^jlddZddlZddlZddlZddlZddlmZmZddlm Z ddl m Z ddl mZmZddlmZmZmZmZdd lmZdd lmZmZdd lmZd d lmZej<eZ d3de!e!e"ffdZ#GddejHZ%GddejLZ'GddejPZ)GddejTZ+GddejPZ,GddejPZ-d4dZ.GddejPZ/Gdd ejPZ0Gd!d"ejPZ1Gd#d$ejPZ2Gd%d&ejPZ3eGd'd(eZ4eGd)d*e4Z5ed+,Gd-d.e4Z6ed/,Gd0d1ee4Z7gd2Z8y)5z9PyTorch BiT model. Also supports backbone for ViT hybrid.N)Tensornn)initialization)ACT2FN) BackboneMixinfilter_output_hidden_states)BackboneOutputBaseModelOutputWithNoAttention(BaseModelOutputWithPoolingAndNoAttention$ImageClassifierOutputWithNoAttention)PreTrainedModel)auto_docstringlogging)can_return_tuple) BitConfigreturncd}||dz ||dz zzdz}||fSt|tra|j}|dk(r0|dk(r#||dz zdzdk(r|dz ||dz zzdz}||fSd}d}||fS|dk(rd}||fS|dz ||dz zzdz}||fS)al Utility function to get the tuple padding value given the kernel_size and padding. Args: padding (Union[`str`, `int`], *optional*): Padding value, can be either `"same"`, `"valid"`. If a different value is provided the default padding from PyTorch is used. kernel_size (`int`, *optional*, defaults to 7): Kernel size of the convolution layers. stride (`int`, *optional*, defaults to 1): Stride value of the convolution layers. dilation (`int`, *optional*, defaults to 1): Dilation value of the convolution layers. FrsamerTvalid) isinstancestrlower)padding kernel_sizestridedilationdynamics o/var/www/ramen.bs-engineer-server.com/venv/lib/python3.12/site-packages/transformers/models/bit/modeling_bit.pyget_padding_valuer")sGQJ(kAo">>1D'3--/ f {K!O <AQF"QJ(kAo*FF1L G  G   G G  h+/&BBqHG G c6eZdZdZ dfd ZdZxZS)WeightStandardizedConv2dzConv2d with Weight Standardization. Used for ViT Hybrid model. Paper: [Micro-Batch Training with Batch-Channel Normalization and Weight Standardization](https://huggingface.co/papers/1903.10520) c t||||\}} t | ||||||||| rt||||_| |_yd|_| |_y)N)rr)rrrgroupsbias)r"super__init__ DynamicPad2dpadeps) self in_channel out_channelsrrrrr'r(r- is_dynamic __class__s r!r*z!WeightStandardizedConv2d.__init__Yss0V^fg       #KBDHDHr#c |j|j|}tjj|jj d|j ddddd|jj|j}tjj|||j|j|j|j|j}|S)NrT)trainingmomentumr-)r,r functional batch_normweightreshaper0r- reshape_asconv2dr(rrrr')r. hidden_stater:s r!forwardz WeightStandardizedConv2d.forwardvs 88 88L1L)) KK  4#4#4b 94PT_bhlhphp* *T[[ ! }}++ &$))T[[$,, W[WbWb r#)rSAMErrFgư>__name__ __module__ __qualname____doc__r*r? __classcell__r2s@r!r%r%Rs&  : r#r%c*eZdZdZdfd ZdZxZS)BitGroupNormActivationzQ A module that combines group normalization with an activation function. ct||j||||rt|j|_yt j|_y)N)r-affine)r)r* num_groupsr hidden_act activationrIdentity)r.config num_channelsr-rKapply_activationr2s r!r*zBitGroupNormActivation.__init__sA **Lc&Q $V%6%67DO kkmDOr#ctjj||j|j|j |j }|j|}|SN)rr8 group_normrLr:r(r-rN)r.r>s r!r?zBitGroupNormActivation.forwardsH}}// doot{{\`\e\egkgogop |4 r#)gh㈵>TTrArGs@r!rIrIs,r#rIc*eZdZdZdfd ZdZxZS)r+z A module that wraps dynamic padding of any input, given the parameters of the convolutional layer and the input hidden states. ct|t|tr||f}t|tr||f}t|tr||f}||_||_||_||_d}||_y)Ncpttj||z dz |z|dz |zzdz|z dS)Nrr)maxmathceil)xrrrs r!compute_paddingz.DynamicPad2d.