Add testing agains each feat PT version

.github/workflows/ci_test-conda.yml

@@ -0,0 +1,76 @@
+name: PyTorch & Conda
+
+# see: https://help.github.com/en/actions/reference/events-that-trigger-workflows
+on: # Trigger the workflow on push or pull request, but only for the master branch
+ push:
+ branches: [master, "release/*"]
+ pull_request:
+ branches: [master, "release/*"]
+
+jobs:
+ conda:
+ runs-on: ubuntu-20.04
+ strategy:
+ fail-fast: false
+ matrix:
+ python-version: [3.7]
+ pytorch-version: [1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9]
+
+ # Timeout: https://stackoverflow.com/a/59076067/4521646
+ timeout-minutes: 35
+ steps:
+ - uses: actions/checkout@v2
+
+ - name: Cache conda
+ uses: actions/cache@v2
+ with:
+ path: ~/conda_pkgs_dir
+ key: conda-py${{ matrix.python-version }}-pt${{ matrix.pytorch-version }}-${{ hashFiles('environment.yml') }}
+ restore-keys: conda-py${{ matrix.python-version }}-pt${{ matrix.pytorch-version }}-
+
+ # Add another cache for Pip as not all packages lives in Conda env
+ - name: Cache pip
+ uses: actions/cache@v2
+ with:
+ path: ~/.cache/pip
+ key: pip-py${{ matrix.python-version }}-pt${{ matrix.pytorch-version }}-${{ hashFiles('requirements/base.txt') }}
+ restore-keys: pip-py${{ matrix.python-version }}-pt${{ matrix.pytorch-version }}-
+
+ # https://docs.conda.io/projects/conda/en/4.6.0/_downloads/52a95608c49671267e40c689e0bc00ca/conda-cheatsheet.pdf
+ # https://gist.github.com/mwouts/9842452d020c08faf9e84a3bba38a66f
+ - name: Setup Miniconda
+ uses: conda-incubator/setup-miniconda@v2
+ with:
+ miniconda-version: "4.7.12"
+ python-version: ${{ matrix.python-version }}
+ channels: conda-forge,pytorch,pytorch-test,pytorch-nightly
+ channel-priority: true
+ auto-activate-base: true
+ # environment-file: ./environment.yml
+ use-only-tar-bz2: true # IMPORTANT: This needs to be set for caching to work properly!
+
+ - name: Update Environment
+ run: |
+ conda info
+ conda install pytorch=${{ matrix.pytorch-version }} cpuonly
+ conda list
+ pip --version
+ pip install --requirement requirements.txt --upgrade-strategy only-if-needed --quiet
+ pip install --requirement requirements/test.txt --upgrade-strategy only-if-needed --quiet
+ pip list
+ python -c "import torch; assert torch.__version__[:3] == '${{ matrix.pytorch-version }}', torch.__version__"
+ shell: bash -l {0}
+
+ - name: Testing
+ run: |
+ # NOTE: run coverage on tests does not propagare faler status for Win, https://github.com/nedbat/coveragepy/issues/1003
+ python -m pytest torchmetrics tests -v --durations=35 --junitxml=junit/test-conda-py${{ matrix.python-version }}-pt${{ matrix.pytorch-version }}.xml
+ shell: bash -l {0}
+
+ - name: Upload pytest test results
+ uses: actions/upload-artifact@master
+ with:
+ name: test-conda-py${{ matrix.python-version }}-pt${{ matrix.pytorch-version }}.xml
+ path: junit/test-conda-py${{ matrix.python-version }}-pt${{ matrix.pytorch-version }}.xml
+ # Use always() to always run this step to publish test results when there are test failures
+ if: failure()

MANIFEST.in

@@ -38,3 +38,4 @@ prune notebook*
 prune temp*
 prune test*
 prune benchmark*
+prune integration*

azure-pipelines.yml

@@ -84,5 +84,5 @@ jobs:
  condition: succeededOrFailed()
 
  - bash: |
- python -m pytest integrations --durations=25
+ python -m pytest integrations -v --durations=25
  displayName: 'Integrations'

integrations/__init__.py

@@ -0,0 +1,3 @@
+from torchmetrics.utilities.imports import _module_available
+
+_PL_AVAILABLE = _module_available('pytorch_lightning')

integrations/lightning_models.py

@@ -11,6 +11,7 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
+
 import torch
 from pytorch_lightning import LightningModule
 from torch.utils.data import Dataset

setup.cfg

@@ -21,7 +21,10 @@ exclude_lines =
 
 [flake8]
 max-line-length = 120
-exclude = .tox,*.egg,build,temp
+exclude =
+ *.egg
+ build
+ temp
 select = E,W,F
 doctests = True
 verbose = 2

