# 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 import numbers import logging __all__ = [ 'Conv2D', 'Conv3D', 'Pool2D', 'FC', 'Linear', 'BatchNorm', 'Embedding', 'GRUUnit', 'LayerNorm', 'NCE', 'PRelu', 'BilinearTensorProduct', 'Conv2DTranspose', 'Conv3DTranspose', 'GroupNorm', 'SpectralNorm', 'TreeConv' ] class Conv2D(layers.Layer): """ This interface is used to construct a callable object of the ``Conv2D`` class. For more details, refer to code examples. 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 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 `_ 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 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. 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 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(3, 2, 3) data = to_variable(data) conv = conv2d(data) """ def __init__(self, num_channels, 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__() self._num_channels = num_channels 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: self._l_type = 'conv2d' self._num_channels = num_channels 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, 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()) self._bias_param = self.create_parameter( attr=self._bias_attr, shape=[self._num_filters], dtype=self._dtype, is_bias=True) @property def weight(self): return self._filter_param @weight.setter def weight(self, value): self._filter_param = value @property def bias(self): return self._bias_param @bias.setter def bias(self, value): self._bias_param = value 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 multidimensional tensors with a shape of :math:`[N, C, D, H, W]` . 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 or NDHWC 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 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 image channel. filter_size (int|tuple, optional): 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, filter_size_depth = filter_size_height = filter_size_width = filter_size. stride (int|tuple, optional): 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. The default value is 1. padding (int|tuple, optional): 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. The default value is 0. dilation (int|tuple, optional): 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. The default value is 1. groups (int, optional): 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. The default value is 1. param_attr (ParamAttr, optional): 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}`. The default value is None. bias_attr (ParamAttr|bool, optional): 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. The default value is None. use_cudnn (bool, optional): Use cudnn kernel or not, it is valid only when the cudnn library is installed. The default value is True. act (str, optional): Activation type, if it is set to None, activation is not appended. The default value is 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): the learnable bias of this layer. Returns: None. 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( num_channels=3, num_filters=2, filter_size=3, act="relu") ret = conv3d(fluid.dygraph.base.to_variable(data)) """ def __init__(self, num_channels, 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(Conv3D, self).__init__() self._num_channels = num_channels 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 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._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, 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] * 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()) self._bias_param = self.create_parameter( attr=self._bias_attr, shape=[self._num_filters], dtype=self._dtype, is_bias=True) @property def weight(self): return self._filter_param @weight.setter def weight(self, value): self._filter_param = value @property def bias(self): return self._bias_param @bias.setter def bias(self, value): self._bias_param = value 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 }) 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 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 `_. 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^\prime_{out} &= (D_{in} - 1) * strides[0] - 2 * paddings[0] + dilations[0] * (D_f - 1) + 1 \\\\ H^\prime_{out} &= (H_{in} - 1) * strides[1] - 2 * paddings[1] + dilations[1] * (H_f - 1) + 1 \\\\ W^\prime_{out} &= (W_{in} - 1) * strides[2] - 2 * paddings[2] + dilations[2] * (W_f - 1) + 1 \\\\ D_{out} &\in [ D^\prime_{out}, D^\prime_{out} + strides[0] ] \\\\ H_{out} &\in [ H^\prime_{out}, H^\prime_{out} + strides[1] ] \\\\ **Note**: The conv3d_transpose can be seen as the backward of the conv3d. For conv3d, when stride > 1, conv3d maps multiple input shape to the same output shape, so for conv3d_transpose, when stride > 1, input shape maps multiple output shape. If output_size is None, :math:`H_{out} = H^\prime_{out}, :math:`H_{out} = \ H^\prime_{out}, W_{out} = W^\prime_{out}`; else, the :math:`D_{out}` of the output size must between :math:`D^\prime_{out}` and :math:`D^\prime_{out} + strides[0]`, the :math:`H_{out}` of the output size must between :math:`H^\prime_{out}` and :math:`H^\prime_{out} + strides[1]`, and the :math:`W_{out}` of the output size must between :math:`W^\prime_{out}` and :math:`W^\prime_{out} + strides[2]`, conv3d_transpose can compute the kernel size automatically. 