From 1cb78a1fdd57ed2b7d6acb216e346da01da0e849 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 15 Jul 2024 22:02:36 +0000 Subject: [PATCH 01/63] Bump setuptools from 69.1.1 to 70.0.0 in /requirements Bumps [setuptools](https://github.com/pypa/setuptools) from 69.1.1 to 70.0.0. - [Release notes](https://github.com/pypa/setuptools/releases) - [Changelog](https://github.com/pypa/setuptools/blob/main/NEWS.rst) - [Commits](https://github.com/pypa/setuptools/compare/v69.1.1...v70.0.0) --- updated-dependencies: - dependency-name: setuptools dependency-type: direct:production ... Signed-off-by: dependabot[bot] --- requirements/requirements.txt | 2 +- requirements/requirements_dev.txt | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 51085e9bb..20a63719b 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -23,7 +23,7 @@ rasterio>=1.3.6 requests==2.32.2 scikit-learn==1.5.0 segmentation-models-pytorch==0.3.3 -setuptools==69.1.1 +setuptools==70.0.0 starlette==0.37.2 # not directly required, pinned by Dependabot to avoid a vulnerability. tabulate==0.9.0 tensorflow==2.16.2 diff --git a/requirements/requirements_dev.txt b/requirements/requirements_dev.txt index 9e0e5d2dc..211357904 100644 --- a/requirements/requirements_dev.txt +++ b/requirements/requirements_dev.txt @@ -33,7 +33,7 @@ rasterio>=1.3.6 requests==2.32.2 scikit-learn==1.5.0 segmentation-models-pytorch==0.3.3 -setuptools==69.1.1 +setuptools==70.0.0 sphinx==7.2.6 sphinx-rtd-theme==2.0.0 starlette>=0.25.0 # not directly required, pinned by Dependabot to avoid a vulnerability. From 2eb3ac4f96c1f753dddbfbe53194e70e51e46c45 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 15 Jul 2024 23:40:42 +0000 Subject: [PATCH 02/63] Bump onnx2torch from 1.5.13 to 1.5.14 in /requirements Bumps [onnx2torch](https://github.com/ENOT-AutoDL/onnx2torch) from 1.5.13 to 1.5.14. - [Release notes](https://github.com/ENOT-AutoDL/onnx2torch/releases) - [Commits](https://github.com/ENOT-AutoDL/onnx2torch/compare/v1.5.13...v1.5.14) --- updated-dependencies: - dependency-name: onnx2torch dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] --- requirements/requirements.txt | 2 +- requirements/requirements_dev.txt | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 20a63719b..100d295a9 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -13,7 +13,7 @@ nptyping==2.5.0 numba>=0.57.0 # not directly required but pinned to ensure Python 3.11 compatibility. numpy==1.26.4 onnx==1.16.1 -onnx2torch==1.5.13 +onnx2torch==1.5.14 overload==1.1 pandas==2.2.1 protobuf>=3.19.5 # not directly required, pinned by Snyk to avoid a vulnerability. diff --git a/requirements/requirements_dev.txt b/requirements/requirements_dev.txt index 211357904..e3065d5b3 100644 --- a/requirements/requirements_dev.txt +++ b/requirements/requirements_dev.txt @@ -18,7 +18,7 @@ nptyping==2.5.0 numba>=0.57.0 # not directly required but pinned to ensure Python 3.11 compatibility. numpy==1.26.4 onnx==1.16.1 -onnx2torch==1.5.13 +onnx2torch==1.5.14 overload==1.1 pandas==2.2.1 pre-commit==3.6.2 From df10b20298973b8d68e532687ae208b495eb12c2 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 15 Jul 2024 23:40:48 +0000 Subject: [PATCH 03/63] Bump sphinx from 7.2.6 to 7.4.4 in /requirements Bumps [sphinx](https://github.com/sphinx-doc/sphinx) from 7.2.6 to 7.4.4. - [Release notes](https://github.com/sphinx-doc/sphinx/releases) - [Changelog](https://github.com/sphinx-doc/sphinx/blob/master/CHANGES.rst) - [Commits](https://github.com/sphinx-doc/sphinx/compare/v7.2.6...v7.4.4) --- updated-dependencies: - dependency-name: sphinx dependency-type: direct:development update-type: version-update:semver-minor ... Signed-off-by: dependabot[bot] --- requirements/requirements_dev.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements_dev.txt b/requirements/requirements_dev.txt index 211357904..be2d8d587 100644 --- a/requirements/requirements_dev.txt +++ b/requirements/requirements_dev.txt @@ -34,7 +34,7 @@ requests==2.32.2 scikit-learn==1.5.0 segmentation-models-pytorch==0.3.3 setuptools==70.0.0 -sphinx==7.2.6 +sphinx==7.4.4 sphinx-rtd-theme==2.0.0 starlette>=0.25.0 # not directly required, pinned by Dependabot to avoid a vulnerability. tabulate==0.9.0 From 16872ad3ad6dd792141c2bb22ca75a51c2a6b6f9 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 15 Jul 2024 23:40:52 +0000 Subject: [PATCH 04/63] Bump requests from 2.32.2 to 2.32.3 in /requirements Bumps [requests](https://github.com/psf/requests) from 2.32.2 to 2.32.3. - [Release notes](https://github.com/psf/requests/releases) - [Changelog](https://github.com/psf/requests/blob/main/HISTORY.md) - [Commits](https://github.com/psf/requests/compare/v2.32.2...v2.32.3) --- updated-dependencies: - dependency-name: requests dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] --- requirements/requirements.txt | 2 +- requirements/requirements_dev.txt | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 20a63719b..25b3c4780 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -20,7 +20,7 @@ protobuf>=3.19.5 # not directly required, pinned by Snyk to avoid a vulnerabilit psutil==5.9.8 pyyaml==6.0.1 rasterio>=1.3.6 -requests==2.32.2 +requests==2.32.3 scikit-learn==1.5.0 segmentation-models-pytorch==0.3.3 setuptools==70.0.0 diff --git a/requirements/requirements_dev.txt b/requirements/requirements_dev.txt index 211357904..a504a323e 100644 --- a/requirements/requirements_dev.txt +++ b/requirements/requirements_dev.txt @@ -30,7 +30,7 @@ pytest-cov==4.1.0 pytest-lazy-fixture==0.6.3 pyyaml==6.0.1 rasterio>=1.3.6 -requests==2.32.2 +requests==2.32.3 scikit-learn==1.5.0 segmentation-models-pytorch==0.3.3 setuptools==70.0.0 From 1ebfef7ff68e25df195dce07353c031afedb08b3 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 22 Jul 2024 23:44:04 +0000 Subject: [PATCH 05/63] Bump psutil from 5.9.8 to 6.0.0 in /requirements Bumps [psutil](https://github.com/giampaolo/psutil) from 5.9.8 to 6.0.0. - [Changelog](https://github.com/giampaolo/psutil/blob/master/HISTORY.rst) - [Commits](https://github.com/giampaolo/psutil/compare/release-5.9.8...release-6.0.0) --- updated-dependencies: - dependency-name: psutil dependency-type: direct:production update-type: version-update:semver-major ... Signed-off-by: dependabot[bot] --- requirements/requirements.txt | 2 +- requirements/requirements_dev.txt | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index a540f0fa3..5718067b6 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -17,7 +17,7 @@ onnx2torch==1.5.14 overload==1.1 pandas==2.2.1 protobuf>=3.19.5 # not directly required, pinned by Snyk to avoid a vulnerability. -psutil==5.9.8 +psutil==6.0.0 pyyaml==6.0.1 rasterio>=1.3.6 requests==2.32.3 diff --git a/requirements/requirements_dev.txt b/requirements/requirements_dev.txt index ea6216ea3..b44e19574 100644 --- a/requirements/requirements_dev.txt +++ b/requirements/requirements_dev.txt @@ -23,7 +23,7 @@ overload==1.1 pandas==2.2.1 pre-commit==3.6.2 protobuf>=3.19.5 # not directly required, pinned by Snyk to avoid a vulnerability. -psutil==5.9.8 +psutil==6.0.0 pygments>=2.7.4 # not directly required, pinned by Snyk to avoid a vulnerability. pytest==7.4.4 pytest-cov==4.1.0 From cbb6d1edc65bfb2011c632e30c75997ccc90ced3 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 22 Jul 2024 23:44:07 +0000 Subject: [PATCH 06/63] Bump pre-commit from 3.6.2 to 3.7.1 in /requirements Bumps [pre-commit](https://github.com/pre-commit/pre-commit) from 3.6.2 to 3.7.1. - [Release notes](https://github.com/pre-commit/pre-commit/releases) - [Changelog](https://github.com/pre-commit/pre-commit/blob/main/CHANGELOG.md) - [Commits](https://github.com/pre-commit/pre-commit/compare/v3.6.2...v3.7.1) --- updated-dependencies: - dependency-name: pre-commit dependency-type: direct:development update-type: version-update:semver-minor ... Signed-off-by: dependabot[bot] --- requirements/requirements_dev.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements_dev.txt b/requirements/requirements_dev.txt index ea6216ea3..ad02d80bd 100644 --- a/requirements/requirements_dev.txt +++ b/requirements/requirements_dev.txt @@ -21,7 +21,7 @@ onnx==1.16.1 onnx2torch==1.5.14 overload==1.1 pandas==2.2.1 -pre-commit==3.6.2 +pre-commit==3.7.1 protobuf>=3.19.5 # not directly required, pinned by Snyk to avoid a vulnerability. psutil==5.9.8 pygments>=2.7.4 # not directly required, pinned by Snyk to avoid a vulnerability. From 134cfecdadf908a4e4c8121f8a3ce70395be6d1e Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 22 Jul 2024 23:44:10 +0000 Subject: [PATCH 07/63] Bump tox from 4.14.2 to 4.16.0 in /requirements Bumps [tox](https://github.com/tox-dev/tox) from 4.14.2 to 4.16.0. - [Release notes](https://github.com/tox-dev/tox/releases) - [Changelog](https://github.com/tox-dev/tox/blob/main/docs/changelog.rst) - [Commits](https://github.com/tox-dev/tox/compare/4.14.2...4.16.0) --- updated-dependencies: - dependency-name: tox dependency-type: direct:development update-type: version-update:semver-minor ... Signed-off-by: dependabot[bot] --- requirements/requirements_dev.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements_dev.txt b/requirements/requirements_dev.txt index ea6216ea3..b811b877b 100644 --- a/requirements/requirements_dev.txt +++ b/requirements/requirements_dev.txt @@ -45,7 +45,7 @@ torchgeo==0.5.1 torchinfo==1.8.0 torchvision==0.17.2 tornado>=6.3.3 -tox==4.14.2 +tox==4.16.0 tqdm==4.66.4 types-PyYAML==6.0.12.20240311 types-requests==2.31.0.20240218 From 37528f8282ac9a52e0ffbf87d570e669b8dba60c Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 29 Jul 2024 23:23:59 +0000 Subject: [PATCH 08/63] Bump ipykernel from 6.29.3 to 6.29.5 in /requirements Bumps [ipykernel](https://github.com/ipython/ipykernel) from 6.29.3 to 6.29.5. - [Release notes](https://github.com/ipython/ipykernel/releases) - [Changelog](https://github.com/ipython/ipykernel/blob/v6.29.5/CHANGELOG.md) - [Commits](https://github.com/ipython/ipykernel/compare/v6.29.3...v6.29.5) --- updated-dependencies: - dependency-name: ipykernel dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] --- requirements/requirements.txt | 2 +- requirements/requirements_dev.txt | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 5718067b6..494bbc59c 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -5,7 +5,7 @@ geopy==2.4.1 GitPython>=3.1.35 imageio==2.34.2 inputimeout==1.0.4 -ipykernel==6.29.3 +ipykernel==6.29.5 lightly==1.4.25 matplotlib==3.9.0 mlflow==2.13.2 diff --git a/requirements/requirements_dev.txt b/requirements/requirements_dev.txt index c4f63d975..5df9b7527 100644 --- a/requirements/requirements_dev.txt +++ b/requirements/requirements_dev.txt @@ -8,7 +8,7 @@ GitPython>=3.1.35 imageio==2.34.2 inputimeout==1.0.4 internet-sabotage3==0.1.6 -ipykernel==6.29.3 +ipykernel==6.29.5 lightly==1.4.25 matplotlib==3.9.0 mlflow==2.13.2 From 6f7ffa82c88724196314fda33b920f19ae1c840f Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 29 Jul 2024 23:24:06 +0000 Subject: [PATCH 09/63] Bump setuptools from 70.0.0 to 72.1.0 in /requirements Bumps [setuptools](https://github.com/pypa/setuptools) from 70.0.0 to 72.1.0. - [Release notes](https://github.com/pypa/setuptools/releases) - [Changelog](https://github.com/pypa/setuptools/blob/main/NEWS.rst) - [Commits](https://github.com/pypa/setuptools/compare/v70.0.0...v72.1.0) --- updated-dependencies: - dependency-name: setuptools dependency-type: direct:production update-type: version-update:semver-major ... Signed-off-by: dependabot[bot] --- requirements/requirements.txt | 2 +- requirements/requirements_dev.txt | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 5718067b6..68eef1601 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -23,7 +23,7 @@ rasterio>=1.3.6 requests==2.32.3 scikit-learn==1.5.0 segmentation-models-pytorch==0.3.3 -setuptools==70.0.0 +setuptools==72.1.0 starlette==0.37.2 # not directly required, pinned by Dependabot to avoid a vulnerability. tabulate==0.9.0 tensorflow==2.16.2 diff --git a/requirements/requirements_dev.txt b/requirements/requirements_dev.txt index c4f63d975..38ed02630 100644 --- a/requirements/requirements_dev.txt +++ b/requirements/requirements_dev.txt @@ -33,7 +33,7 @@ rasterio>=1.3.6 requests==2.32.3 scikit-learn==1.5.0 segmentation-models-pytorch==0.3.3 -setuptools==70.0.0 +setuptools==72.1.0 sphinx==7.4.4 sphinx-rtd-theme==2.0.0 starlette>=0.25.0 # not directly required, pinned by Dependabot to avoid a vulnerability. From 5eff0ae790898c20035285fba1523dc526899e16 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Mon, 5 Aug 2024 23:20:19 +0000 Subject: [PATCH 10/63] [pre-commit.ci] pre-commit autoupdate MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit updates: - [github.com/psf/black: 24.4.2 → 24.8.0](https://github.com/psf/black/compare/24.4.2...24.8.0) --- .pre-commit-config.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 772a54862..4917e48f1 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -18,7 +18,7 @@ repos: - id: requirements-txt-fixer - repo: https://github.com/psf/black - rev: 24.4.2 + rev: 24.8.0 hooks: - id: black From 6249e7e91bbd48522f503d5969e36f0496902e4c Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 5 Aug 2024 23:40:05 +0000 Subject: [PATCH 11/63] Bump onnx from 1.16.1 to 1.16.2 in /requirements Bumps [onnx](https://github.com/onnx/onnx) from 1.16.1 to 1.16.2. - [Release notes](https://github.com/onnx/onnx/releases) - [Changelog](https://github.com/onnx/onnx/blob/main/docs/Changelog-ml.md) - [Commits](https://github.com/onnx/onnx/compare/v1.16.1...v1.16.2) --- updated-dependencies: - dependency-name: onnx dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] --- requirements/requirements.txt | 2 +- requirements/requirements_dev.txt | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 25ad6e8eb..42173f211 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -12,7 +12,7 @@ mlflow==2.13.2 nptyping==2.5.0 numba>=0.57.0 # not directly required but pinned to ensure Python 3.11 compatibility. numpy==1.26.4 -onnx==1.16.1 +onnx==1.16.2 onnx2torch==1.5.14 overload==1.1 pandas==2.2.1 diff --git a/requirements/requirements_dev.txt b/requirements/requirements_dev.txt index 6641a1b4b..840b348f6 100644 --- a/requirements/requirements_dev.txt +++ b/requirements/requirements_dev.txt @@ -17,7 +17,7 @@ myst_parser==2.0.0 nptyping==2.5.0 numba>=0.57.0 # not directly required but pinned to ensure Python 3.11 compatibility. numpy==1.26.4 -onnx==1.16.1 +onnx==1.16.2 onnx2torch==1.5.14 overload==1.1 pandas==2.2.1 From a0231415c376eef2f09b50f5a858fd193937e873 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 5 Aug 2024 23:40:08 +0000 Subject: [PATCH 12/63] Bump pre-commit from 3.7.1 to 3.8.0 in /requirements Bumps [pre-commit](https://github.com/pre-commit/pre-commit) from 3.7.1 to 3.8.0. - [Release notes](https://github.com/pre-commit/pre-commit/releases) - [Changelog](https://github.com/pre-commit/pre-commit/blob/main/CHANGELOG.md) - [Commits](https://github.com/pre-commit/pre-commit/compare/v3.7.1...v3.8.0) --- updated-dependencies: - dependency-name: pre-commit dependency-type: direct:development update-type: version-update:semver-minor ... Signed-off-by: dependabot[bot] --- requirements/requirements_dev.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements_dev.txt b/requirements/requirements_dev.txt index 6641a1b4b..1de1f7247 100644 --- a/requirements/requirements_dev.txt +++ b/requirements/requirements_dev.txt @@ -21,7 +21,7 @@ onnx==1.16.1 onnx2torch==1.5.14 overload==1.1 pandas==2.2.1 -pre-commit==3.7.1 +pre-commit==3.8.0 protobuf>=3.19.5 # not directly required, pinned by Snyk to avoid a vulnerability. psutil==6.0.0 pygments>=2.7.4 # not directly required, pinned by Snyk to avoid a vulnerability. From 8877e8c4cf5481e7f4b9d3dfc721acdd5ddc5428 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 12 Aug 2024 23:58:24 +0000 Subject: [PATCH 13/63] Bump mlflow from 2.13.2 to 2.15.1 in /requirements Bumps [mlflow](https://github.com/mlflow/mlflow) from 2.13.2 to 2.15.1. - [Release notes](https://github.com/mlflow/mlflow/releases) - [Changelog](https://github.com/mlflow/mlflow/blob/master/CHANGELOG.md) - [Commits](https://github.com/mlflow/mlflow/compare/v2.13.2...v2.15.1) --- updated-dependencies: - dependency-name: mlflow dependency-type: direct:production update-type: version-update:semver-minor ... Signed-off-by: dependabot[bot] --- requirements/requirements.txt | 2 +- requirements/requirements_dev.txt | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 42173f211..9890940cd 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -8,7 +8,7 @@ inputimeout==1.0.4 ipykernel==6.29.5 lightly==1.4.25 matplotlib==3.9.0 -mlflow==2.13.2 +mlflow==2.15.1 nptyping==2.5.0 numba>=0.57.0 # not directly required but pinned to ensure Python 3.11 compatibility. numpy==1.26.4 diff --git a/requirements/requirements_dev.txt b/requirements/requirements_dev.txt index b0b0fd1c7..fc9e2bc32 100644 --- a/requirements/requirements_dev.txt +++ b/requirements/requirements_dev.txt @@ -11,7 +11,7 @@ internet-sabotage3==0.1.6 ipykernel==6.29.5 lightly==1.4.25 matplotlib==3.9.0 -mlflow==2.13.2 +mlflow==2.15.1 mypy==1.10.1 myst_parser==2.0.0 nptyping==2.5.0 From cd50786b5492d25618fd6375ac72f96360bc87ec Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 12 Aug 2024 23:58:46 +0000 Subject: [PATCH 14/63] Bump lightly from 1.4.25 to 1.5.11 in /requirements Bumps [lightly](https://github.com/lightly-ai/lightly) from 1.4.25 to 1.5.11. - [Release notes](https://github.com/lightly-ai/lightly/releases) - [Commits](https://github.com/lightly-ai/lightly/compare/v1.4.25...v1.5.11) --- updated-dependencies: - dependency-name: lightly dependency-type: direct:production update-type: version-update:semver-minor ... Signed-off-by: dependabot[bot] --- requirements/requirements.txt | 2 +- requirements/requirements_dev.txt | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 42173f211..4a2e5a9b1 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -6,7 +6,7 @@ GitPython>=3.1.35 imageio==2.34.2 inputimeout==1.0.4 ipykernel==6.29.5 -lightly==1.4.25 +lightly==1.5.11 matplotlib==3.9.0 mlflow==2.13.2 nptyping==2.5.0 diff --git a/requirements/requirements_dev.txt b/requirements/requirements_dev.txt index b0b0fd1c7..c8cc6f2ba 100644 --- a/requirements/requirements_dev.txt +++ b/requirements/requirements_dev.txt @@ -9,7 +9,7 @@ imageio==2.34.2 inputimeout==1.0.4 internet-sabotage3==0.1.6 ipykernel==6.29.5 -lightly==1.4.25 +lightly==1.5.11 matplotlib==3.9.0 mlflow==2.13.2 mypy==1.10.1 From bd0af8b4192d42a3bfa9194eb172eb5cdbfe4afe Mon Sep 17 00:00:00 2001 From: Harry Baker Date: Tue, 20 Aug 2024 00:53:43 +0100 Subject: [PATCH 15/63] Use `hydra` to instantiate transforms --- minerva/datasets/factory.py | 2 +- minerva/inbuilt_cfgs/example_3rd_party.yaml | 12 ++--- minerva/inbuilt_cfgs/example_CNN_config.yaml | 12 ++--- .../inbuilt_cfgs/example_ChangeDetector.yaml | 22 ++++---- .../inbuilt_cfgs/example_GSConvNet-II.yaml | 42 ++++++++------- .../inbuilt_cfgs/example_GeoCLR_config.yaml | 40 +++++++------- .../inbuilt_cfgs/example_GeoSimConvNet.yaml | 42 ++++++++------- minerva/inbuilt_cfgs/example_MultiLabel.yaml | 52 ++++++++++--------- minerva/inbuilt_cfgs/example_PSP.yaml | 46 ++++++++-------- .../inbuilt_cfgs/example_SceneClassifier.yaml | 44 ++++++++-------- .../inbuilt_cfgs/example_UNetR_config.yaml | 6 +-- .../example_autoencoder_config.yaml | 6 +-- minerva/transforms.py | 45 ++++------------ 13 files changed, 179 insertions(+), 192 deletions(-) diff --git a/minerva/datasets/factory.py b/minerva/datasets/factory.py index eaaedf027..524bb4c39 100644 --- a/minerva/datasets/factory.py +++ b/minerva/datasets/factory.py @@ -592,7 +592,7 @@ def _add_class_transform( ) -> Dict[str, Any]: class_transform = { "ClassTransform": { - "module": "minerva.transforms", + "_target_": "minerva.transforms.ClassTransform", "transform": class_matrix, } } diff --git a/minerva/inbuilt_cfgs/example_3rd_party.yaml b/minerva/inbuilt_cfgs/example_3rd_party.yaml index 4db007b28..8ac328113 100644 --- a/minerva/inbuilt_cfgs/example_3rd_party.yaml +++ b/minerva/inbuilt_cfgs/example_3rd_party.yaml @@ -105,7 +105,7 @@ tasks: image: transforms: ToRGB: - module: minerva.transforms + _target_: minerva.transforms.ToRGB images_1: module: minerva.datasets.__testing @@ -124,7 +124,7 @@ tasks: mask: transforms: SingleLabel: - module: minerva.transforms + _target_: minerva.transforms.SingleLabel module: minerva.datasets.__testing name: TstMaskDataset paths: Chesapeake7 @@ -153,7 +153,7 @@ tasks: image: transforms: ToRGB: - module: minerva.transforms + _target_: minerva.transforms.ToRGB module: minerva.datasets.__testing name: TstImgDataset paths: NAIP @@ -163,7 +163,7 @@ tasks: mask: transforms: SingleLabel: - module: minerva.transforms + _target_: minerva.transforms.SingleLabel module: minerva.datasets.__testing name: TstMaskDataset paths: Chesapeake7 @@ -192,7 +192,7 @@ tasks: image: transforms: ToRGB: - module: minerva.transforms + _target_: minerva.transforms.ToRGB module: minerva.datasets.__testing name: TstImgDataset paths: NAIP @@ -202,7 +202,7 @@ tasks: mask: transforms: SingleLabel: - module: minerva.transforms + _target_: minerva.transforms.SingleLabel module: minerva.datasets.__testing name: TstMaskDataset paths: Chesapeake7 diff --git a/minerva/inbuilt_cfgs/example_CNN_config.yaml b/minerva/inbuilt_cfgs/example_CNN_config.yaml index 3936e48e4..50a817af3 100644 --- a/minerva/inbuilt_cfgs/example_CNN_config.yaml +++ b/minerva/inbuilt_cfgs/example_CNN_config.yaml @@ -101,7 +101,7 @@ tasks: image: transforms: Normalise: - module: minerva.transforms + _target_: minerva.transforms.Normalise norm_value: 255 module: minerva.datasets.__testing name: TstImgDataset @@ -111,7 +111,7 @@ tasks: mask: transforms: SingleLabel: - module: minerva.transforms + _target_: minerva.transforms.SingleLabel mode: modal module: minerva.datasets.__testing name: TstMaskDataset @@ -140,7 +140,7 @@ tasks: image: transforms: Normalise: - module: minerva.transforms + _target_: minerva.transforms.Normalise norm_value: 255 module: minerva.datasets.__testing name: TstImgDataset @@ -151,7 +151,7 @@ tasks: mask: transforms: SingleLabel: - module: minerva.transforms + _target_: minerva.transforms.SingleLabel mode: modal module: minerva.datasets.__testing name: TstMaskDataset @@ -180,7 +180,7 @@ tasks: image: transforms: Normalise: - module: minerva.transforms + _target_: minerva.transforms.Normalise norm_value: 255 module: minerva.datasets.__testing name: TstImgDataset @@ -191,7 +191,7 @@ tasks: mask: transforms: SingleLabel: - module: minerva.transforms + _target_: minerva.transforms.SingleLabel mode: modal module: minerva.datasets.__testing name: TstMaskDataset diff --git a/minerva/inbuilt_cfgs/example_ChangeDetector.yaml b/minerva/inbuilt_cfgs/example_ChangeDetector.yaml index 244b3a7e5..992d9efeb 100644 --- a/minerva/inbuilt_cfgs/example_ChangeDetector.yaml +++ b/minerva/inbuilt_cfgs/example_ChangeDetector.yaml @@ -113,17 +113,17 @@ tasks: transforms: RandomCrop: - module: kornia.augmentation + _target_: kornia.augmentation.RandomCrop size: ${patch_size} keepdim: true image: transforms: - Normalise: - module: minerva.transforms + - Normalise: + _target_: minerva.transforms.Normalise norm_value: 4095 - SelectChannels: - module: minerva.transforms + - SelectChannels: + _target_: minerva.transforms.SelectChannels channels: - 1 - 2 @@ -167,17 +167,17 @@ tasks: transforms: RandomCrop: - module: kornia.augmentation + _target_: kornia.augmentation.RandomCrop size: ${patch_size} keepdim: true image: transforms: - Normalise: - module: minerva.transforms + - Normalise: + _target_: minerva.transforms.Normalise norm_value: 4095 - SelectChannels: - module: minerva.transforms + - SelectChannels: + _target_: minerva.transforms.SelectChannels channels: - 1 - 2 @@ -199,7 +199,7 @@ tasks: mask: transforms: SingleLabel: - module: minerva.transforms + _target_: minerva.transforms.SingleLabel mode: centre # === PLOTTING OPTIONS ======================================================== diff --git a/minerva/inbuilt_cfgs/example_GSConvNet-II.yaml b/minerva/inbuilt_cfgs/example_GSConvNet-II.yaml index d04bd52b1..eac9da52d 100644 --- a/minerva/inbuilt_cfgs/example_GSConvNet-II.yaml +++ b/minerva/inbuilt_cfgs/example_GSConvNet-II.yaml @@ -119,40 +119,42 @@ tasks: image: transforms: - Normalise: - module: minerva.transforms - norm_value: 10000 - RandomApply: + - normalise: + _target_: minerva.transforms.Normalise + norm_value: 255 + - random_apply: + _target_: torchvision.transforms.RandomApply p: 0.25 - DetachedColorJitter: - module: minerva.transforms - brightness: 0.2 - contrast: 0.1 - saturation: 0.1 - hue: 0.15 - RandomResizedCrop: - module: kornia.augmentation + transforms: + - jitter: + _target_: minerva.transforms.DetachedColorJitter + brightness: 0.2 + contrast: 0.1 + saturation: 0.1 + hue: 0.15 + - resize_crop: + _target_: kornia.augmentation.RandomResizedCrop p: 0.2 size: ${patch_size} cropping_mode: resample keepdim: true - RandomHorizontalFlip: - module: kornia.augmentation + - horizontal_flip: + _target_: kornia.augmentation.RandomHorizontalFlip p: 0.2 keepdim: true - RandomGaussianBlur: - module: kornia.augmentation + - gaussian_blur: + _target_: kornia.augmentation.RandomGaussianBlur kernel_size: 9 p: 0.2 sigma: [0.01, 0.2] keepdim: true - RandomGaussianNoise: - module: kornia.augmentation + - gaussian_noise: + _target_: kornia.augmentation.RandomGaussianNoise p: 0.2 std: 0.05 keepdim: true - RandomErasing: - module: kornia.augmentation + - random_erasing: + _target_: kornia.augmentation.RandomErasing p: 0.2 keepdim: true diff --git a/minerva/inbuilt_cfgs/example_GeoCLR_config.yaml b/minerva/inbuilt_cfgs/example_GeoCLR_config.yaml index f474b26fe..77f19ff10 100644 --- a/minerva/inbuilt_cfgs/example_GeoCLR_config.yaml +++ b/minerva/inbuilt_cfgs/example_GeoCLR_config.yaml @@ -111,40 +111,42 @@ tasks: image: transforms: - Normalise: - module: minerva.transforms + - normalise: + _target_: minerva.transforms.Normalise norm_value: 255 - RandomApply: + - random_apply: + _target_: torchvision.transforms.RandomApply p: 0.25 - DetachedColorJitter: - module: minerva.transforms - brightness: 0.2 - contrast: 0.1 - saturation: 0.1 - hue: 0.15 - RandomResizedCrop: - module: kornia.augmentation + transforms: + - jitter: + _target_: minerva.transforms.DetachedColorJitter + brightness: 0.2 + contrast: 0.1 + saturation: 0.1 + hue: 0.15 + - resize_crop: + _target_: kornia.augmentation.RandomResizedCrop p: 0.2 size: ${patch_size} cropping_mode: resample keepdim: true - RandomHorizontalFlip: - module: kornia.augmentation + - horizontal_flip: + _target_: kornia.augmentation.RandomHorizontalFlip p: 0.2 keepdim: true - RandomGaussianBlur: - module: kornia.augmentation + - gaussian_blur: + _target_: kornia.augmentation.RandomGaussianBlur kernel_size: 9 p: 0.2 sigma: [0.01, 0.2] keepdim: true - RandomGaussianNoise: - module: kornia.augmentation + - gaussian_noise: + _target_: kornia.augmentation.RandomGaussianNoise p: 0.2 std: 0.05 keepdim: true - RandomErasing: - module: kornia.augmentation + - random_erasing: + _target_: kornia.augmentation.RandomErasing p: 0.2 keepdim: true diff --git a/minerva/inbuilt_cfgs/example_GeoSimConvNet.yaml b/minerva/inbuilt_cfgs/example_GeoSimConvNet.yaml index b9536e839..ea3bc08b9 100644 --- a/minerva/inbuilt_cfgs/example_GeoSimConvNet.yaml +++ b/minerva/inbuilt_cfgs/example_GeoSimConvNet.yaml @@ -111,40 +111,42 @@ tasks: image: transforms: - Normalise: - module: minerva.transforms - norm_value: 10000 - RandomApply: + - normalise: + _target_: minerva.transforms.Normalise + norm_value: 255 + - random_apply: + _target_: torchvision.transforms.RandomApply p: 0.25 - DetachedColorJitter: - module: minerva.transforms - brightness: 0.2 - contrast: 0.1 - saturation: 0.1 - hue: 0.15 - RandomResizedCrop: - module: kornia.augmentation + transforms: + - jitter: + _target_: minerva.transforms.DetachedColorJitter + brightness: 0.2 + contrast: 0.1 + saturation: 0.1 + hue: 0.15 + - resize_crop: + _target_: kornia.augmentation.RandomResizedCrop p: 0.2 size: ${patch_size} cropping_mode: resample keepdim: true - RandomHorizontalFlip: - module: kornia.augmentation + - horizontal_flip: + _target_: kornia.augmentation.RandomHorizontalFlip p: 0.2 keepdim: true - RandomGaussianBlur: - module: kornia.augmentation + - gaussian_blur: + _target_: kornia.augmentation.RandomGaussianBlur kernel_size: 9 p: 0.2 sigma: [0.01, 0.2] keepdim: true - RandomGaussianNoise: - module: kornia.augmentation + - gaussian_noise: + _target_: kornia.augmentation.RandomGaussianNoise p: 0.2 std: 0.05 keepdim: true - RandomErasing: - module: kornia.augmentation + - random_erasing: + _target_: kornia.augmentation.RandomErasing p: 0.2 keepdim: true diff --git a/minerva/inbuilt_cfgs/example_MultiLabel.yaml b/minerva/inbuilt_cfgs/example_MultiLabel.yaml index ed33d12fb..0596e9c31 100644 --- a/minerva/inbuilt_cfgs/example_MultiLabel.yaml +++ b/minerva/inbuilt_cfgs/example_MultiLabel.yaml @@ -113,47 +113,49 @@ tasks: image: transforms: - SelectChannels: - module: minerva.transforms + - SelectChannels: + _target_: minerva.transforms.SelectChannels channels: - 1 - 2 - 3 - 7 - Normalise: - module: minerva.transforms + - Normalise: + _target_: minerva.transforms.Normalise norm_value: 4095 - RandomApply: + - RandomApply: p: 0.25 - DetachedColorJitter: - module: minerva.transforms - brightness: 0.2 - contrast: 0.1 - saturation: 0.1 - hue: 0.15 - RandomResizedCrop: - module: kornia.augmentation + _target_: torchvision.transform.RandomApply + transforms: + - DetachedColorJitter: + _target_: minerva.transforms.DetachedColorJitter + brightness: 0.2 + contrast: 0.1 + saturation: 0.1 + hue: 0.15 + - resize_crop: + _target_: kornia.augmentation.RandomResizedCrop p: 0.2 size: ${patch_size} cropping_mode: resample keepdim: true - RandomHorizontalFlip: - module: kornia.augmentation + - horizontal_flip: + _target_: kornia.augmentation.RandomHorizontalFlip p: 0.2 keepdim: true - RandomGaussianBlur: - module: kornia.augmentation + - gaussian_blur: + _target_: kornia.augmentation.RandomGaussianBlur kernel_size: 9 p: 0.2 sigma: [0.01, 0.2] keepdim: true - RandomGaussianNoise: - module: kornia.augmentation + - gaussian_noise: + _target_: kornia.augmentation.RandomGaussianNoise p: 0.2 std: 0.05 keepdim: true - RandomErasing: - module: kornia.augmentation + - random_erasing: + _target_: kornia.augmentation.RandomErasing p: 0.2 keepdim: true @@ -187,15 +189,15 @@ tasks: image: transforms: - SelectChannels: - module: minerva.transforms + - SelectChannels: + _target_: minerva.transforms.SelectChannels channels: - 1 - 2 - 3 - 7 - Normalise: - module: minerva.transforms + - Normalise: + _target_: minerva.transforms.Normalise norm_value: 4095 module: torchgeo.datasets diff --git a/minerva/inbuilt_cfgs/example_PSP.yaml b/minerva/inbuilt_cfgs/example_PSP.yaml index a8483852a..0ca4b2986 100644 --- a/minerva/inbuilt_cfgs/example_PSP.yaml +++ b/minerva/inbuilt_cfgs/example_PSP.yaml @@ -101,43 +101,45 @@ tasks: num_samples: 32 transforms: - RandomResizedCrop: - module: kornia.augmentation + - RandomResizedCrop: + _target_: kornia.augmentation.RandomResizedCrop p: 1.0 size: ${patch_size} cropping_mode: resample keepdim: true - RandomHorizontalFlip: - module: kornia.augmentation + - RandomHorizontalFlip: + _target_: kornia.augmentation.RandomHorizontalFlip p: 0.2 keepdim: true image: transforms: - Normalise: - module: minerva.transforms + - Normalise: + _target_: minerva.transforms.Normalise norm_value: 4095 - RandomApply: + - RandomApply: + _target_: torchvision.transforms.RandomApply p: 0.25 - DetachedColorJitter: - module: minerva.transforms - brightness: 0.2 - contrast: 0.1 - saturation: 0.1 - hue: 0.15 - RandomGaussianBlur: - module: kornia.augmentation + transforms: + - DetachedColorJitter: + _target_: minerva.transforms + brightness: 0.2 + contrast: 0.1 + saturation: 0.1 + hue: 0.15 + - gaussian_blur: + _target_: kornia.augmentation.RandomGaussianBlur kernel_size: 9 p: 0.2 sigma: [0.01, 0.2] keepdim: true - RandomGaussianNoise: - module: kornia.augmentation + - gaussian_noise: + _target_: kornia.augmentation.RandomGaussianNoise p: 0.2 std: 0.05 keepdim: true - RandomErasing: - module: kornia.augmentation + - random_erasing: + _target_: kornia.augmentation.RandomErasing p: 0.2 keepdim: true @@ -172,8 +174,8 @@ tasks: image: transforms: - Normalise: - module: minerva.transforms + - Normalise: + _target_: minerva.transforms.Normalise norm_value: 4095 module: minerva.datasets @@ -187,7 +189,7 @@ tasks: mask: transforms: MaskResize: - module: minerva.transforms + _target_: minerva.transforms.MaskResize size: 64 # === PLOTTING OPTIONS ======================================================== diff --git a/minerva/inbuilt_cfgs/example_SceneClassifier.yaml b/minerva/inbuilt_cfgs/example_SceneClassifier.yaml index 5f35abcdb..3fb45141b 100644 --- a/minerva/inbuilt_cfgs/example_SceneClassifier.yaml +++ b/minerva/inbuilt_cfgs/example_SceneClassifier.yaml @@ -111,40 +111,42 @@ tasks: image: transforms: - Normalise: - module: minerva.transforms - norm_value: 4095 - RandomApply: + - normalise: + _target_: minerva.transforms.Normalise + norm_value: 255 + - random_apply: + _target_: torchvision.transforms.RandomApply p: 0.25 - DetachedColorJitter: - module: minerva.transforms - brightness: 0.2 - contrast: 0.1 - saturation: 0.1 - hue: 0.15 - RandomResizedCrop: - module: kornia.augmentation + transforms: + - jitter: + _target_: minerva.transforms.DetachedColorJitter + brightness: 0.2 + contrast: 0.1 + saturation: 0.1 + hue: 0.15 + - resize_crop: + _target_: kornia.augmentation.RandomResizedCrop p: 0.2 size: ${patch_size} cropping_mode: resample keepdim: true - RandomHorizontalFlip: - module: kornia.augmentation + - horizontal_flip: + _target_: kornia.augmentation.RandomHorizontalFlip p: 0.2 keepdim: true - RandomGaussianBlur: - module: kornia.augmentation + - gaussian_blur: + _target_: kornia.augmentation.RandomGaussianBlur kernel_size: 9 p: 0.2 sigma: [0.01, 0.2] keepdim: true - RandomGaussianNoise: - module: kornia.augmentation + - gaussian_noise: + _target_: kornia.augmentation.RandomGaussianNoise p: 0.2 std: 0.05 keepdim: true - RandomErasing: - module: kornia.augmentation + - random_erasing: + _target_: kornia.augmentation.RandomErasing p: 0.2 keepdim: true @@ -181,7 +183,7 @@ tasks: image: transforms: Normalise: - module: minerva.transforms + _target_: minerva.transforms.Normalise norm_value: 4095 module: torchgeo.datasets diff --git a/minerva/inbuilt_cfgs/example_UNetR_config.yaml b/minerva/inbuilt_cfgs/example_UNetR_config.yaml index b720f76ba..24de24b30 100644 --- a/minerva/inbuilt_cfgs/example_UNetR_config.yaml +++ b/minerva/inbuilt_cfgs/example_UNetR_config.yaml @@ -105,7 +105,7 @@ tasks: image: transforms: Normalise: - module: minerva.transforms + _target_: minerva.transforms.Normalise norm_value: 255 module: minerva.datasets.__testing name: TstImgDataset @@ -141,7 +141,7 @@ tasks: image: transforms: Normalise: - module: minerva.transforms + _target_: minerva.transforms.Normalise norm_value: 255 module: minerva.datasets.__testing name: TstImgDataset @@ -176,7 +176,7 @@ tasks: image: transforms: Normalise: - module: minerva.transforms + _target_: minerva.transforms.Normalise norm_value: 255 module: minerva.datasets.__testing name: TstImgDataset diff --git a/minerva/inbuilt_cfgs/example_autoencoder_config.yaml b/minerva/inbuilt_cfgs/example_autoencoder_config.yaml index cb162c699..a35d69085 100644 --- a/minerva/inbuilt_cfgs/example_autoencoder_config.yaml +++ b/minerva/inbuilt_cfgs/example_autoencoder_config.yaml @@ -107,7 +107,7 @@ tasks: image: transforms: Normalise: - module: minerva.transforms + _target_: minerva.transforms.Normalise norm_value: 255 module: minerva.datasets.__testing name: TstImgDataset @@ -145,7 +145,7 @@ tasks: image: transforms: Normalise: - module: minerva.transforms + _target_: minerva.transforms.Normalise norm_value: 255 module: minerva.datasets.__testing name: TstImgDataset @@ -183,7 +183,7 @@ tasks: image: transforms: Normalise: - module: minerva.transforms + _target_: minerva.transforms.Normalise norm_value: 255 module: minerva.datasets.__testing name: TstImgDataset diff --git a/minerva/transforms.py b/minerva/transforms.py index 7768d091e..fa73f7bda 100644 --- a/minerva/transforms.py +++ b/minerva/transforms.py @@ -69,6 +69,7 @@ overload, ) +import hydra import numpy as np import rasterio import torch @@ -80,14 +81,12 @@ ColorJitter, ConvertImageDtype, Normalize, - RandomApply, Resize, ) from torchvision.transforms.v2 import functional as ft from minerva.utils.utils import ( find_tensor_mode, - func_by_str, get_centre_pixel_value, mask_transform, ) @@ -839,16 +838,6 @@ def forward(self, img: Tensor) -> Tensor: # ===================================================================================================================== # METHODS # ===================================================================================================================== -def _construct_random_transforms(random_params: Dict[str, Any]) -> Any: - p = random_params.pop("p", 0.5) - - random_transforms = [] - for ran_name in random_params: - random_transforms.append(get_transform(ran_name, random_params[ran_name])) - - return RandomApply(random_transforms, p=p) - - def init_auto_norm( dataset: RasterDataset, params: Dict[str, Any] = {} ) -> RasterDataset: @@ -887,35 +876,24 @@ def init_auto_norm( return dataset -def get_transform(name: str, transform_params: Dict[str, Any]) -> Callable[..., Any]: +def get_transform(transform_params: Dict[str, Any]) -> Callable[..., Any]: """Creates a transform object based on config parameters. Args: - name (str): Name of transform object to import e.g :class:`~torchvision.transforms.RandomResizedCrop`. transform_params (dict[str, ~typing.Any]): Arguements to construct transform with. - Should also include ``"module"`` key defining the import path to the transform object. + Should also include ``"_target_"`` key defining the import path to the transform object. Returns: Initialised transform object specified by config parameters. - .. note:: - If ``transform_params`` contains no ``"module"`` key, it defaults to ``torchvision.transforms``. - Example: - >>> name = "RandomResizedCrop" - >>> params = {"module": "torchvision.transforms", "size": 128} - >>> transform = get_transform(name, params) + >>> params = {"_target": "torchvision.transforms.RandomResizedCrop", "size": 128} + >>> transform = get_transform(params) Raises: TypeError: If created transform object is itself not :class:`~typing.Callable`. """ - params = transform_params.copy() - module = params.pop("module", "torchvision.transforms") - - # Gets the transform requested by config parameters. - _transform: Callable[..., Any] = func_by_str(module, name) - - transform: Callable[..., Any] = _transform(**params) + transform: Callable[..., Any] = hydra.utils.instantiate(transform_params) if callable(transform): return transform else: @@ -934,8 +912,8 @@ def make_transformations( Example: >>> transform_params = { - >>> "CenterCrop": {"module": "torchvision.transforms", "size": 128}, - >>> "RandomHorizontalFlip": {"module": "torchvision.transforms", "p": 0.7} + >>> "crop": {"_target_": "torchvision.transforms.CenterCrop", "size": 128}, + >>> "flip": {"module": "torchvision.transforms.RandomHorizontalFlip", "p": 0.7} >>> } >>> transforms = make_transformations(transform_params) @@ -950,17 +928,14 @@ def construct(type_transform_params: Dict[str, Any]) -> List[Callable[..., Any]] # Get each transform. for _name in type_transform_params: - if _name == "RandomApply": - random_params = type_transform_params[_name].copy() - type_transformations.append(_construct_random_transforms(random_params)) # AutoNorm needs to be handled separately. - elif _name == "AutoNorm": + if _name == "AutoNorm": continue else: type_transformations.append( - get_transform(_name, type_transform_params[_name]) + get_transform(type_transform_params[_name]) ) return type_transformations From 88ed09c37d0d6394bffccc055b23688ef3332dec Mon Sep 17 00:00:00 2001 From: Harry Baker Date: Tue, 20 Aug 2024 01:29:02 +0100 Subject: [PATCH 16/63] Cut back UNet test to 2 epochs --- minerva/inbuilt_cfgs/example_UNetR_config.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/minerva/inbuilt_cfgs/example_UNetR_config.yaml b/minerva/inbuilt_cfgs/example_UNetR_config.yaml index 24de24b30..7c5212386 100644 --- a/minerva/inbuilt_cfgs/example_UNetR_config.yaml +++ b/minerva/inbuilt_cfgs/example_UNetR_config.yaml @@ -27,7 +27,7 @@ patch_size: '${to_patch_size: ${input_size}}' # 2D tuple or float. n_classes: &n_classes 8 # Number of classes in dataset. # ---+ Experiment Execution +-------------------------------------------------- -max_epochs: 5 # Maximum number of training epochs. +max_epochs: 2 # Maximum number of training epochs. elim: true # Eliminates empty classes from schema. balance: true # Balances dataset classes. pre_train: false # Activate pre-training mode. From 031d490c0e9cf1d7a019be60759150687bcef4ba Mon Sep 17 00:00:00 2001 From: Harry Baker Date: Tue, 20 Aug 2024 14:50:10 +0100 Subject: [PATCH 17/63] Fixed `RandomApply` transforms special case --- .../inbuilt_cfgs/example_ChangeDetector.yaml | 8 ++--- .../inbuilt_cfgs/example_GSConvNet-II.yaml | 28 +++++++-------- .../inbuilt_cfgs/example_GeoCLR_config.yaml | 28 +++++++-------- .../inbuilt_cfgs/example_GeoSimConvNet.yaml | 28 +++++++-------- minerva/inbuilt_cfgs/example_MultiLabel.yaml | 34 +++++++++---------- minerva/inbuilt_cfgs/example_PSP.yaml | 34 +++++++++---------- .../inbuilt_cfgs/example_SceneClassifier.yaml | 28 +++++++-------- minerva/transforms.py | 23 +++++++++++-- 8 files changed, 110 insertions(+), 101 deletions(-) diff --git a/minerva/inbuilt_cfgs/example_ChangeDetector.yaml b/minerva/inbuilt_cfgs/example_ChangeDetector.yaml index 992d9efeb..eacb13e94 100644 --- a/minerva/inbuilt_cfgs/example_ChangeDetector.yaml +++ b/minerva/inbuilt_cfgs/example_ChangeDetector.yaml @@ -119,10 +119,10 @@ tasks: image: transforms: - - Normalise: + Normalise: _target_: minerva.transforms.Normalise norm_value: 4095 - - SelectChannels: + SelectChannels: _target_: minerva.transforms.SelectChannels channels: - 1 @@ -173,10 +173,10 @@ tasks: image: transforms: - - Normalise: + Normalise: _target_: minerva.transforms.Normalise norm_value: 4095 - - SelectChannels: + SelectChannels: _target_: minerva.transforms.SelectChannels channels: - 1 diff --git a/minerva/inbuilt_cfgs/example_GSConvNet-II.yaml b/minerva/inbuilt_cfgs/example_GSConvNet-II.yaml index eac9da52d..fbb1c8962 100644 --- a/minerva/inbuilt_cfgs/example_GSConvNet-II.yaml +++ b/minerva/inbuilt_cfgs/example_GSConvNet-II.yaml @@ -119,41 +119,39 @@ tasks: image: transforms: - - normalise: + normalise: _target_: minerva.transforms.Normalise norm_value: 255 - - random_apply: - _target_: torchvision.transforms.RandomApply + RandomApply: p: 0.25 - transforms: - - jitter: - _target_: minerva.transforms.DetachedColorJitter - brightness: 0.2 - contrast: 0.1 - saturation: 0.1 - hue: 0.15 - - resize_crop: + jitter: + _target_: minerva.transforms.DetachedColorJitter + brightness: 0.2 + contrast: 0.1 + saturation: 0.1 + hue: 0.15 + resize_crop: _target_: kornia.augmentation.RandomResizedCrop p: 0.2 size: ${patch_size} cropping_mode: resample keepdim: true - - horizontal_flip: + horizontal_flip: _target_: kornia.augmentation.RandomHorizontalFlip p: 0.2 keepdim: true - - gaussian_blur: + gaussian_blur: _target_: kornia.augmentation.RandomGaussianBlur kernel_size: 9 p: 0.2 sigma: [0.01, 0.2] keepdim: true - - gaussian_noise: + gaussian_noise: _target_: kornia.augmentation.RandomGaussianNoise p: 0.2 std: 0.05 keepdim: true - - random_erasing: + random_erasing: _target_: kornia.augmentation.RandomErasing p: 0.2 keepdim: true diff --git a/minerva/inbuilt_cfgs/example_GeoCLR_config.yaml b/minerva/inbuilt_cfgs/example_GeoCLR_config.yaml index 77f19ff10..31b9523be 100644 --- a/minerva/inbuilt_cfgs/example_GeoCLR_config.yaml +++ b/minerva/inbuilt_cfgs/example_GeoCLR_config.yaml @@ -111,41 +111,39 @@ tasks: image: transforms: - - normalise: + normalise: _target_: minerva.transforms.Normalise norm_value: 255 - - random_apply: - _target_: torchvision.transforms.RandomApply + RandomApply: p: 0.25 - transforms: - - jitter: - _target_: minerva.transforms.DetachedColorJitter - brightness: 0.2 - contrast: 0.1 - saturation: 0.1 - hue: 0.15 - - resize_crop: + jitter: + _target_: minerva.transforms.DetachedColorJitter + brightness: 0.2 + contrast: 0.1 + saturation: 0.1 + hue: 0.15 + resize_crop: _target_: kornia.augmentation.RandomResizedCrop p: 0.2 size: ${patch_size} cropping_mode: resample keepdim: true - - horizontal_flip: + horizontal_flip: _target_: kornia.augmentation.RandomHorizontalFlip p: 0.2 keepdim: true - - gaussian_blur: + gaussian_blur: _target_: kornia.augmentation.RandomGaussianBlur kernel_size: 9 p: 0.2 sigma: [0.01, 0.2] keepdim: true - - gaussian_noise: + gaussian_noise: _target_: kornia.augmentation.RandomGaussianNoise p: 0.2 std: 0.05 keepdim: true - - random_erasing: + random_erasing: _target_: kornia.augmentation.RandomErasing p: 0.2 keepdim: true diff --git a/minerva/inbuilt_cfgs/example_GeoSimConvNet.yaml b/minerva/inbuilt_cfgs/example_GeoSimConvNet.yaml index ea3bc08b9..4fc754ab8 100644 --- a/minerva/inbuilt_cfgs/example_GeoSimConvNet.yaml +++ b/minerva/inbuilt_cfgs/example_GeoSimConvNet.yaml @@ -111,41 +111,39 @@ tasks: image: transforms: - - normalise: + normalise: _target_: minerva.transforms.Normalise norm_value: 255 - - random_apply: - _target_: torchvision.transforms.RandomApply + RandomApply: p: 0.25 - transforms: - - jitter: - _target_: minerva.transforms.DetachedColorJitter - brightness: 0.2 - contrast: 0.1 - saturation: 0.1 - hue: 0.15 - - resize_crop: + jitter: + _target_: minerva.transforms.DetachedColorJitter + brightness: 0.2 + contrast: 0.1 + saturation: 0.1 + hue: 0.15 + resize_crop: _target_: kornia.augmentation.RandomResizedCrop p: 0.2 size: ${patch_size} cropping_mode: resample keepdim: true - - horizontal_flip: + horizontal_flip: _target_: kornia.augmentation.RandomHorizontalFlip p: 0.2 keepdim: true - - gaussian_blur: + gaussian_blur: _target_: kornia.augmentation.RandomGaussianBlur kernel_size: 9 p: 0.2 sigma: [0.01, 0.2] keepdim: true - - gaussian_noise: + gaussian_noise: _target_: kornia.augmentation.RandomGaussianNoise p: 0.2 std: 0.05 keepdim: true - - random_erasing: + random_erasing: _target_: kornia.augmentation.RandomErasing p: 0.2 keepdim: true diff --git a/minerva/inbuilt_cfgs/example_MultiLabel.yaml b/minerva/inbuilt_cfgs/example_MultiLabel.yaml index 0596e9c31..c42475297 100644 --- a/minerva/inbuilt_cfgs/example_MultiLabel.yaml +++ b/minerva/inbuilt_cfgs/example_MultiLabel.yaml @@ -113,48 +113,46 @@ tasks: image: transforms: - - SelectChannels: + SelectChannels: _target_: minerva.transforms.SelectChannels channels: - 1 - 2 - 3 - 7 - - Normalise: + Normalise: _target_: minerva.transforms.Normalise norm_value: 4095 - - RandomApply: + RandomApply: p: 0.25 - _target_: torchvision.transform.RandomApply - transforms: - - DetachedColorJitter: - _target_: minerva.transforms.DetachedColorJitter - brightness: 0.2 - contrast: 0.1 - saturation: 0.1 - hue: 0.15 - - resize_crop: + DetachedColorJitter: + _target_: minerva.transforms.DetachedColorJitter + brightness: 0.2 + contrast: 0.1 + saturation: 0.1 + hue: 0.15 + resize_crop: _target_: kornia.augmentation.RandomResizedCrop p: 0.2 size: ${patch_size} cropping_mode: resample keepdim: true - - horizontal_flip: + horizontal_flip: _target_: kornia.augmentation.RandomHorizontalFlip p: 0.2 keepdim: true - - gaussian_blur: + gaussian_blur: _target_: kornia.augmentation.RandomGaussianBlur kernel_size: 9 p: 0.2 sigma: [0.01, 0.2] keepdim: true - - gaussian_noise: + gaussian_noise: _target_: kornia.augmentation.RandomGaussianNoise p: 0.2 std: 0.05 keepdim: true - - random_erasing: + random_erasing: _target_: kornia.augmentation.RandomErasing p: 0.2 keepdim: true @@ -189,14 +187,14 @@ tasks: image: transforms: - - SelectChannels: + SelectChannels: _target_: minerva.transforms.SelectChannels channels: - 1 - 2 - 3 - 7 - - Normalise: + Normalise: _target_: minerva.transforms.Normalise norm_value: 4095 diff --git a/minerva/inbuilt_cfgs/example_PSP.yaml b/minerva/inbuilt_cfgs/example_PSP.yaml index 0ca4b2986..0720a22bb 100644 --- a/minerva/inbuilt_cfgs/example_PSP.yaml +++ b/minerva/inbuilt_cfgs/example_PSP.yaml @@ -101,44 +101,42 @@ tasks: num_samples: 32 transforms: - - RandomResizedCrop: + RandomResizedCrop: _target_: kornia.augmentation.RandomResizedCrop p: 1.0 size: ${patch_size} cropping_mode: resample keepdim: true - - RandomHorizontalFlip: + RandomHorizontalFlip: _target_: kornia.augmentation.RandomHorizontalFlip p: 0.2 keepdim: true image: transforms: - - Normalise: + normalise: _target_: minerva.transforms.Normalise norm_value: 4095 - - RandomApply: - _target_: torchvision.transforms.RandomApply + RandomApply: p: 0.25 - transforms: - - DetachedColorJitter: - _target_: minerva.transforms - brightness: 0.2 - contrast: 0.1 - saturation: 0.1 - hue: 0.15 - - gaussian_blur: + jitter: + _target_: minerva.transforms.DetachedColorJitter + brightness: 0.2 + contrast: 0.1 + saturation: 0.1 + hue: 0.15 + gaussian_blur: _target_: kornia.augmentation.RandomGaussianBlur kernel_size: 9 p: 0.2 sigma: [0.01, 0.2] keepdim: true - - gaussian_noise: + gaussian_noise: _target_: kornia.augmentation.RandomGaussianNoise p: 0.2 std: 0.05 keepdim: true - - random_erasing: + random_erasing: _target_: kornia.augmentation.RandomErasing p: 0.2 keepdim: true @@ -153,8 +151,9 @@ tasks: mask: transforms: MaskResize: - module: minerva.transforms + _target_: minerva.transforms.MaskResize size: 64 + interpolation: NEAREST test-test: name: StandardEpoch @@ -174,7 +173,7 @@ tasks: image: transforms: - - Normalise: + Normalise: _target_: minerva.transforms.Normalise norm_value: 4095 @@ -191,6 +190,7 @@ tasks: MaskResize: _target_: minerva.transforms.MaskResize size: 64 + interpolation: NEAREST # === PLOTTING OPTIONS ======================================================== plots: diff --git a/minerva/inbuilt_cfgs/example_SceneClassifier.yaml b/minerva/inbuilt_cfgs/example_SceneClassifier.yaml index 3fb45141b..17f22966d 100644 --- a/minerva/inbuilt_cfgs/example_SceneClassifier.yaml +++ b/minerva/inbuilt_cfgs/example_SceneClassifier.yaml @@ -111,41 +111,39 @@ tasks: image: transforms: - - normalise: + normalise: _target_: minerva.transforms.Normalise norm_value: 255 - - random_apply: - _target_: torchvision.transforms.RandomApply + RandomApply: p: 0.25 - transforms: - - jitter: - _target_: minerva.transforms.DetachedColorJitter - brightness: 0.2 - contrast: 0.1 - saturation: 0.1 - hue: 0.15 - - resize_crop: + jitter: + _target_: minerva.transforms.DetachedColorJitter + brightness: 0.2 + contrast: 0.1 + saturation: 0.1 + hue: 0.15 + resize_crop: _target_: kornia.augmentation.RandomResizedCrop p: 0.2 size: ${patch_size} cropping_mode: resample keepdim: true - - horizontal_flip: + horizontal_flip: _target_: kornia.augmentation.RandomHorizontalFlip p: 0.2 keepdim: true - - gaussian_blur: + gaussian_blur: _target_: kornia.augmentation.RandomGaussianBlur kernel_size: 9 p: 0.2 sigma: [0.01, 0.2] keepdim: true - - gaussian_noise: + gaussian_noise: _target_: kornia.augmentation.RandomGaussianNoise p: 0.2 std: 0.05 keepdim: true - - random_erasing: + random_erasing: _target_: kornia.augmentation.RandomErasing p: 0.2 keepdim: true diff --git a/minerva/transforms.py b/minerva/transforms.py index fa73f7bda..17b369bcd 100644 --- a/minerva/transforms.py +++ b/minerva/transforms.py @@ -80,8 +80,10 @@ from torchvision.transforms import ( ColorJitter, ConvertImageDtype, + InterpolationMode, Normalize, Resize, + RandomApply, ) from torchvision.transforms.v2 import functional as ft @@ -807,6 +809,10 @@ def __init__(self, dtype: str) -> None: class MaskResize(Resize): """Wrapper of :class:`torchvision.transforms.Resize` for use with masks that have no channel dimension.""" + def __init__(self, size, interpolation: str = "NEAREST", max_size=None, antialias: bool = True) -> None: + interpolation_mode = getattr(InterpolationMode, interpolation) + super().__init__(size, interpolation_mode, max_size, antialias) + def forward(self, img: Tensor) -> Tensor: """ Args: @@ -838,6 +844,16 @@ def forward(self, img: Tensor) -> Tensor: # ===================================================================================================================== # METHODS # ===================================================================================================================== +def _construct_random_transforms(random_params: Dict[str, Any]) -> Any: + p = random_params.pop("p", 0.5) + + random_transforms = [] + for ran_name in random_params: + random_transforms.append(get_transform(random_params[ran_name])) + + return RandomApply(random_transforms, p=p) + + def init_auto_norm( dataset: RasterDataset, params: Dict[str, Any] = {} ) -> RasterDataset: @@ -913,7 +929,7 @@ def make_transformations( Example: >>> transform_params = { >>> "crop": {"_target_": "torchvision.transforms.CenterCrop", "size": 128}, - >>> "flip": {"module": "torchvision.transforms.RandomHorizontalFlip", "p": 0.7} + >>> "flip": {"_target_": "torchvision.transforms.RandomHorizontalFlip", "p": 0.7} >>> } >>> transforms = make_transformations(transform_params) @@ -928,9 +944,12 @@ def construct(type_transform_params: Dict[str, Any]) -> List[Callable[..., Any]] # Get each transform. for _name in type_transform_params: + if _name == "RandomApply": + random_params = type_transform_params[_name].copy() + type_transformations.append(_construct_random_transforms(random_params)) # AutoNorm needs to be handled separately. - if _name == "AutoNorm": + elif _name == "AutoNorm": continue else: From a559460dc302412b9660d3747cf28e63897deba0 Mon Sep 17 00:00:00 2001 From: Harry Date: Tue, 20 Aug 2024 20:04:58 +0100 Subject: [PATCH 18/63] Use `hydra.utils.instantiate` to init the samplers --- minerva/datasets/factory.py | 66 +++++++------------ minerva/inbuilt_cfgs/example_3rd_party.yaml | 24 +++---- minerva/inbuilt_cfgs/example_CNN_config.yaml | 24 +++---- .../inbuilt_cfgs/example_ChangeDetector.yaml | 14 ++-- .../inbuilt_cfgs/example_GSConvNet-II.yaml | 26 +++----- .../inbuilt_cfgs/example_GeoCLR_config.yaml | 34 ++++------ .../inbuilt_cfgs/example_GeoSimConvNet.yaml | 30 ++++----- minerva/inbuilt_cfgs/example_MultiLabel.yaml | 12 ++-- minerva/inbuilt_cfgs/example_PSP.yaml | 12 ++-- .../inbuilt_cfgs/example_SceneClassifier.yaml | 12 ++-- .../inbuilt_cfgs/example_UNetR_config.yaml | 24 +++---- .../example_autoencoder_config.yaml | 24 +++---- minerva/inbuilt_cfgs/example_config.yaml | 24 +++---- minerva/samplers.py | 45 ++++++++++++- 14 files changed, 161 insertions(+), 210 deletions(-) diff --git a/minerva/datasets/factory.py b/minerva/datasets/factory.py index 524bb4c39..ee196cb69 100644 --- a/minerva/datasets/factory.py +++ b/minerva/datasets/factory.py @@ -60,12 +60,11 @@ from omegaconf import OmegaConf from pandas import DataFrame from rasterio.crs import CRS -from torch.utils.data import DataLoader, Sampler +from torch.utils.data import DataLoader from torchgeo.datasets import GeoDataset, NonGeoDataset, RasterDataset -from torchgeo.samplers import BatchGeoSampler, GeoSampler from torchgeo.samplers.utils import _to_tuple -from minerva.samplers import DistributedSamplerWrapper +from minerva.samplers import DistributedSamplerWrapper, get_sampler from minerva.transforms import MinervaCompose, init_auto_norm, make_transformations from minerva.utils import universal_path, utils @@ -78,7 +77,6 @@ intersect_datasets, load_all_samples, load_dataset_from_cache, - make_bounding_box, masks_or_labels, unionise_datasets, ) @@ -524,34 +522,18 @@ def construct_dataloader( ) # --+ MAKE SAMPLERS +=============================================================================================+ - _sampler: Callable[..., Union[BatchGeoSampler, GeoSampler]] = utils.func_by_str( - module_path=sampler_params["module"], func=sampler_params["name"] - ) + per_device_batch_size = None - batch_sampler = True if re.search(r"Batch", sampler_params["name"]) else False - if batch_sampler: - sampler_params["params"]["batch_size"] = batch_size - - if dist.is_available() and dist.is_initialized(): # type: ignore[attr-defined] - assert ( - sampler_params["params"]["batch_size"] % world_size == 0 - ) # pragma: no cover - per_device_batch_size = ( - sampler_params["params"]["batch_size"] // world_size - ) # pragma: no cover - sampler_params["params"][ - "batch_size" - ] = per_device_batch_size # pragma: no cover - - sampler: Sampler[Any] - if "roi" in signature(_sampler).parameters: - sampler = _sampler( - subdatasets[0], - roi=make_bounding_box(sampler_params["roi"]), - **sampler_params["params"], - ) - else: - sampler = _sampler(subdatasets[0], **sampler_params["params"]) + batch_sampler = True if re.search(r"Batch", sampler_params["_target_"]) else False + if batch_sampler and dist.is_available() and dist.is_initialized(): # type: ignore[attr-defined] + assert sampler_params["batch_size"] % world_size == 0 # pragma: no cover + per_device_batch_size = ( + sampler_params["batch_size"] // world_size + ) # pragma: no cover + + sampler = get_sampler( + sampler_params, subdatasets[0], batch_size=per_device_batch_size + ) # --+ MAKE DATALOADERS +==========================================================================================+ collator = get_collator(collator_params) @@ -651,9 +633,9 @@ def _make_loader( # Calculates number of batches. assert hasattr(loaders.dataset, "__len__") n_batches = int( - sampler_params["params"].get( + sampler_params.get( "length", - sampler_params["params"].get("num_samples", len(loaders.dataset)), + sampler_params.get("num_samples", len(loaders.dataset)), ) / batch_size ) @@ -1025,24 +1007,22 @@ def delete_transforms(params: Dict[str, Any]) -> None: if OmegaConf.is_config(_dataset_params): _dataset_params = OmegaConf.to_object(_dataset_params) # type: ignore[assignment] - if _sampler_params["name"] in ( + if _sampler_params["_target_"].rpartition(".")