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Commit 5fcd6ed2 authored by Christina Floristean's avatar Christina Floristean
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Unit test fixes for when AF2 is not installed

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Showing with 249 additions and 229 deletions
openfold/utils/geometry/test_utils.py
View file @ 5fcd6ed2
...@@ -14,84 +14,84 @@ ...@@ -14,84 +14,84 @@
"""Shared utils for tests.""" """Shared utils for tests."""
import dataclasses import dataclasses
import torch
from alphafold.model.geometry import rigid_matrix_vector from openfold.utils.geometry import rigid_matrix_vector
from alphafold.model.geometry import rotation_matrix from openfold.utils.geometry import rotation_matrix
from alphafold.model.geometry import vector from openfold.utils.geometry import vector
import numpy as np
def assert_rotation_matrix_equal(matrix1: rotation_matrix.Rot3Array, def assert_rotation_matrix_equal(matrix1: rotation_matrix.Rot3Array,
matrix2: rotation_matrix.Rot3Array): matrix2: rotation_matrix.Rot3Array):
for field in dataclasses.fields(rotation_matrix.Rot3Array): for field in dataclasses.fields(rotation_matrix.Rot3Array):
field = field.name field = field.name
np.testing.assert_array_equal( assert torch.equal(
getattr(matrix1, field), getattr(matrix2, field)) getattr(matrix1, field), getattr(matrix2, field))
def assert_rotation_matrix_close(mat1: rotation_matrix.Rot3Array, def assert_rotation_matrix_close(mat1: rotation_matrix.Rot3Array,
mat2: rotation_matrix.Rot3Array): mat2: rotation_matrix.Rot3Array):
np.testing.assert_array_almost_equal(mat1.to_array(), mat2.to_array(), 6) assert torch.allclose(mat1.to_tensor(), mat2.to_tensor(), atol=1e-6)
def assert_array_equal_to_rotation_matrix(array: np.ndarray, def assert_array_equal_to_rotation_matrix(array: torch.Tensor,
matrix: rotation_matrix.Rot3Array): matrix: rotation_matrix.Rot3Array):
"""Check that array and Matrix match.""" """Check that array and Matrix match."""
np.testing.assert_array_equal(matrix.xx, array[..., 0, 0]) assert torch.equal(matrix.xx, array[..., 0, 0])
np.testing.assert_array_equal(matrix.xy, array[..., 0, 1]) assert torch.equal(matrix.xy, array[..., 0, 1])
np.testing.assert_array_equal(matrix.xz, array[..., 0, 2]) assert torch.equal(matrix.xz, array[..., 0, 2])
np.testing.assert_array_equal(matrix.yx, array[..., 1, 0]) assert torch.equal(matrix.yx, array[..., 1, 0])
np.testing.assert_array_equal(matrix.yy, array[..., 1, 1]) assert torch.equal(matrix.yy, array[..., 1, 1])
np.testing.assert_array_equal(matrix.yz, array[..., 1, 2]) assert torch.equal(matrix.yz, array[..., 1, 2])
np.testing.assert_array_equal(matrix.zx, array[..., 2, 0]) assert torch.equal(matrix.zx, array[..., 2, 0])
np.testing.assert_array_equal(matrix.zy, array[..., 2, 1]) assert torch.equal(matrix.zy, array[..., 2, 1])
np.testing.assert_array_equal(matrix.zz, array[..., 2, 2]) assert torch.equal(matrix.zz, array[..., 2, 2])
def assert_array_close_to_rotation_matrix(array: np.ndarray, def assert_array_close_to_rotation_matrix(array: torch.Tensor,
matrix: rotation_matrix.Rot3Array): matrix: rotation_matrix.Rot3Array):
np.testing.assert_array_almost_equal(matrix.to_array(), array, 6) assert torch.allclose(matrix.to_tensor(), array, atol=1e-6)
def assert_vectors_equal(vec1: vector.Vec3Array, vec2: vector.Vec3Array): def assert_vectors_equal(vec1: vector.Vec3Array, vec2: vector.Vec3Array):
np.testing.assert_array_equal(vec1.x, vec2.x) assert torch.equal(vec1.x, vec2.x)
np.testing.assert_array_equal(vec1.y, vec2.y) assert torch.equal(vec1.y, vec2.y)
np.testing.assert_array_equal(vec1.z, vec2.z) assert torch.equal(vec1.z, vec2.z)
def assert_vectors_close(vec1: vector.Vec3Array, vec2: vector.Vec3Array): def assert_vectors_close(vec1: vector.Vec3Array, vec2: vector.Vec3Array):
np.testing.assert_allclose(vec1.x, vec2.x, atol=1e-6, rtol=0.) assert torch.allclose(vec1.x, vec2.x, atol=1e-6, rtol=0.)
np.testing.assert_allclose(vec1.y, vec2.y, atol=1e-6, rtol=0.) assert torch.allclose(vec1.y, vec2.y, atol=1e-6, rtol=0.)