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 image channel. filter_size(int|tuple): 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. padding(int|tuple, optional): The padding size. The padding argument effectively adds `dilation * (kernel - 1)` amount of zero-padding on both sides of input. If `padding` is a string, either 'VALID' or 'SAME' supported, which is the padding algorithm. If `padding` is a tuple or list, it could be in three forms: `[pad_depth, pad_height, pad_width]` or `[pad_depth_front, pad_depth_back, pad_height_top, pad_height_bottom, pad_width_left, pad_width_right]`, and when `data_format` is `'NCDHW'`, `padding` can be in the form `[[0,0], [0,0], [pad_depth_front, pad_depth_back], [pad_height_top, pad_height_bottom], [pad_width_left, pad_width_right]]`. when `data_format` is `'NDHWC'`, `padding` can be in the form `[[0,0], [pad_depth_front, pad_depth_back], [pad_height_top, pad_height_bottom], [pad_width_left, pad_width_right], [0,0]]`. The default value is 0. stride(int|tuple, optional): The stride size. It means the stride in transposed convolution. If stride is a tuple, it must contain three integers, (stride_depth, stride_height, stride_width). Otherwise, stride_depth = stride_height = stride_width = stride. The default value is 1. dilation(int|tuple, optional): 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. The default value is 1. groups(int, optional): 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. The default value is 1. param_attr (ParamAttr, optional): 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. The default value is None. bias_attr (ParamAttr|bool, optional): 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. The default value is None. use_cudnn(bool, optional): Use cudnn kernel or not, it is valid only when the cudnn library is installed. The default value is True. act (str, optional): Activation type, if it is set to None, activation is not appended. The default value is None. name(str, optional): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name`. Attribute: **weight** (Parameter): the learnable weights of filters of this layer. **bias** (Parameter): the learnable bias of this layer. Returns: None. 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( num_channels=3, num_filters=12, filter_size=12, use_cudnn=False) ret = conv3dTranspose(fluid.dygraph.base.to_variable(data)) """ def __init__(self, num_channels, num_filters, filter_size, padding=0, stride=1, dilation=1, groups=None, param_attr=None, bias_attr=None, use_cudnn=True, act=None, dtype='float32'): super(Conv3DTranspose, self).__init__() 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._num_channels = num_channels self._filter_size = filter_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 self._dtype = dtype self._filter_size = utils.convert_to_list( self._filter_size, 3, 'conv3d_transpose.filter_size') filter_shape = [self._num_channels, 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) @property def weight(self): return self._img_filter @weight.setter def weight(self, value): self._img_filter = value @property def bias(self): return self._bias_param @bias.setter def bias(self, value): self._bias_param = value 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): """ This interface is used to construct a callable object of the ``Pool2D`` class. For more details, refer to code examples. The pooling2d operation calculates the output based on the input, pool_type and pool_size, pool_stride, pool_padding parameters.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. Parameters(ksize, strides, paddings) are two elements. These two elements represent height and width, respectively. The input(X) size and output(Out) size may be different. Example: - Input: Input shape: :math:`(N, C, H_{in}, W_{in})` - Output: Output shape: :math:`(N, C, H_{out}, W_{out})` If ``ceil_mode`` = False: .. math:: H_{out} = \\frac{(H_{in} - ksize[0] + 2 * paddings[0])}{strides[0]} + 1 \\\\ W_{out} = \\frac{(W_{in} - ksize[1] + 2 * paddings[1])}{strides[1]} + 1 If ``ceil_mode`` = True: .. math:: H_{out} = \\frac{(H_{in} - ksize[0] + 2 * paddings[0] + strides[0] - 1)}{strides[0]} + 1 \\\\ W_{out} = \\frac{(W_{in} - ksize[1] + 2 * paddings[1] + strides[1] - 1)}{strides[1]} + 1 If ``exclusive`` = False: .. math:: hstart &= i * strides[0] - paddings[0] \\\\ hend &= hstart + ksize[0] \\\\ wstart &= j * strides[1] - paddings[1] \\\\ wend &= wstart + ksize[1] \\\\ Output(i ,j) &= \\frac{sum(Input[hstart:hend, wstart:wend])}{ksize[0] * ksize[1]} If ``exclusive`` = True: .. math:: hstart &= max(0, i * strides[0] - paddings[0])\\\\ hend &= min(H, hstart + ksize[0]) \\\\ wstart &= max(0, j * strides[1] - paddings[1]) \\\\ wend & = min(W, wstart + ksize[1]) \\\\ Output(i ,j) & = \\frac{sum(Input[hstart:hend, wstart:wend])}{(hend - hstart) * (wend - wstart)} Parameters: pool_size (int or list or tuple, optional): 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. Default: -1. pool_type(str, optional) : The pooling type, can be "max" for max-pooling and "avg" for average-pooling. Default: max. pool_stride (int or list or tuple, optional): 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. Default: 1. pool_padding (int or list or tuple, optional): The padding size for pooling operation. If ``pool_padding`` is a tuple, it must contain two integers, (pool_padding_on_Height, pool_padding_on_Width). Otherwise, the padding size for pooling operation will be a square of an int. Default: 0. global_pooling (bool, optional): Whether to use the global pooling. If global_pooling = true, kernel size and paddings will be ignored. Default: False. use_cudnn (bool, optional): Only used in cudnn kernel, need install cudnn. Default: True. ceil_mode (bool, optional): Whether to use the ceil function to calculate output height and width. False is the default. If it is set to False, the floor function will be used. Default: False. exclusive (bool, optional): Whether to exclude padding points in average pooling mode. Default: True. Returns: None 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 import paddle.fluid as fluid from paddle.fluid.dygraph.base import to_variable import numpy as np with fluid.dygraph.guard(): data = numpy.random.random((3, 32, 32, 5)).astype('float32') pool2d = fluid.dygraph.Pool2D(pool_size=2, pool_type='max', pool_stride=1, global_pooling=False) pool2d_res = pool2d(to_variable(data)) """ def __init__(self, pool_size=-1, pool_type="max", pool_stride=1, pool_padding=0, global_pooling=False, use_cudnn=True, ceil_mode=False, exclusive=True): 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__() 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 Linear(layers.Layer): """ Fully-connected linear transformation layer: .. math:: Out = Act({XW + b}) where :math:`X` is the input Tensor, :math:`W` and :math:`b` are weight and bias respectively. Different from FC layer, Linear layer takes only one ``Tensor`` input. The Linear layer multiplies input tensor with weight matrix and produces an output Tensor of shape [N, *, `output_dim`], where N is batch size and `*` means any number of additional dimensions. If ``bias_attr`` is not None, a bias variable will be created and added to the output. Finally, if ``act`` is not None, it will be applied to the output as well. Parameters: input_dim(int): The number of input units in this layer. output_dim(int): The number of output units in this layer. param_attr(ParamAttr or list of ParamAttr, optional): The parameter attribute for learnable weights(Parameter) of this layer. Default: None. bias_attr(ParamAttr or list of ParamAttr, optional): The 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, optional): Activation to be applied to the output of this layer. Default: None. dtype(str, optional): Dtype used for weight, it can be "float32" or "float64". Default: "float32". Attributes: **weight** (Parameter): the learnable weights of this layer. **bias** (Parameter or None): the learnable bias of this layer. Returns: None Examples: .. code-block:: python from paddle.fluid.dygraph.base import to_variable import paddle.fluid as fluid from paddle.fluid.dygraph import Linear import numpy as np data = np.random.uniform(-1, 1, [30, 10, 32]).astype('float32') with fluid.dygraph.guard(): linear = Linear(32, 64) data = to_variable(data) res = linear(data) # [30, 10, 64] """ def __init__(self, input_dim, output_dim, param_attr=None, bias_attr=None, act=None, dtype="float32"): super(Linear, self).__init__() self._act = act self._dtype = dtype self.weight = self.create_parameter( shape=[input_dim, output_dim], attr=param_attr, dtype=dtype, is_bias=False) self.bias = self.create_parameter( shape=[output_dim], attr=bias_attr, dtype=dtype, is_bias=True) def forward(self, input): tmp = self._helper.create_variable_for_type_inference(self._dtype) self._helper.append_op( type="matmul", inputs={"X": input, "Y": self.weight}, outputs={"Out": tmp}, attrs={ "transpose_X": False, "transpose_Y": False, "alpha": 1, }) if self.bias: pre_activation = self._helper.create_variable_for_type_inference( dtype=self._dtype) self._helper.append_op( type='elementwise_add', inputs={'X': [tmp], 'Y': [self.bias]}, outputs={'Out': [pre_activation]}, attrs={'axis': len(input.shape) - 1}) else: pre_activation = tmp return self._helper.append_activation(pre_activation, act=self._act) class FC(layers.Layer): """ This interface is used to construct a callable object of the ``FC`` class. For more details, refer to code examples. It creates a fully connected layer in the network. It can take one or multiple ``Tensor`` as its inputs. 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 [N, `size`], where N is batch size. If multiple input tensors are given, the results of multiple output tensors with shape [N, `size`] will be summed up. If ``bias_attr`` is not None, a bias variable will be created and added to the output. Finally, if ``act`` 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 ``Tensor`` : .. 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 ``Tensor`` . * :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]]] data_1.shape = (1, 1, 2) # 1 is batch_size data_2.data = [[[0.1, 0.2, 0.3]]] data_2.shape = (1, 1, 3) # 1 is batch_size fc = FC("fc", 2, num_flatten_dims=2) out = fc(input=[data_1, data_2]) Then: out.data = [[[0.182996 -0.474117]]] out.shape = (1, 1, 2) Parameters: name_scope(str): The name of this class. size(int): The number of output units in this layer. num_flatten_dims (int, optional): The fc layer can accept an input tensor with more than two dimensions. If this happens, the multi-dimension 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]. Default: 1 param_attr (ParamAttr or list of ParamAttr, optional): The parameter attribute for learnable weights(Parameter) of this layer. Default: None. bias_attr (ParamAttr or list of ParamAttr, optional): The 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, optional): Activation to be applied to the output of this layer. Default: None. is_test(bool, optional): A flag indicating whether execution is in test phase. Default: False. dtype(str, optional): Dtype used for weight, it can be "float32" or "float64". Default: "float32". Attribute: **weight** (list of Parameter): the learnable weights of this layer. **bias** (Parameter or None): the learnable bias of this layer. Returns: None 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() 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) # TODO(songyouwei): We should remove _w property @property def _w(self, i=0): return self.__w[i] @_w.setter def _w(self, value, i=0): assert isinstance(self.__w[i], Variable) self.__w[i].set_value(value) @property def weight(self): if len(self.__w) > 1: return self.__w else: return self.__w[0] @weight.setter def weight(self, value): if len(self.__w) == 1: self.__w[0] = value @property def bias(self): return self._b @bias.setter def bias(self, value): self._b = value 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): """ This interface is used to construct a callable object of the ``BatchNorm`` class. For more details, refer to code examples. It implements the function of the Batch Normalization Layer and can be used as a normalizer function for conv2d and fully connected operations. The data is normalized by the mean and variance of the channel based on the current batch data. Refer to `Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift `_ for more details. When use_global_stats = False, the :math:`\\mu_{\\beta}` and :math:`\\sigma_{\\beta}^{2}` are the statistics of one mini-batch. Calculated as follows: .. 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 \\\\ - :math:`x` : mini-batch data - :math:`m` : the size of the mini-batch data 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 (moving_mean and moving_variance). It usually got from the pre-trained model. Calculated as follows: .. math:: moving\_mean = moving\_mean * momentum + \mu_{\beta} * (1. - momentum) \quad &// global mean \\ moving\_variance = moving\_variance * momentum + \sigma_{\beta}^{2} * (1. - momentum) \quad &// global variance \\ The normalization function formula is as follows: .. math:: \\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 - :math:`\\epsilon` : add a smaller value to the variance to prevent division by zero - :math:`\\gamma` : trainable proportional parameter - :math:`\\beta` : trainable deviation parameter Parameters: num_channels(int): Indicate the number of channels of the input ``Tensor``. act(str, optional): Activation to be applied to the output of batch normalizaiton. Default: None. is_test (bool, optional): A flag indicating whether it is in test phrase or not. Default: False. momentum(float, optional): The value used for the moving_mean and moving_var computation. Default: 0.9. epsilon(float, optional): The small value added to the variance to prevent division by zero. Default: 1e-5. param_attr(ParamAttr, optional): 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, optional): 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. dtype(str, optional): Indicate the data type of the input ``Tensor``, which can be float32 or float64. Default: float32. data_layout(str, optional): Specify the input data format, the data format can be "NCHW" or "NHWC". Default: NCHW. in_place(bool, optional): Make the input and output of batch norm reuse memory. Default: False. moving_mean_name(str, optional): The name of moving_mean which store the global Mean. Default: None. moving_variance_name(str, optional): The name of the moving_variance which store the global Variance. Default: None. do_model_average_for_mean_and_var(bool, optional): Whether parameter mean and variance should do model average when model average is enabled. Default: True. use_global_stats(bool, optional): 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. Default: False. trainable_statistics(bool, optional): 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. Default: False. Returns: None Examples: .. code-block:: python import paddle.fluid as fluid from paddle.fluid.dygraph.base import to_variable import numpy as np x = np.random.random(size=(3, 10, 3, 7)).astype('float32') with fluid.dygraph.guard(): x = to_variable(x) batch_norm = fluid.BatchNorm(10) hidden1 = batch_norm(x) """ 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(BatchNorm, self).__init__() 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._data_layout = data_layout self._momentum = momentum self._epsilon = epsilon self._is_test = is_test self._fuse_with_relu = False self._use_global_stats = use_global_stats self._trainable_statistics = trainable_statistics 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, "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 }) # 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 interface is used to construct a callable object of the ``Embedding`` class. For specific usage, refer to code examples. It implements the function of the Embedding Layer. This layer is used to lookup embeddings vector of ids provided by :attr:`input` . It automatically constructs a 2D embedding matrix based on the input :attr:`size` (vocab_size, emb_size) and :attr:`dtype` . The shape of output Tensor is generated by appending an emb_size dimension to the last dimension of the input Tensor shape. **Note:** The id in :attr:`input` must satisfy :math:`0 =< id < size[0]` , otherwise the program will throw an exception and exit. .. code-block:: text Case 1: input is a Tensor. padding_idx = -1 input.data = [[1, 3], [2, 4], [4, 127] input.shape = [3, 2] Given size = [128, 16] output is a Tensor: out.shape = [3, 2, 16] out.data = [[[0.129435295, 0.244512452, ..., 0.436322452], [0.345421456, 0.524563927, ..., 0.144534654]], [[0.345249859, 0.124939536, ..., 0.194353745], [0.945345345, 0.435394634, ..., 0.435345365]], [[0.945345345, 0.435394634, ..., 0.435345365], [0.0, 0.0, ..., 0.0 ]]] # padding data The input padding_idx is less than 0, it is automatically converted to padding_idx = -1 + 128 = 127 It will pad all-zero data when ids is 127. Parameters: 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. This parameter only affects the performance of the backwards gradient update. It is recommended to set True because sparse update is faster. But some optimizer does not support sparse update, such as :ref:`api_fluid_optimizer_AdadeltaOptimizer` , :ref:`api_fluid_optimizer_AdamaxOptimizer` , :ref:`api_fluid_optimizer_DecayedAdagradOptimizer` , :ref:`api_fluid_optimizer_FtrlOptimizer` , :ref:`api_fluid_optimizer_LambOptimizer` and :ref:`api_fluid_optimizer_LarsMomentumOptimizer` . In these case, is_sparse must be False. Default: False. is_distributed(bool): Whether to store the embedding matrix in a distributed manner. Only used in multi-machine distributed CPU training. Default: False. padding_idx(int|long|None): padding_idx needs to be in the interval [-vocab_size, vocab_size). If :math:`padding\_idx < 0`, the :math:`padding\_idx` will automatically be converted to :math:`vocab\_size + padding\_idx` . It will output all-zero padding data whenever lookup encounters :math:`padding\_idx` in id. And the padding data will not be updated while training. If set None, it makes no effect to output. Default: None. param_attr(ParamAttr): To specify the weight parameter property. Default: None, which means the default weight parameter property is used. See usage for details in :ref:`api_fluid_ParamAttr` . In addition, user-defined or pre-trained word vectors can be loaded with the :attr:`param_attr` parameter. The local word vector needs to be transformed into numpy format, and the shape of local word vector shoud be consistent with :attr:`size` . Then :ref:`api_fluid_initializer_NumpyArrayInitializer` is used to load custom or pre-trained word vectors. See code example 2 for details. dtype(np.dtype|core.VarDesc.VarType|str): It refers to the data type of output Tensor. It must be "float32" or "float64". Default: "float32". Attribute: **weight** (Parameter): the learnable weights of this layer. Returns: Variable: Embedding Tensor or LoDTensor mapped by input. The data type is the same as :attr:`dtype` . Examples: .. code-block:: python import paddle.fluid as fluid import paddle.fluid.dygraph.base as base import numpy as np # example 1 inp_word = np.array([[2, 3, 5], [4, 2, 1]]).astype('int64') inp_word.shape # [2, 3] dict_size = 20 with fluid.dygraph.guard(): emb = fluid.dygraph.Embedding( size=[dict_size, 32], param_attr='emb.w', is_sparse=False) static_rlt3 = emb(base.to_variable(inp_word)) static_rlt3.shape # [2, 3, 32] # example 2: load custom or pre-trained word vectors weight_data = np.random.random(size=(128, 100)) # word vectors with numpy format w_param_attrs = fluid.ParamAttr( name="emb_weight", learning_rate=0.5, initializer=fluid.initializer.NumpyArrayInitializer(weight_data), trainable=True) with fluid.dygraph.guard(): emb = fluid.dygraph.Embedding( size=[128, 100], param_attr= w_param_attrs, is_sparse=False) static_rlt3 = emb(base.to_variable(inp_word)) """ def __init__(self, size, is_sparse=False, is_distributed=False, padding_idx=None, param_attr=None, dtype='float32'): super(Embedding, self).__init__() 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) @property def weight(self): return self._w @weight.setter def weight(self, value): self._w = value def forward(self, input): out = self._helper.create_variable_for_type_inference(self._dtype) self._helper.append_op( type='lookup_table_v2', 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): """ This interface is used to construct a callable object of the ``LayerNorm`` class. For more details, refer to code examples. It implements the function of the Layer Normalization Layer and can be applied to mini-batch input data. Refer to `Layer Normalization `_ The formula is as follows: .. math:: \\mu & = \\frac{1}{H}\\sum_{i=1}^{H} x_i \\sigma & = \\sqrt{\\frac{1}{H}\sum_{i=1}^{H}{(x_i - \\mu)^2} + \\epsilon} y & = f(\\frac{g}{\\sigma}(x - \\mu) + b) - :math:`x`: the vector representation of the summed inputs to the neurons in that layer. - :math:`H`: the number of hidden units in a layers - :math:`\\epsilon`: the small value added to the variance to prevent division by zero. - :math:`g`: the trainable scale parameter. - :math:`b`: the trainable bias parameter. Parameters: normalized_shape(int or list or tuple): Input shape from an expected input of size :math:`[*, normalized_shape[0], normalized_shape[1], ..., normalized_shape[-1]]`. If it is a single integer, this module will normalize over the last dimension which is expected to be of that specific size. scale(bool, optional): Whether to learn the adaptive gain :math:`g` after normalization. Default: True. shift(bool, optional): Whether to learn the adaptive bias :math:`b` after normalization. Default: True. epsilon(float, optional): The small value added to the variance to prevent division by zero. Default: 1e-05. param_attr(ParamAttr, optional): 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, optional): 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, optional): Activation to be applied to the output of layer normalizaiton. Default: None. dtype (str, optional): Data type, it can be "float32" or "float64". Default: "float32". Returns: None Examples: .. code-block:: python import paddle.fluid as fluid from paddle.fluid.dygraph.base import to_variable import numpy x = numpy.random.random((3, 32, 32)).astype('float32') with fluid.dygraph.guard(): x = to_variable(x) layerNorm = fluid.LayerNorm([32, 32]) ret = layerNorm(x) """ def __init__(self, normalized_shape, scale=True, shift=True, epsilon=1e-05, param_attr=None, bias_attr=None, act=None, dtype='float32'): super(LayerNorm, self).__init__() if isinstance(normalized_shape, numbers.Integral): normalized_shape = [normalized_shape] self._normalized_shape = list(normalized_shape) self._scale = scale self._shift = shift self._epsilon = epsilon self._param_attr = param_attr self._bias_attr = bias_attr self._act = act self._dtype = dtype param_shape = [np.prod(self._normalized_shape)] if self._scale: self._scale_w = self.create_parameter( attr=self._param_attr, shape=param_shape, dtype=self._dtype, default_initializer=Constant(1.0)) else: if self._param_attr: logging.warn("param_attr are only avaliable with scale is True") 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) else: if self._bias_attr: logging.warn("bias_attr are only avaliable with shift is True") 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 if input_ndim < normalized_ndim or input_shape[ self._begin_norm_axis:] != self._normalized_shape: str_normalized_shape = str(self._normalized_shape) raise ValueError( 'Given normalized_shape is ' + str_normalized_shape + ', expected input with shape [*, ' + str_normalized_shape[ 1:] + ', but got input shape ' + str(input_shape)) 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, act=self._act) class GRUUnit(layers.Layer): """ **GRU unit layer** It creates a callable object from GRUUnit class. 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 `_ .. 