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| from typing import List, Optional | |
| from torch import Tensor | |
| from .module import Module | |
| from .utils import _single, _pair, _triple | |
| from .. import functional as F | |
| from ..common_types import (_size_any_t, _size_1_t, _size_2_t, _size_3_t, | |
| _ratio_3_t, _ratio_2_t, _size_any_opt_t, _size_2_opt_t, _size_3_opt_t) | |
| __all__ = ['MaxPool1d', 'MaxPool2d', 'MaxPool3d', 'MaxUnpool1d', 'MaxUnpool2d', 'MaxUnpool3d', | |
| 'AvgPool1d', 'AvgPool2d', 'AvgPool3d', 'FractionalMaxPool2d', 'FractionalMaxPool3d', 'LPPool1d', | |
| 'LPPool2d', 'LPPool3d', 'AdaptiveMaxPool1d', 'AdaptiveMaxPool2d', 'AdaptiveMaxPool3d', | |
| 'AdaptiveAvgPool1d', 'AdaptiveAvgPool2d', 'AdaptiveAvgPool3d'] | |
| class _MaxPoolNd(Module): | |
| __constants__ = ['kernel_size', 'stride', 'padding', 'dilation', | |
| 'return_indices', 'ceil_mode'] | |
| return_indices: bool | |
| ceil_mode: bool | |
| def __init__(self, kernel_size: _size_any_t, stride: Optional[_size_any_t] = None, | |
| padding: _size_any_t = 0, dilation: _size_any_t = 1, | |
| return_indices: bool = False, ceil_mode: bool = False) -> None: | |
| super().__init__() | |
| self.kernel_size = kernel_size | |
| self.stride = stride if (stride is not None) else kernel_size | |
| self.padding = padding | |
| self.dilation = dilation | |
| self.return_indices = return_indices | |
| self.ceil_mode = ceil_mode | |
| def extra_repr(self) -> str: | |
| return 'kernel_size={kernel_size}, stride={stride}, padding={padding}' \ | |
| ', dilation={dilation}, ceil_mode={ceil_mode}'.format(**self.__dict__) | |
| 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 padded with negative infinity on both sides | |
| for :attr:`padding` number of points. :attr:`dilation` is the stride between the elements within the | |
| sliding window. This `link`_ has a nice visualization of the pooling parameters. | |
| Note: | |
| When ceil_mode=True, sliding windows are allowed to go off-bounds if they start within the left padding | |
| or the input. Sliding windows that would start in the right padded region are ignored. | |
| Args: | |
| kernel_size: The size of the sliding window, must be > 0. | |
| stride: The stride of the sliding window, must be > 0. Default value is :attr:`kernel_size`. | |
| padding: Implicit negative infinity padding to be added on both sides, must be >= 0 and <= kernel_size / 2. | |
| dilation: The stride between elements within a sliding window, must be > 0. | |
| return_indices: If ``True``, will return the argmax along with the max values. | |
| Useful for :class:`torch.nn.MaxUnpool1d` later | |
| ceil_mode: If ``True``, will use `ceil` instead of `floor` to compute the output shape. This | |
| ensures that every element in the input tensor is covered by a sliding window. | |
| Shape: | |
| - Input: :math:`(N, C, L_{in})` or :math:`(C, L_{in})`. | |
| - Output: :math:`(N, C, L_{out})` or :math:`(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 | |
| """ | |
| kernel_size: _size_1_t | |
| stride: _size_1_t | |
| padding: _size_1_t | |
| dilation: _size_1_t | |
| def forward(self, input: Tensor): | |
| return F.max_pool1d(input, self.kernel_size, self.stride, | |
| self.padding, self.dilation, ceil_mode=self.ceil_mode, | |
| return_indices=self.return_indices) | |
| 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 padded with negative infinity 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. | |
| Note: | |
| When ceil_mode=True, sliding windows are allowed to go off-bounds if they start within the left padding | |
| or the input. Sliding windows that would start in the right padded region are ignored. | |
