tests: move reset seed to fixture [2/2] (#2706)

src/torchmetrics/image/_deprecated.py

@@ -17,11 +17,11 @@
 class _ErrorRelativeGlobalDimensionlessSynthesis(ErrorRelativeGlobalDimensionlessSynthesis):
     """Wrapper for deprecated import.
 
-    >>> import torch
-    >>> preds = torch.rand([16, 1, 16, 16], generator=torch.manual_seed(42))
+    >>> from torch import rand
+    >>> preds = rand([16, 1, 16, 16])
     >>> target = preds * 0.75
     >>> ergas = _ErrorRelativeGlobalDimensionlessSynthesis()
-    >>> torch.round(ergas(preds, target))
+    >>> ergas(preds, target).round()
     tensor(10.)
 
     """
@@ -39,8 +39,8 @@ def __init__(
 class _MultiScaleStructuralSimilarityIndexMeasure(MultiScaleStructuralSimilarityIndexMeasure):
     """Wrapper for deprecated import.
 
-    >>> import torch
-    >>> preds = torch.rand([3, 3, 256, 256], generator=torch.manual_seed(42))
+    >>> from torch import rand
+    >>> preds = rand([3, 3, 256, 256])
     >>> target = preds * 0.75
     >>> ms_ssim = _MultiScaleStructuralSimilarityIndexMeasure(data_range=1.0)
     >>> ms_ssim(preds, target)
@@ -103,13 +103,12 @@ def __init__(
 class _RelativeAverageSpectralError(RelativeAverageSpectralError):
     """Wrapper for deprecated import.
 
-    >>> import torch
-    >>> g = torch.manual_seed(22)
-    >>> preds = torch.rand(4, 3, 16, 16)
-    >>> target = torch.rand(4, 3, 16, 16)
+    >>> from torch import rand
+    >>> preds = rand(4, 3, 16, 16)
+    >>> target = rand(4, 3, 16, 16)
     >>> rase = _RelativeAverageSpectralError()
     >>> rase(preds, target)
-    tensor(5114.66...)
+    tensor(5326.40...)
 
     """
 
@@ -125,13 +124,12 @@ def __init__(
 class _RootMeanSquaredErrorUsingSlidingWindow(RootMeanSquaredErrorUsingSlidingWindow):
     """Wrapper for deprecated import.
 
-    >>> import torch
-    >>> g = torch.manual_seed(22)
-    >>> preds = torch.rand(4, 3, 16, 16)
-    >>> target = torch.rand(4, 3, 16, 16)
+    >>> from torch import rand
+    >>> preds = rand(4, 3, 16, 16)
+    >>> target = rand(4, 3, 16, 16)
     >>> rmse_sw = RootMeanSquaredErrorUsingSlidingWindow()
     >>> rmse_sw(preds, target)
-    tensor(0.3999)
+    tensor(0.4158)
 
     """
 
@@ -147,10 +145,9 @@ def __init__(
 class _SpectralAngleMapper(SpectralAngleMapper):
     """Wrapper for deprecated import.
 
-    >>> import torch
-    >>> gen = torch.manual_seed(42)
-    >>> preds = torch.rand([16, 3, 16, 16], generator=gen)
-    >>> target = torch.rand([16, 3, 16, 16], generator=gen)
+    >>> from torch import rand
+    >>> preds = rand([16, 3, 16, 16])
+    >>> target = rand([16, 3, 16, 16])
     >>> sam = _SpectralAngleMapper()
     >>> sam(preds, target)
     tensor(0.5914)
@@ -169,10 +166,9 @@ def __init__(
 class _SpectralDistortionIndex(SpectralDistortionIndex):
     """Wrapper for deprecated import.
 
-    >>> import torch
-    >>> _ = torch.manual_seed(42)
-    >>> preds = torch.rand([16, 3, 16, 16])
-    >>> target = torch.rand([16, 3, 16, 16])
+    >>> from torch import rand
+    >>> preds = rand([16, 3, 16, 16])
+    >>> target = rand([16, 3, 16, 16])
     >>> sdi = _SpectralDistortionIndex()
     >>> sdi(preds, target)
     tensor(0.0234)
@@ -229,10 +225,9 @@ def __init__(
 class _TotalVariation(TotalVariation):
     """Wrapper for deprecated import.
 
-    >>> import torch
-    >>> _ = torch.manual_seed(42)
+    >>> from torch import rand
     >>> tv = _TotalVariation()
-    >>> img = torch.rand(5, 3, 28, 28)
+    >>> img = rand(5, 3, 28, 28)
     >>> tv(img)
     tensor(7546.8018)
 

src/torchmetrics/image/d_lambda.py

@@ -53,11 +53,10 @@ class SpectralDistortionIndex(Metric):
         kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.
 
