diversity_SR/conditionalMoments/util/tensorflowUtils.py
def diversity_image_example(index,data_LR,h_LR,w_LR,data_HR,h_HR,w_HR,c,mean,std):
    """
    Creates a tf.Example message ready to be written to a file.
    For writing files with LR, HR and conditional moments
    """
    # Create a dictionary mapping the feature name to the tf.Example-compatible
    # data type.

    feature = {
            'index'    : _int64_feature(index),
            'data_LR'  : _bytes_feature(data_LR),
            'h_LR'     : _int64_feature(h_LR),
            'w_LR'     : _int64_feature(w_LR),
            'data_HR'  : _bytes_feature(data_HR),
            'h_HR'     : _int64_feature(h_HR),
            'w_HR'     : _int64_feature(w_HR),
            'c'        : _int64_feature(c),
            'mean'     : _bytes_feature(mean),
            'std'      : _bytes_feature(std),
    }

    return tf.train.Example(features=tf.train.Features(feature=feature))

    Creates a tf.Example message ready to be written to a file.
    For writing files with LR, HR and conditional moments
    
Creates a tf.Example message ready to be written to a file.
For writing files with LR, HR and conditional moments
Creates a tf . Example message ready to be written to a file . For writing files with LR HR and conditional moments
def diversity_image_example(index,data_LR,h_LR,w_LR,data_HR,h_HR,w_HR,c,mean,std):
    feature = {
            'index'    : _int64_feature(index),
            'data_LR'  : _bytes_feature(data_LR),
            'h_LR'     : _int64_feature(h_LR),
            'w_LR'     : _int64_feature(w_LR),
            'data_HR'  : _bytes_feature(data_HR),
            'h_HR'     : _int64_feature(h_HR),
            'w_HR'     : _int64_feature(w_HR),
            'c'        : _int64_feature(c),
            'mean'     : _bytes_feature(mean),
            'std'      : _bytes_feature(std),
    }
    return tf.train.Example(features=tf.train.Features(feature=feature))
def diversity_image_example ( index , data_LR , h_LR , w_LR , data_HR , h_HR , w_HR , c , mean , std ) : feature = { 'index' : _int64_feature ( index ) , 'data_LR' : _bytes_feature ( data_LR ) , 'h_LR' : _int64_feature ( h_LR ) , 'w_LR' : _int64_feature ( w_LR ) , 'data_HR' : _bytes_feature ( data_HR ) , 'h_HR' : _int64_feature ( h_HR ) , 'w_HR' : _int64_feature ( w_HR ) , 'c' : _int64_feature ( c ) , 'mean' : _bytes_feature ( mean ) , 'std' : _bytes_feature ( std ) , } return tf . train . Example ( features = tf . train . Features ( feature = feature ) )
Creates a tf.Example message ready to be written to a file.
Creates a tf . Example message ready to be written to a file .
"""
    Creates a tf.Example message ready to be written to a file.
    For writing files with LR, HR and conditional moments
    """ # Create a dictionary mapping the feature name to the tf.Example-compatible # data type.
index
data_LR
h_LR
w_LR
data_HR
h_HR
w_HR
c
mean
std