# coding=utf-8 # Copyright 2025 The Meta AI Authors and The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """PyTorch SAM 2 model.""" from typing import Optional, Union import torch import torch.nn as nn import torch.utils.checkpoint from transformers.models.sam2.configuration_sam2 import Sam2Config, Sam2MaskDecoderConfig, Sam2PromptEncoderConfig from transformers.models.sam2.modeling_sam2 import ( Sam2Attention, Sam2FeedForward, Sam2LayerNorm, Sam2Model, Sam2PreTrainedModel, Sam2TwoWayAttentionBlock, Sam2VisionEncoderOutput, Sam2VisionModel, ) from transformers.utils.generic import TransformersKwargs, check_model_inputs from ...configuration_utils import PretrainedConfig from ...processing_utils import Unpack from ...utils import ( auto_docstring, ) from ..auto import CONFIG_MAPPING, AutoConfig # fix this in modular if True: from transformers.models.timm_wrapper.modeling_timm_wrapper import TimmWrapperModel class EdgeTamVisionConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`EdgeTamVisionModel`]. It is used to instantiate a SAM vision encoder according to the specified arguments, defining the model architecture. Instantiating a configuration defaults will yield a similar configuration to that of SAM 2.1 Hiera-tiny [facebook/EdgeTAM](https://huggingface.co/facebook/EdgeTAM) architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: backbone_config (`Union[dict, "PretrainedConfig"]`, *optional*): Configuration for the vision backbone. This is used to instantiate the backbone using `AutoModel.from_config`. backbone_channel_list (`List[int]`, *optional*, defaults to `[384, 192, 96, 48]`): The list of channel dimensions for the backbone. backbone_feature_sizes (`List[List[int]]`, *optional*, defaults to `[[256, 256], [128, 128], [64, 64]]`): The spatial sizes of the feature maps from the backbone. fpn_hidden_size (`int`, *optional*, defaults to 256): The hidden dimension of the FPN. fpn_kernel_size (`int`, *optional*, defaults to 1): The kernel size for the convolutions in the neck. fpn_stride (`int`, *optional*, defaults to 1): The stride for the convolutions in the neck. fpn_padding (`int`, *optional*, defaults to 0): The padding for the convolutions in the neck. fpn_top_down_levels (`List[int]`, *optional*, defaults to `[2, 3]`): The levels for the top-down FPN connections. num_feature_levels (`int`, *optional*, defaults to 3): The number of feature levels from the FPN to use. hidden_act (`str`, *optional*, defaults to `"gelu"`): The non-linear activation function in the neck. layer_norm_eps (`float`, *optional*, defaults to 1e-06): The epsilon for the layer normalization. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. """ base_config_key = "vision_config" model_type = "edgetam_vision_model" sub_configs = { "backbone_config": AutoConfig, } def __init__( self, backbone_config=None, backbone_channel_list=None, backbone_feature_sizes=None, fpn_hidden_size=256, fpn_kernel_size=1, fpn_stride=1, fpn_padding=0, fpn_top_down_levels=None, num_feature_levels=3, hidden_act="gelu", layer_norm_eps=1e-6, initializer_range=0.02, **kwargs, ): super().__init__(**kwargs) backbone_channel_list = [384, 192, 96, 48] if backbone_channel_list is None else backbone_channel_list backbone_feature_sizes = ( [[256, 256], [128, 128], [64, 64]] if backbone_feature_sizes is None else backbone_feature_sizes ) fpn_top_down_levels = [2, 3] if fpn_top_down_levels is None else fpn_top_down_levels if isinstance(backbone_config, dict): backbone_config["model_type"] = backbone_config.get("model_type", "timm_wrapper") backbone_config = CONFIG_MAPPING[backbone_config["model_type"]](**backbone_config) elif isinstance(backbone_config, AutoConfig): backbone_config = backbone_config elif backbone_config is None: backbone_config = AutoConfig.from_pretrained( "timm/repvit_m1.dist_in1k", model_args={"in_chans": 3, "features_only": True, "out_indices": [0, 1, 2, 3]}, ) self.backbone_config = backbone_config # Neck self.backbone_channel_list = backbone_channel_list self.backbone_feature_sizes = backbone_feature_sizes self.fpn_hidden_size = fpn_hidden_size self.fpn_kernel_size = fpn_kernel_size self.fpn_stride = fpn_stride self.fpn_padding = fpn_padding self.fpn_top_down_levels = fpn_top_down_levels self.num_feature_levels = num_feature_levels self.hidden_act = hidden_act self.layer_norm_eps = layer_norm_eps self.initializer_range = initializer_range class EdgeTamPromptEncoderConfig(Sam2PromptEncoderConfig): pass class EdgeTamMaskDecoderConfig(Sam2MaskDecoderConfig): pass class EdgeTamConfig(Sam2Config): pass class EdgeTamLayerNorm(Sam2LayerNorm): pass class EdgeTamVisionEncoderOutput(Sam2VisionEncoderOutput): pass class EdgeTamAttention(Sam2Attention): pass class EdgeTamTwoWayAttentionBlock(Sam2TwoWayAttentionBlock): pass class EdgeTamFeedForward(Sam2FeedForward): pass @auto_docstring class EdgeTamPreTrainedModel(Sam2PreTrainedModel): def _init_weights(self, module): std = self.config.initializer_range if isinstance(module, (nn.Linear, nn.Conv2d, nn.ConvTranspose2d)): module.weight.data.normal_(mean=0.0, std=std) if module.bias is not None: module.bias.data.zero_() elif isinstance(module, nn.Embedding): module.weight.data.normal_(mean=0.0, std=std) if module.padding_idx is not None: module.weight.data[module.padding_idx].zero_() elif isinstance(module, (nn.LayerNorm, EdgeTamLayerNorm)): module.weight.data.fill_(1.0) module.bias.data.zero_() if isinstance(module, EdgeTamModel): if module.no_memory_embedding is not None: module.no_memory_embedding.data.zero_() @auto_docstring( custom_intro=""" The vision model from EdgeTAM without any head or projection on top. """ ) class EdgeTamVisionModel(Sam2VisionModel): config_class = EdgeTamVisionConfig main_input_name = "pixel_values" _can_record_outputs = {"hidden_states": TimmWrapperModel, "attentions": TimmWrapperModel} def get_input_embeddings(self): raise NotImplementedError("Can't get input embeddings from timm wrapper model") @check_model_inputs def forward( self, pixel_values: Optional[torch.FloatTensor] = None, **kwargs: Unpack[TransformersKwargs], ) -> Union[tuple, EdgeTamVisionEncoderOutput]: if pixel_values is None: raise ValueError("You have to specify pixel_values") # Forward through backbone backbone_output = self.backbone(pixel_values) intermediate_hidden_states = backbone_output.last_hidden_state intermediate_hidden_states = [hidden_state.permute(0, 2, 3, 1) for hidden_state in intermediate_hidden_states] fpn_hidden_states, fpn_position_encoding = self.neck(intermediate_hidden_states) # Select last `num_feature_levels` feature levels from FPN and reverse order to get features from high to low resolution fpn_hidden_states = fpn_hidden_states[-self.num_feature_levels :][::-1] fpn_position_encoding = fpn_position_encoding[-self.num_feature_levels :][::-1] return EdgeTamVisionEncoderOutput( last_hidden_state=intermediate_hidden_states[-1], fpn_hidden_states=fpn_hidden_states, fpn_position_encoding=fpn_position_encoding, ) class EdgeTamModel(Sam2Model): _keys_to_ignore_on_load_unexpected = [ r"^memory_.*", r"^mask_downsample.*", r"spatial_perceiver.*", r"^object_pointer_proj.*", r"^temporal_positional_encoding_projection_layer.*", "no_memory_positional_encoding", "no_object_pointer", "occlusion_spatial_embedding_parameter", ] def get_input_embeddings(self): raise NotImplementedError("Can't get input embeddings from timm wrapper model") __all__ = [ "EdgeTamModel", "EdgeTamVisionModel", "EdgeTamPreTrainedModel", "EdgeTamConfig", "EdgeTamVisionConfig", "EdgeTamPromptEncoderConfig", "EdgeTamMaskDecoderConfig", ]