snapshot...

modules/deeplearning/srcnn_l1b_l2.py

 import glob
 import tensorflow as tf
 from util.setup import logdir, modeldir, cachepath, now, ancillary_path
-from util.util import EarlyStop, normalize, denormalize, resample, resample_one, get_grid_values_all
+from util.util import EarlyStop, normalize, denormalize, resample, resample_2d_linear, resample_one, get_grid_values_all
 import os, datetime
 import numpy as np
 import pickle
@@ -47,14 +47,16 @@ f.close()
 mean_std_dct.update(mean_std_dct_l1b)
 mean_std_dct.update(mean_std_dct_l2)
 
-params = ['temp_11_0um_nom', 'temp_12_0um_nom', 'refl_0_65um_nom', 'cloud_fraction']
-data_params = ['temp_11_0um_nom', 'temp_12_0um_nom', 'refl_0_65um_nom']
+#params = ['temp_11_0um_nom', 'temp_12_0um_nom', 'refl_0_65um_nom', 'cloud_fraction']
+#data_params = ['temp_11_0um_nom', 'temp_12_0um_nom', 'refl_0_65um_nom']
+params = ['temp_11_0um_nom', 'temp_12_0um_nom', 'cloud_fraction']
+data_params = ['temp_11_0um_nom']
 label_params = ['cloud_fraction']
 
 
 DO_ZERO_OUT = False
 
-label_idx = 3
+label_idx = 2
 label_param = params[label_idx]
 print('data_params: ', data_params)
 print('label_params: ', label_params)
@@ -214,12 +216,13 @@ class SRCNN:
         for param in data_params:
             idx = params.index(param)
             tmp = input_data[:, idx, 3:131:2, 3:131:2]
-            tmp = resample(y_64, x_64, tmp, s, t)
+            # tmp = resample(y_64, x_64, tmp, s, t)
+            tmp = resample_2d_linear(y_64, x_64, tmp, s, t)
             tmp = normalize(tmp, param, mean_std_dct, add_noise=add_noise, noise_scale=noise_scale)
             data_norm.append(tmp)
         # --------
-        tmp = input_data[:, 2, 3:131, 3:131]
-        tmp = normalize(tmp, 'refl_0_65um_nom', mean_std_dct)
+        tmp = input_data[:, 0, 3:131, 3:131]
+        tmp = normalize(tmp, 'temp_11_0um_nom', mean_std_dct)
         data_norm.append(tmp)
         # ---------
         data = np.stack(data_norm, axis=3)