o h*@sddlZddlmZddlZddlmZddlmZmZddl m Z m Z ddl m Z ddlmZgd ZGd d d eZGd d d eZGdddeZGdddeZGddde eZdS)N)Any)Tensor) functionalinit) ParameterUninitializedParameter)LazyModuleMixin)Module)BilinearIdentity LazyLinearLinearcs@eZdZdZdededdffdd Zdedefd d ZZS) r aA placeholder identity operator that is argument-insensitive. Args: args: any argument (unused) kwargs: any keyword argument (unused) Shape: - Input: :math:`(*)`, where :math:`*` means any number of dimensions. - Output: :math:`(*)`, same shape as the input. Examples:: >>> m = nn.Identity(54, unused_argument1=0.1, unused_argument2=False) >>> input = torch.randn(128, 20) >>> output = m(input) >>> print(output.size()) torch.Size([128, 20]) argskwargsreturnNcstdSNsuper__init__)selfrr __class__k/var/www/html/construction_image-detection-poc/venv/lib/python3.10/site-packages/torch/nn/modules/linear.pyr+szIdentity.__init__inputcCs|Srrrrrrrforward.szIdentity.forward) __name__ __module__ __qualname____doc__rrrr __classcell__rrrrr sr c seZdZUdZddgZeed<eed<eed<   ddedededdffd d Z dd d Z d edefddZ de fddZ ZS)raCApplies an affine linear transformation to the incoming data: :math:`y = xA^T + b`. This module supports :ref:`TensorFloat32`. On certain ROCm devices, when using float16 inputs this module will use :ref:`different precision` for backward. Args: in_features: size of each input sample out_features: size of each output sample bias: If set to ``False``, the layer will not learn an additive bias. Default: ``True`` Shape: - Input: :math:`(*, H_\text{in})` where :math:`*` means any number of dimensions including none and :math:`H_\text{in} = \text{in\_features}`. - Output: :math:`(*, H_\text{out})` where all but the last dimension are the same shape as the input and :math:`H_\text{out} = \text{out\_features}`. Attributes: weight: the learnable weights of the module of shape :math:`(\text{out\_features}, \text{in\_features})`. The values are initialized from :math:`\mathcal{U}(-\sqrt{k}, \sqrt{k})`, where :math:`k = \frac{1}{\text{in\_features}}` bias: the learnable bias of the module of shape :math:`(\text{out\_features})`. If :attr:`bias` is ``True``, the values are initialized from :math:`\mathcal{U}(-\sqrt{k}, \sqrt{k})` where :math:`k = \frac{1}{\text{in\_features}}` Examples:: >>> m = nn.Linear(20, 30) >>> input = torch.randn(128, 20) >>> output = m(input) >>> print(output.size()) torch.Size([128, 30]) in_features out_featuresweightTNbiasrcsr||d}t||_||_ttj||ffi||_|r-ttj|fi||_n| dd| dSNdevicedtyper&) rrr#r$rtorchemptyr%r&register_parameterreset_parameters)rr#r$r&r)r*factory_kwargsrrrr]s    zLinear.__init__cCsdtj|jtdd|jdur0t|j\}}|dkr#dt|nd}t|j| |dSdS)N)arr)rkaiming_uniform_r%mathsqrtr&_calculate_fan_in_and_fan_outuniform_)rfan_in_boundrrrr.rs  zLinear.reset_parametersrcCst||j|jSr)Flinearr%r&rrrrr|szLinear.forwardcCs d|jd|jd|jduS)Nz in_features=, out_features=, bias=)r#r$r&rrrr extra_reprs zLinear.extra_reprTNNrNrrr r! __constants__int__annotations__rboolrr.rstrr?r"rrrrr2s* %  rc s6eZdZ   d dedededdffdd ZZS) NonDynamicallyQuantizableLinearTNr#r$r&rcstj|||||ddS)N)r&r)r*r)rr#r$r&r)r*rrrrs  z(NonDynamicallyQuantizableLinear.