File size: 4,994 Bytes
6c3f19f | 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 | import json
import os
import sys
from pathlib import Path
import torch
import yaml
PROJECT_ROOT = Path(__file__).resolve().parents[1]
CONFIG_PATH = PROJECT_ROOT / "conf" / "config.yaml"
PROBLEMS = ("doublepipe", "diffuser", "rugby", "pipebend")
def ensure_onescience_path(explicit_src=None):
candidates = []
if explicit_src:
candidates.append(Path(explicit_src).expanduser())
if os.environ.get("ONESCIENCE_SRC"):
candidates.append(Path(os.environ["ONESCIENCE_SRC"]).expanduser())
for parent in (PROJECT_ROOT, *PROJECT_ROOT.parents):
candidates.append(parent / "refactor" / "onescience" / "src")
candidates.append(parent / "onescience" / "src")
for candidate in candidates:
if (candidate / "onescience").is_dir():
path = str(candidate.resolve())
if path not in sys.path:
sys.path.insert(0, path)
return candidate.resolve()
return None
def load_config():
with CONFIG_PATH.open("r", encoding="utf-8") as f:
return yaml.safe_load(f)["root"]
def resolve_path(path_value):
path = Path(path_value)
return path if path.is_absolute() else PROJECT_ROOT / path
def select_device(section):
requested = section.get("device", "auto")
gpu = int(section.get("gpu", 0))
if requested == "auto":
return torch.device(f"cuda:{gpu}" if torch.cuda.is_available() else "cpu")
if requested.startswith("cuda") and not torch.cuda.is_available():
print(f"Requested {requested}, but CUDA is not available. Falling back to CPU.")
return torch.device("cpu")
return torch.device(requested)
def dtype_from_config(name):
mapping = {
"float32": torch.float32,
"float": torch.float32,
"float64": torch.float64,
"double": torch.float64,
}
if name not in mapping:
raise ValueError(f"Unsupported dtype: {name}")
return mapping[name]
def set_nested(cfg, section, key, value):
if value is not None:
cfg[section][key] = value
def build_models(cfg, device, *, n_col_domain=None, n_train_per_bc=None, problem=None):
ensure_onescience_path(cfg.get("runtime", {}).get("onescience_src"))
from model import GPPLUS
from onescience.utils.GP_TO import get_data_fluid, set_seed
problem = problem or cfg["problem"]
n_col_domain = int(n_col_domain or cfg["data"]["n_col_domain"])
n_train_per_bc = int(n_train_per_bc or cfg["data"]["n_train_per_bc"])
dtype = dtype_from_config(cfg["model"].get("dtype", "float32"))
set_seed(int(cfg["seed"]))
x_col, x_train, sol_train = get_data_fluid(
problem=problem,
N_col_domain=n_col_domain,
N_train=n_train_per_bc,
)
collocation_x = x_col.to(device=device, dtype=dtype).clone().requires_grad_(True)
models = []
for i, name in enumerate(cfg["output_names"]):
model = GPPLUS(
train_x=x_train[i].type(dtype),
train_y=sol_train[i].type(dtype),
collocation_x=collocation_x,
basis=cfg["model"]["mean_function"],
NN_layers_base=cfg["model"]["nn_layers_base"],
name_output=name,
device=device,
dtype=dtype,
).to(device=device, dtype=dtype)
models.append(model)
return models, {
"problem": problem,
"n_col_domain": n_col_domain,
"n_train_per_bc": n_train_per_bc,
"x_col_shape": tuple(x_col.shape),
"x_train_shapes": [tuple(x.shape) for x in x_train],
"sol_train_shapes": [tuple(y.shape) for y in sol_train],
}
def save_checkpoint(path, model_list, cfg, metadata, loss_history):
path = resolve_path(path)
path.parent.mkdir(parents=True, exist_ok=True)
torch.save(
{
"model_state_dicts": [model.state_dict() for model in model_list],
"config": cfg,
"metadata": metadata,
"loss_history": loss_history,
},
path,
)
return path
def load_checkpoint(path, model_list, device):
path = resolve_path(path)
if not path.is_file():
raise FileNotFoundError(f"Missing checkpoint: {path}. Run scripts/train.py first.")
try:
checkpoint = torch.load(path, map_location=device, weights_only=False)
except TypeError:
checkpoint = torch.load(path, map_location=device)
state_dicts = checkpoint.get("model_state_dicts")
if state_dicts is None:
raise KeyError(f"Checkpoint does not contain model_state_dicts: {path}")
for model, state_dict in zip(model_list, state_dicts):
model.load_state_dict(state_dict)
return checkpoint
def dump_json(data, path):
path = resolve_path(path)
path.parent.mkdir(parents=True, exist_ok=True)
with path.open("w", encoding="utf-8") as f:
json.dump(data, f, indent=2)
return path
def tensor_to_numpy_dict(fields):
return {key: value.detach().cpu().numpy() for key, value in fields.items()}
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