From 73d227c18cb949ad8876eb491369b266f417b05b Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 28 Mar 2023 09:04:24 +0200
Subject: [PATCH 1/9] build(deps): update scikit-learn requirement from
 <1.2.2,>1.0 to >1.0,<1.2.3 in /requirements (#1657)

Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/test.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/test.txt b/requirements/test.txt
index 3248b0f7ff6..4b62db7b8f1 100644
--- a/requirements/test.txt
+++ b/requirements/test.txt
@@ -14,4 +14,4 @@ requests <=2.28.2
 fire <=0.5.0
 
 cloudpickle >1.3, <=2.2.1
-scikit-learn >1.0, <1.2.2
+scikit-learn >1.0, <1.2.3

From 685391d6c314981c536c54392783762ae545466c Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 28 Mar 2023 08:25:59 +0000
Subject: [PATCH 2/9] build(deps): update netcal requirement from
 <=1.3.3,>1.0.0 to >1.0.0,<=1.3.4 in /requirements (#1658)

Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/classification_test.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/classification_test.txt b/requirements/classification_test.txt
index 0a232c25173..08bbd5047ed 100644
--- a/requirements/classification_test.txt
+++ b/requirements/classification_test.txt
@@ -2,5 +2,5 @@
 #  in case you want to preserve/enforce restrictions on the latest compatible version, add "strict" as an in-line comment
 
 pandas >1.0.0, <=1.5.3
-netcal >1.0.0, <=1.3.3  # calibration_error
+netcal >1.0.0, <=1.3.4  # calibration_error
 fairlearn # group_fairness

From 7015b947ced62f79b0ca0510a628ceec18085936 Mon Sep 17 00:00:00 2001
From: Nicki Skafte Detlefsen <skaftenicki@gmail.com>
Date: Tue, 28 Mar 2023 12:09:51 +0200
Subject: [PATCH 3/9] Allow FID with torch.float64 (#1628)

* license + docstring + test
* chlog

---------

Co-authored-by: Jirka <jirka.borovec@seznam.cz>
Co-authored-by: Jirka Borovec <6035284+Borda@users.noreply.github.com>
Co-authored-by: mergify[bot] <37929162+mergify[bot]@users.noreply.github.com>
---
 CHANGELOG.md                      |   3 +
 src/torchmetrics/image/fid.py     | 108 +++++++++++++++++++++++++++++-
 tests/unittests/image/test_fid.py |  12 ++++
 3 files changed, 122 insertions(+), 1 deletion(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index afefa880f8e..05dfeda60f5 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -96,6 +96,9 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - Changed `__iter__` method from raising `NotImplementedError` to `TypeError` by setting to `None` ([#1538](https://github.com/Lightning-AI/metrics/pull/1538))
 
 
+- Allowed FID with `torch.float64` ([#1628](https://github.com/Lightning-AI/metrics/pull/1628))
+
+
 ### Deprecated
 
 -
diff --git a/src/torchmetrics/image/fid.py b/src/torchmetrics/image/fid.py
index 52c75bc59b8..469c15a6e9a 100644
--- a/src/torchmetrics/image/fid.py
+++ b/src/torchmetrics/image/fid.py
@@ -19,6 +19,7 @@
 from torch import Tensor
 from torch.autograd import Function
 from torch.nn import Module
+from torch.nn.functional import adaptive_avg_pool2d
 
 from torchmetrics.metric import Metric
 from torchmetrics.utilities import rank_zero_info
@@ -30,11 +31,16 @@
 
 if _TORCH_FIDELITY_AVAILABLE:
     from torch_fidelity.feature_extractor_inceptionv3 import FeatureExtractorInceptionV3 as _FeatureExtractorInceptionV3
+    from torch_fidelity.helpers import vassert
+    from torch_fidelity.interpolate_compat_tensorflow import interpolate_bilinear_2d_like_tensorflow1x
 else:
 
     class _FeatureExtractorInceptionV3(Module):
         pass
 
+    vassert = None
+    interpolate_bilinear_2d_like_tensorflow1x = None
+
     __doctest_skip__ = ["FrechetInceptionDistance", "FrechetInceptionDistance.plot"]
 
 
@@ -59,9 +65,94 @@ def train(self, mode: bool) -> "NoTrainInceptionV3":
         """Force network to always be in evaluation mode."""
         return super().train(False)
 
