Source code for mxnet.gluon.nn.conv_layers

# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements.  See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership.  The ASF licenses this file
# to you 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.

# coding: utf-8
# pylint: disable= arguments-differ, too-many-lines
"""Convolutional neural network layers."""
__all__ = ['Conv1D', 'Conv2D', 'Conv3D',
           'Conv1DTranspose', 'Conv2DTranspose', 'Conv3DTranspose',
           'MaxPool1D', 'MaxPool2D', 'MaxPool3D',
           'AvgPool1D', 'AvgPool2D', 'AvgPool3D',
           'GlobalMaxPool1D', 'GlobalMaxPool2D', 'GlobalMaxPool3D',
           'GlobalAvgPool1D', 'GlobalAvgPool2D', 'GlobalAvgPool3D',
           'ReflectionPad2D']

from ..block import HybridBlock
from ... import symbol
from ...base import numeric_types
from .activations import Activation


def _infer_weight_shape(op_name, data_shape, kwargs):
    op = getattr(symbol, op_name)
    sym = op(symbol.var('data', shape=data_shape), **kwargs)
    return sym.infer_shape_partial()[0]


class _Conv(HybridBlock):
    """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.
    """
    def __init__(self, channels, kernel_size, strides, padding, dilation,
                 groups, layout, in_channels=0, activation=None, use_bias=True,
                 weight_initializer=None, bias_initializer='zeros',
                 op_name='Convolution', adj=None, prefix=None, params=None):
        super(_Conv, self).__init__(prefix=prefix, params=params)
        with self.name_scope():
            self._channels = channels
            self._in_channels = in_channels
            if isinstance(strides, numeric_types):
                strides = (strides,)*len(kernel_size)
            if isinstance(padding, numeric_types):
                padding = (padding,)*len(kernel_size)
            if isinstance(dilation, numeric_types):
                dilation = (dilation,)*len(kernel_size)
            self._op_name = op_name
            self._kwargs = {
                'kernel': kernel_size, 'stride': strides, 'dilate': dilation,
                'pad': padding, 'num_filter': channels, 'num_group': groups,
                'no_bias': not use_bias, 'layout': layout}
            if adj is not None:
                self._kwargs['adj'] = adj

            dshape = [0]*(len(kernel_size) + 2)
            dshape[layout.find('N')] = 1
            dshape[layout.find('C')] = in_channels
            wshapes = _infer_weight_shape(op_name, dshape, self._kwargs)
            self.weight = self.params.get('weight', shape=wshapes[1],
                                          init=weight_initializer,
                                          allow_deferred_init=True)
            if use_bias:
                self.bias = self.params.get('bias', shape=wshapes[2],
                                            init=bias_initializer,
                                            allow_deferred_init=True)
            else:
                self.bias = None

            if activation is not None:
                self.act = Activation(activation, prefix=activation+'_')
            else:
                self.act = None

    def hybrid_forward(self, F, x, weight, bias=None):
        if bias is None:
            act = getattr(F, self._op_name)(x, weight, name='fwd', **self._kwargs)
        else:
            act = getattr(F, self._op_name)(x, weight, bias, name='fwd', **self._kwargs)
        if self.act is not None:
            act = self.act(act)
        return act

    def _alias(self):
        return 'conv'

    def __repr__(self):
        s = '{name}({mapping}, kernel_size={kernel}, stride={stride}'
        len_kernel_size = len(self._kwargs['kernel'])
        if self._kwargs['pad'] != (0,) * len_kernel_size:
            s += ', padding={pad}'
        if self._kwargs['dilate'] != (1,) * len_kernel_size:
            s += ', dilation={dilate}'
        if hasattr(self, 'out_pad') and self.out_pad != (0,) * len_kernel_size:
            s += ', output_padding={out_pad}'.format(out_pad=self.out_pad)
        if self._kwargs['num_group'] != 1:
            s += ', groups={num_group}'
        if self.bias is None:
            s += ', bias=False'
        s += ')'
        shape = self.weight.shape
        return s.format(name=self.__class__.__name__,
                        mapping='{0} -> {1}'.format(shape[1] if shape[1] else None, shape[0]),
                        **self._kwargs)


