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538 lines
20 KiB
538 lines
20 KiB
# Copyright 2025 Meta AI 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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"""SAM3 model configuration"""
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from transformers import CLIPTextConfig
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from ...configuration_utils import PreTrainedConfig
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from ..auto import CONFIG_MAPPING, AutoConfig
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class Sam3ViTConfig(PreTrainedConfig):
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r"""
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Configuration class for SAM3 Vision Encoder (ViT backbone).
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Instantiating a configuration defaults will yield a similar configuration to that of SAM 3
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[facebook/sam3](https://huggingface.co/facebook/sam3) architecture.
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Args:
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hidden_size (`int`, *optional*, defaults to 1024):
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Dimensionality of the encoder layers.
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intermediate_size (`int`, *optional*, defaults to 4736):
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Dimensionality of the feedforward (MLP) layers.
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num_hidden_layers (`int`, *optional*, defaults to 32):
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Number of hidden layers in the Transformer encoder.
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num_attention_heads (`int`, *optional*, defaults to 16):
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Number of attention heads for each attention layer.
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num_channels (`int`, *optional*, defaults to 3):
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Number of input image channels.
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image_size (`int`, *optional*, defaults to 1008):
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Expected input image size.
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patch_size (`int`, *optional*, defaults to 14):
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Size of image patches.
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hidden_act (`str`, *optional*, defaults to `"gelu"`):
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The non-linear activation function.
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layer_norm_eps (`float`, *optional*, defaults to 1e-06):
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The epsilon used by layer normalization layers.
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attention_dropout (`float`, *optional*, defaults to 0.0):
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The dropout ratio for attention probabilities.
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rope_theta (`float`, *optional*, defaults to 10000.0):
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Base frequency for RoPE.
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window_size (`int`, *optional*, defaults to 24):
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Window size for windowed attention.
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global_attn_indexes (`list[int]`, *optional*, defaults to `[7, 15, 23, 31]`):
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Indexes of layers with global attention.
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layer_scale_init_value (`float`, *optional*):
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Initial value for layer scale. None means no layer scale.
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pretrain_image_size (`int`, *optional*, defaults to 336):
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Pretrained model image size for position embedding initialization.
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hidden_dropout (`float`, *optional*, defaults to 0.0):
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Dropout probability for hidden states.
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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 weight matrices.
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"""
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base_config_key = "backbone_config"
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model_type = "sam3_vit_model"
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def __init__(
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self,
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hidden_size=1024,
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intermediate_size=4736,
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num_hidden_layers=32,
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num_attention_heads=16,
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num_channels=3,
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image_size=1008,
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patch_size=14,
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hidden_act="gelu",
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layer_norm_eps=1e-6,
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attention_dropout=0.0,
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rope_theta=10000.0,
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window_size=24,
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global_attn_indexes=None,
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layer_scale_init_value=None,
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pretrain_image_size=336,
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hidden_dropout=0.0,
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initializer_range=0.02,
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**kwargs,
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):
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super().__init__(**kwargs)
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if global_attn_indexes is None:
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global_attn_indexes = [7, 15, 23, 31]
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self.hidden_size = hidden_size
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self.intermediate_size = intermediate_size
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self.num_hidden_layers = num_hidden_layers
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self.num_attention_heads = num_attention_heads
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self.num_channels = num_channels
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self.image_size = image_size
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self.patch_size = patch_size
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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.attention_dropout = attention_dropout
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self.rope_theta = rope_theta
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self.window_size = window_size
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self.global_attn_indexes = global_attn_indexes
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self.layer_scale_init_value = layer_scale_init_value
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self.pretrain_image_size = pretrain_image_size
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self.hidden_dropout = hidden_dropout
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self.initializer_range = initializer_range
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class Sam3VisionConfig(PreTrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`Sam3VisionModel`]. 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 3
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[facebook/sam3](https://huggingface.co/facebook/sam3) 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 `Sam3ViTConfig()`):
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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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fpn_hidden_size (`int`, *optional*, defaults to 256):
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The hidden dimension of the FPN.
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backbone_feature_sizes (`List[List[int]]`, *optional*, defaults to `[[288, 288], [144, 144], [72, 72]]`):
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The spatial sizes (height, width) of the feature maps from the backbone at different scales.
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scale_factors (`list[float]`, *optional*, defaults to `[4.0, 2.0, 1.0, 0.5]`):
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Scale factors for FPN multi-scale features. List of scaling factors for each FPN level.
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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 = "sam3_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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fpn_hidden_size=256,
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backbone_feature_sizes=None,
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scale_factors=None,
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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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scale_factors = [4.0, 2.0, 1.0, 0.5] if scale_factors is None else scale_factors
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if backbone_feature_sizes is None:
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backbone_feature_sizes = [[288, 288], [144, 144], [72, 72]]
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if isinstance(backbone_config, dict):
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backbone_config["model_type"] = backbone_config.get("model_type", "sam3_vit_model")
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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 = CONFIG_MAPPING["sam3_vit_model"]()
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self.backbone_config = backbone_config
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# Neck
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self.fpn_hidden_size = fpn_hidden_size
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self.scale_factors = scale_factors
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self.backbone_feature_sizes = backbone_feature_sizes
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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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@property
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def image_size(self):
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"""Image size for the vision encoder."""
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return self.backbone_config.image_size
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@image_size.setter
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def image_size(self, value):
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"""Set the image size and propagate to backbone."""
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self.backbone_config.image_size = value
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class Sam3GeometryEncoderConfig(PreTrainedConfig):
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r"""
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Configuration class for SAM3 Geometry Encoder.
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Args:
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hidden_size (`int`, *optional*, defaults to 256):
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Dimensionality of the encoder layers.
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num_layers (`int`, *optional*, defaults to 3):
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Number of transformer encoder layers for processing geometry prompts.
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num_attention_heads (`int`, *optional*, defaults to 8):
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Number of attention heads in the geometry encoder.
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intermediate_size (`int`, *optional*, defaults to 2048):
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Dimensionality of the feedforward layers.
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dropout (`float`, *optional*, defaults to 0.1):
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Dropout probability.
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hidden_act (`str`, *optional*, defaults to `"relu"`):
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Activation function in FFN.
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hidden_dropout (`float`, *optional*, defaults to 0.0):
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Dropout probability for hidden states.
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layer_norm_eps (`float`, *optional*, defaults to 1e-06):
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Epsilon for layer normalization.
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roi_size (`int`, *optional*, defaults to 7):
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ROI size for box pooling operations.
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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 weight matrices.
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"""
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model_type = "sam3_geometry_encoder"
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def __init__(
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self,
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hidden_size=256,
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num_layers=3,
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num_attention_heads=8,
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intermediate_size=2048,
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dropout=0.1,
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hidden_act="relu",
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hidden_dropout=0.0,
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layer_norm_eps=1e-6,
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roi_size=7,
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initializer_range=0.02,
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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_layers = num_layers
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self.num_attention_heads = num_attention_heads
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self.intermediate_size = intermediate_size
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self.dropout = dropout
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self.hidden_act = hidden_act
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self.hidden_dropout = hidden_dropout
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self.layer_norm_eps = layer_norm_eps
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self.roi_size = roi_size
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self.initializer_range = initializer_range
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class Sam3DETREncoderConfig(PreTrainedConfig):
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r"""
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Configuration class for SAM3 DETR Encoder (vision-text fusion encoder).
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Args:
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hidden_size (`int`, *optional*, defaults to 256):
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Dimensionality of the encoder layers.
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num_layers (`int`, *optional*, defaults to 6):
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Number of encoder layers.
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num_attention_heads (`int`, *optional*, defaults to 8):
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Number of attention heads.
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intermediate_size (`int`, *optional*, defaults to 2048):
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Dimensionality of the feedforward layers.
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dropout (`float`, *optional*, defaults to 0.1):
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Dropout probability.
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hidden_act (`str`, *optional*, defaults to `"relu"`):
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Activation function in FFN.
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hidden_dropout (`float`, *optional*, defaults to 0.0):
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Dropout probability for hidden states.
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layer_norm_eps (`float`, *optional*, defaults to 1e-06):
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Epsilon for 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 weight matrices.
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"""
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model_type = "sam3_detr_encoder"
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def __init__(
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self,
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hidden_size=256,
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num_layers=6,
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num_attention_heads=8,
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intermediate_size=2048,
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dropout=0.1,
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hidden_act="relu",
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hidden_dropout=0.0,
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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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super().__init__(**kwargs)
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self.hidden_size = hidden_size
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self.num_layers = num_layers
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self.num_attention_heads = num_attention_heads
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self.intermediate_size = intermediate_size
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self.dropout = dropout
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self.hidden_act = hidden_act
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self.hidden_dropout = hidden_dropout
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self.layer_norm_eps = layer_norm_eps
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self.initializer_range = initializer_range
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class Sam3DETRDecoderConfig(PreTrainedConfig):
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r"""
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Configuration class for SAM3 DETR Decoder (object query decoder).
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Args:
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hidden_size (`int`, *optional*, defaults to 256):
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Dimensionality of the decoder layers.
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num_layers (`int`, *optional*, defaults to 6):
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Number of decoder layers.
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num_queries (`int`, *optional*, defaults to 200):
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Number of object queries.
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num_attention_heads (`int`, *optional*, defaults to 8):
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Number of attention heads.
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intermediate_size (`int`, *optional*, defaults to 2048):
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Dimensionality of the feedforward layers.
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dropout (`float`, *optional*, defaults to 0.1):
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Dropout probability.
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hidden_act (`str`, *optional*, defaults to `"relu"`):
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Activation function in FFN.
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hidden_dropout (`float`, *optional*, defaults to 0.0):
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Dropout probability for hidden states.
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layer_norm_eps (`float`, *optional*, defaults to 1e-06):
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Epsilon for 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 weight matrices.
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"""
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model_type = "sam3_detr_decoder"
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def __init__(
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self,
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hidden_size=256,
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num_layers=6,
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num_queries=200,
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num_attention_heads=8,
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intermediate_size=2048,
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dropout=0.1,
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hidden_act="relu",
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hidden_dropout=0.0,
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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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super().__init__(**kwargs)
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self.hidden_size = hidden_size
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self.num_layers = num_layers
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self.num_queries = num_queries
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self.num_attention_heads = num_attention_heads
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self.intermediate_size = intermediate_size
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self.dropout = dropout
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self.hidden_act = hidden_act
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self.hidden_dropout = hidden_dropout
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self.layer_norm_eps = layer_norm_eps
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self.initializer_range = initializer_range
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class Sam3MaskDecoderConfig(PreTrainedConfig):
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r"""
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Configuration class for SAM3 Mask Decoder (pixel-level mask prediction).
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Args:
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hidden_size (`int`, *optional*, defaults to 256):
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Dimensionality of the mask decoder.
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num_upsampling_stages (`int`, *optional*, defaults to 3):
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Number of upsampling stages in the pixel decoder (FPN).
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layer_norm_eps (`float`, *optional*, defaults to 1e-06):
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Epsilon for layer normalization.
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dropout (`float`, *optional*, defaults to 0.0):
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Dropout probability for prompt cross-attention.
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num_attention_heads (`int`, *optional*, defaults to 8):
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Number of attention heads for prompt cross-attention.
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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 weight matrices.
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"""
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model_type = "sam3_mask_decoder"
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def __init__(
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self,
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hidden_size=256,
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num_upsampling_stages=3,
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layer_norm_eps=1e-6,
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dropout=0.0,
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num_attention_heads=8,
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initializer_range=0.02,
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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_upsampling_stages = num_upsampling_stages
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self.layer_norm_eps = layer_norm_eps
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self.dropout = dropout
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self.num_attention_heads = num_attention_heads
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self.initializer_range = initializer_range
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class Sam3Config(PreTrainedConfig):
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r"""
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Configuration class to store the configuration of a [`Sam3Model`].
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Instantiating a configuration defaults will yield a similar configuration to that of SAM 3
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[facebook/sam3](https://huggingface.co/facebook/sam3) architecture.
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This is the main configuration class that combines all sub-configurations for the SAM3 model.
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Args:
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vision_config (`dict` or `Sam3VisionConfig`, *optional*):
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Configuration for the vision encoder.
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text_config (`dict` or `Sam3TextConfig`, *optional*):
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Configuration for the text encoder.
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geometry_encoder_config (`dict` or `Sam3GeometryEncoderConfig`, *optional*):
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Configuration for the geometry encoder.
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detr_encoder_config (`dict` or `Sam3DETREncoderConfig`, *optional*):
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Configuration for the DETR encoder.
