File size: 12,190 Bytes
807a08b | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 | """OneScience ERA5Dataset adapter for ClimODE's 32x64 global grid."""
from __future__ import annotations
from pathlib import Path
from typing import Iterable, Sequence
import h5py
import numpy as np
import torch
import torch.nn.functional as F
from torch.utils.data import DataLoader, Dataset
try:
from onescience.datapipes.climate.era5 import ERA5Dataset
except ImportError as exc: # pragma: no cover - exercised only without OneScience
ERA5Dataset = None
_ERA5_IMPORT_ERROR = exc
else:
_ERA5_IMPORT_ERROR = None
OFFICIAL_VARIABLES = ("z", "t", "t2m", "u10", "v10")
def _require_era5dataset() -> None:
if ERA5Dataset is None:
raise ImportError(
"ClimODE data loading requires OneScience ERA5Dataset; "
"activate an environment containing onescience before running."
) from _ERA5_IMPORT_ERROR
def _as_channel_vector(values: np.ndarray | torch.Tensor) -> torch.Tensor:
tensor = torch.as_tensor(values, dtype=torch.float32)
return tensor.reshape(-1)
def _regrid_periodic(frame: torch.Tensor, target_size: tuple[int, int]) -> torch.Tensor:
"""Bilinearly sample [C,H,W] on WeatherBench cell centers."""
if frame.ndim != 3:
raise ValueError(f"Expected [C,H,W], got {tuple(frame.shape)}")
target_height, target_width = target_size
if frame.shape[-2:] == target_size:
return frame
periodic = torch.cat([frame, frame[..., :1]], dim=-1).unsqueeze(0)
latitude = torch.linspace(
90.0 - 90.0 / target_height,
-90.0 + 90.0 / target_height,
target_height,
device=frame.device,
dtype=frame.dtype,
)
longitude = (
torch.arange(target_width, device=frame.device, dtype=frame.dtype)
* (360.0 / target_width)
)
lat2d, lon2d = torch.meshgrid(latitude, longitude, indexing="ij")
grid = torch.stack([lon2d / 180.0 - 1.0, -lat2d / 90.0], dim=-1)
return F.grid_sample(
periodic,
grid.unsqueeze(0),
mode="bilinear",
padding_mode="border",
align_corners=True,
)[0]
def _load_stats(stats_dir: str | Path, channels: int) -> tuple[torch.Tensor, torch.Tensor]:
stats_path = Path(stats_dir)
minimum = _as_channel_vector(np.load(stats_path / "min_values.npy"))
maximum = _as_channel_vector(np.load(stats_path / "max_values.npy"))
if minimum.numel() != channels or maximum.numel() != channels:
raise ValueError(
f"Expected {channels} channel statistics, got {minimum.numel()} and {maximum.numel()}"
)
if torch.any(maximum <= minimum):
raise ValueError("All max_values must be greater than min_values")
return minimum, maximum
def _normalize(frame: torch.Tensor, minimum: torch.Tensor, maximum: torch.Tensor) -> torch.Tensor:
scale = (maximum - minimum).clamp_min(torch.finfo(frame.dtype).eps)
return (frame - minimum[:, None, None]) / scale[:, None, None]
class ClimODEDataset(Dataset):
"""Return three history frames and the following target frame.
The underlying annual files are always read by OneScience ``ERA5Dataset``.
No direct HDF5 field access is used here, which keeps the model adapter
compatible with the OneScience ERA5 contract.
"""
def __init__(
self,
data_dir: str | Path,
years: Sequence[int],
used_variables: Sequence[str] = OFFICIAL_VARIABLES,
stats_dir: str | Path | None = None,
model_size: tuple[int, int] = (32, 64),
normalize: bool = True,
input_steps: int = 1,
output_steps: int = 1,
) -> None:
_require_era5dataset()
if tuple(used_variables) != OFFICIAL_VARIABLES:
raise ValueError(
"ClimODE requires the exact channel order ['z','t','t2m','u10','v10']"
)
if input_steps != 1 or output_steps != 1:
raise ValueError("The ClimODE adapter currently uses one input and one target step")
if len(years) == 0:
raise ValueError("At least one year is required")
# ``data_dir`` is the OneScience dataset root containing data/*.h5,
# rather than the nested data/ directory itself.
self.data_dir = Path(data_dir)
self.years = [int(year) for year in years]
self.variables = tuple(used_variables)
self.model_size = tuple(model_size)
self.normalize = normalize
self.era5 = ERA5Dataset(
dataset_dir=str(self.data_dir),
used_years=self.years,
used_variables=list(self.variables),
input_steps=1,
output_steps=1,
normalize=False,
)
if (self.era5.H, self.era5.W) != (721, 1440):
raise ValueError(
"ClimODE raw-data adapter expects (721,1440), "
f"got ({self.era5.H},{self.era5.W})"
)
self.samples_per_year = self.era5.samples_per_year
if self.samples_per_year < 3:
raise ValueError("Each year needs at least four frames for a three-frame history and target")
self.samples_per_year_with_history = self.samples_per_year - 2
self.minimum, self.maximum = (
_load_stats(stats_dir or self.data_dir / "static", len(self.variables))
if normalize
else (torch.zeros(len(self.variables)), torch.ones(len(self.variables)))
)
def __len__(self) -> int:
return len(self.years) * self.samples_per_year_with_history
def _frame(self, sample_index: int, target: bool = False) -> torch.Tensor:
invar, outvar, _, _, _ = self.era5[sample_index]
frame = outvar if target else invar
frame = _regrid_periodic(torch.as_tensor(frame, dtype=torch.float32), self.model_size)
if self.normalize:
frame = _normalize(frame, self.minimum, self.maximum)
return frame
def __getitem__(self, index: int) -> dict[str, torch.Tensor | int | str]:
if index < 0:
index += len(self)
if index < 0 or index >= len(self):
raise IndexError(index)
year_index = index // self.samples_per_year_with_history
local_index = index % self.samples_per_year_with_history
base = year_index * self.samples_per_year + local_index + 2
history = torch.stack([self._frame(base - 2), self._frame(base - 1), self._frame(base)])
target = self._frame(base, target=True)
return {
"history": history,
"input": history[-1],
"target": target,
"year": self.years[year_index],
"step_index": local_index + 2,
}
class ClimODESeriesDataset(Dataset):
"""Official-style sequence batches with years as the inner batch axis.
