remove test warning (#584)

tests/common_aev_test.py

 import torch
 import torchani
 import os
+from torchani.testing import TestCase
 
 
-class _TestAEVBase(torchani.testing.TestCase):
+class _TestAEVBase(TestCase):
 
     def setUp(self):
         path = os.path.dirname(os.path.realpath(__file__))

tests/test_aev.py

@@ -9,6 +9,7 @@ import ase.io
 import math
 import traceback
 from common_aev_test import _TestAEVBase
+from torchani.testing import TestCase
 
 
 path = os.path.dirname(os.path.realpath(__file__))
@@ -16,7 +17,7 @@ const_file = os.path.join(path, '../torchani/resources/ani-1x_8x/rHCNO-5.2R_16-3
 N = 97
 
 
-class TestAEVConstructor(torchani.testing.TestCase):
+class TestAEVConstructor(TestCase):
     # Test that checks that the friendly constructor
     # reproduces the values from ANI1x with the correct parameters
     def testCoverLinearly(self):
@@ -38,7 +39,7 @@ class TestAEVConstructor(torchani.testing.TestCase):
             self.assertEqual(c, ca)
 
 
-class TestIsolated(torchani.testing.TestCase):
+class TestIsolated(TestCase):
     # Tests that there is no error when atoms are separated
     # a distance greater than the cutoff radius from all other atoms
     # this can throw an IndexError for large distances or lone atoms
@@ -157,7 +158,7 @@ class TestAEVJIT(TestAEV):
         self.aev_computer = torch.jit.script(self.aev_computer)
 
 
-class TestPBCSeeEachOther(torchani.testing.TestCase):
+class TestPBCSeeEachOther(TestCase):
     def setUp(self):
         consts = torchani.neurochem.Constants(const_file)
         self.aev_computer = torchani.AEVComputer(**consts).to(torch.double)
@@ -261,7 +262,7 @@ class TestPBCSeeEachOther(torchani.testing.TestCase):
         self.assertEqual(atom_index2.tolist(), [1])
 
 
-class TestAEVOnBoundary(torchani.testing.TestCase):
+class TestAEVOnBoundary(TestCase):
 
     def setUp(self):
         self.eps = 1e-9
@@ -307,7 +308,7 @@ class TestAEVOnBoundary(torchani.testing.TestCase):
             self.assertEqual(aev, self.aev)
 
 
-class TestAEVOnBenzenePBC(torchani.testing.TestCase):
+class TestAEVOnBenzenePBC(TestCase):
 
     def setUp(self):
         consts = torchani.neurochem.Constants(const_file)

tests/test_al.py

@@ -2,9 +2,10 @@ import torch
 import torchani
 import math
 import unittest
+from torchani.testing import TestCase
 
 
-class TestALAtomic(torchani.testing.TestCase):
+class TestALAtomic(TestCase):
     def setUp(self):
         self.device = torch.device(
             'cuda' if torch.cuda.is_available() else 'cpu')

tests/test_ase.py

@@ -4,6 +4,7 @@ from ase.md.nptberendsen import NPTBerendsen
 from ase import units
 from ase.io import read
 from ase.calculators.test import numeric_force
+from torchani.testing import TestCase
 import numpy as np
 import torch
 import torchani
@@ -21,7 +22,7 @@ def get_numeric_force(atoms, eps):
     return fn
 
 
-class TestASE(torchani.testing.TestCase):
+class TestASE(TestCase):
 
     def setUp(self):
         self.model = torchani.models.ANI1x(model_index=0).double()

tests/test_data.py

@@ -2,6 +2,7 @@ import os
 import torch
 import torchani
 import unittest
+from torchani.testing import TestCase
 
 path = os.path.dirname(os.path.realpath(__file__))
 dataset_path = os.path.join(path, '../dataset/ani-1x/sample.h5')
@@ -9,7 +10,7 @@ batch_size = 256
 ani1x_sae_dict = {'H': -0.60095298, 'C': -38.08316124, 'N': -54.7077577, 'O': -75.19446356}
 
 
-class TestData(torchani.testing.TestCase):
+class TestData(TestCase):
 
     def testTensorShape(self):
         ds = torchani.data.load(dataset_path).subtract_self_energies(ani1x_sae_dict).species_to_indices().shuffle().collate(batch_size).cache()

tests/test_energies.py

@@ -3,13 +3,14 @@ import torchani
 import unittest
 import os
 import pickle
+from torchani.testing import TestCase
 
 
 path = os.path.dirname(os.path.realpath(__file__))
 N = 97
 
 
-class TestCorrectInput(torchani.testing.TestCase):
+class TestCorrectInput(TestCase):
 
     def setUp(self):
         self.model = torchani.models.ANI1x(model_index=0, periodic_table_index=False)
@@ -32,7 +33,7 @@ class TestCorrectInput(torchani.testing.TestCase):
         self.assertRaises(AssertionError, self.model, (torch.tensor([0, 1, 2, 3]), torch.zeros((4, 3))))
 
