NAME
AI::MXNet::Gluon::NN::Conv
DESCRIPTION
Abstract nD convolution layer (private, used as implementation base).
This layer creates a convolution kernel that is convolved
with the layer input to produce a tensor of outputs.
If `use_bias` is `True`, a bias vector is created and added to the outputs.
Finally, if `activation` is not `None`,
it is applied to the outputs as well.
Parameters
----------
channels : int
The dimensionality of the output space
i.e. the number of output channels in the convolution.
kernel_size : int or tuple/list of n ints
Specifies the dimensions of the convolution window.
strides: int or tuple/list of n ints,
Specifies the strides of the convolution.
padding : int or tuple/list of n ints,
If padding is non-zero, then the input is implicitly zero-padded
on both sides for padding number of points
dilation: int or tuple/list of n ints,
Specifies the dilation rate to use for dilated convolution.
groups : int
Controls the connections between inputs and outputs.
At groups=1, all inputs are convolved to all outputs.
At groups=2, the operation becomes equivalent to having two convolution
layers side by side, each seeing half the input channels, and producing
half the output channels, and both subsequently concatenated.
layout : str,
Dimension ordering of data and weight. Can be 'NCW', 'NWC', 'NCHW',
'NHWC', 'NCDHW', 'NDHWC', etc. 'N', 'C', 'H', 'W', 'D' stands for
batch, channel, height, width and depth dimensions respectively.
Convolution is performed over 'D', 'H', and 'W' dimensions.
in_channels : int, default 0
The number of input channels to this layer. If not specified,
initialization will be deferred to the first time `forward` is called
and `in_channels` will be inferred from the shape of input data.
activation : str
Activation function to use. See :func:`~mxnet.ndarray.Activation`.
If you don't specify anything, no activation is applied
(ie. "linear" activation: `a(x) = x`).
use_bias: bool
Whether the layer uses a bias vector.
weight_initializer : str or `Initializer`
Initializer for the `weight` weights matrix.
bias_initializer: str or `Initializer`
Initializer for the bias vector.
NAME
AI::MXNet::Gluon::NN::Conv1D
DESCRIPTION
1D convolution layer (e.g. temporal convolution).
This layer creates a convolution kernel that is convolved
with the layer input over a single spatial (or temporal) dimension
to produce a tensor of outputs.
If `use_bias` is True, a bias vector is created and added to the outputs.
Finally, if `activation` is not `None`,
it is applied to the outputs as well.
If `in_channels` is not specified, `Parameter` initialization will be
deferred to the first time `forward` is called and `in_channels` will be
inferred from the shape of input data.
Parameters
----------
channels : int
The dimensionality of the output space, i.e. the number of output
channels (filters) in the convolution.
kernel_size :int or tuple/list of 1 int
Specifies the dimensions of the convolution window.
strides : int or tuple/list of 1 int,
Specify the strides of the convolution.
padding : int or a tuple/list of 1 int,
If padding is non-zero, then the input is implicitly zero-padded
on both sides for padding number of points
dilation : int or tuple/list of 1 int
Specifies the dilation rate to use for dilated convolution.
groups : int
Controls the connections between inputs and outputs.
At groups=1, all inputs are convolved to all outputs.
At groups=2, the operation becomes equivalent to having two conv
layers side by side, each seeing half the input channels, and producing
half the output channels, and both subsequently concatenated.
layout: str, default 'NCW'
Dimension ordering of data and weight. Can be 'NCW', 'NWC', etc.
'N', 'C', 'W' stands for batch, channel, and width (time) dimensions
respectively. Convolution is applied on the 'W' dimension.
in_channels : int, default 0
The number of input channels to this layer. If not specified,
initialization will be deferred to the first time `forward` is called
and `in_channels` will be inferred from the shape of input data.
activation : str
Activation function to use. See :func:`~mxnet.ndarray.Activation`.
If you don't specify anything, no activation is applied
(ie. "linear" activation: `a(x) = x`).
use_bias : bool
Whether the layer uses a bias vector.
weight_initializer : str or `Initializer`
Initializer for the `weight` weights matrix.
bias_initializer : str or `Initializer`
Initializer for the bias vector.
