Move contrib metrics files

README.md

@@ -241,7 +241,7 @@ def function_before_backprop(engine):
 ## Out-of-the-box metrics
 
 - [Metrics](https://pytorch.org/ignite/metrics.html#complete-list-of-metrics) for various tasks:
-  Precision, Recall, Accuracy, Confusion Matrix, IoU etc, ~20 [regression metrics](https://pytorch.org/ignite/contrib/metrics.html#regression-metrics).
+  Precision, Recall, Accuracy, Confusion Matrix, IoU etc, ~20 [regression metrics](https://pytorch.org/ignite/metrics.html#complete-list-of-metrics).
 
 - Users can also [compose their metrics](https://pytorch.org/ignite/metrics.html#metric-arithmetics) with ease from
   existing ones using arithmetic operations or torch methods.
@@ -315,24 +315,27 @@ List of available pre-built images
 </summary>
 
 Base
-- `pytorchignite/base:latest` 
+
+- `pytorchignite/base:latest`
 - `pytorchignite/apex:latest`
 - `pytorchignite/hvd-base:latest`
-- `pytorchignite/hvd-apex:latest` 
+- `pytorchignite/hvd-apex:latest`
 - `pytorchignite/msdp-apex:latest`
 
 Vision:
+
 - `pytorchignite/vision:latest`
 - `pytorchignite/hvd-vision:latest`
 - `pytorchignite/apex-vision:latest`
 - `pytorchignite/hvd-apex-vision:latest`
 - `pytorchignite/msdp-apex-vision:latest`
 
 NLP:
+
 - `pytorchignite/nlp:latest`
 - `pytorchignite/hvd-nlp:latest`
-- `pytorchignite/apex-nlp:latest` 
-- `pytorchignite/hvd-apex-nlp:latest` 
+- `pytorchignite/apex-nlp:latest`
+- `pytorchignite/hvd-apex-nlp:latest`
 - `pytorchignite/msdp-apex-nlp:latest`
 
 </details>
@@ -416,8 +419,8 @@ Features:
 ## Code-Generator application
 
 The easiest way to create your training scripts with PyTorch-Ignite:
-- https://code-generator.pytorch-ignite.ai/
 
+- https://code-generator.pytorch-ignite.ai/
 
 <!-- ############################################################################################################### -->
 
@@ -502,7 +505,7 @@ Blog articles, tutorials, books
 - [The Hero Rises: Build Your Own SSD](https://allegro.ai/blog/the-hero-rises-build-your-own-ssd/)
 - [Using Optuna to Optimize PyTorch Ignite Hyperparameters](https://medium.com/pytorch/using-optuna-to-optimize-pytorch-ignite-hyperparameters-626ffe6d4783)
 - [PyTorch Ignite - Classifying Tiny ImageNet with EfficientNet](https://towardsdatascience.com/pytorch-ignite-classifying-tiny-imagenet-with-efficientnet-e5b1768e5e8f)
- 
+
 </details>
 
 <details>
@@ -516,7 +519,7 @@ Toolkits
 - [Nussl - a flexible, object-oriented Python audio source separation library](https://github.com/nussl/nussl)
 - [PyTorch Adapt - A fully featured and modular domain adaptation library](https://github.com/KevinMusgrave/pytorch-adapt)
 - [gnina-torch: PyTorch implementation of GNINA scoring function](https://github.com/RMeli/gnina-torch)
- 
+
 </details>
 
 <details>

docs/source/contrib/handlers.rst

@@ -28,5 +28,5 @@ Time profilers [deprecated]
 Loggers [deprecated]
 --------------------
 
-.. deprecated:: 0.4.14
+.. deprecated:: 0.5.1
     Loggers moved to :ref:`Loggers`.

docs/source/contrib/metrics.rst

@@ -1,56 +1,15 @@
 ignite.contrib.metrics
-======================
+=======================
 
-Contrib module metrics
-----------------------
+Contrib module metrics [deprecated]
+-----------------------------------
 
-.. currentmodule:: ignite.contrib.metrics
+.. deprecated:: 0.5.1
+    All metrics moved to :ref:`Complete list of metrics`.
 
