codingutf8 2023 the huggingface inc team 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 orglicenseslicense2 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 configurationutils import pretrainedconfig from utils import logging from utils backboneutils import backboneconfigmixin getalignedoutputfeaturesoutputindices logger logging getloggername vitdetpretrainedconfigarchivemap facebookvitdetbase https huggingface cofacebookvitdetbaseresolvemainconfig json class vitdetconfigbackboneconfigmixin 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 googlevitdetbasepatch16224https huggingface cogooglevitdetbasepatch16224 architecture configuration objects inherit from pretrainedconfig and can be used to control the model outputs read the documentation from pretrainedconfig for more information args hiddensize int optional defaults to 768 dimensionality of the encoder layers and the pooler layer numhiddenlayers int optional defaults to 12 number of hidden layers in the transformer encoder numattentionheads int optional defaults to 12 number of attention heads for each attention layer in the transformer encoder mlpratio int optional defaults to 4 ratio of mlp hidden dim to embedding dim hiddenact str or function optional defaults to gelu the nonlinear activation function function or string in the encoder and pooler if string gelu relu selu and gelunew are supported dropoutprob float optional defaults to 0 0 the dropout probability for all fully connected layers in the embeddings encoder and pooler initializerrange float optional defaults to 0 02 the standard deviation of the truncatednormalinitializer for initializing all weight matrices layernormeps float optional defaults to 1e06 the epsilon used by the layer normalization layers imagesize int optional defaults to 224 the size resolution of each image pretrainimagesize int optional defaults to 224 the size resolution of each image during pretraining patchsize int optional defaults to 16 the size resolution of each patch numchannels int optional defaults to 3 the number of input channels qkvbias bool optional defaults to true whether to add a bias to the queries keys and values droppathrate float optional defaults to 0 0 stochastic depth rate windowblockindices listint optional defaults to list of indices of blocks that should have window attention instead of regular global selfattention residualblockindices listint optional defaults to list of indices of blocks that should have an extra residual block after the mlp useabsolutepositionembeddings bool optional defaults to true whether to add absolute position embeddings to the patch embeddings userelativepositionembeddings bool optional defaults to false whether to add relative position embeddings to the attention maps windowsize int optional defaults to 0 the size of the attention window outfeatures liststr 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 outindices is set will default to the corresponding stages if unset and outindices is unset will default to the last stage outindices listint 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 outfeatures is set will default to the corresponding stages if unset and outfeatures is unset will default to the last stage example python from transformers import vitdetconfig vitdetmodel initializing a vitdet configuration configuration vitdetconfig initializing a model with random weights from the configuration model vitdetmodelconfiguration accessing the model configuration configuration model config modeltype vitdet def init self hiddensize768 numhiddenlayers12 numattentionheads12 mlpratio4 hiddenactgelu dropoutprob0 0 initializerrange0 02 layernormeps1e6 imagesize224 pretrainimagesize224 patchsize16 numchannels3 qkvbiastrue droppathrate0 0 windowblockindices residualblockindices useabsolutepositionembeddingstrue userelativepositionembeddingsfalse windowsize0 outfeaturesnone outindicesnone kwargs super initkwargs self hiddensize hiddensize self numhiddenlayers numhiddenlayers self numattentionheads numattentionheads self mlpratio mlpratio self hiddenact hiddenact self dropoutprob dropoutprob self initializerrange initializerrange self layernormeps layernormeps self imagesize imagesize self pretrainimagesize pretrainimagesize self patchsize patchsize self numchannels numchannels self qkvbias qkvbias self droppathrate droppathrate self windowblockindices windowblockindices self residualblockindices residualblockindices self useabsolutepositionembeddings useabsolutepositionembeddings self userelativepositionembeddings userelativepositionembeddings self windowsize windowsize self stagenames stem fstageidx for idx in range1 self numhiddenlayers 1 self outfeatures self outindices getalignedoutputfeaturesoutputindices outfeaturesoutfeatures outindicesoutindices stagenamesself stagenames coding utf 8 2023 the huggingface inc team 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 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 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 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
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__)

VITDET_PRETRAINED_CONFIG_ARCHIVE_MAP = {
    "facebook/vit-det-base": "https://huggingface.co/facebook/vit-det-base/resolve/main/config.json",
}


class VitDetConfig(BackboneConfigMixin, PretrainedConfig):
    r

    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
        )