tfrecord格式数据能高效的组织数据,提升训练时的IO性能
1,2步骤定义了函数,3步骤生成tfrecord格式的数据 1.TF-Feature 将数据(values)封装于tf.train.Featurepython
def int64_feature(values):
"""Returns a TF-Feature of int64s. Args: values: A scalar or list of values. Returns: A TF-Feature. """
if not isinstance(values, (tuple, list)):
values = [values]
return tf.train.Feature(int64_list=tf.train.Int64List(value=values))
def bytes_feature(values):
"""Returns a TF-Feature of bytes. Args: values: A string. Returns: A TF-Feature. """
return tf.train.Feature(bytes_list=tf.train.BytesList(value=[values]))
def float_feature(values):
"""Returns a TF-Feature of floats. Args: values: A scalar of list of values. Returns: A TF-Feature. """
if not isinstance(values, (tuple, list)):
values = [values]
return tf.train.Feature(float_list=tf.train.FloatList(value=values))
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2.tf.train.Examplebash
def create_tf_example(features_dict):
''' :param features_dict: { img_query_bytes:bytes :return: tf.train.Example '''
feature_map={ 'img_query':bytes_feature(features_dict['img_query_bytes'])
}
return tf.train.Example(features=tf.train.Features(feature=feature_map))
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3.写入tfrecord文件 使用tf.python_io.TFRecordWriter(out_path) 写入 tf.train.Example函数
out_path = './dataset/train.record'
with tf.python_io.TFRecordWriter(out_path) as writer:
features_dict=dict()
with tf.gfile.GFile(img_path,'rb') as fid:
features_dict['img_query_bytes']=fid.read()
example=create_tf_example(features_dict)
writer.write(example.SerializeToString())
if iter_num%1000==0:
print('done : {} % {}'.format(iter_num,iter_steps))
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dict 表示字典类型性能
tf.train.Example {
features: tf.train.Features{
feature: dict{
'feature_name':tf.train.Feature{
int64_list:tf.train.Int64List{value:list}
bytes_list:tf.train.BytesList{value:list}
float_list:tf.train.FloatList{value:list}
}
}
}
}
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