+    def _torch_fidelity_forward(self, x: Tensor) -> Tensor:
+        """Forward method of inception net.
+
+        Copy of the forward method from this file:
+        https://github.com/toshas/torch-fidelity/blob/master/torch_fidelity/feature_extractor_inceptionv3.py
+        with a single line change regarding the casting of `x` in the beginning.
+
+        Corresponding license file (Apache License, Version 2.0):
+        https://github.com/toshas/torch-fidelity/blob/master/LICENSE.md
+        """
+        vassert(torch.is_tensor(x) and x.dtype == torch.uint8, "Expecting image as torch.Tensor with dtype=torch.uint8")
+        features = {}
+        remaining_features = self.features_list.copy()
+
+        x = x.to(self._dtype) if hasattr(self, "_dtype") else x.to(torch.float)
+        x = interpolate_bilinear_2d_like_tensorflow1x(
+            x,
+            size=(self.INPUT_IMAGE_SIZE, self.INPUT_IMAGE_SIZE),
+            align_corners=False,
+        )
+        x = (x - 128) / 128
+
+        x = self.Conv2d_1a_3x3(x)
+        x = self.Conv2d_2a_3x3(x)
+        x = self.Conv2d_2b_3x3(x)
+        x = self.MaxPool_1(x)
+
+        if "64" in remaining_features:
+            features["64"] = adaptive_avg_pool2d(x, output_size=(1, 1)).squeeze(-1).squeeze(-1)
+            remaining_features.remove("64")
+            if len(remaining_features) == 0:
+                return tuple(features[a] for a in self.features_list)
+
+        x = self.Conv2d_3b_1x1(x)
+        x = self.Conv2d_4a_3x3(x)
+        x = self.MaxPool_2(x)
+
+        if "192" in remaining_features:
+            features["192"] = adaptive_avg_pool2d(x, output_size=(1, 1)).squeeze(-1).squeeze(-1)
+            remaining_features.remove("192")
+            if len(remaining_features) == 0:
+                return tuple(features[a] for a in self.features_list)
+
+        x = self.Mixed_5b(x)
+        x = self.Mixed_5c(x)
+        x = self.Mixed_5d(x)
+        x = self.Mixed_6a(x)
+        x = self.Mixed_6b(x)
+        x = self.Mixed_6c(x)
+        x = self.Mixed_6d(x)
+        x = self.Mixed_6e(x)
+
+        if "768" in remaining_features:
+            features["768"] = adaptive_avg_pool2d(x, output_size=(1, 1)).squeeze(-1).squeeze(-1)
+            remaining_features.remove("768")
+            if len(remaining_features) == 0:
+                return tuple(features[a] for a in self.features_list)
+
+        x = self.Mixed_7a(x)
+        x = self.Mixed_7b(x)
+        x = self.Mixed_7c(x)
+        x = self.AvgPool(x)
+        x = torch.flatten(x, 1)
+
+        if "2048" in remaining_features:
+            features["2048"] = x
+            remaining_features.remove("2048")
+            if len(remaining_features) == 0:
+                return tuple(features[a] for a in self.features_list)
+
+        if "logits_unbiased" in remaining_features:
+            x = x.mm(self.fc.weight.T)
+            # N x 1008 (num_classes)
+            features["logits_unbiased"] = x
+            remaining_features.remove("logits_unbiased")
+            if len(remaining_features) == 0:
+                return tuple(features[a] for a in self.features_list)
+
+            x = x + self.fc.bias.unsqueeze(0)
+        else:
+            x = self.fc(x)
+
+        features["logits"] = x
+        return tuple(features[a] for a in self.features_list)
+
     def forward(self, x: Tensor) -> Tensor:
         """Forward pass of neural network with reshaping of output."""
-        out = super().forward(x)
+        out = self._torch_fidelity_forward(x)
         return out[0].reshape(x.shape[0], -1)
 
 
@@ -151,6 +242,10 @@ class FrechetInceptionDistance(Metric):
     flag ``real`` determines if the images should update the statistics of the real distribution or the
     fake distribution.
 
+    This metric is known to be unstable in its calculatations, and we recommend for the best results using this metric
+    that you calculate using `torch.float64` (default is `torch.float32`) which can be set using the `.set_dtype`
+    method of the metric.
+
     .. note:: using this metrics requires you to have ``scipy`` install. Either install as ``pip install
         torchmetrics[image]`` or ``pip install scipy``
 
@@ -307,6 +402,17 @@ def reset(self) -> None:
         else:
             super().reset()
 
+    def set_dtype(self, dst_type: Union[str, torch.dtype]) -> "Metric":
+        """Transfer all metric state to specific dtype. Special version of standard `type` method.
+
+        Arguments:
+            dst_type (type or string): the desired type.
+        """
+        out = super().set_dtype(dst_type)
+        if isinstance(out.inception, NoTrainInceptionV3):
+            out.inception._dtype = dst_type
+        return out
+
     def plot(
         self, val: Optional[Union[Tensor, Sequence[Tensor]]] = None, ax: Optional[_AX_TYPE] = None
     ) -> _PLOT_OUT_TYPE:
diff --git a/tests/unittests/image/test_fid.py b/tests/unittests/image/test_fid.py
index 824c1e5c7eb..24b88f5c862 100644
--- a/tests/unittests/image/test_fid.py
+++ b/tests/unittests/image/test_fid.py
@@ -199,3 +199,15 @@ def test_normalize_arg_false():
     metric = FrechetInceptionDistance(normalize=False)
     with pytest.raises(ValueError, match="Expecting image as torch.Tensor with dtype=torch.uint8"):
         metric.update(img, real=True)
+
+
+def test_dtype_transfer_to_submodule():
+    """Test that change in dtype also changes the default inception net."""
+    imgs = torch.randn(1, 3, 256, 256)
+    imgs = ((imgs.clamp(-1, 1) / 2 + 0.5) * 255).to(torch.uint8)
+
+    metric = FrechetInceptionDistance(feature=64)
+    metric.set_dtype(torch.float64)
+
+    out = metric.inception(imgs)
+    assert out.dtype == torch.float64

