# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import paddle
from .. import core
from ..layers import utils
from ..layers import nn as F
from .. import dygraph_utils
from . import layers
from ..framework import (
Variable,
_non_static_mode,
OpProtoHolder,
Parameter,
_dygraph_tracer,
_varbase_creator,
default_main_program,
_global_flags,
in_dygraph_mode,
_in_legacy_dygraph,
)
from ..data_feeder import (
convert_dtype,
check_variable_and_dtype,
check_type,
check_dtype,
)
from ..param_attr import ParamAttr
from ..initializer import Normal, Constant, NumpyArrayInitializer
from .. import unique_name
from .layer_object_helper import LayerObjectHelper
from ..data_feeder import check_variable_and_dtype, check_type
import numpy as np
import numbers
import logging
import os
import paddle.utils.deprecated as deprecated
from paddle import _C_ops, _legacy_C_ops
__all__ = [
'Conv2D',
'Conv3D',
'Pool2D',
'Linear',
'BatchNorm',
'Dropout',
'Embedding',
'GRUUnit',
'InstanceNorm',
'LayerNorm',
'NCE',
'PRelu',
'BilinearTensorProduct',
'Conv2DTranspose',
'Conv3DTranspose',
'GroupNorm',
'SpectralNorm',
'TreeConv',
'Flatten',
]
class Conv2D(layers.Layer):
r"""
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 details.
If bias attribution and activation type are provided, bias is added to the
output of the convolution, and the corresponding activation function is
applied to the final result.
For each input :math:`X`, the equation is:
.. math::
Out = \\sigma (W \\ast X + b)
Where:
* :math:`X`: Input value, a ``Tensor`` with NCHW format.
* :math:`W`: Filter value, a ``Tensor`` with shape [MCHW] .
* :math:`\\ast`: Convolution operation.
* :math:`b`: Bias value, a 2-D ``Tensor`` with shape [M, 1].
* :math:`\\sigma`: Activation function.
* :math:`Out`: Output value, the shape of :math:`Out` and :math:`X` may be different.
Example:
- Input:
Input shape: :math:`(N, C_{in}, H_{in}, W_{in})`
Filter shape: :math:`(C_{out}, C_{in}, H_f, W_f)`
- Output:
Output shape: :math:`(N, C_{out}, H_{out}, W_{out})`
Where
.. math::
H_{out}&= \\frac{(H_{in} + 2 * paddings[0] - (dilations[0] * (H_f - 1) + 1))}{strides[0]} + 1 \\\\
W_{out}&= \\frac{(W_{in} + 2 * paddings[1] - (dilations[1] * (W_f - 1) + 1))}{strides[1]} + 1
Parameters:
num_channels(int): The number of channels in the input image.
num_filters(int): The number of filter. It is as same as the output
feature map.
filter_size (int or tuple): The filter size. If filter_size is a tuple,
it must contain two integers, (filter_size_H, filter_size_W).
Otherwise, the filter will be a square.
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__()
if (
core.is_compiled_with_cuda()
and paddle.fluid.get_flags("FLAGS_conv2d_disable_cudnn")[
"FLAGS_conv2d_disable_cudnn"
]
):
use_cudnn = False
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._use_mkldnn = _global_flags()["FLAGS_use_mkldnn"]
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
and not self._use_mkldnn
):
self._l_type = 'depthwise_conv2d'
else:
self._l_type = 'conv2d'
# NPU only supports depthwise_conv2d when "input_channel = output_channel = groups"
if core.is_compiled_with_npu():
if (
self._num_channels == self._groups
and self._num_channels == self._num_filters
):
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.weight = self.create_parameter(
attr=self._param_attr,
shape=filter_shape,
dtype=self._dtype,
default_initializer=_get_default_param_initializer(),
)
self.bias = self.create_parameter(
attr=self._bias_attr,
shape=[self._num_filters],
dtype=self._dtype,
is_bias=True,
)
def forward(self, input):
if in_dygraph_mode() and self._l_type == "conv2d":
pre_bias = _C_ops.conv2d(
input,
self.weight,
self._stride,
self._padding,
"EXPLICIT",
self._dilation,
self._groups if self._groups else 1,
"NCHW",
)
if self.bias is not None:
pre_act = F.elementwise_add(pre_bias, self.bias, axis=1)
else:
pre_act = pre_bias
return dygraph_utils._append_activation_in_dygraph(
pre_act, self._act, use_mkldnn=self._use_mkldnn
)
if _non_static_mode() and (
self._l_type == 'conv2d' or self._l_type == 'depthwise_conv2d'
):
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',
self._use_mkldnn,
)
out = _legacy_C_ops.conv2d(input, self.weight, *attrs)
pre_bias = out
pre_act = dygraph_utils._append_bias_in_dygraph(
pre_bias, self.bias, 1, use_mkldnn=self._use_mkldnn
)
return dygraph_utils._append_activation_in_dygraph(
pre_act, self._act, use_mkldnn=self._use_mkldnn
)
inputs = {
'Input': [input],
'Filter': [self.weight],
}
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': self._use_mkldnn,
}
check_variable_and_dtype(
input, 'input', ['float16', 'float32', 'float64'], 'Conv2D'
)
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.weight,
},
outputs={"Output": pre_bias},
attrs=attrs,
)
