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from unittest import TestCase
import pytest
import torch
from chroma.layers.structure.backbone import (
BackboneBuilder,
LossBackboneResidueDistance,
ProteinBackbone,
RigidTransform,
RigidTransformer,
)
class TestProteinBackbone(TestCase):
def test_cuda(self):
if torch.cuda.is_available():
try:
protein_backbone = ProteinBackbone(1).cuda()
except Exception:
protein_backbone = None
self.assertTrue(protein_backbone is not None)
def test_sample(self):
protein_backbone = ProteinBackbone(1)
expected = torch.Tensor(
[
[
[
[0.1331, -1.6303, -0.7377],
[0.0414, -0.1759, -0.8080],
[-0.3710, 0.4114, 0.5376],
[0.1965, 1.3947, 1.0081],
]
]
]
)
predicted = protein_backbone()
self.assertEqual((1, 1, 4, 3), predicted.shape)
self.assertTrue(torch.allclose(expected, predicted, rtol=1e-03))
def test_random_init_backbone(self):
protein_backbone = ProteinBackbone(1, init_state="")
predicted = protein_backbone()
self.assertEqual((1, 1, 4, 3), predicted.shape)
def test_sample_cartesian(self):
protein_backbone = ProteinBackbone(1, use_internal_coords=False)
expected = torch.Tensor(
[
[
[
[0.1331, -1.6303, -0.7377],
[0.0414, -0.1759, -0.8080],
[-0.3710, 0.4114, 0.5376],
[0.1965, 1.3947, 1.0081],
]
]
]
)
predicted = protein_backbone()
self.assertEqual((1, 1, 4, 3), predicted.shape)
self.assertTrue(torch.allclose(expected, predicted, rtol=1e-03))
def test_initialized_sample(self):
torch.manual_seed(7)
input_x = torch.rand(1, 2, 4, 3)
predicted = ProteinBackbone(1, use_internal_coords=False, X_init=input_x)()
expected = torch.Tensor(
[
[
[
[-5.3644e-07, -2.6469e-01, 1.4716e-01],
[1.2197e-01, -2.3073e-01, -8.6988e-02],
[-3.2784e-01, 1.6625e-01, -1.4673e-01],
[3.1635e-01, 3.9145e-01, 3.8886e-02],
],
[
[-2.4808e-01, -2.5717e-01, -6.6959e-02],
[-1.7564e-01, 2.5689e-01, -4.3900e-01],
[4.3500e-01, -3.5570e-01, 3.7083e-01],
[-1.2175e-01, 2.9370e-01, 1.8280e-01],
],
]
]
)
self.assertTrue(torch.allclose(predicted, expected, rtol=1e-03))
class TestRigidTransform(TestCase):
def test_sample(self):
# Default behavior should be identity transformation
rigid_transform = RigidTransform()
torch.manual_seed(7)
input_x = torch.rand(1, 1, 4, 3)
predicted = rigid_transform(input_x)
self.assertTrue(torch.allclose(predicted, input_x, rtol=1e-3))
class TestRigidTransformer(TestCase):
def test_sample(self):
rigid_transformer = RigidTransformer(center_rotation=True, keep_centered=True)
input_x = torch.rand(1, 1, 4, 3)
mean_centered = input_x - torch.mean(input_x.reshape(1, -1, 3), axis=-2)
# Test Identity
no_translation = torch.zeros(1, 3)
identity_q = torch.Tensor([[1.0, 0, 0, 0]])
predicted = rigid_transformer(input_x, no_translation, identity_q)
self.assertTrue(torch.allclose(predicted, mean_centered, rtol=1e-3))
# Test Translation
x_translation = torch.Tensor([[1, 0, 0]])
expected = mean_centered + x_translation
predicted = rigid_transformer(input_x, x_translation, identity_q)
self.assertTrue(torch.allclose(predicted, expected, rtol=1e-3))
class TestBackboneBuilder(TestCase):
def test_sample(self):
phi_tensor = torch.Tensor([[-1.0472]])
psi_tensor = torch.Tensor([[-0.7854]])
backbone_builder = BackboneBuilder()
expected = torch.Tensor(
[
[
[
[-1.2286, 0.2223, -1.2286],
[-1.3203, 1.6767, -1.2989],
[-1.7327, 2.2640, 0.0468],
[-1.1652, 3.2473, 0.5172],
]
]
]
)
predicted = backbone_builder(phi_tensor, psi_tensor)
self.assertTrue(torch.allclose(expected, predicted, rtol=1e-3))
def test_custom_sample(self):
num_residues = 1
phi_tensor = torch.Tensor([[-1.0472]])
psi_tensor = torch.Tensor([[-0.7854]])
backbone_builder = BackboneBuilder()
expected = torch.Tensor(
[
[
[
[-1.2286, 0.2223, -1.2286],
[-1.3203, 1.6767, -1.2989],
[-1.7327, 2.2640, 0.0468],
[-1.1652, 3.2473, 0.5172],
]
]
]
)
predicted = backbone_builder(phi_tensor, psi_tensor)
lengths = torch.tensor(
[[backbone_builder.lengths[key] for key in ["C_N", "N_CA", "CA_C"]]],
dtype=torch.float32,
)
lengths = lengths.repeat(1, 1) # (1,3)
angles = torch.tensor(
[[backbone_builder.angles[key] for key in ["CA_C_N", "C_N_CA", "N_CA_C"]]],
dtype=torch.float32,
)
angles = angles.repeat(1, 1) # (1,3)
omega = backbone_builder.angles["omega"] * torch.ones(1, 1) # (1,1)
predicted = backbone_builder(phi_tensor, psi_tensor, omega, angles, lengths)
self.assertTrue(torch.allclose(expected, predicted, rtol=1e-3))
lengths = torch.tensor(
[[backbone_builder.lengths[key] for key in ["C_N", "N_CA", "CA_C"]]],
dtype=torch.float32,
)
lengths = lengths.repeat(1, num_residues) # (1,3)
angles = torch.tensor(
[[backbone_builder.angles[key] for key in ["CA_C_N", "C_N_CA", "N_CA_C"]]],
dtype=torch.float32,
)
angles = angles.repeat(1, num_residues) # (1,3)
omega = backbone_builder.angles["omega"] * torch.ones(1, num_residues) # (1,1)
predicted = backbone_builder(phi_tensor, psi_tensor, omega, angles, lengths)
self.assertTrue(torch.allclose(expected, predicted, rtol=1e-3))
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