From bcef6d5f2399a918189aeea8b18903b52733b132 Mon Sep 17 00:00:00 2001 From: Lorenzo Pisani Date: Tue, 4 Nov 2025 08:17:39 -0800 Subject: [PATCH 01/57] initial uv structure --- .envrc | 11 ++ .gitignore | 4 + README.md | 1 + pyproject.toml | 179 ++++++++++++++++++++ src/snraware/__init__.py | 0 test/snraware_test/__init__.py | 0 test/snraware_test/test_basics.py | 6 + uv.lock | 266 ++++++++++++++++++++++++++++++ 8 files changed, 467 insertions(+) create mode 100644 .envrc create mode 100644 README.md create mode 100644 pyproject.toml create mode 100644 src/snraware/__init__.py create mode 100644 test/snraware_test/__init__.py create mode 100644 test/snraware_test/test_basics.py create mode 100644 uv.lock diff --git a/.envrc b/.envrc new file mode 100644 index 0000000..936677d --- /dev/null +++ b/.envrc @@ -0,0 +1,11 @@ +source_up +layout_uv + +export PIP_DISABLE_PIP_VERSION_CHECK=1 +export PYTHONDONTWRITEBYTECODE=1 + +# Should give us the path to the root of the component even if someone CDs deeper into the path +SCRIPT_DIR=$( cd -- "$( dirname -- "${BASH_SOURCE[0]}" )" &> /dev/null && pwd ) + +# If we want executables outside of python, we put them in the `bin/` directory +export PATH="${SCRIPT_DIR}/bin:$PATH" diff --git a/.gitignore b/.gitignore index ce89292..fd5ab85 100644 --- a/.gitignore +++ b/.gitignore @@ -416,3 +416,7 @@ FodyWeavers.xsd *.msix *.msm *.msp + +# added by the team +.idea +.python-version diff --git a/README.md b/README.md new file mode 100644 index 0000000..3c32470 --- /dev/null +++ b/README.md @@ -0,0 +1 @@ +# SNRAware diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 0000000..87d4f8c --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,179 @@ +[project] +name = "snraware" +description = "" +version = "0.1.0" +readme = "README.md" +requires-python = ">=3.12,<4.0" +authors = [ + {name = "Hui Xue", email = "xueh@microsoft.com"}, +] +keywords = [] +license = "MIT" +dependencies = [] + +[project.urls] +repository = "https://github.com/microsoft/SNRAware" +documentation = "https://microsoft.github.io/SNRAware" + +[dependency-groups] +dev = [ + "coverage", + "pyright", + "pytest", + "pytest-cov", + "pytest-nunit", + "setuptools", + "ruff", +] + +[build-system] +requires = ["uv_build>=0.9.2,<0.10.0"] +build-backend = "uv_build" + +[tool.uv.build-backend] +module-name = ["snraware"] + +[project.scripts] + +[tool.pyright] +#pythonVersion = "3.10" +include = ["src", "test"] +exclude = ["**/__pycache__"] + +strictListInference = true +strictDictionaryInference = true +strictSetInference = true +deprecateTypingAliases = true +reportPropertyTypeMismatch = true +reportUnusedImport = true +reportUnusedClass = true +reportUnusedVariable = true +reportDuplicateImport = true +reportUntypedFunctionDecorator = true +reportUntypedClassDecorator = true +reportUntypedBaseClass = true +reportUntypedNamedTuple = true +reportConstantRedefinition = true +reportDeprecated = true +reportInconsistentConstructor = true +reportUnknownParameterType = true +reportMissingParameterType = true +reportMissingTypeArgument = true +reportUnnecessaryCast = true +reportUnnecessaryContains = true +reportImplicitStringConcatenation = true +reportInvalidStubStatement = true +reportIncompleteStub = true +reportUnnecessaryTypeIgnoreComment = true +reportMatchNotExhaustive = true +reportShadowedImports = true + +reportMissingTypeStubs = false +reportImportCycles = true +reportUnusedFunction = false +reportMissingSuperCall = false +reportPrivateUsage = true +reportUninitializedInstanceVariable = false # disabled so we can check test directories +reportUnknownArgumentType = true +reportUnknownLambdaType = true +reportUnknownVariableType = true +reportUnknownMemberType = true +reportCallInDefaultInitializer = true +reportUnnecessaryIsInstance = true +reportUnnecessaryComparison = true +reportUnusedCallResult = false +reportUnusedExpression = true +reportImplicitOverride = false +reportIncompatibleVariableOverride = true +reportIncompatibleMethodOverride = true + +[tool.ruff] +cache-dir = ".tmp/.ruff_cache" +include = [ + "src/**/*.py", + "test/**/*.py", + "pyproject.toml", +] +fix = true +show-fixes = true +output-format = "full" +line-length = 99 +indent-width = 4 +target-version = "py312" + +[tool.ruff.lint] +select = [ + "B", # Bugbear + "D", # Docstring + "E", # Pycodestyle + "F", # Pyflakes + "I", # Import + "Q", # Quotes + "W", # PEP8 warnings + "NPY", # Numpy + "PD", # Pydocstyle + "PTH", # Path + "RUF", # Ruff + "UP", # Unittest + "D213", # D213: Multi-line docstring summary should start at the second line +] +ignore = [ + "D212", # D212: Multi-line docstring summary should start at the second line + # This rule seems buggy: https://github.com/astral-sh/ruff/issues/2480 + "PD011", # PD011: Use `.to_numpy()` instead of `.values` +] + +# Allow fix for all enabled rules (when `--fix`) is provided. +fixable = ["ALL"] +unfixable = [] + +# Allow unused variables when underscore-prefixed. +dummy-variable-rgx = "^(_+|(_+[a-zA-Z0-9_]*[a-zA-Z0-9]+?))$" + +[tool.ruff.lint.per-file-ignores] +"test/*" = [ + "D100", # Missing docstring in public module + "D101", # Missing docstring in public class + "D102", # Missing docstring in public method + "D103", # Missing docstring in public function + "D104", # Missing docstring in public package +] + +[tool.ruff.lint.pydocstyle] +convention = "google" + +[tool.ruff.lint.isort] +known-first-party = ["snraware", "snraware_test"] + +[tool.ruff.format] +quote-style = "double" # Like Black, use double quotes for strings. +indent-style = "space" # Like Black, indent with spaces, rather than tabs. +skip-magic-trailing-comma = false # Like Black, respect magic trailing commas. +line-ending = "auto" # Like Black, automatically detect the appropriate line ending. + +# Enable auto-formatting of code examples in docstrings. Markdown, +# reStructuredText code/literal blocks and doctests are all supported. +docstring-code-format = true + +[tool.coverage.run] +data_file = ".tmp/.coverage" +relative_files = true + +[tool.coverage.report] +show_missing = true + +[tool.coverage.xml] +output = ".tmp/reports/coverage.xml" + +[tool.pytest.ini_options] +cache_dir = ".tmp/.pytest_cache" +testpaths = ["test"] +addopts = """ + --cov=src/snraware --cov=test/snraware_test + --cov-report=term + --cov-report=xml + --junit-xml .tmp/reports/pytest-junit.xml + --capture=no + --cov-branch + --cache-clear +""" diff --git a/src/snraware/__init__.py b/src/snraware/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/test/snraware_test/__init__.py b/test/snraware_test/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/test/snraware_test/test_basics.py b/test/snraware_test/test_basics.py new file mode 100644 index 0000000..86a08cd --- /dev/null +++ b/test/snraware_test/test_basics.py @@ -0,0 +1,6 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for light mode + - media: "(prefers-color-scheme: light)" + scheme: default + primary: teal + accent: light blue + toggle: + icon: material/brightness-7 + name: Switch to dark mode + + # Palette toggle for dark mode + - media: "(prefers-color-scheme: dark)" + scheme: slate + primary: teal + accent: light blue + toggle: + icon: material/brightness-4 + name: Switch to light mode + icon: + repo: fontawesome/brands/github-alt + features: + - navigation.instant + - navigation.instant.prefetch + - navigation.path + - content.code.copy + - content.tabs.link + - toc.follow + - navigation.top +extra_css: + - css/extra.css +extra_javascript: + - js/mathjax.js + - https://unpkg.com/mathjax@3/es5/tex-mml-chtml.js +watch: + - "src" +plugins: + - search + - autorefs + - mkdocstrings: + default_handler: python + handlers: + python: + paths: + - "src" + options: + show_if_no_docstring: true + show_symbol_type_toc: true + show_symbol_type_heading: true + show_signature_annotations: false + group_by_category: true + show_category_heading: false # adds a lot of nesting in TOC navs + show_root_heading: true # adds nice grouping on pages showing more than one element + signature_crossrefs: true + merge_init_into_class: true + annotations_path: source + members_order: source + inventories: + - "https://docs.python.org/3/objects.inv" + - awesome-pages + - mkdocs-video: + is_video: true + video_controls: true +markdown_extensions: + # Allows for nicely styled notes and warnings: !!! note + - admonition + # Allows for adding attributes to markdown content: !