148 lines
6.7 KiB
Python
148 lines
6.7 KiB
Python
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# coding=utf-8
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# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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""" VilT model configuration"""
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from ...configuration_utils import PretrainedConfig
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from ...utils import logging
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logger = logging.get_logger(__name__)
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from ..deprecated._archive_maps import VILT_PRETRAINED_CONFIG_ARCHIVE_MAP # noqa: F401, E402
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class ViltConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`ViLTModel`]. It is used to instantiate an ViLT
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model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
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defaults will yield a similar configuration to that of the ViLT
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[dandelin/vilt-b32-mlm](https://huggingface.co/dandelin/vilt-b32-mlm) architecture.
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Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
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documentation from [`PretrainedConfig`] for more information.
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Args:
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vocab_size (`int`, *optional*, defaults to 30522):
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Vocabulary size of the text part of the model. Defines the number of different tokens that can be
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represented by the `inputs_ids` passed when calling [`ViltModel`].
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type_vocab_size (`int`, *optional*, defaults to 2):
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The vocabulary size of the `token_type_ids` passed when calling [`ViltModel`]. This is used when encoding
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text.
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modality_type_vocab_size (`int`, *optional*, defaults to 2):
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The vocabulary size of the modalities passed when calling [`ViltModel`]. This is used after concatening the
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embeddings of the text and image modalities.
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max_position_embeddings (`int`, *optional*, defaults to 40):
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The maximum sequence length that this model might ever be used with.
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hidden_size (`int`, *optional*, defaults to 768):
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Dimensionality of the encoder layers and the pooler layer.
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num_hidden_layers (`int`, *optional*, defaults to 12):
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Number of hidden layers in the Transformer encoder.
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num_attention_heads (`int`, *optional*, defaults to 12):
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Number of attention heads for each attention layer in the Transformer encoder.
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intermediate_size (`int`, *optional*, defaults to 3072):
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Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
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hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
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The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
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`"relu"`, `"selu"` and `"gelu_new"` are supported.
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hidden_dropout_prob (`float`, *optional*, defaults to 0.0):
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The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
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attention_probs_dropout_prob (`float`, *optional*, defaults to 0.0):
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The dropout ratio for the attention probabilities.
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initializer_range (`float`, *optional*, defaults to 0.02):
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The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
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layer_norm_eps (`float`, *optional*, defaults to 1e-12):
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The epsilon used by the layer normalization layers.
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image_size (`int`, *optional*, defaults to 384):
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The size (resolution) of each image.
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patch_size (`int`, *optional*, defaults to 32):
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The size (resolution) of each patch.
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num_channels (`int`, *optional*, defaults to 3):
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The number of input channels.
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qkv_bias (`bool`, *optional*, defaults to `True`):
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Whether to add a bias to the queries, keys and values.
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max_image_length (`int`, *optional*, defaults to -1):
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The maximum number of patches to take as input for the Transformer encoder. If set to a positive integer,
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the encoder will sample `max_image_length` patches at maximum. If set to -1, will not be taken into
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account.
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num_images (`int`, *optional*, defaults to -1):
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The number of images to use for natural language visual reasoning. If set to a positive integer, will be
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used by [`ViltForImagesAndTextClassification`] for defining the classifier head.
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Example:
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```python
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>>> from transformers import ViLTModel, ViLTConfig
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>>> # Initializing a ViLT dandelin/vilt-b32-mlm style configuration
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>>> configuration = ViLTConfig()
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>>> # Initializing a model from the dandelin/vilt-b32-mlm style configuration
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>>> model = ViLTModel(configuration)
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>>> # Accessing the model configuration
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>>> configuration = model.config
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```"""
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model_type = "vilt"
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def __init__(
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self,
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vocab_size=30522,
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type_vocab_size=2,
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modality_type_vocab_size=2,
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max_position_embeddings=40,
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hidden_size=768,
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num_hidden_layers=12,
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num_attention_heads=12,
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intermediate_size=3072,
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hidden_act="gelu",
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hidden_dropout_prob=0.0,
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attention_probs_dropout_prob=0.0,
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initializer_range=0.02,
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layer_norm_eps=1e-12,
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image_size=384,
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patch_size=32,
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num_channels=3,
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qkv_bias=True,
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max_image_length=-1,
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tie_word_embeddings=False,
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num_images=-1,
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**kwargs,
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):
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super().__init__(tie_word_embeddings=tie_word_embeddings, **kwargs)
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self.vocab_size = vocab_size
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self.type_vocab_size = type_vocab_size
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self.modality_type_vocab_size = modality_type_vocab_size
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self.max_position_embeddings = max_position_embeddings
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self.hidden_size = hidden_size
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self.num_hidden_layers = num_hidden_layers
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self.num_attention_heads = num_attention_heads
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self.intermediate_size = intermediate_size
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self.hidden_act = hidden_act
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self.hidden_dropout_prob = hidden_dropout_prob
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self.attention_probs_dropout_prob = attention_probs_dropout_prob
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self.initializer_range = initializer_range
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self.layer_norm_eps = layer_norm_eps
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self.image_size = image_size
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self.patch_size = patch_size
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self.num_channels = num_channels
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self.qkv_bias = qkv_bias
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self.max_image_length = max_image_length
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self.num_images = num_images
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