Source code for transformers.models.beit.configuration_beit

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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. 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. out_indices (:obj:`List[int]`, `optional`, defaults to :obj:`[3, 5, 7, 11]`): Indices of the feature maps to use for semantic segmentation. pool_scales (:obj:`Tuple[int]`, `optional`, defaults to :obj:`[1, 2, 3, 6]`): Pooling scales used in Pooling Pyramid Module applied on the last feature map. use_auxiliary_head (:obj:`bool`, `optional`, defaults to :obj:`True`): Whether to use an auxiliary head during training. auxiliary_loss_weight (:obj:`float`, `optional`, defaults to 0.4): Weight of the cross-entropy loss of the auxiliary head. auxiliary_channels (:obj:`int`, `optional`, defaults to 256): Number of channels to use in the auxiliary head. auxiliary_num_convs (:obj:`int`, `optional`, defaults to 1): Number of convolutional layers to use in the auxiliary head. auxiliary_concat_input (:obj:`bool`, `optional`, defaults to :obj:`False`): Whether to concatenate the output of the auxiliary head with the input before the classification layer. semantic_loss_ignore_index (:obj:`int`, `optional`, defaults to 255): The index that is ignored by the loss function of the semantic segmentation model. 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, out_indices=[3, 5, 7, 11], pool_scales=[1, 2, 3, 6], use_auxiliary_head=True, auxiliary_loss_weight=0.4, auxiliary_channels=256, auxiliary_num_convs=1, auxiliary_concat_input=False, semantic_loss_ignore_index=255, **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 # decode head attributes (semantic segmentation) self.out_indices = out_indices self.pool_scales = pool_scales # auxiliary head attributes (semantic segmentation) self.use_auxiliary_head = use_auxiliary_head self.auxiliary_loss_weight = auxiliary_loss_weight self.auxiliary_channels = auxiliary_channels self.auxiliary_num_convs = auxiliary_num_convs self.auxiliary_concat_input = auxiliary_concat_input self.semantic_loss_ignore_index = semantic_loss_ignore_index