310 lines
14 KiB
Python
310 lines
14 KiB
Python
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# coding=utf-8
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# Copyright 2023 The HuggingFace Inc. 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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""" SAM model configuration"""
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from ...configuration_utils import PretrainedConfig
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from ...utils import logging
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logger = logging.get_logger(__name__)
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from ..deprecated._archive_maps import SAM_PRETRAINED_CONFIG_ARCHIVE_MAP # noqa: F401, E402
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class SamPromptEncoderConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`SamPromptEncoder`]. The [`SamPromptEncoder`]
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module is used to encode the input 2D points and bounding boxes. Instantiating a configuration defaults will yield
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a similar configuration to that of the SAM-vit-h
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[facebook/sam-vit-huge](https://huggingface.co/facebook/sam-vit-huge) 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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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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"""
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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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**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.image_embedding_size = image_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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class SamMaskDecoderConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`SamMaskDecoder`]. It is used to instantiate a SAM
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mask decoder to the specified arguments, defining the model architecture. Instantiating a configuration defaults
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will yield a similar configuration to that of the SAM-vit-h
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[facebook/sam-vit-huge](https://huggingface.co/facebook/sam-vit-huge) 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 `"relu"`):
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The non-linear activation function used inside the `SamMaskDecoder` module.
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mlp_dim (`int`, *optional*, defaults to 2048):
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Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
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num_hidden_layers (`int`, *optional*, defaults to 2):
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Number of hidden layers in the Transformer encoder.
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num_attention_heads (`int`, *optional*, defaults to 8):
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Number of attention heads for each attention layer in the Transformer encoder.
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attention_downsample_rate (`int`, *optional*, defaults to 2):
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The downsampling rate of the attention layer.
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num_multimask_outputs (`int`, *optional*, defaults to 3):
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The number of outputs from the `SamMaskDecoder` module. In the Segment Anything paper, this is set to 3.
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iou_head_depth (`int`, *optional*, defaults to 3):
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The number of layers in the IoU head module.
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iou_head_hidden_dim (`int`, *optional*, defaults to 256):
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The dimensionality of the hidden states in the IoU head module.
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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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"""
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def __init__(
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self,
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hidden_size=256,
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hidden_act="relu",
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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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layer_norm_eps=1e-6,
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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.hidden_act = hidden_act
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self.mlp_dim = mlp_dim
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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.attention_downsample_rate = attention_downsample_rate
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self.num_multimask_outputs = num_multimask_outputs
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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.layer_norm_eps = layer_norm_eps
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class SamVisionConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`SamVisionModel`]. 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 the SAM ViT-h
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[facebook/sam-vit-huge](https://huggingface.co/facebook/sam-vit-huge) 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 768):
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Dimensionality of the encoder layers and the pooler layer.
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output_channels (`int`, *optional*, defaults to 256):
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Dimensionality of the output channels in the Patch Encoder.
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num_hidden_layers (`int`, *optional*, defaults to 12):
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Number of hidden layers in the Transformer encoder.
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num_attention_heads (`int`, *optional*, defaults to 12):
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Number of attention heads for each attention layer in the Transformer encoder.
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num_channels (`int`, *optional*, defaults to 3):
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Number of channels in the input image.
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image_size (`int`, *optional*, defaults to 1024):
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Expected resolution. Target size of the resized input image.
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patch_size (`int`, *optional*, defaults to 16):
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Size of the patches to be extracted from the input image.
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hidden_act (`str`, *optional*, defaults to `"gelu"`):
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The non-linear activation function (function or string)
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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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attention_dropout (`float`, *optional*, defaults to 0.0):
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The dropout ratio for the attention probabilities.
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initializer_range (`float`, *optional*, defaults to 1e-10):
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The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
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qkv_bias (`bool`, *optional*, defaults to `True`):
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Whether to add a bias to query, key, value projections.
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mlp_ratio (`float`, *optional*, defaults to 4.0):
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Ratio of mlp hidden dim to embedding dim.
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use_abs_pos (`bool`, *optional*, defaults to `True`):
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Whether to use absolute position embedding.
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use_rel_pos (`bool`, *optional*, defaults to `True`):
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Whether to use relative position embedding.
