#   Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

### NOTE: the API of this file is based on Paddle1.8, the API in layers.py is based on Paddle2.0

import numpy as np
import logging
import paddle.fluid as fluid
import paddle.fluid.core as core
import paddle.fluid.dygraph_utils as dygraph_utils
from paddle.fluid.data_feeder import check_variable_and_dtype
from paddle.fluid.framework import _varbase_creator
from paddle.fluid.dygraph.nn import InstanceNorm, Conv2D, Conv2DTranspose, BatchNorm

from ...common import get_logger
from .utils.utils import compute_start_end, get_same_padding, convert_to_list
from .layers_base import *

__all__ = [
    'SuperConv2D', 'SuperConv2DTranspose', 'SuperSeparableConv2D',
    'SuperBatchNorm', 'SuperLinear', 'SuperInstanceNorm', 'SuperGroupConv2D',
    'SuperDepthwiseConv2D', 'SuperGroupConv2DTranspose',
    'SuperDepthwiseConv2DTranspose', 'SuperLayerNorm', 'SuperEmbedding'
]

_logger = get_logger(__name__, level=logging.INFO)

### TODO: if task is elastic width, need to add re_organize_middle_weight in 1x1 conv in MBBlock


class SuperConv2D(fluid.dygraph.Conv2D):
    """
    This interface is used to construct a callable object of the ``SuperConv2D``  class.
    The difference between ```SuperConv2D``` and ```Conv2D``` is: ```SuperConv2D``` need 
    to feed a config dictionary with the format of {'channel', num_of_channel} represents 
    the channels of the outputs, used to change the first dimension of weight and bias, 
    only train the first channels of the weight and bias.

    Note: the channel in config need to less than first defined.

    The super 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
    the feature map, H is the height of the feature map, and W is the width of the feature map.
    Filter's shape is [MCHW] , where M is the number of output feature map,
    C is the number of input feature map, 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 feature map divided by the groups.
    Please refer to UFLDL's `convolution
    <http://ufldl.stanford.edu/tutorial/supervised/FeatureExtractionUsingConvolution/>`_
    for more details.
    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 shape [MCHW] .
    * :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
    Parameters:
        num_channels(int): The number of channels in the input image.
        num_filters(int): The number of filter. It is as same as the output
            feature map.
        filter_size (int or tuple): 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.
        candidate_config(dict, optional): Dictionary descripts candidate config of this layer,
            such as {'kernel_size': (3, 5, 7), 'channel': (4, 6, 8)}, means the kernel size of 
            this layer can be choose from (3, 5, 7), the key of candidate_config
            only can be 'kernel_size', 'channel' and 'expand_ratio', 'channel' and 'expand_ratio'
            CANNOT be set at the same time. Default: None.
        transform_kernel(bool, optional): Whether to use transform matrix to transform a large filter
            to a small filter. Default: False.
        stride (int or tuple, optional): 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: 1.
        padding (int or tuple, optional): 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: 0.
        dilation (int or tuple, optional): 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: 1.
        groups (int, optional): 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: 1.
        param_attr (ParamAttr, optional): The parameter attribute for learnable weights(Parameter)
            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 or bool, optional): The 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, optional): Use cudnn kernel or not, it is valid only when the cudnn
            library is installed. Default: True.
        act (str, optional): Activation type, if it is set to None, activation is not appended.
            Default: None.
        dtype (str, optional): Data type, it can be "float32" or "float64". Default: "float32".
    Attribute:
        **weight** (Parameter): the learnable weights of filter of this layer.
        **bias** (Parameter or None): the learnable bias of this layer.
    Returns:
        None
    
    Raises:
        ValueError: if ``use_cudnn`` is not a bool value.
    Examples:
        .. code-block:: python
          from paddle.fluid.dygraph.base import to_variable
          import paddle.fluid as fluid
          from paddleslim.core.layers import SuperConv2D
          import numpy as np
          data = np.random.uniform(-1, 1, [10, 3, 32, 32]).astype('float32')
          with fluid.dygraph.guard():
              super_conv2d = SuperConv2D(3, 10, 3)
              config = {'channel': 5}
              data = to_variable(data)
              conv = super_conv2d(data, config)

    """

    ### NOTE: filter_size, num_channels and num_filters must be the max of candidate to define a largest network.
    def __init__(self,
                 num_channels,
                 num_filters,
                 filter_size,
                 candidate_config={},
                 transform_kernel=False,
                 stride=1,
                 dilation=1,
                 padding=0,
                 groups=None,
                 param_attr=None,
                 bias_attr=None,
                 use_cudnn=True,
                 act=None,
                 dtype='float32'):
        ### NOTE: padding always is 0, add padding in forward because of kernel size is uncertain
        super(SuperConv2D, self).__init__(
            num_channels, num_filters, filter_size, stride, padding, dilation,
            groups, param_attr, bias_attr, use_cudnn, act, dtype)

        if isinstance(self._filter_size, int):
            self._filter_size = convert_to_list(self._filter_size, 2)

        self.candidate_config = candidate_config
        if len(candidate_config.items()) != 0:
            for k, v in candidate_config.items():
                candidate_config[k] = list(set(v))

        self.ks_set = candidate_config[
            'kernel_size'] if 'kernel_size' in candidate_config else None

        self.expand_ratio = candidate_config[
            'expand_ratio'] if 'expand_ratio' in candidate_config else None
        self.channel = candidate_config[
            'channel'] if 'channel' in candidate_config else None
        self.base_channel = self._num_filters
        if self.expand_ratio != None:
            self.base_channel = int(self._num_filters / max(self.expand_ratio))

        self.transform_kernel = transform_kernel
        if self.ks_set != None:
            self.ks_set.sort()
        if self.transform_kernel != False:
            scale_param = dict()
            ### create parameter to transform kernel
            for i in range(len(self.ks_set) - 1):
                ks_small = self.ks_set[i]
                ks_large = self.ks_set[i + 1]
                param_name = '%dto%d_matrix' % (ks_large, ks_small)
                ks_t = ks_small**2
                scale_param[param_name] = self.create_parameter(
                    attr=fluid.ParamAttr(
                        name=self._full_name + param_name,
                        initializer=fluid.initializer.NumpyArrayInitializer(
                            np.eye(ks_t))),
                    shape=(ks_t, ks_t),
                    dtype=self._dtype)

            for name, param in scale_param.items():
                setattr(self, name, param)

    def get_active_filter(self, in_nc, out_nc, kernel_size):
        ### Unsupport for asymmetric kernels
        if self._filter_size[0] != self._filter_size[1]:
            return self.weight[:out_nc, :in_nc, :, :]
        start, end = compute_start_end(self._filter_size[0], kernel_size)
        ### if NOT transform kernel, intercept a center filter with kernel_size from largest filter
        filters = self.weight[:out_nc, :in_nc, start:end, start:end]
        if self.transform_kernel != False and kernel_size < self._filter_size[
                0]:
            ### if transform kernel, then use matrix to transform
            start_filter = self.weight[:out_nc, :in_nc, :, :]
            for i in range(len(self.ks_set) - 1, 0, -1):
                src_ks = self.ks_set[i]
                if src_ks <= kernel_size:
                    break
                target_ks = self.ks_set[i - 1]
                start, end = compute_start_end(src_ks, target_ks)
                _input_filter = start_filter[:, :, start:end, start:end]
                _input_filter = fluid.layers.reshape(
                    _input_filter,
                    shape=[(_input_filter.shape[0] * _input_filter.shape[1]),
                           -1])
                _tmp_filter = _varbase_creator(dtype=_input_filter.dtype)
                core.ops.matmul(_input_filter,
                                self.__getattr__('%dto%d_matrix' %
                                                 (src_ks, target_ks)),
                                _tmp_filter, 'transpose_X', False,
                                'transpose_Y', False, "alpha", 1)
                _tmp_filter = fluid.layers.reshape(
                    _tmp_filter,
                    shape=[
                        filters.shape[0], filters.shape[1], target_ks, target_ks
                    ])
                start_filter = _tmp_filter
            filters = start_filter
        return filters

    def get_groups_in_out_nc(self, in_nc, out_nc):
        if self._groups == 1 or self._groups == None:
            ### standard conv
            return self._groups, in_nc, out_nc
        elif self._groups == self._num_channels:
            ### depthwise convolution
            if in_nc != out_nc:
                _logger.debug(
                    "input channel and output channel in depthwise conv is different, change output channel to input channel! origin channel:(in_nc {}, out_nc {}): ".
                    format(in_nc, out_nc))
            groups = in_nc
            out_nc = in_nc
            return groups, in_nc, out_nc
        else:
            ### groups convolution
            ### conv: weight: (Cout, Cin/G, Kh, Kw)
            groups = self._groups
            in_nc = int(in_nc // groups)
            return groups, in_nc, out_nc

    def forward(self, input, kernel_size=None, expand_ratio=None, channel=None):
        self.cur_config = {
            'kernel_size': kernel_size,
            'expand_ratio': expand_ratio,
            'channel': channel
        }
        in_nc = int(input.shape[1])
        assert (
            expand_ratio == None or channel == None
        ), "expand_ratio and channel CANNOT be NOT None at the same time."
        if expand_ratio != None:
            out_nc = int(expand_ratio * self.base_channel)
        elif channel != None:
            out_nc = int(channel)
        else:
            out_nc = self._num_filters
        ks = int(self._filter_size[0]) if kernel_size == None else int(
            kernel_size)

