snapshot...

modules/deeplearning/srcnn_cld_frac.py

@@ -138,45 +138,43 @@ def upsample(tmp):
     return tmp
 
 
-def upsample_nearest(tmp):
-    bsize = tmp.shape[0]
-    tmp_2 = tmp[:, slc_y_2, slc_x_2]
-    up = np.zeros(bsize, t.size, s.size)
-    for k in range(bsize):
-        for j in range(t.size/2):
-            for i in range(s.size/2):
-                up[k, j, i] = tmp_2[k, j, i]
-                up[k, j, i+1] = tmp_2[k, j, i]
-                up[k, j+1, i] = tmp_2[k, j, i]
-                up[k, j+1, i+1] = tmp_2[k, j, i]
-    return up
-
+# def get_label_data(grd_k):
+#     grd_k = np.where(np.isnan(grd_k), 0, grd_k)
+#     grd_k = np.where(grd_k < 0.5, 0, 1)
+#
+#     a = grd_k[:, 0::2, 0::2]
+#     b = grd_k[:, 1::2, 0::2]
+#     c = grd_k[:, 0::2, 1::2]
+#     d = grd_k[:, 1::2, 1::2]
+#     s_t = a + b + c + d
+#     s_t = np.where(s_t == 0, 0, s_t)
+#     s_t = np.where(s_t == 1, 1, s_t)
+#     s_t = np.where(s_t == 2, 1, s_t)
+#     s_t = np.where(s_t == 3, 1, s_t)
+#     s_t = np.where(s_t == 4, 2, s_t)
+#
+#     return s_t
 
-def get_label_data(grd_k):
+def get_grid_cell_mean(grd_k):
     grd_k = np.where(np.isnan(grd_k), 0, grd_k)
-    grd_k = np.where(grd_k < 0.5, 0, 1)
 
     a = grd_k[:, 0::2, 0::2]
     b = grd_k[:, 1::2, 0::2]
     c = grd_k[:, 0::2, 1::2]
     d = grd_k[:, 1::2, 1::2]
-    s_t = a + b + c + d
-    s_t = np.where(s_t == 0, 0, s_t)
-    s_t = np.where(s_t == 1, 1, s_t)
-    s_t = np.where(s_t == 2, 1, s_t)
-    s_t = np.where(s_t == 3, 1, s_t)
-    s_t = np.where(s_t == 4, 2, s_t)
+    s = a + b + c + d
+    s /= 4.0
 
-    return s_t
+    return s
 
 
-# def get_label_data(grd_k):
-#     grd_k = np.where(np.isnan(grd_k), 0, grd_k)
-#     grd_k = np.where((grd_k >= 0.0) & (grd_k < 0.3), 0, grd_k)
-#     grd_k = np.where((grd_k >= 0.3) & (grd_k < 0.7), 1, grd_k)
-#     grd_k = np.where((grd_k >= 0.7) & (grd_k <= 1.0), 2, grd_k)
-#
-#     return grd_k
+def get_label_data(grd_k):
+    grd_k = np.where(np.isnan(grd_k), 0, grd_k)
+    grd_k = np.where((grd_k >= 0.0) & (grd_k < 0.3), 0, grd_k)
+    grd_k = np.where((grd_k >= 0.3) & (grd_k < 0.7), 1, grd_k)
+    grd_k = np.where((grd_k >= 0.7) & (grd_k <= 1.0), 2, grd_k)
+
+    return grd_k
 
 
 class SRCNN: