add unit test for pb2pth model

Author: ChiahsinChu

deepmd/pt/train/training.py

@@ -615,13 +615,14 @@ def single_model_finetune(
             frz_model = torch.jit.load(init_frz_model, map_location=DEVICE)
             try:
                 self.model.load_state_dict(frz_model.state_dict())
-            except RuntimeError as e:
-                if "Missing key(s) in state_dict" in str(e):
+            except RuntimeError as err_msg:
+                if "Missing key(s) in state_dict" in str(
+                    err_msg
+                ) or "Unexpected key(s) in state_dict" in str(err_msg):
                     self.model.load_state_dict(frz_model.state_dict(), strict=False)
-                    log.warning("Use strict=False to ignore non-matching keys.")
-                    log.warning(f"Model state_dict mismatch detected: {e}")
+                    log.warning("Loaded with strict=False to ignore non-matching keys.")
                 else:
-                    raise e
+                    raise
 
         # Get model prob for multi-task
         if self.multi_task:

source/tests/pt/model/models/se_e2_a.json

@@ -0,0 +1,77 @@
+{
+  "model": {
+    "type_map": [
+      "O",
+      "H"
+    ],
+    "descriptor": {
+      "type": "se_e2_a",
+      "sel": [
+        23,
+        46
+      ],
+      "rcut_smth": 0.50,
+      "rcut": 4.00,
+      "neuron": [
+        2,
+        4
+      ],
+      "resnet_dt": false,
+      "axis_neuron": 4,
+      "type_one_side": true,
+      "seed": 1,
+      "_comment": " that's all"
+    },
+    "fitting_net": {
+      "neuron": [
+        120
+      ],
+      "resnet_dt": true,
+      "seed": 1,
+      "_comment": " that's all"
+    },
+    "data_stat_nbatch": 20,
+    "_comment": " that's all"
+  },
+  "learning_rate": {
+    "type": "exp",
+    "decay_steps": 5000,
+    "start_lr": 0.001,
+    "stop_lr": 3.51e-8,
+    "_comment": "that's all"
+  },
+  "loss": {
+    "type": "ener",
+    "start_pref_e": 0.02,
+    "limit_pref_e": 1,
+    "start_pref_f": 1000,
+    "limit_pref_f": 1,
+    "_comment": " that's all"
+  },
+  "training": {
+    "training_data": {
+      "systems": [
+        "../data/data_0",
+        "../data/data_1",
+        "../data/data_2"
+      ],
+      "batch_size": 1,
+      "_comment": "that's all"
+    },
+    "validation_data": {
+      "systems": [
+        "../data/data_3"
+      ],
+      "batch_size": 1,
+      "numb_btch": 3,
+      "_comment": "that's all"
+    },
+    "numb_steps": 100000,
+    "seed": 10,
+    "disp_file": "lcurve.out",
+    "disp_freq": 100,
+    "save_freq": 10000,
+    "_comment": "that's all"
+  },
+  "_comment": "that's all"
+}