| 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 negative infinity 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})` or :math:`(C, H_{in}, W_{in})` | |
| - Output: :math:`(N, C, H_{out}, W_{out})` or :math:`(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 | |
| """ | |
| kernel_size: _size_2_t | |
| stride: _size_2_t | |
| padding: _size_2_t | |
| dilation: _size_2_t | |
| def forward(self, input: Tensor): | |
| return F.max_pool2d(input, self.kernel_size, self.stride, | |
| self.padding, self.dilation, ceil_mode=self.ceil_mode, | |
| return_indices=self.return_indices) | |
| 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 padded with negative infinity 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. | |
| Note: | |
| When ceil_mode=True, sliding windows are allowed to go off-bounds if they start within the left padding | |
| or the input. Sliding windows that would start in the right padded region are ignored. | |
| 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 negative infinity 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})` or :math:`(C, D_{in}, H_{in}, W_{in})`. | |
| - Output: :math:`(N, C, D_{out}, H_{out}, W_{out})` or :math:`(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 | |
| kernel_size: _size_3_t | |
| stride: _size_3_t | |
| padding: _size_3_t | |
| dilation: _size_3_t | |
| def forward(self, input: Tensor): | |
| return F.max_pool3d(input, self.kernel_size, self.stride, | |
| self.padding, self.dilation, ceil_mode=self.ceil_mode, | |
| return_indices=self.return_indices) | |
| class _MaxUnpoolNd(Module): | |
| def extra_repr(self) -> str: | |
| return f'kernel_size={self.kernel_size}, stride={self.stride}, padding={self.padding}' | |
| 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: | |
| This operation may behave nondeterministically when the input indices has repeat values. | |
| See https://github.com/pytorch/pytorch/issues/80827 and :doc:`/notes/randomness` for more information. | |
| .. 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})` or :math:`(C, H_{in})`. | |
| - Output: :math:`(N, C, H_{out})` or :math:`(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:: | |
| >>> # xdoctest: +IGNORE_WANT("do other tests modify the global state?") | |
| >>> 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.]]]) | |
| """ | |
| kernel_size: _size_1_t | |
| stride: _size_1_t | |
| padding: _size_1_t | |
| def __init__(self, kernel_size: _size_1_t, stride: Optional[_size_1_t] = None, padding: _size_1_t = 0) -> None: | |
| super().__init__() | |
| self.kernel_size = _single(kernel_size) | |
| self.stride = _single(stride if (stride is not None) else kernel_size) | |
| self.padding = _single(padding) | |
| def forward(self, input: Tensor, indices: Tensor, output_size: Optional[List[int]] = None) -> Tensor: | |
| return F.max_unpool1d(input, indices, self.kernel_size, self.stride, | |
| self.padding, output_size) | |
| 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: | |
| This operation may behave nondeterministically when the input indices has repeat values. | |
| See https://github.com/pytorch/pytorch/issues/80827 and :doc:`/notes/randomness` for more information. | |
| .. 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})` or :math:`(C, H_{in}, W_{in})`. | |
| - Output: :math:`(N, C, H_{out}, W_{out})` or :math:`(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.]]]]) | |
| >>> # Now using output_size to resolve an ambiguous size for the inverse | |
| >>> input = torch.torch.tensor([[[[ 1., 2., 3., 4., 5.], | |
| [ 6., 7., 8., 9., 10.], | |
| [11., 12., 13., 14., 15.], | |
| [16., 17., 18., 19., 20.]]]]) | |
| >>> output, indices = pool(input) | |
| >>> # This call will not work without specifying output_size | |
