285 lines
13 KiB
Python
285 lines
13 KiB
Python
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# coding=utf-8
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# Copyright 2021 Facebook AI Research and The HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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""" DETR model configuration"""
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from collections import OrderedDict
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from typing import Mapping
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from packaging import version
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from ...configuration_utils import PretrainedConfig
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from ...onnx import OnnxConfig
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from ...utils import logging
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from ..auto import CONFIG_MAPPING
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logger = logging.get_logger(__name__)
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from ..deprecated._archive_maps import DETR_PRETRAINED_CONFIG_ARCHIVE_MAP # noqa: F401, E402
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class DetrConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`DetrModel`]. It is used to instantiate a DETR
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model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
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defaults will yield a similar configuration to that of the DETR
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[facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) architecture.
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Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
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documentation from [`PretrainedConfig`] for more information.
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Args:
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use_timm_backbone (`bool`, *optional*, defaults to `True`):
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Whether or not to use the `timm` library for the backbone. If set to `False`, will use the [`AutoBackbone`]
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API.
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backbone_config (`PretrainedConfig` or `dict`, *optional*):
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The configuration of the backbone model. Only used in case `use_timm_backbone` is set to `False` in which
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case it will default to `ResNetConfig()`.
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num_channels (`int`, *optional*, defaults to 3):
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The number of input channels.
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num_queries (`int`, *optional*, defaults to 100):
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Number of object queries, i.e. detection slots. This is the maximal number of objects [`DetrModel`] can
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detect in a single image. For COCO, we recommend 100 queries.
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d_model (`int`, *optional*, defaults to 256):
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Dimension of the layers.
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encoder_layers (`int`, *optional*, defaults to 6):
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Number of encoder layers.
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decoder_layers (`int`, *optional*, defaults to 6):
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Number of decoder layers.
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encoder_attention_heads (`int`, *optional*, defaults to 8):
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Number of attention heads for each attention layer in the Transformer encoder.
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decoder_attention_heads (`int`, *optional*, defaults to 8):
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Number of attention heads for each attention layer in the Transformer decoder.
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decoder_ffn_dim (`int`, *optional*, defaults to 2048):
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Dimension of the "intermediate" (often named feed-forward) layer in decoder.
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encoder_ffn_dim (`int`, *optional*, defaults to 2048):
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Dimension of the "intermediate" (often named feed-forward) layer in decoder.
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activation_function (`str` or `function`, *optional*, defaults to `"relu"`):
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The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
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`"relu"`, `"silu"` and `"gelu_new"` are supported.
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dropout (`float`, *optional*, defaults to 0.1):
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The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
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attention_dropout (`float`, *optional*, defaults to 0.0):
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The dropout ratio for the attention probabilities.
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activation_dropout (`float`, *optional*, defaults to 0.0):
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The dropout ratio for activations inside the fully connected layer.
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init_std (`float`, *optional*, defaults to 0.02):
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The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
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init_xavier_std (`float`, *optional*, defaults to 1):
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The scaling factor used for the Xavier initialization gain in the HM Attention map module.
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encoder_layerdrop (`float`, *optional*, defaults to 0.0):
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The LayerDrop probability for the encoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556)
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for more details.
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decoder_layerdrop (`float`, *optional*, defaults to 0.0):
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The LayerDrop probability for the decoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556)
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for more details.
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auxiliary_loss (`bool`, *optional*, defaults to `False`):
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Whether auxiliary decoding losses (loss at each decoder layer) are to be used.
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position_embedding_type (`str`, *optional*, defaults to `"sine"`):
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Type of position embeddings to be used on top of the image features. One of `"sine"` or `"learned"`.
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backbone (`str`, *optional*, defaults to `"resnet50"`):
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Name of backbone to use when `backbone_config` is `None`. If `use_pretrained_backbone` is `True`, this
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will load the corresponding pretrained weights from the timm or transformers library. If `use_pretrained_backbone`
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is `False`, this loads the backbone's config and uses that to initialize the backbone with random weights.
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use_pretrained_backbone (`bool`, *optional*, `True`):
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Whether to use pretrained weights for the backbone.
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backbone_kwargs (`dict`, *optional*):
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Keyword arguments to be passed to AutoBackbone when loading from a checkpoint
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e.g. `{'out_indices': (0, 1, 2, 3)}`. Cannot be specified if `backbone_config` is set.
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dilation (`bool`, *optional*, defaults to `False`):
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Whether to replace stride with dilation in the last convolutional block (DC5). Only supported when
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`use_timm_backbone` = `True`.
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class_cost (`float`, *optional*, defaults to 1):
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Relative weight of the classification error in the Hungarian matching cost.
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bbox_cost (`float`, *optional*, defaults to 5):
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Relative weight of the L1 error of the bounding box coordinates in the Hungarian matching cost.
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giou_cost (`float`, *optional*, defaults to 2):
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Relative weight of the generalized IoU loss of the bounding box in the Hungarian matching cost.
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mask_loss_coefficient (`float`, *optional*, defaults to 1):
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Relative weight of the Focal loss in the panoptic segmentation loss.
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dice_loss_coefficient (`float`, *optional*, defaults to 1):
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Relative weight of the DICE/F-1 loss in the panoptic segmentation loss.
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bbox_loss_coefficient (`float`, *optional*, defaults to 5):
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Relative weight of the L1 bounding box loss in the object detection loss.
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giou_loss_coefficient (`float`, *optional*, defaults to 2):
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Relative weight of the generalized IoU loss in the object detection loss.
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eos_coefficient (`float`, *optional*, defaults to 0.1):
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Relative classification weight of the 'no-object' class in the object detection loss.
