# Source code for torch.nn.modules.pooling

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals

from .module import Module
from .utils import _single, _pair, _triple
from .. import functional as F

class _MaxPoolNd(Module):
__constants__ = ['kernel_size', 'stride', 'padding', 'dilation',
'return_indices', 'ceil_mode']

def __init__(self, kernel_size, stride=None, padding=0, dilation=1,
return_indices=False, ceil_mode=False):
super(_MaxPoolNd, self).__init__()
self.kernel_size = kernel_size
self.stride = stride or kernel_size
self.dilation = dilation
self.return_indices = return_indices
self.ceil_mode = ceil_mode

def extra_repr(self):
', dilation={dilation}, ceil_mode={ceil_mode}'.format(**self.__dict__)

[docs]class MaxPool1d(_MaxPoolNd): r"""Applies a 1D max pooling over an input signal composed of several input planes. In the simplest case, the output value of the layer with input size :math:(N, C, L) and output :math:(N, C, L_{out}) can be precisely described as: .. math:: out(N_i, C_j, k) = \max_{m=0, \ldots, \text{kernel\_size} - 1} input(N_i, C_j, stride \times k + m) If :attr:padding is non-zero, then the input is implicitly zero-padded on both sides for :attr:padding number of points. :attr:dilation controls the spacing between the kernel points. It is harder to describe, but this link_ has a nice visualization of what :attr:dilation does. Args: kernel_size: the size of the window to take a max over stride: the stride of the window. Default value is :attr:kernel_size padding: implicit zero padding to be added on both sides dilation: a parameter that controls the stride of elements in the window return_indices: if True, will return the max indices along with the outputs. Useful for :class:torch.nn.MaxUnpool1d later ceil_mode: when True, will use ceil instead of floor to compute the output shape Shape: - Input: :math:(N, C, L_{in}) - Output: :math:(N, C, L_{out}), where .. math:: L_{out} = \left\lfloor \frac{L_{in} + 2 \times \text{padding} - \text{dilation} \times (\text{kernel\_size} - 1) - 1}{\text{stride}} + 1\right\rfloor Examples:: >>> # pool of size=3, stride=2 >>> m = nn.MaxPool1d(3, stride=2) >>> input = torch.randn(20, 16, 50) >>> output = m(input) .. _link: https://github.com/vdumoulin/conv_arithmetic/blob/master/README.md """ def forward(self, input): return F.max_pool1d(input, self.kernel_size, self.stride, self.padding, self.dilation, self.ceil_mode, self.return_indices)
[docs]class MaxPool2d(_MaxPoolNd): r"""Applies a 2D max pooling over an input signal composed of several input planes. In the simplest case, the output value of the layer with input size :math:(N, C, H, W), output :math:(N, C, H_{out}, W_{out}) and :attr:kernel_size :math:(kH, kW) can be precisely described as: .. math:: \begin{aligned} out(N_i, C_j, h, w) ={} & \max_{m=0, \ldots, kH-1} \max_{n=0, \ldots, kW-1} \\ & \text{input}(N_i, C_j, \text{stride[0]} \times h + m, \text{stride[1]} \times w + n) \end{aligned} If :attr:padding is non-zero, then the input is implicitly zero-padded on both sides for :attr:padding number of points. :attr:dilation controls the spacing between the kernel points. It is harder to describe, but this link_ has a nice visualization of what :attr:dilation does. The parameters :attr:kernel_size, :attr:stride, :attr:padding, :attr:dilation can either be: - a single int -- in which case the same value is used for the height and width dimension - a tuple of two ints -- in which case, the first int is used for the height dimension, and the second int for the width dimension Args: kernel_size: the size of the window to take a max over stride: the stride of the window. Default value is :attr:kernel_size padding: implicit zero padding to be added on both sides dilation: a parameter that controls the stride of elements in the window return_indices: if True, will return the max indices along with the outputs. Useful for :class:torch.nn.MaxUnpool2d later ceil_mode: when True, will use ceil instead of floor to compute the output shape Shape: - Input: :math:(N, C, H_{in}, W_{in}) - Output: :math:(N, C, H_{out}, W_{out}), where .. math:: H_{out} = \left\lfloor\frac{H_{in} + 2 * \text{padding[0]} - \text{dilation[0]} \times (\text{kernel\_size[0]} - 1) - 1}{\text{stride[0]}} + 1\right\rfloor .. math:: W_{out} = \left\lfloor\frac{W_{in} + 2 * \text{padding[1]} - \text{dilation[1]} \times (\text{kernel\_size[1]} - 1) - 1}{\text{stride[1]}} + 1\right\rfloor Examples:: >>> # pool of square window of size=3, stride=2 >>> m = nn.MaxPool2d(3, stride=2) >>> # pool of non-square window >>> m = nn.MaxPool2d((3, 2), stride=(2, 1)) >>> input = torch.randn(20, 16, 50, 32) >>> output = m(input) .. _link: https://github.com/vdumoulin/conv_arithmetic/blob/master/README.md """ def forward(self, input): return F.max_pool2d(input, self.kernel_size, self.stride, self.padding, self.dilation, self.ceil_mode, self.return_indices)
