Add MetricGroup feature

docs/source/metrics.rst

@@ -335,6 +335,7 @@ Complete list of metrics
     MeanPairwiseDistance
     MeanSquaredError
     metric.Metric
+    metric_group.MetricGroup
     metrics_lambda.MetricsLambda
     MultiLabelConfusionMatrix
     MutualInformation

ignite/metrics/__init__.py

@@ -22,6 +22,7 @@
 from ignite.metrics.mean_pairwise_distance import MeanPairwiseDistance
 from ignite.metrics.mean_squared_error import MeanSquaredError
 from ignite.metrics.metric import BatchFiltered, BatchWise, EpochWise, Metric, MetricUsage
+from ignite.metrics.metric_group import MetricGroup
 from ignite.metrics.metrics_lambda import MetricsLambda
 from ignite.metrics.multilabel_confusion_matrix import MultiLabelConfusionMatrix
 from ignite.metrics.mutual_information import MutualInformation
@@ -41,6 +42,7 @@
     "Metric",
     "Accuracy",
     "Loss",
+    "MetricGroup",
     "MetricsLambda",
     "MeanAbsoluteError",
     "MeanPairwiseDistance",

ignite/metrics/metric_group.py

@@ -0,0 +1,51 @@
+from typing import Any, Callable, Dict, Sequence
+
+import torch
+
+from ignite.metrics import Metric
+
+
+class MetricGroup(Metric):
+    """
+    A class for grouping metrics so that user could manage them easier.
+
+    Args:
+        metrics: a dictionary of names to metric instances.
+        output_transform: a callable that is used to transform the
+            :class:`~ignite.engine.engine.Engine`'s ``process_function``'s output into the
+            form expected by the metric. `output_transform` of each metric in the group is also
+            called upon its update.
+
+    Examples:
+        We construct a group of metrics, attach them to the engine at once and retrieve their result.
+
+        .. code-block:: python
+            metric_group = MetricGroup({'acc': Accuracy(), 'precision': Precision(), 'loss': Loss(nn.NLLLoss())})
+            metric_group.attach(default_evaluator, "eval_metrics")
+            y_true = torch.tensor([1, 0, 1, 1, 0, 1])
+            y_pred = torch.tensor([1, 0, 1, 0, 1, 1])
+            state = default_evaluator.run([[y_pred, y_true]])
+
+            # Metrics individually available in `state.metrics`
+            state.metrics["acc"], state.metrics["precision"], state.metrics["loss"]
+
+            # And also altogether
+            state.metrics["eval_metrics]
+    """
+
+    _state_dict_all_req_keys = ("metrics",)
+
+    def __init__(self, metrics: Dict[str, Metric], output_transform: Callable = lambda x: x):
+        self.metrics = metrics
+        super(MetricGroup, self).__init__(output_transform=output_transform)
+
+    def reset(self):
+        for m in self.metrics.values():
+            m.reset()
+
+    def update(self, output: Sequence[torch.Tensor]):
+        for m in self.metrics.values():
+            m.update(m._output_transform(output))
+
+    def compute(self) -> Dict[str, Any]:
+        return {k: m.compute() for k, m in self.metrics.items()}

tests/ignite/metrics/test_metric_group.py

@@ -0,0 +1,118 @@
+import pytest
+import torch
+
+from ignite import distributed as idist
+from ignite.engine import Engine
+from ignite.metrics import Accuracy, MetricGroup, Precision
+
+torch.manual_seed(41)
+
+
+def test_update():
+    precision = Precision()
+    accuracy = Accuracy()
+
+    group = MetricGroup({"precision": Precision(), "accuracy": Accuracy()})
+
+    y_pred = torch.randint(0, 2, (100,))
+    y = torch.randint(0, 2, (100,))
+
+    precision.update((y_pred, y))
+    accuracy.update((y_pred, y))
+    group.update((y_pred, y))
+
+    assert precision.state_dict() == group.metrics["precision"].state_dict()
+    assert accuracy.state_dict() == group.metrics["accuracy"].state_dict()
+
+
+def test_output_transform():
+    def drop_first(output):
+        y_pred, y = output
+        return (y_pred[1:], y[1:])
+
+    precision = Precision(output_transform=drop_first)
+    accuracy = Accuracy(output_transform=drop_first)
+
+    group = MetricGroup(
+        {"precision": Precision(output_transform=drop_first), "accuracy": Accuracy(output_transform=drop_first)}
+    )
+
+    y_pred = torch.randint(0, 2, (100,))
+    y = torch.randint(0, 2, (100,))
+
+    precision.update(drop_first(drop_first((y_pred, y))))
+    accuracy.update(drop_first(drop_first((y_pred, y))))
+    group.update(drop_first((y_pred, y)))
+
+    assert precision.state_dict() == group.metrics["precision"].state_dict()
+    assert accuracy.state_dict() == group.metrics["accuracy"].state_dict()
+
+
+def test_compute():
+    precision = Precision()
+    accuracy = Accuracy()
+
+    group = MetricGroup({"precision": Precision(), "accuracy": Accuracy()})
+
+    for _ in range(3):
+        y_pred = torch.randint(0, 2, (100,))
+        y = torch.randint(0, 2, (100,))
+
+        precision.update((y_pred, y))
+        accuracy.update((y_pred, y))
+        group.update((y_pred, y))
+
+    assert group.compute() == {"precision": precision.compute(), "accuracy": accuracy.compute()}
+
+    precision.reset()
+    accuracy.reset()
+    group.reset()
+
+    assert precision.state_dict() == group.metrics["precision"].state_dict()
+    assert accuracy.state_dict() == group.metrics["accuracy"].state_dict()
+
+
+@pytest.mark.usefixtures("distributed")
+class TestDistributed:
+    def test_integration(self):
+        rank = idist.get_rank()
+        torch.manual_seed(12 + rank)
+
+        n_epochs = 3
+        n_iters = 5
+        batch_size = 10
+        device = idist.device()
+
+        y_true = torch.randint(0, 2, size=(n_iters * batch_size,)).to(device)
+        y_pred = torch.randint(0, 2, (n_iters * batch_size,)).to(device)
+
+        def update(_, i):
+            return (
+                y_pred[i * batch_size : (i + 1) * batch_size],
+                y_true[i * batch_size : (i + 1) * batch_size],
+            )
+
+        engine = Engine(update)
+
+        precision = Precision()
+        precision.attach(engine, "precision")
+
+        accuracy = Accuracy()
+        accuracy.attach(engine, "accuracy")
+
+        group = MetricGroup({"eval_metrics.accuracy": Accuracy(), "eval_metrics.precision": Precision()})
+        group.attach(engine, "eval_metrics")
+
+        data = list(range(n_iters))
+        engine.run(data=data, max_epochs=n_epochs)
+
+        assert "eval_metrics" in engine.state.metrics
+        assert "eval_metrics.accuracy" in engine.state.metrics
+        assert "eval_metrics.precision" in engine.state.metrics
+
+        assert engine.state.metrics["eval_metrics"] == {
+            "eval_metrics.accuracy": engine.state.metrics["accuracy"],
+            "eval_metrics.precision": engine.state.metrics["precision"],
+        }
+        assert engine.state.metrics["eval_metrics.accuracy"] == engine.state.metrics["accuracy"]
+        assert engine.state.metrics["eval_metrics.precision"] == engine.state.metrics["precision"]