Update min Pytorch to 1.10 (#2145)

Co-authored-by: mergify[bot] <37929162+mergify[bot]@users.noreply.github.com>
Co-authored-by: Jirka Borovec <6035284+Borda@users.noreply.github.com>

.azure/gpu-unittests.yml

@@ -19,9 +19,9 @@ jobs:
  matrix:
  "PyTorch | old":
  # Torch does not have build wheels with old Torch versions for newer CUDA
- docker-image: "pytorchlightning/torchmetrics:ubuntu20.04-cuda11.1.1-py3.8-torch1.8.1"
+ docker-image: "pytorchlightning/torchmetrics:ubuntu22.04-cuda11.8.0-py3.9-torch1.10"
  agent-pool: "lit-rtx-3090"
- torch-ver: "1.8.1"
+ torch-ver: "1.10.2"
  "PyTorch | 1.X":
  docker-image: "pytorchlightning/torchmetrics:ubuntu22.04-cuda11.8.0-py3.9-torch1.13"
  agent-pool: "lit-rtx-3090"

.github/workflows/ci-tests.yml

@@ -32,7 +32,7 @@ jobs:
  matrix:
  os: ["ubuntu-20.04"]
  python-version: ["3.9"]
- pytorch-version: ["1.9.1", "1.10.2", "1.11.0", "1.12.1", "1.13.1", "2.0.1", "2.1.0"]
+ pytorch-version: ["1.10.2", "1.11.0", "1.12.1", "1.13.1", "2.0.1", "2.1.0"]
  include:
  - { os: "ubuntu-22.04", python-version: "3.8", pytorch-version: "1.13.1" }
  - { os: "ubuntu-22.04", python-version: "3.10", pytorch-version: "1.13.1" }
@@ -47,10 +47,6 @@ jobs:
  - { os: "windows-2022", python-version: "3.9", pytorch-version: "1.13.1" }
  - { os: "windows-2022", python-version: "3.10", pytorch-version: "2.0.1" }
  - { os: "windows-2022", python-version: "3.11", pytorch-version: "2.1.0" }
- # the oldest configurations
- - { os: "ubuntu-20.04", python-version: "3.8", pytorch-version: "1.8.1", requires: "oldest" }
- - { os: "macOS-11", python-version: "3.8", pytorch-version: "1.8.1", requires: "oldest" }
- - { os: "windows-2019", python-version: "3.8", pytorch-version: "1.8.1", requires: "oldest" }
  env:
  PYTORCH_URL: "https://download.pytorch.org/whl/cpu/torch_stable.html"
  FREEZE_REQUIREMENTS: ${{ ! (github.ref == 'refs/heads/master' || startsWith(github.ref, 'refs/heads/release/')) }}

.github/workflows/docker-build.yml

@@ -66,7 +66,6 @@ jobs:
  include:
  # These are the base images for PL release docker images,
  # so include at least all of the combinations in release-dockers.yml.
- - { python: "3.8", pytorch: "1.8.1", cuda: "11.1.1", ubuntu: "20.04" }
  - { python: "3.9", pytorch: "1.10", cuda: "11.8.0", ubuntu: "22.04" }
  - { python: "3.9", pytorch: "1.11", cuda: "11.8.0", ubuntu: "22.04" }
  - { python: "3.9", pytorch: "1.13", cuda: "11.8.0", ubuntu: "22.04" }

CHANGELOG.md

@@ -23,6 +23,8 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 - Change default state of `SpectralAngleMapper` and `UniversalImageQualityIndex` to be tensors ([#2089](https://github.com/Lightning-AI/torchmetrics/pull/2089))
 
+- Changed minimum supported Pytorch version from 1.8 to 1.10 ([#2145](https://github.com/Lightning-AI/torchmetrics/pull/2145))
+
 
 ### Removed
 

requirements/base.txt

@@ -3,6 +3,7 @@
 
 numpy >1.20.0
 packaging >17.1
-torch >=1.8.1, <=2.1.0
+torch >=1.10.0, <=2.0.1
+torch >=1.10.0, <=2.1.0
 typing-extensions; python_version < '3.9'
 lightning-utilities >=0.8.0, <0.10.0

src/torchmetrics/functional/image/helper.py

@@ -4,9 +4,6 @@
 from torch import Tensor
 from torch.nn import functional as F # noqa: N812
 
