o h7v@sddlmZddlmmZddlmZddlm Z m Z m Z ddl m Z ddlmZmZmZgdZGd d d e ZGd d d eZGd ddeZGdddeZGddde ZGdddeZGdddeZGdddeZGddde ZGdddeZGdddeZGdd d eZGd!d"d"e ZGd#d$d$eZ Gd%d&d&eZ!Gd'd(d(eZ"Gd)d*d*eZ#Gd+d,d,eZ$Gd-d.d.eZ%dS)/)SequenceN)Tensor) _size_2_t _size_4_t _size_6_t)Module)_ntuple_pair _quadruple) CircularPad1d CircularPad2d CircularPad3d ConstantPad1d ConstantPad2d ConstantPad3dReflectionPad1dReflectionPad2dReflectionPad3dReplicationPad1dReplicationPad2dReplicationPad3d ZeroPad1d ZeroPad2d ZeroPad3dc@sHeZdZUdgZeeed<ddZdedefddZ de fdd Z d S) _CircularPadNdpaddingcCstN)NotImplementedErrorselfinputr"l/var/www/html/construction_image-detection-poc/venv/lib/python3.10/site-packages/torch/nn/modules/padding.py_check_input_dim%sz_CircularPadNd._check_input_dimr!returncCs||t||jdS)Ncircular)r$Fpadrrr"r"r#forward(s z_CircularPadNd.forwardcC|jSrrr r"r"r# extra_repr,z_CircularPadNd.extra_reprN) __name__ __module__ __qualname__ __constants__rint__annotations__r$rr)strr-r"r"r"r#r!s  rcsDeZdZUdZeeefed<deddffdd ZddZ Z S) r aPads the input tensor using circular padding of the input boundary. Tensor values at the beginning of the dimension are used to pad the end, and values at the end are used to pad the beginning. If negative padding is applied then the ends of the tensor get removed. For `N`-dimensional padding, use :func:`torch.nn.functional.pad()`. Args: padding (int, tuple): the size of the padding. If is `int`, uses the same padding in all boundaries. If a 2-`tuple`, uses (:math:`\text{padding\_left}`, :math:`\text{padding\_right}`) Shape: - Input: :math:`(C, W_{in})` or :math:`(N, C, W_{in})`. - Output: :math:`(C, W_{out})` or :math:`(N, C, W_{out})`, where :math:`W_{out} = W_{in} + \text{padding\_left} + \text{padding\_right}` Examples:: >>> # xdoctest: +IGNORE_WANT("not sure why xdoctest is choking on this") >>> m = nn.CircularPad1d(2) >>> input = torch.arange(8, dtype=torch.float).reshape(1, 2, 4) >>> input tensor([[[0., 1., 2., 3.], [4., 5., 6., 7.]]]) >>> m(input) tensor([[[2., 3., 0., 1., 2., 3., 0., 1.], [6., 7., 4., 5., 6., 7., 4., 5.]]]) >>> # using different paddings for different sides >>> m = nn.CircularPad1d((3, 1)) >>> m(input) tensor([[[1., 2., 3., 0., 1., 2., 3., 0.], [5., 6., 7., 4., 5., 6., 7., 4.]]]) rr%Nctt||_dSrsuper__init__r rr r __class__r"r#r9X zCircularPad1d.__init__cC4|dkr|dkrtd|ddSdS)Nzexpected 2D or 3D input (got D input)dim ValueErrorrr"r"r#r$\zCircularPad1d._check_input_dim) r/r0r1__doc__tupler3r4rr9r$ __classcell__r"r"r;r#r 0s %r csHeZdZUdZeeeeefed<deddffdd ZddZ Z S) r aPads the input tensor using circular padding of the input boundary. Tensor values at the beginning of the dimension are used to pad the end, and values at the end are used to pad the beginning. If negative padding is applied then the ends of the tensor get removed. For `N`-dimensional padding, use :func:`torch.nn.functional.pad()`. Args: padding (int, tuple): the size of the padding. If is `int`, uses the same padding in all boundaries. If a 4-`tuple`, uses (:math:`\text{padding\_left}`, :math:`\text{padding\_right}`, :math:`\text{padding\_top}`, :math:`\text{padding\_bottom}`) 