{-j(LddlmZddlZddlZddlmZmZddlm Z ddl m Z ddl m Z dd lmZmZdd lmZmZdd lmZdd lmZdd lmZmZd6dZ d7d8dZ d7d8dZ d9d:d"Z d9d:d#Z d;dd?d,Z"d@d.Z#dAd/Z$dAd0Z% dBdCd3Z&dAd4Z' dBdDd5Z(dS)E) annotationsN)in_dygraph_mode in_pir_modeConstantDirac)calculate_gain) KaimingNormalKaimingUniform)NormalTruncatedNormal OrthogonalUniform) XavierNormal XavierUniformvar paddle.Tensorreturntuple[int, int]ch|j}|rt|dkrdx}}nt|dkr |dx}}npt|dkr|d}|d}nLtj|dd}t |d|z}t |d|z}||fS)aCompute the fan_in and the fan_out for layers This method computes the fan_in and the fan_out for neural network layers, if not specified. It is not possible to perfectly estimate fan_in and fan_out. This method will estimate it correctly for matrix multiply and convolutions. Args: var: variable for which fan_in and fan_out have to be computed. Returns: tuple of two integers (fan_in, fan_out). rrrN)shapelennpprodint)rrfan_infan_outreceptive_field_sizes N/var/www/html/banglarbhumi/venv/lib/python3.11/site-packages/paddle/nn/init.py_calculate_fan_in_and_fan_outr% s IE 7CJJ!OO Uq 8# Uqq( "wuQRRy11U1X 4455eAh!5566 G r! leaky_relutensorafloatmodestr nonlinearitypaddle.Tensor | Nonec<t|||}||S)aModify tensor inplace using Kaiming uniform method. Args: tensor (Tensor): Paddle Tensor. a (float, optional): The negative slope of the rectifier used after this layer. Defaults to 0. mode (str, optional): Mode to compute the fan. Choose from ["fan_in", "fan_out"]. When set to 'fan_in', the fan_in parameter is used for initialization. When set to 'fan_out', the out_features of trainable Tensor will be used. Default is 'fan_in'. nonlinearity (str, optional): Nonlinearity method name. Defaults to "leaky_relu". Returns: Tensor: Initialized tensor. negative_sloper-r+)rr(r)r+r-inits r$kaiming_uniform_r4Cs/* |$   D 4<<r&c<t|||}||S)aModify tensor inplace using Kaiming normal method. Args: tensor (Tensor): Paddle Tensor. a (float, optional): The negative slope of the rectifier used after this layer. Defaults to 0. mode (str, optional): Mode to compute the fan. Choose from ["fan_in", "fan_out"]. When set to 'fan_in', the fan_in parameter is used for initialization. When set to 'fan_out', the out_features of trainable Tensor will be used. Default is 'fan_in'. nonlinearity (str, optional): Nonlinearity method name. Defaults to "leaky_relu". Returns: Tensor: Initialized tensor. r0)r r2s r$kaiming_normal_r6_s&*  4 P P PD 4<<r&?gain float | Noner"c<t|||}||S)aModify tensor inplace using Xavier uniform method. Args: tensor (Tensor): Paddle Tensor. gain (float, optional): Scaling Tensor. Default is 1.0. fan_in (float|None, optional): fan_in for Xavier initialization, which is inferred from the Tensor. Default is None. fan_out (float|None, optional): fan_out for Xavier initialization, which is inferred from the Tensor. Default is None. Returns: Tensor: Initialized tensor. r8r!r")rr(r8r!r"r3s r$xavier_uniform_r=ys1&     D 4<<r&c<t|||}||S)aModify tensor inplace using Xavier normal method. Args: tensor (Tensor): Paddle Tensor. gain (float, optional): Scaling Tensor. Default is 1.0. fan_in (float|None, optional): fan_in for Xavier initialization, which is inferred from the Tensor. Default is None. fan_out (float|None, optional): fan_out for Xavier initialization, which is inferred from the Tensor. Default is None. Returns: Tensor: Initialized tensor. r;)rr<s r$xavier_normal_r?s1&     D 