Repository: ultralytics/yolov3 Branch: master Commit: 5de397bef75c Files: 136 Total size: 1.2 MB Directory structure: gitextract_9q02duc2/ ├── .dockerignore ├── .github/ │ ├── ISSUE_TEMPLATE/ │ │ ├── bug-report.yml │ │ ├── config.yml │ │ ├── feature-request.yml │ │ └── question.yml │ ├── dependabot.yml │ └── workflows/ │ ├── ci-testing.yml │ ├── cla.yml │ ├── docker.yml │ ├── format.yml │ ├── links.yml │ ├── merge-main-into-prs.yml │ └── stale.yml ├── .gitignore ├── CITATION.cff ├── CONTRIBUTING.md ├── LICENSE ├── README.md ├── README.zh-CN.md ├── benchmarks.py ├── classify/ │ ├── predict.py │ ├── train.py │ ├── tutorial.ipynb │ └── val.py ├── data/ │ ├── Argoverse.yaml │ ├── GlobalWheat2020.yaml │ ├── ImageNet.yaml │ ├── SKU-110K.yaml │ ├── VisDrone.yaml │ ├── coco.yaml │ ├── coco128-seg.yaml │ ├── coco128.yaml │ ├── hyps/ │ │ ├── hyp.Objects365.yaml │ │ ├── hyp.VOC.yaml │ │ ├── hyp.no-augmentation.yaml │ │ ├── hyp.scratch-high.yaml │ │ ├── hyp.scratch-low.yaml │ │ └── hyp.scratch-med.yaml │ ├── objects365.yaml │ ├── scripts/ │ │ ├── download_weights.sh │ │ ├── get_coco.sh │ │ ├── get_coco128.sh │ │ └── get_imagenet.sh │ ├── voc.yaml │ └── xView.yaml ├── detect.py ├── export.py ├── hubconf.py ├── models/ │ ├── __init__.py │ ├── common.py │ ├── experimental.py │ ├── hub/ │ │ ├── anchors.yaml │ │ ├── yolov5-bifpn.yaml │ │ ├── yolov5-fpn.yaml │ │ ├── yolov5-p2.yaml │ │ ├── yolov5-p34.yaml │ │ ├── yolov5-p6.yaml │ │ ├── yolov5-p7.yaml │ │ ├── yolov5-panet.yaml │ │ ├── yolov5l6.yaml │ │ ├── yolov5m6.yaml │ │ ├── yolov5n6.yaml │ │ ├── yolov5s-LeakyReLU.yaml │ │ ├── yolov5s-ghost.yaml │ │ ├── yolov5s-transformer.yaml │ │ ├── yolov5s6.yaml │ │ └── yolov5x6.yaml │ ├── segment/ │ │ ├── yolov5l-seg.yaml │ │ ├── yolov5m-seg.yaml │ │ ├── yolov5n-seg.yaml │ │ ├── yolov5s-seg.yaml │ │ └── yolov5x-seg.yaml │ ├── tf.py │ ├── yolo.py │ ├── yolov3-spp.yaml │ ├── yolov3-tiny.yaml │ ├── yolov3.yaml │ ├── yolov5l.yaml │ ├── yolov5m.yaml │ ├── yolov5n.yaml │ ├── yolov5s.yaml │ └── yolov5x.yaml ├── pyproject.toml ├── requirements.txt ├── segment/ │ ├── predict.py │ ├── train.py │ ├── tutorial.ipynb │ └── val.py ├── train.py ├── tutorial.ipynb ├── utils/ │ ├── __init__.py │ ├── activations.py │ ├── augmentations.py │ ├── autoanchor.py │ ├── autobatch.py │ ├── aws/ │ │ ├── __init__.py │ │ ├── mime.sh │ │ ├── resume.py │ │ └── userdata.sh │ ├── callbacks.py │ ├── dataloaders.py │ ├── docker/ │ │ ├── Dockerfile │ │ ├── Dockerfile-arm64 │ │ └── Dockerfile-cpu │ ├── downloads.py │ ├── flask_rest_api/ │ │ ├── README.md │ │ ├── example_request.py │ │ └── restapi.py │ ├── general.py │ ├── google_app_engine/ │ │ ├── Dockerfile │ │ ├── additional_requirements.txt │ │ └── app.yaml │ ├── loggers/ │ │ ├── __init__.py │ │ ├── clearml/ │ │ │ ├── README.md │ │ │ ├── __init__.py │ │ │ ├── clearml_utils.py │ │ │ └── hpo.py │ │ ├── comet/ │ │ │ ├── README.md │ │ │ ├── __init__.py │ │ │ ├── comet_utils.py │ │ │ └── hpo.py │ │ └── wandb/ │ │ ├── __init__.py │ │ └── wandb_utils.py │ ├── loss.py │ ├── metrics.py │ ├── plots.py │ ├── segment/ │ │ ├── __init__.py │ │ ├── augmentations.py │ │ ├── dataloaders.py │ │ ├── general.py │ │ ├── loss.py │ │ ├── metrics.py │ │ └── plots.py │ ├── torch_utils.py │ └── triton.py └── val.py ================================================ FILE CONTENTS ================================================ ================================================ FILE: .dockerignore ================================================ # Repo-specific DockerIgnore ------------------------------------------------------------------------------------------- .git .cache .idea runs output coco storage.googleapis.com data/samples/* **/results*.csv *.jpg # Neural Network weights ----------------------------------------------------------------------------------------------- **/*.pt **/*.pth **/*.onnx **/*.engine **/*.mlmodel **/*.torchscript **/*.torchscript.pt **/*.tflite **/*.h5 **/*.pb *_saved_model/ *_web_model/ *_openvino_model/ # Below Copied From .gitignore ----------------------------------------------------------------------------------------- # Below Copied From .gitignore ----------------------------------------------------------------------------------------- # GitHub Python GitIgnore ---------------------------------------------------------------------------------------------- # Byte-compiled / optimized / DLL files __pycache__/ *.py[cod] *$py.class # C extensions *.so # Distribution / packaging .Python env/ build/ develop-eggs/ dist/ downloads/ eggs/ .eggs/ lib/ lib64/ parts/ sdist/ var/ wheels/ *.egg-info/ wandb/ .installed.cfg *.egg # PyInstaller # Usually these files are written by a python script from a template # before PyInstaller builds the exe, so as to inject date/other infos into it. *.manifest *.spec # Installer logs pip-log.txt pip-delete-this-directory.txt # Unit test / coverage reports htmlcov/ .tox/ .coverage .coverage.* .cache nosetests.xml coverage.xml *.cover .hypothesis/ # Translations *.mo *.pot # Django stuff: *.log local_settings.py # Flask stuff: instance/ .webassets-cache # Scrapy stuff: .scrapy # Sphinx documentation docs/_build/ # PyBuilder target/ # Jupyter Notebook .ipynb_checkpoints # pyenv .python-version # celery beat schedule file celerybeat-schedule # SageMath parsed files *.sage.py # dotenv .env # virtualenv .venv* venv*/ ENV*/ # Spyder project settings .spyderproject .spyproject # Rope project settings .ropeproject # mkdocs documentation /site # mypy .mypy_cache/ # https://github.com/github/gitignore/blob/master/Global/macOS.gitignore ----------------------------------------------- # General .DS_Store .AppleDouble .LSOverride # Icon must end with two \r Icon Icon? # Thumbnails ._* # Files that might appear in the root of a volume .DocumentRevisions-V100 .fseventsd .Spotlight-V100 .TemporaryItems .Trashes .VolumeIcon.icns .com.apple.timemachine.donotpresent # Directories potentially created on remote AFP share .AppleDB .AppleDesktop Network Trash Folder Temporary Items .apdisk # https://github.com/github/gitignore/blob/master/Global/JetBrains.gitignore # Covers JetBrains IDEs: IntelliJ, RubyMine, PhpStorm, AppCode, PyCharm, CLion, Android Studio and WebStorm # Reference: https://intellij-support.jetbrains.com/hc/en-us/articles/206544839 # User-specific stuff: .idea/* .idea/**/workspace.xml .idea/**/tasks.xml .idea/dictionaries .html # Bokeh Plots .pg # TensorFlow Frozen Graphs .avi # videos # Sensitive or high-churn files: .idea/**/dataSources/ .idea/**/dataSources.ids .idea/**/dataSources.local.xml .idea/**/sqlDataSources.xml .idea/**/dynamic.xml .idea/**/uiDesigner.xml # Gradle: .idea/**/gradle.xml .idea/**/libraries # CMake cmake-build-debug/ cmake-build-release/ # Mongo Explorer plugin: .idea/**/mongoSettings.xml ## File-based project format: *.iws ## Plugin-specific files: # IntelliJ out/ # mpeltonen/sbt-idea plugin .idea_modules/ # JIRA plugin atlassian-ide-plugin.xml # Cursive Clojure plugin .idea/replstate.xml # Crashlytics plugin (for Android Studio and IntelliJ) com_crashlytics_export_strings.xml crashlytics.properties crashlytics-build.properties fabric.properties ================================================ FILE: .github/ISSUE_TEMPLATE/bug-report.yml ================================================ # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license name: 🐛 Bug Report description: "Problems with Ultralytics YOLOv3" labels: [bug, triage] type: "bug" body: - type: markdown attributes: value: | Thank you for submitting an Ultralytics YOLOv3 🐛 Bug Report! - type: checkboxes attributes: label: Search before asking description: > 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. options: - label: > I have searched the [issues](https://github.com/ultralytics/yolov3/issues) and did not find a similar report. required: true - type: dropdown attributes: label: Project area description: | Help us route the report to the right maintainers. multiple: true options: - "Training" - "Inference" - "Export/deployment" - "Documentation" - "Other" validations: required: false - type: textarea attributes: label: Bug 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. placeholder: | 💡 ProTip! Include as much information as possible (logs, tracebacks, screenshots, etc.) to receive the most helpful response. validations: required: true - type: textarea attributes: label: Environment description: Share the platform and version information relevant to your report. placeholder: | Please include: - OS (e.g., Ubuntu 20.04, macOS 13.5, Windows 11) - Language or framework version (Python, Swift, Flutter, etc.) - Package or app version - Hardware (e.g., CPU, GPU model, device model) - Any other environment details validations: required: true - type: textarea attributes: label: Minimal Reproducible Example description: > Provide the smallest possible snippet, command, or steps required to reproduce the issue. This helps us pinpoint problems faster. placeholder: | ```python # Code or commands to reproduce your issue here ``` validations: required: true - type: textarea attributes: label: Additional description: Anything else you would like to share? - type: checkboxes attributes: label: Are you willing to submit a PR? description: > (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. See the Ultralytics [Contributing Guide](https://docs.ultralytics.com/help/contributing/) to get started. options: - label: Yes I'd like to help by submitting a PR! ================================================ FILE: .github/ISSUE_TEMPLATE/config.yml ================================================ # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license blank_issues_enabled: true contact_links: - name: 📘 YOLOv3 README url: https://github.com/ultralytics/yolov3#readme about: Usage guide and background for YOLOv3 - name: 💬 Forum url: https://community.ultralytics.com/ about: Ask the Ultralytics community for workflow help - name: 🎧 Discord url: https://ultralytics.com/discord about: Chat with the Ultralytics team and other builders - name: ⌨️ Reddit url: https://reddit.com/r/ultralytics about: Discuss Ultralytics projects on Reddit ================================================ FILE: .github/ISSUE_TEMPLATE/feature-request.yml ================================================ # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license name: 🚀 Feature Request description: "Suggest an Ultralytics YOLOv3 improvement" labels: [enhancement] type: "feature" body: - type: markdown attributes: value: | Thank you for submitting an Ultralytics YOLOv3 🚀 Feature Request! - type: checkboxes attributes: label: Search before asking description: > 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. options: - label: > I have searched https://github.com/ultralytics/yolov3/issues and did not find a similar request. required: true - type: textarea attributes: label: Description description: Briefly describe the feature you would like to see added to Ultralytics YOLOv3. placeholder: | What new capability or improvement are you proposing? validations: required: true - type: textarea attributes: label: Use case description: Explain how this feature would be used and who benefits from it. Screenshots or mockups are welcome. placeholder: | How would this feature improve your workflow? - type: textarea attributes: label: Additional description: Anything else you would like to share? - type: checkboxes attributes: label: Are you willing to submit a PR? description: > (Optional) We encourage you to submit a [Pull Request](https://github.com/ultralytics/yolov3/pulls) to help improve Ultralytics YOLOv3. See the Ultralytics [Contributing Guide](https://docs.ultralytics.com/help/contributing/) to get started. options: - label: Yes I'd like to help by submitting a PR! ================================================ FILE: .github/ISSUE_TEMPLATE/question.yml ================================================ # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license name: ❓ Question description: "Ask an Ultralytics YOLOv3 question" labels: [question] body: - type: markdown attributes: value: | Thank you for asking an Ultralytics YOLOv3 ❓ Question! - type: checkboxes attributes: label: Search before asking description: > 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. options: - label: > I checked the docs, issues, and discussions and could not find an answer. required: true - type: textarea attributes: label: Question 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. placeholder: | 💡 ProTip! Include as much information as possible (logs, tracebacks, screenshots, etc.) to receive the most helpful response. validations: required: true - type: textarea attributes: label: Additional description: Anything else you would like to share? ================================================ FILE: .github/dependabot.yml ================================================ # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license # Dependabot for package version updates # https://docs.github.com/github/administering-a-repository/configuration-options-for-dependency-updates version: 2 updates: - package-ecosystem: pip directory: "/" schedule: interval: weekly time: "04:00" open-pull-requests-limit: 10 labels: - dependencies - package-ecosystem: github-actions directory: "/.github/workflows" schedule: interval: weekly time: "04:00" open-pull-requests-limit: 5 labels: - dependencies ================================================ FILE: .github/workflows/ci-testing.yml ================================================ # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license # YOLOv3 Continuous Integration (CI) GitHub Actions tests name: YOLOv3 CI permissions: contents: read on: push: branches: [master] pull_request: branches: [master] schedule: - cron: "0 0 * * *" # runs at 00:00 UTC every day workflow_dispatch: jobs: Tests: timeout-minutes: 60 runs-on: ${{ matrix.os }} strategy: fail-fast: false matrix: os: [ubuntu-latest, windows-latest] # macos-latest bug https://github.com/ultralytics/yolov5/pull/9049 python-version: ["3.11"] model: [yolov5n] include: # - os: ubuntu-latest # python-version: "3.8" # '3.6.8' min (warning, this test is failing) # model: yolov5n - os: ubuntu-latest python-version: "3.9" model: yolov5n - os: ubuntu-latest python-version: "3.8" # torch 1.8.0 requires python >=3.6, <=3.8 model: yolov5n torch: "1.8.0" # min torch version CI https://pypi.org/project/torchvision/ steps: - uses: actions/checkout@v6 - uses: actions/setup-python@v6 with: python-version: ${{ matrix.python-version }} cache: "pip" # caching pip dependencies - name: Install requirements run: | python -m pip install --upgrade pip wheel torch="" if [ "${{ matrix.torch }}" == "1.8.0" ]; then torch="torch==1.8.0 torchvision==0.9.0" fi pip install -r requirements.txt $torch --extra-index-url https://download.pytorch.org/whl/cpu shell: bash # for Windows compatibility - name: Check environment run: | yolo checks pip list - name: Test detection shell: bash # for Windows compatibility run: | # export PYTHONPATH="$PWD" # to run '$ python *.py' files in subdirectories m=${{ matrix.model }} # official weights b=runs/train/exp/weights/best # best.pt checkpoint python train.py --imgsz 64 --batch 32 --weights $m.pt --cfg $m.yaml --epochs 1 --device cpu # train for d in cpu; do # devices for w in $m $b; do # weights python val.py --imgsz 64 --batch 32 --weights $w.pt --device $d # val python detect.py --imgsz 64 --weights $w.pt --device $d # detect done done python hubconf.py --model $m # hub # python models/tf.py --weights $m.pt # build TF model python models/yolo.py --cfg $m.yaml # build PyTorch model python export.py --weights $m.pt --img 64 --include torchscript # export 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" ================================================ FILE: .github/workflows/cla.yml ================================================ # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license # Ultralytics Contributor License Agreement (CLA) action https://docs.ultralytics.com/help/CLA # This workflow automatically requests Pull Requests (PR) authors to sign the Ultralytics CLA before PRs can be merged name: CLA Assistant on: issue_comment: types: - created pull_request_target: types: - reopened - opened - synchronize permissions: actions: write contents: write pull-requests: write statuses: write jobs: CLA: if: github.repository == 'ultralytics/yolov3' runs-on: ubuntu-latest steps: - name: CLA Assistant 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' uses: contributor-assistant/github-action@v2.6.1 env: GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} # Must be repository secret PAT PERSONAL_ACCESS_TOKEN: ${{ secrets._GITHUB_TOKEN }} with: path-to-signatures: "signatures/version1/cla.json" path-to-document: "https://docs.ultralytics.com/help/CLA" # CLA document # Branch must not be protected branch: cla-signatures allowlist: dependabot[bot],github-actions,[pre-commit*,pre-commit*,bot* remote-organization-name: ultralytics remote-repository-name: cla custom-pr-sign-comment: "I have read the CLA Document and I sign the CLA" custom-allsigned-prcomment: All Contributors have signed the CLA. ✅ ================================================ FILE: .github/workflows/docker.yml ================================================ # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license # Builds ultralytics/yolov3:latest images on DockerHub https://hub.docker.com/r/ultralytics/yolov3 name: Publish Docker Images permissions: contents: read on: push: branches: [master] workflow_dispatch: jobs: docker: if: github.repository == 'ultralytics/yolov3' name: Push Docker image to Docker Hub runs-on: ubuntu-latest steps: - name: Checkout repo uses: actions/checkout@v6 - name: Set up QEMU uses: docker/setup-qemu-action@v4 - name: Set up Docker Buildx uses: docker/setup-buildx-action@v4 - name: Login to Docker Hub uses: docker/login-action@v4 with: username: ${{ secrets.DOCKERHUB_USERNAME }} password: ${{ secrets.DOCKERHUB_TOKEN }} - name: Build and push arm64 image uses: docker/build-push-action@v7 continue-on-error: true with: context: . platforms: linux/arm64 file: utils/docker/Dockerfile-arm64 push: true tags: ultralytics/yolov3:latest-arm64 - name: Build and push CPU image uses: docker/build-push-action@v7 continue-on-error: true with: context: . file: utils/docker/Dockerfile-cpu push: true tags: ultralytics/yolov3:latest-cpu - name: Build and push GPU image uses: docker/build-push-action@v7 continue-on-error: true with: context: . file: utils/docker/Dockerfile push: true tags: ultralytics/yolov3:latest ================================================ FILE: .github/workflows/format.yml ================================================ # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license # Ultralytics Actions https://github.com/ultralytics/actions # This workflow formats code and documentation in PRs to Ultralytics standards name: Ultralytics Actions on: issues: types: [opened] pull_request: branches: [main, master] types: [opened, closed, synchronize, review_requested] permissions: contents: write # Modify code in PRs pull-requests: write # Add comments and labels to PRs issues: write # Add comments and labels to issues jobs: actions: runs-on: ubuntu-latest steps: - name: Run Ultralytics Actions uses: ultralytics/actions@main with: token: ${{ secrets._GITHUB_TOKEN || secrets.GITHUB_TOKEN }} # Auto-generated token labels: true # Auto-label issues/PRs using AI python: true # Format Python with Ruff and docformatter prettier: true # Format YAML, JSON, Markdown, CSS spelling: true # Check spelling with codespell links: false # Check broken links with Lychee summary: true # Generate AI-powered PR summaries openai_api_key: ${{ secrets.OPENAI_API_KEY }} # Powers PR summaries, labels and comments brave_api_key: ${{ secrets.BRAVE_API_KEY }} # Used for broken link resolution ================================================ FILE: .github/workflows/links.yml ================================================ # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license # Continuous Integration (CI) GitHub Actions tests broken link checker using https://github.com/lycheeverse/lychee # Ignores the following status codes to reduce false positives: # - 403(OpenVINO, 'forbidden') # - 429(Instagram, 'too many requests') # - 500(Zenodo, 'cached') # - 502(Zenodo, 'bad gateway') # - 999(LinkedIn, 'unknown status code') name: Check Broken links permissions: contents: read on: workflow_dispatch: schedule: - cron: "0 0 * * *" # runs at 00:00 UTC every day jobs: Links: runs-on: ubuntu-latest steps: - uses: actions/checkout@v6 - name: Install lychee 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 - name: Test Markdown and HTML links with retry uses: ultralytics/actions/retry@main with: timeout_minutes: 5 retry_delay_seconds: 60 retries: 2 run: | lychee \ --scheme 'https' \ --timeout 60 \ --insecure \ --accept 100..=103,200..=299,401,403,429,500,502,999 \ --exclude-all-private \ --exclude 'https?://(www\.)?(linkedin\.com|twitter\.com|x\.com|instagram\.com|kaggle\.com|fonts\.gstatic\.com|url\.com)' \ --exclude-path './**/ci.yml' \ --github-token ${{ secrets.GITHUB_TOKEN }} \ --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" \ './**/*.md' \ './**/*.html' | tee -a $GITHUB_STEP_SUMMARY # Raise error if broken links found if ! grep -q "0 Errors" $GITHUB_STEP_SUMMARY; then exit 1 fi - name: Test Markdown, HTML, YAML, Python and Notebook links with retry if: github.event_name == 'workflow_dispatch' uses: ultralytics/actions/retry@main with: timeout_minutes: 5 retry_delay_seconds: 60 retries: 2 run: | lychee \ --scheme 'https' \ --timeout 60 \ --insecure \ --accept 100..=103,200..=299,429,999 \ --exclude-all-private \ --exclude 'https?://(www\.)?(linkedin\.com|twitter\.com|x\.com|instagram\.com|kaggle\.com|fonts\.gstatic\.com|url\.com)' \ --exclude-path './**/ci.yml' \ --github-token ${{ secrets.GITHUB_TOKEN }} \ --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" \ './**/*.md' \ './**/*.html' \ './**/*.yml' \ './**/*.yaml' \ './**/*.py' \ './**/*.ipynb' | tee -a $GITHUB_STEP_SUMMARY # Raise error if broken links found if ! grep -q "0 Errors" $GITHUB_STEP_SUMMARY; then exit 1 fi ================================================ FILE: .github/workflows/merge-main-into-prs.yml ================================================ # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license # Automatically merges repository 'main' branch into all open PRs to keep them up-to-date # Action runs on updates to main branch so when one PR merges to main all others update name: Merge main into PRs on: workflow_dispatch: # push: # branches: # - ${{ github.event.repository.default_branch }} jobs: Merge: if: github.repository == 'ultralytics/yolov3' runs-on: ubuntu-latest permissions: contents: read pull-requests: write steps: - name: Checkout repository uses: actions/checkout@v6 with: fetch-depth: 0 - uses: actions/setup-python@v6 with: python-version: "3.x" cache: "pip" - name: Install requirements run: | pip install pygithub - name: Merge default branch into PRs shell: python run: | from github import Github import os g = Github(os.getenv('GITHUB_TOKEN')) repo = g.get_repo(os.getenv('GITHUB_REPOSITORY')) # Fetch the default branch name default_branch_name = repo.default_branch default_branch = repo.get_branch(default_branch_name) for pr in repo.get_pulls(state='open', sort='created'): try: # Get full names for repositories and branches base_repo_name = repo.full_name head_repo_name = pr.head.repo.full_name base_branch_name = pr.base.ref head_branch_name = pr.head.ref # Check if PR is behind the default branch comparison = repo.compare(default_branch.commit.sha, pr.head.sha) if comparison.behind_by > 0: 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).") # Attempt to update the branch try: success = pr.update_branch() assert success, "Branch update failed" print(f"✅ Successfully merged '{default_branch_name}' into PR #{pr.number} ({head_repo_name}:{head_branch_name} -> {base_repo_name}:{base_branch_name}).") except Exception as update_error: print(f"❌ Could not update PR #{pr.number} ({head_repo_name}:{head_branch_name} -> {base_repo_name}:{base_branch_name}): {update_error}") print(" This might be due to branch protection rules or insufficient permissions.") else: 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}.") except Exception as e: print(f"❌ Could not process PR #{pr.number}: {e}") env: GITHUB_TOKEN: ${{ secrets._GITHUB_TOKEN }} GITHUB_REPOSITORY: ${{ github.repository }} ================================================ FILE: .github/workflows/stale.yml ================================================ # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license name: Close stale issues permissions: contents: read issues: write pull-requests: write on: schedule: - cron: "0 0 * * *" # Runs at 00:00 UTC every day jobs: stale: runs-on: ubuntu-latest steps: - uses: actions/stale@v10 with: repo-token: ${{ secrets.GITHUB_TOKEN }} stale-issue-message: | 👋 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. For additional resources and information, please see the links below: - **Docs**: https://docs.ultralytics.com - **Platform**: https://platform.ultralytics.com - **Community**: https://community.ultralytics.com 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! Thank you for your contributions to YOLO 🚀 and Vision AI ⭐ stale-pr-message: | 👋 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. 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. For additional resources and information, please see the links below: - **Docs**: https://docs.ultralytics.com - **Platform**: https://platform.ultralytics.com - **Community**: https://community.ultralytics.com Thank you for your contributions to YOLO 🚀 and Vision AI ⭐ days-before-issue-stale: 30 days-before-issue-close: 10 days-before-pr-stale: 90 days-before-pr-close: 30 exempt-issue-labels: "documentation,tutorial,TODO" operations-per-run: 300 # The maximum number of operations per run, used to control rate limiting. ================================================ FILE: .gitignore ================================================ # Repo-specific GitIgnore ---------------------------------------------------------------------------------------------- *.jpg *.jpeg *.png *.bmp *.tif *.tiff *.heic *.JPG *.JPEG *.PNG *.BMP *.TIF *.TIFF *.HEIC *.mp4 *.mov *.MOV *.avi *.data *.json *.cfg !setup.cfg !cfg/yolov3*.cfg storage.googleapis.com runs/* data/* data/images/* !data/*.yaml !data/hyps !data/scripts !data/images !data/images/zidane.jpg !data/images/bus.jpg !data/*.sh results*.csv # Datasets ------------------------------------------------------------------------------------------------------------- coco/ coco128/ VOC/ # MATLAB GitIgnore ----------------------------------------------------------------------------------------------------- *.m~ *.mat !targets*.mat # Neural Network weights ----------------------------------------------------------------------------------------------- *.weights *.pt *.pb *.onnx *.engine *.mlmodel *.torchscript *.tflite *.h5 *_saved_model/ *_web_model/ *_openvino_model/ *_paddle_model/ darknet53.conv.74 yolov3-tiny.conv.15 # GitHub Python GitIgnore ---------------------------------------------------------------------------------------------- # Byte-compiled / optimized / DLL files __pycache__/ *.py[cod] *$py.class # C extensions *.so # Distribution / packaging .Python env/ build/ develop-eggs/ dist/ downloads/ eggs/ .eggs/ lib/ lib64/ parts/ sdist/ var/ wheels/ *.egg-info/ /wandb/ .installed.cfg *.egg # PyInstaller # Usually these files are written by a python script from a template # before PyInstaller builds the exe, so as to inject date/other infos into it. *.manifest *.spec # Installer logs pip-log.txt pip-delete-this-directory.txt # Unit test / coverage reports htmlcov/ .tox/ .coverage .coverage.* .cache nosetests.xml coverage.xml *.cover .hypothesis/ # Translations *.mo *.pot # Django stuff: *.log local_settings.py # Flask stuff: instance/ .webassets-cache # Scrapy stuff: .scrapy # Sphinx documentation docs/_build/ # PyBuilder target/ # Jupyter Notebook .ipynb_checkpoints # pyenv .python-version # celery beat schedule file celerybeat-schedule # SageMath parsed files *.sage.py # dotenv .env # virtualenv .venv* venv*/ ENV*/ # Spyder project settings .spyderproject .spyproject # Rope project settings .ropeproject # mkdocs documentation /site # mypy .mypy_cache/ # https://github.com/github/gitignore/blob/master/Global/macOS.gitignore ----------------------------------------------- # General .DS_Store .AppleDouble .LSOverride # Icon must end with two \r Icon Icon? # Thumbnails ._* # Files that might appear in the root of a volume .DocumentRevisions-V100 .fseventsd .Spotlight-V100 .TemporaryItems .Trashes .VolumeIcon.icns .com.apple.timemachine.donotpresent # Directories potentially created on remote AFP share .AppleDB .AppleDesktop Network Trash Folder Temporary Items .apdisk # https://github.com/github/gitignore/blob/master/Global/JetBrains.gitignore # Covers JetBrains IDEs: IntelliJ, RubyMine, PhpStorm, AppCode, PyCharm, CLion, Android Studio and WebStorm # Reference: https://intellij-support.jetbrains.com/hc/en-us/articles/206544839 # User-specific stuff: .idea/* .idea/**/workspace.xml .idea/**/tasks.xml .idea/dictionaries .html # Bokeh Plots .pg # TensorFlow Frozen Graphs .avi # videos # Sensitive or high-churn files: .idea/**/dataSources/ .idea/**/dataSources.ids .idea/**/dataSources.local.xml .idea/**/sqlDataSources.xml .idea/**/dynamic.xml .idea/**/uiDesigner.xml # Gradle: .idea/**/gradle.xml .idea/**/libraries # CMake cmake-build-debug/ cmake-build-release/ # Mongo Explorer plugin: .idea/**/mongoSettings.xml ## File-based project format: *.iws ## Plugin-specific files: # IntelliJ out/ # mpeltonen/sbt-idea plugin .idea_modules/ # JIRA plugin atlassian-ide-plugin.xml # Cursive Clojure plugin .idea/replstate.xml # Crashlytics plugin (for Android Studio and IntelliJ) com_crashlytics_export_strings.xml crashlytics.properties crashlytics-build.properties fabric.properties ================================================ FILE: CITATION.cff ================================================ cff-version: 1.2.0 preferred-citation: type: software message: If you use YOLOv5, please cite it as below. authors: - family-names: Jocher given-names: Glenn orcid: "https://orcid.org/0000-0001-5950-6979" title: "YOLOv5 by Ultralytics" version: 7.0 doi: 10.5281/zenodo.3908559 date-released: 2020-5-29 license: AGPL-3.0 url: "https://github.com/ultralytics/yolov5" ================================================ FILE: CONTRIBUTING.md ================================================ # Contributing To YOLOv3 🚀 We value your input and welcome your contributions to Ultralytics YOLOv3! Whether you're interested in: - Reporting a bug - Discussing the current state of the codebase - Submitting a fix - Proposing a new feature - Becoming a maintainer Ultralytics 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! 😃 ## Submitting A Pull Request (PR) 🛠️ Contributing a PR is straightforward! Here’s a step-by-step example for updating `requirements.txt`: ### 1. Select The File To Update Click on `requirements.txt` in the GitHub repository to open it.

PR_step1

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

PR_step2

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

PR_step3

### 4. Preview And Submit Your PR Switch 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! 😃

PR_step4

### PR Best Practices To ensure your contribution is integrated smoothly, please: - ✅ 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.

Screenshot 2022-08-29 at 22 47 15

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

Screenshot 2022-08-29 at 22 47 03

- ✅ Limit your changes to the **minimum required** for your bug fix or feature. _"It is not daily increase but daily decrease, hack away the unessential. The closer to the source, the less wastage there is."_ — Bruce Lee ## Submitting A Bug Report 🐛 If you encounter an issue with Ultralytics YOLOv3, please submit a bug report! To help us investigate, please provide a [minimum reproducible example](https://docs.ultralytics.com/help/minimum-reproducible-example/). Your code should be: - ✅ **Minimal** – Use as little code as possible that still produces the issue. - ✅ **Complete** – Include all parts needed for someone else to reproduce the problem. - ✅ **Reproducible** – Test your code to ensure it reliably triggers the issue. Additionally, for [Ultralytics](https://www.ultralytics.com/) to assist, your code should be: - ✅ **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. - ✅ **Unmodified** – The problem must be reproducible without custom modifications to the repository. [Ultralytics](https://www.ultralytics.com/) does not provide support for custom code. If 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. ## License By contributing, you agree that your submissions will be licensed under the [AGPL-3.0 license](https://choosealicense.com/licenses/agpl-3.0/). --- Thank 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/). ================================================ FILE: LICENSE ================================================ GNU AFFERO GENERAL PUBLIC LICENSE Version 3, 19 November 2007 Copyright (C) 2007 Free Software Foundation, Inc. Everyone is permitted to copy and distribute verbatim copies of this license document, but changing it is not allowed. Preamble The GNU Affero General Public License is a free, copyleft license for software and other kinds of works, specifically designed to ensure cooperation with the community in the case of network server software. The licenses for most software and other practical works are designed to take away your freedom to share and change the works. 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Ultralytics 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. Explore 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! For Enterprise License requests, please complete the form at [Ultralytics Licensing](https://www.ultralytics.com/license).
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## 🚀 YOLO11: The Next Evolution We 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. Get started today and unlock the full potential of YOLO11! Visit the [Ultralytics Docs](https://docs.ultralytics.com/) for comprehensive guides and resources: [![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) ```bash # Install the ultralytics package pip install ultralytics ``` ## 📚 Documentation See 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.
Install Clone 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). ```bash # Clone the YOLOv3 repository git clone https://github.com/ultralytics/yolov3 # Navigate to the cloned directory cd yolov3 # Install required packages pip install -r requirements.txt ```
Inference with PyTorch Hub Use 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. ```python import torch # Load a YOLOv3 model (e.g., yolov3, yolov3-spp) model = torch.hub.load("ultralytics/yolov3", "yolov3", pretrained=True) # specify 'yolov3' or other variants # Define the input image source (URL, local file, PIL image, OpenCV frame, numpy array, or list) img = "https://ultralytics.com/images/zidane.jpg" # Example image # Perform inference results = model(img) # Process the results (options: .print(), .show(), .save(), .crop(), .pandas()) results.print() # Print results to console results.show() # Display results in a window results.save() # Save results to runs/detect/exp ```
Inference with detect.py The `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`. ```bash # Run inference using a webcam with yolov3-tiny python detect.py --weights yolov3-tiny.pt --source 0 # Run inference on a local image file with yolov3 python detect.py --weights yolov3.pt --source img.jpg # Run inference on a local video file with yolov3-spp python detect.py --weights yolov3-spp.pt --source vid.mp4 # Run inference on a screen capture python detect.py --weights yolov3.pt --source screen # Run inference on a directory of images python detect.py --weights yolov3.pt --source path/to/images/ # Run inference on a text file listing image paths python detect.py --weights yolov3.pt --source list.txt # Run inference on a text file listing stream URLs python detect.py --weights yolov3.pt --source list.streams # Run inference using a glob pattern for images python detect.py --weights yolov3.pt --source 'path/to/*.jpg' # Run inference on a YouTube video URL python detect.py --weights yolov3.pt --source 'https://youtu.be/LNwODJXcvt4' # Run inference on an RTSP, RTMP, or HTTP stream python detect.py --weights yolov3.pt --source 'rtsp://example.com/media.mp4' ```
Training The 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. ```bash # Train YOLOv3-tiny on COCO for 300 epochs (example settings) python train.py --data coco.yaml --epochs 300 --weights '' --cfg yolov3-tiny.yaml --batch-size 64 # Train YOLOv3 on COCO for 300 epochs (example settings) python train.py --data coco.yaml --epochs 300 --weights '' --cfg yolov3.yaml --batch-size 32 # Train YOLOv3-SPP on COCO for 300 epochs (example settings) python train.py --data coco.yaml --epochs 300 --weights '' --cfg yolov3-spp.yaml --batch-size 16 ```
Tutorials Note: These tutorials primarily use YOLOv5 examples but the principles often apply to YOLOv3 within the Ultralytics framework. - **[Train Custom Data](https://docs.ultralytics.com/yolov5/tutorials/train_custom_data/)** 🚀 **RECOMMENDED**: Learn how to train models on your own datasets. - **[Tips for Best Training Results](https://docs.ultralytics.com/guides/model-training-tips/)** ☘️: Improve your model's performance with expert tips. - **[Multi-GPU Training](https://docs.ultralytics.com/yolov5/tutorials/multi_gpu_training/)**: Speed up training using multiple GPUs. - **[PyTorch Hub Integration](https://docs.ultralytics.com/yolov5/tutorials/pytorch_hub_model_loading/)** 🌟 **NEW**: Easily load models using PyTorch Hub. - **[Model Export (TFLite, ONNX, CoreML, TensorRT)](https://docs.ultralytics.com/yolov5/tutorials/model_export/)** 🚀: Convert your models to various deployment formats. - **[NVIDIA Jetson Deployment](https://docs.ultralytics.com/guides/nvidia-jetson/)** 🌟 **NEW**: Deploy models on NVIDIA Jetson devices. - **[Test-Time Augmentation (TTA)](https://docs.ultralytics.com/yolov5/tutorials/test_time_augmentation/)**: Enhance prediction accuracy with TTA. - **[Model Ensembling](https://docs.ultralytics.com/yolov5/tutorials/model_ensembling/)**: Combine multiple models for better performance. - **[Model Pruning/Sparsity](https://docs.ultralytics.com/yolov5/tutorials/model_pruning_and_sparsity/)**: Optimize models for size and speed. - **[Hyperparameter Evolution](https://docs.ultralytics.com/yolov5/tutorials/hyperparameter_evolution/)**: Automatically find the best training hyperparameters. - **[Transfer Learning with Frozen Layers](https://docs.ultralytics.com/yolov5/tutorials/transfer_learning_with_frozen_layers/)**: Adapt pretrained models to new tasks efficiently. - **[Architecture Summary](https://docs.ultralytics.com/yolov5/tutorials/architecture_description/)** 🌟 **NEW**: Understand the model architecture (focus on YOLOv3 principles). - **[Ultralytics Platform Training](https://platform.ultralytics.com)** 🚀 **RECOMMENDED**: Train and deploy YOLO models using Ultralytics Platform. - **[ClearML Logging](https://docs.ultralytics.com/yolov5/tutorials/clearml_logging_integration/)**: Integrate with ClearML for experiment tracking. - **[Neural Magic DeepSparse Integration](https://docs.ultralytics.com/yolov5/tutorials/neural_magic_pruning_quantization/)**: Accelerate inference with DeepSparse. - **[Comet Logging](https://docs.ultralytics.com/yolov5/tutorials/comet_logging_integration/)** 🌟 **NEW**: Log experiments using Comet ML.
## 🧩 Integrations Ultralytics 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/). Ultralytics active learning integrations

| Ultralytics Platform 🌟 | Weights & Biases | Comet | Neural Magic | | :--------------------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------------: | | 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/). | ## ⭐ Ultralytics Platform Experience 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! Ultralytics Platform Platform Screenshot ## 🤔 Why YOLOv3? YOLOv3 marked a major leap forward in real-time object detection at its release. Key advantages include: - **Improved Accuracy:** Enhanced detection of small objects compared to YOLOv2. - **Multi-Scale Predictions:** Detects objects at three different scales, boosting performance across varied object sizes. - **Class Prediction:** Uses logistic classifiers for object classes, enabling multi-label classification. - **Feature Extractor:** Employs a deeper network (Darknet-53) versus the Darknet-19 used in YOLOv2. While newer models like YOLOv5 and YOLO11 offer further advancements, YOLOv3 remains a reliable and widely adopted baseline, efficiently implemented in PyTorch by Ultralytics. ## ☁️ Environments Get started quickly with our pre-configured environments. Click the icons below for setup details. ## 🤝 Contribute We 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! [![Ultralytics open-source contributors](https://raw.githubusercontent.com/ultralytics/assets/main/im/image-contributors.png)](https://github.com/ultralytics/yolov5/graphs/contributors) ## 📜 License Ultralytics provides two licensing options to meet different needs: - **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. - **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). ## 📧 Contact For 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)!
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Ultralytics 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。 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！ 如需企业许可证，请填写 [Ultralytics 许可申请](https://www.ultralytics.com/license)。
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## 🚀 YOLO11：下一代进化 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。 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： [![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) ```bash # 安装 ultralytics 包 pip install ultralytics ``` ## 📚 文档 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。
安装 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。 ```bash # 克隆 YOLOv3 仓库 git clone https://github.com/ultralytics/yolov3 # 进入目录 cd yolov3 # 安装依赖 pip install -r requirements.txt ```
使用 PyTorch Hub 进行推理 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。 ```python import torch # 加载 YOLOv3 模型（如 yolov3, yolov3-spp） model = torch.hub.load("ultralytics/yolov3", "yolov3", pretrained=True) # 输入图像（支持 URL、本地文件、PIL、OpenCV、numpy 数组或列表） img = "https://ultralytics.com/images/zidane.jpg" # 推理 results = model(img) # 结果处理（.print(), .show(), .save(), .crop(), .pandas()） results.print() results.show() results.save() ```
使用 detect.py 进行推理 `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`。 ```bash # 使用 yolov3-tiny 和摄像头推理 python detect.py --weights yolov3-tiny.pt --source 0 # 使用 yolov3 推理本地图像 python detect.py --weights yolov3.pt --source img.jpg # 使用 yolov3-spp 推理本地视频 python detect.py --weights yolov3-spp.pt --source vid.mp4 # 推理屏幕截图 python detect.py --weights yolov3.pt --source screen # 推理图像目录 python detect.py --weights yolov3.pt --source path/to/images/ # 推理图像路径列表文件 python detect.py --weights yolov3.pt --source list.txt # 推理流 URL 列表文件 python detect.py --weights yolov3.pt --source list.streams # 使用 glob 模式推理 python detect.py --weights yolov3.pt --source 'path/to/*.jpg' # 推理 YouTube 视频 python detect.py --weights yolov3.pt --source 'https://youtu.be/LNwODJXcvt4' # 推理 RTSP、RTMP 或 HTTP 流 python detect.py --weights yolov3.pt --source 'rtsp://example.com/media.mp4' ```
训练 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`。 ```bash # 在 COCO 上训练 YOLOv3-tiny 300 轮 python train.py --data coco.yaml --epochs 300 --weights '' --cfg yolov3-tiny.yaml --batch-size 64 # 在 COCO 上训练 YOLOv3 300 轮 python train.py --data coco.yaml --epochs 300 --weights '' --cfg yolov3.yaml --batch-size 32 # 在 COCO 上训练 YOLOv3-SPP 300 轮 python train.py --data coco.yaml --epochs 300 --weights '' --cfg yolov3-spp.yaml --batch-size 16 ```
教程 注意：这些教程多以 YOLOv5 为例，但原理同样适用于 YOLOv3。 - **[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。 - **[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。 - **[多 GPU 训练](https://docs.ultralytics.com/guides/model-training-tips/)**：加速大规模训练。 - **[PyTorch Hub 集成](https://docs.ultralytics.com/integrations/jupyterlab/)** 🌟 **新增**：一键加载模型。 - **[模型导出 (TFLite, ONNX, CoreML, TensorRT)](https://docs.ultralytics.com/modes/export/)** 🚀：多格式部署支持。 - **[NVIDIA Jetson 部署](https://docs.ultralytics.com/guides/nvidia-jetson/)** 🌟 **新增**：边缘设备推理。 - **[测试时增强 (TTA)](https://docs.ultralytics.com/guides/model-evaluation-insights/)**：提升预测准确率。 - **[模型集成](https://docs.ultralytics.com/guides/model-deployment-options/)**：多模型融合提升表现。 - **[模型剪枝/稀疏化](https://docs.ultralytics.com/guides/model-deployment-practices/)**：优化模型体积与速度。 - **[超参数进化](https://docs.ultralytics.com/guides/hyperparameter-tuning/)**：自动优化训练参数。 - **[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。 - **[架构总结](https://docs.ultralytics.com/models/yolov3/)** 🌟 **新增**：理解 YOLOv3 设计原理。 - **[Ultralytics Platform 训练](https://platform.ultralytics.com)** 🚀 **推荐**：无代码训练与部署。 - **[ClearML 日志集成](https://docs.ultralytics.com/integrations/clearml/)**：实验可追溯。 - **[Neural Magic DeepSparse 集成](https://docs.ultralytics.com/integrations/neural-magic/)**：极致推理加速。 - **[Comet 日志集成](https://docs.ultralytics.com/integrations/comet/)** 🌟 **新增**：实验可视化与管理。
## 🧩 集成 Ultralytics 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。 Ultralytics active learning integrations

