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import tensorflow as tf | |
import time | |
from datetime import datetime | |
import numpy as np | |
FLAGS = tf.app.flags.FLAGS | |
tf.app.flags.DEFINE_bool('cluster', False, '') | |
def log(obj): | |
print datetime.utcnow().strftime('%Y-%m-%d %H:%M:%S.%f')[:-3], obj | |
def main(_): | |
server = tf.train.Server.create_local_server() | |
raw_shape = [576, 3, 220, 220] | |
shape = tf.TensorShape(raw_shape) | |
if FLAGS.cluster: | |
sess = tf.Session(server.target) | |
else: | |
sess = tf.Session() | |
with tf.device('/cpu:0'): | |
q = tf.FIFOQueue(10, tf.float32, shape, name = 'Q', shared_name = 'share_queue') | |
rand_data = tf.zeros(shape) | |
init_op = tf.initialize_local_variables() | |
sess.run(init_op) | |
result = q.dequeue() | |
x = tf.placeholder(tf.float32, shape, 'data') | |
enqueue_op = q.enqueue(x) | |
while True: | |
time.sleep(1) | |
log('pushing') | |
sess.run(enqueue_op, feed_dict = {x: np.zeros(raw_shape)}) | |
log('push done') | |
sess.run(result) | |
log('pop done') | |
if __name__ == '__main__': | |
tf.logging.set_verbosity(tf.logging.INFO) | |
tf.app.run() |
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