snapshot...

modules/deeplearning/cloud_opd_srcnn.py

@@ -69,8 +69,8 @@ KERNEL_SIZE = 3  # target size: (128, 128)
 N_X = N_Y = 1
 
 if KERNEL_SIZE == 3:
-    slc_x = slice(2, N_X*128 + 4)
-    slc_y = slice(2, N_Y*128 + 4)
+    slc_x = slice(3, N_X*128 + 5)
+    slc_y = slice(3, N_Y*128 + 5)
     slc_x_2 = slice(1, int((N_X*128)/2) + 4)
     slc_y_2 = slice(1, int((N_Y*128)/2) + 4)
     x_2 = np.arange(int((N_X*128)/2) + 3)
@@ -79,8 +79,8 @@ if KERNEL_SIZE == 3:
     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)
+    x_128 = slice(4, N_X*128 + 4)
+    y_128 = slice(4, N_Y*128 + 4)
 elif KERNEL_SIZE == 5:
     slc_x = slice(3, 135)
     slc_y = slice(3, 135)
@@ -301,17 +301,20 @@ class SRCNN:
             tmp = input_label[:, idx, :, :]
             tmp = np.where(np.isnan(tmp), 0, tmp)
 
-            lo, hi, std, avg = get_min_max_std(tmp)
-            lo = upsample_nearest(lo)
-            hi = upsample_nearest(hi)
-            avg = upsample_nearest(avg)
-            lo = normalize(lo, param, mean_std_dct)
-            hi = normalize(hi, param, mean_std_dct)
-            avg = normalize(avg, param, mean_std_dct)
-
-            data_norm.append(lo[:, slc_y, slc_x])
-            data_norm.append(hi[:, slc_y, slc_x])
-            data_norm.append(avg[:, slc_y, slc_x])
+            # lo, hi, std, avg = get_min_max_std(tmp)
+            # lo = upsample_nearest(lo)
+            # hi = upsample_nearest(hi)
+            # avg = upsample_nearest(avg)
+            # lo = normalize(lo, param, mean_std_dct)
+            # hi = normalize(hi, param, mean_std_dct)
+            # avg = normalize(avg, param, mean_std_dct)
+            #
+            # data_norm.append(lo[:, slc_y, slc_x])
+            # data_norm.append(hi[:, slc_y, slc_x])
+            # data_norm.append(avg[:, slc_y, slc_x])
+
+            tmp = normalize(tmp, param, mean_std_dct)
+            data_norm.append(tmp[:, slc_y, slc_x])
         # ---------------------------------------------------
         tmp = input_data[:, label_idx, :, :]
         tmp = np.where(np.isnan(tmp), 0, tmp)