o hd@sdZddlZddlZddlmZmZmZmZm Z ddl m Z ddl Z ddl m Z gdZedZe dZed Zed Z dPd e d ed edee jde f ddZ dPd e dededee jde f ddZ dPd e deded ed edee jde fddZd e dede fddZd e de fddZ dPdedee eefdefdd Z ! " dQd e d ed edee jde f d#d$Z ! " dQd e dededee jde f d%d&Z ! " ' ( dRd e deded ed edee jde fd)d*Zd e dede fd+d,Zd e de fd-d.Z d e de fd/d0Z!d e de fd1d2Z"dSd e d4ede fd5d6Z#d e de$eeffd7d8Z% " dTd e d9edee jde fd:d;Z& " dTd e d9edee jde fdedefd?d@Z(  A B dUd e d ed>ededee jde f dCdDZ)  A B dUd e d ed>ededee jde f dEdFZ* 3 dVd e d9edee jde fdGdHZ+ I dWd e dJededee jde f dKdLZ,dMeeefdeeeffdNdOZ-e-eZ.e-eZ/e-eZ0e-e"Z1e-e#Z2e-e&Z3e-e'Z4e-e)Z5e-e*Z6e-e+Z7e-e,Z8dS)XzHThis file contains utilities for initializing neural network parameters.N)CallableLiteralOptionalTypeVarUnion) ParamSpec)Tensor)calculate_gainuniform_normal_ trunc_normal_ constant_ones_zeros_eye_dirac_xavier_uniform_xavier_normal_kaiming_uniform_kaiming_normal_ orthogonal_sparse_uniformnormalconstanteyediracxavier_uniform xavier_normalkaiming_uniformkaiming_normal orthogonalsparse_R_P) linearconv1dconv2dconv3dconv_transpose1dconv_transpose2dconv_transpose3dsigmoidtanhrelu leaky_reluselu)fan_infan_outtensorab generatorreturncC<t|j|||dWdS1swYdSNr6)torchno_gradr r3r4r5r6r>S/home/www/facesmatcher.com/frenv_anti/lib/python3.10/site-packages/torch/nn/init.py_no_grad_uniform_Ds $r@meanstdcCr8r9)r;r<r r3rArBr6r>r>r?_no_grad_normal_Ks $rDc Csdtdtfdd}||d|ks||d|kr tjdddtD||||}||||}|jd|dd|d|d |||t d | ||j ||d |WdS1skwYdS) Nxr7cSsdt|tddS)N?@)matherfsqrt)rEr>r>r?norm_cdf^sz(_no_grad_trunc_normal_..norm_cdfzjmean is more than 2 std from [a, b] in nn.init.trunc_normal_. The distribution of values may be incorrect. stacklevelr:rG)minmax) floatwarningswarnr;r<r Zerfinv_mul_rHrJZadd_Zclamp_) r3rArBr4r5r6rKlur>r>r?_no_grad_trunc_normal_Us     $rXvalcCs6t ||WdS1swYdSN)r;r<Zfill_r3rYr>r>r?_no_grad_fill_s $r\cCs4t |WdS1swYdSrZ)r;r<zero_r3r>r>r?_no_grad_zero_s $r_ nonlinearityparamcCsgd}||vs |dkrdS|dkrdS|dkrtdS|dkrM|d ur(d }nt|ts2t|ts7t|tr:|}ntd |d tdd|d S|dkrT dStd|)aReturn the recommended gain value for the given nonlinearity function. The values are as follows: ================= ==================================================== nonlinearity gain ================= ==================================================== Linear / Identity :math:`1` Conv{1,2,3}D :math:`1` Sigmoid :math:`1` Tanh :math:`\frac{5}{3}` ReLU :math:`\sqrt{2}` Leaky Relu :math:`\sqrt{\frac{2}{1 + \text{negative\_slope}^2}}` SELU :math:`\frac{3}{4}` ================= ==================================================== .. warning:: In order to implement `Self-Normalizing Neural Networks`_ , you should use ``nonlinearity='linear'`` instead of ``nonlinearity='selu'``. This gives the initial weights a variance of ``1 / N``, which is necessary to induce a stable fixed point in the forward pass. In contrast, the default gain for ``SELU`` sacrifices the normalization effect for more stable gradient flow in rectangular layers. Args: nonlinearity: the non-linear function (`nn.functional` name) param: optional parameter for the non-linear function Examples: >>> gain = nn.init.calculate_gain( ... "leaky_relu", 0.2 ... ) # leaky_relu with negative_slope=0.2 .. _Self-Normalizing Neural Networks: https://papers.nips.cc/paper/2017/hash/5d44ee6f2c3f71b73125876103c8f6c4-Abstract.html )r%r&r'r(r)r*r+r,rOr-g?r.rGr/N{Gz?znegative_slope z not a valid numberrLr0g?zUnsupported nonlinearity )rHrJ isinstanceboolintrR ValueError)r`raZ linear_fnsZnegative_sloper>r>r?r s.