弱物体消失了_这是什么意思？答案

【问题标题】：weak object has gone away_what does it mean?弱物体消失了_这是什么意思？
【发布时间】：2019-10-31 13:56:11
【问题描述】：

我正在使用 tensorflow 解决一个问题，其中有一个函数被调用一次并且它工作正常，但第二次调用它时出现错误“弱对象已消失”，我不明白这是什么意思以及问题可能出在哪里。

完整的引用如下：

----------------------------------------------------------------------
TypeError                            Traceback (most recent call last)
~/.local/share/virtualenvs/tf-tRAPLeXL/lib/python3.6/site-packages/tensorflow_core/python/eager/function.py in _hash_fix(self, elem)
    108     try:
--> 109       hash(elem)
    110     except TypeError:

TypeError: weak object has gone away

During handling of the above exception, another exception occurred:

AttributeError                       Traceback (most recent call last)
<ipython-input-23-f1a95ac20255> in <module>
----> 1 default_settings['sur_model'].predict_with_grad(np.atleast_2d(xx))

<ipython-input-5-7e554cb74b1d> in predict_with_grad(self, x)
    127 
    128         with tf.GradientTape() as t:
--> 129             m, v = self.predict(x)
    130             dmdx = t.gradient(m, x)
    131         with tf.GradientTape() as t:

<ipython-input-5-7e554cb74b1d> in predict(self, X)
    116         """
    117 
--> 118         X_embed = self.embedding_model(X)
    119         #print('X_embed',X_embed)
    120         m, v = self.Pr_model.predict_y(X_embed)

~/.local/share/virtualenvs/tf-tRAPLeXL/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/base_layer.py in __call__(self, inputs, *args, **kwargs)
    889           with base_layer_utils.autocast_context_manager(
    890               self._compute_dtype):
--> 891             outputs = self.call(cast_inputs, *args, **kwargs)
    892           self._handle_activity_regularization(inputs, outputs)
    893           self._set_mask_metadata(inputs, outputs, input_masks)

~/.local/share/virtualenvs/tf-tRAPLeXL/lib/python3.6/site-packages/tensorflow_core/python/eager/def_function.py in __call__(self, *args, **kwds)
    455 
    456     tracing_count = self._get_tracing_count()
--> 457     result = self._call(*args, **kwds)
    458     if tracing_count == self._get_tracing_count():
    459       self._call_counter.called_without_tracing()

~/.local/share/virtualenvs/tf-tRAPLeXL/lib/python3.6/site-packages/tensorflow_core/python/eager/def_function.py in _call(self, *args, **kwds)
    492       # In this case we have not created variables on the first call. So we can
    493       # run the first trace but we should fail if variables are created.
--> 494       results = self._stateful_fn(*args, **kwds)
    495       if self._created_variables:
    496         raise ValueError("Creating variables on a non-first call to a function"

~/.local/share/virtualenvs/tf-tRAPLeXL/lib/python3.6/site-packages/tensorflow_core/python/eager/function.py in __call__(self, *args, **kwargs)
   1820   def __call__(self, *args, **kwargs):
   1821     """Calls a graph function specialized to the inputs."""
-> 1822     graph_function, args, kwargs = self._maybe_define_function(args, kwargs)
   1823     return graph_function._filtered_call(args, kwargs)  # pylint: disable=protected-access
   1824 

~/.local/share/virtualenvs/tf-tRAPLeXL/lib/python3.6/site-packages/tensorflow_core/python/eager/function.py in _maybe_define_function(self, args, kwargs)
   2117 
   2118     with self._lock:
-> 2119       graph_function = self._function_cache.primary.get(cache_key, None)
   2120       if graph_function is not None:
   2121         return graph_function, args, kwargs

~/.local/share/virtualenvs/tf-tRAPLeXL/lib/python3.6/site-packages/tensorflow_core/python/eager/function.py in __eq__(self, other)
    115 
    116   def __eq__(self, other):
--> 117     return self._fields_safe == other._fields_safe  # pylint: disable=protected-access
    118 
    119 

~/.local/share/virtualenvs/tf-tRAPLeXL/lib/python3.6/site-packages/tensorflow_core/python/eager/function.py in _fields_safe(self)
     91   def _fields_safe(self):
     92     """Hash & equality-safe version of all the namedtuple fields."""
---> 93     return (self._hash_fix(self.input_signature), self.parent_graph,
     94             self.device_functions, self.colocation_stack,
     95             self.in_cross_replica_context)

~/.local/share/virtualenvs/tf-tRAPLeXL/lib/python3.6/site-packages/tensorflow_core/python/eager/function.py in _hash_fix(self, elem)
     99     # Descend into tuples
    100     if isinstance(elem, tuple):
--> 101       return tuple(self._hash_fix(i) for i in elem)
    102 
    103     if isinstance(elem, set):

