Merge branch 'master' into psnrb

.azure/gpu-pipeline.yml

@@ -37,6 +37,7 @@ jobs:
 
     variables:
       DEVICES: $( python -c 'name = "$(Agent.Name)" ; gpus = name.split("_")[-1] if "_" in name else "0,1"; print(gpus)' )
+      HF_CACHE_DIR: "$(Pipeline.Workspace)/ci-cache_huggingface"
 
     container:
       image: "$(docker-image)"
@@ -95,6 +96,19 @@ jobs:
         python -c "import torch ; mgpu = torch.cuda.device_count() ; assert mgpu >= 2, f'GPU: {mgpu}'"
       displayName: 'Sanity check'
 
+    - task: Cache@2
+      inputs:
+        key: transformers | "$(Agent.OS)"
+        restoreKeys: transformers
+        path: $(HF_CACHE_DIR)
+        cacheHitVar: HF_CACHE_RESTORED
+    - bash: |
+        printf "cache location: $(HF_CACHE_DIR)\n"
+        printf "hit the HF cache: $(variables.HF_CACHE_RESTORED)\n"
+        mkdir -p $(HF_CACHE_DIR)  # in case cache was void
+        ls -lh $(HF_CACHE_DIR)  # show what was restored...
+      displayName: 'Show HF cache'
+
     - bash: python -m pytest torchmetrics --cov=torchmetrics --timeout=120 --durations=50
       workingDirectory: src
       displayName: 'DocTesting'
@@ -120,6 +134,8 @@ jobs:
         python -m codecov --token=$(CODECOV_TOKEN) --commit=$(Build.SourceVersion) --flags=gpu,unittest --name="GPU-coverage" --env=linux,azure
         ls -l
       workingDirectory: tests
+      env:
+        TRANSFORMERS_CACHE: $(HF_CACHE_DIR)
       displayName: 'Statistics'
 
     - task: PublishTestResults@2

.github/workflows/publish-pypi.yml

@@ -73,7 +73,7 @@ jobs:
     # We do this, since failures on test.pypi aren't that bad
     - name: Publish to Test PyPI
       if: startsWith(github.event.ref, 'refs/tags') || github.event_name == 'release'
-      uses: pypa/gh-action-pypi-publish@v1.5.2
+      uses: pypa/gh-action-pypi-publish@v1.6.4
       with:
         user: __token__
         password: ${{ secrets.test_pypi_password }}
@@ -82,7 +82,7 @@ jobs:
 
     - name: Publish distribution 📦 to PyPI
       if: startsWith(github.event.ref, 'refs/tags') || github.event_name == 'release'
-      uses: pypa/gh-action-pypi-publish@v1.5.2
+      uses: pypa/gh-action-pypi-publish@v1.6.4
       with:
         user: __token__
         password: ${{ secrets.pypi_password }}

.pre-commit-config.yaml

@@ -45,13 +45,13 @@ repos:
         name: Upgrade code
 
   - repo: https://github.com/PyCQA/docformatter
-    rev: v1.5.0
+    rev: v1.5.1
     hooks:
       - id: docformatter
         args: [--in-place, --wrap-summaries=115, --wrap-descriptions=120]
 
   - repo: https://github.com/PyCQA/isort
-    rev: 5.11.2
+    rev: 5.11.4
     hooks:
       - id: isort
         name: imports

docs/source/image/error_relative_global_dimensionless_synthesis.rst

@@ -3,6 +3,8 @@
    :image: https://pl-flash-data.s3.amazonaws.com/assets/thumbnails/image_classification.svg
    :tags: Image
 
+.. include:: ../links.rst
+
 ############################################
 Error Relative Global Dim. Synthesis (ERGAS)
 ############################################
@@ -12,6 +14,8 @@ ________________
 
 .. autoclass:: torchmetrics.image.ergas.ErrorRelativeGlobalDimensionlessSynthesis
     :noindex:
+    :exclude-members: update, compute
+
 