| Ultralytics Platform 🌟 | Weights & Biases | Comet | Neural Magic | | :--------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------------------: | | 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。 | ## ⭐ Ultralytics Platform 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！ Ultralytics Platform Platform Screenshot ## 🤔 为何选择 YOLOv3？ YOLOv3 发布时推动了实时目标检测的进步。其核心优势包括： - **更高准确率：** 对小目标检测表现优异。 - **多尺度预测：** 支持三种不同尺度，提升多尺寸目标检测能力。 - **多标签分类：** 采用逻辑分类器而非 softmax，支持多标签输出。 - **强大特征提取器：** 使用更深的 Darknet-53 网络替代 YOLOv2 的 Darknet-19。 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。 ## ☁️ 环境 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。 ## 🤝 贡献 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！ [![Ultralytics open-source contributors](https://raw.githubusercontent.com/ultralytics/assets/main/im/image-contributors.png)](https://github.com/ultralytics/yolov5/graphs/contributors) ## 📜 许可证 Ultralytics 提供两种许可选项以满足不同需求： - **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)。 - **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。 ## 📧 联系 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)！
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================================================ FILE: benchmarks.py ================================================ # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license """ Run YOLOv3 benchmarks on all supported export formats. Format | `export.py --include` | Model --- | --- | --- PyTorch | - | yolov5s.pt TorchScript | `torchscript` | yolov5s.torchscript ONNX | `onnx` | yolov5s.onnx OpenVINO | `openvino` | yolov5s_openvino_model/ TensorRT | `engine` | yolov5s.engine CoreML | `coreml` | yolov5s.mlmodel TensorFlow SavedModel | `saved_model` | yolov5s_saved_model/ TensorFlow GraphDef | `pb` | yolov5s.pb TensorFlow Lite | `tflite` | yolov5s.tflite TensorFlow Edge TPU | `edgetpu` | yolov5s_edgetpu.tflite TensorFlow.js | `tfjs` | yolov5s_web_model/ Requirements: $ pip install -r requirements.txt coremltools onnx onnx-simplifier onnxruntime openvino-dev tensorflow-cpu # CPU $ pip install -r requirements.txt coremltools onnx onnx-simplifier onnxruntime-gpu openvino-dev tensorflow # GPU $ pip install -U nvidia-tensorrt --index-url https://pypi.ngc.nvidia.com # TensorRT Usage: $ python benchmarks.py --weights yolov5s.pt --img 640 """ import argparse import platform import sys import time from pathlib import Path import pandas as pd FILE = Path(__file__).resolve() ROOT = FILE.parents[0] # YOLOv3 root directory if str(ROOT) not in sys.path: sys.path.append(str(ROOT)) # add ROOT to PATH # ROOT = ROOT.relative_to(Path.cwd()) # relative import export from models.experimental import attempt_load from models.yolo import SegmentationModel from segment.val import run as val_seg from utils import notebook_init from utils.general import LOGGER, check_yaml, file_size, print_args from utils.torch_utils import select_device from val import run as val_det def run( weights=ROOT / "yolov5s.pt", # weights path imgsz=640, # inference size (pixels) batch_size=1, # batch size data=ROOT / "data/coco128.yaml", # dataset.yaml path device="", # cuda device, i.e. 0 or 0,1,2,3 or cpu half=False, # use FP16 half-precision inference test=False, # test exports only pt_only=False, # test PyTorch only hard_fail=False, # throw error on benchmark failure ): """Run YOLOv3 benchmarks on multiple export formats and validate performance metrics. Args: weights (str | Path): Path to the weights file. Defaults to 'yolov5s.pt'. imgsz (int): Inference image size in pixels. Defaults to 640. batch_size (int): Batch size for inference. Defaults to 1. data (str | Path): Path to the dataset configuration file (dataset.yaml). Defaults to 'data/coco128.yaml'. device (str): Device to be used for inference, e.g., '0' or '0,1,2,3' for GPU or 'cpu' for CPU. Defaults to ''. half (bool): Use FP16 half-precision for inference. Defaults to False. test (bool): Test exports only without running benchmarks. Defaults to False. pt_only (bool): Run benchmarks only for PyTorch format. Defaults to False. hard_fail (bool): Raise an error if any benchmark test fails. Defaults to False. Returns: None Examples: ```python # Run benchmarks on the default 'yolov5s.pt' model with an image size of 640 pixels run() # Run benchmarks on a specific model with GPU and half-precision enabled run(weights='custom_model.pt', device='0', half=True) # Test only PyTorch export run(pt_only=True) ``` Notes: This function iterates over multiple export formats, performs the export, and then validates the model's performance using appropriate validation functions for detection and segmentation models. The results are logged, and optionally, benchmarks can be configured to raise errors on failures using the `hard_fail` argument. """ y, t = [], time.time() device = select_device(device) model_type = type(attempt_load(weights, fuse=False)) # DetectionModel, SegmentationModel, etc. for i, (name, f, suffix, cpu, gpu) in export.export_formats().iterrows(): # index, (name, file, suffix, CPU, GPU) try: assert i not in (9, 10), "inference not supported" # Edge TPU and TF.js are unsupported assert i != 5 or platform.system() == "Darwin", "inference only supported on macOS>=10.13" # CoreML if "cpu" in device.type: assert cpu, "inference not supported on CPU" if "cuda" in device.type: assert gpu, "inference not supported on GPU" # Export if f == "-": w = weights # PyTorch format else: w = export.run( weights=weights, imgsz=[imgsz], include=[f], batch_size=batch_size, device=device, half=half )[-1] # all others assert suffix in str(w), "export failed" # Validate if model_type == SegmentationModel: result = val_seg(data, w, batch_size, imgsz, plots=False, device=device, task="speed", half=half) metric = result[0][7] # (box(p, r, map50, map), mask(p, r, map50, map), *loss(box, obj, cls)) else: # DetectionModel: result = val_det(data, w, batch_size, imgsz, plots=False, device=device, task="speed", half=half) metric = result[0][3] # (p, r, map50, map, *loss(box, obj, cls)) speed = result[2][1] # times (preprocess, inference, postprocess) y.append([name, round(file_size(w), 1), round(metric, 4), round(speed, 2)]) # MB, mAP, t_inference except Exception as e: if hard_fail: assert type(e) is AssertionError, f"Benchmark --hard-fail for {name}: {e}" LOGGER.warning(f"WARNING ⚠️ Benchmark failure for {name}: {e}") y.append([name, None, None, None]) # mAP, t_inference if pt_only and i == 0: break # break after PyTorch # Print results LOGGER.info("\n") parse_opt() notebook_init() # print system info c = ["Format", "Size (MB)", "mAP50-95", "Inference time (ms)"] if map else ["Format", "Export", "", ""] py = pd.DataFrame(y, columns=c) LOGGER.info(f"\nBenchmarks complete ({time.time() - t:.2f}s)") LOGGER.info(str(py if map else py.iloc[:, :2])) if hard_fail and isinstance(hard_fail, str): metrics = py["mAP50-95"].array # values to compare to floor floor = eval(hard_fail) # minimum metric floor to pass, i.e. = 0.29 mAP for YOLOv5n assert all(x > floor for x in metrics if pd.notna(x)), f"HARD FAIL: mAP50-95 < floor {floor}" return py def test( weights=ROOT / "yolov5s.pt", # weights path imgsz=640, # inference size (pixels) batch_size=1, # batch size data=ROOT / "data/coco128.yaml", # dataset.yaml path device="", # cuda device, i.e. 0 or 0,1,2,3 or cpu half=False, # use FP16 half-precision inference test=False, # test exports only pt_only=False, # test PyTorch only hard_fail=False, # throw error on benchmark failure ): """Run YOLOv3 export tests for various formats and log the results, including export success status. Args: weights (str | Path): Path to the weights file. Defaults to ROOT / "yolov5s.pt". imgsz (int): Inference size in pixels. Defaults to 640. batch_size (int): Number of images per batch. Defaults to 1. data (str | Path): Path to the dataset yaml file. Defaults to ROOT / "data/coco128.yaml". device (str): Device for inference. Accepts cuda device (e.g., "0" or "0,1,2,3") or "cpu". Defaults to "". half (bool): Use FP16 half-precision inference. Defaults to False. test (bool): Run export tests only, no inference. Defaults to False. pt_only (bool): Run tests on PyTorch format only. Defaults to False. hard_fail (bool): Raise an error on benchmark failure. Defaults to False. Returns: pd.DataFrame: A DataFrame containing the export formats and their success status. Examples: ```python from ultralytics import test results = test( weights="path/to/yolov5s.pt", imgsz=640, batch_size=1, data="path/to/coco128.yaml", device="0", half=False, test=True, pt_only=False, hard_fail=True, ) print(results) ``` Notes: Ensure all required packages are installed as specified in the Ultralytics YOLOv3 documentation: https://github.com/ultralytics/ultralytics """ y, t = [], time.time() device = select_device(device) for i, (name, f, suffix, gpu) in export.export_formats().iterrows(): # index, (name, file, suffix, gpu-capable) try: w = ( weights if f == "-" else export.run(weights=weights, imgsz=[imgsz], include=[f], device=device, half=half)[-1] ) # weights assert suffix in str(w), "export failed" y.append([name, True]) except Exception: y.append([name, False]) # mAP, t_inference # Print results LOGGER.info("\n") parse_opt() notebook_init() # print system info py = pd.DataFrame(y, columns=["Format", "Export"]) LOGGER.info(f"\nExports complete ({time.time() - t:.2f}s)") LOGGER.info(str(py)) return py def parse_opt(): """Parses command line arguments for YOLOv3 inference and export configurations. Args: --weights (str): Path to the weights file. Default is 'ROOT / "yolov3-tiny.pt"'. --imgsz | --img | --img-size (int): Inference image size in pixels. Default is 640. --batch-size (int): Batch size for inference. Default is 1. --data (str): Path to the dataset configuration file (dataset.yaml). Default is 'ROOT / "data/coco128.yaml"'. --device (str): CUDA device identifier, e.g., '0' for single GPU, '0,1,2,3' for multiple GPUs, or 'cpu' for CPU inference. Default is "". --half (bool): If set, use FP16 half-precision inference. Default is False. --test (bool): If set, test only exports without running inference. Default is False. --pt-only (bool): If set, test only the PyTorch model without exporting to other formats. Default is False. --hard-fail (str | bool): If set, raise an exception on benchmark failure. Can also be a string representing the minimum metric floor for success. Default is False. Returns: argparse.Namespace: The parsed arguments as a namespace object. Examples: To run inference on the YOLOv3-tiny model with a different image size: ```python $ python benchmarks.py --weights yolov3-tiny.pt --imgsz 512 --device 0 ``` Notes: The `--hard-fail` argument can be a boolean or a string. If a string is provided, it should be an expression that represents the minimum acceptable metric value, such as '0.29' for mAP (mean Average Precision). Links: https://github.com/ultralytics/ultralytics """ parser = argparse.ArgumentParser() parser.add_argument("--weights", type=str, default=ROOT / "yolov3-tiny.pt", help="weights path") parser.add_argument("--imgsz", "--img", "--img-size", type=int, default=640, help="inference size (pixels)") parser.add_argument("--batch-size", type=int, default=1, help="batch size") parser.add_argument("--data", type=str, default=ROOT / "data/coco128.yaml", help="dataset.yaml path") parser.add_argument("--device", default="", help="cuda device, i.e. 0 or 0,1,2,3 or cpu") parser.add_argument("--half", action="store_true", help="use FP16 half-precision inference") parser.add_argument("--test", action="store_true", help="test exports only") parser.add_argument("--pt-only", action="store_true", help="test PyTorch only") parser.add_argument("--hard-fail", nargs="?", const=True, default=False, help="Exception on error or < min metric") opt = parser.parse_args() opt.data = check_yaml(opt.data) # check YAML print_args(vars(opt)) return opt def main(opt): """Executes the export and benchmarking pipeline for YOLOv3 models, testing multiple export formats and validating performance metrics. Args: opt (argparse.Namespace): Parsed command line arguments, including options for weights, image size, batch size, dataset path, device, half-precision inference, test mode, PyTorch-only testing, and hard fail conditions. Returns: pd.DataFrame: A DataFrame containing benchmarking results with columns: - Format: Name of the export format - Size (MB): File size of the exported model - mAP50-95: Mean Average Precision for the model - Inference time (ms): Time taken for inference Examples: Running the function from command line with required arguments: ```python $ python benchmarks.py --weights yolov5s.pt --img 640 ``` For more details, visit the Ultralytics YOLOv3 repository on [GitHub](https://github.com/ultralytics/ultralytics). Notes: The function runs the main pipeline by exporting the YOLOv3 model to various formats and running benchmarks to evaluate performance. If `opt.test` is set to True, it only tests the export process and logs the results. """ test(**vars(opt)) if opt.test else run(**vars(opt)) if __name__ == "__main__": opt = parse_opt() main(opt) ================================================ FILE: classify/predict.py ================================================ # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license """ Run YOLOv3 classification inference on images, videos, directories, globs, YouTube, webcam, streams, etc. Usage - sources: $ python classify/predict.py --weights yolov5s-cls.pt --source 0 # webcam img.jpg # image vid.mp4 # video screen # screenshot path/ # directory list.txt # list of images list.streams # list of streams 'path/*.jpg' # glob 'https://youtu.be/LNwODJXcvt4' # YouTube 'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream Usage - formats: $ python classify/predict.py --weights yolov5s-cls.pt # PyTorch yolov5s-cls.torchscript # TorchScript yolov5s-cls.onnx # ONNX Runtime or OpenCV DNN with --dnn yolov5s-cls_openvino_model # OpenVINO yolov5s-cls.engine # TensorRT yolov5s-cls.mlmodel # CoreML (macOS-only) yolov5s-cls_saved_model # TensorFlow SavedModel yolov5s-cls.pb # TensorFlow GraphDef yolov5s-cls.tflite # TensorFlow Lite yolov5s-cls_edgetpu.tflite # TensorFlow Edge TPU yolov5s-cls_paddle_model # PaddlePaddle """ import argparse import os import platform import sys from pathlib import Path import torch import torch.nn.functional as F FILE = Path(__file__).resolve() ROOT = FILE.parents[1] # YOLOv3 root directory if str(ROOT) not in sys.path: sys.path.append(str(ROOT)) # add ROOT to PATH ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative from ultralytics.utils.plotting import Annotator from models.common import DetectMultiBackend from utils.augmentations import classify_transforms from utils.dataloaders import IMG_FORMATS, VID_FORMATS, LoadImages, LoadScreenshots, LoadStreams from utils.general import ( LOGGER, Profile, check_file, check_img_size, check_imshow, check_requirements, colorstr, cv2, increment_path, print_args, strip_optimizer, ) from utils.torch_utils import select_device, smart_inference_mode @smart_inference_mode() def run( weights=ROOT / "yolov5s-cls.pt", # model.pt path(s) source=ROOT / "data/images", # file/dir/URL/glob/screen/0(webcam) data=ROOT / "data/coco128.yaml", # dataset.yaml path imgsz=(224, 224), # inference size (height, width) device="", # cuda device, i.e. 0 or 0,1,2,3 or cpu view_img=False, # show results save_txt=False, # save results to *.txt nosave=False, # do not save images/videos augment=False, # augmented inference visualize=False, # visualize features update=False, # update all models project=ROOT / "runs/predict-cls", # save results to project/name name="exp", # save results to project/name exist_ok=False, # existing project/name ok, do not increment half=False, # use FP16 half-precision inference dnn=False, # use OpenCV DNN for ONNX inference vid_stride=1, # video frame-rate stride ): """Performs YOLOv3 classification inference on various input sources and saves or displays results.""" source = str(source) save_img = not nosave and not source.endswith(".txt") # save inference images is_file = Path(source).suffix[1:] in (IMG_FORMATS + VID_FORMATS) is_url = source.lower().startswith(("rtsp://", "rtmp://", "http://", "https://")) webcam = source.isnumeric() or source.endswith(".streams") or (is_url and not is_file) screenshot = source.lower().startswith("screen") if is_url and is_file: source = check_file(source) # download # Directories save_dir = increment_path(Path(project) / name, exist_ok=exist_ok) # increment run (save_dir / "labels" if save_txt else save_dir).mkdir(parents=True, exist_ok=True) # make dir # Load model device = select_device(device) model = DetectMultiBackend(weights, device=device, dnn=dnn, data=data, fp16=half) stride, names, pt = model.stride, model.names, model.pt imgsz = check_img_size(imgsz, s=stride) # check image size # Dataloader bs = 1 # batch_size if webcam: view_img = check_imshow(warn=True) dataset = LoadStreams(source, img_size=imgsz, transforms=classify_transforms(imgsz[0]), vid_stride=vid_stride) bs = len(dataset) elif screenshot: dataset = LoadScreenshots(source, img_size=imgsz, stride=stride, auto=pt) else: dataset = LoadImages(source, img_size=imgsz, transforms=classify_transforms(imgsz[0]), vid_stride=vid_stride) vid_path, vid_writer = [None] * bs, [None] * bs # Run inference model.warmup(imgsz=(1 if pt else bs, 3, *imgsz)) # warmup seen, windows, dt = 0, [], (Profile(), Profile(), Profile()) for path, im, im0s, vid_cap, s in dataset: with dt[0]: im = torch.Tensor(im).to(model.device) im = im.half() if model.fp16 else im.float() # uint8 to fp16/32 if len(im.shape) == 3: im = im[None] # expand for batch dim # Inference with dt[1]: results = model(im) # Post-process with dt[2]: pred = F.softmax(results, dim=1) # probabilities # Process predictions for i, prob in enumerate(pred): # per image seen += 1 if webcam: # batch_size >= 1 p, im0, frame = path[i], im0s[i].copy(), dataset.count s += f"{i}: " else: p, im0, frame = path, im0s.copy(), getattr(dataset, "frame", 0) p = Path(p) # to Path save_path = str(save_dir / p.name) # im.jpg txt_path = str(save_dir / "labels" / p.stem) + ("" if dataset.mode == "image" else f"_{frame}") # im.txt s += "{:g}x{:g} ".format(*im.shape[2:]) # print string annotator = Annotator(im0, example=str(names), pil=True) # Print results top5i = prob.argsort(0, descending=True)[:5].tolist() # top 5 indices s += f"{', '.join(f'{names[j]} {prob[j]:.2f}' for j in top5i)}, " # Write results text = "\n".join(f"{prob[j]:.2f} {names[j]}" for j in top5i) if save_img or view_img: # Add bbox to image annotator.text([32, 32], text, txt_color=(255, 255, 255)) if save_txt: # Write to file with open(f"{txt_path}.txt", "a") as f: f.write(text + "\n") # Stream results im0 = annotator.result() if view_img: if platform.system() == "Linux" and p not in windows: windows.append(p) cv2.namedWindow(str(p), cv2.WINDOW_NORMAL | cv2.WINDOW_KEEPRATIO) # allow window resize (Linux) cv2.resizeWindow(str(p), im0.shape[1], im0.shape[0]) cv2.imshow(str(p), im0) cv2.waitKey(1) # 1 millisecond # Save results (image with detections) if save_img: if dataset.mode == "image": cv2.imwrite(save_path, im0) else: # 'video' or 'stream' if vid_path[i] != save_path: # new video vid_path[i] = save_path if isinstance(vid_writer[i], cv2.VideoWriter): vid_writer[i].release() # release previous video writer if vid_cap: # video fps = vid_cap.get(cv2.CAP_PROP_FPS) w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH)) h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) else: # stream fps, w, h = 30, im0.shape[1], im0.shape[0] save_path = str(Path(save_path).with_suffix(".mp4")) # force *.mp4 suffix on results videos vid_writer[i] = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*"mp4v"), fps, (w, h)) vid_writer[i].write(im0) # Print time (inference-only) LOGGER.info(f"{s}{dt[1].dt * 1e3:.1f}ms") # Print results t = tuple(x.t / seen * 1e3 for x in dt) # speeds per image LOGGER.info(f"Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {(1, 3, *imgsz)}" % t) if save_txt or save_img: s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else "" LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}{s}") if update: strip_optimizer(weights[0]) # update model (to fix SourceChangeWarning) def parse_opt(): """Parses command line arguments for model inference settings, returns a Namespace of options.""" parser = argparse.ArgumentParser() parser.add_argument("--weights", nargs="+", type=str, default=ROOT / "yolov5s-cls.pt", help="model path(s)") parser.add_argument("--source", type=str, default=ROOT / "data/images", help="file/dir/URL/glob/screen/0(webcam)") parser.add_argument("--data", type=str, default=ROOT / "data/coco128.yaml", help="(optional) dataset.yaml path") parser.add_argument("--imgsz", "--img", "--img-size", nargs="+", type=int, default=[224], help="inference size h,w") parser.add_argument("--device", default="", help="cuda device, i.e. 0 or 0,1,2,3 or cpu") parser.add_argument("--view-img", action="store_true", help="show results") parser.add_argument("--save-txt", action="store_true", help="save results to *.txt") parser.add_argument("--nosave", action="store_true", help="do not save images/videos") parser.add_argument("--augment", action="store_true", help="augmented inference") parser.add_argument("--visualize", action="store_true", help="visualize features") parser.add_argument("--update", action="store_true", help="update all models") parser.add_argument("--project", default=ROOT / "runs/predict-cls", help="save results to project/name") parser.add_argument("--name", default="exp", help="save results to project/name") parser.add_argument("--exist-ok", action="store_true", help="existing project/name ok, do not increment") parser.add_argument("--half", action="store_true", help="use FP16 half-precision inference") parser.add_argument("--dnn", action="store_true", help="use OpenCV DNN for ONNX inference") parser.add_argument("--vid-stride", type=int, default=1, help="video frame-rate stride") opt = parser.parse_args() opt.imgsz *= 2 if len(opt.imgsz) == 1 else 1 # expand print_args(vars(opt)) return opt def main(opt): """Entry point for running the model; checks requirements and calls `run` with options parsed from CLI.""" check_requirements(ROOT / "requirements.txt", exclude=("tensorboard", "thop")) run(**vars(opt)) if __name__ == "__main__": opt = parse_opt() main(opt) ================================================ FILE: classify/train.py ================================================ # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license """ Train a YOLOv3 classifier model on a classification dataset. Usage - Single-GPU training: $ python classify/train.py --model yolov5s-cls.pt --data imagenette160 --epochs 5 --img 224 Usage - Multi-GPU DDP training: $ 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 Datasets: --data mnist, fashion-mnist, cifar10, cifar100, imagenette, imagewoof, imagenet, or 'path/to/data' YOLOv3-cls models: --model yolov5n-cls.pt, yolov5s-cls.pt, yolov5m-cls.pt, yolov5l-cls.pt, yolov5x-cls.pt Torchvision models: --model resnet50, efficientnet_b0, etc. See https://pytorch.org/vision/stable/models.html """ import argparse import os import subprocess import sys import time from copy import deepcopy from datetime import datetime from pathlib import Path import torch import torch.distributed as dist import torch.hub as hub import torch.optim.lr_scheduler as lr_scheduler import torchvision from torch.cuda import amp from tqdm import tqdm FILE = Path(__file__).resolve() ROOT = FILE.parents[1] # YOLOv3 root directory if str(ROOT) not in sys.path: sys.path.append(str(ROOT)) # add ROOT to PATH ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative from classify import val as validate from models.experimental import attempt_load from models.yolo import ClassificationModel, DetectionModel from utils.dataloaders import create_classification_dataloader from utils.general import ( DATASETS_DIR, LOGGER, TQDM_BAR_FORMAT, WorkingDirectory, check_git_info, check_git_status, check_requirements, colorstr, download, increment_path, init_seeds, print_args, yaml_save, ) from utils.loggers import GenericLogger from utils.plots import imshow_cls from utils.torch_utils import ( ModelEMA, de_parallel, model_info, reshape_classifier_output, select_device, smart_DDP, smart_optimizer, smartCrossEntropyLoss, torch_distributed_zero_first, ) LOCAL_RANK = int(os.getenv("LOCAL_RANK", -1)) # https://pytorch.org/docs/stable/elastic/run.html RANK = int(os.getenv("RANK", -1)) WORLD_SIZE = int(os.getenv("WORLD_SIZE", 1)) GIT_INFO = check_git_info() def train(opt, device): """Trains a model on a given dataset using specified options and device, handling data loading, model optimization, and logging. """ init_seeds(opt.seed + 1 + RANK, deterministic=True) save_dir, data, bs, epochs, nw, imgsz, pretrained = ( opt.save_dir, Path(opt.data), opt.batch_size, opt.epochs, min(os.cpu_count() - 1, opt.workers), opt.imgsz, str(opt.pretrained).lower() == "true", ) cuda = device.type != "cpu" # Directories wdir = save_dir / "weights" wdir.mkdir(parents=True, exist_ok=True) # make dir last, best = wdir / "last.pt", wdir / "best.pt" # Save run settings yaml_save(save_dir / "opt.yaml", vars(opt)) # Logger logger = GenericLogger(opt=opt, console_logger=LOGGER) if RANK in {-1, 0} else None # Download Dataset with torch_distributed_zero_first(LOCAL_RANK), WorkingDirectory(ROOT): data_dir = data if data.is_dir() else (DATASETS_DIR / data) if not data_dir.is_dir(): LOGGER.info(f"\nDataset not found ⚠️, missing path {data_dir}, attempting download...") t = time.time() if str(data) == "imagenet": subprocess.run(["bash", str(ROOT / "data/scripts/get_imagenet.sh")], shell=True, check=True) else: url = f"https://github.com/ultralytics/assets/releases/download/v0.0.0/{data}.zip" download(url, dir=data_dir.parent) s = f"Dataset download success ✅ ({time.time() - t:.1f}s), saved to {colorstr('bold', data_dir)}\n" LOGGER.info(s) # Dataloaders nc = len([x for x in (data_dir / "train").glob("*") if x.is_dir()]) # number of classes trainloader = create_classification_dataloader( path=data_dir / "train", imgsz=imgsz, batch_size=bs // WORLD_SIZE, augment=True, cache=opt.cache, rank=LOCAL_RANK, workers=nw, ) test_dir = data_dir / "test" if (data_dir / "test").exists() else data_dir / "val" # data/test or data/val if RANK in {-1, 0}: testloader = create_classification_dataloader( path=test_dir, imgsz=imgsz, batch_size=bs // WORLD_SIZE * 2, augment=False, cache=opt.cache, rank=-1, workers=nw, ) # Model with torch_distributed_zero_first(LOCAL_RANK), WorkingDirectory(ROOT): if Path(opt.model).is_file() or opt.model.endswith(".pt"): model = attempt_load(opt.model, device="cpu", fuse=False) elif opt.model in torchvision.models.__dict__: # TorchVision models i.e. resnet50, efficientnet_b0 model = torchvision.models.__dict__[opt.model](weights="IMAGENET1K_V1" if pretrained else None) else: m = hub.list("ultralytics/yolov5") # + hub.list('pytorch/vision') # models raise ModuleNotFoundError(f"--model {opt.model} not found. Available models are: \n" + "\n".join(m)) if isinstance(model, DetectionModel): LOGGER.warning("WARNING ⚠️ pass YOLOv3 classifier model with '-cls' suffix, i.e. '--model yolov5s-cls.pt'") model = ClassificationModel(model=model, nc=nc, cutoff=opt.cutoff or 10) # convert to classification model reshape_classifier_output(model, nc) # update class count for m in model.modules(): if not pretrained and hasattr(m, "reset_parameters"): m.reset_parameters() if isinstance(m, torch.nn.Dropout) and opt.dropout is not None: m.p = opt.dropout # set dropout for p in model.parameters(): p.requires_grad = True # for training model = model.to(device) # Info if RANK in {-1, 0}: model.names = trainloader.dataset.classes # attach class names model.transforms = testloader.dataset.torch_transforms # attach inference transforms model_info(model) if opt.verbose: LOGGER.info(model) images, labels = next(iter(trainloader)) file = imshow_cls(images[:25], labels[:25], names=model.names, f=save_dir / "train_images.jpg") logger.log_images(file, name="Train Examples") logger.log_graph(model, imgsz) # log model # Optimizer optimizer = smart_optimizer(model, opt.optimizer, opt.lr0, momentum=0.9, decay=opt.decay) # Scheduler lrf = 0.01 # final lr (fraction of lr0) # lf = lambda x: ((1 + math.cos(x * math.pi / epochs)) / 2) * (1 - lrf) + lrf # cosine def lf(x): """Linear learning rate scheduler function, scaling learning rate from initial value to `lrf` over `epochs`.""" return (1 - x / epochs) * (1 - lrf) + lrf # linear scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lf) # scheduler = lr_scheduler.OneCycleLR(optimizer, max_lr=lr0, total_steps=epochs, pct_start=0.1, # final_div_factor=1 / 25 / lrf) # EMA ema = ModelEMA(model) if RANK in {-1, 0} else None # DDP mode if cuda and RANK != -1: model = smart_DDP(model) # Train t0 = time.time() criterion = smartCrossEntropyLoss(label_smoothing=opt.label_smoothing) # loss function best_fitness = 0.0 scaler = amp.GradScaler(enabled=cuda) val = test_dir.stem # 'val' or 'test' LOGGER.info( f"Image sizes {imgsz} train, {imgsz} test\n" f"Using {nw * WORLD_SIZE} dataloader workers\n" f"Logging results to {colorstr('bold', save_dir)}\n" f"Starting {opt.model} training on {data} dataset with {nc} classes for {epochs} epochs...\n\n" f"{'Epoch':>10}{'GPU_mem':>10}{'train_loss':>12}{f'{val}_loss':>12}{'top1_acc':>12}{'top5_acc':>12}" ) for epoch in range(epochs): # loop over the dataset multiple times tloss, vloss, fitness = 0.0, 0.0, 0.0 # train loss, val loss, fitness model.train() if RANK != -1: trainloader.sampler.set_epoch(epoch) pbar = enumerate(trainloader) if RANK in {-1, 0}: pbar = tqdm(enumerate(trainloader), total=len(trainloader), bar_format=TQDM_BAR_FORMAT) for i, (images, labels) in pbar: # progress bar images, labels = images.to(device, non_blocking=True), labels.to(device) # Forward with amp.autocast(enabled=cuda): # stability issues when enabled loss = criterion(model(images), labels) # Backward scaler.scale(loss).backward() # Optimize scaler.unscale_(optimizer) # unscale gradients torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=10.0) # clip gradients scaler.step(optimizer) scaler.update() optimizer.zero_grad() if ema: ema.update(model) if RANK in {-1, 0}: # Print tloss = (tloss * i + loss.item()) / (i + 1) # update mean losses mem = "%.3gG" % (torch.cuda.memory_reserved() / 1e9 if torch.cuda.is_available() else 0) # (GB) pbar.desc = f"{f'{epoch + 1}/{epochs}':>10}{mem:>10}{tloss:>12.3g}" + " " * 36 # Test if i == len(pbar) - 1: # last batch top1, top5, vloss = validate.run( model=ema.ema, dataloader=testloader, criterion=criterion, pbar=pbar ) # test accuracy, loss fitness = top1 # define fitness as top1 accuracy # Scheduler scheduler.step() # Log metrics if RANK in {-1, 0}: # Best fitness if fitness > best_fitness: best_fitness = fitness # Log metrics = { "train/loss": tloss, f"{val}/loss": vloss, "metrics/accuracy_top1": top1, "metrics/accuracy_top5": top5, "lr/0": optimizer.param_groups[0]["lr"], } # learning rate logger.log_metrics(metrics, epoch) # Save model final_epoch = epoch + 1 == epochs if (not opt.nosave) or final_epoch: ckpt = { "epoch": epoch, "best_fitness": best_fitness, "model": deepcopy(ema.ema).half(), # deepcopy(de_parallel(model)).half(), "ema": None, # deepcopy(ema.ema).half(), "updates": ema.updates, "optimizer": None, # optimizer.state_dict(), "opt": vars(opt), "git": GIT_INFO, # {remote, branch, commit} if a git repo "date": datetime.now().isoformat(), } # Save last, best and delete torch.save(ckpt, last) if best_fitness == fitness: torch.save(ckpt, best) del ckpt # Train complete if RANK in {-1, 0} and final_epoch: LOGGER.info( f"\nTraining complete ({(time.time() - t0) / 3600:.3f} hours)" f"\nResults saved to {colorstr('bold', save_dir)}" f"\nPredict: python classify/predict.py --weights {best} --source im.jpg" f"\nValidate: python classify/val.py --weights {best} --data {data_dir}" f"\nExport: python export.py --weights {best} --include onnx" f"\nPyTorch Hub: model = torch.hub.load('ultralytics/yolov5', 'custom', '{best}')" f"\nVisualize: https://netron.app\n" ) # Plot examples images, labels = (x[:25] for x in next(iter(testloader))) # first 25 images and labels pred = torch.max(ema.ema(images.to(device)), 1)[1] file = imshow_cls(images, labels, pred, de_parallel(model).names, verbose=False, f=save_dir / "test_images.jpg") # Log results meta = {"epochs": epochs, "top1_acc": best_fitness, "date": datetime.now().isoformat()} logger.log_images(file, name="Test Examples (true-predicted)", epoch=epoch) logger.log_model(best, epochs, metadata=meta) def parse_opt(known=False): """Parses command line arguments for model configuration and training options.""" parser = argparse.ArgumentParser() parser.add_argument("--model", type=str, default="yolov5s-cls.pt", help="initial weights path") parser.add_argument("--data", type=str, default="imagenette160", help="cifar10, cifar100, mnist, imagenet, ...") parser.add_argument("--epochs", type=int, default=10, help="total training epochs") parser.add_argument("--batch-size", type=int, default=64, help="total batch size for all GPUs") parser.add_argument("--imgsz", "--img", "--img-size", type=int, default=224, help="train, val image size (pixels)") parser.add_argument("--nosave", action="store_true", help="only save final checkpoint") parser.add_argument("--cache", type=str, nargs="?", const="ram", help='--cache images in "ram" (default) or "disk"') parser.add_argument("--device", default="", help="cuda device, i.e. 0 or 0,1,2,3 or cpu") parser.add_argument("--workers", type=int, default=8, help="max dataloader workers (per RANK in DDP mode)") parser.add_argument("--project", default=ROOT / "runs/train-cls", help="save to project/name") parser.add_argument("--name", default="exp", help="save to project/name") parser.add_argument("--exist-ok", action="store_true", help="existing project/name ok, do not increment") parser.add_argument("--pretrained", nargs="?", const=True, default=True, help="start from i.e. --pretrained False") parser.add_argument("--optimizer", choices=["SGD", "Adam", "AdamW", "RMSProp"], default="Adam", help="optimizer") parser.add_argument("--lr0", type=float, default=0.001, help="initial learning rate") parser.add_argument("--decay", type=float, default=5e-5, help="weight decay") parser.add_argument("--label-smoothing", type=float, default=0.1, help="Label smoothing epsilon") parser.add_argument("--cutoff", type=int, default=None, help="Model layer cutoff index for Classify() head") parser.add_argument("--dropout", type=float, default=None, help="Dropout (fraction)") parser.add_argument("--verbose", action="store_true", help="Verbose mode") parser.add_argument("--seed", type=int, default=0, help="Global training seed") parser.add_argument("--local_rank", type=int, default=-1, help="Automatic DDP Multi-GPU argument, do not modify") return parser.parse_known_args()[0] if known else parser.parse_args() def main(opt): """Initializes training environment, checks, DDP mode setup, and starts training with given options.""" if RANK in {-1, 0}: print_args(vars(opt)) check_git_status() check_requirements(ROOT / "requirements.txt") # DDP mode device = select_device(opt.device, batch_size=opt.batch_size) if LOCAL_RANK != -1: assert opt.batch_size != -1, "AutoBatch is coming soon for classification, please pass a valid --batch-size" assert opt.batch_size % WORLD_SIZE == 0, f"--batch-size {opt.batch_size} must be multiple of WORLD_SIZE" assert torch.cuda.device_count() > LOCAL_RANK, "insufficient CUDA devices for DDP command" torch.cuda.set_device(LOCAL_RANK) device = torch.device("cuda", LOCAL_RANK) dist.init_process_group(backend="nccl" if dist.is_nccl_available() else "gloo") # Parameters opt.save_dir = increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok) # increment run # Train train(opt, device) def run(**kwargs): """Executes YOLOv5 model training with dynamic options, e.g., `run(data='mnist', imgsz=320, model='yolov5m')`.""" opt = parse_opt(True) for k, v in kwargs.items(): setattr(opt, k, v) main(opt) return opt if __name__ == "__main__": opt = parse_opt() main(opt) ================================================ FILE: classify/tutorial.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": { "id": "t6MPjfT5NrKQ" }, "source": [ "
\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", " \"Ultralytics\n", " \"Run\n", " \"Open\n", " \"Open\n", "\n", " \"Discord\"\n", " \"Ultralytics\n", " \"Ultralytics\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", "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", "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", "Request an Enterprise License for commercial use at [Ultralytics Licensing](https://www.ultralytics.com/license).\n", "\n", "
\n", "
\n", " \n", " \"Ultralytics\n", " \n", "\n", "

