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 dinov2 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 from utils backboneutils import backboneconfigmixin getalignedoutputfeaturesoutputindices logger logging getloggername dinov2pretrainedconfigarchivemap facebookdinov2base https huggingface cofacebookdinov2baseresolvemainconfig json class dinov2configbackboneconfigmixin pretrainedconfig r this is the configuration class to store the configuration of a dinov2model it is used to instantiate an dinov2 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 dinov2 googledinov2basepatch16224https huggingface cogoogledinov2basepatch16224 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 the hidden size of the mlps relative to the hiddensize 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 1e06 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 layerscalevalue float optional defaults to 1 0 initial value to use for layer scale droppathrate float optional defaults to 0 0 stochastic depth rate per sample when applied in the main path of residual layers useswigluffn bool optional defaults to false whether to use the swiglu feedforward neural network 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 applylayernorm bool optional defaults to true whether to apply layer normalization to the feature maps in case the model is used as backbone reshapehiddenstates bool optional defaults to true whether to reshape the feature maps to 4d tensors of shape batchsize hiddensize height width in case the model is used as backbone if false the feature maps will be 3d tensors of shape batchsize seqlen hiddensize example python from transformers import dinov2config dinov2model initializing a dinov2 dinov2basepatch16224 style configuration configuration dinov2config initializing a model with random weights from the dinov2basepatch16224 style configuration model dinov2modelconfiguration accessing the model configuration configuration model config modeltype dinov2 def init self hiddensize768 numhiddenlayers12 numattentionheads12 mlpratio4 hiddenactgelu hiddendropoutprob0 0 attentionprobsdropoutprob0 0 initializerrange0 02 layernormeps1e6 imagesize224 patchsize16 numchannels3 qkvbiastrue layerscalevalue1 0 droppathrate0 0 useswigluffnfalse outfeaturesnone outindicesnone applylayernormtrue reshapehiddenstatestrue kwargs super initkwargs self hiddensize hiddensize self numhiddenlayers numhiddenlayers self numattentionheads numattentionheads self mlpratio mlpratio 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 layerscalevalue layerscalevalue self droppathrate droppathrate self useswigluffn useswigluffn self stagenames stem fstageidx for idx in range1 numhiddenlayers 1 self outfeatures self outindices getalignedoutputfeaturesoutputindices outfeaturesoutfeatures outindicesoutindices stagenamesself stagenames self applylayernorm applylayernorm self reshapehiddenstates reshapehiddenstates class dinov2onnxconfigonnxconfig 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 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 dinov2 model configuration this is the configuration class to store the configuration of a dinov2model it is used to instantiate an dinov2 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 dinov2 google dinov2 base patch16 224 https huggingface co google dinov2 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 the hidden size of the mlps relative to the hidden_size 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 06 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 layerscale_value float optional defaults to 1 0 initial value to use for layer scale drop_path_rate float optional defaults to 0 0 stochastic depth rate per sample when applied in the main path of residual layers use_swiglu_ffn bool optional defaults to false whether to use the swiglu feedforward neural network 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 apply_layernorm bool optional defaults to true whether to apply layer normalization to the feature maps in case the model is used as backbone reshape_hidden_states bool optional defaults to true whether to reshape the feature maps to 4d tensors of shape batch_size hidden_size height width in case the model is used as backbone if false the feature maps will be 3d tensors of shape batch_size seq_len hidden_size example python from transformers import dinov2config dinov2model initializing a dinov2 dinov2 base patch16 224 style configuration configuration dinov2config initializing a model with random weights from the dinov2 base patch16 224 style configuration model dinov2model configuration accessing the model configuration configuration model config
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
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


logger = logging.get_logger(__name__)

DINOV2_PRETRAINED_CONFIG_ARCHIVE_MAP = {
    "facebook/dinov2-base": "https://huggingface.co/facebook/dinov2-base/resolve/main/config.json",
}


class Dinov2Config(BackboneConfigMixin, PretrainedConfig):
    r

    model_type = "dinov2"

    def __init__(
        self,
        hidden_size=768,
        num_hidden_layers=12,
        num_attention_heads=12,
        mlp_ratio=4,
        hidden_act="gelu",
        hidden_dropout_prob=0.0,
        attention_probs_dropout_prob=0.0,
        initializer_range=0.02,
        layer_norm_eps=1e-6,
        image_size=224,
        patch_size=16,
        num_channels=3,
        qkv_bias=True,
        layerscale_value=1.0,
        drop_path_rate=0.0,
        use_swiglu_ffn=False,
        out_features=None,
        out_indices=None,
        apply_layernorm=True,
        reshape_hidden_states=True,
        **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.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.layerscale_value = layerscale_value
        self.drop_path_rate = drop_path_rate
        self.use_swiglu_ffn = use_swiglu_ffn
        self.stage_names = ["stem"] + [f"stage{idx}" for idx in range(1, 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
        )
        self.apply_layernorm = apply_layernorm
        self.reshape_hidden_states = reshape_hidden_states


class Dinov2OnnxConfig(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