我在加快tf.data.dataset训练的数据管道时遇到了麻烦，我想我在这里遗漏了一些东西。使用数据集内的不同选项来预加载数据，数据集速度仍然很慢。
我有一个复杂的数据管道，但我简化为下面的一个小例子。我试着微调num_parallel_calls，cycle_length，prefetch等，但似乎无法顺利生成数据集。我错过了什么？有什么建议吗？

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