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| # coding=utf-8 | |
| # 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 ...configuration_utils import PretrainedConfig | |
| from ...utils import logging | |
| logger = logging.get_logger(__name__) | |
| LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP = { | |
| "unc-nlp/lxmert-base-uncased": "", | |
| } | |
| class LxmertConfig(PretrainedConfig): | |
| r""" | |
| This is the configuration class to store the configuration of a :class:`~transformers.LxmertModel` or a | |
| :class:`~transformers.TFLxmertModel`. It is used to instantiate a LXMERT model according to the specified | |
| arguments, defining the model 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 LXMERT model. Defines the number of different tokens that can be represented by the | |
| :obj:`inputs_ids` passed when calling :class:`~transformers.LxmertModel` or | |
| :class:`~transformers.TFLxmertModel`. | |
| hidden_size (:obj:`int`, `optional`, defaults to 768): | |
| Dimensionality of the encoder layers and the pooler layer. | |
| r_layers (:obj:`int`, `optional`, defaults to 5): | |
| Number of hidden layers in the Transformer visual encoder. | |
| l_layers (:obj:`int`, `optional`, defaults to 9): | |
| Number of hidden layers in the Transformer language encoder. | |
| x_layers (:obj:`int`, `optional`, defaults to 5): | |
| Number of hidden layers in the Transformer cross modality encoder. | |
| num_attention_heads (:obj:`int`, `optional`, defaults to 5): | |
| Number of attention heads for each attention layer in the Transformer encoder. | |
| intermediate_size (:obj:`int`, `optional`, defaults to 3072): | |
| Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder. | |
| hidden_act (:obj:`str` or :obj:`Callable`, `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:`"silu"` and :obj:`"gelu_new"` are supported. | |
| hidden_dropout_prob (:obj:`float`, `optional`, defaults to 0.1): | |
| The dropout probability 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 `token_type_ids` passed into :class:`~transformers.BertModel`. | |
| 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. | |
| visual_feat_dim (:obj:`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 (:obj:`int`, `optional`, defaults to 4): | |
| This represents the number of spacial 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 (:obj:`float`, `optional`, defaults to 1/15): | |
| 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. | |
| num_qa_labels (:obj:`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 (:obj:`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 (:obj:`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. | |
| task_matched (:obj:`bool`, `optional`, defaults to :obj:`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 (:obj:`bool`, `optional`, defaults to :obj:`True`): | |
| Whether or not to add masked language modeling (as used in pretraining models such as BERT) to the loss | |
| objective. | |
| task_obj_predict (:obj:`bool`, `optional`, defaults to :obj:`True`): | |
| Whether or not to add object prediction, attribute prediction and feature regression to the loss objective. | |
| task_qa (:obj:`bool`, `optional`, defaults to :obj:`True`): | |
| Whether or not to add the question-answering loss to the objective | |
| visual_obj_loss (:obj:`bool`, `optional`, defaults to :obj:`True`): | |
| Whether or not to calculate the object-prediction loss objective | |
| visual_attr_loss (:obj:`bool`, `optional`, defaults to :obj:`True`): | |
| Whether or not to calculate the attribute-prediction loss objective | |
| visual_feat_loss (:obj:`bool`, `optional`, defaults to :obj:`True`): | |
| Whether or not to calculate the feature-regression loss objective | |
| output_attentions (:obj:`bool`, `optional`, defaults to :obj:`False`): | |
| Whether or not the model should return the attentions from the vision, language, and cross-modality layers | |
| should be returned. | |
| output_hidden_states (:obj:`bool`, `optional`, defaults to :obj:`False`): | |
| Whether or not the model should return the hidden states from the vision, language, and cross-modality | |
| layers should be returned. | |
| """ | |
| model_type = "lxmert" | |
| def __init__( | |
| self, | |
| vocab_size=30522, | |
| hidden_size=768, | |
| num_attention_heads=12, | |
| num_labels=2, | |
| num_qa_labels=9500, | |
| num_object_labels=1600, | |
| num_attr_labels=400, | |
| 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, | |
| pad_token_id=0, | |
| l_layers=9, | |
| x_layers=5, | |
| r_layers=5, | |
| visual_feat_dim=2048, | |
| visual_pos_dim=4, | |
| visual_loss_normalizer=6.67, | |
| task_matched=True, | |
| task_mask_lm=True, | |
| task_obj_predict=True, | |
| task_qa=True, | |
| visual_obj_loss=True, | |
| visual_attr_loss=True, | |
| visual_feat_loss=True, | |
| output_attentions=False, | |
| output_hidden_states=False, | |
| **kwargs, | |
| ): | |
| super().__init__(**kwargs) | |
| self.vocab_size = vocab_size | |
| self.hidden_size = hidden_size | |
| self.num_attention_heads = num_attention_heads | |
| self.num_labels = num_labels | |
| self.hidden_act = hidden_act | |
| self.intermediate_size = intermediate_size | |
| self.hidden_dropout_prob = hidden_dropout_prob | |
| self.attention_probs_dropout_prob = attention_probs_dropout_prob | |
| self.max_position_embeddings = max_position_embeddings | |
| self.type_vocab_size = type_vocab_size | |
| self.initializer_range = initializer_range | |
| self.layer_norm_eps = layer_norm_eps | |
| self.num_qa_labels = num_qa_labels | |
| self.num_object_labels = num_object_labels | |
| self.num_attr_labels = num_attr_labels | |
| self.l_layers = l_layers | |
| self.x_layers = x_layers | |
| self.r_layers = r_layers | |
| self.visual_feat_dim = visual_feat_dim | |
| self.visual_pos_dim = visual_pos_dim | |
| self.visual_loss_normalizer = visual_loss_normalizer | |
| self.task_matched = task_matched | |
| self.task_mask_lm = task_mask_lm | |
| self.task_obj_predict = task_obj_predict | |
| self.task_qa = task_qa | |
| self.visual_obj_loss = visual_obj_loss | |
| self.visual_attr_loss = visual_attr_loss | |
| self.visual_feat_loss = visual_feat_loss | |
| self.output_hidden_states = output_hidden_states | |
| self.output_attentions = self.output_attentions | |
| self.num_hidden_layers = {"vision": r_layers, "cross_encoder": x_layers, "language": l_layers} | |