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 `_ .. 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`. Parameters: size (int): The input dimension value. param_attr(ParamAttr, optional): The parameter attribute for the learnable hidden-hidden weight matrix. **Note**: 1. The shape of the weight matrix is :math:`[T, 3*D]`, where D is the hidden size. 2. 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, 2*D]`, and the second part are weights for candidate hidden state with shape :math:`[D, 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. The default value is None. bias_attr (ParamAttr|bool, optional): The parameter attribute for the bias of GRU.Note that the bias with :math:`[1, 3*D]` 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. The default value is None. activation (str): The activation type for cell (actNode). The default value is 'tanh'. gate_activation (str): The activation type for gates (actGate). The default value is 'sigmoid'. dtype(str): The dtype of the layers. The data type can be set as 'float32', 'float64'. The default value is 'float32'. Attribute: **weight** (Parameter): the learnable weights of this layer. **bias** (Parameter): the learnable bias of this layer. Returns: tuple: The hidden value, reset-hidden value and gate values. The hidden value is a 2-D tensor with shape :math:`[T, D]` . The reset-hidden value is a 2-D tensor with shape :math:`[T, D]` . The gate value is a 2-D tensor with shape :math:`[T, 3*D]`. Examples: .. code-block:: python import paddle.fluid as fluid import paddle.fluid.dygraph.base as base import numpy lod = [[2, 4, 3]] D = 5 T = sum(lod[0]) input = numpy.random.rand(T, 3 * D).astype('float32') hidden_input = numpy.random.rand(T, D).astype('float32') with fluid.dygraph.guard(): x = numpy.random.random((3, 32, 32)).astype('float32') gru = fluid.dygraph.GRUUnit(size=D * 3) dy_ret = gru( base.to_variable(input), base.to_variable(hidden_input)) """ def __init__(self, size, param_attr=None, bias_attr=None, activation='tanh', gate_activation='sigmoid', origin_mode=False, dtype='float32'): super(GRUUnit, self).__init__() self._bias_attr = bias_attr 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_size = bias_size self._bias = self.create_parameter( attr=bias_attr, shape=bias_size, dtype=dtype, is_bias=True) @property def weight(self): return self._weight @weight.setter def weight(self, value): self._weight = value @property def bias(self): return self._bias @bias.setter def bias(self, value): self._bias = value 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): """ This interface is used to construct a callable object of the ``NCE`` class. For more details, refer to code examples. It implements the function of the ``NCE`` loss function. By default this function uses a uniform distribution for sampling, and it compute and return the noise-contrastive estimation training loss. See `Noise-contrastive estimation: A new estimation principle for unnormalized statistical models `_ . Parameters: num_total_classes (int): Total number of classes in all samples. dim (int): Dimension of input (possibly embedding dim). param_attr (ParamAttr, optional): The parameter attribute for learnable weights(Parameter) 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 or bool, optional): The 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, optional): The number of negative classes. The default value is 10. sampler (str, optional): The sampler used to sample class from negtive classes. It can be 'uniform', 'log_uniform' or 'custom_dist'. default: 'uniform'. custom_dist (float[], optional): A float[] with size=num_total_classes. It is used when sampler is set to 'custom_dist'. custom_dist[i] is the probability of i-th class to be sampled. Default: None. seed (int, optional): The seed used in sampler. Default: 0. is_sparse(bool, optional): The flag indicating whether to use sparse update. If is_sparse is True, the weight@GRAD and bias@GRAD will be changed to SelectedRows. Default: False. dtype (str, optional): Data type, it can be "float32" or "float64". Default: "float32". Attribute: **weight** (Parameter): the learnable weights of this layer. **bias** (Parameter or None): the learnable bias of this layer. Returns: None 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( 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( num_total_classes=dict_size, dim=embs3.shape[1], num_neg_samples=2, sampler="custom_dist", custom_dist=nid_freq_arr.tolist(), seed=1, param_attr='nce.w', bias_attr='nce.b') wl = fluid.layers.unsqueeze(words[label_word], axes=[0]) nce_loss3 = nce(embs3, wl) """ def __init__(self, num_total_classes, dim, sample_weight=None, param_attr=None, bias_attr=None, num_neg_samples=None, sampler="uniform", custom_dist=None, seed=0, is_sparse=False, dtype='float32'): super(NCE, self).__init__() self._param_attr = param_attr self._bias_attr = bias_attr self._num_total_classes = num_total_classes self._dtype = dtype self._inputs = dict() self._inputs['SampleWeight'] = sample_weight if sample_weight is not None else [] 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 } self._w = self.create_parameter( attr=self._param_attr, shape=[self._num_total_classes, dim], is_bias=False, dtype=self._dtype) if self._bias_attr: self._b = self.create_parameter( attr=self._bias_attr, shape=[self._num_total_classes, 1], is_bias=True, dtype=self._dtype) self._inputs['Bias'] = self._b self._inputs['Weight'] = self._w @property def weight(self): return self._w @weight.setter def weight(self, value): self._w = value @property def bias(self): return self._b @bias.setter def bias(self, value): self._b = value 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): """ This interface is used to construct a callable object of the ``PRelu`` class. For more details, refer to code examples. It implements three activation methods of the ``PRelu`` activation function. Equation: .. math:: y = \max(0, x) + \\alpha * \min(0, x) Parameters: mode (str): 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 input_shape (list or tuple, optional): The shape of input. This parameter is required when mode is not "all". Default: None. param_attr(ParamAttr, optional): The parameter attribute for the learnable weight (alpha). Default: None. dtype (str, optional): Data type, it can be "float32" or "float64". Default: "float32". Attribute: **weight** (Parameter): the learnable weights of this layer. Returns: None Examples: .. code-block:: python import paddle.fluid as fluid from paddle.fluid.dygraph.base import to_variable import numpy as np inp_np = np.ones([5, 200, 100, 100]).astype('float32') with fluid.dygraph.guard(): inp_np = to_variable(inp_np) mode = 'channel' prelu = fluid.PRelu( mode=mode, input_shape=inp_np.shape, param_attr=fluid.ParamAttr(initializer=fluid.initializer.Constant(1.0))) dy_rlt = prelu(inp_np) """ def __init__(self, mode, input_shape=None, param_attr=None, dtype='float32'): super(PRelu, self).__init__() 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._dtype = dtype self._alpha_shape = [1] if mode is not 'all': assert input_shape is not None input_shape = list(input_shape) if self._mode == 'channel': self._alpha_shape = [1, input_shape[1], 1, 1] elif self._mode == 'element': self._alpha_shape = input_shape self._alpha = self.create_parameter( attr=self._param_attr, shape=self._alpha_shape, dtype='float32', is_bias=False, default_initializer=Constant(1.0)) @property def weight(self): return self._alpha @weight.setter def weight(self, value): self._alpha = value 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`. Parameters: input1_dim (int): The dimension of each first input. input2_dim (int): The dimension of each second input. output_dim (int): The dimension of output of this layer. name (str, optional): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name`. Default: None. act (str, optional): Activation to be applied to the output of this layer. The default value is None. param_attr (ParamAttr, optional): The parameter attribute for the learnable w, parameters/weights of this layer. The default value is None. bias_attr (ParamAttr, optional): 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. The default value is None. dtype (str, optional): Data type, it can be "float32" or "float64". Default: "float32". Attribute: **weight** (Parameter): the learnable weights of this layer. **bias** (Parameter): the learnable bias of this layer. 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( input1_dim=5, input2_dim=4, output_dim=1000) ret = bilinearTensorProduct(fluid.dygraph.base.to_variable(layer1), fluid.dygraph.base.to_variable(layer2)) """ def __init__(self, input1_dim, input2_dim, output_dim, name=None, act=None, param_attr=None, bias_attr=None, dtype='float32'): super(BilinearTensorProduct, self).__init__() self._param_attr = param_attr self._bias_attr = bias_attr self._act = act self._name = name self._input1_dim = input1_dim self._input2_dim = input2_dim self._output_dim = output_dim self._inputs = dict() self._dtype = dtype param_shape = [self._output_dim, self._input1_dim, self._input2_dim] self._w = self.create_parameter( attr=self._param_attr, shape=param_shape, dtype=self._dtype, is_bias=False) bias_size = [1, self._output_dim] self._bias_param = self.create_parameter( attr=self._bias_attr, shape=bias_size, dtype=self._dtype, is_bias=True) @property def weight(self): return self._w @weight.setter def weight(self, value): self._w = value @property def bias(self): return self._bias_param @bias.setter def bias(self, value): self._bias_param = value def forward(self, x, y): self._inputs = {"X": x, "Y": y, "Weight": self._w} if self._bias_param: self._inputs["Bias"] = self._bias_param 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, act=self._act) class Conv2DTranspose(layers.Layer): """ This interface is used to construct a callable object of the ``Conv2DTranspose`` class. For more details, refer to code examples. The 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 `_ . 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. 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 import numpy as np with fluid.dygraph.guard(): data = np.random.random((3, 32, 32, 5)).astype('float32') conv2DTranspose = fluid.dygraph.nn.Conv2DTranspose( num_channels=32, num_filters=2, filter_size=3) ret = conv2DTranspose(fluid.dygraph.base.to_variable(data)) """ def __init__(self, num_channels, num_filters, filter_size, output_size=None, padding=0, stride=1, dilation=1, groups=None, param_attr=None, bias_attr=None, use_cudnn=True, act=None, dtype='float32'): super(Conv2DTranspose, self).__init__() 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._act = act self._groups = groups self._num_channels = num_channels 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._dtype = dtype if (self._num_channels == self._groups and self._num_filters == self._num_channels and not self._use_cudnn): self._op_type = 'depthwise_conv2d_transpose' else: self._op_type = 'conv2d_transpose' 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') 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 = [self._num_channels, self._num_filters // self._groups ] + self._filter_size self._img_filter = self.create_parameter( dtype=self._dtype, shape=filter_shape, attr=self._param_attr) self._bias_param = self.create_parameter( attr=self._bias_attr, shape=[self._num_filters], dtype=self._dtype, is_bias=True) @property def weight(self): return self._img_filter @weight.setter def weight(self, value): self._img_filter = value @property def bias(self): return self._bias_param @bias.setter def bias(self, value): self._bias_param = value 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 }) 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 out = self._helper.append_activation(pre_act, act=self._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. Parameters: name_scope(str): The name of this class. num_filters (int): number of filters. filter_size (int): the filter size (H and W). Default: 3. filter_stride (int): stride of the filter. Default: 1. padding (bool|None): if True, add paddings. Default: None 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. Attributes: weight (Parameter): the learnable weights of filters of this layer. bias (Parameter|None): the learnable bias of this layer. 