# mypy: allow-untyped-defsimportmathfromtypingimportOptional,Unionfromtyping_extensionsimportdeprecatedimporttorchfromtorchimportTensorfromtorch._torch_docsimportreproducibility_notesfromtorch.nnimportfunctionalasF,initfromtorch.nn.common_typesimport_size_1_t,_size_2_t,_size_3_tfromtorch.nn.parameterimportParameter,UninitializedParameterfrom.lazyimportLazyModuleMixinfrom.moduleimportModulefrom.utilsimport_pair,_reverse_repeat_tuple,_single,_triple__all__=["Conv1d","Conv2d","Conv3d","ConvTranspose1d","ConvTranspose2d","ConvTranspose3d","LazyConv1d","LazyConv2d","LazyConv3d","LazyConvTranspose1d","LazyConvTranspose2d","LazyConvTranspose3d",]convolution_notes={"groups_note":r"""* :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}}`).""","depthwise_separable_note":r"""When `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})`.""",}# noqa: B950class_ConvNd(Module):__constants__=["stride","padding","dilation","groups","padding_mode","output_padding","in_channels","out_channels","kernel_size",]__annotations__={"bias":Optional[torch.Tensor]}def_conv_forward(self,input:Tensor,weight:Tensor,bias:Optional[Tensor])->Tensor:# type: ignore[empty-body]...in_channels:int_reversed_padding_repeated_twice:list[int]out_channels:intkernel_size:tuple[int,...]stride:tuple[int,...]padding:Union[str,tuple[int,...]]dilation:tuple[int,...]transposed:booloutput_padding:tuple[int,...]groups:intpadding_mode:strweight:Tensorbias:Optional[Tensor]def__init__(self,in_channels:int,out_channels:int,kernel_size:tuple[int,...],stride:tuple[int,...],padding:Union[str,tuple[int,...]],dilation:tuple[int,...],transposed:bool,output_padding:tuple[int,...],groups:int,bias:bool,padding_mode:str,device=None,dtype=None,)->None:factory_kwargs={"device":device,"dtype":dtype}super().__init__()ifgroups<=0:raiseValueError("groups must be a positive integer")ifin_channels%groups!=0:raiseValueError("in_channels must be divisible by groups")ifout_channels%groups!=0:raiseValueError("out_channels must be divisible by groups")valid_padding_strings={"same","valid"}ifisinstance(padding,str):ifpaddingnotinvalid_padding_strings:raiseValueError(f"Invalid padding string {padding!r}, should be one of {valid_padding_strings}")ifpadding=="same"andany(s!=1forsinstride):raiseValueError("padding='same' is not supported for strided convolutions")valid_padding_modes={"zeros","reflect","replicate","circular"}ifpadding_modenotinvalid_padding_modes:raiseValueError(f"padding_mode must be one of {valid_padding_modes}, but got padding_mode='{padding_mode}'")self.in_channels=in_channelsself.out_channels=out_channelsself.kernel_size=kernel_sizeself.stride=strideself.padding=paddingself.dilation=dilationself.transposed=transposedself.output_padding=output_paddingself.groups=groupsself.padding_mode=padding_mode# `_reversed_padding_repeated_twice` is the padding to be passed to# `F.pad` if needed (e.g., for non-zero padding types that are# implemented as two ops: padding + conv). `F.pad` accepts paddings in# reverse order than the dimension.ifisinstance(self.padding,str):self._reversed_padding_repeated_twice=[0,0]*len(kernel_size)ifpadding=="same":ford,k,iinzip(dilation,kernel_size,range(len(kernel_size)-1,-1,-1)):total_padding=d*(k-1)left_pad=total_padding//2self._reversed_padding_repeated_twice[2*i]=left_padself._reversed_padding_repeated_twice[2*i+1]=(total_padding-left_pad)else:self._reversed_padding_repeated_twice=_reverse_repeat_tuple(self.padding,2)iftransposed:self.weight=Parameter(torch.empty((in_channels,out_channels//groups,*kernel_size),**factory_kwargs,))else:self.weight=Parameter(torch.empty((out_channels,in_channels//groups,*kernel_size),**factory_kwargs,))ifbias:self.bias=Parameter(torch.empty(out_channels,**factory_kwargs))else:self.register_parameter("bias",None)self.reset_parameters()defreset_parameters(self)->None:# Setting a=sqrt(5) in kaiming_uniform is the same as initializing with# uniform(-1/sqrt(k), 1/sqrt(k)), where k = weight.size(1) * prod(*kernel_size)# For more details see: https://github.com/pytorch/pytorch/issues/15314#issuecomment-477448573init.kaiming_uniform_(self.weight,a=math.sqrt(5))ifself.biasisnotNone:fan_in,_=init._calculate_fan_in_and_fan_out(self.weight)iffan_in!=0:bound=1/math.sqrt(fan_in)init.uniform_(self.bias,-bound,bound)defextra_repr(self):s=("{in_channels}, {out_channels}, kernel_size={kernel_size}"", stride={stride}")ifself.padding!=(0,)*len(self.padding):s+=", padding={padding}"ifself.dilation!=(1,)*len(self.dilation):s+=", dilation={dilation}"ifself.output_padding!=(0,)*len(self.output_padding):s+=", output_padding={output_padding}"ifself.groups!=1:s+=", groups={groups}"ifself.biasisNone:s+=", bias=False"ifself.padding_mode!="zeros":s+=", padding_mode={padding_mode}"returns.format(**self.__dict__)def__setstate__(self,state):super().__setstate__(state)ifnothasattr(self,"padding_mode"):self.padding_mode="zeros"
[docs]classConv1d(_ConvNd):__doc__=(r"""Applies 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. """+r""" This module supports :ref:`TensorFloat32<tf32_on_ampere>`. On certain ROCm devices, when using float16 inputs this module will use :ref:`different precision<fp16_on_mi200>` 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 \u00e0 trous algorithm. It is harder to describe, but this `link`_ has a nice visualization of what :attr:`dilation` does."""r""" {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'`` """.format(**reproducibility_notes,**convolution_notes)+r""" 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 """)def__init__(self,in_channels:int,out_channels:int,kernel_size:_size_1_t,stride:_size_1_t=1,padding:Union[str,_size_1_t]=0,dilation:_size_1_t=1,groups:int=1,bias:bool=True,padding_mode:str="zeros",# TODO: refine this typedevice=None,dtype=None,)->None:factory_kwargs={"device":device,"dtype":dtype}# we create new variables below to make mypy happy since kernel_size has# type Union[int, Tuple[int]] and kernel_size_ has type Tuple[int]kernel_size_=_single(kernel_size)stride_=_single(stride)padding_=paddingifisinstance(padding,str)else_single(padding)dilation_=_single(dilation)super().__init__(in_channels,out_channels,kernel_size_,stride_,padding_,dilation_,False,_single(0),groups,bias,padding_mode,**factory_kwargs,)def_conv_forward(self,input:Tensor,weight:Tensor,bias:Optional[Tensor]):ifself.padding_mode!="zeros":returnF.conv1d(F.pad(input,self._reversed_padding_repeated_twice,mode=self.padding_mode),weight,bias,self.stride,_single(0),self.dilation,self.groups,)returnF.conv1d(input,weight,bias,self.stride,self.padding,self.dilation,self.groups)defforward(self,input:Tensor)->Tensor:returnself._conv_forward(input,self.weight,self.bias)
[docs]classConv2d(_ConvNd):__doc__=(r"""Applies 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. """+r""" This module supports :ref:`TensorFloat32<tf32_on_ampere>`. On certain ROCm devices, when using float16 inputs this module will use :ref:`different precision<fp16_on_mi200>` 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 \u00e0 trous algorithm. It is harder to describe, but this `link`_ has a nice visualization of what :attr:`dilation` does."""r""" {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'`` """.format(**reproducibility_notes,**convolution_notes)+r""" 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 """)def__init__(self,in_channels:int,out_channels:int,kernel_size:_size_2_t,stride:_size_2_t=1,padding:Union[str,_size_2_t]=0,dilation:_size_2_t=1,groups:int=1,bias:bool=True,padding_mode:str="zeros",# TODO: refine this typedevice=None,dtype=None,)->None:factory_kwargs={"device":device,"dtype":dtype}kernel_size_=_pair(kernel_size)stride_=_pair(stride)padding_=paddingifisinstance(padding,str)else_pair(padding)dilation_=_pair(dilation)super().__init__(in_channels,out_channels,kernel_size_,stride_,padding_,dilation_,False,_pair(0),groups,bias,padding_mode,**factory_kwargs,)def_conv_forward(self,input:Tensor,weight:Tensor,bias:Optional[Tensor]):ifself.padding_mode!="zeros":returnF.conv2d(F.pad(input,self._reversed_padding_repeated_twice,mode=self.padding_mode),weight,bias,self.stride,_pair(0),self.dilation,self.groups,)returnF.conv2d(input,weight,bias,self.stride,self.padding,self.dilation,self.groups)defforward(self,input:Tensor)->Tensor:returnself._conv_forward(input,self.weight,self.bias)
[docs]classConv3d(_ConvNd):__doc__=(r"""Applies 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 """+r""" This module supports :ref:`TensorFloat32<tf32_on_ampere>`. On certain ROCm devices, when using float16 inputs this module will use :ref:`different precision<fp16_on_mi200>` 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 \u00e0 trous algorithm. It is harder to describe, but this `link`_ has a nice visualization of what :attr:`dilation` does."""r""" {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'`` """.format(**reproducibility_notes,**convolution_notes)+r""" 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 """)def__init__(self,in_channels:int,out_channels:int,kernel_size:_size_3_t,stride:_size_3_t=1,padding:Union[str,_size_3_t]=0,dilation:_size_3_t=1,groups:int=1,bias:bool=True,padding_mode:str="zeros",device=None,dtype=None,)->None:factory_kwargs={"device":device,"dtype":dtype}kernel_size_=_triple(kernel_size)stride_=_triple(stride)padding_=paddingifisinstance(padding,str)else_triple(padding)dilation_=_triple(dilation)super().__init__(in_channels,out_channels,kernel_size_,stride_,padding_,dilation_,False,_triple(0),groups,bias,padding_mode,**factory_kwargs,)def_conv_forward(self,input:Tensor,weight:Tensor,bias:Optional[Tensor]):ifself.padding_mode!="zeros":returnF.conv3d(F.pad(input,self._reversed_padding_repeated_twice,mode=self.padding_mode),weight,bias,self.stride,_triple(0),self.dilation,self.groups,)returnF.conv3d(input,weight,bias,self.stride,self.padding,self.dilation,self.groups)defforward(self,input:Tensor)->Tensor:returnself._conv_forward(input,self.weight,self.bias)
class_ConvTransposeNd(_ConvNd):def__init__(self,in_channels,out_channels,kernel_size,stride,padding,dilation,transposed,output_padding,groups,bias,padding_mode,device=None,dtype=None,)->None:ifpadding_mode!="zeros":raiseValueError(f'Only "zeros" padding mode is supported for {self.__class__.__name__}')factory_kwargs={"device":device,"dtype":dtype}super().__init__(in_channels,out_channels,kernel_size,stride,padding,dilation,transposed,output_padding,groups,bias,padding_mode,**factory_kwargs,)# dilation being an optional parameter is for backwards# compatibilitydef_output_padding(self,input:Tensor,output_size:Optional[list[int]],stride:list[int],padding:list[int],kernel_size:list[int],num_spatial_dims:int,dilation:Optional[list[int]]=None,)->list[int]:ifoutput_sizeisNone:ret=_single(self.output_padding)# converting to list if was not alreadyelse:has_batch_dim=input.dim()==num_spatial_dims+2num_non_spatial_dims=2ifhas_batch_dimelse1iflen(output_size)==num_non_spatial_dims+num_spatial_dims:output_size=output_size[num_non_spatial_dims:]iflen(output_size)!=num_spatial_dims:raiseValueError(f"ConvTranspose{num_spatial_dims}D: for {input.dim()}D input, output_size must have {num_spatial_dims} "f"or {num_non_spatial_dims+num_spatial_dims} elements (got {len(output_size)})")min_sizes=torch.jit.annotate(list[int],[])max_sizes=torch.jit.annotate(list[int],[])fordinrange(num_spatial_dims):dim_size=((input.size(d+num_non_spatial_dims)-1)*stride[d]-2*padding[d]+(dilation[d]ifdilationisnotNoneelse1)*(kernel_size[d]-1)+1)min_sizes.append(dim_size)max_sizes.append(min_sizes[d]+stride[d]-1)foriinrange(len(output_size)):size=output_size[i]min_size=min_sizes[i]max_size=max_sizes[i]ifsize<min_sizeorsize>max_size:raiseValueError(f"requested an output size of {output_size}, but valid sizes range "f"from {min_sizes} to {max_sizes} (for an input of {input.size()[2:]})")res=torch.jit.annotate(list[int],[])fordinrange(num_spatial_dims):res.append(output_size[d]-min_sizes[d])ret=resreturnret
