o h'@sddlZddlmZmZddlmZddlZddlmZddlm Z ddl m Z m Z ddlmZmZmZddlmZmZd d lmZd d lmZd d lmZmZmZmZgd ZdddZGdddeZ Gddde Z!Gddde Z"Gddde Z#Gddde Z$Gddde$Z%Gddde$Z&Gdd d e$Z'Gd!d"d"e$Z(Gd#d$d$eZ)Gd%d&d&e)e!Z*Gd'd(d(e)e"Z+Gd)d*d*e)e#Z,Gd+d,d,e)e%Z-Gd-d.d.e)e&Z.Gd/d0d0e)e'Z/dS)1N)OptionalUnion) deprecated)Tensor)reproducibility_notes) functionalinit) _size_1_t _size_2_t _size_3_t) ParameterUninitializedParameter)LazyModuleMixin)Module)_pair_reverse_repeat_tuple_single_triple) Conv1dConv2dConv3dConvTranspose1dConvTranspose2dConvTranspose3d LazyConv1d LazyConv2d LazyConv3dLazyConvTranspose1dLazyConvTranspose2dLazyConvTranspose3da* :attr:`groups` controls the connections between inputs and outputs. :attr:`in_channels` and :attr:`out_channels` must both be divisible by :attr:`groups`. For example, * At groups=1, all inputs are convolved to all outputs. * At groups=2, the operation becomes equivalent to having two conv layers side by side, each seeing half the input channels and producing half the output channels, and both subsequently concatenated. * At groups= :attr:`in_channels`, each input channel is convolved with its own set of filters (of size :math:`\frac{\text{out\_channels}}{\text{in\_channels}}`).aWhen `groups == in_channels` and `out_channels == K * in_channels`, where `K` is a positive integer, this operation is also known as a "depthwise convolution". In other words, for an input of size :math:`(N, C_{in}, L_{in})`, a depthwise convolution with a depthwise multiplier `K` can be performed with the arguments :math:`(C_\text{in}=C_\text{in}, C_\text{out}=C_\text{in} \times \text{K}, ..., \text{groups}=C_\text{in})`.) groups_notedepthwise_separable_notecsxeZdZUgdZdeejiZdededeedefddZe ed<e e ed <e ed <e e d fed <e e d fed <e e e e d ffed<e e d fed<eed<e e d fed<e ed<e ed<eed<eeed<  dde d e d e e d fd e e d fde e e e d ffde e d fdede e d fde dede ddffdd ZdddZddZfddZZS)_ConvNd) stridepaddingdilationgroups padding_modeoutput_padding in_channels out_channels kernel_sizebiasinputweightreturncCsdSNselfr.r/r-r2r2i/var/www/html/construction_image-detection-poc/venv/lib/python3.10/site-packages/torch/nn/modules/conv.py _conv_forwardEz_ConvNd._conv_forwardr* _reversed_padding_repeated_twicer+.r,r$r%r& transposedr)r'r(Ncs| | d}t| dkrtd|| dkrtd|| dkr&tdddh}t|trN||vr=td|d ||dkrNtd d |DrNtd hd }| |vratd|d| d||_||_||_||_ ||_ ||_ ||_ ||_ | |_| |_t|j trddgt||_|dkrt||tt|dddD]!\}}}||d}|d}||jd|<|||jd|d<qnt|j d|_|rttj||| g|Rfi||_nttj||| g|Rfi||_| rttj|fi||_n|dd|dS)Ndevicedtyperz!groups must be a positive integer'in_channels must be divisible by groupsz(out_channels must be divisible by groupssamevalidzInvalid padding string z, should be one of css|]}|dkVqdS)rNr2).0sr2r2r5 tsz#_ConvNd.__init__..z8padding='same' is not supported for strided convolutions>zerosreflectcircular replicatezpadding_mode must be one of z, but got padding_mode=''rr-)super__init__ ValueError isinstancestranyr*r+r,r$r%r&r9r)r'r(lenr8ziprangerr torchemptyr/r-register_parameterreset_parameters)r4r*r+r,r$r%r&r9r)r'r-r(r;r<factory_kwargsvalid_padding_stringsvalid_padding_modesdki total_paddingleft_pad __class__r2r5rKVs         z_ConvNd.__init__cCsdtj|jtdd|jdur.t|j\}}|dkr0dt|}t|j| |dSdSdS)N)arr)rkaiming_uniform_r/mathsqrtr-_calculate_fan_in_and_fan_outuniform_)r4fan_in_boundr2r2r5rVs z_ConvNd.reset_parameterscCsd}|jdt|jkr|d7}|jdt|jkr|d7}|jdt|jkr,|d7}|jdkr5|d7}|jdur>|d 7}|jd krG|d 7}|jd i|jS) NzI{in_channels}, {out_channels}, kernel_size={kernel_size}, stride={stride})rz, padding={padding})rz, dilation={dilation}z!, output_padding={output_padding}rz, groups={groups}z , bias=FalserCz, padding_mode={padding_mode}r2) r%rPr&r)r'r-r(format__dict__)r4rAr2r2r5 extra_reprs   z_ConvNd.extra_reprcs$t|t|dsd|_dSdS)Nr(rC)rJ __setstate__hasattrr()r4stater_r2r5rns   z_ConvNd.__setstate__NNr0N)__name__ __module__ __qualname__ __constants__rrSr__annotations__r6intlisttuplerrNboolrKrVrmrn __classcell__r2r2r_r5r#7s^            \ r#ceZdZddjdieedZ        dd ed ed ed ed e e efdedede de ddffdd Z de de dee fddZde de fddZZS)raApplies a 1D convolution 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_{\text{in}}, L)` and output :math:`(N, C_{\text{out}}, L_{\text{out}})` can be precisely described as: .. math:: \text{out}(N_i, C_{\text{out}_j}) = \text{bias}(C_{\text{out}_j}) + \sum_{k = 0}^{C_{in} - 1} \text{weight}(C_{\text{out}_j}, k) \star \text{input}(N_i, k) where :math:`\star` is the valid `cross-correlation`_ operator, :math:`N` is a batch size, :math:`C` denotes a number of channels, :math:`L` is a length of signal sequence. u This module supports :ref:`TensorFloat32`. On certain ROCm devices, when using float16 inputs this module will use :ref:`different precision` for backward. * :attr:`stride` controls the stride for the cross-correlation, a single number or a one-element tuple. * :attr:`padding` controls the amount of padding applied to the input. It can be either a string {{'valid', 'same'}} or a tuple of ints giving the amount of implicit padding applied on both sides. * :attr:`dilation` controls the spacing between the kernel points; also known as the à trous algorithm. It is harder to describe, but this `link`_ has a nice visualization of what :attr:`dilation` does. {groups_note} Note: {depthwise_separable_note} Note: {cudnn_reproducibility_note} Note: ``padding='valid'`` is the same as no padding. ``padding='same'`` pads the input so the output has the shape as the input. However, this mode doesn't support any stride values other than 1. Note: This module supports complex data types i.e. ``complex32, complex64, complex128``. Args: in_channels (int): Number of channels in the input image out_channels (int): Number of channels produced by the convolution kernel_size (int or tuple): Size of the convolving kernel stride (int or tuple, optional): Stride of the convolution. Default: 1 padding (int, tuple or str, optional): Padding added to both sides of the input. Default: 0 dilation (int or tuple, optional): Spacing between kernel elements. Default: 1 groups (int, optional): Number of blocked connections from input channels to output channels. Default: 1 bias (bool, optional): If ``True``, adds a learnable bias to the output. Default: ``True`` padding_mode (str, optional): ``'zeros'``, ``'reflect'``, ``'replicate'`` or ``'circular'``. Default: ``'zeros'`` au Shape: - Input: :math:`(N, C_{in}, L_{in})` or :math:`(C_{in}, L_{in})` - Output: :math:`(N, C_{out}, L_{out})` or :math:`(C_{out}, 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 Attributes: weight (Tensor): the learnable weights of the module of shape :math:`(\text{out\_channels}, \frac{\text{in\_channels}}{\text{groups}}, \text{kernel\_size})`. The values of these weights are sampled from :math:`\mathcal{U}(-\sqrt{k}, \sqrt{k})` where :math:`k = \frac{groups}{C_\text{in} * \text{kernel\_size}}` bias (Tensor): the learnable bias of the module of shape (out_channels). If :attr:`bias` is ``True``, then the values of these weights are sampled from :math:`\mathcal{U}(-\sqrt{k}, \sqrt{k})` where :math:`k = \frac{groups}{C_\text{in} * \text{kernel\_size}}` Examples:: >>> m = nn.Conv1d(16, 33, 3, stride=2) >>> input = torch.randn(20, 16, 50) >>> output = m(input) .. _cross-correlation: https://en.wikipedia.org/wiki/Cross-correlation .. _link: https://github.com/vdumoulin/conv_arithmetic/blob/master/README.md rrTrCNr*r+r,r$r%r&r'r-r(r0c  h| | d} t|} t|}t|tr|nt|}t|}tj||| |||dtd||| f i| dSNr:Fr)rrMrNrJrKr4r*r+r,r$r%r&r'r-r(r;r<rW kernel_size_stride_padding_ dilation_r_r2r5rKAs(  zConv1d.__init__r.r/c CX|jdkrttj||j|jd|||jtd|j|jSt||||j|j |j|jSNrC)moder) r(Fconv1dpadr8r$rr&r'r%r3r2r2r5r6e   zConv1d._conv_forwardcC|||j|jSr1r6r/r-r4r.r2r2r5forwardvzConv1d.forwardr2rrrrTrCNN)rsrtrurkrconvolution_notes__doc__rxr rrNr{rKrrr6rr|r2r2r_r5rsX233Eo    $rcr})raApplies a 2D convolution 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_{\text{in}}, H, W)` and output :math:`(N, C_{\text{out}}, H_{\text{out}}, W_{\text{out}})` can be precisely described as: .. math:: \text{out}(N_i, C_{\text{out}_j}) = \text{bias}(C_{\text{out}_j}) + \sum_{k = 0}^{C_{\text{in}} - 1} \text{weight}(C_{\text{out}_j}, k) \star \text{input}(N_i, k) where :math:`\star` is the valid 2D `cross-correlation`_ operator, :math:`N` is a batch size, :math:`C` denotes a number of channels, :math:`H` is a height of input planes in pixels, and :math:`W` is width in pixels. uy This module supports :ref:`TensorFloat32`. On certain ROCm devices, when using float16 inputs this module will use :ref:`different precision` for backward. * :attr:`stride` controls the stride for the cross-correlation, a single number or a tuple. * :attr:`padding` controls the amount of padding applied to the input. It can be either a string {{'valid', 'same'}} or an int / a tuple of ints giving the amount of implicit padding applied on both sides. * :attr:`dilation` controls the spacing between the kernel points; also known as the à trous algorithm. It is harder to describe, but this `link`_ has a nice visualization of what :attr:`dilation` does. {groups_note} 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 Note: {depthwise_separable_note} Note: {cudnn_reproducibility_note} Note: ``padding='valid'`` is the same as no padding. ``padding='same'`` pads the input so the output has the shape as the input. However, this mode doesn't support any stride values other than 1. Note: This module supports complex data types i.e. ``complex32, complex64, complex128``. Args: in_channels (int): Number of channels in the input image out_channels (int): Number of channels produced by the convolution kernel_size (int or tuple): Size of the convolving kernel stride (int or tuple, optional): Stride of the convolution. Default: 1 padding (int, tuple or str, optional): Padding added to all four sides of the input. Default: 0 dilation (int or tuple, optional): Spacing between kernel elements. Default: 1 groups (int, optional): Number of blocked connections from input channels to output channels. Default: 1 bias (bool, optional): If ``True``, adds a learnable bias to the output. Default: ``True`` padding_mode (str, optional): ``'zeros'``, ``'reflect'``, ``'replicate'`` or ``'circular'``. Default: ``'zeros'`` aE Shape: - Input: :math:`(N, C_{in}, H_{in}, W_{in})` or :math:`(C_{in}, H_{in}, W_{in})` - Output: :math:`(N, C_{out}, H_{out}, W_{out})` or :math:`(C_{out}, H_{out}, W_{out})`, where .. math:: H_{out} = \left\lfloor\frac{H_{in} + 2 \times \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 \times \text{padding}[1] - \text{dilation}[1] \times (\text{kernel\_size}[1] - 1) - 1}{\text{stride}[1]} + 1\right\rfloor Attributes: weight (Tensor): the learnable weights of the module of shape :math:`(\text{out\_channels}, \frac{\text{in\_channels}}{\text{groups}},` :math:`\text{kernel\_size[0]}, \text{kernel\_size[1]})`. The values of these weights are sampled from :math:`\mathcal{U}(-\sqrt{k}, \sqrt{k})` where :math:`k = \frac{groups}{C_\text{in} * \prod_{i=0}^{1}\text{kernel\_size}[i]}` bias (Tensor): the learnable bias of the module of shape (out_channels). If :attr:`bias` is ``True``, then the values of these weights are sampled from :math:`\mathcal{U}(-\sqrt{k}, \sqrt{k})` where :math:`k = \frac{groups}{C_\text{in} * \prod_{i=0}^{1}\text{kernel\_size}[i]}` Examples: >>> # With square kernels and equal stride >>> m = nn.Conv2d(16, 33, 3, stride=2) >>> # non-square kernels and unequal stride and with padding >>> m = nn.Conv2d(16, 33, (3, 5), stride=(2, 1), padding=(4, 2)) >>> # non-square kernels and unequal stride and with padding and dilation >>> m = nn.Conv2d(16, 33, (3, 5), stride=(2, 1), padding=(4, 2), dilation=(3, 1)) >>> input = torch.randn(20, 16, 50, 100) >>> output = m(input) .. _cross-correlation: https://en.wikipedia.org/wiki/Cross-correlation .. _link: https://github.com/vdumoulin/conv_arithmetic/blob/master/README.md rrTrCNr*r+r,r$r%r&r'r-r(r0c  r~r)rrMrNrJrKrr_r2r5rK(  zConv2d.__init__r.r/c Crr) r(rconv2drr8r$rr&r'r%r3r2r2r5r6rzConv2d._conv_forwardcCrr1rrr2r2r5r)rzConv2d.forwardr2r)rsrtrurkrrrrxr rrNr{rKrrr6rr|r2r2r_r5rzsZ899L    "rcr})raApplies a 3D convolution 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_{in}, D, H, W)` and output :math:`(N, C_{out}, D_{out}, H_{out}, W_{out})` can be precisely described as: .. math:: out(N_i, C_{out_j}) = bias(C_{out_j}) + \sum_{k = 0}^{C_{in} - 1} weight(C_{out_j}, k) \star input(N_i, k) where :math:`\star` is the valid 3D `cross-correlation`_ operator uT This module supports :ref:`TensorFloat32`. On certain ROCm devices, when using float16 inputs this module will use :ref:`different precision` for backward. * :attr:`stride` controls the stride for the cross-correlation. * :attr:`padding` controls the amount of padding applied to the input. It can be either a string {{'valid', 'same'}} or a tuple of ints giving the amount of implicit padding applied on both sides. * :attr:`dilation` controls the spacing between the kernel points; also known as the à trous algorithm. It is harder to describe, but this `link`_ has a nice visualization of what :attr:`dilation` does. {groups_note} 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 Note: {depthwise_separable_note} Note: {cudnn_reproducibility_note} Note: ``padding='valid'`` is the same as no padding. ``padding='same'`` pads the input so the output has the shape as the input. However, this mode doesn't support any stride values other than 1. Note: This module supports complex data types i.e. ``complex32, complex64, complex128``. Args: in_channels (int): Number of channels in the input image out_channels (int): Number of channels produced by the convolution kernel_size (int or tuple): Size of the convolving kernel stride (int or tuple, optional): Stride of the convolution. Default: 1 padding (int, tuple or str, optional): Padding added to all six sides of the input. Default: 0 dilation (int or tuple, optional): Spacing between kernel elements. Default: 1 groups (int, optional): Number of blocked connections from input channels to output channels. Default: 1 bias (bool, optional): If ``True``, adds a learnable bias to the output. Default: ``True`` padding_mode (str, optional): ``'zeros'``, ``'reflect'``, ``'replicate'`` or ``'circular'``. Default: ``'zeros'`` a Shape: - Input: :math:`(N, C_{in}, D_{in}, H_{in}, W_{in})` or :math:`(C_{in}, D_{in}, H_{in}, W_{in})` - Output: :math:`(N, C_{out}, D_{out}, H_{out}, W_{out})` or :math:`(C_{out}, 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 Attributes: weight (Tensor): the learnable weights of the module of shape :math:`(\text{out\_channels}, \frac{\text{in\_channels}}{\text{groups}},` :math:`\text{kernel\_size[0]}, \text{kernel\_size[1]}, \text{kernel\_size[2]})`. The values of these weights are sampled from :math:`\mathcal{U}(-\sqrt{k}, \sqrt{k})` where :math:`k = \frac{groups}{C_\text{in} * \prod_{i=0}^{2}\text{kernel\_size}[i]}` bias (Tensor): the learnable bias of the module of shape (out_channels). If :attr:`bias` is ``True``, then the values of these weights are sampled from :math:`\mathcal{U}(-\sqrt{k}, \sqrt{k})` where :math:`k = \frac{groups}{C_\text{in} * \prod_{i=0}^{2}\text{kernel\_size}[i]}` Examples:: >>> # With square kernels and equal stride >>> m = nn.Conv3d(16, 33, 3, stride=2) >>> # non-square kernels and unequal stride and with padding >>> m = nn.Conv3d(16, 33, (3, 5, 2), stride=(2, 1, 1), padding=(4, 2, 0)) >>> input = torch.randn(20, 16, 10, 50, 100) >>> output = m(input) .. _cross-correlation: https://en.wikipedia.org/wiki/Cross-correlation .. _link: https://github.com/vdumoulin/conv_arithmetic/blob/master/README.md rrTrCNr*r+r,r$r%r&r'r-r(r0c  r~r)rrMrNrJrKrr_r2r5rKrzConv3d.