     Example:
-        >>> import torch
-        >>> _ = torch.manual_seed(42)
+        >>> from torch import rand
         >>> from torchmetrics.image import SpectralDistortionIndex
-        >>> preds = torch.rand([16, 3, 16, 16])
-        >>> target = torch.rand([16, 3, 16, 16])
+        >>> preds = rand([16, 3, 16, 16])
+        >>> target = rand([16, 3, 16, 16])
         >>> sdi = SpectralDistortionIndex()
         >>> sdi(preds, target)
         tensor(0.0234)
@@ -126,11 +125,10 @@ def plot(
             :scale: 75
 
             >>> # Example plotting a single value
-            >>> import torch
-            >>> _ = torch.manual_seed(42)
+            >>> from torch import rand
             >>> from torchmetrics.image import SpectralDistortionIndex
-            >>> preds = torch.rand([16, 3, 16, 16])
-            >>> target = torch.rand([16, 3, 16, 16])
+            >>> preds = rand([16, 3, 16, 16])
+            >>> target = rand([16, 3, 16, 16])
             >>> metric = SpectralDistortionIndex()
             >>> metric.update(preds, target)
             >>> fig_, ax_ = metric.plot()
@@ -139,11 +137,10 @@ def plot(
             :scale: 75
 
             >>> # Example plotting multiple values
-            >>> import torch
-            >>> _ = torch.manual_seed(42)
+            >>> from torch import rand
             >>> from torchmetrics.image import SpectralDistortionIndex
-            >>> preds = torch.rand([16, 3, 16, 16])
-            >>> target = torch.rand([16, 3, 16, 16])
+            >>> preds = rand([16, 3, 16, 16])
+            >>> target = rand([16, 3, 16, 16])
             >>> metric = SpectralDistortionIndex()
             >>> values = [ ]
             >>> for _ in range(10):

src/torchmetrics/image/d_s.py

@@ -74,13 +74,12 @@ class SpatialDistortionIndex(Metric):
         kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.
 
     Example:
-        >>> import torch
-        >>> _ = torch.manual_seed(42)
+        >>> from torch import rand
         >>> from torchmetrics.image import SpatialDistortionIndex
-        >>> preds = torch.rand([16, 3, 32, 32])
+        >>> preds = rand([16, 3, 32, 32])
         >>> target = {
-        ...     'ms': torch.rand([16, 3, 16, 16]),
-        ...     'pan': torch.rand([16, 3, 32, 32]),
+        ...     'ms': rand([16, 3, 16, 16]),
+        ...     'pan': rand([16, 3, 32, 32]),
         ... }
         >>> sdi = SpatialDistortionIndex()
         >>> sdi(preds, target)
@@ -191,13 +190,12 @@ def plot(
             :scale: 75
 
             >>> # Example plotting a single value
-            >>> import torch
-            >>> _ = torch.manual_seed(42)
+            >>> from torch import rand
             >>> from torchmetrics.image import SpatialDistortionIndex
-            >>> preds = torch.rand([16, 3, 32, 32])
+            >>> preds = rand([16, 3, 32, 32])
             >>> target = {
-            ...     'ms': torch.rand([16, 3, 16, 16]),
-            ...     'pan': torch.rand([16, 3, 32, 32]),
+            ...     'ms': rand([16, 3, 16, 16]),
+            ...     'pan': rand([16, 3, 32, 32]),
             ... }
             >>> metric = SpatialDistortionIndex()
             >>> metric.update(preds, target)
@@ -207,13 +205,12 @@ def plot(
             :scale: 75
 
             >>> # Example plotting multiple values
-            >>> import torch
-            >>> _ = torch.manual_seed(42)
+            >>> from torch import rand
             >>> from torchmetrics.image import SpatialDistortionIndex
-            >>> preds = torch.rand([16, 3, 32, 32])
+            >>> preds = rand([16, 3, 32, 32])
             >>> target = {
-            ...     'ms': torch.rand([16, 3, 16, 16]),
-            ...     'pan': torch.rand([16, 3, 32, 32]),
+            ...     'ms': rand([16, 3, 16, 16]),
+            ...     'pan': rand([16, 3, 32, 32]),
             ... }
             >>> metric = SpatialDistortionIndex()
             >>> values = [ ]

src/torchmetrics/image/ergas.py

@@ -62,12 +62,12 @@ class ErrorRelativeGlobalDimensionlessSynthesis(Metric):
         kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.
 