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} + \text{padding\_top} + \text{padding\_bottom}` :math:`W_{out} = W_{in} + \text{padding\_left} + \text{padding\_right}` Examples:: >>> m = nn.CircularPad2d(2) >>> input = torch.arange(9, dtype=torch.float).reshape(1, 1, 3, 3) >>> input tensor([[[[0., 1., 2.], [3., 4., 5.], [6., 7., 8.]]]]) >>> m(input) tensor([[[[4., 5., 3., 4., 5., 3., 4.], [7., 8., 6., 7., 8., 6., 7.], [1., 2., 0., 1., 2., 0., 1.], [4., 5., 3., 4., 5., 3., 4.], [7., 8., 6., 7., 8., 6., 7.], [1., 2., 0., 1., 2., 0., 1.], [4., 5., 3., 4., 5., 3., 4.]]]]) >>> # using different paddings for different sides >>> m = nn.CircularPad2d((1, 1, 2, 0)) >>> m(input) tensor([[[[5., 3., 4., 5., 3.], [8., 6., 7., 8., 6.], [2., 0., 1., 2., 0.], [5., 3., 4., 5., 3.], [8., 6., 7., 8., 6.]]]]) rr%Ncr6rr8r9r rr:r;r"r#r9r=zCircularPad2d.__init__cCr>)Nr@zexpected 3D or 4D input (got rArBrr"r"r#r$rEzCircularPad2d._check_input_dim) r/r0r1rFrGr3r4rr9r$rHr"r"r;r#r as /r csLeZdZUdZeeeeeeefed<deddffdd ZddZ Z S) raPads the input tensor using circular padding of the input boundary. Tensor values at the beginning of the dimension are used to pad the end, and values at the end are used to pad the beginning. If negative padding is applied then the ends of the tensor get removed. For `N`-dimensional padding, use :func:`torch.nn.functional.pad()`. Args: padding (int, tuple): the size of the padding. If is `int`, uses the same padding in all boundaries. If a 6-`tuple`, uses (:math:`\text{padding\_left}`, :math:`\text{padding\_right}`, :math:`\text{padding\_top}`, :math:`\text{padding\_bottom}`, :math:`\text{padding\_front}`, :math:`\text{padding\_back}`) 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} + \text{padding\_front} + \text{padding\_back}` :math:`H_{out} = H_{in} + \text{padding\_top} + \text{padding\_bottom}` :math:`W_{out} = W_{in} + \text{padding\_left} + \text{padding\_right}` Examples:: >>> # xdoctest: +IGNORE_WANT("non-deterministic") >>> m = nn.CircularPad3d(3) >>> input = torch.randn(16, 3, 8, 320, 480) >>> output = m(input) >>> # using different paddings for different sides >>> m = nn.CircularPad3d((3, 3, 6, 6, 1, 1)) >>> output = m(input) rr%Ncttd||_dSNr8r9r rr:r;r"r#r9 zCircularPad3d.__init__cCr>)NrJzexpected 4D or 5D input (got rArBrr"r"r#r$rEzCircularPad3d._check_input_dim) r/r0r1rFrGr3r4rr9r$rHr"r"r;r#rs %rcsdeZdZUddgZeed<eeed<deddffdd Zde de fdd Z de fd d Z Z S) _ConstantPadNdrvaluer%Ncst||_dSr)r8r9rR)r rRr;r"r#r9s  z_ConstantPadNd.__init__r!cCst||jd|jS)Nconstant)r'r(rrRrr"r"r#r)z_ConstantPadNd.forwardcCsd|jd|jS)Nzpadding=z, value=)rrRr,r"r"r#r-rTz_ConstantPadNd.extra_repr)r/r0r1r2floatr4rr3r9rr)r5r-rHr"r"r;r#rQs  rQcs<eZdZUdZeeefed<dedeffdd Z Z S)raPads the input tensor boundaries with a constant value. For `N`-dimensional padding, use :func:`torch.nn.functional.pad()`. Args: padding (int, tuple): the size of the padding. If is `int`, uses the same padding in both boundaries. If a 2-`tuple`, uses (:math:`\text{padding\_left}`, :math:`\text{padding\_right}`) Shape: - Input: :math:`(C, W_{in})` or :math:`(N, C, W_{in})`. - Output: :math:`(C, W_{out})` or :math:`(N, C, W_{out})`, where :math:`W_{out} = W_{in} + \text{padding\_left} + \text{padding\_right}` Examples:: >>> # xdoctest: +IGNORE_WANT("non-deterministic") >>> m = nn.ConstantPad1d(2, 3.5) >>> input = torch.randn(1, 2, 4) >>> input tensor([[[-1.0491, -0.7152, -0.0749, 0.8530], [-1.3287, 1.8966, 0.1466, -0.2771]]]) >>> m(input) tensor([[[ 3.5000, 3.5000, -1.0491, -0.7152, -0.0749, 0.8530, 3.5000, 3.5000], [ 3.5000, 3.5000, -1.3287, 1.8966, 0.1466, -0.2771, 3.5000, 3.5000]]]) >>> m = nn.ConstantPad1d(2, 3.5) >>> input = torch.randn(1, 2, 3) >>> input tensor([[[ 1.6616, 1.4523, -1.1255], [-3.6372, 0.1182, -1.8652]]]) >>> m(input) tensor([[[ 3.5000, 3.5000, 1.6616, 1.4523, -1.1255, 3.5000, 3.5000], [ 3.5000, 3.5000, -3.6372, 0.1182, -1.8652, 3.5000, 3.5000]]]) >>> # using different paddings for different sides >>> m = nn.ConstantPad1d((3, 1), 3.5) >>> m(input) tensor([[[ 3.5000, 3.5000, 3.5000, 1.6616, 1.4523, -1.1255, 3.5000], [ 3.5000, 3.5000, 3.5000, -3.6372, 0.1182, -1.8652, 3.5000]]]) rrRct|t||_dSrr7r rrRr;r"r#r9  zConstantPad1d.__init__) r/r0r1rFrGr3r4rrUr9rHr"r"r;r#rs +rcsLeZdZUdZddgZeeeeefed<dede ddffdd Z Z S)raPads the input tensor boundaries with a constant value. For `N`-dimensional padding, use :func:`torch.nn.functional.pad()`. Args: padding (int, tuple): the size of the padding. If is `int`, uses the same padding in all boundaries. If a 4-`tuple`, uses (:math:`\text{padding\_left}`, :math:`\text{padding\_right}`, :math:`\text{padding\_top}`, :math:`\text{padding\_bottom}`) 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} + \text{padding\_top} + \text{padding\_bottom}` :math:`W_{out} = W_{in} + \text{padding\_left} + \text{padding\_right}` Examples:: >>> # xdoctest: +IGNORE_WANT("non-deterministic") >>> m = nn.ConstantPad2d(2, 3.5) >>> input = torch.randn(1, 2, 2) >>> input tensor([[[ 1.6585, 0.4320], [-0.8701, -0.4649]]]) >>> m(input) tensor([[[ 3.5000, 3.5000, 3.5000, 3.5000, 3.5000, 3.5000], [ 3.5000, 3.5000, 3.5000, 3.5000, 3.5000, 3.5000], [ 3.5000, 3.5000, 1.6585, 0.4320, 3.5000, 3.5000], [ 3.5000, 3.5000, -0.8701, -0.4649, 3.5000, 3.5000], [ 3.5000, 3.5000, 3.5000, 3.5000, 3.5000, 3.5000], [ 3.5000, 3.5000, 3.5000, 3.5000, 3.5000, 3.5000]]]) >>> # using different paddings for different sides >>> m = nn.ConstantPad2d((3, 0, 2, 1), 3.5) >>> m(input) tensor([[[ 3.5000, 3.5000, 3.5000, 3.5000, 3.5000], [ 3.5000, 3.5000, 3.5000, 3.5000, 3.5000], [ 3.5000, 3.5000, 3.5000, 1.6585, 0.4320], [ 3.5000, 3.5000, 3.5000, -0.8701, -0.4649], [ 3.5000, 3.5000, 3.5000, 3.5000, 3.5000]]]) rrRr%NcrVrrIrWr;r"r#r9>rXzConstantPad2d.__init__) r/r0r1rFr2rGr3r4rrUr9rHr"r"r;r#rs *"rcsHeZdZUdZeeeeeeefed<dededdffdd Z Z S)raPads the input tensor boundaries with a constant value. For `N`-dimensional padding, use :func:`torch.nn.functional.pad()`. Args: padding (int, tuple): the size of the padding. If is `int`, uses the same padding in all boundaries. If a 6-`tuple`, uses (:math:`\text{padding\_left}`, :math:`\text{padding\_right}`, :math:`\text{padding\_top}`, :math:`\text{padding\_bottom}`, :math:`\text{padding\_front}`, :math:`\text{padding\_back}`) 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} + \text{padding\_front} + \text{padding\_back}` :math:`H_{out} = H_{in} + \text{padding\_top} + \text{padding\_bottom}` :math:`W_{out} = W_{in} + \text{padding\_left} + \text{padding\_right}` Examples:: >>> m = nn.ConstantPad3d(3, 3.5) >>> input = torch.randn(16, 3, 10, 20, 30) >>> output = m(input) >>> # using different paddings for different sides >>> m = nn.ConstantPad3d((3, 3, 6, 6, 0, 1), 3.5) >>> output = m(input) rrRr%Ncst|td||_dSrLrNrWr;r"r#r9fs zConstantPad3d.