Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

mdtraj xyz unit #17

Merged
merged 1 commit into from
Nov 6, 2020
Merged

mdtraj xyz unit #17

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
9 changes: 9 additions & 0 deletions ani/BenchmarkCudaANISymmetryFunctions.cu
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -6,6 +6,8 @@
#include <stdexcept>
#include <string>
#include <vector>
#include <stdio.h>
#include <time.h>

using namespace std;

Expand Down Expand Up @@ -149,7 +151,14 @@ int main(int argc, char* argv[]) {
{12.5, 3.1625, 14.1, 2.74889}
};
CudaANISymmetryFunctions ani(species.size(), 7, 5.1, 3.5, periodicBoxVectors.size() > 0, species, radialFunctions, angularFunctions, true);
clock_t start, finish;
double duration;
start = clock();
runBenchmark(ani, stoi(argv[2]), positions, species, periodicBoxVectors);
finish = clock();
duration = (double)(finish - start) / CLOCKS_PER_SEC;
printf(" %f s\n", duration);
printf(" %f ms/it\n", duration/stoi(argv[2])*1000);
}
catch (const exception& e) {
cout << e.what() << endl;
Expand Down
2 changes: 1 addition & 1 deletion pytorch/BenchmarkTorchANISymmetryFunctions.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -9,7 +9,7 @@

mol = mdtraj.load('molecules/2iuz_ligand.mol2')
species = torch.tensor([[atom.element.atomic_number for atom in mol.top.atoms]], device=device)
positions = torch.tensor(mol.xyz, dtype=torch.float32, requires_grad=True, device=device)
positions = torch.tensor(mol.xyz * 10, dtype=torch.float32, requires_grad=True, device=device)

nnp = torchani.models.ANI2x(periodic_table_index=True, model_index=None).to(device)
speciesPositions = nnp.species_converter((species, positions))
Expand Down
2 changes: 1 addition & 1 deletion pytorch/README.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -20,7 +20,7 @@ device = torch.device('cuda')
# Load a molecule
molecule = mdtraj.load('molecule.mol2')
species = torch.tensor([[atom.element.atomic_number for atom in molecule.top.atoms]], device=device)
positions = torch.tensor(molecule.xyz, dtype=torch.float32, requires_grad=True, device=device)
positions = torch.tensor(molecule.xyz * 10, dtype=torch.float32, requires_grad=True, device=device)

# Construct ANI-2x and replace its native featurizer with NNPOps implementation
nnp = torchani.models.ANI2x(periodic_table_index=True).to(device)
Expand Down
2 changes: 1 addition & 1 deletion pytorch/SymmetryFunctions.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -48,7 +48,7 @@ class TorchANISymmetryFunctions(torch.nn.Module):
# Load a molecule
>>> molecule = mdtraj.load('molecule.mol2')
>>> species = torch.tensor([[atom.element.atomic_number for atom in molecule.top.atoms]], device=device)
>>> positions = torch.tensor(molecule.xyz, dtype=torch.float32, requires_grad=True, device=device)
>>> positions = torch.tensor(molecule.xyz * 10, dtype=torch.float32, requires_grad=True, device=device)

# Construct ANI-2x and replace its native featurizer with NNPOps implementation
>>> nnp = torchani.models.ANI2x(periodic_table_index=True).to(device)
Expand Down
4 changes: 2 additions & 2 deletions pytorch/TestSymmetryFunctions.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -37,7 +37,7 @@ def test_compare_with_native(deviceString, molFile):

mol = mdtraj.load(f'molecules/{molFile}_ligand.mol2')
atomicNumbers = torch.tensor([[atom.element.atomic_number for atom in mol.top.atoms]], device=device)
atomicPositions = torch.tensor(mol.xyz, dtype=torch.float32, requires_grad=True, device=device)
atomicPositions = torch.tensor(mol.xyz * 10, dtype=torch.float32, requires_grad=True, device=device)

nnp = torchani.models.ANI2x(periodic_table_index=True).to(device)
energy_ref = nnp((atomicNumbers, atomicPositions)).energies
Expand All @@ -64,7 +64,7 @@ def test_model_serialization(deviceString, molFile):

mol = mdtraj.load(f'molecules/{molFile}_ligand.mol2')
atomicNumbers = torch.tensor([[atom.element.atomic_number for atom in mol.top.atoms]], device=device)
atomicPositions = torch.tensor(mol.xyz, dtype=torch.float32, requires_grad=True, device=device)
atomicPositions = torch.tensor(mol.xyz * 10, dtype=torch.float32, requires_grad=True, device=device)

nnp_ref = torchani.models.ANI2x(periodic_table_index=True).to(device)
nnp_ref.aev_computer = TorchANISymmetryFunctions(nnp_ref.aev_computer)
Expand Down