# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 # This file was automatically generated from src/transformers/models/sam3_tracker/modular_sam3_tracker.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_sam3_tracker.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 class Sam3TrackerPromptEncoderConfig(PreTrainedConfig): r""" This is the configuration class to store the configuration of a [`Sam3TrackerPromptEncoder`]. The [`Sam3TrackerPromptEncoder`] 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 1008): The expected output resolution of the image. patch_size (`int`, *optional*, defaults to 14): 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. """ base_config_key = "prompt_encoder_config" def __init__( self, hidden_size=256, image_size=1008, patch_size=14, mask_input_channels=16, num_point_embeddings=4, hidden_act="gelu", layer_norm_eps=1e-6, scale=1, **kwargs, ): super().__init__(**kwargs) self.hidden_size = hidden_size self.image_size = image_size self.patch_size = patch_size self.mask_input_channels = mask_input_channels self.num_point_embeddings = num_point_embeddings self.hidden_act = hidden_act self.layer_norm_eps = layer_norm_eps self.scale = scale class Sam3TrackerMaskDecoderConfig(PreTrainedConfig): r""" This is the configuration class to store the configuration of a [`Sam3TrackerMaskDecoder`]. It is used to instantiate a SAM3_TRACKER 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 SAM3_TRACKER 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. """ base_config_key = "mask_decoder_config" def __init__( self, hidden_size=256, hidden_act="gelu", mlp_dim=2048, num_hidden_layers=2, num_attention_heads=8, attention_downsample_rate=2, num_multimask_outputs=3, iou_head_depth=3, iou_head_hidden_dim=256, dynamic_multimask_via_stability=True, dynamic_multimask_stability_delta=0.05, dynamic_multimask_stability_thresh=0.98, **kwargs, ): super().__init__(**kwargs) self.hidden_size = hidden_size self.num_multimask_outputs = num_multimask_outputs self.hidden_act = hidden_act self.iou_head_depth = iou_head_depth self.iou_head_hidden_dim = iou_head_hidden_dim self.dynamic_multimask_via_stability = dynamic_multimask_via_stability self.dynamic_multimask_stability_delta = dynamic_multimask_stability_delta self.dynamic_multimask_stability_thresh = dynamic_multimask_stability_thresh # TwoWayTransformer configuration self.num_hidden_layers = num_hidden_layers self.hidden_size = hidden_size self.num_attention_heads = num_attention_heads self.mlp_dim = mlp_dim self.attention_downsample_rate = attention_downsample_rate class Sam3TrackerConfig(PreTrainedConfig): r""" [`Sam3TrackerConfig`] is the configuration class to store the configuration of a [`Sam3TrackerModel`]. It is used to instantiate a SAM3_TRACKER 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/sam3_tracker.1-hiera-tiny](https://huggingface.co/facebook/sam3_tracker.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. SAM3 Tracker checkpoints with `model_type="sam3_tracker_video"` are compatible with `Sam3TrackerModel` since the video variant weights are a superset of the image-only model weights. You may see a warning about model type mismatch when loading such checkpoints, which can be safely ignored in this case. Args: vision_config (Union[`dict`, `Sam3TrackerVisionConfig`], *optional*): Dictionary of configuration options used to initialize [`Sam3TrackerVisionConfig`]. prompt_encoder_config (Union[`dict`, `Sam3TrackerPromptEncoderConfig`], *optional*): Dictionary of configuration options used to initialize [`Sam3TrackerPromptEncoderConfig`]. mask_decoder_config (Union[`dict`, `Sam3TrackerMaskDecoderConfig`], *optional*): Dictionary of configuration options used to initialize [`Sam3TrackerMaskDecoderConfig`]. initializer_range (`float`, *optional*, defaults to 0.02): Standard deviation for parameter initialization. Example: ```python >>> from transformers import ( ... Sam3TrackerVisionConfig, ... Sam3TrackerPromptEncoderConfig, ... Sam3TrackerMaskDecoderConfig, ... Sam3TrackerModel, ... ) >>> # Initializing a Sam3TrackerConfig with `"facebook/sam3_tracker.1_hiera_tiny"` style configuration >>> configuration = Sam3TrackerConfig() >>> # Initializing a Sam3TrackerModel (with random weights) from the `"facebook/sam3_tracker.1_hiera_tiny"` style configuration >>> model = Sam3TrackerModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config >>> # We can also initialize a Sam3TrackerConfig from a Sam3TrackerVisionConfig, Sam3TrackerPromptEncoderConfig, and Sam3TrackerMaskDecoderConfig >>> # Initializing SAM3_TRACKER vision encoder, memory attention, and memory encoder configurations >>> vision_config = Sam3TrackerVisionConfig() >>> prompt_encoder_config = Sam3TrackerPromptEncoderConfig() >>> mask_decoder_config = Sam3TrackerMaskDecoderConfig() >>> config = Sam3TrackerConfig(vision_config, prompt_encoder_config, mask_decoder_config) ``` """ model_type = "sam3_tracker" sub_configs = { "vision_config": AutoConfig, "prompt_encoder_config": Sam3TrackerPromptEncoderConfig, "mask_decoder_config": Sam3TrackerMaskDecoderConfig, } def __init__( self, vision_config=None, prompt_encoder_config=None, mask_decoder_config=None, initializer_range=0.02, **kwargs, ): vision_config = ( vision_config if vision_config is not None else {"backbone_feature_sizes": [[288, 288], [144, 144], [72, 72]]} ) prompt_encoder_config = prompt_encoder_config if prompt_encoder_config is not None else {} mask_decoder_config = mask_decoder_config if mask_decoder_config is not None else {} if isinstance(vision_config, dict): vision_config["model_type"] = vision_config.get("model_type", "sam3_vision_model") vision_config = CONFIG_MAPPING[vision_config["model_type"]](**vision_config) if isinstance(prompt_encoder_config, Sam3TrackerPromptEncoderConfig): prompt_encoder_config = prompt_encoder_config.to_dict() if isinstance(mask_decoder_config, Sam3TrackerMaskDecoderConfig): mask_decoder_config = mask_decoder_config.to_dict() self.vision_config = vision_config self.prompt_encoder_config = Sam3TrackerPromptEncoderConfig(**prompt_encoder_config) self.mask_decoder_config = Sam3TrackerMaskDecoderConfig(**mask_decoder_config) self.initializer_range = initializer_range super().__init__(**kwargs) __all__ = ["Sam3TrackerConfig", "Sam3TrackerPromptEncoderConfig", "Sam3TrackerMaskDecoderConfig"]