for this layer(optional). If set None, the layer
            will be named automatically. Default: None.

    Returns:
        Variable: The tensor variable storing the convolution and \
                  non-linearity activation result.

    Raises:
        ValueError: If the shapes of input, filter_size, stride, padding and
                    groups mismatch.

    Examples:
        .. code-block:: python

          import paddle.fluid as fluid
          import numpy

          with fluid.dygraph.guard():
              data = numpy.random.random((5, 3, 12, 32, 32)).astype('float32')

              conv3d = fluid.dygraph.nn.Conv3D(
                    'Conv3D', num_filters=2, filter_size=3, act="relu")
              ret = conv3d(fluid.dygraph.base.to_variable(data))

    """

    def __init__(self,
                 name_scope,
                 num_filters,
                 filter_size,
                 stride=1,
                 padding=0,
                 dilation=1,
                 groups=None,
                 param_attr=None,
                 bias_attr=None,
                 use_cudnn=True,
                 act=None):
        assert param_attr is not False, "param_attr should not be False here."
        super(Conv3D, self).__init__(name_scope)
        self._groups = groups
        self._stride = utils.convert_to_list(stride, 3, 'stride')
        self._padding = utils.convert_to_list(padding, 3, 'padding')
        self._dilation = utils.convert_to_list(dilation, 3, 'dilation')
        self._act = act
        if not isinstance(use_cudnn, bool):
            raise ValueError("use_cudnn should be True or False")
        self._use_cudnn = use_cudnn
        self._filter_size = filter_size
        self._num_filters = num_filters
        self._param_attr = param_attr
        self._bias_attr = bias_attr

    def _build_once(self, input):
        num_channels = input.shape[1]
        self._dtype = self._helper.input_dtype(input)

        if self._groups is None:
            num_filter_channels = num_channels
        else:
            if num_channels % self._groups != 0:
                raise ValueError("num_channels must be divisible by groups.")
            num_filter_channels = num_channels // self._groups

        filter_size = utils.convert_to_list(self._filter_size, 3, 'filter_size')

        filter_shape = [self._num_filters, num_filter_channels] + filter_size

        def _get_default_param_initializer():
            filter_elem_num = filter_size[0] * filter_size[1] * filter_size[
                2] * num_channels
            std = (2.0 / filter_elem_num)**0.5
            return Normal(0.0, std, 0)

        self._filter_param = self.create_parameter(
            attr=self._param_attr,
            shape=filter_shape,
            dtype=self._dtype,
            default_initializer=_get_default_param_initializer())

        self._bias_param = self.create_parameter(
            attr=self._bias_attr,
            shape=[self._num_filters],
            dtype=self._dtype,
            is_bias=True)

    def forward(self, input):
        pre_bias = self._helper.create_variable_for_type_inference(
            dtype=self._dtype)

        self._helper.append_op(
            type='conv3d',
            inputs={
                'Input': input,
                'Filter': self._filter_param,
            },
            outputs={"Output": pre_bias},
            attrs={
                'strides': self._stride,
                'paddings': self._padding,
                'dilations': self._dilation,
                'groups': self._groups if self._groups else 1,
                'use_cudnn': self._use_cudnn,
                'use_mkldnn': False
            })

        pre_act = self._helper.create_variable_for_type_inference(
            dtype=self._dtype)

        self._helper.append_op(
            type='elementwise_add',
            inputs={'X': [pre_bias],
                    'Y': [self._bias_param]},
            outputs={'Out': [pre_act]},
            attrs={'axis': 1})

        return self._helper.append_activation(pre_act, act=self._act)


class Conv3DTranspose(layers.Layer):
    """
    **Convlution3D transpose layer**

    The convolution3D transpose layer calculates the output based on the input,
    filter, and dilations, strides, paddings. Input(Input) and output(Output)
    are in NCDHW format. Where N is batch size, C is the number of channels,
    D is the depth of the feature, H is the height of the feature, and W
    is the width of the feature. Parameters(dilations, strides, paddings) are
    two elements. These two elements represent height and width, respectively.
    The details of convolution transpose layer, please refer to the following
    explanation and references `therein <http://www.matthewzeiler.com/wp-content/uploads/2017/07/cvpr2010.pdf>`_.
    If bias attribution and activation type are provided, bias is added to
    the output of the convolution, and the corresponding activation function
    is applied to the final result.

    For each input :math:`X`, the equation is:

    .. math::

        Out = \sigma (W \\ast X + b)

    In the above equation:

    * :math:`X`: Input value, a tensor with NCDHW format.
    * :math:`W`: Filter value, a tensor with MCDHW format.
    * :math:`\\ast`: Convolution operation.
    * :math:`b`: Bias value, a 2-D tensor with shape [M, 1].
    * :math:`\\sigma`: Activation function.
    * :math:`Out`: Output value, the shape of :math:`Out` and :math:`X` may be different.

    Example:

        - Input:

          Input shape: :math:`(N, C_{in}, D_{in}, H_{in}, W_{in})`

          Filter shape: :math:`(C_{in}, C_{out}, D_f, H_f, W_f)`

        - Output:

          Output shape: :math:`(N, C_{out}, D_{out}, H_{out}, W_{out})`

        Where

        .. math::

           D_{out} &= (D_{in} - 1) * strides[0] - 2 * paddings[0] + dilations[0] * (D_f - 1) + 1 \\\\
           H_{out} &= (H_{in} - 1) * strides[1] - 2 * paddings[1] + dilations[1] * (H_f - 1) + 1 \\\\
           W_{out} &= (W_{in} - 1) * strides[2] - 2 * paddings[2] + dilations[2] * (W_f - 1) + 1

    Args:
        num_filters(int): The number of the filter. It is as same as the output
            image channel.
        output_size(int|tuple|None): The output image size. If output size is a
            tuple, it must contain three integers, (image_D, image_H, image_W). This
            parameter only works when filter_size is None.
        filter_size(int|tuple|None): The filter size. If filter_size is a tuple,
            it must contain three integers, (filter_size_D, filter_size_H, filter_size_W).
            Otherwise, the filter will be a square. None if use output size to
            calculate filter_size.
        padding(int|tuple): The padding size. If padding is a tuple, it must
            contain three integers, (padding_D, padding_H, padding_W). Otherwise, the
            padding_D = padding_H = padding_W = padding. Default: padding = 0.
        stride(int|tuple): The stride size. If stride is a tuple, it must
            contain three integers, (stride_D, stride_H, stride_W). Otherwise, the
            stride_D = stride_H = stride_W = stride. Default: stride = 1.
        dilation(int|tuple): The dilation size. If dilation is a tuple, it must
            contain three integers, (dilation_D, dilation_H, dilation_W). Otherwise, the
            dilation_D = dilation_H = dilation_W = dilation. Default: dilation = 1.
        groups(int): The groups number of the Conv3d transpose layer. Inspired by
            grouped convolution in Alex Krizhevsky's Deep CNN paper, in which
            when group=2, the first half of the filters is only connected to the
            first half of the input channels, while the second half of the
            filters is only connected to the second half of the input channels.
            Default: groups=1
        param_attr (ParamAttr|None): The parameter attribute for learnable parameters/weights
            of conv3d_transpose. If it is set to None or one attribute of ParamAttr, conv3d_transpose
            will create ParamAttr as param_attr. If the Initializer of the param_attr
            is not set, the parameter is initialized with Xavier. Default: None.
        bias_attr (ParamAttr|bool|None): The parameter attribute for the bias of conv3d_transpose.
            If it is set to False, no bias will be added to the output units.
            If it is set to None or one attribute of ParamAttr, conv3d_transpose
            will create ParamAttr as bias_attr. If the Initializer of the bias_attr
            is not set, the bias is initialized zero. Default: None.
        use_cudnn(bool): Use cudnn kernel or not, it is valid only when the cudnn
            library is installed. Default: True
        act (str): Activation type, if it is set to None, activation is not appended.
            Default: None.
        name(str|None): A name for this layer(optional). If set None, the layer
            will be named automatically.

    Returns:
        Variable: The tensor variable storing the convolution transpose result.

    Raises:
        ValueError: If the shapes of input, filter_size, stride, padding and
                    groups mismatch.

    Examples:
       .. code-block:: python

         import paddle.fluid as fluid
         import numpy

         with fluid.dygraph.guard():
             data = numpy.random.random((5, 3, 12, 32, 32)).astype('float32')

             conv3dTranspose = fluid.dygraph.nn.Conv3DTranspose(
                    'Conv3DTranspose',
                    num_filters=12,
                    filter_size=12,
                    use_cudnn=False)
             ret = conv3dTranspose(fluid.dygraph.base.to_variable(data))