From 6756b74664cee7c3301e56e3ca46709834927fef Mon Sep 17 00:00:00 2001
From: Nicki Skafte Detlefsen <skaftenicki@gmail.com>
Date: Tue, 28 Mar 2023 12:10:45 +0200
Subject: [PATCH 4/9] Fix precision-recall curve based computations for float
 target (#1642)

---
 CHANGELOG.md                                                | 3 +++
 .../functional/classification/precision_recall_curve.py     | 6 +++---
 2 files changed, 6 insertions(+), 3 deletions(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 05dfeda60f5..2257d0f18ed 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -114,6 +114,9 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - Fixed support in `MetricTracker` for `MultioutputWrapper` and nested structures ([#1608](https://github.com/Lightning-AI/metrics/pull/1608))
 
 
+- Fix precision-recall curve based computations for float target ([#1642](https://github.com/Lightning-AI/metrics/pull/1642))
+
+
 ## [0.11.4] - 2023-03-10
 
 ### Fixed
diff --git a/src/torchmetrics/functional/classification/precision_recall_curve.py b/src/torchmetrics/functional/classification/precision_recall_curve.py
index 3369eeb3a2c..91d296ac83d 100644
--- a/src/torchmetrics/functional/classification/precision_recall_curve.py
+++ b/src/torchmetrics/functional/classification/precision_recall_curve.py
@@ -214,7 +214,7 @@ def _binary_precision_recall_curve_update_vectorized(
     """
     len_t = len(thresholds)
     preds_t = (preds.unsqueeze(-1) >= thresholds.unsqueeze(0)).long()  # num_samples x num_thresholds
-    unique_mapping = preds_t + 2 * target.unsqueeze(-1) + 4 * torch.arange(len_t, device=target.device)
+    unique_mapping = preds_t + 2 * target.long().unsqueeze(-1) + 4 * torch.arange(len_t, device=target.device)
     bins = _bincount(unique_mapping.flatten(), minlength=4 * len_t)
     return bins.reshape(len_t, 2, 2)
 
@@ -469,7 +469,7 @@ def _multiclass_precision_recall_curve_update_vectorized(
     len_t = len(thresholds)
     preds_t = (preds.unsqueeze(-1) >= thresholds.unsqueeze(0).unsqueeze(0)).long()
     target_t = torch.nn.functional.one_hot(target, num_classes=num_classes)
-    unique_mapping = preds_t + 2 * target_t.unsqueeze(-1)
+    unique_mapping = preds_t + 2 * target_t.long().unsqueeze(-1)
     unique_mapping += 4 * torch.arange(num_classes, device=preds.device).unsqueeze(0).unsqueeze(-1)
     unique_mapping += 4 * num_classes * torch.arange(len_t, device=preds.device)
     bins = _bincount(unique_mapping.flatten(), minlength=4 * num_classes * len_t)
@@ -714,7 +714,7 @@ def _multilabel_precision_recall_curve_update(
     len_t = len(thresholds)
     # num_samples x num_labels x num_thresholds
     preds_t = (preds.unsqueeze(-1) >= thresholds.unsqueeze(0).unsqueeze(0)).long()
-    unique_mapping = preds_t + 2 * target.unsqueeze(-1)
+    unique_mapping = preds_t + 2 * target.long().unsqueeze(-1)
     unique_mapping += 4 * torch.arange(num_labels, device=preds.device).unsqueeze(0).unsqueeze(-1)
     unique_mapping += 4 * num_labels * torch.arange(len_t, device=preds.device)
     unique_mapping = unique_mapping[unique_mapping >= 0]

From ae7a755674ca45dc9d13c9d404cfb724650f8486 Mon Sep 17 00:00:00 2001
From: Nicki Skafte Detlefsen <skaftenicki@gmail.com>
Date: Tue, 28 Mar 2023 12:11:53 +0200
Subject: [PATCH 5/9] Fix corner case of calibration error (#1648)

---
 CHANGELOG.md                                              | 3 +++
 .../functional/classification/calibration_error.py        | 8 ++++----
 tests/unittests/classification/test_calibration_error.py  | 7 +++++++
 3 files changed, 14 insertions(+), 4 deletions(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 2257d0f18ed..fa6511d42ae 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -114,6 +114,9 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - Fixed support in `MetricTracker` for `MultioutputWrapper` and nested structures ([#1608](https://github.com/Lightning-AI/metrics/pull/1608))
 