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window_size (`int`, *optional*, defaults to 14):
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Window size for relative position.
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global_attn_indexes (`List[int]`, *optional*, defaults to `[2, 5, 8, 11]`):
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The indexes of the global attention layers.
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num_pos_feats (`int`, *optional*, defaults to 128):
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The dimensionality of the position embedding.
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mlp_dim (`int`, *optional*):
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The dimensionality of the MLP layer in the Transformer encoder. If `None`, defaults to `mlp_ratio *
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hidden_size`.
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"""
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def __init__(
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self,
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hidden_size=768,
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output_channels=256,
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num_hidden_layers=12,
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num_attention_heads=12,
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num_channels=3,
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image_size=1024,
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patch_size=16,
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hidden_act="gelu",
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layer_norm_eps=1e-06,
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attention_dropout=0.0,
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initializer_range=1e-10,
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qkv_bias=True,
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mlp_ratio=4.0,
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use_abs_pos=True,
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use_rel_pos=True,
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window_size=14,
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global_attn_indexes=[2, 5, 8, 11],
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num_pos_feats=128,
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mlp_dim=None,
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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.output_channels = output_channels
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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.initializer_range = initializer_range
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self.qkv_bias = qkv_bias
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self.mlp_ratio = mlp_ratio
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self.use_abs_pos = use_abs_pos
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self.use_rel_pos = use_rel_pos
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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.num_pos_feats = num_pos_feats
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self.mlp_dim = int(hidden_size * mlp_ratio) if mlp_dim is None else mlp_dim
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class SamConfig(PretrainedConfig):
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r"""
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[`SamConfig`] is the configuration class to store the configuration of a [`SamModel`]. It is used to instantiate a
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SAM model according to the specified arguments, defining the vision model, prompt-encoder model and mask decoder
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configs. Instantiating a configuration with the defaults will yield a similar configuration to that of the
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SAM-ViT-H [facebook/sam-vit-huge](https://huggingface.co/facebook/sam-vit-huge) 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`, `SamVisionConfig`], *optional*):
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Dictionary of configuration options used to initialize [`SamVisionConfig`].
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prompt_encoder_config (Union[`dict`, `SamPromptEncoderConfig`], *optional*):
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Dictionary of configuration options used to initialize [`SamPromptEncoderConfig`].
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mask_decoder_config (Union[`dict`, `SamMaskDecoderConfig`], *optional*):
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Dictionary of configuration options used to initialize [`SamMaskDecoderConfig`].
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kwargs (*optional*):
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Dictionary of keyword arguments.
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Example:
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```python
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>>> from transformers import (
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... SamVisionConfig,
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... SamPromptEncoderConfig,
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... SamMaskDecoderConfig,
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... SamModel,
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... )
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>>> # Initializing a SamConfig with `"facebook/sam-vit-huge"` style configuration
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>>> configuration = SamConfig()
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>>> # Initializing a SamModel (with random weights) from the `"facebook/sam-vit-huge"` style configuration
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>>> model = SamModel(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 SamConfig from a SamVisionConfig, SamPromptEncoderConfig, and SamMaskDecoderConfig
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>>> # Initializing SAM vision, SAM Q-Former and language model configurations
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>>> vision_config = SamVisionConfig()
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>>> prompt_encoder_config = SamPromptEncoderConfig()
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>>> mask_decoder_config = SamMaskDecoderConfig()
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>>> config = SamConfig(vision_config, prompt_encoder_config, mask_decoder_config)
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```"""
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model_type = "sam"
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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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super().__init__(**kwargs)
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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, SamVisionConfig):
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vision_config = vision_config.to_dict()
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if isinstance(prompt_encoder_config, SamPromptEncoderConfig):
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prompt_encoder_config = prompt_encoder_config.to_dict()
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if isinstance(mask_decoder_config, SamMaskDecoderConfig):
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mask_decoder_config = mask_decoder_config.to_dict()
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self.vision_config = SamVisionConfig(**vision_config)
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self.prompt_encoder_config = SamPromptEncoderConfig(**prompt_encoder_config)
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self.mask_decoder_config = SamMaskDecoderConfig(**mask_decoder_config)
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self.initializer_range = initializer_range
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