[2] in ( "RandomGeoSampler", "RandomPairGeoSampler", "RandomBatchGeoSampler", "RandomPairBatchGeoSampler", ): - _sampler_params["module"] = "torchgeo.samplers" - _sampler_params["name"] = "GridGeoSampler" - _sampler_params["params"]["stride"] = [ - 0.9 * x for x in _to_tuple(_sampler_params["params"]["size"]) + _sampler_params["_target_"] = "torchgeo.samplers.GridGeoSampler" + _sampler_params["stride"] = [ + 0.9 * x for x in _to_tuple(_sampler_params["size"]) ] - if _sampler_params["name"] == "RandomSampler": - _sampler_params["name"] = "SequentialSampler" - _sampler_params["params"] = {} + if _sampler_params["_target_"].rpartition(".")[2] == "RandomSampler": + _sampler_params = {"_target_": "torch.utils.data.sampler.SequentialSampler"} - if "length" in _sampler_params["params"]: - del _sampler_params["params"]["length"] + if "length" in _sampler_params: + del _sampler_params["length"] # Ensure there are no errant `ClassTransform` transforms in the parameters from previous runs. # A `ClassTransform` can only be defined with a correct manifest so we cannot use an old one to diff --git a/minerva/inbuilt_cfgs/example_3rd_party.yaml b/minerva/inbuilt_cfgs/example_3rd_party.yaml index 8ac328113..f35340974 100644 --- a/minerva/inbuilt_cfgs/example_3rd_party.yaml +++ b/minerva/inbuilt_cfgs/example_3rd_party.yaml @@ -95,12 +95,10 @@ tasks: # ---+ Dataset Parameters +-------------------------------------------- dataset_params: sampler: - module: torchgeo.samplers - name: RandomGeoSampler + _target_: torchgeo.samplers.RandomGeoSampler roi: false - params: - size: ${patch_size} - length: 120 + size: ${patch_size} + length: 120 image: transforms: @@ -143,12 +141,10 @@ tasks: # ---+ Dataset Parameters +-------------------------------------------- dataset_params: sampler: - module: torchgeo.samplers - name: RandomGeoSampler + _target_: torchgeo.samplers.RandomGeoSampler roi: false - params: - size: ${patch_size} - length: 32 + size: ${patch_size} + length: 32 image: transforms: @@ -182,12 +178,10 @@ tasks: # ---+ Dataset Parameters +-------------------------------------------- dataset_params: sampler: - module: torchgeo.samplers - name: RandomGeoSampler + _target_: torchgeo.samplers.RandomGeoSampler roi: false - params: - size: ${patch_size} - length: 32 + size: ${patch_size} + length: 32 image: transforms: diff --git a/minerva/inbuilt_cfgs/example_CNN_config.yaml b/minerva/inbuilt_cfgs/example_CNN_config.yaml index 50a817af3..7bbde3fb3 100644 --- a/minerva/inbuilt_cfgs/example_CNN_config.yaml +++ b/minerva/inbuilt_cfgs/example_CNN_config.yaml @@ -91,12 +91,10 @@ tasks: dataset_params: sampler: - module: torchgeo.samplers - name: RandomGeoSampler + _target_: torchgeo.samplers.RandomGeoSampler roi: false - params: - size: ${patch_size} - length: 320 + size: ${patch_size} + length: 320 image: transforms: @@ -130,12 +128,10 @@ tasks: dataset_params: sampler: - module: torchgeo.samplers - name: RandomGeoSampler + _target_: torchgeo.samplers.RandomGeoSampler roi: false - params: - size: ${patch_size} - length: 80 + size: ${patch_size} + length: 80 image: transforms: @@ -170,12 +166,10 @@ tasks: dataset_params: sampler: - module: torchgeo.samplers - name: RandomGeoSampler + _target_: torchgeo.samplers.RandomGeoSampler roi: false - params: - size: ${patch_size} - length: 160 + size: ${patch_size} + length: 160 image: transforms: diff --git a/minerva/inbuilt_cfgs/example_ChangeDetector.yaml b/minerva/inbuilt_cfgs/example_ChangeDetector.yaml index eacb13e94..c698f6f72 100644 --- a/minerva/inbuilt_cfgs/example_ChangeDetector.yaml +++ b/minerva/inbuilt_cfgs/example_ChangeDetector.yaml @@ -106,10 +106,8 @@ tasks: # ---+ Dataset Parameters +-------------------------------------------- dataset_params: sampler: - module: torch.utils.data - name: RandomSampler - params: - num_samples: 32 + _target_: torch.utils.data.RandomSampler + num_samples: 32 transforms: RandomCrop: @@ -145,7 +143,7 @@ tasks: mask: transforms: SingleLabel: - module: minerva.transforms + _target_: minerva.transforms.SingleLabel mode: centre test-test: @@ -160,10 +158,8 @@ tasks: # ---+ Dataset Parameters +-------------------------------------------- dataset_params: sampler: - module: torch.utils.data - name: RandomSampler - params: - num_samples: 16 + _target_: torch.utils.data.RandomSampler + num_samples: 16 transforms: RandomCrop: diff --git a/minerva/inbuilt_cfgs/example_GSConvNet-II.yaml b/minerva/inbuilt_cfgs/example_GSConvNet-II.yaml index fbb1c8962..e6bddd7ca 100644 --- a/minerva/inbuilt_cfgs/example_GSConvNet-II.yaml +++ b/minerva/inbuilt_cfgs/example_GSConvNet-II.yaml @@ -112,10 +112,8 @@ tasks: # ---+ Dataset Parameters +---------------------------------------- dataset_params: sampler: - module: torch.utils.data - name: RandomSampler - params: - num_samples: 32 + _target_: torch.utils.data.RandomSampler + num_samples: 32 image: transforms: @@ -182,10 +180,8 @@ tasks: dataset_params: features: sampler: - module: torch.utils.data - name: RandomSampler - params: - num_samples: 16 + _target_: torch.utils.data.RandomSampler + num_samples: 16 image: module: minerva.datasets @@ -198,10 +194,8 @@ tasks: test: sampler: - module: torch.utils.data - name: RandomSampler - params: - num_samples: 16 + _target_: torch.utils.data.RandomSampler + num_samples: 16 image: module: minerva.datasets @@ -226,12 +220,10 @@ tasks: # ---+ Dataset Parameters +---------------------------------------- dataset_params: sampler: - module: torchgeo.samplers - name: RandomGeoSampler + _target_: torchgeo.samplers.RandomGeoSampler roi: false - params: - size: ${patch_size} - length: 16 + size: ${patch_size} + length: 16 image: module: minerva.datasets.__testing diff --git a/minerva/inbuilt_cfgs/example_GeoCLR_config.yaml b/minerva/inbuilt_cfgs/example_GeoCLR_config.yaml index 31b9523be..b219a743f 100644 --- a/minerva/inbuilt_cfgs/example_GeoCLR_config.yaml +++ b/minerva/inbuilt_cfgs/example_GeoCLR_config.yaml @@ -101,13 +101,11 @@ tasks: # ---+ Dataset Parameters +---------------------------------------- dataset_params: sampler: - module: minerva.samplers - name: RandomPairGeoSampler + _target_: minerva.samplers.RandomPairGeoSampler roi: false - params: - size: ${patch_size} - length: 120 - max_r: *max_r + size: ${patch_size} + length: 120 + max_r: *max_r image: transforms: @@ -181,12 +179,10 @@ tasks: dataset_params: features: sampler: - module: torchgeo.samplers - name: RandomGeoSampler + _target_: torchgeo.samplers.RandomGeoSampler roi: false - params: - size: ${patch_size} - length: 32 + size: ${patch_size} + length: 32 image: module: minerva.datasets.__testing @@ -204,12 +200,10 @@ tasks: test: sampler: - module: torchgeo.samplers - name: RandomGeoSampler + _target_: torchgeo.samplers.RandomGeoSampler roi: false - params: - size: ${patch_size} - length: 32 + size: ${patch_size} + length: 32 image: module: minerva.datasets.__testing @@ -243,12 +237,10 @@ tasks: # ---+ Dataset Parameters +---------------------------------------- dataset_params: sampler: - module: torchgeo.samplers - name: RandomGeoSampler + _target_: torchgeo.samplers.RandomGeoSampler roi: false - params: - size: ${patch_size} - length: 32 + size: ${patch_size} + length: 32 image: module: minerva.datasets.__testing diff --git a/minerva/inbuilt_cfgs/example_GeoSimConvNet.yaml b/minerva/inbuilt_cfgs/example_GeoSimConvNet.yaml index 4fc754ab8..7d97dbd57 100644 --- a/minerva/inbuilt_cfgs/example_GeoSimConvNet.yaml +++ b/minerva/inbuilt_cfgs/example_GeoSimConvNet.yaml @@ -104,10 +104,8 @@ tasks: # ---+ Dataset Parameters +---------------------------------------- dataset_params: sampler: - module: torch.utils.data - name: RandomSampler - params: - num_samples: 32 + _target_: torch.utils.data.RandomSampler + num_samples: 32 image: transforms: @@ -176,12 +174,10 @@ tasks: dataset_params: features: sampler: - module: torchgeo.samplers - name: RandomGeoSampler + _target_: torchgeo.samplers.RandomGeoSampler roi: false - params: - size: ${patch_size} - length: 16 + size: ${patch_size} + length: 16 image: module: minerva.datasets.__testing @@ -199,12 +195,10 @@ tasks: test: sampler: - module: torchgeo.samplers - name: RandomGeoSampler + _target_: torchgeo.samplers.RandomGeoSampler roi: false - params: - size: ${patch_size} - length: 16 + size: ${patch_size} + length: 16 image: module: minerva.datasets.__testing @@ -237,12 +231,10 @@ tasks: # ---+ Dataset Parameters +---------------------------------------- dataset_params: sampler: - module: torchgeo.samplers - name: RandomGeoSampler + _target_: torchgeo.samplers.RandomGeoSampler roi: false - params: - size: ${patch_size} - length: 16 + size: ${patch_size} + length: 16 image: module: minerva.datasets.__testing diff --git a/minerva/inbuilt_cfgs/example_MultiLabel.yaml b/minerva/inbuilt_cfgs/example_MultiLabel.yaml index c42475297..9081cfc86 100644 --- a/minerva/inbuilt_cfgs/example_MultiLabel.yaml +++ b/minerva/inbuilt_cfgs/example_MultiLabel.yaml @@ -106,10 +106,8 @@ tasks: # ---+ Dataset Parameters +-------------------------------------------- dataset_params: sampler: - module: torch.utils.data - name: RandomSampler - params: - num_samples: 32 + _target_: torch.utils.data.RandomSampler + num_samples: 32 image: transforms: @@ -180,10 +178,8 @@ tasks: # ---+ Dataset Parameters +-------------------------------------------- dataset_params: sampler: - module: torch.utils.data - name: RandomSampler - params: - num_samples: 16 + _target_: torch.utils.data.RandomSampler + num_samples: 16 image: transforms: diff --git a/minerva/inbuilt_cfgs/example_PSP.yaml b/minerva/inbuilt_cfgs/example_PSP.yaml index 0720a22bb..2a2d88f9b 100644 --- a/minerva/inbuilt_cfgs/example_PSP.yaml +++ b/minerva/inbuilt_cfgs/example_PSP.yaml @@ -95,10 +95,8 @@ tasks: # ---+ Dataset Parameters +-------------------------------------------- dataset_params: sampler: - module: torch.utils.data - name: RandomSampler - params: - num_samples: 32 + _target_: torch.utils.data.RandomSampler + num_samples: 32 transforms: RandomResizedCrop: @@ -166,10 +164,8 @@ tasks: # ---+ Dataset Parameters +-------------------------------------------- dataset_params: sampler: - module: torch.utils.data - name: RandomSampler - params: - num_samples: 16 + _target_: torch.utils.data.RandomSampler + num_samples: 16 image: transforms: diff --git a/minerva/inbuilt_cfgs/example_SceneClassifier.yaml b/minerva/inbuilt_cfgs/example_SceneClassifier.yaml index 17f22966d..02b35bc0e 100644 --- a/minerva/inbuilt_cfgs/example_SceneClassifier.yaml +++ b/minerva/inbuilt_cfgs/example_SceneClassifier.yaml @@ -104,10 +104,8 @@ tasks: # ---+ Dataset Parameters +-------------------------------------------- dataset_params: sampler: - module: torch.utils.data - name: RandomSampler - params: - num_samples: 32 + _target_: torch.utils.data.RandomSampler + num_samples: 32 image: transforms: @@ -173,10 +171,8 @@ tasks: # ---+ Dataset Parameters +-------------------------------------------- dataset_params: sampler: - module: torch.utils.data - name: RandomSampler - params: - num_samples: 16 + _target_: torch.utils.data.RandomSampler + num_samples: 16 image: transforms: diff --git a/minerva/inbuilt_cfgs/example_UNetR_config.yaml b/minerva/inbuilt_cfgs/example_UNetR_config.yaml index 7c5212386..cabe250a8 100644 --- a/minerva/inbuilt_cfgs/example_UNetR_config.yaml +++ b/minerva/inbuilt_cfgs/example_UNetR_config.yaml @@ -95,12 +95,10 @@ tasks: # ---+ Dataset Parameters +---------------------------------------- dataset_params: sampler: - module: torchgeo.samplers - name: RandomGeoSampler + _target_: torchgeo.samplers.RandomGeoSampler roi: false - params: - size: ${patch_size} - length: 320 + size: ${patch_size} + length: 320 image: transforms: @@ -131,12 +129,10 @@ tasks: # ---+ Dataset Parameters +---------------------------------------- dataset_params: sampler: - module: torchgeo.samplers - name: RandomGeoSampler + _target_: torchgeo.samplers.RandomGeoSampler roi: false - params: - size: ${patch_size} - length: 80 + size: ${patch_size} + length: 80 image: transforms: @@ -167,12 +163,10 @@ tasks: # ---+ Dataset Parameters +---------------------------------------- dataset_params: sampler: - module: torchgeo.samplers - name: RandomGeoSampler + _target_: torchgeo.samplers.RandomGeoSampler roi: false - params: - size: ${patch_size} - length: 160 + size: ${patch_size} + length: 160 image: transforms: Normalise: diff --git a/minerva/inbuilt_cfgs/example_autoencoder_config.yaml b/minerva/inbuilt_cfgs/example_autoencoder_config.yaml index a35d69085..ff06bb234 100644 --- a/minerva/inbuilt_cfgs/example_autoencoder_config.yaml +++ b/minerva/inbuilt_cfgs/example_autoencoder_config.yaml @@ -97,12 +97,10 @@ tasks: # ---+ Dataset Parameters +-------------------------------------------- dataset_params: sampler: - module: torchgeo.samplers - name: RandomGeoSampler + _target_: torchgeo.samplers.RandomGeoSampler roi: false - params: - size: ${patch_size} - length: 36 + size: ${patch_size} + length: 36 image: transforms: @@ -135,12 +133,10 @@ tasks: # ---+ Dataset Parameters +-------------------------------------------- dataset_params: sampler: - module: torchgeo.samplers - name: RandomGeoSampler + _target_: torchgeo.samplers.RandomGeoSampler roi: false - params: - size: ${patch_size} - length: 18 + size: ${patch_size} + length: 18 image: transforms: @@ -173,12 +169,10 @@ tasks: # ---+ Dataset Parameters +-------------------------------------------- dataset_params: sampler: - module: torchgeo.samplers - name: RandomGeoSampler + _target_: torchgeo.samplers.RandomGeoSampler roi: false - params: - size: ${patch_size} - length: 18 + size: ${patch_size} + length: 18 image: transforms: diff --git a/minerva/inbuilt_cfgs/example_config.yaml b/minerva/inbuilt_cfgs/example_config.yaml index 65991055e..689fbbf67 100644 --- a/minerva/inbuilt_cfgs/example_config.yaml +++ b/minerva/inbuilt_cfgs/example_config.yaml @@ -93,12 +93,10 @@ tasks: # ---+ Dataset Parameters +---------------------------------------- dataset_params: sampler: - module: torchgeo.samplers - name: RandomGeoSampler + _target_: torchgeo.samplers.RandomGeoSampler roi: false - params: - size: ${patch_size} - length: 120 + size: ${patch_size} + length: 120 image: transforms: false @@ -141,12 +139,10 @@ tasks: # ---+ Dataset Parameters +---------------------------------------- dataset_params: sampler: - module: torchgeo.samplers - name: RandomGeoSampler + _target_: torchgeo.samplers.RandomGeoSampler roi: false - params: - size: ${patch_size} - length: 32 + size: ${patch_size} + length: 32 image: transforms: false @@ -180,12 +176,10 @@ tasks: # ---+ Dataset Parameters +---------------------------------------- dataset_params: sampler: - module: torchgeo.samplers - name: RandomGeoSampler + _target_: torchgeo.samplers.RandomGeoSampler roi: false - params: - size: ${patch_size} - length: 32 + size: ${patch_size} + length: 32 image: transforms: false diff --git a/minerva/samplers.py b/minerva/samplers.py index 33bce7b3e..7dc7140b4 100644 --- a/minerva/samplers.py +++ b/minerva/samplers.py @@ -41,22 +41,26 @@ "DistributedSamplerWrapper", "get_greater_bbox", "get_pair_bboxes", + "get_sampler", ] # ===================================================================================================================== # IMPORTS # ===================================================================================================================== import random +import re from operator import itemgetter -from typing import Any, Iterator, List, Optional, Sequence, Tuple, Union +from typing import Any, Dict, Iterator, List, Optional, Sequence, Tuple, Union +import hydra import torch from torch.utils.data import Dataset, DistributedSampler, Sampler -from torchgeo.datasets import GeoDataset +from torchgeo.datasets import GeoDataset, NonGeoDataset from torchgeo.datasets.utils import BoundingBox from torchgeo.samplers import BatchGeoSampler, RandomGeoSampler, Units from torchgeo.samplers.utils import _to_tuple, get_random_bounding_box +from minerva.datasets.utils import make_bounding_box from minerva.utils import utils @@ -366,3 +370,40 @@ def __len__(self) -> int: int: Length of the dataset """ return len(self.sampler) # type: ignore[arg-type] + + +# ===================================================================================================================== +# METHODS +# ===================================================================================================================== +def get_sampler( + params: Dict[str, Any], + dataset: Union[GeoDataset, NonGeoDataset], + batch_size: Optional[int] = None, +) -> Sampler: + """Use :meth:`hydra.utils.instantiate` to get the sampler using config parameters. + + Args: + params (dict[str, ~typing.Any]): Sampler parameters. Must include the ``_target_`` key pointing to + the sampler class. + dataset (~torchgeo.datasets.GeoDataset, ~torchgeo.datasets.NonGeoDataset]): Dataset to sample. + batch_size (int): Optional; Batch size to sample if using a batch sampler. + Use if you need to overwrite the config parameters due to distributed computing as the batch size + needs to modified to split the batch across devices. Defaults to ``None``. + + Returns: + ~torch.utils.data.Sampler: Sampler requested by config parameters. + """ + + batch_sampler = True if re.search(r"Batch", params["_target_"]) else False + if batch_sampler and batch_size is not None: + params["batch_size"] = batch_size + + if "roi" in params: + return hydra.utils.instantiate( + params, dataset=dataset, roi=make_bounding_box(params["roi"]) + ) + else: + if "torchgeo" in params["_target_"]: + return hydra.utils.instantiate(params, dataset=dataset) + else: + return hydra.utils.instantiate(params, data_source=dataset) From e49fdb3d08c48e75a3803f4bee22e6f9f49cbe71 Mon Sep 17 00:00:00 2001 From: Harry Baker Date: Wed, 21 Aug 2024 00:00:02 +0100 Subject: [PATCH 19/63] Ensure that `Trainer.model` is updated from last training epoch --- minerva/trainer.py | 20 +++++++++++--------- 1 file changed, 11 insertions(+), 9 deletions(-) diff --git a/minerva/trainer.py b/minerva/trainer.py index 0f35b99fa..da732c222 100644 --- a/minerva/trainer.py +++ b/minerva/trainer.py @@ -649,21 +649,19 @@ def fit(self) -> None: # Conduct training or validation epoch. for mode in tasks.keys(): # Only run a validation epoch at set frequency of epochs. Goes to next epoch if not. - if ( - utils.check_substrings_in_string(mode, "val") - and (self.epoch_no) % self.val_freq != 0 - ): - tasks[mode].log_null(self.epoch_no - 1) - break + if utils.check_substrings_in_string(mode, "val"): + tasks[mode].model = self.model + + if self.epoch_no % self.val_freq != 0: + tasks[mode].log_null(self.epoch_no - 1) + break if tasks[mode].train: self.model.train() else: self.model.eval() - results: Optional[Dict[str, Any]] - - results = tasks[mode](self.epoch_no - 1) + results: Optional[Dict[str, Any]] = tasks[mode](self.epoch_no - 1) # Print epoch results. if self.gpu == 0: @@ -671,6 +669,10 @@ def fit(self) -> None: if not self.stopper and self.checkpoint_experiment: self.save_checkpoint() + # Update Trainer's copy of the model from the training task. + if utils.check_substrings_in_string(mode, "train"): + self.model = tasks[mode].model + # Sends validation loss to the stopper and updates early stop bool. if ( utils.check_substrings_in_string(mode, "val") From bef77ad9c722cc099499d9af8303dfffc0a10dd3 Mon Sep 17 00:00:00 2001 From: Harry Baker Date: Fri, 23 Aug 2024 02:11:27 +0100 Subject: [PATCH 20/63] Use `hydra.utils.instantiate` to create datasets --- minerva/datasets/factory.py | 195 +++++++----------- minerva/inbuilt_cfgs/example_3rd_party.yaml | 48 ++--- minerva/inbuilt_cfgs/example_CNN_config.yaml | 40 ++-- .../inbuilt_cfgs/example_ChangeDetector.yaml | 24 +-- .../inbuilt_cfgs/example_GSConvNet-II.yaml | 48 ++--- .../inbuilt_cfgs/example_GeoCLR_config.yaml | 54 ++--- .../inbuilt_cfgs/example_GeoSimConvNet.yaml | 48 ++--- minerva/inbuilt_cfgs/example_MultiLabel.yaml | 32 ++- minerva/inbuilt_cfgs/example_PSP.yaml | 24 +-- .../inbuilt_cfgs/example_SceneClassifier.yaml | 44 ++-- .../inbuilt_cfgs/example_UNetR_config.yaml | 40 ++-- .../example_autoencoder_config.yaml | 36 ++-- minerva/inbuilt_cfgs/example_config.yaml | 47 ++--- tests/conftest.py | 20 +- tests/test_datasets/test_factory.py | 48 +++-- tests/test_transforms.py | 36 +++- 16 files changed, 316 insertions(+), 468 deletions(-) diff --git a/minerva/datasets/factory.py b/minerva/datasets/factory.py index ee196cb69..6ece29ddf 100644 --- a/minerva/datasets/factory.py +++ b/minerva/datasets/factory.py @@ -49,10 +49,10 @@ import re from copy import deepcopy from datetime import timedelta -from inspect import signature from pathlib import Path -from typing import Any, Callable, Dict, Iterable, List, Literal, Optional, Tuple, Union +from typing import Any, Dict, Iterable, List, Literal, Optional, Tuple, Union +import hydra import numpy as np import pandas as pd import torch @@ -86,7 +86,6 @@ # METHODS # ===================================================================================================================== def create_subdataset( - dataset_class: Union[Callable[..., GeoDataset], Callable[..., NonGeoDataset]], paths: Union[str, Iterable[str]], subdataset_params: Dict[Literal["params"], Dict[str, Any]], transformations: Optional[Any], @@ -95,72 +94,38 @@ def create_subdataset( """Creates a sub-dataset based on the parameters supplied. Args: - dataset_class (Callable[..., ~typing.datasets.GeoDataset]): Constructor for the sub-dataset. paths (str | ~typing.Iterable[str]): Paths to where the data for the dataset is located. subdataset_params (dict[Literal[params], dict[str, ~typing.Any]]): Parameters for the sub-dataset. transformations (~typing.Any): Transformations to apply to this sub-dataset. sample_pairs (bool): Will configure the dataset for paired sampling. Defaults to False. Returns: - ~torchgeo.datasets.GeoDataset: Subdataset requested. + ~torchgeo.datasets.GeoDataset | ~torchgeo.datasets.NonGeoDataset: Subdataset requested. """ - copy_params = deepcopy(subdataset_params) - - if "crs" in copy_params["params"]: - copy_params["params"]["crs"] = CRS.from_epsg(copy_params["params"]["crs"]) - - if sample_pairs: - if "paths" in signature(dataset_class).parameters: - return PairedGeoDataset( - dataset_class, # type: ignore[arg-type] - paths=paths, - transforms=transformations, - **copy_params["params"], - ) - - elif "season_transform" in signature(dataset_class).parameters: - if isinstance(paths, list): - paths = paths[0] - assert isinstance(paths, str) - del copy_params["params"]["season_transform"] - return PairedNonGeoDataset( - dataset_class, # type: ignore[arg-type] - root=paths, - transforms=transformations, - season=True, - season_transform="pair", - **copy_params["params"], - ) - elif "root" in signature(dataset_class).parameters: - if isinstance(paths, list): - paths = paths[0] - assert isinstance(paths, str) - return PairedNonGeoDataset( - dataset_class, # type: ignore[arg-type] - root=paths, - transforms=transformations, - **copy_params["params"], - ) - else: - raise TypeError + crs = None + if "crs" in subdataset_params: + crs = CRS.from_epsg(subdataset_params["crs"]) + + subdataset: Union[GeoDataset, NonGeoDataset] + if "paths" in subdataset_params: + subdataset = hydra.utils.instantiate( + subdataset_params, paths=paths, transforms=transformations, crs=crs + ) + if sample_pairs: + subdataset = PairedGeoDataset(subdataset) + elif "root" in subdataset_params: + if isinstance(paths, list): + paths = paths[0] + assert isinstance(paths, str) + subdataset = hydra.utils.instantiate( + subdataset_params, root=paths, transforms=transformations + ) + if sample_pairs: + subdataset = PairedNonGeoDataset(subdataset) else: - if "paths" in signature(dataset_class).parameters: - return dataset_class( - paths=paths, - transforms=transformations, - **copy_params["params"], - ) - elif "root" in signature(dataset_class).parameters: - if isinstance(paths, list): - paths = paths[0] - assert isinstance(paths, str) - return dataset_class( - root=paths, - transforms=transformations, - **copy_params["params"], - ) - else: - raise TypeError + raise TypeError + + return subdataset def get_subdataset( @@ -171,6 +136,7 @@ def get_subdataset( sample_pairs: bool = False, cache: bool = True, cache_dir: Union[str, Path] = "", + auto_norm: bool = False, ) -> Union[GeoDataset, NonGeoDataset]: """Get a subdataset based on the parameters specified. @@ -194,14 +160,10 @@ def get_subdataset( # Get the params for this sub-dataset. sub_dataset_params = dataset_params[key] - # Get the constructor for the class of dataset defined in params. - _sub_dataset: Callable[..., GeoDataset] = utils.func_by_str( - module_path=sub_dataset_params["module"], func=sub_dataset_params["name"] - ) - # Construct the path to the sub-dataset's files. sub_dataset_paths = utils.compile_dataset_paths( - universal_path(data_directory), sub_dataset_params["paths"] + universal_path(data_directory), + sub_dataset_params.get("paths", sub_dataset_params.get("root")), ) sub_dataset: Optional[Union[GeoDataset, NonGeoDataset]] @@ -228,7 +190,6 @@ def get_subdataset( if rank == 0: print(f"\nCreating dataset on {rank}...") sub_dataset = create_subdataset( - _sub_dataset, sub_dataset_paths, sub_dataset_params, transformations, @@ -253,7 +214,6 @@ def get_subdataset( else: print("\nCreating dataset...") sub_dataset = create_subdataset( - _sub_dataset, sub_dataset_paths, sub_dataset_params, transformations, @@ -265,7 +225,6 @@ def get_subdataset( else: sub_dataset = create_subdataset( - _sub_dataset, sub_dataset_paths, sub_dataset_params, transformations, @@ -273,6 +232,16 @@ def get_subdataset( ) assert sub_dataset is not None + + if auto_norm: + if isinstance(sub_dataset, RasterDataset): + init_auto_norm(sub_dataset, auto_norm) + else: + raise TypeError( # pragma: no cover + "AutoNorm only supports normalisation of data " + + f"from RasterDatasets, not {type(sub_dataset)}!" + ) + return sub_dataset @@ -343,36 +312,39 @@ def make_dataset( auto_norm = None master_transforms: Optional[Any] = None - for area_key in type_dataset_params.keys(): + + for sub_type_key in type_dataset_params.keys(): # If any of these keys are present, this must be a parameter set for a singular dataset at this level. - if area_key in ("module", "name", "params", "paths"): + if sub_type_key in ("_target_", "paths", "root"): multi_datasets_exist = False continue # If there are transforms specified, make them. These could cover a single dataset or many. - elif area_key == "transforms": - if isinstance(type_dataset_params[area_key], dict): - transform_params = type_dataset_params[area_key] + elif sub_type_key == "transforms": + if isinstance(type_dataset_params[sub_type_key], dict): + transform_params = type_dataset_params[sub_type_key] auto_norm = transform_params.get("AutoNorm") # If transforms aren't specified for a particular modality of the sample, # assume they're for the same type as the dataset. if ( not ("image", "mask", "label") - in type_dataset_params[area_key].keys() + in type_dataset_params[sub_type_key].keys() ): - transform_params = {type_key: type_dataset_params[area_key]} + transform_params = {type_key: type_dataset_params[sub_type_key]} else: transform_params = False master_transforms = make_transformations(transform_params) # Assuming that these keys are names of datasets. - else: + elif sub_type_key == "subdatasets": multi_datasets_exist = True - if isinstance(type_dataset_params[area_key].get("transforms"), dict): - transform_params = type_dataset_params[area_key]["transforms"] + if isinstance( + type_dataset_params[sub_type_key].get("transforms"), dict + ): + transform_params = type_dataset_params[sub_type_key]["transforms"] auto_norm = transform_params.get("AutoNorm") else: transform_params = False @@ -380,31 +352,25 @@ def make_dataset( transformations = make_transformations({type_key: transform_params}) # Send the params for this area key back through this function to make the sub-dataset. - sub_dataset = get_subdataset( - data_directory, - type_dataset_params, - area_key, - transformations, - sample_pairs=sample_pairs, - cache=cache, - cache_dir=cache_dir, - ) - - # Performs an auto-normalisation initialisation which finds the mean and std of the dataset - # to make a transform, then adds the transform to the dataset's existing transforms. - if auto_norm: - if isinstance(sub_dataset, RasterDataset): - init_auto_norm(sub_dataset, auto_norm) - else: - raise TypeError( # pragma: no cover - "AutoNorm only supports normalisation of data " - + f"from RasterDatasets, not {type(sub_dataset)}!" - ) + for area_key in type_dataset_params[sub_type_key]: + sub_dataset = get_subdataset( + data_directory, + type_dataset_params[sub_type_key], + area_key, + transformations, + sample_pairs=sample_pairs, + cache=cache, + cache_dir=cache_dir, + auto_norm=auto_norm, + ) # Reset back to None. auto_norm = None - type_subdatasets.append(sub_dataset) # type: ignore[arg-type] + type_subdatasets.append(sub_dataset) # type: ignore[arg-type] + + else: + continue # Unionise all the sub-datsets of this modality together. if multi_datasets_exist: @@ -423,20 +389,11 @@ def make_dataset( sample_pairs=sample_pairs, cache=cache, cache_dir=cache_dir, + auto_norm=auto_norm, ) - # Performs an auto-normalisation initialisation which finds the mean and std of the dataset - # to make a transform, then adds the transform to the dataset's existing transforms. - if auto_norm: - if isinstance(sub_dataset, RasterDataset): - init_auto_norm(sub_dataset, auto_norm) - - # Reset back to None. - auto_norm = None - else: - raise TypeError( # pragma: no cover - f"AutoNorm only supports normalisation of data from RasterDatasets, not {type(sub_dataset)}!" - ) + # Reset back to None. + auto_norm = None sub_datasets.append(sub_dataset) # type: ignore[arg-type] @@ -525,11 +482,13 @@ def construct_dataloader( per_device_batch_size = None batch_sampler = True if re.search(r"Batch", sampler_params["_target_"]) else False - if batch_sampler and dist.is_available() and dist.is_initialized(): # type: ignore[attr-defined] - assert sampler_params["batch_size"] % world_size == 0 # pragma: no cover - per_device_batch_size = ( - sampler_params["batch_size"] // world_size - ) # pragma: no cover + if batch_sampler: + per_device_batch_size = sampler_params.get("batch_size", batch_size) + if dist.is_available() and dist.is_initialized(): # type: ignore[attr-defined] + assert per_device_batch_size % world_size == 0 # pragma: no cover + per_device_batch_size = ( + per_device_batch_size // world_size + ) # pragma: no cover sampler = get_sampler( sampler_params, subdatasets[0], batch_size=per_device_batch_size diff --git a/minerva/inbuilt_cfgs/example_3rd_party.yaml b/minerva/inbuilt_cfgs/example_3rd_party.yaml index f35340974..f4bbce925 100644 --- a/minerva/inbuilt_cfgs/example_3rd_party.yaml +++ b/minerva/inbuilt_cfgs/example_3rd_party.yaml @@ -104,30 +104,24 @@ tasks: transforms: ToRGB: _target_: minerva.transforms.ToRGB - - images_1: - module: minerva.datasets.__testing - name: TstImgDataset - paths: NAIP - params: + subdatasets: + images_1: + _target_: minerva.datasets.__testing.TstImgDataset + paths: NAIP res: 1.0 - image2: - module: minerva.datasets.__testing - name: TstImgDataset - paths: NAIP - params: + image2: + _target_: minerva.datasets.__testing.TstImgDataset + paths: NAIP res: 1.0 mask: transforms: SingleLabel: _target_: minerva.transforms.SingleLabel - module: minerva.datasets.__testing - name: TstMaskDataset + _target_: minerva.datasets.__testing.TstMaskDataset paths: Chesapeake7 - params: - res: 1.0 + res: 1.0 fit-val: name: StandardEpoch @@ -150,21 +144,17 @@ tasks: transforms: ToRGB: _target_: minerva.transforms.ToRGB - module: minerva.datasets.__testing - name: TstImgDataset + _target_: minerva.datasets.__testing.TstImgDataset paths: NAIP - params: - res: 1.0 + res: 1.0 mask: transforms: SingleLabel: _target_: minerva.transforms.SingleLabel - module: minerva.datasets.__testing - name: TstMaskDataset + _target_: minerva.datasets.__testing.TstMaskDataset paths: Chesapeake7 - params: - res: 1.0 + res: 1.0 test-test: name: StandardEpoch @@ -187,21 +177,17 @@ tasks: transforms: ToRGB: _target_: minerva.transforms.ToRGB - module: minerva.datasets.__testing - name: TstImgDataset + _target_: minerva.datasets.__testing.TstImgDataset paths: NAIP - params: - res: 1.0 + res: 1.0 mask: transforms: SingleLabel: _target_: minerva.transforms.SingleLabel - module: minerva.datasets.__testing - name: TstMaskDataset + _target_: minerva.datasets.__testing.TstMaskDataset paths: Chesapeake7 - params: - res: 1.0 + res: 1.0 # === PLOTTING OPTIONS ======================================================== plots: diff --git a/minerva/inbuilt_cfgs/example_CNN_config.yaml b/minerva/inbuilt_cfgs/example_CNN_config.yaml index 7bbde3fb3..f6f1adedf 100644 --- a/minerva/inbuilt_cfgs/example_CNN_config.yaml +++ b/minerva/inbuilt_cfgs/example_CNN_config.yaml @@ -24,7 +24,7 @@ model_type: scene-classifier batch_size: 8 # Number of samples in each batch. input_size: [4, 32, 32] # patch_size plus leading channel dim. patch_size: '${to_patch_size: ${input_size}}' # 2D tuple or float. -n_classes: &n_classes 8 # Number of classes in dataset. +n_classes: 8 # Number of classes in dataset. # ---+ Experiment Execution +-------------------------------------------------- max_epochs: 3 # Maximum number of training epochs. @@ -44,7 +44,7 @@ model_params: module: params: input_size: ${input_size} - n_classes: *n_classes + n_classes: ${n_classes} # ---+ Optimiser Parameters +-------------------------------------------------- optimiser: @@ -101,21 +101,17 @@ tasks: Normalise: _target_: minerva.transforms.Normalise norm_value: 255 - module: minerva.datasets.__testing - name: TstImgDataset + _target_: minerva.datasets.__testing.TstImgDataset paths: NAIP - params: - res: 1.0 + res: 1.0 mask: transforms: SingleLabel: _target_: minerva.transforms.SingleLabel mode: modal - module: minerva.datasets.__testing - name: TstMaskDataset + _target_: minerva.datasets.__testing.TstMaskDataset paths: Chesapeake7 - params: - res: 1.0 + res: 1.0 fit-val: name: StandardEpoch @@ -138,22 +134,18 @@ tasks: Normalise: _target_: minerva.transforms.Normalise norm_value: 255 - module: minerva.datasets.__testing - name: TstImgDataset + _target_: minerva.datasets.__testing.TstImgDataset paths: NAIP - params: - res: 1.0 + res: 1.0 mask: transforms: SingleLabel: _target_: minerva.transforms.SingleLabel mode: modal - module: minerva.datasets.__testing - name: TstMaskDataset + _target_: minerva.datasets.__testing.TstMaskDataset paths: Chesapeake7 - params: - res: 1.0 + res: 1.0 test-test: name: StandardEpoch @@ -176,22 +168,18 @@ tasks: Normalise: _target_: minerva.transforms.Normalise norm_value: 255 - module: minerva.datasets.__testing - name: TstImgDataset + _target_: minerva.datasets.__testing.TstImgDataset paths: NAIP - params: - res: 1.0 + res: 1.0 mask: transforms: SingleLabel: _target_: minerva.transforms.SingleLabel mode: modal - module: minerva.datasets.__testing - name: TstMaskDataset + _target_: minerva.datasets.__testing.TstMaskDataset paths: Chesapeake7 - params: - res: 1.0 + res: 1.0 # === PLOTTING OPTIONS ======================================================== plots: diff --git a/minerva/inbuilt_cfgs/example_ChangeDetector.yaml b/minerva/inbuilt_cfgs/example_ChangeDetector.yaml index c698f6f72..2e2ef92fb 100644 --- a/minerva/inbuilt_cfgs/example_ChangeDetector.yaml +++ b/minerva/inbuilt_cfgs/example_ChangeDetector.yaml @@ -132,13 +132,11 @@ tasks: - 16 - 20 - module: torchgeo.datasets - name: OSCD - paths: OSCD - params: - split: train - bands: all - download: true + _target_: torchgeo.datasets.OSCD + root: OSCD + split: train + bands: all + download: true mask: transforms: @@ -184,13 +182,11 @@ tasks: - 16 - 20 - module: torchgeo.datasets - name: OSCD - paths: OSCD - params: - split: test - bands: all - download: true + _target_: torchgeo.datasets.OSCD + root: OSCD + split: test + bands: all + download: true mask: transforms: diff --git a/minerva/inbuilt_cfgs/example_GSConvNet-II.yaml b/minerva/inbuilt_cfgs/example_GSConvNet-II.yaml index e6bddd7ca..efb5b4f8c 100644 --- a/minerva/inbuilt_cfgs/example_GSConvNet-II.yaml +++ b/minerva/inbuilt_cfgs/example_GSConvNet-II.yaml @@ -154,13 +154,11 @@ tasks: p: 0.2 keepdim: true - module: minerva.datasets - name: NonGeoSSL4EOS12Sentinel2 - paths: SSL4EO-S12 - params: - bands: [B2, B3, B4, B8] - size: ${patch_size} - max_r: *max_r + _target_: minerva.datasets.NonGeoSSL4EOS12Sentinel2 + root: SSL4EO-S12 + bands: [B2, B3, B4, B8] + size: ${patch_size} + max_r: *max_r fit-val: name: WeightedKNN @@ -184,13 +182,11 @@ tasks: num_samples: 16 image: - module: minerva.datasets - name: DFC2020 - paths: DFC/DFC2020 - params: - split: val - use_s2hr: true - labels: true + _target_: minerva.datasets.DFC2020 + root: DFC/DFC2020 + split: val + use_s2hr: true + labels: true test: sampler: @@ -198,13 +194,11 @@ tasks: num_samples: 16 image: - module: minerva.datasets - name: DFC2020 - paths: DFC/DFC2020 - params: - split: test - use_s2hr: true - labels: true + _target_: minerva.datasets.DFC2020 + root: DFC/DFC2020 + split: test + use_s2hr: true + labels: true test-test: name: StandardEpoch @@ -226,18 +220,14 @@ tasks: length: 16 image: - module: minerva.datasets.__testing - name: TstImgDataset + _target_: minerva.datasets.__testing.TstImgDataset paths: NAIP - params: - res: 1.0 + res: 1.0 mask: - module: minerva.datasets.__testing - name: TstMaskDataset + _target_: minerva.datasets.__testing.TstMaskDataset paths: Chesapeake7 - params: - res: 1.0 + res: 1.0 # === PLOTTING OPTIONS ======================================================== plots: diff --git a/minerva/inbuilt_cfgs/example_GeoCLR_config.yaml b/minerva/inbuilt_cfgs/example_GeoCLR_config.yaml index b219a743f..e0298a025 100644 --- a/minerva/inbuilt_cfgs/example_GeoCLR_config.yaml +++ b/minerva/inbuilt_cfgs/example_GeoCLR_config.yaml @@ -145,18 +145,14 @@ tasks: _target_: kornia.augmentation.RandomErasing p: 0.2 keepdim: true - - image1: - module: minerva.datasets.__testing - name: TstImgDataset - paths: NAIP - params: + subdatasets: + image1: + _target_: minerva.datasets.__testing.TstImgDataset + paths: NAIP res: 1.0 - image2: - module: minerva.datasets.__testing - name: TstImgDataset - paths: NAIP - params: + image2: + _target_: minerva.datasets.__testing.TstImgDataset + paths: NAIP res: 1.0 fit-val: @@ -185,18 +181,14 @@ tasks: length: 32 image: - module: minerva.datasets.__testing - name: TstImgDataset + _target_: minerva.datasets.__testing.TstImgDataset paths: NAIP - params: - res: 1.0 + res: 1.0 mask: - module: minerva.datasets.__testing - name: TstMaskDataset + _target_: minerva.datasets.__testing.TstMaskDataset paths: Chesapeake7 - params: - res: 1.0 + res: 1.0 test: sampler: @@ -206,18 +198,14 @@ tasks: length: 32 image: - module: minerva.datasets.__testing - name: TstImgDataset + _target_: minerva.datasets.__testing.TstImgDataset paths: NAIP - params: - res: 1.0 + res: 1.0 mask: - module: minerva.datasets.__testing - name: TstMaskDataset + _target_: minerva.datasets.__testing.TstMaskDataset paths: Chesapeake7 - params: - res: 1.0 + res: 1.0 test-test: name: StandardEpoch @@ -243,18 +231,14 @@ tasks: length: 32 image: - module: minerva.datasets.__testing - name: TstImgDataset + _target_: minerva.datasets.__testing.TstImgDataset paths: NAIP - params: - res: 1.0 + res: 1.0 mask: - module: minerva.datasets.__testing - name: TstMaskDataset + _target_: minerva.datasets.__testing.TstMaskDataset paths: Chesapeake7 - params: - res: 1.0 + res: 1.0 # === PLOTTING OPTIONS ======================================================== plots: diff --git a/minerva/inbuilt_cfgs/example_GeoSimConvNet.yaml b/minerva/inbuilt_cfgs/example_GeoSimConvNet.yaml index 7d97dbd57..16bb56123 100644 --- a/minerva/inbuilt_cfgs/example_GeoSimConvNet.yaml +++ b/minerva/inbuilt_cfgs/example_GeoSimConvNet.yaml @@ -146,13 +146,11 @@ tasks: p: 0.2 keepdim: true - module: minerva.datasets - name: NonGeoSSL4EOS12Sentinel2 - paths: SSL4EO-S12 - params: - bands: [B2, B3, B4, B8] - size: ${patch_size} - max_r: *max_r + _target_: minerva.datasets.NonGeoSSL4EOS12Sentinel2 + root: SSL4EO-S12 + bands: [B2, B3, B4, B8] + size: ${patch_size} + max_r: *max_r fit-val: name: WeightedKNN @@ -180,18 +178,14 @@ tasks: length: 16 image: - module: minerva.datasets.__testing - name: TstImgDataset + _target_: minerva.datasets.__testing.TstImgDataset paths: NAIP - params: - res: 1.0 + res: 1.0 mask: - module: minerva.datasets.__testing - name: TstMaskDataset + _target_: minerva.datasets.__testing.TstMaskDataset paths: Chesapeake7 - params: - res: 1.0 + res: 1.0 test: sampler: @@ -201,18 +195,14 @@ tasks: length: 16 image: - module: minerva.datasets.__testing - name: TstImgDataset + _target_: minerva.datasets.__testing.TstImgDataset paths: NAIP - params: - res: 1.0 + res: 1.0 mask: - module: minerva.datasets.__testing - name: TstMaskDataset + _target_: minerva.datasets.__testing.TstMaskDataset paths: Chesapeake7 - params: - res: 1.0 + res: 1.0 test-test: name: StandardEpoch @@ -237,18 +227,14 @@ tasks: length: 16 image: - module: minerva.datasets.__testing - name: TstImgDataset + _target_: minerva.datasets.__testing.TstImgDataset paths: NAIP - params: - res: 1.0 + res: 1.0 mask: - module: minerva.datasets.__testing - name: TstMaskDataset + _target_: minerva.datasets.__testing.TstMaskDataset paths: Chesapeake7 - params: - res: 1.0 + res: 1.0 # === PLOTTING OPTIONS ======================================================== plots: diff --git a/minerva/inbuilt_cfgs/example_MultiLabel.yaml b/minerva/inbuilt_cfgs/example_MultiLabel.yaml index 9081cfc86..5483ed0f8 100644 --- a/minerva/inbuilt_cfgs/example_MultiLabel.yaml +++ b/minerva/inbuilt_cfgs/example_MultiLabel.yaml @@ -24,7 +24,7 @@ model_type: multilabel-scene-classifier batch_size: 2 # Number of samples in each batch. input_size: [4, 120, 120] # patch_size plus leading channel dim. patch_size: '${to_patch_size: ${input_size}}' # 2D tuple or float. -n_classes: &n_classes 19 # Number of classes in dataset. +n_classes: 19 # Number of classes in dataset. # ---+ Experiment Execution +-------------------------------------------------- max_epochs: 2 # Maximum number of training epochs. @@ -44,7 +44,7 @@ model_params: module: params: input_size: ${input_size} - n_classes: *n_classes + n_classes: ${n_classes} encoder_on: true filter_dim: -1 fc_dim: 512 @@ -155,14 +155,12 @@ tasks: p: 0.2 keepdim: true - module: torchgeo.datasets - name: BigEarthNet - paths: BigEarthNet-mini - params: - split: train - bands: s2 - download: true - num_classes: *n_classes + _target_: torchgeo.datasets.BigEarthNet + root: BigEarthNet-mini + split: train + bands: s2 + download: true + num_classes: ${n_classes} label: @@ -194,14 +192,12 @@ tasks: _target_: minerva.transforms.Normalise norm_value: 4095 - module: torchgeo.datasets - name: BigEarthNet - paths: BigEarthNet-mini - params: - split: test - bands: s2 - download: true - num_classes: *n_classes + _target_: torchgeo.datasets.BigEarthNet + root: BigEarthNet-mini + split: test + bands: s2 + download: true + num_classes: ${n_classes} label: diff --git a/minerva/inbuilt_cfgs/example_PSP.yaml b/minerva/inbuilt_cfgs/example_PSP.yaml index 2a2d88f9b..65f28b91b 100644 --- a/minerva/inbuilt_cfgs/example_PSP.yaml +++ b/minerva/inbuilt_cfgs/example_PSP.yaml @@ -139,13 +139,11 @@ tasks: p: 0.2 keepdim: true - module: minerva.datasets - name: DFC2020 - paths: DFC/DFC2020 - params: - split: test - use_s2hr: true - labels: true + _target_: minerva.datasets.DFC2020 + root: DFC/DFC2020 + split: test + use_s2hr: true + labels: true mask: transforms: MaskResize: @@ -173,13 +171,11 @@ tasks: _target_: minerva.transforms.Normalise norm_value: 4095 - module: minerva.datasets - name: DFC2020 - paths: DFC/DFC2020 - params: - split: test - use_s2hr: true - labels: true + _target_: minerva.datasets.DFC2020 + root: DFC/DFC2020 + split: test + use_s2hr: true + labels: true mask: transforms: diff --git a/minerva/inbuilt_cfgs/example_SceneClassifier.yaml b/minerva/inbuilt_cfgs/example_SceneClassifier.yaml index 02b35bc0e..b56ed8469 100644 --- a/minerva/inbuilt_cfgs/example_SceneClassifier.yaml +++ b/minerva/inbuilt_cfgs/example_SceneClassifier.yaml @@ -24,7 +24,7 @@ model_type: scene-classifier batch_size: 2 # Number of samples in each batch. input_size: [4, 64, 64] # patch_size plus leading channel dim. patch_size: '${to_patch_size: ${input_size}}' # 2D tuple or float. -n_classes: &n_classes 10 # Number of classes in dataset. +n_classes: 10 # Number of classes in dataset. # ---+ Experiment Execution +-------------------------------------------------- max_epochs: 2 # Maximum number of training epochs. @@ -44,7 +44,7 @@ model_params: module: params: input_size: ${input_size} - n_classes: *n_classes + n_classes: ${n_classes} encoder_on: true filter_dim: -1 fc_dim: 512 @@ -146,17 +146,15 @@ tasks: p: 0.2 keepdim: true - module: torchgeo.datasets - name: EuroSAT100 - paths: EuroSAT100 - params: - split: train - bands: - - B02 - - B03 - - B04 - - B08 - download: true + _target_: torchgeo.datasets.EuroSAT100 + root: EuroSAT100 + split: train + bands: + - B02 + - B03 + - B04 + - B08 + download: true label: @@ -180,17 +178,15 @@ tasks: _target_: minerva.transforms.Normalise norm_value: 4095 - module: torchgeo.datasets - name: EuroSAT100 - paths: EuroSAT100 - params: - split: test - bands: - - B02 - - B03 - - B04 - - B08 - download: true + _target_: torchgeo.datasets.EuroSAT100 + root: EuroSAT100 + split: test + bands: + - B02 + - B03 + - B04 + - B08 + download: true label: diff --git a/minerva/inbuilt_cfgs/example_UNetR_config.yaml b/minerva/inbuilt_cfgs/example_UNetR_config.yaml index cabe250a8..1eb3f863c 100644 --- a/minerva/inbuilt_cfgs/example_UNetR_config.yaml +++ b/minerva/inbuilt_cfgs/example_UNetR_config.yaml @@ -24,7 +24,7 @@ model_type: segmentation batch_size: 8 # Number of samples in each batch. input_size: [4, 224, 224] # patch_size plus leading channel dim. patch_size: '${to_patch_size: ${input_size}}' # 2D tuple or float. -n_classes: &n_classes 8 # Number of classes in dataset. +n_classes: 8 # Number of classes in dataset. # ---+ Experiment Execution +-------------------------------------------------- max_epochs: 2 # Maximum number of training epochs. @@ -44,7 +44,7 @@ model_params: module: params: input_size: ${input_size} - n_classes: *n_classes + n_classes: ${n_classes} backbone_kwargs: torch_weights: true @@ -105,17 +105,13 @@ tasks: Normalise: _target_: minerva.transforms.Normalise norm_value: 255 - module: minerva.datasets.__testing - name: TstImgDataset + _target_: minerva.datasets.__testing.TstImgDataset paths: NAIP - params: - res: 1.0 + res: 1.0 mask: - module: minerva.datasets.__testing - name: TstMaskDataset + _target_: minerva.datasets.__testing.TstMaskDataset paths: Chesapeake7 - params: - res: 1.0 + res: 1.0 fit-val: name: StandardEpoch @@ -139,17 +135,13 @@ tasks: Normalise: _target_: minerva.transforms.Normalise norm_value: 255 - module: minerva.datasets.__testing - name: TstImgDataset + _target_: minerva.datasets.__testing.TstImgDataset paths: NAIP - params: - res: 1.0 + res: 1.0 mask: - module: minerva.datasets.__testing - name: TstMaskDataset + _target_: minerva.datasets.__testing.TstMaskDataset paths: Chesapeake7 - params: - res: 1.0 + res: 1.0 test-test: name: StandardEpoch @@ -172,17 +164,13 @@ tasks: Normalise: _target_: minerva.transforms.Normalise norm_value: 255 - module: minerva.datasets.__testing - name: TstImgDataset + _target_: minerva.datasets.__testing.TstImgDataset paths: NAIP - params: - res: 1.0 + res: 1.0 mask: - module: minerva.datasets.__testing - name: TstMaskDataset + _target_: minerva.datasets.__testing.TstMaskDataset paths: Chesapeake7 - params: - res: 1.0 + res: 1.0 # === PLOTTING OPTIONS ======================================================== plots: diff --git a/minerva/inbuilt_cfgs/example_autoencoder_config.yaml b/minerva/inbuilt_cfgs/example_autoencoder_config.yaml index ff06bb234..664ab5624 100644 --- a/minerva/inbuilt_cfgs/example_autoencoder_config.yaml +++ b/minerva/inbuilt_cfgs/example_autoencoder_config.yaml @@ -107,18 +107,14 @@ tasks: Normalise: _target_: minerva.transforms.Normalise norm_value: 255 - module: minerva.datasets.__testing - name: TstImgDataset + _target_: minerva.datasets.__testing.TstImgDataset paths: NAIP - params: - res: 1.0 + res: 1.0 mask: - module: minerva.datasets.__testing - name: TstMaskDataset + _target_: minerva.datasets.__testing.TstMaskDataset paths: Chesapeake7 - params: - res: 1.0 + res: 1.0 fit-val: name: StandardEpoch @@ -143,18 +139,14 @@ tasks: Normalise: _target_: minerva.transforms.Normalise norm_value: 255 - module: minerva.datasets.__testing - name: TstImgDataset + _target_: minerva.datasets.__testing.TstImgDataset paths: NAIP - params: - res: 1.0 + res: 1.0 mask: - module: minerva.datasets.__testing - name: TstMaskDataset + _target_: minerva.datasets.__testing.TstMaskDataset paths: Chesapeake7 - params: - res: 1.0 + res: 1.0 test-test: name: StandardEpoch @@ -179,18 +171,14 @@ tasks: Normalise: _target_: minerva.transforms.Normalise norm_value: 255 - module: minerva.datasets.__testing - name: TstImgDataset + _target_: minerva.datasets.__testing.TstImgDataset paths: NAIP - params: - res: 1.0 + res: 1.0 mask: - module: minerva.datasets.__testing - name: TstMaskDataset + _target_: minerva.datasets.__testing.TstMaskDataset paths: Chesapeake7 - params: - res: 1.0 + res: 1.0 # === PLOTTING OPTIONS ======================================================== plots: diff --git a/minerva/inbuilt_cfgs/example_config.yaml b/minerva/inbuilt_cfgs/example_config.yaml index 689fbbf67..df38e79bf 100644 --- a/minerva/inbuilt_cfgs/example_config.yaml +++ b/minerva/inbuilt_cfgs/example_config.yaml @@ -100,27 +100,22 @@ tasks: image: transforms: false - images_1: - module: minerva.datasets.__testing - name: TstImgDataset - paths: NAIP - params: + subdatasets: + images_1: + _target_: minerva.datasets.__testing.TstImgDataset + paths: NAIP res: 1.0 - image2: - module: minerva.datasets.__testing - name: TstImgDataset - paths: NAIP - params: + image2: + _target_: minerva.datasets.__testing.TstImgDataset + paths: NAIP res: 1.0 mask: transforms: false - module: minerva.datasets.__testing - name: TstMaskDataset + _target_: minerva.datasets.__testing.TstMaskDataset paths: Chesapeake7 - params: - res: 1.0 + res: 1.0 fit-val: name: StandardEpoch @@ -146,19 +141,15 @@ tasks: image: transforms: false - module: minerva.datasets.__testing - name: TstImgDataset + _target_: minerva.datasets.__testing.TstImgDataset paths: NAIP - params: - res: 1.0 + res: 1.0 mask: transforms: false - module: minerva.datasets.__testing - name: TstMaskDataset + _target_: minerva.datasets.__testing.TstMaskDataset paths: Chesapeake7 - params: - res: 1.0 + res: 1.0 test-test: name: StandardEpoch @@ -183,19 +174,15 @@ tasks: image: transforms: false - module: minerva.datasets.__testing - name: TstImgDataset + _target_: minerva.datasets.__testing.TstImgDataset paths: NAIP - params: - res: 1.0 + res: 1.0 mask: transforms: false - module: minerva.datasets.__testing - name: TstMaskDataset + _target_: minerva.datasets.__testing.TstMaskDataset paths: Chesapeake7 - params: - res: 1.0 + res: 1.0 # === PLOTTING OPTIONS ======================================================== plots: diff --git a/tests/conftest.py b/tests/conftest.py index 393afd48a..0cbfa53a8 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -425,17 +425,16 @@ def exp_dataset_params() -> Dict[str, Any]: return { "image": { "transforms": {"AutoNorm": {"length": 12}}, - "module": "minerva.datasets.__testing", - "name": "TstImgDataset", + "_target_": "minerva.datasets.__testing.TstImgDataset", "paths": "NAIP", - "params": {"res": 1.0, "crs": 26918}, + "res": 1.0, + "crs": 26918, }, "mask": { "transforms": False, - "module": "minerva.datasets.__testing", - "name": "TstMaskDataset", + "_target_": "minerva.datasets.__testing.TstMaskDataset", "paths": "Chesapeake7", - "params": {"res": 1.0}, + "res": 1.0, }, } @@ -443,13 +442,10 @@ def exp_dataset_params() -> Dict[str, Any]: @pytest.fixture def exp_sampler_params(small_patch_size: Tuple[int, int]): return { - "module": "torchgeo.samplers", - "name": "RandomGeoSampler", + "_target_": "torchgeo.samplers.RandomGeoSampler", "roi": False, - "params": { - "size": small_patch_size, - "length": 120, - }, + "size": small_patch_size, + "length": 120, } diff --git a/tests/test_datasets/test_factory.py b/tests/test_datasets/test_factory.py index a3ac47387..a5de7cdfc 100644 --- a/tests/test_datasets/test_factory.py +++ b/tests/test_datasets/test_factory.py @@ -60,8 +60,10 @@ def test_make_dataset(exp_dataset_params: Dict[str, Any], data_root: Path) -> No dataset_params2 = { "image": { - "image_1": exp_dataset_params["image"], - "image_2": exp_dataset_params["image"], + "subdatasets": { + "image_1": exp_dataset_params["image"], + "image_2": exp_dataset_params["image"], + }, }, "mask": exp_dataset_params["mask"], } @@ -80,8 +82,12 @@ def test_make_dataset(exp_dataset_params: Dict[str, Any], data_root: Path) -> No assert isinstance(subdatasets_4[0], UnionDataset) dataset_params3 = dataset_params2 - dataset_params3["image"]["image_1"]["transforms"] = {"AutoNorm": {"length": 12}} - dataset_params3["image"]["image_2"]["transforms"] = {"AutoNorm": {"length": 12}} + dataset_params3["image"]["subdatasets"]["image_1"]["transforms"] = { + "AutoNorm": {"length": 12} + } + dataset_params3["image"]["subdatasets"]["image_2"]["transforms"] = { + "AutoNorm": {"length": 12} + } dataset_params3["image"]["transforms"] = {"AutoNorm": {"length": 12}} dataset_5, subdatasets_5 = mdt.make_dataset(data_root, dataset_params3, cache=False) @@ -160,37 +166,28 @@ def test_caching_datasets( [ ( { - "module": "torchgeo.samplers", - "name": "RandomBatchGeoSampler", + "_target_": "torchgeo.samplers.RandomBatchGeoSampler", "roi": False, - "params": { - "size": 224, - "length": 4096, - }, + "size": 224, + "length": 4096, }, {}, ), ( { - "module": "minerva.samplers", - "name": "RandomPairGeoSampler", + "_target_": "minerva.samplers.RandomPairGeoSampler", "roi": False, - "params": { - "size": 224, - "length": 4096, - }, + "size": 224, + "length": 4096, }, {"sample_pairs": True}, ), ( { - "module": "torchgeo.samplers", - "name": "RandomBatchGeoSampler", + "_target_": "torchgeo.samplers.RandomBatchGeoSampler", "roi": False, - "params": { - "size": 224, - "length": 4096, - }, + "size": 224, + "length": 4096, }, {"world_size": 2}, ), @@ -236,9 +233,10 @@ def test_make_loaders(default_config: DictConfig) -> None: old_params_2 = OmegaConf.to_object(deepcopy(default_config)) assert isinstance(old_params_2, dict) dataset_params = old_params_2["tasks"]["fit-val"]["dataset_params"].copy() - old_params_2["tasks"]["fit-val"]["dataset_params"] = {} - old_params_2["tasks"]["fit-val"]["dataset_params"]["val-1"] = dataset_params - old_params_2["tasks"]["fit-val"]["dataset_params"]["val-2"] = dataset_params + old_params_2["tasks"]["fit-val"]["dataset_params"] = { + "val-1": dataset_params, + "val-2": dataset_params, + } loaders, n_batches, class_dist, params = mdt.make_loaders( # type: ignore[arg-type] **old_params_2, diff --git a/tests/test_transforms.py b/tests/test_transforms.py index 62d210c54..479f2cce1 100644 --- a/tests/test_transforms.py +++ b/tests/test_transforms.py @@ -384,14 +384,13 @@ def test_init_auto_norm(default_image_dataset: RasterDataset, transforms) -> Non def test_get_transform() -> None: - name = "RandomResizedCrop" - params = {"module": "torchvision.transforms", "size": 128} - transform = get_transform(name, params) + params = {"_target_": "torchvision.transforms.RandomResizedCrop", "size": 128} + transform = get_transform(params) assert callable(transform) with pytest.raises(TypeError): - _ = get_transform("DataFrame", {"module": "pandas"}) + _ = get_transform({"_target_": "pandas.DataFrame"}) @pytest.mark.parametrize( @@ -399,29 +398,44 @@ def test_get_transform() -> None: [ ( { - "CenterCrop": {"module": "torchvision.transforms", "size": 128}, - "RandomHorizontalFlip": {"module": "torchvision.transforms", "p": 0.7}, + "crop": {"_target_": "torchvision.transforms.CenterCrop", "size": 128}, + "flip": { + "_target_": "torchvision.transforms.RandomHorizontalFlip", + "p": 0.7, + }, }, "mask", ), ( { "RandomApply": { - "CenterCrop": {"module": "torchvision.transforms", "size": 128}, + "crop": { + "_target_": "torchvision.transforms.CenterCrop", + "size": 128, + }, "p": 0.3, }, - "RandomHorizontalFlip": {"module": "torchvision.transforms", "p": 0.7}, + "flip": { + "_target_": "torchvision.transforms.RandomHorizontalFlip", + "p": 0.7, + }, }, "image", ), ( { - "CenterCrop": {"module": "torchvision.transforms", "size": 128}, + "crop": {"_target_": "torchvision.transforms.CenterCrop", "size": 128}, "RandomApply": { - "CenterCrop": {"module": "torchvision.transforms", "size": 128}, + "crop": { + "_target_": "torchvision.transforms.CenterCrop", + "size": 128, + }, "p": 0.3, }, - "RandomHorizontalFlip": {"module": "torchvision.transforms", "p": 0.7}, + "flip": { + "_target_": "torchvision.transforms.RandomHorizontalFlip", + "p": 0.7, + }, }, "image", ), From 545c38c1d61721086396b084cbfe716160d23cf5 Mon Sep 17 00:00:00 2001 From: Harry Baker Date: Fri, 23 Aug 2024 17:26:17 +0100 Subject: [PATCH 21/63] Fixed `RecursionError` with paired datasets --- minerva/datasets/factory.py | 30 ++++++---- minerva/datasets/paired.py | 96 ++++++++++++++++++++++++++---- minerva/datasets/utils.py | 10 ++++ tests/test_datasets/test_paired.py | 1 - 4 files changed, 115 insertions(+), 22 deletions(-) diff --git a/minerva/datasets/factory.py b/minerva/datasets/factory.py index 6ece29ddf..c629dc05e 100644 --- a/minerva/datasets/factory.py +++ b/minerva/datasets/factory.py @@ -69,7 +69,6 @@ from minerva.utils import universal_path, utils from .collators import get_collator, stack_sample_pairs -from .paired import PairedGeoDataset, PairedNonGeoDataset from .utils import ( MinervaConcatDataset, cache_dataset, @@ -102,26 +101,35 @@ def create_subdataset( Returns: ~torchgeo.datasets.GeoDataset | ~torchgeo.datasets.NonGeoDataset: Subdataset requested. """ + params = deepcopy(subdataset_params) + crs = None - if "crs" in subdataset_params: - crs = CRS.from_epsg(subdataset_params["crs"]) + if "crs" in params: + crs = CRS.from_epsg(params["crs"]) subdataset: Union[GeoDataset, NonGeoDataset] - if "paths" in subdataset_params: + if "paths" in params: + if sample_pairs: + params["dataset"] = params["_target_"] + params["_target_"] = "minerva.datasets.paired.PairedGeoDataset" + subdataset = hydra.utils.instantiate( - subdataset_params, paths=paths, transforms=transformations, crs=crs + params, paths=paths, transforms=transformations, crs=crs ) - if sample_pairs: - subdataset = PairedGeoDataset(subdataset) - elif "root" in subdataset_params: + + elif "root" in params: if isinstance(paths, list): paths = paths[0] assert isinstance(paths, str) + + if sample_pairs: + params["dataset"] = params["_target_"] + params["_target_"] = "minerva.datasets.paired.PairedNonGeoDataset" + subdataset = hydra.utils.instantiate( - subdataset_params, root=paths, transforms=transformations + params, root=paths, transforms=transformations ) - if sample_pairs: - subdataset = PairedNonGeoDataset(subdataset) + else: raise TypeError diff --git a/minerva/datasets/paired.py b/minerva/datasets/paired.py index 278069561..e23211654 100644 --- a/minerva/datasets/paired.py +++ b/minerva/datasets/paired.py @@ -46,6 +46,7 @@ from inspect import signature from typing import Any, Callable, Dict, Optional, Sequence, Tuple, Union, overload +import hydra import matplotlib.pyplot as plt import torch from matplotlib.figure import Figure @@ -116,6 +117,9 @@ def __init__( self._args = args self._kwargs = kwargs + if isinstance(dataset, str): + dataset = hydra.utils.get_method(dataset) + if isinstance(dataset, GeoDataset): self.dataset = dataset self._res = dataset.res @@ -170,11 +174,11 @@ def __and__(self, other: "PairedGeoDataset") -> IntersectionDataset: # type: ig self, other, collate_fn=utils.pair_collate(concat_samples) ) - def __or__(self, other: "PairedGeoDataset") -> "PairedUnionDataset": # type: ignore[override] - """Take the union of two :class:`PairedGeoDataset`. + def __or__(self, other: GeoDataset) -> "PairedUnionDataset": # type: ignore[override] + """Take the union of two :class:~`torchgeo.datasets.GeoDataset`. Args: - other (PairedGeoDataset): Another dataset. + other (~torchgeo.datasets.GeoDataset): Another dataset. Returns: PairedUnionDataset: A single dataset. @@ -183,13 +187,35 @@ def __or__(self, other: "PairedGeoDataset") -> "PairedUnionDataset": # type: ig """ return PairedUnionDataset(self, other) - def __getattr__(self, item): - if item in self.dataset.