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"""Table Transformer model configuration""" from ...backbone_utils import consolidate_backbone_kwargs_to_config from ...configuration_utils import PreTrainedConfig from ...utils import logging from ..auto import AutoConfig logger = logging.get_logger(__name__) class TableTransformerConfig(PreTrainedConfig): r""" This is the configuration class to store the configuration of a [`TableTransformerModel`]. It is used to instantiate a Table Transformer 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 Table Transformer [microsoft/table-transformer-detection](https://huggingface.co/microsoft/table-transformer-detection) 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*, defaults to `ResNetConfig()`): The configuration of the backbone model. Only used in case `use_timm_backbone` is set to `False` in which case it will default to `ResNetConfig()`. num_channels (`int`, *optional*, defaults to 3): The number of input channels. num_queries (`int`, *optional*, defaults to 100): Number of object queries, i.e. detection slots. This is the maximal number of objects [`TableTransformerModel`] can detect in a single image. For COCO, we recommend 100 queries. d_model (`int`, *optional*, defaults to 256): Dimension of the layers. encoder_layers (`int`, *optional*, defaults to 6): Number of encoder layers. decoder_layers (`int`, *optional*, defaults to 6): Number of decoder layers. encoder_attention_heads (`int`, *optional*, defaults to 8): Number of attention heads for each attention layer in the Transformer encoder. decoder_attention_heads (`int`, *optional*, defaults to 8): Number of attention heads for each attention layer in the Transformer decoder. decoder_ffn_dim (`int`, *optional*, defaults to 2048): Dimension of the "intermediate" (often named feed-forward) layer in decoder. encoder_ffn_dim (`int`, *optional*, defaults to 2048): Dimension of the "intermediate" (often named feed-forward) layer in decoder. 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. 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. init_std (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. init_xavier_std (`float`, *optional*, defaults to 1): The scaling factor used for the Xavier initialization gain in the HM Attention map module. encoder_layerdrop (`float`, *optional*, defaults to 0.0): The LayerDrop probability for the encoder. See the [LayerDrop paper](see https://huggingface.co/papers/1909.11556) for more details. decoder_layerdrop (`float`, *optional*, defaults to 0.0): The LayerDrop probability for the decoder. See the [LayerDrop paper](see https://huggingface.co/papers/1909.11556) for more details. 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"`. dilation (`bool`, *optional*, defaults to `False`): Whether to replace stride with dilation in the last convolutional block (DC5). Only supported when `use_timm_backbone` = `True`. class_cost (`float`, *optional*, defaults to 1): Relative weight of the classification error in the Hungarian matching cost. bbox_cost (`float`, *optional*, defaults to 5): Relative weight of the L1 error of the bounding box coordinates in the Hungarian matching cost. giou_cost (`float`, *optional*, defaults to 2): Relative weight of the generalized IoU loss of the bounding box in the Hungarian matching cost. mask_loss_coefficient (`float`, *optional*, defaults to 1): Relative weight of the Focal loss in the panoptic segmentation loss. dice_loss_coefficient (`float`, *optional*, defaults to 1): Relative weight of the DICE/F-1 loss in the panoptic segmentation loss. bbox_loss_coefficient (`float`, *optional*, defaults to 5): Relative weight of the L1 bounding box loss in the object detection loss. giou_loss_coefficient (`float`, *optional*, defaults to 2): Relative weight of the generalized IoU loss in the object detection loss. eos_coefficient (`float`, *optional*, defaults to 0.1): Relative classification weight of the 'no-object' class in the object detection loss. Examples: ```python >>> from transformers import TableTransformerModel, TableTransformerConfig >>> # Initializing a Table Transformer microsoft/table-transformer-detection style configuration >>> configuration = TableTransformerConfig() >>> # Initializing a model from the microsoft/table-transformer-detection style configuration >>> model = TableTransformerModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "table-transformer" sub_configs = {"backbone_config": AutoConfig} keys_to_ignore_at_inference = ["past_key_values"] attribute_map = { "hidden_size": "d_model", "num_attention_heads": "encoder_attention_heads", } # Copied from transformers.models.detr.configuration_detr.DetrConfig.__init__ def __init__( self, backbone_config=None, num_channels=3, num_queries=100, encoder_layers=6, encoder_ffn_dim=2048, encoder_attention_heads=8, decoder_layers=6, decoder_ffn_dim=2048, decoder_attention_heads=8, encoder_layerdrop=0.0, decoder_layerdrop=0.0, is_encoder_decoder=True, activation_function="relu", d_model=256, dropout=0.1, attention_dropout=0.0, activation_dropout=0.0, init_std=0.02, init_xavier_std=1.0, auxiliary_loss=False, position_embedding_type="sine", dilation=False, class_cost=1, bbox_cost=5, giou_cost=2, mask_loss_coefficient=1, dice_loss_coefficient=1, bbox_loss_coefficient=5, giou_loss_coefficient=2, eos_coefficient=0.1, **kwargs, ): backbone_kwargs = kwargs.get("backbone_kwargs", {}) timm_default_kwargs = { "num_channels": backbone_kwargs.get("num_channels", num_channels), "features_only": True, "use_pretrained_backbone": False, "out_indices": backbone_kwargs.get("out_indices", [1, 2, 3, 4]), } if dilation: timm_default_kwargs["output_stride"] = backbone_kwargs.get("output_stride", 16) backbone_config, kwargs = consolidate_backbone_kwargs_to_config( backbone_config=backbone_config, default_backbone="resnet50", default_config_type="resnet", default_config_kwargs={"out_features": ["stage4"]}, timm_default_kwargs=timm_default_kwargs, **kwargs, ) self.backbone_config = backbone_config self.num_channels = num_channels 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.init_std = init_std self.init_xavier_std = init_xavier_std self.encoder_layerdrop = encoder_layerdrop self.decoder_layerdrop = decoder_layerdrop self.num_hidden_layers = encoder_layers self.auxiliary_loss = auxiliary_loss self.position_embedding_type = position_embedding_type # Hungarian matcher self.class_cost = class_cost self.bbox_cost = bbox_cost self.giou_cost = giou_cost # Loss coefficients self.mask_loss_coefficient = mask_loss_coefficient self.dice_loss_coefficient = dice_loss_coefficient self.bbox_loss_coefficient = bbox_loss_coefficient self.giou_loss_coefficient = giou_loss_coefficient self.eos_coefficient = eos_coefficient super().__init__(is_encoder_decoder=is_encoder_decoder, **kwargs) __all__ = ["TableTransformerConfig"]