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If needed, it is also used to downsample the input using `stride=2`. r"rrrcttj||d|d|_tj||_dS)NrF)rrr$)r%r&rr'r(r)r*)r,rrrr-s r.r&zResNetShortCut.__init__jsP 9[,AV\chiii^L99r/r0r1cZ||}||}|Sr3)r(r*r4s r.r6zResNetShortCut.forwardos.''.. )),77 r/)r") r7r8r9rNr:r&rr6r<r=s@r.rPrPds ::C:s:C:::::: Vr/rPc <eZdZdZd dedededeffd Zd ZxZS) ResNetBasicLayerzO A classic ResNet's residual layer composed by two `3x3` convolutions. rrrrrr cPt||kp|dk}|rt|||ntj|_tjt|||t||d|_t||_ dS)Nrrr r%r&rPrr+shortcut Sequentialrlayerr r )r,rrrr should_apply_shortcutr-s r.r&zResNetBasicLayer.__init__zs  +| ; Jv{H] pN; V D D D Dcecncpcp ] Kf E E E L,4 H H H   !,r/c|}||}||}||z }||}|Sr3r[rYr r,r5residuals r.r6zResNetBasicLayer.forwardJzz,// ==** |44 r/)rr) r7r8r9rNr:r;r&r6r<r=s@r.rTrTusx - -C -s -C -Y\ - - - - - -r/rTc LeZdZdZ ddededed ed ed ef fd Zd ZxZ S)ResNetBottleNeckLayera 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`. If `downsample_in_bottleneck` is true, downsample will be in the first layer instead of the second layer. rrFrrrr reductiondownsample_in_bottleneckc t||kp|dk}||z}|rt|||ntj|_tjt||d|r|ndt|||s|ndt||dd|_t||_ dS)NrrV)rr)rr rX) r,rrrr rerfr\reduces_channelsr-s r.r&zResNetBottleNeckLayer.__init__s  +| ; Jv{'94H] pN; V D D D Dcecncpcp ] -1OgEnVVmn    ,.>UmGtvvst u u u ,lVZ [ [ [    !,r/c|}||}||}||z }||}|Sr3r^r_s r.r6zResNetBottleNeckLayer.forwardrar/)rrrdF) r7r8r9rNr:r;boolr&r6r<r=s@r.rcrcs ).---- -  -  -#'------0r/rcc NeZdZdZ d dededededef fd Zd ed efd ZxZ S) ResNetStagez4 A ResNet stage composed by stacked layers. r"r@rrrdepthc^tjdkrtntjdkr||jj}n||j}tj|gfdt|dz DR|_ dS)N bottleneck)rr rf)rr c6g|]}jS)rW)rD).0_r@r[rs r. z(ResNetStage.__init__..s.uuu_`55|HYZZZuuur/r) r%r& layer_typercrTrDrfrrZrangelayers) r,r@rrrrm first_layerr[r-s ` ` @r.r&zResNetStage.__init__s )/):l)J)J%%P`   , ,%!,)/)H KK % \&U[UfgggKm  uuuuuudijorsjsdtdtuuu    r/r0r1c4|}|jD] }||}|Sr3)rv)r,r0r5r[s r.r6zResNetStage.forwards/ [ / /E 5..LLr/)r"r") r7r8r9rNrr:r&rr6r<r=s@r.rlrls                4Vr/rlc @eZdZdeffd Z d dedededefd ZxZ S) ResNetEncoderr@c ttjg|_|jt ||j|jd|j rdnd|j dt|j|jdd}t||j ddD]3\\}}}|jt ||||4dS)Nrr"r)rrm)rm) r%r&r ModuleListstagesappendrlrC hidden_sizesdownsample_in_first_stagedepthszip)r,r@in_out_channelsrrrmr-s r.r&zResNetEncoder.__init__s  mB''   %#A&"<Cqq!mA&       f163Fqrr3JKK25ov}UVUWUWGX2Y2Y \ \ . '[, K  {6; TYZZZ [ [ [ [ \ \r/FTr5output_hidden_states return_dictr1c|rdnd}|jD]}|r||fz}||}|r||fz}|std||fDSt||S)Nc3K|]}||V dSr3r)rqvs r. z(ResNetEncoder.forward..s"SSqQ]]]]]SSr/)last_hidden_state hidden_states)r}tupler )r,r5rrr stage_modules r.r6zResNetEncoder.forwards3<  K 6 6L# @ - ? '< 55LL  <)\O;M TSS\=$ASSSSS S-*'    r/)FT) r7r8r9rr&rrjr r6r<r=s@r.rzrzs\|\\\\\\$]a  " :> UY '        r/rzc,eZdZdZeZdZdZddgZdZ dS)ResNetPreTrainedModelz An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. resnetrIrrPct|tjr)tj|jdddSt|tjrtj|jtj d|j otj |j\}}|dkrdtj |z nd}tj |j | |dSdSt|tj tjfrLtj|jdtj|j ddSdS)Nfan_outr)mode nonlinearity)arr) isinstancerr'initkaiming_normal_weightLinearkaiming_uniform_mathsqrtr$_calculate_fan_in_and_fan_outuniform_r) GroupNorm constant_)r,modulefan_inrrbounds r. _init_weightsz#ResNetPreTrainedModel._init_weightssF fbi ( ( . G # #FM PV # W W W W W  * * . G $ $V]dill $ C C C{&GAA&-PP 17!DIf----  ufe<<<<<'& > ? ? . G  fmQ / / / G  fk1 - - - - - . .r/N) r7r8r9rNr config_classbase_model_prefixmain_input_name_no_split_modulesrrr/r.rrsK  L $O*,<= . . . . .r/raH 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 ([`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 [`~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. zOThe bare ResNet model outputting raw features without any specific head on top.c eZdZfdZeeeeee de  d de de e de e defd ZxZS) ResNetModelct|||_t||_t ||_tjd|_ | dS)N)rr) r%r&r@r?rErzencoderrAdaptiveAvgPool2drG post_initrHs r.r&zResNetModel.__init__@sh     (00 $V,, *622  r/vision) checkpoint output_typermodalityexpected_outputNrIrrr1c&||n |jj}||n |jj}||}||||}|d}||}|s||f|ddzSt |||jS)Nrrrr)r pooler_outputr)r@ruse_return_dictrErrGr r)r,rIrrembedding_outputencoder_outputsr pooled_outputs r.r6zResNetModel.forwardIs%9$D $+Jj &1% 1` a classification loss is computed (Cross-Entropy). Nrr regressionsingle_label_classificationmulti_label_classificationrr")losslogitsr)r@rrrr problem_typerdtypetorchlongr:rsqueezerviewrrr) r,rIrrroutputsrrrloss_fctoutputs r.r6z$ResNetForImageClassification.forwards&&1%>> 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("microsoft/resnet-50") >>> model = AutoBackbone.from_pretrained( ... "microsoft/resnet-50", out_features=["stage1", "stage2", "stage3", "stage4"] ... ) >>> inputs = processor(image, return_tensors="pt") >>> outputs = model(**inputs) >>> feature_maps = outputs.feature_maps >>> list(feature_maps[-1].shape) [1, 2048, 7, 7] ```NTrr) feature_mapsr attentions) r@rrrErr enumerate stage_names out_featuresr ) r,rIrrrrrridxstagers r.r6zResNetBackbone.forwards>&1%XXXos7 7 "7 :B4.7 ^fgk^l7 7 7 7 YXDC7 7 7 7 7 r/r)5rNrtypingrrtorch.utils.checkpointrrtorch.nnrrr activationsr modeling_outputsr r r rmodeling_utilsrutilsrrrrrutils.backbone_utilsrconfiguration_resnetr get_loggerr7loggerrrrrrModulerr?rPrTrcrlrzrRESNET_START_DOCSTRINGrrrrrr/r.rs  AAAAAAAAAA!!!!!! .-----211111......  H % %!,(0*bi$ry0RY"ry4'''''BI'''T#####")###L& & & & & BI& & & R.....O...4  U+ + + + + '+ + + \ CsCsCsCsCs#8CsCsCsL  E E E E E *ME E  E E E r/