[ { "path": ".dockerignore", "content": "# Repo-specific DockerIgnore -------------------------------------------------------------------------------------------\n.git\n.cache\n.idea\nruns\noutput\ncoco\nstorage.googleapis.com\n\ndata/samples/*\n**/results*.csv\n*.jpg\n\n# Neural Network weights -----------------------------------------------------------------------------------------------\n**/*.pt\n**/*.pth\n**/*.onnx\n**/*.engine\n**/*.mlmodel\n**/*.torchscript\n**/*.torchscript.pt\n**/*.tflite\n**/*.h5\n**/*.pb\n*_saved_model/\n*_web_model/\n*_openvino_model/\n\n# Below Copied From .gitignore -----------------------------------------------------------------------------------------\n# Below Copied From .gitignore -----------------------------------------------------------------------------------------\n\n\n# GitHub Python GitIgnore ----------------------------------------------------------------------------------------------\n# Byte-compiled / optimized / DLL files\n__pycache__/\n*.py[cod]\n*$py.class\n\n# C extensions\n*.so\n\n# Distribution / packaging\n.Python\nenv/\nbuild/\ndevelop-eggs/\ndist/\ndownloads/\neggs/\n.eggs/\nlib/\nlib64/\nparts/\nsdist/\nvar/\nwheels/\n*.egg-info/\nwandb/\n.installed.cfg\n*.egg\n\n# PyInstaller\n# Usually these files are written by a python script from a template\n# before PyInstaller builds the exe, so as to inject date/other infos into it.\n*.manifest\n*.spec\n\n# Installer logs\npip-log.txt\npip-delete-this-directory.txt\n\n# Unit test / coverage reports\nhtmlcov/\n.tox/\n.coverage\n.coverage.*\n.cache\nnosetests.xml\ncoverage.xml\n*.cover\n.hypothesis/\n\n# Translations\n*.mo\n*.pot\n\n# Django stuff:\n*.log\nlocal_settings.py\n\n# Flask stuff:\ninstance/\n.webassets-cache\n\n# Scrapy stuff:\n.scrapy\n\n# Sphinx documentation\ndocs/_build/\n\n# PyBuilder\ntarget/\n\n# Jupyter Notebook\n.ipynb_checkpoints\n\n# pyenv\n.python-version\n\n# celery beat schedule file\ncelerybeat-schedule\n\n# SageMath parsed files\n*.sage.py\n\n# dotenv\n.env\n\n# virtualenv\n.venv*\nvenv*/\nENV*/\n\n# Spyder project settings\n.spyderproject\n.spyproject\n\n# Rope project settings\n.ropeproject\n\n# mkdocs documentation\n/site\n\n# mypy\n.mypy_cache/\n\n\n# https://github.com/github/gitignore/blob/master/Global/macOS.gitignore -----------------------------------------------\n\n# General\n.DS_Store\n.AppleDouble\n.LSOverride\n\n# Icon must end with two \\r\nIcon\nIcon?\n\n# Thumbnails\n._*\n\n# Files that might appear in the root of a volume\n.DocumentRevisions-V100\n.fseventsd\n.Spotlight-V100\n.TemporaryItems\n.Trashes\n.VolumeIcon.icns\n.com.apple.timemachine.donotpresent\n\n# Directories potentially created on remote AFP share\n.AppleDB\n.AppleDesktop\nNetwork Trash Folder\nTemporary Items\n.apdisk\n\n\n# https://github.com/github/gitignore/blob/master/Global/JetBrains.gitignore\n# Covers JetBrains IDEs: IntelliJ, RubyMine, PhpStorm, AppCode, PyCharm, CLion, Android Studio and WebStorm\n# Reference: https://intellij-support.jetbrains.com/hc/en-us/articles/206544839\n\n# User-specific stuff:\n.idea/*\n.idea/**/workspace.xml\n.idea/**/tasks.xml\n.idea/dictionaries\n.html # Bokeh Plots\n.pg # TensorFlow Frozen Graphs\n.avi # videos\n\n# Sensitive or high-churn files:\n.idea/**/dataSources/\n.idea/**/dataSources.ids\n.idea/**/dataSources.local.xml\n.idea/**/sqlDataSources.xml\n.idea/**/dynamic.xml\n.idea/**/uiDesigner.xml\n\n# Gradle:\n.idea/**/gradle.xml\n.idea/**/libraries\n\n# CMake\ncmake-build-debug/\ncmake-build-release/\n\n# Mongo Explorer plugin:\n.idea/**/mongoSettings.xml\n\n## File-based project format:\n*.iws\n\n## Plugin-specific files:\n\n# IntelliJ\nout/\n\n# mpeltonen/sbt-idea plugin\n.idea_modules/\n\n# JIRA plugin\natlassian-ide-plugin.xml\n\n# Cursive Clojure plugin\n.idea/replstate.xml\n\n# Crashlytics plugin (for Android Studio and IntelliJ)\ncom_crashlytics_export_strings.xml\ncrashlytics.properties\ncrashlytics-build.properties\nfabric.properties\n" }, { "path": ".github/ISSUE_TEMPLATE/bug-report.yml", "content": "# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license\n\nname: 🐛 Bug Report\ndescription: \"Problems with Ultralytics YOLOv3\"\nlabels: [bug, triage]\ntype: \"bug\"\nbody:\n - type: markdown\n attributes:\n value: |\n Thank you for submitting an Ultralytics YOLOv3 🐛 Bug Report!\n\n - type: checkboxes\n attributes:\n label: Search before asking\n description: >\n Please search the Ultralytics YOLOv3 [README](https://github.com/ultralytics/yolov3#readme) and [issues](https://github.com/ultralytics/yolov3/issues) to see if a similar bug report already exists.\n options:\n - label: >\n I have searched the [issues](https://github.com/ultralytics/yolov3/issues) and did not find a similar report.\n required: true\n\n - type: dropdown\n attributes:\n label: Project area\n description: |\n Help us route the report to the right maintainers.\n multiple: true\n options:\n - \"Training\"\n - \"Inference\"\n - \"Export/deployment\"\n - \"Documentation\"\n - \"Other\"\n validations:\n required: false\n\n - type: textarea\n attributes:\n label: Bug\n description: Please describe the issue in detail so we can reproduce it in Ultralytics YOLOv3. Include logs, screenshots, console output, and any context that helps explain the problem.\n placeholder: |\n 💡 ProTip! Include as much information as possible (logs, tracebacks, screenshots, etc.) to receive the most helpful response.\n validations:\n required: true\n\n - type: textarea\n attributes:\n label: Environment\n description: Share the platform and version information relevant to your report.\n placeholder: |\n Please include:\n - OS (e.g., Ubuntu 20.04, macOS 13.5, Windows 11)\n - Language or framework version (Python, Swift, Flutter, etc.)\n - Package or app version\n - Hardware (e.g., CPU, GPU model, device model)\n - Any other environment details\n validations:\n required: true\n\n - type: textarea\n attributes:\n label: Minimal Reproducible Example\n description: >\n Provide the smallest possible snippet, command, or steps required to reproduce the issue. This helps us pinpoint problems faster.\n placeholder: |\n ```python\n # Code or commands to reproduce your issue here\n ```\n validations:\n required: true\n\n - type: textarea\n attributes:\n label: Additional\n description: Anything else you would like to share?\n\n - type: checkboxes\n attributes:\n label: Are you willing to submit a PR?\n description: >\n (Optional) We encourage you to submit a [Pull Request](https://github.com/ultralytics/yolov3/pulls) to help improve Ultralytics YOLOv3, especially if you know how to fix the issue.\n See the Ultralytics [Contributing Guide](https://docs.ultralytics.com/help/contributing/) to get started.\n options:\n - label: Yes I'd like to help by submitting a PR!\n" }, { "path": ".github/ISSUE_TEMPLATE/config.yml", "content": "# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license\n\nblank_issues_enabled: true\ncontact_links:\n - name: 📘 YOLOv3 README\n url: https://github.com/ultralytics/yolov3#readme\n about: Usage guide and background for YOLOv3\n - name: 💬 Forum\n url: https://community.ultralytics.com/\n about: Ask the Ultralytics community for workflow help\n - name: 🎧 Discord\n url: https://ultralytics.com/discord\n about: Chat with the Ultralytics team and other builders\n - name: ⌨️ Reddit\n url: https://reddit.com/r/ultralytics\n about: Discuss Ultralytics projects on Reddit\n" }, { "path": ".github/ISSUE_TEMPLATE/feature-request.yml", "content": "# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license\n\nname: 🚀 Feature Request\ndescription: \"Suggest an Ultralytics YOLOv3 improvement\"\nlabels: [enhancement]\ntype: \"feature\"\nbody:\n - type: markdown\n attributes:\n value: |\n Thank you for submitting an Ultralytics YOLOv3 🚀 Feature Request!\n\n - type: checkboxes\n attributes:\n label: Search before asking\n description: >\n Please search the Ultralytics YOLOv3 [README](https://github.com/ultralytics/yolov3#readme) and [issues](https://github.com/ultralytics/yolov3/issues) to see if a similar feature request already exists.\n options:\n - label: >\n I have searched https://github.com/ultralytics/yolov3/issues and did not find a similar request.\n required: true\n\n - type: textarea\n attributes:\n label: Description\n description: Briefly describe the feature you would like to see added to Ultralytics YOLOv3.\n placeholder: |\n What new capability or improvement are you proposing?\n validations:\n required: true\n\n - type: textarea\n attributes:\n label: Use case\n description: Explain how this feature would be used and who benefits from it. Screenshots or mockups are welcome.\n placeholder: |\n How would this feature improve your workflow?\n\n - type: textarea\n attributes:\n label: Additional\n description: Anything else you would like to share?\n\n - type: checkboxes\n attributes:\n label: Are you willing to submit a PR?\n description: >\n (Optional) We encourage you to submit a [Pull Request](https://github.com/ultralytics/yolov3/pulls) to help improve Ultralytics YOLOv3.\n See the Ultralytics [Contributing Guide](https://docs.ultralytics.com/help/contributing/) to get started.\n options:\n - label: Yes I'd like to help by submitting a PR!\n" }, { "path": ".github/ISSUE_TEMPLATE/question.yml", "content": "# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license\n\nname: ❓ Question\ndescription: \"Ask an Ultralytics YOLOv3 question\"\nlabels: [question]\nbody:\n - type: markdown\n attributes:\n value: |\n Thank you for asking an Ultralytics YOLOv3 ❓ Question!\n\n - type: checkboxes\n attributes:\n label: Search before asking\n description: >\n Please search the Ultralytics YOLOv3 [README](https://github.com/ultralytics/yolov3#readme), [issues](https://github.com/ultralytics/yolov3/issues), and [Ultralytics discussions](https://github.com/orgs/ultralytics/discussions) to see if a similar question already exists.\n options:\n - label: >\n I checked the docs, issues, and discussions and could not find an answer.\n required: true\n\n - type: textarea\n attributes:\n label: Question\n description: What is your question? Provide as much detail as possible so we can assist with Ultralytics YOLOv3. Include code snippets, screenshots, logs, or links to notebooks/demos.\n placeholder: |\n 💡 ProTip! Include as much information as possible (logs, tracebacks, screenshots, etc.) to receive the most helpful response.\n validations:\n required: true\n\n - type: textarea\n attributes:\n label: Additional\n description: Anything else you would like to share?\n" }, { "path": ".github/dependabot.yml", "content": "# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license\n\n# Dependabot for package version updates\n# https://docs.github.com/github/administering-a-repository/configuration-options-for-dependency-updates\n\nversion: 2\nupdates:\n - package-ecosystem: pip\n directory: \"/\"\n schedule:\n interval: weekly\n time: \"04:00\"\n open-pull-requests-limit: 10\n labels:\n - dependencies\n\n - package-ecosystem: github-actions\n directory: \"/.github/workflows\"\n schedule:\n interval: weekly\n time: \"04:00\"\n open-pull-requests-limit: 5\n labels:\n - dependencies\n" }, { "path": ".github/workflows/ci-testing.yml", "content": "# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license\n\n# YOLOv3 Continuous Integration (CI) GitHub Actions tests\n\nname: YOLOv3 CI\n\npermissions:\n contents: read\n\non:\n push:\n branches: [master]\n pull_request:\n branches: [master]\n schedule:\n - cron: \"0 0 * * *\" # runs at 00:00 UTC every day\n workflow_dispatch:\n\njobs:\n Tests:\n timeout-minutes: 60\n runs-on: ${{ matrix.os }}\n strategy:\n fail-fast: false\n matrix:\n os: [ubuntu-latest, windows-latest] # macos-latest bug https://github.com/ultralytics/yolov5/pull/9049\n python-version: [\"3.11\"]\n model: [yolov5n]\n include:\n # - os: ubuntu-latest\n # python-version: \"3.8\" # '3.6.8' min (warning, this test is failing)\n # model: yolov5n\n - os: ubuntu-latest\n python-version: \"3.9\"\n model: yolov5n\n - os: ubuntu-latest\n python-version: \"3.8\" # torch 1.8.0 requires python >=3.6, <=3.8\n model: yolov5n\n torch: \"1.8.0\" # min torch version CI https://pypi.org/project/torchvision/\n steps:\n - uses: actions/checkout@v6\n - uses: actions/setup-python@v6\n with:\n python-version: ${{ matrix.python-version }}\n cache: \"pip\" # caching pip dependencies\n - name: Install requirements\n run: |\n python -m pip install --upgrade pip wheel\n torch=\"\"\n if [ \"${{ matrix.torch }}\" == \"1.8.0\" ]; then\n torch=\"torch==1.8.0 torchvision==0.9.0\"\n fi\n pip install -r requirements.txt $torch --extra-index-url https://download.pytorch.org/whl/cpu\n shell: bash # for Windows compatibility\n - name: Check environment\n run: |\n yolo checks\n pip list\n - name: Test detection\n shell: bash # for Windows compatibility\n run: |\n # export PYTHONPATH=\"$PWD\" # to run '$ python *.py' files in subdirectories\n m=${{ matrix.model }} # official weights\n b=runs/train/exp/weights/best # best.pt checkpoint\n python train.py --imgsz 64 --batch 32 --weights $m.pt --cfg $m.yaml --epochs 1 --device cpu # train\n for d in cpu; do # devices\n for w in $m $b; do # weights\n python val.py --imgsz 64 --batch 32 --weights $w.pt --device $d # val\n python detect.py --imgsz 64 --weights $w.pt --device $d # detect\n done\n done\n python hubconf.py --model $m # hub\n # python models/tf.py --weights $m.pt # build TF model\n python models/yolo.py --cfg $m.yaml # build PyTorch model\n python export.py --weights $m.pt --img 64 --include torchscript # export\n python - < GitHub Actions error for ${{ github.workflow }} ❌\\n\\n\\n*Repository:* https://github.com/${{ github.repository }}\\n*Action:* https://github.com/${{ github.repository }}/actions/runs/${{ github.run_id }}\\n*Author:* ${{ github.actor }}\\n*Event:* ${{ github.event_name }}\\n\"\n" }, { "path": ".github/workflows/cla.yml", "content": "# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license\n\n# Ultralytics Contributor License Agreement (CLA) action https://docs.ultralytics.com/help/CLA\n# This workflow automatically requests Pull Requests (PR) authors to sign the Ultralytics CLA before PRs can be merged\n\nname: CLA Assistant\non:\n issue_comment:\n types:\n - created\n pull_request_target:\n types:\n - reopened\n - opened\n - synchronize\n\npermissions:\n actions: write\n contents: write\n pull-requests: write\n statuses: write\n\njobs:\n CLA:\n if: github.repository == 'ultralytics/yolov3'\n runs-on: ubuntu-latest\n steps:\n - name: CLA Assistant\n if: (github.event.comment.body == 'recheck' || github.event.comment.body == 'I have read the CLA Document and I sign the CLA') || github.event_name == 'pull_request_target'\n uses: contributor-assistant/github-action@v2.6.1\n env:\n GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}\n # Must be repository secret PAT\n PERSONAL_ACCESS_TOKEN: ${{ secrets._GITHUB_TOKEN }}\n with:\n path-to-signatures: \"signatures/version1/cla.json\"\n path-to-document: \"https://docs.ultralytics.com/help/CLA\" # CLA document\n # Branch must not be protected\n branch: cla-signatures\n allowlist: dependabot[bot],github-actions,[pre-commit*,pre-commit*,bot*\n\n remote-organization-name: ultralytics\n remote-repository-name: cla\n custom-pr-sign-comment: \"I have read the CLA Document and I sign the CLA\"\n custom-allsigned-prcomment: All Contributors have signed the CLA. ✅\n" }, { "path": ".github/workflows/docker.yml", "content": "# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license\n\n# Builds ultralytics/yolov3:latest images on DockerHub https://hub.docker.com/r/ultralytics/yolov3\n\nname: Publish Docker Images\n\npermissions:\n contents: read\n\non:\n push:\n branches: [master]\n workflow_dispatch:\n\njobs:\n docker:\n if: github.repository == 'ultralytics/yolov3'\n name: Push Docker image to Docker Hub\n runs-on: ubuntu-latest\n steps:\n - name: Checkout repo\n uses: actions/checkout@v6\n\n - name: Set up QEMU\n uses: docker/setup-qemu-action@v4\n\n - name: Set up Docker Buildx\n uses: docker/setup-buildx-action@v4\n\n - name: Login to Docker Hub\n uses: docker/login-action@v4\n with:\n username: ${{ secrets.DOCKERHUB_USERNAME }}\n password: ${{ secrets.DOCKERHUB_TOKEN }}\n\n - name: Build and push arm64 image\n uses: docker/build-push-action@v7\n continue-on-error: true\n with:\n context: .\n platforms: linux/arm64\n file: utils/docker/Dockerfile-arm64\n push: true\n tags: ultralytics/yolov3:latest-arm64\n\n - name: Build and push CPU image\n uses: docker/build-push-action@v7\n continue-on-error: true\n with:\n context: .\n file: utils/docker/Dockerfile-cpu\n push: true\n tags: ultralytics/yolov3:latest-cpu\n\n - name: Build and push GPU image\n uses: docker/build-push-action@v7\n continue-on-error: true\n with:\n context: .\n file: utils/docker/Dockerfile\n push: true\n tags: ultralytics/yolov3:latest\n" }, { "path": ".github/workflows/format.yml", "content": "# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license\n\n# Ultralytics Actions https://github.com/ultralytics/actions\n# This workflow formats code and documentation in PRs to Ultralytics standards\n\nname: Ultralytics Actions\n\non:\n issues:\n types: [opened]\n pull_request:\n branches: [main, master]\n types: [opened, closed, synchronize, review_requested]\n\npermissions:\n contents: write # Modify code in PRs\n pull-requests: write # Add comments and labels to PRs\n issues: write # Add comments and labels to issues\n\njobs:\n actions:\n runs-on: ubuntu-latest\n steps:\n - name: Run Ultralytics Actions\n uses: ultralytics/actions@main\n with:\n token: ${{ secrets._GITHUB_TOKEN || secrets.GITHUB_TOKEN }} # Auto-generated token\n labels: true # Auto-label issues/PRs using AI\n python: true # Format Python with Ruff and docformatter\n prettier: true # Format YAML, JSON, Markdown, CSS\n spelling: true # Check spelling with codespell\n links: false # Check broken links with Lychee\n summary: true # Generate AI-powered PR summaries\n openai_api_key: ${{ secrets.OPENAI_API_KEY }} # Powers PR summaries, labels and comments\n brave_api_key: ${{ secrets.BRAVE_API_KEY }} # Used for broken link resolution\n" }, { "path": ".github/workflows/links.yml", "content": "# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license\n\n# Continuous Integration (CI) GitHub Actions tests broken link checker using https://github.com/lycheeverse/lychee\n# Ignores the following status codes to reduce false positives:\n# - 403(OpenVINO, 'forbidden')\n# - 429(Instagram, 'too many requests')\n# - 500(Zenodo, 'cached')\n# - 502(Zenodo, 'bad gateway')\n# - 999(LinkedIn, 'unknown status code')\n\nname: Check Broken links\n\npermissions:\n contents: read\n\non:\n workflow_dispatch:\n schedule:\n - cron: \"0 0 * * *\" # runs at 00:00 UTC every day\n\njobs:\n Links:\n runs-on: ubuntu-latest\n steps:\n - uses: actions/checkout@v6\n\n - name: Install lychee\n run: curl -sSfL \"https://github.com/lycheeverse/lychee/releases/latest/download/lychee-x86_64-unknown-linux-gnu.tar.gz\" | sudo tar xz -C /usr/local/bin\n\n - name: Test Markdown and HTML links with retry\n uses: ultralytics/actions/retry@main\n with:\n timeout_minutes: 5\n retry_delay_seconds: 60\n retries: 2\n run: |\n lychee \\\n --scheme 'https' \\\n --timeout 60 \\\n --insecure \\\n --accept 100..=103,200..=299,401,403,429,500,502,999 \\\n --exclude-all-private \\\n --exclude 'https?://(www\\.)?(linkedin\\.com|twitter\\.com|x\\.com|instagram\\.com|kaggle\\.com|fonts\\.gstatic\\.com|url\\.com)' \\\n --exclude-path './**/ci.yml' \\\n --github-token ${{ secrets.GITHUB_TOKEN }} \\\n --header \"User-Agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/126.0.6478.183 Safari/537.36\" \\\n './**/*.md' \\\n './**/*.html' | tee -a $GITHUB_STEP_SUMMARY\n\n # Raise error if broken links found\n if ! grep -q \"0 Errors\" $GITHUB_STEP_SUMMARY; then\n exit 1\n fi\n\n - name: Test Markdown, HTML, YAML, Python and Notebook links with retry\n if: github.event_name == 'workflow_dispatch'\n uses: ultralytics/actions/retry@main\n with:\n timeout_minutes: 5\n retry_delay_seconds: 60\n retries: 2\n run: |\n lychee \\\n --scheme 'https' \\\n --timeout 60 \\\n --insecure \\\n --accept 100..=103,200..=299,429,999 \\\n --exclude-all-private \\\n --exclude 'https?://(www\\.)?(linkedin\\.com|twitter\\.com|x\\.com|instagram\\.com|kaggle\\.com|fonts\\.gstatic\\.com|url\\.com)' \\\n --exclude-path './**/ci.yml' \\\n --github-token ${{ secrets.GITHUB_TOKEN }} \\\n --header \"User-Agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/126.0.6478.183 Safari/537.36\" \\\n './**/*.md' \\\n './**/*.html' \\\n './**/*.yml' \\\n './**/*.yaml' \\\n './**/*.py' \\\n './**/*.ipynb' | tee -a $GITHUB_STEP_SUMMARY\n\n # Raise error if broken links found\n if ! grep -q \"0 Errors\" $GITHUB_STEP_SUMMARY; then\n exit 1\n fi\n" }, { "path": ".github/workflows/merge-main-into-prs.yml", "content": "# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license\n\n# Automatically merges repository 'main' branch into all open PRs to keep them up-to-date\n# Action runs on updates to main branch so when one PR merges to main all others update\n\nname: Merge main into PRs\n\non:\n workflow_dispatch:\n # push:\n # branches:\n # - ${{ github.event.repository.default_branch }}\n\njobs:\n Merge:\n if: github.repository == 'ultralytics/yolov3'\n runs-on: ubuntu-latest\n permissions:\n contents: read\n pull-requests: write\n steps:\n - name: Checkout repository\n uses: actions/checkout@v6\n with:\n fetch-depth: 0\n - uses: actions/setup-python@v6\n with:\n python-version: \"3.x\"\n cache: \"pip\"\n - name: Install requirements\n run: |\n pip install pygithub\n - name: Merge default branch into PRs\n shell: python\n run: |\n from github import Github\n import os\n\n g = Github(os.getenv('GITHUB_TOKEN'))\n repo = g.get_repo(os.getenv('GITHUB_REPOSITORY'))\n\n # Fetch the default branch name\n default_branch_name = repo.default_branch\n default_branch = repo.get_branch(default_branch_name)\n\n for pr in repo.get_pulls(state='open', sort='created'):\n try:\n # Get full names for repositories and branches\n base_repo_name = repo.full_name\n head_repo_name = pr.head.repo.full_name\n base_branch_name = pr.base.ref\n head_branch_name = pr.head.ref\n\n # Check if PR is behind the default branch\n comparison = repo.compare(default_branch.commit.sha, pr.head.sha)\n \n if comparison.behind_by > 0:\n print(f\"⚠️ PR #{pr.number} ({head_repo_name}:{head_branch_name} -> {base_repo_name}:{base_branch_name}) is behind {default_branch_name} by {comparison.behind_by} commit(s).\")\n \n # Attempt to update the branch\n try:\n success = pr.update_branch()\n assert success, \"Branch update failed\"\n print(f\"✅ Successfully merged '{default_branch_name}' into PR #{pr.number} ({head_repo_name}:{head_branch_name} -> {base_repo_name}:{base_branch_name}).\")\n except Exception as update_error:\n print(f\"❌ Could not update PR #{pr.number} ({head_repo_name}:{head_branch_name} -> {base_repo_name}:{base_branch_name}): {update_error}\")\n print(\" This might be due to branch protection rules or insufficient permissions.\")\n else:\n print(f\"✅ PR #{pr.number} ({head_repo_name}:{head_branch_name} -> {base_repo_name}:{base_branch_name}) is up to date with {default_branch_name}.\")\n except Exception as e:\n print(f\"❌ Could not process PR #{pr.number}: {e}\")\n\n env:\n GITHUB_TOKEN: ${{ secrets._GITHUB_TOKEN }}\n GITHUB_REPOSITORY: ${{ github.repository }}\n" }, { "path": ".github/workflows/stale.yml", "content": "# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license\n\nname: Close stale issues\n\npermissions:\n contents: read\n issues: write\n pull-requests: write\n\non:\n schedule:\n - cron: \"0 0 * * *\" # Runs at 00:00 UTC every day\n\njobs:\n stale:\n runs-on: ubuntu-latest\n steps:\n - uses: actions/stale@v10\n with:\n repo-token: ${{ secrets.GITHUB_TOKEN }}\n\n stale-issue-message: |\n 👋 Hello there! We wanted to give you a friendly reminder that this issue has not had any recent activity and may be closed soon, but don't worry - you can always reopen it if needed. If you still have any questions or concerns, please feel free to let us know how we can help.\n\n For additional resources and information, please see the links below:\n\n - **Docs**: https://docs.ultralytics.com\n - **Platform**: https://platform.ultralytics.com\n - **Community**: https://community.ultralytics.com\n\n Feel free to inform us of any other **issues** you discover or **feature requests** that come to mind in the future. Pull Requests (PRs) are also always welcomed!\n\n Thank you for your contributions to YOLO 🚀 and Vision AI ⭐\n\n stale-pr-message: |\n 👋 Hello there! We wanted to let you know that we've decided to close this pull request due to inactivity. We appreciate the effort you put into contributing to our project, but unfortunately, not all contributions are suitable or aligned with our product roadmap.\n\n We hope you understand our decision, and please don't let it discourage you from contributing to open source projects in the future. We value all of our community members and their contributions, and we encourage you to keep exploring new projects and ways to get involved.\n\n For additional resources and information, please see the links below:\n\n - **Docs**: https://docs.ultralytics.com\n - **Platform**: https://platform.ultralytics.com\n - **Community**: https://community.ultralytics.com\n\n Thank you for your contributions to YOLO 🚀 and Vision AI ⭐\n\n days-before-issue-stale: 30\n days-before-issue-close: 10\n days-before-pr-stale: 90\n days-before-pr-close: 30\n exempt-issue-labels: \"documentation,tutorial,TODO\"\n operations-per-run: 300 # The maximum number of operations per run, used to control rate limiting.\n" }, { "path": ".gitignore", "content": "# Repo-specific GitIgnore ----------------------------------------------------------------------------------------------\n*.jpg\n*.jpeg\n*.png\n*.bmp\n*.tif\n*.tiff\n*.heic\n*.JPG\n*.JPEG\n*.PNG\n*.BMP\n*.TIF\n*.TIFF\n*.HEIC\n*.mp4\n*.mov\n*.MOV\n*.avi\n*.data\n*.json\n*.cfg\n!setup.cfg\n!cfg/yolov3*.cfg\n\nstorage.googleapis.com\nruns/*\ndata/*\ndata/images/*\n!data/*.yaml\n!data/hyps\n!data/scripts\n!data/images\n!data/images/zidane.jpg\n!data/images/bus.jpg\n!data/*.sh\n\nresults*.csv\n\n# Datasets -------------------------------------------------------------------------------------------------------------\ncoco/\ncoco128/\nVOC/\n\n# MATLAB GitIgnore -----------------------------------------------------------------------------------------------------\n*.m~\n*.mat\n!targets*.mat\n\n# Neural Network weights -----------------------------------------------------------------------------------------------\n*.weights\n*.pt\n*.pb\n*.onnx\n*.engine\n*.mlmodel\n*.torchscript\n*.tflite\n*.h5\n*_saved_model/\n*_web_model/\n*_openvino_model/\n*_paddle_model/\ndarknet53.conv.74\nyolov3-tiny.conv.15\n\n# GitHub Python GitIgnore ----------------------------------------------------------------------------------------------\n# Byte-compiled / optimized / DLL files\n__pycache__/\n*.py[cod]\n*$py.class\n\n# C extensions\n*.so\n\n# Distribution / packaging\n.Python\nenv/\nbuild/\ndevelop-eggs/\ndist/\ndownloads/\neggs/\n.eggs/\nlib/\nlib64/\nparts/\nsdist/\nvar/\nwheels/\n*.egg-info/\n/wandb/\n.installed.cfg\n*.egg\n\n\n# PyInstaller\n# Usually these files are written by a python script from a template\n# before PyInstaller builds the exe, so as to inject date/other infos into it.\n*.manifest\n*.spec\n\n# Installer logs\npip-log.txt\npip-delete-this-directory.txt\n\n# Unit test / coverage reports\nhtmlcov/\n.tox/\n.coverage\n.coverage.*\n.cache\nnosetests.xml\ncoverage.xml\n*.cover\n.hypothesis/\n\n# Translations\n*.mo\n*.pot\n\n# Django stuff:\n*.log\nlocal_settings.py\n\n# Flask stuff:\ninstance/\n.webassets-cache\n\n# Scrapy stuff:\n.scrapy\n\n# Sphinx documentation\ndocs/_build/\n\n# PyBuilder\ntarget/\n\n# Jupyter Notebook\n.ipynb_checkpoints\n\n# pyenv\n.python-version\n\n# celery beat schedule file\ncelerybeat-schedule\n\n# SageMath parsed files\n*.sage.py\n\n# dotenv\n.env\n\n# virtualenv\n.venv*\nvenv*/\nENV*/\n\n# Spyder project settings\n.spyderproject\n.spyproject\n\n# Rope project settings\n.ropeproject\n\n# mkdocs documentation\n/site\n\n# mypy\n.mypy_cache/\n\n\n# https://github.com/github/gitignore/blob/master/Global/macOS.gitignore -----------------------------------------------\n\n# General\n.DS_Store\n.AppleDouble\n.LSOverride\n\n# Icon must end with two \\r\nIcon\nIcon?\n\n# Thumbnails\n._*\n\n# Files that might appear in the root of a volume\n.DocumentRevisions-V100\n.fseventsd\n.Spotlight-V100\n.TemporaryItems\n.Trashes\n.VolumeIcon.icns\n.com.apple.timemachine.donotpresent\n\n# Directories potentially created on remote AFP share\n.AppleDB\n.AppleDesktop\nNetwork Trash Folder\nTemporary Items\n.apdisk\n\n\n# https://github.com/github/gitignore/blob/master/Global/JetBrains.gitignore\n# Covers JetBrains IDEs: IntelliJ, RubyMine, PhpStorm, AppCode, PyCharm, CLion, Android Studio and WebStorm\n# Reference: https://intellij-support.jetbrains.com/hc/en-us/articles/206544839\n\n# User-specific stuff:\n.idea/*\n.idea/**/workspace.xml\n.idea/**/tasks.xml\n.idea/dictionaries\n.html # Bokeh Plots\n.pg # TensorFlow Frozen Graphs\n.avi # videos\n\n# Sensitive or high-churn files:\n.idea/**/dataSources/\n.idea/**/dataSources.ids\n.idea/**/dataSources.local.xml\n.idea/**/sqlDataSources.xml\n.idea/**/dynamic.xml\n.idea/**/uiDesigner.xml\n\n# Gradle:\n.idea/**/gradle.xml\n.idea/**/libraries\n\n# CMake\ncmake-build-debug/\ncmake-build-release/\n\n# Mongo Explorer plugin:\n.idea/**/mongoSettings.xml\n\n## File-based project format:\n*.iws\n\n## Plugin-specific files:\n\n# IntelliJ\nout/\n\n# mpeltonen/sbt-idea plugin\n.idea_modules/\n\n# JIRA plugin\natlassian-ide-plugin.xml\n\n# Cursive Clojure plugin\n.idea/replstate.xml\n\n# Crashlytics plugin (for Android Studio and IntelliJ)\ncom_crashlytics_export_strings.xml\ncrashlytics.properties\ncrashlytics-build.properties\nfabric.properties\n" }, { "path": "CITATION.cff", "content": "cff-version: 1.2.0\npreferred-citation:\n type: software\n message: If you use YOLOv5, please cite it as below.\n authors:\n - family-names: Jocher\n given-names: Glenn\n orcid: \"https://orcid.org/0000-0001-5950-6979\"\n title: \"YOLOv5 by Ultralytics\"\n version: 7.0\n doi: 10.5281/zenodo.3908559\n date-released: 2020-5-29\n license: AGPL-3.0\n url: \"https://github.com/ultralytics/yolov5\"\n" }, { "path": "CONTRIBUTING.md", "content": "# Contributing To YOLOv3 🚀\n\nWe value your input and welcome your contributions to Ultralytics YOLOv3! Whether you're interested in:\n\n- Reporting a bug\n- Discussing the current state of the codebase\n- Submitting a fix\n- Proposing a new feature\n- Becoming a maintainer\n\nUltralytics YOLO models are successful thanks to the collective efforts of our community. Every improvement you contribute helps advance the possibilities of AI and computer vision! 😃\n\n## Submitting A Pull Request (PR) 🛠️\n\nContributing a PR is straightforward! Here’s a step-by-step example for updating `requirements.txt`:\n\n### 1. Select The File To Update\n\nClick on `requirements.txt` in the GitHub repository to open it.\n\n

\"PR_step1\"

\n\n### 2. Click 'Edit This File'\n\nUse the pencil icon in the top-right corner to begin editing.\n\n

\"PR_step2\"

\n\n### 3. Make Your Changes\n\nFor example, update the `matplotlib` version from `3.2.2` to `3.3`.\n\n

\"PR_step3\"

\n\n### 4. Preview And Submit Your PR\n\nSwitch to the **Preview changes** tab to review your edits. At the bottom, select 'Create a new branch for this commit', give your branch a descriptive name like `fix/matplotlib_version`, and click the green **Propose changes** button. Your PR is now submitted for review! 😃\n\n

\"PR_step4\"

\n\n### PR Best Practices\n\nTo ensure your contribution is integrated smoothly, please:\n\n- ✅ Ensure your PR is **up-to-date** with the `ultralytics/yolov3` `master` branch. If your PR is behind, update your code by clicking the 'Update branch' button or by running `git pull` and `git merge master` locally.\n\n

\"Screenshot

\n\n- ✅ Confirm that all Continuous Integration (CI) **checks are passing**.\n\n

\"Screenshot

\n\n- ✅ Limit your changes to the **minimum required** for your bug fix or feature. \n _\"It is not daily increase but daily decrease, hack away the unessential. The closer to the source, the less wastage there is.\"_ — Bruce Lee\n\n## Submitting A Bug Report 🐛\n\nIf you encounter an issue with Ultralytics YOLOv3, please submit a bug report!\n\nTo help us investigate, please provide a [minimum reproducible example](https://docs.ultralytics.com/help/minimum-reproducible-example/). Your code should be:\n\n- ✅ **Minimal** – Use as little code as possible that still produces the issue.\n- ✅ **Complete** – Include all parts needed for someone else to reproduce the problem.\n- ✅ **Reproducible** – Test your code to ensure it reliably triggers the issue.\n\nAdditionally, for [Ultralytics](https://www.ultralytics.com/) to assist, your code should be:\n\n- ✅ **Current** – Ensure your code is up-to-date with the latest [master branch](https://github.com/ultralytics/yolov3/tree/master). Use `git pull` or `git clone` to get the latest version.\n- ✅ **Unmodified** – The problem must be reproducible without custom modifications to the repository. [Ultralytics](https://www.ultralytics.com/) does not provide support for custom code.\n\nIf your issue meets these criteria, please close your current issue and open a new one using the 🐛 **Bug Report** [template](https://github.com/ultralytics/yolov3/issues/new/choose), including your [minimum reproducible example](https://docs.ultralytics.com/help/minimum-reproducible-example/) to help us diagnose and resolve your problem.\n\n## License\n\nBy contributing, you agree that your submissions will be licensed under the [AGPL-3.0 license](https://choosealicense.com/licenses/agpl-3.0/).\n\n---\n\nThank you for helping improve Ultralytics YOLOv3! Your contributions make a difference. For more on open-source best practices, check out the [Ultralytics open-source community](https://www.ultralytics.com/blog/tips-to-start-contributing-to-ultralytics-open-source-projects) and [GitHub's open source guides](https://opensource.guide/how-to-contribute/).\n" }, { "path": "LICENSE", "content": " GNU AFFERO GENERAL PUBLIC LICENSE\n Version 3, 19 November 2007\n\n Copyright (C) 2007 Free Software Foundation, Inc. \n Everyone is permitted to copy and distribute verbatim copies\n of this license document, but changing it is not allowed.\n\n Preamble\n\n The GNU Affero General Public License is a free, copyleft license for\nsoftware and other kinds of works, specifically designed to ensure\ncooperation with the community in the case of network server software.\n\n The licenses for most software and other practical works are designed\nto take away your freedom to share and change the works. By contrast,\nour General Public Licenses are intended to guarantee your freedom to\nshare and change all versions of a program--to make sure it remains free\nsoftware for all its users.\n\n When we speak of free software, we are referring to freedom, not\nprice. Our General Public Licenses are designed to make sure that you\nhave the freedom to distribute copies of free software (and charge for\nthem if you wish), that you receive source code or can get it if you\nwant it, that you can change the software or use pieces of it in new\nfree programs, and that you know you can do these things.\n\n Developers that use our General Public Licenses protect your rights\nwith two steps: (1) assert copyright on the software, and (2) offer\nyou this License which gives you legal permission to copy, distribute\nand/or modify the software.\n\n A secondary benefit of defending all users' freedom is that\nimprovements made in alternate versions of the program, if they\nreceive widespread use, become available for other developers to\nincorporate. Many developers of free software are heartened and\nencouraged by the resulting cooperation. However, in the case of\nsoftware used on network servers, this result may fail to come about.\nThe GNU General Public License permits making a modified version and\nletting the public access it on a server without ever releasing its\nsource code to the public.\n\n The GNU Affero General Public License is designed specifically to\nensure that, in such cases, the modified source code becomes available\nto the community. It requires the operator of a network server to\nprovide the source code of the modified version running there to the\nusers of that server. Therefore, public use of a modified version, on\na publicly accessible server, gives the public access to the source\ncode of the modified version.\n\n An older license, called the Affero General Public License and\npublished by Affero, was designed to accomplish similar goals. 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If not, see .\n\nAlso add information on how to contact you by electronic and paper mail.\n\n If your software can interact with users remotely through a computer\nnetwork, you should also make sure that it provides a way for users to\nget its source. For example, if your program is a web application, its\ninterface could display a \"Source\" link that leads users to an archive\nof the code. There are many ways you could offer source, and different\nsolutions will be better for different programs; see section 13 for the\nspecific requirements.\n\n You should also get your employer (if you work as a programmer) or school,\nif any, to sign a \"copyright disclaimer\" for the program, if necessary.\nFor more information on this, and how to apply and follow the GNU AGPL, see\n.\n" }, { "path": "README.md", "content": "
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\n \n \"Ultralytics\n

\n\n[中文](https://docs.ultralytics.com/zh/) | [한국어](https://docs.ultralytics.com/ko/) | [日本語](https://docs.ultralytics.com/ja/) | [Русский](https://docs.ultralytics.com/ru/) | [Deutsch](https://docs.ultralytics.com/de/) | [Français](https://docs.ultralytics.com/fr/) | [Español](https://docs.ultralytics.com/es) | [Português](https://docs.ultralytics.com/pt/) | [Türkçe](https://docs.ultralytics.com/tr/) | [Tiếng Việt](https://docs.ultralytics.com/vi/) | [العربية](https://docs.ultralytics.com/ar/)\n\n
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\n\nUltralytics YOLOv3 is a robust and efficient [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) model developed by [Ultralytics](https://www.ultralytics.com/). Built on the [PyTorch](https://pytorch.org/) framework, this implementation extends the original YOLOv3 architecture, renowned for its improvements in [object detection](https://www.ultralytics.com/glossary/object-detection) speed and accuracy over earlier versions. It incorporates best practices and insights from extensive research, making it a reliable choice for a wide range of vision AI applications.\n\nExplore the [Ultralytics Docs](https://docs.ultralytics.com/) for in-depth guidance (YOLOv3-specific docs may be limited, but general YOLO principles apply), open an issue on [GitHub](https://github.com/ultralytics/yolov5/issues/new/choose) for support, and join our [Discord community](https://discord.com/invite/ultralytics) for questions and discussions!\n\nFor Enterprise License requests, please complete the form at [Ultralytics Licensing](https://www.ultralytics.com/license).\n\n
\n \"Ultralytics\n \"space\"\n \"Ultralytics\n \"space\"\n \"Ultralytics\n \"space\"\n \"Ultralytics\n \"space\"\n \"Ultralytics\n \"space\"\n \"Ultralytics\n \"space\"\n \"Ultralytics\n
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\n\n## 🚀 YOLO11: The Next Evolution\n\nWe are thrilled to introduce **Ultralytics YOLO11** 🚀, the latest advancement in our state-of-the-art vision models! Available now at the [Ultralytics YOLO GitHub repository](https://github.com/ultralytics/ultralytics), YOLO11 continues our legacy of speed, precision, and user-friendly design. Whether you're working on [object detection](https://docs.ultralytics.com/tasks/detect/), [instance segmentation](https://docs.ultralytics.com/tasks/segment/), [pose estimation](https://docs.ultralytics.com/tasks/pose/), [image classification](https://docs.ultralytics.com/tasks/classify/), or [oriented object detection (OBB)](https://docs.ultralytics.com/tasks/obb/), YOLO11 delivers the performance and flexibility needed for modern computer vision tasks.\n\nGet started today and unlock the full potential of YOLO11! Visit the [Ultralytics Docs](https://docs.ultralytics.com/) for comprehensive guides and resources:\n\n[![PyPI version](https://badge.fury.io/py/ultralytics.svg)](https://badge.fury.io/py/ultralytics) [![Downloads](https://static.pepy.tech/badge/ultralytics)](https://pepy.tech/projects/ultralytics)\n\n```bash\n# Install the ultralytics package\npip install ultralytics\n```\n\n
\n \n \"Ultralytics\n
\n\n## 📚 Documentation\n\nSee the [Ultralytics Docs for YOLOv3](https://docs.ultralytics.com/models/yolov3/) for full documentation on training, testing, and deployment using the Ultralytics framework. While YOLOv3-specific documentation may be limited, the general YOLO principles apply. Below are quickstart examples adapted for YOLOv3 concepts.\n\n
\nInstall\n\nClone the repository and install dependencies from `requirements.txt` in a [**Python>=3.8.0**](https://www.python.org/) environment. Ensure you have [**PyTorch>=1.8**](https://pytorch.org/get-started/locally/) installed. (Note: This repo is originally YOLOv5, dependencies should be compatible but tailored testing for YOLOv3 is recommended).\n\n```bash\n# Clone the YOLOv3 repository\ngit clone https://github.com/ultralytics/yolov3\n\n# Navigate to the cloned directory\ncd yolov3\n\n# Install required packages\npip install -r requirements.txt\n```\n\n
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\nInference with PyTorch Hub\n\nUse YOLOv3 via [PyTorch Hub](https://docs.ultralytics.com/yolov5/tutorials/pytorch_hub_model_loading/) for inference. [Models](https://github.com/ultralytics/yolov5/tree/master/models) like `yolov3.pt`, `yolov3-spp.pt`, `yolov3-tiny.pt` can be loaded.\n\n```python\nimport torch\n\n# Load a YOLOv3 model (e.g., yolov3, yolov3-spp)\nmodel = torch.hub.load(\"ultralytics/yolov3\", \"yolov3\", pretrained=True) # specify 'yolov3' or other variants\n\n# Define the input image source (URL, local file, PIL image, OpenCV frame, numpy array, or list)\nimg = \"https://ultralytics.com/images/zidane.jpg\" # Example image\n\n# Perform inference\nresults = model(img)\n\n# Process the results (options: .print(), .show(), .save(), .crop(), .pandas())\nresults.print() # Print results to console\nresults.show() # Display results in a window\nresults.save() # Save results to runs/detect/exp\n```\n\n
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\nInference with detect.py\n\nThe `detect.py` script runs inference on various sources. Use `--weights yolov3.pt` or other YOLOv3 variants. It automatically downloads models and saves results to `runs/detect`.\n\n```bash\n# Run inference using a webcam with yolov3-tiny\npython detect.py --weights yolov3-tiny.pt --source 0\n\n# Run inference on a local image file with yolov3\npython detect.py --weights yolov3.pt --source img.jpg\n\n# Run inference on a local video file with yolov3-spp\npython detect.py --weights yolov3-spp.pt --source vid.mp4\n\n# Run inference on a screen capture\npython detect.py --weights yolov3.pt --source screen\n\n# Run inference on a directory of images\npython detect.py --weights yolov3.pt --source path/to/images/\n\n# Run inference on a text file listing image paths\npython detect.py --weights yolov3.pt --source list.txt\n\n# Run inference on a text file listing stream URLs\npython detect.py --weights yolov3.pt --source list.streams\n\n# Run inference using a glob pattern for images\npython detect.py --weights yolov3.pt --source 'path/to/*.jpg'\n\n# Run inference on a YouTube video URL\npython detect.py --weights yolov3.pt --source 'https://youtu.be/LNwODJXcvt4'\n\n# Run inference on an RTSP, RTMP, or HTTP stream\npython detect.py --weights yolov3.pt --source 'rtsp://example.com/media.mp4'\n```\n\n
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\nTraining\n\nThe commands below show how to train YOLOv3 models on the [COCO dataset](https://docs.ultralytics.com/datasets/detect/coco/). Models and datasets are downloaded automatically. Use the largest `--batch-size` your hardware allows.\n\n```bash\n# Train YOLOv3-tiny on COCO for 300 epochs (example settings)\npython train.py --data coco.yaml --epochs 300 --weights '' --cfg yolov3-tiny.yaml --batch-size 64\n\n# Train YOLOv3 on COCO for 300 epochs (example settings)\npython train.py --data coco.yaml --epochs 300 --weights '' --cfg yolov3.yaml --batch-size 32\n\n# Train YOLOv3-SPP on COCO for 300 epochs (example settings)\npython train.py --data coco.yaml --epochs 300 --weights '' --cfg yolov3-spp.yaml --batch-size 16\n```\n\n
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\nTutorials\n\nNote: These tutorials primarily use YOLOv5 examples but the principles often apply to YOLOv3 within the Ultralytics framework.\n\n- **[Train Custom Data](https://docs.ultralytics.com/yolov5/tutorials/train_custom_data/)** 🚀 **RECOMMENDED**: Learn how to train models on your own datasets.\n- **[Tips for Best Training Results](https://docs.ultralytics.com/guides/model-training-tips/)** ☘️: Improve your model's performance with expert tips.\n- **[Multi-GPU Training](https://docs.ultralytics.com/yolov5/tutorials/multi_gpu_training/)**: Speed up training using multiple GPUs.\n- **[PyTorch Hub Integration](https://docs.ultralytics.com/yolov5/tutorials/pytorch_hub_model_loading/)** 🌟 **NEW**: Easily load models using PyTorch Hub.\n- **[Model Export (TFLite, ONNX, CoreML, TensorRT)](https://docs.ultralytics.com/yolov5/tutorials/model_export/)** 🚀: Convert your models to various deployment formats.\n- **[NVIDIA Jetson Deployment](https://docs.ultralytics.com/guides/nvidia-jetson/)** 🌟 **NEW**: Deploy models on NVIDIA Jetson devices.\n- **[Test-Time Augmentation (TTA)](https://docs.ultralytics.com/yolov5/tutorials/test_time_augmentation/)**: Enhance prediction accuracy with TTA.\n- **[Model Ensembling](https://docs.ultralytics.com/yolov5/tutorials/model_ensembling/)**: Combine multiple models for better performance.\n- **[Model Pruning/Sparsity](https://docs.ultralytics.com/yolov5/tutorials/model_pruning_and_sparsity/)**: Optimize models for size and speed.\n- **[Hyperparameter Evolution](https://docs.ultralytics.com/yolov5/tutorials/hyperparameter_evolution/)**: Automatically find the best training hyperparameters.\n- **[Transfer Learning with Frozen Layers](https://docs.ultralytics.com/yolov5/tutorials/transfer_learning_with_frozen_layers/)**: Adapt pretrained models to new tasks efficiently.\n- **[Architecture Summary](https://docs.ultralytics.com/yolov5/tutorials/architecture_description/)** 🌟 **NEW**: Understand the model architecture (focus on YOLOv3 principles).\n- **[Ultralytics Platform Training](https://platform.ultralytics.com)** 🚀 **RECOMMENDED**: Train and deploy YOLO models using Ultralytics Platform.\n- **[ClearML Logging](https://docs.ultralytics.com/yolov5/tutorials/clearml_logging_integration/)**: Integrate with ClearML for experiment tracking.\n- **[Neural Magic DeepSparse Integration](https://docs.ultralytics.com/yolov5/tutorials/neural_magic_pruning_quantization/)**: Accelerate inference with DeepSparse.\n- **[Comet Logging](https://docs.ultralytics.com/yolov5/tutorials/comet_logging_integration/)** 🌟 **NEW**: Log experiments using Comet ML.\n\n
\n\n## 🧩 Integrations\n\nUltralytics offers robust integrations with leading AI platforms to enhance your workflow, including dataset labeling, training, visualization, and model management. Discover how Ultralytics, in collaboration with partners like [Weights & Biases](https://docs.ultralytics.com/integrations/weights-biases/), [Comet ML](https://docs.ultralytics.com/integrations/comet/), [Roboflow](https://docs.ultralytics.com/integrations/roboflow/), and [Intel OpenVINO](https://docs.ultralytics.com/integrations/openvino/), can optimize your AI projects. Explore more at [Ultralytics Integrations](https://docs.ultralytics.com/integrations/).\n\n\n \"Ultralytics\n\n
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\n\n| Ultralytics Platform 🌟 | Weights & Biases | Comet | Neural Magic |\n| :--------------------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------------: |\n| Streamline YOLO workflows: Label, train, and deploy effortlessly with [Ultralytics Platform](https://platform.ultralytics.com). Try now! | Track experiments, hyperparameters, and results with [Weights & Biases](https://docs.ultralytics.com/integrations/weights-biases/). | Free forever, [Comet ML](https://docs.ultralytics.com/integrations/comet/) lets you save YOLO models, resume training, and interactively visualize predictions. | Run YOLO inference up to 6x faster with [Neural Magic DeepSparse](https://docs.ultralytics.com/integrations/neural-magic/). |\n\n## ⭐ Ultralytics Platform\n\nExperience seamless AI development with [Ultralytics Platform](https://platform.ultralytics.com) ⭐, the ultimate platform for building, training, and deploying computer vision models. Visualize datasets, train YOLOv3, YOLOv5, and YOLOv8 🚀 models, and deploy them to real-world applications without writing any code. Transform images into actionable insights using our advanced tools and user-friendly [Ultralytics App](https://www.ultralytics.com/app-install). Start your journey for **Free** today!\n\n\n\"Ultralytics\n\n## 🤔 Why YOLOv3?\n\nYOLOv3 marked a major leap forward in real-time object detection at its release. Key advantages include:\n\n- **Improved Accuracy:** Enhanced detection of small objects compared to YOLOv2.\n- **Multi-Scale Predictions:** Detects objects at three different scales, boosting performance across varied object sizes.\n- **Class Prediction:** Uses logistic classifiers for object classes, enabling multi-label classification.\n- **Feature Extractor:** Employs a deeper network (Darknet-53) versus the Darknet-19 used in YOLOv2.\n\nWhile newer models like YOLOv5 and YOLO11 offer further advancements, YOLOv3 remains a reliable and widely adopted baseline, efficiently implemented in PyTorch by Ultralytics.\n\n## ☁️ Environments\n\nGet started quickly with our pre-configured environments. Click the icons below for setup details.\n\n
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\n\n## 🤝 Contribute\n\nWe welcome your contributions! Making YOLO models accessible and effective is a community effort. Please see our [Contributing Guide](https://docs.ultralytics.com/help/contributing/) to get started. Share your feedback through the [Ultralytics Survey](https://www.ultralytics.com/survey?utm_source=github&utm_medium=social&utm_campaign=Survey). Thank you to all our contributors for making Ultralytics YOLO better!\n\n[![Ultralytics open-source contributors](https://raw.githubusercontent.com/ultralytics/assets/main/im/image-contributors.png)](https://github.com/ultralytics/yolov5/graphs/contributors)\n\n## 📜 License\n\nUltralytics provides two licensing options to meet different needs:\n\n- **AGPL-3.0 License**: An [OSI-approved](https://opensource.org/license/agpl-v3) open-source license ideal for academic research, personal projects, and testing. It promotes open collaboration and knowledge sharing. See the [LICENSE](https://github.com/ultralytics/yolov5/blob/master/LICENSE) file for details.\n- **Enterprise License**: Tailored for commercial applications, this license allows seamless integration of Ultralytics software and AI models into commercial products and services, bypassing the open-source requirements of AGPL-3.0. For commercial use cases, please contact us via [Ultralytics Licensing](https://www.ultralytics.com/license).\n\n## 📧 Contact\n\nFor bug reports and feature requests related to Ultralytics YOLO implementations, please visit [GitHub Issues](https://github.com/ultralytics/yolov5/issues). For general questions, discussions, and community support, join our [Discord server](https://discord.com/invite/ultralytics)!\n\n
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\n" }, { "path": "README.zh-CN.md", "content": "\"Ultralytics\n\n
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\n\n[English](https://docs.ultralytics.com/) | [한국어](https://docs.ultralytics.com/ko/) | [日本語](https://docs.ultralytics.com/ja/) | [Русский](https://docs.ultralytics.com/ru/) | [Deutsch](https://docs.ultralytics.com/de/) | [Français](https://docs.ultralytics.com/fr/) | [Español](https://docs.ultralytics.com/es) | [Português](https://docs.ultralytics.com/pt/) | [Türkçe](https://docs.ultralytics.com/tr/) | [Tiếng Việt](https://docs.ultralytics.com/vi/) | [العربية](https://docs.ultralytics.com/ar/)\n\n
\n \"YOLOv3\n \"YOLOv3\n \"Docker\n \"Discord\"\n \"Ultralytics\n \"Ultralytics\n
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\n\nUltralytics YOLOv3 是由 [Ultralytics](https://www.ultralytics.com/) 开发的高效、强大的[计算机视觉](https://www.ultralytics.com/glossary/computer-vision-cv)模型。该实现基于 [PyTorch](https://pytorch.org/) 框架，构建于原始 YOLOv3 架构之上。与早期版本相比，YOLOv3 在[目标检测](https://www.ultralytics.com/glossary/object-detection)速度与准确性方面表现卓越，融合了前沿研究和最佳实践，成为多种视觉 AI 任务的可靠选择。\n\n欢迎您充分利用本项目资源！请访问 [Ultralytics 文档](https://docs.ultralytics.com/)获取详细指南（注意：YOLOv3 专属文档有限，建议参考通用 YOLO 原则），在 [GitHub Issues](https://github.com/ultralytics/yolov5/issues/new/choose) 提问获取支持，并加入 [Discord 社区](https://discord.com/invite/ultralytics)参与讨论！\n\n如需企业许可证，请填写 [Ultralytics 许可申请](https://www.ultralytics.com/license)。\n\n
\n \"Ultralytics\n \"space\"\n \"Ultralytics\n \"space\"\n \"Ultralytics\n \"space\"\n \"Ultralytics\n \"space\"\n \"Ultralytics\n \"space\"\n \"Ultralytics\n \"space\"\n \"Ultralytics\n
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\n\n## 🚀 YOLO11：下一代进化\n\n我们隆重推出 **Ultralytics YOLO11** 🚀，这是我们最新的 SOTA 视觉模型！YOLO11 已在 [Ultralytics YOLO GitHub 仓库](https://github.com/ultralytics/ultralytics)发布，延续了速度、精度与易用性的卓越传统。无论您在进行[目标检测](https://docs.ultralytics.com/tasks/detect/)、[实例分割](https://docs.ultralytics.com/tasks/segment/)、[姿态估计](https://docs.ultralytics.com/tasks/pose/)、[图像分类](https://docs.ultralytics.com/tasks/classify/)还是[旋转目标检测 (OBB)](https://docs.ultralytics.com/tasks/obb/)，YOLO11 都能为您的应用带来卓越性能和多功能性。\n\n立即体验，释放 YOLO11 的全部潜能！访问 [Ultralytics 文档](https://docs.ultralytics.com/)获取全面指南和资源：\n\n[![PyPI version](https://badge.fury.io/py/ultralytics.svg)](https://badge.fury.io/py/ultralytics) [![Downloads](https://static.pepy.tech/badge/ultralytics)](https://pepy.tech/projects/ultralytics)\n\n```bash\n# 安装 ultralytics 包\npip install ultralytics\n```\n\n
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\n\n## 📚 文档\n\n请参阅 [Ultralytics YOLOv3 文档](https://docs.ultralytics.com/models/yolov3/)，了解如何使用 Ultralytics 框架进行训练、测试和部署。虽然 YOLOv3 专属文档有限，但通用 YOLO 原则同样适用。以下为 YOLOv3 快速入门示例。\n\n
\n安装\n\n克隆仓库并在 [**Python>=3.8.0**](https://www.python.org/) 环境下，从 [requirements.txt](https://github.com/ultralytics/yolov5/blob/master/requirements.txt) 安装依赖。确保已安装 [**PyTorch>=1.8**](https://pytorch.org/get-started/locally/)。建议针对 YOLOv3 进行专门测试以确保兼容性。\n\n```bash\n# 克隆 YOLOv3 仓库\ngit clone https://github.com/ultralytics/yolov3\n\n# 进入目录\ncd yolov3\n\n# 安装依赖\npip install -r requirements.txt\n```\n\n
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\n使用 PyTorch Hub 进行推理\n\n通过 [PyTorch Hub](https://docs.ultralytics.com/integrations/jupyterlab/) 可便捷加载 YOLOv3 进行推理。[模型权重](https://github.com/ultralytics/yolov5/tree/master/models)如 `yolov3.pt`、`yolov3-spp.pt`、`yolov3-tiny.pt` 均可直接使用。\n\n```python\nimport torch\n\n# 加载 YOLOv3 模型（如 yolov3, yolov3-spp）\nmodel = torch.hub.load(\"ultralytics/yolov3\", \"yolov3\", pretrained=True)\n\n# 输入图像（支持 URL、本地文件、PIL、OpenCV、numpy 数组或列表）\nimg = \"https://ultralytics.com/images/zidane.jpg\"\n\n# 推理\nresults = model(img)\n\n# 结果处理（.print(), .show(), .save(), .crop(), .pandas()）\nresults.print()\nresults.show()\nresults.save()\n```\n\n
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\n使用 detect.py 进行推理\n\n`detect.py` 脚本支持多种输入源推理。使用 `--weights yolov3.pt` 或其他变体，模型会自动下载，结果保存至 `runs/detect`。\n\n```bash\n# 使用 yolov3-tiny 和摄像头推理\npython detect.py --weights yolov3-tiny.pt --source 0\n\n# 使用 yolov3 推理本地图像\npython detect.py --weights yolov3.pt --source img.jpg\n\n# 使用 yolov3-spp 推理本地视频\npython detect.py --weights yolov3-spp.pt --source vid.mp4\n\n# 推理屏幕截图\npython detect.py --weights yolov3.pt --source screen\n\n# 推理图像目录\npython detect.py --weights yolov3.pt --source path/to/images/\n\n# 推理图像路径列表文件\npython detect.py --weights yolov3.pt --source list.txt\n\n# 推理流 URL 列表文件\npython detect.py --weights yolov3.pt --source list.streams\n\n# 使用 glob 模式推理\npython detect.py --weights yolov3.pt --source 'path/to/*.jpg'\n\n# 推理 YouTube 视频\npython detect.py --weights yolov3.pt --source 'https://youtu.be/LNwODJXcvt4'\n\n# 推理 RTSP、RTMP 或 HTTP 流\npython detect.py --weights yolov3.pt --source 'rtsp://example.com/media.mp4'\n```\n\n
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\n训练\n\n以下命令展示如何在 [COCO 数据集](https://docs.ultralytics.com/datasets/detect/coco/)上训练 YOLOv3。模型和数据集会自动下载。请根据硬件选择合适的 `--batch-size`。\n\n```bash\n# 在 COCO 上训练 YOLOv3-tiny 300 轮\npython train.py --data coco.yaml --epochs 300 --weights '' --cfg yolov3-tiny.yaml --batch-size 64\n\n# 在 COCO 上训练 YOLOv3 300 轮\npython train.py --data coco.yaml --epochs 300 --weights '' --cfg yolov3.yaml --batch-size 32\n\n# 在 COCO 上训练 YOLOv3-SPP 300 轮\npython train.py --data coco.yaml --epochs 300 --weights '' --cfg yolov3-spp.yaml --batch-size 16\n```\n\n
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\n教程\n\n注意：这些教程多以 YOLOv5 为例，但原理同样适用于 YOLOv3。\n\n- **[训练自定义数据](https://docs.ultralytics.com/guides/data-collection-and-annotation/)** 🚀 **推荐**：学习如何在自有数据集上训练模型。\n- **[最佳训练技巧](https://docs.ultralytics.com/guides/model-training-tips/)** ☘️：提升模型性能的专家建议。\n- **[多 GPU 训练](https://docs.ultralytics.com/guides/model-training-tips/)**：加速大规模训练。\n- **[PyTorch Hub 集成](https://docs.ultralytics.com/integrations/jupyterlab/)** 🌟 **新增**：一键加载模型。\n- **[模型导出 (TFLite, ONNX, CoreML, TensorRT)](https://docs.ultralytics.com/modes/export/)** 🚀：多格式部署支持。\n- **[NVIDIA Jetson 部署](https://docs.ultralytics.com/guides/nvidia-jetson/)** 🌟 **新增**：边缘设备推理。\n- **[测试时增强 (TTA)](https://docs.ultralytics.com/guides/model-evaluation-insights/)**：提升预测准确率。\n- **[模型集成](https://docs.ultralytics.com/guides/model-deployment-options/)**：多模型融合提升表现。\n- **[模型剪枝/稀疏化](https://docs.ultralytics.com/guides/model-deployment-practices/)**：优化模型体积与速度。\n- **[超参数进化](https://docs.ultralytics.com/guides/hyperparameter-tuning/)**：自动优化训练参数。\n- **[迁移学习与冻结层](https://docs.ultralytics.com/guides/model-training-tips/)**：高效迁移预训练模型。\n- **[架构总结](https://docs.ultralytics.com/models/yolov3/)** 🌟 **新增**：理解 YOLOv3 设计原理。\n- **[Ultralytics Platform 训练](https://platform.ultralytics.com)** 🚀 **推荐**：无代码训练与部署。\n- **[ClearML 日志集成](https://docs.ultralytics.com/integrations/clearml/)**：实验可追溯。\n- **[Neural Magic DeepSparse 集成](https://docs.ultralytics.com/integrations/neural-magic/)**：极致推理加速。\n- **[Comet 日志集成](https://docs.ultralytics.com/integrations/comet/)** 🌟 **新增**：实验可视化与管理。\n\n
\n\n## 🧩 集成\n\nUltralytics 与领先 AI 平台深度集成，扩展了数据集标注、训练、可视化和模型管理等能力。了解如何通过 [Weights & Biases](https://docs.ultralytics.com/integrations/weights-biases/)、[Comet ML](https://docs.ultralytics.com/integrations/comet/)、[Roboflow](https://docs.ultralytics.com/integrations/roboflow/) 和 [Intel OpenVINO](https://docs.ultralytics.com/integrations/openvino/) 等合作伙伴优化您的 AI 工作流。探索 [Ultralytics 集成](https://docs.ultralytics.com/integrations/) 了解更多。\n\n\n \"Ultralytics\n\n
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\n\n| Ultralytics Platform 🌟 | Weights & Biases | Comet | Neural Magic |\n| :--------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------------------: |\n| 简化 YOLO 工作流：使用 [Ultralytics Platform](https://platform.ultralytics.com) 轻松标注、训练和部署。立即体验！ | 使用 [Weights & Biases](https://docs.ultralytics.com/integrations/weights-biases/) 跟踪实验与超参数。 | [Comet ML](https://docs.ultralytics.com/integrations/comet/) 永久免费，支持模型保存、训练恢复与预测可视化。 | [Neural Magic DeepSparse](https://docs.ultralytics.com/integrations/neural-magic/) 可将 YOLO 推理速度提升至 6 倍。 |\n\n## ⭐ Ultralytics Platform\n\n通过 [Ultralytics Platform](https://platform.ultralytics.com) ⭐ 体验无缝 AI 开发，轻松构建、训练和部署计算机视觉模型。无需代码，即可可视化数据集、训练 YOLOv3、YOLOv5 和 YOLOv8 🚀，并将模型部署到实际场景。借助 [Ultralytics App](https://www.ultralytics.com/app-install) 和创新工具，将图像转化为可操作见解。立即开启您的**免费** AI 之旅！\n\n\n\"Ultralytics\n\n## 🤔 为何选择 YOLOv3？\n\nYOLOv3 发布时推动了实时目标检测的进步。其核心优势包括：\n\n- **更高准确率：** 对小目标检测表现优异。\n- **多尺度预测：** 支持三种不同尺度，提升多尺寸目标检测能力。\n- **多标签分类：** 采用逻辑分类器而非 softmax，支持多标签输出。\n- **强大特征提取器：** 使用更深的 Darknet-53 网络替代 YOLOv2 的 Darknet-19。\n\n尽管后续如 YOLOv5 和 YOLO11 等模型带来更多创新，YOLOv3 依然是坚实且广泛理解的基准，Ultralytics 在 PyTorch 中实现高效。\n\n## ☁️ 环境\n\n使用预配置环境快速上手。点击下方图标了解各平台设置详情。\n\n
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\n\n## 🤝 贡献\n\n欢迎您的贡献！Ultralytics 致力于让 YOLO 模型更易用、更高效。请参阅[贡献指南](https://docs.ultralytics.com/help/contributing/)开始参与。通过 [Ultralytics 调查](https://www.ultralytics.com/survey?utm_source=github&utm_medium=social&utm_campaign=Survey) 分享您的反馈。感谢所有为 Ultralytics YOLO 发展做出贡献的朋友！\n\n[![Ultralytics open-source contributors](https://raw.githubusercontent.com/ultralytics/assets/main/im/image-contributors.png)](https://github.com/ultralytics/yolov5/graphs/contributors)\n\n## 📜 许可证\n\nUltralytics 提供两种许可选项以满足不同需求：\n\n- **AGPL-3.0 许可证**：经 [OSI 批准](https://opensource.org/license/agpl-v3)的开源协议，适合学术、个人项目和测试，促进开放合作。详情见 [LICENSE](https://github.com/ultralytics/yolov5/blob/master/LICENSE)。\n- **企业许可证**：专为商业应用设计，允许将 Ultralytics 软件和模型集成到商业产品和服务，无需遵守 AGPL-3.0 的开源要求。请通过 [Ultralytics 许可](https://www.ultralytics.com/license) 联系我们。\n\n## 📧 联系\n\n如需报告 Ultralytics YOLO 实现的 bug 或功能请求，请访问 [GitHub Issues](https://github.com/ultralytics/yolov5/issues)。如有一般问题、讨论或社区支持，欢迎加入 [Discord 服务器](https://discord.com/invite/ultralytics)！\n\n
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\n \"Ultralytics\n \"space\"\n \"Ultralytics\n \"space\"\n \"Ultralytics\n \"space\"\n \"Ultralytics\n \"space\"\n \"Ultralytics\n \"space\"\n \"Ultralytics\n \"space\"\n \"Ultralytics\n
\n" }, { "path": "benchmarks.py", "content": "# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license\n\"\"\"\nRun YOLOv3 benchmarks on all supported export formats.\n\nFormat | `export.py --include` | Model\n--- | --- | ---\nPyTorch | - | yolov5s.pt\nTorchScript | `torchscript` | yolov5s.torchscript\nONNX | `onnx` | yolov5s.onnx\nOpenVINO | `openvino` | yolov5s_openvino_model/\nTensorRT | `engine` | yolov5s.engine\nCoreML | `coreml` | yolov5s.mlmodel\nTensorFlow SavedModel | `saved_model` | yolov5s_saved_model/\nTensorFlow GraphDef | `pb` | yolov5s.pb\nTensorFlow Lite | `tflite` | yolov5s.tflite\nTensorFlow Edge TPU | `edgetpu` | yolov5s_edgetpu.tflite\nTensorFlow.js | `tfjs` | yolov5s_web_model/\n\nRequirements:\n $ pip install -r requirements.txt coremltools onnx onnx-simplifier onnxruntime openvino-dev tensorflow-cpu # CPU\n $ pip install -r requirements.txt coremltools onnx onnx-simplifier onnxruntime-gpu openvino-dev tensorflow # GPU\n $ pip install -U nvidia-tensorrt --index-url https://pypi.ngc.nvidia.com # TensorRT\n\nUsage:\n $ python benchmarks.py --weights yolov5s.pt --img 640\n\"\"\"\n\nimport argparse\nimport platform\nimport sys\nimport time\nfrom pathlib import Path\n\nimport pandas as pd\n\nFILE = Path(__file__).resolve()\nROOT = FILE.parents[0] # YOLOv3 root directory\nif str(ROOT) not in sys.path:\n sys.path.append(str(ROOT)) # add ROOT to PATH\n# ROOT = ROOT.relative_to(Path.cwd()) # relative\n\nimport export\nfrom models.experimental import attempt_load\nfrom models.yolo import SegmentationModel\nfrom segment.val import run as val_seg\nfrom utils import notebook_init\nfrom utils.general import LOGGER, check_yaml, file_size, print_args\nfrom utils.torch_utils import select_device\nfrom val import run as val_det\n\n\ndef run(\n weights=ROOT / \"yolov5s.pt\", # weights path\n imgsz=640, # inference size (pixels)\n batch_size=1, # batch size\n data=ROOT / \"data/coco128.yaml\", # dataset.yaml path\n device=\"\", # cuda device, i.e. 0 or 0,1,2,3 or cpu\n half=False, # use FP16 half-precision inference\n test=False, # test exports only\n pt_only=False, # test PyTorch only\n hard_fail=False, # throw error on benchmark failure\n):\n \"\"\"Run YOLOv3 benchmarks on multiple export formats and validate performance metrics.\n\n Args:\n weights (str | Path): Path to the weights file. Defaults to 'yolov5s.pt'.\n imgsz (int): Inference image size in pixels. Defaults to 640.\n batch_size (int): Batch size for inference. Defaults to 1.\n data (str | Path): Path to the dataset configuration file (dataset.yaml). Defaults to 'data/coco128.yaml'.\n device (str): Device to be used for inference, e.g., '0' or '0,1,2,3' for GPU or 'cpu' for CPU. Defaults to ''.\n half (bool): Use FP16 half-precision for inference. Defaults to False.\n test (bool): Test exports only without running benchmarks. Defaults to False.\n pt_only (bool): Run benchmarks only for PyTorch format. Defaults to False.\n hard_fail (bool): Raise an error if any benchmark test fails. Defaults to False.\n\n Returns:\n None\n\n Examples:\n ```python\n # Run benchmarks on the default 'yolov5s.pt' model with an image size of 640 pixels\n run()\n\n # Run benchmarks on a specific model with GPU and half-precision enabled\n run(weights='custom_model.pt', device='0', half=True)\n\n # Test only PyTorch export\n run(pt_only=True)\n ```\n\n Notes:\n This function iterates over multiple export formats, performs the export, and then validates the model's performance\n using appropriate validation functions for detection and segmentation models. The results are logged, and optionally,\n benchmarks can be configured to raise errors on failures using the `hard_fail` argument.\n \"\"\"\n y, t = [], time.time()\n device = select_device(device)\n model_type = type(attempt_load(weights, fuse=False)) # DetectionModel, SegmentationModel, etc.\n for i, (name, f, suffix, cpu, gpu) in export.export_formats().iterrows(): # index, (name, file, suffix, CPU, GPU)\n try:\n assert i not in (9, 10), \"inference not supported\" # Edge TPU and TF.js are unsupported\n assert i != 5 or platform.system() == \"Darwin\", \"inference only supported on macOS>=10.13\" # CoreML\n if \"cpu\" in device.type:\n assert cpu, \"inference not supported on CPU\"\n if \"cuda\" in device.type:\n assert gpu, \"inference not supported on GPU\"\n\n # Export\n if f == \"-\":\n w = weights # PyTorch format\n else:\n w = export.run(\n weights=weights, imgsz=[imgsz], include=[f], batch_size=batch_size, device=device, half=half\n )[-1] # all others\n assert suffix in str(w), \"export failed\"\n\n # Validate\n if model_type == SegmentationModel:\n result = val_seg(data, w, batch_size, imgsz, plots=False, device=device, task=\"speed\", half=half)\n metric = result[0][7] # (box(p, r, map50, map), mask(p, r, map50, map), *loss(box, obj, cls))\n else: # DetectionModel:\n result = val_det(data, w, batch_size, imgsz, plots=False, device=device, task=\"speed\", half=half)\n metric = result[0][3] # (p, r, map50, map, *loss(box, obj, cls))\n speed = result[2][1] # times (preprocess, inference, postprocess)\n y.append([name, round(file_size(w), 1), round(metric, 4), round(speed, 2)]) # MB, mAP, t_inference\n except Exception as e:\n if hard_fail:\n assert type(e) is AssertionError, f\"Benchmark --hard-fail for {name}: {e}\"\n LOGGER.warning(f\"WARNING ⚠️ Benchmark failure for {name}: {e}\")\n y.append([name, None, None, None]) # mAP, t_inference\n if pt_only and i == 0:\n break # break after PyTorch\n\n # Print results\n LOGGER.info(\"\\n\")\n parse_opt()\n notebook_init() # print system info\n c = [\"Format\", \"Size (MB)\", \"mAP50-95\", \"Inference time (ms)\"] if map else [\"Format\", \"Export\", \"\", \"\"]\n py = pd.DataFrame(y, columns=c)\n LOGGER.info(f\"\\nBenchmarks complete ({time.time() - t:.2f}s)\")\n LOGGER.info(str(py if map else py.iloc[:, :2]))\n if hard_fail and isinstance(hard_fail, str):\n metrics = py[\"mAP50-95\"].array # values to compare to floor\n floor = eval(hard_fail) # minimum metric floor to pass, i.e. = 0.29 mAP for YOLOv5n\n assert all(x > floor for x in metrics if pd.notna(x)), f\"HARD FAIL: mAP50-95 < floor {floor}\"\n return py\n\n\ndef test(\n weights=ROOT / \"yolov5s.pt\", # weights path\n imgsz=640, # inference size (pixels)\n batch_size=1, # batch size\n data=ROOT / \"data/coco128.yaml\", # dataset.yaml path\n device=\"\", # cuda device, i.e. 0 or 0,1,2,3 or cpu\n half=False, # use FP16 half-precision inference\n test=False, # test exports only\n pt_only=False, # test PyTorch only\n hard_fail=False, # throw error on benchmark failure\n):\n \"\"\"Run YOLOv3 export tests for various formats and log the results, including export success status.\n\n Args:\n weights (str | Path): Path to the weights file. Defaults to ROOT / \"yolov5s.pt\".\n imgsz (int): Inference size in pixels. Defaults to 640.\n batch_size (int): Number of images per batch. Defaults to 1.\n data (str | Path): Path to the dataset yaml file. Defaults to ROOT / \"data/coco128.yaml\".\n device (str): Device for inference. Accepts cuda device (e.g., \"0\" or \"0,1,2,3\") or \"cpu\". Defaults to \"\".\n half (bool): Use FP16 half-precision inference. Defaults to False.\n test (bool): Run export tests only, no inference. Defaults to False.\n pt_only (bool): Run tests on PyTorch format only. Defaults to False.\n hard_fail (bool): Raise an error on benchmark failure. Defaults to False.\n\n Returns:\n pd.DataFrame: A DataFrame containing the export formats and their success status.\n\n Examples:\n ```python\n from ultralytics import test\n\n results = test(\n weights=\"path/to/yolov5s.pt\",\n imgsz=640,\n batch_size=1,\n data=\"path/to/coco128.yaml\",\n device=\"0\",\n half=False,\n test=True,\n pt_only=False,\n hard_fail=True,\n )\n print(results)\n ```\n\n Notes:\n Ensure all required packages are installed as specified in the Ultralytics YOLOv3 documentation:\n https://github.com/ultralytics/ultralytics\n \"\"\"\n y, t = [], time.time()\n device = select_device(device)\n for i, (name, f, suffix, gpu) in export.export_formats().iterrows(): # index, (name, file, suffix, gpu-capable)\n try:\n w = (\n weights\n if f == \"-\"\n else export.run(weights=weights, imgsz=[imgsz], include=[f], device=device, half=half)[-1]\n ) # weights\n assert suffix in str(w), \"export failed\"\n y.append([name, True])\n except Exception:\n y.append([name, False]) # mAP, t_inference\n\n # Print results\n LOGGER.info(\"\\n\")\n parse_opt()\n notebook_init() # print system info\n py = pd.DataFrame(y, columns=[\"Format\", \"Export\"])\n LOGGER.info(f\"\\nExports complete ({time.time() - t:.2f}s)\")\n LOGGER.info(str(py))\n return py\n\n\ndef parse_opt():\n \"\"\"Parses command line arguments for YOLOv3 inference and export configurations.\n\n Args:\n --weights (str): Path to the weights file. Default is 'ROOT / \"yolov3-tiny.pt\"'.\n --imgsz | --img | --img-size (int): Inference image size in pixels. Default is 640.\n --batch-size (int): Batch size for inference. Default is 1.\n --data (str): Path to the dataset configuration file (dataset.yaml). Default is 'ROOT / \"data/coco128.yaml\"'.\n --device (str): CUDA device identifier, e.g., '0' for single GPU, '0,1,2,3' for multiple GPUs, or 'cpu' for CPU\n inference. Default is \"\".\n --half (bool): If set, use FP16 half-precision inference. Default is False.\n --test (bool): If set, test only exports without running inference. Default is False.\n --pt-only (bool): If set, test only the PyTorch model without exporting to other formats. Default is False.\n --hard-fail (str | bool): If set, raise an exception on benchmark failure. Can also be a string representing the\n minimum metric floor for success. Default is False.\n\n Returns:\n argparse.Namespace: The parsed arguments as a namespace object.\n\n Examples:\n To run inference on the YOLOv3-tiny model with a different image size:\n\n ```python\n $ python benchmarks.py --weights yolov3-tiny.pt --imgsz 512 --device 0\n ```\n\n Notes:\n The `--hard-fail` argument can be a boolean or a string. If a string is provided, it should be an expression that\n represents the minimum acceptable metric value, such as '0.29' for mAP (mean Average Precision).\n\n Links:\n https://github.com/ultralytics/ultralytics\n \"\"\"\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--weights\", type=str, default=ROOT / \"yolov3-tiny.pt\", help=\"weights path\")\n parser.add_argument(\"--imgsz\", \"--img\", \"--img-size\", type=int, default=640, help=\"inference size (pixels)\")\n parser.add_argument(\"--batch-size\", type=int, default=1, help=\"batch size\")\n parser.add_argument(\"--data\", type=str, default=ROOT / \"data/coco128.yaml\", help=\"dataset.yaml path\")\n parser.add_argument(\"--device\", default=\"\", help=\"cuda device, i.e. 0 or 0,1,2,3 or cpu\")\n parser.add_argument(\"--half\", action=\"store_true\", help=\"use FP16 half-precision inference\")\n parser.add_argument(\"--test\", action=\"store_true\", help=\"test exports only\")\n parser.add_argument(\"--pt-only\", action=\"store_true\", help=\"test PyTorch only\")\n parser.add_argument(\"--hard-fail\", nargs=\"?\", const=True, default=False, help=\"Exception on error or < min metric\")\n opt = parser.parse_args()\n opt.data = check_yaml(opt.data) # check YAML\n print_args(vars(opt))\n return opt\n\n\ndef main(opt):\n \"\"\"Executes the export and benchmarking pipeline for YOLOv3 models, testing multiple export formats and validating\n performance metrics.\n\n Args:\n opt (argparse.Namespace): Parsed command line arguments, including options for weights, image size, batch size,\n dataset path, device, half-precision inference, test mode, PyTorch-only testing, and hard fail conditions.\n\n Returns:\n pd.DataFrame: A DataFrame containing benchmarking results with columns:\n - Format: Name of the export format\n - Size (MB): File size of the exported model\n - mAP50-95: Mean Average Precision for the model\n - Inference time (ms): Time taken for inference\n\n Examples:\n Running the function from command line with required arguments:\n\n ```python\n $ python benchmarks.py --weights yolov5s.pt --img 640\n ```\n\n For more details, visit the Ultralytics YOLOv3 repository on [GitHub](https://github.com/ultralytics/ultralytics).\n\n Notes:\n The function runs the main pipeline by exporting the YOLOv3 model to various formats and running benchmarks to\n evaluate performance. If `opt.test` is set to True, it only tests the export process and logs the results.\n \"\"\"\n test(**vars(opt)) if opt.test else run(**vars(opt))\n\n\nif __name__ == \"__main__\":\n opt = parse_opt()\n main(opt)\n" }, { "path": "classify/predict.py", "content": "# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license\n\"\"\"\nRun YOLOv3 classification inference on images, videos, directories, globs, YouTube, webcam, streams, etc.\n\nUsage - sources:\n $ python classify/predict.py --weights yolov5s-cls.pt --source 0 # webcam\n img.jpg # image\n vid.mp4 # video\n screen # screenshot\n path/ # directory\n list.txt # list of images\n list.streams # list of streams\n 'path/*.jpg' # glob\n 'https://youtu.be/LNwODJXcvt4' # YouTube\n 'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream\n\nUsage - formats:\n $ python classify/predict.py --weights yolov5s-cls.pt # PyTorch\n yolov5s-cls.torchscript # TorchScript\n yolov5s-cls.onnx # ONNX Runtime or OpenCV DNN with --dnn\n yolov5s-cls_openvino_model # OpenVINO\n yolov5s-cls.engine # TensorRT\n yolov5s-cls.mlmodel # CoreML (macOS-only)\n yolov5s-cls_saved_model # TensorFlow SavedModel\n yolov5s-cls.pb # TensorFlow GraphDef\n yolov5s-cls.tflite # TensorFlow Lite\n yolov5s-cls_edgetpu.tflite # TensorFlow Edge TPU\n yolov5s-cls_paddle_model # PaddlePaddle\n\"\"\"\n\nimport argparse\nimport os\nimport platform\nimport sys\nfrom pathlib import Path\n\nimport torch\nimport torch.nn.functional as F\n\nFILE = Path(__file__).resolve()\nROOT = FILE.parents[1] # YOLOv3 root directory\nif str(ROOT) not in sys.path:\n sys.path.append(str(ROOT)) # add ROOT to PATH\nROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative\n\nfrom ultralytics.utils.plotting import Annotator\n\nfrom models.common import DetectMultiBackend\nfrom utils.augmentations import classify_transforms\nfrom utils.dataloaders import IMG_FORMATS, VID_FORMATS, LoadImages, LoadScreenshots, LoadStreams\nfrom utils.general import (\n LOGGER,\n Profile,\n check_file,\n check_img_size,\n check_imshow,\n check_requirements,\n colorstr,\n cv2,\n increment_path,\n print_args,\n strip_optimizer,\n)\nfrom utils.torch_utils import select_device, smart_inference_mode\n\n\n@smart_inference_mode()\ndef run(\n weights=ROOT / \"yolov5s-cls.pt\", # model.pt path(s)\n source=ROOT / \"data/images\", # file/dir/URL/glob/screen/0(webcam)\n data=ROOT / \"data/coco128.yaml\", # dataset.yaml path\n imgsz=(224, 224), # inference size (height, width)\n device=\"\", # cuda device, i.e. 0 or 0,1,2,3 or cpu\n view_img=False, # show results\n save_txt=False, # save results to *.txt\n nosave=False, # do not save images/videos\n augment=False, # augmented inference\n visualize=False, # visualize features\n update=False, # update all models\n project=ROOT / \"runs/predict-cls\", # save results to project/name\n name=\"exp\", # save results to project/name\n exist_ok=False, # existing project/name ok, do not increment\n half=False, # use FP16 half-precision inference\n dnn=False, # use OpenCV DNN for ONNX inference\n vid_stride=1, # video frame-rate stride\n):\n \"\"\"Performs YOLOv3 classification inference on various input sources and saves or displays results.\"\"\"\n source = str(source)\n save_img = not nosave and not source.endswith(\".txt\") # save inference images\n is_file = Path(source).suffix[1:] in (IMG_FORMATS + VID_FORMATS)\n is_url = source.lower().startswith((\"rtsp://\", \"rtmp://\", \"http://\", \"https://\"))\n webcam = source.isnumeric() or source.endswith(\".streams\") or (is_url and not is_file)\n screenshot = source.lower().startswith(\"screen\")\n if is_url and is_file:\n source = check_file(source) # download\n\n # Directories\n save_dir = increment_path(Path(project) / name, exist_ok=exist_ok) # increment run\n (save_dir / \"labels\" if save_txt else save_dir).mkdir(parents=True, exist_ok=True) # make dir\n\n # Load model\n device = select_device(device)\n model = DetectMultiBackend(weights, device=device, dnn=dnn, data=data, fp16=half)\n stride, names, pt = model.stride, model.names, model.pt\n imgsz = check_img_size(imgsz, s=stride) # check image size\n\n # Dataloader\n bs = 1 # batch_size\n if webcam:\n view_img = check_imshow(warn=True)\n dataset = LoadStreams(source, img_size=imgsz, transforms=classify_transforms(imgsz[0]), vid_stride=vid_stride)\n bs = len(dataset)\n elif screenshot:\n dataset = LoadScreenshots(source, img_size=imgsz, stride=stride, auto=pt)\n else:\n dataset = LoadImages(source, img_size=imgsz, transforms=classify_transforms(imgsz[0]), vid_stride=vid_stride)\n vid_path, vid_writer = [None] * bs, [None] * bs\n\n # Run inference\n model.warmup(imgsz=(1 if pt else bs, 3, *imgsz)) # warmup\n seen, windows, dt = 0, [], (Profile(), Profile(), Profile())\n for path, im, im0s, vid_cap, s in dataset:\n with dt[0]:\n im = torch.Tensor(im).to(model.device)\n im = im.half() if model.fp16 else im.float() # uint8 to fp16/32\n if len(im.shape) == 3:\n im = im[None] # expand for batch dim\n\n # Inference\n with dt[1]:\n results = model(im)\n\n # Post-process\n with dt[2]:\n pred = F.softmax(results, dim=1) # probabilities\n\n # Process predictions\n for i, prob in enumerate(pred): # per image\n seen += 1\n if webcam: # batch_size >= 1\n p, im0, frame = path[i], im0s[i].copy(), dataset.count\n s += f\"{i}: \"\n else:\n p, im0, frame = path, im0s.copy(), getattr(dataset, \"frame\", 0)\n\n p = Path(p) # to Path\n save_path = str(save_dir / p.name) # im.jpg\n txt_path = str(save_dir / \"labels\" / p.stem) + (\"\" if dataset.mode == \"image\" else f\"_{frame}\") # im.txt\n\n s += \"{:g}x{:g} \".format(*im.shape[2:]) # print string\n annotator = Annotator(im0, example=str(names), pil=True)\n\n # Print results\n top5i = prob.argsort(0, descending=True)[:5].tolist() # top 5 indices\n s += f\"{', '.join(f'{names[j]} {prob[j]:.2f}' for j in top5i)}, \"\n\n # Write results\n text = \"\\n\".join(f\"{prob[j]:.2f} {names[j]}\" for j in top5i)\n if save_img or view_img: # Add bbox to image\n annotator.text([32, 32], text, txt_color=(255, 255, 255))\n if save_txt: # Write to file\n with open(f\"{txt_path}.txt\", \"a\") as f:\n f.write(text + \"\\n\")\n\n # Stream results\n im0 = annotator.result()\n if view_img:\n if platform.system() == \"Linux\" and p not in windows:\n windows.append(p)\n cv2.namedWindow(str(p), cv2.WINDOW_NORMAL | cv2.WINDOW_KEEPRATIO) # allow window resize (Linux)\n cv2.resizeWindow(str(p), im0.shape[1], im0.shape[0])\n cv2.imshow(str(p), im0)\n cv2.waitKey(1) # 1 millisecond\n\n # Save results (image with detections)\n if save_img:\n if dataset.mode == \"image\":\n cv2.imwrite(save_path, im0)\n else: # 'video' or 'stream'\n if vid_path[i] != save_path: # new video\n vid_path[i] = save_path\n if isinstance(vid_writer[i], cv2.VideoWriter):\n vid_writer[i].release() # release previous video writer\n if vid_cap: # video\n fps = vid_cap.get(cv2.CAP_PROP_FPS)\n w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH))\n h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))\n else: # stream\n fps, w, h = 30, im0.shape[1], im0.shape[0]\n save_path = str(Path(save_path).with_suffix(\".mp4\")) # force *.mp4 suffix on results videos\n vid_writer[i] = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*\"mp4v\"), fps, (w, h))\n vid_writer[i].write(im0)\n\n # Print time (inference-only)\n LOGGER.info(f\"{s}{dt[1].dt * 1e3:.1f}ms\")\n\n # Print results\n t = tuple(x.t / seen * 1e3 for x in dt) # speeds per image\n LOGGER.info(f\"Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {(1, 3, *imgsz)}\" % t)\n if save_txt or save_img:\n s = f\"\\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}\" if save_txt else \"\"\n LOGGER.info(f\"Results saved to {colorstr('bold', save_dir)}{s}\")\n if update:\n strip_optimizer(weights[0]) # update model (to fix SourceChangeWarning)\n\n\ndef parse_opt():\n \"\"\"Parses command line arguments for model inference settings, returns a Namespace of options.\"\"\"\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--weights\", nargs=\"+\", type=str, default=ROOT / \"yolov5s-cls.pt\", help=\"model path(s)\")\n parser.add_argument(\"--source\", type=str, default=ROOT / \"data/images\", help=\"file/dir/URL/glob/screen/0(webcam)\")\n parser.add_argument(\"--data\", type=str, default=ROOT / \"data/coco128.yaml\", help=\"(optional) dataset.yaml path\")\n parser.add_argument(\"--imgsz\", \"--img\", \"--img-size\", nargs=\"+\", type=int, default=[224], help=\"inference size h,w\")\n parser.add_argument(\"--device\", default=\"\", help=\"cuda device, i.e. 0 or 0,1,2,3 or cpu\")\n parser.add_argument(\"--view-img\", action=\"store_true\", help=\"show results\")\n parser.add_argument(\"--save-txt\", action=\"store_true\", help=\"save results to *.txt\")\n parser.add_argument(\"--nosave\", action=\"store_true\", help=\"do not save images/videos\")\n parser.add_argument(\"--augment\", action=\"store_true\", help=\"augmented inference\")\n parser.add_argument(\"--visualize\", action=\"store_true\", help=\"visualize features\")\n parser.add_argument(\"--update\", action=\"store_true\", help=\"update all models\")\n parser.add_argument(\"--project\", default=ROOT / \"runs/predict-cls\", help=\"save results to project/name\")\n parser.add_argument(\"--name\", default=\"exp\", help=\"save results to project/name\")\n parser.add_argument(\"--exist-ok\", action=\"store_true\", help=\"existing project/name ok, do not increment\")\n parser.add_argument(\"--half\", action=\"store_true\", help=\"use FP16 half-precision inference\")\n parser.add_argument(\"--dnn\", action=\"store_true\", help=\"use OpenCV DNN for ONNX inference\")\n parser.add_argument(\"--vid-stride\", type=int, default=1, help=\"video frame-rate stride\")\n opt = parser.parse_args()\n opt.imgsz *= 2 if len(opt.imgsz) == 1 else 1 # expand\n print_args(vars(opt))\n return opt\n\n\ndef main(opt):\n \"\"\"Entry point for running the model; checks requirements and calls `run` with options parsed from CLI.\"\"\"\n check_requirements(ROOT / \"requirements.txt\", exclude=(\"tensorboard\", \"thop\"))\n run(**vars(opt))\n\n\nif __name__ == \"__main__\":\n opt = parse_opt()\n main(opt)\n" }, { "path": "classify/train.py", "content": "# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license\n\"\"\"\nTrain a YOLOv3 classifier model on a classification dataset.\n\nUsage - Single-GPU training:\n $ python classify/train.py --model yolov5s-cls.pt --data imagenette160 --epochs 5 --img 224\n\nUsage - Multi-GPU DDP training:\n $ python -m torch.distributed.run --nproc_per_node 4 --master_port 2022 classify/train.py --model yolov5s-cls.pt --data imagenet --epochs 5 --img 224 --device 0,1,2,3\n\nDatasets: --data mnist, fashion-mnist, cifar10, cifar100, imagenette, imagewoof, imagenet, or 'path/to/data'\nYOLOv3-cls models: --model yolov5n-cls.pt, yolov5s-cls.pt, yolov5m-cls.pt, yolov5l-cls.pt, yolov5x-cls.pt\nTorchvision models: --model resnet50, efficientnet_b0, etc. See https://pytorch.org/vision/stable/models.html\n\"\"\"\n\nimport argparse\nimport os\nimport subprocess\nimport sys\nimport time\nfrom copy import deepcopy\nfrom datetime import datetime\nfrom pathlib import Path\n\nimport torch\nimport torch.distributed as dist\nimport torch.hub as hub\nimport torch.optim.lr_scheduler as lr_scheduler\nimport torchvision\nfrom torch.cuda import amp\nfrom tqdm import tqdm\n\nFILE = Path(__file__).resolve()\nROOT = FILE.parents[1] # YOLOv3 root directory\nif str(ROOT) not in sys.path:\n sys.path.append(str(ROOT)) # add ROOT to PATH\nROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative\n\nfrom classify import val as validate\nfrom models.experimental import attempt_load\nfrom models.yolo import ClassificationModel, DetectionModel\nfrom utils.dataloaders import create_classification_dataloader\nfrom utils.general import (\n DATASETS_DIR,\n LOGGER,\n TQDM_BAR_FORMAT,\n WorkingDirectory,\n check_git_info,\n check_git_status,\n check_requirements,\n colorstr,\n download,\n increment_path,\n init_seeds,\n print_args,\n yaml_save,\n)\nfrom utils.loggers import GenericLogger\nfrom utils.plots import imshow_cls\nfrom utils.torch_utils import (\n ModelEMA,\n de_parallel,\n model_info,\n reshape_classifier_output,\n select_device,\n smart_DDP,\n smart_optimizer,\n smartCrossEntropyLoss,\n torch_distributed_zero_first,\n)\n\nLOCAL_RANK = int(os.getenv(\"LOCAL_RANK\", -1)) # https://pytorch.org/docs/stable/elastic/run.html\nRANK = int(os.getenv(\"RANK\", -1))\nWORLD_SIZE = int(os.getenv(\"WORLD_SIZE\", 1))\nGIT_INFO = check_git_info()\n\n\ndef train(opt, device):\n \"\"\"Trains a model on a given dataset using specified options and device, handling data loading, model optimization,\n and logging.\n \"\"\"\n init_seeds(opt.seed + 1 + RANK, deterministic=True)\n save_dir, data, bs, epochs, nw, imgsz, pretrained = (\n opt.save_dir,\n Path(opt.data),\n opt.batch_size,\n opt.epochs,\n min(os.cpu_count() - 1, opt.workers),\n opt.imgsz,\n str(opt.pretrained).lower() == \"true\",\n )\n cuda = device.type != \"cpu\"\n\n # Directories\n wdir = save_dir / \"weights\"\n wdir.mkdir(parents=True, exist_ok=True) # make dir\n last, best = wdir / \"last.pt\", wdir / \"best.pt\"\n\n # Save run settings\n yaml_save(save_dir / \"opt.yaml\", vars(opt))\n\n # Logger\n logger = GenericLogger(opt=opt, console_logger=LOGGER) if RANK in {-1, 0} else None\n\n # Download Dataset\n with torch_distributed_zero_first(LOCAL_RANK), WorkingDirectory(ROOT):\n data_dir = data if data.is_dir() else (DATASETS_DIR / data)\n if not data_dir.is_dir():\n LOGGER.info(f\"\\nDataset not found ⚠️, missing path {data_dir}, attempting download...\")\n t = time.time()\n if str(data) == \"imagenet\":\n subprocess.run([\"bash\", str(ROOT / \"data/scripts/get_imagenet.sh\")], shell=True, check=True)\n else:\n url = f\"https://github.com/ultralytics/assets/releases/download/v0.0.0/{data}.zip\"\n download(url, dir=data_dir.parent)\n s = f\"Dataset download success ✅ ({time.time() - t:.1f}s), saved to {colorstr('bold', data_dir)}\\n\"\n LOGGER.info(s)\n\n # Dataloaders\n nc = len([x for x in (data_dir / \"train\").glob(\"*\") if x.is_dir()]) # number of classes\n trainloader = create_classification_dataloader(\n path=data_dir / \"train\",\n imgsz=imgsz,\n batch_size=bs // WORLD_SIZE,\n augment=True,\n cache=opt.cache,\n rank=LOCAL_RANK,\n workers=nw,\n )\n\n test_dir = data_dir / \"test\" if (data_dir / \"test\").exists() else data_dir / \"val\" # data/test or data/val\n if RANK in {-1, 0}:\n testloader = create_classification_dataloader(\n path=test_dir,\n imgsz=imgsz,\n batch_size=bs // WORLD_SIZE * 2,\n augment=False,\n cache=opt.cache,\n rank=-1,\n workers=nw,\n )\n\n # Model\n with torch_distributed_zero_first(LOCAL_RANK), WorkingDirectory(ROOT):\n if Path(opt.model).is_file() or opt.model.endswith(\".pt\"):\n model = attempt_load(opt.model, device=\"cpu\", fuse=False)\n elif opt.model in torchvision.models.__dict__: # TorchVision models i.e. resnet50, efficientnet_b0\n model = torchvision.models.__dict__[opt.model](weights=\"IMAGENET1K_V1\" if pretrained else None)\n else:\n m = hub.list(\"ultralytics/yolov5\") # + hub.list('pytorch/vision') # models\n raise ModuleNotFoundError(f\"--model {opt.model} not found. Available models are: \\n\" + \"\\n\".join(m))\n if isinstance(model, DetectionModel):\n LOGGER.warning(\"WARNING ⚠️ pass YOLOv3 classifier model with '-cls' suffix, i.e. '--model yolov5s-cls.pt'\")\n model = ClassificationModel(model=model, nc=nc, cutoff=opt.cutoff or 10) # convert to classification model\n reshape_classifier_output(model, nc) # update class count\n for m in model.modules():\n if not pretrained and hasattr(m, \"reset_parameters\"):\n m.reset_parameters()\n if isinstance(m, torch.nn.Dropout) and opt.dropout is not None:\n m.p = opt.dropout # set dropout\n for p in model.parameters():\n p.requires_grad = True # for training\n model = model.to(device)\n\n # Info\n if RANK in {-1, 0}:\n model.names = trainloader.dataset.classes # attach class names\n model.transforms = testloader.dataset.torch_transforms # attach inference transforms\n model_info(model)\n if opt.verbose:\n LOGGER.info(model)\n images, labels = next(iter(trainloader))\n file = imshow_cls(images[:25], labels[:25], names=model.names, f=save_dir / \"train_images.jpg\")\n logger.log_images(file, name=\"Train Examples\")\n logger.log_graph(model, imgsz) # log model\n\n # Optimizer\n optimizer = smart_optimizer(model, opt.optimizer, opt.lr0, momentum=0.9, decay=opt.decay)\n\n # Scheduler\n lrf = 0.01 # final lr (fraction of lr0)\n\n # lf = lambda x: ((1 + math.cos(x * math.pi / epochs)) / 2) * (1 - lrf) + lrf # cosine\n def lf(x):\n \"\"\"Linear learning rate scheduler function, scaling learning rate from initial value to `lrf` over `epochs`.\"\"\"\n return (1 - x / epochs) * (1 - lrf) + lrf # linear\n\n scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lf)\n # scheduler = lr_scheduler.OneCycleLR(optimizer, max_lr=lr0, total_steps=epochs, pct_start=0.1,\n # final_div_factor=1 / 25 / lrf)\n\n # EMA\n ema = ModelEMA(model) if RANK in {-1, 0} else None\n\n # DDP mode\n if cuda and RANK != -1:\n model = smart_DDP(model)\n\n # Train\n t0 = time.time()\n criterion = smartCrossEntropyLoss(label_smoothing=opt.label_smoothing) # loss function\n best_fitness = 0.0\n scaler = amp.GradScaler(enabled=cuda)\n val = test_dir.stem # 'val' or 'test'\n LOGGER.info(\n f\"Image sizes {imgsz} train, {imgsz} test\\n\"\n f\"Using {nw * WORLD_SIZE} dataloader workers\\n\"\n f\"Logging results to {colorstr('bold', save_dir)}\\n\"\n f\"Starting {opt.model} training on {data} dataset with {nc} classes for {epochs} epochs...\\n\\n\"\n f\"{'Epoch':>10}{'GPU_mem':>10}{'train_loss':>12}{f'{val}_loss':>12}{'top1_acc':>12}{'top5_acc':>12}\"\n )\n for epoch in range(epochs): # loop over the dataset multiple times\n tloss, vloss, fitness = 0.0, 0.0, 0.0 # train loss, val loss, fitness\n model.train()\n if RANK != -1:\n trainloader.sampler.set_epoch(epoch)\n pbar = enumerate(trainloader)\n if RANK in {-1, 0}:\n pbar = tqdm(enumerate(trainloader), total=len(trainloader), bar_format=TQDM_BAR_FORMAT)\n for i, (images, labels) in pbar: # progress bar\n images, labels = images.to(device, non_blocking=True), labels.to(device)\n\n # Forward\n with amp.autocast(enabled=cuda): # stability issues when enabled\n loss = criterion(model(images), labels)\n\n # Backward\n scaler.scale(loss).backward()\n\n # Optimize\n scaler.unscale_(optimizer) # unscale gradients\n torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=10.0) # clip gradients\n scaler.step(optimizer)\n scaler.update()\n optimizer.zero_grad()\n if ema:\n ema.update(model)\n\n if RANK in {-1, 0}:\n # Print\n tloss = (tloss * i + loss.item()) / (i + 1) # update mean losses\n mem = \"%.3gG\" % (torch.cuda.memory_reserved() / 1e9 if torch.cuda.is_available() else 0) # (GB)\n pbar.desc = f\"{f'{epoch + 1}/{epochs}':>10}{mem:>10}{tloss:>12.3g}\" + \" \" * 36\n\n # Test\n if i == len(pbar) - 1: # last batch\n top1, top5, vloss = validate.run(\n model=ema.ema, dataloader=testloader, criterion=criterion, pbar=pbar\n ) # test accuracy, loss\n fitness = top1 # define fitness as top1 accuracy\n\n # Scheduler\n scheduler.step()\n\n # Log metrics\n if RANK in {-1, 0}:\n # Best fitness\n if fitness > best_fitness:\n best_fitness = fitness\n\n # Log\n metrics = {\n \"train/loss\": tloss,\n f\"{val}/loss\": vloss,\n \"metrics/accuracy_top1\": top1,\n \"metrics/accuracy_top5\": top5,\n \"lr/0\": optimizer.param_groups[0][\"lr\"],\n } # learning rate\n logger.log_metrics(metrics, epoch)\n\n # Save model\n final_epoch = epoch + 1 == epochs\n if (not opt.nosave) or final_epoch:\n ckpt = {\n \"epoch\": epoch,\n \"best_fitness\": best_fitness,\n \"model\": deepcopy(ema.ema).half(), # deepcopy(de_parallel(model)).half(),\n \"ema\": None, # deepcopy(ema.ema).half(),\n \"updates\": ema.updates,\n \"optimizer\": None, # optimizer.state_dict(),\n \"opt\": vars(opt),\n \"git\": GIT_INFO, # {remote, branch, commit} if a git repo\n \"date\": datetime.now().isoformat(),\n }\n\n # Save last, best and delete\n torch.save(ckpt, last)\n if best_fitness == fitness:\n torch.save(ckpt, best)\n del ckpt\n\n # Train complete\n if RANK in {-1, 0} and final_epoch:\n LOGGER.info(\n f\"\\nTraining complete ({(time.time() - t0) / 3600:.3f} hours)\"\n f\"\\nResults saved to {colorstr('bold', save_dir)}\"\n f\"\\nPredict: python classify/predict.py --weights {best} --source im.jpg\"\n f\"\\nValidate: python classify/val.py --weights {best} --data {data_dir}\"\n f\"\\nExport: python export.py --weights {best} --include onnx\"\n f\"\\nPyTorch Hub: model = torch.hub.load('ultralytics/yolov5', 'custom', '{best}')\"\n f\"\\nVisualize: https://netron.app\\n\"\n )\n\n # Plot examples\n images, labels = (x[:25] for x in next(iter(testloader))) # first 25 images and labels\n pred = torch.max(ema.ema(images.to(device)), 1)[1]\n file = imshow_cls(images, labels, pred, de_parallel(model).names, verbose=False, f=save_dir / \"test_images.jpg\")\n\n # Log results\n meta = {\"epochs\": epochs, \"top1_acc\": best_fitness, \"date\": datetime.now().isoformat()}\n logger.log_images(file, name=\"Test Examples (true-predicted)\", epoch=epoch)\n logger.log_model(best, epochs, metadata=meta)\n\n\ndef parse_opt(known=False):\n \"\"\"Parses command line arguments for model configuration and training options.\"\"\"\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--model\", type=str, default=\"yolov5s-cls.pt\", help=\"initial weights path\")\n parser.add_argument(\"--data\", type=str, default=\"imagenette160\", help=\"cifar10, cifar100, mnist, imagenet, ...\")\n parser.add_argument(\"--epochs\", type=int, default=10, help=\"total training epochs\")\n parser.add_argument(\"--batch-size\", type=int, default=64, help=\"total batch size for all GPUs\")\n parser.add_argument(\"--imgsz\", \"--img\", \"--img-size\", type=int, default=224, help=\"train, val image size (pixels)\")\n parser.add_argument(\"--nosave\", action=\"store_true\", help=\"only save final checkpoint\")\n parser.add_argument(\"--cache\", type=str, nargs=\"?\", const=\"ram\", help='--cache images in \"ram\" (default) or \"disk\"')\n parser.add_argument(\"--device\", default=\"\", help=\"cuda device, i.e. 0 or 0,1,2,3 or cpu\")\n parser.add_argument(\"--workers\", type=int, default=8, help=\"max dataloader workers (per RANK in DDP mode)\")\n parser.add_argument(\"--project\", default=ROOT / \"runs/train-cls\", help=\"save to project/name\")\n parser.add_argument(\"--name\", default=\"exp\", help=\"save to project/name\")\n parser.add_argument(\"--exist-ok\", action=\"store_true\", help=\"existing project/name ok, do not increment\")\n parser.add_argument(\"--pretrained\", nargs=\"?\", const=True, default=True, help=\"start from i.e. --pretrained False\")\n parser.add_argument(\"--optimizer\", choices=[\"SGD\", \"Adam\", \"AdamW\", \"RMSProp\"], default=\"Adam\", help=\"optimizer\")\n parser.add_argument(\"--lr0\", type=float, default=0.001, help=\"initial learning rate\")\n parser.add_argument(\"--decay\", type=float, default=5e-5, help=\"weight decay\")\n parser.add_argument(\"--label-smoothing\", type=float, default=0.1, help=\"Label smoothing epsilon\")\n parser.add_argument(\"--cutoff\", type=int, default=None, help=\"Model layer cutoff index for Classify() head\")\n parser.add_argument(\"--dropout\", type=float, default=None, help=\"Dropout (fraction)\")\n parser.add_argument(\"--verbose\", action=\"store_true\", help=\"Verbose mode\")\n parser.add_argument(\"--seed\", type=int, default=0, help=\"Global training seed\")\n parser.add_argument(\"--local_rank\", type=int, default=-1, help=\"Automatic DDP Multi-GPU argument, do not modify\")\n return parser.parse_known_args()[0] if known else parser.parse_args()\n\n\ndef main(opt):\n \"\"\"Initializes training environment, checks, DDP mode setup, and starts training with given options.\"\"\"\n if RANK in {-1, 0}:\n print_args(vars(opt))\n check_git_status()\n check_requirements(ROOT / \"requirements.txt\")\n\n # DDP mode\n device = select_device(opt.device, batch_size=opt.batch_size)\n if LOCAL_RANK != -1:\n assert opt.batch_size != -1, \"AutoBatch is coming soon for classification, please pass a valid --batch-size\"\n assert opt.batch_size % WORLD_SIZE == 0, f\"--batch-size {opt.batch_size} must be multiple of WORLD_SIZE\"\n assert torch.cuda.device_count() > LOCAL_RANK, \"insufficient CUDA devices for DDP command\"\n torch.cuda.set_device(LOCAL_RANK)\n device = torch.device(\"cuda\", LOCAL_RANK)\n dist.init_process_group(backend=\"nccl\" if dist.is_nccl_available() else \"gloo\")\n\n # Parameters\n opt.save_dir = increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok) # increment run\n\n # Train\n train(opt, device)\n\n\ndef run(**kwargs):\n \"\"\"Executes YOLOv5 model training with dynamic options, e.g., `run(data='mnist', imgsz=320, model='yolov5m')`.\"\"\"\n opt = parse_opt(True)\n for k, v in kwargs.items():\n setattr(opt, k, v)\n main(opt)\n return opt\n\n\nif __name__ == \"__main__\":\n opt = parse_opt()\n main(opt)\n" }, { "path": "classify/tutorial.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"t6MPjfT5NrKQ\"\n },\n \"source\": [\n \"
\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"\\n\",\n \" [中文](https://docs.ultralytics.com/zh/) | [한국어](https://docs.ultralytics.com/ko/) | [日本語](https://docs.ultralytics.com/ja/) | [Русский](https://docs.ultralytics.com/ru/) | [Deutsch](https://docs.ultralytics.com/de/) | [Français](https://docs.ultralytics.com/fr/) | [Español](https://docs.ultralytics.com/es/) | [Português](https://docs.ultralytics.com/pt/) | [Türkçe](https://docs.ultralytics.com/tr/) | [Tiếng Việt](https://docs.ultralytics.com/vi/) | [العربية](https://docs.ultralytics.com/ar/)\\n\",\n \"\\n\",\n \" \\\"Ultralytics\\n\",\n \" \\\"Run\\n\",\n \" \\\"Open\\n\",\n \" \\\"Open\\n\",\n \"\\n\",\n \" \\\"Discord\\\"\\n\",\n \" \\\"Ultralytics\\n\",\n \" \\\"Ultralytics\\n\",\n \"
\\n\",\n \"\\n\",\n \"This **Ultralytics YOLOv5 Classification Notebook** is the easiest way to get started with [YOLO models](https://www.ultralytics.com/yolo)—no installation needed. Built by [Ultralytics](https://www.ultralytics.com/), the creators of YOLO, this notebook walks you through running **state-of-the-art** models directly in your browser.\\n\",\n \"\\n\",\n \"Ultralytics models are constantly updated for performance and flexibility. They're **fast**, **accurate**, and **easy to use**, and they excel at [object detection](https://docs.ultralytics.com/tasks/detect/), [tracking](https://docs.ultralytics.com/modes/track/), [instance segmentation](https://docs.ultralytics.com/tasks/segment/), [image classification](https://docs.ultralytics.com/tasks/classify/), and [pose estimation](https://docs.ultralytics.com/tasks/pose/).\\n\",\n \"\\n\",\n \"Find detailed documentation in the [Ultralytics Docs](https://docs.ultralytics.com/). Get support via [GitHub Issues](https://github.com/ultralytics/ultralytics/issues/new/choose). Join discussions on [Discord](https://discord.com/invite/ultralytics), [Reddit](https://www.reddit.com/r/ultralytics/), and the [Ultralytics Community Forums](https://community.ultralytics.com/)!\\n\",\n \"\\n\",\n \"Request an Enterprise License for commercial use at [Ultralytics Licensing](https://www.ultralytics.com/license).\\n\",\n \"\\n\",\n \"
\\n\",\n \"
\\n\",\n \" \\n\",\n \" \\\"Ultralytics\\n\",\n \" \\n\",\n \"\\n\",\n \"

\\n\",\n \" Watch: How to Train\\n\",\n \" Ultralytics\\n\",\n \" YOLO11 Model on Custom Dataset using Google Colab Notebook 🚀\\n\",\n \"

\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"7mGmQbAO5pQb\"\n },\n \"source\": [\n \"# Setup\\n\",\n \"\\n\",\n \"Clone GitHub [repository](https://github.com/ultralytics/yolov5), install [dependencies](https://github.com/ultralytics/yolov5/blob/master/requirements.txt) and check PyTorch and GPU.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\"\n },\n \"id\": \"wbvMlHd_QwMG\",\n \"outputId\": \"0806e375-610d-4ec0-c867-763dbb518279\"\n },\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"YOLOv5 🚀 v7.0-3-g61ebf5e Python-3.7.15 torch-1.12.1+cu113 CUDA:0 (Tesla T4, 15110MiB)\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Setup complete ✅ (2 CPUs, 12.7 GB RAM, 22.6/78.2 GB disk)\\n\"\n ]\n }\n ],\n \"source\": [\n \"!git clone https://github.com/ultralytics/yolov5 # clone\\n\",\n \"%cd yolov5\\n\",\n \"%pip install -qr requirements.txt # install\\n\",\n \"\\n\",\n \"import torch\\n\",\n \"\\n\",\n \"import utils\\n\",\n \"\\n\",\n \"display = utils.notebook_init() # checks\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"4JnkELT0cIJg\"\n },\n \"source\": [\n \"# 1. Predict\\n\",\n \"\\n\",\n \"`classify/predict.py` runs YOLOv5 Classification inference on a variety of sources, downloading models automatically from the [latest YOLOv5 release](https://github.com/ultralytics/yolov5/releases), and saving results to `runs/predict-cls`. Example inference sources are:\\n\",\n \"\\n\",\n \"```shell\\n\",\n \"python classify/predict.py --source 0 # webcam\\n\",\n \" img.jpg # image \\n\",\n \" vid.mp4 # video\\n\",\n \" screen # screenshot\\n\",\n \" path/ # directory\\n\",\n \" 'path/*.jpg' # glob\\n\",\n \" 'https://youtu.be/LNwODJXcvt4' # YouTube\\n\",\n \" 'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream\\n\",\n \"```\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\"\n },\n \"id\": \"zR9ZbuQCH7FX\",\n \"outputId\": \"50504ef7-aa3e-4281-a4e3-d0c7df3c0ffe\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\u001B[34m\\u001B[1mclassify/predict: \\u001B[0mweights=['yolov5s-cls.pt'], source=data/images, data=data/coco128.yaml, imgsz=[224, 224], device=, view_img=False, save_txt=False, nosave=False, augment=False, visualize=False, update=False, project=runs/predict-cls, name=exp, exist_ok=False, half=False, dnn=False, vid_stride=1\\n\",\n \"YOLOv5 🚀 v7.0-3-g61ebf5e Python-3.7.15 torch-1.12.1+cu113 CUDA:0 (Tesla T4, 15110MiB)\\n\",\n \"\\n\",\n \"Downloading https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s-cls.pt to yolov5s-cls.pt...\\n\",\n \"100% 10.5M/10.5M [00:00<00:00, 12.3MB/s]\\n\",\n \"\\n\",\n \"Fusing layers... \\n\",\n \"Model summary: 117 layers, 5447688 parameters, 0 gradients, 11.4 GFLOPs\\n\",\n \"image 1/2 /content/yolov5/data/images/bus.jpg: 224x224 minibus 0.39, police van 0.24, amphibious vehicle 0.05, recreational vehicle 0.04, trolleybus 0.03, 3.9ms\\n\",\n \"image 2/2 /content/yolov5/data/images/zidane.jpg: 224x224 suit 0.38, bow tie 0.19, bridegroom 0.18, rugby ball 0.04, stage 0.02, 4.6ms\\n\",\n \"Speed: 0.3ms pre-process, 4.3ms inference, 1.5ms NMS per image at shape (1, 3, 224, 224)\\n\",\n \"Results saved to \\u001B[1mruns/predict-cls/exp\\u001B[0m\\n\"\n ]\n }\n ],\n \"source\": [\n \"!python classify/predict.py --weights yolov5s-cls.pt --img 224 --source data/images\\n\",\n \"# display.Image(filename='runs/predict-cls/exp/zidane.jpg', width=600)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"hkAzDWJ7cWTr\"\n },\n \"source\": [\n \"        \\n\",\n \"\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"0eq1SMWl6Sfn\"\n },\n \"source\": [\n \"# 2. Validate\\n\",\n \"Validate a model's accuracy on the [Imagenet](https://image-net.org/) dataset's `val` or `test` splits. Models are downloaded automatically from the [latest YOLOv5 release](https://github.com/ultralytics/yolov5/releases). To show results by class use the `--verbose` flag.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\"\n },\n \"id\": \"WQPtK1QYVaD_\",\n \"outputId\": \"20fc0630-141e-4a90-ea06-342cbd7ce496\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"--2022-11-22 19:53:40-- https://image-net.org/data/ILSVRC/2012/ILSVRC2012_img_val.tar\\n\",\n \"Resolving image-net.org (image-net.org)... 171.64.68.16\\n\",\n \"Connecting to image-net.org (image-net.org)|171.64.68.16|:443... connected.\\n\",\n \"HTTP request sent, awaiting response... 200 OK\\n\",\n \"Length: 6744924160 (6.3G) [application/x-tar]\\n\",\n \"Saving to: ‘ILSVRC2012_img_val.tar’\\n\",\n \"\\n\",\n \"ILSVRC2012_img_val. 100%[===================>] 6.28G 16.1MB/s in 10m 52s \\n\",\n \"\\n\",\n \"2022-11-22 20:04:32 (9.87 MB/s) - ‘ILSVRC2012_img_val.tar’ saved [6744924160/6744924160]\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"# Download Imagenet val (6.3G, 50000 images)\\n\",\n \"!bash data/scripts/get_imagenet.sh --val\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\"\n },\n \"id\": \"X58w8JLpMnjH\",\n \"outputId\": \"41843132-98e2-4c25-d474-4cd7b246fb8e\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\u001B[34m\\u001B[1mclassify/val: \\u001B[0mdata=../datasets/imagenet, weights=['yolov5s-cls.pt'], batch_size=128, imgsz=224, device=, workers=8, verbose=True, project=runs/val-cls, name=exp, exist_ok=False, half=True, dnn=False\\n\",\n \"YOLOv5 🚀 v7.0-3-g61ebf5e Python-3.7.15 torch-1.12.1+cu113 CUDA:0 (Tesla T4, 15110MiB)\\n\",\n \"\\n\",\n \"Fusing layers... \\n\",\n \"Model summary: 117 layers, 5447688 parameters, 0 gradients, 11.4 GFLOPs\\n\",\n \"validating: 100% 391/391 [04:57<00:00, 1.31it/s]\\n\",\n \" Class Images top1_acc top5_acc\\n\",\n \" all 50000 0.715 0.902\\n\",\n \" tench 50 0.94 0.98\\n\",\n \" goldfish 50 0.88 0.92\\n\",\n \" great white shark 50 0.78 0.96\\n\",\n \" tiger shark 50 0.68 0.96\\n\",\n \" hammerhead shark 50 0.82 0.92\\n\",\n \" electric ray 50 0.76 0.9\\n\",\n \" stingray 50 0.7 0.9\\n\",\n \" cock 50 0.78 0.92\\n\",\n \" hen 50 0.84 0.96\\n\",\n \" ostrich 50 0.98 1\\n\",\n \" brambling 50 0.9 0.96\\n\",\n \" goldfinch 50 0.92 0.98\\n\",\n \" house finch 50 0.88 0.96\\n\",\n \" junco 50 0.94 0.98\\n\",\n \" indigo bunting 50 0.86 0.88\\n\",\n \" American robin 50 0.9 0.96\\n\",\n \" bulbul 50 0.84 0.96\\n\",\n \" jay 50 0.9 0.96\\n\",\n \" magpie 50 0.84 0.96\\n\",\n \" chickadee 50 0.9 1\\n\",\n \" American dipper 50 0.82 0.92\\n\",\n \" kite 50 0.76 0.94\\n\",\n \" bald eagle 50 0.92 1\\n\",\n \" vulture 50 0.96 1\\n\",\n \" great grey owl 50 0.94 0.98\\n\",\n \" fire salamander 50 0.96 0.98\\n\",\n \" smooth newt 50 0.58 0.94\\n\",\n \" newt 50 0.74 0.9\\n\",\n \" spotted salamander 50 0.86 0.94\\n\",\n \" axolotl 50 0.86 0.96\\n\",\n \" American bullfrog 50 0.78 0.92\\n\",\n \" tree frog 50 0.84 0.96\\n\",\n \" tailed frog 50 0.48 0.8\\n\",\n \" loggerhead sea turtle 50 0.68 0.94\\n\",\n \" leatherback sea turtle 50 0.5 0.8\\n\",\n \" mud turtle 50 0.64 0.84\\n\",\n \" terrapin 50 0.52 0.98\\n\",\n \" box turtle 50 0.84 0.98\\n\",\n \" banded gecko 50 0.7 0.88\\n\",\n \" green iguana 50 0.76 0.94\\n\",\n \" Carolina anole 50 0.58 0.96\\n\",\n \"desert grassland whiptail lizard 50 0.82 0.94\\n\",\n \" agama 50 0.74 0.92\\n\",\n \" frilled-necked lizard 50 0.84 0.86\\n\",\n \" alligator lizard 50 0.58 0.78\\n\",\n \" Gila monster 50 0.72 0.8\\n\",\n \" European green lizard 50 0.42 0.9\\n\",\n \" chameleon 50 0.76 0.84\\n\",\n \" Komodo dragon 50 0.86 0.96\\n\",\n \" Nile crocodile 50 0.7 0.84\\n\",\n \" American alligator 50 0.76 0.96\\n\",\n \" triceratops 50 0.9 0.94\\n\",\n \" worm snake 50 0.76 0.88\\n\",\n \" ring-necked snake 50 0.8 0.92\\n\",\n \" eastern hog-nosed snake 50 0.58 0.88\\n\",\n \" smooth green snake 50 0.6 0.94\\n\",\n \" kingsnake 50 0.82 0.9\\n\",\n \" garter snake 50 0.88 0.94\\n\",\n \" water snake 50 0.7 0.94\\n\",\n \" vine snake 50 0.66 0.76\\n\",\n \" night snake 50 0.34 0.82\\n\",\n \" boa constrictor 50 0.8 0.96\\n\",\n \" African rock python 50 0.48 0.76\\n\",\n \" Indian cobra 50 0.82 0.94\\n\",\n \" green mamba 50 0.54 0.86\\n\",\n \" sea snake 50 0.62 0.9\\n\",\n \" Saharan horned viper 50 0.56 0.86\\n\",\n \"eastern diamondback rattlesnake 50 0.6 0.86\\n\",\n \" sidewinder 50 0.28 0.86\\n\",\n \" trilobite 50 0.98 0.98\\n\",\n \" harvestman 50 0.86 0.94\\n\",\n \" scorpion 50 0.86 0.94\\n\",\n \" yellow garden spider 50 0.92 0.96\\n\",\n \" barn spider 50 0.38 0.98\\n\",\n \" European garden spider 50 0.62 0.98\\n\",\n \" southern black widow 50 0.88 0.94\\n\",\n \" tarantula 50 0.94 1\\n\",\n \" wolf spider 50 0.82 0.92\\n\",\n \" tick 50 0.74 0.84\\n\",\n \" centipede 50 0.68 0.82\\n\",\n \" black grouse 50 0.88 0.98\\n\",\n \" ptarmigan 50 0.78 0.94\\n\",\n \" ruffed grouse 50 0.88 1\\n\",\n \" prairie grouse 50 0.92 1\\n\",\n \" peacock 50 0.88 0.9\\n\",\n \" quail 50 0.9 0.94\\n\",\n \" partridge 50 0.74 0.96\\n\",\n \" grey parrot 50 0.9 0.96\\n\",\n \" macaw 50 0.88 0.98\\n\",\n \"sulphur-crested cockatoo 50 0.86 0.92\\n\",\n \" lorikeet 50 0.96 1\\n\",\n \" coucal 50 0.82 0.88\\n\",\n \" bee eater 50 0.96 0.98\\n\",\n \" hornbill 50 0.9 0.96\\n\",\n \" hummingbird 50 0.88 0.96\\n\",\n \" jacamar 50 0.92 0.94\\n\",\n \" toucan 50 0.84 0.94\\n\",\n \" duck 50 0.76 0.94\\n\",\n \" red-breasted merganser 50 0.86 0.96\\n\",\n \" goose 50 0.74 0.96\\n\",\n \" black swan 50 0.94 0.98\\n\",\n \" tusker 50 0.54 0.92\\n\",\n \" echidna 50 0.98 1\\n\",\n \" platypus 50 0.72 0.84\\n\",\n \" wallaby 50 0.78 0.88\\n\",\n \" koala 50 0.84 0.92\\n\",\n \" wombat 50 0.78 0.84\\n\",\n \" jellyfish 50 0.88 0.96\\n\",\n \" sea anemone 50 0.72 0.9\\n\",\n \" brain coral 50 0.88 0.96\\n\",\n \" flatworm 50 0.8 0.98\\n\",\n \" nematode 50 0.86 0.9\\n\",\n \" conch 50 0.74 0.88\\n\",\n \" snail 50 0.78 0.88\\n\",\n \" slug 50 0.74 0.82\\n\",\n \" sea slug 50 0.88 0.98\\n\",\n \" chiton 50 0.88 0.98\\n\",\n \" chambered nautilus 50 0.88 0.92\\n\",\n \" Dungeness crab 50 0.78 0.94\\n\",\n \" rock crab 50 0.68 0.86\\n\",\n \" fiddler crab 50 0.64 0.86\\n\",\n \" red king crab 50 0.76 0.96\\n\",\n \" American lobster 50 0.78 0.96\\n\",\n \" spiny lobster 50 0.74 0.88\\n\",\n \" crayfish 50 0.56 0.86\\n\",\n \" hermit crab 50 0.78 0.96\\n\",\n \" isopod 50 0.66 0.78\\n\",\n \" white stork 50 0.88 0.96\\n\",\n \" black stork 50 0.84 0.98\\n\",\n \" spoonbill 50 0.96 1\\n\",\n \" flamingo 50 0.94 1\\n\",\n \" little blue heron 50 0.92 0.98\\n\",\n \" great egret 50 0.9 0.96\\n\",\n \" bittern 50 0.86 0.94\\n\",\n \" crane (bird) 50 0.62 0.9\\n\",\n \" limpkin 50 0.98 1\\n\",\n \" common gallinule 50 0.92 0.96\\n\",\n \" American coot 50 0.9 0.98\\n\",\n \" bustard 50 0.92 0.96\\n\",\n \" ruddy turnstone 50 0.94 1\\n\",\n \" dunlin 50 0.86 0.94\\n\",\n \" common redshank 50 0.9 0.96\\n\",\n \" dowitcher 50 0.84 0.96\\n\",\n \" oystercatcher 50 0.86 0.94\\n\",\n \" pelican 50 0.92 0.96\\n\",\n \" king penguin 50 0.88 0.96\\n\",\n \" albatross 50 0.9 1\\n\",\n \" grey whale 50 0.84 0.92\\n\",\n \" killer whale 50 0.92 1\\n\",\n \" dugong 50 0.84 0.96\\n\",\n \" sea lion 50 0.82 0.92\\n\",\n \" Chihuahua 50 0.66 0.84\\n\",\n \" Japanese Chin 50 0.72 0.98\\n\",\n \" Maltese 50 0.76 0.94\\n\",\n \" Pekingese 50 0.84 0.94\\n\",\n \" Shih Tzu 50 0.74 0.96\\n\",\n \" King Charles Spaniel 50 0.88 0.98\\n\",\n \" Papillon 50 0.86 0.94\\n\",\n \" toy terrier 50 0.48 0.94\\n\",\n \" Rhodesian Ridgeback 50 0.76 0.98\\n\",\n \" Afghan Hound 50 0.84 1\\n\",\n \" Basset Hound 50 0.8 0.92\\n\",\n \" Beagle 50 0.82 0.96\\n\",\n \" Bloodhound 50 0.48 0.72\\n\",\n \" Bluetick Coonhound 50 0.86 0.94\\n\",\n \" Black and Tan Coonhound 50 0.54 0.8\\n\",\n \"Treeing Walker Coonhound 50 0.66 0.98\\n\",\n \" English foxhound 50 0.32 0.84\\n\",\n \" Redbone Coonhound 50 0.62 0.94\\n\",\n \" borzoi 50 0.92 1\\n\",\n \" Irish Wolfhound 50 0.48 0.88\\n\",\n \" Italian Greyhound 50 0.76 0.98\\n\",\n \" Whippet 50 0.74 0.92\\n\",\n \" Ibizan Hound 50 0.6 0.86\\n\",\n \" Norwegian Elkhound 50 0.88 0.98\\n\",\n \" Otterhound 50 0.62 0.9\\n\",\n \" Saluki 50 0.72 0.92\\n\",\n \" Scottish Deerhound 50 0.86 0.98\\n\",\n \" Weimaraner 50 0.88 0.94\\n\",\n \"Staffordshire Bull Terrier 50 0.66 0.98\\n\",\n \"American Staffordshire Terrier 50 0.64 0.92\\n\",\n \" Bedlington Terrier 50 0.9 0.92\\n\",\n \" Border Terrier 50 0.86 0.92\\n\",\n \" Kerry Blue Terrier 50 0.78 0.98\\n\",\n \" Irish Terrier 50 0.7 0.96\\n\",\n \" Norfolk Terrier 50 0.68 0.9\\n\",\n \" Norwich Terrier 50 0.72 1\\n\",\n \" Yorkshire Terrier 50 0.66 0.9\\n\",\n \" Wire Fox Terrier 50 0.64 0.98\\n\",\n \" Lakeland Terrier 50 0.74 0.92\\n\",\n \" Sealyham Terrier 50 0.76 0.9\\n\",\n \" Airedale Terrier 50 0.82 0.92\\n\",\n \" Cairn Terrier 50 0.76 0.9\\n\",\n \" Australian Terrier 50 0.48 0.84\\n\",\n \" Dandie Dinmont Terrier 50 0.82 0.92\\n\",\n \" Boston Terrier 50 0.92 1\\n\",\n \" Miniature Schnauzer 50 0.68 0.9\\n\",\n \" Giant Schnauzer 50 0.72 0.98\\n\",\n \" Standard Schnauzer 50 0.74 1\\n\",\n \" Scottish Terrier 50 0.76 0.96\\n\",\n \" Tibetan Terrier 50 0.48 1\\n\",\n \"Australian Silky Terrier 50 0.66 0.96\\n\",\n \"Soft-coated Wheaten Terrier 50 0.74 0.96\\n\",\n \"West Highland White Terrier 50 0.88 0.96\\n\",\n \" Lhasa Apso 50 0.68 0.96\\n\",\n \" Flat-Coated Retriever 50 0.72 0.94\\n\",\n \" Curly-coated Retriever 50 0.82 0.94\\n\",\n \" Golden Retriever 50 0.86 0.94\\n\",\n \" Labrador Retriever 50 0.82 0.94\\n\",\n \"Chesapeake Bay Retriever 50 0.76 0.96\\n\",\n \"German Shorthaired Pointer 50 0.8 0.96\\n\",\n \" Vizsla 50 0.68 0.96\\n\",\n \" English Setter 50 0.7 1\\n\",\n \" Irish Setter 50 0.8 0.9\\n\",\n \" Gordon Setter 50 0.84 0.92\\n\",\n \" Brittany 50 0.84 0.96\\n\",\n \" Clumber Spaniel 50 0.92 0.96\\n\",\n \"English Springer Spaniel 50 0.88 1\\n\",\n \" Welsh Springer Spaniel 50 0.92 1\\n\",\n \" Cocker Spaniels 50 0.7 0.94\\n\",\n \" Sussex Spaniel 50 0.72 0.92\\n\",\n \" Irish Water Spaniel 50 0.88 0.98\\n\",\n \" Kuvasz 50 0.66 0.9\\n\",\n \" Schipperke 50 0.9 0.98\\n\",\n \" Groenendael 50 0.8 0.94\\n\",\n \" Malinois 50 0.86 0.98\\n\",\n \" Briard 50 0.52 0.8\\n\",\n \" Australian Kelpie 50 0.6 0.88\\n\",\n \" Komondor 50 0.88 0.94\\n\",\n \" Old English Sheepdog 50 0.94 0.98\\n\",\n \" Shetland Sheepdog 50 0.74 0.9\\n\",\n \" collie 50 0.6 0.96\\n\",\n \" Border Collie 50 0.74 0.96\\n\",\n \" Bouvier des Flandres 50 0.78 0.94\\n\",\n \" Rottweiler 50 0.88 0.96\\n\",\n \" German Shepherd Dog 50 0.8 0.98\\n\",\n \" Dobermann 50 0.68 0.96\\n\",\n \" Miniature Pinscher 50 0.76 0.88\\n\",\n \"Greater Swiss Mountain Dog 50 0.68 0.94\\n\",\n \" Bernese Mountain Dog 50 0.96 1\\n\",\n \" Appenzeller Sennenhund 50 0.22 1\\n\",\n \" Entlebucher Sennenhund 50 0.64 0.98\\n\",\n \" Boxer 50 0.7 0.92\\n\",\n \" Bullmastiff 50 0.78 0.98\\n\",\n \" Tibetan Mastiff 50 0.88 0.96\\n\",\n \" French Bulldog 50 0.84 0.94\\n\",\n \" Great Dane 50 0.54 0.9\\n\",\n \" St. Bernard 50 0.92 1\\n\",\n \" husky 50 0.46 0.98\\n\",\n \" Alaskan Malamute 50 0.76 0.96\\n\",\n \" Siberian Husky 50 0.46 0.98\\n\",\n \" Dalmatian 50 0.94 0.98\\n\",\n \" Affenpinscher 50 0.78 0.9\\n\",\n \" Basenji 50 0.92 0.94\\n\",\n \" pug 50 0.94 0.98\\n\",\n \" Leonberger 50 1 1\\n\",\n \" Newfoundland 50 0.78 0.96\\n\",\n \" Pyrenean Mountain Dog 50 0.78 0.96\\n\",\n \" Samoyed 50 0.96 1\\n\",\n \" Pomeranian 50 0.98 1\\n\",\n \" Chow Chow 50 0.9 0.96\\n\",\n \" Keeshond 50 0.88 0.94\\n\",\n \" Griffon Bruxellois 50 0.84 0.98\\n\",\n \" Pembroke Welsh Corgi 50 0.82 0.94\\n\",\n \" Cardigan Welsh Corgi 50 0.66 0.98\\n\",\n \" Toy Poodle 50 0.52 0.88\\n\",\n \" Miniature Poodle 50 0.52 0.92\\n\",\n \" Standard Poodle 50 0.8 1\\n\",\n \" Mexican hairless dog 50 0.88 0.98\\n\",\n \" grey wolf 50 0.82 0.92\\n\",\n \" Alaskan tundra wolf 50 0.78 0.98\\n\",\n \" red wolf 50 0.48 0.9\\n\",\n \" coyote 50 0.64 0.86\\n\",\n \" dingo 50 0.76 0.88\\n\",\n \" dhole 50 0.9 0.98\\n\",\n \" African wild dog 50 0.98 1\\n\",\n \" hyena 50 0.88 0.96\\n\",\n \" red fox 50 0.54 0.92\\n\",\n \" kit fox 50 0.72 0.98\\n\",\n \" Arctic fox 50 0.94 1\\n\",\n \" grey fox 50 0.7 0.94\\n\",\n \" tabby cat 50 0.54 0.92\\n\",\n \" tiger cat 50 0.22 0.94\\n\",\n \" Persian cat 50 0.9 0.98\\n\",\n \" Siamese cat 50 0.96 1\\n\",\n \" Egyptian Mau 50 0.54 0.8\\n\",\n \" cougar 50 0.9 1\\n\",\n \" lynx 50 0.72 0.88\\n\",\n \" leopard 50 0.78 0.98\\n\",\n \" snow leopard 50 0.9 0.98\\n\",\n \" jaguar 50 0.7 0.94\\n\",\n \" lion 50 0.9 0.98\\n\",\n \" tiger 50 0.92 0.98\\n\",\n \" cheetah 50 0.94 0.98\\n\",\n \" brown bear 50 0.94 0.98\\n\",\n \" American black bear 50 0.8 1\\n\",\n \" polar bear 50 0.84 0.96\\n\",\n \" sloth bear 50 0.72 0.92\\n\",\n \" mongoose 50 0.7 0.92\\n\",\n \" meerkat 50 0.82 0.92\\n\",\n \" tiger beetle 50 0.92 0.94\\n\",\n \" ladybug 50 0.86 0.94\\n\",\n \" ground beetle 50 0.64 0.94\\n\",\n \" longhorn beetle 50 0.62 0.88\\n\",\n \" leaf beetle 50 0.64 0.98\\n\",\n \" dung beetle 50 0.86 0.98\\n\",\n \" rhinoceros beetle 50 0.86 0.94\\n\",\n \" weevil 50 0.9 1\\n\",\n \" fly 50 0.78 0.94\\n\",\n \" bee 50 0.68 0.94\\n\",\n \" ant 50 0.68 0.78\\n\",\n \" grasshopper 50 0.5 0.92\\n\",\n \" cricket 50 0.64 0.92\\n\",\n \" stick insect 50 0.64 0.92\\n\",\n \" cockroach 50 0.72 0.8\\n\",\n \" mantis 50 0.64 0.86\\n\",\n \" cicada 50 0.9 0.96\\n\",\n \" leafhopper 50 0.88 0.94\\n\",\n \" lacewing 50 0.78 0.92\\n\",\n \" dragonfly 50 0.82 0.98\\n\",\n \" damselfly 50 0.82 1\\n\",\n \" red admiral 50 0.94 0.96\\n\",\n \" ringlet 50 0.86 0.98\\n\",\n \" monarch butterfly 50 0.9 0.92\\n\",\n \" small white 50 0.9 1\\n\",\n \" sulfur butterfly 50 0.92 1\\n\",\n \"gossamer-winged butterfly 50 0.88 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\" throne 50 0.72 0.82\\n\",\n \" tile roof 50 0.72 0.94\\n\",\n \" toaster 50 0.66 0.84\\n\",\n \" tobacco shop 50 0.42 0.7\\n\",\n \" toilet seat 50 0.62 0.88\\n\",\n \" torch 50 0.64 0.84\\n\",\n \" totem pole 50 0.92 0.98\\n\",\n \" tow truck 50 0.62 0.88\\n\",\n \" toy store 50 0.6 0.94\\n\",\n \" tractor 50 0.76 0.98\\n\",\n \" semi-trailer truck 50 0.78 0.92\\n\",\n \" tray 50 0.46 0.64\\n\",\n \" trench coat 50 0.54 0.72\\n\",\n \" tricycle 50 0.72 0.94\\n\",\n \" trimaran 50 0.7 0.98\\n\",\n \" tripod 50 0.58 0.86\\n\",\n \" triumphal arch 50 0.92 0.98\\n\",\n \" trolleybus 50 0.9 1\\n\",\n \" trombone 50 0.54 0.88\\n\",\n \" tub 50 0.24 0.82\\n\",\n \" turnstile 50 0.84 0.94\\n\",\n \" typewriter keyboard 50 0.68 0.98\\n\",\n \" umbrella 50 0.52 0.7\\n\",\n \" unicycle 50 0.74 0.96\\n\",\n \" upright piano 50 0.76 0.9\\n\",\n \" vacuum cleaner 50 0.62 0.9\\n\",\n \" vase 50 0.5 0.78\\n\",\n \" vault 50 0.76 0.92\\n\",\n \" velvet 50 0.2 0.42\\n\",\n \" vending machine 50 0.9 1\\n\",\n \" vestment 50 0.54 0.82\\n\",\n \" viaduct 50 0.78 0.86\\n\",\n \" violin 50 0.68 0.78\\n\",\n \" volleyball 50 0.86 1\\n\",\n \" waffle iron 50 0.72 0.88\\n\",\n \" wall clock 50 0.54 0.88\\n\",\n \" wallet 50 0.52 0.9\\n\",\n \" wardrobe 50 0.68 0.88\\n\",\n \" military aircraft 50 0.9 0.98\\n\",\n \" sink 50 0.72 0.96\\n\",\n \" washing machine 50 0.78 0.94\\n\",\n \" water bottle 50 0.54 0.74\\n\",\n \" water jug 50 0.22 0.74\\n\",\n \" water tower 50 0.9 0.96\\n\",\n \" whiskey jug 50 0.64 0.74\\n\",\n \" whistle 50 0.72 0.84\\n\",\n \" wig 50 0.84 0.9\\n\",\n \" window screen 50 0.68 0.8\\n\",\n \" window shade 50 0.52 0.76\\n\",\n \" Windsor tie 50 0.22 0.66\\n\",\n \" wine bottle 50 0.42 0.82\\n\",\n \" wing 50 0.54 0.96\\n\",\n \" wok 50 0.46 0.82\\n\",\n \" wooden spoon 50 0.58 0.8\\n\",\n \" wool 50 0.32 0.82\\n\",\n \" split-rail fence 50 0.74 0.9\\n\",\n \" shipwreck 50 0.84 0.96\\n\",\n \" yawl 50 0.78 0.96\\n\",\n \" yurt 50 0.84 1\\n\",\n \" website 50 0.98 1\\n\",\n \" comic book 50 0.62 0.9\\n\",\n \" crossword 50 0.84 0.88\\n\",\n \" traffic sign 50 0.78 0.9\\n\",\n \" traffic light 50 0.8 0.94\\n\",\n \" dust jacket 50 0.72 0.94\\n\",\n \" menu 50 0.82 0.96\\n\",\n \" plate 50 0.44 0.88\\n\",\n \" guacamole 50 0.8 0.92\\n\",\n \" consomme 50 0.54 0.88\\n\",\n \" hot pot 50 0.86 0.98\\n\",\n \" trifle 50 0.92 0.98\\n\",\n \" ice cream 50 0.68 0.94\\n\",\n \" ice pop 50 0.62 0.84\\n\",\n \" baguette 50 0.62 0.88\\n\",\n \" bagel 50 0.64 0.92\\n\",\n \" pretzel 50 0.72 0.88\\n\",\n \" cheeseburger 50 0.9 1\\n\",\n \" hot dog 50 0.74 0.94\\n\",\n \" mashed potato 50 0.74 0.9\\n\",\n \" cabbage 50 0.84 0.96\\n\",\n \" broccoli 50 0.9 0.96\\n\",\n \" cauliflower 50 0.82 1\\n\",\n \" zucchini 50 0.74 0.9\\n\",\n \" spaghetti squash 50 0.8 0.96\\n\",\n \" acorn squash 50 0.82 0.96\\n\",\n \" butternut squash 50 0.7 0.94\\n\",\n \" cucumber 50 0.6 0.96\\n\",\n \" artichoke 50 0.84 0.94\\n\",\n \" bell pepper 50 0.84 0.98\\n\",\n \" cardoon 50 0.88 0.94\\n\",\n \" mushroom 50 0.38 0.92\\n\",\n \" Granny Smith 50 0.9 0.96\\n\",\n \" strawberry 50 0.6 0.88\\n\",\n \" orange 50 0.7 0.92\\n\",\n \" lemon 50 0.78 0.98\\n\",\n \" fig 50 0.82 0.96\\n\",\n \" pineapple 50 0.86 0.96\\n\",\n \" banana 50 0.84 0.96\\n\",\n \" jackfruit 50 0.9 0.98\\n\",\n \" custard apple 50 0.86 0.96\\n\",\n \" pomegranate 50 0.82 0.98\\n\",\n \" hay 50 0.8 0.92\\n\",\n \" carbonara 50 0.88 0.94\\n\",\n \" chocolate syrup 50 0.46 0.84\\n\",\n \" dough 50 0.4 0.6\\n\",\n \" meatloaf 50 0.58 0.84\\n\",\n \" pizza 50 0.84 0.96\\n\",\n \" pot pie 50 0.68 0.9\\n\",\n \" burrito 50 0.8 0.98\\n\",\n \" red wine 50 0.54 0.82\\n\",\n \" espresso 50 0.64 0.88\\n\",\n \" cup 50 0.38 0.7\\n\",\n \" eggnog 50 0.38 0.7\\n\",\n \" alp 50 0.54 0.88\\n\",\n \" bubble 50 0.8 0.96\\n\",\n \" cliff 50 0.64 1\\n\",\n \" coral reef 50 0.72 0.96\\n\",\n \" geyser 50 0.94 1\\n\",\n \" lakeshore 50 0.54 0.88\\n\",\n \" promontory 50 0.58 0.94\\n\",\n \" shoal 50 0.6 0.96\\n\",\n \" seashore 50 0.44 0.78\\n\",\n \" valley 50 0.72 0.94\\n\",\n \" volcano 50 0.78 0.96\\n\",\n \" baseball player 50 0.72 0.94\\n\",\n \" bridegroom 50 0.72 0.88\\n\",\n \" scuba diver 50 0.8 1\\n\",\n \" rapeseed 50 0.94 0.98\\n\",\n \" daisy 50 0.96 0.98\\n\",\n \" yellow lady's slipper 50 1 1\\n\",\n \" corn 50 0.4 0.88\\n\",\n \" acorn 50 0.92 0.98\\n\",\n \" rose hip 50 0.92 0.98\\n\",\n \" horse chestnut seed 50 0.94 0.98\\n\",\n \" coral fungus 50 0.96 0.96\\n\",\n \" agaric 50 0.82 0.94\\n\",\n \" gyromitra 50 0.98 1\\n\",\n \" stinkhorn mushroom 50 0.8 0.94\\n\",\n \" earth star 50 0.98 1\\n\",\n \" hen-of-the-woods 50 0.8 0.96\\n\",\n \" bolete 50 0.74 0.94\\n\",\n \" ear 50 0.48 0.94\\n\",\n \" toilet paper 50 0.36 0.68\\n\",\n \"Speed: 0.1ms pre-process, 0.3ms inference, 0.0ms post-process per image at shape (1, 3, 224, 224)\\n\",\n \"Results saved to \\u001B[1mruns/val-cls/exp\\u001B[0m\\n\"\n ]\n }\n ],\n \"source\": [\n \"# Validate YOLOv5s on Imagenet val\\n\",\n \"!python classify/val.py --weights yolov5s-cls.pt --data ../datasets/imagenet --img 224 --half\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"ZY2VXXXu74w5\"\n },\n \"source\": [\n \"# 3. Train\\n\",\n \"\\n\",\n \"\\n\",\n \" \\\"Ultralytics\\n\",\n \"\\n\",\n \"

\\n\",\n \"\\n\",\n \"Train a YOLOv5s Classification model on the [Imagenette](https://image-net.org/) dataset with `--data imagenet`, starting from pretrained `--pretrained yolov5s-cls.pt`.\\n\",\n \"\\n\",\n \"- **Pretrained [Models](https://github.com/ultralytics/yolov5/tree/master/models)** are downloaded\\n\",\n \"automatically from the [latest YOLOv5 release](https://github.com/ultralytics/yolov5/releases)\\n\",\n \"- **Training Results** are saved to `runs/train-cls/` with incrementing run directories, i.e. `runs/train-cls/exp2`, `runs/train-cls/exp3` etc.\\n\",\n \"

\\n\",\n \"\\n\",\n \"A **Mosaic Dataloader** is used for training which combines 4 images into 1 mosaic.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"i3oKtE4g-aNn\"\n },\n \"outputs\": [],\n \"source\": [\n \"# @title Select YOLOv5 🚀 logger {run: 'auto'}\\n\",\n \"logger = \\\"Comet\\\" # @param ['Comet', 'ClearML', 'TensorBoard']\\n\",\n \"\\n\",\n \"if logger == \\\"Comet\\\":\\n\",\n \" %pip install -q comet_ml\\n\",\n \" import comet_ml\\n\",\n \"\\n\",\n \" comet_ml.init()\\n\",\n \"elif logger == \\\"ClearML\\\":\\n\",\n \" %pip install -q clearml\\n\",\n \" import clearml\\n\",\n \"\\n\",\n \" clearml.browser_login()\\n\",\n \"elif logger == \\\"TensorBoard\\\":\\n\",\n \" %load_ext tensorboard\\n\",\n \" %tensorboard --logdir runs/train\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\"\n },\n \"id\": \"1NcFxRcFdJ_O\",\n \"outputId\": \"77c8d487-16db-4073-b3ea-06cabf2e7766\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\u001B[34m\\u001B[1mclassify/train: \\u001B[0mmodel=yolov5s-cls.pt, data=imagenette160, epochs=5, batch_size=64, imgsz=224, nosave=False, cache=ram, device=, workers=8, project=runs/train-cls, name=exp, exist_ok=False, pretrained=True, optimizer=Adam, lr0=0.001, decay=5e-05, label_smoothing=0.1, cutoff=None, dropout=None, verbose=False, seed=0, local_rank=-1\\n\",\n \"\\u001B[34m\\u001B[1mgithub: \\u001B[0mup to date with https://github.com/ultralytics/yolov5 ✅\\n\",\n \"YOLOv5 🚀 v7.0-3-g61ebf5e Python-3.7.15 torch-1.12.1+cu113 CUDA:0 (Tesla T4, 15110MiB)\\n\",\n \"\\n\",\n \"\\u001B[34m\\u001B[1mTensorBoard: \\u001B[0mStart with 'tensorboard --logdir runs/train-cls', view at http://localhost:6006/\\n\",\n \"\\n\",\n \"Dataset not found ⚠️, missing path /content/datasets/imagenette160, attempting download...\\n\",\n \"Downloading https://github.com/ultralytics/assets/releases/download/v0.0.0/imagenette160.zip to /content/datasets/imagenette160.zip...\\n\",\n \"100% 103M/103M [00:00<00:00, 347MB/s] \\n\",\n \"Unzipping /content/datasets/imagenette160.zip...\\n\",\n \"Dataset download success ✅ (3.3s), saved to \\u001B[1m/content/datasets/imagenette160\\u001B[0m\\n\",\n \"\\n\",\n \"\\u001B[34m\\u001B[1malbumentations: \\u001B[0mRandomResizedCrop(p=1.0, height=224, width=224, scale=(0.08, 1.0), ratio=(0.75, 1.3333333333333333), interpolation=1), HorizontalFlip(p=0.5), ColorJitter(p=0.5, brightness=[0.6, 1.4], contrast=[0.6, 1.4], saturation=[0.6, 1.4], hue=[0, 0]), Normalize(p=1.0, mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225), max_pixel_value=255.0), ToTensorV2(always_apply=True, p=1.0, transpose_mask=False)\\n\",\n \"Model summary: 149 layers, 4185290 parameters, 4185290 gradients, 10.5 GFLOPs\\n\",\n \"\\u001B[34m\\u001B[1moptimizer:\\u001B[0m Adam(lr=0.001) with parameter groups 32 weight(decay=0.0), 33 weight(decay=5e-05), 33 bias\\n\",\n \"Image sizes 224 train, 224 test\\n\",\n \"Using 1 dataloader workers\\n\",\n \"Logging results to \\u001B[1mruns/train-cls/exp\\u001B[0m\\n\",\n \"Starting yolov5s-cls.pt training on imagenette160 dataset with 10 classes for 5 epochs...\\n\",\n \"\\n\",\n \" Epoch GPU_mem train_loss val_loss top1_acc top5_acc\\n\",\n \" 1/5 1.47G 1.05 0.974 0.828 0.975: 100% 148/148 [00:38<00:00, 3.82it/s]\\n\",\n \" 2/5 1.73G 0.895 0.766 0.911 0.994: 100% 148/148 [00:36<00:00, 4.03it/s]\\n\",\n \" 3/5 1.73G 0.82 0.704 0.934 0.996: 100% 148/148 [00:35<00:00, 4.20it/s]\\n\",\n \" 4/5 1.73G 0.766 0.664 0.951 0.998: 100% 148/148 [00:36<00:00, 4.05it/s]\\n\",\n \" 5/5 1.73G 0.724 0.634 0.959 0.997: 100% 148/148 [00:37<00:00, 3.94it/s]\\n\",\n \"\\n\",\n \"Training complete (0.052 hours)\\n\",\n \"Results saved to \\u001B[1mruns/train-cls/exp\\u001B[0m\\n\",\n \"Predict: python classify/predict.py --weights runs/train-cls/exp/weights/best.pt --source im.jpg\\n\",\n \"Validate: python classify/val.py --weights runs/train-cls/exp/weights/best.pt --data /content/datasets/imagenette160\\n\",\n \"Export: python export.py --weights runs/train-cls/exp/weights/best.pt --include onnx\\n\",\n \"PyTorch Hub: model = torch.hub.load('ultralytics/yolov5', 'custom', 'runs/train-cls/exp/weights/best.pt')\\n\",\n \"Visualize: https://netron.app\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"# Train YOLOv5s Classification on Imagenette160 for 3 epochs\\n\",\n \"!python classify/train.py --model yolov5s-cls.pt --data imagenette160 --epochs 5 --img 224 --cache\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"15glLzbQx5u0\"\n },\n \"source\": [\n \"# 4. Visualize\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"nWOsI5wJR1o3\"\n },\n \"source\": [\n \"## Comet Logging and Visualization 🌟 NEW\\n\",\n \"\\n\",\n \"[Comet](https://www.comet.com/site/lp/yolov5-with-comet/?utm_source=yolov5&utm_medium=partner&utm_campaign=partner_yolov5_2022&utm_content=yolov5_colab) is now fully integrated with YOLOv5. Track and visualize model metrics in real time, save your hyperparameters, datasets, and model checkpoints, and visualize your model predictions with [Comet Custom Panels](https://www.comet.com/docs/v2/guides/comet-dashboard/code-panels/about-panels/?utm_source=yolov5&utm_medium=partner&utm_campaign=partner_yolov5_2022&utm_content=yolov5_colab)! Comet makes sure you never lose track of your work and makes it easy to share results and collaborate across teams of all sizes!\\n\",\n \"\\n\",\n \"Getting started is easy:\\n\",\n \"```shell\\n\",\n \"pip install comet_ml # 1. install\\n\",\n \"export COMET_API_KEY= # 2. paste API key\\n\",\n \"python train.py --img 640 --epochs 3 --data coco128.yaml --weights yolov5s.pt # 3. train\\n\",\n \"```\\n\",\n \"To learn more about all of the supported Comet features for this integration, check out the [Comet Tutorial](https://docs.ultralytics.com/yolov5/tutorials/comet_logging_integration). If you'd like to learn more about Comet, head over to our [documentation](https://www.comet.com/docs/v2/?utm_source=yolov5&utm_medium=partner&utm_campaign=partner_yolov5_2022&utm_content=yolov5_colab). Get started by trying out the Comet Colab Notebook:\\n\",\n \"[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1RG0WOQyxlDlo5Km8GogJpIEJlg_5lyYO?usp=sharing)\\n\",\n \"\\n\",\n \"\\n\",\n \"\\\"Comet\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"Lay2WsTjNJzP\"\n },\n \"source\": [\n \"## ClearML Logging and Automation 🌟 NEW\\n\",\n \"\\n\",\n \"[ClearML](https://cutt.ly/yolov5-notebook-clearml) is completely integrated into YOLOv5 to track your experimentation, manage dataset versions and even remotely execute training runs. To enable ClearML (check cells above):\\n\",\n \"\\n\",\n \"- `pip install clearml`\\n\",\n \"- run `clearml-init` to connect to a ClearML server (**deploy your own [open-source server](https://github.com/allegroai/clearml-server)**, or use our [free hosted server](https://cutt.ly/yolov5-notebook-clearml))\\n\",\n \"\\n\",\n \"You'll get all the great expected features from an experiment manager: live updates, model upload, experiment comparison etc. but ClearML also tracks uncommitted changes and installed packages for example. Thanks to that ClearML Tasks (which is what we call experiments) are also reproducible on different machines! With only 1 extra line, we can schedule a YOLOv5 training task on a queue to be executed by any number of ClearML Agents (workers).\\n\",\n \"\\n\",\n \"You can use ClearML Data to version your dataset and then pass it to YOLOv5 simply using its unique ID. This will help you keep track of your data without adding extra hassle. Explore the [ClearML Tutorial](https://docs.ultralytics.com/yolov5/tutorials/clearml_logging_integration) for details!\\n\",\n \"\\n\",\n \"\\n\",\n \"\\\"ClearML\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"-WPvRbS5Swl6\"\n },\n \"source\": [\n \"## Local Logging\\n\",\n \"\\n\",\n \"Training results are automatically logged with [Tensorboard](https://www.tensorflow.org/tensorboard) and [CSV](https://github.com/ultralytics/yolov5/pull/4148) loggers to `runs/train`, with a new experiment directory created for each new training as `runs/train/exp2`, `runs/train/exp3`, etc.\\n\",\n \"\\n\",\n \"This directory contains train and val statistics, mosaics, labels, predictions and augmentated mosaics, as well as metrics and charts including precision-recall (PR) curves and confusion matrices. \\n\",\n \"\\n\",\n \"\\\"Local\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"Zelyeqbyt3GD\"\n },\n \"source\": [\n \"# Environments\\n\",\n \"\\n\",\n \"YOLOv5 may be run in any of the following up-to-date verified environments (with all dependencies including [CUDA](https://developer.nvidia.com/cuda)/[CUDNN](https://developer.nvidia.com/cudnn), [Python](https://www.python.org/) and [PyTorch](https://pytorch.org/) preinstalled):\\n\",\n \"\\n\",\n \"- **Notebooks** with free GPU: \\\"Run \\\"Open \\\"Open\\n\",\n \"- **Google Cloud** Deep Learning VM. See [GCP Quickstart Guide](https://docs.ultralytics.com/yolov5/environments/google_cloud_quickstart_tutorial/)\\n\",\n \"- **Amazon** Deep Learning AMI. See [AWS Quickstart Guide](https://docs.ultralytics.com/yolov5/environments/aws_quickstart_tutorial/)\\n\",\n \"- **Docker Image**. See [Docker Quickstart Guide](https://docs.ultralytics.com/yolov5/environments/docker_image_quickstart_tutorial/) \\\"Docker\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"6Qu7Iesl0p54\"\n },\n \"source\": [\n \"# Status\\n\",\n \"\\n\",\n \"![YOLOv5 CI](https://github.com/ultralytics/yolov3/actions/workflows/ci-testing.yml/badge.svg)\\n\",\n \"\\n\",\n \"If this badge is green, all [YOLOv3 GitHub Actions](https://github.com/ultralytics/yolov3/actions) Continuous Integration (CI) tests are currently passing. CI tests verify correct operation of YOLOv5 training ([train.py](https://github.com/ultralytics/yolov5/blob/master/train.py)), testing ([val.py](https://github.com/ultralytics/yolov5/blob/master/val.py)), inference ([detect.py](https://github.com/ultralytics/yolov5/blob/master/detect.py)) and export ([export.py](https://github.com/ultralytics/yolov5/blob/master/export.py)) on macOS, Windows, and Ubuntu every 24 hours and on every commit.\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"IEijrePND_2I\"\n },\n \"source\": [\n \"# Appendix\\n\",\n \"\\n\",\n \"Additional content below.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"GMusP4OAxFu6\"\n },\n \"outputs\": [],\n \"source\": [\n \"# YOLOv5 PyTorch HUB Inference (DetectionModels only)\\n\",\n \"\\n\",\n \"model = torch.hub.load(\\\"ultralytics/yolov5\\\", \\\"yolov5s\\\") # yolov5n - yolov5x6 or custom\\n\",\n \"im = \\\"https://ultralytics.com/images/zidane.jpg\\\" # file, Path, PIL.Image, OpenCV, nparray, list\\n\",\n \"results = model(im) # inference\\n\",\n \"results.print() # or .show(), .save(), .crop(), .pandas(), etc.\"\n ]\n }\n ],\n \"metadata\": {\n \"accelerator\": \"GPU\",\n \"colab\": {\n \"name\": \"YOLOv5 Classification Tutorial\",\n \"provenance\": []\n },\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.12\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n" }, { "path": "classify/val.py", "content": "# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license\n\"\"\"\nValidate a trained YOLOv3 classification model on a classification dataset.\n\nUsage:\n $ bash data/scripts/get_imagenet.sh --val # download ImageNet val split (6.3G, 50000 images)\n $ python classify/val.py --weights yolov5m-cls.pt --data ../datasets/imagenet --img 224 # validate ImageNet\n\nUsage - formats:\n $ python classify/val.py --weights yolov5s-cls.pt # PyTorch\n yolov5s-cls.torchscript # TorchScript\n yolov5s-cls.onnx # ONNX Runtime or OpenCV DNN with --dnn\n yolov5s-cls_openvino_model # OpenVINO\n yolov5s-cls.engine # TensorRT\n yolov5s-cls.mlmodel # CoreML (macOS-only)\n yolov5s-cls_saved_model # TensorFlow SavedModel\n yolov5s-cls.pb # TensorFlow GraphDef\n yolov5s-cls.tflite # TensorFlow Lite\n yolov5s-cls_edgetpu.tflite # TensorFlow Edge TPU\n yolov5s-cls_paddle_model # PaddlePaddle\n\"\"\"\n\nimport argparse\nimport os\nimport sys\nfrom pathlib import Path\n\nimport torch\nfrom tqdm import tqdm\n\nFILE = Path(__file__).resolve()\nROOT = FILE.parents[1] # YOLOv3 root directory\nif str(ROOT) not in sys.path:\n sys.path.append(str(ROOT)) # add ROOT to PATH\nROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative\n\nfrom models.common import DetectMultiBackend\nfrom utils.dataloaders import create_classification_dataloader\nfrom utils.general import (\n LOGGER,\n TQDM_BAR_FORMAT,\n Profile,\n check_img_size,\n check_requirements,\n colorstr,\n increment_path,\n print_args,\n)\nfrom utils.torch_utils import select_device, smart_inference_mode\n\n\n@smart_inference_mode()\ndef run(\n data=ROOT / \"../datasets/mnist\", # dataset dir\n weights=ROOT / \"yolov5s-cls.pt\", # model.pt path(s)\n batch_size=128, # batch size\n imgsz=224, # inference size (pixels)\n device=\"\", # cuda device, i.e. 0 or 0,1,2,3 or cpu\n workers=8, # max dataloader workers (per RANK in DDP mode)\n verbose=False, # verbose output\n project=ROOT / \"runs/val-cls\", # save to project/name\n name=\"exp\", # save to project/name\n exist_ok=False, # existing project/name ok, do not increment\n half=False, # use FP16 half-precision inference\n dnn=False, # use OpenCV DNN for ONNX inference\n model=None,\n dataloader=None,\n criterion=None,\n pbar=None,\n):\n \"\"\"Evaluate a YOLOv3 classification model on the specified dataset, providing accuracy metrics.\"\"\"\n # Initialize/load model and set device\n training = model is not None\n if training: # called by train.py\n device, pt, jit, engine = next(model.parameters()).device, True, False, False # get model device, PyTorch model\n half &= device.type != \"cpu\" # half precision only supported on CUDA\n model.half() if half else model.float()\n else: # called directly\n device = select_device(device, batch_size=batch_size)\n\n # Directories\n save_dir = increment_path(Path(project) / name, exist_ok=exist_ok) # increment run\n save_dir.mkdir(parents=True, exist_ok=True) # make dir\n\n # Load model\n model = DetectMultiBackend(weights, device=device, dnn=dnn, fp16=half)\n stride, pt, jit, engine = model.stride, model.pt, model.jit, model.engine\n imgsz = check_img_size(imgsz, s=stride) # check image size\n half = model.fp16 # FP16 supported on limited backends with CUDA\n if engine:\n batch_size = model.batch_size\n else:\n device = model.device\n if not (pt or jit):\n batch_size = 1 # export.py models default to batch-size 1\n LOGGER.info(f\"Forcing --batch-size 1 square inference (1,3,{imgsz},{imgsz}) for non-PyTorch models\")\n\n # Dataloader\n data = Path(data)\n test_dir = data / \"test\" if (data / \"test\").exists() else data / \"val\" # data/test or data/val\n dataloader = create_classification_dataloader(\n path=test_dir, imgsz=imgsz, batch_size=batch_size, augment=False, rank=-1, workers=workers\n )\n\n model.eval()\n pred, targets, loss, dt = [], [], 0, (Profile(), Profile(), Profile())\n n = len(dataloader) # number of batches\n action = \"validating\" if dataloader.dataset.root.stem == \"val\" else \"testing\"\n desc = f\"{pbar.desc[:-36]}{action:>36}\" if pbar else f\"{action}\"\n bar = tqdm(dataloader, desc, n, not training, bar_format=TQDM_BAR_FORMAT, position=0)\n with torch.cuda.amp.autocast(enabled=device.type != \"cpu\"):\n for images, labels in bar:\n with dt[0]:\n images, labels = images.to(device, non_blocking=True), labels.to(device)\n\n with dt[1]:\n y = model(images)\n\n with dt[2]:\n pred.append(y.argsort(1, descending=True)[:, :5])\n targets.append(labels)\n if criterion:\n loss += criterion(y, labels)\n\n loss /= n\n pred, targets = torch.cat(pred), torch.cat(targets)\n correct = (targets[:, None] == pred).float()\n acc = torch.stack((correct[:, 0], correct.max(1).values), dim=1) # (top1, top5) accuracy\n top1, top5 = acc.mean(0).tolist()\n\n if pbar:\n pbar.desc = f\"{pbar.desc[:-36]}{loss:>12.3g}{top1:>12.3g}{top5:>12.3g}\"\n if verbose: # all classes\n LOGGER.info(f\"{'Class':>24}{'Images':>12}{'top1_acc':>12}{'top5_acc':>12}\")\n LOGGER.info(f\"{'all':>24}{targets.shape[0]:>12}{top1:>12.3g}{top5:>12.3g}\")\n for i, c in model.names.items():\n acc_i = acc[targets == i]\n top1i, top5i = acc_i.mean(0).tolist()\n LOGGER.info(f\"{c:>24}{acc_i.shape[0]:>12}{top1i:>12.3g}{top5i:>12.3g}\")\n\n # Print results\n t = tuple(x.t / len(dataloader.dataset.samples) * 1e3 for x in dt) # speeds per image\n shape = (1, 3, imgsz, imgsz)\n LOGGER.info(f\"Speed: %.1fms pre-process, %.1fms inference, %.1fms post-process per image at shape {shape}\" % t)\n LOGGER.info(f\"Results saved to {colorstr('bold', save_dir)}\")\n\n return top1, top5, loss\n\n\ndef parse_opt():\n \"\"\"Parses command-line options for model configuration and returns an argparse.Namespace of options.\"\"\"\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--data\", type=str, default=ROOT / \"../datasets/mnist\", help=\"dataset path\")\n parser.add_argument(\"--weights\", nargs=\"+\", type=str, default=ROOT / \"yolov5s-cls.pt\", help=\"model.pt path(s)\")\n parser.add_argument(\"--batch-size\", type=int, default=128, help=\"batch size\")\n parser.add_argument(\"--imgsz\", \"--img\", \"--img-size\", type=int, default=224, help=\"inference size (pixels)\")\n parser.add_argument(\"--device\", default=\"\", help=\"cuda device, i.e. 0 or 0,1,2,3 or cpu\")\n parser.add_argument(\"--workers\", type=int, default=8, help=\"max dataloader workers (per RANK in DDP mode)\")\n parser.add_argument(\"--verbose\", nargs=\"?\", const=True, default=True, help=\"verbose output\")\n parser.add_argument(\"--project\", default=ROOT / \"runs/val-cls\", help=\"save to project/name\")\n parser.add_argument(\"--name\", default=\"exp\", help=\"save to project/name\")\n parser.add_argument(\"--exist-ok\", action=\"store_true\", help=\"existing project/name ok, do not increment\")\n parser.add_argument(\"--half\", action=\"store_true\", help=\"use FP16 half-precision inference\")\n parser.add_argument(\"--dnn\", action=\"store_true\", help=\"use OpenCV DNN for ONNX inference\")\n opt = parser.parse_args()\n print_args(vars(opt))\n return opt\n\n\ndef main(opt):\n \"\"\"Executes the main pipeline, checks and installs requirements, then runs inference or training based on provided\n options.\n \"\"\"\n check_requirements(ROOT / \"requirements.txt\", exclude=(\"tensorboard\", \"thop\"))\n run(**vars(opt))\n\n\nif __name__ == \"__main__\":\n opt = parse_opt()\n main(opt)\n" }, { "path": "data/Argoverse.yaml", "content": "# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license\n\n# Argoverse-HD dataset (ring-front-center camera) http://www.cs.cmu.edu/~mengtial/proj/streaming/ by Argo AI\n# Example usage: python train.py --data Argoverse.yaml\n# parent\n# ├── yolov5\n# └── datasets\n# └── Argoverse ← downloads here (31.3 GB)\n\n# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]\npath: ../datasets/Argoverse # dataset root dir\ntrain: Argoverse-1.1/images/train/ # train images (relative to 'path') 39384 images\nval: Argoverse-1.1/images/val/ # val images (relative to 'path') 15062 images\ntest: Argoverse-1.1/images/test/ # test images (optional) https://eval.ai/web/challenges/challenge-page/800/overview\n\n# Classes\nnames:\n 0: person\n 1: bicycle\n 2: car\n 3: motorcycle\n 4: bus\n 5: truck\n 6: traffic_light\n 7: stop_sign\n\n# Download script/URL (optional) ---------------------------------------------------------------------------------------\ndownload: |\n import json\n\n from tqdm import tqdm\n from utils.general import download, Path\n\n\n def argoverse2yolo(set):\n labels = {}\n a = json.load(open(set, \"rb\"))\n for annot in tqdm(a['annotations'], desc=f\"Converting {set} to YOLOv5 format...\"):\n img_id = annot['image_id']\n img_name = a['images'][img_id]['name']\n img_label_name = f'{img_name[:-3]}txt'\n\n cls = annot['category_id'] # instance class id\n x_center, y_center, width, height = annot['bbox']\n x_center = (x_center + width / 2) / 1920.0 # offset and scale\n y_center = (y_center + height / 2) / 1200.0 # offset and scale\n width /= 1920.0 # scale\n height /= 1200.0 # scale\n\n img_dir = set.parents[2] / 'Argoverse-1.1' / 'labels' / a['seq_dirs'][a['images'][annot['image_id']]['sid']]\n if not img_dir.exists():\n img_dir.mkdir(parents=True, exist_ok=True)\n\n k = str(img_dir / img_label_name)\n if k not in labels:\n labels[k] = []\n labels[k].append(f\"{cls} {x_center} {y_center} {width} {height}\\n\")\n\n for k in labels:\n with open(k, \"w\") as f:\n f.writelines(labels[k])\n\n\n # Download\n dir = Path(yaml['path']) # dataset root dir\n urls = ['https://argoverse-hd.s3.us-east-2.amazonaws.com/Argoverse-HD-Full.zip']\n download(urls, dir=dir, delete=False)\n\n # Convert\n annotations_dir = 'Argoverse-HD/annotations/'\n (dir / 'Argoverse-1.1' / 'tracking').rename(dir / 'Argoverse-1.1' / 'images') # rename 'tracking' to 'images'\n for d in \"train.json\", \"val.json\":\n argoverse2yolo(dir / annotations_dir / d) # convert VisDrone annotations to YOLO labels\n" }, { "path": "data/GlobalWheat2020.yaml", "content": "# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license\n\n# Global Wheat 2020 dataset http://www.global-wheat.com/ by University of Saskatchewan\n# Example usage: python train.py --data GlobalWheat2020.yaml\n# parent\n# ├── yolov5\n# └── datasets\n# └── GlobalWheat2020 ← downloads here (7.0 GB)\n\n# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]\npath: ../datasets/GlobalWheat2020 # dataset root dir\ntrain: # train images (relative to 'path') 3422 images\n - images/arvalis_1\n - images/arvalis_2\n - images/arvalis_3\n - images/ethz_1\n - images/rres_1\n - images/inrae_1\n - images/usask_1\nval: # val images (relative to 'path') 748 images (WARNING: train set contains ethz_1)\n - images/ethz_1\ntest: # test images (optional) 1276 images\n - images/utokyo_1\n - images/utokyo_2\n - images/nau_1\n - images/uq_1\n\n# Classes\nnames:\n 0: wheat_head\n\n# Download script/URL (optional) ---------------------------------------------------------------------------------------\ndownload: |\n from utils.general import download, Path\n\n\n # Download\n dir = Path(yaml['path']) # dataset root dir\n urls = ['https://zenodo.org/record/4298502/files/global-wheat-codalab-official.zip',\n 'https://github.com/ultralytics/assets/releases/download/v0.0.0/GlobalWheat2020_labels.zip']\n download(urls, dir=dir)\n\n # Make Directories\n for p in 'annotations', 'images', 'labels':\n (dir / p).mkdir(parents=True, exist_ok=True)\n\n # Move\n for p in 'arvalis_1', 'arvalis_2', 'arvalis_3', 'ethz_1', 'rres_1', 'inrae_1', 'usask_1', \\\n 'utokyo_1', 'utokyo_2', 'nau_1', 'uq_1':\n (dir / p).rename(dir / 'images' / p) # move to /images\n f = (dir / p).with_suffix('.json') # json file\n if f.exists():\n f.rename((dir / 'annotations' / p).with_suffix('.json')) # move to /annotations\n" }, { "path": "data/ImageNet.yaml", "content": "# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license\n\n# ImageNet-1k dataset https://www.image-net.org/index.php by Stanford University\n# Simplified class names from https://github.com/anishathalye/imagenet-simple-labels\n# Example usage: python classify/train.py --data imagenet\n# parent\n# ├── yolov5\n# └── datasets\n# └── imagenet ← downloads here (144 GB)\n\n# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]\npath: ../datasets/imagenet # dataset root dir\ntrain: train # train images (relative to 'path') 1281167 images\nval: val # val images (relative to 'path') 50000 images\ntest: # test images (optional)\n\n# Classes\nnames:\n 0: tench\n 1: goldfish\n 2: great white shark\n 3: tiger shark\n 4: hammerhead shark\n 5: electric ray\n 6: stingray\n 7: cock\n 8: hen\n 9: ostrich\n 10: brambling\n 11: goldfinch\n 12: house finch\n 13: junco\n 14: indigo bunting\n 15: American robin\n 16: bulbul\n 17: jay\n 18: magpie\n 19: chickadee\n 20: American dipper\n 21: kite\n 22: bald eagle\n 23: vulture\n 24: great grey owl\n 25: fire salamander\n 26: smooth newt\n 27: newt\n 28: spotted salamander\n 29: axolotl\n 30: American bullfrog\n 31: tree frog\n 32: tailed frog\n 33: loggerhead sea turtle\n 34: leatherback sea turtle\n 35: mud turtle\n 36: terrapin\n 37: box turtle\n 38: banded gecko\n 39: green iguana\n 40: Carolina anole\n 41: desert grassland whiptail lizard\n 42: agama\n 43: frilled-necked lizard\n 44: alligator lizard\n 45: Gila monster\n 46: European green lizard\n 47: chameleon\n 48: Komodo dragon\n 49: Nile crocodile\n 50: American alligator\n 51: triceratops\n 52: worm snake\n 53: ring-necked snake\n 54: eastern hog-nosed snake\n 55: smooth green snake\n 56: kingsnake\n 57: garter snake\n 58: water snake\n 59: vine snake\n 60: night snake\n 61: boa constrictor\n 62: African rock python\n 63: Indian cobra\n 64: green mamba\n 65: sea snake\n 66: Saharan horned viper\n 67: eastern diamondback rattlesnake\n 68: sidewinder\n 69: trilobite\n 70: harvestman\n 71: scorpion\n 72: yellow garden spider\n 73: barn spider\n 74: European garden spider\n 75: southern black widow\n 76: tarantula\n 77: wolf spider\n 78: tick\n 79: centipede\n 80: black grouse\n 81: ptarmigan\n 82: ruffed grouse\n 83: prairie grouse\n 84: peacock\n 85: quail\n 86: partridge\n 87: grey parrot\n 88: macaw\n 89: sulphur-crested cockatoo\n 90: lorikeet\n 91: coucal\n 92: bee eater\n 93: hornbill\n 94: hummingbird\n 95: jacamar\n 96: toucan\n 97: duck\n 98: red-breasted merganser\n 99: goose\n 100: black swan\n 101: tusker\n 102: echidna\n 103: platypus\n 104: wallaby\n 105: koala\n 106: wombat\n 107: jellyfish\n 108: sea anemone\n 109: brain coral\n 110: flatworm\n 111: nematode\n 112: conch\n 113: snail\n 114: slug\n 115: sea slug\n 116: chiton\n 117: chambered nautilus\n 118: Dungeness crab\n 119: rock crab\n 120: fiddler crab\n 121: red king crab\n 122: American lobster\n 123: spiny lobster\n 124: crayfish\n 125: hermit crab\n 126: isopod\n 127: white stork\n 128: black stork\n 129: spoonbill\n 130: flamingo\n 131: little blue heron\n 132: great egret\n 133: bittern\n 134: crane (bird)\n 135: limpkin\n 136: common gallinule\n 137: American coot\n 138: bustard\n 139: ruddy turnstone\n 140: dunlin\n 141: common redshank\n 142: dowitcher\n 143: oystercatcher\n 144: pelican\n 145: king penguin\n 146: albatross\n 147: grey whale\n 148: killer whale\n 149: dugong\n 150: sea lion\n 151: Chihuahua\n 152: Japanese Chin\n 153: Maltese\n 154: Pekingese\n 155: Shih Tzu\n 156: King Charles Spaniel\n 157: Papillon\n 158: toy terrier\n 159: Rhodesian Ridgeback\n 160: Afghan Hound\n 161: Basset Hound\n 162: Beagle\n 163: Bloodhound\n 164: Bluetick Coonhound\n 165: Black and Tan Coonhound\n 166: Treeing Walker Coonhound\n 167: English foxhound\n 168: Redbone Coonhound\n 169: borzoi\n 170: Irish Wolfhound\n 171: Italian Greyhound\n 172: Whippet\n 173: Ibizan Hound\n 174: Norwegian Elkhound\n 175: Otterhound\n 176: Saluki\n 177: Scottish Deerhound\n 178: Weimaraner\n 179: Staffordshire Bull Terrier\n 180: American Staffordshire Terrier\n 181: Bedlington Terrier\n 182: Border Terrier\n 183: Kerry Blue Terrier\n 184: Irish Terrier\n 185: Norfolk Terrier\n 186: Norwich Terrier\n 187: Yorkshire Terrier\n 188: Wire Fox Terrier\n 189: Lakeland Terrier\n 190: Sealyham Terrier\n 191: Airedale Terrier\n 192: Cairn Terrier\n 193: Australian Terrier\n 194: Dandie Dinmont Terrier\n 195: Boston Terrier\n 196: Miniature Schnauzer\n 197: Giant Schnauzer\n 198: Standard Schnauzer\n 199: Scottish Terrier\n 200: Tibetan Terrier\n 201: Australian Silky Terrier\n 202: Soft-coated Wheaten Terrier\n 203: West Highland White Terrier\n 204: Lhasa Apso\n 205: Flat-Coated Retriever\n 206: Curly-coated Retriever\n 207: Golden Retriever\n 208: Labrador Retriever\n 209: Chesapeake Bay Retriever\n 210: German Shorthaired Pointer\n 211: Vizsla\n 212: English Setter\n 213: Irish Setter\n 214: Gordon Setter\n 215: Brittany\n 216: Clumber Spaniel\n 217: English Springer Spaniel\n 218: Welsh Springer Spaniel\n 219: Cocker Spaniels\n 220: Sussex Spaniel\n 221: Irish Water Spaniel\n 222: Kuvasz\n 223: Schipperke\n 224: Groenendael\n 225: Malinois\n 226: Briard\n 227: Australian Kelpie\n 228: Komondor\n 229: Old English Sheepdog\n 230: Shetland Sheepdog\n 231: collie\n 232: Border Collie\n 233: Bouvier des Flandres\n 234: Rottweiler\n 235: German Shepherd Dog\n 236: Dobermann\n 237: Miniature Pinscher\n 238: Greater Swiss Mountain Dog\n 239: Bernese Mountain Dog\n 240: Appenzeller Sennenhund\n 241: Entlebucher Sennenhund\n 242: Boxer\n 243: Bullmastiff\n 244: Tibetan Mastiff\n 245: French Bulldog\n 246: Great Dane\n 247: St. Bernard\n 248: husky\n 249: Alaskan Malamute\n 250: Siberian Husky\n 251: Dalmatian\n 252: Affenpinscher\n 253: Basenji\n 254: pug\n 255: Leonberger\n 256: Newfoundland\n 257: Pyrenean Mountain Dog\n 258: Samoyed\n 259: Pomeranian\n 260: Chow Chow\n 261: Keeshond\n 262: Griffon Bruxellois\n 263: Pembroke Welsh Corgi\n 264: Cardigan Welsh Corgi\n 265: Toy Poodle\n 266: Miniature Poodle\n 267: Standard Poodle\n 268: Mexican hairless dog\n 269: grey wolf\n 270: Alaskan tundra wolf\n 271: red wolf\n 272: coyote\n 273: dingo\n 274: dhole\n 275: African wild dog\n 276: hyena\n 277: red fox\n 278: kit fox\n 279: Arctic fox\n 280: grey fox\n 281: tabby cat\n 282: tiger cat\n 283: Persian cat\n 284: Siamese cat\n 285: Egyptian Mau\n 286: cougar\n 287: lynx\n 288: leopard\n 289: snow leopard\n 290: jaguar\n 291: lion\n 292: tiger\n 293: cheetah\n 294: brown bear\n 295: American black bear\n 296: polar bear\n 297: sloth bear\n 298: mongoose\n 299: meerkat\n 300: tiger beetle\n 301: ladybug\n 302: ground beetle\n 303: longhorn beetle\n 304: leaf beetle\n 305: dung beetle\n 306: rhinoceros beetle\n 307: weevil\n 308: fly\n 309: bee\n 310: ant\n 311: grasshopper\n 312: cricket\n 313: stick insect\n 314: cockroach\n 315: mantis\n 316: cicada\n 317: leafhopper\n 318: lacewing\n 319: dragonfly\n 320: damselfly\n 321: red admiral\n 322: ringlet\n 323: monarch butterfly\n 324: small white\n 325: sulfur butterfly\n 326: gossamer-winged butterfly\n 327: starfish\n 328: sea urchin\n 329: sea cucumber\n 330: cottontail rabbit\n 331: hare\n 332: Angora rabbit\n 333: hamster\n 334: porcupine\n 335: fox squirrel\n 336: marmot\n 337: beaver\n 338: guinea pig\n 339: common sorrel\n 340: zebra\n 341: pig\n 342: wild boar\n 343: warthog\n 344: hippopotamus\n 345: ox\n 346: water buffalo\n 347: bison\n 348: ram\n 349: bighorn sheep\n 350: Alpine ibex\n 351: hartebeest\n 352: impala\n 353: gazelle\n 354: dromedary\n 355: llama\n 356: weasel\n 357: mink\n 358: European polecat\n 359: black-footed ferret\n 360: otter\n 361: skunk\n 362: badger\n 363: armadillo\n 364: three-toed sloth\n 365: orangutan\n 366: gorilla\n 367: chimpanzee\n 368: gibbon\n 369: siamang\n 370: guenon\n 371: patas monkey\n 372: baboon\n 373: macaque\n 374: langur\n 375: black-and-white colobus\n 376: proboscis monkey\n 377: marmoset\n 378: white-headed capuchin\n 379: howler monkey\n 380: titi\n 381: Geoffroy's spider monkey\n 382: common squirrel monkey\n 383: ring-tailed lemur\n 384: indri\n 385: Asian elephant\n 386: African bush elephant\n 387: red panda\n 388: giant panda\n 389: snoek\n 390: eel\n 391: coho salmon\n 392: rock beauty\n 393: clownfish\n 394: sturgeon\n 395: garfish\n 396: lionfish\n 397: pufferfish\n 398: abacus\n 399: abaya\n 400: academic gown\n 401: accordion\n 402: acoustic guitar\n 403: aircraft carrier\n 404: airliner\n 405: airship\n 406: altar\n 407: ambulance\n 408: amphibious vehicle\n 409: analog clock\n 410: apiary\n 411: apron\n 412: waste container\n 413: assault rifle\n 414: backpack\n 415: bakery\n 416: balance beam\n 417: balloon\n 418: ballpoint pen\n 419: Band-Aid\n 420: banjo\n 421: baluster\n 422: barbell\n 423: barber chair\n 424: barbershop\n 425: barn\n 426: barometer\n 427: barrel\n 428: wheelbarrow\n 429: baseball\n 430: basketball\n 431: bassinet\n 432: bassoon\n 433: swimming cap\n 434: bath towel\n 435: bathtub\n 436: station wagon\n 437: lighthouse\n 438: beaker\n 439: military cap\n 440: beer bottle\n 441: beer glass\n 442: bell-cot\n 443: bib\n 444: tandem bicycle\n 445: bikini\n 446: ring binder\n 447: binoculars\n 448: birdhouse\n 449: boathouse\n 450: bobsleigh\n 451: bolo tie\n 452: poke bonnet\n 453: bookcase\n 454: bookstore\n 455: bottle cap\n 456: bow\n 457: bow tie\n 458: brass\n 459: bra\n 460: breakwater\n 461: breastplate\n 462: broom\n 463: bucket\n 464: buckle\n 465: bulletproof vest\n 466: high-speed train\n 467: butcher shop\n 468: taxicab\n 469: cauldron\n 470: candle\n 471: cannon\n 472: canoe\n 473: can opener\n 474: cardigan\n 475: car mirror\n 476: carousel\n 477: tool kit\n 478: carton\n 479: car wheel\n 480: automated teller machine\n 481: cassette\n 482: cassette player\n 483: castle\n 484: catamaran\n 485: CD player\n 486: cello\n 487: mobile phone\n 488: chain\n 489: chain-link fence\n 490: chain mail\n 491: chainsaw\n 492: chest\n 493: chiffonier\n 494: chime\n 495: china cabinet\n 496: Christmas stocking\n 497: church\n 498: movie theater\n 499: cleaver\n 500: cliff dwelling\n 501: cloak\n 502: clogs\n 503: cocktail shaker\n 504: coffee mug\n 505: coffeemaker\n 506: coil\n 507: combination lock\n 508: computer keyboard\n 509: confectionery store\n 510: container ship\n 511: convertible\n 512: corkscrew\n 513: cornet\n 514: cowboy boot\n 515: cowboy hat\n 516: cradle\n 517: crane (machine)\n 518: crash helmet\n 519: crate\n 520: infant bed\n 521: Crock Pot\n 522: croquet ball\n 523: crutch\n 524: cuirass\n 525: dam\n 526: desk\n 527: desktop computer\n 528: rotary dial telephone\n 529: diaper\n 530: digital clock\n 531: digital watch\n 532: dining table\n 533: dishcloth\n 534: dishwasher\n 535: disc brake\n 536: dock\n 537: dog sled\n 538: dome\n 539: doormat\n 540: drilling rig\n 541: drum\n 542: drumstick\n 543: dumbbell\n 544: Dutch oven\n 545: electric fan\n 546: electric guitar\n 547: electric locomotive\n 548: entertainment center\n 549: envelope\n 550: espresso machine\n 551: face powder\n 552: feather boa\n 553: filing cabinet\n 554: fireboat\n 555: fire engine\n 556: fire screen sheet\n 557: flagpole\n 558: flute\n 559: folding chair\n 560: football helmet\n 561: forklift\n 562: fountain\n 563: fountain pen\n 564: four-poster bed\n 565: freight car\n 566: French horn\n 567: frying pan\n 568: fur coat\n 569: garbage truck\n 570: gas mask\n 571: gas pump\n 572: goblet\n 573: go-kart\n 574: golf ball\n 575: golf cart\n 576: gondola\n 577: gong\n 578: gown\n 579: grand piano\n 580: greenhouse\n 581: grille\n 582: grocery store\n 583: guillotine\n 584: barrette\n 585: hair spray\n 586: half-track\n 587: hammer\n 588: hamper\n 589: hair dryer\n 590: hand-held computer\n 591: handkerchief\n 592: hard disk drive\n 593: harmonica\n 594: harp\n 595: harvester\n 596: hatchet\n 597: holster\n 598: home theater\n 599: honeycomb\n 600: hook\n 601: hoop skirt\n 602: horizontal bar\n 603: horse-drawn vehicle\n 604: hourglass\n 605: iPod\n 606: clothes iron\n 607: jack-o'-lantern\n 608: jeans\n 609: jeep\n 610: T-shirt\n 611: jigsaw puzzle\n 612: pulled rickshaw\n 613: joystick\n 614: kimono\n 615: knee pad\n 616: knot\n 617: lab coat\n 618: ladle\n 619: lampshade\n 620: laptop computer\n 621: lawn mower\n 622: lens cap\n 623: paper knife\n 624: library\n 625: lifeboat\n 626: lighter\n 627: limousine\n 628: ocean liner\n 629: lipstick\n 630: slip-on shoe\n 631: lotion\n 632: speaker\n 633: loupe\n 634: sawmill\n 635: magnetic compass\n 636: mail bag\n 637: mailbox\n 638: tights\n 639: tank suit\n 640: manhole cover\n 641: maraca\n 642: marimba\n 643: mask\n 644: match\n 645: maypole\n 646: maze\n 647: measuring cup\n 648: medicine chest\n 649: megalith\n 650: microphone\n 651: microwave oven\n 652: military uniform\n 653: milk can\n 654: minibus\n 655: miniskirt\n 656: minivan\n 657: missile\n 658: mitten\n 659: mixing bowl\n 660: mobile home\n 661: Model T\n 662: modem\n 663: monastery\n 664: monitor\n 665: moped\n 666: mortar\n 667: square academic cap\n 668: mosque\n 669: mosquito net\n 670: scooter\n 671: mountain bike\n 672: tent\n 673: computer mouse\n 674: mousetrap\n 675: moving van\n 676: muzzle\n 677: nail\n 678: neck brace\n 679: necklace\n 680: nipple\n 681: notebook computer\n 682: obelisk\n 683: oboe\n 684: ocarina\n 685: odometer\n 686: oil filter\n 687: organ\n 688: oscilloscope\n 689: overskirt\n 690: bullock cart\n 691: oxygen mask\n 692: packet\n 693: paddle\n 694: paddle wheel\n 695: padlock\n 696: paintbrush\n 697: pajamas\n 698: palace\n 699: pan flute\n 700: paper towel\n 701: parachute\n 702: parallel bars\n 703: park bench\n 704: parking meter\n 705: passenger car\n 706: patio\n 707: payphone\n 708: pedestal\n 709: pencil case\n 710: pencil sharpener\n 711: perfume\n 712: Petri dish\n 713: photocopier\n 714: plectrum\n 715: Pickelhaube\n 716: picket fence\n 717: pickup truck\n 718: pier\n 719: piggy bank\n 720: pill bottle\n 721: pillow\n 722: ping-pong ball\n 723: pinwheel\n 724: pirate ship\n 725: pitcher\n 726: hand plane\n 727: planetarium\n 728: plastic bag\n 729: plate rack\n 730: plow\n 731: plunger\n 732: Polaroid camera\n 733: pole\n 734: police van\n 735: poncho\n 736: billiard table\n 737: soda bottle\n 738: pot\n 739: potter's wheel\n 740: power drill\n 741: prayer rug\n 742: printer\n 743: prison\n 744: projectile\n 745: projector\n 746: hockey puck\n 747: punching bag\n 748: purse\n 749: quill\n 750: quilt\n 751: race car\n 752: racket\n 753: radiator\n 754: radio\n 755: radio telescope\n 756: rain barrel\n 757: recreational vehicle\n 758: reel\n 759: reflex camera\n 760: refrigerator\n 761: remote control\n 762: restaurant\n 763: revolver\n 764: rifle\n 765: rocking chair\n 766: rotisserie\n 767: eraser\n 768: rugby ball\n 769: ruler\n 770: running shoe\n 771: safe\n 772: safety pin\n 773: salt shaker\n 774: sandal\n 775: sarong\n 776: saxophone\n 777: scabbard\n 778: weighing scale\n 779: school bus\n 780: schooner\n 781: scoreboard\n 782: CRT screen\n 783: screw\n 784: screwdriver\n 785: seat belt\n 786: sewing machine\n 787: shield\n 788: shoe store\n 789: shoji\n 790: shopping basket\n 791: shopping cart\n 792: shovel\n 793: shower cap\n 794: shower curtain\n 795: ski\n 796: ski mask\n 797: sleeping bag\n 798: slide rule\n 799: sliding door\n 800: slot machine\n 801: snorkel\n 802: snowmobile\n 803: snowplow\n 804: soap dispenser\n 805: soccer ball\n 806: sock\n 807: solar thermal collector\n 808: sombrero\n 809: soup bowl\n 810: space bar\n 811: space heater\n 812: space shuttle\n 813: spatula\n 814: motorboat\n 815: spider web\n 816: spindle\n 817: sports car\n 818: spotlight\n 819: stage\n 820: steam locomotive\n 821: through arch bridge\n 822: steel drum\n 823: stethoscope\n 824: scarf\n 825: stone wall\n 826: stopwatch\n 827: stove\n 828: strainer\n 829: tram\n 830: stretcher\n 831: couch\n 832: stupa\n 833: submarine\n 834: suit\n 835: sundial\n 836: sunglass\n 837: sunglasses\n 838: sunscreen\n 839: suspension bridge\n 840: mop\n 841: sweatshirt\n 842: swimsuit\n 843: swing\n 844: switch\n 845: syringe\n 846: table lamp\n 847: tank\n 848: tape player\n 849: teapot\n 850: teddy bear\n 851: television\n 852: tennis ball\n 853: thatched roof\n 854: front curtain\n 855: thimble\n 856: threshing machine\n 857: throne\n 858: tile roof\n 859: toaster\n 860: tobacco shop\n 861: toilet seat\n 862: torch\n 863: totem pole\n 864: tow truck\n 865: toy store\n 866: tractor\n 867: semi-trailer truck\n 868: tray\n 869: trench coat\n 870: tricycle\n 871: trimaran\n 872: tripod\n 873: triumphal arch\n 874: trolleybus\n 875: trombone\n 876: tub\n 877: turnstile\n 878: typewriter keyboard\n 879: umbrella\n 880: unicycle\n 881: upright piano\n 882: vacuum cleaner\n 883: vase\n 884: vault\n 885: velvet\n 886: vending machine\n 887: vestment\n 888: viaduct\n 889: violin\n 890: volleyball\n 891: waffle iron\n 892: wall clock\n 893: wallet\n 894: wardrobe\n 895: military aircraft\n 896: sink\n 897: washing machine\n 898: water bottle\n 899: water jug\n 900: water tower\n 901: whiskey jug\n 902: whistle\n 903: wig\n 904: window screen\n 905: window shade\n 906: Windsor tie\n 907: wine bottle\n 908: wing\n 909: wok\n 910: wooden spoon\n 911: wool\n 912: split-rail fence\n 913: shipwreck\n 914: yawl\n 915: yurt\n 916: website\n 917: comic book\n 918: crossword\n 919: traffic sign\n 920: traffic light\n 921: dust jacket\n 922: menu\n 923: plate\n 924: guacamole\n 925: consomme\n 926: hot pot\n 927: trifle\n 928: ice cream\n 929: ice pop\n 930: baguette\n 931: bagel\n 932: pretzel\n 933: cheeseburger\n 934: hot dog\n 935: mashed potato\n 936: cabbage\n 937: broccoli\n 938: cauliflower\n 939: zucchini\n 940: spaghetti squash\n 941: acorn squash\n 942: butternut squash\n 943: cucumber\n 944: artichoke\n 945: bell pepper\n 946: cardoon\n 947: mushroom\n 948: Granny Smith\n 949: strawberry\n 950: orange\n 951: lemon\n 952: fig\n 953: pineapple\n 954: banana\n 955: jackfruit\n 956: custard apple\n 957: pomegranate\n 958: hay\n 959: carbonara\n 960: chocolate syrup\n 961: dough\n 962: meatloaf\n 963: pizza\n 964: pot pie\n 965: burrito\n 966: red wine\n 967: espresso\n 968: cup\n 969: eggnog\n 970: alp\n 971: bubble\n 972: cliff\n 973: coral reef\n 974: geyser\n 975: lakeshore\n 976: promontory\n 977: shoal\n 978: seashore\n 979: valley\n 980: volcano\n 981: baseball player\n 982: bridegroom\n 983: scuba diver\n 984: rapeseed\n 985: daisy\n 986: yellow lady's slipper\n 987: corn\n 988: acorn\n 989: rose hip\n 990: horse chestnut seed\n 991: coral fungus\n 992: agaric\n 993: gyromitra\n 994: stinkhorn mushroom\n 995: earth star\n 996: hen-of-the-woods\n 997: bolete\n 998: ear\n 999: toilet paper\n\n# Download script/URL (optional)\ndownload: data/scripts/get_imagenet.sh\n" }, { "path": "data/SKU-110K.yaml", "content": "# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license\n\n# SKU-110K retail items dataset https://github.com/eg4000/SKU110K_CVPR19 by Trax Retail\n# Example usage: python train.py --data SKU-110K.yaml\n# parent\n# ├── yolov5\n# └── datasets\n# └── SKU-110K ← downloads here (13.6 GB)\n\n# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]\npath: ../datasets/SKU-110K # dataset root dir\ntrain: train.txt # train images (relative to 'path') 8219 images\nval: val.txt # val images (relative to 'path') 588 images\ntest: test.txt # test images (optional) 2936 images\n\n# Classes\nnames:\n 0: object\n\n# Download script/URL (optional) ---------------------------------------------------------------------------------------\ndownload: |\n import shutil\n from tqdm import tqdm\n from utils.general import np, pd, Path, download, xyxy2xywh\n\n\n # Download\n dir = Path(yaml['path']) # dataset root dir\n parent = Path(dir.parent) # download dir\n urls = ['http://trax-geometry.s3.amazonaws.com/cvpr_challenge/SKU110K_fixed.tar.gz']\n download(urls, dir=parent, delete=False)\n\n # Rename directories\n if dir.exists():\n shutil.rmtree(dir)\n (parent / 'SKU110K_fixed').rename(dir) # rename dir\n (dir / 'labels').mkdir(parents=True, exist_ok=True) # create labels dir\n\n # Convert labels\n names = 'image', 'x1', 'y1', 'x2', 'y2', 'class', 'image_width', 'image_height' # column names\n for d in 'annotations_train.csv', 'annotations_val.csv', 'annotations_test.csv':\n x = pd.read_csv(dir / 'annotations' / d, names=names).values # annotations\n images, unique_images = x[:, 0], np.unique(x[:, 0])\n with open((dir / d).with_suffix('.txt').__str__().replace('annotations_', ''), 'w') as f:\n f.writelines(f'./images/{s}\\n' for s in unique_images)\n for im in tqdm(unique_images, desc=f'Converting {dir / d}'):\n cls = 0 # single-class dataset\n with open((dir / 'labels' / im).with_suffix('.txt'), 'a') as f:\n for r in x[images == im]:\n w, h = r[6], r[7] # image width, height\n xywh = xyxy2xywh(np.array([[r[1] / w, r[2] / h, r[3] / w, r[4] / h]]))[0] # instance\n f.write(f\"{cls} {xywh[0]:.5f} {xywh[1]:.5f} {xywh[2]:.5f} {xywh[3]:.5f}\\n\") # write label\n" }, { "path": "data/VisDrone.yaml", "content": "# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license\n\n# VisDrone2019-DET dataset https://github.com/VisDrone/VisDrone-Dataset by Tianjin University\n# Example usage: python train.py --data VisDrone.yaml\n# parent\n# ├── yolov5\n# └── datasets\n# └── VisDrone ← downloads here (2.3 GB)\n\n# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]\npath: ../datasets/VisDrone # dataset root dir\ntrain: VisDrone2019-DET-train/images # train images (relative to 'path') 6471 images\nval: VisDrone2019-DET-val/images # val images (relative to 'path') 548 images\ntest: VisDrone2019-DET-test-dev/images # test images (optional) 1610 images\n\n# Classes\nnames:\n 0: pedestrian\n 1: people\n 2: bicycle\n 3: car\n 4: van\n 5: truck\n 6: tricycle\n 7: awning-tricycle\n 8: bus\n 9: motor\n\n# Download script/URL (optional) ---------------------------------------------------------------------------------------\ndownload: |\n from utils.general import download, os, Path\n\n def visdrone2yolo(dir):\n from PIL import Image\n from tqdm import tqdm\n\n def convert_box(size, box):\n # Convert VisDrone box to YOLO xywh box\n dw = 1. / size[0]\n dh = 1. / size[1]\n return (box[0] + box[2] / 2) * dw, (box[1] + box[3] / 2) * dh, box[2] * dw, box[3] * dh\n\n (dir / 'labels').mkdir(parents=True, exist_ok=True) # make labels directory\n pbar = tqdm((dir / 'annotations').glob('*.txt'), desc=f'Converting {dir}')\n for f in pbar:\n img_size = Image.open((dir / 'images' / f.name).with_suffix('.jpg')).size\n lines = []\n with open(f, 'r') as file: # read annotation.txt\n for row in [x.split(',') for x in file.read().strip().splitlines()]:\n if row[4] == '0': # VisDrone 'ignored regions' class 0\n continue\n cls = int(row[5]) - 1\n box = convert_box(img_size, tuple(map(int, row[:4])))\n lines.append(f\"{cls} {' '.join(f'{x:.6f}' for x in box)}\\n\")\n with open(str(f).replace(os.sep + 'annotations' + os.sep, os.sep + 'labels' + os.sep), 'w') as fl:\n fl.writelines(lines) # write label.txt\n\n\n # Download\n dir = Path(yaml['path']) # dataset root dir\n urls = ['https://github.com/ultralytics/assets/releases/download/v0.0.0/VisDrone2019-DET-train.zip',\n 'https://github.com/ultralytics/assets/releases/download/v0.0.0/VisDrone2019-DET-val.zip',\n 'https://github.com/ultralytics/assets/releases/download/v0.0.0/VisDrone2019-DET-test-dev.zip',\n 'https://github.com/ultralytics/assets/releases/download/v0.0.0/VisDrone2019-DET-test-challenge.zip']\n download(urls, dir=dir, curl=True, threads=4)\n\n # Convert\n for d in 'VisDrone2019-DET-train', 'VisDrone2019-DET-val', 'VisDrone2019-DET-test-dev':\n visdrone2yolo(dir / d) # convert VisDrone annotations to YOLO labels\n" }, { "path": "data/coco.yaml", "content": "# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license\n\n# COCO 2017 dataset http://cocodataset.org by Microsoft\n# Example usage: python train.py --data coco.yaml\n# parent\n# ├── yolov5\n# └── datasets\n# └── coco ← downloads here (20.1 GB)\n\n# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]\npath: ../datasets/coco # dataset root dir\ntrain: train2017.txt # train images (relative to 'path') 118287 images\nval: val2017.txt # val images (relative to 'path') 5000 images\ntest: test-dev2017.txt # 20288 of 40670 images, submit to https://competitions.codalab.org/competitions/20794\n\n# Classes\nnames:\n 0: person\n 1: bicycle\n 2: car\n 3: motorcycle\n 4: airplane\n 5: bus\n 6: train\n 7: truck\n 8: boat\n 9: traffic light\n 10: fire hydrant\n 11: stop sign\n 12: parking meter\n 13: bench\n 14: bird\n 15: cat\n 16: dog\n 17: horse\n 18: sheep\n 19: cow\n 20: elephant\n 21: bear\n 22: zebra\n 23: giraffe\n 24: backpack\n 25: umbrella\n 26: handbag\n 27: tie\n 28: suitcase\n 29: frisbee\n 30: skis\n 31: snowboard\n 32: sports ball\n 33: kite\n 34: baseball bat\n 35: baseball glove\n 36: skateboard\n 37: surfboard\n 38: tennis racket\n 39: bottle\n 40: wine glass\n 41: cup\n 42: fork\n 43: knife\n 44: spoon\n 45: bowl\n 46: banana\n 47: apple\n 48: sandwich\n 49: orange\n 50: broccoli\n 51: carrot\n 52: hot dog\n 53: pizza\n 54: donut\n 55: cake\n 56: chair\n 57: couch\n 58: potted plant\n 59: bed\n 60: dining table\n 61: toilet\n 62: tv\n 63: laptop\n 64: mouse\n 65: remote\n 66: keyboard\n 67: cell phone\n 68: microwave\n 69: oven\n 70: toaster\n 71: sink\n 72: refrigerator\n 73: book\n 74: clock\n 75: vase\n 76: scissors\n 77: teddy bear\n 78: hair drier\n 79: toothbrush\n\n# Download script/URL (optional)\ndownload: |\n from utils.general import download, Path\n\n\n # Download labels\n segments = False # segment or box labels\n dir = Path(yaml['path']) # dataset root dir\n url = 'https://github.com/ultralytics/assets/releases/download/v0.0.0/'\n urls = [url + ('coco2017labels-segments.zip' if segments else 'coco2017labels.zip')] # labels\n download(urls, dir=dir.parent)\n\n # Download data\n urls = ['http://images.cocodataset.org/zips/train2017.zip', # 19G, 118k images\n 'http://images.cocodataset.org/zips/val2017.zip', # 1G, 5k images\n 'http://images.cocodataset.org/zips/test2017.zip'] # 7G, 41k images (optional)\n download(urls, dir=dir / 'images', threads=3)\n" }, { "path": "data/coco128-seg.yaml", "content": "# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license\n\n# COCO128-seg dataset https://www.kaggle.com/datasets/ultralytics/coco128 (first 128 images from COCO train2017) by Ultralytics\n# Example usage: python train.py --data coco128.yaml\n# parent\n# ├── yolov5\n# └── datasets\n# └── coco128-seg ← downloads here (7 MB)\n\n# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]\npath: ../datasets/coco128-seg # dataset root dir\ntrain: images/train2017 # train images (relative to 'path') 128 images\nval: images/train2017 # val images (relative to 'path') 128 images\ntest: # test images (optional)\n\n# Classes\nnames:\n 0: person\n 1: bicycle\n 2: car\n 3: motorcycle\n 4: airplane\n 5: bus\n 6: train\n 7: truck\n 8: boat\n 9: traffic light\n 10: fire hydrant\n 11: stop sign\n 12: parking meter\n 13: bench\n 14: bird\n 15: cat\n 16: dog\n 17: horse\n 18: sheep\n 19: cow\n 20: elephant\n 21: bear\n 22: zebra\n 23: giraffe\n 24: backpack\n 25: umbrella\n 26: handbag\n 27: tie\n 28: suitcase\n 29: frisbee\n 30: skis\n 31: snowboard\n 32: sports ball\n 33: kite\n 34: baseball bat\n 35: baseball glove\n 36: skateboard\n 37: surfboard\n 38: tennis racket\n 39: bottle\n 40: wine glass\n 41: cup\n 42: fork\n 43: knife\n 44: spoon\n 45: bowl\n 46: banana\n 47: apple\n 48: sandwich\n 49: orange\n 50: broccoli\n 51: carrot\n 52: hot dog\n 53: pizza\n 54: donut\n 55: cake\n 56: chair\n 57: couch\n 58: potted plant\n 59: bed\n 60: dining table\n 61: toilet\n 62: tv\n 63: laptop\n 64: mouse\n 65: remote\n 66: keyboard\n 67: cell phone\n 68: microwave\n 69: oven\n 70: toaster\n 71: sink\n 72: refrigerator\n 73: book\n 74: clock\n 75: vase\n 76: scissors\n 77: teddy bear\n 78: hair drier\n 79: toothbrush\n\n# Download script/URL (optional)\ndownload: https://github.com/ultralytics/assets/releases/download/v0.0.0/coco128-seg.zip\n" }, { "path": "data/coco128.yaml", "content": "# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license\n\n# COCO128 dataset https://www.kaggle.com/datasets/ultralytics/coco128 (first 128 images from COCO train2017) by Ultralytics\n# Example usage: python train.py --data coco128.yaml\n# parent\n# ├── yolov5\n# └── datasets\n# └── coco128 ← downloads here (7 MB)\n\n# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]\npath: ../datasets/coco128 # dataset root dir\ntrain: images/train2017 # train images (relative to 'path') 128 images\nval: images/train2017 # val images (relative to 'path') 128 images\ntest: # test images (optional)\n\n# Classes\nnames:\n 0: person\n 1: bicycle\n 2: car\n 3: motorcycle\n 4: airplane\n 5: bus\n 6: train\n 7: truck\n 8: boat\n 9: traffic light\n 10: fire hydrant\n 11: stop sign\n 12: parking meter\n 13: bench\n 14: bird\n 15: cat\n 16: dog\n 17: horse\n 18: sheep\n 19: cow\n 20: elephant\n 21: bear\n 22: zebra\n 23: giraffe\n 24: backpack\n 25: umbrella\n 26: handbag\n 27: tie\n 28: suitcase\n 29: frisbee\n 30: skis\n 31: snowboard\n 32: sports ball\n 33: kite\n 34: baseball bat\n 35: baseball glove\n 36: skateboard\n 37: surfboard\n 38: tennis racket\n 39: bottle\n 40: wine glass\n 41: cup\n 42: fork\n 43: knife\n 44: spoon\n 45: bowl\n 46: banana\n 47: apple\n 48: sandwich\n 49: orange\n 50: broccoli\n 51: carrot\n 52: hot dog\n 53: pizza\n 54: donut\n 55: cake\n 56: chair\n 57: couch\n 58: potted plant\n 59: bed\n 60: dining table\n 61: toilet\n 62: tv\n 63: laptop\n 64: mouse\n 65: remote\n 66: keyboard\n 67: cell phone\n 68: microwave\n 69: oven\n 70: toaster\n 71: sink\n 72: refrigerator\n 73: book\n 74: clock\n 75: vase\n 76: scissors\n 77: teddy bear\n 78: hair drier\n 79: toothbrush\n\n# Download script/URL (optional)\ndownload: https://github.com/ultralytics/assets/releases/download/v0.0.0/coco128.zip\n" }, { "path": "data/hyps/hyp.Objects365.yaml", "content": "# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license\n\n# Hyperparameters for Objects365 training\n# python train.py --weights yolov5m.pt --data Objects365.yaml --evolve\n# See Hyperparameter Evolution tutorial for details https://github.com/ultralytics/yolov5#tutorials\n\nlr0: 0.00258\nlrf: 0.17\nmomentum: 0.779\nweight_decay: 0.00058\nwarmup_epochs: 1.33\nwarmup_momentum: 0.86\nwarmup_bias_lr: 0.0711\nbox: 0.0539\ncls: 0.299\ncls_pw: 0.825\nobj: 0.632\nobj_pw: 1.0\niou_t: 0.2\nanchor_t: 3.44\nanchors: 3.2\nfl_gamma: 0.0\nhsv_h: 0.0188\nhsv_s: 0.704\nhsv_v: 0.36\ndegrees: 0.0\ntranslate: 0.0902\nscale: 0.491\nshear: 0.0\nperspective: 0.0\nflipud: 0.0\nfliplr: 0.5\nmosaic: 1.0\nmixup: 0.0\ncopy_paste: 0.0\n" }, { "path": "data/hyps/hyp.VOC.yaml", "content": "# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license\n\n# Hyperparameters for VOC training\n# python train.py --batch 128 --weights yolov5m6.pt --data VOC.yaml --epochs 50 --img 512 --hyp hyp.scratch-med.yaml --evolve\n# See Hyperparameter Evolution tutorial for details https://github.com/ultralytics/yolov5#tutorials\n\n# YOLOv3 Hyperparameter Evolution Results\n# Best generation: 467\n# Last generation: 996\n# metrics/precision, metrics/recall, metrics/mAP_0.5, metrics/mAP_0.5:0.95, val/box_loss, val/obj_loss, val/cls_loss\n# 0.87729, 0.85125, 0.91286, 0.72664, 0.0076739, 0.0042529, 0.0013865\n\nlr0: 0.00334\nlrf: 0.15135\nmomentum: 0.74832\nweight_decay: 0.00025\nwarmup_epochs: 3.3835\nwarmup_momentum: 0.59462\nwarmup_bias_lr: 0.18657\nbox: 0.02\ncls: 0.21638\ncls_pw: 0.5\nobj: 0.51728\nobj_pw: 0.67198\niou_t: 0.2\nanchor_t: 3.3744\nfl_gamma: 0.0\nhsv_h: 0.01041\nhsv_s: 0.54703\nhsv_v: 0.27739\ndegrees: 0.0\ntranslate: 0.04591\nscale: 0.75544\nshear: 0.0\nperspective: 0.0\nflipud: 0.0\nfliplr: 0.5\nmosaic: 0.85834\nmixup: 0.04266\ncopy_paste: 0.0\nanchors: 3.412\n" }, { "path": "data/hyps/hyp.no-augmentation.yaml", "content": "# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license\n\n# Hyperparameters when using Albumentations frameworks\n# python train.py --hyp hyp.no-augmentation.yaml\n# See https://github.com/ultralytics/yolov5/pull/3882 for YOLOv3 + Albumentations Usage examples\n\nlr0: 0.01 # initial learning rate (SGD=1E-2, Adam=1E-3)\nlrf: 0.1 # final OneCycleLR learning rate (lr0 * lrf)\nmomentum: 0.937 # SGD momentum/Adam beta1\nweight_decay: 0.0005 # optimizer weight decay 5e-4\nwarmup_epochs: 3.0 # warmup epochs (fractions ok)\nwarmup_momentum: 0.8 # warmup initial momentum\nwarmup_bias_lr: 0.1 # warmup initial bias lr\nbox: 0.05 # box loss gain\ncls: 0.3 # cls loss gain\ncls_pw: 1.0 # cls BCELoss positive_weight\nobj: 0.7 # obj loss gain (scale with pixels)\nobj_pw: 1.0 # obj BCELoss positive_weight\niou_t: 0.20 # IoU training threshold\nanchor_t: 4.0 # anchor-multiple threshold\n# anchors: 3 # anchors per output layer (0 to ignore)\n# this parameters are all zero since we want to use albumentation framework\nfl_gamma: 0.0 # focal loss gamma (efficientDet default gamma=1.5)\nhsv_h: 0 # image HSV-Hue augmentation (fraction)\nhsv_s: 0 # image HSV-Saturation augmentation (fraction)\nhsv_v: 0 # image HSV-Value augmentation (fraction)\ndegrees: 0.0 # image rotation (+/- deg)\ntranslate: 0 # image translation (+/- fraction)\nscale: 0 # image scale (+/- gain)\nshear: 0 # image shear (+/- deg)\nperspective: 0.0 # image perspective (+/- fraction), range 0-0.001\nflipud: 0.0 # image flip up-down (probability)\nfliplr: 0.0 # image flip left-right (probability)\nmosaic: 0.0 # image mosaic (probability)\nmixup: 0.0 # image mixup (probability)\ncopy_paste: 0.0 # segment copy-paste (probability)\n" }, { "path": "data/hyps/hyp.scratch-high.yaml", "content": "# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license\n\n# Hyperparameters for high-augmentation COCO training from scratch\n# python train.py --batch 32 --cfg yolov5m6.yaml --weights '' --data coco.yaml --img 1280 --epochs 300\n# See tutorials for hyperparameter evolution https://github.com/ultralytics/yolov5#tutorials\n\nlr0: 0.01 # initial learning rate (SGD=1E-2, Adam=1E-3)\nlrf: 0.1 # final OneCycleLR learning rate (lr0 * lrf)\nmomentum: 0.937 # SGD momentum/Adam beta1\nweight_decay: 0.0005 # optimizer weight decay 5e-4\nwarmup_epochs: 3.0 # warmup epochs (fractions ok)\nwarmup_momentum: 0.8 # warmup initial momentum\nwarmup_bias_lr: 0.1 # warmup initial bias lr\nbox: 0.05 # box loss gain\ncls: 0.3 # cls loss gain\ncls_pw: 1.0 # cls BCELoss positive_weight\nobj: 0.7 # obj loss gain (scale with pixels)\nobj_pw: 1.0 # obj BCELoss positive_weight\niou_t: 0.20 # IoU training threshold\nanchor_t: 4.0 # anchor-multiple threshold\n# anchors: 3 # anchors per output layer (0 to ignore)\nfl_gamma: 0.0 # focal loss gamma (efficientDet default gamma=1.5)\nhsv_h: 0.015 # image HSV-Hue augmentation (fraction)\nhsv_s: 0.7 # image HSV-Saturation augmentation (fraction)\nhsv_v: 0.4 # image HSV-Value augmentation (fraction)\ndegrees: 0.0 # image rotation (+/- deg)\ntranslate: 0.1 # image translation (+/- fraction)\nscale: 0.9 # image scale (+/- gain)\nshear: 0.0 # image shear (+/- deg)\nperspective: 0.0 # image perspective (+/- fraction), range 0-0.001\nflipud: 0.0 # image flip up-down (probability)\nfliplr: 0.5 # image flip left-right (probability)\nmosaic: 1.0 # image mosaic (probability)\nmixup: 0.1 # image mixup (probability)\ncopy_paste: 0.1 # segment copy-paste (probability)\n" }, { "path": "data/hyps/hyp.scratch-low.yaml", "content": "# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license\n\n# Hyperparameters for low-augmentation COCO training from scratch\n# python train.py --batch 64 --cfg yolov5n6.yaml --weights '' --data coco.yaml --img 640 --epochs 300 --linear\n# See tutorials for hyperparameter evolution https://github.com/ultralytics/yolov5#tutorials\n\nlr0: 0.01 # initial learning rate (SGD=1E-2, Adam=1E-3)\nlrf: 0.01 # final OneCycleLR learning rate (lr0 * lrf)\nmomentum: 0.937 # SGD momentum/Adam beta1\nweight_decay: 0.0005 # optimizer weight decay 5e-4\nwarmup_epochs: 3.0 # warmup epochs (fractions ok)\nwarmup_momentum: 0.8 # warmup initial momentum\nwarmup_bias_lr: 0.1 # warmup initial bias lr\nbox: 0.05 # box loss gain\ncls: 0.5 # cls loss gain\ncls_pw: 1.0 # cls BCELoss positive_weight\nobj: 1.0 # obj loss gain (scale with pixels)\nobj_pw: 1.0 # obj BCELoss positive_weight\niou_t: 0.20 # IoU training threshold\nanchor_t: 4.0 # anchor-multiple threshold\n# anchors: 3 # anchors per output layer (0 to ignore)\nfl_gamma: 0.0 # focal loss gamma (efficientDet default gamma=1.5)\nhsv_h: 0.015 # image HSV-Hue augmentation (fraction)\nhsv_s: 0.7 # image HSV-Saturation augmentation (fraction)\nhsv_v: 0.4 # image HSV-Value augmentation (fraction)\ndegrees: 0.0 # image rotation (+/- deg)\ntranslate: 0.1 # image translation (+/- fraction)\nscale: 0.5 # image scale (+/- gain)\nshear: 0.0 # image shear (+/- deg)\nperspective: 0.0 # image perspective (+/- fraction), range 0-0.001\nflipud: 0.0 # image flip up-down (probability)\nfliplr: 0.5 # image flip left-right (probability)\nmosaic: 1.0 # image mosaic (probability)\nmixup: 0.0 # image mixup (probability)\ncopy_paste: 0.0 # segment copy-paste (probability)\n" }, { "path": "data/hyps/hyp.scratch-med.yaml", "content": "# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license\n\n# Hyperparameters for medium-augmentation COCO training from scratch\n# python train.py --batch 32 --cfg yolov5m6.yaml --weights '' --data coco.yaml --img 1280 --epochs 300\n# See tutorials for hyperparameter evolution https://github.com/ultralytics/yolov5#tutorials\n\nlr0: 0.01 # initial learning rate (SGD=1E-2, Adam=1E-3)\nlrf: 0.1 # final OneCycleLR learning rate (lr0 * lrf)\nmomentum: 0.937 # SGD momentum/Adam beta1\nweight_decay: 0.0005 # optimizer weight decay 5e-4\nwarmup_epochs: 3.0 # warmup epochs (fractions ok)\nwarmup_momentum: 0.8 # warmup initial momentum\nwarmup_bias_lr: 0.1 # warmup initial bias lr\nbox: 0.05 # box loss gain\ncls: 0.3 # cls loss gain\ncls_pw: 1.0 # cls BCELoss positive_weight\nobj: 0.7 # obj loss gain (scale with pixels)\nobj_pw: 1.0 # obj BCELoss positive_weight\niou_t: 0.20 # IoU training threshold\nanchor_t: 4.0 # anchor-multiple threshold\n# anchors: 3 # anchors per output layer (0 to ignore)\nfl_gamma: 0.0 # focal loss gamma (efficientDet default gamma=1.5)\nhsv_h: 0.015 # image HSV-Hue augmentation (fraction)\nhsv_s: 0.7 # image HSV-Saturation augmentation (fraction)\nhsv_v: 0.4 # image HSV-Value augmentation (fraction)\ndegrees: 0.0 # image rotation (+/- deg)\ntranslate: 0.1 # image translation (+/- fraction)\nscale: 0.9 # image scale (+/- gain)\nshear: 0.0 # image shear (+/- deg)\nperspective: 0.0 # image perspective (+/- fraction), range 0-0.001\nflipud: 0.0 # image flip up-down (probability)\nfliplr: 0.5 # image flip left-right (probability)\nmosaic: 1.0 # image mosaic (probability)\nmixup: 0.1 # image mixup (probability)\ncopy_paste: 0.0 # segment copy-paste (probability)\n" }, { "path": "data/objects365.yaml", "content": "# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license\n\n# Objects365 dataset https://www.objects365.org/ by Megvii\n# Example usage: python train.py --data Objects365.yaml\n# parent\n# ├── yolov5\n# └── datasets\n# └── Objects365 ← downloads here (712 GB = 367G data + 345G zips)\n\n# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]\npath: ../datasets/Objects365 # dataset root dir\ntrain: images/train # train images (relative to 'path') 1742289 images\nval: images/val # val images (relative to 'path') 80000 images\ntest: # test images (optional)\n\n# Classes\nnames:\n 0: Person\n 1: Sneakers\n 2: Chair\n 3: Other Shoes\n 4: Hat\n 5: Car\n 6: Lamp\n 7: Glasses\n 8: Bottle\n 9: Desk\n 10: Cup\n 11: Street Lights\n 12: Cabinet/shelf\n 13: Handbag/Satchel\n 14: Bracelet\n 15: Plate\n 16: Picture/Frame\n 17: Helmet\n 18: Book\n 19: Gloves\n 20: Storage box\n 21: Boat\n 22: Leather Shoes\n 23: Flower\n 24: Bench\n 25: Potted Plant\n 26: Bowl/Basin\n 27: Flag\n 28: Pillow\n 29: Boots\n 30: Vase\n 31: Microphone\n 32: Necklace\n 33: Ring\n 34: SUV\n 35: Wine Glass\n 36: Belt\n 37: Monitor/TV\n 38: Backpack\n 39: Umbrella\n 40: Traffic Light\n 41: Speaker\n 42: Watch\n 43: Tie\n 44: Trash bin Can\n 45: Slippers\n 46: Bicycle\n 47: Stool\n 48: Barrel/bucket\n 49: Van\n 50: Couch\n 51: Sandals\n 52: Basket\n 53: Drum\n 54: Pen/Pencil\n 55: Bus\n 56: Wild Bird\n 57: High Heels\n 58: Motorcycle\n 59: Guitar\n 60: Carpet\n 61: Cell Phone\n 62: Bread\n 63: Camera\n 64: Canned\n 65: Truck\n 66: Traffic cone\n 67: Cymbal\n 68: Lifesaver\n 69: Towel\n 70: Stuffed Toy\n 71: Candle\n 72: Sailboat\n 73: Laptop\n 74: Awning\n 75: Bed\n 76: Faucet\n 77: Tent\n 78: Horse\n 79: Mirror\n 80: Power outlet\n 81: Sink\n 82: Apple\n 83: Air Conditioner\n 84: Knife\n 85: Hockey Stick\n 86: Paddle\n 87: Pickup Truck\n 88: Fork\n 89: Traffic Sign\n 90: Balloon\n 91: Tripod\n 92: Dog\n 93: Spoon\n 94: Clock\n 95: Pot\n 96: Cow\n 97: Cake\n 98: Dinning Table\n 99: Sheep\n 100: Hanger\n 101: Blackboard/Whiteboard\n 102: Napkin\n 103: Other Fish\n 104: Orange/Tangerine\n 105: Toiletry\n 106: Keyboard\n 107: Tomato\n 108: Lantern\n 109: Machinery Vehicle\n 110: Fan\n 111: Green Vegetables\n 112: Banana\n 113: Baseball Glove\n 114: Airplane\n 115: Mouse\n 116: Train\n 117: Pumpkin\n 118: Soccer\n 119: Skiboard\n 120: Luggage\n 121: Nightstand\n 122: Tea pot\n 123: Telephone\n 124: Trolley\n 125: Head Phone\n 126: Sports Car\n 127: Stop Sign\n 128: Dessert\n 129: Scooter\n 130: Stroller\n 131: Crane\n 132: Remote\n 133: Refrigerator\n 134: Oven\n 135: Lemon\n 136: Duck\n 137: Baseball Bat\n 138: Surveillance Camera\n 139: Cat\n 140: Jug\n 141: Broccoli\n 142: Piano\n 143: Pizza\n 144: Elephant\n 145: Skateboard\n 146: Surfboard\n 147: Gun\n 148: Skating and Skiing shoes\n 149: Gas stove\n 150: Donut\n 151: Bow Tie\n 152: Carrot\n 153: Toilet\n 154: Kite\n 155: Strawberry\n 156: Other Balls\n 157: Shovel\n 158: Pepper\n 159: Computer Box\n 160: Toilet Paper\n 161: Cleaning Products\n 162: Chopsticks\n 163: Microwave\n 164: Pigeon\n 165: Baseball\n 166: Cutting/chopping Board\n 167: Coffee Table\n 168: Side Table\n 169: Scissors\n 170: Marker\n 171: Pie\n 172: Ladder\n 173: Snowboard\n 174: Cookies\n 175: Radiator\n 176: Fire Hydrant\n 177: Basketball\n 178: Zebra\n 179: Grape\n 180: Giraffe\n 181: Potato\n 182: Sausage\n 183: Tricycle\n 184: Violin\n 185: Egg\n 186: Fire Extinguisher\n 187: Candy\n 188: Fire Truck\n 189: Billiards\n 190: Converter\n 191: Bathtub\n 192: Wheelchair\n 193: Golf Club\n 194: Briefcase\n 195: Cucumber\n 196: Cigar/Cigarette\n 197: Paint Brush\n 198: Pear\n 199: Heavy Truck\n 200: Hamburger\n 201: Extractor\n 202: Extension Cord\n 203: Tong\n 204: Tennis Racket\n 205: Folder\n 206: American Football\n 207: earphone\n 208: Mask\n 209: Kettle\n 210: Tennis\n 211: Ship\n 212: Swing\n 213: Coffee Machine\n 214: Slide\n 215: Carriage\n 216: Onion\n 217: Green beans\n 218: Projector\n 219: Frisbee\n 220: Washing Machine/Drying Machine\n 221: Chicken\n 222: Printer\n 223: Watermelon\n 224: Saxophone\n 225: Tissue\n 226: Toothbrush\n 227: Ice cream\n 228: Hot-air balloon\n 229: Cello\n 230: French Fries\n 231: Scale\n 232: Trophy\n 233: Cabbage\n 234: Hot dog\n 235: Blender\n 236: Peach\n 237: Rice\n 238: Wallet/Purse\n 239: Volleyball\n 240: Deer\n 241: Goose\n 242: Tape\n 243: Tablet\n 244: Cosmetics\n 245: Trumpet\n 246: Pineapple\n 247: Golf Ball\n 248: Ambulance\n 249: Parking meter\n 250: Mango\n 251: Key\n 252: Hurdle\n 253: Fishing Rod\n 254: Medal\n 255: Flute\n 256: Brush\n 257: Penguin\n 258: Megaphone\n 259: Corn\n 260: Lettuce\n 261: Garlic\n 262: Swan\n 263: Helicopter\n 264: Green Onion\n 265: Sandwich\n 266: Nuts\n 267: Speed Limit Sign\n 268: Induction Cooker\n 269: Broom\n 270: Trombone\n 271: Plum\n 272: Rickshaw\n 273: Goldfish\n 274: Kiwi fruit\n 275: Router/modem\n 276: Poker Card\n 277: Toaster\n 278: Shrimp\n 279: Sushi\n 280: Cheese\n 281: Notepaper\n 282: Cherry\n 283: Pliers\n 284: CD\n 285: Pasta\n 286: Hammer\n 287: Cue\n 288: Avocado\n 289: Hamimelon\n 290: Flask\n 291: Mushroom\n 292: Screwdriver\n 293: Soap\n 294: Recorder\n 295: Bear\n 296: Eggplant\n 297: Board Eraser\n 298: Coconut\n 299: Tape Measure/Ruler\n 300: Pig\n 301: Showerhead\n 302: Globe\n 303: Chips\n 304: Steak\n 305: Crosswalk Sign\n 306: Stapler\n 307: Camel\n 308: Formula 1\n 309: Pomegranate\n 310: Dishwasher\n 311: Crab\n 312: Hoverboard\n 313: Meat ball\n 314: Rice Cooker\n 315: Tuba\n 316: Calculator\n 317: Papaya\n 318: Antelope\n 319: Parrot\n 320: Seal\n 321: Butterfly\n 322: Dumbbell\n 323: Donkey\n 324: Lion\n 325: Urinal\n 326: Dolphin\n 327: Electric Drill\n 328: Hair Dryer\n 329: Egg tart\n 330: Jellyfish\n 331: Treadmill\n 332: Lighter\n 333: Grapefruit\n 334: Game board\n 335: Mop\n 336: Radish\n 337: Baozi\n 338: Target\n 339: French\n 340: Spring Rolls\n 341: Monkey\n 342: Rabbit\n 343: Pencil Case\n 344: Yak\n 345: Red Cabbage\n 346: Binoculars\n 347: Asparagus\n 348: Barbell\n 349: Scallop\n 350: Noddles\n 351: Comb\n 352: Dumpling\n 353: Oyster\n 354: Table Tennis paddle\n 355: Cosmetics Brush/Eyeliner Pencil\n 356: Chainsaw\n 357: Eraser\n 358: Lobster\n 359: Durian\n 360: Okra\n 361: Lipstick\n 362: Cosmetics Mirror\n 363: Curling\n 364: Table Tennis\n\n# Download script/URL (optional) ---------------------------------------------------------------------------------------\ndownload: |\n from tqdm import tqdm\n\n from utils.general import Path, check_requirements, download, np, xyxy2xywhn\n\n check_requirements('pycocotools>=2.0')\n from pycocotools.coco import COCO\n\n # Make Directories\n dir = Path(yaml['path']) # dataset root dir\n for p in 'images', 'labels':\n (dir / p).mkdir(parents=True, exist_ok=True)\n for q in 'train', 'val':\n (dir / p / q).mkdir(parents=True, exist_ok=True)\n\n # Train, Val Splits\n for split, patches in [('train', 50 + 1), ('val', 43 + 1)]:\n print(f\"Processing {split} in {patches} patches ...\")\n images, labels = dir / 'images' / split, dir / 'labels' / split\n\n # Download\n url = f\"https://dorc.ks3-cn-beijing.ksyun.com/data-set/2020Objects365%E6%95%B0%E6%8D%AE%E9%9B%86/{split}/\"\n if split == 'train':\n download([f'{url}zhiyuan_objv2_{split}.tar.gz'], dir=dir, delete=False) # annotations json\n download([f'{url}patch{i}.tar.gz' for i in range(patches)], dir=images, curl=True, delete=False, threads=8)\n elif split == 'val':\n download([f'{url}zhiyuan_objv2_{split}.json'], dir=dir, delete=False) # annotations json\n download([f'{url}images/v1/patch{i}.tar.gz' for i in range(15 + 1)], dir=images, curl=True, delete=False, threads=8)\n download([f'{url}images/v2/patch{i}.tar.gz' for i in range(16, patches)], dir=images, curl=True, delete=False, threads=8)\n\n # Move\n for f in tqdm(images.rglob('*.jpg'), desc=f'Moving {split} images'):\n f.rename(images / f.name) # move to /images/{split}\n\n # Labels\n coco = COCO(dir / f'zhiyuan_objv2_{split}.json')\n names = [x[\"name\"] for x in coco.loadCats(coco.getCatIds())]\n for cid, cat in enumerate(names):\n catIds = coco.getCatIds(catNms=[cat])\n imgIds = coco.getImgIds(catIds=catIds)\n for im in tqdm(coco.loadImgs(imgIds), desc=f'Class {cid + 1}/{len(names)} {cat}'):\n width, height = im[\"width\"], im[\"height\"]\n path = Path(im[\"file_name\"]) # image filename\n try:\n with open(labels / path.with_suffix('.txt').name, 'a') as file:\n annIds = coco.getAnnIds(imgIds=im[\"id\"], catIds=catIds, iscrowd=False)\n for a in coco.loadAnns(annIds):\n x, y, w, h = a['bbox'] # bounding box in xywh (xy top-left corner)\n xyxy = np.array([x, y, x + w, y + h])[None] # pixels(1,4)\n x, y, w, h = xyxy2xywhn(xyxy, w=width, h=height, clip=True)[0] # normalized and clipped\n file.write(f\"{cid} {x:.5f} {y:.5f} {w:.5f} {h:.5f}\\n\")\n except Exception as e:\n print(e)\n" }, { "path": "data/scripts/download_weights.sh", "content": "#!/bin/bash\n# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license\n\n# Download latest models from https://github.com/ultralytics/yolov5/releases\n# Example usage: bash data/scripts/download_weights.sh\n# parent\n# └── yolov5\n# ├── yolov5s.pt ← downloads here\n# ├── yolov5m.pt\n# └── ...\n\npython - << EOF\nfrom utils.downloads import attempt_download\n\np5 = list('nsmlx') # P5 models\np6 = [f'{x}6' for x in p5] # P6 models\ncls = [f'{x}-cls' for x in p5] # classification models\nseg = [f'{x}-seg' for x in p5] # classification models\n\nfor x in p5 + p6 + cls + seg:\n attempt_download(f'weights/yolov5{x}.pt')\n\nEOF\n" }, { "path": "data/scripts/get_coco.sh", "content": "#!/bin/bash\n# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license\n\n# Download COCO 2017 dataset http://cocodataset.org\n# Example usage: bash data/scripts/get_coco.sh\n# parent\n# ├── yolov5\n# └── datasets\n# └── coco ← downloads here\n\n# Arguments (optional) Usage: bash data/scripts/get_coco.sh --train --val --test --segments\nif [ \"$#\" -gt 0 ]; then\n for opt in \"$@\"; do\n case \"${opt}\" in\n --train) train=true ;;\n --val) val=true ;;\n --test) test=true ;;\n --segments) segments=true ;;\n esac\n done\nelse\n train=true\n val=true\n test=false\n segments=false\nfi\n\n# Download/unzip labels\nd='../datasets' # unzip directory\nurl=https://github.com/ultralytics/yolov5/releases/download/v1.0/\nif [ \"$segments\" == \"true\" ]; then\n f='coco2017labels-segments.zip' # 168 MB\nelse\n f='coco2017labels.zip' # 46 MB\nfi\necho 'Downloading' $url$f ' ...'\ncurl -L $url$f -o $f -# && unzip -q $f -d $d && rm $f &\n\n# Download/unzip images\nd='../datasets/coco/images' # unzip directory\nurl=http://images.cocodataset.org/zips/\nif [ \"$train\" == \"true\" ]; then\n f='train2017.zip' # 19G, 118k images\n echo 'Downloading' $url$f '...'\n curl -L $url$f -o $f -# && unzip -q $f -d $d && rm $f &\nfi\nif [ \"$val\" == \"true\" ]; then\n f='val2017.zip' # 1G, 5k images\n echo 'Downloading' $url$f '...'\n curl -L $url$f -o $f -# && unzip -q $f -d $d && rm $f &\nfi\nif [ \"$test\" == \"true\" ]; then\n f='test2017.zip' # 7G, 41k images (optional)\n echo 'Downloading' $url$f '...'\n curl -L $url$f -o $f -# && unzip -q $f -d $d && rm $f &\nfi\nwait # finish background tasks\n" }, { "path": "data/scripts/get_coco128.sh", "content": "#!/bin/bash\n# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license\n\n# Download COCO128 dataset https://www.kaggle.com/ultralytics/coco128 (first 128 images from COCO train2017)\n# Example usage: bash data/scripts/get_coco128.sh\n# parent\n# ├── yolov5\n# └── datasets\n# └── coco128 ← downloads here\n\n# Download/unzip images and labels\nd='../datasets' # unzip directory\nurl=https://github.com/ultralytics/yolov5/releases/download/v1.0/\nf='coco128.zip' # or 'coco128-segments.zip', 68 MB\necho 'Downloading' $url$f ' ...'\ncurl -L $url$f -o $f -# && unzip -q $f -d $d && rm $f &\n\nwait # finish background tasks\n" }, { "path": "data/scripts/get_imagenet.sh", "content": "#!/bin/bash\n# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license\n\n# Download ILSVRC2012 ImageNet dataset https://image-net.org\n# Example usage: bash data/scripts/get_imagenet.sh\n# parent\n# ├── yolov5\n# └── datasets\n# └── imagenet ← downloads here\n\n# Arguments (optional) Usage: bash data/scripts/get_imagenet.sh --train --val\nif [ \"$#\" -gt 0 ]; then\n for opt in \"$@\"; do\n case \"${opt}\" in\n --train) train=true ;;\n --val) val=true ;;\n esac\n done\nelse\n train=true\n val=true\nfi\n\n# Make dir\nd='../datasets/imagenet' # unzip directory\nmkdir -p $d && cd $d\n\n# Download/unzip train\nif [ \"$train\" == \"true\" ]; then\n wget https://image-net.org/data/ILSVRC/2012/ILSVRC2012_img_train.tar # download 138G, 1281167 images\n mkdir train && mv ILSVRC2012_img_train.tar train/ && cd train\n tar -xf ILSVRC2012_img_train.tar && rm -f ILSVRC2012_img_train.tar\n find . -name \"*.tar\" | while read NAME; do\n mkdir -p \"${NAME%.tar}\"\n tar -xf \"${NAME}\" -C \"${NAME%.tar}\"\n rm -f \"${NAME}\"\n done\n cd ..\nfi\n\n# Download/unzip val\nif [ \"$val\" == \"true\" ]; then\n wget https://image-net.org/data/ILSVRC/2012/ILSVRC2012_img_val.tar # download 6.3G, 50000 images\n mkdir val && mv ILSVRC2012_img_val.tar val/ && cd val && tar -xf ILSVRC2012_img_val.tar\n wget -qO- https://raw.githubusercontent.com/soumith/imagenetloader.torch/master/valprep.sh | bash # move into subdirs\nfi\n\n# Delete corrupted image (optional: PNG under JPEG name that may cause dataloaders to fail)\n# rm train/n04266014/n04266014_10835.JPEG\n\n# TFRecords (optional)\n# wget https://raw.githubusercontent.com/tensorflow/models/master/research/slim/datasets/imagenet_lsvrc_2015_synsets.txt\n" }, { "path": "data/voc.yaml", "content": "# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license\n\n# PASCAL VOC dataset http://host.robots.ox.ac.uk/pascal/VOC by University of Oxford\n# Example usage: python train.py --data VOC.yaml\n# parent\n# ├── yolov5\n# └── datasets\n# └── VOC ← downloads here (2.8 GB)\n\n# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]\npath: ../datasets/VOC\ntrain: # train images (relative to 'path') 16551 images\n - images/train2012\n - images/train2007\n - images/val2012\n - images/val2007\nval: # val images (relative to 'path') 4952 images\n - images/test2007\ntest: # test images (optional)\n - images/test2007\n\n# Classes\nnames:\n 0: aeroplane\n 1: bicycle\n 2: bird\n 3: boat\n 4: bottle\n 5: bus\n 6: car\n 7: cat\n 8: chair\n 9: cow\n 10: diningtable\n 11: dog\n 12: horse\n 13: motorbike\n 14: person\n 15: pottedplant\n 16: sheep\n 17: sofa\n 18: train\n 19: tvmonitor\n\n# Download script/URL (optional) ---------------------------------------------------------------------------------------\ndownload: |\n import xml.etree.ElementTree as ET\n\n from tqdm import tqdm\n from utils.general import download, Path\n\n\n def convert_label(path, lb_path, year, image_id):\n def convert_box(size, box):\n dw, dh = 1. / size[0], 1. / size[1]\n x, y, w, h = (box[0] + box[1]) / 2.0 - 1, (box[2] + box[3]) / 2.0 - 1, box[1] - box[0], box[3] - box[2]\n return x * dw, y * dh, w * dw, h * dh\n\n in_file = open(path / f'VOC{year}/Annotations/{image_id}.xml')\n out_file = open(lb_path, 'w')\n tree = ET.parse(in_file)\n root = tree.getroot()\n size = root.find('size')\n w = int(size.find('width').text)\n h = int(size.find('height').text)\n\n names = list(yaml['names'].values()) # names list\n for obj in root.iter('object'):\n cls = obj.find('name').text\n if cls in names and int(obj.find('difficult').text) != 1:\n xmlbox = obj.find('bndbox')\n bb = convert_box((w, h), [float(xmlbox.find(x).text) for x in ('xmin', 'xmax', 'ymin', 'ymax')])\n cls_id = names.index(cls) # class id\n out_file.write(\" \".join([str(a) for a in (cls_id, *bb)]) + '\\n')\n\n\n # Download\n dir = Path(yaml['path']) # dataset root dir\n url = 'https://github.com/ultralytics/assets/releases/download/v0.0.0/'\n urls = [f'{url}VOCtrainval_06-Nov-2007.zip', # 446MB, 5012 images\n f'{url}VOCtest_06-Nov-2007.zip', # 438MB, 4953 images\n f'{url}VOCtrainval_11-May-2012.zip'] # 1.95GB, 17126 images\n download(urls, dir=dir / 'images', delete=False, curl=True, threads=3)\n\n # Convert\n path = dir / 'images/VOCdevkit'\n for year, image_set in ('2012', 'train'), ('2012', 'val'), ('2007', 'train'), ('2007', 'val'), ('2007', 'test'):\n imgs_path = dir / 'images' / f'{image_set}{year}'\n lbs_path = dir / 'labels' / f'{image_set}{year}'\n imgs_path.mkdir(exist_ok=True, parents=True)\n lbs_path.mkdir(exist_ok=True, parents=True)\n\n with open(path / f'VOC{year}/ImageSets/Main/{image_set}.txt') as f:\n image_ids = f.read().strip().split()\n for id in tqdm(image_ids, desc=f'{image_set}{year}'):\n f = path / f'VOC{year}/JPEGImages/{id}.jpg' # old img path\n lb_path = (lbs_path / f.name).with_suffix('.txt') # new label path\n f.rename(imgs_path / f.name) # move image\n convert_label(path, lb_path, year, id) # convert labels to YOLO format\n" }, { "path": "data/xView.yaml", "content": "# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license\n\n# DIUx xView 2018 Challenge https://challenge.xviewdataset.org by U.S. National Geospatial-Intelligence Agency (NGA)\n# -------- DOWNLOAD DATA MANUALLY and jar xf val_images.zip to 'datasets/xView' before running train command! --------\n# Example usage: python train.py --data xView.yaml\n# parent\n# ├── yolov5\n# └── datasets\n# └── xView ← downloads here (20.7 GB)\n\n# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]\npath: ../datasets/xView # dataset root dir\ntrain: images/autosplit_train.txt # train images (relative to 'path') 90% of 847 train images\nval: images/autosplit_val.txt # train images (relative to 'path') 10% of 847 train images\n\n# Classes\nnames:\n 0: Fixed-wing Aircraft\n 1: Small Aircraft\n 2: Cargo Plane\n 3: Helicopter\n 4: Passenger Vehicle\n 5: Small Car\n 6: Bus\n 7: Pickup Truck\n 8: Utility Truck\n 9: Truck\n 10: Cargo Truck\n 11: Truck w/Box\n 12: Truck Tractor\n 13: Trailer\n 14: Truck w/Flatbed\n 15: Truck w/Liquid\n 16: Crane Truck\n 17: Railway Vehicle\n 18: Passenger Car\n 19: Cargo Car\n 20: Flat Car\n 21: Tank car\n 22: Locomotive\n 23: Maritime Vessel\n 24: Motorboat\n 25: Sailboat\n 26: Tugboat\n 27: Barge\n 28: Fishing Vessel\n 29: Ferry\n 30: Yacht\n 31: Container Ship\n 32: Oil Tanker\n 33: Engineering Vehicle\n 34: Tower crane\n 35: Container Crane\n 36: Reach Stacker\n 37: Straddle Carrier\n 38: Mobile Crane\n 39: Dump Truck\n 40: Haul Truck\n 41: Scraper/Tractor\n 42: Front loader/Bulldozer\n 43: Excavator\n 44: Cement Mixer\n 45: Ground Grader\n 46: Hut/Tent\n 47: Shed\n 48: Building\n 49: Aircraft Hangar\n 50: Damaged Building\n 51: Facility\n 52: Construction Site\n 53: Vehicle Lot\n 54: Helipad\n 55: Storage Tank\n 56: Shipping container lot\n 57: Shipping Container\n 58: Pylon\n 59: Tower\n\n# Download script/URL (optional) ---------------------------------------------------------------------------------------\ndownload: |\n import json\n import os\n from pathlib import Path\n\n import numpy as np\n from PIL import Image\n from tqdm import tqdm\n\n from utils.dataloaders import autosplit\n from utils.general import download, xyxy2xywhn\n\n\n def convert_labels(fname=Path('xView/xView_train.geojson')):\n # Convert xView geoJSON labels to YOLO format\n path = fname.parent\n with open(fname) as f:\n print(f'Loading {fname}...')\n data = json.load(f)\n\n # Make dirs\n labels = Path(path / 'labels' / 'train')\n os.system(f'rm -rf {labels}')\n labels.mkdir(parents=True, exist_ok=True)\n\n # xView classes 11-94 to 0-59\n xview_class2index = [-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 0, 1, 2, -1, 3, -1, 4, 5, 6, 7, 8, -1, 9, 10, 11,\n 12, 13, 14, 15, -1, -1, 16, 17, 18, 19, 20, 21, 22, -1, 23, 24, 25, -1, 26, 27, -1, 28, -1,\n 29, 30, 31, 32, 33, 34, 35, 36, 37, -1, 38, 39, 40, 41, 42, 43, 44, 45, -1, -1, -1, -1, 46,\n 47, 48, 49, -1, 50, 51, -1, 52, -1, -1, -1, 53, 54, -1, 55, -1, -1, 56, -1, 57, -1, 58, 59]\n\n shapes = {}\n for feature in tqdm(data['features'], desc=f'Converting {fname}'):\n p = feature['properties']\n if p['bounds_imcoords']:\n id = p['image_id']\n file = path / 'train_images' / id\n if file.exists(): # 1395.tif missing\n try:\n box = np.array([int(num) for num in p['bounds_imcoords'].split(\",\")])\n assert box.shape[0] == 4, f'incorrect box shape {box.shape[0]}'\n cls = p['type_id']\n cls = xview_class2index[int(cls)] # xView class to 0-60\n assert 59 >= cls >= 0, f'incorrect class index {cls}'\n\n # Write YOLO label\n if id not in shapes:\n shapes[id] = Image.open(file).size\n box = xyxy2xywhn(box[None].astype(np.float), w=shapes[id][0], h=shapes[id][1], clip=True)\n with open((labels / id).with_suffix('.txt'), 'a') as f:\n f.write(f\"{cls} {' '.join(f'{x:.6f}' for x in box[0])}\\n\") # write label.txt\n except Exception as e:\n print(f'WARNING: skipping one label for {file}: {e}')\n\n\n # Download manually from https://challenge.xviewdataset.org\n dir = Path(yaml['path']) # dataset root dir\n # urls = ['https://d307kc0mrhucc3.cloudfront.net/train_labels.zip', # train labels\n # 'https://d307kc0mrhucc3.cloudfront.net/train_images.zip', # 15G, 847 train images\n # 'https://d307kc0mrhucc3.cloudfront.net/val_images.zip'] # 5G, 282 val images (no labels)\n # download(urls, dir=dir, delete=False)\n\n # Convert labels\n convert_labels(dir / 'xView_train.geojson')\n\n # Move images\n images = Path(dir / 'images')\n images.mkdir(parents=True, exist_ok=True)\n Path(dir / 'train_images').rename(dir / 'images' / 'train')\n Path(dir / 'val_images').rename(dir / 'images' / 'val')\n\n # Split\n autosplit(dir / 'images' / 'train')\n" }, { "path": "detect.py", "content": "# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license\n\"\"\"\nRun YOLOv3 detection inference on images, videos, directories, globs, YouTube, webcam, streams, etc.\n\nUsage - sources:\n $ python detect.py --weights yolov5s.pt --source 0 # webcam\n img.jpg # image\n vid.mp4 # video\n screen # screenshot\n path/ # directory\n list.txt # list of images\n list.streams # list of streams\n 'path/*.jpg' # glob\n 'https://youtu.be/LNwODJXcvt4' # YouTube\n 'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream\n\nUsage - formats:\n $ python detect.py --weights yolov5s.pt # PyTorch\n yolov5s.torchscript # TorchScript\n yolov5s.onnx # ONNX Runtime or OpenCV DNN with --dnn\n yolov5s_openvino_model # OpenVINO\n yolov5s.engine # TensorRT\n yolov5s.mlmodel # CoreML (macOS-only)\n yolov5s_saved_model # TensorFlow SavedModel\n yolov5s.pb # TensorFlow GraphDef\n yolov5s.tflite # TensorFlow Lite\n yolov5s_edgetpu.tflite # TensorFlow Edge TPU\n yolov5s_paddle_model # PaddlePaddle\n\"\"\"\n\nimport argparse\nimport os\nimport platform\nimport sys\nfrom pathlib import Path\n\nimport torch\n\nFILE = Path(__file__).resolve()\nROOT = FILE.parents[0] # YOLOv3 root directory\nif str(ROOT) not in sys.path:\n sys.path.append(str(ROOT)) # add ROOT to PATH\nROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative\n\nfrom ultralytics.utils.plotting import Annotator, colors, save_one_box\n\nfrom models.common import DetectMultiBackend\nfrom utils.dataloaders import IMG_FORMATS, VID_FORMATS, LoadImages, LoadScreenshots, LoadStreams\nfrom utils.general import (\n LOGGER,\n Profile,\n check_file,\n check_img_size,\n check_imshow,\n check_requirements,\n colorstr,\n cv2,\n increment_path,\n non_max_suppression,\n print_args,\n scale_boxes,\n strip_optimizer,\n xyxy2xywh,\n)\nfrom utils.torch_utils import select_device, smart_inference_mode\n\n\n@smart_inference_mode()\ndef run(\n weights=ROOT / \"yolov5s.pt\", # model path or triton URL\n source=ROOT / \"data/images\", # file/dir/URL/glob/screen/0(webcam)\n data=ROOT / \"data/coco128.yaml\", # dataset.yaml path\n imgsz=(640, 640), # inference size (height, width)\n conf_thres=0.25, # confidence threshold\n iou_thres=0.45, # NMS IOU threshold\n max_det=1000, # maximum detections per image\n device=\"\", # cuda device, i.e. 0 or 0,1,2,3 or cpu\n view_img=False, # show results\n save_txt=False, # save results to *.txt\n save_conf=False, # save confidences in --save-txt labels\n save_crop=False, # save cropped prediction boxes\n nosave=False, # do not save images/videos\n classes=None, # filter by class: --class 0, or --class 0 2 3\n agnostic_nms=False, # class-agnostic NMS\n augment=False, # augmented inference\n visualize=False, # visualize features\n update=False, # update all models\n project=ROOT / \"runs/detect\", # save results to project/name\n name=\"exp\", # save results to project/name\n exist_ok=False, # existing project/name ok, do not increment\n line_thickness=3, # bounding box thickness (pixels)\n hide_labels=False, # hide labels\n hide_conf=False, # hide confidences\n half=False, # use FP16 half-precision inference\n dnn=False, # use OpenCV DNN for ONNX inference\n vid_stride=1, # video frame-rate stride\n):\n \"\"\"Run YOLOv3 detection inference on various input sources such as images, videos, streams, and YouTube URLs.\n\n Args:\n weights (str | Path): Path to the model weights file or a Triton URL (default: 'yolov5s.pt').\n source (str | Path): Source of input data such as a file, directory, URL, glob pattern, or device identifier\n (default: 'data/images').\n data (str | Path): Path to the dataset YAML file (default: 'data/coco128.yaml').\n imgsz (tuple[int, int]): Inference size as a tuple (height, width) (default: (640, 640)).\n conf_thres (float): Confidence threshold for detection (default: 0.25).\n iou_thres (float): Intersection Over Union (IOU) threshold for Non-Max Suppression (NMS) (default: 0.45).\n max_det (int): Maximum number of detections per image (default: 1000).\n device (str): CUDA device identifier, e.g., '0', '0,1,2,3', or 'cpu' (default: '').\n view_img (bool): Whether to display results during inference (default: False).\n save_txt (bool): Whether to save detection results to text files (default: False).\n save_conf (bool): Whether to save detection confidences in the text labels (default: False).\n save_crop (bool): Whether to save cropped detection boxes (default: False).\n nosave (bool): Whether to prevent saving images or videos with detections (default: False).\n classes (list[int] | None): List of class indices to filter, e.g., [0, 2, 3] (default: None).\n agnostic_nms (bool): Whether to perform class-agnostic NMS (default: False).\n augment (bool): Whether to apply augmented inference (default: False).\n visualize (bool): Whether to visualize feature maps (default: False).\n update (bool): Whether to update all models (default: False).\n project (str | Path): Path to the project directory where results will be saved (default: 'runs/detect').\n name (str): Name for the specific run within the project directory (default: 'exp').\n exist_ok (bool): Whether to allow existing project/name directory without incrementing run index (default:\n False).\n line_thickness (int): Thickness of bounding box lines in pixels (default: 3).\n hide_labels (bool): Whether to hide labels in the results (default: False).\n hide_conf (bool): Whether to hide confidences in the results (default: False).\n half (bool): Whether to use half-precision (FP16) for inference (default: False).\n dnn (bool): Whether to use OpenCV DNN for ONNX inference (default: False).\n vid_stride (int): Stride for video frame rate (default: 1).\n\n Returns:\n None\n\n Examples:\n ```python\n # Run YOLOv3 inference on an image\n run(weights='yolov5s.pt', source='data/images/bus.jpg')\n\n # Run YOLOv3 inference on a video\n run(weights='yolov5s.pt', source='data/videos/video.mp4', view_img=True)\n\n # Run YOLOv3 inference on a webcam\n run(weights='yolov5s.pt', source='0', view_img=True)\n ```\n\n Notes:\n This function supports a variety of input sources such as image files, video files, directories, URL patterns,\n webcam streams, and YouTube links. It also supports multiple model formats including PyTorch, ONNX, OpenVINO,\n TensorRT, CoreML, TensorFlow, PaddlePaddle, and others. The results can be visualized in real-time or saved to\n specified directories. Use command-line arguments to modify the behavior of the function.\n \"\"\"\n source = str(source)\n save_img = not nosave and not source.endswith(\".txt\") # save inference images\n is_file = Path(source).suffix[1:] in (IMG_FORMATS + VID_FORMATS)\n is_url = source.lower().startswith((\"rtsp://\", \"rtmp://\", \"http://\", \"https://\"))\n webcam = source.isnumeric() or source.endswith(\".streams\") or (is_url and not is_file)\n screenshot = source.lower().startswith(\"screen\")\n if is_url and is_file:\n source = check_file(source) # download\n\n # Directories\n save_dir = increment_path(Path(project) / name, exist_ok=exist_ok) # increment run\n (save_dir / \"labels\" if save_txt else save_dir).mkdir(parents=True, exist_ok=True) # make dir\n\n # Load model\n device = select_device(device)\n model = DetectMultiBackend(weights, device=device, dnn=dnn, data=data, fp16=half)\n stride, names, pt = model.stride, model.names, model.pt\n imgsz = check_img_size(imgsz, s=stride) # check image size\n\n # Dataloader\n bs = 1 # batch_size\n if webcam:\n view_img = check_imshow(warn=True)\n dataset = LoadStreams(source, img_size=imgsz, stride=stride, auto=pt, vid_stride=vid_stride)\n bs = len(dataset)\n elif screenshot:\n dataset = LoadScreenshots(source, img_size=imgsz, stride=stride, auto=pt)\n else:\n dataset = LoadImages(source, img_size=imgsz, stride=stride, auto=pt, vid_stride=vid_stride)\n vid_path, vid_writer = [None] * bs, [None] * bs\n\n # Run inference\n model.warmup(imgsz=(1 if pt or model.triton else bs, 3, *imgsz)) # warmup\n seen, windows, dt = 0, [], (Profile(), Profile(), Profile())\n for path, im, im0s, vid_cap, s in dataset:\n with dt[0]:\n im = torch.from_numpy(im).to(model.device)\n im = im.half() if model.fp16 else im.float() # uint8 to fp16/32\n im /= 255 # 0 - 255 to 0.0 - 1.0\n if len(im.shape) == 3:\n im = im[None] # expand for batch dim\n\n # Inference\n with dt[1]:\n visualize = increment_path(save_dir / Path(path).stem, mkdir=True) if visualize else False\n pred = model(im, augment=augment, visualize=visualize)\n\n # NMS\n with dt[2]:\n pred = non_max_suppression(pred, conf_thres, iou_thres, classes, agnostic_nms, max_det=max_det)\n\n # Second-stage classifier (optional)\n # pred = utils.general.apply_classifier(pred, classifier_model, im, im0s)\n\n # Process predictions\n for i, det in enumerate(pred): # per image\n seen += 1\n if webcam: # batch_size >= 1\n p, im0, frame = path[i], im0s[i].copy(), dataset.count\n s += f\"{i}: \"\n else:\n p, im0, frame = path, im0s.copy(), getattr(dataset, \"frame\", 0)\n\n p = Path(p) # to Path\n save_path = str(save_dir / p.name) # im.jpg\n txt_path = str(save_dir / \"labels\" / p.stem) + (\"\" if dataset.mode == \"image\" else f\"_{frame}\") # im.txt\n s += \"{:g}x{:g} \".format(*im.shape[2:]) # print string\n gn = torch.tensor(im0.shape)[[1, 0, 1, 0]] # normalization gain whwh\n imc = im0.copy() if save_crop else im0 # for save_crop\n annotator = Annotator(im0, line_width=line_thickness, example=str(names))\n if len(det):\n # Rescale boxes from img_size to im0 size\n det[:, :4] = scale_boxes(im.shape[2:], det[:, :4], im0.shape).round()\n\n # Print results\n for c in det[:, 5].unique():\n n = (det[:, 5] == c).sum() # detections per class\n s += f\"{n} {names[int(c)]}{'s' * (n > 1)}, \" # add to string\n\n # Write results\n for *xyxy, conf, cls in reversed(det):\n if save_txt: # Write to file\n xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() # normalized xywh\n line = (cls, *xywh, conf) if save_conf else (cls, *xywh) # label format\n with open(f\"{txt_path}.txt\", \"a\") as f:\n f.write((\"%g \" * len(line)).rstrip() % line + \"\\n\")\n\n if save_img or save_crop or view_img: # Add bbox to image\n c = int(cls) # integer class\n label = None if hide_labels else (names[c] if hide_conf else f\"{names[c]} {conf:.2f}\")\n annotator.box_label(xyxy, label, color=colors(c, True))\n if save_crop:\n save_one_box(xyxy, imc, file=save_dir / \"crops\" / names[c] / f\"{p.stem}.jpg\", BGR=True)\n\n # Stream results\n im0 = annotator.result()\n if view_img:\n if platform.system() == \"Linux\" and p not in windows:\n windows.append(p)\n cv2.namedWindow(str(p), cv2.WINDOW_NORMAL | cv2.WINDOW_KEEPRATIO) # allow window resize (Linux)\n cv2.resizeWindow(str(p), im0.shape[1], im0.shape[0])\n cv2.imshow(str(p), im0)\n cv2.waitKey(1) # 1 millisecond\n\n # Save results (image with detections)\n if save_img:\n if dataset.mode == \"image\":\n cv2.imwrite(save_path, im0)\n else: # 'video' or 'stream'\n if vid_path[i] != save_path: # new video\n vid_path[i] = save_path\n if isinstance(vid_writer[i], cv2.VideoWriter):\n vid_writer[i].release() # release previous video writer\n if vid_cap: # video\n fps = vid_cap.get(cv2.CAP_PROP_FPS)\n w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH))\n h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))\n else: # stream\n fps, w, h = 30, im0.shape[1], im0.shape[0]\n save_path = str(Path(save_path).with_suffix(\".mp4\")) # force *.mp4 suffix on results videos\n vid_writer[i] = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*\"mp4v\"), fps, (w, h))\n vid_writer[i].write(im0)\n\n # Print time (inference-only)\n LOGGER.info(f\"{s}{'' if len(det) else '(no detections), '}{dt[1].dt * 1e3:.1f}ms\")\n\n # Print results\n t = tuple(x.t / seen * 1e3 for x in dt) # speeds per image\n LOGGER.info(f\"Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {(1, 3, *imgsz)}\" % t)\n if save_txt or save_img:\n s = f\"\\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}\" if save_txt else \"\"\n LOGGER.info(f\"Results saved to {colorstr('bold', save_dir)}{s}\")\n if update:\n strip_optimizer(weights[0]) # update model (to fix SourceChangeWarning)\n\n\ndef parse_opt():\n \"\"\"Parses and returns command-line options for running YOLOv3 model detection.\n\n Args:\n --weights (list[str]): Model path or Triton URL. Default: ROOT / \"yolov3-tiny.pt\".\n --source (str): Input data source like file/dir/URL/glob/screen/0(webcam). Default: ROOT / \"data/images\".\n --data (str): Optional path to dataset.yaml. Default: ROOT / \"data/coco128.yaml\".\n --imgsz (list[int]): Inference size as height, width. Accepts multiple values. Default: [640].\n --conf-thres (float): Confidence threshold for predictions. Default: 0.25.\n --iou-thres (float): IoU threshold for Non-Maximum Suppression (NMS). Default: 0.45.\n --max-det (int): Maximum number of detections per image. Default: 1000.\n --device (str): CUDA device identifier, e.g. \"0\" or \"0,1,2,3\" or \"cpu\". Default: \"\" (auto-select).\n --view-img (bool): Display results. Default: False.\n --save-txt (bool): Save results to *.txt files. Default: False.\n --save-conf (bool): Save confidence scores in text labels. Default: False.\n --save-crop (bool): Save cropped prediction boxes. Default: False.\n --nosave (bool): Do not save images/videos. Default: False.\n --classes (list[int] | None): Filter results by class, e.g. [0, 2, 3]. Default: None.\n --agnostic-nms (bool): Perform class-agnostic NMS. Default: False.\n --augment (bool): Apply augmented inference. Default: False.\n --visualize (bool): Visualize feature maps. Default: False.\n --update (bool): Update all models. Default: False.\n --project (str): Directory to save results; results saved to \"project/name\". Default: ROOT / \"runs/detect\".\n --name (str): Name of the specific run; results saved to \"project/name\". Default: \"exp\".\n --exist-ok (bool): Allow results to be saved in an existing directory without incrementing. Default: False.\n --line-thickness (int): Bounding box line thickness in pixels. Default: 3.\n --hide-labels (bool): Hide labels on detections. Default: False.\n --hide-conf (bool): Hide confidence scores on labels. Default: False.\n --half (bool): Use FP16 half-precision inference. Default: False.\n --dnn (bool): Use OpenCV DNN backend for ONNX inference. Default: False.\n --vid-stride (int): Frame-rate stride for video input. Default: 1.\n\n Returns:\n argparse.Namespace: Parsed command-line arguments for YOLOv3 inference configurations.\n\n Examples:\n ```python\n options = parse_opt()\n run(**vars(options))\n ```\n \"\"\"\n parser = argparse.ArgumentParser()\n parser.add_argument(\n \"--weights\", nargs=\"+\", type=str, default=ROOT / \"yolov3-tiny.pt\", help=\"model path or triton URL\"\n )\n parser.add_argument(\"--source\", type=str, default=ROOT / \"data/images\", help=\"file/dir/URL/glob/screen/0(webcam)\")\n parser.add_argument(\"--data\", type=str, default=ROOT / \"data/coco128.yaml\", help=\"(optional) dataset.yaml path\")\n parser.add_argument(\"--imgsz\", \"--img\", \"--img-size\", nargs=\"+\", type=int, default=[640], help=\"inference size h,w\")\n parser.add_argument(\"--conf-thres\", type=float, default=0.25, help=\"confidence threshold\")\n parser.add_argument(\"--iou-thres\", type=float, default=0.45, help=\"NMS IoU threshold\")\n parser.add_argument(\"--max-det\", type=int, default=1000, help=\"maximum detections per image\")\n parser.add_argument(\"--device\", default=\"\", help=\"cuda device, i.e. 0 or 0,1,2,3 or cpu\")\n parser.add_argument(\"--view-img\", action=\"store_true\", help=\"show results\")\n parser.add_argument(\"--save-txt\", action=\"store_true\", help=\"save results to *.txt\")\n parser.add_argument(\"--save-conf\", action=\"store_true\", help=\"save confidences in --save-txt labels\")\n parser.add_argument(\"--save-crop\", action=\"store_true\", help=\"save cropped prediction boxes\")\n parser.add_argument(\"--nosave\", action=\"store_true\", help=\"do not save images/videos\")\n parser.add_argument(\"--classes\", nargs=\"+\", type=int, help=\"filter by class: --classes 0, or --classes 0 2 3\")\n parser.add_argument(\"--agnostic-nms\", action=\"store_true\", help=\"class-agnostic NMS\")\n parser.add_argument(\"--augment\", action=\"store_true\", help=\"augmented inference\")\n parser.add_argument(\"--visualize\", action=\"store_true\", help=\"visualize features\")\n parser.add_argument(\"--update\", action=\"store_true\", help=\"update all models\")\n parser.add_argument(\"--project\", default=ROOT / \"runs/detect\", help=\"save results to project/name\")\n parser.add_argument(\"--name\", default=\"exp\", help=\"save results to project/name\")\n parser.add_argument(\"--exist-ok\", action=\"store_true\", help=\"existing project/name ok, do not increment\")\n parser.add_argument(\"--line-thickness\", default=3, type=int, help=\"bounding box thickness (pixels)\")\n parser.add_argument(\"--hide-labels\", default=False, action=\"store_true\", help=\"hide labels\")\n parser.add_argument(\"--hide-conf\", default=False, action=\"store_true\", help=\"hide confidences\")\n parser.add_argument(\"--half\", action=\"store_true\", help=\"use FP16 half-precision inference\")\n parser.add_argument(\"--dnn\", action=\"store_true\", help=\"use OpenCV DNN for ONNX inference\")\n parser.add_argument(\"--vid-stride\", type=int, default=1, help=\"video frame-rate stride\")\n opt = parser.parse_args()\n opt.imgsz *= 2 if len(opt.imgsz) == 1 else 1 # expand\n print_args(vars(opt))\n return opt\n\n\ndef main(opt):\n \"\"\"Entry point for running the YOLO model; checks requirements and calls `run` with parsed options.\n\n Args:\n opt (argparse.Namespace): Parsed command-line options, which include:\n - weights (str | list of str): Path to the model weights or Triton server URL.\n - source (str): Input source, can be a file, directory, URL, glob, screen, or webcam index.\n - data (str): Path to the dataset configuration file (.yaml).\n - imgsz (tuple of int): Inference image size as (height, width).\n - conf_thres (float): Confidence threshold for detections.\n - iou_thres (float): Intersection over Union (IoU) threshold for Non-Maximum Suppression (NMS).\n - max_det (int): Maximum number of detections per image.\n - device (str): Device to run inference on; options are CUDA device id(s) or 'cpu'\n - view_img (bool): Flag to display inference results.\n - save_txt (bool): Save detection results in .txt format.\n - save_conf (bool): Save detection confidences in .txt labels.\n - save_crop (bool): Save cropped bounding box predictions.\n - nosave (bool): Do not save images/videos with detections.\n - classes (list of int): Filter results by class, e.g., --class 0 2 3.\n - agnostic_nms (bool): Use class-agnostic NMS.\n - augment (bool): Enable augmented inference.\n - visualize (bool): Visualize feature maps.\n - update (bool): Update the model during inference.\n - project (str): Directory to save results.\n - name (str): Name for the results directory.\n - exist_ok (bool): Allow existing project/name directories without incrementing.\n - line_thickness (int): Thickness of bounding box lines.\n - hide_labels (bool): Hide class labels on bounding boxes.\n - hide_conf (bool): Hide confidence scores on bounding boxes.\n - half (bool): Use FP16 half-precision inference.\n - dnn (bool): Use OpenCV DNN backend for ONNX inference.\n - vid_stride (int): Video frame-rate stride.\n\n Returns:\n None\n\n Examples:\n ```python\n if __name__ == \"__main__\":\n opt = parse_opt()\n main(opt)\n ```\n\n Notes:\n Run this function as the entry point for using YOLO for object detection on a variety of input sources such as\n images, videos, directories, webcams, streams, etc. This function ensures all requirements are checked and\n subsequently initiates the detection process by calling the `run` function with appropriate options.\n \"\"\"\n check_requirements(ROOT / \"requirements.txt\", exclude=(\"tensorboard\", \"thop\"))\n run(**vars(opt))\n\n\nif __name__ == \"__main__\":\n opt = parse_opt()\n main(opt)\n" }, { "path": "export.py", "content": "# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license\n\"\"\"\nExport a YOLOv3 PyTorch model to other formats. TensorFlow exports authored by https://github.com/zldrobit.\n\nFormat | `export.py --include` | Model\n--- | --- | ---\nPyTorch | - | yolov5s.pt\nTorchScript | `torchscript` | yolov5s.torchscript\nONNX | `onnx` | yolov5s.onnx\nOpenVINO | `openvino` | yolov5s_openvino_model/\nTensorRT | `engine` | yolov5s.engine\nCoreML | `coreml` | yolov5s.mlmodel\nTensorFlow SavedModel | `saved_model` | yolov5s_saved_model/\nTensorFlow GraphDef | `pb` | yolov5s.pb\nTensorFlow Lite | `tflite` | yolov5s.tflite\nTensorFlow Edge TPU | `edgetpu` | yolov5s_edgetpu.tflite\nTensorFlow.js | `tfjs` | yolov5s_web_model/\nPaddlePaddle | `paddle` | yolov5s_paddle_model/\n\nRequirements:\n $ pip install -r requirements.txt coremltools onnx onnx-simplifier onnxruntime openvino-dev tensorflow-cpu # CPU\n $ pip install -r requirements.txt coremltools onnx onnx-simplifier onnxruntime-gpu openvino-dev tensorflow # GPU\n\nUsage:\n $ python export.py --weights yolov5s.pt --include torchscript onnx openvino engine coreml tflite ...\n\nInference:\n $ python detect.py --weights yolov5s.pt # PyTorch\n yolov5s.torchscript # TorchScript\n yolov5s.onnx # ONNX Runtime or OpenCV DNN with --dnn\n yolov5s_openvino_model # OpenVINO\n yolov5s.engine # TensorRT\n yolov5s.mlmodel # CoreML (macOS-only)\n yolov5s_saved_model # TensorFlow SavedModel\n yolov5s.pb # TensorFlow GraphDef\n yolov5s.tflite # TensorFlow Lite\n yolov5s_edgetpu.tflite # TensorFlow Edge TPU\n yolov5s_paddle_model # PaddlePaddle\n\nTensorFlow.js:\n $ cd .. && git clone https://github.com/zldrobit/tfjs-yolov5-example.git && cd tfjs-yolov5-example\n $ npm install\n $ ln -s ../../yolov5/yolov5s_web_model public/yolov5s_web_model\n $ npm start\n\"\"\"\n\nimport argparse\nimport contextlib\nimport json\nimport os\nimport platform\nimport re\nimport subprocess\nimport sys\nimport time\nimport warnings\nfrom pathlib import Path\n\nimport pandas as pd\nimport torch\nfrom torch.utils.mobile_optimizer import optimize_for_mobile\n\nFILE = Path(__file__).resolve()\nROOT = FILE.parents[0] # YOLOv3 root directory\nif str(ROOT) not in sys.path:\n sys.path.append(str(ROOT)) # add ROOT to PATH\nif platform.system() != \"Windows\":\n ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative\n\nfrom models.experimental import attempt_load\nfrom models.yolo import ClassificationModel, Detect, DetectionModel, SegmentationModel\nfrom utils.dataloaders import LoadImages\nfrom utils.general import (\n LOGGER,\n Profile,\n check_dataset,\n check_img_size,\n check_requirements,\n check_version,\n check_yaml,\n colorstr,\n file_size,\n get_default_args,\n print_args,\n url2file,\n yaml_save,\n)\nfrom utils.torch_utils import select_device, smart_inference_mode\n\nMACOS = platform.system() == \"Darwin\" # macOS environment\n\n\nclass iOSModel(torch.nn.Module):\n \"\"\"Exports a PyTorch model to an iOS-compatible format with normalized input dimensions and class configurations.\"\"\"\n\n def __init__(self, model, im):\n \"\"\"Initializes an iOSModel with normalized input dimensions and number of classes from a PyTorch model.\n\n Args:\n model (torch.nn.Module): The PyTorch model from which to initialize the iOS model. This should include\n attributes like `nc` (number of classes) which will be used to configure the iOS model.\n im (torch.Tensor): A Tensor representing a sample input image. The shape of this tensor should be\n (batch_size, channels, height, width). This is used to extract dimensions for input normalization.\n\n Returns:\n None\n\n Notes:\n - This class is specifically designed for use in exporting a PyTorch model for deployment on iOS platforms, optimizing\n input dimensions and class configurations to suit mobile requirements.\n - Normalization factor is derived from the input image dimensions, which impacts the model's performance during\n inference on iOS devices.\n - Ensure the sample input image `im` provided has correct dimensions and shape for accurate model configuration.\n \"\"\"\n super().__init__()\n _b, _c, h, w = im.shape # batch, channel, height, width\n self.model = model\n self.nc = model.nc # number of classes\n if w == h:\n self.normalize = 1.0 / w\n else:\n self.normalize = torch.tensor([1.0 / w, 1.0 / h, 1.0 / w, 1.0 / h]) # broadcast (slower, smaller)\n # np = model(im)[0].shape[1] # number of points\n # self.normalize = torch.tensor([1. / w, 1. / h, 1. / w, 1. / h]).expand(np, 4) # explicit (faster, larger)\n\n def forward(self, x):\n \"\"\"Performs a forward pass, returning scaled confidences and normalized coordinates given an input tensor.\n\n Args:\n x (torch.Tensor): Input tensor representing a batch of images, with dimensions [batch_size, channels,\n height, width].\n\n Returns:\n tuple[torch.Tensor, torch.Tensor, torch.Tensor]: A tuple containing three elements:\n - xywh (torch.Tensor): Tensor of shape [batch_size, num_detections, 4] containing normalized x, y, width,\n and height coordinates.\n - conf (torch.Tensor): Tensor of shape [batch_size, num_detections, 1] containing confidence scores for\n each detection.\n - cls (torch.Tensor): Tensor of shape [batch_size, num_detections, num_classes] containing class\n probabilities.\n\n Examples:\n ```python\n model = iOSModel(trained_model, input_image_tensor)\n detection_results = model.forward(input_tensor)\n xywh, conf, cls = detection_results\n ```\n\n Further reading on exporting models to different formats:\n https://github.com/ultralytics/ultralytics\n\n See Also:\n `export.py` for exporting a YOLOv3 PyTorch model to various formats.\n https://github.com/zldrobit for TensorFlow export scripts.\n\n Notes:\n The dimensions of `x` should match the input dimensions used during the model's initialization to ensure\n proper scaling and normalization.\n \"\"\"\n xywh, conf, cls = self.model(x)[0].squeeze().split((4, 1, self.nc), 1)\n return cls * conf, xywh * self.normalize # confidence (3780, 80), coordinates (3780, 4)\n\n\ndef export_formats():\n \"\"\"Lists supported YOLOv3 model export formats including file suffixes and CPU/GPU compatibility.\n\n Returns:\n list: A list of lists where each sublist contains information about a specific export format. Each sublist\n includes\n the following elements:\n - str: The name of the format.\n - str: The command-line argument for including this format.\n - str: The file suffix used for this format.\n - bool: Indicates if the format is compatible with CPU.\n - bool: Indicates if the format is compatible with GPU.\n\n Examples:\n ```python\n formats = export_formats()\n for format in formats:\n print(f\"Format: {format[0]}, Suffix: {format[2]}, CPU Compatible: {format[3]}, GPU Compatible: {format[4]}\")\n ```\n \"\"\"\n x = [\n [\"PyTorch\", \"-\", \".pt\", True, True],\n [\"TorchScript\", \"torchscript\", \".torchscript\", True, True],\n [\"ONNX\", \"onnx\", \".onnx\", True, True],\n [\"OpenVINO\", \"openvino\", \"_openvino_model\", True, False],\n [\"TensorRT\", \"engine\", \".engine\", False, True],\n [\"CoreML\", \"coreml\", \".mlmodel\", True, False],\n [\"TensorFlow SavedModel\", \"saved_model\", \"_saved_model\", True, True],\n [\"TensorFlow GraphDef\", \"pb\", \".pb\", True, True],\n [\"TensorFlow Lite\", \"tflite\", \".tflite\", True, False],\n [\"TensorFlow Edge TPU\", \"edgetpu\", \"_edgetpu.tflite\", False, False],\n [\"TensorFlow.js\", \"tfjs\", \"_web_model\", False, False],\n [\"PaddlePaddle\", \"paddle\", \"_paddle_model\", True, True],\n ]\n return pd.DataFrame(x, columns=[\"Format\", \"Argument\", \"Suffix\", \"CPU\", \"GPU\"])\n\n\ndef try_export(inner_func):\n \"\"\"Profiles and logs the export process of YOLOv3 models, capturing success or failure details.\n\n Args:\n inner_func (Callable): The function that performs the actual export process and returns the model file path and\n the exported model.\n\n Returns:\n Callable: A wrapped function that profiles and logs the export process, handling successes and failures.\n\n Examples:\n ```python\n @try_export\n def export_onnx(py_model_path: str, output_path: str):\n # Export logic here\n return output_path, model\n ```\n\n Notes:\n Applying this decorator to an export function will log the export results, including export success or failure,\n along with associated time and file size details.\n \"\"\"\n inner_args = get_default_args(inner_func)\n\n def outer_func(*args, **kwargs):\n \"\"\"Profiles and logs the export process of YOLOv3 models, capturing success or failure details.\"\"\"\n prefix = inner_args[\"prefix\"]\n try:\n with Profile() as dt:\n f, model = inner_func(*args, **kwargs)\n LOGGER.info(f\"{prefix} export success ✅ {dt.t:.1f}s, saved as {f} ({file_size(f):.1f} MB)\")\n return f, model\n except Exception as e:\n LOGGER.info(f\"{prefix} export failure ❌ {dt.t:.1f}s: {e}\")\n return None, None\n\n return outer_func\n\n\n@try_export\ndef export_torchscript(model, im, file, optimize, prefix=colorstr(\"TorchScript:\")):\n \"\"\"Export a YOLOv3 model to TorchScript format, with optional optimization for mobile deployment.\n\n Args:\n model (torch.nn.Module): The YOLOv3 model to be exported.\n im (torch.Tensor): A tensor representing the input image for the model, typically with shape (N, 3, H, W).\n file (pathlib.Path): The file path where the TorchScript model will be saved.\n optimize (bool): A boolean flag indicating whether to optimize the model for mobile devices.\n prefix (str): A prefix for logging messages. Defaults to `colorstr(\"TorchScript:\")`.\n\n Returns:\n (pathlib.Path | None, torch.nn.Module | None): Tuple containing the path to the saved TorchScript model and the\n model itself. Returns `(None, None)` if the export fails.\n\n Raises:\n Exception: If there is an error during export, it logs the error and returns `(None, None)`.\n\n Examples:\n ```python\n from pathlib import Path\n import torch\n\n model = ... # Assume model is loaded or created\n im = torch.randn(1, 3, 640, 640) # A sample input tensor\n file = Path(\"model.torchscript\")\n optimize = True\n\n export_torchscript(model, im, file, optimize)\n ```\n\n For more information, visit: https://ultralytics.com/.\n\n Notes:\n The function uses `torch.jit.trace` to trace the model with the input image tensor (`im`). Required metadata such as\n input shape, stride, and class names are stored in an extra file included in the TorchScript model.\n \"\"\"\n LOGGER.info(f\"\\n{prefix} starting export with torch {torch.__version__}...\")\n f = file.with_suffix(\".torchscript\")\n\n ts = torch.jit.trace(model, im, strict=False)\n d = {\"shape\": im.shape, \"stride\": int(max(model.stride)), \"names\": model.names}\n extra_files = {\"config.txt\": json.dumps(d)} # torch._C.ExtraFilesMap()\n if optimize: # https://pytorch.org/tutorials/recipes/mobile_interpreter.html\n optimize_for_mobile(ts)._save_for_lite_interpreter(str(f), _extra_files=extra_files)\n else:\n ts.save(str(f), _extra_files=extra_files)\n return f, None\n\n\n@try_export\ndef export_onnx(model, im, file, opset, dynamic, simplify, prefix=colorstr(\"ONNX:\")):\n \"\"\"Export a YOLOv3 model to ONNX format with dynamic shape and simplification options.\n\n Args:\n model (torch.nn.Module): The YOLOv3 model to be exported.\n im (torch.Tensor): A sample input tensor for tracing the model.\n file (pathlib.Path): The file path where the ONNX model will be saved.\n opset (int): The ONNX opset version to use for the export.\n dynamic (bool): If `True`, enables dynamic shape support.\n simplify (bool): If `True`, simplifies the ONNX model using onnx-simplifier.\n prefix (str): A prefix for logging messages.\n\n Returns:\n tuple[pathlib.Path, None]: The path to the saved ONNX model, None as the second tuple element (kept for consistency).\n\n Examples:\n ```python\n from pathlib import Path\n import torch\n\n model = ... # Assume model is loaded or created\n im = torch.randn(1, 3, 640, 640) # A sample input tensor\n file = Path(\"model.onnx\")\n opset = 12\n dynamic = True\n simplify = True\n\n export_onnx(model, im, file, opset, dynamic, simplify)\n ```\n\n Notes:\n Ensure `onnx`, `onnx-simplifier`, and suitable runtime packages are installed.\n This function uses `torch.onnx.export` to create the ONNX model, followed by optional simplification using\n `onnx-simplifier`. If `dynamic` is enabled, dynamic axes mappings are added to support variable input shapes.\n Relevant YOLO model metadata like `stride` and `names` are included as part of the ONNX model's metadata.\n\n For more details on exporting and running inferences, visit:\n - https://github.com/ultralytics/ultralytics\n - https://github.com/zldrobit for TensorFlow export scripts.\n \"\"\"\n check_requirements(\"onnx>=1.12.0\")\n import onnx\n\n LOGGER.info(f\"\\n{prefix} starting export with onnx {onnx.__version__}...\")\n f = file.with_suffix(\".onnx\")\n\n output_names = [\"output0\", \"output1\"] if isinstance(model, SegmentationModel) else [\"output0\"]\n if dynamic:\n dynamic = {\"images\": {0: \"batch\", 2: \"height\", 3: \"width\"}} # shape(1,3,640,640)\n if isinstance(model, SegmentationModel):\n dynamic[\"output0\"] = {0: \"batch\", 1: \"anchors\"} # shape(1,25200,85)\n dynamic[\"output1\"] = {0: \"batch\", 2: \"mask_height\", 3: \"mask_width\"} # shape(1,32,160,160)\n elif isinstance(model, DetectionModel):\n dynamic[\"output0\"] = {0: \"batch\", 1: \"anchors\"} # shape(1,25200,85)\n\n torch.onnx.export(\n model.cpu() if dynamic else model, # --dynamic only compatible with cpu\n im.cpu() if dynamic else im,\n f,\n verbose=False,\n opset_version=opset,\n do_constant_folding=True, # WARNING: DNN inference with torch>=1.12 may require do_constant_folding=False\n input_names=[\"images\"],\n output_names=output_names,\n dynamic_axes=dynamic or None,\n )\n\n # Checks\n model_onnx = onnx.load(f) # load onnx model\n onnx.checker.check_model(model_onnx) # check onnx model\n\n # Metadata\n d = {\"stride\": int(max(model.stride)), \"names\": model.names}\n for k, v in d.items():\n meta = model_onnx.metadata_props.add()\n meta.key, meta.value = k, str(v)\n onnx.save(model_onnx, f)\n\n # Simplify\n if simplify:\n try:\n cuda = torch.cuda.is_available()\n check_requirements((\"onnxruntime-gpu\" if cuda else \"onnxruntime\", \"onnx-simplifier>=0.4.1\"))\n import onnxsim\n\n LOGGER.info(f\"{prefix} simplifying with onnx-simplifier {onnxsim.__version__}...\")\n model_onnx, check = onnxsim.simplify(model_onnx)\n assert check, \"assert check failed\"\n onnx.save(model_onnx, f)\n except Exception as e:\n LOGGER.info(f\"{prefix} simplifier failure: {e}\")\n return f, model_onnx\n\n\n@try_export\ndef export_openvino(file, metadata, half, int8, data, prefix=colorstr(\"OpenVINO:\")):\n \"\"\"Export a YOLOv3 model to OpenVINO format with optional INT8 quantization and inference metadata.\n\n Args:\n file (Path): Path to the output file.\n metadata (dict): Inference metadata to include in the exported model.\n half (bool): Indicates if FP16 precision should be used.\n int8 (bool): Indicates if INT8 quantization should be applied.\n data (str): Path to the dataset file (.yaml) for post-training quantization.\n\n Returns:\n tuple[Path | None, openvino.runtime.Model | None]: Tuple containing the path to the exported model and the OpenVINO\n model object, or None if the export failed.\n\n Examples:\n ```python\n model_file = Path('/path/to/model.onnx')\n metadata = {'names': ['class1', 'class2'], 'stride': 32}\n export_openvino(model_file, metadata, half=True, int8=False, data='/path/to/dataset.yaml')\n ```\n\n Notes:\n - Requires the `openvino-dev>=2023.0` and optional `nncf>=2.4.0` package for INT8 quantization.\n - Refer to OpenVINO documentation for further details: https://docs.openvino.ai/latest/index.html.\n \"\"\"\n check_requirements(\"openvino-dev>=2023.0\") # requires openvino-dev: https://pypi.org/project/openvino-dev/\n import openvino.runtime as ov\n from openvino.tools import mo\n\n LOGGER.info(f\"\\n{prefix} starting export with openvino {ov.__version__}...\")\n f = str(file).replace(file.suffix, f\"_openvino_model{os.sep}\")\n f_onnx = file.with_suffix(\".onnx\")\n f_ov = str(Path(f) / file.with_suffix(\".xml\").name)\n if int8:\n check_requirements(\"nncf>=2.4.0\") # requires at least version 2.4.0 to use the post-training quantization\n import nncf\n import numpy as np\n from openvino.runtime import Core\n\n from utils.dataloaders import create_dataloader\n\n core = Core()\n onnx_model = core.read_model(f_onnx) # export\n\n def prepare_input_tensor(image: np.ndarray):\n \"\"\"Prepares the input tensor by normalizing pixel values and converting the datatype to float32.\"\"\"\n input_tensor = image.astype(np.float32) # uint8 to fp16/32\n input_tensor /= 255.0 # 0 - 255 to 0.0 - 1.0\n\n if input_tensor.ndim == 3:\n input_tensor = np.expand_dims(input_tensor, 0)\n return input_tensor\n\n def gen_dataloader(yaml_path, task=\"train\", imgsz=640, workers=4):\n \"\"\"Generates a PyTorch dataloader for the specified task using dataset configurations from a YAML file.\"\"\"\n data_yaml = check_yaml(yaml_path)\n data = check_dataset(data_yaml)\n dataloader = create_dataloader(\n data[task], imgsz=imgsz, batch_size=1, stride=32, pad=0.5, single_cls=False, rect=False, workers=workers\n )[0]\n return dataloader\n\n def transform_fn(data_item):\n \"\"\"Quantization transform function.\n\n Extracts and preprocess input data from dataloader item for quantization.\n\n Parameters:\n data_item: Tuple with data item produced by DataLoader during iteration\n\n Returns:\n input_tensor: Input data for quantization\n \"\"\"\n img = data_item[0].numpy()\n input_tensor = prepare_input_tensor(img)\n return input_tensor\n\n ds = gen_dataloader(data)\n quantization_dataset = nncf.Dataset(ds, transform_fn)\n ov_model = nncf.quantize(onnx_model, quantization_dataset, preset=nncf.QuantizationPreset.MIXED)\n else:\n ov_model = mo.convert_model(f_onnx, model_name=file.stem, framework=\"onnx\", compress_to_fp16=half) # export\n\n ov.serialize(ov_model, f_ov) # save\n yaml_save(Path(f) / file.with_suffix(\".yaml\").name, metadata) # add metadata.yaml\n return f, None\n\n\n@try_export\ndef export_paddle(model, im, file, metadata, prefix=colorstr(\"PaddlePaddle:\")):\n \"\"\"Export a YOLOv3 model to PaddlePaddle format using X2Paddle, saving to a specified directory and including model\n metadata.\n\n Args:\n model (torch.nn.Module): The YOLOv3 model to be exported.\n im (torch.Tensor): A sample input tensor used for tracing the model.\n file (pathlib.Path): Destination file path for the exported model, with `.pt` suffix.\n metadata (dict): Additional metadata to be saved in YAML format alongside the exported model.\n prefix (str, optional): Log message prefix. Defaults to a colored \"PaddlePaddle:\" string.\n\n Returns:\n tuple: A tuple containing the directory path (str) where the PaddlePaddle model is saved, and `None`.\n Requirements:\n - paddlepaddle: Install via `pip install paddlepaddle`.\n - x2paddle: Install via `pip install x2paddle`.\n\n Examples:\n ```python\n from pathlib import Path\n import torch\n from models.yolo import DetectionModel\n\n model = DetectionModel() # Example model initialization\n im = torch.rand(1, 3, 640, 640) # Example input tensor\n file = Path(\"path/to/save/model.pt\")\n metadata = {\"nc\": 80, \"names\": [\"class1\", \"class2\", ...]} # Example metadata\n\n export_paddle(model, im, file, metadata)\n ```\n\n Notes:\n The function first checks for required packages `paddlepaddle` and `x2paddle`. It then uses X2Paddle to trace\n the model and export it to a PaddlePaddle format, saving the resulting files in the specified directory with\n included metadata in a YAML file.\n \"\"\"\n check_requirements((\"paddlepaddle\", \"x2paddle\"))\n import x2paddle\n from x2paddle.convert import pytorch2paddle\n\n LOGGER.info(f\"\\n{prefix} starting export with X2Paddle {x2paddle.__version__}...\")\n f = str(file).replace(\".pt\", f\"_paddle_model{os.sep}\")\n\n pytorch2paddle(module=model, save_dir=f, jit_type=\"trace\", input_examples=[im]) # export\n yaml_save(Path(f) / file.with_suffix(\".yaml\").name, metadata) # add metadata.yaml\n return f, None\n\n\n@try_export\ndef export_coreml(model, im, file, int8, half, nms, prefix=colorstr(\"CoreML:\")):\n \"\"\"Export a YOLOv3 model to CoreML format with optional quantization and Non-Maximum Suppression (NMS).\n\n Args:\n model (torch.nn.Module): The YOLOv3 model to be exported.\n im (torch.Tensor): Input tensor used for tracing the model. Shape should be (batch_size, channels, height,\n width).\n file (pathlib.Path): Destination file path where the CoreML model will be saved.\n int8 (bool): Whether to use INT8 quantization. If True, quantizes the model to 8-bit integers.\n half (bool): Whether to use FP16 quantization. If True, converts the model to 16-bit floating point numbers.\n nms (bool): Whether to include Non-Maximum Suppression in the CoreML model.\n prefix (str): Prefix string for logging purposes. Default is colorstr(\"CoreML:\").\n\n Returns:\n str: Path to the saved CoreML model (.mlmodel).\n\n Raises:\n Exception: If there is an error during export, logs the error and stops the process.\n\n Examples:\n ```python\n from ultralytics.utils import export_coreml\n from pathlib import Path\n import torch\n\n model = ... # Assume model is loaded or created\n im = torch.randn(1, 3, 640, 640) # A sample input tensor\n file = Path(\"model.mlmodel\")\n export_coreml(model, im, file, int8=False, half=True, nms=True)\n ```\n\n Notes:\n - This function requires `coremltools` to be installed.\n - If `nms` is enabled, the model is wrapped with `iOSModel` to include NMS.\n - Quantization only works on macOS.\n \"\"\"\n check_requirements(\"coremltools\")\n import coremltools as ct\n\n LOGGER.info(f\"\\n{prefix} starting export with coremltools {ct.__version__}...\")\n f = file.with_suffix(\".mlmodel\")\n\n if nms:\n model = iOSModel(model, im)\n ts = torch.jit.trace(model, im, strict=False) # TorchScript model\n ct_model = ct.convert(ts, inputs=[ct.ImageType(\"image\", shape=im.shape, scale=1 / 255, bias=[0, 0, 0])])\n bits, mode = (8, \"kmeans_lut\") if int8 else (16, \"linear\") if half else (32, None)\n if bits < 32:\n if MACOS: # quantization only supported on macOS\n with warnings.catch_warnings():\n warnings.filterwarnings(\"ignore\", category=DeprecationWarning) # suppress numpy==1.20 float warning\n ct_model = ct.models.neural_network.quantization_utils.quantize_weights(ct_model, bits, mode)\n else:\n print(f\"{prefix} quantization only supported on macOS, skipping...\")\n ct_model.save(f)\n return f, ct_model\n\n\n@try_export\ndef export_engine(model, im, file, half, dynamic, simplify, workspace=4, verbose=False, prefix=colorstr(\"TensorRT:\")):\n \"\"\"Export a YOLOv3 model to TensorRT engine format, optimizing it for GPU inference.\n\n Args:\n model (torch.nn.Module): The YOLOv3 model to be exported.\n im (torch.Tensor): Sample input tensor used for tracing the model.\n file (Path): File path where the exported TensorRT engine will be saved.\n half (bool): Whether to use FP16 precision. Requires a supported GPU.\n dynamic (bool): Whether to use dynamic input shapes.\n simplify (bool): Whether to simplify the model during the ONNX export.\n workspace (int): The maximum workspace size in GB. Default is 4.\n verbose (bool): Whether to print detailed export logs.\n prefix (str): Prefix string for log messages. Default is \"TensorRT:\".\n\n Returns:\n tuple[Path, None]: The output file path (Path) and None.\n\n Raises:\n AssertionError: If the model is running on CPU instead of GPU.\n RuntimeError: If the ONNX file failed to load.\n\n Examples:\n ```python\n from pathlib import Path\n import torch\n\n # Initialize model and dummy input\n model = YOLOv3(...) # or another correct initialization\n im = torch.randn(1, 3, 640, 640)\n\n # Export the model\n export_engine(model, im, Path(\"yolov3.engine\"), half=True, dynamic=True, simplify=True)\n ```\n\n Notes:\n Requires TensorRT installation to execute. Nvidia TensorRT: https://developer.nvidia.com/tensorrt\n \"\"\"\n assert im.device.type != \"cpu\", \"export running on CPU but must be on GPU, i.e. `python export.py --device 0`\"\n try:\n import tensorrt as trt\n except Exception:\n if platform.system() == \"Linux\":\n check_requirements(\"nvidia-tensorrt\", cmds=\"-U --index-url https://pypi.ngc.nvidia.com\")\n import tensorrt as trt\n\n if trt.__version__[0] == \"7\": # TensorRT 7 handling https://github.com/ultralytics/yolov5/issues/6012\n grid = model.model[-1].anchor_grid\n model.model[-1].anchor_grid = [a[..., :1, :1, :] for a in grid]\n export_onnx(model, im, file, 12, dynamic, simplify) # opset 12\n model.model[-1].anchor_grid = grid\n else: # TensorRT >= 8\n check_version(trt.__version__, \"8.0.0\", hard=True) # require tensorrt>=8.0.0\n export_onnx(model, im, file, 12, dynamic, simplify) # opset 12\n onnx = file.with_suffix(\".onnx\")\n\n LOGGER.info(f\"\\n{prefix} starting export with TensorRT {trt.__version__}...