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import omegaconf
import torch
import pandas as pd
from omegaconf import ValueNode, OmegaConf
from torch.utils.data import Dataset
import os
from torch_geometric.data import Data
import pickle
import numpy as np
from diffcsp.common.utils import PROJECT_ROOT
from diffcsp.common.data_utils import (
preprocess, preprocess_tensors, add_scaled_lattice_prop)
class CrystDataset(Dataset):
def __init__(self, name: ValueNode, path: ValueNode,
prop: ValueNode, niggli: ValueNode, primitive: ValueNode,
graph_method: ValueNode, preprocess_workers: ValueNode,
lattice_scale_method: ValueNode, save_path: ValueNode, tolerance: ValueNode, use_space_group: ValueNode, use_pos_index: ValueNode,
require_order: ValueNode = True,
**kwargs):
super().__init__()
self.path = path
self.name = name
if os.path.exists(path):
self.df = pd.read_csv(path)
self.prop = prop
if not isinstance(self.prop, str):
self.prop = OmegaConf.to_container(self.prop)
self.require_order = require_order
self.niggli = niggli
self.primitive = primitive
self.graph_method = graph_method
self.lattice_scale_method = lattice_scale_method
self.use_space_group = use_space_group
self.use_pos_index = use_pos_index
self.tolerance = tolerance
self.preprocess(save_path, preprocess_workers, prop)
add_scaled_lattice_prop(self.cached_data, lattice_scale_method)
self.lattice_scaler = None
self.scaler = None
def preprocess(self, save_path, preprocess_workers, prop):
if os.path.exists(save_path):
self.cached_data = torch.load(save_path)
else:
cached_data = preprocess(
self.path,
preprocess_workers,
niggli=self.niggli,
primitive=self.primitive,
graph_method=self.graph_method,
prop_list=[prop] if isinstance(prop, str) else prop,
use_space_group=self.use_space_group,
tol=self.tolerance,
require_order=self.require_order)
torch.save(cached_data, save_path)
self.cached_data = cached_data
def __len__(self) -> int:
return len(self.cached_data)
def __getitem__(self, index):
data_dict = self.cached_data[index]
# scaler is set in DataModule set stage
if isinstance(self.prop, str):
prop = {"y": self.scaler.transform(data_dict[self.prop]).view(1, -1)}
elif isinstance(self.prop, list):
prop = {}
for prop_name in self.prop:
prop_val = data_dict[prop_name]
if isinstance(prop_val, str):
prop_val = eval(prop_val)
prop[prop_name] = torch.tensor(prop_val, dtype = torch.float32)
if prop_name in ["energy", "stress"]:
prop[prop_name] = prop[prop_name].unsqueeze(0)
else:
raise ValueError(f"prop should be string or list of string, but get {type(prop)}")
# 解包 graph_arrays，处理可能缺少 edge_indices 和 to_jimages 的情况
graph_arrays = data_dict['graph_arrays']
n_elements = len(graph_arrays)
if n_elements == 7:
# 正常情况，包含所有元素
(frac_coords, atom_types, lengths, angles,
edge_indices, to_jimages, num_atoms) = graph_arrays
elif n_elements == 5:
# 缺少 edge_indices 和 to_jimages，设为空数组
(frac_coords, atom_types, lengths, angles,
num_atoms) = graph_arrays
edge_indices = np.empty((0, 2), dtype=np.int64) # 空的边索引
to_jimages = np.empty((0, 3), dtype=np.int64) # 空的晶像偏移
else:
raise ValueError(f"Unexpected number of elements in graph_arrays: {n_elements}")
# atom_coords are fractional coordinates
# edge_index is incremented during batching
# https://pytorch-geometric.readthedocs.io/en/latest/notes/batching.html
data = Data(
frac_coords=torch.Tensor(frac_coords),
atom_types=torch.LongTensor(atom_types),
lengths=torch.Tensor(lengths).view(1, -1),
angles=torch.Tensor(angles).view(1, -1),
edge_index=torch.LongTensor(
edge_indices.T).contiguous(), # shape (2, num_edges)
to_jimages=torch.LongTensor(to_jimages),
num_atoms=num_atoms,
num_bonds=edge_indices.shape[0],
num_nodes=num_atoms, # special attribute used for batching in pytorch geometric
**prop
)
if "t_flow" in data_dict:
data.t_flow = torch.tensor(data_dict["t_flow"], dtype = torch.float32)
if "graph_arrays_stable" in data_dict:
(frac_coords_stable, atom_types_stable, lengths_stable, angles_stable, num_atoms_stable) = data_dict['graph_arrays_stable']
data.frac_coords_stable = torch.Tensor(frac_coords_stable)
data.atom_types_stable = torch.LongTensor(atom_types_stable)
data.lengths_stable = torch.Tensor(lengths_stable).view(1, -1)
data.angles_stable = torch.Tensor(angles_stable).view(1, -1)
data.num_atoms_stable = num_atoms_stable