| >>> unpool(output, indices, output_size=input.size()) | |
| tensor([[[[ 0., 0., 0., 0., 0.], | |
| [ 0., 7., 0., 9., 0.], | |
| [ 0., 0., 0., 0., 0.], | |
| [ 0., 17., 0., 19., 0.]]]]) | |
| """ | |
| kernel_size: _size_2_t | |
| stride: _size_2_t | |
| padding: _size_2_t | |
| def __init__(self, kernel_size: _size_2_t, stride: Optional[_size_2_t] = None, padding: _size_2_t = 0) -> None: | |
| super().__init__() | |
| self.kernel_size = _pair(kernel_size) | |
| self.stride = _pair(stride if (stride is not None) else kernel_size) | |
| self.padding = _pair(padding) | |
| def forward(self, input: Tensor, indices: Tensor, output_size: Optional[List[int]] = None) -> Tensor: | |
| return F.max_unpool2d(input, indices, self.kernel_size, self.stride, | |
| self.padding, output_size) | |
| 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: | |
| This operation may behave nondeterministically when the input indices has repeat values. | |
| See https://github.com/pytorch/pytorch/issues/80827 and :doc:`/notes/randomness` for more information. | |
| .. 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})` or :math:`(C, D_{in}, H_{in}, W_{in})`. | |
| - Output: :math:`(N, C, D_{out}, H_{out}, W_{out})` or :math:`(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]) | |
| """ | |
| kernel_size: _size_3_t | |
| stride: _size_3_t | |
| padding: _size_3_t | |
| def __init__(self, kernel_size: _size_3_t, stride: Optional[_size_3_t] = None, padding: _size_3_t = 0) -> None: | |
| super().__init__() | |
| self.kernel_size = _triple(kernel_size) | |
| self.stride = _triple(stride if (stride is not None) else kernel_size) | |
| self.padding = _triple(padding) | |
| def forward(self, input: Tensor, indices: Tensor, output_size: Optional[List[int]] = None) -> Tensor: | |
| return F.max_unpool3d(input, indices, self.kernel_size, self.stride, | |
| self.padding, output_size) | |
| class _AvgPoolNd(Module): | |
| __constants__ = ['kernel_size', 'stride', 'padding', 'ceil_mode', 'count_include_pad'] | |
| def extra_repr(self) -> str: | |
| return f'kernel_size={self.kernel_size}, stride={self.stride}, padding={self.padding}' | |
| 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. | |
| Note: | |
| When ceil_mode=True, sliding windows are allowed to go off-bounds if they start within the left padding | |
| or the input. Sliding windows that would start in the right padded region are ignored. | |
| 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})` or :math:`(C, L_{in})`. | |
| - Output: :math:`(N, C, L_{out})` or :math:`(C, L_{out})`, where | |
| .. math:: | |
| L_{out} = \left\lfloor \frac{L_{in} + | |
| 2 \times \text{padding} - \text{kernel\_size}}{\text{stride}} + 1\right\rfloor | |
| Per the note above, if ``ceil_mode`` is True and :math:`(L_{out} - 1) \times \text{stride} \geq L_{in} | |
| + \text{padding}`, we skip the last window as it would start in the right padded region, resulting in | |
| :math:`L_{out}` being reduced by one. | |
| 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.]]]) | |
| """ | |
| kernel_size: _size_1_t | |
| stride: _size_1_t | |
| padding: _size_1_t | |
| ceil_mode: bool | |
| count_include_pad: bool | |
| def __init__(self, kernel_size: _size_1_t, stride: _size_1_t = None, padding: _size_1_t = 0, ceil_mode: bool = False, | |
| count_include_pad: bool = True) -> None: | |
| super().__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: Tensor) -> Tensor: | |
| return F.avg_pool1d( | |
| input, self.kernel_size, self.stride, self.padding, self.ceil_mode, | |
| self.count_include_pad) | |
| 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. | |
| Note: | |
| When ceil_mode=True, sliding windows are allowed to go off-bounds if they start within the left padding | |
