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 owlvit model configuration import os from collections import ordereddict from typing import typechecking any dict mapping optional union if typechecking from processingutils import processormixin from utils import tensortype from configurationutils import pretrainedconfig from onnx import onnxconfig from utils import logging logger logging getloggername owlvitpretrainedconfigarchivemap googleowlvitbasepatch32 https huggingface cogoogleowlvitbasepatch32resolvemainconfig json googleowlvitbasepatch16 https huggingface cogoogleowlvitbasepatch16resolvemainconfig json googleowlvitlargepatch14 https huggingface cogoogleowlvitlargepatch14resolvemainconfig json class owlvittextconfigpretrainedconfig r this is the configuration class to store the configuration of an owlvittextmodel it is used to instantiate an owlvit text encoder according to the specified arguments defining the model architecture instantiating a configuration with the defaults will yield a similar configuration to that of the owlvit googleowlvitbasepatch32https huggingface cogoogleowlvitbasepatch32 architecture configuration objects inherit from pretrainedconfig and can be used to control the model outputs read the documentation from pretrainedconfig for more information args vocabsize int optional defaults to 49408 vocabulary size of the owlvit text model defines the number of different tokens that can be represented by the inputsids passed when calling owlvittextmodel hiddensize int optional defaults to 512 dimensionality of the encoder layers and the pooler layer intermediatesize int optional defaults to 2048 dimensionality of the intermediate i e feedforward layer in the transformer encoder numhiddenlayers int optional defaults to 12 number of hidden layers in the transformer encoder numattentionheads int optional defaults to 8 number of attention heads for each attention layer in the transformer encoder maxpositionembeddings int optional defaults to 16 the maximum sequence length that this model might ever be used with typically set this to something large just in case e g 512 or 1024 or 2048 hiddenact str or function optional defaults to quickgelu the nonlinear activation function function or string in the encoder and pooler if string gelu relu selu and gelunew quickgelu are supported layernormeps float optional defaults to 1e05 the epsilon used by the layer normalization layers attentiondropout 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 initializerfactor float optional defaults to 1 0 a factor for initializing all weight matrices should be kept to 1 used internally for initialization testing padtokenid int optional defaults to 0 the id of the padding token in the input sequences bostokenid int optional defaults to 49406 the id of the beginningofsequence token in the input sequences eostokenid int optional defaults to 49407 the id of the endofsequence token in the input sequences example python from transformers import owlvittextconfig owlvittextmodel initializing a owlvittextmodel with googleowlvitbasepatch32 style configuration configuration owlvittextconfig initializing a owlvittextconfig from the googleowlvitbasepatch32 style configuration model owlvittextmodelconfiguration accessing the model configuration configuration model config modeltype owlvittextmodel def init self vocabsize49408 hiddensize512 intermediatesize2048 numhiddenlayers12 numattentionheads8 maxpositionembeddings16 hiddenactquickgelu layernormeps1e5 attentiondropout0 0 initializerrange0 02 initializerfactor1 0 padtokenid0 bostokenid49406 eostokenid49407 kwargs super initpadtokenidpadtokenid bostokenidbostokenid eostokenideostokenid kwargs self vocabsize vocabsize self hiddensize hiddensize self intermediatesize intermediatesize self numhiddenlayers numhiddenlayers self numattentionheads numattentionheads self maxpositionembeddings maxpositionembeddings self hiddenact hiddenact self layernormeps layernormeps self attentiondropout attentiondropout self initializerrange initializerrange self initializerfactor initializerfactor classmethod def frompretrainedcls pretrainedmodelnameorpath unionstr os pathlike kwargs pretrainedconfig cls settokeninkwargskwargs configdict kwargs cls getconfigdictpretrainedmodelnameorpath kwargs get the text config dict if we are loading from owlvitconfig if configdict getmodeltype owlvit configdict configdicttextconfig if modeltype in configdict and hasattrcls modeltype and configdictmodeltype cls modeltype logger warning fyou are using a model of type configdict modeltype to instantiate a model of type fcls modeltype this is not supported for all configurations of models and can yield errors return cls fromdictconfigdict kwargs class owlvitvisionconfigpretrainedconfig r this is the configuration class to store the configuration of an owlvitvisionmodel it is used to instantiate an owlvit image encoder according to the specified arguments defining the model architecture instantiating a configuration with the defaults will yield a similar configuration to that of the owlvit googleowlvitbasepatch32https huggingface cogoogleowlvitbasepatch32 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 intermediatesize int optional defaults to 3072 dimensionality of the intermediate i e feedforward layer in the transformer encoder 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 