configuration_table_transformer.py

# coding=utf-8
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""" Table Transformer model configuration"""

from collections import OrderedDict
from typing import Mapping

from packaging import version

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


logger = logging.get_logger(__name__)

TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP = {
    "microsoft/table-transformer-detection": (
        "https://huggingface.co/microsoft/table-transformer-detection/resolve/main/config.json"
    ),
}


class TableTransformerConfig(PretrainedConfig):
    r"""
    This is the configuration class to store the configuration of a [`TableTransformerModel`]. It is used to
    instantiate a Table Transformer 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 Table Transformer
    [microsoft/table-transformer-detection](https://huggingface.co/microsoft/table-transformer-detection) architecture.

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

    Args:
        use_timm_backbone (`bool`, *optional*, defaults to `True`):
            Whether or not to use the `timm` library for the backbone. If set to `False`, will use the [`AutoBackbone`]
            API.
        backbone_config (`PretrainedConfig` or `dict`, *optional*):
            The configuration of the backbone model. Only used in case `use_timm_backbone` is set to `False` in which
            case it will default to `ResNetConfig()`.
        num_channels (`int`, *optional*, defaults to 3):
            The number of input channels.
        num_queries (`int`, *optional*, defaults to 100):
            Number of object queries, i.e. detection slots. This is the maximal number of objects
            [`TableTransformerModel`] can detect in a single image. For COCO, we recommend 100 queries.
        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.
        decoder_layerdrop (`float`, *optional*, defaults to 0.0):
            The LayerDrop probability for the decoder. 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"`.
        backbone (`str`, *optional*, defaults to `"resnet50"`):
            Name of convolutional backbone to use in case `use_timm_backbone` = `True`. Supports any convolutional
            backbone from the timm package. For a list of all available models, see [this
            page](https://rwightman.github.io/pytorch-image-models/#load-a-pretrained-model).
        use_pretrained_backbone (`bool`, *optional*, defaults to `True`):
            Whether to use pretrained weights for the backbone. Only supported when `use_timm_backbone` = `True`.
        dilation (`bool`, *optional*, defaults to `False`):
            Whether to replace stride with dilation in the last convolutional block (DC5). Only supported when
            `use_timm_backbone` = `True`.
        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.

    Examples:

    ```python
    >>> from transformers import TableTransformerModel, TableTransformerConfig

    >>> # Initializing a Table Transformer microsoft/table-transformer-detection style configuration
    >>> configuration = TableTransformerConfig()

    >>> # Initializing a model from the microsoft/table-transformer-detection style configuration
    >>> model = TableTransformerModel(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```"""
    model_type = "table-transformer"
    keys_to_ignore_at_inference = ["past_key_values"]
    attribute_map = {
        "hidden_size": "d_model",
        "num_attention_heads": "encoder_attention_heads",
    }

    # Copied from transformers.models.detr.configuration_detr.DetrConfig.__init__
    def __init__(
        self,
        use_timm_backbone=True,
        backbone_config=None,
        num_channels=3,
        num_queries=100,
        encoder_layers=6,
        encoder_ffn_dim=2048,
        encoder_attention_heads=8,
        decoder_layers=6,
        decoder_ffn_dim=2048,
        decoder_attention_heads=8,
        encoder_layerdrop=0.0,
        decoder_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,
        auxiliary_loss=False,
        position_embedding_type="sine",
        backbone="resnet50",
        use_pretrained_backbone=True,
        dilation=False,
        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,
        **kwargs,
    ):
        if backbone_config is not None and use_timm_backbone:
            raise ValueError("You can't specify both `backbone_config` and `use_timm_backbone`.")

        if not use_timm_backbone:
            if backbone_config is None:
                logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.")
                backbone_config = CONFIG_MAPPING["resnet"](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)
            # set timm attributes to None
            dilation, backbone, use_pretrained_backbone = None, None, None

        self.use_timm_backbone = use_timm_backbone
        self.backbone_config = backbone_config
        self.num_channels = num_channels
        self.num_queries = num_queries
        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.decoder_layerdrop = decoder_layerdrop
        self.num_hidden_layers = encoder_layers
        self.auxiliary_loss = auxiliary_loss
        self.position_embedding_type = position_embedding_type
        self.backbone = backbone
        self.use_pretrained_backbone = use_pretrained_backbone
        self.dilation = dilation
        # 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
        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


# Copied from transformers.models.detr.configuration_detr.DetrOnnxConfig
class TableTransformerOnnxConfig(OnnxConfig):
    torch_onnx_minimum_version = version.parse("1.11")

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

    @property
    def atol_for_validation(self) -> float:
        return 1e-5

    @property
    def default_onnx_opset(self) -> int:
        return 12