[docs]class Conv1D(_Conv): r"""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. Only supports 'NCW' layout for now. '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. Inputs: - **data**: 3D input tensor with shape `(batch_size, in_channels, width)` when `layout` is `NCW`. For other layouts shape is permuted accordingly. Outputs: - **out**: 3D output tensor with shape `(batch_size, channels, out_width)` when `layout` is `NCW`. out_width is calculated as:: out_width = floor((width+2*padding-dilation*(kernel_size-1)-1)/stride)+1 """ def __init__(self, channels, kernel_size, strides=1, padding=0, dilation=1, groups=1, layout='NCW', activation=None, use_bias=True, weight_initializer=None, bias_initializer='zeros', in_channels=0, **kwargs): assert layout == 'NCW', "Only supports 'NCW' layout for now" if isinstance(kernel_size, numeric_types): kernel_size = (kernel_size,) assert len(kernel_size) == 1, "kernel_size must be a number or a list of 1 ints" super(Conv1D, self).__init__( channels, kernel_size, strides, padding, dilation, groups, layout, in_channels, activation, use_bias, weight_initializer, bias_initializer, **kwargs)
[docs]class Conv2D(_Conv): r"""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. Only supports 'NCHW' and 'NHWC' layout for now. '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. Inputs: - **data**: 4D input tensor with shape `(batch_size, in_channels, height, width)` when `layout` is `NCHW`. For other layouts shape is permuted accordingly. Outputs: - **out**: 4D output tensor with shape `(batch_size, channels, out_height, out_width)` when `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 """ def __init__(self, channels, kernel_size, strides=(1, 1), padding=(0, 0), dilation=(1, 1), groups=1, layout='NCHW', activation=None, use_bias=True, weight_initializer=None, bias_initializer='zeros', in_channels=0, **kwargs): assert layout == 'NCHW' or layout == 'NHWC', \ "Only supports 'NCHW' and 'NHWC' layout for now" if isinstance(kernel_size, numeric_types): kernel_size = (kernel_size,)*2 assert len(kernel_size) == 2, "kernel_size must be a number or a list of 2 ints" super(Conv2D, self).__init__( channels, kernel_size, strides, padding, dilation, groups, layout, in_channels, activation, use_bias, weight_initializer, bias_initializer, **kwargs)
[docs]class Conv3D(_Conv): """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. Only supports 'NCDHW' and 'NDHWC' layout for now. '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. Inputs: - **data**: 5D input tensor with shape `(batch_size, in_channels, depth, height, width)` when `layout` is `NCDHW`. For other layouts shape is permuted accordingly. Outputs: - **out**: 5D output tensor with shape `(batch_size, channels, out_depth, out_height, out_width)` when `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 """ def __init__(self, channels, kernel_size, strides=(1, 1, 1), padding=(0, 0, 0), dilation=(1, 1, 1), groups=1, layout='NCDHW', activation=None, use_bias=True, weight_initializer=None, bias_initializer='zeros', in_channels=0, **kwargs): assert layout == 'NCDHW' or layout == 'NDHWC', \ "Only supports 'NCDHW' and 'NDHWC' layout for now" if isinstance(kernel_size, numeric_types): kernel_size = (kernel_size,)*3 assert len(kernel_size) == 3, "kernel_size must be a number or a list of 3 ints" super(Conv3D, self).__init__( channels, kernel_size, strides, padding, dilation, groups, layout, in_channels, activation, use_bias, weight_initializer, bias_initializer, **kwargs)
[docs]class Conv1DTranspose(_Conv): """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 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 output_padding: int or a tuple/list of 1 int Controls the amount of implicit zero-paddings on both sides of the output for output_padding number of points for each dimension. dilation : int or tuple/list of 1 int Controls the spacing between the kernel points; also known as the a trous algorithm 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. Only supports 'NCW' layout for now. '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. Inputs: - **data**: 3D input tensor with shape `(batch_size, in_channels, width)` when `layout` is `NCW`. For other layouts shape is permuted accordingly. Outputs: - **out**: 3D output tensor with shape `(batch_size, channels, out_width)` when `layout` is `NCW`. out_width is calculated as:: out_width = (width-1)*strides-2*padding+kernel_size+output_padding """ def __init__(self, channels, kernel_size, strides=1, padding=0, output_padding=0, dilation=1, groups=1, layout='NCW', activation=None, use_bias=True, weight_initializer=None, bias_initializer='zeros', in_channels=0, **kwargs): assert layout == 'NCW', "Only supports 'NCW' layout for now" if isinstance(kernel_size, numeric_types): kernel_size = (kernel_size,) if isinstance(output_padding, numeric_types): output_padding = (output_padding,) assert len(kernel_size) == 1, "kernel_size must be a number or a list of 1 ints" assert len(output_padding) == 1, "output_padding must be a number or a list of 1 ints" super(Conv1DTranspose, self).__init__( channels, kernel_size, strides, padding, dilation, groups, layout, in_channels, activation, use_bias, weight_initializer, bias_initializer, op_name='Deconvolution', adj=output_padding, **kwargs) self.outpad = output_padding
[docs]class Conv2DTranspose(_Conv): """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 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 output_padding: int or a tuple/list of 2 int Controls the amount of implicit zero-paddings on both sides of the output for output_padding number of points for each dimension. dilation : int or tuple/list of 2 int Controls the spacing between the kernel points; also known as the a trous algorithm 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. Only supports 'NCHW' and 'NHWC' layout for now. '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. Inputs: - **data**: 4D input tensor with shape `(batch_size, in_channels, height, width)` when `layout` is `NCHW`. For other layouts shape is permuted accordingly. Outputs: - **out**: 4D output tensor with shape `(batch_size, channels, out_height, out_width)` when `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] """ def __init__(self, channels, kernel_size, strides=(1, 1), padding=(0, 0), output_padding=(0, 0), dilation=(1, 1), groups=1, layout='NCHW', activation=None, use_bias=True, weight_initializer=None, bias_initializer='zeros', in_channels=0, **kwargs): assert layout == 'NCHW' or layout == 'NHWC', \ "Only supports 'NCHW' and 'NHWC' layout for now" if isinstance(kernel_size, numeric_types): kernel_size = (kernel_size,)*2 if isinstance(output_padding, numeric_types): output_padding = (output_padding,)*2 assert len(kernel_size) == 2, "kernel_size must be a number or a list of 2 ints" assert len(output_padding) == 2, "output_padding must be a number or a list of 2 ints" super(Conv2DTranspose, self).__init__( channels, kernel_size, strides, padding, dilation, groups, layout, in_channels, activation, use_bias, weight_initializer, bias_initializer, op_name='Deconvolution', adj=output_padding, **kwargs) self.outpad = output_padding
[docs]class Conv3DTranspose(_Conv): """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 output_padding: int or a tuple/list of 3 int Controls the amount of implicit zero-paddings on both sides of the output for output_padding number of points for each dimension. dilation : int or tuple/list of 3 int Controls the spacing between the kernel points; also known as the a trous algorithm. 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. Only supports 'NCDHW' and 'NDHWC' layout for now. '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. Inputs: - **data**: 5D input tensor with shape `(batch_size, in_channels, depth, height, width)` when `layout` is `NCDHW`. For other layouts shape is permuted accordingly. Outputs: - **out**: 5D output tensor with shape `(batch_size, channels, out_depth, out_height, out_width)` when `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] """ def __init__(self, channels, kernel_size, strides=(1, 1, 1), padding=(0, 0, 0), output_padding=(0, 0, 0), dilation=(1, 1, 1), groups=1, layout='NCDHW', activation=None, use_bias=True, weight_initializer=None, bias_initializer='zeros', in_channels=0, **kwargs): assert layout == 'NCDHW' or layout == 'NDHWC', \ "Only supports 'NCDHW' and 'NDHWC' layout for now" if isinstance(kernel_size, numeric_types): kernel_size = (kernel_size,)*3 if isinstance(output_padding, numeric_types): output_padding = (output_padding,)*3 assert len(kernel_size) == 3, "kernel_size must be a number or a list of 3 ints" assert len(output_padding) == 3, "output_padding must be a number or a list of 3 ints" super(Conv3DTranspose, self).__init__( channels, kernel_size, strides, padding, dilation, groups, layout, in_channels, activation, use_bias, weight_initializer, bias_initializer, op_name='Deconvolution', adj=output_padding, **kwargs) self.outpad = output_padding
class _Pooling(HybridBlock): """Abstract class for different pooling layers.""" def __init__(self, pool_size, strides, padding, ceil_mode, global_pool, pool_type, count_include_pad=None, **kwargs): super(_Pooling, self).__init__(**kwargs) if strides is None: strides = pool_size if isinstance(strides, numeric_types): strides = (strides,)*len(pool_size) if isinstance(padding, numeric_types): padding = (padding,)*len(pool_size) self._kwargs = { 'kernel': pool_size, 'stride': strides, 'pad': padding, 'global_pool': global_pool, 'pool_type': pool_type, 'pooling_convention': 'full' if ceil_mode else 'valid'} if count_include_pad is not None: self._kwargs['count_include_pad'] = count_include_pad def _alias(self): return 'pool' def hybrid_forward(self, F, x): return F.Pooling(x, name='fwd', **self._kwargs) def __repr__(self): s = '{name}(size={kernel}, stride={stride}, padding={pad}, ceil_mode={ceil_mode})' return s.format(name=self.__class__.__name__, ceil_mode=self._kwargs['pooling_convention'] == 'full', **self._kwargs)
[docs]class MaxPool1D(_Pooling): """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. Only supports 'NCW' layout for now. '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. Inputs: - **data**: 3D input tensor with shape `(batch_size, in_channels, width)` when `layout` is `NCW`. For other layouts shape is permuted accordingly. Outputs: - **out**: 3D output tensor with shape `(batch_size, channels, out_width)` when `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. """ def __init__(self, pool_size=2, strides=None, padding=0, layout='NCW', ceil_mode=False, **kwargs): assert layout == 'NCW', "Only supports 'NCW' layout for now" if isinstance(pool_size, numeric_types): pool_size = (pool_size,) assert len(pool_size) == 1, "pool_size must be a number or a list of 1 ints" super(MaxPool1D, self).__init__( pool_size, strides, padding, ceil_mode, False, 'max', **kwargs)
[docs]class MaxPool2D(_Pooling): """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. Only supports 'NCHW' layout for now. '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. Inputs: - **data**: 4D input tensor with shape `(batch_size, in_channels, height, width)` when `layout` is `NCHW`. For other layouts shape is permuted accordingly. Outputs: - **out**: 4D output tensor with shape `(batch_size, channels, out_height, out_width)` when `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. """ def __init__(self, pool_size=(2, 2), strides=None, padding=0, layout='NCHW', ceil_mode=False, **kwargs): assert layout == 'NCHW', "Only supports 'NCHW' layout for now" if isinstance(pool_size, numeric_types): pool_size = (pool_size,)*2 assert len(pool_size) == 2, "pool_size must be a number or a list of 2 ints" super(MaxPool2D, self).__init__( pool_size, strides, padding, ceil_mode, False, 'max', **kwargs)
[docs]class MaxPool3D(_Pooling): """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. Only supports 'NCDHW' layout for now. '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. Inputs: - **data**: 5D input tensor with shape `(batch_size, in_channels, depth, height, width)` when `layout` is `NCW`. For other layouts shape is permuted accordingly. Outputs: - **out**: 5D output tensor with shape `(batch_size, channels, out_depth, out_height, out_width)` when `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. """ def __init__(self, pool_size=(2, 2, 2), strides=None, padding=0, ceil_mode=False, layout='NCDHW', **kwargs): assert layout == 'NCDHW', "Only supports 'NCDHW' layout for now" if isinstance(pool_size, numeric_types): pool_size = (pool_size,)*3 assert len(pool_size) == 3, "pool_size must be a number or a list of 3 ints" super(MaxPool3D, self).__init__( pool_size, strides, padding, ceil_mode, False, 'max', **kwargs)
[docs]class AvgPool1D(_Pooling): """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. Only supports 'NCW' layout for now. '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. Inputs: - **data**: 3D input tensor with shape `(batch_size, in_channels, width)` when `layout` is `NCW`. For other layouts shape is permuted accordingly. Outputs: - **out**: 3D output tensor with shape `(batch_size, channels, out_width)` when `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. """ def __init__(self, pool_size=2, strides=None, padding=0, layout='NCW', ceil_mode=False, count_include_pad=True, **kwargs): assert layout == 'NCW', "Only supports 'NCW' layout for now" if isinstance(pool_size, numeric_types): pool_size = (pool_size,) assert len(pool_size) == 1, "pool_size must be a number or a list of 1 ints" super(AvgPool1D, self).__init__( pool_size, strides, padding, ceil_mode, False, 'avg', count_include_pad, **kwargs)
[docs]class AvgPool2D(_Pooling): """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. Only supports 'NCHW' layout for now. '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. Inputs: - **data**: 4D input tensor with shape `(batch_size, in_channels, height, width)` when `layout` is `NCHW`. For other layouts shape is permuted accordingly. Outputs: - **out**: 4D output tensor with shape `(batch_size, channels, out_height, out_width)` when `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. """ def __init__(self, pool_size=(2, 2), strides=None, padding=0, ceil_mode=False, layout='NCHW', count_include_pad=True, **kwargs): assert layout == 'NCHW', "Only supports 'NCHW' layout for now" if isinstance(pool_size, numeric_types): pool_size = (pool_size,)*2 assert len(pool_size) == 2, "pool_size must be a number or a list of 2 ints" super(AvgPool2D, self).__init__( pool_size, strides, padding, ceil_mode, False, 'avg', count_include_pad, **kwargs)
[docs]class AvgPool3D(_Pooling): """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. Inputs: - **data**: 5D input tensor with shape `(batch_size, in_channels, depth, height, width)` when `layout` is `NCDHW`. For other layouts shape is permuted accordingly. Outputs: - **out**: 5D output tensor with shape `(batch_size, channels, out_depth, out_height, out_width)` when `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. """ def __init__(self, pool_size=(2, 2, 2), strides=None, padding=0, ceil_mode=False, layout='NCDHW', count_include_pad=True, **kwargs): assert layout == 'NCDHW', "Only supports 'NCDHW' layout for now" if isinstance(pool_size, numeric_types): pool_size = (pool_size,)*3 assert len(pool_size) == 3, "pool_size must be a number or a list of 3 ints" super(AvgPool3D, self).__init__( pool_size, strides, padding, ceil_mode, False, 'avg', count_include_pad, **kwargs)
[docs]class GlobalMaxPool1D(_Pooling): """Gloabl max pooling operation for one dimensional (temporal) data. Parameters ---------- layout : str, default 'NCW' Dimension ordering of data and weight. Only supports 'NCW' layout for now. 'N', 'C', 'W' stands for batch, channel, and width (time) dimensions respectively. Pooling is applied on the W dimension. Inputs: - **data**: 3D input tensor with shape `(batch_size, in_channels, width)` when `layout` is `NCW`. For other layouts shape is permuted accordingly. Outputs: - **out**: 3D output tensor with shape `(batch_size, channels, 1)` when `layout` is `NCW`. """ def __init__(self, layout='NCW', **kwargs): assert layout == 'NCW', "Only supports 'NCW' layout for now" super(GlobalMaxPool1D, self).__init__( (1,), None, 0, True, True, 'max', **kwargs)
[docs]class GlobalMaxPool2D(_Pooling): """Global max pooling operation for two dimensional (spatial) data. Parameters ---------- layout : str, default 'NCHW' Dimension ordering of data and weight. Only supports 'NCHW' layout for now. 'N', 'C', 'H', 'W' stands for batch, channel, height, and width dimensions respectively. padding is applied on 'H' and 'W' dimension. Inputs: - **data**: 4D input tensor with shape `(batch_size, in_channels, height, width)` when `layout` is `NCHW`. For other layouts shape is permuted accordingly. Outputs: - **out**: 4D output tensor with shape `(batch_size, channels, 1, 1)` when `layout` is `NCHW`. """ def __init__(self, layout='NCHW', **kwargs): assert layout == 'NCHW', "Only supports 'NCHW' layout for now" super(GlobalMaxPool2D, self).__init__( (1, 1), None, 0, True, True, 'max', **kwargs)
