Question

在使用tf.data.dataset进行训练时，我无法加快数据管道的速度，我想这里缺少一些东西。由于数据集中有不同的选项可以预加载数据，因此数据集速度仍然很慢。

我有一个复杂的数据管道，但是我在下面简化了一个小例子。我尝试微调num_parallel_calls，cycle_length，预取等，但似乎无法顺利进行数据集生成。我想念什么？有什么建议么？在此处输入代码

import tensorflow as tf
tf.enable_eager_execution()
from timeit import default_timer as timer

feature_count = 400
batch_size = 1024
look_back = 100
target_groups = 21

def random_data_generator(x=0):
    while True:
        x_data = tf.random.uniform(
            shape=(batch_size,  look_back, feature_count),
            minval=-1.0,
            maxval=5,
            dtype=tf.dtypes.float32)

        Y_data = tf.random.uniform(
            shape=(batch_size, target_groups),
            minval=1,
            maxval=21,
            dtype=tf.dtypes.int32)

        yield x_data, Y_data


def get_simple_Dataset_generator(): 

    dataset = tf.data.Dataset.from_tensor_slices([0,1,2])
    dataset = dataset.interleave(lambda x: tf.data.Dataset.from_generator(random_data_generator, 
                            output_types=(tf.float32, tf.float32), args=(x,)),
                            cycle_length=3,
                            block_length=3,
                            num_parallel_calls= tf.data.experimental.AUTOTUNE)

    #dataset = dataset.prefetch(2)
    while True: 
        for x, Y in dataset:
            yield x, Y


def test_speed():
    generator = get_simple_Dataset_generator()
    print("Testing generator speed ")

    for i in range(1,100): 
        start_time = timer()
        next(generator)
        lap_time = timer()-start_time
        print("%s Time - %fsec "%(i, lap_time))


if __name__ == '__main__': 
    test_speed()```




I was hoping to see consistent generator speed but it still very erratic.
Output
1 Time - 3.417578sec
2 Time - 1.257846sec
3 Time - 1.286210sec
4 Time - 0.000456sec
5 Time - 0.027772sec
6 Time - 0.058985sec
7 Time - 0.000416sec
8 Time - 0.026721sec
9 Time - 0.027316sec
10 Time - 0.777332sec
11 Time - 1.379266sec
12 Time - 1.172304sec
13 Time - 0.000365sec
14 Time - 0.026909sec
15 Time - 0.045409sec
16 Time - 0.000708sec
17 Time - 0.025682sec
18 Time - 0.027223sec
19 Time - 0.577131sec
20 Time - 1.220682sec
21 Time - 1.189601sec
22 Time - 0.000573sec
23 Time - 0.079531sec
24 Time - 0.624080sec
25 Time - 0.038932sec

Answer 1

我相信，急于解决的纠结尚待解决。将您的代码更改为基于图形的执行，我得到的每次迭代大约需要0.06秒，而不是急切版本的1秒。

这是代码段：

import tensorflow as tf
from timeit import default_timer as timer

feature_count = 400
batch_size = 1024
look_back = 100
target_groups = 21

def random_data(x=0):
    x_data = tf.random.uniform(
            shape=(batch_size,  look_back, feature_count),
            minval=-1.0,
            maxval=5,
            dtype=tf.dtypes.float32)

    Y_data = tf.random.uniform(
            shape=(batch_size, target_groups),
            minval=1,
            maxval=21,
            dtype=tf.dtypes.int32)

    return x_data, Y_data


def get_simple_Dataset(): 

    dataset = tf.data.Dataset.from_tensor_slices(tf.zeros(100))
    dataset = dataset.map(random_data)

    dataset = dataset.prefetch(10)

    return dataset

def test_speed():
    dataset = get_simple_Dataset()
    iterator = dataset.make_one_shot_iterator()
    fetch = iterator.get_next()

    with tf.Session() as sess:     
        for i in range(1,100): 
            start_time = timer()
            sess.run(fetch)
            lap_time = timer()-start_time
            print("%s Time - %fsec "%(i, lap_time))


if __name__ == '__main__': 
    test_speed()

1 Time - 0.108968sec 
2 Time - 0.071986sec 
3 Time - 0.068198sec 
4 Time - 0.065433sec 
5 Time - 0.066582sec 
6 Time - 0.064175sec 
7 Time - 0.067372sec 
8 Time - 0.064265sec 
9 Time - 0.065510sec 
10 Time - 0.068043sec

使用tf.data.dataset API加速数据管道

1 个答案: