o ¾e¦i#ã@sXdZddlmZddlmZddlmZddlmZe  e ¡Z Gdd„deƒZ dgZ d S) zUperNet model configurationé)Ú%consolidate_backbone_kwargs_to_config)ÚPreTrainedConfig)Úloggingé)Ú AutoConfigc sHeZdZdZdZdeiZdddgd¢dd dd d d d f ‡fdd„ Z‡ZS)Ú UperNetConfigaÖ This is the configuration class to store the configuration of an [`UperNetForSemanticSegmentation`]. It is used to instantiate an UperNet 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 UperNet [openmmlab/upernet-convnext-tiny](https://huggingface.co/openmmlab/upernet-convnext-tiny) architecture. Configuration objects inherit from [`PreTrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PreTrainedConfig`] for more information. Args: backbone_config (`PreTrainedConfig` or `dict`, *optional*, defaults to `ResNetConfig()`): The configuration of the backbone model. hidden_size (`int`, *optional*, defaults to 512): The number of hidden units in the convolutional layers. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. pool_scales (`tuple[int]`, *optional*, defaults to `[1, 2, 3, 6]`): Pooling scales used in Pooling Pyramid Module applied on the last feature map. use_auxiliary_head (`bool`, *optional*, defaults to `True`): Whether to use an auxiliary head during training. auxiliary_loss_weight (`float`, *optional*, defaults to 0.4): Weight of the cross-entropy loss of the auxiliary head. auxiliary_channels (`int`, *optional*, defaults to 256): Number of channels to use in the auxiliary head. auxiliary_num_convs (`int`, *optional*, defaults to 1): Number of convolutional layers to use in the auxiliary head. auxiliary_concat_input (`bool`, *optional*, defaults to `False`): Whether to concatenate the output of the auxiliary head with the input before the classification layer. loss_ignore_index (`int`, *optional*, defaults to 255): The index that is ignored by the loss function. Examples: ```python >>> from transformers import UperNetConfig, UperNetForSemanticSegmentation >>> # Initializing a configuration >>> configuration = UperNetConfig() >>> # Initializing a model (with random weights) from the configuration >>> model = UperNetForSemanticSegmentation(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```ÚupernetÚbackbone_configNig{®Gáz”?)érréTgš™™™™™Ù?ér Féÿc  sztd|ddgd¢idœ| ¤Ž\}} ||_||_||_||_||_||_||_||_| |_ | |_ | |_ t ƒj di| ¤ŽdS)NÚresnetÚ out_features)Ústage1Ústage2Ústage3Ústage4)r Údefault_config_typeÚdefault_config_kwargs©)rr Ú hidden_sizeÚinitializer_rangeÚ pool_scalesÚuse_auxiliary_headÚauxiliary_loss_weightÚauxiliary_in_channelsÚauxiliary_channelsÚauxiliary_num_convsÚauxiliary_concat_inputÚloss_ignore_indexÚsuperÚ__init__) Úselfr rrrrrrrrrr Úkwargs©Ú __class__rúo/home/ubuntu/transcripts/venv/lib/python3.10/site-packages/transformers/models/upernet/configuration_upernet.pyr"Ks(ÿý ú zUperNetConfig.__init__) Ú__name__Ú __module__Ú __qualname__Ú__doc__Ú model_typerÚ sub_configsr"Ú __classcell__rrr%r'rs .ôrN)r+Úbackbone_utilsrÚconfiguration_utilsrÚutilsrÚauto.configuration_autorÚ get_loggerr(ÚloggerrÚ__all__rrrr'Ús      Y