~/.local/share/virtualenvs/tf-tRAPLeXL/lib/python3.6/site-packages/tensorflow_core/python/eager/function.py in <genexpr>(.0)
     99     # Descend into tuples
    100     if isinstance(elem, tuple):
--> 101       return tuple(self._hash_fix(i) for i in elem)
    102 
    103     if isinstance(elem, set):

~/.local/share/virtualenvs/tf-tRAPLeXL/lib/python3.6/site-packages/tensorflow_core/python/eager/function.py in _hash_fix(self, elem)
     99     # Descend into tuples
    100     if isinstance(elem, tuple):
--> 101       return tuple(self._hash_fix(i) for i in elem)
    102 
    103     if isinstance(elem, set):

~/.local/share/virtualenvs/tf-tRAPLeXL/lib/python3.6/site-packages/tensorflow_core/python/eager/function.py in <genexpr>(.0)
     99     # Descend into tuples
    100     if isinstance(elem, tuple):
--> 101       return tuple(self._hash_fix(i) for i in elem)
    102 
    103     if isinstance(elem, set):

~/.local/share/virtualenvs/tf-tRAPLeXL/lib/python3.6/site-packages/tensorflow_core/python/eager/function.py in _hash_fix(self, elem)
    110     except TypeError:
    111       v = elem()
--> 112       return (v.__class__, tensor_spec.TensorSpec(v.shape, v.dtype))
    113 
    114     return elem

AttributeError: 'NoneType' object has no attribute 'shape'

我觉得它很奇怪，还没有找到线索知道我应该从哪里修复它。

【问题讨论】：

您能否编辑问题并提供/添加返回此错误的代码 sn-p？
@stephen_mugisha 这个问题我写了很多代码导致这个
好的，我会根据错误信息尝试回答

标签： python python-3.x tensorflow weak-references

【解决方案1】：

我想您在代码中的某处使用了tf.function /@tf.function，并且您可能正在尝试重新定义一个导致@tf.function 不回溯您的图表的对象。这是因为两个对象共享相同的迹线，即使使用不同的参数也没有效果。以下 sn-p 再现了上述行为

import tensorflow as tf
@tf.function
def square(x):
    return x**2

a = square(tf.Variable(2))
print(a)
a = square(tf.Variable(3))
print(a)

但是，如果您希望不同的对象具有不同的跟踪，即不共享跟踪，您可以使用不同的@tf.function 对象，如下所示；

@tf.function
def square1(x):
    return x**2

@tf.function
def square2(x):
    return x**2

print(square1(tf.Variable(2)))
print(square2(tf.Variable(3)))

更多详情，请查看official_documentation。希望这会有所帮助

【讨论】：

很好，停用 @tf.function 有效，谢谢，这似乎是一个非常罕见的错误，我自己无法弄清楚它来自哪里。您是否还知道是否有一种方法可以使这些 TensorFlow 模型的深度复制或多处理成为可能？
我不知道如何对模型进行深度复制。
好的，但是如果我需要多个独立版本的函数怎么办？手动复制/粘贴函数并像这样重命名对于任何重要的函数来说都是一种糟糕的解决方案。难道没有更好的方法来强制图表被回溯吗？我正在对一个函数进行一些拟合，并且需要针对各种不同的输入重复拟合。我可以将整个东西放在另一个函数中以根据命令动态生成 tf.function 吗？我想我会尝试...好的，不，不起作用。那还能做什么呢？

【解决方案2】：

万一有人像我一样来到这里，如果您使用 imageio，也会出现此错误。使用 imageio 对象调用该函数两次会给你这个错误。但是如果你把它转换成一个 numpy 数组，它就可以工作了

import tensorflow as tf
import imageio

@tf.function
def square(x):
    return x**2

# wont work
# a = square(imageio.imread('myimage.png'))
# a = square(imageio.imread('myimage.png'))

# will work!
a = square(np.asarray(imageio.imread('myimage.png')))
a = square(np.asarray(imageio.imread('myimage.png')))

【讨论】：

【解决方案3】：

只是为了扩展答案，我对不得不复制/粘贴 python 函数来解决这个问题非常不满意。幸运的是，似乎简单地放弃装饰器方法并将tf.function作为函数调用也可以解决问题：

def square(x):
    return x**2

traced1 = tf.function(square)
traced2 = tf.function(square)

print(traced1(tf.Variable(2)))
print(traced2(tf.Variable(3)))

这更加程序化，意味着您不必为了重新运行某些东西而进行任何愚蠢的代码复制/粘贴。

【讨论】：