np.testing.assert_allclose(vec1.z, vec2.z, atol=1e-6, rtol=0.) assert torch.allclose(vec1.z, vec2.z, atol=1e-6, rtol=0.)
def assert_array_close_to_vector(array: np.ndarray, vec: vector.Vec3Array): def assert_array_close_to_vector(array: torch.Tensor, vec: vector.Vec3Array):
np.testing.assert_allclose(vec.to_array(), array, atol=1e-6, rtol=0.) assert torch.allclose(vec.to_tensor(), array, atol=1e-6, rtol=0.)
def assert_array_equal_to_vector(array: np.ndarray, vec: vector.Vec3Array): def assert_array_equal_to_vector(array: torch.Tensor, vec: vector.Vec3Array):
np.testing.assert_array_equal(vec.to_array(), array) assert torch.equal(vec.to_tensor(), array)
def assert_rigid_equal_to_rigid(rigid1: rigid_matrix_vector.Rigid3Array, def assert_rigid_equal_to_rigid(rigid1: rigid_matrix_vector.Rigid3Array,
rigid2: rigid_matrix_vector.Rigid3Array): rigid2: rigid_matrix_vector.Rigid3Array):
assert_rot_trans_equal_to_rigid(rigid1.rotation, rigid1.translation, rigid2) assert_rot_trans_equal_to_rigid(rigid1.rotation, rigid1.translation, rigid2)
def assert_rigid_close_to_rigid(rigid1: rigid_matrix_vector.Rigid3Array, def assert_rigid_close_to_rigid(rigid1: rigid_matrix_vector.Rigid3Array,
rigid2: rigid_matrix_vector.Rigid3Array): rigid2: rigid_matrix_vector.Rigid3Array):
assert_rot_trans_close_to_rigid(rigid1.rotation, rigid1.translation, rigid2) assert_rot_trans_close_to_rigid(rigid1.rotation, rigid1.translation, rigid2)
def assert_rot_trans_equal_to_rigid(rot: rotation_matrix.Rot3Array, def assert_rot_trans_equal_to_rigid(rot: rotation_matrix.Rot3Array,
trans: vector.Vec3Array, trans: vector.Vec3Array,
rigid: rigid_matrix_vector.Rigid3Array): rigid: rigid_matrix_vector.Rigid3Array):
assert_rotation_matrix_equal(rot, rigid.rotation) assert_rotation_matrix_equal(rot, rigid.rotation)
assert_vectors_equal(trans, rigid.translation) assert_vectors_equal(trans, rigid.translation)
def assert_rot_trans_close_to_rigid(rot: rotation_matrix.Rot3Array, def assert_rot_trans_close_to_rigid(rot: rotation_matrix.Rot3Array,
trans: vector.Vec3Array, trans: vector.Vec3Array,
rigid: rigid_matrix_vector.Rigid3Array): rigid: rigid_matrix_vector.Rigid3Array):
assert_rotation_matrix_close(rot, rigid.rotation) assert_rotation_matrix_close(rot, rigid.rotation)
assert_vectors_close(trans, rigid.translation) assert_vectors_close(trans, rigid.translation)
tests/test_feats.py
View file @ 5fcd6ed2
...@@ -45,16 +45,17 @@ if compare_utils.alphafold_is_installed(): ...@@ -45,16 +45,17 @@ if compare_utils.alphafold_is_installed():
class TestFeats(unittest.TestCase): class TestFeats(unittest.TestCase):
@classmethod @classmethod
def setUpClass(cls): def setUpClass(cls):
if consts.is_multimer: if compare_utils.alphafold_is_installed():
cls.am_atom = alphafold.model.all_atom_multimer if consts.is_multimer:
cls.am_fold = alphafold.model.folding_multimer cls.am_atom = alphafold.model.all_atom_multimer
cls.am_modules = alphafold.model.modules_multimer cls.am_fold = alphafold.model.folding_multimer
cls.am_rigid = alphafold.model.geometry cls.am_modules = alphafold.model.modules_multimer
else: cls.am_rigid = alphafold.model.geometry
cls.am_atom = alphafold.model.all_atom else:
cls.am_fold = alphafold.model.folding cls.am_atom = alphafold.model.all_atom
cls.am_modules = alphafold.model.modules cls.am_fold = alphafold.model.folding
cls.am_rigid = alphafold.model.r3 cls.am_modules = alphafold.model.modules
cls.am_rigid = alphafold.model.r3
@compare_utils.skip_unless_alphafold_installed() @compare_utils.skip_unless_alphafold_installed()
def test_pseudo_beta_fn_compare(self): def test_pseudo_beta_fn_compare(self):
......
