ó <±hÐãó\•SSKJr SSKJr \R"\5r"SS\5rS/rg)é)ÚPretrainedConfig)Úloggingcót^•\rSrSrSrSr/SQSSSSSS S S 4 S\\S\S \S\S\S\S\S\4U4Sjjjr Sr U=r$)ÚSuperPointConfigéaY This is the configuration class to store the configuration of a [`SuperPointForKeypointDetection`]. It is used to instantiate a SuperPoint 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 SuperPoint [magic-leap-community/superpoint](https://huggingface.co/magic-leap-community/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: encoder_hidden_sizes (`List`, *optional*, defaults to `[64, 64, 128, 128]`): The number of channels in each convolutional layer in the encoder. decoder_hidden_size (`int`, *optional*, defaults to 256): The hidden size of the decoder. keypoint_decoder_dim (`int`, *optional*, defaults to 65): The output dimension of the keypoint decoder. descriptor_decoder_dim (`int`, *optional*, defaults to 256): The output dimension of the descriptor decoder. keypoint_threshold (`float`, *optional*, defaults to 0.005): The threshold to use for extracting keypoints. max_keypoints (`int`, *optional*, defaults to -1): The maximum number of keypoints to extract. If `-1`, will extract all keypoints. nms_radius (`int`, *optional*, defaults to 4): The radius for non-maximum suppression. border_removal_distance (`int`, *optional*, defaults to 4): The distance from the border to remove keypoints. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. Example: ```python >>> from transformers import SuperPointConfig, SuperPointForKeypointDetection >>> # Initializing a SuperPoint superpoint style configuration >>> configuration = SuperPointConfig() >>> # Initializing a model from the superpoint style configuration >>> model = SuperPointForKeypointDetection(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```Ú superpoint)é@r é€r ééAg{®Gázt?éÿÿÿÿég{®Gáz”?Úencoder_hidden_sizesÚdecoder_hidden_sizeÚkeypoint_decoder_dimÚdescriptor_decoder_dimÚkeypoint_thresholdÚ max_keypointsÚ nms_radiusÚborder_removal_distancec ó’>•XlX lX0lX@lXPlX`lXplX€lXl[TU]("S0U D6 g)N©)rrrrrrrrÚinitializer_rangeÚsuperÚ__init__)ÚselfrrrrrrrrrÚkwargsÚ __class__s €Úo/var/www/html/shao/venv/lib/python3.13/site-packages/transformers/models/superpoint/configuration_superpoint.pyrÚSuperPointConfig.__init__?sJø€ð%9Ô!Ø#6Ô Ø$8Ô!Ø&<Ô#Ø"4ÔØ*ÔØ$ŒØ'>Ô$Ø!2Ôä ‰ÒÑ"˜6Ó"ó) rrrrrrrrr)Ú__name__Ú __module__Ú__qualname__Ú__firstlineno__Ú__doc__Ú model_typeÚlistÚintÚfloatrÚ__static_attributes__Ú __classcell__)rs@rrrs‰ø†ñ$ðL€Jò+=Ø#&Ø$&Ø&)Ø$)ØØØ'(Øñ#à" 3™ið#ð!ð#ð"ð #ð !$ð#ð"ð #ðð#ðð#ð"%÷#ö#r!rN) Úconfiguration_utilsrÚutilsrÚ get_loggerr"ÚloggerrÚ__all__rr!rÚr2s9ðõ4Ýð × Ò ˜HÓ %€ô@#Ð'ô@#ðFÐ r!