__init__..compute_paddingsB !f*-1V;{QRZ>ZZ]^^abbdef fr#) r)r*rintrrrvaluer])r.rrrr_r]r2s r!r*zDynamicPad2d.__init__sw  k3 '& 4K fc "f%F h $ (+H&     g /r#c |jdd\}}|j||jd|jd|jd}|j||jd|jd|jd}|dkDs|dkDrBt j j||dz||dzz |dz||dzz g|j}|S)Nrrr)r_) sizer]rrrrr8r,r_)r.input input_height input_widthpadding_height padding_widths r!r?zDynamicPad2d.forwards$)JJL$5! k--lD)rrr-rr)rrrpr@)rrrrr5)rr_ preactivationrQ)r)r*r%rQembedding_sizeglobal_padding convolutionriembedding_dynamic_paddingpoolerupperrrOr, ConstantPad2d layer_typerInormr.rPr2s r!r*zBitEmbeddings.__init__s 3     ! !))  #qPVPpPpq   ,1F1F1L1L1NRX1X{{}DH'' CHDH    /.vFDYDYZDI DI"//r# pixel_valuesrc|jd}||jk7r td|j|}|j |}|j |}|j |}|S)NrzeMake sure that the channel dimension of the pixel values match with the one set in the configuration.)shaperQ ValueErrorr}r,rr)r.rrQ embeddings r!r?zBitEmbeddings.forwardsr#))!, 4,, ,w $$\2 HHY' IIi( KK * r#) rBrCrDrErr*rr?rFrGs@r!rurus'0y06Fvr#rucreZdZdZd deddffd ZdejdejfdZde fdZ xZ S) BitDropPathzStochastic depth (DropPath) per sample, for residual blocks. Identity when ``drop_prob`` is 0 or outside training. See `Deep Networks with Stochastic Depth `_. drop_probrNc0t|||_yrT)r)r*r)r.rr2s r!r*zBitDropPath.__init__s "r#rscP|jdk(s |js|Sd|jz }|jdfd|jdz zz}t j ||j |j}t j||z}|j||zS)Nr5rr)r)dtypedevice) rr6rndimtorchrandrrfloordiv)r.rs keep_probr random_tensors r!r?zBitDropPath.forward s >>S   & $$Q')DM4F4F4J,KK 5 0C0CML`L`a  MI$=>   +m;;r#c d|jS)Nzp=)r)r.s r! extra_reprzBitDropPath.extra_repr)sDNN#$$r#)r5) rBrCrDrEfloatr*rrr?rrrFrGs@r!rrsB #%#$#eZdZdZ dfd ZdZdedefdZxZS)BitStagez7 A ResNet v2 stage composed by stacked layers. c ^t||dvrdnd} |jdk(rt} nt} |} t j |_t|D]Q} |j| ||\}} }|jjt| | || ||||| | | |} |} Sy)N)rrrr bottleneck)rrrrrr) r)r*rrrr Sequentiallayersrange_get_updated_hyperparameters add_moduler)r.rPrr0rrdepthr layer_dropoutr layer_clsprev_chs layer_idxrrr2s r!r*zBitStage.__init__s &&0a    ,*I7Immo u &I595V5V6=6 2FNN KK " "I !%!-#1#1#1  $H%N+ &r#c8|r||}nd}|dk7rd}|dk(}|||fS)zt Get the new hyper-parameters with respect to the previous ones and the index of the current layer. r5rr)r.rrrrrs r!rz%BitStage._get_updated_hyperparameters s8 *95N N >F"a~~55r#rcrcT|}t|jD] \}}||}|SrT) enumerater)r.rcr>_layers r!r?zBitStage.forwards3 !$++. /HAu .L /r#)rN) rBrCrDrEr*rrr?rFrGs@r!rrs.,&\6 Vr#rc FeZdZdeffd ZdZ d dedededefdZ xZ S) BitEncoderrPc t|tjg|_|j }d}d}t jtjd|jt|jj|jDcgc]}|j}}tt!|j|j"|D]`\}\}} } |j%||| ||\} } }t'||| | ||| } | }|| z}|jj)t+|| bycc}w)Nrr)rrrr)r)r*r ModuleListstagesr{rrnplinspacersumdepthssplittolistrzip hidden_sizesrrrr)r.rPrcurrent_striderr\layer_dropouts stage_idx current_depthcurrent_hidden_sizerr0rstager2s r!r*zBitEncoder.