image-match/python/py/Lib/site-packages/transformers/models/hiera/configuration_hiera.py

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"""Hiera model configuration"""

from huggingface_hub.dataclasses import strict

from ...backbone_utils import BackboneConfigMixin
from ...configuration_utils import PreTrainedConfig
from ...utils import auto_docstring


@auto_docstring(checkpoint="facebook/hiera-base-224")
@strict
class HieraConfig(BackboneConfigMixin, PreTrainedConfig):
    r"""
    patch_stride (`list(int)`, *optional*, defaults to `[4, 4]`):
        The stride of the patch.
    patch_padding (`list(int)`, *optional*, defaults to `[3, 3]`):
        The padding of the patch.
    num_heads (`list(int)`, *optional*, defaults to `[1, 2, 4, 8]`):
        Number of attention heads in each layer of the Transformer encoder.
    embed_dim_multiplier (`float`, *optional*, defaults to 2.0):
        The multiplier to the dimensionality of patch embedding in each layer of the Transformer encoder.
    num_query_pool (`int`, *optional*, defaults to 3):
        The number of query pool stages.
    query_stride (`list(int)`, *optional*, defaults to `[2, 2]`):
        The stride of the query pool.
    masked_unit_size (`list(int)`, *optional*, defaults to `[8, 8]`):
        The size of the masked unit.
    masked_unit_attention (`list(bool)`, *optional*, defaults to `[True, True, False, False]`):
        Whether to use masked unit attention in each layer of the Transformer encoder.
    layer_norm_init (`float`, *optional*, defaults to 1.0):
        The initial weight value for layer normalization layers.
    decoder_depth (`int`, *optional*):
        Depth of the decoder for MAE pretraining.
    normalize_pixel_loss (`bool`, *optional*, defaults to `True`):
        Whether to normalize the pixel loss by the number of pixels.
    mask_ratio (`float`, *optional*, defaults to 0.6):
        The ratio of masked tokens in the input.

    Example:

    ```python
    >>> from transformers import HieraConfig, HieraModel

    >>> # Initializing a Hiera hiera-base-patch16-224 style configuration
    >>> configuration = HieraConfig()

    >>> # Initializing a model (with random weights) from the hiera-base-patch16-224 style configuration
    >>> model = HieraModel(configuration)

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

    model_type = "hiera"

    attribute_map = {"num_hidden_layers": "num_layers"}

    embed_dim: int = 96
    image_size: list[int] | tuple[int, ...] = (224, 224)
    patch_size: list[int] | tuple[int, ...] = (7, 7)
    patch_stride: list[int] | tuple[int, ...] = (4, 4)
    patch_padding: list[int] | tuple[int, ...] = (3, 3)
    mlp_ratio: float = 4.0
    depths: list[int] | tuple[int, ...] = (2, 3, 16, 3)
    num_heads: list[int] | tuple[int, ...] = (1, 2, 4, 8)
    embed_dim_multiplier: float | int = 2.0
    num_query_pool: int = 3
    query_stride: list[int] | tuple[int, ...] = (2, 2)
    masked_unit_size: list[int] | tuple[int, ...] = (8, 8)
    masked_unit_attention: list[bool] | tuple[bool, ...] = (True, True, False, False)
    drop_path_rate: float | int = 0.0
    num_channels: int = 3
    hidden_act: str = "gelu"
    initializer_range: float = 0.02
    layer_norm_init: float = 1.0
    layer_norm_eps: float = 1e-6
    decoder_hidden_size: int | None = None
    decoder_depth: int | None = None
    decoder_num_heads: int | None = None
    normalize_pixel_loss: bool | None = True
    mask_ratio: float = 0.6
    _out_features: list[str] | None = None
    _out_indices: list[int] | None = None

    def __post_init__(self, **kwargs):
        # we set the hidden_size attribute in order to make Hiera work with VisionEncoderDecoderModel
        # this indicates the channel dimension after the last stage of the model
        self.hidden_size = int(self.embed_dim * self.embed_dim_multiplier ** (len(self.depths) - 1))
        self.stage_names = ["stem"] + [f"stage{idx}" for idx in range(1, len(self.depths) + 1)]
        self.set_output_features_output_indices(
            out_indices=kwargs.pop("out_indices", None), out_features=kwargs.pop("out_features", None)
        )
        super().__post_init__(**kwargs)

    def validate_architecture(self):
        """Part of `@strict`-powered validation. Validates the architecture of the config."""
        if self.masked_unit_size[0] % self.query_stride[0] ** (len(self.depths) - 1) != 0:
            raise ValueError(
                f"masked_unit_size[0] ({self.masked_unit_size[0]}) must be divisible by query_stride[0] ({self.query_stride[0]}) "
                f"raised to the power of the number of layers ({len(self.depths) - 1})"
            )

        if self.num_query_pool >= len(self.depths):
            raise ValueError(
                f"num_query_pool ({self.num_query_pool}) must be less than the number of layers ({len(self.depths)})"
            )


__all__ = ["HieraConfig"]