Commit 3f76cf40 authored by Marie Tahon's avatar Marie Tahon
Browse files

minor changes

parents b50c73d4 d348c754
# 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)
#-*- coding: utf-8 -*- #-*- coding: utf-8 -*-
# #
# This file is part of DnCnn4Holo. # This file is part of DnCnn4Holo.
...@@ -37,7 +36,6 @@ import sys ...@@ -37,7 +36,6 @@ import sys
import re import re
import pathlib import pathlib
import numpy as np import numpy as np
#import tensorflow as tf
from PIL import Image from PIL import Image
from scipy.io import loadmat, savemat from scipy.io import loadmat, savemat
from glob import glob from glob import glob
......
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