ёi(*SSKJr SSKrSSKrSSKJrJr SSKJ r SSK J r SSK J r SS KJrJr SS KJrJr SS KJr SS KJr SS KJrJr SSjrSSSjjrSSSjjrSS SjjrSS SjjrS!S"Sjjr S!S#Sjjr!S$S%Sjjr"S&Sjr#S'Sjr$S'Sjr%S(S)Sjjr&S'Sjr'S(S*Sjjr(g)+) annotationsN)in_dygraph_mode in_pir_modeConstantDirac)calculate_gain) KaimingNormalKaimingUniform)NormalTruncatedNormal OrthogonalUniform) XavierNormal XavierUniformc6URnU(a[U5S:XaS=p#X#4$[U5S:Xa US=p#X#4$[U5S:Xa USnUSnX#4$[R"USS5n[ USU-5n[ USU-5nX#4$)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)varrfan_infan_outreceptive_field_sizes N/var/www/html/banglarbhumi/venv/lib/python3.13/site-packages/paddle/nn/init.py_calculate_fan_in_and_fan_outr" s IIE CJ!O   Uq 8#   Uqq(   "wwuQRy1U1X 445eAh!556  c&[XUS9nU"U5$)a_Modify 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_slope nonlinearitymode)rtensorar(r'inits r!kaiming_uniform_r-Cs* $ D <r#c&[XUS9nU"U5$)a^Modify tensor inplace using Kaiming normal method. Args: tensor (Tensor): Paddle Tensor. a (float, optional): The negative slope of the rectifier used after this layer. Defaults to 0. mode (str, optional): Mode to compute the fan. Choose from ["fan_in", "fan_out"]. When set to 'fan_in', the fan_in parameter is used for initialization. When set to 'fan_out', the out_features of trainable Tensor will be used. Default is 'fan_in'. nonlinearity (str, optional): Nonlinearity method name. Defaults to "leaky_relu". Returns: Tensor: Initialized tensor. r%)r r)s r!kaiming_normal_r/_s* 4 PD <r#c([UUUS9nU"U5$)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. gainrr)rr*r2rrr,s r!xavier_uniform_r4ys#&   D <r#c([UUUS9nU"U5$)aModify tensor inplace using Xavier normal method. Args: tensor (Tensor): Paddle Tensor. gain (float, optional): Scaling Tensor. Default is 1.0. fan_in (float|None, optional): fan_in for Xavier initialization, which is inferred from the Tensor. Default is None. fan_out (float|None, optional): fan_out for Xavier initialization, which is inferred from the Tensor. Default is None. Returns: Tensor: Initialized tensor. r1)rr3s r!xavier_normal_r6s#&   D <r#c$[XS9nU"U5$)aBModify tensor inplace using uniform method. Args: tensor (Tensor): Paddle Tensor. low (float, optional): Lower boundary of the uniform distribution. Default is :math:`-1.0`. high (float, optional): Upper boundary of the uniform distribution. Default is :math:`1.0`. Returns: Tensor: Initialized tensor. )lowhighr)r*r+br,s r!uniform_r;s q !D <r#c$[XS9nU"U5$)a0Modify tensor inplace using normal method. Args: tensor (Tensor): Paddle Tensor. mean (float|complex, optional): mean of the normal distribution. Default is 0.0. std (float, optional): standard deviation of the normal distribution. Default is 1.0. Returns: Tensor: Initialized tensor. )meanstd)r)r*r=r>r,s r!normal_r?s t %D <r#c&[XX4S9nU"U5$)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. )r=r>r+r:)r)r*r=r>r+r:r,s r! trunc_normal_rAs&  8D <r#c$[US9nU"U5$)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*valr,s r! constant_rFs # D <r#c$[SS9nU"U5$)zFill the input Tensor with the scalar value 1. Args: tensor (Tensor): Paddle Tensor. Returns: Tensor: Initialized tensor. ?rCrr*r,s r!ones_rJ # D <r#c$[SS9nU"U5$)zFill the input Tensor with the scalar value 0. Args: tensor (Tensor): Paddle Tensor. Returns: Tensor: Initialized tensor. rCrrIs r!zeros_rNrKr#c$[US9nU"U5$)aInitialize 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. )groupsr )r*rPr,s r!dirac_rQ#s  D <r#c[UR5S:wa"[S[UR5S35e[5(aL[R "URSURSUR S9nURU5 g[5(a<[R "URSURSUR S9nU$[S5e) zFill the 2-dimensional input Tensor with the identity matrix. Args: tensor (Tensor): Paddle Tensor. Returns: Tensor: Initialized tensor. rz+Only support 2 dimensional tensor, but got .rr)dtypeNz-Only support run in dygraph mode or PIR mode.) rrAssertionErrorrpaddleeyerT_share_underline_tensor_torNotImplementedError)r* new_tensors r!eye_r[5s 6<<A9#fll:K9LA N  ZZ LLOV\\!_FLL  --f5 ZZ LLOV\\!_FLL ! ;  r#c$[US9nU"U5$)zFill 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. )r2r)r*r2r,s r! orthogonal_r]Vs 4 D <r#)r paddle.Tensorreturnztuple[int, int])rr leaky_relu) r*r^r+floatr(strr'rbr_paddle.Tensor | None)rHNN) r*r^r2rar float | Nonerrdr_rc)rMrH)r*r^r+rar:rar_rc)r*r^r=rar>rar_rc)rMrHgg@) r*r^r=rar>rar+rar:rar_rc)r*r^rErar_rc)r*r^r_rc)r)r*r^rPrr_rc)r*r^r2rar_rc)) __future__rnumpyrrVbase.frameworkrrinitializer.constantr initializer.diracr initializer.initializerr initializer.kaimingr rinitializer.normalrrinitializer.orthogonalrinitializer.uniformrinitializer.xavierrrr"r-r/r4r6r;r?rArFrJrNrQr[r]r#r!rqs}# 9*$3>7.(; J$       <$       8       <       <    ,    ,         0  $        $  $   F      r#