T^(h\JdZddlmZmZmZddlZddlmZddl m Z m Z m Z ddl mZmZmZmZmZddlmZdd lmZmZmZmZd d lmZej8eZd Zd Z gdZ!d Z"dZ#GddejHjJZ&GddejHjJZ'GddejHjJZ(GddejHjJZ)GddejHjJZ*GddejHjJZ+GddejHjJZ,GddeZ-d Z.d!Z/eGd"d#ejHjJZ0ed$e.Gd%d&e-Z1ed'e.Gd(d)e-eZ2gd*Z3y)+zTensorFlow ResNet model.)OptionalTupleUnionN)ACT2FN) TFBaseModelOutputWithNoAttention*TFBaseModelOutputWithPoolingAndNoAttention&TFImageClassifierOutputWithNoAttention)TFPreTrainedModelTFSequenceClassificationLosskeraskeras_serializable unpack_inputs) shape_list)add_code_sample_docstringsadd_start_docstrings%add_start_docstrings_to_model_forwardlogging) ResNetConfigrzmicrosoft/resnet-50)rirz tiger catc eZdZ ddedededededdf fd Zd ejdejfd Zdd ejd e dejfd Z dd Z xZ S)TFResNetConvLayer in_channels out_channels kernel_sizestride activationreturnNc Tt|d i||dz|_tjj |||ddd|_tjjddd |_| t|ntjjd |_ ||_ ||_ y) NvalidF convolution)rstridespaddinguse_biasnameh㈵>? normalizationepsilonmomentumr'linear)super__init__ pad_valuer layersConv2DconvBatchNormalizationr*r Activationrrr)selfrrrrrkwargs __class__s k/var/www/skyplay_api_hub/venv/lib/python3.12/site-packages/transformers/models/resnet/modeling_tf_resnet.pyr1zTFResNetConvLayer.__init__6s "6"$)LL'' k67]biv(  #\\< -PS -  -dryy *r<rwc eZdZdZ ddedededededdf fd Zdd ejd e dejfd Z dd Z xZ S)TFResNetBottleNeckLayera% A classic ResNet's bottleneck layer composed by three `3x3` convolutions. The first `1x1` convolution reduces the input by a factor of `reduction` in order to make the second `3x3` convolution faster. 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) Nrry)rr'r{rzzlayer.2)rrr'r}r.r~r/) r0r1rconv0rrrqr r3r7r}rr) r8rrrrrr9rreduces_channelsr:s r;r1z TFResNetBottleNeckLayer.__init__s "6" +| ; Jv{'94&{4DRSZcd &'79IRX_hi &'7STaeluv % [,vJ W(( (C !,r<r=rCc|}|j||}|j||}|j||}|j||}||z }|j |}|SrE)rrrr}rrs r;rGzTFResNetBottleNeckLayer.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)NTrrrr}) rIrJr?rKrr'rLrrr}rMs r;rLzTFResNetBottleNeckLayer.buildsG ::  4$ ' 3tzz/ '   & ' 4$ ' 3tzz/ '   & ' 4$ ' 3tzz/ '   & ' 4T * 6t}}112 * ##D) * * 7 ' ' ' ' ' ' * *s0 E?%F ?FF$?F  FF!$F-)rrPrQrRrr[s@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/)r0r1 layer_typerrwrerange stage_layers) r8r^rrrrr9layerr3ir:s r;r1zTFResNetStage.__init__s "6"+1+<+< +L'Rd \&VM^M^eopq519%  , 9J9JSZ[\_`[`ZaQb c  #  s#Br=rCc<|jD] }|||}|SrE)r)r8r=rCrs r;rGzTFResNetStage.call's+&& BE AL Br<c|jryd|_t|ddK|jD];}tj|j 5|j dddd=yy#1swYIxYw)NTr)rIrJrr?rKr'rLr8rNrs r;rLzTFResNetStage.build,sr ::  4 . :** &]]5::.&KK%&& & ;&& A..A7 )r!r!rQrR) rSrTrUrorrVr1r?rXrYrGrLrZr[s@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/) r0r1rrd hidden_sizesdownsample_in_first_stagedepthsstages enumeratezipappend)r8r^r9rrrrr:s r;r1zTFResNetEncoder.__init__7s "6" %%##A&"<O P6  v 1A1 \5 KK  }V[,V[dklmpqlqkrbst u vr<r=output_hidden_states return_dictrCc|rdnd}|jD]}|r||fz}|||}|r||fz}|std||fDSt||S)Nr/rFc3&K|] }|| ywrRr/).0vs r; z'TFResNetEncoder.call..