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configuration_visual_bert.py
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configuration_visual_bert.py
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# 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__)
VISUAL_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP = {
"uclanlp/visualbert-vqa": "https://huggingface.co/uclanlp/visualbert-vqa/resolve/main/config.json",
"uclanlp/visualbert-vqa-pre": "https://huggingface.co/uclanlp/visualbert-vqa-pre/resolve/main/config.json",
"uclanlp/visualbert-vqa-coco-pre": (
"https://huggingface.co/uclanlp/visualbert-vqa-coco-pre/resolve/main/config.json"
),
"uclanlp/visualbert-vcr": "https://huggingface.co/uclanlp/visualbert-vcr/resolve/main/config.json",
"uclanlp/visualbert-vcr-pre": "https://huggingface.co/uclanlp/visualbert-vcr-pre/resolve/main/config.json",
"uclanlp/visualbert-vcr-coco-pre": (
"https://huggingface.co/uclanlp/visualbert-vcr-coco-pre/resolve/main/config.json"
),
"uclanlp/visualbert-nlvr2": "https://huggingface.co/uclanlp/visualbert-nlvr2/resolve/main/config.json",
"uclanlp/visualbert-nlvr2-pre": "https://huggingface.co/uclanlp/visualbert-nlvr2-pre/resolve/main/config.json",
"uclanlp/visualbert-nlvr2-coco-pre": (
"https://huggingface.co/uclanlp/visualbert-nlvr2-coco-pre/resolve/main/config.json"
)
# See all VisualBERT models at https://huggingface.co/models?filter=visual_bert
}
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 probabilitiy 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 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