rh\FdZddlmZmZddlZddlmZddlm Z m Z m Z ddl m Z mZmZmZmZddlmZdd lmZmZmZmZd d lmZej6eZd Zd ZgdZ d Z!dZ"GddejFjHZ%GddejFjHZ&GddejFjHZ'GddejFjHZ(GddejFjHZ)GddejFjHZ*GddejFjHZ+Gdde Z,d Z-d!Z.eGd"d#ejFjHZ/ed$e-Gd%d&e,Z0ed'e-Gd(d)e,eZ1gd*Z2y)+zTensorFlow ResNet model.)OptionalUnionN)ACT2FN) TFBaseModelOutputWithNoAttention*TFBaseModelOutputWithPoolingAndNoAttention&TFImageClassifierOutputWithNoAttention)TFPreTrainedModelTFSequenceClassificationLosskeraskeras_serializable unpack_inputs) shape_list)add_code_sample_docstringsadd_start_docstrings%add_start_docstrings_to_model_forwardlogging) ResNetConfigrzmicrosoft/resnet-50)rirz tiger catc eZdZ ddedededededdf fd Zd ejdejfd Zdd ejd e dejfd Z dd Z xZ S)TFResNetConvLayer in_channels out_channels kernel_sizestride activationreturnNc Tt|d i||dz|_tjj |||ddd|_tjjddd |_| t|ntjjd |_ ||_ ||_ y) NvalidF convolution)rstridespaddinguse_biasnameh㈵>? normalizationepsilonmomentumr&linear)super__init__ pad_valuer layersConv2DconvBatchNormalizationr)r Activationrrr)selfrrrrrkwargs __class__s /var/www/html/ai-insurance-compliance-backend/venv/lib/python3.12/site-packages/transformers/models/resnet/modeling_tf_resnet.pyr0zTFResNetConvLayer.__init__6s "6"$)LL'' k67]biv(  #\\< name_scoper4r&buildrr)rr7 input_shapes r:rKzTFResNetConvLayer.buildWs ::  4 & 2tyy~~. F tT43C3C DE F 4$ / ;t11667 P""(($dDTensorr"boolrFrK __classcell__r9s@r:rr5s  ))) )  )  ) )( biidryy Pr;rcreZdZdZdeddffd Zd dejdedejfdZ d d Z xZ S) TFResNetEmbeddingszO ResNet Embeddings (stem) composed of a single aggressive convolution. configrNc t|d i|t|j|jdd|j d|_tjjdddd|_ |j|_y) Nrr embedder)rrrr&rr!pooler) pool_sizer#r$r&r.) r/r0r num_channelsembedding_size hidden_actr_r r2 MaxPool2Dr`r7r]r8r9s r:r0zTFResNetEmbeddings.__init__hss "6")     ! !((   ll,,q!W[c,d "//r; pixel_valuesrBct|\}}}}tjr||jk7r t d|}|j |}tj |ddgddgddgddgg}|j|}|S)NzeMake sure that the channel dimension of the pixel values match with the one set in the configuration.rr)rr>executing_eagerlyrb ValueErrorr_r?r`)r7rgrB_rbr<s r:rFzTFResNetEmbeddings.callus *< 81a   !ld6G6G&Gw $ }}\2 vvlaVaVaVaV,LM {{<0 r;c|jryd|_t|ddMtj|jj 5|jj ddddt|ddNtj|jj 5|jj ddddyy#1swYexYw#1swYyxYw)NTr_r`)rHrIr>rJr_r&rKr`rLs r:rKzTFResNetEmbeddings.builds ::  4T * 6t}}112 * ##D) * 44 ( 4t{{//0 ( !!$' ( ( 5 * * ( ( C %C CC rPrQ) rRrSrT__doc__rr0r>rWrXrFrKrYrZs@r:r\r\csB 0| 0$ 0  d ryy  (r;r\c |eZdZdZd dedededdffd Zd dejd edejfd Z dd Z xZ S)TFResNetShortCutz ResNet shortcut, used to project the residual features to the correct size. If needed, it is also used to downsample the input using `stride=2`. rrrrNc t|d i|tjj |d|dd|_tjj ddd|_||_||_ y) NrFr")rr#r%r&r'r(r)r*r.) r/r0r r2r3r"r5r)rr)r7rrrr8r9s r:r0zTFResNetShortCut.__init__sl "6" <<.. a%m/ #\\<rJr"r&rKrr)rrLs r:rKzTFResNetShortCut.builds ::  4 - 9t//445 M  &&dD$:J:J'KL M 4$ / ;t11667 P""(($dDrWrXrFrKrYrZs@r:rprpsR )C)s)C)Z^)bii4BII Pr;rpc eZdZdZ d dededededdf fd Zdd ejd e dejfd Z dd Z xZ S)TFResNetBasicLayerzO A classic ResNet's residual layer composed by two `3x3` convolutions. rrrrrNc t|d i|||k7xs|dk7}t|||d|_t||dd|_|rt |||dn t jjdd|_ t||_ y) Nrlayer.0rr&layer.1rr&shortcutr-r&r.) r/r0rconv1conv2rpr r2r6r|rr)r7rrrrr8should_apply_shortcutr9s r:r0zTFResNetBasicLayer.