Python tensorflow 模块，FIFOQueue() 实例源码

我们从Python开源项目中，提取了以下50个代码示例，用于说明如何使用tensorflow.FIFOQueue()。

项目：ml 作者：hohoins | 项目源码 | 文件源码

def testSimple(self):
    labels = [9, 3, 0]
    records = [self._record(labels[0], 0, 128, 255),
               self._record(labels[1], 255, 0, 1),
               self._record(labels[2], 254, 255, 0)]
    contents = b"".join([record for record, _ in records])
    expected = [expected for _, expected in records]
    filename = os.path.join(self.get_temp_dir(), "cifar")
    open(filename, "wb").write(contents)

    with self.test_session() as sess:
      q = tf.FIFOQueue(99, [tf.string], shapes=())
      q.enqueue([filename]).run()
      q.close().run()
      result = cifar10_input.read_cifar10(q)

      for i in range(3):
        key, label, uint8image = sess.run([
            result.key, result.label, result.uint8image])
        self.assertEqual("%s:%d" % (filename, i), tf.compat.as_text(key))
        self.assertEqual(labels[i], label)
        self.assertAllEqual(expected[i], uint8image)

      with self.assertRaises(tf.errors.OutOfRangeError):
        sess.run([result.key, result.uint8image])

项目：tf_base 作者：ozansener | 项目源码 | 文件源码

def setup_reader(self, image_paths, image_shape, num_concurrent, batch_size):
    # Path queue is list of image paths which will further be processed by another queue
    num_images = len(image_paths)
    indices = tf.range(0, num_images, 1)

    self.path_queue = tf.FIFOQueue(capacity=num_images, dtypes=[tf.int32, tf.string], name='path_queue')
    self.enqueue_path = self.path_queue.enqueue_many([indices, image_paths])
    self.close_path = self.path_queue.close()

    processed_queue = tf.FIFOQueue(capacity=num_images,
                       dtypes=[tf.int32, tf.float32],
                       shapes=[(), image_shape],
                       name='processed_queue')

    (idx, processed_image) = self.process()
    enqueue_process = processed_queue.enqueue([idx, processed_image])
    self.dequeue_batch = processed_queue.dequeue_many(batch_size)

    self.queue_runner = tf.train.QueueRunner(processed_queue, [enqueue_process] * num_concurrent)

项目：facial-emotion-detection-dl 作者：dllatas | 项目源码 | 文件源码

def testSimple(self):
    labels = [9, 3, 0]
    records = [self._record(labels[0], 0, 128, 255),
               self._record(labels[1], 255, 0, 1),
               self._record(labels[2], 254, 255, 0)]
    contents = b"".join([record for record, _ in records])
    expected = [expected for _, expected in records]
    filename = os.path.join(self.get_temp_dir(), "cifar")
    open(filename, "wb").write(contents)

    with self.test_session() as sess:
      q = tf.FIFOQueue(99, [tf.string], shapes=())
      q.enqueue([filename]).run()
      q.close().run()
      result = cifar10_input.read_cifar10(q)

      for i in range(3):
        key, label, uint8image = sess.run([
            result.key, result.label, result.uint8image])
        self.assertEqual("%s:%d" % (filename, i), tf.compat.as_text(key))
        self.assertEqual(labels[i], label)
        self.assertAllEqual(expected[i], uint8image)

      with self.assertRaises(tf.errors.OutOfRangeError):
        sess.run([result.key, result.uint8image])

项目：KittiClass 作者：MarvinTeichmann | 项目源码 | 文件源码

def create_queues(hypes, phase):
    """Create Queues."""
    arch = hypes['arch']
    dtypes = [tf.float32, tf.int32]

    height = 224
    width = 224
    channel = 3
    shapes = [[height, width, channel], []]

    capacity = 50
    q = tf.FIFOQueue(capacity=50, dtypes=dtypes, shapes=shapes)
    tf.summary.scalar("queue/%s/fraction_of_%d_full" %
                      (q.name + "_" + phase, capacity),
                      math_ops.cast(q.size(), tf.float32) * (1. / capacity))

