# coding=utf-8 # Copyright 2021 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. """VisualBERT model configuration""" from ...configuration_utils import PretrainedConfig from ...utils import logging logger = logging.get_logger(__name__) class VisualBertConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`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 [uclanlp/visualbert-vqa-coco-pre](https://huggingface.co/uclanlp/visualbert-vqa-coco-pre) architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: vocab_size (`int`, *optional*, defaults to 30522): Vocabulary size of the VisualBERT model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`VisualBertModel`]. Vocabulary size of the model. Defines the different tokens that can be represented by the `inputs_ids` passed to the forward method of [`VisualBertModel`]. hidden_size (`int`, *optional*, defaults to 768): Dimensionality of the encoder layers and the pooler layer. visual_embedding_dim (`int`, *optional*, defaults to 512): Dimensionality of the visual embeddings to be passed to the model. num_hidden_layers (`int`, *optional*, defaults to 12): Number of hidden layers in the Transformer encoder. num_attention_heads (`int`, *optional*, defaults to 12): Number of attention heads for each attention layer in the Transformer encoder. intermediate_size (`int`, *optional*, defaults to 3072): Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder. hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`): The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, `"relu"`, `"selu"` and `"gelu_new"` are supported. hidden_dropout_prob (`float`, *optional*, defaults to 0.1): The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1): The dropout ratio for the attention probabilities. max_position_embeddings (`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 (`int`, *optional*, defaults to 2): The vocabulary size of the `token_type_ids` passed when calling [`VisualBertModel`]. 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-12): The epsilon used by the layer normalization layers. bypass_transformer (`bool`, *optional*, defaults to `False`): Whether or not the model should bypass the transformer for the visual embeddings. If set to `True`, the model directly concatenates the visual embeddings from [`VisualBertEmbeddings`] with text output from transformers, and then pass it to a self-attention layer. special_visual_initialize (`bool`, *optional*, defaults to `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 `True`, the weights of the textual token type and position type embeddings are copied to the respective visual embedding layers. Example: ```python >>> from transformers import VisualBertConfig, VisualBertModel >>> # Initializing a VisualBERT visualbert-vqa-coco-pre style configuration >>> configuration = VisualBertConfig.from_pretrained("uclanlp/visualbert-vqa-coco-pre") >>> # Initializing a model (with random weights) 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 __all__ = ["VisualBertConfig"]