(some-image){: align=left } + - attr_list + - md_in_html + - pymdownx.details + - pymdownx.highlight: + anchor_linenums: true + line_spans: __span + pygments_lang_class: true + - pymdownx.inlinehilite + - pymdownx.snippets: + # https://facelessuser.github.io/pymdown-extensions/extensions/snippets/#snippets-notation + base_path: + # can include anything from root of repo + - !relative $config_dir + # fail the mkdocs build if any snippets link to missing files + # this delays painful bugs being found late + check_paths: true + - pymdownx.magiclink + - pymdownx.superfences: + custom_fences: + - name: mermaid + class: mermaid + format: !!python/name:pymdownx.superfences.fence_code_format + - pymdownx.tabbed: + alternate_style: true + slugify: !!python/object/apply:pymdownx.slugs.slugify + kwds: + case: lower + - pymdownx.emoji: + emoji_index: !!python/name:material.extensions.emoji.twemoji + emoji_generator: !!python/name:material.extensions.emoji.to_svg + - toc: + permalink: true + - def_list # definition / description lists + - pymdownx.tasklist: # Allows us to make a Tasklist: [x] some checked thing + custom_checkbox: true + - pymdownx.arithmatex: + generic: true + # See: https://github.com/mkdocs/mkdocs/issues/545 + - mdx_truly_sane_lists diff --git a/pyproject.toml b/pyproject.toml index 87d4f8c..aafb28d 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -24,6 +24,15 @@ dev = [ "pytest-nunit", 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++++++++++++ .github/workflows/release.yaml | 22 ++++++ 11 files changed, 386 insertions(+) create mode 100644 .github/actions/setup-environment/action.yaml create mode 100644 .github/dependabot.yaml create mode 100644 .github/policies/branch-protection.yml create mode 100644 .github/workflows/automerge.yml create mode 100644 .github/workflows/build-docs.yaml create mode 100644 .github/workflows/build-wheels.yaml create mode 100644 .github/workflows/checks.yaml create mode 100644 .github/workflows/codeql.yaml create mode 100644 .github/workflows/main.yaml create mode 100644 .github/workflows/pr.yaml create mode 100644 .github/workflows/release.yaml diff --git a/.github/actions/setup-environment/action.yaml b/.github/actions/setup-environment/action.yaml new file mode 100644 index 0000000..bc93e7e --- /dev/null +++ b/.github/actions/setup-environment/action.yaml @@ -0,0 +1,69 @@ +name: "Setup Environment" +description: | + Install tools and define common variables. +outputs: + package-version: + description: "a generated version usable for publishing packages and containers" + value: ${{ steps.runner-context.outputs.package-version }} +runs: + using: "composite" + steps: + - id: runner-context + name: runner-context + shell: bash + env: + COMMIT_TIMESTAMP: ${{ github.event.head_commit.timestamp }} + # github.head_ref only exists in PR builds + # github.ref_name only exists in branch builds (main) + COMMIT_REF: "${{ github.head_ref || github.ref_name }}" + RUN_ID: "${{ github.run_id }}" + IS_PR_BUILD: "${{ github.event_name == 'pull_request' && '1' || '0' }}" + # we are calling tagged versions and main branch builds both "main" builds + IS_MAIN_BUILD: "${{ github.event_name == 'push' && '1' || '0' }}" + BASE_SHA: "${{ github.event.pull_request.base.sha || github.event.before }}" + HEAD_SHA: "${{ github.event.pull_request.head.sha || github.event.after }}" + run: | + # make sure people are able to use packages installed with `uv tool install` + export PATH="${HOME}/.local/bin:$PATH" + echo "${HOME}/.local/bin" >> $GITHUB_PATH + export PATH="${PWD}/bin:$PATH" + echo "${PWD}/bin" >> $GITHUB_PATH + + TOML_VERSION=$(grep -m 1 version pyproject.toml | tr -s " " | tr -d '"' | tr -d "'" | cut -d" " -f3) + MAIN_BUILD_VERSION="$TOML_VERSION.pre$RUN_ID" + PR_BUILD_VERSION="$(date --date "$COMMIT_TIMESTAMP" "+$TOML_VERSION.pre$RUN_ID.dev%Y%m%d%H%M%S")" + + echo "timestamp: $COMMIT_TIMESTAMP" + echo "commit ref: $COMMIT_REF" + echo "version found in toml: $TOML_VERSION" + echo "version calculated for main branch builds: $MAIN_BUILD_VERSION" + echo "version calculated for pr builds: $PR_BUILD_VERSION" + echo "runid: $RUN_ID" + + if [[ $COMMIT_REF == "v$TOML_VERSION" ]] ; + then + echo "detected tag build: $COMMIT_REF" + PACKAGE_VERSION=$TOML_VERSION + elif [[ $COMMIT_REF == "main" ]] ; + then + echo "detected main branch build" + echo "will generate a release candidate" + # if the package is installed with "allow-prereleases", the latest version of this branch is used + PACKAGE_VERSION=$MAIN_BUILD_VERSION + else + echo "detected branch build" + echo "will generate a dev version" + # these version numbers are only installable by specifying the full version string + PACKAGE_VERSION=$PR_BUILD_VERSION + fi + + echo "generating context for version: $PACKAGE_VERSION" + echo "package-version=$PACKAGE_VERSION" >> $GITHUB_OUTPUT + echo "is-pr-build=$IS_PR_BUILD" >> $GITHUB_OUTPUT + echo "is-main-build=$IS_MAIN_BUILD" >> $GITHUB_OUTPUT + echo "base-sha=$BASE_SHA" >> $GITHUB_OUTPUT + echo "head-sha=$HEAD_SHA" >> $GITHUB_OUTPUT + - name: "install-uv" + uses: astral-sh/setup-uv@v6 + with: + version: "0.9" diff --git a/.github/dependabot.yaml b/.github/dependabot.yaml new file mode 100644 index 0000000..b778454 --- /dev/null +++ b/.github/dependabot.yaml @@ -0,0 +1,26 @@ +version: 2 +updates: + - package-ecosystem: github-actions + directory: / + schedule: + interval: weekly + target-branch: main + groups: + github-actions: + patterns: + - "*" + commit-message: + prefix: ":arrow_up: dep-bump" + include: scope + - package-ecosystem: pip + directory: / + schedule: + interval: weekly + target-branch: main + groups: + package-dependencies: + patterns: + - "*" + commit-message: + prefix: ":arrow_up: dep-bump" + include: scope diff --git a/.github/policies/branch-protection.yml b/.github/policies/branch-protection.yml new file mode 100644 index 0000000..d831dc4 --- /dev/null +++ b/.github/policies/branch-protection.yml @@ -0,0 +1,13 @@ +# Documentation for branch policy: https://aka.ms/gim/docs/policy/branch-protection + +name: main branch policy +description: restrict permissions to main branch +resource: repository +where: +configuration: + branchProtectionRules: + - branchNamePattern: "main" + requiredApprovingReviewsCount: 0 + restrictsPushes: false + allowsForcePushes: false + allowsDeletions: false diff --git a/.github/workflows/automerge.yml b/.github/workflows/automerge.yml new file mode 100644 index 0000000..bcf8df5 --- /dev/null +++ b/.github/workflows/automerge.yml @@ -0,0 +1,23 @@ +name: Dependabot auto-merge +on: pull_request + +permissions: + contents: write + pull-requests: write + +jobs: + dependabot: + runs-on: "ubuntu-24.04" + if: github.event.pull_request.user.login == 'dependabot[bot]' && github.repository == 'microsoft/SNRAware' + steps: + - name: Dependabot metadata + id: metadata + uses: dependabot/fetch-metadata@v2 + with: + github-token: ${{ secrets.GITHUB_TOKEN }} + - name: Enable auto-merge for Dependabot PRs + if: steps.metadata.outputs.update-type == 'version-update:semver-patch' || steps.metadata.outputs.update-type == 'version-update:semver-minor' + run: gh pr merge --auto --rebase "$PR_URL" + env: + PR_URL: ${{ github.event.pull_request.html_url }} + GH_TOKEN: ${{ secrets.GITHUB_TOKEN }} diff --git a/.github/workflows/build-docs.yaml b/.github/workflows/build-docs.yaml new file mode 100644 index 0000000..c1b71f1 --- /dev/null +++ b/.github/workflows/build-docs.yaml @@ -0,0 +1,31 @@ +name: Build Docs +on: + workflow_call: + inputs: + sha: + description: 'the git sha to checkout' + required: true + type: string +jobs: + generate-docs: + runs-on: "ubuntu-24.04" + name: "generate-docs" + steps: + - uses: actions/checkout@v5 + with: + ref: ${{ inputs.sha }} + fetch-depth: 0 + fetch-tags: true + - uses: ./.github/actions/setup-environment + id: runner-context + - name: mkdocs-build + env: + PACKAGE_VERSION: "${{ steps.runner-context.outputs.package-version }}" + run: | + uv version "$PACKAGE_VERSION" + uv sync + mkdocs build + - name: "upload-gh-pages" + uses: actions/upload-pages-artifact@v4 + with: + path: "dist/site" diff --git a/.github/workflows/build-wheels.yaml b/.github/workflows/build-wheels.yaml new file mode 100644 index 0000000..64ed923 --- /dev/null +++ b/.github/workflows/build-wheels.yaml @@ -0,0 +1,32 @@ +name: Build Wheels +on: + workflow_call: + inputs: + sha: + description: 'the git sha to checkout' + required: true + type: string +jobs: + build-components: + runs-on: "ubuntu-24.04" + name: "build-wheels" + steps: + - uses: actions/checkout@v5 + with: + ref: ${{ inputs.sha }} + fetch-depth: 0 + fetch-tags: true + - uses: ./.github/actions/setup-environment + id: runner-context + - name: build-wheel + env: + PACKAGE_VERSION: "${{ steps.runner-context.outputs.package-version }}" + run: | + uv version "$PACKAGE_VERSION" + uv sync + uv build + - name: "upload-artifacts" + uses: actions/upload-artifact@v4 + with: + name: "pkg-wheels" + path: "dist/*.whl" diff --git a/.github/workflows/checks.yaml b/.github/workflows/checks.yaml new file mode 100644 index 0000000..bd968d1 --- /dev/null +++ b/.github/workflows/checks.yaml @@ -0,0 +1,36 @@ +name: Check +on: + workflow_call: + inputs: + sha: + description: 'the git sha to checkout' + required: true + type: string +jobs: + check-components: + runs-on: "ubuntu-24.04" + name: "check" + steps: + - uses: actions/checkout@v5 + with: + ref: ${{ inputs.sha }} + fetch-depth: 0 + fetch-tags: true + - uses: ./.github/actions/setup-environment + id: runner-context + - name: check + env: + PACKAGE_VERSION: "${{ steps.runner-context.outputs.package-version }}" + run: | + uv version "$PACKAGE_VERSION" + uv run ruff format --check + uv run ruff check + uv run pyright + uv run pytest + - name: upload-coverage + uses: codecov/codecov-action@v5.5.1 + with: + token: ${{ secrets.CODECOV_TOKEN }} + slug: microsoft/SNRAware + name: snraware + verbose: true diff --git a/.github/workflows/codeql.yaml b/.github/workflows/codeql.yaml new file mode 100644 index 0000000..bd4d96c --- /dev/null +++ b/.github/workflows/codeql.yaml @@ -0,0 +1,42 @@ +name: "CodeQL" +on: + workflow_dispatch: + inputs: + sha: + description: 'the git sha to checkout' + required: true + type: string + workflow_call: + inputs: + sha: + description: 'the git sha to checkout' + required: true + type: string +jobs: + analyze: + name: Analyze + runs-on: "ubuntu-24.04" + permissions: + security-events: write + actions: read + contents: read + strategy: + fail-fast: false + matrix: + language: [ "javascript-typescript", "python" ] + steps: + - uses: actions/checkout@v5 + with: + ref: ${{ inputs.sha }} + fetch-depth: 0 + fetch-tags: true + - name: Initialize CodeQL + uses: github/codeql-action/init@v3 + with: + languages: ${{ matrix.language }} + - name: Autobuild + uses: github/codeql-action/autobuild@v3 + - name: Perform CodeQL Analysis + uses: github/codeql-action/analyze@v3 + with: + category: "/language:${{matrix.language}}" diff --git a/.github/workflows/main.yaml b/.github/workflows/main.yaml new file mode 100644 index 0000000..7a27738 --- /dev/null +++ b/.github/workflows/main.yaml @@ -0,0 +1,47 @@ +name: Main +on: + push: + branches: [ "main" ] + tags: + - "v[0-9]+.[0-9]+.[0-9]+" +permissions: + contents: write + actions: read + id-token: write + pages: write + security-events: write +concurrency: + group: ${{ github.workflow }}-${{ github.ref }} + cancel-in-progress: true +jobs: + checks: + uses: ./.github/workflows/checks.yaml + secrets: inherit + with: + sha: ${{ github.sha }} + build-wheels: + uses: ./.github/workflows/build-wheels.yaml + secrets: inherit + with: + sha: ${{ github.sha }} + build-docs: + uses: ./.github/workflows/build-docs.yaml + secrets: inherit + with: + sha: ${{ github.sha }} + codeql: + uses: ./.github/workflows/codeql.yaml + secrets: inherit + with: + sha: ${{ github.sha }} + release: + uses: ./.github/workflows/release.yaml + secrets: inherit + needs: + - build-docs + - build-wheels + - checks + - codeql + with: + sha: ${{ github.sha }} + publish-docs: true diff --git a/.github/workflows/pr.yaml b/.github/workflows/pr.yaml new file mode 100644 index 0000000..49fbac3 --- /dev/null +++ b/.github/workflows/pr.yaml @@ -0,0 +1,45 @@ +name: PR +on: + pull_request: + branches: [ main ] +permissions: + contents: read + actions: read + id-token: write + pages: write + security-events: write +concurrency: + group: ${{ github.workflow }}-${{ github.ref }} + cancel-in-progress: true +jobs: + checks: + uses: ./.github/workflows/checks.yaml + secrets: inherit + with: + sha: ${{ github.sha }} + build-wheels: + uses: ./.github/workflows/build-wheels.yaml + secrets: inherit + with: + sha: ${{ github.sha }} + build-docs: + uses: ./.github/workflows/build-docs.yaml + secrets: inherit + with: + sha: ${{ github.sha }} + codeql: + uses: ./.github/workflows/codeql.yaml + secrets: inherit + with: + sha: ${{ github.sha }} + release: + uses: ./.github/workflows/release.yaml + secrets: inherit + needs: + - build-docs + - build-wheels + - checks + - codeql + with: + sha: ${{ github.sha }} + publish-docs: false diff --git a/.github/workflows/release.yaml b/.github/workflows/release.yaml new file mode 100644 index 0000000..0dd1317 --- /dev/null +++ b/.github/workflows/release.yaml @@ -0,0 +1,22 @@ +name: Release +on: + workflow_call: + inputs: + sha: + description: 'the git sha to checkout' + required: true + type: string + publish-docs: + description: 'whether to publish docs to the github pages' + required: true + type: boolean +jobs: + publish-docs: + name: "deploy-docs" + runs-on: "ubuntu-latest" + if: "${{ inputs.publish-docs }}" + timeout-minutes: 10 + steps: + - name: "deploy-gh-pages" + id: deployment + uses: actions/deploy-pages@v4 From f2adf10ea0307d8293c7b867582b69da222ccf93 Mon Sep 17 00:00:00 2001 From: Lorenzo Pisani Date: Tue, 4 Nov 2025 08:58:25 -0800 Subject: [PATCH 04/57] add justfile and fix ci errors --- .github/workflows/build-docs.yaml | 2 +- justfile | 12 ++++++++++++ src/snraware/__init__.py | 1 + test/snraware_test/test_basics.py | 5 +++-- 4 files changed, 17 insertions(+), 3 deletions(-) create mode 100644 justfile diff --git a/.github/workflows/build-docs.yaml