snapshot...

modules/deeplearning/srcnn_l1b_l2.py

@@ -59,8 +59,8 @@ label_param = 'cld_opd_dcomp'
 # label_param = 'cloud_probability'
 
 params = ['temp_11_0um_nom', 'temp_12_0um_nom', 'refl_0_65um_nom', label_param]
-# data_params = ['temp_11_0um_nom']
-data_params = []
+data_params = ['temp_11_0um_nom']
+# data_params = []
 
 label_idx = params.index(label_param)
 
@@ -260,9 +260,10 @@ class SRCNN:
             tmp = tmp.copy()
             tmp = np.where(np.isnan(tmp), 0, tmp)
             # tmp = smooth_2d(tmp, sigma=1.0)
-            tmp = tmp[:, slc_y_2, slc_x_2]
-            tmp = resample_2d_linear(x_2, y_2, tmp, t, s)
-            tmp = tmp[:, y_k, x_k]
+            tmp = tmp[:, slc_y, slc_x]
+            # tmp = tmp[:, slc_y_2, slc_x_2]
+            # tmp = resample_2d_linear(x_2, y_2, tmp, t, s)
+            # tmp = tmp[:, y_k, x_k]
             tmp = normalize(tmp, param, mean_std_dct)
             if DO_ADD_NOISE:
                 tmp = add_noise(tmp, noise_scale=NOISE_STDDEV)