&  r rFcC4tj|rtjjt|f||||dSt||||S)aFill the input Tensor with values drawn from the uniform distribution. :math:`\mathcal{U}(a, b)`. Args: tensor: an n-dimensional `torch.Tensor` a: the lower bound of the uniform distribution b: the upper bound of the uniform distribution generator: the torch Generator to sample from (default: None) Examples: >>> w = torch.empty(3, 5) >>> nn.init.uniform_(w) r=)r; overrideshas_torch_function_variadichandle_torch_functionr r@r=r>r>r?r r cCrh)aFill the input Tensor with values drawn from the normal distribution. :math:`\mathcal{N}(\text{mean}, \text{std}^2)`. Args: tensor: an n-dimensional `torch.Tensor` mean: the mean of the normal distribution std: the standard deviation of the normal distribution generator: the torch Generator to sample from (default: None) Examples: >>> w = torch.empty(3, 5) >>> nn.init.normal_(w) rC)r;rirjrkr rDrCr>r>r?r rlr rGcCst||||||dS)aFill the input Tensor with values drawn from a truncated normal distribution. The values are effectively drawn from the normal distribution :math:`\mathcal{N}(\text{mean}, \text{std}^2)` with values outside :math:`[a, b]` redrawn until they are within the bounds. The method used for generating the random values works best when :math:`a \leq \text{mean} \leq b`. Args: tensor: an n-dimensional `torch.Tensor` mean: the mean of the normal distribution std: the standard deviation of the normal distribution a: the minimum cutoff value b: the maximum cutoff value generator: the torch Generator to sample from (default: None) Examples: >>> w = torch.empty(3, 5) >>> nn.init.trunc_normal_(w) r:)rX)r3rArBr4r5r6r>r>r?r sr cCs,tj|rtjjt|f||dSt||S)zFill the input Tensor with the value :math:`\text{val}`. Args: tensor: an n-dimensional `torch.Tensor` val: the value to fill the tensor with Examples: >>> w = torch.empty(3, 5) >>> nn.init.constant_(w, 0.3) r[)r;rirjrkr r\r[r>r>r?r *s   r cCs t|dS)zFill the input Tensor with the scalar value `1`. Args: tensor: an n-dimensional `torch.Tensor` Examples: >>> w = torch.empty(3, 5) >>> nn.init.ones_(w) rF)r\r^r>r>r?r<s rcCst|S)zFill the input Tensor with the scalar value `0`. Args: tensor: an n-dimensional `torch.Tensor` Examples: >>> w = torch.empty(3, 5) >>> nn.init.zeros_(w) )r_r^r>r>r?rIs rcCsX|dkr tdttj|j||jdWd|S1s%wY|S)a=Fill the 2-dimensional input `Tensor` with the identity matrix. Preserves the identity of the inputs in `Linear` layers, where as many inputs are preserved as possible. Args: tensor: a 2-dimensional `torch.Tensor` Examples: >>> w = torch.empty(3, 5) >>> nn.init.eye_(w) rL,Only tensors with 2 dimensions are supported)out requires_gradN) ndimensionrfr;r<rshaperpr^r>r>r?rVs   rrOgroupsc Cs<|}|dvr td|}|d|dkrtd|d|}t||d}tg|t|D]U}t|D]N}|dkrSd||||||ddf<q<|dkrnd||||||dd|ddf<q1, each group of channels preserves identity Args: tensor: a {3, 4, 5}-dimensional `torch.Tensor` groups (int, optional): number of groups in the conv layer (default: 1) Examples: >>> w = torch.empty(3, 16, 5, 5) >>> nn.init.dirac_(w) >>> w = torch.empty(3, 24, 5, 5) >>> nn.init.dirac_(w, 3) )z5Only tensors with 3, 4, or 5 dimensions are supportedrz!dim 0 must be divisible by groupsrOrtrLruN)rqrfsizerPr;r<r]range)r3rs dimensionssizesZout_chans_per_grpZmin_dimgdr>r>r?rksL    "         rcCsp|}|dkr td|d}|d}d}|dkr,|jddD]}||9}q%||}||}||fS)NrLzNFan in and fan out can not be computed for tensor with fewer than 2 dimensionsrOr)dimrfrwrr)r3ryZnum_input_fmapsZnum_output_fmapsZreceptive_field_sizesr1r2r>r>r?