Input shape:
This depends on the `layout` parameter. Input is 3D array of shape
(batch_size, in_channels, width) if `layout` is `NCW`.
Output shape:
This depends on the `layout` parameter. Output is 3D array of shape
(batch_size, channels, out_width) if `layout` is `NCW`.
out_width is calculated as::
out_width = floor((width+2*padding-dilation*(kernel_size-1)-1)/stride)+1
NAME
AI::MXNet::Gluon::NN::Conv2D
DESCRIPTION
2D convolution layer (e.g. spatial convolution over images).
This layer creates a convolution kernel that is convolved
with the layer input to produce a tensor of
outputs. If `use_bias` is True,
a bias vector is created and added to the outputs. Finally, if
`activation` is not `None`, it is applied to the outputs as well.
If `in_channels` is not specified, `Parameter` initialization will be
deferred to the first time `forward` is called and `in_channels` will be
inferred from the shape of input data.
Parameters
----------
channels : int
The dimensionality of the output space, i.e. the number of output
channels (filters) in the convolution.
kernel_size :int or tuple/list of 2 int
Specifies the dimensions of the convolution window.
strides : int or tuple/list of 2 int,
Specify the strides of the convolution.
padding : int or a tuple/list of 2 int,
If padding is non-zero, then the input is implicitly zero-padded
on both sides for padding number of points
dilation : int or tuple/list of 2 int
Specifies the dilation rate to use for dilated convolution.
groups : int
Controls the connections between inputs and outputs.
At groups=1, all inputs are convolved to all outputs.
At groups=2, the operation becomes equivalent to having two conv
layers side by side, each seeing half the input channels, and producing
half the output channels, and both subsequently concatenated.
layout : str, default 'NCHW'
Dimension ordering of data and weight. Can be 'NCHW', 'NHWC', etc.
'N', 'C', 'H', 'W' stands for batch, channel, height, and width
dimensions respectively. Convolution is applied on the 'H' and
'W' dimensions.
in_channels : int, default 0
The number of input channels to this layer. If not specified,
initialization will be deferred to the first time `forward` is called
and `in_channels` will be inferred from the shape of input data.
activation : str
Activation function to use. See :func:`~mxnet.ndarray.Activation`.
If you don't specify anything, no activation is applied
(ie. "linear" activation: `a(x) = x`).
use_bias : bool
Whether the layer uses a bias vector.
weight_initializer : str or `Initializer`
Initializer for the `weight` weights matrix.
bias_initializer : str or `Initializer`
Initializer for the bias vector.
Input shape:
This depends on the `layout` parameter. Input is 4D array of shape
(batch_size, in_channels, height, width) if `layout` is `NCHW`.
Output shape:
This depends on the `layout` parameter. Output is 4D array of shape
(batch_size, channels, out_height, out_width) if `layout` is `NCHW`.
out_height and out_width are calculated as::
out_height = floor((height+2*padding[0]-dilation[0]*(kernel_size[0]-1)-1)/stride[0])+1
out_width = floor((width+2*padding[1]-dilation[1]*(kernel_size[1]-1)-1)/stride[1])+1
NAME
AI::MXNet::Gluon::NN::Conv3D
DESCRIPTION
3D convolution layer (e.g. spatial convolution over volumes).
This layer creates a convolution kernel that is convolved
with the layer input to produce a tensor of
outputs. If `use_bias` is `True`,
a bias vector is created and added to the outputs. Finally, if
`activation` is not `None`, it is applied to the outputs as well.
If `in_channels` is not specified, `Parameter` initialization will be
deferred to the first time `forward` is called and `in_channels` will be
inferred from the shape of input data.
Parameters
----------
channels : int
The dimensionality of the output space, i.e. the number of output
channels (filters) in the convolution.
kernel_size :int or tuple/list of 3 int
Specifies the dimensions of the convolution window.
strides : int or tuple/list of 3 int,
Specify the strides of the convolution.
padding : int or a tuple/list of 3 int,
If padding is non-zero, then the input is implicitly zero-padded
on both sides for padding number of points
dilation : int or tuple/list of 3 int
Specifies the dilation rate to use for dilated convolution.
groups : int
Controls the connections between inputs and outputs.