__dict__: - return getattr(self.dataset, item) # pragma: no cover - elif item in self.__dict__: - return getattr(self, item) - else: - raise AttributeError + def __getattr__(self, name: str) -> Any: + """ + Called only if the attribute 'name' is not found by usual means. + Checks if 'name' exists in the dataset attribute. + """ + # Instead of calling __getattr__ directly, access the dataset attribute directly + dataset = super().__getattribute__("dataset") + if hasattr(dataset, name): + return getattr(dataset, name) + # If not found in dataset, raise an AttributeError + raise AttributeError(f"{name} cannot be found in self or dataset") + + def __getattribute__(self, name: str) -> Any: + """ + Overrides default attribute access method to prevent recursion. + Checks 'self' first and uses __getattr__ for fallback. + """ + try: + # First, try to get the attribute from the current instance + return super().__getattribute__(name) + except AttributeError: + # If not found in self, __getattr__ will check in the dataset + dataset = super().__getattribute__("dataset") + if hasattr(dataset, name): + return getattr(dataset, name) + # Raise AttributeError if not found in dataset either + raise AttributeError( + f"'{self.__class__.__name__}' object has no attribute '{name}'" + ) def __repr__(self) -> Any: return self.dataset.__repr__() @@ -277,6 +303,13 @@ def __init__( ] = merge_samples, transforms: Optional[Callable[[dict[str, Any]], dict[str, Any]]] = None, ) -> None: + + # Extract the actual dataset out of the paired dataset (otherwise we'll be pairing the datasets twice!) + if isinstance(dataset1, PairedGeoDataset): + dataset1 = dataset1.dataset + if isinstance(dataset2, PairedGeoDataset): + dataset2 = dataset2.dataset + super().__init__(dataset1, dataset2, collate_fn, transforms) new_datasets = [] @@ -320,6 +353,16 @@ def __or__(self, other: "PairedGeoDataset") -> "PairedUnionDataset": # type: ig """ return PairedUnionDataset(self, other) + def __getattr__(self, name: str) -> Any: + if name in self.__dict__: + return getattr(self, name) + elif name in self.datasets[0].__dict__: + return getattr(self.datasets[0], name) # pragma: no cover + elif name in self.datasets[1].__dict__: + return getattr(self.datasets[1], name) # pragma: no cover + else: + raise AttributeError + class PairedNonGeoDataset(NonGeoDataset): """Custom dataset to act as a wrapper to other datasets to handle paired sampling. @@ -396,6 +439,9 @@ def __init__( if isinstance(dataset, PairedNonGeoDataset): raise ValueError("Cannot pair an already paired dataset!") + if isinstance(dataset, str): + dataset = hydra.utils.get_method(dataset) + if isinstance(dataset, NonGeoDataset): self.dataset = dataset @@ -450,6 +496,36 @@ def __len__(self) -> int: def __repr__(self) -> Any: return self.dataset.__repr__() + def __getattr__(self, name: str) -> Any: + """ + Called only if the attribute 'name' is not found by usual means. + Checks if 'name' exists in the dataset attribute. + """ + # Instead of calling __getattr__ directly, access the dataset attribute directly + dataset = super().__getattribute__("dataset") + if hasattr(dataset, name): + return getattr(dataset, name) + # If not found in dataset, raise an AttributeError + raise AttributeError(f"{name} cannot be found in self or dataset") + + def __getattribute__(self, name: str) -> Any: + """ + Overrides default attribute access method to prevent recursion. + Checks 'self' first and uses __getattr__ for fallback. + """ + try: + # First, try to get the attribute from the current instance + return super().__getattribute__(name) + except AttributeError: + # If not found in self, __getattr__ will check in the dataset + dataset = super().__getattribute__("dataset") + if hasattr(dataset, name): + return getattr(dataset, name) + # Raise AttributeError if not found in dataset either + raise AttributeError( + f"'{self.__class__.__name__}' object has no attribute '{name}'" + ) + @staticmethod def plot( sample: Dict[str, Any], diff --git a/minerva/datasets/utils.py b/minerva/datasets/utils.py index a2f4c86e0..16eb7bf3e 100644 --- a/minerva/datasets/utils.py +++ b/minerva/datasets/utils.py @@ -113,6 +113,16 @@ def __or__( """ return MinervaConcatDataset([self, other]) + def __getattr__(self, name: str) -> Any: + if name in self.__dict__: + return getattr(self, name) + elif name in self.datasets[0].__dict__: + return getattr(self.datasets[0], name) # pragma: no cover + elif name in self.datasets[1].__dict__: + return getattr(self.datasets[1], name) # pragma: no cover + else: + raise AttributeError + # ===================================================================================================================== # METHODS diff --git a/tests/test_datasets/test_paired.py b/tests/test_datasets/test_paired.py index cf916f7cf..27d434507 100644 --- a/tests/test_datasets/test_paired.py +++ b/tests/test_datasets/test_paired.py @@ -94,7 +94,6 @@ def test_paired_geodatasets(img_root: Path) -> None: assert isinstance(dataset.crs, CRS) assert isinstance(getattr(dataset, "crs"), CRS) assert isinstance(dataset.dataset, TstImgDataset) - assert isinstance(dataset.__getattr__("dataset"), TstImgDataset) with pytest.raises(AttributeError): _ = dataset.roi From 611ec86684f269937407a8a5608a76384c0f2946 Mon Sep 17 00:00:00 2001 From: Harry Baker Date: Tue, 27 Aug 2024 01:37:54 +0100 Subject: [PATCH 22/63] Replace `module` and `name` fields with `_target_` for defining `task` class in configs --- minerva/inbuilt_cfgs/example_3rd_party.yaml | 9 ++---- minerva/inbuilt_cfgs/example_CNN_config.yaml | 9 ++---- .../inbuilt_cfgs/example_ChangeDetector.yaml | 6 ++-- .../inbuilt_cfgs/example_GSConvNet-II.yaml | 9 ++---- .../inbuilt_cfgs/example_GeoCLR_config.yaml | 9 ++---- .../inbuilt_cfgs/example_GeoSimConvNet.yaml | 9 ++---- minerva/inbuilt_cfgs/example_MultiLabel.yaml | 6 ++-- minerva/inbuilt_cfgs/example_PSP.yaml | 6 ++-- .../inbuilt_cfgs/example_SceneClassifier.yaml | 6 ++-- .../inbuilt_cfgs/example_UNetR_config.yaml | 9 ++---- .../example_autoencoder_config.yaml | 9 ++---- minerva/inbuilt_cfgs/example_config.yaml | 9 ++---- minerva/tasks/core.py | 29 +++++++++++++------ minerva/trainer.py | 10 +++---- 14 files changed, 57 insertions(+), 78 deletions(-) diff --git a/minerva/inbuilt_cfgs/example_3rd_party.yaml b/minerva/inbuilt_cfgs/example_3rd_party.yaml index f4bbce925..39f930915 100644 --- a/minerva/inbuilt_cfgs/example_3rd_party.yaml +++ b/minerva/inbuilt_cfgs/example_3rd_party.yaml @@ -84,8 +84,7 @@ collator: # === TASKS =================================================================== tasks: fit-train: - name: StandardEpoch - module: minerva.tasks + _target_: minerva.tasks.StandardEpoch train: true record_float: true @@ -124,8 +123,7 @@ tasks: res: 1.0 fit-val: - name: StandardEpoch - module: minerva.tasks + _target_: minerva.tasks.StandardEpoch train: false record_float: true @@ -157,8 +155,7 @@ tasks: res: 1.0 test-test: - name: StandardEpoch - module: minerva.tasks + _target_: minerva.tasks.StandardEpoch train: false record_float: true diff --git a/minerva/inbuilt_cfgs/example_CNN_config.yaml b/minerva/inbuilt_cfgs/example_CNN_config.yaml index f6f1adedf..8ba4f4ea2 100644 --- a/minerva/inbuilt_cfgs/example_CNN_config.yaml +++ b/minerva/inbuilt_cfgs/example_CNN_config.yaml @@ -81,8 +81,7 @@ collator: # === TASKS =================================================================== tasks: fit-train: - name: StandardEpoch - module: minerva.tasks + _target_: minerva.tasks.StandardEpoch train: true record_float: true @@ -114,8 +113,7 @@ tasks: res: 1.0 fit-val: - name: StandardEpoch - module: minerva.tasks + _target_: minerva.tasks.StandardEpoch train: false record_float: true @@ -148,8 +146,7 @@ tasks: res: 1.0 test-test: - name: StandardEpoch - module: minerva.tasks + _target_: minerva.tasks.StandardEpoch train: false record_float: true diff --git a/minerva/inbuilt_cfgs/example_ChangeDetector.yaml b/minerva/inbuilt_cfgs/example_ChangeDetector.yaml index 2e2ef92fb..164e1ba7d 100644 --- a/minerva/inbuilt_cfgs/example_ChangeDetector.yaml +++ b/minerva/inbuilt_cfgs/example_ChangeDetector.yaml @@ -95,8 +95,7 @@ collator: # === TASKS =================================================================== tasks: fit-train: - name: StandardEpoch - module: minerva.tasks + _target_: minerva.tasks.StandardEpoch train: true record_float: true sample_pairs: false @@ -145,8 +144,7 @@ tasks: mode: centre test-test: - name: StandardEpoch - module: minerva.tasks + _target_: minerva.tasks.StandardEpoch train: false record_float: true sample_pairs: false diff --git a/minerva/inbuilt_cfgs/example_GSConvNet-II.yaml b/minerva/inbuilt_cfgs/example_GSConvNet-II.yaml index efb5b4f8c..748b19220 100644 --- a/minerva/inbuilt_cfgs/example_GSConvNet-II.yaml +++ b/minerva/inbuilt_cfgs/example_GSConvNet-II.yaml @@ -99,8 +99,7 @@ collator: # === TASKS =================================================================== tasks: fit-train: - name: StandardEpoch - module: minerva.tasks + _target_: minerva.tasks.StandardEpoch train: true record_float: true sample_pairs: true @@ -161,8 +160,7 @@ tasks: max_r: *max_r fit-val: - name: WeightedKNN - module: minerva.tasks + _target_: minerva.tasks.WeightedKNN train: false record_float: true sample_pairs: false @@ -201,8 +199,7 @@ tasks: labels: true test-test: - name: StandardEpoch - module: minerva.tasks + _target_: minerva.tasks.StandardEpoch train: false record_float: true sample_pairs: false diff --git a/minerva/inbuilt_cfgs/example_GeoCLR_config.yaml b/minerva/inbuilt_cfgs/example_GeoCLR_config.yaml index e0298a025..7a2a4fb64 100644 --- a/minerva/inbuilt_cfgs/example_GeoCLR_config.yaml +++ b/minerva/inbuilt_cfgs/example_GeoCLR_config.yaml @@ -84,8 +84,7 @@ collator: # === TASKS =================================================================== tasks: fit-train: - name: StandardEpoch - module: minerva.tasks + _target_: minerva.tasks.StandardEpoch train: true record_float: true sample_pairs: true @@ -156,8 +155,7 @@ tasks: res: 1.0 fit-val: - name: WeightedKNN - module: minerva.tasks + _target_: minerva.tasks.WeightedKNN train: false sample_pairs: false n_classes: 8 @@ -208,8 +206,7 @@ tasks: res: 1.0 test-test: - name: StandardEpoch - module: minerva.tasks + _target_: minerva.tasks.StandardEpoch train: false record_float: true sample_pairs: false diff --git a/minerva/inbuilt_cfgs/example_GeoSimConvNet.yaml b/minerva/inbuilt_cfgs/example_GeoSimConvNet.yaml index 16bb56123..6d7fa2aaf 100644 --- a/minerva/inbuilt_cfgs/example_GeoSimConvNet.yaml +++ b/minerva/inbuilt_cfgs/example_GeoSimConvNet.yaml @@ -91,8 +91,7 @@ collator: # === TASKS =================================================================== tasks: fit-train: - name: StandardEpoch - module: minerva.tasks + _target_: minerva.tasks.StandardEpoch train: true record_float: true sample_pairs: true @@ -153,8 +152,7 @@ tasks: max_r: *max_r fit-val: - name: WeightedKNN - module: minerva.tasks + _target_: minerva.tasks.WeightedKNN train: false record_float: true sample_pairs: false @@ -205,8 +203,7 @@ tasks: res: 1.0 test-test: - name: StandardEpoch - module: minerva.tasks + _target_: minerva.tasks.StandardEpoch train: false record_float: true sample_pairs: false diff --git a/minerva/inbuilt_cfgs/example_MultiLabel.yaml b/minerva/inbuilt_cfgs/example_MultiLabel.yaml index 5483ed0f8..475590688 100644 --- a/minerva/inbuilt_cfgs/example_MultiLabel.yaml +++ b/minerva/inbuilt_cfgs/example_MultiLabel.yaml @@ -95,8 +95,7 @@ collator: # === TASKS =================================================================== tasks: fit-train: - name: StandardEpoch - module: minerva.tasks + _target_: minerva.tasks.StandardEpoch train: true record_float: true cache_dataset: false @@ -165,8 +164,7 @@ tasks: label: test-test: - name: StandardEpoch - module: minerva.tasks + _target_: minerva.tasks.StandardEpoch train: false record_float: true cache_dataset: false diff --git a/minerva/inbuilt_cfgs/example_PSP.yaml b/minerva/inbuilt_cfgs/example_PSP.yaml index 65f28b91b..3847b1412 100644 --- a/minerva/inbuilt_cfgs/example_PSP.yaml +++ b/minerva/inbuilt_cfgs/example_PSP.yaml @@ -85,8 +85,7 @@ collator: # === TASKS =================================================================== tasks: fit-train: - name: StandardEpoch - module: minerva.tasks + _target_: minerva.tasks.StandardEpoch train: true record_float: true @@ -152,8 +151,7 @@ tasks: interpolation: NEAREST test-test: - name: StandardEpoch - module: minerva.tasks + _target_: minerva.tasks.StandardEpoch train: false record_float: true diff --git a/minerva/inbuilt_cfgs/example_SceneClassifier.yaml b/minerva/inbuilt_cfgs/example_SceneClassifier.yaml index b56ed8469..f8f6c8942 100644 --- a/minerva/inbuilt_cfgs/example_SceneClassifier.yaml +++ b/minerva/inbuilt_cfgs/example_SceneClassifier.yaml @@ -94,8 +94,7 @@ collator: # === TASKS =================================================================== tasks: fit-train: - name: StandardEpoch - module: minerva.tasks + _target_: minerva.tasks.StandardEpoch train: true record_float: true @@ -159,8 +158,7 @@ tasks: label: test-test: - name: StandardEpoch - module: minerva.tasks + _target_: minerva.tasks.StandardEpoch train: false record_float: true diff --git a/minerva/inbuilt_cfgs/example_UNetR_config.yaml b/minerva/inbuilt_cfgs/example_UNetR_config.yaml index 1eb3f863c..6cc5fdd65 100644 --- a/minerva/inbuilt_cfgs/example_UNetR_config.yaml +++ b/minerva/inbuilt_cfgs/example_UNetR_config.yaml @@ -84,8 +84,7 @@ collator: # === TASKS =================================================================== tasks: fit-train: - name: StandardEpoch - module: minerva.tasks + _target_: minerva.tasks.StandardEpoch train: true record_float: true @@ -114,8 +113,7 @@ tasks: res: 1.0 fit-val: - name: StandardEpoch - module: minerva.tasks + _target_: minerva.tasks.StandardEpoch train: true record_float: true @@ -144,8 +142,7 @@ tasks: res: 1.0 test-test: - name: StandardEpoch - module: minerva.tasks + _target_: minerva.tasks.StandardEpoch train: true record_float: true diff --git a/minerva/inbuilt_cfgs/example_autoencoder_config.yaml b/minerva/inbuilt_cfgs/example_autoencoder_config.yaml index 664ab5624..96251a5ba 100644 --- a/minerva/inbuilt_cfgs/example_autoencoder_config.yaml +++ b/minerva/inbuilt_cfgs/example_autoencoder_config.yaml @@ -85,8 +85,7 @@ collator: # === TASKS =================================================================== tasks: fit-train: - name: StandardEpoch - module: minerva.tasks + _target_: minerva.tasks.StandardEpoch train: true record_float: true autoencoder_data_key: mask @@ -117,8 +116,7 @@ tasks: res: 1.0 fit-val: - name: StandardEpoch - module: minerva.tasks + _target_: minerva.tasks.StandardEpoch train: false record_float: true autoencoder_data_key: mask @@ -149,8 +147,7 @@ tasks: res: 1.0 test-test: - name: StandardEpoch - module: minerva.tasks + _target_: minerva.tasks.StandardEpoch train: false record_float: true autoencoder_data_key: mask diff --git a/minerva/inbuilt_cfgs/example_config.yaml b/minerva/inbuilt_cfgs/example_config.yaml index df38e79bf..2644510fa 100644 --- a/minerva/inbuilt_cfgs/example_config.yaml +++ b/minerva/inbuilt_cfgs/example_config.yaml @@ -82,8 +82,7 @@ collator: # === TASKS =================================================================== tasks: fit-train: - name: StandardEpoch - module: minerva.tasks + _target_: minerva.tasks.StandardEpoch train: true record_float: true @@ -118,8 +117,7 @@ tasks: res: 1.0 fit-val: - name: StandardEpoch - module: minerva.tasks + _target_: minerva.tasks.StandardEpoch train: false record_float: true @@ -152,8 +150,7 @@ tasks: res: 1.0 test-test: - name: StandardEpoch - module: minerva.tasks + _target_: minerva.tasks.StandardEpoch record_float: true imagery_config: '${oc.create:${cfg_load: minerva/inbuilt_cfgs/dataset/NAIP.yaml}}' # yamllint disable-line rule:line-length diff --git a/minerva/tasks/core.py b/minerva/tasks/core.py index 67faac873..c27027456 100644 --- a/minerva/tasks/core.py +++ b/minerva/tasks/core.py @@ -181,9 +181,12 @@ def __init__( self.model = model # Gets the datasets, number of batches, class distribution and the modfied parameters for the experiment. - loaders, n_batches, class_dist, task_params, = make_loaders( - rank, world_size, task_name=name, **global_params - ) + ( + loaders, + n_batches, + class_dist, + task_params, + ) = make_loaders(rank, world_size, task_name=name, **global_params) # If there are multiple modes and therefore number of batches, just take the value of the first one. if isinstance(n_batches, dict): @@ -319,18 +322,24 @@ def make_criterion(self) -> Any: _weights = Tensor(weights) # Use hydra to instantiate the loss function with the weights and return. - return hydra.utils.instantiate(fallback_params("loss_params", self.params, self.global_params), weight=_weights) + return hydra.utils.instantiate( + fallback_params("loss_params", self.params, self.global_params), + weight=_weights, + ) else: # Use hydra to instantiate the loss function based of the config, without weights. - return hydra.utils.instantiate(fallback_params("loss_params", self.params, self.global_params)) + return hydra.utils.instantiate( + fallback_params("loss_params", self.params, self.global_params) + ) def make_optimiser(self) -> None: """Creates a :mod:`torch` optimiser based on config parameters and sets optimiser.""" # Constructs and sets the optimiser for the model based on supplied config parameters. optimiser = hydra.utils.instantiate( - fallback_params("optimiser", self.params, self.global_params), params=self.model.parameters() + fallback_params("optimiser", self.params, self.global_params), + params=self.model.parameters(), ) self.model.set_optimiser(optimiser) @@ -491,7 +500,9 @@ def plot( colours=utils.fallback_params("colours", self.params, self.global_params), save=save, show=show, - model_name=utils.fallback_params("model_name", self.params, self.global_params), + model_name=utils.fallback_params( + "model_name", self.params, self.global_params + ), timestamp=self.global_params["timestamp"], results_dir=self.task_dir, task_cfg=self.params, @@ -545,7 +556,7 @@ def __repr__(self) -> str: # ===================================================================================================================== # METHODS # ===================================================================================================================== -def get_task(task_name: str, task_module: str = "minerva.tasks", *args, **params) -> MinervaTask: +def get_task(task_name: str, *args, **params) -> MinervaTask: """Get the requested :class:`MinervaTask` by name. Args: @@ -555,7 +566,7 @@ def get_task(task_name: str, task_module: str = "minerva.tasks", *args, **params Returns: MinervaTask: Constructed :class:`MinervaTask` object. """ - _task = func_by_str(task_module, task_name) + _task = hydra.utils.get_class(task_name) task = _task(*args, **params) assert isinstance(task, MinervaTask) diff --git a/minerva/trainer.py b/minerva/trainer.py index da732c222..05eed3ffc 100644 --- a/minerva/trainer.py +++ b/minerva/trainer.py @@ -409,7 +409,9 @@ def __init__( else: pass - self.checkpoint_path = self.exp_fn / (self.params["exp_name"] + "-checkpoint.pt") + self.checkpoint_path = self.exp_fn / ( + self.params["exp_name"] + "-checkpoint.pt" + ) self.backbone_path = self.exp_fn / (self.params["exp_name"] + "-backbone.pt") self.print("Checkpoint will be saved to " + str(self.checkpoint_path)) @@ -623,8 +625,7 @@ def fit(self) -> None: tasks: Dict[str, MinervaTask] = {} for mode in fit_params.keys(): tasks[mode] = get_task( - fit_params[mode]["name"], - fit_params[mode].get("module", "minerva.tasks"), + fit_params[mode]["_target_"], mode, self.model, self.device, @@ -744,8 +745,7 @@ def test(self, save: bool = True, show: bool = False) -> None: self.params["plot_last_epoch"] = True for task_name in test_params.keys(): task = get_task( - test_params[task_name]["name"], - test_params[task_name].get("module", "minerva.tasks"), + test_params[task_name]["_target_"], task_name, self.model, self.device, From 49f9cf8be7c645ae3c0f9fa2f93a221f917ae18a Mon Sep 17 00:00:00 2001 From: Harry Baker Date: Tue, 27 Aug 2024 14:42:18 +0100 Subject: [PATCH 23/63] Added gridlines to `DFC2020.plot` subplots --- minerva/datasets/dfc.py | 37 +++++++++++++++++++++++++++++++++---- 1 file changed, 33 insertions(+), 4 deletions(-) diff --git a/minerva/datasets/dfc.py b/minerva/datasets/dfc.py index bbc5bf277..a3078d71c 100644 --- a/minerva/datasets/dfc.py +++ b/minerva/datasets/dfc.py @@ -425,6 +425,9 @@ def plot( image = bgr_to_rgb(sample["image"][:3]) image = image.permute(1, 2, 0).numpy() + block_x_size = image.shape[0] // 8 + block_y_size = image.shape[1] // 8 + # Use inbuilt class colours and classes mappings. if classes is None or colours is None: classes = self.classes @@ -454,7 +457,13 @@ def plot( # Plot the image. axs[0].imshow(image) - axs[0].axis("off") + + # Sets tick intervals to block size. + axs[0].set_xticks(np.arange(0, image.shape[0] + 1, block_x_size)) + axs[0].set_yticks(np.arange(0, image.shape[1] + 1, block_y_size)) + + # Add grid overlay. + axs[0].grid(which="both", color="#CCCCCC", linestyle=":") # Plot the ground truth mask and predicted mask. mask_plot: Axes @@ -462,17 +471,37 @@ def plot( mask_plot = axs[1].imshow( mask, cmap=cmap, vmin=vmin, vmax=vmax, interpolation="none" ) - axs[1].axis("off") + + # Sets tick intervals to block size. + axs[1].set_xticks(np.arange(0, image.shape[0] + 1, block_x_size)) + axs[1].set_yticks(np.arange(0, image.shape[1] + 1, block_y_size)) + + # Add grid overlay. + axs[1].grid(which="both", color="#CCCCCC", linestyle=":") + if showing_prediction: axs[2].imshow( pred, cmap=cmap, vmin=vmin, vmax=vmax, interpolation="none" ) - axs[2].axis("off") + + # Sets tick intervals to block size. + axs[2].set_xticks(np.arange(0, image.shape[0] + 1, block_x_size)) + axs[2].set_yticks(np.arange(0, image.shape[1] + 1, block_y_size)) + + # Add grid overlay. + axs[2].grid(which="both", color="#CCCCCC", linestyle=":") + elif showing_prediction: mask_plot = axs[1].imshow( pred, cmap=cmap, vmin=vmin, vmax=vmax, interpolation="none" ) - axs[1].axis("off") + + # Sets tick intervals to block size. + axs[1].set_xticks(np.arange(0, image.shape[0] + 1, block_x_size)) + axs[1].set_yticks(np.arange(0, image.shape[1] + 1, block_y_size)) + + # Add grid overlay. + axs[1].grid(which="both", color="#CCCCCC", linestyle=":") if showing_mask or showing_prediction: # Plots colour bar onto figure. From e197f98aae73dc086340f9ee1befbd89c615aa5f Mon Sep 17 00:00:00 2001 From: Harry Baker Date: Tue, 27 Aug 2024 14:52:54 +0100 Subject: [PATCH 24/63] Added `AdjustGamma` to `DFC2020.plot` --- minerva/datasets/dfc.py | 5 ++++- minerva/transforms.py | 29 ++++++++++++++++++++++++++++- 2 files changed, 32 insertions(+), 2 deletions(-) diff --git a/minerva/datasets/dfc.py b/minerva/datasets/dfc.py index a3078d71c..bba20503e 100644 --- a/minerva/datasets/dfc.py +++ b/minerva/datasets/dfc.py @@ -419,10 +419,13 @@ def plot( """ # Reorder image from BGR to RGB. from kornia.color import BgrToRgb + from minerva.transforms import AdjustGamma bgr_to_rgb = BgrToRgb() + adjust_gamma = AdjustGamma(gamma=0.8) - image = bgr_to_rgb(sample["image"][:3]) + # Reorder channels from BGR to RGB and adjust the gamma ready for plotting. + image = adjust_gamma(bgr_to_rgb(sample["image"][:3])) image = image.permute(1, 2, 0).numpy() block_x_size = image.shape[0] // 8 diff --git a/minerva/transforms.py b/minerva/transforms.py index 17b369bcd..cbd3fe2c6 100644 --- a/minerva/transforms.py +++ b/minerva/transforms.py @@ -767,6 +767,8 @@ class SeasonTransform: season (str): How to handle what seasons to return: * ``pair``: Randomly pick 2 seasons to return that will form a pair. * ``random``: Randomly pick a single season to return. + + .. versionadded:: 0.28 """ def __init__(self, season: str = "random") -> None: @@ -800,6 +802,8 @@ class ConvertDtypeFromStr(ConvertImageDtype): Args: dtype (str): A tensor type as :class:`str`. + + .. versionadded:: 0.28 """ def __init__(self, dtype: str) -> None: @@ -807,7 +811,10 @@ def __init__(self, dtype: str) -> None: class MaskResize(Resize): - """Wrapper of :class:`torchvision.transforms.Resize` for use with masks that have no channel dimension.""" + """Wrapper of :class:`torchvision.transforms.Resize` for use with masks that have no channel dimension. + + .. versionadded:: 0.28 + """ def __init__(self, size, interpolation: str = "NEAREST", max_size=None, antialias: bool = True) -> None: interpolation_mode = getattr(InterpolationMode, interpolation) @@ -841,6 +848,26 @@ def forward(self, img: Tensor) -> Tensor: return torch.squeeze(super().forward(torch.reshape(img, tmp_shape))) +class AdjustGamma(): + """Callable version of :meth:`torchvision.transforms.functional.adjust_gamma` + + Args: + gamma (float): Optional; Gamma factor. + gain (float): Optional; Gain scalar. + + .. versionadded:: 0.28 + """ + def __init__(self, gamma: float = 1.0, gain: float = 1.0) -> None: + self.gamma = gamma + self.gain = gain + + def forward(self, img: Tensor) -> Tensor: + return ft.adjust_gamma(img, gamma=self.gamma, gain=self.gain) + + def __call__(self, img: Tensor) -> Tensor: + return self.forward(img) + + # ===================================================================================================================== # METHODS # ===================================================================================================================== From e6d901578d11f7a1a2ecc34cdf50520eace38bc2 Mon Sep 17 00:00:00 2001 From: Harry Baker Date: Tue, 27 Aug 2024 15:49:21 +0100 Subject: [PATCH 25/63] Use each subplot's shape to determine gridlines --- minerva/datasets/dfc.py | 19 +++++++++---------- 1 file changed, 9 insertions(+), 10 deletions(-) diff --git a/minerva/datasets/dfc.py b/minerva/datasets/dfc.py index bba20503e..d10eb91c9 100644 --- a/minerva/datasets/dfc.py +++ b/minerva/datasets/dfc.py @@ -428,8 +428,7 @@ def plot( image = adjust_gamma(bgr_to_rgb(sample["image"][:3])) image = image.permute(1, 2, 0).numpy() - block_x_size = image.shape[0] // 8 - block_y_size = image.shape[1] // 8 + block_size_factor = 8 # Use inbuilt class colours and classes mappings. if classes is None or colours is None: @@ -462,8 +461,8 @@ def plot( axs[0].imshow(image) # Sets tick intervals to block size. - axs[0].set_xticks(np.arange(0, image.shape[0] + 1, block_x_size)) - axs[0].set_yticks(np.arange(0, image.shape[1] + 1, block_y_size)) + axs[0].set_xticks(np.arange(0, image.shape[0] + 1, image.shape[0] // block_size_factor)) + axs[0].set_yticks(np.arange(0, image.shape[1] + 1, image.shape[1] // block_size_factor)) # Add grid overlay. axs[0].grid(which="both", color="#CCCCCC", linestyle=":") @@ -476,8 +475,8 @@ def plot( ) # Sets tick intervals to block size. - axs[1].set_xticks(np.arange(0, image.shape[0] + 1, block_x_size)) - axs[1].set_yticks(np.arange(0, image.shape[1] + 1, block_y_size)) + axs[1].set_xticks(np.arange(0, mask.shape[0] + 1, mask.shape[0] // block_size_factor)) + axs[1].set_yticks(np.arange(0, mask.shape[1] + 1, mask.shape[1] // block_size_factor)) # Add grid overlay. axs[1].grid(which="both", color="#CCCCCC", linestyle=":") @@ -488,8 +487,8 @@ def plot( ) # Sets tick intervals to block size. - axs[2].set_xticks(np.arange(0, image.shape[0] + 1, block_x_size)) - axs[2].set_yticks(np.arange(0, image.shape[1] + 1, block_y_size)) + axs[2].set_xticks(np.arange(0, pred.shape[0] + 1, pred.shape[0] // block_size_factor)) + axs[2].set_yticks(np.arange(0, pred.shape[1] + 1, pred.shape[1] // block_size_factor)) # Add grid overlay. axs[2].grid(which="both", color="#CCCCCC", linestyle=":") @@ -500,8 +499,8 @@ def plot( ) # Sets tick intervals to block size. - axs[1].set_xticks(np.arange(0, image.shape[0] + 1, block_x_size)) - axs[1].set_yticks(np.arange(0, image.shape[1] + 1, block_y_size)) + axs[1].set_xticks(np.arange(0, pred.shape[0] + 1, pred.shape[0] // block_size_factor)) + axs[1].set_yticks(np.arange(0, pred.shape[1] + 1, pred.shape[1] // block_size_factor)) # Add grid overlay. axs[1].grid(which="both", color="#CCCCCC", linestyle=":") From 728e14fb82e45b20663325184fb377f7b796b384 Mon Sep 17 00:00:00 2001 From: Harry Baker Date: Tue, 27 Aug 2024 16:42:03 +0100 Subject: [PATCH 26/63] Use `hydra.utils.get_method` to import the collator --- minerva/datasets/collators.py | 25 ++++-------- minerva/datasets/factory.py | 38 +++++++++---------- minerva/inbuilt_cfgs/example_3rd_party.yaml | 4 +- minerva/inbuilt_cfgs/example_CNN_config.yaml | 4 +- .../inbuilt_cfgs/example_ChangeDetector.yaml | 4 +- .../inbuilt_cfgs/example_GSConvNet-II.yaml | 4 +- .../inbuilt_cfgs/example_GeoCLR_config.yaml | 4 +- .../inbuilt_cfgs/example_GeoSimConvNet.yaml | 4 +- minerva/inbuilt_cfgs/example_MultiLabel.yaml | 4 +- minerva/inbuilt_cfgs/example_PSP.yaml | 4 +- .../inbuilt_cfgs/example_SceneClassifier.yaml | 4 +- .../inbuilt_cfgs/example_UNetR_config.yaml | 4 +- minerva/inbuilt_cfgs/example_config.yaml | 4 +- 13 files changed, 35 insertions(+), 72 deletions(-) diff --git a/minerva/datasets/collators.py b/minerva/datasets/collators.py index 7d00b88f3..da6d22899 100644 --- a/minerva/datasets/collators.py +++ b/minerva/datasets/collators.py @@ -39,38 +39,27 @@ # ===================================================================================================================== # IMPORTS # ===================================================================================================================== -from typing import Any, Callable, Dict, Iterable, Optional, Tuple +from typing import Any, Callable, Dict, Iterable, Tuple +from hydra.utils import get_method from torchgeo.datasets.utils import stack_samples -from minerva.utils import utils - # ===================================================================================================================== # METHODS # ===================================================================================================================== -def get_collator( - collator_params: Optional[Dict[str, str]] = None -) -> Callable[..., Any]: +def get_collator(collator_target: str = "torchgeo.datasets.stack_samples") -> Callable[..., Any]: """Gets the function defined in parameters to collate samples together to form a batch. Args: - collator_params (dict[str, str]): Optional; Dictionary that must contain keys for - ``'module'`` and ``'name'`` of the collation function. Defaults to ``config['collator']``. + collator_target (str): Dot based import path for collator method. + Defaults to :meth:`torchgeo.datasets.stack_samples` Returns: - ~typing.Callable[..., ~typing.Any]: Collation function found from parameters given. + ~typing.Callable[..., ~typing.Any]: Collation function found from target path given. """ collator: Callable[..., Any] - if collator_params is not None: - module = collator_params.pop("module", "") - if module == "": - collator = globals()[collator_params["name"]] - else: - collator = utils.func_by_str(module, collator_params["name"]) - else: - collator = stack_samples - + collator = get_method(collator_target) assert callable(collator) return collator diff --git a/minerva/datasets/factory.py b/minerva/datasets/factory.py index c629dc05e..b5fb2781e 100644 --- a/minerva/datasets/factory.py +++ b/minerva/datasets/factory.py @@ -450,7 +450,7 @@ def construct_dataloader( sampler_params: Dict[str, Any], dataloader_params: Dict[str, Any], batch_size: int, - collator_params: Optional[Dict[str, Any]] = None, + collator_target: Optional[str] = None, rank: int = 0, world_size: int = 1, sample_pairs: bool = False, @@ -468,7 +468,7 @@ def construct_dataloader( to sample from the dataset. dataloader_params (dict[str, ~typing.Any]): Dictionary of parameters for the DataLoader itself. batch_size (int): Number of samples per (global) batch. - collator_params (dict[str, ~typing.Any]): Optional; Dictionary of parameters defining the function to collate + collator_target (str): Optional; Import target path for collator function to collate and stack samples from the sampler. rank (int): Optional; The rank of this process for distributed computing. world_size (int): Optional; The total number of processes within a distributed run. @@ -503,7 +503,7 @@ def construct_dataloader( ) # --+ MAKE DATALOADERS +==========================================================================================+ - collator = get_collator(collator_params) + collator = get_collator(collator_target) # Add batch size from top-level parameters to the dataloader parameters. dataloader_params["batch_size"] = batch_size @@ -564,7 +564,7 @@ def _make_loader( dataset_params, sampler_params, dataloader_params, - collator_params, + collator_target, class_matrix, batch_size, model_type, @@ -589,7 +589,7 @@ def _make_loader( sampler_params, dataloader_params, batch_size, - collator_params=collator_params, + collator_target=collator_target, rank=rank, world_size=world_size, sample_pairs=sample_pairs, @@ -645,9 +645,7 @@ def make_loaders( sampler_params (dict[str, ~typing.Any]): Parameters to construct the samplers for each mode of model fitting. transform_params (dict[str, ~typing.Any]): Parameters to construct the transforms for each dataset. See documentation for the structure of these. - collator (dict[str, ~typing.Any]): Defines the collator to use that will collate samples together into batches. - Contains the ``module`` key to define the import path and the ``name`` key - for name of the collation function. + collator (str): Optional; Defines the collator to use that will collate samples together into batches. sample_pairs (bool): Activates paired sampling for Siamese models. Only used for ``train`` datasets. Returns: @@ -710,9 +708,7 @@ def make_loaders( n_batches: Union[Dict[str, int], int] loaders: Union[Dict[str, DataLoader[Iterable[Any]]], DataLoader[Iterable[Any]]] - collator_params = deepcopy(utils.fallback_params("collator", task_params, params)) - if OmegaConf.is_config(collator_params): - collator_params = OmegaConf.to_object(collator_params) + collator_target = utils.fallback_params("collator", task_params, params, None) if "sampler" in dataset_params.keys(): sampler_params: Dict[str, Any] = dataset_params["sampler"] @@ -727,7 +723,7 @@ def make_loaders( dataset_params, sampler_params, dataloader_params, - collator_params, + collator_target, elim=elim, ) @@ -740,7 +736,7 @@ def make_loaders( dataset_params, sampler_params, dataloader_params, - collator_params, + collator_target, class_matrix, batch_size, model_type, @@ -770,7 +766,7 @@ def make_loaders( dataset_params[mode], mode_sampler_params, dataloader_params, - collator_params, + collator_target, elim=elim, ) @@ -784,7 +780,7 @@ def make_loaders( dataset_params[mode], mode_sampler_params, dataloader_params, - collator_params, + collator_target, class_matrix, batch_size, model_type, @@ -840,7 +836,7 @@ def get_data_specs( dataset_params: Optional[Dict[str, Any]] = None, sampler_params: Optional[Dict[str, Any]] = None, dataloader_params: Optional[Dict[str, Any]] = None, - collator_params: Optional[Dict[str, Any]] = None, + collator_target: Optional[str] = None, elim: bool = True, ): # Load manifest from cache for this dataset. @@ -850,7 +846,7 @@ def get_data_specs( dataset_params, sampler_params, dataloader_params, - collator_params=collator_params, + collator_target=collator_target, ) class_dist = utils.modes_from_manifest(manifest, classes) @@ -877,7 +873,7 @@ def get_manifest( dataset_params: Optional[Dict[str, Any]] = None, sampler_params: Optional[Dict[str, Any]] = None, loader_params: Optional[Dict[str, Any]] = None, - collator_params: Optional[Dict[str, Any]] = None, + collator_target: Optional[str] = None, ) -> DataFrame: """Attempts to return the :class:`~pandas.DataFrame` located at ``manifest_path``. @@ -918,7 +914,7 @@ def get_manifest( dataset_params, sampler_params, loader_params, - collator_params=collator_params, + collator_target=collator_target, ) print(f"MANIFEST TO FILE -----> {manifest_path}") @@ -936,7 +932,7 @@ def make_manifest( dataset_params: Dict[str, Any], sampler_params: Dict[str, Any], loader_params: Dict[str, Any], - collator_params: Optional[Dict[str, Any]] = None, + collator_target: Optional[str] = None, ) -> DataFrame: """Constructs a manifest of the dataset detailing each sample therein. @@ -1010,7 +1006,7 @@ def delete_transforms(params: Dict[str, Any]) -> None: _sampler_params, loader_params, batch_size=1, # To prevent issues with stacking different sized patches, set batch size to 1. - collator_params=collator_params, + collator_target=collator_target, cache=False, ) diff --git a/minerva/inbuilt_cfgs/example_3rd_party.yaml b/minerva/inbuilt_cfgs/example_3rd_party.yaml index 39f930915..479a4a79f 100644 --- a/minerva/inbuilt_cfgs/example_3rd_party.yaml +++ b/minerva/inbuilt_cfgs/example_3rd_party.yaml @@ -77,9 +77,7 @@ record_int: true # Store integer results in memory. record_float: true # Store floating point results too. Beware memory overload! # ---+ Collator +-------------------------------------------------------------- -collator: - module: torchgeo.datasets - name: stack_samples +collator: torchgeo.datasets.stack_samples # === TASKS =================================================================== tasks: diff --git a/minerva/inbuilt_cfgs/example_CNN_config.yaml b/minerva/inbuilt_cfgs/example_CNN_config.yaml index 8ba4f4ea2..2bfe31305 100644 --- a/minerva/inbuilt_cfgs/example_CNN_config.yaml +++ b/minerva/inbuilt_cfgs/example_CNN_config.yaml @@ -74,9 +74,7 @@ record_int: true # Store integer results in memory. record_float: false # Store floating point results too. Beware memory overload! # ---+ Collator +-------------------------------------------------------------- -collator: - module: torchgeo.datasets - name: stack_samples +collator: torchgeo.datasets.stack_samples # === TASKS =================================================================== tasks: diff --git a/minerva/inbuilt_cfgs/example_ChangeDetector.yaml b/minerva/inbuilt_cfgs/example_ChangeDetector.yaml index 164e1ba7d..0bad8ad9a 100644 --- a/minerva/inbuilt_cfgs/example_ChangeDetector.yaml +++ b/minerva/inbuilt_cfgs/example_ChangeDetector.yaml @@ -88,9 +88,7 @@ record_int: true # Store integer results in memory. record_float: false # Store floating point results too. Beware memory overload! # ---+ Collator +-------------------------------------------------------------- -collator: - module: torchgeo.datasets - name: stack_samples +collator: torchgeo.datasets.stack_samples # === TASKS =================================================================== tasks: diff --git a/minerva/inbuilt_cfgs/example_GSConvNet-II.yaml b/minerva/inbuilt_cfgs/example_GSConvNet-II.yaml index 748b19220..611ad11c0 100644 --- a/minerva/inbuilt_cfgs/example_GSConvNet-II.yaml +++ b/minerva/inbuilt_cfgs/example_GSConvNet-II.yaml @@ -92,9 +92,7 @@ record_int: true # Store integer results in memory. record_float: false # Store floating point results too. Beware memory overload! # ---+ Collator +-------------------------------------------------------------- -collator: - module: torchgeo.datasets - name: stack_samples +collator: torchgeo.datasets.stack_samples # === TASKS =================================================================== tasks: diff --git a/minerva/inbuilt_cfgs/example_GeoCLR_config.yaml b/minerva/inbuilt_cfgs/example_GeoCLR_config.yaml index 7a2a4fb64..178e85917 100644 --- a/minerva/inbuilt_cfgs/example_GeoCLR_config.yaml +++ b/minerva/inbuilt_cfgs/example_GeoCLR_config.yaml @@ -77,9 +77,7 @@ record_int: true # Store integer results in memory. record_float: true # Store floating point results too. Beware memory overload! # ---+ Collator +-------------------------------------------------------------- -collator: - module: torchgeo.datasets - name: stack_samples +collator: torchgeo.datasets.stack_samples # === TASKS =================================================================== tasks: diff --git a/minerva/inbuilt_cfgs/example_GeoSimConvNet.yaml b/minerva/inbuilt_cfgs/example_GeoSimConvNet.yaml index 6d7fa2aaf..f3e991a54 100644 --- a/minerva/inbuilt_cfgs/example_GeoSimConvNet.yaml +++ b/minerva/inbuilt_cfgs/example_GeoSimConvNet.yaml @@ -84,9 +84,7 @@ record_int: true # Store integer results in memory. record_float: false # Store floating point results too. Beware memory overload! # ---+ Collator +-------------------------------------------------------------- -collator: - module: torchgeo.datasets - name: stack_samples +collator: torchgeo.datasets.stack_samples # === TASKS =================================================================== tasks: diff --git a/minerva/inbuilt_cfgs/example_MultiLabel.yaml b/minerva/inbuilt_cfgs/example_MultiLabel.yaml index 475590688..b597a5d73 100644 --- a/minerva/inbuilt_cfgs/example_MultiLabel.yaml +++ b/minerva/inbuilt_cfgs/example_MultiLabel.yaml @@ -88,9 +88,7 @@ record_int: true # Store integer results in memory. record_float: false # Store floating point results too. Beware memory overload! # ---+ Collator +-------------------------------------------------------------- -collator: - module: torchgeo.datasets - name: stack_samples +collator: torchgeo.datasets.stack_samples # === TASKS =================================================================== tasks: diff --git a/minerva/inbuilt_cfgs/example_PSP.yaml b/minerva/inbuilt_cfgs/example_PSP.yaml index 3847b1412..2f96e5cf1 100644 --- a/minerva/inbuilt_cfgs/example_PSP.yaml +++ b/minerva/inbuilt_cfgs/example_PSP.yaml @@ -78,9 +78,7 @@ record_int: true # Store integer results in memory. record_float: false # Store floating point results too. Beware memory overload! # ---+ Collator +-------------------------------------------------------------- -collator: - module: torchgeo.datasets - name: stack_samples +collator: torchgeo.datasets.stack_samples # === TASKS =================================================================== tasks: diff --git a/minerva/inbuilt_cfgs/example_SceneClassifier.yaml b/minerva/inbuilt_cfgs/example_SceneClassifier.yaml index f8f6c8942..12c676acc 100644 --- a/minerva/inbuilt_cfgs/example_SceneClassifier.yaml +++ b/minerva/inbuilt_cfgs/example_SceneClassifier.yaml @@ -87,9 +87,7 @@ record_int: true # Store integer results in memory. record_float: false # Store floating point results too. Beware memory overload! # ---+ Collator +-------------------------------------------------------------- -collator: - module: torchgeo.datasets - name: stack_samples +collator: torchgeo.datasets.stack_samples # === TASKS =================================================================== tasks: diff --git a/minerva/inbuilt_cfgs/example_UNetR_config.yaml b/minerva/inbuilt_cfgs/example_UNetR_config.yaml index 6cc5fdd65..a92522ebc 100644 --- a/minerva/inbuilt_cfgs/example_UNetR_config.yaml +++ b/minerva/inbuilt_cfgs/example_UNetR_config.yaml @@ -77,9 +77,7 @@ record_int: true # Store integer results in memory. record_float: false # Store floating point results too. Beware memory overload! # ---+ Collator +-------------------------------------------------------------- -collator: - module: torchgeo.datasets - name: stack_samples +collator: torchgeo.datasets.stack_samples # === TASKS =================================================================== tasks: diff --git a/minerva/inbuilt_cfgs/example_config.yaml b/minerva/inbuilt_cfgs/example_config.yaml index 2644510fa..70d1b3839 100644 --- a/minerva/inbuilt_cfgs/example_config.yaml +++ b/minerva/inbuilt_cfgs/example_config.yaml @@ -75,9 +75,7 @@ project: pytest # Define the project name for wandb. wandb_dir: /test/tmp/wandb # Directory to store wandb logs locally. # ---+ Collator +-------------------------------------------------------------- -collator: - module: torchgeo.datasets - name: stack_samples +collator: torchgeo.datasets.stack_samples # === TASKS =================================================================== tasks: From 2a06b93e70c8b0fd2f5d35d3410091f95d7f418b Mon Sep 17 00:00:00 2001 From: Harry Date: Tue, 27 Aug 2024 16:48:57 +0100 Subject: [PATCH 27/63] Update `example_autoencoder_config` --- minerva/inbuilt_cfgs/example_autoencoder_config.yaml | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/minerva/inbuilt_cfgs/example_autoencoder_config.yaml b/minerva/inbuilt_cfgs/example_autoencoder_config.yaml index 96251a5ba..36c8b2c94 100644 --- a/minerva/inbuilt_cfgs/example_autoencoder_config.yaml +++ b/minerva/inbuilt_cfgs/example_autoencoder_config.yaml @@ -78,9 +78,7 @@ record_int: true # Store integer results in memory. record_float: false # Store floating point results too. Beware memory overload! # ---+ Collator +-------------------------------------------------------------- -collator: - module: torchgeo.datasets - name: stack_samples +collator: torchgeo.datasets.stack_samples # === TASKS =================================================================== tasks: From de1910cf749ac3f9d153bf84f4b1d2c35a62cbb1 Mon Sep 17 00:00:00 2001 From: Harry Date: Tue, 27 Aug 2024 17:07:07 +0100 Subject: [PATCH 28/63] Update collator tests --- tests/test_datasets/test_collators.py | 16 ++++++++++------ 1 file changed, 10 insertions(+), 6 deletions(-) diff --git a/tests/test_datasets/test_collators.py b/tests/test_datasets/test_collators.py index 31666f396..60953dcb0 100644 --- a/tests/test_datasets/test_collators.py +++ b/tests/test_datasets/test_collators.py @@ -39,6 +39,7 @@ from collections import defaultdict from typing import Any, Dict, List, Union +import pytest import torch from numpy.testing import assert_array_equal from torch import Tensor @@ -50,12 +51,15 @@ # ===================================================================================================================== # TESTS # ===================================================================================================================== -def test_get_collator() -> None: - collator_params_1 = {"module": "torchgeo.datasets.utils", "name": "stack_samples"} - collator_params_2 = {"name": "stack_sample_pairs"} - - assert callable(mdt.get_collator(collator_params_1)) - assert callable(mdt.get_collator(collator_params_2)) +@pytest.mark.parametrize( + "target", + ( + "torchgeo.datasets.utils.stack_samples", + "minerva.datasets.collators.stack_sample_pairs", + ), +) +def test_get_collator(target: str) -> None: + assert callable(mdt.get_collator(target)) def test_stack_sample_pairs() -> None: From c7e250c8021e8f6eafe3098e42ba083ad1889874 Mon Sep 17 00:00:00 2001 From: Harry Date: Tue, 27 Aug 2024 17:12:20 +0100 Subject: [PATCH 29/63] Updated tests for new collator finding --- minerva/datasets/factory.py | 6 +++--- tests/test_datasets/test_factory.py | 1 - tests/test_tasks/test_tasks_core.py | 2 +- 3 files changed, 4 insertions(+), 5 deletions(-) diff --git a/minerva/datasets/factory.py b/minerva/datasets/factory.py index b5fb2781e..293883213 100644 --- a/minerva/datasets/factory.py +++ b/minerva/datasets/factory.py @@ -450,7 +450,7 @@ def construct_dataloader( sampler_params: Dict[str, Any], dataloader_params: Dict[str, Any], batch_size: int, - collator_target: Optional[str] = None, + collator_target: str = "torchgeo.datasets.stack_samples", rank: int = 0, world_size: int = 1, sample_pairs: bool = False, @@ -468,7 +468,7 @@ def construct_dataloader( to sample from the dataset. dataloader_params (dict[str, ~typing.Any]): Dictionary of parameters for the DataLoader itself. batch_size (int): Number of samples per (global) batch. - collator_target (str): Optional; Import target path for collator function to collate + collator_target (str): Import target path for collator function to collate and stack samples from the sampler. rank (int): Optional; The rank of this process for distributed computing. world_size (int): Optional; The total number of processes within a distributed run. @@ -873,7 +873,7 @@ def get_manifest( dataset_params: Optional[Dict[str, Any]] = None, sampler_params: Optional[Dict[str, Any]] = None, loader_params: Optional[Dict[str, Any]] = None, - collator_target: Optional[str] = None, + collator_target: str = "torchgeo.datasets.stack_samples", ) -> DataFrame: """Attempts to return the :class:`~pandas.DataFrame` located at ``manifest_path``. diff --git a/tests/test_datasets/test_factory.py b/tests/test_datasets/test_factory.py index a5de7cdfc..75e5f995e 100644 --- a/tests/test_datasets/test_factory.py +++ b/tests/test_datasets/test_factory.py @@ -254,7 +254,6 @@ def test_make_loaders(default_config: DictConfig) -> None: def test_get_manifest( data_root: Path, exp_dataset_params: Dict[str, Any], - exp_loader_params: Dict[str, Any], exp_sampler_params: Dict[str, Any], ) -> None: manifest_path = Path("tests", "tmp", "cache", "Chesapeake7_Manifest.csv") diff --git a/tests/test_tasks/test_tasks_core.py b/tests/test_tasks/test_tasks_core.py index f2924fc7f..353fb3b04 100644 --- a/tests/test_tasks/test_tasks_core.py +++ b/tests/test_tasks/test_tasks_core.py @@ -45,4 +45,4 @@ # ===================================================================================================================== def test_get_task(): with pytest.raises(TypeError): - _ = get_task("MinervaTask") + _ = get_task("minerva.tasks.MinervaTask") From 1f5ecf219a2d61244afd5f141c24587e289a3e66 Mon Sep 17 00:00:00 2001 From: Harry Date: Tue, 27 Aug 2024 17:20:32 +0100 Subject: [PATCH 30/63] Fixed scheduler params in tests --- minerva/inbuilt_cfgs/example_GeoSimConvNet.yaml | 11 +++++------ minerva/inbuilt_cfgs/example_config.yaml | 11 +++++------ 2 files changed, 10 insertions(+), 12 deletions(-) diff --git a/minerva/inbuilt_cfgs/example_GeoSimConvNet.yaml b/minerva/inbuilt_cfgs/example_GeoSimConvNet.yaml index f3e991a54..b1687af66 100644 --- a/minerva/inbuilt_cfgs/example_GeoSimConvNet.yaml +++ b/minerva/inbuilt_cfgs/example_GeoSimConvNet.yaml @@ -59,12 +59,11 @@ optimiser: lr: ${lr} # ---+ Scheduler Parameters +-------------------------------------------------- -scheduler_params: - name: LinearLR - params: - start_factor: *lr - end_factor: 5.0E-4 - total_iters: 2 +scheduler: + _target_: torch.optim.lr_scheduler.LinearLR + start_factor: ${lr} + end_factor: 5.0E-4 + total_iters: 2 # ---+ Loss Function Parameters +---------------------------------------------- loss_params: diff --git a/minerva/inbuilt_cfgs/example_config.yaml b/minerva/inbuilt_cfgs/example_config.yaml index 70d1b3839..2ece2382e 100644 --- a/minerva/inbuilt_cfgs/example_config.yaml +++ b/minerva/inbuilt_cfgs/example_config.yaml @@ -52,12 +52,11 @@ optimiser: lr: ${lr} # ---+ Scheduler Parameters +-------------------------------------------------- -scheduler_params: - name: LinearLR - params: - start_factor: 1.0 - end_factor: 0.5 - total_iters: 5 +scheduler: + _target_: torch.optim.lr_scheduler.LinearLR + start_factor: 1.0 + end_factor: 0.5 + total_iters: 5 # ---+ Loss Function Parameters +---------------------------------------------- loss_params: From 61190f65b810a8b605a3405efa40d7e2b511481e Mon Sep 17 00:00:00 2001 From: Harry Baker Date: Tue, 27 Aug 2024 22:55:12 +0100 Subject: [PATCH 31/63] Fixed `mypy` issues --- minerva/datasets/dfc.py | 2 +- minerva/datasets/factory.py | 32 ++++++++++++++++---------------- minerva/datasets/paired.py | 20 ++++++++++++++++++-- minerva/datasets/utils.py | 2 +- minerva/samplers.py | 11 +++++++---- minerva/tasks/epoch.py | 4 ++-- minerva/tasks/knn.py | 4 ++++ minerva/tasks/tsne.py | 5 +++-- minerva/transforms.py | 11 ++++++++--- 9 files changed, 60 insertions(+), 31 deletions(-) diff --git a/minerva/datasets/dfc.py b/minerva/datasets/dfc.py index d10eb91c9..fd0dc944b 100644 --- a/minerva/datasets/dfc.py +++ b/minerva/datasets/dfc.py @@ -508,7 +508,7 @@ def plot( if showing_mask or showing_prediction: # Plots colour bar onto figure. clb = fig.colorbar( - mask_plot, + mask_plot, # type: ignore[arg-type] ax=axs, location="top", ticks=np.arange(0, len(colours)), diff --git a/minerva/datasets/factory.py b/minerva/datasets/factory.py index 293883213..dc76651a7 100644 --- a/minerva/datasets/factory.py +++ b/minerva/datasets/factory.py @@ -50,7 +50,7 @@ from copy import deepcopy from datetime import timedelta from pathlib import Path -from typing import Any, Dict, Iterable, List, Literal, Optional, Tuple, Union +from typing import Any, Dict, Iterable, List, Optional, Tuple, Union import hydra import numpy as np @@ -86,7 +86,7 @@ # ===================================================================================================================== def create_subdataset( paths: Union[str, Iterable[str]], - subdataset_params: Dict[Literal["params"], Dict[str, Any]], + subdataset_params: Dict[str, Any], transformations: Optional[Any], sample_pairs: bool = False, ) -> Union[GeoDataset, NonGeoDataset]: @@ -243,7 +243,7 @@ def get_subdataset( if auto_norm: if isinstance(sub_dataset, RasterDataset): - init_auto_norm(sub_dataset, auto_norm) + init_auto_norm(sub_dataset, length=sub_dataset_params.get("length"), roi=sub_dataset_params.get("roi")) else: raise TypeError( # pragma: no cover "AutoNorm only supports normalisation of data " @@ -318,7 +318,7 @@ def make_dataset( multi_datasets_exist = False - auto_norm = None + auto_norm = False master_transforms: Optional[Any] = None for sub_type_key in type_dataset_params.keys(): @@ -331,7 +331,7 @@ def make_dataset( elif sub_type_key == "transforms": if isinstance(type_dataset_params[sub_type_key], dict): transform_params = type_dataset_params[sub_type_key] - auto_norm = transform_params.get("AutoNorm") + auto_norm = transform_params.get("AutoNorm", False) # If transforms aren't specified for a particular modality of the sample, # assume they're for the same type as the dataset. @@ -353,7 +353,7 @@ def make_dataset( type_dataset_params[sub_type_key].get("transforms"), dict ): transform_params = type_dataset_params[sub_type_key]["transforms"] - auto_norm = transform_params.get("AutoNorm") + auto_norm = transform_params.get("AutoNorm", False) else: transform_params = False @@ -372,8 +372,8 @@ def make_dataset( auto_norm=auto_norm, ) - # Reset back to None. - auto_norm = None + # Reset back to False. + auto_norm = False type_subdatasets.append(sub_dataset) # type: ignore[arg-type] @@ -400,8 +400,8 @@ def make_dataset( auto_norm=auto_norm, ) - # Reset back to None. - auto_norm = None + # Reset back to False. + auto_norm = False sub_datasets.append(sub_dataset) # type: ignore[arg-type] @@ -418,9 +418,9 @@ def make_dataset( if hasattr(dataset, "transforms"): if isinstance(dataset.transforms, MinervaCompose): assert target_transforms is not None - dataset.transforms += target_transforms + dataset.transforms += target_transforms # type: ignore[union-attr] else: - dataset.transforms = target_transforms + dataset.transforms = target_transforms # type: ignore[union-attr] else: raise TypeError( f"dataset of type {type(dataset)} has no ``transforms`` atttribute!" @@ -433,9 +433,9 @@ def make_dataset( if hasattr(dataset, "transforms"): if isinstance(dataset.transforms, MinervaCompose): assert multi_modal_transforms is not None - dataset.transforms += multi_modal_transforms + dataset.transforms += multi_modal_transforms # type: ignore[union-attr] else: - dataset.transforms = multi_modal_transforms + dataset.transforms = multi_modal_transforms # type: ignore[union-attr] else: raise TypeError( f"dataset of type {type(dataset)} has no ``transforms`` atttribute!" @@ -836,7 +836,7 @@ def get_data_specs( dataset_params: Optional[Dict[str, Any]] = None, sampler_params: Optional[Dict[str, Any]] = None, dataloader_params: Optional[Dict[str, Any]] = None, - collator_target: Optional[str] = None, + collator_target: str = "torchgeo.datasets.stack_samples", elim: bool = True, ): # Load manifest from cache for this dataset. @@ -932,7 +932,7 @@ def make_manifest( dataset_params: Dict[str, Any], sampler_params: Dict[str, Any], loader_params: Dict[str, Any], - collator_target: Optional[str] = None, + collator_target: str = "torchgeo.datasets.stack_samples", ) -> DataFrame: """Constructs a manifest of the dataset detailing each sample therein. diff --git a/minerva/datasets/paired.py b/minerva/datasets/paired.py index e23211654..8239397c9 100644 --- a/minerva/datasets/paired.py +++ b/minerva/datasets/paired.py @@ -107,9 +107,14 @@ def __init__( self, dataset: GeoDataset, *args, **kwargs ) -> None: ... # pragma: no cover + @overload + def __init__( + self, dataset: str, *args, **kwargs + ) -> None: ... # pragma: no cover + def __init__( self, - dataset: Union[Callable[..., GeoDataset], GeoDataset], + dataset: Union[Callable[..., GeoDataset], GeoDataset, str], *args, **kwargs, ) -> None: @@ -416,9 +421,20 @@ def __init__( **kwargs, ) -> None: ... # pragma: no cover + @overload + def __init__( + self, + dataset: str, + size: Union[Tuple[int, int], int], + max_r: int, + season: bool = False, + *args, + **kwargs, + ) -> None: ... # pragma: no cover + def __init__( self, - dataset: Union[Callable[..., NonGeoDataset], NonGeoDataset], + dataset: Union[Callable[..., NonGeoDataset], NonGeoDataset, str], size: Union[Tuple[int, int], int], max_r: int, season: bool = False, diff --git a/minerva/datasets/utils.py b/minerva/datasets/utils.py index 16eb7bf3e..58c9b23b9 100644 --- a/minerva/datasets/utils.py +++ b/minerva/datasets/utils.py @@ -201,7 +201,7 @@ def concatenate_datasets( master_dataset = master_dataset | datasets[i + 1] # type: ignore[operator] if hasattr(master_dataset, "transforms"): - master_dataset.transforms = transforms + master_dataset.transforms = transforms # type: ignore[union-attr] assert isinstance(master_dataset, MinervaConcatDataset) return master_dataset diff --git a/minerva/samplers.py b/minerva/samplers.py index 7dc7140b4..99d4be8cd 100644 --- a/minerva/samplers.py +++ b/minerva/samplers.py @@ -379,7 +379,7 @@ def get_sampler( params: Dict[str, Any], dataset: Union[GeoDataset, NonGeoDataset], batch_size: Optional[int] = None, -) -> Sampler: +) -> Sampler[Any]: """Use :meth:`hydra.utils.instantiate` to get the sampler using config parameters. Args: @@ -399,11 +399,14 @@ def get_sampler( params["batch_size"] = batch_size if "roi" in params: - return hydra.utils.instantiate( + sampler = hydra.utils.instantiate( params, dataset=dataset, roi=make_bounding_box(params["roi"]) ) else: if "torchgeo" in params["_target_"]: - return hydra.utils.instantiate(params, dataset=dataset) + sampler = hydra.utils.instantiate(params, dataset=dataset) else: - return hydra.utils.instantiate(params, data_source=dataset) + sampler = hydra.utils.instantiate(params, data_source=dataset) + + assert isinstance(sampler, Sampler) + return sampler diff --git a/minerva/tasks/epoch.py b/minerva/tasks/epoch.py index 19363a8b3..64704b4fe 100644 --- a/minerva/tasks/epoch.py +++ b/minerva/tasks/epoch.py @@ -72,7 +72,7 @@ def step(self) -> None: self.model.eval() # Ensure gradients will not be calculated if this is not a training task. - with torch.no_grad() if not self.train else nullcontext(): + with torch.no_grad() if not self.train else nullcontext(): # type: ignore[attr-defined] # Core of the epoch. for batch in self.loaders: @@ -85,7 +85,7 @@ def step(self) -> None: ) if self.local_step_num % self.log_rate == 0: - if dist.is_available() and dist.is_initialized(): # type: ignore[attr-defined] # pragma: no cover + if dist.is_available() and dist.is_initialized(): # type: ignore[attr-defined] # pragma: no cover # noqa: E501 loss = results[0].data.clone() dist.all_reduce(loss.div_(dist.get_world_size())) # type: ignore[attr-defined] results = (loss, *results[1:]) diff --git a/minerva/tasks/knn.py b/minerva/tasks/knn.py index 984faab7f..689419702 100644 --- a/minerva/tasks/knn.py +++ b/minerva/tasks/knn.py @@ -178,6 +178,8 @@ def generate_feature_bank(self) -> Tuple[Tensor, Tensor]: feature_list = [] target_list = [] + assert isinstance(self.loaders, dict) + for batch in tqdm(self.loaders["features"]): val_data: Tensor = batch["image"].to(self.device, non_blocking=True) val_target: Tensor = batch["mask"].to(self.device, non_blocking=True) @@ -231,6 +233,8 @@ def step(self) -> None: # Generate feature bank and target bank. feature_bank, feature_labels = self.generate_feature_bank() + assert isinstance(self.loaders, dict) + # Loop test data to predict the label by weighted KNN search. for batch in tqdm(self.loaders["test"]): test_data: Tensor = batch["image"].to(self.device, non_blocking=True) diff --git a/minerva/tasks/tsne.py b/minerva/tasks/tsne.py index 38bd37f26..3ab4f9ea4 100644 --- a/minerva/tasks/tsne.py +++ b/minerva/tasks/tsne.py @@ -38,7 +38,7 @@ # IMPORTS # ===================================================================================================================== from pathlib import Path -from typing import Optional, Union +from typing import Any, Dict, Optional, Union import torch from torch import Tensor @@ -103,7 +103,8 @@ def step(self) -> None: Passes these embeddings to :mod:`visutils` to train a TSNE algorithm and then visual the cluster. """ # Get a batch of data. - data = next(iter(self.loaders)) + assert isinstance(self.loaders, torch.utils.data.DataLoader) + data: Dict[str, Any] = next(iter(self.loaders)) # Make sure the model is in evaluation mode. self.model.eval() diff --git a/minerva/transforms.py b/minerva/transforms.py index cbd3fe2c6..42120bd38 100644 --- a/minerva/transforms.py +++ b/minerva/transforms.py @@ -862,7 +862,9 @@ def __init__(self, gamma: float = 1.0, gain: float = 1.0) -> None: self.gain = gain def forward(self, img: Tensor) -> Tensor: - return ft.adjust_gamma(img, gamma=self.gamma, gain=self.gain) + img = ft.adjust_gamma(img, gamma=self.gamma, gain=self.gain) + assert isinstance(img, Tensor) + return img def __call__(self, img: Tensor) -> Tensor: return self.forward(img) @@ -882,7 +884,10 @@ def _construct_random_transforms(random_params: Dict[str, Any]) -> Any: def init_auto_norm( - dataset: RasterDataset, params: Dict[str, Any] = {} + dataset: RasterDataset, + length: int = 128, + roi: Optional[BoundingBox] = None, + inplace=False, ) -> RasterDataset: """Uses :class:~`minerva.transforms.AutoNorm` to automatically find the mean and standard deviation of `dataset` to create a normalisation transform that is then added to the existing transforms of `dataset`. @@ -896,7 +901,7 @@ def init_auto_norm( added to it's :attr:~`torchgeo.datasets.RasterDataset.transforms` attribute. """ # Creates the AutoNorm transform by sampling `dataset` for its mean and standard deviation stats. - auto_norm = AutoNorm(dataset, **params) + auto_norm = AutoNorm(dataset, length=length, roi=roi, inplace=inplace) if dataset.transforms is None: dataset.transforms = MinervaCompose(auto_norm) From e703e8df841936294cc22a22fc61c7b781bb862f Mon Sep 17 00:00:00 2001 From: Harry Baker Date: Wed, 28 Aug 2024 01:39:57 +0100 Subject: [PATCH 32/63] Use `hydra.utils.instantiate` for making models --- minerva/inbuilt_cfgs/example_3rd_party.yaml | 11 +++--- minerva/inbuilt_cfgs/example_CNN_config.yaml | 7 ++-- .../inbuilt_cfgs/example_ChangeDetector.yaml | 35 +++++++++--------- .../inbuilt_cfgs/example_GSConvNet-II.yaml | 7 ++-- .../inbuilt_cfgs/example_GeoCLR_config.yaml | 7 ++-- .../inbuilt_cfgs/example_GeoSimConvNet.yaml | 5 +-- minerva/inbuilt_cfgs/example_MultiLabel.yaml | 37 +++++++++---------- minerva/inbuilt_cfgs/example_PSP.yaml | 19 +++++----- .../inbuilt_cfgs/example_SceneClassifier.yaml | 35 +++++++++--------- .../inbuilt_cfgs/example_UNetR_config.yaml | 11 +++--- .../example_autoencoder_config.yaml | 11 +++--- minerva/inbuilt_cfgs/example_config.yaml | 11 +++--- minerva/models/core.py | 2 + minerva/trainer.py | 35 +++++++++--------- 14 files changed, 111 insertions(+), 122 deletions(-) diff --git a/minerva/inbuilt_cfgs/example_3rd_party.yaml b/minerva/inbuilt_cfgs/example_3rd_party.yaml index 479a4a79f..c0325ecb8 100644 --- a/minerva/inbuilt_cfgs/example_3rd_party.yaml +++ b/minerva/inbuilt_cfgs/example_3rd_party.yaml @@ -41,12 +41,11 @@ optim_func: SGD # Name of the optimiser function. # ---+ Model Parameters +------------------------------------------------------ model_params: - module: lightly.models - params: - input_size: ${input_size} - n_classes: ${n_classes} - num_classes: ${n_classes} - name: resnet-9 + _target_: lightly.models.ResNetGenerator + input_size: ${input_size} + n_classes: ${n_classes} + num_classes: ${n_classes} + name: resnet-9 # ---+ Optimiser Parameters +-------------------------------------------------- optimiser: diff --git a/minerva/inbuilt_cfgs/example_CNN_config.yaml b/minerva/inbuilt_cfgs/example_CNN_config.yaml index 2bfe31305..899c36d2d 100644 --- a/minerva/inbuilt_cfgs/example_CNN_config.yaml +++ b/minerva/inbuilt_cfgs/example_CNN_config.yaml @@ -41,10 +41,9 @@ optim_func: SGD # Name of the optimiser function. # ---+ Model Parameters +------------------------------------------------------ model_params: - module: - params: - input_size: ${input_size} - n_classes: ${n_classes} + _target_: minerva.models.CNN + input_size: ${input_size} + n_classes: ${n_classes} # ---+ Optimiser Parameters +-------------------------------------------------- optimiser: diff --git a/minerva/inbuilt_cfgs/example_ChangeDetector.yaml b/minerva/inbuilt_cfgs/example_ChangeDetector.yaml index 0bad8ad9a..8ad281681 100644 --- a/minerva/inbuilt_cfgs/example_ChangeDetector.yaml +++ b/minerva/inbuilt_cfgs/example_ChangeDetector.yaml @@ -42,25 +42,24 @@ optim_func: Adam # Name of the optimiser function. # ---+ Model Parameters +------------------------------------------------------ model_params: - module: - params: + _target_: minerva.models.ChangeDetector + input_size: ${input_size} + n_classes: ${n_classes} + encoder_on: true + filter_dim: -1 + fc_dim: 512 + freeze_backbone: false + backbone_args: + module: minerva.models + name: MinervaPSP input_size: ${input_size} - n_classes: ${n_classes} - encoder_on: true - filter_dim: -1 - fc_dim: 512 - freeze_backbone: false - backbone_args: - module: minerva.models - name: MinervaPSP - input_size: ${input_size} - n_classes: 1 - encoder_name: resnet18 - encoder_weights: - psp_out_channels: 512 - segmentation_on: false - classification_on: false - encoder: false + n_classes: 1 + encoder_name: resnet18 + encoder_weights: + psp_out_channels: 512 + segmentation_on: false + classification_on: false + encoder: false # ---+ Optimiser Parameters +-------------------------------------------------- optimiser: diff --git a/minerva/inbuilt_cfgs/example_GSConvNet-II.yaml b/minerva/inbuilt_cfgs/example_GSConvNet-II.yaml index 611ad11c0..474fca398 100644 --- a/minerva/inbuilt_cfgs/example_GSConvNet-II.yaml +++ b/minerva/inbuilt_cfgs/example_GSConvNet-II.yaml @@ -49,10 +49,9 @@ val_freq: 1 # Validation epoch every ``val_freq`` training epochs. # ---+ Model Parameters +------------------------------------------------------ model_params: - module: - params: - input_size: ${input_size} - encoder_weights: imagenet + _target_: minerva.models.SimConv + input_size: ${input_size} + encoder_weights: imagenet # ---+ Optimiser Parameters +-------------------------------------------------- optimiser: diff --git a/minerva/inbuilt_cfgs/example_GeoCLR_config.yaml b/minerva/inbuilt_cfgs/example_GeoCLR_config.yaml index 178e85917..53c3e17e3 100644 --- a/minerva/inbuilt_cfgs/example_GeoCLR_config.yaml +++ b/minerva/inbuilt_cfgs/example_GeoCLR_config.yaml @@ -47,10 +47,9 @@ val_freq: 2 # Validation epoch every ``val_freq`` training epochs. # ---+ Model Parameters +------------------------------------------------------ model_params: - module: - params: - input_size: ${input_size} - # any other params... + _target_: minerva.models.SimCLR18 + input_size: ${input_size} + # any other params... # ---+ Optimiser Parameters +-------------------------------------------------- optimiser: diff --git a/minerva/inbuilt_cfgs/example_GeoSimConvNet.yaml b/minerva/inbuilt_cfgs/example_GeoSimConvNet.yaml index b1687af66..6ce4b3fdb 100644 --- a/minerva/inbuilt_cfgs/example_GeoSimConvNet.yaml +++ b/minerva/inbuilt_cfgs/example_GeoSimConvNet.yaml @@ -49,9 +49,8 @@ val_freq: 1 # Validation epoch every ``val_freq`` training epochs. # ---+ Model Parameters +------------------------------------------------------ model_params: - module: - params: - input_size: ${input_size} + _target_: minerva.models.SimConv + input_size: ${input_size} # ---+ Optimiser Parameters +-------------------------------------------------- optimiser: diff --git a/minerva/inbuilt_cfgs/example_MultiLabel.yaml b/minerva/inbuilt_cfgs/example_MultiLabel.yaml index b597a5d73..b0e120c20 100644 --- a/minerva/inbuilt_cfgs/example_MultiLabel.yaml +++ b/minerva/inbuilt_cfgs/example_MultiLabel.yaml @@ -41,26 +41,25 @@ optim_func: SGD # Name of the optimiser function. # ---+ Model Parameters +------------------------------------------------------ model_params: - module: - params: + _target_: minerva.models.FlexiSceneClassifier + input_size: ${input_size} + n_classes: ${n_classes} + encoder_on: true + filter_dim: -1 + fc_dim: 512 + freeze_backbone: false + clamp_outputs: true + backbone_args: + module: minerva.models + name: MinervaPSP input_size: ${input_size} - n_classes: ${n_classes} - encoder_on: true - filter_dim: -1 - fc_dim: 512 - freeze_backbone: false - clamp_outputs: true - backbone_args: - module: minerva.models - name: MinervaPSP - input_size: ${input_size} - n_classes: 1 - encoder_name: resnet18 - encoder_weights: - psp_out_channels: 512 - segmentation_on: false - classification_on: false - encoder: false + n_classes: 1 + encoder_name: resnet18 + encoder_weights: + psp_out_channels: 512 + segmentation_on: false + classification_on: false + encoder: false # ---+ Optimiser Parameters +-------------------------------------------------- optimiser: diff --git a/minerva/inbuilt_cfgs/example_PSP.yaml b/minerva/inbuilt_cfgs/example_PSP.yaml index 2f96e5cf1..1359f066d 100644 --- a/minerva/inbuilt_cfgs/example_PSP.yaml +++ b/minerva/inbuilt_cfgs/example_PSP.yaml @@ -41,16 +41,15 @@ optim_func: SGD # Name of the optimiser function. # ---+ Model Parameters +------------------------------------------------------ model_params: - module: - params: - input_size: ${input_size} - n_classes: ${n_classes} - encoder: true - segmentation_on: true - classification_on: true - upsampling: 8 - aux_params: - classes: ${n_classes} + _target_: minerva.models.MinervaPSP + input_size: ${input_size} + n_classes: ${n_classes} + encoder: true + segmentation_on: true + classification_on: true + upsampling: 8 + aux_params: + classes: ${n_classes} # ---+ Optimiser Parameters +-------------------------------------------------- optimiser: diff --git a/minerva/inbuilt_cfgs/example_SceneClassifier.yaml b/minerva/inbuilt_cfgs/example_SceneClassifier.yaml index 12c676acc..716355437 100644 --- a/minerva/inbuilt_cfgs/example_SceneClassifier.yaml +++ b/minerva/inbuilt_cfgs/example_SceneClassifier.yaml @@ -41,25 +41,24 @@ optim_func: SGD # Name of the optimiser function. # ---+ Model Parameters +------------------------------------------------------ model_params: - module: - params: + _target_: minerva.models.FlexiSceneClassifier + input_size: ${input_size} + n_classes: ${n_classes} + encoder_on: true + filter_dim: -1 + fc_dim: 512 + freeze_backbone: false + backbone_args: + module: minerva.models + name: MinervaPSP input_size: ${input_size} - n_classes: ${n_classes} - encoder_on: true - filter_dim: -1 - fc_dim: 512 - freeze_backbone: false - backbone_args: - module: minerva.models - name: MinervaPSP - input_size: ${input_size} - n_classes: 1 - encoder_name: resnet18 - encoder_weights: - psp_out_channels: 512 - segmentation_on: false - classification_on: false - encoder: false + n_classes: 1 + encoder_name: resnet18 + encoder_weights: + psp_out_channels: 512 + segmentation_on: false + classification_on: false + encoder: false # ---+ Optimiser Parameters +-------------------------------------------------- optimiser: diff --git a/minerva/inbuilt_cfgs/example_UNetR_config.yaml b/minerva/inbuilt_cfgs/example_UNetR_config.yaml index a92522ebc..4ca263c8e 100644 --- a/minerva/inbuilt_cfgs/example_UNetR_config.yaml +++ b/minerva/inbuilt_cfgs/example_UNetR_config.yaml @@ -41,12 +41,11 @@ optim_func: SGD # Name of the optimiser function. # ---+ Model Parameters +------------------------------------------------------ model_params: - module: - params: - input_size: ${input_size} - n_classes: ${n_classes} - backbone_kwargs: - torch_weights: true + _target_: minerva.models.UNetR18 + input_size: ${input_size} + n_classes: ${n_classes} + backbone_kwargs: + torch_weights: true # ---+ Optimiser Parameters +-------------------------------------------------- optimiser: diff --git a/minerva/inbuilt_cfgs/example_autoencoder_config.yaml b/minerva/inbuilt_cfgs/example_autoencoder_config.yaml index 36c8b2c94..0713cde30 100644 --- a/minerva/inbuilt_cfgs/example_autoencoder_config.yaml +++ b/minerva/inbuilt_cfgs/example_autoencoder_config.yaml @@ -41,12 +41,11 @@ optim_func: SGD # Name of the optimiser function. # ---+ Model Parameters +------------------------------------------------------ model_params: - module: - params: - input_size: ${input_size} - n_classes: ${n_classes} - backbone_kwargs: - torch_weights: false + _target_: minerva.models.UNetR18 + input_size: ${input_size} + n_classes: ${n_classes} + backbone_kwargs: + torch_weights: false # ---+ Optimiser Parameters +-------------------------------------------------- optimiser: diff --git a/minerva/inbuilt_cfgs/example_config.yaml b/minerva/inbuilt_cfgs/example_config.yaml index 2ece2382e..a3c648eae 100644 --- a/minerva/inbuilt_cfgs/example_config.yaml +++ b/minerva/inbuilt_cfgs/example_config.yaml @@ -14,7 +14,7 @@ cache_dir: tests/tmp/cache # === HYPERPARAMETERS ========================================================= # ---+ Model Specification +--------------------------------------------------- -# Name of model. Substring before hyphen is model class. +# Name of model. This no longer used for model class (see model_params). model_name: FCN32ResNet18-test # Type of model. Can be mlp, scene classifier, segmentation, ssl or siamese. @@ -40,11 +40,10 @@ optim_func: SGD # Name of the optimiser function. # ---+ Model Parameters +------------------------------------------------------ model_params: - module: - params: - input_size: ${input_size} - n_classes: *n_classes - # any other params... + _target_: minerva.models.FCN32ResNet18 + input_size: ${input_size} + n_classes: ${n_classes} + # any other params... # ---+ Optimiser Parameters +-------------------------------------------------- optimiser: diff --git a/minerva/models/core.py b/minerva/models/core.py index f06513979..c5700e46c 100644 --- a/minerva/models/core.py +++ b/minerva/models/core.py @@ -146,6 +146,8 @@ def __init__( self.input_size = input_size self.n_classes = n_classes self.scaler = scaler + + print(f"{self.input_size=}") # Output shape initialised as None. Should be set by calling determine_output_dim. self.output_shape: Optional[Tuple[int, ...]] = None diff --git a/minerva/trainer.py b/minerva/trainer.py index 05eed3ffc..cbfa2bab0 100644 --- a/minerva/trainer.py +++ b/minerva/trainer.py @@ -43,6 +43,7 @@ from copy import deepcopy from pathlib import Path from platform import python_version +import re from typing import TYPE_CHECKING, Any, Callable, Dict, Optional, Tuple, Union import hydra @@ -503,39 +504,37 @@ def make_model(self) -> MinervaModel: if OmegaConf.is_config(model_params): model_params = OmegaConf.to_object(model_params) # type: ignore[assignment] - module = model_params.pop("module", "minerva.models") - if not module: - module = "minerva.models" - is_minerva = True if module == "minerva.models" else False - - # Gets the model requested by config parameters. - _model = utils.func_by_str(module, self.params["model_name"].split("-")[0]) + is_minerva = True if re.search(r"minerva", model_params["_target_"]) else False + + print(f"{is_minerva=}") if self.fine_tune: # Add the path to the pre-trained weights to the model params. model_params["backbone_weight_path"] = f"{self.get_weights_path()}.pt" - params = model_params.get("params", {}) - if "n_classes" in params.keys(): + if "n_classes" in model_params.keys(): # Updates the number of classes in case it has been altered by class balancing. - params["n_classes"] = self.params["n_classes"] + model_params["n_classes"] = self.params["n_classes"] - if "num_classes" in params.keys(): + if "num_classes" in model_params.keys(): # Updates the number of classes in case it has been altered by class balancing. - params["num_classes"] = self.params["n_classes"] + model_params["num_classes"] = self.params["n_classes"] if self.params.get("mix_precision", False): - params["scaler"] = torch.cuda.amp.grad_scaler.GradScaler() + model_params["scaler"] = torch.cuda.amp.grad_scaler.GradScaler() + print(model_params["input_size"]) # Initialise model. model: MinervaModel if is_minerva: - model = _model(self.make_criterion(), **params) + model = hydra.utils.instantiate(model_params, criterion=self.make_criterion()) + print(model) else: - model = MinervaWrapper( - _model, - self.make_criterion(), - **params, + model_params["model"] = model_params["_target_"] + model_params["_target_"] = "minerva.models.MinervaWrapper" + model = hydra.utils.instantiate( + model_params, + criterion=self.make_criterion(), ) if self.params.get("reload", False): From 20b914adcaf25770cef75240401ecd7f09ad1840 Mon Sep 17 00:00:00 2001 From: Harry Baker Date: Wed, 28 Aug 2024 14:04:39 +0100 Subject: [PATCH 33/63] Specify dist backend protocol from config (and default to `GLOO`) --- minerva/utils/runner.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/minerva/utils/runner.py b/minerva/utils/runner.py index edba8f1ba..2ba065050 100644 --- a/minerva/utils/runner.py +++ b/minerva/utils/runner.py @@ -301,7 +301,7 @@ def _run_preamble( if cfg.world_size > 1: dist.init_process_group( # type: ignore[attr-defined] - backend="nccl", + backend=cfg.get("dist_backend", "gloo"), init_method=cfg.dist_url, world_size=cfg.world_size, rank=cfg.rank, From 16ddddcdbe4c8d60b4d186b8389c1c2951d9bd9b Mon Sep 17 00:00:00 2001 From: Harry Date: Wed, 28 Aug 2024 14:28:22 +0100 Subject: [PATCH 34/63] Switch to `opencv-python-headless` to remove potential external dependencies issue --- requirements/requirements.txt | 2 +- requirements/requirements_dev.txt | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 2a0cfb08b..a8096605b 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -18,7 +18,7 @@ numba>=0.57.0 # not directly required but pinned to ensure Python 3.11 compatibi numpy==1.26.4 onnx==1.16.2 onnx2torch==1.5.14 -opencv-python==4.9.0.80 +opencv-python-headless==4.10.0.84 overload==1.1 pandas==2.2.2 patch-ng==1.17.4 diff --git a/requirements/requirements_dev.txt b/requirements/requirements_dev.txt index ab1932016..bf5a3d4af 100644 --- a/requirements/requirements_dev.txt +++ b/requirements/requirements_dev.txt @@ -23,7 +23,7 @@ numba>=0.57.0 # not directly required but pinned to ensure Python 3.11 compatibi numpy==1.26.4 onnx==1.16.1 onnx2torch==1.5.14 -opencv-python==4.9.0.80 +opencv-python-headless==4.10.0.84 overload==1.1 pandas==2.2.2 patch-ng==1.17.4 From 32bbda945110c6c0a7f8c0365cee86096f1b60f6 Mon Sep 17 00:00:00 2001 From: Harry Date: Thu, 29 Aug 2024 15:37:37 +0100 Subject: [PATCH 35/63] Fixed `trainer` tests --- minerva/inbuilt_cfgs/example_3rd_party.yaml | 2 +- minerva/models/core.py | 2 -- minerva/trainer.py | 19 ++++++++++--------- tests/test_trainer.py | 11 +++++++++-- 4 files changed, 20 insertions(+), 14 deletions(-) diff --git a/minerva/inbuilt_cfgs/example_3rd_party.yaml b/minerva/inbuilt_cfgs/example_3rd_party.yaml index c0325ecb8..258953fb0 100644 --- a/minerva/inbuilt_cfgs/example_3rd_party.yaml +++ b/minerva/inbuilt_cfgs/example_3rd_party.yaml @@ -41,7 +41,7 @@ optim_func: SGD # Name of the optimiser function. # ---+ Model Parameters +------------------------------------------------------ model_params: - _target_: lightly.models.ResNetGenerator + _target_: lightly.models.resnet.ResNetGenerator input_size: ${input_size} n_classes: ${n_classes} num_classes: ${n_classes} diff --git a/minerva/models/core.py b/minerva/models/core.py index c5700e46c..f06513979 100644 --- a/minerva/models/core.py +++ b/minerva/models/core.py @@ -146,8 +146,6 @@ def __init__( self.input_size = input_size self.n_classes = n_classes self.scaler = scaler - - print(f"{self.input_size=}") # Output shape initialised as None. Should be set by calling determine_output_dim. self.output_shape: Optional[Tuple[int, ...]] = None diff --git a/minerva/trainer.py b/minerva/trainer.py index cbfa2bab0..c98327322 100644 --- a/minerva/trainer.py +++ b/minerva/trainer.py @@ -39,11 +39,11 @@ # IMPORTS # ===================================================================================================================== import os +import re import warnings from copy import deepcopy from pathlib import Path from platform import python_version -import re from typing import TYPE_CHECKING, Any, Callable, Dict, Optional, Tuple, Union import hydra @@ -67,7 +67,6 @@ MinervaDataParallel, MinervaModel, MinervaOnnxModel, - MinervaWrapper, extract_wrapped_model, wrap_model, ) @@ -341,6 +340,7 @@ def __init__( self.model: Union[ MinervaModel, MinervaDataParallel, MinervaBackbone, OptimizedModule ] + if Path(self.params.get("pre_train_name", "none")).suffix == ".onnx": # Loads model from `onnx` format. self.model = self.load_onnx_model() @@ -505,8 +505,6 @@ def make_model(self) -> MinervaModel: model_params = OmegaConf.to_object(model_params) # type: ignore[assignment] is_minerva = True if re.search(r"minerva", model_params["_target_"]) else False - - print(f"{is_minerva=}") if self.fine_tune: # Add the path to the pre-trained weights to the model params. @@ -523,14 +521,14 @@ def make_model(self) -> MinervaModel: if self.params.get("mix_precision", False): model_params["scaler"] = torch.cuda.amp.grad_scaler.GradScaler() - print(model_params["input_size"]) # Initialise model. model: MinervaModel if is_minerva: - model = hydra.utils.instantiate(model_params, criterion=self.make_criterion()) - print(model) + model = hydra.utils.instantiate( + model_params, criterion=self.make_criterion() + ) else: - model_params["model"] = model_params["_target_"] + model_params["model"] = hydra.utils.get_method(model_params["_target_"]) model_params["_target_"] = "minerva.models.MinervaWrapper" model = hydra.utils.instantiate( model_params, @@ -563,7 +561,10 @@ def load_onnx_model(self) -> MinervaModel: package="onnx2torch", ) - model_params = self.params["model_params"].get("params", {}) + model_params = deepcopy(self.params["model_params"]) + + if "_target_" in model_params: + del model_params["_target_"] onnx_model = convert(onnx_load(f"{self.get_weights_path()}.onnx")) model = MinervaOnnxModel(onnx_model, self.make_criterion(), **model_params) diff --git a/tests/test_trainer.py b/tests/test_trainer.py index 7015b0a8f..14f952337 100644 --- a/tests/test_trainer.py +++ b/tests/test_trainer.py @@ -145,10 +145,17 @@ def test_trainer_3(default_config: DictConfig) -> None: params1 = deepcopy(default_config) trainer1 = Trainer(0, **params1) - trainer1.save_model(fn=trainer1.get_model_cache_path()) + + pre_train_path = trainer1.get_model_cache_path() + trainer1.save_model(fn=pre_train_path, fmt="onnx") params2 = deepcopy(default_config) - OmegaConf.update(params2, "pre_train_name", params1["model_name"], force_add=True) + OmegaConf.update( + params2, + "pre_train_name", + str(pre_train_path.with_suffix(".onnx")), + force_add=True, + ) params2["fine_tune"] = True params2["max_epochs"] = 2 params2["elim"] = False From 6c21b21bfc984e7faff97116df234f817a8a47d2 Mon Sep 17 00:00:00 2001 From: Harry Date: Thu, 29 Aug 2024 15:38:38 +0100 Subject: [PATCH 36/63] Fixed `test_transforms` --- tests/test_transforms.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tests/test_transforms.py b/tests/test_transforms.py index 479f2cce1..491471ac5 100644 --- a/tests/test_transforms.py +++ b/tests/test_transforms.py @@ -376,9 +376,9 @@ def test_init_auto_norm(default_image_dataset: RasterDataset, transforms) -> Non and transforms is not None ): with pytest.raises(TypeError): - _ = init_auto_norm(default_image_dataset, params) + _ = init_auto_norm(default_image_dataset, **params) else: - dataset = init_auto_norm(default_image_dataset, params) + dataset = init_auto_norm(default_image_dataset, **params) assert isinstance(dataset, RasterDataset) assert isinstance(dataset.transforms.transforms[-1], AutoNorm) # type: ignore[union-attr] From e0aa04cca0a47d4c45f51ae634c836a21bbcb8ba Mon Sep 17 00:00:00 2001 From: Harry Date: Fri, 30 Aug 2024 14:39:09 +0100 Subject: [PATCH 37/63] Reduce number of new variables in `modelio` --- minerva/modelio.py | 44 ++++++++++++++++++-------------------------- 1 file changed, 18 insertions(+), 26 deletions(-) diff --git a/minerva/modelio.py b/minerva/modelio.py index 2fe6ac934..27e83ae74 100644 --- a/minerva/modelio.py +++ b/minerva/modelio.py @@ -94,31 +94,30 @@ def sup_tg( multilabel = True if check_substrings_in_string(model_type, "multilabel") else False # Extracts the x and y batches from the dict. - images: Tensor = batch["image"] - targets: Tensor = batch[target_key] + x: Tensor = batch["image"] + y: Tensor = batch[target_key] # Check that none of the data is NaN or infinity. if kwargs.get("validate_variables", False): - assert not images.isnan().any() - assert not images.isinf().any() - assert not targets.isnan().any() - assert not targets.isinf().any() + assert not x.isnan().any() + assert not x.isinf().any() + assert not y.isnan().any() + assert not y.isinf().any() # Re-arranges the x and y batches. - x_batch: Tensor = images.to(float_dtype) # type: ignore[attr-defined] - y_batch: Tensor + x = x.to(float_dtype) # type: ignore[attr-defined] # Squeeze out axis 1 if only 1 element wide. if target_key == "mask": - targets = targets.squeeze() + y = y.squeeze() - if isinstance(targets, Tensor): - targets = targets.detach().cpu() - y_batch = torch.tensor(targets, dtype=torch.float if multilabel else torch.long) # type: ignore[attr-defined] + if isinstance(y, Tensor): + y = y.detach().cpu() + y = y.to(dtype=torch.float if multilabel else torch.long) # type: ignore[attr-defined] # Transfer to GPU. - x: Tensor = x_batch.to(device) - y: Tensor = y_batch.to(device) + x = x.to(device) + y = y.to(device) # Runs a step of the epoch. loss, z = model.step(x, y, train=train) @@ -273,13 +272,9 @@ def ssl_pair_tg( """ float_dtype = _determine_float_dtype(device, kwargs.get("mix_precision", False)) - # Extracts the x_i batch from the dict. - x_i_batch: Tensor = batch[0]["image"] - x_j_batch: Tensor = batch[1]["image"] - - # Ensures images are floats. - x_i_batch = x_i_batch.to(float_dtype) # type: ignore[attr-defined] - x_j_batch = x_j_batch.to(float_dtype) # type: ignore[attr-defined] + # Extracts both batches from the dict and ensures images are floats. + x_i_batch: Tensor = batch[0]["image"].to(float_dtype) # type: ignore[attr-defined] + x_j_batch: Tensor = batch[1]["image"].to(float_dtype) # type: ignore[attr-defined] if kwargs.get("validate_variables", False): try: @@ -287,11 +282,8 @@ def ssl_pair_tg( except AssertionError: print("WARNING: Batches are the same!") - # Stacks each side of the pair batches together. - x_batch = torch.stack([x_i_batch, x_j_batch]) - - # Transfer to GPU. - x = x_batch.to(device, non_blocking=True) + # Stacks each side of the pair batches together and transfer to GPU. + x = torch.stack([x_i_batch, x_j_batch]).to(device, non_blocking=True) # Check that none of the data is NaN or infinity. if kwargs.get("validate_variables", False): From 361a1104bf027ff8e31254687ba137b7e99e5eb0 Mon Sep 17 00:00:00 2001 From: Harry Date: Fri, 30 Aug 2024 16:49:02 +0100 Subject: [PATCH 38/63] Minor depenendcy updates --- requirements/requirements.txt | 34 +++++++++++++-------------- requirements/requirements_dev.txt | 38 +++++++++++++++---------------- 2 files changed, 36 insertions(+), 36 deletions(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index a8096605b..ffdffd5a4 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -1,38 +1,38 @@ -argcomplete==3.4.0 +argcomplete==3.5.0 fastapi>=0.101.0 fiona>=1.9.1 geopy==2.4.1 GitPython>=3.1.35 hydra-core==1.3.2 -imagecodecs==2024.1.1 -imageio==2.34.2 +imagecodecs==2024.6.1 +imageio==2.35.1 inputimeout==1.0.4 ipykernel==6.29.5 -kornia==0.7.2 -lightly==1.5.11 -lmdb==1.4.1 -matplotlib==3.9.0 -mlflow-skinny==2.14.0 +kornia==0.7.3 +lightly==1.5.12 +lmdb==1.5.1 +matplotlib==3.9.2 +mlflow-skinny==2.16.0 nptyping==2.5.0 numba>=0.57.0 # not directly required but pinned to ensure Python 3.11 compatibility. numpy==1.26.4 onnx==1.16.2 -onnx2torch==1.5.14 +onnx2torch==1.5.15 opencv-python-headless==4.10.0.84 overload==1.1 pandas==2.2.2 patch-ng==1.17.4 protobuf>=3.19.5 # not directly required, pinned by Snyk to avoid a vulnerability. psutil==6.0.0 -pyyaml==6.0.1 +pyyaml==6.0.2 rasterio>=1.3.6 requests==2.32.3 -scikit-learn==1.5.0 +scikit-learn==1.5.1 segmentation-models-pytorch==0.3.3 -setuptools==72.1.0 +setuptools==74.0.0 starlette==0.37.2 # not directly required, pinned by Dependabot to avoid a vulnerability. tabulate==0.9.0 -tensorflow==2.16.2 +tensorflow==2.17.0 tifffile==2024.1.30 timm==0.9.2 torch==2.3.1 @@ -41,10 +41,10 @@ torchgeo==0.5.2 torchinfo==1.8.0 torchvision==0.18.1 tornado>=6.3.3 -tqdm==4.66.4 -types-PyYAML==6.0.12.20240311 -types-requests==2.32.0.20240602 +tqdm==4.66.5 +types-PyYAML==6.0.12.20240808 +types-requests==2.32.0.20240712 types-tabulate==0.9.0.20240106 -wandb==0.17.4 +wandb==0.17.8 Werkzeug>=2.2.3 # Patches a potential security vulnerability. wheel>=0.38.0 # not directly required, pinned by Snyk to avoid a vulnerability. diff --git a/requirements/requirements_dev.txt b/requirements/requirements_dev.txt index bf5a3d4af..847c68f02 100644 --- a/requirements/requirements_dev.txt +++ b/requirements/requirements_dev.txt @@ -1,28 +1,28 @@ -argcomplete==3.4.0 +argcomplete==3.5.0 certifi>=2022.12.7 # not directly required, pinned by Snyk to avoid a vulnerability. fastapi>=0.101.0 fiona>=1.9.1 -flake8==7.1.0 +flake8==7.1.1 geopy==2.4.1 GitPython>=3.1.35 hydra-core==1.3.2 -imagecodecs==2024.1.1 -imageio==2.34.2 +imagecodecs==2024.6.1 +imageio==2.35.1 inputimeout==1.0.4 internet-sabotage3==0.1.6 ipykernel==6.29.5 -kornia==0.7.2 -lightly==1.5.11 -lmdb==1.4.1 -matplotlib==3.9.0 -mlflow-skinny==2.14.0 -mypy==1.10.1 +kornia==0.7.3 +lightly==1.5.12 +lmdb==1.5.1 +matplotlib==3.9.2 +mlflow-skinny==2.16.0 +mypy==1.11.2 myst_parser==3.0.1 nptyping==2.5.0 numba>=0.57.0 # not directly required but pinned to ensure Python 3.11 compatibility. numpy==1.26.4 -onnx==1.16.1 -onnx2torch==1.5.14 +onnx==1.16.2 +onnx2torch==1.5.15 opencv-python-headless==4.10.0.84 overload==1.1 pandas==2.2.2 @@ -34,17 +34,17 @@ pygments>=2.7.4 # not directly required, pinned by Snyk to avoid a vulnerability pytest==7.4.4 pytest-cov==5.0.0 pytest-lazy-fixture==0.6.3 -pyyaml==6.0.1 +pyyaml==6.0.2 rasterio>=1.3.6 requests==2.32.3 -scikit-learn==1.5.0 +scikit-learn==1.5.1 segmentation-models-pytorch==0.3.3 setuptools==72.1.0 sphinx==7.4.4 sphinx-rtd-theme==2.0.0 starlette>=0.25.0 # not directly required, pinned by Dependabot to avoid a vulnerability. tabulate==0.9.0 -tensorflow==2.16.2 +tensorflow==2.17.0 tifffile==2024.1.30 timm==0.9.2 torch==2.3.1 @@ -54,10 +54,10 @@ torchinfo==1.8.0 torchvision==0.18.1 tornado>=6.3.3 tox==4.16.0 -tqdm==4.66.4 -types-PyYAML==6.0.12.20240311 -types-requests==2.32.0.20240602 +tqdm==4.66.5 +types-PyYAML==6.0.12.20240808 +types-requests==2.32.0.20240712 types-tabulate==0.9.0.20240106 -wandb==0.17.4 +wandb==0.17.8 Werkzeug>=2.2.3 # Patches a potential security vulnerability. wheel>=0.38.0 # not directly required, pinned by Snyk to avoid a vulnerability. From 238dfc80d2cb19cf5d922b39b82416828a03240c Mon Sep 17 00:00:00 2001 From: Harry Date: Fri, 30 Aug 2024 17:35:24 +0100 Subject: [PATCH 39/63] Bump `torch` --> `2.4.0` and `torchvision` --> `0.19.0` --- requirements/requirements.txt | 4 ++-- requirements/requirements_dev.txt | 4 ++-- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index ffdffd5a4..d565f1193 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -35,11 +35,11 @@ tabulate==0.9.0 tensorflow==2.17.0 tifffile==2024.1.30 timm==0.9.2 -torch==2.3.1 +torch==2.4.0 torcheval==0.0.7 torchgeo==0.5.2 torchinfo==1.8.0 -torchvision==0.18.1 +torchvision==0.19.0 tornado>=6.3.3 tqdm==4.66.5 types-PyYAML==6.0.12.20240808 diff --git a/requirements/requirements_dev.txt b/requirements/requirements_dev.txt index 847c68f02..11cf70d3f 100644 --- a/requirements/requirements_dev.txt +++ b/requirements/requirements_dev.txt @@ -47,11 +47,11 @@ tabulate==0.9.0 tensorflow==2.17.0 tifffile==2024.1.30 timm==0.9.2 -torch==2.3.1 +torch==2.4.0 torcheval==0.0.7 torchgeo==0.5.2 torchinfo==1.8.0 -torchvision==0.18.1 +torchvision==0.19.0 tornado>=6.3.3 tox==4.16.0 tqdm==4.66.5 From 040a439fcf1967c0264412e8fbb099fe64198ed6 Mon Sep 17 00:00:00 2001 From: Harry Date: Fri, 30 Aug 2024 17:37:05 +0100 Subject: [PATCH 40/63] Bump `segmentation-models-pytorch` --> `0.3.4` --- requirements/requirements.txt | 4 ++-- requirements/requirements_dev.txt | 4 ++-- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index d565f1193..4c480ce9a 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -28,13 +28,13 @@ pyyaml==6.0.2 rasterio>=1.3.6 requests==2.32.3 scikit-learn==1.5.1 -segmentation-models-pytorch==0.3.3 +segmentation-models-pytorch==0.3.4 setuptools==74.0.0 starlette==0.37.2 # not directly required, pinned by Dependabot to avoid a vulnerability. tabulate==0.9.0 tensorflow==2.17.0 tifffile==2024.1.30 -timm==0.9.2 +timm==0.9.7 torch==2.4.0 torcheval==0.0.7 torchgeo==0.5.2 diff --git a/requirements/requirements_dev.txt b/requirements/requirements_dev.txt index 11cf70d3f..d6d047b0e 100644 --- a/requirements/requirements_dev.txt +++ b/requirements/requirements_dev.txt @@ -38,7 +38,7 @@ pyyaml==6.0.2 rasterio>=1.3.6 requests==2.32.3 scikit-learn==1.5.1 -segmentation-models-pytorch==0.3.3 +segmentation-models-pytorch==0.3.4 setuptools==72.1.0 sphinx==7.4.4 sphinx-rtd-theme==2.0.0 @@ -46,7 +46,7 @@ starlette>=0.25.0 # not directly required, pinned by Dependabot to avoid a vulne tabulate==0.9.0 tensorflow==2.17.0 tifffile==2024.1.30 -timm==0.9.2 +timm==0.9.7 torch==2.4.0 torcheval==0.0.7 torchgeo==0.5.2 From 279a6df8ba1bd3f8f0cd7b7f4d186ace51268228 Mon Sep 17 00:00:00 2001 From: Harry Date: Fri, 30 Aug 2024 17:39:20 +0100 Subject: [PATCH 41/63] Minor dependency updates --- requirements/requirements.txt | 2 +- requirements/requirements_dev.txt | 6 +++--- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 4c480ce9a..cfc30729f 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -21,7 +21,7 @@ onnx2torch==1.5.15 opencv-python-headless==4.10.0.84 overload==1.1 pandas==2.2.2 -patch-ng==1.17.4 +patch-ng==1.18.0 protobuf>=3.19.5 # not directly required, pinned by Snyk to avoid a vulnerability. psutil==6.0.0 pyyaml==6.0.2 diff --git a/requirements/requirements_dev.txt b/requirements/requirements_dev.txt index d6d047b0e..075af7e78 100644 --- a/requirements/requirements_dev.txt +++ b/requirements/requirements_dev.txt @@ -17,7 +17,7 @@ lmdb==1.5.1 matplotlib==3.9.2 mlflow-skinny==2.16.0 mypy==1.11.2 -myst_parser==3.0.1 +myst_parser==4.0.0 nptyping==2.5.0 numba>=0.57.0 # not directly required but pinned to ensure Python 3.11 compatibility. numpy==1.26.4 @@ -26,7 +26,7 @@ onnx2torch==1.5.15 opencv-python-headless==4.10.0.84 overload==1.1 pandas==2.2.2 -patch-ng==1.17.4 +patch-ng==1.18.0 pre-commit==3.8.0 protobuf>=3.19.5 # not directly required, pinned by Snyk to avoid a vulnerability. psutil==6.0.0 @@ -53,7 +53,7 @@ torchgeo==0.5.2 torchinfo==1.8.0 torchvision==0.19.0 tornado>=6.3.3 -tox==4.16.0 +tox==4.18.0 tqdm==4.66.5 types-PyYAML==6.0.12.20240808 types-requests==2.32.0.20240712 From d623a6a3273eb1db7855aca3434d6ab40b33d2c8 Mon Sep 17 00:00:00 2001 From: Harry Date: Fri, 30 Aug 2024 17:55:17 +0100 Subject: [PATCH 42/63] Added `opencv-python` to `minerva` dependencies --- pyproject.toml | 1 + setup.cfg | 1 + 2 files changed, 2 insertions(+) diff --git a/pyproject.toml b/pyproject.toml index 7c84e03cb..196d81f65 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -38,6 +38,7 @@ dependencies = [ "numba>=0.57.0; python_version>='3.11'", "numpy", "overload", + "opencv-python", "pandas", "psutil", "pyyaml", diff --git a/setup.cfg b/setup.cfg index a48cea02c..9f22b5678 100644 --- a/setup.cfg +++ b/setup.cfg @@ -34,6 +34,7 @@ install_requires = psutil geopy overload + opencv-python nptyping lightly argcomplete From 0c4b8a39306f7f4b92161c7db56a46d135ea7523 Mon Sep 17 00:00:00 2001 From: Harry Date: Mon, 2 Sep 2024 12:16:38 +0100 Subject: [PATCH 43/63] Bump `torchgeo` --> `0.6.0` --- minerva/modelio.py | 43 ++++++++++++------------------- minerva/tasks/tsne.py | 3 ++- minerva/utils/utils.py | 27 +++++++++++++++++++ requirements/requirements.txt | 2 +- requirements/requirements_dev.txt | 2 +- 5 files changed, 48 insertions(+), 29 deletions(-) diff --git a/minerva/modelio.py b/minerva/modelio.py index 27e83ae74..e0a99b9d1 100644 --- a/minerva/modelio.py +++ b/minerva/modelio.py @@ -48,7 +48,11 @@ from torchgeo.datasets.utils import BoundingBox from minerva.models import MinervaModel -from minerva.utils.utils import check_substrings_in_string, mask_to_ohe +from minerva.utils.utils import ( + check_substrings_in_string, + get_sample_index, + mask_to_ohe, +) # ===================================================================================================================== @@ -70,7 +74,7 @@ def sup_tg( Args: batch (dict[~typing.Any, ~typing.Any]): Batch of data in a :class:`dict`. - Must have ``"image"``, ``"mask"``/ ``"label"`` and ``"bbox"`` / ``"id"`` keys. + Must have ``"image"``, ``"mask"``/ ``"label"`` and ``"bbox"`` / ``"bounds"`` / ``"id"`` keys. model (MinervaModel): Model being fitted. device (~torch.device): `torch` device object to send data to (e.g. CUDA device). train (bool): True to run a step of the model in training mode. False for eval mode. @@ -122,14 +126,10 @@ def sup_tg( # Runs a step of the epoch. loss, z = model.step(x, y, train=train) - # Get the indices of the batch. Either bounding boxes or filenames. - index: Optional[Union[Sequence[str], Sequence[BoundingBox]]] - if "bbox" in batch: - index = batch["bbox"] - elif "id" in batch: - index = batch["id"] - else: - index = None + # Get the indices of the batch. Either bounding boxes, filenames or index number. + index: Optional[Union[Sequence[str], Sequence[int], Sequence[BoundingBox]]] = ( + get_sample_index(batch) + ) return loss, z, y, index @@ -151,7 +151,7 @@ def autoencoder_io( Args: batch (dict[~typing.Any, ~typing.Any]): Batch of data in a :class:`dict`. - Must have ``"image"``, ``"mask"``/ ``"label"`` and ``"bbox"`` / ``"id"`` keys. + Must have ``"image"``, ``"mask"``/ ``"label"`` and ``"bbox"`` / ``"bounds"`` / ``"id"`` keys. model (MinervaModel): Model being fitted. device (~torch.device): `torch` device object to send data to (e.g. CUDA device). train (bool): True to run a step of the model in training mode. False for eval mode. @@ -227,14 +227,10 @@ def autoencoder_io( # Runs a step of the epoch. loss, z = model.step(x, y, train=train) - # Get the indices of the batch. Either bounding boxes or filenames. - index: Optional[Union[Sequence[str], Sequence[BoundingBox]]] - if "bbox" in batch: - index = batch["bbox"] - elif "id" in batch: - index = batch["id"] - else: - index = None + # Get the indices of the batch. Either bounding boxes, filenames or index number. + index: Optional[Union[Sequence[str], Sequence[int], Sequence[BoundingBox]]] = ( + get_sample_index(batch) + ) return loss, z, y, index @@ -255,7 +251,7 @@ def ssl_pair_tg( Args: batch (tuple[dict[str, ~typing.Any], dict[str, ~typing.Any]]): Pair of batches of data in :class:`dict` (s). - Must have ``"image"`` and ``"bbox"`` keys. + Must have ``"image"`` and ``"bbox"`` / ``"bounds"`` / ``"id"`` keys. model (MinervaModel): Model being fitted. device (~torch.device): :mod:`torch` device object to send data to (e.g. ``CUDA`` device). train (bool): True to run a step of the model in training mode. False for eval mode. @@ -293,12 +289,7 @@ def ssl_pair_tg( # Runs a step of the epoch. loss, z = model.step(x, train=train) - if "bbox" in batch[0].keys(): - return loss, z, None, batch[0]["bbox"] + batch[1]["bbox"] - elif "id" in batch[0].keys(): - return loss, z, None, batch[0]["id"] + batch[1]["id"] - else: - return loss, z, None, None + return loss, z, get_sample_index(batch[0]) + get_sample_index(batch[1]) def _determine_float_dtype(device: torch.device, mix_precision: bool) -> torch.dtype: diff --git a/minerva/tasks/tsne.py b/minerva/tasks/tsne.py index 3ab4f9ea4..a10de5785 100644 --- a/minerva/tasks/tsne.py +++ b/minerva/tasks/tsne.py @@ -46,6 +46,7 @@ from wandb.sdk.wandb_run import Run from minerva.models import MinervaDataParallel, MinervaModel +from minerva.utils.utils import get_sample_index from minerva.utils.visutils import plot_embedding from .core import MinervaTask @@ -119,7 +120,7 @@ def step(self) -> None: plot_embedding( embeddings.detach().cpu(), - data["bbox"], + get_sample_index(data), self.global_params["data_root"], self.params["dataset_params"], show=True, diff --git a/minerva/utils/utils.py b/minerva/utils/utils.py index b8c563867..c5fe98147 100644 --- a/minerva/utils/utils.py +++ b/minerva/utils/utils.py @@ -1958,3 +1958,30 @@ def closest_factors(n): best_pair = (best_pair[1], best_pair[0]) return best_pair + + +def get_sample_index(sample: Dict[str, Any]) -> Optional[Any]: + """Get the index for a sample with unkown index key. + + Will try: + * ``bbox`` (:mod:`torchgeo` < 0.6.0) for :class:~`torchgeo.datasets.GeoDataset` + * ``bounds`` (:mod:`torchgeo` >= 0.6.0) for :class:~`torchgeo.datasets.GeoDataset` + * ``id`` for :class:~`torchgeo.datasets.NonGeoDataset` + + Args: + sample (dict[str, ~typing.Any]): Sample dictionary to find index in. + + Returns: + None | ~typing.Any: Sample index or ``None`` if not found. + + """ + if "bbox" in sample: + index = sample["bbox"] + elif "bounds" in sample: + index = sample["bounds"] + elif "id" in sample: + index = sample["id"] + else: + index = None + + return index diff --git a/requirements/requirements.txt b/requirements/requirements.txt index cfc30729f..e1de41ede 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -37,7 +37,7 @@ tifffile==2024.1.30 timm==0.9.7 torch==2.4.0 torcheval==0.0.7 -torchgeo==0.5.2 +torchgeo==0.6.0 torchinfo==1.8.0 torchvision==0.19.0 tornado>=6.3.3 diff --git a/requirements/requirements_dev.txt b/requirements/requirements_dev.txt index 075af7e78..e9e00a78f 100644 --- a/requirements/requirements_dev.txt +++ b/requirements/requirements_dev.txt @@ -49,7 +49,7 @@ tifffile==2024.1.30 timm==0.9.7 torch==2.4.0 torcheval==0.0.7 -torchgeo==0.5.2 +torchgeo==0.6.0 torchinfo==1.8.0 torchvision==0.19.0 tornado>=6.3.3 From 7a85297b6389053590ce1917bfd0b2c40a130e42 Mon Sep 17 00:00:00 2001 From: Harry Date: Mon, 2 Sep 2024 13:53:50 +0100 Subject: [PATCH 44/63] Account for case where index is not found --- minerva/modelio.py | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/minerva/modelio.py b/minerva/modelio.py index e0a99b9d1..1ccd2e072 100644 --- a/minerva/modelio.py +++ b/minerva/modelio.py @@ -289,7 +289,12 @@ def ssl_pair_tg( # Runs a step of the epoch. loss, z = model.step(x, train=train) - return loss, z, get_sample_index(batch[0]) + get_sample_index(batch[1]) + index_0, index_1 = get_sample_index(batch[0]), get_sample_index(batch[1]) + + if index_0 is None or index_1 is None: + return loss, z, None, None + else: + return loss, z, None, index_0 + index_1 def _determine_float_dtype(device: torch.device, mix_precision: bool) -> torch.dtype: From 2145bcd721f26fc8b7a48875f847c02559a2d414 Mon Sep 17 00:00:00 2001 From: Harry Date: Mon, 2 Sep 2024 14:10:58 +0100 Subject: [PATCH 45/63] Updated example for new `OSCD` signature --- .../inbuilt_cfgs/example_ChangeDetector.yaml | 26 ++----------------- 1 file changed, 2 insertions(+), 24 deletions(-) diff --git a/minerva/inbuilt_cfgs/example_ChangeDetector.yaml b/minerva/inbuilt_cfgs/example_ChangeDetector.yaml index 8ad281681..c62b0a999 100644 --- a/minerva/inbuilt_cfgs/example_ChangeDetector.yaml +++ b/minerva/inbuilt_cfgs/example_ChangeDetector.yaml @@ -116,22 +116,11 @@ tasks: Normalise: _target_: minerva.transforms.Normalise norm_value: 4095 - SelectChannels: - _target_: minerva.transforms.SelectChannels - channels: - - 1 - - 2 - - 3 - - 7 - - 14 - - 15 - - 16 - - 20 _target_: torchgeo.datasets.OSCD root: OSCD split: train - bands: all + bands: [B02, B03, B04, B08] download: true mask: @@ -165,22 +154,11 @@ tasks: Normalise: _target_: minerva.transforms.Normalise norm_value: 4095 - SelectChannels: - _target_: minerva.transforms.SelectChannels - channels: - - 1 - - 2 - - 3 - - 7 - - 14 - - 15 - - 16 - - 20 _target_: torchgeo.datasets.OSCD root: OSCD split: test - bands: all + bands: [B02, B03, B04, B08] download: true mask: From 91238b8ea1ec0df9236ebb4e161ce3db635dba70 Mon Sep 17 00:00:00 2001 From: Harry Date: Mon, 2 Sep 2024 17:27:14 +0100 Subject: [PATCH 46/63] Update `n_classes` only if `elim=True` and `train=True` for a task --- minerva/tasks/core.py | 2 +- minerva/trainer.py | 15 +++++++++++++-- 2 files changed, 14 insertions(+), 3 deletions(-) diff --git a/minerva/tasks/core.py b/minerva/tasks/core.py index c27027456..5bd157a00 100644 --- a/minerva/tasks/core.py +++ b/minerva/tasks/core.py @@ -262,7 +262,7 @@ def __init__( self.model.to(self.device) # To eliminate classes, we're going to have to do a fair bit of rebuilding of the model... - if self.elim: + if self.elim and self.train: # Update the stored number of classes within the model and # then rebuild the classification layers that are dependent on the number of classes. self.model.update_n_classes(self.n_classes) diff --git a/minerva/trainer.py b/minerva/trainer.py index c98327322..3ca9f3384 100644 --- a/minerva/trainer.py +++ b/minerva/trainer.py @@ -638,7 +638,9 @@ def fit(self) -> None: **self.params, ) - if tasks[mode].params.get("elim", False): + # Update the number of classes from the task + # if class elimination and training is active in the task. + if tasks[mode].elim and tasks[mode].train: self.params["n_classes"] = tasks[mode].n_classes while self.epoch_no < self.max_epochs: @@ -667,7 +669,11 @@ def fit(self) -> None: # Print epoch results. if self.gpu == 0: tasks[mode].print_epoch_results(self.epoch_no - 1) - if not self.stopper and self.checkpoint_experiment: + if ( + not self.stopper + and self.checkpoint_experiment + and utils.check_substrings_in_string(mode, "train") + ): self.save_checkpoint() # Update Trainer's copy of the model from the training task. @@ -874,6 +880,11 @@ def load_checkpoint(self) -> None: if self.model.scheduler is not None and "scheduler_state_dict" in checkpoint: self.model.scheduler.load_state_dict(checkpoint["scheduler_state_dict"]) + # Calculate the output dimensions of the model. + self.model.determine_output_dim( + sample_pairs=self.sample_pairs, change_detection=self.change_detection + ) + # Transfer to GPU. self.model.to(self.device) From 54bd2989aca98ca0d945ff55fb98ac881966e48b Mon Sep 17 00:00:00 2001 From: Harry Date: Mon, 2 Sep 2024 17:29:23 +0100 Subject: [PATCH 47/63] Added `compare_models` function --- minerva/utils/utils.py | 37 ++++++++++++++++++++++++++++++++++--- 1 file changed, 34 insertions(+), 3 deletions(-) diff --git a/minerva/utils/utils.py b/minerva/utils/utils.py index c5fe98147..61a51a8dd 100644 --- a/minerva/utils/utils.py +++ b/minerva/utils/utils.py @@ -1964,9 +1964,9 @@ def get_sample_index(sample: Dict[str, Any]) -> Optional[Any]: """Get the index for a sample with unkown index key. Will try: - * ``bbox`` (:mod:`torchgeo` < 0.6.0) for :class:~`torchgeo.datasets.GeoDataset` - * ``bounds`` (:mod:`torchgeo` >= 0.6.0) for :class:~`torchgeo.datasets.GeoDataset` - * ``id`` for :class:~`torchgeo.datasets.NonGeoDataset` + * ``bbox`` (:mod:`torchgeo` < 0.6.0) for :class:`~torchgeo.datasets.GeoDataset` + * ``bounds`` (:mod:`torchgeo` >= 0.6.0) for :class:`~torchgeo.datasets.GeoDataset` + * ``id`` for :class:`~torchgeo.datasets.NonGeoDataset` Args: sample (dict[str, ~typing.Any]): Sample dictionary to find index in. @@ -1974,6 +1974,7 @@ def get_sample_index(sample: Dict[str, Any]) -> Optional[Any]: Returns: None | ~typing.Any: Sample index or ``None`` if not found. + .. versionadded:: 0.28 """ if "bbox" in sample: index = sample["bbox"] @@ -1985,3 +1986,33 @@ def get_sample_index(sample: Dict[str, Any]) -> Optional[Any]: index = None return index + + +def compare_models(model_1: Module, model_2: Module) -> None: + """Compare two models weight-by-weight. + + Source: https://discuss.pytorch.org/t/check-if-models-have-same-weights/4351/5 + + Args: + model_1 (torch.nn.Module): First model. + model_2 (torch.nn.Module): Second model. + + Raises: + AssertionError: If models do not match exactly. + + .. versionadded:: 0.28 + """ + models_differ = 0 + for key_item_1, key_item_2 in zip( + model_1.state_dict().items(), model_2.state_dict().items() + ): + if torch.equal(key_item_1[1], key_item_2[1]): + pass + else: + models_differ += 1 + if key_item_1[0] == key_item_2[0]: + print("Mismtach found at", key_item_1[0]) + else: + raise AssertionError + if models_differ == 0: + print("Models match perfectly! :)") From d3e9d1bf9eb2e32f0bbe86dc53a3d89d6e525c53 Mon Sep 17 00:00:00 2001 From: Harry Date: Wed, 4 Sep 2024 00:49:22 +0100 Subject: [PATCH 48/63] Construct missing paths for datasets --- minerva/utils/utils.py | 9 ++++++++- 1 file changed, 8 insertions(+), 1 deletion(-) diff --git a/minerva/utils/utils.py b/minerva/utils/utils.py index 61a51a8dd..b849ec46c 100644 --- a/minerva/utils/utils.py +++ b/minerva/utils/utils.py @@ -1889,7 +1889,14 @@ def compile_dataset_paths( for path in out_paths: compiled_paths.extend(glob.glob(str(path), recursive=True)) - # For each path, get the absolute path, convert to string and return. + # Convert to paths + raw_paths = [Path(path) for path in compiled_paths] + + # Check if each path exists. If not, make the path. + for path in raw_paths: + path.mkdir(parents=True, exist_ok=True) + + # For each path, get the absolute path, make the path if it does not exist then convert to string and return. return [str(Path(path).absolute()) for path in compiled_paths] From 4efc7d5b0e4afd48fcecb43a0bca2771875de0c9 Mon Sep 17 00:00:00 2001 From: Harry Date: Wed, 4 Sep 2024 14:00:02 +0100 Subject: [PATCH 49/63] Fixed change detection handling for `torchgeo==0.6.0` --- minerva/datasets/factory.py | 50 ++++++++++-- .../inbuilt_cfgs/example_ChangeDetector.yaml | 3 +- minerva/modelio.py | 80 +++++++++++++++++++ minerva/models/change_detector.py | 4 +- minerva/models/core.py | 5 +- minerva/tasks/core.py | 3 + minerva/trainer.py | 5 +- minerva/transforms.py | 66 ++++++++++----- 8 files changed, 177 insertions(+), 39 deletions(-) diff --git a/minerva/datasets/factory.py b/minerva/datasets/factory.py index dc76651a7..0c959af00 100644 --- a/minerva/datasets/factory.py +++ b/minerva/datasets/factory.py @@ -243,7 +243,11 @@ def get_subdataset( if auto_norm: if isinstance(sub_dataset, RasterDataset): - init_auto_norm(sub_dataset, length=sub_dataset_params.get("length"), roi=sub_dataset_params.get("roi")) + init_auto_norm( + sub_dataset, + length=sub_dataset_params.get("length"), + roi=sub_dataset_params.get("roi"), + ) else: raise TypeError( # pragma: no cover "AutoNorm only supports normalisation of data " @@ -257,6 +261,7 @@ def make_dataset( data_directory: Union[Iterable[str], str, Path], dataset_params: Dict[Any, Any], sample_pairs: bool = False, + change_detection: bool = False, cache: bool = True, cache_dir: Union[str, Path] = "", ) -> Tuple[Any, List[Any]]: @@ -268,6 +273,8 @@ def make_dataset( dataset_params (dict[~typing.Any, ~typing.Any]): Dictionary of parameters defining each sub-datasets to be used. sample_pairs (bool): Optional; ``True`` if paired sampling. This will ensure paired samples are handled correctly in the datasets. + change_detection (bool): Flag for a change detection dataset which has + ``"image1"`` and ``"image2"`` keys rather than ``"image"``. cache (bool): Cache the dataset or load from cache if pre-existing. Defaults to True. cache_dir (str | ~pathlib.Path): Path to the directory to save the cached dataset (if ``cache==True``). Defaults to CWD. @@ -343,7 +350,9 @@ def make_dataset( else: transform_params = False - master_transforms = make_transformations(transform_params) + master_transforms = make_transformations( + transform_params, change_detection=change_detection + ) # Assuming that these keys are names of datasets. elif sub_type_key == "subdatasets": @@ -357,7 +366,9 @@ def make_dataset( else: transform_params = False - transformations = make_transformations({type_key: transform_params}) + transformations = make_transformations( + {type_key: transform_params}, change_detection=change_detection + ) # Send the params for this area key back through this function to make the sub-dataset. for area_key in type_dataset_params[sub_type_key]: @@ -413,7 +424,9 @@ def make_dataset( if add_target_transforms is not None: target_key = masks_or_labels(dataset_params) - target_transforms = make_transformations({target_key: add_target_transforms}) + target_transforms = make_transformations( + {target_key: add_target_transforms}, change_detection=change_detection + ) if hasattr(dataset, "transforms"): if isinstance(dataset.transforms, MinervaCompose): @@ -428,7 +441,8 @@ def make_dataset( if add_multi_modal_transforms is not None: multi_modal_transforms = make_transformations( - {"both": add_multi_modal_transforms} + {"both": add_multi_modal_transforms}, + change_detection=change_detection, ) if hasattr(dataset, "transforms"): if isinstance(dataset.transforms, MinervaCompose): @@ -454,6 +468,7 @@ def construct_dataloader( rank: int = 0, world_size: int = 1, sample_pairs: bool = False, + change_detection: bool = False, cache: bool = True, cache_dir: Union[Path, str] = "", ) -> DataLoader[Iterable[Any]]: @@ -474,6 +489,8 @@ def construct_dataloader( world_size (int): Optional; The total number of processes within a distributed run. sample_pairs (bool): Optional; True if paired sampling. This will wrap the collation function for paired samples. + change_detection (bool): Flag for if using a change detection dataset which has + ``"image1"`` and ``"image2"`` keys rather than ``"image"``. Returns: ~torch.utils.data.DataLoader: Object to handle the returning of batched samples from the dataset. @@ -482,6 +499,7 @@ def construct_dataloader( data_directory, dataset_params, sample_pairs=sample_pairs, + change_detection=change_detection, cache=cache, cache_dir=cache_dir, ) @@ -570,6 +588,7 @@ def _make_loader( model_type, elim, sample_pairs, + change_detection, cache, ): target_key = None @@ -593,6 +612,7 @@ def _make_loader( rank=rank, world_size=world_size, sample_pairs=sample_pairs, + change_detection=change_detection, cache=cache, cache_dir=cache_dir, ) @@ -699,8 +719,10 @@ def make_loaders( sample_pairs: Union[bool, Any] = utils.fallback_params( "sample_pairs", task_params, params, False ) - if not isinstance(sample_pairs, bool): # pragma: no cover - sample_pairs = False + + change_detection: Union[bool, Any] = utils.fallback_params( + "change_detection", task_params, params, False + ) elim = utils.fallback_params("elim", task_params, params, False) cache = utils.fallback_params("cache_dataset", task_params, params, True) @@ -724,6 +746,7 @@ def make_loaders( sampler_params, dataloader_params, collator_target, + change_detection=change_detection, elim=elim, ) @@ -742,6 +765,7 @@ def make_loaders( model_type, elim=elim, sample_pairs=sample_pairs, + change_detection=change_detection, cache=cache, ) @@ -767,6 +791,7 @@ def make_loaders( mode_sampler_params, dataloader_params, collator_target, + change_detection=change_detection, elim=elim, ) @@ -786,6 +811,7 @@ def make_loaders( model_type, elim=elim, sample_pairs=sample_pairs if mode == "train" else False, + change_detection=change_detection, cache=cache, ) @@ -837,6 +863,7 @@ def get_data_specs( sampler_params: Optional[Dict[str, Any]] = None, dataloader_params: Optional[Dict[str, Any]] = None, collator_target: str = "torchgeo.datasets.stack_samples", + change_detection: bool = False, elim: bool = True, ): # Load manifest from cache for this dataset. @@ -847,6 +874,7 @@ def get_data_specs( sampler_params, dataloader_params, collator_target=collator_target, + change_detection=change_detection, ) class_dist = utils.modes_from_manifest(manifest, classes) @@ -874,6 +902,7 @@ def get_manifest( sampler_params: Optional[Dict[str, Any]] = None, loader_params: Optional[Dict[str, Any]] = None, collator_target: str = "torchgeo.datasets.stack_samples", + change_detection: bool = False, ) -> DataFrame: """Attempts to return the :class:`~pandas.DataFrame` located at ``manifest_path``. @@ -893,6 +922,8 @@ def get_manifest( manifest_path (str | ~pathlib.Path): Path (including filename and extension) to the manifest saved as a ``csv``. task_name (str): Optional; Name of the task to which the dataset to create a manifest of belongs to. + change_detection (bool): Flag for if using a change detection dataset which has + ``"image1"`` and ``"image2"`` keys rather than ``"image"``. Returns: ~pandas.DataFrame: Manifest either loaded from ``manifest_path`` or created from parameters in :data:`CONFIG`. @@ -915,6 +946,7 @@ def get_manifest( sampler_params, loader_params, collator_target=collator_target, + change_detection=change_detection, ) print(f"MANIFEST TO FILE -----> {manifest_path}") @@ -933,6 +965,7 @@ def make_manifest( sampler_params: Dict[str, Any], loader_params: Dict[str, Any], collator_target: str = "torchgeo.datasets.stack_samples", + change_detection: bool = False, ) -> DataFrame: """Constructs a manifest of the dataset detailing each sample therein. @@ -941,6 +974,8 @@ def make_manifest( Args: mf_config (dict[~typing.Any, ~typing.Any]): Config to use to construct the manifest with. task_name (str): Optional; Name of the task to which the dataset to create a manifest of belongs to. + change_detection (bool): Flag for if using a change detection dataset which has + ``"image1"`` and ``"image2"`` keys rather than ``"image"``. Returns: ~pandas.DataFrame: The completed manifest as a :class:`~pandas.DataFrame`. @@ -1007,6 +1042,7 @@ def delete_transforms(params: Dict[str, Any]) -> None: loader_params, batch_size=1, # To prevent issues with stacking different sized patches, set batch size to 1. collator_target=collator_target, + change_detection=change_detection, cache=False, ) diff --git a/minerva/inbuilt_cfgs/example_ChangeDetector.yaml b/minerva/inbuilt_cfgs/example_ChangeDetector.yaml index c62b0a999..d1fca43bd 100644 --- a/minerva/inbuilt_cfgs/example_ChangeDetector.yaml +++ b/minerva/inbuilt_cfgs/example_ChangeDetector.yaml @@ -34,6 +34,7 @@ pre_train: false # Activate pre-training mode. fine_tune: false # Activate fine-tuning mode. torch_compile: false # Wrap model in `torch.compile`. sample_pairs: false +change_detection: true # ---+ Loss and Optimisers +--------------------------------------------------- loss_func: CrossEntropyLoss # Name of the loss function to use. @@ -82,7 +83,7 @@ wandb_dir: /test/tmp/wandb # Directory to store wandb logs locally. # === MODEL IO & LOGGING ====================================================== # ---+ Minerva Inbuilt Logging Functions +------------------------------------- - +model_io: change_detection record_int: true # Store integer results in memory. record_float: false # Store floating point results too. Beware memory overload! diff --git a/minerva/modelio.py b/minerva/modelio.py index 1ccd2e072..c238283e0 100644 --- a/minerva/modelio.py +++ b/minerva/modelio.py @@ -134,6 +134,86 @@ def sup_tg( return loss, z, y, index +def change_detection( + batch: Dict[Any, Any], + model: MinervaModel, + device: torch.device, # type: ignore[name-defined] + train: bool, + **kwargs, +) -> Tuple[ + Tensor, + Union[Tensor, Tuple[Tensor, ...]], + Tensor, + Optional[Union[Sequence[str], Sequence[BoundingBox]]], +]: + """Provides IO functionality for a change_detection model. + + Args: + batch (dict[~typing.Any, ~typing.Any]): Batch of data in a :class:`dict`. + Must have ``"image1"``, ``"image2"``, ``"mask"``/ ``"label"`` and ``"bbox"`` / ``"bounds"`` / ``"id"`` keys. + model (MinervaModel): Model being fitted. + device (~torch.device): `torch` device object to send data to (e.g. CUDA device). + train (bool): True to run a step of the model in training mode. False for eval mode. + + Kwargs: + mix_precision (bool): Use mixed-precision. Will set the floating tensors to 16-bit + rather than the default 32-bit. + target_key (str): Should be either ``"mask"`` or ``"label"``. + + Returns: + tuple[ + ~torch.Tensor, ~torch.Tensor, ~torch.Tensor, ~typing.Sequence[~torchgeo.datasets.utils.BoundingBox] | None + ]: + The ``loss``, the model output ``z``, the ground truth ``y`` supplied and the bounding boxes + of the input images supplied. + """ + float_dtype = _determine_float_dtype(device, kwargs.get("mix_precision", False)) + target_key = kwargs.get("target_key", "mask") + + model_type = kwargs.get("model_type", "") + multilabel = True if check_substrings_in_string(model_type, "multilabel") else False + + # Extracts the x and y batches from the dict. + x1: Tensor = batch["image1"] + x2: Tensor = batch["image2"] + y: Tensor = batch[target_key] + + # Check that none of the data is NaN or infinity. + if kwargs.get("validate_variables", False): + assert not x1.isnan().any() + assert not x1.isinf().any() + assert not x2.isnan().any() + assert not x2.isinf().any() + assert not y.isnan().any() + assert not y.isinf().any() + + # Re-arranges the x and y batches. + x1 = x1.to(float_dtype) # type: ignore[attr-defined] + x2 = x2.to(float_dtype) # type: ignore[attr-defined] + + # Squeeze out axis 1 if only 1 element wide. + if target_key == "mask": + y = y.squeeze() + + if isinstance(y, Tensor): + y = y.detach().cpu() + y = y.to(dtype=torch.float if multilabel else torch.long) # type: ignore[attr-defined] + + # Transfer to GPU. + x = torch.stack([x1, x2]).to(device) + y = y.to(device) + + # Runs a step of the epoch. + loss, z = model.step(x, y, train=train) + + # Get the indices of the batch. Either bounding boxes, filenames or index number. + index: Optional[Union[Sequence[str], Sequence[int], Sequence[BoundingBox]]] = ( + get_sample_index(batch) + ) + + return loss, z, y, index + + def autoencoder_io( batch: Dict[Any, Any], model: MinervaModel, diff --git a/minerva/models/change_detector.py b/minerva/models/change_detector.py index 5ece466a5..b1f2fe8d7 100644 --- a/minerva/models/change_detector.py +++ b/minerva/models/change_detector.py @@ -128,8 +128,7 @@ def forward(self, x: Tensor) -> Tensor: ~torch.Tensor: Likelihoods the network places on the input ``x`` being of each class. """ - x = torch.squeeze(x) - x_0, x_1 = torch.chunk(x, 2, dim=1) + x_0, x_1 = x[0], x[1] f_0 = self.backbone(x_0) f_1 = self.backbone(x_1) @@ -140,7 +139,6 @@ def forward(self, x: Tensor) -> Tensor: f = torch.cat((f_0, f_1), 1) - # f = f.view(f.size(0), -1) z: Tensor = self.classification_head(f) assert isinstance(z, Tensor) diff --git a/minerva/models/core.py b/minerva/models/core.py index f06513979..317dd4bd8 100644 --- a/minerva/models/core.py +++ b/minerva/models/core.py @@ -542,12 +542,9 @@ def get_output_shape( if not hasattr(_image_dim, "__len__"): assert isinstance(_image_dim, int) random_input = torch.rand([4, _image_dim]) - elif sample_pairs: + elif sample_pairs or change_detection: assert isinstance(_image_dim, Iterable) random_input = torch.rand([2, 4, *_image_dim]) - elif change_detection: - assert isinstance(_image_dim, Iterable) - random_input = torch.rand([4, 2 * _image_dim[0], *_image_dim[1:]]) else: assert isinstance(_image_dim, Iterable) random_input = torch.rand([4, *_image_dim]) diff --git a/minerva/tasks/core.py b/minerva/tasks/core.py index 5bd157a00..bb08751d1 100644 --- a/minerva/tasks/core.py +++ b/minerva/tasks/core.py @@ -229,6 +229,9 @@ def __init__( self.sample_pairs = fallback_params( "sample_pairs", self.params, self.global_params ) + self.change_detection = fallback_params( + "change_detection", self.params, self.global_params + ) self.n_classes = fallback_params("n_classes", self.params, self.global_params) diff --git a/minerva/trainer.py b/minerva/trainer.py index 3ca9f3384..fa6686343 100644 --- a/minerva/trainer.py +++ b/minerva/trainer.py @@ -468,12 +468,9 @@ def get_input_size(self) -> Tuple[int, ...]: assert input_shape is not None input_size: Tuple[int, ...] = (self.batch_size, *input_shape) - if self.sample_pairs: + if self.sample_pairs or self.change_detection: input_size = (2, *input_size) - if self.change_detection: - input_size = (input_size[0], 2 * input_size[1], *input_size[2:]) - return input_size def get_model_cache_path(self) -> Path: diff --git a/minerva/transforms.py b/minerva/transforms.py index 42120bd38..7426e8fa5 100644 --- a/minerva/transforms.py +++ b/minerva/transforms.py @@ -82,16 +82,12 @@ ConvertImageDtype, InterpolationMode, Normalize, - Resize, RandomApply, + Resize, ) from torchvision.transforms.v2 import functional as ft -from minerva.utils.utils import ( - find_tensor_mode, - get_centre_pixel_value, - mask_transform, -) +from minerva.utils.utils import find_tensor_mode, get_centre_pixel_value, mask_transform # ===================================================================================================================== @@ -502,8 +498,8 @@ class MinervaCompose: Args: transforms (~typing.Sequence[~typing.Callable[..., ~typing.Any]] | ~typing.Callable[..., ~typing.Any]): List of transforms to compose. - key (str): Optional; For use with :mod:`torchgeo` samples and must be assigned a value if using. - The key of the data type in the sample dict to transform. + change_detection (bool): Flag for if transforming a change detection dataset which has + ``"image1"`` and ``"image2"`` keys rather than ``"image"``. Example: >>> transforms.MinervaCompose([ @@ -520,16 +516,23 @@ def __init__( Callable[..., Any], Dict[str, Union[List[Callable[..., Any]], Callable[..., Any]]], ], + change_detection: bool = False, ) -> None: self.transforms: Union[ List[Callable[..., Any]], Dict[str, List[Callable[..., Any]]] ] + self.change_detection = change_detection + if isinstance(transforms, Sequence): self.transforms = list(transforms) elif callable(transforms): self.transforms = [transforms] elif isinstance(transforms, dict): + if self.change_detection and "image" in transforms: + transforms["image1"] = transforms["image"] + transforms["image2"] = transforms["image"] + del transforms["image"] self.transforms = transforms # type: ignore[assignment] assert isinstance(self.transforms, dict) for key in transforms.keys(): @@ -564,11 +567,24 @@ def __call__( # Assumes the keys must be "image" and "mask" # We need to apply these first before applying any seperate transforms for modalities. if "both" in self.transforms: + if self.change_detection: + # Transform images1 with new random states (if applicable). + sample["image1"] = self._transform_input( + sample["image1"], self.transforms["both"] + ) - # Transform images with new random states (if applicable). - sample["image"] = self._transform_input( - sample["image"], self.transforms["both"] - ) + # Transform images1 with new random states (if applicable). + sample["image2"] = self._transform_input( + sample["image2"], + self.transforms["both"], + reapply=True, + ) + + else: + # Transform images with new random states (if applicable). + sample["image"] = self._transform_input( + sample["image"], self.transforms["both"] + ) # We'll have to convert the masks to float for these transforms to work # so need to store the current dtype to cast back to after. @@ -816,7 +832,13 @@ class MaskResize(Resize): .. versionadded:: 0.28 """ - def __init__(self, size, interpolation: str = "NEAREST", max_size=None, antialias: bool = True) -> None: + def __init__( + self, + size, + interpolation: str = "NEAREST", + max_size=None, + antialias: bool = True, + ) -> None: interpolation_mode = getattr(InterpolationMode, interpolation) super().__init__(size, interpolation_mode, max_size, antialias) @@ -848,7 +870,7 @@ def forward(self, img: Tensor) -> Tensor: return torch.squeeze(super().forward(torch.reshape(img, tmp_shape))) -class AdjustGamma(): +class AdjustGamma: """Callable version of :meth:`torchvision.transforms.functional.adjust_gamma` Args: @@ -857,6 +879,7 @@ class AdjustGamma(): .. versionadded:: 0.28 """ + def __init__(self, gamma: float = 1.0, gain: float = 1.0) -> None: self.gamma = gamma self.gain = gain @@ -949,7 +972,8 @@ def get_transform(transform_params: Dict[str, Any]) -> Callable[..., Any]: def make_transformations( - transform_params: Union[Dict[str, Any], Literal[False]] + transform_params: Union[Dict[str, Any], Literal[False]], + change_detection: bool = False, ) -> Optional[MinervaCompose]: """Constructs a transform or series of transforms based on parameters provided. @@ -957,6 +981,8 @@ def make_transformations( transform_params (dict[str, ~typing.Any] | ~typing.Literal[False]): Parameters defining transforms desired. The name of each transform should be the key, while the kwargs for the transform should be the value of that key as a dict. + change_detection (bool): Flag for if transforming a change detection dataset which has + ``"image1"`` and ``"image2"`` keys rather than ``"image"``. Example: >>> transform_params = { @@ -985,9 +1011,7 @@ def construct(type_transform_params: Dict[str, Any]) -> List[Callable[..., Any]] continue else: - type_transformations.append( - get_transform(type_transform_params[_name]) - ) + type_transformations.append(get_transform(type_transform_params[_name])) return type_transformations @@ -1006,6 +1030,8 @@ def construct(type_transform_params: Dict[str, Any]) -> List[Callable[..., Any]] else: transformations[name] = construct(transform_params[name]) else: - return MinervaCompose(construct(transform_params)) + return MinervaCompose( + construct(transform_params), change_detection=change_detection + ) - return MinervaCompose(transformations) + return MinervaCompose(transformations, change_detection=change_detection) From f88546c8c125a6ca331ac4db3839e3d9905026c6 Mon Sep 17 00:00:00 2001 From: Harry Baker Date: Wed, 4 Sep 2024 14:20:15 +0100 Subject: [PATCH 50/63] Renamed `modelio` functions to clarify their purpose --- minerva/inbuilt_cfgs/example_3rd_party.yaml | 5 ----- minerva/inbuilt_cfgs/example_CNN_config.yaml | 2 +- minerva/inbuilt_cfgs/example_ChangeDetector.yaml | 2 +- minerva/inbuilt_cfgs/example_GSConvNet-II.yaml | 6 +++--- minerva/inbuilt_cfgs/example_GeoCLR_config.yaml | 6 +++--- minerva/inbuilt_cfgs/example_GeoSimConvNet.yaml | 6 +++--- minerva/inbuilt_cfgs/example_UNetR_config.yaml | 6 ------ minerva/inbuilt_cfgs/example_config.yaml | 4 ++-- minerva/modelio.py | 12 +++++++----- minerva/tasks/core.py | 2 +- tests/test_logging.py | 6 +++--- tests/test_modelio.py | 10 +++++----- 12 files changed, 29 insertions(+), 38 deletions(-) diff --git a/minerva/inbuilt_cfgs/example_3rd_party.yaml b/minerva/inbuilt_cfgs/example_3rd_party.yaml index 258953fb0..1dcd35933 100644 --- a/minerva/inbuilt_cfgs/example_3rd_party.yaml +++ b/minerva/inbuilt_cfgs/example_3rd_party.yaml @@ -67,11 +67,6 @@ wandb_log: true # Activates wandb logging. project: pytest # Define the project name for wandb. wandb_dir: /test/tmp/wandb # Directory to store wandb logs locally. -# ---+ Minerva Inbuilt Logging Functions +------------------------------------- -# task_logger: SupervisedTaskLogger -# step_logger: SupervisedGeoStepLogger -# model_io: sup_tg - record_int: true # Store integer results in memory. record_float: true # Store floating point results too. Beware memory overload! diff --git a/minerva/inbuilt_cfgs/example_CNN_config.yaml b/minerva/inbuilt_cfgs/example_CNN_config.yaml index 899c36d2d..459ef1f42 100644 --- a/minerva/inbuilt_cfgs/example_CNN_config.yaml +++ b/minerva/inbuilt_cfgs/example_CNN_config.yaml @@ -67,7 +67,7 @@ wandb_dir: /test/tmp/wandb # Directory to store wandb logs locally. # === MODEL IO & LOGGING ====================================================== # ---+ Minerva Inbuilt Logging Functions +------------------------------------- task_logger: SupervisedTaskLogger -model_io: sup_tg +model_io: supervised_torchgeo_io record_int: true # Store integer results in memory. record_float: false # Store floating point results too. Beware memory overload! diff --git a/minerva/inbuilt_cfgs/example_ChangeDetector.yaml b/minerva/inbuilt_cfgs/example_ChangeDetector.yaml index d1fca43bd..dc752ba89 100644 --- a/minerva/inbuilt_cfgs/example_ChangeDetector.yaml +++ b/minerva/inbuilt_cfgs/example_ChangeDetector.yaml @@ -83,7 +83,7 @@ wandb_dir: /test/tmp/wandb # Directory to store wandb logs locally. # === MODEL IO & LOGGING ====================================================== # ---+ Minerva Inbuilt Logging Functions +------------------------------------- -model_io: change_detection +model_io: change_detection_io record_int: true # Store integer results in memory. record_float: false # Store floating point results too. Beware memory overload! diff --git a/minerva/inbuilt_cfgs/example_GSConvNet-II.yaml b/minerva/inbuilt_cfgs/example_GSConvNet-II.yaml index 474fca398..6d6bfe40a 100644 --- a/minerva/inbuilt_cfgs/example_GSConvNet-II.yaml +++ b/minerva/inbuilt_cfgs/example_GSConvNet-II.yaml @@ -103,7 +103,7 @@ tasks: # ---+ Minerva Inbuilt Logging Functions +------------------------- task_logger: SSLTaskLogger - model_io: ssl_pair_tg + model_io: ssl_pair_torchgeo_io # ---+ Dataset Parameters +---------------------------------------- dataset_params: @@ -167,7 +167,7 @@ tasks: task_logger: SSLTaskLogger step_logger: name: KNNStepLogger - model_io: sup_tg + model_io: supervised_torchgeo_io # ---+ Dataset Parameters +---------------------------------------- dataset_params: @@ -203,7 +203,7 @@ tasks: # ---+ Minerva Inbuilt Logging Functions +------------------------- task_logger: SupervisedTaskLogger - model_io: sup_tg + model_io: supervised_torchgeo_io # ---+ Dataset Parameters +---------------------------------------- dataset_params: diff --git a/minerva/inbuilt_cfgs/example_GeoCLR_config.yaml b/minerva/inbuilt_cfgs/example_GeoCLR_config.yaml index 53c3e17e3..4bffa9277 100644 --- a/minerva/inbuilt_cfgs/example_GeoCLR_config.yaml +++ b/minerva/inbuilt_cfgs/example_GeoCLR_config.yaml @@ -92,7 +92,7 @@ tasks: task_logger: SSLTaskLogger step_logger: name: SSLStepLogger - model_io: ssl_pair_tg + model_io: ssl_pair_torchgeo_io # ---+ Dataset Parameters +---------------------------------------- dataset_params: @@ -164,7 +164,7 @@ tasks: task_logger: SSLTaskLogger step_logger: name: KNNStepLogger - model_io: ssl_pair_tg + model_io: ssl_pair_torchgeo_io # ---+ Dataset Parameters +---------------------------------------- dataset_params: @@ -214,7 +214,7 @@ tasks: # ---+ Minerva Inbuilt Logging Functions +------------------------- task_logger: SupervisedTaskLogger - model_io: sup_tg + model_io: supervised_torchgeo_io # ---+ Dataset Parameters +---------------------------------------- dataset_params: diff --git a/minerva/inbuilt_cfgs/example_GeoSimConvNet.yaml b/minerva/inbuilt_cfgs/example_GeoSimConvNet.yaml index 6ce4b3fdb..1f62b1881 100644 --- a/minerva/inbuilt_cfgs/example_GeoSimConvNet.yaml +++ b/minerva/inbuilt_cfgs/example_GeoSimConvNet.yaml @@ -94,7 +94,7 @@ tasks: # ---+ Minerva Inbuilt Logging Functions +------------------------- task_logger: SSLTaskLogger - model_io: ssl_pair_tg + model_io: ssl_pair_torchgeo_io # ---+ Dataset Parameters +---------------------------------------- dataset_params: @@ -160,7 +160,7 @@ tasks: task_logger: SSLTaskLogger step_logger: name: KNNStepLogger - model_io: ssl_pair_tg + model_io: ssl_pair_torchgeo_io # ---+ Dataset Parameters +---------------------------------------- dataset_params: @@ -209,7 +209,7 @@ tasks: # ---+ Minerva Inbuilt Logging Functions +------------------------- task_logger: SSLTaskLogger - model_io: ssl_pair_tg + model_io: ssl_pair_torchgeo_io # ---+ Dataset Parameters +---------------------------------------- dataset_params: diff --git a/minerva/inbuilt_cfgs/example_UNetR_config.yaml b/minerva/inbuilt_cfgs/example_UNetR_config.yaml index 4ca263c8e..338488294 100644 --- a/minerva/inbuilt_cfgs/example_UNetR_config.yaml +++ b/minerva/inbuilt_cfgs/example_UNetR_config.yaml @@ -66,12 +66,6 @@ wandb_log: true # Activates wandb logging. project: pytest # Define the project name for wandb. wandb_dir: /test/tmp/wandb # Directory to store wandb logs locally. -# === MODEL IO & LOGGING ====================================================== -# ---+ Minerva Inbuilt Logging Functions +------------------------------------- -# task_logger: SupervisedTaskLogger -# step_logger: SupervisedGeoStepLogger -# model_io: sup_tg - record_int: true # Store integer results in memory. record_float: false # Store floating point results too. Beware memory overload! diff --git a/minerva/inbuilt_cfgs/example_config.yaml b/minerva/inbuilt_cfgs/example_config.yaml index a3c648eae..e50f47bcc 100644 --- a/minerva/inbuilt_cfgs/example_config.yaml +++ b/minerva/inbuilt_cfgs/example_config.yaml @@ -123,7 +123,7 @@ tasks: # ---+ Minerva Inbuilt Logging Functions +------------------------- # logger: STGLogger # metrics: SPMetrics - # model_io: sup_tg + # model_io: supervised_torchgeo_io # ---+ Dataset Parameters +---------------------------------------- dataset_params: @@ -155,7 +155,7 @@ tasks: # ---+ Minerva Inbuilt Logging Functions +------------------------- # logger: STGLogger # metrics: SPMetrics - # model_io: sup_tg + # model_io: supervised_torchgeo_io # ---+ Dataset Parameters +---------------------------------------- dataset_params: diff --git a/minerva/modelio.py b/minerva/modelio.py index c238283e0..67a37652b 100644 --- a/minerva/modelio.py +++ b/minerva/modelio.py @@ -32,8 +32,10 @@ __license__ = "MIT License" __copyright__ = "Copyright (C) 2024 Harry Baker" __all__ = [ - "sup_tg", - "ssl_pair_tg", + "supervised_torchgeo_io", + "change_detection_io", + "autoencoder_io", + "ssl_pair_torchgeo_io", ] # ===================================================================================================================== @@ -58,7 +60,7 @@ # ===================================================================================================================== # METHODS # ===================================================================================================================== -def sup_tg( +def supervised_torchgeo_io( batch: Dict[Any, Any], model: MinervaModel, device: torch.device, # type: ignore[name-defined] @@ -134,7 +136,7 @@ def sup_tg( return loss, z, y, index -def change_detection( +def change_detection_io( batch: Dict[Any, Any], model: MinervaModel, device: torch.device, # type: ignore[name-defined] @@ -315,7 +317,7 @@ def autoencoder_io( return loss, z, y, index -def ssl_pair_tg( +def ssl_pair_torchgeo_io( batch: Tuple[Dict[str, Any], Dict[str, Any]], model: MinervaModel, device: torch.device, # type: ignore[name-defined] diff --git a/minerva/tasks/core.py b/minerva/tasks/core.py index bb08751d1..906919a3b 100644 --- a/minerva/tasks/core.py +++ b/minerva/tasks/core.py @@ -158,7 +158,7 @@ class MinervaTask(ABC): """ logger_cls: str = "SupervisedTaskLogger" - model_io_name: str = "sup_tg" + model_io_name: str = "supervised_torchgeo_io" def __init__( self, diff --git a/tests/test_logging.py b/tests/test_logging.py index f265ee440..0d7aabafc 100644 --- a/tests/test_logging.py +++ b/tests/test_logging.py @@ -62,7 +62,7 @@ from minerva.logger.steplog import SupervisedStepLogger from minerva.logger.tasklog import SSLTaskLogger, SupervisedTaskLogger from minerva.loss import SegBarlowTwinsLoss -from minerva.modelio import ssl_pair_tg, sup_tg +from minerva.modelio import ssl_pair_torchgeo_io, supervised_torchgeo_io from minerva.models import FCN16ResNet18, MinervaSiamese, SimCLR18, SimConv from minerva.utils import utils @@ -156,7 +156,7 @@ def test_SupervisedStepLogger( } data.append(batch) - logger.step(i, i, *sup_tg(batch, model, device=default_device, train=train)) # type: ignore[arg-type] + logger.step(i, i, *supervised_torchgeo_io(batch, model, device=default_device, train=train)) # type: ignore[arg-type] logs = logger.get_logs assert logs["batch_num"] == std_n_batches - 1 @@ -297,7 +297,7 @@ def test_SSLStepLogger( logger.step( i, i, - *ssl_pair_tg((batch, batch), model, device=default_device, train=train), # type: ignore[arg-type] + *ssl_pair_torchgeo_io((batch, batch), model, device=default_device, train=train), # type: ignore[arg-type] ) logs = logger.get_logs diff --git a/tests/test_modelio.py b/tests/test_modelio.py index e7f77d300..03c5ef746 100644 --- a/tests/test_modelio.py +++ b/tests/test_modelio.py @@ -53,14 +53,14 @@ from torch import Tensor from torchgeo.datasets.utils import BoundingBox -from minerva.modelio import autoencoder_io, ssl_pair_tg, sup_tg +from minerva.modelio import autoencoder_io, ssl_pair_torchgeo_io, supervised_torchgeo_io from minerva.models import FCN32ResNet18, SimCLR34 # ===================================================================================================================== # TESTS # ===================================================================================================================== -def test_sup_tg( +def test_supervised_torchgeo_io( simple_bbox: BoundingBox, random_rgbi_batch: Tensor, random_mask_batch: Tensor, @@ -82,7 +82,7 @@ def test_sup_tg( "bbox": bboxes, } - results = sup_tg(batch, model, default_device, train) + results = supervised_torchgeo_io(batch, model, default_device, train) assert isinstance(results[0], Tensor) assert isinstance(results[1], Tensor) @@ -95,7 +95,7 @@ def test_sup_tg( assert results[3] == batch["bbox"] -def test_ssl_pair_tg( +def test_ssl_pair_torchgeo_io( simple_bbox: BoundingBox, std_batch_size: int, rgbi_input_size: Tuple[int, int, int], @@ -123,7 +123,7 @@ def test_ssl_pair_tg( "bbox": bboxes_2, } - results = ssl_pair_tg((batch_1, batch_2), model, default_device, train) + results = ssl_pair_torchgeo_io((batch_1, batch_2), model, default_device, train) assert isinstance(results[0], Tensor) assert isinstance(results[1], Tensor) From dfa74aacfbd7145b8aa71eb39ed5c126ceaf0db1 Mon Sep 17 00:00:00 2001 From: Harry Baker Date: Wed, 4 Sep 2024 14:48:58 +0100 Subject: [PATCH 51/63] Import `modelio` function via `hydra` --- minerva/inbuilt_cfgs/example_CNN_config.yaml | 2 +- minerva/inbuilt_cfgs/example_ChangeDetector.yaml | 2 +- minerva/inbuilt_cfgs/example_GSConvNet-II.yaml | 6 +++--- minerva/inbuilt_cfgs/example_GeoCLR_config.yaml | 6 +++--- minerva/inbuilt_cfgs/example_GeoSimConvNet.yaml | 6 +++--- minerva/inbuilt_cfgs/example_autoencoder_config.yaml | 2 +- minerva/inbuilt_cfgs/example_config.yaml | 12 ++++++------ minerva/tasks/core.py | 5 ++--- 8 files changed, 20 insertions(+), 21 deletions(-) diff --git a/minerva/inbuilt_cfgs/example_CNN_config.yaml b/minerva/inbuilt_cfgs/example_CNN_config.yaml index 459ef1f42..345e568fc 100644 --- a/minerva/inbuilt_cfgs/example_CNN_config.yaml +++ b/minerva/inbuilt_cfgs/example_CNN_config.yaml @@ -67,7 +67,7 @@ wandb_dir: /test/tmp/wandb # Directory to store wandb logs locally. # === MODEL IO & LOGGING ====================================================== # ---+ Minerva Inbuilt Logging Functions +------------------------------------- task_logger: SupervisedTaskLogger -model_io: supervised_torchgeo_io +model_io: minerva.modelio.supervised_torchgeo_io record_int: true # Store integer results in memory. record_float: false # Store floating point results too. Beware memory overload! diff --git a/minerva/inbuilt_cfgs/example_ChangeDetector.yaml b/minerva/inbuilt_cfgs/example_ChangeDetector.yaml index dc752ba89..e060b3c97 100644 --- a/minerva/inbuilt_cfgs/example_ChangeDetector.yaml +++ b/minerva/inbuilt_cfgs/example_ChangeDetector.yaml @@ -83,7 +83,7 @@ wandb_dir: /test/tmp/wandb # Directory to store wandb logs locally. # === MODEL IO & LOGGING ====================================================== # ---+ Minerva Inbuilt Logging Functions +------------------------------------- -model_io: change_detection_io +model_io: minerva.modelio.change_detection_io record_int: true # Store integer results in memory. record_float: false # Store floating point results too. Beware memory overload! diff --git a/minerva/inbuilt_cfgs/example_GSConvNet-II.yaml b/minerva/inbuilt_cfgs/example_GSConvNet-II.yaml index 6d6bfe40a..88ff5945c 100644 --- a/minerva/inbuilt_cfgs/example_GSConvNet-II.yaml +++ b/minerva/inbuilt_cfgs/example_GSConvNet-II.yaml @@ -103,7 +103,7 @@ tasks: # ---+ Minerva Inbuilt Logging Functions +------------------------- task_logger: SSLTaskLogger - model_io: ssl_pair_torchgeo_io + model_io: minerva.modelio.ssl_pair_torchgeo_io # ---+ Dataset Parameters +---------------------------------------- dataset_params: @@ -167,7 +167,7 @@ tasks: task_logger: SSLTaskLogger step_logger: name: KNNStepLogger - model_io: supervised_torchgeo_io + model_io: minerva.modelio.supervised_torchgeo_io # ---+ Dataset Parameters +---------------------------------------- dataset_params: @@ -203,7 +203,7 @@ tasks: # ---+ Minerva Inbuilt Logging Functions +------------------------- task_logger: SupervisedTaskLogger - model_io: supervised_torchgeo_io + model_io: minerva.modelio.supervised_torchgeo_io # ---+ Dataset Parameters +---------------------------------------- dataset_params: diff --git a/minerva/inbuilt_cfgs/example_GeoCLR_config.yaml b/minerva/inbuilt_cfgs/example_GeoCLR_config.yaml index 4bffa9277..ded292463 100644 --- a/minerva/inbuilt_cfgs/example_GeoCLR_config.yaml +++ b/minerva/inbuilt_cfgs/example_GeoCLR_config.yaml @@ -92,7 +92,7 @@ tasks: task_logger: SSLTaskLogger step_logger: name: SSLStepLogger - model_io: ssl_pair_torchgeo_io + model_io: minerva.modelio.ssl_pair_torchgeo_io # ---+ Dataset Parameters +---------------------------------------- dataset_params: @@ -164,7 +164,7 @@ tasks: task_logger: SSLTaskLogger step_logger: name: KNNStepLogger - model_io: ssl_pair_torchgeo_io + model_io: minerva.modelio.ssl_pair_torchgeo_io # ---+ Dataset Parameters +---------------------------------------- dataset_params: @@ -214,7 +214,7 @@ tasks: # ---+ Minerva Inbuilt Logging Functions +------------------------- task_logger: SupervisedTaskLogger - model_io: supervised_torchgeo_io + model_io: minerva.modelio.supervised_torchgeo_io # ---+ Dataset Parameters +---------------------------------------- dataset_params: diff --git a/minerva/inbuilt_cfgs/example_GeoSimConvNet.yaml b/minerva/inbuilt_cfgs/example_GeoSimConvNet.yaml index 1f62b1881..0eac60336 100644 --- a/minerva/inbuilt_cfgs/example_GeoSimConvNet.yaml +++ b/minerva/inbuilt_cfgs/example_GeoSimConvNet.yaml @@ -94,7 +94,7 @@ tasks: # ---+ Minerva Inbuilt Logging Functions +------------------------- task_logger: SSLTaskLogger - model_io: ssl_pair_torchgeo_io + model_io: minerva.modelio.ssl_pair_torchgeo_io # ---+ Dataset Parameters +---------------------------------------- dataset_params: @@ -160,7 +160,7 @@ tasks: task_logger: SSLTaskLogger step_logger: name: KNNStepLogger - model_io: ssl_pair_torchgeo_io + model_io: minerva.modelio.ssl_pair_torchgeo_io # ---+ Dataset Parameters +---------------------------------------- dataset_params: @@ -209,7 +209,7 @@ tasks: # ---+ Minerva Inbuilt Logging Functions +------------------------- task_logger: SSLTaskLogger - model_io: ssl_pair_torchgeo_io + model_io: minerva.modelio.ssl_pair_torchgeo_io # ---+ Dataset Parameters +---------------------------------------- dataset_params: diff --git a/minerva/inbuilt_cfgs/example_autoencoder_config.yaml b/minerva/inbuilt_cfgs/example_autoencoder_config.yaml index 0713cde30..7431bc4e3 100644 --- a/minerva/inbuilt_cfgs/example_autoencoder_config.yaml +++ b/minerva/inbuilt_cfgs/example_autoencoder_config.yaml @@ -71,7 +71,7 @@ wandb_dir: /test/tmp/wandb # Directory to store wandb logs locally. task_logger: SupervisedTaskLogger step_logger: name: SupervisedStepLogger -model_io: autoencoder_io +model_io: minerva.modelio.autoencoder_io record_int: true # Store integer results in memory. record_float: false # Store floating point results too. Beware memory overload! diff --git a/minerva/inbuilt_cfgs/example_config.yaml b/minerva/inbuilt_cfgs/example_config.yaml index e50f47bcc..6c1898545 100644 --- a/minerva/inbuilt_cfgs/example_config.yaml +++ b/minerva/inbuilt_cfgs/example_config.yaml @@ -121,9 +121,9 @@ tasks: data_config: '${oc.create:${cfg_load: minerva/inbuilt_cfgs/dataset/Chesapeake7.yaml}}' # yamllint disable-line rule:line-length # ---+ Minerva Inbuilt Logging Functions +------------------------- - # logger: STGLogger - # metrics: SPMetrics - # model_io: supervised_torchgeo_io + logger: STGLogger + metrics: SPMetrics + model_io: minerva.modelio.supervised_torchgeo_io # ---+ Dataset Parameters +---------------------------------------- dataset_params: @@ -153,9 +153,9 @@ tasks: data_config: '${oc.create:${cfg_load: minerva/inbuilt_cfgs/dataset/Chesapeake7.yaml}}' # yamllint disable-line rule:line-length # ---+ Minerva Inbuilt Logging Functions +------------------------- - # logger: STGLogger - # metrics: SPMetrics - # model_io: supervised_torchgeo_io + logger: STGLogger + metrics: SPMetrics + model_io: minerva.modelio.supervised_torchgeo_io # ---+ Dataset Parameters +---------------------------------------- dataset_params: diff --git a/minerva/tasks/core.py b/minerva/tasks/core.py index 906919a3b..d39e48441 100644 --- a/minerva/tasks/core.py +++ b/minerva/tasks/core.py @@ -158,7 +158,7 @@ class MinervaTask(ABC): """ logger_cls: str = "SupervisedTaskLogger" - model_io_name: str = "supervised_torchgeo_io" + model_io_name: str = "minerva.modelio.supervised_torchgeo_io" def __init__( self, @@ -394,8 +394,7 @@ def get_io_func(self) -> Callable[..., Any]: Returns: ~typing.Callable[..., ~typing.Any]: Model IO function requested from parameters. """ - io_func: Callable[..., Any] = func_by_str( - "minerva.modelio", + io_func: Callable[..., Any] = hydra.utils.get_method( utils.fallback_params( "model_io", self.params, self.global_params, self.model_io_name ), From a0a27d7936060e2e9d8d11dcf0358b4b6af1d1b0 Mon Sep 17 00:00:00 2001 From: Harry Baker Date: Wed, 4 Sep 2024 17:29:00 +0100 Subject: [PATCH 52/63] Use `hydra` for initing the `task_logger` and `step_logger` --- minerva/inbuilt_cfgs/example_CNN_config.yaml | 2 +- .../inbuilt_cfgs/example_GSConvNet-II.yaml | 8 ++--- .../inbuilt_cfgs/example_GeoCLR_config.yaml | 10 +++--- .../inbuilt_cfgs/example_GeoSimConvNet.yaml | 8 ++--- .../example_autoencoder_config.yaml | 4 +-- minerva/logger/tasklog.py | 32 +++++++++---------- minerva/tasks/core.py | 3 +- tests/test_logging.py | 2 +- 8 files changed, 33 insertions(+), 36 deletions(-) diff --git a/minerva/inbuilt_cfgs/example_CNN_config.yaml b/minerva/inbuilt_cfgs/example_CNN_config.yaml index 345e568fc..2a5daf2dc 100644 --- a/minerva/inbuilt_cfgs/example_CNN_config.yaml +++ b/minerva/inbuilt_cfgs/example_CNN_config.yaml @@ -66,7 +66,7 @@ wandb_dir: /test/tmp/wandb # Directory to store wandb logs locally. # === MODEL IO & LOGGING ====================================================== # ---+ Minerva Inbuilt Logging Functions +------------------------------------- -task_logger: SupervisedTaskLogger +task_logger: minerva.logger.tasklog.SupervisedTaskLogger model_io: minerva.modelio.supervised_torchgeo_io record_int: true # Store integer results in memory. diff --git a/minerva/inbuilt_cfgs/example_GSConvNet-II.yaml b/minerva/inbuilt_cfgs/example_GSConvNet-II.yaml index 88ff5945c..aba7d61e0 100644 --- a/minerva/inbuilt_cfgs/example_GSConvNet-II.yaml +++ b/minerva/inbuilt_cfgs/example_GSConvNet-II.yaml @@ -102,7 +102,7 @@ tasks: sample_pairs: true # ---+ Minerva Inbuilt Logging Functions +------------------------- - task_logger: SSLTaskLogger + task_logger: minerva.logger.tasklog.SSLTaskLogger model_io: minerva.modelio.ssl_pair_torchgeo_io # ---+ Dataset Parameters +---------------------------------------- @@ -164,9 +164,9 @@ tasks: n_classes: 11 # ---+ Minerva Inbuilt Logging Functions +------------------------- - task_logger: SSLTaskLogger + task_logger: minerva.logger.tasklog.SSLTaskLogger step_logger: - name: KNNStepLogger + _target_: minerva.logger.steplog.KNNStepLogger model_io: minerva.modelio.supervised_torchgeo_io # ---+ Dataset Parameters +---------------------------------------- @@ -202,7 +202,7 @@ tasks: sample_pairs: false # ---+ Minerva Inbuilt Logging Functions +------------------------- - task_logger: SupervisedTaskLogger + task_logger: minerva.logger.tasklog.SupervisedTaskLogger model_io: minerva.modelio.supervised_torchgeo_io # ---+ Dataset Parameters +---------------------------------------- diff --git a/minerva/inbuilt_cfgs/example_GeoCLR_config.yaml b/minerva/inbuilt_cfgs/example_GeoCLR_config.yaml index ded292463..3cabddad4 100644 --- a/minerva/inbuilt_cfgs/example_GeoCLR_config.yaml +++ b/minerva/inbuilt_cfgs/example_GeoCLR_config.yaml @@ -89,9 +89,9 @@ tasks: imagery_config: '${oc.create:${cfg_load: minerva/inbuilt_cfgs/dataset/NAIP.yaml}}' # yamllint disable-line rule:line-length # ---+ Minerva Inbuilt Logging Functions +------------------------- - task_logger: SSLTaskLogger + task_logger: minerva.logger.tasklog.SSLTaskLogger step_logger: - name: SSLStepLogger + _target_: minerva.logger.steplog.SSLStepLogger model_io: minerva.modelio.ssl_pair_torchgeo_io # ---+ Dataset Parameters +---------------------------------------- @@ -161,9 +161,9 @@ tasks: data_config: '${oc.create:${cfg_load: minerva/inbuilt_cfgs/dataset/Chesapeake7.yaml}}' # yamllint disable-line rule:line-length # ---+ Minerva Inbuilt Logging Functions +------------------------- - task_logger: SSLTaskLogger + task_logger: minerva.logger.tasklog.SSLTaskLogger step_logger: - name: KNNStepLogger + _target_: minerva.logger.steplog.KNNStepLogger model_io: minerva.modelio.ssl_pair_torchgeo_io # ---+ Dataset Parameters +---------------------------------------- @@ -213,7 +213,7 @@ tasks: data_config: '${oc.create:${cfg_load: minerva/inbuilt_cfgs/dataset/Chesapeake7.yaml}}' # yamllint disable-line rule:line-length # ---+ Minerva Inbuilt Logging Functions +------------------------- - task_logger: SupervisedTaskLogger + task_logger: minerva.logger.tasklog.SupervisedTaskLogger model_io: minerva.modelio.supervised_torchgeo_io # ---+ Dataset Parameters +---------------------------------------- diff --git a/minerva/inbuilt_cfgs/example_GeoSimConvNet.yaml b/minerva/inbuilt_cfgs/example_GeoSimConvNet.yaml index 0eac60336..8305e6bab 100644 --- a/minerva/inbuilt_cfgs/example_GeoSimConvNet.yaml +++ b/minerva/inbuilt_cfgs/example_GeoSimConvNet.yaml @@ -93,7 +93,7 @@ tasks: sample_pairs: true # ---+ Minerva Inbuilt Logging Functions +------------------------- - task_logger: SSLTaskLogger + task_logger: minerva.logger.tasklog.SSLTaskLogger model_io: minerva.modelio.ssl_pair_torchgeo_io # ---+ Dataset Parameters +---------------------------------------- @@ -157,9 +157,9 @@ tasks: data_config: '${oc.create:${cfg_load: minerva/inbuilt_cfgs/dataset/Chesapeake7.yaml}}' # yamllint disable-line rule:line-length # ---+ Minerva Inbuilt Logging Functions +------------------------- - task_logger: SSLTaskLogger + task_logger: minerva.logger.tasklog.SSLTaskLogger step_logger: - name: KNNStepLogger + _target_: minerva.logger.steplog.KNNStepLogger model_io: minerva.modelio.ssl_pair_torchgeo_io # ---+ Dataset Parameters +---------------------------------------- @@ -208,7 +208,7 @@ tasks: data_config: '${oc.create:${cfg_load: minerva/inbuilt_cfgs/dataset/Chesapeake7.yaml}}' # yamllint disable-line rule:line-length # ---+ Minerva Inbuilt Logging Functions +------------------------- - task_logger: SSLTaskLogger + task_logger: minerva.logger.tasklog.SSLTaskLogger model_io: minerva.modelio.ssl_pair_torchgeo_io # ---+ Dataset Parameters +---------------------------------------- diff --git a/minerva/inbuilt_cfgs/example_autoencoder_config.yaml b/minerva/inbuilt_cfgs/example_autoencoder_config.yaml index 7431bc4e3..78e45157d 100644 --- a/minerva/inbuilt_cfgs/example_autoencoder_config.yaml +++ b/minerva/inbuilt_cfgs/example_autoencoder_config.yaml @@ -68,9 +68,9 @@ wandb_dir: /test/tmp/wandb # Directory to store wandb logs locally. # === MODEL IO & LOGGING ====================================================== # ---+ Minerva Inbuilt Logging Functions +------------------------------------- -task_logger: SupervisedTaskLogger +task_logger: minerva.logger.tasklog.SupervisedTaskLogger step_logger: - name: SupervisedStepLogger + _target_: minerva.logger.steplog.SupervisedStepLogger model_io: minerva.modelio.autoencoder_io record_int: true # Store integer results in memory. diff --git a/minerva/logger/tasklog.py b/minerva/logger/tasklog.py index 43f4c42dc..975f59908 100644 --- a/minerva/logger/tasklog.py +++ b/minerva/logger/tasklog.py @@ -49,6 +49,7 @@ else: # pragma: no cover SummaryWriter = None +import hydra import numpy as np from torch import Tensor from torchgeo.datasets.utils import BoundingBox @@ -119,15 +120,14 @@ def __init__( self.writer = writer - if isinstance(step_logger_params, dict): - self._logger = get_logger(step_logger_params.get("name", self.logger_cls)) - if "params" not in step_logger_params: - step_logger_params["params"] = {} - + if not isinstance(step_logger_params, dict): + step_logger_params = {"_target_": self.logger_cls} + elif "_target_" not in step_logger_params: + step_logger_params["_target_"] = self.logger_cls else: - step_logger_params = {"params": {}} + pass - step_logger_params["params"]["n_classes"] = self.n_classes + step_logger_params["n_classes"] = self.n_classes self.step_logger_params = step_logger_params @@ -147,7 +147,8 @@ def _make_logger(self) -> None: .. note:: Will overwrite ``self.logger`` with new logger. """ - self.step_logger: MinervaStepLogger = self._logger( + self.step_logger: MinervaStepLogger = hydra.utils.instantiate( + self.step_logger_params, task_name=self.task_name, n_batches=self.n_batches, batch_size=self.batch_size, @@ -156,7 +157,6 @@ def _make_logger(self) -> None: record_float=self.record_float, writer=self.writer, model_type=self.model_type, - **self.step_logger_params.get("params", {}), ) def refresh_step_logger(self) -> None: @@ -316,7 +316,7 @@ class SupervisedTaskLogger(MinervaTaskLogger): """ metric_types: List[str] = ["loss", "acc", "miou"] - logger_cls = "SupervisedStepLogger" + logger_cls = "minerva.logger.tasklog.SupervisedStepLogger" def __init__( self, @@ -415,7 +415,7 @@ class SSLTaskLogger(MinervaTaskLogger): metric_types = ["loss", "acc", "top5_acc"] special_metric_types = ["collapse_level", "euc_dist"] - logger_cls = "SSLStepLogger" + logger_cls = "minerva.logger.steplog.SSLStepLogger" def __init__( self, @@ -432,13 +432,11 @@ def __init__( **params, ) -> None: if not step_logger_params: - step_logger_params = {} - if "params" not in step_logger_params: - step_logger_params["params"] = {} + step_logger_params = {"_target_": self.logger_cls} - step_logger_params["params"]["sample_pairs"] = sample_pairs - step_logger_params["params"]["collapse_level"] = sample_pairs - step_logger_params["params"]["euclidean"] = sample_pairs + step_logger_params["sample_pairs"] = step_logger_params.get("sample_pairs", sample_pairs) + step_logger_params["collapse_level"] = step_logger_params.get("collapse_level", sample_pairs) + step_logger_params["euclidean"] = step_logger_params.get("euclidean", sample_pairs) super(SSLTaskLogger, self).__init__( task_name, diff --git a/minerva/tasks/core.py b/minerva/tasks/core.py index d39e48441..687c42467 100644 --- a/minerva/tasks/core.py +++ b/minerva/tasks/core.py @@ -362,8 +362,7 @@ def make_logger(self) -> MinervaTaskLogger: """ # Gets constructor of the metric logger from name in the config. - _logger_cls = func_by_str( - "minerva.logger.tasklog", + _logger_cls = hydra.utils.get_class( utils.fallback_params( "task_logger", self.params, self.global_params, self.logger_cls ), diff --git a/tests/test_logging.py b/tests/test_logging.py index 0d7aabafc..df073bcdf 100644 --- a/tests/test_logging.py +++ b/tests/test_logging.py @@ -134,7 +134,7 @@ def test_SupervisedStepLogger( record_float=True, writer=writer, model_type=model_type, - step_logger_params={"params": {"n_classes": std_n_classes}}, + step_logger_params={"n_classes": std_n_classes}, ) correct_loss: Dict[str, List[float]] = {"x": [], "y": []} From 00bff82a82e5592a20d21dc2de937dd8ad484815 Mon Sep 17 00:00:00 2001 From: Harry Date: Wed, 4 Sep 2024 18:07:25 +0100 Subject: [PATCH 53/63] Removed `minerva.logger.steplog.get_logger` --- minerva/logger/steplog.py | 19 +------------------ minerva/logger/tasklog.py | 16 +++++++++++----- minerva/tasks/core.py | 2 +- minerva/tasks/epoch.py | 2 +- minerva/tasks/knn.py | 2 +- 5 files changed, 15 insertions(+), 26 deletions(-) diff --git a/minerva/logger/steplog.py b/minerva/logger/steplog.py index 20cb0b13e..a8bfa73ec 100644 --- a/minerva/logger/steplog.py +++ b/minerva/logger/steplog.py @@ -38,7 +38,6 @@ "SupervisedStepLogger", "SSLStepLogger", "KNNStepLogger", - "get_logger", ] # ===================================================================================================================== @@ -76,7 +75,7 @@ from wandb.sdk.wandb_run import Run from minerva.utils import utils -from minerva.utils.utils import check_substrings_in_string, func_by_str +from minerva.utils.utils import check_substrings_in_string # ===================================================================================================================== # GLOBALS @@ -798,19 +797,3 @@ def log( # Writes the loss to the writer. self.write_metric("loss", ls, step_num=global_step_num) - - -# ===================================================================================================================== -# METHODS -# ===================================================================================================================== -def get_logger(name) -> Callable[..., Any]: - """Gets the constructor for a step logger to log the results from each step of model fitting during an epoch. - - Returns: - ~typing.Callable[..., ~typing.Any]: The constructor of :class:`~logging.step.log.MinervaStepLogger` - to be intialised within the epoch. - - .. versionadded:: 0.27 - """ - logger: Callable[..., Any] = func_by_str("minerva.logger.steplog", name) - return logger diff --git a/minerva/logger/tasklog.py b/minerva/logger/tasklog.py index 975f59908..e341eb629 100644 --- a/minerva/logger/tasklog.py +++ b/minerva/logger/tasklog.py @@ -57,7 +57,7 @@ from minerva.utils.utils import check_substrings_in_string -from .steplog import MinervaStepLogger, get_logger +from .steplog import MinervaStepLogger # ===================================================================================================================== @@ -316,7 +316,7 @@ class SupervisedTaskLogger(MinervaTaskLogger): """ metric_types: List[str] = ["loss", "acc", "miou"] - logger_cls = "minerva.logger.tasklog.SupervisedStepLogger" + logger_cls = "minerva.logger.steplog.SupervisedStepLogger" def __init__( self, @@ -434,9 +434,15 @@ def __init__( if not step_logger_params: step_logger_params = {"_target_": self.logger_cls} - step_logger_params["sample_pairs"] = step_logger_params.get("sample_pairs", sample_pairs) - step_logger_params["collapse_level"] = step_logger_params.get("collapse_level", sample_pairs) - step_logger_params["euclidean"] = step_logger_params.get("euclidean", sample_pairs) + step_logger_params["sample_pairs"] = step_logger_params.get( + "sample_pairs", sample_pairs + ) + step_logger_params["collapse_level"] = step_logger_params.get( + "collapse_level", sample_pairs + ) + step_logger_params["euclidean"] = step_logger_params.get( + "euclidean", sample_pairs + ) super(SSLTaskLogger, self).__init__( task_name, diff --git a/minerva/tasks/core.py b/minerva/tasks/core.py index 687c42467..482b2de0b 100644 --- a/minerva/tasks/core.py +++ b/minerva/tasks/core.py @@ -66,7 +66,7 @@ wrap_model, ) from minerva.utils import utils, visutils -from minerva.utils.utils import fallback_params, func_by_str +from minerva.utils.utils import fallback_params # ===================================================================================================================== diff --git a/minerva/tasks/epoch.py b/minerva/tasks/epoch.py index 64704b4fe..98e64fb33 100644 --- a/minerva/tasks/epoch.py +++ b/minerva/tasks/epoch.py @@ -60,7 +60,7 @@ class StandardEpoch(MinervaTask): .. versionadded:: 0.27 """ - logger_cls = "SupervisedTaskLogger" + logger_cls = "minerva.logger.tasklog.SupervisedTaskLogger" def step(self) -> None: # Initialises a progress bar for the epoch. diff --git a/minerva/tasks/knn.py b/minerva/tasks/knn.py index 689419702..7914e88e6 100644 --- a/minerva/tasks/knn.py +++ b/minerva/tasks/knn.py @@ -137,7 +137,7 @@ class WeightedKNN(MinervaTask): .. versionadded:: 0.27 """ - logger_cls = "SSLTaskLogger" + logger_cls = "minerva.logger.tasklog.SSLTaskLogger" def __init__( self, From a790e004ed8bdcdb3ce512cd4fcd202eddffa01b Mon Sep 17 00:00:00 2001 From: Harry Baker Date: Thu, 5 Sep 2024 16:35:53 +0100 Subject: [PATCH 54/63] Added 4 seasons of a patch for `SSL4EO_S12` tests --- .gitignore | 9 +++++++-- .../B1.tif | Bin 0 -> 4238 bytes .../B11.tif | Bin 0 -> 35244 bytes .../B12.tif | Bin 0 -> 35244 bytes .../B2.tif | Bin 0 -> 139866 bytes .../B3.tif | Bin 0 -> 139866 bytes .../B4.tif | Bin 0 -> 139866 bytes .../B5.tif | Bin 0 -> 35244 bytes .../B6.tif | Bin 0 -> 35244 bytes .../B7.tif | Bin 0 -> 35244 bytes .../B8.tif | Bin 0 -> 139866 bytes .../B8A.tif | Bin 0 -> 35244 bytes .../B9.tif | Bin 0 -> 4238 bytes .../metadata.json | 1 + .../B1.tif 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tests/fixtures/data/SSL4EO-S12/s2a/0000099/20200614T181931_20200614T183418_T11SMS/metadata.json diff --git a/.gitignore b/.gitignore index a4d961a62..3f9bbe3c3 100644 --- a/.gitignore +++ b/.gitignore @@ -152,8 +152,7 @@ lightning_logs/ **/results/* **/cache/* **/outputs/* -tests/tmp/cache/* -tests/tmp/results/* +tests/tmp/* user_cfgs/* inbuilt_cfgs/config.yml @@ -162,8 +161,14 @@ inbuilt_cfgs/mf_config.yml .vscode/settings.json # Exclusions +!docs/ +docs/* +!docs/images/ +docs/images/* !docs/images/*.png + !tests/fixtures/data/**/*.png !tests/fixtures/data/**/*.txt !tests/fixtures/data/**/*.csv !tests/fixtures/data/**/*.tif +!tests/fixtures/data/**/*.json diff --git a/tests/fixtures/data/SSL4EO-S12/s2a/0000042/20200424T094029_20200424T094123_T33TYN/B1.tif b/tests/fixtures/data/SSL4EO-S12/s2a/0000042/20200424T094029_20200424T094123_T33TYN/B1.tif new file mode 100644 index 0000000000000000000000000000000000000000..8fdc0fda9c85089ff097eced040122e92dbd705c GIT binary patch literal 4238 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399960, 0, -10, 3700020]}}, "system:footprint": {"type": "LinearRing", "coordinates": [[-116.89627078890739, 32.44924419582073], [-116.89626737417575, 32.44925898927161], [-116.8951045650362, 33.43953070750837], [-116.89514832775068, 33.439572205468394], [-116.89518786695115, 33.4396207947493], [-117.19903171329149, 33.43950534084795], [-117.19907137449762, 33.43947601948668], [-117.19912234182809, 33.4394638167782], [-117.19913391715312, 33.43944350739506], [-117.19977701964083, 33.43781570659065], [-117.20298056753498, 33.42698251736538], [-117.45058635492418, 32.49597355187842], [-117.45371990599305, 32.48405448940864], [-117.46060922124882, 32.45750750785478], [-117.4618614719186, 32.452630471418175], [-117.46248822151223, 32.44991226060029], [-117.46248078368934, 32.44846000384899], [-117.46243723209412, 32.4484187108336], [-117.46239789227033, 32.448370362912655], [-116.8963741294445, 32.44917659271196], [-116.89632497529956, 32.44921309854787], [-116.89627078890739, 32.44924419582073]]}, "system:id": "COPERNICUS/S2_SR/20200614T181931_20200614T183418_T11SMS", "system:index": "20200614T181931_20200614T183418_T11SMS", "system:time_end": 1592159723525, "system:time_start": 1592159723525, "system:version": 1592325576909226} From 81320facb6fd454e70e137917c559b5892477925 Mon Sep 17 00:00:00 2001 From: Harry Baker Date: Thu, 5 Sep 2024 16:37:23 +0100 Subject: [PATCH 55/63] Added season transform to test --- minerva/inbuilt_cfgs/example_GSConvNet-II.yaml | 5 ++++- minerva/inbuilt_cfgs/example_config.yaml | 6 ++---- 2 files changed, 6 insertions(+), 5 deletions(-) diff --git a/minerva/inbuilt_cfgs/example_GSConvNet-II.yaml b/minerva/inbuilt_cfgs/example_GSConvNet-II.yaml index aba7d61e0..9d2467358 100644 --- a/minerva/inbuilt_cfgs/example_GSConvNet-II.yaml +++ b/minerva/inbuilt_cfgs/example_GSConvNet-II.yaml @@ -150,11 +150,14 @@ tasks: p: 0.2 keepdim: true - _target_: minerva.datasets.NonGeoSSL4EOS12Sentinel2 + _target_: minerva.datasets.MinervaSSL4EO root: SSL4EO-S12 + mode: s2a bands: [B2, B3, B4, B8] size: ${patch_size} max_r: *max_r + season: true + season_transform: pair fit-val: _target_: minerva.tasks.WeightedKNN diff --git a/minerva/inbuilt_cfgs/example_config.yaml b/minerva/inbuilt_cfgs/example_config.yaml index 6c1898545..b1370f014 100644 --- a/minerva/inbuilt_cfgs/example_config.yaml +++ b/minerva/inbuilt_cfgs/example_config.yaml @@ -121,8 +121,7 @@ tasks: data_config: '${oc.create:${cfg_load: minerva/inbuilt_cfgs/dataset/Chesapeake7.yaml}}' # yamllint disable-line rule:line-length # ---+ Minerva Inbuilt Logging Functions +------------------------- - logger: STGLogger - metrics: SPMetrics + task_logger: minerva.logger.tasklog.SupervisedTaskLogger model_io: minerva.modelio.supervised_torchgeo_io # ---+ Dataset Parameters +---------------------------------------- @@ -153,8 +152,7 @@ tasks: data_config: '${oc.create:${cfg_load: minerva/inbuilt_cfgs/dataset/Chesapeake7.yaml}}' # yamllint disable-line rule:line-length # ---+ Minerva Inbuilt Logging Functions +------------------------- - logger: STGLogger - metrics: SPMetrics + task_logger: minerva.logger.tasklog.SupervisedTaskLogger model_io: minerva.modelio.supervised_torchgeo_io # ---+ Dataset Parameters +---------------------------------------- From 409253efab427a8e8cd9256619c1c20519798da2 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Thu, 5 Sep 2024 23:40:08 +0000 Subject: [PATCH 56/63] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- minerva/datasets/collators.py | 4 +++- minerva/datasets/dfc.py | 33 +++++++++++++++++++++++++-------- minerva/datasets/paired.py | 4 +--- minerva/loss.py | 10 ++++++++-- minerva/utils/runner.py | 6 ++++-- 5 files changed, 41 insertions(+), 16 deletions(-) diff --git a/minerva/datasets/collators.py b/minerva/datasets/collators.py index da6d22899..3268709e0 100644 --- a/minerva/datasets/collators.py +++ b/minerva/datasets/collators.py @@ -48,7 +48,9 @@ # ===================================================================================================================== # METHODS # ===================================================================================================================== -def get_collator(collator_target: str = "torchgeo.datasets.stack_samples") -> Callable[..., Any]: +def get_collator( + collator_target: str = "torchgeo.datasets.stack_samples", +) -> Callable[..., Any]: """Gets the function defined in parameters to collate samples together to form a batch. Args: diff --git a/minerva/datasets/dfc.py b/minerva/datasets/dfc.py index fd0dc944b..288139be8 100644 --- a/minerva/datasets/dfc.py +++ b/minerva/datasets/dfc.py @@ -419,6 +419,7 @@ def plot( """ # Reorder image from BGR to RGB. from kornia.color import BgrToRgb + from minerva.transforms import AdjustGamma bgr_to_rgb = BgrToRgb() @@ -461,8 +462,12 @@ def plot( axs[0].imshow(image) # Sets tick intervals to block size. - axs[0].set_xticks(np.arange(0, image.shape[0] + 1, image.shape[0] // block_size_factor)) - axs[0].set_yticks(np.arange(0, image.shape[1] + 1, image.shape[1] // block_size_factor)) + axs[0].set_xticks( + np.arange(0, image.shape[0] + 1, image.shape[0] // block_size_factor) + ) + axs[0].set_yticks( + np.arange(0, image.shape[1] + 1, image.shape[1] // block_size_factor) + ) # Add grid overlay. axs[0].grid(which="both", color="#CCCCCC", linestyle=":") @@ -475,8 +480,12 @@ def plot( ) # Sets tick intervals to block size. - axs[1].set_xticks(np.arange(0, mask.shape[0] + 1, mask.shape[0] // block_size_factor)) - axs[1].set_yticks(np.arange(0, mask.shape[1] + 1, mask.shape[1] // block_size_factor)) + axs[1].set_xticks( + np.arange(0, mask.shape[0] + 1, mask.shape[0] // block_size_factor) + ) + axs[1].set_yticks( + np.arange(0, mask.shape[1] + 1, mask.shape[1] // block_size_factor) + ) # Add grid overlay. axs[1].grid(which="both", color="#CCCCCC", linestyle=":") @@ -487,8 +496,12 @@ def plot( ) # Sets tick intervals to block size. - axs[2].set_xticks(np.arange(0, pred.shape[0] + 1, pred.shape[0] // block_size_factor)) - axs[2].set_yticks(np.arange(0, pred.shape[1] + 1, pred.shape[1] // block_size_factor)) + axs[2].set_xticks( + np.arange(0, pred.shape[0] + 1, pred.shape[0] // block_size_factor) + ) + axs[2].set_yticks( + np.arange(0, pred.shape[1] + 1, pred.shape[1] // block_size_factor) + ) # Add grid overlay. axs[2].grid(which="both", color="#CCCCCC", linestyle=":") @@ -499,8 +512,12 @@ def plot( ) # Sets tick intervals to block size. - axs[1].set_xticks(np.arange(0, pred.shape[0] + 1, pred.shape[0] // block_size_factor)) - axs[1].set_yticks(np.arange(0, pred.shape[1] + 1, pred.shape[1] // block_size_factor)) + axs[1].set_xticks( + np.arange(0, pred.shape[0] + 1, pred.shape[0] // block_size_factor) + ) + axs[1].set_yticks( + np.arange(0, pred.shape[1] + 1, pred.shape[1] // block_size_factor) + ) # Add grid overlay. axs[1].grid(which="both", color="#CCCCCC", linestyle=":") diff --git a/minerva/datasets/paired.py b/minerva/datasets/paired.py index 8239397c9..474f1ad29 100644 --- a/minerva/datasets/paired.py +++ b/minerva/datasets/paired.py @@ -108,9 +108,7 @@ def __init__( ) -> None: ... # pragma: no cover @overload - def __init__( - self, dataset: str, *args, **kwargs - ) -> None: ... # pragma: no cover + def __init__(self, dataset: str, *args, **kwargs) -> None: ... # pragma: no cover def __init__( self, diff --git a/minerva/loss.py b/minerva/loss.py index 111d28b55..1c7d13162 100644 --- a/minerva/loss.py +++ b/minerva/loss.py @@ -33,11 +33,12 @@ __copyright__ = "Copyright (C) 2024 Harry Baker" __all__ = ["SegBarlowTwinsLoss", "AuxCELoss"] +import importlib + # ===================================================================================================================== # IMPORTS # ===================================================================================================================== from typing import Optional -import importlib import torch from torch import Tensor @@ -91,7 +92,12 @@ class AuxCELoss(Module): Source: https://github.com/xitongpu/PSPNet/blob/main/src/model/cell.py """ - def __init__(self, weight: Optional[Tensor] = None, ignore_index: int = 255, alpha: float = 0.4) -> None: + def __init__( + self, + weight: Optional[Tensor] = None, + ignore_index: int = 255, + alpha: float = 0.4, + ) -> None: super().__init__() self.loss = CrossEntropyLoss(weight=weight, ignore_index=ignore_index) self.alpha = alpha diff --git a/minerva/utils/runner.py b/minerva/utils/runner.py index 2ba065050..89d3d1d6b 100644 --- a/minerva/utils/runner.py +++ b/minerva/utils/runner.py @@ -62,7 +62,7 @@ import torch.multiprocessing as mp import wandb import yaml -from omegaconf import DictConfig, OmegaConf, ListConfig +from omegaconf import DictConfig, ListConfig, OmegaConf from wandb.sdk.lib import RunDisabled from wandb.sdk.wandb_run import Run @@ -334,7 +334,9 @@ def distributed_run( OmegaConf.register_new_resolver("cfg_load", _config_load_resolver, replace=True) OmegaConf.register_new_resolver("eval", eval, replace=True) - OmegaConf.register_new_resolver("to_patch_size", _construct_patch_size, replace=True) + OmegaConf.register_new_resolver( + "to_patch_size", _construct_patch_size, replace=True + ) @functools.wraps(run) def inner_decorator(cfg: DictConfig): From bb72b6d64916de1c7de52787140b74e8983e1d19 Mon Sep 17 00:00:00 2001 From: Harry Date: Fri, 6 Sep 2024 00:48:15 +0100 Subject: [PATCH 57/63] Dropped support for Python 3.9 --- .github/workflows/tests.yml | 2 +- README.md | 2 +- pyproject.toml | 1 - setup.cfg | 1 - tox.ini | 7 +------ 5 files changed, 3 insertions(+), 10 deletions(-) diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml index 60d06fde0..8f5474d0f 100644 --- a/.github/workflows/tests.yml +++ b/.github/workflows/tests.yml @@ -15,7 +15,7 @@ jobs: fail-fast: false matrix: os: [ubuntu-latest, windows-latest, macos-latest] - python-version: ['3.9', '3.10', '3.11', '3.12'] + python-version: ['3.10', '3.11', '3.12'] exclude: - os: windows-latest python-version: '3.12' diff --git a/README.md b/README.md index 4beccd94b..3b6c917e4 100644 --- a/README.md +++ b/README.md @@ -57,7 +57,7 @@ datasets with upcoming support for [torchvision](https://pytorch.org/vision/stab Required Python modules for `minerva` are stated in the `setup.cfg`. -`minerva` currently only supports `python` 3.9 -- 3.12. +`minerva` currently only supports `python` 3.10 -- 3.12.

