cCixPSrSSKJr SSKJr SSKJrJr \R"\ 5r "SS\5r "SS \5r "S S \5r "S S \5r"SS\5r/SQrg)zSAM2 model configuration)PretrainedConfig)logging)CONFIG_MAPPING AutoConfigc^^\rSrSrSrSrSrSU4SjjrSrU=r $)Sam2HieraDetConfiga\ This is the configuration class to store the configuration of a [`Sam2HieraDetModel`]. It is used to instantiate a HieraDet model as defined in the original sam2 repo according to the specified arguments, defining the model architecture. Instantiating a configuration defaults will yield a similar configuration to that of SAM 2.1 Hiera-tiny [facebook/sam2.1-hiera-tiny](https://huggingface.co/facebook/sam2.1-hiera-tiny) architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: hidden_size (`int`, *optional*, defaults to 96): The hidden dimension of the image encoder. num_attention_heads (`int`, *optional*, defaults to 1): Number of attention heads for each attention layer in the Transformer encoder. num_channels (`int`, *optional*, defaults to 3): The number of channels in the image. image_size (`list[int]`, *optional*, defaults to `[1024, 1024]`): The size of the image. patch_kernel_size (`list[int]`, *optional*, defaults to `[7, 7]`): The kernel size of the patch. patch_stride (`list[int]`, *optional*, defaults to `[4, 4]`): The stride of the patch. patch_padding (`list[int]`, *optional*, defaults to `[3, 3]`): The padding of the patch. query_stride (`list[int]`, *optional*, defaults to `[2, 2]`): The downsample stride between stages. window_positional_embedding_background_size (`list[int]`, *optional*, defaults to `[7, 7]`): The window size per stage when not using global attention. num_query_pool_stages (`int`, *optional*, defaults to 3): The number of query pool stages. blocks_per_stage (`list[int]`, *optional*, defaults to `[1, 2, 7, 2]`): The number of blocks per stage. embed_dim_per_stage (`list[int]`, *optional*, defaults to `[96, 192, 384, 768]`): The embedding dimension per stage. num_attention_heads_per_stage (`list[int]`, *optional*, defaults to `[1, 2, 4, 8]`): The number of attention heads per stage. window_size_per_stage (`list[int]`, *optional*, defaults to `[8, 4, 14, 7]`): The window size per stage. global_attention_blocks (`list[int]`, *optional*, defaults to `[5, 7, 9]`): The blocks where global attention is used. mlp_ratio (`float`, *optional*, defaults to 4.0): The ratio of the MLP hidden dimension to the embedding dimension. hidden_act (`str`, *optional*, defaults to `"gelu"`): The non-linear activation function in the neck. layer_norm_eps (`float`, *optional*, defaults to 1e-06): The epsilon for the layer normalization. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. backbone_configsam2_hiera_det_modelc >[TU]"S 0UD6 UbUOSS/nUbUOSS/nUbUOSS/nUbUOSS/nUbUOSS/nU bU OSS/n U bU O/SQn U bU O/SQn U bU O/SQn UbUO/S QnUbUO/S QnXlX lX0lX@lXPlX`lXplXl Xl Xl Xl Xl XlXlXlUUlUUlUUlUUlg) Nrr)rrr)`)rrr)rrr)r )super__init__ hidden_sizenum_attention_heads num_channels image_sizepatch_kernel_size patch_stride patch_padding query_stride+window_positional_embedding_background_sizenum_query_pool_stagesblocks_per_stageembed_dim_per_stagenum_attention_heads_per_stagewindow_size_per_stageglobal_attention_blocks mlp_ratio hidden_actlayer_norm_epsinitializer_range)selfrrrr r!r"r#r$r%r&r'r(r)r*r+r,r-r.r/kwargs __class__s e/home/james-whalen/.local/lib/python3.13/site-packages/transformers/models/sam2/configuration_sam2.pyrSam2HieraDetConfig.__init__PsM. "6"#-#9Zd| 1B1N-UVXYTZ'3'?|aV )6)B A '3'?