numchannels int optional defaults to 3 number of channels in the input images imagesize int optional defaults to 768 the size resolution of each image patchsize int optional defaults to 32 the size resolution of each patch hiddenact str or function optional defaults to quickgelu the nonlinear activation function function or string in the encoder and pooler if string gelu relu selu and gelunew quickgelu are supported layernormeps float optional defaults to 1e05 the epsilon used by the layer normalization layers attentiondropout 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 initializerfactor float optional defaults to 1 0 a factor for initializing all weight matrices should be kept to 1 used internally for initialization testing example python from transformers import owlvitvisionconfig owlvitvisionmodel initializing a owlvitvisionmodel with googleowlvitbasepatch32 style configuration configuration owlvitvisionconfig initializing a owlvitvisionmodel model from the googleowlvitbasepatch32 style configuration model owlvitvisionmodelconfiguration accessing the model configuration configuration model config modeltype owlvitvisionmodel def init self hiddensize768 intermediatesize3072 numhiddenlayers12 numattentionheads12 numchannels3 imagesize768 patchsize32 hiddenactquickgelu layernormeps1e5 attentiondropout0 0 initializerrange0 02 initializerfactor1 0 kwargs super initkwargs self hiddensize hiddensize self intermediatesize intermediatesize self numhiddenlayers numhiddenlayers self numattentionheads numattentionheads self numchannels numchannels self imagesize imagesize self patchsize patchsize self hiddenact hiddenact self layernormeps layernormeps self attentiondropout attentiondropout self initializerrange initializerrange self initializerfactor initializerfactor classmethod def frompretrainedcls pretrainedmodelnameorpath unionstr os pathlike kwargs pretrainedconfig cls settokeninkwargskwargs configdict kwargs cls getconfigdictpretrainedmodelnameorpath kwargs get the vision config dict if we are loading from owlvitconfig if configdict getmodeltype owlvit configdict configdictvisionconfig if modeltype in configdict and hasattrcls modeltype and configdictmodeltype cls modeltype logger warning fyou are using a model of type configdict modeltype to instantiate a model of type fcls modeltype this is not supported for all configurations of models and can yield errors return cls fromdictconfigdict kwargs class owlvitconfigpretrainedconfig r owlvitconfig is the configuration class to store the configuration of an owlvitmodel it is used to instantiate an owlvit model according to the specified arguments defining the text model and vision model configs instantiating a configuration with the defaults will yield a similar configuration to that of the owlvit googleowlvitbasepatch32https huggingface cogoogleowlvitbasepatch32 architecture configuration objects inherit from pretrainedconfig and can be used to control the model outputs read the documentation from pretrainedconfig for more information args textconfig dict optional dictionary of configuration options used to initialize owlvittextconfig visionconfig dict optional dictionary of configuration options used to initialize owlvitvisionconfig projectiondim int optional defaults to 512 dimensionality of text and vision projection layers logitscaleinitvalue float optional defaults to 2 6592 the inital value of the logitscale parameter default is used as per the original owlvit implementation returndict bool optional defaults to true whether or not the model should return a dictionary if false returns a tuple kwargs optional dictionary of keyword arguments configdict configdicttextconfig textconfig configdictvisionconfig visionconfig return cls fromdictconfigdict kwargs class owlvitonnxconfigonnxconfig property def inputsself mappingstr mappingint str return ordereddict inputids 0 batch 1 sequence pixelvalues 0 batch 1 numchannels 2 height 3 width attentionmask 0 batch 1 sequence property def outputsself mappingstr mappingint str return ordereddict logitsperimage 0 batch logitspertext 0 batch textembeds 0 batch imageembeds 0 batch property def atolforvalidationself float return 1e4 def generatedummyinputs self processor processormixin batchsize int 1 seqlength int 1 framework optionaltensortype none mappingstr any textinputdict super generatedummyinputs processor tokenizer batchsizebatchsize seqlengthseqlength frameworkframework imageinputdict super generatedummyinputs processor imageprocessor batchsizebatchsize frameworkframework return textinputdict imageinputdict property def defaultonnxopsetself int return 14 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 owl vit model configuration this is the configuration class to store the configuration of an owlvittextmodel it is used to instantiate an owlvit text encoder according to the specified arguments defining the model architecture instantiating a configuration with the defaults will yield a similar configuration to that of the owlvit google owlvit base patch32 https huggingface co google owlvit base patch32 architecture configuration objects inherit from pretrainedconfig and can be used to control the model outputs read the documentation from pretrainedconfig for more information args vocab_size int optional defaults to 49408 vocabulary size of