if self.bias 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]},
outputs={'Out': [pre_act]},
attrs={'axis': 1, 'use_mkldnn': self._use_mkldnn},
)
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):
r"""
**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.weight = self.create_parameter(
attr=self._param_attr,
shape=filter_shape,
dtype=self._dtype,
default_initializer=_get_default_param_initializer(),
)
self.bias = self.create_parameter(
attr=self._bias_attr,
shape=[self._num_filters],
dtype=self._dtype,
is_bias=True,
)
def forward(self, input):
pre_bias = self._helper.create_variable_for_type_inference(
dtype=self._dtype
)
self._helper.append_op(
type='conv3d',
inputs={
'Input': input,
'Filter': self.weight,
},
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 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]},
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):
r"""
**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.weight = self.create_parameter(
dtype=self._dtype, shape=filter_shape, attr=self._param_attr
)
self.bias = self.create_parameter(
attr=self._bias_attr,
shape=[self._num_filters],
dtype=self._dtype,
is_bias=True,
)
def forward(self, input):
pre_bias = self._helper.create_variable_for_type_inference(
dtype=self._dtype
)
self._helper.append_op(
type="conv3d_transpose",
inputs={'Input': [input], 'Filter': [self.weight]},
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]},
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):
r"""
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.
data_format (string): The data format of the input and output data. An optional string from: `"NCHW"`, `"NHWC"`.
The default is `"NCHW"`. When it is `"NCHW"`, the data is stored in the order of:
``[batch_size, input_channels, input_height, input_width]``. When it is `"NHWC"`, the data is
stored in the order of: ``[batch_size, input_height, input_width, input_channels]``
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.
ValueError: If ``data_format`` is not "NCHW" nor "NHWC".
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,
data_format="NCHW",
):
data_format = data_format.upper() # supprt NHWC, nhwc, etc.
pool_type = pool_type.lower() # supprt max, Max, etc.
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")
self._use_mkldnn = _global_flags()["FLAGS_use_mkldnn"]
if data_format not in ["NCHW", "NHWC"]:
raise ValueError(
"Attr(data_format) should be 'NCHW' or 'NHWC'. Received "
"Attr(data_format): %s." % str(data_format)
)
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._data_format = data_format
self._l_type = 'pool2d'
def forward(self, input):
if _non_static_mode():
if not self._use_mkldnn and in_dygraph_mode():
return _C_ops.pool2d(
input,
self._pool_size,
self._pool_stride,
self._pool_padding,
self._ceil_mode,
self._exclusive,
self._data_format,
self._pool_type,
self._global_pooling,
False,
"EXPLICIT",
self._use_cudnn,
)
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',
self._use_mkldnn,
'exclusive',
self._exclusive,
'data_format',
self._data_format,
)
return _legacy_C_ops.pool2d(input, *attrs)
check_variable_and_dtype(
input,
'input',
['int8', 'uint8', 'float16', 'float32', 'float64'],
'Pool2D',
)
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": self._use_mkldnn,
"exclusive": self._exclusive,
"data_format": self._data_format,
}
inputs = {"X": [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=attrs,
)
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.
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
)
self._use_mkldnn = _global_flags()["FLAGS_use_mkldnn"]
def forward(self, input):
if _non_static_mode():
pre_bias = _varbase_creator(dtype=input.dtype)
_legacy_C_ops.matmul(
input,
self.weight,
pre_bias,
'transpose_X',
False,
'transpose_Y',
False,
"alpha",
1,
"use_mkldnn",
self._use_mkldnn,
)
pre_act = dygraph_utils._append_bias_in_dygraph(
pre_bias,
self.bias,
axis=len(input.shape) - 1,
use_mkldnn=self._use_mkldnn,
)
return dygraph_utils._append_activation_in_dygraph(
pre_act, self._act, use_mkldnn=self._use_mkldnn
)
check_variable_and_dtype(
input, 'input', ['float16', 'float32', 'float64'], "Linear"
)
attrs = {
"transpose_X": False,
"transpose_Y": False,
"alpha": 1,
"use_mkldnn": self._use_mkldnn,
}
inputs = {"X": [input], "Y": [self.weight]}
tmp = self._helper.create_variable_for_type_inference(self._dtype)
self._helper.append_op(
type="matmul", inputs=inputs, outputs={"Out": tmp}, attrs=attrs
)
if self.bias is not None:
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,
'use_mkldnn': self._use_mkldnn,
},
)
else:
pre_activation = tmp
return self._helper.append_activation(pre_activation, act=self._act)
class InstanceNorm(layers.Layer):
r"""
This interface is used to construct a callable object of the ``InstanceNorm`` class.
