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Refactor all_atom_test.py for clarity, reusability, and modular structure #1078

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Refactor all_atom_test.py for clarity, reusability, and modular structure #1078

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238 changes: 126 additions & 112 deletions alphafold/model/all_atom_test.py
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Expand Up @@ -12,124 +12,138 @@
# See the License for the specific language governing permissions and
# limitations under the License.

"""Tests for all_atom."""
"""Unit tests for all_atom.frame_aligned_point_error."""

import numpy as np
from absl.testing import absltest
from absl.testing import parameterized

from alphafold.model import all_atom
from alphafold.model import r3
import numpy as np

L1_CLAMP_DISTANCE = 10


def get_identity_rigid(shape):
"""Returns identity rigid transform."""

ones = np.ones(shape)
zeros = np.zeros(shape)
rot = r3.Rots(ones, zeros, zeros,
zeros, ones, zeros,
zeros, zeros, ones)
trans = r3.Vecs(zeros, zeros, zeros)
return r3.Rigids(rot, trans)


def get_global_rigid_transform(rot_angle, translation, bcast_dims):
"""Returns rigid transform that globally rotates/translates by same amount."""

rot_angle = np.asarray(rot_angle)
translation = np.asarray(translation)
if bcast_dims:
for _ in range(bcast_dims):
rot_angle = np.expand_dims(rot_angle, 0)
translation = np.expand_dims(translation, 0)
sin_angle = np.sin(np.deg2rad(rot_angle))
cos_angle = np.cos(np.deg2rad(rot_angle))
ones = np.ones_like(sin_angle)
zeros = np.zeros_like(sin_angle)
rot = r3.Rots(ones, zeros, zeros,
zeros, cos_angle, -sin_angle,
zeros, sin_angle, cos_angle)
trans = r3.Vecs(translation[..., 0], translation[..., 1], translation[..., 2])
return r3.Rigids(rot, trans)


class AllAtomTest(parameterized.TestCase, absltest.TestCase):

@parameterized.named_parameters(
('identity', 0, [0, 0, 0]),
('rot_90', 90, [0, 0, 0]),
('trans_10', 0, [0, 0, 10]),
('rot_174_trans_1', 174, [1, 1, 1]))
def test_frame_aligned_point_error_perfect_on_global_transform(
self, rot_angle, translation):
"""Tests global transform between target and preds gives perfect score."""

# pylint: disable=bad-whitespace
target_positions = np.array(
[[ 21.182, 23.095, 19.731],
[ 22.055, 20.919, 17.294],
[ 24.599, 20.005, 15.041],
[ 25.567, 18.214, 12.166],
[ 28.063, 17.082, 10.043],
[ 28.779, 15.569, 6.985],
[ 30.581, 13.815, 4.612],
[ 29.258, 12.193, 2.296]])
# pylint: enable=bad-whitespace
global_rigid_transform = get_global_rigid_transform(
rot_angle, translation, 1)

target_positions = r3.vecs_from_tensor(target_positions)
pred_positions = r3.rigids_mul_vecs(
global_rigid_transform, target_positions)
positions_mask = np.ones(target_positions.x.shape[0])

target_frames = get_identity_rigid(10)
pred_frames = r3.rigids_mul_rigids(global_rigid_transform, target_frames)
frames_mask = np.ones(10)

fape = all_atom.frame_aligned_point_error(
pred_frames, target_frames, frames_mask, pred_positions,
target_positions, positions_mask, L1_CLAMP_DISTANCE,
L1_CLAMP_DISTANCE, epsilon=0)
self.assertAlmostEqual(fape, 0.)

@parameterized.named_parameters(
('identity',
[[0, 0, 0], [5, 0, 0], [10, 0, 0]],
[[0, 0, 0], [5, 0, 0], [10, 0, 0]],
0.),
('shift_2.5',
[[0, 0, 0], [5, 0, 0], [10, 0, 0]],
[[2.5, 0, 0], [7.5, 0, 0], [7.5, 0, 0]],
0.25),
('shift_5',
[[0, 0, 0], [5, 0, 0], [10, 0, 0]],
[[5, 0, 0], [10, 0, 0], [15, 0, 0]],
0.5),
('shift_10',
[[0, 0, 0], [5, 0, 0], [10, 0, 0]],
[[10, 0, 0], [15, 0, 0], [0, 0, 0]],
1.))
def test_frame_aligned_point_error_matches_expected(
self, target_positions, pred_positions, expected_alddt):
"""Tests score matches expected."""

