{-j8ddlmZddlZddlmZmZddlmZmZmZddl m Z ddl m Z m Z mZdd lmZdd lmZgZGd d eZGd deZGddeZGddeZdS)) annotationsN)_C_opspir)core framework unique_name)check_variable_and_dtype)_current_expected_placein_dygraph_mode in_pir_mode) Initializer)lazy_init_helperc4eZdZdZ ddfd Z dddZxZS)NormalInitializeraAImplements the Random Normal(Gaussian) distribution initializer Args: loc (float|complex, optional): mean of the normal distribution. Default is 0.0. scale (float, optional): standard deviation of the normal distribution. Default is 1.0. seed (int, optional): random seed. Default is 0. ?rlocfloatscaleseedintreturnNonecj|J|J|Jt||_||_||_t |jt rV|jj|jjkr)td|jjd|jj|jj|_dSdS)NzVif mean is a complex number, its real part should equal imag part, but got real part: z != imag part: ) super__init___mean_std_dev_seed isinstancecomplexrealimag ValueError)selfrrr __class__s \/var/www/html/banglarbhumi/venv/lib/python3.11/site-packages/paddle/nn/initializer/normal.pyrzNormalInitializer.__init__+s       dj' * * )z$*/11 \*.*/\\JN*/\\DJJJ  ) )Nvar paddle.Tensorblockpir.Block | Nonepaddle.Tensor | Nonec <t|tjr|r Jd||}t|tjt jfsJt|dgdd|jdkr|j j |_trWt}tj|j|j|j|j|j|}||dSt)rBt}tj|j|j|j|j|j|}|S|dd|i|j|j|j|j|jdd }||_|S) a\Initialize the input tensor with Normal distribution. 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. zCCurrently, normal initializer not support lazy init for dist param.Out)uint16float16float32float64 complex64 complex128gaussian_randomrN)shapedtypemeanstdrTtypeoutputsattrs stop_gradient)r"rEagerParamBaseis_dist _check_blockBlockrr r!program random_seedr r rgaussianr9rr r:_share_underline_tensor_tor append_opop)r'r+r-placeout_varrKs r)forwardzNormalInitializer.forward=s sI4 5 5 Q:=++-- Q Q P Q Q !!%((%)/39!=>>>>>        :??2DJ   % +--Eo      G  . .s 3 3 34 ]] +--Eo      GN&  Y Y J= J #!  BCFIr*)rrr)rrrrrrrrNr+r,r-r.rr/__name__ __module__ __qualname____doc__rrN __classcell__r(s@r)rr!s{AB)))))))&=AJJJJJJJJJr*rc(eZdZdZ d dfd ZxZS)NormalamThe Random Normal (Gaussian) distribution initializer. Args: 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. name(str|None, optional): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name`. Default: None. Returns: A parameter initialized by Random Normal (Gaussian) distribution. Examples: .. code-block:: python >>> import paddle >>> data = paddle.ones(shape=[3, 1, 2], dtype='float32') >>> weight_attr = paddle.framework.ParamAttr( ... name="linear_weight", ... initializer=paddle.nn.initializer.Normal(mean=0.0, std=2.0)) >>> bias_attr = paddle.framework.ParamAttr( ... name="linear_bias", ... initializer=paddle.nn.initializer.Normal(mean=0.0, std=2.0)) >>> # doctest: +SKIP('name has been used') >>> linear = paddle.nn.Linear(2, 2, weight_attr=weight_attr, bias_attr=bias_attr) >>> print(linear.weight) Parameter containing: Tensor(shape=[2, 2], dtype=float32, place=Place(cpu), stop_gradient=False, [[ 2.1973135 -2.2697184], [-1.9104223 -1.0541488]]) >>> print(linear.bias) Parameter containing: Tensor(shape=[2], dtype=float32, place=Place(cpu), stop_gradient=False, [ 0.7885926 -0.74719954]) >>> res = linear(data) >>> print(res) Tensor(shape=[3, 1, 2], dtype=float32, place=Place(cpu), stop_gradient=False, [[[ 1.0754838 -4.071067 ]], [[ 1.0754838 -4.071067 ]], [[ 1.0754838 -4.071067 ]]]) rrNr;rr<name str | Nonerrc| Jd| Jdt||ddS)Nmean should not be Nonestd should not be Noner)rrrrr)r'r;r<rZr(s r)rzNormal.