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328 lines
15 KiB
328 lines
15 KiB
# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
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# This file was automatically generated from src/transformers/models/edgetam/modular_edgetam.py.
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# Do NOT edit this file manually as any edits will be overwritten by the generation of
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# the file from the modular. If any change should be done, please apply the change to the
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# modular_edgetam.py file directly. One of our CI enforces this.
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# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
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# Copyright 2025 The Meta AI Authors and The HuggingFace Team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from ...configuration_utils import PreTrainedConfig
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from ..auto import CONFIG_MAPPING, AutoConfig
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class EdgeTamVisionConfig(PreTrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`EdgeTamVisionModel`]. It is used to instantiate a SAM
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vision encoder according to the specified arguments, defining the model architecture. Instantiating a configuration
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defaults will yield a similar configuration to that of SAM 2.1 Hiera-tiny
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[facebook/EdgeTAM](https://huggingface.co/facebook/EdgeTAM) architecture.
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Configuration objects inherit from [`PreTrainedConfig`] and can be used to control the model outputs. Read the
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documentation from [`PreTrainedConfig`] for more information.
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Args:
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backbone_config (`Union[dict, "PreTrainedConfig"]`, *optional*, defaults to `timm/repvit_m1.dist_in1k`):
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Configuration for the vision backbone. This is used to instantiate the backbone using
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`AutoModel.from_config`.
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backbone_channel_list (`List[int]`, *optional*, defaults to `[384, 192, 96, 48]`):
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The list of channel dimensions for the backbone.
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backbone_feature_sizes (`List[List[int]]`, *optional*, defaults to `[[256, 256], [128, 128], [64, 64]]`):
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The spatial sizes of the feature maps from the backbone.
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fpn_hidden_size (`int`, *optional*, defaults to 256):
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The hidden dimension of the FPN.
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fpn_kernel_size (`int`, *optional*, defaults to 1):
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The kernel size for the convolutions in the neck.
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fpn_stride (`int`, *optional*, defaults to 1):
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The stride for the convolutions in the neck.
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fpn_padding (`int`, *optional*, defaults to 0):
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The padding for the convolutions in the neck.
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fpn_top_down_levels (`List[int]`, *optional*, defaults to `[2, 3]`):
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The levels for the top-down FPN connections.
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num_feature_levels (`int`, *optional*, defaults to 3):
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The number of feature levels from the FPN to use.
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hidden_act (`str`, *optional*, defaults to `"gelu"`):
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The non-linear activation function in the neck.
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layer_norm_eps (`float`, *optional*, defaults to 1e-06):
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The epsilon for the layer normalization.
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initializer_range (`float`, *optional*, defaults to 0.02):
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The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
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"""
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base_config_key = "vision_config"
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model_type = "edgetam_vision_model"
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sub_configs = {
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"backbone_config": AutoConfig,
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}
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def __init__(
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self,
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backbone_config=None,
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backbone_channel_list=None,
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backbone_feature_sizes=None,
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fpn_hidden_size=256,
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fpn_kernel_size=1,
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fpn_stride=1,
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fpn_padding=0,
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fpn_top_down_levels=None,
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num_feature_levels=3,
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hidden_act="gelu",
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layer_norm_eps=1e-6,
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initializer_range=0.02,
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**kwargs,
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):
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backbone_channel_list = [384, 192, 96, 48] if backbone_channel_list is None else backbone_channel_list
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backbone_feature_sizes = (
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[[256, 256], [128, 128], [64, 64]] if backbone_feature_sizes is None else backbone_feature_sizes
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)
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fpn_top_down_levels = [2, 3] if fpn_top_down_levels is None else fpn_top_down_levels
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if isinstance(backbone_config, dict):
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backbone_config["model_type"] = backbone_config.get("model_type", "timm_wrapper")
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backbone_config = CONFIG_MAPPING[backbone_config["model_type"]](**backbone_config)
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elif backbone_config is None:
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backbone_config = AutoConfig.from_pretrained(
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"timm/repvit_m1.dist_in1k",
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model_args={"in_chans": 3, "features_only": True, "out_indices": [0, 1, 2, 3]},
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)
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self.backbone_config = backbone_config
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# Neck
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self.backbone_channel_list = backbone_channel_list
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self.backbone_feature_sizes = backbone_feature_sizes
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self.fpn_hidden_size = fpn_hidden_size
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self.fpn_kernel_size = fpn_kernel_size
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self.fpn_stride = fpn_stride
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self.fpn_padding = fpn_padding
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self.fpn_top_down_levels = fpn_top_down_levels
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self.num_feature_levels = num_feature_levels
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self.hidden_act = hidden_act
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self.layer_norm_eps = layer_norm_eps
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self.initializer_range = initializer_range
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super().__init__(**kwargs)
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class EdgeTamPromptEncoderConfig(PreTrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`EdgeTamPromptEncoder`]. The [`EdgeTamPromptEncoder`]
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module is used to encode the input 2D points and bounding boxes.