For more details, refer to code examples.
Can be used as a normalizer function for convolution or fully_connected operations.
The required data format for this layer is one of the following:
DataLayout: NCHW `[batch, in_channels, in_height, in_width]`
Refer to `Instance Normalization: The Missing Ingredient for Fast Stylization `_
for more details.
:math:`input` is the input features over a mini-batch.
.. math::
\\mu_{\\beta} &\\gets \\frac{1}{HW} \\sum_{i=1}^{HW} x_i \\qquad &//\\
\\ mean\ of\ one\ feature\ map\ in\ mini-batch \\\\
\\sigma_{\\beta}^{2} &\\gets \\frac{1}{HW} \\sum_{i=1}^{HW}(x_i - \\
\\mu_{\\beta})^2 \\qquad &//\ variance\ of\ one\ feature\ map\ in\ mini-batch \\\\
\\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
Note:
`H` means height of feature map, `W` means width of feature map.
Parameters:
num_channels(int): Indicate the number of channels of the input ``Tensor``.
epsilon(float, optional): A value added to the denominator for
numerical stability. Default is 1e-5.
param_attr(ParamAttr|bool, optional): The parameter attribute for Parameter `scale`
of instance_norm. If it is set to None or one attribute of ParamAttr, instance_norm
will create ParamAttr as param_attr, the name of scale can be set in ParamAttr.
If the Initializer of the param_attr is not set, the parameter is initialized
one. If it is set to False, will not create param_attr. Default: None.
bias_attr(ParamAttr|bool, optional): The parameter attribute for the bias of instance_norm.
If it is set to None or one attribute of ParamAttr, instance_norm
will create ParamAttr as bias_attr, the name of bias can be set in ParamAttr.
If the Initializer of the bias_attr is not set, the bias is initialized zero.
If it is set to False, will not create bias_attr. Default: None.
dtype(str, optional): Indicate the data type of the input ``Tensor``,
which 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 as np
import paddle
# x's shape is [1, 3, 1, 2]
x = np.array([[[[1.0, 8.0]], [[10.0, 5.0]], [[4.0, 6.0]]]]).astype('float32')
with fluid.dygraph.guard():
x = to_variable(x)
instanceNorm = paddle.nn.InstanceNorm(3)
ret = instanceNorm(x)
# ret's shape is [1, 3, 1, 2]; value is [-1 1 0.999999 -0.999999 -0.999995 0.999995]
print(ret)
"""
def __init__(
self,
num_channels,
epsilon=1e-5,
param_attr=None,
bias_attr=None,
dtype='float32',
):
super(InstanceNorm, self).__init__()
if param_attr == False or bias_attr == False:
assert (
bias_attr == param_attr
), "param_attr and bias_attr must be set to False at the same time in InstanceNorm"
self._epsilon = epsilon
self._param_attr = param_attr
self._bias_attr = bias_attr
self._dtype = dtype
if param_attr != False and bias_attr != False:
self.scale = self.create_parameter(
attr=self._param_attr,
shape=[num_channels],
dtype=self._dtype,
default_initializer=Constant(1.0),
is_bias=False,
)
self.bias = self.create_parameter(
attr=self._bias_attr,
shape=[num_channels],
dtype=self._dtype,
default_initializer=Constant(0.0),
is_bias=True,
)
else:
self.scale = None
self.bias = None
def forward(self, input):
if in_dygraph_mode():
out = _C_ops.instance_norm(
input, self.scale, self.bias, self._epsilon
)
return out
if _in_legacy_dygraph():
out, _, _ = _legacy_C_ops.instance_norm(
input, self.scale, self.bias, 'epsilon', self._epsilon
)
return out
check_variable_and_dtype(
input, 'input', ['float32', 'float64'], "InstanceNorm"
)
attrs = {"epsilon": self._epsilon}
if self.scale and self.bias:
inputs = {"X": [input], "Scale": [self.scale], "Bias": [self.bias]}
else:
inputs = {"X": [input]}
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
)
instance_norm_out = self._helper.create_variable_for_type_inference(
self._dtype
)
outputs = {
"Y": [instance_norm_out],
"SavedMean": [saved_mean],
"SavedVariance": [saved_variance],
}
self._helper.append_op(
type="instance_norm", inputs=inputs, outputs=outputs, attrs=attrs
)
return instance_norm_out
class BatchNorm(layers.Layer):
r"""
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 normalization. Default: None.
is_test (bool, optional): A flag indicating whether it is in test phrase or not.