| or the input. Sliding windows that would start in the right padded region are ignored. | |
| 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 size of the pooling region will be used. | |
| Shape: | |
| - Input: :math:`(N, C, H_{in}, W_{in})` or :math:`(C, H_{in}, W_{in})`. | |
| - Output: :math:`(N, C, H_{out}, W_{out})` or :math:`(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 | |
| Per the note above, if ``ceil_mode`` is True and :math:`(H_{out} - 1)\times \text{stride}[0]\geq H_{in} | |
| + \text{padding}[0]`, we skip the last window as it would start in the bottom padded region, | |
| resulting in :math:`H_{out}` being reduced by one. | |
| The same applies for :math:`W_{out}`. | |
| 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'] | |
| kernel_size: _size_2_t | |
| stride: _size_2_t | |
| padding: _size_2_t | |
| ceil_mode: bool | |
| count_include_pad: bool | |
| def __init__(self, kernel_size: _size_2_t, stride: Optional[_size_2_t] = None, padding: _size_2_t = 0, | |
| ceil_mode: bool = False, count_include_pad: bool = True, divisor_override: Optional[int] = None) -> None: | |
| super().__init__() | |
| self.kernel_size = kernel_size | |
| self.stride = stride if (stride is not None) else 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: Tensor) -> Tensor: | |
| return F.avg_pool2d(input, self.kernel_size, self.stride, | |
| self.padding, self.ceil_mode, self.count_include_pad, self.divisor_override) | |
| 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. | |
| Note: | |
| When ceil_mode=True, sliding windows are allowed to go off-bounds if they start within the left padding | |
| or the input. Sliding windows that would start in the right padded region are ignored. | |
| 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})` or :math:`(C, D_{in}, H_{in}, W_{in})`. | |
| - Output: :math:`(N, C, D_{out}, H_{out}, W_{out})` or | |
| :math:`(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 | |
| Per the note above, if ``ceil_mode`` is True and :math:`(D_{out} - 1)\times \text{stride}[0]\geq D_{in} | |
| + \text{padding}[0]`, we skip the last window as it would start in the padded region, | |
| resulting in :math:`D_{out}` being reduced by one. | |
| The same applies for :math:`W_{out}` and :math:`H_{out}`. | |
| 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'] | |
| kernel_size: _size_3_t | |
| stride: _size_3_t | |
| padding: _size_3_t | |
| ceil_mode: bool | |
| count_include_pad: bool | |
| def __init__(self, kernel_size: _size_3_t, stride: Optional[_size_3_t] = None, padding: _size_3_t = 0, | |
| ceil_mode: bool = False, count_include_pad: bool = True, divisor_override: Optional[int] = None) -> None: | |
| super().__init__() | |
| self.kernel_size = kernel_size | |
| self.stride = stride if (stride is not None) else 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: Tensor) -> Tensor: | |
| 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().__setstate__(d) | |
| self.__dict__.setdefault('padding', 0) | |
| self.__dict__.setdefault('ceil_mode', False) | |
| self.__dict__.setdefault('count_include_pad', True) | |
| 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. | |
| .. note:: Exactly one of ``output_size`` or ``output_ratio`` must be defined. | |
| 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`. | |
| Note that we must have :math:`kH + oH - 1 <= H_{in}` and :math:`kW + oW - 1 <= W_{in}` | |
| 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). | |
| Note that we must have :math:`kH + (output\_ratio\_H * H_{in}) - 1 <= H_{in}` | |
| and :math:`kW + (output\_ratio\_W * W_{in}) - 1 <= W_{in}` | |
