g+\ dZddlmZmZmZddlZddlZddlmZddlm Z m Z m Z ddl m Z ddlmZmZmZmZdd lmZdd lmZmZmZmZmZdd lmZd d lmZeje Z!dZ"dZ#gdZ$dZ%dZ&d4dej'de(de)dej'fdZ*Gddej+Z,Gddej+Z-Gddej+Z.Gdd ej+Z/Gd!d"ej+Z0Gd#d$ej+Z1Gd%d&ej+Z2Gd'd(eZ3d)Z4d*Z5ed+e4Gd,d-e3Z6ed.e4Gd/d0e3Z7ed1e4Gd2d3e3eZ8dS)5zPyTorch ConvNextV2 model.)OptionalTupleUnionN)nn)BCEWithLogitsLossCrossEntropyLossMSELoss)ACT2FN)BackboneOutputBaseModelOutputWithNoAttention(BaseModelOutputWithPoolingAndNoAttention$ImageClassifierOutputWithNoAttention)PreTrainedModel)add_code_sample_docstringsadd_start_docstrings%add_start_docstrings_to_model_forwardloggingreplace_return_docstrings) BackboneMixin)ConvNextV2Configrzfacebook/convnextv2-tiny-1k-224)rirztabby, tabby catFinput drop_probtrainingreturnc|dks|s|Sd|z }|jdfd|jdz zz}|tj||j|jz}||||z}|S)aF Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks). Comment by Ross Wightman: This is the same as the DropConnect impl I created for EfficientNet, etc networks, however, the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper... See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ... I've opted for changing the layer and argument names to 'drop path' rather than mix DropConnect as a layer name and use 'survival rate' as the argument. rrr)r)dtypedevice)shapendimtorchrandr r!floor_div)rrr keep_probr" random_tensoroutputs n/var/www/html/ai-engine/env/lib/python3.11/site-packages/transformers/models/convnextv2/modeling_convnextv2.py drop_pathr,:sCx I I [^  Q 7 7E 5 EL Y Y YYM YYy ! !M 1F McjeZdZdZd deeddffd ZdejdejfdZ de fdZ xZ S) ConvNextV2DropPathzXDrop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).NrrcVt||_dSN)super__init__r)selfr __class__s r+r3zConvNextV2DropPath.__init__Rs$ "r- hidden_statesc8t||j|jSr1)r,rrr4r6s r+forwardzConvNextV2DropPath.forwardVs FFFr-c6d|jS)Nzp={})formatr)r4s r+ extra_reprzConvNextV2DropPath.extra_reprYs}}T^,,,r-r1) __name__ __module__ __qualname____doc__rfloatr3r$Tensorr9strr< __classcell__r5s@r+r/r/Osbb##(5/#T######GU\GelGGGG-C--------r-r/cLeZdZdZdeffd ZdejdejfdZxZ S) ConvNextV2GRNz)GRN (Global Response Normalization) layerdimcttjt jddd||_tjt jddd||_dS)Nr)r2r3r Parameterr$zerosweightbias)r4rHr5s r+r3zConvNextV2GRN.__init__`s_ l5;q!Q#<#<== LQ1c!:!:;; r-r6rctj|ddd}||dddzz }|j||zz|jz|z}|S)N)rrOT)prHkeepdim)rHrQư>)r$normmeanrLrM)r4r6global_features norm_featuress r+r9zConvNextV2GRN.forwardesb*]aVTRRR'?+?+?BPT+?+U+UX\+\]  }}'DE QTaa r-) r=r>r?r@intr3r$ FloatTensorr9rDrEs@r+rGrG]sr33#?#?@@ L-=!>!>?? &  #F F F%&T$BR&T&TUU U!1 3r-xrc*|jdkr=tjj||j|j|j|j}n|jdkr|j }| }| dd}||z d dd}||z tj ||jzz }||}|jddddf|z|jddddfz}|S)Nr\r^rT)rQrO)r )rar$r functional layer_normrcrLrMr`r rArUpowsqrtto)r4rd input_dtypeuss r+r9zConvNextV2LayerNorm.forwards   . .#..q$2GVZV_aeaijjAA  !1 1 1'K Aq$''AQ A##At#44AQ%*Q\222A;''A AAAtTM*Q.111dD=1IIAr-)rSr\) r=r>r?r@r3r$rBr9rDrEs@r+r[r[osm 444444  %,        r-r[cFeZdZdZfdZdejdejfdZxZ S)ConvNextV2EmbeddingszThis class is comparable to (and inspired by) the SwinEmbeddings class found in src/transformers/models/swin/modeling_swin.py. cttj|j|jd|j|j|_t|jddd|_ |j|_dS)Nr kernel_sizestriderSr^r`ra) r2r3rConv2d num_channels hidden_sizes patch_sizepatch_embeddingsr[ layernormr4configr5s r+r3zConvNextV2Embeddings.__init__s  "  !4Q!7VEV_e_p! ! ! -V-@-C[klll"/r- pixel_valuesrc|jd}||jkrtd||}||}|S)NrzeMake sure that the channel dimension of the pixel values match with the one set in the configuration.)r"rv ValueErrorryrz)r4r}rv embeddingss r+r9zConvNextV2Embeddings.forwards^#)!, 4, , ,w **<88 ^^J// r- r=r>r?r@r3r$rYrBr9rDrEs@r+rorosi00000E$5%,r-rocHeZdZdZdfd ZdejdejfdZxZ S)ConvNextV2Layera5This corresponds to the `Block` class in the original implementation. There are two equivalent implementations: [DwConv, LayerNorm (channels_first), Conv, GELU,1x1 Conv]; all in (N, C, H, W) (2) [DwConv, Permute to (N, H, W, C), LayerNorm (channels_last), Linear, GELU, Linear]; Permute back The authors used (2) as they find it slightly faster in PyTorch. Args: config ([`ConvNextV2Config`]): Model configuration class. dim (`int`): Number of input channels. drop_path (`float`): Stochastic depth rate. Default: 0.0. rcttj||dd||_t |d|_tj|d|z|_t|j |_ td|z|_ tjd|z||_|dkrt|ntj|_dS)Nrr )rrpaddinggroupsrSr`r)r2r3rrudwconvr[rzLinearpwconv1r hidden_actactrGgrnpwconv2r/Identityr,)r4r|rHr,r5s r+r3zConvNextV2Layer.__init__s iSa3OOO ,Sd;;;ya#g.. &+, S))yS#.. :Cc//+I666r{}}r-r6rc|}||}|dddd}||}||}||}||}||}|dddd}|||z}|S)NrrOr r)rpermuterzrrrrr,)r4r6rrds r+r9zConvNextV2Layer.forwards KK & & IIaAq ! ! NN1   LLOO HHQKK HHQKK LLOO IIaAq ! ! DNN1%% %r-)rrrEs@r+rrst   ] ] ] ] ] ]U%65<r-rcHeZdZdZdfd ZdejdejfdZxZ S) ConvNextV2StageaConvNeXTV2 stage, consisting of an optional downsampling layer + multiple residual blocks. Args: config ([`ConvNextV2Config`]): Model configuration class. in_channels (`int`): Number of input channels. out_channels (`int`): Number of output channels. depth (`int`): Number of residual blocks. drop_path_rates(`List[float]`): Stochastic depth rates for each layer. rONc t|ks|dkrAtjt |ddtj||||_ntj|_pdg|ztjfdt|D|_ dS)NrrSr^rtrqrc@g|]}t|S))rHr,)r).0jr|drop_path_rates out_channelss r+ z,ConvNextV2Stage.__init__..s/ l l lZ[of,/RSBTUUU l l lr-) r2r3r Sequentialr[rudownsampling_layerrrangelayers) r4r| in_channelsrrrrsdepthrr5s ` ` `r+r3zConvNextV2Stage.__init__s  , & &&1**&(m#KTGWXXX +|U[\\\''D # # ')kmmD #):cUU]m l l l l l l_dej_k_k l l l  r-r6rcZ||}||}|Sr1)rrr8s r+r9zConvNextV2Stage.forwards,// >>  M22 r-)rOrOrONrrEs@r+rrsn       U%65<r-rc neZdZfdZ d dejdeedeedee e ffdZ xZ S) ConvNextV2Encoderc  ttj|_dt jd|jt|j  |j D}|j d}t|j D]Y}|j |}t||||dkrdnd|j |||}|j||}ZdS)Nc6g|]}|S)tolist)rrds r+rz.ConvNextV2Encoder.