Bug fixings related to using validate dataset

deepmd/entrypoints/train.py

@@ -249,10 +249,10 @@ def _do_work(jdata: Dict[str, Any], run_opt: RunOptions):
 
     # init data
     train_data = get_data(jdata["training"]["training_data"], rcut, ipt_type_map, modifier)
-    train_data.print_summary()
-    if "validation_data" in jdata["training"]:
+    train_data.print_summary("training")
+    if jdata["training"]["validation_data"] is not None:
         valid_data = get_data(jdata["training"]["validation_data"], rcut, ipt_type_map, modifier)
-        valid_data.print_summary()
+        valid_data.print_summary("validation")
     else:
         valid_data = None
 
@@ -309,4 +309,4 @@ def get_modifier(modi_data=None):
             raise RuntimeError("unknown modifier type " + str(modi_data["type"]))
     else:
         modifier = None
-    return modifier
+    return modifier

deepmd/train/trainer.py

@@ -235,35 +235,17 @@ def _init_param(self, jdata):
             raise RuntimeError('get unknown fitting type when building loss function')
 
         # training
-        training_param = j_must_have(jdata, 'training')
-
-        # ! first .add() altered by Marián Rynik
-        tr_args = ClassArg()\
-                  .add('disp_file',     str,    default = 'lcurve.out')\
-                  .add('disp_freq',     int,    default = 100)\
-                  .add('save_freq',     int,    default = 1000)\
-                  .add('save_ckpt',     str,    default = 'model.ckpt')\
-                  .add('display_in_training', bool, default = True)\
-                  .add('timing_in_training',  bool, default = True)\
-                  .add('profiling',     bool,   default = False)\
-                  .add('profiling_file',str,    default = 'timeline.json')\
-                  .add('tensorboard',     bool,   default = False)\
-                  .add('tensorboard_log_dir',str,    default = 'log')
-                  # .add('sys_probs',   list    )\
-                  # .add('auto_prob', str, default = "prob_sys_size")
-        tr_data = tr_args.parse(training_param)
-        # not needed
-        # self.numb_test = tr_data['numb_test']
-        self.disp_file = tr_data['disp_file']
-        self.disp_freq = tr_data['disp_freq']
-        self.save_freq = tr_data['save_freq']
-        self.save_ckpt = tr_data['save_ckpt']
-        self.display_in_training = tr_data['display_in_training']
-        self.timing_in_training  = tr_data['timing_in_training']
-        self.profiling = tr_data['profiling']
-        self.profiling_file = tr_data['profiling_file']
-        self.tensorboard = tr_data['tensorboard']
-        self.tensorboard_log_dir = tr_data['tensorboard_log_dir']
+        tr_data = jdata['training']
+        self.disp_file = tr_data.get('disp_file', 'lcurve.out')
+        self.disp_freq = tr_data.get('disp_freq', 1000)
+        self.save_freq = tr_data.get('save_freq', 1000)
+        self.save_ckpt = tr_data.get('save_ckpt', 'model.ckpt')
+        self.display_in_training = tr_data.get('disp_training', True)
+        self.timing_in_training  = tr_data.get('time_training', True)
+        self.profiling = tr_data.get('profiling', False)
+        self.profiling_file = tr_data.get('profiling_file', 'timeline.json')
+        self.tensorboard = tr_data.get('tensorboard', False)
+        self.tensorboard_log_dir = tr_data.get('tensorboard_log_dir', 'log')
         # self.sys_probs = tr_data['sys_probs']
         # self.auto_prob_style = tr_data['auto_prob']
         self.useBN = False
@@ -272,11 +254,12 @@ def _init_param(self, jdata):
         else :
             self.numb_fparam = 0
 
-        if "validation_data" in tr_data.keys():  # if validation set specified
+        if tr_data.get("validation_data", None) is not None:
             self.valid_numb_batch = tr_data["validation_data"].get("numb_btch", 1)
         else:
             self.valid_numb_batch = 1
 
+
     def build (self, 
                data, 
                stop_batch = 0) :
@@ -622,7 +605,7 @@ def get_evaluation_results(self, data, numb_batch):
             feed_dict = self.get_feed_dict(batch, is_training=False)
             results = self.loss.eval(self.sess, feed_dict, natoms)
 
-            for k, v in results:
+            for k, v in results.items():
                 sum_results[k] = sum_results.get(k, 0.) + v * results["natoms"]
         avg_results = {k: v / sum_results["natoms"] for k, v in sum_results.items() if not k == "natoms"}
-        return avg_results
+        return avg_results

deepmd/utils/data_system.py

@@ -420,10 +420,10 @@ def _format_name_length(self, name, width) :
             name = '-- ' + name
             return name 
 
-    def print_summary(self) :
+    def print_summary(self, name) :
         # width 65
         sys_width = 42
-        log.info("---Summary of DataSystem--------------------------------------------------------------")
+        log.info(f"---Summary of DataSystem: {name:13s}-----------------------------------------------")
         log.info("found %d system(s):" % self.nsystems)
         log.info(("%s  " % self._format_name_length('system', sys_width)) + 
                  ("%6s  %6s  %6s  %6s  %5s  %3s" % ('natoms', 'bch_sz', 'n_bch', "n_test", 'prob', 'pbc')))