snapshot..

modules/deeplearning/srcnn_l1b_l2.py

@@ -93,8 +93,8 @@ x_130 = slice(2, 132)
 y_130 = slice(2, 132)
 slc_x_2 = x_134_2
 slc_y_2 = y_134_2
-slc_x = x_128
-slc_y = y_128
+x_128 = slice(3, 131)
+y_128 = slice(3, 131)
 t = np.arange(1, 66, 0.5)
 s = np.arange(1, 66, 0.5)
 x_2 = x_67
@@ -104,10 +104,10 @@ y_2 = y_67
 # Kernel size: 5, target_size: (128, 128)
 # x_132 = slice(3, 135)
 # y_132 = slice(3, 135)
-# slc_x = slice(5, 133)
-# slc_y = slice(5, 133)
 # slc_x_2 = slice(2, 137, 2)
 # slc_y_2 = slice(2, 137, 2)
+# x_128 = slice(5, 133)
+# y_128 = slice(5, 133)
 #
 # t = np.arange(1, 67, 0.5)
 # s = np.arange(1, 67, 0.5)
@@ -306,7 +306,7 @@ class SRCNN:
         data = data.astype(np.float32)
         # -----------------------------------------------------
         # -----------------------------------------------------
-        label = input_data[:, label_idx, slc_y, slc_x]
+        label = input_data[:, label_idx, y_128, x_128]
         if label_param != 'cloud_probability':
             label = normalize(label, label_param, mean_std_dct)
         else: