o Zh@sHddlmZddlmZddlmZeeZGdddeZ dgZ dS))List)PretrainedConfig)loggingcsbeZdZdZdZgddddddddd f d eed ed ed ededededeffdd ZZ S)SuperPointConfiga 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 ```Z superpoint)@rrAg{Gzt?g{Gz?encoder_hidden_sizesdecoder_hidden_sizekeypoint_decoder_dimdescriptor_decoder_dimkeypoint_threshold max_keypoints nms_radiusborder_removal_distancec  sL||_||_||_||_||_||_||_||_| |_t j di| dS)N) r rrrrrrrinitializer_rangesuper__init__) selfr rrrrrrrrkwargs __class__rf/var/www/auris/lib/python3.10/site-packages/transformers/models/superpoint/configuration_superpoint.pyr@s zSuperPointConfig.__init__) __name__ __module__ __qualname____doc__Z model_typerintfloatr __classcell__rrrrrs:& rN) typingrZconfiguration_utilsrutilsrZ get_loggerrloggerr__all__rrrrs    C