255 lines
12 KiB
Python
255 lines
12 KiB
Python
# Copyright 2021 AlQuraishi Laboratory
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# Dingquan Yu @ EMBL-Hamburg Kosinski group
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import math
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import torch
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import unittest
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from openfold.utils.multi_chain_permutation import (pad_features, get_least_asym_entity_or_longest_length,
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compute_permutation_alignment, split_ground_truth_labels,
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merge_labels)
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class TestPermutation(unittest.TestCase):
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def setUp(self):
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"""
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create fake input structure features
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and rotation matrices
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"""
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theta = math.pi / 4
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device = 'cpu'
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self.rotation_matrix_z = torch.tensor([
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[math.cos(theta), -math.sin(theta), 0],
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[math.sin(theta), math.cos(theta), 0],
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[0, 0, 1]
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], device=device)
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self.rotation_matrix_x = torch.tensor([
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[1, 0, 0],
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[0, math.cos(theta), -math.sin(theta)],
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[0, math.sin(theta), math.cos(theta)],
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], device=device)
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self.rotation_matrix_y = torch.tensor([
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[math.cos(theta), 0, math.sin(theta)],
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[0, 1, 0],
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[-math.sin(theta), 1, math.cos(theta)],
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], device=device)
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self.chain_a_num_res = 9
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self.chain_b_num_res = 13
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# below create default fake ground truth structures for a hetero-pentamer A2B3
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self.residue_index = list(
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range(self.chain_a_num_res)) * 2 + list(range(self.chain_b_num_res)) * 3
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self.num_res = self.chain_a_num_res * 2 + self.chain_b_num_res * 3
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self.asym_id = torch.tensor([[1] * self.chain_a_num_res + [2] * self.chain_a_num_res + [
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3] * self.chain_b_num_res + [4] * self.chain_b_num_res + [5] * self.chain_b_num_res], device=device)
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self.sym_id = self.asym_id
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self.entity_id = torch.tensor([[1] * (self.chain_a_num_res * 2) + [2] * (self.chain_b_num_res * 3)],
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device=device)
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def test_1_selecting_anchors(self):
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batch = {
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'asym_id': self.asym_id,
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'sym_id': self.sym_id,
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'entity_id': self.entity_id,
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'seq_length': torch.tensor([57])
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}
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anchor_gt_asym, anchor_pred_asym = get_least_asym_entity_or_longest_length(
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batch, batch['asym_id'])
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anchor_gt_asym = int(anchor_gt_asym)
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anchor_pred_asym = {int(i) for i in anchor_pred_asym}
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expected_anchors = {1, 2}
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expected_non_anchors = {3, 4, 5}
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self.assertIn(anchor_gt_asym, expected_anchors)
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self.assertNotIn(anchor_gt_asym, expected_non_anchors)
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# Check that predicted anchors are within expected anchor set
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self.assertEqual(anchor_pred_asym, expected_anchors & anchor_pred_asym)
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self.assertEqual(set(), anchor_pred_asym & expected_non_anchors)
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def test_2_permutation_pentamer(self):
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"""
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Test the permutation results on a pentamer A2B3, in which protein A has 9 residues
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and protein B has 13 residues.
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Expected outputs:
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Only protein A should be selected as an anchor thus, in the output list, either [(0,1), (1,0)] or [(0,0), (1,1)] are allowed
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The 3 chains from protein B should ALWAYS be aligned in a way that predicted b1 to be aligned with ground truth b1, pred b2 to ground truth b2
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as shown below:
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predicted structure: a2 - a1 - b2 - b3 - b1
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indexes in the predicted list: 0 1 2 3 4
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ground truth structure: a1 - a2 - b1 - b2 - b3
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indexes in the ground truth list: 0 1 2 3 4
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then the 2 protein A chains are free to be aligned by either order, thus either [(0,1),(1,0)] or [(0,0),(1,1)] is valid.
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However, the 3 protein B chains should be strictly aligned in the following order:
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[(2,3), (3,4), (4,2)], regardless of how protein A chains are aligned.
