snapshot...

modules/deeplearning/srcnn_l1b_l2.py

@@ -37,6 +37,7 @@ NOISE_STDDEV = 0.01
 DO_AUGMENT = True
 
 DO_SMOOTH = False
+SIGMA = 0.5
 DO_ZERO_OUT = False
 DO_ESPCN = False  # Note: If True, cannot do mixed resolution input fields (Adjust accordingly below)
 
@@ -63,7 +64,6 @@ label_param = 'cld_opd_dcomp'
 params = ['temp_11_0um_nom', 'temp_12_0um_nom', 'refl_0_65um_nom', label_param]
 data_params_half = ['temp_11_0um_nom']
 data_params_full = ['refl_0_65um_nom']
-# data_params = []
 
 label_idx = params.index(label_param)
 
@@ -307,7 +307,7 @@ class SRCNN:
         tmp = tmp.copy()
         tmp = np.where(np.isnan(tmp), 0, tmp)
         if DO_SMOOTH:
-            tmp = smooth_2d(tmp, sigma=0.5)
+            tmp = smooth_2d(tmp, sigma=SIGMA)
         if DO_ESPCN:
             tmp = tmp[:, slc_y_2, slc_x_2]
         else:  # Half res upsampled to full res:
@@ -331,7 +331,7 @@ class SRCNN:
         label = label.copy()
         if DO_SMOOTH:
             label = np.where(np.isnan(label), 0, label)
-            label = smooth_2d(label, sigma=0.5)
+            label = smooth_2d(label, sigma=SIGMA)
         label = label[:, y_128, x_128]
 
         if label_param != 'cloud_probability':