__init__..s-   AHHJJ   r-rrOr)rrrsrr)r2r3r ModuleListstagesr$linspacedrop_path_ratesumdepthssplitrwr num_stagesrappend)r4r|rprev_chsiout_chsstager5s r+r3zConvNextV2Encoder.__init__s moo   %q&2GV]I[I[ \ \ b bcicp q q   &q)v())  A)!,G#$$EEqqqmA& / 2 E K  u % % %HH  r-FTr6output_hidden_states return_dictrc|rdnd}t|jD]\}}|r||fz}||}|r||fz}|std||fDSt||S)Nrc3K|]}||V dSr1r)rvs r+ z,ConvNextV2Encoder.forward..s"XXq!-----XXr-)last_hidden_stater6) enumeratertupler )r4r6rrall_hidden_statesr layer_modules r+r9zConvNextV2Encoder.forwards #7@BBD(55 8 8OA|# I$58H$H!(L77MM  E 1]4D D  YXX]4E$FXXXXX X-++    r-)FT) r=r>r?r3r$rYrboolrrr r9rDrEs@r+rrs.05&*   ( 'tn d^  u44 5         r-rc*eZdZdZeZdZdZdgZdZ dS)ConvNextV2PreTrainedModelz An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. convnextv2r}rct|tjtjfrT|jjd|jj|j |j j dSdSt|tj r?|j j |jj ddSdS)zInitialize the weightsr)rUstdNg?) isinstancerrrurLdatanormal_r|initializer_rangerMzero_ LayerNormfill_)r4modules r+ _init_weightsz'ConvNextV2PreTrainedModel._init_weights.s fry")4 5 5 * M  & &CT[5R & S S S{&  &&((((('&  - - * K  " " $ $ $ M  $ $S ) ) ) ) ) * *r-N) r=r>r?r@r config_classbase_model_prefixmain_input_name_no_split_modulesrrr-r+rr#sH $L$$O*+ * * * * *r-raL This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior. Parameters: config ([`ConvNextV2Config`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. aI Args: pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): Pixel values. Pixel values can be obtained using [`ConvNextImageProcessor`]. See [`ConvNextImageProcessor.__call__`] for details. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. zSThe bare ConvNextV2 model outputting raw features without any specific head on top.c eZdZfdZeeeeee de  d de j de ede edeeeffd ZxZS) ConvNextV2Modelc&t|||_t||_t ||_tj|j d|j |_ | dS)NrRr) r2r3r|rorrencoderrrrwlayer_norm_epsrz post_initr{s r+r3zConvNextV2Model.__init__Ys{     .v66(00 f&9"&=6CXYYY r-vision) checkpoint output_typermodalityexpected_outputNr}rrrcr||n |jj}||n |jj}|td||}||||}|d}||ddg}|s||f|ddzSt|||j S)Nz You have to specify pixel_valuesrrrrRr)r pooler_outputr6) r|ruse_return_dictrrrrzrUrr6)r4r}rrembedding_outputencoder_outputsr pooled_outputs r+r9zConvNextV2Model.forwardfs%9$D $+Jj &1%r?r3rCONVNEXTV2_INPUTS_DOCSTRINGr_CHECKPOINT_FOR_DOCr_CONFIG_FOR_DOC_EXPECTED_OUTPUT_SHAPEr$rYrrrrr9rDrEs@r+rrSs     +*+FGG&<$. +//3&* " " '" 'tn" d^ " u>> ? " " " HG" " " " " r-rz ConvNextV2 Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for ImageNet. ceZdZfdZeeeeee e  d de j de e jde ede edeeeff d ZxZS) ConvNextV2ForImageClassificationcBt||j|_t||_|jdkr%t j|jd|jnt j|_ | dS)NrrR) r2r3 num_labelsrrrrrwr classifierrr{s r+r3z)ConvNextV2ForImageClassification.__init__s     +)&11FLEVYZEZEZBIf)"-v/@ A A A`b`k`m`m  r-)rrrrNr}labelsrrrc||n |jj}||||}|r|jn|d}||}d}|Z|jjf|jdkr d|j_nN|jdkr7|jtj ks|jtj kr d|j_n d|j_|jjdkrWt} |jdkr1| | | }n| ||}n|jjdkrGt} | |d|j|d}n*|jjdkrt} | ||}|s|f|ddz} ||f| zn| St!|||j S) a labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for computing the image classification/regression loss. Indices should be in `[0, ..., config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If `config.num_labels > 1` a classification loss is computed (Cross-Entropy). Nrr regressionsingle_label_classificationmulti_label_classificationrRrO)losslogitsr6)r|rrrr problem_typerr r$longrXr squeezerviewrrr6) r4r}rrroutputsrrrloss_fctr*s r+r9z(ConvNextV2ForImageClassification.forwards(&1%r?r3rrr_IMAGE_CLASS_CHECKPOINTrr_IMAGE_CLASS_EXPECTED_OUTPUTr$rYr LongTensorrrrr9rDrEs@r+rrs     +*+FGG*8$4 +/-1/3&* 3 3 '3 )*3 'tn 3 d^ 3 u:: ; 3 3 3 HG3 3 3 3 3 r-rzT ConvNeXT V2 backbone, to be used with frameworks like DETR and MaskFormer. c eZdZfdZeeeee d de j de e de e defdZ xZS) ConvNextV2Backbonect|t|t||_t ||_|jdg|jz|_i}t|j |j D]\}}t|d||<tj||_|dS)Nrr^)ra)r2r3_init_backbonerorrrrw num_featureszip _out_featureschannelsr[r ModuleDicthidden_states_normsr)r4r|r rrvr5s r+r3zConvNextV2Backbone.__init__s     v&&&.v66(00 #034v7JJ!#&t'94=#I#I i i E<)<\Wg)h)h)h  & &#%=1D#E#E  r-)rrNr}rrrc||n |jj}||n |jj}||}||d|}|r|jn|d}d}t |j|D]*\}} ||jvr|j || } || fz }+|s |f} |r| |fz } | St||r|nddS)a Returns: Examples: ```python >>> from transformers import AutoImageProcessor, AutoBackbone >>> import torch >>> from PIL import Image >>> import requests >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" >>> image = Image.open(requests.get(url, stream=True).raw) >>> processor = AutoImageProcessor.from_pretrained("facebook/convnextv2-tiny-1k-224") >>> model = AutoBackbone.from_pretrained("facebook/convnextv2-tiny-1k-224") >>> inputs = processor(image, return_tensors="pt") >>> outputs = model(**inputs) ```NTrrr) feature_mapsr6 attentions) r|rrrrr6r stage_names out_featuresr r ) r4r}rrrrr6r r hidden_stater*s r+r9zConvNextV2Backbone.forwards&8&1%t7>|LL /  "_F# +=**M%+?I--T    r-)NN)r=r>r?r3rrrr rr$rBrrr9rDrEs@r+rrs"+*+FGG>XXX04&* 9 9 l9 'tn9 d^ 9  9 9 9 YXHG9 9 9 9 9 r-r)rF)9r@typingrrrr$torch.utils.checkpointrtorch.nnrrr activationsr modeling_outputsr r rrmodeling_utilsrutilsrrrrrutils.backbone_utilsrconfiguration_convnextv2r get_loggerr=loggerrrrrrrBrArr,Moduler/rGr[rorrrrCONVNEXTV2_START_DOCSTRINGrrrrrr-r+rs4 )))))))))) AAAAAAAAAA!!!!!! .-----211111666666  H % %%8'<1U\eTV[Vb* - - - - - - - -BI$")>290(((((bi(((XbiB, , , , ,  , , , `********0  Y 8 8 8 8 8 /8 8  8 v I I I I I '@I I I X M M M M M 2MM M M M M r-