""" Copyright (c) Facebook, Inc. and its affiliates. This source code is licensed under the MIT license found in the LICENSE file in the root directory of this source tree. """ import os import ase import numpy as np import pytest import torch from ase.io import read from ase.lattice.cubic import FaceCenteredCubic from ase.build import molecule from pymatgen.io.ase import AseAtomsAdaptor from torch_geometric.transforms.radius_graph import RadiusGraph from torch_geometric.utils.sort_edge_index import sort_edge_index from ocpmodels.common.utils import get_pbc_distances, radius_graph_pbc from ocpmodels.datasets import data_list_collater from ocpmodels.preprocessing import AtomsToGraphs @pytest.fixture(scope="class") def load_data(request): atoms = read( os.path.join(os.path.dirname(os.path.abspath(__file__)), "atoms.json"), index=0, format="json", ) a2g = AtomsToGraphs( max_neigh=200, radius=6, r_energy=True, r_forces=True, r_distances=True, ) data_list = a2g.convert_all([atoms]) request.cls.data = data_list[0] @pytest.mark.usefixtures("load_data") class TestRadiusGraphPBC: def test_radius_graph_pbc(self): data = self.data batch = data_list_collater([data] * 5) out = radius_graph_pbc( batch, radius=6, max_num_neighbors_threshold=200, pbc=[True, True, False], ) edge_index, cell_offsets, neighbors = out # Combine both edge indices and offsets to one tensor a2g_features = torch.cat( (batch.edge_index, batch.cell_offsets.T), dim=0 ).T rgpbc_features = torch.cat( (edge_index, cell_offsets.T), dim=0 ).T.long() # Convert rows of tensors to sets. The order of edges is not guaranteed a2g_features = {tuple(x.tolist()) for x in a2g_features} rgpbc_features = {tuple(x.tolist()) for x in rgpbc_features} # Ensure sets are not empty assert len(a2g_features) > 0 assert len(rgpbc_features) > 0 # Ensure sets are the same assert a2g_features == rgpbc_features def test_bulk(self): radius = 10 # Must be sufficiently large to ensure all edges are retained max_neigh = 2000 a2g = AtomsToGraphs(radius=radius, max_neigh=max_neigh) structure = FaceCenteredCubic("Pt", size=[1, 2, 3]) # Use the radius as a multiplier to ensure adequate distance between repeated cells structure.cell[0] *= radius structure.cell[1] *= radius structure.cell[2] *= radius data = a2g.convert(structure) non_pbc = data.edge_index.shape[1] # Get number of neighbors for all possible PBC combinations structure.cell[0] /= radius data = a2g.convert(structure) pbc_x = data.edge_index.shape[1] structure.cell[1] /= radius data = a2g.convert(structure) pbc_xy = data.edge_index.shape[1] structure.cell[0] *= radius data = a2g.convert(structure) pbc_y = data.edge_index.shape[1] structure.cell[2] /= radius data = a2g.convert(structure) pbc_yz = data.edge_index.shape[1] structure.cell[1] *= radius data = a2g.convert(structure) pbc_z = data.edge_index.shape[1] structure.cell[0] /= radius data = a2g.convert(structure) pbc_xz = data.edge_index.shape[1] structure.cell[1] /= radius data = a2g.convert(structure) pbc_all = data.edge_index.shape[1] # Ensure edges are actually found assert non_pbc > 0 assert pbc_x > non_pbc assert pbc_y > non_pbc assert pbc_z > non_pbc assert pbc_xy > max(pbc_x, pbc_y) assert pbc_yz > max(pbc_y, pbc_z) assert pbc_xz > max(pbc_x, pbc_z) assert pbc_all > max(pbc_xy, pbc_yz, pbc_xz) structure = FaceCenteredCubic("Pt", size=[1, 2, 3]) data = a2g.convert(structure) batch = data_list_collater([data]) # Ensure radius_graph_pbc matches radius_graph for non-PBC condition RG = RadiusGraph(r=radius, max_num_neighbors=max_neigh) out = radius_graph_pbc( batch, radius=radius, max_num_neighbors_threshold=max_neigh, pbc=[False, False, False], ) assert out[-1].item() == non_pbc radgraph = RG(batch) assert ( sort_edge_index(out[0]) == sort_edge_index(radgraph.edge_index) ).all() # Ensure radius_graph_pbc matches AtomsToGraphs for all PBC combinations out = radius_graph_pbc( batch, radius=radius, max_num_neighbors_threshold=max_neigh, pbc=[True, False, False], ) assert out[-1].item() == pbc_x out = radius_graph_pbc( batch, radius=radius, max_num_neighbors_threshold=max_neigh, pbc=[False, True, False], ) assert out[-1].item() == pbc_y out = radius_graph_pbc( batch, radius=radius, max_num_neighbors_threshold=max_neigh, pbc=[False, False, True], ) assert out[-1].item() == pbc_z out = radius_graph_pbc( batch, radius=radius, max_num_neighbors_threshold=max_neigh, pbc=[True, True, False], ) assert out[-1].item() == pbc_xy out = radius_graph_pbc( batch, radius=radius, max_num_neighbors_threshold=max_neigh, pbc=[False, True, True], ) assert out[-1].item() == pbc_yz out = radius_graph_pbc( batch, radius=radius, max_num_neighbors_threshold=max_neigh, pbc=[True, False, True], ) assert out[-1].item() == pbc_xz out = radius_graph_pbc( batch, radius=radius, max_num_neighbors_threshold=max_neigh, pbc=[True, True, True], ) assert out[-1].item() == pbc_all def test_molecule(self): radius = 6 max_neigh = 100 a2g = AtomsToGraphs(radius=radius, max_neigh=max_neigh) structure = molecule("CH3COOH") structure.cell = [[20, 0, 0], [0, 20, 0], [0, 0, 20]] data = a2g.convert(structure) batch = data_list_collater([data] * 5) out = radius_graph_pbc( batch, radius=radius, max_num_neighbors_threshold=max_neigh, pbc=[False, False, False], ) edge_index, cell_offsets, neighbors = out # Combine both edge indices and offsets to one tensor a2g_features = torch.cat( (batch.edge_index, batch.cell_offsets.T), dim=0 ).T rgpbc_features = torch.cat( (edge_index, cell_offsets.T), dim=0 ).T.long() # Convert rows of tensors to sets. The order of edges is not guaranteed a2g_features = {tuple(x.tolist()) for x in a2g_features} rgpbc_features = {tuple(x.tolist()) for x in rgpbc_features} # Ensure sets are not empty assert len(a2g_features) > 0 assert len(rgpbc_features) > 0 # Ensure sets are the same assert a2g_features == rgpbc_features