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

\n", "
" ] }, { "cell_type": "markdown", "metadata": { "id": "7mGmQbAO5pQb" }, "source": [ "# Setup\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." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "wbvMlHd_QwMG", "outputId": "0806e375-610d-4ec0-c867-763dbb518279" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "YOLOv5 🚀 v7.0-3-g61ebf5e Python-3.7.15 torch-1.12.1+cu113 CUDA:0 (Tesla T4, 15110MiB)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Setup complete ✅ (2 CPUs, 12.7 GB RAM, 22.6/78.2 GB disk)\n" ] } ], "source": [ "!git clone https://github.com/ultralytics/yolov5 # clone\n", "%cd yolov5\n", "%pip install -qr requirements.txt # install\n", "\n", "import torch\n", "\n", "import utils\n", "\n", "display = utils.notebook_init() # checks" ] }, { "cell_type": "markdown", "metadata": { "id": "4JnkELT0cIJg" }, "source": [ "# 1. Predict\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", "```shell\n", "python classify/predict.py --source 0 # webcam\n", " img.jpg # image \n", " vid.mp4 # video\n", " screen # screenshot\n", " path/ # directory\n", " 'path/*.jpg' # glob\n", " 'https://youtu.be/LNwODJXcvt4' # YouTube\n", " 'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream\n", "```" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "zR9ZbuQCH7FX", "outputId": "50504ef7-aa3e-4281-a4e3-d0c7df3c0ffe" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\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", "YOLOv5 🚀 v7.0-3-g61ebf5e Python-3.7.15 torch-1.12.1+cu113 CUDA:0 (Tesla T4, 15110MiB)\n", "\n", "Downloading https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s-cls.pt to yolov5s-cls.pt...\n", "100% 10.5M/10.5M [00:00<00:00, 12.3MB/s]\n", "\n", "Fusing layers... \n", "Model summary: 117 layers, 5447688 parameters, 0 gradients, 11.4 GFLOPs\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", "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", "Speed: 0.3ms pre-process, 4.3ms inference, 1.5ms NMS per image at shape (1, 3, 224, 224)\n", "Results saved to \u001B[1mruns/predict-cls/exp\u001B[0m\n" ] } ], "source": [ "!python classify/predict.py --weights yolov5s-cls.pt --img 224 --source data/images\n", "# display.Image(filename='runs/predict-cls/exp/zidane.jpg', width=600)" ] }, { "cell_type": "markdown", "metadata": { "id": "hkAzDWJ7cWTr" }, "source": [ "        \n", "" ] }, { "cell_type": "markdown", "metadata": { "id": "0eq1SMWl6Sfn" }, "source": [ "# 2. Validate\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." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "WQPtK1QYVaD_", "outputId": "20fc0630-141e-4a90-ea06-342cbd7ce496" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "--2022-11-22 19:53:40-- https://image-net.org/data/ILSVRC/2012/ILSVRC2012_img_val.tar\n", "Resolving image-net.org (image-net.org)... 171.64.68.16\n", "Connecting to image-net.org (image-net.org)|171.64.68.16|:443... connected.\n", "HTTP request sent, awaiting response... 200 OK\n", "Length: 6744924160 (6.3G) [application/x-tar]\n", "Saving to: ‘ILSVRC2012_img_val.tar’\n", "\n", "ILSVRC2012_img_val. 100%[===================>] 6.28G 16.1MB/s in 10m 52s \n", "\n", "2022-11-22 20:04:32 (9.87 MB/s) - ‘ILSVRC2012_img_val.tar’ saved [6744924160/6744924160]\n", "\n" ] } ], "source": [ "# Download Imagenet val (6.3G, 50000 images)\n", "!bash data/scripts/get_imagenet.sh --val" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "X58w8JLpMnjH", "outputId": "41843132-98e2-4c25-d474-4cd7b246fb8e" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\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", "YOLOv5 🚀 v7.0-3-g61ebf5e Python-3.7.15 torch-1.12.1+cu113 CUDA:0 (Tesla T4, 15110MiB)\n", "\n", "Fusing layers... \n", "Model summary: 117 layers, 5447688 parameters, 0 gradients, 11.4 GFLOPs\n", "validating: 100% 391/391 [04:57<00:00, 1.31it/s]\n", " Class Images top1_acc top5_acc\n", " all 50000 0.715 0.902\n", " tench 50 0.94 0.98\n", " goldfish 50 0.88 0.92\n", " great white shark 50 0.78 0.96\n", " tiger shark 50 0.68 0.96\n", " hammerhead shark 50 0.82 0.92\n", " electric ray 50 0.76 0.9\n", " stingray 50 0.7 0.9\n", " cock 50 0.78 0.92\n", " hen 50 0.84 0.96\n", " ostrich 50 0.98 1\n", " brambling 50 0.9 0.96\n", " goldfinch 50 0.92 0.98\n", " house finch 50 0.88 0.96\n", " junco 50 0.94 0.98\n", " indigo bunting 50 0.86 0.88\n", " American robin 50 0.9 0.96\n", " bulbul 50 0.84 0.96\n", " jay 50 0.9 0.96\n", " magpie 50 0.84 0.96\n", " chickadee 50 0.9 1\n", " American dipper 50 0.82 0.92\n", " kite 50 0.76 0.94\n", " bald eagle 50 0.92 1\n", " vulture 50 0.96 1\n", " great grey owl 50 0.94 0.98\n", " fire salamander 50 0.96 0.98\n", " smooth newt 50 0.58 0.94\n", " newt 50 0.74 0.9\n", " spotted salamander 50 0.86 0.94\n", " axolotl 50 0.86 0.96\n", " American bullfrog 50 0.78 0.92\n", " tree frog 50 0.84 0.96\n", " tailed frog 50 0.48 0.8\n", " loggerhead sea turtle 50 0.68 0.94\n", " leatherback sea turtle 50 0.5 0.8\n", " mud turtle 50 0.64 0.84\n", " terrapin 50 0.52 0.98\n", " box turtle 50 0.84 0.98\n", " banded gecko 50 0.7 0.88\n", " green iguana 50 0.76 0.94\n", " Carolina anole 50 0.58 0.96\n", "desert grassland whiptail lizard 50 0.82 0.94\n", " agama 50 0.74 0.92\n", " frilled-necked lizard 50 0.84 0.86\n", " alligator lizard 50 0.58 0.78\n", " Gila monster 50 0.72 0.8\n", " European green lizard 50 0.42 0.9\n", " chameleon 50 0.76 0.84\n", " Komodo dragon 50 0.86 0.96\n", " Nile crocodile 50 0.7 0.84\n", " American alligator 50 0.76 0.96\n", " triceratops 50 0.9 0.94\n", " worm snake 50 0.76 0.88\n", " ring-necked snake 50 0.8 0.92\n", " eastern hog-nosed snake 50 0.58 0.88\n", " smooth green snake 50 0.6 0.94\n", " kingsnake 50 0.82 0.9\n", " garter snake 50 0.88 0.94\n", " water snake 50 0.7 0.94\n", " vine snake 50 0.66 0.76\n", " night snake 50 0.34 0.82\n", " boa constrictor 50 0.8 0.96\n", " African rock python 50 0.48 0.76\n", " Indian cobra 50 0.82 0.94\n", " green mamba 50 0.54 0.86\n", " sea snake 50 0.62 0.9\n", " Saharan horned viper 50 0.56 0.86\n", "eastern diamondback rattlesnake 50 0.6 0.86\n", " sidewinder 50 0.28 0.86\n", " trilobite 50 0.98 0.98\n", " harvestman 50 0.86 0.94\n", " scorpion 50 0.86 0.94\n", " yellow garden spider 50 0.92 0.96\n", " barn spider 50 0.38 0.98\n", " European garden spider 50 0.62 0.98\n", " southern black widow 50 0.88 0.94\n", " tarantula 50 0.94 1\n", " wolf spider 50 0.82 0.92\n", " tick 50 0.74 0.84\n", " centipede 50 0.68 0.82\n", " black grouse 50 0.88 0.98\n", " ptarmigan 50 0.78 0.94\n", " ruffed grouse 50 0.88 1\n", " prairie grouse 50 0.92 1\n", " peacock 50 0.88 0.9\n", " quail 50 0.9 0.94\n", " partridge 50 0.74 0.96\n", " grey parrot 50 0.9 0.96\n", " macaw 50 0.88 0.98\n", "sulphur-crested cockatoo 50 0.86 0.92\n", " lorikeet 50 0.96 1\n", " coucal 50 0.82 0.88\n", " bee eater 50 0.96 0.98\n", " hornbill 50 0.9 0.96\n", " hummingbird 50 0.88 0.96\n", " jacamar 50 0.92 0.94\n", " toucan 50 0.84 0.94\n", " duck 50 0.76 0.94\n", " red-breasted merganser 50 0.86 0.96\n", " goose 50 0.74 0.96\n", " black swan 50 0.94 0.98\n", " tusker 50 0.54 0.92\n", " echidna 50 0.98 1\n", " platypus 50 0.72 0.84\n", " wallaby 50 0.78 0.88\n", " koala 50 0.84 0.92\n", " wombat 50 0.78 0.84\n", " jellyfish 50 0.88 0.96\n", " sea anemone 50 0.72 0.9\n", " brain coral 50 0.88 0.96\n", " flatworm 50 0.8 0.98\n", " nematode 50 0.86 0.9\n", " conch 50 0.74 0.88\n", " snail 50 0.78 0.88\n", " slug 50 0.74 0.82\n", " sea slug 50 0.88 0.98\n", " chiton 50 0.88 0.98\n", " chambered nautilus 50 0.88 0.92\n", " Dungeness crab 50 0.78 0.94\n", " rock crab 50 0.68 0.86\n", " fiddler crab 50 0.64 0.86\n", " red king crab 50 0.76 0.96\n", " American lobster 50 0.78 0.96\n", " spiny lobster 50 0.74 0.88\n", " crayfish 50 0.56 0.86\n", " hermit crab 50 0.78 0.96\n", " isopod 50 0.66 0.78\n", " white stork 50 0.88 0.96\n", " black stork 50 0.84 0.98\n", " spoonbill 50 0.96 1\n", " flamingo 50 0.94 1\n", " little blue heron 50 0.92 0.98\n", " great egret 50 0.9 0.96\n", " bittern 50 0.86 0.94\n", " crane (bird) 50 0.62 0.9\n", " limpkin 50 0.98 1\n", " common gallinule 50 0.92 0.96\n", " American coot 50 0.9 0.98\n", " bustard 50 0.92 0.96\n", " ruddy turnstone 50 0.94 1\n", " dunlin 50 0.86 0.94\n", " common redshank 50 0.9 0.96\n", " dowitcher 50 0.84 0.96\n", " oystercatcher 50 0.86 0.94\n", " pelican 50 0.92 0.96\n", " king penguin 50 0.88 0.96\n", " albatross 50 0.9 1\n", " grey whale 50 0.84 0.92\n", " killer whale 50 0.92 1\n", " dugong 50 0.84 0.96\n", " sea lion 50 0.82 0.92\n", " Chihuahua 50 0.66 0.84\n", " Japanese Chin 50 0.72 0.98\n", " Maltese 50 0.76 0.94\n", " Pekingese 50 0.84 0.94\n", " Shih Tzu 50 0.74 0.96\n", " King Charles Spaniel 50 0.88 0.98\n", " Papillon 50 0.86 0.94\n", " toy terrier 50 0.48 0.94\n", " Rhodesian Ridgeback 50 0.76 0.98\n", " Afghan Hound 50 0.84 1\n", " Basset Hound 50 0.8 0.92\n", " Beagle 50 0.82 0.96\n", " Bloodhound 50 0.48 0.72\n", " Bluetick Coonhound 50 0.86 0.94\n", " Black and Tan Coonhound 50 0.54 0.8\n", "Treeing Walker Coonhound 50 0.66 0.98\n", " English foxhound 50 0.32 0.84\n", " Redbone Coonhound 50 0.62 0.94\n", " borzoi 50 0.92 1\n", " Irish Wolfhound 50 0.48 0.88\n", " Italian Greyhound 50 0.76 0.98\n", " Whippet 50 0.74 0.92\n", " Ibizan Hound 50 0.6 0.86\n", " Norwegian Elkhound 50 0.88 0.98\n", " Otterhound 50 0.62 0.9\n", " Saluki 50 0.72 0.92\n", " Scottish Deerhound 50 0.86 0.98\n", " Weimaraner 50 0.88 0.94\n", "Staffordshire Bull Terrier 50 0.66 0.98\n", "American Staffordshire Terrier 50 0.64 0.92\n", " Bedlington Terrier 50 0.9 0.92\n", " Border Terrier 50 0.86 0.92\n", " Kerry Blue Terrier 50 0.78 0.98\n", " Irish Terrier 50 0.7 0.96\n", " Norfolk Terrier 50 0.68 0.9\n", " Norwich Terrier 50 0.72 1\n", " Yorkshire Terrier 50 0.66 0.9\n", " Wire Fox Terrier 50 0.64 0.98\n", " Lakeland Terrier 50 0.74 0.92\n", " Sealyham Terrier 50 0.76 0.9\n", " Airedale Terrier 50 0.82 0.92\n", " Cairn Terrier 50 0.76 0.9\n", " Australian Terrier 50 0.48 0.84\n", " Dandie Dinmont Terrier 50 0.82 0.92\n", " Boston Terrier 50 0.92 1\n", " Miniature Schnauzer 50 0.68 0.9\n", " Giant Schnauzer 50 0.72 0.98\n", " Standard Schnauzer 50 0.74 1\n", " Scottish Terrier 50 0.76 0.96\n", " Tibetan Terrier 50 0.48 1\n", "Australian Silky Terrier 50 0.66 0.96\n", "Soft-coated Wheaten Terrier 50 0.74 0.96\n", "West Highland White Terrier 50 0.88 0.96\n", " Lhasa Apso 50 0.68 0.96\n", " Flat-Coated Retriever 50 0.72 0.94\n", " Curly-coated Retriever 50 0.82 0.94\n", " Golden Retriever 50 0.86 0.94\n", " Labrador Retriever 50 0.82 0.94\n", "Chesapeake Bay Retriever 50 0.76 0.96\n", "German Shorthaired Pointer 50 0.8 0.96\n", " Vizsla 50 0.68 0.96\n", " English Setter 50 0.7 1\n", " Irish Setter 50 0.8 0.9\n", " Gordon Setter 50 0.84 0.92\n", " Brittany 50 0.84 0.96\n", " Clumber Spaniel 50 0.92 0.96\n", "English Springer Spaniel 50 0.88 1\n", " Welsh Springer Spaniel 50 0.92 1\n", " Cocker Spaniels 50 0.7 0.94\n", " Sussex Spaniel 50 0.72 0.92\n", " Irish Water Spaniel 50 0.88 0.98\n", " Kuvasz 50 0.66 0.9\n", " Schipperke 50 0.9 0.98\n", " Groenendael 50 0.8 0.94\n", " Malinois 50 0.86 0.98\n", " Briard 50 0.52 0.8\n", " Australian Kelpie 50 0.6 0.88\n", " Komondor 50 0.88 0.94\n", " Old English Sheepdog 50 0.94 0.98\n", " Shetland Sheepdog 50 0.74 0.9\n", " collie 50 0.6 0.96\n", " Border Collie 50 0.74 0.96\n", " Bouvier des Flandres 50 0.78 0.94\n", " Rottweiler 50 0.88 0.96\n", " German Shepherd Dog 50 0.8 0.98\n", " Dobermann 50 0.68 0.96\n", " Miniature Pinscher 50 0.76 0.88\n", "Greater Swiss Mountain Dog 50 0.68 0.94\n", " Bernese Mountain Dog 50 0.96 1\n", " Appenzeller Sennenhund 50 0.22 1\n", " Entlebucher Sennenhund 50 0.64 0.98\n", " Boxer 50 0.7 0.92\n", " Bullmastiff 50 0.78 0.98\n", " Tibetan Mastiff 50 0.88 0.96\n", " French Bulldog 50 0.84 0.94\n", " Great Dane 50 0.54 0.9\n", " St. Bernard 50 0.92 1\n", " husky 50 0.46 0.98\n", " Alaskan Malamute 50 0.76 0.96\n", " Siberian Husky 50 0.46 0.98\n", " Dalmatian 50 0.94 0.98\n", " Affenpinscher 50 0.78 0.9\n", " Basenji 50 0.92 0.94\n", " pug 50 0.94 0.98\n", " Leonberger 50 1 1\n", " Newfoundland 50 0.78 0.96\n", " Pyrenean Mountain Dog 50 0.78 0.96\n", " Samoyed 50 0.96 1\n", " Pomeranian 50 0.98 1\n", " Chow Chow 50 0.9 0.96\n", " Keeshond 50 0.88 0.94\n", " Griffon Bruxellois 50 0.84 0.98\n", " Pembroke Welsh Corgi 50 0.82 0.94\n", " Cardigan Welsh Corgi 50 0.66 0.98\n", " Toy Poodle 50 0.52 0.88\n", " Miniature Poodle 50 0.52 0.92\n", " Standard Poodle 50 0.8 1\n", " Mexican hairless dog 50 0.88 0.98\n", " grey wolf 50 0.82 0.92\n", " Alaskan tundra wolf 50 0.78 0.98\n", " red wolf 50 0.48 0.9\n", " coyote 50 0.64 0.86\n", " dingo 50 0.76 0.88\n", " dhole 50 0.9 0.98\n", " African wild dog 50 0.98 1\n", " hyena 50 0.88 0.96\n", " red fox 50 0.54 0.92\n", " kit fox 50 0.72 0.98\n", " Arctic fox 50 0.94 1\n", " grey fox 50 0.7 0.94\n", " tabby cat 50 0.54 0.92\n", " tiger cat 50 0.22 0.94\n", " Persian cat 50 0.9 0.98\n", " Siamese cat 50 0.96 1\n", " Egyptian Mau 50 0.54 0.8\n", " cougar 50 0.9 1\n", " lynx 50 0.72 0.88\n", " leopard 50 0.78 0.98\n", " snow leopard 50 0.9 0.98\n", " jaguar 50 0.7 0.94\n", " lion 50 0.9 0.98\n", " tiger 50 0.92 0.98\n", " cheetah 50 0.94 0.98\n", " brown bear 50 0.94 0.98\n", " American black bear 50 0.8 1\n", " polar bear 50 0.84 0.96\n", " sloth bear 50 0.72 0.92\n", " mongoose 50 0.7 0.92\n", " meerkat 50 0.82 0.92\n", " tiger beetle 50 0.92 0.94\n", " ladybug 50 0.86 0.94\n", " ground beetle 50 0.64 0.94\n", " longhorn beetle 50 0.62 0.88\n", " leaf beetle 50 0.64 0.98\n", " dung beetle 50 0.86 0.98\n", " rhinoceros beetle 50 0.86 0.94\n", " weevil 50 0.9 1\n", " fly 50 0.78 0.94\n", " bee 50 0.68 0.94\n", " ant 50 0.68 0.78\n", " grasshopper 50 0.5 0.92\n", " cricket 50 0.64 0.92\n", " stick insect 50 0.64 0.92\n", " cockroach 50 0.72 0.8\n", " mantis 50 0.64 0.86\n", " cicada 50 0.9 0.96\n", " leafhopper 50 0.88 0.94\n", " lacewing 50 0.78 0.92\n", " dragonfly 50 0.82 0.98\n", " damselfly 50 0.82 1\n", " red admiral 50 0.94 0.96\n", " ringlet 50 0.86 0.98\n", " monarch butterfly 50 0.9 0.92\n", " small white 50 0.9 1\n", " sulfur butterfly 50 0.92 1\n", "gossamer-winged butterfly 50 0.88 1\n", " starfish 50 0.88 0.92\n", " sea urchin 50 0.84 0.94\n", " sea cucumber 50 0.66 0.84\n", " cottontail rabbit 50 0.72 0.94\n", " hare 50 0.84 0.96\n", " Angora rabbit 50 0.94 0.98\n", " hamster 50 0.96 1\n", " porcupine 50 0.88 0.98\n", " fox squirrel 50 0.76 0.94\n", " marmot 50 0.92 0.96\n", " beaver 50 0.78 0.94\n", " guinea pig 50 0.78 0.94\n", " common sorrel 50 0.96 0.98\n", " zebra 50 0.94 0.96\n", " pig 50 0.5 0.76\n", " wild boar 50 0.84 0.96\n", " warthog 50 0.84 0.96\n", " hippopotamus 50 0.88 0.96\n", " ox 50 0.48 0.94\n", " water buffalo 50 0.78 0.94\n", " bison 50 0.88 0.96\n", " ram 50 0.58 0.92\n", " bighorn sheep 50 0.66 1\n", " Alpine ibex 50 0.92 0.98\n", " hartebeest 50 0.94 1\n", " impala 50 0.82 0.96\n", " gazelle 50 0.7 0.96\n", " dromedary 50 0.9 1\n", " llama 50 0.82 0.94\n", " weasel 50 0.44 0.92\n", " mink 50 0.78 0.96\n", " European polecat 50 0.46 0.9\n", " black-footed ferret 50 0.68 0.96\n", " otter 50 0.66 0.88\n", " skunk 50 0.96 0.96\n", " badger 50 0.86 0.92\n", " armadillo 50 0.88 0.9\n", " three-toed sloth 50 0.96 1\n", " orangutan 50 0.78 0.92\n", " gorilla 50 0.82 0.94\n", " chimpanzee 50 0.84 0.94\n", " gibbon 50 0.76 0.86\n", " siamang 50 0.68 0.94\n", " guenon 50 0.8 0.94\n", " patas monkey 50 0.62 0.82\n", " baboon 50 0.9 0.98\n", " macaque 50 0.8 0.86\n", " langur 50 0.6 0.82\n", " black-and-white colobus 50 0.86 0.9\n", " proboscis monkey 50 1 1\n", " marmoset 50 0.74 0.98\n", " white-headed capuchin 50 0.72 0.9\n", " howler monkey 50 0.86 0.94\n", " titi 50 0.5 0.9\n", "Geoffroy's spider monkey 50 0.42 0.8\n", " common squirrel monkey 50 0.76 0.92\n", " ring-tailed lemur 50 0.72 0.94\n", " indri 50 0.9 0.96\n", " Asian elephant 50 0.58 0.92\n", " African bush elephant 50 0.7 0.98\n", " red panda 50 0.94 0.94\n", " giant panda 50 0.94 0.98\n", " snoek 50 0.74 0.9\n", " eel 50 0.6 0.84\n", " coho salmon 50 0.84 0.96\n", " rock beauty 50 0.88 0.98\n", " clownfish 50 0.78 0.98\n", " sturgeon 50 0.68 0.94\n", " garfish 50 0.62 0.8\n", " lionfish 50 0.96 0.96\n", " pufferfish 50 0.88 0.96\n", " abacus 50 0.74 0.88\n", " abaya 50 0.84 0.92\n", " academic gown 50 0.42 0.86\n", " accordion 50 0.8 0.9\n", " acoustic guitar 50 0.5 0.76\n", " aircraft carrier 50 0.8 0.96\n", " airliner 50 0.92 1\n", " airship 50 0.76 0.82\n", " altar 50 0.64 0.98\n", " ambulance 50 0.88 0.98\n", " amphibious vehicle 50 0.64 0.94\n", " analog clock 50 0.52 0.92\n", " apiary 50 0.82 0.96\n", " apron 50 0.7 0.84\n", " waste container 50 0.4 0.8\n", " assault rifle 50 0.42 0.84\n", " backpack 50 0.34 0.64\n", " bakery 50 0.4 0.68\n", " balance beam 50 0.8 0.98\n", " balloon 50 0.86 0.96\n", " ballpoint pen 50 0.52 0.96\n", " Band-Aid 50 0.7 0.9\n", " banjo 50 0.84 1\n", " baluster 50 0.68 0.94\n", " barbell 50 0.56 0.9\n", " barber chair 50 0.7 0.92\n", " barbershop 50 0.54 0.86\n", " barn 50 0.96 0.96\n", " barometer 50 0.84 0.98\n", " barrel 50 0.56 0.88\n", " wheelbarrow 50 0.66 0.88\n", " baseball 50 0.74 0.98\n", " basketball 50 0.88 0.98\n", " bassinet 50 0.66 0.92\n", " bassoon 50 0.74 0.98\n", " swimming cap 50 0.62 0.88\n", " bath towel 50 0.54 0.78\n", " bathtub 50 0.4 0.88\n", " station wagon 50 0.66 0.84\n", " lighthouse 50 0.78 0.94\n", " beaker 50 0.52 0.68\n", " military cap 50 0.84 0.96\n", " beer bottle 50 0.66 0.88\n", " beer glass 50 0.6 0.84\n", " bell-cot 50 0.56 0.96\n", " bib 50 0.58 0.82\n", " tandem bicycle 50 0.86 0.96\n", " bikini 50 0.56 0.88\n", " ring binder 50 0.64 0.84\n", " binoculars 50 0.54 0.78\n", " birdhouse 50 0.86 0.94\n", " boathouse 50 0.74 0.92\n", " bobsleigh 50 0.92 0.96\n", " bolo tie 50 0.8 0.94\n", " poke bonnet 50 0.64 0.86\n", " bookcase 50 0.66 0.92\n", " bookstore 50 0.62 0.88\n", " bottle cap 50 0.58 0.7\n", " bow 50 0.72 0.86\n", " bow tie 50 0.7 0.9\n", " brass 50 0.92 0.96\n", " bra 50 0.5 0.7\n", " breakwater 50 0.62 0.86\n", " breastplate 50 0.4 0.9\n", " broom 50 0.6 0.86\n", " bucket 50 0.66 0.8\n", " buckle 50 0.5 0.68\n", " bulletproof vest 50 0.5 0.78\n", " high-speed train 50 0.94 0.96\n", " butcher shop 50 0.74 0.94\n", " taxicab 50 0.64 0.86\n", " cauldron 50 0.44 0.66\n", " candle 50 0.48 0.74\n", " cannon 50 0.88 0.94\n", " canoe 50 0.94 1\n", " can opener 50 0.66 0.86\n", " cardigan 50 0.68 0.8\n", " car mirror 50 0.94 0.96\n", " carousel 50 0.94 0.98\n", " tool kit 50 0.56 0.78\n", " carton 50 0.42 0.7\n", " car wheel 50 0.38 0.74\n", "automated teller machine 50 0.76 0.94\n", " cassette 50 0.52 0.8\n", " cassette player 50 0.28 0.9\n", " castle 50 0.78 0.88\n", " catamaran 50 0.78 1\n", " CD player 50 0.52 0.82\n", " cello 50 0.82 1\n", " mobile phone 50 0.68 0.86\n", " chain 50 0.38 0.66\n", " chain-link fence 50 0.7 0.84\n", " chain mail 50 0.64 0.9\n", " chainsaw 50 0.84 0.92\n", " chest 50 0.68 0.92\n", " chiffonier 50 0.26 0.64\n", " chime 50 0.62 0.84\n", " china cabinet 50 0.82 0.96\n", " Christmas stocking 50 0.92 0.94\n", " church 50 0.62 0.9\n", " movie theater 50 0.58 0.88\n", " cleaver 50 0.32 0.62\n", " cliff dwelling 50 0.88 1\n", " cloak 50 0.32 0.64\n", " clogs 50 0.58 0.88\n", " cocktail shaker 50 0.62 0.7\n", " coffee mug 50 0.44 0.72\n", " coffeemaker 50 0.64 0.92\n", " coil 50 0.66 0.84\n", " combination lock 50 0.64 0.84\n", " computer keyboard 50 0.7 0.82\n", " confectionery store 50 0.54 0.86\n", " container ship 50 0.82 0.98\n", " convertible 50 0.78 0.98\n", " corkscrew 50 0.82 0.92\n", " cornet 50 0.46 0.88\n", " cowboy boot 50 0.64 0.8\n", " cowboy hat 50 0.64 0.82\n", " cradle 50 0.38 0.8\n", " crane (machine) 50 0.78 0.94\n", " crash helmet 50 0.92 0.96\n", " crate 50 0.52 0.82\n", " infant bed 50 0.74 1\n", " Crock Pot 50 0.78 0.9\n", " croquet ball 50 0.9 0.96\n", " crutch 50 0.46 0.7\n", " cuirass 50 0.54 0.86\n", " dam 50 0.74 0.92\n", " desk 50 0.6 0.86\n", " desktop computer 50 0.54 0.94\n", " rotary dial telephone 50 0.88 0.94\n", " diaper 50 0.68 0.84\n", " digital clock 50 0.54 0.76\n", " digital watch 50 0.58 0.86\n", " dining table 50 0.76 0.9\n", " dishcloth 50 0.94 1\n", " dishwasher 50 0.44 0.78\n", " disc brake 50 0.98 1\n", " dock 50 0.54 0.94\n", " dog sled 50 0.84 1\n", " dome 50 0.72 0.92\n", " doormat 50 0.56 0.82\n", " drilling rig 50 0.84 0.96\n", " drum 50 0.38 0.68\n", " drumstick 50 0.56 0.72\n", " dumbbell 50 0.62 0.9\n", " Dutch oven 50 0.7 0.84\n", " electric fan 50 0.82 0.86\n", " electric guitar 50 0.62 0.84\n", " electric locomotive 50 0.92 0.98\n", " entertainment center 50 0.9 0.98\n", " envelope 50 0.44 0.86\n", " espresso machine 50 0.72 0.94\n", " face powder 50 0.7 0.92\n", " feather boa 50 0.7 0.84\n", " filing cabinet 50 0.88 0.98\n", " fireboat 50 0.94 0.98\n", " fire engine 50 0.84 0.9\n", " fire screen sheet 50 0.62 0.76\n", " flagpole 50 0.74 0.88\n", " flute 50 0.36 0.72\n", " folding chair 50 0.62 0.84\n", " football helmet 50 0.86 0.94\n", " forklift 50 0.8 0.92\n", " fountain 50 0.84 0.94\n", " fountain pen 50 0.76 0.92\n", " four-poster bed 50 0.78 0.94\n", " freight car 50 0.96 1\n", " French horn 50 0.76 0.92\n", " frying pan 50 0.36 0.78\n", " fur coat 50 0.84 0.96\n", " garbage truck 50 0.9 0.98\n", " gas mask 50 0.84 0.92\n", " gas pump 50 0.9 0.98\n", " goblet 50 0.68 0.82\n", " go-kart 50 0.9 1\n", " golf ball 50 0.84 0.9\n", " golf cart 50 0.78 0.86\n", " gondola 50 0.98 0.98\n", " gong 50 0.74 0.92\n", " gown 50 0.62 0.96\n", " grand piano 50 0.7 0.96\n", " greenhouse 50 0.8 0.98\n", " grille 50 0.72 0.9\n", " grocery store 50 0.66 0.94\n", " guillotine 50 0.86 0.92\n", " barrette 50 0.52 0.66\n", " hair spray 50 0.5 0.74\n", " half-track 50 0.78 0.9\n", " hammer 50 0.56 0.76\n", " hamper 50 0.64 0.84\n", " hair dryer 50 0.56 0.74\n", " hand-held computer 50 0.42 0.86\n", " handkerchief 50 0.78 0.94\n", " hard disk drive 50 0.76 0.84\n", " harmonica 50 0.7 0.88\n", " harp 50 0.88 0.96\n", " harvester 50 0.78 1\n", " hatchet 50 0.54 0.74\n", " holster 50 0.66 0.84\n", " home theater 50 0.64 0.94\n", " honeycomb 50 0.56 0.88\n", " hook 50 0.3 0.6\n", " hoop skirt 50 0.64 0.86\n", " horizontal bar 50 0.68 0.98\n", " horse-drawn vehicle 50 0.88 0.94\n", " hourglass 50 0.88 0.96\n", " iPod 50 0.76 0.94\n", " clothes iron 50 0.82 0.88\n", " jack-o'-lantern 50 0.98 0.98\n", " jeans 50 0.68 0.84\n", " jeep 50 0.72 0.9\n", " T-shirt 50 0.72 0.96\n", " jigsaw puzzle 50 0.84 0.94\n", " pulled rickshaw 50 0.86 0.94\n", " joystick 50 0.8 0.9\n", " kimono 50 0.84 0.96\n", " knee pad 50 0.62 0.88\n", " knot 50 0.66 0.8\n", " lab coat 50 0.8 0.96\n", " ladle 50 0.36 0.64\n", " lampshade 50 0.48 0.84\n", " laptop computer 50 0.26 0.88\n", " lawn mower 50 0.78 0.96\n", " lens cap 50 0.46 0.72\n", " paper knife 50 0.26 0.5\n", " library 50 0.54 0.9\n", " lifeboat 50 0.92 0.98\n", " lighter 50 0.56 0.78\n", " limousine 50 0.76 0.92\n", " ocean liner 50 0.88 0.94\n", " lipstick 50 0.74 0.9\n", " slip-on shoe 50 0.74 0.92\n", " lotion 50 0.5 0.86\n", " speaker 50 0.52 0.68\n", " loupe 50 0.32 0.52\n", " sawmill 50 0.72 0.9\n", " magnetic compass 50 0.52 0.82\n", " mail bag 50 0.68 0.92\n", " mailbox 50 0.82 0.92\n", " tights 50 0.22 0.94\n", " tank suit 50 0.24 0.9\n", " manhole cover 50 0.96 0.98\n", " maraca 50 0.74 0.9\n", " marimba 50 0.84 0.94\n", " mask 50 0.44 0.82\n", " match 50 0.66 0.9\n", " maypole 50 0.96 1\n", " maze 50 0.8 0.96\n", " measuring cup 50 0.54 0.76\n", " medicine chest 50 0.6 0.84\n", " megalith 50 0.8 0.92\n", " microphone 50 0.52 0.7\n", " microwave oven 50 0.48 0.72\n", " military uniform 50 0.62 0.84\n", " milk can 50 0.68 0.82\n", " minibus 50 0.7 1\n", " miniskirt 50 0.46 0.76\n", " minivan 50 0.38 0.8\n", " missile 50 0.4 0.84\n", " mitten 50 0.76 0.88\n", " mixing bowl 50 0.8 0.92\n", " mobile home 50 0.54 0.78\n", " Model T 50 0.92 0.96\n", " modem 50 0.58 0.86\n", " monastery 50 0.44 0.9\n", " monitor 50 0.4 0.86\n", " moped 50 0.56 0.94\n", " mortar 50 0.68 0.94\n", " square academic cap 50 0.5 0.84\n", " mosque 50 0.9 1\n", " mosquito net 50 0.9 0.98\n", " scooter 50 0.9 0.98\n", " mountain bike 50 0.78 0.96\n", " tent 50 0.88 0.96\n", " computer mouse 50 0.42 0.82\n", " mousetrap 50 0.76 0.88\n", " moving van 50 0.4 0.72\n", " muzzle 50 0.5 0.72\n", " nail 50 0.68 0.74\n", " neck brace 50 0.56 0.68\n", " necklace 50 0.86 1\n", " nipple 50 0.7 0.88\n", " notebook computer 50 0.34 0.84\n", " obelisk 50 0.8 0.92\n", " oboe 50 0.6 0.84\n", " ocarina 50 0.8 0.86\n", " odometer 50 0.96 1\n", " oil filter 50 0.58 0.82\n", " organ 50 0.82 0.9\n", " oscilloscope 50 0.9 0.96\n", " overskirt 50 0.2 0.7\n", " bullock cart 50 0.7 0.94\n", " oxygen mask 50 0.46 0.84\n", " packet 50 0.5 0.78\n", " paddle 50 0.56 0.94\n", " paddle wheel 50 0.86 0.96\n", " padlock 50 0.74 0.78\n", " paintbrush 50 0.62 0.8\n", " pajamas 50 0.56 0.92\n", " palace 50 0.64 0.96\n", " pan flute 50 0.84 0.86\n", " paper towel 50 0.66 0.84\n", " parachute 50 0.92 0.94\n", " parallel bars 50 0.62 0.96\n", " park bench 50 0.74 0.9\n", " parking meter 50 0.84 0.92\n", " passenger car 50 0.5 0.82\n", " patio 50 0.58 0.84\n", " payphone 50 0.74 0.92\n", " pedestal 50 0.52 0.9\n", " pencil case 50 0.64 0.92\n", " pencil sharpener 50 0.52 0.78\n", " perfume 50 0.7 0.9\n", " Petri dish 50 0.6 0.8\n", " photocopier 50 0.88 0.98\n", " plectrum 50 0.7 0.84\n", " Pickelhaube 50 0.72 0.86\n", " picket fence 50 0.84 0.94\n", " pickup truck 50 0.64 0.92\n", " pier 50 0.52 0.82\n", " piggy bank 50 0.82 0.94\n", " pill bottle 50 0.76 0.86\n", " pillow 50 0.76 0.9\n", " ping-pong ball 50 0.84 0.88\n", " pinwheel 50 0.76 0.88\n", " pirate ship 50 0.76 0.94\n", " pitcher 50 0.46 0.84\n", " hand plane 50 0.84 0.94\n", " planetarium 50 0.88 0.98\n", " plastic bag 50 0.36 0.62\n", " plate rack 50 0.52 0.78\n", " plow 50 0.78 0.88\n", " plunger 50 0.42 0.7\n", " Polaroid camera 50 0.84 0.92\n", " pole 50 0.38 0.74\n", " police van 50 0.76 0.94\n", " poncho 50 0.58 0.86\n", " billiard table 50 0.8 0.88\n", " soda bottle 50 0.56 0.94\n", " pot 50 0.78 0.92\n", " potter's wheel 50 0.9 0.94\n", " power drill 50 0.42 0.72\n", " prayer rug 50 0.7 0.86\n", " printer 50 0.54 0.86\n", " prison 50 0.7 0.9\n", " projectile 50 0.28 0.9\n", " projector 50 0.62 0.84\n", " hockey puck 50 0.92 0.96\n", " punching bag 50 0.6 0.68\n", " purse 50 0.42 0.78\n", " quill 50 0.68 0.84\n", " quilt 50 0.64 0.9\n", " race car 50 0.72 0.92\n", " racket 50 0.72 0.9\n", " radiator 50 0.66 0.76\n", " radio 50 0.64 0.92\n", " radio telescope 50 0.9 0.96\n", " rain barrel 50 0.8 0.98\n", " recreational vehicle 50 0.84 0.94\n", " reel 50 0.72 0.82\n", " reflex camera 50 0.72 0.92\n", " refrigerator 50 0.7 0.9\n", " remote control 50 0.7 0.88\n", " restaurant 50 0.5 0.66\n", " revolver 50 0.82 1\n", " rifle 50 0.38 0.7\n", " rocking chair 50 0.62 0.84\n", " rotisserie 50 0.88 0.92\n", " eraser 50 0.54 0.76\n", " rugby ball 50 0.86 0.94\n", " ruler 50 0.68 0.86\n", " running shoe 50 0.78 0.94\n", " safe 50 0.82 0.92\n", " safety pin 50 0.4 0.62\n", " salt shaker 50 0.66 0.9\n", " sandal 50 0.66 0.86\n", " sarong 50 0.64 0.86\n", " saxophone 50 0.66 0.88\n", " scabbard 50 0.76 0.92\n", " weighing scale 50 0.58 0.78\n", " school bus 50 0.92 1\n", " schooner 50 0.84 1\n", " scoreboard 50 0.9 0.96\n", " CRT screen 50 0.14 0.7\n", " screw 50 0.9 0.98\n", " screwdriver 50 0.3 0.58\n", " seat belt 50 0.88 0.94\n", " sewing machine 50 0.76 0.9\n", " shield 50 0.56 0.82\n", " shoe store 50 0.78 0.96\n", " shoji 50 0.8 0.92\n", " shopping basket 50 0.52 0.88\n", " shopping cart 50 0.76 0.92\n", " shovel 50 0.62 0.84\n", " shower cap 50 0.7 0.84\n", " shower curtain 50 0.64 0.82\n", " ski 50 0.74 0.92\n", " ski mask 50 0.72 0.88\n", " sleeping bag 50 0.68 0.8\n", " slide rule 50 0.72 0.88\n", " sliding door 50 0.44 0.78\n", " slot machine 50 0.94 0.98\n", " snorkel 50 0.86 0.98\n", " snowmobile 50 0.88 1\n", " snowplow 50 0.84 0.98\n", " soap dispenser 50 0.56 0.86\n", " soccer ball 50 0.86 0.96\n", " sock 50 0.62 0.76\n", " solar thermal collector 50 0.72 0.96\n", " sombrero 50 0.6 0.84\n", " soup bowl 50 0.56 0.94\n", " space bar 50 0.34 0.88\n", " space heater 50 0.52 0.74\n", " space shuttle 50 0.82 0.96\n", " spatula 50 0.3 0.6\n", " motorboat 50 0.86 1\n", " spider web 50 0.7 0.9\n", " spindle 50 0.86 0.98\n", " sports car 50 0.6 0.94\n", " spotlight 50 0.26 0.6\n", " stage 50 0.68 0.86\n", " steam locomotive 50 0.94 1\n", " through arch bridge 50 0.84 0.96\n", " steel drum 50 0.82 0.9\n", " stethoscope 50 0.6 0.82\n", " scarf 50 0.5 0.92\n", " stone wall 50 0.76 0.9\n", " stopwatch 50 0.58 0.9\n", " stove 50 0.46 0.74\n", " strainer 50 0.64 0.84\n", " tram 50 0.88 0.96\n", " stretcher 50 0.6 0.8\n", " couch 50 0.8 0.96\n", " stupa 50 0.88 0.88\n", " submarine 50 0.72 0.92\n", " suit 50 0.4 0.78\n", " sundial 50 0.58 0.74\n", " sunglass 50 0.14 0.58\n", " sunglasses 50 0.28 0.58\n", " sunscreen 50 0.32 0.7\n", " suspension bridge 50 0.6 0.94\n", " mop 50 0.74 0.92\n", " sweatshirt 50 0.28 0.66\n", " swimsuit 50 0.52 0.82\n", " swing 50 0.76 0.84\n", " switch 50 0.56 0.76\n", " syringe 50 0.62 0.82\n", " table lamp 50 0.6 0.88\n", " tank 50 0.8 0.96\n", " tape player 50 0.46 0.76\n", " teapot 50 0.84 1\n", " teddy bear 50 0.82 0.94\n", " television 50 0.6 0.9\n", " tennis ball 50 0.7 0.94\n", " thatched roof 50 0.88 0.9\n", " front curtain 50 0.8 0.92\n", " thimble 50 0.6 0.8\n", " threshing machine 50 0.56 0.88\n", " throne 50 0.72 0.82\n", " tile roof 50 0.72 0.94\n", " toaster 50 0.66 0.84\n", " tobacco shop 50 0.42 0.7\n", " toilet seat 50 0.62 0.88\n", " torch 50 0.64 0.84\n", " totem pole 50 0.92 0.98\n", " tow truck 50 0.62 0.88\n", " toy store 50 0.6 0.94\n", " tractor 50 0.76 0.98\n", " semi-trailer truck 50 0.78 0.92\n", " tray 50 0.46 0.64\n", " trench coat 50 0.54 0.72\n", " tricycle 50 0.72 0.94\n", " trimaran 50 0.7 0.98\n", " tripod 50 0.58 0.86\n", " triumphal arch 50 0.92 0.98\n", " trolleybus 50 0.9 1\n", " trombone 50 0.54 0.88\n", " tub 50 0.24 0.82\n", " turnstile 50 0.84 0.94\n", " typewriter keyboard 50 0.68 0.98\n", " umbrella 50 0.52 0.7\n", " unicycle 50 0.74 0.96\n", " upright piano 50 0.76 0.9\n", " vacuum cleaner 50 0.62 0.9\n", " vase 50 0.5 0.78\n", " vault 50 0.76 0.92\n", " velvet 50 0.2 0.42\n", " vending machine 50 0.9 1\n", " vestment 50 0.54 0.82\n", " viaduct 50 0.78 0.86\n", " violin 50 0.68 0.78\n", " volleyball 50 0.86 1\n", " waffle iron 50 0.72 0.88\n", " wall clock 50 0.54 0.88\n", " wallet 50 0.52 0.9\n", " wardrobe 50 0.68 0.88\n", " military aircraft 50 0.9 0.98\n", " sink 50 0.72 0.96\n", " washing machine 50 0.78 0.94\n", " water bottle 50 0.54 0.74\n", " water jug 50 0.22 0.74\n", " water tower 50 0.9 0.96\n", " whiskey jug 50 0.64 0.74\n", " whistle 50 0.72 0.84\n", " wig 50 0.84 0.9\n", " window screen 50 0.68 0.8\n", " window shade 50 0.52 0.76\n", " Windsor tie 50 0.22 0.66\n", " wine bottle 50 0.42 0.82\n", " wing 50 0.54 0.96\n", " wok 50 0.46 0.82\n", " wooden spoon 50 0.58 0.8\n", " wool 50 0.32 0.82\n", " split-rail fence 50 0.74 0.9\n", " shipwreck 50 0.84 0.96\n", " yawl 50 0.78 0.96\n", " yurt 50 0.84 1\n", " website 50 0.98 1\n", " comic book 50 0.62 0.9\n", " crossword 50 0.84 0.88\n", " traffic sign 50 0.78 0.9\n", " traffic light 50 0.8 0.94\n", " dust jacket 50 0.72 0.94\n", " menu 50 0.82 0.96\n", " plate 50 0.44 0.88\n", " guacamole 50 0.8 0.92\n", " consomme 50 0.54 0.88\n", " hot pot 50 0.86 0.98\n", " trifle 50 0.92 0.98\n", " ice cream 50 0.68 0.94\n", " ice pop 50 0.62 0.84\n", " baguette 50 0.62 0.88\n", " bagel 50 0.64 0.92\n", " pretzel 50 0.72 0.88\n", " cheeseburger 50 0.9 1\n", " hot dog 50 0.74 0.94\n", " mashed potato 50 0.74 0.9\n", " cabbage 50 0.84 0.96\n", " broccoli 50 0.9 0.96\n", " cauliflower 50 0.82 1\n", " zucchini 50 0.74 0.9\n", " spaghetti squash 50 0.8 0.96\n", " acorn squash 50 0.82 0.96\n", " butternut squash 50 0.7 0.94\n", " cucumber 50 0.6 0.96\n", " artichoke 50 0.84 0.94\n", " bell pepper 50 0.84 0.98\n", " cardoon 50 0.88 0.94\n", " mushroom 50 0.38 0.92\n", " Granny Smith 50 0.9 0.96\n", " strawberry 50 0.6 0.88\n", " orange 50 0.7 0.92\n", " lemon 50 0.78 0.98\n", " fig 50 0.82 0.96\n", " pineapple 50 0.86 0.96\n", " banana 50 0.84 0.96\n", " jackfruit 50 0.9 0.98\n", " custard apple 50 0.86 0.96\n", " pomegranate 50 0.82 0.98\n", " hay 50 0.8 0.92\n", " carbonara 50 0.88 0.94\n", " chocolate syrup 50 0.46 0.84\n", " dough 50 0.4 0.6\n", " meatloaf 50 0.58 0.84\n", " pizza 50 0.84 0.96\n", " pot pie 50 0.68 0.9\n", " burrito 50 0.8 0.98\n", " red wine 50 0.54 0.82\n", " espresso 50 0.64 0.88\n", " cup 50 0.38 0.7\n", " eggnog 50 0.38 0.7\n", " alp 50 0.54 0.88\n", " bubble 50 0.8 0.96\n", " cliff 50 0.64 1\n", " coral reef 50 0.72 0.96\n", " geyser 50 0.94 1\n", " lakeshore 50 0.54 0.88\n", " promontory 50 0.58 0.94\n", " shoal 50 0.6 0.96\n", " seashore 50 0.44 0.78\n", " valley 50 0.72 0.94\n", " volcano 50 0.78 0.96\n", " baseball player 50 0.72 0.94\n", " bridegroom 50 0.72 0.88\n", " scuba diver 50 0.8 1\n", " rapeseed 50 0.94 0.98\n", " daisy 50 0.96 0.98\n", " yellow lady's slipper 50 1 1\n", " corn 50 0.4 0.88\n", " acorn 50 0.92 0.98\n", " rose hip 50 0.92 0.98\n", " horse chestnut seed 50 0.94 0.98\n", " coral fungus 50 0.96 0.96\n", " agaric 50 0.82 0.94\n", " gyromitra 50 0.98 1\n", " stinkhorn mushroom 50 0.8 0.94\n", " earth star 50 0.98 1\n", " hen-of-the-woods 50 0.8 0.96\n", " bolete 50 0.74 0.94\n", " ear 50 0.48 0.94\n", " toilet paper 50 0.36 0.68\n", "Speed: 0.1ms pre-process, 0.3ms inference, 0.0ms post-process per image at shape (1, 3, 224, 224)\n", "Results saved to \u001B[1mruns/val-cls/exp\u001B[0m\n" ] } ], "source": [ "# Validate YOLOv5s on Imagenet val\n", "!python classify/val.py --weights yolov5s-cls.pt --data ../datasets/imagenet --img 224 --half" ] }, { "cell_type": "markdown", "metadata": { "id": "ZY2VXXXu74w5" }, "source": [ "# 3. Train\n", "\n", "\n", " \"Ultralytics\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", "- **Pretrained [Models](https://github.com/ultralytics/yolov5/tree/master/models)** are downloaded\n", "automatically from the [latest YOLOv5 release](https://github.com/ultralytics/yolov5/releases)\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", "A **Mosaic Dataloader** is used for training which combines 4 images into 1 mosaic." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "i3oKtE4g-aNn" }, "outputs": [], "source": [ "# @title Select YOLOv5 🚀 logger {run: 'auto'}\n", "logger = \"Comet\" # @param ['Comet', 'ClearML', 'TensorBoard']\n", "\n", "if logger == \"Comet\":\n", " %pip install -q comet_ml\n", " import comet_ml\n", "\n", " comet_ml.init()\n", "elif logger == \"ClearML\":\n", " %pip install -q clearml\n", " import clearml\n", "\n", " clearml.browser_login()\n", "elif logger == \"TensorBoard\":\n", " %load_ext tensorboard\n", " %tensorboard --logdir runs/train" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "1NcFxRcFdJ_O", "outputId": "77c8d487-16db-4073-b3ea-06cabf2e7766" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\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", "\u001B[34m\u001B[1mgithub: \u001B[0mup to date with https://github.com/ultralytics/yolov5 ✅\n", "YOLOv5 🚀 v7.0-3-g61ebf5e Python-3.7.15 torch-1.12.1+cu113 CUDA:0 (Tesla T4, 15110MiB)\n", "\n", "\u001B[34m\u001B[1mTensorBoard: \u001B[0mStart with 'tensorboard --logdir runs/train-cls', view at http://localhost:6006/\n", "\n", "Dataset not found ⚠️, missing path /content/datasets/imagenette160, attempting download...\n", "Downloading https://github.com/ultralytics/assets/releases/download/v0.0.0/imagenette160.zip to /content/datasets/imagenette160.zip...\n", "100% 103M/103M [00:00<00:00, 347MB/s] \n", "Unzipping /content/datasets/imagenette160.zip...\n", "Dataset download success ✅ (3.3s), saved to \u001B[1m/content/datasets/imagenette160\u001B[0m\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", "Model summary: 149 layers, 4185290 parameters, 4185290 gradients, 10.5 GFLOPs\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", "Image sizes 224 train, 224 test\n", "Using 1 dataloader workers\n", "Logging results to \u001B[1mruns/train-cls/exp\u001B[0m\n", "Starting yolov5s-cls.pt training on imagenette160 dataset with 10 classes for 5 epochs...\n", "\n", " Epoch GPU_mem train_loss val_loss top1_acc top5_acc\n", " 1/5 1.47G 1.05 0.974 0.828 0.975: 100% 148/148 [00:38<00:00, 3.82it/s]\n", " 2/5 1.73G 0.895 0.766 0.911 0.994: 100% 148/148 [00:36<00:00, 4.03it/s]\n", " 3/5 1.73G 0.82 0.704 0.934 0.996: 100% 148/148 [00:35<00:00, 4.20it/s]\n", " 4/5 1.73G 0.766 0.664 0.951 0.998: 100% 148/148 [00:36<00:00, 4.05it/s]\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", "Training complete (0.052 hours)\n", "Results saved to \u001B[1mruns/train-cls/exp\u001B[0m\n", "Predict: python classify/predict.py --weights runs/train-cls/exp/weights/best.pt --source im.jpg\n", "Validate: python classify/val.py --weights runs/train-cls/exp/weights/best.pt --data /content/datasets/imagenette160\n", "Export: python export.py --weights runs/train-cls/exp/weights/best.pt --include onnx\n", "PyTorch Hub: model = torch.hub.load('ultralytics/yolov5', 'custom', 'runs/train-cls/exp/weights/best.pt')\n", "Visualize: https://netron.app\n", "\n" ] } ], "source": [ "# Train YOLOv5s Classification on Imagenette160 for 3 epochs\n", "!python classify/train.py --model yolov5s-cls.pt --data imagenette160 --epochs 5 --img 224 --cache" ] }, { "cell_type": "markdown", "metadata": { "id": "15glLzbQx5u0" }, "source": [ "# 4. Visualize" ] }, { "cell_type": "markdown", "metadata": { "id": "nWOsI5wJR1o3" }, "source": [ "## Comet Logging and Visualization 🌟 NEW\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", "Getting started is easy:\n", "```shell\n", "pip install comet_ml # 1. install\n", "export COMET_API_KEY= # 2. paste API key\n", "python train.py --img 640 --epochs 3 --data coco128.yaml --weights yolov5s.pt # 3. train\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", "[![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", "\"Comet" ] }, { "cell_type": "markdown", "metadata": { "id": "Lay2WsTjNJzP" }, "source": [ "## ClearML Logging and Automation 🌟 NEW\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", "- `pip install clearml`\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", "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", "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", "\"ClearML" ] }, { "cell_type": "markdown", "metadata": { "id": "-WPvRbS5Swl6" }, "source": [ "## Local Logging\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", "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", "\"Local\n" ] }, { "cell_type": "markdown", "metadata": { "id": "Zelyeqbyt3GD" }, "source": [ "# Environments\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", "- **Notebooks** with free GPU: \"Run \"Open \"Open\n", "- **Google Cloud** Deep Learning VM. See [GCP Quickstart Guide](https://docs.ultralytics.com/yolov5/environments/google_cloud_quickstart_tutorial/)\n", "- **Amazon** Deep Learning AMI. See [AWS Quickstart Guide](https://docs.ultralytics.com/yolov5/environments/aws_quickstart_tutorial/)\n", "- **Docker Image**. See [Docker Quickstart Guide](https://docs.ultralytics.com/yolov5/environments/docker_image_quickstart_tutorial/) \"Docker\n" ] }, { "cell_type": "markdown", "metadata": { "id": "6Qu7Iesl0p54" }, "source": [ "# Status\n", "\n", "![YOLOv5 CI](https://github.com/ultralytics/yolov3/actions/workflows/ci-testing.yml/badge.svg)\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" ] }, { "cell_type": "markdown", "metadata": { "id": "IEijrePND_2I" }, "source": [ "# Appendix\n", "\n", "Additional content below." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "GMusP4OAxFu6" }, "outputs": [], "source": [ "# YOLOv5 PyTorch HUB Inference (DetectionModels only)\n", "\n", "model = torch.hub.load(\"ultralytics/yolov5\", \"yolov5s\") # yolov5n - yolov5x6 or custom\n", "im = \"https://ultralytics.com/images/zidane.jpg\" # file, Path, PIL.Image, OpenCV, nparray, list\n", "results = model(im) # inference\n", "results.print() # or .show(), .save(), .crop(), .pandas(), etc." ] } ], "metadata": { "accelerator": "GPU", "colab": { "name": "YOLOv5 Classification Tutorial", "provenance": [] }, "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.12" } }, "nbformat": 4, "nbformat_minor": 0 } ================================================ FILE: classify/val.py ================================================ # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license """ Validate a trained YOLOv3 classification model on a classification dataset. Usage: $ bash data/scripts/get_imagenet.sh --val # download ImageNet val split (6.3G, 50000 images) $ python classify/val.py --weights yolov5m-cls.pt --data ../datasets/imagenet --img 224 # validate ImageNet Usage - formats: $ python classify/val.py --weights yolov5s-cls.pt # PyTorch yolov5s-cls.torchscript # TorchScript yolov5s-cls.onnx # ONNX Runtime or OpenCV DNN with --dnn yolov5s-cls_openvino_model # OpenVINO yolov5s-cls.engine # TensorRT yolov5s-cls.mlmodel # CoreML (macOS-only) yolov5s-cls_saved_model # TensorFlow SavedModel yolov5s-cls.pb # TensorFlow GraphDef yolov5s-cls.tflite # TensorFlow Lite yolov5s-cls_edgetpu.tflite # TensorFlow Edge TPU yolov5s-cls_paddle_model # PaddlePaddle """ import argparse import os import sys from pathlib import Path import torch from tqdm import tqdm FILE = Path(__file__).resolve() ROOT = FILE.parents[1] # YOLOv3 root directory if str(ROOT) not in sys.path: sys.path.append(str(ROOT)) # add ROOT to PATH ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative from models.common import DetectMultiBackend from utils.dataloaders import create_classification_dataloader from utils.general import ( LOGGER, TQDM_BAR_FORMAT, Profile, check_img_size, check_requirements, colorstr, increment_path, print_args, ) from utils.torch_utils import select_device, smart_inference_mode @smart_inference_mode() def run( data=ROOT / "../datasets/mnist", # dataset dir weights=ROOT / "yolov5s-cls.pt", # model.pt path(s) batch_size=128, # batch size imgsz=224, # inference size (pixels) device="", # cuda device, i.e. 0 or 0,1,2,3 or cpu workers=8, # max dataloader workers (per RANK in DDP mode) verbose=False, # verbose output project=ROOT / "runs/val-cls", # save to project/name name="exp", # save to project/name exist_ok=False, # existing project/name ok, do not increment half=False, # use FP16 half-precision inference dnn=False, # use OpenCV DNN for ONNX inference model=None, dataloader=None, criterion=None, pbar=None, ): """Evaluate a YOLOv3 classification model on the specified dataset, providing accuracy metrics.""" # Initialize/load model and set device training = model is not None if training: # called by train.py device, pt, jit, engine = next(model.parameters()).device, True, False, False # get model device, PyTorch model half &= device.type != "cpu" # half precision only supported on CUDA model.half() if half else model.float() else: # called directly device = select_device(device, batch_size=batch_size) # Directories save_dir = increment_path(Path(project) / name, exist_ok=exist_ok) # increment run save_dir.mkdir(parents=True, exist_ok=True) # make dir # Load model model = DetectMultiBackend(weights, device=device, dnn=dnn, fp16=half) stride, pt, jit, engine = model.stride, model.pt, model.jit, model.engine imgsz = check_img_size(imgsz, s=stride) # check image size half = model.fp16 # FP16 supported on limited backends with CUDA if engine: batch_size = model.batch_size else: device = model.device if not (pt or jit): batch_size = 1 # export.py models default to batch-size 1 LOGGER.info(f"Forcing --batch-size 1 square inference (1,3,{imgsz},{imgsz}) for non-PyTorch models") # Dataloader data = Path(data) test_dir = data / "test" if (data / "test").exists() else data / "val" # data/test or data/val dataloader = create_classification_dataloader( path=test_dir, imgsz=imgsz, batch_size=batch_size, augment=False, rank=-1, workers=workers ) model.eval() pred, targets, loss, dt = [], [], 0, (Profile(), Profile(), Profile()) n = len(dataloader) # number of batches action = "validating" if dataloader.dataset.root.stem == "val" else "testing" desc = f"{pbar.desc[:-36]}{action:>36}" if pbar else f"{action}" bar = tqdm(dataloader, desc, n, not training, bar_format=TQDM_BAR_FORMAT, position=0) with torch.cuda.amp.autocast(enabled=device.type != "cpu"): for images, labels in bar: with dt[0]: images, labels = images.to(device, non_blocking=True), labels.to(device) with dt[1]: y = model(images) with dt[2]: pred.append(y.argsort(1, descending=True)[:, :5]) targets.append(labels) if criterion: loss += criterion(y, labels) loss /= n pred, targets = torch.cat(pred), torch.cat(targets) correct = (targets[:, None] == pred).float() acc = torch.stack((correct[:, 0], correct.max(1).values), dim=1) # (top1, top5) accuracy top1, top5 = acc.mean(0).tolist() if pbar: pbar.desc = f"{pbar.desc[:-36]}{loss:>12.3g}{top1:>12.3g}{top5:>12.3g}" if verbose: # all classes LOGGER.info(f"{'Class':>24}{'Images':>12}{'top1_acc':>12}{'top5_acc':>12}") LOGGER.info(f"{'all':>24}{targets.shape[0]:>12}{top1:>12.3g}{top5:>12.3g}") for i, c in model.names.items(): acc_i = acc[targets == i] top1i, top5i = acc_i.mean(0).tolist() LOGGER.info(f"{c:>24}{acc_i.shape[0]:>12}{top1i:>12.3g}{top5i:>12.3g}") # Print results t = tuple(x.t / len(dataloader.dataset.samples) * 1e3 for x in dt) # speeds per image shape = (1, 3, imgsz, imgsz) LOGGER.info(f"Speed: %.1fms pre-process, %.1fms inference, %.1fms post-process per image at shape {shape}" % t) LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}") return top1, top5, loss def parse_opt(): """Parses command-line options for model configuration and returns an argparse.Namespace of options.""" parser = argparse.ArgumentParser() parser.add_argument("--data", type=str, default=ROOT / "../datasets/mnist", help="dataset path") parser.add_argument("--weights", nargs="+", type=str, default=ROOT / "yolov5s-cls.pt", help="model.pt path(s)") parser.add_argument("--batch-size", type=int, default=128, help="batch size") parser.add_argument("--imgsz", "--img", "--img-size", type=int, default=224, help="inference size (pixels)") parser.add_argument("--device", default="", help="cuda device, i.e. 0 or 0,1,2,3 or cpu") parser.add_argument("--workers", type=int, default=8, help="max dataloader workers (per RANK in DDP mode)") parser.add_argument("--verbose", nargs="?", const=True, default=True, help="verbose output") parser.add_argument("--project", default=ROOT / "runs/val-cls", help="save to project/name") parser.add_argument("--name", default="exp", help="save to project/name") parser.add_argument("--exist-ok", action="store_true", help="existing project/name ok, do not increment") parser.add_argument("--half", action="store_true", help="use FP16 half-precision inference") parser.add_argument("--dnn", action="store_true", help="use OpenCV DNN for ONNX inference") opt = parser.parse_args() print_args(vars(opt)) return opt def main(opt): """Executes the main pipeline, checks and installs requirements, then runs inference or training based on provided options. """ check_requirements(ROOT / "requirements.txt", exclude=("tensorboard", "thop")) run(**vars(opt)) if __name__ == "__main__": opt = parse_opt() main(opt) ================================================ FILE: data/Argoverse.yaml ================================================ # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license # Argoverse-HD dataset (ring-front-center camera) http://www.cs.cmu.edu/~mengtial/proj/streaming/ by Argo AI # Example usage: python train.py --data Argoverse.yaml # parent # ├── yolov5 # └── datasets # └── Argoverse ← downloads here (31.3 GB) # 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, ..] path: ../datasets/Argoverse # dataset root dir train: Argoverse-1.1/images/train/ # train images (relative to 'path') 39384 images val: Argoverse-1.1/images/val/ # val images (relative to 'path') 15062 images test: Argoverse-1.1/images/test/ # test images (optional) https://eval.ai/web/challenges/challenge-page/800/overview # Classes names: 0: person 1: bicycle 2: car 3: motorcycle 4: bus 5: truck 6: traffic_light 7: stop_sign # Download script/URL (optional) --------------------------------------------------------------------------------------- download: | import json from tqdm import tqdm from utils.general import download, Path def argoverse2yolo(set): labels = {} a = json.load(open(set, "rb")) for annot in tqdm(a['annotations'], desc=f"Converting {set} to YOLOv5 format..."): img_id = annot['image_id'] img_name = a['images'][img_id]['name'] img_label_name = f'{img_name[:-3]}txt' cls = annot['category_id'] # instance class id x_center, y_center, width, height = annot['bbox'] x_center = (x_center + width / 2) / 1920.0 # offset and scale y_center = (y_center + height / 2) / 1200.0 # offset and scale width /= 1920.0 # scale height /= 1200.0 # scale img_dir = set.parents[2] / 'Argoverse-1.1' / 'labels' / a['seq_dirs'][a['images'][annot['image_id']]['sid']] if not img_dir.exists(): img_dir.mkdir(parents=True, exist_ok=True) k = str(img_dir / img_label_name) if k not in labels: labels[k] = [] labels[k].append(f"{cls} {x_center} {y_center} {width} {height}\n") for k in labels: with open(k, "w") as f: f.writelines(labels[k]) # Download dir = Path(yaml['path']) # dataset root dir urls = ['https://argoverse-hd.s3.us-east-2.amazonaws.com/Argoverse-HD-Full.zip'] download(urls, dir=dir, delete=False) # Convert annotations_dir = 'Argoverse-HD/annotations/' (dir / 'Argoverse-1.1' / 'tracking').rename(dir / 'Argoverse-1.1' / 'images') # rename 'tracking' to 'images' for d in "train.json", "val.json": argoverse2yolo(dir / annotations_dir / d) # convert VisDrone annotations to YOLO labels ================================================ FILE: data/GlobalWheat2020.yaml ================================================ # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license # Global Wheat 2020 dataset http://www.global-wheat.com/ by University of Saskatchewan # Example usage: python train.py --data GlobalWheat2020.yaml # parent # ├── yolov5 # └── datasets # └── GlobalWheat2020 ← downloads here (7.0 GB) # 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, ..] path: ../datasets/GlobalWheat2020 # dataset root dir train: # train images (relative to 'path') 3422 images - images/arvalis_1 - images/arvalis_2 - images/arvalis_3 - images/ethz_1 - images/rres_1 - images/inrae_1 - images/usask_1 val: # val images (relative to 'path') 748 images (WARNING: train set contains ethz_1) - images/ethz_1 test: # test images (optional) 1276 images - images/utokyo_1 - images/utokyo_2 - images/nau_1 - images/uq_1 # Classes names: 0: wheat_head # Download script/URL (optional) --------------------------------------------------------------------------------------- download: | from utils.general import download, Path # Download dir = Path(yaml['path']) # dataset root dir urls = ['https://zenodo.org/record/4298502/files/global-wheat-codalab-official.zip', 'https://github.com/ultralytics/assets/releases/download/v0.0.0/GlobalWheat2020_labels.zip'] download(urls, dir=dir) # Make Directories for p in 'annotations', 'images', 'labels': (dir / p).mkdir(parents=True, exist_ok=True) # Move for p in 'arvalis_1', 'arvalis_2', 'arvalis_3', 'ethz_1', 'rres_1', 'inrae_1', 'usask_1', \ 'utokyo_1', 'utokyo_2', 'nau_1', 'uq_1': (dir / p).rename(dir / 'images' / p) # move to /images f = (dir / p).with_suffix('.json') # json file if f.exists(): f.rename((dir / 'annotations' / p).with_suffix('.json')) # move to /annotations ================================================ FILE: data/ImageNet.yaml ================================================ # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license # ImageNet-1k dataset https://www.image-net.org/index.php by Stanford University # Simplified class names from https://github.com/anishathalye/imagenet-simple-labels # Example usage: python classify/train.py --data imagenet # parent # ├── yolov5 # └── datasets # └── imagenet ← downloads here (144 GB) # 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, ..] path: ../datasets/imagenet # dataset root dir train: train # train images (relative to 'path') 1281167 images val: val # val images (relative to 'path') 50000 images test: # test images (optional) # Classes names: 0: tench 1: goldfish 2: great white shark 3: tiger shark 4: hammerhead shark 5: electric ray 6: stingray 7: cock 8: hen 9: ostrich 10: brambling 11: goldfinch 12: house finch 13: junco 14: indigo bunting 15: American robin 16: bulbul 17: jay 18: magpie 19: chickadee 20: American dipper 21: kite 22: bald eagle 23: vulture 24: great grey owl 25: fire salamander 26: smooth newt 27: newt 28: spotted salamander 29: axolotl 30: American bullfrog 31: tree frog 32: tailed frog 33: loggerhead sea turtle 34: leatherback sea turtle 35: mud turtle 36: terrapin 37: box turtle 38: banded gecko 39: green iguana 40: Carolina anole 41: desert grassland whiptail lizard 42: agama 43: frilled-necked lizard 44: alligator lizard 45: Gila monster 46: European green lizard 47: chameleon 48: Komodo dragon 49: Nile crocodile 50: American alligator 51: triceratops 52: worm snake 53: ring-necked snake 54: eastern hog-nosed snake 55: smooth green snake 56: kingsnake 57: garter snake 58: water snake 59: vine snake 60: night snake 61: boa constrictor 62: African rock python 63: Indian cobra 64: green mamba 65: sea snake 66: Saharan horned viper 67: eastern diamondback rattlesnake 68: sidewinder 69: trilobite 70: harvestman 71: scorpion 72: yellow garden spider 73: barn spider 74: European garden spider 75: southern black widow 76: tarantula 77: wolf spider 78: tick 79: centipede 80: black grouse 81: ptarmigan 82: ruffed grouse 83: prairie grouse 84: peacock 85: quail 86: partridge 87: grey parrot 88: macaw 89: sulphur-crested cockatoo 90: lorikeet 91: coucal 92: bee eater 93: hornbill 94: hummingbird 95: jacamar 96: toucan 97: duck 98: red-breasted merganser 99: goose 100: black swan 101: tusker 102: echidna 103: platypus 104: wallaby 105: koala 106: wombat 107: jellyfish 108: sea anemone 109: brain coral 110: flatworm 111: nematode 112: conch 113: snail 114: slug 115: sea slug 116: chiton 117: chambered nautilus 118: Dungeness crab 119: rock crab 120: fiddler crab 121: red king crab 122: American lobster 123: spiny lobster 124: crayfish 125: hermit crab 126: isopod 127: white stork 128: black stork 129: spoonbill 130: flamingo 131: little blue heron 132: great egret 133: bittern 134: crane (bird) 135: limpkin 136: common gallinule 137: American coot 138: bustard 139: ruddy turnstone 140: dunlin 141: common redshank 142: dowitcher 143: oystercatcher 144: pelican 145: king penguin 146: albatross 147: grey whale 148: killer whale 149: dugong 150: sea lion 151: Chihuahua 152: Japanese Chin 153: Maltese 154: Pekingese 155: Shih Tzu 156: King Charles Spaniel 157: Papillon 158: toy terrier 159: Rhodesian Ridgeback 160: Afghan Hound 161: Basset Hound 162: Beagle 163: Bloodhound 164: Bluetick Coonhound 165: Black and Tan Coonhound 166: Treeing Walker Coonhound 167: English foxhound 168: Redbone Coonhound 169: borzoi 170: Irish Wolfhound 171: Italian Greyhound 172: Whippet 173: Ibizan Hound 174: Norwegian Elkhound 175: Otterhound 176: Saluki 177: Scottish Deerhound 178: Weimaraner 179: Staffordshire Bull Terrier 180: American Staffordshire Terrier 181: Bedlington Terrier 182: Border Terrier 183: Kerry Blue Terrier 184: Irish Terrier 185: Norfolk Terrier 186: Norwich Terrier 187: Yorkshire Terrier 188: Wire Fox Terrier 189: Lakeland Terrier 190: Sealyham Terrier 191: Airedale Terrier 192: Cairn Terrier 193: Australian Terrier 194: Dandie Dinmont Terrier 195: Boston Terrier 196: Miniature Schnauzer 197: Giant Schnauzer 198: Standard Schnauzer 199: Scottish Terrier 200: Tibetan Terrier 201: Australian Silky Terrier 202: Soft-coated Wheaten Terrier 203: West Highland White Terrier 204: Lhasa Apso 205: Flat-Coated Retriever 206: Curly-coated Retriever 207: Golden Retriever 208: Labrador Retriever 209: Chesapeake Bay Retriever 210: German Shorthaired Pointer 211: Vizsla 212: English Setter 213: Irish Setter 214: Gordon Setter 215: Brittany 216: Clumber Spaniel 217: English Springer Spaniel 218: Welsh Springer Spaniel 219: Cocker Spaniels 220: Sussex Spaniel 221: Irish Water Spaniel 222: Kuvasz 223: Schipperke 224: Groenendael 225: Malinois 226: Briard 227: Australian Kelpie 228: Komondor 229: Old English Sheepdog 230: Shetland Sheepdog 231: collie 232: Border Collie 233: Bouvier des Flandres 234: Rottweiler 235: German Shepherd Dog 236: Dobermann 237: Miniature Pinscher 238: Greater Swiss Mountain Dog 239: Bernese Mountain Dog 240: Appenzeller Sennenhund 241: Entlebucher Sennenhund 242: Boxer 243: Bullmastiff 244: Tibetan Mastiff 245: French Bulldog 246: Great Dane 247: St. Bernard 248: husky 249: Alaskan Malamute 250: Siberian Husky 251: Dalmatian 252: Affenpinscher 253: Basenji 254: pug 255: Leonberger 256: Newfoundland 257: Pyrenean Mountain Dog 258: Samoyed 259: Pomeranian 260: Chow Chow 261: Keeshond 262: Griffon Bruxellois 263: Pembroke Welsh Corgi 264: Cardigan Welsh Corgi 265: Toy Poodle 266: Miniature Poodle 267: Standard Poodle 268: Mexican hairless dog 269: grey wolf 270: Alaskan tundra wolf 271: red wolf 272: coyote 273: dingo 274: dhole 275: African wild dog 276: hyena 277: red fox 278: kit fox 279: Arctic fox 280: grey fox 281: tabby cat 282: tiger cat 283: Persian cat 284: Siamese cat 285: Egyptian Mau 286: cougar 287: lynx 288: leopard 289: snow leopard 290: jaguar 291: lion 292: tiger 293: cheetah 294: brown bear 295: American black bear 296: polar bear 297: sloth bear 298: mongoose 299: meerkat 300: tiger beetle 301: ladybug 302: ground beetle 303: longhorn beetle 304: leaf beetle 305: dung beetle 306: rhinoceros beetle 307: weevil 308: fly 309: bee 310: ant 311: grasshopper 312: cricket 313: stick insect 314: cockroach 315: mantis 316: cicada 317: leafhopper 318: lacewing 319: dragonfly 320: damselfly 321: red admiral 322: ringlet 323: monarch butterfly 324: small white 325: sulfur butterfly 326: gossamer-winged butterfly 327: starfish 328: sea urchin 329: sea cucumber 330: cottontail rabbit 331: hare 332: Angora rabbit 333: hamster 334: porcupine 335: fox squirrel 336: marmot 337: beaver 338: guinea pig 339: common sorrel 340: zebra 341: pig 342: wild boar 343: warthog 344: hippopotamus 345: ox 346: water buffalo 347: bison 348: ram 349: bighorn sheep 350: Alpine ibex 351: hartebeest 352: impala 353: gazelle 354: dromedary 355: llama 356: weasel 357: mink 358: European polecat 359: black-footed ferret 360: otter 361: skunk 362: badger 363: armadillo 364: three-toed sloth 365: orangutan 366: gorilla 367: chimpanzee 368: gibbon 369: siamang 370: guenon 371: patas monkey 372: baboon 373: macaque 374: langur 375: black-and-white colobus 376: proboscis monkey 377: marmoset 378: white-headed capuchin 379: howler monkey 380: titi 381: Geoffroy's spider monkey 382: common squirrel monkey 383: ring-tailed lemur 384: indri 385: Asian elephant 386: African bush elephant 387: red panda 388: giant panda 389: snoek 390: eel 391: coho salmon 392: rock beauty 393: clownfish 394: sturgeon 395: garfish 396: lionfish 397: pufferfish 398: abacus 399: abaya 400: academic gown 401: accordion 402: acoustic guitar 403: aircraft carrier 404: airliner 405: airship 406: altar 407: ambulance 408: amphibious vehicle 409: analog clock 410: apiary 411: apron 412: waste container 413: assault rifle 414: backpack 415: bakery 416: balance beam 417: balloon 418: ballpoint pen 419: Band-Aid 420: banjo 421: baluster 422: barbell 423: barber chair 424: barbershop 425: barn 426: barometer 427: barrel 428: wheelbarrow 429: baseball 430: basketball 431: bassinet 432: bassoon 433: swimming cap 434: bath towel 435: bathtub 436: station wagon 437: lighthouse 438: beaker 439: military cap 440: beer bottle 441: beer glass 442: bell-cot 443: bib 444: tandem bicycle 445: bikini 446: ring binder 447: binoculars 448: birdhouse 449: boathouse 450: bobsleigh 451: bolo tie 452: poke bonnet 453: bookcase 454: bookstore 455: bottle cap 456: bow 457: bow tie 458: brass 459: bra 460: breakwater 461: breastplate 462: broom 463: bucket 464: buckle 465: bulletproof vest 466: high-speed train 467: butcher shop 468: taxicab 469: cauldron 470: candle 471: cannon 472: canoe 473: can opener 474: cardigan 475: car mirror 476: carousel 477: tool kit 478: carton 479: car wheel 480: automated teller machine 481: cassette 482: cassette player 483: castle 484: catamaran 485: CD player 486: cello 487: mobile phone 488: chain 489: chain-link fence 490: chain mail 491: chainsaw 492: chest 493: chiffonier 494: chime 495: china cabinet 496: Christmas stocking 497: church 498: movie theater 499: cleaver 500: cliff dwelling 501: cloak 502: clogs 503: cocktail shaker 504: coffee mug 505: coffeemaker 506: coil 507: combination lock 508: computer keyboard 509: confectionery store 510: container ship 511: convertible 512: corkscrew 513: cornet 514: cowboy boot 515: cowboy hat 516: cradle 517: crane (machine) 518: crash helmet 519: crate 520: infant bed 521: Crock Pot 522: croquet ball 523: crutch 524: cuirass 525: dam 526: desk 527: desktop computer 528: rotary dial telephone 529: diaper 530: digital clock 531: digital watch 532: dining table 533: dishcloth 534: dishwasher 535: disc brake 536: dock 537: dog sled 538: dome 539: doormat 540: drilling rig 541: drum 542: drumstick 543: dumbbell 544: Dutch oven 545: electric fan 546: electric guitar 547: electric locomotive 548: entertainment center 549: envelope 550: espresso machine 551: face powder 552: feather boa 553: filing cabinet 554: fireboat 555: fire engine 556: fire screen sheet 557: flagpole 558: flute 559: folding chair 560: football helmet 561: forklift 562: fountain 563: fountain pen 564: four-poster bed 565: freight car 566: French horn 567: frying pan 568: fur coat 569: garbage truck 570: gas mask 571: gas pump 572: goblet 573: go-kart 574: golf ball 575: golf cart 576: gondola 577: gong 578: gown 579: grand piano 580: greenhouse 581: grille 582: grocery store 583: guillotine 584: barrette 585: hair spray 586: half-track 587: hammer 588: hamper 589: hair dryer 590: hand-held computer 591: handkerchief 592: hard disk drive 593: harmonica 594: harp 595: harvester 596: hatchet 597: holster 598: home theater 599: honeycomb 600: hook 601: hoop skirt 602: horizontal bar 603: horse-drawn vehicle 604: hourglass 605: iPod 606: clothes iron 607: jack-o'-lantern 608: jeans 609: jeep 610: T-shirt 611: jigsaw puzzle 612: pulled rickshaw 613: joystick 614: kimono 615: knee pad 616: knot 617: lab coat 618: ladle 619: lampshade 620: laptop computer 621: lawn mower 622: lens cap 623: paper knife 624: library 625: lifeboat 626: lighter 627: limousine 628: ocean liner 629: lipstick 630: slip-on shoe 631: lotion 632: speaker 633: loupe 634: sawmill 635: magnetic compass 636: mail bag 637: mailbox 638: tights 639: tank suit 640: manhole cover 641: maraca 642: marimba 643: mask 644: match 645: maypole 646: maze 647: measuring cup 648: medicine chest 649: megalith 650: microphone 651: microwave oven 652: military uniform 653: milk can 654: minibus 655: miniskirt 656: minivan 657: missile 658: mitten 659: mixing bowl 660: mobile home 661: Model T 662: modem 663: monastery 664: monitor 665: moped 666: mortar 667: square academic cap 668: mosque 669: mosquito net 670: scooter 671: mountain bike 672: tent 673: computer mouse 674: mousetrap 675: moving van 676: muzzle 677: nail 678: neck brace 679: necklace 680: nipple 681: notebook computer 682: obelisk 683: oboe 684: ocarina 685: odometer 686: oil filter 687: organ 688: oscilloscope 689: overskirt 690: bullock cart 691: oxygen mask 692: packet 693: paddle 694: paddle wheel 695: padlock 696: paintbrush 697: pajamas 698: palace 699: pan flute 700: paper towel 701: parachute 702: parallel bars 703: park bench 704: parking meter 705: passenger car 706: patio 707: payphone 708: pedestal 709: pencil case 710: pencil sharpener 711: perfume 712: Petri dish 713: photocopier 714: plectrum 715: Pickelhaube 716: picket fence 717: pickup truck 718: pier 719: piggy bank 720: pill bottle 721: pillow 722: ping-pong ball 723: pinwheel 724: pirate ship 725: pitcher 726: hand plane 727: planetarium 728: plastic bag 729: plate rack 730: plow 731: plunger 732: Polaroid camera 733: pole 734: police van 735: poncho 736: billiard table 737: soda bottle 738: pot 739: potter's wheel 740: power drill 741: prayer rug 742: printer 743: prison 744: projectile 745: projector 746: hockey puck 747: punching bag 748: purse 749: quill 750: quilt 751: race car 752: racket 753: radiator 754: radio 755: radio telescope 756: rain barrel 757: recreational vehicle 758: reel 759: reflex camera 760: refrigerator 761: remote control 762: restaurant 763: revolver 764: rifle 765: rocking chair 766: rotisserie 767: eraser 768: rugby ball 769: ruler 770: running shoe 771: safe 772: safety pin 773: salt shaker 774: sandal 775: sarong 776: saxophone 777: scabbard 778: weighing scale 779: school bus 780: schooner 781: scoreboard 782: CRT screen 783: screw 784: screwdriver 785: seat belt 786: sewing machine 787: shield 788: shoe store 789: shoji 790: shopping basket 791: shopping cart 792: shovel 793: shower cap 794: shower curtain 795: ski 796: ski mask 797: sleeping bag 798: slide rule 799: sliding door 800: slot machine 801: snorkel 802: snowmobile 803: snowplow 804: soap dispenser 805: soccer ball 806: sock 807: solar thermal collector 808: sombrero 809: soup bowl 810: space bar 811: space heater 812: space shuttle 813: spatula 814: motorboat 815: spider web 816: spindle 817: sports car 818: spotlight 819: stage 820: steam locomotive 821: through arch bridge 822: steel drum 823: stethoscope 824: scarf 825: stone wall 826: stopwatch 827: stove 828: strainer 829: tram 830: stretcher 831: couch 832: stupa 833: submarine 834: suit 835: sundial 836: sunglass 837: sunglasses 838: sunscreen 839: suspension bridge 840: mop 841: sweatshirt 842: swimsuit 843: swing 844: switch 845: syringe 846: table lamp 847: tank 848: tape player 849: teapot 850: teddy bear 851: television 852: tennis ball 853: thatched roof 854: front curtain 855: thimble 856: threshing machine 857: throne 858: tile roof 859: toaster 860: tobacco shop 861: toilet seat 862: torch 863: totem pole 864: tow truck 865: toy store 866: tractor 867: semi-trailer truck 868: tray 869: trench coat 870: tricycle 871: trimaran 872: tripod 873: triumphal arch 874: trolleybus 875: trombone 876: tub 877: turnstile 878: typewriter keyboard 879: umbrella 880: unicycle 881: upright piano 882: vacuum cleaner 883: vase 884: vault 885: velvet 886: vending machine 887: vestment 888: viaduct 889: violin 890: volleyball 891: waffle iron 892: wall clock 893: wallet 894: wardrobe 895: military aircraft 896: sink 897: washing machine 898: water bottle 899: water jug 900: water tower 901: whiskey jug 902: whistle 903: wig 904: window screen 905: window shade 906: Windsor tie 907: wine bottle 908: wing 909: wok 910: wooden spoon 911: wool 912: split-rail fence 913: shipwreck 914: yawl 915: yurt 916: website 917: comic book 918: crossword 919: traffic sign 920: traffic light 921: dust jacket 922: menu 923: plate 924: guacamole 925: consomme 926: hot pot 927: trifle 928: ice cream 929: ice pop 930: baguette 931: bagel 932: pretzel 933: cheeseburger 934: hot dog 935: mashed potato 936: cabbage 937: broccoli 938: cauliflower 939: zucchini 940: spaghetti squash 941: acorn squash 942: butternut squash 943: cucumber 944: artichoke 945: bell pepper 946: cardoon 947: mushroom 948: Granny Smith 949: strawberry 950: orange 951: lemon 952: fig 953: pineapple 954: banana 955: jackfruit 956: custard apple 957: pomegranate 958: hay 959: carbonara 960: chocolate syrup 961: dough 962: meatloaf 963: pizza 964: pot pie 965: burrito 966: red wine 967: espresso 968: cup 969: eggnog 970: alp 971: bubble 972: cliff 973: coral reef 974: geyser 975: lakeshore 976: promontory 977: shoal 978: seashore 979: valley 980: volcano 981: baseball player 982: bridegroom 983: scuba diver 984: rapeseed 985: daisy 986: yellow lady's slipper 987: corn 988: acorn 989: rose hip 990: horse chestnut seed 991: coral fungus 992: agaric 993: gyromitra 994: stinkhorn mushroom 995: earth star 996: hen-of-the-woods 997: bolete 998: ear 999: toilet paper # Download script/URL (optional) download: data/scripts/get_imagenet.sh ================================================ FILE: data/SKU-110K.yaml ================================================ # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license # SKU-110K retail items dataset https://github.com/eg4000/SKU110K_CVPR19 by Trax Retail # Example usage: python train.py --data SKU-110K.yaml # parent # ├── yolov5 # └── datasets # └── SKU-110K ← downloads here (13.6 GB) # 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, ..] path: ../datasets/SKU-110K # dataset root dir train: train.txt # train images (relative to 'path') 8219 images val: val.txt # val images (relative to 'path') 588 images test: test.txt # test images (optional) 2936 images # Classes names: 0: object # Download script/URL (optional) --------------------------------------------------------------------------------------- download: | import shutil from tqdm import tqdm from utils.general import np, pd, Path, download, xyxy2xywh # Download dir = Path(yaml['path']) # dataset root dir parent = Path(dir.parent) # download dir urls = ['http://trax-geometry.s3.amazonaws.com/cvpr_challenge/SKU110K_fixed.tar.gz'] download(urls, dir=parent, delete=False) # Rename directories if dir.exists(): shutil.rmtree(dir) (parent / 'SKU110K_fixed').rename(dir) # rename dir (dir / 'labels').mkdir(parents=True, exist_ok=True) # create labels dir # Convert labels names = 'image', 'x1', 'y1', 'x2', 'y2', 'class', 'image_width', 'image_height' # column names for d in 'annotations_train.csv', 'annotations_val.csv', 'annotations_test.csv': x = pd.read_csv(dir / 'annotations' / d, names=names).values # annotations images, unique_images = x[:, 0], np.unique(x[:, 0]) with open((dir / d).with_suffix('.txt').