    """

    def __init__(self,
                 name_scope,
                 num_filters,
                 output_size=None,
                 filter_size=None,
                 padding=0,
                 stride=1,
                 dilation=1,
                 groups=None,
                 param_attr=None,
                 bias_attr=None,
                 use_cudnn=True,
                 act=None,
                 name=None):
        super(Conv3DTranspose, self).__init__(name_scope)
        if not isinstance(use_cudnn, bool):
            raise ValueError("use_cudnn should be True or False")
        assert param_attr is not False, "param_attr should not be False in conv3d_transpose."
        self._padding = utils.convert_to_list(padding, 3, 'padding')
        self._stride = utils.convert_to_list(stride, 3, 'stride')
        self._dilation = utils.convert_to_list(dilation, 3, 'dilation')
        self._param_attr = param_attr
        self._filter_size = filter_size
        self._output_size = output_size
        self._groups = 1 if groups is None else groups
        self._num_filters = num_filters
        self._use_cudnn = use_cudnn
        self._bias_attr = bias_attr
        self._act = act

    def _build_once(self, input):
        self._dtype = self._helper.input_dtype(input)
        self._input_channel = input.shape[1]

        if self._filter_size is None:
            if self._output_size is None:
                raise ValueError(
                    "output_size must be set when filter_size is None")
            if isinstance(self._output_size, int):
                self._output_size = [self._output_size, self._output_size]

            d_in = input.shape[2]
            h_in = input.shape[3]
            w_in = input.shape[4]

            filter_size_d = (self._output_size[0] -
                             (d_in - 1) * self._stride[0] + 2 * self._padding[0]
                             - 1) // self._dilation[0] + 1
            filter_size_h = (self._output_size[1] -
                             (h_in - 1) * self._stride[1] + 2 * self._padding[1]
                             - 1) // self._dilation[1] + 1
            filter_size_w = (self._output_size[2] -
                             (w_in - 1) * self._stride[2] + 2 * self._padding[2]
                             - 1) // self._dilation[2] + 1
            self._filter_size = [filter_size_d, filter_size_h, filter_size_w]
        else:
            self._filter_size = utils.convert_to_list(
                self._filter_size, 3, 'conv3d_transpose.filter_size')

        filter_shape = [
            self._input_channel, self._num_filters // self._groups
        ] + self._filter_size
        self._img_filter = self.create_parameter(
            dtype=self._dtype, shape=filter_shape, attr=self._param_attr)
        if self._bias_attr:
            self._bias_param = self.create_parameter(
                attr=self._bias_attr,
                shape=[self._num_filters],
                dtype=self._dtype,
                is_bias=True)

    def forward(self, input):
        pre_bias = self._helper.create_variable_for_type_inference(
            dtype=self._dtype)
        self._helper.append_op(
            type="conv3d_transpose",
            inputs={'Input': [input],
                    'Filter': [self._img_filter]},
            outputs={'Output': pre_bias},
            attrs={
                'strides': self._stride,
                'paddings': self._padding,
                'dilations': self._dilation,
                'groups': self._groups if self._groups else 1,
                'use_cudnn': self._use_cudnn
            })

        if self._bias_attr:
            pre_act = self._helper.create_variable_for_type_inference(
                dtype=self._dtype)
            self._helper.append_op(
                type='elementwise_add',
                inputs={'X': [pre_bias],
                        'Y': [self._bias_param]},
                outputs={'Out': [pre_act]},
                attrs={'axis': 1})
        else:
            pre_act = pre_bias

        # Currently, we don't support inplace in imperative mode
        return self._helper.append_activation(pre_act, act=self._act)


class Pool2D(layers.Layer):
    # TODO, should delete this class
    """
    ${comment}

    Args:
        name_scope(str) : The name of this class.
        pool_size (int|list|tuple): The pool kernel size. If pool kernel size is a tuple or list,
            it must contain two integers, (pool_size_Height, pool_size_Width).
            Otherwise, the pool kernel size will be a square of an int.
        pool_type: ${pooling_type_comment}
        pool_stride (int|list|tuple): The pool stride size. If pool stride size is a tuple or list,
            it must contain two integers, (pool_stride_Height, pool_stride_Width).
            Otherwise, the pool stride size will be a square of an int.
        pool_padding (int|list|tuple): The pool padding size. If pool padding size is a tuple,
            it must contain two integers, (pool_padding_on_Height, pool_padding_on_Width).
            Otherwise, the pool padding size will be a square of an int.
        global_pooling (bool): ${global_pooling_comment}
        use_cudnn (bool): ${use_cudnn_comment}
        ceil_mode (bool): ${ceil_mode_comment}
        name (str|None): A name for this layer(optional). If set None, the
                        layer will be named automatically.
        exclusive (bool): Whether to exclude padding points in average pooling
                          mode, default is true

    Returns:
        Variable: The pooling result.

    Raises:
        ValueError: If 'pool_type' is not "max" nor "avg"
        ValueError: If 'global_pooling' is False and 'pool_size' is -1
        ValueError: If 'use_cudnn' is not a bool value.

    Examples:

        .. code-block:: python

          data = fluid.layers.data(
              name='data', shape=[3, 32, 32], dtype='float32')
          pool2d = fluid.Pool2D("pool2d",pool_size=2,
                            pool_type='max',
                            pool_stride=1,
                            global_pooling=False)

          pool2d_res = pool2d(data)
    """

    def __init__(self,
                 name_scope,
                 pool_size=-1,
                 pool_type="max",
                 pool_stride=1,
                 pool_padding=0,
                 global_pooling=False,
                 use_cudnn=True,
                 ceil_mode=False,
                 exclusive=True,
                 dtype=core.VarDesc.VarType.FP32):
        if pool_type not in ["max", "avg"]:
            raise ValueError(
                "Unknown pool_type: '%s'. It can only be 'max' or 'avg'.",
                str(pool_type))

        if global_pooling is False and pool_size == -1:
            raise ValueError(
                "When the global_pooling is False, pool_size must be passed "
                "and be a valid value. Received pool_size: " + str(pool_size))

        if not isinstance(use_cudnn, bool):
            raise ValueError("use_cudnn should be True or False")

        super(Pool2D, self).__init__(name_scope, dtype=dtype)

        self._pool_type = pool_type
        self._pool_size = utils.convert_to_list(pool_size, 2, 'pool_size')
        self._pool_padding = utils.convert_to_list(pool_padding, 2,
                                                   'pool_padding')
        self._pool_stride = utils.convert_to_list(pool_stride, 2, 'pool_stride')
        self._global_pooling = global_pooling
        self._use_cudnn = use_cudnn
        self._ceil_mode = ceil_mode
        self._exclusive = exclusive
        self._l_type = 'pool2d'

    def forward(self, input):
        pool_out = self._helper.create_variable_for_type_inference(self._dtype)

        self._helper.append_op(
            type=self._l_type,
            inputs={"X": input},
            outputs={"Out": pool_out},
            attrs={
                "pooling_type": self._pool_type,
                "ksize": self._pool_size,
                "global_pooling": self._global_pooling,
                "strides": self._pool_stride,
                "paddings": self._pool_padding,
                "use_cudnn": self._use_cudnn,
                "ceil_mode": self._ceil_mode,
                "use_mkldnn": False,
                "exclusive": self._exclusive,
            })
        return pool_out


class FC(layers.Layer):
    """
    **Fully Connected Layer**

    This function creates a fully connected layer in the network. It can take
    one or multiple tensors as its inputs(input can be a list of Variable, see
    Args in detail). It creates a variable called weights for each input tensor,
    which represents a fully connected weight matrix from each input unit to
    each output unit. The fully connected layer multiplies each input tensor
    with its corresponding weight to produce an output Tensor with shape [M, `size`],
    where M is batch size. If multiple input tensors are given, the results of
    multiple output tensors with shape [M, `size`] will be summed up. If bias_attr
    is not None, a bias variable will be created and added to the output.
    Finally, if activation is not None, it will be applied to the output as well.

    When the input is single tensor:

    .. math::

        Out = Act({XW + b})

    When the input are multiple tensors:

    .. math::

        Out = Act({\sum_{i=0}^{N-1}X_iW_i + b})

    In the above equation:

    * :math:`N`: Number of the input. N equals to len(input) if input is list of Variable.
    * :math:`X_i`: The i-th input tensor.
    * :math:`W_i`: The i-th weights matrix corresponding i-th input tensor.
    * :math:`b`: The bias parameter created by this layer (if needed).
    * :math:`Act`: The activation function.
    * :math:`Out`: The output tensor.

    See below for an example.

    .. code-block:: text

        Given:
            data_1.data = [[[0.1, 0.2],
                           [0.3, 0.4]]]
            data_1.shape = (1, 2, 2) # 1 is batch_size

            data_2 = [[[0.1, 0.2, 0.3]]]
            data_2.shape = (1, 1, 3)

            out = fluid.layers.fc(input=[data_1, data_2], size=2)

        Then:
            out.data = [[0.18669507, 0.1893476]]
            out.shape = (1, 2)

    Args:
        name(str): The name of this class.
        size(int): The number of output units in this layer.
        num_flatten_dims (int, default 1): The fc layer can accept an input tensor with more than
            two dimensions. If this happens, the multidimensional tensor will first be flattened
            into a 2-dimensional matrix. The parameter `num_flatten_dims` determines how the input
            tensor is flattened: the first `num_flatten_dims` (inclusive, index starts from 1)
            dimensions will be flatten to form the first dimension of the final matrix (height of
            the matrix), and the rest `rank(X) - num_flatten_dims` dimensions are flattened to
            form the second dimension of the final matrix (width of the matrix). For example, suppose
            `X` is a 5-dimensional tensor with a shape [2, 3, 4, 5, 6], and `num_flatten_dims` = 3.
            Then, the flattened matrix will have a shape [2 x 3 x 4, 5 x 6] = [24, 30].
        param_attr (ParamAttr|list of ParamAttr, default None): The parameter attribute for learnable
            parameters/weights of this layer.
        bias_attr (ParamAttr|list of ParamAttr, default None): The parameter attribute for the bias
            of this layer. If it is set to False, no bias will be added to the output units.
            If it is set to None, the bias is initialized zero. Default: None.
        act (str, default None): Activation to be applied to the output of this layer.
        is_test(bool): A flag indicating whether execution is in test phase.
        dtype(str): Dtype used for weight

    Raises:
        ValueError: If rank of the input tensor is less than 2.

    Examples:
        .. code-block:: python
        
          from paddle.fluid.dygraph.base import to_variable
          import paddle.fluid as fluid
          from paddle.fluid.dygraph import FC
          import numpy as np
          data = np.random.uniform( -1, 1, [30, 10, 32] ).astype('float32')
          with fluid.dygraph.guard():
              fc = FC( "fc", 64, num_flatten_dims=2)
              data = to_variable( data )
              conv = fc( data )

    """

    def __init__(self,
                 name_scope,
                 size,
                 num_flatten_dims=1,
                 param_attr=None,
                 bias_attr=None,
                 act=None,
                 is_test=False,
                 dtype="float32"):
        super(FC, self).__init__(name_scope, dtype)

        self._size = size
        self._num_flatten_dims = num_flatten_dims
        self._dtype = dtype
        self._param_attr = param_attr
        self._bias_attr = bias_attr
        self._act = act
        self.__w = list()

    @property
    def _w(self, i=0):
        return self.__w[i]

    @_w.setter
    def _w(self, value, i=0):
        assert isinstance(value, Parameter)
        self.__w[i] = value

    def _build_once(self, input):
        i = 0
        for inp, param in self._helper.iter_inputs_and_params(input,
                                                              self._param_attr):
            input_shape = inp.shape

            param_shape = [
                reduce(lambda a, b: a * b, input_shape[self._num_flatten_dims:],
                       1)
            ] + [self._size]
            self.__w.append(
                self.add_parameter(
                    '_w%d' % i,
                    self.create_parameter(
                        attr=param,
                        shape=param_shape,
                        dtype=self._dtype,
                        is_bias=False)))
            i += 1

        size = list([self._size])
        self._b = self.create_parameter(
            attr=self._bias_attr, shape=size, dtype=self._dtype, is_bias=True)

    def forward(self, input):
        mul_results = list()
        i = 0
        for inp, param in self._helper.iter_inputs_and_params(input,
                                                              self._param_attr):
            tmp = self._helper.create_variable_for_type_inference(self._dtype)
            self._helper.append_op(
                type="mul",
                inputs={"X": inp,
                        "Y": self.__w[i]},
                outputs={"Out": tmp},
                attrs={
                    "x_num_col_dims": self._num_flatten_dims,
                    "y_num_col_dims": 1
                })
            i += 1
            mul_results.append(tmp)

        if len(mul_results) == 1:
            pre_bias = mul_results[0]
        else:
            pre_bias = self._helper.create_variable_for_type_inference(
                self._dtype)
            self._helper.append_op(
                type="sum",
                inputs={"X": mul_results},
                outputs={"Out": pre_bias},
                attrs={"use_mkldnn": False})

        if self._b:
            pre_activation = self._helper.create_variable_for_type_inference(
                dtype=self._dtype)
            self._helper.append_op(
                type='elementwise_add',
                inputs={'X': [pre_bias],
                        'Y': [self._b]},
                outputs={'Out': [pre_activation]},
                attrs={'axis': self._num_flatten_dims})
        else:
            pre_activation = pre_bias
        # Currently, we don't support inplace in dygraph mode
        return self._helper.append_activation(pre_activation, act=self._act)


class BatchNorm(layers.Layer):
    """
    **Batch Normalization Layer**

    Can be used as a normalizer function for conv2d and fully_connected operations.
    The required data format for this layer is one of the following:

    1. NHWC `[batch, in_height, in_width, in_channels]`

    2. NCHW `[batch, in_channels, in_height, in_width]`

    Refer to `Batch Normalization: Accelerating Deep Network Training by Reducing
    Internal Covariate Shift <https://arxiv.org/pdf/1502.03167.pdf>`_
    for more details.

    :math:`input` is the input features over a mini-batch.

    ..  math::

        \\mu_{\\beta} &\\gets \\frac{1}{m} \\sum_{i=1}^{m} x_i \\qquad &//\\
        \ mini-batch\ mean \\\\
        \\sigma_{\\beta}^{2} &\\gets \\frac{1}{m} \\sum_{i=1}^{m}(x_i - \\
        \\mu_{\\beta})^2 \\qquad &//\ mini-batch\ variance \\\\
        \\hat{x_i} &\\gets \\frac{x_i - \\mu_\\beta} {\\sqrt{\\
        \\sigma_{\\beta}^{2} + \\epsilon}} \\qquad &//\ normalize \\\\
        y_i &\\gets \\gamma \\hat{x_i} + \\beta \\qquad &//\ scale\ and\ shift


    When use_global_stats = True, the :math:`\\mu_{\\beta}`
    and :math:`\\sigma_{\\beta}^{2}` are not the statistics of one mini-batch.
    They are global (or running) statistics. (It usually got from the
    pre-trained model.)
    The training and testing (or inference) have the same behavior:

    ..  math::

        \\hat{x_i} &\\gets \\frac{x_i - \\mu_\\beta} {\\sqrt{\\
        \\sigma_{\\beta}^{2} + \\epsilon}}  \\\\
        y_i &\\gets \\gamma \\hat{x_i} + \\beta

    Args:
        input(variable): The rank of input variable can be 2, 3, 4, 5.
        act(string, Default None): Activation type, linear|relu|prelu|...
        is_test (bool, Default False): A flag indicating whether it is in
            test phrase or not.
        momentum(float, Default 0.9): The value used for the moving_mean and
            moving_var computation. The updated formula is:
            :math:`moving\_mean = moving\_mean * momentum + new\_mean * (1. - momentum)`
            :math:`moving\_var = moving\_var * momentum + new\_var * (1. - momentum)`
            Default is 0.9.
        epsilon(float, Default 1e-05): A value added to the denominator for
            numerical stability. Default is 1e-5.
        param_attr(ParamAttr|None): The parameter attribute for Parameter `scale`
             of batch_norm. If it is set to None or one attribute of ParamAttr, batch_norm
             will create ParamAttr as param_attr. If the Initializer of the param_attr
             is not set, the parameter is initialized with Xavier. Default: None.
        bias_attr(ParamAttr|None): The parameter attribute for the bias of batch_norm.
             If it is set to None or one attribute of ParamAttr, batch_norm
             will create ParamAttr as bias_attr. If the Initializer of the bias_attr
             is not set, the bias is initialized zero. Default: None.
        data_layout(string, default NCHW): NCHW|NHWC
        in_place(bool, Default False): Make the input and output of batch norm reuse memory.
        name(string, Default None): A name for this layer(optional). If set None, the layer
            will be named automatically.
        moving_mean_name(string, Default None): The name of moving_mean which store the global Mean.
        moving_variance_name(string, Default None): The name of the moving_variance which store the global Variance.
        do_model_average_for_mean_and_var(bool, Default False): Do model average for mean and variance or not.
        fuse_with_relu (bool): if True, this OP performs relu after batch norm.
        use_global_stats(bool, Default False): Whether to use global mean and
            variance. In inference or test mode, set use_global_stats to true
            or is_test to true, and the behavior is equivalent.
            In train mode, when setting use_global_stats True, the global mean
            and variance are also used during train period.
        trainable_statistics(bool, Default False): Whether to calculate mean and var in eval mode. In eval mode, when
            setting trainable_statistics True, mean and variance will be calculated by current batch statistics.

    Returns:
        Variable: A tensor variable which is the result after applying batch normalization on the input.

    Examples:

        .. code-block:: python
            fc = fluid.FC('fc', size=200, param_attr='fc1.w')
            hidden1 = fc(x)
            batch_norm = fluid.BatchNorm("batch_norm", 10)
            hidden2 = batch_norm(hidden1)
    """

    def __init__(self,
                 name_scope,
                 num_channels,
                 act=None,
                 is_test=False,
                 momentum=0.9,
                 epsilon=1e-05,
                 param_attr=None,
                 bias_attr=None,
                 dtype='float32',
                 data_layout='NCHW',
                 in_place=False,
                 moving_mean_name=None,
                 moving_variance_name=None,
                 do_model_average_for_mean_and_var=False,
                 fuse_with_relu=False,
                 use_global_stats=False,
                 trainable_statistics=False):
        super(BatchNorm, self).__init__(name_scope, dtype)
        self._param_attr = param_attr
        self._bias_attr = bias_attr
        self._act = act

        assert bias_attr is not False, "bias_attr should not be False in batch_norm."

        if dtype == "float16":
            self._dtype = "float32"
        else:
            self._dtype = dtype

        param_shape = [num_channels]

        # create parameter
        self._scale = self.create_parameter(
            attr=self._param_attr,
            shape=param_shape,
            dtype=self._dtype,
            default_initializer=Constant(1.0))
        if use_global_stats and self._param_attr.learning_rate == 0.:
            self._scale.stop_gradient = True

        self._bias = self.create_parameter(
            attr=self._bias_attr,
            shape=param_shape,
            dtype=self._dtype,
            is_bias=True)
        if use_global_stats and self._param_attr.learning_rate == 0.:
            self._bias.stop_gradient = True

        self._mean = self.create_parameter(
            attr=ParamAttr(
                name=moving_mean_name,
                initializer=Constant(0.0),
                trainable=False,
                do_model_average=do_model_average_for_mean_and_var),
            shape=param_shape,
            dtype=self._dtype)
        self._mean.stop_gradient = True

        self._variance = self.create_parameter(
            attr=ParamAttr(
                name=moving_variance_name,
                initializer=Constant(1.0),
                trainable=False,
                do_model_average=do_model_average_for_mean_and_var),
            shape=param_shape,
            dtype=self._dtype)
        self._variance.stop_gradient = True

        self._in_place = in_place
        self._momentum = momentum
        self._epsilon = epsilon
        self._is_test = is_test
        self._fuse_with_relu = fuse_with_relu
        self._use_global_stats = use_global_stats
        self._trainable_statistics = trainable_statistics

    def _build_once(self, input):
        pass

    def forward(self, input):
        # create output
        # mean and mean_out share the same memory
        mean_out = self._mean
        # variance and variance out share the same memory
        variance_out = self._variance

        saved_mean = self._helper.create_variable_for_type_inference(
            dtype=self._dtype, stop_gradient=True)
        saved_variance = self._helper.create_variable_for_type_inference(
            dtype=self._dtype, stop_gradient=True)
        batch_norm_out = input if self._in_place else self._helper.create_variable_for_type_inference(
            self._dtype)

        self._helper.append_op(
            type="batch_norm",
            inputs={
                "X": input,
                "Scale": self._scale,
                "Bias": self._bias,
                "Mean": self._mean,
                "Variance": self._variance
            },
            outputs={
                "Y": batch_norm_out,
                "MeanOut": mean_out,
                "VarianceOut": variance_out,
                "SavedMean": saved_mean,
                "SavedVariance": saved_variance
            },
            attrs={
                "momentum": self._momentum,
                "epsilon": self._epsilon,
                "is_test": self._is_test,
                "use_mkldnn": False,
                "fuse_with_relu": self._fuse_with_relu,
                "use_global_stats": self._use_global_stats,
                "trainable_statistics": self._trainable_statistics
            })

        # Currently, we don't support inplace in dygraph mode
        return self._helper.append_activation(batch_norm_out, self._act)


class Embedding(layers.Layer):
    """
    **Embedding Layer**

    This layer is used to lookup embeddings of IDs, provided by :attr:`input`, in
    a lookup table. The result of this lookup is the embedding of each ID in the
    :attr:`input`.
    All the input variables are passed in as local variables to the LayerHelper constructor

    Args:
        name_scope: See base class.
        size(tuple|list): The shape of the look up table parameter. It should have two elements which indicate the size of the dictionary of embeddings and the size of each embedding vector respectively.

        is_sparse(bool): The flag indicating whether to use sparse update.
        is_distributed(bool): Whether to run lookup table from remote parameter server.
        padding_idx(int|long|None): If :attr:`None`, it makes no effect to lookup. Otherwise the given :attr:`padding_idx` indicates padding the output with zeros whenever lookup encounters it in :attr:`input`. If :math:`padding_idx < 0`, the :attr:`padding_idx` to use in lookup is :math:`size[0] + dim`.
        param_attr(ParamAttr): Parameters for this layer
        dtype(np.dtype|core.VarDesc.VarType|str): The type of data : float32, float_16, int etc

    Returns:
        Variable: The tensor variable storing the embeddings of the \
                  supplied inputs.

