# coding=utf-8 # Copyright 2022 SenseTime and The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Deformable DETR model configuration""" from ...configuration_utils import PretrainedConfig from ...utils import logging from ...utils.backbone_utils import verify_backbone_config_arguments from ..auto import CONFIG_MAPPING logger = logging.get_logger(__name__) class DeformableDetrConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`DeformableDetrModel`]. It is used to instantiate a Deformable DETR 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 Deformable DETR [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: 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 300): Number of object queries, i.e. detection slots. This is the maximal number of objects [`DeformableDetrModel`] 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 1024): Dimension of the "intermediate" (often named feed-forward) layer in decoder. encoder_ffn_dim (`int`, *optional*, defaults to 1024): 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"`. backbone (`str`, *optional*, defaults to `"resnet50"`): 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 `True`): Whether to use pretrained weights for the backbone. 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. 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. num_feature_levels (`int`, *optional*, defaults to 4): 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 `False`): Whether to apply a two-stage deformable DETR, where the region proposals are also generated by a variant of Deformable DETR, 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 `False`): 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. disable_custom_kernels (`bool`, *optional*, defaults to `False`): 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 DeformableDetrConfig, DeformableDetrModel >>> # Initializing a Deformable DETR SenseTime/deformable-detr style configuration >>> configuration = DeformableDetrConfig() >>> # Initializing a model (with random weights) from the SenseTime/deformable-detr style configuration >>> model = DeformableDetrModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "deformable_detr" attribute_map = { "hidden_size": "d_model", "num_attention_heads": "encoder_attention_heads", } def __init__( self, use_timm_backbone=True, backbone_config=None, num_channels=3, num_queries=300, max_position_embeddings=1024, encoder_layers=6, encoder_ffn_dim=1024, 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", backbone="resnet50", use_pretrained_backbone=True, backbone_kwargs=None, dilation=False, num_feature_levels=4, encoder_n_points=4, decoder_n_points=4, two_stage=False, two_stage_num_proposals=300, with_box_refine=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, focal_alpha=0.25, disable_custom_kernels=False, **kwargs, ): # We default to values which were previously hard-coded in the model. This enables configurability of the config # while keeping the default behavior the same. if use_timm_backbone and backbone_kwargs is None: backbone_kwargs = {} if dilation: backbone_kwargs["output_stride"] = 16 backbone_kwargs["out_indices"] = [2, 3, 4] if num_feature_levels > 1 else [4] backbone_kwargs["in_chans"] = num_channels # Backwards compatibility elif not use_timm_backbone and backbone in (None, "resnet50"): 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) 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.use_timm_backbone = use_timm_backbone self.backbone_config = backbone_config self.num_channels = num_channels 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 self.backbone = backbone self.use_pretrained_backbone = use_pretrained_backbone self.backbone_kwargs = backbone_kwargs self.dilation = dilation # 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 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 __all__ = ["DeformableDetrConfig"]