__str__().replace('annotations_', ''), 'w') as f: f.writelines(f'./images/{s}\n' for s in unique_images) for im in tqdm(unique_images, desc=f'Converting {dir / d}'): cls = 0 # single-class dataset with open((dir / 'labels' / im).with_suffix('.txt'), 'a') as f: for r in x[images == im]: w, h = r[6], r[7] # image width, height xywh = xyxy2xywh(np.array([[r[1] / w, r[2] / h, r[3] / w, r[4] / h]]))[0] # instance f.write(f"{cls} {xywh[0]:.5f} {xywh[1]:.5f} {xywh[2]:.5f} {xywh[3]:.5f}\n") # write label ================================================ FILE: data/VisDrone.yaml ================================================ # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license # VisDrone2019-DET dataset https://github.com/VisDrone/VisDrone-Dataset by Tianjin University # Example usage: python train.py --data VisDrone.yaml # parent # ├── yolov5 # └── datasets # └── VisDrone ← downloads here (2.3 GB) # 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, ..] path: ../datasets/VisDrone # dataset root dir train: VisDrone2019-DET-train/images # train images (relative to 'path') 6471 images val: VisDrone2019-DET-val/images # val images (relative to 'path') 548 images test: VisDrone2019-DET-test-dev/images # test images (optional) 1610 images # Classes names: 0: pedestrian 1: people 2: bicycle 3: car 4: van 5: truck 6: tricycle 7: awning-tricycle 8: bus 9: motor # Download script/URL (optional) --------------------------------------------------------------------------------------- download: | from utils.general import download, os, Path def visdrone2yolo(dir): from PIL import Image from tqdm import tqdm def convert_box(size, box): # Convert VisDrone box to YOLO xywh box dw = 1. / size[0] dh = 1. / size[1] return (box[0] + box[2] / 2) * dw, (box[1] + box[3] / 2) * dh, box[2] * dw, box[3] * dh (dir / 'labels').mkdir(parents=True, exist_ok=True) # make labels directory pbar = tqdm((dir / 'annotations').glob('*.txt'), desc=f'Converting {dir}') for f in pbar: img_size = Image.open((dir / 'images' / f.name).with_suffix('.jpg')).size lines = [] with open(f, 'r') as file: # read annotation.txt for row in [x.split(',') for x in file.read().strip().splitlines()]: if row[4] == '0': # VisDrone 'ignored regions' class 0 continue cls = int(row[5]) - 1 box = convert_box(img_size, tuple(map(int, row[:4]))) lines.append(f"{cls} {' '.join(f'{x:.6f}' for x in box)}\n") with open(str(f).replace(os.sep + 'annotations' + os.sep, os.sep + 'labels' + os.sep), 'w') as fl: fl.writelines(lines) # write label.txt # Download dir = Path(yaml['path']) # dataset root dir urls = ['https://github.com/ultralytics/assets/releases/download/v0.0.0/VisDrone2019-DET-train.zip', 'https://github.com/ultralytics/assets/releases/download/v0.0.0/VisDrone2019-DET-val.zip', 'https://github.com/ultralytics/assets/releases/download/v0.0.0/VisDrone2019-DET-test-dev.zip', 'https://github.com/ultralytics/assets/releases/download/v0.0.0/VisDrone2019-DET-test-challenge.zip'] download(urls, dir=dir, curl=True, threads=4) # Convert for d in 'VisDrone2019-DET-train', 'VisDrone2019-DET-val', 'VisDrone2019-DET-test-dev': visdrone2yolo(dir / d) # convert VisDrone annotations to YOLO labels ================================================ FILE: data/coco.yaml ================================================ # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license # COCO 2017 dataset http://cocodataset.org by Microsoft # Example usage: python train.py --data coco.yaml # parent # ├── yolov5 # └── datasets # └── coco ← downloads here (20.1 GB) # 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, ..] path: ../datasets/coco # dataset root dir train: train2017.txt # train images (relative to 'path') 118287 images val: val2017.txt # val images (relative to 'path') 5000 images test: test-dev2017.txt # 20288 of 40670 images, submit to https://competitions.codalab.org/competitions/20794 # Classes names: 0: person 1: bicycle 2: car 3: motorcycle 4: airplane 5: bus 6: train 7: truck 8: boat 9: traffic light 10: fire hydrant 11: stop sign 12: parking meter 13: bench 14: bird 15: cat 16: dog 17: horse 18: sheep 19: cow 20: elephant 21: bear 22: zebra 23: giraffe 24: backpack 25: umbrella 26: handbag 27: tie 28: suitcase 29: frisbee 30: skis 31: snowboard 32: sports ball 33: kite 34: baseball bat 35: baseball glove 36: skateboard 37: surfboard 38: tennis racket 39: bottle 40: wine glass 41: cup 42: fork 43: knife 44: spoon 45: bowl 46: banana 47: apple 48: sandwich 49: orange 50: broccoli 51: carrot 52: hot dog 53: pizza 54: donut 55: cake 56: chair 57: couch 58: potted plant 59: bed 60: dining table 61: toilet 62: tv 63: laptop 64: mouse 65: remote 66: keyboard 67: cell phone 68: microwave 69: oven 70: toaster 71: sink 72: refrigerator 73: book 74: clock 75: vase 76: scissors 77: teddy bear 78: hair drier 79: toothbrush # Download script/URL (optional) download: | from utils.general import download, Path # Download labels segments = False # segment or box labels dir = Path(yaml['path']) # dataset root dir url = 'https://github.com/ultralytics/assets/releases/download/v0.0.0/' urls = [url + ('coco2017labels-segments.zip' if segments else 'coco2017labels.zip')] # labels download(urls, dir=dir.parent) # Download data urls = ['http://images.cocodataset.org/zips/train2017.zip', # 19G, 118k images 'http://images.cocodataset.org/zips/val2017.zip', # 1G, 5k images 'http://images.cocodataset.org/zips/test2017.zip'] # 7G, 41k images (optional) download(urls, dir=dir / 'images', threads=3) ================================================ FILE: data/coco128-seg.yaml ================================================ # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license # COCO128-seg dataset https://www.kaggle.com/datasets/ultralytics/coco128 (first 128 images from COCO train2017) by Ultralytics # Example usage: python train.py --data coco128.yaml # parent # ├── yolov5 # └── datasets # └── coco128-seg ← downloads here (7 MB) # 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, ..] path: ../datasets/coco128-seg # dataset root dir train: images/train2017 # train images (relative to 'path') 128 images val: images/train2017 # val images (relative to 'path') 128 images test: # test images (optional) # Classes names: 0: person 1: bicycle 2: car 3: motorcycle 4: airplane 5: bus 6: train 7: truck 8: boat 9: traffic light 10: fire hydrant 11: stop sign 12: parking meter 13: bench 14: bird 15: cat 16: dog 17: horse 18: sheep 19: cow 20: elephant 21: bear 22: zebra 23: giraffe 24: backpack 25: umbrella 26: handbag 27: tie 28: suitcase 29: frisbee 30: skis 31: snowboard 32: sports ball 33: kite 34: baseball bat 35: baseball glove 36: skateboard 37: surfboard 38: tennis racket 39: bottle 40: wine glass 41: cup 42: fork 43: knife 44: spoon 45: bowl 46: banana 47: apple 48: sandwich 49: orange 50: broccoli 51: carrot 52: hot dog 53: pizza 54: donut 55: cake 56: chair 57: couch 58: potted plant 59: bed 60: dining table 61: toilet 62: tv 63: laptop 64: mouse 65: remote 66: keyboard 67: cell phone 68: microwave 69: oven 70: toaster 71: sink 72: refrigerator 73: book 74: clock 75: vase 76: scissors 77: teddy bear 78: hair drier 79: toothbrush # Download script/URL (optional) download: https://github.com/ultralytics/assets/releases/download/v0.0.0/coco128-seg.zip ================================================ FILE: data/coco128.yaml ================================================ # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license # COCO128 dataset https://www.kaggle.com/datasets/ultralytics/coco128 (first 128 images from COCO train2017) by Ultralytics # Example usage: python train.py --data coco128.yaml # parent # ├── yolov5 # └── datasets # └── coco128 ← downloads here (7 MB) # 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, ..] path: ../datasets/coco128 # dataset root dir train: images/train2017 # train images (relative to 'path') 128 images val: images/train2017 # val images (relative to 'path') 128 images test: # test images (optional) # Classes names: 0: person 1: bicycle 2: car 3: motorcycle 4: airplane 5: bus 6: train 7: truck 8: boat 9: traffic light 10: fire hydrant 11: stop sign 12: parking meter 13: bench 14: bird 15: cat 16: dog 17: horse 18: sheep 19: cow 20: elephant 21: bear 22: zebra 23: giraffe 24: backpack 25: umbrella 26: handbag 27: tie 28: suitcase 29: frisbee 30: skis 31: snowboard 32: sports ball 33: kite 34: baseball bat 35: baseball glove 36: skateboard 37: surfboard 38: tennis racket 39: bottle 40: wine glass 41: cup 42: fork 43: knife 44: spoon 45: bowl 46: banana 47: apple 48: sandwich 49: orange 50: broccoli 51: carrot 52: hot dog 53: pizza 54: donut 55: cake 56: chair 57: couch 58: potted plant 59: bed 60: dining table 61: toilet 62: tv 63: laptop 64: mouse 65: remote 66: keyboard 67: cell phone 68: microwave 69: oven 70: toaster 71: sink 72: refrigerator 73: book 74: clock 75: vase 76: scissors 77: teddy bear 78: hair drier 79: toothbrush # Download script/URL (optional) download: https://github.com/ultralytics/assets/releases/download/v0.0.0/coco128.zip ================================================ FILE: data/hyps/hyp.Objects365.yaml ================================================ # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license # Hyperparameters for Objects365 training # python train.py --weights yolov5m.pt --data Objects365.yaml --evolve # See Hyperparameter Evolution tutorial for details https://github.com/ultralytics/yolov5#tutorials lr0: 0.00258 lrf: 0.17 momentum: 0.779 weight_decay: 0.00058 warmup_epochs: 1.33 warmup_momentum: 0.86 warmup_bias_lr: 0.0711 box: 0.0539 cls: 0.299 cls_pw: 0.825 obj: 0.632 obj_pw: 1.0 iou_t: 0.2 anchor_t: 3.44 anchors: 3.2 fl_gamma: 0.0 hsv_h: 0.0188 hsv_s: 0.704 hsv_v: 0.36 degrees: 0.0 translate: 0.0902 scale: 0.491 shear: 0.0 perspective: 0.0 flipud: 0.0 fliplr: 0.5 mosaic: 1.0 mixup: 0.0 copy_paste: 0.0 ================================================ FILE: data/hyps/hyp.VOC.yaml ================================================ # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license # Hyperparameters for VOC training # python train.py --batch 128 --weights yolov5m6.pt --data VOC.yaml --epochs 50 --img 512 --hyp hyp.scratch-med.yaml --evolve # See Hyperparameter Evolution tutorial for details https://github.com/ultralytics/yolov5#tutorials # YOLOv3 Hyperparameter Evolution Results # Best generation: 467 # Last generation: 996 # metrics/precision, metrics/recall, metrics/mAP_0.5, metrics/mAP_0.5:0.95, val/box_loss, val/obj_loss, val/cls_loss # 0.87729, 0.85125, 0.91286, 0.72664, 0.0076739, 0.0042529, 0.0013865 lr0: 0.00334 lrf: 0.15135 momentum: 0.74832 weight_decay: 0.00025 warmup_epochs: 3.3835 warmup_momentum: 0.59462 warmup_bias_lr: 0.18657 box: 0.02 cls: 0.21638 cls_pw: 0.5 obj: 0.51728 obj_pw: 0.67198 iou_t: 0.2 anchor_t: 3.3744 fl_gamma: 0.0 hsv_h: 0.01041 hsv_s: 0.54703 hsv_v: 0.27739 degrees: 0.0 translate: 0.04591 scale: 0.75544 shear: 0.0 perspective: 0.0 flipud: 0.0 fliplr: 0.5 mosaic: 0.85834 mixup: 0.04266 copy_paste: 0.0 anchors: 3.412 ================================================ FILE: data/hyps/hyp.no-augmentation.yaml ================================================ # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license # Hyperparameters when using Albumentations frameworks # python train.py --hyp hyp.no-augmentation.yaml # See https://github.com/ultralytics/yolov5/pull/3882 for YOLOv3 + Albumentations Usage examples lr0: 0.01 # initial learning rate (SGD=1E-2, Adam=1E-3) lrf: 0.1 # final OneCycleLR learning rate (lr0 * lrf) momentum: 0.937 # SGD momentum/Adam beta1 weight_decay: 0.0005 # optimizer weight decay 5e-4 warmup_epochs: 3.0 # warmup epochs (fractions ok) warmup_momentum: 0.8 # warmup initial momentum warmup_bias_lr: 0.1 # warmup initial bias lr box: 0.05 # box loss gain cls: 0.3 # cls loss gain cls_pw: 1.0 # cls BCELoss positive_weight obj: 0.7 # obj loss gain (scale with pixels) obj_pw: 1.0 # obj BCELoss positive_weight iou_t: 0.20 # IoU training threshold anchor_t: 4.0 # anchor-multiple threshold # anchors: 3 # anchors per output layer (0 to ignore) # this parameters are all zero since we want to use albumentation framework fl_gamma: 0.0 # focal loss gamma (efficientDet default gamma=1.5) hsv_h: 0 # image HSV-Hue augmentation (fraction) hsv_s: 0 # image HSV-Saturation augmentation (fraction) hsv_v: 0 # image HSV-Value augmentation (fraction) degrees: 0.0 # image rotation (+/- deg) translate: 0 # image translation (+/- fraction) scale: 0 # image scale (+/- gain) shear: 0 # image shear (+/- deg) perspective: 0.0 # image perspective (+/- fraction), range 0-0.001 flipud: 0.0 # image flip up-down (probability) fliplr: 0.0 # image flip left-right (probability) mosaic: 0.0 # image mosaic (probability) mixup: 0.0 # image mixup (probability) copy_paste: 0.0 # segment copy-paste (probability) ================================================ FILE: data/hyps/hyp.scratch-high.yaml ================================================ # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license # Hyperparameters for high-augmentation COCO training from scratch # python train.py --batch 32 --cfg yolov5m6.yaml --weights '' --data coco.yaml --img 1280 --epochs 300 # See tutorials for hyperparameter evolution https://github.com/ultralytics/yolov5#tutorials lr0: 0.01 # initial learning rate (SGD=1E-2, Adam=1E-3) lrf: 0.1 # final OneCycleLR learning rate (lr0 * lrf) momentum: 0.937 # SGD momentum/Adam beta1 weight_decay: 0.0005 # optimizer weight decay 5e-4 warmup_epochs: 3.0 # warmup epochs (fractions ok) warmup_momentum: 0.8 # warmup initial momentum warmup_bias_lr: 0.1 # warmup initial bias lr box: 0.05 # box loss gain cls: 0.3 # cls loss gain cls_pw: 1.0 # cls BCELoss positive_weight obj: 0.7 # obj loss gain (scale with pixels) obj_pw: 1.0 # obj BCELoss positive_weight iou_t: 0.20 # IoU training threshold anchor_t: 4.0 # anchor-multiple threshold # anchors: 3 # anchors per output layer (0 to ignore) fl_gamma: 0.0 # focal loss gamma (efficientDet default gamma=1.5) hsv_h: 0.015 # image HSV-Hue augmentation (fraction) hsv_s: 0.7 # image HSV-Saturation augmentation (fraction) hsv_v: 0.4 # image HSV-Value augmentation (fraction) degrees: 0.0 # image rotation (+/- deg) translate: 0.1 # image translation (+/- fraction) scale: 0.9 # image scale (+/- gain) shear: 0.0 # image shear (+/- deg) perspective: 0.0 # image perspective (+/- fraction), range 0-0.001 flipud: 0.0 # image flip up-down (probability) fliplr: 0.5 # image flip left-right (probability) mosaic: 1.0 # image mosaic (probability) mixup: 0.1 # image mixup (probability) copy_paste: 0.1 # segment copy-paste (probability) ================================================ FILE: data/hyps/hyp.scratch-low.yaml ================================================ # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license # Hyperparameters for low-augmentation COCO training from scratch # python train.py --batch 64 --cfg yolov5n6.yaml --weights '' --data coco.yaml --img 640 --epochs 300 --linear # See tutorials for hyperparameter evolution https://github.com/ultralytics/yolov5#tutorials lr0: 0.01 # initial learning rate (SGD=1E-2, Adam=1E-3) lrf: 0.01 # final OneCycleLR learning rate (lr0 * lrf) momentum: 0.937 # SGD momentum/Adam beta1 weight_decay: 0.0005 # optimizer weight decay 5e-4 warmup_epochs: 3.0 # warmup epochs (fractions ok) warmup_momentum: 0.8 # warmup initial momentum warmup_bias_lr: 0.1 # warmup initial bias lr box: 0.05 # box loss gain cls: 0.5 # cls loss gain cls_pw: 1.0 # cls BCELoss positive_weight obj: 1.0 # obj loss gain (scale with pixels) obj_pw: 1.0 # obj BCELoss positive_weight iou_t: 0.20 # IoU training threshold anchor_t: 4.0 # anchor-multiple threshold # anchors: 3 # anchors per output layer (0 to ignore) fl_gamma: 0.0 # focal loss gamma (efficientDet default gamma=1.5) hsv_h: 0.015 # image HSV-Hue augmentation (fraction) hsv_s: 0.7 # image HSV-Saturation augmentation (fraction) hsv_v: 0.4 # image HSV-Value augmentation (fraction) degrees: 0.0 # image rotation (+/- deg) translate: 0.1 # image translation (+/- fraction) scale: 0.5 # image scale (+/- gain) shear: 0.0 # image shear (+/- deg) perspective: 0.0 # image perspective (+/- fraction), range 0-0.001 flipud: 0.0 # image flip up-down (probability) fliplr: 0.5 # image flip left-right (probability) mosaic: 1.0 # image mosaic (probability) mixup: 0.0 # image mixup (probability) copy_paste: 0.0 # segment copy-paste (probability) ================================================ FILE: data/hyps/hyp.scratch-med.yaml ================================================ # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license # Hyperparameters for medium-augmentation COCO training from scratch # python train.py --batch 32 --cfg yolov5m6.yaml --weights '' --data coco.yaml --img 1280 --epochs 300 # See tutorials for hyperparameter evolution https://github.com/ultralytics/yolov5#tutorials lr0: 0.01 # initial learning rate (SGD=1E-2, Adam=1E-3) lrf: 0.1 # final OneCycleLR learning rate (lr0 * lrf) momentum: 0.937 # SGD momentum/Adam beta1 weight_decay: 0.0005 # optimizer weight decay 5e-4 warmup_epochs: 3.0 # warmup epochs (fractions ok) warmup_momentum: 0.8 # warmup initial momentum warmup_bias_lr: 0.1 # warmup initial bias lr box: 0.05 # box loss gain cls: 0.3 # cls loss gain cls_pw: 1.0 # cls BCELoss positive_weight obj: 0.7 # obj loss gain (scale with pixels) obj_pw: 1.0 # obj BCELoss positive_weight iou_t: 0.20 # IoU training threshold anchor_t: 4.0 # anchor-multiple threshold # anchors: 3 # anchors per output layer (0 to ignore) fl_gamma: 0.0 # focal loss gamma (efficientDet default gamma=1.5) hsv_h: 0.015 # image HSV-Hue augmentation (fraction) hsv_s: 0.7 # image HSV-Saturation augmentation (fraction) hsv_v: 0.4 # image HSV-Value augmentation (fraction) degrees: 0.0 # image rotation (+/- deg) translate: 0.1 # image translation (+/- fraction) scale: 0.9 # image scale (+/- gain) shear: 0.0 # image shear (+/- deg) perspective: 0.0 # image perspective (+/- fraction), range 0-0.001 flipud: 0.0 # image flip up-down (probability) fliplr: 0.5 # image flip left-right (probability) mosaic: 1.0 # image mosaic (probability) mixup: 0.1 # image mixup (probability) copy_paste: 0.0 # segment copy-paste (probability) ================================================ FILE: data/objects365.yaml ================================================ # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license # Objects365 dataset https://www.objects365.org/ by Megvii # Example usage: python train.py --data Objects365.yaml # parent # ├── yolov5 # └── datasets # └── Objects365 ← downloads here (712 GB = 367G data + 345G zips) # 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, ..] path: ../datasets/Objects365 # dataset root dir train: images/train # train images (relative to 'path') 1742289 images val: images/val # val images (relative to 'path') 80000 images test: # test images (optional) # Classes names: 0: Person 1: Sneakers 2: Chair 3: Other Shoes 4: Hat 5: Car 6: Lamp 7: Glasses 8: Bottle 9: Desk 10: Cup 11: Street Lights 12: Cabinet/shelf 13: Handbag/Satchel 14: Bracelet 15: Plate 16: Picture/Frame 17: Helmet 18: Book 19: Gloves 20: Storage box 21: Boat 22: Leather Shoes 23: Flower 24: Bench 25: Potted Plant 26: Bowl/Basin 27: Flag 28: Pillow 29: Boots 30: Vase 31: Microphone 32: Necklace 33: Ring 34: SUV 35: Wine Glass 36: Belt 37: Monitor/TV 38: Backpack 39: Umbrella 40: Traffic Light 41: Speaker 42: Watch 43: Tie 44: Trash bin Can 45: Slippers 46: Bicycle 47: Stool 48: Barrel/bucket 49: Van 50: Couch 51: Sandals 52: Basket 53: Drum 54: Pen/Pencil 55: Bus 56: Wild Bird 57: High Heels 58: Motorcycle 59: Guitar 60: Carpet 61: Cell Phone 62: Bread 63: Camera 64: Canned 65: Truck 66: Traffic cone 67: Cymbal 68: Lifesaver 69: Towel 70: Stuffed Toy 71: Candle 72: Sailboat 73: Laptop 74: Awning 75: Bed 76: Faucet 77: Tent 78: Horse 79: Mirror 80: Power outlet 81: Sink 82: Apple 83: Air Conditioner 84: Knife 85: Hockey Stick 86: Paddle 87: Pickup Truck 88: Fork 89: Traffic Sign 90: Balloon 91: Tripod 92: Dog 93: Spoon 94: Clock 95: Pot 96: Cow 97: Cake 98: Dinning Table 99: Sheep 100: Hanger 101: Blackboard/Whiteboard 102: Napkin 103: Other Fish 104: Orange/Tangerine 105: Toiletry 106: Keyboard 107: Tomato 108: Lantern 109: Machinery Vehicle 110: Fan 111: Green Vegetables 112: Banana 113: Baseball Glove 114: Airplane 115: Mouse 116: Train 117: Pumpkin 118: Soccer 119: Skiboard 120: Luggage 121: Nightstand 122: Tea pot 123: Telephone 124: Trolley 125: Head Phone 126: Sports Car 127: Stop Sign 128: Dessert 129: Scooter 130: Stroller 131: Crane 132: Remote 133: Refrigerator 134: Oven 135: Lemon 136: Duck 137: Baseball Bat 138: Surveillance Camera 139: Cat 140: Jug 141: Broccoli 142: Piano 143: Pizza 144: Elephant 145: Skateboard 146: Surfboard 147: Gun 148: Skating and Skiing shoes 149: Gas stove 150: Donut 151: Bow Tie 152: Carrot 153: Toilet 154: Kite 155: Strawberry 156: Other Balls 157: Shovel 158: Pepper 159: Computer Box 160: Toilet Paper 161: Cleaning Products 162: Chopsticks 163: Microwave 164: Pigeon 165: Baseball 166: Cutting/chopping Board 167: Coffee Table 168: Side Table 169: Scissors 170: Marker 171: Pie 172: Ladder 173: Snowboard 174: Cookies 175: Radiator 176: Fire Hydrant 177: Basketball 178: Zebra 179: Grape 180: Giraffe 181: Potato 182: Sausage 183: Tricycle 184: Violin 185: Egg 186: Fire Extinguisher 187: Candy 188: Fire Truck 189: Billiards 190: Converter 191: Bathtub 192: Wheelchair 193: Golf Club 194: Briefcase 195: Cucumber 196: Cigar/Cigarette 197: Paint Brush 198: Pear 199: Heavy Truck 200: Hamburger 201: Extractor 202: Extension Cord 203: Tong 204: Tennis Racket 205: Folder 206: American Football 207: earphone 208: Mask 209: Kettle 210: Tennis 211: Ship 212: Swing 213: Coffee Machine 214: Slide 215: Carriage 216: Onion 217: Green beans 218: Projector 219: Frisbee 220: Washing Machine/Drying Machine 221: Chicken 222: Printer 223: Watermelon 224: Saxophone 225: Tissue 226: Toothbrush 227: Ice cream 228: Hot-air balloon 229: Cello 230: French Fries 231: Scale 232: Trophy 233: Cabbage 234: Hot dog 235: Blender 236: Peach 237: Rice 238: Wallet/Purse 239: Volleyball 240: Deer 241: Goose 242: Tape 243: Tablet 244: Cosmetics 245: Trumpet 246: Pineapple 247: Golf Ball 248: Ambulance 249: Parking meter 250: Mango 251: Key 252: Hurdle 253: Fishing Rod 254: Medal 255: Flute 256: Brush 257: Penguin 258: Megaphone 259: Corn 260: Lettuce 261: Garlic 262: Swan 263: Helicopter 264: Green Onion 265: Sandwich 266: Nuts 267: Speed Limit Sign 268: Induction Cooker 269: Broom 270: Trombone 271: Plum 272: Rickshaw 273: Goldfish 274: Kiwi fruit 275: Router/modem 276: Poker Card 277: Toaster 278: Shrimp 279: Sushi 280: Cheese 281: Notepaper 282: Cherry 283: Pliers 284: CD 285: Pasta 286: Hammer 287: Cue 288: Avocado 289: Hamimelon 290: Flask 291: Mushroom 292: Screwdriver 293: Soap 294: Recorder 295: Bear 296: Eggplant 297: Board Eraser 298: Coconut 299: Tape Measure/Ruler 300: Pig 301: Showerhead 302: Globe 303: Chips 304: Steak 305: Crosswalk Sign 306: Stapler 307: Camel 308: Formula 1 309: Pomegranate 310: Dishwasher 311: Crab 312: Hoverboard 313: Meat ball 314: Rice Cooker 315: Tuba 316: Calculator 317: Papaya 318: Antelope 319: Parrot 320: Seal 321: Butterfly 322: Dumbbell 323: Donkey 324: Lion 325: Urinal 326: Dolphin 327: Electric Drill 328: Hair Dryer 329: Egg tart 330: Jellyfish 331: Treadmill 332: Lighter 333: Grapefruit 334: Game board 335: Mop 336: Radish 337: Baozi 338: Target 339: French 340: Spring Rolls 341: Monkey 342: Rabbit 343: Pencil Case 344: Yak 345: Red Cabbage 346: Binoculars 347: Asparagus 348: Barbell 349: Scallop 350: Noddles 351: Comb 352: Dumpling 353: Oyster 354: Table Tennis paddle 355: Cosmetics Brush/Eyeliner Pencil 356: Chainsaw 357: Eraser 358: Lobster 359: Durian 360: Okra 361: Lipstick 362: Cosmetics Mirror 363: Curling 364: Table Tennis # Download script/URL (optional) --------------------------------------------------------------------------------------- download: | from tqdm import tqdm from utils.general import Path, check_requirements, download, np, xyxy2xywhn check_requirements('pycocotools>=2.0') from pycocotools.coco import COCO # Make Directories dir = Path(yaml['path']) # dataset root dir for p in 'images', 'labels': (dir / p).mkdir(parents=True, exist_ok=True) for q in 'train', 'val': (dir / p / q).mkdir(parents=True, exist_ok=True) # Train, Val Splits for split, patches in [('train', 50 + 1), ('val', 43 + 1)]: print(f"Processing {split} in {patches} patches ...") images, labels = dir / 'images' / split, dir / 'labels' / split # Download url = f"https://dorc.ks3-cn-beijing.ksyun.com/data-set/2020Objects365%E6%95%B0%E6%8D%AE%E9%9B%86/{split}/" if split == 'train': download([f'{url}zhiyuan_objv2_{split}.tar.gz'], dir=dir, delete=False) # annotations json download([f'{url}patch{i}.tar.gz' for i in range(patches)], dir=images, curl=True, delete=False, threads=8) elif split == 'val': download([f'{url}zhiyuan_objv2_{split}.json'], dir=dir, delete=False) # annotations json download([f'{url}images/v1/patch{i}.tar.gz' for i in range(15 + 1)], dir=images, curl=True, delete=False, threads=8) download([f'{url}images/v2/patch{i}.tar.gz' for i in range(16, patches)], dir=images, curl=True, delete=False, threads=8) # Move for f in tqdm(images.rglob('*.jpg'), desc=f'Moving {split} images'): f.rename(images / f.name) # move to /images/{split} # Labels coco = COCO(dir / f'zhiyuan_objv2_{split}.json') names = [x["name"] for x in coco.loadCats(coco.getCatIds())] for cid, cat in enumerate(names): catIds = coco.getCatIds(catNms=[cat]) imgIds = coco.getImgIds(catIds=catIds) for im in tqdm(coco.loadImgs(imgIds), desc=f'Class {cid + 1}/{len(names)} {cat}'): width, height = im["width"], im["height"] path = Path(im["file_name"]) # image filename try: with open(labels / path.with_suffix('.txt').name, 'a') as file: annIds = coco.getAnnIds(imgIds=im["id"], catIds=catIds, iscrowd=False) for a in coco.loadAnns(annIds): x, y, w, h = a['bbox'] # bounding box in xywh (xy top-left corner) xyxy = np.array([x, y, x + w, y + h])[None] # pixels(1,4) x, y, w, h = xyxy2xywhn(xyxy, w=width, h=height, clip=True)[0] # normalized and clipped file.write(f"{cid} {x:.5f} {y:.5f} {w:.5f} {h:.5f}\n") except Exception as e: print(e) ================================================ FILE: data/scripts/download_weights.sh ================================================ #!/bin/bash # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license # Download latest models from https://github.com/ultralytics/yolov5/releases # Example usage: bash data/scripts/download_weights.sh # parent # └── yolov5 # ├── yolov5s.pt ← downloads here # ├── yolov5m.pt # └── ... python - << EOF from utils.downloads import attempt_download p5 = list('nsmlx') # P5 models p6 = [f'{x}6' for x in p5] # P6 models cls = [f'{x}-cls' for x in p5] # classification models seg = [f'{x}-seg' for x in p5] # classification models for x in p5 + p6 + cls + seg: attempt_download(f'weights/yolov5{x}.pt') EOF ================================================ FILE: data/scripts/get_coco.sh ================================================ #!/bin/bash # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license # Download COCO 2017 dataset http://cocodataset.org # Example usage: bash data/scripts/get_coco.sh # parent # ├── yolov5 # └── datasets # └── coco ← downloads here # Arguments (optional) Usage: bash data/scripts/get_coco.sh --train --val --test --segments if [ "$#" -gt 0 ]; then for opt in "$@"; do case "${opt}" in --train) train=true ;; --val) val=true ;; --test) test=true ;; --segments) segments=true ;; esac done else train=true val=true test=false segments=false fi # Download/unzip labels d='../datasets' # unzip directory url=https://github.com/ultralytics/yolov5/releases/download/v1.0/ if [ "$segments" == "true" ]; then f='coco2017labels-segments.zip' # 168 MB else f='coco2017labels.zip' # 46 MB fi echo 'Downloading' $url$f ' ...' curl -L $url$f -o $f -# && unzip -q $f -d $d && rm $f & # Download/unzip images d='../datasets/coco/images' # unzip directory url=http://images.cocodataset.org/zips/ if [ "$train" == "true" ]; then f='train2017.zip' # 19G, 118k images echo 'Downloading' $url$f '...' curl -L $url$f -o $f -# && unzip -q $f -d $d && rm $f & fi if [ "$val" == "true" ]; then f='val2017.zip' # 1G, 5k images echo 'Downloading' $url$f '...' curl -L $url$f -o $f -# && unzip -q $f -d $d && rm $f & fi if [ "$test" == "true" ]; then f='test2017.zip' # 7G, 41k images (optional) echo 'Downloading' $url$f '...' curl -L $url$f -o $f -# && unzip -q $f -d $d && rm $f & fi wait # finish background tasks ================================================ FILE: data/scripts/get_coco128.sh ================================================ #!/bin/bash # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license # Download COCO128 dataset https://www.kaggle.com/ultralytics/coco128 (first 128 images from COCO train2017) # Example usage: bash data/scripts/get_coco128.sh # parent # ├── yolov5 # └── datasets # └── coco128 ← downloads here # Download/unzip images and labels d='../datasets' # unzip directory url=https://github.com/ultralytics/yolov5/releases/download/v1.0/ f='coco128.zip' # or 'coco128-segments.zip', 68 MB echo 'Downloading' $url$f ' ...' curl -L $url$f -o $f -# && unzip -q $f -d $d && rm $f & wait # finish background tasks ================================================ FILE: data/scripts/get_imagenet.sh ================================================ #!/bin/bash # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license # Download ILSVRC2012 ImageNet dataset https://image-net.org # Example usage: bash data/scripts/get_imagenet.sh # parent # ├── yolov5 # └── datasets # └── imagenet ← downloads here # Arguments (optional) Usage: bash data/scripts/get_imagenet.sh --train --val if [ "$#" -gt 0 ]; then for opt in "$@"; do case "${opt}" in --train) train=true ;; --val) val=true ;; esac done else train=true val=true fi # Make dir d='../datasets/imagenet' # unzip directory mkdir -p $d && cd $d # Download/unzip train if [ "$train" == "true" ]; then wget https://image-net.org/data/ILSVRC/2012/ILSVRC2012_img_train.tar # download 138G, 1281167 images mkdir train && mv ILSVRC2012_img_train.tar train/ && cd train tar -xf ILSVRC2012_img_train.tar && rm -f ILSVRC2012_img_train.tar find . -name "*.tar" | while read NAME; do mkdir -p "${NAME%.tar}" tar -xf "${NAME}" -C "${NAME%.tar}" rm -f "${NAME}" done cd .. fi # Download/unzip val if [ "$val" == "true" ]; then wget https://image-net.org/data/ILSVRC/2012/ILSVRC2012_img_val.tar # download 6.3G, 50000 images mkdir val && mv ILSVRC2012_img_val.tar val/ && cd val && tar -xf ILSVRC2012_img_val.tar wget -qO- https://raw.githubusercontent.com/soumith/imagenetloader.torch/master/valprep.sh | bash # move into subdirs fi # Delete corrupted image (optional: PNG under JPEG name that may cause dataloaders to fail) # rm train/n04266014/n04266014_10835.JPEG # TFRecords (optional) # wget https://raw.githubusercontent.com/tensorflow/models/master/research/slim/datasets/imagenet_lsvrc_2015_synsets.txt ================================================ FILE: data/voc.yaml ================================================ # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license # PASCAL VOC dataset http://host.robots.ox.ac.uk/pascal/VOC by University of Oxford # Example usage: python train.py --data VOC.yaml # parent # ├── yolov5 # └── datasets # └── VOC ← downloads here (2.8 GB) # 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, ..] path: ../datasets/VOC train: # train images (relative to 'path') 16551 images - images/train2012 - images/train2007 - images/val2012 - images/val2007 val: # val images (relative to 'path') 4952 images - images/test2007 test: # test images (optional) - images/test2007 # Classes names: 0: aeroplane 1: bicycle 2: bird 3: boat 4: bottle 5: bus 6: car 7: cat 8: chair 9: cow 10: diningtable 11: dog 12: horse 13: motorbike 14: person 15: pottedplant 16: sheep 17: sofa 18: train 19: tvmonitor # Download script/URL (optional) --------------------------------------------------------------------------------------- download: | import xml.etree.ElementTree as ET from tqdm import tqdm from utils.general import download, Path def convert_label(path, lb_path, year, image_id): def convert_box(size, box): dw, dh = 1. / size[0], 1. / size[1] 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] return x * dw, y * dh, w * dw, h * dh in_file = open(path / f'VOC{year}/Annotations/{image_id}.xml') out_file = open(lb_path, 'w') tree = ET.parse(in_file) root = tree.getroot() size = root.find('size') w = int(size.find('width').text) h = int(size.find('height').text) names = list(yaml['names'].values()) # names list for obj in root.iter('object'): cls = obj.find('name').text if cls in names and int(obj.find('difficult').text) != 1: xmlbox = obj.find('bndbox') bb = convert_box((w, h), [float(xmlbox.find(x).text) for x in ('xmin', 'xmax', 'ymin', 'ymax')]) cls_id = names.index(cls) # class id out_file.write(" ".join([str(a) for a in (cls_id, *bb)]) + '\n') # Download dir = Path(yaml['path']) # dataset root dir url = 'https://github.com/ultralytics/assets/releases/download/v0.0.0/' urls = [f'{url}VOCtrainval_06-Nov-2007.zip', # 446MB, 5012 images f'{url}VOCtest_06-Nov-2007.zip', # 438MB, 4953 images f'{url}VOCtrainval_11-May-2012.zip'] # 1.95GB, 17126 images download(urls, dir=dir / 'images', delete=False, curl=True, threads=3) # Convert path = dir / 'images/VOCdevkit' for year, image_set in ('2012', 'train'), ('2012', 'val'), ('2007', 'train'), ('2007', 'val'), ('2007', 'test'): imgs_path = dir / 'images' / f'{image_set}{year}' lbs_path = dir / 'labels' / f'{image_set}{year}' imgs_path.mkdir(exist_ok=True, parents=True) lbs_path.mkdir(exist_ok=True, parents=True) with open(path / f'VOC{year}/ImageSets/Main/{image_set}.txt') as f: image_ids = f.read().strip().split() for id in tqdm(image_ids, desc=f'{image_set}{year}'): f = path / f'VOC{year}/JPEGImages/{id}.jpg' # old img path lb_path = (lbs_path / f.name).with_suffix('.txt') # new label path f.rename(imgs_path / f.name) # move image convert_label(path, lb_path, year, id) # convert labels to YOLO format ================================================ FILE: data/xView.yaml ================================================ # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license # DIUx xView 2018 Challenge https://challenge.xviewdataset.org by U.S. National Geospatial-Intelligence Agency (NGA) # -------- DOWNLOAD DATA MANUALLY and jar xf val_images.zip to 'datasets/xView' before running train command! -------- # Example usage: python train.py --data xView.yaml # parent # ├── yolov5 # └── datasets # └── xView ← downloads here (20.7 GB) # 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, ..] path: ../datasets/xView # dataset root dir train: images/autosplit_train.txt # train images (relative to 'path') 90% of 847 train images val: images/autosplit_val.txt # train images (relative to 'path') 10% of 847 train images # Classes names: 0: Fixed-wing Aircraft 1: Small Aircraft 2: Cargo Plane 3: Helicopter 4: Passenger Vehicle 5: Small Car 6: Bus 7: Pickup Truck 8: Utility Truck 9: Truck 10: Cargo Truck 11: Truck w/Box 12: Truck Tractor 13: Trailer 14: Truck w/Flatbed 15: Truck w/Liquid 16: Crane Truck 17: Railway Vehicle 18: Passenger Car 19: Cargo Car 20: Flat Car 21: Tank car 22: Locomotive 23: Maritime Vessel 24: Motorboat 25: Sailboat 26: Tugboat 27: Barge 28: Fishing Vessel 29: Ferry 30: Yacht 31: Container Ship 32: Oil Tanker 33: Engineering Vehicle 34: Tower crane 35: Container Crane 36: Reach Stacker 37: Straddle Carrier 38: Mobile Crane 39: Dump Truck 40: Haul Truck 41: Scraper/Tractor 42: Front loader/Bulldozer 43: Excavator 44: Cement Mixer 45: Ground Grader 46: Hut/Tent 47: Shed 48: Building 49: Aircraft Hangar 50: Damaged Building 51: Facility 52: Construction Site 53: Vehicle Lot 54: Helipad 55: Storage Tank 56: Shipping container lot 57: Shipping Container 58: Pylon 59: Tower # Download script/URL (optional) --------------------------------------------------------------------------------------- download: | import json import os from pathlib import Path import numpy as np from PIL import Image from tqdm import tqdm from utils.dataloaders import autosplit from utils.general import download, xyxy2xywhn def convert_labels(fname=Path('xView/xView_train.geojson')): # Convert xView geoJSON labels to YOLO format path = fname.parent with open(fname) as f: print(f'Loading {fname}...') data = json.load(f) # Make dirs labels = Path(path / 'labels' / 'train') os.system(f'rm -rf {labels}') labels.mkdir(parents=True, exist_ok=True) # xView classes 11-94 to 0-59 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, 12, 13, 14, 15, -1, -1, 16, 17, 18, 19, 20, 21, 22, -1, 23, 24, 25, -1, 26, 27, -1, 28, -1, 29, 30, 31, 32, 33, 34, 35, 36, 37, -1, 38, 39, 40, 41, 42, 43, 44, 45, -1, -1, -1, -1, 46, 47, 48, 49, -1, 50, 51, -1, 52, -1, -1, -1, 53, 54, -1, 55, -1, -1, 56, -1, 57, -1, 58, 59] shapes = {} for feature in tqdm(data['features'], desc=f'Converting {fname}'): p = feature['properties'] if p['bounds_imcoords']: id = p['image_id'] file = path / 'train_images' / id if file.exists(): # 1395.tif missing try: box = np.array([int(num) for num in p['bounds_imcoords'].split(",")]) assert box.shape[0] == 4, f'incorrect box shape {box.shape[0]}' cls = p['type_id'] cls = xview_class2index[int(cls)] # xView class to 0-60 assert 59 >= cls >= 0, f'incorrect class index {cls}' # Write YOLO label if id not in shapes: shapes[id] = Image.open(file).size box = xyxy2xywhn(box[None].astype(np.float), w=shapes[id][0], h=shapes[id][1], clip=True) with open((labels / id).with_suffix('.txt'), 'a') as f: f.write(f"{cls} {' '.join(f'{x:.6f}' for x in box[0])}\n") # write label.txt except Exception as e: print(f'WARNING: skipping one label for {file}: {e}') # Download manually from https://challenge.xviewdataset.org dir = Path(yaml['path']) # dataset root dir # urls = ['https://d307kc0mrhucc3.cloudfront.net/train_labels.zip', # train labels # 'https://d307kc0mrhucc3.cloudfront.net/train_images.zip', # 15G, 847 train images # 'https://d307kc0mrhucc3.cloudfront.net/val_images.zip'] # 5G, 282 val images (no labels) # download(urls, dir=dir, delete=False) # Convert labels convert_labels(dir / 'xView_train.geojson') # Move images images = Path(dir / 'images') images.mkdir(parents=True, exist_ok=True) Path(dir / 'train_images').rename(dir / 'images' / 'train') Path(dir / 'val_images').rename(dir / 'images' / 'val') # Split autosplit(dir / 'images' / 'train') ================================================ FILE: detect.py ================================================ # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license """ Run YOLOv3 detection inference on images, videos, directories, globs, YouTube, webcam, streams, etc. Usage - sources: $ python detect.py --weights yolov5s.pt --source 0 # webcam img.jpg # image vid.mp4 # video screen # screenshot path/ # directory list.txt # list of images list.streams # list of streams 'path/*.jpg' # glob 'https://youtu.be/LNwODJXcvt4' # YouTube 'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream Usage - formats: $ python detect.py --weights yolov5s.pt # PyTorch yolov5s.torchscript # TorchScript yolov5s.onnx # ONNX Runtime or OpenCV DNN with --dnn yolov5s_openvino_model # OpenVINO yolov5s.engine # TensorRT yolov5s.mlmodel # CoreML (macOS-only) yolov5s_saved_model # TensorFlow SavedModel yolov5s.pb # TensorFlow GraphDef yolov5s.tflite # TensorFlow Lite yolov5s_edgetpu.tflite # TensorFlow Edge TPU yolov5s_paddle_model # PaddlePaddle """ import argparse import os import platform import sys from pathlib import Path import torch FILE = Path(__file__).resolve() ROOT = FILE.parents[0] # YOLOv3 root directory if str(ROOT) not in sys.path: sys.path.append(str(ROOT)) # add ROOT to PATH ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative from ultralytics.utils.plotting import Annotator, colors, save_one_box from models.common import DetectMultiBackend from utils.dataloaders import IMG_FORMATS, VID_FORMATS, LoadImages, LoadScreenshots, LoadStreams from utils.general import ( LOGGER, Profile, check_file, check_img_size, check_imshow, check_requirements, colorstr, cv2, increment_path, non_max_suppression, print_args, scale_boxes, strip_optimizer, xyxy2xywh, ) from utils.torch_utils import select_device, smart_inference_mode @smart_inference_mode() def run( weights=ROOT / "yolov5s.pt", # model path or triton URL source=ROOT / "data/images", # file/dir/URL/glob/screen/0(webcam) data=ROOT / "data/coco128.yaml", # dataset.yaml path imgsz=(640, 640), # inference size (height, width) conf_thres=0.25, # confidence threshold iou_thres=0.45, # NMS IOU threshold max_det=1000, # maximum detections per image device="", # cuda device, i.e. 0 or 0,1,2,3 or cpu view_img=False, # show results save_txt=False, # save results to *.txt save_conf=False, # save confidences in --save-txt labels save_crop=False, # save cropped prediction boxes nosave=False, # do not save images/videos classes=None, # filter by class: --class 0, or --class 0 2 3 agnostic_nms=False, # class-agnostic NMS augment=False, # augmented inference visualize=False, # visualize features update=False, # update all models project=ROOT / "runs/detect", # save results to project/name name="exp", # save results to project/name exist_ok=False, # existing project/name ok, do not increment line_thickness=3, # bounding box thickness (pixels) hide_labels=False, # hide labels hide_conf=False, # hide confidences half=False, # use FP16 half-precision inference dnn=False, # use OpenCV DNN for ONNX inference vid_stride=1, # video frame-rate stride ): """Run YOLOv3 detection inference on various input sources such as images, videos, streams, and YouTube URLs. Args: weights (str | Path): Path to the model weights file or a Triton URL (default: 'yolov5s.pt'). source (str | Path): Source of input data such as a file, directory, URL, glob pattern, or device identifier (default: 'data/images'). data (str | Path): Path to the dataset YAML file (default: 'data/coco128.yaml'). imgsz (tuple[int, int]): Inference size as a tuple (height, width) (default: (640, 640)). conf_thres (float): Confidence threshold for detection (default: 0.25). iou_thres (float): Intersection Over Union (IOU) threshold for Non-Max Suppression (NMS) (default: 0.45). max_det (int): Maximum number of detections per image (default: 1000). device (str): CUDA device identifier, e.g., '0', '0,1,2,3', or 'cpu' (default: ''). view_img (bool): Whether to display results during inference (default: False). save_txt (bool): Whether to save detection results to text files (default: False). save_conf (bool): Whether to save detection confidences in the text labels (default: False). save_crop (bool): Whether to save cropped detection boxes (default: False). nosave (bool): Whether to prevent saving images or videos with detections (default: False). classes (list[int] | None): List of class indices to filter, e.g., [0, 2, 3] (default: None). agnostic_nms (bool): Whether to perform class-agnostic NMS (default: False). augment (bool): Whether to apply augmented inference (default: False). visualize (bool): Whether to visualize feature maps (default: False). update (bool): Whether to update all models (default: False). project (str | Path): Path to the project directory where results will be saved (default: 'runs/detect'). name (str): Name for the specific run within the project directory (default: 'exp'). exist_ok (bool): Whether to allow existing project/name directory without incrementing run index (default: False). line_thickness (int): Thickness of bounding box lines in pixels (default: 3). hide_labels (bool): Whether to hide labels in the results (default: False). hide_conf (bool): Whether to hide confidences in the results (default: False). half (bool): Whether to use half-precision (FP16) for inference (default: False). dnn (bool): Whether to use OpenCV DNN for ONNX inference (default: False). vid_stride (int): Stride for video frame rate (default: 1). Returns: None Examples: ```python # Run YOLOv3 inference on an image run(weights='yolov5s.pt', source='data/images/bus.jpg') # Run YOLOv3 inference on a video run(weights='yolov5s.pt', source='data/videos/video.mp4', view_img=True) # Run YOLOv3 inference on a webcam run(weights='yolov5s.pt', source='0', view_img=True) ``` Notes: This function supports a variety of input sources such as image files, video files, directories, URL patterns, webcam streams, and YouTube links. It also supports multiple model formats including PyTorch, ONNX, OpenVINO, TensorRT, CoreML, TensorFlow, PaddlePaddle, and others. The results can be visualized in real-time or saved to specified directories. Use command-line arguments to modify the behavior of the function. """ source = str(source) save_img = not nosave and not source.endswith(".txt") # save inference images is_file = Path(source).suffix[1:] in (IMG_FORMATS + VID_FORMATS) is_url = source.lower().startswith(("rtsp://", "rtmp://", "http://", "https://")) webcam = source.isnumeric() or source.endswith(".streams") or (is_url and not is_file) screenshot = source.lower().startswith("screen") if is_url and is_file: source = check_file(source) # download # Directories save_dir = increment_path(Path(project) / name, exist_ok=exist_ok) # increment run (save_dir / "labels" if save_txt else save_dir).mkdir(parents=True, exist_ok=True) # make dir # Load model device = select_device(device) model = DetectMultiBackend(weights, device=device, dnn=dnn, data=data, fp16=half) stride, names, pt = model.stride, model.names, model.pt imgsz = check_img_size(imgsz, s=stride) # check image size # Dataloader bs = 1 # batch_size if webcam: view_img = check_imshow(warn=True) dataset = LoadStreams(source, img_size=imgsz, stride=stride, auto=pt, vid_stride=vid_stride) bs = len(dataset) elif screenshot: dataset = LoadScreenshots(source, img_size=imgsz, stride=stride, auto=pt) else: dataset = LoadImages(source, img_size=imgsz, stride=stride, auto=pt, vid_stride=vid_stride) vid_path, vid_writer = [None] * bs, [None] * bs # Run inference model.warmup(imgsz=(1 if pt or model.triton else bs, 3, *imgsz)) # warmup seen, windows, dt = 0, [], (Profile(), Profile(), Profile()) for path, im, im0s, vid_cap, s in dataset: with dt[0]: im = torch.from_numpy(im).to(model.device) im = im.half() if model.fp16 else im.float() # uint8 to fp16/32 im /= 255 # 0 - 255 to 0.0 - 1.0 if len(im.shape) == 3: im = im[None] # expand for batch dim # Inference with dt[1]: visualize = increment_path(save_dir / Path(path).stem, mkdir=True) if visualize else False pred = model(im, augment=augment, visualize=visualize) # NMS with dt[2]: pred = non_max_suppression(pred, conf_thres, iou_thres, classes, agnostic_nms, max_det=max_det) # Second-stage classifier (optional) # pred = utils.general.apply_classifier(pred, classifier_model, im, im0s) # Process predictions for i, det in enumerate(pred): # per image seen += 1 if webcam: # batch_size >= 1 p, im0, frame = path[i], im0s[i].copy(), dataset.count s += f"{i}: " else: p, im0, frame = path, im0s.copy(), getattr(dataset, "frame", 0) p = Path(p) # to Path save_path = str(save_dir / p.name) # im.jpg txt_path = str(save_dir / "labels" / p.stem) + ("" if dataset.mode == "image" else f"_{frame}") # im.txt s += "{:g}x{:g} ".format(*im.shape[2:]) # print string gn = torch.tensor(im0.shape)[[1, 0, 1, 0]] # normalization gain whwh imc = im0.copy() if save_crop else im0 # for save_crop annotator = Annotator(im0, line_width=line_thickness, example=str(names)) if len(det): # Rescale boxes from img_size to im0 size det[:, :4] = scale_boxes(im.shape[2:], det[:, :4], im0.shape).round() # Print results for c in det[:, 5].unique(): n = (det[:, 5] == c).sum() # detections per class s += f"{n} {names[int(c)]}{'s' * (n > 1)}, " # add to string # Write results for *xyxy, conf, cls in reversed(det): if save_txt: # Write to file xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() # normalized xywh line = (cls, *xywh, conf) if save_conf else (cls, *xywh) # label format with open(f"{txt_path}.txt", "a") as f: f.write(("%g " * len(line)).rstrip() % line + "\n") if save_img or save_crop or view_img: # Add bbox to image c = int(cls) # integer class label = None if hide_labels else (names[c] if hide_conf else f"{names[c]} {conf:.2f}") annotator.box_label(xyxy, label, color=colors(c, True)) if save_crop: save_one_box(xyxy, imc, file=save_dir / "crops" / names[c] / f"{p.stem}.jpg", BGR=True) # Stream results im0 = annotator.result() if view_img: if platform.system() == "Linux" and p not in windows: windows.append(p) cv2.namedWindow(str(p), cv2.WINDOW_NORMAL | cv2.WINDOW_KEEPRATIO) # allow window resize (Linux) cv2.resizeWindow(str(p), im0.shape[1], im0.shape[0]) cv2.imshow(str(p), im0) cv2.waitKey(1) # 1 millisecond # Save results (image with detections) if save_img: if dataset.mode == "image": cv2.imwrite(save_path, im0) else: # 'video' or 'stream' if vid_path[i] != save_path: # new video vid_path[i] = save_path if isinstance(vid_writer[i], cv2.VideoWriter): vid_writer[i].release() # release previous video writer if vid_cap: # video fps = vid_cap.get(cv2.CAP_PROP_FPS) w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH)) h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) else: # stream fps, w, h = 30, im0.shape[1], im0.shape[0] save_path = str(Path(save_path).with_suffix(".mp4")) # force *.mp4 suffix on results videos vid_writer[i] = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*"mp4v"), fps, (w, h)) vid_writer[i].write(im0) # Print time (inference-only) LOGGER.info(f"{s}{'' if len(det) else '(no detections), '}{dt[1].dt * 1e3:.1f}ms") # Print results t = tuple(x.t / seen * 1e3 for x in dt) # speeds per image LOGGER.info(f"Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {(1, 3, *imgsz)}" % t) if save_txt or save_img: s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else "" LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}{s}") if update: strip_optimizer(weights[0]) # update model (to fix SourceChangeWarning) def parse_opt(): """Parses and returns command-line options for running YOLOv3 model detection. Args: --weights (list[str]): Model path or Triton URL. Default: ROOT / "yolov3-tiny.pt". --source (str): Input data source like file/dir/URL/glob/screen/0(webcam). Default: ROOT / "data/images". --data (str): Optional path to dataset.yaml. Default: ROOT / "data/coco128.yaml". --imgsz (list[int]): Inference size as height, width. Accepts multiple values. Default: [640]. --conf-thres (float): Confidence threshold for predictions. Default: 0.25. --iou-thres (float): IoU threshold for Non-Maximum Suppression (NMS). Default: 0.45. --max-det (int): Maximum number of detections per image. Default: 1000. --device (str): CUDA device identifier, e.g. "0" or "0,1,2,3" or "cpu". Default: "" (auto-select). --view-img (bool): Display results. Default: False. --save-txt (bool): Save results to *.txt files. Default: False. --save-conf (bool): Save confidence scores in text labels. Default: False. --save-crop (bool): Save cropped prediction boxes. Default: False. --nosave (bool): Do not save images/videos. Default: False. --classes (list[int] | None): Filter results by class, e.g. [0, 2, 3]. Default: None. --agnostic-nms (bool): Perform class-agnostic NMS. Default: False. --augment (bool): Apply augmented inference. Default: False. --visualize (bool): Visualize feature maps. Default: False. --update (bool): Update all models. Default: False. --project (str): Directory to save results; results saved to "project/name". Default: ROOT / "runs/detect". --name (str): Name of the specific run; results saved to "project/name". Default: "exp". --exist-ok (bool): Allow results to be saved in an existing directory without incrementing. Default: False. --line-thickness (int): Bounding box line thickness in pixels. Default: 3. --hide-labels (bool): Hide labels on detections. Default: False. --hide-conf (bool): Hide confidence scores on labels. Default: False. --half (bool): Use FP16 half-precision inference. Default: False. --dnn (bool): Use OpenCV DNN backend for ONNX inference. Default: False. --vid-stride (int): Frame-rate stride for video input. Default: 1. Returns: argparse.Namespace: Parsed command-line arguments for YOLOv3 inference configurations. Examples: ```python options = parse_opt() run(**vars(options)) ``` """ parser = argparse.ArgumentParser() parser.add_argument( "--weights", nargs="+", type=str, default=ROOT / "yolov3-tiny.pt", help="model path or triton URL" ) parser.add_argument("--source", type=str, default=ROOT / "data/images", help="file/dir/URL/glob/screen/0(webcam)") parser.add_argument("--data", type=str, default=ROOT / "data/coco128.yaml", help="(optional) dataset.yaml path") parser.add_argument("--imgsz", "--img", "--img-size", nargs="+", type=int, default=[640], help="inference size h,w") parser.add_argument("--conf-thres", type=float, default=0.25, help="confidence threshold") parser.add_argument("--iou-thres", type=float, default=0.45, help="NMS IoU threshold") parser.add_argument("--max-det", type=int, default=1000, help="maximum detections per image") parser.add_argument("--device", default="", help="cuda device, i.e. 0 or 0,1,2,3 or cpu") parser.add_argument("--view-img", action="store_true", help="show results") parser.add_argument("--save-txt", action="store_true", help="save results to *.txt") parser.add_argument("--save-conf", action="store_true", help="save confidences in --save-txt labels") parser.add_argument("--save-crop", action="store_true", help="save cropped prediction boxes") parser.add_argument("--nosave", action="store_true", help="do not save images/videos") parser.add_argument("--classes", nargs="+", type=int, help="filter by class: --classes 0, or --classes 0 2 3") parser.add_argument("--agnostic-nms", action="store_true", help="class-agnostic NMS") parser.add_argument("--augment", action="store_true", help="augmented inference") parser.add_argument("--visualize", action="store_true", help="visualize features") parser.add_argument("--update", action="store_true", help="update all models") parser.add_argument("--project", default=ROOT / "runs/detect", help="save results to project/name") parser.add_argument("--name", default="exp", help="save results to project/name") parser.add_argument("--exist-ok", action="store_true", help="existing project/name ok, do not increment") parser.add_argument("--line-thickness", default=3, type=int, help="bounding box thickness (pixels)") parser.add_argument("--hide-labels", default=False, action="store_true", help="hide labels") parser.add_argument("--hide-conf", default=False, action="store_true", help="hide confidences") parser.add_argument("--half", action="store_true", help="use FP16 half-precision inference") parser.add_argument("--dnn", action="store_true", help="use OpenCV DNN for ONNX inference") parser.add_argument("--vid-stride", type=int, default=1, help="video frame-rate stride") opt = parser.parse_args() opt.imgsz *= 2 if len(opt.imgsz) == 1 else 1 # expand print_args(vars(opt)) return opt def main(opt): """Entry point for running the YOLO model; checks requirements and calls `run` with parsed options. Args: opt (argparse.Namespace): Parsed command-line options, which include: - weights (str | list of str): Path to the model weights or Triton server URL. - source (str): Input source, can be a file, directory, URL, glob, screen, or webcam index. - data (str): Path to the dataset configuration file (.yaml). - imgsz (tuple of int): Inference image size as (height, width). - conf_thres (float): Confidence threshold for detections. - iou_thres (float): Intersection over Union (IoU) threshold for Non-Maximum Suppression (NMS). - max_det (int): Maximum number of detections per image. - device (str): Device to run inference on; options are CUDA device id(s) or 'cpu' - view_img (bool): Flag to display inference results. - save_txt (bool): Save detection results in .txt format. - save_conf (bool): Save detection confidences in .txt labels. - save_crop (bool): Save cropped bounding box predictions. - nosave (bool): Do not save images/videos with detections. - classes (list of int): Filter results by class, e.g., --class 0 2 3. - agnostic_nms (bool): Use class-agnostic NMS. - augment (bool): Enable augmented inference. - visualize (bool): Visualize feature maps. - update (bool): Update the model during inference. - project (str): Directory to save results. - name (str): Name for the results directory. - exist_ok (bool): Allow existing project/name directories without incrementing. - line_thickness (int): Thickness of bounding box lines. - hide_labels (bool): Hide class labels on bounding boxes. - hide_conf (bool): Hide confidence scores on bounding boxes. - half (bool): Use FP16 half-precision inference. - dnn (bool): Use OpenCV DNN backend for ONNX inference. - vid_stride (int): Video frame-rate stride. Returns: None Examples: ```python if __name__ == "__main__": opt = parse_opt() main(opt) ``` Notes: Run this function as the entry point for using YOLO for object detection on a variety of input sources such as images, videos, directories, webcams, streams, etc. This function ensures all requirements are checked and subsequently initiates the detection process by calling the `run` function with appropriate options. """ check_requirements(ROOT / "requirements.txt", exclude=("tensorboard", "thop")) run(**vars(opt)) if __name__ == "__main__": opt = parse_opt() main(opt) ================================================ FILE: export.py ================================================ # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license """ Export a YOLOv3 PyTorch model to other formats. TensorFlow exports authored by https://github.com/zldrobit. Format | `export.py --include` | Model --- | --- | --- PyTorch | - | yolov5s.pt TorchScript | `torchscript` | yolov5s.torchscript ONNX | `onnx` | yolov5s.onnx OpenVINO | `openvino` | yolov5s_openvino_model/ TensorRT | `engine` | yolov5s.engine CoreML | `coreml` | yolov5s.mlmodel TensorFlow SavedModel | `saved_model` | yolov5s_saved_model/ TensorFlow GraphDef | `pb` | yolov5s.pb TensorFlow Lite | `tflite` | yolov5s.tflite TensorFlow Edge TPU | `edgetpu` | yolov5s_edgetpu.tflite TensorFlow.js | `tfjs` | yolov5s_web_model/ PaddlePaddle | `paddle` | yolov5s_paddle_model/ Requirements: $ pip install -r requirements.txt coremltools onnx onnx-simplifier onnxruntime openvino-dev tensorflow-cpu # CPU $ pip install -r requirements.txt coremltools onnx onnx-simplifier onnxruntime-gpu openvino-dev tensorflow # GPU Usage: $ python export.py --weights yolov5s.pt --include torchscript onnx openvino engine coreml tflite ... Inference: $ python detect.py --weights yolov5s.pt # PyTorch yolov5s.torchscript # TorchScript yolov5s.onnx # ONNX Runtime or OpenCV DNN with --dnn yolov5s_openvino_model # OpenVINO yolov5s.engine # TensorRT yolov5s.mlmodel # CoreML (macOS-only) yolov5s_saved_model # TensorFlow SavedModel yolov5s.pb # TensorFlow GraphDef yolov5s.tflite # TensorFlow Lite yolov5s_edgetpu.tflite # TensorFlow Edge TPU yolov5s_paddle_model # PaddlePaddle TensorFlow.js: $ cd .. && git clone https://github.com/zldrobit/tfjs-yolov5-example.git && cd tfjs-yolov5-example $ npm install $ ln -s ../../yolov5/yolov5s_web_model public/yolov5s_web_model $ npm start """ import argparse import contextlib import json import os import platform import re import subprocess import sys import time import warnings from pathlib import Path import pandas as pd import torch from torch.utils.mobile_optimizer import optimize_for_mobile FILE = Path(__file__).resolve() ROOT = FILE.parents[0] # YOLOv3 root directory if str(ROOT) not in sys.path: sys.path.append(str(ROOT)) # add ROOT to PATH if platform.system() != "Windows": ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative from models.experimental import attempt_load from models.yolo import ClassificationModel, Detect, DetectionModel, SegmentationModel from utils.dataloaders import LoadImages from utils.general import ( LOGGER, Profile, check_dataset, check_img_size, check_requirements, check_version, check_yaml, colorstr, file_size, get_default_args, print_args, url2file, yaml_save, ) from utils.torch_utils import select_device, smart_inference_mode MACOS = platform.system() == "Darwin" # macOS environment class iOSModel(torch.nn.Module): """Exports a PyTorch model to an iOS-compatible format with normalized input dimensions and class configurations.""" def __init__(self, model, im): """Initializes an iOSModel with normalized input dimensions and number of classes from a PyTorch model. Args: model (torch.nn.Module): The PyTorch model from which to initialize the iOS model. This should include attributes like `nc` (number of classes) which will be used to configure the iOS model. im (torch.Tensor): A Tensor representing a sample input image. The shape of this tensor should be (batch_size, channels, height, width). This is used to extract dimensions for input normalization. Returns: None Notes: - This class is specifically designed for use in exporting a PyTorch model for deployment on iOS platforms, optimizing input dimensions and class configurations to suit mobile requirements. - Normalization factor is derived from the input image dimensions, which impacts the model's performance during inference on iOS devices. - Ensure the sample input image `im` provided has correct dimensions and shape for accurate model configuration. """ super().__init__() _b, _c, h, w = im.shape # batch, channel, height, width self.model = model self.nc = model.nc # number of classes if w == h: self.normalize = 1.0 / w else: self.normalize = torch.tensor([1.0 / w, 1.0 / h, 1.0 / w, 1.0 / h]) # broadcast (slower, smaller) # np = model(im)[0].shape[1] # number of points # self.normalize = torch.tensor([1. / w, 1. / h, 1. / w, 1. / h]).expand(np, 4) # explicit (faster, larger) def forward(self, x): """Performs a forward pass, returning scaled confidences and normalized coordinates given an input tensor. Args: x (torch.Tensor): Input tensor representing a batch of images, with dimensions [batch_size, channels, height, width]. Returns: tuple[torch.Tensor, torch.Tensor, torch.Tensor]: A tuple containing three elements: - xywh (torch.Tensor): Tensor of shape [batch_size, num_detections, 4] containing normalized x, y, width, and height coordinates. - conf (torch.Tensor): Tensor of shape [batch_size, num_detections, 1] containing confidence scores for each detection. - cls (torch.Tensor): Tensor of shape [batch_size, num_detections, num_classes] containing class probabilities. Examples: ```python model = iOSModel(trained_model, input_image_tensor) detection_results = model.forward(input_tensor) xywh, conf, cls = detection_results ``` Further reading on exporting models to different formats: https://github.com/ultralytics/ultralytics See Also: `export.py` for exporting a YOLOv3 PyTorch model to various formats. https://github.com/zldrobit for TensorFlow export scripts. Notes: The dimensions of `x` should match the input dimensions used during the model's initialization to ensure proper scaling and normalization. """ xywh, conf, cls = self.model(x)[0].squeeze().split((4, 1, self.nc), 1) return cls * conf, xywh * self.normalize # confidence (3780, 80), coordinates (3780, 4) def export_formats(): """Lists supported YOLOv3 model export formats including file suffixes and CPU/GPU compatibility. Returns: list: A list of lists where each sublist contains information about a specific export format. Each sublist includes the following elements: - str: The name of the format. - str: The command-line argument for including this format. - str: The file suffix used for this format. - bool: Indicates if the format is compatible with CPU. - bool: Indicates if the format is compatible with GPU. Examples: ```python formats = export_formats() for format in formats: print(f"Format: {format[0]}, Suffix: {format[2]}, CPU Compatible: {format[3]}, GPU Compatible: {format[4]}") ``` """ x = [ ["PyTorch", "-", ".pt", True, True], ["TorchScript", "torchscript", ".torchscript", True, True], ["ONNX", "onnx", ".onnx", True, True], ["OpenVINO", "openvino", "_openvino_model", True, False], ["TensorRT", "engine", ".engine", False, True], ["CoreML", "coreml", ".mlmodel", True, False], ["TensorFlow SavedModel", "saved_model", "_saved_model", True, True], ["TensorFlow GraphDef", "pb", ".pb", True, True], ["TensorFlow Lite", "tflite", ".tflite", True, False], ["TensorFlow Edge TPU", "edgetpu", "_edgetpu.tflite", False, False], ["TensorFlow.js", "tfjs", "_web_model", False, False], ["PaddlePaddle", "paddle", "_paddle_model", True, True], ] return pd.DataFrame(x, columns=["Format", "Argument", "Suffix", "CPU", "GPU"]) def try_export(inner_func): """Profiles and logs the export process of YOLOv3 models, capturing success or failure details. Args: inner_func (Callable): The function that performs the actual export process and returns the model file path and the exported model. Returns: Callable: A wrapped function that profiles and logs the export process, handling successes and failures. Examples: ```python @try_export def export_onnx(py_model_path: str, output_path: str): # Export logic here return output_path, model ``` Notes: Applying this decorator to an export function will log the export results, including export success or failure, along with associated time and file size details. """ inner_args = get_default_args(inner_func) def outer_func(*args, **kwargs): """Profiles and logs the export process of YOLOv3 models, capturing success or failure details.""" prefix = inner_args["prefix"] try: with Profile() as dt: f, model = inner_func(*args, **kwargs) LOGGER.info(f"{prefix} export success ✅ {dt.t:.1f}s, saved as {f} ({file_size(f):.1f} MB)") return f, model except Exception as e: LOGGER.info(f"{prefix} export failure ❌ {dt.t:.1f}s: {e}") return None, None return outer_func @try_export def export_torchscript(model, im, file, optimize, prefix=colorstr("TorchScript:")): """Export a YOLOv3 model to TorchScript format, with optional optimization for mobile deployment. Args: model (torch.nn.Module): The YOLOv3 model to be exported. im (torch.Tensor): A tensor representing the input image for the model, typically with shape (N, 3, H, W). file (pathlib.Path): The file path where the TorchScript model will be saved. optimize (bool): A boolean flag indicating whether to optimize the model for mobile devices. prefix (str): A prefix for logging messages. Defaults to `colorstr("TorchScript:")`. Returns: (pathlib.Path | None, torch.nn.Module | None): Tuple containing the path to the saved TorchScript model and the model itself. Returns `(None, None)` if the export fails. Raises: Exception: If there is an error during export, it logs the error and returns `(None, None)`. Examples: ```python from pathlib import Path import torch model = ... # Assume model is loaded or created im = torch.randn(1, 3, 640, 640) # A sample input tensor file = Path("model.torchscript") optimize = True export_torchscript(model, im, file, optimize) ``` For more information, visit: https://ultralytics.com/. Notes: The function uses `torch.jit.trace` to trace the model with the input image tensor (`im`). Required metadata such as input shape, stride, and class names are stored in an extra file included in the TorchScript model. """ LOGGER.info(f"\n{prefix} starting export with torch {torch.__version__}...") f = file.with_suffix(".torchscript") ts = torch.jit.trace(model, im, strict=False) d = {"shape": im.shape, "stride": int(max(model.stride)), "names": model.names} extra_files = {"config.txt": json.dumps(d)} # torch._C.ExtraFilesMap() if optimize: # https://pytorch.org/tutorials/recipes/mobile_interpreter.html optimize_for_mobile(ts)._save_for_lite_interpreter(str(f), _extra_files=extra_files) else: ts.save(str(f), _extra_files=extra_files) return f, None @try_export def export_onnx(model, im, file, opset, dynamic, simplify, prefix=colorstr("ONNX:")): """Export a YOLOv3 model to ONNX format with dynamic shape and simplification options. Args: model (torch.nn.Module): The YOLOv3 model to be exported. im (torch.Tensor): A sample input tensor for tracing the model. file (pathlib.Path): The file path where the ONNX model will be saved. opset (int): The ONNX opset version to use for the export. dynamic (bool): If `True`, enables dynamic shape support. simplify (bool): If `True`, simplifies the ONNX model using onnx-simplifier. prefix (str): A prefix for logging messages. Returns: tuple[pathlib.Path, None]: The path to the saved ONNX model, None as the second tuple element (kept for consistency). Examples: ```python from pathlib import Path import torch model = ... # Assume model is loaded or created im = torch.randn(1, 3, 640, 640) # A sample input tensor file = Path("model.onnx") opset = 12 dynamic = True simplify = True export_onnx(model, im, file, opset, dynamic, simplify) ``` Notes: Ensure `onnx`, `onnx-simplifier`, and suitable runtime packages are installed. This function uses `torch.onnx.export` to create the ONNX model, followed by optional simplification using `onnx-simplifier`. If `dynamic` is enabled, dynamic axes mappings are added to support variable input shapes. Relevant YOLO model metadata like `stride` and `names` are included as part of the ONNX model's metadata. For more details on exporting and running inferences, visit: - https://github.com/ultralytics/ultralytics - https://github.com/zldrobit for TensorFlow export scripts. """ check_requirements("onnx>=1.12.0") import onnx LOGGER.info(f"\n{prefix} starting export with onnx {onnx.__version__}...") f = file.with_suffix(".onnx") output_names = ["output0", "output1"] if isinstance(model, SegmentationModel) else ["output0"] if dynamic: dynamic = {"images": {0: "batch", 2: "height", 3: "width"}} # shape(1,3,640,640) if isinstance(model, SegmentationModel): dynamic["output0"] = {0: "batch", 1: "anchors"} # shape(1,25200,85) dynamic["output1"] = {0: "batch", 2: "mask_height", 3: "mask_width"} # shape(1,32,160,160) elif isinstance(model, DetectionModel): dynamic["output0"] = {0: "batch", 1: "anchors"} # shape(1,25200,85) torch.onnx.export( model.cpu() if dynamic else model, # --dynamic only compatible with cpu im.cpu() if dynamic else im, f, verbose=False, opset_version=opset, do_constant_folding=True, # WARNING: DNN inference with torch>=1.12 may require do_constant_folding=False input_names=["images"], output_names=output_names, dynamic_axes=dynamic or None, ) # Checks model_onnx = onnx.load(f) # load onnx model onnx.checker.check_model(model_onnx) # check onnx model # Metadata d = {"stride": int(max(model.stride)), "names": model.names} for k, v in d.items(): meta = model_onnx.metadata_props.add() meta.key, meta.value = k, str(v) onnx.save(model_onnx, f) # Simplify if simplify: try: cuda = torch.cuda.is_available() check_requirements(("onnxruntime-gpu" if cuda else "onnxruntime", "onnx-simplifier>=0.4.1")) import onnxsim LOGGER.info(f"{prefix} simplifying with onnx-simplifier {onnxsim.__version__}...") model_onnx, check = onnxsim.simplify(model_onnx) assert check, "assert check failed" onnx.save(model_onnx, f) except Exception as e: LOGGER.info(f"{prefix} simplifier failure: {e}") return f, model_onnx @try_export def export_openvino(file, metadata, half, int8, data, prefix=colorstr("OpenVINO:")): """Export a YOLOv3 model to OpenVINO format with optional INT8 quantization and inference metadata. Args: file (Path): Path to the output file. metadata (dict): Inference metadata to include in the exported model. half (bool): Indicates if FP16 precision should be used. int8 (bool): Indicates if INT8 quantization should be applied. data (str): Path to the dataset file (.yaml) for post-training quantization. Returns: tuple[Path | None, openvino.runtime.Model | None]: Tuple containing the path to the exported model and the OpenVINO model object, or None if the export failed. Examples: ```python model_file = Path('/path/to/model.onnx') metadata = {'names': ['class1', 'class2'], 'stride': 32} export_openvino(model_file, metadata, half=True, int8=False, data='/path/to/dataset.yaml') ``` Notes: - Requires the `openvino-dev>=2023.0` and optional `nncf>=2.4.0` package for INT8 quantization. - Refer to OpenVINO documentation for further details: https://docs.openvino.ai/latest/index.html. """ check_requirements("openvino-dev>=2023.0") # requires openvino-dev: https://pypi.org/project/openvino-dev/ import openvino.runtime as ov from openvino.tools import mo LOGGER.info(f"\n{prefix} starting export with openvino {ov.__version__}...") f = str(file).replace(file.suffix, f"_openvino_model{os.sep}") f_onnx = file.with_suffix(".onnx") f_ov = str(Path(f) / file.with_suffix(".xml").name) if int8: check_requirements("nncf>=2.4.0") # requires at least version 2.4.0 to use the post-training quantization import nncf import numpy as np from openvino.runtime import Core from utils.dataloaders import create_dataloader core = Core() onnx_model = core.read_model(f_onnx) # export def prepare_input_tensor(image: np.ndarray): """Prepares the input tensor by normalizing pixel values and converting the datatype to float32.""" input_tensor = image.astype(np.float32) # uint8 to fp16/32 input_tensor /= 255.0 # 0 - 255 to 0.0 - 1.0 if input_tensor.ndim == 3: input_tensor = np.expand_dims(input_tensor, 0) return input_tensor def gen_dataloader(yaml_path, task="train", imgsz=640, workers=4): """Generates a PyTorch dataloader for the specified task using dataset configurations from a YAML file.""" data_yaml = check_yaml(yaml_path) data = check_dataset(data_yaml) dataloader = create_dataloader( data[task], imgsz=imgsz, batch_size=1, stride=32, pad=0.5, single_cls=False, rect=False, workers=workers )[0] return dataloader def transform_fn(data_item): """Quantization transform function. Extracts and preprocess input data from dataloader item for quantization. Parameters: data_item: Tuple with data item produced by DataLoader during iteration Returns: input_tensor: Input data for quantization """ img = data_item[0].numpy() input_tensor = prepare_input_tensor(img) return input_tensor ds = gen_dataloader(data) quantization_dataset = nncf.Dataset(ds, transform_fn) ov_model = nncf.quantize(onnx_model, quantization_dataset, preset=nncf.QuantizationPreset.MIXED) else: ov_model = mo.convert_model(f_onnx, model_name=file.stem, framework="onnx", compress_to_fp16=half) # export ov.serialize(ov_model, f_ov) # save yaml_save(Path(f) / file.with_suffix(".yaml").name, metadata) # add metadata.yaml return f, None @try_export def export_paddle(model, im, file, metadata, prefix=colorstr("PaddlePaddle:")): """Export a YOLOv3 model to PaddlePaddle format using X2Paddle, saving to a specified directory and including model metadata. Args: model (torch.nn.Module): The YOLOv3 model to be exported. im (torch.Tensor): A sample input tensor used for tracing the model. file (pathlib.Path): Destination file path for the exported model, with `.pt` suffix. metadata (dict): Additional metadata to be saved in YAML format alongside the exported model. prefix (str, optional): Log message prefix. Defaults to a colored "PaddlePaddle:" string. Returns: tuple: A tuple containing the directory path (str) where the PaddlePaddle model is saved, and `None`. Requirements: - paddlepaddle: Install via `pip install paddlepaddle`. - x2paddle: Install via `pip install x2paddle`. Examples: ```python from pathlib import Path import torch from models.yolo import DetectionModel model = DetectionModel() # Example model initialization im = torch.rand(1, 3, 640, 640) # Example input tensor file = Path("path/to/save/model.pt") metadata = {"nc": 80, "names": ["class1", "class2", ...]} # Example metadata export_paddle(model, im, file, metadata) ``` Notes: The function first checks for required packages `paddlepaddle` and `x2paddle`. It then uses X2Paddle to trace the model and export it to a PaddlePaddle format, saving the resulting files in the specified directory with included metadata in a YAML file. """ check_requirements(("paddlepaddle", "x2paddle")) import x2paddle from x2paddle.convert import pytorch2paddle LOGGER.info(f"\n{prefix} starting export with X2Paddle {x2paddle.__version__}...") f = str(file).replace(".pt", f"_paddle_model{os.sep}") pytorch2paddle(module=model, save_dir=f, jit_type="trace", input_examples=[im]) # export yaml_save(Path(f) / file.with_suffix(".yaml").name, metadata) # add metadata.yaml return f, None @try_export def export_coreml(model, im, file, int8, half, nms, prefix=colorstr("CoreML:")): """Export a YOLOv3 model to CoreML format with optional quantization and Non-Maximum Suppression (NMS). Args: model (torch.nn.Module): The YOLOv3 model to be exported. im (torch.Tensor): Input tensor used for tracing the model. Shape should be (batch_size, channels, height, width). file (pathlib.Path): Destination file path where the CoreML model will be saved. int8 (bool): Whether to use INT8 quantization. If True, quantizes the model to 8-bit integers. half (bool): Whether to use FP16 quantization. If True, converts the model to 16-bit floating point numbers. nms (bool): Whether to include Non-Maximum Suppression in the CoreML model. prefix (str): Prefix string for logging purposes. Default is colorstr("CoreML:"). Returns: str: Path to the saved CoreML model (.mlmodel). Raises: Exception: If there is an error during export, logs the error and stops the process. Examples: ```python from ultralytics.utils import export_coreml from pathlib import Path import torch model = ... # Assume model is loaded or created im = torch.randn(1, 3, 640, 640) # A sample input tensor file = Path("model.mlmodel") export_coreml(model, im, file, int8=False, half=True, nms=True) ``` Notes: - This function requires `coremltools` to be installed. - If `nms` is enabled, the model is wrapped with `iOSModel` to include NMS. - Quantization only works on macOS. """ check_requirements("coremltools") import coremltools as ct LOGGER.info(f"\n{prefix} starting export with coremltools {ct.__version__}...") f = file.with_suffix(".mlmodel") if nms: model = iOSModel(model, im) ts = torch.jit.trace(model, im, strict=False) # TorchScript model ct_model = ct.convert(ts, inputs=[ct.ImageType("image", shape=im.shape, scale=1 / 255, bias=[0, 0, 0])]) bits, mode = (8, "kmeans_lut") if int8 else (16, "linear") if half else (32, None) if bits < 32: if MACOS: # quantization only supported on macOS with warnings.catch_warnings(): warnings.filterwarnings("ignore", category=DeprecationWarning) # suppress numpy==1.20 float warning ct_model = ct.models.neural_network.quantization_utils.quantize_weights(ct_model, bits, mode) else: print(f"{prefix} quantization only supported on macOS, skipping...") ct_model.save(f) return f, ct_model @try_export def export_engine(model, im, file, half, dynamic, simplify, workspace=4, verbose=False, prefix=colorstr("TensorRT:")): """Export a YOLOv3 model to TensorRT engine format, optimizing it for GPU inference. Args: model (torch.nn.Module): The YOLOv3 model to be exported. im (torch.Tensor): Sample input tensor used for tracing the model. file (Path): File path where the exported TensorRT engine will be saved. half (bool): Whether to use FP16 precision. Requires a supported GPU. dynamic (bool): Whether to use dynamic input shapes. simplify (bool): Whether to simplify the model during the ONNX export. workspace (int): The maximum workspace size in GB. Default is 4. verbose (bool): Whether to print detailed export logs. prefix (str): Prefix string for log messages. Default is "TensorRT:". Returns: tuple[Path, None]: The output file path (Path) and None. Raises: AssertionError: If the model is running on CPU instead of GPU. RuntimeError: If the ONNX file failed to load. Examples: ```python from pathlib import Path import torch # Initialize model and dummy input model = YOLOv3(...) # or another correct initialization im = torch.randn(1, 3, 640, 640) # Export the model export_engine(model, im, Path("yolov3.engine"), half=True, dynamic=True, simplify=True) ``` Notes: Requires TensorRT installation to execute. Nvidia TensorRT: https://developer.nvidia.com/tensorrt """ assert im.device.type != "cpu", "export running on CPU but must be on GPU, i.e. `python export.py --device 0`" try: import tensorrt as trt except Exception: if platform.system() == "Linux": check_requirements("nvidia-tensorrt", cmds="-U --index-url https://pypi.ngc.nvidia.com") import tensorrt as trt if trt.__version__[0] == "7": # TensorRT 7 handling https://github.com/ultralytics/yolov5/issues/6012 grid = model.model[-1].anchor_grid model.model[-1].anchor_grid = [a[..., :1, :1, :] for a in grid] export_onnx(model, im, file, 12, dynamic, simplify) # opset 12 model.model[-1].anchor_grid = grid else: # TensorRT >= 8 check_version(trt.__version__, "8.0.0", hard=True) # require tensorrt>=8.0.0 export_onnx(model, im, file, 12, dynamic, simplify) # opset 12 onnx = file.with_suffix(".onnx") LOGGER.info(f"\n{prefix} starting export with TensorRT {trt.__version__}...") assert onnx.exists(), f"failed to export ONNX file: {onnx}" f = file.with_suffix(".engine") # TensorRT engine file logger = trt.Logger(trt.Logger.INFO) if verbose: logger.min_severity = trt.Logger.Severity.VERBOSE builder = trt.Builder(logger) config = builder.create_builder_config() config.max_workspace_size = workspace * 1 << 30 # config.set_memory_pool_limit(trt.MemoryPoolType.WORKSPACE, workspace << 30) # fix TRT 8.4 deprecation notice flag = 1 << int(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH) network = builder.create_network(flag) parser = trt.OnnxParser(network, logger) if not parser.parse_from_file(str(onnx)): raise RuntimeError(f"failed to load ONNX file: {onnx}") inputs = [network.get_input(i) for i in range(network.num_inputs)] outputs = [network.get_output(i) for i in range(network.num_outputs)] for inp in inputs: LOGGER.info(f'{prefix} input "{inp.name}" with shape{inp.shape} {inp.dtype}') for out in outputs: LOGGER.info(f'{prefix} output "{out.name}" with shape{out.shape} {out.dtype}') if dynamic: if im.shape[0] <= 1: LOGGER.warning(f"{prefix} WARNING ⚠️ --dynamic model requires maximum --batch-size argument") profile = builder.create_optimization_profile() for inp in inputs: profile.set_shape(inp.name, (1, *im.shape[1:]), (max(1, im.shape[0] // 2), *im.shape[1:]), im.shape) config.add_optimization_profile(profile) LOGGER.info(f"{prefix} building FP{16 if builder.platform_has_fast_fp16 and half else 32} engine as {f}") if builder.platform_has_fast_fp16 and half: config.set_flag(trt.BuilderFlag.FP16) with builder.build_engine(network, config) as engine, open(f, "wb") as t: t.write(engine.serialize()) return f, None @try_export def export_saved_model( model, im, file, dynamic, tf_nms=False, agnostic_nms=False, topk_per_class=100, topk_all=100, iou_thres=0.45, conf_thres=0.25, keras=False, prefix=colorstr("TensorFlow SavedModel:"), ): """Exports a YOLOv3 model to TensorFlow SavedModel format, including optional settings for Non-Max Suppression (NMS). Args: model (torch.nn.Module): The YOLOv3 PyTorch model to be exported. im (torch.Tensor): Tensor of sample input data used for tracing the model. file (pathlib.Path): File path where the exported TensorFlow SavedModel will be saved. dynamic (bool): If `True`, supports dynamic input shapes. tf_nms (bool, optional): If `True`, includes TensorFlow NMS in the exported model. Defaults to `False`. agnostic_nms (bool, optional): If `True`, uses class-agnostic NMS. Defaults to `False`. topk_per_class (int, optional): Number of top-K predictions to keep per class after NMS. Defaults to `100`. topk_all (int, optional): Number of top-K predictions to keep overall after NMS. Defaults to `100`. iou_thres (float, optional): Intersection over Union (IoU) threshold for NMS. Defaults to `0.45`. conf_thres (float, optional): Confidence threshold for NMS. Defaults to `0.25`. keras (bool, optional): If `True`, saves the model in Keras format. Defaults to `False`. prefix (str, optional): Prefix for logging messages. Defaults to `colorstr("TensorFlow SavedModel:")`. Returns: (str, None): Path to the saved TensorFlow model as a string and `None` (kept for interface consistency). Raises: ImportError: If the required TensorFlow libraries are not installed. Examples: ```python from pathlib import Path from models.common import DetectMultiBackend import torch model = DetectMultiBackend(weights='yolov5s.pt') im = torch.zeros(1, 3, 640, 640) # Sample input tensor file = Path("output/saved_model") export_saved_model(model, im, file, dynamic=True) ``` Notes: - Ensure that required TensorFlow libraries are installed (e.g., `pip install tensorflow`). - For more information, visit https://github.com/ultralytics/yolov5. """ # YOLOv3 TensorFlow SavedModel export try: import tensorflow as tf except Exception: check_requirements(f"tensorflow{'' if torch.cuda.is_available() else '-macos' if MACOS else '-cpu'}") import tensorflow as tf from tensorflow.python.framework.convert_to_constants import convert_variables_to_constants_v2 from models.tf import TFModel LOGGER.info(f"\n{prefix} starting export with tensorflow {tf.__version__}...") f = str(file).replace(".pt", "_saved_model") batch_size, ch, *imgsz = list(im.shape) # BCHW tf_model = TFModel(cfg=model.yaml, model=model, nc=model.nc, imgsz=imgsz) im = tf.zeros((batch_size, *imgsz, ch)) # BHWC order for TensorFlow _ = tf_model.predict(im, tf_nms, agnostic_nms, topk_per_class, topk_all, iou_thres, conf_thres) inputs = tf.keras.Input(shape=(*imgsz, ch), batch_size=None if dynamic else batch_size) outputs = tf_model.predict(inputs, tf_nms, agnostic_nms, topk_per_class, topk_all, iou_thres, conf_thres) keras_model = tf.keras.Model(inputs=inputs, outputs=outputs) keras_model.trainable = False keras_model.summary() if keras: keras_model.save(f, save_format="tf") else: spec = tf.TensorSpec(keras_model.inputs[0].shape, keras_model.inputs[0].dtype) m = tf.function(lambda x: keras_model(x)) # full model m = m.get_concrete_function(spec) frozen_func = convert_variables_to_constants_v2(m) tfm = tf.Module() tfm.__call__ = tf.function(lambda x: frozen_func(x)[:4] if tf_nms else frozen_func(x), [spec]) tfm.__call__(im) tf.saved_model.save( tfm, f, options=tf.saved_model.SaveOptions(experimental_custom_gradients=False) if check_version(tf.__version__, "2.6") else tf.saved_model.SaveOptions(), ) return f, keras_model @try_export def export_pb(keras_model, file, prefix=colorstr("TensorFlow GraphDef:")): """Export a Keras model to TensorFlow GraphDef (*.pb) format, which is compatible with YOLOv3. Args: keras_model (tf.keras.Model): The trained Keras model to be exported. file (pathlib.Path): The target file path for saving the exported model. prefix (str, optional): Prefix string for logging. Defaults to colorstr("TensorFlow GraphDef:"). Returns: tuple[pathlib.Path, None]: The file path where the model is saved and None. Examples: ```python from tensorflow.keras.models import load_model from pathlib import Path export_pb(load_model('model.h5'), Path('model.pb')) ``` See Also: For more details on TensorFlow GraphDef, visit https://github.com/leimao/Frozen_Graph_TensorFlow. Notes: Ensure TensorFlow is properly installed in your environment as it is required for this function to execute. TensorFlow's version should be compatible with the version used to train your model to avoid any compatibility issues. """ import tensorflow as tf from tensorflow.python.framework.convert_to_constants import convert_variables_to_constants_v2 LOGGER.info(f"\n{prefix} starting export with tensorflow {tf.__version__}...") f = file.with_suffix(".pb") m = tf.function(lambda x: keras_model(x)) # full model m = m.get_concrete_function(tf.TensorSpec(keras_model.inputs[0].shape, keras_model.inputs[0].dtype)) frozen_func = convert_variables_to_constants_v2(m) frozen_func.graph.as_graph_def() tf.io.write_graph(graph_or_graph_def=frozen_func.graph, logdir=str(f.parent), name=f.name, as_text=False) return f, None @try_export def export_tflite(keras_model, im, file, int8, data, nms, agnostic_nms, prefix=colorstr("TensorFlow Lite:")): """Export a YOLOv3 PyTorch model to TensorFlow Lite (TFLite) format. Args: keras_model (tf.keras.Model): The Keras model obtained after converting the PyTorch model. im (torch.Tensor): Sample input tensor to determine model input size. file (pathlib.Path): Desired file path for saving the exported TFLite model. int8 (bool): Flag to enable INT8 quantization for the TFLite model. data (str): Path to dataset YAML file for representative data generation used in quantization. nms (bool): Flag to include Non-Maximum Suppression (NMS) in the exported TFLite model. agnostic_nms (bool): Flag to apply class-agnostic NMS during inference. prefix (str, optional): Prefix for logging messages. Defaults to colorstr("TensorFlow Lite:"). Returns: (str | None): File path of the saved TensorFlow Lite model file or None if export fails. Examples: ```python import torch from pathlib import Path from models.experimental import attempt_load # Load and prepare model model = attempt_load('yolov5s.pt', map_location='cpu') im = torch.zeros(1, 3, 640, 640) # Dummy input tensor # Export model export_tflite(model, im, Path('yolov5s'), int8=False, data=None, nms=True, agnostic_nms=False) ``` For more details, refer to: TensorFlow Lite Developer Guide: https://www.tensorflow.org/lite/guide Model Conversion Reference: https://github.com/leimao/Frozen_Graph_TensorFlow Notes: - Ensure TensorFlow is installed to perform the export. - INT8 quantization requires a representative dataset to provide accurate calibration for the model. - Including Non-Max Suppression (NMS) modifies the exported model to handle post-processing. """ import tensorflow as tf LOGGER.info(f"\n{prefix} starting export with tensorflow {tf.__version__}...") _batch_size, _ch, *imgsz = list(im.shape) # BCHW f = str(file).replace(".pt", "-fp16.tflite") converter = tf.lite.TFLiteConverter.from_keras_model(keras_model) converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS] converter.target_spec.supported_types = [tf.float16] converter.optimizations = [tf.lite.Optimize.DEFAULT] if int8: from models.tf import representative_dataset_gen dataset = LoadImages(check_dataset(check_yaml(data))["train"], img_size=imgsz, auto=False) converter.representative_dataset = lambda: representative_dataset_gen(dataset, ncalib=100) converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8] converter.target_spec.supported_types = [] converter.inference_input_type = tf.uint8 # or tf.int8 converter.inference_output_type = tf.uint8 # or tf.int8 converter.experimental_new_quantizer = True f = str(file).replace(".pt", "-int8.tflite") if nms or agnostic_nms: converter.target_spec.supported_ops.append(tf.lite.OpsSet.SELECT_TF_OPS) tflite_model = converter.convert() open(f, "wb").write(tflite_model) return f, None @try_export def export_edgetpu(file, prefix=colorstr("Edge TPU:")): """Export a YOLOv5 model to TensorFlow Edge TPU format with INT8 quantization. Args: file (Path): The file path for the PyTorch model to be exported, with a `.pt` suffix. prefix (str): A prefix to be used for logging output. Defaults to "Edge TPU:" Returns: Tuple[Path | None, None]: A tuple containing the file path of the exported model with the `-int8_edgetpu.tflite` suffix and `None`, if successful. If unsuccessful, returns `(None, None)`. Raises: AssertionError: If the export is not executed on a Linux system. subprocess.CalledProcessError: If there are issues with subprocess execution, particularly around Edge TPU compiler installation or model conversion. Examples: ```python from pathlib import Path from ultralytics import export_edgetpu model_file = Path('yolov5s.pt') exported_model, _ = export_edgetpu(model_file) print(f"Model exported to {exported_model}") ``` For additional details, visit the Edge TPU compiler documentation: https://coral.ai/docs/edgetpu/compiler/ Notes: This function is designed to work exclusively on Linux systems and requires the Edge TPU compiler to be installed. If the compiler is not found, the function attempts to install it. """ cmd = "edgetpu_compiler --version" help_url = "https://coral.ai/docs/edgetpu/compiler/" assert platform.system() == "Linux", f"export only supported on Linux. See {help_url}" if subprocess.run(f"{cmd} > /dev/null 2>&1", shell=True).returncode != 0: LOGGER.info(f"\n{prefix} export requires Edge TPU compiler. Attempting install from {help_url}") sudo = subprocess.run("sudo --version >/dev/null", shell=True).returncode == 0 # sudo installed on system for c in ( "curl https://packages.cloud.google.com/apt/doc/apt-key.gpg | sudo apt-key add -", 'echo "deb https://packages.cloud.google.com/apt coral-edgetpu-stable main" | sudo tee /etc/apt/sources.list.d/coral-edgetpu.list', "sudo apt-get update", "sudo apt-get install edgetpu-compiler", ): subprocess.run(c if sudo else c.replace("sudo ", ""), shell=True, check=True) ver = subprocess.run(cmd, shell=True, capture_output=True, check=True).stdout.decode().split()[-1] LOGGER.info(f"\n{prefix} starting export with Edge TPU compiler {ver}...") f = str(file).replace(".pt", "-int8_edgetpu.tflite") # Edge TPU model f_tfl = str(file).replace(".pt", "-int8.tflite") # TFLite model subprocess.run( [ "edgetpu_compiler", "-s", "-d", "-k", "10", "--out_dir", str(file.parent), f_tfl, ], check=True, ) return f, None @try_export def export_tfjs(file, int8, prefix=colorstr("TensorFlow.js:")): """Export a YOLOv3 model to TensorFlow.js format, with an optional quantization to uint8. Args: file (Path): The path to the model file to be exported. int8 (bool): Boolean flag to determine if the model should be quantized to uint8. prefix (str): String prefix for logging, by default "TensorFlow.js". Returns: (tuple[str, None]): The directory path where the TensorFlow.js model files are saved and `None` placeholder to match the expected return type from 'try_export' decorator. Raises: ImportError: If the required 'tensorflowjs' package is not installed. Examples: ```python from pathlib import Path export_tfjs(file=Path("yolov5s.pt"), int8=False) ``` The converted model can be used directly in JavaScript environments using the TensorFlow.js library. For usage in web applications: - Clone the example repository: ```bash cd .. && git clone https://github.com/zldrobit/tfjs-yolov5-example.git && cd tfjs-yolov5-example ``` - Install dependencies: ```bash npm install ``` - Create a symbolic link to the exported web model: ```bash ln -s ../../yolov5/yolov5s_web_model public/yolov5s_web_model ``` - Start the example application: ```bash npm start ``` Notes: Ensure that you have TensorFlow.js installed in your environment. Install the package via: ```bash pip install tensorflowjs ``` For more details on using the converted model: Refer to the official TensorFlow.js documentation: https://www.tensorflow.org/js. """ check_requirements("tensorflowjs") import tensorflowjs as tfjs LOGGER.info(f"\n{prefix} starting export with tensorflowjs {tfjs.__version__}...") f = str(file).replace(".pt", "_web_model") # js dir f_pb = file.with_suffix(".pb") # *.pb path f_json = f"{f}/model.json" # *.json path args = [ "tensorflowjs_converter", "--input_format=tf_frozen_model", "--quantize_uint8" if int8 else "", "--output_node_names=Identity,Identity_1,Identity_2,Identity_3", str(f_pb), f, ] subprocess.run([arg for arg in args if arg], check=True) json = Path(f_json).read_text() with open(f_json, "w") as j: # sort JSON Identity_* in ascending order subst = re.sub( r'{"outputs": {"Identity.?.?": {"name": "Identity.?.?"}, ' r'"Identity.?.?": {"name": "Identity.?.?"}, ' r'"Identity.?.?": {"name": "Identity.?.?"}, ' r'"Identity.?.?": {"name": "Identity.?.?"}}}', r'{"outputs": {"Identity": {"name": "Identity"}, ' r'"Identity_1": {"name": "Identity_1"}, ' r'"Identity_2": {"name": "Identity_2"}, ' r'"Identity_3": {"name": "Identity_3"}}}', json, ) j.write(subst) return f, None def add_tflite_metadata(file, metadata, num_outputs): """Adds metadata to a TensorFlow Lite model to enhance its usability with `tflite_support`. Args: file (str): Path to the TensorFlow Lite model file. metadata (dict): Dictionary of metadata to add, including descriptions of inputs, outputs, and other relevant info. num_outputs (int): Number of output tensors in the model. Returns: None Examples: ```python metadata = { "input": {"description": "Input image tensor"}, "output": [{"name": "scores", "description": "Detection scores"}], } add_tflite_metadata("/path/to/model.tflite", metadata, num_outputs=1) ``` Notes: Requires the `tflite_support` library for adding metadata to the TensorFlow Lite model. Installation: `pip install tflite-support` ```python from tflite_support import flatbuffers from tflite_support import metadata as _metadata from tflite_support import metadata_schema_py_generated as _metadata_fb tmp_file = Path("/tmp/meta.txt") with open(tmp_file, "w") as meta_f: meta_f.write(str(metadata)) model_meta = _metadata_fb.ModelMetadataT() label_file = _metadata_fb.AssociatedFileT() label_file.name = tmp_file.name model_meta.associatedFiles = [label_file] subgraph = _metadata_fb.SubGraphMetadataT() subgraph.inputTensorMetadata = [_metadata_fb.TensorMetadataT()] subgraph.outputTensorMetadata = [_metadata_fb.TensorMetadataT()] * num_outputs model_meta.subgraphMetadata = [subgraph] b = flatbuffers.Builder(0) b.Finish(model_meta.Pack(b), _metadata.MetadataPopulator.METADATA_FILE_IDENTIFIER) metadata_buf = b.Output() populator = _metadata.MetadataPopulator.with_model_file(file) populator.load_metadata_buffer(metadata_buf) populator.load_associated_files([str(tmp_file)]) populator.populate() ``` This function is a helper to add metadata to a TFLite model, making it easier to interpret and process for tasks like object detection or classification. It leverages `tflite_support` to load and attach the metadata directly to the model file. """ with contextlib.suppress(ImportError): # check_requirements('tflite_support') from tflite_support import flatbuffers from tflite_support import metadata as _metadata from tflite_support import metadata_schema_py_generated as _metadata_fb tmp_file = Path("/tmp/meta.txt") with open(tmp_file, "w") as meta_f: meta_f.write(str(metadata)) model_meta = _metadata_fb.ModelMetadataT() label_file = _metadata_fb.AssociatedFileT() label_file.name = tmp_file.name model_meta.associatedFiles = [label_file] subgraph = _metadata_fb.SubGraphMetadataT() subgraph.inputTensorMetadata = [_metadata_fb.TensorMetadataT()] subgraph.outputTensorMetadata = [_metadata_fb.TensorMetadataT()] * num_outputs model_meta.subgraphMetadata = [subgraph] b = flatbuffers.Builder(0) b.Finish(model_meta.Pack(b), _metadata.MetadataPopulator.METADATA_FILE_IDENTIFIER) metadata_buf = b.Output() populator = _metadata.MetadataPopulator.with_model_file(file) populator.load_metadata_buffer(metadata_buf) populator.load_associated_files([str(tmp_file)]) populator.populate() tmp_file.unlink() def pipeline_coreml(model, im, file, names, y, prefix=colorstr("CoreML Pipeline:")): """Processes and exports a YOLOv3 model into the CoreML model format, applying metadata and non-maximum suppression (NMS). Args: model (coremltools.models.MLModel): The pre-trained YOLOv3 CoreML model to be used for the pipeline. im (torch.Tensor): Input image tensor in BCHW (Batch, Channel, Height, Width) format with a shape (B, 3, H, W). file (pathlib.Path): Destination file path where the CoreML model will be saved. names (dict): A dictionary that maps class indices to class names. y (torch.Tensor): Output detection tensor from the YOLO model, containing predictions. prefix (str): Prefix for logging messages, default is "CoreML Pipeline:". Returns: pathlib.Path | None: The path to the saved CoreML model if successful, otherwise None. Examples: ```python from pathlib import Path import torch from coremltools.models import MLModel # Load example CoreML model model = MLModel('path/to/pretrained/model.mlmodel') # Create example input tensor: B, C, H, W format im = torch.randn(1, 3, 640, 640) # Define where the CoreML model will be saved file = Path('path/to/save/model.mlmodel') # Define example class names names = {0: 'class0', 1: 'class1'} # Dummy YOLO model output prediction having similar dimensions to y y = torch.randn(1, 25200, 85) # Execute CoreML pipeline pipeline_coreml(model, im, file, names, y) ``` Notes: - The function adds NMS to the CoreML model, supporting dynamic thresholds for IoU and confidence. - Metadata fields are updated to include class names, thresholds, and additional information. - The pipeline exports the final enhanced model into the specified file path in CoreML (`.mlmodel`) format. - Ensure that `coremltools` is installed and properly configured in your environment. - This function is designed to work primarily on macOS systems as CoreML is macOS-specific. References: - `coremltools`: https://github.com/apple/coremltools - YOLOv3: https://github.com/ultralytics/yolov5 """ import coremltools as ct from PIL import Image print(f"{prefix} starting pipeline with coremltools {ct.__version__}...") _batch_size, _ch, h, w = list(im.shape) # BCHW t = time.time() # YOLOv3 Output shapes spec = model.get_spec() out0, out1 = iter(spec.description.output) if platform.system() == "Darwin": img = Image.new("RGB", (w, h)) # img(192 width, 320 height) # img = torch.zeros((*opt.img_size, 3)).numpy() # img size(320,192,3) iDetection out = model.predict({"image": img}) out0_shape, out1_shape = out[out0.name].shape, out[out1.name].shape else: # linux and windows can not run model.predict(), get sizes from pytorch output y s = tuple(y[0].shape) out0_shape, out1_shape = (s[1], s[2] - 5), (s[1], 4) # (3780, 80), (3780, 4) # Checks nx, ny = spec.description.input[0].type.imageType.width, spec.description.input[0].type.imageType.height _na, nc = out0_shape # na, nc = out0.type.multiArrayType.shape # number anchors, classes assert len(names) == nc, f"{len(names)} names found for nc={nc}" # check # Define output shapes (missing) out0.type.multiArrayType.shape[:] = out0_shape # (3780, 80) out1.type.multiArrayType.shape[:] = out1_shape # (3780, 4) # spec.neuralNetwork.preprocessing[0].featureName = '0' # Flexible input shapes # from coremltools.models.neural_network import flexible_shape_utils # s = [] # shapes # s.append(flexible_shape_utils.NeuralNetworkImageSize(320, 192)) # s.append(flexible_shape_utils.NeuralNetworkImageSize(640, 384)) # (height, width) # flexible_shape_utils.add_enumerated_image_sizes(spec, feature_name='image', sizes=s) # r = flexible_shape_utils.NeuralNetworkImageSizeRange() # shape ranges # r.add_height_range((192, 640)) # r.add_width_range((192, 640)) # flexible_shape_utils.update_image_size_range(spec, feature_name='image', size_range=r) # Print print(spec.description) # Model from spec model = ct.models.MLModel(spec) # 3. Create NMS protobuf nms_spec = ct.proto.Model_pb2.Model() nms_spec.specificationVersion = 5 for i in range(2): decoder_output = model._spec.description.output[i].SerializeToString() nms_spec.description.input.add() nms_spec.description.input[i].ParseFromString(decoder_output) nms_spec.description.output.add() nms_spec.description.output[i].ParseFromString(decoder_output) nms_spec.description.output[0].name = "confidence" nms_spec.description.output[1].name = "coordinates" output_sizes = [nc, 4] for i in range(2): ma_type = nms_spec.description.output[i].type.multiArrayType ma_type.shapeRange.sizeRanges.add() ma_type.shapeRange.sizeRanges[0].lowerBound = 0 ma_type.shapeRange.sizeRanges[0].upperBound = -1 ma_type.shapeRange.sizeRanges.add() ma_type.shapeRange.sizeRanges[1].lowerBound = output_sizes[i] ma_type.shapeRange.sizeRanges[1].upperBound = output_sizes[i] del ma_type.shape[:] nms = nms_spec.nonMaximumSuppression nms.confidenceInputFeatureName = out0.name # 1x507x80 nms.coordinatesInputFeatureName = out1.name # 1x507x4 nms.confidenceOutputFeatureName = "confidence" nms.coordinatesOutputFeatureName = "coordinates" nms.iouThresholdInputFeatureName = "iouThreshold" nms.confidenceThresholdInputFeatureName = "confidenceThreshold" nms.iouThreshold = 0.45 nms.confidenceThreshold = 0.25 nms.pickTop.perClass = True nms.stringClassLabels.vector.extend(names.values()) nms_model = ct.models.MLModel(nms_spec) # 4. Pipeline models together pipeline = ct.models.pipeline.Pipeline( input_features=[ ("image", ct.models.datatypes.Array(3, ny, nx)), ("iouThreshold", ct.models.datatypes.Double()), ("confidenceThreshold", ct.models.datatypes.Double()), ], output_features=["confidence", "coordinates"], ) pipeline.add_model(model) pipeline.add_model(nms_model) # Correct datatypes pipeline.spec.description.input[0].ParseFromString(model._spec.description.input[0].SerializeToString()) pipeline.spec.description.output[0].ParseFromString(nms_model._spec.description.output[0].SerializeToString()) pipeline.spec.description.output[1].ParseFromString(nms_model._spec.description.output[1].SerializeToString()) # Update metadata pipeline.spec.specificationVersion = 5 pipeline.spec.description.metadata.versionString = "https://github.com/ultralytics/yolov5" pipeline.spec.description.metadata.shortDescription = "https://github.com/ultralytics/yolov5" pipeline.spec.description.metadata.author = "glenn.jocher@ultralytics.com" pipeline.spec.description.metadata.license = "https://github.com/ultralytics/yolov5/blob/master/LICENSE" pipeline.spec.description.metadata.userDefined.update( { "classes": ",".join(names.values()), "iou_threshold": str(nms.iouThreshold), "confidence_threshold": str(nms.confidenceThreshold), } ) # Save the model f = file.with_suffix(".mlmodel") # filename model = ct.models.MLModel(pipeline.spec) model.input_description["image"] = "Input image" model.input_description["iouThreshold"] = f"(optional) IOU Threshold override (default: {nms.iouThreshold})" model.input_description["confidenceThreshold"] = ( f"(optional) Confidence Threshold override (default: {nms.confidenceThreshold})" ) model.output_description["confidence"] = 'Boxes × Class confidence (see user-defined metadata "classes")' model.output_description["coordinates"] = "Boxes × [x, y, width, height] (relative to image size)" model.save(f) # pipelined print(f"{prefix} pipeline success ({time.time() - t:.2f}s), saved as {f} ({file_size(f):.1f} MB)") @smart_inference_mode() def run( data=ROOT / "data/coco128.yaml", # 'dataset.yaml path' weights=ROOT / "yolov5s.pt", # weights path imgsz=(640, 640), # image (height, width) batch_size=1, # batch size device="cpu", # cuda device, i.e. 0 or 0,1,2,3 or cpu include=("torchscript", "onnx"), # include formats half=False, # FP16 half-precision export inplace=False, # set YOLOv3 Detect() inplace=True keras=False, # use Keras optimize=False, # TorchScript: optimize for mobile int8=False, # CoreML/TF INT8 quantization dynamic=False, # ONNX/TF/TensorRT: dynamic axes simplify=False, # ONNX: simplify model opset=12, # ONNX: opset version verbose=False, # TensorRT: verbose log workspace=4, # TensorRT: workspace size (GB) nms=False, # TF: add NMS to model agnostic_nms=False, # TF: add agnostic NMS to model topk_per_class=100, # TF.js NMS: topk per class to keep topk_all=100, # TF.js NMS: topk for all classes to keep iou_thres=0.45, # TF.js NMS: IoU threshold conf_thres=0.25, # TF.js NMS: confidence threshold ): """Export a PyTorch model to various formats like ONNX, CoreML, and TensorRT. Args: data (str | Path): Path to dataset configuration file. weights (str | Path): Path to model weights file in PyTorch format. imgsz (tuple[int, int]): Tuple specifying image height and width for input dimensions. batch_size (int): Batch size for model inference. device (str): Device to use for inference (e.g., '0', '0,1,2,3', 'cpu'). include (tuple[str]): Formats to include for model export (e.g., 'torchscript', 'onnx', etc.). half (bool): Whether to export model with FP16 precision. inplace (bool): Set YOLOv3 Detect module inplace option to True. keras (bool): Save Keras model when exporting TensorFlow SavedModel format. optimize (bool): Optimize the TorchScript model for mobile inference. int8 (bool): Apply INT8 quantization for CoreML/TF models. dynamic (bool): Enable dynamic axes for ONNX/TF/TensorRT models. simplify (bool): Simplify the ONNX model after export. opset (int): ONNX opset version. verbose (bool): Enable verbose logging for TensorRT engine export. workspace (int): Workspace size in GB for TensorRT engine. nms (bool): Enable Non-Maximum Suppression (NMS) in TensorFlow models. agnostic_nms (bool): Enable class-agnostic NMS in TensorFlow models. topk_per_class (int): Top-K per class to keep in TensorFlow JSON model. topk_all (int): Top-K for all classes to keep in TensorFlow JSON model. iou_thres (float): IOU threshold for TensorFlow JSON model. conf_thres (float): Confidence threshold for TensorFlow JSON model. Returns: None Examples: ```python run( data='data/coco128.yaml', weights='yolov5s.pt', imgsz=(640, 640), batch_size=1, device='cpu', include=('torchscript', 'onnx'), half=False, dynamic=True, opset=12 ) ``` Notes: - Requires various packages installed for different export formats, e.g., `onnx`, `coremltools`, etc. - Some formats have additional dependencies (e.g., TensorFlow, TensorRT, etc.) """ t = time.time() include = [x.lower() for x in include] # to lowercase fmts = tuple(export_formats()["Argument"][1:]) # --include arguments flags = [x in include for x in fmts] assert sum(flags) == len(include), f"ERROR: Invalid --include {include}, valid --include arguments are {fmts}" jit, onnx, xml, engine, coreml, saved_model, pb, tflite, edgetpu, tfjs, paddle = flags # export booleans file = Path(url2file(weights) if str(weights).startswith(("http:/", "https:/")) else weights) # PyTorch weights # Load PyTorch model device = select_device(device) if half: assert device.type != "cpu" or coreml, "--half only compatible with GPU export, i.e. use --device 0" assert not dynamic, "--half not compatible with --dynamic, i.e. use either --half or --dynamic but not both" model = attempt_load(weights, device=device, inplace=True, fuse=True) # load FP32 model # Checks imgsz *= 2 if len(imgsz) == 1 else 1 # expand if optimize: assert device.type == "cpu", "--optimize not compatible with cuda devices, i.e. use --device cpu" # Input gs = int(max(model.stride)) # grid size (max stride) imgsz = [check_img_size(x, gs) for x in imgsz] # verify img_size are gs-multiples im = torch.zeros(batch_size, 3, *imgsz).to(device) # image size(1,3,320,192) BCHW iDetection # Update model model.eval() for k, m in model.named_modules(): if isinstance(m, Detect): m.inplace = inplace m.dynamic = dynamic m.export = True for _ in range(2): y = model(im) # dry runs if half and not coreml: im, model = im.half(), model.half() # to FP16 shape = tuple((y[0] if isinstance(y, tuple) else y).shape) # model output shape metadata = {"stride": int(max(model.stride)), "names": model.names} # model metadata LOGGER.info(f"\n{colorstr('PyTorch:')} starting from {file} with output shape {shape} ({file_size(file):.1f} MB)") # Exports f = [""] * len(fmts) # exported filenames warnings.filterwarnings(action="ignore", category=torch.jit.TracerWarning) # suppress TracerWarning if jit: # TorchScript f[0], _ = export_torchscript(model, im, file, optimize) if engine: # TensorRT required before ONNX f[1], _ = export_engine(model, im, file, half, dynamic, simplify, workspace, verbose) if onnx or xml: # OpenVINO requires ONNX f[2], _ = export_onnx(model, im, file, opset, dynamic, simplify) if xml: # OpenVINO f[3], _ = export_openvino(file, metadata, half, int8, data) if coreml: # CoreML f[4], ct_model = export_coreml(model, im, file, int8, half, nms) if nms: pipeline_coreml(ct_model, im, file, model.names, y) if any((saved_model, pb, tflite, edgetpu, tfjs)): # TensorFlow formats assert not tflite or not tfjs, "TFLite and TF.js models must be exported separately, please pass only one type." assert not isinstance(model, ClassificationModel), "ClassificationModel export to TF formats not yet supported." f[5], s_model = export_saved_model( model.cpu(), im, file, dynamic, tf_nms=nms or agnostic_nms or tfjs, agnostic_nms=agnostic_nms or tfjs, topk_per_class=topk_per_class, topk_all=topk_all, iou_thres=iou_thres, conf_thres=conf_thres, keras=keras, ) if pb or tfjs: # pb prerequisite to tfjs f[6], _ = export_pb(s_model, file) if tflite or edgetpu: f[7], _ = export_tflite(s_model, im, file, int8 or edgetpu, data=data, nms=nms, agnostic_nms=agnostic_nms) if edgetpu: f[8], _ = export_edgetpu(file) add_tflite_metadata(f[8] or f[7], metadata, num_outputs=len(s_model.outputs)) if tfjs: f[9], _ = export_tfjs(file, int8) if paddle: # PaddlePaddle f[10], _ = export_paddle(model, im, file, metadata) # Finish f = [str(x) for x in f if x] # filter out '' and None if any(f): cls, det, seg = (isinstance(model, x) for x in (ClassificationModel, DetectionModel, SegmentationModel)) # type det &= not seg # segmentation models inherit from SegmentationModel(DetectionModel) dir = Path("segment" if seg else "classify" if cls else "") h = "--half" if half else "" # --half FP16 inference arg s = ( "# WARNING ⚠️ ClassificationModel not yet supported for PyTorch Hub AutoShape inference" if cls else "# WARNING ⚠️ SegmentationModel not yet supported for PyTorch Hub AutoShape inference" if seg else "" ) LOGGER.info( f"\nExport complete ({time.time() - t:.1f}s)" f"\nResults saved to {colorstr('bold', file.parent.resolve())}" f"\nDetect: python {dir / ('detect.py' if det else 'predict.py')} --weights {f[-1]} {h}" f"\nValidate: python {dir / 'val.py'} --weights {f[-1]} {h}" f"\nPyTorch Hub: model = torch.hub.load('ultralytics/yolov5', 'custom', '{f[-1]}') {s}" f"\nVisualize: https://netron.app" ) return f # return list of exported files/dirs def parse_opt(known=False): """Parse command-line arguments for model export configuration. Args: known (bool): If True, parse only known arguments and ignore others. Default is False. Returns: argparse.Namespace: Namespace object containing export configuration parameters. Examples: ```python from ultralytics.export import parse_opt options = parse_opt(known=True) print(options) ``` Notes: This function leverages `argparse` to handle command-line arguments for various model export configurations, allowing users to specify export formats, model parameters, and optimization settings. """ parser = argparse.ArgumentParser() parser.add_argument("--data", type=str, default=ROOT / "data/coco128.yaml", help="dataset.yaml path") parser.add_argument("--weights", nargs="+", type=str, default=ROOT / "yolov3-tiny.pt", help="model.pt path(s)") parser.add_argument("--imgsz", "--img", "--img-size", nargs="+", type=int, default=[640, 640], help="image (h, w)") parser.add_argument("--batch-size", type=int, default=1, help="batch size") parser.add_argument("--device", default="cpu", help="cuda device, i.e. 0 or 0,1,2,3 or cpu") parser.add_argument("--half", action="store_true", help="FP16 half-precision export") parser.add_argument("--inplace", action="store_true", help="set YOLOv3 Detect() inplace=True") parser.add_argument("--keras", action="store_true", help="TF: use Keras") parser.add_argument("--optimize", action="store_true", help="TorchScript: optimize for mobile") parser.add_argument("--int8", action="store_true", help="CoreML/TF/OpenVINO INT8 quantization") parser.add_argument("--dynamic", action="store_true", help="ONNX/TF/TensorRT: dynamic axes") parser.add_argument("--simplify", action="store_true", help="ONNX: simplify model") parser.add_argument("--opset", type=int, default=17, help="ONNX: opset version") parser.add_argument("--verbose", action="store_true", help="TensorRT: verbose log") parser.add_argument("--workspace", type=int, default=4, help="TensorRT: workspace size (GB)") parser.add_argument("--nms", action="store_true", help="TF: add NMS to model") parser.add_argument("--agnostic-nms", action="store_true", help="TF: add agnostic NMS to model") parser.add_argument("--topk-per-class", type=int, default=100, help="TF.js NMS: topk per class to keep") parser.add_argument("--topk-all", type=int, default=100, help="TF.js NMS: topk for all classes to keep") parser.add_argument("--iou-thres", type=float, default=0.45, help="TF.js NMS: IoU threshold") parser.add_argument("--conf-thres", type=float, default=0.25, help="TF.js NMS: confidence threshold") parser.add_argument( "--include", nargs="+", default=["torchscript"], help="torchscript, onnx, openvino, engine, coreml, saved_model, pb, tflite, edgetpu, tfjs, paddle", ) opt = parser.parse_known_args()[0] if known else parser.parse_args() print_args(vars(opt)) return opt def main(opt): """Run(**vars(opt)).""" for opt.weights in opt.weights if isinstance(opt.weights, list) else [opt.weights]: run(**vars(opt)) if __name__ == "__main__": opt = parse_opt() main(opt) ================================================ FILE: hubconf.py ================================================ # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license """ PyTorch Hub models https://pytorch.org/hub/ultralytics_yolov5. Usage: import torch model = torch.hub.load('ultralytics/yolov5', 'yolov5s') # official model model = torch.hub.load('ultralytics/yolov5:master', 'yolov5s') # from branch model = torch.hub.load('ultralytics/yolov5', 'custom', 'yolov5s.pt') # custom/local model model = torch.hub.load('.', 'custom', 'yolov5s.pt', source='local') # local repo """ from ultralytics.utils.patches import torch_load def _create(name, pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None): """Creates or loads a YOLOv3 model with specified configurations and optional pretrained weights. Args: name (str): Model name such as 'yolov5s' or a path to a model checkpoint file, e.g., 'path/to/best.pt'. pretrained (bool): Whether to load pretrained weights into the model. Default is True. channels (int): Number of input channels. Default is 3. classes (int): Number of model classes. Default is 80. autoshape (bool): Whether to apply the YOLOv3 .autoshape() wrapper to the model for handling multiple input types. Default is True. verbose (bool): If True, print all information to the screen. Default is True. device (str | torch.device | None): Device to use for model parameters ('cpu', 'cuda', etc.). If None, defaults to the best available device. Returns: torch.nn.Module: YOLOv3 model loaded with or without pretrained weights. Raises: Exception: If an error occurs while loading the model, returns an error message with a helpful URL: "https: //docs.ultralytics.com/yolov5/tutorials/pytorch_hub_model_loading". Examples: ```python import torch model = _create('yolov5s') ``` """ from pathlib import Path from models.common import AutoShape, DetectMultiBackend from models.experimental import attempt_load from models.yolo import ClassificationModel, DetectionModel, SegmentationModel from utils.downloads import attempt_download from utils.general import LOGGER, ROOT, check_requirements, intersect_dicts, logging from utils.torch_utils import select_device if not verbose: LOGGER.setLevel(logging.WARNING) check_requirements(ROOT / "requirements.txt", exclude=("opencv-python", "tensorboard", "thop")) name = Path(name) path = name.with_suffix(".pt") if name.suffix == "" and not name.is_dir() else name # checkpoint path try: device = select_device(device) if pretrained and channels == 3 and classes == 80: try: model = DetectMultiBackend(path, device=device, fuse=autoshape) # detection model if autoshape: if model.pt and isinstance(model.model, ClassificationModel): LOGGER.warning( "WARNING ⚠️ YOLOv3 ClassificationModel is not yet AutoShape compatible. " "You must pass torch tensors in BCHW to this model, i.e. shape(1,3,224,224)." ) elif model.pt and isinstance(model.model, SegmentationModel): LOGGER.warning( "WARNING ⚠️ YOLOv3 SegmentationModel is not yet AutoShape compatible. " "You will not be able to run inference with this model." ) else: model = AutoShape(model) # for file/URI/PIL/cv2/np inputs and NMS except Exception: model = attempt_load(path, device=device, fuse=False) # arbitrary model else: cfg = next(iter((Path(__file__).parent / "models").rglob(f"{path.stem}.yaml"))) # model.yaml path model = DetectionModel(cfg, channels, classes) # create model if pretrained: ckpt = torch_load(attempt_download(path), map_location=device) # load csd = ckpt["model"].float().state_dict() # checkpoint state_dict as FP32 csd = intersect_dicts(csd, model.state_dict(), exclude=["anchors"]) # intersect model.load_state_dict(csd, strict=False) # load if len(ckpt["model"].names) == classes: model.names = ckpt["model"].names # set class names attribute if not verbose: LOGGER.setLevel(logging.INFO) # reset to default return model.to(device) except Exception as e: help_url = "https://docs.ultralytics.com/yolov5/tutorials/pytorch_hub_model_loading" s = f"{e}. Cache may be out of date, try `force_reload=True` or see {help_url} for help." raise Exception(s) from e def custom(path="path/to/model.pt", autoshape=True, _verbose=True, device=None): """Loads a custom or local YOLOv3 model from a specified path, with options for autoshaping and device assignment. Args: path (str): Path to the model file. Supports both local and URL paths. autoshape (bool): If True, applies the YOLOv3 `.autoshape()` wrapper to allow for various input formats. Default is True. _verbose (bool): If True, outputs detailed information. Otherwise, limits verbosity. Default is True. device (str | torch.device | None): Device to load the model on. Default is None, which uses the available GPU if possible. Returns: (torch.nn.Module): The loaded YOLOv3 model, either with or without autoshaping applied. Raises: Exception: If the model loading fails due to invalid path or incompatible model state, with helpful suggestions including a reference to the troubleshooting page: https://docs.ultralytics.com/yolov5/tutorials/pytorch_hub_model_loading Examples: ```python import torch model = torch.hub.load('ultralytics/yolov5', 'custom', 'path/to/best.pt') model = torch.hub.load('ultralytics/yolov5', 'custom', 'path/to/best.pt', autoshape=False, device='cpu') ``` """ return _create(path, autoshape=autoshape, verbose=_verbose, device=device) def yolov5n(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None): """Instantiates a YOLOv5n model with optional pretrained weights, configurable input channels, number of classes, autoshaping, and device selection. Args: pretrained (bool): If True, loads pretrained weights into the model. Defaults to True. channels (int): Number of input channels. Defaults to 3. classes (int): Number of detection classes. Defaults to 80. autoshape (bool): If True, applies YOLOv5 .autoshape() wrapper to the model for various input formats like file/URI/PIL/cv2/np and adds non-maximum suppression (NMS). Defaults to True. _verbose (bool): If True, prints detailed information to the screen. Defaults to True. device (str | torch.device | None): Device to use for model computations (e.g., 'cpu', 'cuda'). If None, the best available device is automatically selected. Defaults to None. Returns: torch.nn.Module: The instantiated YOLOv5n model. Examples: ```python import torch model = torch.hub.load('ultralytics/yolov5', 'yolov5n') # using official model model = torch.hub.load('ultralytics/yolov5:master', 'yolov5n') # from specific branch model = torch.hub.load('ultralytics/yolov5', 'custom', 'yolov5n.pt') # using custom/local model model = torch.hub.load('.', 'custom', 'yolov5n.pt', source='local') # from local repository ``` Notes: PyTorch Hub models can be explored at https://pytorch.org/hub/ultralytics_yolov5. This allows easy model loading and usage. """ return _create("yolov5n", pretrained, channels, classes, autoshape, _verbose, device) def yolov5s(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None): """Load the YOLOv5s model with customizable options for pretrained weights, input channels, number of classes, autoshape functionality, and device selection. Args: pretrained (bool, optional): If True, loads model with pretrained weights. Default is True. channels (int, optional): Specifies the number of input channels. Default is 3. classes (int, optional): Defines the number of model classes. Default is 80. autoshape (bool, optional): Applies YOLOv5 .autoshape() wrapper to the model for enhanced usability. Default is True. _verbose (bool, optional): If True, prints detailed information during model loading. Default is True. device (str | torch.device | None, optional): Specifies the device to load the model on. Accepts 'cpu', 'cuda', or torch.device. Default is None, which automatically selects the best available option. Returns: torch.nn.Module: The initialized YOLOv5s model loaded with the specified options. Examples: ```python import torch model = torch.hub.load('ultralytics/yolov5', 'yolov5s', pretrained=True) ``` For more information, refer to [PyTorch Hub models](https://pytorch.org/hub/ultralytics_yolov5/). """ return _create("yolov5s", pretrained, channels, classes, autoshape, _verbose, device) def yolov5m(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None): """Loads the YOLOv5m model with options for pretrained weights, input channels, number of classes, autoshape functionality, and device selection. Args: pretrained (bool, optional): If True, loads pretrained weights into the model. Default is True. channels (int, optional): Number of input channels for the model. Default is 3. classes (int, optional): Number of model classes. Default is 80. autoshape (bool, optional): If True, applies the YOLOv5 .autoshape() wrapper for handling multiple input types and NMS. Default is True. _verbose (bool, optional): If True, prints detailed information during model loading. Default is True. device (str | torch.device | None, optional): Device for model computations (e.g., 'cpu', 'cuda'). Automatically selects the best available device if None. Default is None. Returns: torch.nn.Module: The instantiated YOLOv5m model. Examples: ```python import torch model = torch.hub.load('ultralytics/yolov5', 'yolov5m', pretrained=True) ``` """ return _create("yolov5m", pretrained, channels, classes, autoshape, _verbose, device) def yolov5l(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None): """Load the YOLOv5l model with customizable options for pretrained weights, input channels, number of classes, autoshape functionality, and device selection. Args: pretrained (bool, optional): If True, load model with pretrained weights. Default is True. channels (int, optional): Specifies the number of input channels. Default is 3. classes (int, optional): Defines the number of model classes. Default is 80. autoshape (bool, optional): Applies the YOLOv5 .autoshape() wrapper to the model for enhanced usability. Default is True. _verbose (bool, optional): If True, prints detailed information during model loading. Default is True. device (str | torch.device | None, optional): Specifies the device to load the model on. Accepts 'cpu', 'cuda', or torch.device. Default is None, which automatically selects the best available option. Returns: torch.nn.Module: The initialized YOLOv5l model loaded with the specified options. Examples: ```python import torch model = torch.hub.load('ultralytics/yolov5', 'yolov5l', pretrained=True) ``` For more information, refer to [PyTorch Hub models](https://pytorch.org/hub/ultralytics_yolov5/). """ return _create("yolov5l", pretrained, channels, classes, autoshape, _verbose, device) def yolov5x(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None): """Load the YOLOv5x model with options for pretrained weights, number of input channels, classes, autoshaping, and device selection. Args: pretrained (bool, optional): If True, loads the model with pretrained weights. Defaults to True. channels (int, optional): Number of input channels. Defaults to 3. classes (int, optional): Number of detection classes. Defaults to 80. autoshape (bool, optional): If True, applies the YOLOv5 .autoshape() wrapper, enabling various input formats and non-maximum suppression (NMS). Defaults to True. _verbose (bool, optional): If True, prints detailed information during model loading. Defaults to True. device (str | torch.device | None, optional): Device to use for model parameters (e.g., 'cpu', 'cuda'). Defaults to None, selecting the best available device automatically. Returns: torch.nn.Module: The YOLOv5x model loaded with the specified configuration. Examples: ```python import torch # Load YOLOv5x model with default settings model = torch.hub.load('ultralytics/yolov5', 'yolov5x') # Load YOLOv5x model with custom device model = torch.hub.load('ultralytics/yolov5', 'yolov5x', device='cuda:0') ``` For more details, refer to [PyTorch Hub models](https://pytorch.org/hub/ultralytics_yolov5/). """ return _create("yolov5x", pretrained, channels, classes, autoshape, _verbose, device) def yolov5n6(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None): """Loads the YOLOv5n6 model with options for pretrained weights, input channels, classes, autoshaping, verbosity, and device assignment. Args: pretrained (bool, optional): If True, loads pretrained weights into the model. Default is True. channels (int, optional): Number of input channels. Default is 3. classes (int, optional): Number of model classes. Default is 80. autoshape (bool, optional): If True, applies the YOLOv3 .autoshape() wrapper to the model. Default is True. _verbose (bool, optional): If True, prints all information to the screen. Default is True. device (str | torch.device | None, optional): Device to use for model parameters, e.g., 'cpu', '0', or torch.device. Default is None. Returns: torch.nn.Module: YOLOv5n6 model loaded on the specified device and configured as per the provided options. Examples: ```python model = yolov5n6(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device='cuda') ``` Notes: For more information on PyTorch Hub models, refer to: https://pytorch.org/hub/ultralytics_yolov5 """ return _create("yolov5n6", pretrained, channels, classes, autoshape, _verbose, device) def yolov5s6(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None): """Loads the YOLOv5s6 model with options for weights, channels, classes, autoshaping, and device selection. Args: pretrained (bool, optional): If True, loads pretrained weights into the model. Defaults to True. channels (int, optional): Number of input channels. Defaults to 3. classes (int, optional): Number of model classes. Defaults to 80. autoshape (bool, optional): Apply YOLOv5 .autoshape() wrapper to the model. Defaults to True. _verbose (bool, optional): If True, prints detailed information to the screen. Defaults to True. device (str | torch.device | None, optional): Device to use for model parameters, e.g., 'cpu', 'cuda:0'. If None, it will select the appropriate device automatically. Defaults to None. Returns: torch.nn.Module: The YOLOv5s6 model, ready for inference or further training. Examples: ```python import torch model = torch.hub.load('ultralytics/yolov5', 'yolov5s6', pretrained=True, channels=3, classes=80) model.eval() # Set the model to evaluation mode ``` For more details, see the official documentation at: https://github.com/ultralytics/yolov5 """ return _create("yolov5s6", pretrained, channels, classes, autoshape, _verbose, device) def yolov5m6(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None): """Loads YOLOv5m6 model with options for pretrained weights, input channels, number of classes, autoshaping, and device selection. Args: pretrained (bool): Whether to load pretrained weights into the model. Default is True. channels (int): Number of input channels. Default is 3. classes (int): Number of model classes. Default is 80. autoshape (bool): Whether to apply YOLOv5 .autoshape() wrapper to the model. Default is True. _verbose (bool): Whether to print all information to the screen. Default is True. device (str | torch.device | None): Device to use for model parameters, e.g., 'cpu', 'cuda', 'mps', or torch device. Default is None. Returns: YOLOv5m6 model (torch.nn.Module): The instantiated YOLOv5m6 model with specified options. Examples: ```python import torch # Load YOLOv5m6 model with default settings model = torch.hub.load('ultralytics/yolov5', 'yolov5m6') # Load custom YOLOv5m6 model from a local path with specific options model = torch.hub.load('.', 'yolov5m6', pretrained=False, channels=1, classes=10, device='cuda') ``` Notes: For more detailed documentation, visit https://github.com/ultralytics/yolov5 """ return _create("yolov5m6", pretrained, channels, classes, autoshape, _verbose, device) def yolov5l6(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None): """Loads the YOLOv5l6 model with options for pretrained weights, input channels, the number of classes, autoshaping, and device selection. Args: pretrained (bool, optional): If True, loads pretrained weights into the model. Default is True. channels (int, optional): Number of input channels. Default is 3. classes (int, optional): Number of model classes. Default is 80. autoshape (bool, optional): If True, applies the YOLOv5 .autoshape() wrapper to the model for automatic shape inference. Default is True. _verbose (bool, optional): If True, prints all information to the screen. Default is True. device (str | torch.device | None, optional): Device to use for the model parameters, e.g., 'cpu', 'cuda', or a specific GPU like 'cuda:0'. Default is None, which means the best available device will be selected automatically. Returns: yolov5.models.yolo.DetectionModel: YOLOv5l6 model initialized with defined custom configurations. Examples: ```python import torch model = torch.hub.load('ultralytics/yolov5', 'yolov5l6') # Load YOLOv5l6 model ``` Notes: For more details, visit the [Ultralytics YOLOv5 GitHub repository](https://github.com/ultralytics/yolov5). """ return _create("yolov5l6", pretrained, channels, classes, autoshape, _verbose, device) def yolov5x6(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None): """Loads the YOLOv5x6 model, allowing customization for pretrained weights, input channels, and model classes. Args: pretrained (bool): If True, loads the model with pretrained weights. Default is True. channels (int): Number of input channels. Default is 3. classes (int): Number of output classes for the model. Default is 80. autoshape (bool): If True, applies the .autoshape() wrapper for inference on diverse input formats. Default is True. _verbose (bool): If True, prints detailed information during model loading. Default is True. device (str | torch.device | None): Specifies the device to load the model on ('cpu', 'cuda', etc.). Default is None, which uses the best available device. Returns: torch.nn.Module: The YOLOv5x6 model with the specified configurations. Examples: ```python from ultralytics import yolov5x6 # Load the model with default settings model = yolov5x6() # Load the model with custom configurations model = yolov5x6(pretrained=False, channels=1, classes=10, autoshape=False, device='cuda') ``` Notes: For more information, refer to the YOLOv5 repository: https://github.com/ultralytics/yolov5 """ return _create("yolov5x6", pretrained, channels, classes, autoshape, _verbose, device) if __name__ == "__main__": import argparse from pathlib import Path import numpy as np from PIL import Image from utils.general import cv2, print_args # Argparser parser = argparse.ArgumentParser() parser.add_argument("--model", type=str, default="yolov5s", help="model name") opt = parser.parse_args() print_args(vars(opt)) # Model model = _create(name=opt.model, pretrained=True, channels=3, classes=80, autoshape=True, verbose=True) # model = custom(path='path/to/model.pt') # custom # Images imgs = [ "data/images/zidane.jpg", # filename Path("data/images/zidane.jpg"), # Path "https://ultralytics.com/images/zidane.jpg", # URI cv2.imread("data/images/bus.jpg")[:, :, ::-1], # OpenCV Image.open("data/images/bus.jpg"), # PIL np.zeros((320, 640, 3)), ] # numpy # Inference results = model(imgs, size=320) # batched inference # Results results.print() results.save() ================================================ FILE: models/__init__.py ================================================ # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license ================================================ FILE: models/common.py ================================================ # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license """Common modules.""" import ast import contextlib import json import math import platform import warnings import zipfile from collections import OrderedDict, namedtuple from copy import copy from pathlib import Path from urllib.parse import urlparse import cv2 import numpy as np import pandas as pd import requests import torch import torch.nn as nn from PIL import Image from torch.cuda import amp from ultralytics.utils.plotting import Annotator, colors, save_one_box from utils import TryExcept from utils.dataloaders import exif_transpose, letterbox from utils.general import ( LOGGER, ROOT, Profile, check_requirements, check_suffix, check_version, colorstr, increment_path, is_jupyter, make_divisible, non_max_suppression, scale_boxes, xywh2xyxy, xyxy2xywh, yaml_load, ) from utils.torch_utils import copy_attr, smart_inference_mode def autopad(k, p=None, d=1): # kernel, padding, dilation """Automatically calculates same shape padding for convolutional layers, optionally adjusts for dilation.""" if d > 1: k = d * (k - 1) + 1 if isinstance(k, int) else [d * (x - 1) + 1 for x in k] # actual kernel-size if p is None: p = k // 2 if isinstance(k, int) else [x // 2 for x in k] # auto-pad return p class Conv(nn.Module): """A standard Conv2D layer with batch normalization and optional activation for neural networks.""" default_act = nn.SiLU() # default activation def __init__(self, c1, c2, k=1, s=1, p=None, g=1, d=1, act=True): """Initializes a standard Conv2D layer with batch normalization and optional activation; args are channel_in, channel_out, kernel_size, stride, padding, groups, dilation, and activation. """ super().__init__() self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p, d), groups=g, dilation=d, bias=False) self.bn = nn.BatchNorm2d(c2) self.act = self.default_act if act is True else act if isinstance(act, nn.Module) else nn.Identity() def forward(self, x): """Applies convolution, batch normalization, and activation to input `x`; `x` shape: [N, C_in, H, W] -> [N, C_out, H_out, W_out]. """ return self.act(self.bn(self.conv(x))) def forward_fuse(self, x): """Applies fused convolution and activation to input `x`; input shape: [N, C_in, H, W] -> [N, C_out, H_out, W_out]. """ return self.act(self.conv(x)) class DWConv(Conv): """Implements depth-wise convolution for efficient spatial feature extraction in neural networks.""" def __init__(self, c1, c2, k=1, s=1, d=1, act=True): # ch_in, ch_out, kernel, stride, dilation, activation """Initializes depth-wise convolution with optional activation; parameters are channel in/out, kernel, stride, dilation. """ super().__init__(c1, c2, k, s, g=math.gcd(c1, c2), d=d, act=act) class DWConvTranspose2d(nn.ConvTranspose2d): """Implements a depth-wise transpose convolution layer with specified channels, kernel size, stride, and padding.""" def __init__(self, c1, c2, k=1, s=1, p1=0, p2=0): # ch_in, ch_out, kernel, stride, padding, padding_out """Initializes a depth-wise or transpose convolution layer with specified in/out channels, kernel size, stride, and padding. """ super().__init__(c1, c2, k, s, p1, p2, groups=math.gcd(c1, c2)) class TransformerLayer(nn.Module): """Transformer layer with multi-head attention and feed-forward network, optimized by removing LayerNorm.""" def __init__(self, c, num_heads): """Initializes a Transformer layer as per https://arxiv.org/abs/2010.11929, sans LayerNorm, with specified embedding dimension and number of heads. """ super().__init__() self.q = nn.Linear(c, c, bias=False) self.k = nn.Linear(c, c, bias=False) self.v = nn.Linear(c, c, bias=False) self.ma = nn.MultiheadAttention(embed_dim=c, num_heads=num_heads) self.fc1 = nn.Linear(c, c, bias=False) self.fc2 = nn.Linear(c, c, bias=False) def forward(self, x): """Performs forward pass with multi-head attention and residual connections on input tensor 'x' [batch, seq_len, features]. """ x = self.ma(self.q(x), self.k(x), self.v(x))[0] + x x = self.fc2(self.fc1(x)) + x return x class TransformerBlock(nn.Module): """Implements a Vision Transformer block with transformer layers; https://arxiv.org/abs/2010.11929.""" def __init__(self, c1, c2, num_heads, num_layers): """Initializes a Transformer block with optional convolution, linear, and transformer layers.""" super().__init__() self.conv = None if c1 != c2: self.conv = Conv(c1, c2) self.linear = nn.Linear(c2, c2) # learnable position embedding self.tr = nn.Sequential(*(TransformerLayer(c2, num_heads) for _ in range(num_layers))) self.c2 = c2 def forward(self, x): """Applies an optional convolution, transforms features, and reshapes output matching input dimensions.""" if self.conv is not None: x = self.conv(x) b, _, w, h = x.shape p = x.flatten(2).permute(2, 0, 1) return self.tr(p + self.linear(p)).permute(1, 2, 0).reshape(b, self.c2, w, h) class Bottleneck(nn.Module): """Implements a bottleneck layer with optional shortcut for efficient feature extraction in neural networks.""" def __init__(self, c1, c2, shortcut=True, g=1, e=0.5): # ch_in, ch_out, shortcut, groups, expansion """Initializes a standard bottleneck layer with optional shortcut; args: input channels (c1), output channels (c2), shortcut (bool), groups (g), expansion factor (e). """ super().__init__() c_ = int(c2 * e) # hidden channels self.cv1 = Conv(c1, c_, 1, 1) self.cv2 = Conv(c_, c2, 3, 1, g=g) self.add = shortcut and c1 == c2 def forward(self, x): """Executes forward pass, performing convolutional ops and optional shortcut addition; expects input tensor x. """ return x + self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x)) class BottleneckCSP(nn.Module): """Implements a CSP Bottleneck layer for feature extraction.""" def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion """Initializes CSP Bottleneck with channel in/out, optional shortcut, groups, expansion; see https://github.com/WongKinYiu/CrossStagePartialNetworks. """ super().__init__() c_ = int(c2 * e) # hidden channels self.cv1 = Conv(c1, c_, 1, 1) self.cv2 = nn.Conv2d(c1, c_, 1, 1, bias=False) self.cv3 = nn.Conv2d(c_, c_, 1, 1, bias=False) self.cv4 = Conv(2 * c_, c2, 1, 1) self.bn = nn.BatchNorm2d(2 * c_) # applied to cat(cv2, cv3) self.act = nn.SiLU() self.m = nn.Sequential(*(Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n))) def forward(self, x): """Processes input through layers, combining outputs with activation and normalization for feature extraction. """ y1 = self.cv3(self.m(self.cv1(x))) y2 = self.cv2(x) return self.cv4(self.act(self.bn(torch.cat((y1, y2), 1)))) class CrossConv(nn.Module): """Implements Cross Convolution Downsample with 1D and 2D convolutions and optional shortcut.""" def __init__(self, c1, c2, k=3, s=1, g=1, e=1.0, shortcut=False): """Initializes CrossConv with downsample options, combining 1D and 2D convolutions, optional shortcut if input/output channels match. """ super().__init__() c_ = int(c2 * e) # hidden channels self.cv1 = Conv(c1, c_, (1, k), (1, s)) self.cv2 = Conv(c_, c2, (k, 1), (s, 1), g=g) self.add = shortcut and c1 == c2 def forward(self, x): """Performs forward pass using sequential 1D and 2D convolutions with optional shortcut addition.""" return x + self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x)) class C3(nn.Module): """Implements a CSP Bottleneck with 3 convolutions, optional shortcuts, group convolutions, and expansion factor.""" def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion """Initializes CSP Bottleneck with 3 convolutions, optional shortcuts, group convolutions, and expansion factor. """ super().__init__() c_ = int(c2 * e) # hidden channels self.cv1 = Conv(c1, c_, 1, 1) self.cv2 = Conv(c1, c_, 1, 1) self.cv3 = Conv(2 * c_, c2, 1) # optional act=FReLU(c2) self.m = nn.Sequential(*(Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n))) def forward(self, x): """Processes input tensor `x` through convolutions and bottlenecks, returning the concatenated output tensor.""" return self.cv3(torch.cat((self.m(self.cv1(x)), self.cv2(x)), 1)) class C3x(C3): """Extends the C3 module with cross-convolutions for enhanced feature extraction and flexibility.""" def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): """Initializes a C3x module with cross-convolutions, extending the C3 module with customizable parameters.""" super().__init__(c1, c2, n, shortcut, g, e) c_ = int(c2 * e) self.m = nn.Sequential(*(CrossConv(c_, c_, 3, 1, g, 1.0, shortcut) for _ in range(n))) class C3TR(C3): """C3 module with TransformerBlock for integrating attention mechanisms in CNNs.""" def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): """Initializes a C3 module with TransformerBlock, extending C3 for attention mechanisms.""" super().__init__(c1, c2, n, shortcut, g, e) c_ = int(c2 * e) self.m = TransformerBlock(c_, c_, 4, n) class C3SPP(C3): """Extends C3 with Spatial Pyramid Pooling (SPP) for enhanced feature extraction in CNNs.""" def __init__(self, c1, c2, k=(5, 9, 13), n=1, shortcut=True, g=1, e=0.5): """Initializes C3SPP module, extending C3 with Spatial Pyramid Pooling for enhanced feature extraction.""" super().__init__(c1, c2, n, shortcut, g, e) c_ = int(c2 * e) self.m = SPP(c_, c_, k) class C3Ghost(C3): """Implements a C3 module with Ghost Bottlenecks for efficient feature extraction in neural networks.""" def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): """Initializes C3Ghost module with Ghost Bottlenecks for efficient feature extraction.""" super().__init__(c1, c2, n, shortcut, g, e) c_ = int(c2 * e) # hidden channels self.m = nn.Sequential(*(GhostBottleneck(c_, c_) for _ in range(n))) class SPP(nn.Module): """Implements Spatial Pyramid Pooling (SPP) for enhanced feature extraction; see https://arxiv.org/abs/1406.4729.""" def __init__(self, c1, c2, k=(5, 9, 13)): """Initializes SPP layer with specified channels and kernels. More at https://arxiv.org/abs/1406.4729 """ super().__init__() c_ = c1 // 2 # hidden channels self.cv1 = Conv(c1, c_, 1, 1) self.cv2 = Conv(c_ * (len(k) + 1), c2, 1, 1) self.m = nn.ModuleList([nn.MaxPool2d(kernel_size=x, stride=1, padding=x // 2) for x in k]) def forward(self, x): """Applies convolution and max pooling layers to the input tensor `x`, concatenates results for feature extraction. `x` is a tensor of shape [N, C, H, W]. See https://arxiv.org/abs/1406.4729 for more details. """ x = self.cv1(x) with warnings.catch_warnings(): warnings.simplefilter("ignore") # suppress torch 1.9.0 max_pool2d() warning return self.cv2(torch.cat([x] + [m(x) for m in self.m], 1)) class SPPF(nn.Module): """Implements a fast Spatial Pyramid Pooling (SPPF) layer for efficient feature extraction in YOLOv3 models.""" def __init__(self, c1, c2, k=5): # equivalent to SPP(k=(5, 9, 13)) """Initializes the SPPF layer with specified input/output channels and kernel size for YOLOv3.""" super().__init__() c_ = c1 // 2 # hidden channels self.cv1 = Conv(c1, c_, 1, 1) self.cv2 = Conv(c_ * 4, c2, 1, 1) self.m = nn.MaxPool2d(kernel_size=k, stride=1, padding=k // 2) def forward(self, x): """Performs forward pass combining convolutions and max pooling on input `x` of shape [N, C, H, W] to produce feature map. """ x = self.cv1(x) with warnings.catch_warnings(): warnings.simplefilter("ignore") # suppress torch 1.9.0 max_pool2d() warning y1 = self.m(x) y2 = self.m(y1) return self.cv2(torch.cat((x, y1, y2, self.m(y2)), 1)) class Focus(nn.Module): """Focuses spatial information into channel space using configurable convolution.""" def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True): # ch_in, ch_out, kernel, stride, padding, groups """Initializes Focus module to focus width and height information into channel space with configurable convolution parameters. """ super().__init__() self.conv = Conv(c1 * 4, c2, k, s, p, g, act=act) # self.contract = Contract(gain=2) def forward(self, x): # x(b,c,w,h) -> y(b,4c,w/2,h/2) """Applies focused downsampling to input tensor, returning a convolved output with increased channel depth.""" return self.conv(torch.cat((x[..., ::2, ::2], x[..., 1::2, ::2], x[..., ::2, 1::2], x[..., 1::2, 1::2]), 1)) # return self.conv(self.contract(x)) class GhostConv(nn.Module): """Implements Ghost Convolution for efficient feature extraction; see github.com/huawei-noah/ghostnet.""" def __init__(self, c1, c2, k=1, s=1, g=1, act=True): # ch_in, ch_out, kernel, stride, groups """Initializes GhostConv with in/out channels, kernel size, stride, groups; see https://github.com/huawei-noah/ghostnet. """ super().__init__() c_ = c2 // 2 # hidden channels self.cv1 = Conv(c1, c_, k, s, None, g, act=act) self.cv2 = Conv(c_, c_, 5, 1, None, c_, act=act) def forward(self, x): """Executes forward pass, applying convolutions and concatenating results; input `x` is a tensor.""" y = self.cv1(x) return torch.cat((y, self.cv2(y)), 1) class GhostBottleneck(nn.Module): """Implements a Ghost Bottleneck layer for efficient feature extraction from GhostNet.""" def __init__(self, c1, c2, k=3, s=1): # ch_in, ch_out, kernel, stride """Initializes GhostBottleneck module with in/out channels, kernel size, and stride; see https://github.com/huawei-noah/ghostnet. """ super().__init__() c_ = c2 // 2 self.conv = nn.Sequential( GhostConv(c1, c_, 1, 1), # pw DWConv(c_, c_, k, s, act=False) if s == 2 else nn.Identity(), # dw GhostConv(c_, c2, 1, 1, act=False), ) # pw-linear self.shortcut = ( nn.Sequential(DWConv(c1, c1, k, s, act=False), Conv(c1, c2, 1, 1, act=False)) if s == 2 else nn.Identity() ) def forward(self, x): """Performs a forward pass through the network, returning the sum of convolution and shortcut outputs.""" return self.conv(x) + self.shortcut(x) class Contract(nn.Module): """Contracts spatial dimensions into channels, e.g., (1,64,80,80) to (1,256,40,40) with a specified gain.""" def __init__(self, gain=2): """Initializes Contract module to refine input dimensions, e.g., from (1,64,80,80) to (1,256,40,40) with a default gain of 2. """ super().__init__() self.gain = gain def forward(self, x): """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., (1,64,80,80) to (1,256,40,40). """ b, c, h, w = x.size() # assert (h / s == 0) and (W / s == 0), 'Indivisible gain' s = self.gain x = x.view(b, c, h // s, s, w // s, s) # x(1,64,40,2,40,2) x = x.permute(0, 3, 5, 1, 2, 4).contiguous() # x(1,2,2,64,40,40) return x.view(b, c * s * s, h // s, w // s) # x(1,256,40,40) class Expand(nn.Module): """Expands spatial dimensions of input tensor by a factor while reducing channels correspondingly.""" def __init__(self, gain=2): """Initializes Expand module to increase spatial dimensions by factor `gain` while reducing channels correspondingly. """ super().__init__() self.gain = gain def forward(self, x): """Expands spatial dimensions of input tensor `x` by factor `gain` while reducing channels, transforming shape `(B,C,H,W)` to `(B,C/gain^2,H*gain,W*gain)`. """ b, c, h, w = x.size() # assert C / s ** 2 == 0, 'Indivisible gain' s = self.gain x = x.view(b, s, s, c // s**2, h, w) # x(1,2,2,16,80,80) x = x.permute(0, 3, 4, 1, 5, 2).contiguous() # x(1,16,80,2,80,2) return x.view(b, c // s**2, h * s, w * s) # x(1,16,160,160) class Concat(nn.Module): """Concatenates a list of tensors along a specified dimension for efficient feature aggregation.""" def __init__(self, dimension=1): """Initializes a module to concatenate tensors along a specified dimension.""" super().__init__() self.d = dimension def forward(self, x): """Concatenates a list of tensors along a specified dimension; x is a list of tensors to concatenate, dimension defaults to 1. """ return torch.cat(x, self.d) class DetectMultiBackend(nn.Module): """YOLOv3 multi-backend class for inference on frameworks like PyTorch, ONNX, TensorRT, and more.""" def __init__(self, weights="yolov5s.pt", device=torch.device("cpu"), dnn=False, data=None, fp16=False, fuse=True): """Initializes multi-backend detection with options for various frameworks and devices, also handles model download. """ # PyTorch: weights = *.pt # TorchScript: *.torchscript # ONNX Runtime: *.onnx # ONNX OpenCV DNN: *.onnx --dnn # OpenVINO: *_openvino_model # CoreML: *.mlmodel # TensorRT: *.engine # TensorFlow SavedModel: *_saved_model # TensorFlow GraphDef: *.pb # TensorFlow Lite: *.tflite # TensorFlow Edge TPU: *_edgetpu.tflite # PaddlePaddle: *_paddle_model from models.experimental import attempt_download, attempt_load # scoped to avoid circular import super().__init__() w = str(weights[0] if isinstance(weights, list) else weights) pt, jit, onnx, xml, engine, coreml, saved_model, pb, tflite, edgetpu, tfjs, paddle, triton = self._model_type(w) fp16 &= pt or jit or onnx or engine or triton # FP16 nhwc = coreml or saved_model or pb or tflite or edgetpu # BHWC formats (vs torch BCWH) stride = 32 # default stride cuda = torch.cuda.is_available() and device.type != "cpu" # use CUDA if not (pt or triton): w = attempt_download(w) # download if not local if pt: # PyTorch model = attempt_load(weights if isinstance(weights, list) else w, device=device, inplace=True, fuse=fuse) stride = max(int(model.stride.max()), 32) # model stride names = model.module.names if hasattr(model, "module") else model.names # get class names model.half() if fp16 else model.float() self.model = model # explicitly assign for to(), cpu(), cuda(), half() elif jit: # TorchScript LOGGER.info(f"Loading {w} for TorchScript inference...") extra_files = {"config.txt": ""} # model metadata model = torch.jit.load(w, _extra_files=extra_files, map_location=device) model.half() if fp16 else model.float() if extra_files["config.txt"]: # load metadata dict d = json.loads( extra_files["config.txt"], object_hook=lambda d: {int(k) if k.isdigit() else k: v for k, v in d.items()}, ) stride, names = int(d["stride"]), d["names"] elif dnn: # ONNX OpenCV DNN LOGGER.info(f"Loading {w} for ONNX OpenCV DNN inference...") check_requirements("opencv-python>=4.5.4") net = cv2.dnn.readNetFromONNX(w) elif onnx: # ONNX Runtime LOGGER.info(f"Loading {w} for ONNX Runtime inference...") check_requirements(("onnx", "onnxruntime-gpu" if cuda else "onnxruntime")) import onnxruntime providers = ["CUDAExecutionProvider", "CPUExecutionProvider"] if cuda else ["CPUExecutionProvider"] session = onnxruntime.InferenceSession(w, providers=providers) output_names = [x.name for x in session.get_outputs()] meta = session.get_modelmeta().custom_metadata_map # metadata if "stride" in meta: stride, names = int(meta["stride"]), eval(meta["names"]) elif xml: # OpenVINO LOGGER.info(f"Loading {w} for OpenVINO inference...") check_requirements("openvino>=2023.0") # requires openvino-dev: https://pypi.org/project/openvino-dev/ from openvino.runtime import Core, Layout, get_batch core = Core() if not Path(w).is_file(): # if not *.xml w = next(Path(w).glob("*.xml")) # get *.xml file from *_openvino_model dir ov_model = core.read_model(model=w, weights=Path(w).with_suffix(".bin")) if ov_model.get_parameters()[0].get_layout().empty: ov_model.get_parameters()[0].set_layout(Layout("NCHW")) batch_dim = get_batch(ov_model) if batch_dim.is_static: batch_size = batch_dim.get_length() ov_compiled_model = core.compile_model(ov_model, device_name="AUTO") # AUTO selects best available device stride, names = self._load_metadata(Path(w).with_suffix(".yaml")) # load metadata elif engine: # TensorRT LOGGER.info(f"Loading {w} for TensorRT inference...") import tensorrt as trt # https://developer.nvidia.com/nvidia-tensorrt-download check_version(trt.__version__, "7.0.0", hard=True) # require tensorrt>=7.0.0 if device.type == "cpu": device = torch.device("cuda:0") Binding = namedtuple("Binding", ("name", "dtype", "shape", "data", "ptr")) logger = trt.Logger(trt.Logger.INFO) with open(w, "rb") as f, trt.Runtime(logger) as runtime: model = runtime.deserialize_cuda_engine(f.read()) context = model.create_execution_context() bindings = OrderedDict() output_names = [] fp16 = False # default updated below dynamic = False for i in range(model.num_bindings): name = model.get_binding_name(i) dtype = trt.nptype(model.get_binding_dtype(i)) if model.binding_is_input(i): if -1 in tuple(model.get_binding_shape(i)): # dynamic dynamic = True context.set_binding_shape(i, tuple(model.get_profile_shape(0, i)[2])) if dtype == np.float16: fp16 = True else: # output output_names.append(name) shape = tuple(context.get_binding_shape(i)) im = torch.from_numpy(np.empty(shape, dtype=dtype)).to(device) bindings[name] = Binding(name, dtype, shape, im, int(im.data_ptr())) binding_addrs = OrderedDict((n, d.ptr) for n, d in bindings.items()) batch_size = bindings["images"].shape[0] # if dynamic, this is instead max batch size elif coreml: # CoreML LOGGER.info(f"Loading {w} for CoreML inference...") import coremltools as ct model = ct.models.MLModel(w) elif saved_model: # TF SavedModel LOGGER.info(f"Loading {w} for TensorFlow SavedModel inference...") import tensorflow as tf keras = False # assume TF1 saved_model model = tf.keras.models.load_model(w) if keras else tf.saved_model.load(w) elif pb: # GraphDef https://www.tensorflow.org/guide/migrate#a_graphpb_or_graphpbtxt LOGGER.info(f"Loading {w} for TensorFlow GraphDef inference...") import tensorflow as tf def wrap_frozen_graph(gd, inputs, outputs): """Wraps a frozen TensorFlow GraphDef for inference, returning a pruned function for specified inputs and outputs. """ x = tf.compat.v1.wrap_function(lambda: tf.compat.v1.import_graph_def(gd, name=""), []) # wrapped ge = x.graph.as_graph_element return x.prune(tf.nest.map_structure(ge, inputs), tf.nest.map_structure(ge, outputs)) def gd_outputs(gd): """Extracts and sorts non-input (output) tensor names from a TensorFlow GraphDef, excluding 'NoOp' prefixed tensors. """ name_list, input_list = [], [] for node in gd.node: # tensorflow.core.framework.node_def_pb2.NodeDef name_list.append(node.name) input_list.extend(node.input) return sorted(f"{x}:0" for x in list(set(name_list) - set(input_list)) if not x.startswith("NoOp")) gd = tf.Graph().as_graph_def() # TF GraphDef with open(w, "rb") as f: gd.ParseFromString(f.read()) frozen_func = wrap_frozen_graph(gd, inputs="x:0", outputs=gd_outputs(gd)) elif tflite or edgetpu: # https://www.tensorflow.org/lite/guide/python#install_tensorflow_lite_for_python try: # https://coral.ai/docs/edgetpu/tflite-python/#update-existing-tf-lite-code-for-the-edge-tpu from tflite_runtime.interpreter import Interpreter, load_delegate except ImportError: import tensorflow as tf Interpreter, load_delegate = ( tf.lite.Interpreter, tf.lite.experimental.load_delegate, ) if edgetpu: # TF Edge TPU https://coral.ai/software/#edgetpu-runtime LOGGER.info(f"Loading {w} for TensorFlow Lite Edge TPU inference...") delegate = {"Linux": "libedgetpu.so.1", "Darwin": "libedgetpu.1.dylib", "Windows": "edgetpu.dll"}[ platform.system() ] interpreter = Interpreter(model_path=w, experimental_delegates=[load_delegate(delegate)]) else: # TFLite LOGGER.info(f"Loading {w} for TensorFlow Lite inference...") interpreter = Interpreter(model_path=w) # load TFLite model interpreter.allocate_tensors() # allocate input_details = interpreter.get_input_details() # inputs output_details = interpreter.get_output_details() # outputs # load metadata with contextlib.suppress(zipfile.BadZipFile): with zipfile.ZipFile(w, "r") as model: meta_file = model.namelist()[0] meta = ast.literal_eval(model.read(meta_file).decode("utf-8")) stride, names = int(meta["stride"]), meta["names"] elif tfjs: # TF.js raise NotImplementedError("ERROR: YOLOv3 TF.js inference is not supported") elif paddle: # PaddlePaddle LOGGER.info(f"Loading {w} for PaddlePaddle inference...") check_requirements("paddlepaddle-gpu" if cuda else "paddlepaddle") import paddle.inference as pdi if not Path(w).is_file(): # if not *.pdmodel w = next(Path(w).rglob("*.pdmodel")) # get *.pdmodel file from *_paddle_model dir weights = Path(w).with_suffix(".pdiparams") config = pdi.Config(str(w), str(weights)) if cuda: config.enable_use_gpu(memory_pool_init_size_mb=2048, device_id=0) predictor = pdi.create_predictor(config) input_handle = predictor.get_input_handle(predictor.get_input_names()[0]) output_names = predictor.get_output_names() elif triton: # NVIDIA Triton Inference Server LOGGER.info(f"Using {w} as Triton Inference Server...") check_requirements("tritonclient[all]") from utils.triton import TritonRemoteModel model = TritonRemoteModel(url=w) nhwc = model.runtime.startswith("tensorflow") else: raise NotImplementedError(f"ERROR: {w} is not a supported format") # class names if "names" not in locals(): names = yaml_load(data)["names"] if data else {i: f"class{i}" for i in range(999)} if names[0] == "n01440764" and len(names) == 1000: # ImageNet names = yaml_load(ROOT / "data/ImageNet.yaml")["names"] # human-readable names self.__dict__.update(locals()) # assign all variables to self def forward(self, im, augment=False, visualize=False): """Performs YOLOv3 inference on an input image tensor, optionally with augmentation and visualization.""" _b, _ch, h, w = im.shape # batch, channel, height, width if self.fp16 and im.dtype != torch.float16: im = im.half() # to FP16 if self.nhwc: im = im.permute(0, 2, 3, 1) # torch BCHW to numpy BHWC shape(1,320,192,3) if self.pt: # PyTorch y = self.model(im, augment=augment, visualize=visualize) if augment or visualize else self.model(im) elif self.jit: # TorchScript y = self.model(im) elif self.dnn: # ONNX OpenCV DNN im = im.cpu().numpy() # torch to numpy self.net.setInput(im) y = self.net.forward() elif self.onnx: # ONNX Runtime im = im.cpu().numpy() # torch to numpy y = self.session.run(self.output_names, {self.session.get_inputs()[0].name: im}) elif self.xml: # OpenVINO im = im.cpu().numpy() # FP32 y = list(self.ov_compiled_model(im).values()) elif self.engine: # TensorRT if self.dynamic and im.shape != self.bindings["images"].shape: i = self.model.get_binding_index("images") self.context.set_binding_shape(i, im.shape) # reshape if dynamic self.bindings["images"] = self.bindings["images"]._replace(shape=im.shape) for name in self.output_names: i = self.model.get_binding_index(name) self.bindings[name].data.resize_(tuple(self.context.get_binding_shape(i))) s = self.bindings["images"].shape assert im.shape == s, f"input size {im.shape} {'>' if self.dynamic else 'not equal to'} max model size {s}" self.binding_addrs["images"] = int(im.data_ptr()) self.context.execute_v2(list(self.binding_addrs.values())) y = [self.bindings[x].data for x in sorted(self.output_names)] elif self.coreml: # CoreML im = im.cpu().numpy() im = Image.fromarray((im[0] * 255).astype("uint8")) # im = im.resize((192, 320), Image.BILINEAR) y = self.model.predict({"image": im}) # coordinates are xywh normalized if "confidence" in y: box = xywh2xyxy(y["coordinates"] * [[w, h, w, h]]) # xyxy pixels conf, cls = y["confidence"].max(1), y["confidence"].argmax(1).astype(np.float) y = np.concatenate((box, conf.reshape(-1, 1), cls.reshape(-1, 1)), 1) else: y = list(reversed(y.values())) # reversed for segmentation models (pred, proto) elif self.paddle: # PaddlePaddle im = im.cpu().numpy().astype(np.float32) self.input_handle.copy_from_cpu(im) self.predictor.run() y = [self.predictor.get_output_handle(x).copy_to_cpu() for x in self.output_names] elif self.triton: # NVIDIA Triton Inference Server y = self.model(im) else: # TensorFlow (SavedModel, GraphDef, Lite, Edge TPU) im = im.cpu().numpy() if self.saved_model: # SavedModel y = self.model(im, training=False) if self.keras else self.model(im) elif self.pb: # GraphDef y = self.frozen_func(x=self.tf.constant(im)) else: # Lite or Edge TPU input = self.input_details[0] int8 = input["dtype"] == np.uint8 # is TFLite quantized uint8 model if int8: scale, zero_point = input["quantization"] im = (im / scale + zero_point).astype(np.uint8) # de-scale self.interpreter.set_tensor(input["index"], im) self.interpreter.invoke() y = [] for output in self.output_details: x = self.interpreter.get_tensor(output["index"]) if int8: scale, zero_point = output["quantization"] x = (x.astype(np.float32) - zero_point) * scale # re-scale y.append(x) y = [x if isinstance(x, np.ndarray) else x.numpy() for x in y] y[0][..., :4] *= [w, h, w, h] # xywh normalized to pixels if isinstance(y, (list, tuple)): return self.from_numpy(y[0]) if len(y) == 1 else [self.from_numpy(x) for x in y] else: return self.from_numpy(y) def from_numpy(self, x): """Converts a Numpy array to a PyTorch tensor on the specified device, else returns the input if not a Numpy array. """ return torch.from_numpy(x).to(self.device) if isinstance(x, np.ndarray) else x def warmup(self, imgsz=(1, 3, 640, 640)): """Warms up the model by running inference once with a dummy input of shape imgsz.""" warmup_types = self.pt, self.jit, self.onnx, self.engine, self.saved_model, self.pb, self.triton if any(warmup_types) and (self.device.type != "cpu" or self.triton): im = torch.empty(*imgsz, dtype=torch.half if self.fp16 else torch.float, device=self.device) # input for _ in range(2 if self.jit else 1): # self.forward(im) # warmup @staticmethod def _model_type(p="path/to/model.pt"): """Determines model type from filepath or URL, supports various formats including ONNX, PT, JIT. See `export_formats` for all. """ # types = [pt, jit, onnx, xml, engine, coreml, saved_model, pb, tflite, edgetpu, tfjs, paddle] from export import export_formats from utils.downloads import is_url sf = list(export_formats().Suffix) # export suffixes if not is_url(p, check=False): check_suffix(p, sf) # checks url = urlparse(p) # if url may be Triton inference server types = [s in Path(p).name for s in sf] types[8] &= not types[9] # tflite &= not edgetpu triton = not any(types) and all([any(s in url.scheme for s in ["http", "grpc"]), url.netloc]) return [*types, triton] @staticmethod def _load_metadata(f=Path("path/to/meta.yaml")): """Loads metadata from a YAML file, returning 'stride' and 'names' if the file exists, else 'None'.""" if f.exists(): d = yaml_load(f) return d["stride"], d["names"] # assign stride, names return None, None class AutoShape(nn.Module): """A wrapper for YOLOv3 models to handle diverse input types with preprocessing, inference, and NMS.""" conf = 0.25 # NMS confidence threshold iou = 0.45 # NMS IoU threshold agnostic = False # NMS class-agnostic multi_label = False # NMS multiple labels per box classes = None # (optional list) filter by class, i.e. = [0, 15, 16] for COCO persons, cats and dogs max_det = 1000 # maximum number of detections per image amp = False # Automatic Mixed Precision (AMP) inference def __init__(self, model, verbose=True): """Initializes the model for inference, setting attributes, and preparing for multithreaded execution with optional verbose logging. """ super().__init__() if verbose: LOGGER.info("Adding AutoShape... ") copy_attr(self, model, include=("yaml", "nc", "hyp", "names", "stride", "abc"), exclude=()) # copy attributes self.dmb = isinstance(model, DetectMultiBackend) # DetectMultiBackend() instance self.pt = not self.dmb or model.pt # PyTorch model self.model = model.eval() if self.pt: m = self.model.model.model[-1] if self.dmb else self.model.model[-1] # Detect() m.inplace = False # Detect.inplace=False for safe multithread inference m.export = True # do not output loss values def _apply(self, fn): """Applies given function `fn` to model tensors excluding parameters or registered buffers, adjusting strides and grids. """ self = super()._apply(fn) if self.pt: m = self.model.model.model[-1] if self.dmb else self.model.model[-1] # Detect() m.stride = fn(m.stride) m.grid = list(map(fn, m.grid)) if isinstance(m.anchor_grid, list): m.anchor_grid = list(map(fn, m.anchor_grid)) return self @smart_inference_mode() def forward(self, ims, size=640, augment=False, profile=False): """Performs inference on various input sources with optional augmentation and profiling; see `https://ultralytics.com`. """ # file: ims = 'data/images/zidane.jpg' # str or PosixPath # URI: = 'https://ultralytics.com/images/zidane.jpg' # OpenCV: = cv2.imread('image.jpg')[:,:,::-1] # HWC BGR to RGB x(640,1280,3) # PIL: = Image.open('image.jpg') or ImageGrab.grab() # HWC x(640,1280,3) # numpy: = np.zeros((640,1280,3)) # HWC # torch: = torch.zeros(16,3,320,640) # BCHW (scaled to size=640, 0-1 values) # multiple: = [Image.open('image1.jpg'), Image.open('image2.jpg'), ...] # list of images dt = (Profile(), Profile(), Profile()) with dt[0]: if isinstance(size, int): # expand size = (size, size) p = next(self.model.parameters()) if self.pt else torch.empty(1, device=self.model.device) # param autocast = self.amp and (p.device.type != "cpu") # Automatic Mixed Precision (AMP) inference if isinstance(ims, torch.Tensor): # torch with amp.autocast(autocast): return self.model(ims.to(p.device).type_as(p), augment=augment) # inference # Pre-process n, ims = (len(ims), list(ims)) if isinstance(ims, (list, tuple)) else (1, [ims]) # number, list of images shape0, shape1, files = [], [], [] # image and inference shapes, filenames for i, im in enumerate(ims): f = f"image{i}" # filename if isinstance(im, (str, Path)): # filename or uri im, f = Image.open(requests.get(im, stream=True).raw if str(im).startswith("http") else im), im im = np.asarray(exif_transpose(im)) elif isinstance(im, Image.Image): # PIL Image im, f = np.asarray(exif_transpose(im)), getattr(im, "filename", f) or f files.append(Path(f).with_suffix(".jpg").name) if im.shape[0] < 5: # image in CHW im = im.transpose((1, 2, 0)) # reverse dataloader .transpose(2, 0, 1) im = im[..., :3] if im.ndim == 3 else cv2.cvtColor(im, cv2.COLOR_GRAY2BGR) # enforce 3ch input s = im.shape[:2] # HWC shape0.append(s) # image shape g = max(size) / max(s) # gain shape1.append([int(y * g) for y in s]) ims[i] = im if im.data.contiguous else np.ascontiguousarray(im) # update shape1 = [make_divisible(x, self.stride) for x in np.array(shape1).max(0)] # inf shape x = [letterbox(im, shape1, auto=False)[0] for im in ims] # pad x = np.ascontiguousarray(np.array(x).transpose((0, 3, 1, 2))) # stack and BHWC to BCHW x = torch.from_numpy(x).to(p.device).type_as(p) / 255 # uint8 to fp16/32 with amp.autocast(autocast): # Inference with dt[1]: y = self.model(x, augment=augment) # forward # Post-process with dt[2]: y = non_max_suppression( y if self.dmb else y[0], self.conf, self.iou, self.classes, self.agnostic, self.multi_label, max_det=self.max_det, ) # NMS for i in range(n): scale_boxes(shape1, y[i][:, :4], shape0[i]) return Detections(ims, y, files, dt, self.names, x.shape) class Detections: """Handles YOLOv3 detection results with methods for visualization, saving, cropping, and format conversion.""" def __init__(self, ims, pred, files, times=(0, 0, 0), names=None, shape=None): """Initializes YOLOv3 detections with image data, predictions, filenames, profiling times, class names, and shapes. """ super().__init__() d = pred[0].device # device gn = [torch.tensor([*(im.shape[i] for i in [1, 0, 1, 0]), 1, 1], device=d) for im in ims] # normalizations self.ims = ims # list of images as numpy arrays self.pred = pred # list of tensors pred[0] = (xyxy, conf, cls) self.names = names # class names self.files = files # image filenames self.times = times # profiling times self.xyxy = pred # xyxy pixels self.xywh = [xyxy2xywh(x) for x in pred] # xywh pixels self.xyxyn = [x / g for x, g in zip(self.xyxy, gn)] # xyxy normalized self.xywhn = [x / g for x, g in zip(self.xywh, gn)] # xywh normalized self.n = len(self.pred) # number of images (batch size) self.t = tuple(x.t / self.n * 1e3 for x in times) # timestamps (ms) self.s = tuple(shape) # inference BCHW shape def _run(self, pprint=False, show=False, save=False, crop=False, render=False, labels=True, save_dir=Path("")): """Executes inference on images, annotates detections, and can optionally show, save, or crop output images.""" s, crops = "", [] for i, (im, pred) in enumerate(zip(self.ims, self.pred)): s += f"\nimage {i + 1}/{len(self.pred)}: {im.shape[0]}x{im.shape[1]} " # string if pred.shape[0]: for c in pred[:, -1].unique(): n = (pred[:, -1] == c).sum() # detections per class s += f"{n} {self.names[int(c)]}{'s' * (n > 1)}, " # add to string s = s.rstrip(", ") if show or save or render or crop: annotator = Annotator(im, example=str(self.names)) for *box, conf, cls in reversed(pred): # xyxy, confidence, class label = f"{self.names[int(cls)]} {conf:.2f}" if crop: file = save_dir / "crops" / self.names[int(cls)] / self.files[i] if save else None crops.append( { "box": box, "conf": conf, "cls": cls, "label": label, "im": save_one_box(box, im, file=file, save=save), } ) else: # all others annotator.box_label(box, label if labels else "", color=colors(cls)) im = annotator.im else: s += "(no detections)" im = Image.fromarray(im.astype(np.uint8)) if isinstance(im, np.ndarray) else im # from np if show: if is_jupyter(): from IPython.display import display display(im) else: im.show(self.files[i]) if save: f = self.files[i] im.save(save_dir / f) # save if i == self.n - 1: LOGGER.info(f"Saved {self.n} image{'s' * (self.n > 1)} to {colorstr('bold', save_dir)}") if render: self.ims[i] = np.asarray(im) if pprint: s = s.lstrip("\n") return f"{s}\nSpeed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {self.s}" % self.t if crop: if save: LOGGER.info(f"Saved results to {save_dir}\n") return crops @TryExcept("Showing images is not supported in this environment") def show(self, labels=True): """Displays image results with optional labels. Usage: `show(labels=True)` """ self._run(show=True, labels=labels) # show results def save(self, labels=True, save_dir="runs/detect/exp", exist_ok=False): """Saves image results with optional labels to a specified directory. Usage: `save(labels=True, save_dir='runs/detect/exp', exist_ok=False)` """ save_dir = increment_path(save_dir, exist_ok, mkdir=True) # increment save_dir self._run(save=True, labels=labels, save_dir=save_dir) # save results def crop(self, save=True, save_dir="runs/detect/exp", exist_ok=False): """Crops detection results; can save to `save_dir`. Usage: `crop(save=True, save_dir='runs/detect/exp')`. """ save_dir = increment_path(save_dir, exist_ok, mkdir=True) if save else None return self._run(crop=True, save=save, save_dir=save_dir) # crop results def render(self, labels=True): """Renders detection results, optionally displaying labels. Usage: `render(labels=True)`. """ self._run(render=True, labels=labels) # render results return self.ims def pandas(self): """Returns a copy of the detection results as pandas DataFrames for various bounding box formats.""" new = copy(self) # return copy ca = "xmin", "ymin", "xmax", "ymax", "confidence", "class", "name" # xyxy columns cb = "xcenter", "ycenter", "width", "height", "confidence", "class", "name" # xywh columns for k, c in zip(["xyxy", "xyxyn", "xywh", "xywhn"], [ca, ca, cb, cb]): a = [[[*x[:5], int(x[5]), self.names[int(x[5])]] for x in x.tolist()] for x in getattr(self, k)] # update setattr(new, k, [pd.DataFrame(x, columns=c) for x in a]) return new def tolist(self): """Converts Detections object to a list of individual Detection objects for iteration.""" r = range(self.n) # iterable return [ Detections( [self.ims[i]], [self.pred[i]], [self.files[i]], self.times, self.names, self.s, ) for i in r ] def print(self): """Logs the string representation of the current object state to the LOGGER.""" LOGGER.info(self.