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"  []]DNNWYYDN&( +***** d O!y')'!   D  &')'!DO=??DL $$ ))I$/%1$% !%"    ))(gLQQQ ))L[QRSSS ' +!- !!!      ))L$/ L % %gr~l/K/K L L L(0#)L#8#8KKa+o* "}D (K77 --3[QWXXX --I$/%>$% !%"      ) )G:STTT      ) ) 9!- +!!      ) )% 1M1M N N N            r(cd|jr(|||S||}t |dr|||z }|||z }|||z }||S)Nr)rrrrhasattrrrr)r!inputsouts r'r2zDiverseBranchBlock.forwardks > <>>$"2"26":":;; ;oof%% 4 # # ( 4<<'' 'C t||F### t'''~~c"""r(c*t|dr4tjj|jjj|t|dr4tjj|jjj|t|dr4tjj|j j j|t|dr6tjj|j j j|dSdS)Nrrrr) rrreinit constant_rrBrrrrrr)r! gamma_values r' init_gammazDiverseBranchBlock.init_gammavs 4 & & L HM # #DO$6$={ K K K 4 # # I HM # #DLO$:K H H H 4 # # L HM # #DL$6$={ K K K 4 ' ' N HM # #D$4$8$? 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M}%%N(;(;;;s=?Q?QTU?U?U%%%%#0%%%M #M"L"L"Lm"L"L"L @555555..???  #   l + + +  ! OOJ ' ' ' ' ' " !r(cJ|r|rd}|dur}t|tr|g}t|trOt|t kst |dkrfd|D}fd|D}|r||dSdS)NTrcFg|]}|dk|tk|Srrrs r'rz)TheseusLayer.init_res..'s>!!!axxC#n*=*=$=$=$=$=$=r(c g|] }| Srrrs r'rz)TheseusLayer.init_res..,sHHHQ~a0HHHr()rrrrrrr)r!rrr s ` r'init_reszTheseusLayer.init_ress  !} ! M D ,O mS ) ) ,*OM mT * * I=!!C$7$7773};M;MPQ;Q;Q!!!!,!!! IHHH-HHHO  - OOO , , , , , - -r(returnc$d}t|)NzTThe function 'replace_sub()' is deprecated, please use 'upgrade_sublayer()' instead.)DeprecationWarning)r!rrmsgs r' replace_subzTheseusLayer.replace_sub1sd %%%r(layer_name_pattern handle_funccFt|ts|g}g}|D]}t||}|st|dkr|ddn|}|dd}|dd}|dd} |||} | rjt| dkr=t ||| d}| ddD] } || } | || d<n+| t ||| d<nt ||| |||S) ause 'handle_func' to modify the sub-layer(s) specified by 'layer_name_pattern'. Args: layer_name_pattern (Union[str, List[str]]): The name of layer to be modified by 'handle_func'. handle_func (Callable[[nn.Module, str], nn.Module]): The function to modify target layer specified by 'layer_name_pattern'. The formal params are the layer(nn.Module) and pattern(str) that is (a member of) layer_name_pattern (when layer_name_pattern is List type). And the return is the layer processed. Returns: Dict[str, nn.Module]: The key is the pattern and corresponding value is the result returned by 'handle_func()'. Examples: from paddle import nn import paddleclas def rep_func(layer: nn.Module, pattern: str): new_layer = nn.Conv2d( in_channels=layer._in_channels, out_channels=layer._out_channels, kernel_size=5, padding=2 ) return new_layer net = paddleclas.MobileNetV1() res = net.upgrade_sublayer(layer_name_pattern=["blocks[11].depthwise_conv.conv", "blocks[12].depthwise_conv.conv"], handle_func=rep_func) print(res) # {'blocks[11].depthwise_conv.conv': the corresponding new_layer, 'blocks[12].depthwise_conv.conv': the corresponding new_layer} )pattern parent_layerr rrHname index_listr)rrparse_pattern_strrgetattrsetattrr~) r!rrhit_layer_pattern_listr layer_listsub_layer_parent sub_layersub_layer_namesub_layer_index_list new_sub_layersub_layer_indexs r'upgrade_sublayerzTheseusLayer.upgrade_sublayer5sD,d33 6"4!5 !#) 3 3G*7NNNJ :=j//A:M:Mz"~g66SW "2w/I'^F3N#-b>,#? 'K 7;;M# I+,,q00'./?'P'P,Q/($,@"+EMM+;O+L((AN$%9"%=>>&,n==,Q/(.