__init__sO!: 8 T155555r*)rrN)r;rr<rrZr[rrrRrSrTrUrrVrWs@r)rYrYsT((VGK66666666666r*rYc<eZdZdZ ddfd Z dddZxZS)TruncatedNormalInitializera|Implements the Random TruncatedNormal(Gaussian) distribution initializer Note: It is better to set `a <= mean <= b`. If `mean < a - 2*std` or `mean > b + 2*std`, the distribution of values may be incorrect. Args: loc (float, optional): Mean of the normal distribution. Default is :math:`0.0`. scale (float, optional): Standard deviation of the normal distribution. Default is :math:`1.0`. seed (int, optional): random seed. Default is 0. a (float, optional): The minimum cutoff value. Default is -2.0. b (float, optional): The maximum cutoff value. Default is 2.0. rrr@rrrrrabrrc|J|J|J|J|Jt||_||_||_||_||_dSrO)rrrr r!_a_b)r'rrrrerfr(s r)rz#TruncatedNormalInitializer.__init__su   }}}}}}    r*Nr+r,r-r.r/c ||}tjr3tjt jjjtj j f}n2tjt jj t jjjf}t||sJt|tj t j fsJ|jdkr|jj|_|jtjjjtjjjfvrwtjjj}|t1jdd|jdg|j|tjjjd}n |j}|}t=rt?j |j|j!|j"|j|j#|j$|tK}|jtjjjtjjjfvr0t?j&||j}|'|n|'|dStQrt?j |j|j!|j"|j|j#|j$|tK}|jtjjjtjjjfvr/t?j&||j}|'||S|)dd|i|j||j!|j"|j|j#|j$d d }|jtjjjtjjjfvr*|)d d |id|i|j|jd||_*|S)adInitialize the input tensor with TruncatedNormal distribution. 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 r.truncated_gaussian_randomtmpF)rZr9r:r> persistableNr1)r9r:r;r<rrerfTr=castX)in_dtype out_dtype)r>inputsr?r@)+rDrstaterVariablepaddlerr ParameterMetaeagerTensorValuer"rEr!rFrGr:VarDescVarTypeFP16BF16FP32 create_varr generatejoinrZr9 DENSE_TENSORr rrlrr rhrir rorIr rJrK)r'r+r-expectedrrrMvar_tmprKs r)rNz"TruncatedNormalInitializer.forwards~!!%((    # " - !HH"   -H #x(((((%)/39!=>>>>> :??2DJ 9-2DL4H4MN N N ,1I&& )HH938UKLLi\)6!'GG IG   C 6    '))  Gy $) $)!+gsy9922377772237774 ]]. 6    '))  Gy $) $)!+gsy99223777N0( Y& J= J#!  By $) $)>"CL'.}39MM   CFIr*)rrrrcrd) rrrrrrrerrfrrrrOrPrQrWs@r)rbrbs  " *=Atttttttttr*rbc0eZdZdZ ddfd ZxZS)TruncatedNormalaThe truncated normal distribution (Gaussian distribution) initializer. Note: It is better to set `a <= mean <= b`. If `mean < a - 2*std` or `mean > b + 2*std`, the distribution of values may be incorrect. Args: mean (float, optional): Mean of the normal distribution. Default is :math:`0.0`. std (float, optional): Standard deviation of the normal distribution. Default is :math:`1.0`. a (float, optional): The minimum cutoff value. Default is -2.0. b (float, optional): The maximum cutoff value. Default is 2.0. name (str|None, optional): For details, please refer to :ref:`api_guide_Name`. Generally, no setting is required. Default: None. Returns: A parameter initialized by truncated normal distribution (Gaussian distribution). Examples: .. code-block:: python >>> import paddle >>> data = paddle.ones(shape=[3, 1, 2], dtype='float32') >>> weight_attr = paddle.framework.ParamAttr( ... name="linear_weight", ... initializer=paddle.nn.initializer.TruncatedNormal(mean=0.0, std=2.0)) >>> bias_attr = paddle.framework.ParamAttr( ... name="linear_bias", ... initializer=paddle.nn.initializer.TruncatedNormal(mean=0.0, std=2.0)) >>> # doctest: +SKIP('name has been used') >>> linear = paddle.nn.Linear(2, 2, weight_attr=weight_attr, bias_attr=bias_attr) >>> print(linear.weight) Parameter containing: Tensor(shape=[2, 2], dtype=float32, place=Place(cpu), stop_gradient=False, [[-1.0981836 1.4140984], [ 3.1390522 -2.8266568]]) >>> print(linear.bias) Parameter containing: Tensor(shape=[2], dtype=float32, place=Place(cpu), stop_gradient=False, [ -2.1546738 -1.6570673]) >>> res = linear(data) >>> print(res) Tensor(shape=[3, 1, 2], dtype=float32, place=Place(cpu), stop_gradient=False, [[[-0.11380529 -3.0696259 ]], [[-0.11380529 -3.0696259 ]], [[-0.11380529 -3.0696259 ]]]) rrrcrdNr;rr<rerfrZr[rrc| Jd| Jd| Jd| Jdt||d||dS)Nr]r^za should not be Nonezb should not be Noner)rrrrerfr_)r'r;r<rerfrZr(s r)rzTruncatedNormal.__init__ss!: 8}}4}}}}}4}}} T1Q?????r*)rrrcrdN) r;rr<rrerrfrrZr[rrr`rWs@r)rrXsi--b @ @ @ @ @ @ @ @ @ @ @r*r) __future__rrvrrbaserrr base.data_feederr base.frameworkr r r initializerr lazy_initr__all__rrYrbrr*r)rs#""""" 0000000000888888 %$$$$$'''''' fffff fffR0606060606 060606fXXXXXXXXv<@<@<@<@<@0<@<@<@<@<@r*