releasing 1.7.0 (#3023)

* releasing `1.7.0`
* fix tests for tracker
* fix docs

---------

Co-authored-by: Nicki Skafte <skaftenicki@gmail.com>

Author: Borda

CHANGELOG.md

@@ -6,47 +6,34 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 **Note: we move fast, but still we preserve 0.1 version (one feature release) back compatibility.**
 
+
 ---
 
-## [UnReleased] - 2024-MM-DD
+## [1.7.0] - 2025-03-20
 
 ### Added
 
+- Additions to image domain:
+  - Added `ARNIQA` metric ([#2953](https://github.com/Lightning-AI/torchmetrics/pull/2953))
+  - Added `DeepImageStructureAndTextureSimilarity` ([#2993](https://github.com/Lightning-AI/torchmetrics/pull/2993))
+  - Added support for more models and processors in `CLIPScore` ([#2978](https://github.com/Lightning-AI/torchmetrics/pull/2978))
 - Added `JensenShannonDivergence` metric to regression package ([#2992](https://github.com/Lightning-AI/torchmetrics/pull/2992))
-
-
 - Added `ClusterAccuracy` metric to cluster package ([#2777](https://github.com/Lightning-AI/torchmetrics/pull/2777))
-
-
-- Added `ARNIQA` metric to image domain ([#2953](https://github.com/PyTorchLightning/metrics/pull/2953))
-
-
-- Added support for more models and processors in `CLIPScore` ([#2978](https://github.com/PyTorchLightning/metrics/pull/2978))
-
-
-- Added `DeepImageStructureAndTextureSimilarity` to image package ([#2993](https://github.com/PyTorchLightning/metrics/pull/2993))
-
-
-- Added `Equal Error Rate (EER)` to classification package ([#3013](https://github.com/PyTorchLightning/metrics/pull/3013))
-
-
+- Added `Equal Error Rate (EER)` to classification package ([#3013](https://github.com/Lightning-AI/torchmetrics/pull/3013))
 - Added functional interface to `MeanAveragePrecision` metric ([#3011](https://github.com/Lightning-AI/torchmetrics/pull/3011))
 
-
 ### Changed
 
 - Making `num_classes` optional for `one-hot` inputs in `MeanIoU` ([#3012](https://github.com/Lightning-AI/torchmetrics/pull/3012))
 
-
 ### Removed
 
-- Removed `Dice` from classification ([#3017](https://github.com/PyTorchLightning/metrics/pull/3017))
-
+- Removed `Dice` from classification ([#3017](https://github.com/Lightning-AI/torchmetrics/pull/3017))
 
 ### Fixed
 
-- Fix edge case in integration between classwise wrapper and metric tracker ([#3008](https://github.com/Lightning-AI/torchmetrics/pull/3008))
-- Fix IndexError in MultiClassAccuracy when using top_k with single sample  ([#3021](https://github.com/Lightning-AI/torchmetrics/pull/3021))
+- Fixed edge case in integration between class-wise wrapper and metric tracker ([#3008](https://github.com/Lightning-AI/torchmetrics/pull/3008))
+- Fixed `IndexError` in `MultiClassAccuracy` when using `top_k` with single sample  ([#3021](https://github.com/Lightning-AI/torchmetrics/pull/3021))
 
 ---
 

docs/source/pages/plotting.rst

@@ -224,7 +224,7 @@ to rely on ``MetricTracker`` to keep track of the metrics over multiple steps.
     )
 
     # Define tracker over the collection to easy keep track of the metrics over multiple steps
-    tracker = torchmetrics.wrappers.MetricTracker(collection)
+    tracker = torchmetrics.wrappers.MetricTracker(collection, maximize=True)
 
     # Run "training" loop
     for step in range(num_steps):

docs/source/pyplots/tracker_binary.py

@@ -20,7 +20,8 @@
         torchmetrics.Precision(task="binary"),
         confmat,
         roc,
-    )
+    ),
+    maximize=True,
 )
 
 fig = plt.figure(layout="constrained", figsize=(6.8, 4.8), dpi=500)

src/torchmetrics/__about__.py

@@ -1,4 +1,4 @@
-__version__ = "1.7.0dev"
+__version__ = "1.7.0"
 __author__ = "Lightning-AI et al."
 __author_email__ = "name@pytorchlightning.ai"
 __license__ = "Apache-2.0"

src/torchmetrics/clustering/davies_bouldin_score.py

@@ -119,7 +119,7 @@ def plot(self, val: Union[Tensor, Sequence[Tensor], None] = None, ax: Optional[_
             >>> import torch
             >>> from torchmetrics.clustering import DaviesBouldinScore
             >>> metric = DaviesBouldinScore()
-            >>> metric.update(torch.randn(10, 3), torch.randint(0, 2, (10,)))
+            >>> metric.update(torch.randn(20, 3), torch.randint(0, 2, (20,)))
             >>> fig_, ax_ = metric.plot(metric.compute())
 
         .. plot::
@@ -131,7 +131,7 @@ def plot(self, val: Union[Tensor, Sequence[Tensor], None] = None, ax: Optional[_
             >>> metric = DaviesBouldinScore()
             >>> values = [ ]
             >>> for _ in range(10):
-            ...     values.append(metric(torch.randn(10, 3), torch.randint(0, 2, (10,))))
+            ...     values.append(metric(torch.randn(20, 3), torch.randint(0, 2, (20,))))
             >>> fig_, ax_ = metric.plot(values)
 