\")\n assert onnx.exists(), f\"failed to export ONNX file: {onnx}\"\n f = file.with_suffix(\".engine\") # TensorRT engine file\n logger = trt.Logger(trt.Logger.INFO)\n if verbose:\n logger.min_severity = trt.Logger.Severity.VERBOSE\n\n builder = trt.Builder(logger)\n config = builder.create_builder_config()\n config.max_workspace_size = workspace * 1 << 30\n # config.set_memory_pool_limit(trt.MemoryPoolType.WORKSPACE, workspace << 30) # fix TRT 8.4 deprecation notice\n\n flag = 1 << int(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH)\n network = builder.create_network(flag)\n parser = trt.OnnxParser(network, logger)\n if not parser.parse_from_file(str(onnx)):\n raise RuntimeError(f\"failed to load ONNX file: {onnx}\")\n\n inputs = [network.get_input(i) for i in range(network.num_inputs)]\n outputs = [network.get_output(i) for i in range(network.num_outputs)]\n for inp in inputs:\n LOGGER.info(f'{prefix} input \"{inp.name}\" with shape{inp.shape} {inp.dtype}')\n for out in outputs:\n LOGGER.info(f'{prefix} output \"{out.name}\" with shape{out.shape} {out.dtype}')\n\n if dynamic:\n if im.shape[0] <= 1:\n LOGGER.warning(f\"{prefix} WARNING ⚠️ --dynamic model requires maximum --batch-size argument\")\n profile = builder.create_optimization_profile()\n for inp in inputs:\n profile.set_shape(inp.name, (1, *im.shape[1:]), (max(1, im.shape[0] // 2), *im.shape[1:]), im.shape)\n config.add_optimization_profile(profile)\n\n LOGGER.info(f\"{prefix} building FP{16 if builder.platform_has_fast_fp16 and half else 32} engine as {f}\")\n if builder.platform_has_fast_fp16 and half:\n config.set_flag(trt.BuilderFlag.FP16)\n with builder.build_engine(network, config) as engine, open(f, \"wb\") as t:\n t.write(engine.serialize())\n return f, None\n\n\n@try_export\ndef export_saved_model(\n model,\n im,\n file,\n dynamic,\n tf_nms=False,\n agnostic_nms=False,\n topk_per_class=100,\n topk_all=100,\n iou_thres=0.45,\n conf_thres=0.25,\n keras=False,\n prefix=colorstr(\"TensorFlow SavedModel:\"),\n):\n \"\"\"Exports a YOLOv3 model to TensorFlow SavedModel format, including optional settings for Non-Max Suppression\n (NMS).\n\n Args:\n model (torch.nn.Module): The YOLOv3 PyTorch model to be exported.\n im (torch.Tensor): Tensor of sample input data used for tracing the model.\n file (pathlib.Path): File path where the exported TensorFlow SavedModel will be saved.\n dynamic (bool): If `True`, supports dynamic input shapes.\n tf_nms (bool, optional): If `True`, includes TensorFlow NMS in the exported model. Defaults to `False`.\n agnostic_nms (bool, optional): If `True`, uses class-agnostic NMS. Defaults to `False`.\n topk_per_class (int, optional): Number of top-K predictions to keep per class after NMS. Defaults to `100`.\n topk_all (int, optional): Number of top-K predictions to keep overall after NMS. Defaults to `100`.\n iou_thres (float, optional): Intersection over Union (IoU) threshold for NMS. Defaults to `0.45`.\n conf_thres (float, optional): Confidence threshold for NMS. Defaults to `0.25`.\n keras (bool, optional): If `True`, saves the model in Keras format. Defaults to `False`.\n prefix (str, optional): Prefix for logging messages. Defaults to `colorstr(\"TensorFlow SavedModel:\")`.\n\n Returns:\n (str, None): Path to the saved TensorFlow model as a string and `None` (kept for interface consistency).\n\n Raises:\n ImportError: If the required TensorFlow libraries are not installed.\n\n Examples:\n ```python\n from pathlib import Path\n from models.common import DetectMultiBackend\n import torch\n\n model = DetectMultiBackend(weights='yolov5s.pt')\n im = torch.zeros(1, 3, 640, 640) # Sample input tensor\n file = Path(\"output/saved_model\")\n\n export_saved_model(model, im, file, dynamic=True)\n ```\n\n Notes:\n - Ensure that required TensorFlow libraries are installed (e.g., `pip install tensorflow`).\n - For more information, visit https://github.com/ultralytics/yolov5.\n \"\"\"\n # YOLOv3 TensorFlow SavedModel export\n try:\n import tensorflow as tf\n except Exception:\n check_requirements(f\"tensorflow{'' if torch.cuda.is_available() else '-macos' if MACOS else '-cpu'}\")\n import tensorflow as tf\n from tensorflow.python.framework.convert_to_constants import convert_variables_to_constants_v2\n\n from models.tf import TFModel\n\n LOGGER.info(f\"\\n{prefix} starting export with tensorflow {tf.__version__}...\")\n f = str(file).replace(\".pt\", \"_saved_model\")\n batch_size, ch, *imgsz = list(im.shape) # BCHW\n\n tf_model = TFModel(cfg=model.yaml, model=model, nc=model.nc, imgsz=imgsz)\n im = tf.zeros((batch_size, *imgsz, ch)) # BHWC order for TensorFlow\n _ = tf_model.predict(im, tf_nms, agnostic_nms, topk_per_class, topk_all, iou_thres, conf_thres)\n inputs = tf.keras.Input(shape=(*imgsz, ch), batch_size=None if dynamic else batch_size)\n outputs = tf_model.predict(inputs, tf_nms, agnostic_nms, topk_per_class, topk_all, iou_thres, conf_thres)\n keras_model = tf.keras.Model(inputs=inputs, outputs=outputs)\n keras_model.trainable = False\n keras_model.summary()\n if keras:\n keras_model.save(f, save_format=\"tf\")\n else:\n spec = tf.TensorSpec(keras_model.inputs[0].shape, keras_model.inputs[0].dtype)\n m = tf.function(lambda x: keras_model(x)) # full model\n m = m.get_concrete_function(spec)\n frozen_func = convert_variables_to_constants_v2(m)\n tfm = tf.Module()\n tfm.__call__ = tf.function(lambda x: frozen_func(x)[:4] if tf_nms else frozen_func(x), [spec])\n tfm.__call__(im)\n tf.saved_model.save(\n tfm,\n f,\n options=tf.saved_model.SaveOptions(experimental_custom_gradients=False)\n if check_version(tf.__version__, \"2.6\")\n else tf.saved_model.SaveOptions(),\n )\n return f, keras_model\n\n\n@try_export\ndef export_pb(keras_model, file, prefix=colorstr(\"TensorFlow GraphDef:\")):\n \"\"\"Export a Keras model to TensorFlow GraphDef (*.pb) format, which is compatible with YOLOv3.\n\n Args:\n keras_model (tf.keras.Model): The trained Keras model to be exported.\n file (pathlib.Path): The target file path for saving the exported model.\n prefix (str, optional): Prefix string for logging. Defaults to colorstr(\"TensorFlow GraphDef:\").\n\n Returns:\n tuple[pathlib.Path, None]: The file path where the model is saved and None.\n\n Examples:\n ```python\n from tensorflow.keras.models import load_model\n from pathlib import Path\n export_pb(load_model('model.h5'), Path('model.pb'))\n ```\n\n See Also:\n For more details on TensorFlow GraphDef, visit\n https://github.com/leimao/Frozen_Graph_TensorFlow.\n\n Notes:\n Ensure TensorFlow is properly installed in your environment as it is required for this function to execute.\n TensorFlow's version should be compatible with the version used to train your model to avoid any compatibility\n issues.\n \"\"\"\n import tensorflow as tf\n from tensorflow.python.framework.convert_to_constants import convert_variables_to_constants_v2\n\n LOGGER.info(f\"\\n{prefix} starting export with tensorflow {tf.__version__}...\")\n f = file.with_suffix(\".pb\")\n\n m = tf.function(lambda x: keras_model(x)) # full model\n m = m.get_concrete_function(tf.TensorSpec(keras_model.inputs[0].shape, keras_model.inputs[0].dtype))\n frozen_func = convert_variables_to_constants_v2(m)\n frozen_func.graph.as_graph_def()\n tf.io.write_graph(graph_or_graph_def=frozen_func.graph, logdir=str(f.parent), name=f.name, as_text=False)\n return f, None\n\n\n@try_export\ndef export_tflite(keras_model, im, file, int8, data, nms, agnostic_nms, prefix=colorstr(\"TensorFlow Lite:\")):\n \"\"\"Export a YOLOv3 PyTorch model to TensorFlow Lite (TFLite) format.\n\n Args:\n keras_model (tf.keras.Model): The Keras model obtained after converting the PyTorch model.\n im (torch.Tensor): Sample input tensor to determine model input size.\n file (pathlib.Path): Desired file path for saving the exported TFLite model.\n int8 (bool): Flag to enable INT8 quantization for the TFLite model.\n data (str): Path to dataset YAML file for representative data generation used in quantization.\n nms (bool): Flag to include Non-Maximum Suppression (NMS) in the exported TFLite model.\n agnostic_nms (bool): Flag to apply class-agnostic NMS during inference.\n prefix (str, optional): Prefix for logging messages. Defaults to colorstr(\"TensorFlow Lite:\").\n\n Returns:\n (str | None): File path of the saved TensorFlow Lite model file or None if export fails.\n\n Examples:\n ```python\n import torch\n from pathlib import Path\n from models.experimental import attempt_load\n\n # Load and prepare model\n model = attempt_load('yolov5s.pt', map_location='cpu')\n im = torch.zeros(1, 3, 640, 640) # Dummy input tensor\n\n # Export model\n export_tflite(model, im, Path('yolov5s'), int8=False, data=None, nms=True, agnostic_nms=False)\n ```\n\n For more details, refer to:\n TensorFlow Lite Developer Guide: https://www.tensorflow.org/lite/guide\n Model Conversion Reference: https://github.com/leimao/Frozen_Graph_TensorFlow\n\n Notes:\n - Ensure TensorFlow is installed to perform the export.\n - INT8 quantization requires a representative dataset to provide accurate calibration for the model.\n - Including Non-Max Suppression (NMS) modifies the exported model to handle post-processing.\n \"\"\"\n import tensorflow as tf\n\n LOGGER.info(f\"\\n{prefix} starting export with tensorflow {tf.__version__}...\")\n _batch_size, _ch, *imgsz = list(im.shape) # BCHW\n f = str(file).replace(\".pt\", \"-fp16.tflite\")\n\n converter = tf.lite.TFLiteConverter.from_keras_model(keras_model)\n converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS]\n converter.target_spec.supported_types = [tf.float16]\n converter.optimizations = [tf.lite.Optimize.DEFAULT]\n if int8:\n from models.tf import representative_dataset_gen\n\n dataset = LoadImages(check_dataset(check_yaml(data))[\"train\"], img_size=imgsz, auto=False)\n converter.representative_dataset = lambda: representative_dataset_gen(dataset, ncalib=100)\n converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]\n converter.target_spec.supported_types = []\n converter.inference_input_type = tf.uint8 # or tf.int8\n converter.inference_output_type = tf.uint8 # or tf.int8\n converter.experimental_new_quantizer = True\n f = str(file).replace(\".pt\", \"-int8.tflite\")\n if nms or agnostic_nms:\n converter.target_spec.supported_ops.append(tf.lite.OpsSet.SELECT_TF_OPS)\n\n tflite_model = converter.convert()\n open(f, \"wb\").write(tflite_model)\n return f, None\n\n\n@try_export\ndef export_edgetpu(file, prefix=colorstr(\"Edge TPU:\")):\n \"\"\"Export a YOLOv5 model to TensorFlow Edge TPU format with INT8 quantization.\n\n Args:\n file (Path): The file path for the PyTorch model to be exported, with a `.pt` suffix.\n prefix (str): A prefix to be used for logging output. Defaults to \"Edge TPU:\"\n\n Returns:\n Tuple[Path | None, None]: A tuple containing the file path of the exported model with the `-int8_edgetpu.tflite`\n suffix and `None`, if successful. If unsuccessful, returns `(None, None)`.\n\n Raises:\n AssertionError: If the export is not executed on a Linux system.\n subprocess.CalledProcessError: If there are issues with subprocess execution, particularly around Edge TPU\n compiler installation or model conversion.\n\n Examples:\n ```python\n from pathlib import Path\n from ultralytics import export_edgetpu\n\n model_file = Path('yolov5s.pt')\n exported_model, _ = export_edgetpu(model_file)\n print(f\"Model exported to {exported_model}\")\n ```\n\n For additional details, visit the Edge TPU compiler documentation:\n https://coral.ai/docs/edgetpu/compiler/\n\n Notes:\n This function is designed to work exclusively on Linux systems and requires the Edge TPU compiler to be installed.\n If the compiler is not found, the function attempts to install it.\n \"\"\"\n cmd = \"edgetpu_compiler --version\"\n help_url = \"https://coral.ai/docs/edgetpu/compiler/\"\n assert platform.system() == \"Linux\", f\"export only supported on Linux. See {help_url}\"\n if subprocess.run(f\"{cmd} > /dev/null 2>&1\", shell=True).returncode != 0:\n LOGGER.info(f\"\\n{prefix} export requires Edge TPU compiler. Attempting install from {help_url}\")\n sudo = subprocess.run(\"sudo --version >/dev/null\", shell=True).returncode == 0 # sudo installed on system\n for c in (\n \"curl https://packages.cloud.google.com/apt/doc/apt-key.gpg | sudo apt-key add -\",\n 'echo \"deb https://packages.cloud.google.com/apt coral-edgetpu-stable main\" | sudo tee /etc/apt/sources.list.d/coral-edgetpu.list',\n \"sudo apt-get update\",\n \"sudo apt-get install edgetpu-compiler\",\n ):\n subprocess.run(c if sudo else c.replace(\"sudo \", \"\"), shell=True, check=True)\n ver = subprocess.run(cmd, shell=True, capture_output=True, check=True).stdout.decode().split()[-1]\n\n LOGGER.info(f\"\\n{prefix} starting export with Edge TPU compiler {ver}...\")\n f = str(file).replace(\".pt\", \"-int8_edgetpu.tflite\") # Edge TPU model\n f_tfl = str(file).replace(\".pt\", \"-int8.tflite\") # TFLite model\n\n subprocess.run(\n [\n \"edgetpu_compiler\",\n \"-s\",\n \"-d\",\n \"-k\",\n \"10\",\n \"--out_dir\",\n str(file.parent),\n f_tfl,\n ],\n check=True,\n )\n return f, None\n\n\n@try_export\ndef export_tfjs(file, int8, prefix=colorstr(\"TensorFlow.js:\")):\n \"\"\"Export a YOLOv3 model to TensorFlow.js format, with an optional quantization to uint8.\n\n Args:\n file (Path): The path to the model file to be exported.\n int8 (bool): Boolean flag to determine if the model should be quantized to uint8.\n prefix (str): String prefix for logging, by default \"TensorFlow.js\".\n\n Returns:\n (tuple[str, None]): The directory path where the TensorFlow.js model files are saved and `None` placeholder to\n match the expected return type from 'try_export' decorator.\n\n Raises:\n ImportError: If the required 'tensorflowjs' package is not installed.\n\n Examples:\n ```python\n from pathlib import Path\n export_tfjs(file=Path(\"yolov5s.pt\"), int8=False)\n ```\n\n The converted model can be used directly in JavaScript environments using the TensorFlow.js library.\n\n For usage in web applications:\n - Clone the example repository:\n ```bash\n cd .. && git clone https://github.com/zldrobit/tfjs-yolov5-example.git && cd tfjs-yolov5-example\n ```\n - Install dependencies:\n ```bash\n npm install\n ```\n - Create a symbolic link to the exported web model:\n ```bash\n ln -s ../../yolov5/yolov5s_web_model public/yolov5s_web_model\n ```\n - Start the example application:\n ```bash\n npm start\n ```\n\n Notes:\n Ensure that you have TensorFlow.js installed in your environment. Install the package via:\n ```bash\n pip install tensorflowjs\n ```\n\n For more details on using the converted model:\n Refer to the official TensorFlow.js documentation: https://www.tensorflow.org/js.\n \"\"\"\n check_requirements(\"tensorflowjs\")\n import tensorflowjs as tfjs\n\n LOGGER.info(f\"\\n{prefix} starting export with tensorflowjs {tfjs.__version__}...\")\n f = str(file).replace(\".pt\", \"_web_model\") # js dir\n f_pb = file.with_suffix(\".pb\") # *.pb path\n f_json = f\"{f}/model.json\" # *.json path\n\n args = [\n \"tensorflowjs_converter\",\n \"--input_format=tf_frozen_model\",\n \"--quantize_uint8\" if int8 else \"\",\n \"--output_node_names=Identity,Identity_1,Identity_2,Identity_3\",\n str(f_pb),\n f,\n ]\n subprocess.run([arg for arg in args if arg], check=True)\n\n json = Path(f_json).read_text()\n with open(f_json, \"w\") as j: # sort JSON Identity_* in ascending order\n subst = re.sub(\n r'{\"outputs\": {\"Identity.?.?\": {\"name\": \"Identity.?.?\"}, '\n r'\"Identity.?.?\": {\"name\": \"Identity.?.?\"}, '\n r'\"Identity.?.?\": {\"name\": \"Identity.?.?\"}, '\n r'\"Identity.?.?\": {\"name\": \"Identity.?.?\"}}}',\n r'{\"outputs\": {\"Identity\": {\"name\": \"Identity\"}, '\n r'\"Identity_1\": {\"name\": \"Identity_1\"}, '\n r'\"Identity_2\": {\"name\": \"Identity_2\"}, '\n r'\"Identity_3\": {\"name\": \"Identity_3\"}}}',\n json,\n )\n j.write(subst)\n return f, None\n\n\ndef add_tflite_metadata(file, metadata, num_outputs):\n \"\"\"Adds metadata to a TensorFlow Lite model to enhance its usability with `tflite_support`.\n\n Args:\n file (str): Path to the TensorFlow Lite model file.\n metadata (dict): Dictionary of metadata to add, including descriptions of inputs, outputs, and other relevant\n info.\n num_outputs (int): Number of output tensors in the model.\n\n Returns:\n None\n\n Examples:\n ```python\n metadata = {\n \"input\": {\"description\": \"Input image tensor\"},\n \"output\": [{\"name\": \"scores\", \"description\": \"Detection scores\"}],\n }\n add_tflite_metadata(\"/path/to/model.tflite\", metadata, num_outputs=1)\n ```\n\n Notes:\n Requires the `tflite_support` library for adding metadata to the TensorFlow Lite model.\n Installation: `pip install tflite-support`\n\n ```python\n from tflite_support import flatbuffers\n from tflite_support import metadata as _metadata\n from tflite_support import metadata_schema_py_generated as _metadata_fb\n\n tmp_file = Path(\"/tmp/meta.txt\")\n with open(tmp_file, \"w\") as meta_f:\n meta_f.write(str(metadata))\n\n model_meta = _metadata_fb.ModelMetadataT()\n label_file = _metadata_fb.AssociatedFileT()\n label_file.name = tmp_file.name\n model_meta.associatedFiles = [label_file]\n\n subgraph = _metadata_fb.SubGraphMetadataT()\n subgraph.inputTensorMetadata = [_metadata_fb.TensorMetadataT()]\n subgraph.outputTensorMetadata = [_metadata_fb.TensorMetadataT()] * num_outputs\n model_meta.subgraphMetadata = [subgraph]\n\n b = flatbuffers.Builder(0)\n b.Finish(model_meta.Pack(b), _metadata.MetadataPopulator.METADATA_FILE_IDENTIFIER)\n metadata_buf = b.Output()\n\n populator = _metadata.MetadataPopulator.with_model_file(file)\n populator.load_metadata_buffer(metadata_buf)\n populator.load_associated_files([str(tmp_file)])\n populator.populate()\n ```\n\n This function is a helper to add metadata to a TFLite model, making it easier to interpret and process for tasks like\n object detection or classification. It leverages `tflite_support` to load and attach the metadata directly to the\n model file.\n \"\"\"\n with contextlib.suppress(ImportError):\n # check_requirements('tflite_support')\n from tflite_support import flatbuffers\n from tflite_support import metadata as _metadata\n from tflite_support import metadata_schema_py_generated as _metadata_fb\n\n tmp_file = Path(\"/tmp/meta.txt\")\n with open(tmp_file, \"w\") as meta_f:\n meta_f.write(str(metadata))\n\n model_meta = _metadata_fb.ModelMetadataT()\n label_file = _metadata_fb.AssociatedFileT()\n label_file.name = tmp_file.name\n model_meta.associatedFiles = [label_file]\n\n subgraph = _metadata_fb.SubGraphMetadataT()\n subgraph.inputTensorMetadata = [_metadata_fb.TensorMetadataT()]\n subgraph.outputTensorMetadata = [_metadata_fb.TensorMetadataT()] * num_outputs\n model_meta.subgraphMetadata = [subgraph]\n\n b = flatbuffers.Builder(0)\n b.Finish(model_meta.Pack(b), _metadata.MetadataPopulator.METADATA_FILE_IDENTIFIER)\n metadata_buf = b.Output()\n\n populator = _metadata.MetadataPopulator.with_model_file(file)\n populator.load_metadata_buffer(metadata_buf)\n populator.load_associated_files([str(tmp_file)])\n populator.populate()\n tmp_file.unlink()\n\n\ndef pipeline_coreml(model, im, file, names, y, prefix=colorstr(\"CoreML Pipeline:\")):\n \"\"\"Processes and exports a YOLOv3 model into the CoreML model format, applying metadata and non-maximum suppression\n (NMS).\n\n Args:\n model (coremltools.models.MLModel): The pre-trained YOLOv3 CoreML model to be used for the pipeline.\n im (torch.Tensor): Input image tensor in BCHW (Batch, Channel, Height, Width) format with a shape (B, 3, H, W).\n file (pathlib.Path): Destination file path where the CoreML model will be saved.\n names (dict): A dictionary that maps class indices to class names.\n y (torch.Tensor): Output detection tensor from the YOLO model, containing predictions.\n prefix (str): Prefix for logging messages, default is \"CoreML Pipeline:\".\n\n Returns:\n pathlib.Path | None: The path to the saved CoreML model if successful, otherwise None.\n\n Examples:\n ```python\n from pathlib import Path\n import torch\n from coremltools.models import MLModel\n\n # Load example CoreML model\n model = MLModel('path/to/pretrained/model.mlmodel')\n\n # Create example input tensor: B, C, H, W format\n im = torch.randn(1, 3, 640, 640)\n\n # Define where the CoreML model will be saved\n file = Path('path/to/save/model.mlmodel')\n\n # Define example class names\n names = {0: 'class0', 1: 'class1'}\n\n # Dummy YOLO model output prediction having similar dimensions to y\n y = torch.randn(1, 25200, 85)\n\n # Execute CoreML pipeline\n pipeline_coreml(model, im, file, names, y)\n ```\n\n Notes:\n - The function adds NMS to the CoreML model, supporting dynamic thresholds for IoU and confidence.\n - Metadata fields are updated to include class names, thresholds, and additional information.\n - The pipeline exports the final enhanced model into the specified file path in CoreML (`.mlmodel`) format.\n - Ensure that `coremltools` is installed and properly configured in your environment.\n - This function is designed to work primarily on macOS systems as CoreML is macOS-specific.\n\n References:\n - `coremltools`: https://github.com/apple/coremltools\n - YOLOv3: https://github.com/ultralytics/yolov5\n \"\"\"\n import coremltools as ct\n from PIL import Image\n\n print(f\"{prefix} starting pipeline with coremltools {ct.__version__}...\")\n _batch_size, _ch, h, w = list(im.shape) # BCHW\n t = time.time()\n\n # YOLOv3 Output shapes\n spec = model.get_spec()\n out0, out1 = iter(spec.description.output)\n if platform.system() == \"Darwin\":\n img = Image.new(\"RGB\", (w, h)) # img(192 width, 320 height)\n # img = torch.zeros((*opt.img_size, 3)).numpy() # img size(320,192,3) iDetection\n out = model.predict({\"image\": img})\n out0_shape, out1_shape = out[out0.name].shape, out[out1.name].shape\n else: # linux and windows can not run model.predict(), get sizes from pytorch output y\n s = tuple(y[0].shape)\n out0_shape, out1_shape = (s[1], s[2] - 5), (s[1], 4) # (3780, 80), (3780, 4)\n\n # Checks\n nx, ny = spec.description.input[0].type.imageType.width, spec.description.input[0].type.imageType.height\n _na, nc = out0_shape\n # na, nc = out0.type.multiArrayType.shape # number anchors, classes\n assert len(names) == nc, f\"{len(names)} names found for nc={nc}\" # check\n\n # Define output shapes (missing)\n out0.type.multiArrayType.shape[:] = out0_shape # (3780, 80)\n out1.type.multiArrayType.shape[:] = out1_shape # (3780, 4)\n # spec.neuralNetwork.preprocessing[0].featureName = '0'\n\n # Flexible input shapes\n # from coremltools.models.neural_network import flexible_shape_utils\n # s = [] # shapes\n # s.append(flexible_shape_utils.NeuralNetworkImageSize(320, 192))\n # s.append(flexible_shape_utils.NeuralNetworkImageSize(640, 384)) # (height, width)\n # flexible_shape_utils.add_enumerated_image_sizes(spec, feature_name='image', sizes=s)\n # r = flexible_shape_utils.NeuralNetworkImageSizeRange() # shape ranges\n # r.add_height_range((192, 640))\n # r.add_width_range((192, 640))\n # flexible_shape_utils.update_image_size_range(spec, feature_name='image', size_range=r)\n\n # Print\n print(spec.description)\n\n # Model from spec\n model = ct.models.MLModel(spec)\n\n # 3. Create NMS protobuf\n nms_spec = ct.proto.Model_pb2.Model()\n nms_spec.specificationVersion = 5\n for i in range(2):\n decoder_output = model._spec.description.output[i].SerializeToString()\n nms_spec.description.input.add()\n nms_spec.description.input[i].ParseFromString(decoder_output)\n nms_spec.description.output.add()\n nms_spec.description.output[i].ParseFromString(decoder_output)\n\n nms_spec.description.output[0].name = \"confidence\"\n nms_spec.description.output[1].name = \"coordinates\"\n\n output_sizes = [nc, 4]\n for i in range(2):\n ma_type = nms_spec.description.output[i].type.multiArrayType\n ma_type.shapeRange.sizeRanges.add()\n ma_type.shapeRange.sizeRanges[0].lowerBound = 0\n ma_type.shapeRange.sizeRanges[0].upperBound = -1\n ma_type.shapeRange.sizeRanges.add()\n ma_type.shapeRange.sizeRanges[1].lowerBound = output_sizes[i]\n ma_type.shapeRange.sizeRanges[1].upperBound = output_sizes[i]\n del ma_type.shape[:]\n\n nms = nms_spec.nonMaximumSuppression\n nms.confidenceInputFeatureName = out0.name # 1x507x80\n nms.coordinatesInputFeatureName = out1.name # 1x507x4\n nms.confidenceOutputFeatureName = \"confidence\"\n nms.coordinatesOutputFeatureName = \"coordinates\"\n nms.iouThresholdInputFeatureName = \"iouThreshold\"\n nms.confidenceThresholdInputFeatureName = \"confidenceThreshold\"\n nms.iouThreshold = 0.45\n nms.confidenceThreshold = 0.25\n nms.pickTop.perClass = True\n nms.stringClassLabels.vector.extend(names.values())\n nms_model = ct.models.MLModel(nms_spec)\n\n # 4. Pipeline models together\n pipeline = ct.models.pipeline.Pipeline(\n input_features=[\n (\"image\", ct.models.datatypes.Array(3, ny, nx)),\n (\"iouThreshold\", ct.models.datatypes.Double()),\n (\"confidenceThreshold\", ct.models.datatypes.Double()),\n ],\n output_features=[\"confidence\", \"coordinates\"],\n )\n pipeline.add_model(model)\n pipeline.add_model(nms_model)\n\n # Correct datatypes\n pipeline.spec.description.input[0].ParseFromString(model._spec.description.input[0].SerializeToString())\n pipeline.spec.description.output[0].ParseFromString(nms_model._spec.description.output[0].SerializeToString())\n pipeline.spec.description.output[1].ParseFromString(nms_model._spec.description.output[1].SerializeToString())\n\n # Update metadata\n pipeline.spec.specificationVersion = 5\n pipeline.spec.description.metadata.versionString = \"https://github.com/ultralytics/yolov5\"\n pipeline.spec.description.metadata.shortDescription = \"https://github.com/ultralytics/yolov5\"\n pipeline.spec.description.metadata.author = \"glenn.jocher@ultralytics.com\"\n pipeline.spec.description.metadata.license = \"https://github.com/ultralytics/yolov5/blob/master/LICENSE\"\n pipeline.spec.description.metadata.userDefined.update(\n {\n \"classes\": \",\".join(names.values()),\n \"iou_threshold\": str(nms.iouThreshold),\n \"confidence_threshold\": str(nms.confidenceThreshold),\n }\n )\n\n # Save the model\n f = file.with_suffix(\".mlmodel\") # filename\n model = ct.models.MLModel(pipeline.spec)\n model.input_description[\"image\"] = \"Input image\"\n model.input_description[\"iouThreshold\"] = f\"(optional) IOU Threshold override (default: {nms.iouThreshold})\"\n model.input_description[\"confidenceThreshold\"] = (\n f\"(optional) Confidence Threshold override (default: {nms.confidenceThreshold})\"\n )\n model.output_description[\"confidence\"] = 'Boxes × Class confidence (see user-defined metadata \"classes\")'\n model.output_description[\"coordinates\"] = \"Boxes × [x, y, width, height] (relative to image size)\"\n model.save(f) # pipelined\n print(f\"{prefix} pipeline success ({time.time() - t:.2f}s), saved as {f} ({file_size(f):.1f} MB)\")\n\n\n@smart_inference_mode()\ndef run(\n data=ROOT / \"data/coco128.yaml\", # 'dataset.yaml path'\n weights=ROOT / \"yolov5s.pt\", # weights path\n imgsz=(640, 640), # image (height, width)\n batch_size=1, # batch size\n device=\"cpu\", # cuda device, i.e. 0 or 0,1,2,3 or cpu\n include=(\"torchscript\", \"onnx\"), # include formats\n half=False, # FP16 half-precision export\n inplace=False, # set YOLOv3 Detect() inplace=True\n keras=False, # use Keras\n optimize=False, # TorchScript: optimize for mobile\n int8=False, # CoreML/TF INT8 quantization\n dynamic=False, # ONNX/TF/TensorRT: dynamic axes\n simplify=False, # ONNX: simplify model\n opset=12, # ONNX: opset version\n verbose=False, # TensorRT: verbose log\n workspace=4, # TensorRT: workspace size (GB)\n nms=False, # TF: add NMS to model\n agnostic_nms=False, # TF: add agnostic NMS to model\n topk_per_class=100, # TF.js NMS: topk per class to keep\n topk_all=100, # TF.js NMS: topk for all classes to keep\n iou_thres=0.45, # TF.js NMS: IoU threshold\n conf_thres=0.25, # TF.js NMS: confidence threshold\n):\n \"\"\"Export a PyTorch model to various formats like ONNX, CoreML, and TensorRT.\n\n Args:\n data (str | Path): Path to dataset configuration file.\n weights (str | Path): Path to model weights file in PyTorch format.\n imgsz (tuple[int, int]): Tuple specifying image height and width for input dimensions.\n batch_size (int): Batch size for model inference.\n device (str): Device to use for inference (e.g., '0', '0,1,2,3', 'cpu').\n include (tuple[str]): Formats to include for model export (e.g., 'torchscript', 'onnx', etc.).\n half (bool): Whether to export model with FP16 precision.\n inplace (bool): Set YOLOv3 Detect module inplace option to True.\n keras (bool): Save Keras model when exporting TensorFlow SavedModel format.\n optimize (bool): Optimize the TorchScript model for mobile inference.\n int8 (bool): Apply INT8 quantization for CoreML/TF models.\n dynamic (bool): Enable dynamic axes for ONNX/TF/TensorRT models.\n simplify (bool): Simplify the ONNX model after export.\n opset (int): ONNX opset version.\n verbose (bool): Enable verbose logging for TensorRT engine export.\n workspace (int): Workspace size in GB for TensorRT engine.\n nms (bool): Enable Non-Maximum Suppression (NMS) in TensorFlow models.\n agnostic_nms (bool): Enable class-agnostic NMS in TensorFlow models.\n topk_per_class (int): Top-K per class to keep in TensorFlow JSON model.\n topk_all (int): Top-K for all classes to keep in TensorFlow JSON model.\n iou_thres (float): IOU threshold for TensorFlow JSON model.\n conf_thres (float): Confidence threshold for TensorFlow JSON model.\n\n Returns:\n None\n\n Examples:\n ```python\n run(\n data='data/coco128.yaml',\n weights='yolov5s.pt',\n imgsz=(640, 640),\n batch_size=1,\n device='cpu',\n include=('torchscript', 'onnx'),\n half=False,\n dynamic=True,\n opset=12\n )\n ```\n\n Notes:\n - Requires various packages installed for different export formats, e.g., `onnx`, `coremltools`, etc.\n - Some formats have additional dependencies (e.g., TensorFlow, TensorRT, etc.)\n \"\"\"\n t = time.time()\n include = [x.lower() for x in include] # to lowercase\n fmts = tuple(export_formats()[\"Argument\"][1:]) # --include arguments\n flags = [x in include for x in fmts]\n assert sum(flags) == len(include), f\"ERROR: Invalid --include {include}, valid --include arguments are {fmts}\"\n jit, onnx, xml, engine, coreml, saved_model, pb, tflite, edgetpu, tfjs, paddle = flags # export booleans\n file = Path(url2file(weights) if str(weights).startswith((\"http:/\", \"https:/\")) else weights) # PyTorch weights\n\n # Load PyTorch model\n device = select_device(device)\n if half:\n assert device.type != \"cpu\" or coreml, \"--half only compatible with GPU export, i.e. use --device 0\"\n assert not dynamic, \"--half not compatible with --dynamic, i.e. use either --half or --dynamic but not both\"\n model = attempt_load(weights, device=device, inplace=True, fuse=True) # load FP32 model\n\n # Checks\n imgsz *= 2 if len(imgsz) == 1 else 1 # expand\n if optimize:\n assert device.type == \"cpu\", \"--optimize not compatible with cuda devices, i.e. use --device cpu\"\n\n # Input\n gs = int(max(model.stride)) # grid size (max stride)\n imgsz = [check_img_size(x, gs) for x in imgsz] # verify img_size are gs-multiples\n im = torch.zeros(batch_size, 3, *imgsz).to(device) # image size(1,3,320,192) BCHW iDetection\n\n # Update model\n model.eval()\n for k, m in model.named_modules():\n if isinstance(m, Detect):\n m.inplace = inplace\n m.dynamic = dynamic\n m.export = True\n\n for _ in range(2):\n y = model(im) # dry runs\n if half and not coreml:\n im, model = im.half(), model.half() # to FP16\n shape = tuple((y[0] if isinstance(y, tuple) else y).shape) # model output shape\n metadata = {\"stride\": int(max(model.stride)), \"names\": model.names} # model metadata\n LOGGER.info(f\"\\n{colorstr('PyTorch:')} starting from {file} with output shape {shape} ({file_size(file):.1f} MB)\")\n\n # Exports\n f = [\"\"] * len(fmts) # exported filenames\n warnings.filterwarnings(action=\"ignore\", category=torch.jit.TracerWarning) # suppress TracerWarning\n if jit: # TorchScript\n f[0], _ = export_torchscript(model, im, file, optimize)\n if engine: # TensorRT required before ONNX\n f[1], _ = export_engine(model, im, file, half, dynamic, simplify, workspace, verbose)\n if onnx or xml: # OpenVINO requires ONNX\n f[2], _ = export_onnx(model, im, file, opset, dynamic, simplify)\n if xml: # OpenVINO\n f[3], _ = export_openvino(file, metadata, half, int8, data)\n if coreml: # CoreML\n f[4], ct_model = export_coreml(model, im, file, int8, half, nms)\n if nms:\n pipeline_coreml(ct_model, im, file, model.names, y)\n if any((saved_model, pb, tflite, edgetpu, tfjs)): # TensorFlow formats\n assert not tflite or not tfjs, \"TFLite and TF.js models must be exported separately, please pass only one type.\"\n assert not isinstance(model, ClassificationModel), \"ClassificationModel export to TF formats not yet supported.\"\n f[5], s_model = export_saved_model(\n model.cpu(),\n im,\n file,\n dynamic,\n tf_nms=nms or agnostic_nms or tfjs,\n agnostic_nms=agnostic_nms or tfjs,\n topk_per_class=topk_per_class,\n topk_all=topk_all,\n iou_thres=iou_thres,\n conf_thres=conf_thres,\n keras=keras,\n )\n if pb or tfjs: # pb prerequisite to tfjs\n f[6], _ = export_pb(s_model, file)\n if tflite or edgetpu:\n f[7], _ = export_tflite(s_model, im, file, int8 or edgetpu, data=data, nms=nms, agnostic_nms=agnostic_nms)\n if edgetpu:\n f[8], _ = export_edgetpu(file)\n add_tflite_metadata(f[8] or f[7], metadata, num_outputs=len(s_model.outputs))\n if tfjs:\n f[9], _ = export_tfjs(file, int8)\n if paddle: # PaddlePaddle\n f[10], _ = export_paddle(model, im, file, metadata)\n\n # Finish\n f = [str(x) for x in f if x] # filter out '' and None\n if any(f):\n cls, det, seg = (isinstance(model, x) for x in (ClassificationModel, DetectionModel, SegmentationModel)) # type\n det &= not seg # segmentation models inherit from SegmentationModel(DetectionModel)\n dir = Path(\"segment\" if seg else \"classify\" if cls else \"\")\n h = \"--half\" if half else \"\" # --half FP16 inference arg\n s = (\n \"# WARNING ⚠️ ClassificationModel not yet supported for PyTorch Hub AutoShape inference\"\n if cls\n else \"# WARNING ⚠️ SegmentationModel not yet supported for PyTorch Hub AutoShape inference\"\n if seg\n else \"\"\n )\n LOGGER.info(\n f\"\\nExport complete ({time.time() - t:.1f}s)\"\n f\"\\nResults saved to {colorstr('bold', file.parent.resolve())}\"\n f\"\\nDetect: python {dir / ('detect.py' if det else 'predict.py')} --weights {f[-1]} {h}\"\n f\"\\nValidate: python {dir / 'val.py'} --weights {f[-1]} {h}\"\n f\"\\nPyTorch Hub: model = torch.hub.load('ultralytics/yolov5', 'custom', '{f[-1]}') {s}\"\n f\"\\nVisualize: https://netron.app\"\n )\n return f # return list of exported files/dirs\n\n\ndef parse_opt(known=False):\n \"\"\"Parse command-line arguments for model export configuration.\n\n Args:\n known (bool): If True, parse only known arguments and ignore others. Default is False.\n\n Returns:\n argparse.Namespace: Namespace object containing export configuration parameters.\n\n Examples:\n ```python\n from ultralytics.export import parse_opt\n\n options = parse_opt(known=True)\n print(options)\n ```\n\n Notes:\n This function leverages `argparse` to handle command-line arguments for various model export configurations, allowing\n users to specify export formats, model parameters, and optimization settings.\n \"\"\"\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--data\", type=str, default=ROOT / \"data/coco128.yaml\", help=\"dataset.yaml path\")\n parser.add_argument(\"--weights\", nargs=\"+\", type=str, default=ROOT / \"yolov3-tiny.pt\", help=\"model.pt path(s)\")\n parser.add_argument(\"--imgsz\", \"--img\", \"--img-size\", nargs=\"+\", type=int, default=[640, 640], help=\"image (h, w)\")\n parser.add_argument(\"--batch-size\", type=int, default=1, help=\"batch size\")\n parser.add_argument(\"--device\", default=\"cpu\", help=\"cuda device, i.e. 0 or 0,1,2,3 or cpu\")\n parser.add_argument(\"--half\", action=\"store_true\", help=\"FP16 half-precision export\")\n parser.add_argument(\"--inplace\", action=\"store_true\", help=\"set YOLOv3 Detect() inplace=True\")\n parser.add_argument(\"--keras\", action=\"store_true\", help=\"TF: use Keras\")\n parser.add_argument(\"--optimize\", action=\"store_true\", help=\"TorchScript: optimize for mobile\")\n parser.add_argument(\"--int8\", action=\"store_true\", help=\"CoreML/TF/OpenVINO INT8 quantization\")\n parser.add_argument(\"--dynamic\", action=\"store_true\", help=\"ONNX/TF/TensorRT: dynamic axes\")\n parser.add_argument(\"--simplify\", action=\"store_true\", help=\"ONNX: simplify model\")\n parser.add_argument(\"--opset\", type=int, default=17, help=\"ONNX: opset version\")\n parser.add_argument(\"--verbose\", action=\"store_true\", help=\"TensorRT: verbose log\")\n parser.add_argument(\"--workspace\", type=int, default=4, help=\"TensorRT: workspace size (GB)\")\n parser.add_argument(\"--nms\", action=\"store_true\", help=\"TF: add NMS to model\")\n parser.add_argument(\"--agnostic-nms\", action=\"store_true\", help=\"TF: add agnostic NMS to model\")\n parser.add_argument(\"--topk-per-class\", type=int, default=100, help=\"TF.js NMS: topk per class to keep\")\n parser.add_argument(\"--topk-all\", type=int, default=100, help=\"TF.js NMS: topk for all classes to keep\")\n parser.add_argument(\"--iou-thres\", type=float, default=0.45, help=\"TF.js NMS: IoU threshold\")\n parser.add_argument(\"--conf-thres\", type=float, default=0.25, help=\"TF.js NMS: confidence threshold\")\n parser.add_argument(\n \"--include\",\n nargs=\"+\",\n default=[\"torchscript\"],\n help=\"torchscript, onnx, openvino, engine, coreml, saved_model, pb, tflite, edgetpu, tfjs, paddle\",\n )\n opt = parser.parse_known_args()[0] if known else parser.parse_args()\n print_args(vars(opt))\n return opt\n\n\ndef main(opt):\n \"\"\"Run(**vars(opt)).\"\"\"\n for opt.weights in opt.weights if isinstance(opt.weights, list) else [opt.weights]:\n run(**vars(opt))\n\n\nif __name__ == \"__main__\":\n opt = parse_opt()\n main(opt)\n" }, { "path": "hubconf.py", "content": "# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license\n\"\"\"\nPyTorch Hub models https://pytorch.org/hub/ultralytics_yolov5.\n\nUsage:\n import torch\n model = torch.hub.load('ultralytics/yolov5', 'yolov5s') # official model\n model = torch.hub.load('ultralytics/yolov5:master', 'yolov5s') # from branch\n model = torch.hub.load('ultralytics/yolov5', 'custom', 'yolov5s.pt') # custom/local model\n model = torch.hub.load('.', 'custom', 'yolov5s.pt', source='local') # local repo\n\"\"\"\n\nfrom ultralytics.utils.patches import torch_load\n\n\ndef _create(name, pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None):\n \"\"\"Creates or loads a YOLOv3 model with specified configurations and optional pretrained weights.\n\n Args:\n name (str): Model name such as 'yolov5s' or a path to a model checkpoint file, e.g., 'path/to/best.pt'.\n pretrained (bool): Whether to load pretrained weights into the model. Default is True.\n channels (int): Number of input channels. Default is 3.\n classes (int): Number of model classes. Default is 80.\n autoshape (bool): Whether to apply the YOLOv3 .autoshape() wrapper to the model for handling multiple input\n types. Default is True.\n verbose (bool): If True, print all information to the screen. Default is True.\n device (str | torch.device | None): Device to use for model parameters ('cpu', 'cuda', etc.). If None, defaults\n to the best available device.\n\n Returns:\n torch.nn.Module: YOLOv3 model loaded with or without pretrained weights.\n\n Raises:\n Exception: If an error occurs while loading the model, returns an error message with a helpful URL:\n \"https: //docs.ultralytics.com/yolov5/tutorials/pytorch_hub_model_loading\".\n\n Examples:\n ```python\n import torch\n model = _create('yolov5s')\n ```\n \"\"\"\n from pathlib import Path\n\n from models.common import AutoShape, DetectMultiBackend\n from models.experimental import attempt_load\n from models.yolo import ClassificationModel, DetectionModel, SegmentationModel\n from utils.downloads import attempt_download\n from utils.general import LOGGER, ROOT, check_requirements, intersect_dicts, logging\n from utils.torch_utils import select_device\n\n if not verbose:\n LOGGER.setLevel(logging.WARNING)\n check_requirements(ROOT / \"requirements.txt\", exclude=(\"opencv-python\", \"tensorboard\", \"thop\"))\n name = Path(name)\n path = name.with_suffix(\".pt\") if name.suffix == \"\" and not name.is_dir() else name # checkpoint path\n try:\n device = select_device(device)\n if pretrained and channels == 3 and classes == 80:\n try:\n model = DetectMultiBackend(path, device=device, fuse=autoshape) # detection model\n if autoshape:\n if model.pt and isinstance(model.model, ClassificationModel):\n LOGGER.warning(\n \"WARNING ⚠️ YOLOv3 ClassificationModel is not yet AutoShape compatible. \"\n \"You must pass torch tensors in BCHW to this model, i.e. shape(1,3,224,224).\"\n )\n elif model.pt and isinstance(model.model, SegmentationModel):\n LOGGER.warning(\n \"WARNING ⚠️ YOLOv3 SegmentationModel is not yet AutoShape compatible. \"\n \"You will not be able to run inference with this model.\"\n )\n else:\n model = AutoShape(model) # for file/URI/PIL/cv2/np inputs and NMS\n except Exception:\n model = attempt_load(path, device=device, fuse=False) # arbitrary model\n else:\n cfg = next(iter((Path(__file__).parent / \"models\").rglob(f\"{path.stem}.yaml\"))) # model.yaml path\n model = DetectionModel(cfg, channels, classes) # create model\n if pretrained:\n ckpt = torch_load(attempt_download(path), map_location=device) # load\n csd = ckpt[\"model\"].float().state_dict() # checkpoint state_dict as FP32\n csd = intersect_dicts(csd, model.state_dict(), exclude=[\"anchors\"]) # intersect\n model.load_state_dict(csd, strict=False) # load\n if len(ckpt[\"model\"].names) == classes:\n model.names = ckpt[\"model\"].names # set class names attribute\n if not verbose:\n LOGGER.setLevel(logging.INFO) # reset to default\n return model.to(device)\n\n except Exception as e:\n help_url = \"https://docs.ultralytics.com/yolov5/tutorials/pytorch_hub_model_loading\"\n s = f\"{e}. Cache may be out of date, try `force_reload=True` or see {help_url} for help.\"\n raise Exception(s) from e\n\n\ndef custom(path=\"path/to/model.pt\", autoshape=True, _verbose=True, device=None):\n \"\"\"Loads a custom or local YOLOv3 model from a specified path, with options for autoshaping and device assignment.\n\n Args:\n path (str): Path to the model file. Supports both local and URL paths.\n autoshape (bool): If True, applies the YOLOv3 `.autoshape()` wrapper to allow for various input formats. Default\n is True.\n _verbose (bool): If True, outputs detailed information. Otherwise, limits verbosity. Default is True.\n device (str | torch.device | None): Device to load the model on. Default is None, which uses the available GPU\n if possible.\n\n Returns:\n (torch.nn.Module): The loaded YOLOv3 model, either with or without autoshaping applied.\n\n Raises:\n Exception: If the model loading fails due to invalid path or incompatible model state, with helpful suggestions\n including a reference to the troubleshooting page:\n https://docs.ultralytics.com/yolov5/tutorials/pytorch_hub_model_loading\n\n Examples:\n ```python\n import torch\n model = torch.hub.load('ultralytics/yolov5', 'custom', 'path/to/best.pt')\n model = torch.hub.load('ultralytics/yolov5', 'custom', 'path/to/best.pt', autoshape=False, device='cpu')\n ```\n \"\"\"\n return _create(path, autoshape=autoshape, verbose=_verbose, device=device)\n\n\ndef yolov5n(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None):\n \"\"\"Instantiates a YOLOv5n model with optional pretrained weights, configurable input channels, number of classes,\n autoshaping, and device selection.\n\n Args:\n pretrained (bool): If True, loads pretrained weights into the model. Defaults to True.\n channels (int): Number of input channels. Defaults to 3.\n classes (int): Number of detection classes. Defaults to 80.\n autoshape (bool): If True, applies YOLOv5 .autoshape() wrapper to the model for various input formats like\n file/URI/PIL/cv2/np and adds non-maximum suppression (NMS). Defaults to True.\n _verbose (bool): If True, prints detailed information to the screen. Defaults to True.\n device (str | torch.device | None): Device to use for model computations (e.g., 'cpu', 'cuda'). If None, the\n best available device is automatically selected. Defaults to None.\n\n Returns:\n torch.nn.Module: The instantiated YOLOv5n model.\n\n Examples:\n ```python\n import torch\n model = torch.hub.load('ultralytics/yolov5', 'yolov5n') # using official model\n model = torch.hub.load('ultralytics/yolov5:master', 'yolov5n') # from specific branch\n model = torch.hub.load('ultralytics/yolov5', 'custom', 'yolov5n.pt') # using custom/local model\n model = torch.hub.load('.', 'custom', 'yolov5n.pt', source='local') # from local repository\n ```\n\n Notes:\n PyTorch Hub models can be explored at https://pytorch.org/hub/ultralytics_yolov5. This allows easy model loading and usage.\n \"\"\"\n return _create(\"yolov5n\", pretrained, channels, classes, autoshape, _verbose, device)\n\n\ndef yolov5s(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None):\n \"\"\"Load the YOLOv5s model with customizable options for pretrained weights, input channels, number of classes,\n autoshape functionality, and device selection.\n\n Args:\n pretrained (bool, optional): If True, loads model with pretrained weights. Default is True.\n channels (int, optional): Specifies the number of input channels. Default is 3.\n classes (int, optional): Defines the number of model classes. Default is 80.\n autoshape (bool, optional): Applies YOLOv5 .autoshape() wrapper to the model for enhanced usability. Default is\n True.\n _verbose (bool, optional): If True, prints detailed information during model loading. Default is True.\n device (str | torch.device | None, optional): Specifies the device to load the model on. Accepts 'cpu', 'cuda',\n or torch.device. Default is None, which automatically selects the best available option.\n\n Returns:\n torch.nn.Module: The initialized YOLOv5s model loaded with the specified options.\n\n Examples:\n ```python\n import torch\n model = torch.hub.load('ultralytics/yolov5', 'yolov5s', pretrained=True)\n ```\n\n For more information, refer to [PyTorch Hub models](https://pytorch.org/hub/ultralytics_yolov5/).\n \"\"\"\n return _create(\"yolov5s\", pretrained, channels, classes, autoshape, _verbose, device)\n\n\ndef yolov5m(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None):\n \"\"\"Loads the YOLOv5m model with options for pretrained weights, input channels, number of classes, autoshape\n functionality, and device selection.\n\n Args:\n pretrained (bool, optional): If True, loads pretrained weights into the model. Default is True.\n channels (int, optional): Number of input channels for the model. Default is 3.\n classes (int, optional): Number of model classes. Default is 80.\n autoshape (bool, optional): If True, applies the YOLOv5 .autoshape() wrapper for handling multiple input types\n and NMS. Default is True.\n _verbose (bool, optional): If True, prints detailed information during model loading. Default is True.\n device (str | torch.device | None, optional): Device for model computations (e.g., 'cpu', 'cuda'). Automatically\n selects the best available device if None. Default is None.\n\n Returns:\n torch.nn.Module: The instantiated YOLOv5m model.\n\n Examples:\n ```python\n import torch\n model = torch.hub.load('ultralytics/yolov5', 'yolov5m', pretrained=True)\n ```\n \"\"\"\n return _create(\"yolov5m\", pretrained, channels, classes, autoshape, _verbose, device)\n\n\ndef yolov5l(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None):\n \"\"\"Load the YOLOv5l model with customizable options for pretrained weights, input channels, number of classes,\n autoshape functionality, and device selection.\n\n Args:\n pretrained (bool, optional): If True, load model with pretrained weights. Default is True.\n channels (int, optional): Specifies the number of input channels. Default is 3.\n classes (int, optional): Defines the number of model classes. Default is 80.\n autoshape (bool, optional): Applies the YOLOv5 .autoshape() wrapper to the model for enhanced usability. Default\n is True.\n _verbose (bool, optional): If True, prints detailed information during model loading. Default is True.\n device (str | torch.device | None, optional): Specifies the device to load the model on. Accepts 'cpu', 'cuda',\n or torch.device. Default is None, which automatically selects the best available option.\n\n Returns:\n torch.nn.Module: The initialized YOLOv5l model loaded with the specified options.\n\n Examples:\n ```python\n import torch\n model = torch.hub.load('ultralytics/yolov5', 'yolov5l', pretrained=True)\n ```\n\n For more information, refer to [PyTorch Hub models](https://pytorch.org/hub/ultralytics_yolov5/).\n \"\"\"\n return _create(\"yolov5l\", pretrained, channels, classes, autoshape, _verbose, device)\n\n\ndef yolov5x(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None):\n \"\"\"Load the YOLOv5x model with options for pretrained weights, number of input channels, classes, autoshaping, and\n device selection.\n\n Args:\n pretrained (bool, optional): If True, loads the model with pretrained weights. Defaults to True.\n channels (int, optional): Number of input channels. Defaults to 3.\n classes (int, optional): Number of detection classes. Defaults to 80.\n autoshape (bool, optional): If True, applies the YOLOv5 .autoshape() wrapper, enabling various input formats and\n non-maximum suppression (NMS). Defaults to True.\n _verbose (bool, optional): If True, prints detailed information during model loading. Defaults to True.\n device (str | torch.device | None, optional): Device to use for model parameters (e.g., 'cpu', 'cuda'). Defaults\n to None, selecting the best available device automatically.\n\n Returns:\n torch.nn.Module: The YOLOv5x model loaded with the specified configuration.\n\n Examples:\n ```python\n import torch\n\n # Load YOLOv5x model with default settings\n model = torch.hub.load('ultralytics/yolov5', 'yolov5x')\n\n # Load YOLOv5x model with custom device\n model = torch.hub.load('ultralytics/yolov5', 'yolov5x', device='cuda:0')\n ```\n\n For more details, refer to [PyTorch Hub models](https://pytorch.org/hub/ultralytics_yolov5/).\n \"\"\"\n return _create(\"yolov5x\", pretrained, channels, classes, autoshape, _verbose, device)\n\n\ndef yolov5n6(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None):\n \"\"\"Loads the YOLOv5n6 model with options for pretrained weights, input channels, classes, autoshaping, verbosity,\n and device assignment.\n\n Args:\n pretrained (bool, optional): If True, loads pretrained weights into the model. Default is True.\n channels (int, optional): Number of input channels. Default is 3.\n classes (int, optional): Number of model classes. Default is 80.\n autoshape (bool, optional): If True, applies the YOLOv3 .autoshape() wrapper to the model. Default is True.\n _verbose (bool, optional): If True, prints all information to the screen. Default is True.\n device (str | torch.device | None, optional): Device to use for model parameters, e.g., 'cpu', '0', or\n torch.device. Default is None.\n\n Returns:\n torch.nn.Module: YOLOv5n6 model loaded on the specified device and configured as per the provided options.\n\n Examples:\n ```python\n model = yolov5n6(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device='cuda')\n ```\n\n Notes:\n For more information on PyTorch Hub models, refer to: https://pytorch.org/hub/ultralytics_yolov5\n \"\"\"\n return _create(\"yolov5n6\", pretrained, channels, classes, autoshape, _verbose, device)\n\n\ndef yolov5s6(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None):\n \"\"\"Loads the YOLOv5s6 model with options for weights, channels, classes, autoshaping, and device selection.\n\n Args:\n pretrained (bool, optional): If True, loads pretrained weights into the model. Defaults to True.\n channels (int, optional): Number of input channels. Defaults to 3.\n classes (int, optional): Number of model classes. Defaults to 80.\n autoshape (bool, optional): Apply YOLOv5 .autoshape() wrapper to the model. Defaults to True.\n _verbose (bool, optional): If True, prints detailed information to the screen. Defaults to True.\n device (str | torch.device | None, optional): Device to use for model parameters, e.g., 'cpu', 'cuda:0'. If\n None, it will select the appropriate device automatically. Defaults to None.\n\n Returns:\n torch.nn.Module: The YOLOv5s6 model, ready for inference or further training.\n\n Examples:\n ```python\n import torch\n model = torch.hub.load('ultralytics/yolov5', 'yolov5s6', pretrained=True, channels=3, classes=80)\n model.eval() # Set the model to evaluation mode\n ```\n\n For more details, see the official documentation at:\n https://github.com/ultralytics/yolov5\n \"\"\"\n return _create(\"yolov5s6\", pretrained, channels, classes, autoshape, _verbose, device)\n\n\ndef yolov5m6(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None):\n \"\"\"Loads YOLOv5m6 model with options for pretrained weights, input channels, number of classes, autoshaping, and\n device selection.\n\n Args:\n pretrained (bool): Whether to load pretrained weights into the model. Default is True.\n channels (int): Number of input channels. Default is 3.\n classes (int): Number of model classes. Default is 80.\n autoshape (bool): Whether to apply YOLOv5 .autoshape() wrapper to the model. Default is True.\n _verbose (bool): Whether to print all information to the screen. Default is True.\n device (str | torch.device | None): Device to use for model parameters, e.g., 'cpu', 'cuda', 'mps', or torch\n device. Default is None.\n\n Returns:\n YOLOv5m6 model (torch.nn.Module): The instantiated YOLOv5m6 model with specified options.\n\n Examples:\n ```python\n import torch\n\n # Load YOLOv5m6 model with default settings\n model = torch.hub.load('ultralytics/yolov5', 'yolov5m6')\n\n # Load custom YOLOv5m6 model from a local path with specific options\n model = torch.hub.load('.', 'yolov5m6', pretrained=False, channels=1, classes=10, device='cuda')\n ```\n\n Notes:\n For more detailed documentation, visit https://github.com/ultralytics/yolov5\n \"\"\"\n return _create(\"yolov5m6\", pretrained, channels, classes, autoshape, _verbose, device)\n\n\ndef yolov5l6(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None):\n \"\"\"Loads the YOLOv5l6 model with options for pretrained weights, input channels, the number of classes, autoshaping,\n and device selection.\n\n Args:\n pretrained (bool, optional): If True, loads pretrained weights into the model. Default is True.\n channels (int, optional): Number of input channels. Default is 3.\n classes (int, optional): Number of model classes. Default is 80.\n autoshape (bool, optional): If True, applies the YOLOv5 .autoshape() wrapper to the model for automatic shape\n inference. Default is True.\n _verbose (bool, optional): If True, prints all information to the screen. Default is True.\n device (str | torch.device | None, optional): Device to use for the model parameters, e.g., 'cpu', 'cuda', or\n a specific GPU like 'cuda:0'. Default is None, which means the best available device will be selected\n automatically.\n\n Returns:\n yolov5.models.yolo.DetectionModel: YOLOv5l6 model initialized with defined custom configurations.\n\n Examples:\n ```python\n import torch\n model = torch.hub.load('ultralytics/yolov5', 'yolov5l6') # Load YOLOv5l6 model\n ```\n\n Notes:\n For more details, visit the [Ultralytics YOLOv5 GitHub repository](https://github.com/ultralytics/yolov5).\n \"\"\"\n return _create(\"yolov5l6\", pretrained, channels, classes, autoshape, _verbose, device)\n\n\ndef yolov5x6(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None):\n \"\"\"Loads the YOLOv5x6 model, allowing customization for pretrained weights, input channels, and model classes.\n\n Args:\n pretrained (bool): If True, loads the model with pretrained weights. Default is True.\n channels (int): Number of input channels. Default is 3.\n classes (int): Number of output classes for the model. Default is 80.\n autoshape (bool): If True, applies the .autoshape() wrapper for inference on diverse input formats. Default is\n True.\n _verbose (bool): If True, prints detailed information during model loading. Default is True.\n device (str | torch.device | None): Specifies the device to load the model on ('cpu', 'cuda', etc.). Default is\n None, which uses the best available device.\n\n Returns:\n torch.nn.Module: The YOLOv5x6 model with the specified configurations.\n\n Examples:\n ```python\n from ultralytics import yolov5x6\n\n # Load the model with default settings\n model = yolov5x6()\n\n # Load the model with custom configurations\n model = yolov5x6(pretrained=False, channels=1, classes=10, autoshape=False, device='cuda')\n ```\n\n Notes:\n For more information, refer to the YOLOv5 repository: https://github.com/ultralytics/yolov5\n \"\"\"\n return _create(\"yolov5x6\", pretrained, channels, classes, autoshape, _verbose, device)\n\n\nif __name__ == \"__main__\":\n import argparse\n from pathlib import Path\n\n import numpy as np\n from PIL import Image\n\n from utils.general import cv2, print_args\n\n # Argparser\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--model\", type=str, default=\"yolov5s\", help=\"model name\")\n opt = parser.parse_args()\n print_args(vars(opt))\n\n # Model\n model = _create(name=opt.model, pretrained=True, channels=3, classes=80, autoshape=True, verbose=True)\n # model = custom(path='path/to/model.pt') # custom\n\n # Images\n imgs = [\n \"data/images/zidane.jpg\", # filename\n Path(\"data/images/zidane.jpg\"), # Path\n \"https://ultralytics.com/images/zidane.jpg\", # URI\n cv2.imread(\"data/images/bus.jpg\")[:, :, ::-1], # OpenCV\n Image.open(\"data/images/bus.jpg\"), # PIL\n np.zeros((320, 640, 3)),\n ] # numpy\n\n # Inference\n results = model(imgs, size=320) # batched inference\n\n # Results\n results.print()\n results.save()\n" }, { "path": "models/__init__.py", "content": "# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license\n" }, { "path": "models/common.py", "content": "# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license\n\"\"\"Common modules.\"\"\"\n\nimport ast\nimport contextlib\nimport json\nimport math\nimport platform\nimport warnings\nimport zipfile\nfrom collections import OrderedDict, namedtuple\nfrom copy import copy\nfrom pathlib import Path\nfrom urllib.parse import urlparse\n\nimport cv2\nimport numpy as np\nimport pandas as pd\nimport requests\nimport torch\nimport torch.nn as nn\nfrom PIL import Image\nfrom torch.cuda import amp\nfrom ultralytics.utils.plotting import Annotator, colors, save_one_box\n\nfrom utils import TryExcept\nfrom utils.dataloaders import exif_transpose, letterbox\nfrom utils.general import (\n LOGGER,\n ROOT,\n Profile,\n check_requirements,\n check_suffix,\n check_version,\n colorstr,\n increment_path,\n is_jupyter,\n make_divisible,\n non_max_suppression,\n scale_boxes,\n xywh2xyxy,\n xyxy2xywh,\n yaml_load,\n)\nfrom utils.torch_utils import copy_attr, smart_inference_mode\n\n\ndef autopad(k, p=None, d=1): # kernel, padding, dilation\n \"\"\"Automatically calculates same shape padding for convolutional layers, optionally adjusts for dilation.\"\"\"\n if d > 1:\n k = d * (k - 1) + 1 if isinstance(k, int) else [d * (x - 1) + 1 for x in k] # actual kernel-size\n if p is None:\n p = k // 2 if isinstance(k, int) else [x // 2 for x in k] # auto-pad\n return p\n\n\nclass Conv(nn.Module):\n \"\"\"A standard Conv2D layer with batch normalization and optional activation for neural networks.\"\"\"\n\n default_act = nn.SiLU() # default activation\n\n def __init__(self, c1, c2, k=1, s=1, p=None, g=1, d=1, act=True):\n \"\"\"Initializes a standard Conv2D layer with batch normalization and optional activation; args are channel_in,\n channel_out, kernel_size, stride, padding, groups, dilation, and activation.\n \"\"\"\n super().__init__()\n self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p, d), groups=g, dilation=d, bias=False)\n self.bn = nn.BatchNorm2d(c2)\n self.act = self.default_act if act is True else act if isinstance(act, nn.Module) else nn.Identity()\n\n def forward(self, x):\n \"\"\"Applies convolution, batch normalization, and activation to input `x`; `x` shape: [N, C_in, H, W] -> [N,\n C_out, H_out, W_out].\n \"\"\"\n return self.act(self.bn(self.conv(x)))\n\n def forward_fuse(self, x):\n \"\"\"Applies fused convolution and activation to input `x`; input shape: [N, C_in, H, W] -> [N, C_out, H_out,\n W_out].\n \"\"\"\n return self.act(self.conv(x))\n\n\nclass DWConv(Conv):\n \"\"\"Implements depth-wise convolution for efficient spatial feature extraction in neural networks.\"\"\"\n\n def __init__(self, c1, c2, k=1, s=1, d=1, act=True): # ch_in, ch_out, kernel, stride, dilation, activation\n \"\"\"Initializes depth-wise convolution with optional activation; parameters are channel in/out, kernel, stride,\n dilation.\n \"\"\"\n super().__init__(c1, c2, k, s, g=math.gcd(c1, c2), d=d, act=act)\n\n\nclass DWConvTranspose2d(nn.ConvTranspose2d):\n \"\"\"Implements a depth-wise transpose convolution layer with specified channels, kernel size, stride, and padding.\"\"\"\n\n def __init__(self, c1, c2, k=1, s=1, p1=0, p2=0): # ch_in, ch_out, kernel, stride, padding, padding_out\n \"\"\"Initializes a depth-wise or transpose convolution layer with specified in/out channels, kernel size, stride,\n and padding.\n \"\"\"\n super().__init__(c1, c2, k, s, p1, p2, groups=math.gcd(c1, c2))\n\n\nclass TransformerLayer(nn.Module):\n \"\"\"Transformer layer with multi-head attention and feed-forward network, optimized by removing LayerNorm.\"\"\"\n\n def __init__(self, c, num_heads):\n \"\"\"Initializes a Transformer layer as per https://arxiv.org/abs/2010.11929, sans LayerNorm, with specified\n embedding dimension and number of heads.\n \"\"\"\n super().__init__()\n self.q = nn.Linear(c, c, bias=False)\n self.k = nn.Linear(c, c, bias=False)\n self.v = nn.Linear(c, c, bias=False)\n self.ma = nn.MultiheadAttention(embed_dim=c, num_heads=num_heads)\n self.fc1 = nn.Linear(c, c, bias=False)\n self.fc2 = nn.Linear(c, c, bias=False)\n\n def forward(self, x):\n \"\"\"Performs forward pass with multi-head attention and residual connections on input tensor 'x' [batch, seq_len,\n features].\n \"\"\"\n x = self.ma(self.q(x), self.k(x), self.v(x))[0] + x\n x = self.fc2(self.fc1(x)) + x\n return x\n\n\nclass TransformerBlock(nn.Module):\n \"\"\"Implements a Vision Transformer block with transformer layers; https://arxiv.org/abs/2010.11929.\"\"\"\n\n def __init__(self, c1, c2, num_heads, num_layers):\n \"\"\"Initializes a Transformer block with optional convolution, linear, and transformer layers.\"\"\"\n super().__init__()\n self.conv = None\n if c1 != c2:\n self.conv = Conv(c1, c2)\n self.linear = nn.Linear(c2, c2) # learnable position embedding\n self.tr = nn.Sequential(*(TransformerLayer(c2, num_heads) for _ in range(num_layers)))\n self.c2 = c2\n\n def forward(self, x):\n \"\"\"Applies an optional convolution, transforms features, and reshapes output matching input dimensions.\"\"\"\n if self.conv is not None:\n x = self.conv(x)\n b, _, w, h = x.shape\n p = x.flatten(2).permute(2, 0, 1)\n return self.tr(p + self.linear(p)).permute(1, 2, 0).reshape(b, self.c2, w, h)\n\n\nclass Bottleneck(nn.Module):\n \"\"\"Implements a bottleneck layer with optional shortcut for efficient feature extraction in neural networks.\"\"\"\n\n def __init__(self, c1, c2, shortcut=True, g=1, e=0.5): # ch_in, ch_out, shortcut, groups, expansion\n \"\"\"Initializes a standard bottleneck layer with optional shortcut; args: input channels (c1), output channels\n (c2), shortcut (bool), groups (g), expansion factor (e).\n \"\"\"\n super().__init__()\n c_ = int(c2 * e) # hidden channels\n self.cv1 = Conv(c1, c_, 1, 1)\n self.cv2 = Conv(c_, c2, 3, 1, g=g)\n self.add = shortcut and c1 == c2\n\n def forward(self, x):\n \"\"\"Executes forward pass, performing convolutional ops and optional shortcut addition; expects input tensor x.\n \"\"\"\n return x + self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x))\n\n\nclass BottleneckCSP(nn.Module):\n \"\"\"Implements a CSP Bottleneck layer for feature extraction.\"\"\"\n\n def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion\n \"\"\"Initializes CSP Bottleneck with channel in/out, optional shortcut, groups, expansion; see\n https://github.com/WongKinYiu/CrossStagePartialNetworks.\n \"\"\"\n super().__init__()\n c_ = int(c2 * e) # hidden channels\n self.cv1 = Conv(c1, c_, 1, 1)\n self.cv2 = nn.Conv2d(c1, c_, 1, 1, bias=False)\n self.cv3 = nn.Conv2d(c_, c_, 1, 1, bias=False)\n self.cv4 = Conv(2 * c_, c2, 1, 1)\n self.bn = nn.BatchNorm2d(2 * c_) # applied to cat(cv2, cv3)\n self.act = nn.SiLU()\n self.m = nn.Sequential(*(Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)))\n\n def forward(self, x):\n \"\"\"Processes input through layers, combining outputs with activation and normalization for feature extraction.\n \"\"\"\n y1 = self.cv3(self.m(self.cv1(x)))\n y2 = self.cv2(x)\n return self.cv4(self.act(self.bn(torch.cat((y1, y2), 1))))\n\n\nclass CrossConv(nn.Module):\n \"\"\"Implements Cross Convolution Downsample with 1D and 2D convolutions and optional shortcut.\"\"\"\n\n def __init__(self, c1, c2, k=3, s=1, g=1, e=1.0, shortcut=False):\n \"\"\"Initializes CrossConv with downsample options, combining 1D and 2D convolutions, optional shortcut if\n input/output channels match.\n \"\"\"\n super().__init__()\n c_ = int(c2 * e) # hidden channels\n self.cv1 = Conv(c1, c_, (1, k), (1, s))\n self.cv2 = Conv(c_, c2, (k, 1), (s, 1), g=g)\n self.add = shortcut and c1 == c2\n\n def forward(self, x):\n \"\"\"Performs forward pass using sequential 1D and 2D convolutions with optional shortcut addition.\"\"\"\n return x + self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x))\n\n\nclass C3(nn.Module):\n \"\"\"Implements a CSP Bottleneck with 3 convolutions, optional shortcuts, group convolutions, and expansion factor.\"\"\"\n\n def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion\n \"\"\"Initializes CSP Bottleneck with 3 convolutions, optional shortcuts, group convolutions, and expansion factor.\n \"\"\"\n super().__init__()\n c_ = int(c2 * e) # hidden channels\n self.cv1 = Conv(c1, c_, 1, 1)\n self.cv2 = Conv(c1, c_, 1, 1)\n self.cv3 = Conv(2 * c_, c2, 1) # optional act=FReLU(c2)\n self.m = nn.Sequential(*(Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)))\n\n def forward(self, x):\n \"\"\"Processes input tensor `x` through convolutions and bottlenecks, returning the concatenated output tensor.\"\"\"\n return self.cv3(torch.cat((self.m(self.cv1(x)), self.cv2(x)), 1))\n\n\nclass C3x(C3):\n \"\"\"Extends the C3 module with cross-convolutions for enhanced feature extraction and flexibility.\"\"\"\n\n def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):\n \"\"\"Initializes a C3x module with cross-convolutions, extending the C3 module with customizable parameters.\"\"\"\n super().__init__(c1, c2, n, shortcut, g, e)\n c_ = int(c2 * e)\n self.m = nn.Sequential(*(CrossConv(c_, c_, 3, 1, g, 1.0, shortcut) for _ in range(n)))\n\n\nclass C3TR(C3):\n \"\"\"C3 module with TransformerBlock for integrating attention mechanisms in CNNs.\"\"\"\n\n def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):\n \"\"\"Initializes a C3 module with TransformerBlock, extending C3 for attention mechanisms.\"\"\"\n super().__init__(c1, c2, n, shortcut, g, e)\n c_ = int(c2 * e)\n self.m = TransformerBlock(c_, c_, 4, n)\n\n\nclass C3SPP(C3):\n \"\"\"Extends C3 with Spatial Pyramid Pooling (SPP) for enhanced feature extraction in CNNs.\"\"\"\n\n def __init__(self, c1, c2, k=(5, 9, 13), n=1, shortcut=True, g=1, e=0.5):\n \"\"\"Initializes C3SPP module, extending C3 with Spatial Pyramid Pooling for enhanced feature extraction.\"\"\"\n super().__init__(c1, c2, n, shortcut, g, e)\n c_ = int(c2 * e)\n self.m = SPP(c_, c_, k)\n\n\nclass C3Ghost(C3):\n \"\"\"Implements a C3 module with Ghost Bottlenecks for efficient feature extraction in neural networks.\"\"\"\n\n def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):\n \"\"\"Initializes C3Ghost module with Ghost Bottlenecks for efficient feature extraction.\"\"\"\n super().__init__(c1, c2, n, shortcut, g, e)\n c_ = int(c2 * e) # hidden channels\n self.m = nn.Sequential(*(GhostBottleneck(c_, c_) for _ in range(n)))\n\n\nclass SPP(nn.Module):\n \"\"\"Implements Spatial Pyramid Pooling (SPP) for enhanced feature extraction; see https://arxiv.org/abs/1406.4729.\"\"\"\n\n def __init__(self, c1, c2, k=(5, 9, 13)):\n \"\"\"Initializes SPP layer with specified channels and kernels.\n\n More at https://arxiv.org/abs/1406.4729\n \"\"\"\n super().__init__()\n c_ = c1 // 2 # hidden channels\n self.cv1 = Conv(c1, c_, 1, 1)\n self.cv2 = Conv(c_ * (len(k) + 1), c2, 1, 1)\n self.m = nn.ModuleList([nn.MaxPool2d(kernel_size=x, stride=1, padding=x // 2) for x in k])\n\n def forward(self, x):\n \"\"\"Applies convolution and max pooling layers to the input tensor `x`, concatenates results for feature\n extraction.\n\n `x` is a tensor of shape [N, C, H, W]. See https://arxiv.org/abs/1406.4729 for more details.\n \"\"\"\n x = self.cv1(x)\n with warnings.catch_warnings():\n warnings.simplefilter(\"ignore\") # suppress torch 1.9.0 max_pool2d() warning\n return self.cv2(torch.cat([x] + [m(x) for m in self.m], 1))\n\n\nclass SPPF(nn.Module):\n \"\"\"Implements a fast Spatial Pyramid Pooling (SPPF) layer for efficient feature extraction in YOLOv3 models.\"\"\"\n\n def __init__(self, c1, c2, k=5): # equivalent to SPP(k=(5, 9, 13))\n \"\"\"Initializes the SPPF layer with specified input/output channels and kernel size for YOLOv3.\"\"\"\n super().__init__()\n c_ = c1 // 2 # hidden channels\n self.cv1 = Conv(c1, c_, 1, 1)\n self.cv2 = Conv(c_ * 4, c2, 1, 1)\n self.m = nn.MaxPool2d(kernel_size=k, stride=1, padding=k // 2)\n\n def forward(self, x):\n \"\"\"Performs forward pass combining convolutions and max pooling on input `x` of shape [N, C, H, W] to produce\n feature map.\n \"\"\"\n x = self.cv1(x)\n with warnings.catch_warnings():\n warnings.simplefilter(\"ignore\") # suppress torch 1.9.0 max_pool2d() warning\n y1 = self.m(x)\n y2 = self.m(y1)\n return self.cv2(torch.cat((x, y1, y2, self.m(y2)), 1))\n\n\nclass Focus(nn.Module):\n \"\"\"Focuses spatial information into channel space using configurable convolution.\"\"\"\n\n def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True): # ch_in, ch_out, kernel, stride, padding, groups\n \"\"\"Initializes Focus module to focus width and height information into channel space with configurable\n convolution parameters.\n \"\"\"\n super().__init__()\n self.conv = Conv(c1 * 4, c2, k, s, p, g, act=act)\n # self.contract = Contract(gain=2)\n\n def forward(self, x): # x(b,c,w,h) -> y(b,4c,w/2,h/2)\n \"\"\"Applies focused downsampling to input tensor, returning a convolved output with increased channel depth.\"\"\"\n return self.conv(torch.cat((x[..., ::2, ::2], x[..., 1::2, ::2], x[..., ::2, 1::2], x[..., 1::2, 1::2]), 1))\n # return self.conv(self.contract(x))\n\n\nclass GhostConv(nn.Module):\n \"\"\"Implements Ghost Convolution for efficient feature extraction; see github.com/huawei-noah/ghostnet.\"\"\"\n\n def __init__(self, c1, c2, k=1, s=1, g=1, act=True): # ch_in, ch_out, kernel, stride, groups\n \"\"\"Initializes GhostConv with in/out channels, kernel size, stride, groups; see\n https://github.com/huawei-noah/ghostnet.\n \"\"\"\n super().__init__()\n c_ = c2 // 2 # hidden channels\n self.cv1 = Conv(c1, c_, k, s, None, g, act=act)\n self.cv2 = Conv(c_, c_, 5, 1, None, c_, act=act)\n\n def forward(self, x):\n \"\"\"Executes forward pass, applying convolutions and concatenating results; input `x` is a tensor.\"\"\"\n y = self.cv1(x)\n return torch.cat((y, self.cv2(y)), 1)\n\n\nclass GhostBottleneck(nn.Module):\n \"\"\"Implements a Ghost Bottleneck layer for efficient feature extraction from GhostNet.\"\"\"\n\n def __init__(self, c1, c2, k=3, s=1): # ch_in, ch_out, kernel, stride\n \"\"\"Initializes GhostBottleneck module with in/out channels, kernel size, and stride; see\n https://github.com/huawei-noah/ghostnet.\n \"\"\"\n super().__init__()\n c_ = c2 // 2\n self.conv = nn.Sequential(\n GhostConv(c1, c_, 1, 1), # pw\n DWConv(c_, c_, k, s, act=False) if s == 2 else nn.Identity(), # dw\n GhostConv(c_, c2, 1, 1, act=False),\n ) # pw-linear\n self.shortcut = (\n nn.Sequential(DWConv(c1, c1, k, s, act=False), Conv(c1, c2, 1, 1, act=False)) if s == 2 else nn.Identity()\n )\n\n def forward(self, x):\n \"\"\"Performs a forward pass through the network, returning the sum of convolution and shortcut outputs.\"\"\"\n return self.conv(x) + self.shortcut(x)\n\n\nclass Contract(nn.Module):\n \"\"\"Contracts spatial dimensions into channels, e.g., (1,64,80,80) to (1,256,40,40) with a specified gain.\"\"\"\n\n def __init__(self, gain=2):\n \"\"\"Initializes Contract module to refine input dimensions, e.g., from (1,64,80,80) to (1,256,40,40) with a\n default gain of 2.\n \"\"\"\n super().__init__()\n self.gain = gain\n\n def forward(self, x):\n \"\"\"Processes input tensor (b,c,h,w) to contracted shape (b,c*s^2,h/s,w/s) with default gain s=2, e.g.,\n (1,64,80,80) to (1,256,40,40).\n \"\"\"\n b, c, h, w = x.size() # assert (h / s == 0) and (W / s == 0), 'Indivisible gain'\n s = self.gain\n x = x.view(b, c, h // s, s, w // s, s) # x(1,64,40,2,40,2)\n x = x.permute(0, 3, 5, 1, 2, 4).contiguous() # x(1,2,2,64,40,40)\n return x.view(b, c * s * s, h // s, w // s) # x(1,256,40,40)\n\n\nclass Expand(nn.Module):\n \"\"\"Expands spatial dimensions of input tensor by a factor while reducing channels correspondingly.\"\"\"\n\n def __init__(self, gain=2):\n \"\"\"Initializes Expand module to increase spatial dimensions by factor `gain` while reducing channels\n correspondingly.\n \"\"\"\n super().__init__()\n self.gain = gain\n\n def forward(self, x):\n \"\"\"Expands spatial dimensions of input tensor `x` by factor `gain` while reducing channels, transforming shape\n `(B,C,H,W)` to `(B,C/gain^2,H*gain,W*gain)`.\n \"\"\"\n b, c, h, w = x.size() # assert C / s ** 2 == 0, 'Indivisible gain'\n s = self.gain\n x = x.view(b, s, s, c // s**2, h, w) # x(1,2,2,16,80,80)\n x = x.permute(0, 3, 4, 1, 5, 2).contiguous() # x(1,16,80,2,80,2)\n return x.view(b, c // s**2, h * s, w * s) # x(1,16,160,160)\n\n\nclass Concat(nn.Module):\n \"\"\"Concatenates a list of tensors along a specified dimension for efficient feature aggregation.\"\"\"\n\n def __init__(self, dimension=1):\n \"\"\"Initializes a module to concatenate tensors along a specified dimension.\"\"\"\n super().__init__()\n self.d = dimension\n\n def forward(self, x):\n \"\"\"Concatenates a list of tensors along a specified dimension; x is a list of tensors to concatenate, dimension\n defaults to 1.\n \"\"\"\n return torch.cat(x, self.d)\n\n\nclass DetectMultiBackend(nn.Module):\n \"\"\"YOLOv3 multi-backend class for inference on frameworks like PyTorch, ONNX, TensorRT, and more.\"\"\"\n\n def __init__(self, weights=\"yolov5s.pt\", device=torch.device(\"cpu\"), dnn=False, data=None, fp16=False, fuse=True):\n \"\"\"Initializes multi-backend detection with options for various frameworks and devices, also handles model\n download.\n \"\"\"\n # PyTorch: weights = *.pt\n # TorchScript: *.torchscript\n # ONNX Runtime: *.onnx\n # ONNX OpenCV DNN: *.onnx --dnn\n # OpenVINO: *_openvino_model\n # CoreML: *.mlmodel\n # TensorRT: *.engine\n # TensorFlow SavedModel: *_saved_model\n # TensorFlow GraphDef: *.pb\n # TensorFlow Lite: *.tflite\n # TensorFlow Edge TPU: *_edgetpu.tflite\n # PaddlePaddle: *_paddle_model\n from models.experimental import attempt_download, attempt_load # scoped to avoid circular import\n\n super().__init__()\n w = str(weights[0] if isinstance(weights, list) else weights)\n pt, jit, onnx, xml, engine, coreml, saved_model, pb, tflite, edgetpu, tfjs, paddle, triton = self._model_type(w)\n fp16 &= pt or jit or onnx or engine or triton # FP16\n nhwc = coreml or saved_model or pb or tflite or edgetpu # BHWC formats (vs torch BCWH)\n stride = 32 # default stride\n cuda = torch.cuda.is_available() and device.type != \"cpu\" # use CUDA\n if not (pt or triton):\n w = attempt_download(w) # download if not local\n\n if pt: # PyTorch\n model = attempt_load(weights if isinstance(weights, list) else w, device=device, inplace=True, fuse=fuse)\n stride = max(int(model.stride.max()), 32) # model stride\n names = model.module.names if hasattr(model, \"module\") else model.names # get class names\n model.half() if fp16 else model.float()\n self.model = model # explicitly assign for to(), cpu(), cuda(), half()\n elif jit: # TorchScript\n LOGGER.info(f\"Loading {w} for TorchScript inference...\")\n extra_files = {\"config.txt\": \"\"} # model metadata\n model = torch.jit.load(w, _extra_files=extra_files, map_location=device)\n model.half() if fp16 else model.float()\n if extra_files[\"config.txt\"]: # load metadata dict\n d = json.loads(\n extra_files[\"config.txt\"],\n object_hook=lambda d: {int(k) if k.isdigit() else k: v for k, v in d.items()},\n )\n stride, names = int(d[\"stride\"]), d[\"names\"]\n elif dnn: # ONNX OpenCV DNN\n LOGGER.info(f\"Loading {w} for ONNX OpenCV DNN inference...\")\n check_requirements(\"opencv-python>=4.5.4\")\n net = cv2.dnn.readNetFromONNX(w)\n elif onnx: # ONNX Runtime\n LOGGER.info(f\"Loading {w} for ONNX Runtime inference...\")\n check_requirements((\"onnx\", \"onnxruntime-gpu\" if cuda else \"onnxruntime\"))\n import onnxruntime\n\n providers = [\"CUDAExecutionProvider\", \"CPUExecutionProvider\"] if cuda else [\"CPUExecutionProvider\"]\n session = onnxruntime.InferenceSession(w, providers=providers)\n output_names = [x.name for x in session.get_outputs()]\n meta = session.get_modelmeta().custom_metadata_map # metadata\n if \"stride\" in meta:\n stride, names = int(meta[\"stride\"]), eval(meta[\"names\"])\n elif xml: # OpenVINO\n LOGGER.info(f\"Loading {w} for OpenVINO inference...\")\n check_requirements(\"openvino>=2023.0\") # requires openvino-dev: https://pypi.org/project/openvino-dev/\n from openvino.runtime import Core, Layout, get_batch\n\n core = Core()\n if not Path(w).is_file(): # if not *.xml\n w = next(Path(w).glob(\"*.xml\")) # get *.xml file from *_openvino_model dir\n ov_model = core.read_model(model=w, weights=Path(w).with_suffix(\".bin\"))\n if ov_model.get_parameters()[0].get_layout().empty:\n ov_model.get_parameters()[0].set_layout(Layout(\"NCHW\"))\n batch_dim = get_batch(ov_model)\n if batch_dim.is_static:\n batch_size = batch_dim.get_length()\n ov_compiled_model = core.compile_model(ov_model, device_name=\"AUTO\") # AUTO selects best available device\n stride, names = self._load_metadata(Path(w).with_suffix(\".yaml\")) # load metadata\n elif engine: # TensorRT\n LOGGER.info(f\"Loading {w} for TensorRT inference...\")\n import tensorrt as trt # https://developer.nvidia.com/nvidia-tensorrt-download\n\n check_version(trt.__version__, \"7.0.0\", hard=True) # require tensorrt>=7.0.0\n if device.type == \"cpu\":\n device = torch.device(\"cuda:0\")\n Binding = namedtuple(\"Binding\", (\"name\", \"dtype\", \"shape\", \"data\", \"ptr\"))\n logger = trt.Logger(trt.Logger.INFO)\n with open(w, \"rb\") as f, trt.Runtime(logger) as runtime:\n model = runtime.deserialize_cuda_engine(f.read())\n context = model.create_execution_context()\n bindings = OrderedDict()\n output_names = []\n fp16 = False # default updated below\n dynamic = False\n for i in range(model.num_bindings):\n name = model.get_binding_name(i)\n dtype = trt.nptype(model.get_binding_dtype(i))\n if model.binding_is_input(i):\n if -1 in tuple(model.get_binding_shape(i)): # dynamic\n dynamic = True\n context.set_binding_shape(i, tuple(model.get_profile_shape(0, i)[2]))\n if dtype == np.float16:\n fp16 = True\n else: # output\n output_names.append(name)\n shape = tuple(context.get_binding_shape(i))\n im = torch.from_numpy(np.empty(shape, dtype=dtype)).to(device)\n bindings[name] = Binding(name, dtype, shape, im, int(im.data_ptr()))\n binding_addrs = OrderedDict((n, d.ptr) for n, d in bindings.items())\n batch_size = bindings[\"images\"].shape[0] # if dynamic, this is instead max batch size\n elif coreml: # CoreML\n LOGGER.info(f\"Loading {w} for CoreML inference...\")\n import coremltools as ct\n\n model = ct.models.MLModel(w)\n elif saved_model: # TF SavedModel\n LOGGER.info(f\"Loading {w} for TensorFlow SavedModel inference...\")\n import tensorflow as tf\n\n keras = False # assume TF1 saved_model\n model = tf.keras.models.load_model(w) if keras else tf.saved_model.load(w)\n elif pb: # GraphDef https://www.tensorflow.org/guide/migrate#a_graphpb_or_graphpbtxt\n LOGGER.info(f\"Loading {w} for TensorFlow GraphDef inference...\")\n import tensorflow as tf\n\n def wrap_frozen_graph(gd, inputs, outputs):\n \"\"\"Wraps a frozen TensorFlow GraphDef for inference, returning a pruned function for specified inputs\n and outputs.\n \"\"\"\n x = tf.compat.v1.wrap_function(lambda: tf.compat.v1.import_graph_def(gd, name=\"\"), []) # wrapped\n ge = x.graph.as_graph_element\n return x.prune(tf.nest.map_structure(ge, inputs), tf.nest.map_structure(ge, outputs))\n\n def gd_outputs(gd):\n \"\"\"Extracts and sorts non-input (output) tensor names from a TensorFlow GraphDef, excluding 'NoOp'\n prefixed tensors.\n \"\"\"\n name_list, input_list = [], []\n for node in gd.node: # tensorflow.core.framework.node_def_pb2.NodeDef\n name_list.append(node.name)\n input_list.extend(node.input)\n return sorted(f\"{x}:0\" for x in list(set(name_list) - set(input_list)) if not x.startswith(\"NoOp\"))\n\n gd = tf.Graph().as_graph_def() # TF GraphDef\n with open(w, \"rb\") as f:\n gd.ParseFromString(f.read())\n frozen_func = wrap_frozen_graph(gd, inputs=\"x:0\", outputs=gd_outputs(gd))\n elif tflite or edgetpu: # https://www.tensorflow.org/lite/guide/python#install_tensorflow_lite_for_python\n try: # https://coral.ai/docs/edgetpu/tflite-python/#update-existing-tf-lite-code-for-the-edge-tpu\n from tflite_runtime.interpreter import Interpreter, load_delegate\n except ImportError:\n import tensorflow as tf\n\n Interpreter, load_delegate = (\n tf.lite.Interpreter,\n tf.lite.experimental.load_delegate,\n )\n if edgetpu: # TF Edge TPU https://coral.ai/software/#edgetpu-runtime\n LOGGER.info(f\"Loading {w} for TensorFlow Lite Edge TPU inference...\")\n delegate = {\"Linux\": \"libedgetpu.so.1\", \"Darwin\": \"libedgetpu.1.dylib\", \"Windows\": \"edgetpu.dll\"}[\n platform.system()\n ]\n interpreter = Interpreter(model_path=w, experimental_delegates=[load_delegate(delegate)])\n else: # TFLite\n LOGGER.info(f\"Loading {w} for TensorFlow Lite inference...\")\n interpreter = Interpreter(model_path=w) # load TFLite model\n interpreter.allocate_tensors() # allocate\n input_details = interpreter.get_input_details() # inputs\n output_details = interpreter.get_output_details() # outputs\n # load metadata\n with contextlib.suppress(zipfile.BadZipFile):\n with zipfile.ZipFile(w, \"r\") as model:\n meta_file = model.namelist()[0]\n meta = ast.literal_eval(model.read(meta_file).decode(\"utf-8\"))\n stride, names = int(meta[\"stride\"]), meta[\"names\"]\n elif tfjs: # TF.js\n raise NotImplementedError(\"ERROR: YOLOv3 TF.js inference is not supported\")\n elif paddle: # PaddlePaddle\n LOGGER.info(f\"Loading {w} for PaddlePaddle inference...\")\n check_requirements(\"paddlepaddle-gpu\" if cuda else \"paddlepaddle\")\n import paddle.inference as pdi\n\n if not Path(w).is_file(): # if not *.pdmodel\n w = next(Path(w).rglob(\"*.pdmodel\")) # get *.pdmodel file from *_paddle_model dir\n weights = Path(w).with_suffix(\".pdiparams\")\n config = pdi.Config(str(w), str(weights))\n if cuda:\n config.enable_use_gpu(memory_pool_init_size_mb=2048, device_id=0)\n predictor = pdi.create_predictor(config)\n input_handle = predictor.get_input_handle(predictor.get_input_names()[0])\n output_names = predictor.get_output_names()\n elif triton: # NVIDIA Triton Inference Server\n LOGGER.info(f\"Using {w} as Triton Inference Server...\")\n check_requirements(\"tritonclient[all]\")\n from utils.triton import TritonRemoteModel\n\n model = TritonRemoteModel(url=w)\n nhwc = model.runtime.startswith(\"tensorflow\")\n else:\n raise NotImplementedError(f\"ERROR: {w} is not a supported format\")\n\n # class names\n if \"names\" not in locals():\n names = yaml_load(data)[\"names\"] if data else {i: f\"class{i}\" for i in range(999)}\n if names[0] == \"n01440764\" and len(names) == 1000: # ImageNet\n names = yaml_load(ROOT / \"data/ImageNet.yaml\")[\"names\"] # human-readable names\n\n self.__dict__.update(locals()) # assign all variables to self\n\n def forward(self, im, augment=False, visualize=False):\n \"\"\"Performs YOLOv3 inference on an input image tensor, optionally with augmentation and visualization.\"\"\"\n _b, _ch, h, w = im.shape # batch, channel, height, width\n if self.fp16 and im.dtype != torch.float16:\n im = im.half() # to FP16\n if self.nhwc:\n im = im.permute(0, 2, 3, 1) # torch BCHW to numpy BHWC shape(1,320,192,3)\n\n if self.pt: # PyTorch\n y = self.model(im, augment=augment, visualize=visualize) if augment or visualize else self.model(im)\n elif self.jit: # TorchScript\n y = self.model(im)\n elif self.dnn: # ONNX OpenCV DNN\n im = im.cpu().numpy() # torch to numpy\n self.net.setInput(im)\n y = self.net.forward()\n elif self.onnx: # ONNX Runtime\n im = im.cpu().numpy() # torch to numpy\n y = self.session.run(self.output_names, {self.session.get_inputs()[0].name: im})\n elif self.xml: # OpenVINO\n im = im.cpu().numpy() # FP32\n y = list(self.ov_compiled_model(im).values())\n elif self.engine: # TensorRT\n if self.dynamic and im.shape != self.bindings[\"images\"].shape:\n i = self.model.get_binding_index(\"images\")\n self.context.set_binding_shape(i, im.shape) # reshape if dynamic\n self.bindings[\"images\"] = self.bindings[\"images\"]._replace(shape=im.shape)\n for name in self.output_names:\n i = self.model.get_binding_index(name)\n self.bindings[name].data.resize_(tuple(self.context.get_binding_shape(i)))\n s = self.bindings[\"images\"].shape\n assert im.shape == s, f\"input size {im.shape} {'>' if self.dynamic else 'not equal to'} max model size {s}\"\n self.binding_addrs[\"images\"] = int(im.data_ptr())\n self.context.execute_v2(list(self.binding_addrs.values()))\n y = [self.bindings[x].data for x in sorted(self.output_names)]\n elif self.coreml: # CoreML\n im = im.cpu().numpy()\n im = Image.fromarray((im[0] * 255).astype(\"uint8\"))\n # im = im.resize((192, 320), Image.BILINEAR)\n y = self.model.predict({\"image\": im}) # coordinates are xywh normalized\n if \"confidence\" in y:\n box = xywh2xyxy(y[\"coordinates\"] * [[w, h, w, h]]) # xyxy pixels\n conf, cls = y[\"confidence\"].max(1), y[\"confidence\"].argmax(1).astype(np.float)\n y = np.concatenate((box, conf.reshape(-1, 1), cls.reshape(-1, 1)), 1)\n else:\n y = list(reversed(y.values())) # reversed for segmentation models (pred, proto)\n elif self.paddle: # PaddlePaddle\n im = im.cpu().numpy().astype(np.float32)\n self.input_handle.copy_from_cpu(im)\n self.predictor.run()\n y = [self.predictor.get_output_handle(x).copy_to_cpu() for x in self.output_names]\n elif self.triton: # NVIDIA Triton Inference Server\n y = self.model(im)\n else: # TensorFlow (SavedModel, GraphDef, Lite, Edge TPU)\n im = im.cpu().numpy()\n if self.saved_model: # SavedModel\n y = self.model(im, training=False) if self.keras else self.model(im)\n elif self.pb: # GraphDef\n y = self.frozen_func(x=self.tf.constant(im))\n else: # Lite or Edge TPU\n input = self.input_details[0]\n int8 = input[\"dtype\"] == np.uint8 # is TFLite quantized uint8 model\n if int8:\n scale, zero_point = input[\"quantization\"]\n im = (im / scale + zero_point).astype(np.uint8) # de-scale\n self.interpreter.set_tensor(input[\"index\"], im)\n self.interpreter.invoke()\n y = []\n for output in self.output_details:\n x = self.interpreter.get_tensor(output[\"index\"])\n if int8:\n scale, zero_point = output[\"quantization\"]\n x = (x.astype(np.float32) - zero_point) * scale # re-scale\n y.append(x)\n y = [x if isinstance(x, np.ndarray) else x.numpy() for x in y]\n y[0][..., :4] *= [w, h, w, h] # xywh normalized to pixels\n\n if isinstance(y, (list, tuple)):\n return self.from_numpy(y[0]) if len(y) == 1 else [self.from_numpy(x) for x in y]\n else:\n return self.from_numpy(y)\n\n def from_numpy(self, x):\n \"\"\"Converts a Numpy array to a PyTorch tensor on the specified device, else returns the input if not a Numpy\n array.\n \"\"\"\n return torch.from_numpy(x).to(self.device) if isinstance(x, np.ndarray) else x\n\n def warmup(self, imgsz=(1, 3, 640, 640)):\n \"\"\"Warms up the model by running inference once with a dummy input of shape imgsz.\"\"\"\n warmup_types = self.pt, self.jit, self.onnx, self.engine, self.saved_model, self.pb, self.triton\n if any(warmup_types) and (self.device.type != \"cpu\" or self.triton):\n im = torch.empty(*imgsz, dtype=torch.half if self.fp16 else torch.float, device=self.device) # input\n for _ in range(2 if self.jit else 1): #\n self.forward(im) # warmup\n\n @staticmethod\n def _model_type(p=\"path/to/model.pt\"):\n \"\"\"Determines model type from filepath or URL, supports various formats including ONNX, PT, JIT.\n\n See `export_formats` for all.\n \"\"\"\n # types = [pt, jit, onnx, xml, engine, coreml, saved_model, pb, tflite, edgetpu, tfjs, paddle]\n from export import export_formats\n from utils.downloads import is_url\n\n sf = list(export_formats().Suffix) # export suffixes\n if not is_url(p, check=False):\n check_suffix(p, sf) # checks\n url = urlparse(p) # if url may be Triton inference server\n types = [s in Path(p).name for s in sf]\n types[8] &= not types[9] # tflite &= not edgetpu\n triton = not any(types) and all([any(s in url.scheme for s in [\"http\", \"grpc\"]), url.netloc])\n return [*types, triton]\n\n @staticmethod\n def _load_metadata(f=Path(\"path/to/meta.yaml\")):\n \"\"\"Loads metadata from a YAML file, returning 'stride' and 'names' if the file exists, else 'None'.\"\"\"\n if f.exists():\n d = yaml_load(f)\n return d[\"stride\"], d[\"names\"] # assign stride, names\n return None, None\n\n\nclass AutoShape(nn.Module):\n \"\"\"A wrapper for YOLOv3 models to handle diverse input types with preprocessing, inference, and NMS.\"\"\"\n\n conf = 0.25 # NMS confidence threshold\n iou = 0.45 # NMS IoU threshold\n agnostic = False # NMS class-agnostic\n multi_label = False # NMS multiple labels per box\n classes = None # (optional list) filter by class, i.e. = [0, 15, 16] for COCO persons, cats and dogs\n max_det = 1000 # maximum number of detections per image\n amp = False # Automatic Mixed Precision (AMP) inference\n\n def __init__(self, model, verbose=True):\n \"\"\"Initializes the model for inference, setting attributes, and preparing for multithreaded execution with\n optional verbose logging.\n \"\"\"\n super().__init__()\n if verbose:\n LOGGER.info(\"Adding AutoShape... \")\n copy_attr(self, model, include=(\"yaml\", \"nc\", \"hyp\", \"names\", \"stride\", \"abc\"), exclude=()) # copy attributes\n self.dmb = isinstance(model, DetectMultiBackend) # DetectMultiBackend() instance\n self.pt = not self.dmb or model.pt # PyTorch model\n self.model = model.eval()\n if self.pt:\n m = self.model.model.model[-1] if self.dmb else self.model.model[-1] # Detect()\n m.inplace = False # Detect.inplace=False for safe multithread inference\n m.export = True # do not output loss values\n\n def _apply(self, fn):\n \"\"\"Applies given function `fn` to model tensors excluding parameters or registered buffers, adjusting strides\n and grids.\n \"\"\"\n self = super()._apply(fn)\n if self.pt:\n m = self.model.model.model[-1] if self.dmb else self.model.model[-1] # Detect()\n m.stride = fn(m.stride)\n m.grid = list(map(fn, m.grid))\n if isinstance(m.anchor_grid, list):\n m.anchor_grid = list(map(fn, m.anchor_grid))\n return self\n\n @smart_inference_mode()\n def forward(self, ims, size=640, augment=False, profile=False):\n \"\"\"Performs inference on various input sources with optional augmentation and profiling; see\n `https://ultralytics.com`.\n \"\"\"\n # file: ims = 'data/images/zidane.jpg' # str or PosixPath\n # URI: = 'https://ultralytics.com/images/zidane.jpg'\n # OpenCV: = cv2.imread('image.jpg')[:,:,::-1] # HWC BGR to RGB x(640,1280,3)\n # PIL: = Image.open('image.jpg') or ImageGrab.grab() # HWC x(640,1280,3)\n # numpy: = np.zeros((640,1280,3)) # HWC\n # torch: = torch.zeros(16,3,320,640) # BCHW (scaled to size=640, 0-1 values)\n # multiple: = [Image.open('image1.jpg'), Image.open('image2.jpg'), ...] # list of images\n\n dt = (Profile(), Profile(), Profile())\n with dt[0]:\n if isinstance(size, int): # expand\n size = (size, size)\n p = next(self.model.parameters()) if self.pt else torch.empty(1, device=self.model.device) # param\n autocast = self.amp and (p.device.type != \"cpu\") # Automatic Mixed Precision (AMP) inference\n if isinstance(ims, torch.Tensor): # torch\n with amp.autocast(autocast):\n return self.model(ims.to(p.device).type_as(p), augment=augment) # inference\n\n # Pre-process\n n, ims = (len(ims), list(ims)) if isinstance(ims, (list, tuple)) else (1, [ims]) # number, list of images\n shape0, shape1, files = [], [], [] # image and inference shapes, filenames\n for i, im in enumerate(ims):\n f = f\"image{i}\" # filename\n if isinstance(im, (str, Path)): # filename or uri\n im, f = Image.open(requests.get(im, stream=True).raw if str(im).startswith(\"http\") else im), im\n im = np.asarray(exif_transpose(im))\n elif isinstance(im, Image.Image): # PIL Image\n im, f = np.asarray(exif_transpose(im)), getattr(im, \"filename\", f) or f\n files.append(Path(f).with_suffix(\".jpg\").name)\n if im.shape[0] < 5: # image in CHW\n im = im.transpose((1, 2, 0)) # reverse dataloader .transpose(2, 0, 1)\n im = im[..., :3] if im.ndim == 3 else cv2.cvtColor(im, cv2.COLOR_GRAY2BGR) # enforce 3ch input\n s = im.shape[:2] # HWC\n shape0.append(s) # image shape\n g = max(size) / max(s) # gain\n shape1.append([int(y * g) for y in s])\n ims[i] = im if im.data.contiguous else np.ascontiguousarray(im) # update\n shape1 = [make_divisible(x, self.stride) for x in np.array(shape1).max(0)] # inf shape\n x = [letterbox(im, shape1, auto=False)[0] for im in ims] # pad\n x = np.ascontiguousarray(np.array(x).transpose((0, 3, 1, 2))) # stack and BHWC to BCHW\n x = torch.from_numpy(x).to(p.device).type_as(p) / 255 # uint8 to fp16/32\n\n with amp.autocast(autocast):\n # Inference\n with dt[1]:\n y = self.model(x, augment=augment) # forward\n\n # Post-process\n with dt[2]:\n y = non_max_suppression(\n y if self.dmb else y[0],\n self.conf,\n self.iou,\n self.classes,\n self.agnostic,\n self.multi_label,\n max_det=self.max_det,\n ) # NMS\n for i in range(n):\n scale_boxes(shape1, y[i][:, :4], shape0[i])\n\n return Detections(ims, y, files, dt, self.names, x.shape)\n\n\nclass Detections:\n \"\"\"Handles YOLOv3 detection results with methods for visualization, saving, cropping, and format conversion.\"\"\"\n\n def __init__(self, ims, pred, files, times=(0, 0, 0), names=None, shape=None):\n \"\"\"Initializes YOLOv3 detections with image data, predictions, filenames, profiling times, class names, and\n shapes.\n \"\"\"\n super().__init__()\n d = pred[0].device # device\n gn = [torch.tensor([*(im.shape[i] for i in [1, 0, 1, 0]), 1, 1], device=d) for im in ims] # normalizations\n self.ims = ims # list of images as numpy arrays\n self.pred = pred # list of tensors pred[0] = (xyxy, conf, cls)\n self.names = names # class names\n self.files = files # image filenames\n self.times = times # profiling times\n self.xyxy = pred # xyxy pixels\n self.xywh = [xyxy2xywh(x) for x in pred] # xywh pixels\n self.xyxyn = [x / g for x, g in zip(self.xyxy, gn)] # xyxy normalized\n self.xywhn = [x / g for x, g in zip(self.xywh, gn)] # xywh normalized\n self.n = len(self.pred) # number of images (batch size)\n self.t = tuple(x.t / self.n * 1e3 for x in times) # timestamps (ms)\n self.s = tuple(shape) # inference BCHW shape\n\n def _run(self, pprint=False, show=False, save=False, crop=False, render=False, labels=True, save_dir=Path(\"\")):\n \"\"\"Executes inference on images, annotates detections, and can optionally show, save, or crop output images.\"\"\"\n s, crops = \"\", []\n for i, (im, pred) in enumerate(zip(self.ims, self.pred)):\n s += f\"\\nimage {i + 1}/{len(self.pred)}: {im.shape[0]}x{im.shape[1]} \" # string\n if pred.shape[0]:\n for c in pred[:, -1].unique():\n n = (pred[:, -1] == c).sum() # detections per class\n s += f\"{n} {self.names[int(c)]}{'s' * (n > 1)}, \" # add to string\n s = s.rstrip(\", \")\n if show or save or render or crop:\n annotator = Annotator(im, example=str(self.names))\n for *box, conf, cls in reversed(pred): # xyxy, confidence, class\n label = f\"{self.names[int(cls)]} {conf:.2f}\"\n if crop:\n file = save_dir / \"crops\" / self.names[int(cls)] / self.files[i] if save else None\n crops.append(\n {\n \"box\": box,\n \"conf\": conf,\n \"cls\": cls,\n \"label\": label,\n \"im\": save_one_box(box, im, file=file, save=save),\n }\n )\n else: # all others\n annotator.box_label(box, label if labels else \"\", color=colors(cls))\n im = annotator.im\n else:\n s += \"(no detections)\"\n\n im = Image.fromarray(im.astype(np.uint8)) if isinstance(im, np.ndarray) else im # from np\n if show:\n if is_jupyter():\n from IPython.display import display\n\n display(im)\n else:\n im.show(self.files[i])\n if save:\n f = self.files[i]\n im.save(save_dir / f) # save\n if i == self.n - 1:\n LOGGER.info(f\"Saved {self.n} image{'s' * (self.n > 1)} to {colorstr('bold', save_dir)}\")\n if render:\n self.ims[i] = np.asarray(im)\n if pprint:\n s = s.lstrip(\"\\n\")\n return f\"{s}\\nSpeed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {self.s}\" % self.t\n if crop:\n if save:\n LOGGER.info(f\"Saved results to {save_dir}\\n\")\n return crops\n\n @TryExcept(\"Showing images is not supported in this environment\")\n def show(self, labels=True):\n \"\"\"Displays image results with optional labels.\n\n Usage: `show(labels=True)`\n \"\"\"\n self._run(show=True, labels=labels) # show results\n\n def save(self, labels=True, save_dir=\"runs/detect/exp\", exist_ok=False):\n \"\"\"Saves image results with optional labels to a specified directory.\n\n Usage: `save(labels=True, save_dir='runs/detect/exp', exist_ok=False)`\n \"\"\"\n save_dir = increment_path(save_dir, exist_ok, mkdir=True) # increment save_dir\n self._run(save=True, labels=labels, save_dir=save_dir) # save results\n\n def crop(self, save=True, save_dir=\"runs/detect/exp\", exist_ok=False):\n \"\"\"Crops detection results; can save to `save_dir`.\n\n Usage: `crop(save=True, save_dir='runs/detect/exp')`.\n \"\"\"\n save_dir = increment_path(save_dir, exist_ok, mkdir=True) if save else None\n return self._run(crop=True, save=save, save_dir=save_dir) # crop results\n\n def render(self, labels=True):\n \"\"\"Renders detection results, optionally displaying labels.\n\n Usage: `render(labels=True)`.\n \"\"\"\n self._run(render=True, labels=labels) # render results\n return self.ims\n\n def pandas(self):\n \"\"\"Returns a copy of the detection results as pandas DataFrames for various bounding box formats.\"\"\"\n new = copy(self) # return copy\n ca = \"xmin\", \"ymin\", \"xmax\", \"ymax\", \"confidence\", \"class\", \"name\" # xyxy columns\n cb = \"xcenter\", \"ycenter\", \"width\", \"height\", \"confidence\", \"class\", \"name\" # xywh columns\n for k, c in zip([\"xyxy\", \"xyxyn\", \"xywh\", \"xywhn\"], [ca, ca, cb, cb]):\n a = [[[*x[:5], int(x[5]), self.names[int(x[5])]] for x in x.tolist()] for x in getattr(self, k)] # update\n setattr(new, k, [pd.DataFrame(x, columns=c) for x in a])\n return new\n\n def tolist(self):\n \"\"\"Converts Detections object to a list of individual Detection objects for iteration.\"\"\"\n r = range(self.n) # iterable\n return [\n Detections(\n [self.ims[i]],\n [self.pred[i]],\n [self.files[i]],\n self.times,\n self.names,\n self.s,\n )\n for i in r\n ]\n\n def print(self):\n \"\"\"Logs the string representation of the current object state to the LOGGER.\"\"\"\n LOGGER.info(self.__str__())\n\n def __len__(self): # override len(results)\n \"\"\"Returns the number of results stored in the instance.\"\"\"\n return self.n\n\n def __str__(self): # override print(results)\n \"\"\"Returns a string representation of the current object state, printing the results.\"\"\"\n return self._run(pprint=True) # print results\n\n def __repr__(self):\n \"\"\"Returns a string representation for debugging, including class info and current object state.\"\"\"\n return f\"YOLOv3 {self.__class__} instance\\n\" + self.__str__()\n\n\nclass Proto(nn.Module):\n \"\"\"Implements the YOLOv3 mask Proto module for segmentation, including convolutional layers and upsampling.\"\"\"\n\n def __init__(self, c1, c_=256, c2=32): # ch_in, number of protos, number of masks\n \"\"\"Initializes the Proto module for YOLOv3 segmentation, setting up convolutional layers and upsampling.\"\"\"\n super().__init__()\n self.cv1 = Conv(c1, c_, k=3)\n self.upsample = nn.Upsample(scale_factor=2, mode=\"nearest\")\n self.cv2 = Conv(c_, c_, k=3)\n self.cv3 = Conv(c_, c2)\n\n def forward(self, x):\n \"\"\"Performs forward pass, upsampling and applying convolutions for YOLOv3 segmentation.\"\"\"\n return self.cv3(self.cv2(self.upsample(self.cv1(x))))\n\n\nclass Classify(nn.Module):\n \"\"\"Performs image classification using YOLOv3-based architecture with convolutional, pooling, and dropout layers.\"\"\"\n\n def __init__(\n self, c1, c2, k=1, s=1, p=None, g=1, dropout_p=0.0\n ): # ch_in, ch_out, kernel, stride, padding, groups, dropout probability\n \"\"\"Initializes YOLOv3 classification head with convolution, pooling and dropout layers for feature extraction\n and classification.\n \"\"\"\n super().__init__()\n c_ = 1280 # efficientnet_b0 size\n self.conv = Conv(c1, c_, k, s, autopad(k, p), g)\n self.pool = nn.AdaptiveAvgPool2d(1) # to x(b,c_,1,1)\n self.drop = nn.Dropout(p=dropout_p, inplace=True)\n self.linear = nn.Linear(c_, c2) # to x(b,c2)\n\n def forward(self, x):\n \"\"\"Processes input tensor `x` through convolutions and pooling, optionally concatenating lists of tensors, and\n returns linear output.\n \"\"\"\n if isinstance(x, list):\n x = torch.cat(x, 1)\n return self.linear(self.drop(self.pool(self.conv(x)).flatten(1)))\n" }, { "path": "models/experimental.py", "content": "# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license\n\"\"\"Experimental modules.\"\"\"\n\nimport math\n\nimport numpy as np\nimport torch\nimport torch.nn as nn\nfrom ultralytics.utils.patches import torch_load\n\nfrom utils.downloads import attempt_download\n\n\nclass Sum(nn.Module):\n \"\"\"Computes the weighted or unweighted sum of multiple input layers per https://arxiv.org/abs/1911.09070.\"\"\"\n\n def __init__(self, n, weight=False): # n: number of inputs\n \"\"\"Initializes a module to compute weighted/unweighted sum of n inputs, with optional learning weights.\n\n https://arxiv.org/abs/1911.09070\n \"\"\"\n super().__init__()\n self.weight = weight # apply weights boolean\n self.iter = range(n - 1) # iter object\n if weight:\n self.w = nn.Parameter(-torch.arange(1.0, n) / 2, requires_grad=True) # layer weights\n\n def forward(self, x):\n \"\"\"Performs forward pass, blending `x` elements with optional learnable weights.\n\n See https://arxiv.org/abs/1911.09070 for more.\n \"\"\"\n y = x[0] # no weight\n if self.weight:\n w = torch.sigmoid(self.w) * 2\n for i in self.iter:\n y = y + x[i + 1] * w[i]\n else:\n for i in self.iter:\n y = y + x[i + 1]\n return y\n\n\nclass MixConv2d(nn.Module):\n \"\"\"Implements mixed depth-wise convolutions for efficient neural networks; see https://arxiv.org/abs/1907.09595.\"\"\"\n\n def __init__(self, c1, c2, k=(1, 3), s=1, equal_ch=True): # ch_in, ch_out, kernel, stride, ch_strategy\n \"\"\"Initializes MixConv2d with mixed depth-wise convolution layers; details at https://arxiv.org/abs/1907.09595.\n \"\"\"\n super().__init__()\n n = len(k) # number of convolutions\n if equal_ch: # equal c_ per group\n i = torch.linspace(0, n - 1e-6, c2).floor() # c2 indices\n c_ = [(i == g).sum() for g in range(n)] # intermediate channels\n else: # equal weight.numel() per group\n b = [c2] + [0] * n\n a = np.eye(n + 1, n, k=-1)\n a -= np.roll(a, 1, axis=1)\n a *= np.array(k) ** 2\n a[0] = 1\n c_ = np.linalg.lstsq(a, b, rcond=None)[0].round() # solve for equal weight indices, ax = b\n\n self.m = nn.ModuleList(\n [nn.Conv2d(c1, int(c_), k, s, k // 2, groups=math.gcd(c1, int(c_)), bias=False) for k, c_ in zip(k, c_)]\n )\n self.bn = nn.BatchNorm2d(c2)\n self.act = nn.SiLU()\n\n def forward(self, x):\n \"\"\"Applies a series of convolutions, batch normalization, and SiLU activation to input tensor `x`.\"\"\"\n return self.act(self.bn(torch.cat([m(x) for m in self.m], 1)))\n\n\nclass Ensemble(nn.ModuleList):\n \"\"\"Combines outputs from multiple models to improve inference results.\"\"\"\n\n def __init__(self):\n \"\"\"Initializes an ensemble of models to combine their outputs.\"\"\"\n super().__init__()\n\n def forward(self, x, augment=False, profile=False, visualize=False):\n \"\"\"Applies ensemble of models on input `x`, with options for augmentation, profiling, and visualization,\n returning inference outputs.\n \"\"\"\n y = [module(x, augment, profile, visualize)[0] for module in self]\n # y = torch.stack(y).max(0)[0] # max ensemble\n # y = torch.stack(y).mean(0) # mean ensemble\n y = torch.cat(y, 1) # nms ensemble\n return y, None # inference, train output\n\n\ndef attempt_load(weights, device=None, inplace=True, fuse=True):\n \"\"\"Loads an ensemble or single model weights, supports device placement and model fusion.\"\"\"\n from models.yolo import Detect, Model\n\n model = Ensemble()\n for w in weights if isinstance(weights, list) else [weights]:\n ckpt = torch_load(attempt_download(w), map_location=\"cpu\") # load\n ckpt = (ckpt.get(\"ema\") or ckpt[\"model\"]).to(device).float() # FP32 model\n\n # Model compatibility updates\n if not hasattr(ckpt, \"stride\"):\n ckpt.stride = torch.tensor([32.0])\n if hasattr(ckpt, \"names\") and isinstance(ckpt.names, (list, tuple)):\n ckpt.names = dict(enumerate(ckpt.names)) # convert to dict\n\n model.append(ckpt.fuse().eval() if fuse and hasattr(ckpt, \"fuse\") else ckpt.eval()) # model in eval mode\n\n # Module compatibility updates\n for m in model.modules():\n t = type(m)\n if t in (nn.Hardswish, nn.LeakyReLU, nn.ReLU, nn.ReLU6, nn.SiLU, Detect, Model):\n m.inplace = inplace # torch 1.7.0 compatibility\n if t is Detect and not isinstance(m.anchor_grid, list):\n delattr(m, \"anchor_grid\")\n setattr(m, \"anchor_grid\", [torch.zeros(1)] * m.nl)\n elif t is nn.Upsample and not hasattr(m, \"recompute_scale_factor\"):\n m.recompute_scale_factor = None # torch 1.11.0 compatibility\n\n # Return model\n if len(model) == 1:\n return model[-1]\n\n # Return detection ensemble\n print(f\"Ensemble created with {weights}\\n\")\n for k in \"names\", \"nc\", \"yaml\":\n setattr(model, k, getattr(model[0], k))\n model.stride = model[torch.argmax(torch.tensor([m.stride.max() for m in model])).int()].stride # max stride\n assert all(model[0].nc == m.nc for m in model), f\"Models have different class counts: {[m.nc for m in model]}\"\n return model\n" }, { "path": "models/hub/anchors.yaml", "content": "# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license\n\n# Default anchors for COCO data\n\n# P5 -------------------------------------------------------------------------------------------------------------------\n# P5-640:\nanchors_p5_640:\n - [10, 13, 16, 30, 33, 23] # P3/8\n - [30, 61, 62, 45, 59, 119] # P4/16\n - [116, 90, 156, 198, 373, 326] # P5/32\n\n# P6 -------------------------------------------------------------------------------------------------------------------\n# P6-640: thr=0.25: 0.9964 BPR, 5.54 anchors past thr, n=12, img_size=640, metric_all=0.281/0.716-mean/best, past_thr=0.469-mean: 9,11, 21,19, 17,41, 43,32, 39,70, 86,64, 65,131, 134,130, 120,265, 282,180, 247,354, 512,387\nanchors_p6_640:\n - [9, 11, 21, 19, 17, 41] # P3/8\n - [43, 32, 39, 70, 86, 64] # P4/16\n - [65, 131, 134, 130, 120, 265] # P5/32\n - [282, 180, 247, 354, 512, 387] # P6/64\n\n# P6-1280: thr=0.25: 0.9950 BPR, 5.55 anchors past thr, n=12, img_size=1280, metric_all=0.281/0.714-mean/best, past_thr=0.468-mean: 19,27, 44,40, 38,94, 96,68, 86,152, 180,137, 140,301, 303,264, 238,542, 436,615, 739,380, 925,792\nanchors_p6_1280:\n - [19, 27, 44, 40, 38, 94] # P3/8\n - [96, 68, 86, 152, 180, 137] # P4/16\n - [140, 301, 303, 264, 238, 542] # P5/32\n - [436, 615, 739, 380, 925, 792] # P6/64\n\n# P6-1920: thr=0.25: 0.9950 BPR, 5.55 anchors past thr, n=12, img_size=1920, metric_all=0.281/0.714-mean/best, past_thr=0.468-mean: 28,41, 67,59, 57,141, 144,103, 129,227, 270,205, 209,452, 455,396, 358,812, 653,922, 1109,570, 1387,1187\nanchors_p6_1920:\n - [28, 41, 67, 59, 57, 141] # P3/8\n - [144, 103, 129, 227, 270, 205] # P4/16\n - [209, 452, 455, 396, 358, 812] # P5/32\n - [653, 922, 1109, 570, 1387, 1187] # P6/64\n\n# P7 -------------------------------------------------------------------------------------------------------------------\n# P7-640: thr=0.25: 0.9962 BPR, 6.76 anchors past thr, n=15, img_size=640, metric_all=0.275/0.733-mean/best, past_thr=0.466-mean: 11,11, 13,30, 29,20, 30,46, 61,38, 39,92, 78,80, 146,66, 79,163, 149,150, 321,143, 157,303, 257,402, 359,290, 524,372\nanchors_p7_640:\n - [11, 11, 13, 30, 29, 20] # P3/8\n - [30, 46, 61, 38, 39, 92] # P4/16\n - [78, 80, 146, 66, 79, 163] # P5/32\n - [149, 150, 321, 143, 157, 303] # P6/64\n - [257, 402, 359, 290, 524, 372] # P7/128\n\n# P7-1280: thr=0.25: 0.9968 BPR, 6.71 anchors past thr, n=15, img_size=1280, metric_all=0.273/0.732-mean/best, past_thr=0.463-mean: 19,22, 54,36, 32,77, 70,83, 138,71, 75,173, 165,159, 148,334, 375,151, 334,317, 251,626, 499,474, 750,326, 534,814, 1079,818\nanchors_p7_1280:\n - [19, 22, 54, 36, 32, 77] # P3/8\n - [70, 83, 138, 71, 75, 173] # P4/16\n - [165, 159, 148, 334, 375, 151] # P5/32\n - [334, 317, 251, 626, 499, 474] # P6/64\n - [750, 326, 534, 814, 1079, 818] # P7/128\n\n# P7-1920: thr=0.25: 0.9968 BPR, 6.71 anchors past thr, n=15, img_size=1920, metric_all=0.273/0.732-mean/best, past_thr=0.463-mean: 29,34, 81,55, 47,115, 105,124, 207,107, 113,259, 247,238, 222,500, 563,227, 501,476, 376,939, 749,711, 1126,489, 801,1222, 1618,1227\nanchors_p7_1920:\n - [29, 34, 81, 55, 47, 115] # P3/8\n - [105, 124, 207, 107, 113, 259] # P4/16\n - [247, 238, 222, 500, 563, 227] # P5/32\n - [501, 476, 376, 939, 749, 711] # P6/64\n - [1126, 489, 801, 1222, 1618, 1227] # P7/128\n" }, { "path": "models/hub/yolov5-bifpn.yaml", "content": "# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license\n\n# Parameters\nnc: 80 # number of classes\ndepth_multiple: 1.0 # model depth multiple\nwidth_multiple: 1.0 # layer channel multiple\nanchors:\n - [10, 13, 16, 30, 33, 23] # P3/8\n - [30, 61, 62, 45, 59, 119] # P4/16\n - [116, 90, 156, 198, 373, 326] # P5/32\n\n# YOLOv5 v6.0 backbone\nbackbone:\n # [from, number, module, args]\n [\n [-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2\n [-1, 1, Conv, [128, 3, 2]], # 1-P2/4\n [-1, 3, C3, [128]],\n [-1, 1, Conv, [256, 3, 2]], # 3-P3/8\n [-1, 6, C3, [256]],\n [-1, 1, Conv, [512, 3, 2]], # 5-P4/16\n [-1, 9, C3, [512]],\n [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32\n [-1, 3, C3, [1024]],\n [-1, 1, SPPF, [1024, 5]], # 9\n ]\n\n# YOLOv5 v6.0 BiFPN head\nhead: [\n [-1, 1, Conv, [512, 1, 1]],\n [-1, 1, nn.Upsample, [None, 2, \"nearest\"]],\n [[-1, 6], 1, Concat, [1]], # cat backbone P4\n [-1, 3, C3, [512, False]], # 13\n\n [-1, 1, Conv, [256, 1, 1]],\n [-1, 1, nn.Upsample, [None, 2, \"nearest\"]],\n [[-1, 4], 1, Concat, [1]], # cat backbone P3\n [-1, 3, C3, [256, False]], # 17 (P3/8-small)\n\n [-1, 1, Conv, [256, 3, 2]],\n [[-1, 14, 6], 1, Concat, [1]], # cat P4 <--- BiFPN change\n [-1, 3, C3, [512, False]], # 20 (P4/16-medium)\n\n [-1, 1, Conv, [512, 3, 2]],\n [[-1, 10], 1, Concat, [1]], # cat head P5\n [-1, 3, C3, [1024, False]], # 23 (P5/32-large)\n\n [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5)\n ]\n" }, { "path": "models/hub/yolov5-fpn.yaml", "content": "# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license\n\n# Parameters\nnc: 80 # number of classes\ndepth_multiple: 1.0 # model depth multiple\nwidth_multiple: 1.0 # layer channel multiple\nanchors:\n - [10, 13, 16, 30, 33, 23] # P3/8\n - [30, 61, 62, 45, 59, 119] # P4/16\n - [116, 90, 156, 198, 373, 326] # P5/32\n\n# YOLOv5 v6.0 backbone\nbackbone:\n # [from, number, module, args]\n [\n [-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2\n [-1, 1, Conv, [128, 3, 2]], # 1-P2/4\n [-1, 3, C3, [128]],\n [-1, 1, Conv, [256, 3, 2]], # 3-P3/8\n [-1, 6, C3, [256]],\n [-1, 1, Conv, [512, 3, 2]], # 5-P4/16\n [-1, 9, C3, [512]],\n [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32\n [-1, 3, C3, [1024]],\n [-1, 1, SPPF, [1024, 5]], # 9\n ]\n\n# YOLOv5 v6.0 FPN head\nhead: [\n [-1, 3, C3, [1024, False]], # 10 (P5/32-large)\n\n [-1, 1, nn.Upsample, [None, 2, \"nearest\"]],\n [[-1, 6], 1, Concat, [1]], # cat backbone P4\n [-1, 1, Conv, [512, 1, 1]],\n [-1, 3, C3, [512, False]], # 14 (P4/16-medium)\n\n [-1, 1, nn.Upsample, [None, 2, \"nearest\"]],\n [[-1, 4], 1, Concat, [1]], # cat backbone P3\n [-1, 1, Conv, [256, 1, 1]],\n [-1, 3, C3, [256, False]], # 18 (P3/8-small)\n\n [[18, 14, 10], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5)\n ]\n" }, { "path": "models/hub/yolov5-p2.yaml", "content": "# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license\n\n# Parameters\nnc: 80 # number of classes\ndepth_multiple: 1.0 # model depth multiple\nwidth_multiple: 1.0 # layer channel multiple\nanchors: 3 # AutoAnchor evolves 3 anchors per P output layer\n\n# YOLOv5 v6.0 backbone\nbackbone:\n # [from, number, module, args]\n [\n [-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2\n [-1, 1, Conv, [128, 3, 2]], # 1-P2/4\n [-1, 3, C3, [128]],\n [-1, 1, Conv, [256, 3, 2]], # 3-P3/8\n [-1, 6, C3, [256]],\n [-1, 1, Conv, [512, 3, 2]], # 5-P4/16\n [-1, 9, C3, [512]],\n [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32\n [-1, 3, C3, [1024]],\n [-1, 1, SPPF, [1024, 5]], # 9\n ]\n\n# YOLOv5 v6.0 head with (P2, P3, P4, P5) outputs\nhead: [\n [-1, 1, Conv, [512, 1, 1]],\n [-1, 1, nn.Upsample, [None, 2, \"nearest\"]],\n [[-1, 6], 1, Concat, [1]], # cat backbone P4\n [-1, 3, C3, [512, False]], # 13\n\n [-1, 1, Conv, [256, 1, 1]],\n [-1, 1, nn.Upsample, [None, 2, \"nearest\"]],\n [[-1, 4], 1, Concat, [1]], # cat backbone P3\n [-1, 3, C3, [256, False]], # 17 (P3/8-small)\n\n [-1, 1, Conv, [128, 1, 1]],\n [-1, 1, nn.Upsample, [None, 2, \"nearest\"]],\n [[-1, 2], 1, Concat, [1]], # cat backbone P2\n [-1, 1, C3, [128, False]], # 21 (P2/4-xsmall)\n\n [-1, 1, Conv, [128, 3, 2]],\n [[-1, 18], 1, Concat, [1]], # cat head P3\n [-1, 3, C3, [256, False]], # 24 (P3/8-small)\n\n [-1, 1, Conv, [256, 3, 2]],\n [[-1, 14], 1, Concat, [1]], # cat head P4\n [-1, 3, C3, [512, False]], # 27 (P4/16-medium)\n\n [-1, 1, Conv, [512, 3, 2]],\n [[-1, 10], 1, Concat, [1]], # cat head P5\n [-1, 3, C3, [1024, False]], # 30 (P5/32-large)\n\n [[21, 24, 27, 30], 1, Detect, [nc, anchors]], # Detect(P2, P3, P4, P5)\n ]\n" }, { "path": "models/hub/yolov5-p34.yaml", "content": "# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license\n\n# Parameters\nnc: 80 # number of classes\ndepth_multiple: 0.33 # model depth multiple\nwidth_multiple: 0.50 # layer channel multiple\nanchors: 3 # AutoAnchor evolves 3 anchors per P output layer\n\n# YOLOv5 v6.0 backbone\nbackbone:\n # [from, number, module, args]\n [\n [-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2\n [-1, 1, Conv, [128, 3, 2]], # 1-P2/4\n [-1, 3, C3, [128]],\n [-1, 1, Conv, [256, 3, 2]], # 3-P3/8\n [-1, 6, C3, [256]],\n [-1, 1, Conv, [512, 3, 2]], # 5-P4/16\n [-1, 9, C3, [512]],\n [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32\n [-1, 3, C3, [1024]],\n [-1, 1, SPPF, [1024, 5]], # 9\n ]\n\n# YOLOv5 v6.0 head with (P3, P4) outputs\nhead: [\n [-1, 1, Conv, [512, 1, 1]],\n [-1, 1, nn.Upsample, [None, 2, \"nearest\"]],\n [[-1, 6], 1, Concat, [1]], # cat backbone P4\n [-1, 3, C3, [512, False]], # 13\n\n [-1, 1, Conv, [256, 1, 1]],\n [-1, 1, nn.Upsample, [None, 2, \"nearest\"]],\n [[-1, 4], 1, Concat, [1]], # cat backbone P3\n [-1, 3, C3, [256, False]], # 17 (P3/8-small)\n\n [-1, 1, Conv, [256, 3, 2]],\n [[-1, 14], 1, Concat, [1]], # cat head P4\n [-1, 3, C3, [512, False]], # 20 (P4/16-medium)\n\n [[17, 20], 1, Detect, [nc, anchors]], # Detect(P3, P4)\n ]\n" }, { "path": "models/hub/yolov5-p6.yaml", "content": "# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license\n\n# Parameters\nnc: 80 # number of classes\ndepth_multiple: 1.0 # model depth multiple\nwidth_multiple: 1.0 # layer channel multiple\nanchors: 3 # AutoAnchor evolves 3 anchors per P output layer\n\n# YOLOv5 v6.0 backbone\nbackbone:\n # [from, number, module, args]\n [\n [-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2\n [-1, 1, Conv, [128, 3, 2]], # 1-P2/4\n [-1, 3, C3, [128]],\n [-1, 1, Conv, [256, 3, 2]], # 3-P3/8\n [-1, 6, C3, [256]],\n [-1, 1, Conv, [512, 3, 2]], # 5-P4/16\n [-1, 9, C3, [512]],\n [-1, 1, Conv, [768, 3, 2]], # 7-P5/32\n [-1, 3, C3, [768]],\n [-1, 1, Conv, [1024, 3, 2]], # 9-P6/64\n [-1, 3, C3, [1024]],\n [-1, 1, SPPF, [1024, 5]], # 11\n ]\n\n# YOLOv5 v6.0 head with (P3, P4, P5, P6) outputs\nhead: [\n [-1, 1, Conv, [768, 1, 1]],\n [-1, 1, nn.Upsample, [None, 2, \"nearest\"]],\n [[-1, 8], 1, Concat, [1]], # cat backbone P5\n [-1, 3, C3, [768, False]], # 15\n\n [-1, 1, Conv, [512, 1, 1]],\n [-1, 1, nn.Upsample, [None, 2, \"nearest\"]],\n [[-1, 6], 1, Concat, [1]], # cat backbone P4\n [-1, 3, C3, [512, False]], # 19\n\n [-1, 1, Conv, [256, 1, 1]],\n [-1, 1, nn.Upsample, [None, 2, \"nearest\"]],\n [[-1, 4], 1, Concat, [1]], # cat backbone P3\n [-1, 3, C3, [256, False]], # 23 (P3/8-small)\n\n [-1, 1, Conv, [256, 3, 2]],\n [[-1, 20], 1, Concat, [1]], # cat head P4\n [-1, 3, C3, [512, False]], # 26 (P4/16-medium)\n\n [-1, 1, Conv, [512, 3, 2]],\n [[-1, 16], 1, Concat, [1]], # cat head P5\n [-1, 3, C3, [768, False]], # 29 (P5/32-large)\n\n [-1, 1, Conv, [768, 3, 2]],\n [[-1, 12], 1, Concat, [1]], # cat head P6\n [-1, 3, C3, [1024, False]], # 32 (P6/64-xlarge)\n\n [[23, 26, 29, 32], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5, P6)\n ]\n" }, { "path": "models/hub/yolov5-p7.yaml", "content": "# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license\n\n# Parameters\nnc: 80 # number of classes\ndepth_multiple: 1.0 # model depth multiple\nwidth_multiple: 1.0 # layer channel multiple\nanchors: 3 # AutoAnchor evolves 3 anchors per P output layer\n\n# YOLOv5 v6.0 backbone\nbackbone:\n # [from, number, module, args]\n [\n [-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2\n [-1, 1, Conv, [128, 3, 2]], # 1-P2/4\n [-1, 3, C3, [128]],\n [-1, 1, Conv, [256, 3, 2]], # 3-P3/8\n [-1, 6, C3, [256]],\n [-1, 1, Conv, [512, 3, 2]], # 5-P4/16\n [-1, 9, C3, [512]],\n [-1, 1, Conv, [768, 3, 2]], # 7-P5/32\n [-1, 3, C3, [768]],\n [-1, 1, Conv, [1024, 3, 2]], # 9-P6/64\n [-1, 3, C3, [1024]],\n [-1, 1, Conv, [1280, 3, 2]], # 11-P7/128\n [-1, 3, C3, [1280]],\n [-1, 1, SPPF, [1280, 5]], # 13\n ]\n\n# YOLOv5 v6.0 head with (P3, P4, P5, P6, P7) outputs\nhead: [\n [-1, 1, Conv, [1024, 1, 1]],\n [-1, 1, nn.Upsample, [None, 2, \"nearest\"]],\n [[-1, 10], 1, Concat, [1]], # cat backbone P6\n [-1, 3, C3, [1024, False]], # 17\n\n [-1, 1, Conv, [768, 1, 1]],\n [-1, 1, nn.Upsample, [None, 2, \"nearest\"]],\n [[-1, 8], 1, Concat, [1]], # cat backbone P5\n [-1, 3, C3, [768, False]], # 21\n\n [-1, 1, Conv, [512, 1, 1]],\n [-1, 1, nn.Upsample, [None, 2, \"nearest\"]],\n [[-1, 6], 1, Concat, [1]], # cat backbone P4\n [-1, 3, C3, [512, False]], # 25\n\n [-1, 1, Conv, [256, 1, 1]],\n [-1, 1, nn.Upsample, [None, 2, \"nearest\"]],\n [[-1, 4], 1, Concat, [1]], # cat backbone P3\n [-1, 3, C3, [256, False]], # 29 (P3/8-small)\n\n [-1, 1, Conv, [256, 3, 2]],\n [[-1, 26], 1, Concat, [1]], # cat head P4\n [-1, 3, C3, [512, False]], # 32 (P4/16-medium)\n\n [-1, 1, Conv, [512, 3, 2]],\n [[-1, 22], 1, Concat, [1]], # cat head P5\n [-1, 3, C3, [768, False]], # 35 (P5/32-large)\n\n [-1, 1, Conv, [768, 3, 2]],\n [[-1, 18], 1, Concat, [1]], # cat head P6\n [-1, 3, C3, [1024, False]], # 38 (P6/64-xlarge)\n\n [-1, 1, Conv, [1024, 3, 2]],\n [[-1, 14], 1, Concat, [1]], # cat head P7\n [-1, 3, C3, [1280, False]], # 41 (P7/128-xxlarge)\n\n [[29, 32, 35, 38, 41], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5, P6, P7)\n ]\n" }, { "path": "models/hub/yolov5-panet.yaml", "content": "# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license\n\n# Parameters\nnc: 80 # number of classes\ndepth_multiple: 1.0 # model depth multiple\nwidth_multiple: 1.0 # layer channel multiple\nanchors:\n - [10, 13, 16, 30, 33, 23] # P3/8\n - [30, 61, 62, 45, 59, 119] # P4/16\n - [116, 90, 156, 198, 373, 326] # P5/32\n\n# YOLOv5 v6.0 backbone\nbackbone:\n # [from, number, module, args]\n [\n [-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2\n [-1, 1, Conv, [128, 3, 2]], # 1-P2/4\n [-1, 3, C3, [128]],\n [-1, 1, Conv, [256, 3, 2]], # 3-P3/8\n [-1, 6, C3, [256]],\n [-1, 1, Conv, [512, 3, 2]], # 5-P4/16\n [-1, 9, C3, [512]],\n [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32\n [-1, 3, C3, [1024]],\n [-1, 1, SPPF, [1024, 5]], # 9\n ]\n\n# YOLOv5 v6.0 PANet head\nhead: [\n [-1, 1, Conv, [512, 1, 1]],\n [-1, 1, nn.Upsample, [None, 2, \"nearest\"]],\n [[-1, 6], 1, Concat, [1]], # cat backbone P4\n [-1, 3, C3, [512, False]], # 13\n\n [-1, 1, Conv, [256, 1, 1]],\n [-1, 1, nn.Upsample, [None, 2, \"nearest\"]],\n [[-1, 4], 1, Concat, [1]], # cat backbone P3\n [-1, 3, C3, [256, False]], # 17 (P3/8-small)\n\n [-1, 1, Conv, [256, 3, 2]],\n [[-1, 14], 1, Concat, [1]], # cat head P4\n [-1, 3, C3, [512, False]], # 20 (P4/16-medium)\n\n [-1, 1, Conv, [512, 3, 2]],\n [[-1, 10], 1, Concat, [1]], # cat head P5\n [-1, 3, C3, [1024, False]], # 23 (P5/32-large)\n\n [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5)\n ]\n" }, { "path": "models/hub/yolov5l6.yaml", "content": "# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license\n\n# Parameters\nnc: 80 # number of classes\ndepth_multiple: 1.0 # model depth multiple\nwidth_multiple: 1.0 # layer channel multiple\nanchors:\n - [19, 27, 44, 40, 38, 94] # P3/8\n - [96, 68, 86, 152, 180, 137] # P4/16\n - [140, 301, 303, 264, 238, 542] # P5/32\n - [436, 615, 739, 380, 925, 792] # P6/64\n\n# YOLOv5 v6.0 backbone\nbackbone:\n # [from, number, module, args]\n [\n [-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2\n [-1, 1, Conv, [128, 3, 2]], # 1-P2/4\n [-1, 3, C3, [128]],\n [-1, 1, Conv, [256, 3, 2]], # 3-P3/8\n [-1, 6, C3, [256]],\n [-1, 1, Conv, [512, 3, 2]], # 5-P4/16\n [-1, 9, C3, [512]],\n [-1, 1, Conv, [768, 3, 2]], # 7-P5/32\n [-1, 3, C3, [768]],\n [-1, 1, Conv, [1024, 3, 2]], # 9-P6/64\n [-1, 3, C3, [1024]],\n [-1, 1, SPPF, [1024, 5]], # 11\n ]\n\n# YOLOv5 v6.0 head\nhead: [\n [-1, 1, Conv, [768, 1, 1]],\n [-1, 1, nn.Upsample, [None, 2, \"nearest\"]],\n [[-1, 8], 1, Concat, [1]], # cat backbone P5\n [-1, 3, C3, [768, False]], # 15\n\n [-1, 1, Conv, [512, 1, 1]],\n [-1, 1, nn.Upsample, [None, 2, \"nearest\"]],\n [[-1, 6], 1, Concat, [1]], # cat backbone P4\n [-1, 3, C3, [512, False]], # 19\n\n [-1, 1, Conv, [256, 1, 1]],\n [-1, 1, nn.Upsample, [None, 2, \"nearest\"]],\n [[-1, 4], 1, Concat, [1]], # cat backbone P3\n [-1, 3, C3, [256, False]], # 23 (P3/8-small)\n\n [-1, 1, Conv, [256, 3, 2]],\n [[-1, 20], 1, Concat, [1]], # cat head P4\n [-1, 3, C3, [512, False]], # 26 (P4/16-medium)\n\n [-1, 1, Conv, [512, 3, 2]],\n [[-1, 16], 1, Concat, [1]], # cat head P5\n [-1, 3, C3, [768, False]], # 29 (P5/32-large)\n\n [-1, 1, Conv, [768, 3, 2]],\n [[-1, 12], 1, Concat, [1]], # cat head P6\n [-1, 3, C3, [1024, False]], # 32 (P6/64-xlarge)\n\n [[23, 26, 29, 32], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5, P6)\n ]\n" }, { "path": "models/hub/yolov5m6.yaml", "content": "# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license\n\n# Parameters\nnc: 80 # number of classes\ndepth_multiple: 0.67 # model depth multiple\nwidth_multiple: 0.75 # layer channel multiple\nanchors:\n - [19, 27, 44, 40, 38, 94] # P3/8\n - [96, 68, 86, 152, 180, 137] # P4/16\n - [140, 301, 303, 264, 238, 542] # P5/32\n - [436, 615, 739, 380, 925, 792] # P6/64\n\n# YOLOv5 v6.0 backbone\nbackbone:\n # [from, number, module, args]\n [\n [-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2\n [-1, 1, Conv, [128, 3, 2]], # 1-P2/4\n [-1, 3, C3, [128]],\n [-1, 1, Conv, [256, 3, 2]], # 3-P3/8\n [-1, 6, C3, [256]],\n [-1, 1, Conv, [512, 3, 2]], # 5-P4/16\n [-1, 9, C3, [512]],\n [-1, 1, Conv, [768, 3, 2]], # 7-P5/32\n [-1, 3, C3, [768]],\n [-1, 1, Conv, [1024, 3, 2]], # 9-P6/64\n [-1, 3, C3, [1024]],\n [-1, 1, SPPF, [1024, 5]], # 11\n ]\n\n# YOLOv5 v6.0 head\nhead: [\n [-1, 1, Conv, [768, 1, 1]],\n [-1, 1, nn.Upsample, [None, 2, \"nearest\"]],\n [[-1, 8], 1, Concat, [1]], # cat backbone P5\n [-1, 3, C3, [768, False]], # 15\n\n [-1, 1, Conv, [512, 1, 1]],\n [-1, 1, nn.Upsample, [None, 2, \"nearest\"]],\n [[-1, 6], 1, Concat, [1]], # cat backbone P4\n [-1, 3, C3, [512, False]], # 19\n\n [-1, 1, Conv, [256, 1, 1]],\n [-1, 1, nn.Upsample, [None, 2, \"nearest\"]],\n [[-1, 4], 1, Concat, [1]], # cat backbone P3\n [-1, 3, C3, [256, False]], # 23 (P3/8-small)\n\n [-1, 1, Conv, [256, 3, 2]],\n [[-1, 20], 1, Concat, [1]], # cat head P4\n [-1, 3, C3, [512, False]], # 26 (P4/16-medium)\n\n [-1, 1, Conv, [512, 3, 2]],\n [[-1, 16], 1, Concat, [1]], # cat head P5\n [-1, 3, C3, [768, False]], # 29 (P5/32-large)\n\n [-1, 1, Conv, [768, 3, 2]],\n [[-1, 12], 1, Concat, [1]], # cat head P6\n [-1, 3, C3, [1024, False]], # 32 (P6/64-xlarge)\n\n [[23, 26, 29, 32], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5, P6)\n ]\n" }, { "path": "models/hub/yolov5n6.yaml", "content": "# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license\n\n# Parameters\nnc: 80 # number of classes\ndepth_multiple: 0.33 # model depth multiple\nwidth_multiple: 0.25 # layer channel multiple\nanchors:\n - [19, 27, 44, 40, 38, 94] # P3/8\n - [96, 68, 86, 152, 180, 137] # P4/16\n - [140, 301, 303, 264, 238, 542] # P5/32\n - [436, 615, 739, 380, 925, 792] # P6/64\n\n# YOLOv5 v6.0 backbone\nbackbone:\n # [from, number, module, args]\n [\n [-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2\n [-1, 1, Conv, [128, 3, 2]], # 1-P2/4\n [-1, 3, C3, [128]],\n [-1, 1, Conv, [256, 3, 2]], # 3-P3/8\n [-1, 6, C3, [256]],\n [-1, 1, Conv, [512, 3, 2]], # 5-P4/16\n [-1, 9, C3, [512]],\n [-1, 1, Conv, [768, 3, 2]], # 7-P5/32\n [-1, 3, C3, [768]],\n [-1, 1, Conv, [1024, 3, 2]], # 9-P6/64\n [-1, 3, C3, [1024]],\n [-1, 1, SPPF, [1024, 5]], # 11\n ]\n\n# YOLOv5 v6.0 head\nhead: [\n [-1, 1, Conv, [768, 1, 1]],\n [-1, 1, nn.Upsample, [None, 2, \"nearest\"]],\n [[-1, 8], 1, Concat, [1]], # cat backbone P5\n [-1, 3, C3, [768, False]], # 15\n\n [-1, 1, Conv, [512, 1, 1]],\n [-1, 1, nn.Upsample, [None, 2, \"nearest\"]],\n [[-1, 6], 1, Concat, [1]], # cat backbone P4\n [-1, 3, C3, [512, False]], # 19\n\n [-1, 1, Conv, [256, 1, 1]],\n [-1, 1, nn.Upsample, [None, 2, \"nearest\"]],\n [[-1, 4], 1, Concat, [1]], # cat backbone P3\n [-1, 3, C3, [256, False]], # 23 (P3/8-small)\n\n [-1, 1, Conv, [256, 3, 2]],\n [[-1, 20], 1, Concat, [1]], # cat head P4\n [-1, 3, C3, [512, False]], # 26 (P4/16-medium)\n\n [-1, 1, Conv, [512, 3, 2]],\n [[-1, 16], 1, Concat, [1]], # cat head P5\n [-1, 3, C3, [768, False]], # 29 (P5/32-large)\n\n [-1, 1, Conv, [768, 3, 2]],\n [[-1, 12], 1, Concat, [1]], # cat head P6\n [-1, 3, C3, [1024, False]], # 32 (P6/64-xlarge)\n\n [[23, 26, 29, 32], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5, P6)\n ]\n" }, { "path": "models/hub/yolov5s-LeakyReLU.yaml", "content": "# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license\n\n# Parameters\nnc: 80 # number of classes\nactivation: nn.LeakyReLU(0.1) # <----- Conv() activation used throughout entire YOLOv5 model\ndepth_multiple: 0.33 # model depth multiple\nwidth_multiple: 0.50 # layer channel multiple\nanchors:\n - [10, 13, 16, 30, 33, 23] # P3/8\n - [30, 61, 62, 45, 59, 119] # P4/16\n - [116, 90, 156, 198, 373, 326] # P5/32\n\n# YOLOv5 v6.0 backbone\nbackbone:\n # [from, number, module, args]\n [\n [-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2\n [-1, 1, Conv, [128, 3, 2]], # 1-P2/4\n [-1, 3, C3, [128]],\n [-1, 1, Conv, [256, 3, 2]], # 3-P3/8\n [-1, 6, C3, [256]],\n [-1, 1, Conv, [512, 3, 2]], # 5-P4/16\n [-1, 9, C3, [512]],\n [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32\n [-1, 3, C3, [1024]],\n [-1, 1, SPPF, [1024, 5]], # 9\n ]\n\n# YOLOv5 v6.0 head\nhead: [\n [-1, 1, Conv, [512, 1, 1]],\n [-1, 1, nn.Upsample, [None, 2, \"nearest\"]],\n [[-1, 6], 1, Concat, [1]], # cat backbone P4\n [-1, 3, C3, [512, False]], # 13\n\n [-1, 1, Conv, [256, 1, 1]],\n [-1, 1, nn.Upsample, [None, 2, \"nearest\"]],\n [[-1, 4], 1, Concat, [1]], # cat backbone P3\n [-1, 3, C3, [256, False]], # 17 (P3/8-small)\n\n [-1, 1, Conv, [256, 3, 2]],\n [[-1, 14], 1, Concat, [1]], # cat head P4\n [-1, 3, C3, [512, False]], # 20 (P4/16-medium)\n\n [-1, 1, Conv, [512, 3, 2]],\n [[-1, 10], 1, Concat, [1]], # cat head P5\n [-1, 3, C3, [1024, False]], # 23 (P5/32-large)\n\n [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5)\n ]\n" }, { "path": "models/hub/yolov5s-ghost.yaml", "content": "# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license\n\n# Parameters\nnc: 80 # number of classes\ndepth_multiple: 0.33 # model depth multiple\nwidth_multiple: 0.50 # layer channel multiple\nanchors:\n - [10, 13, 16, 30, 33, 23] # P3/8\n - [30, 61, 62, 45, 59, 119] # P4/16\n - [116, 90, 156, 198, 373, 326] # P5/32\n\n# YOLOv5 v6.0 backbone\nbackbone:\n # [from, number, module, args]\n [\n [-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2\n [-1, 1, GhostConv, [128, 3, 2]], # 1-P2/4\n [-1, 3, C3Ghost, [128]],\n [-1, 1, GhostConv, [256, 3, 2]], # 3-P3/8\n [-1, 6, C3Ghost, [256]],\n [-1, 1, GhostConv, [512, 3, 2]], # 5-P4/16\n [-1, 9, C3Ghost, [512]],\n [-1, 1, GhostConv, [1024, 3, 2]], # 7-P5/32\n [-1, 3, C3Ghost, [1024]],\n [-1, 1, SPPF, [1024, 5]], # 9\n ]\n\n# YOLOv5 v6.0 head\nhead: [\n [-1, 1, GhostConv, [512, 1, 1]],\n [-1, 1, nn.Upsample, [None, 2, \"nearest\"]],\n [[-1, 6], 1, Concat, [1]], # cat backbone P4\n [-1, 3, C3Ghost, [512, False]], # 13\n\n [-1, 1, GhostConv, [256, 1, 1]],\n [-1, 1, nn.Upsample, [None, 2, \"nearest\"]],\n [[-1, 4], 1, Concat, [1]], # cat backbone P3\n [-1, 3, C3Ghost, [256, False]], # 17 (P3/8-small)\n\n [-1, 1, GhostConv, [256, 3, 2]],\n [[-1, 14], 1, Concat, [1]], # cat head P4\n [-1, 3, C3Ghost, [512, False]], # 20 (P4/16-medium)\n\n [-1, 1, GhostConv, [512, 3, 2]],\n [[-1, 10], 1, Concat, [1]], # cat head P5\n [-1, 3, C3Ghost, [1024, False]], # 23 (P5/32-large)\n\n [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5)\n ]\n" }, { "path": "models/hub/yolov5s-transformer.yaml", "content": "# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license\n\n# Parameters\nnc: 80 # number of classes\ndepth_multiple: 0.33 # model depth multiple\nwidth_multiple: 0.50 # layer channel multiple\nanchors:\n - [10, 13, 16, 30, 33, 23] # P3/8\n - [30, 61, 62, 45, 59, 119] # P4/16\n - [116, 90, 156, 198, 373, 326] # P5/32\n\n# YOLOv5 v6.0 backbone\nbackbone:\n # [from, number, module, args]\n [\n [-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2\n [-1, 1, Conv, [128, 3, 2]], # 1-P2/4\n [-1, 3, C3, [128]],\n [-1, 1, Conv, [256, 3, 2]], # 3-P3/8\n [-1, 6, C3, [256]],\n [-1, 1, Conv, [512, 3, 2]], # 5-P4/16\n [-1, 9, C3, [512]],\n [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32\n [-1, 3, C3TR, [1024]], # 9 <--- C3TR() Transformer module\n [-1, 1, SPPF, [1024, 5]], # 9\n ]\n\n# YOLOv5 v6.0 head\nhead: [\n [-1, 1, Conv, [512, 1, 1]],\n [-1, 1, nn.Upsample, [None, 2, \"nearest\"]],\n [[-1, 6], 1, Concat, [1]], # cat backbone P4\n [-1, 3, C3, [512, False]], # 13\n\n [-1, 1, Conv, [256, 1, 1]],\n [-1, 1, nn.Upsample, [None, 2, \"nearest\"]],\n [[-1, 4], 1, Concat, [1]], # cat backbone P3\n [-1, 3, C3, [256, False]], # 17 (P3/8-small)\n\n [-1, 1, Conv, [256, 3, 2]],\n [[-1, 14], 1, Concat, [1]], # cat head P4\n [-1, 3, C3, [512, False]], # 20 (P4/16-medium)\n\n [-1, 1, Conv, [512, 3, 2]],\n [[-1, 10], 1, Concat, [1]], # cat head P5\n [-1, 3, C3, [1024, False]], # 23 (P5/32-large)\n\n [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5)\n ]\n" }, { "path": "models/hub/yolov5s6.yaml", "content": "# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license\n\n# Parameters\nnc: 80 # number of classes\ndepth_multiple: 0.33 # model depth multiple\nwidth_multiple: 0.50 # layer channel multiple\nanchors:\n - [19, 27, 44, 40, 38, 94] # P3/8\n - [96, 68, 86, 152, 180, 137] # P4/16\n - [140, 301, 303, 264, 238, 542] # P5/32\n - [436, 615, 739, 380, 925, 792] # P6/64\n\n# YOLOv5 v6.0 backbone\nbackbone:\n # [from, number, module, args]\n [\n [-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2\n [-1, 1, Conv, [128, 3, 2]], # 1-P2/4\n [-1, 3, C3, [128]],\n [-1, 1, Conv, [256, 3, 2]], # 3-P3/8\n [-1, 6, C3, [256]],\n [-1, 1, Conv, [512, 3, 2]], # 5-P4/16\n [-1, 9, C3, [512]],\n [-1, 1, Conv, [768, 3, 2]], # 7-P5/32\n [-1, 3, C3, [768]],\n [-1, 1, Conv, [1024, 3, 2]], # 9-P6/64\n [-1, 3, C3, [1024]],\n [-1, 1, SPPF, [1024, 5]], # 11\n ]\n\n# YOLOv5 v6.0 head\nhead: [\n [-1, 1, Conv, [768, 1, 1]],\n [-1, 1, nn.Upsample, [None, 2, \"nearest\"]],\n [[-1, 8], 1, Concat, [1]], # cat backbone P5\n [-1, 3, C3, [768, False]], # 15\n\n [-1, 1, Conv, [512, 1, 1]],\n [-1, 1, nn.Upsample, [None, 2, \"nearest\"]],\n [[-1, 6], 1, Concat, [1]], # cat backbone P4\n [-1, 3, C3, [512, False]], # 19\n\n [-1, 1, Conv, [256, 1, 1]],\n [-1, 1, nn.Upsample, [None, 2, \"nearest\"]],\n [[-1, 4], 1, Concat, [1]], # cat backbone P3\n [-1, 3, C3, [256, False]], # 23 (P3/8-small)\n\n [-1, 1, Conv, [256, 3, 2]],\n [[-1, 20], 1, Concat, [1]], # cat head P4\n [-1, 3, C3, [512, False]], # 26 (P4/16-medium)\n\n [-1, 1, Conv, [512, 3, 2]],\n [[-1, 16], 1, Concat, [1]], # cat head P5\n [-1, 3, C3, [768, False]], # 29 (P5/32-large)\n\n [-1, 1, Conv, [768, 3, 2]],\n [[-1, 12], 1, Concat, [1]], # cat head P6\n [-1, 3, C3, [1024, False]], # 32 (P6/64-xlarge)\n\n [[23, 26, 29, 32], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5, P6)\n ]\n" }, { "path": "models/hub/yolov5x6.yaml", "content": "# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license\n\n# Parameters\nnc: 80 # number of classes\ndepth_multiple: 1.33 # model depth multiple\nwidth_multiple: 1.25 # layer channel multiple\nanchors:\n - [19, 27, 44, 40, 38, 94] # P3/8\n - [96, 68, 86, 152, 180, 137] # P4/16\n - [140, 301, 303, 264, 238, 542] # P5/32\n - [436, 615, 739, 380, 925, 792] # P6/64\n\n# YOLOv5 v6.0 backbone\nbackbone:\n # [from, number, module, args]\n [\n [-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2\n [-1, 1, Conv, [128, 3, 2]], # 1-P2/4\n [-1, 3, C3, [128]],\n [-1, 1, Conv, [256, 3, 2]], # 3-P3/8\n [-1, 6, C3, [256]],\n [-1, 1, Conv, [512, 3, 2]], # 5-P4/16\n [-1, 9, C3, [512]],\n [-1, 1, Conv, [768, 3, 2]], # 7-P5/32\n [-1, 3, C3, [768]],\n [-1, 1, Conv, [1024, 3, 2]], # 9-P6/64\n [-1, 3, C3, [1024]],\n [-1, 1, SPPF, [1024, 5]], # 11\n ]\n\n# YOLOv5 v6.0 head\nhead: [\n [-1, 1, Conv, [768, 1, 1]],\n [-1, 1, nn.Upsample, [None, 2, \"nearest\"]],\n [[-1, 8], 1, Concat, [1]], # cat backbone P5\n [-1, 3, C3, [768, False]], # 15\n\n [-1, 1, Conv, [512, 1, 1]],\n [-1, 1, nn.Upsample, [None, 2, \"nearest\"]],\n [[-1, 6], 1, Concat, [1]], # cat backbone P4\n [-1, 3, C3, [512, False]], # 19\n\n [-1, 1, Conv, [256, 1, 1]],\n [-1, 1, nn.Upsample, [None, 2, \"nearest\"]],\n [[-1, 4], 1, Concat, [1]], # cat backbone P3\n [-1, 3, C3, [256, False]], # 23 (P3/8-small)\n\n [-1, 1, Conv, [256, 3, 2]],\n [[-1, 20], 1, Concat, [1]], # cat head P4\n [-1, 3, C3, [512, False]], # 26 (P4/16-medium)\n\n [-1, 1, Conv, [512, 3, 2]],\n [[-1, 16], 1, Concat, [1]], # cat head P5\n [-1, 3, C3, [768, False]], # 29 (P5/32-large)\n\n [-1, 1, Conv, [768, 3, 2]],\n [[-1, 12], 1, Concat, [1]], # cat head P6\n [-1, 3, C3, [1024, False]], # 32 (P6/64-xlarge)\n\n [[23, 26, 29, 32], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5, P6)\n ]\n" }, { "path": "models/segment/yolov5l-seg.yaml", "content": "# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license\n\n# Parameters\nnc: 80 # number of classes\ndepth_multiple: 1.0 # model depth multiple\nwidth_multiple: 1.0 # layer channel multiple\nanchors:\n - [10, 13, 16, 30, 33, 23] # P3/8\n - [30, 61, 62, 45, 59, 119] # P4/16\n - [116, 90, 156, 198, 373, 326] # P5/32\n\n# YOLOv5 v6.0 backbone\nbackbone:\n # [from, number, module, args]\n [\n [-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2\n [-1, 1, Conv, [128, 3, 2]], # 1-P2/4\n [-1, 3, C3, [128]],\n [-1, 1, Conv, [256, 3, 2]], # 3-P3/8\n [-1, 6, C3, [256]],\n [-1, 1, Conv, [512, 3, 2]], # 5-P4/16\n [-1, 9, C3, [512]],\n [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32\n [-1, 3, C3, [1024]],\n [-1, 1, SPPF, [1024, 5]], # 9\n ]\n\n# YOLOv5 v6.0 head\nhead: [\n [-1, 1, Conv, [512, 1, 1]],\n [-1, 1, nn.Upsample, [None, 2, \"nearest\"]],\n [[-1, 6], 1, Concat, [1]], # cat backbone P4\n [-1, 3, C3, [512, False]], # 13\n\n [-1, 1, Conv, [256, 1, 1]],\n [-1, 1, nn.Upsample, [None, 2, \"nearest\"]],\n [[-1, 4], 1, Concat, [1]], # cat backbone P3\n [-1, 3, C3, [256, False]], # 17 (P3/8-small)\n\n [-1, 1, Conv, [256, 3, 2]],\n [[-1, 14], 1, Concat, [1]], # cat head P4\n [-1, 3, C3, [512, False]], # 20 (P4/16-medium)\n\n [-1, 1, Conv, [512, 3, 2]],\n [[-1, 10], 1, Concat, [1]], # cat head P5\n [-1, 3, C3, [1024, False]], # 23 (P5/32-large)\n\n [[17, 20, 23], 1, Segment, [nc, anchors, 32, 256]], # Detect(P3, P4, P5)\n ]\n" }, { "path": "models/segment/yolov5m-seg.yaml", "content": "# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license\n\n# Parameters\nnc: 80 # number of classes\ndepth_multiple: 0.67 # model depth multiple\nwidth_multiple: 0.75 # layer channel multiple\nanchors:\n - [10, 13, 16, 30, 33, 23] # P3/8\n - [30, 61, 62, 45, 59, 119] # P4/16\n - [116, 90, 156, 198, 373, 326] # P5/32\n\n# YOLOv5 v6.0 backbone\nbackbone:\n # [from, number, module, args]\n [\n [-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2\n [-1, 1, Conv, [128, 3, 2]], # 1-P2/4\n [-1, 3, C3, [128]],\n [-1, 1, Conv, [256, 3, 2]], # 3-P3/8\n [-1, 6, C3, [256]],\n [-1, 1, Conv, [512, 3, 2]], # 5-P4/16\n [-1, 9, C3, [512]],\n [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32\n [-1, 3, C3, [1024]],\n [-1, 1, SPPF, [1024, 5]], # 9\n ]\n\n# YOLOv5 v6.0 head\nhead: [\n [-1, 1, Conv, [512, 1, 1]],\n [-1, 1, nn.Upsample, [None, 2, \"nearest\"]],\n [[-1, 6], 1, Concat, [1]], # cat backbone P4\n [-1, 3, C3, [512, False]], # 13\n\n [-1, 1, Conv, [256, 1, 1]],\n [-1, 1, nn.Upsample, [None, 2, \"nearest\"]],\n [[-1, 4], 1, Concat, [1]], # cat backbone P3\n [-1, 3, C3, [256, False]], # 17 (P3/8-small)\n\n [-1, 1, Conv, [256, 3, 2]],\n [[-1, 14], 1, Concat, [1]], # cat head P4\n [-1, 3, C3, [512, False]], # 20 (P4/16-medium)\n\n [-1, 1, Conv, [512, 3, 2]],\n [[-1, 10], 1, Concat, [1]], # cat head P5\n [-1, 3, C3, [1024, False]], # 23 (P5/32-large)\n\n [[17, 20, 23], 1, Segment, [nc, anchors, 32, 256]], # Detect(P3, P4, P5)\n ]\n" }, { "path": "models/segment/yolov5n-seg.yaml", "content": "# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license\n\n# Parameters\nnc: 80 # number of classes\ndepth_multiple: 0.33 # model depth multiple\nwidth_multiple: 0.25 # layer channel multiple\nanchors:\n - [10, 13, 16, 30, 33, 23] # P3/8\n - [30, 61, 62, 45, 59, 119] # P4/16\n - [116, 90, 156, 198, 373, 326] # P5/32\n\n# YOLOv5 v6.0 backbone\nbackbone:\n # [from, number, module, args]\n [\n [-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2\n [-1, 1, Conv, [128, 3, 2]], # 1-P2/4\n [-1, 3, C3, [128]],\n [-1, 1, Conv, [256, 3, 2]], # 3-P3/8\n [-1, 6, C3, [256]],\n [-1, 1, Conv, [512, 3, 2]], # 5-P4/16\n [-1, 9, C3, [512]],\n [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32\n [-1, 3, C3, [1024]],\n [-1, 1, SPPF, [1024, 5]], # 9\n ]\n\n# YOLOv5 v6.0 head\nhead: [\n [-1, 1, Conv, [512, 1, 1]],\n [-1, 1, nn.Upsample, [None, 2, \"nearest\"]],\n [[-1, 6], 1, Concat, [1]], # cat backbone P4\n [-1, 3, C3, [512, False]], # 13\n\n [-1, 1, Conv, [256, 1, 1]],\n [-1, 1, nn.Upsample, [None, 2, \"nearest\"]],\n [[-1, 4], 1, Concat, [1]], # cat backbone P3\n [-1, 3, C3, [256, False]], # 17 (P3/8-small)\n\n [-1, 1, Conv, [256, 3, 2]],\n [[-1, 14], 1, Concat, [1]], # cat head P4\n [-1, 3, C3, [512, False]], # 20 (P4/16-medium)\n\n [-1, 1, Conv, [512, 3, 2]],\n [[-1, 10], 1, Concat, [1]], # cat head P5\n [-1, 3, C3, [1024, False]], # 23 (P5/32-large)\n\n [[17, 20, 23], 1, Segment, [nc, anchors, 32, 256]], # Detect(P3, P4, P5)\n ]\n" }, { "path": "models/segment/yolov5s-seg.yaml", "content": "# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license\n\n# Parameters\nnc: 80 # number of classes\ndepth_multiple: 0.33 # model depth multiple\nwidth_multiple: 0.5 # layer channel multiple\nanchors:\n - [10, 13, 16, 30, 33, 23] # P3/8\n - [30, 61, 62, 45, 59, 119] # P4/16\n - [116, 90, 156, 198, 373, 326] # P5/32\n\n# YOLOv5 v6.0 backbone\nbackbone:\n # [from, number, module, args]\n [\n [-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2\n [-1, 1, Conv, [128, 3, 2]], # 1-P2/4\n [-1, 3, C3, [128]],\n [-1, 1, Conv, [256, 3, 2]], # 3-P3/8\n [-1, 6, C3, [256]],\n [-1, 1, Conv, [512, 3, 2]], # 5-P4/16\n [-1, 9, C3, [512]],\n [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32\n [-1, 3, C3, [1024]],\n [-1, 1, SPPF, [1024, 5]], # 9\n ]\n\n# YOLOv5 v6.0 head\nhead: [\n [-1, 1, Conv, [512, 1, 1]],\n [-1, 1, nn.Upsample, [None, 2, \"nearest\"]],\n [[-1, 6], 1, Concat, [1]], # cat backbone P4\n [-1, 3, C3, [512, False]], # 13\n\n [-1, 1, Conv, [256, 1, 1]],\n [-1, 1, nn.Upsample, [None, 2, \"nearest\"]],\n [[-1, 4], 1, Concat, [1]], # cat backbone P3\n [-1, 3, C3, [256, False]], # 17 (P3/8-small)\n\n [-1, 1, Conv, [256, 3, 2]],\n [[-1, 14], 1, Concat, [1]], # cat head P4\n [-1, 3, C3, [512, False]], # 20 (P4/16-medium)\n\n [-1, 1, Conv, [512, 3, 2]],\n [[-1, 10], 1, Concat, [1]], # cat head P5\n [-1, 3, C3, [1024, False]], # 23 (P5/32-large)\n\n [[17, 20, 23], 1, Segment, [nc, anchors, 32, 256]], # Detect(P3, P4, P5)\n ]\n" }, { "path": "models/segment/yolov5x-seg.yaml", "content": "# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license\n\n# Parameters\nnc: 80 # number of classes\ndepth_multiple: 1.33 # model depth multiple\nwidth_multiple: 1.25 # layer channel multiple\nanchors:\n - [10, 13, 16, 30, 33, 23] # P3/8\n - [30, 61, 62, 45, 59, 119] # P4/16\n - [116, 90, 156, 198, 373, 326] # P5/32\n\n# YOLOv5 v6.0 backbone\nbackbone:\n # [from, number, module, args]\n [\n [-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2\n [-1, 1, Conv, [128, 3, 2]], # 1-P2/4\n [-1, 3, C3, [128]],\n [-1, 1, Conv, [256, 3, 2]], # 3-P3/8\n [-1, 6, C3, [256]],\n [-1, 1, Conv, [512, 3, 2]], # 5-P4/16\n [-1, 9, C3, [512]],\n [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32\n [-1, 3, C3, [1024]],\n [-1, 1, SPPF, [1024, 5]], # 9\n ]\n\n# YOLOv5 v6.0 head\nhead: [\n [-1, 1, Conv, [512, 1, 1]],\n [-1, 1, nn.Upsample, [None, 2, \"nearest\"]],\n [[-1, 6], 1, Concat, [1]], # cat backbone P4\n [-1, 3, C3, [512, False]], # 13\n\n [-1, 1, Conv, [256, 1, 1]],\n [-1, 1, nn.Upsample, [None, 2, \"nearest\"]],\n [[-1, 4], 1, Concat, [1]], # cat backbone P3\n [-1, 3, C3, [256, False]], # 17 (P3/8-small)\n\n [-1, 1, Conv, [256, 3, 2]],\n [[-1, 14], 1, Concat, [1]], # cat head P4\n [-1, 3, C3, [512, False]], # 20 (P4/16-medium)\n\n [-1, 1, Conv, [512, 3, 2]],\n [[-1, 10], 1, Concat, [1]], # cat head P5\n [-1, 3, C3, [1024, False]], # 23 (P5/32-large)\n\n [[17, 20, 23], 1, Segment, [nc, anchors, 32, 256]], # Detect(P3, P4, P5)\n ]\n" }, { "path": "models/tf.py", "content": "# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license\n\"\"\"\nTensorFlow, Keras and TFLite versions of YOLOv3\nAuthored by https://github.com/zldrobit in PR https://github.com/ultralytics/yolov5/pull/1127.