|aV ;F 8Q 4 0@/K+Q]5H5T1Zm-J-V )\h &:O9Z 5`m=T=`"9fo&#6 ($!2(*(;f8%:" 0#6 -J*%:"'>$"$,!2)r'r(r+r-rr r/r.r,rr)rr&r!r#r"r$r%r*)rrrNNNNNNrNNNNNg@geluư>{Gz?) __name__ __module__ __qualname____firstlineno____doc__base_config_key model_typer__static_attributes__ __classcell__r2s@r3r r sZ1f(O'J48 &*" $)=3=3r5r cX^\rSrSrSrSrSrS\0rSU4Sjjr Sr U=r $) Sam2VisionConfigan This is the configuration class to store the configuration of a [`Sam2VisionModel`]. It is used to instantiate a SAM vision encoder according to the specified arguments, defining the model architecture. Instantiating a configuration defaults will yield a similar configuration to that of SAM 2.1 Hiera-tiny [facebook/sam2.1-hiera-tiny](https://huggingface.co/facebook/sam2.1-hiera-tiny) architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: backbone_config (`Union[dict, "PretrainedConfig"]`, *optional*): Configuration for the vision backbone. This is used to instantiate the backbone using `AutoModel.from_config`. backbone_channel_list (`List[int]`, *optional*, defaults to `[768, 384, 192, 96]`): The list of channel dimensions for the backbone. backbone_feature_sizes (`List[List[int]]`, *optional*, defaults to `[[256, 256], [128, 128], [64, 64]]`): The spatial sizes of the feature maps from the backbone. fpn_hidden_size (`int`, *optional*, defaults to 256): The hidden dimension of the FPN. fpn_kernel_size (`int`, *optional*, defaults to 1): The kernel size for the convolutions in the neck. fpn_stride (`int`, *optional*, defaults to 1): The stride for the convolutions in the neck. fpn_padding (`int`, *optional*, defaults to 0): The padding for the convolutions in the neck. fpn_top_down_levels (`List[int]`, *optional*, defaults to `[2, 3]`): The levels for the top-down FPN connections. num_feature_levels (`int`, *optional*, defaults to 3): The number of feature levels from the FPN to use. hidden_act (`str`, *optional*, defaults to `"gelu"`): The non-linear activation function in the neck. layer_norm_eps (`float`, *optional*, defaults to 1e-06): The epsilon for the layer normalization. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. vision_configsam2_vision_modelr c  >[TU]"S 0U D6 Uc/SQOUnUc SS/SS/SS//OUnUcSS/OUn[U[5(a(UR SS5US'[ US"S 0UD6nO#[U[ 5(aO Uc [ 5nXlX lX0l X@l XPl X`l Xpl XlXlXlXlXlg) N)rrrr@rrr?r r)rr isinstancedictgetrr r backbone_channel_listbackbone_feature_sizesfpn_hidden_sizefpn_kernel_size fpn_stride fpn_paddingfpn_top_down_levelsnum_feature_levelsr-r.r/)r0r rOrPrQrRrSrTrUrVr-r.r/r1r2s r3rSam2VisionConfig.__init__s "6"7L7T 3Zo2H2Pc3Z#sb"X .Vl )<(Cq!fI\ ot , ,,;,?,? Nd,eOL ),_\-JK^o^O ); < <   $02O.&;"&<#..$&#6 "4$,!2r5) rOr rPrQrRrTrSrUr-r/r.rV) NNNrIrrNrr6r7r8) r9r:r;r<r=r>r?r sub_configsrr@rArBs@r3rDrDsQ$L&O$J:K "# .3.3r5rDcD^\rSrSrSrSrSU4SjjrSrU=r$)Sam2PromptEncoderConfiga This is the configuration class to store the configuration of a [`Sam2PromptEncoder`]. The [`Sam2PromptEncoder`] module is used to encode the input 2D points and bounding boxes. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: hidden_size (`int`, *optional*, defaults to 256): Dimensionality of the hidden states. image_size (`int`, *optional*, defaults to 1024): The expected output resolution of the image. patch_size (`int`, *optional*, defaults to 16): The size (resolution) of each patch. mask_input_channels (`int`, *optional*, defaults to 16): The number of channels to be fed to the `MaskDecoder` module. num_point_embeddings (`int`, *optional*, defaults to 4): The number of point embeddings to be used. hidden_act (`str`, *optional*, defaults to `"gelu"`): The non-linear activation function in the encoder and pooler. layer_norm_eps (`float`, *optional*, defaults to 1e-06): The epsilon used by the layer normalization layers. scale (`float`, *optional*, defaults to 1): The scale factor for the prompt encoder. prompt_encoder_configc  >[T U]"S0U D6 XlX lX0lX@lXPlX`lXplXl gNr) rrrr patch_sizemask_input_channelsnum_point_embeddingsr-r.scale) r0rr r`rarbr-r.rcr1r2s r3r Sam2PromptEncoderConfig.__init__ sA "6"&$$#6 $8!$, r5)r-rr r.rarbr`rc)rIrrerr6r7r r9r:r;r<r=r>rr@rArBs@r3r[r[s34.Or5r[cL^\rSrSrSrSrSU4SjjrSrU=r$)Sam2MaskDecoderConfigi"a  This is the configuration class to store the configuration of a [`Sam2MaskDecoder`]. It is used to instantiate a SAM2 memory encoder according to the specified arguments, defining the model architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: hidden_size (`int`, *optional*, defaults to 256): Dimensionality of the hidden states. hidden_act (`str`, *optional*, defaults to `"gelu"`): The non-linear activation function in the SAM2 mask decoder. mlp_dim (`int`, *optional*, defaults