[docs]class MaxPool3d(_MaxPoolNd): r"""Applies a 3D max pooling over an input signal composed of several input planes. In the simplest case, the output value of the layer with input size :math:(N, C, D, H, W), output :math:(N, C, D_{out}, H_{out}, W_{out}) and :attr:kernel_size :math:(kD, kH, kW) can be precisely described as: .. math:: \begin{aligned} \text{out}(N_i, C_j, d, h, w) ={} & \max_{k=0, \ldots, kD-1} \max_{m=0, \ldots, kH-1} \max_{n=0, \ldots, kW-1} \\ & \text{input}(N_i, C_j, \text{stride[0]} \times d + k, \text{stride[1]} \times h + m, \text{stride[2]} \times w + n) \end{aligned} If :attr:padding is non-zero, then the input is implicitly zero-padded on both sides for :attr:padding number of points. :attr:dilation controls the spacing between the kernel points. It is harder to describe, but this link_ has a nice visualization of what :attr:dilation does. The parameters :attr:kernel_size, :attr:stride, :attr:padding, :attr:dilation can either be: - a single int -- in which case the same value is used for the depth, height and width dimension - a tuple of three ints -- in which case, the first int is used for the depth dimension, the second int for the height dimension and the third int for the width dimension Args: kernel_size: the size of the window to take a max over stride: the stride of the window. Default value is :attr:kernel_size padding: implicit zero padding to be added on all three sides dilation: a parameter that controls the stride of elements in the window return_indices: if True, will return the max indices along with the outputs. Useful for :class:torch.nn.MaxUnpool3d later ceil_mode: when True, will use ceil instead of floor to compute the output shape Shape: - Input: :math:(N, C, D_{in}, H_{in}, W_{in}) - Output: :math:(N, C, D_{out}, H_{out}, W_{out}), where .. math:: D_{out} = \left\lfloor\frac{D_{in} + 2 \times \text{padding}[0] - \text{dilation}[0] \times (\text{kernel\_size}[0] - 1) - 1}{\text{stride}[0]} + 1\right\rfloor .. math:: H_{out} = \left\lfloor\frac{H_{in} + 2 \times \text{padding}[1] - \text{dilation}[1] \times (\text{kernel\_size}[1] - 1) - 1}{\text{stride}[1]} + 1\right\rfloor .. math:: W_{out} = \left\lfloor\frac{W_{in} + 2 \times \text{padding}[2] - \text{dilation}[2] \times (\text{kernel\_size}[2] - 1) - 1}{\text{stride}[2]} + 1\right\rfloor Examples:: >>> # pool of square window of size=3, stride=2 >>> m = nn.MaxPool3d(3, stride=2) >>> # pool of non-square window >>> m = nn.MaxPool3d((3, 2, 2), stride=(2, 1, 2)) >>> input = torch.randn(20, 16, 50,44, 31) >>> output = m(input) .. _link: https://github.com/vdumoulin/conv_arithmetic/blob/master/README.md """ # noqa: E501 def forward(self, input): return F.max_pool3d(input, self.kernel_size, self.stride, self.padding, self.dilation, self.ceil_mode, self.return_indices)
class _MaxUnpoolNd(Module): def extra_repr(self): return 'kernel_size={}, stride={}, padding={}'.format( self.kernel_size, self.stride, self.padding )
[docs]class MaxUnpool1d(_MaxUnpoolNd): r"""Computes a partial inverse of :class:MaxPool1d. :class:MaxPool1d is not fully invertible, since the non-maximal values are lost. :class:MaxUnpool1d takes in as input the output of :class:MaxPool1d including the indices of the maximal values and computes a partial inverse in which all non-maximal values are set to zero. .. note:: :class:MaxPool1d can map several input sizes to the same output sizes. Hence, the inversion process can get ambiguous. To accommodate this, you can provide the needed output size as an additional argument :attr:output_size in the forward call. See the Inputs and Example below. Args: kernel_size (int or tuple): Size of the max pooling window. stride (int or tuple): Stride of the max pooling window. It is set to :attr:kernel_size by default. padding (int or tuple): Padding that was added to the input Inputs: - input: the input Tensor to invert - indices: the indices given out by :class:~torch.nn.MaxPool1d - output_size (optional): the targeted output size Shape: - Input: :math:(N, C, H_{in}) - Output: :math:(N, C, H_{out}), where .. math:: H_{out} = (H_{in} - 1) \times \text{stride}[0] - 2 \times \text{padding}[0] + \text{kernel\_size}[0] or as given by :attr:output_size in the call operator Example:: >>> pool = nn.MaxPool1d(2, stride=2, return_indices=True) >>> unpool = nn.MaxUnpool1d(2, stride=2) >>> input = torch.tensor([[[1., 2, 3, 4, 5, 6, 7, 8]]]) >>> output, indices = pool(input) >>> unpool(output, indices) tensor([[[ 0., 2., 0., 4., 0., 6., 0., 8.]]]) >>> # Example showcasing the use of output_size >>> input = torch.tensor([[[1., 2, 3, 4, 5, 6, 7, 8, 9]]]) >>> output, indices = pool(input) >>> unpool(output, indices, output_size=input.size()) tensor([[[ 0., 2., 0., 4., 0., 6., 0., 8., 0.]]]) >>> unpool(output, indices) tensor([[[ 0., 2., 0., 4., 0., 6., 0., 8.]]]) """ def __init__(self, kernel_size, stride=None, padding=0): super(MaxUnpool1d, self).__init__() self.kernel_size = _single(kernel_size) self.stride = _single(stride or kernel_size) self.padding = _single(padding) def forward(self, input, indices, output_size=None): # type: (Tensor, Tensor, Optional[List[int]]) -> Tensor return F.max_unpool1d(input, indices, self.kernel_size, self.stride, self.padding, output_size)
[docs]class MaxUnpool2d(_MaxUnpoolNd): r"""Computes a partial inverse of :class:MaxPool2d. :class:MaxPool2d is not fully invertible, since the non-maximal values are lost. :class:MaxUnpool2d takes in as input the output of :class:MaxPool2d including the indices of the maximal values and computes a partial inverse in which all non-maximal values are set to zero. .. note:: :class:MaxPool2d can map several input sizes to the same output sizes. Hence, the inversion process can get ambiguous. To accommodate this, you can provide the needed output size as an additional argument :attr:output_size in the forward call. See the Inputs and Example below. Args: kernel_size (int or tuple): Size of the max pooling window. stride (int or tuple): Stride of the max pooling window. It is set to :attr:kernel_size by default. padding (int or tuple): Padding that was added to the input Inputs: - input: the input Tensor to invert - indices: the indices given out by :class:~torch.nn.MaxPool2d - output_size (optional): the targeted output size Shape: - Input: :math:(N, C, H_{in}, W_{in}) - Output: :math:(N, C, H_{out}, W_{out}), where .. math:: H_{out} = (H_{in} - 1) \times \text{stride[0]} - 2 \times \text{padding[0]} + \text{kernel\_size[0]} .. math:: W_{out} = (W_{in} - 1) \times \text{stride[1]} - 2 \times \text{padding[1]} + \text{kernel\_size[1]} or as given by :attr:output_size in the call operator Example:: >>> pool = nn.MaxPool2d(2, stride=2, return_indices=True) >>> unpool = nn.MaxUnpool2d(2, stride=2) >>> input = torch.tensor([[[[ 1., 2, 3, 4], [ 5, 6, 7, 8], [ 9, 10, 11, 12], [13, 14, 15, 16]]]]) >>> output, indices = pool(input) >>> unpool(output, indices) tensor([[[[ 0., 0., 0., 0.], [ 0., 6., 0., 8.], [ 0., 0., 0., 0.], [ 0., 14., 0., 16.]]]]) >>> # specify a different output size than input size >>> unpool(output, indices, output_size=torch.Size([1, 1, 5, 5])) tensor([[[[ 0., 0., 0., 0., 0.], [ 6., 0., 8., 0., 0.], [ 0., 0., 0., 14., 0.], [ 16., 0., 0., 0., 0.], [ 0., 0., 0., 0., 0.]]]]) """ def __init__(self, kernel_size, stride=None, padding=0): super(MaxUnpool2d, self).__init__() self.kernel_size = _pair(kernel_size) self.stride = _pair(stride or kernel_size) self.padding = _pair(padding) def forward(self, input, indices, output_size=None): # type: (Tensor, Tensor, Optional[List[int]]) -> Tensor return F.max_unpool2d(input, indices, self.kernel_size, self.stride, self.padding, output_size)
[docs]class MaxUnpool3d(_MaxUnpoolNd): r"""Computes a partial inverse of :class:MaxPool3d. :class:MaxPool3d is not fully invertible, since the non-maximal values are lost. :class:MaxUnpool3d takes in as input the output of :class:MaxPool3d including the indices of the maximal values and computes a partial inverse in which all non-maximal values are set to zero. .. note:: :class:MaxPool3d can map several input sizes to the same output sizes. Hence, the inversion process can get ambiguous. To accommodate this, you can provide the needed output size as an additional argument :attr:output_size in the forward call. See the Inputs section below. Args: kernel_size (int or tuple): Size of the max pooling window. stride (int or tuple): Stride of the max pooling window. It is set to :attr:kernel_size by default. padding (int or