b/.github/workflows/build-docs.yaml index c1b71f1..da9dc59 100644 --- a/.github/workflows/build-docs.yaml +++ b/.github/workflows/build-docs.yaml @@ -24,7 +24,7 @@ jobs: run: | uv version "$PACKAGE_VERSION" uv sync - mkdocs build + uv run mkdocs build - name: "upload-gh-pages" uses: actions/upload-pages-artifact@v4 with: diff --git a/justfile b/justfile new file mode 100644 index 0000000..d37b23a --- /dev/null +++ b/justfile @@ -0,0 +1,12 @@ +set shell := ['bash', '-ceuo', 'pipefail'] + +@default: lint test + +@lint: + ruff check + ruff format --check + ruff check + pyright + +@test: + pytest diff --git a/src/snraware/__init__.py b/src/snraware/__init__.py index e69de29..ab7c46f 100644 --- a/src/snraware/__init__.py +++ b/src/snraware/__init__.py @@ -0,0 +1 @@ +"""SNRAware Module.""" diff --git a/test/snraware_test/test_basics.py b/test/snraware_test/test_basics.py index 86a08cd..4465da3 100644 --- a/test/snraware_test/test_basics.py +++ b/test/snraware_test/test_basics.py @@ -1,6 +1,7 @@ -import snraware from types import ModuleType +import snraware + def test_basics() -> None: - assert type(snraware) == ModuleType + assert type(snraware) is ModuleType From 21ca909e494482d993017c17399d10b85587fcb3 Mon Sep 17 00:00:00 2001 From: Lorenzo Pisani Date: Tue, 4 Nov 2025 09:05:04 -0800 Subject: [PATCH 05/57] move built docs site to dist directory --- .gitignore | 1 + mkdocs.yaml | 1 + 2 files changed, 2 insertions(+) diff --git a/.gitignore b/.gitignore index fd5ab85..1b4b92b 100644 --- a/.gitignore +++ b/.gitignore @@ -420,3 +420,4 @@ FodyWeavers.xsd # added by the team .idea .python-version +dist diff --git a/mkdocs.yaml b/mkdocs.yaml index 6528c76..40f7667 100644 --- a/mkdocs.yaml +++ b/mkdocs.yaml @@ -2,6 +2,7 @@ site_name: SNRAware repo_url: "https://github.com/health-futures/SNRAware" repo_name: "SNRAware" docs_dir: "docs" +site_dir: "dist/site" theme: name: material logo: images/logo.png From 6c5864f64c5c703218850f2e39dc505a4b530d9d Mon Sep 17 00:00:00 2001 From: Lorenzo Pisani Date: Tue, 4 Nov 2025 09:08:44 -0800 Subject: [PATCH 06/57] disable code coverage integration --- .github/workflows/checks.yaml | 7 ------- 1 file changed, 7 deletions(-) diff --git a/.github/workflows/checks.yaml b/.github/workflows/checks.yaml index bd968d1..f667a24 100644 --- a/.github/workflows/checks.yaml +++ b/.github/workflows/checks.yaml @@ -27,10 +27,3 @@ jobs: uv run ruff check uv run pyright uv run pytest - - name: upload-coverage - uses: codecov/codecov-action@v5.5.1 - with: - token: ${{ secrets.CODECOV_TOKEN }} - slug: microsoft/SNRAware - name: snraware - verbose: true From c1a2701b9182f34ce9ed44d30c42e5bad572c940 Mon Sep 17 00:00:00 2001 From: Lorenzo Pisani Date: Tue, 4 Nov 2025 10:35:15 -0800 Subject: [PATCH 07/57] fix repo url --- mkdocs.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/mkdocs.yaml b/mkdocs.yaml index 40f7667..a99ab25 100644 --- a/mkdocs.yaml +++ b/mkdocs.yaml @@ -1,5 +1,5 @@ site_name: SNRAware -repo_url: "https://github.com/health-futures/SNRAware" +repo_url: "https://github.com/microsoft/SNRAware" repo_name: "SNRAware" docs_dir: "docs" site_dir: "dist/site" From a78078166558bd7d700345f934ec7f9f3e25c39b Mon Sep 17 00:00:00 2001 From: Lorenzo Pisani Date: Tue, 4 Nov 2025 10:55:52 -0800 Subject: [PATCH 08/57] add basic docker building to test installation of our pkg --- .dockerignore | 10 +++++ .github/workflows/build-containers.yaml | 31 +++++++++++++++ .github/workflows/main.yaml | 6 +++ .github/workflows/pr.yaml | 6 +++ Containerfile | 50 +++++++++++++++++++++++++ 5 files changed, 103 insertions(+) create mode 100644 .dockerignore create mode 100644 .github/workflows/build-containers.yaml create mode 100644 Containerfile diff --git a/.dockerignore b/.dockerignore new file mode 100644 index 0000000..465f539 --- /dev/null +++ b/.dockerignore @@ -0,0 +1,10 @@ +**/.venv +**/.tmp +**/.idea +**/.vscode +**/*.pyc + +*/src/python/*.egg-info +*/dist/ + +.git diff --git a/.github/workflows/build-containers.yaml b/.github/workflows/build-containers.yaml new file mode 100644 index 0000000..cd17aa2 --- /dev/null +++ b/.github/workflows/build-containers.yaml @@ -0,0 +1,31 @@ +name: Build Containers +on: + workflow_call: + inputs: + sha: + description: 'the git sha to checkout' + required: true + type: string +jobs: + build-containers: + runs-on: "ubuntu-24.04" + steps: + # Checkout the repository code + - name: Checkout Code + uses: actions/checkout@v4 + with: + ref: ${{ inputs.sha }} + fetch-depth: 0 + fetch-tags: true + - uses: ./.github/actions/setup-environment + id: runner-context + # Build the Docker image + - name: Build Docker Image + run: | + uv version "$PACKAGE_VERSION" + echo "building image: snraware:${PACKAGE_VERSION}" + docker build -f Containerfile -t snraware:${PACKAGE_VERSION} . + echo "testing installation in image" + docker run snraware:${PACKAGE_VERSION} python -c "import snraware" + env: + PACKAGE_VERSION: "${{ steps.runner-context.outputs.package-version }}" diff --git a/.github/workflows/main.yaml b/.github/workflows/main.yaml index 7a27738..60e319c 100644 --- a/.github/workflows/main.yaml +++ b/.github/workflows/main.yaml @@ -24,6 +24,11 @@ jobs: secrets: inherit with: sha: ${{ github.sha }} + build-containers: + uses: ./.github/workflows/build-containers.yaml + secrets: inherit + with: + sha: ${{ github.sha }} build-docs: uses: ./.github/workflows/build-docs.yaml secrets: inherit @@ -38,6 +43,7 @@ jobs: uses: ./.github/workflows/release.yaml secrets: inherit needs: + - build-containers - build-docs - build-wheels - checks diff --git a/.github/workflows/pr.yaml b/.github/workflows/pr.yaml index 49fbac3..fb0e667 100644 --- a/.github/workflows/pr.yaml +++ b/.github/workflows/pr.yaml @@ -22,6 +22,11 @@ jobs: secrets: inherit with: sha: ${{ github.sha }} + build-containers: + uses: ./.github/workflows/build-containers.yaml + secrets: inherit + with: + sha: ${{ github.sha }} build-docs: uses: ./.github/workflows/build-docs.yaml secrets: inherit @@ -36,6 +41,7 @@ jobs: uses: ./.github/workflows/release.yaml secrets: inherit needs: + - build-containers - build-docs - build-wheels - checks diff --git a/Containerfile b/Containerfile new file mode 100644 index 0000000..f023e72 --- /dev/null +++ b/Containerfile @@ -0,0 +1,50 @@ +FROM mcr.microsoft.com/mirror/docker/library/ubuntu:24.04 + +RUN < Date: Wed, 5 Nov 2025 09:44:53 -0800 Subject: [PATCH 09/57] add to justfile from feedback --- justfile | 13 +++++++++++++ 1 file changed, 13 insertions(+) diff --git a/justfile b/justfile index d37b23a..b114349 100644 --- a/justfile +++ b/justfile @@ -10,3 +10,16 @@ set shell := ['bash', '-ceuo', 'pipefail'] @test: pytest + +@build-package: + uv build + +@build-docs: + mkdocs build + +@serve-docs: + mkdocs serve + +@setup-env: + uv sync + direnv allow . From c688229b18a1494aba02a8743759448013984545 Mon Sep 17 00:00:00 2001 From: Hui Xue Date: Wed, 5 Nov 2025 10:05:23 -0800 Subject: [PATCH 10/57] starting to add contents --- docs/index.md | 54 +- pyproject.toml | 53 +- uv.lock | 3738 +++++++++++++++++++++++++++++++++++++++++++++--- 3 files changed, 3619 insertions(+), 226 deletions(-) diff --git a/docs/index.md b/docs/index.md index 4fd6880..a3404d6 100644 --- a/docs/index.md +++ b/docs/index.md @@ -1,4 +1,52 @@ ---- -title: about ---- # SNRAware + +This repository contains the Pytorch code in our paper [SNRAware: Improved Deep Learning MRI Denoising with Signal-to-noise Ratio Unit Training and G-factor Map Augmentation](https://pubs.rsna.org/doi/full/10.1148/ryai.250227) published at the