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NB: this script is for illustration purposes only and is not runnable as our
full dataset is not provided as part of the submission, due to size constraints.
Many of the relevant paths to the data have been thus replaced by dummy paths.
"""
import os
import sys
import pickle
import argparse
import torch
import torch.multiprocessing as mp
from torch.utils.data import DataLoader
import torch.utils.data.distributed
from torch.utils.data.distributed import DistributedSampler
from torch.distributed import init_process_group, destroy_process_group
from trainer import DDPTrainer
from loss_functions import WeightedRmseLoss, PressureWeightedRmseLoss, RmseLoss
from misc_downscaling_functionality import ConvCNPWeatherOnToOff, DownscalingRmseLoss
from loader import *
from models import *
from unet_wrap_padding import *
sys.path.append("../npw/data")
torch.set_float32_matmul_precision("medium")
def ddp_setup(rank, world_size, master_port):
"""
Args:
rank: Unique identifier of each process
world_size: Total number of processes
"""
os.environ["MASTER_ADDR"] = "localhost"
os.environ["MASTER_PORT"] = master_port
init_process_group(backend="nccl", rank=rank, world_size=world_size)
torch.cuda.set_device(rank)
def start_date(name):
if name == "train":
return "2007-01-02"
elif name == "val":
return "2019-01-01"
elif name == "test":
return "2018-01-01"
else:
raise Exception(f"Unrecognised split name {name}")
def end_date(name):
if name == "train":
return "2017-12-31"
elif name == "val":
return "2019-11-01"
elif name == "test":
return "2018-12-21"
else:
raise Exception(f"Unrecognised split name {name}")
def main(rank, world_size, output_dir, args):
"""
Primary training script for the encoder, processor and decoder modules.
"""
master_port = args.master_port
lead_time = args.lead_time
era5_mode = args.era5_mode
weights_dir = args.weights_dir
ddp_setup(rank, world_size, master_port)
# Instantiate loss function
if args.loss == "lw_rmse":
lf = WeightedRmseLoss(
args.res,
start_ind=args.start_ind,
end_ind=args.end_ind,
weight_per_variable=bool(args.weight_per_variable),
)
elif args.loss == "lw_rmse_pressure_weighted":
lf = PressureWeightedRmseLoss(args.res, era5_mode)
elif args.loss == "rmse":
lf = RmseLoss()
elif args.loss == "downscaling_rmse":
lf = DownscalingRmseLoss()
# Setup datasets
# Case 1: training encoder
if args.mode == "assimilation":
train_dataset = WeatherDatasetAssimilation(
device="cuda",
hadisd_mode="train",
start_date="2007-01-02",
end_date="2017-12-31",
lead_time=0,
era5_mode="4u",
res=args.res,
var_start=args.start_ind,
var_end=args.end_ind,
diff=bool(args.diff),
two_frames=bool(args.two_frames),
)
val_dataset = WeatherDatasetAssimilation(
device="cuda",
hadisd_mode="train",
start_date="2019-01-01",
end_date="2019-12-31",
lead_time=0,
era5_mode="4u",
res=args.res,
var_start=args.start_ind,
var_end=args.end_ind,
diff=bool(args.diff),
two_frames=bool(args.two_frames),
)
# Case 2: training processor
elif args.mode == "forecast":
if args.ic == "aardvark":
train_dataset = FineTuneForecastLoaderNew(
device="cuda",
mode="train",
lead_time=lead_time,
era5_mode=era5_mode,
res=args.res,
frequency=args.frequency,
diff=bool(args.diff),
aardvark_ic_path=args.aardvark_ic_path,
random_lt=True,
)
val_dataset = FineTuneForecastLoaderNew(
device="cuda",
mode="val",
lead_time=lead_time,
era5_mode=era5_mode,
res=args.res,
frequency=args.frequency,
diff=bool(args.diff),
aardvark_ic_path=args.aardvark_ic_path,
)
else:
train_dataset = ForecastLoader(
device="cuda",
mode="train",
lead_time=lead_time,
era5_mode=era5_mode,
res=args.res,
frequency=args.frequency,
diff=bool(args.diff),
u_only=False,
random_lt=False,
)
val_dataset = ForecastLoader(
device="cuda",
mode="val",
lead_time=lead_time,