 
+- Fixed corner case in calibration error for zero confidence input ([#1648](https://github.com/Lightning-AI/metrics/pull/1648))
+
+
 - Fix precision-recall curve based computations for float target ([#1642](https://github.com/Lightning-AI/metrics/pull/1642))
 
 
diff --git a/src/torchmetrics/functional/classification/calibration_error.py b/src/torchmetrics/functional/classification/calibration_error.py
index c81f4bf78cd..674df77cc03 100644
--- a/src/torchmetrics/functional/classification/calibration_error.py
+++ b/src/torchmetrics/functional/classification/calibration_error.py
@@ -40,11 +40,11 @@ def _binning_bucketize(
         tuple with binned accuracy, binned confidence and binned probabilities
     """
     accuracies = accuracies.to(dtype=confidences.dtype)
-    acc_bin = torch.zeros(len(bin_boundaries) - 1, device=confidences.device, dtype=confidences.dtype)
-    conf_bin = torch.zeros(len(bin_boundaries) - 1, device=confidences.device, dtype=confidences.dtype)
-    count_bin = torch.zeros(len(bin_boundaries) - 1, device=confidences.device, dtype=confidences.dtype)
+    acc_bin = torch.zeros(len(bin_boundaries), device=confidences.device, dtype=confidences.dtype)
+    conf_bin = torch.zeros(len(bin_boundaries), device=confidences.device, dtype=confidences.dtype)
+    count_bin = torch.zeros(len(bin_boundaries), device=confidences.device, dtype=confidences.dtype)
 
-    indices = torch.bucketize(confidences, bin_boundaries) - 1
+    indices = torch.bucketize(confidences, bin_boundaries, right=True) - 1
 
     count_bin.scatter_add_(dim=0, index=indices, src=torch.ones_like(confidences))
 
diff --git a/tests/unittests/classification/test_calibration_error.py b/tests/unittests/classification/test_calibration_error.py
index dcd28b25d92..5ebe811f094 100644
--- a/tests/unittests/classification/test_calibration_error.py
+++ b/tests/unittests/classification/test_calibration_error.py
@@ -133,6 +133,13 @@ def test_binary_calibration_error_dtype_gpu(self, input, dtype):
         )
 
 
+def test_binary_with_zero_pred():
+    """Test that metric works with edge case where confidence is zero for a bin."""
+    preds = torch.tensor([1.0, 1.0, 1.0, 1.0, 0.0])
+    target = torch.tensor([0, 0, 1, 1, 1])
+    assert binary_calibration_error(preds, target, n_bins=2, norm="l1") == torch.tensor(0.6)
+
+
 def _netcal_multiclass_calibration_error(preds, target, n_bins, norm, ignore_index):
     preds = preds.numpy()
     target = target.numpy().flatten()

From bf4574e1daae0f4f8b302502301a637b1c46e74b Mon Sep 17 00:00:00 2001
From: Nicki Skafte Detlefsen <skaftenicki@gmail.com>
Date: Tue, 28 Mar 2023 12:16:21 +0200
Subject: [PATCH 6/9] Fid: error on too few samples (#1655)

---
 CHANGELOG.md                      |  3 +++
 src/torchmetrics/image/fid.py     |  2 ++
 tests/unittests/image/test_fid.py | 12 ++++++++++++
 3 files changed, 17 insertions(+)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index fa6511d42ae..ba546fa4282 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -96,6 +96,9 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - Changed `__iter__` method from raising `NotImplementedError` to `TypeError` by setting to `None` ([#1538](https://github.com/Lightning-AI/metrics/pull/1538))
 
 
+- `FID` metric will now raise an error if too few samples are provided ([#1655](https://github.com/Lightning-AI/metrics/pull/1655))
+
+
 - Allowed FID with `torch.float64` ([#1628](https://github.com/Lightning-AI/metrics/pull/1628))
 
 
diff --git a/src/torchmetrics/image/fid.py b/src/torchmetrics/image/fid.py
index 469c15a6e9a..99c5579d938 100644
--- a/src/torchmetrics/image/fid.py
+++ b/src/torchmetrics/image/fid.py
@@ -380,6 +380,8 @@ def update(self, imgs: Tensor, real: bool) -> None:
 
     def compute(self) -> Tensor:
         """Calculate FID score based on accumulated extracted features from the two distributions."""
+        if self.real_features_num_samples < 2 or self.fake_features_num_samples < 2:
+            raise RuntimeError("More than one sample is required for both the real and fake distributed to compute FID")
         mean_real = (self.real_features_sum / self.real_features_num_samples).unsqueeze(0)
         mean_fake = (self.fake_features_sum / self.fake_features_num_samples).unsqueeze(0)
 
diff --git a/tests/unittests/image/test_fid.py b/tests/unittests/image/test_fid.py
index 24b88f5c862..a80523001fe 100644
--- a/tests/unittests/image/test_fid.py
+++ b/tests/unittests/image/test_fid.py
@@ -201,6 +201,18 @@ def test_normalize_arg_false():
         metric.update(img, real=True)
 
 
+def test_not_enough_samples():
+    """Test that an error is raised if not enough samples were provided."""
+    img = torch.randint(0, 255, (1, 3, 299, 299), dtype=torch.uint8)
+    metric = FrechetInceptionDistance()
+    metric.update(img, real=True)
+    metric.update(img, real=False)
+    with pytest.raises(
+        RuntimeError, match="More than one sample is required for both the real and fake distributed to compute FID"
+    ):
+        metric.compute()
+
+
 def test_dtype_transfer_to_submodule():
     """Test that change in dtype also changes the default inception net."""
     imgs = torch.randn(1, 3, 256, 256)

