codingutf8 meta platforms 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 data2vecvision model configuration from collections import ordereddict from typing import mapping from packaging import version from configurationutils import pretrainedconfig from onnx import onnxconfig from utils import logging logger logging getloggername data2vecvisionpretrainedconfigarchivemap facebookdata2vecvisionbaseft https huggingface cofacebookdata2vecvisionbaseftresolvemainconfig json class data2vecvisionconfigpretrainedconfig r this is the configuration class to store the configuration of a data2vecvisionmodel it is used to instantiate an data2vecvision 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 data2vecvision facebookdata2vecvisionbasehttps huggingface cofacebookdata2vecvisionbase architecture 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 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 16 the size resolution of each patch numchannels int optional defaults to 3 the number of input channels usemasktoken bool optional defaults to false whether to use a mask token for masked image modeling useabsolutepositionembeddings bool optional defaults to false whether to use bertstyle absolute position embeddings userelativepositionbias bool optional defaults to false whether to use t5style relative position embeddings in the selfattention layers usesharedrelativepositionbias bool optional defaults to false whether to use the same relative position embeddings across all selfattention layers of the transformer layerscaleinitvalue float optional defaults to 0 1 scale to use in the selfattention layers 0 1 for base 1e5 for large set 0 to disable layer scale droppathrate float optional defaults to 0 1 stochastic depth rate per sample when applied in the main path of residual layers usemeanpooling bool optional defaults to true whether to mean pool the final hidden states of the patches instead of using the final hidden state of the cls token before applying the classification head outindices listint optional defaults to 3 5 7 11 indices of the feature maps to use for semantic segmentation poolscales tupleint optional defaults to 1 2 3 6 pooling scales used in pooling pyramid module applied on the last feature map useauxiliaryhead bool optional defaults to true whether to use an auxiliary head during training auxiliarylossweight float optional defaults to 0 4 weight of the crossentropy loss of the auxiliary head auxiliarychannels int optional defaults to 256 number of channels to use in the auxiliary head auxiliarynumconvs int optional defaults to 1 number of convolutional layers to use in the auxiliary head auxiliaryconcatinput bool optional defaults to false whether to concatenate the output of the auxiliary head with the input before the classification layer semanticlossignoreindex int optional defaults to 255 the index that is ignored by the loss function of the semantic segmentation model example python from transformers import data2vecvisionconfig data2vecvisionmodel initializing a data2vecvision data2vecvisionbasepatch16224in22k style configuration configuration data2vecvisionconfig initializing a model with random weights from the data2vecvisionbasepatch16224in22k style configuration model data2vecvisionmodelconfiguration accessing the model configuration configuration model config modeltype data2vecvision def init self hiddensize768 numhiddenlayers12 numattentionheads12 intermediatesize3072 hiddenactgelu hiddendropoutprob0 0 attentionprobsdropoutprob0 0 initializerrange0 02 layernormeps1e12 imagesize224 patchsize16 numchannels3 usemasktokenfalse useabsolutepositionembeddingsfalse userelativepositionbiasfalse usesharedrelativepositionbiasfalse layerscaleinitvalue0 1 droppathrate0 1 usemeanpoolingtrue outindices3 5 7 11 poolscales1 2 3 6 useauxiliaryheadtrue auxiliarylossweight0 4 auxiliarychannels256 auxiliarynumconvs1 auxiliaryconcatinputfalse semanticlossignoreindex255 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 usemasktoken usemasktoken self useabsolutepositionembeddings useabsolutepositionembeddings self userelativepositionbias userelativepositionbias self usesharedrelativepositionbias usesharedrelativepositionbias self layerscaleinitvalue layerscaleinitvalue self droppathrate droppathrate self usemeanpooling usemeanpooling decode head attributes semantic segmentation self outindices outindices self poolscales poolscales auxiliary head attributes semantic segmentation self useauxiliaryhead useauxiliaryhead self auxiliarylossweight auxiliarylossweight self auxiliarychannels auxiliarychannels self auxiliarynumconvs auxiliarynumconvs self auxiliaryconcatinput auxiliaryconcatinput self semanticlossignoreindex semanticlossignoreindex copied from transformers models vit configurationvit vitonnxconfig class data2vecvisiononnxconfigonnxconfig torchonnxminimumversion version parse1 11 property def inputsself mappingstr mappingint str return ordereddict pixelvalues 0 batch 1 numchannels 2 height 3 width property def atolforvalidationself float return 1e4 coding utf 8 meta platforms 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 data2vecvision model configuration this is the configuration class to store the configuration of a data2vecvisionmodel it is used to instantiate an data2vecvision 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 data2vecvision facebook data2vec vision base https huggingface co facebook data2vec vision base architecture 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 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 16 the size resolution of each patch num_channels int optional defaults to 3 the number of input channels use_mask_token bool optional defaults to false whether to use a mask token for masked image modeling use_absolute_position_embeddings bool optional defaults to false whether to use bert style absolute position embeddings use_relative_position_bias bool optional defaults to false whether to use t5 style relative position embeddings in the self attention layers use_shared_relative_position_bias bool optional defaults to false whether to use the same relative position embeddings across all self attention layers of the transformer layer_scale_init_value float optional defaults to 0 1 scale to use in the self attention layers 0 1 for base 1e 5 for large set 0 to disable layer scale drop_path_rate float optional defaults to 0 1 stochastic depth rate per sample when applied in the main path of residual layers use_mean_pooling bool optional defaults to true whether to mean pool the final hidden states of the patches instead of using the final hidden state of the cls token before applying the classification head out_indices list int optional defaults to 3 5 7 11 indices of the feature maps to use for semantic segmentation pool_scales tuple int optional defaults to 1 2 3 6 pooling scales used in pooling pyramid module applied on the last feature map use_auxiliary_head bool optional defaults to true whether to use an auxiliary head during training auxiliary_loss_weight float optional defaults to 0 4 weight of the cross entropy loss of the auxiliary head auxiliary_channels int optional defaults to 256 number of channels to use in the auxiliary head auxiliary_num_convs int optional defaults to 1 number of convolutional layers to use in the auxiliary head auxiliary_concat_input bool optional defaults to false whether to concatenate the output of the auxiliary head with the input before the classification layer semantic_loss_ignore_index int optional defaults to 255 the index that is ignored by the loss function of the semantic segmentation model example python from transformers import data2vecvisionconfig data2vecvisionmodel initializing a data2vecvision data2vec_vision base patch16 224 in22k style configuration configuration data2vecvisionconfig initializing a model with random weights from the data2vec_vision base patch16 224 in22k style configuration model data2vecvisionmodel configuration accessing the model configuration configuration model config decode head attributes semantic segmentation auxiliary head attributes semantic segmentation copied from transformers models vit configuration_vit vitonnxconfig
from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