__init__) r/r0r1rFrGr3r4rrUr9rHr"r"r;r#rCs  "rc@@eZdZUdgZeeed<dedefddZde fddZ dS) _ReflectionPadNdrr!r%cCt||jdS)Nreflectr'r(rrr"r"r#r)oz_ReflectionPadNd.forwardcCr*rr+r,r"r"r#r-rr.z_ReflectionPadNd.extra_reprN r/r0r1r2rr3r4rr)r5r-r"r"r"r#rZk  rZc<eZdZUdZeeefed<deddffdd ZZ S)raPads the input tensor using the reflection of the input boundary. For `N`-dimensional padding, use :func:`torch.nn.functional.pad()`. Args: padding (int, tuple): the size of the padding. If is `int`, uses the same padding in all boundaries. If a 2-`tuple`, uses (:math:`\text{padding\_left}`, :math:`\text{padding\_right}`) Shape: - Input: :math:`(C, W_{in})` or :math:`(N, C, W_{in})`. - Output: :math:`(C, W_{out})` or :math:`(N, C, W_{out})`, where :math:`W_{out} = W_{in} + \text{padding\_left} + \text{padding\_right}` Examples:: >>> m = nn.ReflectionPad1d(2) >>> # xdoctest: +IGNORE_WANT("other tests seem to modify printing styles") >>> input = torch.arange(8, dtype=torch.float).reshape(1, 2, 4) >>> input tensor([[[0., 1., 2., 3.], [4., 5., 6., 7.]]]) >>> m(input) tensor([[[2., 1., 0., 1., 2., 3., 2., 1.], [6., 5., 4., 5., 6., 7., 6., 5.]]]) >>> # using different paddings for different sides >>> m = nn.ReflectionPad1d((3, 1)) >>> m(input) tensor([[[3., 2., 1., 0., 1., 2., 3., 2.], [7., 6., 5., 4., 5., 6., 7., 6.]]]) rr%Ncr6rr7r:r;r"r#r9r=zReflectionPad1d.__init__ r/r0r1rFrGr3r4rr9rHr"r"r;r#rv !rc@eZdZUdZeeeeefed<deddffdd ZZ S)raaPads the input tensor using the reflection of the input boundary. For `N`-dimensional padding, use :func:`torch.nn.functional.pad()`. Args: padding (int, tuple): the size of the padding. If is `int`, uses the same padding in all boundaries. If a 4-`tuple`, uses (:math:`\text{padding\_left}`, :math:`\text{padding\_right}`, :math:`\text{padding\_top}`, :math:`\text{padding\_bottom}`) Note that padding size should be less than the corresponding input dimension. 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} + \text{padding\_top} + \text{padding\_bottom}` :math:`W_{out} = W_{in} + \text{padding\_left} + \text{padding\_right}` Examples:: >>> # xdoctest: +IGNORE_WANT("not sure why xdoctest is choking on this") >>> m = nn.ReflectionPad2d(2) >>> input = torch.arange(9, dtype=torch.float).reshape(1, 1, 3, 3) >>> input tensor([[[[0., 1., 2.], [3., 4., 5.], [6., 7., 8.]]]]) >>> m(input) tensor([[[[8., 7., 6., 7., 8., 7., 6.], [5., 4., 3., 4., 5., 4., 3.], [2., 1., 0., 1., 2., 1., 0.], [5., 4., 3., 4., 5., 4., 3.], [8., 7., 6., 7., 8., 7., 6.], [5., 4., 3., 4., 5., 4., 3.], [2., 1., 0., 1., 2., 1., 0.]]]]) >>> # using different paddings for different sides >>> m = nn.ReflectionPad2d((1, 1, 2, 0)) >>> m(input) tensor([[[[7., 6., 7., 8., 7.], [4., 3., 4., 5., 4.], [1., 0., 1., 2., 1.], [4., 3., 4., 5., 4.], [7., 6., 7., 8., 7.]]]]) rr%Ncr6rrIr:r;r"r#r9r=zReflectionPad2d.