This flag only has effect on static graph mode. For dygraph mode, please use ``eval()``.
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
self._use_mkldnn = _global_flags()["FLAGS_use_mkldnn"]
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.weight = self.create_parameter(
attr=self._param_attr,
shape=param_shape,
dtype=self._dtype,
default_initializer=Constant(1.0),
)
self.weight.stop_gradient = (
use_global_stats and self._param_attr.learning_rate == 0.0
)
self.bias = self.create_parameter(
attr=self._bias_attr,
shape=param_shape,
dtype=self._dtype,
is_bias=True,
)
self.bias.stop_gradient = (
use_global_stats and self._param_attr.learning_rate == 0.0
)
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
if _non_static_mode():
if in_dygraph_mode():
batch_norm_out, t1, t2, t3, t4, _ = _C_ops.batch_norm(
input,
self._mean,
self._variance,
self.weight,
self.bias,
not self.training,
self._momentum,
self._epsilon,
self._data_layout,
self._use_global_stats,
self._trainable_statistics,
)
return dygraph_utils._append_activation_in_dygraph(
batch_norm_out, act=self._act, use_mkldnn=self._use_mkldnn
)
elif _in_legacy_dygraph():
attrs = (
"momentum",
self._momentum,
"epsilon",
self._epsilon,
"is_test",
not self.training,
"data_layout",
self._data_layout,
"use_mkldnn",
self._use_mkldnn,
"fuse_with_relu",
self._fuse_with_relu,
"use_global_stats",
self._use_global_stats,
'trainable_statistics',
self._trainable_statistics,
)
batch_norm_out, _, _, _, _, _ = _legacy_C_ops.batch_norm(
input,
self.weight,
self.bias,
self._mean,
self._variance,
None,
mean_out,
variance_out,
*attrs
)
return dygraph_utils._append_activation_in_dygraph(
batch_norm_out, act=self._act, use_mkldnn=self._use_mkldnn
)
check_variable_and_dtype(
input, 'input', ['float16', 'float32', 'float64'], 'BatchNorm'
)
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,
}
inputs = {
"X": [input],
"Scale": [self.weight],
"Bias": [self.bias],
"Mean": [self._mean],
"Variance": [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
)
reserve_space = self._helper.create_variable_for_type_inference(
dtype=self._helper.input_dtype(input), stop_gradient=True
)
batch_norm_out = (
input
if self._in_place
else self._helper.create_variable_for_type_inference(self._dtype)
)
outputs = {
"Y": [batch_norm_out],
"MeanOut": [mean_out],
"VarianceOut": [variance_out],
"SavedMean": [saved_mean],
"SavedVariance": [saved_variance],
}
if reserve_space is not None:
outputs["ReserveSpace"] = [reserve_space]
self._helper.append_op(
type="batch_norm", inputs=inputs, outputs=outputs, attrs=attrs
)
# Currently, we don't support inplace in dygraph mode
return self._helper.append_activation(batch_norm_out, self._act)
class Dropout(layers.Layer):
"""
This interface is used to construct a callable object of the ``Dropout`` class.
For more details, refer to code examples.
Drop or keep each element of input independently. Dropout is a regularization
technique for reducing overfitting by preventing neuron co-adaption during
training. The dropout operator randomly sets (according to the given dropout
probability) the outputs of some units to zero, while others are remain
unchanged.
Dropout layer can be removed for efficiency concern.
Parameters:
p (float, optional): Probability of setting units to zero. Default: 0.5
seed (int, optional): A Python integer used to create random seeds. If this
parameter is set to None, a random seed is used.
NOTE: If an integer seed is given, always the same output
units will be dropped. DO NOT use a fixed seed in training. Default: None.
dropout_implementation(string, optional): ['downgrade_in_infer'(default)|'upscale_in_train']
1. downgrade_in_infer(default), downgrade the outcome at inference
- train: out = input * mask
- inference: out = input * (1.0 - p)
(mask is a tensor same shape with input, value is 0 or 1
ratio of 0 is dropout_prob)
2. upscale_in_train, upscale the outcome at training time
- train: out = input * mask / ( 1.0 - p )
- inference: out = input
(mask is a tensor same shape with input, value is 0 or 1
ratio of 0 is p)
is_test (bool, optional): A flag indicating whether it is in test phrase or not.