logger = logging.get_logger(__name__)

DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP = {
    "facebook/data2vec-vision-base-ft": (
        "https://huggingface.co/facebook/data2vec-vision-base-ft/resolve/main/config.json"
    ),
}


class Data2VecVisionConfig(PretrainedConfig):
    r

    model_type = "data2vec-vision"

    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,
        use_mask_token=False,
        use_absolute_position_embeddings=False,
        use_relative_position_bias=False,
        use_shared_relative_position_bias=False,
        layer_scale_init_value=0.1,
        drop_path_rate=0.1,
        use_mean_pooling=True,
        out_indices=[3, 5, 7, 11],
        pool_scales=[1, 2, 3, 6],
        use_auxiliary_head=True,
        auxiliary_loss_weight=0.4,
        auxiliary_channels=256,
        auxiliary_num_convs=1,
        auxiliary_concat_input=False,
        semantic_loss_ignore_index=255,
        **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.use_mask_token = use_mask_token
        self.use_absolute_position_embeddings = use_absolute_position_embeddings
        self.use_relative_position_bias = use_relative_position_bias
        self.use_shared_relative_position_bias = use_shared_relative_position_bias
        self.layer_scale_init_value = layer_scale_init_value
        self.drop_path_rate = drop_path_rate
        self.use_mean_pooling = use_mean_pooling
        
        self.out_indices = out_indices
        self.pool_scales = pool_scales
        
        self.use_auxiliary_head = use_auxiliary_head
        self.auxiliary_loss_weight = auxiliary_loss_weight
        self.auxiliary_channels = auxiliary_channels
        self.auxiliary_num_convs = auxiliary_num_convs
        self.auxiliary_concat_input = auxiliary_concat_input
        self.semantic_loss_ignore_index = semantic_loss_ignore_index



class Data2VecVisionOnnxConfig(OnnxConfig):
    torch_onnx_minimum_version = version.parse("1.11")

    @property
    def inputs(self) -> Mapping[str, Mapping[int, str]]:
        return OrderedDict(
            [
                ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
            ]
        )

    @property
    def atol_for_validation(self) -> float:
        return 1e-4