        if kernel_size is not None and self._filter_size[
                0] != self._filter_size[1]:
            _logger.error("Searching for asymmetric kernels is NOT supported")

        groups, weight_in_nc, weight_out_nc = self.get_groups_in_out_nc(in_nc,
                                                                        out_nc)

        weight = self.get_active_filter(weight_in_nc, weight_out_nc, ks)

        if kernel_size != None or 'kernel_size' in self.candidate_config.keys():
            padding = convert_to_list(get_same_padding(ks), 2)
        else:
            padding = self._padding

        if self._l_type == 'conv2d':
            attrs = ('strides', self._stride, 'paddings', padding, 'dilations',
                     self._dilation, 'groups', groups
                     if groups else 1, 'use_cudnn', self._use_cudnn)
            out = core.ops.conv2d(input, weight, *attrs)
        elif self._l_type == 'depthwise_conv2d':
            attrs = ('strides', self._stride, 'paddings', padding, 'dilations',
                     self._dilation, 'groups', groups
                     if groups else self._groups, 'use_cudnn', self._use_cudnn)
            out = core.ops.depthwise_conv2d(input, weight, *attrs)
        else:
            raise ValueError("conv type error")

        pre_bias = out
        out_nc = int(pre_bias.shape[1])
        if self.bias is not None:
            bias = self.bias[:out_nc]
            pre_act = dygraph_utils._append_bias_in_dygraph(pre_bias, bias, 1)
        else:
            pre_act = pre_bias

        return dygraph_utils._append_activation_in_dygraph(pre_act, self._act)


class SuperGroupConv2D(SuperConv2D):
    def get_groups_in_out_nc(self, in_nc, out_nc):
        ### groups convolution
        ### conv: weight: (Cout, Cin/G, Kh, Kw)
        groups = self._groups
        in_nc = int(in_nc // groups)
        return groups, in_nc, out_nc


class SuperDepthwiseConv2D(SuperConv2D):
    ### depthwise convolution
    def get_groups_in_out_nc(self, in_nc, out_nc):
        if in_nc != out_nc:
            _logger.debug(
                "input channel and output channel in depthwise conv is different, change output channel to input channel! origin channel:(in_nc {}, out_nc {}): ".
                format(in_nc, out_nc))
        groups = in_nc
        out_nc = in_nc
        return groups, in_nc, out_nc


class SuperConv2DTranspose(fluid.dygraph.Conv2DTranspose):
    """
    This interface is used to construct a callable object of the ``SuperConv2DTranspose`` 
    class.
    The difference between ```SuperConv2DTranspose``` and ```Conv2DTranspose``` is: 
    ```SuperConv2DTranspose``` need to feed a config dictionary with the format of 
    {'channel', num_of_channel} represents the channels of the outputs, used to change 
    the first dimension of weight and bias, only train the first channels of the weight 
    and bias.

    Note: the channel in config need to less than first defined.

    The super convolution2D transpose layer calculates the output based on the input,
    filter, and dilations, strides, paddings. Input and output
    are in NCHW format. Where N is batch size, C is the number of feature map,
    H is the height of the feature map, and W is the width of the feature map.
    Filter's shape is [MCHW] , where M is the number of input feature map,
    C is the number of output feature map, 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 feature map divided by the groups.
    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.
    The details of convolution transpose layer, please refer to the following explanation and references
    `conv2dtranspose <http://www.matthewzeiler.com/wp-content/uploads/2017/07/cvpr2010.pdf>`_ .
    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 shape [MCHW] .
    * :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] )
    Parameters:
        num_channels(int): The number of channels in the input image.
        num_filters(int): The number of the filter. It is as same as the output
            feature map.
        filter_size(int or tuple): 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.
        candidate_config(dict, optional): Dictionary descripts candidate config of this layer,
            such as {'kernel_size': (3, 5, 7), 'channel': (4, 6, 8)}, means the kernel size of 
            this layer can be choose from (3, 5, 7), the key of candidate_config
            only can be 'kernel_size', 'channel' and 'expand_ratio', 'channel' and 'expand_ratio'
            CANNOT be set at the same time. Default: None.
        transform_kernel(bool, optional): Whether to use transform matrix to transform a large filter
            to a small filter. Default: False.
        output_size(int or tuple, optional): 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. Default: None.
        padding(int or tuple, optional): 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: 0.
        stride(int or tuple, optional): 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: 1.
        dilation(int or tuple, optional): 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: 1.
        groups(int, optional): 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: 1.
        param_attr (ParamAttr, optional): The parameter attribute for learnable weights(Parameter)
            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 or bool, optional): The 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, optional): Use cudnn kernel or not, it is valid only when the cudnn
            library is installed. Default: True.
        act (str, optional): Activation type, if it is set to None, activation is not appended.
            Default: None.
        dtype (str, optional): Data type, it can be "float32" or "float64". Default: "float32".
    Attribute:
        **weight** (Parameter): the learnable weights of filters of this layer.
        **bias** (Parameter or None): the learnable bias of this layer.
    Returns:
        None
    Examples:
       .. code-block:: python
          import paddle.fluid as fluid
          from paddleslim.core.layers import SuperConv2DTranspose
          import numpy as np
          with fluid.dygraph.guard():
              data = np.random.random((3, 32, 32, 5)).astype('float32')
              config = {'channel': 5
              super_convtranspose = SuperConv2DTranspose(num_channels=32, num_filters=10, filter_size=3)
              ret = super_convtranspose(fluid.dygraph.base.to_variable(data), config)
    """

