codingutf8 2022 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 vit hybrid model configuration from configurationutils import pretrainedconfig from utils import logging from auto configurationauto import configmapping from bit import bitconfig logger logging getloggername vithybridpretrainedconfigarchivemap googlevithybridbasebit384 https huggingface covithybridbasebit384resolvemainconfig json see all vit hybrid models at https huggingface comodels filtervit class vithybridconfigpretrainedconfig r this is the configuration class to store the configuration of a vithybridmodel it is used to instantiate a vit hybrid 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 vit hybrid googlevithybridbasebit384https huggingface cogooglevithybridbasebit384 architecture configuration objects inherit from pretrainedconfig and can be used to control the model outputs read the documentation from pretrainedconfig for more information args backboneconfig uniondictstr any pretrainedconfig optional the configuration of the backbone in a dictionary or the config object of the backbone 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 intermediatesize int optional defaults to 3072 dimensionality of the intermediate i e feedforward layer in the transformer encoder 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 hiddendropoutprob float optional defaults to 0 0 the dropout probability for all fully connected layers in the embeddings encoder and pooler attentionprobsdropoutprob float optional defaults to 0 0 the dropout ratio for the attention probabilities initializerrange float optional defaults to 0 02 the standard deviation of the truncatednormalinitializer for initializing all weight matrices layernormeps float optional defaults to 1e12 the epsilon used by the layer normalization layers imagesize int optional defaults to 224 the size resolution of each image patchsize int optional defaults to 1 the size resolution of each patch numchannels int optional defaults to 3 the number of input channels backbonefeatmapshape listint optional defaults to 1 1024 24 24 used only for the hybrid embedding type the shape of the feature maps of the backbone qkvbias bool optional defaults to true whether to add a bias to the queries keys and values example python from transformers import vithybridconfig vithybridmodel initializing a vit hybrid vithybridbasebit384 style configuration configuration vithybridconfig initializing a model with random weights from the vithybridbasebit384 style configuration model vithybridmodelconfiguration accessing the model configuration configuration model config modeltype vithybrid def init self backboneconfignone hiddensize768 numhiddenlayers12 numattentionheads12 intermediatesize3072 hiddenactgelu hiddendropoutprob0 0 attentionprobsdropoutprob0 0 initializerrange0 02 layernormeps1e12 imagesize224 patchsize1 numchannels3 backbonefeatmapshape1 1024 24 24 qkvbiastrue kwargs super initkwargs if backboneconfig is none logger infobackboneconfig is none initializing the config with a bit backbone backboneconfig globalpadding same layertype bottleneck depths 3 4 9 outfeatures stage3 embeddingdynamicpadding true if isinstancebackboneconfig dict if modeltype in backboneconfig backboneconfigclass configmappingbackboneconfigmodeltype else logger info modeltype is not found in backboneconfig use bit as the backbone configuration class backboneconfigclass bitconfig backboneconfig backboneconfigclassbackboneconfig self backbonefeatmapshape backbonefeatmapshape self backboneconfig backboneconfig self hiddensize hiddensize self numhiddenlayers numhiddenlayers self numattentionheads numattentionheads self intermediatesize intermediatesize self hiddenact hiddenact self hiddendropoutprob hiddendropoutprob self attentionprobsdropoutprob attentionprobsdropoutprob self initializerrange initializerrange self layernormeps layernormeps self imagesize imagesize self patchsize patchsize self numchannels numchannels self qkvbias qkvbias coding utf 8 2022 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 vit hybrid model configuration see all vit hybrid models at https huggingface co models filter vit this is the configuration class to store the configuration of a vithybridmodel it is used to instantiate a vit hybrid 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 vit hybrid google vit hybrid base bit 384 https huggingface co google vit hybrid base bit 384 architecture configuration objects inherit from pretrainedconfig and can be used to control the model outputs read the documentation from pretrainedconfig for more information args backbone_config union dict str any pretrainedconfig optional the configuration of the backbone in a dictionary or the config object of the backbone 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 intermediate_size int optional defaults to 3072 dimensionality of the intermediate i e feed forward layer in the transformer encoder 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 hidden_dropout_prob float optional defaults to 0 0 the dropout probability for all fully connected layers in the embeddings encoder and pooler attention_probs_dropout_prob float optional defaults to 0 0 the dropout ratio for the attention probabilities 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 12 the epsilon used by the layer normalization layers image_size int optional defaults to 224 the size resolution of each image patch_size int optional defaults to 1 the size resolution of each patch num_channels int optional defaults to 3 the number of input channels backbone_featmap_shape list int optional defaults to 1 1024 24 24 used only for the hybrid embedding type the shape of the feature maps of the backbone qkv_bias bool optional defaults to true whether to add a bias to the queries keys and values example python from transformers import vithybridconfig vithybridmodel initializing a vit hybrid vit hybrid base bit 384 style configuration configuration vithybridconfig initializing a model with random weights from the vit hybrid base bit 384 style configuration model vithybridmodel configuration accessing the model configuration configuration model config
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
from ..bit import BitConfig


logger = logging.get_logger(__name__)

VIT_HYBRID_PRETRAINED_CONFIG_ARCHIVE_MAP = {
    "google/vit-hybrid-base-bit-384": "https://huggingface.co/vit-hybrid-base-bit-384/resolve/main/config.json",
    
}


class ViTHybridConfig(PretrainedConfig):
    r

    model_type = "vit-hybrid"

    def __init__(
        self,
        backbone_config=None,
        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,
        image_size=224,
        patch_size=1,
        num_channels=3,
        backbone_featmap_shape=[1, 1024, 24, 24],
        qkv_bias=True,
        **kwargs,
    ):
        super().__init__(**kwargs)

        if backbone_config is None:
            logger.info("`backbone_config` is `None`. Initializing the config with a `BiT` backbone.")
            backbone_config = {
                "global_padding": "same",
                "layer_type": "bottleneck",
                "depths": [3, 4, 9],
                "out_features": ["stage3"],
                "embedding_dynamic_padding": True,
            }

        if isinstance(backbone_config, dict):
            if "model_type" in backbone_config:
                backbone_config_class = CONFIG_MAPPING[backbone_config["model_type"]]
            else:
                logger.info(
                    "`model_type` is not found in `backbone_config`. Use `Bit` as the backbone configuration class."
                )
                backbone_config_class = BitConfig
            backbone_config = backbone_config_class(**backbone_config)

        self.backbone_featmap_shape = backbone_featmap_shape
        self.backbone_config = backbone_config
        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.qkv_bias = qkv_bias