gfddlmZddlmZddlmZejeZGddeZ dS))List)PretrainedConfig)loggingcleZdZdZdZgddddddddd f d eed ed ed ededededeffd ZxZ 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 ``` superpoint)@r r Ag{Gzt?g{Gz?encoder_hidden_sizesdecoder_hidden_sizekeypoint_decoder_dimdescriptor_decoder_dimkeypoint_threshold max_keypoints nms_radiusborder_removal_distancec  ||_||_||_||_||_||_||_||_| |_tj di| dS)N) rrrrrrrrinitializer_rangesuper__init__) selfrrrrrrrrrkwargs __class__s s/var/www/html/ai-engine/env/lib/python3.11/site-packages/transformers/models/superpoint/configuration_superpoint.pyrzSuperPointConfig.__init__@so%9!#6 $8!&<#"4*$'>$!2""6""""") __name__ __module__ __qualname____doc__ model_typerintfloatr __classcell__)rs@r rrs$ $ LJ+=*<*<#&$&&)$)'(##"3i#!#" # !$ # " ###"%##########r!rN) typingrconfiguration_utilsrutilsr get_loggerr"loggerrrr!r r/s333333  H % %@#@#@#@#@#'@#@#@#@#@#r!