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534 lines
20 KiB
Python
534 lines
20 KiB
Python
"""Functional interface"""
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import torch
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from . import _functions
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from .modules import utils
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from torch.nn._functions.conv import ConvNd
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from .modules.utils import _single, _pair, _triple
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# Convolutions
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def conv2d(input, weight, bias=None, stride=1, padding=0, dilation=1,
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groups=1):
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"""Applies a 2D convolution over an input image composed of several input
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planes.
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```
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The output value of the layer with input (b x iC x H x W) and filters
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(oC x iC x kH x kW) can be precisely described as:
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output[b_i][oc_i][h_i][w_i] = bias[oc_i]
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+ sum_iC sum_{oh = 0, oH-1} sum_{ow = 0, oW-1} \
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sum_{kh = 0 to kH-1} sum_{kw = 0 to kW-1}
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weight[oc_i][ic_i][kh][kw]
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* input[b_i][ic_i][stride_h * oh + kh)][stride_w * ow + kw)]
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```
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Note that depending of the size of your kernel, several (of the last)
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columns or rows of the input image might be lost. It is up to the user
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to add proper padding in images.
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Args:
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input: input tensor (minibatch x in_channels x iH x iW)
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weight: filters tensor (out_channels, in_channels/groups, kH, kW)
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bias: optional bias tensor (out_channels)
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stride: the stride of the convolving kernel. Can be a single number or
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a tuple (sh x sw). Default: 1
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padding: implicit zero padding on the input. Can be a single number or
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a tuple. Default: 0
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groups: split input into groups, in_channels should be divisible by
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the number of groups
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Output Shape: [ * , out_channels , * , * ] : Output shape is precisely
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minibatch
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x out_channels
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x floor((iH + 2*padH - kH) / dH + 1)
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x floor((iW + 2*padW - kW) / dW + 1)
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Examples:
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>>> # With square kernels and equal stride
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>>> filters = autograd.Variable(torch.randn(8,4,3,3))
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>>> inputs = autograd.Variable(torch.randn(1,4,5,5))
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>>> F.conv2d(inputs, filters, padding=1)
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"""
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f = ConvNd(_pair(stride), _pair(padding), _pair(dilation), False,
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_pair(0), groups)
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return f(input, weight, bias) if bias is not None else f(input, weight)
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def conv1d(input, weight, bias=None, stride=1, padding=0, dilation=1,
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groups=1):
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"""Applies a 1D convolution over an input signal composed of several input
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planes.
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```
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The output value of the layer with input (b x iC x W) and filters
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(oC x oC x kw) can be precisely described as:
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output[b_i][oc_i][w_i] = bias[oc_i]
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+ sum_iC sum_{ow = 0, oW-1} sum_{kw = 0 to kW-1}
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weight[oc_i][ic_i][kw] * input[b_i][ic_i][stride_w * ow + kw)]
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```
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Note that depending on the size of your kernel, several (of the last)
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columns of the input might be lost. It is up to the user
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to add proper padding.
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Args:
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input: input tensor of shape (minibatch x in_channels x iW)
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weight: filters of shape (out_channels, in_channels, kW)
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bias: optional bias of shape (out_channels)
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stride: the stride of the convolving kernel, default 1
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Output Shape:[ * , out_channels , * ] : Output shape is precisely
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minibatch
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x out_channels
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x floor((iW + 2*padW - kW) / dW + 1)
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Examples:
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>>> filters = autograd.Variable(torch.randn(33, 16, 3))
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>>> inputs = autograd.Variable(torch.randn(20, 16, 50))
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>>> F.conv1d(inputs)
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"""
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f = ConvNd(_single(stride), _single(padding), _single(dilation), False,
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_single(0), groups)
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return f(input, weight, bias) if bias is not None else f(input, weight)
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def conv3d(input, weight, bias=None, stride=1, padding=0, dilation=1,
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groups=1):
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"""Applies a 3D convolution over an input image composed of several input
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planes.
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Note that depending of the size of your kernel, several (of the last)
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columns or rows of the input image might be lost. It is up to the user
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to add proper padding in images.