Radiology: Artificial Intelligence: + +```latex +@article{doi:10.1148/ryai.250227, +author = {Xue, Hui and Hooper, Sarah M. and Pierce, Iain and Davies, Rhodri H. and Stairs, John and Naegele, Joseph and Campbell-Washburn, Adrienne E. and Manisty, Charlotte and Moon, James C. and Treibel, Thomas A. and Hansen, Michael S. and Kellman, Peter}, +title = {SNRAware: Improved Deep Learning MRI Denoising with Signal-to-noise Ratio Unit Training and G-factor Map Augmentation}, +journal = {Radiology: Artificial Intelligence}, +volume = {0}, +number = {ja}, +pages = {e250227}, +year = {0}, +doi = {10.1148/ryai.250227}, + note ={PMID: 41123451}, +URL = { + https://doi.org/10.1148/ryai.250227 +}, +eprint = { + https://doi.org/10.1148/ryai.250227 +}, +abstract = { Purpose To develop and evaluate a novel deep learning-based MRI denoising method using quantitative noise distribution information obtained during image reconstruction to improve model performance and generalization. Materials and Methods This retrospective study included a training set of 2885236 images from 96605 cardiac cine series acquired on 3T MRI scanners from January 2018 to December 2020. 95\% of these data were used for training and 5\% for validation. The hold-out test set included 3000 cine series, acquired in the same period. Fourteen model architectures were evaluated by instantiating each of the two backbone types with seven transformer and convolution block types. The proposed SNRAware training scheme leveraged MRI reconstruction knowledge to enhance denoising by simulating diverse synthetic datasets and providing quantitative noise distribution information. Internal testing measured performance using peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM), whereas external tests conducted on 1.5T real-time cardiac cine, first-pass cardiac perfusion, brain, and spine MRIs assessed generalization across various sequences, contrasts, anatomies, and field strengths. Results SNRAware improved performance on internal tests conducted on a hold-out dataset of 3000 cine series. Models trained without reconstruction knowledge achieved the worst performance metrics. Improvement was architecture-agnostic for both convolution and transformer models; however, transformer models outperformed their convolutional counterparts. Additionally, 3D input tensors showed improved performance over 2D images. The best-performing model from the internal testing generalized well to external samples, delivering 6.5 × and 2.9 × contrast-to-noise ratio improvement for real-time cine and perfusion imaging, respectively. The model trained using only cardiac cine data generalized well to T1 MPRAGE (Magnetization-Prepared Rapid Gradient-Echo) brain 3D and T2 TSE (turbo spin-echo) spine MRIs. Conclusion The SNRAware training scheme leveraged data obtained during the image reconstruction process for deep learning-based MRI denoising training, resulting in improved performance and good generalization. © The Author(s) 2025. Published by the Radiological Society of North America under a CC BY 4.0 license. } +} +``` + +- Model type: Imaging AI, non-generative +- License: MIT + +## Get started + + + +## Data +Dataset for MR denoising training is not opened at this moment. + +## Run model training + +## Direct intended uses +SNRAware is shared for research purposes only, namely, benchmarking and inference on downstream +tasks. This is a research model which should not be used in any clinical or production scenario. + +## License and Usage Notices +The data, code, and model checkpoints described in this repository is provided for research use +only. The data, code, and model checkpoints is not intended for use in clinical use. + +## Trademarks + +This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft +trademarks or logos is subject to and must follow +[Microsoft's Trademark & Brand Guidelines](https://www.microsoft.com/en-us/legal/intellectualproperty/trademarks/usage/general). +Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. +Any use of third-party trademarks or logos are subject to those third-party's policies. \ No newline at end of file diff --git a/pyproject.toml b/pyproject.toml index aafb28d..ad25c66 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [project] name = "snraware" -description = "" +description = "A deep learning imaging AI framework" version = "0.1.0" readme = "README.md" requires-python = ">=3.12,<4.0" @@ -9,7 +9,43 @@ authors = [ ] keywords = [] license = "MIT" -dependencies = [] +dependencies = [ + "mkl-fft>=2.0.0", + "mkl-service>=2.4.2", + "typing_extensions", + "click", + "h5py", + "opencv-python", + "pathlib", + "PyWavelets", + "pytorch-wavelets", + "torchmetrics==1.7.3", + "torch-interpol", + "piq", + "imageio", + "pyyaml", + "wandb", + "wandb[media]", + "scipy", + "cffi", + "numpy>=2.2.6", + "torch>=2.8.0", + "setuptools", + "torchvision", + "torchinfo", + "prettytable", + "scikit-image", + "nibabel", + "matplotlib", + "einops", + "pandas", + "lightning", + "hydra-core", + "omegaconf", + "hydra_colorlog", + "pytest>=8.4.2", + "dvc[azure]", +] [project.urls] repository = "https://github.com/microsoft/SNRAware" @@ -42,6 +78,15 @@ build-backend = "uv_build" [tool.uv.build-backend] module-name = ["snraware"] +[[tool.uv.index]] +name = "mkl" +url = "https://urob.github.io/numpy-mkl" + +[tool.uv.sources] +numpy = { index = "mkl" } +scipy = { index = "mkl" } +mkl-service = { index = "mkl" } + [project.scripts] [tool.pyright] @@ -186,3 +231,7 @@ addopts = """ --cov-branch --cache-clear """ +markers = [ + "gpu: a test needs gpus to run", + "slow: mark a test as slow", +] diff --git a/uv.lock b/uv.lock index 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abstract = { Purpose To develop and evaluate a novel deep learning-based MRI den ## Get started +[just](https://github.com/casey/just) is used in this project. If not, please install this tool: +```bash +# install just +wget -qO - 'https://proget.makedeb.org/debian-feeds/prebuilt-mpr.pub' | gpg --dearmor | sudo tee /usr/share/keyrings/prebuilt-mpr-archive-keyring.gpg 1> /dev/null +echo "deb [arch=all,$(dpkg --print-architecture) signed-by=/usr/share/keyrings/prebuilt-mpr-archive-keyring.gpg] https://proget.makedeb.org prebuilt-mpr $(lsb_release -cs)" | sudo tee /etc/apt/sources.list.d/prebuilt-mpr.list +sudo apt update +sudo apt install just -y +``` + +Then, please set up the virtual environment and run tests: + +```bash +# show the list +just --list + +# set up virtual environment +just setup-env + +# review documentation +just serve-docs + +# run test +just test +``` ## Data Dataset for MR denoising training is not opened at this moment. From f2954808bd27a1a658abd26fb85adcf6437408b1 Mon Sep 17 00:00:00 2001 From: Hui Xue Date: Wed, 5 Nov 2025 10:23:22 -0800 Subject: [PATCH 12/57] update doc --- docs/index.md | 27 +++++++++++---------------- 1 file changed, 11 insertions(+), 16 deletions(-) diff --git a/docs/index.md b/docs/index.md index 1c3f43c..e4d7748 100644 --- a/docs/index.md +++ b/docs/index.md @@ -3,23 +3,18 @@ This repository contains the Pytorch code in our paper [SNRAware: Improved Deep Learning MRI Denoising with Signal-to-noise Ratio Unit Training and G-factor Map Augmentation](https://pubs.rsna.org/doi/full/10.1148/ryai.250227) published at the Radiology: Artificial Intelligence: ```latex -@article{doi:10.1148/ryai.250227, -author = {Xue, Hui and Hooper, Sarah M. and Pierce, Iain and Davies, Rhodri H. and Stairs, John and Naegele, Joseph and Campbell-Washburn, Adrienne E. and