Chooses static padding if you have specified an image size, and dynamic padding otherwise. Static padding is necessary for ONNX exporting of mo

Calculate and round number of filters based on width multiplier. Use width_coefficient, depth_divisor and min_depth of global_params. Args

Builds train and test transform functions. Args: height (int): target image height. width (int): target image width.E nor

r""" Overridden.

Calculates the output image size when using Conv2dSamePadding with a stride. Necessary for static padding. Thanks to mannatsingh for pointing t

Adjust model's first convolution layer to in_channels, if in_channels not equals 3. Args: in_channels (int): Input data's channel

Validates model name. Args: model_name (str): Name for efficientnet. Returns: bool: Is a valid name or not.

r""" Overridden.

Builds train and test transform functions. Args: height (int): target image height. width (int): target image width.E nor

Builds train and test transform functions. Args: height (int): target image height. width (int): target image width.E nor

Map EfficientNet model name to parameter coefficients. Args: model_name (str): Model name to be queried. Returns: params_dic

create an efficientnet model according to name. Args: model_name (str): Name for efficientnet. in_channels (int): Inp

:param landmarks_file: input landmarks json file name :return: all_landmarks: having the shape of 64x2 list. represent left eye,

:param modelname: :return: model, image size, pretraining<yes/no>, input_list

Flip vertically and horizontally

Flip vertically and horizontally

Flip vertically and horizontally

Flip vertically and horizontally

anticlockwise rotation 90 180 270

anticlockwise rotation 90 180 270

clockwise rotation 90 180 270

clockwise rotation 90 180 270

Get a block through a string notation of arguments. Args: block_string (str): A string notation of arguments.

Encode a block to a string. Args: block (namedtuple): A BlockArgs type argument. Returns: block_string: A St

Override to build the mask

Override to build the mask

Returns the equal error rate for a binary classifier output.

Decode a list of string notations to specify blocks inside the network. Args: string_list (list[str]): A list of strings, each st

Drop connect. Args: input (tensor: BCWH): Input of this structure. p (float: 0.0~1.0): Probability of drop connection. tr

Create BlockArgs and GlobalParams for efficientnet model. Args: width_coefficient (float) depth_coefficient (float) image

use convolution layer to extract feature . Args: inputs (tensor): Input tensor. Returns: Output of the final

create an efficientnet model according to name. Args: model_name (str): Name for efficientnet. weights_path (None or