File size: 4,230 Bytes
8b64ae3 | 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 | """Train SmaAt-UNet for six-frame precipitation nowcasting."""
import json
import os
import sys
from pathlib import Path
import numpy as np
import torch
import yaml
from torch.nn.parallel import DistributedDataParallel
from torch.utils.data import DataLoader, Dataset, DistributedSampler
ROOT = Path(__file__).resolve().parents[1]
sys.path.insert(0, str(ROOT))
from model.smaatunet import SmaAtUNet
class PrecipitationDataset(Dataset):
def __init__(self, path, config):
self.data = np.load(path)
self.config = config
if str(self.data["format_version"]) != config["data"]["format_version"]:
raise ValueError("incompatible precipitation data format")
height, width = int(config["data"]["height"]), int(config["data"]["width"])
if self.data["inputs"].shape[1:] != (int(config["data"]["input_frames"]), height, width):
raise ValueError("input dimensions do not match the paper precipitation data")
if self.data["targets"].shape[1:] != (int(config["data"]["output_frames"]), height, width):
raise ValueError("target dimensions do not match the paper precipitation data")
def __len__(self):
return len(self.data["inputs"])
def __getitem__(self, index):
return torch.from_numpy(self.data["inputs"][index]).float(), torch.from_numpy(self.data["targets"][index]).float()
def device_from_config(config, rank=0):
if config["runtime"]["device"] == "auto":
return torch.device("cuda", rank) if torch.cuda.is_available() else torch.device("cpu")
return torch.device(config["runtime"]["device"])
def main():
config = yaml.safe_load((ROOT / "conf/config.yaml").read_text())
torch.manual_seed(int(config["seed"]))
distributed = int(os.environ.get("WORLD_SIZE", "1")) > 1
local_rank = int(os.environ.get("LOCAL_RANK", "0"))
if distributed:
torch.distributed.init_process_group("nccl" if torch.cuda.is_available() else "gloo")
rank = torch.distributed.get_rank() if distributed else 0
device = device_from_config(config, local_rank)
dataset = PrecipitationDataset(ROOT / config["data"]["root"] / "train.npz", config)
sampler = DistributedSampler(dataset, shuffle=True) if distributed else None
loader = DataLoader(dataset, batch_size=int(config["train"]["batch_size"]), sampler=sampler,
shuffle=sampler is None, num_workers=int(config["train"]["num_workers"]))
model = SmaAtUNet(config["model"]).to(device)
if distributed:
model = DistributedDataParallel(model, device_ids=[local_rank] if device.type == "cuda" else None)
optimizer = torch.optim.Adam(model.parameters(), lr=float(config["train"]["learning_rate"]),
weight_decay=float(config["train"]["weight_decay"]))
history = []
for epoch in range(int(config["train"]["epochs"])):
model.train()
total, steps = 0.0, 0
for inputs, targets in loader:
prediction = model(inputs.to(device))
loss = torch.nn.functional.mse_loss(prediction, targets.to(device))
optimizer.zero_grad(set_to_none=True)
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
optimizer.step()
total += float(loss.detach())
steps += 1
metrics = {"epoch": epoch + 1, "mse": total / max(steps, 1)}
history.append(metrics)
if rank == 0:
print(f"epoch={epoch + 1} mse={metrics['mse']:.6f}")
if rank == 0:
checkpoint = ROOT / config["paths"]["checkpoint"]
metrics_path = ROOT / config["paths"]["training_metrics"]
checkpoint.parent.mkdir(parents=True, exist_ok=True)
metrics_path.parent.mkdir(parents=True, exist_ok=True)
state = model.module.state_dict() if distributed else model.state_dict()
torch.save({"model": state, "model_config": config["model"],
"format_version": config["data"]["format_version"]}, checkpoint)
metrics_path.write_text(json.dumps({"history": history}, indent=2) + "\n")
if distributed:
torch.distributed.destroy_process_group()
if __name__ == "__main__":
main()
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