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configuration_vilt.py
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configuration_vilt.py
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# coding=utf-8
# Copyright 2022 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.
""" VilT model configuration"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
logger = logging.get_logger(__name__)
class ViltConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`ViLTModel`]. It is used to instantiate an ViLT
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 ViLT
[dandelin/vilt-b32-mlm](https://huggingface.co/dandelin/vilt-b32-mlm) 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 text part of the model. Defines the number of different tokens that can be
represented by the `inputs_ids` passed when calling [`ViltModel`].
type_vocab_size (`int`, *optional*, defaults to 2):
The vocabulary size of the `token_type_ids` passed when calling [`ViltModel`]. This is used when encoding
text.
modality_type_vocab_size (`int`, *optional*, defaults to 2):
The vocabulary size of the modalities passed when calling [`ViltModel`]. This is used after concatening the
embeddings of the text and image modalities.
max_position_embeddings (`int`, *optional*, defaults to 40):
The maximum sequence length that this model might ever be used with.
hidden_size (`int`, *optional*, defaults to 768):
Dimensionality of the encoder layers and the pooler layer.
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 0.0):
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
attention_probs_dropout_prob (`float`, *optional*, defaults to 0.0):
The dropout ratio for the attention probabilities.
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.
image_size (`int`, *optional*, defaults to 384):
The size (resolution) of each image.
patch_size (`int`, *optional*, defaults to 32):
The size (resolution) of each patch.
num_channels (`int`, *optional*, defaults to 3):
The number of input channels.
qkv_bias (`bool`, *optional*, defaults to `True`):
Whether to add a bias to the queries, keys and values.
max_image_length (`int`, *optional*, defaults to -1):
The maximum number of patches to take as input for the Transformer encoder. If set to a positive integer,
the encoder will sample `max_image_length` patches at maximum. If set to -1, will not be taken into
account.
num_images (`int`, *optional*, defaults to -1):
The number of images to use for natural language visual reasoning. If set to a positive integer, will be
used by [`ViltForImagesAndTextClassification`] for defining the classifier head.
Example:
```python
>>> from transformers import ViLTModel, ViLTConfig
>>> # Initializing a ViLT dandelin/vilt-b32-mlm style configuration
>>> configuration = ViLTConfig()
>>> # Initializing a model from the dandelin/vilt-b32-mlm style configuration
>>> model = ViLTModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "vilt"
def __init__(
self,
vocab_size=30522,
type_vocab_size=2,
modality_type_vocab_size=2,
max_position_embeddings=40,
hidden_size=768,
num_hidden_layers=12,
num_attention_heads=12,
intermediate_size=3072,
hidden_act="gelu",
hidden_dropout_prob=0.0,
attention_probs_dropout_prob=0.0,
initializer_range=0.02,
layer_norm_eps=1e-12,
image_size=384,
patch_size=32,
num_channels=3,
qkv_bias=True,
max_image_length=-1,
tie_word_embeddings=False,
num_images=-1,
**kwargs,
):
super().__init__(tie_word_embeddings=tie_word_embeddings, **kwargs)
self.vocab_size = vocab_size
self.type_vocab_size = type_vocab_size
self.modality_type_vocab_size = modality_type_vocab_size
self.max_position_embeddings = max_position_embeddings
self.hidden_size = hidden_size
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.layer_norm_eps = layer_norm_eps
self.image_size = image_size
self.patch_size = patch_size
self.num_channels = num_channels
self.qkv_bias = qkv_bias
self.max_image_length = max_image_length
self.num_images = num_images