From 82a5f6dfb0914cc72ec62197c6b15eec796e2e77 Mon Sep 17 00:00:00 2001
From: Nicki Skafte Detlefsen <skaftenicki@gmail.com>
Date: Tue, 28 Mar 2023 12:17:22 +0200
Subject: [PATCH 7/9] Enchance use of `data_range` in image metrics (#1606)

---
 CHANGELOG.md                              |  3 +++
 src/torchmetrics/functional/image/psnr.py | 13 ++++++++++---
 src/torchmetrics/functional/image/ssim.py | 22 +++++++++++++++-------
 src/torchmetrics/image/psnr.py            | 14 ++++++++++++--
 src/torchmetrics/image/ssim.py            | 13 +++++++++----
 tests/unittests/image/test_psnr.py        |  5 +++++
 tests/unittests/image/test_ssim.py        | 11 ++++++++---
 7 files changed, 62 insertions(+), 19 deletions(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index ba546fa4282..fde45018870 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -76,6 +76,9 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - Added support for plotting of aggregation metrics through `.plot()` method ([#1485](https://github.com/Lightning-AI/metrics/pull/1485))
 
 
+- Added support for auto clamping of input for metrics that uses the `data_range` ([#1606](argument https://github.com/Lightning-AI/metrics/pull/1606))
+
+
 - Added `ModifiedPanopticQuality` metric to detection package ([#1627](https://github.com/Lightning-AI/metrics/pull/1627))
 
 
diff --git a/src/torchmetrics/functional/image/psnr.py b/src/torchmetrics/functional/image/psnr.py
index a38c05525bb..35b733d0a30 100644
--- a/src/torchmetrics/functional/image/psnr.py
+++ b/src/torchmetrics/functional/image/psnr.py
@@ -88,7 +88,7 @@ def _psnr_update(
 def peak_signal_noise_ratio(
     preds: Tensor,
     target: Tensor,
-    data_range: Optional[float] = None,
+    data_range: Optional[Union[float, Tuple[float, float]]] = None,
     base: float = 10.0,
     reduction: Literal["elementwise_mean", "sum", "none", None] = "elementwise_mean",
     dim: Optional[Union[int, Tuple[int, ...]]] = None,
@@ -98,8 +98,10 @@ def peak_signal_noise_ratio(
     Args:
         preds: estimated signal
         target: groun truth signal
-        data_range: the range of the data. If None, it is determined from the data (max - min).
-            ``data_range`` must be given when ``dim`` is not None.
+        data_range:
+            the range of the data. If None, it is determined from the data (max - min). If a tuple is provided then
+            the range is calculated as the difference and input is clamped between the values.
+            The ``data_range`` must be given when ``dim`` is not None.
         base: a base of a logarithm to use
         reduction: a method to reduce metric score over labels.
 
@@ -138,7 +140,12 @@ def peak_signal_noise_ratio(
             raise ValueError("The `data_range` must be given when `dim` is not None.")
 
         data_range = target.max() - target.min()
+    elif isinstance(data_range, tuple):
+        preds = torch.clamp(preds, min=data_range[0], max=data_range[1])
+        target = torch.clamp(target, min=data_range[0], max=data_range[1])
+        data_range = tensor(data_range[1] - data_range[0])
     else:
         data_range = tensor(float(data_range))
+
     sum_squared_error, n_obs = _psnr_update(preds, target, dim=dim)
     return _psnr_compute(sum_squared_error, n_obs, data_range, base=base, reduction=reduction)
diff --git a/src/torchmetrics/functional/image/ssim.py b/src/torchmetrics/functional/image/ssim.py
index 91f4d9fd385..7dfebf11f8b 100644
--- a/src/torchmetrics/functional/image/ssim.py
+++ b/src/torchmetrics/functional/image/ssim.py
@@ -47,7 +47,7 @@ def _ssim_update(
     gaussian_kernel: bool = True,
     sigma: Union[float, Sequence[float]] = 1.5,
     kernel_size: Union[int, Sequence[int]] = 11,
-    data_range: Optional[float] = None,
+    data_range: Optional[Union[float, Tuple[float, float]]] = None,
     k1: float = 0.01,
     k2: float = 0.03,
     return_full_image: bool = False,
@@ -109,6 +109,10 @@ def _ssim_update(
 
     if data_range is None:
         data_range = max(preds.max() - preds.min(), target.max() - target.min())
+    elif isinstance(data_range, tuple):
+        preds = torch.clamp(preds, min=data_range[0], max=data_range[1])
+        target = torch.clamp(target, min=data_range[0], max=data_range[1])
+        data_range = data_range[1] - data_range[0]
 
     c1 = pow(k1 * data_range, 2)
     c2 = pow(k2 * data_range, 2)
@@ -202,7 +206,7 @@ def structural_similarity_index_measure(
     sigma: Union[float, Sequence[float]] = 1.5,
     kernel_size: Union[int, Sequence[int]] = 11,
     reduction: Literal["elementwise_mean", "sum", "none", None] = "elementwise_mean",
-    data_range: Optional[float] = None,
+    data_range: Optional[Union[float, Tuple[float, float]]] = None,
     k1: float = 0.01,
     k2: float = 0.03,
     return_full_image: bool = False,
@@ -224,7 +228,9 @@ def structural_similarity_index_measure(
             - ``'sum'``: takes the sum
             - ``'none'`` or ``None``: no reduction will be applied
 
