Commit bde612fd authored by Noury Robin's avatar Noury Robin
Browse files

Message de validation

parent a9b51b74
......@@ -81,7 +81,7 @@ def generate_patches(isDebug=True):
print(hparams_debug_string())
#filepaths = [x for x in src_path.glob('*.tiff')] #('*.mat')
#noisyfilepaths = [x for x in noisy_path.glob('*.tiff')] #('*.mat')
cleanmat, noisymat = from_DATABASE(hparams.eval_dir, hparams.train_noise, hparams.train_patterns)
cleanmat, noisymat = from_DATABASE(hparams.train_dir, hparams.train_noise, hparams.train_patterns)
#ipdb.set_trace()
print("number of clean training data {0} and noisy {1}".format( len(cleanmat), len(noisymat)))
scales = 1 #et on ne le bouge pas !!!! hparams.scales #old version [1, 0.9, 0.8, 0.7]
......@@ -171,7 +171,7 @@ def generate_patches(isDebug=True):
print('shape of inputs: ', cleaninputs.shape)
print('amplitude of inputs: ', np.max(cleaninputs), np.min(cleaninputs))
sess_name = extract_sess_name(hparams.train_patterns, hparams.train_noise, hparams.phase_type, hparams.stride, hparams.patch_size, hparams.patch_per_image)
sess_name = extract_sess_name(hparams.train_noise, hparams.train_noise, hparams.phase_type, hparams.stride, hparams.patch_size, hparams.patch_per_image)
if not os.path.exists(args.save_dir):
os.mkdir(args.save_dir)
np.save(os.path.join(args.save_dir, "img_clean_train_" + sess_name), cleaninputs)
......
......@@ -36,10 +36,10 @@ __status__ = "Production"
# Default hyperparameters:
hparams = tf.contrib.training.HParams(
#to train on HOLODEEP tiff images
noise_src_dir = '/info/etu/slbm/e161513/dncnn-tensorflow-holography/DB_Train2/',
clean_src_dir = '/info/etu/slbm/e161513/dncnn-tensorflow-holography/DB_Train2/',
noise_src_dir = '/info/etu/slbm/e161513/dncnn-tensorflow-holography/DB_Train2',
clean_src_dir = '/info/etu/slbm/e161513/dncnn-tensorflow-holography/DB_Train2',
eval_dir = '/info/etu/slbm/e161513/dncnn-tensorflow-holography/HOLODEEPmat/',
train_dir='/info/etu/slbm/e161513/dncnn-tensorflow-holography/DB_Train2/',
train_dir='/info/etu/slbm/e161513/dncnn-tensorflow-holography/DB_Train12/',
#to train on matlab images
#eval_dir = '/lium/raid01_c/tahon/holography/HOLODEEPmat/',
#to train on natural images
......@@ -53,8 +53,9 @@ hparams = tf.contrib.training.HParams(
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, 5,6,7], #number of images from 1 to 5
train_noise = '1-1.5-2-2.5-3', # [0.5, 1, 1.5, 2, 2.5,3],
#train_patterns = [1, 2, 3, 4, 5,6,7], #number of images from 1 to 5
train_patterns = [x+1 for x in range(300)],
train_noise ='3', # [0.5, 1, 1.5, 2, 2.5,3],
#select images for evaluation (during training)
eval_patterns = [1, 2, 3, 4, 5],
eval_noise = '0-1-1.5-2-2.5',
......
......@@ -68,7 +68,7 @@ hparams.parse(args.params)
def denoiser_train(denoiser, lr):
#avec load_data les images sont déjà normalisée par 255.0
sess_name = extract_sess_name(hparams.train_patterns, hparams.train_noise, hparams.phase_type, hparams.stride, hparams.patch_size, hparams.patch_per_image)
sess_name = extract_sess_name(hparams.train_noise, hparams.train_noise, hparams.phase_type, hparams.stride, hparams.patch_size, hparams.patch_per_image)
#for training with natural images
#sess_name = 'natural_phi'
print('session name: ', sess_name)
......
......@@ -240,7 +240,7 @@ class denoiser(object):
#self.evaluate(iter_num, eval_data, sample_dir=sample_dir, summary_merged=summary_psnr, summary_writer=writer, sess_name=sess_name, phase_type=phase_type, nb_layers=nb_layers) # eval_data value range is 0-255
for epoch in range(start_epoch, epoch):
#np.random.shuffle(data) #no shuffle for the moment
#shuffle target and source synchronously with random permutation at each epoch.
#shuffle targese and source synchronously with random permutation at each epoch.
ind = np.random.permutation(numPatch)
data_clean = data_clean[ind, :,:,:]
data_noisy = data_noisy[ind, :,:,:]
......