__str__()) def __len__(self): # override len(results) """Returns the number of results stored in the instance.""" return self.n def __str__(self): # override print(results) """Returns a string representation of the current object state, printing the results.""" return self._run(pprint=True) # print results def __repr__(self): """Returns a string representation for debugging, including class info and current object state.""" return f"YOLOv3 {self.__class__} instance\n" + self.__str__() class Proto(nn.Module): """Implements the YOLOv3 mask Proto module for segmentation, including convolutional layers and upsampling.""" def __init__(self, c1, c_=256, c2=32): # ch_in, number of protos, number of masks """Initializes the Proto module for YOLOv3 segmentation, setting up convolutional layers and upsampling.""" super().__init__() self.cv1 = Conv(c1, c_, k=3) self.upsample = nn.Upsample(scale_factor=2, mode="nearest") self.cv2 = Conv(c_, c_, k=3) self.cv3 = Conv(c_, c2) def forward(self, x): """Performs forward pass, upsampling and applying convolutions for YOLOv3 segmentation.""" return self.cv3(self.cv2(self.upsample(self.cv1(x)))) class Classify(nn.Module): """Performs image classification using YOLOv3-based architecture with convolutional, pooling, and dropout layers.""" def __init__( self, c1, c2, k=1, s=1, p=None, g=1, dropout_p=0.0 ): # ch_in, ch_out, kernel, stride, padding, groups, dropout probability """Initializes YOLOv3 classification head with convolution, pooling and dropout layers for feature extraction and classification. """ super().__init__() c_ = 1280 # efficientnet_b0 size self.conv = Conv(c1, c_, k, s, autopad(k, p), g) self.pool = nn.AdaptiveAvgPool2d(1) # to x(b,c_,1,1) self.drop = nn.Dropout(p=dropout_p, inplace=True) self.linear = nn.Linear(c_, c2) # to x(b,c2) def forward(self, x): """Processes input tensor `x` through convolutions and pooling, optionally concatenating lists of tensors, and returns linear output. """ if isinstance(x, list): x = torch.cat(x, 1) return self.linear(self.drop(self.pool(self.conv(x)).flatten(1))) ================================================ FILE: models/experimental.py ================================================ # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license """Experimental modules.""" import math import numpy as np import torch import torch.nn as nn from ultralytics.utils.patches import torch_load from utils.downloads import attempt_download class Sum(nn.Module): """Computes the weighted or unweighted sum of multiple input layers per https://arxiv.org/abs/1911.09070.""" def __init__(self, n, weight=False): # n: number of inputs """Initializes a module to compute weighted/unweighted sum of n inputs, with optional learning weights. https://arxiv.org/abs/1911.09070 """ super().__init__() self.weight = weight # apply weights boolean self.iter = range(n - 1) # iter object if weight: self.w = nn.Parameter(-torch.arange(1.0, n) / 2, requires_grad=True) # layer weights def forward(self, x): """Performs forward pass, blending `x` elements with optional learnable weights. See https://arxiv.org/abs/1911.09070 for more. """ y = x[0] # no weight if self.weight: w = torch.sigmoid(self.w) * 2 for i in self.iter: y = y + x[i + 1] * w[i] else: for i in self.iter: y = y + x[i + 1] return y class MixConv2d(nn.Module): """Implements mixed depth-wise convolutions for efficient neural networks; see https://arxiv.org/abs/1907.09595.""" def __init__(self, c1, c2, k=(1, 3), s=1, equal_ch=True): # ch_in, ch_out, kernel, stride, ch_strategy """Initializes MixConv2d with mixed depth-wise convolution layers; details at https://arxiv.org/abs/1907.09595. """ super().__init__() n = len(k) # number of convolutions if equal_ch: # equal c_ per group i = torch.linspace(0, n - 1e-6, c2).floor() # c2 indices c_ = [(i == g).sum() for g in range(n)] # intermediate channels else: # equal weight.numel() per group b = [c2] + [0] * n a = np.eye(n + 1, n, k=-1) a -= np.roll(a, 1, axis=1) a *= np.array(k) ** 2 a[0] = 1 c_ = np.linalg.lstsq(a, b, rcond=None)[0].round() # solve for equal weight indices, ax = b self.m = nn.ModuleList( [nn.Conv2d(c1, int(c_), k, s, k // 2, groups=math.gcd(c1, int(c_)), bias=False) for k, c_ in zip(k, c_)] ) self.bn = nn.BatchNorm2d(c2) self.act = nn.SiLU() def forward(self, x): """Applies a series of convolutions, batch normalization, and SiLU activation to input tensor `x`.""" return self.act(self.bn(torch.cat([m(x) for m in self.m], 1))) class Ensemble(nn.ModuleList): """Combines outputs from multiple models to improve inference results.""" def __init__(self): """Initializes an ensemble of models to combine their outputs.""" super().__init__() def forward(self, x, augment=False, profile=False, visualize=False): """Applies ensemble of models on input `x`, with options for augmentation, profiling, and visualization, returning inference outputs. """ y = [module(x, augment, profile, visualize)[0] for module in self] # y = torch.stack(y).max(0)[0] # max ensemble # y = torch.stack(y).mean(0) # mean ensemble y = torch.cat(y, 1) # nms ensemble return y, None # inference, train output def attempt_load(weights, device=None, inplace=True, fuse=True): """Loads an ensemble or single model weights, supports device placement and model fusion.""" from models.yolo import Detect, Model model = Ensemble() for w in weights if isinstance(weights, list) else [weights]: ckpt = torch_load(attempt_download(w), map_location="cpu") # load ckpt = (ckpt.get("ema") or ckpt["model"]).to(device).float() # FP32 model # Model compatibility updates if not hasattr(ckpt, "stride"): ckpt.stride = torch.tensor([32.0]) if hasattr(ckpt, "names") and isinstance(ckpt.names, (list, tuple)): ckpt.names = dict(enumerate(ckpt.names)) # convert to dict model.append(ckpt.fuse().eval() if fuse and hasattr(ckpt, "fuse") else ckpt.eval()) # model in eval mode # Module compatibility updates for m in model.modules(): t = type(m) if t in (nn.Hardswish, nn.LeakyReLU, nn.ReLU, nn.ReLU6, nn.SiLU, Detect, Model): m.inplace = inplace # torch 1.7.0 compatibility if t is Detect and not isinstance(m.anchor_grid, list): delattr(m, "anchor_grid") setattr(m, "anchor_grid", [torch.zeros(1)] * m.nl) elif t is nn.Upsample and not hasattr(m, "recompute_scale_factor"): m.recompute_scale_factor = None # torch 1.11.0 compatibility # Return model if len(model) == 1: return model[-1] # Return detection ensemble print(f"Ensemble created with {weights}\n") for k in "names", "nc", "yaml": setattr(model, k, getattr(model[0], k)) model.stride = model[torch.argmax(torch.tensor([m.stride.max() for m in model])).int()].stride # max stride assert all(model[0].nc == m.nc for m in model), f"Models have different class counts: {[m.nc for m in model]}" return model ================================================ FILE: models/hub/anchors.yaml ================================================ # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license # Default anchors for COCO data # P5 ------------------------------------------------------------------------------------------------------------------- # P5-640: anchors_p5_640: - [10, 13, 16, 30, 33, 23] # P3/8 - [30, 61, 62, 45, 59, 119] # P4/16 - [116, 90, 156, 198, 373, 326] # P5/32 # P6 ------------------------------------------------------------------------------------------------------------------- # 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 anchors_p6_640: - [9, 11, 21, 19, 17, 41] # P3/8 - [43, 32, 39, 70, 86, 64] # P4/16 - [65, 131, 134, 130, 120, 265] # P5/32 - [282, 180, 247, 354, 512, 387] # P6/64 # 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 anchors_p6_1280: - [19, 27, 44, 40, 38, 94] # P3/8 - [96, 68, 86, 152, 180, 137] # P4/16 - [140, 301, 303, 264, 238, 542] # P5/32 - [436, 615, 739, 380, 925, 792] # P6/64 # 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 anchors_p6_1920: - [28, 41, 67, 59, 57, 141] # P3/8 - [144, 103, 129, 227, 270, 205] # P4/16 - [209, 452, 455, 396, 358, 812] # P5/32 - [653, 922, 1109, 570, 1387, 1187] # P6/64 # P7 ------------------------------------------------------------------------------------------------------------------- # 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 anchors_p7_640: - [11, 11, 13, 30, 29, 20] # P3/8 - [30, 46, 61, 38, 39, 92] # P4/16 - [78, 80, 146, 66, 79, 163] # P5/32 - [149, 150, 321, 143, 157, 303] # P6/64 - [257, 402, 359, 290, 524, 372] # P7/128 # 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 anchors_p7_1280: - [19, 22, 54, 36, 32, 77] # P3/8 - [70, 83, 138, 71, 75, 173] # P4/16 - [165, 159, 148, 334, 375, 151] # P5/32 - [334, 317, 251, 626, 499, 474] # P6/64 - [750, 326, 534, 814, 1079, 818] # P7/128 # 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 anchors_p7_1920: - [29, 34, 81, 55, 47, 115] # P3/8 - [105, 124, 207, 107, 113, 259] # P4/16 - [247, 238, 222, 500, 563, 227] # P5/32 - [501, 476, 376, 939, 749, 711] # P6/64 - [1126, 489, 801, 1222, 1618, 1227] # P7/128 ================================================ FILE: models/hub/yolov5-bifpn.yaml ================================================ # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license # Parameters nc: 80 # number of classes depth_multiple: 1.0 # model depth multiple width_multiple: 1.0 # layer channel multiple anchors: - [10, 13, 16, 30, 33, 23] # P3/8 - [30, 61, 62, 45, 59, 119] # P4/16 - [116, 90, 156, 198, 373, 326] # P5/32 # YOLOv5 v6.0 backbone backbone: # [from, number, module, args] [ [-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 [-1, 3, C3, [128]], [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 [-1, 6, C3, [256]], [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 [-1, 9, C3, [512]], [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 [-1, 3, C3, [1024]], [-1, 1, SPPF, [1024, 5]], # 9 ] # YOLOv5 v6.0 BiFPN head head: [ [-1, 1, Conv, [512, 1, 1]], [-1, 1, nn.Upsample, [None, 2, "nearest"]], [[-1, 6], 1, Concat, [1]], # cat backbone P4 [-1, 3, C3, [512, False]], # 13 [-1, 1, Conv, [256, 1, 1]], [-1, 1, nn.Upsample, [None, 2, "nearest"]], [[-1, 4], 1, Concat, [1]], # cat backbone P3 [-1, 3, C3, [256, False]], # 17 (P3/8-small) [-1, 1, Conv, [256, 3, 2]], [[-1, 14, 6], 1, Concat, [1]], # cat P4 <--- BiFPN change [-1, 3, C3, [512, False]], # 20 (P4/16-medium) [-1, 1, Conv, [512, 3, 2]], [[-1, 10], 1, Concat, [1]], # cat head P5 [-1, 3, C3, [1024, False]], # 23 (P5/32-large) [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) ] ================================================ FILE: models/hub/yolov5-fpn.yaml ================================================ # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license # Parameters nc: 80 # number of classes depth_multiple: 1.0 # model depth multiple width_multiple: 1.0 # layer channel multiple anchors: - [10, 13, 16, 30, 33, 23] # P3/8 - [30, 61, 62, 45, 59, 119] # P4/16 - [116, 90, 156, 198, 373, 326] # P5/32 # YOLOv5 v6.0 backbone backbone: # [from, number, module, args] [ [-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 [-1, 3, C3, [128]], [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 [-1, 6, C3, [256]], [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 [-1, 9, C3, [512]], [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 [-1, 3, C3, [1024]], [-1, 1, SPPF, [1024, 5]], # 9 ] # YOLOv5 v6.0 FPN head head: [ [-1, 3, C3, [1024, False]], # 10 (P5/32-large) [-1, 1, nn.Upsample, [None, 2, "nearest"]], [[-1, 6], 1, Concat, [1]], # cat backbone P4 [-1, 1, Conv, [512, 1, 1]], [-1, 3, C3, [512, False]], # 14 (P4/16-medium) [-1, 1, nn.Upsample, [None, 2, "nearest"]], [[-1, 4], 1, Concat, [1]], # cat backbone P3 [-1, 1, Conv, [256, 1, 1]], [-1, 3, C3, [256, False]], # 18 (P3/8-small) [[18, 14, 10], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) ] ================================================ FILE: models/hub/yolov5-p2.yaml ================================================ # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license # Parameters nc: 80 # number of classes depth_multiple: 1.0 # model depth multiple width_multiple: 1.0 # layer channel multiple anchors: 3 # AutoAnchor evolves 3 anchors per P output layer # YOLOv5 v6.0 backbone backbone: # [from, number, module, args] [ [-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 [-1, 3, C3, [128]], [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 [-1, 6, C3, [256]], [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 [-1, 9, C3, [512]], [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 [-1, 3, C3, [1024]], [-1, 1, SPPF, [1024, 5]], # 9 ] # YOLOv5 v6.0 head with (P2, P3, P4, P5) outputs head: [ [-1, 1, Conv, [512, 1, 1]], [-1, 1, nn.Upsample, [None, 2, "nearest"]], [[-1, 6], 1, Concat, [1]], # cat backbone P4 [-1, 3, C3, [512, False]], # 13 [-1, 1, Conv, [256, 1, 1]], [-1, 1, nn.Upsample, [None, 2, "nearest"]], [[-1, 4], 1, Concat, [1]], # cat backbone P3 [-1, 3, C3, [256, False]], # 17 (P3/8-small) [-1, 1, Conv, [128, 1, 1]], [-1, 1, nn.Upsample, [None, 2, "nearest"]], [[-1, 2], 1, Concat, [1]], # cat backbone P2 [-1, 1, C3, [128, False]], # 21 (P2/4-xsmall) [-1, 1, Conv, [128, 3, 2]], [[-1, 18], 1, Concat, [1]], # cat head P3 [-1, 3, C3, [256, False]], # 24 (P3/8-small) [-1, 1, Conv, [256, 3, 2]], [[-1, 14], 1, Concat, [1]], # cat head P4 [-1, 3, C3, [512, False]], # 27 (P4/16-medium) [-1, 1, Conv, [512, 3, 2]], [[-1, 10], 1, Concat, [1]], # cat head P5 [-1, 3, C3, [1024, False]], # 30 (P5/32-large) [[21, 24, 27, 30], 1, Detect, [nc, anchors]], # Detect(P2, P3, P4, P5) ] ================================================ FILE: models/hub/yolov5-p34.yaml ================================================ # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license # Parameters nc: 80 # number of classes depth_multiple: 0.33 # model depth multiple width_multiple: 0.50 # layer channel multiple anchors: 3 # AutoAnchor evolves 3 anchors per P output layer # YOLOv5 v6.0 backbone backbone: # [from, number, module, args] [ [-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 [-1, 3, C3, [128]], [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 [-1, 6, C3, [256]], [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 [-1, 9, C3, [512]], [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 [-1, 3, C3, [1024]], [-1, 1, SPPF, [1024, 5]], # 9 ] # YOLOv5 v6.0 head with (P3, P4) outputs head: [ [-1, 1, Conv, [512, 1, 1]], [-1, 1, nn.Upsample, [None, 2, "nearest"]], [[-1, 6], 1, Concat, [1]], # cat backbone P4 [-1, 3, C3, [512, False]], # 13 [-1, 1, Conv, [256, 1, 1]], [-1, 1, nn.Upsample, [None, 2, "nearest"]], [[-1, 4], 1, Concat, [1]], # cat backbone P3 [-1, 3, C3, [256, False]], # 17 (P3/8-small) [-1, 1, Conv, [256, 3, 2]], [[-1, 14], 1, Concat, [1]], # cat head P4 [-1, 3, C3, [512, False]], # 20 (P4/16-medium) [[17, 20], 1, Detect, [nc, anchors]], # Detect(P3, P4) ] ================================================ FILE: models/hub/yolov5-p6.yaml ================================================ # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license # Parameters nc: 80 # number of classes depth_multiple: 1.0 # model depth multiple width_multiple: 1.0 # layer channel multiple anchors: 3 # AutoAnchor evolves 3 anchors per P output layer # YOLOv5 v6.0 backbone backbone: # [from, number, module, args] [ [-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 [-1, 3, C3, [128]], [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 [-1, 6, C3, [256]], [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 [-1, 9, C3, [512]], [-1, 1, Conv, [768, 3, 2]], # 7-P5/32 [-1, 3, C3, [768]], [-1, 1, Conv, [1024, 3, 2]], # 9-P6/64 [-1, 3, C3, [1024]], [-1, 1, SPPF, [1024, 5]], # 11 ] # YOLOv5 v6.0 head with (P3, P4, P5, P6) outputs head: [ [-1, 1, Conv, [768, 1, 1]], [-1, 1, nn.Upsample, [None, 2, "nearest"]], [[-1, 8], 1, Concat, [1]], # cat backbone P5 [-1, 3, C3, [768, False]], # 15 [-1, 1, Conv, [512, 1, 1]], [-1, 1, nn.Upsample, [None, 2, "nearest"]], [[-1, 6], 1, Concat, [1]], # cat backbone P4 [-1, 3, C3, [512, False]], # 19 [-1, 1, Conv, [256, 1, 1]], [-1, 1, nn.Upsample, [None, 2, "nearest"]], [[-1, 4], 1, Concat, [1]], # cat backbone P3 [-1, 3, C3, [256, False]], # 23 (P3/8-small) [-1, 1, Conv, [256, 3, 2]], [[-1, 20], 1, Concat, [1]], # cat head P4 [-1, 3, C3, [512, False]], # 26 (P4/16-medium) [-1, 1, Conv, [512, 3, 2]], [[-1, 16], 1, Concat, [1]], # cat head P5 [-1, 3, C3, [768, False]], # 29 (P5/32-large) [-1, 1, Conv, [768, 3, 2]], [[-1, 12], 1, Concat, [1]], # cat head P6 [-1, 3, C3, [1024, False]], # 32 (P6/64-xlarge) [[23, 26, 29, 32], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5, P6) ] ================================================ FILE: models/hub/yolov5-p7.yaml ================================================ # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license # Parameters nc: 80 # number of classes depth_multiple: 1.0 # model depth multiple width_multiple: 1.0 # layer channel multiple anchors: 3 # AutoAnchor evolves 3 anchors per P output layer # YOLOv5 v6.0 backbone backbone: # [from, number, module, args] [ [-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 [-1, 3, C3, [128]], [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 [-1, 6, C3, [256]], [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 [-1, 9, C3, [512]], [-1, 1, Conv, [768, 3, 2]], # 7-P5/32 [-1, 3, C3, [768]], [-1, 1, Conv, [1024, 3, 2]], # 9-P6/64 [-1, 3, C3, [1024]], [-1, 1, Conv, [1280, 3, 2]], # 11-P7/128 [-1, 3, C3, [1280]], [-1, 1, SPPF, [1280, 5]], # 13 ] # YOLOv5 v6.0 head with (P3, P4, P5, P6, P7) outputs head: [ [-1, 1, Conv, [1024, 1, 1]], [-1, 1, nn.Upsample, [None, 2, "nearest"]], [[-1, 10], 1, Concat, [1]], # cat backbone P6 [-1, 3, C3, [1024, False]], # 17 [-1, 1, Conv, [768, 1, 1]], [-1, 1, nn.Upsample, [None, 2, "nearest"]], [[-1, 8], 1, Concat, [1]], # cat backbone P5 [-1, 3, C3, [768, False]], # 21 [-1, 1, Conv, [512, 1, 1]], [-1, 1, nn.Upsample, [None, 2, "nearest"]], [[-1, 6], 1, Concat, [1]], # cat backbone P4 [-1, 3, C3, [512, False]], # 25 [-1, 1, Conv, [256, 1, 1]], [-1, 1, nn.Upsample, [None, 2, "nearest"]], [[-1, 4], 1, Concat, [1]], # cat backbone P3 [-1, 3, C3, [256, False]], # 29 (P3/8-small) [-1, 1, Conv, [256, 3, 2]], [[-1, 26], 1, Concat, [1]], # cat head P4 [-1, 3, C3, [512, False]], # 32 (P4/16-medium) [-1, 1, Conv, [512, 3, 2]], [[-1, 22], 1, Concat, [1]], # cat head P5 [-1, 3, C3, [768, False]], # 35 (P5/32-large) [-1, 1, Conv, [768, 3, 2]], [[-1, 18], 1, Concat, [1]], # cat head P6 [-1, 3, C3, [1024, False]], # 38 (P6/64-xlarge) [-1, 1, Conv, [1024, 3, 2]], [[-1, 14], 1, Concat, [1]], # cat head P7 [-1, 3, C3, [1280, False]], # 41 (P7/128-xxlarge) [[29, 32, 35, 38, 41], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5, P6, P7) ] ================================================ FILE: models/hub/yolov5-panet.yaml ================================================ # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license # Parameters nc: 80 # number of classes depth_multiple: 1.0 # model depth multiple width_multiple: 1.0 # layer channel multiple anchors: - [10, 13, 16, 30, 33, 23] # P3/8 - [30, 61, 62, 45, 59, 119] # P4/16 - [116, 90, 156, 198, 373, 326] # P5/32 # YOLOv5 v6.0 backbone backbone: # [from, number, module, args] [ [-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 [-1, 3, C3, [128]], [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 [-1, 6, C3, [256]], [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 [-1, 9, C3, [512]], [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 [-1, 3, C3, [1024]], [-1, 1, SPPF, [1024, 5]], # 9 ] # YOLOv5 v6.0 PANet head head: [ [-1, 1, Conv, [512, 1, 1]], [-1, 1, nn.Upsample, [None, 2, "nearest"]], [[-1, 6], 1, Concat, [1]], # cat backbone P4 [-1, 3, C3, [512, False]], # 13 [-1, 1, Conv, [256, 1, 1]], [-1, 1, nn.Upsample, [None, 2, "nearest"]], [[-1, 4], 1, Concat, [1]], # cat backbone P3 [-1, 3, C3, [256, False]], # 17 (P3/8-small) [-1, 1, Conv, [256, 3, 2]], [[-1, 14], 1, Concat, [1]], # cat head P4 [-1, 3, C3, [512, False]], # 20 (P4/16-medium) [-1, 1, Conv, [512, 3, 2]], [[-1, 10], 1, Concat, [1]], # cat head P5 [-1, 3, C3, [1024, False]], # 23 (P5/32-large) [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) ] ================================================ FILE: models/hub/yolov5l6.yaml ================================================ # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license # Parameters nc: 80 # number of classes depth_multiple: 1.0 # model depth multiple width_multiple: 1.0 # layer channel multiple anchors: - [19, 27, 44, 40, 38, 94] # P3/8 - [96, 68, 86, 152, 180, 137] # P4/16 - [140, 301, 303, 264, 238, 542] # P5/32 - [436, 615, 739, 380, 925, 792] # P6/64 # YOLOv5 v6.0 backbone backbone: # [from, number, module, args] [ [-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 [-1, 3, C3, [128]], [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 [-1, 6, C3, [256]], [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 [-1, 9, C3, [512]], [-1, 1, Conv, [768, 3, 2]], # 7-P5/32 [-1, 3, C3, [768]], [-1, 1, Conv, [1024, 3, 2]], # 9-P6/64 [-1, 3, C3, [1024]], [-1, 1, SPPF, [1024, 5]], # 11 ] # YOLOv5 v6.0 head head: [ [-1, 1, Conv, [768, 1, 1]], [-1, 1, nn.Upsample, [None, 2, "nearest"]], [[-1, 8], 1, Concat, [1]], # cat backbone P5 [-1, 3, C3, [768, False]], # 15 [-1, 1, Conv, [512, 1, 1]], [-1, 1, nn.Upsample, [None, 2, "nearest"]], [[-1, 6], 1, Concat, [1]], # cat backbone P4 [-1, 3, C3, [512, False]], # 19 [-1, 1, Conv, [256, 1, 1]], [-1, 1, nn.Upsample, [None, 2, "nearest"]], [[-1, 4], 1, Concat, [1]], # cat backbone P3 [-1, 3, C3, [256, False]], # 23 (P3/8-small) [-1, 1, Conv, [256, 3, 2]], [[-1, 20], 1, Concat, [1]], # cat head P4 [-1, 3, C3, [512, False]], # 26 (P4/16-medium) [-1, 1, Conv, [512, 3, 2]], [[-1, 16], 1, Concat, [1]], # cat head P5 [-1, 3, C3, [768, False]], # 29 (P5/32-large) [-1, 1, Conv, [768, 3, 2]], [[-1, 12], 1, Concat, [1]], # cat head P6 [-1, 3, C3, [1024, False]], # 32 (P6/64-xlarge) [[23, 26, 29, 32], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5, P6) ] ================================================ FILE: models/hub/yolov5m6.yaml ================================================ # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license # Parameters nc: 80 # number of classes depth_multiple: 0.67 # model depth multiple width_multiple: 0.75 # layer channel multiple anchors: - [19, 27, 44, 40, 38, 94] # P3/8 - [96, 68, 86, 152, 180, 137] # P4/16 - [140, 301, 303, 264, 238, 542] # P5/32 - [436, 615, 739, 380, 925, 792] # P6/64 # YOLOv5 v6.0 backbone backbone: # [from, number, module, args] [ [-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 [-1, 3, C3, [128]], [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 [-1, 6, C3, [256]], [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 [-1, 9, C3, [512]], [-1, 1, Conv, [768, 3, 2]], # 7-P5/32 [-1, 3, C3, [768]], [-1, 1, Conv, [1024, 3, 2]], # 9-P6/64 [-1, 3, C3, [1024]], [-1, 1, SPPF, [1024, 5]], # 11 ] # YOLOv5 v6.0 head head: [ [-1, 1, Conv, [768, 1, 1]], [-1, 1, nn.Upsample, [None, 2, "nearest"]], [[-1, 8], 1, Concat, [1]], # cat backbone P5 [-1, 3, C3, [768, False]], # 15 [-1, 1, Conv, [512, 1, 1]], [-1, 1, nn.Upsample, [None, 2, "nearest"]], [[-1, 6], 1, Concat, [1]], # cat backbone P4 [-1, 3, C3, [512, False]], # 19 [-1, 1, Conv, [256, 1, 1]], [-1, 1, nn.Upsample, [None, 2, "nearest"]], [[-1, 4], 1, Concat, [1]], # cat backbone P3 [-1, 3, C3, [256, False]], # 23 (P3/8-small) [-1, 1, Conv, [256, 3, 2]], [[-1, 20], 1, Concat, [1]], # cat head P4 [-1, 3, C3, [512, False]], # 26 (P4/16-medium) [-1, 1, Conv, [512, 3, 2]], [[-1, 16], 1, Concat, [1]], # cat head P5 [-1, 3, C3, [768, False]], # 29 (P5/32-large) [-1, 1, Conv, [768, 3, 2]], [[-1, 12], 1, Concat, [1]], # cat head P6 [-1, 3, C3, [1024, False]], # 32 (P6/64-xlarge) [[23, 26, 29, 32], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5, P6) ] ================================================ FILE: models/hub/yolov5n6.yaml ================================================ # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license # Parameters nc: 80 # number of classes depth_multiple: 0.33 # model depth multiple width_multiple: 0.25 # layer channel multiple anchors: - [19, 27, 44, 40, 38, 94] # P3/8 - [96, 68, 86, 152, 180, 137] # P4/16 - [140, 301, 303, 264, 238, 542] # P5/32 - [436, 615, 739, 380, 925, 792] # P6/64 # YOLOv5 v6.0 backbone backbone: # [from, number, module, args] [ [-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 [-1, 3, C3, [128]], [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 [-1, 6, C3, [256]], [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 [-1, 9, C3, [512]], [-1, 1, Conv, [768, 3, 2]], # 7-P5/32 [-1, 3, C3, [768]], [-1, 1, Conv, [1024, 3, 2]], # 9-P6/64 [-1, 3, C3, [1024]], [-1, 1, SPPF, [1024, 5]], # 11 ] # YOLOv5 v6.0 head head: [ [-1, 1, Conv, [768, 1, 1]], [-1, 1, nn.Upsample, [None, 2, "nearest"]], [[-1, 8], 1, Concat, [1]], # cat backbone P5 [-1, 3, C3, [768, False]], # 15 [-1, 1, Conv, [512, 1, 1]], [-1, 1, nn.Upsample, [None, 2, "nearest"]], [[-1, 6], 1, Concat, [1]], # cat backbone P4 [-1, 3, C3, [512, False]], # 19 [-1, 1, Conv, [256, 1, 1]], [-1, 1, nn.Upsample, [None, 2, "nearest"]], [[-1, 4], 1, Concat, [1]], # cat backbone P3 [-1, 3, C3, [256, False]], # 23 (P3/8-small) [-1, 1, Conv, [256, 3, 2]], [[-1, 20], 1, Concat, [1]], # cat head P4 [-1, 3, C3, [512, False]], # 26 (P4/16-medium) [-1, 1, Conv, [512, 3, 2]], [[-1, 16], 1, Concat, [1]], # cat head P5 [-1, 3, C3, [768, False]], # 29 (P5/32-large) [-1, 1, Conv, [768, 3, 2]], [[-1, 12], 1, Concat, [1]], # cat head P6 [-1, 3, C3, [1024, False]], # 32 (P6/64-xlarge) [[23, 26, 29, 32], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5, P6) ] ================================================ FILE: models/hub/yolov5s-LeakyReLU.yaml ================================================ # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license # Parameters nc: 80 # number of classes activation: nn.LeakyReLU(0.1) # <----- Conv() activation used throughout entire YOLOv5 model depth_multiple: 0.33 # model depth multiple width_multiple: 0.50 # layer channel multiple anchors: - [10, 13, 16, 30, 33, 23] # P3/8 - [30, 61, 62, 45, 59, 119] # P4/16 - [116, 90, 156, 198, 373, 326] # P5/32 # YOLOv5 v6.0 backbone backbone: # [from, number, module, args] [ [-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 [-1, 3, C3, [128]], [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 [-1, 6, C3, [256]], [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 [-1, 9, C3, [512]], [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 [-1, 3, C3, [1024]], [-1, 1, SPPF, [1024, 5]], # 9 ] # YOLOv5 v6.0 head head: [ [-1, 1, Conv, [512, 1, 1]], [-1, 1, nn.Upsample, [None, 2, "nearest"]], [[-1, 6], 1, Concat, [1]], # cat backbone P4 [-1, 3, C3, [512, False]], # 13 [-1, 1, Conv, [256, 1, 1]], [-1, 1, nn.Upsample, [None, 2, "nearest"]], [[-1, 4], 1, Concat, [1]], # cat backbone P3 [-1, 3, C3, [256, False]], # 17 (P3/8-small) [-1, 1, Conv, [256, 3, 2]], [[-1, 14], 1, Concat, [1]], # cat head P4 [-1, 3, C3, [512, False]], # 20 (P4/16-medium) [-1, 1, Conv, [512, 3, 2]], [[-1, 10], 1, Concat, [1]], # cat head P5 [-1, 3, C3, [1024, False]], # 23 (P5/32-large) [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) ] ================================================ FILE: models/hub/yolov5s-ghost.yaml ================================================ # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license # Parameters nc: 80 # number of classes depth_multiple: 0.33 # model depth multiple width_multiple: 0.50 # layer channel multiple anchors: - [10, 13, 16, 30, 33, 23] # P3/8 - [30, 61, 62, 45, 59, 119] # P4/16 - [116, 90, 156, 198, 373, 326] # P5/32 # YOLOv5 v6.0 backbone backbone: # [from, number, module, args] [ [-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 [-1, 1, GhostConv, [128, 3, 2]], # 1-P2/4 [-1, 3, C3Ghost, [128]], [-1, 1, GhostConv, [256, 3, 2]], # 3-P3/8 [-1, 6, C3Ghost, [256]], [-1, 1, GhostConv, [512, 3, 2]], # 5-P4/16 [-1, 9, C3Ghost, [512]], [-1, 1, GhostConv, [1024, 3, 2]], # 7-P5/32 [-1, 3, C3Ghost, [1024]], [-1, 1, SPPF, [1024, 5]], # 9 ] # YOLOv5 v6.0 head head: [ [-1, 1, GhostConv, [512, 1, 1]], [-1, 1, nn.Upsample, [None, 2, "nearest"]], [[-1, 6], 1, Concat, [1]], # cat backbone P4 [-1, 3, C3Ghost, [512, False]], # 13 [-1, 1, GhostConv, [256, 1, 1]], [-1, 1, nn.Upsample, [None, 2, "nearest"]], [[-1, 4], 1, Concat, [1]], # cat backbone P3 [-1, 3, C3Ghost, [256, False]], # 17 (P3/8-small) [-1, 1, GhostConv, [256, 3, 2]], [[-1, 14], 1, Concat, [1]], # cat head P4 [-1, 3, C3Ghost, [512, False]], # 20 (P4/16-medium) [-1, 1, GhostConv, [512, 3, 2]], [[-1, 10], 1, Concat, [1]], # cat head P5 [-1, 3, C3Ghost, [1024, False]], # 23 (P5/32-large) [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) ] ================================================ FILE: models/hub/yolov5s-transformer.yaml ================================================ # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license # Parameters nc: 80 # number of classes depth_multiple: 0.33 # model depth multiple width_multiple: 0.50 # layer channel multiple anchors: - [10, 13, 16, 30, 33, 23] # P3/8 - [30, 61, 62, 45, 59, 119] # P4/16 - [116, 90, 156, 198, 373, 326] # P5/32 # YOLOv5 v6.0 backbone backbone: # [from, number, module, args] [ [-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 [-1, 3, C3, [128]], [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 [-1, 6, C3, [256]], [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 [-1, 9, C3, [512]], [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 [-1, 3, C3TR, [1024]], # 9 <--- C3TR() Transformer module [-1, 1, SPPF, [1024, 5]], # 9 ] # YOLOv5 v6.0 head head: [ [-1, 1, Conv, [512, 1, 1]], [-1, 1, nn.Upsample, [None, 2, "nearest"]], [[-1, 6], 1, Concat, [1]], # cat backbone P4 [-1, 3, C3, [512, False]], # 13 [-1, 1, Conv, [256, 1, 1]], [-1, 1, nn.Upsample, [None, 2, "nearest"]], [[-1, 4], 1, Concat, [1]], # cat backbone P3 [-1, 3, C3, [256, False]], # 17 (P3/8-small) [-1, 1, Conv, [256, 3, 2]], [[-1, 14], 1, Concat, [1]], # cat head P4 [-1, 3, C3, [512, False]], # 20 (P4/16-medium) [-1, 1, Conv, [512, 3, 2]], [[-1, 10], 1, Concat, [1]], # cat head P5 [-1, 3, C3, [1024, False]], # 23 (P5/32-large) [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) ] ================================================ FILE: models/hub/yolov5s6.yaml ================================================ # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license # Parameters nc: 80 # number of classes depth_multiple: 0.33 # model depth multiple width_multiple: 0.50 # layer channel multiple anchors: - [19, 27, 44, 40, 38, 94] # P3/8 - [96, 68, 86, 152, 180, 137] # P4/16 - [140, 301, 303, 264, 238, 542] # P5/32 - [436, 615, 739, 380, 925, 792] # P6/64 # YOLOv5 v6.0 backbone backbone: # [from, number, module, args] [ [-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 [-1, 3, C3, [128]], [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 [-1, 6, C3, [256]], [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 [-1, 9, C3, [512]], [-1, 1, Conv, [768, 3, 2]], # 7-P5/32 [-1, 3, C3, [768]], [-1, 1, Conv, [1024, 3, 2]], # 9-P6/64 [-1, 3, C3, [1024]], [-1, 1, SPPF, [1024, 5]], # 11 ] # YOLOv5 v6.0 head head: [ [-1, 1, Conv, [768, 1, 1]], [-1, 1, nn.Upsample, [None, 2, "nearest"]], [[-1, 8], 1, Concat, [1]], # cat backbone P5 [-1, 3, C3, [768, False]], # 15 [-1, 1, Conv, [512, 1, 1]], [-1, 1, nn.Upsample, [None, 2, "nearest"]], [[-1, 6], 1, Concat, [1]], # cat backbone P4 [-1, 3, C3, [512, False]], # 19 [-1, 1, Conv, [256, 1, 1]], [-1, 1, nn.Upsample, [None, 2, "nearest"]], [[-1, 4], 1, Concat, [1]], # cat backbone P3 [-1, 3, C3, [256, False]], # 23 (P3/8-small) [-1, 1, Conv, [256, 3, 2]], [[-1, 20], 1, Concat, [1]], # cat head P4 [-1, 3, C3, [512, False]], # 26 (P4/16-medium) [-1, 1, Conv, [512, 3, 2]], [[-1, 16], 1, Concat, [1]], # cat head P5 [-1, 3, C3, [768, False]], # 29 (P5/32-large) [-1, 1, Conv, [768, 3, 2]], [[-1, 12], 1, Concat, [1]], # cat head P6 [-1, 3, C3, [1024, False]], # 32 (P6/64-xlarge) [[23, 26, 29, 32], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5, P6) ] ================================================ FILE: models/hub/yolov5x6.yaml ================================================ # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license # Parameters nc: 80 # number of classes depth_multiple: 1.33 # model depth multiple width_multiple: 1.25 # layer channel multiple anchors: - [19, 27, 44, 40, 38, 94] # P3/8 - [96, 68, 86, 152, 180, 137] # P4/16 - [140, 301, 303, 264, 238, 542] # P5/32 - [436, 615, 739, 380, 925, 792] # P6/64 # YOLOv5 v6.0 backbone backbone: # [from, number, module, args] [ [-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 [-1, 3, C3, [128]], [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 [-1, 6, C3, [256]], [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 [-1, 9, C3, [512]], [-1, 1, Conv, [768, 3, 2]], # 7-P5/32 [-1, 3, C3, [768]], [-1, 1, Conv, [1024, 3, 2]], # 9-P6/64 [-1, 3, C3, [1024]], [-1, 1, SPPF, [1024, 5]], # 11 ] # YOLOv5 v6.0 head head: [ [-1, 1, Conv, [768, 1, 1]], [-1, 1, nn.Upsample, [None, 2, "nearest"]], [[-1, 8], 1, Concat, [1]], # cat backbone P5 [-1, 3, C3, [768, False]], # 15 [-1, 1, Conv, [512, 1, 1]], [-1, 1, nn.Upsample, [None, 2, "nearest"]], [[-1, 6], 1, Concat, [1]], # cat backbone P4 [-1, 3, C3, [512, False]], # 19 [-1, 1, Conv, [256, 1, 1]], [-1, 1, nn.Upsample, [None, 2, "nearest"]], [[-1, 4], 1, Concat, [1]], # cat backbone P3 [-1, 3, C3, [256, False]], # 23 (P3/8-small) [-1, 1, Conv, [256, 3, 2]], [[-1, 20], 1, Concat, [1]], # cat head P4 [-1, 3, C3, [512, False]], # 26 (P4/16-medium) [-1, 1, Conv, [512, 3, 2]], [[-1, 16], 1, Concat, [1]], # cat head P5 [-1, 3, C3, [768, False]], # 29 (P5/32-large) [-1, 1, Conv, [768, 3, 2]], [[-1, 12], 1, Concat, [1]], # cat head P6 [-1, 3, C3, [1024, False]], # 32 (P6/64-xlarge) [[23, 26, 29, 32], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5, P6) ] ================================================ FILE: models/segment/yolov5l-seg.yaml ================================================ # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license # Parameters nc: 80 # number of classes depth_multiple: 1.0 # model depth multiple width_multiple: 1.0 # layer channel multiple anchors: - [10, 13, 16, 30, 33, 23] # P3/8 - [30, 61, 62, 45, 59, 119] # P4/16 - [116, 90, 156, 198, 373, 326] # P5/32 # YOLOv5 v6.0 backbone backbone: # [from, number, module, args] [ [-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 [-1, 3, C3, [128]], [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 [-1, 6, C3, [256]], [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 [-1, 9, C3, [512]], [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 [-1, 3, C3, [1024]], [-1, 1, SPPF, [1024, 5]], # 9 ] # YOLOv5 v6.0 head head: [ [-1, 1, Conv, [512, 1, 1]], [-1, 1, nn.Upsample, [None, 2, "nearest"]], [[-1, 6], 1, Concat, [1]], # cat backbone P4 [-1, 3, C3, [512, False]], # 13 [-1, 1, Conv, [256, 1, 1]], [-1, 1, nn.Upsample, [None, 2, "nearest"]], [[-1, 4], 1, Concat, [1]], # cat backbone P3 [-1, 3, C3, [256, False]], # 17 (P3/8-small) [-1, 1, Conv, [256, 3, 2]], [[-1, 14], 1, Concat, [1]], # cat head P4 [-1, 3, C3, [512, False]], # 20 (P4/16-medium) [-1, 1, Conv, [512, 3, 2]], [[-1, 10], 1, Concat, [1]], # cat head P5 [-1, 3, C3, [1024, False]], # 23 (P5/32-large) [[17, 20, 23], 1, Segment, [nc, anchors, 32, 256]], # Detect(P3, P4, P5) ] ================================================ FILE: models/segment/yolov5m-seg.yaml ================================================ # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license # Parameters nc: 80 # number of classes depth_multiple: 0.67 # model depth multiple width_multiple: 0.75 # layer channel multiple anchors: - [10, 13, 16, 30, 33, 23] # P3/8 - [30, 61, 62, 45, 59, 119] # P4/16 - [116, 90, 156, 198, 373, 326] # P5/32 # YOLOv5 v6.0 backbone backbone: # [from, number, module, args] [ [-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 [-1, 3, C3, [128]], [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 [-1, 6, C3, [256]], [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 [-1, 9, C3, [512]], [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 [-1, 3, C3, [1024]], [-1, 1, SPPF, [1024, 5]], # 9 ] # YOLOv5 v6.0 head head: [ [-1, 1, Conv, [512, 1, 1]], [-1, 1, nn.Upsample, [None, 2, "nearest"]], [[-1, 6], 1, Concat, [1]], # cat backbone P4 [-1, 3, C3, [512, False]], # 13 [-1, 1, Conv, [256, 1, 1]], [-1, 1, nn.Upsample, [None, 2, "nearest"]], [[-1, 4], 1, Concat, [1]], # cat backbone P3 [-1, 3, C3, [256, False]], # 17 (P3/8-small) [-1, 1, Conv, [256, 3, 2]], [[-1, 14], 1, Concat, [1]], # cat head P4 [-1, 3, C3, [512, False]], # 20 (P4/16-medium) [-1, 1, Conv, [512, 3, 2]], [[-1, 10], 1, Concat, [1]], # cat head P5 [-1, 3, C3, [1024, False]], # 23 (P5/32-large) [[17, 20, 23], 1, Segment, [nc, anchors, 32, 256]], # Detect(P3, P4, P5) ] ================================================ FILE: models/segment/yolov5n-seg.yaml ================================================ # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license # Parameters nc: 80 # number of classes depth_multiple: 0.33 # model depth multiple width_multiple: 0.25 # layer channel multiple anchors: - [10, 13, 16, 30, 33, 23] # P3/8 - [30, 61, 62, 45, 59, 119] # P4/16 - [116, 90, 156, 198, 373, 326] # P5/32 # YOLOv5 v6.0 backbone backbone: # [from, number, module, args] [ [-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 [-1, 3, C3, [128]], [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 [-1, 6, C3, [256]], [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 [-1, 9, C3, [512]], [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 [-1, 3, C3, [1024]], [-1, 1, SPPF, [1024, 5]], # 9 ] # YOLOv5 v6.0 head head: [ [-1, 1, Conv, [512, 1, 1]], [-1, 1, nn.Upsample, [None, 2, "nearest"]], [[-1, 6], 1, Concat, [1]], # cat backbone P4 [-1, 3, C3, [512, False]], # 13 [-1, 1, Conv, [256, 1, 1]], [-1, 1, nn.Upsample, [None, 2, "nearest"]], [[-1, 4], 1, Concat, [1]], # cat backbone P3 [-1, 3, C3, [256, False]], # 17 (P3/8-small) [-1, 1, Conv, [256, 3, 2]], [[-1, 14], 1, Concat, [1]], # cat head P4 [-1, 3, C3, [512, False]], # 20 (P4/16-medium) [-1, 1, Conv, [512, 3, 2]], [[-1, 10], 1, Concat, [1]], # cat head P5 [-1, 3, C3, [1024, False]], # 23 (P5/32-large) [[17, 20, 23], 1, Segment, [nc, anchors, 32, 256]], # Detect(P3, P4, P5) ] ================================================ FILE: models/segment/yolov5s-seg.yaml ================================================ # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license # Parameters nc: 80 # number of classes depth_multiple: 0.33 # model depth multiple width_multiple: 0.5 # layer channel multiple anchors: - [10, 13, 16, 30, 33, 23] # P3/8 - [30, 61, 62, 45, 59, 119] # P4/16 - [116, 90, 156, 198, 373, 326] # P5/32 # YOLOv5 v6.0 backbone backbone: # [from, number, module, args] [ [-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 [-1, 3, C3, [128]], [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 [-1, 6, C3, [256]], [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 [-1, 9, C3, [512]], [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 [-1, 3, C3, [1024]], [-1, 1, SPPF, [1024, 5]], # 9 ] # YOLOv5 v6.0 head head: [ [-1, 1, Conv, [512, 1, 1]], [-1, 1, nn.Upsample, [None, 2, "nearest"]], [[-1, 6], 1, Concat, [1]], # cat backbone P4 [-1, 3, C3, [512, False]], # 13 [-1, 1, Conv, [256, 1, 1]], [-1, 1, nn.Upsample, [None, 2, "nearest"]], [[-1, 4], 1, Concat, [1]], # cat backbone P3 [-1, 3, C3, [256, False]], # 17 (P3/8-small) [-1, 1, Conv, [256, 3, 2]], [[-1, 14], 1, Concat, [1]], # cat head P4 [-1, 3, C3, [512, False]], # 20 (P4/16-medium) [-1, 1, Conv, [512, 3, 2]], [[-1, 10], 1, Concat, [1]], # cat head P5 [-1, 3, C3, [1024, False]], # 23 (P5/32-large) [[17, 20, 23], 1, Segment, [nc, anchors, 32, 256]], # Detect(P3, P4, P5) ] ================================================ FILE: models/segment/yolov5x-seg.yaml ================================================ # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license # Parameters nc: 80 # number of classes depth_multiple: 1.33 # model depth multiple width_multiple: 1.25 # layer channel multiple anchors: - [10, 13, 16, 30, 33, 23] # P3/8 - [30, 61, 62, 45, 59, 119] # P4/16 - [116, 90, 156, 198, 373, 326] # P5/32 # YOLOv5 v6.0 backbone backbone: # [from, number, module, args] [ [-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 [-1, 3, C3, [128]], [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 [-1, 6, C3, [256]], [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 [-1, 9, C3, [512]], [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 [-1, 3, C3, [1024]], [-1, 1, SPPF, [1024, 5]], # 9 ] # YOLOv5 v6.0 head head: [ [-1, 1, Conv, [512, 1, 1]], [-1, 1, nn.Upsample, [None, 2, "nearest"]], [[-1, 6], 1, Concat, [1]], # cat backbone P4 [-1, 3, C3, [512, False]], # 13 [-1, 1, Conv, [256, 1, 1]], [-1, 1, nn.Upsample, [None, 2, "nearest"]], [[-1, 4], 1, Concat, [1]], # cat backbone P3 [-1, 3, C3, [256, False]], # 17 (P3/8-small) [-1, 1, Conv, [256, 3, 2]], [[-1, 14], 1, Concat, [1]], # cat head P4 [-1, 3, C3, [512, False]], # 20 (P4/16-medium) [-1, 1, Conv, [512, 3, 2]], [[-1, 10], 1, Concat, [1]], # cat head P5 [-1, 3, C3, [1024, False]], # 23 (P5/32-large) [[17, 20, 23], 1, Segment, [nc, anchors, 32, 256]], # Detect(P3, P4, P5) ] ================================================ FILE: models/tf.py ================================================ # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license """ TensorFlow, Keras and TFLite versions of YOLOv3 Authored by https://github.com/zldrobit in PR https://github.com/ultralytics/yolov5/pull/1127. Usage: $ python models/tf.py --weights yolov5s.pt Export: $ python export.py --weights yolov5s.pt --include saved_model pb tflite tfjs """ import argparse import sys from copy import deepcopy from pathlib import Path FILE = Path(__file__).resolve() ROOT = FILE.parents[1] # YOLOv3 root directory if str(ROOT) not in sys.path: sys.path.append(str(ROOT)) # add ROOT to PATH # ROOT = ROOT.relative_to(Path.cwd()) # relative import numpy as np import tensorflow as tf import torch import torch.nn as nn from tensorflow import keras from models.common import ( C3, SPP, SPPF, Bottleneck, BottleneckCSP, C3x, Concat, Conv, CrossConv, DWConv, DWConvTranspose2d, Focus, autopad, ) from models.experimental import MixConv2d, attempt_load from models.yolo import Detect, Segment from utils.activations import SiLU from utils.general import LOGGER, make_divisible, print_args class TFBN(keras.layers.Layer): """A TensorFlow BatchNormalization wrapper layer initialized with specific weights for YOLOv3 models.""" def __init__(self, w=None): """Initializes TFBN with weights, wrapping TensorFlow's BatchNormalization layer with specific initializers.""" super().__init__() self.bn = keras.layers.BatchNormalization( beta_initializer=keras.initializers.Constant(w.bias.numpy()), gamma_initializer=keras.initializers.Constant(w.weight.numpy()), moving_mean_initializer=keras.initializers.Constant(w.running_mean.numpy()), moving_variance_initializer=keras.initializers.Constant(w.running_var.numpy()), epsilon=w.eps, ) def call(self, inputs): """Applies batch normalization on inputs using initialized parameters.""" return self.bn(inputs) class TFPad(keras.layers.Layer): """Pads inputs in spatial dimensions 1 and 2 using specified padding width as an int or (int, int) tuple/list.""" def __init__(self, pad): """Initializes a padding layer for spatial dimensions 1 and 2, with `pad` as int or (int, int) tuple/list.""" super().__init__() if isinstance(pad, int): self.pad = tf.constant([[0, 0], [pad, pad], [pad, pad], [0, 0]]) else: # tuple/list self.pad = tf.constant([[0, 0], [pad[0], pad[0]], [pad[1], pad[1]], [0, 0]]) def call(self, inputs): """Applies constant padding to inputs with `pad` specifying padding width; `pad` can be an int or (int, int) tuple/list. """ return tf.pad(inputs, self.pad, mode="constant", constant_values=0) class TFConv(keras.layers.Layer): """Implements a standard convolutional layer with optional batch normalization and activation for TensorFlow.""" def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True, w=None): """Initializes a convolutional layer with customizable filters, kernel size, stride, padding, groups, and activation. """ super().__init__() assert g == 1, "TF v2.2 Conv2D does not support 'groups' argument" # TensorFlow convolution padding is inconsistent with PyTorch (e.g. k=3 s=2 'SAME' padding) # see https://stackoverflow.com/questions/52975843/comparing-conv2d-with-padding-between-tensorflow-and-pytorch conv = keras.layers.Conv2D( filters=c2, kernel_size=k, strides=s, padding="SAME" if s == 1 else "VALID", use_bias=not hasattr(w, "bn"), kernel_initializer=keras.initializers.Constant(w.conv.weight.permute(2, 3, 1, 0).numpy()), bias_initializer="zeros" if hasattr(w, "bn") else keras.initializers.Constant(w.conv.bias.numpy()), ) self.conv = conv if s == 1 else keras.Sequential([TFPad(autopad(k, p)), conv]) self.bn = TFBN(w.bn) if hasattr(w, "bn") else tf.identity self.act = activations(w.act) if act else tf.identity def call(self, inputs): """Executes the convolution, batch normalization, and activation on the input data.""" return self.act(self.bn(self.conv(inputs))) class TFDWConv(keras.layers.Layer): """Implements a depthwise convolutional layer with optional batch normalization and activation for TensorFlow.""" def __init__(self, c1, c2, k=1, s=1, p=None, act=True, w=None): """Initializes a depthwise convolutional layer with optional batch normalization and activation.""" super().__init__() assert c2 % c1 == 0, f"TFDWConv() output={c2} must be a multiple of input={c1} channels" conv = keras.layers.DepthwiseConv2D( kernel_size=k, depth_multiplier=c2 // c1, strides=s, padding="SAME" if s == 1 else "VALID", use_bias=not hasattr(w, "bn"), depthwise_initializer=keras.initializers.Constant(w.conv.weight.permute(2, 3, 1, 0).numpy()), bias_initializer="zeros" if hasattr(w, "bn") else keras.initializers.Constant(w.conv.bias.numpy()), ) self.conv = conv if s == 1 else keras.Sequential([TFPad(autopad(k, p)), conv]) self.bn = TFBN(w.bn) if hasattr(w, "bn") else tf.identity self.act = activations(w.act) if act else tf.identity def call(self, inputs): """Applies convolution, batch normalization, and activation to the input tensor.""" return self.act(self.bn(self.conv(inputs))) class TFDWConvTranspose2d(keras.layers.Layer): """Implements a depthwise transposed convolutional layer for TensorFlow with equal input and output channels.""" def __init__(self, c1, c2, k=1, s=1, p1=0, p2=0, w=None): """Initializes TFDWConvTranspose2d with ch_in=c1=ch_out, k=4, p1=1; sets up depthwise Conv2DTranspose layers.""" super().__init__() assert c1 == c2, f"TFDWConv() output={c2} must be equal to input={c1} channels" assert k == 4 and p1 == 1, "TFDWConv() only valid for k=4 and p1=1" weight, bias = w.weight.permute(2, 3, 1, 0).numpy(), w.bias.numpy() self.c1 = c1 self.conv = [ keras.layers.Conv2DTranspose( filters=1, kernel_size=k, strides=s, padding="VALID", output_padding=p2, use_bias=True, kernel_initializer=keras.initializers.Constant(weight[..., i : i + 1]), bias_initializer=keras.initializers.Constant(bias[i]), ) for i in range(c1) ] def call(self, inputs): """Performs a forward pass by applying parallel convolutions to split input tensors and concatenates the results. """ return tf.concat([m(x) for m, x in zip(self.conv, tf.split(inputs, self.c1, 3))], 3)[:, 1:-1, 1:-1] class TFFocus(keras.layers.Layer): """Focuses spatial information into channel space using a convolutional layer for efficient feature extraction.""" def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True, w=None): """Initializes TFFocus layer for efficient information focusing into channel-space with customizable convolution parameters. """ super().__init__() self.conv = TFConv(c1 * 4, c2, k, s, p, g, act, w.conv) def call(self, inputs): # x(b,w,h,c) -> y(b,w/2,h/2,4c) """Executes TFFocus layer operation, reducing spatial dimensions by 2 and quadrupling channels, input shape (b,w,h,c). """ inputs = [inputs[:, ::2, ::2, :], inputs[:, 1::2, ::2, :], inputs[:, ::2, 1::2, :], inputs[:, 1::2, 1::2, :]] return self.conv(tf.concat(inputs, 3)) class TFBottleneck(keras.layers.Layer): """A TensorFlow bottleneck layer with optional shortcut connections, channel expansion, and group convolutions.""" def __init__(self, c1, c2, shortcut=True, g=1, e=0.5, w=None): # ch_in, ch_out, shortcut, groups, expansion """Initializes a standard bottleneck layer with optional shortcut, channel expansion, and group convolutions.""" super().__init__() c_ = int(c2 * e) # hidden channels self.cv1 = TFConv(c1, c_, 1, 1, w=w.cv1) self.cv2 = TFConv(c_, c2, 3, 1, g=g, w=w.cv2) self.add = shortcut and c1 == c2 def call(self, inputs): """Executes a bottleneck layer with optional shortcut; returns either input + convoluted input or just convoluted input. """ return inputs + self.cv2(self.cv1(inputs)) if self.add else self.cv2(self.cv1(inputs)) class TFCrossConv(keras.layers.Layer): """Implements a cross convolutional layer with customizable channels, kernel size, stride, groups, and shortcut.""" def __init__(self, c1, c2, k=3, s=1, g=1, e=1.0, shortcut=False, w=None): """Initializes cross convolutional layer with parameters for channel sizes, kernel size, stride, groups, expansion factor, shortcut option, and weights. """ super().__init__() c_ = int(c2 * e) # hidden channels self.cv1 = TFConv(c1, c_, (1, k), (1, s), w=w.cv1) self.cv2 = TFConv(c_, c2, (k, 1), (s, 1), g=g, w=w.cv2) self.add = shortcut and c1 == c2 def call(self, inputs): """Executes the function, optionally adding input to output if shapes match; inputs: tensor [B, C, H, W].""" return inputs + self.cv2(self.cv1(inputs)) if self.add else self.cv2(self.cv1(inputs)) class TFConv2d(keras.layers.Layer): """Implements a TensorFlow 2.2+ Conv2D layer as a substitute for PyTorch's Conv2D with customizable parameters.""" def __init__(self, c1, c2, k, s=1, g=1, bias=True, w=None): """Initializes TFConv2d layer for TensorFlow 2.2+, substituting PyTorch Conv2D; c1, c2: channels, k: kernel size, s: stride. """ super().__init__() assert g == 1, "TF v2.2 Conv2D does not support 'groups' argument" self.conv = keras.layers.Conv2D( filters=c2, kernel_size=k, strides=s, padding="VALID", use_bias=bias, kernel_initializer=keras.initializers.Constant(w.weight.permute(2, 3, 1, 0).numpy()), bias_initializer=keras.initializers.Constant(w.bias.numpy()) if bias else None, ) def call(self, inputs): """Applies convolution to the inputs using initialized weights and biases, returning the convolved output.""" return self.conv(inputs) class TFBottleneckCSP(keras.layers.Layer): """Implements a Cross Stage Partial (CSP) Bottleneck layer for efficient feature extraction in neural networks.""" def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5, w=None): """Initializes CSP Bottleneck layer with channel configurations and optional shortcut, groups, expansion, and weights. """ super().__init__() c_ = int(c2 * e) # hidden channels self.cv1 = TFConv(c1, c_, 1, 1, w=w.cv1) self.cv2 = TFConv2d(c1, c_, 1, 1, bias=False, w=w.cv2) self.cv3 = TFConv2d(c_, c_, 1, 1, bias=False, w=w.cv3) self.cv4 = TFConv(2 * c_, c2, 1, 1, w=w.cv4) self.bn = TFBN(w.bn) self.act = lambda x: keras.activations.swish(x) self.m = keras.Sequential([TFBottleneck(c_, c_, shortcut, g, e=1.0, w=w.m[j]) for j in range(n)]) def call(self, inputs): """Executes the forward pass by combining features through convolutions, activation, and batch normalization.""" y1 = self.cv3(self.m(self.cv1(inputs))) y2 = self.cv2(inputs) return self.cv4(self.act(self.bn(tf.concat((y1, y2), axis=3)))) class TFC3(keras.layers.Layer): """CSP Bottleneck layer with 3 convolutions for enhanced feature extraction and integration in TensorFlow models.""" def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5, w=None): """Initializes a CSP Bottleneck layer with 3 convolutions for channel manipulation and feature integration.""" super().__init__() c_ = int(c2 * e) # hidden channels self.cv1 = TFConv(c1, c_, 1, 1, w=w.cv1) self.cv2 = TFConv(c1, c_, 1, 1, w=w.cv2) self.cv3 = TFConv(2 * c_, c2, 1, 1, w=w.cv3) self.m = keras.Sequential([TFBottleneck(c_, c_, shortcut, g, e=1.0, w=w.m[j]) for j in range(n)]) def call(self, inputs): """Executes model forwarding, combining features using TF layers and concatenation, returning the resulting tensor. """ return self.cv3(tf.concat((self.m(self.cv1(inputs)), self.cv2(inputs)), axis=3)) class TFC3x(keras.layers.Layer): """Implements a CSP Bottleneck layer with cross-convolutions for enhanced feature extraction in YOLOv3 models.""" def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5, w=None): """Initializes a TFC3x layer with cross-convolutions, expanding and concatenating features for given channel inputs and outputs. """ super().__init__() c_ = int(c2 * e) # hidden channels self.cv1 = TFConv(c1, c_, 1, 1, w=w.cv1) self.cv2 = TFConv(c1, c_, 1, 1, w=w.cv2) self.cv3 = TFConv(2 * c_, c2, 1, 1, w=w.cv3) self.m = keras.Sequential( [TFCrossConv(c_, c_, k=3, s=1, g=g, e=1.0, shortcut=shortcut, w=w.m[j]) for j in range(n)] ) def call(self, inputs): """Executes model forwarding, combining features through conv layers and concatenation.""" return self.cv3(tf.concat((self.m(self.cv1(inputs)), self.cv2(inputs)), axis=3)) class TFSPP(keras.layers.Layer): """Implements Spatial Pyramid Pooling (SPP) for YOLOv3-SPP with configurable channels and kernel sizes.""" def __init__(self, c1, c2, k=(5, 9, 13), w=None): """Initializes a Spatial Pyramid Pooling layer for YOLOv3-SPP with configurable in/out channels and kernel sizes. """ super().__init__() c_ = c1 // 2 # hidden channels self.cv1 = TFConv(c1, c_, 1, 1, w=w.cv1) self.cv2 = TFConv(c_ * (len(k) + 1), c2, 1, 1, w=w.cv2) self.m = [keras.layers.MaxPool2D(pool_size=x, strides=1, padding="SAME") for x in k] def call(self, inputs): """Applies transformations and concatenates feature maps from multiple kernel-sized max-poolings.""" x = self.cv1(inputs) return self.cv2(tf.concat([x] + [m(x) for m in self.m], 3)) class TFSPPF(keras.layers.Layer): """Implements a fast spatial pyramid pooling layer for efficient multi-scale feature extraction in YOLOv3 models.""" def __init__(self, c1, c2, k=5, w=None): """Initializes a Spatial Pyramid Pooling-Fast layer with specified channels, kernel size, and optional weights. """ super().__init__() c_ = c1 // 2 # hidden channels self.cv1 = TFConv(c1, c_, 1, 1, w=w.cv1) self.cv2 = TFConv(c_ * 4, c2, 1, 1, w=w.cv2) self.m = keras.layers.MaxPool2D(pool_size=k, strides=1, padding="SAME") def call(self, inputs): """Applies two TFConvs and max pooling with concatenation, returning the processed tensor.""" x = self.cv1(inputs) y1 = self.m(x) y2 = self.m(y1) return self.cv2(tf.concat([x, y1, y2, self.m(y2)], 3)) class TFDetect(keras.layers.Layer): """Implements YOLOv3 detection layer in TensorFlow for object detection with configurable classes and anchors.""" def __init__(self, nc=80, anchors=(), ch=(), imgsz=(640, 640), w=None): # detection layer """Initializes a YOLOv3 detection layer with specified classes, anchors, channels, image size, and weights.""" super().__init__() self.stride = tf.convert_to_tensor(w.stride.numpy(), dtype=tf.float32) self.nc = nc # number of classes self.no = nc + 5 # number of outputs per anchor self.nl = len(anchors) # number of detection layers self.na = len(anchors[0]) // 2 # number of anchors self.grid = [tf.zeros(1)] * self.nl # init grid self.anchors = tf.convert_to_tensor(w.anchors.numpy(), dtype=tf.float32) self.anchor_grid = tf.reshape(self.anchors * tf.reshape(self.stride, [self.nl, 1, 1]), [self.nl, 1, -1, 1, 2]) self.m = [TFConv2d(x, self.no * self.na, 1, w=w.m[i]) for i, x in enumerate(ch)] self.training = False # set to False after building model self.imgsz = imgsz for i in range(self.nl): ny, nx = self.imgsz[0] // self.stride[i], self.imgsz[1] // self.stride[i] self.grid[i] = self._make_grid(nx, ny) def call(self, inputs): """Performs inference on inputs, transforming shape to (batch_size, ny*nx, num_anchors, num_outputs) for each layer. """ z = [] # inference output x = [] for i in range(self.nl): x.append(self.m[i](inputs[i])) # x(bs,20,20,255) to x(bs,3,20,20,85) ny, nx = self.imgsz[0] // self.stride[i], self.imgsz[1] // self.stride[i] x[i] = tf.reshape(x[i], [-1, ny * nx, self.na, self.no]) if not self.training: # inference y = x[i] grid = tf.transpose(self.grid[i], [0, 2, 1, 3]) - 0.5 anchor_grid = tf.transpose(self.anchor_grid[i], [0, 2, 1, 3]) * 4 xy = (tf.sigmoid(y[..., 0:2]) * 2 + grid) * self.stride[i] # xy wh = tf.sigmoid(y[..., 2:4]) ** 2 * anchor_grid # Normalize xywh to 0-1 to reduce calibration error xy /= tf.constant([[self.imgsz[1], self.imgsz[0]]], dtype=tf.float32) wh /= tf.constant([[self.imgsz[1], self.imgsz[0]]], dtype=tf.float32) y = tf.concat([xy, wh, tf.sigmoid(y[..., 4 : 5 + self.nc]), y[..., 5 + self.nc :]], -1) z.append(tf.reshape(y, [-1, self.na * ny * nx, self.no])) return tf.transpose(x, [0, 2, 1, 3]) if self.training else (tf.concat(z, 1),) @staticmethod def _make_grid(nx=20, ny=20): """Generates a grid of shape [1, 1, ny * nx, 2] with ranges [0, nx) and [0, ny) for object detection.""" # return torch.stack((xv, yv), 2).view((1, 1, ny, nx, 2)).float() xv, yv = tf.meshgrid(tf.range(nx), tf.range(ny)) return tf.cast(tf.reshape(tf.stack([xv, yv], 2), [1, 1, ny * nx, 2]), dtype=tf.float32) class TFSegment(TFDetect): """Implements YOLOv3 segmentation head for object detection and segmentation tasks using TensorFlow.""" def __init__(self, nc=80, anchors=(), nm=32, npr=256, ch=(), imgsz=(640, 640), w=None): """Initializes a YOLOv3 Segment head with customizable parameters for segmentation models.""" super().__init__(nc, anchors, ch, imgsz, w) self.nm = nm # number of masks self.npr = npr # number of protos self.no = 5 + nc + self.nm # number of outputs per anchor self.m = [TFConv2d(x, self.no * self.na, 1, w=w.m[i]) for i, x in enumerate(ch)] # output conv self.proto = TFProto(ch[0], self.npr, self.nm, w=w.proto) # protos self.detect = TFDetect.call def call(self, x): """Executes model's forward pass, returning predictions and optionally full-size protos if training.""" p = self.proto(x[0]) # p = TFUpsample(None, scale_factor=4, mode='nearest')(self.proto(x[0])) # (optional) full-size protos p = tf.transpose(p, [0, 3, 1, 2]) # from shape(1,160,160,32) to shape(1,32,160,160) x = self.detect(self, x) return (x, p) if self.training else (x[0], p) class TFProto(keras.layers.Layer): """Implements a TensorFlow layer for feature processing with convolution and upsample operations.""" def __init__(self, c1, c_=256, c2=32, w=None): """Initializes a TFProto layer with convolution and upsample operations for feature processing.""" super().__init__() self.cv1 = TFConv(c1, c_, k=3, w=w.cv1) self.upsample = TFUpsample(None, scale_factor=2, mode="nearest") self.cv2 = TFConv(c_, c_, k=3, w=w.cv2) self.cv3 = TFConv(c_, c2, w=w.cv3) def call(self, inputs): """Performs convolution and upsample operations on input features, returning processed features.""" return self.cv3(self.cv2(self.upsample(self.cv1(inputs)))) class TFUpsample(keras.layers.Layer): """Implements an upsample layer using TensorFlow with specified size, scale factor, and interpolation mode.""" def __init__(self, size, scale_factor, mode, w=None): # warning: all arguments needed including 'w' """Initializes an upsample layer with specific size, doubling scale factor (>0, even), interpolation mode, and optional weights. """ super().__init__() assert scale_factor % 2 == 0, "scale_factor must be multiple of 2" self.upsample = lambda x: tf.image.resize(x, (x.shape[1] * scale_factor, x.shape[2] * scale_factor), mode) # self.upsample = keras.layers.UpSampling2D(size=scale_factor, interpolation=mode) # with default arguments: align_corners=False, half_pixel_centers=False # self.upsample = lambda x: tf.raw_ops.ResizeNearestNeighbor(images=x, # size=(x.shape[1] * 2, x.shape[2] * 2)) def call(self, inputs): """Applies upsample lambda function to the input tensor, returning the upsampled tensor.""" return self.upsample(inputs) class TFConcat(keras.layers.Layer): """Concatenates input tensors along the specified dimension (NHWC format) using TensorFlow.""" def __init__(self, dimension=1, w=None): """Initializes a TensorFlow layer to concatenate tensors along the NHWC dimension, requiring dimension=1.""" super().