File size: 3,077 Bytes
e9b87a5 | 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 | import torch
import os
import sys
from pathlib import Path
root_path = Path(__file__).parent.parent
sys.path.append(str(root_path))
import glob
import numpy as np
import h5py
from tqdm import tqdm
from model.ClimaX import ClimaX
from onescience.utils.YParams import YParams
from onescience.datapipes.climate import ERA5Datapipe
def get_stats(data_dir, channels):
"""Read variable list and normalization params (mean/std) from h5 file."""
h5_files = sorted(glob.glob(os.path.join(data_dir, "data", "*.h5")))
with h5py.File(h5_files[0], "r") as f:
ds = f["fields"]
all_variables = [
v.decode() if isinstance(v, bytes) else v for v in ds.attrs["variables"]
]
mu = f["global_means"][:] # [1, C, 1, 1]
std = f["global_stds"][:]
channel_indices = [all_variables.index(v) for v in channels]
means = mu[:, channel_indices, :, :]
stds = std[:, channel_indices, :, :]
return means, stds
if __name__ == "__main__":
current_path = os.getcwd()
sys.path.append(current_path)
## Model config init
config_file_path = os.path.join(current_path, "conf/config.yaml")
cfg = YParams(config_file_path, "model")
## DataLoader init
cfg_data = YParams(config_file_path, "datapipe")
all_vars = cfg_data.dataset.channels
out_vars = cfg_data.dataset.out_variables
means, stds = get_stats(cfg_data.dataset.data_dir, out_vars)
datapipe = ERA5Datapipe(
dataset_dir=cfg_data.dataset.data_dir,
used_variables=all_vars,
used_years=cfg_data.dataset.test_time,
distributed=False,
batch_size=1,
num_workers=4,
)
test_dataloader, _ = datapipe.get_dataloader("test")
ckpt = torch.load(
f"{cfg.checkpoint_dir}/model_bak.pth",
map_location="cuda:0",
weights_only=False,
)
model = ClimaX(
default_vars=all_vars,
img_size=cfg.img_size,
patch_size=cfg.patch_size,
embed_dim=cfg.embed_dim,
depth=cfg.depth,
decoder_depth=cfg.decoder_depth,
num_heads=cfg.num_heads,
mlp_ratio=cfg.mlp_ratio,
drop_path=cfg.drop_path,
drop_rate=cfg.drop_rate,
).to('cuda:0')
model.load_state_dict(ckpt["model_state_dict"])
model.eval()
os.makedirs('result/output/', exist_ok=True)
print(f"Saving predictions to './result/output/'")
predict_range = cfg.predict_range
hrs_each_step = cfg.hrs_each_step
lead_time_val = (predict_range * hrs_each_step) / 100.0
with torch.no_grad():
for data in tqdm(test_dataloader, desc="Inferring testset", unit="batch"):
invar = data[0].to("cuda:0", dtype=torch.float32)
filename = data[4][-1][0] # time string from dataloader (input timestamp)
preds = model(invar, all_vars, out_vars, lead_time_val)
pred_var = preds.cpu().numpy()
# Denormalize
pred_var = pred_var * stds + means
np.save(f"result/output/{filename}.npy", pred_var)
print("Inference complete.")
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