o ei@sNdZddlmZddlmZddlmZeeZ GdddeeZ dgZ dS)zResNet model configuration)BackboneConfigMixin)PreTrainedConfig)loggingc sJeZdZdZdZddgZddgdgddd d d d d f fd d 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. downsample_in_bottleneck (`bool`, *optional*, defaults to `False`): If `True`, the first conv 1x1 in ResNetBottleNeckLayer 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. Must be in the same order as defined in the `stage_names` attribute. 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. Must be in the same order as defined in the `stage_names` attribute. 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 ``` resnetbasic bottleneckr@)iii)rrreluFNc  stjd i| ||jvrtd|dd|j||_||_||_||_||_ ||_ ||_ ||_ dgddt dt|dD|_|j| | ddS) Nz layer_type=z is not one of ,stemcSsg|]}d|qS)stage).0idxrrm/home/ubuntu/transcripts/venv/lib/python3.10/site-packages/transformers/models/resnet/configuration_resnet.py ksz)ResNetConfig.__init__..) out_indices out_featuresr)super__init__ layer_types ValueErrorjoin num_channelsembedding_size hidden_sizesdepths layer_type hidden_actdownsample_in_first_stagedownsample_in_bottleneckrangelen stage_names"set_output_features_output_indices) selfrrr r!r"r#r$r%rrkwargs __class__rrrRs $zResNetConfig.__init__)__name__ __module__ __qualname____doc__ model_typerr __classcell__rrr,rrs6rN) r1backbone_utilsrconfiguration_utilsrutilsr get_loggerr.loggerr__all__rrrrs     W