configuration_deta.py

# coding=utf-8
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"""DETA model configuration"""

from ....configuration_utils import PretrainedConfig
from ....utils import logging
from ...auto import CONFIG_MAPPING


logger = logging.get_logger(__name__)


class DetaConfig(PretrainedConfig):
    r"""
    This is the configuration class to store the configuration of a [`DetaModel`]. It is used to instantiate a DETA
    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 DETA
    [SenseTime/deformable-detr](https://huggingface.co/SenseTime/deformable-detr) 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 `ResNetConfig()`):
            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*, `False`):
            Whether to use pretrained weights for the backbone.
        use_timm_backbone (`bool`, *optional*, `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.
        num_queries (`int`, *optional*, defaults to 900):
            Number of object queries, i.e. detection slots. This is the maximal number of objects [`DetaModel`] can
            detect in a single image. In case `two_stage` is set to `True`, we use `two_stage_num_proposals` instead.
        d_model (`int`, *optional*, defaults to 256):
            Dimension of the layers.
        encoder_layers (`int`, *optional*, defaults to 6):
            Number of encoder layers.
        decoder_layers (`int`, *optional*, defaults to 6):
            Number of decoder layers.
        encoder_attention_heads (`int`, *optional*, defaults to 8):
            Number of attention heads for each attention layer in the Transformer encoder.
        decoder_attention_heads (`int`, *optional*, defaults to 8):
            Number of attention heads for each attention layer in the Transformer decoder.
        decoder_ffn_dim (`int`, *optional*, defaults to 2048):
            Dimension of the "intermediate" (often named feed-forward) layer in decoder.
        encoder_ffn_dim (`int`, *optional*, defaults to 2048):
            Dimension of the "intermediate" (often named feed-forward) layer in decoder.
        activation_function (`str` or `function`, *optional*, defaults to `"relu"`):
            The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
            `"relu"`, `"silu"` and `"gelu_new"` are supported.
        dropout (`float`, *optional*, defaults to 0.1):
            The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
        attention_dropout (`float`, *optional*, defaults to 0.0):
            The dropout ratio for the attention probabilities.
        activation_dropout (`float`, *optional*, defaults to 0.0):
            The dropout ratio for activations inside the fully connected layer.
        init_std (`float`, *optional*, defaults to 0.02):
            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
        init_xavier_std (`float`, *optional*, defaults to 1):
            The scaling factor used for the Xavier initialization gain in the HM Attention map module.
        encoder_layerdrop (`float`, *optional*, defaults to 0.0):
            The LayerDrop probability for the encoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556)
            for more details.
        auxiliary_loss (`bool`, *optional*, defaults to `False`):
            Whether auxiliary decoding losses (loss at each decoder layer) are to be used.
        position_embedding_type (`str`, *optional*, defaults to `"sine"`):
            Type of position embeddings to be used on top of the image features. One of `"sine"` or `"learned"`.
        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.
        mask_loss_coefficient (`float`, *optional*, defaults to 1):
            Relative weight of the Focal loss in the panoptic segmentation loss.
        dice_loss_coefficient (`float`, *optional*, defaults to 1):
            Relative weight of the DICE/F-1 loss in the panoptic segmentation loss.
        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.
        num_feature_levels (`int`, *optional*, defaults to 5):
            The number of input feature levels.
        encoder_n_points (`int`, *optional*, defaults to 4):
            The number of sampled keys in each feature level for each attention head in the encoder.
        decoder_n_points (`int`, *optional*, defaults to 4):
            The number of sampled keys in each feature level for each attention head in the decoder.
        two_stage (`bool`, *optional*, defaults to `True`):
            Whether to apply a two-stage deformable DETR, where the region proposals are also generated by a variant of
            DETA, which are further fed into the decoder for iterative bounding box refinement.
        two_stage_num_proposals (`int`, *optional*, defaults to 300):
            The number of region proposals to be generated, in case `two_stage` is set to `True`.
        with_box_refine (`bool`, *optional*, defaults to `True`):
            Whether to apply iterative bounding box refinement, where each decoder layer refines the bounding boxes
            based on the predictions from the previous layer.
        focal_alpha (`float`, *optional*, defaults to 0.25):
            Alpha parameter in the focal loss.
        assign_first_stage (`bool`, *optional*, defaults to `True`):
            Whether to assign each prediction i to the highest overlapping ground truth object if the overlap is larger than a threshold 0.7.
        assign_second_stage (`bool`, *optional*, defaults to `True`):
            Whether to assign second assignment procedure in the second stage closely follows the first stage assignment procedure.
        disable_custom_kernels (`bool`, *optional*, defaults to `True`):
            Disable the use of custom CUDA and CPU kernels. This option is necessary for the ONNX export, as custom
            kernels are not supported by PyTorch ONNX export.