    def __init__(self,
                 num_channels,
                 num_filters,
                 filter_size,
                 output_size=None,
                 candidate_config={},
                 transform_kernel=False,
                 stride=1,
                 dilation=1,
                 padding=0,
                 groups=None,
                 param_attr=None,
                 bias_attr=None,
                 use_cudnn=True,
                 act=None,
                 dtype='float32'):
        super(SuperConv2DTranspose, self).__init__(
            num_channels, num_filters, filter_size, output_size, padding,
            stride, dilation, groups, param_attr, bias_attr, use_cudnn, act,
            dtype)
        self.candidate_config = candidate_config
        if len(self.candidate_config.items()) != 0:
            for k, v in candidate_config.items():
                candidate_config[k] = list(set(v))
        self.ks_set = candidate_config[
            'kernel_size'] if 'kernel_size' in candidate_config else None

        if isinstance(self._filter_size, int):
            self._filter_size = convert_to_list(self._filter_size, 2)

        self.expand_ratio = candidate_config[
            'expand_ratio'] if 'expand_ratio' in candidate_config else None
        self.channel = candidate_config[
            'channel'] if 'channel' in candidate_config else None
        self.base_channel = self._num_filters
        if self.expand_ratio:
            self.base_channel = int(self._num_filters / max(self.expand_ratio))

        self.transform_kernel = transform_kernel
        if self.ks_set != None:
            self.ks_set.sort()
        if self.transform_kernel != False:
            scale_param = dict()
            ### create parameter to transform kernel
            for i in range(len(self.ks_set) - 1):
                ks_small = self.ks_set[i]
                ks_large = self.ks_set[i + 1]
                param_name = '%dto%d_matrix' % (ks_large, ks_small)
                ks_t = ks_small**2
                scale_param[param_name] = self.create_parameter(
                    attr=fluid.ParamAttr(
                        name=self._full_name + param_name,
                        initializer=fluid.initializer.NumpyArrayInitializer(
                            np.eye(ks_t))),
                    shape=(ks_t, ks_t),
                    dtype=self._dtype)

            for name, param in scale_param.items():
                setattr(self, name, param)

    def get_active_filter(self, in_nc, out_nc, kernel_size):
        ### Unsupport for asymmetric kernels
        if self._filter_size[0] != self._filter_size[1]:
            return self.weight[:out_nc, :in_nc, :, :]
        start, end = compute_start_end(self._filter_size[0], kernel_size)
        filters = self.weight[:in_nc, :out_nc, start:end, start:end]
        if self.transform_kernel != False and kernel_size < self._filter_size[
                0]:
            start_filter = self.weight[:in_nc, :out_nc, :, :]
            for i in range(len(self.ks_set) - 1, 0, -1):
                src_ks = self.ks_set[i]
                if src_ks <= kernel_size:
                    break
                target_ks = self.ks_set[i - 1]
                start, end = compute_start_end(src_ks, target_ks)
                _input_filter = start_filter[:, :, start:end, start:end]
                _input_filter = fluid.layers.reshape(
                    _input_filter,
                    shape=[(_input_filter.shape[0] * _input_filter.shape[1]),
                           -1])
                _tmp_filter = _varbase_creator(dtype=_input_filter.dtype)
                core.ops.matmul(_input_filter,
                                self.__getattr__('%dto%d_matrix' %
                                                 (src_ks, target_ks)),
                                _tmp_filter, 'transpose_X', False,
                                'transpose_Y', False, "alpha", 1)
                _tmp_filter = fluid.layers.reshape(
                    _tmp_filter,
                    shape=[
                        filters.shape[0], filters.shape[1], target_ks, target_ks
                    ])
                start_filter = _tmp_filter
            filters = start_filter
        return filters

    def get_groups_in_out_nc(self, in_nc, out_nc):
        if self._groups == 1 or self._groups == None:
            ### standard conv
            return self._groups, in_nc, out_nc
        elif self._groups == self._num_channels:
            ### depthwise convolution
            if in_nc != out_nc:
                _logger.debug(
                    "input channel and output channel in depthwise conv is different, change output channel to input channel! origin channel:(in_nc {}, out_nc {}): ".
                    format(in_nc, out_nc))
            groups = in_nc
            out_nc = in_nc
            return groups, in_nc, out_nc
        else:
            ### groups convolution
            ### groups conv transpose: weight: (Cin, Cout/G, Kh, Kw)
            groups = self._groups
            out_nc = int(out_nc // groups)
            return groups, in_nc, out_nc

    def forward(self, input, kernel_size=None, expand_ratio=None, channel=None):
        self.cur_config = {
            'kernel_size': kernel_size,
            'expand_ratio': expand_ratio,
            'channel': channel
        }
        in_nc = int(input.shape[1])
        assert (
            expand_ratio == None or channel == None
        ), "expand_ratio and channel CANNOT be NOT None at the same time."
        if expand_ratio != None:
            out_nc = int(expand_ratio * self.base_channel)
        elif channel != None:
            out_nc = int(channel)
        else:
            out_nc = self._num_filters

        ks = int(self._filter_size[0]) if kernel_size == None else int(
            kernel_size)

        if kernel_size is not None and self._filter_size[
                0] != self._filter_size[1]:
            _logger.error("Searching for asymmetric kernels is NOT supported")

        groups, weight_in_nc, weight_out_nc = self.get_groups_in_out_nc(in_nc,
                                                                        out_nc)

        weight = self.get_active_filter(weight_in_nc, weight_out_nc, ks)
        if kernel_size != None or 'kernel_size' in self.candidate_config.keys():
            padding = convert_to_list(get_same_padding(ks), 2)
        else:
            padding = self._padding

        op = getattr(core.ops, self._op_type)
        out = op(input, weight, 'output_size', self._output_size, 'strides',
                 self._stride, 'paddings', padding, 'dilations', self._dilation,
                 'groups', groups, 'use_cudnn', self._use_cudnn)
        pre_bias = out
        out_nc = int(pre_bias.shape[1])
        if self.bias is not None:
            bias = self.bias[:out_nc]
            pre_act = dygraph_utils._append_bias_in_dygraph(pre_bias, bias, 1)
        else:
            pre_act = pre_bias

        return dygraph_utils._append_activation_in_dygraph(
            pre_act, act=self._act)


class SuperGroupConv2DTranspose(SuperConv2DTranspose):
    def get_groups_in_out_nc(self, in_nc, out_nc):
        ### groups convolution
        ### groups conv transpose: weight: (Cin, Cout/G, Kh, Kw)
        groups = self._groups
        out_nc = int(out_nc // groups)
        return groups, in_nc, out_nc


class SuperDepthwiseConv2DTranspose(SuperConv2DTranspose):
    def get_groups_in_out_nc(self, in_nc, out_nc):
        if in_nc != out_nc:
            _logger.debug(
                "input channel and output channel in depthwise conv transpose is different, change output channel to input channel! origin channel:(in_nc {}, out_nc {}): ".
                format(in_nc, out_nc))
        groups = in_nc
        out_nc = in_nc
        return groups, in_nc, out_nc


### NOTE: only search channel, write for GAN-compression, maybe change to SuperDepthwiseConv and SuperConv after.
class SuperSeparableConv2D(fluid.dygraph.Layer):
    """
    This interface is used to construct a callable object of the ``SuperSeparableConv2D``
    class.
    The difference between ```SuperSeparableConv2D``` and ```SeparableConv2D``` is: 
    ```SuperSeparableConv2D``` need to feed a config dictionary with the format of 
    {'channel', num_of_channel} represents the channels of the first conv's outputs and
    the second conv's inputs, used to change the first dimension of weight and bias, 
    only train the first channels of the weight and bias.

    The architecture of super separable convolution2D op is [Conv2D, norm layer(may be BatchNorm
    or InstanceNorm), Conv2D]. The first conv is depthwise conv, the filter number is input channel
    multiply scale_factor, the group is equal to the number of input channel. The second conv
    is standard conv, which filter size and stride size are 1. 