-HHH " ) )' 2 2 2 2%%r(stop_layer_namect||}|sdS|}|D]7}|d|d}}t|||s d|d|d}dS|d}8dS) zstop forward and backward after 'stop_layer_name'. Args: stop_layer_name (str): The name of layer that stop forward and backward after this layer. Returns: bool: 'True' if successful, 'False' otherwise. Frrz5Failed to set the layers that after stop_layer_name('z0') to IdentityLayer. The error layer's name is 'z'.rT)r set_identity)r!r*r"r layer_dictrrrs r'r zTheseusLayer.stop_afterzs'==  5 $ / /J)&1:l3K*D dJ?? XoXXPTXXXuu%g.LLtr( layer_namecfd}|||}t|dkrd}dSdS)zfreeze the layer named layer_name and its previous layer. Args: layer_name (str): The name of layer that would be freezed. Returns: bool: 'True' if successful, 'False' otherwise. cPGfddtj}|}|S)Nc(eZdZfdZdZxZS)CTheseusLayer.freeze_befor..stop_grad..StopGradLayercVt|_dSr4)rrr)r!r&rs r'rzLTheseusLayer.freeze_befor..stop_grad..StopGradLayer.__init__s$GG$$&&&!&DJJJr(c>||}d|_|S)NT)r stop_gradientr!r^s r'r2zKTheseusLayer.freeze_befor..stop_grad..StopGradLayer.forwards 1 A&*AOHr(r)r&rs@r' StopGradLayerr2sM''''''r(r7)reModule)rrr7 new_layers` r' stop_gradz,TheseusLayer.freeze_befor..stop_gradsL           & I r(rzAFailed to stop the gradient before the layer named '{layer_name}'FT)r)r)r!r.r:resrs r'r zTheseusLayer.freeze_beforsF   ##J :: s88q==UC5tr(rci|_Gddt}||j}|||}t|dr|j||j|_|S)aupdate the result(s) to be returned. Args: return_patterns (Union[str, List[str]]): The name of layer to return output. Returns: Dict[str, nn.Module]: The pattern(str) and corresponding layer(nn.Module) that have been set successfully. ceZdZdZdZdS)(TheseusLayer.update_res..Handlerc||_dSr4)r)r!rs r'rz1TheseusLayer.update_res..Handler.__init__s ( r(c|j|_||_t|dr|j|t |_|S)Nhook_remove_helper)rrrrAremoveregister_forward_post_hooksave_sub_res_hook)r!rrs r'__call__z1TheseusLayer.update_res..Handler.__call__s\!%!(5"6776,33555+0+K+K%,,( r(N)r8r9r:rrErr(r'Handlerr>s2 ) ) )     r(rF)rrA)robjectr)rrArBrCr)r!rrFrr!s r'rzTheseusLayer.update_ress      f   gdm,, !%!6!6 "7" "  4- . . -  # * * , , ,"&"A"A  "# # &%r()NNNNN)NN)rN)r8r9r:rrrrrr strrrrer8rr)boolr r rr;r<s@r'rrsx''''' ,(,(,(,(\----,&&&&C&!#tCy.1C&ry#. 9:C& c29n  C&C&C&C&J#$0st<*&$S$s)^4*& c29n *&*&*&*&*&*&*&*&r(rc$||j|j<dSr4)rr)rr/rVs r'rDrDs%+EN5>"""r(rr.layer_index_listrcRd}|jD]#}|rt|j|<||krd}$|rw|ru|j|}t|D]X\}}d}t|D]}|||}|jD])} |rt|j|| <!|| krd}*Y|S)aset the layer specified by layer_name and layer_index_list to Identity. Args: parent_layer (nn.Module): The parent layer of target layer specified by layer_name and layer_index_list. layer_name (str): The name of target layer to be set to Identity. layer_index_list (str, optional): The index of target layer to be set to Identity in parent_layer. Defaults to None. Returns: bool: True if successfully, False otherwise. FT) _sub_layersr enumerater) rr.rKr r%layer_containernum layer_indexr%r(s r'r,r,sJ&2  7?zzL $^ 4  Z ' 'J &J &&2:> )*: ; ; & & CJ3ZZ G G"12B12E"F#2#> & &LTJJL,Z8I/11!