U ,-e1@sdZddlmZddlmZddlmZddlmZddl m Z ddl m Z d d l mZe eZd d iZGd ddeZGddde ZdS)z DETR model configuration OrderedDict)Mapping)version)PretrainedConfig) OnnxConfig)logging)CONFIG_MAPPINGzfacebook/detr-resnet-50zGhttps://huggingface.co/facebook/detr-resnet-50/resolve/main/config.jsonc!sleZdZdZdZdgZdddZd$fdd Zee dddZ ee ddd Z e e d!d"d#ZZS)% DetrConfiga This is the configuration class to store the configuration of a [`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 [`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 [`DetrModel`] 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 DetrConfig, DetrModel >>> # Initializing a DETR facebook/detr-resnet-50 style configuration >>> configuration = DetrConfig() >>> # Initializing a model (with random weights) from the facebook/detr-resnet-50 style configuration >>> model = DetrModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```ZdetrZpast_key_valuesd_modelencoder_attention_heads) hidden_sizenum_attention_headsTNrdrelu皙?{Gz??Fsineresnet50r c"% sL|dk r|rtd|sl|dkrr?r@rArBrCrDrEkwargsZbackbone_model_typeZ config_class __class__l/var/www/html/Darija-Ai-Train/env/lib/python3.8/site-packages/transformers/models/detr/configuration_detr.pyrGsZ%      zDetrConfig.__init__returncCs|jSN)rrHrLrLrMrszDetrConfig.num_attention_headscCs|jSrP)r rQrLrLrMrszDetrConfig.hidden_size)r)cKs|fd|i|S)a-Instantiate a [`DetrConfig`] (or a derived class) from a pre-trained backbone model configuration. Args: backbone_config ([`PretrainedConfig`]): The backbone configuration. Returns: [`DetrConfig`]: An instance of a configuration object r)rL)clsr)rIrLrLrMfrom_backbone_configs zDetrConfig.from_backbone_config)!TNrrrrrrrrrrTrrrrrrrFrrTFrrr rrrr r)__name__ __module__ __qualname____doc__rZkeys_to_ignore_at_inferenceZ attribute_maprGpropertyintrr classmethodrrS __classcell__rLrLrJrMr $s\bXr c@s\eZdZedZeeeee effdddZ ee dddZ ee dddZ d S) DetrOnnxConfigz1.11rNcCs"tddddddfdddifgS) NZ pixel_valuesbatchr*heightwidth)rrr rZ pixel_maskrrrQrLrLrMinputss  zDetrOnnxConfig.inputscCsdS)Ngh㈵>rLrQrLrLrMatol_for_validationsz"DetrOnnxConfig.atol_for_validationcCsdS)N rLrQrLrLrMdefault_onnx_opset sz!DetrOnnxConfig.default_onnx_opsetN)rTrUrVrparseZtorch_onnx_minimum_versionrXrstrrYr`floatrarcrLrLrLrMr\s  r\N)rW collectionsrtypingr packagingrZconfiguration_utilsrZonnxrutilsr autor Z get_loggerrTr"Z"DETR_PRETRAINED_CONFIG_ARCHIVE_MAPr r\rLrLrLrMs        X