 Functional Interface
 ____________________

docs/source/image/frechet_inception_distance.rst

@@ -14,3 +14,4 @@ ________________
 
 .. autoclass:: torchmetrics.image.fid.FrechetInceptionDistance
     :noindex:
+    :exclude-members: update, compute

docs/source/image/inception_score.rst

@@ -3,6 +3,8 @@
    :image: https://pl-flash-data.s3.amazonaws.com/assets/thumbnails/image_classification.svg
    :tags: Image
 
+.. include:: ../links.rst
+
 ###############
 Inception Score
 ###############
@@ -12,3 +14,4 @@ ________________
 
 .. autoclass:: torchmetrics.image.inception.InceptionScore
     :noindex:
+    :exclude-members: update, compute

docs/source/image/kernel_inception_distance.rst

@@ -14,3 +14,4 @@ ________________
 
 .. autoclass:: torchmetrics.image.kid.KernelInceptionDistance
     :noindex:
+    :exclude-members: update, compute

docs/source/image/learned_perceptual_image_patch_similarity.rst

@@ -12,3 +12,4 @@ ________________
 
 .. autoclass:: torchmetrics.image.lpip.LearnedPerceptualImagePatchSimilarity
     :noindex:
+    :exclude-members: update, compute

docs/source/image/multi_scale_structural_similarity.rst

@@ -14,6 +14,7 @@ ________________
 
 .. autoclass:: torchmetrics.MultiScaleStructuralSimilarityIndexMeasure
     :noindex:
+    :exclude-members: update, compute
 
 Functional Interface
 ____________________

docs/source/image/peak_signal_noise_ratio.rst

@@ -14,6 +14,7 @@ ________________
 
 .. autoclass:: torchmetrics.PeakSignalNoiseRatio
     :noindex:
+    :exclude-members: update, compute
 
 Functional Interface
 ____________________

docs/source/image/spectral_angle_mapper.rst

@@ -12,6 +12,7 @@ ________________
 
 .. autoclass:: torchmetrics.SpectralAngleMapper
     :noindex:
+    :exclude-members: update, compute
 
 Functional Interface
 ____________________

docs/source/image/spectral_distortion_index.rst

@@ -14,6 +14,7 @@ ________________
 
 .. autoclass:: torchmetrics.SpectralDistortionIndex
     :noindex:
+    :exclude-members: update, compute
 
 Functional Interface
 ____________________

docs/source/image/structural_similarity.rst

@@ -14,6 +14,7 @@ ________________
 
 .. autoclass:: torchmetrics.StructuralSimilarityIndexMeasure
     :noindex:
+    :exclude-members: update, compute
 
 Functional Interface
 ____________________

docs/source/image/total_variation.rst

@@ -14,6 +14,7 @@ ________________
 
 .. autoclass:: torchmetrics.TotalVariation
     :noindex:
+    :exclude-members: update, compute
 
 Functional Interface
 ____________________

docs/source/image/universal_image_quality_index.rst

@@ -14,6 +14,7 @@ ________________
 
 .. autoclass:: torchmetrics.UniversalImageQualityIndex
     :noindex:
+    :exclude-members: update, compute
 
 Functional Interface
 ____________________

docs/source/links.rst

@@ -115,5 +115,15 @@
 .. _Scale-invariant signal-to-noise ratio: https://arxiv.org/abs/1711.00541
 .. _Signal-to-noise ratio: https://arxiv.org/abs/1811.02508
 .. _Permutation invariant training: https://arxiv.org/abs/1607.00325
+.. _ranking ref1: https://link.springer.com/chapter/10.1007/978-0-387-09823-4_34
+.. _Spectral Distortion Index: https://www.ingentaconnect.com/content/asprs/pers/2008/00000074/00000002/art00003;jsessionid=nzjnb3v9xxr1.x-ic-live-03
+.. _Relative dimensionless global error synthesis: https://ieeexplore.ieee.org/document/4317530
+.. _fid ref1: https://arxiv.org/abs/1512.00567
+.. _fid ref2: https://arxiv.org/abs/1706.08500
+.. _inception ref1: https://arxiv.org/abs/1606.03498
+.. _inception ref2: https://arxiv.org/abs/1706.08500
+.. _kid ref1: https://arxiv.org/abs/1801.01401
+.. _kid ref2: https://arxiv.org/abs/1706.08500
+.. _Spectral Angle Mapper: https://ntrs.nasa.gov/citations/19940012238
 .. _Multilabel coverage error: https://link.springer.com/chapter/10.1007/978-0-387-09823-4_34
 .. _Peak Signal to Noise Ratio with Blocked Effect:https://ieeexplore.ieee.org/abstract/document/5535179

