# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 # This file was automatically generated from src/transformers/models/eomt_dinov3/modular_eomt_dinov3.py. # Do NOT edit this file manually as any edits will be overwritten by the generation of # the file from the modular. If any change should be done, please apply the change to the # modular_eomt_dinov3.py file directly. One of our CI enforces this. # 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 # Copyright 2026 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. from ...configuration_utils import PreTrainedConfig from ...modeling_rope_utils import RopeParameters class EomtDinov3Config(PreTrainedConfig): r""" This is the configuration class to store the configuration of a [`EomtDinov3ForUniversalSegmentation`]. It is used to instantiate an EoMT-DINOv3 model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the EoMT-DINOv3 [tue-mps/coco_panoptic_eomt_large_640_dinov3](https://huggingface.co/tue-mps/coco_panoptic_eomt_large_640_dinov3) architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: hidden_size (`int`, *optional*, defaults to 1024): Dimensionality of the hidden representations. num_hidden_layers (`int`, *optional*, defaults to 24): Number of hidden layers in the Transformer encoder. num_attention_heads (`int`, *optional*, defaults to 16): Number of attention heads in each attention layer. intermediate_size (`int`, *optional*, defaults to 4096): The intermediate size of the MLP. If not provided, defaults to `hidden_size * 4`. hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`): The non-linear activation function (function or string) in the encoder. hidden_dropout_prob (`float`, *optional*, defaults to 0.0): The dropout probability for all fully connected layers in the embeddings and encoder. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. layer_norm_eps (`float`, *optional*, defaults to 1e-06): The epsilon used by the layer normalization layers. image_size (`int`, *optional*, defaults to 640): The size (resolution) of each input image. patch_size (`int`, *optional*, defaults to 16): The size (resolution) of each patch. num_channels (`int`, *optional*, defaults to 3): The number of input channels. layerscale_value (`float`, *optional*, defaults to 1.0): Initial value for the LayerScale parameter. drop_path_rate (`float`, *optional*, defaults to 0.0): The stochastic depth rate (drop path) used during training. num_upscale_blocks (`int`, *optional*, defaults to 2): Number of upsampling blocks used in the decoder or segmentation head. attention_dropout (`float`, *optional*, defaults to 0.0): Dropout probability applied after attention projection. num_blocks (`int`, *optional*, defaults to 4): Number of feature blocks or stages in the architecture. no_object_weight (`float`, *optional*, defaults to 0.1): Loss weight for the "no object" class in panoptic/instance segmentation. class_weight (`float`, *optional*, defaults to 2.0): Loss weight for classification targets. mask_weight (`float`, *optional*, defaults to 5.0): Loss weight for mask prediction. dice_weight (`float`, *optional*, defaults to 5.0): Loss weight for the dice loss component. train_num_points (`int`, *optional*, defaults to 12544): Number of points to sample for mask loss computation during training. oversample_ratio (`float`, *optional*, defaults to 3.0): Oversampling ratio used in point sampling for mask training. importance_sample_ratio (`float`, *optional*, defaults to 0.75): Ratio of points to sample based on importance during training. num_queries (`int`, *optional*, defaults to 200): Number of object queries in the Transformer. num_register_tokens (`int`, *optional*, defaults to 4): Number of learnable register tokens added to the transformer input. rope_parameters (`RopeParameters`, *optional*): Dictionary containing the configuration parameters for the RoPE embeddings. The dictionary should contain a value for `rope_theta` and optionally parameters used for scaling. query_bias (`bool`, *optional*, defaults to `True`): Whether to use bias in query projection. key_bias (`bool`, *optional*, defaults to `False`): Whether to use bias in key projection. value_bias (`bool`, *optional*, defaults to `True`): Whether to use bias in value projection. proj_bias (`bool`, *optional*, defaults to `True`): Whether to use bias in output projection. mlp_bias (`bool`, *optional*, defaults to `True`): Whether to use bias in MLP layers. use_gated_mlp (`bool`, *optional*, defaults to `False`): Whether to use gated MLP layers. pos_embed_shift (`float`, *optional*): Shift value for position embeddings. pos_embed_jitter (`float`, *optional*): Jitter value for position embeddings. pos_embed_rescale (`float`, *optional*, defaults to 2.0): Rescale value for position embeddings. """ model_type = "eomt_dinov3" default_theta = 100.0 def __init__( self, hidden_size=1024, num_hidden_layers=24, num_attention_heads=16, intermediate_size=4096, hidden_act="gelu", hidden_dropout_prob=0.0, initializer_range=0.02, layer_norm_eps=1e-6, image_size=640, patch_size=16, num_channels=3, layerscale_value=1.0, drop_path_rate=0.0, num_upscale_blocks=2, attention_dropout=0.0, num_blocks=4, no_object_weight: float = 0.1, class_weight: float = 2.0, mask_weight: float = 5.0, dice_weight: float = 5.0, train_num_points: int = 12544, oversample_ratio: float = 3.0, importance_sample_ratio: float = 0.75, num_queries=200, num_register_tokens=4, rope_parameters: RopeParameters | dict[str, RopeParameters] | None = None, query_bias: bool = True, key_bias: bool = False, value_bias: bool = True, proj_bias: bool = True, mlp_bias: bool = True, use_gated_mlp: bool = False, pos_embed_shift: float | None = None, pos_embed_jitter: float | None = None, pos_embed_rescale: float | None = 2.0, **kwargs, ): self.intermediate_size = intermediate_size self.attention_dropout = attention_dropout self.layerscale_value = layerscale_value self.drop_path_rate = drop_path_rate self.num_upscale_blocks = num_upscale_blocks self.num_blocks = num_blocks self.no_object_weight = no_object_weight self.class_weight = class_weight self.mask_weight = mask_weight self.dice_weight = dice_weight self.train_num_points = train_num_points self.oversample_ratio = oversample_ratio self.importance_sample_ratio = importance_sample_ratio self.num_queries = num_queries self.num_register_tokens = num_register_tokens self.rope_parameters = rope_parameters self.query_bias = query_bias self.key_bias = key_bias self.value_bias = value_bias self.proj_bias = proj_bias self.mlp_bias = mlp_bias self.use_gated_mlp = use_gated_mlp self.pos_embed_shift = pos_embed_shift self.pos_embed_jitter = pos_embed_jitter self.pos_embed_rescale = pos_embed_rescale self.attention_dropout = attention_dropout self.layerscale_value = layerscale_value self.drop_path_rate = drop_path_rate self.num_upscale_blocks = num_upscale_blocks self.num_blocks = num_blocks self.no_object_weight = no_object_weight self.class_weight = class_weight self.mask_weight = mask_weight self.dice_weight = dice_weight self.train_num_points = train_num_points self.oversample_ratio = oversample_ratio self.importance_sample_ratio = importance_sample_ratio self.num_queries = num_queries self.num_register_tokens = num_register_tokens self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.hidden_act = hidden_act self.hidden_dropout_prob = hidden_dropout_prob self.initializer_range = initializer_range self.layer_norm_eps = layer_norm_eps self.image_size = image_size self.patch_size = patch_size self.num_channels = num_channels super().__init__(**kwargs) __all__ = ["EomtDinov3Config"]