configuration_owlvit.py

# coding=utf-8
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""" OWL-ViT model configuration"""

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"""
    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
    ```"""

    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)

        # get the text config dict if we are loading from OwlViTConfig
        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"""
    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
    ```"""

    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)

        # get the vision config dict if we are loading from OwlViTConfig
        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"""
    [`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.
    """

    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"""
        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
        """
        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