fix tests

Signed-off-by: Zhiyuan Chen <[email protected]>

.gitignore

@@ -416,6 +416,7 @@ danling/_version.py
 # pytest
 data
 examples
+.coverage*
 
 # experiments
 experiments

danling/metrics/average_meter.py

@@ -219,41 +219,26 @@ class MultiTaskAverageMeters(MultiTaskDict):
     Examples:
         >>> meters = MultiTaskAverageMeters()
         >>> meters.update({"loss": 0.6, "dataset1.cls.auroc": 0.7, "dataset1.reg.r2": 0.8, "dataset2.r2": 0.9})
-        >>> print(f"{meters:.4f}")
-        loss: 0.6000 (0.6000)
-        dataset1.cls.auroc: 0.7000 (0.7000)
-        dataset1.reg.r2: 0.8000 (0.8000)
-        dataset2.r2: 0.9000 (0.9000)
+        >>> f"{meters:.4f}"
+        'loss: 0.6000 (0.6000)\ndataset1.cls.auroc: 0.7000 (0.7000)\ndataset1.reg.r2: 0.8000 (0.8000)\ndataset2.r2: 0.9000 (0.9000)'
         >>> meters['loss'].update(0.9, n=1)
-        >>> print(f"{meters:.4f}")
-        loss: 0.9000 (0.7500)
-        dataset1.cls.auroc: 0.7000 (0.7000)
-        dataset1.reg.r2: 0.8000 (0.8000)
-        dataset2.r2: 0.9000 (0.9000)
+        >>> f"{meters:.4f}"
+        'loss: 0.9000 (0.7500)\ndataset1.cls.auroc: 0.7000 (0.7000)\ndataset1.reg.r2: 0.8000 (0.8000)\ndataset2.r2: 0.9000 (0.9000)'
         >>> meters.sum.dict()
         {'loss': 1.5, 'dataset1': {'cls': {'auroc': 0.7}, 'reg': {'r2': 0.8}}, 'dataset2': {'r2': 0.9}}
         >>> meters.count.dict()
         {'loss': 2, 'dataset1': {'cls': {'auroc': 1}, 'reg': {'r2': 1}}, 'dataset2': {'r2': 1}}
         >>> meters.reset()
-        >>> print(f"{meters:.4f}")
-        loss: 0.0000 (nan)
-        dataset1.cls.auroc: 0.0000 (nan)
-        dataset1.reg.r2: 0.0000 (nan)
-        dataset2.r2: 0.0000 (nan)
+        >>> f"{meters:.4f}"
+        'loss: 0.0000 (nan)\ndataset1.cls.auroc: 0.0000 (nan)\ndataset1.reg.r2: 0.0000 (nan)\ndataset2.r2: 0.0000 (nan)'
         >>> meters = MultiTaskAverageMeters(return_average=True)
         >>> meters.update({"loss": 0.6, "dataset1.a.auroc": 0.7, "dataset1.b.auroc": 0.8, "dataset2.auroc": 0.9})
-        >>> print(f"{meters:.4f}")
-        loss: 0.6000 (0.6000)
-        dataset1.a.auroc: 0.7000 (0.7000)
-        dataset1.b.auroc: 0.8000 (0.8000)
-        dataset2.auroc: 0.9000 (0.9000)
+        >>> f"{meters:.4f}"
+        'loss: 0.6000 (0.6000)\ndataset1.a.auroc: 0.7000 (0.7000)\ndataset1.b.auroc: 0.8000 (0.8000)\ndataset2.auroc: 0.9000 (0.9000)'
         >>> meters.update({"loss": 0.9, "dataset1.a.auroc": 0.8, "dataset1.b.auroc": 0.9, "dataset2.auroc": 1.0})
-        >>> print(f"{meters:.4f}")
-        loss: 0.9000 (0.7500)
-        dataset1.a.auroc: 0.8000 (0.7500)
-        dataset1.b.auroc: 0.9000 (0.8500)
-        dataset2.auroc: 1.0000 (0.9500)
-    """
+        >>> f"{meters:.4f}"
+        'loss: 0.9000 (0.7500)\ndataset1.a.auroc: 0.8000 (0.7500)\ndataset1.b.auroc: 0.9000 (0.8500)\ndataset2.auroc: 1.0000 (0.9500)'
+    """  # noqa: E501
 
     @property
     def sum(self) -> NestedDict[str, float]:

danling/metrics/metric_meter.py

@@ -136,9 +136,9 @@ class MetricMeters(AverageMeters):
     >>> meters['auroc'].update([0.4, 0.8, 0.6, 0.2], [0, 1, 1, 0])
     >>> meters.avg.dict()
     {'acc': 0.6, 'auroc': 0.775, 'auprc': 0.55}
-    >>> meters.update(dict(loss=""))
+    >>> meters.update(dict(loss=""))  # doctest: +ELLIPSIS
     Traceback (most recent call last):
-    TypeError: MetricMeters.update() missing 1 required positional argument: 'target'
+    TypeError: ...update() missing 1 required positional argument: 'target'
     """
 