    (back to top)

    diff --git a/pyproject.toml b/pyproject.toml index 196d81f65..b3fb51d41 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -16,7 +16,6 @@ classifiers = [ "Programming Language :: Python :: 3.12", "Programming Language :: Python :: 3.11", "Programming Language :: Python :: 3.10", - "Programming Language :: Python :: 3.9", "Programming Language :: Python :: 3 :: Only", "License :: OSI Approved :: MIT License", "Development Status :: 4 - Beta", diff --git a/setup.cfg b/setup.cfg index 9f22b5678..ea926089e 100644 --- a/setup.cfg +++ b/setup.cfg @@ -13,7 +13,6 @@ classifiers = Programming Language :: Python :: 3.12 Programming Language :: Python :: 3.11 Programming Language :: Python :: 3.10 - Programming Language :: Python :: 3.9 Programming Language :: Python :: 3 :: Only License :: OSI Approved :: MIT License Development Status :: 4 - Beta diff --git a/tox.ini b/tox.ini index bfea17c61..6e9b2d9ec 100644 --- a/tox.ini +++ b/tox.ini @@ -1,6 +1,6 @@ [tox] minversion = 3.8.0 -envlist = minerva-{312, 311, 310, 39} +envlist = minerva-{312, 311, 310} isolated_build = true skip_missing_interpreters = true @@ -9,7 +9,6 @@ python = 3.12: minerva-312 3.11: minerva-311 3.10: minerva-310 - 3.9: minerva-39 [testenv] skip_install = true @@ -33,7 +32,3 @@ deps = -r{toxinidir}/requirements/requirements_dev.txt [testenv:minerva-310] basepython = python3.10 deps = -r{toxinidir}/requirements/requirements_dev.txt - -[testenv:minerva-39] -basepython = python3.9 -deps = -r{toxinidir}/requirements/requirements_dev.txt From 4b53ef6431158d8bc4996db9b91aa78aa5f81466 Mon Sep 17 00:00:00 2001 From: Harry Baker Date: Fri, 6 Sep 2024 16:52:47 +0100 Subject: [PATCH 58/63] Removed globbing of paths in `utils.compile_dataset_paths` (now handled by `torchgeo`) --- minerva/utils/utils.py | 11 ++--------- 1 file changed, 2 insertions(+), 9 deletions(-) diff --git a/minerva/utils/utils.py b/minerva/utils/utils.py index b849ec46c..6179789cd 100644 --- a/minerva/utils/utils.py +++ b/minerva/utils/utils.py @@ -1885,19 +1885,12 @@ def compile_dataset_paths( else: out_paths = [universal_path(data_dir) / in_paths] - compiled_paths = [] - for path in out_paths: - compiled_paths.extend(glob.glob(str(path), recursive=True)) - - # Convert to paths - raw_paths = [Path(path) for path in compiled_paths] - # Check if each path exists. If not, make the path. - for path in raw_paths: + for path in out_paths: path.mkdir(parents=True, exist_ok=True) # For each path, get the absolute path, make the path if it does not exist then convert to string and return. - return [str(Path(path).absolute()) for path in compiled_paths] + return [str(Path(path).absolute()) for path in out_paths] def make_hash(obj: Dict[Any, Any]) -> str: From a78b6f12cf45b0df9d2c8f26ee732aa83de9a86d Mon Sep 17 00:00:00 2001 From: Harry Baker Date: Fri, 6 Sep 2024 18:55:36 +0100 Subject: [PATCH 59/63] `mypy` fixes --- minerva/datasets/dfc.py | 3 +- minerva/datasets/multispectral.py | 6 ++-- minerva/datasets/ssl4eos12.py | 30 +++++++++-------- minerva/modelio.py | 54 +++++++++++++++---------------- minerva/models/core.py | 2 +- minerva/tasks/tsne.py | 14 ++++---- minerva/utils/utils.py | 1 - tests/test_logging.py | 4 +-- 8 files changed, 56 insertions(+), 58 deletions(-) diff --git a/minerva/datasets/dfc.py b/minerva/datasets/dfc.py index 288139be8..9b8257d00 100644 --- a/minerva/datasets/dfc.py +++ b/minerva/datasets/dfc.py @@ -426,8 +426,7 @@ def plot( adjust_gamma = AdjustGamma(gamma=0.8) # Reorder channels from BGR to RGB and adjust the gamma ready for plotting. - image = adjust_gamma(bgr_to_rgb(sample["image"][:3])) - image = image.permute(1, 2, 0).numpy() + image = adjust_gamma(bgr_to_rgb(sample["image"][:3])).permute(1, 2, 0).numpy() block_size_factor = 8 diff --git a/minerva/datasets/multispectral.py b/minerva/datasets/multispectral.py index 9ece53eb2..de2749845 100644 --- a/minerva/datasets/multispectral.py +++ b/minerva/datasets/multispectral.py @@ -56,14 +56,14 @@ class MultiSpectralDataset(VisionDataset, MinervaNonGeoDataset): """Generic dataset class for multi-spectral images that works within :mod:`torchgeo`""" - all_bands: List[str] = [] - rgb_bands: List[str] = [] + all_bands: tuple[str, ...] = () + rgb_bands: tuple[str, ...] = () def __init__( self, root: str, transforms: Optional[Callable[..., Any]] = None, - bands: Optional[List[str]] = None, + bands: Optional[tuple[str, ...]] = None, as_type=np.float32, ) -> None: super().__init__(root, transform=transforms, target_transform=None) diff --git a/minerva/datasets/ssl4eos12.py b/minerva/datasets/ssl4eos12.py index 74849618c..2698f1f87 100644 --- a/minerva/datasets/ssl4eos12.py +++ b/minerva/datasets/ssl4eos12.py @@ -69,7 +69,7 @@ class GeoSSL4EOS12Sentinel2(Sentinel2): filename_glob = "{}.*" filename_regex = r"""(?PB[^[0-1]?[0-9]|B[^[1]?[0-9][\dA])\..*$""" date_format = "" - all_bands = [ + all_bands: tuple[str, ...] = ( "B1", "B2", "B3", @@ -82,12 +82,12 @@ class GeoSSL4EOS12Sentinel2(Sentinel2): "B9", "B11", "B12", - ] - rgb_bands = ["B4", "B3", "B2"] + ) + rgb_bands: tuple[str, str, str] = ("B4", "B3", "B2") class NonGeoSSL4EOS12Sentinel2(MultiSpectralDataset): - all_bands = [ + all_bands: tuple[str, ...] = ( "B1", "B2", "B3", @@ -100,8 +100,8 @@ class NonGeoSSL4EOS12Sentinel2(MultiSpectralDataset): "B9", "B11", "B12", - ] - rgb_bands = ["B4", "B3", "B2"] + ) + rgb_bands: tuple[str, str, str] = ("B4", "B3", "B2") class MinervaSSL4EO(VisionDataset, MinervaNonGeoDataset): @@ -110,7 +110,7 @@ class MinervaSSL4EO(VisionDataset, MinervaNonGeoDataset): Source: https://github.com/zhu-xlab/SSL4EO-S12/tree/main/src/benchmark/pretrain_ssl/datasets/SSL4EO """ - ALL_BANDS_S2_L2A = [ + ALL_BANDS_S2_L2A: tuple[str, ...] = ( "B1", "B2", "B3", @@ -123,8 +123,8 @@ class MinervaSSL4EO(VisionDataset, MinervaNonGeoDataset): "B9", "B11", "B12", - ] - ALL_BANDS_S2_L1C = [ + ) + ALL_BANDS_S2_L1C: tuple[str, ...] = ( "B1", "B2", "B3", @@ -138,9 +138,9 @@ class MinervaSSL4EO(VisionDataset, MinervaNonGeoDataset): "B10", "B11", "B12", - ] - RGB_BANDS = ["B4", "B3", "B2"] - ALL_BANDS_S1_GRD = ["VV", "VH"] + ) + RGB_BANDS: tuple[str, str, str] = ("B4", "B3", "B2") + ALL_BANDS_S1_GRD: tuple[str, str] = ("VV", "VH") # Band statistics: mean & std # Calculated from 50k data @@ -215,7 +215,7 @@ def __init__( lmdb_file: Optional[str] = None, normalize: bool = False, mode: str = "s2a", - bands: Optional[List[str]] = None, + bands: Optional[tuple[str, ...]] = None, dtype: str = "uint8", is_slurm_job=False, transforms=None, @@ -363,7 +363,9 @@ def __getitem__(self, index: int) -> Dict[str, Union[Tuple[Tensor, ...], Tensor] return {"image": img_4s} - def get_array(self, patch_id: str, mode: str, bands: Optional[List[str]] = None): + def get_array( + self, patch_id: str, mode: str, bands: Optional[tuple[str, ...]] = None + ): data_root_patch = os.path.join(self.root, mode, patch_id) patch_seasons = os.listdir(data_root_patch) seasons = [] diff --git a/minerva/modelio.py b/minerva/modelio.py index 67a37652b..e539ec3e5 100644 --- a/minerva/modelio.py +++ b/minerva/modelio.py @@ -41,7 +41,7 @@ # ===================================================================================================================== # IMPORTS # ===================================================================================================================== -from typing import Any, Dict, Optional, Sequence, Tuple, Union +from typing import Any, Optional, Sequence import numpy as np import torch @@ -61,16 +61,16 @@ # METHODS # ===================================================================================================================== def supervised_torchgeo_io( - batch: Dict[Any, Any], + batch: dict[Any, Any], model: MinervaModel, device: torch.device, # type: ignore[name-defined] train: bool, **kwargs, -) -> Tuple[ +) -> tuple[ Tensor, - Union[Tensor, Tuple[Tensor, ...]], + Tensor | tuple[Tensor, ...], Tensor, - Optional[Union[Sequence[str], Sequence[BoundingBox]]], + Optional[Sequence[str] | Sequence[int] | Sequence[BoundingBox]], ]: """Provides IO functionality for a supervised model using :mod:`torchgeo` datasets. @@ -129,7 +129,7 @@ def supervised_torchgeo_io( loss, z = model.step(x, y, train=train) # Get the indices of the batch. Either bounding boxes, filenames or index number. - index: Optional[Union[Sequence[str], Sequence[int], Sequence[BoundingBox]]] = ( + index: Optional[Sequence[str] | Sequence[int] | Sequence[BoundingBox]] = ( get_sample_index(batch) ) @@ -137,16 +137,16 @@ def supervised_torchgeo_io( def change_detection_io( - batch: Dict[Any, Any], + batch: dict[Any, Any], model: MinervaModel, device: torch.device, # type: ignore[name-defined] train: bool, **kwargs, -) -> Tuple[ +) -> tuple[ Tensor, - Union[Tensor, Tuple[Tensor, ...]], + Tensor | tuple[Tensor, ...], Tensor, - Optional[Union[Sequence[str], Sequence[BoundingBox]]], + Optional[Sequence[str] | Sequence[int] | Sequence[BoundingBox]], ]: """Provides IO functionality for a change_detection model. @@ -209,7 +209,7 @@ def change_detection_io( loss, z = model.step(x, y, train=train) # Get the indices of the batch. Either bounding boxes, filenames or index number. - index: Optional[Union[Sequence[str], Sequence[int], Sequence[BoundingBox]]] = ( + index: Optional[Sequence[str] | Sequence[int] | Sequence[BoundingBox]] = ( get_sample_index(batch) ) @@ -217,16 +217,16 @@ def change_detection_io( def autoencoder_io( - batch: Dict[Any, Any], + batch: dict[Any, Any], model: MinervaModel, device: torch.device, # type: ignore[name-defined] train: bool, **kwargs, -) -> Tuple[ +) -> tuple[ Tensor, - Union[Tensor, Tuple[Tensor, ...]], + Tensor | tuple[Tensor, ...], Tensor, - Optional[Union[Sequence[str], Sequence[BoundingBox]]], + Optional[Sequence[str] | Sequence[int] | Sequence[BoundingBox]], ]: """Provides IO functionality for an autoencoder using :mod:`torchgeo` datasets by only using the same data for input and ground truth. @@ -286,15 +286,13 @@ def autoencoder_io( ) output_masks: LongTensor = masks - if isinstance(input_masks, Tensor): - input_masks = input_masks.detach().cpu().numpy() - - if isinstance(output_masks, Tensor): - output_masks = output_masks.detach().cpu().numpy() - # Transfer to GPU and cast to correct dtypes. - x = torch.tensor(input_masks, dtype=float_dtype, device=device) - y = torch.tensor(output_masks, dtype=torch.long, device=device) + x = torch.tensor( + input_masks.detach().cpu().numpy(), dtype=float_dtype, device=device + ) + y = torch.tensor( + output_masks.detach().cpu().numpy(), dtype=torch.long, device=device + ) elif key == "image": # Extract the images from the batch, set to float, transfer to GPU and make x and y. @@ -310,7 +308,7 @@ def autoencoder_io( loss, z = model.step(x, y, train=train) # Get the indices of the batch. Either bounding boxes, filenames or index number. - index: Optional[Union[Sequence[str], Sequence[int], Sequence[BoundingBox]]] = ( + index: Optional[Sequence[str] | Sequence[int] | Sequence[BoundingBox]] = ( get_sample_index(batch) ) @@ -318,16 +316,16 @@ def autoencoder_io( def ssl_pair_torchgeo_io( - batch: Tuple[Dict[str, Any], Dict[str, Any]], + batch: tuple[dict[str, Any], dict[str, Any]], model: MinervaModel, device: torch.device, # type: ignore[name-defined] train: bool, **kwargs, -) -> Tuple[ +) -> tuple[ Tensor, - Union[Tensor, Tuple[Tensor, ...]], + Tensor | tuple[Tensor, ...], None, - Optional[Union[Sequence[BoundingBox], Sequence[int]]], + Optional[Sequence[BoundingBox] | Sequence[int]], ]: """Provides IO functionality for a self-supervised Siamese model using :mod:`torchgeo` datasets. diff --git a/minerva/models/core.py b/minerva/models/core.py index 317dd4bd8..8a0653498 100644 --- a/minerva/models/core.py +++ b/minerva/models/core.py @@ -81,8 +81,8 @@ from torch.nn.modules import Module from torch.nn.parallel import DataParallel from torch.nn.parallel import DistributedDataParallel as DDP -from torch.optim import Optimizer from torch.optim.lr_scheduler import LRScheduler +from torch.optim.optimizer import Optimizer from torchvision.models._api import WeightsEnum from minerva.utils.utils import func_by_str diff --git a/minerva/tasks/tsne.py b/minerva/tasks/tsne.py index a10de5785..d3b88fed9 100644 --- a/minerva/tasks/tsne.py +++ b/minerva/tasks/tsne.py @@ -38,7 +38,7 @@ # IMPORTS # ===================================================================================================================== from pathlib import Path -from typing import Any, Dict, Optional, Union +from typing import Any, Optional import torch from torch import Tensor @@ -65,14 +65,14 @@ class TSNEVis(MinervaTask): def __init__( self, name: str, - model: Union[MinervaModel, MinervaDataParallel], + model: MinervaModel | MinervaDataParallel, device: torch.device, exp_fn: Path, gpu: int = 0, rank: int = 0, world_size: int = 1, - writer: Union[SummaryWriter, Run, None] = None, - backbone_weight_path: Optional[Union[str, Path]] = None, + writer: Optional[SummaryWriter | Run] = None, + backbone_weight_path: Optional[str | Path] = None, record_int: bool = True, record_float: bool = False, **params, @@ -80,7 +80,7 @@ def __init__( backbone = model.get_backbone() # type: ignore[assignment, operator] # Set dummy optimiser. It won't be used as this is a test. - backbone.set_optimiser(torch.optim.SGD(backbone.parameters(), lr=1.0e-3)) + backbone.set_optimiser(torch.optim.sgd.SGD(backbone.parameters(), lr=1.0e-3)) super().__init__( name, @@ -105,7 +105,7 @@ def step(self) -> None: """ # Get a batch of data. assert isinstance(self.loaders, torch.utils.data.DataLoader) - data: Dict[str, Any] = next(iter(self.loaders)) + data: dict[str, Any] = next(iter(self.loaders)) # Make sure the model is in evaluation mode. self.model.eval() @@ -120,7 +120,7 @@ def step(self) -> None: plot_embedding( embeddings.detach().cpu(), - get_sample_index(data), + get_sample_index(data), # type: ignore[arg-type] self.global_params["data_root"], self.params["dataset_params"], show=True, diff --git a/minerva/utils/utils.py b/minerva/utils/utils.py index 6179789cd..405af5259 100644 --- a/minerva/utils/utils.py +++ b/minerva/utils/utils.py @@ -107,7 +107,6 @@ # ---+ Inbuilt +------------------------------------------------------------------------------------------------------- import cmath import functools -import glob import hashlib import importlib import inspect diff --git a/tests/test_logging.py b/tests/test_logging.py index df073bcdf..5d89f4279 100644 --- a/tests/test_logging.py +++ b/tests/test_logging.py @@ -156,7 +156,7 @@ def test_SupervisedStepLogger( } data.append(batch) - logger.step(i, i, *supervised_torchgeo_io(batch, model, device=default_device, train=train)) # type: ignore[arg-type] + logger.step(i, i, *supervised_torchgeo_io(batch, model, device=default_device, train=train)) # type: ignore[arg-type] # noqa: E501 logs = logger.get_logs assert logs["batch_num"] == std_n_batches - 1 @@ -297,7 +297,7 @@ def test_SSLStepLogger( logger.step( i, i, - *ssl_pair_torchgeo_io((batch, batch), model, device=default_device, train=train), # type: ignore[arg-type] + *ssl_pair_torchgeo_io((batch, batch), model, device=default_device, train=train), # type: ignore[arg-type] # noqa: E501 ) logs = logger.get_logs From da54cf36c53b5f00c84352ac4550e384521ff14b Mon Sep 17 00:00:00 2001 From: Harry Baker Date: Fri, 6 Sep 2024 20:23:11 +0100 Subject: [PATCH 60/63] Eliminated `Dict`, `List`, `Tuple` and `Union` type hints --- minerva/datasets/collators.py | 6 +- minerva/datasets/dfc.py | 20 +-- minerva/datasets/factory.py | 120 ++++++++--------- minerva/datasets/paired.py | 46 +++---- minerva/datasets/ssl4eos12.py | 4 +- minerva/datasets/utils.py | 43 +++--- minerva/logger/steplog.py | 55 ++++---- minerva/logger/tasklog.py | 44 +++---- minerva/models/__depreciated.py | 18 +-- minerva/models/change_detector.py | 8 +- minerva/models/classifiers.py | 8 +- minerva/models/core.py | 45 +++---- minerva/models/fcn.py | 8 +- minerva/models/psp.py | 26 ++-- minerva/models/resnet.py | 36 +++-- minerva/models/siamese.py | 32 ++--- minerva/models/unet.py | 8 +- minerva/optimisers.py | 4 +- minerva/pytorchtools.py | 6 +- minerva/samplers.py | 24 ++-- minerva/scheduler.py | 3 +- minerva/tasks/core.py | 18 +-- minerva/tasks/knn.py | 10 +- minerva/trainer.py | 36 +++-- minerva/transforms.py | 126 ++++++++---------- minerva/utils/runner.py | 16 +-- minerva/utils/utils.py | 182 +++++++++++++------------- minerva/utils/visutils.py | 142 ++++++++++---------- scripts/MinervaExp.py | 6 +- scripts/MinervaPipe.py | 4 +- scripts/RunTensorBoard.py | 4 +- scripts/TorchWeightDownloader.py | 2 +- tests/conftest.py | 44 +++---- tests/test_datasets/test_collators.py | 6 +- tests/test_datasets/test_factory.py | 16 +-- tests/test_datasets/test_paired.py | 4 +- tests/test_logging.py | 24 ++-- tests/test_modelio.py | 16 +-- tests/test_models/test_core.py | 5 +- tests/test_models/test_fcn.py | 4 +- tests/test_models/test_resnets.py | 10 +- tests/test_models/test_unet.py | 8 +- tests/test_samplers.py | 6 +- tests/test_trainer.py | 10 +- tests/test_transforms.py | 8 +- tests/test_utils/test_runner.py | 4 +- tests/test_utils/test_utils.py | 40 +++--- tests/test_utils/test_visutils.py | 32 ++--- 48 files changed, 638 insertions(+), 709 deletions(-) diff --git a/minerva/datasets/collators.py b/minerva/datasets/collators.py index 3268709e0..2534e2b15 100644 --- a/minerva/datasets/collators.py +++ b/minerva/datasets/collators.py @@ -39,7 +39,7 @@ # ===================================================================================================================== # IMPORTS # ===================================================================================================================== -from typing import Any, Callable, Dict, Iterable, Tuple +from typing import Any, Callable, Iterable from hydra.utils import get_method from torchgeo.datasets.utils import stack_samples @@ -67,8 +67,8 @@ def get_collator( def stack_sample_pairs( - samples: Iterable[Tuple[Dict[Any, Any], Dict[Any, Any]]] -) -> Tuple[Dict[Any, Any], Dict[Any, Any]]: + samples: Iterable[tuple[dict[Any, Any], dict[Any, Any]]] +) -> tuple[dict[Any, Any], dict[Any, Any]]: """Takes a list of paired sample dicts and stacks them into a tuple of batches of sample dicts. Args: diff --git a/minerva/datasets/dfc.py b/minerva/datasets/dfc.py index 9b8257d00..49f8255f7 100644 --- a/minerva/datasets/dfc.py +++ b/minerva/datasets/dfc.py @@ -38,7 +38,7 @@ # ===================================================================================================================== from glob import glob from pathlib import Path -from typing import Any, Callable, Dict, List, Optional, Tuple +from typing import Any, Callable, Optional import numpy as np import pandas as pd @@ -84,7 +84,7 @@ class BaseSenS12MS(NonGeoDataset): S2_BANDS_MR = [5, 6, 7, 9, 12, 13] S2_BANDS_LR = [1, 10, 11] - splits: List[str] = [] + splits: list[str] = [] igbp = False @@ -133,13 +133,13 @@ def __init__( # Make sure parent dir exists. assert self.root.exists() - self.samples: List[Dict[str, str]] + self.samples: list[dict[str, str]] def load_sample( self, - sample: Dict[str, str], + sample: dict[str, str], index: int, - ) -> Dict[str, Any]: + ) -> dict[str, Any]: """Util function for reading data from single sample. Args: @@ -192,7 +192,7 @@ def get_ninputs(self) -> int: n_inputs += 2 return n_inputs - def get_display_channels(self) -> Tuple[List[int], int]: + def get_display_channels(self) -> tuple[list[int], int]: """Select channels for preview images. Returns: @@ -303,7 +303,7 @@ def load_lc(self, path: str) -> Tensor: return lc - def __getitem__(self, index: int) -> Dict[str, Any]: + def __getitem__(self, index: int) -> dict[str, Any]: """Get a single example from the dataset""" # Get and load sample from index file. @@ -393,11 +393,11 @@ def __init__( def plot( self, - sample: Dict[str, Tensor], + sample: dict[str, Tensor], show_titles: bool = True, suptitle: Optional[str] = None, - classes: Optional[Dict[int, str]] = None, - colours: Optional[Dict[int, str]] = None, + classes: Optional[dict[int, str]] = None, + colours: Optional[dict[int, str]] = None, ) -> Figure: """Plot a sample from the dataset. diff --git a/minerva/datasets/factory.py b/minerva/datasets/factory.py index 0c959af00..c3e4b3adb 100644 --- a/minerva/datasets/factory.py +++ b/minerva/datasets/factory.py @@ -50,7 +50,7 @@ from copy import deepcopy from datetime import timedelta from pathlib import Path -from typing import Any, Dict, Iterable, List, Optional, Tuple, Union +from typing import Any, Iterable, Optional import hydra import numpy as np @@ -85,11 +85,11 @@ # METHODS # ===================================================================================================================== def create_subdataset( - paths: Union[str, Iterable[str]], - subdataset_params: Dict[str, Any], + paths: str | Iterable[str], + subdataset_params: dict[str, Any], transformations: Optional[Any], sample_pairs: bool = False, -) -> Union[GeoDataset, NonGeoDataset]: +) -> GeoDataset | NonGeoDataset: """Creates a sub-dataset based on the parameters supplied. Args: @@ -107,7 +107,7 @@ def create_subdataset( if "crs" in params: crs = CRS.from_epsg(params["crs"]) - subdataset: Union[GeoDataset, NonGeoDataset] + subdataset: GeoDataset | NonGeoDataset if "paths" in params: if sample_pairs: params["dataset"] = params["_target_"] @@ -137,15 +137,15 @@ def create_subdataset( def get_subdataset( - data_directory: Union[Iterable[str], str, Path], - dataset_params: Dict[str, Any], + data_directory: Iterable[str] | str | Path, + dataset_params: dict[str, Any], key: str, transformations: Optional[Any], sample_pairs: bool = False, cache: bool = True, - cache_dir: Union[str, Path] = "", + cache_dir: str | Path = "", auto_norm: bool = False, -) -> Union[GeoDataset, NonGeoDataset]: +) -> GeoDataset | NonGeoDataset: """Get a subdataset based on the parameters specified. If ``cache==True``, this will attempt to load a cached version of the dataset instance. @@ -174,7 +174,7 @@ def get_subdataset( sub_dataset_params.get("paths", sub_dataset_params.get("root")), ) - sub_dataset: Optional[Union[GeoDataset, NonGeoDataset]] + sub_dataset: Optional[GeoDataset | NonGeoDataset] if cache or sub_dataset_params.get("cache_dataset"): this_hash = utils.make_hash(sub_dataset_params) @@ -258,13 +258,13 @@ def get_subdataset( def make_dataset( - data_directory: Union[Iterable[str], str, Path], - dataset_params: Dict[Any, Any], + data_directory: Iterable[str] | str | Path, + dataset_params: dict[Any, Any], sample_pairs: bool = False, change_detection: bool = False, cache: bool = True, - cache_dir: Union[str, Path] = "", -) -> Tuple[Any, List[Any]]: + cache_dir: str | Path = "", +) -> tuple[Any, list[Any]]: """Constructs a dataset object from ``n`` sub-datasets given by the parameters supplied. Args: @@ -285,9 +285,7 @@ def make_dataset( """ # --+ MAKE SUB-DATASETS +=========================================================================================+ # List to hold all the sub-datasets defined by dataset_params to be intersected together into a single dataset. - sub_datasets: Union[ - List[GeoDataset], List[Union[NonGeoDataset, MinervaConcatDataset]] - ] = [] + sub_datasets: list[GeoDataset] | list[NonGeoDataset | MinervaConcatDataset] = [] if OmegaConf.is_config(dataset_params): dataset_params = OmegaConf.to_object(dataset_params) # type: ignore[assignment] @@ -321,7 +319,7 @@ def make_dataset( add_target_transforms = type_dataset_params["transforms"] continue - type_subdatasets: Union[List[GeoDataset], List[NonGeoDataset]] = [] + type_subdatasets: list[GeoDataset] | list[NonGeoDataset] = [] multi_datasets_exist = False @@ -459,10 +457,10 @@ def make_dataset( def construct_dataloader( - data_directory: Union[Iterable[str], str, Path], - dataset_params: Dict[str, Any], - sampler_params: Dict[str, Any], - dataloader_params: Dict[str, Any], + data_directory: Iterable[str] | str | Path, + dataset_params: dict[str, Any], + sampler_params: dict[str, Any], + dataloader_params: dict[str, Any], batch_size: int, collator_target: str = "torchgeo.datasets.stack_samples", rank: int = 0, @@ -470,7 +468,7 @@ def construct_dataloader( sample_pairs: bool = False, change_detection: bool = False, cache: bool = True, - cache_dir: Union[Path, str] = "", + cache_dir: Path | str = "", ) -> DataLoader[Iterable[Any]]: """Constructs a :class:`~torch.utils.data.DataLoader` object from the parameters provided for the datasets, sampler, collator and transforms. @@ -555,8 +553,8 @@ def construct_dataloader( def _add_class_transform( - class_matrix: Dict[int, int], dataset_params: Dict[str, Any], target_key: str -) -> Dict[str, Any]: + class_matrix: dict[int, int], dataset_params: dict[str, Any], target_key: str +) -> dict[str, Any]: class_transform = { "ClassTransform": { "_target_": "minerva.transforms.ClassTransform", @@ -636,11 +634,11 @@ def make_loaders( p_dist: bool = False, task_name: Optional[str] = None, **params, -) -> Tuple[ - Union[Dict[str, DataLoader[Iterable[Any]]], DataLoader[Iterable[Any]]], - Union[Dict[str, int], int], - List[Tuple[int, int]], - Dict[Any, Any], +) -> tuple[ + dict[str, DataLoader[Iterable[Any]]] | DataLoader[Iterable[Any]], + dict[str, int] | int, + list[tuple[int, int]], + dict[Any, Any], ]: """Constructs train, validation and test datasets and places into :class:`~torch.utils.data.DataLoader` objects. @@ -684,14 +682,14 @@ def make_loaders( cache_dir = params["cache_dir"] # Gets out the parameters for the DataLoaders from params. - dataloader_params: Dict[Any, Any] = deepcopy( + dataloader_params: dict[Any, Any] = deepcopy( utils.fallback_params("loader_params", task_params, params) ) if OmegaConf.is_config(dataloader_params): dataloader_params = OmegaConf.to_object(dataloader_params) # type: ignore[assignment] - dataset_params: Dict[str, Any] = utils.fallback_params( + dataset_params: dict[str, Any] = utils.fallback_params( "dataset_params", task_params, params ) @@ -701,7 +699,7 @@ def make_loaders( batch_size: int = utils.fallback_params("batch_size", task_params, params) model_type = utils.fallback_params("model_type", task_params, params) - class_dist: List[Tuple[int, int]] = [(0, 0)] + class_dist: list[tuple[int, int]] = [(0, 0)] classes = utils.fallback_params("classes", data_config, params, None) cmap_dict = utils.fallback_params("colours", data_config, params, None) @@ -712,28 +710,26 @@ def make_loaders( if n_classes: classes = {i: f"class {i}" for i in range(n_classes)} - new_classes: Dict[int, str] = {} - new_colours: Dict[int, str] = {} - class_matrix: Dict[int, int] = {} + new_classes: dict[int, str] = {} + new_colours: dict[int, str] = {} + class_matrix: dict[int, int] = {} - sample_pairs: Union[bool, Any] = utils.fallback_params( - "sample_pairs", task_params, params, False - ) + sample_pairs = utils.fallback_params("sample_pairs", task_params, params, False) - change_detection: Union[bool, Any] = utils.fallback_params( + change_detection = utils.fallback_params( "change_detection", task_params, params, False ) elim = utils.fallback_params("elim", task_params, params, False) cache = utils.fallback_params("cache_dataset", task_params, params, True) - n_batches: Union[Dict[str, int], int] - loaders: Union[Dict[str, DataLoader[Iterable[Any]]], DataLoader[Iterable[Any]]] + n_batches: dict[str, int] | int + loaders: dict[str, DataLoader[Iterable[Any]]] | DataLoader[Iterable[Any]] collator_target = utils.fallback_params("collator", task_params, params, None) if "sampler" in dataset_params.keys(): - sampler_params: Dict[str, Any] = dataset_params["sampler"] + sampler_params: dict[str, Any] = dataset_params["sampler"] if not utils.check_substrings_in_string(model_type, "siamese"): new_classes, class_matrix, new_colours, class_dist = get_data_specs( @@ -775,7 +771,7 @@ def make_loaders( loaders = {} for mode in dataset_params.keys(): - mode_sampler_params: Dict[str, Any] = dataset_params[mode]["sampler"] + mode_sampler_params: dict[str, Any] = dataset_params[mode]["sampler"] if ( not utils.check_substrings_in_string(model_type, "siamese") @@ -854,14 +850,14 @@ def make_loaders( def get_data_specs( - manifest_name: Union[str, Path], - classes: Dict[int, str], - cmap_dict: Dict[int, str], - cache_dir: Optional[Union[str, Path]] = None, - data_dir: Optional[Union[str, Path]] = None, - dataset_params: Optional[Dict[str, Any]] = None, - sampler_params: Optional[Dict[str, Any]] = None, - dataloader_params: Optional[Dict[str, Any]] = None, + manifest_name: str | Path, + classes: dict[int, str], + cmap_dict: dict[int, str], + cache_dir: Optional[str | Path] = None, + data_dir: Optional[str | Path] = None, + dataset_params: Optional[dict[str, Any]] = None, + sampler_params: Optional[dict[str, Any]] = None, + dataloader_params: Optional[dict[str, Any]] = None, collator_target: str = "torchgeo.datasets.stack_samples", change_detection: bool = False, elim: bool = True, @@ -896,11 +892,11 @@ def get_data_specs( def get_manifest( - manifest_path: Union[str, Path], - data_dir: Optional[Union[str, Path]] = None, - dataset_params: Optional[Dict[str, Any]] = None, - sampler_params: Optional[Dict[str, Any]] = None, - loader_params: Optional[Dict[str, Any]] = None, + manifest_path: str | Path, + data_dir: Optional[str | Path] = None, + dataset_params: Optional[dict[str, Any]] = None, + sampler_params: Optional[dict[str, Any]] = None, + loader_params: Optional[dict[str, Any]] = None, collator_target: str = "torchgeo.datasets.stack_samples", change_detection: bool = False, ) -> DataFrame: @@ -960,10 +956,10 @@ def get_manifest( def make_manifest( - data_dir: Union[str, Path], - dataset_params: Dict[str, Any], - sampler_params: Dict[str, Any], - loader_params: Dict[str, Any], + data_dir: str | Path, + dataset_params: dict[str, Any], + sampler_params: dict[str, Any], + loader_params: dict[str, Any], collator_target: str = "torchgeo.datasets.stack_samples", change_detection: bool = False, ) -> DataFrame: @@ -981,7 +977,7 @@ def make_manifest( ~pandas.DataFrame: The completed manifest as a :class:`~pandas.DataFrame`. """ - def delete_transforms(params: Dict[str, Any]) -> None: + def delete_transforms(params: dict[str, Any]) -> None: assert target_key is not None if params[target_key] is None: return diff --git a/minerva/datasets/paired.py b/minerva/datasets/paired.py index 474f1ad29..71130e254 100644 --- a/minerva/datasets/paired.py +++ b/minerva/datasets/paired.py @@ -44,7 +44,7 @@ # ===================================================================================================================== import random from inspect import signature -from typing import Any, Callable, Dict, Optional, Sequence, Tuple, Union, overload +from typing import Any, Callable, Optional, Sequence, overload import hydra import matplotlib.pyplot as plt @@ -83,10 +83,10 @@ class PairedGeoDataset(RasterDataset): def __new__( # type: ignore[misc] cls, - dataset: Union[Callable[..., GeoDataset], GeoDataset], + dataset: Callable[..., GeoDataset] | GeoDataset, *args, **kwargs, - ) -> Union["PairedGeoDataset", "PairedUnionDataset"]: + ) -> "PairedGeoDataset" | "PairedUnionDataset": if isinstance(dataset, UnionDataset): return PairedUnionDataset( dataset.datasets[0], dataset.datasets[1], *args, **kwargs @@ -112,7 +112,7 @@ def __init__(self, dataset: str, *args, **kwargs) -> None: ... # pragma: no cov def __init__( self, - dataset: Union[Callable[..., GeoDataset], GeoDataset, str], + dataset: Callable[..., GeoDataset] | GeoDataset | str, *args, **kwargs, ) -> None: @@ -148,8 +148,8 @@ def __init__( ) def __getitem__( # type: ignore[override] - self, queries: Tuple[BoundingBox, BoundingBox] - ) -> Tuple[Dict[str, Any], ...]: + self, queries: tuple[BoundingBox, BoundingBox] + ) -> tuple[dict[str, Any], ...]: return self.dataset.__getitem__(queries[0]), self.dataset.__getitem__( queries[1] ) @@ -225,7 +225,7 @@ def __repr__(self) -> Any: @staticmethod def plot( - sample: Dict[str, Any], + sample: dict[str, Any], show_titles: bool = True, suptitle: Optional[str] = None, ) -> Figure: @@ -264,7 +264,7 @@ def plot( def plot_random_sample( self, - size: Union[Tuple[int, int], int], + size: tuple[int, int] | int, res: float, show_titles: bool = True, suptitle: Optional[str] = None, @@ -327,8 +327,8 @@ def __init__( self.datasets = new_datasets def __getitem__( # type: ignore[override] - self, query: Tuple[BoundingBox, BoundingBox] - ) -> Tuple[Dict[str, Any], Dict[str, Any]]: + self, query: tuple[BoundingBox, BoundingBox] + ) -> tuple[dict[str, Any], dict[str, Any]]: """Retrieve image and metadata indexed by query. Uses :meth:`torchgeo.datasets.UnionDataset.__getitem__` to send each query of the pair off to get a @@ -401,7 +401,7 @@ def __getnewargs__(self): def __init__( self, dataset: Callable[..., NonGeoDataset], - size: Union[Tuple[int, int], int], + size: tuple[int, int] | int, max_r: int, season: bool = False, *args, @@ -412,7 +412,7 @@ def __init__( def __init__( self, dataset: NonGeoDataset, - size: Union[Tuple[int, int], int], + size: tuple[int, int] | int, max_r: int, season: bool = False, *args, @@ -423,7 +423,7 @@ def __init__( def __init__( self, dataset: str, - size: Union[Tuple[int, int], int], + size: tuple[int, int] | int, max_r: int, season: bool = False, *args, @@ -432,8 +432,8 @@ def __init__( def __init__( self, - dataset: Union[Callable[..., NonGeoDataset], NonGeoDataset, str], - size: Union[Tuple[int, int], int], + dataset: Callable[..., NonGeoDataset] | NonGeoDataset | str, + size: tuple[int, int] | int, max_r: int, season: bool = False, *args, @@ -480,7 +480,7 @@ def __init__( self.make_geo_pair = SamplePair(self.size, self.max_r, season=season) - def __getitem__(self, index: int) -> Tuple[Dict[str, Any], ...]: # type: ignore[override] + def __getitem__(self, index: int) -> tuple[dict[str, Any], ...]: # type: ignore[override] patch = self.dataset[index] image_a, image_b = self.make_geo_pair(patch["image"]) @@ -542,7 +542,7 @@ def __getattribute__(self, name: str) -> Any: @staticmethod def plot( - sample: Dict[str, Any], + sample: dict[str, Any], show_titles: bool = True, suptitle: Optional[str] = None, ) -> Figure: @@ -606,8 +606,8 @@ class PairedConcatDataset(MinervaConcatDataset): # type: ignore[type-arg] def __init__( self, - dataset1: Union[NonGeoDataset, "PairedConcatDataset"], - dataset2: Union[NonGeoDataset, "PairedConcatDataset"], + dataset1: NonGeoDataset | "PairedConcatDataset", + dataset2: NonGeoDataset | "PairedConcatDataset", size: Optional[int] = None, max_r: Optional[int] = None, ) -> None: @@ -636,7 +636,7 @@ def __init__( super().__init__(datasets) - def __getitem__(self, index: int) -> Tuple[Dict[str, Any], Dict[str, Any]]: + def __getitem__(self, index: int) -> tuple[dict[str, Any], dict[str, Any]]: """Retrieve image and metadata indexed by query. Uses :meth:`torch.utils.data.ConcatDataset.__getitem__` to get the pair of samples from @@ -694,7 +694,7 @@ def __init__(self, size: int = 64, max_r: int = 20, season: bool = False) -> Non # Transform to cut samples out at the desired output size. self.random_crop = RandomCrop(self.size) - def __call__(self, x: Tensor) -> Tuple[Tensor, Tensor]: + def __call__(self, x: Tensor) -> tuple[Tensor, Tensor]: if self.season: max_width, h, w = self._find_max_width(x[0]) @@ -721,7 +721,7 @@ def __call__(self, x: Tensor) -> Tuple[Tensor, Tensor]: # Now cut out 2 random samples from within that sampling area and return. return self.random_crop(sampling_area), self.random_crop(sampling_area) - def _find_max_width(self, x: Tensor) -> Tuple[int, int, int]: + def _find_max_width(self, x: Tensor) -> tuple[int, int, int]: max_width = self.max_width w = x.shape[-1] @@ -737,7 +737,7 @@ def _find_max_width(self, x: Tensor) -> Tuple[int, int, int]: return max_width, h, w @staticmethod - def _get_random_crop_params(img: Tensor, max_width: int) -> Tuple[int, int]: + def _get_random_crop_params(img: Tensor, max_width: int) -> tuple[int, int]: i = torch.randint(0, img.shape[-1] - max_width + 1, size=(1,)).item() j = torch.randint(0, img.shape[-2] - max_width + 1, size=(1,)).item() diff --git a/minerva/datasets/ssl4eos12.py b/minerva/datasets/ssl4eos12.py index 2698f1f87..3e6a447be 100644 --- a/minerva/datasets/ssl4eos12.py +++ b/minerva/datasets/ssl4eos12.py @@ -32,7 +32,7 @@ # ===================================================================================================================== import os import pickle -from typing import Dict, List, Optional, Tuple, Union +from typing import Optional import cv2 import lmdb @@ -263,7 +263,7 @@ def _init_db(self): with self.env.begin(write=False) as txn: # type: ignore[unreachable] self.length = txn.stat()["entries"] - def __getitem__(self, index: int) -> Dict[str, Union[Tuple[Tensor, ...], Tensor]]: + def __getitem__(self, index: int) -> dict[str, tuple[Tensor, ...] | Tensor]: if self.lmdb_file: if self.is_slurm_job: # Delay loading LMDB data until after initialization diff --git a/minerva/datasets/utils.py b/minerva/datasets/utils.py index 58c9b23b9..0e7935019 100644 --- a/minerva/datasets/utils.py +++ b/minerva/datasets/utils.py @@ -46,18 +46,7 @@ # ===================================================================================================================== import pickle from pathlib import Path -from typing import ( - Any, - Callable, - Dict, - Iterable, - List, - Literal, - Optional, - Sequence, - Tuple, - Union, -) +from typing import Any, Callable, Iterable, Literal, Optional, Sequence import numpy as np from nptyping import NDArray @@ -81,7 +70,7 @@ class MinervaNonGeoDataset(NonGeoDataset): def __or__( self, - other: Union["MinervaNonGeoDataset", "MinervaConcatDataset"], + other: "MinervaNonGeoDataset" | "MinervaConcatDataset", ) -> "MinervaConcatDataset": """Take the union of two :class:`MinervaNonGeoDataset`. @@ -99,7 +88,7 @@ def __or__( class MinervaConcatDataset(ConcatDataset): # type: ignore[type-arg] def __or__( self, - other: Union[MinervaNonGeoDataset, "MinervaConcatDataset"], + other: MinervaNonGeoDataset | "MinervaConcatDataset", ) -> "MinervaConcatDataset": """Take the union of two :class:`MinervaNonGeoDataset`. @@ -139,7 +128,7 @@ def intersect_datasets(datasets: Sequence[GeoDataset]) -> IntersectionDataset: ~torchgeo.datasets.IntersectionDataset: Final dataset object representing an intersection of all the parsed datasets. """ - master_dataset: Union[GeoDataset, IntersectionDataset] = datasets[0] + master_dataset: GeoDataset | IntersectionDataset = datasets[0] for i in range(len(datasets) - 1): master_dataset = master_dataset & datasets[i + 1] @@ -150,7 +139,7 @@ def intersect_datasets(datasets: Sequence[GeoDataset]) -> IntersectionDataset: def unionise_datasets( datasets: Sequence[GeoDataset], - transforms: Optional[Callable[[Dict[str, Any]], Dict[str, Any]]] = None, + transforms: Optional[Callable[[dict[str, Any]], dict[str, Any]]] = None, ) -> UnionDataset: """Unionises a list of :class:`~torchgeo.datasets.GeoDataset` together to return a single dataset object. @@ -166,7 +155,7 @@ def unionise_datasets( Returns: ~torchgeo.datasets.UnionDataset: Final dataset object representing an union of all the parsed datasets. """ - master_dataset: Union[GeoDataset, UnionDataset] = datasets[0] + master_dataset: GeoDataset | UnionDataset = datasets[0] for i in range(len(datasets) - 1): master_dataset = master_dataset | datasets[i + 1] @@ -178,7 +167,7 @@ def unionise_datasets( def concatenate_datasets( datasets: Sequence[NonGeoDataset], - transforms: Optional[Callable[[Dict[str, Any]], Dict[str, Any]]] = None, + transforms: Optional[Callable[[dict[str, Any]], dict[str, Any]]] = None, ) -> MinervaConcatDataset: """Unionises a list of :class:`~torchgeo.datasets.GeoDataset` together to return a single dataset object. @@ -195,7 +184,7 @@ def concatenate_datasets( ~minerva.datasets.MinervaConcatDataset: Final dataset object representing an union of all the parsed datasets. """ assert isinstance(datasets[0], MinervaNonGeoDataset) - master_dataset: Union[MinervaNonGeoDataset, MinervaConcatDataset] = datasets[0] + master_dataset: MinervaNonGeoDataset | MinervaConcatDataset = datasets[0] for i in range(len(datasets) - 1): master_dataset = master_dataset | datasets[i + 1] # type: ignore[operator] @@ -206,9 +195,7 @@ def concatenate_datasets( return master_dataset -def make_bounding_box( - roi: Union[Sequence[float], bool] = False -) -> Optional[BoundingBox]: +def make_bounding_box(roi: Sequence[float] | bool = False) -> Optional[BoundingBox]: """Construct a :class:`~torchgeo.datasets.utils.BoundingBox` object from the corners of the box. ``False`` for no :class:`~torchgeo.datasets.utils.BoundingBox`. @@ -246,7 +233,7 @@ def load_all_samples( ~numpy.ndarray: 2D array of the class modes within every sample defined by the parsed :class:`~torch.utils.data.DataLoader`. """ - sample_modes: List[List[Tuple[int, int]]] = [] + sample_modes: list[list[tuple[int, int]]] = [] for sample in tqdm(dataloader): modes = utils.find_modes(sample[target_key]) sample_modes.append(modes) @@ -255,8 +242,8 @@ def load_all_samples( def get_random_sample( - dataset: GeoDataset, size: Union[Tuple[int, int], int], res: float -) -> Dict[str, Any]: + dataset: GeoDataset, size: tuple[int, int] | int, res: float +) -> dict[str, Any]: """Gets a random sample from the provided dataset of size ``size`` and at ``res`` resolution. Args: @@ -272,7 +259,7 @@ def get_random_sample( def load_dataset_from_cache( cached_dataset_path: Path, -) -> Union[NonGeoDataset, GeoDataset]: +) -> NonGeoDataset | GeoDataset: """Load a pickled dataset object in from a cache. Args: @@ -290,7 +277,7 @@ def load_dataset_from_cache( def cache_dataset( - dataset: Union[GeoDataset, NonGeoDataset], cached_dataset_path: Path + dataset: GeoDataset | NonGeoDataset, cached_dataset_path: Path ) -> None: """Pickle and cache a dataset object. @@ -305,7 +292,7 @@ def cache_dataset( pickle.dump(dataset, fp) -def masks_or_labels(dataset_params: Dict[str, Any]) -> str: +def masks_or_labels(dataset_params: dict[str, Any]) -> str: for key in dataset_params.keys(): if key not in ( "sampler", diff --git a/minerva/logger/steplog.py b/minerva/logger/steplog.py index a8bfa73ec..0b3bc9bde 100644 --- a/minerva/logger/steplog.py +++ b/minerva/logger/steplog.py @@ -46,16 +46,7 @@ import abc import math from abc import ABC -from typing import ( - TYPE_CHECKING, - Any, - Callable, - Dict, - Optional, - SupportsFloat, - Tuple, - Union, -) +from typing import TYPE_CHECKING, Any, Callable, Optional, SupportsFloat import mlflow import numpy as np @@ -132,10 +123,10 @@ def __init__( task_name: str, n_batches: int, batch_size: int, - output_size: Tuple[int, ...], + output_size: tuple[int, ...], record_int: bool = True, record_float: bool = False, - writer: Optional[Union[SummaryWriter, Run]] = None, + writer: Optional[SummaryWriter | Run] = None, model_type: str = "", **kwargs, ) -> None: @@ -156,8 +147,8 @@ def __init__( self.model_type = model_type - self.logs: Dict[str, Any] = {} - self.results: Dict[str, Any] = {} + self.logs: dict[str, Any] = {} + self.results: dict[str, Any] = {} def __call__( self, @@ -185,7 +176,7 @@ def log( loss: Tensor, z: Optional[Tensor] = None, y: Optional[Tensor] = None, - index: Optional[Union[int, BoundingBox]] = None, + index: Optional[int | BoundingBox] = None, *args, **kwargs, ) -> None: @@ -242,7 +233,7 @@ def write_metric( mlflow.log_metric(key, value) # pragma: no cover @property - def get_logs(self) -> Dict[str, Any]: + def get_logs(self) -> dict[str, Any]: """Gets the logs dictionary. Returns: @@ -251,7 +242,7 @@ def get_logs(self) -> Dict[str, Any]: return self.logs @property - def get_results(self) -> Dict[str, Any]: + def get_results(self) -> dict[str, Any]: """Gets the results dictionary. Returns: @@ -308,10 +299,10 @@ def __init__( task_name: str, n_batches: int, batch_size: int, - output_size: Tuple[int, int], + output_size: tuple[int, int], record_int: bool = True, record_float: bool = False, - writer: Optional[Union[SummaryWriter, Run]] = None, + writer: Optional[SummaryWriter | Run] = None, model_type: str = "", n_classes: Optional[int] = None, **kwargs, @@ -329,13 +320,13 @@ def __init__( if n_classes is None: raise ValueError("`n_classes` must be specified for this type of logger!") - self.logs: Dict[str, Any] = { + self.logs: dict[str, Any] = { "batch_num": 0, "total_loss": 0.0, "total_correct": 0.0, } - self.results: Dict[str, Any] = { + self.results: dict[str, Any] = { "y": None, "z": None, "probs": None, @@ -353,7 +344,7 @@ def __init__( # Allocate memory for the integer values to be recorded. if self.record_int: - int_log_shape: Tuple[int, ...] + int_log_shape: tuple[int, ...] if check_substrings_in_string(self.model_type, "scene-classifier"): if check_substrings_in_string(self.model_type, "multilabel"): int_log_shape = (self.n_batches, self.batch_size, n_classes) @@ -374,7 +365,7 @@ def __init__( # Allocate memory for the floating point values to be recorded. if self.record_float: - float_log_shape: Tuple[int, ...] + float_log_shape: tuple[int, ...] if check_substrings_in_string(self.model_type, "scene-classifier"): float_log_shape = (self.n_batches, self.batch_size, n_classes) else: @@ -415,7 +406,7 @@ def log( loss: Tensor, z: Optional[Tensor] = None, y: Optional[Tensor] = None, - index: Optional[Union[int, BoundingBox]] = None, + index: Optional[int | BoundingBox] = None, *args, **kwargs, ) -> None: @@ -542,7 +533,7 @@ def __init__( batch_size: int, record_int: bool = True, record_float: bool = False, - writer: Optional[Union[SummaryWriter, Run]] = None, + writer: Optional[SummaryWriter | Run] = None, model_type: str = "", **kwargs, ) -> None: @@ -557,14 +548,14 @@ def __init__( **kwargs, ) - self.logs: Dict[str, Any] = { + self.logs: dict[str, Any] = { "batch_num": 0, "total_loss": 0.0, "total_correct": 0.0, "total_top5": 0.0, } - self.results: Dict[str, Any] = { + self.results: dict[str, Any] = { "y": None, "z": None, "probs": None, @@ -579,7 +570,7 @@ def log( loss: Tensor, z: Optional[Tensor] = None, y: Optional[Tensor] = None, - index: Optional[Union[int, BoundingBox]] = None, + index: Optional[int | BoundingBox] = None, *args, **kwargs, ) -> None: @@ -662,10 +653,10 @@ def __init__( task_name: str, n_batches: int, batch_size: int, - output_size: Tuple[int, int], + output_size: tuple[int, int], record_int: bool = True, record_float: bool = False, - writer: Optional[Union[SummaryWriter, Run]] = None, + writer: Optional[SummaryWriter | Run] = None, model_type: str = "", **kwargs, ) -> None: @@ -681,7 +672,7 @@ def __init__( **kwargs, ) - self.logs: Dict[str, Any] = { + self.logs: dict[str, Any] = { "batch_num": 0, "total_loss": 0.0, "avg_loss": 0.0, @@ -706,7 +697,7 @@ def log( loss: Tensor, z: Optional[Tensor] = None, y: Optional[Tensor] = None, - index: Optional[Union[int, BoundingBox]] = None, + index: Optional[int | BoundingBox] = None, *args, **kwargs, ) -> None: diff --git a/minerva/logger/tasklog.py b/minerva/logger/tasklog.py index e341eb629..f8ad92735 100644 --- a/minerva/logger/tasklog.py +++ b/minerva/logger/tasklog.py @@ -42,7 +42,7 @@ # ===================================================================================================================== import abc from abc import ABC -from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union +from typing import TYPE_CHECKING, Any, Optional if TYPE_CHECKING: # pragma: no cover from torch.utils.tensorboard.writer import SummaryWriter @@ -86,8 +86,8 @@ class MinervaTaskLogger(ABC): __metaclass__ = abc.ABCMeta - metric_types: List[str] = [] - special_metric_types: List[str] = [] + metric_types: list[str] = [] + special_metric_types: list[str] = [] logger_cls: str def __init__( @@ -95,11 +95,11 @@ def __init__( task_name: str, n_batches: int, batch_size: int, - output_size: Tuple[int, ...], - step_logger_params: Optional[Dict[str, Any]] = None, + output_size: tuple[int, ...], + step_logger_params: Optional[dict[str, Any]] = None, record_int: bool = True, record_float: bool = False, - writer: Optional[Union[SummaryWriter, Run]] = None, + writer: Optional[SummaryWriter | Run] = None, **params, ) -> None: super(MinervaTaskLogger, self).__init__() @@ -137,7 +137,7 @@ def __init__( self.metric_types += self.special_metric_types # Creates a dict to hold the loss and accuracy results from training, validation and testing. - self.metrics: Dict[str, Any] = {} + self.metrics: dict[str, Any] = {} for metric in self.metric_types: self.metrics[f"{self.task_name}_{metric}"] = {"x": [], "y": []} @@ -207,7 +207,7 @@ def calc_metrics(self, epoch_no: int) -> None: self.log_epoch_number(epoch_no) @abc.abstractmethod - def _calc_metrics(self, logs: Dict[str, Any]) -> None: + def _calc_metrics(self, logs: dict[str, Any]) -> None: """Updates metrics with epoch results. Must be defined before use. @@ -226,7 +226,7 @@ def log_epoch_number(self, epoch_no: int) -> None: self.metrics[metric]["x"].append(epoch_no) @property - def get_metrics(self) -> Dict[str, Any]: + def get_metrics(self) -> dict[str, Any]: """Get the ``metrics`` dictionary. Returns: @@ -235,7 +235,7 @@ def get_metrics(self) -> Dict[str, Any]: return self.metrics @property - def get_logs(self) -> Dict[str, Any]: + def get_logs(self) -> dict[str, Any]: """Get the logs of each step from the latest epoch of the task. Returns: @@ -246,7 +246,7 @@ def get_logs(self) -> Dict[str, Any]: return self.step_logger.get_logs @property - def get_results(self) -> Dict[str, Any]: + def get_results(self) -> dict[str, Any]: """Get the results of each step from the latest epoch of the task. Returns: @@ -266,8 +266,8 @@ def log_null(self) -> None: self.metrics[metric]["y"].append(np.NAN) def get_sub_metrics( - self, pattern: Tuple[str, ...] = ("train", "val") - ) -> Dict[str, Any]: + self, pattern: tuple[str, ...] = ("train", "val") + ) -> dict[str, Any]: """Gets a subset of the metrics dictionary with keys containing strings in the pattern. Useful for getting the train and validation metrics for plotting for example. @@ -315,7 +315,7 @@ class SupervisedTaskLogger(MinervaTaskLogger): .. versionadded:: 0.27 """ - metric_types: List[str] = ["loss", "acc", "miou"] + metric_types: list[str] = ["loss", "acc", "miou"] logger_cls = "minerva.logger.steplog.SupervisedStepLogger" def __init__( @@ -323,11 +323,11 @@ def __init__( task_name: str, n_batches: int, batch_size: int, - output_size: Tuple[int, ...], - step_logger_params: Optional[Dict[str, Any]] = None, + output_size: tuple[int, ...], + step_logger_params: Optional[dict[str, Any]] = None, record_int: bool = True, record_float: bool = False, - writer: Optional[Union[SummaryWriter, Run]] = None, + writer: Optional[SummaryWriter | Run] = None, model_type: str = "segmentation", **params, ) -> None: @@ -344,7 +344,7 @@ def __init__( **params, ) - def _calc_metrics(self, logs: Dict[str, Any]) -> None: + def _calc_metrics(self, logs: dict[str, Any]) -> None: """Updates metrics with epoch results. Args: @@ -422,11 +422,11 @@ def __init__( task_name: str, n_batches: int, batch_size: int, - output_size: Tuple[int, ...], - step_logger_params: Optional[Dict[str, Any]] = None, + output_size: tuple[int, ...], + step_logger_params: Optional[dict[str, Any]] = None, record_int: bool = True, record_float: bool = False, - writer: Optional[Union[SummaryWriter, Run]] = None, + writer: Optional[SummaryWriter | Run] = None, model_type: str = "segmentation", sample_pairs: bool = False, **params, @@ -469,7 +469,7 @@ def __init__( if not getattr(self.step_logger, "euclidean", False): del self.metrics[f"{self.task_name}_euc_dist"] - def _calc_metrics(self, logs: Dict[str, Any]) -> None: + def _calc_metrics(self, logs: dict[str, Any]) -> None: """Updates metrics with epoch results. Args: diff --git a/minerva/models/__depreciated.py b/minerva/models/__depreciated.py index 2df888790..544a817d5 100644 --- a/minerva/models/__depreciated.py +++ b/minerva/models/__depreciated.py @@ -37,7 +37,7 @@ # IMPORTS # ===================================================================================================================== from collections import OrderedDict -from typing import Any, List, Optional, Sequence, Tuple, Union +from typing import Any, Optional, Sequence import numpy as np import torch.nn.modules as nn @@ -80,7 +80,7 @@ def __init__( criterion: Optional[Any] = None, input_size: int = 288, n_classes: int = 8, - hidden_sizes: Union[Tuple[int, ...], List[int], int] = (256, 144), + hidden_sizes: tuple[int, ...] | list[int] | int = (256, 144), ) -> None: super(MLP, self).__init__( criterion=criterion, input_size=(input_size,), n_classes=n_classes @@ -161,14 +161,14 @@ class CNN(MinervaModel): def __init__( self, criterion, - input_size: Tuple[int, int, int] = (4, 256, 256), + input_size: tuple[int, int, int] = (4, 256, 256), n_classes: int = 8, - features: Union[Tuple[int, ...], List[int]] = (2, 1, 1), - fc_sizes: Union[Tuple[int, ...], List[int]] = (128, 64), - conv_kernel_size: Union[int, Tuple[int, ...]] = 3, - conv_stride: Union[int, Tuple[int, ...]] = 1, - max_kernel_size: Union[int, Tuple[int, ...]] = 2, - max_stride: Union[int, Tuple[int, ...]] = 2, + features: tuple[int, ...] | list[int] = (2, 1, 1), + fc_sizes: tuple[int, ...] | list[int] = (128, 64), + conv_kernel_size: int | tuple[int, ...] = 3, + conv_stride: int | tuple[int, ...] = 1, + max_kernel_size: int | tuple[int, ...] = 2, + max_stride: int | tuple[int, ...] = 2, conv_do: bool = True, fc_do: bool = True, p_conv_do: float = 0.1, diff --git a/minerva/models/change_detector.py b/minerva/models/change_detector.py index b1f2fe8d7..72d816786 100644 --- a/minerva/models/change_detector.py +++ b/minerva/models/change_detector.py @@ -38,7 +38,7 @@ # IMPORTS # ===================================================================================================================== from pathlib import Path -from typing import Any, Dict, Optional, Tuple, Union +from typing import Any, Optional import torch from torch import Tensor @@ -57,7 +57,7 @@ class ChangeDetector(MinervaModel): def __init__( self, criterion: Optional[Module] = None, - input_size: Optional[Tuple[int]] = None, + input_size: Optional[tuple[int]] = None, n_classes: int = 1, scaler: Optional[GradScaler] = None, fc_dim: int = 512, @@ -65,8 +65,8 @@ def __init__( encoder_on: bool = False, filter_dim: int = 0, freeze_backbone: bool = False, - backbone_weight_path: Optional[Union[str, Path]] = None, - backbone_args: Dict[str, Any] = {}, + backbone_weight_path: Optional[str | Path] = None, + backbone_args: dict[str, Any] = {}, clamp_outputs: bool = False, ) -> None: super().