         """

src/torchmetrics/wrappers/tracker.py

@@ -108,9 +108,7 @@ class MetricTracker(ModuleList):
     maximize: Union[bool, list[bool]]
     _base_metric: Union[Metric, MetricCollection]
 
-    def __init__(
-        self, metric: Union[Metric, MetricCollection], maximize: Optional[Union[bool, list[bool]]] = True
-    ) -> None:
+    def __init__(self, metric: Union[Metric, MetricCollection], maximize: Union[bool, list[bool], None] = None) -> None:
         super().__init__()
         if not isinstance(metric, (Metric, MetricCollection)):
             raise TypeError(
@@ -140,14 +138,10 @@ def __init__(
                         m_higher_is_better = [m.higher_is_better]
                     self.maximize.extend(m_higher_is_better)  # type: ignore[arg-type]  # this is false alarm
         else:
-            rank_zero_warn(
-                "The default value for `maximize` will be changed from `True` to `None` in v1.7.0 of TorchMetrics,"
-                "will automatically infer the value based on the `higher_is_better` attribute of the metric"
-                " (if such attribute exists) or raise an error if it does not. If you are explicitly setting the"
-                " `maximize` argument to either `True` or `False` already, you can ignore this warning.",
-                FutureWarning,
-            )
-
+            # The default value for `maximize` has be changed from `True` to `None` in v1.7.0 of TorchMetrics,
+            # will automatically infer the value based on the `higher_is_better` attribute of the metric
+            # (if such attribute exists) or raise an error if it does not. If you are explicitly setting the
+            # `maximize` argument to either `True` or `False` already, you can ignore this warning.
             if not isinstance(maximize, (bool, list)):
                 raise ValueError("Argument `maximize` should either be a single bool or list of bool")
             if isinstance(maximize, list) and not all(isinstance(m, bool) for m in maximize):
@@ -342,7 +336,7 @@ def plot(
             >>> import torch
             >>> from torchmetrics.wrappers import MetricTracker
             >>> from torchmetrics.classification import BinaryAccuracy
-            >>> tracker = MetricTracker(BinaryAccuracy())
+            >>> tracker = MetricTracker(BinaryAccuracy(), maximize=True)
             >>> for epoch in range(5):
             ...     tracker.increment()
             ...     for batch_idx in range(5):

tests/unittests/wrappers/test_tracker.py

@@ -12,7 +12,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import warnings
 
 import pytest
 import torch
@@ -25,7 +24,6 @@
     MulticlassRecall,
 )
 from torchmetrics.regression import MeanAbsoluteError, MeanSquaredError
-from torchmetrics.utilities.imports import _TORCHMETRICS_GREATER_EQUAL_1_6
 from torchmetrics.wrappers import ClasswiseWrapper, MetricTracker, MultioutputWrapper
 from unittests._helpers import seed_all
 
@@ -154,8 +152,8 @@ def test_tracker(base_metric, metric_input, maximize):
 @pytest.mark.parametrize(
     "base_metric",
     [
-        MulticlassConfusionMatrix(3),
-        MetricCollection([MulticlassConfusionMatrix(3), MulticlassAccuracy(3)]),
+        pytest.param(MulticlassConfusionMatrix(3), id="Multiclass-confusion-matrix"),
+        pytest.param(MetricCollection([MulticlassConfusionMatrix(3), MulticlassAccuracy(3)]), id="Metric-collection"),
     ],
 )
 def test_best_metric_for_not_well_defined_metric_collection(base_metric):
@@ -165,7 +163,7 @@ def test_best_metric_for_not_well_defined_metric_collection(base_metric):
     warning and return None.
 
     """
-    tracker = MetricTracker(base_metric)
+    tracker = MetricTracker(base_metric, maximize=True)
     for _ in range(3):
         tracker.increment()
         for _ in range(5):
@@ -207,7 +205,7 @@ def test_best_metric_for_not_well_defined_metric_collection(base_metric):
 )
 def test_metric_tracker_and_collection_multioutput(input_to_tracker, assert_type):
     """Check that MetricTracker support wrapper inputs and nested structures."""
-    tracker = MetricTracker(input_to_tracker)
+    tracker = MetricTracker(input_to_tracker, maximize=False)
     for _ in range(5):
         tracker.increment()
         for _ in range(5):
@@ -226,22 +224,6 @@ def test_metric_tracker_and_collection_multioutput(input_to_tracker, assert_type
         assert which_epoch is None
 
 
-def test_tracker_futurewarning():
-    """Check that future warning is raised for the maximize argument.
-
-    Also to make sure that we remove it in future versions of TM.
-
-    """
-    if _TORCHMETRICS_GREATER_EQUAL_1_6:
-        # Check that for future versions that we remove the warning
-        with warnings.catch_warnings():
-            warnings.simplefilter("error")
-            MetricTracker(MeanSquaredError(), maximize=True)
-    else:
-        with pytest.warns(FutureWarning, match="The default value for `maximize` will be changed from `True` to.*"):
-            MetricTracker(MeanSquaredError(), maximize=True)
-
-
 @pytest.mark.parametrize(
     "base_metric",
     [