    Parameters:
        num_channels(int): The number of channels in the input image.
        num_filters(int): The number of the second conv's filter. It is as same as the output
            feature map.
        filter_size(int or tuple): The first conv's 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.
        padding(int or tuple, optional): The first conv's padding size. If padding is a tuple, 
            it must contain two integers, (padding_H, padding_W). Otherwise, the
            padding_H = padding_W = padding. Default: 0.
        stride(int or tuple, optional): The first conv's stride size. If stride is a tuple,
            it must contain two integers, (stride_H, stride_W). Otherwise, the
            stride_H = stride_W = stride. Default: 1.
        dilation(int or tuple, optional): The first conv's dilation size. If dilation is a tuple, 
            it must contain two integers, (dilation_H, dilation_W). Otherwise, the
            dilation_H = dilation_W = dilation. Default: 1.
        norm_layer(class): The normalization layer between two convolution. Default: InstanceNorm.
        bias_attr (ParamAttr or bool, optional): The attribute for the bias of convolution.
            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, convolution
            will create ParamAttr as bias_attr. If the Initializer of the bias_attr
            is not set, the bias is initialized zero. Default: None.
        scale_factor(float): The scale factor of the first conv's output channel. Default: 1.
        use_cudnn(bool, optional): Use cudnn kernel or not, it is valid only when the cudnn
            library is installed. Default: True.
    Returns:
        None
    """

    def __init__(self,
                 num_channels,
                 num_filters,
                 filter_size,
                 candidate_config={},
                 stride=1,
                 padding=0,
                 dilation=1,
                 norm_layer=InstanceNorm,
                 bias_attr=None,
                 scale_factor=1,
                 use_cudnn=False):
        super(SuperSeparableConv2D, self).__init__()
        self.conv = fluid.dygraph.LayerList([
            fluid.dygraph.nn.Conv2D(
                num_channels=num_channels,
                num_filters=num_channels * scale_factor,
                filter_size=filter_size,
                stride=stride,
                padding=padding,
                use_cudnn=False,
                groups=num_channels,
                bias_attr=bias_attr)
        ])

        self.conv.extend([norm_layer(num_channels * scale_factor)])

        self.conv.extend([
            fluid.dygraph.nn.Conv2D(
                num_channels=num_channels * scale_factor,
                num_filters=num_filters,
                filter_size=1,
                stride=1,
                use_cudnn=use_cudnn,
                bias_attr=bias_attr)
        ])

        self.candidate_config = candidate_config
        self.expand_ratio = candidate_config[
            'expand_ratio'] if 'expand_ratio' in candidate_config else None
        self.base_output_dim = self.conv[0]._num_filters
        if self.expand_ratio != None:
            self.base_output_dim = int(self.conv[0]._num_filters /
                                       max(self.expand_ratio))

    def forward(self, input, expand_ratio=None, channel=None):
        self.cur_config = {'expand_ratio': expand_ratio, 'channel': channel}
        in_nc = int(input.shape[1])
        assert (
            expand_ratio == None or channel == None
        ), "expand_ratio and channel CANNOT be NOT None at the same time."
        if expand_ratio != None:
            out_nc = int(expand_ratio * self.base_output_dim)
        elif channel != None:
            out_nc = int(channel)
        else:
            out_nc = self.conv[0]._num_filters

        weight = self.conv[0].weight[:in_nc]
        ###  conv1
        if self.conv[0]._l_type == 'conv2d':
            attrs = ('strides', self.conv[0]._stride, 'paddings',
                     self.conv[0]._padding, 'dilations', self.conv[0]._dilation,
                     'groups', in_nc, 'use_cudnn', self.conv[0]._use_cudnn)
            out = core.ops.conv2d(input, weight, *attrs)
        elif self.conv[0]._l_type == 'depthwise_conv2d':
            attrs = ('strides', self.conv[0]._stride, 'paddings',
                     self.conv[0]._padding, 'dilations', self.conv[0]._dilation,
                     'groups', in_nc, 'use_cudnn', self.conv[0]._use_cudnn)
            out = core.ops.depthwise_conv2d(input, weight, *attrs)
        else:
            raise ValueError("conv type error")

        pre_bias = out
        if self.conv[0].bias is not None:
            bias = self.conv[0].bias[:in_nc]
            pre_act = dygraph_utils._append_bias_in_dygraph(pre_bias, bias, 1)
        else:
            pre_act = pre_bias

        conv0_out = dygraph_utils._append_activation_in_dygraph(
            pre_act, self.conv[0]._act)

        norm_out = self.conv[1](conv0_out)

        weight = self.conv[2].weight[:out_nc, :in_nc, :, :]