__init__(criterion, input_size, n_classes, scaler) diff --git a/minerva/models/classifiers.py b/minerva/models/classifiers.py index bb3472798..c95eb42fa 100644 --- a/minerva/models/classifiers.py +++ b/minerva/models/classifiers.py @@ -42,7 +42,7 @@ # IMPORTS # ===================================================================================================================== from pathlib import Path -from typing import Any, Dict, Optional, Tuple, Union +from typing import Any, Optional import torch from torch import Tensor @@ -61,7 +61,7 @@ class FlexiSceneClassifier(MinervaBackbone): def __init__( self, criterion: Optional[Module] = None, - input_size: Optional[Tuple[int]] = None, + input_size: Optional[tuple[int]] = None, n_classes: int = 1, scaler: Optional[GradScaler] = None, fc_dim: int = 512, @@ -69,8 +69,8 @@ def __init__( encoder_on: bool = False, filter_dim: int = 0, freeze_backbone: bool = False, - backbone_weight_path: Optional[Union[str, Path]] = None, - backbone_args: Dict[str, Any] = {}, + backbone_weight_path: Optional[str | Path] = None, + backbone_args: dict[str, Any] = {}, clamp_outputs: bool = False, ) -> None: super().__init__(criterion, input_size, n_classes, scaler) diff --git a/minerva/models/core.py b/minerva/models/core.py index 8a0653498..493ea94c4 100644 --- a/minerva/models/core.py +++ b/minerva/models/core.py @@ -58,18 +58,7 @@ import warnings from abc import ABC from pathlib import Path -from typing import ( - Any, - Callable, - Iterable, - List, - Optional, - Sequence, - Tuple, - Type, - Union, - overload, -) +from typing import Any, Callable, Iterable, Optional, Sequence, Type, overload import numpy as np import torch @@ -101,7 +90,7 @@ class FilterOutputs(Module): indexes (int | list[int]): Index(es) of the inputs to pass forward. """ - def __init__(self, indexes: Union[int, List[int]]) -> None: + def __init__(self, indexes: int | list[int]) -> None: self.indexes = indexes def forward(self, inputs: Tensor) -> Tensor: @@ -134,7 +123,7 @@ class MinervaModel(Module, ABC): def __init__( self, criterion: Optional[Module] = None, - input_size: Optional[Tuple[int, ...]] = None, + input_size: Optional[tuple[int, ...]] = None, n_classes: Optional[int] = None, scaler: Optional[GradScaler] = None, ) -> None: @@ -148,7 +137,7 @@ def __init__( self.scaler = scaler # Output shape initialised as None. Should be set by calling determine_output_dim. - self.output_shape: Optional[Tuple[int, ...]] = None + self.output_shape: Optional[tuple[int, ...]] = None # Optimiser initialised as None as the model parameters created by its init is required to init a # torch optimiser. The optimiser MUST be set by calling set_optimiser before the model can be trained. @@ -206,19 +195,19 @@ def update_n_classes(self, n_classes: int) -> None: @overload def step( self, x: Tensor, y: Tensor, train: bool = False - ) -> Tuple[Tensor, Union[Tensor, Tuple[Tensor, ...]]]: ... # pragma: no cover + ) -> tuple[Tensor, Tensor | tuple[Tensor, ...]]: ... # pragma: no cover @overload def step( self, x: Tensor, *, train: bool = False - ) -> Tuple[Tensor, Union[Tensor, Tuple[Tensor, ...]]]: ... # pragma: no cover + ) -> tuple[Tensor, Tensor | tuple[Tensor, ...]]: ... # pragma: no cover def step( self, x: Tensor, y: Optional[Tensor] = None, train: bool = False, - ) -> Tuple[Tensor, Union[Tensor, Tuple[Tensor, ...]]]: + ) -> tuple[Tensor, Tensor | tuple[Tensor, ...]]: """Generic step of model fitting using a batch of data. Raises: @@ -248,7 +237,7 @@ def step( if train: self.optimiser.zero_grad() - z: Union[Tensor, Tuple[Tensor, ...]] + z: Tensor | tuple[Tensor, ...] loss: Tensor mix_precision: bool = True if self.scaler else False @@ -300,9 +289,9 @@ class MinervaWrapper(MinervaModel): def __init__( self, - model: Union[Module, Callable[..., Module]], + model: Module | Callable[..., Module], criterion: Optional[Module] = None, - input_size: Optional[Tuple[int, ...]] = None, + input_size: Optional[tuple[int, ...]] = None, n_classes: Optional[int] = None, scaler: Optional[GradScaler] = None, *args, @@ -386,7 +375,7 @@ class MinervaDataParallel(Module): # pragma: no cover def __init__( self, model: Module, - paralleliser: Union[Type[DataParallel], Type[DDP]], # type: ignore[type-arg] + paralleliser: Type[DataParallel] | Type[DDP], # type: ignore[type-arg] *args, **kwargs, ) -> None: @@ -396,7 +385,7 @@ def __init__( self.output_shape = model.output_shape self.n_classes = model.n_classes - def forward(self, *inputs: Tuple[Tensor, ...]) -> Tuple[Tensor, ...]: + def forward(self, *inputs: tuple[Tensor, ...]) -> tuple[Tensor, ...]: """Ensures a forward call to the model goes to the actual wrapped model. Args: @@ -410,7 +399,7 @@ def forward(self, *inputs: Tuple[Tensor, ...]) -> Tuple[Tensor, ...]: assert isinstance(z, tuple) and list(map(type, z)) == [Tensor] * len(z) return z - def __call__(self, *inputs) -> Tuple[Tensor, ...]: + def __call__(self, *inputs) -> tuple[Tensor, ...]: return self.forward(*inputs) def __getattr__(self, name): @@ -515,10 +504,10 @@ def get_torch_weights(weights_name: str) -> Optional[WeightsEnum]: def get_output_shape( model: Module, - image_dim: Union[Sequence[int], int], + image_dim: Sequence[int] | int, sample_pairs: bool = False, change_detection: bool = False, -) -> Tuple[int, ...]: +) -> tuple[int, ...]: """Gets the output shape of a model. Args: @@ -530,7 +519,7 @@ def get_output_shape( Returns: tuple[int, ...]: The shape of the output data from the model. """ - _image_dim: Union[Sequence[int], int] = image_dim + _image_dim: Sequence[int] | int = image_dim try: assert not isinstance(image_dim, int) if len(image_dim) == 1: @@ -634,7 +623,7 @@ def is_minerva_subtype(model: Module, subtype: type) -> bool: def extract_wrapped_model( - model: Union[MinervaModel, MinervaDataParallel, OptimizedModule] + model: MinervaModel | MinervaDataParallel | OptimizedModule, ) -> MinervaModel: """ Extracts the actual model object from within :class:`MinervaDataParallel` or diff --git a/minerva/models/fcn.py b/minerva/models/fcn.py index d80bb3962..9c32910b3 100644 --- a/minerva/models/fcn.py +++ b/minerva/models/fcn.py @@ -52,7 +52,7 @@ # ===================================================================================================================== # IMPORTS # ===================================================================================================================== -from typing import Any, Dict, Literal, Optional, Sequence, Tuple +from typing import Any, Literal, Optional, Sequence import torch import torch.nn.modules as nn @@ -100,12 +100,12 @@ class FCN(MinervaBackbone): def __init__( self, criterion: Any, - input_size: Tuple[int, ...] = (4, 256, 256), + input_size: tuple[int, ...] = (4, 256, 256), n_classes: int = 8, scaler: Optional[GradScaler] = None, backbone_weight_path: Optional[str] = None, freeze_backbone: bool = False, - backbone_kwargs: Dict[str, Any] = {}, + backbone_kwargs: dict[str, Any] = {}, ) -> None: super(FCN, self).__init__( criterion=criterion, @@ -294,7 +294,7 @@ def __init__( f"Variant {self.variant} does not match known types" ) - def forward(self, x: Tuple[Tensor, Tensor, Tensor, Tensor, Tensor]) -> Tensor: + def forward(self, x: tuple[Tensor, Tensor, Tensor, Tensor, Tensor]) -> Tensor: """Performs a forward pass of the decoder. Depending on DCN variant, will take multiple inputs throughout pass from the encoder. diff --git a/minerva/models/psp.py b/minerva/models/psp.py index e0c1a2276..d76984f0e 100644 --- a/minerva/models/psp.py +++ b/minerva/models/psp.py @@ -38,7 +38,7 @@ # ===================================================================================================================== # IMPORTS # ===================================================================================================================== -from typing import Any, Callable, Dict, Optional, Tuple, Union +from typing import Any, Callable, Optional import segmentation_models_pytorch as smp import torch @@ -106,9 +106,9 @@ def __init__( psp_dropout: float = 0.2, in_channels: Optional[int] = None, n_classes: int = 1, - activation: Optional[Union[str, Callable[..., Any]]] = None, + activation: Optional[str | Callable[..., Any]] = None, upsampling: int = 8, - aux_params: Optional[Dict[str, Any]] = None, + aux_params: Optional[dict[str, Any]] = None, backbone_weight_path: Optional[str] = None, freeze_backbone: bool = False, encoder: bool = True, @@ -150,7 +150,7 @@ def __init__( def make_segmentation_head( self, n_classes: int, - activation: Optional[Union[str, Callable[..., Any]]] = None, + activation: Optional[str | Callable[..., Any]] = None, upsampling: int = 8, ) -> None: self.segmentation_head = SegmentationHead( @@ -164,7 +164,7 @@ def make_segmentation_head( self.encoder_mode = True self.segmentation_on = True - def make_classification_head(self, aux_params: Dict[str, Any]) -> None: + def make_classification_head(self, aux_params: dict[str, Any]) -> None: # Makes the classification head. self.classification_head = ClassificationHead( in_channels=self.encoder.out_channels[-1], **aux_params @@ -198,7 +198,7 @@ def freeze_backbone(self, freeze: bool = True) -> None: """ self.encoder.requires_grad_(False if freeze else True) - def forward(self, x: Tensor) -> Union[Tensor, Tuple[Tensor, ...]]: + def forward(self, x: Tensor) -> Tensor | tuple[Tensor, ...]: f = self.encoder(x) if not self.encoder_mode: @@ -223,7 +223,7 @@ class MinervaPSP(MinervaWrapper): def __init__( self, criterion: Optional[Module] = None, - input_size: Optional[Tuple[int, ...]] = None, + input_size: Optional[tuple[int, ...]] = None, n_classes: int = 1, scaler: Optional[GradScaler] = None, encoder_name: str = "resnet34", @@ -232,9 +232,9 @@ def __init__( psp_out_channels: int = 512, psp_use_batchnorm: bool = True, psp_dropout: float = 0.2, - activation: Optional[Union[str, Callable[..., Any]]] = None, + activation: Optional[str | Callable[..., Any]] = None, upsampling: int = 8, - aux_params: Optional[Dict[str, Any]] = None, + aux_params: Optional[dict[str, Any]] = None, backbone_weight_path: Optional[str] = None, freeze_backbone: bool = False, encoder: bool = False, @@ -315,7 +315,7 @@ def forward(self, x): class PSPUNetDecoder(Module): def __init__( self, - encoder_channels: Tuple[int, int, int, int, int, int], + encoder_channels: tuple[int, int, int, int, int, int], n_classes: int, use_batchnorm: bool = True, dropout: float = 0.2, @@ -467,7 +467,7 @@ class MinervaPSPUNet(MinervaWrapper): def __init__( self, criterion: Optional[Module] = None, - input_size: Optional[Tuple[int, ...]] = None, + input_size: Optional[tuple[int, ...]] = None, n_classes: int = 1, scaler: Optional[GradScaler] = None, encoder_name: str = "resnet34", @@ -476,9 +476,9 @@ def __init__( psp_out_channels: int = 512, psp_use_batchnorm: bool = True, psp_dropout: float = 0.2, - activation: Optional[Union[str, Callable[..., Any]]] = None, + activation: Optional[str | Callable[..., Any]] = None, upsampling: int = 8, - aux_params: Optional[Dict[str, Any]] = None, + aux_params: Optional[dict[str, Any]] = None, backbone_weight_path: Optional[str] = None, freeze_backbone: bool = False, encoder: bool = False, diff --git a/minerva/models/resnet.py b/minerva/models/resnet.py index d9e437c0a..36c67cc49 100644 --- a/minerva/models/resnet.py +++ b/minerva/models/resnet.py @@ -46,7 +46,7 @@ # IMPORTS # ===================================================================================================================== import abc -from typing import Any, Callable, List, Optional, Tuple, Type, Union +from typing import Any, Callable, Optional, Type import torch import torch.nn.modules as nn @@ -122,14 +122,14 @@ class ResNet(MinervaModel): def __init__( self, - block: Type[Union[BasicBlock, Bottleneck]], - layers: Union[List[int], Tuple[int, int, int, int]], + block: Type[BasicBlock | Bottleneck], + layers: list[int] | tuple[int, int, int, int], in_channels: int = 3, n_classes: int = 8, zero_init_residual: bool = False, groups: int = 1, width_per_group: int = 64, - replace_stride_with_dilation: Optional[Tuple[bool, bool, bool]] = None, + replace_stride_with_dilation: Optional[tuple[bool, bool, bool]] = None, norm_layer: Optional[Callable[..., Module]] = None, encoder: bool = False, ) -> None: @@ -224,7 +224,7 @@ def __init__( def _make_layer( self, - block: Type[Union[BasicBlock, Bottleneck]], + block: Type[BasicBlock | Bottleneck], planes: int, blocks: int, stride: int = 1, @@ -296,7 +296,7 @@ def _make_layer( def _forward_impl( self, x: Tensor - ) -> Union[Tensor, Tuple[Tensor, Tensor, Tensor, Tensor, Tensor]]: + ) -> Tensor | tuple[Tensor, Tensor, Tensor, Tensor, Tensor]: x = self.conv1(x) x = self.bn1(x) x = self.relu(x) @@ -320,7 +320,7 @@ def _forward_impl( def forward( self, x: Tensor - ) -> Union[Tensor, Tuple[Tensor, Tensor, Tensor, Tensor, Tensor]]: + ) -> Tensor | tuple[Tensor, Tensor, Tensor, Tensor, Tensor]: """Performs a forward pass of the :class:`ResNet`. Can be called directly as a method (e.g. ``model.forward``) or when data is parsed @@ -390,19 +390,19 @@ class ResNetX(MinervaModel): """ __metaclass__ = abc.ABCMeta - block_type: Union[Type[BasicBlock], Type[Bottleneck]] = BasicBlock - layer_struct: List[int] = [2, 2, 2, 2] + block_type: Type[BasicBlock] | Type[Bottleneck] = BasicBlock + layer_struct: list[int] = [2, 2, 2, 2] weights_name = "ResNet18_Weights.IMAGENET1K_V1" def __init__( self, criterion: Optional[Any] = None, - input_size: Tuple[int, int, int] = (4, 256, 256), + input_size: tuple[int, int, int] = (4, 256, 256), n_classes: int = 8, zero_init_residual: bool = False, groups: int = 1, width_per_group: int = 64, - replace_stride_with_dilation: Optional[Tuple[bool, bool, bool]] = None, + replace_stride_with_dilation: Optional[tuple[bool, bool, bool]] = None, norm_layer: Optional[Callable[..., Module]] = None, encoder: bool = False, torch_weights: bool = False, @@ -431,7 +431,7 @@ def __init__( def forward( self, x: Tensor - ) -> Union[Tensor, Tuple[Tensor, Tensor, Tensor, Tensor, Tensor]]: + ) -> Tensor | tuple[Tensor, Tensor, Tensor, Tensor, Tensor]: """Performs a forward pass of the :class:`ResNet`. Can be called directly as a method (e.g. :func:`model.forward`) or when data is parsed @@ -445,9 +445,7 @@ def forward( initialised as an encoder, returns a tuple of outputs from each ``layer`` 1-4. Else, returns :class:`~torch.Tensor` of the likelihoods the network places on the input ``x`` being of each class. """ - z: Union[Tensor, Tuple[Tensor, Tensor, Tensor, Tensor, Tensor]] = self.network( - x - ) + z: Tensor | tuple[Tensor, Tensor, Tensor, Tensor, Tensor] = self.network(x) if isinstance(z, Tensor): return z elif isinstance(z, tuple): @@ -460,7 +458,7 @@ class ResNet18(ResNetX): by stripping classification layers away. """ - layer_struct: List[int] = [2, 2, 2, 2] + layer_struct: list[int] = [2, 2, 2, 2] weights_name = "ResNet18_Weights.IMAGENET1K_V1" @@ -469,7 +467,7 @@ class ResNet34(ResNetX): by stripping classification layers away. """ - layer_struct: List[int] = [3, 4, 6, 3] + layer_struct: list[int] = [3, 4, 6, 3] weights_name = "ResNet34_Weights.IMAGENET1K_V1" @@ -508,8 +506,8 @@ class ResNet152(ResNetX): # ===================================================================================================================== def _preload_weights( resnet: ResNet, - weights: Optional[Union[WeightsEnum, Any]], - input_shape: Tuple[int, int, int], + weights: Optional[WeightsEnum | Any], + input_shape: tuple[int, int, int], encoder_on: bool, ) -> ResNet: # pragma: no cover if not weights: diff --git a/minerva/models/siamese.py b/minerva/models/siamese.py index 62d6564cf..7e8b5fd9b 100644 --- a/minerva/models/siamese.py +++ b/minerva/models/siamese.py @@ -53,7 +53,7 @@ # IMPORTS # ===================================================================================================================== import abc -from typing import Any, Dict, Optional, Sequence, Tuple +from typing import Any, Optional, Sequence import numpy as np import torch @@ -86,7 +86,7 @@ def __init__(self, *args, **kwargs) -> None: self.backbone: MinervaModel self.proj_head: Module - def forward(self, x: Tensor) -> Tuple[Tensor, Tensor, Tensor, Tensor, Tensor]: + def forward(self, x: Tensor) -> tuple[Tensor, Tensor, Tensor, Tensor, Tensor]: """Performs a forward pass of the network by using the forward methods of the backbone and feeding its output into the projection heads. @@ -106,7 +106,7 @@ def forward(self, x: Tensor) -> Tuple[Tensor, Tensor, Tensor, Tensor, Tensor]: """ return self.forward_pair(x) - def forward_pair(self, x: Tensor) -> Tuple[Tensor, Tensor, Tensor, Tensor, Tensor]: + def forward_pair(self, x: Tensor) -> tuple[Tensor, Tensor, Tensor, Tensor, Tensor]: """Performs a forward pass of the network by using the forward methods of the backbone and feeding its output into the projection heads. @@ -131,7 +131,7 @@ def forward_pair(self, x: Tensor) -> Tuple[Tensor, Tensor, Tensor, Tensor, Tenso return g, g_a, g_b, f_a, f_b @abc.abstractmethod - def forward_single(self, x: Tensor) -> Tuple[Tensor, Tensor]: + def forward_single(self, x: Tensor) -> tuple[Tensor, Tensor]: """Performs a forward pass of a single head of the network by using the forward methods of the backbone and feeding its output into the projection heads. @@ -171,10 +171,10 @@ class SimCLR(MinervaSiamese): def __init__( self, criterion: Any, - input_size: Tuple[int, int, int] = (4, 256, 256), + input_size: tuple[int, int, int] = (4, 256, 256), feature_dim: int = 128, scaler: Optional[GradScaler] = None, - backbone_kwargs: Dict[str, Any] = {}, + backbone_kwargs: dict[str, Any] = {}, ) -> None: super(SimCLR, self).__init__( criterion=criterion, input_size=input_size, scaler=scaler @@ -196,7 +196,7 @@ def __init__( nn.Linear(512, feature_dim, bias=False), ) - def forward_single(self, x: Tensor) -> Tuple[Tensor, Tensor]: + def forward_single(self, x: Tensor) -> tuple[Tensor, Tensor]: """Performs a forward pass of a single head of the network by using the forward methods of the :attr:`~SimCLR.backbone` and feeding its output into the :attr:`~SimCLR.proj_head`. @@ -214,7 +214,7 @@ def forward_single(self, x: Tensor) -> Tuple[Tensor, Tensor]: return g, f - def step(self, x: Tensor, *args, train: bool = False) -> Tuple[Tensor, Tensor]: + def step(self, x: Tensor, *args, train: bool = False) -> tuple[Tensor, Tensor]: """Overwrites :class:`~models.core.MinervaModel` to account for paired logits. Raises: @@ -318,11 +318,11 @@ class SimSiam(MinervaSiamese): def __init__( self, criterion: Any, - input_size: Tuple[int, int, int] = (4, 256, 256), + input_size: tuple[int, int, int] = (4, 256, 256), feature_dim: int = 128, pred_dim: int = 512, scaler: Optional[GradScaler] = None, - backbone_kwargs: Dict[str, Any] = {}, + backbone_kwargs: dict[str, Any] = {}, ) -> None: super(SimSiam, self).__init__( criterion=criterion, input_size=input_size, scaler=scaler @@ -359,7 +359,7 @@ def __init__( nn.Linear(pred_dim, feature_dim), ) # output layer - def forward_single(self, x: Tensor) -> Tuple[Tensor, Tensor]: + def forward_single(self, x: Tensor) -> tuple[Tensor, Tensor]: """Performs a forward pass of a single head of :class:`SimSiam` by using the forward methods of the backbone and feeding its output into the :attr:`~SimSiam.proj_head`. @@ -376,7 +376,7 @@ def forward_single(self, x: Tensor) -> Tuple[Tensor, Tensor]: return p, z.detach() - def step(self, x: Tensor, *args, train: bool = False) -> Tuple[Tensor, Tensor]: + def step(self, x: Tensor, *args, train: bool = False) -> tuple[Tensor, Tensor]: """Overwrites :class:`~models.core.MinervaModel` to account for paired logits. Raises: @@ -480,12 +480,12 @@ class SimConv(MinervaSiamese): def __init__( self, criterion: Any, - input_size: Tuple[int, int, int] = (4, 256, 256), + input_size: tuple[int, int, int] = (4, 256, 256), feature_dim: int = 2048, projection_dim: int = 512, scaler: Optional[GradScaler] = None, encoder_weights: Optional[str] = None, - backbone_kwargs: Dict[str, Any] = {}, + backbone_kwargs: dict[str, Any] = {}, ) -> None: super(SimConv, self).__init__( criterion=criterion, input_size=input_size, scaler=scaler @@ -518,7 +518,7 @@ def __init__( nn.UpsamplingBilinear2d(scale_factor=4), ) - def forward_single(self, x: Tensor) -> Tuple[Tensor, Tensor]: + def forward_single(self, x: Tensor) -> tuple[Tensor, Tensor]: """Performs a forward pass of a single head of the network by using the forward methods of the :attr:`~SimCLR.backbone` and feeding its output into the :attr:`~SimCLR.proj_head`. @@ -536,7 +536,7 @@ def forward_single(self, x: Tensor) -> Tuple[Tensor, Tensor]: return g, f - def step(self, x: Tensor, *args, train: bool = False) -> Tuple[Tensor, Tensor]: + def step(self, x: Tensor, *args, train: bool = False) -> tuple[Tensor, Tensor]: """Overwrites :class:`~models.core.MinervaModel` to account for paired logits. Raises: diff --git a/minerva/models/unet.py b/minerva/models/unet.py index 200c514ed..b1fb0f641 100644 --- a/minerva/models/unet.py +++ b/minerva/models/unet.py @@ -51,7 +51,7 @@ # IMPORTS # ===================================================================================================================== import abc -from typing import Any, Dict, Optional, Sequence, Tuple +from typing import Any, Optional, Sequence import torch import torch.nn.functional as F @@ -267,7 +267,7 @@ class UNet(MinervaModel): def __init__( self, criterion: Any, - input_size: Tuple[int, ...] = (4, 256, 256), + input_size: tuple[int, ...] = (4, 256, 256), n_classes: int = 8, bilinear: bool = False, scaler: Optional[GradScaler] = None, @@ -356,13 +356,13 @@ class UNetR(MinervaModel): def __init__( self, criterion: Any, - input_size: Tuple[int, ...] = (4, 256, 256), + input_size: tuple[int, ...] = (4, 256, 256), n_classes: int = 8, bilinear: bool = False, scaler: Optional[GradScaler] = None, backbone_weight_path: Optional[str] = None, freeze_backbone: bool = False, - backbone_kwargs: Dict[str, Any] = {}, + backbone_kwargs: dict[str, Any] = {}, ) -> None: super(UNetR, self).__init__( criterion=criterion, diff --git a/minerva/optimisers.py b/minerva/optimisers.py index 90bf4cc7b..5a5a10f9f 100644 --- a/minerva/optimisers.py +++ b/minerva/optimisers.py @@ -31,7 +31,7 @@ # ===================================================================================================================== # IMPORTS # ===================================================================================================================== -from typing import Any, Callable, Dict, Iterable, Optional, Union +from typing import Any, Callable, Iterable, Optional import torch from torch.optim.optimizer import Optimizer @@ -67,7 +67,7 @@ class LARS(Optimizer): def __init__( self, - params: Union[Iterable[Any], Dict[Any, Any]], + params: Iterable[Any] | dict[Any, Any], lr: float, momentum: float = 0.9, weight_decay: float = 0.0005, diff --git a/minerva/pytorchtools.py b/minerva/pytorchtools.py index 538f8b669..1cb61c848 100644 --- a/minerva/pytorchtools.py +++ b/minerva/pytorchtools.py @@ -35,7 +35,7 @@ # IMPORTS # ===================================================================================================================== from pathlib import Path -from typing import Callable, Optional, Union +from typing import Callable, Optional import numpy as np import torch @@ -81,7 +81,7 @@ def __init__( patience: int = 7, verbose: bool = False, delta: float = 0.0, - path: Union[str, Path] = "checkpoint.pt", + path: str | Path = "checkpoint.pt", trace_func: Callable[..., None] = print, external_save: bool = False, ): @@ -92,7 +92,7 @@ def __init__( self.early_stop: bool = False self.val_loss_min: float = np.Inf self.delta: float = delta - self.path: Union[str, Path] = path + self.path: str | Path = path self.trace_func: Callable[..., None] = trace_func self.external_save: bool = external_save self.save_model: bool = False diff --git a/minerva/samplers.py b/minerva/samplers.py index 99d4be8cd..9731961e3 100644 --- a/minerva/samplers.py +++ b/minerva/samplers.py @@ -50,7 +50,7 @@ import random import re from operator import itemgetter -from typing import Any, Dict, Iterator, List, Optional, Sequence, Tuple, Union +from typing import Any, Iterator, Optional, Sequence import hydra import torch @@ -94,7 +94,7 @@ class RandomPairGeoSampler(RandomGeoSampler): def __init__( self, dataset: GeoDataset, - size: Union[Tuple[float, float], float], + size: tuple[float, float] | float, length: int, roi: Optional[BoundingBox] = None, units: Units = Units.PIXELS, @@ -103,7 +103,7 @@ def __init__( super().__init__(dataset, size, length, roi, units) self.max_r = max_r - def __iter__(self) -> Iterator[Tuple[BoundingBox, BoundingBox]]: # type: ignore[override] + def __iter__(self) -> Iterator[tuple[BoundingBox, BoundingBox]]: # type: ignore[override] """Return a pair of :class:`~torchgeo.datasets.utils.BoundingBox` indices of a dataset that are geospatially close. @@ -159,7 +159,7 @@ class RandomPairBatchGeoSampler(BatchGeoSampler): def __init__( self, dataset: GeoDataset, - size: Union[Tuple[float, float], float], + size: tuple[float, float] | float, batch_size: int, length: int, roi: Optional[BoundingBox] = None, @@ -180,7 +180,7 @@ def __init__( else: raise ValueError(f"{tiles_per_batch=} is not a multiple of {batch_size=}") - def __iter__(self) -> Iterator[List[Tuple[BoundingBox, BoundingBox]]]: # type: ignore[override] + def __iter__(self) -> Iterator[list[tuple[BoundingBox, BoundingBox]]]: # type: ignore[override] """Return the indices of a dataset. Returns: @@ -212,7 +212,9 @@ def __len__(self) -> int: def get_greater_bbox( - bbox: BoundingBox, r: float, size: Union[float, int, Sequence[float]] + bbox: BoundingBox, + r: float, + size: float | int | Sequence[float], ) -> BoundingBox: """Return a bounding box at ``r`` distance around the first box. @@ -250,10 +252,10 @@ def get_greater_bbox( def get_pair_bboxes( bounds: BoundingBox, - size: Union[Tuple[float, float], float], + size: tuple[float, float] | float, res: float, max_r: float, -) -> Tuple[BoundingBox, BoundingBox]: +) -> tuple[BoundingBox, BoundingBox]: """Samples a pair of bounding boxes geo-spatially close to each other. Args: @@ -349,7 +351,7 @@ class DatasetFromSampler(Dataset): # type: ignore[type-arg] def __init__(self, sampler: Sampler[Any]): """Initialisation for :class:`DatasetFromSampler`.""" self.sampler = sampler - self.sampler_list: Optional[List[Sampler[Any]]] = None + self.sampler_list: Optional[list[Sampler[Any]]] = None def __getitem__(self, index: int) -> Any: """Gets element of the dataset. @@ -376,8 +378,8 @@ def __len__(self) -> int: # METHODS # ===================================================================================================================== def get_sampler( - params: Dict[str, Any], - dataset: Union[GeoDataset, NonGeoDataset], + params: dict[str, Any], + dataset: GeoDataset | NonGeoDataset, batch_size: Optional[int] = None, ) -> Sampler[Any]: """Use :meth:`hydra.utils.instantiate` to get the sampler using config parameters. diff --git a/minerva/scheduler.py b/minerva/scheduler.py index ff8b33684..c49f0fe9a 100644 --- a/minerva/scheduler.py +++ b/minerva/scheduler.py @@ -37,7 +37,6 @@ # IMPORTS # ===================================================================================================================== import warnings -from typing import List import numpy as np from torch.optim.lr_scheduler import LRScheduler @@ -81,7 +80,7 @@ def __init__( self.n_periods = n_periods super().__init__(optimizer, last_epoch, verbose) - def get_lr(self) -> List[float]: # type: ignore[override] + def get_lr(self) -> list[float]: # type: ignore[override] if not hasattr(self, "_get_lr_called_within_step"): warnings.warn( "To get the last learning rate computed by the scheduler, " diff --git a/minerva/tasks/core.py b/minerva/tasks/core.py index 482b2de0b..fcd6d84d2 100644 --- a/minerva/tasks/core.py +++ b/minerva/tasks/core.py @@ -41,7 +41,7 @@ import abc from abc import ABC from pathlib import Path -from typing import TYPE_CHECKING, Any, Callable, Dict, Optional, Sequence, Union +from typing import TYPE_CHECKING, Any, Callable, Optional, Sequence if TYPE_CHECKING: # pragma: no cover from torch.utils.tensorboard.writer import SummaryWriter @@ -163,14 +163,14 @@ class MinervaTask(ABC): def __init__( self, name: str, - model: Union[MinervaModel, MinervaDataParallel, OptimizedModule], + model: MinervaModel | MinervaDataParallel | OptimizedModule, device: torch.device, exp_fn: Path, gpu: int = 0, rank: int = 0, world_size: int = 1, - writer: Optional[Union[SummaryWriter, Run]] = None, - backbone_weight_path: Optional[Union[str, Path]] = None, + writer: Optional[SummaryWriter | Run] = None, + backbone_weight_path: Optional[str | Path] = None, record_int: bool = True, record_float: bool = False, train: bool = False, @@ -404,7 +404,7 @@ def get_io_func(self) -> Callable[..., Any]: def step(self) -> None: # pragma: no cover raise NotImplementedError - def _generic_step(self, epoch_no: int) -> Optional[Dict[str, Any]]: + def _generic_step(self, epoch_no: int) -> Optional[dict[str, Any]]: self.local_step_num = 0 self.step() @@ -431,11 +431,11 @@ def __call__(self, epoch_no: int) -> Any: return self._generic_step(epoch_no) @property - def get_logs(self) -> Dict[str, Any]: + def get_logs(self) -> dict[str, Any]: return self.logger.get_logs @property - def get_metrics(self) -> Dict[str, Any]: + def get_metrics(self) -> dict[str, Any]: return self.logger.get_metrics def log_null(self, epoch_no: int) -> None: @@ -455,8 +455,8 @@ def print_epoch_results(self, epoch_no: int) -> None: def plot( self, - results: Dict[str, Any], - metrics: Optional[Dict[str, Any]] = None, + results: dict[str, Any], + metrics: Optional[dict[str, Any]] = None, save: bool = True, show: bool = False, ) -> None: diff --git a/minerva/tasks/knn.py b/minerva/tasks/knn.py index 7914e88e6..9c650a139 100644 --- a/minerva/tasks/knn.py +++ b/minerva/tasks/knn.py @@ -41,7 +41,7 @@ # ===================================================================================================================== # IMPORTS # ===================================================================================================================== -from typing import TYPE_CHECKING, Optional, Tuple, Union +from typing import TYPE_CHECKING, Optional import torch import torch.distributed as dist @@ -142,14 +142,14 @@ class WeightedKNN(MinervaTask): def __init__( self, name: str, - model: Union[MinervaModel, MinervaDataParallel], + model: MinervaModel | MinervaDataParallel, device: torch.device, exp_fn: Path, gpu: int = 0, rank: int = 0, world_size: int = 1, - writer: Optional[Union[SummaryWriter, Run]] = None, - backbone_weight_path: Optional[Union[str, Path]] = None, + writer: Optional[SummaryWriter | Run] = None, + backbone_weight_path: Optional[str | Path] = None, record_int: bool = True, record_float: bool = False, k: int = 5, @@ -174,7 +174,7 @@ def __init__( self.temp = temp self.k = k - def generate_feature_bank(self) -> Tuple[Tensor, Tensor]: + def generate_feature_bank(self) -> tuple[Tensor, Tensor]: feature_list = [] target_list = [] diff --git a/minerva/trainer.py b/minerva/trainer.py index fa6686343..88a9cf470 100644 --- a/minerva/trainer.py +++ b/minerva/trainer.py @@ -44,7 +44,7 @@ from copy import deepcopy from pathlib import Path from platform import python_version -from typing import TYPE_CHECKING, Any, Callable, Dict, Optional, Tuple, Union +from typing import TYPE_CHECKING, Any, Callable, Optional import hydra import packaging @@ -237,7 +237,7 @@ def __init__( rank: int = 0, world_size: int = 1, verbose: bool = True, - wandb_run: Optional[Union[Run, RunDisabled]] = None, + wandb_run: Optional[Run | RunDisabled] = None, **params, ) -> None: assert not isinstance(wandb_run, RunDisabled) @@ -264,7 +264,7 @@ def __init__( utils.print_config(params) # type: ignore[arg-type] # Now that we have pretty printed the config, it is easier to handle as a dict. - self.params: Dict[str, Any] = OmegaConf.to_object(params) # type: ignore[assignment] + self.params: dict[str, Any] = OmegaConf.to_object(params) # type: ignore[assignment] assert isinstance(self.params, dict) # Set variables for checkpointing the experiment or loading from a previous checkpoint. @@ -324,7 +324,7 @@ def __init__( # Makes a directory for this experiment. utils.mkexpdir(self.params["exp_name"]) - self.writer: Optional[Union[SummaryWriter, Run]] = None + self.writer: Optional[SummaryWriter | Run] = None if self.params.get("wandb_log", False): # Sets the `wandb` run object (or None). self.writer = wandb_run @@ -337,9 +337,9 @@ def __init__( else: # pragma: no cover self.writer = None - self.model: Union[ - MinervaModel, MinervaDataParallel, MinervaBackbone, OptimizedModule - ] + self.model: ( + MinervaModel | MinervaDataParallel | MinervaBackbone | OptimizedModule + ) if Path(self.params.get("pre_train_name", "none")).suffix == ".onnx": # Loads model from `onnx` format. @@ -352,7 +352,7 @@ def __init__( self.model = self.make_model() # Determines the output shape of the model. - sample_pairs: Union[bool, Any] = self.sample_pairs + sample_pairs: bool | Any = self.sample_pairs if not isinstance(sample_pairs, bool): sample_pairs = False self.params["sample_pairs"] = False @@ -458,15 +458,15 @@ def _setup_writer(self) -> None: if isinstance(self.writer, Run): self.writer.watch(self.model) - def get_input_size(self) -> Tuple[int, ...]: + def get_input_size(self) -> tuple[int, ...]: """Determines the input size of the model. Returns: tuple[int, ...]: :class:`tuple` describing the input shape of the model. """ - input_shape: Optional[Tuple[int, ...]] = self.model.input_size # type: ignore + input_shape: Optional[tuple[int, ...]] = self.model.input_size # type: ignore assert input_shape is not None - input_size: Tuple[int, ...] = (self.batch_size, *input_shape) + input_size: tuple[int, ...] = (self.batch_size, *input_shape) if self.sample_pairs or self.change_detection: input_size = (2, *input_size) @@ -497,7 +497,7 @@ def make_model(self) -> MinervaModel: Returns: MinervaModel: Initialised model. """ - model_params: Dict[str, Any] = deepcopy(self.params["model_params"]) + model_params: dict[str, Any] = deepcopy(self.params["model_params"]) if OmegaConf.is_config(model_params): model_params = OmegaConf.to_object(model_params) # type: ignore[assignment] @@ -619,7 +619,7 @@ def fit(self) -> None: } ) - tasks: Dict[str, MinervaTask] = {} + tasks: dict[str, MinervaTask] = {} for mode in fit_params.keys(): tasks[mode] = get_task( fit_params[mode]["_target_"], @@ -661,7 +661,7 @@ def fit(self) -> None: else: self.model.eval() - results: Optional[Dict[str, Any]] = tasks[mode](self.epoch_no - 1) + results: Optional[dict[str, Any]] = tasks[mode](self.epoch_no - 1) # Print epoch results. if self.gpu == 0: @@ -698,7 +698,7 @@ def fit(self) -> None: self.print("\nEarly stopping triggered") # Create a subset of metrics for plotting model history. - fit_metrics: Dict[str, Any] = {} + fit_metrics: dict[str, Any] = {} for _mode in tasks.keys(): fit_metrics = {**fit_metrics, **tasks[_mode].get_metrics} @@ -970,7 +970,7 @@ def close(self) -> None: # Saves model state dict to PyTorch file. self.save_model_weights() - def save_model_weights(self, fn: Optional[Union[str, Path]] = None) -> None: + def save_model_weights(self, fn: Optional[str | Path] = None) -> None: """Saves model state dict to :mod:`torch` file. Args: @@ -983,9 +983,7 @@ def save_model_weights(self, fn: Optional[Union[str, Path]] = None) -> None: torch.save(model.state_dict(), f"{fn}.pt") - def save_model( - self, fn: Optional[Union[Path, str]] = None, fmt: str = "pt" - ) -> None: + def save_model(self, fn: Optional[Path | str] = None, fmt: str = "pt") -> None: """Saves the model object itself to :mod:`torch` file. Args: diff --git a/minerva/transforms.py b/minerva/transforms.py index 7426e8fa5..bbb857581 100644 --- a/minerva/transforms.py +++ b/minerva/transforms.py @@ -55,19 +55,7 @@ import re from copy import deepcopy from pathlib import Path -from typing import ( - Any, - Callable, - Dict, - List, - Literal, - Optional, - Sequence, - Tuple, - Union, - cast, - overload, -) +from typing import Any, Callable, Literal, Optional, Sequence, cast, overload import hydra import numpy as np @@ -103,7 +91,7 @@ class ClassTransform: transform (dict[int, int]): Mapping from one labelling schema to another. """ - def __init__(self, transform: Dict[int, int]) -> None: + def __init__(self, transform: dict[int, int]) -> None: self.transform = transform def __call__(self, mask: LongTensor) -> LongTensor: @@ -131,14 +119,14 @@ class PairCreate: def __init__(self) -> None: pass - def __call__(self, sample: Any) -> Tuple[Any, Any]: + def __call__(self, sample: Any) -> tuple[Any, Any]: return self.forward(sample) def __repr__(self) -> str: return f"{self.__class__.__name__}()" @staticmethod - def forward(sample: Any) -> Tuple[Any, Any]: + def forward(sample: Any) -> tuple[Any, Any]: """Takes a sample and returns it and a copy as a :class:`tuple` pair. Args: @@ -217,7 +205,7 @@ def __init__( super().__init__(mean, std, inplace) - def _calc_mean_std(self) -> Tuple[List[float], List[float]]: + def _calc_mean_std(self) -> tuple[list[float], list[float]]: per_img_means = [] per_img_stds = [] for query in self.sampler: @@ -233,7 +221,7 @@ def _calc_mean_std(self) -> Tuple[List[float], List[float]]: return per_band_mean, per_band_std - def _get_tile_mean_std(self, query: BoundingBox) -> Tuple[List[float], List[float]]: + def _get_tile_mean_std(self, query: BoundingBox) -> tuple[list[float], list[float]]: hits = self.dataset.index.intersection(tuple(query), objects=True) filepaths = cast(list[str], [hit.object for hit in hits]) @@ -242,8 +230,8 @@ def _get_tile_mean_std(self, query: BoundingBox) -> Tuple[List[float], List[floa f"query: {query} not found in index with bounds: {self.dataset.bounds}" ) - means: List[float] - stds: List[float] + means: list[float] + stds: list[float] if self.dataset.separate_files: filename_regex = re.compile(self.dataset.filename_regex, re.VERBOSE) @@ -274,9 +262,9 @@ def _get_tile_mean_std(self, query: BoundingBox) -> Tuple[List[float], List[floa def _get_image_mean_std( self, - filepaths: List[str], + filepaths: list[str], band_indexes: Optional[Sequence[int]] = None, - ) -> Tuple[List[float], List[float]]: + ) -> tuple[list[float], list[float]]: stats = [self._get_meta_mean_std(fp, band_indexes) for fp in filepaths] means = list(np.mean([stat[0] for stat in stats], axis=0)) @@ -286,7 +274,7 @@ def _get_image_mean_std( def _get_meta_mean_std( self, filepath, band_indexes: Optional[Sequence[int]] = None - ) -> Tuple[List[float], List[float]]: + ) -> tuple[list[float], list[float]]: # Open the Tiff file and get the statistics from the meta (min, max, mean, std). means = [] stds = [] @@ -371,7 +359,7 @@ class ToRGB: """ - def __init__(self, channels: Optional[Tuple[int, int, int]] = None) -> None: + def __init__(self, channels: Optional[tuple[int, int, int]] = None) -> None: self.channels = channels def __call__(self, img: Tensor) -> Tensor: @@ -423,7 +411,7 @@ class SelectChannels: channels (list[int]): Channel indices to keep. """ - def __init__(self, channels: List[int]) -> None: + def __init__(self, channels: list[int]) -> None: self.channels = channels def __call__(self, img: Tensor) -> Tensor: @@ -511,16 +499,14 @@ class MinervaCompose: def __init__( self, - transforms: Union[ - List[Callable[..., Any]], - Callable[..., Any], - Dict[str, Union[List[Callable[..., Any]], Callable[..., Any]]], - ], + transforms: ( + list[Callable[..., Any]] + | Callable[..., Any] + | dict[str, list[Callable[..., Any]] | Callable[..., Any]] + ), change_detection: bool = False, ) -> None: - self.transforms: Union[ - List[Callable[..., Any]], Dict[str, List[Callable[..., Any]]] - ] + self.transforms: list[Callable[..., Any]] | dict[str, list[Callable[..., Any]]] self.change_detection = change_detection @@ -550,12 +536,10 @@ def __call__(self, sample: Tensor) -> Tensor: ... # pragma: no cover @overload def __call__( - self, sample: Dict[str, Any] - ) -> Dict[str, Any]: ... # pragma: no cover + self, sample: dict[str, Any] + ) -> dict[str, Any]: ... # pragma: no cover - def __call__( - self, sample: Union[Tensor, Dict[str, Any]] - ) -> Union[Tensor, Dict[str, Any]]: + def __call__(self, sample: Tensor | dict[str, Any]) -> Tensor | dict[str, Any]: if isinstance(sample, Tensor): assert not isinstance(self.transforms, dict) return self._transform_input(sample, self.transforms) @@ -613,7 +597,7 @@ def __call__( @staticmethod def _transform_input( - img: Tensor, transforms: List[Callable[..., Any]], reapply: bool = False + img: Tensor, transforms: list[Callable[..., Any]], reapply: bool = False ) -> Tensor: if isinstance(transforms, Sequence): for t in transforms: @@ -632,17 +616,17 @@ def _transform_input( def _add( self, - new_transform: Union[ - "MinervaCompose", - Sequence[Callable[..., Any]], - Callable[..., Any], - Dict[str, Union[Sequence[Callable[..., Any]], Callable[..., Any]]], - ], - ) -> Union[Dict[str, List[Callable[..., Any]]], List[Callable[..., Any]]]: + new_transform: ( + "MinervaCompose" + | Sequence[Callable[..., Any]] + | Callable[..., Any] + | dict[str, Sequence[Callable[..., Any]] | Callable[..., Any]] + ), + ) -> dict[str, list[Callable[..., Any]]] | list[Callable[..., Any]]: def add_transforms( - _new_transform: Union[Sequence[Callable[..., Any]], Callable[..., Any]], - old_transform: List[Callable[..., Any]], - ) -> List[Callable[..., Any]]: + _new_transform: Sequence[Callable[..., Any]] | Callable[..., Any], + old_transform: list[Callable[..., Any]], + ) -> list[Callable[..., Any]]: if isinstance(_new_transform, Sequence): old_transform.extend(_new_transform) return old_transform @@ -682,12 +666,12 @@ def add_transforms( def __add__( self, - new_transform: Union[ - "MinervaCompose", - Sequence[Callable[..., Any]], - Callable[..., Any], - Dict[str, Union[Sequence[Callable[..., Any]], Callable[..., Any]]], - ], + new_transform: ( + "MinervaCompose" + | Sequence[Callable[..., Any]] + | Callable[..., Any] + | dict[str, Sequence[Callable[..., Any]] | Callable[..., Any]] + ), ) -> "MinervaCompose": new_compose = deepcopy(self) new_compose.transforms = self._add(new_transform) @@ -695,12 +679,12 @@ def __add__( def __iadd__( self, - new_transform: Union[ - "MinervaCompose", - Sequence[Callable[..., Any]], - Callable[..., Any], - Dict[str, Union[Sequence[Callable[..., Any]], Callable[..., Any]]], - ], + new_transform: ( + "MinervaCompose" + | Sequence[Callable[..., Any]] + | Callable[..., Any] + | dict[str, Sequence[Callable[..., Any]] | Callable[..., Any]] + ), ) -> "MinervaCompose": self.transforms = self._add(new_transform) return self @@ -755,13 +739,13 @@ def __init__(self, from_key: str, to_key: str) -> None: self.from_key = from_key self.to_key = to_key - def __call__(self, sample: Dict[str, Any]) -> Dict[str, Any]: + def __call__(self, sample: dict[str, Any]) -> dict[str, Any]: return self.forward(sample) def __repr__(self) -> str: return f"{self.__class__.__name__}({self.from_key} -> {self.to_key})" - def forward(self, sample: Dict[str, Any]) -> Dict[str, Any]: + def forward(self, sample: dict[str, Any]) -> dict[str, Any]: """Sets the ``to_key`` of ``sample`` to the ``from_key`` and returns. Args: @@ -790,7 +774,7 @@ class SeasonTransform: def __init__(self, season: str = "random") -> None: self.season = season - def __call__(self, x: Tensor) -> Union[Tuple[Tensor, Tensor], Tensor]: + def __call__(self, x: Tensor) -> tuple[Tensor, Tensor] | Tensor: if self.season == "pair": season1 = np.random.choice([0, 1, 2, 3]) @@ -852,7 +836,7 @@ def forward(self, img: Tensor) -> Tensor: """ org_shape = img.shape - tmp_shape: Tuple[int, int, int, int] + tmp_shape: tuple[int, int, int, int] if len(org_shape) == 4: # Mask already has shape [N,C,H,W] so no need to modify shape for Resize. @@ -896,7 +880,7 @@ def __call__(self, img: Tensor) -> Tensor: # ===================================================================================================================== # METHODS # ===================================================================================================================== -def _construct_random_transforms(random_params: Dict[str, Any]) -> Any: +def _construct_random_transforms(random_params: dict[str, Any]) -> Any: p = random_params.pop("p", 0.5) random_transforms = [] @@ -917,7 +901,7 @@ def init_auto_norm( Args: dataset (RasterDataset): Dataset to find and apply the normalisation conditions to. - params (Dict[str, Any]): Parameters for :class:~`minerva.transforms.AutoNorm`. + params (dict[str, Any]): Parameters for :class:~`minerva.transforms.AutoNorm`. Returns: RasterDataset: `dataset` with an additional :class:~`minerva.transforms.AutoNorm` transform @@ -947,7 +931,7 @@ def init_auto_norm( return dataset -def get_transform(transform_params: Dict[str, Any]) -> Callable[..., Any]: +def get_transform(transform_params: dict[str, Any]) -> Callable[..., Any]: """Creates a transform object based on config parameters. Args: @@ -972,7 +956,7 @@ def get_transform(transform_params: Dict[str, Any]) -> Callable[..., Any]: def make_transformations( - transform_params: Union[Dict[str, Any], Literal[False]], + transform_params: dict[str, Any] | Literal[False], change_detection: bool = False, ) -> Optional[MinervaCompose]: """Constructs a transform or series of transforms based on parameters provided. @@ -997,7 +981,7 @@ def make_transformations( If multiple transforms are defined, a Compose object of Transform objects is returned. """ - def construct(type_transform_params: Dict[str, Any]) -> List[Callable[..., Any]]: + def construct(type_transform_params: dict[str, Any]) -> list[Callable[..., Any]]: type_transformations = [] # Get each transform. @@ -1021,7 +1005,7 @@ def construct(type_transform_params: Dict[str, Any]) -> List[Callable[..., Any]] if all(transform_params.values()) is None: return None - transformations: Dict[str, Any] = {} + transformations: dict[str, Any] = {} for name in transform_params: if name in ("image", "mask", "label", "both"): diff --git a/minerva/utils/runner.py b/minerva/utils/runner.py index 89d3d1d6b..61304a959 100644 --- a/minerva/utils/runner.py +++ b/minerva/utils/runner.py @@ -54,18 +54,18 @@ import signal import subprocess from pathlib import Path -from typing import Any, Callable, Optional, Tuple, Union +from typing import Any, Callable, Optional import requests import torch import torch.distributed as dist import torch.multiprocessing as mp -import wandb import yaml from omegaconf import DictConfig, ListConfig, OmegaConf from wandb.sdk.lib import RunDisabled from wandb.sdk.wandb_run import Run +import wandb from minerva.trainer import Trainer from minerva.utils import utils @@ -120,14 +120,14 @@ def _config_load_resolver(path: str): return cfg -def _construct_patch_size(input_size: Tuple[int, int, int]) -> ListConfig: +def _construct_patch_size(input_size: tuple[int, int, int]) -> ListConfig: return ListConfig(input_size[-2:]) def setup_wandb_run( gpu: int, cfg: DictConfig, -) -> Tuple[Optional[Union[Run, RunDisabled]], DictConfig]: +) -> tuple[Optional[Run | RunDisabled], DictConfig]: """Sets up a :mod:`wandb` logger for either every process, the master process or not if not logging. Note: @@ -148,7 +148,7 @@ def setup_wandb_run( ~wandb.sdk.wandb_run.Run | ~wandb.sdk.lib.RunDisabled | None: The :mod:`wandb` run object for this process or ``None`` if ``log_all=False`` and ``rank!=0``. """ - run: Optional[Union[Run, RunDisabled]] = None + run: Optional[Run | RunDisabled] = None if cfg.get("wandb_log", False) or cfg.get("project", None): try: if cfg.get("log_all", False) and cfg.world_size > 1: @@ -290,7 +290,7 @@ def config_args(cfg: DictConfig) -> DictConfig: def _run_preamble( gpu: int, - run: Callable[[int, Optional[Union[Run, RunDisabled]], DictConfig], Any], + run: Callable[[int, Optional[Run | RunDisabled], DictConfig], Any], cfg: DictConfig, ) -> None: # pragma: no cover # Calculates the global rank of this process. @@ -316,7 +316,7 @@ def _run_preamble( def distributed_run( - run: Callable[[int, Optional[Union[Run, RunDisabled]], DictConfig], Any] + run: Callable[[int, Optional[Run | RunDisabled], DictConfig], Any] ) -> Callable[..., Any]: """Runs the supplied function and arguments with distributed computing according to arguments. @@ -362,7 +362,7 @@ def inner_decorator(cfg: DictConfig): def run_trainer( - gpu: int, wandb_run: Optional[Union[Run, RunDisabled]], cfg: DictConfig + gpu: int, wandb_run: Optional[Run | RunDisabled], cfg: DictConfig ) -> None: trainer = Trainer( diff --git a/minerva/utils/utils.py b/minerva/utils/utils.py index 405af5259..8e701b1ad 100644 --- a/minerva/utils/utils.py +++ b/minerva/utils/utils.py @@ -125,7 +125,7 @@ from types import ModuleType from typing import Any, Callable from typing import Counter as CounterType -from typing import Dict, Iterable, List, Optional, Sequence, Tuple, Union, overload +from typing import Iterable, Optional, Sequence, overload # ---+ 3rd Party +----------------------------------------------------------------------------------------------------- import numpy as np @@ -171,8 +171,8 @@ # DECORATORS # ===================================================================================================================== def return_updated_kwargs( - func: Callable[..., Tuple[Any, ...]] -) -> Callable[..., Tuple[Any, ...]]: + func: Callable[..., tuple[Any, ...]] +) -> Callable[..., tuple[Any, ...]]: """Decorator that allows the `kwargs` supplied to the wrapped function to be returned with updated values. Assumes that the wrapped function returns a :class:`dict` in the last position of the @@ -211,7 +211,7 @@ def pair_collate(func: Callable[[Any], Any]) -> Callable[[Any], Any]: """ @functools.wraps(func) - def wrapper(samples: Iterable[Tuple[Any, Any]]) -> Tuple[Any, Any]: + def wrapper(samples: Iterable[tuple[Any, Any]]) -> tuple[Any, Any]: a, b = tuple(zip(*samples)) return func(a), func(b) @@ -226,7 +226,7 @@ class Wrapper: def __init__(self, *args, **kwargs) -> None: self.wrap = cls(*args, **kwargs) - def __call__(self, pair: Tuple[Any, Any]) -> Tuple[Any, Any]: + def __call__(self, pair: tuple[Any, Any]) -> tuple[Any, Any]: a, b = pair return self.wrap.__call__(a), self.wrap.