add nb of layers in params

README.md

@@ -34,6 +34,7 @@ Modifiable parameters are located in hparams.py
     sigma = 25, #noise level for gaussian denoising not used in this project
 
     #Training
+    nb_layers = 4,#nb of intermediate convolutional layers (original size is 16)
     batch_size = 64,#nb of patches per batch
     patch_per_image = 350, # Silvio a utilisé 384 pour des images 1024*1024
     patch_size = 50, #size of training images.

hparams.py

@@ -36,15 +36,15 @@ __status__ = "Production"
 # Default hyperparameters:
 hparams = tf.contrib.training.HParams(
     #noise_src_dir = '/lium/raid01_c/tahon/holography/HOLODEEP/',
-    noise_src_dir = '/lium/raid01_c/tahon/holography/NATURAL/',
+    noise_src_dir = '/lium/raid01_c/tahon/holography/NATURAL/noisy',
     #clean_src_dir = '/lium/raid01_c/tahon/holography/NOISEFREEHOLODEEP/',
-    clean_src_dir = '/lium/raid01_c/tahon/holography/NOISEFREENATURAL/',
+    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
+    phase = 'test', #train or test phase
     #image
     isDebug = False, #True,#reate only 10 patches
-    originalsize = (1024,1024), #1024 for matlab database, 128 for holodeep database
+    originalsize = (180,180), #1024 for matlab database, 128 for holodeep database
     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
@@ -60,8 +60,9 @@ hparams = tf.contrib.training.HParams(
     sigma = 25, #noise level for gaussian denoising
 
     #Training
+    nb_layers = 4,#original number is 16
     batch_size = 64,#128
-    patch_per_image = 384, # Silvio a utilisé 384 pour des images 1024*1024
+    patch_per_image = 9, #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 = 2000,#2000
     lr = 0.001, # learning rate

model.py

@@ -24,7 +24,7 @@ def loss_function(ref, pred, phase_type):
 def dncnn(input, is_training=True, output_channels=1):
     with tf.variable_scope('block1'):
         output = tf.layers.conv2d(input, 64, 3, padding='same', activation=tf.nn.relu)#attention c'etait tf.nn.relu
-    for layers in range(2, 4 + 1): #4 + 1): #16 + 1
+    for layers in range(2, hparams.nb_layers + 1): #4 + 1): #16 + 1
         with tf.variable_scope('block%d' % layers):
             output = tf.layers.conv2d(output, 64, 3, padding='same', name='conv%d' % layers, use_bias=False)
             output = tf.nn.relu(tf.layers.batch_normalization(output, training=is_training))
@@ -138,10 +138,10 @@ class denoiser(object):
         clean_pred, psnr = self.sess.run([self.Y, self.eva_psnr], feed_dict={self.Y_: data_clean, self.X: data_noisy, self.is_training: False})
         return output_clean_image, noisy_image, psnr
 
-    def train(self, data, eval_data, batch_size, ckpt_dir, epoch, lr, sample_dir, phase_type, eval_every_epoch=5):
+    def train(self, data, eval_data, batch_size, ckpt_dir_, epoch, lr, sample_dir, phase_type, eval_every_epoch=5):
         phase_augmentation = True
         sess_name = 'run-test' + str(datetime.now()).replace(' ', '_')
-        ckpt_dir = ckpt_dir + '/' + sess_name + '/'
+        ckpt_dir = ckpt_dir_ + '/' + sess_name + '/'
         sample_dir = sample_dir + '/' + sess_name + '/'
         if not os.path.exists(ckpt_dir):
             os.makedirs(ckpt_dir)
@@ -179,7 +179,7 @@ class denoiser(object):
         fres.write('Nb of batches is :%d \n' %(numBatch))
         print('Nb of batches is :', numBatch)
         # load pretrained model
-        load_model_status, global_step = self.load(ckpt_dir)
+        load_model_status, global_step = self.load(ckpt_dir_)
         if load_model_status:
             iter_num = global_step
             start_epoch = global_step // numBatch