Tensorflow 2.1.0 - 函数构建代码之外的操作正在传递一个“图形”张量

Posted 2023-02-23

【中文标题】Tensorflow 2.1.0 - 函数构建代码之外的操作正在传递一个“图形”张量

【英文标题】：Tensorflow 2.1.0 - An op outside of the function building code is being passed a "Graph" tensor

【发布时间】：2020-06-18 10:14:07

【问题描述】：

我正在尝试实施最近的一篇论文。此实现的一部分涉及从 tf 1.14 迁移到 tf 2.1.0。该代码与 tf 1.14 一起工作，但不再工作。

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注意：如果我禁用 Eager Execution tf.compat.v1.disable_eager_execution()，那么代码将按预期工作。

这是解决方案吗？我之前在 TF 2.x 中制作了很多模型，并且从来不需要禁用 Eager Execution 来实现正常功能。

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我已将问题提炼成一个非常简短的要点，说明正在发生的事情。

链接和代码在前，然后是详细的错误消息

链接到 Gist -- https://gist.github.com/darien-schettler/fd5b25626e9eb5b1330cce670bf9cc17

代码

# version 2.1.0
import tensorflow as tf

# version 1.18.1
import numpy as np


# ######## DEFINE CUSTOM FUNCTION FOR TF LAMBDA LAYER  ######## #
def resize_like(input_tensor, ref_tensor):
    """ Resize an image tensor to the same size/shape as a reference image tensor

    Args:
        input_tensor : (image tensor) Input image tensor that will be resized
        ref_tensor   : (image tensor) Reference image tensor that we want to resize the input tensor to.

    Returns:
        reshaped tensor
    """
    reshaped_tensor = tf.image.resize(images=input_tensor,
                                      size=tf.shape(ref_tensor)[1:3],
                                      method=tf.image.ResizeMethod.NEAREST_NEIGHBOR,
                                      preserve_aspect_ratio=False,
                                      antialias=False,
                                      name=None)
    return reshaped_tensor
# ############################################################# #

# ############ DEFINE MODEL USING TF.KERAS FN API  ############ #

# INPUTS
model_input_1 = tf.keras.layers.Input(shape=(160,160,3))
model_input_2 = tf.keras.layers.Input(shape=(160,160,3))

# OUTPUTS
model_output_1 = tf.keras.layers.Conv2D(filters=64, 
                                        kernel_size=(1, 1), 
                                        use_bias=False,
                                        kernel_initializer='he_normal',
                                        name='conv_name_base')(model_input_1)
model_output_2 = tf.keras.layers.Lambda(function=resize_like,
                                        arguments='ref_tensor': model_output_1)(model_input_2)

# MODEL
model = tf.keras.models.Model(inputs=[model_input_1, model_input_2],
                                      outputs=model_output_2,
                                      name="test_model")

# ############################################################# #

# ######### TRY TO UTILIZE PREDICT WITH DUMMY INPUT  ########## #

dummy_input = [np.ones((1,160,160,3)), np.zeros((1,160,160,3))]
model.predict(x=dummy_input) # >>>>ERROR OCCURS HERE<<<<

# ############################################################# #

完全错误

>>> model.predict(x=dummy_input) # >>>>ERROR OCCURS HERE<<<<

Traceback (most recent call last):
  File "/Users/<username>/.virtualenvs/<venv-name>/lib/python3.7/site-packages/tensorflow_core/python/eager/execute.py", line 61, in quick_execute
    num_outputs)
TypeError: An op outside of the function building code is being passed
a "Graph" tensor. It is possible to have Graph tensors
leak out of the function building context by including a
tf.init_scope in your function building code.
For example, the following function will fail:
  @tf.function
  def has_init_scope():
    my_constant = tf.constant(1.)
    with tf.init_scope():
      added = my_constant * 2
The graph tensor has name: conv_name_base_1/Identity:0

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/Users/<user-name>/.virtualenvs/<venv-name>/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/training.py", line 1013, in predict
    use_multiprocessing=use_multiprocessing)
  File "/Users/<user-name>/.virtualenvs/<venv-name>/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/training_v2.py", line 498, in predict
    workers=workers, use_multiprocessing=use_multiprocessing, **kwargs)
  File "/Users/<user-name>/.virtualenvs/<venv-name>/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/training_v2.py", line 475, in _model_iteration
    total_epochs=1)
  File "/Users/<user-name>/.virtualenvs/<venv-name>/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/training_v2.py", line 128, in run_one_epoch
    batch_outs = execution_function(iterator)
  File "/Users/<user-name>/.virtualenvs/<venv-name>/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/training_v2_utils.py", line 98, in execution_function
    distributed_function(input_fn))
  File "/Users/<user-name>/.virtualenvs/<venv-name>/lib/python3.7/site-packages/tensorflow_core/python/eager/def_function.py", line 568, in __call__
    result = self._call(*args, **kwds)
  File "/Users/<user-name>/.virtualenvs/<venv-name>/lib/python3.7/site-packages/tensorflow_core/python/eager/def_function.py", line 638, in _call
    return self._concrete_stateful_fn._filtered_call(canon_args, canon_kwds)  # pylint: disable=protected-access
  File "/Users/<user-name>/.virtualenvs/<venv-name>/lib/python3.7/site-packages/tensorflow_core/python/eager/function.py", line 1611, in _filtered_call
    self.captured_inputs)
  File "/Users/<user-name>/.virtualenvs/<venv-name>/lib/python3.7/site-packages/tensorflow_core/python/eager/function.py", line 1692, in _call_flat
    ctx, args, cancellation_manager=cancellation_manager))
  File "/Users/<user-name>/.virtualenvs/<venv-name>/lib/python3.7/site-packages/tensorflow_core/python/eager/function.py", line 545, in call
    ctx=ctx)
  File "/Users/<user-name>/.virtualenvs/<venv-name>/lib/python3.7/site-packages/tensorflow_core/python/eager/execute.py", line 75, in quick_execute
    "tensors, but found ".format(keras_symbolic_tensors))
tensorflow.python.eager.core._SymbolicException: Inputs to eager execution function cannot be Keras symbolic tensors, but found [<tf.Tensor 'conv_name_base_1/Identity:0' shape=(None, 160, 160, 64) dtype=float32>]