| return_indices: if ``True``, will return the indices along with the outputs. | |
| Useful to pass to :meth:`nn.MaxUnpool2d`. Default: ``False`` | |
| Shape: | |
| - Input: :math:`(N, C, H_{in}, W_{in})` or :math:`(C, H_{in}, W_{in})`. | |
| - Output: :math:`(N, C, H_{out}, W_{out})` or :math:`(C, H_{out}, W_{out})`, where | |
| :math:`(H_{out}, W_{out})=\text{output\_size}` or | |
| :math:`(H_{out}, W_{out})=\text{output\_ratio} \times (H_{in}, W_{in})`. | |
| 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: | |
| https://arxiv.org/abs/1412.6071 | |
| """ | |
| __constants__ = ['kernel_size', 'return_indices', 'output_size', | |
| 'output_ratio'] | |
| kernel_size: _size_2_t | |
| return_indices: bool | |
| output_size: _size_2_t | |
| output_ratio: _ratio_2_t | |
| def __init__(self, kernel_size: _size_2_t, output_size: Optional[_size_2_t] = None, | |
| output_ratio: Optional[_ratio_2_t] = None, | |
| return_indices: bool = False, _random_samples=None) -> None: | |
| super().__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(f"output_ratio must be between 0 and 1 (got {output_ratio})") | |
| def forward(self, input: Tensor): | |
| 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:`kT \times 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. | |
| .. note:: Exactly one of ``output_size`` or ``output_ratio`` must be defined. | |
| 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`` | |
| Shape: | |
| - Input: :math:`(N, C, T_{in}, H_{in}, W_{in})` or :math:`(C, T_{in}, H_{in}, W_{in})`. | |
| - Output: :math:`(N, C, T_{out}, H_{out}, W_{out})` or :math:`(C, T_{out}, H_{out}, W_{out})`, where | |
| :math:`(T_{out}, H_{out}, W_{out})=\text{output\_size}` or | |
| :math:`(T_{out}, H_{out}, W_{out})=\text{output\_ratio} \times (T_{in}, H_{in}, W_{in})` | |
| 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: | |
| https://arxiv.org/abs/1412.6071 | |
| """ | |
| __constants__ = ['kernel_size', 'return_indices', 'output_size', | |
| 'output_ratio'] | |
| kernel_size: _size_3_t | |
| return_indices: bool | |
| output_size: _size_3_t | |
| output_ratio: _ratio_3_t | |
| def __init__(self, kernel_size: _size_3_t, output_size: Optional[_size_3_t] = None, | |
| output_ratio: Optional[_ratio_3_t] = None, | |
| return_indices: bool = False, _random_samples=None) -> None: | |
| super().__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(f"output_ratio must be between 0 and 1 (got {output_ratio})") | |
| def forward(self, input: Tensor): | |
| 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'] | |
| norm_type: float | |
| ceil_mode: bool | |
| def __init__(self, norm_type: float, kernel_size: _size_any_t, stride: Optional[_size_any_t] = None, | |
| ceil_mode: bool = False) -> None: | |
| super().__init__() | |
| self.norm_type = norm_type | |
| self.kernel_size = kernel_size | |
| self.stride = stride | |
| self.ceil_mode = ceil_mode | |
| def extra_repr(self) -> str: | |
| return 'norm_type={norm_type}, kernel_size={kernel_size}, stride={stride}, ' \ | |
| 'ceil_mode={ceil_mode}'.format(**self.__dict__) | |
| 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})` or :math:`(C, L_{in})`. | |
| - Output: :math:`(N, C, L_{out})` or :math:`(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) | |
| """ | |
| kernel_size: _size_1_t | |
| stride: _size_1_t | |
| def forward(self, input: Tensor) -> Tensor: | |
| return F.lp_pool1d(input, float(self.norm_type), self.kernel_size, | |
| self.stride, self.ceil_mode) | |
| 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})` or :math:`(C, H_{in}, W_{in})`. | |
| - Output: :math:`(N, C, H_{out}, W_{out})` or :math:`(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) | |
| """ | |
| kernel_size: _size_2_t | |
| stride: _size_2_t | |
