Shaun ZhouandClaude Opus 5
be6173c746
- SimCLRTrainer 记录 simclr/loss、loss_avg、lr（按 step）与 epoch_loss、 epoch_seconds、gpu_mem_peak_mb（按 epoch），超参为完整 yaml 配置； 正常结束与 KeyboardInterrupt 两条路径都会 finish() - 配置新增 simclr.swanlab（字段同 train.swanlab），默认 mode: local， 不需要登录，日志写到 <work_dir>/swanlog - 依赖改为 swanlab[dashboard]：此前 `swanlab watch` 在干净环境里会报 "Please install the swanboard package"，README 里检测训练那节的看板命令 同样受影响，现已随 uv sync 装好 - README 补齐本地看板/云端/离线补传三种用法与端口、局域网参数 Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
191 lines
7.3 KiB
Python
191 lines
7.3 KiB
Python
"""SimCLR 自监督预训练：在无标注的薄膜图上学一套特征，供聚类使用。
|
||
|
||
骨架仍然是项目里的纯卷积 backbone（`tiny_conv` 等），预训练完可以直接拿去
|
||
初始化检测模型。支持 cuda / mps / cpu，用 `device: auto` 自动挑最快的后端。
|
||
"""
|
||
|
||
from __future__ import annotations
|
||
|
||
import time
|
||
from collections.abc import Mapping
|
||
from pathlib import Path
|
||
from typing import Any
|
||
|
||
import torch
|
||
from torch import nn
|
||
from torch.nn import functional as F
|
||
from torch.utils.data import DataLoader
|
||
|
||
from engine.metrics import AverageMeter
|
||
from engine.optim import build_optimizer, build_scheduler
|
||
from engine.visualizer import SwanLabLogger
|
||
from models.heads.projection import ProjectionHead
|
||
from utils.device import PeakMemory, resolve_amp, synchronize
|
||
from utils.logging import get_logger
|
||
|
||
logger = get_logger("engine.simclr")
|
||
|
||
|
||
def nt_xent_loss(z1: torch.Tensor, z2: torch.Tensor, temperature: float = 0.2) -> torch.Tensor:
|
||
"""NT-Xent：同一张图的两个视图互为正样本，batch 内其余 2N-2 个都是负样本。"""
|
||
batch_size = z1.shape[0]
|
||
z = F.normalize(torch.cat([z1, z2]), dim=1)
|
||
similarity = z @ z.t() / temperature
|
||
similarity.fill_diagonal_(float("-inf")) # 排除自己和自己
|
||
|
||
targets = torch.cat(
|
||
[
|
||
torch.arange(batch_size, 2 * batch_size, device=z.device),
|
||
torch.arange(0, batch_size, device=z.device),
|
||
]
|
||
)
|
||
return F.cross_entropy(similarity, targets)
|
||
|
||
|
||
class SimCLRTrainer:
|
||
"""Args:
|
||
backbone: 输出特征图的卷积骨架（需带 `out_channels`）。
|
||
dataloader: 每个样本产出 2 个视图的 DataLoader（`UnlabeledImageDataset(views=2)`）。
|
||
cfg: `epochs / temperature / optimizer / scheduler / amp / log_interval / save_interval`
|
||
以及 `swanlab`（可视化，字段同 `train.swanlab`）。
|
||
full_config: 记录到 SwanLab 的超参，一般直接传完整 yaml 配置。
|
||
"""
|
||
|
||
def __init__(
|
||
self,
|
||
backbone: nn.Module,
|
||
dataloader: DataLoader,
|
||
cfg: Mapping[str, Any],
|
||
device: torch.device,
|
||
work_dir: str | Path,
|
||
full_config: dict | None = None,
|
||
) -> None:
|
||
self.backbone = backbone
|
||
self.dataloader = dataloader
|
||
self.cfg = dict(cfg)
|
||
self.device = device
|
||
self.work_dir = Path(work_dir)
|
||
self.work_dir.mkdir(parents=True, exist_ok=True)
|
||
|
||
self.epochs = int(self.cfg.get("epochs", 50))
|
||
self.temperature = float(self.cfg.get("temperature", 0.2))
|
||
self.log_interval = int(self.cfg.get("log_interval", 20))
|
||
# 每隔若干 epoch 落一次盘：自监督动辄跑几十分钟，中途被打断不该把训练成果全丢掉
|
||
self.save_interval = int(self.cfg.get("save_interval", 5))
|
||
|
||
self.projector = ProjectionHead(
|
||
backbone.out_channels, **dict(self.cfg.get("projection", {}))
|
||
).to(device)
|
||
self.model = nn.ModuleDict({"backbone": backbone, "projector": self.projector})
|
||
self.optimizer = build_optimizer(
|
||
self.model, self.cfg.get("optimizer", {"name": "adamw", "lr": 1e-3, "weight_decay": 1e-4})
