Source code for transformers.models.layoutlmv2.configuration_layoutlmv2

# coding=utf-8
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""" LayoutLMv2 model configuration """

from ...configuration_utils import PretrainedConfig
from ...file_utils import is_detectron2_available
from ...utils import logging

logger = logging.get_logger(__name__)

    "layoutlmv2-base-uncased": "",
    "layoutlmv2-large-uncased": "",
    # See all LayoutLMv2 models at

# soft dependency
if is_detectron2_available():
    import detectron2

[docs]class LayoutLMv2Config(PretrainedConfig): r""" This is the configuration class to store the configuration of a :class:`~transformers.LayoutLMv2Model`. It is used to instantiate an LayoutLMv2 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 LayoutLMv2 `microsoft/layoutlmv2-base-uncased <>`__ architecture. Configuration objects inherit from :class:`~transformers.PretrainedConfig` and can be used to control the model outputs. Read the documentation from :class:`~transformers.PretrainedConfig` for more information. Args: vocab_size (:obj:`int`, `optional`, defaults to 30522): Vocabulary size of the LayoutLMv2 model. Defines the number of different tokens that can be represented by the :obj:`inputs_ids` passed when calling :class:`~transformers.LayoutLMv2Model` or :class:`~transformers.TFLayoutLMv2Model`. hidden_size (:obj:`int`, `optional`, defaults to 768): Dimension 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): Dimension 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 probabilitiy 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. max_position_embeddings (:obj:`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 (:obj:`int`, `optional`, defaults to 2): The vocabulary size of the :obj:`token_type_ids` passed when calling :class:`~transformers.LayoutLMv2Model` or :class:`~transformers.TFLayoutLMv2Model`. 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. max_2d_position_embeddings (:obj:`int`, `optional`, defaults to 1024): The maximum value that the 2D position embedding might ever be used with. Typically set this to something large just in case (e.g., 1024). max_rel_pos (:obj:`int`, `optional`, defaults to 128): The maximum number of relative positions to be used in the self-attention mechanism. rel_pos_bins (:obj:`int`, `optional`, defaults to 32): The number of relative position bins to be used in the self-attention mechanism. fast_qkv (:obj:`bool`, `optional`, defaults to :obj:`True`): Whether or not to use a single matrix for the queries, keys, values in the self-attention layers. max_rel_2d_pos (:obj:`int`, `optional`, defaults to 256): The maximum number of relative 2D positions in the self-attention mechanism. rel_2d_pos_bins (:obj:`int`, `optional`, defaults to 64): The number of 2D relative position bins in the self-attention mechanism. image_feature_pool_shape (:obj:`List[int]`, `optional`, defaults to [7, 7, 256]): The shape of the average-pooled feature map. coordinate_size (:obj:`int`, `optional`, defaults to 128): Dimension of the coordinate embeddings. shape_size (:obj:`int`, `optional`, defaults to 128): Dimension of the width and height embeddings. has_relative_attention_bias (:obj:`bool`, `optional`, defaults to :obj:`True`): Whether or not to use a relative attention bias in the self-attention mechanism. has_spatial_attention_bias (:obj:`bool`, `optional`, defaults to :obj:`True`): Whether or not to use a spatial attention bias in the self-attention mechanism. has_visual_segment_embedding (:obj:`bool`, `optional`, defaults to :obj:`False`): Whether or not to add visual segment embeddings. detectron2_config_args (:obj:`dict`, `optional`): Dictionary containing the configuration arguments of the Detectron2 visual