U ,È-eFã@s†dZddlmZddlmZmZddlmZddlm Z ddl m Z ddl m Z e  e¡Zd d iZGd d „d e ƒZGd d„de ƒZdS)z! EfficientNet model configurationé©Ú OrderedDict)ÚListÚMapping)Úversioné)ÚPretrainedConfig)Ú OnnxConfig)Úloggingzgoogle/efficientnet-b7zFhttps://huggingface.co/google/efficientnet-b7/resolve/main/config.jsoncsîeZdZdZdZddddddddddddgd d d d d ddgd d d d dddggdddddddgdddddddgdddddddgdddddddddfeeeeeeeeeeeeeeeeeeeeeeeeeeeedœ‡fdd „ Z ‡Z S)!ÚEfficientNetConfiga# This is the configuration class to store the configuration of a [`EfficientNetModel`]. It is used to instantiate an EfficientNet 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 EfficientNet [google/efficientnet-b7](https://huggingface.co/google/efficientnet-b7) 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 600): The input image size. width_coefficient (`float`, *optional*, defaults to 2.0): Scaling coefficient for network width at each stage. depth_coefficient (`float`, *optional*, defaults to 3.1): Scaling coefficient for network depth at each stage. depth_divisor `int`, *optional*, defaults to 8): A unit of network width. kernel_sizes (`List[int]`, *optional*, defaults to `[3, 3, 5, 3, 5, 5, 3]`): List of kernel sizes to be used in each block. in_channels (`List[int]`, *optional*, defaults to `[32, 16, 24, 40, 80, 112, 192]`): List of input channel sizes to be used in each block for convolutional layers. out_channels (`List[int]`, *optional*, defaults to `[16, 24, 40, 80, 112, 192, 320]`): List of output channel sizes to be used in each block for convolutional layers. depthwise_padding (`List[int]`, *optional*, defaults to `[]`): List of block indices with square padding. strides (`List[int]`, *optional*, defaults to `[1, 2, 2, 2, 1, 2, 1]`): List of stride sizes to be used in each block for convolutional layers. num_block_repeats (`List[int]`, *optional*, defaults to `[1, 2, 2, 3, 3, 4, 1]`): List of the number of times each block is to repeated. expand_ratios (`List[int]`, *optional*, defaults to `[1, 6, 6, 6, 6, 6, 6]`): List of scaling coefficient of each block. squeeze_expansion_ratio (`float`, *optional*, defaults to 0.25): Squeeze expansion ratio. hidden_act (`str` or `function`, *optional*, defaults to `"silu"`): The non-linear activation function (function or string) in each block. If string, `"gelu"`, `"relu"`, `"selu", `"gelu_new"`, `"silu"` and `"mish"` are supported. hiddem_dim (`int`, *optional*, defaults to 1280): The hidden dimension of the layer before the classification head. pooling_type (`str` or `function`, *optional*, defaults to `"mean"`): Type of final pooling to be applied before the dense classification head. Available options are [`"mean"`, `"max"`] initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. batch_norm_eps (`float`, *optional*, defaults to 1e-3): The epsilon used by the batch normalization layers. batch_norm_momentum (`float`, *optional*, defaults to 0.99): The momentum used by the batch normalization layers. dropout_rate (`float`, *optional*, defaults to 0.5): The dropout rate to be applied before final classifier layer. drop_connect_rate (`float`, *optional*, defaults to 0.2): The drop rate for skip connections. Example: ```python >>> from transformers import EfficientNetConfig, EfficientNetModel >>> # Initializing a EfficientNet efficientnet-b7 style configuration >>> configuration = EfficientNetConfig() >>> # Initializing a model (with random weights) from the efficientnet-b7 style configuration >>> model = EfficientNetModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```Z efficientnetriXg@gÍÌÌÌÌÌ@ééé ééé(éPépéÀi@éééégÐ?Zswishi Zmeang{®Gáz”?gü©ñÒMbP?g®Gáz®ï?gà?gš™™™™™É?)Ú num_channelsÚ image_sizeÚwidth_coefficientÚdepth_coefficientÚ depth_divisorÚ kernel_sizesÚ in_channelsÚ out_channelsÚdepthwise_paddingÚstridesÚnum_block_repeatsÚ expand_ratiosÚsqueeze_expansion_ratioÚ hidden_actÚ hidden_dimÚ pooling_typeÚinitializer_rangeÚbatch_norm_epsÚbatch_norm_momentumÚ dropout_rateÚdrop_connect_ratec sžtƒjf|Ž||_||_||_||_||_||_||_||_ | |_ | |_ | |_ | |_ | |_||_||_||_||_||_||_||_||_t| ƒd|_dS)Nr)ÚsuperÚ__init__rrrrrrrr r!r"r#r$r%r&r'r(r)r*r+r,r-ÚsumZnum_hidden_layers)Úselfrrrrrrrr r!r"r#r$r%r&r'r(r)r*r+r,r-Úkwargs©Ú __class__©ú|/var/www/html/Darija-Ai-Train/env/lib/python3.8/site-packages/transformers/models/efficientnet/configuration_efficientnet.pyr/is.zEfficientNetConfig.__init__) Ú__name__Ú __module__Ú __qualname__Ú__doc__Z model_typeÚintÚfloatrÚstrr/Ú __classcell__r5r5r3r6r "s\Dêêr c@sJeZdZe d¡Zeeeee effdœdd„ƒZ ee dœdd„ƒZ dS)ÚEfficientNetOnnxConfigz1.11)ÚreturncCstddddddœfgƒS)NZ pixel_valuesÚbatchrÚheightÚwidth)rrrrr©r1r5r5r6ÚinputsŸsÿÿzEfficientNetOnnxConfig.inputscCsdS)Ngñh㈵øä>r5rDr5r5r6Úatol_for_validation§sz*EfficientNetOnnxConfig.atol_for_validationN) r7r8r9rÚparseZtorch_onnx_minimum_versionÚpropertyrr=r;rEr<rFr5r5r5r6r?œs   r?N)r:Ú collectionsrÚtypingrrÚ packagingrZconfiguration_utilsrZonnxr Úutilsr Z get_loggerr7ÚloggerZ*EFFICIENTNET_PRETRAINED_CONFIG_ARCHIVE_MAPr r?r5r5r5r6Ús      ÿz