+ j;Rt^RIHt^RIt^RIHt^RIHt^RIHt^RI H t H t ^RI H t HtHtHt^RIHt^RIHtHt^R IHt^R IHt]P4!]4t!R R ]P:4t!R R]P:4t!RR]P:4t !RR]P:4t!]!RR]44t"]!RR]"44t#]!RR7!RR]"44t$]!RR7!RR] ]"44t%.ROt&R#)zPyTorch TextNet model.)AnyN)Tensor)ACT2CLS) BackboneMixinfilter_output_hidden_states)BackboneOutputBaseModelOutputWithNoAttention(BaseModelOutputWithPoolingAndNoAttention$ImageClassifierOutputWithNoAttention)PreTrainedModel)auto_docstringlogging)can_return_tuple TextNetConfigcPaa]tRt^'toV3RlV3RlltV3RlRltRtVtV;t#)TextNetConvLayerc <V^8dQhRS[/#configr)format __classdict__s"m/home/luca/screen-checking/.venv/lib/python3.14/site-packages/transformers/models/textnet/modeling_textnet.py __annotate__TextNetConvLayer.__annotate__(sBB}Bc <\SV`4VPVnVPVnVP Vn\VP\4'd5VP^,^,VP^,^,3MVP^,p\P!VPVPVPVPVRR7Vn\P!VPVP 4Vn\P$!4VnVPe$\(VP,!4VnR#R#)F) kernel_sizestridepaddingbiasN)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&& rr$TextNetConvLayer.__init__(s !22(( #)#7#7 &11599   "a '););A)>!)C D((A-  II  $ $  $ $//%%   ..)A)A6CXCXY++-  # # /%d&>&>?ADO 0rcN<V^8dQhRS[PRS[P/#r hidden_statesreturntorchr)rrs"rrrCs#..U\\.ell.rchVPV4pVPV4pVPV4#N)r/r2r4)r5r:s&&rforwardTextNetConvLayer.forwardCs- -0  6 }--r)r4r(r2r/rr __name__ __module__ __qualname____firstlineno__r$r@__static_attributes____classdictcell__ __classcell__r6rs@@rrr's BB6...rrcTaa]tRt^ItoRtV3RlV3RlltV3RlRltRtVtV;t #)TextNetRepConvLayera 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). c 8<V^8dQhRS[RS[RS[RS[RS[/#)rr in_channels out_channelsrr rint)rrs"rr TextNetRepConvLayer.__annotate__Ss27 7 }7 37 c7 `c7 mp7 rc <\S V`4W nW0nW@nWPnV^,^, ^,V^,^, ^,3p\ P!4Vn\ P!VVVVVRR7Vn \ P!W1PR7Vn V^,^, ^,^3p^V^,^, ^,3pV^,^8wdR\ P!VVV^,^3VVRR7Vn\ P!W1PR7VnMRRuVnVnV^,^8wdR\ P!VV^V^,3VVRR7Vn\ P!W1PR7VnMRRuVnVnW28Xd0V^8Xd)\ P!W!PR7VnR#RVnR#)rF)rNrOrr r!r") num_featuresepsN)r#r$ num_channelsrOrr r+ReLUr(r, main_convr0r1main_batch_norm vertical_convvertical_batch_normhorizontal_convhorizontal_batch_norm rbr_identity) r5rrNrOrr r!vertical_paddinghorizontal_paddingr6s &&&&&& rr$TextNetRepConvLayer.__init__Ss '(& NQ&1,{1~/Aa.GH#%779 #%#   "~~Q !#')(^Q/( "D (*~~Q #%99') A/* $D *,\WlWl)mD &?CT 1` a classification loss is computed (Cross-Entropy). Examples: ```python >>> import torch >>> import httpx >>> from io import BytesIO >>> from transformers import TextNetForImageClassification, TextNetImageProcessor >>> from PIL import Image >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" >>> with httpx.stream("GET", url) as response: ... image = Image.open(BytesIO(response.read())) >>> processor = TextNetImageProcessor.from_pretrained("czczup/textnet-base") >>> model = TextNetForImageClassification.from_pretrained("czczup/textnet-base") >>> inputs = processor(images=image, return_tensors="pt") >>> with torch.no_grad(): ... outputs = model(**inputs) >>> outputs.logits.shape torch.Size([1, 2]) ```Nr:rNN)losslogitsr:)rrrrr loss_functionr r:) r5rrrrroutputsrlayerrrrs &&&&&, rr@%TextNetForImageClassification.forwardsH&1%>> import torch >>> import httpx >>> from io import BytesIO >>> from PIL import Image >>> from transformers import AutoImageProcessor, AutoBackbone >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" >>> with httpx.stream("GET", url) as response: ... image = Image.open(BytesIO(response.read())) >>> processor = AutoImageProcessor.from_pretrained("czczup/textnet-base") >>> model = AutoBackbone.from_pretrained("czczup/textnet-base") >>> inputs = processor(image, return_tensors="pt") >>> with torch.no_grad(): >>> outputs = model(**inputs) ```NTr) feature_mapsr: attentionsr) rrrrr: enumerate stage_names out_featuresr) r5rrrrrr:ridxrxrs &&&&, rr@TextNetBackbone.forwardfs>&1%rs% "H .,-0   H %.ryy.DW6"))W6t2990kRYYk:%_%% & )& & R Cs$:Cs  CsL D m%;D  D N ir