diversity_SR/conditionalMoments/util/tensorflowUtils.py
def _parse_image_wind_SFfunction(example_proto):
    """
    Dictionary for reading LR and SF
    """
    image_feature_description = {
        'index': tf.io.FixedLenFeature([], tf.int64),
        'data_LR': tf.io.FixedLenFeature([], tf.string),
        'h_LR': tf.io.FixedLenFeature([], tf.int64),
        'w_LR': tf.io.FixedLenFeature([], tf.int64),
        'data_SF': tf.io.FixedLenFeature([], tf.string),
        'h_SF': tf.io.FixedLenFeature([], tf.int64),
        'w_SF': tf.io.FixedLenFeature([], tf.int64),
        'c': tf.io.FixedLenFeature([], tf.int64),
    }
    example = tf.io.parse_single_example(example_proto, image_feature_description)
    h_SF, w_SF, c = example['h_SF'], example['w_SF'], example['c']
    h_LR, w_LR, c = example['h_LR'], example['w_LR'], example['c']
    data_SF = tf.io.decode_raw(example['data_SF'], tf.float64)
    data_SF = tf.reshape(data_SF, (h_SF, w_SF, c))
    data_LR = tf.io.decode_raw(example['data_LR'], tf.float64)
    data_LR = tf.reshape(data_LR, (h_LR, w_LR, c))
    return data_LR, data_SF

    Dictionary for reading LR and SF
    
Dictionary for reading LR and SF
Dictionary for reading LR and SF
def _parse_image_wind_SFfunction(example_proto):
    image_feature_description = {
        'index': tf.io.FixedLenFeature([], tf.int64),
        'data_LR': tf.io.FixedLenFeature([], tf.string),
        'h_LR': tf.io.FixedLenFeature([], tf.int64),
        'w_LR': tf.io.FixedLenFeature([], tf.int64),
        'data_SF': tf.io.FixedLenFeature([], tf.string),
        'h_SF': tf.io.FixedLenFeature([], tf.int64),
        'w_SF': tf.io.FixedLenFeature([], tf.int64),
        'c': tf.io.FixedLenFeature([], tf.int64),
    }
    example = tf.io.parse_single_example(example_proto, image_feature_description)
    h_SF, w_SF, c = example['h_SF'], example['w_SF'], example['c']
    h_LR, w_LR, c = example['h_LR'], example['w_LR'], example['c']
    data_SF = tf.io.decode_raw(example['data_SF'], tf.float64)
    data_SF = tf.reshape(data_SF, (h_SF, w_SF, c))
    data_LR = tf.io.decode_raw(example['data_LR'], tf.float64)
    data_LR = tf.reshape(data_LR, (h_LR, w_LR, c))
    return data_LR, data_SF
def _parse_image_wind_SFfunction ( example_proto ) : image_feature_description = { 'index' : tf . io . FixedLenFeature ( [ ] , tf . int64 ) , 'data_LR' : tf . io . FixedLenFeature ( [ ] , tf . string ) , 'h_LR' : tf . io . FixedLenFeature ( [ ] , tf . int64 ) , 'w_LR' : tf . io . FixedLenFeature ( [ ] , tf . int64 ) , 'data_SF' : tf . io . FixedLenFeature ( [ ] , tf . string ) , 'h_SF' : tf . io . FixedLenFeature ( [ ] , tf . int64 ) , 'w_SF' : tf . io . FixedLenFeature ( [ ] , tf . int64 ) , 'c' : tf . io . FixedLenFeature ( [ ] , tf . int64 ) , } example = tf . io . parse_single_example ( example_proto , image_feature_description ) h_SF , w_SF , c = example [ 'h_SF' ] , example [ 'w_SF' ] , example [ 'c' ] h_LR , w_LR , c = example [ 'h_LR' ] , example [ 'w_LR' ] , example [ 'c' ] data_SF = tf . io . decode_raw ( example [ 'data_SF' ] , tf . float64 ) data_SF = tf . reshape ( data_SF , ( h_SF , w_SF , c ) ) data_LR = tf . io . decode_raw ( example [ 'data_LR' ] , tf . float64 ) data_LR = tf . reshape ( data_LR , ( h_LR , w_LR , c ) ) return data_LR , data_SF
Dictionary for reading LR and SF
Dictionary for reading LR and SF
"""
    Dictionary for reading LR and SF
    """
example_proto