tests/test_loss.py
View file @ 5fcd6ed2
...@@ -79,16 +79,17 @@ def affine_vector_to_rigid(am_rigid, affine): ...@@ -79,16 +79,17 @@ def affine_vector_to_rigid(am_rigid, affine):
class TestLoss(unittest.TestCase): class TestLoss(unittest.TestCase):
@classmethod @classmethod
def setUpClass(cls): def setUpClass(cls):
if consts.is_multimer: if compare_utils.alphafold_is_installed():
cls.am_atom = alphafold.model.all_atom_multimer if consts.is_multimer:
cls.am_fold = alphafold.model.folding_multimer cls.am_atom = alphafold.model.all_atom_multimer
cls.am_modules = alphafold.model.modules_multimer cls.am_fold = alphafold.model.folding_multimer
cls.am_rigid = alphafold.model.geometry cls.am_modules = alphafold.model.modules_multimer
else: cls.am_rigid = alphafold.model.geometry
cls.am_atom = alphafold.model.all_atom else:
cls.am_fold = alphafold.model.folding cls.am_atom = alphafold.model.all_atom
cls.am_modules = alphafold.model.modules cls.am_fold = alphafold.model.folding
cls.am_rigid = alphafold.model.r3 cls.am_modules = alphafold.model.modules
cls.am_rigid = alphafold.model.r3
def test_run_torsion_angle_loss(self): def test_run_torsion_angle_loss(self):
batch_size = consts.batch_size batch_size = consts.batch_size
......
tests/test_model.py
View file @ 5fcd6ed2
...@@ -38,16 +38,17 @@ if compare_utils.alphafold_is_installed(): ...@@ -38,16 +38,17 @@ if compare_utils.alphafold_is_installed():
class TestModel(unittest.TestCase): class TestModel(unittest.TestCase):
@classmethod @classmethod
def setUpClass(cls): def setUpClass(cls):
if consts.is_multimer: if compare_utils.alphafold_is_installed():
cls.am_atom = alphafold.model.all_atom_multimer if consts.is_multimer:
cls.am_fold = alphafold.model.folding_multimer cls.am_atom = alphafold.model.all_atom_multimer
cls.am_modules = alphafold.model.modules_multimer cls.am_fold = alphafold.model.folding_multimer
cls.am_rigid = alphafold.model.geometry cls.am_modules = alphafold.model.modules_multimer
else: cls.am_rigid = alphafold.model.geometry
cls.am_atom = alphafold.model.all_atom else:
cls.am_fold = alphafold.model.folding cls.am_atom = alphafold.model.all_atom
cls.am_modules = alphafold.model.modules cls.am_fold = alphafold.model.folding
cls.am_rigid = alphafold.model.r3 cls.am_modules = alphafold.model.modules
cls.am_rigid = alphafold.model.r3
def test_dry_run(self): def test_dry_run(self):
n_seq = consts.n_seq n_seq = consts.n_seq
......
tests/test_multimer_datamodule.py
View file @ 5fcd6ed2
...@@ -20,7 +20,8 @@ from openfold.utils.tensor_utils import tensor_tree_map ...@@ -20,7 +20,8 @@ from openfold.utils.tensor_utils import tensor_tree_map
from openfold.config import model_config from openfold.config import model_config
from openfold.data.data_modules import OpenFoldMultimerDataModule from openfold.data.data_modules import OpenFoldMultimerDataModule
from openfold.model.model import AlphaFold from openfold.model.model import AlphaFold
from openfold.utils.loss import AlphaFoldMultimerLoss from openfold.utils.loss import AlphaFoldLoss
from openfold.utils.multi_chain_permutation import multi_chain_permutation_align
from tests.config import consts from tests.config import consts
import logging import logging
logger = logging.getLogger(__name__) logger = logging.getLogger(__name__)
...@@ -61,17 +62,28 @@ class TestMultimerDataModule(unittest.TestCase): ...@@ -61,17 +62,28 @@ class TestMultimerDataModule(unittest.TestCase):
self.c.model.evoformer_stack.blocks_per_ckpt = None # don't want to set up self.c.model.evoformer_stack.blocks_per_ckpt = None # don't want to set up
# deepspeed for this test # deepspeed for this test
self.model = AlphaFold(self.c) self.model = AlphaFold(self.c)
self.multimer_loss = AlphaFoldMultimerLoss(self.c.loss) self.loss = AlphaFoldLoss(self.c.loss)
def testPrepareData(self): def testPrepareData(self):
self.data_module.prepare_data() self.data_module.prepare_data()
self.data_module.setup() self.data_module.setup()
train_dataset = self.data_module.train_dataset train_dataset = self.data_module.train_dataset
all_chain_features,ground_truth = train_dataset[1] all_chain_features = train_dataset[1]
add_batch_size_dimension = lambda t: ( add_batch_size_dimension = lambda t: (
t.unsqueeze(0) t.unsqueeze(0)
) )
all_chain_features = tensor_tree_map(add_batch_size_dimension,all_chain_features) all_chain_features = tensor_tree_map(add_batch_size_dimension, all_chain_features)
with torch.no_grad(): with torch.no_grad():
ground_truth = all_chain_features.pop('gt_features', None)
# Run the model
out = self.model(all_chain_features) out = self.model(all_chain_features)
self.multimer_loss(out,(all_chain_features,ground_truth))
\ No newline at end of file # Remove the recycling dimension
all_chain_features = tensor_tree_map(lambda t: t[..., -1], all_chain_features)
all_chain_features = multi_chain_permutation_align(out=out,
features=all_chain_features,
ground_truth=ground_truth)
self.loss(out, all_chain_features)
\ No newline at end of file
tests/test_permutation.py
View file @ 5fcd6ed2
...@@ -12,14 +12,16 @@ ...@@ -12,14 +12,16 @@
# See the License for the specific language governing permissions and # See the License for the specific language governing permissions and
# limitations under the License. # limitations under the License.
import math
import torch import torch
import unittest import unittest
from openfold.utils.loss import AlphaFoldMultimerLoss from openfold.utils.multi_chain_permutation import (pad_features, get_least_asym_entity_or_longest_length,
from openfold.utils.loss import get_least_asym_entity_or_longest_length,merge_labels,pad_features compute_permutation_alignment, split_ground_truth_labels,
from openfold.utils.tensor_utils import tensor_tree_map merge_labels)
import math
@unittest.skip("Tests need to be fixed post-refactor")
class TestPermutation(unittest.TestCase): class TestPermutation(unittest.TestCase):
def setUp(self): def setUp(self):
""" """
...@@ -27,144 +29,143 @@ class TestPermutation(unittest.TestCase): ...@@ -27,144 +29,143 @@ class TestPermutation(unittest.TestCase):
and rotation matrices and rotation matrices
""" """
theta = math.pi/4 theta = math.pi / 4
device = 'cpu'
self.rotation_matrix_z = torch.tensor([ self.rotation_matrix_z = torch.tensor([
[math.cos(theta),-math.sin(theta),0], [math.cos(theta), -math.sin(theta), 0],
[math.sin(theta),math.cos(theta),0], [math.sin(theta), math.cos(theta), 0],
[0,0,1] [0, 0, 1]
],device='cuda') ], device=device)
self.rotation_matrix_x = torch.tensor([ self.rotation_matrix_x = torch.tensor([
[1,0,0], [1, 0, 0],
[0,math.cos(theta),-math.sin(theta)], [0, math.cos(theta), -math.sin(theta)],
[0,math.sin(theta),math.cos(theta)], [0, math.sin(theta), math.cos(theta)],
],device='cuda') ], device=device)
self.rotation_matrix_y = torch.tensor([ self.rotation_matrix_y = torch.tensor([
[math.cos(theta),0,math.sin(theta)], [math.cos(theta), 0, math.sin(theta)],
[0,1,0], [0, 1, 0],
[-math.sin(theta),1,math.cos(theta)], [-math.sin(theta), 1, math.cos(theta)],
],device='cuda') ], device=device)
self.chain_a_num_res=9 self.chain_a_num_res = 9
self.chain_b_num_res=13 self.chain_b_num_res = 13
# below create default fake ground truth structures for a hetero-pentamer A2B3 # below create default fake ground truth structures for a hetero-pentamer A2B3
self.residue_index=list(range(self.chain_a_num_res))*2 + list(range(self.chain_b_num_res))*3 self.residue_index = list(range(self.chain_a_num_res)) * 2 + list(range(self.chain_b_num_res)) * 3
self.num_res = self.chain_a_num_res*2 + self.chain_b_num_res*3 self.num_res = self.chain_a_num_res * 2 + self.chain_b_num_res * 3
self.asym_id = torch.tensor([[1]*self.chain_a_num_res+[2]*self.chain_a_num_res+[3]*self.chain_b_num_res+[4]*self.chain_b_num_res+[5]*self.chain_b_num_res],device='cuda') self.asym_id = torch.tensor([[1] * self.chain_a_num_res + [2] * self.chain_a_num_res + [
3] * self.chain_b_num_res + [4] * self.chain_b_num_res + [5] * self.chain_b_num_res], device=device)
self.sym_id = self.asym_id self.sym_id = self.asym_id
self.entity_id = torch.tensor([[1]*(self.chain_a_num_res*2)+[2]*(self.chain_b_num_res*3)],device='cuda') self.entity_id = torch.tensor([[1] * (self.chain_a_num_res * 2) + [2] * (self.chain_b_num_res * 3)],
device=device)
def test_1_selecting_anchors(self): def test_1_selecting_anchors(self):
self.batch = { batch = {
'asym_id':self.asym_id, 'asym_id': self.asym_id,
'sym_id':self.sym_id, 'sym_id': self.sym_id,
'entity_id':self.entity_id, 'entity_id': self.entity_id,
'seq_length':torch.tensor([57]) 'seq_length': torch.tensor([57])
} }
anchor_gt_asym, anchor_pred_asym=get_least_asym_entity_or_longest_length(self.batch) anchor_gt_asym, anchor_pred_asym = get_least_asym_entity_or_longest_length(batch, batch['asym_id'])
self.assertIn(int(anchor_gt_asym),[1,2]) self.assertIn(int(anchor_gt_asym), [1, 2])
self.assertNotIn(int(anchor_gt_asym),[3,4,5]) self.assertNotIn(int(anchor_gt_asym), [3, 4, 5])