__init__r.r/c Crr) r(rconv3drr8r$rr&r'r%r3r2r2r5r6rzConv3d._conv_forwardcCrr1rrr2r2r5rrzConv3d.forwardr2r)rsrtrurkrrrrxr rrNr{rKrrr6rr|r2r2r_r5r-sX 344Bx    "rcsteZdZ  d dfdd Z ddedeeedeedeed eed ed eeedeefd d ZZ S)_ConvTransposeNdNr0c sP| dkr td|jj| | d}tj||||||||| | | f i|dS)NrCz+Only "zeros" padding mode is supported for r:)rLr`rsrJrK)r4r*r+r,r$r%r&r9r)r'r-r(r;r<rWr_r2r5rKs(   z_ConvTransposeNd.__init__r. output_sizer$r%r,num_spatial_dimsr&c Cs|dur t|j}|S||dk} | rdnd} t|| |kr'|| d}t||krGtd|d|d|d| |dt|d tjtt g} tjtt g} t |D]9} | | | d|| d|| |dury|| nd|| dd}| || | | || dq]t t|D]-}||}| |}| |}||ks||krtd |d | d | d | ddd qtjtt g}t |D] } | || | | q|}|S) NrIr ConvTransposezD: for zD input, output_size must have z or z elements (got )zrequested an output size of z, but valid sizes range from z to z (for an input of ) rr)dimrPrLrSjitannotateryrxrRsizeappend)r4r.rr$r%r,rr&ret has_batch_dimnum_non_spatial_dims min_sizes max_sizesrZdim_sizer\rmin_sizemax_sizeresr2r2r5_output_paddingsd (        z _ConvTransposeNd._output_paddingrqrrr1) rsrtrurKrrryrxrr|r2r2r_r5rs0/   rceZdZdjdieedZ         dded ed ed ed ed edede dede ddffdd Z dde de eede fddZZS)ruX Applies a 1D transposed convolution operator over an input image composed of several input planes. This module can be seen as the gradient of Conv1d with respect to its input. It is also known as a fractionally-strided convolution or a deconvolution (although it is not an actual deconvolution operation as it does not compute a true inverse of convolution). For more information, see the visualizations `here`_ and the `Deconvolutional Networks`_ paper. This module supports :ref:`TensorFloat32`. On certain ROCm devices, when using float16 inputs this module will use :ref:`different precision` for backward. * :attr:`stride` controls the stride for the cross-correlation. * :attr:`padding` controls the amount of implicit zero padding on both sides for ``dilation * (kernel_size - 1) - padding`` number of points. See note below for details. * :attr:`output_padding` controls the additional size added to one side of the output shape. See note below for details. * :attr:`dilation` controls the spacing between the kernel points; also known as the à trous algorithm. It is harder to describe, but the link `here`_ has a nice visualization of what :attr:`dilation` does. {groups_note} Note: The :attr:`padding` argument effectively adds ``dilation * (kernel_size - 1) - padding`` amount of zero padding to both sizes of the input. This is set so that when a :class:`~torch.nn.Conv1d` and a :class:`~torch.nn.ConvTranspose1d` are initialized with same parameters, they are inverses of each other in regard to the input and output shapes. However, when ``stride > 1``, :class:`~torch.nn.Conv1d` maps multiple input shapes to the same output shape. :attr:`output_padding` is provided to resolve this ambiguity by effectively increasing the calculated output shape on one side. Note that :attr:`output_padding` is only used to find output shape, but does not actually add zero-padding to output. Note: In some circumstances when using the CUDA backend with CuDNN, this operator may select a nondeterministic algorithm to increase performance. If this is undesirable, you can try to make the operation deterministic (potentially at a performance cost) by setting ``torch.backends.cudnn.deterministic = True``. Please see the notes on :doc:`/notes/randomness` for background. Args: in_channels (int): Number of channels in the input image out_channels (int): Number of channels produced by the convolution kernel_size (int or tuple): Size of the convolving kernel stride (int or tuple, optional): Stride of the convolution. Default: 1 padding (int or tuple, optional): ``dilation * (kernel_size - 1) - padding`` zero-padding will be added to both sides of the input. Default: 0 output_padding (int or tuple, optional): Additional size added to one side of the output shape. Default: 0 groups (int, optional): Number of blocked connections from input channels to output channels. Default: 1 bias (bool, optional): If ``True``, adds a learnable bias to the output. Default: ``True`` dilation (int or tuple, optional): Spacing between kernel elements. Default: 1 a~ Shape: - Input: :math:`(N, C_{in}, L_{in})` or :math:`(C_{in}, L_{in})` - Output: :math:`(N, C_{out}, L_{out})` or :math:`(C_{out}, L_{out})`, where .. math:: L_{out} = (L_{in} - 1) \times \text{stride} - 2 \times \text{padding} + \text{dilation} \times (\text{kernel\_size} - 1) + \text{output\_padding} + 1 Attributes: weight (Tensor): the learnable weights of the module of shape :math:`(\text{in\_channels}, \frac{\text{out\_channels}}{\text{groups}},` :math:`\text{kernel\_size})`. The values of these weights are sampled from :math:`\mathcal{U}(-\sqrt{k}, \sqrt{k})` where :math:`k = \frac{groups}{C_\text{out} * \text{kernel\_size}}` bias (Tensor): the learnable bias of the module of shape (out_channels). If :attr:`bias` is ``True``, then the values of these weights are sampled from :math:`\mathcal{U}(-\sqrt{k}, \sqrt{k})` where :math:`k = \frac{groups}{C_\text{out} * \text{kernel\_size}}` .. _`here`: https://github.com/vdumoulin/conv_arithmetic/blob/master/README.md .. _`Deconvolutional Networks`: https://www.matthewzeiler.com/mattzeiler/deconvolutionalnetworks.pdf rrTrCNr*r+r,r$r%r)r'r-r&r(r0c  ^| | d} t|}t|}t|}t| } t|}tj|||||| d|||| f i| dSNr:T)rrJrKr4r*r+r,r$r%r)r'r-r&r(r;r<rWr_r2r5rK*  zConvTranspose1d.__init__r.rc Ch|jdkr tdt|jtsJd}||||j|j|j||j}t ||j |j |j|j||j |jS)NrCz:Only `zeros` padding mode is supported for ConvTranspose1dr)r(rLrMr%rzrr$r,r&rconv_transpose1dr/r-r'r4r.rrr)r2r2r5rs2  zConvTranspose1d.forwardr2 rrrrTrrCNNr1)rsrtrurkrrrrxr r{rNrKrrryrr|r2r2r_r5r6sX>??Ae   ($rcr)ruu Applies a 2D transposed convolution operator over an input image composed of several input planes. This module can be seen as the gradient of Conv2d with respect to its input. It is also known as a fractionally-strided convolution or a deconvolution (although it is not an actual deconvolution operation as it does not compute a true inverse of convolution). For more information, see the visualizations `here`_ and the `Deconvolutional Networks`_ paper. This module supports :ref:`TensorFloat32`. On certain ROCm devices, when using float16 inputs this module will use :ref:`different precision` for backward. * :attr:`stride` controls the stride for the cross-correlation. * :attr:`padding` controls the amount of implicit zero padding on both sides for ``dilation * (kernel_size - 1) - padding`` number of points. See note below for details. * :attr:`output_padding` controls the additional size added to one side of the output shape. See note below for details. * :attr:`dilation` controls the spacing between the kernel points; also known as the à trous algorithm. It is harder to describe, but the link `here`_ has a nice visualization of what :attr:`dilation` does. {groups_note} The parameters :attr:`kernel_size`, :attr:`stride`, :attr:`padding`, :attr:`output_padding` can either be: - a single ``int`` -- in which case the same value is used for the height and width dimensions - 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: The :attr:`padding` argument effectively adds ``dilation * (kernel_size - 1) - padding`` amount of zero padding to both sizes of the input. This is set so that when a :class:`~torch.nn.Conv2d` and a :class:`~torch.nn.ConvTranspose2d` are initialized with same parameters, they are inverses of each other in regard to the input and output shapes. However, when ``stride > 1``, :class:`~torch.nn.Conv2d` maps multiple input shapes to the same output shape. :attr:`output_padding` is provided to resolve this ambiguity by effectively increasing the calculated output shape on one side. Note that :attr:`output_padding` is only used to find output shape, but does not actually add zero-padding to output. Note: {cudnn_reproducibility_note} Args: in_channels (int): Number of channels in the input image out_channels (int): Number of channels produced by the convolution kernel_size (int or tuple): Size of the convolving kernel stride (int or tuple, optional): Stride of the convolution. Default: 1 padding (int or tuple, optional): ``dilation * (kernel_size - 1) - padding`` zero-padding will be added to both sides of each dimension in the input. Default: 0 output_padding (int or tuple, optional): Additional size added to one side of each dimension in the output shape. Default: 0 groups (int, optional): Number of blocked connections from input channels to output channels. Default: 1 bias (bool, optional): If ``True``, adds a learnable bias to the output. Default: ``True`` dilation (int or tuple, optional): Spacing between kernel elements. Default: 1 a Shape: - Input: :math:`(N, C_{in}, H_{in}, W_{in})` or :math:`(C_{in}, H_{in}, W_{in})` - Output: :math:`(N, C_{out}, H_{out}, W_{out})` or :math:`(C_{out}, H_{out}, W_{out})`, where .. math:: H_{out} = (H_{in} - 1) \times \text{stride}[0] - 2 \times \text{padding}[0] + \text{dilation}[0] \times (\text{kernel\_size}[0] - 1) + \text{output\_padding}[0] + 1 .. math:: W_{out} = (W_{in} - 1) \times \text{stride}[1] - 2 \times \text{padding}[1] + \text{dilation}[1] \times (\text{kernel\_size}[1] - 1) + \text{output\_padding}[1] + 1 Attributes: weight (Tensor): the learnable weights of the module of shape :math:`(\text{in\_channels}, \frac{\text{out\_channels}}{\text{groups}},` :math:`\text{kernel\_size[0]}, \text{kernel\_size[1]})`. The values of these weights are sampled from :math:`\mathcal{U}(-\sqrt{k}, \sqrt{k})` where :math:`k = \frac{groups}{C_\text{out} * \prod_{i=0}^{1}\text{kernel\_size}[i]}` bias (Tensor): the learnable bias of the module of shape (out_channels) If :attr:`bias` is ``True``, then the values of these weights are sampled from :math:`\mathcal{U}(-\sqrt{k}, \sqrt{k})` where :math:`k = \frac{groups}{C_\text{out} * \prod_{i=0}^{1}\text{kernel\_size}[i]}` Examples:: >>> # With square kernels and equal stride >>> m = nn.ConvTranspose2d(16, 33, 3, stride=2) >>> # non-square kernels and unequal stride and with padding >>> m = nn.ConvTranspose2d(16, 33, (3, 5), stride=(2, 1), padding=(4, 2)) >>> input = torch.randn(20, 16, 50, 100) >>> output = m(input) >>> # exact output size can be also specified as an argument >>> input = torch.randn(1, 16, 12, 12) >>> downsample = nn.Conv2d(16, 16, 3, stride=2, padding=1) >>> upsample = nn.ConvTranspose2d(16, 16, 3, stride=2, padding=1) >>> h = downsample(input) >>> h.size() torch.Size([1, 16, 6, 6]) >>> output = upsample(h, output_size=input.size()) >>> output.size() torch.Size([1, 16, 12, 12]) .. _`here`: https://github.com/vdumoulin/conv_arithmetic/blob/master/README.md .. _`Deconvolutional Networks`: https://www.matthewzeiler.com/mattzeiler/deconvolutionalnetworks.pdf rrTrCNr*r+r,r$r%r)r'r-r&r(r0c  rr)rrJrKrr_r2r5rKRrzConvTranspose2d.__init__r.rc Cr)NrCz:Only `zeros` padding mode is supported for ConvTranspose2drI)r(rLrMr%rzrr$r,r&rconv_transpose2dr/r-r'rr2r2r5rv2  zConvTranspose2d.forwardr2rr1)rsrtrurkrrrrxr r{rNrKrrryrr|r2r2r_r5rsX?@@B|   ($rcr)ruKApplies a 3D transposed convolution operator over an input image composed of several input planes. The transposed convolution operator multiplies each input value element-wise by a learnable kernel, and sums over the outputs from all input feature planes. This module can be seen as the gradient of Conv3d with respect to its input. It is also known as a fractionally-strided convolution or a deconvolution (although it is not an actual deconvolution operation as it does not compute a true inverse of convolution). For more information, see the visualizations `here`_ and the `Deconvolutional Networks`_ paper. This module supports :ref:`TensorFloat32`. On certain ROCm devices, when using float16 inputs this module will use :ref:`different precision` for backward. * :attr:`stride` controls the stride for the cross-correlation. * :attr:`padding` controls the amount of implicit zero padding on both sides for ``dilation * (kernel_size - 1) - padding`` number of points. See note below for details. * :attr:`output_padding` controls the additional size added to one side of the output shape. See note below for details. * :attr:`dilation` controls the spacing between the kernel points; also known as the à trous algorithm. It is harder to describe, but the link `here`_ has a nice visualization of what :attr:`dilation` does. {groups_note} The parameters :attr:`kernel_size`, :attr:`stride`, :attr:`padding`, :attr:`output_padding` can either be: - a single ``int`` -- in which case the same value is used for the depth, height and width dimensions - 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: The :attr:`padding` argument effectively adds ``dilation * (kernel_size - 1) - padding`` amount of zero padding to both sizes of the input. This is set so that when a :class:`~torch.nn.Conv3d` and a :class:`~torch.nn.ConvTranspose3d` are initialized with same parameters, they are inverses of each other in regard to the input and output shapes. However, when ``stride > 1``, :class:`~torch.nn.Conv3d` maps multiple input shapes to the same output shape. :attr:`output_padding` is provided to resolve this ambiguity by effectively increasing the calculated output shape on one side. Note that :attr:`output_padding` is only used to find output shape, but does not actually add zero-padding to output. Note: {cudnn_reproducibility_note} Args: in_channels (int): Number of channels in the input image out_channels (int): Number of channels produced by the convolution kernel_size (int or tuple): Size of the convolving kernel stride (int or tuple, optional): Stride of the convolution. Default: 1 padding (int or tuple, optional): ``dilation * (kernel_size - 1) - padding`` zero-padding will be added to both sides of each dimension in the input. Default: 0 output_padding (int or tuple, optional): Additional size added to one side of each dimension in the output shape. Default: 0 groups (int, optional): Number of blocked connections from input channels to output channels. Default: 1 bias (bool, optional): If ``True``, adds a learnable bias to the output. Default: ``True`` dilation (int or tuple, optional): Spacing between kernel elements. Default: 1 aY Shape: - Input: :math:`(N, C_{in}, D_{in}, H_{in}, W_{in})` or :math:`(C_{in}, D_{in}, H_{in}, W_{in})` - Output: :math:`(N, C_{out}, D_{out}, H_{out}, W_{out})` or :math:`(C_{out}, D_{out}, H_{out}, W_{out})`, where .. math:: D_{out} = (D_{in} - 1) \times \text{stride}[0] - 2 \times \text{padding}[0] + \text{dilation}[0] \times (\text{kernel\_size}[0] - 1) + \text{output\_padding}[0] + 1 .. math:: H_{out} = (H_{in} - 1) \times \text{stride}[1] - 2 \times \text{padding}[1] + \text{dilation}[1] \times (\text{kernel\_size}[1] - 1) + \text{output\_padding}[1] + 1 .. math:: W_{out} = (W_{in} - 1) \times \text{stride}[2] - 2 \times \text{padding}[2] + \text{dilation}[2] \times (\text{kernel\_size}[2] - 1) + \text{output\_padding}[2] + 1 Attributes: weight (Tensor): the learnable weights of the module of shape :math:`(\text{in\_channels}, \frac{\text{out\_channels}}{\text{groups}},` :math:`\text{kernel\_size[0]}, \text{kernel\_size[1]}, \text{kernel\_size[2]})`. The values of these weights are sampled from :math:`\mathcal{U}(-\sqrt{k}, \sqrt{k})` where :math:`k = \frac{groups}{C_\text{out} * \prod_{i=0}^{2}\text{kernel\_size}[i]}` bias (Tensor): the learnable bias of the module of shape (out_channels) If :attr:`bias` is ``True``, then the values of these weights are sampled from :math:`\mathcal{U}(-\sqrt{k}, \sqrt{k})` where :math:`k = \frac{groups}{C_\text{out} * \prod_{i=0}^{2}\text{kernel\_size}[i]}` Examples:: >>> # With square kernels and equal stride >>> m = nn.ConvTranspose3d(16, 33, 3, stride=2) >>> # non-square kernels and unequal stride and with padding >>> m = nn.ConvTranspose3d(16, 33, (3, 5, 2), stride=(2, 1, 1), padding=(0, 4, 2)) >>> input = torch.randn(20, 16, 10, 50, 100) >>> output = m(input) .. _`here`: https://github.com/vdumoulin/conv_arithmetic/blob/master/README.md .. _`Deconvolutional Networks`: https://www.matthewzeiler.com/mattzeiler/deconvolutionalnetworks.pdf rrTrCNr*r+r,r$r%r)r'r-r&r(r0c  rr)rrJrKrr_r2r5rK rzConvTranspose3d.__init__r.rc Cr)NrCz:Only `zeros` padding mode is supported for ConvTranspose3d)r(rLrMr%rzrr$r,r&rconv_transpose3dr/r-r'rr2r2r5r/rzConvTranspose3d.forwardr2rr1)rsrtrurkrrrrxr r{rNrKrrryrr|r2r2r_r5rsXABBDy   ($rcs(eZdZededfddZZS)_ConvTransposeMixinzX`_ConvTransposeMixin` is a deprecated internal class. Please consider using public APIs.)categorycstj|i|dSr1)rJrK)r4argskwargsr_r2r5rK_sz_ConvTransposeMixin.__init__)rsrtrur FutureWarningrKr|r2r2r_r5r^s rcseZdZUeed<eed<eed<eed<eedfed<eed<eed<dfd d Zd e d d fddZ d e d efddZ d efddZ Z S)_LazyConvXdMixinr'r9r*r+.r,r/r-r0Ncs(|s|jdkrtdSdSdS)Nr)has_uninitialized_paramsr*rJrVr4r_r2r5rVvsz!_LazyConvXdMixin.reset_parametersr.cOs|r_|||_|j|jdkrtdt|jtsJ|jr3|j |j|j |jg|j Rn|j |j |j|jg|j R|j durYt|j tsQJ|j |j f| dSdS)Nrr=)r_get_in_channelsr*r'rLrMr/r r9 materializer+r,r-rV)r4r.rrr2r2r5initialize_parameterss2     z&_LazyConvXdMixin.initialize_parametersc Csn|}|d}|d}|||fvr'td|d|d|jjd|j||kr2|jdS|jdS)Nrz Expected zD (unbatched) or zD (batched) input to z, but got input of size: r)_get_num_spatial_dimsr RuntimeErrorr`rsshape)r4r.rnum_dims_no_batchnum_dims_batchr2r2r5rs z!_LazyConvXdMixin._get_in_channelscCstr1)NotImplementedErrorrr2r2r5rr7z&_LazyConvXdMixin._get_num_spatial_dimsrr)rsrtrurxrwr{rzr rVrrrrr|r2r2r_r5rms  rcjeZdZdZeZ        ddeded ed ed ed ed ede ddffdd Z defddZ Z S)raA :class:`torch.nn.Conv1d` module with lazy initialization of the ``in_channels`` argument. The ``in_channels`` argument of the :class:`Conv1d` is inferred from the ``input.size(1)``. The attributes that will be lazily initialized are `weight` and `bias`. Check the :class:`torch.nn.modules.lazy.LazyModuleMixin` for further documentation on lazy modules and their limitations. Args: out_channels (int): Number of channels produced by the convolution kernel_size (int or tuple): Size of the convolving kernel stride (int or tuple, optional): Stride of the convolution. Default: 1 padding (int or tuple, optional): Zero-padding added to both sides of the input. Default: 0 dilation (int or tuple, optional): Spacing between kernel elements. Default: 1 groups (int, optional): Number of blocked connections from input channels to output channels. Default: 1 bias (bool, optional): If ``True``, adds a learnable bias to the output. Default: ``True`` padding_mode (str, optional): ``'zeros'``, ``'reflect'``, ``'replicate'`` or ``'circular'``. Default: ``'zeros'`` .. seealso:: :class:`torch.nn.Conv1d` and :class:`torch.nn.modules.lazy.LazyModuleMixin` rrTrCNr+r,r$r%r&r'r-r(r0c `| | d} tjdd|||||d|f i| tdi| |_||_|r.tdi| |_dSdSNr:rFr2rJrKr r/r+r- r4r+r,r$r%r&r'r-r(r;r<rWr_r2r5rK&  zLazyConv1d.__init__cCdSNrr2rr2r2r5rr7z LazyConv1d._get_num_spatial_dimsr) rsrtrurr cls_to_becomerxr r{rNrKrr|r2r2r_r5r>  !rcr)raA :class:`torch.nn.Conv2d` module with lazy initialization of the ``in_channels`` argument. The ``in_channels`` argument of the :class:`Conv2d` that is inferred from the ``input.size(1)``. The attributes that will be lazily initialized are `weight` and `bias`. Check the :class:`torch.nn.modules.lazy.LazyModuleMixin` for further documentation on lazy modules and their limitations. Args: out_channels (int): Number of channels produced by the convolution kernel_size (int or tuple): Size of the convolving kernel stride (int or tuple, optional): Stride of the convolution. Default: 1 padding (int or tuple, optional): Zero-padding added to both sides of the input. Default: 0 dilation (int or tuple, optional): Spacing between kernel elements. Default: 1 groups (int, optional): Number of blocked connections from input channels to output channels. Default: 1 bias (bool, optional): If ``True``, adds a learnable bias to the output. Default: ``True`` padding_mode (str, optional): ``'zeros'``, ``'reflect'``, ``'replicate'`` or ``'circular'``. Default: ``'zeros'`` .. seealso:: :class:`torch.nn.Conv2d` and :class:`torch.nn.modules.lazy.LazyModuleMixin` rrTrCNr+r,r$r%r&r'r-r(r0c rrrrr_r2r5rKrzLazyConv2d.__init__cCrNrIr2rr2r2r5r4r7z LazyConv2d._get_num_spatial_dimsr) rsrtrurrrrxr r{rNrKrr|r2r2r_r5rrrcr)ra#A :class:`torch.nn.Conv3d` module with lazy initialization of the ``in_channels`` argument. The ``in_channels`` argument of the :class:`Conv3d` that is inferred from the ``input.size(1)``. The attributes that will be lazily initialized are `weight` and `bias`. Check the :class:`torch.nn.modules.lazy.LazyModuleMixin` for further documentation on lazy modules and their limitations. Args: out_channels (int): Number of channels produced by the convolution kernel_size (int or tuple): Size of the convolving kernel stride (int or tuple, optional): Stride of the convolution. Default: 1 padding (int or tuple, optional): Zero-padding added to both sides of the input. Default: 0 dilation (int or tuple, optional): Spacing between kernel elements. Default: 1 groups (int, optional): Number of blocked connections from input channels to output channels. Default: 1 bias (bool, optional): If ``True``, adds a learnable bias to the output. Default: ``True`` padding_mode (str, optional): ``'zeros'``, ``'reflect'``, ``'replicate'`` or ``'circular'``. Default: ``'zeros'`` .. seealso:: :class:`torch.nn.Conv3d` and :class:`torch.nn.modules.lazy.LazyModuleMixin` rrTrCNr+r,r$r%r&r'r-r(r0c rrrrr_r2r5rKYrzLazyConv3d.