docs/source/pages/lightning.rst

@@ -32,9 +32,9 @@ The example below shows how to use a metric in your `LightningModule <https://py
 
     class MyModel(LightningModule):
 
-        def __init__(self):
+        def __init__(self, num_classes):
             ...
-            self.accuracy = torchmetrics.Accuracy(task='multiclass')
+            self.accuracy = torchmetrics.Accuracy(task="multiclass", num_classes=num_classes)
 
         def training_step(self, batch, batch_idx):
             x, y = batch
@@ -78,10 +78,10 @@ value by calling ``.compute()``.
 
     class MyModule(LightningModule):
 
-        def __init__(self):
+        def __init__(self, num_classes):
             ...
-            self.train_acc = torchmetrics.Accuracy(task='multiclass')
-            self.valid_acc = torchmetrics.Accuracy(task='multiclass')
+            self.train_acc = torchmetrics.Accuracy(task="multiclass", num_classes=num_classes)
+            self.valid_acc = torchmetrics.Accuracy(task="multiclass", num_classes=num_classes)
 
         def training_step(self, batch, batch_idx):
             x, y = batch
@@ -105,8 +105,8 @@ of the metrics.
 
         def __init__(self):
             ...
-            self.train_acc = torchmetrics.Accuracy(task='multiclass')
-            self.valid_acc = torchmetrics.Accuracy(task='multiclass')
+            self.train_acc = torchmetrics.Accuracy(task="multiclass", num_classes=num_classes)
+            self.valid_acc = torchmetrics.Accuracy(task="multiclass", num_classes=num_classes)
 
         def training_step(self, batch, batch_idx):
             x, y = batch
@@ -141,9 +141,9 @@ mixed as it can lead to wrong results.
 
         class MyModule(LightningModule):
 
-            def __init__(self):
+            def __init__(self, num_classes):
                 ...
-                self.valid_acc = torchmetrics.Accuracy(task='multiclass')
+                self.valid_acc = torchmetrics.Accuracy(task="multiclass", num_classes=num_classes)
 
             def validation_step(self, batch, batch_idx):
                 logits = self(x)
@@ -185,9 +185,9 @@ The following contains a list of pitfalls to be aware of:
 
     class MyModule(LightningModule):
 
-        def __init__(self):
+        def __init__(self, num_classes):
             ...
-            self.val_acc = nn.ModuleList([torchmetrics.Accuracy(task='multiclass') for _ in range(2)])
+            self.val_acc = nn.ModuleList([torchmetrics.Accuracy(task="multiclass", num_classes=num_classes) for _ in range(2)])
 
         def val_dataloader(self):
             return [DataLoader(...), DataLoader(...)]

docs/source/pages/overview.rst

@@ -282,9 +282,9 @@ inside your LightningModule. In most cases we just have to replace ``self.log``
     from torchmetrics.classification import MulticlassAccuracy, MulticlassPrecision, MulticlassRecall
 
     class MyModule(LightningModule):
-        def __init__(self):
+        def __init__(self, num_classes):
             metrics = MetricCollection([
-                MulticlassAccuracy(), MulticlassPrecision(), MulticlassRecall()
+                MulticlassAccuracy(num_classes), MulticlassPrecision(num_classes), MulticlassRecall(num_classes)
             ])
             self.train_metrics = metrics.clone(prefix='train_')
             self.valid_metrics = metrics.clone(prefix='val_')

docs/source/pages/quickstart.rst

@@ -64,7 +64,7 @@ The code-snippet below shows a simple example for calculating the accuracy using
     preds = torch.randn(10, 5).softmax(dim=-1)
     target = torch.randint(5, (10,))
 
