# coding=utf-8 # Copyright 2023 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. """VitDet model configuration""" from ...configuration_utils import PretrainedConfig from ...utils import logging from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices logger = logging.get_logger(__name__) class VitDetConfig(BackboneConfigMixin, PretrainedConfig): r""" This is the configuration class to store the configuration of a [`VitDetModel`]. It is used to instantiate an VitDet 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 VitDet [google/vitdet-base-patch16-224](https://huggingface.co/google/vitdet-base-patch16-224) architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: 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. mlp_ratio (`int`, *optional*, defaults to 4): Ratio of mlp hidden dim to embedding dim. 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. dropout_prob (`float`, *optional*, defaults to 0.0): The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. 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-06): The epsilon used by the layer normalization layers. image_size (`int`, *optional*, defaults to 224): The size (resolution) of each image. pretrain_image_size (`int`, *optional*, defaults to 224): The size (resolution) of each image during pretraining. patch_size (`int`, *optional*, defaults to 16): 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. drop_path_rate (`float`, *optional*, defaults to 0.0): Stochastic depth rate. window_block_indices (`List[int]`, *optional*, defaults to `[]`): List of indices of blocks that should have window attention instead of regular global self-attention. residual_block_indices (`List[int]`, *optional*, defaults to `[]`): List of indices of blocks that should have an extra residual block after the MLP. use_absolute_position_embeddings (`bool`, *optional*, defaults to `True`): Whether to add absolute position embeddings to the patch embeddings. use_relative_position_embeddings (`bool`, *optional*, defaults to `False`): Whether to add relative position embeddings to the attention maps. window_size (`int`, *optional*, defaults to 0): The size of the attention window. out_features (`List[str]`, *optional*): If used as backbone, list of features to output. Can be any of `"stem"`, `"stage1"`, `"stage2"`, etc. (depending on how many stages the model has). If unset and `out_indices` is set, will default to the corresponding stages. If unset and `out_indices` is unset, will default to the last stage. Must be in the same order as defined in the `stage_names` attribute. out_indices (`List[int]`, *optional*): If used as backbone, list of indices of features to output. Can be any of 0, 1, 2, etc. (depending on how many stages the model has). If unset and `out_features` is set, will default to the corresponding stages. If unset and `out_features` is unset, will default to the last stage. Must be in the same order as defined in the `stage_names` attribute. Example: ```python >>> from transformers import VitDetConfig, VitDetModel >>> # Initializing a VitDet configuration >>> configuration = VitDetConfig() >>> # Initializing a model (with random weights) from the configuration >>> model = VitDetModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "vitdet" def __init__( self, hidden_size=768, num_hidden_layers=12, num_attention_heads=12, mlp_ratio=4, hidden_act="gelu", dropout_prob=0.0, initializer_range=0.02, layer_norm_eps=1e-6, image_size=224, pretrain_image_size=224, patch_size=16, num_channels=3, qkv_bias=True, drop_path_rate=0.0, window_block_indices=[], residual_block_indices=[], use_absolute_position_embeddings=True, use_relative_position_embeddings=False, window_size=0, out_features=None, out_indices=None, **kwargs, ): super().__init__(**kwargs) self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.mlp_ratio = mlp_ratio self.hidden_act = hidden_act self.dropout_prob = dropout_prob self.initializer_range = initializer_range self.layer_norm_eps = layer_norm_eps self.image_size = image_size self.pretrain_image_size = pretrain_image_size self.patch_size = patch_size self.num_channels = num_channels self.qkv_bias = qkv_bias self.drop_path_rate = drop_path_rate self.window_block_indices = window_block_indices self.residual_block_indices = residual_block_indices self.use_absolute_position_embeddings = use_absolute_position_embeddings self.use_relative_position_embeddings = use_relative_position_embeddings self.window_size = window_size self.stage_names = ["stem"] + [f"stage{idx}" for idx in range(1, self.num_hidden_layers + 1)] self._out_features, self._out_indices = get_aligned_output_features_output_indices( out_features=out_features, out_indices=out_indices, stage_names=self.stage_names ) __all__ = ["VitDetConfig"]