158 lines
5.4 KiB
Python
158 lines
5.4 KiB
Python
import torch
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from tqdm import tqdm
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from lightning import LightningDataModule
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from datasets import load_dataset
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from torch_geometric.data import Data
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from torch_geometric.loader import DataLoader
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from torch_geometric.utils import to_undirected
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from .mesh_data import MeshData
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class GraphDataModule(LightningDataModule):
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def __init__(
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self,
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hf_repo: str,
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split_name: str,
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train_size: float = 0.2,
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val_size: float = 0.1,
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test_size: float = 0.1,
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batch_size: int = 32,
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remove_boundary_edges: bool = True,
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):
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super().__init__()
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self.hf_repo = hf_repo
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self.split_name = split_name
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self.dataset = None
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self.geometry = None
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self.train_size = train_size
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self.val_size = val_size
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self.test_size = test_size
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self.batch_size = batch_size
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self.remove_boundary_edges = remove_boundary_edges
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def prepare_data(self):
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hf_dataset = load_dataset(self.hf_repo, name="snapshots")[
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self.split_name
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]
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self.geometry = load_dataset(self.hf_repo, name="geometry")[
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self.split_name
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]
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self.data = [
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self._build_dataset(snapshot, geometry)
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for snapshot, geometry in tqdm(
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zip(hf_dataset, self.geometry),
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desc="Building graphs",
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total=len(hf_dataset),
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)
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]
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def _compute_boundary_mask(
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self, bottom_ids, right_ids, top_ids, left_ids, temperature
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):
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left_ids = left_ids[~torch.isin(left_ids, bottom_ids)]
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right_ids = right_ids[~torch.isin(right_ids, bottom_ids)]
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left_ids = left_ids[~torch.isin(left_ids, top_ids)]
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right_ids = right_ids[~torch.isin(right_ids, top_ids)]
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bottom_bc = temperature[bottom_ids].median()
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bottom_bc_mask = torch.ones(len(bottom_ids)) * bottom_bc
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left_bc = temperature[left_ids].median()
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left_bc_mask = torch.ones(len(left_ids)) * left_bc
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right_bc = temperature[right_ids].median()
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right_bc_mask = torch.ones(len(right_ids)) * right_bc
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boundary_values = torch.cat(
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[bottom_bc_mask, right_bc_mask, left_bc_mask], dim=0
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)
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boundary_mask = torch.cat([bottom_ids, right_ids, left_ids], dim=0)
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return boundary_mask, boundary_values
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def _build_dataset(
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self,
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snapshot: dict,
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geometry: dict,
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) -> Data:
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conductivity = torch.tensor(
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snapshot["conductivity"], dtype=torch.float32
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)
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temperature = torch.tensor(snapshot["temperature"], dtype=torch.float32)
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edge_index = torch.tensor(geometry["edge_index"], dtype=torch.int64).T
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pos = torch.tensor(geometry["points"], dtype=torch.float32)[:, :2]
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bottom_ids = torch.tensor(
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geometry["bottom_boundary_ids"], dtype=torch.long
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)
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top_ids = torch.tensor(geometry["top_boundary_ids"], dtype=torch.long)
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left_ids = torch.tensor(geometry["left_boundary_ids"], dtype=torch.long)
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right_ids = torch.tensor(
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geometry["right_boundary_ids"], dtype=torch.long
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)
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edge_index = to_undirected(edge_index, num_nodes=pos.size(0))
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boundary_mask, boundary_values = self._compute_boundary_mask(
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bottom_ids, right_ids, top_ids, left_ids, temperature
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)
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if self.remove_boundary_edges:
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boundary_idx = torch.unique(boundary_mask)
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edge_index_mask = ~torch.isin(edge_index[1], boundary_idx)
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edge_index = edge_index[:, edge_index_mask]
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edge_attr = pos[edge_index[0]] - pos[edge_index[1]]
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edge_attr = torch.cat(
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[edge_attr, torch.norm(edge_attr, dim=1).unsqueeze(-1)], dim=1
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)
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x = torch.zeros_like(temperature, dtype=torch.float32).unsqueeze(-1)
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if self.remove_boundary_edges:
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x[boundary_mask] = boundary_values.unsqueeze(-1)
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return MeshData(
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x=x,
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c=conductivity.unsqueeze(-1),
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edge_index=edge_index,
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pos=pos,
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edge_attr=edge_attr,
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y=temperature.unsqueeze(-1),
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boundary_mask=boundary_mask,
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boundary_values=torch.tensor(0),
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)
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return MeshData(
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x=torch.rand_like(temperature).unsqueeze(-1),
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c=conductivity.unsqueeze(-1),
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edge_index=edge_index,
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pos=pos,
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edge_attr=edge_attr,
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boundary_mask=boundary_mask,
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boundary_values=boundary_values.unsqueeze(-1),
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y=temperature.unsqueeze(-1),
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)
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def setup(self, stage: str = None):
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n = len(self.data)
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train_end = int(n * self.train_size)
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val_end = train_end + int(n * self.val_size)
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if stage == "fit" or stage is None:
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self.train_data = self.data[:train_end]
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self.val_data = self.data[train_end:val_end]
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if stage == "test" or stage is None:
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self.test_data = self.data[val_end:]
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def train_dataloader(self):
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return DataLoader(
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self.train_data, batch_size=self.batch_size, shuffle=True
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)
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def val_dataloader(self):
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return DataLoader(
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self.val_data, batch_size=self.batch_size, shuffle=False
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)
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def test_dataloader(self):
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return DataLoader(
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self.test_data, batch_size=self.batch_size, shuffle=False
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)
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