ёi[9SSKJr SSKrSSKJr SSKrSSKJr SSKJrJ r J r SSK J r J r Jr SS KJrJr \(aSS KJr /r"S S \5r"S S\5r"SS\5rg)) annotationsN) TYPE_CHECKING)_C_ops)core framework unique_name)_current_expected_placein_dygraph_mode in_pir_mode) Initializercalculate_gain) _NonLinearityct^\rSrSrSrSSU4SjjjrSS SjjrSrU=r$) MSRAInitializer&aImplements the MSRA initializer a.k.a. Kaiming Initializer This class implements the weight initialization from the paper `Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification `_ by Kaiming He, Xiangyu Zhang, Shaoqing Ren and Jian Sun. This is a robust initialization method that particularly considers the rectifier nonlinearities. In case of Uniform distribution, the range is [-x, x], where .. math:: x = gain \times \sqrt{\frac{3}{fan\_in}} In case of Normal distribution, the mean is 0 and the standard deviation is .. math:: \frac{gain}{\sqrt{{fan\_in}}} Args: uniform (bool, optional): whether to use uniform or normal distribution. Default is True. fan_in (float32|None, optional): fan_in (in_features) of trainable Tensor, If None, it will be inferred automatically. If you don't want to use in_features of the Tensor, you can set the value of 'fan_in' smartly by yourself. Default is None. seed (int32, optional): random seed. Default is 0. negative_slope (float, optional): negative_slope (only used with leaky_relu). Default is 0.0. nonlinearity(str, optional): the non-linear function. Default is relu. mode(str, optional): the mode of initialization, can be 'fan_in' or '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'. Note: It is recommended to set fan_in to None for most cases. c">UceUce[TU]5 XlX lX0lX@lXPlX`lURS;a[SURS35eURS:XaURb [S5egg)zConstructor for MSRAInitializerN)fan_infan_outzWThe mode of KaimingNormal/KaimingUniform should be 'fan_in' or 'fan_out', but received .rzdThe mode of KaimingNormal/KaimingUniform is 'fan_out', but fan_in is set. Please set fan_in to None.) super__init___uniform_fan_in_seed_negative_slope _nonlinearity_mode ValueError)selfuniformrseednegative_slope nonlinearitymode __class__s ]/var/www/html/banglarbhumi/venv/lib/python3.13/site-packages/paddle/nn/initializer/kaiming.pyrMSRAInitializer.__init__Hs"""    -) ::2 2 $ |1.  :: "t||'?@ (@ "c ,[U[R5(aUR5(aS5eUR U5n[U[R [ RR[ RRR45(de[U[R[ RR45(deURU5up4URS:XaURcUO URnURS:XaUnURS:XaUR R"UlUR$nU[R&R(R*:Xd9U[R&R(R,:XaUR.(d[R&R(R0nUR3[4R6"SR9SUR:S/55UR<U[R&R(R>S S 9nOjU[R@RB[R@RD4;a.UR.(d[R@RFnUnOUnUn[I5(Ga,UR.(a[KURLURN5n U [PRR"S [UW5- 5-n [VRX"UR<UU *U URURZR]5(a URZO [_55nO[KURLURN5n U [PRR"[UW55- n URZR]5(a URZO [_5n [VR`"UR<S XRX|5nU[R&R(R*:XdlU[R&R(R,[R@RB[R@RD4;a9UR.(d([VRb"X5n U ReU5 gUReU5 g[g5(GaiUR.(a~[KURLURN5n U [PRR"S [UW5- 5-n [VRX"UR<UU *U UR[_55nOy[KURLURN5n U [PRR"[UW55- n [_5n [VR`"UR<S XRX|5nU[R@RB[R@RD4;a'UR.(d[VRb"X5$U$UR.(a[KURLURN5n U [PRR"S [UW5- 5-n URiS 0SU0UR<[kU5U *U URS.SS9nOy[KURLURN5n U [PRR"[UW55- n URiSSU0UR<[kU5S U URS.SS9nU[R&R(R*:Xd9U[R&R(R,:Xa4UR.(d#URiSSU0SU0UR$US.S9 Xl6U$)a$Initialize the input tensor with MSRA initialization. Args: var(Tensor): Tensor that needs to be initialized. block(Block|None, optional): The block in which initialization ops should be added. Used in static graph only, default None. Returns: The initialization op. zDCurrently, kaiming initializer not support lazy init for dist param.rNrrr masra_inittmpF)nameshapedtypetype persistableg@uniform_randomOut)r/r0minmaxr#T)r1inputsoutputsattrs stop_gradientgaussian_random)r/r0meanstdr#)r1r9r:r;castX)in_dtype out_dtype)r1r8r9r:)7 isinstancerEagerParamBaseis_dist _check_blockVariablepaddlepirValuer ParameterMetaBlock _compute_fansrrrprogram random_seedr0VarDescVarTypeFP16BF16rFP32 create_varr generatejoinr.r/ DENSE_TENSORDataTypeFLOAT16BFLOAT16FLOAT32r rrrmathsqrtfloatrr"place_typer gaussianr?_share_underline_tensor_tor append_opintop)r!varblockf_inf_outr origin_dtyperBout_vargainlimitr>r`var_tmprfs r(forwardMSRAInitializer.forwardfs sI44 5 5#++--  S  !!