At groups=1, all inputs are convolved to all outputs.
At groups=2, the operation becomes equivalent to having two conv
layers side by side, each seeing half the input channels, and producing
half the output channels, and both subsequently concatenated.
layout : str, default 'NCDHW'
Dimension ordering of data and weight. Can be 'NCDHW', 'NDHWC', etc.
'N', 'C', 'H', 'W', 'D' stands for batch, channel, height, width and
depth dimensions respectively. Convolution is applied on the 'D',
'H' and 'W' dimensions.
in_channels : int, default 0
The number of input channels to this layer. If not specified,
initialization will be deferred to the first time `forward` is called
and `in_channels` will be inferred from the shape of input data.
activation : str
Activation function to use. See :func:`~mxnet.ndarray.Activation`.
If you don't specify anything, no activation is applied
(ie. "linear" activation: `a(x) = x`).
use_bias : bool
Whether the layer uses a bias vector.
weight_initializer : str or `Initializer`
Initializer for the `weight` weights matrix.
bias_initializer : str or `Initializer`
Initializer for the bias vector.
Input shape:
This depends on the `layout` parameter. Input is 5D array of shape
(batch_size, in_channels, depth, height, width) if `layout` is `NCDHW`.
Output shape:
This depends on the `layout` parameter. Output is 5D array of shape
(batch_size, channels, out_depth, out_height, out_width) if `layout` is
`NCDHW`.
out_depth, out_height and out_width are calculated as::
out_depth = floor((depth+2*padding[0]-dilation[0]*(kernel_size[0]-1)-1)/stride[0])+1
out_height = floor((height+2*padding[1]-dilation[1]*(kernel_size[1]-1)-1)/stride[1])+1
out_width = floor((width+2*padding[2]-dilation[2]*(kernel_size[2]-1)-1)/stride[2])+1
NAME
AI::MXNet::Gluon::NN::Conv1DTranspose
DESCRIPTION
Transposed 1D convolution layer (sometimes called Deconvolution).
The need for transposed convolutions generally arises
from the desire to use a transformation going in the opposite direction
of a normal convolution, i.e., from something that has the shape of the
output of some convolution to something that has the shape of its input
while maintaining a connectivity pattern that is compatible with
said convolution.
If `in_channels` is not specified, `Parameter` initialization will be
deferred to the first time `forward` is called and `in_channels` will be
inferred from the shape of input data.
Parameters
----------
channels : int
The dimensionality of the output space, i.e. the number of output
channels (filters) in the convolution.
kernel_size :int or tuple/list of 3 int
Specifies the dimensions of the convolution window.
strides : int or tuple/list of 3 int,
Specify the strides of the convolution.
padding : int or a tuple/list of 3 int,
If padding is non-zero, then the input is implicitly zero-padded
on both sides for padding number of points
dilation : int or tuple/list of 3 int
Specifies the dilation rate to use for dilated convolution.
groups : int
Controls the connections between inputs and outputs.
At groups=1, all inputs are convolved to all outputs.
At groups=2, the operation becomes equivalent to having two conv
layers side by side, each seeing half the input channels, and producing
half the output channels, and both subsequently concatenated.
layout : str, default 'NCW'
Dimension ordering of data and weight. Can be 'NCW', 'NWC', etc.
'N', 'C', 'W' stands for batch, channel, and width (time) dimensions
respectively. Convolution is applied on the 'W' dimension.
in_channels : int, default 0
The number of input channels to this layer. If not specified,
initialization will be deferred to the first time `forward` is called
and `in_channels` will be inferred from the shape of input data.
activation : str
Activation function to use. See :func:`~mxnet.ndarray.Activation`.
If you don't specify anything, no activation is applied
(ie. "linear" activation: `a(x) = x`).
use_bias : bool
Whether the layer uses a bias vector.
weight_initializer : str or `Initializer`
Initializer for the `weight` weights matrix.
bias_initializer : str or `Initializer`
Initializer for the bias vector.
