# coding=utf-8 # Copyright 2024 The HuggingFace Inc. 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. """Grounding DINO model configuration""" from ...configuration_utils import PretrainedConfig from ...utils import logging from ...utils.backbone_utils import verify_backbone_config_arguments from ..auto import CONFIG_MAPPING logger = logging.get_logger(__name__) class GroundingDinoConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`GroundingDinoModel`]. It is used to instantiate a Grounding DINO 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 Grounding DINO [IDEA-Research/grounding-dino-tiny](https://huggingface.co/IDEA-Research/grounding-dino-tiny) 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 (`PretrainedConfig` or `dict`, *optional*, defaults to `ResNetConfig()`): The configuration of the backbone model. backbone (`str`, *optional*): Name of backbone to use when `backbone_config` is `None`. If `use_pretrained_backbone` is `True`, this will load the corresponding pretrained weights from the timm or transformers library. If `use_pretrained_backbone` is `False`, this loads the backbone's config and uses that to initialize the backbone with random weights. use_pretrained_backbone (`bool`, *optional*, defaults to `False`): Whether to use pretrained weights for the backbone. use_timm_backbone (`bool`, *optional*, defaults to `False`): Whether to load `backbone` from the timm library. If `False`, the backbone is loaded from the transformers library. backbone_kwargs (`dict`, *optional*): Keyword arguments to be passed to AutoBackbone when loading from a checkpoint e.g. `{'out_indices': (0, 1, 2, 3)}`. Cannot be specified if `backbone_config` is set. text_config (`Union[AutoConfig, dict]`, *optional*, defaults to `BertConfig`): The config object or dictionary of the text backbone. num_queries (`int`, *optional*, defaults to 900): Number of object queries, i.e. detection slots. This is the maximal number of objects [`GroundingDinoModel`] can detect in a single image. encoder_layers (`int`, *optional*, defaults to 6): Number of encoder layers. encoder_ffn_dim (`int`, *optional*, defaults to 2048): Dimension of the "intermediate" (often named feed-forward) layer in decoder. encoder_attention_heads (`int`, *optional*, defaults to 8): Number of attention heads for each attention layer in the Transformer encoder. decoder_layers (`int`, *optional*, defaults to 6): Number of decoder layers. decoder_ffn_dim (`int`, *optional*, defaults to 2048): Dimension of the "intermediate" (often named feed-forward) layer in decoder. decoder_attention_heads (`int`, *optional*, defaults to 8): Number of attention heads for each attention layer in the Transformer decoder. is_encoder_decoder (`bool`, *optional*, defaults to `True`): Whether the model is used as an encoder/decoder or not. activation_function (`str` or `function`, *optional*, defaults to `"relu"`): The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, `"relu"`, `"silu"` and `"gelu_new"` are supported. d_model (`int`, *optional*, defaults to 256): Dimension of the layers. dropout (`float`, *optional*, defaults to 0.1): The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. attention_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for the attention probabilities. activation_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for activations inside the fully connected layer. auxiliary_loss (`bool`, *optional*, defaults to `False`): Whether auxiliary decoding losses (loss at each decoder layer) are to be used. position_embedding_type (`str`, *optional*, defaults to `"sine"`): Type of position embeddings to be used on top of the image features. One of `"sine"` or `"learned"`. num_feature_levels (`int`, *optional*, defaults to 4): The number of input feature levels. encoder_n_points (`int`, *optional*, defaults to 4): The number of sampled keys in each feature level for each attention head in the encoder. decoder_n_points (`int`, *optional*, defaults to 4): The number of sampled keys in each feature level for each attention head in the decoder. two_stage (`bool`, *optional*, defaults to `True`): Whether to apply a two-stage deformable DETR, where the region proposals are also generated by a variant of Grounding DINO, which are further fed into the decoder for iterative bounding box refinement. class_cost (`float`, *optional*, defaults to 1.0): Relative weight of the classification error in the Hungarian matching cost. bbox_cost (`float`, *optional*, defaults to 5.0): Relative weight of the L1 error of the bounding box coordinates in the Hungarian matching cost. giou_cost (`float`, *optional*, defaults to 2.0): Relative weight of the generalized IoU loss of the bounding box in the Hungarian matching cost. bbox_loss_coefficient (`float`, *optional*, defaults to 5.0): Relative weight of the L1 bounding box loss in the object detection loss. giou_loss_coefficient (`float`, *optional*, defaults to 2.0): Relative weight of the generalized IoU loss in the object detection loss. focal_alpha (`float`, *optional*, defaults to 0.25): Alpha parameter in the focal loss. disable_custom_kernels (`bool`, *optional*, defaults to `False`): Disable the use of custom CUDA and CPU kernels. This option is necessary for the ONNX export, as custom kernels are not supported by PyTorch ONNX export. max_text_len (`int`, *optional*, defaults to 256): The maximum length of the text input. text_enhancer_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for the text enhancer. fusion_droppath (`float`, *optional*, defaults to 0.1): The droppath ratio for the fusion module. fusion_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for the fusion module. embedding_init_target (`bool`, *optional*, defaults to `True`): Whether to initialize the target with Embedding weights. query_dim (`int`, *optional*, defaults to 4): The dimension of the query vector. decoder_bbox_embed_share (`bool`, *optional*, defaults to `True`): Whether to share the bbox regression head for all decoder layers. two_stage_bbox_embed_share (`bool`, *optional*, defaults to `False`): Whether to share the bbox embedding between the two-stage bbox generator and the region proposal generation. positional_embedding_temperature (`float`, *optional*, defaults to 20): The temperature for Sine Positional Embedding that is used together with vision backbone. init_std (`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-05): The epsilon used by the layer normalization layers. Examples: ```python >>> from transformers import GroundingDinoConfig, GroundingDinoModel >>> # Initializing a Grounding DINO IDEA-Research/grounding-dino-tiny style configuration >>> configuration = GroundingDinoConfig() >>> # Initializing a model (with random weights) from the IDEA-Research/grounding-dino-tiny style configuration >>> model = GroundingDinoModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "grounding-dino" attribute_map = { "hidden_size": "d_model", "num_attention_heads": "encoder_attention_heads", } def __init__( self, backbone_config=None, backbone=None, use_pretrained_backbone=False, use_timm_backbone=False, backbone_kwargs=None, text_config=None, num_queries=900, encoder_layers=6, encoder_ffn_dim=2048, encoder_attention_heads=8, decoder_layers=6, decoder_ffn_dim=2048, decoder_attention_heads=8, is_encoder_decoder=True, activation_function="relu", d_model=256, dropout=0.1, attention_dropout=0.0, activation_dropout=0.0, auxiliary_loss=False, position_embedding_type="sine", num_feature_levels=4, encoder_n_points=4, decoder_n_points=4, two_stage=True, class_cost=1.0, bbox_cost=5.0, giou_cost=2.0, bbox_loss_coefficient=5.0, giou_loss_coefficient=2.0, focal_alpha=0.25, disable_custom_kernels=False, # other parameters max_text_len=256, text_enhancer_dropout=0.0, fusion_droppath=0.1, fusion_dropout=0.0, embedding_init_target=True, query_dim=4, decoder_bbox_embed_share=True, two_stage_bbox_embed_share=False, positional_embedding_temperature=20, init_std=0.02, layer_norm_eps=1e-5, **kwargs, ): if backbone_config is None and backbone is None: logger.info("`backbone_config` is `None`. Initializing the config with the default `Swin` backbone.") backbone_config = CONFIG_MAPPING["swin"]( window_size=7, image_size=224, embed_dim=96, depths=[2, 2, 6, 2], num_heads=[3, 6, 12, 24], out_indices=[2, 3, 4], ) elif isinstance(backbone_config, dict): backbone_model_type = backbone_config.pop("model_type") config_class = CONFIG_MAPPING[backbone_model_type] backbone_config = config_class.from_dict(backbone_config) verify_backbone_config_arguments( use_timm_backbone=use_timm_backbone, use_pretrained_backbone=use_pretrained_backbone, backbone=backbone, backbone_config=backbone_config, backbone_kwargs=backbone_kwargs, ) if text_config is None: text_config = {} logger.info("text_config is None. Initializing the text config with default values (`BertConfig`).") self.backbone_config = backbone_config self.backbone = backbone self.use_pretrained_backbone = use_pretrained_backbone self.use_timm_backbone = use_timm_backbone self.backbone_kwargs = backbone_kwargs self.num_queries = num_queries self.d_model = d_model self.encoder_ffn_dim = encoder_ffn_dim self.encoder_layers = encoder_layers self.encoder_attention_heads = encoder_attention_heads self.decoder_ffn_dim = decoder_ffn_dim self.decoder_layers = decoder_layers self.decoder_attention_heads = decoder_attention_heads self.dropout = dropout self.attention_dropout = attention_dropout self.activation_dropout = activation_dropout self.activation_function = activation_function self.auxiliary_loss = auxiliary_loss self.position_embedding_type = position_embedding_type # deformable attributes self.num_feature_levels = num_feature_levels self.encoder_n_points = encoder_n_points self.decoder_n_points = decoder_n_points self.two_stage = two_stage # Hungarian matcher self.class_cost = class_cost self.bbox_cost = bbox_cost self.giou_cost = giou_cost # Loss coefficients self.bbox_loss_coefficient = bbox_loss_coefficient self.giou_loss_coefficient = giou_loss_coefficient self.focal_alpha = focal_alpha self.disable_custom_kernels = disable_custom_kernels # Text backbone if isinstance(text_config, dict): text_config["model_type"] = text_config["model_type"] if "model_type" in text_config else "bert" text_config = CONFIG_MAPPING[text_config["model_type"]](**text_config) elif text_config is None: text_config = CONFIG_MAPPING["bert"]() self.text_config = text_config self.max_text_len = max_text_len # Text Enhancer self.text_enhancer_dropout = text_enhancer_dropout # Fusion self.fusion_droppath = fusion_droppath self.fusion_dropout = fusion_dropout # Others self.embedding_init_target = embedding_init_target self.query_dim = query_dim self.decoder_bbox_embed_share = decoder_bbox_embed_share self.two_stage_bbox_embed_share = two_stage_bbox_embed_share if two_stage_bbox_embed_share and not decoder_bbox_embed_share: raise ValueError("If two_stage_bbox_embed_share is True, decoder_bbox_embed_share must be True.") self.positional_embedding_temperature = positional_embedding_temperature self.init_std = init_std self.layer_norm_eps = layer_norm_eps super().__init__(is_encoder_decoder=is_encoder_decoder, **kwargs) @property def num_attention_heads(self) -> int: return self.encoder_attention_heads @property def hidden_size(self) -> int: return self.d_model __all__ = ["GroundingDinoConfig"]