CODEWITHSUNDEEP
pythontensorflowkerasdeep-learningobject-detection-api
golden96371
golden96371

Reputation: 410

Keras failed to load SavedModel: TypeError 'module' object is not callable

I trained an SSD MobileNet v2 network using the TensorFlow Object Detection API with TensorFlow 2 and then converted the trained model into a SavedModel. Now I need to convert the SavedModel to a FrozenGraph in order to make the model compatible with external libraries like OpenCV. I use this example for conversion and I cannot even load the Keras model.

from keras.models import load_model
model = load_model("training/model/saved_model")

Calling load_model() produces an exception:

Exception has occurred: TypeError
'module' object is not callable

Full traceback:
Traceback (most recent call last):
  File "path\to\tensorflow\python\util\dispatch.py", line 201, in wrapper
    return target(*args, **kwargs)
TypeError: 'str' object is not callable

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "path\to\saved_model_to_frozen_graph.py", line 17, in <module>
    model = load_model("saved_model")
  File "path\to\tensorflow\python\keras\saving\save.py", line 187, in load_model
    return saved_model_load.load(filepath, compile, options)
  File "path\to\tensorflow\python\keras\saving\saved_model\load.py", line 121, in load
    path, options=options, loader_cls=KerasObjectLoader)
  File "path\to\tensorflow\python\saved_model\load.py", line 633, in load_internal
    ckpt_options)
  File "path\to\tensorflow\python\keras\saving\saved_model\load.py", line 194, in __init__
    super(KerasObjectLoader, self).__init__(*args, **kwargs)
  File "path\to\tensorflow\python\saved_model\load.py", line 130, in __init__
    self._load_all()
  File "path\to\tensorflow\python\keras\saving\saved_model\load.py", line 221, in _load_all
    self._finalize_objects()
  File "path\to\tensorflow\python\keras\saving\saved_model\load.py", line 530, in _finalize_objects
    self._reconstruct_all_models()
  File "path\to\tensorflow\python\keras\saving\saved_model\load.py", line 548, in _reconstruct_all_models
    self._reconstruct_model(model_id, model, layers)
  File "path\to\tensorflow\python\keras\saving\saved_model\load.py", line 589, in _reconstruct_model
    config, created_layers={layer.name: layer for layer in layers})
  File "path\to\tensorflow\python\keras\engine\functional.py", line 1215, in reconstruct_from_config
    process_node(layer, node_data)
  File "path\to\tensorflow\python\keras\engine\functional.py", line 1163, in process_node
    output_tensors = layer(input_tensors, **kwargs)
  File "path\to\tensorflow\python\keras\engine\base_layer.py", line 926, in __call__
    input_list)
  File "path\to\tensorflow\python\keras\engine\base_layer.py", line 1117, in _functional_construction_call
    outputs = call_fn(cast_inputs, *args, **kwargs)
  File "path\to\tensorflow\python\keras\layers\core.py", line 903, in call
    result = self.function(inputs, **kwargs)
  File "path\to\tensorflow\python\util\dispatch.py", line 205, in wrapper
    result = dispatch(wrapper, args, kwargs)
TypeError: 'module' object is not callable

type(layer) = <class 'tensorflow.python.keras.layers.core.Lambda'>

Anyone knows how to fix that?

Additional information:

Windows 10, Python 3.7, TensorFlow 2.3.1, Keras 2.4.3

# Train the network
python model_main_tf2.py --model_dir="training" --pipeline_config_path="training/pipeline.config"
# Export the network
python exporter_main_v2.py --input_type=image_tensor --pipeline_config_path="training/pipeline.config" --trained_checkpoint_dir="training" --output_directory="training/model"

Folder structure:

training/
-> label_map.pbtxt
-> pipeline.config
-> model
 . -> checkpoint
 . -> saved_model
 .  . -> assets
 .  . -> variables
 .  . -> saved_model.pb
-> checkpoint
-> ckpt.x.data-00000-of-00001
-> ckpt.x.index

Upvotes: 3

Views: 3361

Answers (2)

user15583898
user15583898

Reputation: 1

The reason might be the lambda layer in your model.

There are some issues in Keras that may raise TypeError(str is not callable or module is not callable) while loading a model with lambda layer.

To solve the issue, not save the model with model.save. Instead, save the model weights and recreate the model architecture using the code,then load weights.

Example：

model.save_weights('model_weights.h5')

or

ModelCheckPoint(file,save_weights_only=True)#this will call save_weights

After create the model,then:

model.load_weights('model_weights.h5')

Note: save the model architecture as a JSON file can't solve the error

Upvotes: 0

golden96371
golden96371

Reputation: 410

I have found a way to convert the model without Keras, which is working for me. https://github.com/opencv/opencv/issues/16879

import tensorflow as tf
from tensorflow.python.framework.convert_to_constants import convert_variables_to_constants_v2

loaded = tf.saved_model.load('training/model/saved_model')
infer = loaded.signatures['serving_default']

f = tf.function(infer).get_concrete_function(input_tensor=tf.TensorSpec(shape=[1, None, None, 3], dtype=tf.uint8))
f2 = convert_variables_to_constants_v2(f)
graph_def = f2.graph.as_graph_def()

# Export frozen graph
with tf.io.gfile.GFile('frozen_graph.pb', 'wb') as f:
   f.write(graph_def.SerializeToString())

Upvotes: 3

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