snapshot...

modules/deeplearning/srcnn_l1b_l2.py

@@ -31,7 +31,7 @@ EARLY_STOP = True
 
 NOISE_TRAINING = False
 NOISE_STDDEV = 0.01
-DO_AUGMENT = False
+DO_AUGMENT = True
 
 DO_SMOOTH = False
 SIGMA = 1.0
@@ -70,20 +70,21 @@ print('label_param: ', label_param)
 
 KERNEL_SIZE = 3  # target size: (128, 128)
 N_X = N_Y = 1
+LEN_X = LEN_Y = 128
 
 if KERNEL_SIZE == 3:
-    slc_x = slice(2, N_X*128 + 4)
-    slc_y = slice(2, N_Y*128 + 4)
-    slc_x_2 = slice(1, N_X*128 + 6, 2)
-    slc_y_2 = slice(1, N_Y*128 + 6, 2)
-    x_2 = np.arange(int((N_X*128)/2) + 3)
-    y_2 = np.arange(int((N_Y*128)/2) + 3)
-    t = np.arange(0, int((N_X*128)/2) + 3, 0.5)
-    s = np.arange(0, int((N_Y*128)/2) + 3, 0.5)
-    x_k = slice(1, N_X*128 + 3)
-    y_k = slice(1, N_Y*128 + 3)
-    x_128 = slice(3, N_X*128 + 3)
-    y_128 = slice(3, N_Y*128 + 3)
+    slc_x = slice(2, N_X*LEN_X + 4)
+    slc_y = slice(2, N_Y*LEN_Y + 4)
+    slc_x_2 = slice(1, N_X*LEN_X + 6, 2)
+    slc_y_2 = slice(1, N_Y*LEN_Y + 6, 2)
+    x_2 = np.arange(int((N_X*LEN_X)/2) + 3)
+    y_2 = np.arange(int((N_Y*LEN_Y)/2) + 3)
+    t = np.arange(0, int((N_X*LEN_X)/2) + 3, 0.5)
+    s = np.arange(0, int((N_Y*LEN_Y)/2) + 3, 0.5)
+    x_k = slice(1, N_X*LEN_X + 3)
+    y_k = slice(1, N_Y*LEN_Y + 3)
+    x_128 = slice(3, N_X*LEN_X + 3)
+    y_128 = slice(3, N_Y*LEN_Y + 3)
 elif KERNEL_SIZE == 5:
     slc_x = slice(3, 135)
     slc_y = slice(3, 135)