tuple): Padding that was added to the input Inputs: - input: the input Tensor to invert - indices: the indices given out by :class:~torch.nn.MaxPool3d - output_size (optional): the targeted output size Shape: - Input: :math:(N, C, D_{in}, H_{in}, W_{in}) - Output: :math:(N, C, D_{out}, H_{out}, W_{out}), where .. math:: D_{out} = (D_{in} - 1) \times \text{stride[0]} - 2 \times \text{padding[0]} + \text{kernel\_size[0]} .. math:: H_{out} = (H_{in} - 1) \times \text{stride[1]} - 2 \times \text{padding[1]} + \text{kernel\_size[1]} .. math:: W_{out} = (W_{in} - 1) \times \text{stride[2]} - 2 \times \text{padding[2]} + \text{kernel\_size[2]} or as given by :attr:output_size in the call operator Example:: >>> # pool of square window of size=3, stride=2 >>> pool = nn.MaxPool3d(3, stride=2, return_indices=True) >>> unpool = nn.MaxUnpool3d(3, stride=2) >>> output, indices = pool(torch.randn(20, 16, 51, 33, 15)) >>> unpooled_output = unpool(output, indices) >>> unpooled_output.size() torch.Size([20, 16, 51, 33, 15]) """ def __init__(self, kernel_size, stride=None, padding=0): super(MaxUnpool3d, self).__init__() self.kernel_size = _triple(kernel_size) self.stride = _triple(stride or kernel_size) self.padding = _triple(padding) def forward(self, input, indices, output_size=None): # type: (Tensor, Tensor, Optional[List[int]]) -> Tensor return F.max_unpool3d(input, indices, self.kernel_size, self.stride, self.padding, output_size)
[docs]class AvgPool1d(_AvgPoolNd): r"""Applies a 1D average pooling over an input signal composed of several input planes. In the simplest case, the output value of the layer with input size :math:(N, C, L), output :math:(N, C, L_{out}) and :attr:kernel_size :math:k can be precisely described as: .. math:: \text{out}(N_i, C_j, l) = \frac{1}{k} \sum_{m=0}^{k-1} \text{input}(N_i, C_j, \text{stride} \times l + m) If :attr:padding is non-zero, then the input is implicitly zero-padded on both sides for :attr:padding number of points. The parameters :attr:kernel_size, :attr:stride, :attr:padding can each be an int or a one-element tuple. Args: kernel_size: the size of the window stride: the stride of the window. Default value is :attr:kernel_size padding: implicit zero padding to be added on both sides ceil_mode: when True, will use ceil instead of floor to compute the output shape count_include_pad: when True, will include the zero-padding in the averaging calculation Shape: - Input: :math:(N, C, L_{in}) - Output: :math:(N, C, L_{out}), where .. math:: L_{out} = \left\lfloor \frac{L_{in} + 2 \times \text{padding} - \text{kernel\_size}}{\text{stride}} + 1\right\rfloor Examples:: >>> # pool with window of size=3, stride=2 >>> m = nn.AvgPool1d(3, stride=2) >>> m(torch.tensor([[[1.,2,3,4,5,6,7]]])) tensor([[[ 2., 4., 6.]]]) """ def __init__(self, kernel_size, stride=None, padding=0, ceil_mode=False, count_include_pad=True): super(AvgPool1d, self).__init__() self.kernel_size = _single(kernel_size) self.stride = _single(stride if stride is not None else kernel_size) self.padding = _single(padding) self.ceil_mode = ceil_mode self.count_include_pad = count_include_pad def forward(self, input): return F.avg_pool1d( input, self.kernel_size, self.stride, self.padding, self.ceil_mode, self.count_include_pad)
[docs]class AvgPool2d(_AvgPoolNd): r"""Applies a 2D average pooling over an input signal composed of several input planes. In the simplest case, the output value of the layer with input size :math:(N, C, H, W), output :math:(N, C, H_{out}, W_{out}) and :attr:kernel_size :math:(kH, kW) can be precisely described as: .. math:: out(N_i, C_j, h, w) = \frac{1}{kH * kW} \sum_{m=0}^{kH-1} \sum_{n=0}^{kW-1} input(N_i, C_j, stride[0] \times h + m, stride[1] \times w + n) If :attr:padding is non-zero, then the input is implicitly zero-padded on both sides for :attr:padding number of points. The parameters :attr:kernel_size, :attr:stride, :attr:padding can either be: - a single int -- in which case the same value is used for the height and width dimension - a tuple of two ints -- in which case, the first int is used for the height dimension, and the second int for the width dimension Args: kernel_size: the size of the window stride: the stride of the window. Default value is :attr:kernel_size padding: implicit zero padding to be added on both sides ceil_mode: when True, will use ceil instead of floor to compute the output shape count_include_pad: when True, will include the zero-padding in the averaging