data.num_nodes_stable = num_atoms_stable
if "graph_arrays_initial" in data_dict:
(frac_coords_initial, atom_types_initial, lengths_initial, angles_initial, edge_indices_initial,
to_jimages_initial, num_atoms_initial) = data_dict['graph_arrays_initial']
data.frac_coords_initial = torch.Tensor(frac_coords_initial)
data.atom_types_initial = torch.LongTensor(atom_types_initial)
data.lengths_initial = torch.Tensor(lengths_initial).view(1, -1)
data.angles_initial = torch.Tensor(angles_initial).view(1, -1)
data.edge_index_initial = torch.LongTensor(
edge_indices_initial.T).contiguous()
data.to_jimages_initial = torch.LongTensor(to_jimages_initial)
data.num_atoms_initial = num_atoms_initial
data.num_bonds_initial = edge_indices_initial.shape[0]
data.num_nodes_initial = num_atoms_initial
if self.use_space_group:
data.spacegroup = torch.LongTensor([data_dict['spacegroup']])
data.ops = torch.Tensor(data_dict['wyckoff_ops'])
data.anchor_index = torch.LongTensor(data_dict['anchors'])
if self.use_pos_index:
pos_dic = {}
indexes = []
for atom in atom_types:
pos_dic[atom] = pos_dic.get(atom, 0) + 1
indexes.append(pos_dic[atom] - 1)
data.index = torch.LongTensor(indexes)
return data
def __repr__(self) -> str:
return f"CrystDataset({self.name=}, {self.path=})"
class TensorCrystDataset(Dataset):
def __init__(self, crystal_array_list, niggli, primitive,
graph_method, preprocess_workers,
lattice_scale_method, **kwargs):
super().__init__()
self.niggli = niggli
self.primitive = primitive
self.graph_method = graph_method
self.lattice_scale_method = lattice_scale_method
self.cached_data = preprocess_tensors(
crystal_array_list,
niggli=self.niggli,
primitive=self.primitive,
graph_method=self.graph_method)
add_scaled_lattice_prop(self.cached_data, lattice_scale_method)
self.lattice_scaler = None
self.scaler = None
def __len__(self) -> int:
return len(self.cached_data)
def __getitem__(self, index):
data_dict = self.cached_data[index]
(frac_coords, atom_types, lengths, angles, edge_indices,
to_jimages, num_atoms) = data_dict['graph_arrays']
# atom_coords are fractional coordinates
# edge_index is incremented during batching
# https://pytorch-geometric.readthedocs.io/en/latest/notes/batching.html
data = Data(
frac_coords=torch.Tensor(frac_coords),
atom_types=torch.LongTensor(atom_types),
lengths=torch.Tensor(lengths).view(1, -1),
angles=torch.Tensor(angles).view(1, -1),
edge_index=torch.LongTensor(
edge_indices.T).contiguous(), # shape (2, num_edges)
to_jimages=torch.LongTensor(to_jimages),
num_atoms=num_atoms,
num_bonds=edge_indices.shape[0],
num_nodes=num_atoms, # special attribute used for batching in pytorch geometric
)
if "graph_arrays_initial" in data_dict:
(frac_coords_initial, atom_types_initial, lengths_initial, angles_initial, edge_indices_initial,
to_jimages_initial, num_atoms_initial) = data_dict['graph_arrays_initial']
data.frac_coords_initial = torch.Tensor(frac_coords_initial)
data.atom_types_initial = torch.LongTensor(atom_types_initial)
data.lengths_initial = torch.Tensor(lengths_initial).view(1, -1)
data.angles_initial = torch.Tensor(angles_initial).view(1, -1)
data.edge_index_initial = torch.LongTensor(
edge_indices_initial.T).contiguous()
data.to_jimages_initial = torch.LongTensor(to_jimages_initial)
data.num_atoms_initial = num_atoms_initial
data.num_bonds_initial = edge_indices_initial.shape[0]
data.num_nodes_initial = num_atoms_initial
return data
def __repr__(self) -> str:
return f"TensorCrystDataset(len: {len(self.cached_data)})"
@hydra.main(config_path=str(PROJECT_ROOT / "conf"), config_name="default", version_base="1.1")
def main(cfg: omegaconf.DictConfig):
from torch_geometric.data import Batch
from diffcsp.common.data_utils import get_scaler_from_data_list
dataset: CrystDataset = hydra.utils.instantiate(
cfg.data.datamodule.datasets.train, _recursive_=False
)
lattice_scaler = get_scaler_from_data_list(
dataset.cached_data,
key='scaled_lattice')
scaler = get_scaler_from_data_list(
dataset.cached_data,
key=dataset.prop)
dataset.lattice_scaler = lattice_scaler
dataset.scaler = scaler
data_list = [dataset[i] for i in range(len(dataset))]
batch = Batch.from_data_list(data_list)
return batch
if __name__ == "__main__":
main()
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