snapshot...

modules/deeplearning/srcnn_l1b_l2.py

@@ -213,7 +213,7 @@ class SRCNN:
         self.test_label_nda = None
 
         # self.n_chans = len(data_params) + 2
-        self.n_chans = 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))
@@ -246,18 +246,18 @@ 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)
+        # 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)
@@ -777,7 +777,8 @@ def run_evaluate_static(in_file, out_file, ckpt_dir):
         grd_c = normalize(grd_c, label_param, mean_std_dct)
 
     # data = np.stack([grd_a, grd_b, grd_c], axis=2)
-    data = np.stack([grd_a, grd_c], axis=2)
+    #data = np.stack([grd_a, grd_c], axis=2)
+    data = np.stack([grd_c], axis=2)
     data = np.expand_dims(data, axis=0)
 
     nn = SRCNN()