U MhU @s"dZddlZddlZddlmZddlZddlmZdCddZdDddZ dEd d Z d d Z d dZ dFddZ dGeeeeejedddZdHeeeeejedddZdIeeeeeeejedddZeeeddd Zeed!d"d#Zeed!d$d%Zd&d'ZdJd)d*Zd+d,ZdKeeeejed-d.d/ZdLeeeejed-d0d1Zd2d3ZdMeeeeeejd6d7d8ZdNeeeeeejd6d9d:ZdOeejd;d.norm_cdfzjmean is more than 2 std from [a, b] in nn.init.trunc_normal_. The distribution of values may be incorrect. stacklevelrr)minmax) warningswarnrrr Zerfinv_mul_rrZadd_Zclamp_) r rrr r rrlurrr_no_grad_trunc_normal_s    r(c Cs*t||W5QRSQRXdSN)rrZfill_r valrrr_no_grad_fill_;s r,c Cs(t|W5QRSQRXdSr))rrzero_r rrr_no_grad_zero_@s r/cCsdddddddg}||ks"|dkr&d S|d kr2d S|d krDtd S|dkr|dkrZd}n4t|tsnt|tsxt|tr~|}ntd|dtd d |dS|dkrdStd|dS)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 ZlinearZconv1dZconv2dZconv3dZconv_transpose1dZconv_transpose2dZconv_transpose3dZsigmoidr tanhg?Zrelur leaky_reluN{Gz?znegative_slope z not a valid numberrZselug?zUnsupported nonlinearity )rr isinstanceboolintfloat ValueError) nonlinearityparamZ linear_fnsZnegative_sloperrrcalculate_gainEs"" r:r)r r r rreturncCs4tj|r&tjjt|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 rr rrrr }s r )r rrrr<cCs4tj|r&tjjt|f||||dSt||||S)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) r)rr=r>r?rrrrrrrs rr)r rrr r rr<cCst||||||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)r()r rrr r rrrr trunc_normal_srA)r r+r<cCs,tj|r"tjjt|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*)rr=r>r? constant_r,r*rrrrBs rB)r 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) r)r,r.rrrones_s rCcCst|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.rrrzeros_s rDc CsB|dkrtdttj|j||jdW5QRX|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) r,Only tensors with 2 dimensions are supported)out requires_grad) ndimensionr7rreyeshaperGr.rrreye_s   rKr c Cs&|}|dkrtd|}|d|dkr8td|d|}t||d}t|t|D]}t|D]}|dkrd||||||ddf<qx|dkrd||||||dd|ddf<qxd||||||dd|dd|ddf<qxqlW5QRX|S) aFFill the {3, 4, 5}-dimensional input `Tensor` with the Dirac delta function. Preserves the identity of the inputs in `Convolutional` layers, where as many input channels are preserved as possible. In case of groups>1, 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 groupsr rLrrM)rHr7sizer!rrr-range)r groups dimensionssizesZout_chans_per_grpZmin_dimgdrrrdirac_s2    "  rVcCsp|}|dkrtd|d}|d}d}|dkrX|jddD] }||9}qJ||}||}||fS)NrzNFan in and fan out can not be computed for tensor with fewer than 2 dimensionsr r)dimr7rOrJ)r rRZnum_input_fmapsZnum_output_fmapsZreceptive_field_sizesfan_infan_outrrr_calculate_fan_in_and_fan_out<s    r[)r gainrr<cCsDt|\}}|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')) r@)r[rrr6r)r r\rrYrZrr rrrxavier_uniform_Os r^cCs4t|\}}|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) rr;)r[rrr6r)r r\rrYrZrrrrxavier_normal_ns r_cCsH|}ddg}||kr,td|d|t|\}}|dkrD|S|S)NrYrZzMode z" not supported, please use one of )lowerr7r[)r modeZ valid_modesrYrZrrr_calculate_correct_fans  rbrYr1r r rar8rc Cstj|r(tjjt|f|||||dSd|jkr@td|St||}t ||}|t |}t d|}t |j | ||dW5QRSQRXdS)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') rcr,Initializing zero-element tensors is a no-opr]rN)rr=r>r?kaiming_uniform_rJr#r$rbr:rrrr ) r r rar8rfanr\rboundrrrres&#      rec Csjd|jkrtd|St||}t||}|t|}t|j d||dW5QRSQRXdS)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') rrdrN) rJr#r$rbr:rrrrr)r r rar8rrfr\rrrrkaiming_normal_s#     rhrc Cs|dkrtd|dkr$|S|d}||}|||jdd|d}||krb|tj |\}}t |d}| } || 9}||kr|t | ||||W5QRX|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) rz4Only tensors with 2 or more dimensions are supportedrr r)rHr7ZnumelrOnewrZt_rZlinalgZqrZdiagsignrZview_asZcopy_r%) r r\rrowscolsZ flattenedqrrUphrrr orthogonal_s&      rpr2c Cs|dkrtd|j\}}tt||}tF|jd||dt |D]&}t |}|d|} d|| |f<qRW5QRX|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) rrErrN) rHr7rJr5rceilrrrrPZrandperm) r ZsparsityrrrkrlZ num_zerosZcol_idxZ row_indicesZ zero_indicesrrrsparse_/s      rrcsFjddfdd}dddd|_|_|S)Ncs(tjdddtdd||S)Nz `nn.init.z)` is now deprecated in favor of `nn.init.z`.rr)r#r$ FutureWarning)argskwargsmethnew_nameZold_namerrdeprecated_initZs 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____doc__)rxrzrrwr_make_deprecateVs  r})N)N)N)N)r;rN)r;rN)r;rr@rN)r )rN)rN)rrYr1N)rrYr1N)r N)r2N)-r|rr#rrtypingrZ _Optionalrrr(r,r/r:r6 Generatorr rrArBrCrDrKrVr[r^r_rbstrrerhrprrr}uniformnormalZconstantrIZdiracZxavier_uniformZ xavier_normalZkaiming_uniformZkaiming_normalZ orthogonalsparserrrrs     # :      , !   : / 6 '