[docs]class GlobalMaxPool3D(_Pooling): """Global max pooling operation for 3D data (spatial or spatio-temporal). Parameters ---------- layout : str, default 'NCDHW' Dimension ordering of data and weight. Only supports 'NCDHW' layout for now. 'N', 'C', 'H', 'W', 'D' stands for batch, channel, height, width and depth dimensions respectively. padding is applied on 'D', 'H' and 'W' dimension. Inputs: - **data**: 5D input tensor with shape `(batch_size, in_channels, depth, height, width)` when `layout` is `NCW`. For other layouts shape is permuted accordingly. Outputs: - **out**: 5D output tensor with shape `(batch_size, channels, 1, 1, 1)` when `layout` is `NCDHW`. """ def __init__(self, layout='NCDHW', **kwargs): assert layout == 'NCDHW', "Only supports 'NCDHW' layout for now" super(GlobalMaxPool3D, self).__init__( (1, 1, 1), None, 0, True, True, 'max', **kwargs)
[docs]class GlobalAvgPool1D(_Pooling): """Global average pooling operation for temporal data. Parameters ---------- layout : str, default 'NCW' Dimension ordering of data and weight. Only supports 'NCW' layout for now. 'N', 'C', 'W' stands for batch, channel, and width (time) dimensions respectively. padding is applied on 'W' dimension. Inputs: - **data**: 3D input tensor with shape `(batch_size, in_channels, width)` when `layout` is `NCW`. For other layouts shape is permuted accordingly. Outputs: - **out**: 3D output tensor with shape `(batch_size, channels, 1)`. """ def __init__(self, layout='NCW', **kwargs): assert layout == 'NCW', "Only supports 'NCW' layout for now" super(GlobalAvgPool1D, self).__init__( (1,), None, 0, True, True, 'avg', **kwargs)
[docs]class GlobalAvgPool2D(_Pooling): """Global average pooling operation for spatial data. Parameters ---------- layout : str, default 'NCHW' Dimension ordering of data and weight. Only supports 'NCHW' layout for now. 'N', 'C', 'H', 'W' stands for batch, channel, height, and width dimensions respectively. Inputs: - **data**: 4D input tensor with shape `(batch_size, in_channels, height, width)` when `layout` is `NCHW`. For other layouts shape is permuted accordingly. Outputs: - **out**: 4D output tensor with shape `(batch_size, channels, 1, 1)` when `layout` is `NCHW`. """ def __init__(self, layout='NCHW', **kwargs): assert layout == 'NCHW', "Only supports 'NCHW' layout for now" super(GlobalAvgPool2D, self).__init__( (1, 1), None, 0, True, True, 'avg', **kwargs)
[docs]class GlobalAvgPool3D(_Pooling): """Global average pooling operation for 3D data (spatial or spatio-temporal). Parameters ---------- 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. Inputs: - **data**: 5D input tensor with shape `(batch_size, in_channels, depth, height, width)` when `layout` is `NCDHW`. For other layouts shape is permuted accordingly. Outputs: - **out**: 5D output tensor with shape `(batch_size, channels, 1, 1, 1)` when `layout` is `NCDHW`. """ def __init__(self, layout='NCDHW', **kwargs): assert layout == 'NCDHW', "Only supports 'NCDHW' layout for now" super(GlobalAvgPool3D, self).__init__( (1, 1, 1), None, 0, True, True, 'avg', **kwargs)
[docs]class ReflectionPad2D(HybridBlock): r"""Pads the input tensor using the reflection of the input boundary. Parameters ---------- padding: int An integer padding size Inputs: - **data**: input tensor with the shape :math:`(N, C, H_{in}, W_{in})`. Outputs: - **out**: output tensor with the shape :math:`(N, C, H_{out}, W_{out})`, where .. math:: H_{out} = H_{in} + 2 \cdot padding W_{out} = W_{in} + 2 \cdot padding Examples -------- >>> m = nn.ReflectionPad2D(3) >>> input = mx.nd.random.normal(shape=(16, 3, 224, 224)) >>> output = m(input) """ def __init__(self, padding=0, **kwargs): super(ReflectionPad2D, self).__init__(**kwargs) if isinstance(padding, numeric_types): padding = (0, 0, 0, 0, padding, padding, padding, padding) assert(len(padding) == 8) self._padding = padding def hybrid_forward(self, F, x): return F.pad(x, mode='reflect', pad_width=self._padding)