# coding=utf-8
# Copyright Microsoft Research and The HuggingFace Inc. team. All rights reserved.
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# 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
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# distributed under the License is distributed on an "AS IS" BASIS,
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""" BEiT model configuration """
from ...configuration_utils import PretrainedConfig
from ...utils import logging
logger = logging.get_logger(__name__)
BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP = {
"microsoft/beit-base-patch16-224-in22k": "https://huggingface.co/microsoft/beit-base-patch16-224-in22k/resolve/main/config.json",
# See all BEiT models at https://huggingface.co/models?filter=beit
}
[docs]class BeitConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a :class:`~transformers.BeitModel`. It is used to
instantiate an BEiT 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 BEiT
`microsoft/beit-base-patch16-224-in22k <https://huggingface.co/microsoft/beit-base-patch16-224-in22k>`__
architecture.
Args:
vocab_size (:obj:`int`, `optional`, defaults to 8092):
Vocabulary size of the BEiT model. Defines the number of different image tokens that can be used during
pre-training.
hidden_size (:obj:`int`, `optional`, defaults to 768):
Dimensionality of the encoder layers and the pooler layer.
num_hidden_layers (:obj:`int`, `optional`, defaults to 12):
Number of hidden layers in the Transformer encoder.
num_attention_heads (:obj:`int`, `optional`, defaults to 12):
Number of attention heads for each attention layer in the Transformer encoder.
intermediate_size (:obj:`int`, `optional`, defaults to 3072):
Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
hidden_act (:obj:`str` or :obj:`function`, `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:`"selu"` 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.
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.
gradient_checkpointing (:obj:`bool`, `optional`, defaults to :obj:`False`):
If True, use gradient checkpointing to save memory at the expense of slower backward pass.
image_size (:obj:`int`, `optional`, defaults to :obj:`224`):
The size (resolution) of each image.
patch_size (:obj:`int`, `optional`, defaults to :obj:`16`):
The size (resolution) of each patch.
num_channels (:obj:`int`, `optional`, defaults to :obj:`3`):
The number of input channels.
use_mask_token (:obj:`bool`, `optional`, defaults to :obj:`False`):
Whether to use a mask token for masked image modeling.
use_absolute_position_embeddings (:obj:`bool`, `optional`, defaults to :obj:`False`):
Whether to use BERT-style absolute position embeddings.
use_relative_position_bias (:obj:`bool`, `optional`, defaults to :obj:`False`):
Whether to use T5-style relative position embeddings in the self-attention layers.
use_shared_relative_position_bias (:obj:`bool`, `optional`, defaults to :obj:`False`):
Whether to use the same relative position embeddings across all self-attention layers of the Transformer.
layer_scale_init_value (:obj:`float`, `optional`, defaults to 0.1):
Scale to use in the self-attention layers. 0.1 for base, 1e-5 for large. Set 0 to disable layer scale.
drop_path_rate (:obj:`float`, `optional`, defaults to 0.1):
Stochastic depth rate per sample (when applied in the main path of residual layers).
use_mean_pooling (:obj:`bool`, `optional`, defaults to :obj:`True`):
Whether to mean pool the final hidden states of the patches instead of using the final hidden state of the
CLS token, before applying the classification head.
Example::
>>> from transformers import BeitModel, BeitConfig
>>> # Initializing a BEiT beit-base-patch16-224-in22k style configuration
>>> configuration = BeitConfig()
>>> # Initializing a model from the beit-base-patch16-224-in22k style configuration
>>> model = BeitModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
"""
model_type = "beit"
def __init__(
self,
vocab_size=8192,
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,
is_encoder_decoder=False,
image_size=224,
patch_size=16,
num_channels=3,
use_mask_token=False,
use_absolute_position_embeddings=False,
use_relative_position_bias=False,
use_shared_relative_position_bias=False,
layer_scale_init_value=0.1,
drop_path_rate=0.1,
use_mean_pooling=True,
**kwargs
):
super().__init__(**kwargs)
self.vocab_size = vocab_size
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.use_mask_token = use_mask_token
self.use_absolute_position_embeddings = use_absolute_position_embeddings
self.use_relative_position_bias = use_relative_position_bias
self.use_shared_relative_position_bias = use_shared_relative_position_bias
self.layer_scale_init_value = layer_scale_init_value
self.drop_path_rate = drop_path_rate
self.use_mean_pooling = use_mean_pooling