\sSqQ]Ss)last_hidden_state hidden_states)rtupler)r8r=rrrCr stage_modules r;rGzTFResNetEncoder.callIsu3  KK IL# - ? ' xHL  I )\O;MS\=$ASS S/,^kllr<c|jryd|_t|ddK|jD];}tj|j 5|j dddd=yy#1swYIxYw)NTr)rIrJrr?rKr'rLrs r;rLzTFResNetEncoder.build`sp ::  44 ( 4 &]]5::.&KK%&& & 5&&r)FTFrR) rSrTrUrr1r?rXrYrrGrLrZr[s@r;rr6shv|v$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. resnetrhcdtjd|jjddftjiS)Nrh)shapedtype)r? TensorSpecr^rcfloat32)r8s r;input_signaturez'TFResNetPreTrainedModel.input_signaturets4 T4;;;S;SUXZ]4^fhfpfp qrrr<N) rSrTrUror config_classbase_model_prefixmain_input_namepropertyrr/r<r;rrjs-  L $O ssr<rad This model is a TensorFlow [keras.layers.Layer](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Layer) sub-class. Use it as a regular TensorFlow Module and refer to the TensorFlow documentation for all matter related to general usage and behavior. Parameters: config ([`ResNetConfig`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~TFPreTrainedModel.from_pretrained`] method to load the model weights. a> Args: pixel_values (`tf.Tensor` of shape `(batch_size, num_channels, height, width)`): Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See [`ConvNextImageProcessor.__call__`] for details. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. ceZdZeZdeddffd Ze d dejde e de e de de e eje ff d Zd d ZxZS) TFResNetMainLayerr^rNc t|di|||_t|d|_t |d|_tjjd|_ y)Nr`r~encoderT)keepdimsr/) r0r1r^r]r`rrr r3GlobalAveragePooling2Drargs r;r1zTFResNetMainLayer.__init__sO "6" *6 C &vI> ll9949H r<rhrrrCc||n|jj}||n|jj}tj|gd}|j ||}|j ||||}|d}|j|}tj|d}tj|d}d} |ddD]} | td | Dz} |s||f| zS|r| nd} t||| S) N)rr!rr)permrFrrrCrrrrr!r/rc3HK|]}tj|dyw)rN)r? transpose)rhs r;rz)TFResNetMainLayer.call..s1fTU",,q,2O1fs ")r pooler_outputr) r^ruse_return_dictr?rr`rrarr ) r8rhrrrCembedding_outputencoder_outputsr pooled_outputrr=s r;rGzTFResNetMainLayer.calls!%9$D $++JjJj &1%$. 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.) r0r1 num_labelsrrr r3Denser7classifier_layerr^rgs r;r1z'TFResNetForImageClassification.__init__s *6* ++'X>   1$ LL  v00~  F(((G   r<rscntjj|}|j|}|SrR)r r3Flattenr)r8rslogitss r; classifierz)TFResNetForImageClassification.classifiers. LL "1 %&&q) r<)rrrrrhlabelsrrrCc.||n|jj}|j||||}|r |jn|d}|j |}|dn|j ||} |s|f|ddz} | | f| zS| St | ||jS)a) labels (`tf.Tensor` 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 classification loss is computed (Cross-Entropy). Nrrr!)lossrr)r^rrrrhf_compute_lossr r) r8rhrrrrCoutputsrrroutputs r;rGz#TFResNetForImageClassification.call!s*&1%t4499: X%%++T49Q9QRT9U,VW X X ? ( ( X Xs C%%6C1%C.1C:)NNNNFrR)rSrTrUrr1r?rXrrrr_IMAGE_CLASS_CHECKPOINTr r_IMAGE_CLASS_EXPECTED_OUTPUTrrrYrrrGrLrZr[s@r;rrs | $ BII")) ++BC*:$4  -1&*/3&* uryy)u#u'tn u d^ u  u uRYY!GG Hu Du> Xr<r)rrr)4rotypingrrr tensorflowr?activations_tfrmodeling_tf_outputsrr r modeling_tf_utilsr r r rrtf_utilsrutilsrrrrconfiguration_resnetr get_loggerrSloggerrrrrrr3Layerrr]rqrwrrrrRESNET_START_DOCSTRINGrrrr__all__r/r<r;rs))$ #uu.   H %!,(0*+P **+P\'(++'(TPu||))PD(*++(*V7*ell007*t&ELL&&&D1&ell((1&h s/ s  A) **A)A)HU((+(( ((V BX%<>ZBXBXJ Yr<