ignite.metrics
==============

Metrics provide a way to compute various quantities of interest in an online
fashion without having to store the entire output history of a model.

In practice a user needs to attach the metric instance to an engine. The metric
value is then computed using the output of the engine's ``process_function``:

.. code-block:: python

    from ignite.engine import Engine
    from ignite.metrics import Accuracy

    def process_function(engine, batch):
        # ...
        return y_pred, y

    engine = Engine(process_function)
    metric = Accuracy()
    metric.attach(engine, "accuracy")
    # ...
    state = engine.run(data)
    print(f"Accuracy: {state.metrics['accuracy']}")


If the engine's output is not in the format ``(y_pred, y)`` or ``{'y_pred': y_pred, 'y': y, ...}``, the user can
use the ``output_transform`` argument to transform it:

.. code-block:: python

    from ignite.engine import Engine
    from ignite.metrics import Accuracy

    def process_function(engine, batch):
        # ...
        return {'y_pred': y_pred, 'y_true': y, ...}

    engine = Engine(process_function)

    def output_transform(output):
        # `output` variable is returned by above `process_function`
        y_pred = output['y_pred']
        y = output['y_true']
        return y_pred, y  # output format is according to `Accuracy` docs

    metric = Accuracy(output_transform=output_transform)
    metric.attach(engine, "accuracy")
    # ...
    state = engine.run(data)
    print(f"Accuracy: {state.metrics['accuracy']}")


.. warning::

    Please, be careful when using ``lambda`` functions to setup multiple ``output_transform`` for multiple metrics
    
    .. code-block:: python

        # Wrong
        # metrics_group = [Accuracy(output_transform=lambda output: output[name]) for name in names]
        # As lambda can not store `name` and all `output_transform` will use the last `name`
        
        # A correct way. For example, using functools.partial
        from functools import partial

        def ot_func(output, name):
            return output[name]

        metrics_group = [Accuracy(output_transform=partial(ot_func, name=name)) for name in names]
    
    For more details, see `here <https://discuss.pytorch.org/t/evaluate-multiple-models-with-one-evaluator-results-weird-metrics/96695>`_

.. Note ::

    Most of implemented metrics are adapted to distributed computations and reduce their internal states across supported
    devices before computing metric value. This can be helpful to run the evaluation on multiple nodes/GPU instances/TPUs
    with a distributed data sampler. Following code snippet shows in detail how to use metrics:

    .. code-block:: python

        device = f"cuda:{local_rank}"
        model = torch.nn.parallel.DistributedDataParallel(model,
                                                          device_ids=[local_rank, ],
                                                          output_device=local_rank)
        test_sampler = DistributedSampler(test_dataset)
        test_loader = DataLoader(
            test_dataset,
            batch_size=batch_size,
            sampler=test_sampler,
            num_workers=num_workers,
            pin_memory=True
        )

        evaluator = create_supervised_evaluator(model, metrics={'accuracy': Accuracy()}, device=device)

.. Note ::

   Metrics cannot be serialized using `pickle` module because the implementation is based on lambda functions.
   Therefore, use the third party library `dill` to overcome the limitation of `pickle`.


Reset, Update, Compute API
--------------------------

User can also call directly the following methods on the metric:

- :meth:`~ignite.metrics.Metric.reset()` : resets internal variables and accumulators
- :meth:`~ignite.metrics.Metric.update()` : updates internal variables and accumulators with provided batch output ``(y_pred, y)``
- :meth:`~ignite.metrics.Metric.compute()` : computes custom metric and return the result

This API gives a more fine-grained/custom usage on how to compute a metric. For example:

.. code-block:: python

    from ignite.metrics import Precision

    # Define the metric
    precision = Precision()

    # Start accumulation:
    for x, y in data:
        y_pred = model(x)
        precision.update((y_pred, y))

    # Compute the result
    print("Precision: ", precision.compute())

    # Reset metric 
    precision.reset()

    # Start new accumulation:
    for x, y in data:
        y_pred = model(x)
        precision.update((y_pred, y))

    # Compute new result
    print("Precision: ", precision.compute())


Metric arithmetics
------------------

Metrics could be combined together to form new metrics. This could be done through arithmetics, such
as ``metric1 + metric2``, use PyTorch operators, such as ``(metric1 + metric2).pow(2).mean()``,
or use a lambda function, such as ``MetricsLambda(lambda a, b: torch.mean(a + b), metric1, metric2)``.

