cCi<SrSSKJrJrJr SSKrSSKJr SSKJr SSK J r SSK J r SSK JrJrJrJr SSKJr SS KJr SS KJr S S KJr \R4"\5r"S S\R:5r"SS\R:5r"SS\R:5r "SS\R:5r!\"SS\ 55r"\"SS\"55r#\"SS9"SS\"55r$\"SS9"SS\"\55r%/S Qr&g)!zPyTorch TextNet model.)AnyOptionalUnionN)Tensor)PreTrainedModel)ACT2CLS)BackboneOutputBaseModelOutputWithNoAttention(BaseModelOutputWithPoolingAndNoAttention$ImageClassifierOutputWithNoAttention) TextNetConfig)logging) BackboneMixin)auto_docstringcj^\rSrSrS\4U4SjjrS\RS\R4SjrSr U=r $)TextNetConvLayer)configc >[TU]5 URUlURUlUR Ul[UR[5(a$URSS-URSS-4OURS-n[R"URURURURUSS9Ul[R"URUR 5Ul[R$"5UlURb[(UR"5Ulgg)NrF) kernel_sizestridepaddingbias)super__init__stem_kernel_sizer stem_strider stem_act_funcactivation_function isinstancetuplennConv2dstem_num_channelsstem_out_channelsconv BatchNorm2dbatch_norm_eps batch_normIdentity activationr)selfrr __class__s f/home/james-whalen/.local/lib/python3.13/site-packages/transformers/models/textnet/modeling_textnet.pyrTextNetConvLayer.__init__*s !22(( #)#7#7 &11599   "a '););A)>!)C D((A-  II  $ $  $ $//%%   ..)A)A6CXCXY++-  # # /%d&>&>?ADO 0 hidden_statesreturnchURU5nURU5nURU5$N)r)r,r.)r/r4s r1forwardTextNetConvLayer.forwardEs- -0  6 }--r3)r.r"r,r)rr) __name__ __module__ __qualname____firstlineno__r rtorchrr8__static_attributes__ __classcell__r0s@r1rr)s1B}B6.U\\.ell..r3rc ~^\rSrSrSrS\S\S\S\S\4 U4SjjrS \RS \R4S jr S r U=r $) TextNetRepConvLayerKa This layer supports re-parameterization by combining multiple convolutional branches (e.g., main convolution, vertical, horizontal, and identity branches) during training. At inference time, these branches can be collapsed into a single convolution for efficiency, as per the re-parameterization paradigm. The "Rep" in the name stands for "re-parameterization" (introduced by RepVGG). r in_channels out_channelsrrc P>[T U]5 X lX0lX@lXPlUSS- S-USS- S-4n[ R"5Ul[ R"UUUUUSS9Ul [ R"X1RS9Ul USS- S-S4nSUSS- S-4nUSS:waG[ R"UUUSS4UUSS9Ul[ R"X1RS9UlOSuUlUlUSS:waG[ R"UUSUS4UUSS9Ul[ R"X1RS9UlOSuUlUlX2:Xa*US:Xa$[ R"X!RS9UlgSUlg)NrrrF)rErFrrrr) num_featuresepsNN)rr num_channelsrFrrr%ReLUr"r& main_convr*r+main_batch_norm vertical_convvertical_batch_normhorizontal_convhorizontal_batch_norm rbr_identity) r/rrErFrrrvertical_paddinghorizontal_paddingr0s r1rTextNetRepConvLayer.__init__Us '(& NQ&1,{1~/Aa.GH#%779 #%#   "~~Q !#')(^Q/( "D (*~~Q #%99') A/* $D *,\WlWl)mD &?I rr8r?r@rAs@r1rCrCKsS7 }7 37 c7 `c7 mp7 r6U\\6ell66r3rCc:^\rSrSrS\S\4U4SjjrSrSrU=r $) TextNetStagerdepthcr>[T U]5 URUnURUn[ U5nUR UnUR US-nU/U/US- --nU/U-n /n [ XX45Hn U R[U/U Q765 M [R"U 5Ul g)Nr) rrconv_layer_kernel_sizesconv_layer_strideslen hidden_sizeszipappendrCr% ModuleListstage) r/rrbrr num_layersstage_in_channel_sizestage_out_channel_sizerErFrk stage_configr0s r1rTextNetStage.__init__s 44U; **51% & 3 3E :!'!4!4UQY!?,-1G0HJYZN0[[ ./*<  ;OL LL,VClC DP]]5) r3c<URH nU"U5nM U$r7rk)r/ hidden_stateblocks r1r8TextNetStage.forwards ZZE .L r3rr) r:r;r<r=r r^rr8r?r@rAs@r1r`r`s *}*S*"r3r`c r^\rSrSrS\4U4SjjrS S\RS\\ S\\ S\ 4Sjjr S r U=r $) TextNetEncoderrc>[TU]5 /n[UR5n[ U5HnUR [ X55 M [R"U5Ul gr7) rrrfrdrangerir`r%rjstages)r/rr{ num_stagesstage_ixr0s r1rTextNetEncoder.__init__sU 778 j)H MM,v8 9*mmF+ r3rsoutput_hidden_states return_dictr5cU/nURHnU"U5nURU5 M U(dU4nU(aXd4-$U$[XS9$)N)last_hidden_stater4)r{rir )r/rsrrr4rkoutputs r1r8TextNetEncoder.forwards[ & [[E .L   .!"_F0D6,, P& P- jjr3)r{rJ)r:r;r<r=r rr>rrboolr r8r?r@rAs@r1rwrwsY,},04&* kllk'tnkd^ k ( kkr3rwc.