self.assertIn(int(anchor_pred_asym),[1,2]) self.assertIn(int(anchor_pred_asym), [1, 2])
self.assertNotIn(int(anchor_pred_asym),[3,4,5]) self.assertNotIn(int(anchor_pred_asym), [3, 4, 5])
def test_2_permutation_pentamer(self): def test_2_permutation_pentamer(self):
batch = { batch = {
'asym_id':self.asym_id, 'asym_id': self.asym_id,
'sym_id':self.sym_id, 'sym_id': self.sym_id,
'entity_id':self.entity_id, 'entity_id': self.entity_id,
'seq_length':torch.tensor([57]), 'seq_length': torch.tensor([57]),
'aatype':torch.randint(21,size=(1,57)) 'aatype': torch.randint(21, size=(1, 57))
} }
batch['asym_id'] = batch['asym_id'].reshape(1,self.num_res) batch['asym_id'] = batch['asym_id'].reshape(1, self.num_res)
batch["residue_index"] = torch.tensor([self.residue_index],device='cuda') batch["residue_index"] = torch.tensor([self.residue_index])
# create fake ground truth atom positions # create fake ground truth atom positions
chain_a1_pos = torch.randint(15,(self.chain_a_num_res,3*37), chain_a1_pos = torch.randint(15, (self.chain_a_num_res, 3 * 37),
device='cuda',dtype=torch.float).reshape(1,self.chain_a_num_res,37,3) dtype=torch.float).reshape(1, self.chain_a_num_res, 37, 3)
chain_a2_pos = torch.matmul(chain_a1_pos,self.rotation_matrix_x)+10 chain_a2_pos = torch.matmul(chain_a1_pos, self.rotation_matrix_x) + 10
chain_b1_pos = torch.randint(low=15,high=30,size=(self.chain_b_num_res,3*37), chain_b1_pos = torch.randint(low=15, high=30, size=(self.chain_b_num_res, 3 * 37),
device='cuda',dtype=torch.float).reshape(1,self.chain_b_num_res,37,3) dtype=torch.float).reshape(1, self.chain_b_num_res, 37, 3)
chain_b2_pos = torch.matmul(chain_b1_pos,self.rotation_matrix_y)+10 chain_b2_pos = torch.matmul(chain_b1_pos, self.rotation_matrix_y) + 10
chain_b3_pos = torch.matmul(torch.matmul(chain_b1_pos,self.rotation_matrix_z),self.rotation_matrix_x)+30 chain_b3_pos = torch.matmul(torch.matmul(chain_b1_pos, self.rotation_matrix_z), self.rotation_matrix_x) + 30
# Below permutate predicted chain positions # Below permutate predicted chain positions
pred_atom_position = torch.cat((chain_a2_pos,chain_a1_pos,chain_b2_pos,chain_b3_pos,chain_b1_pos),dim=1) pred_atom_position = torch.cat((chain_a2_pos, chain_a1_pos, chain_b2_pos, chain_b3_pos, chain_b1_pos), dim=1)
pred_atom_mask = torch.ones((1,self.num_res,37),device='cuda') pred_atom_mask = torch.ones((1, self.num_res, 37))
out = { out = {
'final_atom_positions':pred_atom_position, 'final_atom_positions': pred_atom_position,
'final_atom_mask':pred_atom_mask 'final_atom_mask': pred_atom_mask
} }
true_atom_position = torch.cat((chain_a1_pos,chain_a2_pos,chain_b1_pos,chain_b2_pos,chain_b3_pos),dim=1) true_atom_position = torch.cat((chain_a1_pos, chain_a2_pos, chain_b1_pos, chain_b2_pos, chain_b3_pos), dim=1)
true_atom_mask = torch.cat((torch.ones((1,self.chain_a_num_res,37),device='cuda'), true_atom_mask = torch.cat((torch.ones((1, self.chain_a_num_res, 37)),
torch.ones((1,self.chain_a_num_res,37),device='cuda'), torch.ones((1, self.chain_a_num_res, 37)),
torch.ones((1,self.chain_b_num_res,37),device='cuda'), torch.ones((1, self.chain_b_num_res, 37)),
torch.ones((1,self.chain_b_num_res,37),device='cuda'), torch.ones((1, self.chain_b_num_res, 37)),
torch.ones((1,self.chain_b_num_res,37),device='cuda')),dim=1) torch.ones((1, self.chain_b_num_res, 37))), dim=1)
batch['all_atom_positions'] = true_atom_position batch['all_atom_positions'] = true_atom_position
batch['all_atom_mask'] = true_atom_mask batch['all_atom_mask'] = true_atom_mask
dim_dict = AlphaFoldMultimerLoss.determine_split_dim(batch) aligns, _ = compute_permutation_alignment(out, batch,
aligns = AlphaFoldMultimerLoss.multi_chain_perm_align(out,batch, batch)
dim_dict,
permutate_chains=True)
print(f"##### aligns is {aligns}") print(f"##### aligns is {aligns}")
