我在尝试实现一篇最近的论文。实现的一部分涉及从tf 1.14升级到tf 2.1.0。代码在tf 1.14下可以正常工作，但在tf 2.1.0下不再工作。
注意：如果我禁用即时执行tf.compat.v1.disable_eager_execution()，代码可以按预期工作。
这是解决方案吗？我之前用TF 2.x制作过很多模型，从未需要禁用即时执行来实现正常功能。
我已经将问题简化为一个非常简短的示例，展示了发生的情况。
链接和代码在前，详细的错误消息在后
链接到Gist — https://gist.github.com/darien-schettler/fd5b25626e9eb5b1330cce670bf9cc17
代码
# version 2.1.0import tensorflow as tf# version 1.18.1import 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 ############ ## INPUTSmodel_input_1 = tf.keras.layers.Input(shape=(160,160,3))model_input_2 = tf.keras.layers.Input(shape=(160,160,3))# OUTPUTSmodel_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)# MODELmodel = 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<<<<# ############################################################# #完整错误
>>> 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 passeda "Graph" tensor. It is possible to have Graph tensorsleak out of the function building context by including atf.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 * 2The graph tensor has name: conv_name_base_1/Identity:0During 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>]我想到的一个潜在解决方案是用自定义层替换Lambda层…这似乎也可以解决问题。不过，我不确定这方面的最佳实践。代码如下。
# version 2.1.0import tensorflow as tf# version 1.18.1import 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 ############ ## INPUTSmodel_input_1 = tf.keras.layers.Input(shape=(160,160,3))model_input_2 = tf.keras.layers.Input(shape=(160,160,3))# OUTPUTSmodel_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])# MODELmodel = 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<<<<# ############################################################# #有什么想法吗？
提前谢谢了！
如果您需要我提供其他任何东西，请告诉我。
回答：