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 self._act = act 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) 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(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 }) 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 return self._helper.append_activation(pre_act, act=self._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 . Parameters: name_scope(str): The name of this 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. Default: None. act (str): Non-linear activation to be applied to output variable. Default: None. Attributes: weight (Parameter): the learnable weights of this layer. 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): """ This interface is used to construct a callable object of the ``GroupNorm`` class. For more details, refer to code examples. It implements the function of the Group Normalization Layer. Refer to `Group Normalization `_ . Parameters: channels(int): The number of channels of input. groups(int): The number of groups that divided from channels. epsilon(float, optional): The small value added to the variance to prevent division by zero. Default: 1e-05. param_attr(ParamAttr, optional): 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, optional): 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, optional): Activation to be applied to the output of group normalizaiton. Default: None. data_layout(str, optional): Specify the input data format. Only NCHW is supported. Default: NCHW. Returns: None Examples: .. code-block:: python import paddle.fluid as fluid import numpy as np with fluid.dygraph.guard(): x = np.random.random((8, 32, 32)).astype('float32') groupNorm = fluid.dygraph.nn.GroupNorm(channels=32, groups=4) ret = groupNorm(fluid.dygraph.base.to_variable(x)) """ def __init__(self, channels, groups, epsilon=1e-05, param_attr=None, bias_attr=None, act=None, data_layout='NCHW', dtype='float32'): super(GroupNorm, self).__init__() self._param_attr = param_attr self._bias_attr = bias_attr self._epsilon = epsilon self._channels = channels self._groups = groups self._act = act self._dtype = dtype if data_layout != 'NCHW': raise ValueError("unsupported data layout:" + data_layout) param_shape = [self._channels] 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_attr: inputs['Bias'] = self._bias if self._param_attr: 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): """ This interface is used to construct a callable object of the ``SpectralNorm`` class. For more details, refer to code examples. It implements the function of the 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 `_ . Parameters: weight_shape(list or tuple): The shape of weight parameter. dim(int, optional): 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, optional): The number of power iterations to calculate spectral norm. Default: 1. eps(float, optional): The epsilon for numerical stability in calculating norms. Default: 1e-12. name (str, optional): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name` . dtype (str, optional): Data type, it can be "float32" or "float64". Default: "float32". Returns: None Examples: .. code-block:: python import paddle.fluid as fluid import numpy as np with fluid.dygraph.guard(): weight = np.random.random((2, 8, 32, 32)).astype('float32') spectralNorm = fluid.dygraph.nn.SpectralNorm(weight.shape, dim=1, power_iters=2) ret = spectralNorm(fluid.dygraph.base.to_variable(weight)) """ def __init__(self, weight_shape, dim=0, power_iters=1, eps=1e-12, dtype='float32'): super(SpectralNorm, self).__init__() self._power_iters = power_iters self._eps = eps self._dim = dim self._dtype = dtype self._weight_shape = list(weight_shape) h = self._weight_shape[self._dim] w = np.prod(self._weight_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): """ This interface is used to construct a callable object of the ``TreeConv`` class. For more details, refer to code examples. 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: `tree-based convolution `_ . Parameters: feature_size(int): last dimension of nodes_vector. output_size(int): output feature width. num_filters(int, optional): number of filters, Default: 1. max_depth(int, optional): max depth of filters, Default: 2. act(str, optional): activation function, Default: tanh. param_attr(ParamAttr, optional): the parameter attribute for the filters, Default: None. bias_attr(ParamAttr, optional): the parameter attribute for the bias of this layer, Default: None. name(str, optional): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name` . 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 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( feature_size=5, 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, feature_size, output_size, num_filters=1, max_depth=2, act='tanh', param_attr=None, bias_attr=None, name=None, dtype='float32'): super(TreeConv, self).__init__() self._name = name self._feature_size = feature_size 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 self._dtype = dtype w_shape = [self._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) @property def weight(self): return self.W @weight.setter def weight(self, value): self.W = value @property def bias(self): return self._bias_param @bias.setter def bias(self, value): self._bias_param = value 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)