[docs]classConvTranspose1d(_ConvTransposeNd):__doc__=(r"""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<tf32_on_ampere>`. On certain ROCm devices, when using float16 inputs this module will use :ref:`different precision<fp16_on_mi200>` 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 \u00e0 trous algorithm. It is harder to describe, but the link `here`_ has a nice visualization of what :attr:`dilation` does."""r""" {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 """.format(**reproducibility_notes,**convolution_notes)+r""" 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 """)def__init__(self,in_channels:int,out_channels:int,kernel_size:_size_1_t,stride:_size_1_t=1,padding:_size_1_t=0,output_padding:_size_1_t=0,groups:int=1,bias:bool=True,dilation:_size_1_t=1,padding_mode:str="zeros",device=None,dtype=None,)->None:factory_kwargs={"device":device,"dtype":dtype}kernel_size=_single(kernel_size)stride=_single(stride)padding=_single(padding)dilation=_single(dilation)output_padding=_single(output_padding)super().__init__(in_channels,out_channels,kernel_size,stride,padding,dilation,True,output_padding,groups,bias,padding_mode,**factory_kwargs,)defforward(self,input:Tensor,output_size:Optional[list[int]]=None)->Tensor:ifself.padding_mode!="zeros":raiseValueError("Only `zeros` padding mode is supported for ConvTranspose1d")assertisinstance(self.padding,tuple)# One cannot replace List by Tuple or Sequence in "_output_padding" because# TorchScript does not support `Sequence[T]` or `Tuple[T, ...]`.num_spatial_dims=1output_padding=self._output_padding(input,output_size,self.stride,# type: ignore[arg-type]self.padding,# type: ignore[arg-type]self.kernel_size,# type: ignore[arg-type]num_spatial_dims,self.dilation,# type: ignore[arg-type])returnF.conv_transpose1d(input,self.weight,self.bias,self.stride,self.padding,output_padding,self.groups,self.dilation,)
[docs]classConvTranspose2d(_ConvTransposeNd):__doc__=(r"""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<tf32_on_ampere>`. On certain ROCm devices, when using float16 inputs this module will use :ref:`different precision<fp16_on_mi200>` 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 \u00e0 trous algorithm. It is harder to describe, but the link `here`_ has a nice visualization of what :attr:`dilation` does."""r""" {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 """.format(**reproducibility_notes,**convolution_notes)+r""" 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 """)def__init__(self,in_channels:int,out_channels:int,kernel_size:_size_2_t,stride:_size_2_t=1,padding:_size_2_t=0,output_padding:_size_2_t=0,groups:int=1,bias:bool=True,dilation:_size_2_t=1,padding_mode:str="zeros",device=None,dtype=None,)->None:factory_kwargs={"device":device,"dtype":dtype}kernel_size=_pair(kernel_size)stride=_pair(stride)padding=_pair(padding)dilation=_pair(dilation)output_padding=_pair(output_padding)super().__init__(in_channels,out_channels,kernel_size,stride,padding,dilation,True,output_padding,groups,bias,padding_mode,**factory_kwargs,)defforward(self,input:Tensor,output_size:Optional[list[int]]=None)->Tensor:ifself.padding_mode!="zeros":raiseValueError("Only `zeros` padding mode is supported for ConvTranspose2d")assertisinstance(self.padding,tuple)# One cannot replace List by Tuple or Sequence in "_output_padding" because# TorchScript does not support `Sequence[T]` or `Tuple[T, ...]`.num_spatial_dims=2output_padding=self._output_padding(input,output_size,self.stride,# type: ignore[arg-type]self.padding,# type: ignore[arg-type]self.kernel_size,# type: ignore[arg-type]num_spatial_dims,self.dilation,# type: ignore[arg-type])returnF.conv_transpose2d(input,self.weight,self.bias,self.stride,self.padding,output_padding,self.groups,self.dilation,)