    return q

项目：KittiClass 作者：MarvinTeichmann | 项目源码 | 文件源码

def create_queues(hypes, phase):
    """Create Queues."""
    arch = hypes['arch']
    dtypes = [tf.float32, tf.int32]

    shape_known = hypes['jitter']['fix_shape'] or \
        hypes['jitter']['resize_image']

    if shape_known:
        height = hypes['jitter']['image_height']
        width = hypes['jitter']['image_width']
        channel = hypes['arch']['num_channels']
        shapes = [[height, width, channel],
                  []]
    else:
        shapes = None

    capacity = 50
    q = tf.FIFOQueue(capacity=50, dtypes=dtypes, shapes=shapes)
    tf.summary.scalar("queue/%s/fraction_of_%d_full" %
                      (q.name + "_" + phase, capacity),
                      math_ops.cast(q.size(), tf.float32) * (1. / capacity))

    return q

项目：keras_experiments 作者：avolkov1 | 项目源码 | 文件源码

def get_mydevlist(self, ngpus):
        wdev_list = self.get_allworkers_devlist(ngpus)
        mytask_id = self.mytask_id
        #:  :type wdev: tf.DeviceSpec
        mywdev_list = [wdev for wdev in wdev_list if wdev.task == mytask_id]

        return mywdev_list


# =============================================================================
# SIGNAL QUEUES: https://github.com/hustcat/tensorflow_examples/blob/master/mnist_distributed/dist_fifo.py @IgnorePep8
# =============================================================================
# def create_done_queue(i, num_workers=1):
#     """Queue used to signal death for i'th ps shard. Intended to have
#       all workers enqueue an item onto it to signal doneness."""
#
#     with tf.device("/job:ps/task:%d" % (i)):
#         return tf.FIFOQueue(num_workers, tf.int32,
#                             shared_name="done_queue{}".format(i))
#
#
# def create_done_queues(num_ps):
#     return [create_done_queue(i) for i in range(num_ps)]

# Perhaps implement a READY queue just like DONE queues.

项目：keras_experiments 作者：avolkov1 | 项目源码 | 文件源码

def testSimple(self):
    labels = [9, 3, 0]
    records = [self._record(labels[0], 0, 128, 255),
               self._record(labels[1], 255, 0, 1),
               self._record(labels[2], 254, 255, 0)]
    contents = b"".join([record for record, _ in records])
    expected = [expected for _, expected in records]
    filename = os.path.join(self.get_temp_dir(), "cifar")
    open(filename, "wb").write(contents)

    with self.test_session() as sess:
      q = tf.FIFOQueue(99, [tf.string], shapes=())
      q.enqueue([filename]).run()
      q.close().run()
      result = cifar10_input.read_cifar10(q)

      for i in range(3):
        key, label, uint8image = sess.run([
            result.key, result.label, result.uint8image])
        self.assertEqual("%s:%d" % (filename, i), tf.compat.as_text(key))
        self.assertEqual(labels[i], label)
        self.assertAllEqual(expected[i], uint8image)

      with self.assertRaises(tf.errors.OutOfRangeError):
        sess.run([result.key, result.uint8image])

项目：SLAM 作者：sanjeevkumar42 | 项目源码 | 文件源码

def testSimple(self):
    labels = [9, 3, 0]
    records = [self._record(labels[0], 0, 128, 255),
               self._record(labels[1], 255, 0, 1),
               self._record(labels[2], 254, 255, 0)]
    contents = b"".join([record for record, _ in records])
    expected = [expected for _, expected in records]
    filename = os.path.join(self.get_temp_dir(), "cifar")
    open(filename, "wb").write(contents)

    with self.test_session() as sess:
      q = tf.FIFOQueue(99, [tf.string], shapes=())
      q.enqueue([filename]).run()
      q.close().run()
      result = cifar10_input.read_cifar10(q)

      for i in range(3):
        key, label, uint8image = sess.run([
            result.key, result.label, result.uint8image])
        self.assertEqual("%s:%d" % (filename, i), tf.compat.as_text(key))
        self.assertEqual(labels[i], label)
        self.assertAllEqual(expected[i], uint8image)

      with self.assertRaises(tf.errors.OutOfRangeError):
        sess.run([result.key, result.uint8image])