Source code for transformers.models.visual_bert.configuration_visual_bert

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""" VisualBERT model configuration """

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

logger = logging.get_logger(__name__)

    "uclanlp/visualbert-vqa": "",
    "uclanlp/visualbert-vqa-pre": "",
    "uclanlp/visualbert-vqa-coco-pre": "",
    "uclanlp/visualbert-vcr": "",
    "uclanlp/visualbert-vcr-pre": "",
    "uclanlp/visualbert-vcr-coco-pre": "",
    "uclanlp/visualbert-nlvr2": "",
    "uclanlp/visualbert-nlvr2-pre": "",
    "uclanlp/visualbert-nlvr2-coco-pre": ""
    # See all VisualBERT models at

[docs]class VisualBertConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a :class:`~transformers.VisualBertModel`. It is used to instantiate an VisualBERT 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 VisualBERT `visualbert-vqa-coco-pre <>`__ 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: vocab_size (:obj:`int`, `optional`, defaults to 30522): Vocabulary size of the VisualBERT model. Defines the number of different tokens that can be represented by the :obj:`inputs_ids` passed when calling :class:`~transformers.VisualBertModel`. Vocabulary size of the model. Defines the different tokens that can be represented by the ``inputs_ids`` passed to the forward method of :class:`~transformers.VisualBertModel`. hidden_size (:obj:`int`, `optional`, defaults to 768): Dimensionality of the encoder layers and the pooler layer. visual_embedding_dim (:obj:`int`, `optional`, defaults to 512): Dimensionality of the visual embeddings to be passed to the model. num_hidden_layers (:obj:`int`, `optional`, defaults to 12): Number of hidden layers in the Transformer encoder. num_attention_heads (:obj:`int`, `optional`, defaults to 12): Number of attention heads for each attention layer in the Transformer encoder. intermediate_size (:obj:`int`, `optional`, defaults to 3072): Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder. hidden_act (:obj:`str` or :obj:`function`, `optional`, defaults to :obj:`"gelu"`): The non-linear activation function (function or string) in the encoder and pooler. If string, :obj:`"gelu"`, :obj:`"relu"`, :obj:`"selu"` and :obj:`"gelu_new"` are supported. hidden_dropout_prob (:obj:`float`, `optional`, defaults to 0.1): The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler. attention_probs_dropout_prob (:obj:`float`, `optional`, defaults to 0.1): The dropout ratio for the attention probabilities. max_position_embeddings (:obj:`int`, `optional`, defaults to 512): The maximum sequence length that this model might ever be used with. Typically set this to something large just in case (e.g., 512 or 1024 or 2048). type_vocab_size (:obj:`int`, `optional`, defaults to 2): The vocabulary size of the :obj:`token_type_ids` passed when calling :class:`~transformers.VisualBertModel`. initializer_range (:obj:`float`, `optional`, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. layer_norm_eps (:obj:`float`, `optional`, defaults to 1e-12): The epsilon used by the layer normalization layers. bypass_transformer (:obj:`bool`, `optional`, defaults to :obj:`False`): Whether or not the model should bypass the transformer for the visual embeddings. If set to :obj:`True`, the model directly concatenates the visual embeddings from :class:`~transformers.VisualBertEmbeddings` with text output from transformers, and then pass it to a self-attention layer. special_visual_initialize (:obj:`bool`, `optional`, defaults to :obj:`True`): Whether or not the visual token type and position type embedding weights should be initialized the same as the textual token type and positive type embeddings. When set to :obj:`True`, the weights of the textual token type and position type embeddings are copied to the respective visual embedding layers. Example:: >>> from transformers import VisualBertModel, VisualBertConfig >>> # Initializing a VisualBERT visualbert-vqa-coco-pre style configuration >>> configuration = VisualBertConfig.from_pretrained('visualbert-vqa-coco-pre') >>> # Initializing a model from the visualbert-vqa-coco-pre style configuration >>> model = VisualBertModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config """ model_type = "visual_bert" def __init__( self, vocab_size=30522, hidden_size=768, visual_embedding_dim=512, num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072, hidden_act="gelu", hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, max_position_embeddings=512, type_vocab_size=2, initializer_range=0.02, layer_norm_eps=1e-12, bypass_transformer=False, special_visual_initialize=True, pad_token_id=1, bos_token_id=0, eos_token_id=2, **kwargs ): super().__init__(pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs) self.vocab_size = vocab_size self.max_position_embeddings = max_position_embeddings self.hidden_size = hidden_size self.visual_embedding_dim = visual_embedding_dim self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.intermediate_size = intermediate_size self.hidden_act = hidden_act self.hidden_dropout_prob = hidden_dropout_prob self.attention_probs_dropout_prob = attention_probs_dropout_prob self.initializer_range = initializer_range self.type_vocab_size = type_vocab_size self.layer_norm_eps = layer_norm_eps self.bypass_transformer = bypass_transformer self.special_visual_initialize = special_visual_initialize