    Examples:

        .. code-block:: python

          inp_word = np.array([[[1]]]).astype('int64')
          dict_size = 20
          with fluid.dygraph.guard():
              emb = fluid.Embedding(
                  name_scope='embedding',
                  size=[dict_size, 32],
                  param_attr='emb.w',
                  is_sparse=False)
            static_rlt3 = emb2(base.to_variable(inp_word))
    """

    def __init__(self,
                 name_scope,
                 size,
                 is_sparse=False,
                 is_distributed=False,
                 padding_idx=None,
                 param_attr=None,
                 dtype='float32'):

        super(Embedding, self).__init__(name_scope, dtype)
        self._size = size
        self._is_sparse = is_sparse
        self._is_distributed = is_distributed
        self._padding_idx = -1 if padding_idx is None else padding_idx if padding_idx >= 0 else (
            size[0] + padding_idx)

        self._param_attr = param_attr
        self._dtype = dtype
        self._remote_prefetch = self._is_sparse and (not self._is_distributed)
        if self._remote_prefetch:
            assert self._is_sparse is True and self._is_distributed is False

        self._w = self.create_parameter(
            attr=self._param_attr,
            shape=self._size,
            dtype=self._dtype,
            is_bias=False)

    def forward(self, input):
        out = self._helper.create_variable_for_type_inference(self._dtype)
        self._helper.append_op(
            type='lookup_table',
            inputs={'Ids': input,
                    'W': self._w},
            outputs={'Out': out},
            attrs={
                'is_sparse': self._is_sparse,
                'is_distributed': self._is_distributed,
                'remote_prefetch': self._remote_prefetch,
                'padding_idx': self._padding_idx
            })

        return out


class LayerNorm(layers.Layer):
    """
    Assume feature vectors exist on dimensions
    `begin_norm_axis ... rank(input)` and calculate the moment statistics along these dimensions for each feature
    vector `a` with size `H`, then normalize each feature vector using the corresponding
    statistics. After that, apply learnable gain and bias on the normalized
    tensor to scale and shift if `scale` and `shift` are set.

    Refer to `Layer Normalization <https://arxiv.org/pdf/1607.06450v1.pdf>`_

    The formula is as follows:

    ..  math::

        \\mu & = \\frac{1}{H}\\sum_{i=1}^{H} a_i

        \\sigma & = \\sqrt{\\frac{1}{H}\sum_{i=1}^{H}(a_i - \\mu)^2}

        h & = f(\\frac{g}{\\sigma}(a - \\mu) + b)

    * :math:`a`: the vector representation of the summed inputs to the neurons
    in that layer.

    * :math:`H`: the number of hidden units in a layers

    * :math:`g`: the trainable scale parameter.

    * :math:`b`: the trainable bias parameter.

    Args:
        name_scope (str): See base class.
        scale(bool): Whether to learn the adaptive gain :math:`g` after
            normalization. Default True.
        shift(bool): Whether to learn the adaptive bias :math:`b` after
            normalization. Default True.
        begin_norm_axis(int): The normalization will be performed along
            dimensions from :attr:`begin_norm_axis` to :attr:`rank(input)`.
            Default 1.
        epsilon(float): The small value added to the variance to prevent
            division by zero. Default 1e-05.
        param_attr(ParamAttr|None): The parameter attribute for the learnable
            gain :math:`g`. If :attr:`scale` is False, :attr:`param_attr` is
            omitted. If :attr:`scale` is True and :attr:`param_attr` is None,
            a default :code:`ParamAttr` would be added as scale. The
            :attr:`param_attr` is initialized as 1 if it is added. Default None.
        bias_attr(ParamAttr|None): The parameter attribute for the learnable
            bias :math:`b`. If :attr:`shift` is False, :attr:`bias_attr` is
            omitted. If :attr:`shift` is True and :attr:`param_attr` is None,
            a default :code:`ParamAttr` would be added as bias. The
            :attr:`bias_attr` is initialized as 0 if it is added. Default None.
        act(str): Activation to be applied to the output of layer normalizaiton.
                  Default None.
    Returns:
        Result after normalization

    Examples:

        .. code-block:: python

          import paddle.fluid as fluid
          import numpy

          with fluid.dygraph.guard():
              x = numpy.random.random((3, 32, 32)).astype('float32')
              layerNorm = fluid.dygraph.nn.LayerNorm(
                    'LayerNorm', begin_norm_axis=1)
             ret = layerNorm(fluid.dygraph.base.to_variable(x))

    """

    def __init__(self,
                 name_scope,
                 scale=True,
                 shift=True,
                 begin_norm_axis=1,
                 epsilon=1e-05,
                 param_attr=None,
                 bias_attr=None,
                 act=None):
        super(LayerNorm, self).__init__(name_scope)
        self._scale = scale
        self._shift = shift
        self._begin_norm_axis = begin_norm_axis
        self._epsilon = epsilon
        self._param_attr = param_attr
        self._bias_attr = bias_attr
        self._act = act

    def _build_once(self, input):
        self._dtype = self._helper.input_dtype(input)
        input_shape = input.shape
        param_shape = [
            reduce(lambda x, y: x * y, input_shape[self._begin_norm_axis:])
        ]
        if self._scale:
            self._scale_w = self.create_parameter(
                attr=self._param_attr,
                shape=param_shape,
                dtype=self._dtype,
                default_initializer=Constant(1.0))
        if self._shift:
            assert self._bias_attr is not False
            self._bias_w = self.create_parameter(
                attr=self._bias_attr,
                shape=param_shape,
                dtype=self._dtype,
                is_bias=True)

    def forward(self, input):
        inputs = dict()
        inputs['X'] = input
        if self._scale:
            inputs['Scale'] = self._scale_w
        if self._shift:
            inputs['Bias'] = self._bias_w
        # create output
        mean_out = self._helper.create_variable_for_type_inference(
            dtype=self._dtype, stop_gradient=True)
        variance_out = self._helper.create_variable_for_type_inference(
            dtype=self._dtype, stop_gradient=True)
        layer_norm_out = self._helper.create_variable_for_type_inference(
            self._dtype)

        self._helper.append_op(
            type="layer_norm",
            inputs=inputs,
            outputs={
                "Y": layer_norm_out,
                "Mean": mean_out,
                "Variance": variance_out,
            },
            attrs={
                "epsilon": self._epsilon,
                "begin_norm_axis": self._begin_norm_axis
            })

        return self._helper.append_activation(layer_norm_out)


class GRUUnit(layers.Layer):
    """
    **GRU unit layer**

    if origin_mode is True, then the equation of a gru step is from paper
    `Learning Phrase Representations using RNN Encoder-Decoder for Statistical
    Machine Translation <https://arxiv.org/pdf/1406.1078.pdf>`_

        .. math::
            u_t & = actGate(xu_{t} + W_u h_{t-1} + b_u)

            r_t & = actGate(xr_{t} + W_r h_{t-1} + b_r)

            m_t & = actNode(xm_t + W_c dot(r_t, h_{t-1}) + b_m)

            h_t & = dot(u_t, h_{t-1}) + dot((1-u_t), m_t)

    if origin_mode is False, then the equation of a gru step is from paper
    `Empirical Evaluation of Gated Recurrent Neural Networks on Sequence
    Modeling <https://arxiv.org/pdf/1412.3555.pdf>`_

        .. math::
            u_t & = actGate(xu_{t} + W_u h_{t-1} + b_u)

            r_t & = actGate(xr_{t} + W_r h_{t-1} + b_r)

            m_t & = actNode(xm_t + W_c dot(r_t, h_{t-1}) + b_m)

            h_t & = dot((1-u_t), h_{t-1}) + dot(u_t, m_t)


    The inputs of gru unit includes :math:`z_t`, :math:`h_{t-1}`. In terms
    of the equation above, the :math:`z_t` is split into 3 parts -
    :math:`xu_t`, :math:`xr_t` and :math:`xm_t`. This means that in order to
    implement a full GRU unit operator for an input, a fully
    connected layer has to be applied, such that :math:`z_t = W_{fc}x_t`.

    The terms :math:`u_t` and :math:`r_t` represent the update and reset gates
    of the GRU cell. Unlike LSTM, GRU has one lesser gate. However, there is
    an intermediate candidate hidden output, which is denoted by :math:`m_t`.
    This layer has three outputs :math:`h_t`, :math:`dot(r_t, h_{t-1})`
    and concatenation of :math:`u_t`, :math:`r_t` and :math:`m_t`.

    Args:
        input (Variable): The fc transformed input value of current step.
        name_scope (str): See base class.
        hidden (Variable): The hidden value of gru unit from previous step.
        size (integer): The input dimension value.
        param_attr(ParamAttr|None): The parameter attribute for the learnable
            hidden-hidden weight matrix. Note:

            - The shape of the weight matrix is :math:`(T \\times 3D)`, where
              :math:`D` is the hidden size.
            - All elements in the weight matrix can be divided into two parts.
              The first part are weights of the update gate and reset gate with
              shape :math:`(D \\times 2D)`, and the second part are weights for
              candidate hidden state with shape :math:`(D \\times D)`.

