[Tensorflow] TFRecord read and write mechanism

1. tfrecord, when writing is written line by line, when reading is to read to each batch of rows.

2. Writing, by the for loop (e.g.: img_path, cls_label = img_paths [i ], cls_labels [I]) to the inside tf.train.Example feed feature.
   Thus, feature tf.train.Example fed in a single line of data (including a img_path, its corresponding encode_img, cls_label, pts_label).

   example = tf.train.Example(features=tf.train.Features(feature={
       'img_path': _bytes_feature(img_path),
       'encoded': _bytes_feature(image_buffer),
       'cls_label': _int64_feature(cls_label)
   }))
   

   You can print(example)view it.

3. The final data will form a two-dimensional list in tfrecord in:

   [ { [img_path], [img_encode], [cls_label], [pts_label] }
     { [img_path], [img_encode], [cls_label], [pts_label] }
     { [img_path], [img_encode], [cls_label], [pts_label] }
     { [img_path], [img_encode], [cls_label], [pts_label] }
                                                … …
     { [img_path], [img_encode], [cls_label], [pts_label] } ]
   

   Each row in the list, is a complete data element, element = {[img_path], [img_encode], [cls_label], [pts_label]}

4. Specifically, element = {[b '\ path \ to \ img_1.jpg'], [\ df \ sdf \ df \ df \ dfsxc \ d], [1], [21.2, 34.6, 45.1, 56.7]}

5. Every individual is in the type list element, the writing _int64_feature, _float_feature, when _bytes_feature, it must be a one-dimensional list!