target_frames = get_identity_rigid(2)
pred_frames = target_frames
frames_mask = np.ones(2)

target_positions = r3.vecs_from_tensor(np.array(target_positions))
pred_positions = r3.vecs_from_tensor(np.array(pred_positions))
positions_mask = np.ones(target_positions.x.shape[0])

alddt = all_atom.frame_aligned_point_error(
pred_frames, target_frames, frames_mask, pred_positions,
target_positions, positions_mask, L1_CLAMP_DISTANCE,
L1_CLAMP_DISTANCE, epsilon=0)
self.assertAlmostEqual(alddt, expected_alddt)
L1_CLAMP_DISTANCE = 10.0


def vecs_from_xyz(coords):
"""Convert XYZ coords to Vecs object."""
coords = np.asarray(coords)
return r3.vecs_from_tensor(coords)


def identity_rigid_batch(size):
"""Generate identity rigid frames of given batch size."""
ones = np.ones(size)
zeros = np.zeros(size)
rot = r3.Rots(
ones, zeros, zeros,
zeros, ones, zeros,
zeros, zeros, ones,
)
trans = r3.Vecs(zeros, zeros, zeros)
return r3.Rigids(rot, trans)


def global_transform_rigid(angle_deg, translation, batch_dims):
"""Create rigid transform with rotation (about x) and translation."""
angle_deg = np.asarray(angle_deg)
translation = np.asarray(translation)

for _ in range(batch_dims):
angle_deg = np.expand_dims(angle_deg, 0)
translation = np.expand_dims(translation, 0)

sin = np.sin(np.deg2rad(angle_deg))
cos = np.cos(np.deg2rad(angle_deg))
ones = np.ones_like(sin)
zeros = np.zeros_like(sin)

rot = r3.Rots(
ones, zeros, zeros,
zeros, cos, -sin,
zeros, sin, cos
)
trans = r3.Vecs(translation[..., 0], translation[..., 1], translation[..., 2])
return r3.Rigids(rot, trans)


class AllAtomTest(parameterized.TestCase):

@parameterized.named_parameters(
('no_transform', 0, [0, 0, 0]),
('rotation_90', 90, [0, 0, 0]),
('translation_10z', 0, [0, 0, 10]),
('rot_174_trans_xyz', 174, [1, 1, 1]),
)
def test_fape_zero_on_perfect_alignment(self, rot_angle, translation):
"""FAPE should be zero if prediction = target under global transform."""
coords = np.array([
[21.182, 23.095, 19.731],
[22.055, 20.919, 17.294],
[24.599, 20.005, 15.041],
[25.567, 18.214, 12.166],
[28.063, 17.082, 10.043],
[28.779, 15.569, 6.985],
[30.581, 13.815, 4.612],
[29.258, 12.193, 2.296],
])

# Target and predicted positions
target_positions = vecs_from_xyz(coords)
transform = global_transform_rigid(rot_angle, translation, batch_dims=1)
pred_positions = r3.rigids_mul_vecs(transform, target_positions)

# Identity frames, with global transform applied to pred_frames
target_frames = identity_rigid_batch(10)
pred_frames = r3.rigids_mul_rigids(transform, target_frames)

# All atoms and frames are valid
positions_mask = np.ones(target_positions.x.shape[0])
frames_mask = np.ones(10)

error = all_atom.frame_aligned_point_error(
pred_frames, target_frames, frames_mask,
pred_positions, target_positions, positions_mask,
L1_CLAMP_DISTANCE, L1_CLAMP_DISTANCE, epsilon=0
)

self.assertAlmostEqual(error, 0.0)

@parameterized.named_parameters(
('exact_match',
[[0, 0, 0], [5, 0, 0], [10, 0, 0]],
[[0, 0, 0], [5, 0, 0], [10, 0, 0]],
0.0),
('small_shift',
[[0, 0, 0], [5, 0, 0], [10, 0, 0]],
[[2.5, 0, 0], [7.5, 0, 0], [7.5, 0, 0]],
0.25),
('half_shift',
[[0, 0, 0], [5, 0, 0], [10, 0, 0]],
[[5, 0, 0], [10, 0, 0], [15, 0, 0]],
0.5),
('large_shift',
[[0, 0, 0], [5, 0, 0], [10, 0, 0]],
[[10, 0, 0], [15, 0, 0], [0, 0, 0]],
1.0),
)
def test_fape_matches_expected_value(self, target_coords, pred_coords, expected_fape):
"""Verify FAPE outputs correct normalized error for simple inputs."""
target_positions = vecs_from_xyz(target_coords)
pred_positions = vecs_from_xyz(pred_coords)

positions_mask = np.ones(len(target_coords))
target_frames = identity_rigid_batch(2)
pred_frames = target_frames
frames_mask = np.ones(2)

error = all_atom.frame_aligned_point_error(
pred_frames, target_frames, frames_mask,
pred_positions, target_positions, positions_mask,
L1_CLAMP_DISTANCE, L1_CLAMP_DISTANCE, epsilon=0
)

self.assertAlmostEqual(error, expected_fape)


if __name__ == '__main__':
absltest.main()
absltest.main()