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我想到的一个潜在解决方案是用自定义层替换 Lambda 层......这似乎也解决了这个问题。不过，不确定围绕这一点的最佳实践是什么。代码如下。

---

# version 2.1.0
import tensorflow as tf

# version 1.18.1
import numpy as np


# ######## DEFINE CUSTOM LAYER DIRECTLY BY SUBCLASSING  ######## #
class ResizeLike(tf.keras.layers.Layer):
    """ tf.keras layer to resize a tensor to the reference tensor shape.

    Attributes:
        keras.layers.Layer: Base layer class.
            This is the class from which all layers inherit.
                -   A layer is a class implementing common neural networks
                    operations, such as convolution, batch norm, etc.
                -   These operations require managing weights,
                    losses, updates, and inter-layer connectivity.
    """
    def __init__(self, **kwargs):
        super().__init__(**kwargs)

    def call(self, inputs, **kwargs):
        """TODO: docstring

        Args:
            inputs (TODO): TODO

        **kwargs:
            TODO

        Returns:
            TODO
        """

        input_tensor, ref_tensor = inputs

        return self.resize_like(input_tensor, ref_tensor)


    def resize_like(self, input_tensor, ref_tensor):
        """ Resize an image tensor to the same size/shape as a reference image tensor

        Args:
            input_tensor: (image tensor) Input image tensor that will be resized
            ref_tensor: (image tensor) Reference image tensor that we want to resize the input tensor to.

        Returns:
            reshaped tensor
        """
        reshaped_tensor = tf.image.resize(images=input_tensor,
                                          size=tf.shape(ref_tensor)[1:3],
                                          method=tf.image.ResizeMethod.NEAREST_NEIGHBOR,
                                          preserve_aspect_ratio=False,
                                          antialias=False)
        return reshaped_tensor
# ############################################################# #

# ############ DEFINE MODEL USING TF.KERAS FN API  ############ #

# INPUTS
model_input_1 = tf.keras.layers.Input(shape=(160,160,3))
model_input_2 = tf.keras.layers.Input(shape=(160,160,3))

# OUTPUTS
model_output_1 = tf.keras.layers.Conv2D(filters=64, 
                                        kernel_size=(1, 1), 
                                        use_bias=False,
                                        kernel_initializer='he_normal',
                                        name='conv_name_base')(model_input_1)
model_output_2 = ResizeLike(name="resize_layer")([model_input_2, model_output_1])

# MODEL
model = tf.keras.models.Model(inputs=[model_input_1, model_input_2],
                                      outputs=model_output_2,
                                      name="test_model")

# ############################################################# #

# ######### TRY TO UTILIZE PREDICT WITH DUMMY INPUT  ########## #

dummy_input = [np.ones((1,160,160,3)), np.zeros((1,160,160,3))]
model.predict(x=dummy_input) # >>>>ERROR OCCURS HERE<<<<

# ############################################################# #

想法？？

提前致谢！！

如果您希望我提供其他任何内容，请告诉我。

【参考方案1】：

您可以尝试以下步骤：

将resize_like改成如下：

def resize_like(inputs):
    input_tensor, ref_tensor = inputs
    reshaped_tensor = tf.image.resize(images=input_tensor,
                              size=tf.shape(ref_tensor)[1:3],

    method=tf.image.ResizeMethod.NEAREST_NEIGHBOR,
                              preserve_aspect_ratio=False,
                              antialias=False,
                              name=None)
    return reshaped_tensor

然后，在Lambda 层：

model_output_2 = tf.keras.layers.Lambda(function=resize_like)([model_input_2, model_output_1])

【讨论】：

太棒了！谢谢！你认为坚持使用 lambda 层是更好的做法吗？还是要子类化并制作一个自定义层类，如原帖底部所示？

Lambda 图层非常适合快速原型制作，但我更喜欢为具有多个操作的任何内容编写自定义图层。在构建最终模型时，坚持使用自定义层。 Lambda 只会在以后引起问题。

你能解释一下为什么函数的第一行能解决问题吗？

Tensorflow 将层的第一个参数作为其输入。输入的类型在很大程度上与列表、元组、numpy 数组或张量无关。在这种情况下，我们将输入作为列表传递，并在函数中解包。

我可以请您@DarienSchettler 展示您的代码并实现自定义层吗？我有一个与你类似的问题，我不知道如何使用我的自定义层来解决它。