calculation divisor_override: if specified, it will be used as divisor, otherwise attr:kernel_size will be used Shape: - Input: :math:(N, C, H_{in}, W_{in}) - Output: :math:(N, C, H_{out}, W_{out}), where .. math:: H_{out} = \left\lfloor\frac{H_{in} + 2 \times \text{padding}[0] - \text{kernel\_size}[0]}{\text{stride}[0]} + 1\right\rfloor .. math:: W_{out} = \left\lfloor\frac{W_{in} + 2 \times \text{padding}[1] - \text{kernel\_size}[1]}{\text{stride}[1]} + 1\right\rfloor Examples:: >>> # pool of square window of size=3, stride=2 >>> m = nn.AvgPool2d(3, stride=2) >>> # pool of non-square window >>> m = nn.AvgPool2d((3, 2), stride=(2, 1)) >>> input = torch.randn(20, 16, 50, 32) >>> output = m(input) """ __constants__ = ['kernel_size', 'stride', 'padding', 'ceil_mode', 'count_include_pad', 'divisor_override'] def __init__(self, kernel_size, stride=None, padding=0, ceil_mode=False, count_include_pad=True, divisor_override=None): super(AvgPool2d, self).__init__() self.kernel_size = kernel_size self.stride = stride or kernel_size self.padding = padding self.ceil_mode = ceil_mode self.count_include_pad = count_include_pad self.divisor_override = divisor_override def forward(self, input): return F.avg_pool2d(input, self.kernel_size, self.stride, self.padding, self.ceil_mode, self.count_include_pad, self.divisor_override)
[docs]class AvgPool3d(_AvgPoolNd): r"""Applies a 3D average pooling over an input signal composed of several input planes. In the simplest case, the output value of the layer with input size :math:(N, C, D, H, W), output :math:(N, C, D_{out}, H_{out}, W_{out}) and :attr:kernel_size :math:(kD, kH, kW) can be precisely described as: .. math:: \begin{aligned} \text{out}(N_i, C_j, d, h, w) ={} & \sum_{k=0}^{kD-1} \sum_{m=0}^{kH-1} \sum_{n=0}^{kW-1} \\ & \frac{\text{input}(N_i, C_j, \text{stride}[0] \times d + k, \text{stride}[1] \times h + m, \text{stride}[2] \times w + n)} {kD \times kH \times kW} \end{aligned} If :attr:padding is non-zero, then the input is implicitly zero-padded on all three sides for :attr:padding number of points. The parameters :attr:kernel_size, :attr:stride can either be: - a single int -- in which case the same value is used for the depth, height and width dimension - a tuple of three ints -- in which case, the first int is used for the depth dimension, the second int for the height dimension and the third int for the width dimension Args: kernel_size: the size of the window stride: the stride of the window. Default value is :attr:kernel_size padding: implicit zero padding to be added on all three sides ceil_mode: when True, will use ceil instead of floor to compute the output shape count_include_pad: when True, will include the zero-padding in the averaging calculation divisor_override: if specified, it will be used as divisor, otherwise attr:kernel_size will be used Shape: - Input: :math:(N, C, D_{in}, H_{in}, W_{in}) - Output: :math:(N, C, D_{out}, H_{out}, W_{out}), where .. math:: D_{out} = \left\lfloor\frac{D_{in} + 2 \times \text{padding}[0] - \text{kernel\_size}[0]}{\text{stride}[0]} + 1\right\rfloor .. math:: H_{out} = \left\lfloor\frac{H_{in} + 2 \times \text{padding}[1] - \text{kernel\_size}[1]}{\text{stride}[1]} + 1\right\rfloor .. math:: W_{out} = \left\lfloor\frac{W_{in} + 2 \times \text{padding}[2] - \text{kernel\_size}[2]}{\text{stride}[2]} + 1\right\rfloor Examples:: >>> # pool of square window of size=3, stride=2 >>> m = nn.AvgPool3d(3, stride=2) >>> # pool of non-square window >>> m = nn.AvgPool3d((3, 2, 2), stride=(2, 1, 2)) >>> input = torch.randn(20, 16, 50,44, 31) >>> output = m(input) """ __constants__ = ['kernel_size', 'stride', 'padding', 'ceil_mode', 'count_include_pad', 'divisor_override'] def __init__(self, kernel_size, stride=None, padding=0, ceil_mode=False, count_include_pad=True, divisor_override=None): super(AvgPool3d, self).__init__() self.kernel_size = kernel_size self.stride = stride or kernel_size self.padding = padding self.ceil_mode = ceil_mode self.count_include_pad = count_include_pad self.divisor_override = divisor_override def forward(self, input): return F.avg_pool3d(input, self.kernel_size, self.stride, self.padding, self.ceil_mode, self.count_include_pad, self.divisor_override) def __setstate__(self, d): super(AvgPool3d, self).__setstate__(d) self.__dict__.setdefault('padding', 0) self.__dict__.setdefault('ceil_mode', False) self.__dict__.setdefault('count_include_pad', True)
