Cannot load saved model
julibeg opened this issue · 1 comments
julibeg commented
Hi again!
I created a conda environment with the file in md_cnn/model_training/environment_reqs_MDCNN.txt, but was not able to load the model saved in saved_models/MDCNN_saved_model.tar.gz (after unpacking the tar archive of course). This is the error I got:
Click to expand error message
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TypeError Traceback (most recent call last)
~/git/MTB-CNN/saved_models/try-loading-mdnn-model.py in <module>
3 "masked_multi_weighted_bce": tb_cnn.masked_multi_weighted_bce,
4 }
----> 5 m = keras.models.load_model("MDCNN_saved_model", custom_objects=custom_objects)
~/miniconda3/envs/mdcnn/lib/python3.6/site-packages/tensorflow/python/keras/saving/save.py in load_model(filepath, custom_objects, compile)
188 if isinstance(filepath, six.string_types):
189 loader_impl.parse_saved_model(filepath)
--> 190 return saved_model_load.load(filepath, compile)
191
192 raise IOError(
~/miniconda3/envs/mdcnn/lib/python3.6/site-packages/tensorflow/python/keras/saving/saved_model/load.py in load(path, compile)
114 # TODO(kathywu): Add saving/loading of optimizer, compiled losses and metrics.
115 # TODO(kathywu): Add code to load from objects that contain all endpoints
--> 116 model = tf_load.load_internal(path, loader_cls=KerasObjectLoader)
117
118 # pylint: disable=protected-access
~/miniconda3/envs/mdcnn/lib/python3.6/site-packages/tensorflow/python/saved_model/load.py in load_internal(export_dir, tags, loader_cls)
602 loader = loader_cls(object_graph_proto,
603 saved_model_proto,
--> 604 export_dir)
605 root = loader.get(0)
606 if isinstance(loader, Loader):
~/miniconda3/envs/mdcnn/lib/python3.6/site-packages/tensorflow/python/keras/saving/saved_model/load.py in __init__(self, *args, **kwargs)
186 self._models_to_reconstruct = []
187
--> 188 super(KerasObjectLoader, self).__init__(*args, **kwargs)
189
190 # Now that the node object has been fully loaded, and the checkpoint has
~/miniconda3/envs/mdcnn/lib/python3.6/site-packages/tensorflow/python/saved_model/load.py in __init__(self, object_graph_proto, saved_model_proto, export_dir)
121 self._concrete_functions[name] = _WrapperFunction(concrete_function)
122
--> 123 self._load_all()
124 self._restore_checkpoint()
125
~/miniconda3/envs/mdcnn/lib/python3.6/site-packages/tensorflow/python/keras/saving/saved_model/load.py in _load_all(self)
207 # loaded from config may create variables / other objects during
208 # initialization. These are recorded in `_nodes_recreated_from_config`.
--> 209 self._layer_nodes = self._load_layers()
210
211 # Load all other nodes and functions.
~/miniconda3/envs/mdcnn/lib/python3.6/site-packages/tensorflow/python/keras/saving/saved_model/load.py in _load_layers(self)
307 continue
308
--> 309 layers[node_id] = self._load_layer(proto.user_object, node_id)
310
311 for node_id, proto in metric_list:
~/miniconda3/envs/mdcnn/lib/python3.6/site-packages/tensorflow/python/keras/saving/saved_model/load.py in _load_layer(self, proto, node_id)
333 # Detect whether this object can be revived from the config. If not, then
334 # revive from the SavedModel instead.
--> 335 obj, setter = self._revive_from_config(proto.identifier, metadata, node_id)
336 if obj is None:
337 obj, setter = revive_custom_object(proto.identifier, metadata)
~/miniconda3/envs/mdcnn/lib/python3.6/site-packages/tensorflow/python/keras/saving/saved_model/load.py in _revive_from_config(self, identifier, metadata, node_id)
351 obj = (
352 self._revive_graph_network(metadata, node_id) or
--> 353 self._revive_layer_from_config(metadata, node_id))
354
355 if obj is None:
~/miniconda3/envs/mdcnn/lib/python3.6/site-packages/tensorflow/python/keras/saving/saved_model/load.py in _revive_layer_from_config(self, metadata, node_id)
406 try:
407 obj = layers_module.deserialize(
--> 408 generic_utils.serialize_keras_class_and_config(class_name, config))
409 except ValueError:
410 return None
~/miniconda3/envs/mdcnn/lib/python3.6/site-packages/tensorflow/python/keras/layers/serialization.py in deserialize(config, custom_objects)
107 module_objects=globs,
108 custom_objects=custom_objects,
--> 109 printable_module_name='layer')
~/miniconda3/envs/mdcnn/lib/python3.6/site-packages/tensorflow/python/keras/utils/generic_utils.py in deserialize_keras_object(identifier, module_objects, custom_objects, printable_module_name)
373 list(custom_objects.items())))
374 with CustomObjectScope(custom_objects):
--> 375 return cls.from_config(cls_config)
376 else:
377 # Then `cls` may be a function returning a class.
~/miniconda3/envs/mdcnn/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py in from_config(cls, config)
653 A layer instance.
654 """
--> 655 return cls(**config)
656
657 def compute_output_shape(self, input_shape):
~/miniconda3/envs/mdcnn/lib/python3.6/site-packages/tensorflow/python/keras/layers/convolutional.py in __init__(self, filters, kernel_size, strides, padding, data_format, dilation_rate, activation, use_bias, kernel_initializer, bias_initializer, kernel_regularizer, bias_regularizer, activity_regularizer, kernel_constraint, bias_constraint, **kwargs)
597 kernel_constraint=constraints.get(kernel_constraint),
598 bias_constraint=constraints.get(bias_constraint),
--> 599 **kwargs)
600
601
~/miniconda3/envs/mdcnn/lib/python3.6/site-packages/tensorflow/python/keras/layers/convolutional.py in __init__(self, rank, filters, kernel_size, strides, padding, data_format, dilation_rate, activation, use_bias, kernel_initializer, bias_initializer, kernel_regularizer, bias_regularizer, activity_regularizer, kernel_constraint, bias_constraint, trainable, name, **kwargs)
123 name=name,
124 activity_regularizer=regularizers.get(activity_regularizer),
--> 125 **kwargs)
126 self.rank = rank
127 if filters is not None and not isinstance(filters, int):
~/miniconda3/envs/mdcnn/lib/python3.6/site-packages/tensorflow/python/training/tracking/base.py in _method_wrapper(self, *args, **kwargs)
454 self._self_setattr_tracking = False # pylint: disable=protected-access
455 try:
--> 456 result = method(self, *args, **kwargs)
457 finally:
458 self._self_setattr_tracking = previous_value # pylint: disable=protected-access
~/miniconda3/envs/mdcnn/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py in __init__(self, trainable, name, dtype, dynamic, **kwargs)
292 }
293 # Validate optional keyword arguments.
--> 294 generic_utils.validate_kwargs(kwargs, allowed_kwargs)
295
296 # Mutable properties
~/miniconda3/envs/mdcnn/lib/python3.6/site-packages/tensorflow/python/keras/utils/generic_utils.py in validate_kwargs(kwargs, allowed_kwargs, error_message)
790 for kwarg in kwargs:
791 if kwarg not in allowed_kwargs:
--> 792 raise TypeError(error_message, kwarg)
793
794
TypeError: ('Keyword argument not understood:', 'groups')
I investigated a little and turns out that this is probably caused by a mismatch in TensorFlow or Keras versions with the environment that was used to save the model (see here for a related SO question). I had to create a new environment with Python 3.8, installing tensorflow=2.3.0, I was able to load the model.