    Examples:

    ```python
    >>> from transformers import DetaConfig, DetaModel

    >>> # Initializing a DETA SenseTime/deformable-detr style configuration
    >>> configuration = DetaConfig()

    >>> # Initializing a model (with random weights) from the SenseTime/deformable-detr style configuration
    >>> model = DetaModel(configuration)

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

    model_type = "deta"
    attribute_map = {
        "hidden_size": "d_model",
        "num_attention_heads": "encoder_attention_heads",
    }

    def __init__(
        self,
        backbone_config=None,
        backbone=None,
        use_pretrained_backbone=False,
        use_timm_backbone=False,
        backbone_kwargs=None,
        num_queries=900,
        max_position_embeddings=2048,
        encoder_layers=6,
        encoder_ffn_dim=2048,
        encoder_attention_heads=8,
        decoder_layers=6,
        decoder_ffn_dim=1024,
        decoder_attention_heads=8,
        encoder_layerdrop=0.0,
        is_encoder_decoder=True,
        activation_function="relu",
        d_model=256,
        dropout=0.1,
        attention_dropout=0.0,
        activation_dropout=0.0,
        init_std=0.02,
        init_xavier_std=1.0,
        return_intermediate=True,
        auxiliary_loss=False,
        position_embedding_type="sine",
        num_feature_levels=5,
        encoder_n_points=4,
        decoder_n_points=4,
        two_stage=True,
        two_stage_num_proposals=300,
        with_box_refine=True,
        assign_first_stage=True,
        assign_second_stage=True,
        class_cost=1,
        bbox_cost=5,
        giou_cost=2,
        mask_loss_coefficient=1,
        dice_loss_coefficient=1,
        bbox_loss_coefficient=5,
        giou_loss_coefficient=2,
        eos_coefficient=0.1,
        focal_alpha=0.25,
        disable_custom_kernels=True,
        **kwargs,
    ):
        if use_pretrained_backbone:
            raise ValueError("Pretrained backbones are not supported yet.")

        if backbone_config is not None and backbone is not None:
            raise ValueError("You can't specify both `backbone` and `backbone_config`.")

        if backbone_config is None and backbone is None:
            logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.")
            backbone_config = CONFIG_MAPPING["resnet"](out_features=["stage2", "stage3", "stage4"])
        else:
            if isinstance(backbone_config, dict):
                backbone_model_type = backbone_config.pop("model_type")
                config_class = CONFIG_MAPPING[backbone_model_type]
                backbone_config = config_class.from_dict(backbone_config)

        if backbone_kwargs is not None and backbone_kwargs and backbone_config is not None:
            raise ValueError("You can't specify both `backbone_kwargs` and `backbone_config`.")

        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.num_queries = num_queries
        self.max_position_embeddings = max_position_embeddings
        self.d_model = d_model
        self.encoder_ffn_dim = encoder_ffn_dim
        self.encoder_layers = encoder_layers
        self.encoder_attention_heads = encoder_attention_heads
        self.decoder_ffn_dim = decoder_ffn_dim
        self.decoder_layers = decoder_layers
        self.decoder_attention_heads = decoder_attention_heads
        self.dropout = dropout
        self.attention_dropout = attention_dropout
        self.activation_dropout = activation_dropout
        self.activation_function = activation_function
        self.init_std = init_std
        self.init_xavier_std = init_xavier_std
        self.encoder_layerdrop = encoder_layerdrop
        self.auxiliary_loss = auxiliary_loss
        self.position_embedding_type = position_embedding_type
        # deformable attributes
        self.num_feature_levels = num_feature_levels
        self.encoder_n_points = encoder_n_points
        self.decoder_n_points = decoder_n_points
        self.two_stage = two_stage
        self.two_stage_num_proposals = two_stage_num_proposals
        self.with_box_refine = with_box_refine
        self.assign_first_stage = assign_first_stage
        self.assign_second_stage = assign_second_stage
        if two_stage is True and with_box_refine is False:
            raise ValueError("If two_stage is True, with_box_refine must be True.")
        # Hungarian matcher
        self.class_cost = class_cost
        self.bbox_cost = bbox_cost
        self.giou_cost = giou_cost
        # Loss coefficients
        self.mask_loss_coefficient = mask_loss_coefficient
        self.dice_loss_coefficient = dice_loss_coefficient
        self.bbox_loss_coefficient = bbox_loss_coefficient
        self.giou_loss_coefficient = giou_loss_coefficient
        self.eos_coefficient = eos_coefficient
        self.focal_alpha = focal_alpha
        self.disable_custom_kernels = disable_custom_kernels
        super().__init__(is_encoder_decoder=is_encoder_decoder, **kwargs)

    @property
    def num_attention_heads(self) -> int:
        return self.encoder_attention_heads

    @property
    def hidden_size(self) -> int:
        return self.d_model