-from torchmetrics.utilities import rank_zero_warn
-from torchmetrics.utilities.imports import _TORCH_GREATER_EQUAL_1_10
-
 
 def _gaussian(kernel_size: int, sigma: float, dtype: torch.dtype, device: Union[torch.device, str]) -> Tensor:
  """Compute 1D gaussian kernel.
@@ -172,13 +169,4 @@ def _reflection_pad_3d(inputs: Tensor, pad_h: int, pad_w: int, pad_d: int) -> Te
  padded input tensor
 
  """
- if _TORCH_GREATER_EQUAL_1_10:
- inputs = F.pad(inputs, (pad_h, pad_h, pad_w, pad_w, pad_d, pad_d), mode="reflect")
- else:
- rank_zero_warn(
- "An older version of pyTorch is used."
- " For optimal speed, please upgrade to at least PyTorch v1.10 or higher."
- )
- for dim, pad in enumerate([pad_h, pad_w, pad_d]):
- inputs = _single_dimension_pad(inputs, dim + 2, pad, outer_pad=1)
- return inputs
+ return F.pad(inputs, (pad_h, pad_h, pad_w, pad_w, pad_d, pad_d), mode="reflect")

src/torchmetrics/functional/image/perceptual_path_length.py

@@ -18,7 +18,7 @@
 from torch import Tensor, nn
 
 from torchmetrics.functional.image.lpips import _LPIPS
-from torchmetrics.utilities.imports import _TORCH_GREATER_EQUAL_1_10, _TORCHVISION_AVAILABLE
+from torchmetrics.utilities.imports import _TORCHVISION_AVAILABLE
 
 if not _TORCHVISION_AVAILABLE:
  __doctest_skip__ = ["perceptual_path_length"]
@@ -246,8 +246,7 @@ def perceptual_path_length(
  else:
  raise ValueError(f"sim_net must be a nn.Module or one of 'alex', 'vgg', 'squeeze', got {sim_net}")
 
- decorator = torch.inference_mode if _TORCH_GREATER_EQUAL_1_10 else torch.no_grad
- with decorator():
+ with torch.inference_mode():
  distances = []
  num_batches = math.ceil(num_samples / batch_size)
  for batch_idx in range(num_batches):

src/torchmetrics/image/fid.py

@@ -20,13 +20,11 @@
 from torch.nn.functional import adaptive_avg_pool2d
 
 from torchmetrics.metric import Metric
-from torchmetrics.utilities.imports import _MATPLOTLIB_AVAILABLE, _TORCH_FIDELITY_AVAILABLE, _TORCH_GREATER_EQUAL_1_9
+from torchmetrics.utilities.imports import _MATPLOTLIB_AVAILABLE, _TORCH_FIDELITY_AVAILABLE
 from torchmetrics.utilities.plot import _AX_TYPE, _PLOT_OUT_TYPE
 
-__doctest_skip__ = ["FrechetInceptionDistance.__init__"] if not _TORCH_GREATER_EQUAL_1_9 else []
-
 if not _MATPLOTLIB_AVAILABLE:
- __doctest_skip__ += ["FrechetInceptionDistance.plot"]
+ __doctest_skip__ = ["FrechetInceptionDistance.plot"]
 
 if _TORCH_FIDELITY_AVAILABLE:
  from torch_fidelity.feature_extractor_inceptionv3 import FeatureExtractorInceptionV3 as _FeatureExtractorInceptionV3
@@ -42,9 +40,6 @@ class _FeatureExtractorInceptionV3(Module): # type: ignore[no-redef]
 
  __doctest_skip__ = ["FrechetInceptionDistance", "FrechetInceptionDistance.plot"]
 
-if not _TORCH_GREATER_EQUAL_1_9:
- __doctest_skip__ = ["FrechetInceptionDistance", "FrechetInceptionDistance.plot"]
-
 
 class NoTrainInceptionV3(_FeatureExtractorInceptionV3):
  """Module that never leaves evaluation mode."""
@@ -285,10 +280,6 @@ def __init__(
  **kwargs: Any,
  ) -> None:
  super().__init__(**kwargs)
-
- if not _TORCH_GREATER_EQUAL_1_9:
- raise ValueError("FrechetInceptionDistance metric requires that PyTorch is version 1.9.0 or higher.")
-
  if isinstance(feature, int):
  num_features = feature
  if not _TORCH_FIDELITY_AVAILABLE:

src/torchmetrics/image/mifid.py

@@ -20,7 +20,7 @@
 from torchmetrics.image.fid import NoTrainInceptionV3, _compute_fid
 from torchmetrics.metric import Metric
 from torchmetrics.utilities.data import dim_zero_cat
-from torchmetrics.utilities.imports import _MATPLOTLIB_AVAILABLE, _TORCH_FIDELITY_AVAILABLE, _TORCH_GREATER_EQUAL_1_10
+from torchmetrics.utilities.imports import _MATPLOTLIB_AVAILABLE, _TORCH_FIDELITY_AVAILABLE
 from torchmetrics.utilities.plot import _AX_TYPE, _PLOT_OUT_TYPE
 
 __doctest_requires__ = {
@@ -29,12 +29,6 @@
  ]
 }
 
-if not _TORCH_GREATER_EQUAL_1_10:
- __doctest_skip__ = [
- "MemorizationInformedFrechetInceptionDistance",
- "MemorizationInformedFrechetInceptionDistance.plot",
- ]
-
 if not _MATPLOTLIB_AVAILABLE:
  __doctest_skip__ = ["MemorizationInformedFrechetInceptionDistance.plot"]
 
@@ -166,11 +160,6 @@ def __init__(
  **kwargs: Any,
  ) -> None:
  super().__init__(**kwargs)
- if not _TORCH_GREATER_EQUAL_1_10:
- raise RuntimeError(
- "MemorizationInformedFrechetInceptionDistance metric requires PyTorch version greater or equal to 1.10"
- )
-
  if isinstance(feature, int):
  if not _TORCH_FIDELITY_AVAILABLE:
  raise ModuleNotFoundError(

src/torchmetrics/utilities/checks.py

@@ -656,7 +656,7 @@ def check_forward_full_state_property(
 
  Example (states in ``update`` are independent, save to set ``full_state_update=False``)
  >>> from torchmetrics.classification import MulticlassConfusionMatrix
- >>> check_forward_full_state_property( # doctest: +ELLIPSIS
+ >>> check_forward_full_state_property( # doctest: +SKIP
  ... MulticlassConfusionMatrix,
  ... init_args = {'num_classes': 3},
  ... input_args = {'preds': torch.randint(3, (100,)), 'target': torch.randint(3, (100,))},

src/torchmetrics/utilities/compute.py

@@ -16,8 +16,6 @@
 import torch
 from torch import Tensor
 
-from torchmetrics.utilities.imports import _TORCH_GREATER_EQUAL_1_9
-
 
 def _safe_matmul(x: Tensor, y: Tensor) -> Tensor:
  """Safe calculation of matrix multiplication.
@@ -101,7 +99,7 @@ def _auc_compute_without_check(x: Tensor, y: Tensor, direction: float, axis: int
 def _auc_compute(x: Tensor, y: Tensor, reorder: bool = False) -> Tensor:
  with torch.no_grad():
  if reorder:
- x, x_idx = torch.sort(x, stable=True) if _TORCH_GREATER_EQUAL_1_9 else torch.sort(x)
+ x, x_idx = torch.sort(x, stable=True)
  y = y[x_idx]
 
  dx = x[1:] - x[:-1]

src/torchmetrics/utilities/imports.py

@@ -23,8 +23,6 @@
 _PYTHON_VERSION = ".".join(map(str, [sys.version_info.major, sys.version_info.minor, sys.version_info.micro]))
 _PYTHON_LOWER_3_8 = parse(_PYTHON_VERSION) < Version("3.8")
 _TORCH_LOWER_1_12_DEV: Optional[bool] = compare_version("torch", operator.lt, "1.12.0.dev")
-_TORCH_GREATER_EQUAL_1_9: Optional[bool] = compare_version("torch", operator.ge, "1.9.0")
-_TORCH_GREATER_EQUAL_1_10: Optional[bool] = compare_version("torch", operator.ge, "1.10.0")
 _TORCH_GREATER_EQUAL_1_11: Optional[bool] = compare_version("torch", operator.ge, "1.11.0")
 _TORCH_GREATER_EQUAL_1_12: Optional[bool] = compare_version("torch", operator.ge, "1.12.0")
 _TORCH_GREATER_EQUAL_1_13: Optional[bool] = compare_version("torch", operator.ge, "1.13.0")

tests/unittests/classification/test_calibration_error.py

@@ -29,7 +29,7 @@
  multiclass_calibration_error,
 )
 from torchmetrics.metric import Metric
-from torchmetrics.utilities.imports import _TORCH_GREATER_EQUAL_1_9, _TORCH_GREATER_EQUAL_1_13
+from torchmetrics.utilities.imports import _TORCH_GREATER_EQUAL_1_13
 
