tensorflow: AttributeError: 'Tensor' object has no attribute '_in_graph_mode'

I am having an error: ‘Tensor’ object has no attribute ‘_in_graph_mode’. I’ve debugged the code, and I think it’s in this GradientTape function, but I don’t know why. If anyone knows, please help me! 😃

System information

• TensorFlow version: 2.0 - ‘2.2.0-dev20200407’
• OS Platform and Distribution: Linux Mint
• Python version: Python 3.7.4

Describe the current behavior I am trying to minimize a function using opt = tf.keras.optimizers.Adam() and I am getting a TypeError when I apply opt.apply_gradients.

Standalone code to reproduce the issue

def explain(
      self,
      validation_data,
      model,
      class_index,
      layer_name=None,
      colormap=cv2.COLORMAP_VIRIDIS,
      image_weight=0.7,
      _grid=True
  ):


# Returns: numpy.ndarray: Grid of all the inverted image or 4D array (batch_size, height, width, channels)
     
      tf.executing_eagerly()
      images, _ = validation_data

      if layer_name is None:
          layer_name = self.infer_target_layer(model)
      
      inverted_image = InvertedImage.get_optimize_image(
          images, model, class_index, layer_name
      )

      if _grid:
          return grid_display(inverted_image)
      else:
          return inverted_image

@staticmethod
def infer_target_layer(model):
 
     # Returns: str: Name of the target layer

      for layer in reversed(model.layers):
          # Select closest 4D layer to the end of the network.
          if len(layer.output_shape) == 4 and layer.name.count('conv') > 0:
              return layer.name

      raise ValueError(
          "Model does not seem to contain 4D layer. Inverted image cannot be applied."
      )

@tf.function
def get_optimize_image(images, model, class_index, layer_name):
 
      grad_model = tf.keras.models.Model(
          [model.inputs], [model.get_layer(layer_name).output]
      )

      opt = tf.keras.optimizers.SGD(learning_rate=1-4, momentum=0.9)
      dtype = model.get_layer(layer_name).output.dtype
      tensor_image = tf.convert_to_tensor(images)

      opt_img = tf.Variable(1e-1 * tf.random.normal((tensor_image.shape[0], tensor_image.shape[1], tensor_image.shape[2], tensor_image.shape[3])), trainable=True)

      steps = 50
      for i in range(steps):
           with tf.GradientTape() as tape:
              
              inverted_feature = tf.cast(opt_img, dtype)
              content_feature = tf.cast(images, dtype)
                  
              conv_inverted_outputs = grad_model(inverted_feature)
              conv_content_outputs = grad_model(content_feature)
          
              loss = InvertedImage.get_loss(conv_content_outputs, conv_inverted_outputs, content_feature, inverted_feature)
              #print("Initial loss: {:.3f}".format(loss))

          grad = tape.gradient(loss, [conv_inverted_outputs, conv_content_outputs])
          print(grad)
          processed_grads = [g for g in grad]
          opt.apply_gradients(zip(processed_grads, [conv_inverted_outputs, conv_content_outputs]))

return opt_img

Loss function

def get_loss(conv_content_outputs, conv_inverted_outputs, content_feature, inverted_feature):
        euclidian = tf.norm(conv_content_outputs - conv_inverted_outputs, ord='euclidean') / tf.norm(conv_content_outputs, ord='euclidean')
        reg_alpha = 1e-7 * tf.math.reduce_sum(tf.norm(inverted_feature, ord=6))
        total_variation = 1e-8 * tf.math.reduce_sum(tf.image.total_variation(content_feature+inverted_feature))