     Example:
-        >>> import torch
+        >>> from torch import rand
         >>> from torchmetrics.image import ErrorRelativeGlobalDimensionlessSynthesis
-        >>> preds = torch.rand([16, 1, 16, 16], generator=torch.manual_seed(42))
+        >>> preds = rand([16, 1, 16, 16])
         >>> target = preds * 0.75
         >>> ergas = ErrorRelativeGlobalDimensionlessSynthesis()
-        >>> torch.round(ergas(preds, target))
+        >>> ergas(preds, target).round()
         tensor(10.)
 
     """
@@ -131,9 +131,9 @@ def plot(
             :scale: 75
 
             >>> # Example plotting a single value
-            >>> import torch
+            >>> from torch import rand
             >>> from torchmetrics.image import ErrorRelativeGlobalDimensionlessSynthesis
-            >>> preds = torch.rand([16, 1, 16, 16], generator=torch.manual_seed(42))
+            >>> preds = rand([16, 1, 16, 16])
             >>> target = preds * 0.75
             >>> metric = ErrorRelativeGlobalDimensionlessSynthesis()
             >>> metric.update(preds, target)
@@ -143,9 +143,9 @@ def plot(
             :scale: 75
 
             >>> # Example plotting multiple values
-            >>> import torch
+            >>> from torch import rand
             >>> from torchmetrics.image import ErrorRelativeGlobalDimensionlessSynthesis
-            >>> preds = torch.rand([16, 1, 16, 16], generator=torch.manual_seed(42))
+            >>> preds = rand([16, 1, 16, 16])
             >>> target = preds * 0.75
             >>> metric = ErrorRelativeGlobalDimensionlessSynthesis()
             >>> values = [ ]

src/torchmetrics/image/fid.py

@@ -265,8 +265,7 @@ class FrechetInceptionDistance(Metric):
             If ``reset_real_features`` is not an ``bool``
 
     Example:
-        >>> import torch
-        >>> _ = torch.manual_seed(123)
+        >>> from torch import rand
         >>> from torchmetrics.image.fid import FrechetInceptionDistance
         >>> fid = FrechetInceptionDistance(feature=64)
         >>> # generate two slightly overlapping image intensity distributions
@@ -275,7 +274,7 @@ class FrechetInceptionDistance(Metric):
         >>> fid.update(imgs_dist1, real=True)
         >>> fid.update(imgs_dist2, real=False)
         >>> fid.compute()
-        tensor(12.7202)
+        tensor(12.6388)
 
     """
 

src/torchmetrics/image/inception.py

@@ -84,15 +84,14 @@ class InceptionScore(Metric):
             If ``feature`` is not an ``str``, ``int`` or ``torch.nn.Module``
 
     Example:
-        >>> import torch
-        >>> _ = torch.manual_seed(123)
+        >>> from torch import rand
         >>> from torchmetrics.image.inception import InceptionScore
         >>> inception = InceptionScore()
         >>> # generate some images
         >>> imgs = torch.randint(0, 255, (100, 3, 299, 299), dtype=torch.uint8)
         >>> inception.update(imgs)
         >>> inception.compute()
-        (tensor(1.0544), tensor(0.0117))
+        (tensor(1.0549), tensor(0.0121))
 
     """
 

src/torchmetrics/image/kid.py

@@ -148,17 +148,16 @@ class KernelInceptionDistance(Metric):
             If ``reset_real_features`` is not an ``bool``
 
     Example:
-        >>> import torch
-        >>> _ = torch.manual_seed(123)
+        >>> from torch import randint
         >>> from torchmetrics.image.kid import KernelInceptionDistance
         >>> kid = KernelInceptionDistance(subset_size=50)
         >>> # generate two slightly overlapping image intensity distributions
-        >>> imgs_dist1 = torch.randint(0, 200, (100, 3, 299, 299), dtype=torch.uint8)
-        >>> imgs_dist2 = torch.randint(100, 255, (100, 3, 299, 299), dtype=torch.uint8)
+        >>> imgs_dist1 = randint(0, 200, (100, 3, 299, 299), dtype=torch.uint8)
+        >>> imgs_dist2 = randint(100, 255, (100, 3, 299, 299), dtype=torch.uint8)
         >>> kid.update(imgs_dist1, real=True)
         >>> kid.update(imgs_dist2, real=False)
         >>> kid.compute()
-        (tensor(0.0337), tensor(0.0023))
+        (tensor(0.0312), tensor(0.0025))
 
     """
 

src/torchmetrics/image/lpip.py

@@ -78,15 +78,14 @@ class LearnedPerceptualImagePatchSimilarity(Metric):
             If ``reduction`` is not one of ``"mean"`` or ``"sum"``
 