 from unittests import NUM_CLASSES
 from unittests.classification.inputs import _binary_cases, _multiclass_cases
@@ -228,8 +228,6 @@ def test_multiclass_calibration_error_differentiability(self, inputs):
  def test_multiclass_calibration_error_dtype_cpu(self, inputs, dtype):
  """Test dtype support of the metric on CPU."""
  preds, target = inputs
- if dtype == torch.half and not _TORCH_GREATER_EQUAL_1_9:
- pytest.xfail(reason="torch.max in metric not supported before pytorch v1.9 for cpu + half")
  if (preds < 0).any() and dtype == torch.half:
  pytest.xfail(reason="torch.softmax in metric does not support cpu + half precision")
  self.run_precision_test_cpu(

tests/unittests/classification/test_ranking.py

@@ -30,7 +30,6 @@
  multilabel_ranking_average_precision,
  multilabel_ranking_loss,
 )
-from torchmetrics.utilities.imports import _TORCH_GREATER_EQUAL_1_9
 
 from unittests import NUM_CLASSES
 from unittests.classification.inputs import _multilabel_cases
@@ -124,11 +123,6 @@ def test_multilabel_ranking_dtype_cpu(self, inputs, metric, functional_metric, r
  pytest.xfail(
  reason="multilabel_ranking_average_precision requires torch.unique which is not implemented for half"
  )
- if dtype == torch.half and not _TORCH_GREATER_EQUAL_1_9 and functional_metric == multilabel_coverage_error:
- pytest.xfail(
- reason="multilabel_coverage_error requires torch.min which is only implemented for half"
- " in v1.9 or higher of torch."
- )
  self.run_precision_test_cpu(
  preds=preds,
  target=target,

tests/unittests/image/test_fid.py

@@ -20,7 +20,7 @@
 from torch.nn import Module
 from torch.utils.data import Dataset
 from torchmetrics.image.fid import FrechetInceptionDistance, NoTrainInceptionV3
-from torchmetrics.utilities.imports import _TORCH_FIDELITY_AVAILABLE, _TORCH_GREATER_EQUAL_1_9
+from torchmetrics.utilities.imports import _TORCH_FIDELITY_AVAILABLE
 
 torch.manual_seed(42)
 
@@ -34,7 +34,6 @@ def test_no_train_network_missing_torch_fidelity():
  NoTrainInceptionV3(name="inception-v3-compat", features_list=["2048"])
 
 
-@pytest.mark.skipif(not _TORCH_GREATER_EQUAL_1_9, reason="test requires torch>=1.9")
 @pytest.mark.skipif(not _TORCH_FIDELITY_AVAILABLE, reason="test requires torch-fidelity")
 def test_no_train():
  """Assert that metric never leaves evaluation mode."""
@@ -53,7 +52,6 @@ def forward(self, x):
  assert not model.metric.inception.training, "FID metric was changed to training mode which should not happen"
 
 
-@pytest.mark.skipif(not _TORCH_GREATER_EQUAL_1_9, reason="test requires torch>=1.9")
 @pytest.mark.skipif(not _TORCH_FIDELITY_AVAILABLE, reason="test requires torch-fidelity")
 def test_fid_pickle():
  """Assert that we can initialize the metric and pickle it."""
@@ -67,28 +65,21 @@ def test_fid_pickle():
 
 def test_fid_raises_errors_and_warnings():
  """Test that expected warnings and errors are raised."""
- if _TORCH_GREATER_EQUAL_1_9:
- if _TORCH_FIDELITY_AVAILABLE:
- with pytest.raises(ValueError, match="Integer input to argument `feature` must be one of .*"):
- _ = FrechetInceptionDistance(feature=2)
- else:
- with pytest.raises(
- ModuleNotFoundError,
- match="FID metric requires that `Torch-fidelity` is installed."
- " Either install as `pip install torchmetrics[image-quality]` or `pip install torch-fidelity`.",
- ):
- _ = FrechetInceptionDistance()
-
- with pytest.raises(TypeError, match="Got unknown input to argument `feature`"):
- _ = FrechetInceptionDistance(feature=[1, 2])
+ if _TORCH_FIDELITY_AVAILABLE:
+ with pytest.raises(ValueError, match="Integer input to argument `feature` must be one of .*"):
+ _ = FrechetInceptionDistance(feature=2)
  else:
  with pytest.raises(
- ValueError, match="FrechetInceptionDistance metric requires that PyTorch is version 1.9.0 or higher."
+ ModuleNotFoundError,
+ match="FID metric requires that `Torch-fidelity` is installed."
+ " Either install as `pip install torchmetrics[image-quality]` or `pip install torch-fidelity`.",
  ):
  _ = FrechetInceptionDistance()
 
+ with pytest.raises(TypeError, match="Got unknown input to argument `feature`"):
+ _ = FrechetInceptionDistance(feature=[1, 2])
+
 
-@pytest.mark.skipif(not _TORCH_GREATER_EQUAL_1_9, reason="test requires torch>=1.9")
 @pytest.mark.skipif(not _TORCH_FIDELITY_AVAILABLE, reason="test requires torch-fidelity")
 @pytest.mark.parametrize("feature", [64, 192, 768, 2048])
 def test_fid_same_input(feature):
@@ -120,7 +111,6 @@ def __len__(self) -> int:
 
 
 @pytest.mark.skipif(not torch.cuda.is_available(), reason="test is too slow without gpu")
-@pytest.mark.skipif(not _TORCH_GREATER_EQUAL_1_9, reason="test requires torch>=1.9")
 @pytest.mark.skipif(not _TORCH_FIDELITY_AVAILABLE, reason="test requires torch-fidelity")
 @pytest.mark.parametrize("equal_size", [False, True])
 def test_compare_fid(tmpdir, equal_size, feature=768):
@@ -157,7 +147,6 @@ def test_compare_fid(tmpdir, equal_size, feature=768):
  assert torch.allclose(tm_res.cpu(), torch.tensor([torch_fid["frechet_inception_distance"]]), atol=1e-3)
 
 
-@pytest.mark.skipif(not _TORCH_GREATER_EQUAL_1_9, reason="test requires torch>=1.9")
 @pytest.mark.parametrize("reset_real_features", [True, False])
 def test_reset_real_features_arg(reset_real_features):
  """Test that `reset_real_features` argument works as expected."""
@@ -187,7 +176,6 @@ def test_reset_real_features_arg(reset_real_features):
  assert metric.real_features_cov_sum.shape == torch.Size([64, 64])
 
 
-@pytest.mark.skipif(not _TORCH_GREATER_EQUAL_1_9, reason="test requires torch>=1.9")
 @pytest.mark.parametrize("normalize", [True, False])
 def test_normalize_arg(normalize):
  """Test that normalize argument works as expected."""
@@ -206,7 +194,6 @@ def test_normalize_arg(normalize):
  metric.update(img, real=True)
 
 
-@pytest.mark.skipif(not _TORCH_GREATER_EQUAL_1_9, reason="test requires torch>=1.9")
 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)
@@ -219,7 +206,6 @@ def test_not_enough_samples():
  metric.compute()
 
 
-@pytest.mark.skipif(not _TORCH_GREATER_EQUAL_1_9, reason="test requires torch>=1.9")
 def test_dtype_transfer_to_submodule():
  """Test that change in dtype also changes the default inception net."""
  imgs = torch.randn(1, 3, 256, 256)

tests/unittests/image/test_lpips.py

@@ -19,7 +19,7 @@
 from lpips import LPIPS as LPIPS_reference # noqa: N811
 from torch import Tensor
 from torchmetrics.image.lpip import LearnedPerceptualImagePatchSimilarity
-from torchmetrics.utilities.imports import _LPIPS_AVAILABLE, _TORCH_GREATER_EQUAL_1_9
+from torchmetrics.utilities.imports import _LPIPS_AVAILABLE
 
 from unittests.helpers import seed_all
 from unittests.helpers.testers import MetricTester
@@ -73,8 +73,6 @@ def test_lpips_differentiability(self):
  # LPIPS half + cpu does not work due to missing support in torch.min for older version of torch
  def test_lpips_half_cpu(self):
  """Test for half + cpu support."""
- if not _TORCH_GREATER_EQUAL_1_9:
- pytest.xfail(reason="LPIPS metric does not support cpu + half precision for v1.8.1 or lower of Pytorch")
  self.run_precision_test_cpu(_inputs.img1, _inputs.img2, LearnedPerceptualImagePatchSimilarity)
 
  @pytest.mark.skipif(not torch.cuda.is_available(), reason="test requires cuda")