cCi(DSSKJr SSKJr SSKJr "SS\5rS/rg))Optional)PretrainedConfig)rope_config_validationc5^\rSrSrSrSrS S\\\S\\\S\\\S\S\ S \S \S \S \S \S\S\ S\ S\ S\ S\ S\ S\S\S\ S\ S\ S\\ S\S\ S\ 44U4Sjjjr SrU=r$)!EfficientLoFTRConfiga This is the configuration class to store the configuration of a [`EfficientLoFTRFromKeypointMatching`]. It is used to instantiate a EfficientLoFTR 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 EfficientLoFTR [zju-community/efficientloftr](https://huggingface.co/zju-community/efficientloftr) architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: stage_num_blocks (`List`, *optional*, defaults to [1, 2, 4, 14]): The number of blocks in each stages out_features (`List`, *optional*, defaults to [64, 64, 128, 256]): The number of channels in each stage stage_stride (`List`, *optional*, defaults to [2, 1, 2, 2]): The stride used in each stage hidden_size (`int`, *optional*, defaults to 256): The dimension of the descriptors. activation_function (`str`, *optional*, defaults to `"relu"`): The activation function used in the backbone q_aggregation_kernel_size (`int`, *optional*, defaults to 4): The kernel size of the aggregation of query states in the fusion network kv_aggregation_kernel_size (`int`, *optional*, defaults to 4): The kernel size of the aggregation of key and value states in the fusion network q_aggregation_stride (`int`, *optional*, defaults to 4): The stride of the aggregation of query states in the fusion network kv_aggregation_stride (`int`, *optional*, defaults to 4): The stride of the aggregation of key and value states in the fusion network num_attention_layers (`int`, *optional*, defaults to 4): Number of attention layers in the LocalFeatureTransformer num_attention_heads (`int`, *optional*, defaults to 8): The number of heads in the GNN layers. attention_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for the attention probabilities. attention_bias (`bool`, *optional*, defaults to `False`): Whether to use a bias in the query, key, value and output projection layers during attention. mlp_activation_function (`str`, *optional*, defaults to `"leaky_relu"`): Activation function used in the attention mlp layer. coarse_matching_skip_softmax (`bool`, *optional*, defaults to `False`): Whether to skip softmax or not at the coarse matching step. coarse_matching_threshold (`float`, *optional*, defaults to 0.2): The threshold for the minimum score required for a match. coarse_matching_temperature (`float`, *optional*, defaults to 0.1): The temperature to apply to the coarse similarity matrix coarse_matching_border_removal (`int`, *optional*, defaults to 2): The size of the border to remove during coarse matching fine_kernel_size (`int`, *optional*, defaults to 8): Kernel size used for the fine feature matching batch_norm_eps (`float`, *optional*, defaults to 1e-05): The epsilon used by the batch normalization layers. rope_theta (`float`, *optional*, defaults to 10000.0): The base period of the RoPE embeddings. partial_rotary_factor (`float`, *optional*, defaults to 4.0): Dim factor for the RoPE embeddings, in EfficientLoFTR, frequencies should be generated for the whole hidden_size, so this factor is used to compensate. rope_scaling (`Dict`, *optional*): Dictionary containing the scaling configuration for the RoPE embeddings. NOTE: if you apply new rope type and you expect the model to work on longer `max_position_embeddings`, we recommend you to update this value accordingly. Expected contents: `rope_type` (`str`): The sub-variant of RoPE to use. Can be one of ['default', 'linear', 'dynamic', 'yarn', 'longrope', 'llama3', '2d'], with 'default' being the original RoPE implementation. `dim` (`int`): The dimension of the RoPE embeddings. fine_matching_slice_dim (`int`, *optional*, defaults to 8): The size of the slice used to divide the fine features for the first and second fine matching stages. fine_matching_regress_temperature (`float`, *optional*, defaults to 10.0): The temperature to apply to the fine similarity matrix initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. Examples: ```python >>> from transformers import EfficientLoFTRConfig, EfficientLoFTRForKeypointMatching >>> # Initializing a EfficientLoFTR configuration >>> configuration = EfficientLoFTRConfig() >>> # Initializing a model from the EfficientLoFTR configuration >>> model = EfficientLoFTRForKeypointMatching(configuration) >>> # Accessing the model configuration >>> configuration = model.config ``` efficientloftrstage_num_blocks out_features stage_stride hidden_sizeactivation_functionq_aggregation_kernel_sizekv_aggregation_kernel_sizeq_aggregation_stridekv_aggregation_stridenum_attention_layersnum_attention_headsattention_dropoutattention_biasmlp_activation_functioncoarse_matching_skip_softmaxcoarse_matching_thresholdcoarse_matching_temperaturecoarse_matching_border_removalfine_kernel_sizebatch_norm_eps rope_thetapartial_rotary_factor rope_scalingfine_matching_slice_dim!fine_matching_regress_temperatureinitializer_rangec >UbUO/SQUlUbUO/SQUlUbUO/SQUlS/URSS-Ul[ URUR5VVs/sHunnU/S/US- --PM snnUl[ UR5VVs/sHunnURU/U-PM snnUl[[UR55Vs/sH&nURU/URUSS-PM( snUl [[UR55SSUl X@lURURS:wa([SURSURS35eXPlX`lXplXlXlXlXlXlXlURS-UlXlXlUUlUUlUUlUUlUUl UUl!UUl"Xl#UUl$UbUOS S 0Ul%UUl&[OU5 UUl([RTU]"S 0UD6 gs snnfs snnfs snf) N))r'r&r'r')@r*r&zMhidden_size should be equal to the last value in out_features. hidden_size = z, out_features = r' rope_typedefault)+r r r stage_in_channelszipstage_block_stride enumeratestage_block_out_channelsrangelenstage_block_in_channelslistreversedfine_fusion_dimsr ValueErrorrrrrrrrrrintermediate_sizerrrrrrrr"r#num_key_value_headsrr!r rr$super__init__) selfr r r rrrrrrrrrrrrrrrrrrr r!r"r#r$kwargsstride num_blocks stage_idx __class__s y/home/james-whalen/.local/lib/python3.13/site-packages/transformers/models/efficientloftr/configuration_efficientloftr.pyr@EfficientLoFTRConfig.__init__ms>5E4P 0Vc,8,DL,,8,DLJ\"#t'8'8"'=!=ILDL]L]_c_t_tHu# Hu2D&*VHsj1n- -Hu# V__c_t_tUu) Uu( >  # #I . /$2O2OPY2Z[^\^2_ _>( $ !%Xd.?.?%@ A#2 F&   t004 4_`d`p`p_qrCDHDUDUVXDYCZ[ $7 )B&*D'$8!%:"$8!#6 !2,!%!1!1A!5'>$,H))B&+F(.L+ 0,'>$1R.#6 $,8,DL;XaJb&;"t$!2 "6"e# ) ( s'I" I#+-I))!rrrrrrrrr;rr#r"rr$r=rrrrrr>r r rrr!rr8r5r3r1r r )NNNr,relur(r(r(r(r(gF leaky_reluFg?g?r'rJgh㈵>g@g@NrJg$@g{Gz?)__name__ __module__ __qualname____firstlineno____doc__ model_typerr9intstrfloatbooldictr@__static_attributes__ __classcell__)rFs@rGrrsTl"J15,0,0#))**+$%%&$%#$#&$'3-2+.-0./ ! $#'*'+'(37#'7W#"49-W#tCy)W#tCy) W#  W# ! 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