     Example:
-        >>> import torch
-        >>> _ = torch.manual_seed(123)
+        >>> from torch import rand
         >>> from torchmetrics.image.lpip import LearnedPerceptualImagePatchSimilarity
         >>> lpips = LearnedPerceptualImagePatchSimilarity(net_type='squeeze')
         >>> # LPIPS needs the images to be in the [-1, 1] range.
-        >>> img1 = (torch.rand(10, 3, 100, 100) * 2) - 1
-        >>> img2 = (torch.rand(10, 3, 100, 100) * 2) - 1
+        >>> img1 = (rand(10, 3, 100, 100) * 2) - 1
+        >>> img2 = (rand(10, 3, 100, 100) * 2) - 1
         >>> lpips(img1, img2)
-        tensor(0.1046)
+        tensor(0.1024)
 
     """
 

src/torchmetrics/image/mifid.py

@@ -129,13 +129,12 @@ class MemorizationInformedFrechetInceptionDistance(Metric):
             If ``reset_real_features`` is not an ``bool``
 
     Example::
-        >>> import torch
-        >>> _ = torch.manual_seed(42)
+        >>> from torch import randint
         >>> from torchmetrics.image.mifid import MemorizationInformedFrechetInceptionDistance
         >>> mifid = MemorizationInformedFrechetInceptionDistance(feature=64)
         >>> # generate two slightly overlapping image intensity distributions
-        >>> imgs_dist1 = torch.randint(0, 200, (100, 3, 299, 299), dtype=torch.uint8)
-        >>> imgs_dist2 = torch.randint(100, 255, (100, 3, 299, 299), dtype=torch.uint8)
+        >>> imgs_dist1 = randint(0, 200, (100, 3, 299, 299), dtype=torch.uint8)
+        >>> imgs_dist2 = randint(100, 255, (100, 3, 299, 299), dtype=torch.uint8)
         >>> mifid.update(imgs_dist1, real=True)
         >>> mifid.update(imgs_dist2, real=False)
         >>> mifid.compute()

src/torchmetrics/image/perceptual_path_length.py

@@ -23,9 +23,9 @@
     perceptual_path_length,
 )
 from torchmetrics.metric import Metric
-from torchmetrics.utilities.imports import _TORCHVISION_AVAILABLE
+from torchmetrics.utilities.imports import _TORCH_GREATER_EQUAL_2_0, _TORCHVISION_AVAILABLE
 
-if not _TORCHVISION_AVAILABLE:
+if not _TORCHVISION_AVAILABLE or not _TORCH_GREATER_EQUAL_2_0:
     __doctest_skip__ = ["PerceptualPathLength"]
 
 
@@ -98,9 +98,7 @@ class PerceptualPathLength(Metric):
             If ``upper_discard`` is not a float between 0 and 1 or None.
 
     Example::
-        >>> from torchmetrics.image import PerceptualPathLength
         >>> import torch
-        >>> _ = torch.manual_seed(42)
         >>> class DummyGenerator(torch.nn.Module):
         ...    def __init__(self, z_size) -> None:
         ...       super().__init__()
@@ -112,10 +110,8 @@ class PerceptualPathLength(Metric):
         ...      return torch.randn(num_samples, self.z_size)
         >>> generator = DummyGenerator(2)
         >>> ppl = PerceptualPathLength(num_samples=10)
-        >>> ppl(generator)  # doctest: +SKIP
-        (tensor(0.2371),
-        tensor(0.1763),
-        tensor([0.3502, 0.1362, 0.2535, 0.0902, 0.1784, 0.0769, 0.5871, 0.0691, 0.3921]))
+        >>> ppl(generator)
+        (tensor(...), tensor(...), tensor([...]))
 
     """
 

src/torchmetrics/image/psnrb.py

@@ -51,12 +51,10 @@ class PeakSignalNoiseRatioWithBlockedEffect(Metric):
         kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.
 
     Example:
-        >>> import torch
-        >>> from torchmetrics.image import PeakSignalNoiseRatioWithBlockedEffect
+        >>> from torch import rand
         >>> metric = PeakSignalNoiseRatioWithBlockedEffect()
-        >>> _ = torch.manual_seed(42)
-        >>> preds = torch.rand(2, 1, 10, 10)
-        >>> target = torch.rand(2, 1, 10, 10)
+        >>> preds = rand(2, 1, 10, 10)
+        >>> target = rand(2, 1, 10, 10)
         >>> metric(preds, target)
         tensor(7.2893)