Sj4>ddlZddlZddlmZmZddlmZmZddlm Z ddl m Z ddl mZgdZGd d eZGd d eZ eeezezZGd deZGddeZGddeZy)N)SizeTensor) functionalinit) Parameter) CrossMapLRN2d)Module)LocalResponseNormr LayerNorm GroupNormRMSNormc eZdZUdZgdZeed<eed<eed<eed< d dededededdf fd Zd e de fd Z d Z xZ S)r aApplies local response normalization over an input signal. The input signal is composed of several input planes, where channels occupy the second dimension. Applies normalization across channels. .. math:: b_{c} = a_{c}\left(k + \frac{\alpha}{n} \sum_{c'=\max(0, c-n/2)}^{\min(N-1,c+n/2)}a_{c'}^2\right)^{-\beta} Args: size: amount of neighbouring channels used for normalization alpha: multiplicative factor. Default: 0.0001 beta: exponent. Default: 0.75 k: additive factor. Default: 1 Shape: - Input: :math:`(N, C, *)` - Output: :math:`(N, C, *)` (same shape as input) Examples:: >>> lrn = nn.LocalResponseNorm(2) >>> signal_2d = torch.randn(32, 5, 24, 24) >>> signal_4d = torch.randn(16, 5, 7, 7, 7, 7) >>> output_2d = lrn(signal_2d) >>> output_4d = lrn(signal_4d) )sizealphabetakrrrrreturnNcZt|||_||_||_||_yNsuper__init__rrrrselfrrrr __class__s ]/var/www/html/ia-enhanced/venv/lib/python3.12/site-packages/torch/nn/modules/normalization.pyrzLocalResponseNorm.__init__4,    inputctj||j|j|j|j Sz( Runs the forward pass. )Flocal_response_normrrrrrr s rforwardzLocalResponseNorm.forward=s-$$UDIItzz499dffUUrc:djdi|jS@ Return the extra representation of the module. z){size}, alpha={alpha}, beta={beta}, k={k}format__dict__rs r extra_reprzLocalResponseNorm.extra_reprC B:AARDMMRRr)-C6??g?) __name__ __module__ __qualname____doc__ __constants__int__annotations__floatrrr&r/ __classcell__rs@rr r ss:3M I L K HNQ %49EJ VVVV Srr c ~eZdZUeed<eed<eed<eed< d dededededdf fd Zdedefd Zde fd Z xZ S) r rrrrrNcZt|||_||_||_||_yrrrs rrzCrossMapLRN2d.__init__Prrr ctj||j|j|j|j Sr")_cross_map_lrn2dapplyrrrrr%s rr&zCrossMapLRN2d.forwardYs- %%eTYY DIItvvVVrc:djdi|jSr(r+r.s rr/zCrossMapLRN2d.extra_repr_r0r)r1r2r) r3r4r5r8r9r:rrr&strr/r;r<s@rr r Jso I L K HNO %49EJ WVWW SCSrr c eZdZUdZgdZeedfed<eed<e ed< dde dede de d df fd Z dd Z d e d e fd Zd efdZxZS)r a Applies Layer Normalization over a mini-batch of inputs. This layer implements the operation as described in the paper `Layer Normalization `__ .. math:: y = \frac{x - \mathrm{E}[x]}{ \sqrt{\mathrm{Var}[x] + \epsilon}} * \gamma + \beta The mean and standard-deviation are calculated over the last `D` dimensions, where `D` is the dimension of :attr:`normalized_shape`. For example, if :attr:`normalized_shape` is ``(3, 5)`` (a 2-dimensional shape), the mean and standard-deviation are computed over the last 2 dimensions of the input (i.e. ``input.mean((-2, -1))``). :math:`\gamma` and :math:`\beta` are learnable affine transform parameters of :attr:`normalized_shape` if :attr:`elementwise_affine` is ``True``. The variance is calculated via the biased estimator, equivalent to `torch.var(input, correction=0)`. .. note:: Unlike Batch Normalization and Instance Normalization, which applies scalar scale and bias for each entire channel/plane with the :attr:`affine` option, Layer Normalization applies per-element scale and bias with :attr:`elementwise_affine`. This layer uses statistics computed from input data in both training and evaluation modes. Args: normalized_shape (int or list or torch.Size): input shape from an expected input of size .. math:: [* \times \text{normalized\_shape}[0] \times \text{normalized\_shape}[1] \times \ldots \times \text{normalized\_shape}[-1]] If a single integer is used, it is treated as a singleton list, and this module will normalize over the last dimension which is expected to be of that specific size. eps: a value added to the denominator for numerical stability. Default: 1e-5 elementwise_affine: a boolean value that when set to ``True``, this module has learnable per-element affine parameters initialized to ones (for weights) and zeros (for biases). Default: ``True`` bias: If set to ``False``, the layer will not learn an additive bias (only relevant if :attr:`elementwise_affine` is ``True``). Default: ``True`` Attributes: weight: the learnable weights of the module of shape :math:`\text{normalized\_shape}` when :attr:`elementwise_affine` is set to ``True``. The values are initialized to 1. bias: the learnable bias of the module of shape :math:`\text{normalized\_shape}` when :attr:`elementwise_affine` is set to ``True``. The values are initialized to 0. Shape: - Input: :math:`(N, *)` - Output: :math:`(N, *)` (same shape as input) Examples:: >>> # NLP Example >>> batch, sentence_length, embedding_dim = 20, 5, 10 >>> embedding = torch.randn(batch, sentence_length, embedding_dim) >>> layer_norm = nn.LayerNorm(embedding_dim) >>> # Activate module >>> layer_norm(embedding) >>> >>> # Image Example >>> N, C, H, W = 20, 5, 10, 10 >>> input = torch.randn(N, C, H, W) >>> # Normalize over the last three dimensions (i.e. the channel and spatial dimensions) >>> # as shown in the image below >>> layer_norm = nn.LayerNorm([C, H, W]) >>> output = layer_norm(input) .. image:: ../_static/img/nn/layer_norm.jpg :scale: 50 % normalized_shapeepselementwise_affine.rFrGrHNbiasrc||d}t|t|tjr|f}t ||_||_||_|jrrttj|j fi||_ |r/ttj|j fi||_ n7|jddn$|jdd|jdd|jy)NdevicedtyperIweight)rr isinstancenumbersIntegraltuplerFrGrHrtorchemptyrNrIregister_parameterreset_parameters) rrFrGrHrIrLrMfactory_kwargsrs rrzLayerNorm.__init__s%+U;  &(8(8 9 02  %&6 7"4  " "# D11D^DDK%KK 5 5HH ''5  # #Hd 3  # #FD 1 rc|jrLtj|j|j tj |jyyyr)rHrones_rNrIzeros_r.s rrVzLayerNorm.reset_parameterss?  " " JJt{{ #yy$ DII&% #rr ctj||j|j|j|j Sr)r# layer_normrFrNrIrGr%s rr&zLayerNorm.forwards0|| 4(($++tyy$((  rcZdjdi|jd|jduiS)NzW{normalized_shape}, eps={eps}, elementwise_affine={elementwise_affine}, bias={use_bias}use_biasr*r,r-rIr.s rr/zLayerNorm.extra_repr< % $f V'+}} V?CyyPT?T V r)h㈵>TTNNrN)r3r4r5r6r7rRr8r9r:bool_shape_trrVrr&rCr/r;r<s@rr r isKZFMCHo% J #' "  !      B'  V   C rr ceZdZUdZgdZeed<eed<eed<eed< ddd dedededed ed df fd Z dd Z de d e fdZ d e fdZxZS)r aRApplies Group Normalization over a mini-batch of inputs. This layer implements the operation as described in the paper `Group Normalization `__ .. math:: y = \frac{x - \mathrm{E}[x]}{ \sqrt{\mathrm{Var}[x] + \epsilon}} * \gamma + \beta The input channels are separated into :attr:`num_groups` groups, each containing ``num_channels / num_groups`` channels. :attr:`num_channels` must be divisible by :attr:`num_groups`. The mean and standard-deviation are calculated separately over each group. :math:`\gamma` and :math:`\beta` are learnable per-channel affine transform parameter vectors of size :attr:`num_channels` if :attr:`affine` is ``True``. The variance is calculated via the biased estimator, equivalent to `torch.var(input, correction=0)`. This layer uses statistics computed from input data in both training and evaluation modes. Args: num_groups (int): number of groups to separate the channels into num_channels (int): number of channels expected in input eps: a value added to the denominator for numerical stability. Default: 1e-5 affine: a boolean value that when set to ``True``, this module has learnable per-channel affine parameters initialized to ones (for weights) and zeros (for biases). Default: ``True`` bias: If set to ``False``, the layer will not learn an additive bias (only relevant if :attr:`affine` is ``True``). Default: ``True`` Shape: - Input: :math:`(N, C, *)` where :math:`C=\text{num\_channels}` - Output: :math:`(N, C, *)` (same shape as input) Examples:: >>> input = torch.randn(20, 6, 10, 10) >>> # Separate 6 channels into 3 groups >>> m = nn.GroupNorm(3, 6) >>> # Separate 6 channels into 6 groups (equivalent with InstanceNorm) >>> m = nn.GroupNorm(6, 6) >>> # Put all 6 channels into a single group (equivalent with LayerNorm) >>> m = nn.GroupNorm(1, 6) >>> # Activating the module >>> output = m(input) ) num_groups num_channelsrGaffinerfrgrGrhTN)rIrIrc||d}t |||zdk7rtd|d|d||_||_||_||_|j r^ttj|fi||_ |r%ttj|fi||_ n7|jddn$|jdd|jdd|jy)NrKrznum_channels (z#) must be divisible by num_groups ()rIrN)rr ValueErrorrfrgrGrhrrSrTrNrIrUrV) rrfrgrGrhrLrMrIrWrs rrzGroupNorm.__init__%s%+U;  * $ ) .QR\Q]]^_ %( ;;#EKK $O$OPDK%ekk,&Q.&QR ''5  # #Hd 3  # #FD 1 rc|jrLtj|j|j tj |jyyyr)rhrrYrNrIrZr.s rrVzGroupNorm.reset_parametersGs= ;; JJt{{ #yy$ DII&% rr ctj||j|j|j|j Sr)r# group_normrfrNrIrGr%s rr&zGroupNorm.forwardMs)||E4??DKKDHHUUrcZdjdi|jd|jduiS)NzI{num_groups}, {num_channels}, eps={eps}, affine={affine}, bias={use_bias}r^r*r_r.s rr/zGroupNorm.extra_reprPr`r)raTNNrb)r3r4r5r6r7r8r9r:rcrrVrr&rCr/r;r<s@rr r s-^DMO J L                  D' VVVV C rr c eZdZUdZgdZeedfed<edzed<e ed< dde dedzde ddffd Z dd Z d e jde jfd Zdefd ZxZS)raApplies Root Mean Square Layer Normalization over a mini-batch of inputs. This layer implements the operation as described in the paper `Root Mean Square Layer Normalization `__ .. math:: y_i = \frac{x_i}{\mathrm{RMS}(x)} * \gamma_i, \quad \text{where} \quad \text{RMS}(x) = \sqrt{\epsilon + \frac{1}{n} \sum_{i=1}^{n} x_i^2} The RMS is taken over the last ``D`` dimensions, where ``D`` is the dimension of :attr:`normalized_shape`. For example, if :attr:`normalized_shape` is ``(3, 5)`` (a 2-dimensional shape), the RMS is computed over the last 2 dimensions of the input. Args: normalized_shape (int or list or torch.Size): input shape from an expected input of size .. math:: [* \times \text{normalized\_shape}[0] \times \text{normalized\_shape}[1] \times \ldots \times \text{normalized\_shape}[-1]] If a single integer is used, it is treated as a singleton list, and this module will normalize over the last dimension which is expected to be of that specific size. eps (float, optional): a value added to the denominator for numerical stability. If not specified, uses the machine epsilon of the computation (opmath) type: fp16/bf16 and fp32 inputs use ``torch.finfo(torch.float32).eps``, while fp64 inputs use ``torch.finfo(torch.float64).eps``. Default: ``None`` elementwise_affine: a boolean value that when set to ``True``, this module has learnable per-element affine parameters initialized to ones (for weights). Default: ``True``. Shape: - Input: :math:`(N, *)` - Output: :math:`(N, *)` (same shape as input) Examples:: >>> rms_norm = nn.RMSNorm([2, 3]) >>> input = torch.randn(2, 2, 3) >>> rms_norm(input) rE.rFNrGrHrc\||d}t|t|tjr|f}t ||_||_||_|jr/ttj|j fi||_ n|jdd|jy)NrKrN)rrrOrPrQrRrFrGrHrrSrTrNrUrV)rrFrGrHrLrMrWrs rrzRMSNorm.__init__s%+U;  &(8(8 9 02  %&6 7"4  " "# D11D^DDK  # #Hd 3 rc\|jr tj|jyy)zS Resets parameters based on their initialization used in __init__. N)rHrrYrNr.s rrVzRMSNorm.reset_parameterss"  " " JJt{{ # #rxcntj||j|j|jSr")r#rms_normrFrNrG)rrss rr&zRMSNorm.forwards'zz!T22DKKJJrc:djdi|jS)r)zF{normalized_shape}, eps={eps}, elementwise_affine={elementwise_affine}r*r+r.s rr/zRMSNorm.extra_reprs)  = 66r}s *(9 V7S7StSFS8 c?T !C C Le e P] f] r