如何使用tf.function从TensorFlow中的函数集中随机选择

Question

我的问题是:在预处理期间，我想使用tf.data.Dataset和tf.functionAPI从一组函数中随机选择一个函数应用于数据集示例。

具体来说，我的数据是3D体积，我希望从一组24个预定义的旋转函数中应用旋转。我想在tf.function中编写这段代码，这样就限制了numpy和列表索引等包的使用。

例如，我想这样做:

import tensorflow as tf
@tf.function
def func1(tensor):
# Apply some rotation here
...
@tf.function
def func2(tensor):
...
...
@tf.function
def func24(tensor):
...

@tf.function
def apply(tensor):
list_of_funcs = [func1, func2, ..., func24]
# Randomly sample from 0-23
a = tf.random.uniform([1], minval=0, maxval=23, dtype=tf.int32)

return list_of_funcs[a](tensor)

然而，我不能索引list_of_funcs作为TypeError: list indices must be integers or slices, not Tensor。此外，我不能将这些函数(AFAIK)收集到tf.Tensor中并使用tf.gather。

所以我的问题是:如何在tf.function中合理而整齐地从这些函数中采样?

Answer 1

您可以使用一堆嵌套的tf.cond。如果满足某个条件，它将调用true_fn或false_fn。由于您有两个以上的函数，因此可以为任意多的函数嵌套它们。例如，根据随机变量的值，我正在编写将输入乘以2、3、4或5的函数。

import tensorflow as tf
x = 10
@tf.function
def mult_2():
tf.print(f'i was 2, returning {x} multiplied by 2')
return tf.multiply(x, 2)
@tf.function
def mult_3():
tf.print(f'i was 3, returning {x} multiplied by 3')
return tf.multiply(x, 3)

@tf.function
def mult_4():
tf.print(f'i was 4, returning {x} multiplied by 4')
return tf.multiply(x, 4)

@tf.function
def mult_5():
tf.print(f'i was 5, returning {x} multiplied by 5')
return tf.multiply(x, 5)

i = tf.random.uniform((), 1, 5, dtype=tf.int32)
tf.cond(i == 2, mult_2,
lambda: tf.cond(i == 3, mult_3,
lambda: tf.cond(i == 4, mult_4, mult_5)))

I was 3, returning 10 multiplied by 3

<tf.Tensor: shape=(), dtype=int32, numpy=30>

请注意，如果不满足任何条件，mult_5将执行。

Answer 2

您可以使用tf.switch_case像

def func1(tensor):
return tensor * 1
def func2(tensor):
return tensor * 2
def func24(tensor):
return tensor * 24
class Lambda:
def __init__(self, func, arg):
self._func = func
self._arg = arg

def __call__(self):
return self._func(self._arg)
@tf.function
def apply(tensor):
list_of_funcs = [func1, func2, func24]
branch_index = tf.random.uniform(shape=[], minval=0, maxval=len(list_of_funcs), dtype=tf.int32)
output = tf.switch_case(
branch_index=branch_index, 
branch_fns=[Lambda(func, tensor) for func in list_of_funcs], 
)

return output

[e-mail]仅需要用于您希望优化的整个函数，在本例中为apply。如果在tf.data.Dataset.map中使用apply，则根本不需要装饰器。

看到这个讨论来理解为什么我们必须在这里定义Lambda类。

Answer 3

也许可以尝试使用tf.py_function，其中:

将python函数封装到一个优先执行的TensorFlow op中。

例如(在Google Colab上测试):

import tensorflow as tf
import random
@tf.function
def func1(tensor):
print('func1')
return tensor
@tf.function
def func2(tensor):
print('func2')
return tensor
@tf.function
def func3(tensor):
print('func3')
return tensor
@tf.function
def func4(tensor):
print('func4')
return tensor
@tf.function
def apply(tensor):
dispatcher = {
'func1': func1,
'func2': func2,
'func3': func3,
'func4': func4
}
keys = list(dispatcher)

def get_random_function_and_apply(t):
return dispatcher[random.choice(keys)](t)
y = tf.py_function(func=get_random_function_and_apply, inp=[tensor], Tout=tf.float32)

return y

mirrored_strategy = tf.distribute.MirroredStrategy(["GPU:0", "GPU:1"])
with mirrored_strategy.scope():
output = apply(tf.random.normal((5, 5, 5)))
print(output)
'''
INFO:tensorflow:Using MirroredStrategy with devices ('/job:localhost/replica:0/task:0/device:GPU:0', '/job:localhost/replica:0/task:0/device:GPU:1')
func4
tf.Tensor(
[[[ 0.6041213  -2.054427    1.1755397  -0.62914884 -0.00978021]
[ 0.06134182 -1.5529596  -0.3429052  -0.03199977 -1.1796658 ]
[-0.65084136 -1.5009187  -0.43266404 -0.18494445  1.2958355 ]
[-1.6614605  -0.7398612   1.5384725  -0.24926051 -0.5075399 ]
[ 0.7781286  -0.4102168   1.2152135   0.4508075  -1.7295381 ]]
[[-1.0509509  -1.271087    1.9061071   0.61855525  0.58581835]
[ 2.080663    0.43406835  0.32372198 -0.71427256  0.04448809]
[-0.6438594  -1.1245041  -0.4723388  -0.8302859  -2.0056007 ]
[ 1.1778332   0.2977344   0.7516829   1.1387901  -0.71768486]
[-0.44642782 -0.6523012  -0.48157197 -0.8197472   0.3635474 ]]
[[-0.43357274  1.166849   -0.04528571  0.44322303  0.74193203]
[ 1.2332342   0.07857647  1.3399298   0.62153     1.835202  ]
[ 0.48021084  0.36239776  0.16630112  0.59010863  1.8134127 ]
[-1.1444335   1.2445287  -1.2320557   0.08095992 -0.1379302 ]
[-1.101756   -1.8099649   0.18504284  0.15212883  0.33380997]]
[[-0.68228734 -0.82357454 -0.744171   -0.04959428 -1.3200126 ]
[ 0.813062    1.0669035  -0.7924809  -0.0548021   0.8043163 ]
[ 1.6480085  -0.17134379  0.25517386  0.02731211  1.2226027 ]
[-1.9785942  -0.22399756 -0.6814836   1.2065881  -1.7922156 ]
[-0.34833568 -1.0567352   1.5795225   0.14899854  0.5924402 ]]
[[-1.057639   -1.1659449  -0.22045298  0.39324322 -1.3500952 ]
[-0.32044935  0.9534627   0.40809664 -1.0296333  -0.8129102 ]
[-0.13515176 -0.32676768 -0.9333701   0.35130095 -1.5411847 ]
[ 2.090785    0.3497966   0.27694222  0.78199005 -0.08591356]
[ 0.9621986  -2.3930101  -1.1035724   0.27208164 -1.1846163 ]]], shape=(5, 5, 5), dtype=float32)
'''