            If it is set to None or one attribute of ParamAttr, gru_unit will
            create ParamAttr as param_attr. If the Initializer of the param_attr
            is not set, the parameter is initialized with Xavier. Default: None.
        bias_attr (ParamAttr|bool|None): The parameter attribute for the bias
            of GRU.Note that the bias with :math:`(1 \\times 3D)` concatenates
            the bias in the update gate, reset gate and candidate calculations.
            If it is set to False, no bias will be applied to the update gate,
            reset gate and candidate calculations. If it is set to None or one
            attribute of ParamAttr, gru_unit will create ParamAttr as
            bias_attr. If the Initializer of the bias_attr is not set, the bias
            is initialized zero. Default: None.
        activation (string): The activation type for cell (actNode).
                             Default: 'tanh'
        gate_activation (string): The activation type for gates (actGate).
                                  Default: 'sigmoid'
        dtype(string): The dtype of the layers

    Returns:
        tuple: The hidden value, reset-hidden value and gate values.
    """

    def __init__(self,
                 name_scope,
                 size,
                 param_attr=None,
                 bias_attr=None,
                 activation='tanh',
                 gate_activation='sigmoid',
                 origin_mode=False,
                 dtype='float32'):
        super(GRUUnit, self).__init__(name_scope, dtype)

        activation_dict = dict(
            identity=0,
            sigmoid=1,
            tanh=2,
            relu=3, )
        self.activation = activation_dict[activation]
        self.gate_activation = activation_dict[gate_activation]

        self._dtype = dtype
        size = size // 3
        # create weight
        self._weight = self.create_parameter(
            attr=param_attr, shape=[size, 3 * size], dtype=dtype)

        # create bias
        bias_size = [1, 3 * size]
        self._bias = self.create_parameter(
            attr=bias_attr, shape=bias_size, dtype=dtype, is_bias=True)

    def forward(self, input, hidden):
        inputs = {'Input': input, 'HiddenPrev': hidden, 'Weight': self._weight}
        if self._bias:
            inputs['Bias'] = self._bias

        gate = self._helper.create_variable_for_type_inference(self._dtype)
        reset_hidden_pre = self._helper.create_variable_for_type_inference(
            self._dtype)
        updated_hidden = self._helper.create_variable_for_type_inference(
            self._dtype)
        self._helper.append_op(
            type='gru_unit',
            inputs=inputs,
            outputs={
                'Gate': gate,
                'ResetHiddenPrev': reset_hidden_pre,
                'Hidden': updated_hidden,
            },
            attrs={
                'activation': self.activation,
                'gate_activation': self.gate_activation,
            })

        return updated_hidden, reset_hidden_pre, gate


class NCE(layers.Layer):
    """
    Compute and return the noise-contrastive estimation training loss. See
    `Noise-contrastive estimation: A new estimation principle for unnormalized
    statistical models
     <http://www.jmlr.org/proceedings/papers/v9/gutmann10a/gutmann10a.pdf>`_.
    By default this operator uses a uniform distribution for sampling.

    Args:
        name_scope (str): See base class.
        num_total_classes (int): Total number of classes in all samples
        sample_weight (Variable|None): A Variable of shape [batch_size, 1]
            storing a weight for each sample. The default weight for each
            sample is 1.0.
        param_attr (ParamAttr|None): The parameter attribute for learnable parameters/weights
             of nce. If it is set to None or one attribute of ParamAttr, nce
             will create ParamAttr as param_attr. If the Initializer of the param_attr
             is not set, the parameter is initialized with Xavier. Default: None.
        bias_attr (ParamAttr|bool|None): The parameter attribute for the bias of nce.
             If it is set to False, no bias will be added to the output units.
             If it is set to None or one attribute of ParamAttr, nce
             will create ParamAttr as bias_attr. If the Initializer of the bias_attr
             is not set, the bias is initialized zero. Default: None.
        num_neg_samples (int): The number of negative classes. The default value is 10.
        name (str|None): A name for this layer(optional). If set None, the layer
             will be named automatically. Default: None.
        sampler (str): The sampler used to sample class from negtive classes.
                       It can be 'uniform', 'log_uniform' or 'custom_dist'.
                       default: 'uniform'.
        custom_dist (float[]): A float[] with size=num_total_classes.
                       It is used when sampler is set to 'custom_dist'.
                       custom_dist[i] is the probsbility of i-th class to be sampled.
                       default: None.
        seed (int): The seed used in sampler. default: 0.
        is_sparse(bool): The flag indicating whether to use sparse update, the weight@GRAD and bias@GRAD will be changed to SelectedRows.

    Returns:
        Variable: The output nce loss.

    Examples:
        .. code-block:: python

            import numpy as np
            import paddle.fluid as fluid

            window_size = 5
            dict_size = 20
            label_word = int(window_size // 2) + 1
            inp_word = np.array([[[1]], [[2]], [[3]], [[4]], [[5]]]).astype('int64')
            nid_freq_arr = np.random.dirichlet(np.ones(20) * 1000).astype('float32')

            with fluid.dygraph.guard():
                words = []
                for i in range(window_size):
                    words.append(fluid.dygraph.base.to_variable(inp_word[i]))

                emb = fluid.Embedding(
                    'embedding',
                    size=[dict_size, 32],
                    param_attr='emb.w',
                    is_sparse=False)

                embs3 = []
                for i in range(window_size):
                    if i == label_word:
                        continue

                    emb_rlt = emb(words[i])
                    embs3.append(emb_rlt)

                embs3 = fluid.layers.concat(input=embs3, axis=1)
                nce = fluid.NCE('nce',
                             num_total_classes=dict_size,
                             num_neg_samples=2,
                             sampler="custom_dist",
                             custom_dist=nid_freq_arr.tolist(),
                             seed=1,
                             param_attr='nce.w',
                             bias_attr='nce.b')

                nce_loss3 = nce(embs3, words[label_word])

    """

    def __init__(self,
                 name_scope,
                 num_total_classes,
                 param_attr=None,
                 bias_attr=None,
                 num_neg_samples=None,
                 sampler="uniform",
                 custom_dist=None,
                 seed=0,
                 is_sparse=False):
        super(NCE, self).__init__(name_scope)
        self._param_attr = param_attr
        self._bias_attr = bias_attr
        self._num_total_classes = num_total_classes

        self._inputs = dict()

        if sampler == "uniform":
            sampler = 0
        elif sampler == "log_uniform":
            sampler = 1
        elif sampler == "custom_dist":
            assert custom_dist is not None
            # assert isinstance(custom_dist, Variable)

            custom_dist_len = len(custom_dist)
            alias_probs_ = [0] * custom_dist_len
            alias_ = [0] * custom_dist_len
            bigs = []
            littles = []
            for i in range(custom_dist_len):
                normal_prob = custom_dist[i] * custom_dist_len
                if normal_prob - 1.0 > 0:
                    bigs.append((i, normal_prob))
                elif 1.0 - normal_prob > 0:
                    littles.append((i, normal_prob))
                else:
                    alias_probs_[i] = normal_prob
                    alias_[i] = -1

            while len(bigs) and len(littles):
                big = bigs.pop(0)
                little = littles.pop(0)

                big_idx = big[0]
                big_prob = big[1]

                alias_probs_[little[0]] = little[1]
                alias_[little[0]] = big_idx
                big_left = big[1] + little[1] - 1
                if big_left - 1.0 > 0:
                    bigs.append((big_idx, big_left))
                elif 1.0 - big_left > 0:
                    littles.append((big_idx, big_left))
                else:
                    alias_probs_[big_idx] = big_left
                    alias_[big_idx] = -1

            if len(bigs):
                big = bigs.pop(0)
                alias_probs_[big[0]] = 1.0
                alias_[big[0]] = -1
            if len(littles):
                little = littles.pop(0)
                alias_probs_[little[0]] = 1.0
                alias_[little[0]] = -1

            def _init_by_numpy_array(numpy_array):
                ret = self.create_parameter(
                    attr=ParamAttr(),
                    shape=numpy_array.shape,
                    dtype=numpy_array.dtype,
                    default_initializer=NumpyArrayInitializer(numpy_array))
                ret.stop_gradient = True
                return ret

            self._inputs['CustomDistProbs'] = _init_by_numpy_array(
                np.array(custom_dist).astype('float32'))
            self._inputs['CustomDistAlias'] = _init_by_numpy_array(
                np.array(alias_).astype('int32'))
            self._inputs['CustomDistAliasProbs'] = _init_by_numpy_array(
                np.array(alias_probs_).astype('float32'))
            sampler = 2
        else:
            raise Exception("Unsupported sampler type.")

        if num_neg_samples is None:
            num_neg_samples = 10
        else:
            num_neg_samples = int(num_neg_samples)
        self._num_neg_samples = num_neg_samples
        remote_prefetch = is_sparse
        print(
            "With sparse mode, if your models has only small parameter prefetch may cause speed down"
        )
        self._attrs = {
            'num_total_classes': int(num_total_classes),
            'num_neg_samples': num_neg_samples,
            'seed': seed,
            'sampler': sampler,
            'is_sparse': is_sparse,
            'remote_prefetch': remote_prefetch
        }

    def _build_once(self, input, label, sample_weight=None):
        assert isinstance(input, Variable)
        assert isinstance(label, Variable)

        dim = input.shape[1]
        num_true_class = label.shape[1]
        self._w = self.create_parameter(
            attr=self._param_attr,
            shape=[self._num_total_classes, dim],
            is_bias=False,
            dtype=input.dtype)
        if self._bias_attr:
            self._b = self.create_parameter(
                attr=self._bias_attr,
                shape=[self._num_total_classes, 1],
                is_bias=True,
                dtype=input.dtype)
            self._inputs['Bias'] = self._b
        self._inputs['Weight'] = self._w

    def forward(self, input, label, sample_weight=None):
        assert isinstance(input, Variable)
        assert isinstance(label, Variable)

        self._inputs['Input'] = input
        self._inputs['Label'] = label
        self._inputs['SampleWeight'] = sample_weight if sample_weight is not None else []

        cost = self._helper.create_variable_for_type_inference(
            dtype=input.dtype)
        sample_logits = self._helper.create_variable_for_type_inference(
            dtype=input.dtype)
        sample_labels = self._helper.create_variable_for_type_inference(
            dtype=label.dtype)

        self._helper.append_op(
            type='nce',
            inputs=self._inputs,
            outputs={
                'Cost': cost,
                'SampleLogits': sample_logits,
                'SampleLabels': sample_labels
            },
            attrs=self._attrs)
        return cost / (self._num_neg_samples + 1)


class PRelu(layers.Layer):
    """
    Equation:

    .. math::
        y = \max(0, x) + \\alpha * \min(0, x)

    Args:
        name_scope (str): See base class.
        mode (string): The mode for weight sharing. It supports all, channel
          and element. all: all elements share same weight
          channel:elements in a channel share same weight
          element:each element has a weight
        param_attr(ParamAttr|None): The parameter attribute for the learnable
          weight (alpha).
        name(str|None): A name for this layer(optional). If set None, the layer
          will be named automatically.

