def test_builder_add_layer_and_param_groups(ctx):
shape_info = ShapeDictionary()
shape_info[BONDS] = (10, )
shape_info[ANGLES] = (20, )
builder = BoltzmannGeneratorBuilder(shape_info, **ctx)
# transform some fields
builder.add_layer(
CDFTransform(
TruncatedNormalDistribution(torch.zeros(10, **ctx), lower_bound=-torch.tensor(np.infty)),
),
what=[BONDS],
inverse=True,
param_groups=("group1", )
)
# transform all fields
builder.add_layer(
CouplingFlow(
AffineTransformer(
DenseNet([10, 20]), DenseNet([10, 20])
)
),
param_groups=("group1", "group2")
)
builder.targets[BONDS] = NormalDistribution(10, torch.zeros(10, **ctx))
builder.targets[ANGLES] = NormalDistribution(20, torch.zeros(20, **ctx))
generator = builder.build_generator().to(**ctx)
assert builder.param_groups["group1"] == list(generator.parameters())
assert builder.param_groups["group2"] == list(generator._flow._blocks[1].parameters())
generator.sample(10)
generator.kldiv(10)
def test_constrain_chirality(ala2, ctx):
bgmol = pytest.importorskip("bgmol")
top = ala2.system.mdtraj_topology
zmatrix, _ = bgmol.ZMatrixFactory(top).build_naive()
crd_transform = GlobalInternalCoordinateTransformation(zmatrix)
shape_info = ShapeDictionary.from_coordinate_transform(crd_transform)
builder = BoltzmannGeneratorBuilder(shape_info, target=ala2.system.energy_model, **ctx)
chiral_torsions = bgmol.is_chiral_torsion(crd_transform.torsion_indices, top)
builder.add_constrain_chirality(chiral_torsions)
builder.add_map_to_ic_domains()
builder.add_map_to_cartesian(crd_transform)
generator = builder.build_generator()
# play forward and backward
samples = generator.sample(20)
b, a, t, *_ = crd_transform.forward(samples)
assert torch.all(t[:, chiral_torsions] >= 0.5)
assert torch.all(t[:, chiral_torsions] <= 1.0)
def test_builder_augmentation_and_global(ala2, ctx):
pytest.importorskip("nflows")
crd_transform = GlobalInternalCoordinateTransformation(ala2.system.global_z_matrix)
shape_info = ShapeDictionary.from_coordinate_transform(crd_transform, dim_augmented=10)
builder = BoltzmannGeneratorBuilder(shape_info, target=ala2.system.energy_model, **ctx)
for i in range(4):
builder.add_condition(TORSIONS, on=AUGMENTED)
builder.add_condition(AUGMENTED, on=TORSIONS)
for i in range(2):
builder.add_condition(BONDS, on=ANGLES)
builder.add_condition(ANGLES, on=BONDS)
builder.add_condition(ANGLES, on=(TORSIONS, AUGMENTED))
builder.add_condition(BONDS, on=(ANGLES, TORSIONS, AUGMENTED))
builder.add_map_to_ic_domains()
builder.add_map_to_cartesian(crd_transform)
generator = builder.build_generator()
# play forward and backward
samples = generator.sample(2)
assert len(samples) == 2
generator.energy(*samples)
generator.kldiv(10)
def test_builder_api(ala2, ctx):
pytest.importorskip("nflows")
z_matrix = ala2.system.z_matrix
fixed_atoms = ala2.system.rigid_block
crd_transform = MixedCoordinateTransformation(torch.tensor(ala2.xyz, **ctx), z_matrix, fixed_atoms)
shape_info = ShapeDictionary.from_coordinate_transform(crd_transform)
builder = BoltzmannGeneratorBuilder(shape_info, target=ala2.system.energy_model, **ctx)
for i in range(4):
builder.add_condition(TORSIONS, on=FIXED)
builder.add_condition(FIXED, on=TORSIONS)
for i in range(2):
builder.add_condition(BONDS, on=ANGLES)
builder.add_condition(ANGLES, on=BONDS)
builder.add_condition(ANGLES, on=(TORSIONS, FIXED))
builder.add_condition(BONDS, on=(ANGLES, TORSIONS, FIXED))
builder.add_map_to_ic_domains()
builder.add_map_to_cartesian(crd_transform)
generator = builder.build_generator()
# play forward and backward
samples = generator.sample(2)
generator.energy(samples)
generator.kldiv(10)
def test_builder_bond_constraints(ala2, ctx):
# import logging
# logger = logging.getLogger('bgflow')
# logger.setLevel(logging.DEBUG)
# logger.addHandler(
# logging.StreamHandler()
# )
pytest.importorskip("nflows")
crd_transform = GlobalInternalCoordinateTransformation(ala2.system.global_z_matrix)
shape_info = ShapeDictionary.from_coordinate_transform(
crd_transform,
dim_augmented=0,
n_constraints=2,
remove_origin_and_rotation=True
)
builder = BoltzmannGeneratorBuilder(shape_info, target=ala2.system.energy_model, **ctx)
constrained_bond_indices = [0, 1]
constrained_bond_lengths = [0.1, 0.1]
