mirror of
https://github.com/vale981/ray
synced 2025-03-10 13:26:39 -04:00
31 lines
1.1 KiB
Python
31 lines
1.1 KiB
Python
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import tensorflow as tf
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import threading
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class DataQueue(object):
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def __init__(self, placeholder_dict):
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"""Here, placeholder_dict is an OrderedDict."""
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placeholders = placeholder_dict.values()
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shapes = [placeholder.get_shape()[1:].as_list() for placeholder in placeholders]
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types = [placeholder.dtype for placeholder in placeholders]
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self.queue = tf.RandomShuffleQueue(shapes=shapes, dtypes=dtypes, capacity=2000, min_after_dequeue=1000)
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self.enqueue_op = self.queue.enqueue_many(placeholders)
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def thread_main(self, sess, data_iterator):
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for data in data_iterator:
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feed_dict = {placeholder: data[name] for (name, placeholder) in placeholder_dict}
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sess.run(self.enqueue_op, feed_dict=feed_dict)
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def start_thread(self, sess, data_iterator, num_threads=1):
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threads = []
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for n in range(num_thread):
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t = threading.Thread(target=self.train_main, args=(sess, data_iterator))
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t.daemon = True # Thread will close when parent quits
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t.start()
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threads.append(t)
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return threads
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