Input shape:
This depends on the `layout` parameter. Input is 3D array of shape
(batch_size, in_channels, width) if `layout` is `NCW`.
Output shape:
This depends on the `layout` parameter. Output is 3D array of shape
(batch_size, channels, out_width) if `layout` is `NCW`.
out_width is calculated as::
out_width = (width-1)*strides-2*padding+kernel_size+output_padding
NAME
AI::MXNet::Gluon::NN::Conv2DTranspose
DESCRIPTION
Transposed 2D convolution layer (sometimes called Deconvolution).
The need for transposed convolutions generally arises
from the desire to use a transformation going in the opposite direction
of a normal convolution, i.e., from something that has the shape of the
output of some convolution to something that has the shape of its input
while maintaining a connectivity pattern that is compatible with
said convolution.
If `in_channels` is not specified, `Parameter` initialization will be
deferred to the first time `forward` is called and `in_channels` will be
inferred from the shape of input data.
Parameters
----------
channels : int
The dimensionality of the output space, i.e. the number of output
channels (filters) in the convolution.
kernel_size :int or tuple/list of 3 int
Specifies the dimensions of the convolution window.
strides : int or tuple/list of 3 int,
Specify the strides of the convolution.
padding : int or a tuple/list of 3 int,
If padding is non-zero, then the input is implicitly zero-padded
on both sides for padding number of points
dilation : int or tuple/list of 3 int
Specifies the dilation rate to use for dilated convolution.
groups : int
Controls the connections between inputs and outputs.
At groups=1, all inputs are convolved to all outputs.
At groups=2, the operation becomes equivalent to having two conv
layers side by side, each seeing half the input channels, and producing
half the output channels, and both subsequently concatenated.
layout : str, default 'NCHW'
Dimension ordering of data and weight. Can be 'NCHW', 'NHWC', etc.
'N', 'C', 'H', 'W' stands for batch, channel, height, and width
dimensions respectively. Convolution is applied on the 'H' and
'W' dimensions.
in_channels : int, default 0
The number of input channels to this layer. If not specified,
initialization will be deferred to the first time `forward` is called
and `in_channels` will be inferred from the shape of input data.
activation : str
Activation function to use. See :func:`~mxnet.ndarray.Activation`.
If you don't specify anything, no activation is applied
(ie. "linear" activation: `a(x) = x`).
use_bias : bool
Whether the layer uses a bias vector.
weight_initializer : str or `Initializer`
Initializer for the `weight` weights matrix.
bias_initializer : str or `Initializer`
Initializer for the bias vector.
Input shape:
This depends on the `layout` parameter. Input is 4D array of shape
(batch_size, in_channels, height, width) if `layout` is `NCHW`.
Output shape:
This depends on the `layout` parameter. Output is 4D array of shape
(batch_size, channels, out_height, out_width) if `layout` is `NCHW`.
out_height and out_width are calculated as::
out_height = (height-1)*strides[0]-2*padding[0]+kernel_size[0]+output_padding[0]
out_width = (width-1)*strides[1]-2*padding[1]+kernel_size[1]+output_padding[1]
NAME
AI::MXNet::Gluon::NN::Conv3DTranspose
DESCRIPTION
Transposed 3D convolution layer (sometimes called Deconvolution).
The need for transposed convolutions generally arises
from the desire to use a transformation going in the opposite direction
of a normal convolution, i.e., from something that has the shape of the
output of some convolution to something that has the shape of its input
while maintaining a connectivity pattern that is compatible with
said convolution.
If `in_channels` is not specified, `Parameter` initialization will be
deferred to the first time `forward` is called and `in_channels` will be
inferred from the shape of input data.
Parameters
----------
channels : int
The dimensionality of the output space, i.e. the number of output
channels (filters) in the convolution.
kernel_size :int or tuple/list of 3 int
Specifies the dimensions of the convolution window.
strides : int or tuple/list of 3 int,
Specify the strides of the convolution.
padding : int or a tuple/list of 3 int,
If padding is non-zero, then the input is implicitly zero-padded
on both sides for padding number of points
dilation : int or tuple/list of 3 int
Specifies the dilation rate to use for dilated convolution.
groups : int
Controls the connections between inputs and outputs.
At groups=1, all inputs are convolved to all outputs.
At groups=2, the operation becomes equivalent to having two conv
layers side by side, each seeing half the input channels, and producing
half the output channels, and both subsequently concatenated.
layout : str, default 'NCDHW'
Dimension ordering of data and weight. Can be 'NCDHW', 'NDHWC', etc.
'N', 'C', 'H', 'W', 'D' stands for batch, channel, height, width and
depth dimensions respectively. Convolution is applied on the 'D',
'H', and 'W' dimensions.
in_channels : int, default 0
The number of input channels to this layer. If not specified,
initialization will be deferred to the first time `forward` is called
and `in_channels` will be inferred from the shape of input data.
activation : str
Activation function to use. See :func:`~mxnet.ndarray.Activation`.
If you don't specify anything, no activation is applied
(ie. "linear" activation: `a(x) = x`).
use_bias : bool
Whether the layer uses a bias vector.
weight_initializer : str or `Initializer`
Initializer for the `weight` weights matrix.
bias_initializer : str or `Initializer`
Initializer for the bias vector.
Input shape:
This depends on the `layout` parameter. Input is 5D array of shape
(batch_size, in_channels, depth, height, width) if `layout` is `NCDHW`.
Output shape:
This depends on the `layout` parameter. Output is 5D array of shape
(batch_size, channels, out_depth, out_height, out_width) if `layout` is `NCDHW`.
out_depth, out_height and out_width are calculated as::
out_depth = (depth-1)*strides[0]-2*padding[0]+kernel_size[0]+output_padding[0]
out_height = (height-1)*strides[1]-2*padding[1]+kernel_size[1]+output_padding[1]
out_width = (width-1)*strides[2]-2*padding[2]+kernel_size[2]+output_padding[2]
NAME
AI::MXNet::Gluon::NN::MaxPool1D
DESCRIPTION
Max pooling operation for one dimensional data.
Parameters
----------
pool_size: int
Size of the max pooling windows.
strides: int, or None
Factor by which to downscale. E.g. 2 will halve the input size.
If `None`, it will default to `pool_size`.
padding: int
If padding is non-zero, then the input is implicitly
zero-padded on both sides for padding number of points.
layout : str, default 'NCW'
Dimension ordering of data and weight. Can be 'NCW', 'NWC', etc.
'N', 'C', 'W' stands for batch, channel, and width (time) dimensions
respectively. Pooling is applied on the W dimension.
ceil_mode : bool, default False
When `True`, will use ceil instead of floor to compute the output shape.
Input shape:
This depends on the `layout` parameter. Input is 3D array of shape
(batch_size, channels, width) if `layout` is `NCW`.
Output shape:
This depends on the `layout` parameter. Output is 3D array of shape
(batch_size, channels, out_width) if `layout` is `NCW`.
out_width is calculated as::
out_width = floor((width+2*padding-pool_size)/strides)+1
When `ceil_mode` is `True`, ceil will be used instead of floor in this
equation.
NAME
AI::MXNet::Gluon::NN::MaxPool2D
DESCRIPTION
Max pooling operation for two dimensional (spatial) data.
Parameters
----------
pool_size: int or list/tuple of 2 ints,
Size of the max pooling windows.
strides: int, list/tuple of 2 ints, or None.
Factor by which to downscale. E.g. 2 will halve the input size.
If `None`, it will default to `pool_size`.
padding: int or list/tuple of 2 ints,
If padding is non-zero, then the input is implicitly
zero-padded on both sides for padding number of points.
layout : str, default 'NCHW'
Dimension ordering of data and weight. Can be 'NCHW', 'NHWC', etc.
'N', 'C', 'H', 'W' stands for batch, channel, height, and width
dimensions respectively. padding is applied on 'H' and 'W' dimension.
ceil_mode : bool, default False
When `True`, will use ceil instead of floor to compute the output shape.
Input shape:
This depends on the `layout` parameter. Input is 4D array of shape
(batch_size, channels, height, width) if `layout` is `NCHW`.
Output shape:
This depends on the `layout` parameter. Output is 4D array of shape
(batch_size, channels, out_height, out_width) if `layout` is `NCHW`.
out_height and out_width are calculated as::
out_height = floor((height+2*padding[0]-pool_size[0])/strides[0])+1
out_width = floor((width+2*padding[1]-pool_size[1])/strides[1])+1
When `ceil_mode` is `True`, ceil will be used instead of floor in this
equation.
NAME
AI::MXNet::Gluon::NN::MaxPool3D
DESCRIPTION
Max pooling operation for 3D data (spatial or spatio-temporal).
Parameters
----------
pool_size: int or list/tuple of 3 ints,
Size of the max pooling windows.
strides: int, list/tuple of 3 ints, or None.
Factor by which to downscale. E.g. 2 will halve the input size.
If `None`, it will default to `pool_size`.
padding: int or list/tuple of 3 ints,
If padding is non-zero, then the input is implicitly
zero-padded on both sides for padding number of points.
layout : str, default 'NCDHW'
Dimension ordering of data and weight. Can be 'NCDHW', 'NDHWC', etc.
'N', 'C', 'H', 'W', 'D' stands for batch, channel, height, width and
depth dimensions respectively. padding is applied on 'D', 'H' and 'W'
dimension.
ceil_mode : bool, default False
When `True`, will use ceil instead of floor to compute the output shape.
Input shape:
This depends on the `layout` parameter. Input is 5D array of shape
(batch_size, channels, depth, height, width) if `layout` is `NCDHW`.
Output shape:
This depends on the `layout` parameter. Output is 5D array of shape
(batch_size, channels, out_depth, out_height, out_width) if `layout`
is `NCDHW`.
out_depth, out_height and out_width are calculated as ::
out_depth = floor((depth+2*padding[0]-pool_size[0])/strides[0])+1
out_height = floor((height+2*padding[1]-pool_size[1])/strides[1])+1
out_width = floor((width+2*padding[2]-pool_size[2])/strides[2])+1
When `ceil_mode` is `True`, ceil will be used instead of floor in this
equation.
NAME
AI::MXNet::Gluon::NN::AvgPool1D
DESCRIPTION
Average pooling operation for temporal data.
Parameters
----------
pool_size: int
Size of the max pooling windows.
strides: int, or None
Factor by which to downscale. E.g. 2 will halve the input size.
If `None`, it will default to `pool_size`.
padding: int
If padding is non-zero, then the input is implicitly
zero-padded on both sides for padding number of points.
layout : str, default 'NCW'
Dimension ordering of data and weight. Can be 'NCW', 'NWC', etc.
'N', 'C', 'W' stands for batch, channel, and width (time) dimensions
respectively. padding is applied on 'W' dimension.
ceil_mode : bool, default False
When `True`, will use ceil instead of floor to compute the output shape.
count_include_pad : bool, default True
When 'False', will exclude padding elements when computing the average value.
Input shape:
This depends on the `layout` parameter. Input is 3D array of shape
(batch_size, channels, width) if `layout` is `NCW`.
Output shape:
This depends on the `layout` parameter. Output is 3D array of shape
(batch_size, channels, out_width) if `layout` is `NCW`.
out_width is calculated as::
out_width = floor((width+2*padding-pool_size)/strides)+1
When `ceil_mode` is `True`, ceil will be used instead of floor in this
equation.
NAME
AI::MXNet::Gluon::NN::AvgPool2D
DESCRIPTION
Average pooling operation for spatial data.
Parameters
----------
pool_size: int or list/tuple of 2 ints,
Size of the max pooling windows.
strides: int, list/tuple of 2 ints, or None.
Factor by which to downscale. E.g. 2 will halve the input size.
