reading from files

Posted 2020-09-10 武方绿

如果有图会很好理解，最近太忙，以后再加吧

#首先有一个需要读取的文件名列表
#然后将文件名列表通过函数string_input_producer放进文件名队列。
#有时候因为数据量太大，需要把他们放进不同的tfrecord文件中
filename_queue = tf.train.string_input_producer(["file0.csv", "file1.csv"])
#对不同格式的文件有不同的reader
reader = tf.TextLineReader()
#通过reader的read函数extract a record from a file whose name is in the queue,
#如果该文件中所有记录都被抽取完，dequeue这个filename，参考readerbase
#read（）返回下一个record
key, value = reader.read(filename_queue)
# decoded record，decode方式和文件内部record格式相关，然后拼接成需要的格式
record_defaults = [[1], [1], [1], [1], [1]]
col1, col2, col3, col4, col5 = tf.decode_csv(
    value, record_defaults=record_defaults)
features = tf.stack([col1, col2, col3, col4])
with tf.Session() as sess:
  # Start populating the filename queue.
  coord = tf.train.Coordinator()
  threads = tf.train.start_queue_runners(coord=coord)
  for i in range(1200):
    # Retrieve a single instance:
    example, label = sess.run([features, col5])
  coord.request_stop()
  coord.join(threads)

---

提到queue就不得不提两个帮助多线程异步的类:tf.train.Coordinator和tf.train.QueueRunner；

• tf.train.Coordinator：控制多线程，使其同时结束。
  
• tf.train.QueueRunner：包含一些enqueue op，为其create一些线程，每一个op都在一个线程上运行。
  

coordinator

Coordinator方法：should_stop,request_stop,join

# Thread body: loop until the coordinator indicates a stop was requested.
# If some condition becomes true, ask the coordinator to stop.
def MyLoop(coord):
  while not coord.should_stop():#should_stop返回true or false，表示线程是否该结束
    ...do something...
    if ...some condition...:
      coord.request_stop()#当某些条件发生时，一个进程request_stop，其他进程因为should_stop返回true而终止
# Main thread: create a coordinator.
coord = tf.train.Coordinator()
# Create 10 threads that run ‘MyLoop()‘
threads = [threading.Thread(target=MyLoop, args=(coord,)) for i in xrange(10)]
# Start the threads and wait for all of them to stop.
for t in threads:
  t.start()
coord.join(threads)

QueueRunner

example = ...ops to create one example...
# Create a queue, and an op that enqueues examples one at a time in the queue.
#区别于filename queue，这是example queue。可以是接着上面读数据解析然后放进这个queue
queue = tf.RandomShuffleQueue(...)
enqueue_op = queue.enqueue(example)#定义入队操作
# Create a training graph that starts by dequeuing a batch of examples.
inputs = queue.dequeue_many(batch_size)
train_op = ...use ‘inputs‘ to build the training part of the graph...
# Create a queue runner that will run 4 threads in parallel to enqueue
# examples.
#QueueRunner的构造函数，queuerunner是为一个queue的入队操作多线程化服务的，
#第二个参数是入队操作列表
qr = tf.train.QueueRunner(queue, [enqueue_op] * 4)
# Launch the graph.
sess = tf.Session()
# Create a coordinator, launch the queue runner threads.
coord = tf.train.Coordinator()
#queuerunner为queue创造多线程，并且把这些线程的结束交由coordinator管理
enqueue_threads = qr.create_threads(sess, coord=coord, start=True)
  
# Run the training loop, controlling termination with the coordinator.
for step in xrange(1000000):
    if coord.should_stop():
        break
    sess.run(train_op)
# When done, ask the threads to stop.
coord.request_stop()
# And wait for them to actually do it.
coord.join(enqueue_threads)

未完待续。。。

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