File size: 9,215 Bytes
355f250 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 | """Pre-train SatMAE with masked reconstruction; supports torchrun."""
import argparse
import importlib.util
import json
import math
import os
import random
from contextlib import nullcontext
from functools import partial
from pathlib import Path
import numpy as np
import torch
import yaml
from torch import distributed as dist
from torch.nn.parallel import DistributedDataParallel
from torch.utils.data import DataLoader, Dataset, DistributedSampler
ROOT = Path(__file__).resolve().parents[1]
class NPZDataset(Dataset):
def __init__(self, path, mode):
archive = np.load(path)
self.images = archive["images"]
self.timestamps = archive["timestamps"] if "timestamps" in archive else None
if mode == "temporal" and self.timestamps is None:
raise ValueError("temporal datasets must contain timestamps")
def __len__(self):
return len(self.images)
def __getitem__(self, index):
images = torch.from_numpy(self.images[index])
if self.timestamps is None:
return images, torch.empty(0)
return images, torch.from_numpy(self.timestamps[index])
def load_model_class():
spec = importlib.util.spec_from_file_location("satmae", ROOT / "model/satmae.py")
module = importlib.util.module_from_spec(spec)
spec.loader.exec_module(module)
return module.SatMAE
def model_config(config):
return {
key: value for key, value in config["model"].items()
if key not in {"architecture", "runtime_profile"}
}
def parse_args():
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument("--config", type=Path, default=ROOT / "conf/config.yaml")
parser.add_argument("--data", type=Path, default=None)
parser.add_argument("--output", type=Path, default=None)
parser.add_argument("--resume", type=Path, default=None)
parser.add_argument("--epochs", type=int, default=None)
parser.add_argument("--batch-size", type=int, default=None)
parser.add_argument("--device", choices=("auto", "cpu", "cuda"), default=None)
return parser.parse_args()
def cosine_learning_rate(progress, config, peak_lr):
warmup = config["warmup_epochs"]
if warmup > 0 and progress < warmup:
return peak_lr * progress / warmup
span = max(config["epochs"] - warmup, 1)
phase = min(max((progress - warmup) / span, 0.0), 1.0)
return config["min_learning_rate"] + 0.5 * (
peak_lr - config["min_learning_rate"]
) * (1.0 + math.cos(math.pi * phase))
def main():
args = parse_args()
config = yaml.safe_load(args.config.read_text())
train_config = config["training"]
if args.epochs is not None:
train_config["epochs"] = args.epochs
if args.batch_size is not None:
train_config["batch_size"] = args.batch_size
world_size = int(os.environ.get("WORLD_SIZE", "1"))
local_rank = int(os.environ.get("LOCAL_RANK", "0"))
rank = int(os.environ.get("RANK", "0"))
distributed = world_size > 1
requested_device = args.device or config["runtime"]["device"]
use_cuda = torch.cuda.is_available() and requested_device != "cpu"
if requested_device == "cuda" and not torch.cuda.is_available():
raise RuntimeError("CUDA was requested but is unavailable")
if distributed:
dist.init_process_group("nccl" if use_cuda else "gloo")
device = torch.device(f"cuda:{local_rank}" if use_cuda else "cpu")
if use_cuda:
torch.cuda.set_device(local_rank)
seed = config["seed"] + rank
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
data_path = args.data or ROOT / config["data"]["root"] / "train.npz"
if not data_path.exists():
raise FileNotFoundError(f"training data not found: {data_path}")
dataset = NPZDataset(data_path, config["model"]["mode"])
sampler = DistributedSampler(dataset, shuffle=True) if distributed else None
loader = DataLoader(
dataset,
batch_size=train_config["batch_size"],
shuffle=sampler is None,
sampler=sampler,
num_workers=train_config["num_workers"],
pin_memory=use_cuda,
drop_last=False,
)
model = load_model_class()(**model_config(config)).to(device)
