I'm training the PointNet3D object classification model with my own dataset following the Tutorial here in Keras: https://keras.io/examples/vision/pointnet/#point-cloud-classification-with-pointnet
Now for the training part, I've been able to do everything just fine but after training I'm facing issues loading the trained model.
The main issue I think is with this part below, OrthogonalRegularizer class object might not be registered properly when I'm saving the model:
@keras.saving.register_keras_serializable('OrthogonalRegularizer')
class OrthogonalRegularizer(keras.regularizers.Regularizer):
def __init__(self, num_features, **kwargs):
super(OrthogonalRegularizer, self).__init__(**kwargs)
self.num_features = num_features
self.l2reg = 0.001
self.eye = tf.eye(num_features)
def __call__(self, x):
x = tf.reshape(x, (-1, self.num_features, self.num_features))
xxt = tf.tensordot(x, x, axes=(2, 2))
xxt = tf.reshape(xxt, (-1, self.num_features, self.num_features))
return tf.math.reduce_sum(self.l2reg * tf.square(xxt - self.eye))
def get_config(self):
config = {}
config.update({"num_features": self.num_features, "l2reg": self.l2reg, "eye": self.eye})
return config
def tnet(inputs, num_features):
# Initialise bias as the identity matrix
bias = keras.initializers.Constant(np.eye(num_features).flatten())
reg = OrthogonalRegularizer(num_features)
x = conv_bn(inputs, 32)
x = conv_bn(x, 64)
x = conv_bn(x, 512)
x = layers.GlobalMaxPooling1D()(x)
x = dense_bn(x, 256)
x = dense_bn(x, 128)
x = layers.Dense(
num_features * num_features,
kernel_initializer="zeros",
bias_initializer=bias,
activity_regularizer=reg,
)(x)
feat_T = layers.Reshape((num_features, num_features))(x)
# Apply affine transformation to input features
return layers.Dot(axes=(2, 1))([inputs, feat_T])
After training when I try to load the model by the following, I see the following error:
model.save('my_model.h5')
model = keras.models.load_model('my_model.h5', custom_objects={'OrthogonalRegularizer': OrthogonalRegularizer})
The error message:
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-18-05f700f433a8> in <cell line: 2>()
1 model.save('my_model.h5')
----> 2 model = keras.models.load_model('my_model.h5', custom_objects={'OrthogonalRegularizer': OrthogonalRegularizer})
2 frames
/usr/local/lib/python3.10/dist-packages/keras/src/saving/saving_api.py in load_model(filepath, custom_objects, compile, safe_mode, **kwargs)
260
261 # Legacy case.
--> 262 return legacy_sm_saving_lib.load_model(
263 filepath, custom_objects=custom_objects, compile=compile, **kwargs
264 )
/usr/local/lib/python3.10/dist-packages/keras/src/utils/traceback_utils.py in error_handler(*args, **kwargs)
68 # To get the full stack trace, call:
69 # `tf.debugging.disable_traceback_filtering()`
---> 70 raise e.with_traceback(filtered_tb) from None
71 finally:
72 del filtered_tb
/usr/local/lib/python3.10/dist-packages/keras/src/engine/base_layer.py in from_config(cls, config)
868 return cls(**config)
869 except Exception as e:
--> 870 raise TypeError(
871 f"Error when deserializing class '{cls.__name__}' using "
872 f"config={config}.\n\nException encountered: {e}"
TypeError: Error when deserializing class 'Dense' using config={'name': 'dense_2',
'trainable': True, 'dtype': 'float32', 'units': 9, 'activation': 'linear', 'use_bias':
True, 'kernel_initializer': {'module': 'keras.initializers', 'class_name': 'Zeros',
'config': {}, 'registered_name': None}, 'bias_initializer': {'module':
'keras.initializers', 'class_name': 'Constant', 'config': {'value': {'class_name':
'__numpy__', 'config': {'value': [1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0], 'dtype':
'float64'}}}, 'registered_name': None}, 'kernel_regularizer': None, 'bias_regularizer':
None, 'activity_regularizer': {'module': None, 'class_name': 'OrthogonalRegularizer',
'config': {'num_features': 3, 'l2reg': 0.001, 'eye': {'class_name': '__tensor__',
'config': {'value': [[1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, 1.0]], 'dtype':
'float32'}}}, 'registered_name': 'OrthogonalRegularizer>OrthogonalRegularizer'},
'kernel_constraint': None, 'bias_constraint': None}.
Exception encountered: object.__init__() takes exactly one argument (the instance to initialize)
What I understand so far is that while saving I'm not able to save the OrthogonalRegularizer class object properly. Please let me know what I'm doing wrong.
The minimal version of the code is uploaded here in this collab notebook: https://colab.research.google.com/drive/1akpfoOBVAWThsZl7moYywuZIuXt_vWCU?usp=sharing
One possible similar question is this: Load customized regularizer in Keras
You do not need to call
super(OrthogonalRegularizer, self).__init__(**kwargs)
because the constructor in keras.regularizers.Regularizer is not defined.
Also there is no need to store the unserializable tensor eye: self.eye. Moreover, it would be better to create this tensor closer to where it is used.
The modified code should look like:
@keras.saving.register_keras_serializable('OrthogonalRegularizer')
class OrthogonalRegularizer(keras.regularizers.Regularizer):
def __init__(self, num_features, **kwargs):
self.num_features = num_features
self.l2reg = 0.001
def call(self, x):
x = tf.reshape(x, (-1, self.num_features, self.num_features))
xxt = tf.tensordot(x, x, axes=(2, 2))
xxt = tf.reshape(xxt, (-1, self.num_features, self.num_features))
eye = tf.eye(self.num_features)
return tf.math.reduce_sum(self.l2reg * tf.square(xxt - eye))
def get_config(self):
return {"num_features": self.num_features, "l2reg": self.l2reg}
Also it is better to use newer type of save and load model:
model.save('my_model')
model = keras.models.load_model('my_model', custom_objects={'OrthogonalRegularizer': OrthogonalRegularizer})