snapshot...

modules/deeplearning/srcnn_cld_frac.py

@@ -37,8 +37,6 @@ NOISE_TRAINING = False
 NOISE_STDDEV = 0.01
 DO_AUGMENT = True
 
-DO_SMOOTH = False
-SIGMA = 1.0
 DO_ZERO_OUT = False
 DO_ESPCN = False  # Note: If True, cannot do mixed resolution input fields (Adjust accordingly below)
 
@@ -138,7 +136,7 @@ def upsample(tmp):
     return tmp
 
 
-def upsample_nearest(grd):
+def upsample_mean(grd):
     bsize, ylen, xlen = grd.shape
     grd = get_grid_cell_mean(grd)
     up = np.zeros(bsize, ylen, xlen)
@@ -345,13 +343,13 @@ class SRCNN:
         tmp = input_data[:, label_idx, :, :]
         tmp = tmp.copy()
         tmp = np.where(np.isnan(tmp), 0, tmp)
-        if DO_SMOOTH:
-            tmp = smooth_2d(tmp, sigma=SIGMA)
-            # tmp = median_filter_2d(tmp)
+
         if DO_ESPCN:
             tmp = tmp[:, slc_y_2, slc_x_2]
         else:  # Half res upsampled to full res:
-            tmp = upsample(tmp)
+            # tmp = upsample(tmp)
+            tmp = upsample_mean(tmp)
+            tmp = tmp[:, slc_y, slc_x]
         if label_param != 'cloud_probability':
             tmp = normalize(tmp, label_param, mean_std_dct)
             if DO_ADD_NOISE: