snapshot..

modules/deeplearning/srcnn_l1b_l2.py

@@ -208,7 +208,8 @@ class SRCNN:
         self.test_data_nda = None
         self.test_label_nda = None
 
-        self.n_chans = len(data_params) + 2
+        # self.n_chans = len(data_params) + 2
+        self.n_chans = 1
 
         self.X_img = tf.keras.Input(shape=(None, None, self.n_chans))
         # self.X_img = tf.keras.Input(shape=(36, 36, self.n_chans))
@@ -241,25 +242,25 @@ class SRCNN:
             DO_ADD_NOISE = True
 
         data_norm = []
-        for param in data_params:
-            idx = params.index(param)
-            # tmp = input_data[:, idx, slc_y_2, slc_x_2]
-            tmp = input_data[:, idx, slc_y, slc_x]
-            tmp = normalize(tmp, param, mean_std_dct)
-            if DO_ADD_NOISE:
-                tmp = add_noise(tmp, noise_scale=NOISE_STDDEV)
-            # tmp = resample_2d_linear(x_2, y_2, tmp, t, s)
-            data_norm.append(tmp)
-        # --------------------------
-        param = 'refl_0_65um_nom'
-        idx = params.index(param)
-        # tmp = input_data[:, idx, slc_y_2, slc_x_2]
-        tmp = input_data[:, idx, slc_y, slc_x]
-        tmp = normalize(tmp, param, mean_std_dct)
-        if DO_ADD_NOISE:
-            tmp = add_noise(tmp, noise_scale=NOISE_STDDEV)
-        # tmp = resample_2d_linear(x_2, y_2, tmp, t, s)
-        data_norm.append(tmp)
+        # for param in data_params:
+        #     idx = params.index(param)
+        #     # tmp = input_data[:, idx, slc_y_2, slc_x_2]
+        #     tmp = input_data[:, idx, slc_y, slc_x]
+        #     tmp = normalize(tmp, param, mean_std_dct)
+        #     if DO_ADD_NOISE:
+        #         tmp = add_noise(tmp, noise_scale=NOISE_STDDEV)
+        #     # tmp = resample_2d_linear(x_2, y_2, tmp, t, s)
+        #     data_norm.append(tmp)
+        # # --------------------------
+        # param = 'refl_0_65um_nom'
+        # idx = params.index(param)
+        # # tmp = input_data[:, idx, slc_y_2, slc_x_2]
+        # tmp = input_data[:, idx, slc_y, slc_x]
+        # tmp = normalize(tmp, param, mean_std_dct)
+        # if DO_ADD_NOISE:
+        #     tmp = add_noise(tmp, noise_scale=NOISE_STDDEV)
+        # # tmp = resample_2d_linear(x_2, y_2, tmp, t, s)
+        # data_norm.append(tmp)
         # --------
         tmp = input_data[:, label_idx, slc_y_2, slc_x_2]
         if label_param != 'cloud_probability':