     ignored_index: Optional[int] = None
@@ -200,11 +200,11 @@ class MultiTaskMetricMeters(MultiTaskAverageMeters):
           )
           ('dataset2'): MetricMeters('acc',)
         )
-        >>> metrics.update({"dataset1.cls": {"input": [0.2, 0.4, 0.5, 0.7], "target": [0, 1, 0, 1]}, "dataset2": {"input": [0.1, 0.4, 0.6, 0.8], "target": [1, 0, 0, 0]}})
+        >>> metrics.update({"dataset1.cls": {"input": [0.2, 0.4, 0.5, 0.7], "target": [0, 1, 0, 1]}, "dataset2": ([0.1, 0.4, 0.6, 0.8], [1, 0, 0, 0])})
         >>> f"{metrics:.4f}"
         'dataset1.cls: acc: 0.5000 (0.5000)\ndataset2: acc: 0.2500 (0.2500)'
         >>> metrics.setattr("return_average", True)
-        >>> metrics.update({"dataset1.cls": {"input": [0.1, 0.4, 0.6, 0.8], "target": [0, 0, 1, 0]}, "dataset2": {"input": [0.2, 0.3, 0.5, 0.7], "target": [0, 0, 0, 1]}})
+        >>> metrics.update({"dataset1.cls": [[0.1, 0.4, 0.6, 0.8], [0, 0, 1, 0]], "dataset2": {"input": [0.2, 0.3, 0.5, 0.7], "target": [0, 0, 0, 1]}})
         >>> f"{metrics:.4f}"
         'dataset1.cls: acc: 0.7500 (0.6250)\ndataset2: acc: 0.7500 (0.5000)'
     """  # noqa: E501
@@ -225,21 +225,31 @@ def update(  # type: ignore[override] # pylint: disable=W0221
         """
 
         for metric, value in values.items():
-            if isinstance(value, Mapping):
+            if isinstance(value, (Mapping, Sequence)):
                 if metric not in self:
                     raise ValueError(f"Metric {metric} not found in {self}")
                 if isinstance(self[metric], MultiTaskMetricMeters):
                     for met in self[metric].all_values():
-                        met.update(*value)
+                        if isinstance(value, Mapping):
+                            met.update(**value)
+                        elif isinstance(value, Sequence):
+                            met.update(*value)
+                        else:
+                            raise ValueError(f"Expected value to be a Mapping or Sequence, but got {type(value)}")
                 elif isinstance(self[metric], (MetricMeters, MetricMeter)):
-                    self[metric].update(*value)
+                    if isinstance(value, Mapping):
+                        self[metric].update(**value)
+                    elif isinstance(value, Sequence):
+                        self[metric].update(*value)
+                    else:
+                        raise ValueError(f"Expected value to be a Mapping or Sequence, but got {type(value)}")
                 else:
                     raise ValueError(
                         f"Expected {metric} to be an instance of MultiTaskMetricMeters, MetricMeters, "
                         "or MetricMeter, but got {type(self[metric])}"
                     )
             else:
-                raise ValueError(f"Expected values to be a flat dictionary, but got {type(value)}")
+                raise ValueError(f"Expected values to be a Mapping or Sequence, but got {type(value)}")
 
     # MultiTaskAverageMeters.get is hacked
     def get(self, name: Any, default=None) -> Any:

danling/optim/lr_scheduler/lr_scheduler.py

@@ -73,7 +73,14 @@ class LRScheduler(lr_scheduler._LRScheduler):  # pylint: disable=protected-acces
         ...     scheduler.step()
         >>> [round(lr, 10) for lr in lrs]
         [0.3330753446, 0.0187302031, 0.000533897, 3.00232e-05, 1e-09]
-    """
+        >>> scheduler = LRScheduler(optimizer, total_steps=5, final_lr_ratio=1e-5, strategy='linear', method='numerical')
+        >>> lrs = []
+        >>> for epoch in range(5):
+        ...     lrs.append(scheduler.get_lr()[0])
+        ...     scheduler.step()
+        >>> [round(lr, 2) for lr in lrs]
+        [0.8, 0.6, 0.4, 0.2, 0.0]
+    """  # noqa: E501
 
     def __init__(
         self,

danling/runner/accelerate_runner.py

@@ -202,6 +202,8 @@ def train_epoch(self, split: str = "train") -> NestedDict:
                 interval = iteration - last_print_iteration
                 if self.device == torch.device("cuda"):
                     torch.cuda.synchronize()
+                if self.scheduler is not None:
+                    self.meters.lr.update(self.scheduler.get_last_lr()[0])
                 self.meters.time.update((time() - batch_time) / interval)
                 batch_time = time()
                 reduced_loss = self.reduce(loss).item()

demo/vision/torch_mnist.py

@@ -46,6 +46,7 @@ def __init__(self):
         self.optim.weight_decay = 1e-4
 
     def post(self):
+        self.copy_class_attributes()
         self.experiment_name = f"{self.network.name}_{self.optim.name}@{self.optim.lr}"
 
 
@@ -65,6 +66,8 @@ def __init__(self, config: Config):
         self.model = getattr(torchvision.models, self.network.name)(pretrained=False, num_classes=10)
         self.model.conv1 = nn.Conv2d(1, 64, 1, bias=False)
         self.optimizer = OPTIMIZERS.build(params=self.model.parameters(), **self.optim)
+        train_steps = len(self.datasets.train) // self.dataloader.batch_size * (self.epoch_end - self.epoch_begin)
+        self.scheduler = dl.optim.lr_scheduler.LRScheduler(self.optimizer, total_steps=train_steps)
         self.criterion = nn.CrossEntropyLoss()
 
         self.metrics = dl.metrics.multiclass_metrics(num_classes=10)

pyproject.toml

@@ -42,7 +42,7 @@ dynamic = [
 ]
 dependencies = [
   "cached-property; python_version<'3.8'",
-  "chanfig>=0.0.96",
+  "chanfig>=0.0.96,!=0.0.101,!=0.0.102",
   "gitpython",
   "lazy-imports",
   "strenum; python_version<'3.11'",