[docs]class FractionalMaxPool2d(Module): r"""Applies a 2D fractional max pooling over an input signal composed of several input planes. Fractional MaxPooling is described in detail in the paper Fractional MaxPooling_ by Ben Graham The max-pooling operation is applied in :math:kH \times kW regions by a stochastic step size determined by the target output size. The number of output features is equal to the number of input planes. Args: kernel_size: the size of the window to take a max over. Can be a single number k (for a square kernel of k x k) or a tuple (kh, kw) output_size: the target output size of the image of the form oH x oW. Can be a tuple (oH, oW) or a single number oH for a square image oH x oH output_ratio: If one wants to have an output size as a ratio of the input size, this option can be given. This has to be a number or tuple in the range (0, 1) return_indices: if True, will return the indices along with the outputs. Useful to pass to :meth:nn.MaxUnpool2d. Default: False Examples: >>> # pool of square window of size=3, and target output size 13x12 >>> m = nn.FractionalMaxPool2d(3, output_size=(13, 12)) >>> # pool of square window and target output size being half of input image size >>> m = nn.FractionalMaxPool2d(3, output_ratio=(0.5, 0.5)) >>> input = torch.randn(20, 16, 50, 32) >>> output = m(input) .. _Fractional MaxPooling: http://arxiv.org/abs/1412.6071 """ __constants__ = ['kernel_size', 'return_indices', 'output_size', 'output_ratio'] def __init__(self, kernel_size, output_size=None, output_ratio=None, return_indices=False, _random_samples=None): super(FractionalMaxPool2d, self).__init__() self.kernel_size = _pair(kernel_size) self.return_indices = return_indices self.register_buffer('_random_samples', _random_samples) self.output_size = _pair(output_size) if output_size is not None else None self.output_ratio = _pair(output_ratio) if output_ratio is not None else None if output_size is None and output_ratio is None: raise ValueError("FractionalMaxPool2d requires specifying either " "an output size, or a pooling ratio") if output_size is not None and output_ratio is not None: raise ValueError("only one of output_size and output_ratio may be specified") if self.output_ratio is not None: if not (0 < self.output_ratio[0] < 1 and 0 < self.output_ratio[1] < 1): raise ValueError("output_ratio must be between 0 and 1 (got {})" .format(output_ratio)) def forward(self, input): return F.fractional_max_pool2d( input, self.kernel_size, self.output_size, self.output_ratio, self.return_indices, _random_samples=self._random_samples)
class FractionalMaxPool3d(Module): r"""Applies a 3D fractional max pooling over an input signal composed of several input planes. Fractional MaxPooling is described in detail in the paper Fractional MaxPooling_ by Ben Graham The max-pooling operation is applied in :math:kTxkHxkW regions by a stochastic step size determined by the target output size. The number of output features is equal to the number of input planes. Args: kernel_size: the size of the window to take a max over. Can be a single number k (for a square kernel of k x k x k) or a tuple (kt x kh x kw) output_size: the target output size of the image of the form oT x oH x oW. Can be a tuple (oT, oH, oW) or a single number oH for a square image oH x oH x oH output_ratio: If one wants to have an output size as a ratio of the input size, this option can be given. This has to be a number or tuple in the range (0, 1) return_indices: if True, will return the indices along with the outputs. Useful to pass to :meth:nn.MaxUnpool3d. Default: False Examples: >>> # pool of cubic window of size=3, and target output size 13x12x11 >>> m = nn.FractionalMaxPool3d(3, output_size=(13, 12, 11)) >>> # pool of cubic window and target output size being half of input size >>> m = nn.FractionalMaxPool3d(3, output_ratio=(0.5, 0.5, 0.5)) >>> input = torch.randn(20, 16, 50, 32, 16) >>> output = m(input) .. _Fractional MaxPooling: http://arxiv.org/abs/1412.6071 """ __constants__ = ['kernel_size', 'return_indices', 'output_size', 'output_ratio'] def __init__(self, kernel_size, output_size=None, output_ratio=None, return_indices=False, _random_samples=None): super(FractionalMaxPool3d, self).