__init__cCrNrr2rr2r2r5rzr7z LazyConv3d._get_num_spatial_dimsr) rsrtrurrrrxr r{rNrKrr|r2r2r_r5r9s>  !rcpeZdZdZeZ         ddeded ed ed ed ed edede ddffdd Z defddZ Z S)raFA :class:`torch.nn.ConvTranspose1d` module with lazy initialization of the ``in_channels`` argument. The ``in_channels`` argument of the :class:`ConvTranspose1d` that is inferred from the ``input.size(1)``. The attributes that will be lazily initialized are `weight` and `bias`. Check the :class:`torch.nn.modules.lazy.LazyModuleMixin` for further documentation on lazy modules and their limitations. Args: out_channels (int): Number of channels produced by the convolution kernel_size (int or tuple): Size of the convolving kernel stride (int or tuple, optional): Stride of the convolution. Default: 1 padding (int or tuple, optional): ``dilation * (kernel_size - 1) - padding`` zero-padding will be added to both sides of the input. Default: 0 output_padding (int or tuple, optional): Additional size added to one side of the output shape. Default: 0 groups (int, optional): Number of blocked connections from input channels to output channels. Default: 1 bias (bool, optional): If ``True``, adds a learnable bias to the output. Default: ``True`` dilation (int or tuple, optional): Spacing between kernel elements. Default: 1 .. seealso:: :class:`torch.nn.ConvTranspose1d` and :class:`torch.nn.modules.lazy.LazyModuleMixin` rrTrCNr+r,r$r%r)r'r-r&r(r0c b| | d} tjdd|||||d|| f i| tdi| |_||_|r/tdi| |_dSdSrr r4r+r,r$r%r)r'r-r&r(r;r<rWr_r2r5rK(  zLazyConvTranspose1d.__init__cCrrr2rr2r2r5rr7z)LazyConvTranspose1d._get_num_spatial_dimsr) rsrtrurrrrxr r{rNrKrr|r2r2r_r5rD   #rcspeZdZdZeZ         ddeded ed ed ed ed edede ddffdd Z defddZ Z S)raeA :class:`torch.nn.ConvTranspose2d` module with lazy initialization of the ``in_channels`` argument. The ``in_channels`` argument of the :class:`ConvTranspose2d` is inferred from the ``input.size(1)``. The attributes that will be lazily initialized are `weight` and `bias`. Check the :class:`torch.nn.modules.lazy.LazyModuleMixin` for further documentation on lazy modules and their limitations. Args: out_channels (int): Number of channels produced by the convolution kernel_size (int or tuple): Size of the convolving kernel stride (int or tuple, optional): Stride of the convolution. Default: 1 padding (int or tuple, optional): ``dilation * (kernel_size - 1) - padding`` zero-padding will be added to both sides of each dimension in the input. Default: 0 output_padding (int or tuple, optional): Additional size added to one side of each dimension in the output shape. Default: 0 groups (int, optional): Number of blocked connections from input channels to output channels. Default: 1 bias (bool, optional): If ``True``, adds a learnable bias to the output. Default: ``True`` dilation (int or tuple, optional): Spacing between kernel elements. Default: 1 .. seealso:: :class:`torch.nn.ConvTranspose2d` and :class:`torch.nn.modules.lazy.LazyModuleMixin` rrTrCNr+r,r$r%r)r'r-r&r(r0c rrrrr_r2r5rKrzLazyConvTranspose2d.__init__cCrrr2rr2r2r5rr7z)LazyConvTranspose2d._get_num_spatial_dimsr) rsrtrurrrrxr r{rNrKrr|r2r2r_r5rrrcr)r aeA :class:`torch.nn.ConvTranspose3d` module with lazy initialization of the ``in_channels`` argument. The ``in_channels`` argument of the :class:`ConvTranspose3d` is inferred from the ``input.size(1)``. The attributes that will be lazily initialized are `weight` and `bias`. Check the :class:`torch.nn.modules.lazy.LazyModuleMixin` for further documentation on lazy modules and their limitations. Args: out_channels (int): Number of channels produced by the convolution kernel_size (int or tuple): Size of the convolving kernel stride (int or tuple, optional): Stride of the convolution. Default: 1 padding (int or tuple, optional): ``dilation * (kernel_size - 1) - padding`` zero-padding will be added to both sides of each dimension in the input. Default: 0 output_padding (int or tuple, optional): Additional size added to one side of each dimension in the output shape. Default: 0 groups (int, optional): Number of blocked connections from input channels to output channels. Default: 1 bias (bool, optional): If ``True``, adds a learnable bias to the output. Default: ``True`` dilation (int or tuple, optional): Spacing between kernel elements. Default: 1 .. seealso:: :class:`torch.nn.ConvTranspose3d` and :class:`torch.nn.modules.lazy.LazyModuleMixin` rrTrCNr+r,r$r%r)r'r-r&r(r0c rrrrr_r2r5rK&rzLazyConvTranspose3d.__init__cCrrr2rr2r2r5rIr7z)LazyConvTranspose3d._get_num_spatial_dimsr) rsrtrurrrrxr r{rNrKrr|r2r2r_r5r rr )0rdtypingrrtyping_extensionsrrSrtorch._torch_docsrtorch.nnrrrtorch.nn.common_typesr r r torch.nn.parameterr r lazyrmodulerutilsrrrr__all__rr#rrrrrrrrrrrrrrr r2r2r2r5sN       %4,^%=IBEEFEE