This flag only has effect on static graph mode. For dygraph mode, please use ``eval()``.
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)
m = fluid.dygraph.Dropout(p=0.5)
droped_train = m(x)
# switch to eval mode
m.eval()
droped_eval = m(x)
"""
def __init__(
self,
p=0.5,
seed=None,
dropout_implementation="downgrade_in_infer",
is_test=False,
):
super(Dropout, self).__init__()
assert isinstance(p, (float, int)), "p argument should be a number"
assert 0 <= p <= 1, "p argument should between 0 and 1"
self._dropout_prob = p
assert seed is None or isinstance(
seed, int
), "seed argument should be None or a integer"
self._seed = seed
assert dropout_implementation in (
'downgrade_in_infer',
'upscale_in_train',
), "dropout_implementation argument should be 'downgrade_in_infer' or 'upscale_in_train'"
self._dropout_implementation = dropout_implementation
self._is_test = is_test
def forward(self, input):
# fast return for p == 0
if self._dropout_prob == 0:
return input
prog = default_main_program()
if (self._seed is None or self._seed == 0) and prog.random_seed != 0:
self._seed = prog.random_seed
attrs = {
'dropout_prob': self._dropout_prob,
'is_test': not self.training
if _non_static_mode()
else self._is_test,
'fix_seed': self._seed is not None,
'seed': self._seed if self._seed is not None else 0,
'dropout_implementation': self._dropout_implementation,
}
if _non_static_mode():
attrs = sum(attrs.items(), ())
out, mask = _legacy_C_ops.dropout(input, *attrs)
return out
out = self._helper.create_variable_for_type_inference(dtype=input.dtype)
mask = self._helper.create_variable_for_type_inference(
dtype=core.VarDesc.VarType.UINT8, stop_gradient=True
)
self._helper.append_op(
type='dropout',
inputs={'X': [input]},
outputs={'Out': [out], 'Mask': [mask]},
attrs=attrs,
)
return out
class Embedding(layers.Layer):
r"""
:alias_main: paddle.nn.Embedding
:alias: paddle.nn.Embedding,paddle.nn.layer.Embedding,paddle.nn.layer.common.Embedding
:old_api: paddle.fluid.dygraph.Embedding
**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 should 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.weight = self.create_parameter(
attr=self._param_attr,
shape=self._size,
dtype=self._dtype,
is_bias=False,
)
def forward(self, input):
if _non_static_mode():
return _legacy_C_ops.lookup_table_v2(
self.weight,
input,
'is_sparse',
self._is_sparse,
'is_distributed',
self._is_distributed,
'remote_prefetch',
self._remote_prefetch,
'padding_idx',
self._padding_idx,
)
check_variable_and_dtype(
input,
'input',
['uint8', 'int8', 'int16', 'int32', 'int64'],
'Embedding',
)
attrs = {
'is_sparse': self._is_sparse,
'is_distributed': self._is_distributed,
'remote_prefetch': self._remote_prefetch,
'padding_idx': self._padding_idx,
}
out = self._helper.create_variable_for_type_inference(self._dtype)
self._helper.append_op(
type='lookup_table_v2',
inputs={'Ids': input, 'W': self.weight},
outputs={'Out': out},
attrs=attrs,
)
return out
class LayerNorm(layers.Layer):
r"""
:alias_main: paddle.nn.LayerNorm
:alias: paddle.nn.LayerNorm,paddle.nn.layer.LayerNorm,paddle.nn.layer.norm.LayerNorm
:old_api: paddle.fluid.dygraph.LayerNorm
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 normalization.
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.weight = 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 available with scale is True")
self.weight = None
if self._shift:
assert self._bias_attr is not False
self.bias = 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 available with shift is True")
self.bias = None
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)
)
if _non_static_mode():
if in_dygraph_mode():
pre_act, _, _, = _C_ops.layer_norm(
input,
self.weight,
self.bias,
self._epsilon,
self._begin_norm_axis,
False,
)
return dygraph_utils._append_activation_in_dygraph(
pre_act, act=self._act
)
else:
pre_act, _, _ = _legacy_C_ops.layer_norm(
input,
self.weight,
self.bias,
'epsilon',
self._epsilon,
'begin_norm_axis',
self._begin_norm_axis,
)
return dygraph_utils._append_activation_in_dygraph(
pre_act, act=self._act
)
check_variable_and_dtype(
input, 'input', ['float32', 'float64'], 'LayerNorm'
)
inputs = dict()
inputs['X'] = [input]
if self._scale:
inputs['Scale'] = [self.weight]
if self._shift:
inputs['Bias'] = [self.bias]
attrs = {
"epsilon": self._epsilon,
"begin_norm_axis": self._begin_norm_axis,
}
# 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
)
def forward(self, input, hidden):
if _non_static_mode():
gate, reset_hidden_pre, updated_hidden = _legacy_C_ops.gru_unit(
input,
hidden,
self.weight,
self.bias,
'activation',
self.activation,
'gate_activation',
self.gate_activation,
)
return updated_hidden, reset_hidden_pre, gate
check_variable_and_dtype(
input, 'input', ['float32', 'float64'], 'GRUUnit'
)
check_variable_and_dtype(
hidden, 'hidden', ['float32', 'float64'], 'GRUUnit'
)
inputs = {
'Input': [input],
'HiddenPrev': [hidden],
'Weight': [self.weight],
}
if self.bias is not None:
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 negative 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.weight = self.create_parameter(
attr=self._param_attr,
shape=[self._num_total_classes, dim],
is_bias=False,
dtype=self._dtype,
)
if self._bias_attr:
self.bias = self.create_parameter(
attr=self._bias_attr,
shape=[self._num_total_classes, 1],
is_bias=True,
dtype=self._dtype,
)
self._inputs['Bias'] = self.bias
self._inputs['Weight'] = self.weight
def forward(self, input, label, sample_weight=None):
if _non_static_mode():
attrs = (
'num_total_classes',
self._attrs['num_total_classes'],
'num_neg_samples',
self._attrs['num_neg_samples'],
'seed',
self._attrs['seed'],
'sampler',
self._attrs['sampler'],
'is_sparse',
self._attrs['is_sparse'],
'remote_prefetch',
self._attrs['remote_prefetch'],
)
cost, _, _ = _legacy_C_ops.nce(
input,
label,
self.weight,
self.bias,
self._inputs['SampleWeight'],
self._inputs['CustomDistProbs'],
self._inputs['CustomDistAlias'],
self._inputs['CustomDistAliasProbs'],
*attrs
)
return cost / (self._num_neg_samples + 1)
check_variable_and_dtype(input, "input", ['float32', 'float64'], "NCE")
check_variable_and_dtype(label, "label", ['int64'], "NCE")
check_type(
sample_weight, 'sample_weight', (Variable, type(None)), 'NCE'
)
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):
r"""
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
channel (int, optional): The number of channels.
This argument is required when mode is "channel".
Default: None.
input_shape (list or tuple, optional): The shape of input.
This argument is required when mode is "element".
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)
prelu0 = fluid.PRelu(
mode='all',
param_attr=fluid.ParamAttr(initializer=fluid.initializer.Constant(1.0)))
dy_rlt0 = prelu0(inp_np)
prelu1 = fluid.PRelu(
mode='channel',
channel=200,
param_attr=fluid.ParamAttr(initializer=fluid.initializer.Constant(1.0)))
dy_rlt1 = prelu1(inp_np)
prelu2 = fluid.PRelu(
mode='element',
input_shape=inp_np.shape,
param_attr=fluid.ParamAttr(initializer=fluid.initializer.Constant(1.0)))
dy_rlt2 = prelu2(inp_np)
"""
def __init__(
self,
mode,
channel=None,
input_shape=None,
param_attr=None,
dtype='float32',
):
# need specify name_scope since snake-cased 'PRelu' is 'p_relu'
super(PRelu, self).__init__(name_scope='prelu')
self._mode = mode
self._param_attr = param_attr
self._dtype = dtype
if mode == 'all':
self._alpha_shape = [1]
elif mode == 'channel':
assert isinstance(
channel, int
), "channel argument is required when mode is 'channel'."
# NOTE(zhiqiu): The _alpha_shape should be [1, channel] + [1] * len(input_shape[2:]), not [1, channel, 1, 1].
# However, the suffix 1 in the list is useless, since the tensor is viewed as one demension array during kernel calculation.
# And, input_shape is not required when mode is 'channel', so it is simplified.
# NOTE(zhiqiu): Revert shape to [1, channel, 1, 1] for compatibility with saved model of old version.
self._alpha_shape = [1, channel, 1, 1]
elif mode == 'element':
assert isinstance(
input_shape, (list, tuple)
), "input_shape argument is required when mode is 'element'."
self._alpha_shape = [1] + list(input_shape)[1:]
else:
raise ValueError('mode should be one of all, channel, element.')
self.weight = self.create_parameter(
attr=self._param_attr,
shape=self._alpha_shape,
dtype='float32',
is_bias=False,
default_initializer=Constant(1.0),
)
def forward(self, input):
if in_dygraph_mode():
return _C_ops.prelu(input, self.weight, "NCHW", self._mode)
check_variable_and_dtype(input, 'input', ['float32'], 'PRelu')
out = self._helper.create_variable_for_type_inference(self._dtype)
self._helper.append_op(
type="prelu",
inputs={"X": input, 'Alpha': self.weight},
attrs={"mode": self._mode},
outputs={"Out": out},
)
return out
class BilinearTensorProduct(layers.Layer):
r"""
**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:
Tensor: A 2-D Tensor of shape [batch_size, size].