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Args:
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input: input tensor of shape (minibatch x in_channels x iT x iH x iW)
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weight: filters tensor of shape (out_channels, in_channels, kT, kH, kW)
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bias: optional bias tensor of shape (out_channels)
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stride: the stride of the convolving kernel. Can be a single number or
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a tuple (st x sh x sw). Default: 1
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padding: implicit zero padding on the input. Can be a single number or
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a tuple. Default: 0
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Output Shape:[ * , out_channels , * , * , * ] : Output shape is precisely
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minibatch
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x out_channels x floor((iT + 2*padT - kT) / dT + 1)
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x floor((iH + 2*padH - kH) / dH + 1)
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x floor((iW + 2*padW - kW) / dW + 1)
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Examples:
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>>> filters = autograd.Variable(torch.randn(33, 16, 3, 3, 3))
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>>> inputs = autograd.Variable(torch.randn(20, 16, 50, 10, 20))
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>>> F.conv3d(inputs)
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"""
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f = ConvNd(_triple(stride), _triple(padding), _triple(dilation), False,
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_triple(0), groups)
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return f(input, weight, bias) if bias is not None else f(input, weight)
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def conv_transpose1d(input, weight, bias=None, stride=1, padding=0,
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output_padding=0, groups=1):
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f = ConvNd(_single(stride), _single(padding), _single(1), True,
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_single(output_padding), groups)
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return f(input, weight, bias) if bias is not None else f(input, weight)
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def conv_transpose2d(input, weight, bias=None, stride=1, padding=0,
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output_padding=0, groups=1):
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"""Applies a 2D transposed convolution operator over an input image
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composed of several input planes, sometimes also called "deconvolution"
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The operator multiplies each input value element-wise by a
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kernel, and sums over the outputs from all input feature planes.
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This module can be seen as the exact reverse of the conv2d function
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Args:
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input: input tensor of shape (minibatch x in_channels x iH x iW)
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weight: filters of shape (in_channels x out_channels x kH x kW)
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bias: optional bias of shape (out_channels)
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stride: the stride of the convolving kernel, a single number or a
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tuple (sh x sw). Default: 1
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padding: implicit zero padding on the input, a single number or a
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tuple (padh x padw). Default: 0
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groups: split input into groups, in_channels should be divisible by
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the number of groups
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output_padding: A zero-padding of 0 <= padding < stride that should be
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added to the output. Can be a single number or a tuple. Default: 0
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Output Shape:[ * , out_channels , * , * ] : Output shape is
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minibatch x
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out_channels x
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(iH - 1) * sH - 2*padH + kH + output_paddingH x
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(iW - 1) * sW - 2*padW + kW + output_paddingW
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Examples:
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>>> #TODO
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"""
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f = ConvNd(_pair(stride), _pair(padding), _pair(1), True,
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_pair(output_padding), groups)
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return f(input, weight, bias) if bias is not None else f(input, weight)
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def conv_transpose3d(input, weight, bias=None, stride=1, padding=0,
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output_padding=0, groups=1):
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"""Applies a 3D transposed convolution operator over an input image
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composed of several input planes, sometimes also called "deconvolution"
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The operator multiplies each input value element-wise by a
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kernel, and sums over the outputs from all input feature planes.
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This module can be seen as the exact reverse of the conv3d function
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Args:
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input: input tensor of shape (minibatch x in_channels x iT x iH x iW)
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weight: filters of shape (in_channels x out_channels x kH x kW)
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bias: optional bias of shape (out_channels)
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stride: the stride of the convolving kernel, a single number or a
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tuple (sh x sw). Default: 1
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padding: implicit zero padding on the input, a single number or a
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tuple (padh x padw). Default: 0
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Output Shape:[ * , out_channels , * , * , * ] : Output shape is precisely
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minibatch
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x out_channels
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x (iT - 1) * sT - 2*padT + kT
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x (iH - 1) * sH - 2*padH + kH
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x (iW - 1) * sW - 2*padW + kW
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Examples:
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>>> #TODO
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"""
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f = ConvNd(_triple(stride), _triple(padding), _triple(1), True,
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_triple(output_padding), groups)
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return f(input, weight, bias) if bias is not None else f(input, weight)
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# Pooling
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def avg_pool1d(input, kernel_size, stride=None, padding=0,
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ceil_mode=False, count_include_pad=True):
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r"""Applies a 1D average pooling over an input signal composed of several
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input planes.
