1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
|
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
import torch
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) -> None:
for m in self.metrics.values():
m.reset()
def update(self, output: Sequence[torch.Tensor]) -> None:
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()}
|