-.. autosummary::
-    :nosignatures:
-    :toctree: ../generated
 
-    AveragePrecision
-    CohenKappa
-    GpuInfo
-    PrecisionRecallCurve
-    ROC_AUC
-    RocCurve
+Regression metrics [deprecated]
+--------------------------------
 
-Regression metrics
-------------------
-
-.. currentmodule:: ignite.contrib.metrics.regression
-
-.. automodule:: ignite.contrib.metrics.regression
-
-
-Module :mod:`ignite.contrib.metrics.regression` provides implementations of
-metrics useful for regression tasks. Definitions of metrics are based on `Botchkarev 2018`_, page 30 "Appendix 2. Metrics mathematical definitions".
-
-.. _`Botchkarev 2018`:
-        https://arxiv.org/abs/1809.03006
-
-Complete list of metrics:
-
-.. currentmodule:: ignite.contrib.metrics.regression
-
-.. autosummary::
-    :nosignatures:
-    :toctree: ../generated
-
-    CanberraMetric
-    FractionalAbsoluteError
-    FractionalBias
-    GeometricMeanAbsoluteError
-    GeometricMeanRelativeAbsoluteError
-    ManhattanDistance
-    MaximumAbsoluteError
-    MeanAbsoluteRelativeError
-    MeanError
-    MeanNormalizedBias
-    MedianAbsoluteError
-    MedianAbsolutePercentageError
-    MedianRelativeAbsoluteError
-    R2Score
-    WaveHedgesDistance
+.. deprecated:: 0.5.1
+    All metrics moved to :ref:`Complete list of metrics`.

docs/source/defaults.rst

@@ -12,9 +12,8 @@
         from ignite.engine import *
         from ignite.handlers import *
         from ignite.metrics import *
+        from ignite.metrics.regression import *
         from ignite.utils import *
-        from ignite.contrib.metrics.regression import *
-        from ignite.contrib.metrics import *
 
         # create default evaluator for doctests
 
@@ -46,4 +45,4 @@
             ('fc', nn.Linear(2, 1))
         ]))
 
-        manual_seed(666)
+        manual_seed(666)

docs/source/metrics.rst

@@ -352,6 +352,35 @@ Complete list of metrics
     FID
     CosineSimilarity
     Entropy
+    AveragePrecision
+    CohenKappa
+    GpuInfo
+    PrecisionRecallCurve
+    RocCurve
+    ROC_AUC
+    regression.CanberraMetric
+    regression.FractionalAbsoluteError
+    regression.FractionalBias
+    regression.GeometricMeanAbsoluteError
+    regression.GeometricMeanRelativeAbsoluteError
+    regression.ManhattanDistance
+    regression.MaximumAbsoluteError
+    regression.MeanAbsoluteRelativeError
+    regression.MeanError
+    regression.MeanNormalizedBias
+    regression.MedianAbsoluteError
+    regression.MedianAbsolutePercentageError
+    regression.MedianRelativeAbsoluteError
+    regression.R2Score
+    regression.WaveHedgesDistance
+
+
+.. note::
+
+    Module ignite.metrics.regression provides implementations of metrics useful
+    for regression tasks. Definitions of metrics are based on
+    `Botchkarev 2018`_, page 30 "Appendix 2. Metrics mathematical definitions".
+
 