\rSrSr%\\S'SrSrSrSr g)TextNetPreTrainedModelrtextnet pixel_valuesc&[U[R[R45(akURR R SURRS9 URb%URR R5 gg[U[R5(aXURR RS5 URb%URR R5 ggg)Ng)meanstdg?) r#r%Linearr&weightdatanormal_rinitializer_rangerzero_r*fill_)r/modules r1 _init_weights$TextNetPreTrainedModel._init_weightss fryy"))4 5 5 MM   & &CT[[5R5R & S{{&   &&('  / / MM   $ $S ){{&   &&('0r3N) r:r;r<r=r __annotations__base_model_prefixmain_input_namerr?rr3r1rrs !$O)r3rc^\rSrSrU4Sjr\S S\S\\S\\S\ \ \ \ \ 4\ \ \ 44Sjj5rSrU=r$) TextNetModelc>[TU]U5 [U5Ul[ U5Ul[ R"S5UlUR5 g)N)rr) rrrstemrwencoderr%AdaptiveAvgPool2dpooler post_initr/rr0s r1rTextNetModel.__init__sD  $V, %f- **62  r3rrrr5cDUbUOURRnUbUOURRnURU5nUR XBUS9nUSnUR U5nU(dXg4nU(aXS4-$U$[ UUU(aUSS9$SS9$)Nrrrr)r pooler_outputr4)ruse_return_dictrrrrr ) r/rrrrsencoder_outputsr pooled_outputrs r1r8TextNetModel.forwards&1%[TU]U5 URUl[U5Ul[ R "S5Ul[ R"5Ul URS:a.[ R"URSUR5O[ R"5Ul [ R"URUR/5UlUR!5 g)N)rrr)rr num_labelsrrr%ravg_poolFlattenflattenrrgr-fcrj classifierrrs r1r&TextNetForImageClassification.__init__s   ++#F+ ,,V4 zz| KQK\K\_`K`"))F//3V5F5FGfhfqfqfs-- (EF r3rlabelsrrr5cZUbUOURRnURXUS9nUSnURH nU"U5nM UR U5nSn UbUR X(UR5n U(dU4USS-n U bU 4U -$U $[ XURS9$)a labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for computing the image classification/regression loss. Indices should be in `[0, ..., config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If `config.num_labels > 1` a classification loss is computed (Cross-Entropy). Examples: ```python >>> import torch >>> import requests >>> from transformers import TextNetForImageClassification, TextNetImageProcessor >>> from PIL import Image >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" >>> image = Image.open(requests.get(url, stream=True).raw) >>> processor = TextNetImageProcessor.from_pretrained("czczup/textnet-base") >>> model = TextNetForImageClassification.from_pretrained("czczup/textnet-base") >>> inputs = processor(images=image, return_tensors="pt") >>> with torch.no_grad(): ... outputs = model(**inputs) >>> outputs.logits.shape torch.Size([1, 2]) ```Nrrr)losslogitsr4)rrrrr loss_functionr r4) r/rrrroutputsrlayerrrrs r1r8%TextNetForImageClassification.forward&sB&1% FloatTensor LongTensorrr r8r?r@rAs@r1rrs 59-1/3&* 0su0010s))*0s'tn 0s d^ 0s . 0s0sr3rzP TextNet backbone, to be used with frameworks like DETR and MaskFormer. c r^\rSrSrSrU4Sjr\S S\S\\ S\\ S\ \ \ \ 44Sjj5r S rU=r$) TextNetBackboneiZFc>[TU]U5 [TU] U5 [U5UlUR UlUR5 gr7)rr_init_backbonerrrgrHrrs r1rTextNetBackbone.__init__bsC   v&#F+ "// r3rrrr5cUbUOURRnUbUOURRnURUSUS9nU(a UROUSnSn[ UR 5HupxXR;dMXeU4- nM U(d)U4n U(aU(a UROUSnX4- n U $[UU(aURSS9$SSS9$)a Examples: ```python >>> import torch >>> import requests >>> from PIL import Image >>> from transformers import AutoImageProcessor, AutoBackbone >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" >>> image = Image.open(requests.get(url, stream=True).raw) >>> processor = AutoImageProcessor.from_pretrained("czczup/textnet-base") >>> model = AutoBackbone.from_pretrained("czczup/textnet-base") >>> inputs = processor(image, return_tensors="pt") >>> with torch.no_grad(): >>> outputs = model(**inputs) ```NTrrr) feature_mapsr4 attentions) rrrrr4 enumerate stage_names out_featuresr ) r/rrrrr4ridxrkrs r1r8TextNetBackbone.forwardls.&1%torch.nnr%r transformersrtransformers.activationsrtransformers.modeling_outputsr r r r 1transformers.models.textnet.configuration_textnetr transformers.utilsr!transformers.utils.backbone_utilsrutilsr get_loggerr:loggerModulerrCr`rwrrrr__all__rr3r1rs&'' (, L&;#   H %.ryy.DW6"))W6t2990kRYYk: )_ ) ) " )" " J @s$:@s  @sF = ,m=  = @ ir3