possible_outcome = [[(0,1),(1,0),(2,3),(3,4),(4,2)],[(0,0),(1,1),(2,3),(3,4),(4,2)]] possible_outcome = [[(0, 1), (1, 0), (2, 3), (3, 4), (4, 2)], [(0, 0), (1, 1), (2, 3), (3, 4), (4, 2)]]
wrong_outcome = [[(0,1),(1,0),(2,4),(3,2),(4,3)],[(0,0),(1,1),(2,2),(3,3),(4,4)]] wrong_outcome = [[(0, 1), (1, 0), (2, 4), (3, 2), (4, 3)], [(0, 0), (1, 1), (2, 2), (3, 3), (4, 4)]]
self.assertIn(aligns,possible_outcome) self.assertIn(aligns, possible_outcome)
self.assertNotIn(aligns,wrong_outcome) self.assertNotIn(aligns, wrong_outcome)
def test_3_merge_labels(self): def test_3_merge_labels(self):
nres_pad = 325 - 57 # suppose the cropping size is 325 nres_pad = 325 - 57 # suppose the cropping size is 325
batch = { batch = {
'asym_id':pad_features(self.asym_id,nres_pad,pad_dim=1), 'asym_id': pad_features(self.asym_id, nres_pad, pad_dim=1),
'sym_id':pad_features(self.sym_id,nres_pad,pad_dim=1), 'sym_id': pad_features(self.sym_id, nres_pad, pad_dim=1),
'entity_id':pad_features(self.entity_id,nres_pad,pad_dim=1), 'entity_id': pad_features(self.entity_id, nres_pad, pad_dim=1),
'aatype':torch.randint(21,size=(1,325)), 'aatype': torch.randint(21, size=(1, 325)),
'seq_length':torch.tensor([57]) 'seq_length': torch.tensor([57])
} }
batch['asym_id'] = batch['asym_id'].reshape(1,325) batch['asym_id'] = batch['asym_id'].reshape(1, 325)
batch["residue_index"] = pad_features(torch.tensor(self.residue_index).reshape(1,57),nres_pad,pad_dim=1) batch["residue_index"] = pad_features(torch.tensor(self.residue_index).reshape(1, 57), nres_pad, pad_dim=1)
# create fake ground truth atom positions # create fake ground truth atom positions
chain_a1_pos = torch.randint(15,(self.chain_a_num_res,3*37), chain_a1_pos = torch.randint(15, (self.chain_a_num_res, 3 * 37),
device='cuda',dtype=torch.float).reshape(1,self.chain_a_num_res,37,3) dtype=torch.float).reshape(1, self.chain_a_num_res, 37, 3)
chain_a2_pos = torch.matmul(chain_a1_pos,self.rotation_matrix_x)+10 chain_a2_pos = torch.matmul(chain_a1_pos, self.rotation_matrix_x) + 10
chain_b1_pos = torch.randint(low=15,high=30,size=(self.chain_b_num_res,3*37), chain_b1_pos = torch.randint(low=15, high=30, size=(self.chain_b_num_res, 3 * 37),
device='cuda',dtype=torch.float).reshape(1,self.chain_b_num_res,37,3) dtype=torch.float).reshape(1, self.chain_b_num_res, 37, 3)
chain_b2_pos = torch.matmul(chain_b1_pos,self.rotation_matrix_y)+10 chain_b2_pos = torch.matmul(chain_b1_pos, self.rotation_matrix_y) + 10
chain_b3_pos = torch.matmul(torch.matmul(chain_b1_pos,self.rotation_matrix_z),self.rotation_matrix_x)+30 chain_b3_pos = torch.matmul(torch.matmul(chain_b1_pos, self.rotation_matrix_z), self.rotation_matrix_x) + 30
# Below permutate predicted chain positions # Below permutate predicted chain positions
pred_atom_position = torch.cat((chain_a2_pos,chain_a1_pos,chain_b2_pos,chain_b3_pos,chain_b1_pos),dim=1) pred_atom_position = torch.cat((chain_a2_pos, chain_a1_pos, chain_b2_pos, chain_b3_pos, chain_b1_pos), dim=1)
pred_atom_mask = torch.ones((1,self.num_res,37),device='cuda') pred_atom_mask = torch.ones((1, self.num_res, 37))
pred_atom_position = pad_features(pred_atom_position,nres_pad,pad_dim=1) pred_atom_position = pad_features(pred_atom_position, nres_pad, pad_dim=1)
pred_atom_mask = pad_features(pred_atom_mask,nres_pad,pad_dim=1) pred_atom_mask = pad_features(pred_atom_mask, nres_pad, pad_dim=1)
out = { out = {
'final_atom_positions':pred_atom_position, 'final_atom_positions': pred_atom_position,
'final_atom_mask':pred_atom_mask 'final_atom_mask': pred_atom_mask
} }
true_atom_position = torch.cat((chain_a1_pos,chain_a2_pos,chain_b1_pos,chain_b2_pos,chain_b3_pos),dim=1) true_atom_position = torch.cat((chain_a1_pos, chain_a2_pos, chain_b1_pos, chain_b2_pos, chain_b3_pos), dim=1)