%J  & r(rc |d}|sd|d}dSg}t|dkr%d|dvr\|ddd}td|ddddD}n |d}d}t||d}| d |d |d }dS|r\|D]Y}t |dks t |t|krd |d |d t|d}dS||}Z||||d|dd}|}t|dk%|S)aqparse the string type pattern. Args: pattern (str): The pattern to describe layer. parent_layer (nn.Module): The root layer relative to the pattern. Returns: Union[None, List[Dict[str, Union[nn.Module, str, None]]]]: None if failed. If successfully, the members are layers parsed in order: [ {"layer": first layer, "name": first layer's name parsed, "index": first layer's index parsed if exist}, {"layer": second layer, "name": second layer's name parsed, "index": second layer's index parsed if exist}, ... ] .z The pattern('z&') is illegal. Please check and retry.Nr[c3LK|]}|ddV dS)]rN)split)rindexs r' z$parse_pattern_str.."sD++(- C  #++++++r(r zNot found layer named('z') specified in pattern('z').zNot found layer by index('z'). The index should < z and > 0.)rrr)rWrrrrr~) rr pattern_listrr"target_layer_nametarget_layer_index_list target_layertarget_layer_indexs r'rrs%$==%%L MgMMMtJ l  a   ,q/ ! ! ,Q 5 5c : :1 = &*++1=a1F1Fs1K1KABB1O+++'' # #!-Q &* #|->EE  d,=ddX_dddC4 " @&= @ @")**Q..#6H2I2IS NN33a7IaadkaaEHIUEVEVaaaC44+,>? %)5     $ABB' # E l  a  H r(zphttps://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPHGNetV2_B0_ssld_pretrained.pdparamszphttps://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPHGNetV2_B1_ssld_pretrained.pdparamszphttps://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPHGNetV2_B2_ssld_pretrained.pdparamszphttps://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPHGNetV2_B3_ssld_pretrained.pdparamszphttps://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPHGNetV2_B4_ssld_pretrained.pdparamszphttps://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPHGNetV2_B5_ssld_pretrained.pdparamszphttps://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPHGNetV2_B6_ssld_pretrained.pdparams) PPHGNetV2_B0 PPHGNetV2_B1 PPHGNetV2_B2 PPHGNetV2_B3 PPHGNetV2_B4 PPHGNetV2_B5 PPHGNetV2_B6c*eZdZdZdfd ZdZxZS)LearnableAffineBlocka Create a learnable affine block module. This module can significantly improve accuracy on smaller models. Args: scale_value (float): The initial value of the scale parameter, default is 1.0. bias_value (float): The initial value of the bias parameter, default is 0.0. lr_mult (float): The learning rate multiplier, default is 1.0. lab_lr (float): The learning rate, default is 0.01. rr{Gz?cPttjtjtjdtj ||_ | d|j tjtjtjdtj ||_ | d|j dS)Nr )rscalerN) rrr Parameterrerronestofloat32rjregister_parameterrN)r! scale_value bias_valuelr_multlab_lrr&s r'rzLearnableAffineBlock.__init__s _ G  ejmm..u}==;  O O    444O G  ejmm..u}==:  N N     22222r(c&|j|z|jzSr4)rjrNr6s r'r2zLearnableAffineBlock.forwardszA~ ))r()rrrrhr8r9r:__doc__rr2r;r<s@r'rgrgsV3333338*******r(rgc8eZdZdZ d fd ZdZxZS) ConvBNActa ConvBNAct is a combination of convolution and batchnorm layers. Args: in_channels (int): Number of input channels. out_channels (int): Number of output channels. kernel_size (int): Size of the convolution kernel. Defaults to 3. stride (int): Stride of the convolution. Defaults to 1. padding (int/str): Padding or padding type for the convolution. Defaults to 1. groups (int): Number of groups for the convolution. Defaults to 1. use_act: (bool): Whether to use activation function. Defaults to True. use_lab (bool): Whether to use the LAB operation. Defaults to False. lr_mult (float): Learning rate multiplier for the layer. Defaults to 1.0. rLr TFrc t||_||_t j||||t |tr|n|dz dz|d|_t j ||_ |jr6t j |_ |jrt| |_dSdSdS)Nr rIF)rrrNrr)rruse_actuse_labrerkrrHrjrfrBrrrglab) r!rrrrrrr{r|rrr&s r'rzConvBNAct.