Reduce user inputs for training custom models (#460)

* Reduce user inputs for training custom models

* Update nnp_training.py

examples/nnp_training.py

@@ -58,10 +58,10 @@ Zeta = torch.tensor([3.2000000e+01], device=device)
 ShfZ = torch.tensor([1.9634954e-01, 5.8904862e-01, 9.8174770e-01, 1.3744468e+00, 1.7671459e+00, 2.1598449e+00, 2.5525440e+00, 2.9452431e+00], device=device)
 EtaA = torch.tensor([8.0000000e+00], device=device)
 ShfA = torch.tensor([9.0000000e-01, 1.5500000e+00, 2.2000000e+00, 2.8500000e+00], device=device)
-num_species = 4
+species_order = ['H', 'C', 'N', 'O']
+num_species = len(species_order)
 aev_computer = torchani.AEVComputer(Rcr, Rca, EtaR, ShfR, EtaA, Zeta, ShfA, ShfZ, num_species)
 energy_shifter = torchani.utils.EnergyShifter(None)
-species_to_tensor = torchani.utils.ChemicalSymbolsToInts(['H', 'C', 'N', 'O'])
 
 ###############################################################################
 # Now let's setup datasets. These paths assumes the user run this script under
@@ -82,7 +82,7 @@ except NameError:
 dspath = os.path.join(path, '../dataset/ani1-up_to_gdb4/ani_gdb_s01.h5')
 batch_size = 2560
 
-training, validation = torchani.data.load(dspath).subtract_self_energies(energy_shifter).species_to_indices().shuffle().split(0.8, None)
+training, validation = torchani.data.load(dspath).subtract_self_energies(energy_shifter).species_to_indices(species_order).shuffle().split(0.8, None)
 training = training.collate(batch_size).cache()
 validation = validation.collate(batch_size).cache()
 print('Self atomic energies: ', energy_shifter.self_energies)

examples/nnp_training_force.py

@@ -35,10 +35,10 @@ Zeta = torch.tensor([3.2000000e+01], device=device)
 ShfZ = torch.tensor([1.9634954e-01, 5.8904862e-01, 9.8174770e-01, 1.3744468e+00, 1.7671459e+00, 2.1598449e+00, 2.5525440e+00, 2.9452431e+00], device=device)
 EtaA = torch.tensor([8.0000000e+00], device=device)
 ShfA = torch.tensor([9.0000000e-01, 1.5500000e+00, 2.2000000e+00, 2.8500000e+00], device=device)
-num_species = 4
+species_order = ['H', 'C', 'N', 'O']
+num_species = len(species_order)
 aev_computer = torchani.AEVComputer(Rcr, Rca, EtaR, ShfR, EtaA, Zeta, ShfA, ShfZ, num_species)
 energy_shifter = torchani.utils.EnergyShifter(None)
-species_to_tensor = torchani.utils.ChemicalSymbolsToInts(['H', 'C', 'N', 'O'])
 
 
 try:
@@ -49,7 +49,7 @@ dspath = os.path.join(path, '../dataset/ani-1x/sample.h5')
 
 batch_size = 2560
 
-training, validation = torchani.data.load(dspath).subtract_self_energies(energy_shifter).species_to_indices().shuffle().split(0.8, None)
+training, validation = torchani.data.load(dspath).subtract_self_energies(energy_shifter).species_to_indices(species_order).shuffle().split(0.8, None)
 training = training.collate(batch_size).cache()
 validation = validation.collate(batch_size).cache()