修复了mmengine集成中SwanlabVisBackend的save_dir不能为None的问题

swanlab/integration/mmengine.py

@@ -70,7 +70,7 @@ class SwanlabVisBackend(BaseVisBackend):
 
     def __init__(
         self,
-        save_dir: str,
+        save_dir: str = None,
         init_kwargs: Optional[dict] = None,
     ):
         self._save_dir = save_dir
@@ -90,8 +90,9 @@ def experiment(self) -> Any:
 
     def _init_env(self) -> Any:
         """Setup env for swanlab."""
-        if not os.path.exists(self._save_dir):
-            os.makedirs(self._save_dir, exist_ok=True)  # type: ignore
+        if self._save_dir is not None:
+            if not os.path.exists(self._save_dir):
+                os.makedirs(self._save_dir, exist_ok=True)  # type: ignore
         if self._init_kwargs is None:
             self._init_kwargs = {"logdir": self._save_dir}
         else:

test/integration/mmengine/README.md

@@ -0,0 +1,40 @@
+# SwanLab integrate MMEngine test docs
+
+## User`s guidance
+
+There are two scripts to test intgration module
+
+* `mmengine_train.py` for full test. It is a implement for cifar10 classification mission. Use resnet50 as our classifier and use mmengine with our training farmwork. Ref [mmengine docs](https://mmengine.readthedocs.io/en/latest/get_started/15_minutes.html).
+
+* `mmengine_visualizer_import.py` is a simple nad efficient test scripts. It use `mmengine.registry` initialize mmengine 'visualizer' with swanlab backend.  Ref [mmegine docs](https://mmengine.readthedocs.io/en/latest/advanced_tutorials/visualization.html#customize-storage-backends-and-visualizers)
+
+## Install
+
+Refer to [mmengine official document](https://mmengine.readthedocs.io/en/latest/get_started/installation.html).
+
+Install the environment with following command.
+
+```sh
+# with cuda12.1 or you can find torch version you want at pytorch.org
+pip install torch torchvision --index-url https://download.pytorch.org/whl/cu121
+
+pip install -U openmim
+mim install mmengine
+pip install swanlab
+```
+
+Or use `pip install -r requirements.txt` to install env. (hope it can work)
+
+## Start Test
+
+Simple test with init visualizer
+
+```sh
+python mmengine_visualizer_import.py
+```
+
+Full test with training mission
+
+```sh
+python mmengine_train.py
+```

test/integration/mmengine/mmengine_train.py

@@ -0,0 +1,94 @@
+import torch.nn.functional as F
+import torchvision
+import torchvision.transforms as transforms
+from torch.optim import SGD
+from torch.utils.data import DataLoader
+
+from mmengine.evaluator import BaseMetric
+from mmengine.model import BaseModel
+from mmengine.runner import Runner
+from mmengine.visualization import Visualizer
+
+
+from tutils import open_dev_mode
+import swanlab
+from swanlab.integration.mmengine import SwanlabVisBackend
+
+swanlab.login(open_dev_mode())
+
+
+class MMResNet50(BaseModel):
+    def __init__(self):
+        super().__init__()
+        self.resnet = torchvision.models.resnet50()
+
+    def forward(self, imgs, labels, mode):
+        x = self.resnet(imgs)
+        if mode == "loss":
+            return {"loss": F.cross_entropy(x, labels)}
+        elif mode == "predict":
+            return x, labels
+
+
+class Accuracy(BaseMetric):
+    def process(self, data_batch, data_samples):
+        score, gt = data_samples
+        self.results.append(
+            {
+                "batch_size": len(gt),
+                "correct": (score.argmax(dim=1) == gt).sum().cpu(),
+            }
+        )
+
+    def compute_metrics(self, results):
+        total_correct = sum(item["correct"] for item in results)
+        total_size = sum(item["batch_size"] for item in results)
+        return dict(accuracy=100 * total_correct / total_size)
+
+
+norm_cfg = dict(mean=[0.491, 0.482, 0.447], std=[0.202, 0.199, 0.201])
+train_dataloader = DataLoader(
+    batch_size=32,
+    shuffle=True,
+    dataset=torchvision.datasets.CIFAR10(
+        "data/cifar10",
+        train=True,
+        download=True,
+        transform=transforms.Compose(
+            [
+                transforms.RandomCrop(32, padding=4),
+                transforms.RandomHorizontalFlip(),
+                transforms.ToTensor(),
+                transforms.Normalize(**norm_cfg),
+            ]
+        ),
+    ),
+)
+
+val_dataloader = DataLoader(
+    batch_size=32,
+    shuffle=False,
+    dataset=torchvision.datasets.CIFAR10(
+        "data/cifar10",
+        train=False,
+        download=True,
+        transform=transforms.Compose([transforms.ToTensor(), transforms.Normalize(**norm_cfg)]),
+    ),
+)
+
+visualizer = Visualizer(
+    vis_backends=SwanlabVisBackend(init_kwargs={})
+)  # init args can be found in https://docs.swanlab.cn/zh/guide_cloud/integration/integration-mmengine.html
+
+runner = Runner(
+    model=MMResNet50(),
+    work_dir="./work_dir",
+    train_dataloader=train_dataloader,
+    optim_wrapper=dict(optimizer=dict(type=SGD, lr=0.001, momentum=0.9)),
+    train_cfg=dict(by_epoch=True, max_epochs=5, val_interval=1),
+    val_dataloader=val_dataloader,
+    val_cfg=dict(),
+    val_evaluator=dict(type=Accuracy),
+    visualizer=visualizer,
+)
+runner.train()

test/integration/mmengine/mmengine_visualizer_import.py

@@ -0,0 +1,37 @@
+from mmengine.config import Config
+from mmengine.registry import VISUALIZERS
+import mmengine
+import swanlab
+
+print(f"MMEngine Version: {mmengine.__version__}")
+print(f"SwanLab Version: {swanlab.__version__}")
+
+cfg_text = """
+# swanlab visualizer
+custom_imports = dict(  # 引入SwanLab作为日志记录器，对于部分不支持custom_imports的项目可以直接初始化SwanlabVisBackend并加入vis_backends
+    imports=["swanlab.integration.mmengine"], allow_failed_imports=False
+)
+
+vis_backends = [
+    dict(
+        type="SwanlabVisBackend",
+        init_kwargs={ # swanlab.init 参数
+            "project": "swanlab-mmengine",
+            "experiment_name": "Your exp",  # 实验名称
+            "description": "Note whatever you want",  # 实验的描述信息
+        },
+    ),
+]
+
+visualizer = dict(
+    type="Visualizer",
+    vis_backends=vis_backends,
+    name="visualizer",
+)
+
+"""
+
+cfg = Config.fromstring(cfg_text, ".py")
+
+custom_vis = VISUALIZERS.build(cfg.visualizer)
+print(custom_vis)

test/integration/mmengine/requirements.txt

@@ -0,0 +1,4 @@
+torch
+torchvision
+mmengine
+swanlab