snapshot..

modules/deeplearning/srcnn_l1b_l2.py

@@ -63,20 +63,21 @@ print('data_params: ', data_params)
 print('label_param: ', label_param)
 
 KERNEL_SIZE = 3  # target size: (128, 128)
+N = 1
 
 if KERNEL_SIZE == 3:
-    slc_x = slice(2, 132)
-    slc_y = slice(2, 132)
-    slc_x_2 = slice(1, 134, 2)
-    slc_y_2 = slice(1, 134, 2)
-    x_2 = np.arange(67)
-    y_2 = np.arange(67)
-    t = np.arange(0, 67, 0.5)
-    s = np.arange(0, 67, 0.5)
-    x_k = slice(1, 131)
-    y_k = slice(1, 131)
-    x_128 = slice(3, 131)
-    y_128 = slice(3, 131)
+    slc_x = slice(2, N*128 + 4)
+    slc_y = slice(2, N*128 + 4)
+    slc_x_2 = slice(1, N*128 + 6, 2)
+    slc_y_2 = slice(1, N*128 + 6, 2)
+    x_2 = np.arange(int((N*128)/2) + 3)
+    y_2 = np.arange(int((N*128)/2) + 3)
+    t = np.arange(0, int((N*128)/2) + 3, 0.5)
+    s = np.arange(0, int((N*128)/2) + 3, 0.5)
+    x_k = slice(1, N*128 + 3)
+    y_k = slice(1, N*128 + 3)
+    x_128 = slice(3, N*128 + 3)
+    y_128 = slice(3, N*128 + 3)
 elif KERNEL_SIZE == 5:
     slc_x = slice(3, 135)
     slc_y = slice(3, 135)