Source code for transformers.models.detr.configuration_detr

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""" DETR model configuration """

from ...configuration_utils import PretrainedConfig
from ...utils import logging


logger = logging.get_logger(__name__)

DETR_PRETRAINED_CONFIG_ARCHIVE_MAP = {
    "facebook/detr-resnet-50": "https://huggingface.co/facebook/detr-resnet-50/resolve/main/config.json",
    # See all DETR models at https://huggingface.co/models?filter=detr
}


[docs]class DetrConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a :class:`~transformers.DetrModel`. It is used to instantiate a DETR 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 DETR `facebook/detr-resnet-50 <https://huggingface.co/facebook/detr-resnet-50>`__ architecture. Configuration objects inherit from :class:`~transformers.PretrainedConfig` and can be used to control the model outputs. Read the documentation from :class:`~transformers.PretrainedConfig` for more information. Args: num_queries (:obj:`int`, `optional`, defaults to 100): Number of object queries, i.e. detection slots. This is the maximal number of objects :class:`~transformers.DetrModel` can detect in a single image. For COCO, we recommend 100 queries. d_model (:obj:`int`, `optional`, defaults to 256): Dimension of the layers. encoder_layers (:obj:`int`, `optional`, defaults to 6): Number of encoder layers. decoder_layers (:obj:`int`, `optional`, defaults to 6): Number of decoder layers. encoder_attention_heads (:obj:`int`, `optional`, defaults to 8): Number of attention heads for each attention layer in the Transformer encoder. decoder_attention_heads (:obj:`int`, `optional`, defaults to 8): Number of attention heads for each attention layer in the Transformer decoder. decoder_ffn_dim (:obj:`int`, `optional`, defaults to 2048): Dimension of the "intermediate" (often named feed-forward) layer in decoder. encoder_ffn_dim (:obj:`int`, `optional`, defaults to 2048): Dimension of the "intermediate" (often named feed-forward) layer in decoder. activation_function (:obj:`str` or :obj:`function`, `optional`, defaults to :obj:`"relu"`): The non-linear activation function (function or string) in the encoder and pooler. If string, :obj:`"gelu"`, :obj:`"relu"`, :obj:`"silu"` and :obj:`"gelu_new"` are supported. dropout (:obj:`float`, `optional`, defaults to 0.1): The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. attention_dropout (:obj:`float`, `optional`, defaults to 0.0): The dropout ratio for the attention probabilities. activation_dropout (:obj:`float`, `optional`, defaults to 0.0): The dropout ratio for activations inside the fully connected layer. init_std (:obj:`float`, `optional`, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. init_xavier_std (:obj:`float`, `optional`, defaults to 1): The scaling factor used for the Xavier initialization gain in the HM Attention map module. encoder_layerdrop: (:obj:`float`, `optional`, defaults to 0.0): The LayerDrop probability for the encoder. See the `LayerDrop paper <see https://arxiv.org/abs/1909.11556>`__ for more details. decoder_layerdrop: (:obj:`float`, `optional`, defaults to 0.0): The LayerDrop probability for the decoder. See the `LayerDrop paper <see https://arxiv.org/abs/1909.11556>`__ for more details. auxiliary_loss (:obj:`bool`, `optional`, defaults to :obj:`False`): Whether auxiliary decoding losses (loss at each decoder layer) are to be used. position_embedding_type (:obj:`str`, `optional`, defaults to :obj:`"sine"`): Type of position embeddings to be used on top of the image features. One of :obj:`"sine"` or :obj:`"learned"`. backbone (:obj:`str`, `optional`, defaults to :obj:`"resnet50"`): Name of convolutional backbone to use. Supports any convolutional backbone from the timm package. For a list of all available models, see `this page <https://rwightman.github.io/pytorch-image-models/#load-a-pretrained-model>`__. dilation (:obj:`bool`, `optional`, defaults to :obj:`False`): Whether to replace stride with dilation in the last convolutional block (DC5). class_cost (:obj:`float`, `optional`, defaults to 1): Relative weight of the classification error in the Hungarian matching cost. bbox_cost (:obj:`float`, `optional`, defaults to 5): Relative weight of the L1 error of the bounding box coordinates in the Hungarian matching cost. giou_cost (:obj:`float`, `optional`, defaults to 2): Relative weight of the generalized IoU loss of the bounding box in the Hungarian matching cost. mask_loss_coefficient (:obj:`float`, `optional`, defaults to 1): Relative weight of the Focal loss in the panoptic segmentation loss. dice_loss_coefficient (:obj:`float`, `optional`, defaults to 1): Relative weight of the DICE/F-1 loss in the panoptic segmentation loss. bbox_loss_coefficient (:obj:`float`, `optional`, defaults to 5): Relative weight of the L1 bounding box loss in the object detection loss. giou_loss_coefficient (:obj:`float`, `optional`, defaults to 2): Relative weight of the generalized IoU loss in the object detection loss. eos_coefficient (:obj:`float`, `optional`, defaults to 0.1): Relative classification weight of the 'no-object' class in the object detection loss. Examples:: >>> from transformers import DetrModel, DetrConfig >>> # Initializing a DETR facebook/detr-resnet-50 style configuration >>> configuration = DetrConfig() >>> # Initializing a model from the facebook/detr-resnet-50 style configuration >>> model = DetrModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config """ model_type = "detr" keys_to_ignore_at_inference = ["past_key_values"] attribute_map = { "hidden_size": "d_model", "num_attention_heads": "encoder_attention_heads", } def __init__( self, num_queries=100, max_position_embeddings=1024, 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, classifier_dropout=0.0, scale_embedding=False, auxiliary_loss=False, position_embedding_type="sine", backbone="resnet50", 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 ): self.num_queries = num_queries self.max_position_embeddings = max_position_embeddings 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.scale_embedding = scale_embedding # scale factor will be sqrt(d_model) if True self.auxiliary_loss = auxiliary_loss self.position_embedding_type = position_embedding_type self.backbone = backbone self.dilation = dilation # 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) @property def num_attention_heads(self) -> int: return self.encoder_attention_heads @property def hidden_size(self) -> int: return self.d_model