Each item contains a contiguous sequence for every requested year. The
outer DataLoader should use ``batch_size=1``; the sequence length plays the
role of the official training batch of time points.
"""
def __init__(
self,
data_dir: str | Path,
years: Sequence[int],
used_variables: Sequence[str] = OFFICIAL_VARIABLES,
stats_dir: str | Path | None = None,
model_size: tuple[int, int] = (32, 64),
sequence_length: int = 8,
normalize: bool = True,
) -> None:
_require_era5dataset()
if tuple(used_variables) != OFFICIAL_VARIABLES:
raise ValueError("ClimODE requires the exact channel order ['z','t','t2m','u10','v10']")
if sequence_length < 1:
raise ValueError("sequence_length must be positive")
self.data_dir = Path(data_dir)
self.years = [int(year) for year in years]
self.variables = tuple(used_variables)
self.model_size = tuple(model_size)
self.sequence_length = int(sequence_length)
self.normalize = normalize
self.era5 = ERA5Dataset(
dataset_dir=str(self.data_dir),
used_years=self.years,
used_variables=list(self.variables),
input_steps=1,
output_steps=1,
normalize=False,
)
if (self.era5.H, self.era5.W) != (721, 1440):
raise ValueError(
"ClimODE raw-data adapter expects (721,1440), "
f"got ({self.era5.H},{self.era5.W})"
)
self.samples_per_year = self.era5.samples_per_year
self.frames_per_year = self.era5.T
first_start = 2
# The official DataLoader keeps its final, possibly shorter batch.
self.starts = list(range(first_start, self.frames_per_year, self.sequence_length))
if not self.starts:
raise ValueError(
f"Not enough frames ({self.era5.T}) for sequence_length={sequence_length} "
"and a three-frame history"
)
self.minimum, self.maximum = (
_load_stats(stats_dir or self.data_dir / "static", len(self.variables))
if normalize
else (torch.zeros(len(self.variables)), torch.ones(len(self.variables)))
)
def __len__(self) -> int:
return len(self.starts)
def _frame(self, year_index: int, frame_index: int) -> torch.Tensor:
if frame_index < 0 or frame_index >= self.frames_per_year:
raise IndexError(frame_index)
sample_index = year_index * self.samples_per_year + min(
frame_index, self.samples_per_year - 1
)
invar, outvar, _, _, _ = self.era5[sample_index]
# ERA5Dataset's final input index is T-2; its paired target is frame T-1.
frame = outvar if frame_index == self.frames_per_year - 1 else invar
frame = _regrid_periodic(torch.as_tensor(frame, dtype=torch.float32), self.model_size)
if self.normalize:
frame = _normalize(frame, self.minimum, self.maximum)
return frame
def __getitem__(self, index: int) -> dict[str, torch.Tensor | int]:
start = self.starts[index]
history_per_year = []
sequence_per_year = []
for year_index in range(len(self.years)):
history_per_year.append(
torch.stack(
[
self._frame(year_index, start - 2),
self._frame(year_index, start - 1),
self._frame(year_index, start),
]
)
)
stop = min(start + self.sequence_length, self.frames_per_year)
sequence_per_year.append(
torch.stack(
[self._frame(year_index, step) for step in range(start, stop)]
)
)
stop = min(start + self.sequence_length, self.frames_per_year)
return {
"history": torch.stack(history_per_year, dim=0),
"observations": torch.stack(sequence_per_year, dim=1),
"time_steps": torch.arange(start, stop, dtype=torch.float32),
"sequence_index": index,
}
def load_constants(
static_file: str | Path,
expected_size: tuple[int, int] = (32, 64),
) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
"""Load [orography, lsm], latitude and longitude from constants.h5."""
with h5py.File(static_file, "r") as handle:
constants = torch.stack(
[
torch.as_tensor(handle["orography"][:], dtype=torch.float32),
torch.as_tensor(handle["lsm"][:], dtype=torch.float32),
]
).unsqueeze(0)
lat2d = torch.as_tensor(handle["lat2d"][:], dtype=torch.float32)
lon2d = torch.as_tensor(handle["lon2d"][:], dtype=torch.float32)
if tuple(constants.shape[-2:]) != expected_size:
raise ValueError(f"Static constants have shape {tuple(constants.shape[-2:])}")
return constants, lat2d, lon2d
def make_dataloader(
dataset: Dataset,
batch_size: int,
shuffle: bool,
num_workers: int = 0,
pin_memory: bool = False,
) -> DataLoader:
return DataLoader(
dataset,
batch_size=batch_size,
shuffle=shuffle,
num_workers=num_workers,
pin_memory=pin_memory,
drop_last=False,
)
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