\n\nUsage:\n $ python models/tf.py --weights yolov5s.pt\n\nExport:\n $ python export.py --weights yolov5s.pt --include saved_model pb tflite tfjs\n\"\"\"\n\nimport argparse\nimport sys\nfrom copy import deepcopy\nfrom pathlib import Path\n\nFILE = Path(__file__).resolve()\nROOT = FILE.parents[1] # YOLOv3 root directory\nif str(ROOT) not in sys.path:\n sys.path.append(str(ROOT)) # add ROOT to PATH\n# ROOT = ROOT.relative_to(Path.cwd()) # relative\n\nimport numpy as np\nimport tensorflow as tf\nimport torch\nimport torch.nn as nn\nfrom tensorflow import keras\n\nfrom models.common import (\n C3,\n SPP,\n SPPF,\n Bottleneck,\n BottleneckCSP,\n C3x,\n Concat,\n Conv,\n CrossConv,\n DWConv,\n DWConvTranspose2d,\n Focus,\n autopad,\n)\nfrom models.experimental import MixConv2d, attempt_load\nfrom models.yolo import Detect, Segment\nfrom utils.activations import SiLU\nfrom utils.general import LOGGER, make_divisible, print_args\n\n\nclass TFBN(keras.layers.Layer):\n \"\"\"A TensorFlow BatchNormalization wrapper layer initialized with specific weights for YOLOv3 models.\"\"\"\n\n def __init__(self, w=None):\n \"\"\"Initializes TFBN with weights, wrapping TensorFlow's BatchNormalization layer with specific initializers.\"\"\"\n super().__init__()\n self.bn = keras.layers.BatchNormalization(\n beta_initializer=keras.initializers.Constant(w.bias.numpy()),\n gamma_initializer=keras.initializers.Constant(w.weight.numpy()),\n moving_mean_initializer=keras.initializers.Constant(w.running_mean.numpy()),\n moving_variance_initializer=keras.initializers.Constant(w.running_var.numpy()),\n epsilon=w.eps,\n )\n\n def call(self, inputs):\n \"\"\"Applies batch normalization on inputs using initialized parameters.\"\"\"\n return self.bn(inputs)\n\n\nclass TFPad(keras.layers.Layer):\n \"\"\"Pads inputs in spatial dimensions 1 and 2 using specified padding width as an int or (int, int) tuple/list.\"\"\"\n\n def __init__(self, pad):\n \"\"\"Initializes a padding layer for spatial dimensions 1 and 2, with `pad` as int or (int, int) tuple/list.\"\"\"\n super().__init__()\n if isinstance(pad, int):\n self.pad = tf.constant([[0, 0], [pad, pad], [pad, pad], [0, 0]])\n else: # tuple/list\n self.pad = tf.constant([[0, 0], [pad[0], pad[0]], [pad[1], pad[1]], [0, 0]])\n\n def call(self, inputs):\n \"\"\"Applies constant padding to inputs with `pad` specifying padding width; `pad` can be an int or (int, int)\n tuple/list.\n \"\"\"\n return tf.pad(inputs, self.pad, mode=\"constant\", constant_values=0)\n\n\nclass TFConv(keras.layers.Layer):\n \"\"\"Implements a standard convolutional layer with optional batch normalization and activation for TensorFlow.\"\"\"\n\n def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True, w=None):\n \"\"\"Initializes a convolutional layer with customizable filters, kernel size, stride, padding, groups, and\n activation.\n \"\"\"\n super().__init__()\n assert g == 1, \"TF v2.2 Conv2D does not support 'groups' argument\"\n # TensorFlow convolution padding is inconsistent with PyTorch (e.g. k=3 s=2 'SAME' padding)\n # see https://stackoverflow.com/questions/52975843/comparing-conv2d-with-padding-between-tensorflow-and-pytorch\n conv = keras.layers.Conv2D(\n filters=c2,\n kernel_size=k,\n strides=s,\n padding=\"SAME\" if s == 1 else \"VALID\",\n use_bias=not hasattr(w, \"bn\"),\n kernel_initializer=keras.initializers.Constant(w.conv.weight.permute(2, 3, 1, 0).numpy()),\n bias_initializer=\"zeros\" if hasattr(w, \"bn\") else keras.initializers.Constant(w.conv.bias.numpy()),\n )\n self.conv = conv if s == 1 else keras.Sequential([TFPad(autopad(k, p)), conv])\n self.bn = TFBN(w.bn) if hasattr(w, \"bn\") else tf.identity\n self.act = activations(w.act) if act else tf.identity\n\n def call(self, inputs):\n \"\"\"Executes the convolution, batch normalization, and activation on the input data.\"\"\"\n return self.act(self.bn(self.conv(inputs)))\n\n\nclass TFDWConv(keras.layers.Layer):\n \"\"\"Implements a depthwise convolutional layer with optional batch normalization and activation for TensorFlow.\"\"\"\n\n def __init__(self, c1, c2, k=1, s=1, p=None, act=True, w=None):\n \"\"\"Initializes a depthwise convolutional layer with optional batch normalization and activation.\"\"\"\n super().__init__()\n assert c2 % c1 == 0, f\"TFDWConv() output={c2} must be a multiple of input={c1} channels\"\n conv = keras.layers.DepthwiseConv2D(\n kernel_size=k,\n depth_multiplier=c2 // c1,\n strides=s,\n padding=\"SAME\" if s == 1 else \"VALID\",\n use_bias=not hasattr(w, \"bn\"),\n depthwise_initializer=keras.initializers.Constant(w.conv.weight.permute(2, 3, 1, 0).numpy()),\n bias_initializer=\"zeros\" if hasattr(w, \"bn\") else keras.initializers.Constant(w.conv.bias.numpy()),\n )\n self.conv = conv if s == 1 else keras.Sequential([TFPad(autopad(k, p)), conv])\n self.bn = TFBN(w.bn) if hasattr(w, \"bn\") else tf.identity\n self.act = activations(w.act) if act else tf.identity\n\n def call(self, inputs):\n \"\"\"Applies convolution, batch normalization, and activation to the input tensor.\"\"\"\n return self.act(self.bn(self.conv(inputs)))\n\n\nclass TFDWConvTranspose2d(keras.layers.Layer):\n \"\"\"Implements a depthwise transposed convolutional layer for TensorFlow with equal input and output channels.\"\"\"\n\n def __init__(self, c1, c2, k=1, s=1, p1=0, p2=0, w=None):\n \"\"\"Initializes TFDWConvTranspose2d with ch_in=c1=ch_out, k=4, p1=1; sets up depthwise Conv2DTranspose layers.\"\"\"\n super().__init__()\n assert c1 == c2, f\"TFDWConv() output={c2} must be equal to input={c1} channels\"\n assert k == 4 and p1 == 1, \"TFDWConv() only valid for k=4 and p1=1\"\n weight, bias = w.weight.permute(2, 3, 1, 0).numpy(), w.bias.numpy()\n self.c1 = c1\n self.conv = [\n keras.layers.Conv2DTranspose(\n filters=1,\n kernel_size=k,\n strides=s,\n padding=\"VALID\",\n output_padding=p2,\n use_bias=True,\n kernel_initializer=keras.initializers.Constant(weight[..., i : i + 1]),\n bias_initializer=keras.initializers.Constant(bias[i]),\n )\n for i in range(c1)\n ]\n\n def call(self, inputs):\n \"\"\"Performs a forward pass by applying parallel convolutions to split input tensors and concatenates the\n results.\n \"\"\"\n return tf.concat([m(x) for m, x in zip(self.conv, tf.split(inputs, self.c1, 3))], 3)[:, 1:-1, 1:-1]\n\n\nclass TFFocus(keras.layers.Layer):\n \"\"\"Focuses spatial information into channel space using a convolutional layer for efficient feature extraction.\"\"\"\n\n def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True, w=None):\n \"\"\"Initializes TFFocus layer for efficient information focusing into channel-space with customizable convolution\n parameters.\n \"\"\"\n super().__init__()\n self.conv = TFConv(c1 * 4, c2, k, s, p, g, act, w.conv)\n\n def call(self, inputs): # x(b,w,h,c) -> y(b,w/2,h/2,4c)\n \"\"\"Executes TFFocus layer operation, reducing spatial dimensions by 2 and quadrupling channels, input shape\n (b,w,h,c).\n \"\"\"\n inputs = [inputs[:, ::2, ::2, :], inputs[:, 1::2, ::2, :], inputs[:, ::2, 1::2, :], inputs[:, 1::2, 1::2, :]]\n return self.conv(tf.concat(inputs, 3))\n\n\nclass TFBottleneck(keras.layers.Layer):\n \"\"\"A TensorFlow bottleneck layer with optional shortcut connections, channel expansion, and group convolutions.\"\"\"\n\n def __init__(self, c1, c2, shortcut=True, g=1, e=0.5, w=None): # ch_in, ch_out, shortcut, groups, expansion\n \"\"\"Initializes a standard bottleneck layer with optional shortcut, channel expansion, and group convolutions.\"\"\"\n super().__init__()\n c_ = int(c2 * e) # hidden channels\n self.cv1 = TFConv(c1, c_, 1, 1, w=w.cv1)\n self.cv2 = TFConv(c_, c2, 3, 1, g=g, w=w.cv2)\n self.add = shortcut and c1 == c2\n\n def call(self, inputs):\n \"\"\"Executes a bottleneck layer with optional shortcut; returns either input + convoluted input or just\n convoluted input.\n \"\"\"\n return inputs + self.cv2(self.cv1(inputs)) if self.add else self.cv2(self.cv1(inputs))\n\n\nclass TFCrossConv(keras.layers.Layer):\n \"\"\"Implements a cross convolutional layer with customizable channels, kernel size, stride, groups, and shortcut.\"\"\"\n\n def __init__(self, c1, c2, k=3, s=1, g=1, e=1.0, shortcut=False, w=None):\n \"\"\"Initializes cross convolutional layer with parameters for channel sizes, kernel size, stride, groups,\n expansion factor, shortcut option, and weights.\n \"\"\"\n super().__init__()\n c_ = int(c2 * e) # hidden channels\n self.cv1 = TFConv(c1, c_, (1, k), (1, s), w=w.cv1)\n self.cv2 = TFConv(c_, c2, (k, 1), (s, 1), g=g, w=w.cv2)\n self.add = shortcut and c1 == c2\n\n def call(self, inputs):\n \"\"\"Executes the function, optionally adding input to output if shapes match; inputs: tensor [B, C, H, W].\"\"\"\n return inputs + self.cv2(self.cv1(inputs)) if self.add else self.cv2(self.cv1(inputs))\n\n\nclass TFConv2d(keras.layers.Layer):\n \"\"\"Implements a TensorFlow 2.2+ Conv2D layer as a substitute for PyTorch's Conv2D with customizable parameters.\"\"\"\n\n def __init__(self, c1, c2, k, s=1, g=1, bias=True, w=None):\n \"\"\"Initializes TFConv2d layer for TensorFlow 2.2+, substituting PyTorch Conv2D; c1, c2: channels, k: kernel\n size, s: stride.\n \"\"\"\n super().__init__()\n assert g == 1, \"TF v2.2 Conv2D does not support 'groups' argument\"\n self.conv = keras.layers.Conv2D(\n filters=c2,\n kernel_size=k,\n strides=s,\n padding=\"VALID\",\n use_bias=bias,\n kernel_initializer=keras.initializers.Constant(w.weight.permute(2, 3, 1, 0).numpy()),\n bias_initializer=keras.initializers.Constant(w.bias.numpy()) if bias else None,\n )\n\n def call(self, inputs):\n \"\"\"Applies convolution to the inputs using initialized weights and biases, returning the convolved output.\"\"\"\n return self.conv(inputs)\n\n\nclass TFBottleneckCSP(keras.layers.Layer):\n \"\"\"Implements a Cross Stage Partial (CSP) Bottleneck layer for efficient feature extraction in neural networks.\"\"\"\n\n def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5, w=None):\n \"\"\"Initializes CSP Bottleneck layer with channel configurations and optional shortcut, groups, expansion, and\n weights.\n \"\"\"\n super().__init__()\n c_ = int(c2 * e) # hidden channels\n self.cv1 = TFConv(c1, c_, 1, 1, w=w.cv1)\n self.cv2 = TFConv2d(c1, c_, 1, 1, bias=False, w=w.cv2)\n self.cv3 = TFConv2d(c_, c_, 1, 1, bias=False, w=w.cv3)\n self.cv4 = TFConv(2 * c_, c2, 1, 1, w=w.cv4)\n self.bn = TFBN(w.bn)\n self.act = lambda x: keras.activations.swish(x)\n self.m = keras.Sequential([TFBottleneck(c_, c_, shortcut, g, e=1.0, w=w.m[j]) for j in range(n)])\n\n def call(self, inputs):\n \"\"\"Executes the forward pass by combining features through convolutions, activation, and batch normalization.\"\"\"\n y1 = self.cv3(self.m(self.cv1(inputs)))\n y2 = self.cv2(inputs)\n return self.cv4(self.act(self.bn(tf.concat((y1, y2), axis=3))))\n\n\nclass TFC3(keras.layers.Layer):\n \"\"\"CSP Bottleneck layer with 3 convolutions for enhanced feature extraction and integration in TensorFlow models.\"\"\"\n\n def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5, w=None):\n \"\"\"Initializes a CSP Bottleneck layer with 3 convolutions for channel manipulation and feature integration.\"\"\"\n super().__init__()\n c_ = int(c2 * e) # hidden channels\n self.cv1 = TFConv(c1, c_, 1, 1, w=w.cv1)\n self.cv2 = TFConv(c1, c_, 1, 1, w=w.cv2)\n self.cv3 = TFConv(2 * c_, c2, 1, 1, w=w.cv3)\n self.m = keras.Sequential([TFBottleneck(c_, c_, shortcut, g, e=1.0, w=w.m[j]) for j in range(n)])\n\n def call(self, inputs):\n \"\"\"Executes model forwarding, combining features using TF layers and concatenation, returning the resulting\n tensor.\n \"\"\"\n return self.cv3(tf.concat((self.m(self.cv1(inputs)), self.cv2(inputs)), axis=3))\n\n\nclass TFC3x(keras.layers.Layer):\n \"\"\"Implements a CSP Bottleneck layer with cross-convolutions for enhanced feature extraction in YOLOv3 models.\"\"\"\n\n def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5, w=None):\n \"\"\"Initializes a TFC3x layer with cross-convolutions, expanding and concatenating features for given channel\n inputs and outputs.\n \"\"\"\n super().__init__()\n c_ = int(c2 * e) # hidden channels\n self.cv1 = TFConv(c1, c_, 1, 1, w=w.cv1)\n self.cv2 = TFConv(c1, c_, 1, 1, w=w.cv2)\n self.cv3 = TFConv(2 * c_, c2, 1, 1, w=w.cv3)\n self.m = keras.Sequential(\n [TFCrossConv(c_, c_, k=3, s=1, g=g, e=1.0, shortcut=shortcut, w=w.m[j]) for j in range(n)]\n )\n\n def call(self, inputs):\n \"\"\"Executes model forwarding, combining features through conv layers and concatenation.\"\"\"\n return self.cv3(tf.concat((self.m(self.cv1(inputs)), self.cv2(inputs)), axis=3))\n\n\nclass TFSPP(keras.layers.Layer):\n \"\"\"Implements Spatial Pyramid Pooling (SPP) for YOLOv3-SPP with configurable channels and kernel sizes.\"\"\"\n\n def __init__(self, c1, c2, k=(5, 9, 13), w=None):\n \"\"\"Initializes a Spatial Pyramid Pooling layer for YOLOv3-SPP with configurable in/out channels and kernel\n sizes.\n \"\"\"\n super().__init__()\n c_ = c1 // 2 # hidden channels\n self.cv1 = TFConv(c1, c_, 1, 1, w=w.cv1)\n self.cv2 = TFConv(c_ * (len(k) + 1), c2, 1, 1, w=w.cv2)\n self.m = [keras.layers.MaxPool2D(pool_size=x, strides=1, padding=\"SAME\") for x in k]\n\n def call(self, inputs):\n \"\"\"Applies transformations and concatenates feature maps from multiple kernel-sized max-poolings.\"\"\"\n x = self.cv1(inputs)\n return self.cv2(tf.concat([x] + [m(x) for m in self.m], 3))\n\n\nclass TFSPPF(keras.layers.Layer):\n \"\"\"Implements a fast spatial pyramid pooling layer for efficient multi-scale feature extraction in YOLOv3 models.\"\"\"\n\n def __init__(self, c1, c2, k=5, w=None):\n \"\"\"Initializes a Spatial Pyramid Pooling-Fast layer with specified channels, kernel size, and optional weights.\n \"\"\"\n super().__init__()\n c_ = c1 // 2 # hidden channels\n self.cv1 = TFConv(c1, c_, 1, 1, w=w.cv1)\n self.cv2 = TFConv(c_ * 4, c2, 1, 1, w=w.cv2)\n self.m = keras.layers.MaxPool2D(pool_size=k, strides=1, padding=\"SAME\")\n\n def call(self, inputs):\n \"\"\"Applies two TFConvs and max pooling with concatenation, returning the processed tensor.\"\"\"\n x = self.cv1(inputs)\n y1 = self.m(x)\n y2 = self.m(y1)\n return self.cv2(tf.concat([x, y1, y2, self.m(y2)], 3))\n\n\nclass TFDetect(keras.layers.Layer):\n \"\"\"Implements YOLOv3 detection layer in TensorFlow for object detection with configurable classes and anchors.\"\"\"\n\n def __init__(self, nc=80, anchors=(), ch=(), imgsz=(640, 640), w=None): # detection layer\n \"\"\"Initializes a YOLOv3 detection layer with specified classes, anchors, channels, image size, and weights.\"\"\"\n super().__init__()\n self.stride = tf.convert_to_tensor(w.stride.numpy(), dtype=tf.float32)\n self.nc = nc # number of classes\n self.no = nc + 5 # number of outputs per anchor\n self.nl = len(anchors) # number of detection layers\n self.na = len(anchors[0]) // 2 # number of anchors\n self.grid = [tf.zeros(1)] * self.nl # init grid\n self.anchors = tf.convert_to_tensor(w.anchors.numpy(), dtype=tf.float32)\n self.anchor_grid = tf.reshape(self.anchors * tf.reshape(self.stride, [self.nl, 1, 1]), [self.nl, 1, -1, 1, 2])\n self.m = [TFConv2d(x, self.no * self.na, 1, w=w.m[i]) for i, x in enumerate(ch)]\n self.training = False # set to False after building model\n self.imgsz = imgsz\n for i in range(self.nl):\n ny, nx = self.imgsz[0] // self.stride[i], self.imgsz[1] // self.stride[i]\n self.grid[i] = self._make_grid(nx, ny)\n\n def call(self, inputs):\n \"\"\"Performs inference on inputs, transforming shape to (batch_size, ny*nx, num_anchors, num_outputs) for each\n layer.\n \"\"\"\n z = [] # inference output\n x = []\n for i in range(self.nl):\n x.append(self.m[i](inputs[i]))\n # x(bs,20,20,255) to x(bs,3,20,20,85)\n ny, nx = self.imgsz[0] // self.stride[i], self.imgsz[1] // self.stride[i]\n x[i] = tf.reshape(x[i], [-1, ny * nx, self.na, self.no])\n\n if not self.training: # inference\n y = x[i]\n grid = tf.transpose(self.grid[i], [0, 2, 1, 3]) - 0.5\n anchor_grid = tf.transpose(self.anchor_grid[i], [0, 2, 1, 3]) * 4\n xy = (tf.sigmoid(y[..., 0:2]) * 2 + grid) * self.stride[i] # xy\n wh = tf.sigmoid(y[..., 2:4]) ** 2 * anchor_grid\n # Normalize xywh to 0-1 to reduce calibration error\n xy /= tf.constant([[self.imgsz[1], self.imgsz[0]]], dtype=tf.float32)\n wh /= tf.constant([[self.imgsz[1], self.imgsz[0]]], dtype=tf.float32)\n y = tf.concat([xy, wh, tf.sigmoid(y[..., 4 : 5 + self.nc]), y[..., 5 + self.nc :]], -1)\n z.append(tf.reshape(y, [-1, self.na * ny * nx, self.no]))\n\n return tf.transpose(x, [0, 2, 1, 3]) if self.training else (tf.concat(z, 1),)\n\n @staticmethod\n def _make_grid(nx=20, ny=20):\n \"\"\"Generates a grid of shape [1, 1, ny * nx, 2] with ranges [0, nx) and [0, ny) for object detection.\"\"\"\n # return torch.stack((xv, yv), 2).view((1, 1, ny, nx, 2)).float()\n xv, yv = tf.meshgrid(tf.range(nx), tf.range(ny))\n return tf.cast(tf.reshape(tf.stack([xv, yv], 2), [1, 1, ny * nx, 2]), dtype=tf.float32)\n\n\nclass TFSegment(TFDetect):\n \"\"\"Implements YOLOv3 segmentation head for object detection and segmentation tasks using TensorFlow.\"\"\"\n\n def __init__(self, nc=80, anchors=(), nm=32, npr=256, ch=(), imgsz=(640, 640), w=None):\n \"\"\"Initializes a YOLOv3 Segment head with customizable parameters for segmentation models.\"\"\"\n super().__init__(nc, anchors, ch, imgsz, w)\n self.nm = nm # number of masks\n self.npr = npr # number of protos\n self.no = 5 + nc + self.nm # number of outputs per anchor\n self.m = [TFConv2d(x, self.no * self.na, 1, w=w.m[i]) for i, x in enumerate(ch)] # output conv\n self.proto = TFProto(ch[0], self.npr, self.nm, w=w.proto) # protos\n self.detect = TFDetect.call\n\n def call(self, x):\n \"\"\"Executes model's forward pass, returning predictions and optionally full-size protos if training.\"\"\"\n p = self.proto(x[0])\n # p = TFUpsample(None, scale_factor=4, mode='nearest')(self.proto(x[0])) # (optional) full-size protos\n p = tf.transpose(p, [0, 3, 1, 2]) # from shape(1,160,160,32) to shape(1,32,160,160)\n x = self.detect(self, x)\n return (x, p) if self.training else (x[0], p)\n\n\nclass TFProto(keras.layers.Layer):\n \"\"\"Implements a TensorFlow layer for feature processing with convolution and upsample operations.\"\"\"\n\n def __init__(self, c1, c_=256, c2=32, w=None):\n \"\"\"Initializes a TFProto layer with convolution and upsample operations for feature processing.\"\"\"\n super().__init__()\n self.cv1 = TFConv(c1, c_, k=3, w=w.cv1)\n self.upsample = TFUpsample(None, scale_factor=2, mode=\"nearest\")\n self.cv2 = TFConv(c_, c_, k=3, w=w.cv2)\n self.cv3 = TFConv(c_, c2, w=w.cv3)\n\n def call(self, inputs):\n \"\"\"Performs convolution and upsample operations on input features, returning processed features.\"\"\"\n return self.cv3(self.cv2(self.upsample(self.cv1(inputs))))\n\n\nclass TFUpsample(keras.layers.Layer):\n \"\"\"Implements an upsample layer using TensorFlow with specified size, scale factor, and interpolation mode.\"\"\"\n\n def __init__(self, size, scale_factor, mode, w=None): # warning: all arguments needed including 'w'\n \"\"\"Initializes an upsample layer with specific size, doubling scale factor (>0, even), interpolation mode, and\n optional weights.\n \"\"\"\n super().__init__()\n assert scale_factor % 2 == 0, \"scale_factor must be multiple of 2\"\n self.upsample = lambda x: tf.image.resize(x, (x.shape[1] * scale_factor, x.shape[2] * scale_factor), mode)\n # self.upsample = keras.layers.UpSampling2D(size=scale_factor, interpolation=mode)\n # with default arguments: align_corners=False, half_pixel_centers=False\n # self.upsample = lambda x: tf.raw_ops.ResizeNearestNeighbor(images=x,\n # size=(x.shape[1] * 2, x.shape[2] * 2))\n\n def call(self, inputs):\n \"\"\"Applies upsample lambda function to the input tensor, returning the upsampled tensor.\"\"\"\n return self.upsample(inputs)\n\n\nclass TFConcat(keras.layers.Layer):\n \"\"\"Concatenates input tensors along the specified dimension (NHWC format) using TensorFlow.\"\"\"\n\n def __init__(self, dimension=1, w=None):\n \"\"\"Initializes a TensorFlow layer to concatenate tensors along the NHWC dimension, requiring dimension=1.\"\"\"\n super().__init__()\n assert dimension == 1, \"convert only NCHW to NHWC concat\"\n self.d = 3\n\n def call(self, inputs):\n \"\"\"Concatenates tensors along NHWC dimension (3rd axis); `inputs` is a list of tensors.\"\"\"\n return tf.concat(inputs, self.d)\n\n\ndef parse_model(d, ch, model, imgsz): # model_dict, input_channels(3)\n \"\"\"Parses model configuration and constructs Keras model with layer connectivity, returning the model and save list.\n \"\"\"\n LOGGER.info(f\"\\n{'':>3}{'from':>18}{'n':>3}{'params':>10} {'module':<40}{'arguments':<30}\")\n anchors, nc, gd, gw = d[\"anchors\"], d[\"nc\"], d[\"depth_multiple\"], d[\"width_multiple\"]\n na = (len(anchors[0]) // 2) if isinstance(anchors, list) else anchors # number of anchors\n no = na * (nc + 5) # number of outputs = anchors * (classes + 5)\n\n layers, save, c2 = [], [], ch[-1] # layers, savelist, ch out\n for i, (f, n, m, args) in enumerate(d[\"backbone\"] + d[\"head\"]): # from, number, module, args\n m_str = m\n m = eval(m) if isinstance(m, str) else m # eval strings\n for j, a in enumerate(args):\n try:\n args[j] = eval(a) if isinstance(a, str) else a # eval strings\n except NameError:\n pass\n\n n = max(round(n * gd), 1) if n > 1 else n # depth gain\n if m in [\n nn.Conv2d,\n Conv,\n DWConv,\n DWConvTranspose2d,\n Bottleneck,\n SPP,\n SPPF,\n MixConv2d,\n Focus,\n CrossConv,\n BottleneckCSP,\n C3,\n C3x,\n ]:\n c1, c2 = ch[f], args[0]\n c2 = make_divisible(c2 * gw, 8) if c2 != no else c2\n\n args = [c1, c2, *args[1:]]\n if m in [BottleneckCSP, C3, C3x]:\n args.insert(2, n)\n n = 1\n elif m is nn.BatchNorm2d:\n args = [ch[f]]\n elif m is Concat:\n c2 = sum(ch[-1 if x == -1 else x + 1] for x in f)\n elif m in [Detect, Segment]:\n args.append([ch[x + 1] for x in f])\n if isinstance(args[1], int): # number of anchors\n args[1] = [list(range(args[1] * 2))] * len(f)\n if m is Segment:\n args[3] = make_divisible(args[3] * gw, 8)\n args.append(imgsz)\n else:\n c2 = ch[f]\n\n tf_m = eval(\"TF\" + m_str.replace(\"nn.\", \"\"))\n m_ = (\n keras.Sequential([tf_m(*args, w=model.model[i][j]) for j in range(n)])\n if n > 1\n else tf_m(*args, w=model.model[i])\n ) # module\n\n torch_m_ = nn.Sequential(*(m(*args) for _ in range(n))) if n > 1 else m(*args) # module\n t = str(m)[8:-2].replace(\"__main__.\", \"\") # module type\n np = sum(x.numel() for x in torch_m_.parameters()) # number params\n m_.i, m_.f, m_.type, m_.np = i, f, t, np # attach index, 'from' index, type, number params\n LOGGER.info(f\"{i:>3}{f!s:>18}{n!s:>3}{np:>10} {t:<40}{args!s:<30}\") # print\n save.extend(x % i for x in ([f] if isinstance(f, int) else f) if x != -1) # append to savelist\n layers.append(m_)\n ch.append(c2)\n return keras.Sequential(layers), sorted(save)\n\n\nclass TFModel:\n \"\"\"TensorFlow implementation of YOLOv3 for object detection, supporting Keras and TFLite models.\"\"\"\n\n def __init__(self, cfg=\"yolov5s.yaml\", ch=3, nc=None, model=None, imgsz=(640, 640)): # model, channels, classes\n \"\"\"Initializes TF YOLOv3 model with config, channels, classes, optional pre-loaded model, and input image size.\n \"\"\"\n super().__init__()\n if isinstance(cfg, dict):\n self.yaml = cfg # model dict\n else: # is *.yaml\n import yaml # for torch hub\n\n self.yaml_file = Path(cfg).name\n with open(cfg) as f:\n self.yaml = yaml.load(f, Loader=yaml.FullLoader) # model dict\n\n # Define model\n if nc and nc != self.yaml[\"nc\"]:\n LOGGER.info(f\"Overriding {cfg} nc={self.yaml['nc']} with nc={nc}\")\n self.yaml[\"nc\"] = nc # override yaml value\n self.model, self.savelist = parse_model(deepcopy(self.yaml), ch=[ch], model=model, imgsz=imgsz)\n\n def predict(\n self,\n inputs,\n tf_nms=False,\n agnostic_nms=False,\n topk_per_class=100,\n topk_all=100,\n iou_thres=0.45,\n conf_thres=0.25,\n ):\n \"\"\"Performs inference on input data using a YOLOv3 model, including optional TensorFlow NMS.\"\"\"\n y = [] # outputs\n x = inputs\n for m in self.model.layers:\n if m.f != -1: # if not from previous layer\n x = y[m.f] if isinstance(m.f, int) else [x if j == -1 else y[j] for j in m.f] # from earlier layers\n\n x = m(x) # run\n y.append(x if m.i in self.savelist else None) # save output\n\n # Add TensorFlow NMS\n if tf_nms:\n boxes = self._xywh2xyxy(x[0][..., :4])\n probs = x[0][:, :, 4:5]\n classes = x[0][:, :, 5:]\n scores = probs * classes\n if agnostic_nms:\n nms = AgnosticNMS()((boxes, classes, scores), topk_all, iou_thres, conf_thres)\n else:\n boxes = tf.expand_dims(boxes, 2)\n nms = tf.image.combined_non_max_suppression(\n boxes, scores, topk_per_class, topk_all, iou_thres, conf_thres, clip_boxes=False\n )\n return (nms,)\n return x # output [1,6300,85] = [xywh, conf, class0, class1, ...]\n # x = x[0] # [x(1,6300,85), ...] to x(6300,85)\n # xywh = x[..., :4] # x(6300,4) boxes\n # conf = x[..., 4:5] # x(6300,1) confidences\n # cls = tf.reshape(tf.cast(tf.argmax(x[..., 5:], axis=1), tf.float32), (-1, 1)) # x(6300,1) classes\n # return tf.concat([conf, cls, xywh], 1)\n\n @staticmethod\n def _xywh2xyxy(xywh):\n \"\"\"Converts bounding boxes from [x, y, w, h] format to [x1, y1, x2, y2], where xy1=top-left, xy2=bottom- right.\n \"\"\"\n x, y, w, h = tf.split(xywh, num_or_size_splits=4, axis=-1)\n return tf.concat([x - w / 2, y - h / 2, x + w / 2, y + h / 2], axis=-1)\n\n\nclass AgnosticNMS(keras.layers.Layer):\n \"\"\"Applies class-agnostic non-maximum suppression (NMS) to filter detections by IoU and confidence thresholds.\"\"\"\n\n def call(self, input, topk_all, iou_thres, conf_thres):\n \"\"\"Applies non-maximum suppression (NMS) to filter detections based on IoU, confidence thresholds, and top-K.\"\"\"\n return tf.map_fn(\n lambda x: self._nms(x, topk_all, iou_thres, conf_thres),\n input,\n fn_output_signature=(tf.float32, tf.float32, tf.float32, tf.int32),\n name=\"agnostic_nms\",\n )\n\n @staticmethod\n def _nms(x, topk_all=100, iou_thres=0.45, conf_thres=0.25): # agnostic NMS\n \"\"\"Performs non-max suppression on bounding boxes with class, IoU, and confidence thresholds; returns processed\n boxes, scores, classes, and count.\n \"\"\"\n boxes, classes, scores = x\n class_inds = tf.cast(tf.argmax(classes, axis=-1), tf.float32)\n scores_inp = tf.reduce_max(scores, -1)\n selected_inds = tf.image.non_max_suppression(\n boxes, scores_inp, max_output_size=topk_all, iou_threshold=iou_thres, score_threshold=conf_thres\n )\n selected_boxes = tf.gather(boxes, selected_inds)\n padded_boxes = tf.pad(\n selected_boxes,\n paddings=[[0, topk_all - tf.shape(selected_boxes)[0]], [0, 0]],\n mode=\"CONSTANT\",\n constant_values=0.0,\n )\n selected_scores = tf.gather(scores_inp, selected_inds)\n padded_scores = tf.pad(\n selected_scores,\n paddings=[[0, topk_all - tf.shape(selected_boxes)[0]]],\n mode=\"CONSTANT\",\n constant_values=-1.0,\n )\n selected_classes = tf.gather(class_inds, selected_inds)\n padded_classes = tf.pad(\n selected_classes,\n paddings=[[0, topk_all - tf.shape(selected_boxes)[0]]],\n mode=\"CONSTANT\",\n constant_values=-1.0,\n )\n valid_detections = tf.shape(selected_inds)[0]\n return padded_boxes, padded_scores, padded_classes, valid_detections\n\n\ndef activations(act=nn.SiLU):\n \"\"\"Converts PyTorch activation functions (LeakyReLU, Hardswish, SiLU) to their TensorFlow counterparts.\"\"\"\n if isinstance(act, nn.LeakyReLU):\n return lambda x: keras.activations.relu(x, alpha=0.1)\n elif isinstance(act, nn.Hardswish):\n return lambda x: x * tf.nn.relu6(x + 3) * 0.166666667\n elif isinstance(act, (nn.SiLU, SiLU)):\n return lambda x: keras.activations.swish(x)\n else:\n raise Exception(f\"no matching TensorFlow activation found for PyTorch activation {act}\")\n\n\ndef representative_dataset_gen(dataset, ncalib=100):\n \"\"\"Generates a representative dataset for TFLite conversion; yields normalized np arrays from input dataset up to\n `ncalib` samples.\n \"\"\"\n for n, (path, img, im0s, vid_cap, string) in enumerate(dataset):\n im = np.transpose(img, [1, 2, 0])\n im = np.expand_dims(im, axis=0).astype(np.float32)\n im /= 255\n yield [im]\n if n >= ncalib:\n break\n\n\ndef run(\n weights=ROOT / \"yolov5s.pt\", # weights path\n imgsz=(640, 640), # inference size h,w\n batch_size=1, # batch size\n dynamic=False, # dynamic batch size\n):\n # PyTorch model\n \"\"\"Exports and summarizes both PyTorch and TensorFlow models for YOLOv5-based object detection.\"\"\"\n im = torch.zeros((batch_size, 3, *imgsz)) # BCHW image\n model = attempt_load(weights, device=torch.device(\"cpu\"), inplace=True, fuse=False)\n _ = model(im) # inference\n model.info()\n\n # TensorFlow model\n im = tf.zeros((batch_size, *imgsz, 3)) # BHWC image\n tf_model = TFModel(cfg=model.yaml, model=model, nc=model.nc, imgsz=imgsz)\n _ = tf_model.predict(im) # inference\n\n # Keras model\n im = keras.Input(shape=(*imgsz, 3), batch_size=None if dynamic else batch_size)\n keras_model = keras.Model(inputs=im, outputs=tf_model.predict(im))\n keras_model.summary()\n\n LOGGER.info(\"PyTorch, TensorFlow and Keras models successfully verified.\\nUse export.py for TF model export.\")\n\n\ndef parse_opt():\n \"\"\"Parses command line arguments for model configuration including weights path, image size, batch size, and dynamic\n batching.\n \"\"\"\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--weights\", type=str, default=ROOT / \"yolov3-tiny.pt\", help=\"weights path\")\n parser.add_argument(\"--imgsz\", \"--img\", \"--img-size\", nargs=\"+\", type=int, default=[640], help=\"inference size h,w\")\n parser.add_argument(\"--batch-size\", type=int, default=1, help=\"batch size\")\n parser.add_argument(\"--dynamic\", action=\"store_true\", help=\"dynamic batch size\")\n opt = parser.parse_args()\n opt.imgsz *= 2 if len(opt.imgsz) == 1 else 1 # expand\n print_args(vars(opt))\n return opt\n\n\ndef main(opt):\n \"\"\"Executes the model run function with parsed CLI arguments on batch size and dynamic batching option.\"\"\"\n run(**vars(opt))\n\n\nif __name__ == \"__main__\":\n opt = parse_opt()\n main(opt)\n" }, { "path": "models/yolo.py", "content": "# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license\n\"\"\"\nYOLO-specific modules.\n\nUsage:\n $ python models/yolo.py --cfg yolov5s.yaml\n\"\"\"\n\nimport argparse\nimport os\nimport platform\nimport sys\nfrom copy import deepcopy\nfrom pathlib import Path\n\nFILE = Path(__file__).resolve()\nROOT = FILE.parents[1] # YOLOv3 root directory\nif str(ROOT) not in sys.path:\n sys.path.append(str(ROOT)) # add ROOT to PATH\nif platform.system() != \"Windows\":\n ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative\n\nfrom models.common import * # noqa\nfrom models.experimental import * # noqa\nfrom utils.autoanchor import check_anchor_order\nfrom utils.general import LOGGER, check_version, check_yaml, make_divisible, print_args\nfrom utils.plots import feature_visualization\nfrom utils.torch_utils import (\n fuse_conv_and_bn,\n initialize_weights,\n model_info,\n profile,\n scale_img,\n select_device,\n time_sync,\n)\n\ntry:\n import thop # for FLOPs computation\nexcept ImportError:\n thop = None\n\n\nclass Detect(nn.Module):\n \"\"\"YOLOv3 Detect head for processing detection model outputs, including grid and anchor grid generation.\"\"\"\n\n stride = None # strides computed during build\n dynamic = False # force grid reconstruction\n export = False # export mode\n\n def __init__(self, nc=80, anchors=(), ch=(), inplace=True): # detection layer\n \"\"\"Initializes YOLOv3 detection layer with class count, anchors, channels, and operation modes.\"\"\"\n super().__init__()\n self.nc = nc # number of classes\n self.no = nc + 5 # number of outputs per anchor\n self.nl = len(anchors) # number of detection layers\n self.na = len(anchors[0]) // 2 # number of anchors\n self.grid = [torch.empty(0) for _ in range(self.nl)] # init grid\n self.anchor_grid = [torch.empty(0) for _ in range(self.nl)] # init anchor grid\n self.register_buffer(\"anchors\", torch.tensor(anchors).float().view(self.nl, -1, 2)) # shape(nl,na,2)\n self.m = nn.ModuleList(nn.Conv2d(x, self.no * self.na, 1) for x in ch) # output conv\n self.inplace = inplace # use inplace ops (e.g. slice assignment)\n\n def forward(self, x):\n \"\"\"Processes input through convolutional layers, reshaping output for detection.\n\n Expects x as list of tensors with shape(bs, C, H, W).\n \"\"\"\n z = [] # inference output\n for i in range(self.nl):\n x[i] = self.m[i](x[i]) # conv\n bs, _, ny, nx = x[i].shape # x(bs,255,20,20) to x(bs,3,20,20,85)\n x[i] = x[i].view(bs, self.na, self.no, ny, nx).permute(0, 1, 3, 4, 2).contiguous()\n\n if not self.training: # inference\n if self.dynamic or self.grid[i].shape[2:4] != x[i].shape[2:4]:\n self.grid[i], self.anchor_grid[i] = self._make_grid(nx, ny, i)\n\n if isinstance(self, Segment): # (boxes + masks)\n xy, wh, conf, mask = x[i].split((2, 2, self.nc + 1, self.no - self.nc - 5), 4)\n xy = (xy.sigmoid() * 2 + self.grid[i]) * self.stride[i] # xy\n wh = (wh.sigmoid() * 2) ** 2 * self.anchor_grid[i] # wh\n y = torch.cat((xy, wh, conf.sigmoid(), mask), 4)\n else: # Detect (boxes only)\n xy, wh, conf = x[i].sigmoid().split((2, 2, self.nc + 1), 4)\n xy = (xy * 2 + self.grid[i]) * self.stride[i] # xy\n wh = (wh * 2) ** 2 * self.anchor_grid[i] # wh\n y = torch.cat((xy, wh, conf), 4)\n z.append(y.view(bs, self.na * nx * ny, self.no))\n\n return x if self.training else (torch.cat(z, 1),) if self.export else (torch.cat(z, 1), x)\n\n def _make_grid(self, nx=20, ny=20, i=0, torch_1_10=check_version(torch.__version__, \"1.10.0\")):\n \"\"\"Generates a grid and corresponding anchor grid with shape `(1, num_anchors, ny, nx, 2)` for indexing anchors.\n \"\"\"\n d = self.anchors[i].device\n t = self.anchors[i].dtype\n shape = 1, self.na, ny, nx, 2 # grid shape\n y, x = torch.arange(ny, device=d, dtype=t), torch.arange(nx, device=d, dtype=t)\n yv, xv = torch.meshgrid(y, x, indexing=\"ij\") if torch_1_10 else torch.meshgrid(y, x) # torch>=0.7 compatibility\n grid = torch.stack((xv, yv), 2).expand(shape) - 0.5 # add grid offset, i.e. y = 2.0 * x - 0.5\n anchor_grid = (self.anchors[i] * self.stride[i]).view((1, self.na, 1, 1, 2)).expand(shape)\n return grid, anchor_grid\n\n\nclass Segment(Detect):\n \"\"\"YOLOv3 Segment head for segmentation models, adding mask prediction and prototyping to detection.\"\"\"\n\n def __init__(self, nc=80, anchors=(), nm=32, npr=256, ch=(), inplace=True):\n \"\"\"Initializes the YOLOv3 segment head with customizable class count, anchors, masks, protos, channels, and\n inplace option.\n \"\"\"\n super().__init__(nc, anchors, ch, inplace)\n self.nm = nm # number of masks\n self.npr = npr # number of protos\n self.no = 5 + nc + self.nm # number of outputs per anchor\n self.m = nn.ModuleList(nn.Conv2d(x, self.no * self.na, 1) for x in ch) # output conv\n self.proto = Proto(ch[0], self.npr, self.nm) # protos\n self.detect = Detect.forward\n\n def forward(self, x):\n \"\"\"Executes forward pass, returning predictions and protos, with different outputs based on training and export\n states.\n \"\"\"\n p = self.proto(x[0])\n x = self.detect(self, x)\n return (x, p) if self.training else (x[0], p) if self.export else (x[0], p, x[1])\n\n\nclass BaseModel(nn.Module):\n \"\"\"Implements the base YOLOv3 model architecture for object detection tasks.\"\"\"\n\n def forward(self, x, profile=False, visualize=False):\n \"\"\"Performs a single-scale inference or training step on input `x`, with options for profiling and\n visualization.\n \"\"\"\n return self._forward_once(x, profile, visualize) # single-scale inference, train\n\n def _forward_once(self, x, profile=False, visualize=False):\n \"\"\"Executes a single inference or training step, offering profiling and visualization options for input `x`.\"\"\"\n y, dt = [], [] # outputs\n for m in self.model:\n if m.f != -1: # if not from previous layer\n x = y[m.f] if isinstance(m.f, int) else [x if j == -1 else y[j] for j in m.f] # from earlier layers\n if profile:\n self._profile_one_layer(m, x, dt)\n x = m(x) # run\n y.append(x if m.i in self.save else None) # save output\n if visualize:\n feature_visualization(x, m.type, m.i, save_dir=visualize)\n return x\n\n def _profile_one_layer(self, m, x, dt):\n \"\"\"Profiles a single layer of the model by measuring its execution time and computational cost.\"\"\"\n c = m == self.model[-1] # is final layer, copy input as inplace fix\n o = thop.profile(m, inputs=(x.copy() if c else x,), verbose=False)[0] / 1e9 * 2 if thop else 0 # FLOPs\n t = time_sync()\n for _ in range(10):\n m(x.copy() if c else x)\n dt.append((time_sync() - t) * 100)\n if m == self.model[0]:\n LOGGER.info(f\"{'time (ms)':>10s} {'GFLOPs':>10s} {'params':>10s} module\")\n LOGGER.info(f\"{dt[-1]:10.2f} {o:10.2f} {m.np:10.0f} {m.type}\")\n if c:\n LOGGER.info(f\"{sum(dt):10.2f} {'-':>10s} {'-':>10s} Total\")\n\n def fuse(self): # fuse model Conv2d() + BatchNorm2d() layers\n \"\"\"Fuses Conv2d() and BatchNorm2d() layers in the model to optimize inference speed.\"\"\"\n LOGGER.info(\"Fusing layers... \")\n for m in self.model.modules():\n if isinstance(m, (Conv, DWConv)) and hasattr(m, \"bn\"):\n m.conv = fuse_conv_and_bn(m.conv, m.bn) # update conv\n delattr(m, \"bn\") # remove batchnorm\n m.forward = m.forward_fuse # update forward\n self.info()\n return self\n\n def info(self, verbose=False, img_size=640): # print model information\n \"\"\"Prints model information; `verbose` for detailed, `img_size` for input image size (default 640).\"\"\"\n model_info(self, verbose, img_size)\n\n def _apply(self, fn):\n \"\"\"Applies `to()`, `cpu()`, `cuda()`, `half()` to model tensors, excluding parameters or registered buffers.\"\"\"\n self = super()._apply(fn)\n m = self.model[-1] # Detect()\n if isinstance(m, (Detect, Segment)):\n m.stride = fn(m.stride)\n m.grid = list(map(fn, m.grid))\n if isinstance(m.anchor_grid, list):\n m.anchor_grid = list(map(fn, m.anchor_grid))\n return self\n\n\nclass DetectionModel(BaseModel):\n \"\"\"YOLOv3 detection model class for initializing and processing detection models with configurable parameters.\"\"\"\n\n def __init__(self, cfg=\"yolov5s.yaml\", ch=3, nc=None, anchors=None): # model, input channels, number of classes\n \"\"\"Initializes YOLOv3 detection model with configurable YAML, input channels, classes, and anchors.\"\"\"\n super().__init__()\n if isinstance(cfg, dict):\n self.yaml = cfg # model dict\n else: # is *.yaml\n import yaml # for torch hub\n\n self.yaml_file = Path(cfg).name\n with open(cfg, encoding=\"ascii\", errors=\"ignore\") as f:\n self.yaml = yaml.safe_load(f) # model dict\n\n # Define model\n ch = self.yaml[\"ch\"] = self.yaml.get(\"ch\", ch) # input channels\n if nc and nc != self.yaml[\"nc\"]:\n LOGGER.info(f\"Overriding model.yaml nc={self.yaml['nc']} with nc={nc}\")\n self.yaml[\"nc\"] = nc # override yaml value\n if anchors:\n LOGGER.info(f\"Overriding model.yaml anchors with anchors={anchors}\")\n self.yaml[\"anchors\"] = round(anchors) # override yaml value\n self.model, self.save = parse_model(deepcopy(self.yaml), ch=[ch]) # model, savelist\n self.names = [str(i) for i in range(self.yaml[\"nc\"])] # default names\n self.inplace = self.yaml.get(\"inplace\", True)\n\n # Build strides, anchors\n m = self.model[-1] # Detect()\n if isinstance(m, (Detect, Segment)):\n s = 256 # 2x min stride\n m.inplace = self.inplace\n\n def forward(x):\n \"\"\"Passes the input 'x' through the model and returns the processed output.\"\"\"\n return self.forward(x)[0] if isinstance(m, Segment) else self.forward(x)\n\n m.stride = torch.tensor([s / x.shape[-2] for x in forward(torch.zeros(1, ch, s, s))]) # forward\n check_anchor_order(m)\n m.anchors /= m.stride.view(-1, 1, 1)\n self.stride = m.stride\n self._initialize_biases() # only run once\n\n # Init weights, biases\n initialize_weights(self)\n self.info()\n LOGGER.info(\"\")\n\n def forward(self, x, augment=False, profile=False, visualize=False):\n \"\"\"Processes input through the model, with options for augmentation, profiling, and visualization.\"\"\"\n if augment:\n return self._forward_augment(x) # augmented inference, None\n return self._forward_once(x, profile, visualize) # single-scale inference, train\n\n def _forward_augment(self, x):\n \"\"\"Performs augmented inference by scaling and flipping input images, returning concatenated predictions.\"\"\"\n img_size = x.shape[-2:] # height, width\n s = [1, 0.83, 0.67] # scales\n f = [None, 3, None] # flips (2-ud, 3-lr)\n y = [] # outputs\n for si, fi in zip(s, f):\n xi = scale_img(x.flip(fi) if fi else x, si, gs=int(self.stride.max()))\n yi = self._forward_once(xi)[0] # forward\n # cv2.imwrite(f'img_{si}.jpg', 255 * xi[0].cpu().numpy().transpose((1, 2, 0))[:, :, ::-1]) # save\n yi = self._descale_pred(yi, fi, si, img_size)\n y.append(yi)\n y = self._clip_augmented(y) # clip augmented tails\n return torch.cat(y, 1), None # augmented inference, train\n\n def _descale_pred(self, p, flips, scale, img_size):\n \"\"\"Rescales predictions after augmentation by adjusting scales and flips based on image dimensions.\"\"\"\n if self.inplace:\n p[..., :4] /= scale # de-scale\n if flips == 2:\n p[..., 1] = img_size[0] - p[..., 1] # de-flip ud\n elif flips == 3:\n p[..., 0] = img_size[1] - p[..., 0] # de-flip lr\n else:\n x, y, wh = p[..., 0:1] / scale, p[..., 1:2] / scale, p[..., 2:4] / scale # de-scale\n if flips == 2:\n y = img_size[0] - y # de-flip ud\n elif flips == 3:\n x = img_size[1] - x # de-flip lr\n p = torch.cat((x, y, wh, p[..., 4:]), -1)\n return p\n\n def _clip_augmented(self, y):\n \"\"\"Clips augmented inference tails from YOLOv3 predictions, affecting the first and last detection layers.\"\"\"\n nl = self.model[-1].nl # number of detection layers (P3-P5)\n g = sum(4**x for x in range(nl)) # grid points\n e = 1 # exclude layer count\n i = (y[0].shape[1] // g) * sum(4**x for x in range(e)) # indices\n y[0] = y[0][:, :-i] # large\n i = (y[-1].shape[1] // g) * sum(4 ** (nl - 1 - x) for x in range(e)) # indices\n y[-1] = y[-1][:, i:] # small\n return y\n\n def _initialize_biases(self, cf=None): # initialize biases into Detect(), cf is class frequency\n \"\"\"Initializes biases for objectness and classes in Detect() module; optionally uses class frequency `cf`.\"\"\"\n # cf = torch.bincount(torch.tensor(np.concatenate(dataset.labels, 0)[:, 0]).long(), minlength=nc) + 1.\n m = self.model[-1] # Detect() module\n for mi, s in zip(m.m, m.stride): # from\n b = mi.bias.view(m.na, -1) # conv.bias(255) to (3,85)\n b.data[:, 4] += math.log(8 / (640 / s) ** 2) # obj (8 objects per 640 image)\n b.data[:, 5 : 5 + m.nc] += (\n math.log(0.6 / (m.nc - 0.99999)) if cf is None else torch.log(cf / cf.sum())\n ) # cls\n mi.bias = torch.nn.Parameter(b.view(-1), requires_grad=True)\n\n\nModel = DetectionModel # retain YOLOv3 'Model' class for backwards compatibility\n\n\nclass SegmentationModel(DetectionModel):\n \"\"\"Implements a YOLOv3-based segmentation model with customizable configuration, channels, classes, and anchors.\"\"\"\n\n def __init__(self, cfg=\"yolov5s-seg.yaml\", ch=3, nc=None, anchors=None):\n \"\"\"Initializes a SegmentationModel with optional configuration, channel, class count, and anchors parameters.\"\"\"\n super().__init__(cfg, ch, nc, anchors)\n\n\nclass ClassificationModel(BaseModel):\n \"\"\"Implements a YOLOv3-based image classification model with configurable architecture and class count.\"\"\"\n\n def __init__(self, cfg=None, model=None, nc=1000, cutoff=10): # yaml, model, number of classes, cutoff index\n \"\"\"Initializes a ClassificationModel from a detection model or YAML, with configurable classes and cutoff.\"\"\"\n super().__init__()\n self._from_detection_model(model, nc, cutoff) if model is not None else self._from_yaml(cfg)\n\n def _from_detection_model(self, model, nc=1000, cutoff=10):\n \"\"\"Initializes a classification model from a YOLOv3 detection model, configuring classes and cutoff.\"\"\"\n if isinstance(model, DetectMultiBackend):\n model = model.model # unwrap DetectMultiBackend\n model.model = model.model[:cutoff] # backbone\n m = model.model[-1] # last layer\n ch = m.conv.in_channels if hasattr(m, \"conv\") else m.cv1.conv.in_channels # ch into module\n c = Classify(ch, nc) # Classify()\n c.i, c.f, c.type = m.i, m.f, \"models.common.Classify\" # index, from, type\n model.model[-1] = c # replace\n self.model = model.model\n self.stride = model.stride\n self.save = []\n self.nc = nc\n\n def _from_yaml(self, cfg):\n \"\"\"Creates a YOLOv3 classification model from a YAML file configuration.\"\"\"\n self.model = None\n\n\ndef parse_model(d, ch): # model_dict, input_channels(3)\n \"\"\"Parses a YOLOv3 model configuration from a dictionary and constructs the model.\"\"\"\n LOGGER.info(f\"\\n{'':>3}{'from':>18}{'n':>3}{'params':>10} {'module':<40}{'arguments':<30}\")\n anchors, nc, gd, gw, act = d[\"anchors\"], d[\"nc\"], d[\"depth_multiple\"], d[\"width_multiple\"], d.get(\"activation\")\n if act:\n Conv.default_act = eval(act) # redefine default activation, i.e. Conv.default_act = nn.SiLU()\n LOGGER.info(f\"{colorstr('activation:')} {act}\") # print\n na = (len(anchors[0]) // 2) if isinstance(anchors, list) else anchors # number of anchors\n no = na * (nc + 5) # number of outputs = anchors * (classes + 5)\n\n layers, save, c2 = [], [], ch[-1] # layers, savelist, ch out\n for i, (f, n, m, args) in enumerate(d[\"backbone\"] + d[\"head\"]): # from, number, module, args\n m = eval(m) if isinstance(m, str) else m # eval strings\n for j, a in enumerate(args):\n with contextlib.suppress(NameError):\n args[j] = eval(a) if isinstance(a, str) else a # eval strings\n\n n = n_ = max(round(n * gd), 1) if n > 1 else n # depth gain\n if m in {\n Conv,\n GhostConv,\n Bottleneck,\n GhostBottleneck,\n SPP,\n SPPF,\n DWConv,\n MixConv2d,\n Focus,\n CrossConv,\n BottleneckCSP,\n C3,\n C3TR,\n C3SPP,\n C3Ghost,\n nn.ConvTranspose2d,\n DWConvTranspose2d,\n C3x,\n }:\n c1, c2 = ch[f], args[0]\n if c2 != no: # if not output\n c2 = make_divisible(c2 * gw, 8)\n\n args = [c1, c2, *args[1:]]\n if m in {BottleneckCSP, C3, C3TR, C3Ghost, C3x}:\n args.insert(2, n) # number of repeats\n n = 1\n elif m is nn.BatchNorm2d:\n args = [ch[f]]\n elif m is Concat:\n c2 = sum(ch[x] for x in f)\n # TODO: channel, gw, gd\n elif m in {Detect, Segment}:\n args.append([ch[x] for x in f])\n if isinstance(args[1], int): # number of anchors\n args[1] = [list(range(args[1] * 2))] * len(f)\n if m is Segment:\n args[3] = make_divisible(args[3] * gw, 8)\n elif m is Contract:\n c2 = ch[f] * args[0] ** 2\n elif m is Expand:\n c2 = ch[f] // args[0] ** 2\n else:\n c2 = ch[f]\n\n m_ = nn.Sequential(*(m(*args) for _ in range(n))) if n > 1 else m(*args) # module\n t = str(m)[8:-2].replace(\"__main__.\", \"\") # module type\n np = sum(x.numel() for x in m_.parameters()) # number params\n m_.i, m_.f, m_.type, m_.np = i, f, t, np # attach index, 'from' index, type, number params\n LOGGER.info(f\"{i:>3}{f!s:>18}{n_:>3}{np:10.0f} {t:<40}{args!s:<30}\") # print\n save.extend(x % i for x in ([f] if isinstance(f, int) else f) if x != -1) # append to savelist\n layers.append(m_)\n if i == 0:\n ch = []\n ch.append(c2)\n return nn.Sequential(*layers), sorted(save)\n\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--cfg\", type=str, default=\"yolov5s.yaml\", help=\"model.yaml\")\n parser.add_argument(\"--batch-size\", type=int, default=1, help=\"total batch size for all GPUs\")\n parser.add_argument(\"--device\", default=\"\", help=\"cuda device, i.e. 0 or 0,1,2,3 or cpu\")\n parser.add_argument(\"--profile\", action=\"store_true\", help=\"profile model speed\")\n parser.add_argument(\"--line-profile\", action=\"store_true\", help=\"profile model speed layer by layer\")\n parser.add_argument(\"--test\", action=\"store_true\", help=\"test all yolo*.yaml\")\n opt = parser.parse_args()\n opt.cfg = check_yaml(opt.cfg) # check YAML\n print_args(vars(opt))\n device = select_device(opt.device)\n\n # Create model\n im = torch.rand(opt.batch_size, 3, 640, 640).to(device)\n model = Model(opt.cfg).to(device)\n\n # Options\n if opt.line_profile: # profile layer by layer\n model(im, profile=True)\n\n elif opt.profile: # profile forward-backward\n results = profile(input=im, ops=[model], n=3)\n\n elif opt.test: # test all models\n for cfg in Path(ROOT / \"models\").rglob(\"yolo*.yaml\"):\n try:\n _ = Model(cfg)\n except Exception as e:\n print(f\"Error in {cfg}: {e}\")\n\n else: # report fused model summary\n model.fuse()\n" }, { "path": "models/yolov3-spp.yaml", "content": "# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license\n\n# Parameters\nnc: 80 # number of classes\ndepth_multiple: 1.0 # model depth multiple\nwidth_multiple: 1.0 # layer channel multiple\nanchors:\n - [10, 13, 16, 30, 33, 23] # P3/8\n - [30, 61, 62, 45, 59, 119] # P4/16\n - [116, 90, 156, 198, 373, 326] # P5/32\n\n# darknet53 backbone\nbackbone:\n # [from, number, module, args]\n [\n [-1, 1, Conv, [32, 3, 1]], # 0\n [-1, 1, Conv, [64, 3, 2]], # 1-P1/2\n [-1, 1, Bottleneck, [64]],\n [-1, 1, Conv, [128, 3, 2]], # 3-P2/4\n [-1, 2, Bottleneck, [128]],\n [-1, 1, Conv, [256, 3, 2]], # 5-P3/8\n [-1, 8, Bottleneck, [256]],\n [-1, 1, Conv, [512, 3, 2]], # 7-P4/16\n [-1, 8, Bottleneck, [512]],\n [-1, 1, Conv, [1024, 3, 2]], # 9-P5/32\n [-1, 4, Bottleneck, [1024]], # 10\n ]\n\n# YOLOv3-SPP head\nhead: [\n [-1, 1, Bottleneck, [1024, False]],\n [-1, 1, SPP, [512, [5, 9, 13]]],\n [-1, 1, Conv, [1024, 3, 1]],\n [-1, 1, Conv, [512, 1, 1]],\n [-1, 1, Conv, [1024, 3, 1]], # 15 (P5/32-large)\n\n [-2, 1, Conv, [256, 1, 1]],\n [-1, 1, nn.Upsample, [None, 2, \"nearest\"]],\n [[-1, 8], 1, Concat, [1]], # cat backbone P4\n [-1, 1, Bottleneck, [512, False]],\n [-1, 1, Bottleneck, [512, False]],\n [-1, 1, Conv, [256, 1, 1]],\n [-1, 1, Conv, [512, 3, 1]], # 22 (P4/16-medium)\n\n [-2, 1, Conv, [128, 1, 1]],\n [-1, 1, nn.Upsample, [None, 2, \"nearest\"]],\n [[-1, 6], 1, Concat, [1]], # cat backbone P3\n [-1, 1, Bottleneck, [256, False]],\n [-1, 2, Bottleneck, [256, False]], # 27 (P3/8-small)\n\n [[27, 22, 15], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5)\n ]\n" }, { "path": "models/yolov3-tiny.yaml", "content": "# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license\n\n# Parameters\nnc: 80 # number of classes\ndepth_multiple: 1.0 # model depth multiple\nwidth_multiple: 1.0 # layer channel multiple\nanchors:\n - [10, 14, 23, 27, 37, 58] # P4/16\n - [81, 82, 135, 169, 344, 319] # P5/32\n\n# YOLOv3-tiny backbone\nbackbone:\n # [from, number, module, args]\n [\n [-1, 1, Conv, [16, 3, 1]], # 0\n [-1, 1, nn.MaxPool2d, [2, 2, 0]], # 1-P1/2\n [-1, 1, Conv, [32, 3, 1]],\n [-1, 1, nn.MaxPool2d, [2, 2, 0]], # 3-P2/4\n [-1, 1, Conv, [64, 3, 1]],\n [-1, 1, nn.MaxPool2d, [2, 2, 0]], # 5-P3/8\n [-1, 1, Conv, [128, 3, 1]],\n [-1, 1, nn.MaxPool2d, [2, 2, 0]], # 7-P4/16\n [-1, 1, Conv, [256, 3, 1]],\n [-1, 1, nn.MaxPool2d, [2, 2, 0]], # 9-P5/32\n [-1, 1, Conv, [512, 3, 1]],\n [-1, 1, nn.ZeroPad2d, [[0, 1, 0, 1]]], # 11\n [-1, 1, nn.MaxPool2d, [2, 1, 0]], # 12\n ]\n\n# YOLOv3-tiny head\nhead: [\n [-1, 1, Conv, [1024, 3, 1]],\n [-1, 1, Conv, [256, 1, 1]],\n [-1, 1, Conv, [512, 3, 1]], # 15 (P5/32-large)\n\n [-2, 1, Conv, [128, 1, 1]],\n [-1, 1, nn.Upsample, [None, 2, \"nearest\"]],\n [[-1, 8], 1, Concat, [1]], # cat backbone P4\n [-1, 1, Conv, [256, 3, 1]], # 19 (P4/16-medium)\n\n [[19, 15], 1, Detect, [nc, anchors]], # Detect(P4, P5)\n ]\n" }, { "path": "models/yolov3.yaml", "content": "# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license\n\n# Parameters\nnc: 80 # number of classes\ndepth_multiple: 1.0 # model depth multiple\nwidth_multiple: 1.0 # layer channel multiple\nanchors:\n - [10, 13, 16, 30, 33, 23] # P3/8\n - [30, 61, 62, 45, 59, 119] # P4/16\n - [116, 90, 156, 198, 373, 326] # P5/32\n\n# darknet53 backbone\nbackbone:\n # [from, number, module, args]\n [\n [-1, 1, Conv, [32, 3, 1]], # 0\n [-1, 1, Conv, [64, 3, 2]], # 1-P1/2\n [-1, 1, Bottleneck, [64]],\n [-1, 1, Conv, [128, 3, 2]], # 3-P2/4\n [-1, 2, Bottleneck, [128]],\n [-1, 1, Conv, [256, 3, 2]], # 5-P3/8\n [-1, 8, Bottleneck, [256]],\n [-1, 1, Conv, [512, 3, 2]], # 7-P4/16\n [-1, 8, Bottleneck, [512]],\n [-1, 1, Conv, [1024, 3, 2]], # 9-P5/32\n [-1, 4, Bottleneck, [1024]], # 10\n ]\n\n# YOLOv3 head\nhead: [\n [-1, 1, Bottleneck, [1024, False]],\n [-1, 1, Conv, [512, 1, 1]],\n [-1, 1, Conv, [1024, 3, 1]],\n [-1, 1, Conv, [512, 1, 1]],\n [-1, 1, Conv, [1024, 3, 1]], # 15 (P5/32-large)\n\n [-2, 1, Conv, [256, 1, 1]],\n [-1, 1, nn.Upsample, [None, 2, \"nearest\"]],\n [[-1, 8], 1, Concat, [1]], # cat backbone P4\n [-1, 1, Bottleneck, [512, False]],\n [-1, 1, Bottleneck, [512, False]],\n [-1, 1, Conv, [256, 1, 1]],\n [-1, 1, Conv, [512, 3, 1]], # 22 (P4/16-medium)\n\n [-2, 1, Conv, [128, 1, 1]],\n [-1, 1, nn.Upsample, [None, 2, \"nearest\"]],\n [[-1, 6], 1, Concat, [1]], # cat backbone P3\n [-1, 1, Bottleneck, [256, False]],\n [-1, 2, Bottleneck, [256, False]], # 27 (P3/8-small)\n\n [[27, 22, 15], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5)\n ]\n" }, { "path": "models/yolov5l.yaml", "content": "# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license\n\n# Parameters\nnc: 80 # number of classes\ndepth_multiple: 1.0 # model depth multiple\nwidth_multiple: 1.0 # layer channel multiple\nanchors:\n - [10, 13, 16, 30, 33, 23] # P3/8\n - [30, 61, 62, 45, 59, 119] # P4/16\n - [116, 90, 156, 198, 373, 326] # P5/32\n\n# YOLOv5 v6.0 backbone\nbackbone:\n # [from, number, module, args]\n [\n [-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2\n [-1, 1, Conv, [128, 3, 2]], # 1-P2/4\n [-1, 3, C3, [128]],\n [-1, 1, Conv, [256, 3, 2]], # 3-P3/8\n [-1, 6, C3, [256]],\n [-1, 1, Conv, [512, 3, 2]], # 5-P4/16\n [-1, 9, C3, [512]],\n [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32\n [-1, 3, C3, [1024]],\n [-1, 1, SPPF, [1024, 5]], # 9\n ]\n\n# YOLOv5 v6.0 head\nhead: [\n [-1, 1, Conv, [512, 1, 1]],\n [-1, 1, nn.Upsample, [None, 2, \"nearest\"]],\n [[-1, 6], 1, Concat, [1]], # cat backbone P4\n [-1, 3, C3, [512, False]], # 13\n\n [-1, 1, Conv, [256, 1, 1]],\n [-1, 1, nn.Upsample, [None, 2, \"nearest\"]],\n [[-1, 4], 1, Concat, [1]], # cat backbone P3\n [-1, 3, C3, [256, False]], # 17 (P3/8-small)\n\n [-1, 1, Conv, [256, 3, 2]],\n [[-1, 14], 1, Concat, [1]], # cat head P4\n [-1, 3, C3, [512, False]], # 20 (P4/16-medium)\n\n [-1, 1, Conv, [512, 3, 2]],\n [[-1, 10], 1, Concat, [1]], # cat head P5\n [-1, 3, C3, [1024, False]], # 23 (P5/32-large)\n\n [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5)\n ]\n" }, { "path": "models/yolov5m.yaml", "content": "# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license\n\n# Parameters\nnc: 80 # number of classes\ndepth_multiple: 0.67 # model depth multiple\nwidth_multiple: 0.75 # layer channel multiple\nanchors:\n - [10, 13, 16, 30, 33, 23] # P3/8\n - [30, 61, 62, 45, 59, 119] # P4/16\n - [116, 90, 156, 198, 373, 326] # P5/32\n\n# YOLOv5 v6.0 backbone\nbackbone:\n # [from, number, module, args]\n [\n [-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2\n [-1, 1, Conv, [128, 3, 2]], # 1-P2/4\n [-1, 3, C3, [128]],\n [-1, 1, Conv, [256, 3, 2]], # 3-P3/8\n [-1, 6, C3, [256]],\n [-1, 1, Conv, [512, 3, 2]], # 5-P4/16\n [-1, 9, C3, [512]],\n [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32\n [-1, 3, C3, [1024]],\n [-1, 1, SPPF, [1024, 5]], # 9\n ]\n\n# YOLOv5 v6.0 head\nhead: [\n [-1, 1, Conv, [512, 1, 1]],\n [-1, 1, nn.Upsample, [None, 2, \"nearest\"]],\n [[-1, 6], 1, Concat, [1]], # cat backbone P4\n [-1, 3, C3, [512, False]], # 13\n\n [-1, 1, Conv, [256, 1, 1]],\n [-1, 1, nn.Upsample, [None, 2, \"nearest\"]],\n [[-1, 4], 1, Concat, [1]], # cat backbone P3\n [-1, 3, C3, [256, False]], # 17 (P3/8-small)\n\n [-1, 1, Conv, [256, 3, 2]],\n [[-1, 14], 1, Concat, [1]], # cat head P4\n [-1, 3, C3, [512, False]], # 20 (P4/16-medium)\n\n [-1, 1, Conv, [512, 3, 2]],\n [[-1, 10], 1, Concat, [1]], # cat head P5\n [-1, 3, C3, [1024, False]], # 23 (P5/32-large)\n\n [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5)\n ]\n" }, { "path": "models/yolov5n.yaml", "content": "# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license\n\n# Parameters\nnc: 80 # number of classes\ndepth_multiple: 0.33 # model depth multiple\nwidth_multiple: 0.25 # layer channel multiple\nanchors:\n - [10, 13, 16, 30, 33, 23] # P3/8\n - [30, 61, 62, 45, 59, 119] # P4/16\n - [116, 90, 156, 198, 373, 326] # P5/32\n\n# YOLOv5 v6.0 backbone\nbackbone:\n # [from, number, module, args]\n [\n [-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2\n [-1, 1, Conv, [128, 3, 2]], # 1-P2/4\n [-1, 3, C3, [128]],\n [-1, 1, Conv, [256, 3, 2]], # 3-P3/8\n [-1, 6, C3, [256]],\n [-1, 1, Conv, [512, 3, 2]], # 5-P4/16\n [-1, 9, C3, [512]],\n [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32\n [-1, 3, C3, [1024]],\n [-1, 1, SPPF, [1024, 5]], # 9\n ]\n\n# YOLOv5 v6.0 head\nhead: [\n [-1, 1, Conv, [512, 1, 1]],\n [-1, 1, nn.Upsample, [None, 2, \"nearest\"]],\n [[-1, 6], 1, Concat, [1]], # cat backbone P4\n [-1, 3, C3, [512, False]], # 13\n\n [-1, 1, Conv, [256, 1, 1]],\n [-1, 1, nn.Upsample, [None, 2, \"nearest\"]],\n [[-1, 4], 1, Concat, [1]], # cat backbone P3\n [-1, 3, C3, [256, False]], # 17 (P3/8-small)\n\n [-1, 1, Conv, [256, 3, 2]],\n [[-1, 14], 1, Concat, [1]], # cat head P4\n [-1, 3, C3, [512, False]], # 20 (P4/16-medium)\n\n [-1, 1, Conv, [512, 3, 2]],\n [[-1, 10], 1, Concat, [1]], # cat head P5\n [-1, 3, C3, [1024, False]], # 23 (P5/32-large)\n\n [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5)\n ]\n" }, { "path": "models/yolov5s.yaml", "content": "# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license\n\n# Parameters\nnc: 80 # number of classes\ndepth_multiple: 0.33 # model depth multiple\nwidth_multiple: 0.50 # layer channel multiple\nanchors:\n - [10, 13, 16, 30, 33, 23] # P3/8\n - [30, 61, 62, 45, 59, 119] # P4/16\n - [116, 90, 156, 198, 373, 326] # P5/32\n\n# YOLOv5 v6.0 backbone\nbackbone:\n # [from, number, module, args]\n [\n [-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2\n [-1, 1, Conv, [128, 3, 2]], # 1-P2/4\n [-1, 3, C3, [128]],\n [-1, 1, Conv, [256, 3, 2]], # 3-P3/8\n [-1, 6, C3, [256]],\n [-1, 1, Conv, [512, 3, 2]], # 5-P4/16\n [-1, 9, C3, [512]],\n [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32\n [-1, 3, C3, [1024]],\n [-1, 1, SPPF, [1024, 5]], # 9\n ]\n\n# YOLOv5 v6.0 head\nhead: [\n [-1, 1, Conv, [512, 1, 1]],\n [-1, 1, nn.Upsample, [None, 2, \"nearest\"]],\n [[-1, 6], 1, Concat, [1]], # cat backbone P4\n [-1, 3, C3, [512, False]], # 13\n\n [-1, 1, Conv, [256, 1, 1]],\n [-1, 1, nn.Upsample, [None, 2, \"nearest\"]],\n [[-1, 4], 1, Concat, [1]], # cat backbone P3\n [-1, 3, C3, [256, False]], # 17 (P3/8-small)\n\n [-1, 1, Conv, [256, 3, 2]],\n [[-1, 14], 1, Concat, [1]], # cat head P4\n [-1, 3, C3, [512, False]], # 20 (P4/16-medium)\n\n [-1, 1, Conv, [512, 3, 2]],\n [[-1, 10], 1, Concat, [1]], # cat head P5\n [-1, 3, C3, [1024, False]], # 23 (P5/32-large)\n\n [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5)\n ]\n" }, { "path": "models/yolov5x.yaml", "content": "# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license\n\n# Parameters\nnc: 80 # number of classes\ndepth_multiple: 1.33 # model depth multiple\nwidth_multiple: 1.25 # layer channel multiple\nanchors:\n - [10, 13, 16, 30, 33, 23] # P3/8\n - [30, 61, 62, 45, 59, 119] # P4/16\n - [116, 90, 156, 198, 373, 326] # P5/32\n\n# YOLOv5 v6.0 backbone\nbackbone:\n # [from, number, module, args]\n [\n [-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2\n [-1, 1, Conv, [128, 3, 2]], # 1-P2/4\n [-1, 3, C3, [128]],\n [-1, 1, Conv, [256, 3, 2]], # 3-P3/8\n [-1, 6, C3, [256]],\n [-1, 1, Conv, [512, 3, 2]], # 5-P4/16\n [-1, 9, C3, [512]],\n [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32\n [-1, 3, C3, [1024]],\n [-1, 1, SPPF, [1024, 5]], # 9\n ]\n\n# YOLOv5 v6.0 head\nhead: [\n [-1, 1, Conv, [512, 1, 1]],\n [-1, 1, nn.Upsample, [None, 2, \"nearest\"]],\n [[-1, 6], 1, Concat, [1]], # cat backbone P4\n [-1, 3, C3, [512, False]], # 13\n\n [-1, 1, Conv, [256, 1, 1]],\n [-1, 1, nn.Upsample, [None, 2, \"nearest\"]],\n [[-1, 4], 1, Concat, [1]], # cat backbone P3\n [-1, 3, C3, [256, False]], # 17 (P3/8-small)\n\n [-1, 1, Conv, [256, 3, 2]],\n [[-1, 14], 1, Concat, [1]], # cat head P4\n [-1, 3, C3, [512, False]], # 20 (P4/16-medium)\n\n [-1, 1, Conv, [512, 3, 2]],\n [[-1, 10], 1, Concat, [1]], # cat head P5\n [-1, 3, C3, [1024, False]], # 23 (P5/32-large)\n\n [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5)\n ]\n" }, { "path": "pyproject.toml", "content": "# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license\n\n# Overview:\n# This pyproject.toml file manages the build, packaging, and distribution of the Ultralytics library.\n# It defines essential project metadata, dependencies, and settings used to develop and deploy the library.\n\n# Key Sections:\n# - [build-system]: Specifies the build requirements and backend (e.g., setuptools, wheel).\n# - [project]: Includes details like name, version, description, authors, dependencies and more.\n# - [project.optional-dependencies]: Provides additional, optional packages for extended features.\n# - [tool.*]: Configures settings for various tools (pytest, yapf, etc.) used in the project.\n\n# Installation:\n# The Ultralytics library can be installed using the command: 'pip install ultralytics'\n# For development purposes, you can install the package in editable mode with: 'pip install -e .'\n# This approach allows for real-time code modifications without the need for re-installation.\n\n# Documentation:\n# For comprehensive documentation and usage instructions, visit: https://docs.ultralytics.com\n\n[build-system]\nrequires = [\"setuptools>=43.0.0\", \"wheel\"]\nbuild-backend = \"setuptools.build_meta\"\n\n# Project settings -----------------------------------------------------------------------------------------------------\n[project]\nname = \"YOLOv3\"\ndescription = \"Ultralytics YOLOv3 for object detection.\"\nreadme = \"README.md\"\nrequires-python = \">=3.8\"\nlicense = { \"text\" = \"AGPL-3.0\" }\nkeywords = [\"machine-learning\", \"deep-learning\", \"computer-vision\", \"ML\", \"DL\", \"AI\", \"YOLO\", \"YOLOv3\", \"YOLOv5\", \"YOLOv8\", \"HUB\", \"Ultralytics\"]\nauthors = [\n { name = \"Glenn Jocher\" },\n { name = \"Ayush Chaurasia\" },\n { name = \"Jing Qiu\" }\n]\nmaintainers = [\n { name = \"Glenn Jocher\" },\n { name = \"Ayush Chaurasia\" },\n { name = \"Jing Qiu\" }\n]\nclassifiers = [\n \"Development Status :: 4 - Beta\",\n \"Intended Audience :: Developers\",\n \"Intended Audience :: Education\",\n \"Intended Audience :: Science/Research\",\n \"License :: OSI Approved :: GNU Affero General Public License v3 or later (AGPLv3+)\",\n \"Programming Language :: Python :: 3\",\n \"Programming Language :: Python :: 3.8\",\n \"Programming Language :: Python :: 3.9\",\n \"Programming Language :: Python :: 3.10\",\n \"Programming Language :: Python :: 3.11\",\n \"Topic :: Software Development\",\n \"Topic :: Scientific/Engineering\",\n \"Topic :: Scientific/Engineering :: Artificial Intelligence\",\n \"Topic :: Scientific/Engineering :: Image Recognition\",\n \"Operating System :: POSIX :: Linux\",\n \"Operating System :: MacOS\",\n \"Operating System :: Microsoft :: Windows\",\n]\n\n# Required dependencies ------------------------------------------------------------------------------------------------\ndependencies = [\n \"matplotlib>=3.3.0\",\n \"numpy>=1.22.2\",\n \"opencv-python>=4.6.0\",\n \"pillow>=7.1.2\",\n \"pyyaml>=5.3.1\",\n \"requests>=2.23.0\",\n \"scipy>=1.4.1\",\n \"torch>=1.8.0\",\n \"torchvision>=0.9.0\",\n \"tqdm>=4.64.0\", # progress bars\n \"psutil\", # system utilization\n \"py-cpuinfo\", # display CPU info\n \"thop>=0.1.1\", # FLOPs computation\n \"pandas>=1.1.4\",\n \"packaging\", # general utilities\n \"seaborn>=0.11.0\", # plotting\n \"ultralytics>=8.2.64\",\n]\n\n# Optional dependencies ------------------------------------------------------------------------------------------------\n[project.optional-dependencies]\ndev = [\n \"ipython\",\n \"check-manifest\",\n \"pre-commit\",\n \"pytest\",\n \"pytest-cov\",\n \"coverage[toml]\",\n \"mkdocs-material\",\n \"mkdocstrings[python]\",\n \"mkdocs-redirects\", # for 301 redirects\n \"mkdocs-ultralytics-plugin>=0.0.34\", # for meta descriptions and images, dates and authors\n]\nexport = [\n \"onnx>=1.12.0\", # ONNX export\n \"coremltools>=7.0; platform_system != 'Windows'\", # CoreML only supported on macOS and Linux\n \"openvino-dev>=2023.0\", # OpenVINO export\n \"tensorflow>=2.0.0,<=2.19.0\", # TF bug https://github.com/ultralytics/ultralytics/issues/5161\n \"tensorflowjs>=3.9.0\", # TF.js export, automatically installs tensorflow\n]\n# tensorflow>=2.4.1,<=2.13.1 # TF exports (-cpu, -aarch64, -macos)\n# tflite-support # for TFLite model metadata\n# scikit-learn==0.19.2 # CoreML quantization\n# nvidia-pyindex # TensorRT export\n# nvidia-tensorrt # TensorRT export\nlogging = [\n \"comet\", # https://docs.ultralytics.com/integrations/comet/\n \"tensorboard>=2.13.0\",\n \"dvclive>=2.12.0\",\n]\nextra = [\n \"ipython\", # interactive notebook\n \"albumentations>=1.0.3\", # training augmentations\n \"pycocotools>=2.0.6\", # COCO mAP\n]\n\n[project.urls]\n\"Bug Reports\" = \"https://github.com/ultralytics/yolov3/issues\"\n\"Funding\" = \"https://ultralytics.com\"\n\"Source\" = \"https://github.com/ultralytics/yolov3/\"\n\n# Tools settings -------------------------------------------------------------------------------------------------------\n[tool.pytest]\nnorecursedirs = [\".git\", \"dist\", \"build\"]\naddopts = \"--doctest-modules --durations=30 --color=yes\"\n\n[tool.isort]\nline_length = 120\nmulti_line_output = 0\n\n[tool.ruff]\nline-length = 120\n\n[tool.docformatter]\nwrap-summaries = 120\nwrap-descriptions = 120\nin-place = true\npre-summary-newline = true\nclose-quotes-on-newline = true\n\n[tool.codespell]\nignore-words-list = \"crate,nd,strack,dota,ane,segway,fo,gool,winn,commend\"\nskip = '*.csv,*venv*,docs/??/,docs/mkdocs_??.yml'\n" }, { "path": "requirements.txt", "content": "# YOLOv3 requirements\n# Usage: pip install -r requirements.txt\n# Python >= 3.8 recommended\n\n# Base ------------------------------------------------------------------------\ngitpython>=3.1.30 # Git repo interaction for training/versioning\nmatplotlib>=3.5.0 # Plotting results and graphs\nnumpy>=1.23.5 # Fundamental for array/matrix operations\nopencv-python>=4.1.1 # Image/video processing\nPillow>=10.3.0 # Image reading/writing support\npsutil>=5.9.0 # System monitoring (RAM, CPU, etc.)\nPyYAML>=5.3.1 # Reading configs (yaml files)\nrequests>=2.32.2 # HTTP requests, used in model hub/downloads\nscipy>=1.4.1 # Scientific computing (e.g. IoU, metrics)\nthop>=0.1.1 # Model profiling - FLOPs and parameter count\ntorch>=1.8.0 # Core PyTorch for training/inference\ntorchvision>=0.9.0 # Torch utilities for vision (transforms, datasets)\ntqdm>=4.66.3 # Progress bar in CLI\nultralytics>=8.2.64 # YOLO framework library (models, training, utils)\n# protobuf<=3.20.1 # For ONNX/TensorFlow export compatibility\n\n# Logging ---------------------------------------------------------------------\n# tensorboard>=2.4.1 # Visual logging (scalars, images)\n# clearml>=1.2.0 # Experiment tracking\n# comet # Another logging/monitoring tool\n\n# Plotting --------------------------------------------------------------------\npandas>=1.1.4 # Data handling and manipulation\nseaborn>=0.11.0 # Statistical data visualization (confusion matrix, etc.)\n\n# Export (optional) -----------------------------------------------------------\n# coremltools>=6.0 # Apple CoreML export support\n# onnx>=1.10.0 # ONNX export support\n# onnx-simplifier>=0.4.1 # Optimizes ONNX models\n# nvidia-pyindex # Required for installing NVIDIA TensorRT\n# nvidia-tensorrt # TensorRT export and inference\n# scikit-learn<=1.1.2 # Used in CoreML quantization (used in older code)\n# tensorflow>=2.4.0 # TensorFlow export\n# tensorflowjs>=3.9.0 # TensorFlow.js export\n# openvino-dev>=2023.0 # Intel OpenVINO export\n\n# Deploy ----------------------------------------------------------------------\nsetuptools>=70.0.0 # Required to avoid known vulnerabilities\n# tritonclient[all]~=2.24.0 # NVIDIA Triton server deployment (optional)\n\n# Extras ----------------------------------------------------------------------\n# ipython # Enhanced interactive shell\n# mss # Screenshot capturing for inference UI\n# albumentations>=1.0.3 # Powerful image augmentation library\n# pycocotools>=2.0.6 # COCO dataset metrics (mAP, etc.)\n" }, { "path": "segment/predict.py", "content": "# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license\n\"\"\"\nRun YOLOv3 segmentation inference on images, videos, directories, streams, etc.\n\nUsage - sources:\n $ python segment/predict.py --weights yolov5s-seg.pt --source 0 # webcam\n img.jpg # image\n vid.mp4 # video\n screen # screenshot\n path/ # directory\n list.txt # list of images\n list.streams # list of streams\n 'path/*.jpg' # glob\n 'https://youtu.be/LNwODJXcvt4' # YouTube\n 'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream\n\nUsage - formats:\n $ python segment/predict.py --weights yolov5s-seg.pt # PyTorch\n yolov5s-seg.torchscript # TorchScript\n yolov5s-seg.onnx # ONNX Runtime or OpenCV DNN with --dnn\n yolov5s-seg_openvino_model # OpenVINO\n yolov5s-seg.engine # TensorRT\n yolov5s-seg.mlmodel # CoreML (macOS-only)\n yolov5s-seg_saved_model # TensorFlow SavedModel\n yolov5s-seg.pb # TensorFlow GraphDef\n yolov5s-seg.tflite # TensorFlow Lite\n yolov5s-seg_edgetpu.tflite # TensorFlow Edge TPU\n yolov5s-seg_paddle_model # PaddlePaddle\n\"\"\"\n\nimport argparse\nimport os\nimport platform\nimport sys\nfrom pathlib import Path\n\nimport torch\n\nFILE = Path(__file__).resolve()\nROOT = FILE.parents[1] # YOLOv3 root directory\nif str(ROOT) not in sys.path:\n sys.path.append(str(ROOT)) # add ROOT to PATH\nROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative\n\nfrom ultralytics.utils.plotting import Annotator, colors, save_one_box\n\nfrom models.common import DetectMultiBackend\nfrom utils.dataloaders import IMG_FORMATS, VID_FORMATS, LoadImages, LoadScreenshots, LoadStreams\nfrom utils.general import (\n LOGGER,\n Profile,\n check_file,\n check_img_size,\n check_imshow,\n check_requirements,\n colorstr,\n cv2,\n increment_path,\n non_max_suppression,\n print_args,\n scale_boxes,\n scale_segments,\n strip_optimizer,\n)\nfrom utils.segment.general import masks2segments, process_mask, process_mask_native\nfrom utils.torch_utils import select_device, smart_inference_mode\n\n\n@smart_inference_mode()\ndef run(\n weights=ROOT / \"yolov5s-seg.pt\", # model.pt path(s)\n source=ROOT / \"data/images\", # file/dir/URL/glob/screen/0(webcam)\n data=ROOT / \"data/coco128.yaml\", # dataset.yaml path\n imgsz=(640, 640), # inference size (height, width)\n conf_thres=0.25, # confidence threshold\n iou_thres=0.45, # NMS IOU threshold\n max_det=1000, # maximum detections per image\n device=\"\", # cuda device, i.e. 0 or 0,1,2,3 or cpu\n view_img=False, # show results\n save_txt=False, # save results to *.txt\n save_conf=False, # save confidences in --save-txt labels\n save_crop=False, # save cropped prediction boxes\n nosave=False, # do not save images/videos\n classes=None, # filter by class: --class 0, or --class 0 2 3\n agnostic_nms=False, # class-agnostic NMS\n augment=False, # augmented inference\n visualize=False, # visualize features\n update=False, # update all models\n project=ROOT / \"runs/predict-seg\", # save results to project/name\n name=\"exp\", # save results to project/name\n exist_ok=False, # existing project/name ok, do not increment\n line_thickness=3, # bounding box thickness (pixels)\n hide_labels=False, # hide labels\n hide_conf=False, # hide confidences\n half=False, # use FP16 half-precision inference\n dnn=False, # use OpenCV DNN for ONNX inference\n vid_stride=1, # video frame-rate stride\n retina_masks=False,\n):\n \"\"\"Performs YOLOv3 segmentation inference on various sources such as images, videos, and streams.\"\"\"\n source = str(source)\n save_img = not nosave and not source.endswith(\".txt\") # save inference images\n is_file = Path(source).suffix[1:] in (IMG_FORMATS + VID_FORMATS)\n is_url = source.lower().startswith((\"rtsp://\", \"rtmp://\", \"http://\", \"https://\"))\n webcam = source.isnumeric() or source.endswith(\".streams\") or (is_url and not is_file)\n screenshot = source.lower().startswith(\"screen\")\n if is_url and is_file:\n source = check_file(source) # download\n\n # Directories\n save_dir = increment_path(Path(project) / name, exist_ok=exist_ok) # increment run\n (save_dir / \"labels\" if save_txt else save_dir).mkdir(parents=True, exist_ok=True) # make dir\n\n # Load model\n device = select_device(device)\n model = DetectMultiBackend(weights, device=device, dnn=dnn, data=data, fp16=half)\n stride, names, pt = model.stride, model.names, model.pt\n imgsz = check_img_size(imgsz, s=stride) # check image size\n\n # Dataloader\n bs = 1 # batch_size\n if webcam:\n view_img = check_imshow(warn=True)\n dataset = LoadStreams(source, img_size=imgsz, stride=stride, auto=pt, vid_stride=vid_stride)\n bs = len(dataset)\n elif screenshot:\n dataset = LoadScreenshots(source, img_size=imgsz, stride=stride, auto=pt)\n else:\n dataset = LoadImages(source, img_size=imgsz, stride=stride, auto=pt, vid_stride=vid_stride)\n vid_path, vid_writer = [None] * bs, [None] * bs\n\n # Run inference\n model.warmup(imgsz=(1 if pt else bs, 3, *imgsz)) # warmup\n seen, windows, dt = 0, [], (Profile(), Profile(), Profile())\n for path, im, im0s, vid_cap, s in dataset:\n with dt[0]:\n im = torch.from_numpy(im).to(model.device)\n im = im.half() if model.fp16 else im.float() # uint8 to fp16/32\n im /= 255 # 0 - 255 to 0.0 - 1.0\n if len(im.shape) == 3:\n im = im[None] # expand for batch dim\n\n # Inference\n with dt[1]:\n visualize = increment_path(save_dir / Path(path).stem, mkdir=True) if visualize else False\n pred, proto = model(im, augment=augment, visualize=visualize)[:2]\n\n # NMS\n with dt[2]:\n pred = non_max_suppression(pred, conf_thres, iou_thres, classes, agnostic_nms, max_det=max_det, nm=32)\n\n # Second-stage classifier (optional)\n # pred = utils.general.apply_classifier(pred, classifier_model, im, im0s)\n\n # Process predictions\n for i, det in enumerate(pred): # per image\n seen += 1\n if webcam: # batch_size >= 1\n p, im0, frame = path[i], im0s[i].copy(), dataset.count\n s += f\"{i}: \"\n else:\n p, im0, frame = path, im0s.copy(), getattr(dataset, \"frame\", 0)\n\n p = Path(p) # to Path\n save_path = str(save_dir / p.name) # im.jpg\n txt_path = str(save_dir / \"labels\" / p.stem) + (\"\" if dataset.mode == \"image\" else f\"_{frame}\") # im.txt\n s += \"{:g}x{:g} \".format(*im.shape[2:]) # print string\n imc = im0.copy() if save_crop else im0 # for save_crop\n annotator = Annotator(im0, line_width=line_thickness, example=str(names))\n if len(det):\n if retina_masks:\n # scale bbox first the crop masks\n det[:, :4] = scale_boxes(im.shape[2:], det[:, :4], im0.shape).round() # rescale boxes to im0 size\n masks = process_mask_native(proto[i], det[:, 6:], det[:, :4], im0.shape[:2]) # HWC\n else:\n masks = process_mask(proto[i], det[:, 6:], det[:, :4], im.shape[2:], upsample=True) # HWC\n det[:, :4] = scale_boxes(im.shape[2:], det[:, :4], im0.shape).round() # rescale boxes to im0 size\n\n # Segments\n if save_txt:\n segments = [\n scale_segments(im0.shape if retina_masks else im.shape[2:], x, im0.shape, normalize=True)\n for x in reversed(masks2segments(masks))\n ]\n\n # Print results\n for c in det[:, 5].unique():\n n = (det[:, 5] == c).sum() # detections per class\n s += f\"{n} {names[int(c)]}{'s' * (n > 1)}, \" # add to string\n\n # Mask plotting\n annotator.masks(\n masks,\n colors=[colors(x, True) for x in det[:, 5]],\n im_gpu=torch.as_tensor(im0, dtype=torch.float16).to(device).permute(2, 0, 1).flip(0).contiguous()\n / 255\n if retina_masks\n else im[i],\n )\n\n # Write results\n for j, (*xyxy, conf, cls) in enumerate(reversed(det[:, :6])):\n if save_txt: # Write to file\n seg = segments[j].reshape(-1) # (n,2) to (n*2)\n line = (cls, *seg, conf) if save_conf else (cls, *seg) # label format\n with open(f\"{txt_path}.txt\", \"a\") as f:\n f.write((\"%g \" * len(line)).rstrip() % line + \"\\n\")\n\n if save_img or save_crop or view_img: # Add bbox to image\n c = int(cls) # integer class\n label = None if hide_labels else (names[c] if hide_conf else f\"{names[c]} {conf:.2f}\")\n annotator.box_label(xyxy, label, color=colors(c, True))\n # annotator.draw.polygon(segments[j], outline=colors(c, True), width=3)\n if save_crop:\n save_one_box(xyxy, imc, file=save_dir / \"crops\" / names[c] / f\"{p.stem}.jpg\", BGR=True)\n\n # Stream results\n im0 = annotator.result()\n if view_img:\n if platform.system() == \"Linux\" and p not in windows:\n windows.append(p)\n cv2.namedWindow(str(p), cv2.WINDOW_NORMAL | cv2.WINDOW_KEEPRATIO) # allow window resize (Linux)\n cv2.resizeWindow(str(p), im0.shape[1], im0.shape[0])\n cv2.imshow(str(p), im0)\n if cv2.waitKey(1) == ord(\"q\"): # 1 millisecond\n exit()\n\n # Save results (image with detections)\n if save_img:\n if dataset.mode == \"image\":\n cv2.imwrite(save_path, im0)\n else: # 'video' or 'stream'\n if vid_path[i] != save_path: # new video\n vid_path[i] = save_path\n if isinstance(vid_writer[i], cv2.VideoWriter):\n vid_writer[i].release() # release previous video writer\n if vid_cap: # video\n fps = vid_cap.get(cv2.CAP_PROP_FPS)\n w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH))\n h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))\n else: # stream\n fps, w, h = 30, im0.shape[1], im0.shape[0]\n save_path = str(Path(save_path).with_suffix(\".mp4\")) # force *.mp4 suffix on results videos\n vid_writer[i] = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*\"mp4v\"), fps, (w, h))\n vid_writer[i].write(im0)\n\n # Print time (inference-only)\n LOGGER.info(f\"{s}{'' if len(det) else '(no detections), '}{dt[1].dt * 1e3:.1f}ms\")\n\n # Print results\n t = tuple(x.t / seen * 1e3 for x in dt) # speeds per image\n LOGGER.info(f\"Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {(1, 3, *imgsz)}\" % t)\n if save_txt or save_img:\n s = f\"\\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}\" if save_txt else \"\"\n LOGGER.info(f\"Results saved to {colorstr('bold', save_dir)}{s}\")\n if update:\n strip_optimizer(weights[0]) # update model (to fix SourceChangeWarning)\n\n\ndef parse_opt():\n \"\"\"Parses command-line options for YOLOv5 including model paths, source, inference size, and saving options.\"\"\"\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--weights\", nargs=\"+\", type=str, default=ROOT / \"yolov5s-seg.pt\", help=\"model path(s)\")\n parser.add_argument(\"--source\", type=str, default=ROOT / \"data/images\", help=\"file/dir/URL/glob/screen/0(webcam)\")\n parser.add_argument(\"--data\", type=str, default=ROOT / \"data/coco128.yaml\", help=\"(optional) dataset.yaml path\")\n parser.add_argument(\"--imgsz\", \"--img\", \"--img-size\", nargs=\"+\", type=int, default=[640], help=\"inference size h,w\")\n parser.add_argument(\"--conf-thres\", type=float, default=0.25, help=\"confidence threshold\")\n parser.add_argument(\"--iou-thres\", type=float, default=0.45, help=\"NMS IoU threshold\")\n parser.add_argument(\"--max-det\", type=int, default=1000, help=\"maximum detections per image\")\n parser.add_argument(\"--device\", default=\"\", help=\"cuda device, i.e. 0 or 0,1,2,3 or cpu\")\n parser.add_argument(\"--view-img\", action=\"store_true\", help=\"show results\")\n parser.add_argument(\"--save-txt\", action=\"store_true\", help=\"save results to *.txt\")\n parser.add_argument(\"--save-conf\", action=\"store_true\", help=\"save confidences in --save-txt labels\")\n parser.add_argument(\"--save-crop\", action=\"store_true\", help=\"save cropped prediction boxes\")\n parser.add_argument(\"--nosave\", action=\"store_true\", help=\"do not save images/videos\")\n parser.add_argument(\"--classes\", nargs=\"+\", type=int, help=\"filter by class: --classes 0, or --classes 0 2 3\")\n parser.add_argument(\"--agnostic-nms\", action=\"store_true\", help=\"class-agnostic NMS\")\n parser.add_argument(\"--augment\", action=\"store_true\", help=\"augmented inference\")\n parser.add_argument(\"--visualize\", action=\"store_true\", help=\"visualize features\")\n parser.add_argument(\"--update\", action=\"store_true\", help=\"update all models\")\n parser.add_argument(\"--project\", default=ROOT / \"runs/predict-seg\", help=\"save results to project/name\")\n parser.add_argument(\"--name\", default=\"exp\", help=\"save results to project/name\")\n parser.add_argument(\"--exist-ok\", action=\"store_true\", help=\"existing project/name ok, do not increment\")\n parser.add_argument(\"--line-thickness\", default=3, type=int, help=\"bounding box thickness (pixels)\")\n parser.add_argument(\"--hide-labels\", default=False, action=\"store_true\", help=\"hide labels\")\n parser.add_argument(\"--hide-conf\", default=False, action=\"store_true\", help=\"hide confidences\")\n parser.add_argument(\"--half\", action=\"store_true\", help=\"use FP16 half-precision inference\")\n parser.add_argument(\"--dnn\", action=\"store_true\", help=\"use OpenCV DNN for ONNX inference\")\n parser.add_argument(\"--vid-stride\", type=int, default=1, help=\"video frame-rate stride\")\n parser.add_argument(\"--retina-masks\", action=\"store_true\", help=\"whether to plot masks in native resolution\")\n opt = parser.parse_args()\n opt.imgsz *= 2 if len(opt.imgsz) == 1 else 1 # expand\n print_args(vars(opt))\n return opt\n\n\ndef main(opt):\n \"\"\"Executes model inference based on parsed options, checking requirements and excluding specified packages.\"\"\"\n check_requirements(ROOT / \"requirements.txt\", exclude=(\"tensorboard\", \"thop\"))\n run(**vars(opt))\n\n\nif __name__ == \"__main__\":\n opt = parse_opt()\n main(opt)\n" }, { "path": "segment/train.py", "content": "# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license\n\"\"\"\nTrain a YOLOv3 segment model on a segment dataset Models and datasets download automatically from the latest YOLOv3\nrelease.\n\nUsage - Single-GPU training:\n $ python segment/train.py --data coco128-seg.yaml --weights yolov5s-seg.pt --img 640 # from pretrained (recommended)\n $ python segment/train.py --data coco128-seg.yaml --weights '' --cfg yolov5s-seg.yaml --img 640 # from scratch\n\nUsage - Multi-GPU DDP training:\n $ python -m torch.distributed.run --nproc_per_node 4 --master_port 1 segment/train.py --data coco128-seg.yaml --weights yolov5s-seg.pt --img 640 --device 0,1,2,3\n\nModels: https://github.com/ultralytics/yolov5/tree/master/models\nDatasets: https://github.com/ultralytics/yolov5/tree/master/data\nTutorial: https://docs.ultralytics.com/yolov5/tutorials/train_custom_data\n\"\"\"\n\nimport argparse\nimport math\nimport os\nimport random\nimport subprocess\nimport sys\nimport time\nfrom copy import deepcopy\nfrom datetime import datetime\nfrom pathlib import Path\n\nimport numpy as np\nimport torch\nimport torch.distributed as dist\nimport torch.nn as nn\nimport yaml\nfrom torch.optim import lr_scheduler\nfrom tqdm import tqdm\n\nFILE = Path(__file__).resolve()\nROOT = FILE.parents[1] # YOLOv3 root directory\nif str(ROOT) not in sys.path:\n sys.path.append(str(ROOT)) # add ROOT to PATH\nROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative\n\nfrom ultralytics.utils.patches import torch_load\n\nimport segment.val as validate # for end-of-epoch mAP\nfrom models.experimental import attempt_load\nfrom models.yolo import SegmentationModel\nfrom utils.autoanchor import check_anchors\nfrom utils.autobatch import check_train_batch_size\nfrom utils.callbacks import Callbacks\nfrom utils.downloads import attempt_download, is_url\nfrom utils.general import (\n LOGGER,\n TQDM_BAR_FORMAT,\n check_amp,\n check_dataset,\n check_file,\n check_git_info,\n check_git_status,\n check_img_size,\n check_requirements,\n check_suffix,\n check_yaml,\n colorstr,\n get_latest_run,\n increment_path,\n init_seeds,\n intersect_dicts,\n labels_to_class_weights,\n labels_to_image_weights,\n one_cycle,\n print_args,\n print_mutation,\n strip_optimizer,\n yaml_save,\n)\nfrom utils.loggers import GenericLogger\nfrom utils.plots import plot_evolve, plot_labels\nfrom utils.segment.dataloaders import create_dataloader\nfrom utils.segment.loss import ComputeLoss\nfrom utils.segment.metrics import KEYS, fitness\nfrom utils.segment.plots import plot_images_and_masks, plot_results_with_masks\nfrom utils.torch_utils import (\n EarlyStopping,\n ModelEMA,\n de_parallel,\n select_device,\n smart_DDP,\n smart_optimizer,\n smart_resume,\n torch_distributed_zero_first,\n)\n\nLOCAL_RANK = int(os.getenv(\"LOCAL_RANK\", -1)) # https://pytorch.org/docs/stable/elastic/run.html\nRANK = int(os.getenv(\"RANK\", -1))\nWORLD_SIZE = int(os.getenv(\"WORLD_SIZE\", 1))\nGIT_INFO = check_git_info()\n\n\ndef train(hyp, opt, device, callbacks): # hyp is path/to/hyp.yaml or hyp dictionary\n \"\"\"Trains a segmentation model using the provided hyperparameters, options, and callbacks, handling multi-GPU\n setups, data loading, logging, and validation.\n \"\"\"\n (\n save_dir,\n epochs,\n batch_size,\n weights,\n single_cls,\n evolve,\n data,\n cfg,\n resume,\n noval,\n nosave,\n workers,\n freeze,\n mask_ratio,\n ) = (\n Path(opt.save_dir),\n opt.epochs,\n opt.batch_size,\n opt.weights,\n opt.single_cls,\n opt.evolve,\n opt.data,\n opt.cfg,\n opt.resume,\n opt.noval,\n opt.nosave,\n opt.workers,\n opt.freeze,\n opt.mask_ratio,\n )\n # callbacks.run('on_pretrain_routine_start')\n\n # Directories\n w = save_dir / \"weights\" # weights dir\n (w.parent if evolve else w).mkdir(parents=True, exist_ok=True) # make dir\n last, best = w / \"last.pt\", w / \"best.pt\"\n\n # Hyperparameters\n if isinstance(hyp, str):\n with open(hyp, errors=\"ignore\") as f:\n hyp = yaml.safe_load(f) # load hyps dict\n LOGGER.info(colorstr(\"hyperparameters: \") + \", \".join(f\"{k}={v}\" for k, v in hyp.items()))\n opt.hyp = hyp.copy() # for saving hyps to checkpoints\n\n # Save run settings\n if not evolve:\n yaml_save(save_dir / \"hyp.yaml\", hyp)\n yaml_save(save_dir / \"opt.yaml\", vars(opt))\n\n # Loggers\n data_dict = None\n if RANK in {-1, 0}:\n logger = GenericLogger(opt=opt, console_logger=LOGGER)\n\n # Config\n plots = not evolve and not opt.noplots # create plots\n overlap = not opt.no_overlap\n cuda = device.type != \"cpu\"\n init_seeds(opt.seed + 1 + RANK, deterministic=True)\n with torch_distributed_zero_first(LOCAL_RANK):\n data_dict = data_dict or check_dataset(data) # check if None\n train_path, val_path = data_dict[\"train\"], data_dict[\"val\"]\n nc = 1 if single_cls else int(data_dict[\"nc\"]) # number of classes\n names = {0: \"item\"} if single_cls and len(data_dict[\"names\"]) != 1 else data_dict[\"names\"] # class names\n is_coco = isinstance(val_path, str) and val_path.endswith(\"coco/val2017.txt\") # COCO dataset\n\n # Model\n check_suffix(weights, \".pt\") # check weights\n pretrained = weights.endswith(\".pt\")\n if pretrained:\n with torch_distributed_zero_first(LOCAL_RANK):\n weights = attempt_download(weights) # download if not found locally\n ckpt = torch_load(weights, map_location=\"cpu\") # load checkpoint to CPU to avoid CUDA memory leak\n model = SegmentationModel(cfg or ckpt[\"model\"].yaml, ch=3, nc=nc, anchors=hyp.get(\"anchors\")).to(device)\n exclude = [\"anchor\"] if (cfg or hyp.get(\"anchors\")) and not resume else [] # exclude keys\n csd = ckpt[\"model\"].float().state_dict() # checkpoint state_dict as FP32\n csd = intersect_dicts(csd, model.state_dict(), exclude=exclude) # intersect\n model.load_state_dict(csd, strict=False) # load\n LOGGER.info(f\"Transferred {len(csd)}/{len(model.state_dict())} items from {weights}\") # report\n else:\n model = SegmentationModel(cfg, ch=3, nc=nc, anchors=hyp.get(\"anchors\")).to(device) # create\n amp = check_amp(model) # check AMP\n\n # Freeze\n freeze = [f\"model.{x}.\" for x in (freeze if len(freeze) > 1 else range(freeze[0]))] # layers to freeze\n for k, v in model.named_parameters():\n v.requires_grad = True # train all layers\n # v.register_hook(lambda x: torch.nan_to_num(x)) # NaN to 0 (commented for erratic training results)\n if any(x in k for x in freeze):\n LOGGER.info(f\"freezing {k}\")\n v.requires_grad = False\n\n # Image size\n gs = max(int(model.stride.max()), 32) # grid size (max stride)\n imgsz = check_img_size(opt.imgsz, gs, floor=gs * 2) # verify imgsz is gs-multiple\n\n # Batch size\n if RANK == -1 and batch_size == -1: # single-GPU only, estimate best batch size\n batch_size = check_train_batch_size(model, imgsz, amp)\n logger.update_params({\"batch_size\": batch_size})\n # loggers.on_params_update({\"batch_size\": batch_size})\n\n # Optimizer\n nbs = 64 # nominal batch size\n accumulate = max(round(nbs / batch_size), 1) # accumulate loss before optimizing\n hyp[\"weight_decay\"] *= batch_size * accumulate / nbs # scale weight_decay\n optimizer = smart_optimizer(model, opt.optimizer, hyp[\"lr0\"], hyp[\"momentum\"], hyp[\"weight_decay\"])\n\n # Scheduler\n if opt.cos_lr:\n lf = one_cycle(1, hyp[\"lrf\"], epochs) # cosine 1->hyp['lrf']\n else:\n\n def lf(x):\n \"\"\"Linear learning rate scheduler decreasing from 1 to hyp['lrf'] over the course of given epochs.\"\"\"\n return (1 - x / epochs) * (1.0 - hyp[\"lrf\"]) + hyp[\"lrf\"] # linear\n\n scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lf) # plot_lr_scheduler(optimizer, scheduler, epochs)\n\n # EMA\n ema = ModelEMA(model) if RANK in {-1, 0} else None\n\n # Resume\n best_fitness, start_epoch = 0.0, 0\n if pretrained:\n if resume:\n best_fitness, start_epoch, epochs = smart_resume(ckpt, optimizer, ema, weights, epochs, resume)\n del ckpt, csd\n\n # DP mode\n if cuda and RANK == -1 and torch.cuda.device_count() > 1:\n LOGGER.warning(\n \"WARNING ⚠️ DP not recommended, use torch.distributed.run for best DDP Multi-GPU results.\\n\"\n \"See Multi-GPU Tutorial at https://docs.ultralytics.com/yolov5/tutorials/multi_gpu_training to get started.\"\n )\n model = torch.nn.DataParallel(model)\n\n # SyncBatchNorm\n if opt.sync_bn and cuda and RANK != -1:\n model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model).to(device)\n LOGGER.info(\"Using SyncBatchNorm()\")\n\n # Trainloader\n train_loader, dataset = create_dataloader(\n train_path,\n imgsz,\n batch_size // WORLD_SIZE,\n gs,\n single_cls,\n hyp=hyp,\n augment=True,\n cache=None if opt.cache == \"val\" else opt.cache,\n rect=opt.rect,\n rank=LOCAL_RANK,\n workers=workers,\n image_weights=opt.image_weights,\n quad=opt.quad,\n prefix=colorstr(\"train: \"),\n shuffle=True,\n mask_downsample_ratio=mask_ratio,\n overlap_mask=overlap,\n )\n labels = np.concatenate(dataset.labels, 0)\n mlc = int(labels[:, 0].max()) # max label class\n assert mlc < nc, f\"Label class {mlc} exceeds nc={nc} in {data}. Possible class labels are 0-{nc - 1}\"\n\n # Process 0\n if RANK in {-1, 0}:\n val_loader = create_dataloader(\n val_path,\n imgsz,\n batch_size // WORLD_SIZE * 2,\n gs,\n single_cls,\n hyp=hyp,\n cache=None if noval else opt.cache,\n rect=True,\n rank=-1,\n workers=workers * 2,\n pad=0.5,\n mask_downsample_ratio=mask_ratio,\n overlap_mask=overlap,\n prefix=colorstr(\"val: \"),\n )[0]\n\n if not resume:\n if not opt.noautoanchor:\n check_anchors(dataset, model=model, thr=hyp[\"anchor_t\"], imgsz=imgsz) # run AutoAnchor\n model.half().float() # pre-reduce anchor precision\n\n if plots:\n plot_labels(labels, names, save_dir)\n # callbacks.run('on_pretrain_routine_end', labels, names)\n\n # DDP mode\n if cuda and RANK != -1:\n model = smart_DDP(model)\n\n # Model attributes\n nl = de_parallel(model).model[-1].nl # number of detection layers (to scale hyps)\n hyp[\"box\"] *= 3 / nl # scale to layers\n hyp[\"cls\"] *= nc / 80 * 3 / nl # scale to classes and layers\n hyp[\"obj\"] *= (imgsz / 640) ** 2 * 3 / nl # scale to image size and layers\n hyp[\"label_smoothing\"] = opt.label_smoothing\n model.nc = nc # attach number of classes to model\n model.hyp = hyp # attach hyperparameters to model\n model.class_weights = labels_to_class_weights(dataset.labels, nc).to(device) * nc # attach class weights\n model.names = names\n\n # Start training\n t0 = time.time()\n nb = len(train_loader) # number of batches\n nw = max(round(hyp[\"warmup_epochs\"] * nb), 100) # number of warmup iterations, max(3 epochs, 100 iterations)\n # nw = min(nw, (epochs - start_epoch) / 2 * nb) # limit warmup to < 1/2 of training\n last_opt_step = -1\n maps = np.zeros(nc) # mAP per class\n results = (0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0) # P, R, mAP@.5, mAP@.5-.95, val_loss(box, obj, cls)\n scheduler.last_epoch = start_epoch - 1 # do not move\n scaler = torch.cuda.amp.GradScaler(enabled=amp)\n stopper, stop = EarlyStopping(patience=opt.patience), False\n compute_loss = ComputeLoss(model, overlap=overlap) # init loss class\n # callbacks.run('on_train_start')\n LOGGER.info(\n f\"Image sizes {imgsz} train, {imgsz} val\\n\"\n f\"Using {train_loader.num_workers * WORLD_SIZE} dataloader workers\\n\"\n f\"Logging results to {colorstr('bold', save_dir)}\\n\"\n f\"Starting training for {epochs} epochs...\"\n )\n for epoch in range(start_epoch, epochs): # epoch ------------------------------------------------------------------\n # callbacks.run('on_train_epoch_start')\n model.train()\n\n # Update image weights (optional, single-GPU only)\n if opt.image_weights:\n cw = model.class_weights.cpu().numpy() * (1 - maps) ** 2 / nc # class weights\n iw = labels_to_image_weights(dataset.labels, nc=nc, class_weights=cw) # image weights\n dataset.indices = random.choices(range(dataset.n), weights=iw, k=dataset.n) # rand weighted idx\n\n # Update mosaic border (optional)\n # b = int(random.uniform(0.25 * imgsz, 0.75 * imgsz + gs) // gs * gs)\n # dataset.mosaic_border = [b - imgsz, -b] # height, width borders\n\n mloss = torch.zeros(4, device=device) # mean losses\n if RANK != -1:\n train_loader.sampler.set_epoch(epoch)\n pbar = enumerate(train_loader)\n LOGGER.info(\n (\"\\n\" + \"%11s\" * 8)\n % (\"Epoch\", \"GPU_mem\", \"box_loss\", \"seg_loss\", \"obj_loss\", \"cls_loss\", \"Instances\", \"Size\")\n )\n if RANK in {-1, 0}:\n pbar = tqdm(pbar, total=nb, bar_format=TQDM_BAR_FORMAT) # progress bar\n optimizer.zero_grad()\n for i, (imgs, targets, paths, _, masks) in pbar: # batch ------------------------------------------------------\n # callbacks.run('on_train_batch_start')\n ni = i + nb * epoch # number integrated batches (since train start)\n imgs = imgs.to(device, non_blocking=True).float() / 255 # uint8 to float32, 0-255 to 0.0-1.0\n\n # Warmup\n if ni <= nw:\n xi = [0, nw] # x interp\n # compute_loss.gr = np.interp(ni, xi, [0.0, 1.0]) # iou loss ratio (obj_loss = 1.0 or iou)\n accumulate = max(1, np.interp(ni, xi, [1, nbs / batch_size]).round())\n for j, x in enumerate(optimizer.param_groups):\n # bias lr falls from 0.1 to lr0, all other lrs rise from 0.0 to lr0\n x[\"lr\"] = np.interp(ni, xi, [hyp[\"warmup_bias_lr\"] if j == 0 else 0.0, x[\"initial_lr\"] * lf(epoch)])\n if \"momentum\" in x:\n x[\"momentum\"] = np.interp(ni, xi, [hyp[\"warmup_momentum\"], hyp[\"momentum\"]])\n\n # Multi-scale\n if opt.multi_scale:\n sz = random.randrange(int(imgsz * 0.5), int(imgsz * 1.5) + gs) // gs * gs # size\n sf = sz / max(imgs.shape[2:]) # scale factor\n if sf != 1:\n ns = [math.ceil(x * sf / gs) * gs for x in imgs.shape[2:]] # new shape (stretched to gs-multiple)\n imgs = nn.functional.interpolate(imgs, size=ns, mode=\"bilinear\", align_corners=False)\n\n # Forward\n with torch.cuda.amp.autocast(amp):\n pred = model(imgs) # forward\n loss, loss_items = compute_loss(pred, targets.to(device), masks=masks.to(device).float())\n if RANK != -1:\n loss *= WORLD_SIZE # gradient averaged between devices in DDP mode\n if opt.quad:\n loss *= 4.0\n\n # Backward\n scaler.scale(loss).backward()\n\n # Optimize - https://pytorch.org/docs/master/notes/amp_examples.html\n if ni - last_opt_step >= accumulate:\n scaler.unscale_(optimizer) # unscale gradients\n torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=10.0) # clip gradients\n scaler.step(optimizer) # optimizer.step\n scaler.update()\n optimizer.zero_grad()\n if ema:\n ema.update(model)\n last_opt_step = ni\n\n # Log\n if RANK in {-1, 0}:\n mloss = (mloss * i + loss_items) / (i + 1) # update mean losses\n mem = f\"{torch.cuda.memory_reserved() / 1e9 if torch.cuda.is_available() else 0:.3g}G\" # (GB)\n pbar.set_description(\n (\"%11s\" * 2 + \"%11.4g\" * 6)\n % (f\"{epoch}/{epochs - 1}\", mem, *mloss, targets.shape[0], imgs.shape[-1])\n )\n # callbacks.run('on_train_batch_end', model, ni, imgs, targets, paths)\n # if callbacks.stop_training:\n # return\n\n # Mosaic plots\n if plots:\n if ni < 3:\n plot_images_and_masks(imgs, targets, masks, paths, save_dir / f\"train_batch{ni}.jpg\")\n if ni == 10:\n files = sorted(save_dir.glob(\"train*.jpg\"))\n logger.log_images(files, \"Mosaics\", epoch)\n # end batch ------------------------------------------------------------------------------------------------\n\n # Scheduler\n lr = [x[\"lr\"] for x in optimizer.param_groups] # for loggers\n scheduler.step()\n\n if RANK in {-1, 0}:\n # mAP\n # callbacks.run('on_train_epoch_end', epoch=epoch)\n ema.update_attr(model, include=[\"yaml\", \"nc\", \"hyp\", \"names\", \"stride\", \"class_weights\"])\n final_epoch = (epoch + 1 == epochs) or stopper.possible_stop\n if not noval or final_epoch: # Calculate mAP\n results, maps, _ = validate.run(\n data_dict,\n batch_size=batch_size // WORLD_SIZE * 2,\n imgsz=imgsz,\n half=amp,\n model=ema.ema,\n single_cls=single_cls,\n dataloader=val_loader,\n save_dir=save_dir,\n plots=False,\n callbacks=callbacks,\n compute_loss=compute_loss,\n mask_downsample_ratio=mask_ratio,\n overlap=overlap,\n )\n\n # Update best mAP\n fi = fitness(np.array(results).reshape(1, -1)) # weighted combination of [P, R, mAP@.5, mAP@.5-.95]\n stop = stopper(epoch=epoch, fitness=fi) # early stop check\n if fi > best_fitness:\n best_fitness = fi\n log_vals = list(mloss) + list(results) + lr\n # callbacks.run('on_fit_epoch_end', log_vals, epoch, best_fitness, fi)\n # Log val metrics and media\n metrics_dict = dict(zip(KEYS, log_vals))\n logger.log_metrics(metrics_dict, epoch)\n\n # Save model\n if (not nosave) or (final_epoch and not evolve): # if save\n ckpt = {\n \"epoch\": epoch,\n \"best_fitness\": best_fitness,\n \"model\": deepcopy(de_parallel(model)).half(),\n \"ema\": deepcopy(ema.ema).half(),\n \"updates\": ema.updates,\n \"optimizer\": optimizer.state_dict(),\n \"opt\": vars(opt),\n \"git\": GIT_INFO, # {remote, branch, commit} if a git repo\n \"date\": datetime.now().isoformat(),\n }\n\n # Save last, best and delete\n torch.save(ckpt, last)\n if best_fitness == fi:\n torch.save(ckpt, best)\n if opt.save_period > 0 and epoch % opt.save_period == 0:\n torch.save(ckpt, w / f\"epoch{epoch}.pt\")\n logger.log_model(w / f\"epoch{epoch}.pt\")\n del ckpt\n # callbacks.run('on_model_save', last, epoch, final_epoch, best_fitness, fi)\n\n # EarlyStopping\n if RANK != -1: # if DDP training\n broadcast_list = [stop if RANK == 0 else None]\n dist.broadcast_object_list(broadcast_list, 0) # broadcast 'stop' to all ranks\n if RANK != 0:\n stop = broadcast_list[0]\n if stop:\n break # must break all DDP ranks\n\n # end epoch ----------------------------------------------------------------------------------------------------\n # end training -----------------------------------------------------------------------------------------------------\n if RANK in {-1, 0}:\n LOGGER.info(f\"\\n{epoch - start_epoch + 1} epochs completed in {(time.time() - t0) / 3600:.3f} hours.\")\n for f in last, best:\n if f.exists():\n strip_optimizer(f) # strip optimizers\n if f is best:\n LOGGER.info(f\"\\nValidating {f}...\")\n results, _, _ = validate.run(\n data_dict,\n batch_size=batch_size // WORLD_SIZE * 2,\n imgsz=imgsz,\n model=attempt_load(f, device).half(),\n iou_thres=0.65 if is_coco else 0.60, # best pycocotools at iou 0.65\n single_cls=single_cls,\n dataloader=val_loader,\n save_dir=save_dir,\n save_json=is_coco,\n verbose=True,\n plots=plots,\n callbacks=callbacks,\n compute_loss=compute_loss,\n mask_downsample_ratio=mask_ratio,\n overlap=overlap,\n ) # val best model with plots\n if is_coco:\n # callbacks.run('on_fit_epoch_end', list(mloss) + list(results) + lr, epoch, best_fitness, fi)\n metrics_dict = dict(zip(KEYS, list(mloss) + list(results) + lr))\n logger.log_metrics(metrics_dict, epoch)\n\n # callbacks.run('on_train_end', last, best, epoch, results)\n # on train end callback using genericLogger\n logger.log_metrics(dict(zip(KEYS[4:16], results)), epochs)\n if not opt.evolve:\n logger.log_model(best, epoch)\n if plots:\n plot_results_with_masks(file=save_dir / \"results.csv\") # save results.png\n files = [\"results.png\", \"confusion_matrix.png\", *(f\"{x}_curve.png\" for x in (\"F1\", \"PR\", \"P\", \"R\"))]\n files = [(save_dir / f) for f in files if (save_dir / f).exists()] # filter\n LOGGER.info(f\"Results saved to {colorstr('bold', save_dir)}\")\n logger.log_images(files, \"Results\", epoch + 1)\n logger.log_images(sorted(save_dir.glob(\"val*.jpg\")), \"Validation\", epoch + 1)\n torch.cuda.empty_cache()\n return results\n\n\ndef parse_opt(known=False):\n \"\"\"Parses command line arguments for training configurations, supporting optional known args parsing.\"\"\"\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--weights\", type=str, default=ROOT / \"yolov5s-seg.pt\", help=\"initial weights path\")\n parser.add_argument(\"--cfg\", type=str, default=\"\", help=\"model.yaml path\")\n parser.add_argument(\"--data\", type=str, default=ROOT / \"data/coco128-seg.yaml\", help=\"dataset.yaml path\")\n parser.add_argument(\"--hyp\", type=str, default=ROOT / \"data/hyps/hyp.scratch-low.yaml\", help=\"hyperparameters path\")\n parser.add_argument(\"--epochs\", type=int, default=100, help=\"total training epochs\")\n parser.add_argument(\"--batch-size\", type=int, default=16, help=\"total batch size for all GPUs, -1 for autobatch\")\n parser.add_argument(\"--imgsz\", \"--img\", \"--img-size\", type=int, default=640, help=\"train, val image size (pixels)\")\n parser.add_argument(\"--rect\", action=\"store_true\", help=\"rectangular training\")\n parser.add_argument(\"--resume\", nargs=\"?\", const=True, default=False, help=\"resume most recent training\")\n parser.add_argument(\"--nosave\", action=\"store_true\", help=\"only save final checkpoint\")\n parser.add_argument(\"--noval\", action=\"store_true\", help=\"only validate final epoch\")\n parser.add_argument(\"--noautoanchor\", action=\"store_true\", help=\"disable AutoAnchor\")\n parser.add_argument(\"--noplots\", action=\"store_true\", help=\"save no plot files\")\n parser.add_argument(\"--evolve\", type=int, nargs=\"?\", const=300, help=\"evolve hyperparameters for x generations\")\n parser.add_argument(\"--bucket\", type=str, default=\"\", help=\"gsutil bucket\")\n parser.add_argument(\"--cache\", type=str, nargs=\"?\", const=\"ram\", help=\"image --cache ram/disk\")\n parser.add_argument(\"--image-weights\", action=\"store_true\", help=\"use weighted image selection for training\")\n parser.add_argument(\"--device\", default=\"\", help=\"cuda device, i.e. 0 or 0,1,2,3 or cpu\")\n parser.add_argument(\"--multi-scale\", action=\"store_true\", help=\"vary img-size +/- 50%%\")\n parser.add_argument(\"--single-cls\", action=\"store_true\", help=\"train multi-class data as single-class\")\n parser.add_argument(\"--optimizer\", type=str, choices=[\"SGD\", \"Adam\", \"AdamW\"], default=\"SGD\", help=\"optimizer\")\n parser.add_argument(\"--sync-bn\", action=\"store_true\", help=\"use SyncBatchNorm, only available in DDP mode\")\n parser.add_argument(\"--workers\", type=int, default=8, help=\"max dataloader workers (per RANK in DDP mode)\")\n parser.add_argument(\"--project\", default=ROOT / \"runs/train-seg\", help=\"save to project/name\")\n parser.add_argument(\"--name\", default=\"exp\", help=\"save to project/name\")\n parser.add_argument(\"--exist-ok\", action=\"store_true\", help=\"existing project/name ok, do not increment\")\n parser.add_argument(\"--quad\", action=\"store_true\", help=\"quad dataloader\")\n parser.add_argument(\"--cos-lr\", action=\"store_true\", help=\"cosine LR scheduler\")\n parser.add_argument(\"--label-smoothing\", type=float, default=0.0, help=\"Label smoothing epsilon\")\n parser.add_argument(\"--patience\", type=int, default=100, help=\"EarlyStopping patience (epochs without improvement)\")\n parser.add_argument(\"--freeze\", nargs=\"+\", type=int, default=[0], help=\"Freeze layers: backbone=10, first3=0 1 2\")\n parser.add_argument(\"--save-period\", type=int, default=-1, help=\"Save checkpoint every x epochs (disabled if < 1)\")\n parser.add_argument(\"--seed\", type=int, default=0, help=\"Global training seed\")\n parser.add_argument(\"--local_rank\", type=int, default=-1, help=\"Automatic DDP Multi-GPU argument, do not modify\")\n\n # Instance Segmentation Args\n parser.add_argument(\"--mask-ratio\", type=int, default=4, help=\"Downsample the truth masks to saving memory\")\n parser.add_argument(\"--no-overlap\", action=\"store_true\", help=\"Overlap masks train faster at slightly less mAP\")\n\n return parser.parse_known_args()[0] if known else parser.parse_args()\n\n\ndef main(opt, callbacks=Callbacks()):\n \"\"\"Initializes training or evolution of models with given options and callbacks, handling device setup and data\n preparation.\n \"\"\"\n if RANK in {-1, 0}:\n print_args(vars(opt))\n check_git_status()\n check_requirements(ROOT / \"requirements.txt\")\n\n # Resume\n if opt.resume and not opt.evolve: # resume from specified or most recent last.pt\n last = Path(check_file(opt.resume) if isinstance(opt.resume, str) else get_latest_run())\n opt_yaml = last.parent.parent / \"opt.yaml\" # train options yaml\n opt_data = opt.data # original dataset\n if opt_yaml.is_file():\n with open(opt_yaml, errors=\"ignore\") as f:\n d = yaml.safe_load(f)\n else:\n d = torch_load(last, map_location=\"cpu\")[\"opt\"]\n opt = argparse.Namespace(**d) # replace\n opt.cfg, opt.weights, opt.resume = \"\", str(last), True # reinstate\n if is_url(opt_data):\n opt.data = check_file(opt_data) # avoid HUB resume auth timeout\n else:\n opt.data, opt.cfg, opt.hyp, opt.weights, opt.project = (\n check_file(opt.data),\n check_yaml(opt.cfg),\n check_yaml(opt.hyp),\n str(opt.weights),\n str(opt.project),\n ) # checks\n assert len(opt.cfg) or len(opt.weights), \"either --cfg or --weights must be specified\"\n if opt.evolve:\n if opt.project == str(ROOT / \"runs/train-seg\"): # if default project name, rename to runs/evolve-seg\n opt.project = str(ROOT / \"runs/evolve-seg\")\n opt.exist_ok, opt.resume = opt.resume, False # pass resume to exist_ok and disable resume\n if opt.name == \"cfg\":\n opt.name = Path(opt.cfg).stem # use model.yaml as name\n opt.save_dir = str(increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok))\n\n # DDP mode\n device = select_device(opt.device, batch_size=opt.batch_size)\n if LOCAL_RANK != -1:\n msg = \"is not compatible with YOLOv3 Multi-GPU DDP training\"\n assert not opt.image_weights, f\"--image-weights {msg}\"\n assert not opt.evolve, f\"--evolve {msg}\"\n assert opt.batch_size != -1, f\"AutoBatch with --batch-size -1 {msg}, please pass a valid --batch-size\"\n assert opt.batch_size % WORLD_SIZE == 0, f\"--batch-size {opt.batch_size} must be multiple of WORLD_SIZE\"\n assert torch.cuda.device_count() > LOCAL_RANK, \"insufficient CUDA devices for DDP command\"\n torch.cuda.set_device(LOCAL_RANK)\n device = torch.device(\"cuda\", LOCAL_RANK)\n dist.init_process_group(backend=\"nccl\" if dist.is_nccl_available() else \"gloo\")\n\n # Train\n if not opt.evolve:\n train(opt.hyp, opt, device, callbacks)\n\n # Evolve hyperparameters (optional)\n else:\n # Hyperparameter evolution metadata (mutation scale 0-1, lower_limit, upper_limit)\n meta = {\n \"lr0\": (1, 1e-5, 1e-1), # initial learning rate (SGD=1E-2, Adam=1E-3)\n \"lrf\": (1, 0.01, 1.0), # final OneCycleLR learning rate (lr0 * lrf)\n \"momentum\": (0.3, 0.6, 0.98), # SGD momentum/Adam beta1\n \"weight_decay\": (1, 0.0, 0.001), # optimizer weight decay\n \"warmup_epochs\": (1, 0.0, 5.0), # warmup epochs (fractions ok)\n \"warmup_momentum\": (1, 0.0, 0.95), # warmup initial momentum\n \"warmup_bias_lr\": (1, 0.0, 0.2), # warmup initial bias lr\n \"box\": (1, 0.02, 0.2), # box loss gain\n \"cls\": (1, 0.2, 4.0), # cls loss gain\n \"cls_pw\": (1, 0.5, 2.0), # cls BCELoss positive_weight\n \"obj\": (1, 0.2, 4.0), # obj loss gain (scale with pixels)\n \"obj_pw\": (1, 0.5, 2.0), # obj BCELoss positive_weight\n \"iou_t\": (0, 0.1, 0.7), # IoU training threshold\n \"anchor_t\": (1, 2.0, 8.0), # anchor-multiple threshold\n \"anchors\": (2, 2.0, 10.0), # anchors per output grid (0 to ignore)\n \"fl_gamma\": (0, 0.0, 2.0), # focal loss gamma (efficientDet default gamma=1.5)\n \"hsv_h\": (1, 0.0, 0.1), # image HSV-Hue augmentation (fraction)\n \"hsv_s\": (1, 0.0, 0.9), # image HSV-Saturation augmentation (fraction)\n \"hsv_v\": (1, 0.0, 0.9), # image HSV-Value augmentation (fraction)\n \"degrees\": (1, 0.0, 45.0), # image rotation (+/- deg)\n \"translate\": (1, 0.0, 0.9), # image translation (+/- fraction)\n \"scale\": (1, 0.0, 0.9), # image scale (+/- gain)\n \"shear\": (1, 0.0, 10.0), # image shear (+/- deg)\n \"perspective\": (0, 0.0, 0.001), # image perspective (+/- fraction), range 0-0.001\n \"flipud\": (1, 0.0, 1.0), # image flip up-down (probability)\n \"fliplr\": (0, 0.0, 1.0), # image flip left-right (probability)\n \"mosaic\": (1, 0.0, 1.0), # image mixup (probability)\n \"mixup\": (1, 0.0, 1.0), # image mixup (probability)\n \"copy_paste\": (1, 0.0, 1.0),\n } # segment copy-paste (probability)\n\n with open(opt.hyp, errors=\"ignore\") as f:\n hyp = yaml.safe_load(f) # load hyps dict\n if \"anchors\" not in hyp: # anchors commented in hyp.yaml\n hyp[\"anchors\"] = 3\n if opt.noautoanchor:\n del hyp[\"anchors\"], meta[\"anchors\"]\n opt.noval, opt.nosave, save_dir = True, True, Path(opt.save_dir) # only val/save final epoch\n # ei = [isinstance(x, (int, float)) for x in hyp.values()] # evolvable indices\n evolve_yaml, evolve_csv = save_dir / \"hyp_evolve.yaml\", save_dir / \"evolve.csv\"\n if opt.bucket:\n # download evolve.csv if exists\n subprocess.run(\n [\n \"gsutil\",\n \"cp\",\n f\"gs://{opt.bucket}/evolve.csv\",\n str(evolve_csv),\n ]\n )\n\n for _ in range(opt.evolve): # generations to evolve\n if evolve_csv.exists(): # if evolve.csv exists: select best hyps and mutate\n # Select parent(s)\n parent = \"single\" # parent selection method: 'single' or 'weighted'\n x = np.loadtxt(evolve_csv, ndmin=2, delimiter=\",\", skiprows=1)\n n = min(5, len(x)) # number of previous results to consider\n x = x[np.argsort(-fitness(x))][:n] # top n mutations\n w = fitness(x) - fitness(x).min() + 1e-6 # weights (sum > 0)\n if parent == \"single\" or len(x) == 1:\n # x = x[random.randint(0, n - 1)] # random selection\n x = x[random.choices(range(n), weights=w)[0]] # weighted selection\n elif parent == \"weighted\":\n x = (x * w.reshape(n, 1)).sum(0) / w.sum() # weighted combination\n\n # Mutate\n mp, s = 0.8, 0.2 # mutation probability, sigma\n npr = np.random\n npr.seed(int(time.time()))\n g = np.array([meta[k][0] for k in hyp.keys()]) # gains 0-1\n ng = len(meta)\n v = np.ones(ng)\n while all(v == 1): # mutate until a change occurs (prevent duplicates)\n v = (g * (npr.random(ng) < mp) * npr.randn(ng) * npr.random() * s + 1).clip(0.3, 3.0)\n for i, k in enumerate(hyp.keys()): # plt.hist(v.ravel(), 300)\n hyp[k] = float(x[i + 12] * v[i]) # mutate\n\n # Constrain to limits\n for k, v in meta.items():\n hyp[k] = max(hyp[k], v[1]) # lower limit\n hyp[k] = min(hyp[k], v[2]) # upper limit\n hyp[k] = round(hyp[k], 5) # significant digits\n\n # Train mutation\n results = train(hyp.copy(), opt, device, callbacks)\n callbacks = Callbacks()\n # Write mutation results\n print_mutation(KEYS[4:16], results, hyp.copy(), save_dir, opt.bucket)\n\n # Plot results\n plot_evolve(evolve_csv)\n LOGGER.info(\n f\"Hyperparameter evolution finished {opt.evolve} generations\\n\"\n f\"Results saved to {colorstr('bold', save_dir)}\\n\"\n f\"Usage example: $ python train.py --hyp {evolve_yaml}\"\n )\n\n\ndef run(**kwargs):\n \"\"\"Executes model training with specified configurations; see example: `train.run(data='coco128.yaml', imgsz=320,\n weights='yolov5m.pt')`.\n \"\"\"\n opt = parse_opt(True)\n for k, v in kwargs.items():\n setattr(opt, k, v)\n main(opt)\n return opt\n\n\nif __name__ == \"__main__\":\n opt = parse_opt()\n main(opt)\n" }, { "path": "segment/tutorial.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"t6MPjfT5NrKQ\"\n },\n \"source\": [\n \"
\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"\\n\",\n \" [中文](https://docs.ultralytics.com/zh/) | [한국어](https://docs.ultralytics.com/ko/) | [日本語](https://docs.ultralytics.com/ja/) | [Русский](https://docs.ultralytics.com/ru/) | [Deutsch](https://docs.ultralytics.com/de/) | [Français](https://docs.ultralytics.com/fr/) | [Español](https://docs.ultralytics.com/es/) | [Português](https://docs.ultralytics.com/pt/) | [Türkçe](https://docs.ultralytics.com/tr/) | [Tiếng Việt](https://docs.ultralytics.com/vi/) | [العربية](https://docs.ultralytics.com/ar/)\\n\",\n \"\\n\",\n \" \\\"Ultralytics\\n\",\n \" \\\"Run\\n\",\n \" \\\"Open\\n\",\n \" \\\"Open\\n\",\n \"\\n\",\n \" \\\"Discord\\\"\\n\",\n \" \\\"Ultralytics\\n\",\n \" \\\"Ultralytics\\n\",\n \"
\\n\",\n \"\\n\",\n \"This **Ultralytics YOLOv5 Segmentation Colab Notebook** is the easiest way to get started with [YOLO models](https://www.ultralytics.com/yolo)—no installation needed. Built by [Ultralytics](https://www.ultralytics.com/), the creators of YOLO, this notebook walks you through running **state-of-the-art** models directly in your browser.\\n\",\n \"\\n\",\n \"Ultralytics models are constantly updated for performance and flexibility. They're **fast**, **accurate**, and **easy to use**, and they excel at [object detection](https://docs.ultralytics.com/tasks/detect/), [tracking](https://docs.ultralytics.com/modes/track/), [instance segmentation](https://docs.ultralytics.com/tasks/segment/), [image classification](https://docs.ultralytics.com/tasks/classify/), and [pose estimation](https://docs.ultralytics.com/tasks/pose/).\\n\",\n \"\\n\",\n \"Find detailed documentation in the [Ultralytics Docs](https://docs.ultralytics.com/). Get support via [GitHub Issues](https://github.com/ultralytics/ultralytics/issues/new/choose). Join discussions on [Discord](https://discord.com/invite/ultralytics), [Reddit](https://www.reddit.com/r/ultralytics/), and the [Ultralytics Community Forums](https://community.ultralytics.com/)!\\n\",\n \"\\n\",\n \"Request an Enterprise License for commercial use at [Ultralytics Licensing](https://www.ultralytics.com/license).\\n\",\n \"\\n\",\n \"
\\n\",\n \"
\\n\",\n \" \\n\",\n \" \\\"Ultralytics\\n\",\n \" \\n\",\n \"\\n\",\n \"

\\n\",\n \" Watch: How to Train\\n\",\n \" Ultralytics\\n\",\n \" YOLO11 Model on Custom Dataset using Google Colab Notebook 🚀\\n\",\n \"

\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"7mGmQbAO5pQb\"\n },\n \"source\": [\n \"# Setup\\n\",\n \"\\n\",\n \"Clone GitHub [repository](https://github.com/ultralytics/yolov5), install [dependencies](https://github.com/ultralytics/yolov5/blob/master/requirements.txt) and check PyTorch and GPU.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\"\n },\n \"id\": \"wbvMlHd_QwMG\",\n \"outputId\": \"171b23f0-71b9-4cbf-b666-6fa2ecef70c8\"\n },\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"YOLOv5 🚀 v7.0-2-gc9d47ae Python-3.7.15 torch-1.12.1+cu113 CUDA:0 (Tesla T4, 15110MiB)\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Setup complete ✅ (2 CPUs, 12.7 GB RAM, 22.6/78.2 GB disk)\\n\"\n ]\n }\n ],\n \"source\": [\n \"!git clone https://github.com/ultralytics/yolov5 # clone\\n\",\n \"%cd yolov5\\n\",\n \"%pip install -qr requirements.txt comet_ml # install\\n\",\n \"\\n\",\n \"import torch\\n\",\n \"\\n\",\n \"import utils\\n\",\n \"\\n\",\n \"display = utils.notebook_init() # checks\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"4JnkELT0cIJg\"\n },\n \"source\": [\n \"# 1. Predict\\n\",\n \"\\n\",\n \"`segment/predict.py` runs YOLOv5 instance segmentation inference on a variety of sources, downloading models automatically from the [latest YOLOv5 release](https://github.com/ultralytics/yolov5/releases), and saving results to `runs/predict`. Example inference sources are:\\n\",\n \"\\n\",\n \"```shell\\n\",\n \"python segment/predict.py --source 0 # webcam\\n\",\n \" img.jpg # image \\n\",\n \" vid.mp4 # video\\n\",\n \" screen # screenshot\\n\",\n \" path/ # directory\\n\",\n \" 'path/*.jpg' # glob\\n\",\n \" 'https://youtu.be/LNwODJXcvt4' # YouTube\\n\",\n \" 'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream\\n\",\n \"```\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\"\n },\n \"id\": \"zR9ZbuQCH7FX\",\n \"outputId\": \"3f67f1c7-f15e-4fa5-d251-967c3b77eaad\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\u001B[34m\\u001B[1msegment/predict: \\u001B[0mweights=['yolov5s-seg.pt'], source=data/images, data=data/coco128.yaml, imgsz=[640, 640], conf_thres=0.25, iou_thres=0.45, max_det=1000, device=, view_img=False, save_txt=False, save_conf=False, save_crop=False, nosave=False, classes=None, agnostic_nms=False, augment=False, visualize=False, update=False, project=runs/predict-seg, name=exp, exist_ok=False, line_thickness=3, hide_labels=False, hide_conf=False, half=False, dnn=False, vid_stride=1, retina_masks=False\\n\",\n \"YOLOv5 🚀 v7.0-2-gc9d47ae Python-3.7.15 torch-1.12.1+cu113 CUDA:0 (Tesla T4, 15110MiB)\\n\",\n \"\\n\",\n \"Downloading https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s-seg.pt to yolov5s-seg.pt...\\n\",\n \"100% 14.9M/14.9M [00:01<00:00, 12.0MB/s]\\n\",\n \"\\n\",\n \"Fusing layers... \\n\",\n \"YOLOv5s-seg summary: 224 layers, 7611485 parameters, 0 gradients, 26.4 GFLOPs\\n\",\n \"image 1/2 /content/yolov5/data/images/bus.jpg: 640x480 4 persons, 1 bus, 18.2ms\\n\",\n \"image 2/2 /content/yolov5/data/images/zidane.jpg: 384x640 2 persons, 1 tie, 13.4ms\\n\",\n \"Speed: 0.5ms pre-process, 15.8ms inference, 18.5ms NMS per image at shape (1, 3, 640, 640)\\n\",\n \"Results saved to \\u001B[1mruns/predict-seg/exp\\u001B[0m\\n\"\n ]\n }\n ],\n \"source\": [\n \"!python segment/predict.py --weights yolov5s-seg.pt --img 640 --conf 0.25 --source data/images\\n\",\n \"# display.Image(filename='runs/predict-seg/exp/zidane.jpg', width=600)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"hkAzDWJ7cWTr\"\n },\n \"source\": [\n \"        \\n\",\n \"\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"0eq1SMWl6Sfn\"\n },\n \"source\": [\n \"# 2. Validate\\n\",\n \"Validate a model's accuracy on the [COCO](https://cocodataset.org/#home) dataset's `val` or `test` splits. Models are downloaded automatically from the [latest YOLOv5 release](https://github.com/ultralytics/yolov5/releases). To show results by class use the `--verbose` flag.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\"\n },\n \"id\": \"WQPtK1QYVaD_\",\n \"outputId\": \"9d751d8c-bee8-4339-cf30-9854ca530449\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Downloading https://github.com/ultralytics/assets/releases/download/v0.0.0/coco2017labels-segments.zip ...\\n\",\n \"Downloading http://images.cocodataset.org/zips/val2017.zip ...\\n\",\n \"######################################################################## 100.0%\\n\",\n \"######################################################################## 100.0%\\n\"\n ]\n }\n ],\n \"source\": [\n \"# Download COCO val\\n\",\n \"!bash data/scripts/get_coco.sh --val --segments # download (780M - 5000 images)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\"\n },\n \"id\": \"X58w8JLpMnjH\",\n \"outputId\": \"a140d67a-02da-479e-9ddb-7d54bf9e407a\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\u001B[34m\\u001B[1msegment/val: \\u001B[0mdata=/content/yolov5/data/coco.yaml, weights=['yolov5s-seg.pt'], batch_size=32, imgsz=640, conf_thres=0.001, iou_thres=0.6, max_det=300, task=val, device=, workers=8, single_cls=False, augment=False, verbose=False, save_txt=False, save_hybrid=False, save_conf=False, save_json=False, project=runs/val-seg, name=exp, exist_ok=False, half=True, dnn=False\\n\",\n \"YOLOv5 🚀 v7.0-2-gc9d47ae Python-3.7.15 torch-1.12.1+cu113 CUDA:0 (Tesla T4, 15110MiB)\\n\",\n \"\\n\",\n \"Fusing layers... \\n\",\n \"YOLOv5s-seg summary: 224 layers, 7611485 parameters, 0 gradients, 26.4 GFLOPs\\n\",\n \"\\u001B[34m\\u001B[1mval: \\u001B[0mScanning /content/datasets/coco/val2017... 4952 images, 48 backgrounds, 0 corrupt: 100% 5000/5000 [00:03<00:00, 1361.31it/s]\\n\",\n \"\\u001B[34m\\u001B[1mval: \\u001B[0mNew cache created: /content/datasets/coco/val2017.cache\\n\",\n \" Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100% 157/157 [01:54<00:00, 1.37it/s]\\n\",\n \" all 5000 36335 0.673 0.517 0.566 0.373 0.672 0.49 0.532 0.319\\n\",\n \"Speed: 0.6ms pre-process, 4.4ms inference, 2.9ms NMS per image at shape (32, 3, 640, 640)\\n\",\n \"Results saved to \\u001B[1mruns/val-seg/exp\\u001B[0m\\n\"\n ]\n }\n ],\n \"source\": [\n \"# Validate YOLOv5s-seg on COCO val\\n\",\n \"!python segment/val.py --weights yolov5s-seg.pt --data coco.yaml --img 640 --half\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"ZY2VXXXu74w5\"\n },\n \"source\": [\n \"# 3. Train\\n\",\n \"\\n\",\n \"\\n\",\n \" \\\"Ultralytics\\n\",\n \"\\n\",\n \"

\\n\",\n \"\\n\",\n \"Train a YOLOv5s-seg model on the [COCO128](https://www.kaggle.com/datasets/ultralytics/coco128) dataset with `--data coco128-seg.yaml`, starting from pretrained `--weights yolov5s-seg.pt`, or from randomly initialized `--weights '' --cfg yolov5s-seg.yaml`.\\n\",\n \"\\n\",\n \"- **Pretrained [Models](https://github.com/ultralytics/yolov5/tree/master/models)** are downloaded\\n\",\n \"automatically from the [latest YOLOv5 release](https://github.com/ultralytics/yolov5/releases)\\n\",\n \"- **[Datasets](https://github.com/ultralytics/yolov5/tree/master/data)** available for autodownload include: [COCO](https://github.com/ultralytics/yolov5/blob/master/data/coco.yaml), [COCO128](https://github.com/ultralytics/yolov5/blob/master/data/coco128.yaml), [VOC](https://github.com/ultralytics/yolov5/blob/master/data/VOC.yaml), [Argoverse](https://github.com/ultralytics/yolov5/blob/master/data/Argoverse.yaml), [VisDrone](https://github.com/ultralytics/yolov5/blob/master/data/VisDrone.yaml), [GlobalWheat](https://github.com/ultralytics/yolov5/blob/master/data/GlobalWheat2020.yaml), [xView](https://github.com/ultralytics/yolov5/blob/master/data/xView.yaml), [Objects365](https://github.com/ultralytics/yolov5/blob/master/data/Objects365.yaml), [SKU-110K](https://github.com/ultralytics/yolov5/blob/master/data/SKU-110K.yaml).\\n\",\n \"- **Training Results** are saved to `runs/train-seg/` with incrementing run directories, i.e. `runs/train-seg/exp2`, `runs/train-seg/exp3` etc.\\n\",\n \"

\\n\",\n \"\\n\",\n \"A **Mosaic Dataloader** is used for training which combines 4 images into 1 mosaic.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"i3oKtE4g-aNn\"\n },\n \"outputs\": [],\n \"source\": [\n \"# @title Select YOLOv5 🚀 logger {run: 'auto'}\\n\",\n \"logger = \\\"Comet\\\" # @param ['Comet', 'ClearML', 'TensorBoard']\\n\",\n \"\\n\",\n \"if logger == \\\"Comet\\\":\\n\",\n \" %pip install -q comet_ml\\n\",\n \" import comet_ml\\n\",\n \"\\n\",\n \" comet_ml.init()\\n\",\n \"elif logger == \\\"ClearML\\\":\\n\",\n \" %pip install -q clearml\\n\",\n \" import clearml\\n\",\n \"\\n\",\n \" clearml.browser_login()\\n\",\n \"elif logger == \\\"TensorBoard\\\":\\n\",\n \" %load_ext tensorboard\\n\",\n \" %tensorboard --logdir runs/train\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\"\n },\n \"id\": \"1NcFxRcFdJ_O\",\n \"outputId\": \"3a3e0cf7-e79c-47a5-c8e7-2d26eeeab988\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\u001B[34m\\u001B[1msegment/train: \\u001B[0mweights=yolov5s-seg.pt, cfg=, data=coco128-seg.yaml, hyp=data/hyps/hyp.scratch-low.yaml, epochs=3, batch_size=16, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, bucket=, cache=ram, image_weights=False, device=, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=8, project=runs/train-seg, name=exp, exist_ok=False, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, seed=0, local_rank=-1, mask_ratio=4, no_overlap=False\\n\",\n \"\\u001B[34m\\u001B[1mgithub: \\u001B[0mup to date with https://github.com/ultralytics/yolov5 ✅\\n\",\n \"YOLOv5 🚀 v7.0-2-gc9d47ae Python-3.7.15 torch-1.12.1+cu113 CUDA:0 (Tesla T4, 15110MiB)\\n\",\n \"\\n\",\n \"\\u001B[34m\\u001B[1mhyperparameters: \\u001B[0mlr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.5, cls_pw=1.0, obj=1.0, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0\\n\",\n \"\\u001B[34m\\u001B[1mTensorBoard: \\u001B[0mStart with 'tensorboard --logdir runs/train-seg', view at http://localhost:6006/\\n\",\n \"\\n\",\n \"Dataset not found ⚠️, missing paths ['/content/datasets/coco128-seg/images/train2017']\\n\",\n \"Downloading https://github.com/ultralytics/assets/releases/download/v0.0.0/coco128-seg.zip to coco128-seg.zip...\\n\",\n \"100% 6.79M/6.79M [00:01<00:00, 6.73MB/s]\\n\",\n \"Dataset download success ✅ (1.9s), saved to \\u001B[1m/content/datasets\\u001B[0m\\n\",\n \"\\n\",\n \" from n params module arguments \\n\",\n \" 0 -1 1 3520 models.common.Conv [3, 32, 6, 2, 2] \\n\",\n \" 1 -1 1 18560 models.common.Conv [32, 64, 3, 2] \\n\",\n \" 2 -1 1 18816 models.common.C3 [64, 64, 1] \\n\",\n \" 3 -1 1 73984 models.common.Conv [64, 128, 3, 2] \\n\",\n \" 4 -1 2 115712 models.common.C3 [128, 128, 2] \\n\",\n \" 5 -1 1 295424 models.common.Conv [128, 256, 3, 2] \\n\",\n \" 6 -1 3 625152 models.common.C3 [256, 256, 3] \\n\",\n \" 7 -1 1 1180672 models.common.Conv [256, 512, 3, 2] \\n\",\n \" 8 -1 1 1182720 models.common.C3 [512, 512, 1] \\n\",\n \" 9 -1 1 656896 models.common.SPPF [512, 512, 5] \\n\",\n \" 10 -1 1 131584 models.common.Conv [512, 256, 1, 1] \\n\",\n \" 11 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] \\n\",\n \" 12 [-1, 6] 1 0 models.common.Concat [1] \\n\",\n \" 13 -1 1 361984 models.common.C3 [512, 256, 1, False] \\n\",\n \" 14 -1 1 33024 models.common.Conv [256, 128, 1, 1] \\n\",\n \" 15 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] \\n\",\n \" 16 [-1, 4] 1 0 models.common.Concat [1] \\n\",\n \" 17 -1 1 90880 models.common.C3 [256, 128, 1, False] \\n\",\n \" 18 -1 1 147712 models.common.Conv [128, 128, 3, 2] \\n\",\n \" 19 [-1, 14] 1 0 models.common.Concat [1] \\n\",\n \" 20 -1 1 296448 models.common.C3 [256, 256, 1, False] \\n\",\n \" 21 -1 1 590336 models.common.Conv [256, 256, 3, 2] \\n\",\n \" 22 [-1, 10] 1 0 models.common.Concat [1] \\n\",\n \" 23 -1 1 1182720 models.common.C3 [512, 512, 1, False] \\n\",\n \" 24 [17, 20, 23] 1 615133 models.yolo.Segment [80, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], 32, 128, [128, 256, 512]]\\n\",\n \"Model summary: 225 layers, 7621277 parameters, 7621277 gradients, 26.6 GFLOPs\\n\",\n \"\\n\",\n \"Transferred 367/367 items from yolov5s-seg.pt\\n\",\n \"\\u001B[34m\\u001B[1mAMP: \\u001B[0mchecks passed ✅\\n\",\n \"\\u001B[34m\\u001B[1moptimizer:\\u001B[0m SGD(lr=0.01) with parameter groups 60 weight(decay=0.0), 63 weight(decay=0.0005), 63 bias\\n\",\n \"\\u001B[34m\\u001B[1malbumentations: \\u001B[0mBlur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01), CLAHE(p=0.01, clip_limit=(1, 4.0), tile_grid_size=(8, 8))\\n\",\n \"\\u001B[34m\\u001B[1mtrain: \\u001B[0mScanning /content/datasets/coco128-seg/labels/train2017... 126 images, 2 backgrounds, 0 corrupt: 100% 128/128 [00:00<00:00, 1389.59it/s]\\n\",\n \"\\u001B[34m\\u001B[1mtrain: \\u001B[0mNew cache created: /content/datasets/coco128-seg/labels/train2017.cache\\n\",\n \"\\u001B[34m\\u001B[1mtrain: \\u001B[0mCaching images (0.1GB ram): 100% 128/128 [00:00<00:00, 238.86it/s]\\n\",\n \"\\u001B[34m\\u001B[1mval: \\u001B[0mScanning /content/datasets/coco128-seg/labels/train2017.cache... 126 images, 2 backgrounds, 0 corrupt: 100% 128/128 [00:00 # 2. paste API key\\n\",\n \"python train.py --img 640 --epochs 3 --data coco128.yaml --weights yolov5s.pt # 3. train\\n\",\n \"```\\n\",\n \"To learn more about all of the supported Comet features for this integration, check out the [Comet Tutorial](https://docs.ultralytics.com/yolov5/tutorials/comet_logging_integration). If you'd like to learn more about Comet, head over to our [documentation](https://www.comet.com/docs/v2/?utm_source=yolov5&utm_medium=partner&utm_campaign=partner_yolov5_2022&utm_content=yolov5_colab). Get started by trying out the Comet Colab Notebook:\\n\",\n \"[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1RG0WOQyxlDlo5Km8GogJpIEJlg_5lyYO?usp=sharing)\\n\",\n \"\\n\",\n \"\\n\",\n \"\\\"Comet\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"Lay2WsTjNJzP\"\n },\n \"source\": [\n \"## ClearML Logging and Automation 🌟 NEW\\n\",\n \"\\n\",\n \"[ClearML](https://cutt.ly/yolov5-notebook-clearml) is completely integrated into YOLOv5 to track your experimentation, manage dataset versions and even remotely execute training runs. To enable ClearML (check cells above):\\n\",\n \"\\n\",\n \"- `pip install clearml`\\n\",\n \"- run `clearml-init` to connect to a ClearML server (**deploy your own [open-source server](https://github.com/allegroai/clearml-server)**, or use our [free hosted server](https://cutt.ly/yolov5-notebook-clearml))\\n\",\n \"\\n\",\n \"You'll get all the great expected features from an experiment manager: live updates, model upload, experiment comparison etc. but ClearML also tracks uncommitted changes and installed packages for example. Thanks to that ClearML Tasks (which is what we call experiments) are also reproducible on different machines! With only 1 extra line, we can schedule a YOLOv5 training task on a queue to be executed by any number of ClearML Agents (workers).\\n\",\n \"\\n\",\n \"You can use ClearML Data to version your dataset and then pass it to YOLOv5 simply using its unique ID. This will help you keep track of your data without adding extra hassle. Explore the [ClearML Tutorial](https://docs.ultralytics.com/yolov5/tutorials/clearml_logging_integration) for details!\\n\",\n \"\\n\",\n \"\\n\",\n \"\\\"ClearML\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"-WPvRbS5Swl6\"\n },\n \"source\": [\n \"## Local Logging\\n\",\n \"\\n\",\n \"Training results are automatically logged with [Tensorboard](https://www.tensorflow.org/tensorboard) and [CSV](https://github.com/ultralytics/yolov5/pull/4148) loggers to `runs/train`, with a new experiment directory created for each new training as `runs/train/exp2`, `runs/train/exp3`, etc.\\n\",\n \"\\n\",\n \"This directory contains train and val statistics, mosaics, labels, predictions and augmentated mosaics, as well as metrics and charts including precision-recall (PR) curves and confusion matrices. \\n\",\n \"\\n\",\n \"\\\"Local\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"Zelyeqbyt3GD\"\n },\n \"source\": [\n \"# Environments\\n\",\n \"\\n\",\n \"YOLOv5 may be run in any of the following up-to-date verified environments (with all dependencies including [CUDA](https://developer.nvidia.com/cuda)/[CUDNN](https://developer.nvidia.com/cudnn), [Python](https://www.python.org/) and [PyTorch](https://pytorch.org/) preinstalled):\\n\",\n \"\\n\",\n \"- **Notebooks** with free GPU: \\\"Run \\\"Open \\\"Open\\n\",\n \"- **Google Cloud** Deep Learning VM. See [GCP Quickstart Guide](https://docs.ultralytics.com/yolov5/environments/google_cloud_quickstart_tutorial/)\\n\",\n \"- **Amazon** Deep Learning AMI. See [AWS Quickstart Guide](https://docs.ultralytics.com/yolov5/environments/aws_quickstart_tutorial/)\\n\",\n \"- **Docker Image**. See [Docker Quickstart Guide](https://docs.ultralytics.com/yolov5/environments/docker_image_quickstart_tutorial/) \\\"Docker\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"6Qu7Iesl0p54\"\n },\n \"source\": [\n \"# Status\\n\",\n \"\\n\",\n \"![YOLOv5 CI](https://github.com/ultralytics/yolov3/actions/workflows/ci-testing.yml/badge.svg)\\n\",\n \"\\n\",\n \"If this badge is green, all [YOLOv3 GitHub Actions](https://github.com/ultralytics/yolov3/actions) Continuous Integration (CI) tests are currently passing. CI tests verify correct operation of YOLOv5 training ([train.py](https://github.com/ultralytics/yolov5/blob/master/train.py)), testing ([val.py](https://github.com/ultralytics/yolov5/blob/master/val.py)), inference ([detect.py](https://github.com/ultralytics/yolov5/blob/master/detect.py)) and export ([export.py](https://github.com/ultralytics/yolov5/blob/master/export.py)) on macOS, Windows, and Ubuntu every 24 hours and on every commit.\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"IEijrePND_2I\"\n },\n \"source\": [\n \"# Appendix\\n\",\n \"\\n\",\n \"Additional content below.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"GMusP4OAxFu6\"\n },\n \"outputs\": [],\n \"source\": [\n \"# YOLOv5 PyTorch HUB Inference (DetectionModels only)\\n\",\n \"\\n\",\n \"model = torch.hub.load(\\\"ultralytics/yolov5\\\", \\\"yolov5s-seg\\\") # yolov5n - yolov5x6 or custom\\n\",\n \"im = \\\"https://ultralytics.com/images/zidane.jpg\\\" # file, Path, PIL.Image, OpenCV, nparray, list\\n\",\n \"results = model(im) # inference\\n\",\n \"results.print() # or .show(), .save(), .crop(), .pandas(), etc.\"\n ]\n }\n ],\n \"metadata\": {\n \"accelerator\": \"GPU\",\n \"colab\": {\n \"name\": \"YOLOv5 Segmentation Tutorial\",\n \"provenance\": [],\n \"toc_visible\": true\n },\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.12\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n" }, { "path": "segment/val.py", "content": "# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license\n\"\"\"\nValidate a trained YOLOv3 segment model on a segment dataset.\n\nUsage:\n $ bash data/scripts/get_coco.sh --val --segments # download COCO-segments val split (1G, 5000 images)\n $ python segment/val.py --weights yolov5s-seg.pt --data coco.yaml --img 640 # validate COCO-segments\n\nUsage - formats:\n $ python segment/val.py --weights yolov5s-seg.pt # PyTorch\n yolov5s-seg.torchscript # TorchScript\n yolov5s-seg.onnx # ONNX Runtime or OpenCV DNN with --dnn\n yolov5s-seg_openvino_label # OpenVINO\n yolov5s-seg.engine # TensorRT\n yolov5s-seg.mlmodel # CoreML (macOS-only)\n yolov5s-seg_saved_model # TensorFlow SavedModel\n yolov5s-seg.pb # TensorFlow GraphDef\n yolov5s-seg.tflite # TensorFlow Lite\n yolov5s-seg_edgetpu.tflite # TensorFlow Edge TPU\n yolov5s-seg_paddle_model # PaddlePaddle\n\"\"\"\n\nimport argparse\nimport json\nimport os\nimport subprocess\nimport sys\nfrom multiprocessing.pool import ThreadPool\nfrom pathlib import Path\n\nimport numpy as np\nimport torch\nfrom tqdm import tqdm\n\nFILE = Path(__file__).resolve()\nROOT = FILE.parents[1] # YOLOv3 root directory\nif str(ROOT) not in sys.path:\n sys.path.append(str(ROOT)) # add ROOT to PATH\nROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative\n\nimport torch.nn.functional as F\n\nfrom models.common import DetectMultiBackend\nfrom models.yolo import SegmentationModel\nfrom utils.callbacks import Callbacks\nfrom utils.general import (\n LOGGER,\n NUM_THREADS,\n TQDM_BAR_FORMAT,\n Profile,\n check_dataset,\n check_img_size,\n check_requirements,\n check_yaml,\n coco80_to_coco91_class,\n colorstr,\n increment_path,\n non_max_suppression,\n print_args,\n scale_boxes,\n xywh2xyxy,\n xyxy2xywh,\n)\nfrom utils.metrics import ConfusionMatrix, box_iou\nfrom utils.plots import output_to_target, plot_val_study\nfrom utils.segment.dataloaders import create_dataloader\nfrom utils.segment.general import mask_iou, process_mask, process_mask_native, scale_image\nfrom utils.segment.metrics import Metrics, ap_per_class_box_and_mask\nfrom utils.segment.plots import plot_images_and_masks\nfrom utils.torch_utils import de_parallel, select_device, smart_inference_mode\n\n\ndef save_one_txt(predn, save_conf, shape, file):\n \"\"\"Saves detection results in normalized xywh format (with optional confidence) to a txt file.\"\"\"\n gn = torch.tensor(shape)[[1, 0, 1, 0]] # normalization gain whwh\n for *xyxy, conf, cls in predn.tolist():\n xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() # normalized xywh\n line = (cls, *xywh, conf) if save_conf else (cls, *xywh) # label format\n with open(file, \"a\") as f:\n f.write((\"%g \" * len(line)).rstrip() % line + \"\\n\")\n\n\ndef save_one_json(predn, jdict, path, class_map, pred_masks):\n \"\"\"Saves detection results in COCO JSON format, including bbox, category_id and segmentation if available.\"\"\"\n from pycocotools.mask import encode\n\n def single_encode(x):\n \"\"\"Encodes a binary mask to COCO RLE format, converting counts to a UTF-8 string for JSON serialization.\"\"\"\n rle = encode(np.asarray(x[:, :, None], order=\"F\", dtype=\"uint8\"))[0]\n rle[\"counts\"] = rle[\"counts\"].decode(\"utf-8\")\n return rle\n\n image_id = int(path.stem) if path.stem.isnumeric() else path.stem\n box = xyxy2xywh(predn[:, :4]) # xywh\n box[:, :2] -= box[:, 2:] / 2 # xy center to top-left corner\n pred_masks = np.transpose(pred_masks, (2, 0, 1))\n with ThreadPool(NUM_THREADS) as pool:\n rles = pool.map(single_encode, pred_masks)\n for i, (p, b) in enumerate(zip(predn.tolist(), box.tolist())):\n jdict.append(\n {\n \"image_id\": image_id,\n \"category_id\": class_map[int(p[5])],\n \"bbox\": [round(x, 3) for x in b],\n \"score\": round(p[4], 5),\n \"segmentation\": rles[i],\n }\n )\n\n\ndef process_batch(detections, labels, iouv, pred_masks=None, gt_masks=None, overlap=False, masks=False):\n \"\"\"Return correct prediction matrix.\n\n Args:\n detections (array[N, 6]), x1, y1, x2, y2, conf, class: labels (array[M, 5]), class, x1, y1, x2, y2\n\n Returns:\n correct (array[N, 10]), for 10 IoU levels.\n \"\"\"\n if masks:\n if overlap:\n nl = len(labels)\n index = torch.arange(nl, device=gt_masks.device).view(nl, 1, 1) + 1\n gt_masks = gt_masks.repeat(nl, 1, 1) # shape(1,640,640) -> (n,640,640)\n gt_masks = torch.where(gt_masks == index, 1.0, 0.0)\n if gt_masks.shape[1:] != pred_masks.shape[1:]:\n gt_masks = F.interpolate(gt_masks[None], pred_masks.shape[1:], mode=\"bilinear\", align_corners=False)[0]\n gt_masks = gt_masks.gt_(0.5)\n iou = mask_iou(gt_masks.view(gt_masks.shape[0], -1), pred_masks.view(pred_masks.shape[0], -1))\n else: # boxes\n iou = box_iou(labels[:, 1:], detections[:, :4])\n\n correct = np.zeros((detections.shape[0], iouv.shape[0])).astype(bool)\n correct_class = labels[:, 0:1] == detections[:, 5]\n for i in range(len(iouv)):\n x = torch.where((iou >= iouv[i]) & correct_class) # IoU > threshold and classes match\n if x[0].shape[0]:\n matches = torch.cat((torch.stack(x, 1), iou[x[0], x[1]][:, None]), 1).cpu().numpy() # [label, detect, iou]\n if x[0].shape[0] > 1:\n matches = matches[matches[:, 2].argsort()[::-1]]\n matches = matches[np.unique(matches[:, 1], return_index=True)[1]]\n # matches = matches[matches[:, 2].argsort()[::-1]]\n matches = matches[np.unique(matches[:, 0], return_index=True)[1]]\n correct[matches[:, 1].astype(int), i] = True\n return torch.tensor(correct, dtype=torch.bool, device=iouv.device)\n\n\n@smart_inference_mode()\ndef run(\n data,\n weights=None, # model.pt path(s)\n batch_size=32, # batch size\n imgsz=640, # inference size (pixels)\n conf_thres=0.001, # confidence threshold\n iou_thres=0.6, # NMS IoU threshold\n max_det=300, # maximum detections per image\n task=\"val\", # train, val, test, speed or study\n device=\"\", # cuda device, i.e. 0 or 0,1,2,3 or cpu\n workers=8, # max dataloader workers (per RANK in DDP mode)\n single_cls=False, # treat as single-class dataset\n augment=False, # augmented inference\n verbose=False, # verbose output\n save_txt=False, # save results to *.txt\n save_hybrid=False, # save label+prediction hybrid results to *.txt\n save_conf=False, # save confidences in --save-txt labels\n save_json=False, # save a COCO-JSON results file\n project=ROOT / \"runs/val-seg\", # save to project/name\n name=\"exp\", # save to project/name\n exist_ok=False, # existing project/name ok, do not increment\n half=True, # use FP16 half-precision inference\n dnn=False, # use OpenCV DNN for ONNX inference\n model=None,\n dataloader=None,\n save_dir=Path(\"\"),\n plots=True,\n overlap=False,\n mask_downsample_ratio=1,\n compute_loss=None,\n callbacks=Callbacks(),\n):\n \"\"\"Validates a trained YOLOv3 segmentation model using a specified dataset and evaluation metrics.\"\"\"\n if save_json:\n check_requirements(\"pycocotools>=2.0.6\")\n process = process_mask_native # more accurate\n else:\n process = process_mask # faster\n\n # Initialize/load model and set device\n training = model is not None\n if training: # called by train.py\n device, pt, jit, engine = next(model.parameters()).device, True, False, False # get model device, PyTorch model\n half &= device.type != \"cpu\" # half precision only supported on CUDA\n model.half() if half else model.float()\n nm = de_parallel(model).model[-1].nm # number of masks\n else: # called directly\n device = select_device(device, batch_size=batch_size)\n\n # Directories\n save_dir = increment_path(Path(project) / name, exist_ok=exist_ok) # increment run\n (save_dir / \"labels\" if save_txt else save_dir).mkdir(parents=True, exist_ok=True) # make dir\n\n # Load model\n model = DetectMultiBackend(weights, device=device, dnn=dnn, data=data, fp16=half)\n stride, pt, jit, engine = model.stride, model.pt, model.jit, model.engine\n imgsz = check_img_size(imgsz, s=stride) # check image size\n half = model.fp16 # FP16 supported on limited backends with CUDA\n nm = de_parallel(model).model.model[-1].nm if isinstance(model, SegmentationModel) else 32 # number of masks\n if engine:\n batch_size = model.batch_size\n else:\n device = model.device\n if not (pt or jit):\n batch_size = 1 # export.py models default to batch-size 1\n LOGGER.info(f\"Forcing --batch-size 1 square inference (1,3,{imgsz},{imgsz}) for non-PyTorch models\")\n\n # Data\n data = check_dataset(data) # check\n\n # Configure\n model.eval()\n cuda = device.type != \"cpu\"\n is_coco = isinstance(data.get(\"val\"), str) and data[\"val\"].endswith(f\"coco{os.sep}val2017.txt\") # COCO dataset\n nc = 1 if single_cls else int(data[\"nc\"]) # number of classes\n iouv = torch.linspace(0.5, 0.95, 10, device=device) # iou vector for mAP@0.5:0.95\n niou = iouv.numel()\n\n # Dataloader\n if not training:\n if pt and not single_cls: # check --weights are trained on --data\n ncm = model.model.nc\n assert ncm == nc, (\n f\"{weights} ({ncm} classes) trained on different --data than what you passed ({nc} \"\n f\"classes). Pass correct combination of --weights and --data that are trained together.\"\n )\n model.warmup(imgsz=(1 if pt else batch_size, 3, imgsz, imgsz)) # warmup\n pad, rect = (0.0, False) if task == \"speed\" else (0.5, pt) # square inference for benchmarks\n task = task if task in (\"train\", \"val\", \"test\") else \"val\" # path to train/val/test images\n dataloader = create_dataloader(\n data[task],\n imgsz,\n batch_size,\n stride,\n single_cls,\n pad=pad,\n rect=rect,\n workers=workers,\n prefix=colorstr(f\"{task}: \"),\n overlap_mask=overlap,\n mask_downsample_ratio=mask_downsample_ratio,\n )[0]\n\n seen = 0\n confusion_matrix = ConfusionMatrix(nc=nc)\n names = model.names if hasattr(model, \"names\") else model.module.names # get class names\n if isinstance(names, (list, tuple)): # old format\n names = dict(enumerate(names))\n class_map = coco80_to_coco91_class() if is_coco else list(range(1000))\n s = (\"%22s\" + \"%11s\" * 10) % (\n \"Class\",\n \"Images\",\n \"Instances\",\n \"Box(P\",\n \"R\",\n \"mAP50\",\n \"mAP50-95)\",\n \"Mask(P\",\n \"R\",\n \"mAP50\",\n \"mAP50-95)\",\n )\n dt = Profile(), Profile(), Profile()\n metrics = Metrics()\n loss = torch.zeros(4, device=device)\n jdict, stats = [], []\n # callbacks.run('on_val_start')\n pbar = tqdm(dataloader, desc=s, bar_format=TQDM_BAR_FORMAT) # progress bar\n for batch_i, (im, targets, paths, shapes, masks) in enumerate(pbar):\n # callbacks.run('on_val_batch_start')\n with dt[0]:\n if cuda:\n im = im.to(device, non_blocking=True)\n targets = targets.to(device)\n masks = masks.to(device)\n masks = masks.float()\n im = im.half() if half else im.float() # uint8 to fp16/32\n im /= 255 # 0 - 255 to 0.0 - 1.0\n nb, _, height, width = im.shape # batch size, channels, height, width\n\n # Inference\n with dt[1]:\n preds, protos, train_out = model(im) if compute_loss else (*model(im, augment=augment)[:2], None)\n\n # Loss\n if compute_loss:\n loss += compute_loss((train_out, protos), targets, masks)[1] # box, obj, cls\n\n # NMS\n targets[:, 2:] *= torch.tensor((width, height, width, height), device=device) # to pixels\n lb = [targets[targets[:, 0] == i, 1:] for i in range(nb)] if save_hybrid else [] # for autolabelling\n with dt[2]:\n preds = non_max_suppression(\n preds, conf_thres, iou_thres, labels=lb, multi_label=True, agnostic=single_cls, max_det=max_det, nm=nm\n )\n\n # Metrics\n plot_masks = [] # masks for plotting\n for si, (pred, proto) in enumerate(zip(preds, protos)):\n labels = targets[targets[:, 0] == si, 1:]\n nl, npr = labels.shape[0], pred.shape[0] # number of labels, predictions\n path, shape = Path(paths[si]), shapes[si][0]\n correct_masks = torch.zeros(npr, niou, dtype=torch.bool, device=device) # init\n correct_bboxes = torch.zeros(npr, niou, dtype=torch.bool, device=device) # init\n seen += 1\n\n if npr == 0:\n if nl:\n stats.append((correct_masks, correct_bboxes, *torch.zeros((2, 0), device=device), labels[:, 0]))\n if plots:\n confusion_matrix.process_batch(detections=None, labels=labels[:, 0])\n continue\n\n # Masks\n midx = [si] if overlap else targets[:, 0] == si\n gt_masks = masks[midx]\n pred_masks = process(proto, pred[:, 6:], pred[:, :4], shape=im[si].shape[1:])\n\n # Predictions\n if single_cls:\n pred[:, 5] = 0\n predn = pred.clone()\n scale_boxes(im[si].shape[1:], predn[:, :4], shape, shapes[si][1]) # native-space pred\n\n # Evaluate\n if nl:\n tbox = xywh2xyxy(labels[:, 1:5]) # target boxes\n scale_boxes(im[si].shape[1:], tbox, shape, shapes[si][1]) # native-space labels\n labelsn = torch.cat((labels[:, 0:1], tbox), 1) # native-space labels\n correct_bboxes = process_batch(predn, labelsn, iouv)\n correct_masks = process_batch(predn, labelsn, iouv, pred_masks, gt_masks, overlap=overlap, masks=True)\n if plots:\n confusion_matrix.process_batch(predn, labelsn)\n stats.append((correct_masks, correct_bboxes, pred[:, 4], pred[:, 5], labels[:, 0])) # (conf, pcls, tcls)\n\n pred_masks = torch.as_tensor(pred_masks, dtype=torch.uint8)\n if plots and batch_i < 3:\n plot_masks.append(pred_masks[:15]) # filter top 15 to plot\n\n # Save/log\n if save_txt:\n save_one_txt(predn, save_conf, shape, file=save_dir / \"labels\" / f\"{path.stem}.txt\")\n if save_json:\n pred_masks = scale_image(\n im[si].shape[1:], pred_masks.permute(1, 2, 0).contiguous().cpu().numpy(), shape, shapes[si][1]\n )\n save_one_json(predn, jdict, path, class_map, pred_masks) # append to COCO-JSON dictionary\n # callbacks.run('on_val_image_end', pred, predn, path, names, im[si])\n\n # Plot images\n if plots and batch_i < 3:\n if len(plot_masks):\n plot_masks = torch.cat(plot_masks, dim=0)\n plot_images_and_masks(im, targets, masks, paths, save_dir / f\"val_batch{batch_i}_labels.jpg\", names)\n plot_images_and_masks(\n im,\n output_to_target(preds, max_det=15),\n plot_masks,\n paths,\n save_dir / f\"val_batch{batch_i}_pred.jpg\",\n names,\n ) # pred\n\n # callbacks.run('on_val_batch_end')\n\n # Compute metrics\n stats = [torch.cat(x, 0).cpu().numpy() for x in zip(*stats)] # to numpy\n if len(stats) and stats[0].any():\n results = ap_per_class_box_and_mask(*stats, plot=plots, save_dir=save_dir, names=names)\n metrics.update(results)\n nt = np.bincount(stats[4].astype(int), minlength=nc) # number of targets per class\n\n # Print results\n pf = \"%22s\" + \"%11i\" * 2 + \"%11.3g\" * 8 # print format\n LOGGER.info(pf % (\"all\", seen, nt.sum(), *metrics.mean_results()))\n if nt.sum() == 0:\n LOGGER.warning(f\"WARNING ⚠️ no labels found in {task} set, can not compute metrics without labels\")\n\n # Print results per class\n if (verbose or (nc < 50 and not training)) and nc > 1 and len(stats):\n for i, c in enumerate(metrics.ap_class_index):\n LOGGER.info(pf % (names[c], seen, nt[c], *metrics.class_result(i)))\n\n # Print speeds\n t = tuple(x.t / seen * 1e3 for x in dt) # speeds per image\n if not training:\n shape = (batch_size, 3, imgsz, imgsz)\n LOGGER.info(f\"Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {shape}\" % t)\n\n # Plots\n if plots:\n confusion_matrix.plot(save_dir=save_dir, names=list(names.values()))\n # callbacks.run('on_val_end')\n\n mp_bbox, mr_bbox, map50_bbox, map_bbox, mp_mask, mr_mask, map50_mask, map_mask = metrics.mean_results()\n\n # Save JSON\n if save_json and len(jdict):\n w = Path(weights[0] if isinstance(weights, list) else weights).stem if weights is not None else \"\" # weights\n anno_json = str(Path(\"../datasets/coco/annotations/instances_val2017.json\")) # annotations\n pred_json = str(save_dir / f\"{w}_predictions.json\") # predictions\n LOGGER.info(f\"\\nEvaluating pycocotools mAP... saving {pred_json}...\")\n with open(pred_json, \"w\") as f:\n json.dump(jdict, f)\n\n try: # https://github.com/cocodataset/cocoapi/blob/master/PythonAPI/pycocoEvalDemo.ipynb\n from pycocotools.coco import COCO\n from pycocotools.cocoeval import COCOeval\n\n anno = COCO(anno_json) # init annotations api\n pred = anno.loadRes(pred_json) # init predictions api\n results = []\n for eval in COCOeval(anno, pred, \"bbox\"), COCOeval(anno, pred, \"segm\"):\n if is_coco:\n eval.params.imgIds = [int(Path(x).stem) for x in dataloader.dataset.im_files] # img ID to evaluate\n eval.evaluate()\n eval.accumulate()\n eval.summarize()\n results.extend(eval.stats[:2]) # update results (mAP@0.5:0.95, mAP@0.5)\n map_bbox, map50_bbox, map_mask, map50_mask = results\n except Exception as e:\n LOGGER.info(f\"pycocotools unable to run: {e}\")\n\n # Return results\n model.float() # for training\n if not training:\n s = f\"\\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}\" if save_txt else \"\"\n LOGGER.info(f\"Results saved to {colorstr('bold', save_dir)}{s}\")\n final_metric = mp_bbox, mr_bbox, map50_bbox, map_bbox, mp_mask, mr_mask, map50_mask, map_mask\n return (*final_metric, *(loss.cpu() / len(dataloader)).tolist()), metrics.get_maps(nc), t\n\n\ndef parse_opt():\n \"\"\"Parses and validates command-line arguments for configuring model training or inference.\"\"\"\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--data\", type=str, default=ROOT / \"data/coco128-seg.yaml\", help=\"dataset.yaml path\")\n parser.add_argument(\"--weights\", nargs=\"+\", type=str, default=ROOT / \"yolov5s-seg.pt\", help=\"model path(s)\")\n parser.add_argument(\"--batch-size\", type=int, default=32, help=\"batch size\")\n parser.add_argument(\"--imgsz\", \"--img\", \"--img-size\", type=int, default=640, help=\"inference size (pixels)\")\n parser.add_argument(\"--conf-thres\", type=float, default=0.001, help=\"confidence threshold\")\n parser.add_argument(\"--iou-thres\", type=float, default=0.6, help=\"NMS IoU threshold\")\n parser.add_argument(\"--max-det\", type=int, default=300, help=\"maximum detections per image\")\n parser.add_argument(\"--task\", default=\"val\", help=\"train, val, test, speed or study\")\n parser.add_argument(\"--device\", default=\"\", help=\"cuda device, i.e. 0 or 0,1,2,3 or cpu\")\n parser.add_argument(\"--workers\", type=int, default=8, help=\"max dataloader workers (per RANK in DDP mode)\")\n parser.add_argument(\"--single-cls\", action=\"store_true\", help=\"treat as single-class dataset\")\n parser.add_argument(\"--augment\", action=\"store_true\", help=\"augmented inference\")\n parser.add_argument(\"--verbose\", action=\"store_true\", help=\"report mAP by class\")\n parser.add_argument(\"--save-txt\", action=\"store_true\", help=\"save results to *.txt\")\n parser.add_argument(\"--save-hybrid\", action=\"store_true\", help=\"save label+prediction hybrid results to *.txt\")\n parser.add_argument(\"--save-conf\", action=\"store_true\", help=\"save confidences in --save-txt labels\")\n parser.add_argument(\"--save-json\", action=\"store_true\", help=\"save a COCO-JSON results file\")\n parser.add_argument(\"--project\", default=ROOT / \"runs/val-seg\", help=\"save results to project/name\")\n parser.add_argument(\"--name\", default=\"exp\", help=\"save to project/name\")\n parser.add_argument(\"--exist-ok\", action=\"store_true\", help=\"existing project/name ok, do not increment\")\n parser.add_argument(\"--half\", action=\"store_true\", help=\"use FP16 half-precision inference\")\n parser.add_argument(\"--dnn\", action=\"store_true\", help=\"use OpenCV DNN for ONNX inference\")\n opt = parser.parse_args()\n opt.data = check_yaml(opt.data) # check YAML\n # opt.save_json |= opt.data.endswith('coco.yaml')\n opt.save_txt |= opt.save_hybrid\n print_args(vars(opt))\n return opt\n\n\ndef main(opt):\n \"\"\"Executes the primary function based on task, including training, validation, testing, speed, and study\n benchmarks.\n \"\"\"\n check_requirements(ROOT / \"requirements.txt\", exclude=(\"tensorboard\", \"thop\"))\n\n if opt.task in (\"train\", \"val\", \"test\"): # run normally\n if opt.conf_thres > 0.001: # https://github.com/ultralytics/yolov5/issues/1466\n LOGGER.warning(f\"WARNING ⚠️ confidence threshold {opt.conf_thres} > 0.001 produces invalid results\")\n if opt.save_hybrid:\n LOGGER.warning(\"WARNING ⚠️ --save-hybrid returns high mAP from hybrid labels, not from predictions alone\")\n run(**vars(opt))\n\n else:\n weights = opt.weights if isinstance(opt.weights, list) else [opt.weights]\n opt.half = torch.cuda.is_available() and opt.device != \"cpu\" # FP16 for fastest results\n if opt.task == \"speed\": # speed benchmarks\n # python val.py --task speed --data coco.yaml --batch 1 --weights yolov5n.pt yolov5s.pt...\n opt.conf_thres, opt.iou_thres, opt.save_json = 0.25, 0.45, False\n for opt.weights in weights:\n run(**vars(opt), plots=False)\n\n elif opt.task == \"study\": # speed vs mAP benchmarks\n # python val.py --task study --data coco.yaml --iou 0.7 --weights yolov5n.pt yolov5s.pt...\n for opt.weights in weights:\n f = f\"study_{Path(opt.data).stem}_{Path(opt.weights).stem}.txt\" # filename to save to\n x, y = list(range(256, 1536 + 128, 128)), [] # x axis (image sizes), y axis\n for opt.imgsz in x: # img-size\n LOGGER.info(f\"\\nRunning {f} --imgsz {opt.imgsz}...\")\n r, _, t = run(**vars(opt), plots=False)\n y.append(r + t) # results and times\n np.savetxt(f, y, fmt=\"%10.4g\") # save\n subprocess.run([\"zip\", \"-r\", \"study.zip\", \"study_*.txt\"])\n plot_val_study(x=x) # plot\n else:\n raise NotImplementedError(f'--task {opt.task} not in (\"train\", \"val\", \"test\", \"speed\", \"study\")')\n\n\nif __name__ == \"__main__\":\n opt = parse_opt()\n main(opt)\n" }, { "path": "train.py", "content": "# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license\n\"\"\"\nTrain a YOLOv3 model on a custom dataset. Models and datasets download automatically from the latest YOLOv3 release.\n\nUsage - Single-GPU training:\n $ python train.py --data coco128.yaml --weights yolov5s.pt --img 640 # from pretrained (recommended)\n $ python train.py --data coco128.yaml --weights '' --cfg yolov5s.yaml --img 640 # from scratch\n\nUsage - Multi-GPU DDP training:\n $ python -m torch.distributed.run --nproc_per_node 4 --master_port 1 train.py --data coco128.yaml --weights yolov5s.pt --img 640 --device 0,1,2,3\n\nModels: https://github.com/ultralytics/yolov5/tree/master/models\nDatasets: https://github.com/ultralytics/yolov5/tree/master/data\nTutorial: https://docs.ultralytics.com/yolov5/tutorials/train_custom_data\n\"\"\"\n\nimport argparse\nimport math\nimport os\nimport random\nimport subprocess\nimport sys\nimport time\nfrom copy import deepcopy\nfrom datetime import datetime\nfrom pathlib import Path\n\ntry:\n import comet_ml # must be imported before torch (if installed)\nexcept ImportError:\n comet_ml = None\n\nimport numpy as np\nimport torch\nimport torch.distributed as dist\nimport torch.nn as nn\nimport yaml\nfrom torch.optim import lr_scheduler\nfrom tqdm import tqdm\n\nFILE = Path(__file__).resolve()\nROOT = FILE.parents[0] # YOLOv3 root directory\nif str(ROOT) not in sys.path:\n sys.path.append(str(ROOT)) # add ROOT to PATH\nROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative\n\nfrom ultralytics.utils.patches import torch_load\n\nimport val as validate # for end-of-epoch mAP\nfrom models.experimental import attempt_load\nfrom models.yolo import Model\nfrom utils.autoanchor import check_anchors\nfrom utils.autobatch import check_train_batch_size\nfrom utils.callbacks import Callbacks\nfrom utils.dataloaders import create_dataloader\nfrom utils.downloads import attempt_download, is_url\nfrom utils.general import (\n LOGGER,\n TQDM_BAR_FORMAT,\n check_amp,\n check_dataset,\n check_file,\n check_git_info,\n check_git_status,\n check_img_size,\n check_requirements,\n check_suffix,\n check_yaml,\n colorstr,\n get_latest_run,\n increment_path,\n init_seeds,\n intersect_dicts,\n labels_to_class_weights,\n labels_to_image_weights,\n methods,\n one_cycle,\n print_args,\n print_mutation,\n strip_optimizer,\n yaml_save,\n)\nfrom utils.loggers import Loggers\nfrom utils.loggers.comet.comet_utils import check_comet_resume\nfrom utils.loss import ComputeLoss\nfrom utils.metrics import fitness\nfrom utils.plots import plot_evolve\nfrom utils.torch_utils import (\n EarlyStopping,\n ModelEMA,\n de_parallel,\n select_device,\n smart_DDP,\n smart_optimizer,\n smart_resume,\n torch_distributed_zero_first,\n)\n\nLOCAL_RANK = int(os.getenv(\"LOCAL_RANK\", -1)) # https://pytorch.org/docs/stable/elastic/run.html\nRANK = int(os.getenv(\"RANK\", -1))\nWORLD_SIZE = int(os.getenv(\"WORLD_SIZE\", 1))\nGIT_INFO = check_git_info()\n\n\ndef train(hyp, opt, device, callbacks): # hyp is path/to/hyp.yaml or hyp dictionary\n \"\"\"Train a YOLOv3 model on a custom dataset and manage the training process.\n\n Args:\n hyp (str | dict): Path to hyperparameters yaml file or hyperparameters dictionary.\n opt (argparse.Namespace): Parsed command line arguments containing training options.\n device (torch.device): Device to load and train the model on.\n callbacks (Callbacks): Callbacks to handle various stages of the training lifecycle.\n\n Returns:\n None\n Usage - Single-GPU training:\n $ python train.py --data coco128.yaml --weights yolov5s.pt --img 640 # from pretrained (recommended)\n $ python train.py --data coco128.yaml --weights '' --cfg yolov5s.yaml --img 640 # from scratch\n Usage - Multi-GPU DDP training:\n $ python -m torch.distributed.run --nproc_per_node 4 --master_port 1 train.py --data coco128.yaml --weights\n yolov5s.pt --img 640 --device 0,1,2,3\n Models: https://github.com/ultralytics/yolov5/tree/master/models\n Datasets: https://github.com/ultralytics/yolov5/tree/master/data\n Tutorial: https://docs.ultralytics.com/yolov5/tutorials/train_custom_data\n\n Examples:\n ```python\n from ultralytics import train\n import argparse\n import torch\n from utils.callbacks import Callbacks\n\n # Example usage\n args = argparse.Namespace(\n data='coco128.yaml',\n weights='yolov5s.pt',\n cfg='yolov5s.yaml',\n img_size=640,\n epochs=50,\n batch_size=16,\n device='0'\n )\n\n device = torch.device(f'cuda:{args.device}' if torch.cuda.is_available() else 'cpu')\n callbacks = Callbacks()\n\n train(hyp='hyp.scratch.yaml', opt=args, device=device, callbacks=callbacks)\n ```\n \"\"\"\n save_dir, epochs, batch_size, weights, single_cls, evolve, data, cfg, resume, noval, nosave, workers, freeze = (\n Path(opt.save_dir),\n opt.epochs,\n opt.batch_size,\n opt.weights,\n opt.single_cls,\n opt.evolve,\n opt.data,\n opt.cfg,\n opt.resume,\n opt.noval,\n opt.nosave,\n opt.workers,\n opt.freeze,\n )\n callbacks.run(\"on_pretrain_routine_start\")\n\n # Directories\n w = save_dir / \"weights\" # weights dir\n (w.parent if evolve else w).mkdir(parents=True, exist_ok=True) # make dir\n last, best = w / \"last.pt\", w / \"best.pt\"\n\n # Hyperparameters\n if isinstance(hyp, str):\n with open(hyp, errors=\"ignore\") as f:\n hyp = yaml.safe_load(f) # load hyps dict\n LOGGER.info(colorstr(\"hyperparameters: \") + \", \".join(f\"{k}={v}\" for k, v in hyp.items()))\n opt.hyp = hyp.copy() # for saving hyps to checkpoints\n\n # Save run settings\n if not evolve:\n yaml_save(save_dir / \"hyp.yaml\", hyp)\n yaml_save(save_dir / \"opt.yaml\", vars(opt))\n\n # Loggers\n data_dict = None\n if RANK in {-1, 0}:\n loggers = Loggers(save_dir, weights, opt, hyp, LOGGER) # loggers instance\n\n # Register actions\n for k in methods(loggers):\n callbacks.register_action(k, callback=getattr(loggers, k))\n\n # Process custom dataset artifact link\n data_dict = loggers.remote_dataset\n if resume: # If resuming runs from remote artifact\n weights, epochs, hyp, batch_size = opt.weights, opt.epochs, opt.hyp, opt.batch_size\n\n # Config\n plots = not evolve and not opt.noplots # create plots\n cuda = device.type != \"cpu\"\n init_seeds(opt.seed + 1 + RANK, deterministic=True)\n with torch_distributed_zero_first(LOCAL_RANK):\n data_dict = data_dict or check_dataset(data) # check if None\n train_path, val_path = data_dict[\"train\"], data_dict[\"val\"]\n nc = 1 if single_cls else int(data_dict[\"nc\"]) # number of classes\n names = {0: \"item\"} if single_cls and len(data_dict[\"names\"]) != 1 else data_dict[\"names\"] # class names\n is_coco = isinstance(val_path, str) and val_path.endswith(\"coco/val2017.txt\") # COCO dataset\n\n # Model\n check_suffix(weights, \".pt\") # check weights\n pretrained = weights.endswith(\".pt\")\n if pretrained:\n with torch_distributed_zero_first(LOCAL_RANK):\n weights = attempt_download(weights) # download if not found locally\n ckpt = torch_load(weights, map_location=\"cpu\") # load checkpoint to CPU to avoid CUDA memory leak\n model = Model(cfg or ckpt[\"model\"].yaml, ch=3, nc=nc, anchors=hyp.get(\"anchors\")).to(device) # create\n exclude = [\"anchor\"] if (cfg or hyp.get(\"anchors\")) and not resume else [] # exclude keys\n csd = ckpt[\"model\"].float().state_dict() # checkpoint state_dict as FP32\n csd = intersect_dicts(csd, model.state_dict(), exclude=exclude) # intersect\n model.load_state_dict(csd, strict=False) # load\n LOGGER.info(f\"Transferred {len(csd)}/{len(model.state_dict())} items from {weights}\") # report\n else:\n model = Model(cfg, ch=3, nc=nc, anchors=hyp.get(\"anchors\")).to(device) # create\n amp = check_amp(model) # check AMP\n\n # Freeze\n freeze = [f\"model.{x}.\" for x in (freeze if len(freeze) > 1 else range(freeze[0]))] # layers to freeze\n for k, v in model.named_parameters():\n v.requires_grad = True # train all layers\n # v.register_hook(lambda x: torch.nan_to_num(x)) # NaN to 0 (commented for erratic training results)\n if any(x in k for x in freeze):\n LOGGER.info(f\"freezing {k}\")\n v.requires_grad = False\n\n # Image size\n gs = max(int(model.stride.max()), 32) # grid size (max stride)\n imgsz = check_img_size(opt.imgsz, gs, floor=gs * 2) # verify imgsz is gs-multiple\n\n # Batch size\n if RANK == -1 and batch_size == -1: # single-GPU only, estimate best batch size\n batch_size = check_train_batch_size(model, imgsz, amp)\n loggers.on_params_update({\"batch_size\": batch_size})\n\n # Optimizer\n nbs = 64 # nominal batch size\n accumulate = max(round(nbs / batch_size), 1) # accumulate loss before optimizing\n hyp[\"weight_decay\"] *= batch_size * accumulate / nbs # scale weight_decay\n optimizer = smart_optimizer(model, opt.optimizer, hyp[\"lr0\"], hyp[\"momentum\"], hyp[\"weight_decay\"])\n\n # Scheduler\n if opt.cos_lr:\n lf = one_cycle(1, hyp[\"lrf\"], epochs) # cosine 1->hyp['lrf']\n else:\n\n def lf(x):\n \"\"\"Linear learning rate scheduler function with decay calculated by epoch proportion.\"\"\"\n return (1 - x / epochs) * (1.0 - hyp[\"lrf\"]) + hyp[\"lrf\"] # linear\n\n scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lf) # plot_lr_scheduler(optimizer, scheduler, epochs)\n\n # EMA\n ema = ModelEMA(model) if RANK in {-1, 0} else None\n\n # Resume\n best_fitness, start_epoch = 0.0, 0\n if pretrained:\n if resume:\n best_fitness, start_epoch, epochs = smart_resume(ckpt, optimizer, ema, weights, epochs, resume)\n del ckpt, csd\n\n # DP mode\n if cuda and RANK == -1 and torch.cuda.device_count() > 1:\n LOGGER.warning(\n \"WARNING ⚠️ DP not recommended, use torch.distributed.run for best DDP Multi-GPU results.\\n\"\n \"See Multi-GPU Tutorial at https://docs.ultralytics.com/yolov5/tutorials/multi_gpu_training to get started.\"\n )\n model = torch.nn.DataParallel(model)\n\n # SyncBatchNorm\n if opt.sync_bn and cuda and RANK != -1:\n model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model).to(device)\n LOGGER.info(\"Using SyncBatchNorm()\")\n\n # Trainloader\n train_loader, dataset = create_dataloader(\n train_path,\n imgsz,\n batch_size // WORLD_SIZE,\n gs,\n single_cls,\n hyp=hyp,\n augment=True,\n cache=None if opt.cache == \"val\" else opt.cache,\n rect=opt.rect,\n rank=LOCAL_RANK,\n workers=workers,\n image_weights=opt.image_weights,\n quad=opt.quad,\n prefix=colorstr(\"train: \"),\n shuffle=True,\n seed=opt.seed,\n )\n labels = np.concatenate(dataset.labels, 0)\n mlc = int(labels[:, 0].max()) # max label class\n assert mlc < nc, f\"Label class {mlc} exceeds nc={nc} in {data}. Possible class labels are 0-{nc - 1}\"\n\n # Process 0\n if RANK in {-1, 0}:\n val_loader = create_dataloader(\n val_path,\n imgsz,\n batch_size // WORLD_SIZE * 2,\n gs,\n single_cls,\n hyp=hyp,\n cache=None if noval else opt.cache,\n rect=True,\n rank=-1,\n workers=workers * 2,\n pad=0.5,\n prefix=colorstr(\"val: \"),\n )[0]\n\n if not resume:\n if not opt.noautoanchor:\n check_anchors(dataset, model=model, thr=hyp[\"anchor_t\"], imgsz=imgsz) # run AutoAnchor\n model.half().float() # pre-reduce anchor precision\n\n callbacks.run(\"on_pretrain_routine_end\", labels, names)\n\n # DDP mode\n if cuda and RANK != -1:\n model = smart_DDP(model)\n\n # Model attributes\n nl = de_parallel(model).model[-1].nl # number of detection layers (to scale hyps)\n hyp[\"box\"] *= 3 / nl # scale to layers\n hyp[\"cls\"] *= nc / 80 * 3 / nl # scale to classes and layers\n hyp[\"obj\"] *= (imgsz / 640) ** 2 * 3 / nl # scale to image size and layers\n hyp[\"label_smoothing\"] = opt.label_smoothing\n model.nc = nc # attach number of classes to model\n model.hyp = hyp # attach hyperparameters to model\n model.class_weights = labels_to_class_weights(dataset.labels, nc).to(device) * nc # attach class weights\n model.names = names\n\n # Start training\n t0 = time.time()\n nb = len(train_loader) # number of batches\n nw = max(round(hyp[\"warmup_epochs\"] * nb), 100) # number of warmup iterations, max(3 epochs, 100 iterations)\n # nw = min(nw, (epochs - start_epoch) / 2 * nb) # limit warmup to < 1/2 of training\n last_opt_step = -1\n maps = np.zeros(nc) # mAP per class\n results = (0, 0, 0, 0, 0, 0, 0) # P, R, mAP@.5, mAP@.5-.95, val_loss(box, obj, cls)\n scheduler.last_epoch = start_epoch - 1 # do not move\n scaler = torch.cuda.amp.GradScaler(enabled=amp)\n stopper, stop = EarlyStopping(patience=opt.patience), False\n compute_loss = ComputeLoss(model) # init loss class\n callbacks.run(\"on_train_start\")\n LOGGER.info(\n f\"Image sizes {imgsz} train, {imgsz} val\\n\"\n f\"Using {train_loader.num_workers * WORLD_SIZE} dataloader workers\\n\"\n f\"Logging results to {colorstr('bold', save_dir)}\\n\"\n f\"Starting training for {epochs} epochs...\"\n )\n for epoch in range(start_epoch, epochs): # epoch ------------------------------------------------------------------\n callbacks.run(\"on_train_epoch_start\")\n model.train()\n\n # Update image weights (optional, single-GPU only)\n if opt.image_weights:\n cw = model.class_weights.cpu().numpy() * (1 - maps) ** 2 / nc # class weights\n iw = labels_to_image_weights(dataset.labels, nc=nc, class_weights=cw) # image weights\n dataset.indices = random.choices(range(dataset.n), weights=iw, k=dataset.n) # rand weighted idx\n\n # Update mosaic border (optional)\n # b = int(random.uniform(0.25 * imgsz, 0.75 * imgsz + gs) // gs * gs)\n # dataset.mosaic_border = [b - imgsz, -b] # height, width borders\n\n mloss = torch.zeros(3, device=device) # mean losses\n if RANK != -1:\n train_loader.sampler.set_epoch(epoch)\n pbar = enumerate(train_loader)\n LOGGER.info((\"\\n\" + \"%11s\" * 7) % (\"Epoch\", \"GPU_mem\", \"box_loss\", \"obj_loss\", \"cls_loss\", \"Instances\", \"Size\"))\n if RANK in {-1, 0}:\n pbar = tqdm(pbar, total=nb, bar_format=TQDM_BAR_FORMAT) # progress bar\n optimizer.zero_grad()\n for i, (imgs, targets, paths, _) in pbar: # batch -------------------------------------------------------------\n callbacks.run(\"on_train_batch_start\")\n ni = i + nb * epoch # number integrated batches (since train start)\n imgs = imgs.to(device, non_blocking=True).float() / 255 # uint8 to float32, 0-255 to 0.0-1.0\n\n # Warmup\n if ni <= nw:\n xi = [0, nw] # x interp\n # compute_loss.gr = np.interp(ni, xi, [0.0, 1.0]) # iou loss ratio (obj_loss = 1.0 or iou)\n accumulate = max(1, np.interp(ni, xi, [1, nbs / batch_size]).round())\n for j, x in enumerate(optimizer.param_groups):\n # bias lr falls from 0.1 to lr0, all other lrs rise from 0.0 to lr0\n x[\"lr\"] = np.interp(ni, xi, [hyp[\"warmup_bias_lr\"] if j == 0 else 0.0, x[\"initial_lr\"] * lf(epoch)])\n if \"momentum\" in x:\n x[\"momentum\"] = np.interp(ni, xi, [hyp[\"warmup_momentum\"], hyp[\"momentum\"]])\n\n # Multi-scale\n if opt.multi_scale:\n sz = random.randrange(int(imgsz * 0.5), int(imgsz * 1.5) + gs) // gs * gs # size\n sf = sz / max(imgs.shape[2:]) # scale factor\n if sf != 1:\n ns = [math.ceil(x * sf / gs) * gs for x in imgs.shape[2:]] # new shape (stretched to gs-multiple)\n imgs = nn.functional.interpolate(imgs, size=ns, mode=\"bilinear\", align_corners=False)\n\n # Forward\n with torch.cuda.amp.autocast(amp):\n pred = model(imgs) # forward\n loss, loss_items = compute_loss(pred, targets.to(device)) # loss scaled by batch_size\n if RANK != -1:\n loss *= WORLD_SIZE # gradient averaged between devices in DDP mode\n if opt.quad:\n loss *= 4.0\n\n # Backward\n scaler.scale(loss).backward()\n\n # Optimize - https://pytorch.org/docs/master/notes/amp_examples.html\n if ni - last_opt_step >= accumulate:\n scaler.unscale_(optimizer) # unscale gradients\n torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=10.0) # clip gradients\n scaler.step(optimizer) # optimizer.step\n scaler.update()\n optimizer.zero_grad()\n if ema:\n ema.update(model)\n last_opt_step = ni\n\n # Log\n if RANK in {-1, 0}:\n mloss = (mloss * i + loss_items) / (i + 1) # update mean losses\n mem = f\"{torch.cuda.memory_reserved() / 1e9 if torch.cuda.is_available() else 0:.3g}G\" # (GB)\n pbar.set_description(\n (\"%11s\" * 2 + \"%11.4g\" * 5)\n % (f\"{epoch}/{epochs - 1}\", mem, *mloss, targets.shape[0], imgs.shape[-1])\n )\n callbacks.run(\"on_train_batch_end\", model, ni, imgs, targets, paths, list(mloss))\n if callbacks.stop_training:\n return\n # end batch ------------------------------------------------------------------------------------------------\n\n # Scheduler\n lr = [x[\"lr\"] for x in optimizer.param_groups] # for loggers\n scheduler.step()\n\n if RANK in {-1, 0}:\n # mAP\n callbacks.run(\"on_train_epoch_end\", epoch=epoch)\n ema.update_attr(model, include=[\"yaml\", \"nc\", \"hyp\", \"names\", \"stride\", \"class_weights\"])\n final_epoch = (epoch + 1 == epochs) or stopper.possible_stop\n if not noval or final_epoch: # Calculate mAP\n results, maps, _ = validate.run(\n data_dict,\n batch_size=batch_size // WORLD_SIZE * 2,\n imgsz=imgsz,\n half=amp,\n model=ema.ema,\n single_cls=single_cls,\n dataloader=val_loader,\n save_dir=save_dir,\n plots=False,\n callbacks=callbacks,\n compute_loss=compute_loss,\n )\n\n # Update best mAP\n fi = fitness(np.array(results).reshape(1, -1)) # weighted combination of [P, R, mAP@.5, mAP@.5-.95]\n stop = stopper(epoch=epoch, fitness=fi) # early stop check\n if fi > best_fitness:\n best_fitness = fi\n log_vals = list(mloss) + list(results) + lr\n callbacks.run(\"on_fit_epoch_end\", log_vals, epoch, best_fitness, fi)\n\n # Save model\n if (not nosave) or (final_epoch and not evolve): # if save\n ckpt = {\n \"epoch\": epoch,\n \"best_fitness\": best_fitness,\n \"model\": deepcopy(de_parallel(model)).half(),\n \"ema\": deepcopy(ema.ema).half(),\n \"updates\": ema.updates,\n \"optimizer\": optimizer.state_dict(),\n \"opt\": vars(opt),\n \"git\": GIT_INFO, # {remote, branch, commit} if a git repo\n \"date\": datetime.now().isoformat(),\n }\n\n # Save last, best and delete\n torch.save(ckpt, last)\n if best_fitness == fi:\n torch.save(ckpt, best)\n if opt.save_period > 0 and epoch % opt.save_period == 0:\n torch.save(ckpt, w / f\"epoch{epoch}.pt\")\n del ckpt\n callbacks.run(\"on_model_save\", last, epoch, final_epoch, best_fitness, fi)\n\n # EarlyStopping\n if RANK != -1: # if DDP training\n broadcast_list = [stop if RANK == 0 else None]\n dist.broadcast_object_list(broadcast_list, 0) # broadcast 'stop' to all ranks\n if RANK != 0:\n stop = broadcast_list[0]\n if stop:\n break # must break all DDP ranks\n\n # end epoch ----------------------------------------------------------------------------------------------------\n # end training -----------------------------------------------------------------------------------------------------\n if RANK in {-1, 0}:\n LOGGER.info(f\"\\n{epoch - start_epoch + 1} epochs completed in {(time.time() - t0) / 3600:.3f} hours.\")\n for f in last, best:\n if f.exists():\n strip_optimizer(f) # strip optimizers\n if f is best:\n LOGGER.info(f\"\\nValidating {f}...\")\n results, _, _ = validate.run(\n data_dict,\n batch_size=batch_size // WORLD_SIZE * 2,\n imgsz=imgsz,\n model=attempt_load(f, device).half(),\n iou_thres=0.65 if is_coco else 0.60, # best pycocotools at iou 0.65\n single_cls=single_cls,\n dataloader=val_loader,\n save_dir=save_dir,\n save_json=is_coco,\n verbose=True,\n plots=plots,\n callbacks=callbacks,\n compute_loss=compute_loss,\n ) # val best model with plots\n if is_coco:\n callbacks.run(\"on_fit_epoch_end\", list(mloss) + list(results) + lr, epoch, best_fitness, fi)\n\n callbacks.run(\"on_train_end\", last, best, epoch, results)\n\n torch.cuda.empty_cache()\n return results\n\n\ndef parse_opt(known=False):\n \"\"\"Parse command line arguments for configuring the training of a YOLO model.\n\n Args:\n known (bool): Flag to parse known arguments only, defaults to False.\n\n Returns:\n (argparse.Namespace): Parsed command line arguments.\n\n Examples:\n ```python\n options = parse_opt()\n print(options.weights)\n ```\n\n Notes:\n * The default weights path is 'yolov3-tiny.pt'.\n * Set `known` to True for parsing only the known arguments, useful for partial arguments.\n\n References:\n * Models: https://github.com/ultralytics/yolov5/tree/master/models\n * Datasets: https://github.com/ultralytics/yolov5/tree/master/data\n * Training Tutorial: https://docs.ultralytics.com/yolov5/tutorials/train_custom_data\n \"\"\"\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--weights\", type=str, default=ROOT / \"yolov3-tiny.pt\", help=\"initial weights path\")\n parser.add_argument(\"--cfg\", type=str, default=\"\", help=\"model.yaml path\")\n parser.add_argument(\"--data\", type=str, default=ROOT / \"data/coco128.yaml\", help=\"dataset.yaml path\")\n parser.add_argument(\"--hyp\", type=str, default=ROOT / \"data/hyps/hyp.scratch-low.yaml\", help=\"hyperparameters path\")\n parser.add_argument(\"--epochs\", type=int, default=100, help=\"total training epochs\")\n parser.add_argument(\"--batch-size\", type=int, default=16, help=\"total batch size for all GPUs, -1 for autobatch\")\n parser.add_argument(\"--imgsz\", \"--img\", \"--img-size\", type=int, default=640, help=\"train, val image size (pixels)\")\n parser.add_argument(\"--rect\", action=\"store_true\", help=\"rectangular training\")\n parser.add_argument(\"--resume\", nargs=\"?\", const=True, default=False, help=\"resume most recent training\")\n parser.add_argument(\"--nosave\", action=\"store_true\", help=\"only save final checkpoint\")\n parser.add_argument(\"--noval\", action=\"store_true\", help=\"only validate final epoch\")\n parser.add_argument(\"--noautoanchor\", action=\"store_true\", help=\"disable AutoAnchor\")\n parser.add_argument(\"--noplots\", action=\"store_true\", help=\"save no plot files\")\n parser.add_argument(\"--evolve\", type=int, nargs=\"?\", const=300, help=\"evolve hyperparameters for x generations\")\n parser.add_argument(\"--bucket\", type=str, default=\"\", help=\"gsutil bucket\")\n parser.add_argument(\"--cache\", type=str, nargs=\"?\", const=\"ram\", help=\"image --cache ram/disk\")\n parser.add_argument(\"--image-weights\", action=\"store_true\", help=\"use weighted image selection for training\")\n parser.add_argument(\"--device\", default=\"\", help=\"cuda device, i.e. 0 or 0,1,2,3 or cpu\")\n parser.add_argument(\"--multi-scale\", action=\"store_true\", help=\"vary img-size +/- 50%%\")\n parser.add_argument(\"--single-cls\", action=\"store_true\", help=\"train multi-class data as single-class\")\n parser.add_argument(\"--optimizer\", type=str, choices=[\"SGD\", \"Adam\", \"AdamW\"], default=\"SGD\", help=\"optimizer\")\n parser.add_argument(\"--sync-bn\", action=\"store_true\", help=\"use SyncBatchNorm, only available in DDP mode\")\n parser.add_argument(\"--workers\", type=int, default=8, help=\"max dataloader workers (per RANK in DDP mode)\")\n parser.add_argument(\"--project\", default=ROOT / \"runs/train\", help=\"save to project/name\")\n parser.add_argument(\"--name\", default=\"exp\", help=\"save to project/name\")\n parser.add_argument(\"--exist-ok\", action=\"store_true\", help=\"existing project/name ok, do not increment\")\n parser.add_argument(\"--quad\", action=\"store_true\", help=\"quad dataloader\")\n parser.add_argument(\"--cos-lr\", action=\"store_true\", help=\"cosine LR scheduler\")\n parser.add_argument(\"--label-smoothing\", type=float, default=0.0, help=\"Label smoothing epsilon\")\n parser.add_argument(\"--patience\", type=int, default=100, help=\"EarlyStopping patience (epochs without improvement)\")\n parser.add_argument(\"--freeze\", nargs=\"+\", type=int, default=[0], help=\"Freeze layers: backbone=10, first3=0 1 2\")\n parser.add_argument(\"--save-period\", type=int, default=-1, help=\"Save checkpoint every x epochs (disabled if < 1)\")\n parser.add_argument(\"--seed\", type=int, default=0, help=\"Global training seed\")\n parser.add_argument(\"--local_rank\", type=int, default=-1, help=\"Automatic DDP Multi-GPU argument, do not modify\")\n\n # Logger arguments\n parser.add_argument(\"--entity\", default=None, help=\"Entity\")\n parser.add_argument(\"--upload_dataset\", nargs=\"?\", const=True, default=False, help='Upload data, \"val\" option')\n parser.add_argument(\"--bbox_interval\", type=int, default=-1, help=\"Set bounding-box image logging interval\")\n parser.add_argument(\"--artifact_alias\", type=str, default=\"latest\", help=\"Version of dataset artifact to use\")\n\n return parser.parse_known_args()[0] if known else parser.parse_args()\n\n\ndef main(opt, callbacks=Callbacks()):\n \"\"\"Main training/evolution script handling model checks, DDP setup, training, and hyperparameter evolution.\n\n Args:\n opt (argparse.Namespace): Parsed command-line options.\n callbacks (Callbacks, optional): Callback object for handling training events. Defaults to Callbacks().\n\n Returns:\n None\n\n Raises:\n AssertionError: If certain constraints are violated (e.g., when specific options are incompatible with DDP\n training).\n\n Examples:\n Single-GPU training:\n ```python\n $ python train.py --data coco128.yaml --weights yolov5s.pt --img 640 # from pretrained (recommended)\n $ python train.py --data coco128.yaml --weights '' --cfg yolov5s.yaml --img 640 # from scratch\n ```\n\n Multi-GPU DDP training:\n ```python\n $ python -m torch.distributed.run --nproc_per_node 4 --master_port 1 train.py --data coco128.yaml --weights yolov5s.pt --img 640 --device 0,1,2,3\n ```\n\n Models: https://github.com/ultralytics/yolov5/tree/master/models\n Datasets: https://github.com/ultralytics/yolov5/tree/master/data\n Tutorial: https://docs.ultralytics.com/yolov5/tutorials/train_custom_data\n\n Notes:\n - For a tutorial on using Multi-GPU with DDP: https://docs.ultralytics.com/yolov5/tutorials/multi_gpu_training\n \"\"\"\n if RANK in {-1, 0}:\n print_args(vars(opt))\n check_git_status()\n check_requirements(ROOT / \"requirements.txt\")\n\n # Resume (from specified or most recent last.pt)\n if opt.resume and not check_comet_resume(opt) and not opt.evolve:\n last = Path(check_file(opt.resume) if isinstance(opt.resume, str) else get_latest_run())\n opt_yaml = last.parent.parent / \"opt.yaml\" # train options yaml\n opt_data = opt.data # original dataset\n if opt_yaml.is_file():\n with open(opt_yaml, errors=\"ignore\") as f:\n d = yaml.safe_load(f)\n else:\n d = torch_load(last, map_location=\"cpu\")[\"opt\"]\n opt = argparse.Namespace(**d) # replace\n opt.cfg, opt.weights, opt.resume = \"\", str(last), True # reinstate\n if is_url(opt_data):\n opt.data = check_file(opt_data) # avoid HUB resume auth timeout\n else:\n opt.data, opt.cfg, opt.hyp, opt.weights, opt.project = (\n check_file(opt.data),\n check_yaml(opt.cfg),\n check_yaml(opt.hyp),\n str(opt.weights),\n str(opt.project),\n ) # checks\n assert len(opt.cfg) or len(opt.weights), \"either --cfg or --weights must be specified\"\n if opt.evolve:\n if opt.project == str(ROOT / \"runs/train\"): # if default project name, rename to runs/evolve\n opt.project = str(ROOT / \"runs/evolve\")\n opt.exist_ok, opt.resume = opt.resume, False # pass resume to exist_ok and disable resume\n if opt.name == \"cfg\":\n opt.name = Path(opt.cfg).stem # use model.yaml as name\n opt.save_dir = str(increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok))\n\n # DDP mode\n device = select_device(opt.device, batch_size=opt.batch_size)\n if LOCAL_RANK != -1:\n msg = \"is not compatible with YOLOv3 Multi-GPU DDP training\"\n assert not opt.image_weights, f\"--image-weights {msg}\"\n assert not opt.evolve, f\"--evolve {msg}\"\n assert opt.batch_size != -1, f\"AutoBatch with --batch-size -1 {msg}, please pass a valid --batch-size\"\n assert opt.batch_size % WORLD_SIZE == 0, f\"--batch-size {opt.batch_size} must be multiple of WORLD_SIZE\"\n assert torch.cuda.device_count() > LOCAL_RANK, \"insufficient CUDA devices for DDP command\"\n torch.cuda.set_device(LOCAL_RANK)\n device = torch.device(\"cuda\", LOCAL_RANK)\n dist.init_process_group(backend=\"nccl\" if dist.is_nccl_available() else \"gloo\")\n\n # Train\n if not opt.evolve:\n train(opt.hyp, opt, device, callbacks)\n\n # Evolve hyperparameters (optional)\n else:\n # Hyperparameter evolution metadata (mutation scale 0-1, lower_limit, upper_limit)\n meta = {\n \"lr0\": (1, 1e-5, 1e-1), # initial learning rate (SGD=1E-2, Adam=1E-3)\n \"lrf\": (1, 0.01, 1.0), # final OneCycleLR learning rate (lr0 * lrf)\n \"momentum\": (0.3, 0.6, 0.98), # SGD momentum/Adam beta1\n \"weight_decay\": (1, 0.0, 0.001), # optimizer weight decay\n \"warmup_epochs\": (1, 0.0, 5.0), # warmup epochs (fractions ok)\n \"warmup_momentum\": (1, 0.0, 0.95), # warmup initial momentum\n \"warmup_bias_lr\": (1, 0.0, 0.2), # warmup initial bias lr\n \"box\": (1, 0.02, 0.2), # box loss gain\n \"cls\": (1, 0.2, 4.0), # cls loss gain\n \"cls_pw\": (1, 0.5, 2.0), # cls BCELoss positive_weight\n \"obj\": (1, 0.2, 4.0), # obj loss gain (scale with pixels)\n \"obj_pw\": (1, 0.5, 2.0), # obj BCELoss positive_weight\n \"iou_t\": (0, 0.1, 0.7), # IoU training threshold\n \"anchor_t\": (1, 2.0, 8.0), # anchor-multiple threshold\n \"anchors\": (2, 2.0, 10.0), # anchors per output grid (0 to ignore)\n \"fl_gamma\": (0, 0.0, 2.0), # focal loss gamma (efficientDet default gamma=1.5)\n \"hsv_h\": (1, 0.0, 0.1), # image HSV-Hue augmentation (fraction)\n \"hsv_s\": (1, 0.0, 0.9), # image HSV-Saturation augmentation (fraction)\n \"hsv_v\": (1, 0.0, 0.9), # image HSV-Value augmentation (fraction)\n \"degrees\": (1, 0.0, 45.0), # image rotation (+/- deg)\n \"translate\": (1, 0.0, 0.9), # image translation (+/- fraction)\n \"scale\": (1, 0.0, 0.9), # image scale (+/- gain)\n \"shear\": (1, 0.0, 10.0), # image shear (+/- deg)\n \"perspective\": (0, 0.0, 0.001), # image perspective (+/- fraction), range 0-0.001\n \"flipud\": (1, 0.0, 1.0), # image flip up-down (probability)\n \"fliplr\": (0, 0.0, 1.0), # image flip left-right (probability)\n \"mosaic\": (1, 0.0, 1.0), # image mixup (probability)\n \"mixup\": (1, 0.0, 1.0), # image mixup (probability)\n \"copy_paste\": (1, 0.0, 1.0),\n } # segment copy-paste (probability)\n\n with open(opt.hyp, errors=\"ignore\") as f:\n hyp = yaml.safe_load(f) # load hyps dict\n if \"anchors\" not in hyp: # anchors commented in hyp.yaml\n hyp[\"anchors\"] = 3\n if opt.noautoanchor:\n del hyp[\"anchors\"], meta[\"anchors\"]\n opt.noval, opt.nosave, save_dir = True, True, Path(opt.save_dir) # only val/save final epoch\n # ei = [isinstance(x, (int, float)) for x in hyp.values()] # evolvable indices\n evolve_yaml, evolve_csv = save_dir / \"hyp_evolve.yaml\", save_dir / \"evolve.csv\"\n if opt.bucket:\n # download evolve.csv if exists\n subprocess.run(\n [\n \"gsutil\",\n \"cp\",\n f\"gs://{opt.bucket}/evolve.csv\",\n str(evolve_csv),\n ]\n )\n\n for _ in range(opt.evolve): # generations to evolve\n if evolve_csv.exists(): # if evolve.csv exists: select best hyps and mutate\n # Select parent(s)\n parent = \"single\" # parent selection method: 'single' or 'weighted'\n x = np.loadtxt(evolve_csv, ndmin=2, delimiter=\",\", skiprows=1)\n n = min(5, len(x)) # number of previous results to consider\n x = x[np.argsort(-fitness(x))][:n] # top n mutations\n w = fitness(x) - fitness(x).min() + 1e-6 # weights (sum > 0)\n if parent == \"single\" or len(x) == 1:\n # x = x[random.randint(0, n - 1)] # random selection\n x = x[random.choices(range(n), weights=w)[0]] # weighted selection\n elif parent == \"weighted\":\n x = (x * w.reshape(n, 1)).sum(0) / w.sum() # weighted combination\n\n # Mutate\n mp, s = 0.8, 0.2 # mutation probability, sigma\n npr = np.random\n npr.seed(int(time.time()))\n g = np.array([meta[k][0] for k in hyp.keys()]) # gains 0-1\n ng = len(meta)\n v = np.ones(ng)\n while all(v == 1): # mutate until a change occurs (prevent duplicates)\n v = (g * (npr.random(ng) < mp) * npr.randn(ng) * npr.random() * s + 1).clip(0.3, 3.0)\n for i, k in enumerate(hyp.keys()): # plt.hist(v.ravel(), 300)\n hyp[k] = float(x[i + 7] * v[i]) # mutate\n\n # Constrain to limits\n for k, v in meta.items():\n hyp[k] = max(hyp[k], v[1]) # lower limit\n hyp[k] = min(hyp[k], v[2]) # upper limit\n hyp[k] = round(hyp[k], 5) # significant digits\n\n # Train mutation\n results = train(hyp.copy(), opt, device, callbacks)\n callbacks = Callbacks()\n # Write mutation results\n keys = (\n \"metrics/precision\",\n \"metrics/recall\",\n \"metrics/mAP_0.5\",\n \"metrics/mAP_0.5:0.95\",\n \"val/box_loss\",\n \"val/obj_loss\",\n \"val/cls_loss\",\n )\n print_mutation(keys, results, hyp.copy(), save_dir, opt.bucket)\n\n # Plot results\n plot_evolve(evolve_csv)\n LOGGER.info(\n f\"Hyperparameter evolution finished {opt.evolve} generations\\n\"\n f\"Results saved to {colorstr('bold', save_dir)}\\n\"\n f\"Usage example: $ python train.py --hyp {evolve_yaml}\"\n )\n\n\ndef run(**kwargs):\n \"\"\"Run the training process for a YOLOv3 model with the specified configurations.\n\n Args:\n data (str): Path to the dataset YAML file.\n weights (str): Path to the pre-trained weights file or '' to train from scratch.\n cfg (str): Path to the model configuration file.\n hyp (str): Path to the hyperparameters YAML file.\n epochs (int): Total number of training epochs.\n batch_size (int): Total batch size across all GPUs.\n imgsz (int): Image size for training and validation (in pixels).\n rect (bool): Use rectangular training for better aspect ratio preservation.\n resume (bool | str): Resume most recent training if True, or resume training from a specific checkpoint if a\n string.\n nosave (bool): Only save the final checkpoint and not the intermediate ones.\n noval (bool): Only validate model performance in the final epoch.\n noautoanchor (bool): Disable automatic anchor generation.\n noplots (bool): Do not save any plots.\n evolve (int): Number of generations for hyperparameters evolution.\n bucket (str): Google Cloud Storage bucket name for saving run artifacts.\n cache (str | None): Cache images for faster training ('ram' or 'disk').\n image_weights (bool): Use weighted image selection for training.\n device (str): Device to use for training, e.g., '0' for first GPU or 'cpu' for CPU.\n multi_scale (bool): Use multi-scale training.\n single_cls (bool): Train a multi-class dataset as a single-class.\n optimizer (str): Optimizer to use ('SGD', 'Adam', or 'AdamW').\n sync_bn (bool): Use synchronized batch normalization (only in DDP mode).\n workers (int): Maximum number of dataloader workers (per rank in DDP mode).\n project (str): Location of the output directory.\n name (str): Unique name for the run.\n exist_ok (bool): Allow existing output directory.\n quad (bool): Use quad dataloader.\n cos_lr (bool): Use cosine learning rate scheduler.\n label_smoothing (float): Label smoothing epsilon.\n patience (int): EarlyStopping patience (epochs without improvement).\n freeze (list[int]): List of layers to freeze, e.g., [0] to freeze only the first layer.\n save_period (int): Save checkpoint every 'save_period' epochs (disabled if less than 1).\n seed (int): Global training seed for reproducibility.\n local_rank (int): For automatic DDP Multi-GPU argument parsing, do not modify.\n\n Returns:\n None\n\n Examples:\n ```python\n from ultralytics import run\n run(data='coco128.yaml', weights='yolov5m.pt', imgsz=320, epochs=100, batch_size=16)\n ```\n\n Notes:\n - Ensure the dataset YAML file and initial weights are accessible.\n - Refer to the [Ultralytics YOLOv5 repository](https://github.com/ultralytics/yolov5) for model and data configurations.\n - Use the [Training Tutorial](https://docs.ultralytics.com/yolov5/tutorials/train_custom_data/) for custom dataset training.\n \"\"\"\n opt = parse_opt(True)\n for k, v in kwargs.items():\n setattr(opt, k, v)\n main(opt)\n return opt\n\n\nif __name__ == \"__main__\":\n opt = parse_opt()\n main(opt)\n" }, { "path": "tutorial.ipynb", "content": "{\n \"nbformat\": 4,\n \"nbformat_minor\": 0,\n \"metadata\": {\n \"colab\": {\n \"name\": \"YOLOv5 Tutorial\",\n \"provenance\": []\n },\n \"kernelspec\": {\n \"name\": \"python3\",\n \"display_name\": \"Python 3\"\n },\n \"accelerator\": \"GPU\"\n },\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"t6MPjfT5NrKQ\"\n },\n \"source\": [\n \"
\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"\\n\",\n \" [中文](https://docs.ultralytics.com/zh/) | [한국어](https://docs.ultralytics.com/ko/) | [日本語](https://docs.ultralytics.com/ja/) | [Русский](https://docs.ultralytics.com/ru/) | [Deutsch](https://docs.ultralytics.com/de/) | [Français](https://docs.ultralytics.com/fr/) | [Español](https://docs.ultralytics.com/es/) | [Português](https://docs.ultralytics.com/pt/) | [Türkçe](https://docs.ultralytics.com/tr/) | [Tiếng Việt](https://docs.ultralytics.com/vi/) | [العربية](https://docs.ultralytics.com/ar/)\\n\",\n \"\\n\",\n \" \\\"Ultralytics\\n\",\n \" \\\"Run\\n\",\n \" \\\"Open\\n\",\n \" \\\"Open\\n\",\n \"\\n\",\n \" \\\"Discord\\\"\\n\",\n \" \\\"Ultralytics\\n\",\n \" \\\"Ultralytics\\n\",\n \"
\\n\",\n \"\\n\",\n \"This **Ultralytics YOLOv5 Colab Notebook** is the easiest way to get started with [YOLO models](https://www.ultralytics.com/yolo)—no installation needed. Built by [Ultralytics](https://www.ultralytics.com/), the creators of YOLO, this notebook walks you through running **state-of-the-art** models directly in your browser.\\n\",\n \"\\n\",\n \"Ultralytics models are constantly updated for performance and flexibility. They're **fast**, **accurate**, and **easy to use**, and they excel at [object detection](https://docs.ultralytics.com/tasks/detect/), [tracking](https://docs.ultralytics.com/modes/track/), [instance segmentation](https://docs.ultralytics.com/tasks/segment/), [image classification](https://docs.ultralytics.com/tasks/classify/), and [pose estimation](https://docs.ultralytics.com/tasks/pose/).\\n\",\n \"\\n\",\n \"Find detailed documentation in the [Ultralytics Docs](https://docs.ultralytics.com/). Get support via [GitHub Issues](https://github.com/ultralytics/ultralytics/issues/new/choose). Join discussions on [Discord](https://discord.com/invite/ultralytics), [Reddit](https://www.reddit.com/r/ultralytics/), and the [Ultralytics Community Forums](https://community.ultralytics.com/)!\\n\",\n \"\\n\",\n \"Request an Enterprise License for commercial use at [Ultralytics Licensing](https://www.ultralytics.com/license).\\n\",\n \"\\n\",\n \"
\\n\",\n \"
\\n\",\n \" \\n\",\n \" \\\"Ultralytics\\n\",\n \" \\n\",\n \"\\n\",\n \"

\\n\",\n \" Watch: How to Train\\n\",\n \" Ultralytics\\n\",\n \" YOLO11 Model on Custom Dataset using Google Colab Notebook 🚀\\n\",\n \"

\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"7mGmQbAO5pQb\"\n },\n \"source\": [\n \"# Setup\\n\",\n \"\\n\",\n \"Clone GitHub [repository](https://github.com/ultralytics/yolov5), install [dependencies](https://github.com/ultralytics/yolov5/blob/master/requirements.txt) and check PyTorch and GPU.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"metadata\": {\n \"id\": \"wbvMlHd_QwMG\",\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\"\n },\n \"outputId\": \"e8225db4-e61d-4640-8b1f-8bfce3331cea\"\n },\n \"source\": [\n \"!git clone https://github.com/ultralytics/yolov5 # clone\\n\",\n \"%cd yolov5\\n\",\n \"%pip install -qr requirements.txt comet_ml # install\\n\",\n \"\\n\",\n \"import torch\\n\",\n \"import utils\\n\",\n \"display = utils.notebook_init() # checks\"\n ],\n \"execution_count\": 1,\n \"outputs\": [\n {\n \"output_type\": \"stream\",\n \"name\": \"stderr\",\n \"text\": [\n \"YOLOv5 🚀 v7.0-136-g71244ae Python-3.9.16 torch-2.0.0+cu118 CUDA:0 (Tesla T4, 15102MiB)\\n\"\n ]\n },\n {\n \"output_type\": \"stream\",\n \"name\": \"stdout\",\n \"text\": [\n \"Setup complete ✅ (2 CPUs, 12.7 GB RAM, 23.3/166.8 GB disk)\\n\"\n ]\n }\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"4JnkELT0cIJg\"\n },\n \"source\": [\n \"# 1. Detect\\n\",\n \"\\n\",\n \"`detect.py` runs YOLOv5 inference on a variety of sources, downloading models automatically from the [latest YOLOv5 release](https://github.com/ultralytics/yolov5/releases), and saving results to `runs/detect`. Example inference sources are:\\n\",\n \"\\n\",\n \"```shell\\n\",\n \"python detect.py --source 0 # webcam\\n\",\n \" img.jpg # image \\n\",\n \" vid.mp4 # video\\n\",\n \" screen # screenshot\\n\",\n \" path/ # directory\\n\",\n \" 'path/*.jpg' # glob\\n\",\n \" 'https://youtu.be/LNwODJXcvt4' # YouTube\\n\",\n \" 'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream\\n\",\n \"```\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"metadata\": {\n \"id\": \"zR9ZbuQCH7FX\",\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\"\n },\n \"outputId\": \"284ef04b-1596-412f-88f6-948828dd2b49\"\n },\n \"source\": [\n \"!python detect.py --weights yolov5s.pt --img 640 --conf 0.25 --source data/images\\n\",\n \"# display.Image(filename='runs/detect/exp/zidane.jpg', width=600)\"\n ],\n \"execution_count\": 13,\n \"outputs\": [\n {\n \"output_type\": \"stream\",\n \"name\": \"stdout\",\n \"text\": [\n \"\\u001B[34m\\u001B[1mdetect: \\u001B[0mweights=['yolov5s.pt'], source=data/images, data=data/coco128.yaml, imgsz=[640, 640], conf_thres=0.25, iou_thres=0.45, max_det=1000, device=, view_img=False, save_txt=False, save_conf=False, save_crop=False, nosave=False, classes=None, agnostic_nms=False, augment=False, visualize=False, update=False, project=runs/detect, name=exp, exist_ok=False, line_thickness=3, hide_labels=False, hide_conf=False, half=False, dnn=False, vid_stride=1\\n\",\n \"YOLOv5 🚀 v7.0-136-g71244ae Python-3.9.16 torch-2.0.0+cu118 CUDA:0 (Tesla T4, 15102MiB)\\n\",\n \"\\n\",\n \"Downloading https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s.pt to yolov5s.pt...\\n\",\n \"100% 14.1M/14.1M [00:00<00:00, 24.5MB/s]\\n\",\n \"\\n\",\n \"Fusing layers... \\n\",\n \"YOLOv5s summary: 213 layers, 7225885 parameters, 0 gradients\\n\",\n \"image 1/2 /content/yolov5/data/images/bus.jpg: 640x480 4 persons, 1 bus, 41.5ms\\n\",\n \"image 2/2 /content/yolov5/data/images/zidane.jpg: 384x640 2 persons, 2 ties, 60.0ms\\n\",\n \"Speed: 0.5ms pre-process, 50.8ms inference, 37.7ms NMS per image at shape (1, 3, 640, 640)\\n\",\n \"Results saved to \\u001B[1mruns/detect/exp\\u001B[0m\\n\"\n ]\n }\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"hkAzDWJ7cWTr\"\n },\n \"source\": [\n \"        \\n\",\n \"\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"0eq1SMWl6Sfn\"\n },\n \"source\": [\n \"# 2. Validate\\n\",\n \"Validate a model's accuracy on the [COCO](https://cocodataset.org/#home) dataset's `val` or `test` splits. Models are downloaded automatically from the [latest YOLOv5 release](https://github.com/ultralytics/yolov5/releases). To show results by class use the `--verbose` flag.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"metadata\": {\n \"id\": \"WQPtK1QYVaD_\",\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\"\n },\n \"outputId\": \"cf7d52f0-281c-4c96-a488-79f5908f8426\"\n },\n \"source\": [\n \"# Download COCO val\\n\",\n \"torch.hub.download_url_to_file('https://github.com/ultralytics/assets/releases/download/v0.0.0/coco2017val.zip', 'tmp.zip') # download (780M - 5000 images)\\n\",\n \"!unzip -q tmp.zip -d ../datasets && rm tmp.zip # unzip\"\n ],\n \"execution_count\": 3,\n \"outputs\": [\n {\n \"output_type\": \"stream\",\n \"name\": \"stderr\",\n \"text\": [\n \"100%|██████████| 780M/780M [00:12<00:00, 66.6MB/s]\\n\"\n ]\n }\n ]\n },\n {\n \"cell_type\": \"code\",\n \"metadata\": {\n \"id\": \"X58w8JLpMnjH\",\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\"\n },\n \"outputId\": \"3e234e05-ee8b-4ad1-b1a4-f6a55d5e4f3d\"\n },\n \"source\": [\n \"# Validate YOLOv5s on COCO val\\n\",\n \"!python val.py --weights yolov5s.pt --data coco.yaml --img 640 --half\"\n ],\n \"execution_count\": 4,\n \"outputs\": [\n {\n \"output_type\": \"stream\",\n \"name\": \"stdout\",\n \"text\": [\n \"\\u001B[34m\\u001B[1mval: \\u001B[0mdata=/content/yolov5/data/coco.yaml, weights=['yolov5s.pt'], batch_size=32, imgsz=640, conf_thres=0.001, iou_thres=0.6, max_det=300, task=val, device=, workers=8, single_cls=False, augment=False, verbose=False, save_txt=False, save_hybrid=False, save_conf=False, save_json=True, project=runs/val, name=exp, exist_ok=False, half=True, dnn=False\\n\",\n \"YOLOv5 🚀 v7.0-136-g71244ae Python-3.9.16 torch-2.0.0+cu118 CUDA:0 (Tesla T4, 15102MiB)\\n\",\n \"\\n\",\n \"Fusing layers... \\n\",\n \"YOLOv5s summary: 213 layers, 7225885 parameters, 0 gradients\\n\",\n \"\\u001B[34m\\u001B[1mval: \\u001B[0mScanning /content/datasets/coco/val2017... 4952 images, 48 backgrounds, 0 corrupt: 100% 5000/5000 [00:02<00:00, 2024.59it/s]\\n\",\n \"\\u001B[34m\\u001B[1mval: \\u001B[0mNew cache created: /content/datasets/coco/val2017.cache\\n\",\n \" Class Images Instances P R mAP50 mAP50-95: 100% 157/157 [01:25<00:00, 1.84it/s]\\n\",\n \" all 5000 36335 0.671 0.519 0.566 0.371\\n\",\n \"Speed: 0.1ms pre-process, 3.1ms inference, 2.3ms NMS per image at shape (32, 3, 640, 640)\\n\",\n \"\\n\",\n \"Evaluating pycocotools mAP... saving runs/val/exp/yolov5s_predictions.json...\\n\",\n \"loading annotations into memory...\\n\",\n \"Done (t=0.43s)\\n\",\n \"creating index...\\n\",\n \"index created!\\n\",\n \"Loading and preparing results...\\n\",\n \"DONE (t=5.32s)\\n\",\n \"creating index...\\n\",\n \"index created!\\n\",\n \"Running per image evaluation...\\n\",\n \"Evaluate annotation type *bbox*\\n\",\n \"DONE (t=78.89s).\\n\",\n \"Accumulating evaluation results...\\n\",\n \"DONE (t=14.51s).\\n\",\n \" Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.374\\n\",\n \" Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.572\\n\",\n \" Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.402\\n\",\n \" Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.211\\n\",\n \" Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.423\\n\",\n \" Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.489\\n\",\n \" Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.311\\n\",\n \" Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.516\\n\",\n \" Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.566\\n\",\n \" Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.378\\n\",\n \" Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.625\\n\",\n \" Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.722\\n\",\n \"Results saved to \\u001B[1mruns/val/exp\\u001B[0m\\n\"\n ]\n }\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"ZY2VXXXu74w5\"\n },\n \"source\": [\n \"# 3. Train\\n\",\n \"\\n\",\n \"\\n\",\n \" \\\"Ultralytics\\n\",\n \"\\n\",\n \"

