DECIMER/config.py
def initialize_encoder_config(
        self,
        image_embedding_dim,
        preprocessing_fn,
        backbone_fn,
        image_shape,
        do_permute=False,
        pretrained_weights=None,
    ):
        """This functions initializes the Efficient-Net V2 encoder with user defined
        configurations.

        Args:
            image_embedding_dim (int): Embedding dimention of the input image
            preprocessing_fn (method): Efficient Net preprocessing function for input image
            backbone_fn (method): Calls Efficient-Net V2 as backbone for encoder
            image_shape (int): Shape of the input image
            do_permute (bool, optional): . Defaults to False.
            pretrained_weights (keras weights, optional): Use pretrainined efficient net weights or not. Defaults to None.
        """
        self.encoder_config = dict(
            image_embedding_dim=image_embedding_dim,
            preprocessing_fn=preprocessing_fn,
            backbone_fn=backbone_fn,
            image_shape=image_shape,
            do_permute=do_permute,
            pretrained_weights=pretrained_weights,
        )
This functions initializes the Efficient-Net V2 encoder with user defined
        configurations.

        Args:
            image_embedding_dim (int): Embedding dimention of the input image
            preprocessing_fn (method): Efficient Net preprocessing function for input image
            backbone_fn (method): Calls Efficient-Net V2 as backbone for encoder
            image_shape (int): Shape of the input image
            do_permute (bool, optional): . Defaults to False.
            pretrained_weights (keras weights, optional): Use pretrainined efficient net weights or not. Defaults to None.
        
This functions initializes the Efficient-Net V2 encoder with user defined
configurations.
This functions initializes the Efficient - Net V2 encoder with user defined configurations .
def initialize_encoder_config(
        self,
        image_embedding_dim,
        preprocessing_fn,
        backbone_fn,
        image_shape,
        do_permute=False,
        pretrained_weights=None,
    ):
        self.encoder_config = dict(
            image_embedding_dim=image_embedding_dim,
            preprocessing_fn=preprocessing_fn,
            backbone_fn=backbone_fn,
            image_shape=image_shape,
            do_permute=do_permute,
            pretrained_weights=pretrained_weights,
        )
def initialize_encoder_config ( self , image_embedding_dim , preprocessing_fn , backbone_fn , image_shape , do_permute = False , pretrained_weights = None , ) : self . encoder_config = dict ( image_embedding_dim = image_embedding_dim , preprocessing_fn = preprocessing_fn , backbone_fn = backbone_fn , image_shape = image_shape , do_permute = do_permute , pretrained_weights = pretrained_weights , )
This functions initializes the Efficient-Net V2 encoder with user defined
configurations.
This functions initializes the Efficient - Net V2 encoder with user defined configurations .
"""This functions initializes the Efficient-Net V2 encoder with user defined
        configurations.

        Args:
            image_embedding_dim (int): Embedding dimention of the input image
            preprocessing_fn (method): Efficient Net preprocessing function for input image
            backbone_fn (method): Calls Efficient-Net V2 as backbone for encoder
            image_shape (int): Shape of the input image
            do_permute (bool, optional): . Defaults to False.
            pretrained_weights (keras weights, optional): Use pretrainined efficient net weights or not. Defaults to None.
        """
self
image_embedding_dim
Embedding dimention of the input image
Embedding dimention of the input image
preprocessing_fn
Efficient Net preprocessing function for input image
Efficient Net preprocessing function for input image
backbone_fn
Calls Efficient-Net V2 as backbone for encoder
Calls Efficient - Net V2 as backbone for encoder
image_shape
Shape of the input image
Shape of the input image
do_permute
. Defaults to False.
. Defaults to False .
False
pretrained_weights
Use pretrainined efficient net weights or not. Defaults to None.
Use pretrainined efficient net weights or not . Defaults to None .
None