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In the simplest case, the output value of the layer with input size :math:`(N, C, L)`,
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output :math:`(N, C, L_{out})` and :attr:`kernel_size` :math:`k`
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can be precisely described as:
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.. math::
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\begin{array}{ll}
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out(N_i, C_j, l) = 1 / k * \sum_{{m}=0}^{k}
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input(N_i, C_j, stride * l + m)
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\end{array}
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| If :attr:`padding` is non-zero, then the input is implicitly zero-padded on both sides
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for :attr:`padding` number of points
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The parameters :attr:`kernel_size`, :attr:`stride`, :attr:`padding` can each be
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an ``int`` or a one-element tuple.
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Args:
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kernel_size: the size of the window
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stride: the stride of the window. Default value is :attr:`kernel_size`
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padding: implicit zero padding to be added on both sides
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ceil_mode: when True, will use `ceil` instead of `floor` to compute the output shape
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count_include_pad: when True, will include the zero-padding in the averaging calculation
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Shape:
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- Input: :math:`(N, C, L_{in})`
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- Output: :math:`(N, C, L_{out})` where
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:math:`L_{out} = floor((L_{in} + 2 * padding - kernel\_size) / stride + 1)`
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Examples::
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>>> # pool of square window of size=3, stride=2
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>>> input = Variable(torch.Tensor([[[1,2,3,4,5,6,7]]]))
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>>> F.avg_pool1d(input, kernel_size=3, stride=2)
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Variable containing:
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(0 ,.,.) =
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2 4 6
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[torch.FloatTensor of size 1x1x3]
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"""
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if input.dim() != 3:
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raise ValueError('expected 3D input (got {} dimensions)'
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.format(input.dim()))
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kernel_size = _single(kernel_size) + (1,)
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stride = _single(stride) + (1,)
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padding = _single(padding) + (0,)
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f = _functions.thnn.AvgPool2d(kernel_size, stride, padding,
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ceil_mode, count_include_pad)
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return f(input.unsqueeze(3)).squeeze(3)
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def avg_pool2d(input, kernel_size, stride=None, padding=0,
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ceil_mode=False, count_include_pad=True):
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"""Applies 2D average-pooling operation in kh x kw regions by step size
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dh x dw steps. The number of output features is equal to the number of
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input planes.
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By default, the output of each pooling region is divided by the number of
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elements inside the padded image (which is usually kh x kw, except in some
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corner cases in which it can be smaller). You can also divide by the number
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of elements inside the original non-padded image.
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To switch between different division factors, set count_include_pad to
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True or False. If padW=padH=0, both options give the same results.
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Args:
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:param input: input tensor (minibatch x in_channels x iH x iW)
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:param kernel_size: size of the pooling region, a single number or a
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tuple (kh x kw)
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:param stride: stride of the pooling operation, a single number or a
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tuple (sh x sw). Default is equal to kernel size
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:param padding: implicit zero padding on the input, a single number or
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a tuple (padh x padw), Default: 0
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:param ceil_mode: operation that defines spatial output shape
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:param count_include_pad: divide by the number of elements inside the
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original non-padded image or kh * kw
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:return: output tensor of shape
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Output Shape: [ * , in_channels, * , * ] : Output shape is precisely
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minibatch
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x in_channels
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x op((iH + 2*padh - kh) / dh + 1)
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x op((iW + 2*padw - kw) / dw + 1)
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Examples:
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>>> #TODO
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"""
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return _functions.thnn.AvgPool2d(kernel_size, stride, padding,
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ceil_mode, count_include_pad)(input)
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def avg_pool3d(input, kernel_size, stride=None):
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"""Applies 3D average-pooling operation in kt x kh x kw regions by step
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size kt x dh x dw steps. The number of output features is equal to the
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number of input planes / dt.
