restore checkpoint path bug fixed

video_prediction_tools/main_scripts/main_train_models.py

@@ -259,7 +259,10 @@ class TrainModel(object):
         """
         Restore the train and validation losses in the pickle file if checkpoint is given 
         """
-        if not os.path.exists(os.path.join(self.output_dir,"checkpoint")):
+        if (not os.path.exists(os.path.join(self.output_dir,"checkpoint"))) and (self.checkpoint is not None):
+            raise FileNotFoundError("The user-configured checkpoint path {} does not exists!!".format(self.checkpoint))
+
+        if (not os.path.exists(os.path.join(self.output_dir,"checkpoint"))) or (self.checkpoint is None):
             train_losses = []
             val_losses = []
         else:
@@ -294,15 +297,11 @@ class TrainModel(object):
                 self.create_fetches_for_train()             # In addition to the loss, we fetch the optimizer
                 self.results = sess.run(self.fetches)       # ...and run it here!
                 train_losses.append(self.results["total_loss"])
-                print("t_start for training",self.results["inputs"]["T_start"])
-                print("len of t_start per iteration",len(self.results["inputs"]["T_start"]))
                 #Run and fetch losses for validation data
                 val_handle_eval = sess.run(self.val_handle)
                 self.create_fetches_for_val()
                 self.val_results = sess.run(self.val_fetches,feed_dict={self.train_handle: val_handle_eval})
                 val_losses.append(self.val_results["total_loss"])
-                print("t_start for validation",self.val_results["inputs"]["T_start"])
-                print("len of t_start per iteration",len(self.val_results["inputs"]["T_start"]))
                 self.write_to_summary()
                 self.print_results(step,self.results)
                 timeit_end = time.time()