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| # coding=utf-8 | |
| # Copyright 2021 Facebook AI Research 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. | |
| """ DETR model configuration """ | |
| from ...configuration_utils import PretrainedConfig | |
| from ...utils import logging | |
| logger = logging.get_logger(__name__) | |
| DETR_PRETRAINED_CONFIG_ARCHIVE_MAP = { | |
| "facebook/detr-resnet-50": "https://huggingface.co/facebook/detr-resnet-50/resolve/main/config.json", | |
| # See all DETR models at https://huggingface.co/models?filter=detr | |
| } | |
| class DetrConfig(PretrainedConfig): | |
| r""" | |
| This is the configuration class to store the configuration of a :class:`~transformers.DetrModel`. It is used to | |
| instantiate a 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 DETR `facebook/detr-resnet-50 | |
| <https://huggingface.co/facebook/detr-resnet-50>`__ architecture. | |
| Configuration objects inherit from :class:`~transformers.PretrainedConfig` and can be used to control the model | |
| outputs. Read the documentation from :class:`~transformers.PretrainedConfig` for more information. | |
| Args: | |
| num_queries (:obj:`int`, `optional`, defaults to 100): | |
| Number of object queries, i.e. detection slots. This is the maximal number of objects | |
| :class:`~transformers.DetrModel` can detect in a single image. For COCO, we recommend 100 queries. | |
| d_model (:obj:`int`, `optional`, defaults to 256): | |
| Dimension of the layers. | |
| encoder_layers (:obj:`int`, `optional`, defaults to 6): | |
| Number of encoder layers. | |
| decoder_layers (:obj:`int`, `optional`, defaults to 6): | |
| Number of decoder layers. | |
| encoder_attention_heads (:obj:`int`, `optional`, defaults to 8): | |
| Number of attention heads for each attention layer in the Transformer encoder. | |
| decoder_attention_heads (:obj:`int`, `optional`, defaults to 8): | |
| Number of attention heads for each attention layer in the Transformer decoder. | |
| decoder_ffn_dim (:obj:`int`, `optional`, defaults to 2048): | |
| Dimension of the "intermediate" (often named feed-forward) layer in decoder. | |
| encoder_ffn_dim (:obj:`int`, `optional`, defaults to 2048): | |
| Dimension of the "intermediate" (often named feed-forward) layer in decoder. | |
| activation_function (:obj:`str` or :obj:`function`, `optional`, defaults to :obj:`"relu"`): | |
| The non-linear activation function (function or string) in the encoder and pooler. If string, | |
| :obj:`"gelu"`, :obj:`"relu"`, :obj:`"silu"` and :obj:`"gelu_new"` are supported. | |
| dropout (:obj:`float`, `optional`, defaults to 0.1): | |
| The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. | |
| attention_dropout (:obj:`float`, `optional`, defaults to 0.0): | |
| The dropout ratio for the attention probabilities. | |
| activation_dropout (:obj:`float`, `optional`, defaults to 0.0): | |
| The dropout ratio for activations inside the fully connected layer. | |
| init_std (:obj:`float`, `optional`, defaults to 0.02): | |
| The standard deviation of the truncated_normal_initializer for initializing all weight matrices. | |
| init_xavier_std (:obj:`float`, `optional`, defaults to 1): | |
| The scaling factor used for the Xavier initialization gain in the HM Attention map module. | |
| encoder_layerdrop: (:obj:`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: (:obj:`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 (:obj:`bool`, `optional`, defaults to :obj:`False`): | |
| Whether auxiliary decoding losses (loss at each decoder layer) are to be used. | |
| position_embedding_type (:obj:`str`, `optional`, defaults to :obj:`"sine"`): | |
| Type of position embeddings to be used on top of the image features. One of :obj:`"sine"` or | |
| :obj:`"learned"`. | |
| backbone (:obj:`str`, `optional`, defaults to :obj:`"resnet50"`): | |
| Name of convolutional backbone to use. 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>`__. | |
| dilation (:obj:`bool`, `optional`, defaults to :obj:`False`): | |
| Whether to replace stride with dilation in the last convolutional block (DC5). | |
| class_cost (:obj:`float`, `optional`, defaults to 1): | |
| Relative weight of the classification error in the Hungarian matching cost. | |
| bbox_cost (:obj:`float`, `optional`, defaults to 5): | |
| Relative weight of the L1 error of the bounding box coordinates in the Hungarian matching cost. | |
| giou_cost (:obj:`float`, `optional`, defaults to 2): | |
| Relative weight of the generalized IoU loss of the bounding box in the Hungarian matching cost. | |
| mask_loss_coefficient (:obj:`float`, `optional`, defaults to 1): | |
| Relative weight of the Focal loss in the panoptic segmentation loss. | |
| dice_loss_coefficient (:obj:`float`, `optional`, defaults to 1): | |
| Relative weight of the DICE/F-1 loss in the panoptic segmentation loss. | |
| bbox_loss_coefficient (:obj:`float`, `optional`, defaults to 5): | |
| Relative weight of the L1 bounding box loss in the object detection loss. | |
| giou_loss_coefficient (:obj:`float`, `optional`, defaults to 2): | |
| Relative weight of the generalized IoU loss in the object detection loss. | |
| eos_coefficient (:obj:`float`, `optional`, defaults to 0.1): | |
| Relative classification weight of the 'no-object' class in the object detection loss. | |
| Examples:: | |
| >>> from transformers import DetrModel, DetrConfig | |
| >>> # Initializing a DETR facebook/detr-resnet-50 style configuration | |
| >>> configuration = DetrConfig() | |
| >>> # Initializing a model from the facebook/detr-resnet-50 style configuration | |
| >>> model = DetrModel(configuration) | |
| >>> # Accessing the model configuration | |
| >>> configuration = model.config | |
| """ | |
| model_type = "detr" | |
| keys_to_ignore_at_inference = ["past_key_values"] | |
| def __init__( | |
| self, | |
| num_queries=100, | |
| max_position_embeddings=1024, | |
| 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, | |
| classifier_dropout=0.0, | |
| scale_embedding=False, | |
| auxiliary_loss=False, | |
| position_embedding_type="sine", | |
| backbone="resnet50", | |
| 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 | |
| ): | |
| super().__init__(is_encoder_decoder=is_encoder_decoder, **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.decoder_layerdrop = decoder_layerdrop | |
| self.num_hidden_layers = encoder_layers | |
| self.scale_embedding = scale_embedding # scale factor will be sqrt(d_model) if True | |
| self.auxiliary_loss = auxiliary_loss | |
| self.position_embedding_type = position_embedding_type | |
| self.backbone = 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 | |
| def num_attention_heads(self) -> int: | |
| return self.encoder_attention_heads | |
| def hidden_size(self) -> int: | |
| return self.d_model | |