sgLMdZddlZddlmZddlZddlZddlmZmZddlm Z m Z m Z ddl m Z ddlmZmZmZmZdd lmZdd lmZmZmZmZmZdd lmZd d lmZej>e Z!dZ"dZ#gdZ$dZ%dZ&GddejNZ(GddejNZ)GddejNZ*GddejNZ+GddejNZ,GddejNZ-GddejNZ.Gd d!eZ/d"Z0d#Z1ed$e0Gd%d&e/Z2ed'e0Gd(d)e/Z3ed*e0Gd+d,e/eZ4y)-zPyTorch ResNet model.N)Optional)Tensornn)BCEWithLogitsLossCrossEntropyLossMSELoss)ACT2FN)BackboneOutputBaseModelOutputWithNoAttention(BaseModelOutputWithPoolingAndNoAttention$ImageClassifierOutputWithNoAttention)PreTrainedModel)add_code_sample_docstringsadd_start_docstrings%add_start_docstrings_to_model_forwardloggingreplace_return_docstrings) BackboneMixin) ResNetConfigrzmicrosoft/resnet-50)rirz tiger catc HeZdZ d dededededef fd Zdedefd ZxZS) ResNetConvLayer in_channels out_channels kernel_sizestride activationct|tj|||||dzd|_tj ||_|t||_ ytj|_ y)NF)rrpaddingbias) super__init__rConv2d convolution BatchNorm2d normalizationr Identityr)selfrrrrr __class__s ]/var/www/html/venv/lib/python3.12/site-packages/transformers/models/resnet/modeling_resnet.pyr%zResNetConvLayer.__init__;sf 99 ;vWbfgWgns  ^^L90:0F&,BKKMinputreturncl|j|}|j|}|j|}|SN)r'r)rr+r/ hidden_states r-forwardzResNetConvLayer.forwardEs6''. )),7 |4 r.)r rrelu) __name__ __module__ __qualname__intstrr%rr5 __classcell__r,s@r-rr:sLlrZZ.1Z@CZQTZfiZVr.rc8eZdZdZdeffd ZdedefdZxZS)ResNetEmbeddingszO ResNet Embeddings (stem) composed of a single aggressive convolution. configct|t|j|jdd|j |_tjddd|_ |j|_y)Nrr!)rrrr r)rrr") r$r%r num_channelsembedding_size hidden_actembedderr MaxPool2dpoolerr+r@r,s r-r%zResNetEmbeddings.__init__Qs\ '   !6!6Aa\b\m\m  llqAF "//r. pixel_valuesr0c|jd}||jk7r td|j|}|j |}|S)NrzeMake sure that the channel dimension of the pixel values match with the one set in the configuration.)shaperB ValueErrorrErG)r+rIrB embeddings r-r5zResNetEmbeddings.forwardYsT#))!, 4,, ,w MM,/ KK * r.) r7r8r9__doc__rr%rr5r<r=s@r-r?r?Ls'0|0Fvr.r?cBeZdZdZd dededeffd ZdedefdZxZS) ResNetShortCutz 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`. rrrct|tj||d|d|_tj ||_y)NrF)rrr#)r$r%rr&r'r(r))r+rrrr,s r-r%zResNetShortCut.__init__js: 99[,AV\chi^^L9r.r/r0cJ|j|}|j|}|Sr2)r'r)r3s r-r5zResNetShortCut.forwardos(''. )),7 r.)r!) r7r8r9rNr:r%rr5r<r=s@r-rPrPds5 :C:s:C: Vr.rPc <eZdZdZddedededeffd ZdZxZS) ResNetBasicLayerzO A classic ResNet's residual layer composed by two `3x3` convolutions. rrrrct|||k7xs|dk7}|rt|||ntj|_tj t|||t||d|_t||_ y)Nrrr r$r%rPrr*shortcut Sequentialrlayerr r)r+rrrrshould_apply_shortcutr,s r-r%zResNetBasicLayer.__init__zs{  +| ; Jv{H]N; V Dcecncncp ]] Kf E L,4 H  !,r.cz|}|j|}|j|}||z }|j|}|Sr2r[rYrr+r4residuals r-r5zResNetBasicLayer.forwardAzz,/ ==* |4 r.)rr6) r7r8r9rNr:r;r%r5r<r=s@r-rTrTus/ -C -s -C -Y\ -r.rTc LeZdZdZ d dedededededef 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. rrrr reductiondownsample_in_bottleneckc Ft |||k7xs|dk7}||z}|rt|||ntj|_tj t||d|r|ndt|||s|ndt||dd|_t||_ y)NrrV)rr)rrrX) r+rrrrrdrer\reduces_channelsr,s r-r%zResNetBottleNeckLayer.__init__s  +| ; Jv{'94H]N; V Dcecncncp ]] -1OgVmn  ,.>Umvst u ,lVZ [   !,r.cz|}|j|}|j|}||z }|j|}|Sr2r^r_s r-r5zResNetBottleNeckLayer.forwardrar.)rr6F) r7r8r9rNr:r;boolr%r5r<r=s@r-rcrcsZ ).