        if self.conv[2]._l_type == 'conv2d':
            attrs = ('strides', self.conv[2]._stride, 'paddings',
                     self.conv[2]._padding, 'dilations', self.conv[2]._dilation,
                     'groups', self.conv[2]._groups if self.conv[2]._groups else
                     1, 'use_cudnn', self.conv[2]._use_cudnn)
            out = core.ops.conv2d(norm_out, weight, *attrs)
        elif self.conv[2]._l_type == 'depthwise_conv2d':
            attrs = ('strides', self.conv[2]._stride, 'paddings',
                     self.conv[2]._padding, 'dilations', self.conv[2]._dilation,
                     'groups', self.conv[2]._groups, 'use_cudnn',
                     self.conv[2]._use_cudnn)
            out = core.ops.depthwise_conv2d(norm_out, weight, *attrs)
        else:
            raise ValueError("conv type error")

        pre_bias = out
        if self.conv[2].bias is not None:
            bias = self.conv[2].bias[:out_nc]
            pre_act = dygraph_utils._append_bias_in_dygraph(pre_bias, bias, 1)
        else:
            pre_act = pre_bias

        conv1_out = dygraph_utils._append_activation_in_dygraph(
            pre_act, self.conv[2]._act)

        return conv1_out


class SuperLinear(fluid.dygraph.Linear):
    """
    """

    def __init__(self,
                 input_dim,
                 output_dim,
                 candidate_config={},
                 param_attr=None,
                 bias_attr=None,
                 act=None,
                 dtype="float32"):
        super(SuperLinear, self).__init__(input_dim, output_dim, param_attr,
                                          bias_attr, act, dtype)
        self._param_attr = param_attr
        self._bias_attr = bias_attr
        self.output_dim = output_dim
        self.candidate_config = candidate_config
        self.expand_ratio = candidate_config[
            'expand_ratio'] if 'expand_ratio' in candidate_config else None
        self.base_output_dim = self.output_dim
        if self.expand_ratio != None:
            self.base_output_dim = int(self.output_dim / max(self.expand_ratio))

    def forward(self, input, expand_ratio=None, channel=None):
        self.cur_config = {'expand_ratio': expand_ratio, 'channel': channel}
        ### weight: (Cin, Cout)
        in_nc = int(input.shape[-1])
        assert (
            expand_ratio == None or channel == None
        ), "expand_ratio and channel CANNOT be NOT None at the same time."
        if expand_ratio != None:
            out_nc = int(expand_ratio * self.base_output_dim)
        elif channel != None:
            out_nc = int(channel)
        else:
            out_nc = self.output_dim

        weight = self.weight[:in_nc, :out_nc]
        if self._bias_attr != False:
            bias = self.bias[:out_nc]
            use_bias = True

        pre_bias = _varbase_creator(dtype=input.dtype)
        core.ops.matmul(input, weight, pre_bias, 'transpose_X', False,
                        'transpose_Y', False, "alpha", 1)
        if self._bias_attr != False:
            pre_act = dygraph_utils._append_bias_in_dygraph(
                pre_bias, bias, axis=len(input.shape) - 1)
        else:
            pre_act = pre_bias

        return dygraph_utils._append_activation_in_dygraph(pre_act, self._act)


class SuperBatchNorm(fluid.dygraph.BatchNorm):
    """
    add comment
    """

    def __init__(self,
                 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=True,
                 use_global_stats=False,
                 trainable_statistics=False):
        super(SuperBatchNorm, self).__init__(
            num_channels, act, is_test, momentum, epsilon, param_attr,
            bias_attr, dtype, data_layout, in_place, moving_mean_name,
            moving_variance_name, do_model_average_for_mean_and_var,
            use_global_stats, trainable_statistics)

    def forward(self, input):
        feature_dim = int(input.shape[1])

        weight = self.weight[:feature_dim]
        bias = self.bias[:feature_dim]
        mean = self._mean[:feature_dim]
        variance = self._variance[:feature_dim]

        mean_out = self._mean
        variance_out = self._variance
        mean_out_tmp = mean
        variance_out_tmp = variance