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detr_decoder_config (`dict` or `Sam3DETRDecoderConfig`, *optional*):
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Configuration for the DETR decoder.
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mask_decoder_config (`dict` or `Sam3MaskDecoderConfig`, *optional*):
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Configuration for the mask decoder.
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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 weight matrices.
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Example:
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```python
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>>> from transformers import Sam3Config, Sam3Model
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>>> # Initializing a SAM3 configuration
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>>> configuration = Sam3Config()
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>>> # Initializing a model from the configuration
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>>> model = Sam3Model(configuration)
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>>> # Accessing the model configuration
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>>> configuration = model.config
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```
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"""
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model_type = "sam3"
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is_composition = True
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sub_configs = {
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"vision_config": Sam3VisionConfig,
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"text_config": CLIPTextConfig,
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"geometry_encoder_config": Sam3GeometryEncoderConfig,
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"detr_encoder_config": Sam3DETREncoderConfig,
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"detr_decoder_config": Sam3DETRDecoderConfig,
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"mask_decoder_config": Sam3MaskDecoderConfig,
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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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text_config=None,
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geometry_encoder_config=None,
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detr_encoder_config=None,
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detr_decoder_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
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if vision_config is None:
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vision_config = {}
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if isinstance(vision_config, dict):
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self.vision_config = Sam3VisionConfig(**vision_config)
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else:
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self.vision_config = vision_config
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# Text config (CLIPTextModelWithProjection defaults)
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if text_config is None:
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text_config = {
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"vocab_size": 49408,
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"hidden_size": 1024,
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"intermediate_size": 4096, # hidden_size * mlp_ratio (1024 * 4)
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"projection_dim": 512, # CLIP's internal projection dimension
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"num_hidden_layers": 24,
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"num_attention_heads": 16,
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"max_position_embeddings": 32,
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"hidden_act": "gelu",
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}
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if isinstance(text_config, dict):
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self.text_config = CLIPTextConfig(**text_config)
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else:
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self.text_config = text_config
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# Geometry encoder config
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if geometry_encoder_config is None:
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geometry_encoder_config = {}
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if isinstance(geometry_encoder_config, dict):
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self.geometry_encoder_config = Sam3GeometryEncoderConfig(**geometry_encoder_config)
|
|
else:
|
|
self.geometry_encoder_config = geometry_encoder_config
|
|
|
|
# DETR encoder config
|
|
if detr_encoder_config is None:
|
|
detr_encoder_config = {}
|
|
if isinstance(detr_encoder_config, dict):
|
|
self.detr_encoder_config = Sam3DETREncoderConfig(**detr_encoder_config)
|
|
else:
|
|
self.detr_encoder_config = detr_encoder_config
|
|
|
|
# DETR decoder config
|
|
if detr_decoder_config is None:
|
|
detr_decoder_config = {}
|
|
if isinstance(detr_decoder_config, dict):
|
|
self.detr_decoder_config = Sam3DETRDecoderConfig(**detr_decoder_config)
|
|
else:
|
|
self.detr_decoder_config = detr_decoder_config
|
|
|
|
# Mask decoder config
|
|
if mask_decoder_config is None:
|
|
mask_decoder_config = {}
|
|
if isinstance(mask_decoder_config, dict):
|
|
self.mask_decoder_config = Sam3MaskDecoderConfig(**mask_decoder_config)
|
|
else:
|
|
self.mask_decoder_config = mask_decoder_config
|
|
|
|
self.initializer_range = initializer_range
|
|
super().__init__(**kwargs)
|
|
|
|
@property
|
|
def image_size(self):
|
|
"""Image size for the SAM3 model."""
|
|
return self.vision_config.image_size
|
|
|
|
@image_size.setter
|
|
def image_size(self, value):
|
|
"""Set the image size and propagate to vision config."""
|
|
self.vision_config.image_size = value
|
|
|
|
|
|
__all__ = [
|
|
"Sam3Config",
|
|
"Sam3ViTConfig",
|
|
"Sam3VisionConfig",
|
|
"Sam3GeometryEncoderConfig",
|
|
"Sam3DETREncoderConfig",
|
|
"Sam3DETRDecoderConfig",
|
|
"Sam3MaskDecoderConfig",
|
|
]
|