U 9%eA@sdZddlmZddlmZddlmZddlmZddl m Z ddl m Z dd l mZmZe eZd d iZGd d d eeZGddde ZdS)z ResNet model configuration OrderedDict)Mapping)version)PretrainedConfig) OnnxConfig)logging)BackboneConfigMixin*get_aligned_output_features_output_indiceszmicrosoft/resnet-50z@https://huggingface.co/microsoft/resnet-50/blob/main/config.jsonc sPeZdZdZdZddgZddddd d gdd d dgdd dddf fdd ZZS) ResNetConfiga< This is the configuration class to store the configuration of a [`ResNetModel`]. It is used to instantiate an ResNet model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the ResNet [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: num_channels (`int`, *optional*, defaults to 3): The number of input channels. embedding_size (`int`, *optional*, defaults to 64): Dimensionality (hidden size) for the embedding layer. hidden_sizes (`List[int]`, *optional*, defaults to `[256, 512, 1024, 2048]`): Dimensionality (hidden size) at each stage. depths (`List[int]`, *optional*, defaults to `[3, 4, 6, 3]`): Depth (number of layers) for each stage. layer_type (`str`, *optional*, defaults to `"bottleneck"`): The layer to use, it can be either `"basic"` (used for smaller models, like resnet-18 or resnet-34) or `"bottleneck"` (used for larger models like resnet-50 and above). hidden_act (`str`, *optional*, defaults to `"relu"`): The non-linear activation function in each block. If string, `"gelu"`, `"relu"`, `"selu"` and `"gelu_new"` are supported. downsample_in_first_stage (`bool`, *optional*, defaults to `False`): If `True`, the first stage will downsample the inputs using a `stride` of 2. out_features (`List[str]`, *optional*): If used as backbone, list of features to output. Can be any of `"stem"`, `"stage1"`, `"stage2"`, etc. (depending on how many stages the model has). If unset and `out_indices` is set, will default to the corresponding stages. If unset and `out_indices` is unset, will default to the last stage. out_indices (`List[int]`, *optional*): If used as backbone, list of indices of features to output. Can be any of 0, 1, 2, etc. (depending on how many stages the model has). If unset and `out_features` is set, will default to the corresponding stages. If unset and `out_features` is unset, will default to the last stage. Example: ```python >>> from transformers import ResNetConfig, ResNetModel >>> # Initializing a ResNet resnet-50 style configuration >>> configuration = ResNetConfig() >>> # Initializing a model (with random weights) from the resnet-50 style configuration >>> model = ResNetModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ``` ZresnetbasicZ bottleneckr@iiiZreluFNc  stjf| ||jkr4td|dd|j||_||_||_||_||_ ||_ ||_ dgddt dt |dD|_t|| |jd\|_|_dS) Nz layer_type=z is not one of ,stemcSsg|]}d|qS)Zstage).0idxrrn/var/www/html/Darija-Ai-API/env/lib/python3.8/site-packages/transformers/models/resnet/configuration_resnet.py osz)ResNetConfig.__init__..) out_features out_indices stage_names)super__init__ layer_types ValueErrorjoin num_channelsembedding_size hidden_sizesdepths layer_type hidden_actdownsample_in_first_stagerangelenrr Z _out_featuresZ _out_indices) selfr"r#r$r%r&r'r(rrkwargs __class__rrrXs  $zResNetConfig.__init__)__name__ __module__ __qualname____doc__Z model_typerr __classcell__rrr-rr #s1  r c@sJeZdZedZeeeee effdddZ ee dddZ dS)ResNetOnnxConfigz1.11)returncCstddddddfgS)NZ pixel_valuesbatchr"heightwidth)rrrrr+rrrinputsxszResNetOnnxConfig.inputscCsdS)NgMbP?rr:rrratol_for_validationsz$ResNetOnnxConfig.atol_for_validationN) r/r0r1rparseZtorch_onnx_minimum_versionpropertyrstrintr;floatr<rrrrr4us   r4N)r2 collectionsrtypingr packagingrZconfiguration_utilsrZonnxrutilsr Zutils.backbone_utilsr r Z get_loggerr/loggerZ$RESNET_PRETRAINED_CONFIG_ARCHIVE_MAPr r4rrrrs       R