Training settings for abs value tempest

KAIR/data/dataset_ffdnet.py

@@ -30,8 +30,10 @@ class DatasetFFDNet(data.Dataset):
         # -------------------------------------
         # get the path of H, return None if input is None
         # -------------------------------------
-        self.paths_H = util.get_image_paths(opt['dataroot_H'])
+        self.paths_H = util.get_image_paths(opt['dataroot_H'])[:50]   # Edit: overfittear con las primeras 50 imagenes
-        self.paths_L = util.get_image_paths(opt['dataroot_L'])
+        self.paths_L = util.get_image_paths(opt['dataroot_L'])[:50]   # Edit: las primeras 9 imagenes pertenecen a test
+
+        # print('\nNum patches:',self.num_patches_per_image,'\n')
         if self.opt['phase'] == 'train':
             listOfLists = [list(itertools.repeat(path, self.num_patches_per_image)) for path in self.paths_H]
             self.paths_H = list(itertools.chain.from_iterable(listOfLists))
@@ -81,6 +83,11 @@ class DatasetFFDNet(data.Dataset):
             # Get the patch from the simulation
             patch_L = img_L[rnd_h:rnd_h + self.patch_size, rnd_w:rnd_w + self.patch_size, :]
 
+            # Get module of complex image
+            patch_L = patch_L.astype('float')
+            patch_L = np.abs(patch_L[:,:,0]+1j*patch_L[:,:,1]).astype('uint8')
+
+
             # # Commented augmentation with rotating because of TMDS encoding
 
             # ---------------------------------
@@ -119,6 +126,11 @@ class DatasetFFDNet(data.Dataset):
             img_H = img_H[:,:,np.newaxis]
             img_H = util.uint2single(img_H)
 
+            # Get module of complex image
+            img_L = img_L.astype('float')
+            img_L = np.abs(img_L[:,:,0]+1j*img_L[:,:,1]).astype('uint8')
+            img_L = img_L[:,:,np.newaxis]
+
             np.random.seed(seed=0)
             img_L = img_L + np.random.normal(0, self.sigma_test/255.0, img_L.shape)
             noise_level = torch.FloatTensor([self.sigma_test/255.0])

KAIR/models/model_base.py

@@ -9,7 +9,8 @@ class ModelBase():
     def __init__(self, opt):
         self.opt = opt                         # opt
         self.save_dir = opt['path']['models']  # save models
-        self.device = torch.device('cuda' if opt['gpu_ids'] is not None else 'cpu')
+        # self.device = torch.device('cuda' if opt['gpu_ids'] is not None else 'cpu')
+        self.device = torch.device('cuda' if len(opt['gpu_ids']) != 0 else 'cpu')
         self.is_train = opt['is_train']        # training or not
         self.schedulers = []                   # schedulers
 

KAIR/options/train_drunet.json

@@ -4,7 +4,7 @@
   , "gpu_ids": [0]
 
   , "scale": 1       // broadcast to "netG" if SISR
-  , "n_channels": 2  // broadcast to "datasets", 1 for grayscale, 3 for color
+  , "n_channels": 1  // broadcast to "datasets", 1 for grayscale, 3 for color
   , "n_channels_datasetload": 3 // broadcast to image training set
   , "sigma": [0, 50]      // 15, 25, 50 for DnCNN | [0, 75] for FFDNet and FDnCNN
   , "sigma_test": 25 // 15, 25, 50 for DnCNN and ffdnet
@@ -36,7 +36,7 @@
 
   , "netG": {
     "net_type": "drunet" // "dncnn" | "fdncnn" | "ffdnet" | "srmd" | "dpsr" | "srresnet0" |  "srresnet1" | "rrdbnet" 
-    , "in_nc": 2        // input channel number
+    , "in_nc": 1        // input channel number
     , "out_nc": 1       // ouput channel number
     , "nc": [64, 128, 256, 512]          // 64 for "dncnn"
     , "nb": 4          // 17 for "dncnn", 20 for dncnn3, 16 for "srresnet"
@@ -54,7 +54,7 @@
 
   , "train": {
       "epochs": 1000                      // number of epochs to train
-    , "G_lossfn_type": "l1"               // "l1" preferred | "l2sum" | "l2" | "ssim" 
+    , "G_lossfn_type": "tv"               // "l1" preferred | "l2sum" | "l2" | "ssim" 
     , "G_lossfn_weight": 1.0            // default
     , "G_tvloss_weight": 1.0            // total variation weight
 
@@ -70,8 +70,8 @@
     , "G_regularizer_clipstep": null    // unused
     
     // iteration (batch step) checkpoints
-    , "checkpoint_test": 1000           // for testing
+    , "checkpoint_test": 500           // for testing
-    , "checkpoint_save": 1000           // for saving model
+    , "checkpoint_save": 780           // for saving model
-    , "checkpoint_print": 100           // for print
+    , "checkpoint_print": 16           // for print
   }
 }