Manisty, Charlotte and Moon, James C. and Treibel, Thomas A. and Hansen, Michael S. and Kellman, Peter}, -title = {SNRAware: Improved Deep Learning MRI Denoising with Signal-to-noise Ratio Unit Training and G-factor Map Augmentation}, -journal = {Radiology: Artificial Intelligence}, -volume = {0}, -number = {ja}, -pages = {e250227}, -year = {0}, -doi = {10.1148/ryai.250227}, +@article{ + doi:10.1148/ryai.250227, + author = {Xue, Hui and Hooper, Sarah M. and Pierce, Iain and Davies, Rhodri H. and Stairs, John and Naegele, Joseph and Campbell-Washburn, Adrienne E. and Manisty, Charlotte and Moon, James C. and Treibel, Thomas A. and Hansen, Michael S. and Kellman, Peter}, + title = {SNRAware: Improved Deep Learning MRI Denoising with Signal-to-noise Ratio Unit Training and G-factor Map Augmentation}, + journal = {Radiology: Artificial Intelligence}, + volume = {0}, + number = {ja}, + pages = {e250227}, + year = {0}, + doi = {10.1148/ryai.250227}, note ={PMID: 41123451}, -URL = { - https://doi.org/10.1148/ryai.250227 -}, -eprint = { - https://doi.org/10.1148/ryai.250227 -}, -abstract = { Purpose To develop and evaluate a novel deep learning-based MRI denoising method using quantitative noise distribution information obtained during image reconstruction to improve model performance and generalization. Materials and Methods This retrospective study included a training set of 2885236 images from 96605 cardiac cine series acquired on 3T MRI scanners from January 2018 to December 2020. 95\% of these data were used for training and 5\% for validation. The hold-out test set included 3000 cine series, acquired in the same period. Fourteen model architectures were evaluated by instantiating each of the two backbone types with seven transformer and convolution block types. The proposed SNRAware training scheme leveraged MRI reconstruction knowledge to enhance denoising by simulating diverse synthetic datasets and providing quantitative noise distribution information. Internal testing measured performance using peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM), whereas external tests conducted on 1.5T real-time cardiac cine, first-pass cardiac perfusion, brain, and spine MRIs assessed generalization across various sequences, contrasts, anatomies, and field strengths. Results SNRAware improved performance on internal tests conducted on a hold-out dataset of 3000 cine series. Models trained without reconstruction knowledge achieved the worst performance metrics. Improvement was architecture-agnostic for both convolution and transformer models; however, transformer models outperformed their convolutional counterparts. Additionally, 3D input tensors showed improved performance over 2D images. The best-performing model from the internal testing generalized well to external samples, delivering 6.5 × and 2.9 × contrast-to-noise ratio improvement for real-time cine and perfusion imaging, respectively. The model trained using only cardiac cine data generalized well to T1 MPRAGE (Magnetization-Prepared Rapid Gradient-Echo) brain 3D and T2 TSE (turbo spin-echo) spine MRIs. Conclusion The SNRAware training scheme leveraged data obtained during the image reconstruction process for deep learning-based MRI denoising training, resulting in improved performance and good generalization. © The Author(s) 2025. Published by the Radiological Society of North America under a CC BY 4.0 license. } + URL = {https://doi.org/10.1148/ryai.250227} } ``` From bf10ae09779050d3165d71537f3cf63a7ffe5854 Mon Sep 17 00:00:00 2001 From: Hui Xue Date: Wed, 5 Nov 2025 12:08:36 -0800 Subject: [PATCH 13/57] add doc --- .gitignore | 3 + docs/.pages | 8 + docs/attention_layers.md | 49 + docs/backbone.md | 42 + docs/block_and_cell.md | 69 ++ docs/examples_and_tests.md | 89 ++ docs/images/Attention_in_Unet.jpg | Bin 0 -> 148824 bytes docs/images/Cell.jpg | Bin 0 -> 60660 bytes docs/images/HRNet.jpg | Bin 0 -> 2150658 bytes docs/images/LLM.jpg | Bin 0 -> 385946 bytes docs/images/PSNR_small_medium_large.png | Bin 0 -> 468286 bytes docs/images/SOANet.jpg | Bin 0 -> 56532 bytes docs/images/T2Neuro.png | Bin 0 -> 1319491 bytes docs/images/Unet.jpg | Bin 0 -> 736618 bytes docs/images/ch4.gif | Bin 0 -> 5171852 bytes docs/images/chest_x_ray.png | Bin 0 -> 1178254 bytes docs/images/ifm_model_block.png | Bin 0 -> 39947 bytes 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.../mri/denoising/configs/config.yaml | 64 + .../configs/dataset/dataset_base.yaml | 29 + .../configs/logging/logging_base.yaml | 17 + .../denoising/configs/trainer/lightning.yaml | 31 + src/snraware/projects/mri/denoising/data.py | 683 +++++++++++ .../projects/mri/denoising/inference.py | 327 ++++++ .../projects/mri/denoising/inference_model.py | 103 ++ .../mri/denoising/lightning_denoising.py | 524 +++++++++ src/snraware/projects/mri/denoising/model.py | 94 ++ src/snraware/projects/mri/denoising/run.py | 162 +++ .../projects/mri/denoising/run_inference.py | 173 +++ src/snraware/projects/mri/denoising/utils.py | 116 ++ src/snraware/projects/mri/snr/__init__.py | 0 src/snraware/projects/mri/snr/fftc.py | 101 ++ src/snraware/projects/mri/snr/filter.py | 260 ++++ src/snraware/projects/mri/snr/imaging.py | 201 ++++ src/snraware/projects/mri/snr/noise.py | 220 ++++ test/snraware_test/test_basics.py | 7 - test/test_e2e_mri_denoising.py | 171 +++ test/test_e2e_simple_regression.py | 266 +++++ test/test_loss.py | 129 ++ test/test_mri_denoising_data.py | 279 +++++ test/test_snr_noise.py | 302 +++++ 98 files changed, 14264 insertions(+), 9 deletions(-) create mode 100644 docs/.pages create mode 100644 docs/attention_layers.md create mode 100644 docs/backbone.md create mode 100644 docs/block_and_cell.md create mode 100644 docs/examples_and_tests.md create mode 100644 docs/images/Attention_in_Unet.jpg create mode 100644 docs/images/Cell.jpg create mode 100644 docs/images/HRNet.jpg create mode 100644 docs/images/LLM.jpg create mode 100644 docs/images/PSNR_small_medium_large.png create mode 100644 docs/images/SOANet.jpg create mode 100644 docs/images/T2Neuro.png create mode 100644 docs/images/Unet.jpg create mode 100644 docs/images/ch4.gif create mode 100644 docs/images/chest_x_ray.png create mode 100644 docs/images/ifm_model_block.png create mode 100644 docs/images/ifm_model_cell.png create mode 100644 docs/images/ifm_model_index.png create mode 100644 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src/snraware/projects/mri/snr/imaging.py create mode 100644 src/snraware/projects/mri/snr/noise.py delete mode 100644 test/snraware_test/test_basics.py create mode 100644 test/test_e2e_mri_denoising.py create mode 100644 test/test_e2e_simple_regression.py create mode 100644 test/test_loss.py create mode 100644 test/test_mri_denoising_data.py create mode 100644 test/test_snr_noise.py diff --git a/.gitignore b/.gitignore index 1b4b92b..3628940 100644 --- a/.gitignore +++ b/.gitignore @@ -33,6 +33,9 @@ bld/ [Oo]ut/ [Ll]og/ [Ll]ogs/ +lightning_logs/ +run/ +.run/ # Build results on 'Bin' directories **/[Bb]in/* diff --git a/docs/.pages b/docs/.pages new file mode 100644 index 0000000..1db3223 --- /dev/null +++ b/docs/.pages @@ -0,0 +1,8 @@ +title: SNRAware +nav: + - overview.md + - attention_layers.md + - block_and_cell.md + - backbone.md + - examples_and_tests.md + - mri_denoising.md \ No newline at end of file diff --git a/docs/attention_layers.md b/docs/attention_layers.md new file mode 100644 index 0000000..68ddf65 --- /dev/null +++ b/docs/attention_layers.md @@ -0,0 +1,49 @@ + +# Attention layers + +Given the 5D tensor, different imaging attention modules are developed to capture the signal correlation across spatial and frame/temporal dimensions. All these modules receive a 5D tensor and output a 5D tensor. Unlike the token based attention, these imaging attention modules do not convert pixel data to tokens. That is, the computation is performed in the pixel space, not in token space. + +![motivation](images/ifm_model_index.png) + +Full attention across three dimensions (F, H, W) can incur high computing costs and may not be optimal as the locality of information was not exploited. Instead, intra- and inter-frame attention can be separately computed. For spatial (along with H, W) attention, a target patch (marked in yellow) takes in information from both close neighbors (marked in green) and remote patches (marked in pink). + +![attention](images/ifm_model_three_attentions.png) + +Let the image size be $[H, W]$, the windows size be $[w, w]$ and patch size be $[k, k]$. The number of windows will be $[\frac {H}{w}, \frac{W}{w}]$. The number of patches is $[\frac{H}{k}, \frac{W}{k}]$. For example, for an $256 \times 256$ images, $w=32$ and $k=16$, we will have $8 \times 8$ windows and each window will have $2 \times 2$ patches. + +**Spatial Local attention (L)** + +Local attention is computed by attending to the all patches in a window for images or feature maps. The attention matrix is $\frac {w}{k} \times \frac {w}{k}$. + +**Spatial Global attention (G)** + +While the local attention only explores neighboring pixels, global attention looks at more remote pixels. This will help model learn global information over larger field-of-view. All patches with the same color are attended together. So for every patch, attention matrix size is $w^2 \times w^2$ (that is, the number of windows). + +**Frame or temporal attention (F)** + +All frames can attend each other to compute the output values. In this case, the attention matrix size is ${F}\times{F}$. + +These **F, G, T** attention mechanisms are implemented as **Attention** modules. The implemented version supports multi-heads. + +**Spatial Local 3d attention** + +Previous spatial local attention works on every 2D frame independently. For some applications where data acquisition is 3D, it may require to perform 3D attention. In this case, the [F, H, W] volume is split into 3D windows. Every window includes 3D patches. All patches within a window are attended together. + +**Spatial Global 3d attention** + +Similar to the 2D global attention, the tensor is split to 3D windows and patches. Corresponding patches from all windows attend each other. + +## Other attentions + +**Spatial ViT attention** + +The vision transformer method splits the image into windows. All pixels in a window are flattened into a feature vector. Features from all windows are inputted into the attention. This layer works on every 2D frame, as the spatial local attention. + +**ViT 3d attention** + +The image is split to 3D window to compute the attention. + +**Swin 3d attention** + +The shifted window attention is adapted to 3D. The image is split to 3D windows. Every 3d window is split to 3D patches. The attention is computed among all patches within a window or a shifted window. + diff --git a/docs/backbone.md b/docs/backbone.md new file mode 100644 index 0000000..36b4837 --- /dev/null +++ b/docs/backbone.md @@ -0,0 +1,42 @@ + +# Backbone + +Attention - Cell - Block are three levels of building blocks for a model. We can connect many blocks to assemble a backbone. Here the backbone means an "almost" full model. Different pre- and post-processing task heads can be added to a backbone, given a specific application. + +In the LLMs, the stack of attentions proves to be very effective. For imaging, previous researches had explored similar architecture (e.g. [3B swin v2](https://arxiv.org/abs/2111.09883) and [20B ViT](https://arxiv.org/abs/2302.05442)). On the other hand, there are very intuitive model architectures were invented for different convolution models. Comparing to the LLM type backbone, these CNN derived architectures are more tuned for image data format, by utilizing resolution pyramid, up and downsampling, and long range skip connections. + +Different backbone models are implemented by borrowing successful architectures from CNNs. + +### backbone U-Net + +![unet](images/Unet.jpg) + +Here every resolution stage includes one block containing multiple cells. Model can specify number of feature maps (Channel dimension C) at each resolution stage. + +Return tensor of this backbone is the final output tensor after U0 layer. + +The [Unet with attention](https://arxiv.org/abs/1804.03999) is implemented here. Downsample and upsample are implemented with interpolation. + +![Attention_in_Unet](images/Attention_in_Unet.jpg) + +### backbone HR-Net + +This network is modified from the [high-resolution architecture](https://www.microsoft.com/en-us/research/blog/high-resolution-network-a-universal-neural-architecture-for-visual-recognition/). + +![stcnnt_hrnet](images/HRNet.jpg) + +The network is defined as levels and stages. Every block is numbered by its level and stage indexes (starting from 0). The downsample and upsample modules are added to link different blocks. Different up/downsample modules are implemented, with TLG attentions or 1x1 CONV. Bilinear interpolation is used to alter spatial resolution. + +After the fusion stage, the model outputs per-level tensors and the aggregated tensor as a list. This backbone returns a list of tensors. The outputs from every resolution level and the final aggregated tensor are returned as a list *res*. *res[0]* is the output with the highest resolution (*y_hat_0*). *res[1]* is the *y_hat_1* etc. The *res[-1]* is the aggregated tensor. + +### backbone Stack-of-attention (SOANet) + +The classical stack-of-attention model is defined by the stages. Each stage contains one block with many cells. The downsampling layers are optional between stages. + +We can instantiate the typical Swin or ViT model by inserting corresponding cells to SOANet. For example, a four-stage model with block strings ["S3ShS3Sh", "S3ShS3Sh", "S3ShS3ShS3ShS3ShS3ShS3Sh", "S3ShS3Sh"] is a replica of published model in the [swin paper](https://arxiv.org/abs/2103.14030).A one stage model ["V2V2V2V2V2V2V2V2V2V2V2V2"] is a version of 2D ViT base model with 12 cells. Since the backbone takes in the 5D tensor, it is often better to instantiate 3D ViT model, e.g. ["V3V3V3","V3V3V3V3V3V3", "V3V3V3V3V3V3V3V3V3V3V3V3"] for a 3 stage 3D ViT. + +This backbone returns a list of tensors containing outputs from all stages. *res[0]* is the first stage and *res[-1]* has the lowest resolution (if downsampling). + +![soanet](images/SOANet.jpg) + +**Please ref to [examples and tests](./examples_and_tests.md) for how to instantiate the backbone.