File size: 10,832 Bytes
9b92c75 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 | from __future__ import annotations
import argparse
import json
import os
import time
from pathlib import Path
import torch
import torch.distributed as dist
from torch.nn.parallel import DistributedDataParallel
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from torch.utils.data import DataLoader, DistributedSampler
from .config import apply_overrides, load_config, save_config
from .data import build_dataset, detection_collate
from .evaluate import evaluate_coco
from .losses import ObjectModelCriterion
from .model import build_model
from .utils import (
ModelEMA,
learning_rate_factor,
move_targets,
save_checkpoint,
seed_everything,
trainable_parameter_count,
)
def distributed_context() -> tuple[int, int, int]:
world_size = int(os.environ.get("WORLD_SIZE", "1"))
rank = int(os.environ.get("RANK", "0"))
local_rank = int(os.environ.get("LOCAL_RANK", "0"))
if world_size > 1:
if not torch.cuda.is_available():
raise RuntimeError("Distributed training currently requires CUDA")
torch.cuda.set_device(local_rank)
dist.init_process_group(backend="nccl")
return rank, world_size, local_rank
def build_optimizer(model, config: dict) -> AdamW:
train = config["train"]
backbone, other = [], []
for name, parameter in model.named_parameters():
if not parameter.requires_grad:
continue
(backbone if name.startswith("backbone.") else other).append(parameter)
return AdamW(
[
{"params": other, "lr": float(train["lr"])},
{"params": backbone, "lr": float(train.get("backbone_lr", train["lr"]))},
],
weight_decay=float(train["weight_decay"]),
)
def reduce_losses(losses: dict[str, torch.Tensor], world_size: int) -> dict[str, float]:
values = torch.stack([value.detach() for value in losses.values()])
if world_size > 1:
dist.all_reduce(values)
values /= world_size
return {name: float(value) for name, value in zip(losses, values, strict=True)}
def main() -> None:
parser = argparse.ArgumentParser(description="Train ObjectModel-v1")
parser.add_argument("--config", default="configs/objectmodel_v1.yaml")
parser.add_argument("--data-root", required=True)
parser.add_argument("--output", default="outputs/objectmodel_v1")
parser.add_argument("--resume")
parser.add_argument("--device", default="cuda" if torch.cuda.is_available() else "cpu")
parser.add_argument("--set", action="append", default=[])
args = parser.parse_args()
rank, world_size, local_rank = distributed_context()
config = apply_overrides(load_config(args.config), args.set)
train_config = config["train"]
seed_everything(int(train_config["seed"]) + rank)
device = torch.device(f"cuda:{local_rank}" if world_size > 1 else args.device)
if device.type == "cuda":
torch.backends.cudnn.benchmark = True
torch.backends.cuda.matmul.allow_tf32 = True
torch.backends.cudnn.allow_tf32 = True
torch.set_float32_matmul_precision("high")
channels_last = bool(train_config.get("channels_last", False))
compile_model = bool(train_config.get("compile", False))
output_dir = Path(args.output)
if rank == 0:
output_dir.mkdir(parents=True, exist_ok=True)
save_config(config, output_dir / "config.yaml")
train_dataset = build_dataset(config, args.data_root, "train")
train_sampler = DistributedSampler(train_dataset, shuffle=True) if world_size > 1 else None
train_loader = DataLoader(
train_dataset,
batch_size=int(train_config["batch_size"]),
shuffle=train_sampler is None,
sampler=train_sampler,
num_workers=int(train_config["workers"]),
pin_memory=device.type == "cuda",
drop_last=True,
persistent_workers=int(train_config["workers"]) > 0,
prefetch_factor=int(train_config.get("prefetch_factor", 4))
if int(train_config["workers"]) > 0
else None,
collate_fn=detection_collate,
)
model = build_model(config).to(device)
if channels_last:
model = model.to(memory_format=torch.channels_last)
criterion = ObjectModelCriterion(config).to(device)
optimizer = build_optimizer(model, config)
total_steps = int(train_config["epochs"]) * len(train_loader)
scheduler = LambdaLR(
optimizer,
lambda step: learning_rate_factor(
step,
total_steps,
int(train_config["warmup_steps"]),
float(train_config["min_lr_ratio"]),
),
)
ema = ModelEMA(model, float(train_config["ema_decay"])) if rank == 0 else None
start_epoch, global_step, best_ap = 0, 0, -1.0
if args.resume:
checkpoint = torch.load(args.resume, map_location="cpu", weights_only=False)
