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 yolos 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 yolospretrainedconfigarchivemap hustvlyolossmall https huggingface cohustvlyolossmallresolvemainconfig json see all yolos models at https huggingface comodels filteryolos class yolosconfigpretrainedconfig r this is the configuration class to store the configuration of a yolosmodel it is used to instantiate a yolos 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 yolos hustvlyolosbasehttps huggingface cohustvlyolosbase 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 listint optional defaults to 512 864 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 numdetectiontokens int optional defaults to 100 the number of detection tokens usemidpositionembeddings bool optional defaults to true whether to use the midlayer position encodings auxiliaryloss bool optional defaults to false whether auxiliary decoding losses loss at each decoder layer are to be used 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 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 example python from transformers import yolosconfig yolosmodel initializing a yolos hustvlyolosbase style configuration configuration yolosconfig initializing a model with random weights from the hustvlyolosbase style configuration model yolosmodelconfiguration accessing the model configuration configuration model config modeltype yolos def init self hiddensize768 numhiddenlayers12 numattentionheads12 intermediatesize3072 hiddenactgelu hiddendropoutprob0 0 attentionprobsdropoutprob0 0 initializerrange0 02 layernormeps1e12 imagesize512 864 patchsize16 numchannels3 qkvbiastrue numdetectiontokens100 usemidpositionembeddingstrue auxiliarylossfalse classcost1 bboxcost5 gioucost2 bboxlosscoefficient5 gioulosscoefficient2 eoscoefficient0 1 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 numdetectiontokens numdetectiontokens self usemidpositionembeddings usemidpositionembeddings self auxiliaryloss auxiliaryloss hungarian matcher self classcost classcost self bboxcost bboxcost self gioucost gioucost loss coefficients self bboxlosscoefficient bboxlosscoefficient self gioulosscoefficient gioulosscoefficient self eoscoefficient eoscoefficient class yolosonnxconfigonnxconfig 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 property def defaultonnxopsetself int return 12 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 yolos model configuration see all yolos models at https huggingface co models filter yolos this is the configuration class to store the configuration of a yolosmodel it is used to instantiate a yolos 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 yolos hustvl yolos base https huggingface co hustvl yolos 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 list int optional defaults to 512 864 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 num_detection_tokens int optional defaults to 100 the number of detection tokens use_mid_position_embeddings bool optional defaults to true whether to use the mid layer position encodings auxiliary_loss bool optional defaults to false whether auxiliary decoding losses loss at each decoder layer are to be used 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 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 example python from transformers import yolosconfig yolosmodel initializing a yolos hustvl yolos base style configuration configuration yolosconfig initializing a model with random weights from the hustvl yolos base style configuration model yolosmodel configuration accessing the model configuration configuration model config hungarian matcher loss coefficients
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__)

YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP = {
    "hustvl/yolos-small": "https://huggingface.co/hustvl/yolos-small/resolve/main/config.json",
    
}


class YolosConfig(PretrainedConfig):
    r

    model_type = "yolos"

    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=[512, 864],
        patch_size=16,
        num_channels=3,
        qkv_bias=True,
        num_detection_tokens=100,
        use_mid_position_embeddings=True,
        auxiliary_loss=False,
        class_cost=1,
        bbox_cost=5,
        giou_cost=2,
        bbox_loss_coefficient=5,
        giou_loss_coefficient=2,
        eos_coefficient=0.1,
        **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.num_detection_tokens = num_detection_tokens
        self.use_mid_position_embeddings = use_mid_position_embeddings
        self.auxiliary_loss = auxiliary_loss
        
        self.class_cost = class_cost
        self.bbox_cost = bbox_cost
        self.giou_cost = giou_cost
        
        self.bbox_loss_coefficient = bbox_loss_coefficient
        self.giou_loss_coefficient = giou_loss_coefficient
        self.eos_coefficient = eos_coefficient


class YolosOnnxConfig(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

    @property
    def default_onnx_opset(self) -> int:
        return 12