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Examples:
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```python
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>>> from transformers import DetrConfig, DetrModel
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>>> # Initializing a DETR facebook/detr-resnet-50 style configuration
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>>> configuration = DetrConfig()
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>>> # Initializing a model (with random weights) from the facebook/detr-resnet-50 style configuration
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>>> model = DetrModel(configuration)
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>>> # Accessing the model configuration
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>>> configuration = model.config
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```"""
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model_type = "detr"
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keys_to_ignore_at_inference = ["past_key_values"]
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attribute_map = {
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"hidden_size": "d_model",
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"num_attention_heads": "encoder_attention_heads",
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}
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def __init__(
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self,
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use_timm_backbone=True,
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backbone_config=None,
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num_channels=3,
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num_queries=100,
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encoder_layers=6,
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encoder_ffn_dim=2048,
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encoder_attention_heads=8,
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decoder_layers=6,
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decoder_ffn_dim=2048,
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decoder_attention_heads=8,
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encoder_layerdrop=0.0,
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decoder_layerdrop=0.0,
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is_encoder_decoder=True,
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activation_function="relu",
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d_model=256,
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dropout=0.1,
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attention_dropout=0.0,
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activation_dropout=0.0,
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init_std=0.02,
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init_xavier_std=1.0,
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auxiliary_loss=False,
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position_embedding_type="sine",
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backbone="resnet50",
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use_pretrained_backbone=True,
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backbone_kwargs=None,
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dilation=False,
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class_cost=1,
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bbox_cost=5,
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giou_cost=2,
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mask_loss_coefficient=1,
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dice_loss_coefficient=1,
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bbox_loss_coefficient=5,
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giou_loss_coefficient=2,
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eos_coefficient=0.1,
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**kwargs,
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):
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if not use_timm_backbone and use_pretrained_backbone:
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raise ValueError(
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"Loading pretrained backbone weights from the transformers library is not supported yet. `use_timm_backbone` must be set to `True` when `use_pretrained_backbone=True`"
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)
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if backbone_config is not None and backbone is not None:
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raise ValueError("You can't specify both `backbone` and `backbone_config`.")
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if backbone_config is not None and use_timm_backbone:
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raise ValueError("You can't specify both `backbone_config` and `use_timm_backbone`.")
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if backbone_kwargs is not None and backbone_kwargs and backbone_config is not None:
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raise ValueError("You can't specify both `backbone_kwargs` and `backbone_config`.")
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if not use_timm_backbone:
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if backbone_config is None:
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logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.")
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backbone_config = CONFIG_MAPPING["resnet"](out_features=["stage4"])
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elif isinstance(backbone_config, dict):
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backbone_model_type = backbone_config.get("model_type")
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config_class = CONFIG_MAPPING[backbone_model_type]
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backbone_config = config_class.from_dict(backbone_config)
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# set timm attributes to None
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dilation, backbone, use_pretrained_backbone = None, None, None
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self.use_timm_backbone = use_timm_backbone
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self.backbone_config = backbone_config
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self.num_channels = num_channels
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self.num_queries = num_queries
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self.d_model = d_model
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self.encoder_ffn_dim = encoder_ffn_dim
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self.encoder_layers = encoder_layers
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self.encoder_attention_heads = encoder_attention_heads
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self.decoder_ffn_dim = decoder_ffn_dim
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self.decoder_layers = decoder_layers
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self.decoder_attention_heads = decoder_attention_heads
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self.dropout = dropout
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self.attention_dropout = attention_dropout
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self.activation_dropout = activation_dropout
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self.activation_function = activation_function
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self.init_std = init_std
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self.init_xavier_std = init_xavier_std
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self.encoder_layerdrop = encoder_layerdrop
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self.decoder_layerdrop = decoder_layerdrop
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self.num_hidden_layers = encoder_layers
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self.auxiliary_loss = auxiliary_loss
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self.position_embedding_type = position_embedding_type
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self.backbone = backbone
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self.use_pretrained_backbone = use_pretrained_backbone
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self.backbone_kwargs = backbone_kwargs
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self.dilation = dilation
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# Hungarian matcher
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self.class_cost = class_cost
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self.bbox_cost = bbox_cost
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self.giou_cost = giou_cost
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# Loss coefficients
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self.mask_loss_coefficient = mask_loss_coefficient
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self.dice_loss_coefficient = dice_loss_coefficient
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self.bbox_loss_coefficient = bbox_loss_coefficient
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self.giou_loss_coefficient = giou_loss_coefficient
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self.eos_coefficient = eos_coefficient
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super().__init__(is_encoder_decoder=is_encoder_decoder, **kwargs)
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@property
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def num_attention_heads(self) -> int:
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return self.encoder_attention_heads
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@property
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def hidden_size(self) -> int:
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return self.d_model
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@classmethod
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def from_backbone_config(cls, backbone_config: PretrainedConfig, **kwargs):
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"""Instantiate a [`DetrConfig`] (or a derived class) from a pre-trained backbone model configuration.
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Args:
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backbone_config ([`PretrainedConfig`]):
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The backbone configuration.
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Returns:
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[`DetrConfig`]: An instance of a configuration object
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"""
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return cls(backbone_config=backbone_config, **kwargs)
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class DetrOnnxConfig(OnnxConfig):
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torch_onnx_minimum_version = version.parse("1.11")
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@property
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def inputs(self) -> Mapping[str, Mapping[int, str]]:
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return OrderedDict(
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[
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("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
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("pixel_mask", {0: "batch"}),
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]
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)
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@property
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def atol_for_validation(self) -> float:
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return 1e-5
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@property
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def default_onnx_opset(self) -> int:
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return 12
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