AutoAndroidController/py/venv/Lib/site-packages/transformers/models/vitmatte/configuration_vitmatte.py

# coding=utf-8
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# you may not use this file except in compliance with the License.
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#     http://www.apache.org/licenses/LICENSE-2.0
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"""VitMatte model configuration"""

import copy
from typing import Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import verify_backbone_config_arguments
from ..auto.configuration_auto import CONFIG_MAPPING


logger = logging.get_logger(__name__)


class VitMatteConfig(PretrainedConfig):
    r"""
    This is the configuration class to store the configuration of [`VitMatteForImageMatting`]. It is used to
    instantiate a ViTMatte 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 ViTMatte
    [hustvl/vitmatte-small-composition-1k](https://huggingface.co/hustvl/vitmatte-small-composition-1k) architecture.

    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
    documentation from [`PretrainedConfig`] for more information.

    Args:
        backbone_config (`PretrainedConfig` or `dict`, *optional*, defaults to `VitDetConfig()`):
            The configuration of the backbone model.
        backbone (`str`, *optional*):
            Name of backbone to use when `backbone_config` is `None`. If `use_pretrained_backbone` is `True`, this
            will load the corresponding pretrained weights from the timm or transformers library. If `use_pretrained_backbone`
            is `False`, this loads the backbone's config and uses that to initialize the backbone with random weights.
        use_pretrained_backbone (`bool`, *optional*, defaults to `False`):
            Whether to use pretrained weights for the backbone.
        use_timm_backbone (`bool`, *optional*, defaults to `False`):
            Whether to load `backbone` from the timm library. If `False`, the backbone is loaded from the transformers
            library.
        backbone_kwargs (`dict`, *optional*):
            Keyword arguments to be passed to AutoBackbone when loading from a checkpoint
            e.g. `{'out_indices': (0, 1, 2, 3)}`. Cannot be specified if `backbone_config` is set.
        hidden_size (`int`, *optional*, defaults to 384):
            The number of input channels of the decoder.
        batch_norm_eps (`float`, *optional*, defaults to 1e-05):
            The epsilon used by the batch norm layers.
        initializer_range (`float`, *optional*, defaults to 0.02):
            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
        convstream_hidden_sizes (`list[int]`, *optional*, defaults to `[48, 96, 192]`):
            The output channels of the ConvStream module.
        fusion_hidden_sizes (`list[int]`, *optional*, defaults to `[256, 128, 64, 32]`):
            The output channels of the Fusion blocks.

    Example:

    ```python
    >>> from transformers import VitMatteConfig, VitMatteForImageMatting

    >>> # Initializing a ViTMatte hustvl/vitmatte-small-composition-1k style configuration
    >>> configuration = VitMatteConfig()

    >>> # Initializing a model (with random weights) from the hustvl/vitmatte-small-composition-1k style configuration
    >>> model = VitMatteForImageMatting(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```"""

    model_type = "vitmatte"

    def __init__(
        self,
        backbone_config: Optional[PretrainedConfig] = None,
        backbone=None,
        use_pretrained_backbone=False,
        use_timm_backbone=False,
        backbone_kwargs=None,
        hidden_size: int = 384,
        batch_norm_eps: float = 1e-5,
        initializer_range: float = 0.02,
        convstream_hidden_sizes: list[int] = [48, 96, 192],
        fusion_hidden_sizes: list[int] = [256, 128, 64, 32],
        **kwargs,
    ):
        super().__init__(**kwargs)

        if backbone_config is None and backbone is None:
            logger.info("`backbone_config` is `None`. Initializing the config with the default `VitDet` backbone.")
            backbone_config = CONFIG_MAPPING["vitdet"](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)

        verify_backbone_config_arguments(
            use_timm_backbone=use_timm_backbone,
            use_pretrained_backbone=use_pretrained_backbone,
            backbone=backbone,
            backbone_config=backbone_config,
            backbone_kwargs=backbone_kwargs,
        )

        self.backbone_config = backbone_config
        self.backbone = backbone
        self.use_pretrained_backbone = use_pretrained_backbone
        self.use_timm_backbone = use_timm_backbone
        self.backbone_kwargs = backbone_kwargs
        self.batch_norm_eps = batch_norm_eps
        self.hidden_size = hidden_size
        self.initializer_range = initializer_range
        self.convstream_hidden_sizes = convstream_hidden_sizes
        self.fusion_hidden_sizes = fusion_hidden_sizes

    @property
    def sub_configs(self):
        return (
            {"backbone_config": type(self.backbone_config)}
            if getattr(self, "backbone_config", None) is not None
            else {}
        )

    def to_dict(self):
        """
        Serializes this instance to a Python dictionary. Override the default [`~PretrainedConfig.to_dict`]. Returns:
            `dict[str, any]`: Dictionary of all the attributes that make up this configuration instance,
        """
        output = copy.deepcopy(self.__dict__)
        output["backbone_config"] = self.backbone_config.to_dict()
        output["model_type"] = self.__class__.model_type
        return output


__all__ = ["VitMatteConfig"]