__init__s "6" +| ; Jv{&{LV_` &|\dYbc % [,vJ W(( (C !,r;r<rBc|}|j||}|j||}|j||}||z }|j|}|SrD)r~rr|rr7r<rBresiduals r:rFzTFResNetBasicLayer.calls[zz,zB zz,zB ==H== |4 r;c|jryd|_t|ddMtj|jj 5|jj ddddt|ddMtj|jj 5|jj ddddt|ddNtj|jj 5|jj ddddyy#1swYxYw#1swYqxYw#1swYyxYw)NTr~rr|) rHrIr>rJr~r&rKrr|rLs r:rKzTFResNetBasicLayer.builds ::  4$ ' 3tzz/ '   & ' 4$ ' 3tzz/ '   & ' 4T * 6t}}112 * ##D) * * 7  ' ' ' ' * *s$ D%%D1?D=%D.1D:=E)rrOrPrQ rRrSrTrnrUrVr0r>rWrXrFrKrYrZs@r:rvrvsc W] - -.1 -;> -PS -  -dryy *r;rvc eZdZdZ ddedededededdf fd Zdd ejd e dejfd Z dd Z xZ S)TFResNetBottleNeckLayera% A classic ResNet's bottleneck layer composed by three `3x3` convolutions. 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The last `1x1` convolution remaps the reduced features to `out_channels`. rrrr reductionrNc Jt |d i|||k7xs|dk7}||z}t||dd|_t|||d|_t||ddd|_|rt |||dn tjjd d |_ t||_ y) Nrrx)rr&rzryzlayer.2)rrr&r|r-r}r.) r/r0rconv0r~rrpr r2r6r|rr) r7rrrrrr8rreduces_channelsr9s r:r0z TFResNetBottleNeckLayer.__init__s "6" +| ; Jv{'94&{4DRSZcd &'79IRX_hi &'7STaeluv % [,vJ W(( (C !,r;r<rBc|}|j||}|j||}|j||}|j||}||z }|j |}|SrD)rr~rr|rrs r:rFzTFResNetBottleNeckLayer.callsmzz,zB zz,zB zz,zB ==H== |4 r;c`|jryd|_t|ddMtj|jj 5|jj ddddt|ddMtj|jj 5|jj ddddt|ddMtj|jj 5|jj ddddt|ddNtj|jj 5|jj ddddyy#1swYxYw#1swYxYw#1swY~xYw#1swYyxYw)NTrr~rr|) rHrIr>rJrr&rKr~rr|rLs r:rKzTFResNetBottleNeckLayer.buildsG ::  4$ ' 3tzz/ '   & ' 4$ ' 3tzz/ '   & ' 4$ ' 3tzz/ '   & ' 4T * 6t}}112 * ##D) * * 7 ' ' ' ' ' ' * *s0 E?%F ?FF$?F  FF!$F-)rrOrPrQrrZs@r:rrsy  --- -  -  - -,dryy*r;rc eZdZdZ ddedededededdf fd Zdd ejd e dejfd Z dd Z xZ S) TFResNetStagez4 A ResNet stage composed of stacked layers. r]rrrdepthrNc t |di||jdk(rtnt}|||||j dg}|t |dz D cgc]} ||||j d| dz c} z }||_ycc} w)N bottleneckzlayers.0)rrr&rzlayers.r{r.)r/r0 layer_typerrvrdrange stage_layers) r7r]rrrrr8layerr2ir9s r:r0zTFResNetStage.__init__s "6"+1+<+< +L'Rd \&VM^M^eopq519%  , 9J9JSZ[\_`[`ZaQb c  #  s#Br<rBc<|jD] }|||}|SrD)r)r7r<rBrs r:rFzTFResNetStage.call's+&& BE AL Br;c|jryd|_t|ddK|jD];}tj|j 5|j dddd=yy#1swYIxYw)NTr)rHrIrr>rJr&rKr7rMrs r:rKzTFResNetStage.build,sr ::  4 . :** &]]5::.&KK%&& & ;&& A..A7 )r r rPrQ) rRrSrTrnrrUr0r>rWrXrFrKrYrZs@r:rrsk hi #" #14 #DG #QT #ad #  #dryy &r;rc heZdZdeddffd Z d dej dedededef d Z d d Z xZ S) TFResNetEncoderr]rNc t|di|t||j|jd|j rdnd|j ddg|_tt|j|jdd|j ddD]8\}\}}}|jjt||||d|dz:y) Nrr rzstages.0)rrr&zstages.)rr&r.) r/r0rrc hidden_sizesdownsample_in_first_stagedepthsstages enumeratezipappend)r7r]r8rrrrr9s r:r0zTFResNetEncoder.__init__7s "6" %%##A&"<O P6  v 1A1 \5 KK  }V[,V[dklmpqlqkrbst u vr;r<output_hidden_states return_dictrBc|rdnd}|jD]}|r||fz}|||}|r||fz}|std||fDSt||S)Nr.rEc3&K|] }|| ywrQr.).0vs r: z'TFResNetEncoder.call..\sSqQ]Ss)last_hidden_state hidden_states)rtupler)r7r<rrrBr stage_modules r:rFzTFResNetEncoder.callIsu3  KK IL# - ? ' xHL  I )\O;MS\=$ASS S/,^kllr;c|jryd|_t|ddK|jD];}tj|j 5|j dddd=yy#1swYIxYw)NTr)rHrIrr>rJr&rKrs r:rKzTFResNetEncoder.build`sp ::  44 ( 4 &]]5::.