| def forward(self, input: Tensor) -> Tensor: | |
| return F.lp_pool2d(input, float(self.norm_type), self.kernel_size, | |
| self.stride, self.ceil_mode) | |
| class LPPool3d(_LPPoolNd): | |
| r"""Applies a 3D 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, width and depth 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 | |
| .. 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, D_{in}, H_{in}, W_{in})` or :math:`(C, D_{in}, H_{in}, W_{in})`. | |
| - Output: :math:`(N, C, D_{out}, H_{out}, W_{out})` or | |
| :math:`(C, D_{out}, H_{out}, W_{out})`, where | |
| .. math:: | |
| D_{out} = \left\lfloor\frac{D_{in} - \text{kernel\_size}[0]}{\text{stride}[0]} + 1\right\rfloor | |
| .. math:: | |
| H_{out} = \left\lfloor\frac{H_{in} - \text{kernel\_size}[1]}{\text{stride}[1]} + 1\right\rfloor | |
| .. math:: | |
| W_{out} = \left\lfloor\frac{W_{in} - \text{kernel\_size}[2]}{\text{stride}[2]} + 1\right\rfloor | |
| Examples:: | |
| >>> # power-2 pool of square window of size=3, stride=2 | |
| >>> m = nn.LPPool3d(2, 3, stride=2) | |
| >>> # pool of non-square window of power 1.2 | |
| >>> m = nn.LPPool3d(1.2, (3, 2, 2), stride=(2, 1, 2)) | |
| >>> input = torch.randn(20, 16, 50, 44, 31) | |
| >>> output = m(input) | |
| """ | |
| kernel_size: _size_3_t | |
| stride: _size_3_t | |
| def forward(self, input: Tensor) -> Tensor: | |
| return F.lp_pool3d(input, float(self.norm_type), self.kernel_size, | |
| self.stride, self.ceil_mode) | |
| class _AdaptiveMaxPoolNd(Module): | |
| __constants__ = ['output_size', 'return_indices'] | |
| return_indices: bool | |
| def __init__(self, output_size: _size_any_opt_t, return_indices: bool = False) -> None: | |
| super().__init__() | |
| self.output_size = output_size | |
| self.return_indices = return_indices | |
| def extra_repr(self) -> str: | |
| return f'output_size={self.output_size}' | |
| # FIXME (by @ssnl): Improve adaptive pooling docs: specify what the input and | |
| # output shapes are, and how the operation computes output. | |
| class AdaptiveMaxPool1d(_AdaptiveMaxPoolNd): | |
| r"""Applies a 1D adaptive max pooling over an input signal composed of several input planes. | |
| The output size is :math:`L_{out}`, for any input size. | |
| The number of output features is equal to the number of input planes. | |
| Args: | |
| output_size: the target output size :math:`L_{out}`. | |
| return_indices: if ``True``, will return the indices along with the outputs. | |
| Useful to pass to nn.MaxUnpool1d. Default: ``False`` | |
| Shape: | |
| - Input: :math:`(N, C, L_{in})` or :math:`(C, L_{in})`. | |
| - Output: :math:`(N, C, L_{out})` or :math:`(C, L_{out})`, where | |
| :math:`L_{out}=\text{output\_size}`. | |
| Examples: | |
| >>> # target output size of 5 | |
| >>> m = nn.AdaptiveMaxPool1d(5) | |
| >>> input = torch.randn(1, 64, 8) | |
| >>> output = m(input) | |
| """ | |
| output_size: _size_1_t | |
| def forward(self, input: Tensor): | |
| return F.adaptive_max_pool1d(input, self.output_size, self.return_indices) | |
| class AdaptiveMaxPool2d(_AdaptiveMaxPoolNd): | |
| r"""Applies a 2D adaptive max pooling over an input signal composed of several input planes. | |
| The output is of size :math:`H_{out} \times W_{out}`, 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 :math:`H_{out} \times W_{out}`. | |
| Can be a tuple :math:`(H_{out}, W_{out})` or a single :math:`H_{out}` for a | |
| square image :math:`H_{out} \times H_{out}`. :math:`H_{out}` and :math:`W_{out}` | |
| 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`` | |
| Shape: | |
| - Input: :math:`(N, C, H_{in}, W_{in})` or :math:`(C, H_{in}, W_{in})`. | |
| - Output: :math:`(N, C, H_{out}, W_{out})` or :math:`(C, H_{out}, W_{out})`, where | |
