minor changes

hparams.py

-# Home page: https://git-lium.univ-lemans.fr/tahon/dncnn-tensorflow-holography
-#
-# Adapted from https://github.com/wbhu/DnCNN-tensorflow by Hu Wenbo
-#
-# DnCnn4Holo is free software: you can redistribute it and/or modify
-# it under the terms of the GNU LLesser General Public License as
-# published by the Free Software Foundation, either version 3 of the License,
-# or (at your option) any later version.
-#
-# DnCnn4Holo is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
-# GNU Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public License
-# along with DnCnn4Holo.  If not, see <http://www.gnu.org/licenses/>.
-
-"""
-Copyright 2019-2020 Marie Tahon
-
-    :mod:`hparams.py` list of modifiable parameters for generating patches and training model
-
-"""
-
-import tensorflow as tf
-
-__license__ = "LGPL"
-__author__ = "Marie Tahon"
-__copyright__ = "Copyright 2019-2020 Marie Tahon"
-__maintainer__ = "Marie Tahon"
-__email__ = "marie.tahon@univ-lemans.fr"
-__status__ = "Production"
-#__docformat__ = 'reStructuredText'
-
-
-# Default hyperparameters:
-hparams = tf.contrib.training.HParams(
-    #to train on HOLODEEP tiff images
-    noise_src_dir = '/info/etu/m1/s160128/Documents/M1/DnCnn/Portage-reseau-de-neurones-de-Keras-vers-PyTorch/dncnn-tensorflow-holography-master/Holography/DATABASE/',
-    clean_src_dir = '/info/etu/m1/s160128/Documents/M1/DnCnn/Portage-reseau-de-neurones-de-Keras-vers-PyTorch/dncnn-tensorflow-holography-master/Holography/DATABASE/',
-    eval_dir = '/info/etu/m1/s160128/Documents/M1/DnCnn/Portage-reseau-de-neurones-de-Keras-vers-PyTorch/dncnn-tensorflow-holography-master/Holography/DATABASE/',
-    #to train on matlab images
-    #eval_dir = '/lium/raid01_c/tahon/holography/HOLODEEPmat/',
-    #to train on natural images
-    #noise_src_dir = '/lium/raid01_c/tahon/holography/NATURAL/noisy',
-    #clean_src_dir = '/lium/raid01_c/tahon/holography/NATURAL/original',
-    #eval_dir = '/lium/raid01_c/tahon/holography/HOLODEEPmat/',
-    #test_dir = 'lium/raid01_c/tahon/holography/TEST/',
-    phase = 'train', #train or test phase
-    #image
-    isDebug = False, #True,#reate only 10 patches
-    originalsize = (1024,1024), #1024 for matlab database, 128 for holodeep database, 180 for natural images
-    phase_type = 'two', #keep phase between -pi and pi (phi), convert into cosinus (cos) or sinus (sin)
-    #select images for training
-    train_patterns = [1, 2, 3, 4, 5], #number of images from 1 to 5
-    train_noise = '0-1-1.5-2-2.5', #[0, 1, 1.5, 2, 2.5],
-    #select images for evaluation (during training)
-    eval_patterns = [1, 2, 3, 4, 5],
-    eval_noise = '0-1-1.5-2-2.5',
-    #select images for testing
-    test_patterns = [1, 2, 3, 4, 5],
-    test_noise = '0-1-1.5-2-2.5',
-
-    noise_type = 'spkl', #type of noise: speckle or gaussian (spkl|gauss)
-    sigma = 25, #noise level for gaussian denoising
-
-    #Training
-    nb_layers = 4,#original number is 16
-    batch_size = 128,#128
-    patch_per_image = 384, #384, #9 pour des images 180*180 (NATURAL) Silvio a utilisé 384 pour des images 1024*1024 (MATLAB)
-    patch_size = 50, #Silvio a utilisé 50.
-    epoch = 10,#2000
-    lr = 0.0005, # learning rate
-    stride = 50, # spatial step for cropping images values from initial script 10
-    step = 0, #initial spatial setp for cropping
-    scales = [1], #[1, 0.9, 0.8, 0.7] # scale for data augmentation
-    chosenIteration = '' #chosen iteration to load for traning or testing
-)
-
-
-def hparams_debug_string():
-    values = hparams.values()
-    hp = [' %s: %s' % (name, values[name]) for name in sorted(values)]
-    return 'Hyperparameters:\n' + '\n'.join(hp)

utils.py

 
 
 
-
 #-*- coding: utf-8 -*-
 #
 # This file is part of DnCnn4Holo.
@@ -37,7 +36,6 @@ import sys
 import re
 import pathlib
 import numpy as np
-#import tensorflow as tf
 from PIL import Image
 from scipy.io import loadmat, savemat
 from glob import glob