 Helpers for customizing metrics
 -------------------------------
@@ -393,3 +422,6 @@ reinit__is_reduced
 sync_all_reduce
 ~~~~~~~~~~~~~~~
 .. autofunction:: sync_all_reduce
+
+.. _`Botchkarev 2018`:
+        https://arxiv.org/abs/1809.03006

examples/mnist/mnist_with_tensorboard_logger.py

@@ -91,7 +91,7 @@ def run(train_batch_size, val_batch_size, epochs, lr, momentum, log_dir):
     trainer.logger = setup_logger("Trainer")
 
     if sys.version_info > (3,):
-        from ignite.contrib.metrics.gpu_info import GpuInfo
+        from ignite.metrics.gpu_info import GpuInfo
 
         try:
             GpuInfo().attach(trainer)

examples/mnist/mnist_with_tqdm_logger.py

@@ -64,7 +64,7 @@ def run(train_batch_size, val_batch_size, epochs, lr, momentum, display_gpu_info
     RunningAverage(output_transform=lambda x: x).attach(trainer, "loss")
 
     if display_gpu_info:
-        from ignite.contrib.metrics import GpuInfo
+        from ignite.metrics import GpuInfo
 
         GpuInfo().attach(trainer, name="gpu")
 

ignite/contrib/engines/common.py

@@ -15,7 +15,6 @@
     from torch.optim.lr_scheduler import _LRScheduler as PyTorchLRScheduler
 
 import ignite.distributed as idist
-from ignite.contrib.metrics import GpuInfo
 from ignite.engine import Engine, Events
 from ignite.handlers import (
     Checkpoint,
@@ -35,7 +34,7 @@
 from ignite.handlers.base_logger import BaseLogger
 from ignite.handlers.checkpoint import BaseSaveHandler
 from ignite.handlers.param_scheduler import ParamScheduler
-from ignite.metrics import RunningAverage
+from ignite.metrics import GpuInfo, RunningAverage
 from ignite.metrics.metric import RunningBatchWise
 from ignite.utils import deprecated
 
@@ -78,14 +77,14 @@ def setup_common_training_handlers(
             exclusive with ``save_handler``.
         lr_scheduler: learning rate scheduler
             as native torch LRScheduler or ignite's parameter scheduler.
-        with_gpu_stats: if True, :class:`~ignite.contrib.metrics.GpuInfo` is attached to the
+        with_gpu_stats: if True, :class:`~ignite.metrics.GpuInfo` is attached to the
             trainer. This requires `pynvml` package to be installed.
         output_names: list of names associated with `update_function` output dictionary.
         with_pbars: if True, two progress bars on epochs and optionally on iterations are attached.
             Default, True.
         with_pbar_on_iters: if True, a progress bar on iterations is attached to the trainer.
             Default, True.
-        log_every_iters: logging interval for :class:`~ignite.contrib.metrics.GpuInfo` and for
+        log_every_iters: logging interval for :class:`~ignite.metrics.GpuInfo` and for
             epoch-wise progress bar. Default, 100.
         stop_on_nan: if True, :class:`~ignite.handlers.terminate_on_nan.TerminateOnNan` handler is added to the trainer.
             Default, True.

ignite/contrib/metrics/__init__.py

@@ -1,6 +1,7 @@
-import ignite.contrib.metrics.regression
-from ignite.contrib.metrics.average_precision import AveragePrecision
-from ignite.contrib.metrics.cohen_kappa import CohenKappa
-from ignite.contrib.metrics.gpu_info import GpuInfo
-from ignite.contrib.metrics.precision_recall_curve import PrecisionRecallCurve
-from ignite.contrib.metrics.roc_auc import ROC_AUC, RocCurve
+import ignite.metrics.regression
+from ignite.metrics import average_precision, cohen_kappa, gpu_info, precision_recall_curve, roc_auc
+from ignite.metrics.average_precision import AveragePrecision
+from ignite.metrics.cohen_kappa import CohenKappa
+from ignite.metrics.gpu_info import GpuInfo
+from ignite.metrics.precision_recall_curve import PrecisionRecallCurve
+from ignite.metrics.roc_auc import ROC_AUC, RocCurve

ignite/contrib/metrics/average_precision.py

@@ -1,81 +1,22 @@
-from typing import Callable, Union
-
-import torch
-
