File size: 6,237 Bytes
eac1b4e | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 | from __future__ import annotations
import json
import os
import random
import sys
from pathlib import Path
from typing import Any
import numpy as np
import torch
import yaml
ROOT = Path(__file__).resolve().parents[1]
if str(ROOT) not in sys.path:
sys.path.insert(0, str(ROOT))
from model.ho_fno_pipe import HOFNOPipe, count_parameters # noqa: E402
class UnitTransformer:
def __init__(self, tensor: torch.Tensor, axes: tuple[int, ...] = (0, 1), eps: float = 1e-8) -> None:
self.axes = axes
self.eps = eps
self.mean = tensor.mean(dim=axes, keepdim=True)
self.std = tensor.std(dim=axes, keepdim=True) + eps
def encode(self, tensor: torch.Tensor) -> torch.Tensor:
return (tensor - self.mean) / self.std
def decode(self, tensor: torch.Tensor) -> torch.Tensor:
return tensor * self.std + self.mean
def to(self, device: torch.device) -> "UnitTransformer":
self.mean = self.mean.to(device)
self.std = self.std.to(device)
return self
def state_dict(self) -> dict[str, Any]:
return {"mean": self.mean.cpu(), "std": self.std.cpu(), "axes": self.axes, "eps": self.eps}
@classmethod
def from_state_dict(cls, state: dict[str, Any]) -> "UnitTransformer":
obj = cls.__new__(cls)
obj.mean = state["mean"]
obj.std = state["std"]
obj.axes = tuple(state.get("axes", (0, 1)))
obj.eps = float(state.get("eps", 1e-8))
return obj
def load_config(path: str | os.PathLike[str] | None = None) -> dict[str, Any]:
cfg_path = Path(path) if path else ROOT / "config" / "config.yaml"
with cfg_path.open("r", encoding="utf-8") as handle:
cfg = yaml.safe_load(handle)
cfg["_config_path"] = str(cfg_path)
return cfg
def set_seed(seed: int) -> None:
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
if torch.cuda.is_available():
torch.cuda.manual_seed_all(seed)
def resolve(path: str | os.PathLike[str], base: Path | None = None) -> Path:
p = Path(path)
if p.is_absolute():
return p
return (base or ROOT) / p
def select_device(require_accelerator: bool = False) -> torch.device:
if torch.cuda.is_available():
return torch.device("cuda")
if require_accelerator:
raise RuntimeError("accelerator is required, but torch.cuda.is_available() is False")
return torch.device("cpu")
def load_pipe_tensors(cfg: dict[str, Any]) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
data_cfg = cfg["data"]
data_dir = Path(cfg["paths"]["data_dir"])
train_size = int(data_cfg["train_size"])
test_size = int(data_cfg["test_size"])
total_used = int(data_cfg["total_used"])
r1, r2 = (int(v) for v in data_cfg.get("downsample", [1, 1]))
s1 = int(((int(data_cfg["resolution"][0]) - 1) / r1) + 1)
s2 = int(((int(data_cfg["resolution"][1]) - 1) / r2) + 1)
x_raw = np.load(data_dir / data_cfg["x_file"], mmap_mode="r")[:total_used, ::r1, ::r2][:, :s1, :s2]
y_raw = np.load(data_dir / data_cfg["y_file"], mmap_mode="r")[:total_used, ::r1, ::r2][:, :s1, :s2]
q_raw = np.load(data_dir / data_cfg["q_file"], mmap_mode="r")[
:total_used, int(data_cfg["output_channel_index"]), ::r1, ::r2
][:, :s1, :s2]
coords = np.stack((np.asarray(x_raw, dtype=np.float32), np.asarray(y_raw, dtype=np.float32)), axis=-1)
target = np.asarray(q_raw, dtype=np.float32)[..., None]
x_all = torch.from_numpy(coords.copy())
y_all = torch.from_numpy(target.copy())
return x_all[:train_size], y_all[:train_size], x_all[-test_size:], y_all[-test_size:]
def make_normalized_loaders(cfg: dict[str, Any]):
x_train, y_train, x_test, y_test = load_pipe_tensors(cfg)
axes = tuple(int(v) for v in cfg["data"].get("normalizer_axes", [0, 1]))
x_normalizer = UnitTransformer(x_train, axes=axes)
y_normalizer = UnitTransformer(y_train, axes=axes)
x_train_n = x_normalizer.encode(x_train)
x_test_n = x_normalizer.encode(x_test)
y_train_n = y_normalizer.encode(y_train)
y_test_n = y_normalizer.encode(y_test)
batch_size = int(cfg["training"]["batch_size"])
loader_kwargs = {
"batch_size": batch_size,
"num_workers": int(cfg["data"].get("num_workers", 0)),
"pin_memory": bool(cfg["data"].get("pin_memory", False)),
}
train_ds = torch.utils.data.TensorDataset(x_train_n, y_train_n)
test_ds = torch.utils.data.TensorDataset(x_test_n, y_test_n)
train_loader = torch.utils.data.DataLoader(train_ds, shuffle=True, **loader_kwargs)
test_loader = torch.utils.data.DataLoader(test_ds, shuffle=False, **loader_kwargs)
return train_loader, test_loader, x_normalizer, y_normalizer
def build_model(cfg: dict[str, Any]) -> HOFNOPipe:
m = cfg["model"]
return HOFNOPipe(
input_channels=int(m["input_channels"]),
output_channels=int(m["output_channels"]),
order=int(m["order"]),
layers=int(m["layers"]),
width=int(m["width"]),
modes=tuple(int(v) for v in m["modes"]),
add_unit_grid=bool(m.get("add_unit_grid", True)),
mlp_expansion=int(m.get("mlp_expansion", 4)),
mlp_dropout=float(m.get("mlp_dropout", 0.0)),
drop_path_rate=float(m.get("drop_path_rate", 0.0)),
)
def relative_l2_per_sample(pred: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
pred_f = pred.reshape(pred.shape[0], -1)
target_f = target.reshape(target.shape[0], -1)
denom = torch.linalg.vector_norm(target_f, dim=1).clamp_min(1e-12)
return torch.linalg.vector_norm(pred_f - target_f, dim=1) / denom
def relative_l2_sum(pred: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
return relative_l2_per_sample(pred, target).sum()
def save_json(path: Path, payload: dict[str, Any]) -> None:
path.parent.mkdir(parents=True, exist_ok=True)
with path.open("w", encoding="utf-8") as handle:
json.dump(payload, handle, indent=2)
__all__ = [
"ROOT",
"UnitTransformer",
"build_model",
"count_parameters",
"load_config",
"make_normalized_loaders",
"relative_l2_per_sample",
"relative_l2_sum",
"resolve",
"save_json",
"select_device",
"set_seed",
]
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