--- -  -  -#'-0r.rcc NeZdZdZ d dededededef fd Zded efd ZxZ S) ResNetStagez4 A ResNet stage composed by stacked layers. r@rrrdepthc t ||jdk(rtnt}|jdk(r"|||||j |j }n|||||j }tj|gt|dz Dcgc]}||||j c}|_ ycc}w)N bottleneck)rrre)rrrrW) r$r% layer_typercrTrDrerrZrangelayers) r+r@rrrrmr[ first_layer_r,s r-r%zResNetStage.__init__s )/):):l)J%P`    ,!,,)/)H)H K \&U[UfUfgKmm  dijorsjsdtu_`5|HYHYZu  usB; r/r0c<|}|jD] }||} |Sr2)rr)r+r/r4r[s r-r5zResNetStage.forwards* [[ /E .L /r.)r!r!) r7r8r9rNrr:r%rr5r<r=s@r-rlrlsX          4Vr.rlc @eZdZdeffd Z ddedededefdZxZ S) ResNetEncoderr@c t|tjg|_|jj t ||j|jd|jrdnd|jdt|j|jdd}t||jddD]0\\}}}|jj t ||||2y)Nrr!r)rrm)rm) r$r%r ModuleListstagesappendrlrC hidden_sizesdownsample_in_first_stagedepthszip)r+r@in_out_channelsrrrmr,s r-r%zResNetEncoder.__init__s mmB'   %%##A&"<z(ResNetEncoder.forward..sSqQ]Ss)last_hidden_state hidden_states)rztupler )r+r4rrr stage_modules r-r5zResNetEncoder.forwardsv3  KK 6L# - ? ' 5L  6 )\O;MS\=$ASS S-*'  r.)FT) r7r8r9rr%rrjr r5r<r=s@r-rwrws=\|\$]a " :> UY ' r.rwc*eZdZdZeZdZdZddgZdZ y)ResNetPreTrainedModelz An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. resnetrIrrPcJt|tjr-tjj |j ddyt|tj rtjj|j tjd|jxtjj|j \}}|dkDrdtj|z nd}tjj|j| |yyt|tjtjfrUtjj|j dtjj|jdyy)Nfan_outr6)mode nonlinearity)arr) isinstancerr&initkaiming_normal_weightLinearkaiming_uniform_mathsqrtr#_calculate_fan_in_and_fan_outuniform_r( GroupNorm constant_)r+modulefan_inrtbounds r- _init_weightsz#ResNetPreTrainedModel._init_weightss fbii ( GG # #FMM PV # W  * GG $ $V]]diil $ C{{&GGAA&--P 17!DIIf--  ufe<' > ? GG  fmmQ / GG  fkk1 -@r.N) r7r8r9rNr config_classbase_model_prefixmain_input_name_no_split_modulesrrr.r-rrs*  L $O*,<= .r.raH This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior. Parameters: config ([`ResNetConfig`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. aF Args: pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See [`ConvNextImageProcessor.__call__`] for details. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. zOThe bare ResNet model outputting raw features without any specific head on top.c |eZdZfdZeeeeee de  d de de e de e defdZxZS) ResNetModelct||||_t||_t ||_tjd|_ |jy)N)rr) r$r%r@r?rErwencoderrAdaptiveAvgPool2drG post_initrHs r-r%zResNetModel.__init__@sK   (0 $V, **62  r.vision) checkpoint output_typermodalityexpected_outputrIrrr0c(||n|jj}||n|jj}|j|}|j |||}|d}|j |}|s ||f|ddzSt |||jS)Nrrrr)r pooler_outputr)r@ruse_return_dictrErrGr r)r+rIrrembedding_outputencoder_outputsr pooled_outputs r-r5zResNetModel.forwardIs%9$D $++JjJj &1% 1` a classification loss is computed (Cross-Entropy). Nrr regressionsingle_label_classificationmulti_label_classificationrr!)losslogitsr)r@rrrr problem_typerdtypetorchlongr:rsqueezerviewrrr) r+rIrrroutputsrrrloss_fctoutputs r-r5z$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-r5zResNetBackbone.forwards>&1%Xos7 "7 :B4.7 ^fgk^l7 7 YD7 r.r)5rNrtypingrrtorch.utils.checkpointrrtorch.nnrrr activationsr modeling_outputsr r r rmodeling_utilsrutilsrrrrrutils.backbone_utilsrconfiguration_resnetr get_loggerr7loggerrrrrrModulerr?rPrTrcrlrwrRESNET_START_DOCSTRINGrrrrrr.r-rs  AA! .2.   H %!,(0*bii$ryy0RYY"ryy4'BII'T#"))#L& BII& R.O.4  U+ '+ + \ Cs#8CsCsL  E *ME  E r.