    Returns:
        Variable: The output tensor with the same shape as input.

    Examples:

        .. code-block:: python

        inp_np = np.ones([5, 200, 100, 100]).astype('float32')
        with fluid.dygraph.guard():
            mode = 'channel'
            prelu = fluid.PRelu(
                'prelu',
                mode=mode,
                param_attr=fluid.ParamAttr(initializer=fluid.initializer.Constant(1.0)))
            dy_rlt = prelu(fluid.dygraph.base.to_variable(inp_np))
    """

    def __init__(self, name_scope, mode, param_attr=None):

        super(PRelu, self).__init__(name_scope)
        self._mode = mode
        self._param_attr = param_attr
        if self._mode not in ['all', 'channel', 'element']:
            raise ValueError('mode should be one of all, channel, element.')
        self._alpha_shape = [1]

    def _build_once(self, input):
        if self._mode == 'channel':
            self._alpha_shape = [1, input.shape[1], 1, 1]
        elif self._mode == 'element':
            self._alpha_shape = input.shape
        self._dtype = self._helper.input_dtype(input)
        self._alpha = self.create_parameter(
            attr=self._param_attr,
            shape=self._alpha_shape,
            dtype='float32',
            is_bias=False,
            default_initializer=Constant(1.0))

    def forward(self, input):

        out = self._helper.create_variable_for_type_inference(self._dtype)
        self._helper.append_op(
            type="prelu",
            inputs={"X": input,
                    'Alpha': self._alpha},
            attrs={"mode": self._mode},
            outputs={"Out": out})
        return out


class BilinearTensorProduct(layers.Layer):
    """
    **Add Bilinear Tensor Product Layer**

    This layer performs bilinear tensor product on two inputs.
    For example:

    .. math::
      out_{i} = x * W_{i} * {y^\mathrm{T}}, i=0,1,...,size-1

    In this formula:
     - :math:`x`: the first input contains M elements, shape is [batch_size, M].
     - :math:`y`: the second input contains N elements, shape is [batch_size, N].
     - :math:`W_{i}`: the i-th learned weight, shape is [M, N]
     - :math:`out_{i}`: the i-th element of out, shape is [batch_size, size].
     - :math:`y^\mathrm{T}`: the transpose of :math:`y_{2}`.

    Args:
       name_scope (str): See base class.
       size (int): The dimension of this layer.
       act (str, default None): Activation to be applied to the output of this layer.
       name (str, default None): The name of this layer.
       param_attr (ParamAttr, default None): The parameter attribute for the learnable w.
           parameters/weights of this layer.
       bias_attr (ParamAttr, default None): The parameter attribute for the bias
           of this layer. If it is set to False, no bias will be added to the output units.
           If it is set to None, the bias is initialized zero. Default: None.

    Returns:
       Variable: A 2-D Tensor of shape [batch_size, size].

    Examples:
       .. code-block:: python

         import paddle.fluid as fluid
         import numpy

         with fluid.dygraph.guard():
             layer1 = numpy.random.random((5, 5)).astype('float32')
             layer2 = numpy.random.random((5, 4)).astype('float32')
             bilinearTensorProduct = fluid.dygraph.nn.BilinearTensorProduct(
                    'BilinearTensorProduct', size=1000)
             ret = bilinearTensorProduct(fluid.dygraph.base.to_variable(layer1),
                                fluid.dygraph.base.to_variable(layer2))
    """

    def __init__(self,
                 name_scope,
                 size,
                 name=None,
                 act=None,
                 param_attr=None,
                 bias_attr=None):
        super(BilinearTensorProduct, self).__init__(name_scope)
        self._param_attr = param_attr
        self._bias_attr = bias_attr
        self._act = act
        self._size = size
        self._name = name
        self._inputs = dict()

    def _build_once(self, x, y):
        self._dtype = self._helper.input_dtype(x)

        param_shape = [self._size, x.shape[1], y.shape[1]]

        self._w = self.create_parameter(
            attr=self._param_attr,
            shape=param_shape,
            dtype=self._dtype,
            is_bias=False)

        if self._bias_attr:
            bias_size = [1, self._size]
            bias = self.create_parameter(
                attr=self._bias_attr,
                shape=bias_size,
                dtype=self._dtype,
                is_bias=True)
            self._inputs["Bias"] = bias

    def forward(self, x, y):
        self._inputs = {"X": x, "Y": y, "Weight": self._w}
        if self._name is not None:
            out = self._helper.create_variable(
                name=".".join([self.full_name(), self._name]),
                dtype=self._dtype,
                persistable=False)
        else:
            out = self._helper.create_variable(
                dtype=self._dtype, persistable=False)
        self._helper.append_op(
            type="bilinear_tensor_product",
            inputs=self._inputs,
            outputs={"Out": out})

        # add activation
        return self._helper.append_activation(out)


class Conv2DTranspose(layers.Layer):
    """
    **Convlution2D transpose layer**

    The convolution2D transpose layer calculates the output based on the input,
    filter, and dilations, strides, paddings. Input(Input) and output(Output)
    are in NCHW format. Where N is batch size, C is the number of channels,
    H is the height of the feature, and W is the width of the feature.
    Parameters(dilations, strides, paddings) are two elements. These two elements
    represent height and width, respectively. The details of convolution transpose
    layer, please refer to the following explanation and references
    `therein <http://www.matthewzeiler.com/wp-content/uploads/2017/07/cvpr2010.pdf>`_.
    If bias attribution and activation type are provided, bias is added to
    the output of the convolution, and the corresponding activation function
    is applied to the final result.

    For each input :math:`X`, the equation is:

    .. math::

        Out = \sigma (W \\ast X + b)

    Where:

    * :math:`X`: Input value, a tensor with NCHW format.
    * :math:`W`: Filter value, a tensor with MCHW format.
    * :math:`\\ast`: Convolution operation.
    * :math:`b`: Bias value, a 2-D tensor with shape [M, 1].
    * :math:`\\sigma`: Activation function.
    * :math:`Out`: Output value, the shape of :math:`Out` and :math:`X` may be different.

    Example:

        - Input:

          Input shape: :math:`(N, C_{in}, H_{in}, W_{in})`

          Filter shape: :math:`(C_{in}, C_{out}, H_f, W_f)`

        - Output:

          Output shape: :math:`(N, C_{out}, H_{out}, W_{out})`

        Where

        .. math::

           H^\prime_{out} &= (H_{in} - 1) * strides[0] - 2 * paddings[0] + dilations[0] * (H_f - 1) + 1 \\\\
           W^\prime_{out} &= (W_{in} - 1) * strides[1] - 2 * paddings[1] + dilations[1] * (W_f - 1) + 1 \\\\
           H_{out} &\in [ H^\prime_{out}, H^\prime_{out} + strides[0] ) \\\\
           W_{out} &\in [ W^\prime_{out}, W^\prime_{out} + strides[1] )

    Args:
        name_scope (str): See base class.
        num_filters(int): The number of the filter. It is as same as the output
            image channel.
        output_size(int|tuple|None): The output image size. If output size is a
            tuple, it must contain two integers, (image_H, image_W). None if use
            filter_size, padding, and stride to calculate output_size.
            if output_size and filter_size are specified at the same time, They
            should follow the formula above.
        filter_size(int|tuple|None): The filter size. If filter_size is a tuple,
            it must contain two integers, (filter_size_H, filter_size_W).
            Otherwise, the filter will be a square. None if use output size to
            calculate filter_size.
        padding(int|tuple): The padding size. If padding is a tuple, it must
            contain two integers, (padding_H, padding_W). Otherwise, the
            padding_H = padding_W = padding. Default: padding = 0.
        stride(int|tuple): The stride size. If stride is a tuple, it must
            contain two integers, (stride_H, stride_W). Otherwise, the
            stride_H = stride_W = stride. Default: stride = 1.
        dilation(int|tuple): The dilation size. If dilation is a tuple, it must
            contain two integers, (dilation_H, dilation_W). Otherwise, the
            dilation_H = dilation_W = dilation. Default: dilation = 1.
        groups(int): The groups number of the Conv2d transpose layer. Inspired by
            grouped convolution in Alex Krizhevsky's Deep CNN paper, in which
            when group=2, the first half of the filters is only connected to the
            first half of the input channels, while the second half of the
            filters is only connected to the second half of the input channels.
            Default: groups = 1.
        param_attr (ParamAttr|None): The parameter attribute for learnable parameters/weights
            of conv2d_transpose. If it is set to None or one attribute of ParamAttr, conv2d_transpose
            will create ParamAttr as param_attr. If the Initializer of the param_attr
            is not set, the parameter is initialized with Xavier. Default: None.
        bias_attr (ParamAttr|bool|None): The parameter attribute for the bias of conv2d_transpose.
            If it is set to False, no bias will be added to the output units.
            If it is set to None or one attribute of ParamAttr, conv2d_transpose
            will create ParamAttr as bias_attr. If the Initializer of the bias_attr
            is not set, the bias is initialized zero. Default: None.
        use_cudnn(bool): Use cudnn kernel or not, it is valid only when the cudnn
            library is installed. Default: True.
        act (str): Activation type, if it is set to None, activation is not appended.
            Default: None.
        name(str|None): A name for this layer(optional). If set None, the layer
            will be named automatically. Default: True.

