weak object has gone away_what 是什么意思？
weak object has gone away_what does it mean?
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被调用时出现错误“弱对象已经消失”，我不明白是什么这意味着问题可能出在哪里。
完整引用如下：
----------------------------------------------------------------------
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，还没有找到我应该从哪里修复它的线索。
我想您在代码中的某个地方使用了 tf.function /@tf.function，并且您可能正在尝试重新定义一个对象，这导致 @tf.function 没有回溯您的图形。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。以下代码段重现了上述行为
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轨迹。即不共享轨迹，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)))
有关详细信息，请查看 official_documentation。希望这有帮助
只是为了扩展答案，我非常不满意必须 copy/paste python 函数来解决这个问题。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)))
这更具编程性，意味着您不必为了重新运行 某些内容而编写任何愚蠢的copy/pasting代码。
万一有人跟我一样，使用imageio也会出现这个错误。使用 imageio 对象调用该函数两次将会出现此错误。但是如果你把它转换成一个 numpy 数组它会工作
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')))
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被调用时出现错误“弱对象已经消失”，我不明白是什么这意味着问题可能出在哪里。
完整引用如下：
----------------------------------------------------------------------
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，还没有找到我应该从哪里修复它的线索。
我想您在代码中的某个地方使用了 tf.function /@tf.function，并且您可能正在尝试重新定义一个对象，这导致 @tf.function 没有回溯您的图形。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。以下代码段重现了上述行为
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轨迹。即不共享轨迹，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)))
有关详细信息，请查看 official_documentation。希望这有帮助
只是为了扩展答案，我非常不满意必须 copy/paste python 函数来解决这个问题。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)))
这更具编程性，意味着您不必为了重新运行 某些内容而编写任何愚蠢的copy/pasting代码。
万一有人跟我一样，使用imageio也会出现这个错误。使用 imageio 对象调用该函数两次将会出现此错误。但是如果你把它转换成一个 numpy 数组它会工作
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')))