g?U dZddlmZmZmZddlZddlZddlmZddlm Z m Z m Z ddl m Z ddlmZmZmZmZdd lmZdd lmZmZmZmZmZdd lmZd d lmZeje Z!dZ"dZ#gdZ$dZ%dZ&d2dej'de(de)dej'fdZ*Gddej+Z,Gddej+Z-Gddej+Z.Gdd ej+Z/Gd!d"ej+Z0Gd#d$ej+Z1Gd%d&eZ2d'Z3d(Z4ed)e3Gd*d+e2Z5ed,e3Gd-d.e2Z6ed/e3Gd0d1e2eZ7dS)3zPyTorch ConvNext model.)OptionalTupleUnionN)nn)BCEWithLogitsLossCrossEntropyLossMSELoss)ACT2FN)BackboneOutputBaseModelOutputWithNoAttention(BaseModelOutputWithPoolingAndNoAttention$ImageClassifierOutputWithNoAttention)PreTrainedModel)add_code_sample_docstringsadd_start_docstrings%add_start_docstrings_to_model_forwardloggingreplace_return_docstrings) BackboneMixin)ConvNextConfigrzfacebook/convnext-tiny-224)rirztabby, tabby catFinput drop_probtrainingreturnc|dks|s|Sd|z }|jdfd|jdz zz}|tj||j|jz}||||z}|S)aF Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks). Comment by Ross Wightman: This is the same as the DropConnect impl I created for EfficientNet, etc networks, however, the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper... See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ... I've opted for changing the layer and argument names to 'drop path' rather than mix DropConnect as a layer name and use 'survival rate' as the argument. rrr)r)dtypedevice)shapendimtorchrandr r!floor_div)rrr keep_probr" random_tensoroutputs j/var/www/html/ai-engine/env/lib/python3.11/site-packages/transformers/models/convnext/modeling_convnext.py drop_pathr,:sCx I I [^  Q 7 7E 5 EL Y Y YYM YYy ! !M 1F McjeZdZdZd deeddffd ZdejdejfdZ de fdZ xZ S) ConvNextDropPathzXDrop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).NrrcVt||_dSN)super__init__r)selfr __class__s r+r3zConvNextDropPath.__init__Rs$ "r- hidden_statesc8t||j|jSr1)r,rrr4r6s r+forwardzConvNextDropPath.forwardVs FFFr-c6d|jS)Nzp={})formatr)r4s r+ extra_reprzConvNextDropPath.extra_reprYs}}T^,,,r-r1) __name__ __module__ __qualname____doc__rfloatr3r$Tensorr9strr< __classcell__r5s@r+r/r/Osbb##(5/#T######GU\GelGGGG-C--------r-r/cHeZdZdZdfd ZdejdejfdZxZS) ConvNextLayerNormaALayerNorm that supports two data formats: channels_last (default) or channels_first. The ordering of the dimensions in the inputs. channels_last corresponds to inputs with shape (batch_size, height, width, channels) while channels_first corresponds to inputs with shape (batch_size, channels, height, width). ư> channels_lastc`ttjt j||_tjt j||_||_ ||_ |j dvrtd|j |f|_ dS)N)rIchannels_firstzUnsupported data format: ) r2r3r Parameterr$onesweightzerosbiaseps data_formatNotImplementedErrornormalized_shape)r4rTrQrRr5s r+r3zConvNextLayerNorm.__init__cs l5:.>#?#?@@ L-=!>!>?? &  #F F F%&T$BR&T&TUU U!1 3r-xrc*|jdkr=tjj||j|j|j|j}n|jdkr|j }| }| dd}||z d dd}||z tj ||jzz }||}|jddddf|z|jddddfz}|S)NrIrKrT)keepdim)r )rRr$r functional layer_normrTrNrPrQr rAmeanpowsqrtto)r4rU input_dtypeuss r+r9zConvNextLayerNorm.forwardms   . .#..q$2GVZV_aeaijjAA  !1 1 1'K Aq$''AQ A##At#44AQ%*Q\222A;''A AAAtTM*Q.111dD=1IIAr-)rHrI) r=r>r?r@r3r$rBr9rDrEs@r+rGrG]sm 444444  %,        r-rGcFeZdZdZfdZdejdejfdZxZ S)ConvNextEmbeddingszThis class is comparable to (and inspired by) the SwinEmbeddings class found in src/transformers/models/swin/modeling_swin.py. cttj|j|jd|j|j|_t|jddd|_ |j|_dS)Nr kernel_sizestriderHrKrQrR) r2r3rConv2d num_channels hidden_sizes patch_sizepatch_embeddingsrG layernormr4configr5s r+r3zConvNextEmbeddings.