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Configuration objects inherit from [`PreTrainedConfig`] and can be used to control the model outputs. Read the
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documentation from [`PreTrainedConfig`] for more information.
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Args:
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hidden_size (`int`, *optional*, defaults to 256):
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Dimensionality of the hidden states.
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image_size (`int`, *optional*, defaults to 1024):
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The expected output resolution of the image.
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patch_size (`int`, *optional*, defaults to 16):
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The size (resolution) of each patch.
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mask_input_channels (`int`, *optional*, defaults to 16):
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The number of channels to be fed to the `MaskDecoder` module.
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num_point_embeddings (`int`, *optional*, defaults to 4):
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The number of point embeddings to be used.
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hidden_act (`str`, *optional*, defaults to `"gelu"`):
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The non-linear activation function in the encoder and pooler.
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layer_norm_eps (`float`, *optional*, defaults to 1e-06):
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The epsilon used by the layer normalization layers.
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scale (`float`, *optional*, defaults to 1):
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The scale factor for the prompt encoder.
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"""
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base_config_key = "prompt_encoder_config"
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def __init__(
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self,
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hidden_size=256,
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image_size=1024,
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patch_size=16,
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mask_input_channels=16,
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num_point_embeddings=4,
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hidden_act="gelu",
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layer_norm_eps=1e-6,
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scale=1,
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**kwargs,
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):
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super().__init__(**kwargs)
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self.hidden_size = hidden_size
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self.image_size = image_size
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self.patch_size = patch_size
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self.mask_input_channels = mask_input_channels
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self.num_point_embeddings = num_point_embeddings
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self.hidden_act = hidden_act
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self.layer_norm_eps = layer_norm_eps
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self.scale = scale
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class EdgeTamMaskDecoderConfig(PreTrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`EdgeTamMaskDecoder`]. It is used to instantiate a EDGETAM
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memory encoder according to the specified arguments, defining the model architecture.
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Configuration objects inherit from [`PreTrainedConfig`] and can be used to control the model outputs. Read the
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documentation from [`PreTrainedConfig`] for more information.
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Args:
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hidden_size (`int`, *optional*, defaults to 256):
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Dimensionality of the hidden states.
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hidden_act (`str`, *optional*, defaults to `"gelu"`):
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The non-linear activation function in the EDGETAM mask decoder.
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mlp_dim (`int`, *optional*, defaults to 2048):
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The dimension of the MLP in the two-way transformer.
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num_hidden_layers (`int`, *optional*, defaults to 2):
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The number of hidden layers in the two-way transformer.
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num_attention_heads (`int`, *optional*, defaults to 8):
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The number of attention heads in the two-way transformer.
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attention_downsample_rate (`int`, *optional*, defaults to 2):
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The downsample rate for the attention layers.
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num_multimask_outputs (`int`, *optional*, defaults to 3):
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The number of multimask outputs.
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iou_head_depth (`int`, *optional*, defaults to 3):
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The depth of the IoU head.
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iou_head_hidden_dim (`int`, *optional*, defaults to 256):
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The hidden dimension of the IoU head.
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dynamic_multimask_via_stability (`bool`, *optional*, defaults to `True`):
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Whether to use dynamic multimask via stability.
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dynamic_multimask_stability_delta (`float`, *optional*, defaults to 0.05):
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The stability delta for the dynamic multimask.
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dynamic_multimask_stability_thresh (`float`, *optional*, defaults to 0.98):
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The stability threshold for the dynamic multimask.
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"""
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base_config_key = "mask_decoder_config"
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def __init__(
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self,
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hidden_size=256,
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hidden_act="gelu",
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mlp_dim=2048,
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num_hidden_layers=2,
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num_attention_heads=8,
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attention_downsample_rate=2,
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num_multimask_outputs=3,
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iou_head_depth=3,
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iou_head_hidden_dim=256,
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dynamic_multimask_via_stability=True,
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dynamic_multimask_stability_delta=0.05,
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dynamic_multimask_stability_thresh=0.98,
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**kwargs,
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):
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super().__init__(**kwargs)
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self.hidden_size = hidden_size
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self.num_multimask_outputs = num_multimask_outputs
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self.hidden_act = hidden_act
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self.iou_head_depth = iou_head_depth
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self.iou_head_hidden_dim = iou_head_hidden_dim
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self.dynamic_multimask_via_stability = dynamic_multimask_via_stability
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self.dynamic_multimask_stability_delta = dynamic_multimask_stability_delta
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self.dynamic_multimask_stability_thresh = dynamic_multimask_stability_thresh
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# TwoWayTransformer configuration
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self.num_hidden_layers = num_hidden_layers
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self.hidden_size = hidden_size
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self.num_attention_heads = num_attention_heads
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self.mlp_dim = mlp_dim
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self.attention_downsample_rate = attention_downsample_rate
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class EdgeTamConfig(PreTrainedConfig):
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r"""
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[`EdgeTamConfig`] is the configuration class to store the configuration of a [`EdgeTamModel`]. It is used to instantiate a
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EDGETAM model according to the specified arguments, defining the memory attention, memory encoder, and image encoder
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configs. Instantiating a configuration defaults will yield a similar configuration to that of the SAM 2.1 Hiera-tiny
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[facebook/edgetam.1-hiera-tiny](https://huggingface.co/facebook/edgetam.1-hiera-tiny) architecture.