__init__$sA mmB' ((\\"++a1F1FFMMHZ"[\bbcicpcpq  HHJ  OX  v22N CO  : JIJ ':M.2-N-N>+>&. *L&(!#+E$H f $N KK " "3y>5 9+ :  sEczt||jz}|dk(rdnd}||jk\r||z}d}|||fS)Nrrr)r width_factor output_stride)r.rrrrrPr0rs r!rz'BitEncoder._get_updated_hyperparametersJsO 3f6I6I IJ 1n! V11 1  HFVX--r#r>output_hidden_states return_dictrc|rdnd}|jD]}|r||fz}||}|r||fz}|std||fDSt||S)Nrc3&K|] }|| ywrTr).0vs r! z%BitEncoder.forward..asSqQ]Ss)last_hidden_staters)rtupler )r.r>rrrs stage_modules r!r?zBitEncoder.forwardRsv3  KK 6L# - ? ' 5L  6 )\O;MS\=$ASS S-*'  r#)FT) rBrCrDrr*rrboolr r?rFrGs@r!rr#sA$:y$:L.]a " :> UY ' r#rcdeZdZUeed<dZdZdZdgZe jfdZ xZ S)BitPreTrainedModelrPbit)imagerrucDt||t|tjr#t j |jddyt|tjrt j|jtjd|jxtjj j|j\}}|dkDrdtj|z nd}t j|j| |yyy)Nfan_outrelu)mode nonlinearity)arr)r) _init_weightsrrConv2dinitkaiming_normal_r:Linearkaiming_uniform_rZsqrtr(r_calculate_fan_in_and_fan_outuniform_)r.modulefan_inrboundr2s r!rz BitPreTrainedModel._init_weightsqs f% fbii (  YV T  *  ! !&--499Q< @{{&!HHMMGG V 17!DIIf-- fkkE659'+r#) rBrCrDr__annotations__base_model_prefixinput_modalitiesmain_input_name_no_split_modulesrno_gradrrFrGs@r!rris= !$O()U]]_ : :r#rc ReZdZfdZe ddededzdedzdefdZxZ S) BitModelcJt||||_t||_t ||_|jdk(rt||jdntj|_ tjd|_|jy)Nryr4rz)rr)r)r*rPruembedderrencoderrrIrrrOrAdaptiveAvgPool2dr post_initrs r!r*zBitModel.__init__s   %f- !&)   O3 #68K8KB8O P **62  r#Nrrrrc J||n|jj}||n|jj}|j|}|j |||}|d}|j |}|j |}|s ||f|ddzSt|||jS)Nrrrr)r pooler_outputrs) rPrrrrrrr rs) r.rrrkwargsembedding_outputencoder_outputsr pooled_outputs r!r?zBitModel.forwards%9$D $++JjJj &1% 1` a classification loss is computed (Cross-Entropy). Nrrr)losslogitsrs)rPrrr r, loss_functionr rs) r.rr-rrr!outputsr$r0r/outputs r!r?z!BitForImageClassification.forwards&1%R`k(l1<--'!* /  %%ffdkkBDY,F'+'7D7V# CV C3f\c\q\qrrr#)NNNN) rBrCrDr*rr FloatTensor LongTensorrr r?rFrGs@r!r(r(s 26*.,0#' s''$.s  4's#Tk s D[ s .ssr#r(zL BiT backbone, to be used with frameworks like DETR and MaskFormer. c jeZdZdZfdZeee d dede dzde dzde fdZ xZ S) BitBackboneFct||t||_|jg|j z|_|jyrT)r)r*rrr{r num_featuresrrs r!r*zBitBackbone.__init__sD  F##223f6I6II r#Nrrrrc ||n|jj}||n|jj}|j|dd}|j}d}t |j D]\}} | |jvs|||fz }|s|f} |r| |jfz } | St||r|jdSddS)a Examples: ```python >>> from transformers import AutoImageProcessor, AutoBackbone >>> import torch >>> from PIL import Image >>> import httpx >>> from io import BytesIO >>> 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("google/bit-50") >>> model = AutoBackbone.from_pretrained("google/bit-50") >>> inputs = processor(image, return_tensors="pt") >>> outputs = model(**inputs) ```NTrr) feature_mapsrs attentions) rPrrrrsr stage_names out_featuresr ) r.rrrr!r2rsr;idxrr3s r!r?zBitBackbone.forwards<&1%rQs@  &!H .,-(   H %&ERWY]R]L^&R-ryy-`R\\$02990f 2<< 6/BII/f%"))%0A(biiA(HFFR' '.GryyGTC C L:::*2 !2 2 j ,s 2,s  ,s^ B -!3B  B J Yr#