&KK%&& & 5&&r)FTFrQ) rRrSrTrr0r>rWrXrrFrKrYrZs@r:rr6shv|v$v*&+  miim#m m  m * m.&r;rc,eZdZdZeZdZdZedZ y)TFResNetPreTrainedModelz An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. resnetrgcdtjd|jjddftjiS)Nrg)shapedtype)r> TensorSpecr]rbfloat32)r7s r:input_signaturez'TFResNetPreTrainedModel.input_signaturets4 T4;;;S;SUXZ]4^fhfpfp qrrr;N) rRrSrTrnr config_classbase_model_prefixmain_input_namepropertyrr.r;r:rrjs-  L $O ssr;rad This model is a TensorFlow [keras.layers.Layer](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Layer) sub-class. Use it as a regular TensorFlow Module and refer to the TensorFlow documentation for all matter related to general usage and behavior. Parameters: config ([`ResNetConfig`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~TFPreTrainedModel.from_pretrained`] method to load the model weights. a> Args: pixel_values (`tf.Tensor` of shape `(batch_size, num_channels, height, width)`): Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See [`ConvNextImageProcessor.__call__`] for details. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. ceZdZeZdeddffd Ze d dejde e de e de de e eje ff d Zd d ZxZS) TFResNetMainLayerr]rNc t|di|||_t|d|_t |d|_tjjd|_ y)Nr_r}encoderT)keepdimsr.) r/r0r]r\r_rrr r2GlobalAveragePooling2Dr`rfs r:r0zTFResNetMainLayer.__init__sO "6" *6 C &vI> ll9949H r;rgrrrBc||n|jj}||n|jj}tj|gd}|j ||}|j ||||}|d}|j|}tj|d}tj|d}d} |ddD]} | td | Dz} |s||f| zS|r| nd} t||| S) N)rr rr)permrErrrBrrrrr r.rc3HK|]}tj|dyw)rN)r> transpose)rhs r:rz)TFResNetMainLayer.call..s1fTU",,q,2O1fs ")r pooler_outputr) r]ruse_return_dictr>rr_rr`rr) r7rgrrrBembedding_outputencoder_outputsr pooled_outputrr<s r:rFzTFResNetMainLayer.calls!%9$D $++JjJj &1%rJr_r&rKrrLs r:rKzTFResNetMainLayer.builds ::  4T * 6t}}112 * ##D) * 4D ) 5t||001 ) ""4( ) ) 6 * * ) )rmNNFrQ)rRrSrTrrr0rr>rWrrXrrrrFrKrYrZs@r:rrsLI|I$I04&* + ii+ 'tn+ d^ +  + uRYY!KK L + + Z )r;rzOThe bare ResNet model outputting raw features without any specific head on top.ceZdZdeddffd Zeeeee e de e d de jdeed eed edeee je ff d Zdd ZxZS) TFResNetModelr]rNc Jt||fi|t|d|_y)Nr)r]r&)r/r0rrrfs r:r0zTFResNetModel.__init__s# *6*'vHE r;vision) checkpoint output_typermodalityexpected_outputrgrrrBc||n|jj}||n|jj}|j||||}|S)N)rgrrrB)r]rrr)r7rgrrrBresnet_outputss r:rFzTFResNetModel.calls]"%9$D $++JjJj &1%rJrr&rKrLs r:rKzTFResNetModel.buildsi ::  44 ( 4t{{//0 ( !!$' ( ( 5 ( (s A11A:rrQ)rRrSrTrr0rRESNET_INPUTS_DOCSTRINGr_CHECKPOINT_FOR_DOCr_CONFIG_FOR_DOC_EXPECTED_OUTPUT_SHAPErr>rWrrXrrrFrKrYrZs@r:rrs F|F$F++BC&>$. 04&* ii'tnd^    uRYY!KK L D((r;rz ResNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for ImageNet. c6eZdZdeddffd Zdej dej fdZee e e e e ee ddeej d eej d eed eed edeeej e ff d ZddZxZS)TFResNetForImageClassificationr]rNc :t||fi||j|_t|d|_|jdkDr+t j j|jdn t j jdd|_ ||_ y)Nrr}rz classifier.1r-) r/r0 num_labelsrrr r2Denser6classifier_layerr]rfs r:r0z'TFResNetForImageClassification.__init__s *6* ++'X>   1$ LL  v00~  F(((G   r;rrcntjj|}|j|}|SrQ)r r2Flattenr)r7rrlogitss r: classifierz)TFResNetForImageClassification.classifiers. 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