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Args:
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:param input:
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:param kernel_size:
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:param stride:
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:return:
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Examples:
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>>> #TODO
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"""
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return _functions.thnn.AvgPool3d(kernel_size, stride)(input)
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# share the same interface
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def max_pool1d(input, kernel_size, stride=None, padding=0, dilation=1,
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ceil_mode=False, return_indices=False):
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return _functions.thnn.MaxPool1d(kernel_size, stride, padding, dilation,
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return_indices, ceil_mode)(input)
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def max_pool2d(input, kernel_size, stride=None, padding=0, dilation=1,
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ceil_mode=False, return_indices=False):
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return _functions.thnn.MaxPool2d(kernel_size, stride, padding, dilation,
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return_indices, ceil_mode)(input)
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def max_pool3d(input, kernel_size, stride=None, padding=0, dilation=1,
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ceil_mode=False, return_indices=False):
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return _functions.thnn.MaxPool3d(kernel_size, stride, padding, dilation,
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return_indices, ceil_mode)(input)
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def _unpool_output_size(input, kernel_size, stride, padding, output_size):
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input_size = input.size()
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default_size = []
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for d in range(len(kernel_size)):
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default_size.append((input_size[d + 2] - 1) * stride[d]
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+ kernel_size[d] - 2 * padding[d])
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if output_size is None:
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return default_size
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output_size = list(output_size)
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if len(output_size) == len(kernel_size) + 2:
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output_size = output_size[2:]
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if len(output_size) != len(kernel_size):
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raise ValueError("output_size should be a sequence containing "
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"{} or {} elements, but it has a length of '{}'"
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.format(len(kernel_size), len(kernel_size) + 2,
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len(output_size)))
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for d in range(len(kernel_size)):
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min_size = default_size[d] - stride[d]
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max_size = default_size[d] + stride[d]
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if not (min_size < output_size[d] < max_size):
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raise ValueError(
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'invalid output_size "{}" (dim {} must be between {} and {})'
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.format(output_size, d, min_size, max_size))
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return output_size
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def max_unpool1d(input, indices, kernel_size, stride=None, padding=0,
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output_size=None):
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kernel_size = _single(kernel_size)
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stride = _single(stride)
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padding = _single(padding)
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output_size = _unpool_output_size(input, kernel_size, stride, padding,
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output_size)
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f = _functions.thnn.MaxUnpool2d(output_size + [1])
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return f(input.unsqueeze(3), indices.unsqueeze(3)).squeeze(3)
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def max_unpool2d(input, indices, kernel_size, stride=None, padding=0,
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output_size=None):
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kernel_size = _pair(kernel_size)
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stride = _pair(stride)
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padding = _pair(padding)
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output_size = _unpool_output_size(input, kernel_size, stride, padding,
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output_size)
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f = _functions.thnn.MaxUnpool2d(output_size)
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return f(input, indices)
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def max_unpool3d(input, indices, kernel_size, stride=None, padding=0,
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output_size=None):
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kernel_size = _triple(kernel_size)
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stride = _triple(stride)
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padding = _triple(padding)
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output_size = _unpool_output_size(input, kernel_size, stride, padding,
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output_size)
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f = _functions.thnn.MaxUnpool3d(output_size, stride, padding)
