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 dilated neighborhood attention transformer model configuration from configurationutils import pretrainedconfig from utils import logging from utils backboneutils import backboneconfigmixin getalignedoutputfeaturesoutputindices logger logging getloggername dinatpretrainedconfigarchivemap shilabsdinatminiin1k224 https huggingface coshilabsdinatminiin1k224resolvemainconfig json see all dinat models at https huggingface comodels filterdinat class dinatconfigbackboneconfigmixin pretrainedconfig r this is the configuration class to store the configuration of a dinatmodel it is used to instantiate a dinat 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 dinat shilabsdinatminiin1k224https huggingface coshilabsdinatminiin1k224 architecture configuration objects inherit from pretrainedconfig and can be used to control the model outputs read the documentation from pretrainedconfig for more information args patchsize int optional defaults to 4 the size resolution of each patch note only patch size of 4 is supported at the moment numchannels int optional defaults to 3 the number of input channels embeddim int optional defaults to 64 dimensionality of patch embedding depths listint optional defaults to 3 4 6 5 number of layers in each level of the encoder numheads listint optional defaults to 2 4 8 16 number of attention heads in each layer of the transformer encoder kernelsize int optional defaults to 7 neighborhood attention kernel size dilations listlistint optional defaults to 1 8 1 1 4 1 4 1 2 1 2 1 2 1 1 1 1 1 dilation value of each na layer in the transformer encoder mlpratio float optional defaults to 3 0 ratio of mlp hidden dimensionality to embedding dimensionality qkvbias bool optional defaults to true whether or not a learnable bias should be added to the queries keys and values hiddendropoutprob float optional defaults to 0 0 the dropout probability for all fully connected layers in the embeddings and encoder attentionprobsdropoutprob float optional defaults to 0 0 the dropout ratio for the attention probabilities droppathrate float optional defaults to 0 1 stochastic depth rate hiddenact str or function optional defaults to gelu the nonlinear activation function function or string in the encoder if string gelu relu selu and gelunew are supported initializerrange float optional defaults to 0 02 the standard deviation of the truncatednormalinitializer for initializing all weight matrices layernormeps float optional defaults to 1e05 the epsilon used by the layer normalization layers layerscaleinitvalue float optional defaults to 0 0 the initial value for the layer scale disabled if 0 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 dinatconfig dinatmodel initializing a dinat shilabsdinatminiin1k224 style configuration configuration dinatconfig initializing a model with random weights from the shilabsdinatminiin1k224 style configuration model dinatmodelconfiguration accessing the model configuration configuration model config modeltype dinat attributemap numattentionheads numheads numhiddenlayers numlayers def init self patchsize4 numchannels3 embeddim64 depths3 4 6 5 numheads2 4 8 16 kernelsize7 dilations1 8 1 1 4 1 4 1 2 1 2 1 2 1 1 1 1 1 mlpratio3 0 qkvbiastrue hiddendropoutprob0 0 attentionprobsdropoutprob0 0 droppathrate0 1 hiddenactgelu initializerrange0 02 layernormeps1e5 layerscaleinitvalue0 0 outfeaturesnone outindicesnone kwargs super initkwargs self patchsize patchsize self numchannels numchannels self embeddim embeddim self depths depths self numlayers lendepths self numheads numheads self kernelsize kernelsize self dilations dilations self mlpratio mlpratio self qkvbias qkvbias self hiddendropoutprob hiddendropoutprob self attentionprobsdropoutprob attentionprobsdropoutprob self droppathrate droppathrate self hiddenact hiddenact self layernormeps layernormeps self initializerrange initializerrange we set the hiddensize attribute in order to make dinat work with visionencoderdecodermodel this indicates the channel dimension after the last stage of the model self hiddensize intembeddim 2 lendepths 1 self layerscaleinitvalue layerscaleinitvalue self stagenames stem fstageidx for idx in range1 lendepths 1 self outfeatures self outindices getalignedoutputfeaturesoutputindices outfeaturesoutfeatures outindicesoutindices stagenamesself stagenames 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 dilated neighborhood attention transformer model configuration see all dinat models at https huggingface co models filter dinat this is the configuration class to store the configuration of a dinatmodel it is used to instantiate a dinat 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 dinat shi labs dinat mini in1k 224 https huggingface co shi labs dinat mini in1k 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 patch_size int optional defaults to 4 the size resolution of each patch note only patch size of 4 is supported at the moment num_channels int optional defaults to 3 the number of input channels embed_dim int optional defaults to 64 dimensionality of patch embedding depths list int optional defaults to 3 4 6 5 number of layers in each level of the encoder num_heads list int optional defaults to 2 4 8 16 number of attention heads in each layer of the transformer encoder kernel_size int optional defaults to 7 neighborhood attention kernel size dilations list list int optional defaults to 1 8 1 1 4 1 4 1 2 1 2 1 2 1 1 1 1 1 dilation value of each na layer in the transformer encoder mlp_ratio float optional defaults to 3 0 ratio of mlp hidden dimensionality to embedding dimensionality qkv_bias bool optional defaults to true whether or not a learnable bias should be added to the queries keys and values hidden_dropout_prob float optional defaults to 0 0 the dropout probability for all fully connected layers in the embeddings and encoder attention_probs_dropout_prob float optional defaults to 0 0 the dropout ratio for the attention probabilities drop_path_rate float optional defaults to 0 1 stochastic depth rate hidden_act str or function optional defaults to gelu the non linear activation function function or string in the encoder if string gelu relu selu and gelu_new are supported 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 05 the epsilon used by the layer normalization layers layer_scale_init_value float optional defaults to 0 0 the initial value for the layer scale disabled if 0 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 dinatconfig dinatmodel initializing a dinat shi labs dinat mini in1k 224 style configuration configuration dinatconfig initializing a model with random weights from the shi labs dinat mini in1k 224 style configuration model dinatmodel configuration accessing the model configuration configuration model config we set the hidden_size attribute in order to make dinat work with visionencoderdecodermodel this indicates the channel dimension after the last stage of the model
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__)

DINAT_PRETRAINED_CONFIG_ARCHIVE_MAP = {
    "shi-labs/dinat-mini-in1k-224": "https://huggingface.co/shi-labs/dinat-mini-in1k-224/resolve/main/config.json",
    
}


class DinatConfig(BackboneConfigMixin, PretrainedConfig):
    r

    model_type = "dinat"

    attribute_map = {
        "num_attention_heads": "num_heads",
        "num_hidden_layers": "num_layers",
    }

    def __init__(
        self,
        patch_size=4,
        num_channels=3,
        embed_dim=64,
        depths=[3, 4, 6, 5],
        num_heads=[2, 4, 8, 16],
        kernel_size=7,
        dilations=[[1, 8, 1], [1, 4, 1, 4], [1, 2, 1, 2, 1, 2], [1, 1, 1, 1, 1]],
        mlp_ratio=3.0,
        qkv_bias=True,
        hidden_dropout_prob=0.0,
        attention_probs_dropout_prob=0.0,
        drop_path_rate=0.1,
        hidden_act="gelu",
        initializer_range=0.02,
        layer_norm_eps=1e-5,
        layer_scale_init_value=0.0,
        out_features=None,
        out_indices=None,
        **kwargs,
    ):
        super().__init__(**kwargs)

        self.patch_size = patch_size
        self.num_channels = num_channels
        self.embed_dim = embed_dim
        self.depths = depths
        self.num_layers = len(depths)
        self.num_heads = num_heads
        self.kernel_size = kernel_size
        self.dilations = dilations
        self.mlp_ratio = mlp_ratio
        self.qkv_bias = qkv_bias
        self.hidden_dropout_prob = hidden_dropout_prob
        self.attention_probs_dropout_prob = attention_probs_dropout_prob
        self.drop_path_rate = drop_path_rate
        self.hidden_act = hidden_act
        self.layer_norm_eps = layer_norm_eps
        self.initializer_range = initializer_range
        
        
        self.hidden_size = int(embed_dim * 2 ** (len(depths) - 1))
        self.layer_scale_init_value = layer_scale_init_value
        self.stage_names = ["stem"] + [f"stage{idx}" for idx in range(1, len(depths) + 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
        )