image-match/python/py/Lib/site-packages/transformers/models/lxmert/configuration_lxmert.py

# Copyright 2018, Hao Tan, Mohit Bansal
#
# 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.
"""LXMERT model configuration"""

from huggingface_hub.dataclasses import strict

from ...configuration_utils import PreTrainedConfig
from ...utils import auto_docstring


@auto_docstring(checkpoint="unc-nlp/lxmert-base-uncased")
@strict
class LxmertConfig(PreTrainedConfig):
    r"""
    num_qa_labels (`int`, *optional*, defaults to 9500):
        This represents the total number of different question answering (QA) labels there are. If using more than
        one dataset with QA, the user will need to account for the total number of labels that all of the datasets
        have in total.
    num_object_labels (`int`, *optional*, defaults to 1600):
        This represents the total number of semantically unique objects that lxmert will be able to classify a
        pooled-object feature as belonging too.
    num_attr_labels (`int`, *optional*, defaults to 400):
        This represents the total number of semantically unique attributes that lxmert will be able to classify a
        pooled-object feature as possessing.
    l_layers (`int`, *optional*, defaults to 9):
        Number of hidden layers in the Transformer language encoder.
    x_layers (`int`, *optional*, defaults to 5):
        Number of hidden layers in the Transformer cross modality encoder.
    r_layers (`int`, *optional*, defaults to 5):
        Number of hidden layers in the Transformer visual encoder.
    visual_feat_dim (`int`, *optional*, defaults to 2048):
        This represents the last dimension of the pooled-object features used as input for the model, representing
        the size of each object feature itself.
    visual_pos_dim (`int`, *optional*, defaults to 4):
        This represents the number of spatial features that are mixed into the visual features. The default is set
        to 4 because most commonly this will represent the location of a bounding box. i.e., (x, y, width, height)
    visual_loss_normalizer (`float`, *optional*, defaults to 6.67):
        This represents the scaling factor in which each visual loss is multiplied by if during pretraining, one
        decided to train with multiple vision-based loss objectives.
    task_matched (`bool`, *optional*, defaults to `True`):
        This task is used for sentence-image matching. If the sentence correctly describes the image the label will
        be 1. If the sentence does not correctly describe the image, the label will be 0.
    task_mask_lm (`bool`, *optional*, defaults to `True`):
        Whether or not to add masked language modeling (as used in pretraining models such as BERT) to the loss
        objective.
    task_obj_predict (`bool`, *optional*, defaults to `True`):
        Whether or not to add object prediction, attribute prediction and feature regression to the loss objective.
    task_qa (`bool`, *optional*, defaults to `True`):
        Whether or not to add the question-answering loss to the objective
    visual_obj_loss (`bool`, *optional*, defaults to `True`):
        Whether or not to calculate the object-prediction loss objective
    visual_attr_loss (`bool`, *optional*, defaults to `True`):
        Whether or not to calculate the attribute-prediction loss objective
    visual_feat_loss (`bool`, *optional*, defaults to `True`):
        Whether or not to calculate the feature-regression loss objective
    """

    model_type = "lxmert"
    attribute_map = {}

    vocab_size: int = 30522
    hidden_size: int = 768
    num_attention_heads: int = 12
    num_qa_labels: int = 9500
    num_object_labels: int = 1600
    num_attr_labels: int = 400
    intermediate_size: int = 3072
    hidden_act: str = "gelu"
    hidden_dropout_prob: float | int = 0.1
    attention_probs_dropout_prob: float | int = 0.1
    max_position_embeddings: int = 512
    type_vocab_size: int = 2
    initializer_range: float = 0.02
    l_layers: int = 9
    x_layers: int = 5
    r_layers: int = 5
    visual_feat_dim: int = 2048
    visual_pos_dim: int = 4
    visual_loss_normalizer: float = 6.67
    task_matched: bool = True
    task_mask_lm: bool = True
    task_obj_predict: bool = True
    task_qa: bool = True
    visual_obj_loss: bool = True
    visual_attr_loss: bool = True
    visual_feat_loss: bool = True
    pad_token_id: int | None = None
    bos_token_id: int | None = None
    eos_token_id: int | list[int] | None = None
    tie_word_embeddings: bool = True

    def __post_init__(self, **kwargs):
        self.num_hidden_layers = {"vision": self.r_layers, "cross_encoder": self.x_layers, "language": self.l_layers}
        super().__post_init__(**kwargs)


__all__ = ["LxmertConfig"]