File size: 6,034 Bytes
bd3493c | 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 | """Thin, model-local wrapper for the official Aardvark Weather model."""
from __future__ import annotations
import hashlib
import importlib
import os
import pickle
import sys
from pathlib import Path
from typing import Any
import numpy as np
import torch
TOP_LEVEL_KEYS = {"assimilation", "forecast", "downscaling", "y_target"}
REQUIRED_ASSIMILATION_KEYS = {
"x_context_hadisd_current", "y_context_hadisd_current", "climatology_current",
"sat_x_current", "sat_current", "icoads_x_current", "icoads_current",
"igra_x_current", "igra_current", "amsua_current", "amsua_x_current",
"amsub_current", "amsub_x_current", "iasi_current", "iasi_x_current",
"ascat_current", "ascat_x_current", "hirs_current", "hirs_x_current",
"y_target_current", "era5_x_current",
"era5_elev_current", "era5_lonlat_current", "aux_time_current", "lt",
"y_target",
}
REQUIRED_FORECAST_KEYS = {"y_context", "y_target", "lt"}
REQUIRED_DOWNSCALING_KEYS = {
"x_target", "alt_target", "y_target", "y_context", "x_context", "aux_time", "lt",
}
def sha256(path: Path) -> str:
digest = hashlib.sha256()
with path.open("rb") as handle:
for chunk in iter(lambda: handle.read(1024 * 1024), b""):
digest.update(chunk)
return digest.hexdigest()
def validate_sample(sample_path: Path) -> dict[str, Any]:
with sample_path.open("rb") as handle:
sample = pickle.load(handle)
if not isinstance(sample, dict) or set(sample) != TOP_LEVEL_KEYS:
raise ValueError(f"sample top-level keys mismatch: {list(sample) if isinstance(sample, dict) else type(sample)}")
expected = {
"assimilation": REQUIRED_ASSIMILATION_KEYS,
"forecast": REQUIRED_FORECAST_KEYS,
"downscaling": REQUIRED_DOWNSCALING_KEYS,
}
for name, keys in expected.items():
if not isinstance(sample[name], dict) or set(sample[name]) != keys:
raise ValueError(f"sample {name} keys mismatch: {list(sample[name])}")
if not isinstance(sample["y_target"], torch.Tensor) or sample["y_target"].ndim != 2:
raise ValueError("sample y_target must be a rank-2 torch.Tensor")
return {
"top_level_keys": sorted(sample),
"nested_keys": {name: sorted(value) for name, value in expected.items()},
"y_target_shape": list(sample["y_target"].shape),
"nan_counts": {
name: int(value.isnan().sum())
for name, value in sample["downscaling"].items()
if isinstance(value, torch.Tensor) and value.is_floating_point()
},
}
def validate_checkpoint(path: Path) -> dict[str, Any]:
checkpoint = torch.load(path, map_location="cpu")
if not isinstance(checkpoint, dict) or "model_state_dict" not in checkpoint:
raise ValueError(f"checkpoint contract mismatch: {path}")
state = checkpoint["model_state_dict"]
if not isinstance(state, dict) or not state:
raise ValueError(f"empty model_state_dict: {path}")
return {"path": str(path), "key_count": len(state), "has_model_state_dict": True}
def load_sample(sample_path: Path) -> dict[str, Any]:
with sample_path.open("rb") as handle:
return pickle.load(handle)
def build_one_day_model(weights_root: Path, official_root: Path, device: str):
encoder = weights_root / "trained_model/encoder"
processor = weights_root / "trained_model/processor"
decoder = weights_root / "trained_model/decoder/tas"
sys.path.insert(0, str(official_root / "aardvark"))
_install_timm_compatibility()
official_e2e = importlib.import_module("e2e_model")
caller_dir = Path.cwd()
try:
os.chdir(official_root / "aardvark")
model = official_e2e.ConvCNPWeatherE2E(
device=device,
lead_time=1,
se_model_path=str(encoder),
forecast_model_path=str(processor),
sf_model_path=str(decoder) + "/",
return_gridded=True,
aux_data_path=str(official_root / "data") + "/",
)
finally:
os.chdir(caller_dir)
return model
def run_one_day(sample_path: Path, weights_root: Path, official_root: Path, device: str) -> dict[str, Any]:
sample_report = validate_sample(sample_path)
encoder = weights_root / "trained_model/encoder"
processor = weights_root / "trained_model/processor"
decoder = weights_root / "trained_model/decoder/tas"
checkpoint_report = [
validate_checkpoint(encoder / "epoch_96"),
validate_checkpoint(processor / "forecast_1/epoch_0"),
validate_checkpoint(decoder / "lt_1/epoch_18"),
]
sample = load_sample(sample_path)
model = build_one_day_model(weights_root, official_root, device)
model.eval()
with torch.inference_mode():
station, global_forecast, initial_state = model(sample)
for name, tensor in (("station_tas", station), ("global_forecast", global_forecast), ("initial_state", initial_state)):
if not isinstance(tensor, torch.Tensor) or not bool(torch.isfinite(tensor).all()):
raise ValueError(f"{name} contains non-finite values")
return {
"sample": sample_report,
"checkpoints": checkpoint_report,
"device": device,
"lead_time_days": 1,
"station_tas_shape": list(station.shape),
"global_forecast_shape": list(global_forecast.shape),
"initial_state_shape": list(initial_state.shape),
"finite_outputs": True,
}
def _install_timm_compatibility() -> None:
"""Bridge the old timm 0.6 Block constructor used by the official code."""
import timm.models.vision_transformer as vision_transformer
original = vision_transformer.Block
if getattr(original, "_aardvark_compat", False):
return
class AardvarkBlock(original):
_aardvark_compat = True
def __init__(self, *args: Any, drop: float = 0.0, **kwargs: Any) -> None:
kwargs.setdefault("proj_drop", drop)
super().__init__(*args, **kwargs)
vision_transformer.Block = AardvarkBlock
|