test(pt-expt): add .pte and .pt2 tests for dp convert-backend

source/tests/infer/fparam_aparam-testcase.yaml

@@ -26,7 +26,7 @@ model_def_script:
         "set_davg_zero": False,
         "trainable": True,
         "type": "se_e2_a",
-        "type_one_side": False,
+        "type_one_side": True,
       },
     "fitting_net":
       {

source/tests/infer/fparam_aparam.yaml

@@ -526,7 +526,7 @@ model:
     embeddings:
       "@class": NetworkCollection
       "@version": 1
-      ndim: 2
+      ndim: 1
       network_type: embedding_network
       networks:
         - "@class": EmbeddingNetwork
@@ -916,7 +916,7 @@ model:
     type: se_e2_a
     type_map: &id001
       - O
-    type_one_side: false
+    type_one_side: true
   fitting:
     "@class": Fitting
     "@variables":
@@ -2012,7 +2012,7 @@ model_def_script:
     set_davg_zero: false
     trainable: true
     type: se_e2_a
-    type_one_side: false
+    type_one_side: true
   fitting_net:
     activation_function: tanh
     atom_ener: *id004

source/tests/infer/fparam_aparam_default.yaml

@@ -526,7 +526,7 @@ model:
     embeddings:
       "@class": NetworkCollection
       "@version": 1
-      ndim: 2
+      ndim: 1
       network_type: embedding_network
       networks:
         - "@class": EmbeddingNetwork
@@ -916,7 +916,7 @@ model:
     type: se_e2_a
     type_map: &id001
       - O
-    type_one_side: false
+    type_one_side: true
   fitting:
     "@class": Fitting
     "@variables":
@@ -2021,7 +2021,7 @@ model_def_script:
     set_davg_zero: false
     trainable: true
     type: se_e2_a
-    type_one_side: false
+    type_one_side: true
   fitting_net:
     activation_function: tanh
     atom_ener: *id004

source/tests/infer/test_models.py

@@ -13,6 +13,7 @@
 )
 
 from ..consistent.common import (
+    INSTALLED_PT_EXPT,
     parameterized,
 )
 from .case import (
@@ -28,14 +29,24 @@
         "se_e2_r",
         "fparam_aparam",
     ),  # key
-    (".pb", ".pth"),  # model extension
+    (".pb", ".pth", ".pte", ".pt2"),  # model extension
 )
 class TestDeepPot(unittest.TestCase):
     # moved from tests/tf/test_deeppot_a.py
 
     @classmethod
     def setUpClass(cls) -> None:
         key, extension = cls.param
+        if extension in (".pte", ".pt2") and not INSTALLED_PT_EXPT:
+            raise unittest.SkipTest("pt_expt backend not installed")
+        if key in ("se_e2_a", "se_e2_r") and extension in (".pte", ".pt2"):
+            raise unittest.SkipTest(
+                "type_one_side=False is not supported for pt_expt export"
+            )
+        if key == "se_e2_r" and extension == ".pth":
+            raise unittest.SkipTest(
+                "se_e2_r type_one_side is not supported for PyTorch models"
+            )
         cls.case = get_cases()[key]
         cls.model_name = cls.case.get_model(extension)
         cls.dp = DeepEval(cls.model_name)
@@ -44,13 +55,6 @@ def setUpClass(cls) -> None:
     def tearDownClass(cls) -> None:
         cls.dp = None
 
-    def setUp(self) -> None:
-        key, extension = self.param
-        if key == "se_e2_r" and extension == ".pth":
-            self.skipTest(
-                reason="se_e2_r type_one_side is not supported for PyTorch models"
-            )
-
     def test_attrs(self) -> None:
         assert isinstance(self.dp, DeepPot)
         self.assertEqual(self.dp.get_ntypes(), self.case.ntypes)
@@ -153,6 +157,8 @@ def test_1frame_atm(self) -> None:
 
     def test_descriptor(self) -> None:
         _, extension = self.param
+        if extension in (".pte", ".pt2"):
+            self.skipTest("eval_descriptor not supported for pt_expt models")
         for ii, result in enumerate(self.case.results):
             if result.descriptor is None:
                 continue
@@ -166,8 +172,8 @@ def test_descriptor(self) -> None:
 
     def test_fitting_last_layer(self) -> None:
         _, extension = self.param
-        if extension == ".pb":
-            self.skipTest("fitting_last_layer not supported for TensorFlow models")
+        if extension in (".pb", ".pte", ".pt2"):
+            self.skipTest("fitting_last_layer not supported for this backend")
         for ii, result in enumerate(self.case.results):
             if result.fit_ll is None:
                 continue