 
-class TestEnergies(torchani.testing.TestCase):
+class TestEnergies(TestCase):
     # tests the predicions for a torchani.nn.Sequential(AEVComputer(),
     # ANIModel(), EnergyShifter()) against precomputed values
 

tests/test_ensemble.py

@@ -3,12 +3,13 @@ import pickle
 import os
 import torch
 import torchani
+from torchani.testing import TestCase
 
 path = os.path.dirname(os.path.realpath(__file__))
 N = 10
 
 
-class TestEnsemble(torchani.testing.TestCase):
+class TestEnsemble(TestCase):
 
     def setUp(self):
         self.conformations = 20

tests/test_forces.py

@@ -3,12 +3,13 @@ import torchani
 import unittest
 import os
 import pickle
+from torchani.testing import TestCase
 
 path = os.path.dirname(os.path.realpath(__file__))
 N = 97
 
 
-class TestForce(torchani.testing.TestCase):
+class TestForce(TestCase):
 
     def setUp(self):
         model = torchani.models.ANI1x(model_index=0)

tests/test_grad.py

@@ -3,11 +3,12 @@ import torchani
 import unittest
 import os
 import pickle
+from torchani.testing import TestCase
 
 path = os.path.dirname(os.path.realpath(__file__))
 
 
-class TestGrad(torchani.testing.TestCase):
+class TestGrad(TestCase):
     # torch.autograd.gradcheck and torch.autograd.gradgradcheck verify that
     # the numerical and analytical gradient and hessian of a function
     # matches to within a given tolerance.

tests/test_jit_builtin_models.py

@@ -2,13 +2,14 @@ import torch
 import torchani
 import unittest
 import os
+from torchani.testing import TestCase
 
 
 path = os.path.dirname(os.path.realpath(__file__))
 dspath = os.path.join(path, '../dataset/ani-1x/sample.h5')
 
 
-class TestBuiltinModelsJIT(torchani.testing.TestCase):
+class TestBuiltinModelsJIT(TestCase):
     # Tests if JIT compiled models have the same output energies
     # as eager (non JIT) models
 

tests/test_neurochem.py

@@ -2,6 +2,7 @@ import torchani
 import torch
 import os
 import unittest
+from torchani.testing import TestCase
 
 
 path = os.path.dirname(os.path.realpath(__file__))
@@ -9,7 +10,7 @@ iptpath = os.path.join(path, 'test_data/inputtrain.ipt')
 dspath = os.path.join(path, '../dataset/ani1-up_to_gdb4/ani_gdb_s01.h5')
 
 
-class TestNeuroChem(torchani.testing.TestCase):
+class TestNeuroChem(TestCase):
 
     def testNeuroChemTrainer(self):
         d = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

tests/test_padding.py

 import unittest
 import torch
 import torchani
+from torchani.testing import TestCase
 
 
 b = torchani.utils.broadcast_first_dim
 
 
-class TestPaddings(torchani.testing.TestCase):
+class TestPaddings(TestCase):
 
     def testVectorSpecies(self):
         species1 = torch.tensor([[0, 2, 3, 1]])
@@ -90,7 +91,7 @@ class TestPaddings(torchani.testing.TestCase):
         self.assertEqual(expected, present_species)
 
 
-class TestStripRedundantPadding(torchani.testing.TestCase):
+class TestStripRedundantPadding(TestCase):
 
     def testStripRestore(self):
         species1 = torch.randint(4, (5, 4), dtype=torch.long)

tests/test_periodic_table_indexing.py

 import unittest
 import torch
 import torchani
+from torchani.testing import TestCase
 
 
-class TestSpeciesConverter(torchani.testing.TestCase):
+class TestSpeciesConverter(TestCase):
 
     def setUp(self):
         self.c = torchani.SpeciesConverter(['H', 'C', 'N', 'O'])
@@ -29,7 +30,7 @@ class TestSpeciesConverterJIT(TestSpeciesConverter):
         self.c = torch.jit.script(self.c)
 
 
-class TestBuiltinEnsemblePeriodicTableIndex(torchani.testing.TestCase):
+class TestBuiltinEnsemblePeriodicTableIndex(TestCase):
 
     def setUp(self):
         self.model1 = torchani.models.ANI1x()

tests/test_structure_optim.py

@@ -6,12 +6,13 @@ import copy
 import pickle
 from ase.optimize import BFGS
 from ase import Atoms
+from torchani.testing import TestCase
 
 
 path = os.path.dirname(os.path.realpath(__file__))
 
 
-class TestStructureOptimization(torchani.testing.TestCase):
+class TestStructureOptimization(TestCase):
 
     def setUp(self):
         self.tolerance = 1e-6

tests/test_utils.py

 import unittest
 import torch
 import torchani
+from torchani.testing import TestCase
 
 
-class TestUtils(torchani.testing.TestCase):
+class TestUtils(TestCase):
 
     def testChemicalSymbolsToInts(self):
         str2i = torchani.utils.ChemicalSymbolsToInts(['A', 'B', 'C', 'D', 'E', 'F'])

tests/test_vibrational.py

@@ -7,13 +7,14 @@ import ase
 import ase.optimize
 import ase.vibrations
 import numpy
+from torchani.testing import TestCase
 
 
 path = os.path.dirname(os.path.realpath(__file__))
 path = os.path.join(path, '../dataset/xyz_files/H2O.xyz')
 
 
-class TestVibrational(torchani.testing.TestCase):
+class TestVibrational(TestCase):
 
     def testVibrationalWavenumbers(self):
         model = torchani.models.ANI1x().double()

torchani/__init__.py

@@ -38,7 +38,6 @@ from . import models
 from . import units
 from pkg_resources import get_distribution, DistributionNotFound
 import warnings
-from . import testing
 
 try:
     __version__ = get_distribution(__name__).version
@@ -47,7 +46,7 @@ except DistributionNotFound:
     pass
 
 __all__ = ['AEVComputer', 'EnergyShifter', 'ANIModel', 'Ensemble', 'SpeciesConverter',
-           'utils', 'neurochem', 'models', 'units', 'testing']
+           'utils', 'neurochem', 'models', 'units']
 
 try:
     from . import ase  # noqa: F401