o ZŽhÂã@s‚dZddlmZddlmZddlmZddlmZddl m Z ddl m Z e   e¡ZGd d „d eƒZGd d „d e ƒZd d gZd S)zYOLOS model configurationé©Ú OrderedDict)ÚMapping)Úversioné)ÚPretrainedConfig)Ú OnnxConfig)ÚloggingcsVeZdZdZdZddddddddd d d gd d ddddddddddf‡fdd„ Z‡ZS)Ú YolosConfiga¨ This is the configuration class to store the configuration of a [`YolosModel`]. It is used to instantiate a YOLOS 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 YOLOS [hustvl/yolos-base](https://huggingface.co/hustvl/yolos-base) architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: hidden_size (`int`, *optional*, defaults to 768): Dimensionality of the encoder layers and the pooler layer. num_hidden_layers (`int`, *optional*, defaults to 12): Number of hidden layers in the Transformer encoder. num_attention_heads (`int`, *optional*, defaults to 12): Number of attention heads for each attention layer in the Transformer encoder. intermediate_size (`int`, *optional*, defaults to 3072): Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder. hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`): The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, `"relu"`, `"selu"` and `"gelu_new"` are supported. hidden_dropout_prob (`float`, *optional*, defaults to 0.0): The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. attention_probs_dropout_prob (`float`, *optional*, defaults to 0.0): The dropout ratio for the attention probabilities. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. layer_norm_eps (`float`, *optional*, defaults to 1e-12): The epsilon used by the layer normalization layers. image_size (`List[int]`, *optional*, defaults to `[512, 864]`): The size (resolution) of each image. patch_size (`int`, *optional*, defaults to 16): The size (resolution) of each patch. num_channels (`int`, *optional*, defaults to 3): The number of input channels. qkv_bias (`bool`, *optional*, defaults to `True`): Whether to add a bias to the queries, keys and values. num_detection_tokens (`int`, *optional*, defaults to 100): The number of detection tokens. use_mid_position_embeddings (`bool`, *optional*, defaults to `True`): Whether to use the mid-layer position encodings. auxiliary_loss (`bool`, *optional*, defaults to `False`): Whether auxiliary decoding losses (loss at each decoder layer) are to be used. 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. 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. Example: ```python >>> from transformers import YolosConfig, YolosModel >>> # Initializing a YOLOS hustvl/yolos-base style configuration >>> configuration = YolosConfig() >>> # Initializing a model (with random weights) from the hustvl/yolos-base style configuration >>> model = YolosModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```Zyolosié i Zgelugg{®Gáz”?gê-™—q=ii`érTédFééégš™™™™™¹?c sštƒjdi|¤Ž||_||_||_||_||_||_||_||_ | |_ | |_ | |_ | |_ | |_||_||_||_||_||_||_||_||_||_dS)N©)ÚsuperÚ__init__Ú hidden_sizeÚnum_hidden_layersÚnum_attention_headsÚintermediate_sizeÚ hidden_actÚhidden_dropout_probÚattention_probs_dropout_probÚinitializer_rangeÚlayer_norm_epsÚ image_sizeÚ patch_sizeÚ num_channelsÚqkv_biasÚnum_detection_tokensÚuse_mid_position_embeddingsÚauxiliary_lossÚ class_costÚ bbox_costÚ giou_costÚbbox_loss_coefficientÚgiou_loss_coefficientÚeos_coefficient)Úselfrrrrrrrrrrrrr r!r"r#r$r%r&r'r(r)Úkwargs©Ú __class__rú\/var/www/auris/lib/python3.10/site-packages/transformers/models/yolos/configuration_yolos.pyrhs. zYolosConfig.__init__)Ú__name__Ú __module__Ú __qualname__Ú__doc__Z model_typerÚ __classcell__rrr,r.r s4Gér c@s\eZdZe d¡Zedeeee efffdd„ƒZ ede fdd„ƒZ ede fdd„ƒZ d S) ÚYolosOnnxConfigz1.11ÚreturncCstddddddœfgƒS)NZ pixel_valuesÚbatchrÚheightÚwidth)rrrrr©r*rrr.Úinputs¡sÿÿzYolosOnnxConfig.inputscCódS)Ng-Cëâ6?rr9rrr.Úatol_for_validation©óz#YolosOnnxConfig.atol_for_validationcCr;)Nr rr9rrr.Údefault_onnx_opset­r=z"YolosOnnxConfig.default_onnx_opsetN)r/r0r1rÚparseZtorch_onnx_minimum_versionÚpropertyrÚstrÚintr:Úfloatr<r>rrrr.r4žs  r4N)r2Ú collectionsrÚtypingrÚ packagingrZconfiguration_utilsrZonnxrÚutilsr Z get_loggerr/Úloggerr r4Ú__all__rrrr.Ús