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Therefore, the only 2 correct permutations are :
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[(0, 1), (1, 0), (2, 3), (3, 4), (4, 2)] and
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[(0, 0), (1, 1), (2, 3), (3, 4), (4, 2)]
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"""
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batch = {
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'asym_id': self.asym_id,
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'sym_id': self.sym_id,
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'entity_id': self.entity_id,
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'seq_length': torch.tensor([57]),
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'aatype': torch.randint(21, size=(1, 57))
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}
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batch['asym_id'] = batch['asym_id'].reshape(1, self.num_res)
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batch["residue_index"] = torch.tensor([self.residue_index])
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# create fake ground truth atom positions
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chain_a1_pos = torch.randint(15, (self.chain_a_num_res, 3 * 37),
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dtype=torch.float).reshape(1, self.chain_a_num_res, 37, 3)
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chain_a2_pos = torch.matmul(chain_a1_pos, self.rotation_matrix_x) + 10
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chain_b1_pos = torch.randint(low=15, high=30, size=(self.chain_b_num_res, 3 * 37),
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dtype=torch.float).reshape(1, self.chain_b_num_res, 37, 3)
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chain_b2_pos = torch.matmul(chain_b1_pos, self.rotation_matrix_y) + 10
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chain_b3_pos = torch.matmul(torch.matmul(
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chain_b1_pos, self.rotation_matrix_z), self.rotation_matrix_x) + 30
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# Below permutate predicted chain positions
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# here the b2 chain from the ground truth is deliberately put in b1 chain's position, and predicted b3 chain to b2's position
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# and predicted b1 chain to b3's position
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pred_atom_position = torch.cat(
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(chain_a2_pos, chain_a1_pos, chain_b2_pos, chain_b3_pos, chain_b1_pos), dim=1)
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pred_atom_mask = torch.ones((1, self.num_res, 37))
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out = {
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'final_atom_positions': pred_atom_position,
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'final_atom_mask': pred_atom_mask
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}
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true_atom_position = torch.cat(
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(chain_a1_pos, chain_a2_pos, chain_b1_pos, chain_b2_pos, chain_b3_pos), dim=1)
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true_atom_mask = torch.cat((torch.ones((1, self.chain_a_num_res, 37)),
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torch.ones((1, self.chain_a_num_res, 37)),
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torch.ones((1, self.chain_b_num_res, 37)),
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torch.ones((1, self.chain_b_num_res, 37)),
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torch.ones((1, self.chain_b_num_res, 37))), dim=1)
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batch['all_atom_positions'] = true_atom_position
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batch['all_atom_mask'] = true_atom_mask
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aligns, per_asym_residue_index = compute_permutation_alignment(out, batch,
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batch)
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expected_asym_residue_index = {
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1: torch.tensor(list(range(self.chain_a_num_res))),
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2: torch.tensor(list(range(self.chain_a_num_res))),
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3: torch.tensor(list(range(self.chain_b_num_res))),
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4: torch.tensor(list(range(self.chain_b_num_res))),
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5: torch.tensor(list(range(self.chain_b_num_res)))
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}
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chain_a_permutated_chain_b_permutated = [
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(0, 1), (1, 0), (2, 3), (3, 4), (4, 2)]
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chain_a_not_permutated_chain_b_permutated = [
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(0, 0), (1, 1), (2, 3), (3, 4), (4, 2)]
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chain_a_permutated_chain_b_not_permuated = [
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(0, 1), (1, 0), (2, 2), (3, 3), (4, 4)]
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chain_a_not_permutated_chain_b_not_permuated = [
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(0, 0), (1, 1), (2, 2), (3, 3), (4, 4)]
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# test on the permutation alignments
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self.assertIn(aligns, [chain_a_permutated_chain_b_permutated,
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chain_a_not_permutated_chain_b_permutated])
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self.assertNotIn(aligns, [chain_a_permutated_chain_b_not_permuated,
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chain_a_not_permutated_chain_b_not_permuated])
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# test on the per_aysm_residue_index
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for k, v in expected_asym_residue_index.items():
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self.assertTrue(torch.equal(v, per_asym_residue_index[k]))
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def test_3_merge_labels(self):
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nres_pad = 325 - 57 # suppose the cropping size is 325
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batch = {
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'asym_id': self.asym_id,
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'sym_id': self.sym_id,
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'entity_id': self.entity_id,
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'aatype': torch.randint(21, size=(1, 57)),
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'seq_length': torch.tensor([57])
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}
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batch['asym_id'] = batch['asym_id'].reshape(1, 57)
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batch["residue_index"] = torch.tensor([self.residue_index])
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# create fake ground truth atom positions
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chain_a1_pos = torch.randint(15, (self.chain_a_num_res, 3 * 37),
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dtype=torch.float).reshape(1, self.chain_a_num_res, 37, 3)
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chain_a2_pos = torch.matmul(chain_a1_pos, self.rotation_matrix_x) + 10
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chain_b1_pos = torch.randint(low=15, high=30, size=(self.chain_b_num_res, 3 * 37),
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dtype=torch.float).reshape(1, self.chain_b_num_res, 37, 3)
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chain_b2_pos = torch.matmul(chain_b1_pos, self.rotation_matrix_y) + 10
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chain_b3_pos = torch.matmul(torch.matmul(
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chain_b1_pos, self.rotation_matrix_z), self.rotation_matrix_x) + 30
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# Below permutate predicted chain positions
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pred_atom_position = torch.cat(
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(chain_a2_pos, chain_a1_pos, chain_b2_pos, chain_b3_pos, chain_b1_pos), dim=1)
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pred_atom_mask = torch.ones((1, self.num_res, 37))
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pred_atom_position = pad_features(
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pred_atom_position, nres_pad, pad_dim=1)
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pred_atom_mask = pad_features(pred_atom_mask, nres_pad, pad_dim=1)
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out = {
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'final_atom_positions': pred_atom_position,
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'final_atom_mask': pred_atom_mask
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}
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true_atom_position = torch.cat(
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(chain_a1_pos, chain_a2_pos, chain_b1_pos, chain_b2_pos, chain_b3_pos), dim=1)
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true_atom_mask = torch.cat((torch.ones((1, self.chain_a_num_res, 37)),
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torch.ones((1, self.chain_a_num_res, 37)),
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torch.ones((1, self.chain_b_num_res, 37)),
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torch.ones((1, self.chain_b_num_res, 37)),
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torch.ones((1, self.chain_b_num_res, 37))), dim=1)
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batch['all_atom_positions'] = true_atom_position
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batch['all_atom_mask'] = true_atom_mask
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# Below create a fake_input_features
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fake_input_features = {
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'asym_id': pad_features(self.asym_id, nres_pad, pad_dim=1),
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'sym_id': pad_features(self.sym_id, nres_pad, pad_dim=1),
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'entity_id': pad_features(self.entity_id, nres_pad, pad_dim=1),
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'aatype': torch.randint(21, size=(1, 325)),
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'seq_length': torch.tensor([57])
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}
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fake_input_features['asym_id'] = fake_input_features['asym_id'].reshape(
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1, 325)
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fake_input_features["residue_index"] = pad_features(
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torch.tensor(self.residue_index).reshape(1, 57), nres_pad, pad_dim=1)
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fake_input_features['all_atom_positions'] = pad_features(
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true_atom_position, nres_pad, pad_dim=1)
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fake_input_features['all_atom_mask'] = pad_features(
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true_atom_mask, nres_pad=nres_pad, pad_dim=1)
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# NOTE
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# batch: simulates ground_truth features
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# fake_input_features: simulates the data that are going be used as input for model.forward(fake_input_features)
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# out: simulates the output of model.forward(fake_input_features)
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aligns, per_asym_residue_index = compute_permutation_alignment(out,
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fake_input_features,
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batch)
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labels = split_ground_truth_labels(batch)
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labels = merge_labels(per_asym_residue_index, labels, aligns,
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original_nres=batch['aatype'].shape[-1])
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self.assertTrue(torch.equal(
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labels['residue_index'], batch['residue_index']))
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expected_permutated_gt_pos = torch.cat((chain_a2_pos, chain_a1_pos, chain_b2_pos, chain_b3_pos, chain_b1_pos),
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dim=1)
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self.assertTrue(torch.equal(
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labels['all_atom_positions'], expected_permutated_gt_pos))
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