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| Original file line number | Diff line number | Diff line change |
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@@ -21,7 +21,6 @@ dependencies: | |
| - torchvision | ||
| - openff-units | ||
| - pint | ||
| - ase | ||
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| # Testing | ||
| - pytest | ||
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| Original file line number | Diff line number | Diff line change |
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| @@ -1,2 +1,2 @@ | ||
| from .schnet import Schnet | ||
| from .utils import Dense, GaussianRBF, cosine_cutoff, shifted_softplus, scatter_add | ||
| from .utils import GaussianRBF, cosine_cutoff, shifted_softplus, scatter_add |
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| Original file line number | Diff line number | Diff line change |
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| @@ -1,67 +1,155 @@ | ||
| from typing import Dict, List, Optional | ||
| from typing import Dict | ||
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| import lightning as pl | ||
| import torch | ||
| import torch.nn as nn | ||
| from torch.optim import AdamW | ||
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| from modelforge.utils import Inputs, Properties, SpeciesEnergies | ||
| from modelforge.utils import SpeciesEnergies | ||
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| class BaseNNP(nn.Module): | ||
| class PairList(nn.Module): | ||
| def __init__(self, cutoff: float = 5.0): | ||
| """ | ||
| Initialize the PairList class. | ||
| """ | ||
| super().__init__() | ||
| from .utils import neighbor_pairs_nopbc | ||
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| self.calculate_neighbors = neighbor_pairs_nopbc | ||
| self.cutoff = cutoff | ||
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| def compute_distance( | ||
| self, atom_index12: torch.Tensor, R: torch.Tensor | ||
| ) -> torch.Tensor: | ||
| """ | ||
| Compute distances based on atom indices and coordinates. | ||
| Parameters | ||
| ---------- | ||
| atom_index12 : torch.Tensor, shape [n_pairs, 2] | ||
| Atom indices for pairs of atoms | ||
| R : torch.Tensor, shape [batch_size, n_atoms, n_dims] | ||
| Atom coordinates. | ||
| Returns | ||
| ------- | ||
| torch.Tensor, shape [n_pairs] | ||
| Computed distances. | ||
| """ | ||
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| coordinates = R.flatten(0, 1) | ||
| selected_coordinates = coordinates.index_select(0, atom_index12.view(-1)).view( | ||
| 2, -1, 3 | ||
| ) | ||
| vec = selected_coordinates[0] - selected_coordinates[1] | ||
| return vec.norm(2, -1) | ||
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| def forward(self, mask, R) -> Dict[str, torch.Tensor]: | ||
| atom_index12 = self.calculate_neighbors(mask, R, self.cutoff) | ||
| d_ij = self.compute_distance(atom_index12, R) | ||
| return {"atom_index12": atom_index12, "d_ij": d_ij} | ||
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| class BaseNNP(pl.LightningModule): | ||
| """ | ||
| Abstract base class for neural network potentials. | ||
| This class defines the overall structure and ensures that subclasses | ||
| implement the `calculate_energies_and_forces` method. | ||
| Methods | ||
| ------- | ||
| forward(inputs: dict) -> SpeciesEnergies: | ||
| Forward pass for the neural network potential. | ||
| calculate_energy(inputs: dict) -> torch.Tensor: | ||
| Placeholder for the method that should calculate energies and forces. | ||
| training_step(batch, batch_idx) -> torch.Tensor: | ||
| Defines the train loop. | ||
| configure_optimizers() -> AdamW: | ||
| Configures the optimizer. | ||
| """ | ||
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| def __init__(self, dtype: torch.dtype, device: torch.device): | ||
| def __init__(self): | ||
| """ | ||
| Initialize the NeuralNetworkPotential class. | ||
| Parameters | ||
| ---------- | ||
| dtype : torch.dtype | ||
| Data type for the PyTorch tensors. | ||
| device : torch.device | ||
| Device ("cpu" or "cuda") on which computations will be performed. | ||
| Initialize the NNP class. | ||
| """ | ||
| super().__init__() | ||
| self.dtype = dtype | ||
| self.device = device | ||
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| def forward( | ||
| self, | ||
| inputs: Inputs, | ||
| ) -> SpeciesEnergies: | ||
| def forward(self, inputs: Dict[str, torch.Tensor]) -> SpeciesEnergies: | ||
| """ | ||
| Forward pass for the neural network potential. | ||
| Parameters | ||
| ---------- | ||
| inputs : Inputs | ||
| An instance of the Inputs data class containing atomic numbers, positions, etc. | ||
| inputs : dict | ||
| A dictionary containing atomic numbers, positions, etc. | ||
| Returns | ||
| ------- | ||
| SpeciesEnergies | ||
| An instance of the SpeciesEnergies data class containing species and calculated energies. | ||
| """ | ||
| assert isinstance(inputs, Dict) # | ||
| E = self.calculate_energy(inputs) | ||
| return SpeciesEnergies(inputs["Z"], E) | ||
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| E = self.calculate_energies_and_forces(inputs) | ||
| return SpeciesEnergies(inputs.Z, E) | ||
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| def calculate_energies_and_forces(self, inputs: Optional[Inputs] = None): | ||
| def calculate_energy(self, inputs: Dict[str, torch.Tensor]) -> torch.Tensor: | ||
| """ | ||
| Placeholder for the method that should calculate energies and forces. | ||
| This method should be implemented in subclasses. | ||
| Parameters | ||
| ---------- | ||
| inputs : dict | ||
| A dictionary containing atomic numbers, positions, etc. | ||
| Returns | ||
| ------- | ||
| torch.Tensor | ||
| The calculated energy tensor. | ||
| Raises | ||
| ------ | ||
| NotImplementedError | ||
| If the method is not overridden in the subclass. | ||
| """ | ||
| raise NotImplementedError("Subclasses must implement this method.") | ||
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| def training_step( | ||
| self, batch: Dict[str, torch.Tensor], batch_idx: int | ||
| ) -> torch.Tensor: | ||
| """ | ||
| Defines the training loop. | ||
| Parameters | ||
| ---------- | ||
| batch : dict | ||
| Batch data. | ||
| batch_idx : int | ||
| Batch index. | ||
| Returns | ||
| ------- | ||
| torch.Tensor | ||
| The loss tensor. | ||
| """ | ||
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| E_hat = self.forward(batch) # wrap_vals_from_dataloader(batch)) | ||
| loss = nn.functional.mse_loss(E_hat.energies, batch["E"]) | ||
| # Logging to TensorBoard (if installed) by default | ||
| self.log("train_loss", loss) | ||
| return loss | ||
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| def configure_optimizers(self) -> AdamW: | ||
| """ | ||
| Configures the optimizer for training. | ||
| Returns | ||
| ------- | ||
| AdamW | ||
| The AdamW optimizer. | ||
| """ | ||
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| optimizer = AdamW(self.parameters(), lr=1e-3) | ||
| return optimizer |
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