U ,-eQJ@sdZddlmZddlZddlZddlmZmZddlmZm Z m Z ddl m Z ddl mZmZmZmZdd lmZdd lmZmZmZmZmZdd lmZd d lmZeeZ dZ!dZ"d dddgZ#dZ$dZ%dgZ&Gdddej'Z(Gdddej'Z)Gdddej'Z*Gdddej'Z+Gdddej'Z,Gdddej'Z-Gdd d ej'Z.Gd!d"d"eZ/d#Z0d$Z1ed%e0Gd&d'd'e/Z2ed(e0Gd)d*d*e/Z3ed+e0Gd,d-d-e/eZ4dS).z PyTorch ResNet model.)OptionalN)Tensornn)BCEWithLogitsLossCrossEntropyLossMSELoss)ACT2FN)BackboneOutputBaseModelOutputWithNoAttention(BaseModelOutputWithPoolingAndNoAttention$ImageClassifierOutputWithNoAttention)PreTrainedModel)add_code_sample_docstringsadd_start_docstrings%add_start_docstrings_to_model_forwardloggingreplace_return_docstrings) BackboneMixin) ResNetConfigrzmicrosoft/resnet-50iz tiger catcs<eZdZd eeeeedfdd Zeeddd ZZS) ResNetConvLayerrrrelu) in_channels out_channels kernel_sizestride activationcsPttj|||||ddd|_t||_|dk rBt|nt|_ dS)NF)rrpaddingbias) super__init__rConv2d convolution BatchNorm2d normalizationr Identityr)selfrrrrr __class__k/var/www/html/Darija-Ai-Train/env/lib/python3.8/site-packages/transformers/models/resnet/modeling_resnet.pyr#?s  zResNetConvLayer.__init__inputreturncCs"||}||}||}|SN)r%r'rr)r/ hidden_stater,r,r-forwardIs   zResNetConvLayer.forward)rrr) __name__ __module__ __qualname__intstrr#rr4 __classcell__r,r,r*r-r>s rcs6eZdZdZedfdd ZeedddZZS)ResNetEmbeddingszO ResNet Embeddings (stem) composed of a single aggressive convolution. configcsBtt|j|jdd|jd|_tjdddd|_ |j|_dS)Nrr)rrrrr)rrr ) r"r#r num_channelsembedding_size hidden_actembedderrZ MaxPool2dpoolerr)r=r*r,r-r#Us zResNetEmbeddings.__init__) pixel_valuesr0cCs4|jd}||jkrtd||}||}|S)NrzeMake sure that the channel dimension of the pixel values match with the one set in the configuration.)shaper> ValueErrorrArB)r)rDr>Z embeddingr,r,r-r4]s    zResNetEmbeddings.forward) r5r6r7__doc__rr#rr4r:r,r,r*r-r;Psr;cs<eZdZdZd eeedfdd ZeedddZZS) ResNetShortCutz ResNet shortcut, used to project the residual features to the correct size. 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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. 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Indices should be in `[0, ..., config.num_labels - 1]`. If `config.num_labels > 1` a classification loss is computed (Cross-Entropy). Nr~rZ regressionZsingle_label_classificationZmulti_label_classificationrr)losslogitsri)r=rrmrrZ problem_typerZdtypetorchlongr8rZsqueezerviewrr ri) r)rDrrdreoutputsrrrZloss_fctoutputr,r,r-r4qs6     "     z$ResNetForImageClassification.forward)NNNN)r5r6r7r#rrr_IMAGE_CLASS_CHECKPOINTr r_IMAGE_CLASS_EXPECTED_OUTPUTrrZ FloatTensorZ LongTensorrkr4r:r,r,r*r-r]s& rzO ResNet backbone, to be used with frameworks like DETR and MaskFormer. csNeZdZfddZeeeeedde e e e e edddZ Z S) ResNetBackbonecsHt|t||jg|j|_t||_t||_ | dSr1) r"r#Z_init_backboner?rbZ num_featuresr;rAr_rxryrCr*r,r-r#s     zResNetBackbone.__init__)r{rvNr}c Cs|dk r |n|jj}|dk r |n|jj}||}|j|ddd}|j}d}t|jD] \}} | |jkrV|||f7}qV|s|f} |r| |jf7} | St ||r|jndddS)a3 Returns: Examples: ```python >>> 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] ```NTr~r,) feature_mapsriZ attentions) r=rrdrArxri enumerateZ stage_namesZ out_featuresr ) r)rDrdrerrriridxZstagerr,r,r-r4s(    zResNetBackbone.forward)NN)r5r6r7r#rrrr rrrrkr4r:r,r,r*r-rs  r)5rGtypingrrZtorch.utils.checkpointrrZtorch.nnrrrZ activationsr Zmodeling_outputsr r r r Zmodeling_utilsrutilsrrrrrZutils.backbone_utilsrZconfiguration_resnetrZ get_loggerr5loggerrrrrrZ$RESNET_PRETRAINED_MODEL_ARCHIVE_LISTModulerr;rHrIrTrWr_rlZRESNET_START_DOCSTRINGrrwrrr,r,r,r-sX       !) .F