__init__s  "  !4Q!7VEV_e_p! ! ! +6+>q+AtYijjj"/r- pixel_valuesrc|jd}||jkrtd||}||}|S)NrzeMake sure that the channel dimension of the pixel values match with the one set in the configuration.)r"rj ValueErrorrmrn)r4rqrj embeddingss r+r9zConvNextEmbeddings.forwards^#)!, 4, , ,w **<88 ^^J// r- r=r>r?r@r3r$ FloatTensorrBr9rDrEs@r+rcrc{si00000E$5%,r-rccHeZdZdZdfd ZdejdejfdZxZ S) ConvNextLayera3This corresponds to the `Block` class in the original implementation. There are two equivalent implementations: [DwConv, LayerNorm (channels_first), Conv, GELU,1x1 Conv]; all in (N, C, H, W) (2) [DwConv, Permute to (N, H, W, C), LayerNorm (channels_last), Linear, GELU, Linear]; Permute back The authors used (2) as they find it slightly faster in PyTorch. Args: config ([`ConvNextConfig`]): Model configuration class. dim (`int`): Number of input channels. drop_path (`float`): Stochastic depth rate. Default: 0.0. rc0ttj||dd||_t |d|_tj|d|z|_t|j |_ tjd|z||_ |j dkr0tj|j tj|zd nd|_|d krt%|ntj|_dS) Nrr )rfpaddinggroupsrHrQrT) requires_gradr)r2r3rridwconvrGrnLinearpwconv1r hidden_actactpwconv2layer_scale_init_valuerLr$rMlayer_scale_parameterr/Identityr,)r4rpdimr,r5s r+r3zConvNextLayer.__init__s iSa3OOO *3D999ya#g.. &+,yS#.. ,q00 L6S9J9JJZ^ _ _ _ _ " 9BC))444R[]]r-r6rc|}||}|dddd}||}||}||}||}|j |j|z}|dddd}|||z}|S)NrrXr r)rpermuternrrrrr,)r4r6rrUs r+r9zConvNextLayer.forwards KK & & IIaAq ! ! NN1   LLOO HHQKK LLOO  % 1*Q.A IIaAq ! ! DNN1%% %r-)rrurEs@r+rxrxst   [ [ [ [ [ [ U%6 5<        r-rxcHeZdZdZdfd ZdejdejfdZxZ S) ConvNextStageaConvNeXT stage, consisting of an optional downsampling layer + multiple residual blocks. Args: config ([`ConvNextConfig`]): 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. rXNc t|ks|dkrAtjt |ddtj||||_ntj|_pdg|ztjfdt|D|_ dS)NrrHrKrhrerc@g|]}t|S))rr,)rx).0jrpdrop_path_rates out_channelss r+ z*ConvNextStage.__init__..s/ j j jXYmF PQ@RSSS j j jr-) r2r3r SequentialrGridownsampling_layerrrangelayers) r4rp in_channelsrrfrgdepthrr5s ` ` `r+r3zConvNextStage.__init__s  , & &&1**&(m!+4EUVVV +|U[\\\''D # # ')kmmD #):cUU]m j j j j j j]bch]i]i j j j  r-r6rcZ||}||}|Sr1)rrr8s r+r9zConvNextStage.forwards,// >>  M22 r-)rXrXrXNrurEs@r+rrsn       U%65<r-rc neZdZfdZ d dejdeedeedee e ffdZ xZ S) ConvNextEncoderc  ttj|_dt jd|jt|j  |j D}|j d}t|j D]Y}|j |}t||||dkrdnd|j |||}|j||}ZdS)Nc6g|]}|S)tolist)rrUs r+rz,ConvNextEncoder.__init__..s-   AHHJJ   r-rrXr)rrrgrr)r2r3r ModuleListstagesr$linspacedrop_path_ratesumdepthssplitrkr num_stagesrappend)r4rprprev_chsiout_chsstager5s r+r3zConvNextEncoder.__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*ConvNextEncoder.forward..s"XXq!-----XXr-)last_hidden_stater6) enumeratertupler )r4r6rrall_hidden_statesr layer_modules r+r9zConvNextEncoder.forwards #7@BBD(55 8 8OA|# I$58H$H!(L77MM  E 1]4D D  YXX]4E$FXXXXX X-++    r-)FT) r=r>r?r3r$rvrboolrrr r9rDrEs@r+rrs.05&*   ( 'tn d^  u44 5         r-rc*eZdZdZeZdZdZdgZdZ dS)ConvNextPreTrainedModelz An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. convnextrqrxct|tjtjfrT|jjd|jj|j |j j dSdSt|tj r?|j j |jj ddSdS)zInitialize the weightsr)r[stdNg?) isinstancerrrirNdatanormal_rpinitializer_rangerPzero_ LayerNormfill_)r4modules r+ _init_weightsz%ConvNextPreTrainedModel._init_weightss fry")4 5 5 * M  & &CT[5R & S S S{&  &&((((('&  - - * K  " " $ $ $ M  $ $S ) ) ) ) ) * *r-N) r=r>r?r@r config_classbase_model_prefixmain_input_name_no_split_modulesrrr-r+rrsH "L"$O() * * * * *r-raJ 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 ([`ConvNextConfig`]): 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. aF Args: pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. 