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return f(input, indices)
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def lp_pool2d(input, norm_type, kernel_size, stride=None, ceil_mode=False):
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kw, kh = utils._pair(kernel_size)
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out = avg_pool2d(input.pow(norm_type), kernel_size, stride, 0, ceil_mode)
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return out.mul(kw * kh).pow(1./norm_type)
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# Activation functions
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def dropout(input, p=0.5, training=False, inplace=False):
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return _functions.dropout.Dropout(p, training, inplace)(input)
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def threshold(input, threshold, value, inplace=False):
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return _functions.thnn.auto.Threshold(threshold, value, inplace)(input)
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def relu(input, inplace=False):
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return _functions.thnn.auto.Threshold(0, 0, inplace)(input)
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def hardtanh(input, min_val=-1., max_val=1., inplace=False):
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return _functions.thnn.auto.Hardtanh(min_val, max_val, inplace)(input)
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def relu6(input, inplace=False):
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return _functions.thnn.auto.Hardtanh(0, 6, inplace)(input)
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def elu(input, alpha=1., inplace=False):
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return _functions.thnn.auto.ELU(alpha, inplace)(input)
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def leaky_relu(input, negative_slope=1e-2, inplace=False):
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return _functions.thnn.auto.LeakyReLU(negative_slope, inplace)(input)
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def prelu(input, weight):
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return _functions.thnn.PReLU()(input, weight)
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def rrelu(input, lower=1./8, upper=1./3, training=False, inplace=False):
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return _functions.thnn.RReLU(lower, upper, training, inplace)(input)
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def logsigmoid(input):
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return _functions.thnn.LogSigmoid()(input)
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def hardshrink(input, lambd=0.5):
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return _functions.thnn.auto.Hardshrink(lambd)(input)
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def tanhshrink(input):
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return input - torch.tanh(input)
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def softsign(input):
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return _functions.activation.Softsign()(input)
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def softplus(input, beta=1, threshold=20):
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return _functions.thnn.auto.Softplus(beta, threshold)(input)
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|
|
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|
def softmin(input):
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return _functions.thnn.Softmin()(input)
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|
|
|
|
|
def softmax(input):
|
|
return _functions.thnn.auto.Softmax()(input)
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|
|
|
|
|
def softshrink(input, lambd=0.5):
|
|
return _functions.thnn.auto.Softshrink(lambd)(input)
|
|
|
|
|
|
def log_softmax(input):
|
|
return _functions.thnn.LogSoftmax()(input)
|
|
|
|
|
|
def tanh(input):
|
|
return torch.tanh(input)
|
|
|
|
|
|
def sigmoid(input):
|
|
return torch.sigmoid(input)
|
|
|
|
|
|
# etc.
|
|
|
|
def linear(input, weight, bias=None):
|
|
state = _functions.linear.Linear()
|
|
return bias and state(input, weight, bias) or state(input, weight)
|
|
|
|
|
|
def batch_norm(input, running_mean, running_var, weight=None, bias=None,
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|
training=False, momentum=0.1, eps=1e-5):
|
|
state = _functions.batchnorm.BatchNorm(
|
|
running_mean, running_var, training, momentum, eps)
|
|
return weight and state(input, weight, bias) or state(input)
|
|
|
|
|
|
def nll_loss(input, target, weight=None, size_average=True):
|
|
return _functions.thnn.NLLLoss(size_average, weight=weight)(input, target)
|
|
|
|
|
|
def cross_entropy(input, target, weight=None, size_average=True):
|
|
return nll_loss(log_softmax(input), target, weight, size_average)
|
|
|
|
|
|
def binary_cross_entropy(input, target, weight=None, size_average=True):
|
|
return _functions.thnn.BCELoss(size_average, weight=weight)(input, target)
|
|
|
|
|
|
def smooth_l1_loss(input, target, size_average=True):
|
|
return _functions.thnn.SmoothL1Loss(size_average)(input, target)
|
|
|
|
|
|
def pixel_shuffle(input, upscale_factor):
|
|
"""Rearranges elements in a tensor of shape [*, C*r^2, H, W] to a
|
|
tensor of shape [C, H*r, W*r]. This is useful for implementing
|
|
efficient sub-pixel convolution with a stride of 1/r.
|
|
"Real-Time Single Image and Video Super-Resolution Using an Efficient
|
|
Sub-Pixel Convolutional Neural Network" - Shi et. al (2016) for more details
|
|
Args:
|
|
input (Tensor): Input
|
|
upscale_factor (int): factor to increase spatial resolution by
|
|
Input Shape: [*, channels*upscale_factor^2, height, width]
|
|
Output Shape:[*, channels, height*upscale_factor, width*upscale_factor]
|
|
Examples:
|
|
>>> ps = nn.PixelShuffle(3)
|
|
>>> input = autograd.Variable(torch.Tensor(1, 9, 4, 4))
|
|
>>> output = ps(input)
|
|
>>> print(output.size())
|
|
torch.Size([1, 1, 12, 12])
|
|
"""
|
|
batch_size, channels, in_height, in_width = input.size()
|
|
channels //= upscale_factor ** 2
|
|
|
|
out_height = in_height * upscale_factor
|
|
out_width = in_width * upscale_factor
|
|
|
|
input_view = input.contiguous().view(
|
|
batch_size, channels, upscale_factor, upscale_factor,
|
|
in_height, in_width)
|
|
|
|
shuffle_out = input_view.permute(0, 1, 4, 2, 5, 3).contiguous()
|
|
return shuffle_out.view(batch_size, channels, out_height, out_width)
|