remove torchvision from data.py and ref to tf in utils

argument.py

@@ -80,12 +80,12 @@ def parse():
 
 
     parser.add_argument('--isDebug', dest='isDebug', action='store_true')
-    parser.add_argument('--patch_size', dest='patch_size', default=50)
-    parser.add_argument('--stride', dest='stride', default=50)
-    parser.add_argument('--step', dest='step', default=0)
+    parser.add_argument('--patch_size', dest='patch_size', type=int, default=50)
+    parser.add_argument('--stride', dest='stride', type=int, default=50)
+    parser.add_argument('--step', dest='step', type=int, default=0)
     parser.add_argument('--freq_save', dest='freq_save', type=int, default=1)
     parser.add_argument('--phase_type', dest='phase_type', default="two")
-    parser.add_argument('--patch_per_image', dest='patch_per_image', default=384)
+    parser.add_argument('--patch_per_image', dest='patch_per_image', type=int, default=384)
     parser.add_argument('--noise_src_dir', dest='noise_src_dir', default="./chemin/")
     parser.add_argument('--clean_src_dir', dest='clean_src_dir', default="./chemin/")
     

generate_patches_holo_fromMAT.py

@@ -67,7 +67,7 @@ parser.add_argument('--params', dest='params', type=str, default='', help='hyper
 # check output arguments
 #parser.add_argument('--from_file', dest='from_file', default="./data/img_clean_pats.npy", help='get pic from file')
 #parser.add_argument('--num_pic', dest='num_pic', type=int, default=10, help='number of pic to pick')
-args = parser.parse_args()
+#args = parser.parse_args()
 #print(args.params['patch_size'])
 #hparams.parse(args.params)
 

main_holo.py

@@ -171,7 +171,7 @@ def run(args):
 
 
 
-    exp = nt.Experiment(net, adam, statsManager, batch_size=args.batch_size, perform_validation_during_training=args.perform_validation, input_dir=args.input_dir, startEpoch=args.epoch, freq_save=args.freq_save)
+    exp = nt.Experiment(net, adam, statsManager, perform_validation_during_training=args.perform_validation, input_dir=args.input_dir, startEpoch=args.epoch, freq_save=args.freq_save)
 
 
     if not args.test_mode :
@@ -181,7 +181,7 @@ def run(args):
         trainData = TrainDataset(args.clean_train, args.noisy_train, args.image_mode, args.train_image_size, nb_rotation=args.nb_rotation)
         evalData  = EvalDataset(args.eval_dir, args.eval_noises, args.eval_patterns, args.image_mode, args.eval_image_size)
 
-        exp.initData(trainData, evalData)
+        exp.initData(trainData, evalData, batch_size=args.batch_size)
         exp.run(num_epochs=args.num_epochs)
 
         if(args.graph):

nntools.py

@@ -146,7 +146,7 @@ class Experiment(object):
     """
 
     def __init__(self, net, optimizer, stats_manager, startEpoch=None,
-                 input_dir=None, batch_size=16, perform_validation_during_training=False, freq_save=1):
+                 input_dir=None, perform_validation_during_training=False, freq_save=1):
 
         # Initialize history
         history = []
@@ -173,13 +173,14 @@ class Experiment(object):
         #                "I found a checkpoint conflicting with the current setting.")
             self.load(startEpoch)
 
-    def initData(self, train_set, val_set):
+    def initData(self, train_set, val_set, batch_size=16):
 
         self.train_set = train_set
         self.val_set = val_set
+        self.batch_size = batch_size
         # Define data loaders
-        self.train_loader = td.DataLoader(train_set, batch_size=self.batch_size, shuffle=True, drop_last=True, pin_memory=True)
-        self.val_loader = td.DataLoader(val_set, batch_size=self.batch_size, shuffle=False, drop_last=True, pin_memory=True)
+        self.train_loader = td.DataLoader(train_set, batch_size=batch_size, shuffle=True, drop_last=True, pin_memory=True)
+        self.val_loader = td.DataLoader(val_set, batch_size=1, shuffle=False, drop_last=True, pin_memory=True)
 
         self.training_data = train_set.getTrainingName()
 

utils.py

@@ -37,7 +37,7 @@ import sys
 import re
 import pathlib
 import numpy as np
-#import tensorflow as tf
+import tensorflow as tf
 from PIL import Image
 from scipy.io import loadmat, savemat
 from glob import glob
@@ -402,7 +402,7 @@ def normalize_data(data,phase_type, rdm, phase_augmentation = False):
         return data_n
     elif (phase_type == 'two') & (phase_augmentation == True):
         numPatch = data.shape[0]
-        newshape = (numPatch * 4, data.shape[1], data.shape[2], data.shape[3])
+        newshape = (numPatch * 8, data.shape[1], data.shape[2], data.shape[3])
         data_n = np.zeros(shape = newshape)
         cpt = 0
         for k in range(numPatch):
@@ -410,11 +410,11 @@ def normalize_data(data,phase_type, rdm, phase_augmentation = False):
             data_n[1*numPatch + k,:,:,0] =  np.sin(                             data[k,:,:,0])
             data_n[2*numPatch + k,:,:,0] =  np.cos( np.transpose(               data[k,:,:,0]) )
             data_n[3*numPatch + k,:,:,0] =  np.sin( np.transpose(               data[k,:,:,0]) )
-            #data_n[4*numPatch + k,:,:,0] =  np.cos(                 math.pi/4 + data[k,:,:,0])
-            #data_n[5*numPatch + k,:,:,0] =  np.sin(                 math.pi/4 + data[k,:,:,0])
-            #data_n[6*numPatch + k,:,:,0] =  np.cos( np.transpose(   math.pi/4 + data[k,:,:,0]) )
-            #data_n[7*numPatch + k,:,:,0] =  np.sin( np.transpose(   math.pi/4 + data[k,:,:,0]) )
-        print('nb of cos / sin / cos + flipud / sin + flipud: ', numPatch * 4)
+            data_n[4*numPatch + k,:,:,0] =  np.cos(                 math.pi/4 + data[k,:,:,0])
+            data_n[5*numPatch + k,:,:,0] =  np.sin(                 math.pi/4 + data[k,:,:,0])
+            data_n[6*numPatch + k,:,:,0] =  np.cos( np.transpose(   math.pi/4 + data[k,:,:,0]) )
+            data_n[7*numPatch + k,:,:,0] =  np.sin( np.transpose(   math.pi/4 + data[k,:,:,0]) )
+        print('nb of cos / sin / cos + flipud / sin + flipud: ', numPatch * 8)
         return data_n
     else:
         print('[!] phase type not exists (phi|cos|sin|two)')
@@ -593,6 +593,7 @@ def cal_std_phase(im1, im2):
     return dev
 
 
+<<<<<<< HEAD
 def tf_psnr(im1, im2):
     '''
     this function is deprecated
@@ -603,6 +604,8 @@ def tf_psnr(im1, im2):
     return 10.0 * (tf.log(1 / mse) / tf.log(10.0))
 
 
+=======
+>>>>>>> cfbb377a755bc7b5e2b9ead857e01e5576ec3f67