\\n\",\n \"\\n\",\n \"Train a YOLOv5s model on the [COCO128](https://www.kaggle.com/datasets/ultralytics/coco128) dataset with `--data coco128.yaml`, starting from pretrained `--weights yolov5s.pt`, or from randomly initialized `--weights '' --cfg yolov5s.yaml`.\\n\",\n \"\\n\",\n \"- **Pretrained [Models](https://github.com/ultralytics/yolov5/tree/master/models)** are downloaded\\n\",\n \"automatically from the [latest YOLOv5 release](https://github.com/ultralytics/yolov5/releases)\\n\",\n \"- **[Datasets](https://github.com/ultralytics/yolov5/tree/master/data)** available for autodownload include: [COCO](https://github.com/ultralytics/yolov5/blob/master/data/coco.yaml), [COCO128](https://github.com/ultralytics/yolov5/blob/master/data/coco128.yaml), [VOC](https://github.com/ultralytics/yolov5/blob/master/data/VOC.yaml), [Argoverse](https://github.com/ultralytics/yolov5/blob/master/data/Argoverse.yaml), [VisDrone](https://github.com/ultralytics/yolov5/blob/master/data/VisDrone.yaml), [GlobalWheat](https://github.com/ultralytics/yolov5/blob/master/data/GlobalWheat2020.yaml), [xView](https://github.com/ultralytics/yolov5/blob/master/data/xView.yaml), [Objects365](https://github.com/ultralytics/yolov5/blob/master/data/Objects365.yaml), [SKU-110K](https://github.com/ultralytics/yolov5/blob/master/data/SKU-110K.yaml).\\n\",\n \"- **Training Results** are saved to `runs/train/` with incrementing run directories, i.e. `runs/train/exp2`, `runs/train/exp3` etc.\\n\",\n \"
\\n\",\n \"\\n\",\n \"A **Mosaic Dataloader** is used for training which combines 4 images into 1 mosaic.\\n\",\n \"\\n\",\n \"## Label a dataset on Roboflow (optional)\\n\",\n \"\\n\",\n \"[Roboflow](https://roboflow.com/?ref=ultralytics) enables you to easily **organize, label, and prepare** a high quality dataset with your own custom data. Roboflow also makes it easy to establish an active learning pipeline, collaborate with your team on dataset improvement, and integrate directly into your model building workflow with the `roboflow` pip package.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"source\": [\n \"#@title Select YOLOv5 🚀 logger {run: 'auto'}\\n\",\n \"logger = 'Comet' #@param ['Comet', 'ClearML', 'TensorBoard']\\n\",\n \"\\n\",\n \"if logger == 'Comet':\\n\",\n \" %pip install -q comet_ml\\n\",\n \" import comet_ml; comet_ml.init()\\n\",\n \"elif logger == 'ClearML':\\n\",\n \" %pip install -q clearml\\n\",\n \" import clearml; clearml.browser_login()\\n\",\n \"elif logger == 'TensorBoard':\\n\",\n \" %load_ext tensorboard\\n\",\n \" %tensorboard --logdir runs/train\"\n ],\n \"metadata\": {\n \"id\": \"i3oKtE4g-aNn\"\n },\n \"execution_count\": null,\n \"outputs\": []\n },\n {\n \"cell_type\": \"code\",\n \"metadata\": {\n \"id\": \"1NcFxRcFdJ_O\",\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\"\n },\n \"outputId\": \"bbeeea2b-04fc-4185-aa64-258690495b5a\"\n },\n \"source\": [\n \"# Train YOLOv5s on COCO128 for 3 epochs\\n\",\n \"!python train.py --img 640 --batch 16 --epochs 3 --data coco128.yaml --weights yolov5s.pt --cache\"\n ],\n \"execution_count\": 5,\n \"outputs\": [\n {\n \"output_type\": \"stream\",\n \"name\": \"stdout\",\n \"text\": [\n \"2023-04-09 14:11:38.063605: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\\n\",\n \"To enable the following instructions: AVX2 AVX512F FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\\n\",\n \"2023-04-09 14:11:39.026661: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\\n\",\n \"\\u001B[34m\\u001B[1mtrain: \\u001B[0mweights=yolov5s.pt, cfg=, data=coco128.yaml, hyp=data/hyps/hyp.scratch-low.yaml, epochs=3, batch_size=16, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, bucket=, cache=ram, image_weights=False, device=, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=8, project=runs/train, name=exp, exist_ok=False, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, seed=0, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=-1, artifact_alias=latest\\n\",\n \"\\u001B[34m\\u001B[1mgithub: \\u001B[0mup to date with https://github.com/ultralytics/yolov5 ✅\\n\",\n \"YOLOv5 🚀 v7.0-136-g71244ae Python-3.9.16 torch-2.0.0+cu118 CUDA:0 (Tesla T4, 15102MiB)\\n\",\n \"\\n\",\n \"\\u001B[34m\\u001B[1mhyperparameters: \\u001B[0mlr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.5, cls_pw=1.0, obj=1.0, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0\\n\",\n \"\\u001B[34m\\u001B[1mClearML: \\u001B[0mrun 'pip install clearml' to automatically track, visualize and remotely train YOLOv5 🚀 in ClearML\\n\",\n \"\\u001B[34m\\u001B[1mComet: \\u001B[0mrun 'pip install comet_ml' to automatically track and visualize YOLOv5 🚀 runs in Comet\\n\",\n \"\\u001B[34m\\u001B[1mTensorBoard: \\u001B[0mStart with 'tensorboard --logdir runs/train', view at http://localhost:6006/\\n\",\n \"\\n\",\n \"Dataset not found ⚠️, missing paths ['/content/datasets/coco128/images/train2017']\\n\",\n \"Downloading https://github.com/ultralytics/assets/releases/download/v0.0.0/coco128.zip to coco128.zip...\\n\",\n \"100% 6.66M/6.66M [00:00<00:00, 75.6MB/s]\\n\",\n \"Dataset download success ✅ (0.6s), saved to \\u001B[1m/content/datasets\\u001B[0m\\n\",\n \"\\n\",\n \" from n params module arguments \\n\",\n \" 0 -1 1 3520 models.common.Conv [3, 32, 6, 2, 2] \\n\",\n \" 1 -1 1 18560 models.common.Conv [32, 64, 3, 2] \\n\",\n \" 2 -1 1 18816 models.common.C3 [64, 64, 1] \\n\",\n \" 3 -1 1 73984 models.common.Conv [64, 128, 3, 2] \\n\",\n \" 4 -1 2 115712 models.common.C3 [128, 128, 2] \\n\",\n \" 5 -1 1 295424 models.common.Conv [128, 256, 3, 2] \\n\",\n \" 6 -1 3 625152 models.common.C3 [256, 256, 3] \\n\",\n \" 7 -1 1 1180672 models.common.Conv [256, 512, 3, 2] \\n\",\n \" 8 -1 1 1182720 models.common.C3 [512, 512, 1] \\n\",\n \" 9 -1 1 656896 models.common.SPPF [512, 512, 5] \\n\",\n \" 10 -1 1 131584 models.common.Conv [512, 256, 1, 1] \\n\",\n \" 11 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] \\n\",\n \" 12 [-1, 6] 1 0 models.common.Concat [1] \\n\",\n \" 13 -1 1 361984 models.common.C3 [512, 256, 1, False] \\n\",\n \" 14 -1 1 33024 models.common.Conv [256, 128, 1, 1] \\n\",\n \" 15 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] \\n\",\n \" 16 [-1, 4] 1 0 models.common.Concat [1] \\n\",\n \" 17 -1 1 90880 models.common.C3 [256, 128, 1, False] \\n\",\n \" 18 -1 1 147712 models.common.Conv [128, 128, 3, 2] \\n\",\n \" 19 [-1, 14] 1 0 models.common.Concat [1] \\n\",\n \" 20 -1 1 296448 models.common.C3 [256, 256, 1, False] \\n\",\n \" 21 -1 1 590336 models.common.Conv [256, 256, 3, 2] \\n\",\n \" 22 [-1, 10] 1 0 models.common.Concat [1] \\n\",\n \" 23 -1 1 1182720 models.common.C3 [512, 512, 1, False] \\n\",\n \" 24 [17, 20, 23] 1 229245 models.yolo.Detect [80, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]\\n\",\n \"Model summary: 214 layers, 7235389 parameters, 7235389 gradients, 16.6 GFLOPs\\n\",\n \"\\n\",\n \"Transferred 349/349 items from yolov5s.pt\\n\",\n \"\\u001B[34m\\u001B[1mAMP: \\u001B[0mchecks passed ✅\\n\",\n \"\\u001B[34m\\u001B[1moptimizer:\\u001B[0m SGD(lr=0.01) with parameter groups 57 weight(decay=0.0), 60 weight(decay=0.0005), 60 bias\\n\",\n \"\\u001B[34m\\u001B[1malbumentations: \\u001B[0mBlur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01), CLAHE(p=0.01, clip_limit=(1, 4.0), tile_grid_size=(8, 8))\\n\",\n \"\\u001B[34m\\u001B[1mtrain: \\u001B[0mScanning /content/datasets/coco128/labels/train2017... 126 images, 2 backgrounds, 0 corrupt: 100% 128/128 [00:00<00:00, 1709.36it/s]\\n\",\n \"\\u001B[34m\\u001B[1mtrain: \\u001B[0mNew cache created: /content/datasets/coco128/labels/train2017.cache\\n\",\n \"\\u001B[34m\\u001B[1mtrain: \\u001B[0mCaching images (0.1GB ram): 100% 128/128 [00:00<00:00, 264.35it/s]\\n\",\n \"\\u001B[34m\\u001B[1mval: \\u001B[0mScanning /content/datasets/coco128/labels/train2017.cache... 126 images, 2 backgrounds, 0 corrupt: 100% 128/128 [00:00 # 2. paste API key\\n\",\n \"python train.py --img 640 --epochs 3 --data coco128.yaml --weights yolov5s.pt # 3. train\\n\",\n \"```\\n\",\n \"To learn more about all of the supported Comet features for this integration, check out the [Comet Tutorial](https://docs.ultralytics.com/yolov5/tutorials/comet_logging_integration). If you'd like to learn more about Comet, head over to our [documentation](https://www.comet.com/docs/v2/?utm_source=yolov5&utm_medium=partner&utm_campaign=partner_yolov5_2022&utm_content=yolov5_colab). Get started by trying out the Comet Colab Notebook:\\n\",\n \"[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1RG0WOQyxlDlo5Km8GogJpIEJlg_5lyYO?usp=sharing)\\n\",\n \"\\n\",\n \"\\n\",\n \"\\\"Comet\"\n ],\n \"metadata\": {\n \"id\": \"nWOsI5wJR1o3\"\n }\n },\n {\n \"cell_type\": \"markdown\",\n \"source\": [\n \"## ClearML Logging and Automation 🌟 NEW\\n\",\n \"\\n\",\n \"[ClearML](https://cutt.ly/yolov5-notebook-clearml) is completely integrated into YOLOv5 to track your experimentation, manage dataset versions and even remotely execute training runs. To enable ClearML (check cells above):\\n\",\n \"\\n\",\n \"- `pip install clearml`\\n\",\n \"- run `clearml-init` to connect to a ClearML server (**deploy your own [open-source server](https://github.com/allegroai/clearml-server)**, or use our [free hosted server](https://cutt.ly/yolov5-notebook-clearml))\\n\",\n \"\\n\",\n \"You'll get all the great expected features from an experiment manager: live updates, model upload, experiment comparison etc. but ClearML also tracks uncommitted changes and installed packages for example. Thanks to that ClearML Tasks (which is what we call experiments) are also reproducible on different machines! With only 1 extra line, we can schedule a YOLOv5 training task on a queue to be executed by any number of ClearML Agents (workers).\\n\",\n \"\\n\",\n \"You can use ClearML Data to version your dataset and then pass it to YOLOv5 simply using its unique ID. This will help you keep track of your data without adding extra hassle. Explore the [ClearML Tutorial](https://docs.ultralytics.com/yolov5/tutorials/clearml_logging_integration) for details!\\n\",\n \"\\n\",\n \"\\n\",\n \"\\\"ClearML\"\n ],\n \"metadata\": {\n \"id\": \"Lay2WsTjNJzP\"\n }\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"-WPvRbS5Swl6\"\n },\n \"source\": [\n \"## Local Logging\\n\",\n \"\\n\",\n \"Training results are automatically logged with [Tensorboard](https://www.tensorflow.org/tensorboard) and [CSV](https://github.com/ultralytics/yolov5/pull/4148) loggers to `runs/train`, with a new experiment directory created for each new training as `runs/train/exp2`, `runs/train/exp3`, etc.\\n\",\n \"\\n\",\n \"This directory contains train and val statistics, mosaics, labels, predictions and augmentated mosaics, as well as metrics and charts including precision-recall (PR) curves and confusion matrices. \\n\",\n \"\\n\",\n \"\\\"Local\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"Zelyeqbyt3GD\"\n },\n \"source\": [\n \"# Environments\\n\",\n \"\\n\",\n \"YOLOv5 may be run in any of the following up-to-date verified environments (with all dependencies including [CUDA](https://developer.nvidia.com/cuda)/[CUDNN](https://developer.nvidia.com/cudnn), [Python](https://www.python.org/) and [PyTorch](https://pytorch.org/) preinstalled):\\n\",\n \"\\n\",\n \"- **Notebooks** with free GPU: \\\"Run \\\"Open \\\"Open\\n\",\n \"- **Google Cloud** Deep Learning VM. See [GCP Quickstart Guide](https://docs.ultralytics.com/yolov5/environments/google_cloud_quickstart_tutorial/)\\n\",\n \"- **Amazon** Deep Learning AMI. See [AWS Quickstart Guide](https://docs.ultralytics.com/yolov5/environments/aws_quickstart_tutorial/)\\n\",\n \"- **Docker Image**. See [Docker Quickstart Guide](https://docs.ultralytics.com/yolov5/environments/docker_image_quickstart_tutorial/) \\\"Docker\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"6Qu7Iesl0p54\"\n },\n \"source\": [\n \"# Status\\n\",\n \"\\n\",\n \"![YOLOv5 CI](https://github.com/ultralytics/yolov3/actions/workflows/ci-testing.yml/badge.svg)\\n\",\n \"\\n\",\n \"If this badge is green, all [YOLOv3 GitHub Actions](https://github.com/ultralytics/yolov3/actions) Continuous Integration (CI) tests are currently passing. CI tests verify correct operation of YOLOv5 training ([train.py](https://github.com/ultralytics/yolov5/blob/master/train.py)), testing ([val.py](https://github.com/ultralytics/yolov5/blob/master/val.py)), inference ([detect.py](https://github.com/ultralytics/yolov5/blob/master/detect.py)) and export ([export.py](https://github.com/ultralytics/yolov5/blob/master/export.py)) on macOS, Windows, and Ubuntu every 24 hours and on every commit.\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"IEijrePND_2I\"\n },\n \"source\": [\n \"# Appendix\\n\",\n \"\\n\",\n \"Additional content below.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"metadata\": {\n \"id\": \"GMusP4OAxFu6\"\n },\n \"source\": [\n \"# YOLOv5 PyTorch HUB Inference (DetectionModels only)\\n\",\n \"import torch\\n\",\n \"\\n\",\n \"model = torch.hub.load('ultralytics/yolov5', 'yolov5s', force_reload=True) # yolov5n - yolov5x6 or custom\\n\",\n \"im = 'https://ultralytics.com/images/zidane.jpg' # file, Path, PIL.Image, OpenCV, nparray, list\\n\",\n \"results = model(im) # inference\\n\",\n \"results.print() # or .show(), .save(), .crop(), .pandas(), etc.\"\n ],\n \"execution_count\": null,\n \"outputs\": []\n }\n ]\n}\n" }, { "path": "utils/__init__.py", "content": "# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license\n\"\"\"utils/initialization.\"\"\"\n\nimport contextlib\nimport platform\nimport threading\n\n\ndef emojis(str=\"\"):\n \"\"\"Returns platform-dependent emoji-safe version of str; ignores emojis on Windows, else returns original str.\"\"\"\n return str.encode().decode(\"ascii\", \"ignore\") if platform.system() == \"Windows\" else str\n\n\nclass TryExcept(contextlib.ContextDecorator):\n \"\"\"A context manager and decorator for handling exceptions with optional custom messages.\"\"\"\n\n def __init__(self, msg=\"\"):\n \"\"\"Initializes TryExcept with optional custom message, used as decorator or context manager for exception\n handling.\n \"\"\"\n self.msg = msg\n\n def __enter__(self):\n \"\"\"Begin exception-handling block, optionally customizing exception message when used with TryExcept context\n manager.\n \"\"\"\n pass\n\n def __exit__(self, exc_type, value, traceback):\n \"\"\"Ends exception-handling block, optionally prints custom message with exception, suppressing exceptions within\n context.\n \"\"\"\n if value:\n print(emojis(f\"{self.msg}{': ' if self.msg else ''}{value}\"))\n return True\n\n\ndef threaded(func):\n \"\"\"Decorates a function to run in a separate thread, returning the thread object.\n\n Usage: @threaded.\n \"\"\"\n\n def wrapper(*args, **kwargs):\n \"\"\"Runs the decorated function in a separate thread and returns the thread object.\n\n Usage: @threaded.\n \"\"\"\n thread = threading.Thread(target=func, args=args, kwargs=kwargs, daemon=True)\n thread.start()\n return thread\n\n return wrapper\n\n\ndef join_threads(verbose=False):\n \"\"\"Joins all daemon threads, excluding the main thread, with an optional verbose flag for logging.\"\"\"\n main_thread = threading.current_thread()\n for t in threading.enumerate():\n if t is not main_thread:\n if verbose:\n print(f\"Joining thread {t.name}\")\n t.join()\n\n\ndef notebook_init(verbose=True):\n \"\"\"Initializes notebook environment by checking hardware, software requirements, and cleaning up if in Colab.\"\"\"\n print(\"Checking setup...\")\n\n import os\n import shutil\n\n from ultralytics.utils.checks import check_requirements\n\n from utils.general import check_font, is_colab\n from utils.torch_utils import select_device # imports\n\n check_font()\n\n import psutil\n\n if check_requirements(\"wandb\", install=False):\n os.system(\"pip uninstall -y wandb\") # eliminate unexpected account creation prompt with infinite hang\n if is_colab():\n shutil.rmtree(\"/content/sample_data\", ignore_errors=True) # remove colab /sample_data directory\n\n # System info\n display = None\n if verbose:\n gb = 1 << 30 # bytes to GiB (1024 ** 3)\n ram = psutil.virtual_memory().total\n total, _used, free = shutil.disk_usage(\"/\")\n with contextlib.suppress(Exception): # clear display if ipython is installed\n from IPython import display\n\n display.clear_output()\n s = f\"({os.cpu_count()} CPUs, {ram / gb:.1f} GB RAM, {(total - free) / gb:.1f}/{total / gb:.1f} GB disk)\"\n else:\n s = \"\"\n\n select_device(newline=False)\n print(emojis(f\"Setup complete ✅ {s}\"))\n return display\n" }, { "path": "utils/activations.py", "content": "# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license\n\"\"\"Activation functions.\"\"\"\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\n\nclass SiLU(nn.Module):\n \"\"\"Applies the SiLU activation function to the input tensor as described in https://arxiv.org/pdf/1606.08415.pdf.\"\"\"\n\n @staticmethod\n def forward(x):\n \"\"\"Applies the SiLU activation function, as detailed in https://arxiv.org/pdf/1606.08415.pdf, on input tensor\n `x`.\n \"\"\"\n return x * torch.sigmoid(x)\n\n\nclass Hardswish(nn.Module):\n \"\"\"Applies the Hardswish activation function to the input tensor `x`.\"\"\"\n\n @staticmethod\n def forward(x):\n \"\"\"Applies Hardswish activation, suitable for TorchScript, CoreML, ONNX, modifying input `x` as per Hard-SiLU\n definition.\n \"\"\"\n return x * F.hardtanh(x + 3, 0.0, 6.0) / 6.0 # for TorchScript, CoreML and ONNX\n\n\nclass Mish(nn.Module):\n \"\"\"Applies the Mish activation function to improve model performance; see https://github.com/digantamisra98/Mish.\"\"\"\n\n @staticmethod\n def forward(x):\n \"\"\"Applies the Mish activation function, enhancing model performance and convergence.\n\n Reference: https://github.com/digantamisra98/Mish\n \"\"\"\n return x * F.softplus(x).tanh()\n\n\nclass MemoryEfficientMish(nn.Module):\n \"\"\"Applies the memory-efficient Mish activation function for improved model performance and reduced memory usage.\"\"\"\n\n class F(torch.autograd.Function):\n \"\"\"Memory-efficient implementation of the Mish activation function for enhanced model performance.\"\"\"\n\n @staticmethod\n def forward(ctx, x):\n \"\"\"Applies the Mish activation function in a memory-efficient manner, useful for enhancing model\n performance.\n \"\"\"\n ctx.save_for_backward(x)\n return x.mul(torch.tanh(F.softplus(x))) # x * tanh(ln(1 + exp(x)))\n\n @staticmethod\n def backward(ctx, grad_output):\n \"\"\"Computes gradient of the Mish activation function for backpropagation, returning the derivative with\n respect to the input.\n \"\"\"\n x = ctx.saved_tensors[0]\n sx = torch.sigmoid(x)\n fx = F.softplus(x).tanh()\n return grad_output * (fx + x * sx * (1 - fx * fx))\n\n def forward(self, x):\n \"\"\"Applies Mish activation function, useful in neural networks for nonlinear transformation of inputs.\"\"\"\n return self.F.apply(x)\n\n\nclass FReLU(nn.Module):\n \"\"\"Implements the FReLU activation, combining ReLU and convolution from https://arxiv.org/abs/2007.11824.\"\"\"\n\n def __init__(self, c1, k=3): # ch_in, kernel\n \"\"\"Initializes FReLU with specified channel size and kernel, implementing activation from\n https://arxiv.org/abs/2007.11824.\n \"\"\"\n super().__init__()\n self.conv = nn.Conv2d(c1, c1, k, 1, 1, groups=c1, bias=False)\n self.bn = nn.BatchNorm2d(c1)\n\n def forward(self, x):\n \"\"\"Performs FReLU activation on input, returning the max of input and its 2D convolution.\"\"\"\n return torch.max(x, self.bn(self.conv(x)))\n\n\nclass AconC(nn.Module):\n r\"\"\"ACON activation (activate or not) AconC: (p1*x-p2*x) * sigmoid(beta*(p1*x-p2*x)) + p2*x, beta is a learnable\n parameter according to \"Activate or Not: Learning Customized Activation\" .\n \"\"\"\n\n def __init__(self, c1):\n \"\"\"Initializes ACON activation with learnable parameters p1, p2, and beta as per\n https://arxiv.org/pdf/2009.04759.pdf.\n \"\"\"\n super().__init__()\n self.p1 = nn.Parameter(torch.randn(1, c1, 1, 1))\n self.p2 = nn.Parameter(torch.randn(1, c1, 1, 1))\n self.beta = nn.Parameter(torch.ones(1, c1, 1, 1))\n\n def forward(self, x):\n \"\"\"Applies a parametric activation function to tensor x; see https://arxiv.org/pdf/2009.04759.pdf for details.\n \"\"\"\n dpx = (self.p1 - self.p2) * x\n return dpx * torch.sigmoid(self.beta * dpx) + self.p2 * x\n\n\nclass MetaAconC(nn.Module):\n r\"\"\"ACON activation (activate or not) MetaAconC: (p1*x-p2*x) * sigmoid(beta*(p1*x-p2*x)) + p2*x, beta is generated\n by a small network according to \"Activate or Not: Learning Customized\n Activation\" .\n \"\"\"\n\n def __init__(self, c1, k=1, s=1, r=16): # ch_in, kernel, stride, r\n \"\"\"Initializes MetaAconC activation with params c1, optional k (kernel=1), s (stride=1), r (16), defining\n activation dynamics.\n \"\"\"\n super().__init__()\n c2 = max(r, c1 // r)\n self.p1 = nn.Parameter(torch.randn(1, c1, 1, 1))\n self.p2 = nn.Parameter(torch.randn(1, c1, 1, 1))\n self.fc1 = nn.Conv2d(c1, c2, k, s, bias=True)\n self.fc2 = nn.Conv2d(c2, c1, k, s, bias=True)\n # self.bn1 = nn.BatchNorm2d(c2)\n # self.bn2 = nn.BatchNorm2d(c1)\n\n def forward(self, x):\n \"\"\"Applies a forward pass transforming input `x` using parametric operations and returns the modified tensor.\"\"\"\n y = x.mean(dim=2, keepdims=True).mean(dim=3, keepdims=True)\n # batch-size 1 bug/instabilities https://github.com/ultralytics/yolov5/issues/2891\n # beta = torch.sigmoid(self.bn2(self.fc2(self.bn1(self.fc1(y))))) # bug/unstable\n beta = torch.sigmoid(self.fc2(self.fc1(y))) # bug patch BN layers removed\n dpx = (self.p1 - self.p2) * x\n return dpx * torch.sigmoid(beta * dpx) + self.p2 * x\n" }, { "path": "utils/augmentations.py", "content": "# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license\n\"\"\"Image augmentation functions.\"\"\"\n\nimport math\nimport random\n\nimport cv2\nimport numpy as np\nimport torch\nimport torchvision.transforms as T\nimport torchvision.transforms.functional as TF\n\nfrom utils.general import LOGGER, check_version, colorstr, resample_segments, segment2box, xywhn2xyxy\nfrom utils.metrics import bbox_ioa\n\nIMAGENET_MEAN = 0.485, 0.456, 0.406 # RGB mean\nIMAGENET_STD = 0.229, 0.224, 0.225 # RGB standard deviation\n\n\nclass Albumentations:\n \"\"\"Provides optional image augmentation for YOLOv3 using the Albumentations library if installed.\"\"\"\n\n def __init__(self, size=640):\n \"\"\"Initializes Albumentations class for optional YOLOv3 data augmentation with default size 640.\"\"\"\n self.transform = None\n prefix = colorstr(\"albumentations: \")\n try:\n import albumentations as A\n\n check_version(A.__version__, \"1.0.3\", hard=True) # version requirement\n\n T = [\n A.RandomResizedCrop(height=size, width=size, scale=(0.8, 1.0), ratio=(0.9, 1.11), p=0.0),\n A.Blur(p=0.01),\n A.MedianBlur(p=0.01),\n A.ToGray(p=0.01),\n A.CLAHE(p=0.01),\n A.RandomBrightnessContrast(p=0.0),\n A.RandomGamma(p=0.0),\n A.ImageCompression(quality_lower=75, p=0.0),\n ] # transforms\n self.transform = A.Compose(T, bbox_params=A.BboxParams(format=\"yolo\", label_fields=[\"class_labels\"]))\n\n LOGGER.info(prefix + \", \".join(f\"{x}\".replace(\"always_apply=False, \", \"\") for x in T if x.p))\n except ImportError: # package not installed, skip\n pass\n except Exception as e:\n LOGGER.info(f\"{prefix}{e}\")\n\n def __call__(self, im, labels, p=1.0):\n \"\"\"Applies transformations to an image and its bounding boxes with a probability `p`.\"\"\"\n if self.transform and random.random() < p:\n new = self.transform(image=im, bboxes=labels[:, 1:], class_labels=labels[:, 0]) # transformed\n im, labels = new[\"image\"], np.array([[c, *b] for c, b in zip(new[\"class_labels\"], new[\"bboxes\"])])\n return im, labels\n\n\ndef normalize(x, mean=IMAGENET_MEAN, std=IMAGENET_STD, inplace=False):\n \"\"\"Normalizes RGB images in BCHW format using ImageNet stats; use `inplace=True` for in-place normalization.\"\"\"\n return TF.normalize(x, mean, std, inplace=inplace)\n\n\ndef denormalize(x, mean=IMAGENET_MEAN, std=IMAGENET_STD):\n \"\"\"Converts normalized images back to original form using ImageNet stats; inputs in BCHW format.\n\n Example: `denormalize(tensor)`.\n \"\"\"\n for i in range(3):\n x[:, i] = x[:, i] * std[i] + mean[i]\n return x\n\n\ndef augment_hsv(im, hgain=0.5, sgain=0.5, vgain=0.5):\n \"\"\"Applies HSV color-space augmentation with optional gains; expects BGR image input.\n\n Example: `augment_hsv(image)`.\n \"\"\"\n if hgain or sgain or vgain:\n r = np.random.uniform(-1, 1, 3) * [hgain, sgain, vgain] + 1 # random gains\n hue, sat, val = cv2.split(cv2.cvtColor(im, cv2.COLOR_BGR2HSV))\n dtype = im.dtype # uint8\n\n x = np.arange(0, 256, dtype=r.dtype)\n lut_hue = ((x * r[0]) % 180).astype(dtype)\n lut_sat = np.clip(x * r[1], 0, 255).astype(dtype)\n lut_val = np.clip(x * r[2], 0, 255).astype(dtype)\n\n im_hsv = cv2.merge((cv2.LUT(hue, lut_hue), cv2.LUT(sat, lut_sat), cv2.LUT(val, lut_val)))\n cv2.cvtColor(im_hsv, cv2.COLOR_HSV2BGR, dst=im) # no return needed\n\n\ndef hist_equalize(im, clahe=True, bgr=False):\n \"\"\"Equalizes histogram of BGR/RGB image `im` with shape (n,m,3), optionally using CLAHE; returns equalized image.\"\"\"\n yuv = cv2.cvtColor(im, cv2.COLOR_BGR2YUV if bgr else cv2.COLOR_RGB2YUV)\n if clahe:\n c = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8))\n yuv[:, :, 0] = c.apply(yuv[:, :, 0])\n else:\n yuv[:, :, 0] = cv2.equalizeHist(yuv[:, :, 0]) # equalize Y channel histogram\n return cv2.cvtColor(yuv, cv2.COLOR_YUV2BGR if bgr else cv2.COLOR_YUV2RGB) # convert YUV image to RGB\n\n\ndef replicate(im, labels):\n \"\"\"Duplicates half of the smallest bounding boxes in an image to augment dataset; update labels accordingly.\"\"\"\n h, w = im.shape[:2]\n boxes = labels[:, 1:].astype(int)\n x1, y1, x2, y2 = boxes.T\n s = ((x2 - x1) + (y2 - y1)) / 2 # side length (pixels)\n for i in s.argsort()[: round(s.size * 0.5)]: # smallest indices\n x1b, y1b, x2b, y2b = boxes[i]\n bh, bw = y2b - y1b, x2b - x1b\n yc, xc = int(random.uniform(0, h - bh)), int(random.uniform(0, w - bw)) # offset x, y\n x1a, y1a, x2a, y2a = [xc, yc, xc + bw, yc + bh]\n im[y1a:y2a, x1a:x2a] = im[y1b:y2b, x1b:x2b] # im4[ymin:ymax, xmin:xmax]\n labels = np.append(labels, [[labels[i, 0], x1a, y1a, x2a, y2a]], axis=0)\n\n return im, labels\n\n\ndef letterbox(im, new_shape=(640, 640), color=(114, 114, 114), auto=True, scaleFill=False, scaleup=True, stride=32):\n \"\"\"Resizes and pads an image to a new shape with optional scaling, filling, and stride-multiple constraints.\"\"\"\n shape = im.shape[:2] # current shape [height, width]\n if isinstance(new_shape, int):\n new_shape = (new_shape, new_shape)\n\n # Scale ratio (new / old)\n r = min(new_shape[0] / shape[0], new_shape[1] / shape[1])\n if not scaleup: # only scale down, do not scale up (for better val mAP)\n r = min(r, 1.0)\n\n # Compute padding\n ratio = r, r # width, height ratios\n new_unpad = round(shape[1] * r), round(shape[0] * r)\n dw, dh = new_shape[1] - new_unpad[0], new_shape[0] - new_unpad[1] # wh padding\n if auto: # minimum rectangle\n dw, dh = np.mod(dw, stride), np.mod(dh, stride) # wh padding\n elif scaleFill: # stretch\n dw, dh = 0.0, 0.0\n new_unpad = (new_shape[1], new_shape[0])\n ratio = new_shape[1] / shape[1], new_shape[0] / shape[0] # width, height ratios\n\n dw /= 2 # divide padding into 2 sides\n dh /= 2\n\n if shape[::-1] != new_unpad: # resize\n im = cv2.resize(im, new_unpad, interpolation=cv2.INTER_LINEAR)\n top, bottom = round(dh - 0.1), round(dh + 0.1)\n left, right = round(dw - 0.1), round(dw + 0.1)\n im = cv2.copyMakeBorder(im, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color) # add border\n return im, ratio, (dw, dh)\n\n\ndef random_perspective(\n im, targets=(), segments=(), degrees=10, translate=0.1, scale=0.1, shear=10, perspective=0.0, border=(0, 0)\n):\n # torchvision.transforms.RandomAffine(degrees=(-10, 10), translate=(0.1, 0.1), scale=(0.9, 1.1), shear=(-10, 10))\n # targets = [cls, xyxy]\n \"\"\"Applies a random perspective transformation to an image and its bounding boxes for data augmentation.\"\"\"\n height = im.shape[0] + border[0] * 2 # shape(h,w,c)\n width = im.shape[1] + border[1] * 2\n\n # Center\n C = np.eye(3)\n C[0, 2] = -im.shape[1] / 2 # x translation (pixels)\n C[1, 2] = -im.shape[0] / 2 # y translation (pixels)\n\n # Perspective\n P = np.eye(3)\n P[2, 0] = random.uniform(-perspective, perspective) # x perspective (about y)\n P[2, 1] = random.uniform(-perspective, perspective) # y perspective (about x)\n\n # Rotation and Scale\n R = np.eye(3)\n a = random.uniform(-degrees, degrees)\n # a += random.choice([-180, -90, 0, 90]) # add 90deg rotations to small rotations\n s = random.uniform(1 - scale, 1 + scale)\n # s = 2 ** random.uniform(-scale, scale)\n R[:2] = cv2.getRotationMatrix2D(angle=a, center=(0, 0), scale=s)\n\n # Shear\n S = np.eye(3)\n S[0, 1] = math.tan(random.uniform(-shear, shear) * math.pi / 180) # x shear (deg)\n S[1, 0] = math.tan(random.uniform(-shear, shear) * math.pi / 180) # y shear (deg)\n\n # Translation\n T = np.eye(3)\n T[0, 2] = random.uniform(0.5 - translate, 0.5 + translate) * width # x translation (pixels)\n T[1, 2] = random.uniform(0.5 - translate, 0.5 + translate) * height # y translation (pixels)\n\n # Combined rotation matrix\n M = T @ S @ R @ P @ C # order of operations (right to left) is IMPORTANT\n if (border[0] != 0) or (border[1] != 0) or (M != np.eye(3)).any(): # image changed\n if perspective:\n im = cv2.warpPerspective(im, M, dsize=(width, height), borderValue=(114, 114, 114))\n else: # affine\n im = cv2.warpAffine(im, M[:2], dsize=(width, height), borderValue=(114, 114, 114))\n\n if n := len(targets):\n use_segments = any(x.any() for x in segments) and len(segments) == n\n new = np.zeros((n, 4))\n if use_segments: # warp segments\n segments = resample_segments(segments) # upsample\n for i, segment in enumerate(segments):\n xy = np.ones((len(segment), 3))\n xy[:, :2] = segment\n xy = xy @ M.T # transform\n xy = xy[:, :2] / xy[:, 2:3] if perspective else xy[:, :2] # perspective rescale or affine\n\n # clip\n new[i] = segment2box(xy, width, height)\n\n else: # warp boxes\n xy = np.ones((n * 4, 3))\n xy[:, :2] = targets[:, [1, 2, 3, 4, 1, 4, 3, 2]].reshape(n * 4, 2) # x1y1, x2y2, x1y2, x2y1\n xy = xy @ M.T # transform\n xy = (xy[:, :2] / xy[:, 2:3] if perspective else xy[:, :2]).reshape(n, 8) # perspective rescale or affine\n\n # create new boxes\n x = xy[:, [0, 2, 4, 6]]\n y = xy[:, [1, 3, 5, 7]]\n new = np.concatenate((x.min(1), y.min(1), x.max(1), y.max(1))).reshape(4, n).T\n\n # clip\n new[:, [0, 2]] = new[:, [0, 2]].clip(0, width)\n new[:, [1, 3]] = new[:, [1, 3]].clip(0, height)\n\n # filter candidates\n i = box_candidates(box1=targets[:, 1:5].T * s, box2=new.T, area_thr=0.01 if use_segments else 0.10)\n targets = targets[i]\n targets[:, 1:5] = new[i]\n\n return im, targets\n\n\ndef copy_paste(im, labels, segments, p=0.5):\n \"\"\"Applies Copy-Paste augmentation (https://arxiv.org/abs/2012.07177) on image, labels (nx5 np.array(cls, xyxy)),\n and segments.\n \"\"\"\n n = len(segments)\n if p and n:\n _h, w, _c = im.shape # height, width, channels\n im_new = np.zeros(im.shape, np.uint8)\n for j in random.sample(range(n), k=round(p * n)):\n l, s = labels[j], segments[j]\n box = w - l[3], l[2], w - l[1], l[4]\n ioa = bbox_ioa(box, labels[:, 1:5]) # intersection over area\n if (ioa < 0.30).all(): # allow 30% obscuration of existing labels\n labels = np.concatenate((labels, [[l[0], *box]]), 0)\n segments.append(np.concatenate((w - s[:, 0:1], s[:, 1:2]), 1))\n cv2.drawContours(im_new, [segments[j].astype(np.int32)], -1, (1, 1, 1), cv2.FILLED)\n\n result = cv2.flip(im, 1) # augment segments (flip left-right)\n i = cv2.flip(im_new, 1).astype(bool)\n im[i] = result[i] # cv2.imwrite('debug.jpg', im) # debug\n\n return im, labels, segments\n\n\ndef cutout(im, labels, p=0.5):\n \"\"\"Applies cutout augmentation, potentially removing >60% obscured labels; see https://arxiv.org/abs/1708.04552.\"\"\"\n if random.random() < p:\n h, w = im.shape[:2]\n scales = [0.5] * 1 + [0.25] * 2 + [0.125] * 4 + [0.0625] * 8 + [0.03125] * 16 # image size fraction\n for s in scales:\n mask_h = random.randint(1, int(h * s)) # create random masks\n mask_w = random.randint(1, int(w * s))\n\n # box\n xmin = max(0, random.randint(0, w) - mask_w // 2)\n ymin = max(0, random.randint(0, h) - mask_h // 2)\n xmax = min(w, xmin + mask_w)\n ymax = min(h, ymin + mask_h)\n\n # apply random color mask\n im[ymin:ymax, xmin:xmax] = [random.randint(64, 191) for _ in range(3)]\n\n # return unobscured labels\n if len(labels) and s > 0.03:\n box = np.array([xmin, ymin, xmax, ymax], dtype=np.float32)\n ioa = bbox_ioa(box, xywhn2xyxy(labels[:, 1:5], w, h)) # intersection over area\n labels = labels[ioa < 0.60] # remove >60% obscured labels\n\n return labels\n\n\ndef mixup(im, labels, im2, labels2):\n \"\"\"Applies MixUp augmentation by blending images and labels; see https://arxiv.org/pdf/1710.09412.pdf for details.\n \"\"\"\n r = np.random.beta(32.0, 32.0) # mixup ratio, alpha=beta=32.0\n im = (im * r + im2 * (1 - r)).astype(np.uint8)\n labels = np.concatenate((labels, labels2), 0)\n return im, labels\n\n\ndef box_candidates(box1, box2, wh_thr=2, ar_thr=100, area_thr=0.1, eps=1e-16): # box1(4,n), box2(4,n)\n \"\"\"Evaluates candidate boxes based on width, height, aspect ratio, and area thresholds.\"\"\"\n w1, h1 = box1[2] - box1[0], box1[3] - box1[1]\n w2, h2 = box2[2] - box2[0], box2[3] - box2[1]\n ar = np.maximum(w2 / (h2 + eps), h2 / (w2 + eps)) # aspect ratio\n return (w2 > wh_thr) & (h2 > wh_thr) & (w2 * h2 / (w1 * h1 + eps) > area_thr) & (ar < ar_thr) # candidates\n\n\ndef classify_albumentations(\n augment=True,\n size=224,\n scale=(0.08, 1.0),\n ratio=(0.75, 1.0 / 0.75), # 0.75, 1.33\n hflip=0.5,\n vflip=0.0,\n jitter=0.4,\n mean=IMAGENET_MEAN,\n std=IMAGENET_STD,\n auto_aug=False,\n):\n # YOLOv3 classification Albumentations (optional, only used if package is installed)\n \"\"\"Generates an Albumentations transform pipeline for image classification with optional augmentations.\"\"\"\n prefix = colorstr(\"albumentations: \")\n try:\n import albumentations as A\n from albumentations.pytorch import ToTensorV2\n\n check_version(A.__version__, \"1.0.3\", hard=True) # version requirement\n if augment: # Resize and crop\n T = [A.RandomResizedCrop(height=size, width=size, scale=scale, ratio=ratio)]\n if auto_aug:\n # TODO: implement AugMix, AutoAug & RandAug in albumentation\n LOGGER.info(f\"{prefix}auto augmentations are currently not supported\")\n else:\n if hflip > 0:\n T += [A.HorizontalFlip(p=hflip)]\n if vflip > 0:\n T += [A.VerticalFlip(p=vflip)]\n if jitter > 0:\n color_jitter = (float(jitter),) * 3 # repeat value for brightness, contrast, satuaration, 0 hue\n T += [A.ColorJitter(*color_jitter, 0)]\n else: # Use fixed crop for eval set (reproducibility)\n T = [A.SmallestMaxSize(max_size=size), A.CenterCrop(height=size, width=size)]\n T += [A.Normalize(mean=mean, std=std), ToTensorV2()] # Normalize and convert to Tensor\n LOGGER.info(prefix + \", \".join(f\"{x}\".replace(\"always_apply=False, \", \"\") for x in T if x.p))\n return A.Compose(T)\n\n except ImportError: # package not installed, skip\n LOGGER.warning(f\"{prefix}⚠️ not found, install with `pip install albumentations` (recommended)\")\n except Exception as e:\n LOGGER.info(f\"{prefix}{e}\")\n\n\ndef classify_transforms(size=224):\n \"\"\"Applies classification transforms including center cropping, tensor conversion, and normalization.\"\"\"\n assert isinstance(size, int), f\"ERROR: classify_transforms size {size} must be integer, not (list, tuple)\"\n # T.Compose([T.ToTensor(), T.Resize(size), T.CenterCrop(size), T.Normalize(IMAGENET_MEAN, IMAGENET_STD)])\n return T.Compose([CenterCrop(size), ToTensor(), T.Normalize(IMAGENET_MEAN, IMAGENET_STD)])\n\n\nclass LetterBox:\n \"\"\"Resizes and pads images to a specified size while maintaining aspect ratio.\"\"\"\n\n def __init__(self, size=(640, 640), auto=False, stride=32):\n \"\"\"Initializes LetterBox for YOLOv3 image preprocessing with optional auto-sizing and stride; `size` can be int\n or tuple.\n \"\"\"\n super().__init__()\n self.h, self.w = (size, size) if isinstance(size, int) else size\n self.auto = auto # pass max size integer, automatically solve for short side using stride\n self.stride = stride # used with auto\n\n def __call__(self, im): # im = np.array HWC\n \"\"\"Resizes and pads image `im` (np.array HWC) to specified `size` and `stride`, possibly autosizing for the\n short side.\n \"\"\"\n imh, imw = im.shape[:2]\n r = min(self.h / imh, self.w / imw) # ratio of new/old\n h, w = round(imh * r), round(imw * r) # resized image\n hs, ws = (math.ceil(x / self.stride) * self.stride for x in (h, w)) if self.auto else self.h, self.w\n top, left = round((hs - h) / 2 - 0.1), round((ws - w) / 2 - 0.1)\n im_out = np.full((self.h, self.w, 3), 114, dtype=im.dtype)\n im_out[top : top + h, left : left + w] = cv2.resize(im, (w, h), interpolation=cv2.INTER_LINEAR)\n return im_out\n\n\nclass CenterCrop:\n \"\"\"Crops the center of an image to a specified size, maintaining aspect ratio.\"\"\"\n\n def __init__(self, size=640):\n \"\"\"Initializes a CenterCrop object for YOLOv3, to crop images to a specified size, with default 640x640.\"\"\"\n super().__init__()\n self.h, self.w = (size, size) if isinstance(size, int) else size\n\n def __call__(self, im): # im = np.array HWC\n \"\"\"Crops and resizes an image to specified dimensions, defaulting to 640x640, maintaining aspect ratio.\"\"\"\n imh, imw = im.shape[:2]\n m = min(imh, imw) # min dimension\n top, left = (imh - m) // 2, (imw - m) // 2\n return cv2.resize(im[top : top + m, left : left + m], (self.w, self.h), interpolation=cv2.INTER_LINEAR)\n\n\nclass ToTensor:\n \"\"\"Converts a BGR image in numpy format to a PyTorch tensor in RGB format, with optional half precision.\"\"\"\n\n def __init__(self, half=False):\n \"\"\"Initializes ToTensor class for YOLOv3 image preprocessing to convert images to PyTorch tensors, optionally in\n half precision.\n \"\"\"\n super().__init__()\n self.half = half\n\n def __call__(self, im): # im = np.array HWC in BGR order\n \"\"\"Converts a BGR image in numpy format to a PyTorch tensor in RGB format, with options for half precision and\n normalization.\n \"\"\"\n im = np.ascontiguousarray(im.transpose((2, 0, 1))[::-1]) # HWC to CHW -> BGR to RGB -> contiguous\n im = torch.from_numpy(im) # to torch\n im = im.half() if self.half else im.float() # uint8 to fp16/32\n im /= 255.0 # 0-255 to 0.0-1.0\n return im\n" }, { "path": "utils/autoanchor.py", "content": "# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license\n\"\"\"AutoAnchor utils.\"\"\"\n\nimport random\n\nimport numpy as np\nimport torch\nimport yaml\nfrom tqdm import tqdm\n\nfrom utils import TryExcept\nfrom utils.general import LOGGER, TQDM_BAR_FORMAT, colorstr\n\nPREFIX = colorstr(\"AutoAnchor: \")\n\n\ndef check_anchor_order(m):\n \"\"\"Checks and corrects anchor order in YOLOv3's Detect() module if mismatched with stride order.\"\"\"\n a = m.anchors.prod(-1).mean(-1).view(-1) # mean anchor area per output layer\n da = a[-1] - a[0] # delta a\n ds = m.stride[-1] - m.stride[0] # delta s\n if da and (da.sign() != ds.sign()): # same order\n LOGGER.info(f\"{PREFIX}Reversing anchor order\")\n m.anchors[:] = m.anchors.flip(0)\n\n\n@TryExcept(f\"{PREFIX}ERROR\")\ndef check_anchors(dataset, model, thr=4.0, imgsz=640):\n \"\"\"Evaluates anchor fit to dataset and recomputes if below a threshold, enhancing model performance.\"\"\"\n m = model.module.model[-1] if hasattr(model, \"module\") else model.model[-1] # Detect()\n shapes = imgsz * dataset.shapes / dataset.shapes.max(1, keepdims=True)\n scale = np.random.uniform(0.9, 1.1, size=(shapes.shape[0], 1)) # augment scale\n wh = torch.tensor(np.concatenate([l[:, 3:5] * s for s, l in zip(shapes * scale, dataset.labels)])).float() # wh\n\n def metric(k): # compute metric\n \"\"\"Computes and returns best possible recall (bpr) and anchors above threshold (aat) metrics for given anchors.\n \"\"\"\n r = wh[:, None] / k[None]\n x = torch.min(r, 1 / r).min(2)[0] # ratio metric\n best = x.max(1)[0] # best_x\n aat = (x > 1 / thr).float().sum(1).mean() # anchors above threshold\n bpr = (best > 1 / thr).float().mean() # best possible recall\n return bpr, aat\n\n stride = m.stride.to(m.anchors.device).view(-1, 1, 1) # model strides\n anchors = m.anchors.clone() * stride # current anchors\n bpr, aat = metric(anchors.cpu().view(-1, 2))\n s = f\"\\n{PREFIX}{aat:.2f} anchors/target, {bpr:.3f} Best Possible Recall (BPR). \"\n if bpr > 0.98: # threshold to recompute\n LOGGER.info(f\"{s}Current anchors are a good fit to dataset ✅\")\n else:\n LOGGER.info(f\"{s}Anchors are a poor fit to dataset ⚠️, attempting to improve...\")\n na = m.anchors.numel() // 2 # number of anchors\n anchors = kmean_anchors(dataset, n=na, img_size=imgsz, thr=thr, gen=1000, verbose=False)\n new_bpr = metric(anchors)[0]\n if new_bpr > bpr: # replace anchors\n anchors = torch.tensor(anchors, device=m.anchors.device).type_as(m.anchors)\n m.anchors[:] = anchors.clone().view_as(m.anchors)\n check_anchor_order(m) # must be in pixel-space (not grid-space)\n m.anchors /= stride\n s = f\"{PREFIX}Done ✅ (optional: update model *.yaml to use these anchors in the future)\"\n else:\n s = f\"{PREFIX}Done ⚠️ (original anchors better than new anchors, proceeding with original anchors)\"\n LOGGER.info(s)\n\n\ndef kmean_anchors(dataset=\"./data/coco128.yaml\", n=9, img_size=640, thr=4.0, gen=1000, verbose=True):\n \"\"\"Creates kmeans-evolved anchors from training dataset.\n\n Args:\n dataset: path to data.yaml, or a loaded dataset\n n: number of anchors\n img_size: image size used for training\n thr: anchor-label wh ratio threshold hyperparameter hyp['anchor_t'] used for training, default=4.0\n gen: generations to evolve anchors using genetic algorithm\n verbose: print all results\n\n Returns:\n k: kmeans evolved anchors\n\n Examples:\n from utils.autoanchor import *; _ = kmean_anchors()\n \"\"\"\n from scipy.cluster.vq import kmeans\n\n npr = np.random\n thr = 1 / thr\n\n def metric(k, wh): # compute metrics\n \"\"\"Computes best possible recall (BPR) and anchors above threshold (AAT) metrics for given anchor boxes.\"\"\"\n r = wh[:, None] / k[None]\n x = torch.min(r, 1 / r).min(2)[0] # ratio metric\n # x = wh_iou(wh, torch.tensor(k)) # iou metric\n return x, x.max(1)[0] # x, best_x\n\n def anchor_fitness(k): # mutation fitness\n \"\"\"Evaluates the fitness of anchor boxes by computing mean recall weighted by an activation threshold.\"\"\"\n _, best = metric(torch.tensor(k, dtype=torch.float32), wh)\n return (best * (best > thr).float()).mean() # fitness\n\n def print_results(k, verbose=True):\n \"\"\"Displays sorted anchors and their metrics including best possible recall and anchors above threshold.\"\"\"\n k = k[np.argsort(k.prod(1))] # sort small to large\n x, best = metric(k, wh0)\n bpr, aat = (best > thr).float().mean(), (x > thr).float().mean() * n # best possible recall, anch > thr\n s = (\n f\"{PREFIX}thr={thr:.2f}: {bpr:.4f} best possible recall, {aat:.2f} anchors past thr\\n\"\n f\"{PREFIX}n={n}, img_size={img_size}, metric_all={x.mean():.3f}/{best.mean():.3f}-mean/best, \"\n f\"past_thr={x[x > thr].mean():.3f}-mean: \"\n )\n for x in k:\n s += \"%i,%i, \" % (round(x[0]), round(x[1]))\n if verbose:\n LOGGER.info(s[:-2])\n return k\n\n if isinstance(dataset, str): # *.yaml file\n with open(dataset, errors=\"ignore\") as f:\n data_dict = yaml.safe_load(f) # model dict\n from utils.dataloaders import LoadImagesAndLabels\n\n dataset = LoadImagesAndLabels(data_dict[\"train\"], augment=True, rect=True)\n\n # Get label wh\n shapes = img_size * dataset.shapes / dataset.shapes.max(1, keepdims=True)\n wh0 = np.concatenate([l[:, 3:5] * s for s, l in zip(shapes, dataset.labels)]) # wh\n\n # Filter\n i = (wh0 < 3.0).any(1).sum()\n if i:\n LOGGER.info(f\"{PREFIX}WARNING ⚠️ Extremely small objects found: {i} of {len(wh0)} labels are <3 pixels in size\")\n wh = wh0[(wh0 >= 2.0).any(1)].astype(np.float32) # filter > 2 pixels\n # wh = wh * (npr.rand(wh.shape[0], 1) * 0.9 + 0.1) # multiply by random scale 0-1\n\n # Kmeans init\n try:\n LOGGER.info(f\"{PREFIX}Running kmeans for {n} anchors on {len(wh)} points...\")\n assert n <= len(wh) # apply overdetermined constraint\n s = wh.std(0) # sigmas for whitening\n k = kmeans(wh / s, n, iter=30)[0] * s # points\n assert n == len(k) # kmeans may return fewer points than requested if wh is insufficient or too similar\n except Exception:\n LOGGER.warning(f\"{PREFIX}WARNING ⚠️ switching strategies from kmeans to random init\")\n k = np.sort(npr.rand(n * 2)).reshape(n, 2) * img_size # random init\n wh, wh0 = (torch.tensor(x, dtype=torch.float32) for x in (wh, wh0))\n k = print_results(k, verbose=False)\n\n # Plot\n # k, d = [None] * 20, [None] * 20\n # for i in tqdm(range(1, 21)):\n # k[i-1], d[i-1] = kmeans(wh / s, i) # points, mean distance\n # fig, ax = plt.subplots(1, 2, figsize=(14, 7), tight_layout=True)\n # ax = ax.ravel()\n # ax[0].plot(np.arange(1, 21), np.array(d) ** 2, marker='.')\n # fig, ax = plt.subplots(1, 2, figsize=(14, 7)) # plot wh\n # ax[0].hist(wh[wh[:, 0]<100, 0],400)\n # ax[1].hist(wh[wh[:, 1]<100, 1],400)\n # fig.savefig('wh.png', dpi=200)\n\n # Evolve\n f, sh, mp, s = anchor_fitness(k), k.shape, 0.9, 0.1 # fitness, generations, mutation prob, sigma\n pbar = tqdm(range(gen), bar_format=TQDM_BAR_FORMAT) # progress bar\n for _ in pbar:\n v = np.ones(sh)\n while (v == 1).all(): # mutate until a change occurs (prevent duplicates)\n v = ((npr.random(sh) < mp) * random.random() * npr.randn(*sh) * s + 1).clip(0.3, 3.0)\n kg = (k.copy() * v).clip(min=2.0)\n fg = anchor_fitness(kg)\n if fg > f:\n f, k = fg, kg.copy()\n pbar.desc = f\"{PREFIX}Evolving anchors with Genetic Algorithm: fitness = {f:.4f}\"\n if verbose:\n print_results(k, verbose)\n\n return print_results(k).astype(np.float32)\n" }, { "path": "utils/autobatch.py", "content": "# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license\n\"\"\"Auto-batch utils.\"\"\"\n\nfrom copy import deepcopy\n\nimport numpy as np\nimport torch\n\nfrom utils.general import LOGGER, colorstr\nfrom utils.torch_utils import profile\n\n\ndef check_train_batch_size(model, imgsz=640, amp=True):\n \"\"\"Checks and computes the optimal training batch size for YOLOv3, given model and image size.\"\"\"\n with torch.cuda.amp.autocast(amp):\n return autobatch(deepcopy(model).train(), imgsz) # compute optimal batch size\n\n\ndef autobatch(model, imgsz=640, fraction=0.8, batch_size=16):\n \"\"\"Estimates optimal YOLOv3 batch size using available CUDA memory; imgsz:int=640, fraction:float=0.8,\n batch_size:int=16.\n \"\"\"\n # Usage:\n # import torch\n # from utils.autobatch import autobatch\n # model = torch.hub.load('ultralytics/yolov5', 'yolov5s', autoshape=False)\n # print(autobatch(model))\n\n # Check device\n prefix = colorstr(\"AutoBatch: \")\n LOGGER.info(f\"{prefix}Computing optimal batch size for --imgsz {imgsz}\")\n device = next(model.parameters()).device # get model device\n if device.type == \"cpu\":\n LOGGER.info(f\"{prefix}CUDA not detected, using default CPU batch-size {batch_size}\")\n return batch_size\n if torch.backends.cudnn.benchmark:\n LOGGER.info(f\"{prefix} ⚠️ Requires torch.backends.cudnn.benchmark=False, using default batch-size {batch_size}\")\n return batch_size\n\n # Inspect CUDA memory\n gb = 1 << 30 # bytes to GiB (1024 ** 3)\n d = str(device).upper() # 'CUDA:0'\n properties = torch.cuda.get_device_properties(device) # device properties\n t = properties.total_memory / gb # GiB total\n r = torch.cuda.memory_reserved(device) / gb # GiB reserved\n a = torch.cuda.memory_allocated(device) / gb # GiB allocated\n f = t - (r + a) # GiB free\n LOGGER.info(f\"{prefix}{d} ({properties.name}) {t:.2f}G total, {r:.2f}G reserved, {a:.2f}G allocated, {f:.2f}G free\")\n\n # Profile batch sizes\n batch_sizes = [1, 2, 4, 8, 16]\n try:\n img = [torch.empty(b, 3, imgsz, imgsz) for b in batch_sizes]\n results = profile(img, model, n=3, device=device)\n except Exception as e:\n LOGGER.warning(f\"{prefix}{e}\")\n\n # Fit a solution\n y = [x[2] for x in results if x] # memory [2]\n p = np.polyfit(batch_sizes[: len(y)], y, deg=1) # first degree polynomial fit\n b = int((f * fraction - p[1]) / p[0]) # y intercept (optimal batch size)\n if None in results: # some sizes failed\n i = results.index(None) # first fail index\n if b >= batch_sizes[i]: # y intercept above failure point\n b = batch_sizes[max(i - 1, 0)] # select prior safe point\n if b < 1 or b > 1024: # b outside of safe range\n b = batch_size\n LOGGER.warning(f\"{prefix}WARNING ⚠️ CUDA anomaly detected, recommend restart environment and retry command.\")\n\n fraction = (np.polyval(p, b) + r + a) / t # actual fraction predicted\n LOGGER.info(f\"{prefix}Using batch-size {b} for {d} {t * fraction:.2f}G/{t:.2f}G ({fraction * 100:.0f}%) ✅\")\n return b\n" }, { "path": "utils/aws/__init__.py", "content": "# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license\n" }, { "path": "utils/aws/mime.sh", "content": "# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license\n\n# AWS EC2 instance startup 'MIME' script https://aws.amazon.com/premiumsupport/knowledge-center/execute-user-data-ec2/\n# This script will run on every instance restart, not only on first start\n# --- DO NOT COPY ABOVE COMMENTS WHEN PASTING INTO USERDATA ---\n\nContent-Type: multipart/mixed\nboundary=\"//\"\nMIME-Version: 1.0\n\n--//\nContent-Type: text/cloud-config\ncharset=\"us-ascii\"\nMIME-Version: 1.0\nContent-Transfer-Encoding: 7bit\nContent-Disposition: attachment\nfilename=\"cloud-config.txt\"\n\n#cloud-config\ncloud_final_modules:\n- [scripts-user, always]\n\n--//\nContent-Type: text/x-shellscript\ncharset=\"us-ascii\"\nMIME-Version: 1.0\nContent-Transfer-Encoding: 7bit\nContent-Disposition: attachment\nfilename=\"userdata.txt\"\n\n#!/bin/bash\n# --- paste contents of userdata.sh here ---\n--//\n" }, { "path": "utils/aws/resume.py", "content": "# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license\n\n# Resume all interrupted trainings in yolov5/ dir including DDP trainings\n# Usage: $ python utils/aws/resume.py\n\nimport os\nimport sys\nfrom pathlib import Path\n\nimport torch\nimport yaml\nfrom ultralytics.utils.patches import torch_load\n\nFILE = Path(__file__).resolve()\nROOT = FILE.parents[2] # YOLOv3 root directory\nif str(ROOT) not in sys.path:\n sys.path.append(str(ROOT)) # add ROOT to PATH\n\nport = 0 # --master_port\npath = Path(\"\").resolve()\nfor last in path.rglob(\"*/**/last.pt\"):\n ckpt = torch_load(last)\n if ckpt[\"optimizer\"] is None:\n continue\n\n # Load opt.yaml\n with open(last.parent.parent / \"opt.yaml\", errors=\"ignore\") as f:\n opt = yaml.safe_load(f)\n\n # Get device count\n d = opt[\"device\"].split(\",\") # devices\n nd = len(d) # number of devices\n ddp = nd > 1 or (nd == 0 and torch.cuda.device_count() > 1) # distributed data parallel\n\n if ddp: # multi-GPU\n port += 1\n cmd = f\"python -m torch.distributed.run --nproc_per_node {nd} --master_port {port} train.py --resume {last}\"\n else: # single-GPU\n cmd = f\"python train.py --resume {last}\"\n\n cmd += \" > /dev/null 2>&1 &\" # redirect output to dev/null and run in daemon thread\n print(cmd)\n os.system(cmd)\n" }, { "path": "utils/aws/userdata.sh", "content": "#!/bin/bash\n# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license\n\n# AWS EC2 instance startup script https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/user-data.html\n# This script will run only once on first instance start (for a re-start script see mime.sh)\n# /home/ubuntu (ubuntu) or /home/ec2-user (amazon-linux) is working dir\n# Use >300 GB SSD\n\ncd home/ubuntu\nif [ ! -d yolov5 ]; then\n echo \"Running first-time script.\" # install dependencies, download COCO, pull Docker\n git clone https://github.com/ultralytics/yolov5 -b master && sudo chmod -R 777 yolov5\n cd yolov5\n bash data/scripts/get_coco.sh && echo \"COCO done.\" &\n sudo docker pull ultralytics/yolov5:latest && echo \"Docker done.\" &\n python -m pip install --upgrade pip && pip install -r requirements.txt && python detect.py && echo \"Requirements done.\" &\n wait && echo \"All tasks done.\" # finish background tasks\nelse\n echo \"Running re-start script.\" # resume interrupted runs\n i=0\n list=$(sudo docker ps -qa) # container list i.e. $'one\\ntwo\\nthree\\nfour'\n while IFS= read -r id; do\n ((i++))\n echo \"restarting container $i: $id\"\n sudo docker start $id\n # sudo docker exec -it $id python train.py --resume # single-GPU\n sudo docker exec -d $id python utils/aws/resume.py # multi-scenario\n done <<< \"$list\"\nfi\n" }, { "path": "utils/callbacks.py", "content": "# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license\n\"\"\"Callback utils.\"\"\"\n\nimport threading\n\n\nclass Callbacks:\n \"\"\"Handles all registered callbacks for YOLOv3 Hooks.\"\"\"\n\n def __init__(self):\n \"\"\"Initializes a Callbacks object to manage YOLOv3 training hooks with various event triggers.\"\"\"\n self._callbacks = {\n \"on_pretrain_routine_start\": [],\n \"on_pretrain_routine_end\": [],\n \"on_train_start\": [],\n \"on_train_epoch_start\": [],\n \"on_train_batch_start\": [],\n \"optimizer_step\": [],\n \"on_before_zero_grad\": [],\n \"on_train_batch_end\": [],\n \"on_train_epoch_end\": [],\n \"on_val_start\": [],\n \"on_val_batch_start\": [],\n \"on_val_image_end\": [],\n \"on_val_batch_end\": [],\n \"on_val_end\": [],\n \"on_fit_epoch_end\": [], # fit = train + val\n \"on_model_save\": [],\n \"on_train_end\": [],\n \"on_params_update\": [],\n \"teardown\": [],\n }\n self.stop_training = False # set True to interrupt training\n\n def register_action(self, hook, name=\"\", callback=None):\n \"\"\"Register a new action to a callback hook.\n\n Args:\n hook: The callback hook name to register the action to\n name: The name of the action for later reference\n callback: The callback to fire\n \"\"\"\n assert hook in self._callbacks, f\"hook '{hook}' not found in callbacks {self._callbacks}\"\n assert callable(callback), f\"callback '{callback}' is not callable\"\n self._callbacks[hook].append({\"name\": name, \"callback\": callback})\n\n def get_registered_actions(self, hook=None):\n \"\"\"\" Returns all the registered actions by callback hook.\n\n Args:\n hook: The name of the hook to check, defaults to all\n \"\"\"\n return self._callbacks[hook] if hook else self._callbacks\n\n def run(self, hook, *args, thread=False, **kwargs):\n \"\"\"Loop through the registered actions and fire all callbacks on main thread.\n\n Args:\n hook: The name of the hook to check, defaults to all\n args: Arguments to receive from YOLOv3\n thread: (boolean) Run callbacks in daemon thread\n kwargs: Keyword Arguments to receive from YOLOv3\n \"\"\"\n assert hook in self._callbacks, f\"hook '{hook}' not found in callbacks {self._callbacks}\"\n for logger in self._callbacks[hook]:\n if thread:\n threading.Thread(target=logger[\"callback\"], args=args, kwargs=kwargs, daemon=True).start()\n else:\n logger[\"callback\"](*args, **kwargs)\n" }, { "path": "utils/dataloaders.py", "content": "# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license\n\"\"\"Dataloaders and dataset utils.\"\"\"\n\nimport contextlib\nimport glob\nimport hashlib\nimport json\nimport math\nimport os\nimport random\nimport shutil\nimport time\nfrom itertools import repeat\nfrom multiprocessing.pool import Pool, ThreadPool\nfrom pathlib import Path\nfrom threading import Thread\nfrom urllib.parse import urlparse\n\nimport numpy as np\nimport psutil\nimport torch\nimport torch.nn.functional as F\nimport torchvision\nimport yaml\nfrom PIL import ExifTags, Image, ImageOps\nfrom torch.utils.data import DataLoader, Dataset, dataloader, distributed\nfrom tqdm import tqdm\n\nfrom utils.augmentations import (\n Albumentations,\n augment_hsv,\n classify_albumentations,\n classify_transforms,\n copy_paste,\n letterbox,\n mixup,\n random_perspective,\n)\nfrom utils.general import (\n DATASETS_DIR,\n LOGGER,\n NUM_THREADS,\n TQDM_BAR_FORMAT,\n check_dataset,\n check_requirements,\n check_yaml,\n clean_str,\n cv2,\n is_colab,\n is_kaggle,\n segments2boxes,\n unzip_file,\n xyn2xy,\n xywh2xyxy,\n xywhn2xyxy,\n xyxy2xywhn,\n)\nfrom utils.torch_utils import torch_distributed_zero_first\n\n# Parameters\nHELP_URL = \"See https://docs.ultralytics.com/yolov5/tutorials/train_custom_data\"\nIMG_FORMATS = \"bmp\", \"dng\", \"jpeg\", \"jpg\", \"mpo\", \"png\", \"tif\", \"tiff\", \"webp\", \"pfm\" # include image suffixes\nVID_FORMATS = \"asf\", \"avi\", \"gif\", \"m4v\", \"mkv\", \"mov\", \"mp4\", \"mpeg\", \"mpg\", \"ts\", \"wmv\" # include video suffixes\nLOCAL_RANK = int(os.getenv(\"LOCAL_RANK\", -1)) # https://pytorch.org/docs/stable/elastic/run.html\nRANK = int(os.getenv(\"RANK\", -1))\nPIN_MEMORY = str(os.getenv(\"PIN_MEMORY\", True)).lower() == \"true\" # global pin_memory for dataloaders\n\n# Get orientation exif tag\nfor orientation in ExifTags.TAGS.keys():\n if ExifTags.TAGS[orientation] == \"Orientation\":\n break\n\n\ndef get_hash(paths):\n \"\"\"Calculates a SHA256 hash for a list of file or directory paths, combining their total size and path strings.\"\"\"\n size = sum(os.path.getsize(p) for p in paths if os.path.exists(p)) # sizes\n h = hashlib.sha256(str(size).encode()) # hash sizes\n h.update(\"\".join(paths).encode()) # hash paths\n return h.hexdigest() # return hash\n\n\ndef exif_size(img):\n \"\"\"Returns corrected image size (width, height) considering EXIF rotation metadata.\"\"\"\n s = img.size # (width, height)\n with contextlib.suppress(Exception):\n rotation = dict(img._getexif().items())[orientation]\n if rotation in [6, 8]: # rotation 270 or 90\n s = (s[1], s[0])\n return s\n\n\ndef exif_transpose(image):\n \"\"\"\n Transpose a PIL image accordingly if it has an EXIF Orientation tag.\n Inplace version of https://github.com/python-pillow/Pillow/blob/master/src/PIL/ImageOps.py exif_transpose().\n\n :param image: The image to transpose.\n :return: An image.\n \"\"\"\n exif = image.getexif()\n orientation = exif.get(0x0112, 1) # default 1\n if orientation > 1:\n method = {\n 2: Image.FLIP_LEFT_RIGHT,\n 3: Image.ROTATE_180,\n 4: Image.FLIP_TOP_BOTTOM,\n 5: Image.TRANSPOSE,\n 6: Image.ROTATE_270,\n 7: Image.TRANSVERSE,\n 8: Image.ROTATE_90,\n }.get(orientation)\n if method is not None:\n image = image.transpose(method)\n del exif[0x0112]\n image.info[\"exif\"] = exif.tobytes()\n return image\n\n\ndef seed_worker(worker_id):\n \"\"\"Sets the seed for a DataLoader worker to ensure reproducibility.\"\"\"\n worker_seed = torch.initial_seed() % 2**32\n np.random.seed(worker_seed)\n random.seed(worker_seed)\n\n\ndef create_dataloader(\n path,\n imgsz,\n batch_size,\n stride,\n single_cls=False,\n hyp=None,\n augment=False,\n cache=False,\n pad=0.0,\n rect=False,\n rank=-1,\n workers=8,\n image_weights=False,\n quad=False,\n prefix=\"\",\n shuffle=False,\n seed=0,\n):\n \"\"\"Creates a DataLoader for training, with options for augmentation, caching, and parallelization.\"\"\"\n if rect and shuffle:\n LOGGER.warning(\"WARNING ⚠️ --rect is incompatible with DataLoader shuffle, setting shuffle=False\")\n shuffle = False\n with torch_distributed_zero_first(rank): # init dataset *.cache only once if DDP\n dataset = LoadImagesAndLabels(\n path,\n imgsz,\n batch_size,\n augment=augment, # augmentation\n hyp=hyp, # hyperparameters\n rect=rect, # rectangular batches\n cache_images=cache,\n single_cls=single_cls,\n stride=int(stride),\n pad=pad,\n image_weights=image_weights,\n prefix=prefix,\n )\n\n batch_size = min(batch_size, len(dataset))\n nd = torch.cuda.device_count() # number of CUDA devices\n nw = min([os.cpu_count() // max(nd, 1), batch_size if batch_size > 1 else 0, workers]) # number of workers\n sampler = None if rank == -1 else distributed.DistributedSampler(dataset, shuffle=shuffle)\n loader = DataLoader if image_weights else InfiniteDataLoader # only DataLoader allows for attribute updates\n generator = torch.Generator()\n generator.manual_seed(6148914691236517205 + seed + RANK)\n return loader(\n dataset,\n batch_size=batch_size,\n shuffle=shuffle and sampler is None,\n num_workers=nw,\n sampler=sampler,\n pin_memory=PIN_MEMORY,\n collate_fn=LoadImagesAndLabels.collate_fn4 if quad else LoadImagesAndLabels.collate_fn,\n worker_init_fn=seed_worker,\n generator=generator,\n ), dataset\n\n\nclass InfiniteDataLoader(dataloader.DataLoader):\n \"\"\"Dataloader that reuses workers.\n\n Uses same syntax as vanilla DataLoader\n \"\"\"\n\n def __init__(self, *args, **kwargs):\n \"\"\"Initializes an InfiniteDataLoader that reuses workers with standard DataLoader syntax and a repeating\n sampler.\n \"\"\"\n super().__init__(*args, **kwargs)\n object.__setattr__(self, \"batch_sampler\", _RepeatSampler(self.batch_sampler))\n self.iterator = super().__iter__()\n\n def __len__(self):\n \"\"\"Returns the length of the batch sampler's sampler.\"\"\"\n return len(self.batch_sampler.sampler)\n\n def __iter__(self):\n \"\"\"Iterates over the dataset indefinitely, yielding batches from the batch_sampler.\"\"\"\n for _ in range(len(self)):\n yield next(self.iterator)\n\n\nclass _RepeatSampler:\n \"\"\"Sampler that repeats forever.\n\n Args:\n sampler (Sampler)\n \"\"\"\n\n def __init__(self, sampler):\n \"\"\"Initializes an infinitely repeating sampler with a provided `sampler` object.\"\"\"\n self.sampler = sampler\n\n def __iter__(self):\n \"\"\"Provides an iterator that infinitely repeats over a given `sampler` object.\"\"\"\n while True:\n yield from iter(self.sampler)\n\n\nclass LoadScreenshots:\n \"\"\"Loads screenshots as input data for YOLOv3, capturing screen regions specified by coordinates and dimensions.\"\"\"\n\n def __init__(self, source, img_size=640, stride=32, auto=True, transforms=None):\n \"\"\"Initializes a screenshot dataloader for YOLOv3; source format: [screen_number left top width height], default\n img_size=640, stride=32.\n \"\"\"\n check_requirements(\"mss\")\n import mss\n\n source, *params = source.split()\n self.screen, left, top, width, height = 0, None, None, None, None # default to full screen 0\n if len(params) == 1:\n self.screen = int(params[0])\n elif len(params) == 4:\n left, top, width, height = (int(x) for x in params)\n elif len(params) == 5:\n self.screen, left, top, width, height = (int(x) for x in params)\n self.img_size = img_size\n self.stride = stride\n self.transforms = transforms\n self.auto = auto\n self.mode = \"stream\"\n self.frame = 0\n self.sct = mss.mss()\n\n # Parse monitor shape\n monitor = self.sct.monitors[self.screen]\n self.top = monitor[\"top\"] if top is None else (monitor[\"top\"] + top)\n self.left = monitor[\"left\"] if left is None else (monitor[\"left\"] + left)\n self.width = width or monitor[\"width\"]\n self.height = height or monitor[\"height\"]\n self.monitor = {\"left\": self.left, \"top\": self.top, \"width\": self.width, \"height\": self.height}\n\n def __iter__(self):\n \"\"\"Iterates over itself, effectively making the object its own iterator.\"\"\"\n return self\n\n def __next__(self):\n \"\"\"Captures and returns the next screen image as a NumPy array in BGR format, excluding alpha channel.\"\"\"\n im0 = np.array(self.sct.grab(self.monitor))[:, :, :3] # [:, :, :3] BGRA to BGR\n s = f\"screen {self.screen} (LTWH): {self.left},{self.top},{self.width},{self.height}: \"\n\n if self.transforms:\n im = self.transforms(im0) # transforms\n else:\n im = letterbox(im0, self.img_size, stride=self.stride, auto=self.auto)[0] # padded resize\n im = im.transpose((2, 0, 1))[::-1] # HWC to CHW, BGR to RGB\n im = np.ascontiguousarray(im) # contiguous\n self.frame += 1\n return str(self.screen), im, im0, None, s # screen, img, original img, im0s, s\n\n\nclass LoadImages:\n \"\"\"Loads images and videos for YOLOv3 from various sources, including directories and '*.txt' path lists.\"\"\"\n\n def __init__(self, path, img_size=640, stride=32, auto=True, transforms=None, vid_stride=1):\n \"\"\"Initializes the data loader for YOLOv3, supporting image, video, directory, and '*.txt' path lists with\n customizable image sizing.\n \"\"\"\n if isinstance(path, str) and Path(path).suffix == \".txt\": # *.txt file with img/vid/dir on each line\n path = Path(path).read_text().rsplit()\n files = []\n for p in sorted(path) if isinstance(path, (list, tuple)) else [path]:\n p = str(Path(p).resolve())\n if \"*\" in p:\n files.extend(sorted(glob.glob(p, recursive=True))) # glob\n elif os.path.isdir(p):\n files.extend(sorted(glob.glob(os.path.join(p, \"*.*\")))) # dir\n elif os.path.isfile(p):\n files.append(p) # files\n else:\n raise FileNotFoundError(f\"{p} does not exist\")\n\n images = [x for x in files if x.split(\".\")[-1].lower() in IMG_FORMATS]\n videos = [x for x in files if x.split(\".\")[-1].lower() in VID_FORMATS]\n ni, nv = len(images), len(videos)\n\n self.img_size = img_size\n self.stride = stride\n self.files = images + videos\n self.nf = ni + nv # number of files\n self.video_flag = [False] * ni + [True] * nv\n self.mode = \"image\"\n self.auto = auto\n self.transforms = transforms # optional\n self.vid_stride = vid_stride # video frame-rate stride\n if any(videos):\n self._new_video(videos[0]) # new video\n else:\n self.cap = None\n assert self.nf > 0, (\n f\"No images or videos found in {p}. Supported formats are:\\nimages: {IMG_FORMATS}\\nvideos: {VID_FORMATS}\"\n )\n\n def __iter__(self):\n \"\"\"Initializes the iterator by resetting count to zero and returning the iterator instance itself.\"\"\"\n self.count = 0\n return self\n\n def __next__(self):\n \"\"\"Advances to the next file in the dataset, raising StopIteration when all files are processed.\"\"\"\n if self.count == self.nf:\n raise StopIteration\n path = self.files[self.count]\n\n if self.video_flag[self.count]:\n # Read video\n self.mode = \"video\"\n for _ in range(self.vid_stride):\n self.cap.grab()\n ret_val, im0 = self.cap.retrieve()\n while not ret_val:\n self.count += 1\n self.cap.release()\n if self.count == self.nf: # last video\n raise StopIteration\n path = self.files[self.count]\n self._new_video(path)\n ret_val, im0 = self.cap.read()\n\n self.frame += 1\n # im0 = self._cv2_rotate(im0) # for use if cv2 autorotation is False\n s = f\"video {self.count + 1}/{self.nf} ({self.frame}/{self.frames}) {path}: \"\n\n else:\n # Read image\n self.count += 1\n im0 = cv2.imread(path) # BGR\n assert im0 is not None, f\"Image Not Found {path}\"\n s = f\"image {self.count}/{self.nf} {path}: \"\n\n if self.transforms:\n im = self.transforms(im0) # transforms\n else:\n im = letterbox(im0, self.img_size, stride=self.stride, auto=self.auto)[0] # padded resize\n im = im.transpose((2, 0, 1))[::-1] # HWC to CHW, BGR to RGB\n im = np.ascontiguousarray(im) # contiguous\n\n return path, im, im0, self.cap, s\n\n def _new_video(self, path):\n \"\"\"Initializes a video capture object with frame counting and orientation from a given path.\"\"\"\n self.frame = 0\n self.cap = cv2.VideoCapture(path)\n self.frames = int(self.cap.get(cv2.CAP_PROP_FRAME_COUNT) / self.vid_stride)\n self.orientation = int(self.cap.get(cv2.CAP_PROP_ORIENTATION_META)) # rotation degrees\n # self.cap.set(cv2.CAP_PROP_ORIENTATION_AUTO, 0) # disable https://github.com/ultralytics/yolov5/issues/8493\n\n def _cv2_rotate(self, im):\n \"\"\"Rotates a cv2 image based on the video's metadata orientation; returns the rotated image.\"\"\"\n if self.orientation == 0:\n return cv2.rotate(im, cv2.ROTATE_90_CLOCKWISE)\n elif self.orientation == 180:\n return cv2.rotate(im, cv2.ROTATE_90_COUNTERCLOCKWISE)\n elif self.orientation == 90:\n return cv2.rotate(im, cv2.ROTATE_180)\n return im\n\n def __len__(self):\n \"\"\"Returns the number of files in the dataset.\"\"\"\n return self.nf # number of files\n\n\nclass LoadStreams:\n \"\"\"Loads video streams for YOLOv3 inference, supporting multiple sources and customizable frame sizes.\"\"\"\n\n def __init__(self, sources=\"file.streams\", img_size=640, stride=32, auto=True, transforms=None, vid_stride=1):\n \"\"\"Initializes a stream loader for YOLOv3, handling video sources or files with customizable frame sizes and\n intervals.\n \"\"\"\n torch.backends.cudnn.benchmark = True # faster for fixed-size inference\n self.mode = \"stream\"\n self.img_size = img_size\n self.stride = stride\n self.vid_stride = vid_stride # video frame-rate stride\n sources = Path(sources).read_text().rsplit() if os.path.isfile(sources) else [sources]\n n = len(sources)\n self.sources = [clean_str(x) for x in sources] # clean source names for later\n self.imgs, self.fps, self.frames, self.threads = [None] * n, [0] * n, [0] * n, [None] * n\n for i, s in enumerate(sources): # index, source\n # Start thread to read frames from video stream\n st = f\"{i + 1}/{n}: {s}... \"\n if urlparse(s).hostname in (\"www.youtube.com\", \"youtube.com\", \"youtu.be\"): # if source is YouTube video\n # YouTube format i.e. 'https://www.youtube.com/watch?v=Zgi9g1ksQHc' or 'https://youtu.be/LNwODJXcvt4'\n check_requirements((\"pafy\", \"youtube_dl==2020.12.2\"))\n import pafy\n\n s = pafy.new(s).getbest(preftype=\"mp4\").url # YouTube URL\n s = eval(s) if s.isnumeric() else s # i.e. s = '0' local webcam\n if s == 0:\n assert not is_colab(), \"--source 0 webcam unsupported on Colab. Rerun command in a local environment.\"\n assert not is_kaggle(), \"--source 0 webcam unsupported on Kaggle. Rerun command in a local environment.\"\n cap = cv2.VideoCapture(s)\n assert cap.isOpened(), f\"{st}Failed to open {s}\"\n w = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))\n h = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))\n fps = cap.get(cv2.CAP_PROP_FPS) # warning: may return 0 or nan\n self.frames[i] = max(int(cap.get(cv2.CAP_PROP_FRAME_COUNT)), 0) or float(\"inf\") # infinite stream fallback\n self.fps[i] = max((fps if math.isfinite(fps) else 0) % 100, 0) or 30 # 30 FPS fallback\n\n _, self.imgs[i] = cap.read() # guarantee first frame\n self.threads[i] = Thread(target=self.update, args=([i, cap, s]), daemon=True)\n LOGGER.info(f\"{st} Success ({self.frames[i]} frames {w}x{h} at {self.fps[i]:.2f} FPS)\")\n self.threads[i].start()\n LOGGER.info(\"\") # newline\n\n # check for common shapes\n s = np.stack([letterbox(x, img_size, stride=stride, auto=auto)[0].shape for x in self.imgs])\n self.rect = np.unique(s, axis=0).shape[0] == 1 # rect inference if all shapes equal\n self.auto = auto and self.rect\n self.transforms = transforms # optional\n if not self.rect:\n LOGGER.warning(\"WARNING ⚠️ Stream shapes differ. For optimal performance supply similarly-shaped streams.\")\n\n def update(self, i, cap, stream):\n \"\"\"Reads frames from stream `i` into `self.imgs` at intervals defined by `self.vid_stride`, handling\n reconnection if needed.\n \"\"\"\n n, f = 0, self.frames[i] # frame number, frame array\n while cap.isOpened() and n < f:\n n += 1\n cap.grab() # .read() = .grab() followed by .retrieve()\n if n % self.vid_stride == 0:\n success, im = cap.retrieve()\n if success:\n self.imgs[i] = im\n else:\n LOGGER.warning(\"WARNING ⚠️ Video stream unresponsive, please check your IP camera connection.\")\n self.imgs[i] = np.zeros_like(self.imgs[i])\n cap.open(stream) # re-open stream if signal was lost\n time.sleep(0.0) # wait time\n\n def __iter__(self):\n \"\"\"Resets and returns an iterator of the current object for iterating through video frames or images.\"\"\"\n self.count = -1\n return self\n\n def __next__(self):\n \"\"\"Iterates video frames or images; halts if all threads are dead or 'q' is pressed.\"\"\"\n self.count += 1\n if not all(x.is_alive() for x in self.threads) or cv2.waitKey(1) == ord(\"q\"): # q to quit\n cv2.destroyAllWindows()\n raise StopIteration\n\n im0 = self.imgs.copy()\n if self.transforms:\n im = np.stack([self.transforms(x) for x in im0]) # transforms\n else:\n im = np.stack([letterbox(x, self.img_size, stride=self.stride, auto=self.auto)[0] for x in im0]) # resize\n im = im[..., ::-1].transpose((0, 3, 1, 2)) # BGR to RGB, BHWC to BCHW\n im = np.ascontiguousarray(im) # contiguous\n\n return self.sources, im, im0, None, \"\"\n\n def __len__(self):\n \"\"\"Returns the number of sources in the dataset, supporting up to 1E12 frames across streams and scenarios.\"\"\"\n return len(self.sources) # 1E12 frames = 32 streams at 30 FPS for 30 years\n\n\ndef img2label_paths(img_paths):\n \"\"\"Converts image paths to corresponding label paths by replacing `/images/` with `/labels/` and `.jpg` with `.txt`.\n \"\"\"\n sa, sb = f\"{os.sep}images{os.sep}\", f\"{os.sep}labels{os.sep}\" # /images/, /labels/ substrings\n return [sb.join(x.rsplit(sa, 1)).rsplit(\".\", 1)[0] + \".txt\" for x in img_paths]\n\n\nclass LoadImagesAndLabels(Dataset):\n \"\"\"Loads images and labels for YOLOv3 training and validation with support for augmentations and caching.\"\"\"\n\n cache_version = 0.6 # dataset labels *.cache version\n rand_interp_methods = [cv2.INTER_NEAREST, cv2.INTER_LINEAR, cv2.INTER_CUBIC, cv2.INTER_AREA, cv2.INTER_LANCZOS4]\n\n def __init__(\n self,\n path,\n img_size=640,\n batch_size=16,\n augment=False,\n hyp=None,\n rect=False,\n image_weights=False,\n cache_images=False,\n single_cls=False,\n stride=32,\n pad=0.0,\n min_items=0,\n prefix=\"\",\n ):\n \"\"\"Initializes a dataset with images and labels for YOLOv3 training and validation.\"\"\"\n self.img_size = img_size\n self.augment = augment\n self.hyp = hyp\n self.image_weights = image_weights\n self.rect = False if image_weights else rect\n self.mosaic = self.augment and not self.rect # load 4 images at a time into a mosaic (only during training)\n self.mosaic_border = [-img_size // 2, -img_size // 2]\n self.stride = stride\n self.path = path\n self.albumentations = Albumentations(size=img_size) if augment else None\n\n try:\n f = [] # image files\n for p in path if isinstance(path, list) else [path]:\n p = Path(p) # os-agnostic\n if p.is_dir(): # dir\n f += glob.glob(str(p / \"**\" / \"*.*\"), recursive=True)\n # f = list(p.rglob('*.*')) # pathlib\n elif p.is_file(): # file\n with open(p) as t:\n t = t.read().strip().splitlines()\n parent = str(p.parent) + os.sep\n f += [x.replace(\"./\", parent, 1) if x.startswith(\"./\") else x for x in t] # to global path\n # f += [p.parent / x.lstrip(os.sep) for x in t] # to global path (pathlib)\n else:\n raise FileNotFoundError(f\"{prefix}{p} does not exist\")\n self.im_files = sorted(x.replace(\"/\", os.sep) for x in f if x.split(\".\")[-1].lower() in IMG_FORMATS)\n # self.img_files = sorted([x for x in f if x.suffix[1:].lower() in IMG_FORMATS]) # pathlib\n assert self.im_files, f\"{prefix}No images found\"\n except Exception as e:\n raise Exception(f\"{prefix}Error loading data from {path}: {e}\\n{HELP_URL}\") from e\n\n # Check cache\n self.label_files = img2label_paths(self.im_files) # labels\n cache_path = (p if p.is_file() else Path(self.label_files[0]).parent).with_suffix(\".cache\")\n try:\n cache, exists = np.load(cache_path, allow_pickle=True).item(), True # load dict\n assert cache[\"version\"] == self.cache_version # matches current version\n assert cache[\"hash\"] == get_hash(self.label_files + self.im_files) # identical hash\n except Exception:\n cache, exists = self.cache_labels(cache_path, prefix), False # run cache ops\n\n # Display cache\n nf, nm, ne, nc, n = cache.pop(\"results\") # found, missing, empty, corrupt, total\n if exists and LOCAL_RANK in {-1, 0}:\n d = f\"Scanning {cache_path}... {nf} images, {nm + ne} backgrounds, {nc} corrupt\"\n tqdm(None, desc=prefix + d, total=n, initial=n, bar_format=TQDM_BAR_FORMAT) # display cache results\n if cache[\"msgs\"]:\n LOGGER.info(\"\\n\".join(cache[\"msgs\"])) # display warnings\n assert nf > 0 or not augment, f\"{prefix}No labels found in {cache_path}, can not start training. {HELP_URL}\"\n\n # Read cache\n [cache.pop(k) for k in (\"hash\", \"version\", \"msgs\")] # remove items\n labels, shapes, self.segments = zip(*cache.values())\n nl = len(np.concatenate(labels, 0)) # number of labels\n assert nl > 0 or not augment, f\"{prefix}All labels empty in {cache_path}, can not start training. {HELP_URL}\"\n self.labels = list(labels)\n self.shapes = np.array(shapes)\n self.im_files = list(cache.keys()) # update\n self.label_files = img2label_paths(cache.keys()) # update\n\n # Filter images\n if min_items:\n include = np.array([len(x) >= min_items for x in self.labels]).nonzero()[0].astype(int)\n LOGGER.info(f\"{prefix}{n - len(include)}/{n} images filtered from dataset\")\n self.im_files = [self.im_files[i] for i in include]\n self.label_files = [self.label_files[i] for i in include]\n self.labels = [self.labels[i] for i in include]\n self.segments = [self.segments[i] for i in include]\n self.shapes = self.shapes[include] # wh\n\n # Create indices\n n = len(self.shapes) # number of images\n bi = np.floor(np.arange(n) / batch_size).astype(int) # batch index\n nb = bi[-1] + 1 # number of batches\n self.batch = bi # batch index of image\n self.n = n\n self.indices = range(n)\n\n # Update labels\n include_class = [] # filter labels to include only these classes (optional)\n self.segments = list(self.segments)\n include_class_array = np.array(include_class).reshape(1, -1)\n for i, (label, segment) in enumerate(zip(self.labels, self.segments)):\n if include_class:\n j = (label[:, 0:1] == include_class_array).any(1)\n self.labels[i] = label[j]\n if segment:\n self.segments[i] = [segment[idx] for idx, elem in enumerate(j) if elem]\n if single_cls: # single-class training, merge all classes into 0\n self.labels[i][:, 0] = 0\n\n # Rectangular Training\n if self.rect:\n # Sort by aspect ratio\n s = self.shapes # wh\n ar = s[:, 1] / s[:, 0] # aspect ratio\n irect = ar.argsort()\n self.im_files = [self.im_files[i] for i in irect]\n self.label_files = [self.label_files[i] for i in irect]\n self.labels = [self.labels[i] for i in irect]\n self.segments = [self.segments[i] for i in irect]\n self.shapes = s[irect] # wh\n ar = ar[irect]\n\n # Set training image shapes\n shapes = [[1, 1]] * nb\n for i in range(nb):\n ari = ar[bi == i]\n mini, maxi = ari.min(), ari.max()\n if maxi < 1:\n shapes[i] = [maxi, 1]\n elif mini > 1:\n shapes[i] = [1, 1 / mini]\n\n self.batch_shapes = np.ceil(np.array(shapes) * img_size / stride + pad).astype(int) * stride\n\n # Cache images into RAM/disk for faster training\n if cache_images == \"ram\" and not self.check_cache_ram(prefix=prefix):\n cache_images = False\n self.ims = [None] * n\n self.npy_files = [Path(f).with_suffix(\".npy\") for f in self.im_files]\n if cache_images:\n b, gb = 0, 1 << 30 # bytes of cached images, bytes per gigabytes\n self.im_hw0, self.im_hw = [None] * n, [None] * n\n fcn = self.cache_images_to_disk if cache_images == \"disk\" else self.load_image\n results = ThreadPool(NUM_THREADS).imap(fcn, range(n))\n pbar = tqdm(enumerate(results), total=n, bar_format=TQDM_BAR_FORMAT, disable=LOCAL_RANK > 0)\n for i, x in pbar:\n if cache_images == \"disk\":\n b += self.npy_files[i].stat().st_size\n else: # 'ram'\n self.ims[i], self.im_hw0[i], self.im_hw[i] = x # im, hw_orig, hw_resized = load_image(self, i)\n b += self.ims[i].nbytes\n pbar.desc = f\"{prefix}Caching images ({b / gb:.1f}GB {cache_images})\"\n pbar.close()\n\n def check_cache_ram(self, safety_margin=0.1, prefix=\"\"):\n \"\"\"Evaluates if there's enough RAM to cache dataset images, considering a safety margin.\"\"\"\n b, gb = 0, 1 << 30 # bytes of cached images, bytes per gigabytes\n n = min(self.n, 30) # extrapolate from 30 random images\n for _ in range(n):\n im = cv2.imread(random.choice(self.im_files)) # sample image\n ratio = self.img_size / max(im.shape[0], im.shape[1]) # max(h, w) # ratio\n b += im.nbytes * ratio**2\n mem_required = b * self.n / n # GB required to cache dataset into RAM\n mem = psutil.virtual_memory()\n cache = mem_required * (1 + safety_margin) < mem.available # to cache or not to cache, that is the question\n if not cache:\n LOGGER.info(\n f\"{prefix}{mem_required / gb:.1f}GB RAM required, \"\n f\"{mem.available / gb:.1f}/{mem.total / gb:.1f}GB available, \"\n f\"{'caching images ✅' if cache else 'not caching images ⚠️'}\"\n )\n return cache\n\n def cache_labels(self, path=Path(\"./labels.cache\"), prefix=\"\"):\n \"\"\"Caches dataset labels, checks image existence and readability, and records image shapes and segments.\"\"\"\n x = {} # dict\n nm, nf, ne, nc, msgs = 0, 0, 0, 0, [] # number missing, found, empty, corrupt, messages\n desc = f\"{prefix}Scanning {path.parent / path.stem}...\"\n with Pool(NUM_THREADS) as pool:\n pbar = tqdm(\n pool.imap(verify_image_label, zip(self.im_files, self.label_files, repeat(prefix))),\n desc=desc,\n total=len(self.im_files),\n bar_format=TQDM_BAR_FORMAT,\n )\n for im_file, lb, shape, segments, nm_f, nf_f, ne_f, nc_f, msg in pbar:\n nm += nm_f\n nf += nf_f\n ne += ne_f\n nc += nc_f\n if im_file:\n x[im_file] = [lb, shape, segments]\n if msg:\n msgs.append(msg)\n pbar.desc = f\"{desc} {nf} images, {nm + ne} backgrounds, {nc} corrupt\"\n\n pbar.close()\n if msgs:\n LOGGER.info(\"\\n\".join(msgs))\n if nf == 0:\n LOGGER.warning(f\"{prefix}WARNING ⚠️ No labels found in {path}. {HELP_URL}\")\n x[\"hash\"] = get_hash(self.label_files + self.im_files)\n x[\"results\"] = nf, nm, ne, nc, len(self.im_files)\n x[\"msgs\"] = msgs # warnings\n x[\"version\"] = self.cache_version # cache version\n try:\n np.save(path, x) # save cache for next time\n path.with_suffix(\".cache.npy\").rename(path) # remove .npy suffix\n LOGGER.info(f\"{prefix}New cache created: {path}\")\n except Exception as e:\n LOGGER.warning(f\"{prefix}WARNING ⚠️ Cache directory {path.parent} is not writeable: {e}\") # not writeable\n return x\n\n def __len__(self):\n \"\"\"Returns the number of image files in the dataset.\"\"\"\n return len(self.im_files)\n\n # def __iter__(self):\n # self.count = -1\n # print('ran dataset iter')\n # #self.shuffled_vector = np.random.permutation(self.nF) if self.augment else np.arange(self.nF)\n # return self\n\n def __getitem__(self, index):\n \"\"\"Fetches dataset item at `index` after applying indexing via `self.indices`, supporting\n linear/shuffled/image_weights modes.\n \"\"\"\n index = self.indices[index] # linear, shuffled, or image_weights\n\n hyp = self.hyp\n if mosaic := self.mosaic and random.random() < hyp[\"mosaic\"]:\n # Load mosaic\n img, labels = self.load_mosaic(index)\n shapes = None\n\n # MixUp augmentation\n if random.random() < hyp[\"mixup\"]:\n img, labels = mixup(img, labels, *self.load_mosaic(random.randint(0, self.n - 1)))\n\n else:\n # Load image\n img, (h0, w0), (h, w) = self.load_image(index)\n\n # Letterbox\n shape = self.batch_shapes[self.batch[index]] if self.rect else self.img_size # final letterboxed shape\n img, ratio, pad = letterbox(img, shape, auto=False, scaleup=self.augment)\n shapes = (h0, w0), ((h / h0, w / w0), pad) # for COCO mAP rescaling\n\n labels = self.labels[index].copy()\n if labels.size: # normalized xywh to pixel xyxy format\n labels[:, 1:] = xywhn2xyxy(labels[:, 1:], ratio[0] * w, ratio[1] * h, padw=pad[0], padh=pad[1])\n\n if self.augment:\n img, labels = random_perspective(\n img,\n labels,\n degrees=hyp[\"degrees\"],\n translate=hyp[\"translate\"],\n scale=hyp[\"scale\"],\n shear=hyp[\"shear\"],\n perspective=hyp[\"perspective\"],\n )\n\n nl = len(labels) # number of labels\n if nl:\n labels[:, 1:5] = xyxy2xywhn(labels[:, 1:5], w=img.shape[1], h=img.shape[0], clip=True, eps=1e-3)\n\n if self.augment:\n # Albumentations\n img, labels = self.albumentations(img, labels)\n nl = len(labels) # update after albumentations\n\n # HSV color-space\n augment_hsv(img, hgain=hyp[\"hsv_h\"], sgain=hyp[\"hsv_s\"], vgain=hyp[\"hsv_v\"])\n\n # Flip up-down\n if random.random() < hyp[\"flipud\"]:\n img = np.flipud(img)\n if nl:\n labels[:, 2] = 1 - labels[:, 2]\n\n # Flip left-right\n if random.random() < hyp[\"fliplr\"]:\n img = np.fliplr(img)\n if nl:\n labels[:, 1] = 1 - labels[:, 1]\n\n # Cutouts\n # labels = cutout(img, labels, p=0.5)\n # nl = len(labels) # update after cutout\n\n labels_out = torch.zeros((nl, 6))\n if nl:\n labels_out[:, 1:] = torch.from_numpy(labels)\n\n # Convert\n img = img.transpose((2, 0, 1))[::-1] # HWC to CHW, BGR to RGB\n img = np.ascontiguousarray(img)\n\n return torch.from_numpy(img), labels_out, self.im_files[index], shapes\n\n def load_image(self, i):\n \"\"\"Loads a single image by index, returning the image, its original dimensions, and resized dimensions.\"\"\"\n im, f, fn = (\n self.ims[i],\n self.im_files[i],\n self.npy_files[i],\n )\n if im is None: # not cached in RAM\n if fn.exists(): # load npy\n im = np.load(fn)\n else: # read image\n im = cv2.imread(f) # BGR\n assert im is not None, f\"Image Not Found {f}\"\n h0, w0 = im.shape[:2] # orig hw\n r = self.img_size / max(h0, w0) # ratio\n if r != 1: # if sizes are not equal\n interp = cv2.INTER_LINEAR if (self.augment or r > 1) else cv2.INTER_AREA\n im = cv2.resize(im, (math.ceil(w0 * r), math.ceil(h0 * r)), interpolation=interp)\n return im, (h0, w0), im.shape[:2] # im, hw_original, hw_resized\n return self.ims[i], self.im_hw0[i], self.im_hw[i] # im, hw_original, hw_resized\n\n def cache_images_to_disk(self, i):\n \"\"\"Saves an image to disk as an *.npy file for faster future loading.\"\"\"\n f = self.npy_files[i]\n if not f.exists():\n np.save(f.as_posix(), cv2.imread(self.im_files[i]))\n\n def load_mosaic(self, index):\n \"\"\"Loads 4 images into a mosaic for YOLOv3 training, enhancing detection capabilities through data augmentation.\n \"\"\"\n labels4, segments4 = [], []\n s = self.img_size\n yc, xc = (int(random.uniform(-x, 2 * s + x)) for x in self.mosaic_border) # mosaic center x, y\n indices = [index] + random.choices(self.indices, k=3) # 3 additional image indices\n random.shuffle(indices)\n for i, index in enumerate(indices):\n # Load image\n img, _, (h, w) = self.load_image(index)\n\n # place img in img4\n if i == 0: # top left\n img4 = np.full((s * 2, s * 2, img.shape[2]), 114, dtype=np.uint8) # base image with 4 tiles\n x1a, y1a, x2a, y2a = max(xc - w, 0), max(yc - h, 0), xc, yc # xmin, ymin, xmax, ymax (large image)\n x1b, y1b, x2b, y2b = w - (x2a - x1a), h - (y2a - y1a), w, h # xmin, ymin, xmax, ymax (small image)\n elif i == 1: # top right\n x1a, y1a, x2a, y2a = xc, max(yc - h, 0), min(xc + w, s * 2), yc\n x1b, y1b, x2b, y2b = 0, h - (y2a - y1a), min(w, x2a - x1a), h\n elif i == 2: # bottom left\n x1a, y1a, x2a, y2a = max(xc - w, 0), yc, xc, min(s * 2, yc + h)\n x1b, y1b, x2b, y2b = w - (x2a - x1a), 0, w, min(y2a - y1a, h)\n elif i == 3: # bottom right\n x1a, y1a, x2a, y2a = xc, yc, min(xc + w, s * 2), min(s * 2, yc + h)\n x1b, y1b, x2b, y2b = 0, 0, min(w, x2a - x1a), min(y2a - y1a, h)\n\n img4[y1a:y2a, x1a:x2a] = img[y1b:y2b, x1b:x2b] # img4[ymin:ymax, xmin:xmax]\n padw = x1a - x1b\n padh = y1a - y1b\n\n # Labels\n labels, segments = self.labels[index].copy(), self.segments[index].copy()\n if labels.size:\n labels[:, 1:] = xywhn2xyxy(labels[:, 1:], w, h, padw, padh) # normalized xywh to pixel xyxy format\n segments = [xyn2xy(x, w, h, padw, padh) for x in segments]\n labels4.append(labels)\n segments4.extend(segments)\n\n # Concat/clip labels\n labels4 = np.concatenate(labels4, 0)\n for x in (labels4[:, 1:], *segments4):\n np.clip(x, 0, 2 * s, out=x) # clip when using random_perspective()\n # img4, labels4 = replicate(img4, labels4) # replicate\n\n # Augment\n img4, labels4, segments4 = copy_paste(img4, labels4, segments4, p=self.hyp[\"copy_paste\"])\n img4, labels4 = random_perspective(\n img4,\n labels4,\n segments4,\n degrees=self.hyp[\"degrees\"],\n translate=self.hyp[\"translate\"],\n scale=self.hyp[\"scale\"],\n shear=self.hyp[\"shear\"],\n perspective=self.hyp[\"perspective\"],\n border=self.mosaic_border,\n ) # border to remove\n\n return img4, labels4\n\n def load_mosaic9(self, index):\n \"\"\"Loads 1 image + 8 random images into a 9-image mosaic for YOLOv3, returning combined image and labels.\"\"\"\n labels9, segments9 = [], []\n s = self.img_size\n indices = [index] + random.choices(self.indices, k=8) # 8 additional image indices\n random.shuffle(indices)\n hp, wp = -1, -1 # height, width previous\n for i, index in enumerate(indices):\n # Load image\n img, _, (h, w) = self.load_image(index)\n\n # place img in img9\n if i == 0: # center\n img9 = np.full((s * 3, s * 3, img.shape[2]), 114, dtype=np.uint8) # base image with 4 tiles\n h0, w0 = h, w\n c = s, s, s + w, s + h # xmin, ymin, xmax, ymax (base) coordinates\n elif i == 1: # top\n c = s, s - h, s + w, s\n elif i == 2: # top right\n c = s + wp, s - h, s + wp + w, s\n elif i == 3: # right\n c = s + w0, s, s + w0 + w, s + h\n elif i == 4: # bottom right\n c = s + w0, s + hp, s + w0 + w, s + hp + h\n elif i == 5: # bottom\n c = s + w0 - w, s + h0, s + w0, s + h0 + h\n elif i == 6: # bottom left\n c = s + w0 - wp - w, s + h0, s + w0 - wp, s + h0 + h\n elif i == 7: # left\n c = s - w, s + h0 - h, s, s + h0\n elif i == 8: # top left\n c = s - w, s + h0 - hp - h, s, s + h0 - hp\n\n padx, pady = c[:2]\n x1, y1, x2, y2 = (max(x, 0) for x in c) # allocate coords\n\n # Labels\n labels, segments = self.labels[index].copy(), self.segments[index].copy()\n if labels.size:\n labels[:, 1:] = xywhn2xyxy(labels[:, 1:], w, h, padx, pady) # normalized xywh to pixel xyxy format\n segments = [xyn2xy(x, w, h, padx, pady) for x in segments]\n labels9.append(labels)\n segments9.extend(segments)\n\n # Image\n img9[y1:y2, x1:x2] = img[y1 - pady :, x1 - padx :] # img9[ymin:ymax, xmin:xmax]\n hp, wp = h, w # height, width previous\n\n # Offset\n yc, xc = (int(random.uniform(0, s)) for _ in self.mosaic_border) # mosaic center x, y\n img9 = img9[yc : yc + 2 * s, xc : xc + 2 * s]\n\n # Concat/clip labels\n labels9 = np.concatenate(labels9, 0)\n labels9[:, [1, 3]] -= xc\n labels9[:, [2, 4]] -= yc\n c = np.array([xc, yc]) # centers\n segments9 = [x - c for x in segments9]\n\n for x in (labels9[:, 1:], *segments9):\n np.clip(x, 0, 2 * s, out=x) # clip when using random_perspective()\n # img9, labels9 = replicate(img9, labels9) # replicate\n\n # Augment\n img9, labels9, segments9 = copy_paste(img9, labels9, segments9, p=self.hyp[\"copy_paste\"])\n img9, labels9 = random_perspective(\n img9,\n labels9,\n segments9,\n degrees=self.hyp[\"degrees\"],\n translate=self.hyp[\"translate\"],\n scale=self.hyp[\"scale\"],\n shear=self.hyp[\"shear\"],\n perspective=self.hyp[\"perspective\"],\n border=self.mosaic_border,\n ) # border to remove\n\n return img9, labels9\n\n @staticmethod\n def collate_fn(batch):\n \"\"\"Collates batch of images, labels, paths, and shapes, indexing labels for target image identification.\"\"\"\n im, label, path, shapes = zip(*batch) # transposed\n for i, lb in enumerate(label):\n lb[:, 0] = i # add target image index for build_targets()\n return torch.stack(im, 0), torch.cat(label, 0), path, shapes\n\n @staticmethod\n def collate_fn4(batch):\n \"\"\"Batches images, labels, paths, and shapes by grouping every 4 items for dataset loading.\"\"\"\n im, label, path, shapes = zip(*batch) # transposed\n n = len(shapes) // 4\n im4, label4, path4, shapes4 = [], [], path[:n], shapes[:n]\n\n ho = torch.tensor([[0.0, 0, 0, 1, 0, 0]])\n wo = torch.tensor([[0.0, 0, 1, 0, 0, 0]])\n s = torch.tensor([[1, 1, 0.5, 0.5, 0.5, 0.5]]) # scale\n for i in range(n): # zidane torch.zeros(16,3,720,1280) # BCHW\n i *= 4\n if random.random() < 0.5:\n im1 = F.interpolate(im[i].unsqueeze(0).float(), scale_factor=2.0, mode=\"bilinear\", align_corners=False)[\n 0\n ].type(im[i].type())\n lb = label[i]\n else:\n im1 = torch.cat((torch.cat((im[i], im[i + 1]), 1), torch.cat((im[i + 2], im[i + 3]), 1)), 2)\n lb = torch.cat((label[i], label[i + 1] + ho, label[i + 2] + wo, label[i + 3] + ho + wo), 0) * s\n im4.append(im1)\n label4.append(lb)\n\n for i, lb in enumerate(label4):\n lb[:, 0] = i # add target image index for build_targets()\n\n return torch.stack(im4, 0), torch.cat(label4, 0), path4, shapes4\n\n\n# Ancillary functions --------------------------------------------------------------------------------------------------\ndef flatten_recursive(path=DATASETS_DIR / \"coco128\"):\n \"\"\"Flattens a directory recursively by copying all files to a new top-level directory, given an input path.\"\"\"\n new_path = Path(f\"{path!s}_flat\")\n if os.path.exists(new_path):\n shutil.rmtree(new_path) # delete output folder\n os.makedirs(new_path) # make new output folder\n for file in tqdm(glob.glob(f\"{Path(path)!s}/**/*.*\", recursive=True)):\n shutil.copyfile(file, new_path / Path(file).name)\n\n\ndef extract_boxes(path=DATASETS_DIR / \"coco128\"): # from utils.dataloaders import *; extract_boxes()\n \"\"\"Converts detection dataset to classification dataset, creating one directory per class with images cropped to\n bounding boxes.\n \"\"\"\n path = Path(path) # images dir\n shutil.rmtree(path / \"classification\") if (path / \"classification\").is_dir() else None # remove existing\n files = list(path.rglob(\"*.*\"))\n n = len(files) # number of files\n for im_file in tqdm(files, total=n):\n if im_file.suffix[1:] in IMG_FORMATS:\n # image\n im = cv2.imread(str(im_file))[..., ::-1] # BGR to RGB\n h, w = im.shape[:2]\n\n # labels\n lb_file = Path(img2label_paths([str(im_file)])[0])\n if Path(lb_file).exists():\n with open(lb_file) as f:\n lb = np.array([x.split() for x in f.read().strip().splitlines()], dtype=np.float32) # labels\n\n for j, x in enumerate(lb):\n c = int(x[0]) # class\n f = (path / \"classifier\") / f\"{c}\" / f\"{path.stem}_{im_file.stem}_{j}.jpg\" # new filename\n if not f.parent.is_dir():\n f.parent.mkdir(parents=True)\n\n b = x[1:] * [w, h, w, h] # box\n # b[2:] = b[2:].max() # rectangle to square\n b[2:] = b[2:] * 1.2 + 3 # pad\n b = xywh2xyxy(b.reshape(-1, 4)).ravel().astype(int)\n\n b[[0, 2]] = np.clip(b[[0, 2]], 0, w) # clip boxes outside of image\n b[[1, 3]] = np.clip(b[[1, 3]], 0, h)\n assert cv2.imwrite(str(f), im[b[1] : b[3], b[0] : b[2]]), f\"box failure in {f}\"\n\n\ndef autosplit(path=DATASETS_DIR / \"coco128/images\", weights=(0.9, 0.1, 0.0), annotated_only=False):\n \"\"\"Autosplit a dataset into train/val/test splits and save path/autosplit_*.txt files Usage: from utils.dataloaders\n import *; autosplit().\n\n Args:\n path: Path to images directory\n weights: Train, val, test weights (list, tuple)\n annotated_only: Only use images with an annotated txt file\n \"\"\"\n path = Path(path) # images dir\n files = sorted(x for x in path.rglob(\"*.*\") if x.suffix[1:].lower() in IMG_FORMATS) # image files only\n n = len(files) # number of files\n random.seed(0) # for reproducibility\n indices = random.choices([0, 1, 2], weights=weights, k=n) # assign each image to a split\n\n txt = [\"autosplit_train.txt\", \"autosplit_val.txt\", \"autosplit_test.txt\"] # 3 txt files\n for x in txt:\n if (path.parent / x).exists():\n (path.parent / x).unlink() # remove existing\n\n print(f\"Autosplitting images from {path}\" + \", using *.txt labeled images only\" * annotated_only)\n for i, img in tqdm(zip(indices, files), total=n):\n if not annotated_only or Path(img2label_paths([str(img)])[0]).exists(): # check label\n with open(path.parent / txt[i], \"a\") as f:\n f.write(f\"./{img.relative_to(path.parent).as_posix()}\" + \"\\n\") # add image to txt file\n\n\ndef verify_image_label(args):\n \"\"\"Checks and verifies one image-label pair, fixing common issues and reporting anomalies.\"\"\"\n im_file, lb_file, prefix = args\n nm, nf, ne, nc, msg, segments = 0, 0, 0, 0, \"\", [] # number (missing, found, empty, corrupt), message, segments\n try:\n # verify images\n im = Image.open(im_file)\n im.verify() # PIL verify\n shape = exif_size(im) # image size\n assert (shape[0] > 9) & (shape[1] > 9), f\"image size {shape} <10 pixels\"\n assert im.format.lower() in IMG_FORMATS, f\"invalid image format {im.format}\"\n if im.format.lower() in (\"jpg\", \"jpeg\"):\n with open(im_file, \"rb\") as f:\n f.seek(-2, 2)\n if f.read() != b\"\\xff\\xd9\": # corrupt JPEG\n ImageOps.exif_transpose(Image.open(im_file)).save(im_file, \"JPEG\", subsampling=0, quality=100)\n msg = f\"{prefix}WARNING ⚠️ {im_file}: corrupt JPEG restored and saved\"\n\n # verify labels\n if os.path.isfile(lb_file):\n nf = 1 # label found\n with open(lb_file) as f:\n lb = [x.split() for x in f.read().strip().splitlines() if len(x)]\n if any(len(x) > 6 for x in lb): # is segment\n classes = np.array([x[0] for x in lb], dtype=np.float32)\n segments = [np.array(x[1:], dtype=np.float32).reshape(-1, 2) for x in lb] # (cls, xy1...)\n lb = np.concatenate((classes.reshape(-1, 1), segments2boxes(segments)), 1) # (cls, xywh)\n lb = np.array(lb, dtype=np.float32)\n if nl := len(lb):\n assert lb.shape[1] == 5, f\"labels require 5 columns, {lb.shape[1]} columns detected\"\n assert (lb >= 0).all(), f\"negative label values {lb[lb < 0]}\"\n assert (lb[:, 1:] <= 1).all(), f\"non-normalized or out of bounds coordinates {lb[:, 1:][lb[:, 1:] > 1]}\"\n _, i = np.unique(lb, axis=0, return_index=True)\n if len(i) < nl: # duplicate row check\n lb = lb[i] # remove duplicates\n if segments:\n segments = [segments[x] for x in i]\n msg = f\"{prefix}WARNING ⚠️ {im_file}: {nl - len(i)} duplicate labels removed\"\n else:\n ne = 1 # label empty\n lb = np.zeros((0, 5), dtype=np.float32)\n else:\n nm = 1 # label missing\n lb = np.zeros((0, 5), dtype=np.float32)\n return im_file, lb, shape, segments, nm, nf, ne, nc, msg\n except Exception as e:\n nc = 1\n msg = f\"{prefix}WARNING ⚠️ {im_file}: ignoring corrupt image/label: {e}\"\n return [None, None, None, None, nm, nf, ne, nc, msg]\n\n\nclass HUBDatasetStats:\n \"\"\"Class for generating HUB dataset JSON and `-hub` dataset directory.\n\n Args:\n path: Path to data.yaml or data.zip (with data.yaml inside data.zip)\n autodownload: Attempt to download dataset if not found locally\n\n Usage\n from utils.dataloaders import HUBDatasetStats\n stats = HUBDatasetStats('coco128.yaml', autodownload=True) # usage 1\n stats = HUBDatasetStats('path/to/coco128.zip') # usage 2\n stats.get_json(save=False)\n stats.process_images()\n \"\"\"\n\n def __init__(self, path=\"coco128.yaml\", autodownload=False):\n \"\"\"Initializes HUBDatasetStats with dataset path, optionally autodownloads; supports .yaml or .zip formats.\"\"\"\n zipped, data_dir, yaml_path = self._unzip(Path(path))\n try:\n with open(check_yaml(yaml_path), errors=\"ignore\") as f:\n data = yaml.safe_load(f) # data dict\n if zipped:\n data[\"path\"] = data_dir\n except Exception as e:\n raise Exception(\"error/HUB/dataset_stats/yaml_load\") from e\n\n check_dataset(data, autodownload) # download dataset if missing\n self.hub_dir = Path(data[\"path\"] + \"-hub\")\n self.im_dir = self.hub_dir / \"images\"\n self.im_dir.mkdir(parents=True, exist_ok=True) # makes /images\n self.stats = {\"nc\": data[\"nc\"], \"names\": list(data[\"names\"].values())} # statistics dictionary\n self.data = data\n\n @staticmethod\n def _find_yaml(dir):\n \"\"\"Finds a single `data.yaml` file within specified directory, preferring matches to directory name.\"\"\"\n files = list(dir.glob(\"*.yaml\")) or list(dir.rglob(\"*.yaml\")) # try root level first and then recursive\n assert files, f\"No *.yaml file found in {dir}\"\n if len(files) > 1:\n files = [f for f in files if f.stem == dir.stem] # prefer *.yaml files that match dir name\n assert files, f\"Multiple *.yaml files found in {dir}, only 1 *.yaml file allowed\"\n assert len(files) == 1, f\"Multiple *.yaml files found: {files}, only 1 *.yaml file allowed in {dir}\"\n return files[0]\n\n def _unzip(self, path):\n \"\"\"Unzips a .zip file, verifying its integrity and locating the associated YAML file within the unzipped\n directory.\n \"\"\"\n if not str(path).endswith(\".zip\"): # path is data.yaml\n return False, None, path\n assert Path(path).is_file(), f\"Error unzipping {path}, file not found\"\n unzip_file(path, path=path.parent)\n dir = path.with_suffix(\"\") # dataset directory == zip name\n assert dir.is_dir(), f\"Error unzipping {path}, {dir} not found. path/to/abc.zip MUST unzip to path/to/abc/\"\n return True, str(dir), self._find_yaml(dir) # zipped, data_dir, yaml_path\n\n def _hub_ops(self, f, max_dim=1920):\n \"\"\"Resizes and saves an image at reduced quality for web/app viewing; `f`: path to image, `max_dim`=1920 maximum\n dimension.\n \"\"\"\n f_new = self.im_dir / Path(f).name # dataset-hub image filename\n try: # use PIL\n im = Image.open(f)\n r = max_dim / max(im.height, im.width) # ratio\n if r < 1.0: # image too large\n im = im.resize((int(im.width * r), int(im.height * r)))\n im.save(f_new, \"JPEG\", quality=50, optimize=True) # save\n except Exception as e: # use OpenCV\n LOGGER.info(f\"WARNING ⚠️ HUB ops PIL failure {f}: {e}\")\n im = cv2.imread(f)\n im_height, im_width = im.shape[:2]\n r = max_dim / max(im_height, im_width) # ratio\n if r < 1.0: # image too large\n im = cv2.resize(im, (int(im_width * r), int(im_height * r)), interpolation=cv2.INTER_AREA)\n cv2.imwrite(str(f_new), im)\n\n def get_json(self, save=False, verbose=False):\n \"\"\"Generates dataset JSON for Ultralytics Platform, with optional saving and verbosity; rounds labels to int\n class and 6 decimal floats.\n \"\"\"\n\n def _round(labels):\n \"\"\"Update labels to integer class and 6 decimal place floats.\"\"\"\n return [[int(c), *(round(x, 4) for x in points)] for c, *points in labels]\n\n for split in \"train\", \"val\", \"test\":\n if self.data.get(split) is None:\n self.stats[split] = None # i.e. no test set\n continue\n dataset = LoadImagesAndLabels(self.data[split]) # load dataset\n x = np.array(\n [\n np.bincount(label[:, 0].astype(int), minlength=self.data[\"nc\"])\n for label in tqdm(dataset.labels, total=dataset.n, desc=\"Statistics\")\n ]\n ) # shape(128x80)\n self.stats[split] = {\n \"instance_stats\": {\"total\": int(x.sum()), \"per_class\": x.sum(0).tolist()},\n \"image_stats\": {\n \"total\": dataset.n,\n \"unlabelled\": int(np.all(x == 0, 1).sum()),\n \"per_class\": (x > 0).sum(0).tolist(),\n },\n \"labels\": [{str(Path(k).name): _round(v.tolist())} for k, v in zip(dataset.im_files, dataset.labels)],\n }\n\n # Save, print and return\n if save:\n stats_path = self.hub_dir / \"stats.json\"\n print(f\"Saving {stats_path.resolve()}...\")\n with open(stats_path, \"w\") as f:\n json.dump(self.stats, f) # save stats.json\n if verbose:\n print(json.dumps(self.stats, indent=2, sort_keys=False))\n return self.stats\n\n def process_images(self):\n \"\"\"Compresses images for Ultralytics Platform, saving them to specified directory; supports 'train', 'val',\n 'test' splits.\n \"\"\"\n for split in \"train\", \"val\", \"test\":\n if self.data.get(split) is None:\n continue\n dataset = LoadImagesAndLabels(self.data[split]) # load dataset\n desc = f\"{split} images\"\n for _ in tqdm(ThreadPool(NUM_THREADS).imap(self._hub_ops, dataset.im_files), total=dataset.n, desc=desc):\n pass\n print(f\"Done. All images saved to {self.im_dir}\")\n return self.im_dir\n\n\n# Classification dataloaders -------------------------------------------------------------------------------------------\nclass ClassificationDataset(torchvision.datasets.ImageFolder):\n \"\"\"YOLOv3 Classification Dataset.\n\n Args:\n root: Dataset path\n transform: torchvision transforms, used by default\n album_transform: Albumentations transforms, used if installed\n \"\"\"\n\n def __init__(self, root, augment, imgsz, cache=False):\n \"\"\"Initializes classification dataset with optional augmentation, image resizing, caching, inheriting from\n ImageFolder.\n \"\"\"\n super().__init__(root=root)\n self.torch_transforms = classify_transforms(imgsz)\n self.album_transforms = classify_albumentations(augment, imgsz) if augment else None\n self.cache_ram = cache is True or cache == \"ram\"\n self.cache_disk = cache == \"disk\"\n self.samples = [[*list(x), Path(x[0]).with_suffix(\".npy\"), None] for x in self.samples] # file, index, npy, im\n\n def __getitem__(self, i):\n \"\"\"Fetches the item at index `i`, applies caching and transformations, and returns image-sample and index.\"\"\"\n f, j, fn, im = self.samples[i] # filename, index, filename.with_suffix('.npy'), image\n if self.cache_ram and im is None:\n im = self.samples[i][3] = cv2.imread(f)\n elif self.cache_disk:\n if not fn.exists(): # load npy\n np.save(fn.as_posix(), cv2.imread(f))\n im = np.load(fn)\n else: # read image\n im = cv2.imread(f) # BGR\n if self.album_transforms:\n sample = self.album_transforms(image=cv2.cvtColor(im, cv2.COLOR_BGR2RGB))[\"image\"]\n else:\n sample = self.torch_transforms(im)\n return sample, j\n\n\ndef create_classification_dataloader(\n path, imgsz=224, batch_size=16, augment=True, cache=False, rank=-1, workers=8, shuffle=True\n):\n # Returns Dataloader object to be used with YOLOv3 Classifier\n \"\"\"Creates a DataLoader for image classification tasks with options for augmentation, caching, and distributed\n training.\n \"\"\"\n with torch_distributed_zero_first(rank): # init dataset *.cache only once if DDP\n dataset = ClassificationDataset(root=path, imgsz=imgsz, augment=augment, cache=cache)\n batch_size = min(batch_size, len(dataset))\n nd = torch.cuda.device_count()\n nw = min([os.cpu_count() // max(nd, 1), batch_size if batch_size > 1 else 0, workers])\n sampler = None if rank == -1 else distributed.DistributedSampler(dataset, shuffle=shuffle)\n generator = torch.Generator()\n generator.manual_seed(6148914691236517205 + RANK)\n return InfiniteDataLoader(\n dataset,\n batch_size=batch_size,\n shuffle=shuffle and sampler is None,\n num_workers=nw,\n sampler=sampler,\n pin_memory=PIN_MEMORY,\n worker_init_fn=seed_worker,\n generator=generator,\n ) # or DataLoader(persistent_workers=True)\n" }, { "path": "utils/docker/Dockerfile", "content": "# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license\n\n# Builds ultralytics/yolov5:latest image on DockerHub https://hub.docker.com/r/ultralytics/yolov3\n# Image is CUDA-optimized for YOLOv5 single/multi-GPU training and inference\n\n# Start FROM PyTorch image https://hub.docker.com/r/pytorch/pytorch\nFROM pytorch/pytorch:2.8.0-cuda12.8-cudnn9-runtime\n\n# Downloads to user config dir\nADD https://ultralytics.com/assets/Arial.ttf https://ultralytics.com/assets/Arial.Unicode.ttf /root/.config/Ultralytics/\n\n# Install linux packages\nENV DEBIAN_FRONTEND noninteractive\nRUN apt update\nRUN TZ=Etc/UTC apt install -y tzdata\nRUN apt install --no-install-recommends -y gcc git zip curl htop libgl1 libglib2.0-0 libpython3-dev gnupg\n# RUN alias python=python3\n\n# Security updates\n# https://security.snyk.io/vuln/SNYK-UBUNTU1804-OPENSSL-3314796\nRUN apt upgrade --no-install-recommends -y openssl\n\n# Create working directory\nRUN rm -rf /usr/src/app && mkdir -p /usr/src/app\nWORKDIR /usr/src/app\n\n# Copy contents\n# COPY . /usr/src/app (issues as not a .git directory)\nRUN git clone https://github.com/ultralytics/yolov5 /usr/src/app\n\n# Install pip packages\nCOPY requirements.txt .\nRUN python3 -m pip install --upgrade pip wheel\nRUN pip install --no-cache -r requirements.txt albumentations comet gsutil notebook \\\n coremltools onnx onnx-simplifier onnxruntime 'openvino-dev>=2023.0'\n # tensorflow tensorflowjs \\\n\n# Set environment variables\nENV OMP_NUM_THREADS=1\n\n# Cleanup\nENV DEBIAN_FRONTEND teletype\n\n\n# Usage Examples -------------------------------------------------------------------------------------------------------\n\n# Build and Push\n# t=ultralytics/yolov5:latest && sudo docker build -f utils/docker/Dockerfile -t $t . && sudo docker push $t\n\n# Pull and Run\n# t=ultralytics/yolov5:latest && sudo docker pull $t && sudo docker run -it --ipc=host --gpus all $t\n\n# Pull and Run with local directory access\n# t=ultralytics/yolov5:latest && sudo docker pull $t && sudo docker run -it --ipc=host --gpus all -v \"$(pwd)\"/datasets:/usr/src/datasets $t\n\n# Kill all\n# sudo docker kill $(sudo docker ps -q)\n\n# Kill all image-based\n# sudo docker kill $(sudo docker ps -qa --filter ancestor=ultralytics/yolov5:latest)\n\n# DockerHub tag update\n# t=ultralytics/yolov5:latest tnew=ultralytics/yolov5:v6.2 && sudo docker pull $t && sudo docker tag $t $tnew && sudo docker push $tnew\n\n# Clean up\n# sudo docker system prune -a --volumes\n\n# Update Ubuntu drivers\n# https://www.maketecheasier.com/install-nvidia-drivers-ubuntu/\n\n# DDP test\n# python -m torch.distributed.run --nproc_per_node 2 --master_port 1 train.py --epochs 3\n\n# GCP VM from Image\n# docker.io/ultralytics/yolov5:latest\n" }, { "path": "utils/docker/Dockerfile-arm64", "content": "# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license\n\n# Builds ultralytics/yolov5:latest-arm64 image on DockerHub https://hub.docker.com/r/ultralytics/yolov3\n# Image is aarch64-compatible for Apple M1 and other ARM architectures i.e. Jetson Nano and Raspberry Pi\n\n# Start FROM Ubuntu image https://hub.docker.com/_/ubuntu\nFROM arm64v8/ubuntu:22.10\n\n# Downloads to user config dir\nADD https://ultralytics.com/assets/Arial.ttf https://ultralytics.com/assets/Arial.Unicode.ttf /root/.config/Ultralytics/\n\n# Install linux packages\nENV DEBIAN_FRONTEND noninteractive\nRUN apt update\nRUN TZ=Etc/UTC apt install -y tzdata\nRUN apt install --no-install-recommends -y python3-pip git zip curl htop gcc libgl1 libglib2.0-0 libpython3-dev\n# RUN alias python=python3\n\n# Install pip packages\nCOPY requirements.txt .\nRUN python3 -m pip install --upgrade pip wheel\nRUN pip install --no-cache -r requirements.txt albumentations gsutil notebook \\\n coremltools onnx onnxruntime\n # tensorflow-aarch64 tensorflowjs \\\n\n# Create working directory\nRUN mkdir -p /usr/src/app\nWORKDIR /usr/src/app\n\n# Copy contents\n# COPY . /usr/src/app (issues as not a .git directory)\nRUN git clone https://github.com/ultralytics/yolov5 /usr/src/app\nENV DEBIAN_FRONTEND teletype\n\n\n# Usage Examples -------------------------------------------------------------------------------------------------------\n\n# Build and Push\n# t=ultralytics/yolov5:latest-arm64 && sudo docker build --platform linux/arm64 -f utils/docker/Dockerfile-arm64 -t $t . && sudo docker push $t\n\n# Pull and Run\n# t=ultralytics/yolov5:latest-arm64 && sudo docker pull $t && sudo docker run -it --ipc=host -v \"$(pwd)\"/datasets:/usr/src/datasets $t\n" }, { "path": "utils/docker/Dockerfile-cpu", "content": "# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license\n\n# Builds ultralytics/yolov5:latest-cpu image on DockerHub https://hub.docker.com/r/ultralytics/yolov3\n# Image is CPU-optimized for ONNX, OpenVINO and PyTorch YOLOv5 deployments\n\n# Start FROM Ubuntu image https://hub.docker.com/_/ubuntu\nFROM ubuntu:23.10\n\n# Downloads to user config dir\nADD https://ultralytics.com/assets/Arial.ttf https://ultralytics.com/assets/Arial.Unicode.ttf /root/.config/Ultralytics/\n\n# Install linux packages\n# g++ required to build 'tflite_support' and 'lap' packages, libusb-1.0-0 required for 'tflite_support' package\nRUN apt update \\\n && apt install --no-install-recommends -y python3-pip git zip curl htop libgl1 libglib2.0-0 libpython3-dev gnupg g++ libusb-1.0-0\n# RUN alias python=python3\n\n# Remove python3.11/EXTERNALLY-MANAGED or use 'pip install --break-system-packages' avoid 'externally-managed-environment' Ubuntu nightly error\nRUN rm -rf /usr/lib/python3.11/EXTERNALLY-MANAGED\n\n# Install pip packages\nCOPY requirements.txt .\nRUN python3 -m pip install --upgrade pip wheel\nRUN pip install --no-cache -r requirements.txt albumentations gsutil notebook \\\n coremltools onnx onnx-simplifier onnxruntime 'openvino-dev>=2023.0' \\\n # tensorflow tensorflowjs \\\n --extra-index-url https://download.pytorch.org/whl/cpu\n\n# Create working directory\nRUN mkdir -p /usr/src/app\nWORKDIR /usr/src/app\n\n# Copy contents\n# COPY . /usr/src/app (issues as not a .git directory)\nRUN git clone https://github.com/ultralytics/yolov5 /usr/src/app\n\n\n# Usage Examples -------------------------------------------------------------------------------------------------------\n\n# Build and Push\n# t=ultralytics/yolov5:latest-cpu && sudo docker build -f utils/docker/Dockerfile-cpu -t $t . && sudo docker push $t\n\n# Pull and Run\n# t=ultralytics/yolov5:latest-cpu && sudo docker pull $t && sudo docker run -it --ipc=host -v \"$(pwd)\"/datasets:/usr/src/datasets $t\n" }, { "path": "utils/downloads.py", "content": "# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license\n\"\"\"Download utils.\"\"\"\n\nimport logging\nimport subprocess\nimport urllib\nfrom pathlib import Path\n\nimport requests\nimport torch\n\n\ndef is_url(url, check=True):\n \"\"\"Determines if a string is a valid URL and optionally checks its existence online.\"\"\"\n try:\n url = str(url)\n result = urllib.parse.urlparse(url)\n assert all([result.scheme, result.netloc]) # check if is url\n return (urllib.request.urlopen(url).getcode() == 200) if check else True # check if exists online\n except (AssertionError, urllib.request.HTTPError):\n return False\n\n\ndef gsutil_getsize(url=\"\"):\n \"\"\"Returns the size of a file at a 'gs://' URL using gsutil du command; 0 if file not found or command fails.\"\"\"\n output = subprocess.check_output([\"gsutil\", \"du\", url], shell=True, encoding=\"utf-8\")\n return int(output.split()[0]) if output else 0\n\n\ndef url_getsize(url=\"https://ultralytics.com/images/bus.jpg\"):\n \"\"\"Fetches file size in bytes from a URL using an HTTP HEAD request; defaults to -1 if not found.\"\"\"\n response = requests.head(url, allow_redirects=True)\n return int(response.headers.get(\"content-length\", -1))\n\n\ndef curl_download(url, filename, *, silent: bool = False) -> bool:\n \"\"\"Download a file from a url to a filename using curl.\"\"\"\n silent_option = \"sS\" if silent else \"\" # silent\n proc = subprocess.run(\n [\n \"curl\",\n \"-#\",\n f\"-{silent_option}L\",\n url,\n \"--output\",\n filename,\n \"--retry\",\n \"9\",\n \"-C\",\n \"-\",\n ]\n )\n return proc.returncode == 0\n\n\ndef safe_download(file, url, url2=None, min_bytes=1e0, error_msg=\"\"):\n \"\"\"Downloads a file from 'url' or 'url2' to 'file', ensuring size > 'min_bytes'; removes incomplete downloads.\"\"\"\n from utils.general import LOGGER\n\n file = Path(file)\n assert_msg = f\"Downloaded file '{file}' does not exist or size is < min_bytes={min_bytes}\"\n try: # url1\n LOGGER.info(f\"Downloading {url} to {file}...\")\n torch.hub.download_url_to_file(url, str(file), progress=LOGGER.level <= logging.INFO)\n assert file.exists() and file.stat().st_size > min_bytes, assert_msg # check\n except Exception as e: # url2\n if file.exists():\n file.unlink() # remove partial downloads\n LOGGER.info(f\"ERROR: {e}\\nRe-attempting {url2 or url} to {file}...\")\n # curl download, retry and resume on fail\n curl_download(url2 or url, file)\n finally:\n if not file.exists() or file.stat().st_size < min_bytes: # check\n if file.exists():\n file.unlink() # remove partial downloads\n LOGGER.info(f\"ERROR: {assert_msg}\\n{error_msg}\")\n LOGGER.info(\"\")\n\n\ndef attempt_download(file, repo=\"ultralytics/yolov5\", release=\"v7.0\"):\n \"\"\"Attempts to download a file from a specified URL or GitHub release, ensuring file integrity with a minimum size\n check.\n \"\"\"\n from utils.general import LOGGER\n\n def github_assets(repository, version=\"latest\"):\n \"\"\"Returns GitHub tag and assets for a given repository and version from the GitHub API.\"\"\"\n if version != \"latest\":\n version = f\"tags/{version}\" # i.e. tags/v7.0\n response = requests.get(f\"https://api.github.com/repos/{repository}/releases/{version}\").json() # github api\n return response[\"tag_name\"], [x[\"name\"] for x in response[\"assets\"]] # tag, assets\n\n file = Path(str(file).strip().replace(\"'\", \"\"))\n if not file.exists():\n # URL specified\n name = Path(urllib.parse.unquote(str(file))).name # decode '%2F' to '/' etc.\n if str(file).startswith((\"http:/\", \"https:/\")): # download\n url = str(file).replace(\":/\", \"://\") # Pathlib turns :// -> :/\n file = name.split(\"?\")[0] # parse authentication https://url.com/file.txt?auth...\n if Path(file).is_file():\n LOGGER.info(f\"Found {url} locally at {file}\") # file already exists\n else:\n safe_download(file=file, url=url, min_bytes=1e5)\n return file\n\n # GitHub assets\n assets = [f\"yolov5{size}{suffix}.pt\" for size in \"nsmlx\" for suffix in (\"\", \"6\", \"-cls\", \"-seg\")] # default\n try:\n tag, assets = github_assets(repo, release)\n except Exception:\n try:\n tag, assets = github_assets(repo) # latest release\n except Exception:\n try:\n tag = subprocess.check_output(\"git tag\", shell=True, stderr=subprocess.STDOUT).decode().split()[-1]\n except Exception:\n tag = release\n\n if name in assets:\n file.parent.mkdir(parents=True, exist_ok=True) # make parent dir (if required)\n safe_download(\n file,\n url=f\"https://github.com/{repo}/releases/download/{tag}/{name}\",\n min_bytes=1e5,\n error_msg=f\"{file} missing, try downloading from https://github.com/{repo}/releases/{tag}\",\n )\n\n return str(file)\n" }, { "path": "utils/flask_rest_api/README.md", "content": "\"Ultralytics\n\n# Flask REST API Example for YOLO Models\n\n[Representational State Transfer (REST)](https://en.wikipedia.org/wiki/Representational_state_transfer) [Application Programming Interfaces (APIs)](https://developer.mozilla.org/en-US/docs/Web/API) are a standard way to expose [Machine Learning (ML)](https://www.ultralytics.com/glossary/machine-learning-ml) models, allowing other services or applications to interact with them over a network. This directory provides an example REST API built using the [Flask](https://palletsprojects.com/projects/flask/) microframework to serve predictions from an [Ultralytics YOLOv3](https://docs.ultralytics.com/models/yolov3/) model, potentially loaded via [PyTorch Hub](https://pytorch.org/hub/) or other standard PyTorch methods.\n\nDeploying models via APIs is a crucial step in [MLOps](https://www.ultralytics.com/glossary/machine-learning-operations-mlops) and enables integration into larger systems. You can explore various [model deployment options](https://docs.ultralytics.com/guides/model-deployment-options/) for different scenarios.\n\n## 🔧 Requirements\n\nEnsure you have the necessary Python packages installed. The primary requirement is Flask.\n\nInstall Flask using pip:\n\n```shell\npip install Flask torch torchvision\n```\n\n_Note: `torch` and `torchvision` are required for loading and running PyTorch-based models like YOLOv3._\n\n## ▶️ Run the API\n\nOnce Flask and dependencies are installed, you can start the API server.\n\nExecute the Python script:\n\n```shell\npython restapi.py --port 5000\n```\n\nThe API server will start listening on the specified port (default is 5000).\n\n## 🚀 Make a Prediction Request\n\nYou can send prediction requests to the running API using tools like [`curl`](https://curl.se/) or scripting languages.\n\nSend a POST request with an image file (`zidane.jpg` in this example) to the `/v1/object-detection/yolov3` endpoint:\n\n```shell\ncurl -X POST -F image=@zidane.jpg 'http://localhost:5000/v1/object-detection/yolov3'\n```\n\n_Ensure `zidane.jpg` (or your test image) is present in the directory where you run the `curl` command._\n\n## 📄 Understand the Response\n\nThe API processes the image and returns the [object detection](https://www.ultralytics.com/glossary/object-detection) results in [JSON](https://www.ultralytics.com/glossary/json) format. Each object detected includes its class ID, confidence score, bounding box coordinates (normalized), and class name.\n\nExample JSON response:\n\n```json\n[\n {\n \"class\": 0,\n \"confidence\": 0.8900438547,\n \"height\": 0.9318675399,\n \"name\": \"person\",\n \"width\": 0.3264600933,\n \"xcenter\": 0.7438579798,\n \"ycenter\": 0.5207948685\n },\n {\n \"class\": 0,\n \"confidence\": 0.8440024257,\n \"height\": 0.7155083418,\n \"name\": \"person\",\n \"width\": 0.6546785235,\n \"xcenter\": 0.427829951,\n \"ycenter\": 0.6334488392\n },\n {\n \"class\": 27,\n \"confidence\": 0.3771208823,\n \"height\": 0.3902671337,\n \"name\": \"tie\",\n \"width\": 0.0696444362,\n \"xcenter\": 0.3675483763,\n \"ycenter\": 0.7991207838\n },\n {\n \"class\": 27,\n \"confidence\": 0.3527112305,\n \"height\": 0.1540903747,\n \"name\": \"tie\",\n \"width\": 0.0336618312,\n \"xcenter\": 0.7814827561,\n \"ycenter\": 0.5065554976\n }\n]\n```\n\nAn example Python script (`example_request.py`) demonstrating how to send requests using the popular [requests](https://requests.readthedocs.io/en/latest/) library is also included in this directory.\n\n## 🤝 Contributing\n\nContributions to enhance this example or add support for other Ultralytics models are welcome! Please see the main Ultralytics [CONTRIBUTING](https://docs.ultralytics.com/help/contributing/) guide for more information on how to get involved.\n" }, { "path": "utils/flask_rest_api/example_request.py", "content": "# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license\n\"\"\"Perform test request.\"\"\"\n\nimport pprint\n\nimport requests\n\nDETECTION_URL = \"http://localhost:5000/v1/object-detection/yolov5s\"\nIMAGE = \"zidane.jpg\"\n\n# Read image\nwith open(IMAGE, \"rb\") as f:\n image_data = f.read()\n\nresponse = requests.post(DETECTION_URL, files={\"image\": image_data}).json()\n\npprint.pprint(response)\n" }, { "path": "utils/flask_rest_api/restapi.py", "content": "# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license\n\"\"\"Run a Flask REST API exposing one or more YOLOv5s models.\"\"\"\n\nimport argparse\nimport io\n\nimport torch\nfrom flask import Flask, request\nfrom PIL import Image\n\napp = Flask(__name__)\nmodels = {}\n\nDETECTION_URL = \"/v1/object-detection/\"\n\n\n@app.route(DETECTION_URL, methods=[\"POST\"])\ndef predict(model):\n \"\"\"Predicts objects in an image using YOLOv5s models exposed via Flask REST API; expects 'image' file in POST\n request.\n \"\"\"\n if request.method != \"POST\":\n return\n\n if request.files.get(\"image\"):\n # Method 1\n # with request.files[\"image\"] as f:\n # im = Image.open(io.BytesIO(f.read()))\n\n # Method 2\n im_file = request.files[\"image\"]\n im_bytes = im_file.read()\n im = Image.open(io.BytesIO(im_bytes))\n\n if model in models:\n results = models[model](im, size=640) # reduce size=320 for faster inference\n return results.pandas().xyxy[0].to_json(orient=\"records\")\n\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser(description=\"Flask API exposing YOLOv3 model\")\n parser.add_argument(\"--port\", default=5000, type=int, help=\"port number\")\n parser.add_argument(\"--model\", nargs=\"+\", default=[\"yolov5s\"], help=\"model(s) to run, i.e. --model yolov5n yolov5s\")\n opt = parser.parse_args()\n\n for m in opt.model:\n models[m] = torch.hub.load(\"ultralytics/yolov5\", m, force_reload=True, skip_validation=True)\n\n app.run(host=\"0.0.0.0\", port=opt.port) # debug=True causes Restarting with stat\n" }, { "path": "utils/general.py", "content": "# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license\n\"\"\"General utils.\"\"\"\n\nfrom __future__ import annotations\n\nimport contextlib\nimport glob\nimport inspect\nimport logging\nimport logging.config\nimport math\nimport os\nimport platform\nimport random\nimport re\nimport signal\nimport subprocess\nimport sys\nimport time\nimport urllib\nfrom copy import deepcopy\nfrom datetime import datetime\nfrom itertools import repeat\nfrom multiprocessing.pool import ThreadPool\nfrom pathlib import Path\nfrom subprocess import check_output\nfrom tarfile import is_tarfile\nfrom zipfile import ZipFile, is_zipfile\n\nimport cv2\nimport numpy as np\nimport pandas as pd\nimport torch\nimport torchvision\nimport yaml\nfrom packaging.version import parse\nfrom ultralytics.utils.checks import check_requirements\nfrom ultralytics.utils.patches import torch_load\n\nfrom utils import TryExcept, emojis\nfrom utils.downloads import curl_download, gsutil_getsize\nfrom utils.metrics import box_iou, fitness\n\nFILE = Path(__file__).resolve()\nROOT = FILE.parents[1] # YOLOv3 root directory\nRANK = int(os.getenv(\"RANK\", -1))\n\n# Settings\nNUM_THREADS = min(8, max(1, os.cpu_count() - 1)) # number of YOLOv3 multiprocessing threads\nDATASETS_DIR = Path(os.getenv(\"YOLOv5_DATASETS_DIR\", ROOT.parent / \"datasets\")) # global datasets directory\nAUTOINSTALL = str(os.getenv(\"YOLOv5_AUTOINSTALL\", True)).lower() == \"true\" # global auto-install mode\nVERBOSE = str(os.getenv(\"YOLOv5_VERBOSE\", True)).lower() == \"true\" # global verbose mode\nTQDM_BAR_FORMAT = \"{l_bar}{bar:10}{r_bar}\" # tqdm bar format\nFONT = \"Arial.ttf\" # https://github.com/ultralytics/assets/releases/download/v0.0.0/Arial.ttf\n\ntorch.set_printoptions(linewidth=320, precision=5, profile=\"long\")\nnp.set_printoptions(linewidth=320, formatter={\"float_kind\": \"{:11.5g}\".format}) # format short g, %precision=5\npd.options.display.max_columns = 10\ncv2.setNumThreads(0) # prevent OpenCV from multithreading (incompatible with PyTorch DataLoader)\nos.environ[\"NUMEXPR_MAX_THREADS\"] = str(NUM_THREADS) # NumExpr max threads\nos.environ[\"OMP_NUM_THREADS\"] = \"1\" if platform.system() == \"darwin\" else str(NUM_THREADS) # OpenMP (PyTorch and SciPy)\nos.environ[\"TF_CPP_MIN_LOG_LEVEL\"] = \"2\" # suppress verbose TF compiler warnings in Colab\n\n\ndef is_ascii(s=\"\"):\n \"\"\"Checks if input string `s` is composed solely of ASCII characters; compatible with pre-Python 3.7 versions.\"\"\"\n s = str(s) # convert list, tuple, None, etc. to str\n return len(s.encode().decode(\"ascii\", \"ignore\")) == len(s)\n\n\ndef is_chinese(s=\"人工智能\"):\n \"\"\"Determines if a string `s` contains any Chinese characters; returns a boolean.\"\"\"\n return bool(re.search(\"[\\u4e00-\\u9fff]\", str(s)))\n\n\ndef is_colab():\n \"\"\"Checks if the current environment is a Google Colab instance; returns a boolean.\"\"\"\n return \"google.colab\" in sys.modules\n\n\ndef is_jupyter():\n \"\"\"Check if the current script is running inside a Jupyter Notebook. Verified on Colab, Jupyterlab, Kaggle,\n Paperspace.\n\n Returns:\n bool: True if running inside a Jupyter Notebook, False otherwise.\n \"\"\"\n with contextlib.suppress(Exception):\n from IPython import get_ipython\n\n return get_ipython() is not None\n return False\n\n\ndef is_kaggle():\n \"\"\"Determines if the environment is a Kaggle Notebook by checking environment variables.\"\"\"\n return os.environ.get(\"PWD\") == \"/kaggle/working\" and os.environ.get(\"KAGGLE_URL_BASE\") == \"https://www.kaggle.com\"\n\n\ndef is_docker() -> bool:\n \"\"\"Check if the process runs inside a docker container.\"\"\"\n if Path(\"/.dockerenv\").exists():\n return True\n try: # check if docker is in control groups\n with open(\"/proc/self/cgroup\") as file:\n return any(\"docker\" in line for line in file)\n except OSError:\n return False\n\n\ndef is_writeable(dir, test=False):\n \"\"\"Determines if a directory is writeable, optionally tests by writing a file if `test=True`.\"\"\"\n if not test:\n return os.access(dir, os.W_OK) # possible issues on Windows\n file = Path(dir) / \"tmp.txt\"\n try:\n with open(file, \"w\"): # open file with write permissions\n pass\n file.unlink() # remove file\n return True\n except OSError:\n return False\n\n\nLOGGING_NAME = \"yolov5\"\n\n\ndef set_logging(name=LOGGING_NAME, verbose=True):\n \"\"\"Configures logging with specified verbosity; 'name' sets logger identity, 'verbose' toggles logging level.\"\"\"\n rank = int(os.getenv(\"RANK\", -1)) # rank in world for Multi-GPU trainings\n level = logging.INFO if verbose and rank in {-1, 0} else logging.ERROR\n logging.config.dictConfig(\n {\n \"version\": 1,\n \"disable_existing_loggers\": False,\n \"formatters\": {name: {\"format\": \"%(message)s\"}},\n \"handlers\": {\n name: {\n \"class\": \"logging.StreamHandler\",\n \"formatter\": name,\n \"level\": level,\n }\n },\n \"loggers\": {\n name: {\n \"level\": level,\n \"handlers\": [name],\n \"propagate\": False,\n }\n },\n }\n )\n\n\nset_logging(LOGGING_NAME) # run before defining LOGGER\nLOGGER = logging.getLogger(LOGGING_NAME) # define globally (used in train.py, val.py, detect.py, etc.)\nif platform.system() == \"Windows\":\n for fn in LOGGER.info, LOGGER.warning:\n setattr(LOGGER, fn.__name__, lambda x: fn(emojis(x))) # emoji safe logging\n\n\ndef user_config_dir(dir=\"Ultralytics\", env_var=\"YOLOV5_CONFIG_DIR\"):\n \"\"\"Returns user configuration directory path, prefers `env_var` if set, else uses OS-specific path, creates\n directory if needed.\n \"\"\"\n if env := os.getenv(env_var):\n path = Path(env) # use environment variable\n else:\n cfg = {\"Windows\": \"AppData/Roaming\", \"Linux\": \".config\", \"Darwin\": \"Library/Application Support\"} # 3 OS dirs\n path = Path.home() / cfg.get(platform.system(), \"\") # OS-specific config dir\n path = (path if is_writeable(path) else Path(\"/tmp\")) / dir # GCP and AWS lambda fix, only /tmp is writeable\n path.mkdir(exist_ok=True) # make if required\n return path\n\n\nCONFIG_DIR = user_config_dir() # Ultralytics settings dir\n\n\nclass Profile(contextlib.ContextDecorator):\n \"\"\"Profiles code execution time, usable as a context manager or decorator for performance monitoring.\"\"\"\n\n def __init__(self, t=0.0):\n \"\"\"Initializes a profiling context for YOLOv3 with optional timing threshold `t` and checks CUDA availability.\n \"\"\"\n self.t = t\n self.cuda = torch.cuda.is_available()\n\n def __enter__(self):\n \"\"\"Starts the profiling timer, returning the profile instance for use with @Profile() decorator or 'with\n Profile():' context.\n \"\"\"\n self.start = self.time()\n return self\n\n def __exit__(self, type, value, traceback):\n \"\"\"Ends profiling, calculating time delta and updating total time, for use within 'with Profile():' context.\"\"\"\n self.dt = self.time() - self.start # delta-time\n self.t += self.dt # accumulate dt\n\n def time(self):\n \"\"\"Returns current time, ensuring CUDA operations are synchronized if on GPU.\"\"\"\n if self.cuda:\n torch.cuda.synchronize()\n return time.time()\n\n\nclass Timeout(contextlib.ContextDecorator):\n \"\"\"Enforces a timeout on code execution, raising TimeoutError on expiry.\"\"\"\n\n def __init__(self, seconds, *, timeout_msg=\"\", suppress_timeout_errors=True):\n \"\"\"Initializes a timeout context/decorator with specified duration, custom message, and error handling option.\n \"\"\"\n self.seconds = int(seconds)\n self.timeout_message = timeout_msg\n self.suppress = bool(suppress_timeout_errors)\n\n def _timeout_handler(self, signum, frame):\n \"\"\"Raises a TimeoutError with a custom message upon timeout signal reception.\"\"\"\n raise TimeoutError(self.timeout_message)\n\n def __enter__(self):\n \"\"\"Starts a countdown for a signal alarm; not supported on Windows.