snapshot...

modules/deeplearning/srcnn_l1b_l2.py

@@ -212,8 +212,7 @@ class SRCNN:
         self.test_data_nda = None
         self.test_label_nda = None
 
-        # self.n_chans = len(data_params) + 2
-        self.n_chans = 1
+        self.n_chans = len(data_params) + 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))
@@ -246,29 +245,20 @@ class SRCNN:
             DO_ADD_NOISE = True
 
         data_norm = []
-        # for param in data_params:
-        #     idx = params.index(param)
-        #     tmp = input_data[:, idx, :, :]
-        #     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 = normalize(tmp, param, mean_std_dct)
-        #     if DO_ADD_NOISE:
-        #         tmp = add_noise(tmp, noise_scale=NOISE_STDDEV)
-        #     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, :, :]
+            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 = 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, :, :]
         tmp = tmp.copy()
         tmp = np.where(np.isnan(tmp), 0, tmp)