I am trying the SavedModel format export

[famy040326]

With the code below, I was able to output the db_resnet50 model in SavedModel format. Thank you.

import tensorflow as tf
from doctr.models import db_resnet50
model = db_resnet50(pretrained=True, exportable=True)
input_t = tf.random.uniform(shape=[1, 1024, 1024, 3], maxval=1, dtype=tf.float32)
_ = model(input_t, training=False)
tf.saved_model.save(model, '. /doctr_SavedModel/')

I tried to output the model of crnn_vgg16_bn in SavedModel format with the following code in the same way.
However, I get an error. How can I solve this problem?

import tensorflow as tf
from doctr.models import crnn_vgg16_bn
model = crnn_vgg16_bn(pretrained=True, exportable=True)
input_t = tf.random.uniform(shape=[1, 32, 128, 3], maxval=1, dtype=tf.float32)
_ = model(input_t, training=False)
tf.saved_model.save(model, '. /doctr_SavedModel/')

Traceback (most recent call last):
File "", line 1, in
File "C:\Python_dev\env1\lib\site-packages\tensorflow\python\saved_model\save.py", line 1334, in save
save_and_return_nodes(obj, export_dir, signatures, options)
File "C:\Python_dev\env1\lib\site-packages\tensorflow\python\saved_model\save.py", line 1369, in save_and_return_nodes
_build_meta_graph(obj, signatures, options, meta_graph_def))
File "C:\Python_dev\env1\lib\site-packages\tensorflow\python\saved_model\save.py", line 1536, in _build_meta_graph
return _build_meta_graph_impl(obj, signatures, options, meta_graph_def)
File "C:\Python_dev\env1\lib\site-packages\tensorflow\python\saved_model\save.py", line 1477, in _build_meta_graph_impl
signatures = signature_serialization.find_function_to_export(
File "C:\Python_dev\env1\lib\site-packages\tensorflow\python\saved_model\signature_serialization.py", line 103, in find_function_to_export
for name, child in children:
File "C:\Python_dev\env1\lib\site-packages\tensorflow\python\saved_model\save.py", line 133, in list_children
super(_AugmentedGraphView, self).list_children(
File "C:\Python_dev\env1\lib\site-packages\tensorflow\python\training\tracking\graph_view.py", line 256, in list_children
in obj._trackable_children(save_type, **kwargs).items()]
File "C:\Python_dev\env1\lib\site-packages\tensorflow\python\training\tracking\base.py", line 1479, in _trackable_children
return self._get_legacy_saved_model_children(cache)
File "C:\Python_dev\env1\lib\site-packages\tensorflow\python\training\tracking\base.py", line 1490, in _get_legacy_saved_model_children
functions = self._list_functions_for_serialization(serialization_cache)
File "C:\Python_dev\env1\lib\site-packages\keras\engine\training.py", line 3081, in _list_functions_for_serialization
functions = super(
File "C:\Python_dev\env1\lib\site-packages\keras\engine\base_layer.py", line 3168, in _list_functions_for_serialization
return (self._trackable_saved_model_saver
File "C:\Python_dev\env1\lib\site-packages\keras\saving\saved_model\base_serialization.py", line 93, in list_functions_for_serialization
fns = self.functions_to_serialize(serialization_cache)
File "C:\Python_dev\env1\lib\site-packages\keras\saving\saved_model\layer_serialization.py", line 72, in functions_to_serialize
return (self._get_serialized_attributes(
File "C:\Python_dev\env1\lib\site-packages\keras\saving\saved_model\layer_serialization.py", line 88, in _get_serialized_attributes
object_dict, function_dict = self._get_serialized_attributes_internal(
File "C:\Python_dev\env1\lib\site-packages\keras\saving\saved_model\model_serialization.py", line 56, in _get_serialized_attributes_internal
super(ModelSavedModelSaver, self)._get_serialized_attributes_internal(
File "C:\Python_dev\env1\lib\site-packages\keras\saving\saved_model\layer_serialization.py", line 98, in _get_serialized_attributes_internal
functions = save_impl.wrap_layer_functions(self.obj, serialization_cache)
File "C:\Python_dev\env1\lib\site-packages\keras\saving\saved_model\save_impl.py", line 197, in wrap_layer_functions
fn.get_concrete_function()
File "C:\Users\Y.FUJIZONO-BSW\AppData\Local\Programs\Python\Python39\lib\contextlib.py", line 126, in exit
next(self.gen)
File "C:\Python_dev\env1\lib\site-packages\keras\saving\saved_model\save_impl.py", line 359, in tracing_scope
fn.get_concrete_function(*args, **kwargs)
File "C:\Python_dev\env1\lib\site-packages\tensorflow\python\eager\def_function.py", line 1264, in get_concrete_function
concrete = self._get_concrete_function_garbage_collected(*args, **kwargs)