-        data_range: Range of the image. If ``None``, it is determined from the image (max - min)
+        data_range:
+            the range of the data. If None, it is determined from the data (max - min). If a tuple is provided then
+            the range is calculated as the difference and input is clamped between the values.
         k1: Parameter of SSIM.
         k2: Parameter of SSIM.
         return_full_image: If true, the full ``ssim`` image is returned as a second argument.
@@ -283,7 +289,7 @@ def _get_normalized_sim_and_cs(
     gaussian_kernel: bool = True,
     sigma: Union[float, Sequence[float]] = 1.5,
     kernel_size: Union[int, Sequence[int]] = 11,
-    data_range: Optional[float] = None,
+    data_range: Optional[Union[float, Tuple[float, float]]] = None,
     k1: float = 0.01,
     k2: float = 0.03,
     normalize: Optional[Literal["relu", "simple"]] = None,
@@ -311,7 +317,7 @@ def _multiscale_ssim_update(
     gaussian_kernel: bool = True,
     sigma: Union[float, Sequence[float]] = 1.5,
     kernel_size: Union[int, Sequence[int]] = 11,
-    data_range: Optional[float] = None,
+    data_range: Optional[Union[float, Tuple[float, float]]] = None,
     k1: float = 0.01,
     k2: float = 0.03,
     betas: Union[Tuple[float, float, float, float, float], Tuple[float, ...]] = (
@@ -436,7 +442,7 @@ def multiscale_structural_similarity_index_measure(
     sigma: Union[float, Sequence[float]] = 1.5,
     kernel_size: Union[int, Sequence[int]] = 11,
     reduction: Literal["elementwise_mean", "sum", "none", None] = "elementwise_mean",
-    data_range: Optional[float] = None,
+    data_range: Optional[Union[float, Tuple[float, float]]] = None,
     k1: float = 0.01,
     k2: float = 0.03,
     betas: Tuple[float, ...] = (0.0448, 0.2856, 0.3001, 0.2363, 0.1333),
@@ -459,7 +465,9 @@ def multiscale_structural_similarity_index_measure(
             - ``'sum'``: takes the sum
             - ``'none'`` or ``None``: no reduction will be applied
 
-        data_range: Range of the image. If ``None``, it is determined from the image (max - min)
+        data_range:
+            the range of the data. If None, it is determined from the data (max - min). If a tuple is provided then
+            the range is calculated as the difference and input is clamped between the values.
         k1: Parameter of structural similarity index measure.
         k2: Parameter of structural similarity index measure.
         betas: Exponent parameters for individual similarities and contrastive sensitivies returned by different image
diff --git a/src/torchmetrics/image/psnr.py b/src/torchmetrics/image/psnr.py
index b6e2e1aea87..d26b1b279a6 100644
--- a/src/torchmetrics/image/psnr.py
+++ b/src/torchmetrics/image/psnr.py
@@ -11,6 +11,7 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
+from functools import partial
 from typing import Any, Optional, Sequence, Tuple, Union
 
 import torch
@@ -46,7 +47,8 @@ class PeakSignalNoiseRatio(Metric):
 
     Args:
         data_range:
-            the range of the data. If None, it is determined from the data (max - min).
+            the range of the data. If None, it is determined from the data (max - min). If a tuple is provided then
+            the range is calculated as the difference and input is clamped between the values.
             The ``data_range`` must be given when ``dim`` is not None.
         base: a base of a logarithm to use.
         reduction: a method to reduce metric score over labels.
@@ -83,7 +85,7 @@ class PeakSignalNoiseRatio(Metric):
 
     def __init__(
         self,
-        data_range: Optional[float] = None,
+        data_range: Optional[Union[float, Tuple[float, float]]] = None,
         base: float = 10.0,
         reduction: Literal["elementwise_mean", "sum", "none", None] = "elementwise_mean",
         dim: Optional[Union[int, Tuple[int, ...]]] = None,
@@ -101,6 +103,7 @@ def __init__(
             self.add_state("sum_squared_error", default=[], dist_reduce_fx="cat")
             self.add_state("total", default=[], dist_reduce_fx="cat")
 
