snapshot...

modules/deeplearning/cnn_cld_frac.py

@@ -37,7 +37,6 @@ NOISE_TRAINING = False
 NOISE_STDDEV = 0.01
 DO_AUGMENT = True
 
-DO_SMOOTH = True
 SIGMA = 1.0
 DO_ZERO_OUT = False
 DO_ESPCN = False  # Note: If True, cannot do mixed resolution input fields (Adjust accordingly below)
@@ -374,8 +373,6 @@ class SRCNN:
             else:  # Half res upsampled to full res:
                 tmp = upsample(tmp)
             tmp = normalize(tmp, param, mean_std_dct)
-            if DO_ADD_NOISE:
-                tmp = add_noise(tmp, noise_scale=NOISE_STDDEV)
             data_norm.append(tmp)
 
         for param in data_params_full:
@@ -386,8 +383,6 @@ class SRCNN:
             # Full res:
             tmp = tmp[:, slc_y, slc_x]
             tmp = normalize(tmp, param, mean_std_dct)
-            if DO_ADD_NOISE:
-                tmp = add_noise(tmp, noise_scale=NOISE_STDDEV)
             data_norm.append(tmp)
         # ---------------------------------------------------
         tmp = input_data[:, label_idx, :, :]
@@ -401,13 +396,6 @@ class SRCNN:
             tmp = tmp[:, slc_y, slc_x]
         if label_param != 'cloud_probability':
             tmp = normalize(tmp, label_param, mean_std_dct)
-            if DO_ADD_NOISE:
-                tmp = add_noise(tmp, noise_scale=NOISE_STDDEV)
-        else:
-            if DO_ADD_NOISE:
-                tmp = add_noise(tmp, noise_scale=NOISE_STDDEV)
-                tmp = np.where(tmp < 0.0, 0.0, tmp)
-                tmp = np.where(tmp > 1.0, 1.0, tmp)
         data_norm.append(tmp)
         # ---------
         data = np.stack(data_norm, axis=3)