model_without_ddp = model
if distributed:
model = DistributedDataParallel(
model, device_ids=[local_rank] if use_cuda else None
)
model_without_ddp = model.module
effective_batch = (
train_config["batch_size"] * train_config["accum_iter"] * world_size
)
peak_lr = train_config["learning_rate"]
if peak_lr is None:
peak_lr = train_config["base_learning_rate"] * effective_batch / 256
decay, no_decay = [], []
for name, parameter in model_without_ddp.named_parameters():
if not parameter.requires_grad:
continue
(no_decay if parameter.ndim == 1 or name.endswith("bias") else decay).append(parameter)
optimizer = torch.optim.AdamW(
[
{"params": decay, "weight_decay": train_config["weight_decay"]},
{"params": no_decay, "weight_decay": 0.0},
],
lr=peak_lr,
betas=(0.9, 0.95),
)
amp_enabled = bool(config["runtime"].get("amp", True) and use_cuda)
scaler = torch.amp.GradScaler("cuda", enabled=amp_enabled)
start_epoch = 0
history = []
resume_path = args.resume
if resume_path is None and train_config.get("resume"):
resume_path = ROOT / train_config["resume"]
if resume_path is not None:
checkpoint = torch.load(resume_path, map_location="cpu", weights_only=False)
model_without_ddp.load_state_dict(checkpoint["model"])
optimizer.load_state_dict(checkpoint["optimizer"])
if checkpoint.get("scaler") is not None:
scaler.load_state_dict(checkpoint["scaler"])
start_epoch = checkpoint["epoch"] + 1
history = checkpoint.get("history", [])
checkpoint_path = args.output or ROOT / config["paths"]["checkpoint"]
metrics_path = ROOT / config["paths"]["training_metrics"]
optimizer.zero_grad(set_to_none=True)
for epoch in range(start_epoch, train_config["epochs"]):
if sampler is not None:
sampler.set_epoch(epoch)
model.train()
total_loss = 0.0
steps = len(loader)
for step, (images, timestamps) in enumerate(loader):
progress = epoch + step / max(steps, 1)
learning_rate = cosine_learning_rate(progress, train_config, peak_lr)
for group in optimizer.param_groups:
group["lr"] = learning_rate
images = images.to(device, non_blocking=use_cuda)
timestamps = timestamps.to(device, non_blocking=use_cuda)
timestamps = timestamps if timestamps.numel() else None
autocast = partial(torch.amp.autocast, "cuda") if amp_enabled else nullcontext
with autocast():
output = model(images, timestamps=timestamps)
loss = output["loss"] / train_config["accum_iter"]
if not torch.isfinite(loss):
raise ValueError(f"non-finite loss at epoch {epoch}, step {step}")
scaler.scale(loss).backward()
update = (step + 1) % train_config["accum_iter"] == 0 or step + 1 == steps
if update:
scaler.step(optimizer)
scaler.update()
optimizer.zero_grad(set_to_none=True)
total_loss += output["loss"].detach().item()
epoch_loss = total_loss / max(steps, 1)
record = {
"epoch": epoch + 1,
"reconstruction_loss": epoch_loss,
"learning_rate": optimizer.param_groups[0]["lr"],
}
history.append(record)
if rank == 0:
print(
f"epoch={epoch + 1} reconstruction_loss={epoch_loss:.6f} "
f"lr={record['learning_rate']:.3e}"
)
if (epoch + 1) % train_config["save_every"] == 0 or epoch + 1 == train_config["epochs"]:
checkpoint_path.parent.mkdir(parents=True, exist_ok=True)
torch.save(
{
"model": model_without_ddp.state_dict(),
"optimizer": optimizer.state_dict(),
"scaler": scaler.state_dict() if amp_enabled else None,
"epoch": epoch,
"history": history,
"config": config,
},
checkpoint_path,
)
if rank == 0:
metrics_path.parent.mkdir(parents=True, exist_ok=True)
metrics_path.write_text(json.dumps({
"history": history,
"protocol": config["data"]["protocol"],
"data_source": "synthetic" if "synthetic" in data_path.name or (data_path.parent / "format.json").exists() else "provided",
"effective_batch_size": effective_batch,
"peak_learning_rate": peak_lr,
}, indent=2) + "\n")
print("checkpoint=", checkpoint_path)
if distributed:
dist.destroy_process_group()
if __name__ == "__main__":
main()
|