Issue with shapes after saving and loading the model

[FrancoCuevas444]

Hi! I'm having the following issue after loading the model.

I save it as following:

...train loop...

def dummy_graph():
    x = np.ones((11, 6), dtype=np.float32)
    a = np.ones((10, 10), dtype=np.float32)
    e = np.ones((100, 1), dtype=np.float32)
    i = np.zeros((10), dtype=np.int64)
    return [x, a, e, i]

model(dummy_graph())
model.save("...dir...")

I then load the model as following:

model = tf.saved_model.load("...dir...")

If I then try to predict with the same dummy_graph() it works fine:

model(dummy_graph())

But when I try to predict using a graph of different size, for example a with shape (11,11), I get the following error:

def dummy_graph_2():
    x = np.ones((11, 6), dtype=np.float32)
    a = np.ones((11, 11), dtype=np.float32)
    e = np.ones((100, 1), dtype=np.float32)
    i = np.zeros((10), dtype=np.int64)
    return [x, a, e, i]

model(dummy_graph_2())

ERROR:

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
/tmp/ipykernel_1600/2299801205.py in <module>
----> 1 model(dummy_graph_2())

~/.cache/pypoetry/virtualenvs/app-XPlGwO1Q-py3.7/lib/python3.7/site-packages/tensorflow/python/saved_model/load.py in _call_attribute(instance, *args, **kwargs)
    666 
    667 def _call_attribute(instance, *args, **kwargs):
--> 668   return instance.__call__(*args, **kwargs)
    669 
    670 

~/.cache/pypoetry/virtualenvs/app-XPlGwO1Q-py3.7/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py in __call__(self, *args, **kwds)
    826     tracing_count = self.experimental_get_tracing_count()
    827     with trace.Trace(self._name) as tm:
--> 828       result = self._call(*args, **kwds)
    829       compiler = "xla" if self._experimental_compile else "nonXla"
    830       new_tracing_count = self.experimental_get_tracing_count()

~/.cache/pypoetry/virtualenvs/app-XPlGwO1Q-py3.7/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py in _call(self, *args, **kwds)
    860       # In this case we have not created variables on the first call. So we can
    861       # run the first trace but we should fail if variables are created.
--> 862       results = self._stateful_fn(*args, **kwds)
    863       if self._created_variables:
    864         raise ValueError("Creating variables on a non-first call to a function"

~/.cache/pypoetry/virtualenvs/app-XPlGwO1Q-py3.7/lib/python3.7/site-packages/tensorflow/python/eager/function.py in __call__(self, *args, **kwargs)
   2939     with self._lock:
   2940       (graph_function,
-> 2941        filtered_flat_args) = self._maybe_define_function(args, kwargs)
   2942     return graph_function._call_flat(
   2943         filtered_flat_args, captured_inputs=graph_function.captured_inputs)  # pylint: disable=protected-access

~/.cache/pypoetry/virtualenvs/app-XPlGwO1Q-py3.7/lib/python3.7/site-packages/tensorflow/python/eager/function.py in _maybe_define_function(self, args, kwargs)
   3359 
   3360           self._function_cache.missed.add(call_context_key)
-> 3361           graph_function = self._create_graph_function(args, kwargs)
   3362           self._function_cache.primary[cache_key] = graph_function
   3363 

~/.cache/pypoetry/virtualenvs/app-XPlGwO1Q-py3.7/lib/python3.7/site-packages/tensorflow/python/eager/function.py in _create_graph_function(self, args, kwargs, override_flat_arg_shapes)
   3204             arg_names=arg_names,
   3205             override_flat_arg_shapes=override_flat_arg_shapes,
-> 3206             capture_by_value=self._capture_by_value),
   3207         self._function_attributes,
   3208         function_spec=self.function_spec,

~/.cache/pypoetry/virtualenvs/app-XPlGwO1Q-py3.7/lib/python3.7/site-packages/tensorflow/python/framework/func_graph.py in func_graph_from_py_func(name, python_func, args, kwargs, signature, func_graph, autograph, autograph_options, add_control_dependencies, arg_names, op_return_value, collections, capture_by_value, override_flat_arg_shapes)
    988         _, original_func = tf_decorator.unwrap(python_func)
    989 
--> 990       func_outputs = python_func(*func_args, **func_kwargs)
    991 
    992       # invariant: `func_outputs` contains only Tensors, CompositeTensors,

~/.cache/pypoetry/virtualenvs/app-XPlGwO1Q-py3.7/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py in wrapped_fn(*args, **kwds)
    632             xla_context.Exit()
    633         else:
--> 634           out = weak_wrapped_fn().__wrapped__(*args, **kwds)
    635         return out
    636 

~/.cache/pypoetry/virtualenvs/app-XPlGwO1Q-py3.7/lib/python3.7/site-packages/tensorflow/python/saved_model/function_deserialization.py in restored_function_body(*args, **kwargs)
    271         .format(_pretty_format_positional(args), kwargs,
    272                 len(saved_function.concrete_functions),
--> 273                 "\n\n".join(signature_descriptions)))
    274 
    275   concrete_function_objects = []

ValueError: Could not find matching function to call loaded from the SavedModel. Got:
  Positional arguments (2 total):
    * [<tf.Tensor 'inputs:0' shape=(11, 6) dtype=float32>, <tf.Tensor 'inputs_1:0' shape=(11, 11) dtype=float32>, <tf.Tensor 'inputs_2:0' shape=(100, 1) dtype=float32>, <tf.Tensor 'inputs_3:0' shape=(10,) dtype=int64>]
    * False
  Keyword arguments: {}

Expected these arguments to match one of the following 4 option(s):

Option 1:
  Positional arguments (2 total):
    * [TensorSpec(shape=(None, 6), dtype=tf.float32, name='input_1'), TensorSpec(shape=(None, 10), dtype=tf.float32, name='input_2'), TensorSpec(shape=(None, 1), dtype=tf.float32, name='input_3'), TensorSpec(shape=(None,), dtype=tf.int64, name='input_4')]
    * False
  Keyword arguments: {}

Option 2:
  Positional arguments (2 total):
    * [TensorSpec(shape=(None, 6), dtype=tf.float32, name='input_1'), TensorSpec(shape=(None, 10), dtype=tf.float32, name='input_2'), TensorSpec(shape=(None, 1), dtype=tf.float32, name='input_3'), TensorSpec(shape=(None,), dtype=tf.int64, name='input_4')]
    * True
  Keyword arguments: {}

Option 3:
  Positional arguments (2 total):
    * [TensorSpec(shape=(None, 6), dtype=tf.float32, name='inputs/0'), TensorSpec(shape=(None, 10), dtype=tf.float32, name='inputs/1'), TensorSpec(shape=(None, 1), dtype=tf.float32, name='inputs/2'), TensorSpec(shape=(None,), dtype=tf.int64, name='inputs/3')]
    * False
  Keyword arguments: {}

Option 4:
  Positional arguments (2 total):
    * [TensorSpec(shape=(None, 6), dtype=tf.float32, name='inputs/0'), TensorSpec(shape=(None, 10), dtype=tf.float32, name='inputs/1'), TensorSpec(shape=(None, 1), dtype=tf.float32, name='inputs/2'), TensorSpec(shape=(None,), dtype=tf.int64, name='inputs/3')]
    * True
  Keyword arguments: {}

Whay I interpret from this message is that it is expecting an a with shape (None, 10) but I want this model to be able to predict on graphs of all sizes.

Can you help me find the cause of this problem? Thanks!

[danielegrattarola]

Hey,

the second dummy graph is not correct, because it has 10 nodes in the batch index i and still only 100 edges.
It should be:

def dummy_graph_2():
    x = np.ones((11, 6), dtype=np.float32)
    a = np.ones((11, 11), dtype=np.float32)
    e = np.ones((121, 1), dtype=np.float32)
    i = np.zeros((11), dtype=np.int64)
    return [x, a, e, i]

Anyway, saving the entire model is a bit broken at the moment and the recommended way is to save only the model weights and re-create the model using the constructor from scratch.
Sorry about that