model.load_state_dict(checkpoint["model"])
optimizer.load_state_dict(checkpoint["optimizer"])
scheduler.load_state_dict(checkpoint["scheduler"])
start_epoch = int(checkpoint["epoch"]) + 1
global_step = int(checkpoint.get("global_step", start_epoch * len(train_loader)))
best_ap = float(checkpoint.get("best_ap", -1.0))
if ema is not None and "ema" in checkpoint:
ema.model.load_state_dict(checkpoint["ema"])
if rank == 0:
print(
json.dumps(
{
"parameters": trainable_parameter_count(model),
"world_size": world_size,
"device": str(device),
"steps_per_epoch": len(train_loader),
},
indent=2,
)
)
training_model = (
DistributedDataParallel(model, device_ids=[local_rank], find_unused_parameters=False)
if world_size > 1
else model
)
if compile_model:
training_model = torch.compile(training_model, dynamic=False, mode="reduce-overhead")
use_amp = bool(train_config.get("amp", True)) and device.type == "cuda"
amp_dtype = (
torch.bfloat16 if train_config.get("amp_dtype", "float16") == "bfloat16" else torch.float16
)
scaler = torch.amp.GradScaler("cuda", enabled=use_amp and amp_dtype == torch.float16)
history_path = output_dir / "metrics.jsonl"
for epoch in range(start_epoch, int(train_config["epochs"])):
if train_sampler is not None:
train_sampler.set_epoch(epoch)
training_model.train()
epoch_start = time.perf_counter()
log_start = epoch_start
running = torch.zeros((), device=device)
for batch_index, (images, targets) in enumerate(train_loader):
images = images.to(
device,
non_blocking=True,
memory_format=torch.channels_last if channels_last else torch.preserve_format,
)
targets = move_targets(targets, device)
optimizer.zero_grad(set_to_none=True)
with torch.autocast(device_type=device.type, dtype=amp_dtype, enabled=use_amp):
outputs = training_model(images)
losses = criterion(outputs, targets)
scaler.scale(losses["loss_total"]).backward()
scaler.unscale_(optimizer)
torch.nn.utils.clip_grad_norm_(
training_model.parameters(), float(train_config["clip_grad_norm"])
)
scaler.step(optimizer)
scaler.update()
scheduler.step()
global_step += 1
if ema is not None:
ema.update(model)
running += losses["loss_total"].detach()
if rank == 0 and (batch_index + 1) % int(train_config["print_freq"]) == 0:
now = time.perf_counter()
log_steps = int(train_config["print_freq"])
avg_loss = running / (batch_index + 1)
if world_size > 1:
dist.all_reduce(avg_loss)
avg_loss = avg_loss / world_size
print(
f"epoch={epoch + 1} step={batch_index + 1}/{len(train_loader)} "
f"loss={avg_loss.item():.4f} lr={scheduler.get_last_lr()[0]:.3e} "
f"step_seconds={(now - log_start) / log_steps:.3f} "
f"images_per_second={log_steps * len(images) / (now - log_start):.2f}",
flush=True,
)
log_start = now
epoch_avg_loss = running / max(len(train_loader), 1)
if world_size > 1:
dist.all_reduce(epoch_avg_loss)
epoch_avg_loss = epoch_avg_loss / world_size
metrics: dict[str, float] = {
"epoch": epoch + 1,
"train_loss": epoch_avg_loss.item(),
"epoch_seconds": time.perf_counter() - epoch_start,
}
if world_size > 1:
dist.barrier()
should_evaluate = (epoch + 1) % int(train_config["eval_every"]) == 0
if rank == 0 and should_evaluate:
val_dataset = build_dataset(config, args.data_root, "val")
val_loader = DataLoader(
val_dataset,
batch_size=int(train_config.get("eval_batch_size", train_config["batch_size"])),
shuffle=False,
num_workers=int(train_config["workers"]),
pin_memory=device.type == "cuda",
collate_fn=detection_collate,
)
metrics.update(
evaluate_coco(
ema.model if ema is not None else model,
val_loader,
device,
output_dir / f"predictions_epoch_{epoch + 1:03d}.json",
)
)
if rank == 0:
state = {
"epoch": epoch,
"global_step": global_step,
"best_ap": max(best_ap, metrics.get("AP", -1.0)),
"model": model.state_dict(),
"ema": ema.model.state_dict() if ema is not None else model.state_dict(),
"optimizer": optimizer.state_dict(),
"scheduler": scheduler.state_dict(),
"config": config,
}
save_checkpoint(output_dir / "last.pt", **state)
if metrics.get("AP", -1.0) > best_ap:
best_ap = metrics["AP"]
state["best_ap"] = best_ap
save_checkpoint(output_dir / "best.pt", **state)
with history_path.open("a", encoding="utf-8") as handle:
handle.write(json.dumps(metrics) + "\n")
print(json.dumps(metrics))
if world_size > 1:
dist.barrier()
if world_size > 1:
dist.destroy_process_group()
if __name__ == "__main__":
main()
|