Question

继续this问题和讨论here - 我正在尝试使用数据集API获取可变长度张量的数据集，并将它们切割成相等长度的切片（片段）。类似的东西：

Dataset = tf.contrib.data.Dataset
segment_len = 6
batch_size = 16

with tf.Graph().as_default() as g:
    # get the tfrecords dataset
    dataset = tf.contrib.data.TFRecordDataset(filenames).map(
        partial(record_type.parse_single_example, graph=g)).batch(batch_size)
    # zip it with the number of segments we need to slice each tensor
    dataset2 = Dataset.zip((dataset, Dataset.from_tensor_slices(
        tf.constant(num_segments, dtype=tf.int64))))
    it2 = dataset2.make_initializable_iterator()
    def _dataset_generator():
        with g.as_default():
            while True:
                try:
                    (im, length), count = sess.run(it2.get_next())
                    dataset3 = Dataset.zip((
                        # repeat each tensor then use map to take a stridded slice
                        Dataset.from_tensors((im, length)).repeat(count),
                        Dataset.range(count))).map(lambda x, c: (
                            x[0][:, c: c + segment_len],
                            x[0][:, c + 1: (c + 1) + segment_len],
                    ))
                    it = dataset3.make_initializable_iterator()
                    it_init = it.initializer
                    try:
                        yield it_init
                        while True:
                            yield sess.run(it.get_next())
                    except tf.errors.OutOfRangeError:
                        continue
                except tf.errors.OutOfRangeError:
                    return
    # Dataset.from_generator need tensorflow > 1.3 !
    das_dataset = Dataset.from_generator(
        _dataset_generator,
        (tf.float32, tf.float32),
        # (tf.TensorShape([]), tf.TensorShape([]))
    )
    das_dataset_it = das_dataset.make_one_shot_iterator()


with tf.Session(graph=g) as sess:
    while True:
        print(sess.run(it2.initializer))
        print(sess.run(das_dataset_it.get_next()))

当然我不想在生成器中传递会话，但这应该通过链接中给出的技巧（创建虚拟数据集并映射另一个的迭代器）来实现。上面的代码失败了圣经：

tensorflow.python.framework.errors_impl.InvalidArgumentError: TypeError: If shallow structure is a sequence, input must also be a sequence. Input has type: <class 'tensorflow.python.framework.ops.Operation'>.
         [[Node: PyFunc = PyFunc[Tin=[DT_INT64], Tout=[DT_FLOAT, DT_FLOAT], token="pyfunc_1"](arg0)]]
         [[Node: IteratorGetNext = IteratorGetNext[output_shapes=[<unknown>, <unknown>], output_types=[DT_FLOAT, DT_FLOAT], _device="/job:localhost/replica:0/task:0/cpu:0"](OneShotIterator)]]

我猜是因为我试图产生迭代器的初始化器，但我的问题基本上是我可以实现我正在尝试使用数据集API。

Answer 1

从嵌套Dataset构建Dataset的最简单方法是使用Dataset.flat_map()转换。此转换将函数应用于输入数据集的每个元素（示例中为dataset2），该函数返回嵌套的Dataset（在您的示例中最有可能是dataset3），然后转换将所有嵌套数据集展平为单个Dataset。

dataset2 = ...  # As above.

def get_slices(im_and_length, count):
  im, length = im_and_length
  # Repeat each tensor then use map to take a strided slice.
  return Dataset.zip((
      Dataset.from_tensors((im, length)).repeat(count),
      Dataset.range(count))).map(lambda x, c: (
          x[0][:, c + segment_len: (c + 1) + segment_len],
          x[0][:, c + 1 + segment_len: (c + 2) + segment_len],
  ))

das_dataset = dataset2.flat_map(get_slices)

从tfrecords数据集生成stridded切片的数据集

1 个答案: