如何仅在应用程序的特定部分使用tensorflow eager执行？

我有几个文件与不同的文件:

main.py

watch.py

read.py

detect.py < - 使用darkflow依赖于图模式的基于张量流的库

translate.py < - 使用tf eager执行

在darkflow的TFNet初始化期间,我收到此错误:

Traceback (most recent call last):
  File "/home/justin/Projects/comp3931/main.py", line 6, in 
    watcher = Watcher('res/vid/planet_earth_s01e01/video.mp4', 'res/vid/planet_earth_s01e01/english.srt')
  File "/home/justin/Projects/comp3931/watch.py", line 9, in __init__
    self.detector = Detector()
  File "/home/justin/Projects/comp3931/detect.py", line 6, in __init__
    self.tfnet = TFNet(self.options)
  File "/usr/local/lib64/python3.6/site-packages/darkflow/net/build.py", line 75, in __init__
    self.build_forward()
  File "/usr/local/lib64/python3.6/site-packages/darkflow/net/build.py", line 105, in build_forward
    self.inp = tf.placeholder(tf.float32, inp_size, 'input')
  File "/usr/local/lib/python3.6/site-packages/tensorflow/python/ops/array_ops.py", line 1677, in placeholder
    raise RuntimeError("tf.placeholder() is not compatible with "
RuntimeError: tf.placeholder() is not compatible with eager execution.

所以,我认为,当我实例Translator从类translate.py文件时,它调用了整个计划,然后是不是要darkflow的电话兼容急于执行TFNet中使用类Dectector从类detect.py

如果我translate.py独立于其他人运行它工作正常,其他模块如果运行它们没有translate.py参与也可以正常工作.

我猜他们使用不同的上下文(graph/eager)这一事实,整个事情不能在同一个程序中一起运行.我已经尝试查看文档,但在需要时找不到切换回图形模式的方法.

有什么方法可以在不同的地方在同一个应用程序中运行eager和graph模式吗？

最好编写兼容图形模式和急切执行的代码.从文档:

使用tf.data进行输入处理而不是队列.它更快更容易.

使用面向对象的层API(如tf.keras.layers和tf.keras.Model),因为它们具有显式的变量存储.

在热切和图形执行期间,大多数模型代码的工作方式相同,但也有例外.(例如,使用Python控制流的动态模型根据输入更改计算.)

一旦使用tf.enable_eager_execution启用了急切执行,就无法关闭它.启动一个新的Python会话以返回图形执行.

也就是说,通过使用,可以在图形模式下使用急切执行tfe.py_func().这是文档中的代码示例(我刚刚添加了导入和断言):

import tensorflow as tf
import tensorflow.contrib.eager as tfe

def my_py_func(x):
    assert tf.executing_eagerly()
    x = tf.matmul(x, x)  # You can use tf ops
    print(x)  # but it's eager!
    return x

assert not tf.executing_eagerly()
with tf.Session() as sess:
    x = tf.placeholder(dtype=tf.float32)
    # Call eager function in graph!
    pf = tfe.py_func(my_py_func, [x], tf.float32)
    sess.run(pf, feed_dict={x: [[2.0]]})  # [[4.0]]

反过来也是可能的,正如Alex Passos在本视频中所解释的那样.以下是受视频启发的示例:

import tensorflow as tf
import tensorflow.contrib.eager as tfe

tf.enable_eager_execution()

def my_graph_func(x):
    assert not tf.executing_eagerly()
    w = tfe.Variable(2.0)
    b = tfe.Variable(4.0)
    return x * w + b

assert tf.executing_eagerly()
g = tfe.make_template("g", my_graph_func, create_graph_function_=True)
print(g(3))

还有一种非官方的方式来切换模式,使用如下定义的eager_mode和graph_mode上下文tensorflow.python.eager.context:

import tensorflow as tf
import tensorflow.contrib.eager as tfe
from tensorflow.python.eager.context import eager_mode, graph_mode

with eager_mode():
    print("Eager mode")
    assert tf.executing_eagerly()
    x1 = tfe.Variable(5.0)
    print(x1.numpy())

print()
with graph_mode():
    print("Graph mode")
    assert not tf.executing_eagerly()

    x2 = tfe.Variable(5.0)
    with tf.Session():
        x2.initializer.run()
        print(x2.eval())

因为它不是官方的,你应该在生产代码中避免它,但它可能在调试时或在Jupyter笔记本中派上用场.最后一个选项是使用此switch_to()功能:

import tensorflow as tf
import tensorflow.contrib.eager as tfe
from tensorflow.python.eager.context import context, EAGER_MODE, GRAPH_MODE

def switch_to(mode):
    ctx = context()._eager_context
    ctx.mode = mode
    ctx.is_eager = mode == EAGER_MODE

switch_to(EAGER_MODE)
assert tf.executing_eagerly()
v = tfe.Variable(3.0)
print(v.numpy())
assert tf.get_default_graph().get_operations() == []

switch_to(GRAPH_MODE)
assert not tf.executing_eagerly()
v = tfe.Variable(3.0)
init = tf.global_variables_initializer()
assert len(tf.get_default_graph().get_operations()) > 0
with tf.Session():
    init.run()
    print(v.eval())

这真的是一个黑客,但它可能在Jupyter笔记本中很有用,如果你不喜欢将所有代码嵌套在with块中.