    Returns:
        Variable: The tensor variable storing the convolution transpose result.

    Raises:
        ValueError: If the shapes of input, filter_size, stride, padding and
                    groups mismatch.

    Examples:
       .. code-block:: python

          import paddle.fluid as fluid
          import numpy

          with fluid.dygraph.guard():
              data = numpy.random.random((3, 32, 32)).astype('float32')
              conv2DTranspose = fluid.dygraph.nn.Conv2DTranspose(
                    'Conv2DTranspose', num_filters=2, filter_size=3)
              ret = conv2DTranspose(fluid.dygraph.base.to_variable(data))

    """

    def __init__(self,
                 name_scope,
                 num_filters,
                 output_size=None,
                 filter_size=None,
                 padding=0,
                 stride=1,
                 dilation=1,
                 groups=None,
                 param_attr=None,
                 bias_attr=None,
                 use_cudnn=True,
                 act=None):
        super(Conv2DTranspose, self).__init__(name_scope)
        assert param_attr is not False, "param_attr should not be False in conv2d_transpose."
        self._param_attr = param_attr
        self._bias_attr = bias_attr
        self._groups = groups
        self._num_filters = num_filters
        self._use_cudnn = use_cudnn
        self._padding = padding
        self._stride = stride
        self._dilation = dilation
        self._filter_size = filter_size
        self._output_size = output_size
        self._op_type = 'conv2d_transpose'

    def _build_once(self, input):
        input_channel = input.shape[1]
        if (input_channel == self._groups and
                self._num_filters == input_channel and not self._use_cudnn):
            self._op_type = 'depthwise_conv2d_transpose'

        if not isinstance(input, Variable):
            raise TypeError("Input of conv2d_transpose must be Variable")

        self._padding = utils.convert_to_list(self._padding, 2, 'padding')
        self._stride = utils.convert_to_list(self._stride, 2, 'stride')
        self._dilation = utils.convert_to_list(self._dilation, 2, 'dilation')

        if not isinstance(self._use_cudnn, bool):
            raise ValueError("use_cudnn should be True or False")

        if self._filter_size is None:
            if self._output_size is None:
                raise ValueError(
                    "output_size must be set when filter_size is None")
            if isinstance(self._output_size, int):
                self._output_size = [self._output_size, self._output_size]

            h_in = input.shape[2]
            w_in = input.shape[3]

            filter_size_h = (self._output_size[0] -
                             (h_in - 1) * self._stride[0] + 2 * self._padding[0]
                             - 1) // self._dilation[0] + 1
            filter_size_w = (self._output_size[1] -
                             (w_in - 1) * self._stride[1] + 2 * self._padding[1]
                             - 1) // self._dilation[1] + 1
            self._filter_size = [filter_size_h, filter_size_w]
        else:
            self._filter_size = utils.convert_to_list(
                self._filter_size, 2, 'conv2d_transpose.filter_size')

        if self._output_size is None:
            self._output_size = []
        elif isinstance(self._output_size, list) or isinstance(
                self._output_size, int):
            self._output_size = utils.convert_to_list(self._output_size, 2,
                                                      'output_size')
        else:
            raise ValueError("output_size should be list or int")
        self._padding = utils.convert_to_list(self._padding, 2, 'padding')
        self._groups = 1 if self._groups is None else self._groups
        filter_shape = [input_channel, self._num_filters // self._groups
                        ] + self._filter_size

        self._img_filter = self.create_parameter(
            dtype=input.dtype, shape=filter_shape, attr=self._param_attr)

    def forward(self, input):
        pre_bias = self._helper.create_variable_for_type_inference(
            dtype=input.dtype)
        self._helper.append_op(
            type=self._op_type,
            inputs={'Input': [input],
                    'Filter': [self._img_filter]},
            outputs={'Output': pre_bias},
            attrs={
                'output_size': self._output_size,
                'strides': self._stride,
                'paddings': self._padding,
                'dilations': self._dilation,
                'groups': self._groups,
                'use_cudnn': self._use_cudnn
            })

        pre_act = self._helper.append_bias_op(pre_bias, dim_start=1, dim_end=2)
        out = self._helper.append_activation(pre_act)
        return out


class SequenceConv(layers.Layer):
    """
    This function creates the op for sequence_conv, using the inputs and
    other convolutional configurations for the filters and stride as given
    in the input parameters to the function.

    Args:
        name_scope (str): See base class.
        num_filters (int): number of filters.
        filter_size (int): the filter size (H and W).
        filter_stride (int): stride of the filter.
        padding (bool): if True, add paddings.
        bias_attr (ParamAttr|bool|None): The parameter attribute for the bias of sequence_conv.
            If it is set to False, no bias will be added to the output units.
            If it is set to None or one attribute of ParamAttr, sequence_conv
            will create ParamAttr as bias_attr. If the Initializer of the bias_attr
            is not set, the bias is initialized zero. Default: None.
        param_attr (ParamAttr|None): The parameter attribute for learnable parameters/weights
            of sequence_conv. If it is set to None or one attribute of ParamAttr, sequence_conv
            will create ParamAttr as param_attr. If the Initializer of the param_attr
            is not set, the parameter is initialized with Xavier. Default: None.
        act (str): Activation type, if it is set to None, activation is not appended.
            Default: None.
        name (str|None): A name for this layer(optional). If set None, the layer
            will be named automatically. Default: None.

    Returns:
        Variable: output of sequence_conv
    """

    def __init__(self,
                 name_scope,
                 num_filters,
                 filter_size=3,
                 filter_stride=1,
                 padding=None,
                 bias_attr=None,
                 param_attr=None,
                 act=None):
        assert not in_dygraph_mode(
        ), "SequenceConv is not supported by dynamic graph mode yet!"
        super(SequenceConv, self).__init__(name_scope)
        self._num_filters = num_filters
        self._filter_size = filter_size
        self._filter_stride = filter_stride
        self._padding = padding
        self._bias_attr = bias_attr
        self._param_attr = param_attr

    def _build_once(self, input):
        self._dtype = self._helper.input_dtype(input)
        filter_shape = [self._filter_size * input.shape[1], self._num_filters]
        self._filter_param = self.create_parameter(
            attr=self._param_attr, shape=filter_shape, dtype=self._dtype)

    def forward(self, input):
        pre_bias = self._helper.create_variable_for_type_inference(self._dtype)
        self._helper.append_op(
            type='sequence_conv',
            inputs={
                'X': [input],
                'Filter': [self._filter_param],
            },
            outputs={"Out": pre_bias},
            attrs={
                'contextStride': self._filter_stride,
                'contextStart': -int(self._filter_size // 2),
                'contextLength': self._filter_size
            })
        pre_act = self._helper.append_bias_op(pre_bias)
        return self._helper.append_activation(pre_act)


class RowConv(layers.Layer):
    """
    ***Row-convolution operator***

    The row convolution is called lookahead convolution.  This operator was introduced in the following paper for DeepSpeech2:
    http://www.cs.cmu.edu/~dyogatam/papers/wang+etal.iclrworkshop2016.pdf

    The main motivation is that a bidirectional RNN, useful in DeepSpeech like speech models, learns representation for a sequence by performing a
    forward and a backward pass through the entire sequence. However, unlike
    unidirectional RNNs, bidirectional RNNs are challenging to deploy in an online
    and low-latency setting. The lookahead convolution incorporates information
    from future subsequences in a computationally efficient manner to improve
    unidirectional recurrent neural networks. The row convolution operator is
    different from the 1D sequence convolution, and is computed as follows:

    Given an input sequence X of length t and input dimension D, and a filter (W) of size context * D.

    More details about row_conv please refer to the design document https://github.com/PaddlePaddle/Paddle/issues/2228#issuecomment-303903645 .

    Args:
        name_scope (str): See base class.
        future_context_size (int): Future context size. Please note, the shape
            of convolution kernel is [future_context_size + 1, D].
        param_attr (ParamAttr): Attributes of parameters, including
            name, initializer etc.
        act (str): Non-linear activation to be applied to output variable.

    Returns:
        the output(Out) is a LodTensor, which supports variable time-length input sequences. The underlying tensor in this LodTensor is a matrix with shape T x N, i.e., the same shape as X.

    Examples:
        .. code-block:: python

          import paddle.fluid as fluid
          import numpy

          with fluid.dygraph.guard():
              x = numpy.random.random((16)).astype('float32')
              rowConv = fluid.dygraph.nn.RowConv(
                    'RowConv', future_context_size=2)
              ret = rowConv(fluid.dygraph.base.to_variable(x))

    """

    def __init__(self,
                 name_scope,
                 future_context_size,
                 param_attr=None,
                 act=None):
        assert not in_dygraph_mode(
        ), "RowConv is not supported by dynamic graph mode yet!"
        super(RowConv, self).__init__(name_scope)
        self._act = act
        self._param_attr = param_attr
        self._future_context_size = future_context_size

    def _build_once(self, input):
        self._dtype = self._helper.input_dtype(input)
        filter_shape = [self._future_context_size + 1, input.shape[1]]
        self._filter_param = self.create_parameter(
            attr=self._param_attr,
            shape=filter_shape,
            dtype=self._dtype,
            is_bias=False)

    def forward(self, input):
        out = self._helper.create_variable_for_type_inference(self._dtype)
        self._helper.append_op(
            type='row_conv',
            inputs={'X': [input],
                    'Filter': [self._filter_param]},
            outputs={'Out': [out]})
        return self._helper.append_activation(out, act=self._act)


class GroupNorm(layers.Layer):
    """
        **Group Normalization Layer**

        Refer to `Group Normalization <https://arxiv.org/abs/1803.08494>`_ .

        Args:
            name_scope (str): See base class.
            groups(int): The number of groups that divided from channels.
            epsilon(float): The small value added to the variance to prevent
                division by zero.
            param_attr(ParamAttr|None): The parameter attribute for the learnable
                scale :math:`g`. If it is set to False, no scale will be added to the output units.
                If it is set to None, the bias is initialized one. Default: None.
            bias_attr(ParamAttr|None): The parameter attribute for the learnable
                bias :math:`b`. If it is set to False, no bias will be added to the output units.
                If it is set to None, the bias is initialized zero. Default: None.
            act(str): Activation to be applied to the output of group normalizaiton.
            data_layout(string|NCHW): Only NCHW is supported.
            dtype(np.dtype|core.VarDesc.VarType|str): The type of data : float32, float_16, int etc

        Returns:
            Variable: A tensor variable which is the result after applying group normalization on the input.

        Examples:
            .. code-block:: python

              import paddle.fluid as fluid
              import numpy

              with fluid.dygraph.guard():
                  x = numpy.random.random((8, 32, 32)).astype('float32')
                  groupNorm = fluid.dygraph.nn.GroupNorm('GroupNorm', groups=4)
                  ret = groupNorm(fluid.dygraph.base.to_variable(x))

    """

    def __init__(self,
                 name_scope,
                 groups,
                 epsilon=1e-05,
                 param_attr=None,
                 bias_attr=None,
                 act=None,
                 data_layout='NCHW'):
        super(GroupNorm, self).__init__(name_scope)
        self._param_attr = param_attr
        self._bias_attr = bias_attr
        self._epsilon = epsilon
        self._groups = groups
        self._act = act
        if data_layout != 'NCHW':
            raise ValueError("unsupported data layout:" + data_layout)

    def _build_once(self, input):
        self._dtype = self._helper.input_dtype(input)
        param_shape = [input.shape[1]]
        if self._bias_attr:
            self._bias = self.create_parameter(
                attr=self._bias_attr,
                shape=param_shape,
                dtype=self._dtype,
                is_bias=True)

        if self._param_attr:
            self._scale = self.create_parameter(
                attr=self._param_attr,
                shape=param_shape,
                dtype=self._dtype,
                default_initializer=Constant(1.0))

    def forward(self, input):
        inputs = {'X': input}
        if self._bias:
            inputs['Bias'] = self._bias
        if self._scale:
            inputs['Scale'] = self._scale

        # create output
        mean_out = self._helper.create_variable_for_type_inference(
            dtype=self._dtype, stop_gradient=True)
        variance_out = self._helper.create_variable_for_type_inference(
            dtype=self._dtype, stop_gradient=True)
        group_norm_out = self._helper.create_variable_for_type_inference(
            dtype=self._dtype)

        self._helper.append_op(
            type="group_norm",
            inputs=inputs,
            outputs={
                "Y": group_norm_out,
                "Mean": mean_out,
                "Variance": variance_out,
            },
            attrs={"epsilon": self._epsilon,
                   "groups": self._groups})

        return self._helper.append_activation(group_norm_out, self._act)


class SpectralNorm(layers.Layer):
    """
    **Spectral Normalization Layer**

    This layer calculates the spectral normalization value of weight parameters of
    fc, conv1d, conv2d, conv3d layers which should be 2-D, 3-D, 4-D, 5-D
    Parameters. Calculations are showed as follows.

    Step 1:
    Generate vector U in shape of [H], and V in shape of [W].
    While H is the :attr:`dim` th dimension of the input weights,
    and W is the product result of remaining dimensions.

    Step 2:
    :attr:`power_iters` shoule be a positive interger, do following
    calculations with U and V for :attr:`power_iters` rounds.

    .. math::

        \mathbf{v} := \\frac{\mathbf{W}^{T} \mathbf{u}}{\|\mathbf{W}^{T} \mathbf{u}\|_2}

        \mathbf{u} := \\frac{\mathbf{W}^{T} \mathbf{v}}{\|\mathbf{W}^{T} \mathbf{v}\|_2}

    Step 3:
    Calculate :math:`\sigma(\mathbf{W})` and normalize weight values.

    .. math::

        \sigma(\mathbf{W}) = \mathbf{u}^{T} \mathbf{W} \mathbf{v}

        \mathbf{W} = \\frac{\mathbf{W}}{\sigma(\mathbf{W})}


    Refer to `Spectral Normalization <https://arxiv.org/abs/1802.05957>`_ .

    Args:
        name_scope (str): See base class.
        dim(int): The index of dimension which should be permuted to the first before reshaping Input(Weight) to matrix, it should be set as 0 if Input(Weight) is the weight of fc layer, and should be set as 1 if Input(Weight) is the weight of conv layer, default 0
        power_iters(int): number of power iterations to calculate spectral norm, default 1
        eps(float): epsilon for numerical stability in calculating norms
        name (str): The name of this layer. It is optional.

    Returns:
        Variable: A tensor variable of weight parameters after spectral normalization.

    Examples:
       .. code-block:: python

            import paddle.fluid as fluid
            import numpy

            with fluid.dygraph.guard():
                x = numpy.random.random((2, 8, 32, 32)).astype('float32')
                spectralNorm = fluid.dygraph.nn.SpectralNorm('SpectralNorm', dim=1, power_iters=2)
                ret = spectralNorm(fluid.dygraph.base.to_variable(x))

    """

    def __init__(self, name_scope, dim=0, power_iters=1, eps=1e-12, name=None):
        super(SpectralNorm, self).__init__(name_scope)
        self._power_iters = power_iters
        self._eps = eps
        self._dim = dim

    def _build_once(self, weight):
        self._dtype = self._helper.input_dtype(weight)
        input_shape = weight.shape
        h = input_shape[self._dim]
        w = np.prod(input_shape) // h

        self.u = self.create_parameter(
            attr=ParamAttr(),
            shape=[h],
            dtype=self._dtype,
            default_initializer=Normal(0., 1.))
        self.u.stop_gradient = True

        self.v = self.create_parameter(
            attr=ParamAttr(),
            shape=[w],
            dtype=self._dtype,
            default_initializer=Normal(0., 1.))
        self.v.stop_gradient = True

    def forward(self, weight):
        inputs = {'Weight': weight, 'U': self.u, 'V': self.v}
        out = self._helper.create_variable_for_type_inference(self._dtype)
        self._helper.append_op(
            type="spectral_norm",
            inputs=inputs,
            outputs={"Out": out, },
            attrs={
                "dim": self._dim,
                "power_iters": self._power_iters,
                "eps": self._eps,
            })

        return out


class TreeConv(layers.Layer):
    """
        ***Tree-Based Convolution Operator***

        Tree-Based Convolution is a kind of convolution based on tree structure.
        Tree-Based Convolution is a part of Tree-Based Convolution Neural Network(TBCNN),
        which is used to classify tree structures, such as Abstract Syntax Tree.
        Tree-Based Convolution proposed a kind of data structure called continuous binary tree,
        which regards multiway tree as binary tree.
        The paper of Tree-Based Convolution Operator is here: https://arxiv.org/abs/1409.5718v1


        Args:
            name_scope (str): See base class.
            output_size(int): output feature width
            num_filters(int): number of filters, Default 1
            max_depth(int): max depth of filters, Default 2
            act(str): activation function, Default tanh
            param_attr(ParamAttr): the parameter attribute for the filters, Default None
            bias_attr(ParamAttr): the parameter attribute for the bias of this layer, Default None
            name(str): a name of this layer(optional). If set None, the layer will be named automatically, Default None

        Returns:
            out(Variable): (Tensor) The feature vector of subtrees. The shape of the output tensor is [max_tree_node_size, output_size, num_filters]. The output tensor could be a new feature vector for next tree convolution layers

        Examples:
            .. code-block:: python
              import paddle.fluid as fluid
              import numpy

              with fluid.dygraph.guard():
                  nodes_vector = numpy.random.random((1, 10, 5)).astype('float32')
                  edge_set = numpy.random.random((1, 9, 2)).astype('int32')
                  treeConv = fluid.dygraph.nn.TreeConv(
                    'TreeConv', output_size=6, num_filters=1, max_depth=2)
                  ret = treeConv(fluid.dygraph.base.to_variable(nodes_vector), fluid.dygraph.base.to_variable(edge_set))

    """

    def __init__(self,
                 name_scope,
                 output_size,
                 num_filters=1,
                 max_depth=2,
                 act='tanh',
                 param_attr=None,
                 bias_attr=None,
                 name=None):
        super(TreeConv, self).__init__(name_scope)
        self._name = name
        self._output_size = output_size
        self._act = act
        self._max_depth = max_depth
        self._num_filters = num_filters
        self._bias_attr = bias_attr
        self._param_attr = param_attr

    def _build_once(self, nodes_vector, edge_set):
        assert isinstance(nodes_vector, Variable)
        assert isinstance(edge_set, Variable)
        self._dtype = self._helper.input_dtype(nodes_vector)

        feature_size = nodes_vector.shape[2]
        w_shape = [feature_size, 3, self._output_size, self._num_filters]
        if self._bias_attr:
            self._bias_param = self.create_parameter(
                attr=self._bias_attr,
                shape=[self._num_filters],
                dtype=self._dtype,
                is_bias=True)
        self.W = self.create_parameter(
            attr=self._param_attr,
            shape=w_shape,
            dtype=self._dtype,
            is_bias=False)

    def forward(self, nodes_vector, edge_set):
        if self._name:
            out = self.create_variable(
                name=self._name, dtype=self._dtype, persistable=False)
        else:
            out = self._helper.create_variable_for_type_inference(
                dtype=self._dtype)

        self._helper.append_op(
            type='tree_conv',
            inputs={
                'NodesVector': nodes_vector,
                'EdgeSet': edge_set,
                'Filter': self.W
            },
            outputs={'Out': out, },
            attrs={'max_depth': self._max_depth})
        if self._bias_attr:
            pre_activation = self._helper.create_variable_for_type_inference(
                dtype=self._dtype)
            self._helper.append_op(
                type='elementwise_add',
                inputs={'X': [out],
                        'Y': [self._bias_param]},
                outputs={'Out': [pre_activation]},
                attrs={'axis': 1})
        else:
            pre_activation = out
        return self._helper.append_activation(pre_activation, act=self._act)