** \ No newline at end of file diff --git a/docs/block_and_cell.md b/docs/block_and_cell.md new file mode 100644 index 0000000..bec9d2c --- /dev/null +++ b/docs/block_and_cell.md @@ -0,0 +1,69 @@ + +# Cell and Block + +## Cell +A cell is a container to host an attention layer, as the basic processing unit for the 5D tensor. As the following, a cell can host either F, L or G or other attentions. Input tensor goes through the norm layers and attention. The mixer consists of another norm and two convolutions. + +![Cell](./images/ifm_model_cell.png) + +Many attention modules are supported. These modules are represented by its name string: + +| Name | Description | +| ----------- | ----------- | +| V2 | 2D ViT attention, working on [H, W], with the mixer | +| V3 | 3D ViT attention, working on [D/Z/T, H, W], with the mixer | +| L1 | spatial local attention on [H, W], with mixer | +| L0 | spatial local attention on [H, W], without mixer | +| L3 | local 3D attention on [D/Z/T, H, W], with mixer | +| G1 | spatial global attention on [H, W], with mixer | +| G0 | spatial global attention on [H, W], without mixer | +| G3 | global 3D attention on [D/Z/T, H, W], with mixer | +| S3 | swin 3D attention over [D/Z/T, H, W], with the mixer| +| Sh | swin 3D attention over [D/Z/T, H, W], with the mixer, shifted window| + +#### Norms + +Different $Norm$ can be configured: + +| Norm | Description | +| ----------- | ----------- | +| LayerNorm | normalize over [C, H, W] | +| BatchNorm 2D | normalize over [H, W], B*T are batch dimension | +| BatchNorm 3D | normalize over [T, H, W] | +| InstanceNorm 2D | normalize over [H, W]| +| InstanceNorm 3D | normalize over [T, H, W]| + +Except the *layer* norm, all other norms support flexible image sizes, when using with the $CONV$ in attention layers. + +#### Mixers + +The mixers are added after attention layers to increase model power. Two types of mixers are implemented. *Linear* mixer are the same mixer type used in the conventional transformer, with `torch.linear` layers. The "conv" mixer replaced Linear layer with the convolution layer, to better fit for imaging tensors. The conv is performed along the $[C, H, W]$: + +``` +self.mlp = nn.Sequential( + Conv2DExt(C_out, 4*C_out, kernel_size=kernel_size, stride=stride, padding=padding, bias=True), + nn.GELU(), + Conv2DExt(4*C_out, C_out, kernel_size=kernel_size, stride=stride, padding=padding, bias=True), + nn.Dropout(dropout_p), + ) +``` + +User can specify whether a cell has mixer or not. + +## Block + +Many cells are concatenated to enhance the model power. To standardize this configuration, the Block is introduced: + +![Block](./images/ifm_model_block.png) + +A block contains any number of cells and can be scaled up by inserting more cells. + +A block is coded by the acronyms of attention layers in each cell. For example, a block is coded as the block string "L1G1T1". The letter "L", "G" or "T" means the local, global and Frame/Temporal Cell. "1" means the mixer is added on top of the attention mechanism (if "0", mixer is not added; we can have a stack of attention only layers). As an example, "L0L1G0G1T1" means a block with 5 attention layers. Mixers are not added to the first and third attentions, but added after the second and fourth attentions. The last cell is a temporal attention with its mixer added. This method of "block string specification" gives a good amount of flexibility to assemble and test different attention configurations. Depending on the image resolutions and number of feature maps, different blocks in a model can have different attention configuration. Note the convolution layers are formatted into this framework as the "C2" or "C3" cells. "C2" means the 2D conv is used in the cell. "C3" means the 3D conv is used (over F, H, W). By changing the block string from attentions to convs, we can get identical architectures with only differences being the cell structure. + +The block string can also be "S2" or "S3" for 2D or 3D SWIN module. Each SWIN module includes two parts for concatenated normal and shifted window attentions. These attention modules can be concatenated in any order of mixing. For example, *L1G1T1* or *S3V2L1G1T1*, or *G1G3S3V2T1* etc. *S3ShS3ShS3Sh* is the SWIN transformer with normal and shifted window attentions interleaved. *V3V3V3V3* is the ViT transformer. By separating the backbone architecture with attention modules, it is very flexible to instantiate many types of models, tailored to the nature of datasets and amount of computing. + +Two cell layouts were implemented: sequential and parallel: + +![Cell](./images/Cell.jpg) + +The reference for parallel cell is [here](https://arxiv.org/abs/2302.05442). \ No newline at end of file diff --git a/docs/examples_and_tests.md b/docs/examples_and_tests.md new file mode 100644 index 0000000..1ffd435 --- /dev/null +++ b/docs/examples_and_tests.md @@ -0,0 +1,89 @@ + +# Examples and tests + +The modules and backbones in this package can be used to build models for different applications. The key design is to support 5D tensor [B, C, T/D/F/Z, H, W] for 2D, 2D+T, and 3D usecases. + +## Attention modules + +All attention modules are implemented as pytorch modules. They can be defined in the very straight-forward way as the following: + +```python +import numpy as np +import torch +from snraware.components.setup import start_timer, end_timer, get_device +from snraware.components.model.attention import ViT3DAttention + +# find the device, e.g. 'cuda:0' +device = get_device() + +# define an input tensor +B, C, T, H, W = 1, 2, 16, 32, 32 +C_out = 8 +test_in = torch.rand(B, C, T, H, W).to(device=device) +print(test_in.shape) + +# define a vit3d module + +spacial_vit = ViT3DAttention(window_size=None, + num_wind=[8, 8, 4], # here we choose to set number of windows; alternatively, we can set window_size to be [2, 2, 4] + attention_type="conv", # use convolution to compute Q/K/V in the attention + C_in=2, # input channel, 2 + C_out=8, # output channel, 8 + H=H, W=W, D=T, # tensor sizes + stride_qk=[1,1,1], # stride is 1 when computing Q and K + cosine_att=True, # use the cosine attention + normalize_Q_K=False, # normalize Q and K before computing attention matrix; if cosine_att is True, this option + att_with_relative_position_bias=True, + att_with_output_proj=True) + +spacial_vit.to(device=device) + +test_out = spacial_vit(test_in) + +print(f"ViT3DAttention, input tensor {test_in.shape}, output tensor {test_out.shape}") + +``` + +## Backbones + +The backbone models contain multiple Blocks and Cells. They are configured by supplying the block_str to define the attention structures. + +Unlike the attention modules where parameters are listed out, the model has many more parameters. We use the [hydra](https://hydra.cc/) to manage the parameters. The parameter configuration files are in the `src/ifm/configs` folder. To define the model parameters for a backbone model: + +``` +from hydra import initialize, compose +with initialize(version_base=None, config_path="../src/snraware/components/configs"): + config = compose(config_name="config") +``` + +The backbone parameters are in `config.backbone`. The block and cell parameters are in `config.block` and `config.cell`. + +```python + +import torch +from snraware.components.setup import get_device +import hydra +from hydra import initialize, compose + +# get the device, e.g. 'cuda:0' +device = get_device() + +# define the input tensor +B, C, T, H, W = 1, 2, 32, 128, 128 +test_in = torch.rand(B, C, T, H, W).to(device=device) + +# get the mode parameter; note user can override the default configuration +# here we order a hrnet backbone +with initialize(version_base=None, config_path="../src/snraware/components/configs"): + cfg = compose(config_name="config") + +config = hydra.utils.instantiate(cfg.backbone) + +# define the model +model = HRnet(config=config, input_feature_channels=C, H=H, W=W, D=T) +model = model.to(device=device) + +# perform the forward pass +test_out = model(test_in) +print(f"{Fore.YELLOW}The output tensor shape is {test_out[-1].shape}.{Style.RESET_ALL}") +``` \ No newline at end of file diff --git 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