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7180154 | 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 | #!/usr/bin/env python3
"""使用官方 GenCast DPM-Solver++ 执行集合自回归推理。"""
from __future__ import annotations
import argparse
import sys
import warnings
from pathlib import Path
warnings.filterwarnings("ignore", message="Changing the sparsity structure")
PROJECT_ROOT = Path(__file__).resolve().parents[1]
sys.path.insert(0, str(PROJECT_ROOT))
from model.common import configure_jax, load_config, load_stats, resolve_path
def parse_args() -> argparse.Namespace:
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument("--config", default=str(PROJECT_ROOT / "conf/config.yaml"))
parser.add_argument("--checkpoint")
parser.add_argument("--sample-index", type=int, default=0)
parser.add_argument("--num-members", type=int)
parser.add_argument("--prediction-steps", type=int)
parser.add_argument("--output")
return parser.parse_args()
def main() -> None:
args = parse_args()
config = load_config(args.config)
configure_jax(config["runtime"].get("platform", "auto"))
import jax
import numpy as np
import xarray
from model.graphcast import rollout
from model.gencast import GenCastModel, load_model_checkpoint
from model.common import (
load_trainer_checkpoint, validate_checkpoint_config,
)
from model.data_loader import GenCastERA5Dataset
prediction_steps = int(args.prediction_steps or config["inference"]["prediction_steps"])
num_members = int(args.num_members or config["inference"]["num_members"])
stats = load_stats(config["data"]["stats_dir"])
checkpoint_path = args.checkpoint or config["inference"].get("official_checkpoint")
if checkpoint_path:
official = load_model_checkpoint(resolve_path(checkpoint_path))
model = GenCastModel.from_checkpoint_and_stats(
official,
stats,
attention_type=config["inference"].get("attention_type_override"),
)
params, state = official.params, {}
task_config = official.task_config
else:
model = GenCastModel.from_config_and_stats(config, stats)
params, state, _, _, saved_config = load_trainer_checkpoint(
config["checkpoint"]["trainer"]
)
validate_checkpoint_config(config, saved_config, scope="inference")
task_config = model.task_config
dataset = GenCastERA5Dataset(
resolve_path(config["data"]["data_dir"]),
list(config["data"]["test_years"]),
static_dir=resolve_path(config["data"]["static_dir"]),
prediction_steps=prediction_steps,
stride=int(config["data"].get("test_stride", 1)),
task_config=task_config,
precipitation_interval_hours=int(
config["data"]["precipitation_interval_hours"]
),
load_future_targets=False,
)
inputs, targets, forcings = dataset[args.sample_index]
def forward(rng, inputs, targets_template, forcings):
return model.predict(
params, state, rng, inputs, targets_template, forcings
)[0]
forward = jax.jit(forward)
seed = int(config["inference"]["seed"])
rngs = np.stack([jax.random.fold_in(jax.random.PRNGKey(seed), i) for i in range(num_members)])
chunks = rollout.chunked_prediction_generator_multiple_runs(
predictor_fn=forward,
rngs=rngs,
inputs=inputs,
targets_template=targets * np.nan,
forcings=forcings,
num_steps_per_chunk=1,
num_samples=num_members,
pmap_devices=None,
)
output = resolve_path(args.output or config["output"]["prediction"])
if bool(config["inference"].get("stream_chunks", True)):
output_dir = output.with_suffix("")
output_dir.mkdir(parents=True, exist_ok=True)
for chunk_index, chunk in enumerate(chunks):
host_chunk = jax.device_get(chunk)
member = int(host_chunk.coords["sample"])
lead = int(host_chunk.time.values[0] / np.timedelta64(1, "h"))
host_chunk = host_chunk.drop_vars("sample").assign_coords(time=[lead])
host_chunk.coords["time"].attrs = {"long_name": "forecast lead time hours"}
host_chunk.attrs.update(
model="GenCast", target_channel_count=84,
forecast_reference_time=inputs.attrs["forecast_reference_time"],
)
path = output_dir / f"member_{member:03d}_lead_{lead:04d}h.nc"
host_chunk.to_netcdf(path)
print(f"Saved prediction chunk to {path}")
return
chunks = list(chunks)
member_chunks: list[list[xarray.Dataset]] = [[] for _ in range(num_members)]
for chunk in chunks:
host_chunk = jax.device_get(chunk)
member = int(host_chunk.coords["sample"])
member_chunks[member].append(host_chunk.drop_vars("sample"))
members = [
xarray.concat(parts, dim="time").expand_dims(sample=[member])
for member, parts in enumerate(member_chunks)
]
predictions = xarray.concat(members, dim="sample")
predictions.attrs.update(
model="GenCast",
target_channel_count=84,
ensemble_members=num_members,
step_hours=12,
forecast_reference_time=inputs.attrs["forecast_reference_time"],
)
# Store lead time as plain hours; xarray_jax's internal dtype attribute is
# not valid CF metadata and conflicts with decoding after NetCDF round-trip.
lead_hours = (
predictions.coords["time"].values / np.timedelta64(1, "h")
).astype(np.int32)
predictions = predictions.assign_coords(time=("time", lead_hours))
predictions.coords["time"].attrs = {
"long_name": "forecast lead time",
"units": "hours",
}
reference_time = np.datetime64(inputs.attrs["forecast_reference_time"])
predictions = predictions.assign_coords(
valid_time=("time", reference_time + lead_hours.astype("timedelta64[h]"))
)
output.parent.mkdir(parents=True, exist_ok=True)
temporary = output.with_suffix(output.suffix + ".tmp")
predictions.to_netcdf(temporary)
temporary.replace(output)
print(f"Saved predictions to {output}")
if __name__ == "__main__":
main()
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