codingutf8 2022 facebook ai and 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 mae model configuration from configurationutils import pretrainedconfig from utils import logging logger logging getloggername vitmaepretrainedconfigarchivemap facebookvitmaebase https huggingface cofacebookvitmaebaseresolvemainconfig json see all vit mae models at https huggingface comodels filtervitmae class vitmaeconfigpretrainedconfig r this is the configuration class to store the configuration of a vitmaemodel it is used to instantiate an vit mae 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 facebookvitmaebasehttps huggingface cofacebookvitmaebase 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 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 probabilitiy 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 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 decodernumattentionheads int optional defaults to 16 number of attention heads for each attention layer in the decoder decoderhiddensize int optional defaults to 512 dimensionality of the decoder decodernumhiddenlayers int optional defaults to 8 number of hidden layers in the decoder decoderintermediatesize int optional defaults to 2048 dimensionality of the intermediate i e feedforward layer in the decoder maskratio float optional defaults to 0 75 the ratio of the number of masked tokens in the input sequence normpixloss bool optional defaults to false whether or not to train with normalized pixels see table 3 in the paper using normalized pixels improved representation quality in the experiments of the s example python from transformers import vitmaeconfig vitmaemodel initializing a vit mae vitmaebase style configuration configuration vitmaeconfig initializing a model with random weights from the vitmaebase style configuration model vitmaemodelconfiguration accessing the model configuration configuration model config modeltype vitmae def init self hiddensize768 numhiddenlayers12 numattentionheads12 intermediatesize3072 hiddenactgelu hiddendropoutprob0 0 attentionprobsdropoutprob0 0 initializerrange0 02 layernormeps1e12 imagesize224 patchsize16 numchannels3 qkvbiastrue decodernumattentionheads16 decoderhiddensize512 decodernumhiddenlayers8 decoderintermediatesize2048 maskratio0 75 normpixlossfalse kwargs super initkwargs 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 self decodernumattentionheads decodernumattentionheads self decoderhiddensize decoderhiddensize self decodernumhiddenlayers decodernumhiddenlayers self decoderintermediatesize decoderintermediatesize self maskratio maskratio self normpixloss normpixloss coding utf 8 2022 facebook ai and 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 mae model configuration see all vit mae models at https huggingface co models filter vit mae this is the configuration class to store the configuration of a vitmaemodel it is used to instantiate an vit mae 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 facebook vit mae base https huggingface co facebook vit mae base 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 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 probabilitiy 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 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 decoder_num_attention_heads int optional defaults to 16 number of attention heads for each attention layer in the decoder decoder_hidden_size int optional defaults to 512 dimensionality of the decoder decoder_num_hidden_layers int optional defaults to 8 number of hidden layers in the decoder decoder_intermediate_size int optional defaults to 2048 dimensionality of the intermediate i e feed forward layer in the decoder mask_ratio float optional defaults to 0 75 the ratio of the number of masked tokens in the input sequence norm_pix_loss bool optional defaults to false whether or not to train with normalized pixels see table 3 in the paper using normalized pixels improved representation quality in the experiments of the s example python from transformers import vitmaeconfig vitmaemodel initializing a vit mae vit mae base style configuration configuration vitmaeconfig initializing a model with random weights from the vit mae base style configuration model vitmaemodel configuration accessing the model configuration configuration model config
from ...configuration_utils import PretrainedConfig
from ...utils import logging


logger = logging.get_logger(__name__)

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


class ViTMAEConfig(PretrainedConfig):
    r

    model_type = "vit_mae"

    def __init__(
        self,
        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=16,
        num_channels=3,
        qkv_bias=True,
        decoder_num_attention_heads=16,
        decoder_hidden_size=512,
        decoder_num_hidden_layers=8,
        decoder_intermediate_size=2048,
        mask_ratio=0.75,
        norm_pix_loss=False,
        **kwargs,
    ):
        super().__init__(**kwargs)

        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
        self.decoder_num_attention_heads = decoder_num_attention_heads
        self.decoder_hidden_size = decoder_hidden_size
        self.decoder_num_hidden_layers = decoder_num_hidden_layers
        self.decoder_intermediate_size = decoder_intermediate_size
        self.mask_ratio = mask_ratio
        self.norm_pix_loss = norm_pix_loss