File "C:\Python_dev\env1\lib\site-packages\tensorflow\python\eager\def_function.py", line 1255, in _get_concrete_function_garbage_collected
concrete = self._stateful_fn._get_concrete_function_garbage_collected( # pylint: disable=protected-access
File "C:\Python_dev\env1\lib\site-packages\tensorflow\python\eager\function.py", line 3036, in _get_concrete_function_garbage_collected
graph_function, _ = self._maybe_define_function(args, kwargs)
File "C:\Python_dev\env1\lib\site-packages\tensorflow\python\eager\function.py", line 3292, in _maybe_define_function
graph_function = self._create_graph_function(args, kwargs)
File "C:\Python_dev\env1\lib\site-packages\tensorflow\python\eager\function.py", line 3130, in _create_graph_function
func_graph_module.func_graph_from_py_func(
File "C:\Python_dev\env1\lib\site-packages\tensorflow\python\framework\func_graph.py", line 1161, in func_graph_from_py_func
func_outputs = python_func(*func_args, **func_kwargs)
File "C:\Python_dev\env1\lib\site-packages\tensorflow\python\eager\def_function.py", line 677, in wrapped_fn
out = weak_wrapped_fn().wrapped(*args, **kwds)
File "C:\Python_dev\env1\lib\site-packages\keras\saving\saved_model\save_impl.py", line 572, in wrapper
ret = method(*args, **kwargs)
File "C:\Python_dev\env1\lib\site-packages\keras\saving\saved_model\utils.py", line 168, in wrap_with_training_arg
return control_flow_util.smart_cond(
File "C:\Python_dev\env1\lib\site-packages\keras\utils\control_flow_util.py", line 105, in smart_cond
return tf.internal.smart_cond.smart_cond(
File "C:\Python_dev\env1\lib\site-packages\tensorflow\python\framework\smart_cond.py", line 53, in smart_cond
return true_fn()
File "C:\Python_dev\env1\lib\site-packages\keras\saving\saved_model\utils.py", line 169, in
training, lambda: replace_training_and_call(True),
File "C:\Python_dev\env1\lib\site-packages\keras\saving\saved_model\utils.py", line 166, in replace_training_and_call
return wrapped_call(*args, **kwargs)
File "C:\Python_dev\env1\lib\site-packages\keras\saving\saved_model\save_impl.py", line 652, in call
return call_and_return_conditional_losses(inputs, *args, **kwargs)[0]
return self.wrapped_call(*args, **kwargs)
return self.wrapped_call(*args, **kwargs)
File "C:\Python_dev\env1\lib\site-packages\tensorflow\python\util\traceback_utils.py", line 153, in error_handler
raise e.with_traceback(filtered_tb) from None
File "C:\Python_dev\env1\lib\site-packages\keras\saving\saved_model\save_impl.py", line 572, in wrapper
ret = method(*args, **kwargs)
File "C:\Python_dev\env1\lib\site-packages\keras\saving\saved_model\utils.py", line 168, in wrap_with_training_arg
return control_flow_util.smart_cond(
File "C:\Python_dev\env1\lib\site-packages\keras\saving\saved_model\utils.py", line 169, in
training, lambda: replace_training_and_call(True),
File "C:\Python_dev\env1\lib\site-packages\keras\saving\saved_model\utils.py", line 166, in replace_training_and_call
return wrapped_call(*args, **kwargs)
File "C:\Python_dev\env1\lib\site-packages\keras\saving\saved_model\save_impl.py", line 634, in call_and_return_conditional_losses
call_output = layer_call(*args, **kwargs)
File "C:\Python_dev\env1\doctr\doctr\models\recognition\crnn\tensorflow.py", line 195, in call
raise ValueError("Need to provide labels during training")
ValueError: Need to provide labels during training

[felixdittrich92]

Hi @famy040326 👋,
after a short test anything while calling save sets training to True (then it raises the error in our code)

False <-- our call
False <-- our call
False <-- now tf interanl calls
False
True <-- raises
False
True
True
False
True
False
True
False
True

uncomment in our code will solve your problem (not beauty):

doctr/doctr/models/recognition/crnn/tensorflow.py

Line 195 in 3d7c812

raise ValueError("Need to provide labels during training")

A bit weird with the Onnx export i haven't faiced this issue and the pipe is also keras model -> saved model -> onnx 🤔

[famy040326]

Thanks for getting back to me.
I will try to respond with an uncomment.

My question is, can you tell me the intent of the following code as it is not clear to me?

if kwargs.get("training", False) and target is None:
raise ValueError("Need to provide labels during training")

[felixdittrich92]

Yeah we have to ensure that if the model in training mode there are targets provided while training as ground truth data to compute the loss.
While inference we pass only "images" into the model (training is set to False) to predict "words"