o h @sdZddlmZddlmZddlmZddlmZddl m Z ddl m Z e eZd d d d d ddZGdddeZGddde ZdS)z MobileViT model configuration OrderedDict)Mapping)version)PretrainedConfig) OnnxConfig)loggingzEhttps://huggingface.co/apple/mobilevit-small/resolve/main/config.jsonzGhttps://huggingface.co/apple/mobilevit-x-small/resolve/main/config.jsonzHhttps://huggingface.co/apple/mobilevit-xx-small/resolve/main/config.jsonzOhttps://huggingface.co/apple/deeplabv3-mobilevit-small/resolve/main/config.jsonzQhttps://huggingface.co/apple/deeplabv3-mobilevit-x-small/resolve/main/config.jsonzRhttps://huggingface.co/apple/deeplabv3-mobilevit-xx-small/resolve/main/config.json)zapple/mobilevit-smallzapple/mobilevit-x-smallzapple/mobilevit-xx-smallzapple/deeplabv3-mobilevit-smallz!apple/deeplabv3-mobilevit-x-smallz"apple/deeplabv3-mobilevit-xx-smallcs\eZdZdZdZdddgdgddd d d dd d dd ddddgdd dffdd ZZS)MobileViTConfiga This is the configuration class to store the configuration of a [`MobileViTModel`]. It is used to instantiate a MobileViT 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 MobileViT [apple/mobilevit-small](https://huggingface.co/apple/mobilevit-small) architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: num_channels (`int`, *optional*, defaults to 3): The number of input channels. image_size (`int`, *optional*, defaults to 256): The size (resolution) of each image. patch_size (`int`, *optional*, defaults to 2): The size (resolution) of each patch. hidden_sizes (`List[int]`, *optional*, defaults to `[144, 192, 240]`): Dimensionality (hidden size) of the Transformer encoders at each stage. neck_hidden_sizes (`List[int]`, *optional*, defaults to `[16, 32, 64, 96, 128, 160, 640]`): The number of channels for the feature maps of the backbone. num_attention_heads (`int`, *optional*, defaults to 4): Number of attention heads for each attention layer in the Transformer encoder. mlp_ratio (`float`, *optional*, defaults to 2.0): The ratio of the number of channels in the output of the MLP to the number of channels in the input. expand_ratio (`float`, *optional*, defaults to 4.0): Expansion factor for the MobileNetv2 layers. hidden_act (`str` or `function`, *optional*, defaults to `"silu"`): The non-linear activation function (function or string) in the Transformer encoder and convolution layers. conv_kernel_size (`int`, *optional*, defaults to 3): The size of the convolutional kernel in the MobileViT layer. output_stride (`int`, *optional*, defaults to 32): The ratio of the spatial resolution of the output to the resolution of the input image. hidden_dropout_prob (`float`, *optional*, defaults to 0.1): The dropout probabilitiy for all fully connected layers in the Transformer encoder. attention_probs_dropout_prob (`float`, *optional*, defaults to 0.0): The dropout ratio for the attention probabilities. classifier_dropout_prob (`float`, *optional*, defaults to 0.1): The dropout ratio for attached classifiers. 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-05): The epsilon used by the layer normalization layers. qkv_bias (`bool`, *optional*, defaults to `True`): Whether to add a bias to the queries, keys and values. aspp_out_channels (`int`, *optional*, defaults to 256): Number of output channels used in the ASPP layer for semantic segmentation. atrous_rates (`List[int]`, *optional*, defaults to `[6, 12, 18]`): Dilation (atrous) factors used in the ASPP layer for semantic segmentation. aspp_dropout_prob (`float`, *optional*, defaults to 0.1): The dropout ratio for the ASPP layer for semantic segmentation. semantic_loss_ignore_index (`int`, *optional*, defaults to 255): The index that is ignored by the loss function of the semantic segmentation model. Example: ```python >>> from transformers import MobileViTConfig, MobileViTModel >>> # Initializing a mobilevit-small style configuration >>> configuration = MobileViTConfig() >>> # Initializing a model from the mobilevit-small style configuration >>> model = MobileViTModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ``` mobilevitr)) @`ig@g@silurg?gg{Gz?gh㈵>T) c stjdi|||_||_||_||_||_||_||_||_ | |_ | |_ | |_ | |_ | |_||_||_||_||_||_||_||_||_dS)N)super__init__ num_channels image_size patch_size hidden_sizesneck_hidden_sizesnum_attention_heads mlp_ratio expand_ratio hidden_actconv_kernel_size output_stridehidden_dropout_probattention_probs_dropout_probclassifier_dropout_probinitializer_rangelayer_norm_epsqkv_biasaspp_out_channels atrous_ratesaspp_dropout_probsemantic_loss_ignore_index)selfr r!r"r#r$r%r&r'r(r)r*r+r,r-r.r/r0r1r2r3r4kwargs __class__rk/var/www/html/ai/venv/lib/python3.10/site-packages/transformers/models/mobilevit/configuration_mobilevit.pyrus, zMobileViTConfig.__init__)__name__ __module__ __qualname____doc__ model_typer __classcell__rrr7r9r .s2Dr c@sleZdZedZedeeee efffddZ edeeee efffddZ ede fddZ d S) MobileViTOnnxConfigz1.11returncCstddddddfgS)N pixel_valuesbatchr heightwidth)rr rrr5rrr9inputsszMobileViTOnnxConfig.inputscCs8|jdkrtdddifgStdddifdddifgS)Nzimage-classificationlogitsrrClast_hidden_state pooler_output)taskrrGrrr9outputss zMobileViTOnnxConfig.outputscCsdS)Ng-C6?rrGrrr9atol_for_validationsz'MobileViTOnnxConfig.atol_for_validationN)r:r;r<rparsetorch_onnx_minimum_versionpropertyrstrintrHrMfloatrNrrrr9r@s   r@N)r= collectionsrtypingr packagingrconfiguration_utilsronnxrutilsr get_loggerr:logger'MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAPr r@rrrr9s"       {