a hCQ@sdZddlmZddlmZddlmZmZee Z GdddeZ Gdd d eZ Gd d d eZ Gd d d eZGdddeZgdZdS)zSAM2 model configuration)PretrainedConfig)logging)CONFIG_MAPPING AutoConfigcs*eZdZdZdZdZdfd d ZZS)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_model`rN@geluư>{Gz?c sdtjfi||dur|nddg}|dur2|nddg}|durF|nddg}|durZ|nddg}|durn|nddg}| dur| nddg} | dur| ngd} | dur| ngd} | dur| ngd} |dur|ngd }|dur|ngd }||_||_||_||_||_||_||_||_ | |_ | |_ | |_ | |_ | |_||_||_||_||_||_||_dS) Nrr)r rrr)r )r rr)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)selfrrrrr r!r"r#r$r%r&r'r(r)r*r+r,r-r.kwargs __class__g/var/www/html/assistant/venv/lib/python3.9/site-packages/transformers/models/sam2/configuration_sam2.pyrPsFzSam2HieraDetConfig.__init__)r r rNNNNNNrNNNNNr r rr)__name__ __module__ __qualname____doc__base_config_key model_typer __classcell__r3r3r1r4rs.3rc s2eZdZdZdZdZdeiZdfd d ZZ S)Sam2VisionConfiga 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_modelrNr rr rrc  stjfi| |dur"gdn|}|durDddgddgddggn|}|durXddgn|}t|tr|dd|d<t|dfi|}nt|tr|}n|durt}||_||_||_ ||_ ||_ ||_ ||_ ||_| |_| |_| |_| |_dS) N)rrrr r?@rrr:r )rr isinstancedictgetrrrbackbone_channel_listbackbone_feature_sizesfpn_hidden_sizefpn_kernel_size fpn_stride fpn_paddingfpn_top_down_levelsnum_feature_levelsr,r-r.)r/rrFrGrHrIrJrKrLrMr,r-r.r0r1r3r4rs0   zSam2VisionConfig.__init__) NNNr?r r r@Nrr rr) r5r6r7r8r9r:r sub_configsrr;r3r3r1r4r<s$&r<cs&eZdZdZdZd fd d ZZS) 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_configr?rrr rr c  sFtjfi| ||_||_||_||_||_||_||_||_ dSN) rrrr patch_sizemask_input_channelsnum_point_embeddingsr,r-scale) r/rrrSrTrUr,r-rVr0r1r3r4r s z Sam2PromptEncoderConfig.__init__)r?rrQrQrr rr r5r6r7r8r9rr;r3r3r1r4rOsrOc s&eZdZdZdZdfd d ZZS)Sam2MaskDecoderConfiga 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_configr?r rrrT皙?\(\?c  sdtjfi| ||_||_||_||_| |_| |_| |_| |_ ||_ ||_||_ ||_ ||_ dSrR)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)r/rr,rdrcrrer]r^r_r`rarbr0r1r3r4rHszSam2MaskDecoderConfig.__init__) r?r rZrrrrrr?Tr[r\rWr3r3r1r4rX"s#rXcs2eZdZdZdZeeedZdfdd Z Z S) Sam2Configal [`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. kwargs (*optional*): Dictionary of keyword arguments. 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) ```Zsam2)r=rPrYNrc stjfi||dur|ni}|dur.|ni}|dur>|ni}t|trt|dd|d<t|dfi|}nt|tr|}t|tr|}t|t r|}||_ tfi||_ t fi||_ ||_ dS)Nr:r>)rrrCrDrErrrOto_dictrXr=rPrYr.)r/r=rPrYr.r0r1r3r4rs"    zSam2Config.__init__)NNNr) r5r6r7r8r:rrOrXrNrr;r3r3r1r4rfks2rf)rfrr<rOrXN)r8Zconfiguration_utilsrutilsrautorrZ get_loggerr5loggerrr<rOrXrf__all__r3r3r3r4s   w^4IX