For example:

.. code-block:: python

    from ignite.metrics import Precision, Recall

    precision = Precision(average=False)
    recall = Recall(average=False)
    F1 = (precision * recall * 2 / (precision + recall)).mean()

.. note::  This example computes the mean of F1 across classes. To combine
    precision and recall to get F1 or other F metrics, we have to be careful
    that ``average=False``, i.e. to use the unaveraged precision and recall,
    otherwise we will not be computing F-beta metrics.

Metrics also support indexing operation (if metric's result is a vector/matrix/tensor). For example, this can be useful to compute mean metric (e.g. precision, recall or IoU) ignoring the background:

.. code-block:: python

    from ignite.metrics import ConfusionMatrix

    cm = ConfusionMatrix(num_classes=10)
    iou_metric = IoU(cm)
    iou_no_bg_metric = iou_metric[:9]  # We assume that the background index is 9
    mean_iou_no_bg_metric = iou_no_bg_metric.mean()
    # mean_iou_no_bg_metric.compute() -> tensor(0.12345)

How to create a custom metric
-----------------------------

To create a custom metric one needs to create a new class inheriting from :class:`~ignite.metrics.Metric` and override
three methods :

- :meth:`~ignite.metrics.Metric.reset()` : resets internal variables and accumulators
- :meth:`~ignite.metrics.Metric.update()` : updates internal variables and accumulators with provided batch output ``(y_pred, y)``
- :meth:`~ignite.metrics.Metric.compute()` : computes custom metric and return the result

For example, we would like to implement for illustration purposes a multi-class accuracy metric with some
specific condition (e.g. ignore user-defined classes):

.. code-block:: python

    from ignite.metrics import Metric
    from ignite.exceptions import NotComputableError

    # These decorators helps with distributed settings
    from ignite.metrics.metric import sync_all_reduce, reinit__is_reduced


    class CustomAccuracy(Metric):

        def __init__(self, ignored_class, output_transform=lambda x: x, device="cpu"):
            self.ignored_class = ignored_class
            self._num_correct = None
            self._num_examples = None
            super(CustomAccuracy, self).__init__(output_transform=output_transform, device=device)

        @reinit__is_reduced
        def reset(self):
            self._num_correct = torch.tensor(0, device=self._device)
            self._num_examples = 0
            super(CustomAccuracy, self).reset()

        @reinit__is_reduced
        def update(self, output):
            y_pred, y = output[0].detach(), output[1].detach()

            indices = torch.argmax(y_pred, dim=1)

            mask = (y != self.ignored_class)
            mask &= (indices != self.ignored_class)
            y = y[mask]
            indices = indices[mask]
            correct = torch.eq(indices, y).view(-1)

            self._num_correct += torch.sum(correct).to(self._device)
            self._num_examples += correct.shape[0]

        @sync_all_reduce("_num_examples", "_num_correct")
        def compute(self):
            if self._num_examples == 0:
                raise NotComputableError('CustomAccuracy must have at least one example before it can be computed.')
            return self._num_correct.item() / self._num_examples


We imported necessary classes as :class:`~ignite.metrics.Metric`, :class:`~ignite.exceptions.NotComputableError` and
decorators to adapt the metric for distributed setting. In ``reset`` method, we reset internal variables ``_num_correct``
and ``_num_examples`` which are used to compute the custom metric. In ``updated`` method we define how to update
the internal variables. And finally in ``compute`` method, we compute metric value.