\"\"\"\n if platform.system() != \"Windows\": # not supported on Windows\n signal.signal(signal.SIGALRM, self._timeout_handler) # Set handler for SIGALRM\n signal.alarm(self.seconds) # start countdown for SIGALRM to be raised\n\n def __exit__(self, exc_type, exc_val, exc_tb):\n \"\"\"Cancels any scheduled SIGALRM on non-Windows platforms, optionally suppressing TimeoutError.\"\"\"\n if platform.system() != \"Windows\":\n signal.alarm(0) # Cancel SIGALRM if it's scheduled\n if self.suppress and exc_type is TimeoutError: # Suppress TimeoutError\n return True\n\n\nclass WorkingDirectory(contextlib.ContextDecorator):\n \"\"\"Context manager to temporarily change the working directory, reverting to the original on exit.\"\"\"\n\n def __init__(self, new_dir):\n \"\"\"Initializes context manager to temporarily change working directory, reverting on exit.\"\"\"\n self.dir = new_dir # new dir\n self.cwd = Path.cwd().resolve() # current dir\n\n def __enter__(self):\n \"\"\"Temporarily changes the current working directory to `new_dir`, reverting to the original on exit.\"\"\"\n os.chdir(self.dir)\n\n def __exit__(self, exc_type, exc_val, exc_tb):\n \"\"\"Reverts to the original working directory upon exiting the context manager.\"\"\"\n os.chdir(self.cwd)\n\n\ndef methods(instance):\n \"\"\"Returns a list of callable class/instance methods, excluding magic methods.\"\"\"\n return [f for f in dir(instance) if callable(getattr(instance, f)) and not f.startswith(\"__\")]\n\n\ndef print_args(args: dict | None = None, show_file=True, show_func=False):\n \"\"\"Prints function arguments; optionally specify args dict, show file and/or function name.\"\"\"\n x = inspect.currentframe().f_back # previous frame\n file, _, func, _, _ = inspect.getframeinfo(x)\n if args is None: # get args automatically\n args, _, _, frm = inspect.getargvalues(x)\n args = {k: v for k, v in frm.items() if k in args}\n try:\n file = Path(file).resolve().relative_to(ROOT).with_suffix(\"\")\n except ValueError:\n file = Path(file).stem\n s = (f\"{file}: \" if show_file else \"\") + (f\"{func}: \" if show_func else \"\")\n LOGGER.info(colorstr(s) + \", \".join(f\"{k}={v}\" for k, v in args.items()))\n\n\ndef init_seeds(seed=0, deterministic=False):\n \"\"\"Initializes RNG seeds for reproducibility; `seed`: RNG seed, `deterministic`: enforces deterministic behavior if\n True.\n \"\"\"\n random.seed(seed)\n np.random.seed(seed)\n torch.manual_seed(seed)\n torch.cuda.manual_seed(seed)\n torch.cuda.manual_seed_all(seed) # for Multi-GPU, exception safe\n # torch.backends.cudnn.benchmark = True # AutoBatch problem https://github.com/ultralytics/yolov5/issues/9287\n if deterministic and check_version(torch.__version__, \"1.12.0\"): # https://github.com/ultralytics/yolov5/pull/8213\n torch.use_deterministic_algorithms(True)\n torch.backends.cudnn.deterministic = True\n os.environ[\"CUBLAS_WORKSPACE_CONFIG\"] = \":4096:8\"\n os.environ[\"PYTHONHASHSEED\"] = str(seed)\n\n\ndef intersect_dicts(da, db, exclude=()):\n \"\"\"Intersects two dicts by matching keys and shapes, excluding specified keys, and retains values from the first\n dict.\n \"\"\"\n return {k: v for k, v in da.items() if k in db and all(x not in k for x in exclude) and v.shape == db[k].shape}\n\n\ndef get_default_args(func):\n \"\"\"Returns a dict of `func`'s default arguments using inspection.\"\"\"\n signature = inspect.signature(func)\n return {k: v.default for k, v in signature.parameters.items() if v.default is not inspect.Parameter.empty}\n\n\ndef get_latest_run(search_dir=\".\"):\n \"\"\"Returns path to the most recent 'last.pt' file within 'search_dir' for resuming, or an empty string if not found.\n \"\"\"\n last_list = glob.glob(f\"{search_dir}/**/last*.pt\", recursive=True)\n return max(last_list, key=os.path.getctime) if last_list else \"\"\n\n\ndef file_age(path=__file__):\n \"\"\"Returns the number of days since the last update of the file specified by 'path'.\"\"\"\n dt = datetime.now() - datetime.fromtimestamp(Path(path).stat().st_mtime) # delta\n return dt.days # + dt.seconds / 86400 # fractional days\n\n\ndef file_date(path=__file__):\n \"\"\"Returns file modification date in 'YYYY-M-D' format for the file at 'path'.\"\"\"\n t = datetime.fromtimestamp(Path(path).stat().st_mtime)\n return f\"{t.year}-{t.month}-{t.day}\"\n\n\ndef file_size(path):\n \"\"\"Returns the size of a file or total size of files in a directory at 'path' in MB.\"\"\"\n mb = 1 << 20 # bytes to MiB (1024 ** 2)\n path = Path(path)\n if path.is_file():\n return path.stat().st_size / mb\n elif path.is_dir():\n return sum(f.stat().st_size for f in path.glob(\"**/*\") if f.is_file()) / mb\n else:\n return 0.0\n\n\ndef check_online():\n \"\"\"Checks internet connectivity by attempting to connect to \"1.1.1.1\" on port 443 twice; returns True if successful.\n \"\"\"\n import socket\n\n def run_once():\n \"\"\"Attempts a single internet connectivity check to '1.1.1.1' on port 443 and returns True if successful.\"\"\"\n try:\n socket.create_connection((\"1.1.1.1\", 443), 5) # check host accessibility\n return True\n except OSError:\n return False\n\n return run_once() or run_once() # check twice to increase robustness to intermittent connectivity issues\n\n\ndef git_describe(path=ROOT): # path must be a directory\n \"\"\"Returns human-readable git description of a directory if it's a git repository, otherwise an empty string.\"\"\"\n try:\n assert (Path(path) / \".git\").is_dir()\n return check_output(f\"git -C {path} describe --tags --long --always\", shell=True).decode()[:-1]\n except Exception:\n return \"\"\n\n\n@TryExcept()\n@WorkingDirectory(ROOT)\ndef check_git_status(repo=\"ultralytics/yolov5\", branch=\"master\"):\n \"\"\"Checks YOLOv3 code update status against remote, suggests 'git pull' if outdated; requires internet and git\n repository.\n \"\"\"\n url = f\"https://github.com/{repo}\"\n msg = f\", for updates see {url}\"\n s = colorstr(\"github: \") # string\n assert Path(\".git\").exists(), s + \"skipping check (not a git repository)\" + msg\n assert check_online(), s + \"skipping check (offline)\" + msg\n\n splits = re.split(pattern=r\"\\s\", string=check_output(\"git remote -v\", shell=True).decode())\n matches = [repo in s for s in splits]\n if any(matches):\n remote = splits[matches.index(True) - 1]\n else:\n remote = \"ultralytics\"\n check_output(f\"git remote add {remote} {url}\", shell=True)\n check_output(f\"git fetch {remote}\", shell=True, timeout=5) # git fetch\n local_branch = check_output(\"git rev-parse --abbrev-ref HEAD\", shell=True).decode().strip() # checked out\n n = int(check_output(f\"git rev-list {local_branch}..{remote}/{branch} --count\", shell=True)) # commits behind\n if n > 0:\n pull = \"git pull\" if remote == \"origin\" else f\"git pull {remote} {branch}\"\n s += f\"⚠️ YOLOv3 is out of date by {n} commit{'s' * (n > 1)}. Use '{pull}' or 'git clone {url}' to update.\"\n else:\n s += f\"up to date with {url} ✅\"\n LOGGER.info(s)\n\n\n@WorkingDirectory(ROOT)\ndef check_git_info(path=\".\"):\n \"\"\"Checks YOLOv3 git info (remote, branch, commit) in path, requires 'gitpython'.\n\n Returns dict.\n \"\"\"\n check_requirements(\"gitpython\")\n import git\n\n try:\n repo = git.Repo(path)\n remote = repo.remotes.origin.url.replace(\".git\", \"\") # i.e. 'https://github.com/ultralytics/yolov5'\n commit = repo.head.commit.hexsha # i.e. '3134699c73af83aac2a481435550b968d5792c0d'\n try:\n branch = repo.active_branch.name # i.e. 'main'\n except TypeError: # not on any branch\n branch = None # i.e. 'detached HEAD' state\n return {\"remote\": remote, \"branch\": branch, \"commit\": commit}\n except git.exc.InvalidGitRepositoryError: # path is not a git dir\n return {\"remote\": None, \"branch\": None, \"commit\": None}\n\n\ndef check_python(minimum=\"3.7.0\"):\n \"\"\"Checks if current Python version meets the specified minimum requirement, raising error if not.\"\"\"\n check_version(platform.python_version(), minimum, name=\"Python \", hard=True)\n\n\ndef check_version(current=\"0.0.0\", minimum=\"0.0.0\", name=\"version \", pinned=False, hard=False, verbose=False):\n \"\"\"Compares current and minimum version requirements, optionally enforcing minimum version and logging warnings.\"\"\"\n current, minimum = (parse(x) for x in (current, minimum))\n result = (current == minimum) if pinned else (current >= minimum) # bool\n s = f\"WARNING ⚠️ {name}{minimum} is required by YOLOv3, but {name}{current} is currently installed\" # string\n if hard:\n assert result, emojis(s) # assert min requirements met\n if verbose and not result:\n LOGGER.warning(s)\n return result\n\n\ndef check_img_size(imgsz, s=32, floor=0):\n \"\"\"Adjusts image size to be divisible by `s`, ensuring it's above `floor`; returns int for single dim or list for\n dims.\n \"\"\"\n if isinstance(imgsz, int): # integer i.e. img_size=640\n new_size = max(make_divisible(imgsz, int(s)), floor)\n else: # list i.e. img_size=[640, 480]\n imgsz = list(imgsz) # convert to list if tuple\n new_size = [max(make_divisible(x, int(s)), floor) for x in imgsz]\n if new_size != imgsz:\n LOGGER.warning(f\"WARNING ⚠️ --img-size {imgsz} must be multiple of max stride {s}, updating to {new_size}\")\n return new_size\n\n\ndef check_imshow(warn=False):\n \"\"\"Checks if the environment supports image display; warns if `warn=True` and display is unsupported.\"\"\"\n try:\n assert not is_jupyter()\n assert not is_docker()\n cv2.imshow(\"test\", np.zeros((1, 1, 3)))\n cv2.waitKey(1)\n cv2.destroyAllWindows()\n cv2.waitKey(1)\n return True\n except Exception as e:\n if warn:\n LOGGER.warning(f\"WARNING ⚠️ Environment does not support cv2.imshow() or PIL Image.show()\\n{e}\")\n return False\n\n\ndef check_suffix(file=\"yolov5s.pt\", suffix=(\".pt\",), msg=\"\"):\n \"\"\"Checks for acceptable file suffixes, supports batch checking for lists or tuples of filenames.\"\"\"\n if file and suffix:\n if isinstance(suffix, str):\n suffix = [suffix]\n for f in file if isinstance(file, (list, tuple)) else [file]:\n s = Path(f).suffix.lower() # file suffix\n if len(s):\n assert s in suffix, f\"{msg}{f} acceptable suffix is {suffix}\"\n\n\ndef check_yaml(file, suffix=(\".yaml\", \".yml\")):\n \"\"\"Searches/downloads a YAML file and returns its path, ensuring it has a .yaml or .yml suffix.\"\"\"\n return check_file(file, suffix)\n\n\ndef check_file(file, suffix=\"\"):\n \"\"\"Checks for file's existence locally, downloads if a URL, supports ClearML dataset IDs, and enforces optional\n suffix.\n \"\"\"\n check_suffix(file, suffix) # optional\n file = str(file) # convert to str()\n if os.path.isfile(file) or not file: # exists\n return file\n elif file.startswith((\"http:/\", \"https:/\")): # download\n url = file # warning: Pathlib turns :// -> :/\n file = Path(urllib.parse.unquote(file).split(\"?\")[0]).name # '%2F' to '/', split https://url.com/file.txt?auth\n if os.path.isfile(file):\n LOGGER.info(f\"Found {url} locally at {file}\") # file already exists\n else:\n LOGGER.info(f\"Downloading {url} to {file}...\")\n torch.hub.download_url_to_file(url, file)\n assert Path(file).exists() and Path(file).stat().st_size > 0, f\"File download failed: {url}\" # check\n return file\n elif file.startswith(\"clearml://\"): # ClearML Dataset ID\n assert \"clearml\" in sys.modules, (\n \"ClearML is not installed, so cannot use ClearML dataset. Try running 'pip install clearml'.\"\n )\n return file\n else: # search\n files = []\n for d in \"data\", \"models\", \"utils\": # search directories\n files.extend(glob.glob(str(ROOT / d / \"**\" / file), recursive=True)) # find file\n assert len(files), f\"File not found: {file}\" # assert file was found\n assert len(files) == 1, f\"Multiple files match '{file}', specify exact path: {files}\" # assert unique\n return files[0] # return file\n\n\ndef check_font(font=FONT, progress=False):\n \"\"\"Checks and downloads the specified font to CONFIG_DIR if not present, with optional download progress.\"\"\"\n font = Path(font)\n file = CONFIG_DIR / font.name\n if not font.exists() and not file.exists():\n url = f\"https://github.com/ultralytics/assets/releases/download/v0.0.0/{font.name}\"\n LOGGER.info(f\"Downloading {url} to {file}...\")\n torch.hub.download_url_to_file(url, str(file), progress=progress)\n\n\ndef check_dataset(data, autodownload=True):\n \"\"\"Verifies and prepares dataset by downloading if absent, checking, and unzipping; supports auto-downloading.\"\"\"\n # Download (optional)\n extract_dir = \"\"\n if isinstance(data, (str, Path)) and (is_zipfile(data) or is_tarfile(data)):\n download(data, dir=f\"{DATASETS_DIR}/{Path(data).stem}\", unzip=True, delete=False, curl=False, threads=1)\n data = next((DATASETS_DIR / Path(data).stem).rglob(\"*.yaml\"))\n extract_dir, autodownload = data.parent, False\n\n # Read yaml (optional)\n if isinstance(data, (str, Path)):\n data = yaml_load(data) # dictionary\n\n # Checks\n for k in \"train\", \"val\", \"names\":\n assert k in data, emojis(f\"data.yaml '{k}:' field missing ❌\")\n if isinstance(data[\"names\"], (list, tuple)): # old array format\n data[\"names\"] = dict(enumerate(data[\"names\"])) # convert to dict\n assert all(isinstance(k, int) for k in data[\"names\"].keys()), \"data.yaml names keys must be integers, i.e. 2: car\"\n data[\"nc\"] = len(data[\"names\"])\n\n # Resolve paths\n path = Path(extract_dir or data.get(\"path\") or \"\") # optional 'path' default to '.'\n if not path.is_absolute():\n path = (ROOT / path).resolve()\n data[\"path\"] = path # download scripts\n for k in \"train\", \"val\", \"test\":\n if data.get(k): # prepend path\n if isinstance(data[k], str):\n x = (path / data[k]).resolve()\n if not x.exists() and data[k].startswith(\"../\"):\n x = (path / data[k][3:]).resolve()\n data[k] = str(x)\n else:\n data[k] = [str((path / x).resolve()) for x in data[k]]\n\n # Parse yaml\n _train, val, _test, s = (data.get(x) for x in (\"train\", \"val\", \"test\", \"download\"))\n if val:\n val = [Path(x).resolve() for x in (val if isinstance(val, list) else [val])] # val path\n if not all(x.exists() for x in val):\n LOGGER.info(\"\\nDataset not found ⚠️, missing paths %s\" % [str(x) for x in val if not x.exists()])\n if not s or not autodownload:\n raise Exception(\"Dataset not found ❌\")\n t = time.time()\n if s.startswith(\"http\") and s.endswith(\".zip\"): # URL\n f = Path(s).name # filename\n LOGGER.info(f\"Downloading {s} to {f}...\")\n torch.hub.download_url_to_file(s, f)\n Path(DATASETS_DIR).mkdir(parents=True, exist_ok=True) # create root\n unzip_file(f, path=DATASETS_DIR) # unzip\n Path(f).unlink() # remove zip\n r = None # success\n elif s.startswith(\"bash \"): # bash script\n LOGGER.info(f\"Running {s} ...\")\n r = subprocess.run(s, shell=True)\n else: # python script\n r = exec(s, {\"yaml\": data}) # return None\n dt = f\"({round(time.time() - t, 1)}s)\"\n s = f\"success ✅ {dt}, saved to {colorstr('bold', DATASETS_DIR)}\" if r in (0, None) else f\"failure {dt} ❌\"\n LOGGER.info(f\"Dataset download {s}\")\n check_font(\"Arial.ttf\" if is_ascii(data[\"names\"]) else \"Arial.Unicode.ttf\", progress=True) # download fonts\n return data # dictionary\n\n\ndef check_amp(model):\n \"\"\"Checks PyTorch AMP functionality with model and sample image, returning True if AMP operates correctly.\"\"\"\n from models.common import AutoShape, DetectMultiBackend\n\n def amp_allclose(model, im):\n \"\"\"Compares FP32 and AMP inference results for a model and image, ensuring outputs are within 10% tolerance.\"\"\"\n m = AutoShape(model, verbose=False) # model\n a = m(im).xywhn[0] # FP32 inference\n m.amp = True\n b = m(im).xywhn[0] # AMP inference\n return a.shape == b.shape and torch.allclose(a, b, atol=0.1) # close to 10% absolute tolerance\n\n prefix = colorstr(\"AMP: \")\n device = next(model.parameters()).device # get model device\n if device.type in (\"cpu\", \"mps\"):\n return False # AMP only used on CUDA devices\n f = ROOT / \"data\" / \"images\" / \"bus.jpg\" # image to check\n im = f if f.exists() else \"https://ultralytics.com/images/bus.jpg\" if check_online() else np.ones((640, 640, 3))\n try:\n assert amp_allclose(deepcopy(model), im) or amp_allclose(DetectMultiBackend(\"yolov5n.pt\", device), im)\n LOGGER.info(f\"{prefix}checks passed ✅\")\n return True\n except Exception:\n help_url = \"https://github.com/ultralytics/yolov5/issues/7908\"\n LOGGER.warning(f\"{prefix}checks failed ❌, disabling Automatic Mixed Precision. See {help_url}\")\n return False\n\n\ndef yaml_load(file=\"data.yaml\"):\n \"\"\"Safely loads a YAML file, ignoring file errors; default file is 'data.yaml'.\"\"\"\n with open(file, errors=\"ignore\") as f:\n return yaml.safe_load(f)\n\n\ndef yaml_save(file=\"data.yaml\", data=None):\n \"\"\"Safely saves data to a YAML file, converting `Path` objects to strings; defaults to 'data.yaml'.\"\"\"\n if data is None:\n data = {}\n with open(file, \"w\") as f:\n yaml.safe_dump({k: str(v) if isinstance(v, Path) else v for k, v in data.items()}, f, sort_keys=False)\n\n\ndef unzip_file(file, path=None, exclude=(\".DS_Store\", \"__MACOSX\")):\n \"\"\"Unzips '*.zip' to `path` (default: file's parent), excluding files matching `exclude` (`('.DS_Store',\n '__MACOSX')`).\n \"\"\"\n if path is None:\n path = Path(file).parent # default path\n with ZipFile(file) as zipObj:\n for f in zipObj.namelist(): # list all archived filenames in the zip\n if all(x not in f for x in exclude):\n zipObj.extract(f, path=path)\n\n\ndef url2file(url):\n \"\"\"Converts a URL to a filename by extracting the last path segment and removing query parameters.\"\"\"\n url = str(Path(url)).replace(\":/\", \"://\") # Pathlib turns :// -> :/\n return Path(urllib.parse.unquote(url)).name.split(\"?\")[0] # '%2F' to '/', split https://url.com/file.txt?auth\n\n\ndef download(url, dir=\".\", unzip=True, delete=True, curl=False, threads=1, retry=3):\n \"\"\"Downloads files from URLs into a specified directory, optionally unzips, and supports multithreading and retries.\n \"\"\"\n\n def download_one(url, dir):\n \"\"\"Downloads a file from a URL into the specified directory, supporting retries and using curl or torch methods.\n \"\"\"\n success = True\n if os.path.isfile(url):\n f = Path(url) # filename\n else: # does not exist\n f = dir / Path(url).name\n LOGGER.info(f\"Downloading {url} to {f}...\")\n for i in range(retry + 1):\n if curl:\n success = curl_download(url, f, silent=(threads > 1))\n else:\n torch.hub.download_url_to_file(url, f, progress=threads == 1) # torch download\n success = f.is_file()\n if success:\n break\n elif i < retry:\n LOGGER.warning(f\"⚠️ Download failure, retrying {i + 1}/{retry} {url}...\")\n else:\n LOGGER.warning(f\"❌ Failed to download {url}...\")\n\n if unzip and success and (f.suffix == \".gz\" or is_zipfile(f) or is_tarfile(f)):\n LOGGER.info(f\"Unzipping {f}...\")\n if is_zipfile(f):\n unzip_file(f, dir) # unzip\n elif is_tarfile(f):\n subprocess.run([\"tar\", \"xf\", f, \"--directory\", f.parent], check=True) # unzip\n elif f.suffix == \".gz\":\n subprocess.run([\"tar\", \"xfz\", f, \"--directory\", f.parent], check=True) # unzip\n if delete:\n f.unlink() # remove zip\n\n dir = Path(dir)\n dir.mkdir(parents=True, exist_ok=True) # make directory\n if threads > 1:\n pool = ThreadPool(threads)\n pool.imap(lambda x: download_one(*x), zip(url, repeat(dir))) # multithreaded\n pool.close()\n pool.join()\n else:\n for u in [url] if isinstance(url, (str, Path)) else url:\n download_one(u, dir)\n\n\ndef make_divisible(x, divisor):\n \"\"\"Adjusts `x` to be nearest and greater than or equal to value divisible by `divisor`.\"\"\"\n if isinstance(divisor, torch.Tensor):\n divisor = int(divisor.max()) # to int\n return math.ceil(x / divisor) * divisor\n\n\ndef clean_str(s):\n \"\"\"Cleans a string by replacing special characters with underscores, e.g., 'test@string!' to 'test_string_'.\"\"\"\n return re.sub(pattern=\"[|@#!¡·$€%&()=?¿^*;:,¨´><+]\", repl=\"_\", string=s)\n\n\ndef one_cycle(y1=0.0, y2=1.0, steps=100):\n \"\"\"Generates a lambda for a sinusoidal ramp from y1 to y2 over 'steps'; usage: `lambda x: ((1 - math.cos(x * math.pi\n / steps)) / 2) * (y2 - y1) + y1`.\n \"\"\"\n return lambda x: ((1 - math.cos(x * math.pi / steps)) / 2) * (y2 - y1) + y1\n\n\ndef colorstr(*input):\n \"\"\"Colors strings using ANSI escape codes; see usage example `colorstr('blue', 'hello world')`.\n\n [https://en.wikipedia.org/wiki/ANSI_escape_code]\n \"\"\"\n *args, string = input if len(input) > 1 else (\"blue\", \"bold\", input[0]) # color arguments, string\n colors = {\n \"black\": \"\\033[30m\", # basic colors\n \"red\": \"\\033[31m\",\n \"green\": \"\\033[32m\",\n \"yellow\": \"\\033[33m\",\n \"blue\": \"\\033[34m\",\n \"magenta\": \"\\033[35m\",\n \"cyan\": \"\\033[36m\",\n \"white\": \"\\033[37m\",\n \"bright_black\": \"\\033[90m\", # bright colors\n \"bright_red\": \"\\033[91m\",\n \"bright_green\": \"\\033[92m\",\n \"bright_yellow\": \"\\033[93m\",\n \"bright_blue\": \"\\033[94m\",\n \"bright_magenta\": \"\\033[95m\",\n \"bright_cyan\": \"\\033[96m\",\n \"bright_white\": \"\\033[97m\",\n \"end\": \"\\033[0m\", # misc\n \"bold\": \"\\033[1m\",\n \"underline\": \"\\033[4m\",\n }\n return \"\".join(colors[x] for x in args) + f\"{string}\" + colors[\"end\"]\n\n\ndef labels_to_class_weights(labels, nc=80):\n \"\"\"Calculates class weights from labels to counteract dataset imbalance; `labels` is a list of numpy arrays with\n shape `(n, 5)`.\n \"\"\"\n if labels[0] is None: # no labels loaded\n return torch.Tensor()\n\n labels = np.concatenate(labels, 0) # labels.shape = (866643, 5) for COCO\n classes = labels[:, 0].astype(int) # labels = [class xywh]\n weights = np.bincount(classes, minlength=nc) # occurrences per class\n\n # Prepend gridpoint count (for uCE training)\n # gpi = ((320 / 32 * np.array([1, 2, 4])) ** 2 * 3).sum() # gridpoints per image\n # weights = np.hstack([gpi * len(labels) - weights.sum() * 9, weights * 9]) ** 0.5 # prepend gridpoints to start\n\n weights[weights == 0] = 1 # replace empty bins with 1\n weights = 1 / weights # number of targets per class\n weights /= weights.sum() # normalize\n return torch.from_numpy(weights).float()\n\n\ndef labels_to_image_weights(labels, nc=80, class_weights=np.ones(80)):\n \"\"\"Calculates image weights from labels using class weights, for balanced sampling.\"\"\"\n # Usage: index = random.choices(range(n), weights=image_weights, k=1) # weighted image sample\n class_counts = np.array([np.bincount(x[:, 0].astype(int), minlength=nc) for x in labels])\n return (class_weights.reshape(1, nc) * class_counts).sum(1)\n\n\ndef coco80_to_coco91_class(): # converts 80-index (val2014) to 91-index (paper)\n \"\"\"Converts COCO 80-class index to COCO 91-class index.\n\n Reference: https://tech.amikelive.com/node-718/what-object-categories-labels-are-in-coco-dataset/\n \"\"\"\n # a = np.loadtxt('data/coco.names', dtype='str', delimiter='\\n')\n # b = np.loadtxt('data/coco_paper.names', dtype='str', delimiter='\\n')\n # x1 = [list(a[i] == b).index(True) + 1 for i in range(80)] # darknet to coco\n # x2 = [list(b[i] == a).index(True) if any(b[i] == a) else None for i in range(91)] # coco to darknet\n return [\n 1,\n 2,\n 3,\n 4,\n 5,\n 6,\n 7,\n 8,\n 9,\n 10,\n 11,\n 13,\n 14,\n 15,\n 16,\n 17,\n 18,\n 19,\n 20,\n 21,\n 22,\n 23,\n 24,\n 25,\n 27,\n 28,\n 31,\n 32,\n 33,\n 34,\n 35,\n 36,\n 37,\n 38,\n 39,\n 40,\n 41,\n 42,\n 43,\n 44,\n 46,\n 47,\n 48,\n 49,\n 50,\n 51,\n 52,\n 53,\n 54,\n 55,\n 56,\n 57,\n 58,\n 59,\n 60,\n 61,\n 62,\n 63,\n 64,\n 65,\n 67,\n 70,\n 72,\n 73,\n 74,\n 75,\n 76,\n 77,\n 78,\n 79,\n 80,\n 81,\n 82,\n 84,\n 85,\n 86,\n 87,\n 88,\n 89,\n 90,\n ]\n\n\ndef xyxy2xywh(x):\n \"\"\"Converts nx4 bounding boxes from corners [x1, y1, x2, y2] to center format [x, y, w, h].\"\"\"\n y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)\n y[..., 0] = (x[..., 0] + x[..., 2]) / 2 # x center\n y[..., 1] = (x[..., 1] + x[..., 3]) / 2 # y center\n y[..., 2] = x[..., 2] - x[..., 0] # width\n y[..., 3] = x[..., 3] - x[..., 1] # height\n return y\n\n\ndef xywh2xyxy(x):\n \"\"\"Converts bbox format from [x, y, w, h] to [x1, y1, x2, y2], supporting torch.Tensor and np.ndarray.\"\"\"\n y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)\n y[..., 0] = x[..., 0] - x[..., 2] / 2 # top left x\n y[..., 1] = x[..., 1] - x[..., 3] / 2 # top left y\n y[..., 2] = x[..., 0] + x[..., 2] / 2 # bottom right x\n y[..., 3] = x[..., 1] + x[..., 3] / 2 # bottom right y\n return y\n\n\ndef xywhn2xyxy(x, w=640, h=640, padw=0, padh=0):\n \"\"\"Converts boxes from normalized [x, y, w, h] to [x1, y1, x2, y2] format, applies padding.\"\"\"\n y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)\n y[..., 0] = w * (x[..., 0] - x[..., 2] / 2) + padw # top left x\n y[..., 1] = h * (x[..., 1] - x[..., 3] / 2) + padh # top left y\n y[..., 2] = w * (x[..., 0] + x[..., 2] / 2) + padw # bottom right x\n y[..., 3] = h * (x[..., 1] + x[..., 3] / 2) + padh # bottom right y\n return y\n\n\ndef xyxy2xywhn(x, w=640, h=640, clip=False, eps=0.0):\n \"\"\"Converts bounding boxes from [x1, y1, x2, y2] format to normalized [x, y, w, h] format.\"\"\"\n if clip:\n clip_boxes(x, (h - eps, w - eps)) # warning: inplace clip\n y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)\n y[..., 0] = ((x[..., 0] + x[..., 2]) / 2) / w # x center\n y[..., 1] = ((x[..., 1] + x[..., 3]) / 2) / h # y center\n y[..., 2] = (x[..., 2] - x[..., 0]) / w # width\n y[..., 3] = (x[..., 3] - x[..., 1]) / h # height\n return y\n\n\ndef xyn2xy(x, w=640, h=640, padw=0, padh=0):\n \"\"\"Converts normalized segments to pixel segments, shape (n,2), adjusting for width `w`, height `h`, and padding.\"\"\"\n y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)\n y[..., 0] = w * x[..., 0] + padw # top left x\n y[..., 1] = h * x[..., 1] + padh # top left y\n return y\n\n\ndef segment2box(segment, width=640, height=640):\n \"\"\"Converts a single segment to a bounding box using image dimensions, output shape (4,), ensuring coordinates stay\n within image boundaries.\n \"\"\"\n x, y = segment.T # segment xy\n inside = (x >= 0) & (y >= 0) & (x <= width) & (y <= height)\n (\n x,\n y,\n ) = x[inside], y[inside]\n return np.array([x.min(), y.min(), x.max(), y.max()]) if any(x) else np.zeros((1, 4)) # xyxy\n\n\ndef segments2boxes(segments):\n \"\"\"Converts segmentation labels to bounding box labels in format (cls, xywh) from (cls, xy1, xy2, ...).\"\"\"\n boxes = []\n for s in segments:\n x, y = s.T # segment xy\n boxes.append([x.min(), y.min(), x.max(), y.max()]) # cls, xyxy\n return xyxy2xywh(np.array(boxes)) # cls, xywh\n\n\ndef resample_segments(segments, n=1000):\n \"\"\"Resamples segments to a fixed number of points (n), returning up-sampled (n,2) segment arrays.\"\"\"\n for i, s in enumerate(segments):\n s = np.concatenate((s, s[0:1, :]), axis=0)\n x = np.linspace(0, len(s) - 1, n)\n xp = np.arange(len(s))\n segments[i] = np.concatenate([np.interp(x, xp, s[:, i]) for i in range(2)]).reshape(2, -1).T # segment xy\n return segments\n\n\ndef scale_boxes(img1_shape, boxes, img0_shape, ratio_pad=None):\n \"\"\"Rescales bounding boxes from one image shape to another, optionally with ratio and padding adjustments.\"\"\"\n if ratio_pad is None: # calculate from img0_shape\n gain = min(img1_shape[0] / img0_shape[0], img1_shape[1] / img0_shape[1]) # gain = old / new\n pad = (img1_shape[1] - img0_shape[1] * gain) / 2, (img1_shape[0] - img0_shape[0] * gain) / 2 # wh padding\n else:\n gain = ratio_pad[0][0]\n pad = ratio_pad[1]\n\n boxes[..., [0, 2]] -= pad[0] # x padding\n boxes[..., [1, 3]] -= pad[1] # y padding\n boxes[..., :4] /= gain\n clip_boxes(boxes, img0_shape)\n return boxes\n\n\ndef scale_segments(img1_shape, segments, img0_shape, ratio_pad=None, normalize=False):\n \"\"\"Rescales segment coordinates from img1_shape to img0_shape, optionally normalizing, with support for padding\n adjustments.\n \"\"\"\n if ratio_pad is None: # calculate from img0_shape\n gain = min(img1_shape[0] / img0_shape[0], img1_shape[1] / img0_shape[1]) # gain = old / new\n pad = (img1_shape[1] - img0_shape[1] * gain) / 2, (img1_shape[0] - img0_shape[0] * gain) / 2 # wh padding\n else:\n gain = ratio_pad[0][0]\n pad = ratio_pad[1]\n\n segments[:, 0] -= pad[0] # x padding\n segments[:, 1] -= pad[1] # y padding\n segments /= gain\n clip_segments(segments, img0_shape)\n if normalize:\n segments[:, 0] /= img0_shape[1] # width\n segments[:, 1] /= img0_shape[0] # height\n return segments\n\n\ndef clip_boxes(boxes, shape):\n \"\"\"Clips bounding boxes to within the specified image shape; supports both torch.Tensor and np.array.\"\"\"\n if isinstance(boxes, torch.Tensor): # faster individually\n boxes[..., 0].clamp_(0, shape[1]) # x1\n boxes[..., 1].clamp_(0, shape[0]) # y1\n boxes[..., 2].clamp_(0, shape[1]) # x2\n boxes[..., 3].clamp_(0, shape[0]) # y2\n else: # np.array (faster grouped)\n boxes[..., [0, 2]] = boxes[..., [0, 2]].clip(0, shape[1]) # x1, x2\n boxes[..., [1, 3]] = boxes[..., [1, 3]].clip(0, shape[0]) # y1, y2\n\n\ndef clip_segments(segments, shape):\n \"\"\"Clips segments to within image shape (height, width), supporting torch.Tensor and np.array inputs.\"\"\"\n if isinstance(segments, torch.Tensor): # faster individually\n segments[:, 0].clamp_(0, shape[1]) # x\n segments[:, 1].clamp_(0, shape[0]) # y\n else: # np.array (faster grouped)\n segments[:, 0] = segments[:, 0].clip(0, shape[1]) # x\n segments[:, 1] = segments[:, 1].clip(0, shape[0]) # y\n\n\ndef non_max_suppression(\n prediction,\n conf_thres=0.25,\n iou_thres=0.45,\n classes=None,\n agnostic=False,\n multi_label=False,\n labels=(),\n max_det=300,\n nm=0, # number of masks\n):\n \"\"\"Non-Maximum Suppression (NMS) on inference results to reject overlapping detections.\n\n Returns:\n list of detections, on (n,6) tensor per image [xyxy, conf, cls]\n \"\"\"\n # Checks\n assert 0 <= conf_thres <= 1, f\"Invalid Confidence threshold {conf_thres}, valid values are between 0.0 and 1.0\"\n assert 0 <= iou_thres <= 1, f\"Invalid IoU {iou_thres}, valid values are between 0.0 and 1.0\"\n if isinstance(prediction, (list, tuple)): # YOLOv3 model in validation model, output = (inference_out, loss_out)\n prediction = prediction[0] # select only inference output\n\n device = prediction.device\n mps = \"mps\" in device.type # Apple MPS\n if mps: # MPS not fully supported yet, convert tensors to CPU before NMS\n prediction = prediction.cpu()\n bs = prediction.shape[0] # batch size\n nc = prediction.shape[2] - nm - 5 # number of classes\n xc = prediction[..., 4] > conf_thres # candidates\n\n # Settings\n # min_wh = 2 # (pixels) minimum box width and height\n max_wh = 7680 # (pixels) maximum box width and height\n max_nms = 30000 # maximum number of boxes into torchvision.ops.nms()\n time_limit = 0.5 + 0.05 * bs # seconds to quit after\n redundant = True # require redundant detections\n multi_label &= nc > 1 # multiple labels per box (adds 0.5ms/img)\n merge = False # use merge-NMS\n\n t = time.time()\n mi = 5 + nc # mask start index\n output = [torch.zeros((0, 6 + nm), device=prediction.device)] * bs\n for xi, x in enumerate(prediction): # image index, image inference\n # Apply constraints\n # x[((x[..., 2:4] < min_wh) | (x[..., 2:4] > max_wh)).any(1), 4] = 0 # width-height\n x = x[xc[xi]] # confidence\n\n # Cat apriori labels if autolabelling\n if labels and len(labels[xi]):\n lb = labels[xi]\n v = torch.zeros((len(lb), nc + nm + 5), device=x.device)\n v[:, :4] = lb[:, 1:5] # box\n v[:, 4] = 1.0 # conf\n v[range(len(lb)), lb[:, 0].long() + 5] = 1.0 # cls\n x = torch.cat((x, v), 0)\n\n # If none remain process next image\n if not x.shape[0]:\n continue\n\n # Compute conf\n x[:, 5:] *= x[:, 4:5] # conf = obj_conf * cls_conf\n\n # Box/Mask\n box = xywh2xyxy(x[:, :4]) # center_x, center_y, width, height) to (x1, y1, x2, y2)\n mask = x[:, mi:] # zero columns if no masks\n\n # Detections matrix nx6 (xyxy, conf, cls)\n if multi_label:\n i, j = (x[:, 5:mi] > conf_thres).nonzero(as_tuple=False).T\n x = torch.cat((box[i], x[i, 5 + j, None], j[:, None].float(), mask[i]), 1)\n else: # best class only\n conf, j = x[:, 5:mi].max(1, keepdim=True)\n x = torch.cat((box, conf, j.float(), mask), 1)[conf.view(-1) > conf_thres]\n\n # Filter by class\n if classes is not None:\n x = x[(x[:, 5:6] == torch.tensor(classes, device=x.device)).any(1)]\n\n # Apply finite constraint\n # if not torch.isfinite(x).all():\n # x = x[torch.isfinite(x).all(1)]\n\n # Check shape\n n = x.shape[0] # number of boxes\n if not n: # no boxes\n continue\n x = x[x[:, 4].argsort(descending=True)[:max_nms]] # sort by confidence and remove excess boxes\n\n # Batched NMS\n c = x[:, 5:6] * (0 if agnostic else max_wh) # classes\n boxes, scores = x[:, :4] + c, x[:, 4] # boxes (offset by class), scores\n i = torchvision.ops.nms(boxes, scores, iou_thres) # NMS\n i = i[:max_det] # limit detections\n if merge and (1 < n < 3e3): # Merge NMS (boxes merged using weighted mean)\n # update boxes as boxes(i,4) = weights(i,n) * boxes(n,4)\n iou = box_iou(boxes[i], boxes) > iou_thres # iou matrix\n weights = iou * scores[None] # box weights\n x[i, :4] = torch.mm(weights, x[:, :4]).float() / weights.sum(1, keepdim=True) # merged boxes\n if redundant:\n i = i[iou.sum(1) > 1] # require redundancy\n\n output[xi] = x[i]\n if mps:\n output[xi] = output[xi].to(device)\n if (time.time() - t) > time_limit:\n LOGGER.warning(f\"WARNING ⚠️ NMS time limit {time_limit:.3f}s exceeded\")\n break # time limit exceeded\n\n return output\n\n\ndef strip_optimizer(f=\"best.pt\", s=\"\"): # from utils.general import *; strip_optimizer()\n \"\"\"Strips optimizer from a checkpoint file 'f', optionally saving as 's', to finalize training.\"\"\"\n x = torch_load(f, map_location=torch.device(\"cpu\"))\n if x.get(\"ema\"):\n x[\"model\"] = x[\"ema\"] # replace model with ema\n for k in \"optimizer\", \"best_fitness\", \"ema\", \"updates\": # keys\n x[k] = None\n x[\"epoch\"] = -1\n x[\"model\"].half() # to FP16\n for p in x[\"model\"].parameters():\n p.requires_grad = False\n torch.save(x, s or f)\n mb = os.path.getsize(s or f) / 1e6 # filesize\n LOGGER.info(f\"Optimizer stripped from {f},{f' saved as {s},' if s else ''} {mb:.1f}MB\")\n\n\ndef print_mutation(keys, results, hyp, save_dir, bucket, prefix=colorstr(\"evolve: \")):\n \"\"\"Logs mutation results, updates evolve CSV/YAML, optionally syncs with cloud storage.\"\"\"\n evolve_csv = save_dir / \"evolve.csv\"\n evolve_yaml = save_dir / \"hyp_evolve.yaml\"\n keys = tuple(keys) + tuple(hyp.keys()) # [results + hyps]\n keys = tuple(x.strip() for x in keys)\n vals = results + tuple(hyp.values())\n n = len(keys)\n\n # Download (optional)\n if bucket:\n url = f\"gs://{bucket}/evolve.csv\"\n if gsutil_getsize(url) > (evolve_csv.stat().st_size if evolve_csv.exists() else 0):\n subprocess.run([\"gsutil\", \"cp\", f\"{url}\", f\"{save_dir}\"]) # download evolve.csv if larger than local\n\n # Log to evolve.csv\n s = \"\" if evolve_csv.exists() else ((\"%20s,\" * n % keys).rstrip(\",\") + \"\\n\") # add header\n with open(evolve_csv, \"a\") as f:\n f.write(s + (\"%20.5g,\" * n % vals).rstrip(\",\") + \"\\n\")\n\n # Save yaml\n with open(evolve_yaml, \"w\") as f:\n data = pd.read_csv(evolve_csv, skipinitialspace=True)\n data = data.rename(columns=lambda x: x.strip()) # strip keys\n i = np.argmax(fitness(data.values[:, :4])) #\n generations = len(data)\n f.write(\n \"# YOLOv3 Hyperparameter Evolution Results\\n\"\n + f\"# Best generation: {i}\\n\"\n + f\"# Last generation: {generations - 1}\\n\"\n + \"# \"\n + \", \".join(f\"{x.strip():>20s}\" for x in keys[:7])\n + \"\\n\"\n + \"# \"\n + \", \".join(f\"{x:>20.5g}\" for x in data.values[i, :7])\n + \"\\n\\n\"\n )\n yaml.safe_dump(data.loc[i][7:].to_dict(), f, sort_keys=False)\n\n # Print to screen\n LOGGER.info(\n prefix\n + f\"{generations} generations finished, current result:\\n\"\n + prefix\n + \", \".join(f\"{x.strip():>20s}\" for x in keys)\n + \"\\n\"\n + prefix\n + \", \".join(f\"{x:20.5g}\" for x in vals)\n + \"\\n\\n\"\n )\n\n if bucket:\n subprocess.run([\"gsutil\", \"cp\", f\"{evolve_csv}\", f\"{evolve_yaml}\", f\"gs://{bucket}\"]) # upload\n\n\ndef apply_classifier(x, model, img, im0):\n \"\"\"Applies a second stage classifier to YOLO outputs, adjusting box shapes and filtering class matches.\"\"\"\n # Example model = torchvision.models.__dict__['efficientnet_b0'](pretrained=True).to(device).eval()\n im0 = [im0] if isinstance(im0, np.ndarray) else im0\n for i, d in enumerate(x): # per image\n if d is not None and len(d):\n d = d.clone()\n\n # Reshape and pad cutouts\n b = xyxy2xywh(d[:, :4]) # boxes\n b[:, 2:] = b[:, 2:].max(1)[0].unsqueeze(1) # rectangle to square\n b[:, 2:] = b[:, 2:] * 1.3 + 30 # pad\n d[:, :4] = xywh2xyxy(b).long()\n\n # Rescale boxes from img_size to im0 size\n scale_boxes(img.shape[2:], d[:, :4], im0[i].shape)\n\n # Classes\n pred_cls1 = d[:, 5].long()\n ims = []\n for a in d:\n cutout = im0[i][int(a[1]) : int(a[3]), int(a[0]) : int(a[2])]\n im = cv2.resize(cutout, (224, 224)) # BGR\n\n im = im[:, :, ::-1].transpose(2, 0, 1) # BGR to RGB, to 3x416x416\n im = np.ascontiguousarray(im, dtype=np.float32) # uint8 to float32\n im /= 255 # 0 - 255 to 0.0 - 1.0\n ims.append(im)\n\n pred_cls2 = model(torch.Tensor(ims).to(d.device)).argmax(1) # classifier prediction\n x[i] = x[i][pred_cls1 == pred_cls2] # retain matching class detections\n\n return x\n\n\ndef increment_path(path, exist_ok=False, sep=\"\", mkdir=False):\n \"\"\"Increments file or directory path, optionally creating the directory, not thread-safe.\n\n Args: path (str/Path), exist_ok (bool), sep (str), mkdir (bool).\n \"\"\"\n path = Path(path) # os-agnostic\n if path.exists() and not exist_ok:\n path, suffix = (path.with_suffix(\"\"), path.suffix) if path.is_file() else (path, \"\")\n\n # Method 1\n for n in range(2, 9999):\n p = f\"{path}{sep}{n}{suffix}\" # increment path\n if not os.path.exists(p): #\n break\n path = Path(p)\n\n # Method 2 (deprecated)\n # dirs = glob.glob(f\"{path}{sep}*\") # similar paths\n # matches = [re.search(rf\"{path.stem}{sep}(\\d+)\", d) for d in dirs]\n # i = [int(m.groups()[0]) for m in matches if m] # indices\n # n = max(i) + 1 if i else 2 # increment number\n # path = Path(f\"{path}{sep}{n}{suffix}\") # increment path\n\n if mkdir:\n path.mkdir(parents=True, exist_ok=True) # make directory\n\n return path\n\n\n# OpenCV Multilanguage-friendly functions\n# ------------------------------------------------------------------------------------\nimshow_ = cv2.imshow # copy to avoid recursion errors\n\n\ndef imread(filename, flags=cv2.IMREAD_COLOR):\n \"\"\"Reads an image from a file, supporting multilanguage paths, and returns it in the specified color scheme.\"\"\"\n return cv2.imdecode(np.fromfile(filename, np.uint8), flags)\n\n\ndef imwrite(filename, img):\n \"\"\"Writes an image to a file; returns True on success, False on failure.\n\n Args: filename (str), img (ndarray).\n \"\"\"\n try:\n cv2.imencode(Path(filename).suffix, img)[1].tofile(filename)\n return True\n except Exception:\n return False\n\n\ndef imshow(path, im):\n \"\"\"Displays an image; accepts a path (str) and image data (ndarray) as arguments.\"\"\"\n imshow_(path.encode(\"unicode_escape\").decode(), im)\n\n\nif Path(inspect.stack()[0].filename).parent.parent.as_posix() in inspect.stack()[-1].filename:\n cv2.imread, cv2.imwrite, cv2.imshow = imread, imwrite, imshow # redefine\n\n# Variables ------------------------------------------------------------------------------------------------------------\n" }, { "path": "utils/google_app_engine/Dockerfile", "content": "FROM gcr.io/google-appengine/python\n\n# Create a virtualenv for dependencies. This isolates these packages from\n# system-level packages.\n# Use -p python3 or -p python3.7 to select python version. Default is version 2.\nRUN virtualenv /env -p python3\n\n# Setting these environment variables are the same as running\n# source /env/bin/activate.\nENV VIRTUAL_ENV /env\nENV PATH /env/bin:$PATH\n\nRUN apt-get update && apt-get install -y python-opencv\n\n# Copy the application's requirements.txt and run pip to install all\n# dependencies into the virtualenv.\nADD requirements.txt /app/requirements.txt\nRUN pip install -r /app/requirements.txt\n\n# Add the application source code.\nADD . /app\n\n# Run a WSGI server to serve the application. gunicorn must be declared as\n# a dependency in requirements.txt.\nCMD gunicorn -b :$PORT main:app\n" }, { "path": "utils/google_app_engine/additional_requirements.txt", "content": "# add these requirements in your app on top of the existing ones\npip==26.0\nFlask==3.1.3\ngunicorn==23.0.0\nwerkzeug>=3.0.1 # not directly required, pinned by Snyk to avoid a vulnerability\nzipp>=3.19.1 # not directly required, pinned by Snyk to avoid a vulnerability\n" }, { "path": "utils/google_app_engine/app.yaml", "content": "# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license\n\nruntime: custom\nenv: flex\n\nservice: yolov5app\n\nliveness_check:\n initial_delay_sec: 600\n\nmanual_scaling:\n instances: 1\nresources:\n cpu: 1\n memory_gb: 4\n disk_size_gb: 20\n" }, { "path": "utils/loggers/__init__.py", "content": "# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license\n\"\"\"Logging utils.\"\"\"\n\nimport os\nimport warnings\nfrom pathlib import Path\n\nimport torch\nfrom packaging.version import parse\n\nfrom utils.general import LOGGER, colorstr, cv2\nfrom utils.loggers.clearml.clearml_utils import ClearmlLogger\nfrom utils.loggers.wandb.wandb_utils import WandbLogger\nfrom utils.plots import plot_images, plot_labels, plot_results\nfrom utils.torch_utils import de_parallel\n\nLOGGERS = (\"csv\", \"tb\", \"wandb\", \"clearml\", \"comet\") # *.csv, TensorBoard, Weights & Biases, ClearML\nRANK = int(os.getenv(\"RANK\", -1))\n\ntry:\n from torch.utils.tensorboard import SummaryWriter\nexcept ImportError:\n\n def SummaryWriter(*args):\n \"\"\"Imports TensorBoard's SummaryWriter for logging, with a fallback returning None if TensorBoard is not\n installed.\n \"\"\"\n return None # None = SummaryWriter(str)\n\n\ntry:\n import wandb\n\n assert hasattr(wandb, \"__version__\") # verify package import not local dir\n if parse(wandb.__version__) >= parse(\"0.12.2\") and RANK in {0, -1}:\n try:\n wandb_login_success = wandb.login(timeout=30)\n except wandb.errors.UsageError: # known non-TTY terminal issue\n wandb_login_success = False\n if not wandb_login_success:\n wandb = None\nexcept (ImportError, AssertionError):\n wandb = None\n\ntry:\n import clearml\n\n assert hasattr(clearml, \"__version__\") # verify package import not local dir\nexcept (ImportError, AssertionError):\n clearml = None\n\ntry:\n if RANK in {0, -1}:\n import comet_ml\n\n assert hasattr(comet_ml, \"__version__\") # verify package import not local dir\n from utils.loggers.comet import CometLogger\n\n else:\n comet_ml = None\nexcept (ImportError, AssertionError):\n comet_ml = None\n\n\nclass Loggers:\n \"\"\"Manages logging for training and validation using TensorBoard, Weights & Biases, ClearML, and Comet ML.\"\"\"\n\n def __init__(self, save_dir=None, weights=None, opt=None, hyp=None, logger=None, include=LOGGERS):\n \"\"\"Initializes YOLOv3 logging with directory, weights, options, hyperparameters, and includes specified loggers.\n \"\"\"\n self.save_dir = save_dir\n self.weights = weights\n self.opt = opt\n self.hyp = hyp\n self.plots = not opt.noplots # plot results\n self.logger = logger # for printing results to console\n self.include = include\n self.keys = [\n \"train/box_loss\",\n \"train/obj_loss\",\n \"train/cls_loss\", # train loss\n \"metrics/precision\",\n \"metrics/recall\",\n \"metrics/mAP_0.5\",\n \"metrics/mAP_0.5:0.95\", # metrics\n \"val/box_loss\",\n \"val/obj_loss\",\n \"val/cls_loss\", # val loss\n \"x/lr0\",\n \"x/lr1\",\n \"x/lr2\",\n ] # params\n self.best_keys = [\"best/epoch\", \"best/precision\", \"best/recall\", \"best/mAP_0.5\", \"best/mAP_0.5:0.95\"]\n for k in LOGGERS:\n setattr(self, k, None) # init empty logger dictionary\n self.csv = True # always log to csv\n\n # Messages\n if not comet_ml:\n prefix = colorstr(\"Comet: \")\n s = f\"{prefix}run 'pip install comet_ml' to automatically track and visualize YOLOv3 🚀 runs in Comet\"\n self.logger.info(s)\n # TensorBoard\n s = self.save_dir\n if \"tb\" in self.include and not self.opt.evolve:\n prefix = colorstr(\"TensorBoard: \")\n self.logger.info(f\"{prefix}Start with 'tensorboard --logdir {s.parent}', view at http://localhost:6006/\")\n self.tb = SummaryWriter(str(s))\n\n # W&B\n if wandb and \"wandb\" in self.include:\n self.opt.hyp = self.hyp # add hyperparameters\n self.wandb = WandbLogger(self.opt)\n else:\n self.wandb = None\n\n # ClearML\n if clearml and \"clearml\" in self.include:\n try:\n self.clearml = ClearmlLogger(self.opt, self.hyp)\n except Exception:\n self.clearml = None\n prefix = colorstr(\"ClearML: \")\n LOGGER.warning(\n f\"{prefix}WARNING ⚠️ ClearML is installed but not configured, skipping ClearML logging.\"\n f\" See https://docs.ultralytics.com/yolov5/tutorials/clearml_logging_integration#readme\"\n )\n\n else:\n self.clearml = None\n\n # Comet\n if comet_ml and \"comet\" in self.include:\n if isinstance(self.opt.resume, str) and self.opt.resume.startswith(\"comet://\"):\n run_id = self.opt.resume.split(\"/\")[-1]\n self.comet_logger = CometLogger(self.opt, self.hyp, run_id=run_id)\n\n else:\n self.comet_logger = CometLogger(self.opt, self.hyp)\n\n else:\n self.comet_logger = None\n\n @property\n def remote_dataset(self):\n \"\"\"Fetches dataset dictionary from ClearML, W&B, or Comet ML based on the logger instantiated.\"\"\"\n data_dict = None\n if self.clearml:\n data_dict = self.clearml.data_dict\n if self.wandb:\n data_dict = self.wandb.data_dict\n if self.comet_logger:\n data_dict = self.comet_logger.data_dict\n\n return data_dict\n\n def on_train_start(self):\n \"\"\"Calls `on_train_start` method on comet_logger if it's available.\"\"\"\n if self.comet_logger:\n self.comet_logger.on_train_start()\n\n def on_pretrain_routine_start(self):\n \"\"\"Initiates pretraining routine on comet_logger if available.\"\"\"\n if self.comet_logger:\n self.comet_logger.on_pretrain_routine_start()\n\n def on_pretrain_routine_end(self, labels, names):\n \"\"\"Logs pretrain routine end, plots labels if enabled, updates WandB/Comet with images.\n\n Takes `labels` (List of int), `names` (List of str).\n \"\"\"\n if self.plots:\n plot_labels(labels, names, self.save_dir)\n paths = self.save_dir.glob(\"*labels*.jpg\") # training labels\n if self.wandb:\n self.wandb.log({\"Labels\": [wandb.Image(str(x), caption=x.name) for x in paths]})\n # if self.clearml:\n # pass # ClearML saves these images automatically using hooks\n if self.comet_logger:\n self.comet_logger.on_pretrain_routine_end(paths)\n\n def on_train_batch_end(self, model, ni, imgs, targets, paths, vals):\n \"\"\"Logs training batch details, plots initial batches, logs Tensorboard and WandB/ClearML if enabled.\"\"\"\n log_dict = dict(zip(self.keys[:3], vals))\n # Callback runs on train batch end\n # ni: number integrated batches (since train start)\n if self.plots:\n if ni < 3:\n f = self.save_dir / f\"train_batch{ni}.jpg\" # filename\n plot_images(imgs, targets, paths, f)\n if ni == 0 and self.tb and not self.opt.sync_bn:\n log_tensorboard_graph(self.tb, model, imgsz=(self.opt.imgsz, self.opt.imgsz))\n if ni == 10 and (self.wandb or self.clearml):\n files = sorted(self.save_dir.glob(\"train*.jpg\"))\n if self.wandb:\n self.wandb.log({\"Mosaics\": [wandb.Image(str(f), caption=f.name) for f in files if f.exists()]})\n if self.clearml:\n self.clearml.log_debug_samples(files, title=\"Mosaics\")\n\n if self.comet_logger:\n self.comet_logger.on_train_batch_end(log_dict, step=ni)\n\n def on_train_epoch_end(self, epoch):\n \"\"\"Callback that updates the current epoch in wandb at the end of each training epoch.\"\"\"\n if self.wandb:\n self.wandb.current_epoch = epoch + 1\n\n if self.comet_logger:\n self.comet_logger.on_train_epoch_end(epoch)\n\n def on_val_start(self):\n \"\"\"Callback that notifies the comet logger at the start of each validation phase.\"\"\"\n if self.comet_logger:\n self.comet_logger.on_val_start()\n\n def on_val_image_end(self, pred, predn, path, names, im):\n \"\"\"Callback for logging a single validation image and its predictions to WandB or ClearML at the end of\n validation.\n \"\"\"\n if self.wandb:\n self.wandb.val_one_image(pred, predn, path, names, im)\n if self.clearml:\n self.clearml.log_image_with_boxes(path, pred, names, im)\n\n def on_val_batch_end(self, batch_i, im, targets, paths, shapes, out):\n \"\"\"Logs a single validation batch for Comet ML analytics (batch_i: int, im: tensor, targets: tensor, paths:.\n\n list, shapes: list, out: tensor).\n \"\"\"\n if self.comet_logger:\n self.comet_logger.on_val_batch_end(batch_i, im, targets, paths, shapes, out)\n\n def on_val_end(self, nt, tp, fp, p, r, f1, ap, ap50, ap_class, confusion_matrix):\n \"\"\"Logs validation results and images on validation end for visual analytics.\"\"\"\n if self.wandb or self.clearml:\n files = sorted(self.save_dir.glob(\"val*.jpg\"))\n if self.wandb:\n self.wandb.log({\"Validation\": [wandb.Image(str(f), caption=f.name) for f in files]})\n if self.clearml:\n self.clearml.log_debug_samples(files, title=\"Validation\")\n\n if self.comet_logger:\n self.comet_logger.on_val_end(nt, tp, fp, p, r, f1, ap, ap50, ap_class, confusion_matrix)\n\n def on_fit_epoch_end(self, vals, epoch, best_fitness, fi):\n \"\"\"Logs epoch results to CSV if enabled, updating with vals, best_fitness, and fi.\"\"\"\n x = dict(zip(self.keys, vals))\n if self.csv:\n file = self.save_dir / \"results.csv\"\n n = len(x) + 1 # number of cols\n s = \"\" if file.exists() else ((\"%20s,\" * n % tuple([\"epoch\", *self.keys])).rstrip(\",\") + \"\\n\") # add header\n with open(file, \"a\") as f:\n f.write(s + (\"%20.5g,\" * n % tuple([epoch, *vals])).rstrip(\",\") + \"\\n\")\n\n if self.tb:\n for k, v in x.items():\n self.tb.add_scalar(k, v, epoch)\n elif self.clearml: # log to ClearML if TensorBoard not used\n for k, v in x.items():\n title, series = k.split(\"/\")\n self.clearml.task.get_logger().report_scalar(title, series, v, epoch)\n\n if self.wandb:\n if best_fitness == fi:\n best_results = [epoch, *vals[3:7]]\n for i, name in enumerate(self.best_keys):\n self.wandb.wandb_run.summary[name] = best_results[i] # log best results in the summary\n self.wandb.log(x)\n self.wandb.end_epoch()\n\n if self.clearml:\n self.clearml.current_epoch_logged_images = set() # reset epoch image limit\n self.clearml.current_epoch += 1\n\n if self.comet_logger:\n self.comet_logger.on_fit_epoch_end(x, epoch=epoch)\n\n def on_model_save(self, last, epoch, final_epoch, best_fitness, fi):\n \"\"\"Logs model to WandB/ClearML, considering save_period and if not final_epoch, also notes if best model so far.\n \"\"\"\n if (epoch + 1) % self.opt.save_period == 0 and not final_epoch and self.opt.save_period != -1:\n if self.wandb:\n self.wandb.log_model(last.parent, self.opt, epoch, fi, best_model=best_fitness == fi)\n if self.clearml:\n self.clearml.task.update_output_model(\n model_path=str(last), model_name=\"Latest Model\", auto_delete_file=False\n )\n\n if self.comet_logger:\n self.comet_logger.on_model_save(last, epoch, final_epoch, best_fitness, fi)\n\n def on_train_end(self, last, best, epoch, results):\n \"\"\"Callback to execute at training end, saving plots of results and relevant metrics to the specified save\n directory.\n \"\"\"\n if self.plots:\n plot_results(file=self.save_dir / \"results.csv\") # save results.png\n files = [\"results.png\", \"confusion_matrix.png\", *(f\"{x}_curve.png\" for x in (\"F1\", \"PR\", \"P\", \"R\"))]\n files = [(self.save_dir / f) for f in files if (self.save_dir / f).exists()] # filter\n self.logger.info(f\"Results saved to {colorstr('bold', self.save_dir)}\")\n\n if self.tb and not self.clearml: # These images are already captured by ClearML by now, we don't want doubles\n for f in files:\n self.tb.add_image(f.stem, cv2.imread(str(f))[..., ::-1], epoch, dataformats=\"HWC\")\n\n if self.wandb:\n self.wandb.log(dict(zip(self.keys[3:10], results)))\n self.wandb.log({\"Results\": [wandb.Image(str(f), caption=f.name) for f in files]})\n # Calling wandb.log. TODO: Refactor this into WandbLogger.log_model\n if not self.opt.evolve:\n wandb.log_artifact(\n str(best if best.exists() else last),\n type=\"model\",\n name=f\"run_{self.wandb.wandb_run.id}_model\",\n aliases=[\"latest\", \"best\", \"stripped\"],\n )\n self.wandb.finish_run()\n\n if self.clearml and not self.opt.evolve:\n self.clearml.task.update_output_model(\n model_path=str(best if best.exists() else last), name=\"Best Model\", auto_delete_file=False\n )\n\n if self.comet_logger:\n final_results = dict(zip(self.keys[3:10], results))\n self.comet_logger.on_train_end(files, self.save_dir, last, best, epoch, final_results)\n\n def on_params_update(self, params: dict):\n \"\"\"Updates experiment hyperparameters or configs in WandB and Comet logger with provided params dictionary.\"\"\"\n if self.wandb:\n self.wandb.wandb_run.config.update(params, allow_val_change=True)\n if self.comet_logger:\n self.comet_logger.on_params_update(params)\n\n\nclass GenericLogger:\n \"\"\"YOLOv3 General purpose logger for non-task specific logging Usage: from utils.loggers import GenericLogger;\n logger = GenericLogger(...).\n\n Args:\n opt: Run arguments\n console_logger: Console logger\n include: loggers to include\n \"\"\"\n\n def __init__(self, opt, console_logger, include=(\"tb\", \"wandb\")):\n \"\"\"Initializes a generic logger for YOLOv3, including options for TensorBoard and wandb logging.\"\"\"\n self.save_dir = Path(opt.save_dir)\n self.include = include\n self.console_logger = console_logger\n self.csv = self.save_dir / \"results.csv\" # CSV logger\n if \"tb\" in self.include:\n prefix = colorstr(\"TensorBoard: \")\n self.console_logger.info(\n f\"{prefix}Start with 'tensorboard --logdir {self.save_dir.parent}', view at http://localhost:6006/\"\n )\n self.tb = SummaryWriter(str(self.save_dir))\n\n if wandb and \"wandb\" in self.include:\n self.wandb = wandb.init(\n project=web_project_name(str(opt.project)), name=None if opt.name == \"exp\" else opt.name, config=opt\n )\n else:\n self.wandb = None\n\n def log_metrics(self, metrics, epoch):\n \"\"\"Logs metric dictionary to all loggers, including CSV with keys, values, and epoch.\"\"\"\n if self.csv:\n keys, vals = list(metrics.keys()), list(metrics.values())\n n = len(metrics) + 1 # number of cols\n s = \"\" if self.csv.exists() else ((\"%23s,\" * n % tuple([\"epoch\", *keys])).rstrip(\",\") + \"\\n\") # header\n with open(self.csv, \"a\") as f:\n f.write(s + (\"%23.5g,\" * n % tuple([epoch, *vals])).rstrip(\",\") + \"\\n\")\n\n if self.tb:\n for k, v in metrics.items():\n self.tb.add_scalar(k, v, epoch)\n\n if self.wandb:\n self.wandb.log(metrics, step=epoch)\n\n def log_images(self, files, name=\"Images\", epoch=0):\n \"\"\"Logs images to TensorBoard and Weights & Biases, ensuring file existence and supporting various formats.\"\"\"\n files = [Path(f) for f in (files if isinstance(files, (tuple, list)) else [files])] # to Path\n files = [f for f in files if f.exists()] # filter by exists\n\n if self.tb:\n for f in files:\n self.tb.add_image(f.stem, cv2.imread(str(f))[..., ::-1], epoch, dataformats=\"HWC\")\n\n if self.wandb:\n self.wandb.log({name: [wandb.Image(str(f), caption=f.name) for f in files]}, step=epoch)\n\n def log_graph(self, model, imgsz=(640, 640)):\n \"\"\"Logs model graph to all loggers, accepts `model` and `imgsz` (default (640, 640)) as inputs.\"\"\"\n if self.tb:\n log_tensorboard_graph(self.tb, model, imgsz)\n\n def log_model(self, model_path, epoch=0, metadata=None):\n \"\"\"Logs model to all loggers with `model_path`, optional `epoch` (default 0), and `metadata` dictionary.\"\"\"\n if metadata is None:\n metadata = {}\n if self.wandb:\n art = wandb.Artifact(name=f\"run_{wandb.run.id}_model\", type=\"model\", metadata=metadata)\n art.add_file(str(model_path))\n wandb.log_artifact(art)\n\n def update_params(self, params):\n \"\"\"Updates logged parameters in wandb; `params`: dictionary to update, requires `wandb` to be initialized.\"\"\"\n if self.wandb:\n wandb.run.config.update(params, allow_val_change=True)\n\n\ndef log_tensorboard_graph(tb, model, imgsz=(640, 640)):\n \"\"\"Logs a model graph to TensorBoard using an all-zero input image of shape `(1, 3, imgsz, imgsz)`.\"\"\"\n try:\n p = next(model.parameters()) # for device, type\n imgsz = (imgsz, imgsz) if isinstance(imgsz, int) else imgsz # expand\n im = torch.zeros((1, 3, *imgsz)).to(p.device).type_as(p) # input image (WARNING: must be zeros, not empty)\n with warnings.catch_warnings():\n warnings.simplefilter(\"ignore\") # suppress jit trace warning\n tb.add_graph(torch.jit.trace(de_parallel(model), im, strict=False), [])\n except Exception as e:\n LOGGER.warning(f\"WARNING ⚠️ TensorBoard graph visualization failure {e}\")\n\n\ndef web_project_name(project):\n \"\"\"Converts local project name to a web-friendly format by adding a suffix based on its type (classify or segment).\n \"\"\"\n if not project.startswith(\"runs/train\"):\n return project\n suffix = \"-Classify\" if project.endswith(\"-cls\") else \"-Segment\" if project.endswith(\"-seg\") else \"\"\n return f\"YOLOv3{suffix}\"\n" }, { "path": "utils/loggers/clearml/README.md", "content": "\"Ultralytics\n\n# ClearML Integration for Ultralytics YOLO\n\nThis guide details how to integrate [ClearML](https://clear.ml/), a leading open-source MLOps platform, with your Ultralytics YOLO projects. ClearML streamlines the entire machine learning lifecycle—from experiment tracking to deployment—making it easier to manage and scale your computer vision workflows.\n\n\"Clear|ML\"\"Clear|ML\"\n\n## ✨ About ClearML\n\n[ClearML](https://clear.ml/) is an [open-source MLOps suite](https://github.com/clearml/clearml) that enables you to manage, automate, and orchestrate machine learning workflows efficiently. Integrating ClearML with Ultralytics YOLO unlocks several advantages:\n\n- **Experiment Management**: Automatically track every YOLO training run, including code versions, configurations, metrics, and outputs in a centralized dashboard. Explore more about [Ultralytics experiment tracking integrations](https://docs.ultralytics.com/integrations/).\n- **Data Versioning**: Manage and access your custom training datasets with ClearML Data Versioning. See how [Ultralytics datasets](https://docs.ultralytics.com/datasets/) are structured.\n- **Remote Execution**: Train and monitor your YOLO models remotely using ClearML Agent on any machine or cloud instance. Learn about [model deployment options](https://docs.ultralytics.com/guides/model-deployment-options/).\n- **Hyperparameter Optimization**: Use ClearML's HPO tools to optimize your model configurations and improve [mean average precision (mAP)](https://www.ultralytics.com/glossary/mean-average-precision-map). Review the [Ultralytics Hyperparameter Tuning guide](https://docs.ultralytics.com/guides/hyperparameter-tuning/).\n- **Model Deployment**: Deploy trained YOLO models as scalable APIs with ClearML Serving in just a few steps.\n\nYou can leverage any combination of these tools to fit your project requirements.\n\n![ClearML scalars dashboard](https://raw.githubusercontent.com/thepycoder/clearml_screenshots/main/experiment_manager_with_compare.gif)\n\n## 🦾 Setting Up ClearML\n\nTo use ClearML, connect the SDK to a ClearML Server instance. You have two main options:\n\n1. **ClearML Hosted Service**: Register for a free account at the [ClearML Hosted Service](https://app.clear.ml/).\n2. **Self-Hosted Server**: Deploy your own [ClearML Server](https://clear.ml/docs/latest/docs/deploying_clearml/clearml_server) for full control and data privacy.\n\nFollow these steps to get started:\n\n1. Install the `clearml` Python package:\n\n ```bash\n pip install clearml\n ```\n\n2. Connect the ClearML SDK to your server. Generate credentials in the ClearML Web UI (Settings → Workspace → Create new credentials) and run:\n\n ```bash\n clearml-init\n ```\n\n Follow the prompts to complete setup.\n\nOnce configured, ClearML is ready to integrate with your YOLO workflows! 😎\n\n## 🚀 Training YOLO With ClearML\n\nEnabling ClearML experiment tracking for YOLO is simple. Ensure the `clearml` package is installed:\n\n```bash\npip install clearml > =1.2.0\n```\n\nWith ClearML installed, every YOLO [training run](https://docs.ultralytics.com/modes/train/) is automatically logged.\n\nBy default, experiments are organized under the `YOLO` project with the task name `Training`. You can customize these using the `--project` and `--name` arguments in your training command. ClearML uses `/` as a delimiter for subprojects.\n\n**Example Training Command:**\n\n```bash\n# Train with default project/task names\npython train.py --img 640 --batch 16 --epochs 3 --data coco128.yaml --weights yolov5s.pt --cache\n```\n\n**Example with Custom Names:**\n\n```bash\n# Train with custom project and task names\npython train.py --project my_yolo_project --name experiment_001 --img 640 --batch 16 --epochs 3 --data coco128.yaml --weights yolov5s.pt --cache\n```\n\nClearML will automatically capture:\n\n- Git repository details (URL, commit ID, entry point) and local code changes\n- Installed Python packages and versions\n- [Hyperparameters](https://www.ultralytics.com/glossary/hyperparameter-tuning) and script arguments\n- [Model checkpoints](https://www.ultralytics.com/glossary/model-weights) (use `--save-period n` to save every `n` epochs)\n- Console output (stdout and stderr)\n- Performance [metrics and scalars](https://docs.ultralytics.com/guides/yolo-performance-metrics/) such as mAP0.5, mAP0.5:0.95, precision, recall, losses, and learning rates\n- Machine details, runtime, and creation date\n- Generated plots like label correlograms and [confusion matrices](https://www.ultralytics.com/glossary/confusion-matrix)\n- Debug samples: images with bounding boxes, mosaic visualizations, and validation images per epoch\n\nThis comprehensive tracking allows you to visualize progress in the ClearML UI, compare experiments, and easily identify the best-performing models by sorting based on metrics like [mAP](https://www.ultralytics.com/glossary/mean-average-precision-map).\n\n## 🔗 Dataset Version Management\n\nVersioning datasets is essential for reproducibility and collaboration in [machine learning](https://www.ultralytics.com/glossary/machine-learning-ml) projects. ClearML Data helps manage datasets efficiently. YOLO supports using ClearML dataset IDs directly in the training command.\n\n![ClearML Dataset Interface](https://raw.githubusercontent.com/thepycoder/clearml_screenshots/main/clearml_data.gif)\n\n### Prepare Your Dataset\n\nYOLO uses YAML files to define dataset configurations. Datasets are typically stored in a `../datasets` directory relative to your repository root. For example, the [COCO128 dataset](https://docs.ultralytics.com/datasets/detect/coco128/) structure:\n\n```\n../\n├── yolov3/ # Your repository\n└── datasets/\n └── coco128/ # Dataset root folder\n ├── images/\n ├── labels/\n ├── coco128.yaml # Dataset configuration file <--- IMPORTANT\n ├── LICENSE\n └── README.txt\n```\n\nEnsure your custom dataset follows a similar structure.\n\n⚠️ **Important**: Copy the dataset `.yaml` configuration file into the **root directory** of your dataset folder (e.g., `datasets/coco128/coco128.yaml`). This YAML file must include keys like `path`, `train`, `val`, `test`, `nc` (number of classes), and `names` (class names list) for ClearML integration to function correctly.\n\n### Upload Your Dataset\n\nNavigate to your dataset's root folder and use the `clearml-data` CLI tool to upload and version it:\n\n```bash\n# Navigate to the dataset directory\ncd ../datasets/coco128\n\n# Sync the dataset with ClearML (creates a versioned dataset)\nclearml-data sync --project \"YOLO Datasets\" --name coco128 --folder .\n```\n\nThis command creates a new ClearML dataset (or a new version if it exists) named `coco128` within the `YOLO Datasets` project.\n\nAlternatively, use granular commands:\n\n```bash\n# Create a new dataset task\nclearml-data create --project \"YOLO Datasets\" --name coco128\n\n# Add files to the dataset (use '.' for current folder)\nclearml-data add --files .\n\n# Finalize and upload the dataset version\nclearml-data close\n```\n\n### Run Training Using a ClearML Dataset\n\nOnce your dataset is versioned in ClearML, you can reference it directly in your YOLO training command using its unique ID. ClearML will automatically download the dataset if it's not present locally.\n\n```bash\n# Replace with the actual ID from ClearML\npython train.py --img 640 --batch 16 --epochs 3 --data clearml:// yolov5s.pt --cache < your_dataset_id > --weights\n```\n\nThe dataset ID used will be logged as a parameter in your ClearML experiment, ensuring full traceability.\n\n## 👀 Hyperparameter Optimization\n\nClearML's experiment tracking captures all the information needed to reproduce a run, forming the foundation for effective [hyperparameter optimization (HPO)](https://docs.ultralytics.com/guides/hyperparameter-tuning/). ClearML allows you to clone experiments, modify hyperparameters, and rerun them automatically.\n\nTo run HPO locally, Ultralytics provides a sample script. You'll need the ID of a previously executed training task (the \"template task\") to use as a base.\n\n1. Locate the HPO script at `utils/loggers/clearml/hpo.py`.\n2. Edit the script to include the `template task` ID.\n3. Optionally, install [Optuna](https://optuna.org/) (`pip install optuna`) for advanced optimization strategies, or use the default `RandomSearch`.\n4. Run the script:\n\n ```bash\n python utils/loggers/clearml/hpo.py\n ```\n\nThis script clones the template task, applies new hyperparameters suggested by the optimizer, and executes the modified task locally (`task.execute_locally()`). To run HPO remotely, change this to `task.execute()` to enqueue the tasks for a ClearML Agent.\n\n![HPO in ClearML UI](https://raw.githubusercontent.com/thepycoder/clearml_screenshots/main/hpo.png)\n\n## 🤯 Remote Execution (Advanced)\n\nClearML Agent enables running experiments on remote machines, such as on-premises servers or cloud GPUs. The agent fetches tasks from a queue, replicates the original environment (code, packages, uncommitted changes), executes the task, and reports results back to the ClearML Server.\n\n- **Learn More**: Watch the [ClearML Agent Introduction](https://www.youtube.com/watch?v=MX3BrXnaULs) or read the [ClearML Agent documentation](https://clear.ml/docs/latest/docs/clearml_agent).\n\nTurn any machine into a ClearML Agent by running:\n\n```bash\n# Replace with your queue(s) name(s)\nclearml-agent daemon --queue < queues_to_listen_to > [--docker] # Use --docker to run in a Docker container\n```\n\n### Cloning, Editing, and Enqueuing Tasks\n\nYou can manage remote execution tasks through the ClearML Web UI:\n\n1. **Clone**: Right-click an existing experiment to clone it.\n2. **Edit**: Modify hyperparameters or other configurations in the cloned task.\n3. **Enqueue**: Right-click the modified task and select \"Enqueue\" to assign it to a specific queue monitored by your agents.\n\n![Enqueue a task from the ClearML UI](https://raw.githubusercontent.com/thepycoder/clearml_screenshots/main/enqueue.gif)\n\n### Executing a Task Remotely via Code\n\nAlternatively, modify your training script to automatically enqueue the task for remote execution. Add `task.execute_remotely()` after the ClearML logger is initialized in `train.py`:\n\n```python\n# ... inside train.py ...\n\n# Loggers setup\nif RANK in {-1, 0}:\n # Initialize loggers, including ClearML\n loggers = Loggers(save_dir, weights, opt, hyp, LOGGER)\n\n if loggers.clearml:\n # Add this line to send the task to a queue for remote execution\n loggers.clearml.task.execute_remotely(queue_name=\"my_default_queue\")\n\n # Get dataset dictionary if using ClearML datasets\n data_dict = loggers.clearml.data_dict\n# ... rest of the script ...\n```\n\nWhen you run the modified `train.py`, the script execution will pause, package the code and environment, and send the task to the specified queue (`my_default_queue`). A ClearML Agent listening to that queue will then pick it up and run it.\n\n### Autoscaling Agents\n\nClearML also provides **Autoscalers** that automatically provision and manage cloud instances (AWS, GCP, Azure) as ClearML Agents based on queue load. Machines spin up when tasks are pending and shut down when idle, optimizing resource usage and cost.\n\nLearn how to set up autoscalers:\n\n[![Watch the Autoscaler setup video](https://img.youtube.com/vi/j4XVMAaUt3E/0.jpg)](https://youtu.be/j4XVMAaUt3E)\n\n## 👋 Contribute\n\nContributions are welcome! If you'd like to improve this integration or suggest features, please see the Ultralytics [Contributing Guide](https://docs.ultralytics.com/help/contributing/) and submit a Pull Request. Thank you to all our contributors!\n\n[![Ultralytics open-source contributors](https://raw.githubusercontent.com/ultralytics/assets/main/im/image-contributors.png)](https://github.com/ultralytics/ultralytics/graphs/contributors)\n" }, { "path": "utils/loggers/clearml/__init__.py", "content": "# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license\n" }, { "path": "utils/loggers/clearml/clearml_utils.py", "content": "# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license\n\"\"\"Main Logger class for ClearML experiment tracking.\"\"\"\n\nimport glob\nimport re\nfrom pathlib import Path\n\nimport numpy as np\nimport yaml\nfrom ultralytics.utils.plotting import Annotator, colors\n\ntry:\n import clearml\n from clearml import Dataset, Task\n\n assert hasattr(clearml, \"__version__\") # verify package import not local dir\nexcept (ImportError, AssertionError):\n clearml = None\n\n\ndef construct_dataset(clearml_info_string):\n \"\"\"Load in a clearml dataset and fill the internal data_dict with its contents.\"\"\"\n dataset_id = clearml_info_string.replace(\"clearml://\", \"\")\n dataset = Dataset.get(dataset_id=dataset_id)\n dataset_root_path = Path(dataset.get_local_copy())\n\n # We'll search for the yaml file definition in the dataset\n yaml_filenames = list(glob.glob(str(dataset_root_path / \"*.yaml\")) + glob.glob(str(dataset_root_path / \"*.yml\")))\n if len(yaml_filenames) > 1:\n raise ValueError(\n \"More than one yaml file was found in the dataset root, cannot determine which one contains \"\n \"the dataset definition this way.\"\n )\n elif not yaml_filenames:\n raise ValueError(\n \"No yaml definition found in dataset root path, check that there is a correct yaml file \"\n \"inside the dataset root path.\"\n )\n with open(yaml_filenames[0]) as f:\n dataset_definition = yaml.safe_load(f)\n\n assert set(dataset_definition.keys()).issuperset({\"train\", \"test\", \"val\", \"nc\", \"names\"}), (\n \"The right keys were not found in the yaml file, make sure it at least has the following keys: ('train', 'test', 'val', 'nc', 'names')\"\n )\n\n data_dict = {\n \"train\": (\n str((dataset_root_path / dataset_definition[\"train\"]).resolve()) if dataset_definition[\"train\"] else None\n )\n }\n data_dict[\"test\"] = (\n str((dataset_root_path / dataset_definition[\"test\"]).resolve()) if dataset_definition[\"test\"] else None\n )\n data_dict[\"val\"] = (\n str((dataset_root_path / dataset_definition[\"val\"]).resolve()) if dataset_definition[\"val\"] else None\n )\n data_dict[\"nc\"] = dataset_definition[\"nc\"]\n data_dict[\"names\"] = dataset_definition[\"names\"]\n\n return data_dict\n\n\nclass ClearmlLogger:\n \"\"\"Log training runs, datasets, models, and predictions to ClearML.\n\n This logger sends information to ClearML at app.clear.ml or to your own hosted server. By default, this information\n includes hyperparameters, system configuration and metrics, model metrics, code information and basic data metrics\n and analyses.\n\n By providing additional command line arguments to train.py, datasets, models and predictions can also be logged.\n \"\"\"\n\n def __init__(self, opt, hyp):\n \"\"\"- Initialize ClearML Task, this object will capture the experiment - Upload dataset version to ClearML Data\n if opt.upload_dataset is True.\n\n Args:\n opt (namespace) -- Commandline arguments for this run: hyp (dict) -- Hyperparameters for this run\n \"\"\"\n self.current_epoch = 0\n # Keep tracked of amount of logged images to enforce a limit\n self.current_epoch_logged_images = set()\n # Maximum number of images to log to clearML per epoch\n self.max_imgs_to_log_per_epoch = 16\n # Get the interval of epochs when bounding box images should be logged\n self.bbox_interval = opt.bbox_interval\n self.clearml = clearml\n self.task = None\n self.data_dict = None\n if self.clearml:\n self.task = Task.init(\n project_name=opt.project if opt.project != \"runs/train\" else \"YOLOv3\",\n task_name=opt.name if opt.name != \"exp\" else \"Training\",\n tags=[\"YOLOv3\"],\n output_uri=True,\n reuse_last_task_id=opt.exist_ok,\n auto_connect_frameworks={\"pytorch\": False},\n # We disconnect pytorch auto-detection, because we added manual model save points in the code\n )\n # ClearML's hooks will already grab all general parameters\n # Only the hyperparameters coming from the yaml config file\n # will have to be added manually!\n self.task.connect(hyp, name=\"Hyperparameters\")\n self.task.connect(opt, name=\"Args\")\n\n # Make sure the code is easily remotely runnable by setting the docker image to use by the remote agent\n self.task.set_base_docker(\n \"ultralytics/yolov5:latest\",\n docker_arguments='--ipc=host -e=\"CLEARML_AGENT_SKIP_PYTHON_ENV_INSTALL=1\"',\n docker_setup_bash_script=\"pip install clearml\",\n )\n\n # Get ClearML Dataset Version if requested\n if opt.data.startswith(\"clearml://\"):\n # data_dict should have the following keys:\n # names, nc (number of classes), test, train, val (all three relative paths to ../datasets)\n self.data_dict = construct_dataset(opt.data)\n # Set data to data_dict because wandb will crash without this information and opt is the best way\n # to give it to them\n opt.data = self.data_dict\n\n def log_debug_samples(self, files, title=\"Debug Samples\"):\n \"\"\"Log files (images) as debug samples in the ClearML task.\n\n Args:\n files (List(PosixPath)) a list of file paths in PosixPath format: title (str) A title that groups together\n images with the same values\n \"\"\"\n for f in files:\n if f.exists():\n it = re.search(r\"_batch(\\d+)\", f.name)\n iteration = int(it.groups()[0]) if it else 0\n self.task.get_logger().report_image(\n title=title, series=f.name.replace(it.group(), \"\"), local_path=str(f), iteration=iteration\n )\n\n def log_image_with_boxes(self, image_path, boxes, class_names, image, conf_threshold=0.25):\n \"\"\"Draw the bounding boxes on a single image and report the result as a ClearML debug sample.\n\n Args:\n image_path (PosixPath) the path the original image file\n boxes (list): list of scaled predictions in the format - [xmin, ymin, xmax, ymax, confidence, class]\n class_names (dict): dict containing mapping of class int to class name\n image (Tensor): A torch tensor containing the actual image data\n \"\"\"\n if (\n len(self.current_epoch_logged_images) < self.max_imgs_to_log_per_epoch\n and self.current_epoch >= 0\n and (self.current_epoch % self.bbox_interval == 0 and image_path not in self.current_epoch_logged_images)\n ):\n im = np.ascontiguousarray(np.moveaxis(image.mul(255).clamp(0, 255).byte().cpu().numpy(), 0, 2))\n annotator = Annotator(im=im, pil=True)\n for i, (conf, class_nr, box) in enumerate(zip(boxes[:, 4], boxes[:, 5], boxes[:, :4])):\n color = colors(i)\n\n class_name = class_names[int(class_nr)]\n confidence_percentage = round(float(conf) * 100, 2)\n label = f\"{class_name}: {confidence_percentage}%\"\n\n if conf > conf_threshold:\n annotator.rectangle(box.cpu().numpy(), outline=color)\n annotator.box_label(box.cpu().numpy(), label=label, color=color)\n\n annotated_image = annotator.result()\n self.task.get_logger().report_image(\n title=\"Bounding Boxes\", series=image_path.name, iteration=self.current_epoch, image=annotated_image\n )\n self.current_epoch_logged_images.add(image_path)\n" }, { "path": "utils/loggers/clearml/hpo.py", "content": "# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license\n\nfrom clearml import Task\n\n# Connecting ClearML with the current process,\n# from here on everything is logged automatically\nfrom clearml.automation import HyperParameterOptimizer, UniformParameterRange\nfrom clearml.automation.optuna import OptimizerOptuna\n\ntask = Task.init(\n project_name=\"Hyper-Parameter Optimization\",\n task_name=\"YOLOv3\",\n task_type=Task.TaskTypes.optimizer,\n reuse_last_task_id=False,\n)\n\n# Example use case:\noptimizer = HyperParameterOptimizer(\n # This is the experiment we want to optimize\n base_task_id=\"\",\n # here we define the hyper-parameters to optimize\n # Notice: The parameter name should exactly match what you see in the UI: /\n # For Example, here we see in the base experiment a section Named: \"General\"\n # under it a parameter named \"batch_size\", this becomes \"General/batch_size\"\n # If you have `argparse` for example, then arguments will appear under the \"Args\" section,\n # and you should instead pass \"Args/batch_size\"\n hyper_parameters=[\n UniformParameterRange(\"Hyperparameters/lr0\", min_value=1e-5, max_value=1e-1),\n UniformParameterRange(\"Hyperparameters/lrf\", min_value=0.01, max_value=1.0),\n UniformParameterRange(\"Hyperparameters/momentum\", min_value=0.6, max_value=0.98),\n UniformParameterRange(\"Hyperparameters/weight_decay\", min_value=0.0, max_value=0.001),\n UniformParameterRange(\"Hyperparameters/warmup_epochs\", min_value=0.0, max_value=5.0),\n UniformParameterRange(\"Hyperparameters/warmup_momentum\", min_value=0.0, max_value=0.95),\n UniformParameterRange(\"Hyperparameters/warmup_bias_lr\", min_value=0.0, max_value=0.2),\n UniformParameterRange(\"Hyperparameters/box\", min_value=0.02, max_value=0.2),\n UniformParameterRange(\"Hyperparameters/cls\", min_value=0.2, max_value=4.0),\n UniformParameterRange(\"Hyperparameters/cls_pw\", min_value=0.5, max_value=2.0),\n UniformParameterRange(\"Hyperparameters/obj\", min_value=0.2, max_value=4.0),\n UniformParameterRange(\"Hyperparameters/obj_pw\", min_value=0.5, max_value=2.0),\n UniformParameterRange(\"Hyperparameters/iou_t\", min_value=0.1, max_value=0.7),\n UniformParameterRange(\"Hyperparameters/anchor_t\", min_value=2.0, max_value=8.0),\n UniformParameterRange(\"Hyperparameters/fl_gamma\", min_value=0.0, max_value=4.0),\n UniformParameterRange(\"Hyperparameters/hsv_h\", min_value=0.0, max_value=0.1),\n UniformParameterRange(\"Hyperparameters/hsv_s\", min_value=0.0, max_value=0.9),\n UniformParameterRange(\"Hyperparameters/hsv_v\", min_value=0.0, max_value=0.9),\n UniformParameterRange(\"Hyperparameters/degrees\", min_value=0.0, max_value=45.0),\n UniformParameterRange(\"Hyperparameters/translate\", min_value=0.0, max_value=0.9),\n UniformParameterRange(\"Hyperparameters/scale\", min_value=0.0, max_value=0.9),\n UniformParameterRange(\"Hyperparameters/shear\", min_value=0.0, max_value=10.0),\n UniformParameterRange(\"Hyperparameters/perspective\", min_value=0.0, max_value=0.001),\n UniformParameterRange(\"Hyperparameters/flipud\", min_value=0.0, max_value=1.0),\n UniformParameterRange(\"Hyperparameters/fliplr\", min_value=0.0, max_value=1.0),\n UniformParameterRange(\"Hyperparameters/mosaic\", min_value=0.0, max_value=1.0),\n UniformParameterRange(\"Hyperparameters/mixup\", min_value=0.0, max_value=1.0),\n UniformParameterRange(\"Hyperparameters/copy_paste\", min_value=0.0, max_value=1.0),\n ],\n # this is the objective metric we want to maximize/minimize\n objective_metric_title=\"metrics\",\n objective_metric_series=\"mAP_0.5\",\n # now we decide if we want to maximize it or minimize it (accuracy we maximize)\n objective_metric_sign=\"max\",\n # let us limit the number of concurrent experiments,\n # this in turn will make sure we do dont bombard the scheduler with experiments.\n # if we have an auto-scaler connected, this, by proxy, will limit the number of machine\n max_number_of_concurrent_tasks=1,\n # this is the optimizer class (actually doing the optimization)\n # Currently, we can choose from GridSearch, RandomSearch or OptimizerBOHB (Bayesian optimization Hyper-Band)\n optimizer_class=OptimizerOptuna,\n # If specified only the top K performing Tasks will be kept, the others will be automatically archived\n save_top_k_tasks_only=5, # 5,\n compute_time_limit=None,\n total_max_jobs=20,\n min_iteration_per_job=None,\n max_iteration_per_job=None,\n)\n\n# report every 10 seconds, this is way too often, but we are testing here\noptimizer.set_report_period(10 / 60)\n# You can also use the line below instead to run all the optimizer tasks locally, without using queues or agent\n# an_optimizer.start_locally(job_complete_callback=job_complete_callback)\n# set the time limit for the optimization process (2 hours)\noptimizer.set_time_limit(in_minutes=120.0)\n# Start the optimization process in the local environment\noptimizer.start_locally()\n# wait until process is done (notice we are controlling the optimization process in the background)\noptimizer.wait()\n# make sure background optimization stopped\noptimizer.stop()\n\nprint(\"We are done, good bye\")\n" }, { "path": "utils/loggers/comet/README.md", "content": "\"Ultralytics\n\n\n\n# YOLOv3 Integration with Comet\n\nThis guide explains how to seamlessly integrate YOLOv3 with [Comet experiment tracking](https://www.comet.com/site/?utm_source=yolov5&utm_medium=partner&utm_campaign=partner_yolov5_2022&utm_content=github) for enhanced experiment management, model optimization, and collaborative workflows.\n\n## ℹ️ About Comet\n\n[Comet](https://www.comet.com/site/?utm_source=yolov5&utm_medium=partner&utm_campaign=partner_yolov5_2022&utm_content=github) is a leading platform for tracking, visualizing, and optimizing machine learning and deep learning experiments. It empowers data scientists, engineers, and teams to:\n\n- Monitor model metrics in real time\n- Save and version hyperparameters, datasets, and model checkpoints\n- Visualize predictions using [Comet Custom Panels](https://www.comet.com/docs/v2/guides/comet-dashboard/code-panels/about-panels/?utm_source=yolov5&utm_medium=partner&utm_campaign=partner_yolov5_2022&utm_content=github)\n- Collaborate and share results efficiently\n\nComet ensures your work is always accessible and simplifies team collaboration.\n\n## 🚀 Getting Started\n\n### Install Comet\n\nInstall Comet using pip:\n\n```shell\npip install comet_ml\n```\n\n### Configure Comet Credentials\n\nYou can set up Comet credentials for YOLOv3 in two ways:\n\n1. **Environment Variables** \n Set your credentials in your environment:\n\n ```shell\n export COMET_API_KEY=YOUR_COMET_API_KEY\n export COMET_PROJECT_NAME=YOUR_COMET_PROJECT_NAME # Defaults to 'yolov3' if not set\n ```\n\n2. **Comet Configuration File** \n Create a `.comet.config` file in your working directory:\n\n ```\n [comet]\n api_key=YOUR_API_KEY\n project_name=YOUR_PROJECT_NAME # Defaults to 'yolov3' if not set\n ```\n\n### Run the Training Script\n\nRun the [Ultralytics training script](https://docs.ultralytics.com/modes/train/) as usual. Comet will automatically integrate with YOLOv3.\n\n```shell\n# Train YOLOv3 on COCO128 for 5 epochs\npython train.py --img 640 --batch 16 --epochs 5 --data coco128.yaml --weights yolov3.pt\n```\n\nComet will automatically log your hyperparameters, command-line arguments, training metrics, and validation metrics. You can analyze your runs in the Comet UI. For more on metrics like mAP and Recall, see the [YOLO Performance Metrics guide](https://docs.ultralytics.com/guides/yolo-performance-metrics/).\n\n\"Comet\n\n## ✨ Try an Example!\n\nExplore a [completed YOLO run in the Comet UI](https://www.comet.com/examples/comet-example-yolov5/a0e29e0e9b984e4a822db2a62d0cb357?experiment-tab=chart&showOutliers=true&smoothing=0&transformY=smoothing&xAxis=step&utm_source=yolov5&utm_medium=partner&utm_campaign=partner_yolov5_2022&utm_content=github).\n\nOr, try it yourself in Colab:\n\n[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/comet-ml/comet-examples/blob/master/integrations/model-training/yolov5/notebooks/Comet_and_YOLOv5.ipynb)\n\n## 📊 Automatic Logging\n\nBy default, Comet logs the following during YOLOv3 training:\n\n### Metrics\n\n- Box Loss, Object Loss, Classification Loss (training and validation)\n- mAP0.5, mAP0.5:0.95 (validation)\n- Precision and Recall (validation)\n\n### Parameters\n\n- All model hyperparameters\n- All command-line options used during training\n\n### Visualizations\n\n- Confusion matrix of model predictions on validation data\n- PR and F1 curves for all classes\n- Correlogram of class labels\n\n## ⚙️ Configure Comet Logging\n\nYou can customize Comet logging using environment variables:\n\n```shell\n# Comet Logging Configuration\nexport COMET_MODE=online # 'online' or 'offline'. Defaults to online.\nexport COMET_MODEL_NAME=YOUR_MODEL_NAME # Name for the saved model. Defaults to yolov3.\nexport COMET_LOG_CONFUSION_MATRIX=false # Disable confusion matrix logging. Defaults to true.\nexport COMET_MAX_IMAGE_UPLOADS=NUMBER # Max prediction images to log. Defaults to 100.\nexport COMET_LOG_PER_CLASS_METRICS=true # Log per-class metrics. Defaults to false.\nexport COMET_DEFAULT_CHECKPOINT_FILENAME=your_checkpoint.pt # Checkpoint for resuming. Defaults to 'last.pt'.\nexport COMET_LOG_BATCH_LEVEL_METRICS=true # Log batch-level metrics. Defaults to false.\nexport COMET_LOG_PREDICTIONS=true # Set to false to disable prediction logging. Defaults to true.\n```\n\n### Logging Checkpoints with Comet\n\nBy default, [model checkpoints](https://docs.ultralytics.com/guides/model-training-tips/#checkpoints) are not uploaded to Comet. Enable checkpoint logging by using the `--save-period` argument:\n\n```shell\npython train.py \\\n --img 640 \\\n --batch 16 \\\n --epochs 5 \\\n --data coco128.yaml \\\n --weights yolov3.pt \\\n --save-period 1 # Save checkpoints every epoch\n```\n\n### Logging Model Predictions\n\nModel predictions (images, ground truth, bounding boxes) are logged to Comet by default. Control frequency with the `--bbox_interval` argument (log every Nth batch per epoch). Visualize predictions using Comet's Object Detection Custom Panel.\n\n**Note:** The YOLOv3 validation dataloader defaults to a batch size of 32. Adjust logging frequency as needed.\n\nSee an [example Comet project using the Object Detection Panel](https://www.comet.com/examples/comet-example-yolov5?shareable=YcwMiJaZSXfcEXpGOHDD12vA1&utm_source=yolov5&utm_medium=partner&utm_campaign=partner_yolov5_2022&utm_content=github).\n\n```shell\npython train.py \\\n --img 640 \\\n --batch 16 \\\n --epochs 5 \\\n --data coco128.yaml \\\n --weights yolov3.pt \\\n --bbox_interval 2 # Log predictions every 2nd batch per epoch\n```\n\n#### Controlling the Number of Prediction Images Logged\n\nComet logs up to 100 validation images by default. Adjust this with the `COMET_MAX_IMAGE_UPLOADS` variable:\n\n```shell\nenv COMET_MAX_IMAGE_UPLOADS=200 python train.py \\\n --img 640 \\\n --batch 16 \\\n --epochs 5 \\\n --data coco128.yaml \\\n --weights yolov3.pt \\\n --bbox_interval 1\n```\n\n#### Logging Class-Level Metrics\n\nEnable per-class mAP, precision, recall, and F1-score logging:\n\n```shell\nenv COMET_LOG_PER_CLASS_METRICS=true python train.py \\\n --img 640 \\\n --batch 16 \\\n --epochs 5 \\\n --data coco128.yaml \\\n --weights yolov3.pt\n```\n\n## 💾 Uploading a Dataset to Comet Artifacts\n\nStore your [datasets](https://docs.ultralytics.com/datasets/) using [Comet Artifacts](https://www.comet.com/docs/v2/guides/data-management/using-artifacts/#learn-more?utm_source=yolov5&utm_medium=partner&utm_campaign=partner_yolov5_2022&utm_content=github) by adding the `--upload_dataset` flag. Ensure your dataset follows the structure in the [Ultralytics dataset guide](https://docs.ultralytics.com/datasets/). The dataset config YAML file must match the format of `coco128.yaml`.\n\n```shell\npython train.py \\\n --img 640 \\\n --batch 16 \\\n --epochs 5 \\\n --data coco128.yaml \\\n --weights yolov3.pt \\\n --upload_dataset # Uploads the dataset specified in coco128.yaml\n```\n\nFind uploaded datasets in the Artifacts tab in your Comet Workspace.\n\"Comet\n\nPreview data directly in the Comet UI.\n\"Comet\n\nArtifacts are versioned and support metadata. Comet automatically logs metadata from your dataset YAML file.\n\"Comet\n\n### Using a Saved Artifact\n\nTo use a dataset stored in Comet Artifacts, update the `path` variable in your dataset YAML file to the Artifact resource URL:\n\n```yaml\n# contents of artifact.yaml\npath: \"comet:///:\"\ntrain: images/train # train images (relative to 'path')\nval: images/val # val images (relative to 'path')\n# ... other dataset configurations\n```\n\nThen, pass this config file to your training script:\n\n```shell\npython train.py \\\n --img 640 \\\n --batch 16 \\\n --epochs 5 \\\n --data artifact.yaml \\\n --weights yolov3.pt\n```\n\nArtifacts enable tracking data lineage throughout your workflow. The graph below shows experiments using the uploaded dataset.\n\"Comet\n\n## ▶️ Resuming a Training Run\n\nIf your training run is interrupted, resume it with the `--resume` flag and the Comet Run Path (`comet:////`). This restores the model state, hyperparameters, arguments, and downloads necessary Comet Artifacts. Logging continues to the same Comet Experiment.\n\n```shell\npython train.py \\\n --resume \"comet://YOUR_WORKSPACE/YOUR_WORKSPACE/EXPERIMENT_ID\"\n```\n\n## 🔍 Hyperparameter Search with the Comet Optimizer\n\nYOLOv3 integrates with Comet's Optimizer for [hyperparameter tuning](https://docs.ultralytics.com/guides/hyperparameter-tuning/) and visualization.\n\n### Configuring an Optimizer Sweep\n\nCreate a JSON config file for the sweep (e.g., `utils/loggers/comet/optimizer_config.json`):\n\n```json\n{\n \"spec\": {\n \"maxCombo\": 10,\n \"objective\": \"minimize\",\n \"metric\": \"metrics/mAP_0.5\",\n \"algorithm\": \"bayes\",\n \"parameters\": {\n \"lr0\": { \"type\": \"float\", \"min\": 0.001, \"max\": 0.01 },\n \"momentum\": { \"type\": \"float\", \"min\": 0.85, \"max\": 0.95 }\n }\n },\n \"name\": \"YOLOv3 Hyperparameter Sweep\",\n \"trials\": 1\n}\n```\n\nRun the sweep with the `hpo.py` script:\n\n```shell\npython utils/loggers/comet/hpo.py \\\n --comet_optimizer_config utils/loggers/comet/optimizer_config.json\n```\n\nThe `hpo.py` script accepts the same arguments as `train.py`. Add any additional arguments as needed:\n\n```shell\npython utils/loggers/comet/hpo.py \\\n --comet_optimizer_config utils/loggers/comet/optimizer_config.json \\\n --save-period 1 \\\n --bbox_interval 1\n```\n\n### Running a Sweep in Parallel\n\nUse the `comet optimizer` command to run the sweep with multiple workers:\n\n```shell\ncomet optimizer -j \\\n utils/loggers/comet/hpo.py NUMBER_OF_WORKERS utils/loggers/comet/optimizer_config.json\n```\n\n### Visualizing Results\n\nComet provides rich visualizations for sweep results. Explore a [project with a completed sweep](https://www.comet.com/examples/comet-example-yolov5/view/PrlArHGuuhDTKC1UuBmTtOSXD/panels?utm_source=yolov5&utm_medium=partner&utm_campaign=partner_yolov5_2022&utm_content=github).\n\n\"Comet\n\n## 🤝 Contributing\n\nContributions to this integration are welcome! See the [Ultralytics Contributing Guide](https://docs.ultralytics.com/help/contributing/) for details on how to get involved. Thank you for helping improve the Ultralytics ecosystem!\n" }, { "path": "utils/loggers/comet/__init__.py", "content": "# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license\n\nimport glob\nimport json\nimport logging\nimport os\nimport sys\nfrom pathlib import Path\n\nlogger = logging.getLogger(__name__)\n\nFILE = Path(__file__).resolve()\nROOT = FILE.parents[3] # YOLOv3 root directory\nif str(ROOT) not in sys.path:\n sys.path.append(str(ROOT)) # add ROOT to PATH\n\ntry:\n import comet_ml\n\n # Project Configuration\n config = comet_ml.config.get_config()\n COMET_PROJECT_NAME = config.get_string(os.getenv(\"COMET_PROJECT_NAME\"), \"comet.project_name\", default=\"yolov5\")\nexcept ImportError:\n comet_ml = None\n COMET_PROJECT_NAME = None\n\nimport PIL\nimport torch\nimport torchvision.transforms as T\nimport yaml\n\nfrom utils.dataloaders import img2label_paths\nfrom utils.general import check_dataset, scale_boxes, xywh2xyxy\nfrom utils.metrics import box_iou\n\nCOMET_PREFIX = \"comet://\"\n\nCOMET_MODE = os.getenv(\"COMET_MODE\", \"online\")\n\n# Model Saving Settings\nCOMET_MODEL_NAME = os.getenv(\"COMET_MODEL_NAME\", \"yolov5\")\n\n# Dataset Artifact Settings\nCOMET_UPLOAD_DATASET = os.getenv(\"COMET_UPLOAD_DATASET\", \"false\").lower() == \"true\"\n\n# Evaluation Settings\nCOMET_LOG_CONFUSION_MATRIX = os.getenv(\"COMET_LOG_CONFUSION_MATRIX\", \"true\").lower() == \"true\"\nCOMET_LOG_PREDICTIONS = os.getenv(\"COMET_LOG_PREDICTIONS\", \"true\").lower() == \"true\"\nCOMET_MAX_IMAGE_UPLOADS = int(os.getenv(\"COMET_MAX_IMAGE_UPLOADS\", 100))\n\n# Confusion Matrix Settings\nCONF_THRES = float(os.getenv(\"CONF_THRES\", 0.001))\nIOU_THRES = float(os.getenv(\"IOU_THRES\", 0.6))\n\n# Batch Logging Settings\nCOMET_LOG_BATCH_METRICS = os.getenv(\"COMET_LOG_BATCH_METRICS\", \"false\").lower() == \"true\"\nCOMET_BATCH_LOGGING_INTERVAL = os.getenv(\"COMET_BATCH_LOGGING_INTERVAL\", 1)\nCOMET_PREDICTION_LOGGING_INTERVAL = os.getenv(\"COMET_PREDICTION_LOGGING_INTERVAL\", 1)\nCOMET_LOG_PER_CLASS_METRICS = os.getenv(\"COMET_LOG_PER_CLASS_METRICS\", \"false\").lower() == \"true\"\n\nRANK = int(os.getenv(\"RANK\", -1))\n\nto_pil = T.ToPILImage()\n\n\nclass CometLogger:\n \"\"\"Log metrics, parameters, source code, models and much more with Comet.\"\"\"\n\n def __init__(self, opt, hyp, run_id=None, job_type=\"Training\", **experiment_kwargs) -> None:\n \"\"\"Initialize the CometLogger instance with experiment configurations and hyperparameters for logging.\"\"\"\n self.job_type = job_type\n self.opt = opt\n self.hyp = hyp\n\n # Comet Flags\n self.comet_mode = COMET_MODE\n\n self.save_model = opt.save_period > -1\n self.model_name = COMET_MODEL_NAME\n\n # Batch Logging Settings\n self.log_batch_metrics = COMET_LOG_BATCH_METRICS\n self.comet_log_batch_interval = COMET_BATCH_LOGGING_INTERVAL\n\n # Dataset Artifact Settings\n self.upload_dataset = self.opt.upload_dataset or COMET_UPLOAD_DATASET\n self.resume = self.opt.resume\n\n self.default_experiment_kwargs = {\n \"log_code\": False,\n \"log_env_gpu\": True,\n \"log_env_cpu\": True,\n \"project_name\": COMET_PROJECT_NAME,\n } | experiment_kwargs\n self.experiment = self._get_experiment(self.comet_mode, run_id)\n self.experiment.set_name(self.opt.name)\n\n self.data_dict = self.check_dataset(self.opt.data)\n self.class_names = self.data_dict[\"names\"]\n self.num_classes = self.data_dict[\"nc\"]\n\n self.logged_images_count = 0\n self.max_images = COMET_MAX_IMAGE_UPLOADS\n\n if run_id is None:\n self.experiment.log_other(\"Created from\", \"YOLOv3\")\n if not isinstance(self.experiment, comet_ml.OfflineExperiment):\n workspace, project_name, experiment_id = self.experiment.url.split(\"/\")[-3:]\n self.experiment.log_other(\n \"Run Path\",\n f\"{workspace}/{project_name}/{experiment_id}\",\n )\n self.log_parameters(vars(opt))\n self.log_parameters(self.opt.hyp)\n self.log_asset_data(\n self.opt.hyp,\n name=\"hyperparameters.json\",\n metadata={\"type\": \"hyp-config-file\"},\n )\n self.log_asset(\n f\"{self.opt.save_dir}/opt.yaml\",\n metadata={\"type\": \"opt-config-file\"},\n )\n\n self.comet_log_confusion_matrix = COMET_LOG_CONFUSION_MATRIX\n\n if hasattr(self.opt, \"conf_thres\"):\n self.conf_thres = self.opt.conf_thres\n else:\n self.conf_thres = CONF_THRES\n if hasattr(self.opt, \"iou_thres\"):\n self.iou_thres = self.opt.iou_thres\n else:\n self.iou_thres = IOU_THRES\n\n self.log_parameters({\"val_iou_threshold\": self.iou_thres, \"val_conf_threshold\": self.conf_thres})\n\n self.comet_log_predictions = COMET_LOG_PREDICTIONS\n if self.opt.bbox_interval == -1:\n self.comet_log_prediction_interval = 1 if self.opt.epochs < 10 else self.opt.epochs // 10\n else:\n self.comet_log_prediction_interval = self.opt.bbox_interval\n\n if self.comet_log_predictions:\n self.metadata_dict = {}\n self.logged_image_names = []\n\n self.comet_log_per_class_metrics = COMET_LOG_PER_CLASS_METRICS\n\n self.experiment.log_others(\n {\n \"comet_mode\": COMET_MODE,\n \"comet_max_image_uploads\": COMET_MAX_IMAGE_UPLOADS,\n \"comet_log_per_class_metrics\": COMET_LOG_PER_CLASS_METRICS,\n \"comet_log_batch_metrics\": COMET_LOG_BATCH_METRICS,\n \"comet_log_confusion_matrix\": COMET_LOG_CONFUSION_MATRIX,\n \"comet_model_name\": COMET_MODEL_NAME,\n }\n )\n\n # Check if running the Experiment with the Comet Optimizer\n if hasattr(self.opt, \"comet_optimizer_id\"):\n self.experiment.log_other(\"optimizer_id\", self.opt.comet_optimizer_id)\n self.experiment.log_other(\"optimizer_objective\", self.opt.comet_optimizer_objective)\n self.experiment.log_other(\"optimizer_metric\", self.opt.comet_optimizer_metric)\n self.experiment.log_other(\"optimizer_parameters\", json.dumps(self.hyp))\n\n def _get_experiment(self, mode, experiment_id=None):\n \"\"\"Returns a comet_ml Experiment object, either online or offline, existing or new, based on mode and\n experiment_id.\n \"\"\"\n if mode == \"offline\":\n return (\n comet_ml.ExistingOfflineExperiment(\n previous_experiment=experiment_id,\n **self.default_experiment_kwargs,\n )\n if experiment_id is not None\n else comet_ml.OfflineExperiment(\n **self.default_experiment_kwargs,\n )\n )\n try:\n if experiment_id is not None:\n return comet_ml.ExistingExperiment(\n previous_experiment=experiment_id,\n **self.default_experiment_kwargs,\n )\n\n return comet_ml.Experiment(**self.default_experiment_kwargs)\n\n except ValueError:\n logger.warning(\n \"COMET WARNING: \"\n \"Comet credentials have not been set. \"\n \"Comet will default to offline logging. \"\n \"Please set your credentials to enable online logging.\"\n )\n return self._get_experiment(\"offline\", experiment_id)\n\n return\n\n def log_metrics(self, log_dict, **kwargs):\n \"\"\"Logs metrics to the current experiment using a dictionary of metric names and values.\"\"\"\n self.experiment.log_metrics(log_dict, **kwargs)\n\n def log_parameters(self, log_dict, **kwargs):\n \"\"\"Logs parameters to the current experiment using a dictionary of parameter names and values.\"\"\"\n self.experiment.log_parameters(log_dict, **kwargs)\n\n def log_asset(self, asset_path, **kwargs):\n \"\"\"Logs a file or directory at `asset_path` to the current experiment, supporting additional `kwargs`.\"\"\"\n self.experiment.log_asset(asset_path, **kwargs)\n\n def log_asset_data(self, asset, **kwargs):\n \"\"\"Logs binary asset data to the current experiment, supporting additional `kwargs`.\"\"\"\n self.experiment.log_asset_data(asset, **kwargs)\n\n def log_image(self, img, **kwargs):\n \"\"\"Logs an image to the current experiment with optional `kwargs` for additional parameters.\"\"\"\n self.experiment.log_image(img, **kwargs)\n\n def log_model(self, path, opt, epoch, fitness_score, best_model=False):\n \"\"\"Logs a model's state at a given epoch, fitness, and optionality as best, requiring path, options, epoch, and\n fitness score.\n \"\"\"\n if not self.save_model:\n return\n\n model_metadata = {\n \"fitness_score\": fitness_score[-1],\n \"epochs_trained\": epoch + 1,\n \"save_period\": opt.save_period,\n \"total_epochs\": opt.epochs,\n }\n\n model_files = glob.glob(f\"{path}/*.pt\")\n for model_path in model_files:\n name = Path(model_path).name\n\n self.experiment.log_model(\n self.model_name,\n file_or_folder=model_path,\n file_name=name,\n metadata=model_metadata,\n overwrite=True,\n )\n\n def check_dataset(self, data_file):\n \"\"\"Loads and validates the dataset configuration from a YAML file.\"\"\"\n with open(data_file) as f:\n data_config = yaml.safe_load(f)\n\n path = data_config.get(\"path\")\n if path and path.startswith(COMET_PREFIX):\n path = data_config[\"path\"].replace(COMET_PREFIX, \"\")\n return self.download_dataset_artifact(path)\n self.log_asset(self.opt.data, metadata={\"type\": \"data-config-file\"})\n\n return check_dataset(data_file)\n\n def log_predictions(self, image, labelsn, path, shape, predn):\n \"\"\"Logs filtered predictions with IoU above a threshold, discarding if max image log count reached.\"\"\"\n if self.logged_images_count >= self.max_images:\n return\n detections = predn[predn[:, 4] > self.conf_thres]\n iou = box_iou(labelsn[:, 1:], detections[:, :4])\n mask, _ = torch.where(iou > self.iou_thres)\n if len(mask) == 0:\n return\n\n filtered_detections = detections[mask]\n filtered_labels = labelsn[mask]\n\n image_id = path.split(\"/\")[-1].split(\".\")[0]\n image_name = f\"{image_id}_curr_epoch_{self.experiment.curr_epoch}\"\n if image_name not in self.logged_image_names:\n native_scale_image = PIL.Image.open(path)\n self.log_image(native_scale_image, name=image_name)\n self.logged_image_names.append(image_name)\n\n metadata = [\n {\n \"label\": f\"{self.class_names[int(cls)]}-gt\",\n \"score\": 100,\n \"box\": {\"x\": xyxy[0], \"y\": xyxy[1], \"x2\": xyxy[2], \"y2\": xyxy[3]},\n }\n for cls, *xyxy in filtered_labels.tolist()\n ]\n metadata.extend(\n {\n \"label\": f\"{self.class_names[int(cls)]}\",\n \"score\": conf * 100,\n \"box\": {\"x\": xyxy[0], \"y\": xyxy[1], \"x2\": xyxy[2], \"y2\": xyxy[3]},\n }\n for *xyxy, conf, cls in filtered_detections.tolist()\n )\n self.metadata_dict[image_name] = metadata\n self.logged_images_count += 1\n\n return\n\n def preprocess_prediction(self, image, labels, shape, pred):\n \"\"\"Preprocesses predictions by adjusting label and prediction shapes; `image`: input image, `labels`: true\n labels, `shape`: image shape, `pred`: model predictions.\n \"\"\"\n nl, _ = labels.shape[0], pred.shape[0]\n\n # Predictions\n if self.opt.single_cls:\n pred[:, 5] = 0\n\n predn = pred.clone()\n scale_boxes(image.shape[1:], predn[:, :4], shape[0], shape[1])\n\n labelsn = None\n if nl:\n tbox = xywh2xyxy(labels[:, 1:5]) # target boxes\n scale_boxes(image.shape[1:], tbox, shape[0], shape[1]) # native-space labels\n labelsn = torch.cat((labels[:, 0:1], tbox), 1) # native-space labels\n scale_boxes(image.shape[1:], predn[:, :4], shape[0], shape[1]) # native-space pred\n\n return predn, labelsn\n\n def add_assets_to_artifact(self, artifact, path, asset_path, split):\n \"\"\"Adds asset images and labels from `asset_path` to `artifact` by `split`, ensuring paths are sorted.\"\"\"\n img_paths = sorted(glob.glob(f\"{asset_path}/*\"))\n label_paths = img2label_paths(img_paths)\n\n for image_file, label_file in zip(img_paths, label_paths):\n image_logical_path, label_logical_path = map(lambda x: os.path.relpath(x, path), [image_file, label_file])\n\n try:\n artifact.add(\n image_file,\n logical_path=image_logical_path,\n metadata={\"split\": split},\n )\n artifact.add(\n label_file,\n logical_path=label_logical_path,\n metadata={\"split\": split},\n )\n except ValueError as e:\n logger.error(\"COMET ERROR: Error adding file to Artifact. Skipping file.\")\n logger.error(f\"COMET ERROR: {e}\")\n continue\n\n return artifact\n\n def upload_dataset_artifact(self):\n \"\"\"Uploads dataset to Comet as an artifact with optional custom dataset name, defaulting to 'yolov5-dataset'.\"\"\"\n dataset_name = self.data_dict.get(\"dataset_name\", \"yolov5-dataset\")\n path = str((ROOT / Path(self.data_dict[\"path\"])).resolve())\n\n metadata = self.data_dict.copy()\n for key in [\"train\", \"val\", \"test\"]:\n split_path = metadata.get(key)\n if split_path is not None:\n metadata[key] = split_path.replace(path, \"\")\n\n artifact = comet_ml.Artifact(name=dataset_name, artifact_type=\"dataset\", metadata=metadata)\n for key in metadata.keys():\n if key in [\"train\", \"val\", \"test\"]:\n if isinstance(self.upload_dataset, str) and (key != self.upload_dataset):\n continue\n\n asset_path = self.data_dict.get(key)\n if asset_path is not None:\n artifact = self.add_assets_to_artifact(artifact, path, asset_path, key)\n\n self.experiment.log_artifact(artifact)\n\n return\n\n def download_dataset_artifact(self, artifact_path):\n \"\"\"Downloads a dataset artifact to a specified directory, given its path.\"\"\"\n logged_artifact = self.experiment.get_artifact(artifact_path)\n artifact_save_dir = str(Path(self.opt.save_dir) / logged_artifact.name)\n logged_artifact.download(artifact_save_dir)\n\n metadata = logged_artifact.metadata\n data_dict = metadata.copy()\n data_dict[\"path\"] = artifact_save_dir\n\n metadata_names = metadata.get(\"names\")\n if isinstance(metadata_names, dict):\n data_dict[\"names\"] = {int(k): v for k, v in metadata.get(\"names\").items()}\n elif isinstance(metadata_names, list):\n data_dict[\"names\"] = {int(k): v for k, v in zip(range(len(metadata_names)), metadata_names)}\n else:\n raise \"Invalid 'names' field in dataset yaml file. Please use a list or dictionary\"\n\n return self.update_data_paths(data_dict)\n\n def update_data_paths(self, data_dict):\n \"\"\"Updates 'path' in data_dict with provided path, returning modified data_dict.\"\"\"\n path = data_dict.get(\"path\", \"\")\n\n for split in [\"train\", \"val\", \"test\"]:\n if data_dict.get(split):\n split_path = data_dict.get(split)\n data_dict[split] = (\n f\"{path}/{split_path}\" if isinstance(split, str) else [f\"{path}/{x}\" for x in split_path]\n )\n\n return data_dict\n\n def on_pretrain_routine_end(self, paths):\n \"\"\"Called at the end of the pretraining routine to handle paths modification if `opt.resume` is False.\"\"\"\n if self.opt.resume:\n return\n\n for path in paths:\n self.log_asset(str(path))\n\n if self.upload_dataset and not self.resume:\n self.upload_dataset_artifact()\n\n return\n\n def on_train_start(self):\n \"\"\"Logs hyperparameter settings at the start of training.\"\"\"\n self.log_parameters(self.hyp)\n\n def on_train_epoch_start(self):\n \"\"\"Callback function executed at the start of each training epoch.\"\"\"\n return\n\n def on_train_epoch_end(self, epoch):\n \"\"\"Callback function executed at the end of each training epoch, updates current epoch in experiment.\"\"\"\n self.experiment.curr_epoch = epoch\n\n return\n\n def on_train_batch_start(self):\n \"\"\"Callback executed at the start of each training batch without inputs or modifications.\"\"\"\n return\n\n def on_train_batch_end(self, log_dict, step):\n \"\"\"Callback after training batch ends; updates step and logs metrics if conditions met.\"\"\"\n self.experiment.curr_step = step\n if self.log_batch_metrics and (step % self.comet_log_batch_interval == 0):\n self.log_metrics(log_dict, step=step)\n\n return\n\n def on_train_end(self, files, save_dir, last, best, epoch, results):\n \"\"\"Callback at training end; logs image metadata to Comet if comet_log_predictions is True.\"\"\"\n if self.comet_log_predictions:\n curr_epoch = self.experiment.curr_epoch\n self.experiment.log_asset_data(self.metadata_dict, \"image-metadata.json\", epoch=curr_epoch)\n\n for f in files:\n self.log_asset(f, metadata={\"epoch\": epoch})\n self.log_asset(f\"{save_dir}/results.csv\", metadata={\"epoch\": epoch})\n\n if not self.opt.evolve:\n model_path = str(best if best.exists() else last)\n name = Path(model_path).name\n if self.save_model:\n self.experiment.log_model(\n self.model_name,\n file_or_folder=model_path,\n file_name=name,\n overwrite=True,\n )\n\n # Check if running Experiment with Comet Optimizer\n if hasattr(self.opt, \"comet_optimizer_id\"):\n metric = results.get(self.opt.comet_optimizer_metric)\n self.experiment.log_other(\"optimizer_metric_value\", metric)\n\n self.finish_run()\n\n def on_val_start(self):\n \"\"\"Prepares environment for validation phase.\"\"\"\n return\n\n def on_val_batch_start(self):\n \"\"\"Called at the start of each validation batch to prepare the batch environment.\"\"\"\n return\n\n def on_val_batch_end(self, batch_i, images, targets, paths, shapes, outputs):\n \"\"\"Handles end of validation batch, optionally logs predictions to Comet.ml if conditions met.\"\"\"\n if not (self.comet_log_predictions and ((batch_i + 1) % self.comet_log_prediction_interval == 0)):\n return\n\n for si, pred in enumerate(outputs):\n if len(pred) == 0:\n continue\n\n image = images[si]\n labels = targets[targets[:, 0] == si, 1:]\n shape = shapes[si]\n path = paths[si]\n predn, labelsn = self.preprocess_prediction(image, labels, shape, pred)\n if labelsn is not None:\n self.log_predictions(image, labelsn, path, shape, predn)\n\n return\n\n def on_val_end(self, nt, tp, fp, p, r, f1, ap, ap50, ap_class, confusion_matrix):\n \"\"\"Logs per-class metric stats to Comet.ml at validation end; requires class-wise tp, fp, nt, p, r, f1, ap,\n ap50, ap_class, confusion_matrix.\n \"\"\"\n if self.comet_log_per_class_metrics and self.num_classes > 1:\n for i, c in enumerate(ap_class):\n class_name = self.class_names[c]\n self.experiment.log_metrics(\n {\n \"mAP@.5\": ap50[i],\n \"mAP@.5:.95\": ap[i],\n \"precision\": p[i],\n \"recall\": r[i],\n \"f1\": f1[i],\n \"true_positives\": tp[i],\n \"false_positives\": fp[i],\n \"support\": nt[c],\n },\n prefix=class_name,\n )\n\n if self.comet_log_confusion_matrix:\n epoch = self.experiment.curr_epoch\n class_names = list(self.class_names.values())\n class_names.append(\"background\")\n num_classes = len(class_names)\n\n self.experiment.log_confusion_matrix(\n matrix=confusion_matrix.matrix,\n max_categories=num_classes,\n labels=class_names,\n epoch=epoch,\n column_label=\"Actual Category\",\n row_label=\"Predicted Category\",\n file_name=f\"confusion-matrix-epoch-{epoch}.json\",\n )\n\n def on_fit_epoch_end(self, result, epoch):\n \"\"\"Logs metrics at the end of each training epoch with provided result and epoch number.\"\"\"\n self.log_metrics(result, epoch=epoch)\n\n def on_model_save(self, last, epoch, final_epoch, best_fitness, fi):\n \"\"\"Logs and saves model periodically if conditions met, excluding final epoch unless best fitness achieved.\"\"\"\n if ((epoch + 1) % self.opt.save_period == 0 and not final_epoch) and self.opt.save_period != -1:\n self.log_model(last.parent, self.opt, epoch, fi, best_model=best_fitness == fi)\n\n def on_params_update(self, params):\n \"\"\"Updates and logs model parameters.\"\"\"\n self.log_parameters(params)\n\n def finish_run(self):\n \"\"\"Terminates the current experiment and performs necessary cleanup operations.\"\"\"\n self.experiment.end()\n" }, { "path": "utils/loggers/comet/comet_utils.py", "content": "# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license\n\nimport logging\nimport os\nfrom urllib.parse import urlparse\n\ntry:\n import comet_ml\nexcept ImportError:\n comet_ml = None\n\nimport yaml\n\nlogger = logging.getLogger(__name__)\n\nCOMET_PREFIX = \"comet://\"\nCOMET_MODEL_NAME = os.getenv(\"COMET_MODEL_NAME\", \"yolov5\")\nCOMET_DEFAULT_CHECKPOINT_FILENAME = os.getenv(\"COMET_DEFAULT_CHECKPOINT_FILENAME\", \"last.pt\")\n\n\ndef download_model_checkpoint(opt, experiment):\n \"\"\"Downloads the model checkpoint from Comet ML; updates `opt.weights` with the downloaded file path.\"\"\"\n model_dir = f\"{opt.project}/{experiment.name}\"\n os.makedirs(model_dir, exist_ok=True)\n\n model_name = COMET_MODEL_NAME\n model_asset_list = experiment.get_model_asset_list(model_name)\n\n if len(model_asset_list) == 0:\n logger.error(f\"COMET ERROR: No checkpoints found for model name : {model_name}\")\n return\n\n model_asset_list = sorted(\n model_asset_list,\n key=lambda x: x[\"step\"],\n reverse=True,\n )\n logged_checkpoint_map = {asset[\"fileName\"]: asset[\"assetId\"] for asset in model_asset_list}\n\n resource_url = urlparse(opt.weights)\n checkpoint_filename = resource_url.query\n\n if checkpoint_filename:\n asset_id = logged_checkpoint_map.get(checkpoint_filename)\n else:\n asset_id = logged_checkpoint_map.get(COMET_DEFAULT_CHECKPOINT_FILENAME)\n checkpoint_filename = COMET_DEFAULT_CHECKPOINT_FILENAME\n\n if asset_id is None:\n logger.error(f\"COMET ERROR: Checkpoint {checkpoint_filename} not found in the given Experiment\")\n return\n\n try:\n logger.info(f\"COMET INFO: Downloading checkpoint {checkpoint_filename}\")\n asset_filename = checkpoint_filename\n\n model_binary = experiment.get_asset(asset_id, return_type=\"binary\", stream=False)\n model_download_path = f\"{model_dir}/{asset_filename}\"\n with open(model_download_path, \"wb\") as f:\n f.write(model_binary)\n\n opt.weights = model_download_path\n\n except Exception as e:\n logger.warning(\"COMET WARNING: Unable to download checkpoint from Comet\")\n logger.exception(e)\n\n\ndef set_opt_parameters(opt, experiment):\n \"\"\"Update the opts Namespace with parameters from Comet's ExistingExperiment when resuming a run.\n\n Args:\n opt (argparse.Namespace): Namespace of command line options\n experiment (comet_ml.APIExperiment): Comet API Experiment object\n \"\"\"\n asset_list = experiment.get_asset_list()\n resume_string = opt.resume\n\n for asset in asset_list:\n if asset[\"fileName\"] == \"opt.yaml\":\n asset_id = asset[\"assetId\"]\n asset_binary = experiment.get_asset(asset_id, return_type=\"binary\", stream=False)\n opt_dict = yaml.safe_load(asset_binary)\n for key, value in opt_dict.items():\n setattr(opt, key, value)\n opt.resume = resume_string\n\n # Save hyperparameters to YAML file\n # Necessary to pass checks in training script\n save_dir = f\"{opt.project}/{experiment.name}\"\n os.makedirs(save_dir, exist_ok=True)\n\n hyp_yaml_path = f\"{save_dir}/hyp.yaml\"\n with open(hyp_yaml_path, \"w\") as f:\n yaml.dump(opt.hyp, f)\n opt.hyp = hyp_yaml_path\n\n\ndef check_comet_weights(opt):\n \"\"\"Downloads model weights from Comet and updates the weights path to point to saved weights location.\n\n Args:\n opt (argparse.Namespace): Command Line arguments passed to YOLOv3 training script\n\n Returns:\n None/bool: Return True if weights are successfully downloaded else return None\n \"\"\"\n if comet_ml is None:\n return\n\n if isinstance(opt.weights, str) and opt.weights.startswith(COMET_PREFIX):\n api = comet_ml.API()\n resource = urlparse(opt.weights)\n experiment_path = f\"{resource.netloc}{resource.path}\"\n experiment = api.get(experiment_path)\n download_model_checkpoint(opt, experiment)\n return True\n\n return None\n\n\ndef check_comet_resume(opt):\n \"\"\"Restores run parameters to its original state based on the model checkpoint and logged Experiment parameters.\n\n Args:\n opt (argparse.Namespace): Command Line arguments passed to YOLOv3 training script\n\n Returns:\n None/bool: Return True if the run is restored successfully else return None\n \"\"\"\n if comet_ml is None:\n return\n\n if isinstance(opt.resume, str) and opt.resume.startswith(COMET_PREFIX):\n api = comet_ml.API()\n resource = urlparse(opt.resume)\n experiment_path = f\"{resource.netloc}{resource.path}\"\n experiment = api.get(experiment_path)\n set_opt_parameters(opt, experiment)\n download_model_checkpoint(opt, experiment)\n\n return True\n\n return None\n" }, { "path": "utils/loggers/comet/hpo.py", "content": "# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license\n\nimport argparse\nimport json\nimport logging\nimport os\nimport sys\nfrom pathlib import Path\n\nimport comet_ml\n\nlogger = logging.getLogger(__name__)\n\nFILE = Path(__file__).resolve()\nROOT = FILE.parents[3] # YOLOv3 root directory\nif str(ROOT) not in sys.path:\n sys.path.append(str(ROOT)) # add ROOT to PATH\n\nfrom train import train\nfrom utils.callbacks import Callbacks\nfrom utils.general import increment_path\nfrom utils.torch_utils import select_device\n\n# Project Configuration\nconfig = comet_ml.config.get_config()\nCOMET_PROJECT_NAME = config.get_string(os.getenv(\"COMET_PROJECT_NAME\"), \"comet.project_name\", default=\"yolov5\")\n\n\ndef get_args(known=False):\n \"\"\"Parses command line arguments for configuring training options, supporting Comet and W&B integrations.\"\"\"\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--weights\", type=str, default=ROOT / \"yolov3-tiny.pt\", help=\"initial weights path\")\n parser.add_argument(\"--cfg\", type=str, default=\"\", help=\"model.yaml path\")\n parser.add_argument(\"--data\", type=str, default=ROOT / \"data/coco128.yaml\", help=\"dataset.yaml path\")\n parser.add_argument(\"--hyp\", type=str, default=ROOT / \"data/hyps/hyp.scratch-low.yaml\", help=\"hyperparameters path\")\n parser.add_argument(\"--epochs\", type=int, default=300, help=\"total training epochs\")\n parser.add_argument(\"--batch-size\", type=int, default=16, help=\"total batch size for all GPUs, -1 for autobatch\")\n parser.add_argument(\"--imgsz\", \"--img\", \"--img-size\", type=int, default=640, help=\"train, val image size (pixels)\")\n parser.add_argument(\"--rect\", action=\"store_true\", help=\"rectangular training\")\n parser.add_argument(\"--resume\", nargs=\"?\", const=True, default=False, help=\"resume most recent training\")\n parser.add_argument(\"--nosave\", action=\"store_true\", help=\"only save final checkpoint\")\n parser.add_argument(\"--noval\", action=\"store_true\", help=\"only validate final epoch\")\n parser.add_argument(\"--noautoanchor\", action=\"store_true\", help=\"disable AutoAnchor\")\n parser.add_argument(\"--noplots\", action=\"store_true\", help=\"save no plot files\")\n parser.add_argument(\"--evolve\", type=int, nargs=\"?\", const=300, help=\"evolve hyperparameters for x generations\")\n parser.add_argument(\"--bucket\", type=str, default=\"\", help=\"gsutil bucket\")\n parser.add_argument(\"--cache\", type=str, nargs=\"?\", const=\"ram\", help='--cache images in \"ram\" (default) or \"disk\"')\n parser.add_argument(\"--image-weights\", action=\"store_true\", help=\"use weighted image selection for training\")\n parser.add_argument(\"--device\", default=\"\", help=\"cuda device, i.e. 0 or 0,1,2,3 or cpu\")\n parser.add_argument(\"--multi-scale\", action=\"store_true\", help=\"vary img-size +/- 50%%\")\n parser.add_argument(\"--single-cls\", action=\"store_true\", help=\"train multi-class data as single-class\")\n parser.add_argument(\"--optimizer\", type=str, choices=[\"SGD\", \"Adam\", \"AdamW\"], default=\"SGD\", help=\"optimizer\")\n parser.add_argument(\"--sync-bn\", action=\"store_true\", help=\"use SyncBatchNorm, only available in DDP mode\")\n parser.add_argument(\"--workers\", type=int, default=8, help=\"max dataloader workers (per RANK in DDP mode)\")\n parser.add_argument(\"--project\", default=ROOT / \"runs/train\", help=\"save to project/name\")\n parser.add_argument(\"--name\", default=\"exp\", help=\"save to project/name\")\n parser.add_argument(\"--exist-ok\", action=\"store_true\", help=\"existing project/name ok, do not increment\")\n parser.add_argument(\"--quad\", action=\"store_true\", help=\"quad dataloader\")\n parser.add_argument(\"--cos-lr\", action=\"store_true\", help=\"cosine LR scheduler\")\n parser.add_argument(\"--label-smoothing\", type=float, default=0.0, help=\"Label smoothing epsilon\")\n parser.add_argument(\"--patience\", type=int, default=100, help=\"EarlyStopping patience (epochs without improvement)\")\n parser.add_argument(\"--freeze\", nargs=\"+\", type=int, default=[0], help=\"Freeze layers: backbone=10, first3=0 1 2\")\n parser.add_argument(\"--save-period\", type=int, default=-1, help=\"Save checkpoint every x epochs (disabled if < 1)\")\n parser.add_argument(\"--seed\", type=int, default=0, help=\"Global training seed\")\n parser.add_argument(\"--local_rank\", type=int, default=-1, help=\"Automatic DDP Multi-GPU argument, do not modify\")\n\n # Weights & Biases arguments\n parser.add_argument(\"--entity\", default=None, help=\"W&B: Entity\")\n parser.add_argument(\"--upload_dataset\", nargs=\"?\", const=True, default=False, help='W&B: Upload data, \"val\" option')\n parser.add_argument(\"--bbox_interval\", type=int, default=-1, help=\"W&B: Set bounding-box image logging interval\")\n parser.add_argument(\"--artifact_alias\", type=str, default=\"latest\", help=\"W&B: Version of dataset artifact to use\")\n\n # Comet Arguments\n parser.add_argument(\"--comet_optimizer_config\", type=str, help=\"Comet: Path to a Comet Optimizer Config File.\")\n parser.add_argument(\"--comet_optimizer_id\", type=str, help=\"Comet: ID of the Comet Optimizer sweep.\")\n parser.add_argument(\"--comet_optimizer_objective\", type=str, help=\"Comet: Set to 'minimize' or 'maximize'.\")\n parser.add_argument(\"--comet_optimizer_metric\", type=str, help=\"Comet: Metric to Optimize.\")\n parser.add_argument(\n \"--comet_optimizer_workers\",\n type=int,\n default=1,\n help=\"Comet: Number of Parallel Workers to use with the Comet Optimizer.\",\n )\n\n return parser.parse_known_args()[0] if known else parser.parse_args()\n\n\ndef run(parameters, opt):\n \"\"\"Executes training process with given hyperparameters and options, handling device selection and callback\n initialization.\n \"\"\"\n hyp_dict = {k: v for k, v in parameters.items() if k not in [\"epochs\", \"batch_size\"]}\n\n opt.save_dir = str(increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok or opt.evolve))\n opt.batch_size = parameters.get(\"batch_size\")\n opt.epochs = parameters.get(\"epochs\")\n\n device = select_device(opt.device, batch_size=opt.batch_size)\n train(hyp_dict, opt, device, callbacks=Callbacks())\n\n\nif __name__ == \"__main__\":\n opt = get_args(known=True)\n\n opt.weights = str(opt.weights)\n opt.cfg = str(opt.cfg)\n opt.data = str(opt.data)\n opt.project = str(opt.project)\n\n optimizer_id = os.getenv(\"COMET_OPTIMIZER_ID\")\n if optimizer_id is None:\n with open(opt.comet_optimizer_config) as f:\n optimizer_config = json.load(f)\n optimizer = comet_ml.Optimizer(optimizer_config)\n else:\n optimizer = comet_ml.Optimizer(optimizer_id)\n\n opt.comet_optimizer_id = optimizer.id\n status = optimizer.status()\n\n opt.comet_optimizer_objective = status[\"spec\"][\"objective\"]\n opt.comet_optimizer_metric = status[\"spec\"][\"metric\"]\n\n logger.info(\"COMET INFO: Starting Hyperparameter Sweep\")\n for parameter in optimizer.get_parameters():\n run(parameter[\"parameters\"], opt)\n" }, { "path": "utils/loggers/wandb/__init__.py", "content": "# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license\n" }, { "path": "utils/loggers/wandb/wandb_utils.py", "content": "# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license\n\n# WARNING ⚠️ wandb is deprecated and will be removed in future release.\n# See supported integrations at https://github.com/ultralytics/yolov5#integrations\n\nimport logging\nimport os\nimport sys\nfrom contextlib import contextmanager\nfrom pathlib import Path\n\nfrom utils.general import LOGGER, colorstr\n\nFILE = Path(__file__).resolve()\nROOT = FILE.parents[3] # YOLOv3 root directory\nif str(ROOT) not in sys.path:\n sys.path.append(str(ROOT)) # add ROOT to PATH\nRANK = int(os.getenv(\"RANK\", -1))\nDEPRECATION_WARNING = (\n f\"{colorstr('wandb')}: WARNING ⚠️ wandb is deprecated and will be removed in a future release. \"\n f\"See supported integrations at https://github.com/ultralytics/yolov5#integrations.\"\n)\n\ntry:\n import wandb\n\n assert hasattr(wandb, \"__version__\") # verify package import not local dir\n LOGGER.warning(DEPRECATION_WARNING)\nexcept (ImportError, AssertionError):\n wandb = None\n\n\nclass WandbLogger:\n \"\"\"Log training runs, datasets, models, and predictions to Weights & Biases.\n\n This logger sends information to W&B at wandb.ai. By default, this information includes hyperparameters, system\n configuration and metrics, model metrics, and basic data metrics and analyses.\n\n By providing additional command line arguments to train.py, datasets, models and predictions can also be logged.\n\n For more on how this logger is used, see the Weights & Biases documentation:\n https://docs.wandb.com/guides/integrations/yolov5\n \"\"\"\n\n def __init__(self, opt, run_id=None, job_type=\"Training\"):\n \"\"\"- Initialize WandbLogger instance - Upload dataset if opt.upload_dataset is True - Setup training processes\n if job_type is 'Training'.\n\n Args:\n opt (namespace) -- Commandline arguments for this run: run_id (str) -- Run ID of W&B run to be resumed\n job_type (str) -- To set the job_type for this run\n \"\"\"\n # Pre-training routine --\n self.job_type = job_type\n self.wandb, self.wandb_run = wandb, wandb.run if wandb else None\n self.val_artifact, self.train_artifact = None, None\n self.train_artifact_path, self.val_artifact_path = None, None\n self.result_artifact = None\n self.val_table, self.result_table = None, None\n self.max_imgs_to_log = 16\n self.data_dict = None\n if self.wandb:\n self.wandb_run = wandb.run or wandb.init(\n config=opt,\n resume=\"allow\",\n project=\"YOLOv3\" if opt.project == \"runs/train\" else Path(opt.project).stem,\n entity=opt.entity,\n name=opt.name if opt.name != \"exp\" else None,\n job_type=job_type,\n id=run_id,\n allow_val_change=True,\n )\n\n if self.wandb_run and self.job_type == \"Training\":\n if isinstance(opt.data, dict):\n # This means another dataset manager has already processed the dataset info (e.g. ClearML)\n # and they will have stored the already processed dict in opt.data\n self.data_dict = opt.data\n self.setup_training(opt)\n\n def setup_training(self, opt):\n \"\"\"Setup the necessary processes for training YOLO models: - Attempt to download model checkpoint and dataset\n artifacts if opt.resume stats with WANDB_ARTIFACT_PREFIX - Update data_dict, to contain info of previous\n run if resumed and the paths of dataset artifact if downloaded - Setup log_dict,\n initialize bbox_interval.\n\n Args:\n opt (namespace) -- commandline arguments for this run\n \"\"\"\n self.log_dict, self.current_epoch = {}, 0\n self.bbox_interval = opt.bbox_interval\n if isinstance(opt.resume, str):\n model_dir, _ = self.download_model_artifact(opt)\n if model_dir:\n self.weights = Path(model_dir) / \"last.pt\"\n config = self.wandb_run.config\n opt.weights, opt.save_period, opt.batch_size, opt.bbox_interval, opt.epochs, opt.hyp, opt.imgsz = (\n str(self.weights),\n config.save_period,\n config.batch_size,\n config.bbox_interval,\n config.epochs,\n config.hyp,\n config.imgsz,\n )\n\n if opt.bbox_interval == -1:\n self.bbox_interval = opt.bbox_interval = (opt.epochs // 10) if opt.epochs > 10 else 1\n if opt.evolve or opt.noplots:\n self.bbox_interval = opt.bbox_interval = opt.epochs + 1 # disable bbox_interval\n\n def log_model(self, path, opt, epoch, fitness_score, best_model=False):\n \"\"\"Log the model checkpoint as W&B artifact.\n\n Args:\n path (Path) -- Path of directory containing the checkpoints: opt (namespace) -- Command line arguments for\n this run epoch (int) -- Current epoch number fitness_score (float) -- fitness score for current epoch\n best_model (boolean) -- Boolean representing if the current checkpoint is the best yet.\n \"\"\"\n model_artifact = wandb.Artifact(\n f\"run_{wandb.run.id}_model\",\n type=\"model\",\n metadata={\n \"original_url\": str(path),\n \"epochs_trained\": epoch + 1,\n \"save period\": opt.save_period,\n \"project\": opt.project,\n \"total_epochs\": opt.epochs,\n \"fitness_score\": fitness_score,\n },\n )\n model_artifact.add_file(str(path / \"last.pt\"), name=\"last.pt\")\n wandb.log_artifact(\n model_artifact,\n aliases=[\n \"latest\",\n \"last\",\n f\"epoch {self.current_epoch!s}\",\n \"best\" if best_model else \"\",\n ],\n )\n LOGGER.info(f\"Saving model artifact on epoch {epoch + 1}\")\n\n def val_one_image(self, pred, predn, path, names, im):\n \"\"\"Evaluates model's prediction for a single image, updating metrics based on comparison with ground truth.\"\"\"\n pass\n\n def log(self, log_dict):\n \"\"\"Save the metrics to the logging dictionary.\n\n Args:\n log_dict (Dict) -- metrics/media to be logged in current step\n \"\"\"\n if self.wandb_run:\n for key, value in log_dict.items():\n self.log_dict[key] = value\n\n def end_epoch(self):\n \"\"\"Commit the log_dict, model artifacts and Tables to W&B and flush the log_dict.\n\n Args:\n best_result (boolean): Boolean representing if the result of this evaluation is best or not\n \"\"\"\n if self.wandb_run:\n with all_logging_disabled():\n try:\n wandb.log(self.log_dict)\n except BaseException as e:\n LOGGER.info(\n f\"An error occurred in wandb. The training will proceed without interruption. More info\\n{e}\"\n )\n self.wandb_run.finish()\n self.wandb_run = None\n self.log_dict = {}\n\n def finish_run(self):\n \"\"\"Log metrics if any and finish the current W&B run.\"\"\"\n if self.wandb_run:\n if self.log_dict:\n with all_logging_disabled():\n wandb.log(self.log_dict)\n wandb.run.finish()\n LOGGER.warning(DEPRECATION_WARNING)\n\n\n@contextmanager\ndef all_logging_disabled(highest_level=logging.CRITICAL):\n \"\"\"Source - https://gist.github.com/simon-weber/7853144\n A context manager that will prevent any logging messages triggered during the body from being processed.\n :param highest_level: the maximum logging level in use.\n This would only need to be changed if a custom level greater than CRITICAL is defined.\n \"\"\"\n previous_level = logging.root.manager.disable\n logging.disable(highest_level)\n try:\n yield\n finally:\n logging.disable(previous_level)\n" }, { "path": "utils/loss.py", "content": "# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license\n\"\"\"Loss functions.\"\"\"\n\nimport torch\nimport torch.nn as nn\n\nfrom utils.metrics import bbox_iou\nfrom utils.torch_utils import de_parallel\n\n\ndef smooth_BCE(eps=0.1): # https://github.com/ultralytics/yolov3/issues/238#issuecomment-598028441\n \"\"\"Applies label smoothing to BCE targets, returning smoothed positive/negative labels; eps default is 0.1.\"\"\"\n return 1.0 - 0.5 * eps, 0.5 * eps\n\n\nclass BCEBlurWithLogitsLoss(nn.Module):\n \"\"\"Implements BCEWithLogitsLoss with adjustments to mitigate missing label effects using an alpha parameter.\"\"\"\n\n def __init__(self, alpha=0.05):\n \"\"\"Initializes BCEBlurWithLogitsLoss with alpha to reduce missing label effects; default alpha is 0.05.\"\"\"\n super().__init__()\n self.loss_fcn = nn.BCEWithLogitsLoss(reduction=\"none\") # must be nn.BCEWithLogitsLoss()\n self.alpha = alpha\n\n def forward(self, pred, true):\n \"\"\"Calculates modified BCEWithLogitsLoss factoring in missing labels, taking `pred` logits and `true` labels as\n inputs.\n \"\"\"\n loss = self.loss_fcn(pred, true)\n pred = torch.sigmoid(pred) # prob from logits\n dx = pred - true # reduce only missing label effects\n # dx = (pred - true).abs() # reduce missing label and false label effects\n alpha_factor = 1 - torch.exp((dx - 1) / (self.alpha + 1e-4))\n loss *= alpha_factor\n return loss.mean()\n\n\nclass FocalLoss(nn.Module):\n \"\"\"Implements Focal Loss to address class imbalance by modulating the loss based on prediction confidence.