o ¾e¦iÐã@s<ddlmZddlmZe e¡ZGdd„deƒZdgZdS)é)ÚPreTrainedConfig)ÚloggingcsbeZdZdZdZgd¢dddddddd f d eed ed ed ededededef‡fdd„ Z‡Z S)ÚSuperPointConfigaÙ 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@ré€rééAg{®Gázt?éÿÿÿÿég{®Gáz”?Úencoder_hidden_sizesÚdecoder_hidden_sizeÚkeypoint_decoder_dimÚdescriptor_decoder_dimÚkeypoint_thresholdÚ max_keypointsÚ nms_radiusÚborder_removal_distancec  sL||_||_||_||_||_||_||_||_| |_t ƒj di| ¤ŽdS)N©) r r rrrrrrÚinitializer_rangeÚsuperÚ__init__) Úselfr r rrrrrrrÚkwargs©Ú __class__rúu/home/ubuntu/transcripts/venv/lib/python3.10/site-packages/transformers/models/superpoint/configuration_superpoint.pyr?s zSuperPointConfig.__init__) Ú__name__Ú __module__Ú __qualname__Ú__doc__Ú model_typeÚlistÚintÚfloatrÚ __classcell__rrrrrs:&öþýüûúùø ÷rN) Úconfiguration_utilsrÚutilsrÚ get_loggerrÚloggerrÚ__all__rrrrÚs    C