# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 # This file was automatically generated from src/transformers/models/edgetam/modular_edgetam.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_edgetam.py file directly. One of our CI enforces this. # 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 # Copyright 2025 The Meta AI Authors and 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 huggingface_hub.dataclasses import strict from ...configuration_utils import PreTrainedConfig from ...utils import auto_docstring from ..auto import CONFIG_MAPPING, AutoConfig @auto_docstring(checkpoint="yonigozlan/EdgeTAM-hf") @strict class EdgeTamVisionConfig(PreTrainedConfig): r""" backbone_channel_list (`List[int]`, *optional*, defaults to `[384, 192, 96, 48]`): 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. """ base_config_key = "vision_config" model_type = "edgetam_vision_model" sub_configs = { "backbone_config": AutoConfig, } backbone_config: dict | PreTrainedConfig | None = None backbone_channel_list: list[int] | None = None backbone_feature_sizes: list | None = None fpn_hidden_size: int = 256 fpn_kernel_size: int = 1 fpn_stride: int = 1 fpn_padding: int = 0 fpn_top_down_levels: list[int] | None = None num_feature_levels: int = 3 hidden_act: str = "gelu" layer_norm_eps: float = 1e-6 initializer_range: float = 0.02 def __post_init__(self, **kwargs): self.backbone_channel_list = ( [384, 192, 96, 48] if self.backbone_channel_list is None else self.backbone_channel_list ) self.backbone_feature_sizes = ( [[256, 256], [128, 128], [64, 64]] if self.backbone_feature_sizes is None else self.backbone_feature_sizes ) self.fpn_top_down_levels = [2, 3] if self.fpn_top_down_levels is None else self.fpn_top_down_levels if isinstance(self.backbone_config, dict): self.backbone_config["model_type"] = self.backbone_config.get("model_type", "timm_wrapper") self.backbone_config = CONFIG_MAPPING[self.backbone_config["model_type"]](**self.backbone_config) elif self.backbone_config is None: self.backbone_config = AutoConfig.from_pretrained( "timm/repvit_m1.dist_in1k", model_args={"in_chans": 3, "features_only": True, "out_indices": [0, 1, 2, 3]}, ) super().__post_init__(**kwargs) @auto_docstring(checkpoint="yonigozlan/EdgeTAM-hf") @strict class EdgeTamPromptEncoderConfig(PreTrainedConfig): r""" 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. scale (`float`, *optional*, defaults to 1): The scale factor for the prompt encoder. """ base_config_key = "prompt_encoder_config" hidden_size: int = 256 image_size: int | list[int] | tuple[int, int] = 1024 patch_size: int | list[int] | tuple[int, int] = 16 mask_input_channels: int = 16 num_point_embeddings: int = 4 hidden_act: str = "gelu" layer_norm_eps: float = 1e-6 scale: int = 1 @auto_docstring(checkpoint="yonigozlan/EdgeTAM-hf") @strict class EdgeTamMaskDecoderConfig(PreTrainedConfig): r""" mlp_dim (`int`, *optional*, defaults to 2048): The dimension of the MLP 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" hidden_size: int = 256 hidden_act: str = "gelu" mlp_dim: int = 2048 num_hidden_layers: int = 2 num_attention_heads: int = 8 attention_downsample_rate: int = 2 num_multimask_outputs: int = 3 iou_head_depth: int = 3 iou_head_hidden_dim: int = 256 dynamic_multimask_via_stability: bool = True dynamic_multimask_stability_delta: float = 0.05 dynamic_multimask_stability_thresh: float = 0.98 @auto_docstring(checkpoint="yonigozlan/EdgeTAM-hf") @strict class EdgeTamConfig(PreTrainedConfig): r""" prompt_encoder_config (Union[`dict`, `EdgeTamPromptEncoderConfig`], *optional*): Dictionary of configuration options used to initialize [`EdgeTamPromptEncoderConfig`]. mask_decoder_config (Union[`dict`, `EdgeTamMaskDecoderConfig`], *optional*): Dictionary of configuration options used to initialize [`EdgeTamMaskDecoderConfig`]. Example: ```python >>> from transformers import ( ... EdgeTamVisionConfig, ... EdgeTamPromptEncoderConfig, ... EdgeTamMaskDecoderConfig, ... EdgeTamModel, ... ) >>> # Initializing a EdgeTamConfig with `"facebook/edgetam.1_hiera_tiny"` style configuration >>> configuration = EdgeTamConfig() >>> # Initializing a EdgeTamModel (with random weights) from the `"facebook/edgetam.1_hiera_tiny"` style configuration >>> model = EdgeTamModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config >>> # We can also initialize a EdgeTamConfig from a EdgeTamVisionConfig, EdgeTamPromptEncoderConfig, and EdgeTamMaskDecoderConfig >>> # Initializing EDGETAM vision encoder, memory attention, and memory encoder configurations >>> vision_config = EdgeTamVisionConfig() >>> prompt_encoder_config = EdgeTamPromptEncoderConfig() >>> mask_decoder_config = EdgeTamMaskDecoderConfig() >>> config = EdgeTamConfig(vision_config, prompt_encoder_config, mask_decoder_config) ``` """ model_type = "edgetam" sub_configs = { "vision_config": AutoConfig, "prompt_encoder_config": EdgeTamPromptEncoderConfig, "mask_decoder_config": EdgeTamMaskDecoderConfig, } vision_config: dict | PreTrainedConfig | None = None prompt_encoder_config: dict | PreTrainedConfig | None = None mask_decoder_config: dict | PreTrainedConfig | None = None initializer_range: float = 0.02 def __post_init__(self, **kwargs): if isinstance(self.vision_config, dict): self.vision_config["model_type"] = self.vision_config.get("model_type", "edgetam_vision_model") self.vision_config = CONFIG_MAPPING[self.vision_config["model_type"]](**self.vision_config) elif self.vision_config is None: self.vision_config = CONFIG_MAPPING["edgetam_vision_model"]() if isinstance(self.prompt_encoder_config, dict): self.prompt_encoder_config = EdgeTamPromptEncoderConfig(**self.prompt_encoder_config) elif self.prompt_encoder_config is None: self.prompt_encoder_config = EdgeTamPromptEncoderConfig() if isinstance(self.mask_decoder_config, dict): self.mask_decoder_config = EdgeTamMaskDecoderConfig(**self.mask_decoder_config) elif self.mask_decoder_config is None: self.mask_decoder_config = EdgeTamMaskDecoderConfig() super().__post_init__(**kwargs) __all__ = ["EdgeTamConfig", "EdgeTamVisionConfig", "EdgeTamPromptEncoderConfig", "EdgeTamMaskDecoderConfig"]