__init__ r/r0r1rFrGr3r4rr9rHr"r"r;r#rs -rcDeZdZUdZeeeeeeefed<deddffdd ZZ S)ra"Pads the input tensor using the reflection of the input boundary. For `N`-dimensional padding, use :func:`torch.nn.functional.pad()`. Args: padding (int, tuple): the size of the padding. If is `int`, uses the same padding in all boundaries. If a 6-`tuple`, uses (:math:`\text{padding\_left}`, :math:`\text{padding\_right}`, :math:`\text{padding\_top}`, :math:`\text{padding\_bottom}`, :math:`\text{padding\_front}`, :math:`\text{padding\_back}`) 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} + \text{padding\_front} + \text{padding\_back}` :math:`H_{out} = H_{in} + \text{padding\_top} + \text{padding\_bottom}` :math:`W_{out} = W_{in} + \text{padding\_left} + \text{padding\_right}` Examples:: >>> # xdoctest: +IGNORE_WANT("not sure why xdoctest is choking on this") >>> m = nn.ReflectionPad3d(1) >>> input = torch.arange(8, dtype=torch.float).reshape(1, 1, 2, 2, 2) >>> m(input) tensor([[[[[7., 6., 7., 6.], [5., 4., 5., 4.], [7., 6., 7., 6.], [5., 4., 5., 4.]], [[3., 2., 3., 2.], [1., 0., 1., 0.], [3., 2., 3., 2.], [1., 0., 1., 0.]], [[7., 6., 7., 6.], [5., 4., 5., 4.], [7., 6., 7., 6.], [5., 4., 5., 4.]], [[3., 2., 3., 2.], [1., 0., 1., 0.], [3., 2., 3., 2.], [1., 0., 1., 0.]]]]]) rr%NcrKrLrNr:r;r"r#r9rOzReflectionPad3d.__init__ r/r0r1rFrGr3r4rr9rHr"r"r;r#rs .rc@rY) _ReplicationPadNdrr!r%cCr[)N replicater]rr"r"r#r)r^z_ReplicationPadNd.forwardcCr*rr+r,r"r"r#r-r.z_ReplicationPadNd.extra_reprNr_r"r"r"r#rh r`rhcra)raPads the input tensor using replication of the input boundary. For `N`-dimensional padding, use :func:`torch.nn.functional.pad()`. Args: padding (int, tuple): the size of the padding. If is `int`, uses the same padding in all boundaries. If a 2-`tuple`, uses (:math:`\text{padding\_left}`, :math:`\text{padding\_right}`) Shape: - Input: :math:`(C, W_{in})` or :math:`(N, C, W_{in})`. - Output: :math:`(C, W_{out})` or :math:`(N, C, W_{out})`, where :math:`W_{out} = W_{in} + \text{padding\_left} + \text{padding\_right}` Examples:: >>> # xdoctest: +IGNORE_WANT("not sure why xdoctest is choking on this") >>> m = nn.ReplicationPad1d(2) >>> input = torch.arange(8, dtype=torch.float).reshape(1, 2, 4) >>> input tensor([[[0., 1., 2., 3.], [4., 5., 6., 7.]]]) >>> m(input) tensor([[[0., 0., 0., 1., 2., 3., 3., 3.], [4., 4., 4., 5., 6., 7., 7., 7.]]]) >>> # using different paddings for different sides >>> m = nn.ReplicationPad1d((3, 1)) >>> m(input) tensor([[[0., 0., 0., 0., 1., 2., 3., 3.], [4., 4., 4., 4., 5., 6., 7., 7.]]]) rr%Ncr6rr7r:r;r"r#r99r=zReplicationPad1d.__init__rbr"r"r;r#rrcrcrd)raPads the input tensor using replication of the input boundary. For `N`-dimensional padding, use :func:`torch.nn.functional.pad()`. Args: padding (int, tuple): the size of the padding. If is `int`, uses the same padding in all boundaries. If a 4-`tuple`, uses (:math:`\text{padding\_left}`, :math:`\text{padding\_right}`, :math:`\text{padding\_top}`, :math:`\text{padding\_bottom}`) 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} + \text{padding\_top} + \text{padding\_bottom}` :math:`W_{out} = W_{in} + \text{padding\_left} + \text{padding\_right}` Examples:: >>> m = nn.ReplicationPad2d(2) >>> # xdoctest: +IGNORE_WANT("non-deterministic") >>> input = torch.arange(9, dtype=torch.float).reshape(1, 1, 3, 3) >>> input tensor([[[[0., 1., 2.], [3., 4., 5.], [6., 7., 8.]]]]) >>> m(input) tensor([[[[0., 0., 0., 1., 2., 2., 2.], [0., 0., 0., 1., 2., 2., 2.], [0., 0., 0., 1., 2., 2., 2.], [3., 3., 3., 4., 5., 5., 5.], [6., 6., 6., 7., 8., 8., 8.], [6., 6., 6., 7., 8., 8., 8.], [6., 6., 6., 7., 8., 8., 8.]]]]) >>> # using different paddings for different sides >>> m = nn.ReplicationPad2d((1, 1, 2, 0)) >>> m(input) tensor([[[[0., 0., 1., 2., 2.], [0., 0., 1., 2., 2.], [0., 0., 1., 2., 2.], [3., 3., 4., 5., 5.], [6., 6., 7., 8., 8.]]]]) rr%Ncr6rrIr:r;r"r#r9mr=zReplicationPad2d.__init__rer"r"r;r#r>s ,rcrf)ra2Pads the input tensor using replication of the input boundary. For `N`-dimensional padding, use :func:`torch.nn.functional.pad()`. Args: padding (int, tuple): the size of the padding. If is `int`, uses the same padding in all boundaries. If a 6-`tuple`, uses (:math:`\text{padding\_left}`, :math:`\text{padding\_right}`, :math:`\text{padding\_top}`, :math:`\text{padding\_bottom}`, :math:`\text{padding\_front}`, :math:`\text{padding\_back}`) 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} + \text{padding\_front} + \text{padding\_back}` :math:`H_{out} = H_{in} + \text{padding\_top} + \text{padding\_bottom}` :math:`W_{out} = W_{in} + \text{padding\_left} + \text{padding\_right}` Examples:: >>> # xdoctest: +IGNORE_WANT("non-deterministic") >>> m = nn.ReplicationPad3d(3) >>> input = torch.randn(16, 3, 8, 320, 480) >>> output = m(input) >>> # using different paddings for different sides >>> m = nn.ReplicationPad3d((3, 3, 6, 6, 1, 1)) >>> output = m(input) rr%NcrKrLrNr:r;r"r#r9rOzReplicationPad3d.__init__rgr"r"r;r#rrs !rcsJeZdZUdZeeefed<deddffdd Zde fddZ Z S) raPads the input tensor boundaries with zero. For `N`-dimensional padding, use :func:`torch.nn.functional.pad()`. Args: padding (int, tuple): the size of the padding. If is `int`, uses the same padding in both boundaries. If a 2-`tuple`, uses (:math:`\text{padding\_left}`, :math:`\text{padding\_right}`) Shape: - Input: :math:`(C, W_{in})` or :math:`(N, C, W_{in})`. - Output: :math:`(C, W_{out})` or :math:`(N, C, W_{out})`, where :math:`W_{out} = W_{in} + \text{padding\_left} + \text{padding\_right}` Examples:: >>> # xdoctest: +IGNORE_WANT("non-deterministic") >>> m = nn.ZeroPad1d(2) >>> input = torch.randn(1, 2, 4) >>> input tensor([[[-1.0491, -0.7152, -0.0749, 0.8530], [-1.3287, 1.8966, 0.1466, -0.2771]]]) >>> m(input) tensor([[[ 0.0000, 0.0000, -1.0491, -0.7152, -0.0749, 0.8530, 0.0000, 0.0000], [ 0.0000, 0.0000, -1.3287, 1.8966, 0.1466, -0.2771, 0.0000, 0.0000]]]) >>> m = nn.ZeroPad1d(2) >>> input = torch.randn(1, 2, 3) >>> input tensor([[[ 1.6616, 1.4523, -1.1255], [-3.6372, 0.1182, -1.8652]]]) >>> m(input) tensor([[[ 0.0000, 0.0000, 1.6616, 1.4523, -1.1255, 0.0000, 0.0000], [ 0.0000, 0.0000, -3.6372, 0.1182, -1.8652, 0.0000, 0.0000]]]) >>> # using different paddings for different sides >>> m = nn.ZeroPad1d((3, 1)) >>> m(input) tensor([[[ 0.0000, 0.0000, 0.0000, 1.6616, 1.4523, -1.1255, 0.0000], [ 0.0000, 0.0000, 0.0000, -3.6372, 0.1182, -1.8652, 0.0000]]]) rr%Nct|ddSNgr8r9r:r;r"r#r9zZeroPad1d.