feat(simclr): 接入 SwanLab 可视化，并补上本地看板缺失的依赖

- 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>
2026-09-03 09:24:39 +08:00
parent 7d5b6ec423
commit be6173c746
7 changed files with 304 additions and 14 deletions
@@ -127,11 +127,21 @@ train:
 ```

 ```bash
-swanlab login                                                     # 首次使用云端模式需登录
-uv run python train.py --config configs/train/baseline.yaml --set train.swanlab.mode=offline
-uv run swanlab watch runs/baseline/swanlog                        # 本地看板
+# 本地看板：不需要账号，训练时用 local 模式，另开终端起看板
+uv run python train.py --config configs/train/baseline.yaml --set train.swanlab.mode=local
+uv run swanlab watch runs/baseline/swanlog          # 默认 http://127.0.0.1:5092
+
+# 云端
+uv run swanlab login                               # 首次使用需登录
+uv run python train.py --config configs/train/baseline.yaml   # mode 默认 online
+
+# 离线跑完后再补传云端
+uv run swanlab sync runs/baseline/swanlog
 ```

+看板可以在训练**进行中**打开，指标边训边刷。端口冲突用 `-p 5100`，要局域网访问加 `-h 0.0.0.0`。
+本地看板依赖 `swanlab[dashboard]`，已包含在 `uv sync` 里。
+
 未安装 swanlab、未登录或网络不通时会打一条警告并继续训练，不会中断。关掉用
 `--set train.swanlab.enabled=false`。

@@ -290,8 +300,17 @@ view_transforms:            # 生成两个视图的随机增强
 > `RandomResizedCrop` 的 `scale` 下限别低于 0.4：裁太狠会让两个视图都只剩背景，
 > "背景 vs 背景"被当成正样本对，学出来的特征会退化。

-> SimCLR 训练目前**没有接 SwanLab**，只往 `<work_dir>/cluster.log` 打文本日志，
-> 盯 loss 要看日志（`grep "完成 loss" runs/simclr/cluster.log`）。
+SimCLR 训练同样接了 SwanLab（配置在 `simclr.swanlab`，字段与 `train.swanlab` 相同，
+默认 `mode: local` 不需要登录）。训练跑起来后另开一个终端：
+
+```bash
+uv run swanlab watch runs/simclr/swanlog
+```
+
+记录的指标：按 step 的 `simclr/loss`、`simclr/loss_avg`、`simclr/lr`，按 epoch 的
+`simclr/epoch_loss`、`simclr/epoch_seconds`、`simclr/gpu_mem_peak_mb`，超参是完整的 yaml 配置。
+不想要就 `--set simclr.swanlab.enabled=false`；纯看文本日志用
+`grep "完成 loss" runs/simclr/cluster.log`。

 训练出的骨架和检测模型用的是同一套卷积骨架，可以直接拿去初始化 `DefectNet` 的 backbone——
 标注量有限时，通常比 ImageNet 权重更贴合薄膜的成像特性。
@@ -99,6 +99,16 @@ simclr:
  temperature: 0.2              # 越小越关注难负样本
  amp: auto                     # CUDA 上开、MPS 上关
  save_interval: 5              # 每 5 个 epoch 落一次盘，中途中断也能拿到权重
+
+  # 训练可视化，字段与 train.swanlab 相同。默认 local：不需要登录，
+  # 日志写到 <work_dir>/swanlog，另开终端 `uv run swanlab watch runs/simclr/swanlog` 看板。
+  swanlab:
+    enabled: true
+    project: film-simclr
+    name: null                  # 缺省用 work_dir 目录名
+    mode: local                 # online(云端) | local(本地看板) | offline(仅存盘) | disabled
+    tags: [simclr, tiny_conv]
+
  log_interval: 20
  checkpoint: null              # 填已有的 simclr_backbone.pth 可跳过重复训练
  projection: {hidden_channels: 256, out_channels: 128}
@@ -18,6 +18,7 @@ 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
@@ -45,7 +46,9 @@ 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` 等。
+    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__(
@@ -55,6 +58,7 @@ class SimCLRTrainer:
        cfg: Mapping[str, Any],
        device: torch.device,
        work_dir: str | Path,
+        full_config: dict | None = None,
    ) -> None:
        self.backbone = backbone
        self.dataloader = dataloader
@@ -81,6 +85,13 @@ class SimCLRTrainer:
        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()
@@ -101,7 +112,17 @@ class SimCLRTrainer:

            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,
@@ -113,11 +134,22 @@ class SimCLRTrainer:

        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,
-            time.time() - start,
+            elapsed,
            peak,
            "" if self.peak_memory.exact else "（MPS 采样下界）",
        )
@@ -149,8 +181,10 @@ class SimCLRTrainer:
        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
@@ -13,7 +13,7 @@ dependencies = [
    "numpy>=1.26",
    "pillow>=10.3",
    "pyyaml>=6.0",
-    "swanlab>=0.10.0",
+    "swanlab[dashboard]>=0.10.0",
 ]

 [dependency-groups]
@@ -94,7 +94,8 @@ def build_encoder(cfg, device, work_dir):
        num_workers=int(cfg.get("loader", {}).get("num_workers", 4)),
        collate_fn=unlabeled_collate,
    )
-    SimCLRTrainer(backbone, loader, simclr_cfg, device, work_dir).fit()
+    full_config = cfg.to_dict() if hasattr(cfg, "to_dict") else dict(cfg)
+    SimCLRTrainer(backbone, loader, simclr_cfg, device, work_dir, full_config=full_config).fit()
    return StageFeatureExtractor(backbone, stages).to(device).eval()


@@ -104,6 +104,61 @@ def test_simclr_trainer_runs_and_saves_backbone_only(tiny_image_dir, tmp_path):
    backbone.load_state_dict(state["model"])  # 能被干净地加载回去


+def test_simclr_logs_metrics_to_swanlab(tiny_image_dir, tmp_path, monkeypatch):
+    """SwanLab 接线：按 step 记 loss/lr，按 epoch 记平均 loss、耗时与显存峰值，结束时收尾。"""
+    from torch.utils.data import DataLoader
+
+    import engine.simclr as simclr_mod
+    from datasets.unlabeled import UnlabeledImageDataset, unlabeled_collate
+
+    records: list[tuple[dict, int | None, str]] = []
+    finished: list[bool] = []
+
+    class StubLogger:
