codingutf8 2022 sensetime 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 deformable detr model configuration from configurationutils import pretrainedconfig from utils import logging from auto import configmapping logger logging getloggername deformabledetrpretrainedconfigarchivemap sensetimedeformabledetr https huggingface cosensetimedeformabledetrresolvemainconfig json see all deformable detr models at https huggingface comodels filterdeformabledetr class deformabledetrconfigpretrainedconfig r this is the configuration class to store the configuration of a deformabledetrmodel it is used to instantiate a deformable detr 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 deformable detr sensetimedeformabledetrhttps huggingface cosensetimedeformabledetr architecture configuration objects inherit from pretrainedconfig and can be used to control the model outputs read the documentation from pretrainedconfig for more information args usetimmbackbone bool optional defaults to true whether or not to use the timm library for the backbone if set to false will use the autobackbone api backboneconfig pretrainedconfig or dict optional the configuration of the backbone model only used in case usetimmbackbone is set to false in which case it will default to resnetconfig numchannels int optional defaults to 3 the number of input channels numqueries int optional defaults to 300 number of object queries i e detection slots this is the maximal number of objects deformabledetrmodel can detect in a single image in case twostage is set to true we use twostagenumproposals instead dmodel int optional defaults to 256 dimension of the layers encoderlayers int optional defaults to 6 number of encoder layers decoderlayers int optional defaults to 6 number of decoder layers encoderattentionheads int optional defaults to 8 number of attention heads for each attention layer in the transformer encoder decoderattentionheads int optional defaults to 8 number of attention heads for each attention layer in the transformer decoder decoderffndim int optional defaults to 1024 dimension of the intermediate often named feedforward layer in decoder encoderffndim int optional defaults to 1024 dimension of the intermediate often named feedforward layer in decoder activationfunction str or function optional defaults to relu the nonlinear activation function function or string in the encoder and pooler if string gelu relu silu and gelunew are supported dropout float optional defaults to 0 1 the dropout probability for all fully connected layers in the embeddings encoder and pooler attentiondropout float optional defaults to 0 0 the dropout ratio for the attention probabilities activationdropout float optional defaults to 0 0 the dropout ratio for activations inside the fully connected layer initstd float optional defaults to 0 02 the standard deviation of the truncatednormalinitializer for initializing all weight matrices initxavierstd float optional defaults to 1 the scaling factor used for the xavier initialization gain in the hm attention map module encoderlayerdrop float optional defaults to 0 0 the layerdrop probability for the encoder see the layerdrop papersee https arxiv orgabs1909 11556 for more details auxiliaryloss bool optional defaults to false whether auxiliary decoding losses loss at each decoder layer are to be used positionembeddingtype str optional defaults to sine type of position embeddings to be used on top of the image features one of sine or learned backbone str optional defaults to resnet50 name of convolutional backbone to use in case usetimmbackbone true supports any convolutional backbone from the timm package for a list of all available models see this pagehttps rwightman github iopytorchimagemodelsloadapretrainedmodel usepretrainedbackbone bool optional defaults to true whether to use pretrained weights for the backbone only supported when usetimmbackbone true dilation bool optional defaults to false whether to replace stride with dilation in the last convolutional block dc5 only supported when usetimmbackbone true classcost float optional defaults to 1 relative weight of the classification error in the hungarian matching cost bboxcost float optional defaults to 5 relative weight of the l1 error of the bounding box coordinates in the hungarian matching cost gioucost float optional defaults to 2 relative weight of the generalized iou loss of the bounding box in the hungarian matching cost masklosscoefficient float optional defaults to 1 relative weight of the focal loss in the panoptic segmentation loss dicelosscoefficient float optional defaults to 1 relative weight of the dicef1 loss in the panoptic segmentation loss bboxlosscoefficient float optional defaults to 5 relative weight of the l1 bounding box loss in the object detection loss gioulosscoefficient float optional defaults to 2 relative weight of the generalized iou loss in the object detection loss eoscoefficient float optional defaults to 0 1 relative classification weight of the noobject class in the object detection loss numfeaturelevels int optional defaults to 4 the number of input feature levels encodernpoints int optional defaults to 4 the number of sampled keys in each feature level for each attention head in the encoder decodernpoints int optional defaults to 4 the number of sampled keys in each feature level for each attention head in the decoder twostage bool optional defaults