true_atom_mask = torch.cat((torch.ones((1,self.chain_a_num_res,37),device='cuda'), true_atom_mask = torch.cat((torch.ones((1, self.chain_a_num_res, 37)),
torch.ones((1,self.chain_a_num_res,37),device='cuda'), torch.ones((1, self.chain_a_num_res, 37)),
torch.ones((1,self.chain_b_num_res,37),device='cuda'), torch.ones((1, self.chain_b_num_res, 37)),
torch.ones((1,self.chain_b_num_res,37),device='cuda'), torch.ones((1, self.chain_b_num_res, 37)),
torch.ones((1,self.chain_b_num_res,37),device='cuda')),dim=1) torch.ones((1, self.chain_b_num_res, 37))), dim=1)
batch['all_atom_positions'] = pad_features(true_atom_position,nres_pad,pad_dim=1) batch['all_atom_positions'] = pad_features(true_atom_position, nres_pad, pad_dim=1)
batch['all_atom_mask'] = pad_features(true_atom_mask,nres_pad=nres_pad,pad_dim=1) batch['all_atom_mask'] = pad_features(true_atom_mask, nres_pad=nres_pad, pad_dim=1)
tensor_to_cuda = lambda t: t.to('cuda') # tensor_to_cuda = lambda t: t.to('cuda')
batch = tensor_tree_map(tensor_to_cuda,batch) # ground_truth = tensor_tree_map(tensor_to_cuda,ground_truth)
dim_dict = AlphaFoldMultimerLoss.determine_split_dim(batch) aligns, per_asym_residue_index = compute_permutation_alignment(out,
aligns = AlphaFoldMultimerLoss.multi_chain_perm_align(out, batch,
batch, batch)
dim_dict,
permutate_chains=True)
print(f"##### aligns is {aligns}") print(f"##### aligns is {aligns}")
labels = AlphaFoldMultimerLoss.split_ground_truth_labels(batch,dim_dict=dim_dict, labels = split_ground_truth_labels(batch)
REQUIRED_FEATURES=[i for i in batch.keys() if i in dim_dict])
labels = merge_labels(per_asym_residue_index, labels, aligns,
labels = merge_labels(labels,aligns,
original_nres=batch['aatype'].shape[-1]) original_nres=batch['aatype'].shape[-1])
self.assertTrue(torch.equal(labels['residue_index'],batch['residue_index'])) self.assertTrue(torch.equal(labels['residue_index'], batch['residue_index']))
expected_permutated_gt_pos = torch.cat((chain_a2_pos,chain_a1_pos,chain_b2_pos,chain_b3_pos,chain_b1_pos),dim=1) expected_permutated_gt_pos = torch.cat((chain_a2_pos, chain_a1_pos, chain_b2_pos, chain_b3_pos, chain_b1_pos),
expected_permutated_gt_pos = pad_features(expected_permutated_gt_pos,nres_pad,pad_dim=1) dim=1)
self.assertTrue(torch.equal(labels['all_atom_positions'],expected_permutated_gt_pos)) expected_permutated_gt_pos = pad_features(expected_permutated_gt_pos, nres_pad, pad_dim=1)
\ No newline at end of file self.assertTrue(torch.equal(labels['all_atom_positions'], expected_permutated_gt_pos))
tests/test_structure_module.py
View file @ 5fcd6ed2
...@@ -46,16 +46,17 @@ if compare_utils.alphafold_is_installed(): ...@@ -46,16 +46,17 @@ if compare_utils.alphafold_is_installed():
class TestStructureModule(unittest.TestCase): class TestStructureModule(unittest.TestCase):
@classmethod @classmethod
def setUpClass(cls): def setUpClass(cls):
if consts.is_multimer: if compare_utils.alphafold_is_installed():
cls.am_atom = alphafold.model.all_atom_multimer if consts.is_multimer:
cls.am_fold = alphafold.model.folding_multimer cls.am_atom = alphafold.model.all_atom_multimer
cls.am_modules = alphafold.model.modules_multimer cls.am_fold = alphafold.model.folding_multimer
cls.am_rigid = alphafold.model.geometry cls.am_modules = alphafold.model.modules_multimer
else: cls.am_rigid = alphafold.model.geometry
cls.am_atom = alphafold.model.all_atom else:
cls.am_fold = alphafold.model.folding cls.am_atom = alphafold.model.all_atom
cls.am_modules = alphafold.model.modules cls.am_fold = alphafold.model.folding
cls.am_rigid = alphafold.model.r3 cls.am_modules = alphafold.model.modules
cls.am_rigid = alphafold.model.r3
def test_structure_module_shape(self): def test_structure_module_shape(self):
