267 lines
9.5 KiB
Python
267 lines
9.5 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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# from torch.utils.data import Dataset
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from torch_geometric.utils import scatter
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def compute_nodal_area(edge_index, edge_attr, num_nodes):
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"""
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1. Calculates Area ~ (Min Edge Length)^2
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2. Scales by Mean so average cell has size 1.0
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"""
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row, col = edge_index
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dist = edge_attr.squeeze()
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# 1. Get 'h' (Closest neighbor distance)
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# Using 'min' filters out diagonal connections in the quad mesh
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h = scatter(dist, col, dim=0, dim_size=num_nodes, reduce="min")
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# 2. Estimate Raw Area
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raw_area = h.pow(2)
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# 3. Mean Scaling (The Best Normalization)
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# This keeps values near 1.0, preserving stability AND physics ratios.
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# We detach to ensure no gradients flow here (it's static data).
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mean_val = raw_area.mean().detach()
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# Result:
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# Small cells -> approx 0.1
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# Large cells -> approx 5.0
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# Average -> 1.0
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# nodal_area = (raw_area / mean_val).unsqueeze(-1) + 1e-6
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nodal_area = raw_area
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return nodal_area.unsqueeze(-1)
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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 = False,
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build_radial_graph: bool = False,
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radius: float = None,
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start_unrolling_steps: int = 1,
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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_dict = {}
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self.train_dataset, self.val_dataset, self.test_dataset = (
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None,
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None,
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None,
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)
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self.unrolling_steps = start_unrolling_steps
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self.geometry_dict = {}
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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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self.build_radial_graph = build_radial_graph
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self.radius = radius
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def prepare_data(self):
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dataset = load_dataset(self.hf_repo, name="snapshots")[self.split_name]
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geometry = load_dataset(self.hf_repo, name="geometry")[self.split_name]
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total_len = len(dataset)
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train_len = int(self.train_size * total_len)
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valid_len = int(self.val_size * total_len)
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self.dataset_dict = {
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"train": dataset.select(range(0, train_len)),
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"val": dataset.select(range(train_len, train_len + valid_len)),
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"test": dataset.select(range(train_len + valid_len, total_len)),
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}
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self.geometry_dict = {
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"train": geometry.select(range(0, train_len)),
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"val": geometry.select(range(train_len, train_len + valid_len)),
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"test": geometry.select(range(train_len + valid_len, total_len)),
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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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geometry["conductivity"], dtype=torch.float32
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)
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temperatures = torch.tensor(
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snapshot["temperatures"], dtype=torch.float32
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)[:40]
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times = torch.tensor(snapshot["times"], dtype=torch.float32)
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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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if self.build_radial_graph:
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# from pina.graph import RadiusGraph
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# if self.radius is None:
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# raise ValueError("Radius must be specified for radial graph.")
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# edge_index = RadiusGraph.compute_radius_graph(
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# pos, radius=self.radius
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# )
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# from torch_geometric.utils import remove_self_loops
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# edge_index, _ = remove_self_loops(edge_index)
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raise NotImplementedError(
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"Radial graph building not implemented yet."
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)
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else:
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edge_index = torch.tensor(
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geometry["edge_index"], dtype=torch.int64
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).T
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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, temperatures[0, :]
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)
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edge_attr = torch.norm(pos[edge_index[0]] - pos[edge_index[1]], dim=1)
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nodal_area = compute_nodal_area(edge_index, edge_attr, pos.size(0))
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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 = edge_attr[edge_index_mask]
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n_data = temperatures.size(0) - self.unrolling_steps
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data = []
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for i in range(n_data):
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x = temperatures[i, :].unsqueeze(-1)
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y = (
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temperatures[i + 1 : i + 1 + self.unrolling_steps, :]
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.unsqueeze(-1)
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.permute(1, 0, 2)
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)
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data.append(
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MeshData(
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x=x,
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y=y,
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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,
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nodal_area=nodal_area,
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)
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)
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return data
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def setup(self, stage: str = None):
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if stage == "fit" or stage is None:
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self.train_data = [
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self._build_dataset(snap, geom)
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for snap, geom in tqdm(
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zip(
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self.dataset_dict["train"], self.geometry_dict["train"]
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),
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desc="Building train graphs",
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total=len(self.dataset_dict["train"]),
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)
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]
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self.val_data = [
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self._build_dataset(snap, geom)
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for snap, geom in tqdm(
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zip(self.dataset_dict["val"], self.geometry_dict["val"]),
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desc="Building val graphs",
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total=len(self.dataset_dict["val"]),
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)
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]
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if stage == "test" or stage is None:
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self.test_data = [
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self._build_dataset(snap, geom)
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for snap, geom in tqdm(
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zip(self.dataset_dict["test"], self.geometry_dict["test"]),
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desc="Building test graphs",
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total=len(self.dataset_dict["test"]),
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)
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]
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# def create_autoregressive_datasets(self, dataset: str, no_unrolling: bool = False):
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# if dataset == "train":
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# return AutoregressiveDataset(self.train_data, self.unrolling_steps, no_unrolling)
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# if dataset == "val":
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# return AutoregressiveDataset(self.val_data, self.unrolling_steps, no_unrolling)
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# if dataset == "test":
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# return AutoregressiveDataset(self.test_data, self.unrolling_steps, no_unrolling)
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def train_dataloader(self):
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# ds = self.create_autoregressive_datasets(dataset="train")
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# self.train_dataset = ds
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# print(type(self.train_data[0]))
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ds = [i for data in self.train_data for i in data]
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# print(type(ds[0]))
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return DataLoader(
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ds,
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batch_size=self.batch_size,
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shuffle=True,
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num_workers=8,
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pin_memory=True,
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)
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def val_dataloader(self):
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ds = [i for data in self.val_data for i in data]
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return DataLoader(
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ds,
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batch_size=128,
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shuffle=False,
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num_workers=8,
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pin_memory=True,
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)
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def test_dataloader(self):
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ds = self.create_autoregressive_datasets(
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dataset="test", no_unrolling=True
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)
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return DataLoader(
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ds,
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batch_size=self.batch_size,
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shuffle=False,
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num_workers=8,
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pin_memory=True,
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)
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