Bump pytest from 7.4.4 to 8.0.0 in /requirements

pyproject.toml

@@ -64,7 +64,7 @@ test = [
  "mypy",
  "pytest",
  "pytest-cov",
- "pytest-lazy-fixture",
+ "pytest-lazy-fixtures",
  "tox",
 ]
 

requirements/requirements_dev.txt

@@ -6,9 +6,9 @@ mypy==1.11.2
 myst_parser==4.0.0
 pre-commit==3.8.0
 pygments>=2.7.4 # not directly required, pinned by Snyk to avoid a vulnerability.
-pytest==7.4.4
+pytest==8.3.3
 pytest-cov==5.0.0
-pytest-lazy-fixture==0.6.3
+pytest-lazy-fixtures==1.1.1
 sphinx==7.4.4
 sphinx-rtd-theme==2.0.0
-tox==4.18.0
+tox==4.21.2

setup.cfg

@@ -68,7 +68,7 @@ test =
  mypy
  pytest
  pytest-cov
- pytest-lazy-fixture
+ pytest-lazy-fixtures
  tox
 
 

tests/test_models/test_core.py

@@ -45,7 +45,7 @@
 import packaging
 import pytest
 import torch
-from pytest_lazyfixture import lazy_fixture
+from pytest_lazy_fixtures import lf
 from urllib3.exceptions import MaxRetryError, NewConnectionError
 
 # Needed to avoid connection error when importing lightly.
@@ -194,8 +194,8 @@ def test_bilinear_init() -> None:
 @pytest.mark.parametrize(
  ("model", "answer"),
  [
- (lazy_fixture("exp_fcn"), True),
- (lazy_fixture("exp_cnn"), True),
+ (lf("exp_fcn"), True),
+ (lf("exp_cnn"), True),
  (resnet18(), False),
  ],
 )
@@ -214,9 +214,9 @@ def test_is_minerva_model(model: Module, compile_model: bool, answer: bool) -> N
 @pytest.mark.parametrize(
  ("model", "subtype", "answer"),
  [
- (lazy_fixture("exp_fcn"), MinervaBackbone, True),
- (lazy_fixture("exp_cnn"), MinervaSiamese, False),
- (lazy_fixture("exp_simconv"), MinervaSiamese, True),
+ (lf("exp_fcn"), MinervaBackbone, True),
+ (lf("exp_cnn"), MinervaSiamese, False),
+ (lf("exp_simconv"), MinervaSiamese, True),
  (resnet18(), MinervaBackbone, False),
  ],
 )
@@ -236,7 +236,7 @@ def test_is_minerva_subtype(
 
 @pytest.mark.parametrize(
  "model",
- (lazy_fixture("exp_fcn"), lazy_fixture("exp_cnn"), lazy_fixture("exp_simconv")),
+ (lf("exp_fcn"), lf("exp_cnn"), lf("exp_simconv")),
 )
 @pytest.mark.parametrize("compile_model", (True, False))
 def test_extract_wrapped_model(model, compile_model: bool) -> None:

tests/test_transforms.py

@@ -41,7 +41,7 @@
 import pytest
 import torch
 from numpy.testing import assert_array_equal
-from pytest_lazyfixture import lazy_fixture
+from pytest_lazy_fixtures import lf
 from torch import FloatTensor, LongTensor
 from torchgeo.datasets import RasterDataset
 from torchvision.transforms import ColorJitter, RandomHorizontalFlip, RandomVerticalFlip
@@ -69,7 +69,7 @@
 @pytest.mark.parametrize(
  ["input_mask", "output"],
  [
- (lazy_fixture("simple_mask"), torch.tensor([[1, 3, 0], [2, 0, 1], [1, 1, 1]])),
+ (lf("simple_mask"), torch.tensor([[1, 3, 0], [2, 0, 1], [1, 1, 1]])),
  (
  torch.tensor([[5, 3, 5], [4, 5, 1], [1, 3, 1]], dtype=torch.long),
  torch.tensor([[0, 3, 0], [2, 0, 1], [1, 3, 1]]),
@@ -86,9 +86,7 @@ def test_class_transform(
  assert repr(transform) == f"ClassTransform(transform={example_matrix})"
 
 
-@pytest.mark.parametrize(
- "sample", (42, lazy_fixture("simple_mask"), lazy_fixture("example_matrix"))
-)
+@pytest.mark.parametrize("sample", (42, lf("simple_mask"), lf("example_matrix")))
 def test_pair_create(sample: Any) -> None:
  transform = PairCreate()
 
@@ -100,7 +98,7 @@ def test_pair_create(sample: Any) -> None:
 @pytest.mark.parametrize(
  "mask",
  (
- lazy_fixture("simple_mask"),
+ lf("simple_mask"),
  torch.tensor(
  [[1023.0, 3.890, 557.0], [478.0, 5.788, 10009.0], [1.0, 10240.857, 1458.7]]
  ),
@@ -256,22 +254,22 @@ def test_dublicator(
  Normalise,
  None,
  255,
- lazy_fixture("simple_rgb_img"),
- lazy_fixture("norm_simple_rgb_img"),
+ lf("simple_rgb_img"),
+ lf("norm_simple_rgb_img"),
  ),
  (
  Normalise,
  ["image"],
  255,
- lazy_fixture("simple_rgb_img"),
- lazy_fixture("norm_simple_rgb_img"),
+ lf("simple_rgb_img"),
+ lf("norm_simple_rgb_img"),
  ),
  (
  RandomHorizontalFlip,
  ["image", "mask"],
  1.0,
- lazy_fixture("simple_sample"),
- lazy_fixture("flipped_simple_sample"),
+ lf("simple_sample"),
+ lf("flipped_simple_sample"),
  ),
  ],
 )
@@ -349,7 +347,7 @@ def test_swap_keys(random_rgbi_tensor, random_tensor_mask) -> None:
 
 @pytest.mark.parametrize(
  "dataset",
- [lazy_fixture("default_image_dataset"), lazy_fixture("ssl4eo_s12_dataset")],
+ [lf("default_image_dataset"), lf("ssl4eo_s12_dataset")],
 )
 def test_auto_norm(dataset: RasterDataset, random_rgbi_tensor):
  auto_norm = AutoNorm(dataset, 12)

tests/test_utils/test_utils.py

@@ -56,7 +56,7 @@
 from internet_sabotage import no_connection
 from nptyping import Float, NDArray, Shape
 from numpy.testing import assert_array_equal
-from pytest_lazyfixture import lazy_fixture
+from pytest_lazy_fixtures import lf
 from rasterio.crs import CRS
 from torchgeo.datasets.utils import BoundingBox, stack_samples
 from torchvision.datasets import FakeData
@@ -241,7 +241,7 @@ def test_eliminate_classes(
  [2, 1, 5, 1, 6, 3, 0, 1, 1, 5, 7],
  [2, 4, 5, 1, 6, 3, 0, 2, 1, 5, 7],
  [2, 1, 5, 1, 6, 3, 0, 1, 1, 5, 7],
- lazy_fixture("exp_classes"),
+ lf("exp_classes"),
  ),
  ],
 )