Merge branch 'master' of github.com:aiqm/torchani

tests/test_padding.py

@@ -80,29 +80,6 @@ class TestPaddings(unittest.TestCase):
         self.assertEqual((atomic_properties['species'] - expected_species).abs().max().item(), 0)
         self.assertEqual(atomic_properties['coordinates'].abs().max().item(), 0)
 
-    def testPadSpecies(self):
-        species1 = torch.tensor([
-            [0, 2, 3, 1],
-            [0, 2, 3, 1],
-            [0, 2, 3, 1],
-            [0, 2, 3, 1],
-            [0, 2, 3, 1],
-        ])
-        species2 = torch.tensor([[3, 2, 0, 1, 0]]).expand(2, 5)
-        species = torchani.utils.pad([species1, species2])
-        self.assertEqual(species.shape[0], 7)
-        self.assertEqual(species.shape[1], 5)
-        expected_species = torch.tensor([
-            [0, 2, 3, 1, -1],
-            [0, 2, 3, 1, -1],
-            [0, 2, 3, 1, -1],
-            [0, 2, 3, 1, -1],
-            [0, 2, 3, 1, -1],
-            [3, 2, 0, 1, 0],
-            [3, 2, 0, 1, 0],
-        ])
-        self.assertEqual((species - expected_species).abs().max().item(), 0)
-
     def testPresentSpecies(self):
         species = torch.tensor([0, 1, 1, 0, 3, 7, -1, -1])
         present_species = torchani.utils.present_species(species)

torchani/utils.py

@@ -8,33 +8,6 @@ from typing import Tuple, NamedTuple, Optional
 from .nn import SpeciesEnergies
 
 
-def pad(species):
-    """Put different species together into single tensor.
-
-    If the species are from molecules of different number of total atoms, then
-    ghost atoms with atom type -1 will be added to make it fit into the same
-    shape.
-
-    Arguments:
-        species (:class:`collections.abc.Sequence`): sequence of species.
-            Species must be of shape ``(N, A)``, where ``N`` is the number of
-            3D structures, ``A`` is the number of atoms.
-
-    Returns:
-        :class:`torch.Tensor`: species batched together.
-    """
-    max_atoms = max([s.shape[1] for s in species])
-    padded_species = []
-    for s in species:
-        natoms = s.shape[1]
-        if natoms < max_atoms:
-            padding = torch.full((s.shape[0], max_atoms - natoms), -1,
-                                 dtype=torch.long, device=s.device)
-            s = torch.cat([s, padding], dim=1)
-        padded_species.append(s)
-    return torch.cat(padded_species)
-
-
 def pad_atomic_properties(atomic_properties, padding_values=defaultdict(lambda: 0.0, species=-1)):
     """Put a sequence of atomic properties together into single tensor.
 
@@ -350,6 +323,6 @@ def vibrational_analysis(masses, hessian, mode_type='MDU', unit='cm^-1'):
     return VibAnalysis(wavenumbers, modes, fconstants, rmasses)
 
 
-__all__ = ['pad', 'pad_atomic_properties', 'present_species', 'hessian',
+__all__ = ['pad_atomic_properties', 'present_species', 'hessian',
            'vibrational_analysis', 'strip_redundant_padding',
            'ChemicalSymbolsToInts']