# 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 ...backbone_utils import consolidate_backbone_kwargs_to_config from ...configuration_utils import PreTrainedConfig from ...utils import logging from ..auto import AutoConfig 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: backbone_config (`Union[dict, "PreTrainedConfig"]`, *optional*, defaults to `ResNetConfig()`): 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://huggingface.co/papers/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"`. 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. tie_word_embeddings (`bool`, *optional*, defaults to `True`): Whether to tie weight embeddings 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" sub_configs = {"backbone_config": AutoConfig} attribute_map = { "hidden_size": "d_model", "num_attention_heads": "encoder_attention_heads", } def __init__( self, 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", 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, tie_word_embeddings=True, **kwargs, ): # Init timm backbone with hardcoded values for BC timm_default_kwargs = { "num_channels": 3, "features_only": True, "use_pretrained_backbone": False, "out_indices": [2, 3, 4] if num_feature_levels > 1 else [4], } if dilation: timm_default_kwargs["output_stride"] = 16 backbone_config, kwargs = consolidate_backbone_kwargs_to_config( backbone_config=backbone_config, default_backbone="resnet50", default_config_type="resnet50", default_config_kwargs={"out_features": ["stage4"]}, timm_default_kwargs=timm_default_kwargs, **kwargs, ) 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.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 self.tie_word_embeddings = tie_word_embeddings super().__init__(is_encoder_decoder=is_encoder_decoder, **kwargs) __all__ = ["DeformableDetrConfig"]