| :math:`(H_{out}, W_{out})=\text{output\_size}`. | |
| 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) | |
| """ | |
| output_size: _size_2_opt_t | |
| def forward(self, input: Tensor): | |
| return F.adaptive_max_pool2d(input, self.output_size, self.return_indices) | |
| class AdaptiveMaxPool3d(_AdaptiveMaxPoolNd): | |
| r"""Applies a 3D adaptive max pooling over an input signal composed of several input planes. | |
| The output is of size :math:`D_{out} \times H_{out} \times W_{out}`, 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 :math:`D_{out} \times H_{out} \times W_{out}`. | |
| Can be a tuple :math:`(D_{out}, H_{out}, W_{out})` or a single | |
| :math:`D_{out}` for a cube :math:`D_{out} \times D_{out} \times D_{out}`. | |
| :math:`D_{out}`, :math:`H_{out}` and :math:`W_{out}` 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`` | |
| Shape: | |
| - Input: :math:`(N, C, D_{in}, H_{in}, W_{in})` or :math:`(C, D_{in}, H_{in}, W_{in})`. | |
| - Output: :math:`(N, C, D_{out}, H_{out}, W_{out})` or :math:`(C, D_{out}, H_{out}, W_{out})`, | |
| where :math:`(D_{out}, H_{out}, W_{out})=\text{output\_size}`. | |
| 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) | |
| """ | |
| output_size: _size_3_opt_t | |
| def forward(self, input: Tensor): | |
| return F.adaptive_max_pool3d(input, self.output_size, self.return_indices) | |
| class _AdaptiveAvgPoolNd(Module): | |
| __constants__ = ['output_size'] | |
| def __init__(self, output_size: _size_any_opt_t) -> None: | |
| super().__init__() | |
| self.output_size = output_size | |
| def extra_repr(self) -> str: | |
| return f'output_size={self.output_size}' | |
| class AdaptiveAvgPool1d(_AdaptiveAvgPoolNd): | |
| r"""Applies a 1D adaptive average pooling over an input signal composed of several input planes. | |
| The output size is :math:`L_{out}`, for any input size. | |
| The number of output features is equal to the number of input planes. | |
| Args: | |
| output_size: the target output size :math:`L_{out}`. | |
| Shape: | |
| - Input: :math:`(N, C, L_{in})` or :math:`(C, L_{in})`. | |
| - Output: :math:`(N, C, L_{out})` or :math:`(C, L_{out})`, where | |
| :math:`L_{out}=\text{output\_size}`. | |
| Examples: | |
| >>> # target output size of 5 | |
| >>> m = nn.AdaptiveAvgPool1d(5) | |
| >>> input = torch.randn(1, 64, 8) | |
| >>> output = m(input) | |
| """ | |
| output_size: _size_1_t | |
| def forward(self, input: Tensor) -> Tensor: | |
| return F.adaptive_avg_pool1d(input, self.output_size) | |
| 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. | |
| Shape: | |
| - Input: :math:`(N, C, H_{in}, W_{in})` or :math:`(C, H_{in}, W_{in})`. | |
| - Output: :math:`(N, C, S_{0}, S_{1})` or :math:`(C, S_{0}, S_{1})`, where | |
| :math:`S=\text{output\_size}`. | |
| 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.AdaptiveAvgPool2d((None, 7)) | |
| >>> input = torch.randn(1, 64, 10, 9) | |
| >>> output = m(input) | |
| """ | |
| output_size: _size_2_opt_t | |
| def forward(self, input: Tensor) -> Tensor: | |
| return F.adaptive_avg_pool2d(input, self.output_size) | |
| 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. | |
| Shape: | |
| - Input: :math:`(N, C, D_{in}, H_{in}, W_{in})` or :math:`(C, D_{in}, H_{in}, W_{in})`. | |
| - Output: :math:`(N, C, S_{0}, S_{1}, S_{2})` or :math:`(C, S_{0}, S_{1}, S_{2})`, | |
| where :math:`S=\text{output\_size}`. | |
| 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.AdaptiveAvgPool3d((7, None, None)) | |
| >>> input = torch.randn(1, 64, 10, 9, 8) | |
| >>> output = m(input) | |
| """ | |
| output_size: _size_3_opt_t | |
| def forward(self, input: Tensor) -> Tensor: | |
| return F.adaptive_avg_pool3d(input, self.output_size) | |