tests/classification/test_stat_scores.py

@@ -21,7 +21,7 @@
 from torch import Tensor, tensor
 
 from tests.classification.inputs import _input_binary, _input_binary_prob, _input_multiclass
-from tests.classification.inputs import _input_multiclass_prob as _input_mccls_prob
+from tests.classification.inputs import _input_multiclass_prob as _input_mcls_prob
 from tests.classification.inputs import _input_multidim_multiclass as _input_mdmc
 from tests.classification.inputs import _input_multidim_multiclass_prob as _input_mdmc_prob
 from tests.classification.inputs import _input_multilabel as _input_mcls
@@ -104,8 +104,8 @@ def _sk_stat_scores_mdim_mcls(preds, target, reduce, mdmc_reduce, num_classes, i
  ["macro", None, None, _input_binary, None],
  ["micro", None, None, _input_mdmc_prob, None],
  ["micro", None, None, _input_binary_prob, 0],
- ["micro", None, None, _input_mccls_prob, NUM_CLASSES],
- ["micro", None, NUM_CLASSES, _input_mccls_prob, NUM_CLASSES],
+ ["micro", None, None, _input_mcls_prob, NUM_CLASSES],
+ ["micro", None, NUM_CLASSES, _input_mcls_prob, NUM_CLASSES],
  ],
 )
 def test_wrong_params(reduce, mdmc_reduce, num_classes, inputs, ignore_index):
@@ -141,8 +141,8 @@ def test_wrong_threshold():
  (_input_mlb_prob.preds, _input_mlb_prob.target, _sk_stat_scores, None, NUM_CLASSES, None, None),
  (_input_mlb_prob.preds, _input_mlb_prob.target, _sk_stat_scores, None, NUM_CLASSES, None, 2),
  (_input_mcls.preds, _input_mcls.target, _sk_stat_scores, None, NUM_CLASSES, False, None),
- (_input_mccls_prob.preds, _input_mccls_prob.target, _sk_stat_scores, None, NUM_CLASSES, None, None),
- (_input_mccls_prob.preds, _input_mccls_prob.target, _sk_stat_scores, None, NUM_CLASSES, None, 2),
+ (_input_mcls_prob.preds, _input_mcls_prob.target, _sk_stat_scores, None, NUM_CLASSES, None, None),
+ (_input_mcls_prob.preds, _input_mcls_prob.target, _sk_stat_scores, None, NUM_CLASSES, None, 2),
  (_input_multiclass.preds, _input_multiclass.target, _sk_stat_scores, None, NUM_CLASSES, None, None),
  (_input_mdmc.preds, _input_mdmc.target, _sk_stat_scores_mdim_mcls, "samplewise", NUM_CLASSES, None, None),
  (

tests/retrieval/test_map.py

@@ -86,7 +86,7 @@ def test_input_data(torch_class_metric: Metric) -> None:
  """Check PL metrics inputs are controlled correctly. """
 
  device = 'cuda' if torch.cuda.is_available() else 'cpu'
- length = random.randint(0, 20)
+ length = random.randint(1, 20)
 
  # check error when `query_without_relevant_docs='error'` is raised correctly
  indexes = torch.tensor([0] * length, device=device, dtype=torch.int64)

torchmetrics/utilities/imports.py

@@ -1,6 +1,64 @@
+# Copyright The PyTorch Lightning team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
 from distutils.version import LooseVersion
+from importlib import import_module
+from importlib.util import find_spec
 
 import torch
+from pkg_resources import DistributionNotFound
+
+
+def _module_available(module_path: str) -> bool:
+ """
+ Check if a path is available in your environment
+
+ >>> _module_available('os')
+ True
+ >>> _module_available('bla.bla')
+ False
+ """
+ try:
+ return find_spec(module_path) is not None
+ except AttributeError:
+ # Python 3.6
+ return False
+ except ModuleNotFoundError:
+ # Python 3.7+
+ return False
+
+
+def _compare_version(package: str, op, version) -> bool:
+ """
+ Compare package version with some requirements
+
+ >>> import operator
+ >>> _compare_version("torch", operator.ge, "0.1")
+ True
+ """
+ try:
+ pkg = import_module(package)
+ except (ModuleNotFoundError, DistributionNotFound):
+ return False
+ try:
+ pkg_version = LooseVersion(pkg.__version__)
+ except AttributeError:
+ return False
+ if not (hasattr(pkg_version, "vstring") and hasattr(pkg_version, "version")):
+ # this is mock by sphinx, so it shall return True ro generate all summaries
+ return True
+ return op(pkg_version, LooseVersion(version))
+
 
 _TORCH_LOWER_1_4 = LooseVersion(torch.__version__) < LooseVersion("1.4.0")
 _TORCH_LOWER_1_5 = LooseVersion(torch.__version__) < LooseVersion("1.5.0")