#!/bin/bash
#SBATCH -p gpu
#SBATCH --gres gpu:1
#SBATCH --mem 40G
#SBATCH --gres gpu:rtx6000:1
#SBATCH --mail-type=ALL
#SBATCH --mail-user=robin.noury.etu@univ-lemans.fr
#SBATCH -o test_debruitage.out
#SBATCH --time 20-00
#SBATCH --time 40-00
#noisyImg=$1
#cleanImg=$2
#runTest=/lium/raid01_c/tahon/holography/checkpoints/run-test2020-04-12_12\:14\:29.082341/
......@@ -17,22 +16,21 @@ for num in 1 2 3 4 5; do
noisyImg=./HOLODEEPmat/Pattern$num/MFH_$lambda/NoisyPhase.mat
#noisyImg=/lium/raid01_c/tahon/holography/HOLODEEPmat/PATTERN$num/MFH_$lambda/run-test2020-04-12_12\:14\:29.082341/run-test2020-04-12_12\:14\:29.082341/NoisyPhase.mat-27000.mat-27000.mat
cleanImg=./HOLODEEPmat/Pattern$num/PhaseDATA.mat
echo $noisyImg >> TEST-TRAIN_1.res
python main_holo.py --test_noisy_img $noisyImg --test_noisy_key 'NoisyPhase' --test_clean_img $cleanImg --test_clean_key 'Phase' --test_flip False --test_ckpt_index $runTest >> TEST-TRAIN_1.res
echo $noisyImg >> TEST-TRAIN_2_315.res
python main_holo.py --test_noisy_img $noisyImg --test_noisy_key 'NoisyPhase' --test_clean_img $cleanImg --test_clean_key 'Phase' --test_flip False --test_ckpt_index $runTest >> TEST-TRAIN_2_315.res
done
done
test1=./DATAEVAL/DATA_1_Phase_Type1_2_0.25_1.5_4_50.mat
test2=./DATAEVAL/DATA_20_Phase_Type4_2_0.25_2.5_4_100.mat
test3=./DATAEVAL/VibPhaseDATA.mat
test4=./Base_exp_test_double_impact/Temps_200/NoisyPhase.mat
keyNoisy='Phaseb'
keyClean='Phase'
echo $test1
python main_holo.py --test_noisy_img $test1 --test_noisy_key $keyNoisy --test_clean_img $test1 --test_clean_key $keyClean --test_flip False --test_ckpt_index $runTest >> TEST-TRAIN_1.res
python main_holo.py --test_noisy_img $test1 --test_noisy_key $keyNoisy --test_clean_img $test1 --test_clean_key $keyClean --test_flip False --test_ckpt_index $runTest >> TEST-TRAIN_2_315.res
echo $test2
python main_holo.py --test_noisy_img $test2 --test_noisy_key $keyNoisy --test_clean_img $test2 --test_clean_key $keyClean --test_flip False --test_ckpt_index $runTest >> TEST-TRAIN_1.res
python main_holo.py --test_noisy_img $test2 --test_noisy_key $keyNoisy --test_clean_img $test2 --test_clean_key $keyClean --test_flip False --test_ckpt_index $runTest >> TEST-TRAIN_2_315.res
echo $test3
python main_holo.py --test_noisy_img $test3 --test_noisy_key $keyNoisy --test_clean_img $test3 --test_clean_key $keyClean --test_flip False --test_ckpt_index $runTest >> TEST-TRAIN_1.res
echo $test4
python main_holo.py --test_noisy_img $test4 --test_noisy_key $keyNoisy --test_flip False --test_ckpt_index $runTest >> TEST-TRAIN_1.res
python main_holo.py --test_noisy_img $test3 --test_noisy_key $keyNoisy --test_clean_img $test3 --test_clean_key $keyClean --test_flip False --test_ckpt_index $runTest >> TEST-TRAIN_2_315.res
#!/bin/bash
#SBATCH -p gpu
#SBATCH --gres gpu:1
#SBATCH --mem 40G
#SBATCH --mem 80G
#SBATCH --mail-type=ALL
#SBATCH --mail-user=robin.noury.etu@univ-lemans.fr
#SBATCH -o holo_two_train_noise0-1.5.out
#SBATCH --time 20-00
#SBATCH -o holo_train12.out
#SBATCH --time 50-00
source activate hologaphy
#python generate_patches_holo_fromMAT.py --params "phase_type=two"
......
......@@ -31,9 +31,12 @@ import gc
import os
import sys
import re
import math
import mat73
import pathlib
import numpy as np
import tensorflow as tf
import h5py
from PIL import Image
from scipy.io import loadmat, savemat
from glob import glob
......@@ -149,15 +152,17 @@ def from_DATABASE(dir_data, noise_eval, img_eval, flipupdown = False):
clean.append(im)
noisy = []
p=0
for file in select_noisy:
print('noisy eval data: ', file)
im = loadMAT_flip(file, 'NoisyPhase', flipupdown)
im= loadMAT_flip(file,'NoisyPhase', flipupdown)
noisy.append(im)
return clean, noisy
def loadMAT_flip(file, key, flipupdown):
s = loadmat(file)
s =loadmat(file)
#s=h5py.File(file)
if key in s:
im = np.array(s[key])
else:
......
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