+        self.clamping_fn = None
         if data_range is None:
             if dim is not None:
                 # Maybe we could use `torch.amax(target, dim=dim) - torch.amin(target, dim=dim)` in PyTorch 1.7 to
@@ -110,6 +113,9 @@ def __init__(
             self.data_range = None
             self.add_state("min_target", default=tensor(0.0), dist_reduce_fx=torch.min)
             self.add_state("max_target", default=tensor(0.0), dist_reduce_fx=torch.max)
+        elif isinstance(data_range, tuple):
+            self.add_state("data_range", default=tensor(data_range[1] - data_range[0]), dist_reduce_fx="mean")
+            self.clamping_fn = partial(torch.clamp, min=data_range[0], max=data_range[1])
         else:
             self.add_state("data_range", default=tensor(float(data_range)), dist_reduce_fx="mean")
         self.base = base
@@ -118,6 +124,10 @@ def __init__(
 
     def update(self, preds: Tensor, target: Tensor) -> None:
         """Update state with predictions and targets."""
+        if self.clamping_fn is not None:
+            preds = self.clamping_fn(preds)
+            target = self.clamping_fn(target)
+
         sum_squared_error, n_obs = _psnr_update(preds, target, dim=self.dim)
         if self.dim is None:
             if self.data_range is None:
diff --git a/src/torchmetrics/image/ssim.py b/src/torchmetrics/image/ssim.py
index 54bd59bd967..f37f32cc7fb 100644
--- a/src/torchmetrics/image/ssim.py
+++ b/src/torchmetrics/image/ssim.py
@@ -54,7 +54,9 @@ class StructuralSimilarityIndexMeasure(Metric):
             - ``'sum'``: takes the sum
             - ``'none'`` or ``None``: no reduction will be applied
 
-        data_range: Range of the image. If ``None``, it is determined from the image (max - min)
+        data_range:
+            the range of the data. If None, it is determined from the data (max - min). If a tuple is provided then
+            the range is calculated as the difference and input is clamped between the values.
         k1: Parameter of SSIM.
         k2: Parameter of SSIM.
         return_full_image: If true, the full ``ssim`` image is returned as a second argument.
@@ -89,7 +91,7 @@ def __init__(
         sigma: Union[float, Sequence[float]] = 1.5,
         kernel_size: Union[int, Sequence[int]] = 11,
         reduction: Literal["elementwise_mean", "sum", "none", None] = "elementwise_mean",
-        data_range: Optional[float] = None,
+        data_range: Optional[Union[float, Tuple[float, float]]] = None,
         k1: float = 0.01,
         k2: float = 0.03,
         return_full_image: bool = False,
@@ -239,7 +241,10 @@ class MultiScaleStructuralSimilarityIndexMeasure(Metric):
             - ``'sum'``: takes the sum
             - ``'none'`` or ``None``: no reduction will be applied
 
-        data_range: Range of the image. If ``None``, it is determined from the image (max - min)
+        data_range:
+            the range of the data. If None, it is determined from the data (max - min). If a tuple is provided then
+            the range is calculated as the difference and input is clamped between the values.
+            The ``data_range`` must be given when ``dim`` is not None.
         k1: Parameter of structural similarity index measure.
         k2: Parameter of structural similarity index measure.
         betas: Exponent parameters for individual similarities and contrastive sensitivies returned by different image
@@ -285,7 +290,7 @@ def __init__(
         kernel_size: Union[int, Sequence[int]] = 11,
         sigma: Union[float, Sequence[float]] = 1.5,
         reduction: Literal["elementwise_mean", "sum", "none", None] = "elementwise_mean",
-        data_range: Optional[float] = None,
+        data_range: Optional[Union[float, Tuple[float, float]]] = None,
         k1: float = 0.01,
         k2: float = 0.03,
         betas: Tuple[float, ...] = (0.0448, 0.2856, 0.3001, 0.2363, 0.1333),
diff --git a/tests/unittests/image/test_psnr.py b/tests/unittests/image/test_psnr.py
index 2ca5375e570..5c64a11a3f6 100644
--- a/tests/unittests/image/test_psnr.py
+++ b/tests/unittests/image/test_psnr.py
@@ -60,6 +60,10 @@ def _to_sk_peak_signal_noise_ratio_inputs(value, dim):
 
 
 def _skimage_psnr(preds, target, data_range, reduction, dim):
+    if isinstance(data_range, tuple):
+        preds = preds.clamp(min=data_range[0], max=data_range[1])
+        target = target.clamp(min=data_range[0], max=data_range[1])
+        data_range = data_range[1] - data_range[0]
     sk_preds_lists = _to_sk_peak_signal_noise_ratio_inputs(preds, dim=dim)
     sk_target_lists = _to_sk_peak_signal_noise_ratio_inputs(target, dim=dim)
     np_reduce_map = {"elementwise_mean": np.mean, "none": np.array, "sum": np.sum}
@@ -84,6 +88,7 @@ def _base_e_sk_psnr(preds, target, data_range, reduction, dim):
         (_inputs[2].preds, _inputs[2].target, 5, "elementwise_mean", 1),
         (_inputs[2].preds, _inputs[2].target, 5, "elementwise_mean", (1, 2)),
         (_inputs[2].preds, _inputs[2].target, 5, "sum", (1, 2)),
+        (_inputs[0].preds, _inputs[0].target, (0.0, 1.0), "elementwise_mean", None),
     ],
 )
 @pytest.mark.parametrize(
diff --git a/tests/unittests/image/test_ssim.py b/tests/unittests/image/test_ssim.py
index 2170d10d043..7c1d5956ad1 100644
--- a/tests/unittests/image/test_ssim.py
+++ b/tests/unittests/image/test_ssim.py
@@ -65,6 +65,10 @@ def _skimage_ssim(
     gaussian_weights=True,
     reduction_arg="elementwise_mean",
 ):
+    if isinstance(data_range, tuple):
+        preds = preds.clamp(min=data_range[0], max=data_range[1])
+        target = target.clamp(min=data_range[0], max=data_range[1])
+        data_range = data_range[1] - data_range[0]
     if len(preds.shape) == 4:
         c, h, w = preds.shape[-3:]
         sk_preds = preds.view(-1, c, h, w).permute(0, 2, 3, 1).numpy()
@@ -137,17 +141,18 @@ class TestSSIM(MetricTester):
 