the owl vit text model defines the number of different tokens that can be represented by the inputs_ids passed when calling owlvittextmodel hidden_size int optional defaults to 512 dimensionality of the encoder layers and the pooler layer intermediate_size int optional defaults to 2048 dimensionality of the intermediate i e feed forward layer in the transformer encoder num_hidden_layers int optional defaults to 12 number of hidden layers in the transformer encoder num_attention_heads int optional defaults to 8 number of attention heads for each attention layer in the transformer encoder max_position_embeddings int optional defaults to 16 the maximum sequence length that this model might ever be used with typically set this to something large just in case e g 512 or 1024 or 2048 hidden_act str or function optional defaults to quick_gelu the non linear activation function function or string in the encoder and pooler if string gelu relu selu and gelu_new quick_gelu are supported layer_norm_eps float optional defaults to 1e 05 the epsilon used by the layer normalization layers attention_dropout 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 initializer_factor float optional defaults to 1 0 a factor for initializing all weight matrices should be kept to 1 used internally for initialization testing pad_token_id int optional defaults to 0 the id of the padding token in the input sequences bos_token_id int optional defaults to 49406 the id of the beginning of sequence token in the input sequences eos_token_id int optional defaults to 49407 the id of the end of sequence token in the input sequences example python from transformers import owlvittextconfig owlvittextmodel initializing a owlvittextmodel with google owlvit base patch32 style configuration configuration owlvittextconfig initializing a owlvittextconfig from the google owlvit base patch32 style configuration model owlvittextmodel configuration accessing the model configuration configuration model config get the text config dict if we are loading from owlvitconfig this is the configuration class to store the configuration of an owlvitvisionmodel it is used to instantiate an owl vit image encoder according to the specified arguments defining the model architecture instantiating a configuration with the defaults will yield a similar configuration to that of the owl vit google owlvit base patch32 https huggingface co google owlvit base patch32 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 intermediate_size int optional defaults to 3072 dimensionality of the intermediate i e feed forward layer in the transformer encoder 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 num_channels int optional defaults to 3 number of channels in the input images image_size int optional defaults to 768 the size resolution of each image patch_size int optional defaults to 32 the size resolution of each patch hidden_act str or function optional defaults to quick_gelu the non linear activation function function or string in the encoder and pooler if string gelu relu selu and gelu_new quick_gelu are supported layer_norm_eps float optional defaults to 1e 05 the epsilon used by the layer normalization layers attention_dropout 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 initializer_factor float optional defaults to 1 0 a factor for initializing all weight matrices should be kept to 1 used internally for initialization testing example python from transformers import owlvitvisionconfig owlvitvisionmodel initializing a owlvitvisionmodel with google owlvit base patch32 style configuration configuration owlvitvisionconfig initializing a owlvitvisionmodel model from the google owlvit base patch32 style configuration model owlvitvisionmodel configuration accessing the model configuration configuration model config get the vision config dict if we are loading from owlvitconfig owlvitconfig is the configuration class to store the configuration of an owlvitmodel it is used to instantiate an owl vit model according to the specified arguments defining the text model and vision model configs instantiating a configuration with the defaults will yield a similar configuration to that of the owl vit google owlvit base patch32 https huggingface co google owlvit base patch32 architecture configuration objects inherit from pretrainedconfig and can be used to control the model outputs read the documentation from pretrainedconfig for more information args text_config dict optional dictionary of configuration options used to initialize owlvittextconfig vision_config dict optional dictionary of configuration options used to initialize owlvitvisionconfig projection_dim int optional defaults to 512 dimensionality of text and vision projection layers logit_scale_init_value float optional defaults to 2 6592 the inital value of the logit_scale parameter default is used as per the original owl vit implementation return_dict bool optional defaults to true whether or not the model should return a dictionary if false returns a tuple kwargs optional dictionary of keyword arguments instantiate a owlvitconfig or a derived class from owlvit text model configuration and owlvit vision model configuration returns owlvitconfig an instance of a configuration object
import os
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Dict, Mapping, Optional, Union