to 2048): The dimension of the MLP in the two-way transformer. num_hidden_layers (`int`, *optional*, defaults to 2): The number of hidden layers in the two-way transformer. num_attention_heads (`int`, *optional*, defaults to 8): The number of attention heads in the two-way transformer. attention_downsample_rate (`int`, *optional*, defaults to 2): The downsample rate for the attention layers. num_multimask_outputs (`int`, *optional*, defaults to 3): The number of multimask outputs. iou_head_depth (`int`, *optional*, defaults to 3): The depth of the IoU head. iou_head_hidden_dim (`int`, *optional*, defaults to 256): The hidden dimension of the IoU head. dynamic_multimask_via_stability (`bool`, *optional*, defaults to `True`): Whether to use dynamic multimask via stability. dynamic_multimask_stability_delta (`float`, *optional*, defaults to 0.05): The stability delta for the dynamic multimask. dynamic_multimask_stability_thresh (`float`, *optional*, defaults to 0.98): The stability threshold for the dynamic multimask. mask_decoder_configc  >[TU]"S0U D6 XlXplX lXlXlXlXlXl X@l XlXPl X0l X`l gr_)rrrnum_multimask_outputsr-iou_head_depthiou_head_hidden_dimdynamic_multimask_via_stability!dynamic_multimask_stability_delta"dynamic_multimask_stability_threshnum_hidden_layersrmlp_dimattention_downsample_rate)r0rr-rrrqrrsrkrlrmrnrorpr1r2s r3rSam2MaskDecoderConfig.__init__Hsc "6"&%:"$,#6 /N,1R.2T/"3&#6  )B&r5) rsrorprnr-rrlrmrrrrqrk) rIr6irrrrrrITg?g\(\?rfrBs@r3rhrh"sB!F,O"#(,*.+/ C Cr5rhcH^\rSrSrSrSr\\\S.r SU4Sjjr Sr U=r $) Sam2Configika [`Sam2Config`] is the configuration class to store the configuration of a [`Sam2Model`]. It is used to instantiate a SAM2 model according to the specified arguments, defining the memory attention, memory encoder, and image encoder configs. Instantiating a configuration defaults will yield a similar configuration to that of the SAM 2.1 Hiera-tiny [facebook/sam2.1-hiera-tiny](https://huggingface.co/facebook/sam2.1-hiera-tiny) architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: vision_config (Union[`dict`, `Sam2VisionConfig`], *optional*): Dictionary of configuration options used to initialize [`Sam2VisionConfig`]. prompt_encoder_config (Union[`dict`, `Sam2PromptEncoderConfig`], *optional*): Dictionary of configuration options used to initialize [`Sam2PromptEncoderConfig`]. mask_decoder_config (Union[`dict`, `Sam2MaskDecoderConfig`], *optional*): Dictionary of configuration options used to initialize [`Sam2MaskDecoderConfig`]. initializer_range (`float`, *optional*, defaults to 0.02): Standard deviation for parameter initialization. Example: ```python >>> from transformers import ( ... Sam2VisionConfig, ... Sam2PromptEncoderConfig, ... Sam2MaskDecoderConfig, ... Sam2Model, ... ) >>> # Initializing a Sam2Config with `"facebook/sam2.1_hiera_tiny"` style configuration >>> configuration = Sam2config() >>> # Initializing a Sam2Model (with random weights) from the `"facebook/sam2.1_hiera_tiny"` style configuration >>> model = Sam2Model(configuration) >>> # Accessing the model configuration >>> configuration = model.config >>> # We can also initialize a Sam2Config from a Sam2VisionConfig, Sam2PromptEncoderConfig, and Sam2MaskDecoderConfig >>> # Initializing SAM2 vision encoder, memory attention, and memory encoder configurations >>> vision_config = Sam2VisionConfig() >>> prompt_encoder_config = Sam2PromptEncoderConfig() >>> mask_decoder_config = Sam2MaskDecoderConfig() >>> config = Sam2Config(vision_config, prompt_encoder_config, mask_decoder_config) ```sam2)rFr]ric >[TU]"S0UD6 UbUO0nUbUO0nUbUO0n[U[5(a'UR SS5US'[ US"S0UD6n[U[ 5(aUR5n[U[5(aUR5nXl [ S0UD6Ul [S0UD6Ul X@l g)Nr?rGr) rrrLrMrNrr[to_dictrhrFr]rir/)r0rFr]rir/r1r2s r3rSam2Config.__init__s "6")6)B  9N9Z 5`b5H5T1Z\ mT * **7*;*;LJ]*^M, '*=+FGX-XM +-D E E$9$A$A$C ! )+@ A A"5"="="? *%<%U?T%U"#8#O;N#O !2r5)r/rir]rF)NNNr8) r9r:r;r<r=r?rr[rhrYrr@rArBs@r3rvrvks8. `J#!84K"  33r5rv)rvr rDr[rhN)r=configuration_utilsrutilsrautorr get_loggerr9loggerr rDr[rhrv__all__rr5r3rs|3-   H %t3)t3n[3'[3|1.1hFC,FCRQ3!Q3h r5