File size: 5,228 Bytes
d46a58d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 | import os
import sys
from pathlib import Path
import dgl
import torch
from dgl.dataloading import GraphDataLoader
from torch.utils.data import Dataset
PROJECT_ROOT = Path(__file__).resolve().parents[1]
sys.path.insert(0, str(PROJECT_ROOT / "model"))
from onescience.utils.YParams import YParams
def make_graph(num_nodes: int = 12):
src = torch.arange(num_nodes, dtype=torch.int32)
dst = torch.roll(src, shifts=-1)
graph = dgl.to_bidirected(dgl.graph((src, dst), num_nodes=num_nodes, idtype=torch.int32))
pos = torch.stack(
(
torch.linspace(0.0, 1.0, num_nodes),
torch.sin(torch.linspace(0.0, 3.14159, num_nodes)) * 0.2,
),
dim=1,
)
row, col = graph.edges()
disp = pos[row.long()] - pos[col.long()]
graph.edata["x"] = torch.cat(
(disp, torch.linalg.norm(disp, dim=-1, keepdim=True)),
dim=1,
)
velocity = torch.randn(num_nodes, 2) * 0.1
node_type = torch.zeros(num_nodes, 4)
node_type[:, 0] = 1.0
graph.ndata["x"] = torch.cat((velocity, node_type), dim=1)
graph.ndata["y"] = torch.cat(
(torch.randn(num_nodes, 2) * 0.01, torch.randn(num_nodes, 1) * 0.01),
dim=1,
)
graph.ndata["mesh_pos"] = pos
cells = torch.tensor(
[[i, i + 1, min(i + 2, num_nodes - 1)] for i in range(num_nodes - 2)],
dtype=torch.int64,
)
mask = torch.ones(num_nodes, 1, dtype=torch.bool)
return {"graph": graph, "cells": cells, "mask": mask}
class FakeGraphDataset(Dataset):
def __init__(self, samples):
self.samples = samples
def __len__(self):
return len(self.samples)
def __getitem__(self, index):
sample = self.samples[index]
if isinstance(sample, dict) and "graph" in sample:
return sample["graph"]
return sample
def _resolve_path(project_root: Path, path):
path = Path(path)
return path if path.is_absolute() else project_root / path
def _torch_load(path: Path):
try:
return torch.load(path, map_location="cpu", weights_only=False)
except TypeError:
return torch.load(path, map_location="cpu")
class FakeCylinderFlowDatapipe:
def __init__(self, params, project_root: Path):
self.params = params
fake_data_path = _resolve_path(project_root, params.source.fake_data_path)
if not fake_data_path.exists():
raise FileNotFoundError(
f"Fake data file not found: {fake_data_path}. Run scripts/fake_data.py first."
)
payload = _torch_load(fake_data_path)
self.train_dataset = FakeGraphDataset(payload["train"])
self.val_dataset = FakeGraphDataset(payload["val"])
self.test_dataset = FakeGraphDataset(payload["test"])
self.stats = payload.get("stats", {})
def _loader(self, dataset, shuffle=False, drop_last=False):
return GraphDataLoader(
dataset,
batch_size=self.params.dataloader.batch_size,
drop_last=drop_last,
num_workers=self.params.dataloader.num_workers,
pin_memory=True,
shuffle=shuffle,
)
def train_dataloader(self):
return self._loader(self.train_dataset, shuffle=True), None
def val_dataloader(self):
return self._loader(self.val_dataset), None
def test_dataloader(self):
return self._loader(self.test_dataset)
def use_fake_data(params):
return bool(getattr(params.source, "fake_data", False))
def build_cylinder_flow_datapipe(params, distributed: bool, project_root: Path):
if use_fake_data(params):
return FakeCylinderFlowDatapipe(params=params, project_root=project_root)
from onescience.datapipes.cfd import DeepMind_CylinderFlowDatapipe
return DeepMind_CylinderFlowDatapipe(params=params, distributed=distributed)
def main():
os.chdir(PROJECT_ROOT)
config_path = PROJECT_ROOT / "config" / "config.yaml"
cfg_data = YParams(config_path, "datapipe")
output_path = PROJECT_ROOT / cfg_data.source.fake_data_path
output_path.parent.mkdir(parents=True, exist_ok=True)
payload = {
"train": [
make_graph()
for _ in range(cfg_data.data.train_samples * (cfg_data.data.train_steps - 1))
],
"val": [
make_graph()
for _ in range(cfg_data.data.val_samples * (cfg_data.data.val_steps - 1))
],
"test": [
make_graph()
for _ in range(cfg_data.data.test_samples * (cfg_data.data.test_steps - 1))
],
"stats": {
"edge_stats": {
"edge_mean": torch.zeros(3),
"edge_std": torch.ones(3),
},
"node_stats": {
"velocity_mean": torch.zeros(2),
"velocity_std": torch.ones(2),
"velocity_diff_mean": torch.zeros(2),
"velocity_diff_std": torch.ones(2),
"pressure_mean": torch.zeros(1),
"pressure_std": torch.ones(1),
},
},
}
torch.save(payload, output_path)
print(f"Fake data saved to {output_path.relative_to(PROJECT_ROOT)}")
if __name__ == "__main__":
main()
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