问如何将tfrecord拆分成多个tfrecord？

EN

path_list = paths.values # List of the data paths
n_paths = len(path_list) # Gets the lenght

n_items = 10000 # Defines the number of items per TFRecord

# Defines the total number of files, the "1" added here was manually placed by me 
# as the necessary number of files to place the remaining items from. (Basically i have calculated that 1 extra file would fit the remaining
# data that could not be equally distributed over the other files)  
n_files = int(n_paths / n_items) + 1 

rest = n_paths % n_items # In case the number of items can not be equally distributed


file_path = DATA_DIR+'TFRecords/train/train_{}.tfrecords' # Format the output path


for record in range(n_files):
  print('Record: '+ str(record)+' from: ', n_folders + number_of_extra_files)

  fmt_path = file_path.format(record)

  if not sample_index == distributed_total:
    limit = sample_index + n_items

    print('converting from: ' + str(sample_index)+' to: ' + str(limit-1))
    path_subset = path_list[sample_index : (limit -1)]      

    sample_index = limit
    convert(path_subset, None, fmt_path)
  else:
    path_subset = path_list[sample_index : (sample_index + (rest -1))]  

    print('converting from: ' + str(sample_index)+' to: ' + str(sample_index + (rest -1)))

    convert(path_subset, None, fmt_path)
    sample_index = sample_index + rest

def convert(image_paths, labels, out_path):
    # Args:
    # image_paths   List of file-paths for the images.
    # labels        Class-labels for the images.
    # out_path      File-path for the TFRecords output file.
    
    print("Converting: " + out_path)
    
    # Number of images. Used when printing the progress.
    num_images = len(image_paths)
    
    # Open a TFRecordWriter for the output-file.
    with tf.python_io.TFRecordWriter(out_path) as writer:
        
        # Iterate over all the image-paths and class-labels.
        for i in range(num_images):
          # Print the percentage-progress.
          print_progress(count=i, total=num_images-1)
          
          # Load the image-file using matplotlib's imread function.
          path = image_paths[i]
          img = imread(path)
          path = path.split('/')

          # Convert the image to raw bytes.
          img_bytes = img.tostring()

          # Get the label index  
          label = int(path[4])

          # Create a dict with the data we want to save in the
          # TFRecords file. You can add more relevant data here.
          data = \
              {
                  'image': wrap_bytes(img_bytes),
                  'label': wrap_int64(label)
              }

          # Wrap the data as TensorFlow Features.
          feature = tf.train.Features(feature=data)

          # Wrap again as a TensorFlow Example.
          example = tf.train.Example(features=feature)

          # Serialize the data.
          serialized = example.SerializeToString()
            
          # Write the serialized data to the TFRecords file.
          writer.write(serialized)