        return euclidian + reg_alpha + total_variation 

Traceback

Traceback (most recent call last):
  File "/usr/local/lib/python3.7/runpy.py", line 193, in _run_module_as_main
    "__main__", mod_spec)
  File "/usr/local/lib/python3.7/runpy.py", line 85, in _run_code
    exec(code, run_globals)
  File "/home/helena/.vscode/extensions/ms-python.python-2020.2.64397/pythonFiles/lib/python/new_ptvsd/wheels/ptvsd/__main__.py", line 45, in <module>
    cli.main()
  File "/home/helena/.vscode/extensions/ms-python.python-2020.2.64397/pythonFiles/lib/python/new_ptvsd/wheels/ptvsd/../ptvsd/server/cli.py", line 361, in main
    run()
  File "/home/helena/.vscode/extensions/ms-python.python-2020.2.64397/pythonFiles/lib/python/new_ptvsd/wheels/ptvsd/../ptvsd/server/cli.py", line 203, in run_file
    runpy.run_path(options.target, run_name="__main__")
  File "/usr/local/lib/python3.7/runpy.py", line 263, in run_path
    pkg_name=pkg_name, script_name=fname)
  File "/usr/local/lib/python3.7/runpy.py", line 96, in _run_module_code
    mod_name, mod_spec, pkg_name, script_name)
  File "/usr/local/lib/python3.7/runpy.py", line 85, in _run_code
    exec(code, run_globals)
  File "/home/helena/Documents/LAR_Celesc/lar-computer-vision/objdet-api/test_inverted_image.py", line 20, in <module>
    data, model, class_index=tabby_cat_class_index, layer_name="block5_conv3"
  File "/home/helena/Documents/LAR_Celesc/lar-computer-vision/objdet-api/tf_explain/core/inverted_image.py", line 54, in explain
    images, model, class_index, layer_name
  File "/home/helena/Documents/LAR_Celesc/larenv/lib/python3.7/site-packages/tensorflow_core/python/eager/def_function.py", line 568, in __call__
    result = self._call(*args, **kwds)
  File "/home/helena/Documents/LAR_Celesc/larenv/lib/python3.7/site-packages/tensorflow_core/python/eager/def_function.py", line 615, in _call
    self._initialize(args, kwds, add_initializers_to=initializers)
  File "/home/helena/Documents/LAR_Celesc/larenv/lib/python3.7/site-packages/tensorflow_core/python/eager/def_function.py", line 497, in _initialize
    *args, **kwds))
  File "/home/helena/Documents/LAR_Celesc/larenv/lib/python3.7/site-packages/tensorflow_core/python/eager/function.py", line 2389, in _get_concrete_function_internal_garbage_collected
    graph_function, _, _ = self._maybe_define_function(args, kwargs)
  File "/home/helena/Documents/LAR_Celesc/larenv/lib/python3.7/site-packages/tensorflow_core/python/eager/function.py", line 2703, in _maybe_define_function
    graph_function = self._create_graph_function(args, kwargs)
  File "/home/helena/Documents/LAR_Celesc/larenv/lib/python3.7/site-packages/tensorflow_core/python/eager/function.py", line 2593, in _create_graph_function
    capture_by_value=self._capture_by_value),
  File "/home/helena/Documents/LAR_Celesc/larenv/lib/python3.7/site-packages/tensorflow_core/python/framework/func_graph.py", line 978, in func_graph_from_py_func
    func_outputs = python_func(*func_args, **func_kwargs)
  File "/home/helena/Documents/LAR_Celesc/larenv/lib/python3.7/site-packages/tensorflow_core/python/eager/def_function.py", line 439, in wrapped_fn
    return weak_wrapped_fn().__wrapped__(*args, **kwds)
  File "/home/helena/Documents/LAR_Celesc/larenv/lib/python3.7/site-packages/tensorflow_core/python/framework/func_graph.py", line 968, in wrapper
    raise e.ag_error_metadata.to_exception(e)
AttributeError: in converted code:
    /home/helena/Documents/LAR_Celesc/lar-computer-vision/objdet-api/tf_explain/core/inverted_image.py:125 get_optimize_image  *
        opt.apply_gradients(grads_and_vars)
    /home/helena/Documents/LAR_Celesc/larenv/lib/python3.7/site-packages/tensorflow_core/python/keras/optimizer_v2/optimizer_v2.py:434 apply_gradients
        self._create_slots(var_list)
    /home/helena/Documents/LAR_Celesc/larenv/lib/python3.7/site-packages/tensorflow_core/python/keras/optimizer_v2/gradient_descent.py:100 _create_slots
        self.add_slot(var, "momentum")
    /home/helena/Documents/LAR_Celesc/larenv/lib/python3.7/site-packages/tensorflow_core/python/keras/optimizer_v2/optimizer_v2.py:574 add_slot
        var_key = _var_key(var)
    /home/helena/Documents/LAR_Celesc/larenv/lib/python3.7/site-packages/tensorflow_core/python/keras/optimizer_v2/optimizer_v2.py:1065 _var_key
        if var._in_graph_mode:

    AttributeError: 'Tensor' object has no attribute '_in_graph_mode'