New version of E3statistics
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| typed_dataset = idp(self.data.to_dict()) | ||
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| idp.get_irreps() |
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这块get_irreps的时候要设置一下参数，no_parity=False
| for irrep in idp.orbpair_irreps: | ||
| l = int(str(irrep)[2]) | ||
| irrep_slice = slice(index, index+2*l+1) |
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这块的写法其实要保证idp.orbpair_irreps中每个irrep都是1xne/1xno这样的形式，虽然我们的idp.orbpair_irreps是这样写的，但后面很难保证不做修改，这边应该写更general一点
| onsite_tp_mask = onsite_block_mask[typed_dataset["atom_types"].flatten().eq(tp)] | ||
| onsite_tp = features[typed_dataset["atom_types"].flatten().eq(tp)] | ||
| filtered_vecs = torch.where(onsite_tp_mask, onsite_tp, torch.tensor(float('nan'))) |
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主要是占位，给后面统一用irrep_slice取不同type的rme时候用
| typed_norm_std[at] = bt_std | ||
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| return typed_norm_ave, typed_norm_std |
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这边只有norm的std 和 mean，似乎缺了l=0时的mean?
floatingCatty
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Feb 2, 2024
* add data * adapt data and nn module of nequip into deeptb * just modify some imports * update torch-geometry * add kpoint eigenvalue support * add support for nested tensor * update * update data and add batchlize hamiltonian * update se3 rotation * update test * update * debug e3 * update hamileig * delete nequip nn and write our own based on PyG * update nn * nn refactor, write hamiltonian and hop function * update sk hamiltonian and onsite function * refactor sktb and add register for descriptor * update param prototype and dptb * refactor index mapping to data transform * debug sktb and e3tb module * finish debuging sk and e3 * update data interfaces * update r2k and transform * remove dash line in file names * fnishied debugging deeptb module * finish debugging hr2hk * update overlap support * update base trainer and example quantities * update build model * update trainer * update pyproject.toml dependencies * update bond reduction and self-interaction * debug nnsk * nnsk run succeed, add from v1 json model * add nnsk test example of AlAs coupond system * Add 'ABACUSDataset' in data module (#9) * Prototype code for loading Hamiltonian * add 'ABACUSDataset' in data module * modified "basis.dat" storage & can load overlap * recover some original dataset settings * add ABACUSDataset in init * debug new dptb and trainer * debug datasets * pass cmd line train mod to new model and data * add some comments in neighbor_list_and_relative_vec. * add overlap fitting support * update baseline descriptor and debug validationer * update e3deeph module * update deephe3 module * Added ABACUSInMemoryDataset in data module (#11) * Prototype code for loading Hamiltonian * add 'ABACUSDataset' in data module * modified "basis.dat" storage & can load overlap * recover some original dataset settings * add ABACUSDataset in init * Add the in memory version of ABACUSDataset * add ABACUSInMemoryDataset in data package * update dataset and add deephdataset * gpu support and debugging * add dptb+nnsk mix model, debugging build, restart * align run.py, test.py, main.py * debugging * final * add new model backbone on allegro * add new e3 embeding and lr schedular * Added `DefaultDataset` (#12) * Prototype code for loading Hamiltonian * add 'ABACUSDataset' in data module * modified "basis.dat" storage & can load overlap * recover some original dataset settings * add ABACUSDataset in init * Add the in memory version of ABACUSDataset * add ABACUSInMemoryDataset in data package * Added `DefaultDataset` and unified `ABACUSDataset` * improved DefaultDataset & add `dptb data` entrypoint for preprocess * update `build_dataset` * aggregating new data class * debug plugin savor and support atom specific cutoffs * refactor bond reduction and rme parameterization * add E3 fitting analysis and E3 rescale * update LossAnalysis and e3baseline model * update band calc and debug nnsk add orbitals * update datatype switch * Unified dataset IO (#13) * Prototype code for loading Hamiltonian * add 'ABACUSDataset' in data module * modified "basis.dat" storage & can load overlap * recover some original dataset settings * add ABACUSDataset in init * Add the in memory version of ABACUSDataset * add ABACUSInMemoryDataset in data package * Added `DefaultDataset` and unified `ABACUSDataset` * improved DefaultDataset & add `dptb data` entrypoint for preprocess * update `build_dataset` * update `data` entrypoint * Unified dataset IO & added ASE trajectory support * Add support to save `.pth` files with different `info.json` settings. * Bug fix in dealing with "ase" info. * updated `argcheck` for setinfo. * added setinfo check when building dataset. * file IO improvements * bug fix in loading `info.json` * update e3 descriptor and OrbitalMapper * Bug fix in reading trajectory data (#15) * add comment and complete eig loss * update new embedding and dependencies * New version of `E3statistics` (#17) * new version of `E3statistics` function added in DefaultDataset. * fix bug in dealing with scalars in `E3statistics` * add "decay" option in E3statistics to return edge length dependence * fix bug in getting rmes when doing stat & update argcheck * adding statistics initialization * debug nnsk batchlization and eigenvalues loading * debug nnsk * optimizing saving best checkpoint * Pr/44 (#19) * add comments QG * add comment QG * debug nnsk add orbital and strain * update `.npy` files loading procedure in DefaultDataset (#18) * optimizing init and restart param loading * update nnsk push thr * update mix model param and deeptb sktb param * BUG FIX in loading `kpoints.npy` files with `ndim==3` (#20) * bug fix in loading `kpoints.npy` files with `ndim==3` * added tests for nnsk training * main program for test_train * refactor test * update nrl * denote run --------- Co-authored-by: Sharp Londe <93334987+SharpLonde@users.noreply.github.com> Co-authored-by: qqgu <guqq_phy@qq.com> Co-authored-by: Qiangqiang Gu <98570179+QG-phy@users.noreply.github.com>
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New version of
E3statisticsfunction is added in DefaultDataset.It calculates the norm and standard deviation of node / edge features by their irreducible matrix sub-blocks.