feat(pt): add dpa3 alpha descriptor #4476
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| # deserialize repflow | ||
| statistic_repflows = repflow_variable.pop("@variables") | ||
| env_mat = repflow_variable.pop("env_mat") |
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Unused local variable Note
| # cast the input to internal precsion | ||
| extended_coord = extended_coord.to(dtype=self.prec) | ||
| nframes, nloc, nnei = nlist.shape | ||
| nall = extended_coord.view(nframes, -1).shape[1] // 3 |
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Unused local variable Note
| # h2: nb x nloc x nnei x 3 | ||
| # msk: nb x nloc x nnei | ||
| nb, nloc, nnei, _ = edge_ebd.shape | ||
| e_dim = edge_ebd.shape[-1] |
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| def list_update_res_residual( | ||
| self, update_list: list[torch.Tensor], update_name: str = "node" | ||
| ) -> torch.Tensor: | ||
| nitem = len(update_list) |
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| [False], # use_econf_tebd | ||
| ): | ||
| dtype = PRECISION_DICT[prec] | ||
| rtol, atol = get_tols(prec) |
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Unused local variable Note test
| [False], # use_econf_tebd | ||
| ): | ||
| dtype = PRECISION_DICT[prec] | ||
| rtol, atol = get_tols(prec) |
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Unused local variable Note test
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| dd0.repflows.mean = torch.tensor(davg, dtype=dtype, device=env.DEVICE) | ||
| dd0.repflows.stddev = torch.tensor(dstd, dtype=dtype, device=env.DEVICE) | ||
| model = torch.jit.script(dd0) |
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Unused local variable Note test
Codecov ReportAttention: Patch coverage is
Additional details and impacted files@@ Coverage Diff @@
## dpa3-alpha #4476 +/- ##
==============================================
+ Coverage 83.75% 84.48% +0.72%
==============================================
Files 667 674 +7
Lines 61514 62824 +1310
Branches 3486 3486
==============================================
+ Hits 51523 53074 +1551
+ Misses 8866 8623 -243
- Partials 1125 1127 +2 ☔ View full report in Codecov by Sentry. 🚀 New features to boost your workflow:
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| # if mae: | ||
| # mae_e = torch.mean(torch.abs(energy_pred - energy_label)) * atom_norm | ||
| # more_loss["mae_e"] = self.display_if_exist(mae_e.detach(), find_energy) | ||
| # mae_e_all = torch.mean(torch.abs(energy_pred - energy_label)) | ||
| # more_loss["mae_e_all"] = self.display_if_exist( | ||
| # mae_e_all.detach(), find_energy |
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| # if mae: | ||
| # mae_f = torch.mean(torch.abs(diff_f)) | ||
| # more_loss["mae_f"] = self.display_if_exist( | ||
| # mae_f.detach(), find_force |
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| # if mae: | ||
| # mae_v = torch.mean(torch.abs(diff_v)) * atom_norm | ||
| # more_loss["mae_v"] = self.display_if_exist(mae_v.detach(), find_virial) |
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Commented-out code Note
This PR is an early experimental preview version of DPA3. Significant changes may occur in subsequent updates. Please use with caution.