batch_size = consts.batch_size batch_size = consts.batch_size
...@@ -202,16 +203,17 @@ class TestStructureModule(unittest.TestCase): ...@@ -202,16 +203,17 @@ class TestStructureModule(unittest.TestCase):
class TestInvariantPointAttention(unittest.TestCase): class TestInvariantPointAttention(unittest.TestCase):
@classmethod @classmethod
def setUpClass(cls): def setUpClass(cls):
if consts.is_multimer: if compare_utils.alphafold_is_installed():
cls.am_atom = alphafold.model.all_atom_multimer if consts.is_multimer:
cls.am_fold = alphafold.model.folding_multimer cls.am_atom = alphafold.model.all_atom_multimer
cls.am_modules = alphafold.model.modules_multimer cls.am_fold = alphafold.model.folding_multimer
cls.am_rigid = alphafold.model.geometry cls.am_modules = alphafold.model.modules_multimer
else: cls.am_rigid = alphafold.model.geometry
cls.am_atom = alphafold.model.all_atom else:
cls.am_fold = alphafold.model.folding cls.am_atom = alphafold.model.all_atom
cls.am_modules = alphafold.model.modules cls.am_fold = alphafold.model.folding
cls.am_rigid = alphafold.model.r3 cls.am_modules = alphafold.model.modules
cls.am_rigid = alphafold.model.r3
def test_shape(self): def test_shape(self):
c_m = 13 c_m = 13
......
tests/test_template.py
View file @ 5fcd6ed2
...@@ -56,16 +56,17 @@ class TestTemplatePointwiseAttention(unittest.TestCase): ...@@ -56,16 +56,17 @@ class TestTemplatePointwiseAttention(unittest.TestCase):
class TestTemplatePairStack(unittest.TestCase): class TestTemplatePairStack(unittest.TestCase):
@classmethod @classmethod
def setUpClass(cls): def setUpClass(cls):
if consts.is_multimer: if compare_utils.alphafold_is_installed():
cls.am_atom = alphafold.model.all_atom_multimer if consts.is_multimer:
cls.am_fold = alphafold.model.folding_multimer cls.am_atom = alphafold.model.all_atom_multimer
cls.am_modules = alphafold.model.modules_multimer cls.am_fold = alphafold.model.folding_multimer
cls.am_rigid = alphafold.model.geometry cls.am_modules = alphafold.model.modules_multimer
else: cls.am_rigid = alphafold.model.geometry
cls.am_atom = alphafold.model.all_atom else:
cls.am_fold = alphafold.model.folding cls.am_atom = alphafold.model.all_atom
cls.am_modules = alphafold.model.modules cls.am_fold = alphafold.model.folding
cls.am_rigid = alphafold.model.r3 cls.am_modules = alphafold.model.modules
cls.am_rigid = alphafold.model.r3
def test_shape(self): def test_shape(self):
batch_size = consts.batch_size batch_size = consts.batch_size
...@@ -196,16 +197,17 @@ class TestTemplatePairStack(unittest.TestCase): ...@@ -196,16 +197,17 @@ class TestTemplatePairStack(unittest.TestCase):
class Template(unittest.TestCase): class Template(unittest.TestCase):
@classmethod @classmethod
def setUpClass(cls): def setUpClass(cls):
if consts.is_multimer: if compare_utils.alphafold_is_installed():
cls.am_atom = alphafold.model.all_atom_multimer if consts.is_multimer:
cls.am_fold = alphafold.model.folding_multimer cls.am_atom = alphafold.model.all_atom_multimer
cls.am_modules = alphafold.model.modules_multimer cls.am_fold = alphafold.model.folding_multimer
cls.am_rigid = alphafold.model.geometry cls.am_modules = alphafold.model.modules_multimer
else: cls.am_rigid = alphafold.model.geometry
cls.am_atom = alphafold.model.all_atom else:
cls.am_fold = alphafold.model.folding cls.am_atom = alphafold.model.all_atom
cls.am_modules = alphafold.model.modules cls.am_fold = alphafold.model.folding
cls.am_rigid = alphafold.model.r3 cls.am_modules = alphafold.model.modules
cls.am_rigid = alphafold.model.r3
@compare_utils.skip_unless_alphafold_installed() @compare_utils.skip_unless_alphafold_installed()
def test_compare(self): def test_compare(self):
......
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