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. zQThe bare ConvNext model outputting raw features without any specific head on top.c eZdZfdZeeeeee de  d de j de ede edeeeffd ZxZS) ConvNextModelc&t|||_t||_t ||_tj|j d|j |_ | dS)Nr|) r2r3rprcrtrencoderrrrklayer_norm_epsrn post_initros r+r3zConvNextModel.__init__Ds{     ,V44&v.. f&9"&=6CXYYY r-vision) checkpoint output_typermodalityexpected_outputNrqrrrcr||n |jj}||n |jj}|td||}||||}|d}||ddg}|s||f|ddzSt|||j S)Nz You have to specify pixel_valuesrrrrr)r pooler_outputr6) rpruse_return_dictrsrtrrnr[rr6)r4rqrrembedding_outputencoder_outputsr pooled_outputs r+r9zConvNextModel.forwardQs%9$D $+Jj &1%r?r3rCONVNEXT_INPUTS_DOCSTRINGr_CHECKPOINT_FOR_DOCr_CONFIG_FOR_DOC_EXPECTED_OUTPUT_SHAPEr$rvrrrrr9rDrEs@r+rr?s     +*+DEE&<$. +//3&* " " '" 'tn" d^ " u>> ? " " " FE" " " " " r-rz ConvNext Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for ImageNet. ceZdZfdZeeeeee e  d de j de e jde ede edeeeff d ZxZS) ConvNextForImageClassificationcBt||j|_t||_|jdkr%t j|jd|jnt j|_ | dS)Nrr) r2r3 num_labelsrrrrrkr classifierrros r+r3z'ConvNextForImageClassification.__init__s     +%f-- FLEVYZEZEZBIf)"-v/@ A A A`b`k`m`m  r-)rrrrNrqlabelsrrrc||n |jj}||||}|r|jn|d}||}d}|Z|jjf|jdkr d|j_nN|jdkr7|jtj ks|jtj kr d|j_n d|j_|jjdkrWt} |jdkr1| | | }n| ||}n|jjdkrGt} | |d|j|d}n*|jjdkrt} | ||}|s|f|ddz} ||f| zn| St!|||j S) a labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for computing the image classification/regression loss. Indices should be in `[0, ..., config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If `config.num_labels > 1` a classification loss is computed (Cross-Entropy). Nrr regressionsingle_label_classificationmulti_label_classificationrrX)losslogitsr6)rprrrr problem_typerr r$longintr squeezerviewrrr6) r4rqrrroutputsrrrloss_fctr*s r+r9z&ConvNextForImageClassification.forwards(&1%r?r3rrr_IMAGE_CLASS_CHECKPOINTrr_IMAGE_CLASS_EXPECTED_OUTPUTr$rvr LongTensorrrrr9rDrEs@r+rr~s     +*+DEE*8$4 +/-1/3&* 3 3 '3 )*3 'tn 3 d^ 3 u:: ; 3 3 3 FE3 3 3 3 3 r-rzQ ConvNeXt backbone, to be used with frameworks like DETR and MaskFormer. c eZdZfdZeeeee d de j de e de e defdZ xZS) ConvNextBackbonect|t|t||_t ||_|jdg|jz|_i}t|j |j D]\}}t|d||<tj||_|dS)NrrK)rR)r2r3_init_backbonercrtrrrk num_featureszip _out_featureschannelsrGr ModuleDicthidden_states_normsr)r4rprrrjr5s r+r3zConvNextBackbone.__init__s     v&&&,V44&v.. #034v7JJ!#&t'94=#I#I g g E<):>> from transformers import AutoImageProcessor, AutoBackbone >>> import torch >>> from PIL import Image >>> import requests >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" >>> image = Image.open(requests.get(url, stream=True).raw) >>> processor = AutoImageProcessor.from_pretrained("facebook/convnext-tiny-224") >>> model = AutoBackbone.from_pretrained("facebook/convnext-tiny-224") >>> inputs = processor(image, return_tensors="pt") >>> outputs = model(**inputs) ```NTrrr) feature_mapsr6 attentions) rprrrtrr6r stage_names out_featuresrr ) r4rqrrrrr6rr hidden_stater*s r+r9zConvNextBackbone.forwards&8&1%t7>|LL /  "_F# +=**M%+?I--T    r-)NN)r=r>r?r3rrrr rr$rBrrr9rDrEs@r+rrs"+*+DEE>XXX04&* 9 9 l9 'tn9 d^ 9  9 9 9 YXFE9 9 9 9 9 r-r)rF)8r@typingrrrr$torch.utils.checkpointrtorch.nnrrr activationsr modeling_outputsr r rrmodeling_utilsrutilsrrrrrutils.backbone_utilsrconfiguration_convnextr get_loggerr=loggerrrrrrrBrArr,Moduler/rGrcrxrrrCONVNEXT_START_DOCSTRINGrrrrrr-r+rs)))))))))) AAAAAAAAAA!!!!!! .-----211111222222  H % %#3'71U\eTV[Vb* - - - - -ry - - - <0)))))BI)))XBI@, , , , , bi, , , ^*****o***0  W8 8 8 8 8 +8 8 8 v I I I I I %<I I I X  M M M M M . 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