# Copyright or © or Copr. IETR/INSA Rennes (2019)
#
# Contributors :
# Eduardo Fernandes-Montesuma eduardo.fernandes-montesuma@insa-rennes.fr (2019)
# Florian Lemarchand florian.lemarchand@insa-rennes.fr (2019)
#
#
# OpenDenoising is a computer program whose purpose is to benchmark image
# restoration algorithms.
#
# This software is governed by the CeCILL-C license under French law and
# abiding by the rules of distribution of free software. You can use,
# modify and/ or redistribute the software under the terms of the CeCILL-C
# license as circulated by CEA, CNRS and INRIA at the following URL
# "http://www.cecill.info".
#
# As a counterpart to the access to the source code and rights to copy,
# modify and redistribute granted by the license, users are provided only
# with a limited warranty and the software's author, the holder of the
# economic rights, and the successive licensors have only limited
# liability.
#
# In this respect, the user's attention is drawn to the risks associated
# with loading, using, modifying and/or developing or reproducing the
# software by the user in light of its specific status of free software,
# that may mean that it is complicated to manipulate, and that also
# therefore means that it is reserved for developers and experienced
# professionals having in-depth computer knowledge. Users are therefore
# encouraged to load and test the software's suitability as regards their
# requirements in conditions enabling the security of their systems and/or
# data to be ensured and, more generally, to use and operate it in the
# same conditions as regards security.
#
# The fact that you are presently reading this means that you have had
# knowledge of the CeCILL-C license and that you accept its terms.
from abc import abstractmethod
from OpenDenoising.model import AbstractDenoiser
[docs]class AbstractDeepLearningModel(AbstractDenoiser):
"""Common interface for Deep Learning based image denoisers."""
[docs] def __init__(self, model_name="DeepLearningModel", logdir="./training_logs/", framework=None, return_diff=False):
"""Common interface for Deep Learning based image denoisers.
Attributes
----------
model
Object representing the Denoiser Model in the framework used.
logdir : str
String containing the path to the model log directory. Such directory will contain training information,
as well as model checkpoints.
train_info : dict
Dictionary containing the time spent on training, how much parameters the network has, and if it has been
trained.
framework : str
String containing the name of the chosen framework (e.g. Keras, Tensorflow, Pytorch).
return_diff : bool
If True, return the difference between predicted image, and image.
"""
self.model = None
self.logdir = logdir
self.train_info = None
self.framework = framework
self.return_diff = return_diff
super().__init__(model_name)
@abstractmethod
def charge_model(self):
pass
@abstractmethod
def train(self, train_generator, valid_generator=None):
pass
[docs] def __repr__(self):
return "Model name: {}, Framework: {}".format(self, self.framework)
[docs] @abstractmethod
def __call__(self, image):
pass
@abstractmethod
def __len__(self):
pass