_calculate_fan_in_and_fan_outs    rgaincCsDt|\}}|tdt||}td|}t|| ||S)aFill the input `Tensor` with values using a Xavier uniform distribution. The method is described in `Understanding the difficulty of training deep feedforward neural networks` - Glorot, X. & Bengio, Y. (2010). The resulting tensor will have values sampled from :math:`\mathcal{U}(-a, a)` where .. math:: a = \text{gain} \times \sqrt{\frac{6}{\text{fan\_in} + \text{fan\_out}}} Also known as Glorot initialization. Args: tensor: an n-dimensional `torch.Tensor` gain: an optional scaling factor generator: the torch Generator to sample from (default: None) Examples: >>> w = torch.empty(3, 5) >>> nn.init.xavier_uniform_(w, gain=nn.init.calculate_gain("relu")) Note: Be aware that ``fan_in`` and ``fan_out`` are calculated assuming that the weight matrix is used in a transposed manner, (i.e., ``x @ w.T`` in ``Linear`` layers, where ``w.shape = [fan_out, fan_in]``). This is important for correct initialization. If you plan to use ``x @ w``, where ``w.shape = [fan_in, fan_out]``, pass in a transposed weight matrix, i.e. ``nn.init.xavier_uniform_(w.T, ...)``. rG@)rrHrJrRr@)r3rr6r1r2rBr4r>r>r?rs "rcCs4t|\}}|tdt||}t|d||S)aFill the input `Tensor` with values using a Xavier normal distribution. The method is described in `Understanding the difficulty of training deep feedforward neural networks` - Glorot, X. & Bengio, Y. (2010). The resulting tensor will have values sampled from :math:`\mathcal{N}(0, \text{std}^2)` where .. math:: \text{std} = \text{gain} \times \sqrt{\frac{2}{\text{fan\_in} + \text{fan\_out}}} Also known as Glorot initialization. Args: tensor: an n-dimensional `torch.Tensor` gain: an optional scaling factor generator: the torch Generator to sample from (default: None) Examples: >>> w = torch.empty(3, 5) >>> nn.init.xavier_normal_(w) Note: Be aware that ``fan_in`` and ``fan_out`` are calculated assuming that the weight matrix is used in a transposed manner, (i.e., ``x @ w.T`` in ``Linear`` layers, where ``w.shape = [fan_out, fan_in]``). This is important for correct initialization. If you plan to use ``x @ w``, where ``w.shape = [fan_in, fan_out]``, pass in a transposed weight matrix, i.e. ``nn.init.xavier_normal_(w.T, ...)``. rGrg)rrHrJrRrD)r3rr6r1r2rBr>r>r?rs !rmodecCsH|}ddg}||vrtd|d|t|\}}|dkr"|S|S)Nr1r2zMode z" not supported, please use one of )lowerrfr)r3rZ valid_modesr1r2r>r>r?_calculate_correct_fans  rr1r/c Cstj|rtjjt|f|||||dSd|jvr td|St||}t ||}|t |}t d|}t |j | ||dWdS1sPwYdS)aFill the input `Tensor` with values using a Kaiming uniform distribution. The method is described in `Delving deep into rectifiers: Surpassing human-level performance on ImageNet classification` - He, K. et al. (2015). The resulting tensor will have values sampled from :math:`\mathcal{U}(-\text{bound}, \text{bound})` where .. math:: \text{bound} = \text{gain} \times \sqrt{\frac{3}{\text{fan\_mode}}} Also known as He initialization. Args: tensor: an n-dimensional `torch.Tensor` a: the negative slope of the rectifier used after this layer (only used with ``'leaky_relu'``) mode: either ``'fan_in'`` (default) or ``'fan_out'``. Choosing ``'fan_in'`` preserves the magnitude of the variance of the weights in the forward pass. Choosing ``'fan_out'`` preserves the magnitudes in the backwards pass. nonlinearity: the non-linear function (`nn.functional` name), recommended to use only with ``'relu'`` or ``'leaky_relu'`` (default). generator: the torch Generator to sample from (default: None) Examples: >>> w = torch.empty(3, 5) >>> nn.init.kaiming_uniform_(w, mode="fan_in", nonlinearity="relu") Note: Be aware that ``fan_in`` and ``fan_out`` are calculated assuming that the weight matrix is used in a transposed manner, (i.e., ``x @ w.T`` in ``Linear`` layers, where ``w.shape = [fan_out, fan_in]``). This is important for correct initialization. If you plan to use ``x @ w``, where ``w.shape = [fan_in, fan_out]``, pass in a transposed weight matrix, i.e. ``nn.init.kaiming_uniform_(w.T, ...)