+        def __init__(self, **kwargs):
+            self.kwargs = kwargs
+
+        def log(self, metrics, step=None, prefix=""):
+            records.append((metrics, step, prefix))
+
+        def finish(self):
+            finished.append(True)
+
+    monkeypatch.setattr(simclr_mod, "SwanLabLogger", StubLogger)
+
+    dataset = UnlabeledImageDataset(
+        tiny_image_dir,
+        transforms=[{"name": "Resize", "size": [32, 32]}, {"name": "ToTensor"}],
+        views=2,
+    )
+    loader = DataLoader(dataset, batch_size=4, shuffle=True, drop_last=True, collate_fn=unlabeled_collate)
+    backbone = BACKBONES.build({"name": "tiny_conv", "widths": [8, 16], "depths": [1, 1]})
+
+    trainer = simclr_mod.SimCLRTrainer(
+        backbone,
+        loader,
+        {"epochs": 1, "amp": False, "log_interval": 1, "save_interval": 0,
+         "projection": {"hidden_channels": 16, "out_channels": 8},
+         "swanlab": {"enabled": True, "mode": "disabled"}},
+        torch.device("cpu"),
+        tmp_path / "run",
+        full_config={"method": "simclr"},
+    )
+    trainer.fit()
+
+    assert records, "没有任何指标被记录"
+    assert all(prefix == "simclr/" for _, _, prefix in records)
+    step_keys = {k for metrics, _, _ in records for k in metrics}
+    assert {"loss", "loss_avg", "lr"} <= step_keys
+    assert {"epoch", "epoch_loss", "epoch_seconds", "gpu_mem_peak_mb"} <= step_keys
+    assert all(step is not None and step > 0 for _, step, _ in records)
+    assert finished == [True]  # fit() 结束时调用了 finish()
+
+    # 完整 yaml 配置作为超参传给了 SwanLab
+    assert trainer.visualizer.kwargs["config"] == {"method": "simclr"}
+    assert trainer.visualizer.kwargs["mode"] == "disabled"
+
+
 def test_simclr_saves_intermediate_checkpoints(tiny_image_dir, tmp_path):
    """中途落盘：跑到一半被打断也要能拿到权重（此前只在全部 epoch 跑完后才存）。"""
    from torch.utils.data import DataLoader
@@ -10,6 +10,15 @@ resolution-markers = [
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+[[package]]
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+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/5a/8e/38aa427ed5402449e226975b649c5dc73ccadfefeb95e6aecb8f8ea4b6b6/annotated_doc-0.0.5.tar.gz", hash = "sha256:c7e58ce09192557605d8bbd92836d7e1d520ac9580096042c0bfd197efacf1bb", size = 10758, upload-time = "2026-07-28T13:50:58.129Z" }
+wheels = [
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+]
+
 [[package]]
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 version = "0.8.0"
@@ -19,6 +28,19 @@ wheels = [
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+[[package]]
+name = "anyio"
+version = "4.15.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "idna" },
+    { name = "typing-extensions", marker = "python_full_version < '3.15'" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/ea/9a/c15a60547004a3f3cea20296c934f827ddd7bdba225a2e7e9fcb5ec48c80/anyio-4.15.0.tar.gz", hash = "sha256:b5c620ed540725e2579c31b17bb995b3bf02c9281c9cace04c7d186380bab85e", size = 276504, upload-time = "2026-09-02T21:46:36.957Z" }
+wheels = [
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+]
+
 [[package]]
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@@ -252,6 +274,22 @@ nvtx = [
    { name = "nvidia-nvtx", marker = "platform_machine == 'aarch64' or platform_machine == 'x86_64'" },
 ]

+[[package]]
+name = "fastapi"
+version = "0.141.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "annotated-doc" },
+    { name = "pydantic" },
+    { name = "starlette" },
+    { name = "typing-extensions" },
+    { name = "typing-inspection" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/8a/02/91e3416a8fdd715abb903a952a6bec7cdd8d14eed55d415fc8595524c319/fastapi-0.141.1.tar.gz", hash = "sha256:e8822fc40db1e1858054d7a949a888695bc9bdce70139178e33bd2871a453ca1", size = 425799, upload-time = "2026-07-29T17:18:05.568Z" }
+wheels = [
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+]
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+[[package]]
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+wheels = [
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+]
+
 [[package]]
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@@ -433,7 +480,7 @@ dependencies = [
    { name = "numpy" },
    { name = "pillow" },
    { name = "pyyaml" },
-    { name = "swanlab" },
+    { name = "swanlab", extra = ["dashboard"] },
    { name = "torch" },
    { name = "torchvision" },
 ]
@@ -453,7 +500,7 @@ requires-dist = [
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    { name = "pillow", specifier = ">=10.3" },
    { name = "pyyaml", specifier = ">=6.0" },
-    { name = "swanlab", specifier = ">=0.10.0" },
+    { name = "swanlab", extras = ["dashboard"], specifier = ">=0.10.0" },
    { name = "torch", specifier = ">=2.4" },
    { name = "torchvision", specifier = ">=0.19" },
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+]
+
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