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"""
===============================================================================
File: painn
Date: 6/16/2024
Description: Code is adapted from torch geometric implementation https://github.com/pyg-team/pytorch_geometric/blob/master/torch_geometric/nn/models/schnet.py.
All rights reserved to original authors.
===============================================================================
"""
import os
import os.path as osp
import warnings
from math import pi as PI
from typing import Callable, Dict, Optional, Tuple
import numpy as np
import torch
import torch.nn.functional as F
from torch import Tensor, nn
from torch.nn import Embedding, Linear, ModuleList, Sequential
from torch_geometric.nn import MessagePassing, SumAggregation, radius_graph
from torch_geometric.nn.resolver import aggregation_resolver as aggr_resolver
from torch_scatter import scatter
from torch_geometric.utils import remove_isolated_nodes
class SchNet(torch.nn.Module):
def __init__(
self,
hidden_channels: int = 128,
num_filters: int = 128,
num_interactions: int = 4,
num_gaussians: int = 128,
cutoff: float = 5.0,
max_num_neighbors: int = 100,
readout: str = 'mean',
):
super().__init__()
self.max_num_neighbors = max_num_neighbors
self.hidden_channels = hidden_channels
self.num_filters = num_filters
self.num_interactions = num_interactions
self.num_gaussians = num_gaussians
self.cutoff = cutoff
self.sum_aggr = SumAggregation()
self.readout = aggr_resolver(readout)
self.embedding = Embedding(80, hidden_channels)
self.interaction_graph = RadiusInteractionGraph(cutoff, max_num_neighbors)
self.distance_expansion = GaussianSmearing(0.0, cutoff, num_gaussians)
self.interactions = ModuleList()
for _ in range(num_interactions):
block = InteractionBlock(hidden_channels, num_gaussians,
num_filters, cutoff)
self.interactions.append(block)
self.lin1 = Linear(hidden_channels, hidden_channels // 2)
self.act = ShiftedSoftplus()
self.lin2 = Linear(hidden_channels // 2, 1)
#self.force_decoder = nn.Sequential(Linear(hidden_channels, hidden_channels), ShiftedSoftplus(),
# Linear(hidden_channels, 3))
self.reset_parameters()
def reset_parameters(self):
r"""Resets all learnable parameters of the module."""
self.embedding.reset_parameters()
for interaction in self.interactions:
interaction.reset_parameters()
torch.nn.init.xavier_uniform_(self.lin1.weight)
self.lin1.bias.data.fill_(0)
torch.nn.init.xavier_uniform_(self.lin2.weight)
self.lin2.bias.data.fill_(0)
def forward(self, data):
# edge_index = radius_graph(data.pos, r=self.cutoff, batch=data.batch, max_num_neighbors=self.max_num_neighbors)
# edge_index, _, mask = remove_isolated_nodes(edge_index, num_nodes=data.num_nodes)
# data.pos = data.pos[mask]
# data.x = data.x[mask]
# data.batch = data.batch[mask]
batch_size = data.batch.max().item() + 1
edge_index = radius_graph(data.pos, r=self.cutoff, batch=data.batch, max_num_neighbors=self.max_num_neighbors)
edge_index, _, mask = remove_isolated_nodes(edge_index, num_nodes=data.num_nodes)
pos = data.pos[mask]
x = data.x[mask]
batch = data.batch[mask]
pos.requires_grad_(True)
z = x.long().squeeze(-1)
h = self.embedding(z)
#edge_index, edge_weight = self.interaction_graph(pos, batch)
row, col = edge_index
edge_weight = (pos[row] - pos[col]).norm(dim=-1)
edge_attr = self.distance_expansion(edge_weight)
for interaction in self.interactions:
h = h + interaction(h, edge_index, edge_weight, edge_attr)
# forces = self.force_decoder(h)
h = self.lin1(h)
h = self.act(h)
h = self.lin2(h)
#out = self.readout(h, batch, dim=0).squeeze()
out = scatter(h, batch, dim=0, dim_size=batch_size, reduce='sum').squeeze()
forces = -1 * (
torch.autograd.grad(
out,
pos,
grad_outputs=torch.ones_like(out),
create_graph=True,
)[0]
)
return out, forces, mask
class RadiusInteractionGraph(torch.nn.Module):
r"""Creates edges based on atom positions :obj:`pos` to all points within
the cutoff distance.