__init__s   I    )'377SGGkAoRS=S    .    < AwyyDH| A/@@@ A A A Ar(c||}||}|jr1||}|jr||}|Sr4)rjrBr{rr|r}r6s r'r2zConvBNAct.forwardsW IIaLL GGAJJ <  A| HHQKKr()rLr r r TFrrur<s@r'rxrxst  &AAAAAA@r(rxc.eZdZdZ dfd ZdZxZS)LightConvBNActa LightConvBNAct is a combination of pw and dw layers. Args: in_channels (int): Number of input channels. out_channels (int): Number of output channels. kernel_size (int): Size of the depth-wise convolution kernel. use_lab (bool): Whether to use the LAB operation. Defaults to False. lr_mult (float): Learning rate multiplier for the layer. Defaults to 1.0. Frc tt||dd|||_t||||d|||_dS)Nr F)rrrr{r|rrT)rrrrr{r|rr)rrrxrr)r!rrrr|rrrr&s r'rzLightConvBNAct.__init__st #%     $%#    r(cZ||}||}|Sr4)rrr6s r'r2zLightConvBNAct.forwards% JJqMM JJqMMr()Frrur<s@r'rrs^          8r(rc&eZdZdfd ZdZxZS)PaddingSameAsPaddleMaxPool2dr ct||_||_tj||dd|_dS)NrT)r ceil_mode)rrrrrre MaxPool2dpool)r!rrr&s r'rz%PaddingSameAsPaddleMaxPool2d.__init__%sM & H&&{FAQU&VV r(c|j\}}}}tdtj||jz dz |jz|jz|z }tdtj||jz dz |jz|jz|z }|dz}|dz}t jj ||||z |||z g}| |S)Nrr rI) rRrmathceilrrrre functionalrr) r!r^_rZr[ pad_h_total pad_w_totalpad_hpad_ws r'r2z$PaddingSameAsPaddleMaxPool2d.forward+sW 1a  !dk/**Q.$+=@PPSTT    !dk/**Q.$+=@PPSTT  q q  H  # # {U*E;3FG  yy||r(r7rr<s@r'rr$sRWWWWWW       r(rc0eZdZdZ dfd ZdZxZS) StemBlockap StemBlock for PP-HGNetV2. Args: in_channels (int): Number of input channels. mid_channels (int): Number of middle channels. out_channels (int): Number of output channels. use_lab (bool): Whether to use the LAB operation. Defaults to False. lr_mult (float): Learning rate multiplier for the layer. Defaults to 1.0. Frc tt||dd|||_t||dzddd|||_t|dz|ddd|||_t|dz|d|rdnd|||_t||dd|||_tdd|_ dS)NrLrIrrrrr|rrr same)rrrrrr|rr)rr) rrrxstem1stem2astem2bstem3stem4rr)r!r mid_channelsrr|rrtext_recr&s r'rzStemBlock.__init__Gs+ #%      $%*     $)%    $q(% '11a     $%     1    r(c0||}||}||}||}t j||gd}||}||}|S)Nr )rrrrrrUrr)r!r^x2x1s r'r2zStemBlock.forwardsv JJqMM [[^^ [[__ YYq\\ Ir2h " " JJqMM JJqMMr()FrFrur<s@r'rr;sb   7 7 7 7 7 7 r       r(rc6eZdZdZ d fd ZdZxZS) HGV2_BlockaX HGV2_Block, the basic unit that constitutes the HGV2_Stage. Args: in_channels (int): Number of input channels. mid_channels (int): Number of middle channels. out_channels (int): Number of output channels. kernel_size (int): Size of the convolution kernel. Defaults to 3. layer_num (int): Number of layers in the HGV2 block. Defaults to 6. stride (int): Stride of the convolution. Defaults to 1. padding (int/str): Padding or padding type for the convolution. Defaults to 1. groups (int): Number of groups for the convolution. Defaults to 1. use_act (bool): Whether to use activation function. Defaults to True. use_lab (bool): Whether to use the LAB operation. Defaults to False. lr_mult (float): Learning rate multiplier for the layer. Defaults to 1.0. rLFTrc t||_tj|_|rdnd} t |D]@} |jt| | dkr|n||d||| A|||zz} t| |dzdd|| |_ t|dz|dd|| |_ dS)Nrrxrr )rrrrr|rrrIr) rridentityre ModuleListlayersrr~evalrxaggregation_squeeze_convaggregation_excitation_conv)r!rrrr layer_numr light_blockr|rr block_typer%total_channelsr&s r'rzHGV2_Block.