     atol = 6e-3
 
+    @pytest.mark.parametrize("data_range", [1.0, (0.1, 1.0)])
     @pytest.mark.parametrize("ddp", [True, False])
-    def test_ssim_sk(self, preds, target, sigma, ddp):
+    def test_ssim_sk(self, preds, target, sigma, data_range, ddp):
         """Test class implementation of metricvs skimage."""
         self.run_class_metric_test(
             ddp,
             preds,
             target,
             StructuralSimilarityIndexMeasure,
-            partial(_skimage_ssim, data_range=1.0, sigma=sigma, kernel_size=None),
+            partial(_skimage_ssim, data_range=data_range, sigma=sigma, kernel_size=None),
             metric_args={
-                "data_range": 1.0,
+                "data_range": data_range,
                 "sigma": sigma,
             },
         )

From 1778808280e95189908a9ba88669ce4c08b205ae Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 28 Mar 2023 11:53:01 +0000
Subject: [PATCH 8/9] build(deps): update netcal requirement from
 <=1.3.4,>1.0.0 to >1.0.0,<=1.3.5 in /requirements (#1661)

Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/classification_test.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/classification_test.txt b/requirements/classification_test.txt
index 08bbd5047ed..2d87aec322b 100644
--- a/requirements/classification_test.txt
+++ b/requirements/classification_test.txt
@@ -2,5 +2,5 @@
 #  in case you want to preserve/enforce restrictions on the latest compatible version, add "strict" as an in-line comment
 
 pandas >1.0.0, <=1.5.3
-netcal >1.0.0, <=1.3.4  # calibration_error
+netcal >1.0.0, <=1.3.5  # calibration_error
 fairlearn # group_fairness

From 4d5147a083a8972916fbf7f457347fdf10dac7ea Mon Sep 17 00:00:00 2001
From: Bas Veeling <basveeling@gmail.com>
Date: Tue, 28 Mar 2023 14:48:15 +0200
Subject: [PATCH 9/9] Silently failing DDP syncing when initializing Metric
 with jsonargparse (#1651)

* Broken ddp syncing when initializing metric with jsonargparse (LightningCLI)

This happens due the following combination of factors.
- jsonargparse has a docstring parsing function enabled when installed with `pip install jsonargparse[signatures]`
- `torchmetrics.Metric` has a docstring that mentions an optional` distributed_available_fn`
- If `distributed_available_fn` is not set in `**kwargs`, `Metric.__init__` sets a default pytorch function which enables ddp syncing of metrics\
- When a metric is initialized in a yaml config, jsonargparse recognizes the `distributed_available_fn` field in the docstring and passes a default value of `distributed_available_fn=None`
- Hence, any subclass of `Metric` initialized by jsonargparse has `distributed_available_fn = None`. These metrics silently fail to sync across gpus.


---------

Co-authored-by: Nicki Skafte Detlefsen <skaftenicki@gmail.com>
Co-authored-by: mergify[bot] <37929162+mergify[bot]@users.noreply.github.com>
---
 CHANGELOG.md               | 3 +++
 src/torchmetrics/metric.py | 2 +-
 2 files changed, 4 insertions(+), 1 deletion(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index fde45018870..dcd2e714997 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -120,6 +120,9 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - Fixed support in `MetricTracker` for `MultioutputWrapper` and nested structures ([#1608](https://github.com/Lightning-AI/metrics/pull/1608))
 
 
+- Fixed integration with `jsonargparse` and `LightningCLI` ([#1651](https://github.com/Lightning-AI/metrics/pull/1651))
+
+
 - Fixed corner case in calibration error for zero confidence input ([#1648](https://github.com/Lightning-AI/metrics/pull/1648))
 
 
diff --git a/src/torchmetrics/metric.py b/src/torchmetrics/metric.py
index 9a392c8dd5e..0595575a647 100644
--- a/src/torchmetrics/metric.py
+++ b/src/torchmetrics/metric.py
@@ -124,7 +124,7 @@ def __init__(
                 f"Expected keyword argument `dist_sync_fn` to be an callable function but got {self.dist_sync_fn}"
             )
 
-        self.distributed_available_fn = kwargs.pop("distributed_available_fn", jit_distributed_available)
+        self.distributed_available_fn = kwargs.pop("distributed_available_fn", None) or jit_distributed_available
 
         self.sync_on_compute = kwargs.pop("sync_on_compute", True)
         if not isinstance(self.sync_on_compute, bool):