[docs]classConvTranspose3d(_ConvTransposeNd):__doc__=(r"""Applies 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<tf32_on_ampere>`. On certain ROCm devices, when using float16 inputs this module will use :ref:`different precision<fp16_on_mi200>` 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 \u00e0 trous algorithm. It is harder to describe, but the link `here`_ has a nice visualization of what :attr:`dilation` does."""r""" {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 """.format(**reproducibility_notes,**convolution_notes)+r""" 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 """)def__init__(self,in_channels:int,out_channels:int,kernel_size:_size_3_t,stride:_size_3_t=1,padding:_size_3_t=0,output_padding:_size_3_t=0,groups:int=1,bias:bool=True,dilation:_size_3_t=1,padding_mode:str="zeros",device=None,dtype=None,)->None:factory_kwargs={"device":device,"dtype":dtype}kernel_size=_triple(kernel_size)stride=_triple(stride)padding=_triple(padding)dilation=_triple(dilation)output_padding=_triple(output_padding)super().__init__(in_channels,out_channels,kernel_size,stride,padding,dilation,True,output_padding,groups,bias,padding_mode,**factory_kwargs,)defforward(self,input:Tensor,output_size:Optional[list[int]]=None)->Tensor:ifself.padding_mode!="zeros":raiseValueError("Only `zeros` padding mode is supported for ConvTranspose3d")assertisinstance(self.padding,tuple)# One cannot replace List by Tuple or Sequence in "_output_padding" because# TorchScript does not support `Sequence[T]` or `Tuple[T, ...]`.num_spatial_dims=3output_padding=self._output_padding(input,output_size,self.stride,# type: ignore[arg-type]self.padding,# type: ignore[arg-type]self.kernel_size,# type: ignore[arg-type]num_spatial_dims,self.dilation,# type: ignore[arg-type])returnF.conv_transpose3d(input,self.weight,self.bias,self.stride,self.padding,output_padding,self.groups,self.dilation,)
# TODO: Deprecate and remove the following alias `_ConvTransposeMixin`.## `_ConvTransposeMixin` was a mixin that was removed. It is meant to be used# with `_ConvNd` to construct actual module classes that implements conv# transpose ops:## class MyConvTranspose(_ConvNd, _ConvTransposeMixin):# ...## In PyTorch, it has been replaced by `_ConvTransposeNd`, which is a proper# subclass of `_ConvNd`. However, some user code in the wild still (incorrectly)# use the internal class `_ConvTransposeMixin`. Hence, we provide this alias# for BC, because it is cheap and easy for us to do so, even though that# `_ConvTransposeNd` is really not a mixin anymore (but multiple inheritance as# above would still work).class_ConvTransposeMixin(_ConvTransposeNd):@deprecated("`_ConvTransposeMixin` is a deprecated internal class. ""Please consider using public APIs.",category=FutureWarning,)def__init__(self,*args,**kwargs):super().__init__(*args,**kwargs)# TODO: Conv2dLocal# TODO: Conv2dMap# TODO: ConvTranspose2dMapclass_LazyConvXdMixin(LazyModuleMixin):groups:inttransposed:boolin_channels:intout_channels:intkernel_size:tuple[int,...]weight:UninitializedParameterbias:UninitializedParameterdefreset_parameters(self)->None:# has_uninitialized_params is defined in parent class and it is using a protocol on selfifnotself.has_uninitialized_params()andself.in_channels!=0:# type: ignore[misc]# "type:ignore[..]" is required because mypy thinks that "reset_parameters" is undefined# in super class. Turns out that it is defined in _ConvND which is inherited by any class# that also inherits _LazyConvXdMixinsuper().reset_parameters()# type: ignore[misc]# Signature of "initialize_parameters" is incompatible with the definition in supertype LazyModuleMixindefinitialize_parameters(self,input:Tensor,*args,**kwargs)->None:# type: ignore[override]# defined by parent class but using a protocolifself.has_uninitialized_params():# type: ignore[misc]self.in_channels=self._get_in_channels(input)ifself.in_channels%self.groups!