Args:
cutoff (float, optional): Cutoff distance for interatomic interactions.
(default: :obj:`10.0`)
max_num_neighbors (int, optional): The maximum number of neighbors to
collect for each node within the :attr:`cutoff` distance with the
default interaction graph method.
(default: :obj:`32`)
"""
def __init__(self, cutoff: float = 10.0, max_num_neighbors: int = 32):
super().__init__()
self.cutoff = cutoff
self.max_num_neighbors = max_num_neighbors
def forward(self, pos: Tensor, batch: Tensor) -> Tuple[Tensor, Tensor]:
r"""Forward pass.
Args:
pos (Tensor): Coordinates of each atom.
batch (LongTensor, optional): Batch indices assigning each atom to
a separate molecule.
:rtype: (:class:`LongTensor`, :class:`Tensor`)
"""
edge_index = radius_graph(pos, r=self.cutoff, batch=batch,
max_num_neighbors=self.max_num_neighbors)
row, col = edge_index
edge_weight = (pos[row] - pos[col]).norm(dim=-1)
return edge_index, edge_weight
class InteractionBlock(torch.nn.Module):
def __init__(self, hidden_channels: int, num_gaussians: int,
num_filters: int, cutoff: float):
super().__init__()
self.mlp = Sequential(
Linear(num_gaussians, num_filters),
ShiftedSoftplus(),
Linear(num_filters, num_filters),
)
self.conv = CFConv(hidden_channels, hidden_channels, num_filters,
self.mlp, cutoff)
self.act = ShiftedSoftplus()
self.lin = Linear(hidden_channels, hidden_channels)
self.reset_parameters()
def reset_parameters(self):
torch.nn.init.xavier_uniform_(self.mlp[0].weight)
self.mlp[0].bias.data.fill_(0)
torch.nn.init.xavier_uniform_(self.mlp[2].weight)
self.mlp[2].bias.data.fill_(0)
self.conv.reset_parameters()
torch.nn.init.xavier_uniform_(self.lin.weight)
self.lin.bias.data.fill_(0)
def forward(self, x: Tensor, edge_index: Tensor, edge_weight: Tensor,
edge_attr: Tensor) -> Tensor:
x = self.conv(x, edge_index, edge_weight, edge_attr)
x = self.act(x)
x = self.lin(x)
return x
class CFConv(MessagePassing):
def __init__(
self,
in_channels: int,
out_channels: int,
num_filters: int,
nn: Sequential,
cutoff: float,
):
super().__init__(aggr='add')
self.lin1 = Linear(in_channels, num_filters, bias=False)
self.lin2 = Linear(num_filters, out_channels)
self.nn = nn
self.cutoff = cutoff
self.reset_parameters()
def reset_parameters(self):
torch.nn.init.xavier_uniform_(self.lin1.weight)
torch.nn.init.xavier_uniform_(self.lin2.weight)
self.lin2.bias.data.fill_(0)
def forward(self, x: Tensor, edge_index: Tensor, edge_weight: Tensor,
edge_attr: Tensor) -> Tensor:
C = 0.5 * (torch.cos(edge_weight * PI / self.cutoff) + 1.0)
W = self.nn(edge_attr) * C.view(-1, 1)
x = self.lin1(x)
x = self.propagate(edge_index, x=x, W=W)
x = self.lin2(x)
return x
def message(self, x_j: Tensor, W: Tensor) -> Tensor:
return x_j * W
class GaussianSmearing(torch.nn.Module):
def __init__(
self,
start: float = 0.0,
stop: float = 5.0,
num_gaussians: int = 50,
):
super().__init__()
offset = torch.linspace(start, stop, num_gaussians)
self.coeff = -0.5 / (offset[1] - offset[0]).item() ** 2
self.register_buffer('offset', offset)
def forward(self, dist: Tensor) -> Tensor:
dist = dist.view(-1, 1) - self.offset.view(1, -1)
return torch.exp(self.coeff * torch.pow(dist, 2))
class ShiftedSoftplus(torch.nn.Module):
def __init__(self):
super().__init__()
self.shift = torch.log(torch.tensor(2.0)).item()
def forward(self, x: Tensor) -> Tensor:
return F.softplus(x) - self.shift
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