backbone. Refer to `this file <>`__ for details regarding default values. Example:: >>> from transformers import LayoutLMv2Model, LayoutLMv2Config >>> # Initializing a LayoutLMv2 microsoft/layoutlmv2-base-uncased style configuration >>> configuration = LayoutLMv2Config() >>> # Initializing a model from the microsoft/layoutlmv2-base-uncased style configuration >>> model = LayoutLMv2Model(configuration) >>> # Accessing the model configuration >>> configuration = model.config """ model_type = "layoutlmv2" def __init__( self, vocab_size=30522, hidden_size=768, 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, pad_token_id=0, max_2d_position_embeddings=1024, max_rel_pos=128, rel_pos_bins=32, fast_qkv=True, max_rel_2d_pos=256, rel_2d_pos_bins=64, convert_sync_batchnorm=True, image_feature_pool_shape=[7, 7, 256], coordinate_size=128, shape_size=128, has_relative_attention_bias=True, has_spatial_attention_bias=True, has_visual_segment_embedding=False, detectron2_config_args=None, **kwargs ): super().__init__( vocab_size=vocab_size, hidden_size=hidden_size, num_hidden_layers=num_hidden_layers, num_attention_heads=num_attention_heads, intermediate_size=intermediate_size, hidden_act=hidden_act, hidden_dropout_prob=hidden_dropout_prob, attention_probs_dropout_prob=attention_probs_dropout_prob, max_position_embeddings=max_position_embeddings, type_vocab_size=type_vocab_size, initializer_range=initializer_range, layer_norm_eps=layer_norm_eps, pad_token_id=pad_token_id, **kwargs, ) self.max_2d_position_embeddings = max_2d_position_embeddings self.max_rel_pos = max_rel_pos self.rel_pos_bins = rel_pos_bins self.fast_qkv = fast_qkv self.max_rel_2d_pos = max_rel_2d_pos self.rel_2d_pos_bins = rel_2d_pos_bins self.convert_sync_batchnorm = convert_sync_batchnorm self.image_feature_pool_shape = image_feature_pool_shape self.coordinate_size = coordinate_size self.shape_size = shape_size self.has_relative_attention_bias = has_relative_attention_bias self.has_spatial_attention_bias = has_spatial_attention_bias self.has_visual_segment_embedding = has_visual_segment_embedding self.detectron2_config_args = ( detectron2_config_args if detectron2_config_args is not None else self.get_default_detectron2_config() ) @classmethod def get_default_detectron2_config(self): return { "MODEL.MASK_ON": True, "MODEL.PIXEL_STD": [57.375, 57.120, 58.395], "MODEL.BACKBONE.NAME": "build_resnet_fpn_backbone", "MODEL.FPN.IN_FEATURES": ["res2", "res3", "res4", "res5"], "MODEL.ANCHOR_GENERATOR.SIZES": [[32], [64], [128], [256], [512]], "MODEL.RPN.IN_FEATURES": ["p2", "p3", "p4", "p5", "p6"], "MODEL.RPN.PRE_NMS_TOPK_TRAIN": 2000, "MODEL.RPN.PRE_NMS_TOPK_TEST": 1000, "MODEL.RPN.POST_NMS_TOPK_TRAIN": 1000, "MODEL.POST_NMS_TOPK_TEST": 1000, "MODEL.ROI_HEADS.NAME": "StandardROIHeads", "MODEL.ROI_HEADS.NUM_CLASSES": 5, "MODEL.ROI_HEADS.IN_FEATURES": ["p2", "p3", "p4", "p5"], "MODEL.ROI_BOX_HEAD.NAME": "FastRCNNConvFCHead", "MODEL.ROI_BOX_HEAD.NUM_FC": 2, "MODEL.ROI_BOX_HEAD.POOLER_RESOLUTION": 14, "MODEL.ROI_MASK_HEAD.NAME": "MaskRCNNConvUpsampleHead", "MODEL.ROI_MASK_HEAD.NUM_CONV": 4, "MODEL.ROI_MASK_HEAD.POOLER_RESOLUTION": 7, "MODEL.RESNETS.DEPTH": 101, "MODEL.RESNETS.SIZES": [[32], [64], [128], [256], [512]], "MODEL.RESNETS.ASPECT_RATIOS": [[0.5, 1.0, 2.0]], "MODEL.RESNETS.OUT_FEATURES": ["res2", "res3", "res4", "res5"], "MODEL.RESNETS.NUM_GROUPS": 32, "MODEL.RESNETS.WIDTH_PER_GROUP": 8, "MODEL.RESNETS.STRIDE_IN_1X1": False, } def get_detectron2_config(self): detectron2_config = detectron2.config.get_cfg() for k, v in self.detectron2_config_args.items(): attributes = k.split(".") to_set = detectron2_config for attribute in attributes[:-1]: to_set = getattr(to_set, attribute) setattr(to_set, attributes[-1], v) return detectron2_config