|
||
)
|
||
self.scheduler = build_scheduler(self.optimizer, self.cfg.get("scheduler"), self.epochs)
|
||
self.amp = resolve_amp(self.cfg.get("amp", "auto"), device)
|
||
self.scaler = torch.amp.GradScaler(device.type, enabled=self.amp)
|
||
self.peak_memory = PeakMemory(device)
|
||
|
||
self.global_step = 0
|
||
self.visualizer = SwanLabLogger(
|
||
work_dir=self.work_dir,
|
||
config=full_config,
|
||
**dict(self.cfg.get("swanlab", {})),
|
||
)
|
||
|
||
def train_one_epoch(self, epoch: int) -> float:
|
||
self.model.train()
|
||
meter = AverageMeter()
|
||
start = time.time()
|
||
|
||
for step, batch in enumerate(self.dataloader):
|
||
view1, view2 = (view.to(self.device, non_blocking=True) for view in batch["views"][:2])
|
||
|
||
with torch.amp.autocast(self.device.type, enabled=self.amp):
|
||
z1 = self.projector(self.backbone(view1))
|
||
z2 = self.projector(self.backbone(view2))
|
||
loss = nt_xent_loss(z1, z2, self.temperature)
|
||
|
||
self.optimizer.zero_grad(set_to_none=True)
|
||
self.scaler.scale(loss).backward()
|
||
self.scaler.step(self.optimizer)
|
||
self.scaler.update()
|
||
|
||
self.peak_memory.update()
|
||
meter.update(float(loss.detach()), view1.shape[0])
|
||
self.global_step += 1
|
||
if step % self.log_interval == 0:
|
||
self.visualizer.log(
|
||
{
|
||
"loss": float(loss.detach()),
|
||
"loss_avg": meter.avg,
|
||
"lr": self.optimizer.param_groups[0]["lr"],
|
||
},
|
||
step=self.global_step,
|
||
prefix="simclr/",
|
||
)
|
||
logger.info(
|
||
"simclr epoch %d [%d/%d] loss=%.4f lr=%.2e",
|
||
epoch,
|
||
step,
|
||
len(self.dataloader),
|
||
meter.avg,
|
||
self.optimizer.param_groups[0]["lr"],
|
||
)
|
||
|
||
synchronize(self.device)
|
||
peak = self.peak_memory.peak_mb
|
||
elapsed = time.time() - start
|
||
self.visualizer.log(
|
||
{
|
||
"epoch": float(epoch),
|
||
"epoch_loss": meter.avg,
|
||
"epoch_seconds": elapsed,
|
||
"gpu_mem_peak_mb": peak,
|
||
},
|
||
step=self.global_step,
|
||
prefix="simclr/",
|
||
)
|
||
logger.info(
|
||
"simclr epoch %d 完成 loss=%.4f 耗时 %.1fs 显存峰值 %.0f MB%s",
|
||
epoch,
|
||
meter.avg,
|
||
elapsed,
|
||
peak,
|
||
"" if self.peak_memory.exact else "（MPS 采样下界）",
|
||
)
|
||
return meter.avg
|
||
|
||
def save_backbone(self, epoch: int) -> Path:
|
||
"""只存骨架权重，不含投影头——投影头训练成对增强不变，会丢掉下游有用的信息。"""
|
||
path = self.work_dir / "simclr_backbone.pth"
|
||
torch.save({"model": self.backbone.state_dict(), "epochs": epoch + 1}, path)
|
||
return path
|
||
|
||
def fit(self) -> Path:
|
||
"""训练并保存骨架权重（不含投影头），返回权重路径。
|
||
|
||
每 `save_interval` 个 epoch 落一次盘，中途被 Ctrl-C 或 kill 掉也能拿到当前权重继续用。
|
||
"""
|
||
if len(self.dataloader) < 2:
|
||
raise ValueError("SimCLR 需要多个 batch，请调小 batch_size 或增加样本")
|
||
|
||
path = self.work_dir / "simclr_backbone.pth"
|
||
try:
|
||
for epoch in range(self.epochs):
|
||
self.train_one_epoch(epoch)
|
||
if self.scheduler is not None:
|
||
self.scheduler.step()
|
||
if self.save_interval and (epoch + 1) % self.save_interval == 0:
|
||
self.save_backbone(epoch)
|
||
logger.info("已保存中间权重（epoch %d）: %s", epoch, path)
|
||
except KeyboardInterrupt:
|
||
self.save_backbone(epoch)
|
||
logger.warning("训练被中断，已保存 epoch %d 的权重: %s", epoch, path)
|
||
self.visualizer.finish()
|
||
return path
|
||
|
||
self.save_backbone(self.epochs - 1)
|
||
logger.info("自监督骨架已保存: %s", path)
|
||
self.visualizer.finish()
|
||
return path
|