``. )r3r4rr`r6r,Initializing zero-element tensors is a no-oprr:N)r;rirjrkrrrrSrTrr rHrJr<r ) r3r4rr`r6fanrrBboundr>r>r?rs( +    $rcCsvd|jvr td|St||}t||}|t|}t|j d||dWdS1s4wYdS)aFill the input `Tensor` with values using a Kaiming normal distribution. The method is described in `Delving deep into rectifiers: Surpassing human-level performance on ImageNet classification` - He, K. et al. (2015). The resulting tensor will have values sampled from :math:`\mathcal{N}(0, \text{std}^2)` where .. math:: \text{std} = \frac{\text{gain}}{\sqrt{\text{fan\_mode}}} Also known as He initialization. Args: tensor: an n-dimensional `torch.Tensor` a: the negative slope of the rectifier used after this layer (only used with ``'leaky_relu'``) mode: either ``'fan_in'`` (default) or ``'fan_out'``. Choosing ``'fan_in'`` preserves the magnitude of the variance of the weights in the forward pass. Choosing ``'fan_out'`` preserves the magnitudes in the backwards pass. nonlinearity: the non-linear function (`nn.functional` name), recommended to use only with ``'relu'`` or ``'leaky_relu'`` (default). generator: the torch Generator to sample from (default: None) Examples: >>> w = torch.empty(3, 5) >>> nn.init.kaiming_normal_(w, mode="fan_out", nonlinearity="relu") Note: Be aware that ``fan_in`` and ``fan_out`` are calculated assuming that the weight matrix is used in a transposed manner, (i.e., ``x @ w.T`` in ``Linear`` layers, where ``w.shape = [fan_out, fan_in]``). This is important for correct initialization. If you plan to use ``x @ w``, where ``w.shape = [fan_in, fan_out]``, pass in a transposed weight matrix, i.e. ``nn.init.kaiming_normal_(w.T, ...)``. rrr:N) rrrSrTrr rHrJr;r<r )r3r4rr`r6rrrBr>r>r?rPs +    $rc Cs|dkr td|dkr|S|d}||}|||fjdd|d}||kr2|tj |\}}t |d}| } || 9}||krP|t | ||||Wd|S1smwY|S)aFill the input `Tensor` with a (semi) orthogonal matrix. Described in `Exact solutions to the nonlinear dynamics of learning in deep linear neural networks` - Saxe, A. et al. (2013). The input tensor must have at least 2 dimensions, and for tensors with more than 2 dimensions the trailing dimensions are flattened. Args: tensor: an n-dimensional `torch.Tensor`, where :math:`n \geq 2` gain: optional scaling factor generator: the torch Generator to sample from (default: None) Examples: >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_LAPACK) >>> w = torch.empty(3, 5) >>> nn.init.orthogonal_(w) rLz4Only tensors with 2 or more dimensions are supportedrrOr:N)rqrfZnumelrwZ new_emptyr Zt_r;ZlinalgZqrZdiagsignr<Zview_asZcopy_rU) r3rr6rowscolsZ flattenedqrr|phr>r>r?rs,        rrbsparsityc Cs|dkr td|j\}}tt||}t)|jd||dt |D]}t |}|d|} d|| |f<q)Wd|S1sHwY|S)aFill the 2D input `Tensor` as a sparse matrix. The non-zero elements will be drawn from the normal distribution :math:`\mathcal{N}(0, 0.01)`, as described in `Deep learning via Hessian-free optimization` - Martens, J. (2010). Args: tensor: an n-dimensional `torch.Tensor` sparsity: The fraction of elements in each column to be set to zero std: the standard deviation of the normal distribution used to generate the non-zero values generator: the torch Generator to sample from (default: None) Examples: >>> w = torch.empty(3, 5) >>> nn.init.sparse_(w, sparsity=0.1) rLrnrr:N) rqrfrrrerHceilr;r<r rxZrandperm) r3rrBr6rrZ num_zerosZcol_idxZ row_indicesZ zero_indicesr>r>r?rs       rmethcsXjdddtjdtjdtffdd }ddd d |_|_|S) Nargskwargsr7cs,tjdddtdd|i|S)Nz `nn.init.z)` is now deprecated in favor of `nn.init.z`.rLrM)rSrT FutureWarning)rrrnew_nameZold_namer>r?deprecated_inits z(_make_deprecate..deprecated_initz z_(...) .. warning:: This method is now deprecated in favor of :func:`torch.nn.init.z"`. See :func:`~torch.nn.init.z` for details.)__name__r$rrr#__doc__)rrr>rr?_make_deprecates " rrZ)rgrFN)rgrFrmrGN)rO)rFN)rr1r/N)rON)rbN)9rrHrStypingrrrZ _OptionalrrZtyping_extensionsrr;r__all__r#r$Z_NonlinearityTypeZ_FanModerR Generatorr@rDrXr\r_rer r r r r rrrrtuplerrrrrrrrrrrrrrrrrr r!r"r>r>r>r?s      , L     5 + '  C 7 6 "'