fix errors

Signed-off-by: Jinzhe Zeng <[email protected]>

deepmd/pd/train/training.py

@@ -1088,7 +1088,7 @@ def get_data(self, is_train=True, task_key="Default"):
     def print_header(self, fout, train_results, valid_results):
         train_keys = sorted(train_results.keys())
         print_str = ""
-        print_str += "# %5s" % "step"
+        print_str += "# {:5s}".format("step")
         if not self.multi_task:
             if valid_results:
                 prop_fmt = "   %11s %11s"
@@ -1111,15 +1111,15 @@ def print_header(self, fout, train_results, valid_results):
                     prop_fmt = "   %11s"
                     for k in sorted(train_results[model_key].keys()):
                         print_str += prop_fmt % (k + f"_trn_{model_key}")
-        print_str += "   %8s\n" % "lr"
+        print_str += "   {:8s}\n".format("lr")
         print_str += "# If there is no available reference data, rmse_*_{val,trn} will print nan\n"
         fout.write(print_str)
         fout.flush()
 
     def print_on_training(self, fout, step_id, cur_lr, train_results, valid_results):
         train_keys = sorted(train_results.keys())
         print_str = ""
-        print_str += "%7d" % step_id
+        print_str += f"{step_id:7d}"
         if not self.multi_task:
             if valid_results:
                 prop_fmt = "   %11.2e %11.2e"

deepmd/pt/train/training.py

@@ -1132,7 +1132,7 @@ def get_data(self, is_train=True, task_key="Default"):
     def print_header(self, fout, train_results, valid_results) -> None:
         train_keys = sorted(train_results.keys())
         print_str = ""
-        print_str += f"# {'step':5s}"
+        print_str += "# {:5s}".format("step")
         if not self.multi_task:
             if valid_results:
                 prop_fmt = "   %11s %11s"
@@ -1155,7 +1155,7 @@ def print_header(self, fout, train_results, valid_results) -> None:
                     prop_fmt = "   %11s"
                     for k in sorted(train_results[model_key].keys()):
                         print_str += prop_fmt % (k + f"_trn_{model_key}")
-        print_str += f"   {'lr':8s}\n"
+        print_str += "   {:8s}\n".format("lr")
         print_str += "# If there is no available reference data, rmse_*_{val,trn} will print nan\n"
         fout.write(print_str)
         fout.flush()

deepmd/tf/train/trainer.py

@@ -420,7 +420,12 @@ def train(self, train_data=None, valid_data=None) -> None:
         is_first_step = True
         self.cur_batch = cur_batch
         log.info(
-            f"start training at lr {run_sess(self.sess, self.learning_rate):.2e} (== {self.lr.value(cur_batch):.2e}), decay_step {self.lr.decay_steps_}, decay_rate {self.lr.decay_rate_:f}, final lr will be {self.lr.value(stop_batch):.2e}"
+            "start training at lr %.2e (== %.2e), decay_step %d, decay_rate %f, final lr will be %.2e",
+            run_sess(self.sess, self.learning_rate),
+            self.lr.value(cur_batch),
+            self.lr.decay_steps_,
+            self.lr.decay_rate_,
+            self.lr.value(stop_batch),
         )
 
         prf_options = None
@@ -595,7 +600,7 @@ def train(self, train_data=None, valid_data=None) -> None:
         if self.timing_in_training and elapsed_batch // self.disp_freq > 0:
             if elapsed_batch >= 2 * self.disp_freq:
                 log.info(
-                    "average training time: %.4f s/batcsh (exclude first %d batches)",
+                    "average training time: %.4f s/batch (exclude first %d batches)",
                     total_train_time
                     / (
                         elapsed_batch // self.disp_freq * self.disp_freq
@@ -685,7 +690,7 @@ def valid_on_the_fly(
     @staticmethod
     def print_header(fp, train_results, valid_results) -> None:
         print_str = ""
-        print_str += f'# {"step":5s}'
+        print_str += "# {:5s}".format("step")
         if valid_results is not None:
             prop_fmt = "   %11s %11s"
             for k in train_results.keys():
@@ -694,7 +699,7 @@ def print_header(fp, train_results, valid_results) -> None:
             prop_fmt = "   %11s"
             for k in train_results.keys():
                 print_str += prop_fmt % (k + "_trn")
-        print_str += f'   {"lr":8s}\n'
+        print_str += "   {:8s}\n".format("lr")
         print_str += "# If there is no available reference data, rmse_*_{val,trn} will print nan\n"
         fp.write(print_str)
         fp.flush()

deepmd/utils/data_system.py

@@ -664,14 +664,25 @@ def print_summary(
     log.info(
         f"---Summary of DataSystem: {name:13s}-----------------------------------------------"
     )
-    log.info(f"found {nsystems} system(s):")
+    log.info("found %d system(s):", nsystems)
     log.info(
-        ("{}  ".format(_format_name_length("system", sys_width)))
-        + (f'{"natoms":6s}  {"bch_sz":6s}  {"n_bch":6s}  {"prob":9s}  {"pbc":3s}')
+        "%s  %6s  %6s  %6s  %9s  %3s",
+        _format_name_length("system", sys_width),
+        "natoms",
+        "bch_sz",
+        "n_bch",
+        "prob",
+        "pbc",
     )
     for ii in range(nsystems):
         log.info(
-            f'{_format_name_length(system_dirs[ii], sys_width)}  {natoms[ii]:6d}  {batch_size[ii]:6d}  {nbatches[ii]:6d}  {sys_probs[ii]:9.3e}  {"T" if pbc[ii] else "F":3s}'
+            "%s %6d %6d %6d %9.3e %3s",
+            _format_name_length(system_dirs[ii], sys_width),
+            natoms[ii],
+            batch_size[ii],
+            nbatches[ii],
+            sys_probs[ii],
+            "T" if pbc[ii] else "F",
         )
     log.info(
         "--------------------------------------------------------------------------------------"

source/tests/pd/model/test_model.py

@@ -71,14 +71,14 @@ def paddle2tf(paddle_name, last_layer_id=None):
             return None
         layer_id = int(fields[4 + offset]) + 1
         weight_type = fields[5 + offset]
-        ret = "filter_type_all/%s_%d_%d:0" % (weight_type, layer_id, element_id)
+        ret = f"filter_type_all/{weight_type}_{layer_id}_{element_id}:0"
     elif fields[1] == "fitting_net":
         layer_id = int(fields[4 + offset])
         weight_type = fields[5 + offset]
         if layer_id != last_layer_id:
-            ret = "layer_%d_type_%d/%s:0" % (layer_id, element_id, weight_type)
+            ret = f"layer_{layer_id}_type_{element_id}/{weight_type}:0"
         else:
-            ret = "final_layer_type_%d/%s:0" % (element_id, weight_type)
+            ret = f"final_layer_type_{element_id}/{weight_type}:0"
     else:
         raise RuntimeError(f"Unexpected parameter name: {paddle_name}")
     return ret