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245 lines
9.0 KiB
245 lines
9.0 KiB
# 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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"""PyTorch SAM 2 model."""
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import torch
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from ... import initialization as init
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from ...configuration_utils import PreTrainedConfig
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from ...modeling_utils import PreTrainedModel
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from ...processing_utils import Unpack
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from ...utils import (
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auto_docstring,
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)
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from ...utils.generic import TransformersKwargs, check_model_inputs
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from ..auto import CONFIG_MAPPING, AutoConfig
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from ..sam2.configuration_sam2 import Sam2Config, Sam2MaskDecoderConfig, Sam2PromptEncoderConfig
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from ..sam2.modeling_sam2 import (
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Sam2Attention,
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Sam2FeedForward,
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Sam2LayerNorm,
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Sam2Model,
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Sam2PreTrainedModel,
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Sam2TwoWayAttentionBlock,
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Sam2VisionEncoderOutput,
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Sam2VisionModel,
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)
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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(Sam2PromptEncoderConfig):
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pass
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class EdgeTamMaskDecoderConfig(Sam2MaskDecoderConfig):
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pass
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class EdgeTamConfig(Sam2Config):
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pass
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class EdgeTamLayerNorm(Sam2LayerNorm):
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pass
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class EdgeTamVisionEncoderOutput(Sam2VisionEncoderOutput):
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pass
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class EdgeTamAttention(Sam2Attention):
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pass
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class EdgeTamTwoWayAttentionBlock(Sam2TwoWayAttentionBlock):
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pass
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class EdgeTamFeedForward(Sam2FeedForward):
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pass
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@auto_docstring
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class EdgeTamPreTrainedModel(Sam2PreTrainedModel):
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@torch.no_grad()
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def _init_weights(self, module):
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PreTrainedModel._init_weights(self, module)
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if isinstance(module, EdgeTamModel):
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if module.no_memory_embedding is not None:
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init.zeros_(module.no_memory_embedding)
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elif hasattr(module, "positional_embedding"):
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init.normal_(module.positional_embedding, std=module.scale)
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@auto_docstring(
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custom_intro="""
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The vision model from EdgeTAM without any head or projection on top.
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"""
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)
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class EdgeTamVisionModel(Sam2VisionModel):
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config_class = EdgeTamVisionConfig
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main_input_name = "pixel_values"
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# TODO: TimmWrapper models aren't compatible with _can_record_outputs yet. We specifically set this to
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# an empty dict to avoid the _can_record_outputs from Sam2VisionModel being inherited here.
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_can_record_outputs = {}
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def get_input_embeddings(self):
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raise NotImplementedError("Can't get input embeddings from timm wrapper model")
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@check_model_inputs
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def forward(
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self,
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pixel_values: torch.FloatTensor | None = None,
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**kwargs: Unpack[TransformersKwargs],
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) -> tuple | EdgeTamVisionEncoderOutput:
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if pixel_values is None:
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raise ValueError("You have to specify pixel_values")
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# Forward through backbone
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backbone_output = self.backbone(pixel_values, **kwargs)
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intermediate_hidden_states = backbone_output.last_hidden_state
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intermediate_hidden_states = [hidden_state.permute(0, 2, 3, 1) for hidden_state in intermediate_hidden_states]
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fpn_hidden_states, fpn_position_encoding = self.neck(intermediate_hidden_states)
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# Select last `num_feature_levels` feature levels from FPN and reverse order to get features from high to low resolution
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fpn_hidden_states = fpn_hidden_states[-self.num_feature_levels :][::-1]
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fpn_position_encoding = fpn_position_encoding[-self.num_feature_levels :][::-1]
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return EdgeTamVisionEncoderOutput(
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last_hidden_state=intermediate_hidden_states[-1],
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fpn_hidden_states=fpn_hidden_states,
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fpn_position_encoding=fpn_position_encoding,
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hidden_states=backbone_output.hidden_states,
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)
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class EdgeTamModel(Sam2Model):
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_keys_to_ignore_on_load_unexpected = [
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r"^memory_.*",
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r"^mask_downsample.*",
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r"spatial_perceiver.*",
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r"^object_pointer_proj.*",
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r"^temporal_positional_encoding_projection_layer.*",
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"no_memory_positional_encoding",
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"no_object_pointer",
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"occlusion_spatial_embedding_parameter",
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]
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def get_input_embeddings(self):
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raise NotImplementedError("Can't get input embeddings from timm wrapper model")
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__all__ = [
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"EdgeTamModel",
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"EdgeTamVisionModel",
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"EdgeTamPreTrainedModel",
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"EdgeTamConfig",
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"EdgeTamVisionConfig",
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"EdgeTamPromptEncoderConfig",
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"EdgeTamMaskDecoderConfig",
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]
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