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Configuration objects inherit from [`PreTrainedConfig`] and can be used to control the model outputs. Read the
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documentation from [`PreTrainedConfig`] for more information.
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Args:
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vision_config (Union[`dict`, `EdgeTamVisionConfig`], *optional*):
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Dictionary of configuration options used to initialize [`EdgeTamVisionConfig`].
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prompt_encoder_config (Union[`dict`, `EdgeTamPromptEncoderConfig`], *optional*):
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Dictionary of configuration options used to initialize [`EdgeTamPromptEncoderConfig`].
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mask_decoder_config (Union[`dict`, `EdgeTamMaskDecoderConfig`], *optional*):
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Dictionary of configuration options used to initialize [`EdgeTamMaskDecoderConfig`].
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initializer_range (`float`, *optional*, defaults to 0.02):
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Standard deviation for parameter initialization.
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Example:
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```python
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>>> from transformers import (
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... EdgeTamVisionConfig,
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... EdgeTamPromptEncoderConfig,
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... EdgeTamMaskDecoderConfig,
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... EdgeTamModel,
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... )
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>>> # Initializing a EdgeTamConfig with `"facebook/edgetam.1_hiera_tiny"` style configuration
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>>> configuration = EdgeTamConfig()
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>>> # Initializing a EdgeTamModel (with random weights) from the `"facebook/edgetam.1_hiera_tiny"` style configuration
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>>> model = EdgeTamModel(configuration)
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>>> # Accessing the model configuration
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>>> configuration = model.config
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>>> # We can also initialize a EdgeTamConfig from a EdgeTamVisionConfig, EdgeTamPromptEncoderConfig, and EdgeTamMaskDecoderConfig
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>>> # Initializing EDGETAM vision encoder, memory attention, and memory encoder configurations
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>>> vision_config = EdgeTamVisionConfig()
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>>> prompt_encoder_config = EdgeTamPromptEncoderConfig()
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>>> mask_decoder_config = EdgeTamMaskDecoderConfig()
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>>> config = EdgeTamConfig(vision_config, prompt_encoder_config, mask_decoder_config)
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```"""
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model_type = "edgetam"
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sub_configs = {
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"vision_config": AutoConfig,
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"prompt_encoder_config": EdgeTamPromptEncoderConfig,
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"mask_decoder_config": EdgeTamMaskDecoderConfig,
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}
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def __init__(
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self,
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vision_config=None,
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prompt_encoder_config=None,
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mask_decoder_config=None,
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initializer_range=0.02,
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**kwargs,
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):
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vision_config = vision_config if vision_config is not None else {}
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prompt_encoder_config = prompt_encoder_config if prompt_encoder_config is not None else {}
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mask_decoder_config = mask_decoder_config if mask_decoder_config is not None else {}
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if isinstance(vision_config, dict):
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vision_config["model_type"] = vision_config.get("model_type", "edgetam_vision_model")
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vision_config = CONFIG_MAPPING[vision_config["model_type"]](**vision_config)
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if isinstance(prompt_encoder_config, EdgeTamPromptEncoderConfig):
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prompt_encoder_config = prompt_encoder_config.to_dict()
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if isinstance(mask_decoder_config, EdgeTamMaskDecoderConfig):
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mask_decoder_config = mask_decoder_config.to_dict()
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self.vision_config = vision_config
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self.prompt_encoder_config = EdgeTamPromptEncoderConfig(**prompt_encoder_config)
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self.mask_decoder_config = EdgeTamMaskDecoderConfig(**mask_decoder_config)
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self.initializer_range = initializer_range
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super().__init__(**kwargs)
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__all__ = ["EdgeTamConfig", "EdgeTamVisionConfig", "EdgeTamPromptEncoderConfig", "EdgeTamMaskDecoderConfig"]
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