__init__r@)rrr rDrFrr"rrrrrHsrHc seZdZUdZgdZeed<eed<eed<eed<   ddededed ed df fd d Z dd dZ deded efddZ d e fddZ ZS)r aApplies a bilinear transformation to the incoming data: :math:`y = x_1^T A x_2 + b`. Args: in1_features: size of each first input sample in2_features: size of each second input sample out_features: size of each output sample bias: If set to ``False``, the layer will not learn an additive bias. Default: ``True`` Shape: - Input1: :math:`(*, H_\text{in1})` where :math:`H_\text{in1}=\text{in1\_features}` and :math:`*` means any number of additional dimensions including none. All but the last dimension of the inputs should be the same. - Input2: :math:`(*, H_\text{in2})` where :math:`H_\text{in2}=\text{in2\_features}`. - Output: :math:`(*, H_\text{out})` where :math:`H_\text{out}=\text{out\_features}` and all but the last dimension are the same shape as the input. Attributes: weight: the learnable weights of the module of shape :math:`(\text{out\_features}, \text{in1\_features}, \text{in2\_features})`. The values are initialized from :math:`\mathcal{U}(-\sqrt{k}, \sqrt{k})`, where :math:`k = \frac{1}{\text{in1\_features}}` bias: the learnable bias of the module of shape :math:`(\text{out\_features})`. If :attr:`bias` is ``True``, the values are initialized from :math:`\mathcal{U}(-\sqrt{k}, \sqrt{k})`, where :math:`k = \frac{1}{\text{in1\_features}}` Examples:: >>> m = nn.Bilinear(20, 30, 40) >>> input1 = torch.randn(128, 20) >>> input2 = torch.randn(128, 30) >>> output = m(input1, input2) >>> print(output.size()) torch.Size([128, 40]) ) in1_features in2_featuresr$rIrJr$r%TNr&rcsz||d}t||_||_||_ttj|||ffi||_|r1ttj|fi||_ n| dd| dSr') rrrIrJr$rr+r,r%r&r-r.)rrIrJr$r&r)r*r/rrrrs   zBilinear.__init__cCsLdt|jd}t|j| ||jdur$t|j| |dSdS)Nr)r3r4r%sizerr6r&)rr9rrrr.s  zBilinear.reset_parametersinput1input2cCst|||j|jSr)r:bilinearr%r&)rrLrMrrrrszBilinear.forwardc Cs(d|jd|jd|jd|jduS)Nz in1_features=z, in2_features=r<r=)rIrJr$r&r>rrrr?s zBilinear.extra_reprr@rArBrrrrr s0 % r cs`eZdZUdZeZeed<eed< ddede ddffdd Z dfd d Z dd d Z Z S)r aA :class:`torch.nn.Linear` module where `in_features` is inferred. In this module, the `weight` and `bias` are of :class:`torch.nn.UninitializedParameter` class. They will be initialized after the first call to ``forward`` is done and the module will become a regular :class:`torch.nn.Linear` module. The ``in_features`` argument of the :class:`Linear` is inferred from the ``input.shape[-1]``. Check the :class:`torch.nn.modules.lazy.LazyModuleMixin` for further documentation on lazy modules and their limitations. Args: out_features: size of each output sample bias: If set to ``False``, the layer will not learn an additive bias. Default: ``True`` Attributes: weight: the learnable weights of the module of shape :math:`(\text{out\_features}, \text{in\_features})`. The values are initialized from :math:`\mathcal{U}(-\sqrt{k}, \sqrt{k})`, where :math:`k = \frac{1}{\text{in\_features}}` bias: the learnable bias of the module of shape :math:`(\text{out\_features})`. If :attr:`bias` is ``True``, the values are initialized from :math:`\mathcal{U}(-\sqrt{k}, \sqrt{k})` where :math:`k = \frac{1}{\text{in\_features}}` r%r&TNr$rcsL||d}tdddtdi||_||_|r$tdi||_dSdS)Nr(rFr)rrrr%r$r&)rr$r&r)r*r/rrrr s zLazyLinear.__init__cs(|s|jdkrtdSdSdS)Nr)has_uninitialized_paramsr#rr.r>rrrr.szLazyLinear.reset_parameterscCs||rs    VT