Merge branch 'master' into bugfix/segmentation_dice_zero_overlap

Lightning-AI · Jan 8, 2025 · ab5070d · ab5070d
2 parents 5486901 + e690bbd
commit ab5070d
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diff --git a/.github/workflows/ci-integrate.yml b/.github/workflows/ci-integrate.yml
@@ -36,7 +36,7 @@ jobs:
           - { python-version: "3.10", requires: "latest", os: "ubuntu-22.04" }
           # - { python-version: "3.10", requires: "latest", os: "macOS-14" } # M1 machine # todo: crashing for MPS out of memory
     env:
-      PYTORCH_URL: "http://download.pytorch.org/whl/cpu/"
+      PYTORCH_URL: "https://download.pytorch.org/whl/cpu/"
       FREEZE_REQUIREMENTS: ${{ ! (github.ref == 'refs/heads/master' || startsWith(github.ref, 'refs/heads/release/')) }}
       PYPI_CACHE: "_ci-cache_PyPI"
 

diff --git a/.github/workflows/ci-tests.yml b/.github/workflows/ci-tests.yml
@@ -64,7 +64,7 @@ jobs:
       FREEZE_REQUIREMENTS: ${{ ! (github.ref == 'refs/heads/master' || startsWith(github.ref, 'refs/heads/release/')) }}
       TOKENIZERS_PARALLELISM: false
       TEST_DIRS: ${{ needs.check-diff.outputs.test-dirs }}
-      PIP_EXTRA_INDEX_URL: "--extra-index-url=http://download.pytorch.org/whl/cpu/"
+      PIP_EXTRA_INDEX_URL: "--extra-index-url=https://download.pytorch.org/whl/cpu/"
       UNITTEST_TIMEOUT: "" # by default, it is not set
 
     # Timeout: https://stackoverflow.com/a/59076067/4521646

diff --git a/.github/workflows/docs-build.yml b/.github/workflows/docs-build.yml
@@ -18,7 +18,7 @@ defaults:
 
 env:
   FREEZE_REQUIREMENTS: "1"
-  TORCH_URL: "http://download.pytorch.org/whl/cpu/"
+  TORCH_URL: "https://download.pytorch.org/whl/cpu/"
   PYPI_CACHE: "_ci-cache_PyPI"
   PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION: "python"
   TOKENIZERS_PARALLELISM: false

diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
@@ -23,7 +23,7 @@ ci:
 
 repos:
   - repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v4.6.0
+    rev: v5.0.0
     hooks:
       - id: end-of-file-fixer
       - id: trailing-whitespace
@@ -46,11 +46,10 @@ repos:
         exclude: pyproject.toml
 
   - repo: https://github.com/crate-ci/typos
-    rev: v1.22.9
+    rev: dictgen-v0.3.1
     hooks:
       - id: typos
-        # empty to do not write fixes
-        args: []
+        args: [] # empty to do not write fixes
         exclude: pyproject.toml
 
   - repo: https://github.com/PyCQA/docformatter
@@ -61,12 +60,12 @@ repos:
         args: ["--in-place"]
 
   - repo: https://github.com/sphinx-contrib/sphinx-lint
-    rev: v0.9.1
+    rev: v1.0.0
     hooks:
       - id: sphinx-lint
 
   - repo: https://github.com/executablebooks/mdformat
-    rev: 0.7.17
+    rev: 0.7.21
     hooks:
       - id: mdformat
         args: ["--number"]
@@ -113,7 +112,7 @@ repos:
       - id: text-unicode-replacement-char
 
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.5.0
+    rev: v0.8.6
     hooks:
       # try to fix what is possible
       - id: ruff
@@ -124,11 +123,11 @@ repos:
       - id: ruff
 
   - repo: https://github.com/tox-dev/pyproject-fmt
-    rev: 2.1.3
+    rev: v2.5.0
     hooks:
       - id: pyproject-fmt
         additional_dependencies: [tox]
   - repo: https://github.com/abravalheri/validate-pyproject
-    rev: v0.18
+    rev: v0.23
     hooks:
       - id: validate-pyproject
diff --git a/CHANGELOG.md b/CHANGELOG.md
@@ -842,7 +842,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
   * `PearsonCorrcoef`
   * `SpearmanCorrcoef`
 - Removed deprecated functions, and warnings in detection and pairwise ([#804](https://github.com/Lightning-AI/metrics/pull/804))
-  * `MAP` and `functional.pairwise.manhatten`
+  * `MAP` and `functional.pairwise.manhattan`
 - Removed deprecated functions, and warnings in Audio ([#805](https://github.com/Lightning-AI/metrics/pull/805))
   * `PESQ` and `functional.audio.pesq`
   * `PIT` and `functional.audio.pit`
@@ -1032,7 +1032,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
   - `pairwise_cosine_similarity`
   - `pairwise_euclidean_distance`
   - `pairwise_linear_similarity`
-  - `pairwise_manhatten_distance`
+  - `pairwise_manhattan_distance`
 
 ### Changed
 

diff --git a/_samples/bert_score-own_model.py b/_samples/bert_score-own_model.py
@@ -23,6 +23,7 @@
 import torch
 from torch import Tensor, nn
 from torch.nn import Module
+
 from torchmetrics.text.bert import BERTScore
 
 _NUM_LAYERS = 2

diff --git a/_samples/detection_map.py b/_samples/detection_map.py
@@ -14,6 +14,7 @@
 """An example of how the predictions and target should be defined for the MAP object detection metric."""
 
 from torch import BoolTensor, IntTensor, Tensor
+
 from torchmetrics.detection.mean_ap import MeanAveragePrecision
 
 # Preds should be a list of elements, where each element is a dict

diff --git a/docs/source/conf.py b/docs/source/conf.py
@@ -18,9 +18,10 @@
 from typing import Optional
 
 import lai_sphinx_theme
-import torchmetrics
 from lightning_utilities.docs.formatting import _linkcode_resolve, _transform_changelog
 
+import torchmetrics
+
 _PATH_HERE = os.path.abspath(os.path.dirname(__file__))
 _PATH_ROOT = os.path.realpath(os.path.join(_PATH_HERE, "..", ".."))
 sys.path.insert(0, os.path.abspath(_PATH_ROOT))

diff --git a/docs/source/pages/implement.rst b/docs/source/pages/implement.rst
@@ -30,7 +30,7 @@ We provide the remaining interface, such as ``reset()`` that will make sure to c
 states that have been added using ``add_state``. You should therefore not implement ``reset()`` yourself, only in rare
 cases where not all the state variables should be reset to their default value. Adding metric states with ``add_state``
 will make sure that states are correctly synchronized in distributed settings (DDP). To see how metric states are
-synchronized across distributed processes, refer to :meth:`~torchmetrics.Metric.add_state()` docs from the base
+synchronized across distributed processes, refer to :meth:`~torchmetrics.Metric.add_state` docs from the base
 :class:`~torchmetrics.Metric` class.
 
 Below is a basic implementation of a custom accuracy metric. In the ``__init__`` method we add the metric states

diff --git a/docs/source/pyplots/binary_accuracy.py b/docs/source/pyplots/binary_accuracy.py
@@ -1,5 +1,6 @@
 import matplotlib.pyplot as plt
 import torch
+
 import torchmetrics
 
 N = 10

diff --git a/docs/source/pyplots/binary_accuracy_multistep.py b/docs/source/pyplots/binary_accuracy_multistep.py
@@ -1,5 +1,6 @@
 import matplotlib.pyplot as plt
 import torch
+
 import torchmetrics
 
 N = 10

diff --git a/docs/source/pyplots/collection_binary.py b/docs/source/pyplots/collection_binary.py
@@ -1,5 +1,6 @@
 import matplotlib.pyplot as plt
 import torch
+
 import torchmetrics
 
 N = 10

diff --git a/docs/source/pyplots/collection_binary_together.py b/docs/source/pyplots/collection_binary_together.py
@@ -1,5 +1,6 @@
 import matplotlib.pyplot as plt
 import torch
+
 import torchmetrics
 
 N = 10

diff --git a/docs/source/pyplots/confusion_matrix.py b/docs/source/pyplots/confusion_matrix.py
@@ -1,5 +1,6 @@
 import matplotlib.pyplot as plt
 import torch
+
 import torchmetrics
 
 N = 10

diff --git a/docs/source/pyplots/multiclass_accuracy.py b/docs/source/pyplots/multiclass_accuracy.py
@@ -1,5 +1,6 @@
 import matplotlib.pyplot as plt
 import torch
+
 import torchmetrics
 
 N = 10

diff --git a/docs/source/pyplots/tracker_binary.py b/docs/source/pyplots/tracker_binary.py
@@ -1,5 +1,6 @@
 import matplotlib.pyplot as plt
 import torch
+
 import torchmetrics
 
 N = 10

diff --git a/examples/audio/pesq.py b/examples/audio/pesq.py
@@ -19,6 +19,7 @@
 import numpy as np
 import torch
 import torchaudio
+
 from torchmetrics.audio import PerceptualEvaluationSpeechQuality
 
 # %%

diff --git a/examples/audio/signal_to_noise_ratio.py b/examples/audio/signal_to_noise_ratio.py
@@ -13,6 +13,7 @@
 import matplotlib.pyplot as plt
 import numpy as np
 import torch
+
 from torchmetrics.audio import SignalNoiseRatio
 
 # %%

diff --git a/examples/image/clip_score.py b/examples/image/clip_score.py
@@ -15,6 +15,7 @@
 import torch
 from matplotlib.table import Table
 from skimage.data import astronaut, cat, coffee
+
 from torchmetrics.multimodal import CLIPScore
 
 # %%

diff --git a/examples/image/spatial_correlation_coef.py b/examples/image/spatial_correlation_coef.py
@@ -16,6 +16,7 @@
 import torch
 from skimage.data import shepp_logan_phantom
 from skimage.transform import iradon, radon, rescale
+
 from torchmetrics.image import SpatialCorrelationCoefficient
 
 # %%

diff --git a/examples/text/bertscore.py b/examples/text/bertscore.py
@@ -6,9 +6,10 @@
 Let's consider a use case in natural language processing where BERTScore is used to evaluate the quality of a text generation model. In this case we are imaging that we are developing a automated news summarization system. The goal is to create concise summaries of news articles that accurately capture the key points of the original articles. To evaluate the performance of your summarization system, you need a metric that can compare the generated summaries to human-written summaries. This is where the BERTScore can be used.
 """
 
-from torchmetrics.text import BERTScore, ROUGEScore
 from transformers import AutoTokenizer, pipeline
 
+from torchmetrics.text import BERTScore, ROUGEScore
+
 pipe = pipeline("text-generation", model="openai-community/gpt2")
 tokenizer = AutoTokenizer.from_pretrained("openai-community/gpt2")
 

diff --git a/examples/text/perplexity.py b/examples/text/perplexity.py
@@ -12,9 +12,10 @@
 # Here's a hypothetical Python example demonstrating the usage of Perplexity to evaluate a generative language model
 
 import torch
-from torchmetrics.text import Perplexity
 from transformers import AutoModelWithLMHead, AutoTokenizer
 
+from torchmetrics.text import Perplexity
+
 # %%
 # Load the GPT-2 model and tokenizer
 

diff --git a/examples/text/rouge.py b/examples/text/rouge.py
@@ -9,9 +9,10 @@
 # %%
 # Here's a hypothetical Python example demonstrating the usage of unigram ROUGE F-score to evaluate a generative language model:
 
-from torchmetrics.text import ROUGEScore
 from transformers import AutoTokenizer, pipeline
 
+from torchmetrics.text import ROUGEScore
+
 pipe = pipeline("text-generation", model="openai-community/gpt2")
 tokenizer = AutoTokenizer.from_pretrained("openai-community/gpt2")
 

diff --git a/requirements/image.txt b/requirements/image.txt
@@ -1,6 +1,6 @@
 # NOTE: the upper bound for the package version is only set for CI stability, and it is dropped while installing this package
 #  in case you want to preserve/enforce restrictions on the latest compatible version, add "strict" as an in-line comment
 
-scipy >1.0.0, <1.15.0
+scipy >1.0.0, <1.16.0
 torchvision >=0.15.1, <0.22.0
 torch-fidelity <=0.4.0  # bumping to allow install version from master, now used in testing
diff --git a/requirements/nominal_test.txt b/requirements/nominal_test.txt
@@ -3,6 +3,6 @@
 
 pandas >1.4.0, <=2.2.3 # cannot pin version due to numpy version incompatibility
 dython ==0.7.6 ; python_version <"3.9"
-dython ~=0.7.8 ; python_version > "3.8"  # we do not use `> =`
-scipy >1.0.0, <1.15.0 # cannot pin version due to some version conflicts with `oldest` CI configuration
+dython ==0.7.9 ; python_version > "3.8"  # we do not use `> =`
+scipy >1.0.0, <1.16.0 # cannot pin version due to some version conflicts with `oldest` CI configuration
 statsmodels >0.13.5, <0.15.0
diff --git a/requirements/segmentation_test.txt b/requirements/segmentation_test.txt
@@ -1,6 +1,6 @@
 # NOTE: the upper bound for the package version is only set for CI stability, and it is dropped while installing this package
 #  in case you want to preserve/enforce restrictions on the latest compatible version, add "strict" as an in-line comment
 