__init__() self.kernel_size = _triple(kernel_size) self.return_indices = return_indices self.register_buffer('_random_samples', _random_samples) self.output_size = _triple(output_size) if output_size is not None else None self.output_ratio = _triple(output_ratio) if output_ratio is not None else None if output_size is None and output_ratio is None: raise ValueError("FractionalMaxPool3d requires specifying either " "an output size, or a pooling ratio") if output_size is not None and output_ratio is not None: raise ValueError("only one of output_size and output_ratio may be specified") if self.output_ratio is not None: if not (0 < self.output_ratio[0] < 1 and 0 < self.output_ratio[1] < 1 and 0 < self.output_ratio[2] < 1): raise ValueError("output_ratio must be between 0 and 1 (got {})" .format(output_ratio)) def forward(self, input): return F.fractional_max_pool3d( input, self.kernel_size, self.output_size, self.output_ratio, self.return_indices, _random_samples=self._random_samples) class _LPPoolNd(Module): __constants__ = ['norm_type', 'kernel_size', 'stride', 'ceil_mode'] def __init__(self, norm_type, kernel_size, stride=None, ceil_mode=False): super(_LPPoolNd, self).__init__() self.norm_type = norm_type self.kernel_size = kernel_size self.stride = stride self.ceil_mode = ceil_mode def extra_repr(self): return 'norm_type={norm_type}, kernel_size={kernel_size}, stride={stride}, ' \ 'ceil_mode={ceil_mode}'.format(**self.__dict__)
[docs]class LPPool1d(_LPPoolNd): r"""Applies a 1D power-average pooling over an input signal composed of several input planes. On each window, the function computed is: .. math:: f(X) = \sqrt[p]{\sum_{x \in X} x^{p}} - At p = :math:\infty, one gets Max Pooling - At p = 1, one gets Sum Pooling (which is proportional to Average Pooling) .. note:: If the sum to the power of p is zero, the gradient of this function is not defined. This implementation will set the gradient to zero in this case. Args: kernel_size: a single int, the size of the window stride: a single int, the stride of the window. Default value is :attr:kernel_size ceil_mode: when True, will use ceil instead of floor to compute the output shape Shape: - Input: :math:(N, C, L_{in}) - Output: :math:(N, C, L_{out}), where .. math:: L_{out} = \left\lfloor\frac{L_{in} - \text{kernel\_size}}{\text{stride}} + 1\right\rfloor Examples:: >>> # power-2 pool of window of length 3, with stride 2. >>> m = nn.LPPool1d(2, 3, stride=2) >>> input = torch.randn(20, 16, 50) >>> output = m(input) """ def forward(self, input): return F.lp_pool1d(input, float(self.norm_type), self.kernel_size, self.stride, self.ceil_mode)
[docs]class LPPool2d(_LPPoolNd): r"""Applies a 2D power-average pooling over an input signal composed of several input planes. On each window, the function computed is: .. math:: f(X) = \sqrt[p]{\sum_{x \in X} x^{p}} - At p = :math:\infty, one gets Max Pooling - At p = 1, one gets Sum Pooling (which is proportional to average pooling) The parameters :attr:kernel_size, :attr:stride can either be: - a single int -- in which case the same value is used for the height and width dimension - a tuple of two ints -- in which case, the first int is used for the height dimension, and the second int for the width dimension .. note:: If the sum to the power of p is zero, the gradient of this function is not defined. This implementation will set the gradient to zero in this case. Args: kernel_size: the size of the window stride: the stride of the window. Default value is :attr:kernel_size ceil_mode: when True, will use ceil instead of floor to compute the output shape Shape: - Input: :math:(N, C, H_{in}, W_{in}) - Output: :math:(N, C, H_{out}, W_{out}), where .. math:: H_{out} = \left\lfloor\frac{H_{in} - \text{kernel\_size}[0]}{\text{stride}[0]} + 1\right\rfloor .. math:: W_{out} = \left\lfloor\frac{W_{in} - \text{kernel\_size}[1]}{\text{stride}[1]} + 1\right\rfloor Examples:: >>> # power-2 pool of square window of size=3, stride=2 >>> m = nn.LPPool2d(2, 3, stride=2) >>> # pool of non-square window of power 1.2 >>> m = nn.LPPool2d(1.2, (3, 2), stride=(2, 1)) >>> input = torch.randn(20, 16, 50, 32) >>> output = m(input) """ def forward(self, input): return F.lp_pool2d(input, float(self.norm_type), self.kernel_size, self.stride, self.ceil_mode)
class _AdaptiveMaxPoolNd(Module): __constants__ = ['output_size', 'return_indices'] def __init__(self, output_size, return_indices=False): super(_AdaptiveMaxPoolNd, self).__init__() self.output_size = output_size self.return_indices = return_indices def extra_repr(self): return 'output_size={}'.format(self.output_size) # FIXME (by @ssnl): Improve adaptive pooling docs: specify what the input and # output shapes are, and how the operation computes output.