=0:raiseValueError("in_channels must be divisible by groups")assertisinstance(self.weight,UninitializedParameter)ifself.transposed:self.weight.materialize((self.in_channels,self.out_channels//self.groups,*self.kernel_size,))else:self.weight.materialize((self.out_channels,self.in_channels//self.groups,*self.kernel_size,))ifself.biasisnotNone:assertisinstance(self.bias,UninitializedParameter)self.bias.materialize((self.out_channels,))self.reset_parameters()# Function to extract in_channels from first input.def_get_in_channels(self,input:Tensor)->int:num_spatial_dims=self._get_num_spatial_dims()num_dims_no_batch=num_spatial_dims+1# +1 for channels dimnum_dims_batch=num_dims_no_batch+1ifinput.dim()notin(num_dims_no_batch,num_dims_batch):raiseRuntimeError(f"Expected {num_dims_no_batch}D (unbatched) or {num_dims_batch}D (batched) input "f"to {self.__class__.__name__}, but "f"got input of size: {input.shape}")returninput.shape[1]ifinput.dim()==num_dims_batchelseinput.shape[0]# Function to return the number of spatial dims expected for inputs to the module.# This is expected to be implemented by subclasses.def_get_num_spatial_dims(self)->int:raiseNotImplementedError# LazyConv1d defines weight as a Tensor but derived class defines it as UnitializeParameter
[docs]classLazyConv1d(_LazyConvXdMixin,Conv1d):# type: ignore[misc]r"""A :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` """# super class define this variable as None. "type: ignore[..] is required# since we are redefining the variable.cls_to_become=Conv1d# type: ignore[assignment]def__init__(self,out_channels:int,kernel_size:_size_1_t,stride:_size_1_t=1,padding:_size_1_t=0,dilation:_size_1_t=1,groups:int=1,bias:bool=True,padding_mode:str="zeros",device=None,dtype=None,)->None:factory_kwargs={"device":device,"dtype":dtype}super().__init__(0,0,kernel_size,stride,padding,dilation,groups,# bias is hardcoded to False to avoid creating tensor# that will soon be overwritten.False,padding_mode,**factory_kwargs,)self.weight=UninitializedParameter(**factory_kwargs)self.out_channels=out_channelsifbias:self.bias=UninitializedParameter(**factory_kwargs)def_get_num_spatial_dims(self)->int:return1
# LazyConv2d defines weight as a Tensor but derived class defines it as UnitializeParameter
[docs]classLazyConv2d(_LazyConvXdMixin,Conv2d):# type: ignore[misc]r"""A :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` """# super class define this variable as None. "type: ignore[..] is required# since we are redefining the variable.cls_to_become=Conv2d# type: ignore[assignment]def__init__(self,out_channels:int,kernel_size:_size_2_t,stride:_size_2_t=1,padding:_size_2_t=0,dilation:_size_2_t=1,groups:int=1,bias:bool=True,padding_mode:str="zeros",# TODO: refine this typedevice=None,dtype=None,)->None:factory_kwargs={"device":device,"dtype":dtype}super().__init__(0,0,kernel_size,stride,padding,dilation,groups,# bias is hardcoded to False to avoid creating tensor# that will soon be overwritten.False,padding_mode,**factory_kwargs,)self.weight=UninitializedParameter(**factory_kwargs)self.out_channels=out_channelsifbias:self.bias=UninitializedParameter(**factory_kwargs)def_get_num_spatial_dims(self)->int:return2