        attrs = ("momentum", self._momentum, "epsilon", self._epsilon,
                 "is_test", not self.training, "data_layout", self._data_layout,
                 "use_mkldnn", False, "fuse_with_relu", self._fuse_with_relu,
                 "use_global_stats", self._use_global_stats,
                 'trainable_statistics', self._trainable_statistics)
        try:
            from paddle import _C_ops
            from paddle.fluid.framework import in_dygraph_mode, _in_legacy_dygraph
            if in_dygraph_mode():
                if feature_dim != self._mean.shape[0]:
                    batch_norm_out = _C_ops.final_state_batch_norm(
                        input, weight, bias, mean, variance, mean_out_tmp,
                        variance_out_tmp, *attrs)
                    self._mean[:feature_dim] = mean
                    self._variance[:feature_dim] = variance
                    mean_out[:feature_dim] = mean_out_tmp
                    variance_out[:feature_dim] = variance_out_tmp
                else:
                    batch_norm_out = core.ops.batch_norm(
                        input, weight, bias, self._mean, self._variance,
                        mean_out, variance_out, *attrs)
            elif _in_legacy_dygraph():
                if feature_dim != self._mean.shape[0]:
                    batch_norm_out = core.ops.batch_norm(
                        input, weight, bias, mean, variance, None, mean_out_tmp,
                        variance_out_tmp, *attrs)
                    self._mean[:feature_dim].set_value(mean)
                    self._variance[:feature_dim].set_value(variance)
                    mean_out[:feature_dim].set_value(mean_out_tmp)
                    variance_out[:feature_dim].set_value(variance_out_tmp)
                else:
                    batch_norm_out = core.ops.batch_norm(
                        input, weight, bias, self._mean, self._variance, None,
                        mean_out, variance_out, *attrs)
        except:
            if feature_dim != self._mean.shape[0]:
                batch_norm_out = core.ops.batch_norm(input, weight, bias, mean,
                                                     variance, mean_out_tmp,
                                                     variance_out_tmp, *attrs)
                self._mean[:feature_dim].set_value(mean)
                self._variance[:feature_dim].set_value(variance)
                mean_out[:feature_dim].set_value(mean_out_tmp)
                variance_out[:feature_dim].set_value(variance_out_tmp)
            else:
                batch_norm_out = core.ops.batch_norm(
                    input, weight, bias, self._mean, self._variance, mean_out,
                    variance_out, *attrs)

        return dygraph_utils._append_activation_in_dygraph(
            batch_norm_out[0], act=self._act)


class SuperInstanceNorm(fluid.dygraph.InstanceNorm):
    """
    """

    def __init__(self,
                 num_channels,
                 epsilon=1e-05,
                 param_attr=None,
                 bias_attr=None,
                 dtype='float32'):
        super(SuperInstanceNorm, self).__init__(num_channels, epsilon,
                                                param_attr, bias_attr, dtype)

    def forward(self, input):
        feature_dim = int(input.shape[1])

        if self._param_attr == False and self._bias_attr == False:
            scale = None
            bias = None
        else:
            scale = self.scale[:feature_dim]
            bias = self.bias[:feature_dim]

        out, _, _ = core.ops.instance_norm(input, scale, bias, 'epsilon',
                                           self._epsilon)
        return out


class SuperLayerNorm(fluid.dygraph.LayerNorm):
    def __init__(self,
                 normalized_shape,
                 scale=True,
                 shift=True,
                 epsilon=1e-05,
                 param_attr=None,
                 bias_attr=None,
                 act=None,
                 dtype='float32'):
        super(SuperLayerNorm,
              self).__init__(normalized_shape, scale, shift, epsilon,
                             param_attr, bias_attr, act, dtype)

    def forward(self, input):
        input_shape = list(input.shape)
        input_ndim = len(input_shape)
        normalized_ndim = len(self._normalized_shape)
        self._begin_norm_axis = input_ndim - normalized_ndim

        ### TODO(ceci3): fix if normalized_shape is not a single number
        feature_dim = int(input.shape[-1])
        weight = self.weight[:feature_dim]
        bias = self.bias[:feature_dim]
        pre_act, _, _ = core.ops.layer_norm(input, weight, bias, 'epsilon',
                                            self._epsilon, 'begin_norm_axis',
                                            self._begin_norm_axis)
        return dygraph_utils._append_activation_in_dygraph(
            pre_act, act=self._act)


class SuperEmbedding(fluid.dygraph.Embedding):
    def __init__(self,
                 size,
                 candidate_config={},
                 is_sparse=False,
                 is_distributed=False,
                 padding_idx=None,
                 param_attr=None,
                 dtype='float32'):
        super(SuperEmbedding, self).__init__(size, is_sparse, is_distributed,
                                             padding_idx, param_attr, dtype)
        self.candidate_config = candidate_config
        self.expand_ratio = candidate_config[
            'expand_ratio'] if 'expand_ratio' in candidate_config else None
        self.base_output_dim = self._size[-1]
        if self.expand_ratio != None:
            self.base_output_dim = int(self._size[-1] / max(self.expand_ratio))

    def forward(self, input, expand_ratio=None, channel=None):
        assert (
            expand_ratio == None or channel == None
        ), "expand_ratio and channel CANNOT be NOT None at the same time."
        if expand_ratio != None:
            out_nc = int(expand_ratio * self.base_output_dim)
        elif channel != None:
            out_nc = int(channel)
        else:
            out_nc = self._size[-1]

        weight = self.weight[:, :out_nc]
        return core.ops.lookup_table_v2(
            weight, input, 'is_sparse', self._is_sparse, 'is_distributed',
            self._is_distributed, 'remote_prefetch', self._remote_prefetch,
            'padding_idx', self._padding_idx)