Examples:
.. code-block:: python
import paddle
import numpy
layer1 = numpy.random.random((5, 5)).astype('float32')
layer2 = numpy.random.random((5, 4)).astype('float32')
bilinearTensorProduct = paddle.nn.BilinearTensorProduct(
input1_dim=5, input2_dim=4, output_dim=1000)
ret = bilinearTensorProduct(paddle.to_tensor(layer1),
paddle.to_tensor(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.weight = self.create_parameter(
attr=self._param_attr,
shape=param_shape,
dtype=self._dtype,
is_bias=False,
)
bias_size = [1, self._output_dim]
self.bias = self.create_parameter(
attr=self._bias_attr,
shape=bias_size,
dtype=self._dtype,
is_bias=True,
)
@deprecated(
since="2.0.0",
update_to="paddle.nn.Bilinear",
reason="New name and new args in Bilinear, easier to use.",
)
def forward(self, x, y):
check_variable_and_dtype(
x, 'x', ['float32', 'float64'], 'BilinearTensorProduct'
)
check_variable_and_dtype(
y, 'y', ['float32', 'float64'], 'BilinearTensorProduct'
)
self._inputs = {"X": x, "Y": y, "Weight": self.weight}
if self.bias is not None:
self._inputs["Bias"] = self.bias
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):
r"""
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):
if utils._contain_var(self._output_size):
self._output_size = utils._convert_to_tensor_list(
self._output_size
)
else:
self._output_size = utils.convert_to_list(
self._output_size, 2, 'output_size'
)
elif isinstance(self._output_size, int):
self._output_size = utils.convert_to_list(
self._output_size, 2, 'output_size'
)
elif isinstance(self._output_size, Variable):
check_dtype(
self._output_size.dtype,
'output_size',
['int32', 'int64'],
'Conv2DTranspose',
)
if len(self._output_size.shape) == 1 and (
self._output_size.shape[0] == 1
or self._output_size.shape[0] == 2
):
if self._output_size.shape[0] == 1:
self._output_size = [self._output_size, self._output_size]
else:
raise ValueError(
"output_size must contain one or two integers."
)
else:
raise ValueError("output_size should be list or int or Tensor")
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.weight = self.create_parameter(
dtype=self._dtype, shape=filter_shape, attr=self._param_attr
)
self.bias = self.create_parameter(
attr=self._bias_attr,
shape=[self._num_filters],
dtype=self._dtype,
is_bias=True,
)
def forward(self, input):
if _non_static_mode():
op = getattr(_legacy_C_ops, self._op_type)
out = op(
input,
self.weight,
'output_size',
self._output_size,
'strides',
self._stride,
'paddings',
self._padding,
'dilations',
self._dilation,
'groups',
self._groups,
'use_cudnn',
self._use_cudnn,
)
pre_bias = out
pre_act = dygraph_utils._append_bias_in_dygraph(
pre_bias, self.bias, 1
)
return dygraph_utils._append_activation_in_dygraph(
pre_act, act=self._act
)
check_variable_and_dtype(
input, 'input', ['float16', 'float32', 'float64'], "Conv2DTranspose"
)
inputs = {'Input': [input], 'Filter': [self.weight]}
attrs = {
'output_size': self._output_size,
'strides': self._stride,
'paddings': self._padding,
'dilations': self._dilation,
'groups': self._groups,
'use_cudnn': self._use_cudnn,
}
pre_bias = self._helper.create_variable_for_type_inference(
dtype=input.dtype
)
self._helper.append_op(
type=self._op_type,
inputs=inputs,
outputs={'Output': pre_bias},
attrs=attrs,
)
if self.bias 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]},
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 _non_static_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.weight = self.create_parameter(
attr=self._param_attr, shape=filter_shape, dtype=self._dtype
)
self.bias = 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.weight],
},
outputs={"Out": pre_bias},
attrs={
'contextStride': self._filter_stride,
'contextStart': -int(self._filter_size // 2),
'contextLength': self._filter_size,
},
)
if self.bias 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]},
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 _non_static_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.weight = 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.weight]},
outputs={'Out': [out]},
)
return self._helper.append_activation(out, act=self._act)
class GroupNorm(layers.Layer):
"""
:alias_main: paddle.nn.GroupNorm