# LazyConv3d defines weight as a Tensor but derived class defines it as UnitializeParameter
[docs]classLazyConv3d(_LazyConvXdMixin,Conv3d):# type: ignore[misc]r"""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` """# super class define this variable as None. "type: ignore[..] is required# since we are redefining the variable.cls_to_become=Conv3d# type: ignore[assignment]def__init__(self,out_channels:int,kernel_size:_size_3_t,stride:_size_3_t=1,padding:_size_3_t=0,dilation:_size_3_t=1,groups:int=1,bias:bool=True,padding_mode:str="zeros",device=None,dtype=None,)->None:factory_kwargs={"device":device,"dtype":dtype}super().__init__(0,0,kernel_size,stride,padding,dilation,groups,# bias is hardcoded to False to avoid creating tensor# that will soon be overwritten.False,padding_mode,**factory_kwargs,)self.weight=UninitializedParameter(**factory_kwargs)self.out_channels=out_channelsifbias:self.bias=UninitializedParameter(**factory_kwargs)def_get_num_spatial_dims(self)->int:return3
# LazyConvTranspose1d defines weight as a Tensor but derived class defines it as UnitializeParameter
[docs]classLazyConvTranspose1d(_LazyConvXdMixin,ConvTranspose1d):# type: ignore[misc]r"""A :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` """# super class define this variable as None. "type: ignore[..] is required# since we are redefining the variable.cls_to_become=ConvTranspose1d# type: ignore[assignment]def__init__(self,out_channels:int,kernel_size:_size_1_t,stride:_size_1_t=1,padding:_size_1_t=0,output_padding:_size_1_t=0,groups:int=1,bias:bool=True,dilation:_size_1_t=1,padding_mode:str="zeros",device=None,dtype=None,)->None:factory_kwargs={"device":device,"dtype":dtype}super().__init__(0,0,kernel_size,stride,padding,output_padding,groups,# bias is hardcoded to False to avoid creating tensor# that will soon be overwritten.False,dilation,padding_mode,**factory_kwargs,)self.weight=UninitializedParameter(**factory_kwargs)self.out_channels=out_channelsifbias:self.bias=UninitializedParameter(**factory_kwargs)def_get_num_spatial_dims(self)->int:return1
# LazyConvTranspose2d defines weight as a Tensor but derived class defines it as UnitializeParameter
[docs]classLazyConvTranspose2d(_LazyConvXdMixin,ConvTranspose2d):# type: ignore[misc]r"""A :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` """# super class define this variable as None. "type: ignore[..] is required# since we are redefining the variable.cls_to_become=ConvTranspose2d# type: ignore[assignment]def__init__(self,out_channels:int,kernel_size:_size_2_t,stride:_size_2_t=1,padding:_size_2_t=0,output_padding:_size_2_t=0,groups:int=1,bias:bool=True,dilation:int=1,padding_mode:str="zeros",device=None,dtype=None,)->None:factory_kwargs={"device":device,"dtype":dtype}super().__init__(0,0,kernel_size,stride,padding,output_padding,groups,# bias is hardcoded to False to avoid creating tensor# that will soon be overwritten.False,dilation,padding_mode,**factory_kwargs,)self.weight=UninitializedParameter(**factory_kwargs)self.out_channels=out_channelsifbias:self.bias=UninitializedParameter(**factory_kwargs)def_get_num_spatial_dims(self)->int:return2
# LazyConvTranspose3d defines weight as a Tensor but derived class defines it as UnitializeParameter
[docs]classLazyConvTranspose3d(_LazyConvXdMixin,ConvTranspose3d):# type: ignore[misc]r"""A :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` """# super class define this variable as None. "type: ignore[..] is required# since we are redefining the variable.cls_to_become=ConvTranspose3d# type: ignore[assignment]def__init__(self,out_channels:int,kernel_size:_size_3_t,stride:_size_3_t=1,padding:_size_3_t=0,output_padding:_size_3_t=0,groups:int=1,bias:bool=True,dilation:_size_3_t=1,padding_mode:str="zeros",device=None,dtype=None,)->None:factory_kwargs={"device":device,"dtype":dtype}super().__init__(0,0,kernel_size,stride,padding,output_padding,groups,# bias is hardcoded to False to avoid creating tensor# that will soon be overwritten.False,dilation,padding_mode,**factory_kwargs,)self.weight=UninitializedParameter(**factory_kwargs)self.out_channels=out_channelsifbias:self.bias=UninitializedParameter(**factory_kwargs)def_get_num_spatial_dims(self)->int:return3
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