snapshot...

modules/deeplearning/srcnn_l1b_l2.py

@@ -668,10 +668,11 @@ class SRCNN:
         preds = np.concatenate(self.test_preds)
         print(labels.shape, preds.shape)
 
-        labels_denorm = denormalize(labels, label_param, mean_std_dct)
-        preds_denorm = denormalize(preds, label_param, mean_std_dct)
+        # labels_denorm = denormalize(labels, label_param, mean_std_dct)
+        # preds_denorm = denormalize(preds, label_param, mean_std_dct)
 
-        return labels_denorm, preds_denorm
+        # return labels_denorm, preds_denorm
+        return labels, preds
 
     def do_evaluate(self, data, ckpt_dir):
 
@@ -714,9 +715,11 @@ class SRCNN:
 
 def run_restore_static(directory, ckpt_dir, out_file=None):
     nn = SRCNN()
-    labels_denorm, preds_denorm = nn.run_restore(directory, ckpt_dir)
+    # labels_denorm, preds_denorm = nn.run_restore(directory, ckpt_dir)
+    labels, preds = nn.run_restore(directory, ckpt_dir)
     if out_file is not None:
-        np.save(out_file, [labels_denorm, preds_denorm])
+        # np.save(out_file, [labels_denorm, preds_denorm])
+        np.save(out_file, [labels, preds])
 
 
 def run_evaluate_static(in_file, out_file, ckpt_dir):