-    acc = torchmetrics.functional.accuracy(preds, target, task='multiclass', num_classes=5)
+    acc = torchmetrics.functional.accuracy(preds, target, task="multiclass", num_classes=5)
 
 Module metrics
 ~~~~~~~~~~~~~~
@@ -86,7 +86,7 @@ The code below shows how to use the class-based interface:
     import torchmetrics
 
     # initialize metric
-    metric = torchmetrics.Accuracy(task='multiclass', num_classes=5)
+    metric = torchmetrics.Accuracy(task="multiclass", num_classes=5)
 
     n_batches = 10
     for i in range(n_batches):

src/torchmetrics/detection/mean_ap.py

@@ -438,7 +438,6 @@ def _move_list_states_to_cpu(self) -> None:
             setattr(self, key, current_to_cpu)
 
     def _get_safe_item_values(self, item: Dict[str, Any]) -> Union[Tensor, Tuple]:
-
         if self.iou_type == "bbox":
             boxes = _fix_empty_tensors(item["boxes"])
             if boxes.numel() > 0:

src/torchmetrics/functional/image/d_lambda.py

@@ -94,8 +94,8 @@ def spectral_distortion_index(
     p: int = 1,
     reduction: Literal["elementwise_mean", "sum", "none"] = "elementwise_mean",
 ) -> Tensor:
-    """Spectral Distortion Index (SpectralDistortionIndex_) also now as D_lambda is used to compare the spectral
-    distortion between two images.
+    """Calculates `Spectral Distortion Index`_ (SpectralDistortionIndex_) also known as D_lambda that is used to
+    compare the spectral distortion between two images.
 
     Args:
         preds: Low resolution multispectral image

src/torchmetrics/image/d_lambda.py

@@ -26,6 +26,16 @@ class SpectralDistortionIndex(Metric):
     """Computes Spectral Distortion Index (SpectralDistortionIndex_) also now as D_lambda is used to compare the
     spectral distortion between two images.
 
+    As input to ``forward`` and ``update`` the metric accepts the following input
+
+    - ``preds`` (:class:`~torch.Tensor`): Low resolution multispectral image of shape ``(N,C,H,W)``
+    - ``target``(:class:`~torch.Tensor`): High resolution fused image of shape ``(N,C,H,W)``
+
+    As output of `forward` and `compute` the metric returns the following output
+
+    - ``sdi`` (:class:`~torch.Tensor`): if ``reduction!='none'`` returns float scalar tensor with average SDI value
+      over sample else returns tensor of shape ``(N,)`` with SDI values per sample
+
     Args:
         p: Large spectral differences
         reduction: a method to reduce metric score over labels.
@@ -36,7 +46,6 @@ class SpectralDistortionIndex(Metric):
 
         kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.
 
-
     Example:
         >>> import torch
         >>> _ = torch.manual_seed(42)
@@ -46,12 +55,6 @@ class SpectralDistortionIndex(Metric):
         >>> sdi = SpectralDistortionIndex()
         >>> sdi(preds, target)
         tensor(0.0234)
-
-    References:
-        [1] Alparone, Luciano & Aiazzi, Bruno & Baronti, Stefano & Garzelli, Andrea & Nencini,
-        Filippo & Selva, Massimo. (2008). Multispectral and Panchromatic Data Fusion
-        Assessment Without Reference. ASPRS Journal of Photogrammetric Engineering
-        and Remote Sensing. 74. 193-200. 10.14358/PERS.74.2.193.
     """
 
     higher_is_better: bool = True
@@ -82,12 +85,7 @@ def __init__(
         self.add_state("target", default=[], dist_reduce_fx="cat")
 
     def update(self, preds: Tensor, target: Tensor) -> None:
-        """Update state with preds and target.
-
-        Args:
-            preds: Low resolution multispectral image
-            target: High resolution fused image
-        """
+        """Update state with preds and target."""
         preds, target = _spectral_distortion_index_update(preds, target)
         self.preds.append(preds)
         self.target.append(target)