4<<r&bc:t||}||S)a^Modify tensor inplace using uniform method. Args: tensor (Tensor): Paddle Tensor. low (float, optional): Lower boundary of the uniform distribution. Default is :math:`-1.0`. high (float, optional): Upper boundary of the uniform distribution. Default is :math:`1.0`. Returns: Tensor: Initialized tensor. )lowhighr)r(r)rAr3s r$uniform_rEs$ qq ! ! !D 4<<r&meanstdc:t||}||S)aLModify tensor inplace using normal method. Args: tensor (Tensor): Paddle Tensor. mean (float|complex, optional): mean of the normal distribution. Default is 0.0. std (float, optional): standard deviation of the normal distribution. Default is 1.0. Returns: Tensor: Initialized tensor. )rFrG)r)r(rFrGr3s r$normal_rIs$ t % % %D 4<<r&@c>t||||}||S)aModify tensor inplace using truncated normal method. Args: tensor (Tensor): Paddle Tensor. mean (float|complex, optional): mean of the normal distribution. Default is 0.0. std (float, optional): standard deviation of the normal distribution. Default is 1.0. a (float, optional): The minimum cutoff value. Default is -2.0. b (float, optional): The maximum cutoff value. Default is 2.0. Returns: Tensor: Initialized tensor. )rFrGr)rA)r)r(rFrGr)rAr3s r$ trunc_normal_rMs(& #a 8 8 8D 4<<r&valc8t|}||S)zModify tensor inplace using constant method. Args: tensor (Tensor): Paddle Tensor. value (float32|float64, optional): constant value to initialize the parameter. Returns: Tensor: Initialized tensor. valuer)r(rNr3s r$ constant_rRs" #   D 4<<r&c8td}||S)zFill the input Tensor with the scalar value 1. Args: tensor (Tensor): Paddle Tensor. Returns: Tensor: Initialized tensor. r7rPrr(r3s r$ones_rU" #   D 4<<r&c8td}||S)zFill the input Tensor with the scalar value 0. Args: tensor (Tensor): Paddle Tensor. Returns: Tensor: Initialized tensor. r@rPrrTs r$zeros_rXrVr&groupsr c8t|}||S)a4Initialize the 3D/4D/5D Tensor with Dirac delta function. Args: tensor (Tensor): Paddle Tensor. groups (int|None, optional): 0-dimension of the Tensor will be divided by groups, each group has the same value. Default: 1. Returns: Tensor: Initialized tensor. )rYr )r(rYr3s r$dirac_r[#s"    D 4<<r&ct|jdkr%tdt|jdtrIt j|jd|jd|j}||dStr4t j|jd|jd|j}|Std) zFill the 2-dimensional input Tensor with the identity matrix. Args: tensor (Tensor): Paddle Tensor. Returns: Tensor: Initialized tensor. rz+Only support 2 dimensional tensor, but got .rr)dtypeNz-Only support run in dygraph mode or PIR mode.) rrAssertionErrorrpaddleeyer^_share_underline_tensor_torNotImplementedError)r( new_tensors r$eye_re5s 6<A N#fl:K:K N N N    Z LOV\!_FL    --f555t  Z LOV\!_FL   ! ;   r&c8t|}||S)aFill the input Tensor with a (semi) orthogonal matrix. Args: tensor (Tensor): Paddle Tensor. gain(float, optional): The multiplication coefficient for initialized tensor. Default: 1.0. Returns: Tensor: Initialized tensor. )r8r)r(r8r3s r$ orthogonal_rgVs" 4 D 4<<r&)rrrr)rr!r') r(rr)r*r+r,r-r,rr.)r7NN) r(rr8r*r!r9r"r9rr.)r@r7)r(rr)r*rAr*rr.)r(rrFr*rGr*rr.)r@r7rJrK) r(rrFr*rGr*r)r*rAr*rr.)r(rrNr*rr.)r(rrr.)r)r(rrYr rr.)r(rr8r*rr.)) __future__rnumpyrr`base.frameworkrrinitializer.constantr initializer.diracr initializer.initializerr initializer.kaimingr rinitializer.normalrrinitializer.orthogonalrinitializer.uniformrinitializer.xavierrrr%r4r6r=r?rErIrMrRrUrXr[rergr&r$rts#""""" 99999999******$$$$$$333333>>>>>>>>77777777......((((((;;;;;;;;    J$ <$ 8 < <,, 0$        $$    F       r&