:alias: paddle.nn.GroupNorm,paddle.nn.layer.GroupNorm,paddle.nn.layer.norm.GroupNorm
:old_api: paddle.fluid.dygraph.GroupNorm
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 normalization. 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]
self.weight = self.create_parameter(
attr=self._param_attr or False,
shape=param_shape,
dtype=self._dtype,
default_initializer=Constant(1.0),
)
self.bias = self.create_parameter(
attr=self._bias_attr or False,
shape=param_shape,
dtype=self._dtype,
is_bias=True,
)
def forward(self, input):
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
)
if in_dygraph_mode():
out = _C_ops.group_norm(
input,
self.weight,
self.bias,
self._epsilon,
self._groups,
"NCHW",
)
return dygraph_utils._append_activation_in_dygraph(out, self._act)
elif _in_legacy_dygraph():
attrs = ('epsilon', self._epsilon, 'groups', self._groups)
out, _, _ = _legacy_C_ops.group_norm(
input, self.weight, self.bias, mean_out, variance_out, *attrs
)
return dygraph_utils._append_activation_in_dygraph(out, self._act)
else:
inputs = {'X': input}
if self.bias is not None:
inputs['Bias'] = self.bias
if self.weight is not None:
inputs['Scale'] = self.weight
# create output
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):
r"""
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` should be a positive integer, 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
x = paddle.rand((2,8,32,32))
spectral_norm = paddle.nn.SpectralNorm(x.shape, dim=1, power_iters=2)
spectral_norm_out = spectral_norm(x)
print(spectral_norm_out.shape) # [2, 8, 32, 32]
"""
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)
assert (
np.prod(self._weight_shape) > 0
), "Any dimension of `weight_shape` cannot be equal to 0."
assert dim < len(self._weight_shape), (
"The input `dim` should be less than the "
"length of `weight_shape`, but received dim="
"{}".format(dim)
)
h = self._weight_shape[self._dim]
w = np.prod(self._weight_shape) // h
self.weight_u = self.create_parameter(
attr=ParamAttr(),
shape=[h],
dtype=self._dtype,
default_initializer=Normal(0.0, 1.0),
)
self.weight_u.stop_gradient = True
self.weight_v = self.create_parameter(
attr=ParamAttr(),
shape=[w],
dtype=self._dtype,
default_initializer=Normal(0.0, 1.0),
)
self.weight_v.stop_gradient = True
def forward(self, weight):
if in_dygraph_mode():
return _C_ops.spectral_norm(
weight,
self.weight_u,
self.weight_v,
self._dim,
self._power_iters,
self._eps,
)
check_variable_and_dtype(
weight, "weight", ['float32', 'float64'], 'SpectralNorm'
)
inputs = {'Weight': weight, 'U': self.weight_u, 'V': self.weight_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 = self.create_parameter(
attr=self._bias_attr,
shape=[self._num_filters],
dtype=self._dtype,
is_bias=True,
)
self.weight = self.create_parameter(
attr=self._param_attr,
shape=w_shape,
dtype=self._dtype,
is_bias=False,
)
def forward(self, nodes_vector, edge_set):
check_type(nodes_vector, 'nodes_vector', (Variable), 'TreeConv')
check_type(edge_set, 'edge_set', (Variable), 'TreeConv')
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.weight,
},
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]},
outputs={'Out': [pre_activation]},
attrs={'axis': 1},
)
else:
pre_activation = out
return self._helper.append_activation(pre_activation, act=self._act)
class Flatten(layers.Layer):
"""
This interface is used to construct a callable object of the ``FLatten`` class.
For more details, refer to code examples.
It implements flatten a contiguous range of dims into a tensor.
Parameters:
start_axis(int): first dim to flatten (default = 1)
stop_axis(int): last dim to flatten (default = -1).
Returns:
None
Examples:
.. code-block:: python
import paddle
import numpy as np
inp_np = np.ones([5, 2, 3, 4]).astype('float32')
inp_np = paddle.to_tensor(inp_np)
flatten = paddle.nn.Flatten(start_axis=1, stop_axis=2)
flatten_res = flatten(inp_np)
"""
def __init__(self, start_axis=1, stop_axis=-1):
super(Flatten, self).__init__()
self.start_axis = start_axis
self.stop_axis = stop_axis
def forward(self, input):
out = paddle.tensor.manipulation.flatten(
input, start_axis=self.start_axis, stop_axis=self.stop_axis
)
return out