__init__s$   moo )4E%%+ y!!  A K   Z  /0Avv tj | |_ dS)NrLF)rrrrrr{r|rrrT) rrrrrrrr|rr) rr is_downsamplerx downsamplerr~rrerlblocks)r!rrr block_numrrrrr|rrr blocks_listr%r&s r'rzHGV2_Stage.__init__s *   ''("   DO y!!  A   /0Avv T1dD!QA?dAtT1a!Q@ :99999:::;;;   !kk),B%%2B        E Lr(c Hgdgdgdgdd}td gd|dd|}|S) ag PPHGNetV2_B5 Args: pretrained (bool/str): If `True` load pretrained parameters, `False` otherwise. If str, means the path of the pretrained model. use_ssld (bool) Whether using ssld pretrained model when pretrained is True. Returns: model: nn.Module. Specific `PPHGNetV2_B5` model depends on args. )rrrr FFrLr)rrrrITFrLr)rrrrTTrr)rrrrITTrrrrFrrrrs r'rdrd1i766777777888 L  !kk e  OU  E Lr(c Hgdgdgdgdd}td gd|dd|}|S) ag PPHGNetV2_B6 Args: pretrained (bool/str): If `True` load pretrained parameters, `False` otherwise. If str, means the path of the pretrained model. use_ssld (bool) Whether using ssld pretrained model when pretrained is True. Returns: model: nn.Module. Specific `PPHGNetV2_B6` model depends on args. )rrrrIFFrLr)rrrrLTFrLr)rrrrTTrr)rrrrLTTrrrrLrrFrrrrs r'rereIr r(c*eZdZdZdfd ZdZxZS)PPHGNetV2_B4_Formulaa, PPHGNetV2_B4_Formula Args: in_channels (int): Number of input channels. Default is 3 (for RGB images). class_num (int): Number of classes for classification. Default is 1000. Returns: model: nn.Module. Specific `PPHGNetV2_B4` model with defined architecture. rLrct||_d|_gdgdgdgdd}t gd||d |_dS) NrrrrrrrF)rrrr|)rrrrr pphgnet_b4r!rrrr&s r'rzPPHGNetV2_B4_Formula.__init__ks & >=====>>>???   $%++%    r(c|jr|\}}}n t|tr |d}n|}|jd}|dkrt j|dd}||}|j\}}} } |||| | zggd}t|dddd}|jr|||fS|SNrr rL)repeatsrK)rrIr F)last_hidden_state pooler_output hidden_states attentionsreshaped_hidden_states) rrrrRrrepeat_interleaverrSr}r ) r! input_data pixel_valueslabelattention_maskrpphgnet_b4_outputbrYrZr[s r'r2zPPHGNetV2_B4_Formula.forward~ = *2< /L%*d++ *)!} ) #)!, 1   2<PQRRRL OOL99&, 1a-55q!QUmDDNN II  1/#'     = %$e^; ;$ $r(rLrrur<s@r'rraV      &%%%%%%%r(rc*eZdZdZdfd ZdZxZS)PPHGNetV2_B6_Formulaa, PPHGNetV2_B6_Formula Args: in_channels (int): Number of input channels. Default is 3 (for RGB images). class_num (int): Number of classes for classification. Default is 1000. Returns: model: nn.Module. Specific `PPHGNetV2_B6` model with defined architecture. rLrct||_d|_gdgdgdgdd}t gd||d |_dS) Nr) rrrrIFFrLrrI) rrrrLTFrLrrI) rrrrTTrrrI) rrrrLTTrrrIrrF)rrrr|)rrrrr pphgnet_b6rs r'rzPPHGNetV2_B6_Formula.__init__s & >==>>>>>>???   $%++%    r(c|jr|\}}}n t|tr |d}n|}|jd}|dkrt j|dd}||}|j\}}} } |||| | zggd}t|dddd}|jr|||fS|Sr) rrrrRrrr)rSr}r ) r!rrrr rpphgnet_b6_outputr"rYrZr[s r'r2zPPHGNetV2_B6_Formula.forwardr#r(r$rur<s@r'r'r'r%r(r')r rr r rr4)FF)FFFF)=rv __future__rrrrtypingrrrr numpyrrtorch.nnretorch.nn.functionalrr+rec_donut_swinr rkr r8r>rqrur{rrrrrrrrDrHrIr,r MODEL_URLSrkeys__all__rgrxrrrrrrr_r`rarbrcrdrerr'rr(r'r5s A@@@@@@@@@ /........... 100000",",",",",29",",",J6 6 6 6 6 ry6 6 6 z  8%%%4888   sssssssslry@&@&@&@&@&29@&@&@&F,,, GK##)#),#@C# ####L< < " < 4d3bid&: ;;<= =><<<<tGGGGGGG  $z  ! !(*(*(*(*(*<(*(*(*V77777 777t+++++\+++\58?.NNNNN NNNbMMMMMMMM`BBBBBBBBJIIIII IIIX0000!!!!H007%7%7%7%7%297%7%7%t7%7%7%7%7%297%7%7%7%7%r(