\"\"\"\n\n def __init__(self, loss_fcn, gamma=1.5, alpha=0.25):\n \"\"\"Initializes FocalLoss with specified loss function, gamma, and alpha for enhanced training on imbalanced\n datasets.\n \"\"\"\n super().__init__()\n self.loss_fcn = loss_fcn # must be nn.BCEWithLogitsLoss()\n self.gamma = gamma\n self.alpha = alpha\n self.reduction = loss_fcn.reduction\n self.loss_fcn.reduction = \"none\" # required to apply FL to each element\n\n def forward(self, pred, true):\n \"\"\"Computes the focal loss between `pred` and `true` using specific alpha and gamma, not applying the modulating\n factor.\n \"\"\"\n loss = self.loss_fcn(pred, true)\n # p_t = torch.exp(-loss)\n # loss *= self.alpha * (1.000001 - p_t) ** self.gamma # non-zero power for gradient stability\n\n # TF implementation https://github.com/tensorflow/addons/blob/v0.7.1/tensorflow_addons/losses/focal_loss.py\n pred_prob = torch.sigmoid(pred) # prob from logits\n p_t = true * pred_prob + (1 - true) * (1 - pred_prob)\n alpha_factor = true * self.alpha + (1 - true) * (1 - self.alpha)\n modulating_factor = (1.0 - p_t) ** self.gamma\n loss *= alpha_factor * modulating_factor\n\n if self.reduction == \"mean\":\n return loss.mean()\n elif self.reduction == \"sum\":\n return loss.sum()\n else: # 'none'\n return loss\n\n\nclass QFocalLoss(nn.Module):\n \"\"\"Implements Quality Focal Loss to handle class imbalance with a modulating factor and alpha.\"\"\"\n\n def __init__(self, loss_fcn, gamma=1.5, alpha=0.25):\n \"\"\"Initializes QFocalLoss with specified loss function, gamma, and alpha for element-wise focal loss\n application.\n \"\"\"\n super().__init__()\n self.loss_fcn = loss_fcn # must be nn.BCEWithLogitsLoss()\n self.gamma = gamma\n self.alpha = alpha\n self.reduction = loss_fcn.reduction\n self.loss_fcn.reduction = \"none\" # required to apply FL to each element\n\n def forward(self, pred, true):\n \"\"\"Computes focal loss between predictions and true labels using configured loss function, `gamma`, and `alpha`.\n \"\"\"\n loss = self.loss_fcn(pred, true)\n\n pred_prob = torch.sigmoid(pred) # prob from logits\n alpha_factor = true * self.alpha + (1 - true) * (1 - self.alpha)\n modulating_factor = torch.abs(true - pred_prob) ** self.gamma\n loss *= alpha_factor * modulating_factor\n\n if self.reduction == \"mean\":\n return loss.mean()\n elif self.reduction == \"sum\":\n return loss.sum()\n else: # 'none'\n return loss\n\n\nclass ComputeLoss:\n \"\"\"Computes the total loss for YOLO models by aggregating classification, box regression, and objectness losses.\"\"\"\n\n sort_obj_iou = False\n\n # Compute losses\n def __init__(self, model, autobalance=False):\n \"\"\"Initializes ComputeLoss with model's device and hyperparameters, and sets autobalance.\"\"\"\n device = next(model.parameters()).device # get model device\n h = model.hyp # hyperparameters\n\n # Define criteria\n BCEcls = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h[\"cls_pw\"]], device=device))\n BCEobj = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h[\"obj_pw\"]], device=device))\n\n # Class label smoothing https://arxiv.org/pdf/1902.04103.pdf eqn 3\n self.cp, self.cn = smooth_BCE(eps=h.get(\"label_smoothing\", 0.0)) # positive, negative BCE targets\n\n # Focal loss\n g = h[\"fl_gamma\"] # focal loss gamma\n if g > 0:\n BCEcls, BCEobj = FocalLoss(BCEcls, g), FocalLoss(BCEobj, g)\n\n m = de_parallel(model).model[-1] # Detect() module\n self.balance = {3: [4.0, 1.0, 0.4]}.get(m.nl, [4.0, 1.0, 0.25, 0.06, 0.02]) # P3-P7\n self.ssi = list(m.stride).index(16) if autobalance else 0 # stride 16 index\n self.BCEcls, self.BCEobj, self.gr, self.hyp, self.autobalance = BCEcls, BCEobj, 1.0, h, autobalance\n self.na = m.na # number of anchors\n self.nc = m.nc # number of classes\n self.nl = m.nl # number of layers\n self.anchors = m.anchors\n self.device = device\n\n def __call__(self, p, targets): # predictions, targets\n \"\"\"Computes loss given predictions and targets, returning class, box, and object loss as tensors.\"\"\"\n lcls = torch.zeros(1, device=self.device) # class loss\n lbox = torch.zeros(1, device=self.device) # box loss\n lobj = torch.zeros(1, device=self.device) # object loss\n tcls, tbox, indices, anchors = self.build_targets(p, targets) # targets\n\n # Losses\n for i, pi in enumerate(p): # layer index, layer predictions\n b, a, gj, gi = indices[i] # image, anchor, gridy, gridx\n tobj = torch.zeros(pi.shape[:4], dtype=pi.dtype, device=self.device) # target obj\n\n if n := b.shape[0]:\n # pxy, pwh, _, pcls = pi[b, a, gj, gi].tensor_split((2, 4, 5), dim=1) # faster, requires torch 1.8.0\n pxy, pwh, _, pcls = pi[b, a, gj, gi].split((2, 2, 1, self.nc), 1) # target-subset of predictions\n\n # Regression\n pxy = pxy.sigmoid() * 2 - 0.5\n pwh = (pwh.sigmoid() * 2) ** 2 * anchors[i]\n pbox = torch.cat((pxy, pwh), 1) # predicted box\n iou = bbox_iou(pbox, tbox[i], CIoU=True).squeeze() # iou(prediction, target)\n lbox += (1.0 - iou).mean() # iou loss\n\n # Objectness\n iou = iou.detach().clamp(0).type(tobj.dtype)\n if self.sort_obj_iou:\n j = iou.argsort()\n b, a, gj, gi, iou = b[j], a[j], gj[j], gi[j], iou[j]\n if self.gr < 1:\n iou = (1.0 - self.gr) + self.gr * iou\n tobj[b, a, gj, gi] = iou # iou ratio\n\n # Classification\n if self.nc > 1: # cls loss (only if multiple classes)\n t = torch.full_like(pcls, self.cn, device=self.device) # targets\n t[range(n), tcls[i]] = self.cp\n lcls += self.BCEcls(pcls, t) # BCE\n\n obji = self.BCEobj(pi[..., 4], tobj)\n lobj += obji * self.balance[i] # obj loss\n if self.autobalance:\n self.balance[i] = self.balance[i] * 0.9999 + 0.0001 / obji.detach().item()\n\n if self.autobalance:\n self.balance = [x / self.balance[self.ssi] for x in self.balance]\n lbox *= self.hyp[\"box\"]\n lobj *= self.hyp[\"obj\"]\n lcls *= self.hyp[\"cls\"]\n bs = tobj.shape[0] # batch size\n\n return (lbox + lobj + lcls) * bs, torch.cat((lbox, lobj, lcls)).detach()\n\n def build_targets(self, p, targets):\n \"\"\"Generates matching anchor targets for compute_loss() from given images and labels in format\n (image,class,x,y,w,h).\n \"\"\"\n na, nt = self.na, targets.shape[0] # number of anchors, targets\n tcls, tbox, indices, anch = [], [], [], []\n gain = torch.ones(7, device=self.device) # normalized to gridspace gain\n ai = torch.arange(na, device=self.device).float().view(na, 1).repeat(1, nt) # same as .repeat_interleave(nt)\n targets = torch.cat((targets.repeat(na, 1, 1), ai[..., None]), 2) # append anchor indices\n\n g = 0.5 # bias\n off = (\n torch.tensor(\n [\n [0, 0],\n [1, 0],\n [0, 1],\n [-1, 0],\n [0, -1], # j,k,l,m\n # [1, 1], [1, -1], [-1, 1], [-1, -1], # jk,jm,lk,lm\n ],\n device=self.device,\n ).float()\n * g\n ) # offsets\n\n for i in range(self.nl):\n anchors, shape = self.anchors[i], p[i].shape\n gain[2:6] = torch.tensor(shape)[[3, 2, 3, 2]] # xyxy gain\n\n # Match targets to anchors\n t = targets * gain # shape(3,n,7)\n if nt:\n # Matches\n r = t[..., 4:6] / anchors[:, None] # wh ratio\n j = torch.max(r, 1 / r).max(2)[0] < self.hyp[\"anchor_t\"] # compare\n # j = wh_iou(anchors, t[:, 4:6]) > model.hyp['iou_t'] # iou(3,n)=wh_iou(anchors(3,2), gwh(n,2))\n t = t[j] # filter\n\n # Offsets\n gxy = t[:, 2:4] # grid xy\n gxi = gain[[2, 3]] - gxy # inverse\n j, k = ((gxy % 1 < g) & (gxy > 1)).T\n l, m = ((gxi % 1 < g) & (gxi > 1)).T\n j = torch.stack((torch.ones_like(j), j, k, l, m))\n t = t.repeat((5, 1, 1))[j]\n offsets = (torch.zeros_like(gxy)[None] + off[:, None])[j]\n else:\n t = targets[0]\n offsets = 0\n\n # Define\n bc, gxy, gwh, a = t.chunk(4, 1) # (image, class), grid xy, grid wh, anchors\n a, (b, c) = a.long().view(-1), bc.long().T # anchors, image, class\n gij = (gxy - offsets).long()\n gi, gj = gij.T # grid indices\n\n # Append\n indices.append((b, a, gj.clamp_(0, shape[2] - 1), gi.clamp_(0, shape[3] - 1))) # image, anchor, grid\n tbox.append(torch.cat((gxy - gij, gwh), 1)) # box\n anch.append(anchors[a]) # anchors\n tcls.append(c) # class\n\n return tcls, tbox, indices, anch\n" }, { "path": "utils/metrics.py", "content": "# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license\n\"\"\"Model validation metrics.\"\"\"\n\nimport math\nimport warnings\nfrom pathlib import Path\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport torch\n\nfrom utils import TryExcept, threaded\n\n\ndef fitness(x):\n \"\"\"Calculates model fitness as a weighted sum of metrics [P, R, mAP@0.5, mAP@0.5:0.95] with respective weights.\"\"\"\n w = [0.0, 0.0, 0.1, 0.9] # weights for [P, R, mAP@0.5, mAP@0.5:0.95]\n return (x[:, :4] * w).sum(1)\n\n\ndef smooth(y, f=0.05):\n \"\"\"Smooths array `y` using a box filter with fractional size `f`, returning the smoothed array.\"\"\"\n nf = round(len(y) * f * 2) // 2 + 1 # number of filter elements (must be odd)\n p = np.ones(nf // 2) # ones padding\n yp = np.concatenate((p * y[0], y, p * y[-1]), 0) # y padded\n return np.convolve(yp, np.ones(nf) / nf, mode=\"valid\") # y-smoothed\n\n\ndef ap_per_class(tp, conf, pred_cls, target_cls, plot=False, save_dir=\".\", names=(), eps=1e-16, prefix=\"\"):\n \"\"\"Compute the average precision, given the recall and precision curves.\n\n Source: https://github.com/rafaelpadilla/Object-Detection-Metrics. # Arguments\n tp: True positives (nparray, nx1 or nx10).\n conf: Objectness value from 0-1 (nparray).\n pred_cls: Predicted object classes (nparray).\n target_cls: True object classes (nparray).\n plot: Plot precision-recall curve at mAP@0.5\n save_dir: Plot save directory\n # Returns\n The average precision as computed in py-faster-rcnn.\n \"\"\"\n # Sort by objectness\n i = np.argsort(-conf)\n tp, conf, pred_cls = tp[i], conf[i], pred_cls[i]\n\n # Find unique classes\n unique_classes, nt = np.unique(target_cls, return_counts=True)\n nc = unique_classes.shape[0] # number of classes, number of detections\n\n # Create Precision-Recall curve and compute AP for each class\n px, py = np.linspace(0, 1, 1000), [] # for plotting\n ap, p, r = np.zeros((nc, tp.shape[1])), np.zeros((nc, 1000)), np.zeros((nc, 1000))\n for ci, c in enumerate(unique_classes):\n i = pred_cls == c\n n_l = nt[ci] # number of labels\n n_p = i.sum() # number of predictions\n if n_p == 0 or n_l == 0:\n continue\n\n # Accumulate FPs and TPs\n fpc = (1 - tp[i]).cumsum(0)\n tpc = tp[i].cumsum(0)\n\n # Recall\n recall = tpc / (n_l + eps) # recall curve\n r[ci] = np.interp(-px, -conf[i], recall[:, 0], left=0) # negative x, xp because xp decreases\n\n # Precision\n precision = tpc / (tpc + fpc) # precision curve\n p[ci] = np.interp(-px, -conf[i], precision[:, 0], left=1) # p at pr_score\n\n # AP from recall-precision curve\n for j in range(tp.shape[1]):\n ap[ci, j], mpre, mrec = compute_ap(recall[:, j], precision[:, j])\n if plot and j == 0:\n py.append(np.interp(px, mrec, mpre)) # precision at mAP@0.5\n\n # Compute F1 (harmonic mean of precision and recall)\n f1 = 2 * p * r / (p + r + eps)\n names = [v for k, v in names.items() if k in unique_classes] # list: only classes that have data\n names = dict(enumerate(names)) # to dict\n if plot:\n plot_pr_curve(px, py, ap, Path(save_dir) / f\"{prefix}PR_curve.png\", names)\n plot_mc_curve(px, f1, Path(save_dir) / f\"{prefix}F1_curve.png\", names, ylabel=\"F1\")\n plot_mc_curve(px, p, Path(save_dir) / f\"{prefix}P_curve.png\", names, ylabel=\"Precision\")\n plot_mc_curve(px, r, Path(save_dir) / f\"{prefix}R_curve.png\", names, ylabel=\"Recall\")\n\n i = smooth(f1.mean(0), 0.1).argmax() # max F1 index\n p, r, f1 = p[:, i], r[:, i], f1[:, i]\n tp = (r * nt).round() # true positives\n fp = (tp / (p + eps) - tp).round() # false positives\n return tp, fp, p, r, f1, ap, unique_classes.astype(int)\n\n\ndef compute_ap(recall, precision):\n \"\"\"Compute the average precision, given the recall and precision curves # Arguments recall: The recall curve (list)\n precision: The precision curve (list) # Returns Average precision, precision curve, recall curve.\n \"\"\"\n # Append sentinel values to beginning and end\n mrec = np.concatenate(([0.0], recall, [1.0]))\n mpre = np.concatenate(([1.0], precision, [0.0]))\n\n # Compute the precision envelope\n mpre = np.flip(np.maximum.accumulate(np.flip(mpre)))\n\n # Integrate area under curve\n method = \"interp\" # methods: 'continuous', 'interp'\n if method == \"interp\":\n x = np.linspace(0, 1, 101) # 101-point interp (COCO)\n ap = (np.trapezoid if hasattr(np, \"trapezoid\") else np.trapz)(np.interp(x, mrec, mpre), x) # integrate\n else: # 'continuous'\n i = np.where(mrec[1:] != mrec[:-1])[0] # points where x axis (recall) changes\n ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1]) # area under curve\n\n return ap, mpre, mrec\n\n\nclass ConfusionMatrix:\n \"\"\"Computes and visualizes a confusion matrix for object detection tasks with configurable thresholds.\"\"\"\n\n def __init__(self, nc, conf=0.25, iou_thres=0.45):\n \"\"\"Initializes confusion matrix for object detection with adjustable confidence and IoU thresholds.\"\"\"\n self.matrix = np.zeros((nc + 1, nc + 1))\n self.nc = nc # number of classes\n self.conf = conf\n self.iou_thres = iou_thres\n\n def process_batch(self, detections, labels):\n \"\"\"Return intersection-over-union (Jaccard index) of boxes.\n\n Both sets of boxes are expected to be in (x1, y1, x2, y2) format.\n\n Args:\n detections (Array[N, 6]), x1, y1, x2, y2, conf, class: labels (Array[M, 5]), class, x1, y1, x2, y2\n\n Returns:\n None, updates confusion matrix accordingly\n \"\"\"\n if detections is None:\n gt_classes = labels.int()\n for gc in gt_classes:\n self.matrix[self.nc, gc] += 1 # background FN\n return\n\n detections = detections[detections[:, 4] > self.conf]\n gt_classes = labels[:, 0].int()\n detection_classes = detections[:, 5].int()\n iou = box_iou(labels[:, 1:], detections[:, :4])\n\n x = torch.where(iou > self.iou_thres)\n if x[0].shape[0]:\n matches = torch.cat((torch.stack(x, 1), iou[x[0], x[1]][:, None]), 1).cpu().numpy()\n if x[0].shape[0] > 1:\n matches = matches[matches[:, 2].argsort()[::-1]]\n matches = matches[np.unique(matches[:, 1], return_index=True)[1]]\n matches = matches[matches[:, 2].argsort()[::-1]]\n matches = matches[np.unique(matches[:, 0], return_index=True)[1]]\n else:\n matches = np.zeros((0, 3))\n\n n = matches.shape[0] > 0\n m0, m1, _ = matches.transpose().astype(int)\n for i, gc in enumerate(gt_classes):\n j = m0 == i\n if n and sum(j) == 1:\n self.matrix[detection_classes[m1[j]], gc] += 1 # correct\n else:\n self.matrix[self.nc, gc] += 1 # true background\n\n if n:\n for i, dc in enumerate(detection_classes):\n if not any(m1 == i):\n self.matrix[dc, self.nc] += 1 # predicted background\n\n def tp_fp(self):\n \"\"\"Computes true positives and false positives, excluding the background class, from a confusion matrix.\"\"\"\n tp = self.matrix.diagonal() # true positives\n fp = self.matrix.sum(1) - tp # false positives\n # fn = self.matrix.sum(0) - tp # false negatives (missed detections)\n return tp[:-1], fp[:-1] # remove background class\n\n @TryExcept(\"WARNING ⚠️ ConfusionMatrix plot failure\")\n def plot(self, normalize=True, save_dir=\"\", names=()):\n \"\"\"Plots confusion matrix as a heatmap; args: normalize(bool), save_dir(str), names(iterable of str).\"\"\"\n import seaborn as sn\n\n array = self.matrix / ((self.matrix.sum(0).reshape(1, -1) + 1e-9) if normalize else 1) # normalize columns\n array[array < 0.005] = np.nan # don't annotate (would appear as 0.00)\n\n fig, ax = plt.subplots(1, 1, figsize=(12, 9), tight_layout=True)\n nc, nn = self.nc, len(names) # number of classes, names\n sn.set(font_scale=1.0 if nc < 50 else 0.8) # for label size\n labels = (0 < nn < 99) and (nn == nc) # apply names to ticklabels\n ticklabels = ([*names, \"background\"]) if labels else \"auto\"\n with warnings.catch_warnings():\n warnings.simplefilter(\"ignore\") # suppress empty matrix RuntimeWarning: All-NaN slice encountered\n sn.heatmap(\n array,\n ax=ax,\n annot=nc < 30,\n annot_kws={\"size\": 8},\n cmap=\"Blues\",\n fmt=\".2f\",\n square=True,\n vmin=0.0,\n xticklabels=ticklabels,\n yticklabels=ticklabels,\n ).set_facecolor((1, 1, 1))\n ax.set_xlabel(\"True\")\n ax.set_ylabel(\"Predicted\")\n ax.set_title(\"Confusion Matrix\")\n fig.savefig(Path(save_dir) / \"confusion_matrix.png\", dpi=250)\n plt.close(fig)\n\n def print(self):\n \"\"\"Prints each row of the confusion matrix, where matrix elements are separated by spaces.\"\"\"\n for i in range(self.nc + 1):\n print(\" \".join(map(str, self.matrix[i])))\n\n\ndef bbox_iou(box1, box2, xywh=True, GIoU=False, DIoU=False, CIoU=False, eps=1e-7):\n \"\"\"Calculates IoU, GIoU, DIoU, CIoU between two bounding boxes, supporting `xywh` and `xyxy` formats.\"\"\"\n # Get the coordinates of bounding boxes\n if xywh: # transform from xywh to xyxy\n (x1, y1, w1, h1), (x2, y2, w2, h2) = box1.chunk(4, -1), box2.chunk(4, -1)\n w1_, h1_, w2_, h2_ = w1 / 2, h1 / 2, w2 / 2, h2 / 2\n b1_x1, b1_x2, b1_y1, b1_y2 = x1 - w1_, x1 + w1_, y1 - h1_, y1 + h1_\n b2_x1, b2_x2, b2_y1, b2_y2 = x2 - w2_, x2 + w2_, y2 - h2_, y2 + h2_\n else: # x1, y1, x2, y2 = box1\n b1_x1, b1_y1, b1_x2, b1_y2 = box1.chunk(4, -1)\n b2_x1, b2_y1, b2_x2, b2_y2 = box2.chunk(4, -1)\n w1, h1 = b1_x2 - b1_x1, (b1_y2 - b1_y1).clamp(eps)\n w2, h2 = b2_x2 - b2_x1, (b2_y2 - b2_y1).clamp(eps)\n\n # Intersection area\n inter = (b1_x2.minimum(b2_x2) - b1_x1.maximum(b2_x1)).clamp(0) * (\n b1_y2.minimum(b2_y2) - b1_y1.maximum(b2_y1)\n ).clamp(0)\n\n # Union Area\n union = w1 * h1 + w2 * h2 - inter + eps\n\n # IoU\n iou = inter / union\n if CIoU or DIoU or GIoU:\n cw = b1_x2.maximum(b2_x2) - b1_x1.minimum(b2_x1) # convex (smallest enclosing box) width\n ch = b1_y2.maximum(b2_y2) - b1_y1.minimum(b2_y1) # convex height\n if CIoU or DIoU: # Distance or Complete IoU https://arxiv.org/abs/1911.08287v1\n c2 = cw**2 + ch**2 + eps # convex diagonal squared\n rho2 = ((b2_x1 + b2_x2 - b1_x1 - b1_x2) ** 2 + (b2_y1 + b2_y2 - b1_y1 - b1_y2) ** 2) / 4 # center dist ** 2\n if CIoU: # https://github.com/Zzh-tju/DIoU-SSD-pytorch/blob/master/utils/box/box_utils.py#L47\n v = (4 / math.pi**2) * (torch.atan(w2 / h2) - torch.atan(w1 / h1)).pow(2)\n with torch.no_grad():\n alpha = v / (v - iou + (1 + eps))\n return iou - (rho2 / c2 + v * alpha) # CIoU\n return iou - rho2 / c2 # DIoU\n c_area = cw * ch + eps # convex area\n return iou - (c_area - union) / c_area # GIoU https://arxiv.org/pdf/1902.09630.pdf\n return iou # IoU\n\n\ndef box_iou(box1, box2, eps=1e-7):\n # https://github.com/pytorch/vision/blob/master/torchvision/ops/boxes.py\n \"\"\"Return intersection-over-union (Jaccard index) of boxes.\n\n Both sets of boxes are expected to be in (x1, y1, x2, y2) format.\n\n Args:\n box1 (Tensor[N, 4]): box2 (Tensor[M, 4])\n\n Returns:\n iou (Tensor[N, M]): the NxM matrix containing the pairwise IoU values for every element in boxes1 and boxes2\n \"\"\"\n # inter(N,M) = (rb(N,M,2) - lt(N,M,2)).clamp(0).prod(2)\n (a1, a2), (b1, b2) = box1.unsqueeze(1).chunk(2, 2), box2.unsqueeze(0).chunk(2, 2)\n inter = (torch.min(a2, b2) - torch.max(a1, b1)).clamp(0).prod(2)\n\n # IoU = inter / (area1 + area2 - inter)\n return inter / ((a2 - a1).prod(2) + (b2 - b1).prod(2) - inter + eps)\n\n\ndef bbox_ioa(box1, box2, eps=1e-7):\n \"\"\"Returns the intersection over box2 area given box1, box2.\n\n Boxes are x1y1x2y2 box1: np.array of shape(4) box2: np.array of shape(nx4) returns: np.array of shape(n)\n \"\"\"\n # Get the coordinates of bounding boxes\n b1_x1, b1_y1, b1_x2, b1_y2 = box1\n b2_x1, b2_y1, b2_x2, b2_y2 = box2.T\n\n # Intersection area\n inter_area = (np.minimum(b1_x2, b2_x2) - np.maximum(b1_x1, b2_x1)).clip(0) * (\n np.minimum(b1_y2, b2_y2) - np.maximum(b1_y1, b2_y1)\n ).clip(0)\n\n # box2 area\n box2_area = (b2_x2 - b2_x1) * (b2_y2 - b2_y1) + eps\n\n # Intersection over box2 area\n return inter_area / box2_area\n\n\ndef wh_iou(wh1, wh2, eps=1e-7):\n \"\"\"Calculates the IoU of width-height pairs, wh1[n,2] and wh2[m,2], returning an nxm IoU matrix.\"\"\"\n wh1 = wh1[:, None] # [N,1,2]\n wh2 = wh2[None] # [1,M,2]\n inter = torch.min(wh1, wh2).prod(2) # [N,M]\n return inter / (wh1.prod(2) + wh2.prod(2) - inter + eps) # iou = inter / (area1 + area2 - inter)\n\n\n# Plots ----------------------------------------------------------------------------------------------------------------\n\n\n@threaded\ndef plot_pr_curve(px, py, ap, save_dir=Path(\"pr_curve.png\"), names=()):\n \"\"\"Plots precision-recall curve, supports per-class curves if < 21 classes; args: px (recall), py (precision list),\n ap (APs), save_dir, names.\n \"\"\"\n fig, ax = plt.subplots(1, 1, figsize=(9, 6), tight_layout=True)\n py = np.stack(py, axis=1)\n\n if 0 < len(names) < 21: # display per-class legend if < 21 classes\n for i, y in enumerate(py.T):\n ax.plot(px, y, linewidth=1, label=f\"{names[i]} {ap[i, 0]:.3f}\") # plot(recall, precision)\n else:\n ax.plot(px, py, linewidth=1, color=\"grey\") # plot(recall, precision)\n\n ax.plot(px, py.mean(1), linewidth=3, color=\"blue\", label=f\"all classes {ap[:, 0].mean():.3f} mAP@0.5\")\n ax.set_xlabel(\"Recall\")\n ax.set_ylabel(\"Precision\")\n ax.set_xlim(0, 1)\n ax.set_ylim(0, 1)\n ax.legend(bbox_to_anchor=(1.04, 1), loc=\"upper left\")\n ax.set_title(\"Precision-Recall Curve\")\n fig.savefig(save_dir, dpi=250)\n plt.close(fig)\n\n\n@threaded\ndef plot_mc_curve(px, py, save_dir=Path(\"mc_curve.png\"), names=(), xlabel=\"Confidence\", ylabel=\"Metric\"):\n \"\"\"Plots metric-confidence curve for given classes; px, py shapes (N,), (C, N); save_dir: str or Path; names: tuple\n of class names.\n \"\"\"\n fig, ax = plt.subplots(1, 1, figsize=(9, 6), tight_layout=True)\n\n if 0 < len(names) < 21: # display per-class legend if < 21 classes\n for i, y in enumerate(py):\n ax.plot(px, y, linewidth=1, label=f\"{names[i]}\") # plot(confidence, metric)\n else:\n ax.plot(px, py.T, linewidth=1, color=\"grey\") # plot(confidence, metric)\n\n y = smooth(py.mean(0), 0.05)\n ax.plot(px, y, linewidth=3, color=\"blue\", label=f\"all classes {y.max():.2f} at {px[y.argmax()]:.3f}\")\n ax.set_xlabel(xlabel)\n ax.set_ylabel(ylabel)\n ax.set_xlim(0, 1)\n ax.set_ylim(0, 1)\n ax.legend(bbox_to_anchor=(1.04, 1), loc=\"upper left\")\n ax.set_title(f\"{ylabel}-Confidence Curve\")\n fig.savefig(save_dir, dpi=250)\n plt.close(fig)\n" }, { "path": "utils/plots.py", "content": "# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license\n\"\"\"Plotting utils.\"\"\"\n\nimport contextlib\nimport math\nimport os\nfrom copy import copy\nfrom pathlib import Path\n\nimport cv2\nimport matplotlib\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\nimport seaborn as sn\nimport torch\nfrom PIL import Image, ImageDraw\nfrom scipy.ndimage.filters import gaussian_filter1d\nfrom ultralytics.utils.plotting import Annotator\n\nfrom utils import TryExcept, threaded\nfrom utils.general import LOGGER, clip_boxes, increment_path, xywh2xyxy, xyxy2xywh\nfrom utils.metrics import fitness\n\n# Settings\nRANK = int(os.getenv(\"RANK\", -1))\nmatplotlib.rc(\"font\", **{\"size\": 11})\nmatplotlib.use(\"Agg\") # for writing to files only\n\n\nclass Colors:\n \"\"\"Provides a color palette and methods to convert indices to RGB or BGR color tuples.\"\"\"\n\n def __init__(self):\n \"\"\"Initializes the Colors class with a palette from the Ultralytics color palette.\"\"\"\n hexs = (\n \"FF3838\",\n \"FF9D97\",\n \"FF701F\",\n \"FFB21D\",\n \"CFD231\",\n \"48F90A\",\n \"92CC17\",\n \"3DDB86\",\n \"1A9334\",\n \"00D4BB\",\n \"2C99A8\",\n \"00C2FF\",\n \"344593\",\n \"6473FF\",\n \"0018EC\",\n \"8438FF\",\n \"520085\",\n \"CB38FF\",\n \"FF95C8\",\n \"FF37C7\",\n )\n self.palette = [self.hex2rgb(f\"#{c}\") for c in hexs]\n self.n = len(self.palette)\n\n def __call__(self, i, bgr=False):\n \"\"\"Converts index `i` to a color from predefined palette, returning in BGR format if `bgr` is True, else RGB.\"\"\"\n c = self.palette[int(i) % self.n]\n return (c[2], c[1], c[0]) if bgr else c\n\n @staticmethod\n def hex2rgb(h): # rgb order (PIL)\n \"\"\"Converts hexadecimal color `h` to RGB tuple; `h` format should be '#RRGGBB'.\"\"\"\n return tuple(int(h[1 + i : 1 + i + 2], 16) for i in (0, 2, 4))\n\n\ncolors = Colors() # create instance for 'from utils.plots import colors'\n\n\ndef feature_visualization(x, module_type, stage, n=32, save_dir=Path(\"runs/detect/exp\")):\n \"\"\"x: Features to be visualized module_type: Module type stage: Module stage within model n: Maximum number of\n feature maps to plot save_dir: Directory to save results.\n \"\"\"\n if \"Detect\" not in module_type:\n _batch, channels, height, width = x.shape # batch, channels, height, width\n if height > 1 and width > 1:\n f = save_dir / f\"stage{stage}_{module_type.split('.')[-1]}_features.png\" # filename\n\n blocks = torch.chunk(x[0].cpu(), channels, dim=0) # select batch index 0, block by channels\n n = min(n, channels) # number of plots\n _fig, ax = plt.subplots(math.ceil(n / 8), 8, tight_layout=True) # 8 rows x n/8 cols\n ax = ax.ravel()\n plt.subplots_adjust(wspace=0.05, hspace=0.05)\n for i in range(n):\n ax[i].imshow(blocks[i].squeeze()) # cmap='gray'\n ax[i].axis(\"off\")\n\n LOGGER.info(f\"Saving {f}... ({n}/{channels})\")\n plt.savefig(f, dpi=300, bbox_inches=\"tight\")\n plt.close()\n np.save(str(f.with_suffix(\".npy\")), x[0].cpu().numpy()) # npy save\n\n\ndef hist2d(x, y, n=100):\n \"\"\"Generates a 2D log-scaled histogram from input arrays `x` and `y`, with `n` bins for each axis.\"\"\"\n xedges, yedges = np.linspace(x.min(), x.max(), n), np.linspace(y.min(), y.max(), n)\n hist, xedges, yedges = np.histogram2d(x, y, (xedges, yedges))\n xidx = np.clip(np.digitize(x, xedges) - 1, 0, hist.shape[0] - 1)\n yidx = np.clip(np.digitize(y, yedges) - 1, 0, hist.shape[1] - 1)\n return np.log(hist[xidx, yidx])\n\n\ndef butter_lowpass_filtfilt(data, cutoff=1500, fs=50000, order=5):\n \"\"\"Applies a low-pass Butterworth filter using forward-backward method.\n\n See: https://stackoverflow.com/questions/28536191/how-to-filter-smooth-with-scipy-numpy\n \"\"\"\n from scipy.signal import butter, filtfilt\n\n # https://stackoverflow.com/questions/28536191/how-to-filter-smooth-with-scipy-numpy\n def butter_lowpass(cutoff, fs, order):\n \"\"\"Applies a low-pass Butterworth filter to input data using forward-backward method; see\n https://stackoverflow.com/questions/28536191/how-to-filter-smooth-with-scipy-numpy.\n \"\"\"\n nyq = 0.5 * fs\n normal_cutoff = cutoff / nyq\n return butter(order, normal_cutoff, btype=\"low\", analog=False)\n\n b, a = butter_lowpass(cutoff, fs, order=order)\n return filtfilt(b, a, data) # forward-backward filter\n\n\ndef output_to_target(output, max_det=300):\n \"\"\"Converts model output to [batch_id, class_id, x, y, w, h, conf] format for plotting, handling up to `max_det`\n detections.\n \"\"\"\n targets = []\n for i, o in enumerate(output):\n box, conf, cls = o[:max_det, :6].cpu().split((4, 1, 1), 1)\n j = torch.full((conf.shape[0], 1), i)\n targets.append(torch.cat((j, cls, xyxy2xywh(box), conf), 1))\n return torch.cat(targets, 0).numpy()\n\n\n@threaded\ndef plot_images(images, targets, paths=None, fname=\"images.jpg\", names=None):\n \"\"\"Plots a grid of images with labels, optionally resizing and annotating with target boxes and names.\"\"\"\n if isinstance(images, torch.Tensor):\n images = images.cpu().float().numpy()\n if isinstance(targets, torch.Tensor):\n targets = targets.cpu().numpy()\n\n max_size = 1920 # max image size\n max_subplots = 16 # max image subplots, i.e. 4x4\n bs, _, h, w = images.shape # batch size, _, height, width\n bs = min(bs, max_subplots) # limit plot images\n ns = np.ceil(bs**0.5) # number of subplots (square)\n if np.max(images[0]) <= 1:\n images *= 255 # de-normalise (optional)\n\n # Build Image\n mosaic = np.full((int(ns * h), int(ns * w), 3), 255, dtype=np.uint8) # init\n for i, im in enumerate(images):\n if i == max_subplots: # if last batch has fewer images than we expect\n break\n x, y = int(w * (i // ns)), int(h * (i % ns)) # block origin\n im = im.transpose(1, 2, 0)\n mosaic[y : y + h, x : x + w, :] = im\n\n # Resize (optional)\n scale = max_size / ns / max(h, w)\n if scale < 1:\n h = math.ceil(scale * h)\n w = math.ceil(scale * w)\n mosaic = cv2.resize(mosaic, tuple(int(x * ns) for x in (w, h)))\n\n # Annotate\n fs = int((h + w) * ns * 0.01) # font size\n annotator = Annotator(mosaic, line_width=round(fs / 10), font_size=fs, pil=True, example=names)\n for i in range(i + 1):\n x, y = int(w * (i // ns)), int(h * (i % ns)) # block origin\n annotator.rectangle([x, y, x + w, y + h], None, (255, 255, 255), width=2) # borders\n if paths:\n annotator.text([x + 5, y + 5], text=Path(paths[i]).name[:40], txt_color=(220, 220, 220)) # filenames\n if len(targets) > 0:\n ti = targets[targets[:, 0] == i] # image targets\n boxes = xywh2xyxy(ti[:, 2:6]).T\n classes = ti[:, 1].astype(\"int\")\n labels = ti.shape[1] == 6 # labels if no conf column\n conf = None if labels else ti[:, 6] # check for confidence presence (label vs pred)\n\n if boxes.shape[1]:\n if boxes.max() <= 1.01: # if normalized with tolerance 0.01\n boxes[[0, 2]] *= w # scale to pixels\n boxes[[1, 3]] *= h\n elif scale < 1: # absolute coords need scale if image scales\n boxes *= scale\n boxes[[0, 2]] += x\n boxes[[1, 3]] += y\n for j, box in enumerate(boxes.T.tolist()):\n cls = classes[j]\n color = colors(cls)\n cls = names[cls] if names else cls\n if labels or conf[j] > 0.25: # 0.25 conf thresh\n label = f\"{cls}\" if labels else f\"{cls} {conf[j]:.1f}\"\n annotator.box_label(box, label, color=color)\n annotator.im.save(fname) # save\n\n\ndef plot_lr_scheduler(optimizer, scheduler, epochs=300, save_dir=\"\"):\n \"\"\"Simulates and plots LR schedule over epochs, saving figure to `save_dir`.\"\"\"\n optimizer, scheduler = copy(optimizer), copy(scheduler) # do not modify originals\n y = []\n for _ in range(epochs):\n scheduler.step()\n y.append(optimizer.param_groups[0][\"lr\"])\n plt.plot(y, \".-\", label=\"LR\")\n plt.xlabel(\"epoch\")\n plt.ylabel(\"LR\")\n plt.grid()\n plt.xlim(0, epochs)\n plt.ylim(0)\n plt.savefig(Path(save_dir) / \"LR.png\", dpi=200)\n plt.close()\n\n\ndef plot_val_txt(): # from utils.plots import *; plot_val()\n \"\"\"Plots 2D and 1D histograms of object center locations from 'val.txt', saving as 'hist2d.png' and 'hist1d.png'.\"\"\"\n x = np.loadtxt(\"val.txt\", dtype=np.float32)\n box = xyxy2xywh(x[:, :4])\n cx, cy = box[:, 0], box[:, 1]\n\n fig, ax = plt.subplots(1, 1, figsize=(6, 6), tight_layout=True)\n ax.hist2d(cx, cy, bins=600, cmax=10, cmin=0)\n ax.set_aspect(\"equal\")\n plt.savefig(\"hist2d.png\", dpi=300)\n\n _fig, ax = plt.subplots(1, 2, figsize=(12, 6), tight_layout=True)\n ax[0].hist(cx, bins=600)\n ax[1].hist(cy, bins=600)\n plt.savefig(\"hist1d.png\", dpi=200)\n\n\ndef plot_targets_txt(): # from utils.plots import *; plot_targets_txt()\n \"\"\"Plots histograms for target attributes from 'targets.txt' and saves as 'targets.jpg'.\"\"\"\n x = np.loadtxt(\"targets.txt\", dtype=np.float32).T\n s = [\"x targets\", \"y targets\", \"width targets\", \"height targets\"]\n _fig, ax = plt.subplots(2, 2, figsize=(8, 8), tight_layout=True)\n ax = ax.ravel()\n for i in range(4):\n ax[i].hist(x[i], bins=100, label=f\"{x[i].mean():.3g} +/- {x[i].std():.3g}\")\n ax[i].legend()\n ax[i].set_title(s[i])\n plt.savefig(\"targets.jpg\", dpi=200)\n\n\ndef plot_val_study(file=\"\", dir=\"\", x=None): # from utils.plots import *; plot_val_study()\n \"\"\"Plots validation study results from 'study*.txt' files, comparing model performance and speed.\"\"\"\n save_dir = Path(file).parent if file else Path(dir)\n plot2 = False # plot additional results\n if plot2:\n ax = plt.subplots(2, 4, figsize=(10, 6), tight_layout=True)[1].ravel()\n\n _fig2, ax2 = plt.subplots(1, 1, figsize=(8, 4), tight_layout=True)\n # for f in [save_dir / f'study_coco_{x}.txt' for x in ['yolov5n6', 'yolov5s6', 'yolov5m6', 'yolov5l6', 'yolov5x6']]:\n for f in sorted(save_dir.glob(\"study*.txt\")):\n y = np.loadtxt(f, dtype=np.float32, usecols=[0, 1, 2, 3, 7, 8, 9], ndmin=2).T\n x = np.arange(y.shape[1]) if x is None else np.array(x)\n if plot2:\n s = [\"P\", \"R\", \"mAP@.5\", \"mAP@.5:.95\", \"t_preprocess (ms/img)\", \"t_inference (ms/img)\", \"t_NMS (ms/img)\"]\n for i in range(7):\n ax[i].plot(x, y[i], \".-\", linewidth=2, markersize=8)\n ax[i].set_title(s[i])\n\n j = y[3].argmax() + 1\n ax2.plot(\n y[5, 1:j],\n y[3, 1:j] * 1e2,\n \".-\",\n linewidth=2,\n markersize=8,\n label=f.stem.replace(\"study_coco_\", \"\").replace(\"yolo\", \"YOLO\"),\n )\n\n ax2.plot(\n 1e3 / np.array([209, 140, 97, 58, 35, 18]),\n [34.6, 40.5, 43.0, 47.5, 49.7, 51.5],\n \"k.-\",\n linewidth=2,\n markersize=8,\n alpha=0.25,\n label=\"EfficientDet\",\n )\n\n ax2.grid(alpha=0.2)\n ax2.set_yticks(np.arange(20, 60, 5))\n ax2.set_xlim(0, 57)\n ax2.set_ylim(25, 55)\n ax2.set_xlabel(\"GPU Speed (ms/img)\")\n ax2.set_ylabel(\"COCO AP val\")\n ax2.legend(loc=\"lower right\")\n f = save_dir / \"study.png\"\n print(f\"Saving {f}...\")\n plt.savefig(f, dpi=300)\n\n\n@TryExcept() # known issue https://github.com/ultralytics/yolov5/issues/5395\ndef plot_labels(labels, names=(), save_dir=Path(\"\")):\n \"\"\"Plots dataset labels correlogram, class distribution, and label geometry; saves to `save_dir`.\"\"\"\n LOGGER.info(f\"Plotting labels to {save_dir / 'labels.jpg'}... \")\n c, b = labels[:, 0], labels[:, 1:].transpose() # classes, boxes\n nc = int(c.max() + 1) # number of classes\n x = pd.DataFrame(b.transpose(), columns=[\"x\", \"y\", \"width\", \"height\"])\n\n # seaborn correlogram\n sn.pairplot(x, corner=True, diag_kind=\"auto\", kind=\"hist\", diag_kws=dict(bins=50), plot_kws=dict(pmax=0.9))\n plt.savefig(save_dir / \"labels_correlogram.jpg\", dpi=200)\n plt.close()\n\n # matplotlib labels\n matplotlib.use(\"svg\") # faster\n ax = plt.subplots(2, 2, figsize=(8, 8), tight_layout=True)[1].ravel()\n y = ax[0].hist(c, bins=np.linspace(0, nc, nc + 1) - 0.5, rwidth=0.8)\n with contextlib.suppress(Exception): # color histogram bars by class\n [y[2].patches[i].set_color([x / 255 for x in colors(i)]) for i in range(nc)] # known issue #3195\n ax[0].set_ylabel(\"instances\")\n if 0 < len(names) < 30:\n ax[0].set_xticks(range(len(names)))\n ax[0].set_xticklabels(list(names.values()), rotation=90, fontsize=10)\n else:\n ax[0].set_xlabel(\"classes\")\n sn.histplot(x, x=\"x\", y=\"y\", ax=ax[2], bins=50, pmax=0.9)\n sn.histplot(x, x=\"width\", y=\"height\", ax=ax[3], bins=50, pmax=0.9)\n\n # rectangles\n labels[:, 1:3] = 0.5 # center\n labels[:, 1:] = xywh2xyxy(labels[:, 1:]) * 2000\n img = Image.fromarray(np.ones((2000, 2000, 3), dtype=np.uint8) * 255)\n for cls, *box in labels[:1000]:\n ImageDraw.Draw(img).rectangle(box, width=1, outline=colors(cls)) # plot\n ax[1].imshow(img)\n ax[1].axis(\"off\")\n\n for a in [0, 1, 2, 3]:\n for s in [\"top\", \"right\", \"left\", \"bottom\"]:\n ax[a].spines[s].set_visible(False)\n\n plt.savefig(save_dir / \"labels.jpg\", dpi=200)\n matplotlib.use(\"Agg\")\n plt.close()\n\n\ndef imshow_cls(im, labels=None, pred=None, names=None, nmax=25, verbose=False, f=Path(\"images.jpg\")):\n \"\"\"Displays a grid of classification images with optional labels and predictions, saving to file.\"\"\"\n from utils.augmentations import denormalize\n\n names = names or [f\"class{i}\" for i in range(1000)]\n blocks = torch.chunk(\n denormalize(im.clone()).cpu().float(), len(im), dim=0\n ) # select batch index 0, block by channels\n n = min(len(blocks), nmax) # number of plots\n m = min(8, round(n**0.5)) # 8 x 8 default\n _fig, ax = plt.subplots(math.ceil(n / m), m) # 8 rows x n/8 cols\n ax = ax.ravel() if m > 1 else [ax]\n # plt.subplots_adjust(wspace=0.05, hspace=0.05)\n for i in range(n):\n ax[i].imshow(blocks[i].squeeze().permute((1, 2, 0)).numpy().clip(0.0, 1.0))\n ax[i].axis(\"off\")\n if labels is not None:\n s = names[labels[i]] + (f\"—{names[pred[i]]}\" if pred is not None else \"\")\n ax[i].set_title(s, fontsize=8, verticalalignment=\"top\")\n plt.savefig(f, dpi=300, bbox_inches=\"tight\")\n plt.close()\n if verbose:\n LOGGER.info(f\"Saving {f}\")\n if labels is not None:\n LOGGER.info(\"True: \" + \" \".join(f\"{names[i]:3s}\" for i in labels[:nmax]))\n if pred is not None:\n LOGGER.info(\"Predicted:\" + \" \".join(f\"{names[i]:3s}\" for i in pred[:nmax]))\n return f\n\n\ndef plot_evolve(evolve_csv=\"path/to/evolve.csv\"): # from utils.plots import *; plot_evolve()\n \"\"\"Plots evolution of hyperparameters from a CSV file, highlighting best results.\"\"\"\n evolve_csv = Path(evolve_csv)\n data = pd.read_csv(evolve_csv)\n keys = [x.strip() for x in data.columns]\n x = data.values\n f = fitness(x)\n j = np.argmax(f) # max fitness index\n plt.figure(figsize=(10, 12), tight_layout=True)\n matplotlib.rc(\"font\", **{\"size\": 8})\n print(f\"Best results from row {j} of {evolve_csv}:\")\n for i, k in enumerate(keys[7:]):\n v = x[:, 7 + i]\n mu = v[j] # best single result\n plt.subplot(6, 5, i + 1)\n plt.scatter(v, f, c=hist2d(v, f, 20), cmap=\"viridis\", alpha=0.8, edgecolors=\"none\")\n plt.plot(mu, f.max(), \"k+\", markersize=15)\n plt.title(f\"{k} = {mu:.3g}\", fontdict={\"size\": 9}) # limit to 40 characters\n if i % 5 != 0:\n plt.yticks([])\n print(f\"{k:>15}: {mu:.3g}\")\n f = evolve_csv.with_suffix(\".png\") # filename\n plt.savefig(f, dpi=200)\n plt.close()\n print(f\"Saved {f}\")\n\n\ndef plot_results(file=\"path/to/results.csv\", dir=\"\"):\n \"\"\"Plots training results from 'results.csv'; usage: `plot_results('path/to/results.csv')`.\"\"\"\n save_dir = Path(file).parent if file else Path(dir)\n fig, ax = plt.subplots(2, 5, figsize=(12, 6), tight_layout=True)\n ax = ax.ravel()\n files = list(save_dir.glob(\"results*.csv\"))\n assert len(files), f\"No results.csv files found in {save_dir.resolve()}, nothing to plot.\"\n for f in files:\n try:\n data = pd.read_csv(f)\n s = [x.strip() for x in data.columns]\n x = data.values[:, 0]\n for i, j in enumerate([1, 2, 3, 4, 5, 8, 9, 10, 6, 7]):\n y = data.values[:, j].astype(\"float\")\n # y[y == 0] = np.nan # don't show zero values\n ax[i].plot(x, y, marker=\".\", label=f.stem, linewidth=2, markersize=8) # actual results\n ax[i].plot(x, gaussian_filter1d(y, sigma=3), \":\", label=\"smooth\", linewidth=2) # smoothing line\n ax[i].set_title(s[j], fontsize=12)\n # if j in [8, 9, 10]: # share train and val loss y axes\n # ax[i].get_shared_y_axes().join(ax[i], ax[i - 5])\n except Exception as e:\n LOGGER.info(f\"Warning: Plotting error for {f}: {e}\")\n ax[1].legend()\n fig.savefig(save_dir / \"results.png\", dpi=200)\n plt.close()\n\n\ndef profile_idetection(start=0, stop=0, labels=(), save_dir=\"\"):\n \"\"\"Plots iDetection per-image logs from '*.txt', including metrics like storage and FPS; pass start, stop times,\n labels, and save_dir.\n \"\"\"\n ax = plt.subplots(2, 4, figsize=(12, 6), tight_layout=True)[1].ravel()\n s = [\"Images\", \"Free Storage (GB)\", \"RAM Usage (GB)\", \"Battery\", \"dt_raw (ms)\", \"dt_smooth (ms)\", \"real-world FPS\"]\n files = list(Path(save_dir).glob(\"frames*.txt\"))\n for fi, f in enumerate(files):\n try:\n results = np.loadtxt(f, ndmin=2).T[:, 90:-30] # clip first and last rows\n n = results.shape[1] # number of rows\n x = np.arange(start, min(stop, n) if stop else n)\n results = results[:, x]\n t = results[0] - results[0].min() # set t0=0s\n results[0] = x\n for i, a in enumerate(ax):\n if i < len(results):\n label = labels[fi] if len(labels) else f.stem.replace(\"frames_\", \"\")\n a.plot(t, results[i], marker=\".\", label=label, linewidth=1, markersize=5)\n a.set_title(s[i])\n a.set_xlabel(\"time (s)\")\n # if fi == len(files) - 1:\n # a.set_ylim(bottom=0)\n for side in [\"top\", \"right\"]:\n a.spines[side].set_visible(False)\n else:\n a.remove()\n except Exception as e:\n print(f\"Warning: Plotting error for {f}; {e}\")\n ax[1].legend()\n plt.savefig(Path(save_dir) / \"idetection_profile.png\", dpi=200)\n\n\ndef save_one_box(xyxy, im, file=Path(\"im.jpg\"), gain=1.02, pad=10, square=False, BGR=False, save=True):\n \"\"\"Saves/enhances a crop from `im` defined by `xyxy` to `file` or returns it; customizable with `gain`, `pad`,\n `square`, `BGR`.\n \"\"\"\n xyxy = torch.tensor(xyxy).view(-1, 4)\n b = xyxy2xywh(xyxy) # boxes\n if square:\n b[:, 2:] = b[:, 2:].max(1)[0].unsqueeze(1) # attempt rectangle to square\n b[:, 2:] = b[:, 2:] * gain + pad # box wh * gain + pad\n xyxy = xywh2xyxy(b).long()\n clip_boxes(xyxy, im.shape)\n crop = im[int(xyxy[0, 1]) : int(xyxy[0, 3]), int(xyxy[0, 0]) : int(xyxy[0, 2]), :: (1 if BGR else -1)]\n if save:\n file.parent.mkdir(parents=True, exist_ok=True) # make directory\n f = str(increment_path(file).with_suffix(\".jpg\"))\n # cv2.imwrite(f, crop) # save BGR, https://github.com/ultralytics/yolov5/issues/7007 chroma subsampling issue\n Image.fromarray(crop[..., ::-1]).save(f, quality=95, subsampling=0) # save RGB\n return crop\n" }, { "path": "utils/segment/__init__.py", "content": "# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license\n" }, { "path": "utils/segment/augmentations.py", "content": "# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license\n\"\"\"Image augmentation functions.\"\"\"\n\nimport math\nimport random\n\nimport cv2\nimport numpy as np\n\nfrom ..augmentations import box_candidates\nfrom ..general import resample_segments, segment2box\n\n\ndef mixup(im, labels, segments, im2, labels2, segments2):\n \"\"\"Applies MixUp augmentation by blending pairs of images, labels, and segments; see\n https://arxiv.org/pdf/1710.09412.pdf.\n \"\"\"\n r = np.random.beta(32.0, 32.0) # mixup ratio, alpha=beta=32.0\n im = (im * r + im2 * (1 - r)).astype(np.uint8)\n labels = np.concatenate((labels, labels2), 0)\n segments = np.concatenate((segments, segments2), 0)\n return im, labels, segments\n\n\ndef random_perspective(\n im, targets=(), segments=(), degrees=10, translate=0.1, scale=0.1, shear=10, perspective=0.0, border=(0, 0)\n):\n # torchvision.transforms.RandomAffine(degrees=(-10, 10), translate=(.1, .1), scale=(.9, 1.1), shear=(-10, 10))\n # targets = [cls, xyxy]\n \"\"\"Applies random perspective augmentation including rotation, translation, scale, and shear transformations.\"\"\"\n height = im.shape[0] + border[0] * 2 # shape(h,w,c)\n width = im.shape[1] + border[1] * 2\n\n # Center\n C = np.eye(3)\n C[0, 2] = -im.shape[1] / 2 # x translation (pixels)\n C[1, 2] = -im.shape[0] / 2 # y translation (pixels)\n\n # Perspective\n P = np.eye(3)\n P[2, 0] = random.uniform(-perspective, perspective) # x perspective (about y)\n P[2, 1] = random.uniform(-perspective, perspective) # y perspective (about x)\n\n # Rotation and Scale\n R = np.eye(3)\n a = random.uniform(-degrees, degrees)\n # a += random.choice([-180, -90, 0, 90]) # add 90deg rotations to small rotations\n s = random.uniform(1 - scale, 1 + scale)\n # s = 2 ** random.uniform(-scale, scale)\n R[:2] = cv2.getRotationMatrix2D(angle=a, center=(0, 0), scale=s)\n\n # Shear\n S = np.eye(3)\n S[0, 1] = math.tan(random.uniform(-shear, shear) * math.pi / 180) # x shear (deg)\n S[1, 0] = math.tan(random.uniform(-shear, shear) * math.pi / 180) # y shear (deg)\n\n # Translation\n T = np.eye(3)\n T[0, 2] = random.uniform(0.5 - translate, 0.5 + translate) * width # x translation (pixels)\n T[1, 2] = random.uniform(0.5 - translate, 0.5 + translate) * height # y translation (pixels)\n\n # Combined rotation matrix\n M = T @ S @ R @ P @ C # order of operations (right to left) is IMPORTANT\n if (border[0] != 0) or (border[1] != 0) or (M != np.eye(3)).any(): # image changed\n if perspective:\n im = cv2.warpPerspective(im, M, dsize=(width, height), borderValue=(114, 114, 114))\n else: # affine\n im = cv2.warpAffine(im, M[:2], dsize=(width, height), borderValue=(114, 114, 114))\n\n new_segments = []\n if n := len(targets):\n new = np.zeros((n, 4))\n segments = resample_segments(segments) # upsample\n for i, segment in enumerate(segments):\n xy = np.ones((len(segment), 3))\n xy[:, :2] = segment\n xy = xy @ M.T # transform\n xy = xy[:, :2] / xy[:, 2:3] if perspective else xy[:, :2] # perspective rescale or affine\n\n # clip\n new[i] = segment2box(xy, width, height)\n new_segments.append(xy)\n\n # filter candidates\n i = box_candidates(box1=targets[:, 1:5].T * s, box2=new.T, area_thr=0.01)\n targets = targets[i]\n targets[:, 1:5] = new[i]\n new_segments = np.array(new_segments)[i]\n\n return im, targets, new_segments\n" }, { "path": "utils/segment/dataloaders.py", "content": "# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license\n\"\"\"Dataloaders.\"\"\"\n\nimport os\nimport random\n\nimport cv2\nimport numpy as np\nimport torch\nfrom torch.utils.data import DataLoader, distributed\n\nfrom ..augmentations import augment_hsv, copy_paste, letterbox\nfrom ..dataloaders import InfiniteDataLoader, LoadImagesAndLabels, seed_worker\nfrom ..general import LOGGER, xyn2xy, xywhn2xyxy, xyxy2xywhn\nfrom ..torch_utils import torch_distributed_zero_first\nfrom .augmentations import mixup, random_perspective\n\nRANK = int(os.getenv(\"RANK\", -1))\n\n\ndef create_dataloader(\n path,\n imgsz,\n batch_size,\n stride,\n single_cls=False,\n hyp=None,\n augment=False,\n cache=False,\n pad=0.0,\n rect=False,\n rank=-1,\n workers=8,\n image_weights=False,\n quad=False,\n prefix=\"\",\n shuffle=False,\n mask_downsample_ratio=1,\n overlap_mask=False,\n seed=0,\n):\n \"\"\"Creates a DataLoader for images and labels with optional augmentations and distributed sampling.\"\"\"\n if rect and shuffle:\n LOGGER.warning(\"WARNING ⚠️ --rect is incompatible with DataLoader shuffle, setting shuffle=False\")\n shuffle = False\n with torch_distributed_zero_first(rank): # init dataset *.cache only once if DDP\n dataset = LoadImagesAndLabelsAndMasks(\n path,\n imgsz,\n batch_size,\n augment=augment, # augmentation\n hyp=hyp, # hyperparameters\n rect=rect, # rectangular batches\n cache_images=cache,\n single_cls=single_cls,\n stride=int(stride),\n pad=pad,\n image_weights=image_weights,\n prefix=prefix,\n downsample_ratio=mask_downsample_ratio,\n overlap=overlap_mask,\n )\n\n batch_size = min(batch_size, len(dataset))\n nd = torch.cuda.device_count() # number of CUDA devices\n nw = min([os.cpu_count() // max(nd, 1), batch_size if batch_size > 1 else 0, workers]) # number of workers\n sampler = None if rank == -1 else distributed.DistributedSampler(dataset, shuffle=shuffle)\n loader = DataLoader if image_weights else InfiniteDataLoader # only DataLoader allows for attribute updates\n generator = torch.Generator()\n generator.manual_seed(6148914691236517205 + seed + RANK)\n return loader(\n dataset,\n batch_size=batch_size,\n shuffle=shuffle and sampler is None,\n num_workers=nw,\n sampler=sampler,\n pin_memory=True,\n collate_fn=LoadImagesAndLabelsAndMasks.collate_fn4 if quad else LoadImagesAndLabelsAndMasks.collate_fn,\n worker_init_fn=seed_worker,\n generator=generator,\n ), dataset\n\n\nclass LoadImagesAndLabelsAndMasks(LoadImagesAndLabels): # for training/testing\n \"\"\"Loads images, labels, and masks for training/testing with optional augmentations including mosaic and mixup.\"\"\"\n\n def __init__(\n self,\n path,\n img_size=640,\n batch_size=16,\n augment=False,\n hyp=None,\n rect=False,\n image_weights=False,\n cache_images=False,\n single_cls=False,\n stride=32,\n pad=0,\n min_items=0,\n prefix=\"\",\n downsample_ratio=1,\n overlap=False,\n ):\n \"\"\"Initializes image, label, and mask loading for training/testing with optional augmentations.\"\"\"\n super().__init__(\n path,\n img_size,\n batch_size,\n augment,\n hyp,\n rect,\n image_weights,\n cache_images,\n single_cls,\n stride,\n pad,\n min_items,\n prefix,\n )\n self.downsample_ratio = downsample_ratio\n self.overlap = overlap\n\n def __getitem__(self, index):\n \"\"\"Fetches the dataset item at a given index, handling linear, shuffled, or image-weighted indexing.\"\"\"\n index = self.indices[index] # linear, shuffled, or image_weights\n\n hyp = self.hyp\n if mosaic := self.mosaic and random.random() < hyp[\"mosaic\"]:\n # Load mosaic\n img, labels, segments = self.load_mosaic(index)\n shapes = None\n\n # MixUp augmentation\n if random.random() < hyp[\"mixup\"]:\n img, labels, segments = mixup(img, labels, segments, *self.load_mosaic(random.randint(0, self.n - 1)))\n\n else:\n # Load image\n img, (h0, w0), (h, w) = self.load_image(index)\n\n # Letterbox\n shape = self.batch_shapes[self.batch[index]] if self.rect else self.img_size # final letterboxed shape\n img, ratio, pad = letterbox(img, shape, auto=False, scaleup=self.augment)\n shapes = (h0, w0), ((h / h0, w / w0), pad) # for COCO mAP rescaling\n\n labels = self.labels[index].copy()\n # [array, array, ....], array.shape=(num_points, 2), xyxyxyxy\n segments = self.segments[index].copy()\n if len(segments):\n for i_s in range(len(segments)):\n segments[i_s] = xyn2xy(\n segments[i_s],\n ratio[0] * w,\n ratio[1] * h,\n padw=pad[0],\n padh=pad[1],\n )\n if labels.size: # normalized xywh to pixel xyxy format\n labels[:, 1:] = xywhn2xyxy(labels[:, 1:], ratio[0] * w, ratio[1] * h, padw=pad[0], padh=pad[1])\n\n if self.augment:\n img, labels, segments = random_perspective(\n img,\n labels,\n segments=segments,\n degrees=hyp[\"degrees\"],\n translate=hyp[\"translate\"],\n scale=hyp[\"scale\"],\n shear=hyp[\"shear\"],\n perspective=hyp[\"perspective\"],\n )\n\n nl = len(labels) # number of labels\n masks = []\n if nl:\n labels[:, 1:5] = xyxy2xywhn(labels[:, 1:5], w=img.shape[1], h=img.shape[0], clip=True, eps=1e-3)\n if self.overlap:\n masks, sorted_idx = polygons2masks_overlap(\n img.shape[:2], segments, downsample_ratio=self.downsample_ratio\n )\n masks = masks[None] # (640, 640) -> (1, 640, 640)\n labels = labels[sorted_idx]\n else:\n masks = polygons2masks(img.shape[:2], segments, color=1, downsample_ratio=self.downsample_ratio)\n\n masks = (\n torch.from_numpy(masks)\n if len(masks)\n else torch.zeros(\n 1 if self.overlap else nl, img.shape[0] // self.downsample_ratio, img.shape[1] // self.downsample_ratio\n )\n )\n # TODO: albumentations support\n if self.augment:\n # Albumentations\n # there are some augmentation that won't change boxes and masks,\n # so just be it for now.\n img, labels = self.albumentations(img, labels)\n nl = len(labels) # update after albumentations\n\n # HSV color-space\n augment_hsv(img, hgain=hyp[\"hsv_h\"], sgain=hyp[\"hsv_s\"], vgain=hyp[\"hsv_v\"])\n\n # Flip up-down\n if random.random() < hyp[\"flipud\"]:\n img = np.flipud(img)\n if nl:\n labels[:, 2] = 1 - labels[:, 2]\n masks = torch.flip(masks, dims=[1])\n\n # Flip left-right\n if random.random() < hyp[\"fliplr\"]:\n img = np.fliplr(img)\n if nl:\n labels[:, 1] = 1 - labels[:, 1]\n masks = torch.flip(masks, dims=[2])\n\n # Cutouts # labels = cutout(img, labels, p=0.5)\n\n labels_out = torch.zeros((nl, 6))\n if nl:\n labels_out[:, 1:] = torch.from_numpy(labels)\n\n # Convert\n img = img.transpose((2, 0, 1))[::-1] # HWC to CHW, BGR to RGB\n img = np.ascontiguousarray(img)\n\n return (torch.from_numpy(img), labels_out, self.im_files[index], shapes, masks)\n\n def load_mosaic(self, index):\n \"\"\"Loads 4-image mosaic for YOLOv3 training, combining 1 target image with 3 random images within specified\n border constraints.\n \"\"\"\n labels4, segments4 = [], []\n s = self.img_size\n yc, xc = (int(random.uniform(-x, 2 * s + x)) for x in self.mosaic_border) # mosaic center x, y\n\n # 3 additional image indices\n indices = [index] + random.choices(self.indices, k=3) # 3 additional image indices\n for i, index in enumerate(indices):\n # Load image\n img, _, (h, w) = self.load_image(index)\n\n # place img in img4\n if i == 0: # top left\n img4 = np.full((s * 2, s * 2, img.shape[2]), 114, dtype=np.uint8) # base image with 4 tiles\n x1a, y1a, x2a, y2a = max(xc - w, 0), max(yc - h, 0), xc, yc # xmin, ymin, xmax, ymax (large image)\n x1b, y1b, x2b, y2b = w - (x2a - x1a), h - (y2a - y1a), w, h # xmin, ymin, xmax, ymax (small image)\n elif i == 1: # top right\n x1a, y1a, x2a, y2a = xc, max(yc - h, 0), min(xc + w, s * 2), yc\n x1b, y1b, x2b, y2b = 0, h - (y2a - y1a), min(w, x2a - x1a), h\n elif i == 2: # bottom left\n x1a, y1a, x2a, y2a = max(xc - w, 0), yc, xc, min(s * 2, yc + h)\n x1b, y1b, x2b, y2b = w - (x2a - x1a), 0, w, min(y2a - y1a, h)\n elif i == 3: # bottom right\n x1a, y1a, x2a, y2a = xc, yc, min(xc + w, s * 2), min(s * 2, yc + h)\n x1b, y1b, x2b, y2b = 0, 0, min(w, x2a - x1a), min(y2a - y1a, h)\n\n img4[y1a:y2a, x1a:x2a] = img[y1b:y2b, x1b:x2b] # img4[ymin:ymax, xmin:xmax]\n padw = x1a - x1b\n padh = y1a - y1b\n\n labels, segments = self.labels[index].copy(), self.segments[index].copy()\n\n if labels.size:\n labels[:, 1:] = xywhn2xyxy(labels[:, 1:], w, h, padw, padh) # normalized xywh to pixel xyxy format\n segments = [xyn2xy(x, w, h, padw, padh) for x in segments]\n labels4.append(labels)\n segments4.extend(segments)\n\n # Concat/clip labels\n labels4 = np.concatenate(labels4, 0)\n for x in (labels4[:, 1:], *segments4):\n np.clip(x, 0, 2 * s, out=x) # clip when using random_perspective()\n # img4, labels4 = replicate(img4, labels4) # replicate\n\n # Augment\n img4, labels4, segments4 = copy_paste(img4, labels4, segments4, p=self.hyp[\"copy_paste\"])\n img4, labels4, segments4 = random_perspective(\n img4,\n labels4,\n segments4,\n degrees=self.hyp[\"degrees\"],\n translate=self.hyp[\"translate\"],\n scale=self.hyp[\"scale\"],\n shear=self.hyp[\"shear\"],\n perspective=self.hyp[\"perspective\"],\n border=self.mosaic_border,\n ) # border to remove\n return img4, labels4, segments4\n\n @staticmethod\n def collate_fn(batch):\n \"\"\"Batches images, labels, paths, shapes, and masks; modifies label indices for target image association.\"\"\"\n img, label, path, shapes, masks = zip(*batch) # transposed\n batched_masks = torch.cat(masks, 0)\n for i, l in enumerate(label):\n l[:, 0] = i # add target image index for build_targets()\n return torch.stack(img, 0), torch.cat(label, 0), path, shapes, batched_masks\n\n\ndef polygon2mask(img_size, polygons, color=1, downsample_ratio=1):\n \"\"\"\n Args:\n img_size (tuple): The image size.\n polygons (np.ndarray): [N, M], N is the number of polygons, M is the number of points(Be divided by 2).\n \"\"\"\n mask = np.zeros(img_size, dtype=np.uint8)\n polygons = np.asarray(polygons)\n polygons = polygons.astype(np.int32)\n shape = polygons.shape\n polygons = polygons.reshape(shape[0], -1, 2)\n cv2.fillPoly(mask, polygons, color=color)\n nh, nw = (img_size[0] // downsample_ratio, img_size[1] // downsample_ratio)\n # NOTE: fillPoly firstly then resize is trying the keep the same way\n # of loss calculation when mask-ratio=1.\n mask = cv2.resize(mask, (nw, nh))\n return mask\n\n\ndef polygons2masks(img_size, polygons, color, downsample_ratio=1):\n \"\"\"\n Args:\n img_size (tuple): The image size.\n polygons (list[np.ndarray]): each polygon is [N, M], N is the number of polygons, M is the number of points(Be\n divided by 2).\n \"\"\"\n masks = []\n for si in range(len(polygons)):\n mask = polygon2mask(img_size, [polygons[si].reshape(-1)], color, downsample_ratio)\n masks.append(mask)\n return np.array(masks)\n\n\ndef polygons2masks_overlap(img_size, segments, downsample_ratio=1):\n \"\"\"Return a (640, 640) overlap mask.\"\"\"\n masks = np.zeros(\n (img_size[0] // downsample_ratio, img_size[1] // downsample_ratio),\n dtype=np.int32 if len(segments) > 255 else np.uint8,\n )\n areas = []\n ms = []\n for si in range(len(segments)):\n mask = polygon2mask(\n img_size,\n [segments[si].reshape(-1)],\n downsample_ratio=downsample_ratio,\n color=1,\n )\n ms.append(mask)\n areas.append(mask.sum())\n areas = np.asarray(areas)\n index = np.argsort(-areas)\n ms = np.array(ms)[index]\n for i in range(len(segments)):\n mask = ms[i] * (i + 1)\n masks = masks + mask\n masks = np.clip(masks, a_min=0, a_max=i + 1)\n return masks, index\n" }, { "path": "utils/segment/general.py", "content": "# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license\n\nimport cv2\nimport numpy as np\nimport torch\nimport torch.nn.functional as F\n\n\ndef crop_mask(masks, boxes):\n \"\"\"\"Crop\" predicted masks by zeroing out everything not in the predicted bbox. Vectorized by Chong (thanks Chong).\n\n Args:\n - masks should be a size [n, h, w] tensor of masks\n - boxes should be a size [n, 4] tensor of bbox coords in relative point form\n \"\"\"\n _n, h, w = masks.shape\n x1, y1, x2, y2 = torch.chunk(boxes[:, :, None], 4, 1) # x1 shape(1,1,n)\n r = torch.arange(w, device=masks.device, dtype=x1.dtype)[None, None, :] # rows shape(1,w,1)\n c = torch.arange(h, device=masks.device, dtype=x1.dtype)[None, :, None] # cols shape(h,1,1)\n\n return masks * ((r >= x1) * (r < x2) * (c >= y1) * (c < y2))\n\n\ndef process_mask_upsample(protos, masks_in, bboxes, shape):\n \"\"\"Crop after upsample. protos: [mask_dim, mask_h, mask_w] masks_in: [n, mask_dim], n is number of masks after nms\n bboxes: [n, 4], n is number of masks after nms shape: input_image_size, (h, w).\n\n return: h, w, n\n \"\"\"\n c, mh, mw = protos.shape # CHW\n masks = (masks_in @ protos.float().view(c, -1)).sigmoid().view(-1, mh, mw)\n masks = F.interpolate(masks[None], shape, mode=\"bilinear\", align_corners=False)[0] # CHW\n masks = crop_mask(masks, bboxes) # CHW\n return masks.gt_(0.5)\n\n\ndef process_mask(protos, masks_in, bboxes, shape, upsample=False):\n \"\"\"Crop before upsample. proto_out: [mask_dim, mask_h, mask_w] out_masks: [n, mask_dim], n is number of masks after\n nms bboxes: [n, 4], n is number of masks after nms shape:input_image_size, (h, w).\n\n return: h, w, n\n \"\"\"\n c, mh, mw = protos.shape # CHW\n ih, iw = shape\n masks = (masks_in @ protos.float().view(c, -1)).sigmoid().view(-1, mh, mw) # CHW\n\n downsampled_bboxes = bboxes.clone()\n downsampled_bboxes[:, 0] *= mw / iw\n downsampled_bboxes[:, 2] *= mw / iw\n downsampled_bboxes[:, 3] *= mh / ih\n downsampled_bboxes[:, 1] *= mh / ih\n\n masks = crop_mask(masks, downsampled_bboxes) # CHW\n if upsample:\n masks = F.interpolate(masks[None], shape, mode=\"bilinear\", align_corners=False)[0] # CHW\n return masks.gt_(0.5)\n\n\ndef process_mask_native(protos, masks_in, bboxes, shape):\n \"\"\"Crop after upsample. protos: [mask_dim, mask_h, mask_w] masks_in: [n, mask_dim], n is number of masks after nms\n bboxes: [n, 4], n is number of masks after nms shape: input_image_size, (h, w).\n\n return: h, w, n\n \"\"\"\n c, mh, mw = protos.shape # CHW\n masks = (masks_in @ protos.float().view(c, -1)).sigmoid().view(-1, mh, mw)\n gain = min(mh / shape[0], mw / shape[1]) # gain = old / new\n pad = (mw - shape[1] * gain) / 2, (mh - shape[0] * gain) / 2 # wh padding\n top, left = int(pad[1]), int(pad[0]) # y, x\n bottom, right = int(mh - pad[1]), int(mw - pad[0])\n masks = masks[:, top:bottom, left:right]\n\n masks = F.interpolate(masks[None], shape, mode=\"bilinear\", align_corners=False)[0] # CHW\n masks = crop_mask(masks, bboxes) # CHW\n return masks.gt_(0.5)\n\n\ndef scale_image(im1_shape, masks, im0_shape, ratio_pad=None):\n \"\"\"Img1_shape: model input shape, [h, w] img0_shape: origin pic shape, [h, w, 3] masks: [h, w, num].\"\"\"\n # Rescale coordinates (xyxy) from im1_shape to im0_shape\n if ratio_pad is None: # calculate from im0_shape\n gain = min(im1_shape[0] / im0_shape[0], im1_shape[1] / im0_shape[1]) # gain = old / new\n pad = (im1_shape[1] - im0_shape[1] * gain) / 2, (im1_shape[0] - im0_shape[0] * gain) / 2 # wh padding\n else:\n pad = ratio_pad[1]\n top, left = int(pad[1]), int(pad[0]) # y, x\n bottom, right = int(im1_shape[0] - pad[1]), int(im1_shape[1] - pad[0])\n\n if len(masks.shape) < 2:\n raise ValueError(f'\"len of masks shape\" should be 2 or 3, but got {len(masks.shape)}')\n masks = masks[top:bottom, left:right]\n # masks = masks.permute(2, 0, 1).contiguous()\n # masks = F.interpolate(masks[None], im0_shape[:2], mode='bilinear', align_corners=False)[0]\n # masks = masks.permute(1, 2, 0).contiguous()\n masks = cv2.resize(masks, (im0_shape[1], im0_shape[0]))\n\n if len(masks.shape) == 2:\n masks = masks[:, :, None]\n return masks\n\n\ndef mask_iou(mask1, mask2, eps=1e-7):\n \"\"\"mask1: [N, n] m1 means number of predicted objects mask2: [M, n] m2 means number of gt objects Note: n means\n image_w x image_h.\n\n return: masks iou, [N, M]\n \"\"\"\n intersection = torch.matmul(mask1, mask2.t()).clamp(0)\n union = (mask1.sum(1)[:, None] + mask2.sum(1)[None]) - intersection # (area1 + area2) - intersection\n return intersection / (union + eps)\n\n\ndef masks_iou(mask1, mask2, eps=1e-7):\n \"\"\"mask1: [N, n] m1 means number of predicted objects mask2: [N, n] m2 means number of gt objects Note: n means\n image_w x image_h.\n\n return: masks iou, (N, )\n \"\"\"\n intersection = (mask1 * mask2).sum(1).clamp(0) # (N, )\n union = (mask1.sum(1) + mask2.sum(1))[None] - intersection # (area1 + area2) - intersection\n return intersection / (union + eps)\n\n\ndef masks2segments(masks, strategy=\"largest\"):\n \"\"\"Converts binary masks to polygon segments with 'largest' or 'concat' strategies, returning lists of (n,xy)\n coordinates.\n \"\"\"\n segments = []\n for x in masks.int().cpu().numpy().astype(\"uint8\"):\n c = cv2.findContours(x, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)[0]\n if c:\n if strategy == \"concat\": # concatenate all segments\n c = np.concatenate([x.reshape(-1, 2) for x in c])\n elif strategy == \"largest\": # select largest segment\n c = np.array(c[np.array([len(x) for x in c]).argmax()]).reshape(-1, 2)\n else:\n c = np.zeros((0, 2)) # no segments found\n segments.append(c.astype(\"float32\"))\n return segments\n" }, { "path": "utils/segment/loss.py", "content": "# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nfrom ..general import xywh2xyxy\nfrom ..loss import FocalLoss, smooth_BCE\nfrom ..metrics import bbox_iou\nfrom ..torch_utils import de_parallel\nfrom .general import crop_mask\n\n\nclass ComputeLoss:\n \"\"\"Computes classification, box regression, objectness, and segmentation losses for YOLOv3 model predictions.\"\"\"\n\n def __init__(self, model, autobalance=False, overlap=False):\n \"\"\"Initializes ComputeLoss with model settings, optional autobalancing, and overlap handling.\"\"\"\n self.sort_obj_iou = False\n self.overlap = overlap\n device = next(model.parameters()).device # get model device\n h = model.hyp # hyperparameters\n\n # Define criteria\n BCEcls = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h[\"cls_pw\"]], device=device))\n BCEobj = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h[\"obj_pw\"]], device=device))\n\n # Class label smoothing https://arxiv.org/pdf/1902.04103.pdf eqn 3\n self.cp, self.cn = smooth_BCE(eps=h.get(\"label_smoothing\", 0.0)) # positive, negative BCE targets\n\n # Focal loss\n g = h[\"fl_gamma\"] # focal loss gamma\n if g > 0:\n BCEcls, BCEobj = FocalLoss(BCEcls, g), FocalLoss(BCEobj, g)\n\n m = de_parallel(model).model[-1] # Detect() module\n self.balance = {3: [4.0, 1.0, 0.4]}.get(m.nl, [4.0, 1.0, 0.25, 0.06, 0.02]) # P3-P7\n self.ssi = list(m.stride).index(16) if autobalance else 0 # stride 16 index\n self.BCEcls, self.BCEobj, self.gr, self.hyp, self.autobalance = BCEcls, BCEobj, 1.0, h, autobalance\n self.na = m.na # number of anchors\n self.nc = m.nc # number of classes\n self.nl = m.nl # number of layers\n self.nm = m.nm # number of masks\n self.anchors = m.anchors\n self.device = device\n\n def __call__(self, preds, targets, masks): # predictions, targets, model\n \"\"\"Computes losses given predictions, targets, and masks; returns tuple of class, box, object, and segmentation\n losses.\n \"\"\"\n p, proto = preds\n bs, nm, mask_h, mask_w = proto.shape # batch size, number of masks, mask height, mask width\n lcls = torch.zeros(1, device=self.device)\n lbox = torch.zeros(1, device=self.device)\n lobj = torch.zeros(1, device=self.device)\n lseg = torch.zeros(1, device=self.device)\n tcls, tbox, indices, anchors, tidxs, xywhn = self.build_targets(p, targets) # targets\n\n # Losses\n for i, pi in enumerate(p): # layer index, layer predictions\n b, a, gj, gi = indices[i] # image, anchor, gridy, gridx\n tobj = torch.zeros(pi.shape[:4], dtype=pi.dtype, device=self.device) # target obj\n\n if n := b.shape[0]:\n pxy, pwh, _, pcls, pmask = pi[b, a, gj, gi].split((2, 2, 1, self.nc, nm), 1) # subset of predictions\n\n # Box regression\n pxy = pxy.sigmoid() * 2 - 0.5\n pwh = (pwh.sigmoid() * 2) ** 2 * anchors[i]\n pbox = torch.cat((pxy, pwh), 1) # predicted box\n iou = bbox_iou(pbox, tbox[i], CIoU=True).squeeze() # iou(prediction, target)\n lbox += (1.0 - iou).mean() # iou loss\n\n # Objectness\n iou = iou.detach().clamp(0).type(tobj.dtype)\n if self.sort_obj_iou:\n j = iou.argsort()\n b, a, gj, gi, iou = b[j], a[j], gj[j], gi[j], iou[j]\n if self.gr < 1:\n iou = (1.0 - self.gr) + self.gr * iou\n tobj[b, a, gj, gi] = iou # iou ratio\n\n # Classification\n if self.nc > 1: # cls loss (only if multiple classes)\n t = torch.full_like(pcls, self.cn, device=self.device) # targets\n t[range(n), tcls[i]] = self.cp\n lcls += self.BCEcls(pcls, t) # BCE\n\n # Mask regression\n if tuple(masks.shape[-2:]) != (mask_h, mask_w): # downsample\n masks = F.interpolate(masks[None], (mask_h, mask_w), mode=\"nearest\")[0]\n marea = xywhn[i][:, 2:].prod(1) # mask width, height normalized\n mxyxy = xywh2xyxy(xywhn[i] * torch.tensor([mask_w, mask_h, mask_w, mask_h], device=self.device))\n for bi in b.unique():\n j = b == bi # matching index\n if self.overlap:\n mask_gti = torch.where(masks[bi][None] == tidxs[i][j].view(-1, 1, 1), 1.0, 0.0)\n else:\n mask_gti = masks[tidxs[i]][j]\n lseg += self.single_mask_loss(mask_gti, pmask[j], proto[bi], mxyxy[j], marea[j])\n\n obji = self.BCEobj(pi[..., 4], tobj)\n lobj += obji * self.balance[i] # obj loss\n if self.autobalance:\n self.balance[i] = self.balance[i] * 0.9999 + 0.0001 / obji.detach().item()\n\n if self.autobalance:\n self.balance = [x / self.balance[self.ssi] for x in self.balance]\n lbox *= self.hyp[\"box\"]\n lobj *= self.hyp[\"obj\"]\n lcls *= self.hyp[\"cls\"]\n lseg *= self.hyp[\"box\"] / bs\n\n loss = lbox + lobj + lcls + lseg\n return loss * bs, torch.cat((lbox, lseg, lobj, lcls)).detach()\n\n def single_mask_loss(self, gt_mask, pred, proto, xyxy, area):\n \"\"\"Computes single image mask loss using BCE, cropping based on bbox.\n\n Args: gt_mask[n,h,w], pred[n,nm], proto[nm,h,w], xyxy[n,4], area[n].\n \"\"\"\n pred_mask = (pred @ proto.view(self.nm, -1)).view(-1, *proto.shape[1:]) # (n,32) @ (32,80,80) -> (n,80,80)\n loss = F.binary_cross_entropy_with_logits(pred_mask, gt_mask, reduction=\"none\")\n return (crop_mask(loss, xyxy).mean(dim=(1, 2)) / area).mean()\n\n def build_targets(self, p, targets):\n \"\"\"Prepares targets for loss computation by appending anchor indices; supports optional target overlap handling.\n \"\"\"\n na, nt = self.na, targets.shape[0] # number of anchors, targets\n tcls, tbox, indices, anch, tidxs, xywhn = [], [], [], [], [], []\n gain = torch.ones(8, device=self.device) # normalized to gridspace gain\n ai = torch.arange(na, device=self.device).float().view(na, 1).repeat(1, nt) # same as .repeat_interleave(nt)\n if self.overlap:\n batch = p[0].shape[0]\n ti = []\n for i in range(batch):\n num = (targets[:, 0] == i).sum() # find number of targets of each image\n ti.append(torch.arange(num, device=self.device).float().view(1, num).repeat(na, 1) + 1) # (na, num)\n ti = torch.cat(ti, 1) # (na, nt)\n else:\n ti = torch.arange(nt, device=self.device).float().view(1, nt).repeat(na, 1)\n targets = torch.cat((targets.repeat(na, 1, 1), ai[..., None], ti[..., None]), 2) # append anchor indices\n\n g = 0.5 # bias\n off = (\n torch.tensor(\n [\n [0, 0],\n [1, 0],\n [0, 1],\n [-1, 0],\n [0, -1], # j,k,l,m\n # [1, 1], [1, -1], [-1, 1], [-1, -1], # jk,jm,lk,lm\n ],\n device=self.device,\n ).float()\n * g\n ) # offsets\n\n for i in range(self.nl):\n anchors, shape = self.anchors[i], p[i].shape\n gain[2:6] = torch.tensor(shape)[[3, 2, 3, 2]] # xyxy gain\n\n # Match targets to anchors\n t = targets * gain # shape(3,n,7)\n if nt:\n # Matches\n r = t[..., 4:6] / anchors[:, None] # wh ratio\n j = torch.max(r, 1 / r).max(2)[0] < self.hyp[\"anchor_t\"] # compare\n # j = wh_iou(anchors, t[:, 4:6]) > model.hyp['iou_t'] # iou(3,n)=wh_iou(anchors(3,2), gwh(n,2))\n t = t[j] # filter\n\n # Offsets\n gxy = t[:, 2:4] # grid xy\n gxi = gain[[2, 3]] - gxy # inverse\n j, k = ((gxy % 1 < g) & (gxy > 1)).T\n l, m = ((gxi % 1 < g) & (gxi > 1)).T\n j = torch.stack((torch.ones_like(j), j, k, l, m))\n t = t.repeat((5, 1, 1))[j]\n offsets = (torch.zeros_like(gxy)[None] + off[:, None])[j]\n else:\n t = targets[0]\n offsets = 0\n\n # Define\n bc, gxy, gwh, at = t.chunk(4, 1) # (image, class), grid xy, grid wh, anchors\n (a, tidx), (b, c) = at.long().T, bc.long().T # anchors, image, class\n gij = (gxy - offsets).long()\n gi, gj = gij.T # grid indices\n\n # Append\n indices.append((b, a, gj.clamp_(0, shape[2] - 1), gi.clamp_(0, shape[3] - 1))) # image, anchor, grid\n tbox.append(torch.cat((gxy - gij, gwh), 1)) # box\n anch.append(anchors[a]) # anchors\n tcls.append(c) # class\n tidxs.append(tidx)\n xywhn.append(torch.cat((gxy, gwh), 1) / gain[2:6]) # xywh normalized\n\n return tcls, tbox, indices, anch, tidxs, xywhn\n" }, { "path": "utils/segment/metrics.py", "content": "# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license\n\"\"\"Model validation metrics.\"\"\"\n\nimport numpy as np\n\nfrom ..metrics import ap_per_class\n\n\ndef fitness(x):\n \"\"\"Calculates model fitness as a weighted sum of 8 metrics, where `x` is an array of shape [N, 8].\"\"\"\n w = [0.0, 0.0, 0.1, 0.9, 0.0, 0.0, 0.1, 0.9]\n return (x[:, :8] * w).sum(1)\n\n\ndef ap_per_class_box_and_mask(\n tp_m,\n tp_b,\n conf,\n pred_cls,\n target_cls,\n plot=False,\n save_dir=\".\",\n names=(),\n):\n \"\"\"\n Args:\n tp_b: tp of boxes.\n tp_m: tp of masks.\n other arguments see `func: ap_per_class`.\n \"\"\"\n results_boxes = ap_per_class(\n tp_b, conf, pred_cls, target_cls, plot=plot, save_dir=save_dir, names=names, prefix=\"Box\"\n )[2:]\n results_masks = ap_per_class(\n tp_m, conf, pred_cls, target_cls, plot=plot, save_dir=save_dir, names=names, prefix=\"Mask\"\n )[2:]\n\n return {\n \"boxes\": {\n \"p\": results_boxes[0],\n \"r\": results_boxes[1],\n \"ap\": results_boxes[3],\n \"f1\": results_boxes[2],\n \"ap_class\": results_boxes[4],\n },\n \"masks\": {\n \"p\": results_masks[0],\n \"r\": results_masks[1],\n \"ap\": results_masks[3],\n \"f1\": results_masks[2],\n \"ap_class\": results_masks[4],\n },\n }\n\n\nclass Metric:\n \"\"\"Represents model evaluation metrics including precision, recall, F1 score, and average precision (AP) values.\"\"\"\n\n def __init__(self) -> None:\n \"\"\"Initializes Metric class attributes for precision, recall, F1 score, AP values, and AP class indices.\"\"\"\n self.p = [] # (nc, )\n self.r = [] # (nc, )\n self.f1 = [] # (nc, )\n self.all_ap = [] # (nc, 10)\n self.ap_class_index = [] # (nc, )\n\n @property\n def ap50(self):\n \"\"\"AP@0.5 of all classes.\n\n Returns:\n (nc, ) or [].\n \"\"\"\n return self.all_ap[:, 0] if len(self.all_ap) else []\n\n @property\n def ap(self):\n \"\"\"AP@0.5:0.95.\n\n Returns:\n (nc, ) or [].\n \"\"\"\n return self.all_ap.mean(1) if len(self.all_ap) else []\n\n @property\n def mp(self):\n \"\"\"Mean precision of all classes.\n\n Returns:\n float.\n \"\"\"\n return self.p.mean() if len(self.p) else 0.0\n\n @property\n def mr(self):\n \"\"\"Mean recall of all classes.\n\n Returns:\n float.\n \"\"\"\n return self.r.mean() if len(self.r) else 0.0\n\n @property\n def map50(self):\n \"\"\"Mean AP@0.5 of all classes.\n\n Returns:\n float.\n \"\"\"\n return self.all_ap[:, 0].mean() if len(self.all_ap) else 0.0\n\n @property\n def map(self):\n \"\"\"Mean AP@0.5:0.95 of all classes.\n\n Returns:\n float.\n \"\"\"\n return self.all_ap.mean() if len(self.all_ap) else 0.0\n\n def mean_results(self):\n \"\"\"Mean of results, return mp, mr, map50, map.\"\"\"\n return (self.mp, self.mr, self.map50, self.map)\n\n def class_result(self, i):\n \"\"\"Class-aware result, return p[i], r[i], ap50[i], ap[i].\"\"\"\n return (self.p[i], self.r[i], self.ap50[i], self.ap[i])\n\n def get_maps(self, nc):\n \"\"\"Calculates mean average precisions (mAPs) for each class; `nc`: num of classes; returns array of mAPs per\n class.\n \"\"\"\n maps = np.zeros(nc) + self.map\n for i, c in enumerate(self.ap_class_index):\n maps[c] = self.ap[i]\n return maps\n\n def update(self, results):\n \"\"\"\n Args:\n results: tuple(p, r, ap, f1, ap_class).\n \"\"\"\n p, r, all_ap, f1, ap_class_index = results\n self.p = p\n self.r = r\n self.all_ap = all_ap\n self.f1 = f1\n self.ap_class_index = ap_class_index\n\n\nclass Metrics:\n \"\"\"Metric for boxes and masks.\"\"\"\n\n def __init__(self) -> None:\n \"\"\"Initializes the Metrics class with separate Metric instances for boxes and masks.\"\"\"\n self.metric_box = Metric()\n self.metric_mask = Metric()\n\n def update(self, results):\n \"\"\"\n Args:\n results: Dict{'boxes': Dict{}, 'masks': Dict{}}.\n \"\"\"\n self.metric_box.update(list(results[\"boxes\"].values()))\n self.metric_mask.update(list(results[\"masks\"].values()))\n\n def mean_results(self):\n \"\"\"Calculates and returns the sum of mean results from 'metric_box' and 'metric_mask'.\"\"\"\n return self.metric_box.mean_results() + self.metric_mask.mean_results()\n\n def class_result(self, i):\n \"\"\"Combines and returns class-specific results from 'metric_box' and 'metric_mask' for class index 'i'.\"\"\"\n return self.metric_box.class_result(i) + self.metric_mask.class_result(i)\n\n def get_maps(self, nc):\n \"\"\"Returns combined mean Average Precision (mAP) scores for bounding boxes and masks for `nc` classes.\"\"\"\n return self.metric_box.get_maps(nc) + self.metric_mask.get_maps(nc)\n\n @property\n def ap_class_index(self):\n \"\"\"Returns the AP class index, identical for both boxes and masks.\"\"\"\n return self.metric_box.ap_class_index\n\n\nKEYS = [\n \"train/box_loss\",\n \"train/seg_loss\", # train loss\n \"train/obj_loss\",\n \"train/cls_loss\",\n \"metrics/precision(B)\",\n \"metrics/recall(B)\",\n \"metrics/mAP_0.5(B)\",\n \"metrics/mAP_0.5:0.95(B)\", # metrics\n \"metrics/precision(M)\",\n \"metrics/recall(M)\",\n \"metrics/mAP_0.5(M)\",\n \"metrics/mAP_0.5:0.95(M)\", # metrics\n \"val/box_loss\",\n \"val/seg_loss\", # val loss\n \"val/obj_loss\",\n \"val/cls_loss\",\n \"x/lr0\",\n \"x/lr1\",\n \"x/lr2\",\n]\n\nBEST_KEYS = [\n \"best/epoch\",\n \"best/precision(B)\",\n \"best/recall(B)\",\n \"best/mAP_0.5(B)\",\n \"best/mAP_0.5:0.95(B)\",\n \"best/precision(M)\",\n \"best/recall(M)\",\n \"best/mAP_0.5(M)\",\n \"best/mAP_0.5:0.95(M)\",\n]\n" }, { "path": "utils/segment/plots.py", "content": "# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license\n\nimport contextlib\nimport math\nfrom pathlib import Path\n\nimport cv2\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\nimport torch\n\nfrom .. import threaded\nfrom ..general import xywh2xyxy\nfrom ..plots import Annotator, colors\n\n\n@threaded\ndef plot_images_and_masks(images, targets, masks, paths=None, fname=\"images.jpg\", names=None):\n \"\"\"Plots a grid of images with annotations and masks, optionally resizing and saving the result.\"\"\"\n if isinstance(images, torch.Tensor):\n images = images.cpu().float().numpy()\n if isinstance(targets, torch.Tensor):\n targets = targets.cpu().numpy()\n if isinstance(masks, torch.Tensor):\n masks = masks.cpu().numpy().astype(int)\n\n max_size = 1920 # max image size\n max_subplots = 16 # max image subplots, i.e. 4x4\n bs, _, h, w = images.shape # batch size, _, height, width\n bs = min(bs, max_subplots) # limit plot images\n ns = np.ceil(bs**0.5) # number of subplots (square)\n if np.max(images[0]) <= 1:\n images *= 255 # de-normalise (optional)\n\n # Build Image\n mosaic = np.full((int(ns * h), int(ns * w), 3), 255, dtype=np.uint8) # init\n for i, im in enumerate(images):\n if i == max_subplots: # if last batch has fewer images than we expect\n break\n x, y = int(w * (i // ns)), int(h * (i % ns)) # block origin\n im = im.transpose(1, 2, 0)\n mosaic[y : y + h, x : x + w, :] = im\n\n # Resize (optional)\n scale = max_size / ns / max(h, w)\n if scale < 1:\n h = math.ceil(scale * h)\n w = math.ceil(scale * w)\n mosaic = cv2.resize(mosaic, tuple(int(x * ns) for x in (w, h)))\n\n # Annotate\n fs = int((h + w) * ns * 0.01) # font size\n annotator = Annotator(mosaic, line_width=round(fs / 10), font_size=fs, pil=True, example=names)\n for i in range(i + 1):\n x, y = int(w * (i // ns)), int(h * (i % ns)) # block origin\n annotator.rectangle([x, y, x + w, y + h], None, (255, 255, 255), width=2) # borders\n if paths:\n annotator.text([x + 5, y + 5], text=Path(paths[i]).name[:40], txt_color=(220, 220, 220)) # filenames\n if len(targets) > 0:\n idx = targets[:, 0] == i\n ti = targets[idx] # image targets\n\n boxes = xywh2xyxy(ti[:, 2:6]).T\n classes = ti[:, 1].astype(\"int\")\n labels = ti.shape[1] == 6 # labels if no conf column\n conf = None if labels else ti[:, 6] # check for confidence presence (label vs pred)\n\n if boxes.shape[1]:\n if boxes.max() <= 1.01: # if normalized with tolerance 0.01\n boxes[[0, 2]] *= w # scale to pixels\n boxes[[1, 3]] *= h\n elif scale < 1: # absolute coords need scale if image scales\n boxes *= scale\n boxes[[0, 2]] += x\n boxes[[1, 3]] += y\n for j, box in enumerate(boxes.T.tolist()):\n cls = classes[j]\n color = colors(cls)\n cls = names[cls] if names else cls\n if labels or conf[j] > 0.25: # 0.25 conf thresh\n label = f\"{cls}\" if labels else f\"{cls} {conf[j]:.1f}\"\n annotator.box_label(box, label, color=color)\n\n # Plot masks\n if len(masks):\n if masks.max() > 1.0: # mean that masks are overlap\n image_masks = masks[[i]] # (1, 640, 640)\n nl = len(ti)\n index = np.arange(nl).reshape(nl, 1, 1) + 1\n image_masks = np.repeat(image_masks, nl, axis=0)\n image_masks = np.where(image_masks == index, 1.0, 0.0)\n else:\n image_masks = masks[idx]\n\n im = np.asarray(annotator.im).copy()\n for j, box in enumerate(boxes.T.tolist()):\n if labels or conf[j] > 0.25: # 0.25 conf thresh\n color = colors(classes[j])\n mh, mw = image_masks[j].shape\n if mh != h or mw != w:\n mask = image_masks[j].astype(np.uint8)\n mask = cv2.resize(mask, (w, h))\n mask = mask.astype(bool)\n else:\n mask = image_masks[j].astype(bool)\n with contextlib.suppress(Exception):\n im[y : y + h, x : x + w, :][mask] = (\n im[y : y + h, x : x + w, :][mask] * 0.4 + np.array(color) * 0.6\n )\n annotator.fromarray(im)\n annotator.im.save(fname) # save\n\n\ndef plot_results_with_masks(file=\"path/to/results.csv\", dir=\"\", best=True):\n \"\"\"Plots training results from CSV, highlighting best/last metrics; supports custom file paths and directory saving.\n \"\"\"\n save_dir = Path(file).parent if file else Path(dir)\n fig, ax = plt.subplots(2, 8, figsize=(18, 6), tight_layout=True)\n ax = ax.ravel()\n files = list(save_dir.glob(\"results*.csv\"))\n assert len(files), f\"No results.csv files found in {save_dir.resolve()}, nothing to plot.\"\n for f in files:\n try:\n data = pd.read_csv(f)\n index = np.argmax(\n 0.9 * data.values[:, 8] + 0.1 * data.values[:, 7] + 0.9 * data.values[:, 12] + 0.1 * data.values[:, 11]\n )\n s = [x.strip() for x in data.columns]\n x = data.values[:, 0]\n for i, j in enumerate([1, 2, 3, 4, 5, 6, 9, 10, 13, 14, 15, 16, 7, 8, 11, 12]):\n y = data.values[:, j]\n # y[y == 0] = np.nan # don't show zero values\n ax[i].plot(x, y, marker=\".\", label=f.stem, linewidth=2, markersize=2)\n if best:\n # best\n ax[i].scatter(index, y[index], color=\"r\", label=f\"best:{index}\", marker=\"*\", linewidth=3)\n ax[i].set_title(s[j] + f\"\\n{round(y[index], 5)}\")\n else:\n # last\n ax[i].scatter(x[-1], y[-1], color=\"r\", label=\"last\", marker=\"*\", linewidth=3)\n ax[i].set_title(s[j] + f\"\\n{round(y[-1], 5)}\")\n # if j in [8, 9, 10]: # share train and val loss y axes\n # ax[i].get_shared_y_axes().join(ax[i], ax[i - 5])\n except Exception as e:\n print(f\"Warning: Plotting error for {f}: {e}\")\n ax[1].legend()\n fig.savefig(save_dir / \"results.png\", dpi=200)\n plt.close()\n" }, { "path": "utils/torch_utils.py", "content": "# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license\n\"\"\"PyTorch utils.\"\"\"\n\nimport math\nimport os\nimport platform\nimport subprocess\nimport time\nimport warnings\nfrom contextlib import contextmanager\nfrom copy import deepcopy\nfrom pathlib import Path\n\nimport torch\nimport torch.distributed as dist\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torch.nn.parallel import DistributedDataParallel as DDP\n\nfrom utils.general import LOGGER, check_version, colorstr, file_date, git_describe\n\nLOCAL_RANK = int(os.getenv(\"LOCAL_RANK\", -1)) # https://pytorch.org/docs/stable/elastic/run.html\nRANK = int(os.getenv(\"RANK\", -1))\nWORLD_SIZE = int(os.getenv(\"WORLD_SIZE\", 1))\n\ntry:\n import thop # for FLOPs computation\nexcept ImportError:\n thop = None\n\n# Suppress PyTorch warnings\nwarnings.filterwarnings(\"ignore\", message=\"User provided device_type of 'cuda', but CUDA is not available. Disabling\")\nwarnings.filterwarnings(\"ignore\", category=UserWarning)\n\n\ndef smart_inference_mode(torch_1_9=check_version(torch.__version__, \"1.9.0\")):\n \"\"\"Applies torch.inference_mode() if torch>=1.9.0 or torch.no_grad() otherwise as a decorator to functions.\"\"\"\n\n def decorate(fn):\n \"\"\"Applies torch.inference_mode() if torch>=1.9.0, otherwise torch.no_grad(), as a decorator to functions.\"\"\"\n return (torch.inference_mode if torch_1_9 else torch.no_grad)()(fn)\n\n return decorate\n\n\ndef smartCrossEntropyLoss(label_smoothing=0.0):\n \"\"\"Returns CrossEntropyLoss with optional label smoothing for torch>=1.10.0; warns if label smoothing used with\n older versions.\n \"\"\"\n if check_version(torch.__version__, \"1.10.0\"):\n return nn.CrossEntropyLoss(label_smoothing=label_smoothing)\n if label_smoothing > 0:\n LOGGER.warning(f\"WARNING ⚠️ label smoothing {label_smoothing} requires torch>=1.10.0\")\n return nn.CrossEntropyLoss()\n\n\ndef smart_DDP(model):\n \"\"\"Initializes DDP for a model with version checks; fails for torch==1.12.0 due to known issues.\n\n See https://github.com/ultralytics/yolov5/issues/8395.\n \"\"\"\n assert not check_version(torch.__version__, \"1.12.0\", pinned=True), (\n \"torch==1.12.0 torchvision==0.13.0 DDP training is not supported due to a known issue. \"\n \"Please upgrade or downgrade torch to use DDP. See https://github.com/ultralytics/yolov5/issues/8395\"\n )\n if check_version(torch.__version__, \"1.11.0\"):\n return DDP(model, device_ids=[LOCAL_RANK], output_device=LOCAL_RANK, static_graph=True)\n else:\n return DDP(model, device_ids=[LOCAL_RANK], output_device=LOCAL_RANK)\n\n\ndef reshape_classifier_output(model, n=1000):\n \"\"\"Reshapes the last layer of a model to have 'n' outputs; supports YOLOv3, ResNet, EfficientNet, adjusting Linear\n and Conv2d layers.\n \"\"\"\n from models.common import Classify\n\n name, m = list((model.model if hasattr(model, \"model\") else model).named_children())[-1] # last module\n if isinstance(m, Classify): # YOLOv3 Classify() head\n if m.linear.out_features != n:\n m.linear = nn.Linear(m.linear.in_features, n)\n elif isinstance(m, nn.Linear): # ResNet, EfficientNet\n if m.out_features != n:\n setattr(model, name, nn.Linear(m.in_features, n))\n elif isinstance(m, nn.Sequential):\n types = [type(x) for x in m]\n if nn.Linear in types:\n i = types.index(nn.Linear) # nn.Linear index\n if m[i].out_features != n:\n m[i] = nn.Linear(m[i].in_features, n)\n elif nn.Conv2d in types:\n i = types.index(nn.Conv2d) # nn.Conv2d index\n if m[i].out_channels != n:\n m[i] = nn.Conv2d(m[i].in_channels, n, m[i].kernel_size, m[i].stride, bias=m[i].bias is not None)\n\n\n@contextmanager\ndef torch_distributed_zero_first(local_rank: int):\n \"\"\"Context manager ensuring ordered execution in distributed training by synchronizing local masters first.\"\"\"\n if local_rank not in [-1, 0]:\n dist.barrier(device_ids=[local_rank])\n yield\n if local_rank == 0:\n dist.barrier(device_ids=[0])\n\n\ndef device_count():\n \"\"\"Returns the count of available CUDA devices; supports Linux and Windows, using nvidia-smi.\"\"\"\n assert platform.system() in (\"Linux\", \"Windows\"), \"device_count() only supported on Linux or Windows\"\n try:\n cmd = \"nvidia-smi -L | wc -l\" if platform.system() == \"Linux\" else 'nvidia-smi -L | find /c /v \"\"' # Windows\n return int(subprocess.run(cmd, shell=True, capture_output=True, check=True).stdout.decode().split()[-1])\n except Exception:\n return 0\n\n\ndef select_device(device=\"\", batch_size=0, newline=True):\n \"\"\"Selects the device for running models, handling CPU, GPU, and MPS with optional batch size divisibility check.\"\"\"\n s = f\"YOLOv3 🚀 {git_describe() or file_date()} Python-{platform.python_version()} torch-{torch.__version__} \"\n device = str(device).strip().lower().replace(\"cuda:\", \"\").replace(\"none\", \"\") # to string, 'cuda:0' to '0'\n cpu = device == \"cpu\"\n mps = device == \"mps\" # Apple Metal Performance Shaders (MPS)\n if cpu or mps:\n os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"-1\" # force torch.cuda.is_available() = False\n elif device: # non-cpu device requested\n os.environ[\"CUDA_VISIBLE_DEVICES\"] = device # set environment variable - must be before assert is_available()\n assert torch.cuda.is_available() and torch.cuda.device_count() >= len(device.replace(\",\", \"\")), (\n f\"Invalid CUDA '--device {device}' requested, use '--device cpu' or pass valid CUDA device(s)\"\n )\n\n if not cpu and not mps and torch.cuda.is_available(): # prefer GPU if available\n devices = device.split(\",\") if device else \"0\" # range(torch.cuda.device_count()) # i.e. 0,1,6,7\n n = len(devices) # device count\n if n > 1 and batch_size > 0: # check batch_size is divisible by device_count\n assert batch_size % n == 0, f\"batch-size {batch_size} not multiple of GPU count {n}\"\n space = \" \" * (len(s) + 1)\n for i, d in enumerate(devices):\n p = torch.cuda.get_device_properties(i)\n s += f\"{'' if i == 0 else space}CUDA:{d} ({p.name}, {p.total_memory / (1 << 20):.0f}MiB)\\n\" # bytes to MB\n arg = \"cuda:0\"\n elif mps and getattr(torch, \"has_mps\", False) and torch.backends.mps.is_available(): # prefer MPS if available\n s += \"MPS\\n\"\n arg = \"mps\"\n else: # revert to CPU\n s += \"CPU\\n\"\n arg = \"cpu\"\n\n if not newline:\n s = s.rstrip()\n LOGGER.info(s)\n return torch.device(arg)\n\n\ndef time_sync():\n \"\"\"Synchronizes PyTorch across available CUDA devices and returns current time in seconds.\"\"\"\n if torch.cuda.is_available():\n torch.cuda.synchronize()\n return time.time()\n\n\ndef profile(input, ops, n=10, device=None):\n \"\"\"YOLOv3 speed/memory/FLOPs profiler.\n\n Examples:\n input = torch.randn(16, 3, 640, 640)\n m1 = lambda x: x * torch.sigmoid(x)\n m2 = nn.SiLU()\n profile(input, [m1, m2], n=100) # profile over 100 iterations.\n \"\"\"\n results = []\n if not isinstance(device, torch.device):\n device = select_device(device)\n print(\n f\"{'Params':>12s}{'GFLOPs':>12s}{'GPU_mem (GB)':>14s}{'forward (ms)':>14s}{'backward (ms)':>14s}\"\n f\"{'input':>24s}{'output':>24s}\"\n )\n\n for x in input if isinstance(input, list) else [input]:\n x = x.to(device)\n x.requires_grad = True\n for m in ops if isinstance(ops, list) else [ops]:\n m = m.to(device) if hasattr(m, \"to\") else m # device\n m = m.half() if hasattr(m, \"half\") and isinstance(x, torch.Tensor) and x.dtype is torch.float16 else m\n tf, tb, t = 0, 0, [0, 0, 0] # dt forward, backward\n try:\n flops = thop.profile(m, inputs=(x,), verbose=False)[0] / 1e9 * 2 # GFLOPs\n except Exception:\n flops = 0\n\n try:\n for _ in range(n):\n t[0] = time_sync()\n y = m(x)\n t[1] = time_sync()\n try:\n _ = (sum(yi.sum() for yi in y) if isinstance(y, list) else y).sum().backward()\n t[2] = time_sync()\n except Exception: # no backward method\n # print(e) # for debug\n t[2] = float(\"nan\")\n tf += (t[1] - t[0]) * 1000 / n # ms per op forward\n tb += (t[2] - t[1]) * 1000 / n # ms per op backward\n mem = torch.cuda.memory_reserved() / 1e9 if torch.cuda.is_available() else 0 # (GB)\n s_in, s_out = (tuple(x.shape) if isinstance(x, torch.Tensor) else \"list\" for x in (x, y)) # shapes\n p = sum(x.numel() for x in m.parameters()) if isinstance(m, nn.Module) else 0 # parameters\n print(f\"{p:12}{flops:12.4g}{mem:>14.3f}{tf:14.4g}{tb:14.4g}{s_in!s:>24s}{s_out!s:>24s}\")\n results.append([p, flops, mem, tf, tb, s_in, s_out])\n except Exception as e:\n print(e)\n results.append(None)\n torch.cuda.empty_cache()\n return results\n\n\ndef is_parallel(model):\n \"\"\"Checks if a model is using DataParallel (DP) or DistributedDataParallel (DDP).\"\"\"\n return type(model) in (nn.parallel.DataParallel, nn.parallel.DistributedDataParallel)\n\n\ndef de_parallel(model):\n \"\"\"Returns a single-GPU model if input model is using DataParallel (DP) or DistributedDataParallel (DDP).\"\"\"\n return model.module if is_parallel(model) else model\n\n\ndef initialize_weights(model):\n \"\"\"Initializes weights for Conv2D, BatchNorm2d, and activation layers (Hardswish, LeakyReLU, ReLU, ReLU6, SiLU) in a\n model.\n \"\"\"\n for m in model.modules():\n t = type(m)\n if t is nn.Conv2d:\n pass # nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')\n elif t is nn.BatchNorm2d:\n m.eps = 1e-3\n m.momentum = 0.03\n elif t in [nn.Hardswish, nn.LeakyReLU, nn.ReLU, nn.ReLU6, nn.SiLU]:\n m.inplace = True\n\n\ndef find_modules(model, mclass=nn.Conv2d):\n \"\"\"Finds indices of layers in 'model' matching 'mclass'; default searches for 'nn.Conv2d'.\"\"\"\n return [i for i, m in enumerate(model.module_list) if isinstance(m, mclass)]\n\n\ndef sparsity(model):\n \"\"\"Calculates and returns the global sparsity of a model as the ratio of zero-valued parameters to total parameters.\n \"\"\"\n a, b = 0, 0\n for p in model.parameters():\n a += p.numel()\n b += (p == 0).sum()\n return b / a\n\n\ndef prune(model, amount=0.3):\n \"\"\"Prunes Conv2d layers in a model to a specified global sparsity using l1 unstructured pruning.\"\"\"\n import torch.nn.utils.prune as prune\n\n for name, m in model.named_modules():\n if isinstance(m, nn.Conv2d):\n prune.l1_unstructured(m, name=\"weight\", amount=amount) # prune\n prune.remove(m, \"weight\") # make permanent\n LOGGER.info(f\"Model pruned to {sparsity(model):.3g} global sparsity\")\n\n\ndef fuse_conv_and_bn(conv, bn):\n \"\"\"Fuses Conv2d and BatchNorm2d layers for efficiency; see https://tehnokv.com/posts/fusing-batchnorm-and-conv/.\"\"\"\n fusedconv = (\n nn.Conv2d(\n conv.in_channels,\n conv.out_channels,\n kernel_size=conv.kernel_size,\n stride=conv.stride,\n padding=conv.padding,\n dilation=conv.dilation,\n groups=conv.groups,\n bias=True,\n )\n .requires_grad_(False)\n .to(conv.weight.device)\n )\n\n # Prepare filters\n w_conv = conv.weight.clone().view(conv.out_channels, -1)\n w_bn = torch.diag(bn.weight.div(torch.sqrt(bn.eps + bn.running_var)))\n fusedconv.weight.copy_(torch.mm(w_bn, w_conv).view(fusedconv.weight.shape))\n\n # Prepare spatial bias\n b_conv = torch.zeros(conv.weight.size(0), device=conv.weight.device) if conv.bias is None else conv.bias\n b_bn = bn.bias - bn.weight.mul(bn.running_mean).div(torch.sqrt(bn.running_var + bn.eps))\n fusedconv.bias.copy_(torch.mm(w_bn, b_conv.reshape(-1, 1)).reshape(-1) + b_bn)\n\n return fusedconv\n\n\ndef model_info(model, verbose=False, imgsz=640):\n \"\"\"Prints model layers, parameters, gradients, and GFLOPs if verbose; handles various `imgsz`.\n\n Usage: model_info(model).\n \"\"\"\n n_p = sum(x.numel() for x in model.parameters()) # number parameters\n n_g = sum(x.numel() for x in model.parameters() if x.requires_grad) # number gradients\n if verbose:\n print(f\"{'layer':>5} {'name':>40} {'gradient':>9} {'parameters':>12} {'shape':>20} {'mu':>10} {'sigma':>10}\")\n for i, (name, p) in enumerate(model.named_parameters()):\n name = name.replace(\"module_list.\", \"\")\n print(\n \"%5g %40s %9s %12g %20s %10.3g %10.3g\"\n % (i, name, p.requires_grad, p.numel(), list(p.shape), p.mean(), p.std())\n )\n\n try: # FLOPs\n p = next(model.parameters())\n stride = max(int(model.stride.max()), 32) if hasattr(model, \"stride\") else 32 # max stride\n im = torch.empty((1, p.shape[1], stride, stride), device=p.device) # input image in BCHW format\n flops = thop.profile(deepcopy(model), inputs=(im,), verbose=False)[0] / 1e9 * 2 # stride GFLOPs\n imgsz = imgsz if isinstance(imgsz, list) else [imgsz, imgsz] # expand if int/float\n fs = f\", {flops * imgsz[0] / stride * imgsz[1] / stride:.1f} GFLOPs\" # 640x640 GFLOPs\n except Exception:\n fs = \"\"\n\n name = Path(model.yaml_file).stem.replace(\"yolov5\", \"YOLOv3\") if hasattr(model, \"yaml_file\") else \"Model\"\n LOGGER.info(f\"{name} summary: {len(list(model.modules()))} layers, {n_p} parameters, {n_g} gradients{fs}\")\n\n\ndef scale_img(img, ratio=1.0, same_shape=False, gs=32): # img(16,3,256,416)\n \"\"\"Scales and optionally pads an image tensor to a specified ratio, maintaining its aspect ratio constrained by\n `gs`.\n \"\"\"\n if ratio == 1.0:\n return img\n h, w = img.shape[2:]\n s = (int(h * ratio), int(w * ratio)) # new size\n img = F.interpolate(img, size=s, mode=\"bilinear\", align_corners=False) # resize\n if not same_shape: # pad/crop img\n h, w = (math.ceil(x * ratio / gs) * gs for x in (h, w))\n return F.pad(img, [0, w - s[1], 0, h - s[0]], value=0.447) # value = imagenet mean\n\n\ndef copy_attr(a, b, include=(), exclude=()):\n \"\"\"Copies attributes from object b to a, with options to include or exclude specific attributes.\"\"\"\n for k, v in b.__dict__.items():\n if (len(include) and k not in include) or k.startswith(\"_\") or k in exclude:\n continue\n else:\n setattr(a, k, v)\n\n\ndef smart_optimizer(model, name=\"Adam\", lr=0.001, momentum=0.9, decay=1e-5):\n \"\"\"Initializes a smart optimizer for YOLOv3 with custom parameter groups for different weight decays and biases.\"\"\"\n g = [], [], [] # optimizer parameter groups\n bn = tuple(v for k, v in nn.__dict__.items() if \"Norm\" in k) # normalization layers, i.e. BatchNorm2d()\n for v in model.modules():\n for p_name, p in v.named_parameters(recurse=0):\n if p_name == \"bias\": # bias (no decay)\n g[2].append(p)\n elif p_name == \"weight\" and isinstance(v, bn): # weight (no decay)\n g[1].append(p)\n else:\n g[0].append(p) # weight (with decay)\n\n if name == \"Adam\":\n optimizer = torch.optim.Adam(g[2], lr=lr, betas=(momentum, 0.999)) # adjust beta1 to momentum\n elif name == \"AdamW\":\n optimizer = torch.optim.AdamW(g[2], lr=lr, betas=(momentum, 0.999), weight_decay=0.0)\n elif name == \"RMSProp\":\n optimizer = torch.optim.RMSprop(g[2], lr=lr, momentum=momentum)\n elif name == \"SGD\":\n optimizer = torch.optim.SGD(g[2], lr=lr, momentum=momentum, nesterov=True)\n else:\n raise NotImplementedError(f\"Optimizer {name} not implemented.\")\n\n optimizer.add_param_group({\"params\": g[0], \"weight_decay\": decay}) # add g0 with weight_decay\n optimizer.add_param_group({\"params\": g[1], \"weight_decay\": 0.0}) # add g1 (BatchNorm2d weights)\n LOGGER.info(\n f\"{colorstr('optimizer:')} {type(optimizer).__name__}(lr={lr}) with parameter groups \"\n f\"{len(g[1])} weight(decay=0.0), {len(g[0])} weight(decay={decay}), {len(g[2])} bias\"\n )\n return optimizer\n\n\ndef smart_hub_load(repo=\"ultralytics/yolov5\", model=\"yolov5s\", **kwargs):\n \"\"\"Loads YOLO model from Ultralytics repo with smart error handling, supports `force_reload` on failure.\n\n See https://github.com/ultralytics/yolov5\n \"\"\"\n if check_version(torch.__version__, \"1.9.1\"):\n kwargs[\"skip_validation\"] = True # validation causes GitHub API rate limit errors\n if check_version(torch.__version__, \"1.12.0\"):\n kwargs[\"trust_repo\"] = True # argument required starting in torch 0.12\n try:\n return torch.hub.load(repo, model, **kwargs)\n except Exception:\n return torch.hub.load(repo, model, force_reload=True, **kwargs)\n\n\ndef smart_resume(ckpt, optimizer, ema=None, weights=\"yolov5s.pt\", epochs=300, resume=True):\n \"\"\"Resumes or fine-tunes training from a checkpoint with optimizer and EMA support; updates epochs based on\n progress.\n \"\"\"\n best_fitness = 0.0\n start_epoch = ckpt[\"epoch\"] + 1\n if ckpt[\"optimizer\"] is not None:\n optimizer.load_state_dict(ckpt[\"optimizer\"]) # optimizer\n best_fitness = ckpt[\"best_fitness\"]\n if ema and ckpt.get(\"ema\"):\n ema.ema.load_state_dict(ckpt[\"ema\"].float().state_dict()) # EMA\n ema.updates = ckpt[\"updates\"]\n if resume:\n assert start_epoch > 0, (\n f\"{weights} training to {epochs} epochs is finished, nothing to resume.\\n\"\n f\"Start a new training without --resume, i.e. 'python train.py --weights {weights}'\"\n )\n LOGGER.info(f\"Resuming training from {weights} from epoch {start_epoch} to {epochs} total epochs\")\n if epochs < start_epoch:\n LOGGER.info(f\"{weights} has been trained for {ckpt['epoch']} epochs. Fine-tuning for {epochs} more epochs.\")\n epochs += ckpt[\"epoch\"] # finetune additional epochs\n return best_fitness, start_epoch, epochs\n\n\nclass EarlyStopping:\n \"\"\"Monitors training to halt if no improvement in fitness metric is observed for a specified number of epochs.\"\"\"\n\n def __init__(self, patience=30):\n \"\"\"Initializes EarlyStopping to monitor training, halting if no improvement in 'patience' epochs, defaulting to\n 30.\n \"\"\"\n self.best_fitness = 0.0 # i.e. mAP\n self.best_epoch = 0\n self.patience = patience or float(\"inf\") # epochs to wait after fitness stops improving to stop\n self.possible_stop = False # possible stop may occur next epoch\n\n def __call__(self, epoch, fitness):\n \"\"\"Updates stopping criteria based on fitness; returns True to stop if no improvement in 'patience' epochs.\"\"\"\n if fitness >= self.best_fitness: # >= 0 to allow for early zero-fitness stage of training\n self.best_epoch = epoch\n self.best_fitness = fitness\n delta = epoch - self.best_epoch # epochs without improvement\n self.possible_stop = delta >= (self.patience - 1) # possible stop may occur next epoch\n stop = delta >= self.patience # stop training if patience exceeded\n if stop:\n LOGGER.info(\n f\"Stopping training early as no improvement observed in last {self.patience} epochs. \"\n f\"Best results observed at epoch {self.best_epoch}, best model saved as best.pt.\\n\"\n f\"To update EarlyStopping(patience={self.patience}) pass a new patience value, \"\n f\"i.e. `python train.py --patience 300` or use `--patience 0` to disable EarlyStopping.\"\n )\n return stop\n\n\nclass ModelEMA:\n \"\"\"Updated Exponential Moving Average (EMA) from https://github.com/rwightman/pytorch-image-models Keeps a moving\n average of everything in the model state_dict (parameters and buffers) For EMA details\n see https://www.tensorflow.org/api_docs/python/tf/train/ExponentialMovingAverage.\n \"\"\"\n\n def __init__(self, model, decay=0.9999, tau=2000, updates=0):\n \"\"\"Initializes EMA with model, optional decay (default 0.9999), tau (2000), and updates count, setting model to\n eval mode.\n \"\"\"\n self.ema = deepcopy(de_parallel(model)).eval() # FP32 EMA\n self.updates = updates # number of EMA updates\n self.decay = lambda x: decay * (1 - math.exp(-x / tau)) # decay exponential ramp (to help early epochs)\n for p in self.ema.parameters():\n p.requires_grad_(False)\n\n def update(self, model):\n \"\"\"Updates EMA parameters based on model weights, decay factor, and increment update count.\"\"\"\n self.updates += 1\n d = self.decay(self.updates)\n\n msd = de_parallel(model).state_dict() # model state_dict\n for k, v in self.ema.state_dict().items():\n if v.dtype.is_floating_point: # true for FP16 and FP32\n v *= d\n v += (1 - d) * msd[k].detach()\n # assert v.dtype == msd[k].dtype == torch.float32, f'{k}: EMA {v.dtype} and model {msd[k].dtype} must be FP32'\n\n def update_attr(self, model, include=(), exclude=(\"process_group\", \"reducer\")):\n \"\"\"Updates EMA attributes by copying from model, excluding 'process_group' and 'reducer' by default.\"\"\"\n copy_attr(self.ema, model, include, exclude)\n" }, { "path": "utils/triton.py", "content": "# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license\n\"\"\"Utils to interact with the Triton Inference Server.\"\"\"\n\nfrom __future__ import annotations\n\nfrom urllib.parse import urlparse\n\nimport torch\n\n\nclass TritonRemoteModel:\n \"\"\"A wrapper over a model served by the Triton Inference Server.\n\n It can be configured to communicate over GRPC or HTTP. It accepts Torch Tensors as input and returns them as\n outputs.\n \"\"\"\n\n def __init__(self, url: str):\n \"\"\"Keyword Arguments: url: Fully qualified address of the Triton server - for e.g. grpc://localhost:8000.\"\"\"\n parsed_url = urlparse(url)\n if parsed_url.scheme == \"grpc\":\n from tritonclient.grpc import InferenceServerClient, InferInput\n\n self.client = InferenceServerClient(parsed_url.netloc) # Triton GRPC client\n model_repository = self.client.get_model_repository_index()\n self.model_name = model_repository.models[0].name\n self.metadata = self.client.get_model_metadata(self.model_name, as_json=True)\n\n def create_input_placeholders() -> list[InferInput]:\n return [\n InferInput(i[\"name\"], [int(s) for s in i[\"shape\"]], i[\"datatype\"]) for i in self.metadata[\"inputs\"]\n ]\n\n else:\n from tritonclient.http import InferenceServerClient, InferInput\n\n self.client = InferenceServerClient(parsed_url.netloc) # Triton HTTP client\n model_repository = self.client.get_model_repository_index()\n self.model_name = model_repository[0][\"name\"]\n self.metadata = self.client.get_model_metadata(self.model_name)\n\n def create_input_placeholders() -> list[InferInput]:\n return [\n InferInput(i[\"name\"], [int(s) for s in i[\"shape\"]], i[\"datatype\"]) for i in self.metadata[\"inputs\"]\n ]\n\n self._create_input_placeholders_fn = create_input_placeholders\n\n @property\n def runtime(self):\n \"\"\"Returns the model runtime.\"\"\"\n return self.metadata.get(\"backend\", self.metadata.get(\"platform\"))\n\n def __call__(self, *args, **kwargs) -> torch.Tensor | tuple[torch.Tensor, ...]:\n \"\"\"Invokes the model.\n\n Parameters can be provided via args or kwargs. args, if provided, are assumed to match the order of inputs of\n the model. kwargs are matched with the model input names.\n \"\"\"\n inputs = self._create_inputs(*args, **kwargs)\n response = self.client.infer(model_name=self.model_name, inputs=inputs)\n result = []\n for output in self.metadata[\"outputs\"]:\n tensor = torch.as_tensor(response.as_numpy(output[\"name\"]))\n result.append(tensor)\n return result[0] if len(result) == 1 else result\n\n def _create_inputs(self, *args, **kwargs):\n \"\"\"Generates model inputs from args or kwargs, not allowing both; raises error if neither or both are provided.\n \"\"\"\n args_len, kwargs_len = len(args), len(kwargs)\n if not args_len and not kwargs_len:\n raise RuntimeError(\"No inputs provided.\")\n if args_len and kwargs_len:\n raise RuntimeError(\"Cannot specify args and kwargs at the same time\")\n\n placeholders = self._create_input_placeholders_fn()\n if args_len:\n if args_len != len(placeholders):\n raise RuntimeError(f\"Expected {len(placeholders)} inputs, got {args_len}.\")\n for input, value in zip(placeholders, args):\n input.set_data_from_numpy(value.cpu().numpy())\n else:\n for input in placeholders:\n value = kwargs[input.name]\n input.set_data_from_numpy(value.cpu().numpy())\n return placeholders\n" }, { "path": "val.py", "content": "# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license\n\"\"\"\nValidate a trained YOLOv3 detection model on a detection dataset.\n\nUsage:\n $ python val.py --weights yolov5s.pt --data coco128.yaml --img 640\n\nUsage - formats:\n $ python val.py --weights yolov5s.pt # PyTorch\n yolov5s.torchscript # TorchScript\n yolov5s.onnx # ONNX Runtime or OpenCV DNN with --dnn\n yolov5s_openvino_model # OpenVINO\n yolov5s.engine # TensorRT\n yolov5s.mlmodel # CoreML (macOS-only)\n yolov5s_saved_model # TensorFlow SavedModel\n yolov5s.pb # TensorFlow GraphDef\n yolov5s.tflite # TensorFlow Lite\n yolov5s_edgetpu.tflite # TensorFlow Edge TPU\n yolov5s_paddle_model # PaddlePaddle\n\"\"\"\n\nimport argparse\nimport json\nimport os\nimport subprocess\nimport sys\nfrom pathlib import Path\n\nimport numpy as np\nimport torch\nfrom tqdm import tqdm\n\nFILE = Path(__file__).resolve()\nROOT = FILE.parents[0] # YOLOv3 root directory\nif str(ROOT) not in sys.path:\n sys.path.append(str(ROOT)) # add ROOT to PATH\nROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative\n\nfrom models.common import DetectMultiBackend\nfrom utils.callbacks import Callbacks\nfrom utils.dataloaders import create_dataloader\nfrom utils.general import (\n LOGGER,\n TQDM_BAR_FORMAT,\n Profile,\n check_dataset,\n check_img_size,\n check_requirements,\n check_yaml,\n coco80_to_coco91_class,\n colorstr,\n increment_path,\n non_max_suppression,\n print_args,\n scale_boxes,\n xywh2xyxy,\n xyxy2xywh,\n)\nfrom utils.metrics import ConfusionMatrix, ap_per_class, box_iou\nfrom utils.plots import output_to_target, plot_images, plot_val_study\nfrom utils.torch_utils import select_device, smart_inference_mode\n\n\ndef save_one_txt(predn, save_conf, shape, file):\n \"\"\"Saves detection results in a text format, including labels and optionally confidence scores.\n\n Args:\n predn (torch.Tensor): A tensor containing normalized prediction results in the format (x1, y1, x2, y2, conf,\n cls).\n save_conf (bool): A flag indicating whether to save confidence scores.\n shape (tuple[int, int]): Original image shape in the format (height, width).\n file (str | Path): Path to the file where the results will be saved.\n\n Returns:\n None\n\n Examples:\n ```python\n from pathlib import Path\n import torch\n\n predn = torch.tensor([\n [10, 20, 100, 200, 0.9, 1],\n [30, 40, 150, 250, 0.8, 0],\n ])\n save_conf = True\n shape = (416, 416)\n file = Path(\"results.txt\")\n\n save_one_txt(predn, save_conf, shape, file)\n ```\n\n Notes:\n - The function normalizes bounding box coordinates before saving.\n - Each line in the output file will contain class, x-center, y-center, width, height and optionally confidence score.\n - The format is compatible with YOLO training dataset format.\n \"\"\"\n gn = torch.tensor(shape)[[1, 0, 1, 0]] # normalization gain whwh\n for *xyxy, conf, cls in predn.tolist():\n xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() # normalized xywh\n line = (cls, *xywh, conf) if save_conf else (cls, *xywh) # label format\n with open(file, \"a\") as f:\n f.write((\"%g \" * len(line)).rstrip() % line + \"\\n\")\n\n\ndef save_one_json(predn, jdict, path, class_map):\n \"\"\"Save detection results in JSON format containing image_id, category_id, bbox, and score per detection.\n\n Args:\n predn (torch.Tensor): Normalized prediction tensor of shape (N, 6) where N is the number of detections. Each\n detection should contain (x1, y1, x2, y2, confidence, class).\n jdict (list): List to store the JSON serializable detections.\n path (Path): Path object representing the image file path.\n class_map (dict[int, int]): Dictionary mapping class indices to their respective category IDs.\n\n Returns:\n None\n\n Examples:\n ```python\n predn = torch.tensor([[50, 30, 200, 150, 0.9, 0], [30, 20, 180, 150, 0.8, 1]])\n jdict = []\n path = Path('images/000001.jpg')\n class_map = {0: 1, 1: 2}\n save_one_json(predn, jdict, path, class_map)\n ```\n\n Notes:\n - The image_id is extracted from the image file path.\n - Bounding boxes are converted from xyxy format to xywh format.\n - The JSON output format is compatible with COCO dataset evaluation.\n \"\"\"\n image_id = int(path.stem) if path.stem.isnumeric() else path.stem\n box = xyxy2xywh(predn[:, :4]) # xywh\n box[:, :2] -= box[:, 2:] / 2 # xy center to top-left corner\n for p, b in zip(predn.tolist(), box.tolist()):\n jdict.append(\n {\n \"image_id\": image_id,\n \"category_id\": class_map[int(p[5])],\n \"bbox\": [round(x, 3) for x in b],\n \"score\": round(p[4], 5),\n }\n )\n\n\ndef process_batch(detections, labels, iouv):\n \"\"\"Computes correct prediction matrix for detections against ground truth labels at various IoU thresholds.\n\n Args:\n detections (np.ndarray): Array of detections with shape (N, 6), where each detection contains [x1, y1, x2, y2,\n confidence, class].\n labels (np.ndarray): Array of ground truth labels with shape (M, 5), where each label contains [class, x1, y1,\n x2, y2].\n iouv (np.ndarray): Array of IoU thresholds to use for evaluation.\n\n Returns:\n np.ndarray: Boolean array of shape (N, len(iouv)), indicating correct predictions at each IoU threshold.\n\n Examples:\n ```python\n detections = np.array([[50, 50, 150, 150, 0.8, 0],\n [30, 30, 120, 120, 0.7, 1]])\n labels = np.array([[0, 50, 50, 150, 150],\n [1, 30, 30, 120, 120]])\n iouv = np.array([0.5, 0.6, 0.7])\n\n correct = process_batch(detections, labels, iouv)\n ```\n\n Notes:\n - This function compares detections and ground truth labels to establish matches based on IoU and class.\n - It supports multiple IoU thresholds to evaluate prediction accuracy flexibly.\n \"\"\"\n correct = np.zeros((detections.shape[0], iouv.shape[0])).astype(bool)\n iou = box_iou(labels[:, 1:], detections[:, :4])\n correct_class = labels[:, 0:1] == detections[:, 5]\n for i in range(len(iouv)):\n x = torch.where((iou >= iouv[i]) & correct_class) # IoU > threshold and classes match\n if x[0].shape[0]:\n matches = torch.cat((torch.stack(x, 1), iou[x[0], x[1]][:, None]), 1).cpu().numpy() # [label, detect, iou]\n if x[0].shape[0] > 1:\n matches = matches[matches[:, 2].argsort()[::-1]]\n matches = matches[np.unique(matches[:, 1], return_index=True)[1]]\n # matches = matches[matches[:, 2].argsort()[::-1]]\n matches = matches[np.unique(matches[:, 0], return_index=True)[1]]\n correct[matches[:, 1].astype(int), i] = True\n return torch.tensor(correct, dtype=torch.bool, device=iouv.device)\n\n\n@smart_inference_mode()\ndef run(\n data,\n weights=None, # model.pt path(s)\n batch_size=32, # batch size\n imgsz=640, # inference size (pixels)\n conf_thres=0.001, # confidence threshold\n iou_thres=0.6, # NMS IoU threshold\n max_det=300, # maximum detections per image\n task=\"val\", # train, val, test, speed or study\n device=\"\", # cuda device, i.e. 0 or 0,1,2,3 or cpu\n workers=8, # max dataloader workers (per RANK in DDP mode)\n single_cls=False, # treat as single-class dataset\n augment=False, # augmented inference\n verbose=False, # verbose output\n save_txt=False, # save results to *.txt\n save_hybrid=False, # save label+prediction hybrid results to *.txt\n save_conf=False, # save confidences in --save-txt labels\n save_json=False, # save a COCO-JSON results file\n project=ROOT / \"runs/val\", # save to project/name\n name=\"exp\", # save to project/name\n exist_ok=False, # existing project/name ok, do not increment\n half=True, # use FP16 half-precision inference\n dnn=False, # use OpenCV DNN for ONNX inference\n model=None,\n dataloader=None,\n save_dir=Path(\"\"),\n plots=True,\n callbacks=Callbacks(),\n compute_loss=None,\n):\n \"\"\"Validates a trained YOLO model on a dataset and saves detection results in specified formats.\n\n Args:\n data (str | dict): Path to the dataset configuration file (.yaml) or a dictionary containing the dataset paths.\n weights (str | list, optional): Path to the trained model weights file(s). Default is None.\n batch_size (int, optional): Batch size for inference. Default is 32.\n imgsz (int, optional): Input image size for inference in pixels. Default is 640.\n conf_thres (float, optional): Confidence threshold for object detection. Default is 0.001.\n iou_thres (float, optional): IoU threshold for Non-Maximum Suppression (NMS). Default is 0.6.\n max_det (int, optional): Maximum number of detections per image. Default is 300.\n task (str, optional): Task type, can be 'train', 'val', 'test', 'speed', or 'study'. Default is 'val'.\n device (str, optional): Device for computation, e.g., '0' for GPU or 'cpu' for CPU. Default is \"\".\n workers (int, optional): Number of dataloader workers. Default is 8.\n single_cls (bool, optional): Whether to treat the dataset as a single-class dataset. Default is False.\n augment (bool, optional): Whether to apply augmented inference. Default is False.\n verbose (bool, optional): Whether to output verbose information. Default is False.\n save_txt (bool, optional): Whether to save detection results in text format (*.txt). Default is False.\n save_hybrid (bool, optional): Whether to save hybrid results (labels+predictions) in text format (*.txt).\n Default is False.\n save_conf (bool, optional): Whether to save confidence scores in text format labels. Default is False.\n save_json (bool, optional): Whether to save detection results in COCO JSON format. Default is False.\n project (str | Path, optional): Directory path to save validation results. Default is ROOT / 'runs/val'.\n name (str, optional): Directory name to save validation results. Default is 'exp'.\n exist_ok (bool, optional): Whether to overwrite existing project/name directory. Default is False.\n half (bool, optional): Whether to use half-precision (FP16) for inference. Default is True.\n dnn (bool, optional): Whether to use OpenCV DNN for ONNX inference. Default is False.\n model (torch.nn.Module, optional): Existing model instance. Default is None.\n dataloader (torch.utils.data.DataLoader, optional): Existing dataloader instance. Default is None.\n save_dir (Path, optional): Path to directory to save results. Default is Path(\"\").\n plots (bool, optional): Whether to generate plots for visual results. Default is True.\n callbacks (Callbacks, optional): Callbacks instance for event handling. Default is Callbacks().\n compute_loss (Callable, optional): Loss function for computing training loss. Default is None.\n\n Returns:\n (tuple): A tuple containing:\n - metrics (torch.Tensor): Dictionary containing metrics such as precision, recall, mAP, F1 score, etc.\n - times (dict): Dictionary containing times for different parts of the pipeline (e.g., preprocessing, inference, NMS).\n - samples (torch.Tensor): Torch tensor containing validation samples.\n\n Examples:\n ```python\n metrics, times, samples = run(\n data='data/coco.yaml',\n weights='yolov5s.pt',\n batch_size=32,\n imgsz=640,\n conf_thres=0.001,\n iou_thres=0.6,\n max_det=300,\n task='val',\n device='cpu'\n )\n ```\n \"\"\"\n # Initialize/load model and set device\n training = model is not None\n if training: # called by train.py\n device, pt, jit, engine = next(model.parameters()).device, True, False, False # get model device, PyTorch model\n half &= device.type != \"cpu\" # half precision only supported on CUDA\n model.half() if half else model.float()\n else: # called directly\n device = select_device(device, batch_size=batch_size)\n\n # Directories\n save_dir = increment_path(Path(project) / name, exist_ok=exist_ok) # increment run\n (save_dir / \"labels\" if save_txt else save_dir).mkdir(parents=True, exist_ok=True) # make dir\n\n # Load model\n model = DetectMultiBackend(weights, device=device, dnn=dnn, data=data, fp16=half)\n stride, pt, jit, engine = model.stride, model.pt, model.jit, model.engine\n imgsz = check_img_size(imgsz, s=stride) # check image size\n half = model.fp16 # FP16 supported on limited backends with CUDA\n if engine:\n batch_size = model.batch_size\n else:\n device = model.device\n if not (pt or jit):\n batch_size = 1 # export.py models default to batch-size 1\n LOGGER.info(f\"Forcing --batch-size 1 square inference (1,3,{imgsz},{imgsz}) for non-PyTorch models\")\n\n # Data\n data = check_dataset(data) # check\n\n # Configure\n model.eval()\n cuda = device.type != \"cpu\"\n is_coco = isinstance(data.get(\"val\"), str) and data[\"val\"].endswith(f\"coco{os.sep}val2017.txt\") # COCO dataset\n nc = 1 if single_cls else int(data[\"nc\"]) # number of classes\n iouv = torch.linspace(0.5, 0.95, 10, device=device) # iou vector for mAP@0.5:0.95\n niou = iouv.numel()\n\n # Dataloader\n if not training:\n if pt and not single_cls: # check --weights are trained on --data\n ncm = model.model.nc\n assert ncm == nc, (\n f\"{weights} ({ncm} classes) trained on different --data than what you passed ({nc} \"\n f\"classes). Pass correct combination of --weights and --data that are trained together.\"\n )\n model.warmup(imgsz=(1 if pt else batch_size, 3, imgsz, imgsz)) # warmup\n pad, rect = (0.0, False) if task == \"speed\" else (0.5, pt) # square inference for benchmarks\n task = task if task in (\"train\", \"val\", \"test\") else \"val\" # path to train/val/test images\n dataloader = create_dataloader(\n data[task],\n imgsz,\n batch_size,\n stride,\n single_cls,\n pad=pad,\n rect=rect,\n workers=workers,\n prefix=colorstr(f\"{task}: \"),\n )[0]\n\n seen = 0\n confusion_matrix = ConfusionMatrix(nc=nc)\n names = model.names if hasattr(model, \"names\") else model.module.names # get class names\n if isinstance(names, (list, tuple)): # old format\n names = dict(enumerate(names))\n class_map = coco80_to_coco91_class() if is_coco else list(range(1000))\n s = (\"%22s\" + \"%11s\" * 6) % (\"Class\", \"Images\", \"Instances\", \"P\", \"R\", \"mAP50\", \"mAP50-95\")\n tp, fp, p, r, f1, mp, mr, map50, ap50, map = 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0\n dt = Profile(), Profile(), Profile() # profiling times\n loss = torch.zeros(3, device=device)\n jdict, stats, ap, ap_class = [], [], [], []\n callbacks.run(\"on_val_start\")\n pbar = tqdm(dataloader, desc=s, bar_format=TQDM_BAR_FORMAT) # progress bar\n for batch_i, (im, targets, paths, shapes) in enumerate(pbar):\n callbacks.run(\"on_val_batch_start\")\n with dt[0]:\n if cuda:\n im = im.to(device, non_blocking=True)\n targets = targets.to(device)\n im = im.half() if half else im.float() # uint8 to fp16/32\n im /= 255 # 0 - 255 to 0.0 - 1.0\n nb, _, height, width = im.shape # batch size, channels, height, width\n\n # Inference\n with dt[1]:\n preds, train_out = model(im) if compute_loss else (model(im, augment=augment), None)\n\n # Loss\n if compute_loss:\n loss += compute_loss(train_out, targets)[1] # box, obj, cls\n\n # NMS\n targets[:, 2:] *= torch.tensor((width, height, width, height), device=device) # to pixels\n lb = [targets[targets[:, 0] == i, 1:] for i in range(nb)] if save_hybrid else [] # for autolabelling\n with dt[2]:\n preds = non_max_suppression(\n preds, conf_thres, iou_thres, labels=lb, multi_label=True, agnostic=single_cls, max_det=max_det\n )\n\n # Metrics\n for si, pred in enumerate(preds):\n labels = targets[targets[:, 0] == si, 1:]\n nl, npr = labels.shape[0], pred.shape[0] # number of labels, predictions\n path, shape = Path(paths[si]), shapes[si][0]\n correct = torch.zeros(npr, niou, dtype=torch.bool, device=device) # init\n seen += 1\n\n if npr == 0:\n if nl:\n stats.append((correct, *torch.zeros((2, 0), device=device), labels[:, 0]))\n if plots:\n confusion_matrix.process_batch(detections=None, labels=labels[:, 0])\n continue\n\n # Predictions\n if single_cls:\n pred[:, 5] = 0\n predn = pred.clone()\n scale_boxes(im[si].shape[1:], predn[:, :4], shape, shapes[si][1]) # native-space pred\n\n # Evaluate\n if nl:\n tbox = xywh2xyxy(labels[:, 1:5]) # target boxes\n scale_boxes(im[si].shape[1:], tbox, shape, shapes[si][1]) # native-space labels\n labelsn = torch.cat((labels[:, 0:1], tbox), 1) # native-space labels\n correct = process_batch(predn, labelsn, iouv)\n if plots:\n confusion_matrix.process_batch(predn, labelsn)\n stats.append((correct, pred[:, 4], pred[:, 5], labels[:, 0])) # (correct, conf, pcls, tcls)\n\n # Save/log\n if save_txt:\n save_one_txt(predn, save_conf, shape, file=save_dir / \"labels\" / f\"{path.stem}.txt\")\n if save_json:\n save_one_json(predn, jdict, path, class_map) # append to COCO-JSON dictionary\n callbacks.run(\"on_val_image_end\", pred, predn, path, names, im[si])\n\n # Plot images\n if plots and batch_i < 3:\n plot_images(im, targets, paths, save_dir / f\"val_batch{batch_i}_labels.jpg\", names) # labels\n plot_images(im, output_to_target(preds), paths, save_dir / f\"val_batch{batch_i}_pred.jpg\", names) # pred\n\n callbacks.run(\"on_val_batch_end\", batch_i, im, targets, paths, shapes, preds)\n\n # Compute metrics\n stats = [torch.cat(x, 0).cpu().numpy() for x in zip(*stats)] # to numpy\n if len(stats) and stats[0].any():\n tp, fp, p, r, f1, ap, ap_class = ap_per_class(*stats, plot=plots, save_dir=save_dir, names=names)\n ap50, ap = ap[:, 0], ap.mean(1) # AP@0.5, AP@0.5:0.95\n mp, mr, map50, map = p.mean(), r.mean(), ap50.mean(), ap.mean()\n nt = np.bincount(stats[3].astype(int), minlength=nc) # number of targets per class\n\n # Print results\n pf = \"%22s\" + \"%11i\" * 2 + \"%11.3g\" * 4 # print format\n LOGGER.info(pf % (\"all\", seen, nt.sum(), mp, mr, map50, map))\n if nt.sum() == 0:\n LOGGER.warning(f\"WARNING ⚠️ no labels found in {task} set, can not compute metrics without labels\")\n\n # Print results per class\n if (verbose or (nc < 50 and not training)) and nc > 1 and len(stats):\n for i, c in enumerate(ap_class):\n LOGGER.info(pf % (names[c], seen, nt[c], p[i], r[i], ap50[i], ap[i]))\n\n # Print speeds\n t = tuple(x.t / seen * 1e3 for x in dt) # speeds per image\n if not training:\n shape = (batch_size, 3, imgsz, imgsz)\n LOGGER.info(f\"Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {shape}\" % t)\n\n # Plots\n if plots:\n confusion_matrix.plot(save_dir=save_dir, names=list(names.values()))\n callbacks.run(\"on_val_end\", nt, tp, fp, p, r, f1, ap, ap50, ap_class, confusion_matrix)\n\n # Save JSON\n if save_json and len(jdict):\n w = Path(weights[0] if isinstance(weights, list) else weights).stem if weights is not None else \"\" # weights\n anno_json = str(Path(\"../datasets/coco/annotations/instances_val2017.json\")) # annotations\n if not os.path.exists(anno_json):\n anno_json = os.path.join(data[\"path\"], \"annotations\", \"instances_val2017.json\")\n pred_json = str(save_dir / f\"{w}_predictions.json\") # predictions\n LOGGER.info(f\"\\nEvaluating pycocotools mAP... saving {pred_json}...\")\n with open(pred_json, \"w\") as f:\n json.dump(jdict, f)\n\n try: # https://github.com/cocodataset/cocoapi/blob/master/PythonAPI/pycocoEvalDemo.ipynb\n check_requirements(\"pycocotools>=2.0.6\")\n from pycocotools.coco import COCO\n from pycocotools.cocoeval import COCOeval\n\n anno = COCO(anno_json) # init annotations api\n pred = anno.loadRes(pred_json) # init predictions api\n eval = COCOeval(anno, pred, \"bbox\")\n if is_coco:\n eval.params.imgIds = [int(Path(x).stem) for x in dataloader.dataset.im_files] # image IDs to evaluate\n eval.evaluate()\n eval.accumulate()\n eval.summarize()\n map, map50 = eval.stats[:2] # update results (mAP@0.5:0.95, mAP@0.5)\n except Exception as e:\n LOGGER.info(f\"pycocotools unable to run: {e}\")\n\n # Return results\n model.float() # for training\n if not training:\n s = f\"\\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}\" if save_txt else \"\"\n LOGGER.info(f\"Results saved to {colorstr('bold', save_dir)}{s}\")\n maps = np.zeros(nc) + map\n for i, c in enumerate(ap_class):\n maps[c] = ap[i]\n return (mp, mr, map50, map, *(loss.cpu() / len(dataloader)).tolist()), maps, t\n\n\ndef parse_opt():\n \"\"\"Parses and returns command-line options for dataset paths, model parameters, and inference settings.\n\n Args:\n --data (str): Path to the dataset YAML file. Default is 'data/coco128.yaml'.\n --weights (list[str]): Paths to one or more model files. Default is 'yolov3-tiny.pt'.\n --batch-size (int): Number of images per batch during inference. Default is 32.\n --imgsz (int): Inference size (pixels). Default is 640.\n --conf-thres (float): Confidence threshold for object detection. Default is 0.001.\n --iou-thres (float): IoU threshold for non-max suppression (NMS). Default is 0.6.\n --max-det (int): Maximum number of detections per image. Default is 300.\n --task (str): Task to perform: 'train', 'val', 'test', 'speed', or 'study'. Default is 'val'.\n --device (str): CUDA device identifier (e.g., '0' or '0,1,2,3') or 'cpu' for using CPU. Default is \"\".\n --workers (int): Maximum number of dataloader workers (per RANK in DDP mode). Default is 8.\n --single-cls (bool): Treat the dataset as a single-class dataset. Default is False.\n --augment (bool): Apply test-time augmentation during inference. Default is False.\n --verbose (bool): Print mAP by class. Default is False.\n --save-txt (bool): Save detection results in '.txt' format. Default is False.\n --save-hybrid (bool): Save hybrid results containing both label and prediction in '.txt' format. Default is\n False.\n --save-conf (bool): Save confidence scores in the '--save-txt' labels. Default is False.\n --save-json (bool): Save detection results in COCO JSON format. Default is False.\n --project (str): Project directory to save results. Default is 'runs/val'.\n --name (str): Name of the experiment to save results. Default is 'exp'.\n --exist-ok (bool): Whether to overwrite existing project/name without incrementing. Default is False.\n --half (bool): Use FP16 half-precision during inference. Default is False.\n --dnn (bool): Use OpenCV DNN backend for ONNX inference. Default is False.\n\n Returns:\n opt (argparse.Namespace): Parsed command-line options.\n\n Examples:\n Use the following command to run validation with custom settings:\n ```python\n $ python val.py --weights yolov5s.pt --data coco128.yaml --img 640\n ```\n\n Notes:\n - The function uses `argparse` to handle command-line options.\n - It also modifies some options based on specific conditions, such as appending additional flags for saving\n in JSON format and checking for the `coco.yaml` dataset.\n \"\"\"\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--data\", type=str, default=ROOT / \"data/coco128.yaml\", help=\"dataset.yaml path\")\n parser.add_argument(\"--weights\", nargs=\"+\", type=str, default=ROOT / \"yolov3-tiny.pt\", help=\"model path(s)\")\n parser.add_argument(\"--batch-size\", type=int, default=32, help=\"batch size\")\n parser.add_argument(\"--imgsz\", \"--img\", \"--img-size\", type=int, default=640, help=\"inference size (pixels)\")\n parser.add_argument(\"--conf-thres\", type=float, default=0.001, help=\"confidence threshold\")\n parser.add_argument(\"--iou-thres\", type=float, default=0.6, help=\"NMS IoU threshold\")\n parser.add_argument(\"--max-det\", type=int, default=300, help=\"maximum detections per image\")\n parser.add_argument(\"--task\", default=\"val\", help=\"train, val, test, speed or study\")\n parser.add_argument(\"--device\", default=\"\", help=\"cuda device, i.e. 0 or 0,1,2,3 or cpu\")\n parser.add_argument(\"--workers\", type=int, default=8, help=\"max dataloader workers (per RANK in DDP mode)\")\n parser.add_argument(\"--single-cls\", action=\"store_true\", help=\"treat as single-class dataset\")\n parser.add_argument(\"--augment\", action=\"store_true\", help=\"augmented inference\")\n parser.add_argument(\"--verbose\", action=\"store_true\", help=\"report mAP by class\")\n parser.add_argument(\"--save-txt\", action=\"store_true\", help=\"save results to *.txt\")\n parser.add_argument(\"--save-hybrid\", action=\"store_true\", help=\"save label+prediction hybrid results to *.txt\")\n parser.add_argument(\"--save-conf\", action=\"store_true\", help=\"save confidences in --save-txt labels\")\n parser.add_argument(\"--save-json\", action=\"store_true\", help=\"save a COCO-JSON results file\")\n parser.add_argument(\"--project\", default=ROOT / \"runs/val\", help=\"save to project/name\")\n parser.add_argument(\"--name\", default=\"exp\", help=\"save to project/name\")\n parser.add_argument(\"--exist-ok\", action=\"store_true\", help=\"existing project/name ok, do not increment\")\n parser.add_argument(\"--half\", action=\"store_true\", help=\"use FP16 half-precision inference\")\n parser.add_argument(\"--dnn\", action=\"store_true\", help=\"use OpenCV DNN for ONNX inference\")\n opt = parser.parse_args()\n opt.data = check_yaml(opt.data) # check YAML\n opt.save_json |= opt.data.endswith(\"coco.yaml\")\n opt.save_txt |= opt.save_hybrid\n print_args(vars(opt))\n return opt\n\n\ndef main(opt):\n \"\"\"Executes model tasks including training, validation, and speed or study benchmarks based on specified options.\n\n Args:\n opt (argparse.Namespace): Parsed command-line options for dataset paths, model parameters, and inference\n settings.\n\n Returns:\n None\n\n Examples:\n To validate a trained YOLOv3 model:\n\n ```bash\n $ python val.py --weights yolov3.pt --data coco.yaml --img 640 --task val\n ```\n\n For running speed benchmarks:\n\n ```bash\n $ python val.py --task speed --data coco.yaml --weights yolov3.pt --batch-size 1\n ```\n\n Links:\n For more information, visit the official repository: https://github.com/ultralytics/ultralytics\n\n Notes:\n This function orchestrates different tasks based on the user input provided through command-line arguments. It supports tasks\n like `train`, `val`, `test`, `speed`, and `study`. Depending on the task, it validates the model on a dataset, performs speed\n benchmarks, or runs mAP benchmarks.\n \"\"\"\n check_requirements(ROOT / \"requirements.txt\", exclude=(\"tensorboard\", \"thop\"))\n\n if opt.task in (\"train\", \"val\", \"test\"): # run normally\n if opt.conf_thres > 0.001: # https://github.com/ultralytics/yolov5/issues/1466\n LOGGER.info(f\"WARNING ⚠️ confidence threshold {opt.conf_thres} > 0.001 produces invalid results\")\n if opt.save_hybrid:\n LOGGER.info(\"WARNING ⚠️ --save-hybrid will return high mAP from hybrid labels, not from predictions alone\")\n run(**vars(opt))\n\n else:\n weights = opt.weights if isinstance(opt.weights, list) else [opt.weights]\n opt.half = torch.cuda.is_available() and opt.device != \"cpu\" # FP16 for fastest results\n if opt.task == \"speed\": # speed benchmarks\n # python val.py --task speed --data coco.yaml --batch 1 --weights yolov5n.pt yolov5s.pt...\n opt.conf_thres, opt.iou_thres, opt.save_json = 0.25, 0.45, False\n for opt.weights in weights:\n run(**vars(opt), plots=False)\n\n elif opt.task == \"study\": # speed vs mAP benchmarks\n # python val.py --task study --data coco.yaml --iou 0.7 --weights yolov5n.pt yolov5s.pt...\n for opt.weights in weights:\n f = f\"study_{Path(opt.data).stem}_{Path(opt.weights).stem}.txt\" # filename to save to\n x, y = list(range(256, 1536 + 128, 128)), [] # x axis (image sizes), y axis\n for opt.imgsz in x: # img-size\n LOGGER.info(f\"\\nRunning {f} --imgsz {opt.imgsz}...\")\n r, _, t = run(**vars(opt), plots=False)\n y.append(r + t) # results and times\n np.savetxt(f, y, fmt=\"%10.4g\") # save\n subprocess.run([\"zip\", \"-r\", \"study.zip\", \"study_*.txt\"])\n plot_val_study(x=x) # plot\n else:\n raise NotImplementedError(f'--task {opt.task} not in (\"train\", \"val\", \"test\", \"speed\", \"study\")')\n\n\nif __name__ == \"__main__\":\n opt = parse_opt()\n main(opt)\n" } ]