Message de validation

generate_patches_holo_fromMAT.py

@@ -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)

hparams.py

@@ -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',

main_holo.py

@@ -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)

model.py

@@ -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, :,:,:]

run_holo_test.sh

 #!/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
+

run_holo_train2.sh

 #!/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"