-scipy >1.0.0, <1.15.0
+scipy >1.0.0, <1.16.0
 monai ==1.3.2 ; python_version < "3.9"
 monai ==1.4.0 ; python_version > "3.8"
diff --git a/requirements/text_test.txt b/requirements/text_test.txt
@@ -5,7 +5,7 @@ jiwer >=2.3.0, <3.1.0
 rouge-score >0.1.0, <=0.1.2
 bert_score ==0.3.13
 huggingface-hub <0.28
-sacrebleu >=2.3.0, <2.5.0
+sacrebleu >=2.3.0, <2.6.0
 
 mecab-ko >=1.0.0, <1.1.0 ; python_version < "3.12"  # strict # todo: unpin python_version
 mecab-ko-dic >=1.0.0, <1.1.0 ; python_version < "3.12"  # todo: unpin python_version
diff --git a/src/conftest.py b/src/conftest.py
@@ -11,7 +11,7 @@
     MANUAL_SEED = doctest.register_optionflag("MANUAL_SEED")
 
     @pytest.fixture(autouse=True)
-    def reset_random_seed(seed: int = 42) -> None:  # noqa: PT004
+    def reset_random_seed(seed: int = 42) -> None:
         """Reset the random seed before running each doctest."""
         import random
 

diff --git a/src/torchmetrics/__init__.py b/src/torchmetrics/__init__.py
@@ -169,6 +169,7 @@
 
 __all__ = [
     "AUROC",
+    "ROC",
     "Accuracy",
     "AveragePrecision",
     "BLEUScore",
@@ -229,7 +230,6 @@
     "PrecisionAtFixedRecall",
     "PrecisionRecallCurve",
     "R2Score",
-    "ROC",
     "Recall",
     "RecallAtFixedPrecision",
     "RelativeAverageSpectralError",

diff --git a/src/torchmetrics/aggregation.py b/src/torchmetrics/aggregation.py
@@ -65,8 +65,7 @@ def __init__(
         allowed_nan_strategy = ("error", "warn", "ignore")
         if nan_strategy not in allowed_nan_strategy and not isinstance(nan_strategy, float):
             raise ValueError(
-                f"Arg `nan_strategy` should either be a float or one of {allowed_nan_strategy}"
-                f" but got {nan_strategy}."
+                f"Arg `nan_strategy` should either be a float or one of {allowed_nan_strategy} but got {nan_strategy}."
             )
 
         self.nan_strategy = nan_strategy

diff --git a/src/torchmetrics/audio/__init__.py b/src/torchmetrics/audio/__init__.py
@@ -41,13 +41,13 @@
         scipy.signal.hamming = scipy.signal.windows.hamming
 
 __all__ = [
+    "ComplexScaleInvariantSignalNoiseRatio",
     "PermutationInvariantTraining",
     "ScaleInvariantSignalDistortionRatio",
-    "SignalDistortionRatio",
-    "SourceAggregatedSignalDistortionRatio",
     "ScaleInvariantSignalNoiseRatio",
+    "SignalDistortionRatio",
     "SignalNoiseRatio",
-    "ComplexScaleInvariantSignalNoiseRatio",
+    "SourceAggregatedSignalDistortionRatio",
 ]
 
 if _PESQ_AVAILABLE:

diff --git a/src/torchmetrics/audio/srmr.py b/src/torchmetrics/audio/srmr.py
@@ -58,7 +58,7 @@ class SpeechReverberationModulationEnergyRatio(Metric):
         This implementation is experimental, and might not be consistent with the matlab
         implementation `SRMRToolbox`_, especially the fast implementation.
         The slow versions, a) fast=False, norm=False, max_cf=128, b) fast=False, norm=True, max_cf=30, have
-        a relatively small inconsistence.
+        a relatively small inconsistency.
 
     Args:
         fs: the sampling rate