__init__cCr*rr+r,r"r"r#r-r.zZeroPad1d.extra_repr) r/r0r1rFrGr3r4rr9r5r-rHr"r"r;r#rs +rcsNeZdZUdZeeeeefed<deddffdd Zde fddZ Z S) raOPads the input tensor boundaries with zero. For `N`-dimensional padding, use :func:`torch.nn.functional.pad()`. Args: padding (int, tuple): the size of the padding. If is `int`, uses the same padding in all boundaries. If a 4-`tuple`, uses (:math:`\text{padding\_left}`, :math:`\text{padding\_right}`, :math:`\text{padding\_top}`, :math:`\text{padding\_bottom}`) 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} + \text{padding\_top} + \text{padding\_bottom}` :math:`W_{out} = W_{in} + \text{padding\_left} + \text{padding\_right}` Examples:: >>> # xdoctest: +IGNORE_WANT("non-deterministic") >>> m = nn.ZeroPad2d(2) >>> input = torch.randn(1, 1, 3, 3) >>> input tensor([[[[-0.1678, -0.4418, 1.9466], [ 0.9604, -0.4219, -0.5241], [-0.9162, -0.5436, -0.6446]]]]) >>> m(input) tensor([[[[ 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000], [ 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000], [ 0.0000, 0.0000, -0.1678, -0.4418, 1.9466, 0.0000, 0.0000], [ 0.0000, 0.0000, 0.9604, -0.4219, -0.5241, 0.0000, 0.0000], [ 0.0000, 0.0000, -0.9162, -0.5436, -0.6446, 0.0000, 0.0000], [ 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000], [ 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000]]]]) >>> # using different paddings for different sides >>> m = nn.ZeroPad2d((1, 1, 2, 0)) >>> m(input) tensor([[[[ 0.0000, 0.0000, 0.0000, 0.0000, 0.0000], [ 0.0000, 0.0000, 0.0000, 0.0000, 0.0000], [ 0.0000, -0.1678, -0.4418, 1.9466, 0.0000], [ 0.0000, 0.9604, -0.4219, -0.5241, 0.0000], [ 0.0000, -0.9162, -0.5436, -0.6446, 0.0000]]]]) rr%Ncrjrkrlr:r;r"r#r9rmzZeroPad2d.__init__cCr*rr+r,r"r"r#r-r.zZeroPad2d.extra_repr) r/r0r1rFrGr3r4rr9r5r-rHr"r"r;r#rs ,rcsReZdZUdZeeeeeeefed<deddffdd Zde fddZ Z S) raPads the input tensor boundaries with zero. For `N`-dimensional padding, use :func:`torch.nn.functional.pad()`. Args: padding (int, tuple): the size of the padding. If is `int`, uses the same padding in all boundaries. If a 6-`tuple`, uses (:math:`\text{padding\_left}`, :math:`\text{padding\_right}`, :math:`\text{padding\_top}`, :math:`\text{padding\_bottom}`, :math:`\text{padding\_front}`, :math:`\text{padding\_back}`) 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} + \text{padding\_front} + \text{padding\_back}` :math:`H_{out} = H_{in} + \text{padding\_top} + \text{padding\_bottom}` :math:`W_{out} = W_{in} + \text{padding\_left} + \text{padding\_right}` Examples:: >>> m = nn.ZeroPad3d(3) >>> input = torch.randn(16, 3, 10, 20, 30) >>> output = m(input) >>> # using different paddings for different sides >>> m = nn.ZeroPad3d((3, 3, 6, 6, 0, 1)) >>> output = m(input) rr%Ncrjrkrlr:r;r"r#r9)rmzZeroPad3d.__init__cCr*rr+r,r"r"r#r-,r.zZeroPad3d.extra_repr) r/r0r1rFrGr3r4rr9r5r-rHr"r"r;r#rs  r)&collections.abcrtorch.nn.functionalnn functionalr'torchrtorch.nn.common_typesrrrmodulerutilsr r r __all__rr r rrQrrrrZrrrrhrrrrrrr"r"r"r#s4   1;133( )56 )4)56