assert builder.current_dims[BONDS] == (19, )
assert builder.prior_dims[BONDS] == (19, )
builder.add_condition(BONDS, on=(ANGLES, TORSIONS))
builder.add_map_to_ic_domains()
builder.add_merge_constraints(constrained_bond_indices, constrained_bond_lengths)
assert builder.current_dims[BONDS] == (21, )
builder.add_map_to_cartesian(crd_transform)
generator = builder.build_generator()
# play forward and backward
samples = generator.sample(2)
assert samples.shape == (2, 66)
generator.energy(samples)
generator.kldiv(10)
def test_builder_multiple_crd(ala2, ctx):
bgmol = pytest.importorskip("bgmol")
pytest.importorskip("nflows")
# all-atom trafo
z_matrix, fixed = bgmol.ZMatrixFactory(ala2.system.mdtraj_topology, cartesian=[6, 8, 10, 14, 16]).build_naive()
crd_transform = RelativeInternalCoordinateTransformation(z_matrix, fixed)
shape_info = ShapeDictionary.from_coordinate_transform(crd_transform)
# cg trafo
cg_top, _ = bgmol.build_fake_topology(5)
cg_z_matrix, _ = bgmol.ZMatrixFactory(cg_top).build_naive()
cg_crd_transform = GlobalInternalCoordinateTransformation(cg_z_matrix)
cg_shape_info = ShapeDictionary.from_coordinate_transform(cg_crd_transform)
CG_BONDS = cg_shape_info.replace(BONDS, "CG_BONDS")
CG_ANGLES = cg_shape_info.replace(ANGLES, "CG_ANGLES")
CG_TORSIONS = cg_shape_info.replace(TORSIONS, "CG_TORSIONS")
shape_info.update(cg_shape_info)
del shape_info[FIXED]
# factory
#marginals = InternalCoordinateMarginals(builder.current_dims)
builder = BoltzmannGeneratorBuilder(shape_info, target=ala2.system.energy_model, **ctx)
for i in range(2):
builder.add_condition(CG_TORSIONS, on=(CG_ANGLES, CG_BONDS))
builder.add_condition((CG_ANGLES, CG_BONDS), on=CG_TORSIONS)
marginals = InternalCoordinateMarginals(builder.current_dims, builder.ctx, bonds=CG_BONDS, angles=CG_ANGLES, torsions=CG_TORSIONS)
builder.add_map_to_ic_domains(marginals)
builder.add_map_to_cartesian(cg_crd_transform, bonds=CG_BONDS, angles=CG_ANGLES, torsions=CG_TORSIONS, out=FIXED)
builder.transformer_type[FIXED] = bg.AffineTransformer
for i in range(2):
builder.add_condition(TORSIONS, on=FIXED)
builder.add_condition(FIXED, on=TORSIONS)
for i in range(2):
builder.add_condition(BONDS, on=ANGLES)
builder.add_condition(ANGLES, on=BONDS)
builder.add_condition(ANGLES, on=(TORSIONS, FIXED))
builder.add_condition(BONDS, on=(ANGLES, TORSIONS, FIXED))
builder.add_map_to_ic_domains()
builder.add_map_to_cartesian(crd_transform)
generator = builder.build_generator()
# play forward and backward
samples = generator.sample(2)
generator.energy(samples)
generator.kldiv(10)
def test_builder_split_merge(ctx):
pytest.importorskip("nflows")
shape_info = ShapeDictionary()
shape_info[BONDS] = (10, )
shape_info[ANGLES] = (20, )
shape_info[TORSIONS] = (13, )
builder = BoltzmannGeneratorBuilder(shape_info, **ctx)
split1 = TensorInfo("SPLIT_1")
split2 = TensorInfo("SPLIT_2")
split3 = TensorInfo("SPLIT_3")
builder.add_split(ANGLES, (split1, split2, split3), (6, 2, 12))
builder.add_condition(split1, on=split2)
generator = builder.build_generator(zero_parameters=True, check_target=False)
samples = generator.sample(11)
assert len(samples) == 5
assert all(samples[i].shape == (11,j) for i, j in enumerate([10,6,2,12,13]))
# check split + add_merge (with string arguments)
assert builder.layers == []
s1, split_2, s3 = builder.add_split(ANGLES, (split1, "split2", split3), (6, 2, 12))
assert s1 == split1
assert s3 == split3
assert split_2.name == "split2"
assert split_2.is_circular == ANGLES.is_circular
builder.add_condition(split1, on=split_2)
angles = builder.add_merge((split1, split_2, split3), "angles")
assert angles.name == "angles"
assert angles.is_circular == ANGLES.is_circular
assert list(builder.current_dims) == [BONDS, angles, TORSIONS]
generator = builder.build_generator(zero_parameters=True, check_target=False)
samples = generator._prior.sample(11)
assert all(torch.all(s > torch.zeros_like(s)) for s in samples)
assert all(torch.all(s < torch.ones_like(s)) for s in samples)
*output, dlogp = generator._flow.forward(*samples)
assert all(s.shape == o.shape for s, o in zip(samples, output))
assert all(torch.allclose(s, o, atol=0.01, rtol=0.0) for s, o in zip(samples, output))