era5_mode=era5_mode,
res=args.res,
frequency=args.frequency,
diff=bool(args.diff),
u_only=False,
random_lt=False,
)
# Case 3: training decoder
elif args.mode == "downscaling":
train_dataset = ForecasterDatasetDownscaling(
start_date="2007-01-02",
end_date="2017-12-31",
lead_time=args.lead_time,
hadisd_var=args.var,
mode="train",
device="cuda",
forecast_path=None,
)
val_dataset = ForecasterDatasetDownscaling(
start_date="2019-01-01",
end_date="2019-12-21",
lead_time=args.lead_time,
hadisd_var=args.var,
mode="train",
device="cuda",
forecast_path=None,
)
try:
os.mkdir(f"{output_dir}lt_{args.lead_time}")
except FileExistsError:
pass
output_dir = f"{output_dir}lt_{args.lead_time}/"
# Instantiate model
if args.mode == "downscaling":
model = ConvCNPWeatherOnToOff(
in_channels=args.in_channels,
out_channels=args.end_ind - args.start_ind,
int_channels=args.int_channels,
device="cuda",
res=args.res,
decoder=args.decoder,
mode=args.mode,
film=bool(args.film),
)
else:
model = ConvCNPWeather(
in_channels=args.in_channels,
out_channels=args.end_ind - args.start_ind,
int_channels=args.int_channels,
device="cuda",
res=args.res,
gnp=bool(0),
decoder=args.decoder,
mode=args.mode,
film=bool(args.film),
two_frames=bool(args.two_frames),
)
# Instantiate loaders
train_sampler = DistributedSampler(train_dataset)
val_sampler = DistributedSampler(val_dataset)
train_loader = DataLoader(
train_dataset,
batch_size=args.batch_size,
shuffle=False,
sampler=train_sampler,
)
val_loader = DataLoader(
val_dataset,
batch_size=args.batch_size,
shuffle=False,
sampler=val_sampler,
)
# Instantiate trainer
trainer = DDPTrainer(
model,
rank,
train_loader,
val_loader,
lf,
output_dir,
args.lr,
train_sampler,
weight_decay=args.weight_decay,
weights_path=weights_dir,
tune_film=args.film,
)
# Train model
trainer.train(n_epochs=args.epoch)
destroy_process_group()
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--output_dir")
parser.add_argument("--mode")
parser.add_argument("--weights_dir")
parser.add_argument("--in_channels", type=int)
parser.add_argument("--out_channels", type=int)
parser.add_argument("--int_channels", type=int)
parser.add_argument("--loss")
parser.add_argument("--ic")
parser.add_argument("--decoder")
parser.add_argument("--film")
parser.add_argument("--aardvark_ic_path")
parser.add_argument("--two_frames", type=int, default=0)
parser.add_argument("--weight_per_variable", type=int, default=0)
parser.add_argument("--batch_size", type=int, default=128)
parser.add_argument("--epoch", type=int, default=50)
parser.add_argument("--master_port", default="12345")
parser.add_argument("--lr", type=float, default=5e-4)
parser.add_argument("--lead_time", type=int)
parser.add_argument("--era5_mode", default="4u")
parser.add_argument("--weight_decay", type=float, default=1e-6)
parser.add_argument("--res", type=int, default=1)
parser.add_argument("--frequency", type=int, default=6)
parser.add_argument("--diff", type=int, default=1)
parser.add_argument("--start_ind", type=int, default=0)
parser.add_argument("--end_ind", type=int, default=24)
parser.add_argument("--downscaling_train_start_date", default="1979-01-01")
parser.add_argument("--downscaling_train_end_date", default="2017-12-31")
parser.add_argument("--downscaling_context", default="era5")
parser.add_argument("--downscaling_lead_time", type=int)
parser.add_argument("--var", default=None)
args = parser.parse_args()
torch.device("cuda")
# Create results directory
output_dir = args.output_dir
if not os.path.exists(output_dir):
os.mkdir(output_dir)
# Save config
with open(output_dir + "/config.pkl", "wb") as f:
pickle.dump(vars(args), f)
world_size = torch.cuda.device_count()
mp.spawn(main, args=[world_size, output_dir, args], nprocs=world_size)
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