# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 # This file was automatically generated from src/transformers/models/lightglue/modular_lightglue.py. # Do NOT edit this file manually as any edits will be overwritten by the generation of # the file from the modular. If any change should be done, please apply the change to the # modular_lightglue.py file directly. One of our CI enforces this. # 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 # Copyright 2025 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from ...configuration_utils import PretrainedConfig from ..auto import CONFIG_MAPPING, AutoConfig from ..superpoint import SuperPointConfig class LightGlueConfig(PretrainedConfig): r""" 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://arxiv.org/pdf/2305.13245.pdf). 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 ``` """ model_type = "lightglue" sub_configs = {"keypoint_detector_config": AutoConfig} def __init__( self, keypoint_detector_config: SuperPointConfig = None, descriptor_dim: int = 256, num_hidden_layers: int = 9, num_attention_heads: int = 4, num_key_value_heads=None, depth_confidence: float = 0.95, width_confidence: float = 0.99, filter_threshold: float = 0.1, initializer_range: float = 0.02, hidden_act: str = "gelu", attention_dropout=0.0, attention_bias=True, trust_remote_code: bool = False, **kwargs, ): # LightGlue can be used with other models than SuperPoint as keypoint detector # We provide the trust_remote_code argument to allow the use of other models # that are not registered in the CONFIG_MAPPING dictionary (for example DISK) self.trust_remote_code = trust_remote_code if descriptor_dim % num_attention_heads != 0: raise ValueError("descriptor_dim % num_heads is different from zero") self.descriptor_dim = descriptor_dim self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads # for backward compatibility if num_key_value_heads is None: num_key_value_heads = num_attention_heads self.num_key_value_heads = num_key_value_heads self.depth_confidence = depth_confidence self.width_confidence = width_confidence self.filter_threshold = filter_threshold self.initializer_range = initializer_range # Keypoint Detector is forced into eager attention mode because SuperPoint does not have Attention # See https://github.com/huggingface/transformers/pull/31718#discussion_r2109733153 if isinstance(keypoint_detector_config, dict): keypoint_detector_config["model_type"] = keypoint_detector_config.get("model_type", "superpoint") if keypoint_detector_config["model_type"] not in CONFIG_MAPPING: keypoint_detector_config = AutoConfig.from_pretrained( keypoint_detector_config["_name_or_path"], trust_remote_code=self.trust_remote_code ) else: keypoint_detector_config = CONFIG_MAPPING[keypoint_detector_config["model_type"]]( **keypoint_detector_config, attn_implementation="eager" ) if keypoint_detector_config is None: keypoint_detector_config = CONFIG_MAPPING["superpoint"](attn_implementation="eager") self.keypoint_detector_config = keypoint_detector_config self.hidden_size = descriptor_dim self.intermediate_size = descriptor_dim * 2 self.hidden_act = hidden_act self.attention_dropout = attention_dropout self.attention_bias = attention_bias super().__init__(**kwargs) __all__ = ["LightGlueConfig"]