-from ignite.metrics import EpochMetric
-
-
-def average_precision_compute_fn(y_preds: torch.Tensor, y_targets: torch.Tensor) -> float:
-    from sklearn.metrics import average_precision_score
-
-    y_true = y_targets.cpu().numpy()
-    y_pred = y_preds.cpu().numpy()
-    return average_precision_score(y_true, y_pred)
-
-
-class AveragePrecision(EpochMetric):
-    """Computes Average Precision accumulating predictions and the ground-truth during an epoch
-    and applying `sklearn.metrics.average_precision_score <https://scikit-learn.org/stable/modules/generated/
-    sklearn.metrics.average_precision_score.html#sklearn.metrics.average_precision_score>`_ .
-
-    Args:
-        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. This can be useful if, for example, you have a multi-output model and
-            you want to compute the metric with respect to one of the outputs.
-        check_compute_fn: Default False. If True, `average_precision_score
-            <https://scikit-learn.org/stable/modules/generated/sklearn.metrics.average_precision_score.html
-            #sklearn.metrics.average_precision_score>`_ is run on the first batch of data to ensure there are
-            no issues. User will be warned in case there are any issues computing the function.
-        device: optional device specification for internal storage.
-
-    Note:
-        AveragePrecision expects y to be comprised of 0's and 1's. y_pred must either be probability estimates or
-        confidence values. To apply an activation to y_pred, use output_transform as shown below:
-
-        .. code-block:: python
-
-            def activated_output_transform(output):
-                y_pred, y = output
-                y_pred = torch.softmax(y_pred, dim=1)
-                return y_pred, y
-            avg_precision = AveragePrecision(activated_output_transform)
-
-    Examples:
-
-        .. include:: defaults.rst
-            :start-after: :orphan:
-
-        .. testcode::
-
-            y_pred = torch.tensor([[0.79, 0.21], [0.30, 0.70], [0.46, 0.54], [0.16, 0.84]])
-            y_true = torch.tensor([[1, 1], [1, 1], [0, 1], [0, 1]])
-
-            avg_precision = AveragePrecision()
-            avg_precision.attach(default_evaluator, 'average_precision')
-            state = default_evaluator.run([[y_pred, y_true]])
-            print(state.metrics['average_precision'])
-
-        .. testoutput::
-
-            0.9166...
-
-    """
-
-    def __init__(
-        self,
-        output_transform: Callable = lambda x: x,
-        check_compute_fn: bool = False,
-        device: Union[str, torch.device] = torch.device("cpu"),
-    ):
-        try:
-            from sklearn.metrics import average_precision_score  # noqa: F401
-        except ImportError:
-            raise ModuleNotFoundError("This contrib module requires scikit-learn to be installed.")
-
-        super(AveragePrecision, self).__init__(
-            average_precision_compute_fn,
-            output_transform=output_transform,
-            check_compute_fn=check_compute_fn,
-            device=device,
-        )
+""" ``ignite.contrib.metrics.average_precision`` was moved to ``ignite.metrics.average_precision``.
+Note:
+    ``ignite.contrib.metrics.average_precision`` was moved to ``ignite.metrics.average_precision``.
+    Please refer to :mod:`~ignite.metrics.average_precision`.
+"""
+
+import warnings
+
+removed_in = "0.6.0"
+deprecation_warning = (
+    f"{__file__} has been moved to /ignite/metrics/average_precision.py"
+    + (f" and will be removed in version {removed_in}" if removed_in else "")
+    + ".\n Please refer to the documentation for more details."
+)
+warnings.warn(deprecation_warning, DeprecationWarning, stacklevel=2)
+from ignite.metrics.average_precision import AveragePrecision
+
+__all__ = [
+    "AveragePrecision",
+]
+
+AveragePrecision = AveragePrecision