[docs]class AdaptiveMaxPool1d(_AdaptiveMaxPoolNd): r"""Applies a 1D adaptive max pooling over an input signal composed of several input planes. The output size is H, for any input size. The number of output features is equal to the number of input planes. Args: output_size: the target output size H return_indices: if True, will return the indices along with the outputs. Useful to pass to nn.MaxUnpool1d. Default: False Examples: >>> # target output size of 5 >>> m = nn.AdaptiveMaxPool1d(5) >>> input = torch.randn(1, 64, 8) >>> output = m(input) """ def forward(self, input): return F.adaptive_max_pool1d(input, self.output_size, self.return_indices)
[docs]class AdaptiveMaxPool2d(_AdaptiveMaxPoolNd): r"""Applies a 2D adaptive max pooling over an input signal composed of several input planes. The output is of size H x W, for any input size. The number of output features is equal to the number of input planes. Args: output_size: the target output size of the image of the form H x W. Can be a tuple (H, W) or a single H for a square image H x H. H and W can be either a int, or None which means the size will be the same as that of the input. return_indices: if True, will return the indices along with the outputs. Useful to pass to nn.MaxUnpool2d. Default: False Examples: >>> # target output size of 5x7 >>> m = nn.AdaptiveMaxPool2d((5,7)) >>> input = torch.randn(1, 64, 8, 9) >>> output = m(input) >>> # target output size of 7x7 (square) >>> m = nn.AdaptiveMaxPool2d(7) >>> input = torch.randn(1, 64, 10, 9) >>> output = m(input) >>> # target output size of 10x7 >>> m = nn.AdaptiveMaxPool2d((None, 7)) >>> input = torch.randn(1, 64, 10, 9) >>> output = m(input) """ def forward(self, input): return F.adaptive_max_pool2d(input, self.output_size, self.return_indices)
[docs]class AdaptiveMaxPool3d(_AdaptiveMaxPoolNd): r"""Applies a 3D adaptive max pooling over an input signal composed of several input planes. The output is of size D x H x W, for any input size. The number of output features is equal to the number of input planes. Args: output_size: the target output size of the image of the form D x H x W. Can be a tuple (D, H, W) or a single D for a cube D x D x D. D, H and W can be either a int, or None which means the size will be the same as that of the input. return_indices: if True, will return the indices along with the outputs. Useful to pass to nn.MaxUnpool3d. Default: False Examples: >>> # target output size of 5x7x9 >>> m = nn.AdaptiveMaxPool3d((5,7,9)) >>> input = torch.randn(1, 64, 8, 9, 10) >>> output = m(input) >>> # target output size of 7x7x7 (cube) >>> m = nn.AdaptiveMaxPool3d(7) >>> input = torch.randn(1, 64, 10, 9, 8) >>> output = m(input) >>> # target output size of 7x9x8 >>> m = nn.AdaptiveMaxPool3d((7, None, None)) >>> input = torch.randn(1, 64, 10, 9, 8) >>> output = m(input) """ def forward(self, input): return F.adaptive_max_pool3d(input, self.output_size, self.return_indices)
class _AdaptiveAvgPoolNd(Module): __constants__ = ['output_size'] def __init__(self, output_size): super(_AdaptiveAvgPoolNd, self).__init__() self.output_size = output_size def extra_repr(self): return 'output_size={}'.format(self.output_size)
[docs]class AdaptiveAvgPool1d(_AdaptiveAvgPoolNd): r"""Applies a 1D adaptive average pooling over an input signal composed of several input planes. The output size is H, for any input size. The number of output features is equal to the number of input planes. Args: output_size: the target output size H Examples: >>> # target output size of 5 >>> m = nn.AdaptiveAvgPool1d(5) >>> input = torch.randn(1, 64, 8) >>> output = m(input) """ def forward(self, input): return F.adaptive_avg_pool1d(input, self.output_size)
[docs]class AdaptiveAvgPool2d(_AdaptiveAvgPoolNd): r"""Applies a 2D adaptive average pooling over an input signal composed of several input planes. The output is of size H x W, for any input size. The number of output features is equal to the number of input planes. Args: output_size: the target output size of the image of the form H x W. Can be a tuple (H, W) or a single H for a square image H x H. H and W can be either a int, or None which means the size will be the same as that of the input. Examples: >>> # target output size of 5x7 >>> m = nn.AdaptiveAvgPool2d((5,7)) >>> input = torch.randn(1, 64, 8, 9) >>> output = m(input) >>> # target output size of 7x7 (square) >>> m = nn.AdaptiveAvgPool2d(7) >>> input = torch.randn(1, 64, 10, 9) >>> output = m(input) >>> # target output size of 10x7 >>> m = nn.AdaptiveMaxPool2d((None, 7)) >>> input = torch.randn(1, 64, 10, 9) >>> output = m(input) """ def forward(self, input): return F.adaptive_avg_pool2d(input, self.output_size)
[docs]class AdaptiveAvgPool3d(_AdaptiveAvgPoolNd): r"""Applies a 3D adaptive average pooling over an input signal composed of several input planes. The output is of size D x H x W, for any input size. The number of output features is equal to the number of input planes. Args: output_size: the target output size of the form D x H x W. Can be a tuple (D, H, W) or a single number D for a cube D x D x D. D, H and W can be either a int, or None which means the size will be the same as that of the input. Examples: >>> # target output size of 5x7x9 >>> m = nn.AdaptiveAvgPool3d((5,7,9)) >>> input = torch.randn(1, 64, 8, 9, 10) >>> output = m(input) >>> # target output size of 7x7x7 (cube) >>> m = nn.AdaptiveAvgPool3d(7) >>> input = torch.randn(1, 64, 10, 9, 8) >>> output = m(input) >>> # target output size of 7x9x8 >>> m = nn.AdaptiveMaxPool3d((7, None, None)) >>> input = torch.randn(1, 64, 10, 9, 8) >>> output = m(input) """ def forward(self, input): return F.adaptive_avg_pool3d(input, self.output_size)