if TYPE_CHECKING:
    from ...processing_utils import ProcessorMixin
    from ...utils import TensorType

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


logger = logging.get_logger(__name__)

OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP = {
    "google/owlvit-base-patch32": "https://huggingface.co/google/owlvit-base-patch32/resolve/main/config.json",
    "google/owlvit-base-patch16": "https://huggingface.co/google/owlvit-base-patch16/resolve/main/config.json",
    "google/owlvit-large-patch14": "https://huggingface.co/google/owlvit-large-patch14/resolve/main/config.json",
}


class OwlViTTextConfig(PretrainedConfig):
    r

    model_type = "owlvit_text_model"

    def __init__(
        self,
        vocab_size=49408,
        hidden_size=512,
        intermediate_size=2048,
        num_hidden_layers=12,
        num_attention_heads=8,
        max_position_embeddings=16,
        hidden_act="quick_gelu",
        layer_norm_eps=1e-5,
        attention_dropout=0.0,
        initializer_range=0.02,
        initializer_factor=1.0,
        pad_token_id=0,
        bos_token_id=49406,
        eos_token_id=49407,
        **kwargs,
    ):
        super().__init__(pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)

        self.vocab_size = vocab_size
        self.hidden_size = hidden_size
        self.intermediate_size = intermediate_size
        self.num_hidden_layers = num_hidden_layers
        self.num_attention_heads = num_attention_heads
        self.max_position_embeddings = max_position_embeddings
        self.hidden_act = hidden_act
        self.layer_norm_eps = layer_norm_eps
        self.attention_dropout = attention_dropout
        self.initializer_range = initializer_range
        self.initializer_factor = initializer_factor

    @classmethod
    def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs) -> "PretrainedConfig":
        cls._set_token_in_kwargs(kwargs)

        config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs)

        
        if config_dict.get("model_type") == "owlvit":
            config_dict = config_dict["text_config"]

        if "model_type" in config_dict and hasattr(cls, "model_type") and config_dict["model_type"] != cls.model_type:
            logger.warning(
                f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
                f"{cls.model_type}. This is not supported for all configurations of models and can yield errors."
            )

        return cls.from_dict(config_dict, **kwargs)


class OwlViTVisionConfig(PretrainedConfig):
    r

    model_type = "owlvit_vision_model"

    def __init__(
        self,
        hidden_size=768,
        intermediate_size=3072,
        num_hidden_layers=12,
        num_attention_heads=12,
        num_channels=3,
        image_size=768,
        patch_size=32,
        hidden_act="quick_gelu",
        layer_norm_eps=1e-5,
        attention_dropout=0.0,
        initializer_range=0.02,
        initializer_factor=1.0,
        **kwargs,
    ):
        super().__init__(**kwargs)

        self.hidden_size = hidden_size
        self.intermediate_size = intermediate_size
        self.num_hidden_layers = num_hidden_layers
        self.num_attention_heads = num_attention_heads
        self.num_channels = num_channels
        self.image_size = image_size
        self.patch_size = patch_size
        self.hidden_act = hidden_act
        self.layer_norm_eps = layer_norm_eps
        self.attention_dropout = attention_dropout
        self.initializer_range = initializer_range
        self.initializer_factor = initializer_factor

    @classmethod
    def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs) -> "PretrainedConfig":
        cls._set_token_in_kwargs(kwargs)

        config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs)

        
        if config_dict.get("model_type") == "owlvit":
            config_dict = config_dict["vision_config"]

        if "model_type" in config_dict and hasattr(cls, "model_type") and config_dict["model_type"] != cls.model_type:
            logger.warning(
                f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
                f"{cls.model_type}. This is not supported for all configurations of models and can yield errors."
            )

        return cls.from_dict(config_dict, **kwargs)


class OwlViTConfig(PretrainedConfig):
    r

    model_type = "owlvit"

    def __init__(
        self,
        text_config=None,
        vision_config=None,
        projection_dim=512,
        logit_scale_init_value=2.6592,
        return_dict=True,
        **kwargs,
    ):
        super().__init__(**kwargs)

        if text_config is None:
            text_config = {}
            logger.info("text_config is None. Initializing the OwlViTTextConfig with default values.")

        if vision_config is None:
            vision_config = {}
            logger.info("vision_config is None. initializing the OwlViTVisionConfig with default values.")

        self.text_config = OwlViTTextConfig(**text_config)
        self.vision_config = OwlViTVisionConfig(**vision_config)

        self.projection_dim = projection_dim
        self.logit_scale_init_value = logit_scale_init_value
        self.return_dict = return_dict
        self.initializer_factor = 1.0

    @classmethod
    def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs) -> "PretrainedConfig":
        cls._set_token_in_kwargs(kwargs)

        config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs)

        if "model_type" in config_dict and hasattr(cls, "model_type") and config_dict["model_type"] != cls.model_type:
            logger.warning(
                f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
                f"{cls.model_type}. This is not supported for all configurations of models and can yield errors."
            )

        return cls.from_dict(config_dict, **kwargs)

    @classmethod
    def from_text_vision_configs(cls, text_config: Dict, vision_config: Dict, **kwargs):
        r
        config_dict = {}
        config_dict["text_config"] = text_config
        config_dict["vision_config"] = vision_config

        return cls.from_dict(config_dict, **kwargs)


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

    @property
    def outputs(self) -> Mapping[str, Mapping[int, str]]:
        return OrderedDict(
            [
                ("logits_per_image", {0: "batch"}),
                ("logits_per_text", {0: "batch"}),
                ("text_embeds", {0: "batch"}),
                ("image_embeds", {0: "batch"}),
            ]
        )

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

    def generate_dummy_inputs(
        self,
        processor: "ProcessorMixin",
        batch_size: int = -1,
        seq_length: int = -1,
        framework: Optional["TensorType"] = None,
    ) -> Mapping[str, Any]:
        text_input_dict = super().generate_dummy_inputs(
            processor.tokenizer, batch_size=batch_size, seq_length=seq_length, framework=framework
        )
        image_input_dict = super().generate_dummy_inputs(
            processor.image_processor, batch_size=batch_size, framework=framework
        )
        return {**text_input_dict, **image_input_dict}

    @property
    def default_onnx_opset(self) -> int:
        return 14