to false whether to apply a twostage deformable detr where the region proposals are also generated by a variant of deformable detr which are further fed into the decoder for iterative bounding box refinement twostagenumproposals int optional defaults to 300 the number of region proposals to be generated in case twostage is set to true withboxrefine bool optional defaults to false whether to apply iterative bounding box refinement where each decoder layer refines the bounding boxes based on the predictions from the previous layer focalalpha float optional defaults to 0 25 alpha parameter in the focal loss disablecustomkernels bool optional defaults to false disable the use of custom cuda and cpu kernels this option is necessary for the onnx export as custom kernels are not supported by pytorch onnx export examples python from transformers import deformabledetrconfig deformabledetrmodel initializing a deformable detr sensetimedeformabledetr style configuration configuration deformabledetrconfig initializing a model with random weights from the sensetimedeformabledetr style configuration model deformabledetrmodelconfiguration accessing the model configuration configuration model config modeltype deformabledetr attributemap hiddensize dmodel numattentionheads encoderattentionheads def init self usetimmbackbonetrue backboneconfignone numchannels3 numqueries300 maxpositionembeddings1024 encoderlayers6 encoderffndim1024 encoderattentionheads8 decoderlayers6 decoderffndim1024 decoderattentionheads8 encoderlayerdrop0 0 isencoderdecodertrue activationfunctionrelu dmodel256 dropout0 1 attentiondropout0 0 activationdropout0 0 initstd0 02 initxavierstd1 0 returnintermediatetrue auxiliarylossfalse positionembeddingtypesine backboneresnet50 usepretrainedbackbonetrue dilationfalse numfeaturelevels4 encodernpoints4 decodernpoints4 twostagefalse twostagenumproposals300 withboxrefinefalse classcost1 bboxcost5 gioucost2 masklosscoefficient1 dicelosscoefficient1 bboxlosscoefficient5 gioulosscoefficient2 eoscoefficient0 1 focalalpha0 25 disablecustomkernelsfalse kwargs if backboneconfig is not none and usetimmbackbone raise valueerroryou can t specify both backboneconfig and usetimmbackbone if not usetimmbackbone if backboneconfig is none logger infobackboneconfig is none initializing the config with the default resnet backbone backboneconfig configmappingresnetoutfeaturesstage4 elif isinstancebackboneconfig dict backbonemodeltype backboneconfig getmodeltype configclass configmappingbackbonemodeltype backboneconfig configclass fromdictbackboneconfig self usetimmbackbone usetimmbackbone self backboneconfig backboneconfig self numchannels numchannels self numqueries numqueries self maxpositionembeddings maxpositionembeddings self dmodel dmodel self encoderffndim encoderffndim self encoderlayers encoderlayers self encoderattentionheads encoderattentionheads self decoderffndim decoderffndim self decoderlayers decoderlayers self decoderattentionheads decoderattentionheads self dropout dropout self attentiondropout attentiondropout self activationdropout activationdropout self activationfunction activationfunction self initstd initstd self initxavierstd initxavierstd self encoderlayerdrop encoderlayerdrop self auxiliaryloss auxiliaryloss self positionembeddingtype positionembeddingtype self backbone backbone self usepretrainedbackbone usepretrainedbackbone self dilation dilation deformable attributes self numfeaturelevels numfeaturelevels self encodernpoints encodernpoints self decodernpoints decodernpoints self twostage twostage self twostagenumproposals twostagenumproposals self withboxrefine withboxrefine if twostage is true and withboxrefine is false raise valueerrorif twostage is true withboxrefine must be true hungarian matcher self classcost classcost self bboxcost bboxcost self gioucost gioucost loss coefficients self masklosscoefficient masklosscoefficient self dicelosscoefficient dicelosscoefficient self bboxlosscoefficient bboxlosscoefficient self gioulosscoefficient gioulosscoefficient self eoscoefficient eoscoefficient self focalalpha focalalpha self disablecustomkernels disablecustomkernels super initisencoderdecoderisencoderdecoder kwargs property def numattentionheadsself int return self encoderattentionheads property def hiddensizeself int return self dmodel coding utf 8 2022 sensetime 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 deformable detr model configuration see all deformable detr models at https huggingface co models filter deformable detr this is the configuration class to store the configuration of a deformabledetrmodel it is used to instantiate a deformable detr 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 deformable detr sensetime deformable detr https huggingface co sensetime deformable detr architecture configuration objects inherit from pretrainedconfig and can be used to control the model outputs read the documentation from pretrainedconfig for more information args use_timm_backbone bool optional defaults to true whether or not to use the timm library for the backbone if set to false will use the autobackbone api backbone_config pretrainedconfig or dict optional the configuration of the backbone model only used in case use_timm_backbone is set to false in which case it will default to resnetconfig num_channels int optional defaults to 3 the number of input channels num_queries int optional defaults to 300 number of object queries i e detection slots this is the maximal number of objects deformabledetrmodel can detect in a single image in case two_stage is set to true we use two_stage_num_proposals instead d_model int optional defaults to 256 dimension of the layers encoder_layers int optional defaults to 6 number of encoder layers decoder_layers int optional defaults to 6 number of decoder layers encoder_attention_heads int optional defaults to 8 number of attention heads for each attention layer in the transformer encoder decoder_attention_heads int optional defaults to 8 number of attention heads for each attention layer in the transformer decoder decoder_ffn_dim int optional defaults to 1024 dimension of the intermediate often named feed forward layer in decoder encoder_ffn_dim int optional defaults to 1024 dimension of the intermediate often named feed forward layer in decoder activation_function str or function optional defaults to relu the non linear activation function function or string in the encoder and pooler if string gelu relu silu and gelu_new are supported dropout float optional defaults to 0 1 the dropout probability for all fully connected layers in the embeddings encoder and pooler attention_dropout float optional defaults to 0 0 the dropout ratio for the attention probabilities activation_dropout float optional defaults to 0 0 the dropout ratio for activations inside the fully connected layer init_std float optional defaults to 0 02 the standard deviation of the truncated_normal_initializer for initializing all weight matrices init_xavier_std float optional defaults to 1 the scaling factor used for the xavier initialization gain in the hm attention map module encoder_layerdrop float optional defaults to 0 0 the layerdrop probability for the encoder see the layerdrop paper see https arxiv org abs 1909 11556 for more details auxiliary_loss bool optional defaults to false whether auxiliary decoding losses loss at each decoder layer are to be used position_embedding_type str optional defaults to sine type of position embeddings to be used on top of the image features one of sine or learned backbone str optional defaults to resnet50 name of convolutional backbone to use in case use_timm_backbone true supports any convolutional backbone from the timm package for a list of all available models see this page https rwightman github io pytorch image models load a pretrained model use_pretrained_backbone bool optional defaults to true whether to use pretrained weights for the backbone only supported when use_timm_backbone true dilation bool optional defaults to false whether to replace stride with dilation in the last convolutional block dc5 only supported when use_timm_backbone true class_cost float optional defaults to 1 relative weight of the classification error in the hungarian matching cost bbox_cost float optional defaults to 5 relative weight of the l1 error of the bounding box coordinates in the hungarian matching cost giou_cost float optional defaults to 2 relative weight of the generalized iou loss of the bounding box in the hungarian matching cost mask_loss_coefficient float optional defaults to 1 relative weight of the focal loss in the panoptic segmentation loss dice_loss_coefficient float optional defaults to 1 relative weight of the dice f 1 loss in the panoptic segmentation loss bbox_loss_coefficient float optional defaults to 5 relative weight of the l1 bounding box loss in the object detection loss giou_loss_coefficient float optional defaults to 2 relative weight of the generalized iou loss in the object detection loss eos_coefficient float optional defaults to 0 1 relative classification weight of the no object class in the object detection loss num_feature_levels int optional defaults to 4 the number of input feature levels encoder_n_points int optional defaults to 4 the number of sampled keys in each feature level for each attention head in the encoder decoder_n_points int optional defaults to 4 the number of sampled keys in each feature level for each attention head in the decoder two_stage bool optional defaults to false whether to apply a two stage deformable detr where the region proposals are also generated by a variant of deformable detr which are further fed into the decoder for iterative bounding box refinement two_stage_num_proposals int optional defaults to 300 the number of region proposals to be generated in case two_stage is set to true with_box_refine bool optional defaults to false whether to apply iterative bounding box refinement where each decoder layer refines the bounding boxes based on the predictions from the previous layer focal_alpha float optional defaults to 0 25 alpha parameter in the focal loss disable_custom_kernels bool optional defaults to false disable the use of custom cuda and cpu kernels this option is necessary for the onnx export as custom kernels are not supported by pytorch onnx export examples python from transformers import deformabledetrconfig deformabledetrmodel initializing a deformable detr sensetime deformable detr style configuration configuration deformabledetrconfig initializing a model with random weights from the sensetime deformable detr style configuration model deformabledetrmodel configuration accessing the model configuration configuration model config deformable attributes hungarian matcher loss coefficients
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


logger = logging.get_logger(__name__)

DEFORMABLE_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP = {
    "SenseTime/deformable-detr": "https://huggingface.co/sensetime/deformable-detr/resolve/main/config.json",
    
}


class DeformableDetrConfig(PretrainedConfig):
    r

    model_type = "deformable_detr"
    attribute_map = {
        "hidden_size": "d_model",
        "num_attention_heads": "encoder_attention_heads",
    }

    def __init__(
        self,
        use_timm_backbone=True,
        backbone_config=None,
        num_channels=3,
        num_queries=300,
        max_position_embeddings=1024,
        encoder_layers=6,
        encoder_ffn_dim=1024,
        encoder_attention_heads=8,
        decoder_layers=6,
        decoder_ffn_dim=1024,
        decoder_attention_heads=8,
        encoder_layerdrop=0.0,
        is_encoder_decoder=True,
        activation_function="relu",
        d_model=256,
        dropout=0.1,
        attention_dropout=0.0,
        activation_dropout=0.0,
        init_std=0.02,
        init_xavier_std=1.0,
        return_intermediate=True,
        auxiliary_loss=False,
        position_embedding_type="sine",
        backbone="resnet50",
        use_pretrained_backbone=True,
        dilation=False,
        num_feature_levels=4,
        encoder_n_points=4,
        decoder_n_points=4,
        two_stage=False,
        two_stage_num_proposals=300,
        with_box_refine=False,
        class_cost=1,
        bbox_cost=5,
        giou_cost=2,
        mask_loss_coefficient=1,
        dice_loss_coefficient=1,
        bbox_loss_coefficient=5,
        giou_loss_coefficient=2,
        eos_coefficient=0.1,
        focal_alpha=0.25,
        disable_custom_kernels=False,
        **kwargs,
    ):
        if backbone_config is not None and use_timm_backbone:
            raise ValueError("You can't specify both `backbone_config` and `use_timm_backbone`.")

        if not use_timm_backbone:
            if backbone_config is None:
                logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.")
                backbone_config = CONFIG_MAPPING["resnet"](out_features=["stage4"])
            elif isinstance(backbone_config, dict):
                backbone_model_type = backbone_config.get("model_type")
                config_class = CONFIG_MAPPING[backbone_model_type]
                backbone_config = config_class.from_dict(backbone_config)
        self.use_timm_backbone = use_timm_backbone
        self.backbone_config = backbone_config
        self.num_channels = num_channels
        self.num_queries = num_queries
        self.max_position_embeddings = max_position_embeddings
        self.d_model = d_model
        self.encoder_ffn_dim = encoder_ffn_dim
        self.encoder_layers = encoder_layers
        self.encoder_attention_heads = encoder_attention_heads
        self.decoder_ffn_dim = decoder_ffn_dim
        self.decoder_layers = decoder_layers
        self.decoder_attention_heads = decoder_attention_heads
        self.dropout = dropout
        self.attention_dropout = attention_dropout
        self.activation_dropout = activation_dropout
        self.activation_function = activation_function
        self.init_std = init_std
        self.init_xavier_std = init_xavier_std
        self.encoder_layerdrop = encoder_layerdrop
        self.auxiliary_loss = auxiliary_loss
        self.position_embedding_type = position_embedding_type
        self.backbone = backbone
        self.use_pretrained_backbone = use_pretrained_backbone
        self.dilation = dilation
        
        self.num_feature_levels = num_feature_levels
        self.encoder_n_points = encoder_n_points
        self.decoder_n_points = decoder_n_points
        self.two_stage = two_stage
        self.two_stage_num_proposals = two_stage_num_proposals
        self.with_box_refine = with_box_refine
        if two_stage is True and with_box_refine is False:
            raise ValueError("If two_stage is True, with_box_refine must be True.")
        
        self.class_cost = class_cost
        self.bbox_cost = bbox_cost
        self.giou_cost = giou_cost
        
        self.mask_loss_coefficient = mask_loss_coefficient
        self.dice_loss_coefficient = dice_loss_coefficient
        self.bbox_loss_coefficient = bbox_loss_coefficient
        self.giou_loss_coefficient = giou_loss_coefficient
        self.eos_coefficient = eos_coefficient
        self.focal_alpha = focal_alpha
        self.disable_custom_kernels = disable_custom_kernels
        super().__init__(is_encoder_decoder=is_encoder_decoder, **kwargs)

    @property
    def num_attention_heads(self) -> int:
        return self.encoder_attention_heads

    @property
    def hidden_size(self) -> int:
        return self.d_model