__call__(b) @@ -257,26 +257,26 @@ class Wrapper: def __init__(self, *args, **kwargs) -> None: self.wrap: Callable[ [ - Union[Dict[str, Any], Tensor], + dict[str, Any] | Tensor, ], - Dict[str, Any], + dict[str, Any], ] = cls(*args, **kwargs) self.keys = keys @overload def __call__( - self, batch: Dict[str, Any] - ) -> Dict[str, Any]: ... # pragma: no cover + self, batch: dict[str, Any] + ) -> dict[str, Any]: ... # pragma: no cover @overload - def __call__(self, batch: Tensor) -> Dict[str, Any]: ... # pragma: no cover + def __call__(self, batch: Tensor) -> dict[str, Any]: ... # pragma: no cover - def __call__(self, batch: Union[Dict[str, Any], Tensor]) -> Dict[str, Any]: + def __call__(self, batch: dict[str, Any] | Tensor) -> dict[str, Any]: if isinstance(batch, Tensor): return self.wrap(batch) elif isinstance(batch, dict) and isinstance(self.keys, Sequence): - aug_batch: Dict[str, Any] = {} + aug_batch: dict[str, Any] = {} for key in self.keys: aug_batch[key] = self.wrap(batch.pop(key)) @@ -310,7 +310,7 @@ class Wrapper: def __init__(self, *args, **kwargs) -> None: self.wrap = cls(*args, **kwargs) - def __getitem__(self, queries: Any = None) -> Tuple[Any, Any]: + def __getitem__(self, queries: Any = None) -> tuple[Any, Any]: return self.wrap[queries[0]], self.wrap[queries[1]] def __getattr__(self, item): @@ -398,7 +398,7 @@ def _optional_import( def _optional_import( module: str, *, name: Optional[str] = None, package: Optional[str] = None -) -> Union[ModuleType, Callable[..., Any]]: +) -> ModuleType | Callable[..., Any]: try: _module: ModuleType = importlib.import_module(module) return _module if name is None else getattr(_module, name) @@ -461,7 +461,7 @@ def is_notebook() -> bool: return True -def get_cuda_device(device_sig: Union[int, str] = "cuda:0") -> _device: +def get_cuda_device(device_sig: int | str = "cuda:0") -> _device: """Finds and returns the ``CUDA`` device, if one is available. Else, returns CPU as device. Assumes there is at most only one ``CUDA`` device. @@ -477,7 +477,7 @@ def get_cuda_device(device_sig: Union[int, str] = "cuda:0") -> _device: return device -def exist_delete_check(fn: Union[str, Path]) -> None: +def exist_delete_check(fn: str | Path) -> None: """Checks if given file exists then deletes if true. Args: @@ -490,7 +490,7 @@ def exist_delete_check(fn: Union[str, Path]) -> None: Path(fn).unlink(missing_ok=True) -def mkexpdir(name: str, results_dir: Union[Path, str] = "results") -> None: +def mkexpdir(name: str, results_dir: Path | str = "results") -> None: """Makes a new directory below the results directory with name provided. If directory already exists, no action is taken. @@ -520,7 +520,7 @@ def set_seeds(seed: int) -> None: torch.cuda.random.manual_seed_all(seed) -def check_dict_key(dictionary: Dict[Any, Any], key: Any) -> bool: +def check_dict_key(dictionary: dict[Any, Any], key: Any) -> bool: """Checks if a key exists in a dictionary and if it is ``None`` or ``False``. Args: @@ -582,7 +582,7 @@ def transform_coordinates( y: Sequence[float], src_crs: CRS, new_crs: CRS = WGS84, -) -> Tuple[Sequence[float], Sequence[float]]: ... # pragma: no cover +) -> tuple[Sequence[float], Sequence[float]]: ... # pragma: no cover @overload @@ -591,7 +591,7 @@ def transform_coordinates( y: float, src_crs: CRS, new_crs: CRS = WGS84, -) -> Tuple[Sequence[float], Sequence[float]]: ... # pragma: no cover +) -> tuple[Sequence[float], Sequence[float]]: ... # pragma: no cover @overload @@ -600,21 +600,21 @@ def transform_coordinates( y: Sequence[float], src_crs: CRS, new_crs: CRS = WGS84, -) -> Tuple[Sequence[float], Sequence[float]]: ... # pragma: no cover +) -> tuple[Sequence[float], Sequence[float]]: ... # pragma: no cover @overload def transform_coordinates( x: float, y: float, src_crs: CRS, new_crs: CRS = WGS84 -) -> Tuple[float, float]: ... # pragma: no cover +) -> tuple[float, float]: ... # pragma: no cover def transform_coordinates( - x: Union[Sequence[float], float], - y: Union[Sequence[float], float], + x: Sequence[float] | float, + y: Sequence[float] | float, src_crs: CRS, new_crs: CRS = WGS84, -) -> Union[Tuple[Sequence[float], Sequence[float]], Tuple[float, float]]: +) -> tuple[Sequence[float], Sequence[float]] | tuple[float, float]: """Transforms co-ordinates from one :class:`~rasterio.crs.CRS` to another. Args: @@ -640,7 +640,7 @@ def transform_coordinates( y = check_len(y, x) # Transform co-ordinates from source to new CRS and returns a tuple of (x, y) - co_ordinates: Tuple[Sequence[float], Sequence[float]] = rt.warp.transform( # type: ignore + co_ordinates: tuple[Sequence[float], Sequence[float]] = rt.warp.transform( # type: ignore src_crs=src_crs, dst_crs=new_crs, xs=x, ys=y ) @@ -714,9 +714,9 @@ def deg_to_dms(deg: float, axis: str = "lat") -> str: def dec2deg( - dec_co: Union[Sequence[float], NDArray[Shape["*"], Float]], # noqa: F722 + dec_co: Sequence[float] | NDArray[Shape["*"], Float], # noqa: F722 axis: str = "lat", -) -> List[str]: +) -> list[str]: """Wrapper for :func:`deg_to_dms`. Args: @@ -726,14 +726,14 @@ def dec2deg( Returns: list[str]: List of formatted strings in degrees, minutes and seconds. """ - deg_co: List[str] = [] + deg_co: list[str] = [] for co in dec_co: deg_co.append(deg_to_dms(co, axis=axis)) return deg_co -def get_centre_loc(bounds: BoundingBox) -> Tuple[float, float]: +def get_centre_loc(bounds: BoundingBox) -> tuple[float, float]: """Gets the centre co-ordinates of the parsed bounding box. Args: @@ -774,7 +774,7 @@ def get_centre_pixel_value(x: Tensor) -> Any: raise ValueError() -def lat_lon_to_loc(lat: Union[str, float], lon: Union[str, float]) -> str: +def lat_lon_to_loc(lat: str | float, lon: str | float) -> str: """Takes a latitude - longitude co-ordinate and returns a string of the semantic location. Args: @@ -803,7 +803,7 @@ def lat_lon_to_loc(lat: Union[str, float], lon: Union[str, float]) -> str: location = query.raw["properties"] # type: ignore # Attempts to add possible fields to address of the location. Not all will be present for every query. - locs: List[str] = [] + locs: list[str] = [] try: locs.append(location["city"]) except KeyError: @@ -888,8 +888,8 @@ def mask_to_ohe(mask: LongTensor, n_classes: int) -> LongTensor: def class_weighting( - class_dist: List[Tuple[int, int]], normalise: bool = False -) -> Dict[int, float]: + class_dist: list[tuple[int, int]], normalise: bool = False +) -> dict[int, float]: """Constructs weights for each class defined by the distribution provided. Note: @@ -911,7 +911,7 @@ def class_weighting( n_samples += mode[1] # Constructs class weights. Each weight is 1 / number of samples for that class. - class_weights: Dict[int, float] = {} + class_weights: dict[int, float] = {} if normalise: for mode in class_dist: class_weights[mode[0]] = n_samples / mode[1] @@ -923,8 +923,8 @@ def class_weighting( def find_empty_classes( - class_dist: List[Tuple[int, int]], class_names: Dict[int, str] -) -> List[int]: + class_dist: list[tuple[int, int]], class_names: dict[int, str] +) -> list[int]: """Finds which classes defined by config files are not present in the dataset. Args: @@ -935,7 +935,7 @@ def find_empty_classes( Returns: list[int]: List of classes not found in ``class_dist`` and are thus empty/ not present in dataset. """ - empty: List[int] = [] + empty: list[int] = [] # Checks which classes are not present in class_dist for label in class_names.keys(): @@ -947,10 +947,10 @@ def find_empty_classes( def eliminate_classes( - empty_classes: Union[List[int], Tuple[int, ...], NDArray[Any, Int]], - old_classes: Dict[int, str], - old_cmap: Optional[Dict[int, str]] = None, -) -> Tuple[Dict[int, str], Dict[int, int], Optional[Dict[int, str]]]: + empty_classes: list[int] | tuple[int, ...] | NDArray[Any, Int], + old_classes: dict[int, str], + old_cmap: Optional[dict[int, str]] = None, +) -> tuple[dict[int, str], dict[int, int], Optional[dict[int, str]]]: """Eliminates empty classes from the class text label and class colour dictionaries and re-normalise. This should ensure that the remaining list of classes is still a linearly spaced list of numbers. @@ -1019,7 +1019,7 @@ def eliminate_classes( return reordered_classes, conversion, reordered_colours -def class_transform(label: int, matrix: Dict[int, int]) -> int: +def class_transform(label: int, matrix: dict[int, int]) -> int: """Transforms labels from one schema to another mapped by a supplied dictionary. Args: @@ -1034,20 +1034,20 @@ def class_transform(label: int, matrix: Dict[int, int]) -> int: @overload def mask_transform( # type: ignore[overload-overlap] - array: NDArray[Any, Int], matrix: Dict[int, int] + array: NDArray[Any, Int], matrix: dict[int, int] ) -> NDArray[Any, Int]: ... # pragma: no cover @overload def mask_transform( - array: LongTensor, matrix: Dict[int, int] + array: LongTensor, matrix: dict[int, int] ) -> LongTensor: ... # pragma: no cover def mask_transform( - array: Union[NDArray[Any, Int], LongTensor], - matrix: Dict[int, int], -) -> Union[NDArray[Any, Int], LongTensor]: + array: NDArray[Any, Int] | LongTensor, + matrix: dict[int, int], +) -> NDArray[Any, Int] | LongTensor: """Transforms all labels of an N-dimensional array from one schema to another mapped by a supplied dictionary. Args: @@ -1064,11 +1064,11 @@ def mask_transform( def check_test_empty( - pred: Union[Sequence[int], NDArray[Any, Int]], - labels: Union[Sequence[int], NDArray[Any, Int]], - class_labels: Optional[Dict[int, str]] = None, + pred: Sequence[int] | NDArray[Any, Int], + labels: Sequence[int] | NDArray[Any, Int], + class_labels: Optional[dict[int, str]] = None, p_dist: bool = True, -) -> Tuple[NDArray[Any, Int], NDArray[Any, Int], Dict[int, str]]: +) -> tuple[NDArray[Any, Int], NDArray[Any, Int], dict[int, str]]: """Checks if any of the classes in the dataset were not present in both the predictions and ground truth labels. Returns corrected and re-ordered predictions, labels and class labels. @@ -1119,8 +1119,8 @@ def check_test_empty( def class_dist_transform( - class_dist: List[Tuple[int, int]], matrix: Dict[int, int] -) -> List[Tuple[int, int]]: + class_dist: list[tuple[int, int]], matrix: dict[int, int] +) -> list[tuple[int, int]]: """Transforms the class distribution from an old schema to a new one. Args: @@ -1131,14 +1131,14 @@ def class_dist_transform( Returns: list[tuple[int, int]]: Class distribution updated to new labels. """ - new_class_dist: List[Tuple[int, int]] = [] + new_class_dist: list[tuple[int, int]] = [] for mode in class_dist: new_class_dist.append((class_transform(mode[0], matrix), mode[1])) return new_class_dist -def class_frac(patch: pd.Series) -> Dict[Any, Any]: +def class_frac(patch: pd.Series) -> dict[Any, Any]: """Computes the fractional sizes of the classes of the given :term:`patch` and returns a :class:`dict` of the results. @@ -1149,7 +1149,7 @@ def class_frac(patch: pd.Series) -> Dict[Any, Any]: Mapping: Dictionary-like object with keys as class numbers and associated values of fractional size of class plus a key-value pair for the :term:`patch` ID. """ - new_columns: Dict[Any, Any] = dict(patch.to_dict()) + new_columns: dict[Any, Any] = dict(patch.to_dict()) counts = 0 for mode in patch["MODES"]: counts += mode[1] @@ -1172,7 +1172,7 @@ def cloud_cover(scene: NDArray[Any, Any]) -> Any: return np.sum(scene) / scene.size -def threshold_scene_select(df: DataFrame, thres: float = 0.3) -> List[str]: +def threshold_scene_select(df: DataFrame, thres: float = 0.3) -> list[str]: """Selects all scenes in a :term:`patch` with a cloud cover less than the threshold provided. Args: @@ -1190,9 +1190,9 @@ def threshold_scene_select(df: DataFrame, thres: float = 0.3) -> List[str]: def find_best_of( patch_id: str, manifest: DataFrame, - selector: Callable[[DataFrame], List[str]] = threshold_scene_select, + selector: Callable[[DataFrame], list[str]] = threshold_scene_select, **kwargs, -) -> List[str]: +) -> list[str]: """Finds the scenes sorted by cloud cover using selector function supplied. Args: @@ -1233,9 +1233,9 @@ def timestamp_now(fmt: str = "%d-%m-%Y_%H%M") -> str: def find_modes( labels: Iterable[int], plot: bool = False, - classes: Optional[Dict[int, str]] = None, - cmap_dict: Optional[Dict[int, str]] = None, -) -> List[Tuple[int, int]]: + classes: Optional[dict[int, str]] = None, + cmap_dict: Optional[dict[int, str]] = None, +) -> list[tuple[int, int]]: """Finds the modal distribution of the classes within the labels provided. Can plot the results as a pie chart if ``plot=True``. @@ -1248,7 +1248,7 @@ def find_modes( list[tuple[int, int]]: Modal distribution of classes in input in order of most common class. """ # Finds the distribution of the classes within the data - class_dist: List[Tuple[int, int]] = Counter( + class_dist: list[tuple[int, int]] = Counter( np.array(labels).flatten() ).most_common() @@ -1263,10 +1263,10 @@ def find_modes( def modes_from_manifest( manifest: DataFrame, - classes: Dict[int, str], + classes: dict[int, str], plot: bool = False, - cmap_dict: Optional[Dict[int, str]] = None, -) -> List[Tuple[int, int]]: + cmap_dict: Optional[dict[int, str]] = None, +) -> list[tuple[int, int]]: """Uses the dataset manifest to calculate the fractional size of the classes. Args: @@ -1294,7 +1294,7 @@ def count_samples(cls): class_counter[classification] = count except KeyError: continue - class_dist: List[Tuple[int, int]] = class_counter.most_common() + class_dist: list[tuple[int, int]] = class_counter.most_common() if plot: # Plots a pie chart of the distribution of the classes within the given list of patches @@ -1324,7 +1324,7 @@ def func_by_str(module_path: str, func: str) -> Callable[..., Any]: return func -def check_len(param: Any, comparator: Any) -> Union[Any, Sequence[Any]]: +def check_len(param: Any, comparator: Any) -> Any | Sequence[Any]: """Checks the length of one object against a comparator object. Args: @@ -1382,8 +1382,8 @@ def calc_grad(model: Module) -> Optional[float]: def print_class_dist( - class_dist: List[Tuple[int, int]], - class_labels: Optional[Dict[int, str]] = None, + class_dist: list[tuple[int, int]], + class_labels: Optional[dict[int, str]] = None, ) -> None: """Prints the supplied ``class_dist`` in a pretty table format using :mod:`tabulate`. @@ -1434,7 +1434,7 @@ def calc_frac(count: float, total: float) -> str: def batch_flatten( - x: Union[NDArray[Any, Any], ArrayLike] + x: NDArray[Any, Any] | ArrayLike ) -> NDArray[Shape["*"], Any]: # noqa: F722 """Flattens the supplied array with :func:`numpy`. @@ -1454,9 +1454,9 @@ def batch_flatten( def make_classification_report( - pred: Union[Sequence[int], NDArray[Any, Int]], - labels: Union[Sequence[int], NDArray[Any, Int]], - class_labels: Optional[Dict[int, str]] = None, + pred: Sequence[int] | NDArray[Any, Int], + labels: Sequence[int] | NDArray[Any, Int], + class_labels: Optional[dict[int, str]] = None, print_cr: bool = True, p_dist: bool = False, ) -> DataFrame: @@ -1563,9 +1563,9 @@ def calc_contrastive_acc(z: Tensor) -> Tensor: def run_tensorboard( exp_name: str, - path: Union[str, List[str], Tuple[str, ...], Path] = "", + path: str | list[str] | tuple[str, ...] | Path = "", env_name: str = "env", - host_num: Union[str, int] = 6006, + host_num: str | int = 6006, _testing: bool = False, ) -> Optional[int]: """Runs the :mod:`TensorBoard` logs and hosts on a local webpage. @@ -1626,11 +1626,11 @@ def run_tensorboard( def compute_roc_curves( probs: NDArray[Any, Float], - labels: Union[Sequence[int], NDArray[Any, Int]], - class_labels: List[int], + labels: Sequence[int] | NDArray[Any, Int], + class_labels: list[int], micro: bool = True, macro: bool = True, -) -> Tuple[Dict[Any, float], Dict[Any, float], Dict[Any, float]]: +) -> tuple[dict[Any, float], dict[Any, float], dict[Any, float]]: """Computes the false-positive rate, true-positive rate and AUCs for each class using a one-vs-all approach. The micro and macro averages are for each of these variables is also computed. @@ -1659,13 +1659,13 @@ def compute_roc_curves( # Dicts to hold the false-positive rate, true-positive rate and Area Under Curves # of each class and micro, macro averages. - fpr: Dict[Any, Any] = {} - tpr: Dict[Any, Any] = {} - roc_auc: Dict[Any, Any] = {} + fpr: dict[Any, Any] = {} + tpr: dict[Any, Any] = {} + roc_auc: dict[Any, Any] = {} # Holds a list of the classes that were in the targets supplied to the model. # Avoids warnings about empty targets from sklearn! - populated_classes: List[int] = [] + populated_classes: list[int] = [] print("Computing class ROC curves") @@ -1843,8 +1843,8 @@ def calc_norm_euc_dist(a: Tensor, b: Tensor) -> Tensor: def fallback_params( key: str, - params_a: Dict[str, Any], - params_b: Dict[str, Any], + params_a: dict[str, Any], + params_b: dict[str, Any], fallback: Optional[Any] = None, ) -> Any: """Search for a value associated with ``key`` from @@ -1867,9 +1867,9 @@ def fallback_params( def compile_dataset_paths( - data_dir: Union[Path, str], - in_paths: Union[List[Union[Path, str]], Union[Path, str]], -) -> List[str]: + data_dir: Path | str, + in_paths: list[Path | str] | Path | str, +) -> list[str]: """Ensures that a list of paths is returned with the data directory prepended, even if a single string is supplied Args: @@ -1892,7 +1892,7 @@ def compile_dataset_paths( return [str(Path(path).absolute()) for path in out_paths] -def make_hash(obj: Dict[Any, Any]) -> str: +def make_hash(obj: dict[Any, Any]) -> str: """Make a deterministic MD5 hash of a serialisable object using JSON. Source: https://death.andgravity.com/stable-hashing @@ -1959,7 +1959,7 @@ def closest_factors(n): return best_pair -def get_sample_index(sample: Dict[str, Any]) -> Optional[Any]: +def get_sample_index(sample: dict[str, Any]) -> Optional[Any]: """Get the index for a sample with unkown index key. Will try: diff --git a/minerva/utils/visutils.py b/minerva/utils/visutils.py index 8201c7fcd..dd36c6153 100644 --- a/minerva/utils/visutils.py +++ b/minerva/utils/visutils.py @@ -64,7 +64,7 @@ import os import random from pathlib import Path -from typing import Any, Dict, List, Optional, Sequence, Tuple, Union +from typing import Any, Optional, Sequence import imageio import matplotlib as mlp @@ -126,7 +126,7 @@ def de_interlace(x: Sequence[Any], f: int) -> NDArray[Any, Any]: Returns: ~numpy.ndarray[~typing.Any]: De-interlaced array. Each source array is now sequentially connected. """ - new_x: List[NDArray[Any, Any]] = [] + new_x: list[NDArray[Any, Any]] = [] for i in range(f): x_i = [] for j in np.arange(start=i, stop=len(x), step=f): @@ -137,12 +137,12 @@ def de_interlace(x: Sequence[Any], f: int) -> NDArray[Any, Any]: def dec_extent_to_deg( - shape: Tuple[int, int], + shape: tuple[int, int], bounds: BoundingBox, src_crs: CRS, new_crs: CRS = WGS84, spacing: int = 32, -) -> Tuple[Tuple[int, int, int, int], NDArray[Any, Float], NDArray[Any, Float]]: +) -> tuple[tuple[int, int, int, int], NDArray[Any, Float], NDArray[Any, Float]]: """Gets the extent of the image with ``shape`` and with ``bounds`` in latitude, longitude of system ``new_crs``. Args: @@ -194,7 +194,7 @@ def dec_extent_to_deg( def get_mlp_cmap( - cmap_style: Optional[Union[Colormap, str]] = None, n_classes: Optional[int] = None + cmap_style: Optional[Colormap | str] = None, n_classes: Optional[int] = None ) -> Optional[Colormap]: """Creates a cmap from query @@ -225,8 +225,8 @@ def get_mlp_cmap( def discrete_heatmap( data: NDArray[Shape["*, *"], Int], # noqa: F722 - classes: Union[List[str], Tuple[str, ...]], - cmap_style: Optional[Union[str, ListedColormap]] = None, + classes: list[str] | tuple[str, ...], + cmap_style: Optional[str | ListedColormap] = None, block_size: int = 32, ) -> None: """Plots a heatmap with a discrete colour bar. Designed for Radiant Earth MLHub 256x256 SENTINEL images. @@ -329,16 +329,16 @@ def labelled_rgb_image( mask: NDArray[Shape["*, *"], Int], # noqa: F722 bounds: BoundingBox, src_crs: CRS, - path: Union[str, Path], + path: str | Path, name: str, - classes: Union[List[str], Tuple[str, ...]], - cmap_style: Optional[Union[str, ListedColormap]] = None, + classes: list[str] | tuple[str, ...], + cmap_style: Optional[str | ListedColormap] = None, new_crs: Optional[CRS] = WGS84, block_size: int = 32, alpha: float = 0.5, show: bool = True, save: bool = True, - figdim: Tuple[Union[int, float], Union[int, float]] = (8.02, 10.32), + figdim: tuple[int | float, int | float] = (8.02, 10.32), ) -> Path: """Produces a layered image of an RGB image, and it's associated label mask heat map alpha blended on top. @@ -363,7 +363,7 @@ def labelled_rgb_image( str: Path to figure save location. """ # Checks that the mask and image shapes will align. - mask_shape: Tuple[int, int] = mask.shape # type: ignore[assignment] + mask_shape: tuple[int, int] = mask.shape # type: ignore[assignment] assert mask_shape == image.shape[:2] assert new_crs is not None @@ -475,14 +475,14 @@ def make_gif( masks: NDArray[Shape["*, *, *"], Any], # noqa: F722 bounds: BoundingBox, src_crs: CRS, - classes: Union[List[str], Tuple[str, ...]], + classes: list[str] | tuple[str, ...], gif_name: str, - path: Union[str, Path], - cmap_style: Optional[Union[str, ListedColormap]] = None, + path: str | Path, + cmap_style: Optional[str | ListedColormap] = None, fps: float = 1.0, new_crs: Optional[CRS] = WGS84, alpha: float = 0.5, - figdim: Tuple[Union[int, float], Union[int, float]] = (8.02, 10.32), + figdim: tuple[int | float, int | float] = (8.02, 10.32), ) -> None: """Wrapper to :func:`labelled_rgb_image` to make a GIF for a patch out of scenes. @@ -548,19 +548,19 @@ def make_gif( def prediction_plot( - sample: Dict[str, Any], + sample: dict[str, Any], sample_id: str, - classes: Dict[int, str], + classes: dict[int, str], src_crs: CRS, new_crs: CRS = WGS84, path: str = "", - cmap_style: Optional[Union[str, ListedColormap]] = None, + cmap_style: Optional[str | ListedColormap] = None, exp_id: Optional[str] = None, - fig_dim: Optional[Tuple[Union[int, float], Union[int, float]]] = None, + fig_dim: Optional[tuple[int | float, int | float]] = None, block_size: int = 32, show: bool = True, save: bool = True, - fn_prefix: Optional[Union[str, Path]] = None, + fn_prefix: Optional[str | Path] = None, ) -> None: """ Produces a figure containing subplots of the predicted label mask, the ground truth label mask @@ -704,21 +704,21 @@ def prediction_plot( def seg_plot( - z: Union[List[int], NDArray[Any, Any]], - y: Union[List[int], NDArray[Any, Any]], - ids: List[str], - index: Union[Sequence[Any], NDArray[Any, Any]], - data_dir: Union[Path, str], - dataset_params: Dict[str, Any], - classes: Dict[int, str], - colours: Dict[int, str], - fn_prefix: Optional[Union[str, Path]], + z: list[int] | NDArray[Any, Any], + y: list[int] | NDArray[Any, Any], + ids: list[str], + index: Sequence[Any] | NDArray[Any, Any], + data_dir: Path | str, + dataset_params: dict[str, Any], + classes: dict[int, str], + colours: dict[int, str], + fn_prefix: Optional[str | Path], frac: float = 0.05, - fig_dim: Optional[Tuple[Union[int, float], Union[int, float]]] = (9.3, 10.5), + fig_dim: Optional[tuple[int | float, int | float]] = (9.3, 10.5), model_name: str = "", path: str = "", max_pixel_value: int = 255, - cache_dir: Optional[Union[str, Path]] = None, + cache_dir: Optional[str | Path] = None, ) -> None: """Custom function for pre-processing the outputs from image segmentation testing for data visualisation. @@ -840,10 +840,10 @@ def seg_plot( def plot_subpopulations( - class_dist: List[Tuple[int, int]], - class_names: Optional[Dict[int, str]] = None, - cmap_dict: Optional[Dict[int, str]] = None, - filename: Optional[Union[str, Path]] = None, + class_dist: list[tuple[int, int]], + class_names: Optional[dict[int, str]] = None, + cmap_dict: Optional[dict[int, str]] = None, + filename: Optional[str | Path] = None, save: bool = True, show: bool = False, ) -> None: @@ -867,7 +867,7 @@ def plot_subpopulations( counts = [] # List to hold colours of classes in the correct order. - colours: Optional[List[str]] = [] + colours: Optional[list[str]] = [] if class_names is None: class_numbers = [x[0] for x in class_dist] @@ -920,8 +920,8 @@ def plot_subpopulations( def plot_history( - metrics: Dict[str, Any], - filename: Optional[Union[str, Path]] = None, + metrics: dict[str, Any], + filename: Optional[str | Path] = None, save: bool = True, show: bool = False, ) -> None: @@ -972,12 +972,12 @@ def plot_history( def make_confusion_matrix( - pred: Union[List[int], NDArray[Any, Int]], - labels: Union[List[int], NDArray[Any, Int]], - classes: Dict[int, str], - filename: Optional[Union[str, Path]] = None, + pred: list[int] | NDArray[Any, Int], + labels: list[int] | NDArray[Any, Int], + classes: dict[int, str], + filename: Optional[str | Path] = None, cmap_style: str = "Blues", - figsize: Tuple[int, int] = (2, 2), + figsize: tuple[int, int] = (2, 2), show: bool = True, save: bool = False, ) -> None: @@ -1029,12 +1029,12 @@ def make_confusion_matrix( def make_multilabel_confusion_matrix( - preds: Union[List[int], NDArray[Any, Int]], - labels: Union[List[int], NDArray[Any, Int]], - classes: Dict[int, str], - filename: Optional[Union[str, Path]] = None, + preds: list[int] | NDArray[Any, Int], + labels: list[int] | NDArray[Any, Int], + classes: dict[int, str], + filename: Optional[str | Path] = None, cmap_style: str = "Blues", - figsize: Tuple[int, int] = (2, 2), + figsize: tuple[int, int] = (2, 2), show: bool = True, save: bool = False, ) -> None: @@ -1105,12 +1105,12 @@ def make_multilabel_confusion_matrix( def make_roc_curves( probs: ArrayLike, - labels: Union[Sequence[int], NDArray[Any, Int]], - class_names: Dict[int, str], - colours: Dict[int, str], + labels: Sequence[int] | NDArray[Any, Int], + class_names: dict[int, str], + colours: dict[int, str], micro: bool = True, macro: bool = True, - filename: Optional[Union[str, Path]] = None, + filename: Optional[str | Path] = None, show: bool = False, save: bool = True, ) -> None: @@ -1206,15 +1206,15 @@ def make_roc_curves( def plot_embedding( embeddings: Any, - index: Union[Sequence[BoundingBox], Sequence[int]], - data_dir: Union[Path, str], - dataset_params: Dict[str, Any], + index: Sequence[BoundingBox] | Sequence[int], + data_dir: Path | str, + dataset_params: dict[str, Any], title: Optional[str] = None, show: bool = False, save: bool = True, - filename: Optional[Union[Path, str]] = None, + filename: Optional[Path | str] = None, max_pixel_value: int = 255, - cache_dir: Optional[Union[Path, str]] = None, + cache_dir: Optional[Path | str] = None, ) -> None: """Using TSNE Clustering, visualises the embeddings from a model. @@ -1316,8 +1316,8 @@ def plot_embedding( def format_plot_names( - model_name: str, timestamp: str, path: Union[Sequence[str], str, Path] -) -> Dict[str, Path]: + model_name: str, timestamp: str, path: Sequence[str] | str | Path +) -> dict[str, Path]: """Creates unique filenames of plots in a standardised format. Args: @@ -1357,23 +1357,23 @@ def standard_format(plot_type: str, *sub_dir) -> str: def plot_results( - plots: Dict[str, bool], - z: Optional[Union[List[int], NDArray[Any, Int]]] = None, - y: Optional[Union[List[int], NDArray[Any, Int]]] = None, - metrics: Optional[Dict[str, Any]] = None, - ids: Optional[List[str]] = None, + plots: dict[str, bool], + z: Optional[list[int] | NDArray[Any, Int]] = None, + y: Optional[list[int] | NDArray[Any, Int]] = None, + metrics: Optional[dict[str, Any]] = None, + ids: Optional[list[str]] = None, index: Optional[NDArray[Any, Any]] = None, - probs: Optional[Union[List[float], NDArray[Any, Float]]] = None, + probs: Optional[list[float] | NDArray[Any, Float]] = None, embeddings: Optional[NDArray[Any, Any]] = None, - class_names: Optional[Dict[int, str]] = None, - colours: Optional[Dict[int, str]] = None, + class_names: Optional[dict[int, str]] = None, + colours: Optional[dict[int, str]] = None, save: bool = True, show: bool = False, model_name: Optional[str] = None, timestamp: Optional[str] = None, - results_dir: Optional[Union[Sequence[str], str, Path]] = None, - task_cfg: Optional[Dict[str, Any]] = None, - global_cfg: Optional[Dict[str, Any]] = None, + results_dir: Optional[Sequence[str] | str | Path] = None, + task_cfg: Optional[dict[str, Any]] = None, + global_cfg: Optional[dict[str, Any]] = None, ) -> None: """Orchestrates the creation of various plots from the results of a model fitting. diff --git a/scripts/MinervaExp.py b/scripts/MinervaExp.py index fc40d93f5..f8ff132a4 100644 --- a/scripts/MinervaExp.py +++ b/scripts/MinervaExp.py @@ -39,7 +39,7 @@ # ===================================================================================================================== # IMPORTS # ===================================================================================================================== -from typing import Optional, Union +from typing import Optional import hydra from omegaconf import DictConfig @@ -58,9 +58,7 @@ config_name=DEFAULT_CONFIG_NAME, ) @runner.distributed_run -def main( - gpu: int, wandb_run: Optional[Union[Run, RunDisabled]], cfg: DictConfig -) -> None: +def main(gpu: int, wandb_run: Optional[Run | RunDisabled], cfg: DictConfig) -> None: # Due to the nature of multiprocessing and its interaction with hydra, wandb and SLURM, # the actual code excuted in the job is contained in `run_trainer` in `runner`. diff --git a/scripts/MinervaPipe.py b/scripts/MinervaPipe.py index 5a4ced905..6a95f6b91 100644 --- a/scripts/MinervaPipe.py +++ b/scripts/MinervaPipe.py @@ -36,7 +36,7 @@ import shlex import subprocess import sys -from typing import Any, Dict +from typing import Any import yaml @@ -46,7 +46,7 @@ # ===================================================================================================================== def main(config_path: str): with open(config_path) as f: - config: Dict[str, Any] = yaml.safe_load(f) + config: dict[str, Any] = yaml.safe_load(f) for key in config.keys(): print( diff --git a/scripts/RunTensorBoard.py b/scripts/RunTensorBoard.py index 589b1a7d9..3559429ad 100644 --- a/scripts/RunTensorBoard.py +++ b/scripts/RunTensorBoard.py @@ -33,7 +33,7 @@ # IMPORTS # ===================================================================================================================== import argparse -from typing import List, Optional, Union +from typing import Optional from minerva.utils import utils @@ -42,7 +42,7 @@ # MAIN # ===================================================================================================================== def main( - path: Optional[Union[str, List[str]]] = None, + path: Optional[str | list[str]] = None, env_name: str = "env2", exp_name: Optional[str] = None, host_num: int = 6006, diff --git a/scripts/TorchWeightDownloader.py b/scripts/TorchWeightDownloader.py index 2d076b7b8..b0f6f2f5c 100644 --- a/scripts/TorchWeightDownloader.py +++ b/scripts/TorchWeightDownloader.py @@ -21,7 +21,7 @@ """Loads :mod:`torch` weights from Torch Hub into cache. Attributes: - resnets (List[str]): List of tags for ``pytorch`` resnet weights to download. + resnets (list[str]): List of tags for ``pytorch`` resnet weights to download. """ # ===================================================================================================================== diff --git a/tests/conftest.py b/tests/conftest.py index 0cbfa53a8..cbfa31a45 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -41,7 +41,7 @@ import os import shutil from pathlib import Path -from typing import Any, Dict, Generator, Tuple +from typing import Any, Generator import hydra import numpy as np @@ -199,7 +199,7 @@ def std_batch_size() -> int: @pytest.fixture -def std_n_classes(exp_classes: Dict[int, str]) -> int: +def std_n_classes(exp_classes: dict[int, str]) -> int: return len(exp_classes) @@ -214,12 +214,12 @@ def x_entropy_loss() -> nn.CrossEntropyLoss: @pytest.fixture -def small_patch_size() -> Tuple[int, int]: +def small_patch_size() -> tuple[int, int]: return (32, 32) @pytest.fixture -def rgbi_input_size() -> Tuple[int, int, int]: +def rgbi_input_size() -> tuple[int, int, int]: return (4, 32, 32) @@ -230,7 +230,7 @@ def exp_mlp(x_entropy_loss: nn.CrossEntropyLoss) -> MLP: @pytest.fixture def exp_cnn( - x_entropy_loss: nn.CrossEntropyLoss, rgbi_input_size: Tuple[int, int, int] + x_entropy_loss: nn.CrossEntropyLoss, rgbi_input_size: tuple[int, int, int] ) -> CNN: return CNN(x_entropy_loss, rgbi_input_size) @@ -238,7 +238,7 @@ def exp_cnn( @pytest.fixture def exp_fcn( x_entropy_loss: nn.CrossEntropyLoss, - rgbi_input_size: Tuple[int, int, int], + rgbi_input_size: tuple[int, int, int], std_n_classes: int, ) -> FCN32ResNet18: return FCN32ResNet18(x_entropy_loss, rgbi_input_size, std_n_classes) @@ -246,14 +246,14 @@ def exp_fcn( @pytest.fixture def exp_simconv( - rgbi_input_size: Tuple[int, int, int], + rgbi_input_size: tuple[int, int, int], ) -> SimConv: return SimConv(SegBarlowTwinsLoss(), rgbi_input_size, feature_dim=128) @pytest.fixture def random_mask( - small_patch_size: Tuple[int, int], std_n_classes: int + small_patch_size: tuple[int, int], std_n_classes: int ) -> NDArray[Shape["32, 32"], Int]: mask = np.random.randint(0, std_n_classes - 1, size=small_patch_size) assert isinstance(mask, np.ndarray) @@ -262,20 +262,20 @@ def random_mask( @pytest.fixture def random_image( - small_patch_size: Tuple[int, int] + small_patch_size: tuple[int, int] ) -> NDArray[Shape["32, 32, 3"], Float]: return np.random.rand(*small_patch_size, 3) @pytest.fixture def random_rgbi_image( - small_patch_size: Tuple[int, int] + small_patch_size: tuple[int, int] ) -> NDArray[Shape["32, 32, 4"], Float]: return np.random.rand(*small_patch_size, 4) @pytest.fixture -def random_rgbi_tensor(rgbi_input_size: Tuple[int, int, int]) -> Tensor: +def random_rgbi_tensor(rgbi_input_size: tuple[int, int, int]) -> Tensor: return torch.rand(rgbi_input_size) @@ -305,27 +305,27 @@ def flipped_rgb_img() -> Tensor: @pytest.fixture -def simple_sample(simple_rgb_img: Tensor, simple_mask: LongTensor) -> Dict[str, Tensor]: +def simple_sample(simple_rgb_img: Tensor, simple_mask: LongTensor) -> dict[str, Tensor]: return {"image": simple_rgb_img, "mask": simple_mask} @pytest.fixture def flipped_simple_sample( flipped_rgb_img: Tensor, flipped_simple_mask: LongTensor -) -> Dict[str, Tensor]: +) -> dict[str, Tensor]: return {"image": flipped_rgb_img, "mask": flipped_simple_mask} @pytest.fixture def random_rgbi_batch( - rgbi_input_size: Tuple[int, int, int], std_batch_size: int + rgbi_input_size: tuple[int, int, int], std_batch_size: int ) -> Tensor: return torch.rand((std_batch_size, *rgbi_input_size)) @pytest.fixture def random_tensor_mask( - std_n_classes: int, small_patch_size: Tuple[int, int] + std_n_classes: int, small_patch_size: tuple[int, int] ) -> LongTensor: mask = torch.randint(0, std_n_classes - 1, size=small_patch_size, dtype=torch.long) assert isinstance(mask, LongTensor) @@ -334,7 +334,7 @@ def random_tensor_mask( @pytest.fixture def random_mask_batch( - std_batch_size: int, std_n_classes: int, rgbi_input_size: Tuple[int, int, int] + std_batch_size: int, std_n_classes: int, rgbi_input_size: tuple[int, int, int] ) -> LongTensor: mask = torch.randint( 0, @@ -368,7 +368,7 @@ def bounds_for_test_img() -> BoundingBox: @pytest.fixture -def exp_classes() -> Dict[int, str]: +def exp_classes() -> dict[int, str]: return { 0: "No Data", 1: "Water", @@ -382,7 +382,7 @@ def exp_classes() -> Dict[int, str]: @pytest.fixture -def exp_cmap_dict() -> Dict[int, str]: +def exp_cmap_dict() -> dict[int, str]: return { 0: "#000000", # Transparent 1: "#00c5ff", # Light Blue @@ -411,7 +411,7 @@ def flipped_simple_mask() -> LongTensor: @pytest.fixture -def example_matrix() -> Dict[int, int]: +def example_matrix() -> dict[int, int]: return {1: 1, 3: 3, 4: 2, 5: 0} @@ -421,7 +421,7 @@ def simple_bbox() -> BoundingBox: @pytest.fixture -def exp_dataset_params() -> Dict[str, Any]: +def exp_dataset_params() -> dict[str, Any]: return { "image": { "transforms": {"AutoNorm": {"length": 12}}, @@ -440,7 +440,7 @@ def exp_dataset_params() -> Dict[str, Any]: @pytest.fixture -def exp_sampler_params(small_patch_size: Tuple[int, int]): +def exp_sampler_params(small_patch_size: tuple[int, int]): return { "_target_": "torchgeo.samplers.RandomGeoSampler", "roi": False, @@ -474,7 +474,7 @@ def default_dataset( @pytest.fixture def default_image_dataset( - default_config: DictConfig, exp_dataset_params: Dict[str, Any] + default_config: DictConfig, exp_dataset_params: dict[str, Any] ) -> RasterDataset: del exp_dataset_params["mask"] dataset, _ = make_dataset( diff --git a/tests/test_datasets/test_collators.py b/tests/test_datasets/test_collators.py index 60953dcb0..b35218bcb 100644 --- a/tests/test_datasets/test_collators.py +++ b/tests/test_datasets/test_collators.py @@ -37,7 +37,7 @@ # IMPORTS # ===================================================================================================================== from collections import defaultdict -from typing import Any, Dict, List, Union +from typing import Any import pytest import torch @@ -71,13 +71,13 @@ def test_stack_sample_pairs() -> None: mask_2 = torch.randint(0, 8, (52, 52)) # type: ignore[attr-defined] bbox_2 = [BoundingBox(0, 1, 0, 1, 0, 1)] - sample_1: Dict[str, Union[Tensor, List[Any]]] = { + sample_1: dict[str, Tensor | list[Any]] = { "image": image_1, "mask": mask_1, "bbox": bbox_1, } - sample_2: Dict[str, Union[Tensor, List[Any]]] = { + sample_2: dict[str, Tensor | list[Any]] = { "image": image_2, "mask": mask_2, "bbox": bbox_2, diff --git a/tests/test_datasets/test_factory.py b/tests/test_datasets/test_factory.py index 75e5f995e..37d720346 100644 --- a/tests/test_datasets/test_factory.py +++ b/tests/test_datasets/test_factory.py @@ -39,7 +39,7 @@ # ===================================================================================================================== from copy import deepcopy from pathlib import Path -from typing import Any, Dict +from typing import Any import pandas as pd import pytest @@ -56,7 +56,7 @@ # ===================================================================================================================== # TESTS # ===================================================================================================================== -def test_make_dataset(exp_dataset_params: Dict[str, Any], data_root: Path) -> None: +def test_make_dataset(exp_dataset_params: dict[str, Any], data_root: Path) -> None: dataset_params2 = { "image": { @@ -117,7 +117,7 @@ def test_make_dataset(exp_dataset_params: Dict[str, Any], data_root: Path) -> No @pytest.mark.parametrize("sample_pairs", (False, True)) def test_caching_datasets( - exp_dataset_params: Dict[str, Any], + exp_dataset_params: dict[str, Any], data_root: Path, cache_dir: Path, sample_pairs: bool, @@ -194,10 +194,10 @@ def test_caching_datasets( ], ) def test_construct_dataloader( - exp_dataset_params: Dict[str, Any], + exp_dataset_params: dict[str, Any], data_root: Path, - sampler_params: Dict[str, Any], - kwargs: Dict[str, Any], + sampler_params: dict[str, Any], + kwargs: dict[str, Any], ) -> None: batch_size = 256 @@ -253,8 +253,8 @@ def test_make_loaders(default_config: DictConfig) -> None: def test_get_manifest( data_root: Path, - exp_dataset_params: Dict[str, Any], - exp_sampler_params: Dict[str, Any], + exp_dataset_params: dict[str, Any], + exp_sampler_params: dict[str, Any], ) -> None: manifest_path = Path("tests", "tmp", "cache", "Chesapeake7_Manifest.csv") diff --git a/tests/test_datasets/test_paired.py b/tests/test_datasets/test_paired.py index 27d434507..e52a38049 100644 --- a/tests/test_datasets/test_paired.py +++ b/tests/test_datasets/test_paired.py @@ -38,8 +38,6 @@ # ===================================================================================================================== # IMPORTS # ===================================================================================================================== -from typing import Tuple - import matplotlib.pyplot as plt import pytest from rasterio.crs import CRS @@ -162,7 +160,7 @@ def test_paired_nongeodatasets(data_root: Path) -> None: def test_paired_concat_datasets( - data_root: Path, small_patch_size: Tuple[int, int] + data_root: Path, small_patch_size: tuple[int, int] ) -> None: def dataset_test(_dataset) -> None: for sub_dataset in _dataset.datasets: diff --git a/tests/test_logging.py b/tests/test_logging.py index 5d89f4279..eda99d2bb 100644 --- a/tests/test_logging.py +++ b/tests/test_logging.py @@ -41,7 +41,7 @@ # ===================================================================================================================== # IMPORTS # ===================================================================================================================== -from typing import Any, Dict, List, Tuple, Union +from typing import Any import numpy as np import torch @@ -80,7 +80,7 @@ def test_SupervisedStepLogger( std_n_batches: int, std_n_classes: int, std_batch_size: int, - small_patch_size: Tuple[int, int], + small_patch_size: tuple[int, int], default_device: torch.device, train: bool, model_type: str, @@ -137,19 +137,19 @@ def test_SupervisedStepLogger( step_logger_params={"n_classes": std_n_classes}, ) - correct_loss: Dict[str, List[float]] = {"x": [], "y": []} - correct_acc: Dict[str, List[float]] = {"x": [], "y": []} - correct_miou: Dict[str, List[float]] = {"x": [], "y": []} + correct_loss: dict[str, list[float]] = {"x": [], "y": []} + correct_acc: dict[str, list[float]] = {"x": [], "y": []} + correct_miou: dict[str, list[float]] = {"x": [], "y": []} for epoch_no in range(n_epochs): - data: List[Dict[str, Union[Tensor, List[Any]]]] = [] + data: list[dict[str, Tensor | list[Any]]] = [] for i in range(std_n_batches): images = torch.rand(size=(std_batch_size, 4, *small_patch_size)) masks = torch.randint( # type: ignore[attr-defined] 0, std_n_classes, (std_batch_size, *small_patch_size) ) bboxes = [simple_bbox] * std_batch_size - batch: Dict[str, Union[Tensor, List[Any]]] = { + batch: dict[str, Tensor | list[Any]] = { "image": images, "mask": masks, "bbox": bboxes, @@ -184,7 +184,7 @@ def test_SupervisedStepLogger( (std_n_batches, std_batch_size, *output_shape), dtype=np.uint8 ) for i in range(std_n_batches): - mask: Union[Tensor, List[Any]] = data[i]["mask"] + mask: Tensor | list[Any] = data[i]["mask"] assert isinstance(mask, Tensor) y[i] = mask.cpu().numpy() @@ -239,7 +239,7 @@ def test_SSLStepLogger( simple_bbox: BoundingBox, std_n_batches: int, std_batch_size: int, - small_patch_size: Tuple[int, int], + small_patch_size: tuple[int, int], default_device: torch.device, model_cls: MinervaSiamese, model_type: str, @@ -284,9 +284,9 @@ def test_SSLStepLogger( sample_pairs=True, ) - correct_loss: Dict[str, List[float]] = {"x": [], "y": []} - correct_collapse_level: Dict[str, List[float]] = {"x": [], "y": []} - correct_euc_dist: Dict[str, List[float]] = {"x": [], "y": []} + correct_loss: dict[str, list[float]] = {"x": [], "y": []} + correct_collapse_level: dict[str, list[float]] = {"x": [], "y": []} + correct_euc_dist: dict[str, list[float]] = {"x": [], "y": []} for epoch_no in range(n_epochs): for i in range(std_n_batches): diff --git a/tests/test_modelio.py b/tests/test_modelio.py index 03c5ef746..b1164e6b7 100644 --- a/tests/test_modelio.py +++ b/tests/test_modelio.py @@ -37,7 +37,7 @@ # IMPORTS # ===================================================================================================================== import importlib -from typing import Any, Dict, List, Tuple, Union +from typing import Any import torch import torch.nn.modules as nn @@ -66,7 +66,7 @@ def test_supervised_torchgeo_io( random_mask_batch: Tensor, std_batch_size: int, std_n_classes: int, - rgbi_input_size: Tuple[int, int, int], + rgbi_input_size: tuple[int, int, int], default_device: torch.device, ) -> None: criterion = nn.CrossEntropyLoss() @@ -76,7 +76,7 @@ def test_supervised_torchgeo_io( for train in (True, False): bboxes = [simple_bbox] * std_batch_size - batch: Dict[str, Union[Tensor, List[Any]]] = { + batch: dict[str, Tensor | list[Any]] = { "image": random_rgbi_batch, "mask": random_mask_batch, "bbox": bboxes, @@ -98,7 +98,7 @@ def test_supervised_torchgeo_io( def test_ssl_pair_torchgeo_io( simple_bbox: BoundingBox, std_batch_size: int, - rgbi_input_size: Tuple[int, int, int], + rgbi_input_size: tuple[int, int, int], default_device: torch.device, ) -> None: criterion = NTXentLoss(0.5) @@ -138,7 +138,7 @@ def test_mask_autoencoder_io( simple_bbox: BoundingBox, std_batch_size: int, std_n_classes: int, - rgbi_input_size: Tuple[int, int, int], + rgbi_input_size: tuple[int, int, int], default_device: torch.device, ) -> None: criterion = nn.CrossEntropyLoss() @@ -152,7 +152,7 @@ def test_mask_autoencoder_io( images = torch.rand(size=(std_batch_size, *rgbi_input_size)) masks = torch.randint(0, 8, (std_batch_size, *rgbi_input_size[1:])) # type: ignore[attr-defined] bboxes = [simple_bbox] * std_batch_size - batch: Dict[str, Union[Tensor, List[Any]]] = { + batch: dict[str, Tensor | list[Any]] = { "image": images, "mask": masks, "bbox": bboxes, @@ -191,7 +191,7 @@ def test_image_autoencoder_io( random_rgbi_batch: Tensor, random_mask_batch: Tensor, std_batch_size: int, - rgbi_input_size: Tuple[int, int, int], + rgbi_input_size: tuple[int, int, int], default_device: torch.device, ) -> None: criterion = nn.CrossEntropyLoss() @@ -203,7 +203,7 @@ def test_image_autoencoder_io( for train in (True, False): bboxes = [simple_bbox] * std_batch_size - batch: Dict[str, Union[Tensor, List[Any]]] = { + batch: dict[str, Tensor | list[Any]] = { "image": random_rgbi_batch, "mask": random_mask_batch, "bbox": bboxes, diff --git a/tests/test_models/test_core.py b/tests/test_models/test_core.py index 6a2dd5841..181e504ea 100644 --- a/tests/test_models/test_core.py +++ b/tests/test_models/test_core.py @@ -39,7 +39,6 @@ import importlib import os from platform import python_version -from typing import Tuple import internet_sabotage import numpy as np @@ -114,7 +113,7 @@ def test_minerva_model(x_entropy_loss, std_n_classes: int, std_n_batches: int) - assert z.size() == (std_n_batches, std_n_classes) -def test_minerva_backbone(rgbi_input_size: Tuple[int, int, int]) -> None: +def test_minerva_backbone(rgbi_input_size: tuple[int, int, int]) -> None: loss_func = NTXentLoss(0.3) model = SimCLR18(loss_func, input_size=rgbi_input_size) @@ -124,7 +123,7 @@ def test_minerva_backbone(rgbi_input_size: Tuple[int, int, int]) -> None: def test_minerva_wrapper( x_entropy_loss, - small_patch_size: Tuple[int, int], + small_patch_size: tuple[int, int], std_n_classes: int, std_n_batches: int, ) -> None: diff --git a/tests/test_models/test_fcn.py b/tests/test_models/test_fcn.py index 15cb845de..2cf91cfee 100644 --- a/tests/test_models/test_fcn.py +++ b/tests/test_models/test_fcn.py @@ -36,8 +36,6 @@ # ===================================================================================================================== # IMPORTS # ===================================================================================================================== -from typing import Tuple - import pytest import torch from torch import Tensor @@ -86,7 +84,7 @@ def test_fcn( random_mask_batch: Tensor, std_batch_size: int, std_n_classes: int, - rgbi_input_size: Tuple[int, int, int], + rgbi_input_size: tuple[int, int, int], ) -> None: model: MinervaModel = model_cls(x_entropy_loss, input_size=rgbi_input_size) optimiser = torch.optim.SGD(model.parameters(), lr=1.0e-3) diff --git a/tests/test_models/test_resnets.py b/tests/test_models/test_resnets.py index bb4f0fdd6..d6a1f34ca 100644 --- a/tests/test_models/test_resnets.py +++ b/tests/test_models/test_resnets.py @@ -36,8 +36,6 @@ # ===================================================================================================================== # IMPORTS # ===================================================================================================================== -from typing import Tuple - import pytest import torch from torch import LongTensor, Tensor @@ -94,7 +92,7 @@ def test_resnets( random_scene_classification_batch: LongTensor, std_batch_size: int, std_n_classes: int, - rgbi_input_size: Tuple[int, int, int], + rgbi_input_size: tuple[int, int, int], ) -> None: model: MinervaModel = model_cls( x_entropy_loss, input_size=rgbi_input_size, zero_init_residual=zero_init @@ -121,7 +119,7 @@ def test_replace_stride( random_scene_classification_batch: LongTensor, std_batch_size: int, std_n_classes: int, - rgbi_input_size: Tuple[int, int, int], + rgbi_input_size: tuple[int, int, int], ) -> None: for model in ( ResNet50(x_entropy_loss, input_size=rgbi_input_size), @@ -144,7 +142,7 @@ def test_replace_stride( def test_resnet_encoder( x_entropy_loss, random_rgbi_batch: Tensor, - rgbi_input_size: Tuple[int, int, int], + rgbi_input_size: tuple[int, int, int], ) -> None: encoder = ResNet18(x_entropy_loss, input_size=rgbi_input_size, encoder=True) optimiser = torch.optim.SGD(encoder.parameters(), lr=1.0e-3) @@ -157,7 +155,7 @@ def test_resnet_encoder( assert len(encoder(random_rgbi_batch)) == 5 -def test_preload_weights(rgbi_input_size: Tuple[int, int, int]) -> None: +def test_preload_weights(rgbi_input_size: tuple[int, int, int]) -> None: resnet = ResNet(BasicBlock, [2, 2, 2, 2]) new_resnet = _preload_weights(resnet, None, rgbi_input_size, encoder_on=False) diff --git a/tests/test_models/test_unet.py b/tests/test_models/test_unet.py index 18ee9df1a..0c4880b92 100644 --- a/tests/test_models/test_unet.py +++ b/tests/test_models/test_unet.py @@ -36,8 +36,6 @@ # ===================================================================================================================== # IMPORTS # ===================================================================================================================== -from typing import Tuple - import pytest import torch from torch import Tensor @@ -62,7 +60,7 @@ def unet_test( y: Tensor, batch_size: int, n_classes: int, - input_size: Tuple[int, int, int], + input_size: tuple[int, int, int], ) -> None: optimiser = torch.optim.SGD(model.parameters(), lr=1.0e-3) model.set_optimiser(optimiser) @@ -97,7 +95,7 @@ def test_unetrs( random_mask_batch: Tensor, std_batch_size: int, std_n_classes: int, - rgbi_input_size: Tuple[int, int, int], + rgbi_input_size: tuple[int, int, int], ) -> None: model: MinervaModel = model_cls(x_entropy_loss, rgbi_input_size) @@ -117,7 +115,7 @@ def test_unet( random_mask_batch: Tensor, std_batch_size: int, std_n_classes: int, - rgbi_input_size: Tuple[int, int, int], + rgbi_input_size: tuple[int, int, int], ) -> None: model = UNet(x_entropy_loss, input_size=rgbi_input_size) unet_test( diff --git a/tests/test_samplers.py b/tests/test_samplers.py index e01d697ed..42cc08cd3 100644 --- a/tests/test_samplers.py +++ b/tests/test_samplers.py @@ -38,7 +38,7 @@ # ===================================================================================================================== from collections import defaultdict from pathlib import Path -from typing import Any, Dict +from typing import Any import pytest from torch.utils.data import DataLoader @@ -60,7 +60,7 @@ def test_randompairgeosampler(img_root: Path) -> None: dataset = PairedGeoDataset(TstImgDataset, str(img_root), res=1.0) sampler = RandomPairGeoSampler(dataset, size=32, length=32, max_r=52) - loader: DataLoader[Dict[str, Any]] = DataLoader( + loader: DataLoader[dict[str, Any]] = DataLoader( dataset, batch_size=8, sampler=sampler, collate_fn=stack_sample_pairs ) @@ -78,7 +78,7 @@ def test_randompairbatchgeosampler(img_root: Path) -> None: sampler = RandomPairBatchGeoSampler( dataset, size=32, length=32, batch_size=8, max_r=52, tiles_per_batch=1 ) - loader: DataLoader[Dict[str, Any]] = DataLoader( + loader: DataLoader[dict[str, Any]] = DataLoader( dataset, batch_sampler=sampler, collate_fn=stack_sample_pairs ) diff --git a/tests/test_trainer.py b/tests/test_trainer.py index 14f952337..e025728bc 100644 --- a/tests/test_trainer.py +++ b/tests/test_trainer.py @@ -38,7 +38,7 @@ import shutil from copy import deepcopy from pathlib import Path -from typing import Any, Dict, Optional, Union +from typing import Any, Optional import hydra import pytest @@ -56,9 +56,7 @@ # TESTS # ===================================================================================================================== @runner.distributed_run -def run_trainer( - gpu: int, wandb_run: Optional[Union[Run, RunDisabled]], cfg: DictConfig -): +def run_trainer(gpu: int, wandb_run: Optional[Run | RunDisabled], cfg: DictConfig): params = deepcopy(cfg) params["calc_norm"] = True @@ -184,8 +182,8 @@ def test_trainer_3(default_config: DictConfig) -> None: def test_trainer_4( inbuilt_cfg_root: Path, cfg_name: str, - cfg_args: Dict[str, Any], - kwargs: Dict[str, Any], + cfg_args: dict[str, Any], + kwargs: dict[str, Any], ) -> None: with hydra.initialize(version_base="1.3", config_path=str(inbuilt_cfg_root)): diff --git a/tests/test_transforms.py b/tests/test_transforms.py index 491471ac5..96a3c1f54 100644 --- a/tests/test_transforms.py +++ b/tests/test_transforms.py @@ -36,7 +36,7 @@ # ===================================================================================================================== # IMPORTS # ===================================================================================================================== -from typing import Any, Dict, List, Optional +from typing import Any, Optional import pytest import torch @@ -77,7 +77,7 @@ ], ) def test_class_transform( - example_matrix: Dict[int, int], input_mask: LongTensor, output: LongTensor + example_matrix: dict[int, int], input_mask: LongTensor, output: LongTensor ) -> None: transform = ClassTransform(example_matrix) @@ -276,7 +276,7 @@ def test_dublicator( ], ) def test_tg_to_torch( - transform, keys: Optional[List[str]], args: Any, in_img, expected + transform, keys: Optional[list[str]], args: Any, in_img, expected ) -> None: transformation = (utils.tg_to_torch(transform, keys=keys))(args) @@ -441,7 +441,7 @@ def test_get_transform() -> None: ), ], ) -def test_make_transformations(params: Dict[str, Any], key: str) -> None: +def test_make_transformations(params: dict[str, Any], key: str) -> None: if params: transforms = make_transformations({key: params}) assert callable(transforms) diff --git a/tests/test_utils/test_runner.py b/tests/test_utils/test_runner.py index 0d07128e1..980f48eab 100644 --- a/tests/test_utils/test_runner.py +++ b/tests/test_utils/test_runner.py @@ -39,7 +39,7 @@ import os import subprocess import time -from typing import Optional, Union +from typing import Optional import pytest import requests @@ -111,7 +111,7 @@ def test_config_env_vars(default_config: DictConfig) -> None: @runner.distributed_run def _run_func( - gpu: int, wandb_run: Optional[Union[Run, RunDisabled]], cfg: DictConfig + gpu: int, wandb_run: Optional[Run | RunDisabled], cfg: DictConfig ) -> None: time.sleep(0.5) return diff --git a/tests/test_utils/test_utils.py b/tests/test_utils/test_utils.py index 34c6e6f1f..f88d9298e 100644 --- a/tests/test_utils/test_utils.py +++ b/tests/test_utils/test_utils.py @@ -45,7 +45,7 @@ from collections import defaultdict from datetime import datetime from pathlib import Path -from typing import Any, Dict, List, Tuple, Union +from typing import Any import numpy as np import pandas as pd @@ -85,7 +85,7 @@ def test_is_notebook() -> None: def test_return_updated_kwargs() -> None: @utils.return_updated_kwargs - def example_func(*args, **kwargs) -> Tuple[Any, Dict[str, Any]]: + def example_func(*args, **kwargs) -> tuple[Any, dict[str, Any]]: _ = ( kwargs["update_1"] * kwargs["update_3"] - kwargs["static_2"] / args[1] * args[0] @@ -196,13 +196,13 @@ def test_ohe_labels() -> None: assert_array_equal(correct_targets, targets) -def test_empty_classes(exp_classes: Dict[int, str]) -> None: +def test_empty_classes(exp_classes: dict[int, str]) -> None: class_distribution = [(3, 321), (4, 112), (1, 671), (5, 456)] assert utils.find_empty_classes(class_distribution, exp_classes) == [0, 2, 6, 7] def test_eliminate_classes( - exp_classes: Dict[int, str], exp_cmap_dict: Dict[int, str] + exp_classes: dict[int, str], exp_cmap_dict: dict[int, str] ) -> None: empty = [0, 2, 7] new_classes = { @@ -246,12 +246,12 @@ def test_eliminate_classes( ], ) def test_check_test_empty( - exp_classes: Dict[int, str], - in_labels: List[int], - in_pred: List[int], - out_labels: List[int], - out_pred: List[int], - out_classes: Dict[int, str], + exp_classes: dict[int, str], + in_labels: list[int], + in_pred: list[int], + out_labels: list[int], + out_pred: list[int], + out_classes: dict[int, str], ) -> None: results = utils.check_test_empty(in_pred, in_labels, exp_classes) @@ -260,7 +260,7 @@ def test_check_test_empty( assert results[2] == out_classes -def test_find_modes(exp_classes: Dict[int, str]) -> None: +def test_find_modes(exp_classes: dict[int, str]) -> None: labels = [1, 1, 3, 5, 1, 4, 1, 5, 3, 3] class_dist = utils.find_modes(labels, plot=True) @@ -370,12 +370,12 @@ def test_batch_flatten(x, exp_len: int) -> None: ], ) def test_transform_coordinates( - x: Union[List[float], float], - y: Union[List[float], float], + x: list[float] | float, + y: list[float] | float, src_crs: CRS, dest_crs: CRS, - exp_x: Union[List[float], float], - exp_y: Union[List[float], float], + exp_x: list[float] | float, + exp_y: list[float] | float, ) -> None: out_x, out_y = utils.transform_coordinates(x, y, src_crs, dest_crs) @@ -451,9 +451,7 @@ def test_get_centre_loc() -> None: (-77.844504, 166.707506, "McMurdo Station"), # McMurdo Station, Antartica. ], ) -def test_lat_lon_to_loc( - lat: Union[float, str], lon: Union[float, str], loc: str -) -> None: +def test_lat_lon_to_loc(lat: float | str, lon: float | str, loc: str) -> None: try: requests.head("http://www.google.com/", timeout=1.0) except (requests.ConnectionError, requests.ReadTimeout): @@ -546,7 +544,7 @@ def test_find_best_of() -> None: assert scene == ["2018_06_21"] -def test_modes_from_manifest(exp_classes: Dict[int, str]) -> None: +def test_modes_from_manifest(exp_classes: dict[int, str]) -> None: df = pd.DataFrame() class_dist = [ @@ -608,7 +606,7 @@ def test_compute_roc_curves() -> None: labels = [0, 3, 2, 1, 3, 2, 1, 0] class_labels = [0, 1, 2, 3] - fpr: Dict[Any, NDArray[Any, Any]] = { + fpr: dict[Any, NDArray[Any, Any]] = { 0: np.array([0.0, 0.0, 0.0, 0.5, 5.0 / 6.0, 1.0]), 1: np.array([0.0, 0.0, 1.0 / 6.0, 1.0 / 6.0, 1.0]), 2: np.array([0.0, 0.0, 1.0 / 6.0, 0.5, 0.5, 1.0]), @@ -637,7 +635,7 @@ def test_compute_roc_curves() -> None: "macro": np.array([0.0, 1.0 / 6.0, 0.5, 5.0 / 6.0, 1.0]), } - tpr: Dict[Any, NDArray[Any, Any]] = { + tpr: dict[Any, NDArray[Any, Any]] = { 0: np.array([0.0, 0.5, 1.0, 1.0, 1.0, 1.0]), 1: np.array([0.0, 0.5, 0.5, 1.0, 1.0]), 2: np.array([0.0, 0.5, 0.5, 0.5, 1.0, 1.0]), diff --git a/tests/test_utils/test_visutils.py b/tests/test_utils/test_visutils.py index e2820a3f0..8542a8616 100644 --- a/tests/test_utils/test_visutils.py +++ b/tests/test_utils/test_visutils.py @@ -40,7 +40,7 @@ import shutil import tempfile from pathlib import Path -from typing import Any, Dict, Tuple +from typing import Any import matplotlib as mlp import numpy as np @@ -116,7 +116,7 @@ def test_get_mlp_cmap() -> None: def test_discrete_heatmap( - random_mask, exp_classes: Dict[int, str], exp_cmap_dict: Dict[int, str] + random_mask, exp_classes: dict[int, str], exp_cmap_dict: dict[int, str] ) -> None: cmap = ListedColormap(exp_cmap_dict.values()) # type: ignore visutils.discrete_heatmap(random_mask, list(exp_classes.values()), cmap_style=cmap) @@ -151,8 +151,8 @@ def test_labelled_rgb_image( random_mask, random_image, bounds_for_test_img, - exp_classes: Dict[int, str], - exp_cmap_dict: Dict[int, str], + exp_classes: dict[int, str], + exp_cmap_dict: dict[int, str], ) -> None: path = tempfile.gettempdir() name = "pretty_pic" @@ -174,7 +174,7 @@ def test_labelled_rgb_image( def test_make_gif( - bounds_for_test_img, exp_classes: Dict[int, str], exp_cmap_dict: Dict[int, str] + bounds_for_test_img, exp_classes: dict[int, str], exp_cmap_dict: dict[int, str] ) -> None: dates = ["2018-01-15", "2018-07-03", "2018-11-30"] images = np.random.rand(3, 32, 32, 3) @@ -209,7 +209,7 @@ def test_prediction_plot( random_image, random_mask, bounds_for_test_img, - exp_classes: Dict[int, str], + exp_classes: dict[int, str], results_dir: Path, ) -> None: pred = np.random.randint(0, 8, size=(32, 32)) @@ -229,9 +229,9 @@ def test_seg_plot( results_root: Path, data_root: Path, default_dataset: GeoDataset, - exp_dataset_params: Dict[str, Any], - exp_classes: Dict[int, str], - exp_cmap_dict: Dict[int, str], + exp_dataset_params: dict[str, Any], + exp_classes: dict[int, str], + exp_cmap_dict: dict[int, str], cache_dir: Path, monkeypatch, ) -> None: @@ -271,7 +271,7 @@ def test_seg_plot( def test_plot_subpopulations( - exp_classes: Dict[int, str], exp_cmap_dict: Dict[int, str] + exp_classes: dict[int, str], exp_cmap_dict: dict[int, str] ) -> None: class_dist = [(1, 25000), (0, 1300), (2, 100), (3, 2)] @@ -305,7 +305,7 @@ def test_plot_history() -> None: filename.unlink(missing_ok=True) -def test_make_confusion_matrix(exp_classes: Dict[int, str]) -> None: +def test_make_confusion_matrix(exp_classes: dict[int, str]) -> None: batch_size = 2 patch_size = (32, 32) @@ -346,11 +346,11 @@ def test_format_names() -> None: def test_plot_results( default_dataset: GeoDataset, - exp_classes: Dict[int, str], - exp_cmap_dict: Dict[int, str], + exp_classes: dict[int, str], + exp_cmap_dict: dict[int, str], results_dir: Path, - default_config: Dict[str, Any], - small_patch_size: Tuple[int, int], + default_config: dict[str, Any], + small_patch_size: tuple[int, int], std_batch_size: int, std_n_classes: int, ) -> None: @@ -420,7 +420,7 @@ def test_plot_results( def test_plot_embeddings( results_root: Path, default_dataset: GeoDataset, - exp_dataset_params: Dict[str, Any], + exp_dataset_params: dict[str, Any], data_root: Path, cache_dir: Path, ) -> None: From deb49cece9fbfb0e2982c2c5ebbd01ade9f838a5 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Fri, 6 Sep 2024 19:23:36 +0000 Subject: [PATCH 61/63] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- minerva/utils/runner.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/minerva/utils/runner.py b/minerva/utils/runner.py index 61304a959..7f74b18e6 100644 --- a/minerva/utils/runner.py +++ b/minerva/utils/runner.py @@ -60,12 +60,12 @@ import torch import torch.distributed as dist import torch.multiprocessing as mp +import wandb import yaml from omegaconf import DictConfig, ListConfig, OmegaConf from wandb.sdk.lib import RunDisabled from wandb.sdk.wandb_run import Run -import wandb from minerva.trainer import Trainer from minerva.utils import utils From c5f4e789240747b710bb5de725f6a0a40d894585 Mon Sep 17 00:00:00 2001 From: Harry Baker Date: Fri, 6 Sep 2024 20:57:37 +0100 Subject: [PATCH 62/63] Reintroduced `Union` type hint for self-refernecing type hints --- minerva/datasets/paired.py | 8 ++++---- minerva/datasets/utils.py | 6 +++--- 2 files changed, 7 insertions(+), 7 deletions(-) diff --git a/minerva/datasets/paired.py b/minerva/datasets/paired.py index 71130e254..5b2b4e53f 100644 --- a/minerva/datasets/paired.py +++ b/minerva/datasets/paired.py @@ -44,7 +44,7 @@ # ===================================================================================================================== import random from inspect import signature -from typing import Any, Callable, Optional, Sequence, overload +from typing import Any, Callable, Optional, Sequence, Union, overload import hydra import matplotlib.pyplot as plt @@ -86,7 +86,7 @@ def __new__( # type: ignore[misc] dataset: Callable[..., GeoDataset] | GeoDataset, *args, **kwargs, - ) -> "PairedGeoDataset" | "PairedUnionDataset": + ) -> Union["PairedGeoDataset", "PairedUnionDataset"]: if isinstance(dataset, UnionDataset): return PairedUnionDataset( dataset.datasets[0], dataset.datasets[1], *args, **kwargs @@ -606,8 +606,8 @@ class PairedConcatDataset(MinervaConcatDataset): # type: ignore[type-arg] def __init__( self, - dataset1: NonGeoDataset | "PairedConcatDataset", - dataset2: NonGeoDataset | "PairedConcatDataset", + dataset1: Union[NonGeoDataset, "PairedConcatDataset"], + dataset2: Union[NonGeoDataset, "PairedConcatDataset"], size: Optional[int] = None, max_r: Optional[int] = None, ) -> None: diff --git a/minerva/datasets/utils.py b/minerva/datasets/utils.py index 0e7935019..c64f5e843 100644 --- a/minerva/datasets/utils.py +++ b/minerva/datasets/utils.py @@ -46,7 +46,7 @@ # ===================================================================================================================== import pickle from pathlib import Path -from typing import Any, Callable, Iterable, Literal, Optional, Sequence +from typing import Any, Callable, Iterable, Literal, Optional, Sequence, Union import numpy as np from nptyping import NDArray @@ -70,7 +70,7 @@ class MinervaNonGeoDataset(NonGeoDataset): def __or__( self, - other: "MinervaNonGeoDataset" | "MinervaConcatDataset", + other: Union["MinervaNonGeoDataset", "MinervaConcatDataset"], ) -> "MinervaConcatDataset": """Take the union of two :class:`MinervaNonGeoDataset`. @@ -88,7 +88,7 @@ def __or__( class MinervaConcatDataset(ConcatDataset): # type: ignore[type-arg] def __or__( self, - other: MinervaNonGeoDataset | "MinervaConcatDataset", + other: Union[MinervaNonGeoDataset, "MinervaConcatDataset"], ) -> "MinervaConcatDataset": """Take the union of two :class:`MinervaNonGeoDataset`. From 22876986e9c6bf8dcf203bb3f356c6d81e2e2080 Mon Sep 17 00:00:00 2001 From: Harry Baker Date: Fri, 6 Sep 2024 21:31:12 +0100 Subject: [PATCH 63/63] Import `SGD` direct from `torch.optim` --- minerva/tasks/tsne.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/minerva/tasks/tsne.py b/minerva/tasks/tsne.py index d3b88fed9..4db2bd922 100644 --- a/minerva/tasks/tsne.py +++ b/minerva/tasks/tsne.py @@ -80,7 +80,7 @@ def __init__( backbone = model.get_backbone() # type: ignore[assignment, operator] # Set dummy optimiser. It won't be used as this is a test. - backbone.set_optimiser(torch.optim.sgd.SGD(backbone.parameters(), lr=1.0e-3)) + backbone.set_optimiser(torch.optim.SGD(backbone.parameters(), lr=1.0e-3)) # type: ignore[attr-defined] super().__init__( name,

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