If `None`, it will default to `pool_size`.
padding: int or list/tuple of 2 ints,
If padding is non-zero, then the input is implicitly
zero-padded on both sides for padding number of points.
layout : str, default 'NCHW'
Dimension ordering of data and weight. Can be 'NCHW', 'NHWC', etc.
'N', 'C', 'H', 'W' stands for batch, channel, height, and width
dimensions respectively. padding is applied on 'H' and 'W' dimension.
ceil_mode : bool, default False
When True, will use ceil instead of floor to compute the output shape.
count_include_pad : bool, default True
When 'False', will exclude padding elements when computing the average value.
Input shape:
This depends on the `layout` parameter. Input is 4D array of shape
(batch_size, channels, height, width) if `layout` is `NCHW`.
Output shape:
This depends on the `layout` parameter. Output is 4D array of shape
(batch_size, channels, out_height, out_width) if `layout` is `NCHW`.
out_height and out_width are calculated as::
out_height = floor((height+2*padding[0]-pool_size[0])/strides[0])+1
out_width = floor((width+2*padding[1]-pool_size[1])/strides[1])+1
When `ceil_mode` is `True`, ceil will be used instead of floor in this
equation.
NAME
AI::MXNet::Gluon::NN::AvgPool3D
DESCRIPTION
Average pooling operation for 3D data (spatial or spatio-temporal).
Parameters
----------
pool_size: int or list/tuple of 3 ints,
Size of the max pooling windows.
strides: int, list/tuple of 3 ints, or None.
Factor by which to downscale. E.g. 2 will halve the input size.
If `None`, it will default to `pool_size`.
padding: int or list/tuple of 3 ints,
If padding is non-zero, then the input is implicitly
zero-padded on both sides for padding number of points.
layout : str, default 'NCDHW'
Dimension ordering of data and weight. Can be 'NCDHW', 'NDHWC', etc.
'N', 'C', 'H', 'W', 'D' stands for batch, channel, height, width and
depth dimensions respectively. padding is applied on 'D', 'H' and 'W'
dimension.
ceil_mode : bool, default False
When True, will use ceil instead of floor to compute the output shape.
count_include_pad : bool, default True
When 'False', will exclude padding elements when computing the average value.
Input shape:
This depends on the `layout` parameter. Input is 5D array of shape
(batch_size, channels, depth, height, width) if `layout` is `NCDHW`.
Output shape:
This depends on the `layout` parameter. Output is 5D array of shape
(batch_size, channels, out_depth, out_height, out_width) if `layout`
is `NCDHW`.
out_depth, out_height and out_width are calculated as ::
out_depth = floor((depth+2*padding[0]-pool_size[0])/strides[0])+1
out_height = floor((height+2*padding[1]-pool_size[1])/strides[1])+1
out_width = floor((width+2*padding[2]-pool_size[2])/strides[2])+1
When `ceil_mode` is `True,` ceil will be used instead of floor in this
equation.
NAME
AI::MXNet::Gluon::NN::GlobalMaxPool1D
DESCRIPTION
Global max pooling operation for temporal data.
NAME
AI::MXNet::Gluon::NN::GlobalMaxPool2D
DESCRIPTION
Global max pooling operation for spatial data.
NAME
AI::MXNet::Gluon::NN::GlobalMaxPool3D
DESCRIPTION
Global max pooling operation for 3D data.
NAME
AI::MXNet::Gluon::NN::GlobalAvgPool1D
DESCRIPTION
Global average pooling operation for temporal data.
NAME
AI::MXNet::Gluon::NN::GlobalAvgPool2D
DESCRIPTION
Global average pooling operation for spatial data.
NAME
AI::MXNet::Gluon::NN::GlobalAvgPool2D
DESCRIPTION
Global average pooling operation for 3D data.
NAME
AI::MXNet::Gluon::NN::ReflectionPad2D
DESCRIPTION
Pads the input tensor using the reflection of the input boundary.
Parameters
----------
padding: int
An integer padding size
Examples
--------
>>> $m = nn->ReflectionPad2D(3);
>>> $input = mx->nd->random->normal(shape=>[16, 3, 224, 224]);
>>> $output = $m->($input);