| # Copyright 2021 AlQuraishi Laboratory | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| import torch | |
| def gdt(p1, p2, mask, cutoffs): | |
| n = torch.sum(mask, dim=-1) | |
| p1 = p1.float() | |
| p2 = p2.float() | |
| distances = torch.sqrt(torch.sum((p1 - p2)**2, dim=-1)) | |
| scores = [] | |
| for c in cutoffs: | |
| score = torch.sum((distances <= c) * mask, dim=-1) / n | |
| scores.append(score) | |
| return sum(scores) / len(scores) | |
| def gdt_ts(p1, p2, mask): | |
| return gdt(p1, p2, mask, [1., 2., 4., 8.]) | |
| def gdt_ha(p1, p2, mask): | |
| return gdt(p1, p2, mask, [0.5, 1., 2., 4.]) | |