a h@sBddlmZddlmZmZddlmZGdddeZdgZdS))PretrainedConfig)CONFIG_MAPPING AutoConfig)SuperPointConfigc sFeZdZdZdZdeiZdeeeee e e e e e d fdd Z Z S)LightGlueConfiga This is the configuration class to store the configuration of a [`LightGlueForKeypointMatching`]. It is used to instantiate a LightGlue 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 LightGlue [ETH-CVG/lightglue_superpoint](https://huggingface.co/ETH-CVG/lightglue_superpoint) architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: keypoint_detector_config (`Union[AutoConfig, dict]`, *optional*, defaults to `SuperPointConfig`): The config object or dictionary of the keypoint detector. descriptor_dim (`int`, *optional*, defaults to 256): The dimension of the descriptors. num_hidden_layers (`int`, *optional*, defaults to 9): The number of self and cross attention layers. num_attention_heads (`int`, *optional*, defaults to 4): The number of heads in the multi-head attention. num_key_value_heads (`int`, *optional*): This is the number of key_value heads that should be used to implement Grouped Query Attention. If `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if `num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed by meanpooling all the original heads within that group. For more details checkout [this paper](https://huggingface.co/papers/2305.13245). If it is not specified, will default to `num_attention_heads`. depth_confidence (`float`, *optional*, defaults to 0.95): The confidence threshold used to perform early stopping width_confidence (`float`, *optional*, defaults to 0.99): The confidence threshold used to prune points filter_threshold (`float`, *optional*, defaults to 0.1): The confidence threshold used to filter matches initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. hidden_act (`str`, *optional*, defaults to `"gelu"`): The activation function to be used in the hidden layers. attention_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for the attention probabilities. attention_bias (`bool`, *optional*, defaults to `True`): Whether to use a bias in the query, key, value and output projection layers during self-attention. trust_remote_code (`bool`, *optional*, defaults to `False`): Whether to trust remote code when using other models than SuperPoint as keypoint detector. Examples: ```python >>> from transformers import LightGlueConfig, LightGlueForKeypointMatching >>> # Initializing a LightGlue style configuration >>> configuration = LightGlueConfig() >>> # Initializing a model from the LightGlue style configuration >>> model = LightGlueForKeypointMatching(configuration) >>> # Accessing the model configuration >>> configuration = model.config ``` Z lightgluekeypoint_detector_configN ffffff?Gz?皙?{Gz?geluTF) rdescriptor_dimnum_hidden_layersnum_attention_headsdepth_confidencewidth_confidencefilter_thresholdinitializer_range hidden_acttrust_remote_codec s| |_||dkrtd||_||_||_|dur8|}||_||_||_||_| |_ t |t r| dd|d<|dt vrtj|d|jd}nt |dfi|ddi}|durt ddd }||_||_|d |_| |_| |_| |_tjfi|dS) Nz1descriptor_dim % num_heads is different from zero model_type superpointZ _name_or_path)rattn_implementationeager)rr)r ValueErrorrrrnum_key_value_headsrrrr isinstancedictgetrrZfrom_pretrainedrZ hidden_sizeZintermediate_sizerattention_dropoutattention_biassuper__init__)selfrrrrr!rrrrrr%r&rkwargs __class__q/var/www/html/assistant/venv/lib/python3.9/site-packages/transformers/models/lightglue/configuration_lightglue.pyr(YsB     zLightGlueConfig.__init__) Nr r r Nr r rrrrTF)__name__ __module__ __qualname____doc__rrZ sub_configsrintfloatstrboolr( __classcell__r-r-r+r.rs8:rN) Zconfiguration_utilsrautorrrrr__all__r-r-r-r.s