%( ""    --     %)//6::3C3C!DEEEE((-  :: !!\\1Tt||F :: "F ::?22DJyy 4<<//44 4 DLL0055 5dmm ,,11I&& ))HHlCHHe<=ii\\))66!'G T]]22DMM4J4JK KMM --IG$IG   }}%d&8&8$:N:NOtyyuV})<== ..IIFJJyy((II02 &d&8&8$:N:NOTYYuV}55yy((II02 !//MM3ZZt||33888LL((--MM))MM**   ++g<22372237 ]]}}%d&8&8$:N:NOtyyuV})<== ..IIFJJ+- &d&8&8$:N:NOTYYuV}55/1 //MM3ZZ !6!6 8N8N OO {{799N}}%d&8&8$:N:NOtyyuV})<==__)"G,!(!$Y %v$ $ #'% &d&8&8$:N:NOTYYuV}55__*"G,!(!$Y #" $ #'% t||33888 4 4 9 99$-->"CL$+MM%1  FIr*)rrrrrr)TNrrrelur)r"boolr float | Noner#rer$r_r%rr&strreturnNone)N)rgz paddle.Tensorrhzpaddle.pir.Block | Nonervzpaddle.Tensor | None) __name__ __module__ __qualname____firstlineno____doc__rrp__static_attributes__ __classcell__r's@r(rr&sF# !&,    $  >DHt t)@t ttr*rcN^\rSrSrSrSSU4SjjjrSrU=r$) KaimingNormaliaImplements the Kaiming Normal initializer This class implements the weight initialization from the paper `Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification `_ by Kaiming He, Xiangyu Zhang, Shaoqing Ren and Jian Sun. This is a robust initialization method that particularly considers the rectifier nonlinearities. In case of Normal distribution, the mean is 0 and the standard deviation is .. math:: \frac{gain}{\sqrt{{fan\_in}}} Args: fan_in (float32|None, optional): fan_in (in_features) of trainable Tensor, If None, it will be inferred automatically. If you don't want to use in_features of the Tensor, you can set the value of 'fan_in' smartly by yourself. Default is None. negative_slope (float, optional): negative_slope (only used with leaky_relu). Default is 0.0. nonlinearity(str, optional): the non-linear function. Default is relu. mode(str, optional): the mode of initialization, can be 'fan_in' or 'fan_out'. When set to 'fan_in', the fan_in parameter is used for initialization. When set to 'fan_out', the out_features of trainable Tensor will be used. Default is 'fan_in'. Note: It is recommended to set fan_in to None for most cases. Examples: .. code-block:: python >>> import paddle >>> import paddle.nn as nn >>> linear = nn.Linear(2, 4, weight_attr=nn.initializer.KaimingNormal()) >>> data = paddle.rand([30, 10, 2], dtype='float32') >>> res = linear(data) c *>[TU]SUSUUUS9 g)NFrr"rr#r$r%r&rrr!rr$r%r&r's r(rKaimingNormal.__init__Cs) )%  r*Nr3rrr rrtr$r_r%rur&rurvrwrxryrzr{r|rr}r~rs@r(rrsQ#N $ #"           r*rcN^\rSrSrSrSSU4SjjjrSrU=r$)KaimingUniformiTaImplements the Kaiming Uniform initializer This class implements the weight initialization from the paper `Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification `_ by Kaiming He, Xiangyu Zhang, Shaoqing Ren and Jian Sun. This is a robust initialization method that particularly considers the rectifier nonlinearities. In case of Uniform distribution, the range is [-x, x], where .. math:: x = gain \times \sqrt{\frac{3}{fan\_in}} Args: fan_in (float32|None, optional): fan_in (in_features) of trainable Tensor, If None, it will be inferred automatically. If you don't want to use in_features of the Tensor, you can set the value of 'fan_in' smartly by yourself. Default is None. negative_slope (float, optional): negative_slope (only used with leaky_relu). Default is 0.0. nonlinearity(str, optional): the non-linear function. Default is relu. mode(str, optional): the mode of initialization, can be 'fan_in' or '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'. Note: It is recommended to set fan_in to None for most cases. Examples: .. code-block:: python >>> import paddle >>> import paddle.nn as nn >>> linear = nn.Linear(2, 4, weight_attr=nn.initializer.KaimingUniform()) >>> data = paddle.rand([30, 10, 2], dtype='float32') >>> res = linear(data) c *>[TU]SUSUUUS9 g)NTrrrrs r(rKaimingUniform.__init__ys) )%  r*rrrrrs@r(rrTsQ"L $ #"           r*r) __future__rr]typingrrHrbaserrr base.frameworkr r r initializerrrr__all__rrrrr*r(rs`# 00 5* tktn4 O4 n3 _3 r*