Notice that ``_num_correct`` is a tensor, since in ``update`` we accumulate tensor values. ``_num_examples`` is a python
scalar since we accumulate normal integers. For differentiable metrics, you must detach the accumulated values before
adding them to the internal variables.

We can check this implementation in a simple case:

.. code-block:: python

    import torch
    torch.manual_seed(8)

    m = CustomAccuracy(ignored_class=3)

    batch_size = 4
    num_classes = 5

    y_pred = torch.rand(batch_size, num_classes)
    y = torch.randint(0, num_classes, size=(batch_size, ))

    m.update((y_pred, y))
    res = m.compute()

    print(y, torch.argmax(y_pred, dim=1))
    # Out: tensor([2, 2, 2, 3]) tensor([2, 1, 0, 0])

    print(m._num_correct, m._num_examples, res)
    # Out: 1 3 0.3333333333333333

Metrics and its usages
----------------------

By default, `Metrics` are epoch-wise, it means

- :meth:`~ignite.metrics.Metric.reset()` is triggered every ``EPOCH_STARTED`` (See :class:`~ignite.engine.events.Events`).
- :meth:`~ignite.metrics.Metric.update()` is triggered every ``ITERATION_COMPLETED``.
- :meth:`~ignite.metrics.Metric.compute()` is triggered every ``EPOCH_COMPLETED``.

Usages can be user defined by creating a class inheriting for :class:`~ignite.metrics.MetricUsage`. See the list below of usages.

Complete list of usages
```````````````````````

    - :class:`~ignite.metrics.MetricUsage`
    - :class:`~ignite.metrics.EpochWise`
    - :class:`~ignite.metrics.BatchWise`
    - :class:`~ignite.metrics.BatchFiltered`

Metrics and distributed computations
------------------------------------

In the above example, ``CustomAccuracy`` has ``reset``, ``update``, ``compute`` methods decorated
with :meth:`~ignite.metrics.metric.reinit__is_reduced`, :meth:`~ignite.metrics.metric.sync_all_reduce`. The purpose of these features is to adapt metrics in distributed
computations on supported backend and devices (see :doc:`distributed` for more details). More precisely, in the above
example we added ``@sync_all_reduce("_num_examples", "_num_correct")`` over ``compute`` method. This means that when ``compute``
method is called, metric's interal variables ``self._num_examples`` and ``self._num_correct`` are summed up over all participating
devices. Therefore, once collected, these internal variables can be used to compute the final metric value.

Complete list of metrics
------------------------

.. currentmodule:: ignite.metrics

.. autosummary::
    :nosignatures:
    :autolist:

.. autoclass:: Accuracy

.. autoclass:: Average

.. autoclass:: ConfusionMatrix

.. autofunction:: DiceCoefficient

.. autoclass:: EpochMetric

.. autofunction:: Fbeta

.. autoclass:: GeometricAverage

.. autofunction:: IoU

.. autofunction:: mIoU

.. autoclass:: Loss

.. autoclass:: MeanAbsoluteError

.. autoclass:: MeanPairwiseDistance

.. autoclass:: MeanSquaredError

.. autoclass:: Metric
    :members:

.. autoclass:: MetricsLambda

.. autoclass:: PSNR

.. autoclass:: Precision

.. autoclass:: Recall

.. autoclass:: RootMeanSquaredError

.. autoclass:: RunningAverage

.. autoclass:: SSIM

.. autoclass:: TopKCategoricalAccuracy

.. autoclass:: VariableAccumulation

.. autoclass:: MetricUsage

.. autoclass:: EpochWise

.. autoclass:: BatchWise

.. autoclass:: BatchFiltered


.. currentmodule:: ignite.metrics.metric

.. autofunction:: reinit__is_reduced

.. autofunction:: sync_all_reduce