__init__() assert dimension == 1, "convert only NCHW to NHWC concat" self.d = 3 def call(self, inputs): """Concatenates tensors along NHWC dimension (3rd axis); `inputs` is a list of tensors.""" return tf.concat(inputs, self.d) def parse_model(d, ch, model, imgsz): # model_dict, input_channels(3) """Parses model configuration and constructs Keras model with layer connectivity, returning the model and save list. """ LOGGER.info(f"\n{'':>3}{'from':>18}{'n':>3}{'params':>10} {'module':<40}{'arguments':<30}") anchors, nc, gd, gw = d["anchors"], d["nc"], d["depth_multiple"], d["width_multiple"] na = (len(anchors[0]) // 2) if isinstance(anchors, list) else anchors # number of anchors no = na * (nc + 5) # number of outputs = anchors * (classes + 5) layers, save, c2 = [], [], ch[-1] # layers, savelist, ch out for i, (f, n, m, args) in enumerate(d["backbone"] + d["head"]): # from, number, module, args m_str = m m = eval(m) if isinstance(m, str) else m # eval strings for j, a in enumerate(args): try: args[j] = eval(a) if isinstance(a, str) else a # eval strings except NameError: pass n = max(round(n * gd), 1) if n > 1 else n # depth gain if m in [ nn.Conv2d, Conv, DWConv, DWConvTranspose2d, Bottleneck, SPP, SPPF, MixConv2d, Focus, CrossConv, BottleneckCSP, C3, C3x, ]: c1, c2 = ch[f], args[0] c2 = make_divisible(c2 * gw, 8) if c2 != no else c2 args = [c1, c2, *args[1:]] if m in [BottleneckCSP, C3, C3x]: args.insert(2, n) n = 1 elif m is nn.BatchNorm2d: args = [ch[f]] elif m is Concat: c2 = sum(ch[-1 if x == -1 else x + 1] for x in f) elif m in [Detect, Segment]: args.append([ch[x + 1] for x in f]) if isinstance(args[1], int): # number of anchors args[1] = [list(range(args[1] * 2))] * len(f) if m is Segment: args[3] = make_divisible(args[3] * gw, 8) args.append(imgsz) else: c2 = ch[f] tf_m = eval("TF" + m_str.replace("nn.", "")) m_ = ( keras.Sequential([tf_m(*args, w=model.model[i][j]) for j in range(n)]) if n > 1 else tf_m(*args, w=model.model[i]) ) # module torch_m_ = nn.Sequential(*(m(*args) for _ in range(n))) if n > 1 else m(*args) # module t = str(m)[8:-2].replace("__main__.", "") # module type np = sum(x.numel() for x in torch_m_.parameters()) # number params m_.i, m_.f, m_.type, m_.np = i, f, t, np # attach index, 'from' index, type, number params LOGGER.info(f"{i:>3}{f!s:>18}{n!s:>3}{np:>10} {t:<40}{args!s:<30}") # print save.extend(x % i for x in ([f] if isinstance(f, int) else f) if x != -1) # append to savelist layers.append(m_) ch.append(c2) return keras.Sequential(layers), sorted(save) class TFModel: """TensorFlow implementation of YOLOv3 for object detection, supporting Keras and TFLite models.""" def __init__(self, cfg="yolov5s.yaml", ch=3, nc=None, model=None, imgsz=(640, 640)): # model, channels, classes """Initializes TF YOLOv3 model with config, channels, classes, optional pre-loaded model, and input image size. """ super().__init__() if isinstance(cfg, dict): self.yaml = cfg # model dict else: # is *.yaml import yaml # for torch hub self.yaml_file = Path(cfg).name with open(cfg) as f: self.yaml = yaml.load(f, Loader=yaml.FullLoader) # model dict # Define model if nc and nc != self.yaml["nc"]: LOGGER.info(f"Overriding {cfg} nc={self.yaml['nc']} with nc={nc}") self.yaml["nc"] = nc # override yaml value self.model, self.savelist = parse_model(deepcopy(self.yaml), ch=[ch], model=model, imgsz=imgsz) def predict( self, inputs, tf_nms=False, agnostic_nms=False, topk_per_class=100, topk_all=100, iou_thres=0.45, conf_thres=0.25, ): """Performs inference on input data using a YOLOv3 model, including optional TensorFlow NMS.""" y = [] # outputs x = inputs for m in self.model.layers: if m.f != -1: # if not from previous layer 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 x = m(x) # run y.append(x if m.i in self.savelist else None) # save output # Add TensorFlow NMS if tf_nms: boxes = self._xywh2xyxy(x[0][..., :4]) probs = x[0][:, :, 4:5] classes = x[0][:, :, 5:] scores = probs * classes if agnostic_nms: nms = AgnosticNMS()((boxes, classes, scores), topk_all, iou_thres, conf_thres) else: boxes = tf.expand_dims(boxes, 2) nms = tf.image.combined_non_max_suppression( boxes, scores, topk_per_class, topk_all, iou_thres, conf_thres, clip_boxes=False ) return (nms,) return x # output [1,6300,85] = [xywh, conf, class0, class1, ...] # x = x[0] # [x(1,6300,85), ...] to x(6300,85) # xywh = x[..., :4] # x(6300,4) boxes # conf = x[..., 4:5] # x(6300,1) confidences # cls = tf.reshape(tf.cast(tf.argmax(x[..., 5:], axis=1), tf.float32), (-1, 1)) # x(6300,1) classes # return tf.concat([conf, cls, xywh], 1) @staticmethod def _xywh2xyxy(xywh): """Converts bounding boxes from [x, y, w, h] format to [x1, y1, x2, y2], where xy1=top-left, xy2=bottom- right. """ x, y, w, h = tf.split(xywh, num_or_size_splits=4, axis=-1) return tf.concat([x - w / 2, y - h / 2, x + w / 2, y + h / 2], axis=-1) class AgnosticNMS(keras.layers.Layer): """Applies class-agnostic non-maximum suppression (NMS) to filter detections by IoU and confidence thresholds.""" def call(self, input, topk_all, iou_thres, conf_thres): """Applies non-maximum suppression (NMS) to filter detections based on IoU, confidence thresholds, and top-K.""" return tf.map_fn( lambda x: self._nms(x, topk_all, iou_thres, conf_thres), input, fn_output_signature=(tf.float32, tf.float32, tf.float32, tf.int32), name="agnostic_nms", ) @staticmethod def _nms(x, topk_all=100, iou_thres=0.45, conf_thres=0.25): # agnostic NMS """Performs non-max suppression on bounding boxes with class, IoU, and confidence thresholds; returns processed boxes, scores, classes, and count. """ boxes, classes, scores = x class_inds = tf.cast(tf.argmax(classes, axis=-1), tf.float32) scores_inp = tf.reduce_max(scores, -1) selected_inds = tf.image.non_max_suppression( boxes, scores_inp, max_output_size=topk_all, iou_threshold=iou_thres, score_threshold=conf_thres ) selected_boxes = tf.gather(boxes, selected_inds) padded_boxes = tf.pad( selected_boxes, paddings=[[0, topk_all - tf.shape(selected_boxes)[0]], [0, 0]], mode="CONSTANT", constant_values=0.0, ) selected_scores = tf.gather(scores_inp, selected_inds) padded_scores = tf.pad( selected_scores, paddings=[[0, topk_all - tf.shape(selected_boxes)[0]]], mode="CONSTANT", constant_values=-1.0, ) selected_classes = tf.gather(class_inds, selected_inds) padded_classes = tf.pad( selected_classes, paddings=[[0, topk_all - tf.shape(selected_boxes)[0]]], mode="CONSTANT", constant_values=-1.0, ) valid_detections = tf.shape(selected_inds)[0] return padded_boxes, padded_scores, padded_classes, valid_detections def activations(act=nn.SiLU): """Converts PyTorch activation functions (LeakyReLU, Hardswish, SiLU) to their TensorFlow counterparts.""" if isinstance(act, nn.LeakyReLU): return lambda x: keras.activations.relu(x, alpha=0.1) elif isinstance(act, nn.Hardswish): return lambda x: x * tf.nn.relu6(x + 3) * 0.166666667 elif isinstance(act, (nn.SiLU, SiLU)): return lambda x: keras.activations.swish(x) else: raise Exception(f"no matching TensorFlow activation found for PyTorch activation {act}") def representative_dataset_gen(dataset, ncalib=100): """Generates a representative dataset for TFLite conversion; yields normalized np arrays from input dataset up to `ncalib` samples. """ for n, (path, img, im0s, vid_cap, string) in enumerate(dataset): im = np.transpose(img, [1, 2, 0]) im = np.expand_dims(im, axis=0).astype(np.float32) im /= 255 yield [im] if n >= ncalib: break def run( weights=ROOT / "yolov5s.pt", # weights path imgsz=(640, 640), # inference size h,w batch_size=1, # batch size dynamic=False, # dynamic batch size ): # PyTorch model """Exports and summarizes both PyTorch and TensorFlow models for YOLOv5-based object detection.""" im = torch.zeros((batch_size, 3, *imgsz)) # BCHW image model = attempt_load(weights, device=torch.device("cpu"), inplace=True, fuse=False) _ = model(im) # inference model.info() # TensorFlow model im = tf.zeros((batch_size, *imgsz, 3)) # BHWC image tf_model = TFModel(cfg=model.yaml, model=model, nc=model.nc, imgsz=imgsz) _ = tf_model.predict(im) # inference # Keras model im = keras.Input(shape=(*imgsz, 3), batch_size=None if dynamic else batch_size) keras_model = keras.Model(inputs=im, outputs=tf_model.predict(im)) keras_model.summary() LOGGER.info("PyTorch, TensorFlow and Keras models successfully verified.\nUse export.py for TF model export.") def parse_opt(): """Parses command line arguments for model configuration including weights path, image size, batch size, and dynamic batching. """ parser = argparse.ArgumentParser() parser.add_argument("--weights", type=str, default=ROOT / "yolov3-tiny.pt", help="weights path") parser.add_argument("--imgsz", "--img", "--img-size", nargs="+", type=int, default=[640], help="inference size h,w") parser.add_argument("--batch-size", type=int, default=1, help="batch size") parser.add_argument("--dynamic", action="store_true", help="dynamic batch size") opt = parser.parse_args() opt.imgsz *= 2 if len(opt.imgsz) == 1 else 1 # expand print_args(vars(opt)) return opt def main(opt): """Executes the model run function with parsed CLI arguments on batch size and dynamic batching option.""" run(**vars(opt)) if __name__ == "__main__": opt = parse_opt() main(opt) ================================================ FILE: models/yolo.py ================================================ # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license """ YOLO-specific modules. Usage: $ python models/yolo.py --cfg yolov5s.yaml """ import argparse import os import platform import sys from copy import deepcopy from pathlib import Path FILE = Path(__file__).resolve() ROOT = FILE.parents[1] # YOLOv3 root directory if str(ROOT) not in sys.path: sys.path.append(str(ROOT)) # add ROOT to PATH if platform.system() != "Windows": ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative from models.common import * # noqa from models.experimental import * # noqa from utils.autoanchor import check_anchor_order from utils.general import LOGGER, check_version, check_yaml, make_divisible, print_args from utils.plots import feature_visualization from utils.torch_utils import ( fuse_conv_and_bn, initialize_weights, model_info, profile, scale_img, select_device, time_sync, ) try: import thop # for FLOPs computation except ImportError: thop = None class Detect(nn.Module): """YOLOv3 Detect head for processing detection model outputs, including grid and anchor grid generation.""" stride = None # strides computed during build dynamic = False # force grid reconstruction export = False # export mode def __init__(self, nc=80, anchors=(), ch=(), inplace=True): # detection layer """Initializes YOLOv3 detection layer with class count, anchors, channels, and operation modes.""" super().__init__() self.nc = nc # number of classes self.no = nc + 5 # number of outputs per anchor self.nl = len(anchors) # number of detection layers self.na = len(anchors[0]) // 2 # number of anchors self.grid = [torch.empty(0) for _ in range(self.nl)] # init grid self.anchor_grid = [torch.empty(0) for _ in range(self.nl)] # init anchor grid self.register_buffer("anchors", torch.tensor(anchors).float().view(self.nl, -1, 2)) # shape(nl,na,2) self.m = nn.ModuleList(nn.Conv2d(x, self.no * self.na, 1) for x in ch) # output conv self.inplace = inplace # use inplace ops (e.g. slice assignment) def forward(self, x): """Processes input through convolutional layers, reshaping output for detection. Expects x as list of tensors with shape(bs, C, H, W). """ z = [] # inference output for i in range(self.nl): x[i] = self.m[i](x[i]) # conv bs, _, ny, nx = x[i].shape # x(bs,255,20,20) to x(bs,3,20,20,85) x[i] = x[i].view(bs, self.na, self.no, ny, nx).permute(0, 1, 3, 4, 2).contiguous() if not self.training: # inference if self.dynamic or self.grid[i].shape[2:4] != x[i].shape[2:4]: self.grid[i], self.anchor_grid[i] = self._make_grid(nx, ny, i) if isinstance(self, Segment): # (boxes + masks) xy, wh, conf, mask = x[i].split((2, 2, self.nc + 1, self.no - self.nc - 5), 4) xy = (xy.sigmoid() * 2 + self.grid[i]) * self.stride[i] # xy wh = (wh.sigmoid() * 2) ** 2 * self.anchor_grid[i] # wh y = torch.cat((xy, wh, conf.sigmoid(), mask), 4) else: # Detect (boxes only) xy, wh, conf = x[i].sigmoid().split((2, 2, self.nc + 1), 4) xy = (xy * 2 + self.grid[i]) * self.stride[i] # xy wh = (wh * 2) ** 2 * self.anchor_grid[i] # wh y = torch.cat((xy, wh, conf), 4) z.append(y.view(bs, self.na * nx * ny, self.no)) return x if self.training else (torch.cat(z, 1),) if self.export else (torch.cat(z, 1), x) def _make_grid(self, nx=20, ny=20, i=0, torch_1_10=check_version(torch.__version__, "1.10.0")): """Generates a grid and corresponding anchor grid with shape `(1, num_anchors, ny, nx, 2)` for indexing anchors. """ d = self.anchors[i].device t = self.anchors[i].dtype shape = 1, self.na, ny, nx, 2 # grid shape y, x = torch.arange(ny, device=d, dtype=t), torch.arange(nx, device=d, dtype=t) yv, xv = torch.meshgrid(y, x, indexing="ij") if torch_1_10 else torch.meshgrid(y, x) # torch>=0.7 compatibility grid = torch.stack((xv, yv), 2).expand(shape) - 0.5 # add grid offset, i.e. y = 2.0 * x - 0.5 anchor_grid = (self.anchors[i] * self.stride[i]).view((1, self.na, 1, 1, 2)).expand(shape) return grid, anchor_grid class Segment(Detect): """YOLOv3 Segment head for segmentation models, adding mask prediction and prototyping to detection.""" def __init__(self, nc=80, anchors=(), nm=32, npr=256, ch=(), inplace=True): """Initializes the YOLOv3 segment head with customizable class count, anchors, masks, protos, channels, and inplace option. """ super().__init__(nc, anchors, ch, inplace) self.nm = nm # number of masks self.npr = npr # number of protos self.no = 5 + nc + self.nm # number of outputs per anchor self.m = nn.ModuleList(nn.Conv2d(x, self.no * self.na, 1) for x in ch) # output conv self.proto = Proto(ch[0], self.npr, self.nm) # protos self.detect = Detect.forward def forward(self, x): """Executes forward pass, returning predictions and protos, with different outputs based on training and export states. """ p = self.proto(x[0]) x = self.detect(self, x) return (x, p) if self.training else (x[0], p) if self.export else (x[0], p, x[1]) class BaseModel(nn.Module): """Implements the base YOLOv3 model architecture for object detection tasks.""" def forward(self, x, profile=False, visualize=False): """Performs a single-scale inference or training step on input `x`, with options for profiling and visualization. """ return self._forward_once(x, profile, visualize) # single-scale inference, train def _forward_once(self, x, profile=False, visualize=False): """Executes a single inference or training step, offering profiling and visualization options for input `x`.""" y, dt = [], [] # outputs for m in self.model: if m.f != -1: # if not from previous layer 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 if profile: self._profile_one_layer(m, x, dt) x = m(x) # run y.append(x if m.i in self.save else None) # save output if visualize: feature_visualization(x, m.type, m.i, save_dir=visualize) return x def _profile_one_layer(self, m, x, dt): """Profiles a single layer of the model by measuring its execution time and computational cost.""" c = m == self.model[-1] # is final layer, copy input as inplace fix o = thop.profile(m, inputs=(x.copy() if c else x,), verbose=False)[0] / 1e9 * 2 if thop else 0 # FLOPs t = time_sync() for _ in range(10): m(x.copy() if c else x) dt.append((time_sync() - t) * 100) if m == self.model[0]: LOGGER.info(f"{'time (ms)':>10s} {'GFLOPs':>10s} {'params':>10s} module") LOGGER.info(f"{dt[-1]:10.2f} {o:10.2f} {m.np:10.0f} {m.type}") if c: LOGGER.info(f"{sum(dt):10.2f} {'-':>10s} {'-':>10s} Total") def fuse(self): # fuse model Conv2d() + BatchNorm2d() layers """Fuses Conv2d() and BatchNorm2d() layers in the model to optimize inference speed.""" LOGGER.info("Fusing layers... ") for m in self.model.modules(): if isinstance(m, (Conv, DWConv)) and hasattr(m, "bn"): m.conv = fuse_conv_and_bn(m.conv, m.bn) # update conv delattr(m, "bn") # remove batchnorm m.forward = m.forward_fuse # update forward self.info() return self def info(self, verbose=False, img_size=640): # print model information """Prints model information; `verbose` for detailed, `img_size` for input image size (default 640).""" model_info(self, verbose, img_size) def _apply(self, fn): """Applies `to()`, `cpu()`, `cuda()`, `half()` to model tensors, excluding parameters or registered buffers.""" self = super()._apply(fn) m = self.model[-1] # Detect() if isinstance(m, (Detect, Segment)): m.stride = fn(m.stride) m.grid = list(map(fn, m.grid)) if isinstance(m.anchor_grid, list): m.anchor_grid = list(map(fn, m.anchor_grid)) return self class DetectionModel(BaseModel): """YOLOv3 detection model class for initializing and processing detection models with configurable parameters.""" def __init__(self, cfg="yolov5s.yaml", ch=3, nc=None, anchors=None): # model, input channels, number of classes """Initializes YOLOv3 detection model with configurable YAML, input channels, classes, and anchors.""" super().__init__() if isinstance(cfg, dict): self.yaml = cfg # model dict else: # is *.yaml import yaml # for torch hub self.yaml_file = Path(cfg).name with open(cfg, encoding="ascii", errors="ignore") as f: self.yaml = yaml.safe_load(f) # model dict # Define model ch = self.yaml["ch"] = self.yaml.get("ch", ch) # input channels if nc and nc != self.yaml["nc"]: LOGGER.info(f"Overriding model.yaml nc={self.yaml['nc']} with nc={nc}") self.yaml["nc"] = nc # override yaml value if anchors: LOGGER.info(f"Overriding model.yaml anchors with anchors={anchors}") self.yaml["anchors"] = round(anchors) # override yaml value self.model, self.save = parse_model(deepcopy(self.yaml), ch=[ch]) # model, savelist self.names = [str(i) for i in range(self.yaml["nc"])] # default names self.inplace = self.yaml.get("inplace", True) # Build strides, anchors m = self.model[-1] # Detect() if isinstance(m, (Detect, Segment)): s = 256 # 2x min stride m.inplace = self.inplace def forward(x): """Passes the input 'x' through the model and returns the processed output.""" return self.forward(x)[0] if isinstance(m, Segment) else self.forward(x) m.stride = torch.tensor([s / x.shape[-2] for x in forward(torch.zeros(1, ch, s, s))]) # forward check_anchor_order(m) m.anchors /= m.stride.view(-1, 1, 1) self.stride = m.stride self._initialize_biases() # only run once # Init weights, biases initialize_weights(self) self.info() LOGGER.info("") def forward(self, x, augment=False, profile=False, visualize=False): """Processes input through the model, with options for augmentation, profiling, and visualization.""" if augment: return self._forward_augment(x) # augmented inference, None return self._forward_once(x, profile, visualize) # single-scale inference, train def _forward_augment(self, x): """Performs augmented inference by scaling and flipping input images, returning concatenated predictions.""" img_size = x.shape[-2:] # height, width s = [1, 0.83, 0.67] # scales f = [None, 3, None] # flips (2-ud, 3-lr) y = [] # outputs for si, fi in zip(s, f): xi = scale_img(x.flip(fi) if fi else x, si, gs=int(self.stride.max())) yi = self._forward_once(xi)[0] # forward # cv2.imwrite(f'img_{si}.jpg', 255 * xi[0].cpu().numpy().transpose((1, 2, 0))[:, :, ::-1]) # save yi = self._descale_pred(yi, fi, si, img_size) y.append(yi) y = self._clip_augmented(y) # clip augmented tails return torch.cat(y, 1), None # augmented inference, train def _descale_pred(self, p, flips, scale, img_size): """Rescales predictions after augmentation by adjusting scales and flips based on image dimensions.""" if self.inplace: p[..., :4] /= scale # de-scale if flips == 2: p[..., 1] = img_size[0] - p[..., 1] # de-flip ud elif flips == 3: p[..., 0] = img_size[1] - p[..., 0] # de-flip lr else: x, y, wh = p[..., 0:1] / scale, p[..., 1:2] / scale, p[..., 2:4] / scale # de-scale if flips == 2: y = img_size[0] - y # de-flip ud elif flips == 3: x = img_size[1] - x # de-flip lr p = torch.cat((x, y, wh, p[..., 4:]), -1) return p def _clip_augmented(self, y): """Clips augmented inference tails from YOLOv3 predictions, affecting the first and last detection layers.""" nl = self.model[-1].nl # number of detection layers (P3-P5) g = sum(4**x for x in range(nl)) # grid points e = 1 # exclude layer count i = (y[0].shape[1] // g) * sum(4**x for x in range(e)) # indices y[0] = y[0][:, :-i] # large i = (y[-1].shape[1] // g) * sum(4 ** (nl - 1 - x) for x in range(e)) # indices y[-1] = y[-1][:, i:] # small return y def _initialize_biases(self, cf=None): # initialize biases into Detect(), cf is class frequency """Initializes biases for objectness and classes in Detect() module; optionally uses class frequency `cf`.""" # cf = torch.bincount(torch.tensor(np.concatenate(dataset.labels, 0)[:, 0]).long(), minlength=nc) + 1. m = self.model[-1] # Detect() module for mi, s in zip(m.m, m.stride): # from b = mi.bias.view(m.na, -1) # conv.bias(255) to (3,85) b.data[:, 4] += math.log(8 / (640 / s) ** 2) # obj (8 objects per 640 image) b.data[:, 5 : 5 + m.nc] += ( math.log(0.6 / (m.nc - 0.99999)) if cf is None else torch.log(cf / cf.sum()) ) # cls mi.bias = torch.nn.Parameter(b.view(-1), requires_grad=True) Model = DetectionModel # retain YOLOv3 'Model' class for backwards compatibility class SegmentationModel(DetectionModel): """Implements a YOLOv3-based segmentation model with customizable configuration, channels, classes, and anchors.""" def __init__(self, cfg="yolov5s-seg.yaml", ch=3, nc=None, anchors=None): """Initializes a SegmentationModel with optional configuration, channel, class count, and anchors parameters.""" super().__init__(cfg, ch, nc, anchors) class ClassificationModel(BaseModel): """Implements a YOLOv3-based image classification model with configurable architecture and class count.""" def __init__(self, cfg=None, model=None, nc=1000, cutoff=10): # yaml, model, number of classes, cutoff index """Initializes a ClassificationModel from a detection model or YAML, with configurable classes and cutoff.""" super().__init__() self._from_detection_model(model, nc, cutoff) if model is not None else self._from_yaml(cfg) def _from_detection_model(self, model, nc=1000, cutoff=10): """Initializes a classification model from a YOLOv3 detection model, configuring classes and cutoff.""" if isinstance(model, DetectMultiBackend): model = model.model # unwrap DetectMultiBackend model.model = model.model[:cutoff] # backbone m = model.model[-1] # last layer ch = m.conv.in_channels if hasattr(m, "conv") else m.cv1.conv.in_channels # ch into module c = Classify(ch, nc) # Classify() c.i, c.f, c.type = m.i, m.f, "models.common.Classify" # index, from, type model.model[-1] = c # replace self.model = model.model self.stride = model.stride self.save = [] self.nc = nc def _from_yaml(self, cfg): """Creates a YOLOv3 classification model from a YAML file configuration.""" self.model = None def parse_model(d, ch): # model_dict, input_channels(3) """Parses a YOLOv3 model configuration from a dictionary and constructs the model.""" LOGGER.info(f"\n{'':>3}{'from':>18}{'n':>3}{'params':>10} {'module':<40}{'arguments':<30}") anchors, nc, gd, gw, act = d["anchors"], d["nc"], d["depth_multiple"], d["width_multiple"], d.get("activation") if act: Conv.default_act = eval(act) # redefine default activation, i.e. Conv.default_act = nn.SiLU() LOGGER.info(f"{colorstr('activation:')} {act}") # print na = (len(anchors[0]) // 2) if isinstance(anchors, list) else anchors # number of anchors no = na * (nc + 5) # number of outputs = anchors * (classes + 5) layers, save, c2 = [], [], ch[-1] # layers, savelist, ch out for i, (f, n, m, args) in enumerate(d["backbone"] + d["head"]): # from, number, module, args m = eval(m) if isinstance(m, str) else m # eval strings for j, a in enumerate(args): with contextlib.suppress(NameError): args[j] = eval(a) if isinstance(a, str) else a # eval strings n = n_ = max(round(n * gd), 1) if n > 1 else n # depth gain if m in { Conv, GhostConv, Bottleneck, GhostBottleneck, SPP, SPPF, DWConv, MixConv2d, Focus, CrossConv, BottleneckCSP, C3, C3TR, C3SPP, C3Ghost, nn.ConvTranspose2d, DWConvTranspose2d, C3x, }: c1, c2 = ch[f], args[0] if c2 != no: # if not output c2 = make_divisible(c2 * gw, 8) args = [c1, c2, *args[1:]] if m in {BottleneckCSP, C3, C3TR, C3Ghost, C3x}: args.insert(2, n) # number of repeats n = 1 elif m is nn.BatchNorm2d: args = [ch[f]] elif m is Concat: c2 = sum(ch[x] for x in f) # TODO: channel, gw, gd elif m in {Detect, Segment}: args.append([ch[x] for x in f]) if isinstance(args[1], int): # number of anchors args[1] = [list(range(args[1] * 2))] * len(f) if m is Segment: args[3] = make_divisible(args[3] * gw, 8) elif m is Contract: c2 = ch[f] * args[0] ** 2 elif m is Expand: c2 = ch[f] // args[0] ** 2 else: c2 = ch[f] m_ = nn.Sequential(*(m(*args) for _ in range(n))) if n > 1 else m(*args) # module t = str(m)[8:-2].replace("__main__.", "") # module type np = sum(x.numel() for x in m_.parameters()) # number params m_.i, m_.f, m_.type, m_.np = i, f, t, np # attach index, 'from' index, type, number params LOGGER.info(f"{i:>3}{f!s:>18}{n_:>3}{np:10.0f} {t:<40}{args!s:<30}") # print save.extend(x % i for x in ([f] if isinstance(f, int) else f) if x != -1) # append to savelist layers.append(m_) if i == 0: ch = [] ch.append(c2) return nn.Sequential(*layers), sorted(save) if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument("--cfg", type=str, default="yolov5s.yaml", help="model.yaml") parser.add_argument("--batch-size", type=int, default=1, help="total batch size for all GPUs") parser.add_argument("--device", default="", help="cuda device, i.e. 0 or 0,1,2,3 or cpu") parser.add_argument("--profile", action="store_true", help="profile model speed") parser.add_argument("--line-profile", action="store_true", help="profile model speed layer by layer") parser.add_argument("--test", action="store_true", help="test all yolo*.yaml") opt = parser.parse_args() opt.cfg = check_yaml(opt.cfg) # check YAML print_args(vars(opt)) device = select_device(opt.device) # Create model im = torch.rand(opt.batch_size, 3, 640, 640).to(device) model = Model(opt.cfg).to(device) # Options if opt.line_profile: # profile layer by layer model(im, profile=True) elif opt.profile: # profile forward-backward results = profile(input=im, ops=[model], n=3) elif opt.test: # test all models for cfg in Path(ROOT / "models").rglob("yolo*.yaml"): try: _ = Model(cfg) except Exception as e: print(f"Error in {cfg}: {e}") else: # report fused model summary model.fuse() ================================================ FILE: models/yolov3-spp.yaml ================================================ # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license # Parameters nc: 80 # number of classes depth_multiple: 1.0 # model depth multiple width_multiple: 1.0 # layer channel multiple anchors: - [10, 13, 16, 30, 33, 23] # P3/8 - [30, 61, 62, 45, 59, 119] # P4/16 - [116, 90, 156, 198, 373, 326] # P5/32 # darknet53 backbone backbone: # [from, number, module, args] [ [-1, 1, Conv, [32, 3, 1]], # 0 [-1, 1, Conv, [64, 3, 2]], # 1-P1/2 [-1, 1, Bottleneck, [64]], [-1, 1, Conv, [128, 3, 2]], # 3-P2/4 [-1, 2, Bottleneck, [128]], [-1, 1, Conv, [256, 3, 2]], # 5-P3/8 [-1, 8, Bottleneck, [256]], [-1, 1, Conv, [512, 3, 2]], # 7-P4/16 [-1, 8, Bottleneck, [512]], [-1, 1, Conv, [1024, 3, 2]], # 9-P5/32 [-1, 4, Bottleneck, [1024]], # 10 ] # YOLOv3-SPP head head: [ [-1, 1, Bottleneck, [1024, False]], [-1, 1, SPP, [512, [5, 9, 13]]], [-1, 1, Conv, [1024, 3, 1]], [-1, 1, Conv, [512, 1, 1]], [-1, 1, Conv, [1024, 3, 1]], # 15 (P5/32-large) [-2, 1, Conv, [256, 1, 1]], [-1, 1, nn.Upsample, [None, 2, "nearest"]], [[-1, 8], 1, Concat, [1]], # cat backbone P4 [-1, 1, Bottleneck, [512, False]], [-1, 1, Bottleneck, [512, False]], [-1, 1, Conv, [256, 1, 1]], [-1, 1, Conv, [512, 3, 1]], # 22 (P4/16-medium) [-2, 1, Conv, [128, 1, 1]], [-1, 1, nn.Upsample, [None, 2, "nearest"]], [[-1, 6], 1, Concat, [1]], # cat backbone P3 [-1, 1, Bottleneck, [256, False]], [-1, 2, Bottleneck, [256, False]], # 27 (P3/8-small) [[27, 22, 15], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) ] ================================================ FILE: models/yolov3-tiny.yaml ================================================ # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license # Parameters nc: 80 # number of classes depth_multiple: 1.0 # model depth multiple width_multiple: 1.0 # layer channel multiple anchors: - [10, 14, 23, 27, 37, 58] # P4/16 - [81, 82, 135, 169, 344, 319] # P5/32 # YOLOv3-tiny backbone backbone: # [from, number, module, args] [ [-1, 1, Conv, [16, 3, 1]], # 0 [-1, 1, nn.MaxPool2d, [2, 2, 0]], # 1-P1/2 [-1, 1, Conv, [32, 3, 1]], [-1, 1, nn.MaxPool2d, [2, 2, 0]], # 3-P2/4 [-1, 1, Conv, [64, 3, 1]], [-1, 1, nn.MaxPool2d, [2, 2, 0]], # 5-P3/8 [-1, 1, Conv, [128, 3, 1]], [-1, 1, nn.MaxPool2d, [2, 2, 0]], # 7-P4/16 [-1, 1, Conv, [256, 3, 1]], [-1, 1, nn.MaxPool2d, [2, 2, 0]], # 9-P5/32 [-1, 1, Conv, [512, 3, 1]], [-1, 1, nn.ZeroPad2d, [[0, 1, 0, 1]]], # 11 [-1, 1, nn.MaxPool2d, [2, 1, 0]], # 12 ] # YOLOv3-tiny head head: [ [-1, 1, Conv, [1024, 3, 1]], [-1, 1, Conv, [256, 1, 1]], [-1, 1, Conv, [512, 3, 1]], # 15 (P5/32-large) [-2, 1, Conv, [128, 1, 1]], [-1, 1, nn.Upsample, [None, 2, "nearest"]], [[-1, 8], 1, Concat, [1]], # cat backbone P4 [-1, 1, Conv, [256, 3, 1]], # 19 (P4/16-medium) [[19, 15], 1, Detect, [nc, anchors]], # Detect(P4, P5) ] ================================================ FILE: models/yolov3.yaml ================================================ # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license # Parameters nc: 80 # number of classes depth_multiple: 1.0 # model depth multiple width_multiple: 1.0 # layer channel multiple anchors: - [10, 13, 16, 30, 33, 23] # P3/8 - [30, 61, 62, 45, 59, 119] # P4/16 - [116, 90, 156, 198, 373, 326] # P5/32 # darknet53 backbone backbone: # [from, number, module, args] [ [-1, 1, Conv, [32, 3, 1]], # 0 [-1, 1, Conv, [64, 3, 2]], # 1-P1/2 [-1, 1, Bottleneck, [64]], [-1, 1, Conv, [128, 3, 2]], # 3-P2/4 [-1, 2, Bottleneck, [128]], [-1, 1, Conv, [256, 3, 2]], # 5-P3/8 [-1, 8, Bottleneck, [256]], [-1, 1, Conv, [512, 3, 2]], # 7-P4/16 [-1, 8, Bottleneck, [512]], [-1, 1, Conv, [1024, 3, 2]], # 9-P5/32 [-1, 4, Bottleneck, [1024]], # 10 ] # YOLOv3 head head: [ [-1, 1, Bottleneck, [1024, False]], [-1, 1, Conv, [512, 1, 1]], [-1, 1, Conv, [1024, 3, 1]], [-1, 1, Conv, [512, 1, 1]], [-1, 1, Conv, [1024, 3, 1]], # 15 (P5/32-large) [-2, 1, Conv, [256, 1, 1]], [-1, 1, nn.Upsample, [None, 2, "nearest"]], [[-1, 8], 1, Concat, [1]], # cat backbone P4 [-1, 1, Bottleneck, [512, False]], [-1, 1, Bottleneck, [512, False]], [-1, 1, Conv, [256, 1, 1]], [-1, 1, Conv, [512, 3, 1]], # 22 (P4/16-medium) [-2, 1, Conv, [128, 1, 1]], [-1, 1, nn.Upsample, [None, 2, "nearest"]], [[-1, 6], 1, Concat, [1]], # cat backbone P3 [-1, 1, Bottleneck, [256, False]], [-1, 2, Bottleneck, [256, False]], # 27 (P3/8-small) [[27, 22, 15], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) ] ================================================ FILE: models/yolov5l.yaml ================================================ # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license # Parameters nc: 80 # number of classes depth_multiple: 1.0 # model depth multiple width_multiple: 1.0 # layer channel multiple anchors: - [10, 13, 16, 30, 33, 23] # P3/8 - [30, 61, 62, 45, 59, 119] # P4/16 - [116, 90, 156, 198, 373, 326] # P5/32 # YOLOv5 v6.0 backbone backbone: # [from, number, module, args] [ [-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 [-1, 3, C3, [128]], [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 [-1, 6, C3, [256]], [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 [-1, 9, C3, [512]], [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 [-1, 3, C3, [1024]], [-1, 1, SPPF, [1024, 5]], # 9 ] # YOLOv5 v6.0 head head: [ [-1, 1, Conv, [512, 1, 1]], [-1, 1, nn.Upsample, [None, 2, "nearest"]], [[-1, 6], 1, Concat, [1]], # cat backbone P4 [-1, 3, C3, [512, False]], # 13 [-1, 1, Conv, [256, 1, 1]], [-1, 1, nn.Upsample, [None, 2, "nearest"]], [[-1, 4], 1, Concat, [1]], # cat backbone P3 [-1, 3, C3, [256, False]], # 17 (P3/8-small) [-1, 1, Conv, [256, 3, 2]], [[-1, 14], 1, Concat, [1]], # cat head P4 [-1, 3, C3, [512, False]], # 20 (P4/16-medium) [-1, 1, Conv, [512, 3, 2]], [[-1, 10], 1, Concat, [1]], # cat head P5 [-1, 3, C3, [1024, False]], # 23 (P5/32-large) [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) ] ================================================ FILE: models/yolov5m.yaml ================================================ # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license # Parameters nc: 80 # number of classes depth_multiple: 0.67 # model depth multiple width_multiple: 0.75 # layer channel multiple anchors: - [10, 13, 16, 30, 33, 23] # P3/8 - [30, 61, 62, 45, 59, 119] # P4/16 - [116, 90, 156, 198, 373, 326] # P5/32 # YOLOv5 v6.0 backbone backbone: # [from, number, module, args] [ [-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 [-1, 3, C3, [128]], [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 [-1, 6, C3, [256]], [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 [-1, 9, C3, [512]], [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 [-1, 3, C3, [1024]], [-1, 1, SPPF, [1024, 5]], # 9 ] # YOLOv5 v6.0 head head: [ [-1, 1, Conv, [512, 1, 1]], [-1, 1, nn.Upsample, [None, 2, "nearest"]], [[-1, 6], 1, Concat, [1]], # cat backbone P4 [-1, 3, C3, [512, False]], # 13 [-1, 1, Conv, [256, 1, 1]], [-1, 1, nn.Upsample, [None, 2, "nearest"]], [[-1, 4], 1, Concat, [1]], # cat backbone P3 [-1, 3, C3, [256, False]], # 17 (P3/8-small) [-1, 1, Conv, [256, 3, 2]], [[-1, 14], 1, Concat, [1]], # cat head P4 [-1, 3, C3, [512, False]], # 20 (P4/16-medium) [-1, 1, Conv, [512, 3, 2]], [[-1, 10], 1, Concat, [1]], # cat head P5 [-1, 3, C3, [1024, False]], # 23 (P5/32-large) [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) ] ================================================ FILE: models/yolov5n.yaml ================================================ # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license # Parameters nc: 80 # number of classes depth_multiple: 0.33 # model depth multiple width_multiple: 0.25 # layer channel multiple anchors: - [10, 13, 16, 30, 33, 23] # P3/8 - [30, 61, 62, 45, 59, 119] # P4/16 - [116, 90, 156, 198, 373, 326] # P5/32 # YOLOv5 v6.0 backbone backbone: # [from, number, module, args] [ [-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 [-1, 3, C3, [128]], [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 [-1, 6, C3, [256]], [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 [-1, 9, C3, [512]], [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 [-1, 3, C3, [1024]], [-1, 1, SPPF, [1024, 5]], # 9 ] # YOLOv5 v6.0 head head: [ [-1, 1, Conv, [512, 1, 1]], [-1, 1, nn.Upsample, [None, 2, "nearest"]], [[-1, 6], 1, Concat, [1]], # cat backbone P4 [-1, 3, C3, [512, False]], # 13 [-1, 1, Conv, [256, 1, 1]], [-1, 1, nn.Upsample, [None, 2, "nearest"]], [[-1, 4], 1, Concat, [1]], # cat backbone P3 [-1, 3, C3, [256, False]], # 17 (P3/8-small) [-1, 1, Conv, [256, 3, 2]], [[-1, 14], 1, Concat, [1]], # cat head P4 [-1, 3, C3, [512, False]], # 20 (P4/16-medium) [-1, 1, Conv, [512, 3, 2]], [[-1, 10], 1, Concat, [1]], # cat head P5 [-1, 3, C3, [1024, False]], # 23 (P5/32-large) [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) ] ================================================ FILE: models/yolov5s.yaml ================================================ # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license # Parameters nc: 80 # number of classes depth_multiple: 0.33 # model depth multiple width_multiple: 0.50 # layer channel multiple anchors: - [10, 13, 16, 30, 33, 23] # P3/8 - [30, 61, 62, 45, 59, 119] # P4/16 - [116, 90, 156, 198, 373, 326] # P5/32 # YOLOv5 v6.0 backbone backbone: # [from, number, module, args] [ [-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 [-1, 3, C3, [128]], [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 [-1, 6, C3, [256]], [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 [-1, 9, C3, [512]], [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 [-1, 3, C3, [1024]], [-1, 1, SPPF, [1024, 5]], # 9 ] # YOLOv5 v6.0 head head: [ [-1, 1, Conv, [512, 1, 1]], [-1, 1, nn.Upsample, [None, 2, "nearest"]], [[-1, 6], 1, Concat, [1]], # cat backbone P4 [-1, 3, C3, [512, False]], # 13 [-1, 1, Conv, [256, 1, 1]], [-1, 1, nn.Upsample, [None, 2, "nearest"]], [[-1, 4], 1, Concat, [1]], # cat backbone P3 [-1, 3, C3, [256, False]], # 17 (P3/8-small) [-1, 1, Conv, [256, 3, 2]], [[-1, 14], 1, Concat, [1]], # cat head P4 [-1, 3, C3, [512, False]], # 20 (P4/16-medium) [-1, 1, Conv, [512, 3, 2]], [[-1, 10], 1, Concat, [1]], # cat head P5 [-1, 3, C3, [1024, False]], # 23 (P5/32-large) [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) ] ================================================ FILE: models/yolov5x.yaml ================================================ # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license # Parameters nc: 80 # number of classes depth_multiple: 1.33 # model depth multiple width_multiple: 1.25 # layer channel multiple anchors: - [10, 13, 16, 30, 33, 23] # P3/8 - [30, 61, 62, 45, 59, 119] # P4/16 - [116, 90, 156, 198, 373, 326] # P5/32 # YOLOv5 v6.0 backbone backbone: # [from, number, module, args] [ [-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 [-1, 3, C3, [128]], [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 [-1, 6, C3, [256]], [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 [-1, 9, C3, [512]], [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 [-1, 3, C3, [1024]], [-1, 1, SPPF, [1024, 5]], # 9 ] # YOLOv5 v6.0 head head: [ [-1, 1, Conv, [512, 1, 1]], [-1, 1, nn.Upsample, [None, 2, "nearest"]], [[-1, 6], 1, Concat, [1]], # cat backbone P4 [-1, 3, C3, [512, False]], # 13 [-1, 1, Conv, [256, 1, 1]], [-1, 1, nn.Upsample, [None, 2, "nearest"]], [[-1, 4], 1, Concat, [1]], # cat backbone P3 [-1, 3, C3, [256, False]], # 17 (P3/8-small) [-1, 1, Conv, [256, 3, 2]], [[-1, 14], 1, Concat, [1]], # cat head P4 [-1, 3, C3, [512, False]], # 20 (P4/16-medium) [-1, 1, Conv, [512, 3, 2]], [[-1, 10], 1, Concat, [1]], # cat head P5 [-1, 3, C3, [1024, False]], # 23 (P5/32-large) [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) ] ================================================ FILE: pyproject.toml ================================================ # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license # Overview: # This pyproject.toml file manages the build, packaging, and distribution of the Ultralytics library. # It defines essential project metadata, dependencies, and settings used to develop and deploy the library. # Key Sections: # - [build-system]: Specifies the build requirements and backend (e.g., setuptools, wheel). # - [project]: Includes details like name, version, description, authors, dependencies and more. # - [project.optional-dependencies]: Provides additional, optional packages for extended features. # - [tool.*]: Configures settings for various tools (pytest, yapf, etc.) used in the project. # Installation: # The Ultralytics library can be installed using the command: 'pip install ultralytics' # For development purposes, you can install the package in editable mode with: 'pip install -e .' # This approach allows for real-time code modifications without the need for re-installation. # Documentation: # For comprehensive documentation and usage instructions, visit: https://docs.ultralytics.com [build-system] requires = ["setuptools>=43.0.0", "wheel"] build-backend = "setuptools.build_meta" # Project settings ----------------------------------------------------------------------------------------------------- [project] name = "YOLOv3" description = "Ultralytics YOLOv3 for object detection." readme = "README.md" requires-python = ">=3.8" license = { "text" = "AGPL-3.0" } keywords = ["machine-learning", "deep-learning", "computer-vision", "ML", "DL", "AI", "YOLO", "YOLOv3", "YOLOv5", "YOLOv8", "HUB", "Ultralytics"] authors = [ { name = "Glenn Jocher" }, { name = "Ayush Chaurasia" }, { name = "Jing Qiu" } ] maintainers = [ { name = "Glenn Jocher" }, { name = "Ayush Chaurasia" }, { name = "Jing Qiu" } ] classifiers = [ "Development Status :: 4 - Beta", "Intended Audience :: Developers", "Intended Audience :: Education", "Intended Audience :: Science/Research", "License :: OSI Approved :: GNU Affero General Public License v3 or later (AGPLv3+)", "Programming Language :: Python :: 3", "Programming Language :: Python :: 3.8", "Programming Language :: Python :: 3.9", "Programming Language :: Python :: 3.10", "Programming Language :: Python :: 3.11", "Topic :: Software Development", "Topic :: Scientific/Engineering", "Topic :: Scientific/Engineering :: Artificial Intelligence", "Topic :: Scientific/Engineering :: Image Recognition", "Operating System :: POSIX :: Linux", "Operating System :: MacOS", "Operating System :: Microsoft :: Windows", ] # Required dependencies ------------------------------------------------------------------------------------------------ dependencies = [ "matplotlib>=3.3.0", "numpy>=1.22.2", "opencv-python>=4.6.0", "pillow>=7.1.2", "pyyaml>=5.3.1", "requests>=2.23.0", "scipy>=1.4.1", "torch>=1.8.0", "torchvision>=0.9.0", "tqdm>=4.64.0", # progress bars "psutil", # system utilization "py-cpuinfo", # display CPU info "thop>=0.1.1", # FLOPs computation "pandas>=1.1.4", "packaging", # general utilities "seaborn>=0.11.0", # plotting "ultralytics>=8.2.64", ] # Optional dependencies ------------------------------------------------------------------------------------------------ [project.optional-dependencies] dev = [ "ipython", "check-manifest", "pre-commit", "pytest", "pytest-cov", "coverage[toml]", "mkdocs-material", "mkdocstrings[python]", "mkdocs-redirects", # for 301 redirects "mkdocs-ultralytics-plugin>=0.0.34", # for meta descriptions and images, dates and authors ] export = [ "onnx>=1.12.0", # ONNX export "coremltools>=7.0; platform_system != 'Windows'", # CoreML only supported on macOS and Linux "openvino-dev>=2023.0", # OpenVINO export "tensorflow>=2.0.0,<=2.19.0", # TF bug https://github.com/ultralytics/ultralytics/issues/5161 "tensorflowjs>=3.9.0", # TF.js export, automatically installs tensorflow ] # tensorflow>=2.4.1,<=2.13.1 # TF exports (-cpu, -aarch64, -macos) # tflite-support # for TFLite model metadata # scikit-learn==0.19.2 # CoreML quantization # nvidia-pyindex # TensorRT export # nvidia-tensorrt # TensorRT export logging = [ "comet", # https://docs.ultralytics.com/integrations/comet/ "tensorboard>=2.13.0", "dvclive>=2.12.0", ] extra = [ "ipython", # interactive notebook "albumentations>=1.0.3", # training augmentations "pycocotools>=2.0.6", # COCO mAP ] [project.urls] "Bug Reports" = "https://github.com/ultralytics/yolov3/issues" "Funding" = "https://ultralytics.com" "Source" = "https://github.com/ultralytics/yolov3/" # Tools settings ------------------------------------------------------------------------------------------------------- [tool.pytest] norecursedirs = [".git", "dist", "build"] addopts = "--doctest-modules --durations=30 --color=yes" [tool.isort] line_length = 120 multi_line_output = 0 [tool.ruff] line-length = 120 [tool.docformatter] wrap-summaries = 120 wrap-descriptions = 120 in-place = true pre-summary-newline = true close-quotes-on-newline = true [tool.codespell] ignore-words-list = "crate,nd,strack,dota,ane,segway,fo,gool,winn,commend" skip = '*.csv,*venv*,docs/??/,docs/mkdocs_??.yml' ================================================ FILE: requirements.txt ================================================ # YOLOv3 requirements # Usage: pip install -r requirements.txt # Python >= 3.8 recommended # Base ------------------------------------------------------------------------ gitpython>=3.1.30 # Git repo interaction for training/versioning matplotlib>=3.5.0 # Plotting results and graphs numpy>=1.23.5 # Fundamental for array/matrix operations opencv-python>=4.1.1 # Image/video processing Pillow>=10.3.0 # Image reading/writing support psutil>=5.9.0 # System monitoring (RAM, CPU, etc.) PyYAML>=5.3.1 # Reading configs (yaml files) requests>=2.32.2 # HTTP requests, used in model hub/downloads scipy>=1.4.1 # Scientific computing (e.g. IoU, metrics) thop>=0.1.1 # Model profiling - FLOPs and parameter count torch>=1.8.0 # Core PyTorch for training/inference torchvision>=0.9.0 # Torch utilities for vision (transforms, datasets) tqdm>=4.66.3 # Progress bar in CLI ultralytics>=8.2.64 # YOLO framework library (models, training, utils) # protobuf<=3.20.1 # For ONNX/TensorFlow export compatibility # Logging --------------------------------------------------------------------- # tensorboard>=2.4.1 # Visual logging (scalars, images) # clearml>=1.2.0 # Experiment tracking # comet # Another logging/monitoring tool # Plotting -------------------------------------------------------------------- pandas>=1.1.4 # Data handling and manipulation seaborn>=0.11.0 # Statistical data visualization (confusion matrix, etc.) # Export (optional) ----------------------------------------------------------- # coremltools>=6.0 # Apple CoreML export support # onnx>=1.10.0 # ONNX export support # onnx-simplifier>=0.4.1 # Optimizes ONNX models # nvidia-pyindex # Required for installing NVIDIA TensorRT # nvidia-tensorrt # TensorRT export and inference # scikit-learn<=1.1.2 # Used in CoreML quantization (used in older code) # tensorflow>=2.4.0 # TensorFlow export # tensorflowjs>=3.9.0 # TensorFlow.js export # openvino-dev>=2023.0 # Intel OpenVINO export # Deploy ---------------------------------------------------------------------- setuptools>=70.0.0 # Required to avoid known vulnerabilities # tritonclient[all]~=2.24.0 # NVIDIA Triton server deployment (optional) # Extras ---------------------------------------------------------------------- # ipython # Enhanced interactive shell # mss # Screenshot capturing for inference UI # albumentations>=1.0.3 # Powerful image augmentation library # pycocotools>=2.0.6 # COCO dataset metrics (mAP, etc.) ================================================ FILE: segment/predict.py ================================================ # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license """ Run YOLOv3 segmentation inference on images, videos, directories, streams, etc. Usage - sources: $ python segment/predict.py --weights yolov5s-seg.pt --source 0 # webcam img.jpg # image vid.mp4 # video screen # screenshot path/ # directory list.txt # list of images list.streams # list of streams 'path/*.jpg' # glob 'https://youtu.be/LNwODJXcvt4' # YouTube 'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream Usage - formats: $ python segment/predict.py --weights yolov5s-seg.pt # PyTorch yolov5s-seg.torchscript # TorchScript yolov5s-seg.onnx # ONNX Runtime or OpenCV DNN with --dnn yolov5s-seg_openvino_model # OpenVINO yolov5s-seg.engine # TensorRT yolov5s-seg.mlmodel # CoreML (macOS-only) yolov5s-seg_saved_model # TensorFlow SavedModel yolov5s-seg.pb # TensorFlow GraphDef yolov5s-seg.tflite # TensorFlow Lite yolov5s-seg_edgetpu.tflite # TensorFlow Edge TPU yolov5s-seg_paddle_model # PaddlePaddle """ import argparse import os import platform import sys from pathlib import Path import torch FILE = Path(__file__).resolve() ROOT = FILE.parents[1] # YOLOv3 root directory if str(ROOT) not in sys.path: sys.path.append(str(ROOT)) # add ROOT to PATH ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative from ultralytics.utils.plotting import Annotator, colors, save_one_box from models.common import DetectMultiBackend from utils.dataloaders import IMG_FORMATS, VID_FORMATS, LoadImages, LoadScreenshots, LoadStreams from utils.general import ( LOGGER, Profile, check_file, check_img_size, check_imshow, check_requirements, colorstr, cv2, increment_path, non_max_suppression, print_args, scale_boxes, scale_segments, strip_optimizer, ) from utils.segment.general import masks2segments, process_mask, process_mask_native from utils.torch_utils import select_device, smart_inference_mode @smart_inference_mode() def run( weights=ROOT / "yolov5s-seg.pt", # model.pt path(s) source=ROOT / "data/images", # file/dir/URL/glob/screen/0(webcam) data=ROOT / "data/coco128.yaml", # dataset.yaml path imgsz=(640, 640), # inference size (height, width) conf_thres=0.25, # confidence threshold iou_thres=0.45, # NMS IOU threshold max_det=1000, # maximum detections per image device="", # cuda device, i.e. 0 or 0,1,2,3 or cpu view_img=False, # show results save_txt=False, # save results to *.txt save_conf=False, # save confidences in --save-txt labels save_crop=False, # save cropped prediction boxes nosave=False, # do not save images/videos classes=None, # filter by class: --class 0, or --class 0 2 3 agnostic_nms=False, # class-agnostic NMS augment=False, # augmented inference visualize=False, # visualize features update=False, # update all models project=ROOT / "runs/predict-seg", # save results to project/name name="exp", # save results to project/name exist_ok=False, # existing project/name ok, do not increment line_thickness=3, # bounding box thickness (pixels) hide_labels=False, # hide labels hide_conf=False, # hide confidences half=False, # use FP16 half-precision inference dnn=False, # use OpenCV DNN for ONNX inference vid_stride=1, # video frame-rate stride retina_masks=False, ): """Performs YOLOv3 segmentation inference on various sources such as images, videos, and streams.""" source = str(source) save_img = not nosave and not source.endswith(".txt") # save inference images is_file = Path(source).suffix[1:] in (IMG_FORMATS + VID_FORMATS) is_url = source.lower().startswith(("rtsp://", "rtmp://", "http://", "https://")) webcam = source.isnumeric() or source.endswith(".streams") or (is_url and not is_file) screenshot = source.lower().startswith("screen") if is_url and is_file: source = check_file(source) # download # Directories save_dir = increment_path(Path(project) / name, exist_ok=exist_ok) # increment run (save_dir / "labels" if save_txt else save_dir).mkdir(parents=True, exist_ok=True) # make dir # Load model device = select_device(device) model = DetectMultiBackend(weights, device=device, dnn=dnn, data=data, fp16=half) stride, names, pt = model.stride, model.names, model.pt imgsz = check_img_size(imgsz, s=stride) # check image size # Dataloader bs = 1 # batch_size if webcam: view_img = check_imshow(warn=True) dataset = LoadStreams(source, img_size=imgsz, stride=stride, auto=pt, vid_stride=vid_stride) bs = len(dataset) elif screenshot: dataset = LoadScreenshots(source, img_size=imgsz, stride=stride, auto=pt) else: dataset = LoadImages(source, img_size=imgsz, stride=stride, auto=pt, vid_stride=vid_stride) vid_path, vid_writer = [None] * bs, [None] * bs # Run inference model.warmup(imgsz=(1 if pt else bs, 3, *imgsz)) # warmup seen, windows, dt = 0, [], (Profile(), Profile(), Profile()) for path, im, im0s, vid_cap, s in dataset: with dt[0]: im = torch.from_numpy(im).to(model.device) im = im.half() if model.fp16 else im.float() # uint8 to fp16/32 im /= 255 # 0 - 255 to 0.0 - 1.0 if len(im.shape) == 3: im = im[None] # expand for batch dim # Inference with dt[1]: visualize = increment_path(save_dir / Path(path).stem, mkdir=True) if visualize else False pred, proto = model(im, augment=augment, visualize=visualize)[:2] # NMS with dt[2]: pred = non_max_suppression(pred, conf_thres, iou_thres, classes, agnostic_nms, max_det=max_det, nm=32) # Second-stage classifier (optional) # pred = utils.general.apply_classifier(pred, classifier_model, im, im0s) # Process predictions for i, det in enumerate(pred): # per image seen += 1 if webcam: # batch_size >= 1 p, im0, frame = path[i], im0s[i].copy(), dataset.count s += f"{i}: " else: p, im0, frame = path, im0s.copy(), getattr(dataset, "frame", 0) p = Path(p) # to Path save_path = str(save_dir / p.name) # im.jpg txt_path = str(save_dir / "labels" / p.stem) + ("" if dataset.mode == "image" else f"_{frame}") # im.txt s += "{:g}x{:g} ".format(*im.shape[2:]) # print string imc = im0.copy() if save_crop else im0 # for save_crop annotator = Annotator(im0, line_width=line_thickness, example=str(names)) if len(det): if retina_masks: # scale bbox first the crop masks det[:, :4] = scale_boxes(im.shape[2:], det[:, :4], im0.shape).round() # rescale boxes to im0 size masks = process_mask_native(proto[i], det[:, 6:], det[:, :4], im0.shape[:2]) # HWC else: masks = process_mask(proto[i], det[:, 6:], det[:, :4], im.shape[2:], upsample=True) # HWC det[:, :4] = scale_boxes(im.shape[2:], det[:, :4], im0.shape).round() # rescale boxes to im0 size # Segments if save_txt: segments = [ scale_segments(im0.shape if retina_masks else im.shape[2:], x, im0.shape, normalize=True) for x in reversed(masks2segments(masks)) ] # Print results for c in det[:, 5].unique(): n = (det[:, 5] == c).sum() # detections per class s += f"{n} {names[int(c)]}{'s' * (n > 1)}, " # add to string # Mask plotting annotator.masks( masks, colors=[colors(x, True) for x in det[:, 5]], im_gpu=torch.as_tensor(im0, dtype=torch.float16).to(device).permute(2, 0, 1).flip(0).contiguous() / 255 if retina_masks else im[i], ) # Write results for j, (*xyxy, conf, cls) in enumerate(reversed(det[:, :6])): if save_txt: # Write to file seg = segments[j].reshape(-1) # (n,2) to (n*2) line = (cls, *seg, conf) if save_conf else (cls, *seg) # label format with open(f"{txt_path}.txt", "a") as f: f.write(("%g " * len(line)).rstrip() % line + "\n") if save_img or save_crop or view_img: # Add bbox to image c = int(cls) # integer class label = None if hide_labels else (names[c] if hide_conf else f"{names[c]} {conf:.2f}") annotator.box_label(xyxy, label, color=colors(c, True)) # annotator.draw.polygon(segments[j], outline=colors(c, True), width=3) if save_crop: save_one_box(xyxy, imc, file=save_dir / "crops" / names[c] / f"{p.stem}.jpg", BGR=True) # Stream results im0 = annotator.result() if view_img: if platform.system() == "Linux" and p not in windows: windows.append(p) cv2.namedWindow(str(p), cv2.WINDOW_NORMAL | cv2.WINDOW_KEEPRATIO) # allow window resize (Linux) cv2.resizeWindow(str(p), im0.shape[1], im0.shape[0]) cv2.imshow(str(p), im0) if cv2.waitKey(1) == ord("q"): # 1 millisecond exit() # Save results (image with detections) if save_img: if dataset.mode == "image": cv2.imwrite(save_path, im0) else: # 'video' or 'stream' if vid_path[i] != save_path: # new video vid_path[i] = save_path if isinstance(vid_writer[i], cv2.VideoWriter): vid_writer[i].release() # release previous video writer if vid_cap: # video fps = vid_cap.get(cv2.CAP_PROP_FPS) w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH)) h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) else: # stream fps, w, h = 30, im0.shape[1], im0.shape[0] save_path = str(Path(save_path).with_suffix(".mp4")) # force *.mp4 suffix on results videos vid_writer[i] = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*"mp4v"), fps, (w, h)) vid_writer[i].write(im0) # Print time (inference-only) LOGGER.info(f"{s}{'' if len(det) else '(no detections), '}{dt[1].dt * 1e3:.1f}ms") # Print results t = tuple(x.t / seen * 1e3 for x in dt) # speeds per image LOGGER.info(f"Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {(1, 3, *imgsz)}" % t) if save_txt or save_img: s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else "" LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}{s}") if update: strip_optimizer(weights[0]) # update model (to fix SourceChangeWarning) def parse_opt(): """Parses command-line options for YOLOv5 including model paths, source, inference size, and saving options.""" parser = argparse.ArgumentParser() parser.add_argument("--weights", nargs="+", type=str, default=ROOT / "yolov5s-seg.pt", help="model path(s)") parser.add_argument("--source", type=str, default=ROOT / "data/images", help="file/dir/URL/glob/screen/0(webcam)") parser.add_argument("--data", type=str, default=ROOT / "data/coco128.yaml", help="(optional) dataset.yaml path") parser.add_argument("--imgsz", "--img", "--img-size", nargs="+", type=int, default=[640], help="inference size h,w") parser.add_argument("--conf-thres", type=float, default=0.25, help="confidence threshold") parser.add_argument("--iou-thres", type=float, default=0.45, help="NMS IoU threshold") parser.add_argument("--max-det", type=int, default=1000, help="maximum detections per image") parser.add_argument("--device", default="", help="cuda device, i.e. 0 or 0,1,2,3 or cpu") parser.add_argument("--view-img", action="store_true", help="show results") parser.add_argument("--save-txt", action="store_true", help="save results to *.txt") parser.add_argument("--save-conf", action="store_true", help="save confidences in --save-txt labels") parser.add_argument("--save-crop", action="store_true", help="save cropped prediction boxes") parser.add_argument("--nosave", action="store_true", help="do not save images/videos") parser.add_argument("--classes", nargs="+", type=int, help="filter by class: --classes 0, or --classes 0 2 3") parser.add_argument("--agnostic-nms", action="store_true", help="class-agnostic NMS") parser.add_argument("--augment", action="store_true", help="augmented inference") parser.add_argument("--visualize", action="store_true", help="visualize features") parser.add_argument("--update", action="store_true", help="update all models") parser.add_argument("--project", default=ROOT / "runs/predict-seg", help="save results to project/name") parser.add_argument("--name", default="exp", help="save results to project/name") parser.add_argument("--exist-ok", action="store_true", help="existing project/name ok, do not increment") parser.add_argument("--line-thickness", default=3, type=int, help="bounding box thickness (pixels)") parser.add_argument("--hide-labels", default=False, action="store_true", help="hide labels") parser.add_argument("--hide-conf", default=False, action="store_true", help="hide confidences") parser.add_argument("--half", action="store_true", help="use FP16 half-precision inference") parser.add_argument("--dnn", action="store_true", help="use OpenCV DNN for ONNX inference") parser.add_argument("--vid-stride", type=int, default=1, help="video frame-rate stride") parser.add_argument("--retina-masks", action="store_true", help="whether to plot masks in native resolution") opt = parser.parse_args() opt.imgsz *= 2 if len(opt.imgsz) == 1 else 1 # expand print_args(vars(opt)) return opt def main(opt): """Executes model inference based on parsed options, checking requirements and excluding specified packages.""" check_requirements(ROOT / "requirements.txt", exclude=("tensorboard", "thop")) run(**vars(opt)) if __name__ == "__main__": opt = parse_opt() main(opt) ================================================ FILE: segment/train.py ================================================ # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license """ Train a YOLOv3 segment model on a segment dataset Models and datasets download automatically from the latest YOLOv3 release. Usage - Single-GPU training: $ python segment/train.py --data coco128-seg.yaml --weights yolov5s-seg.pt --img 640 # from pretrained (recommended) $ python segment/train.py --data coco128-seg.yaml --weights '' --cfg yolov5s-seg.yaml --img 640 # from scratch Usage - Multi-GPU DDP training: $ 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 Models: https://github.com/ultralytics/yolov5/tree/master/models Datasets: https://github.com/ultralytics/yolov5/tree/master/data Tutorial: https://docs.ultralytics.com/yolov5/tutorials/train_custom_data """ import argparse import math import os import random import subprocess import sys import time from copy import deepcopy from datetime import datetime from pathlib import Path import numpy as np import torch import torch.distributed as dist import torch.nn as nn import yaml from torch.optim import lr_scheduler from tqdm import tqdm FILE = Path(__file__).resolve() ROOT = FILE.parents[1] # YOLOv3 root directory if str(ROOT) not in sys.path: sys.path.append(str(ROOT)) # add ROOT to PATH ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative from ultralytics.utils.patches import torch_load import segment.val as validate # for end-of-epoch mAP from models.experimental import attempt_load from models.yolo import SegmentationModel from utils.autoanchor import check_anchors from utils.autobatch import check_train_batch_size from utils.callbacks import Callbacks from utils.downloads import attempt_download, is_url from utils.general import ( LOGGER, TQDM_BAR_FORMAT, check_amp, check_dataset, check_file, check_git_info, check_git_status, check_img_size, check_requirements, check_suffix, check_yaml, colorstr, get_latest_run, increment_path, init_seeds, intersect_dicts, labels_to_class_weights, labels_to_image_weights, one_cycle, print_args, print_mutation, strip_optimizer, yaml_save, ) from utils.loggers import GenericLogger from utils.plots import plot_evolve, plot_labels from utils.segment.dataloaders import create_dataloader from utils.segment.loss import ComputeLoss from utils.segment.metrics import KEYS, fitness from utils.segment.plots import plot_images_and_masks, plot_results_with_masks from utils.torch_utils import ( EarlyStopping, ModelEMA, de_parallel, select_device, smart_DDP, smart_optimizer, smart_resume, torch_distributed_zero_first, ) LOCAL_RANK = int(os.getenv("LOCAL_RANK", -1)) # https://pytorch.org/docs/stable/elastic/run.html RANK = int(os.getenv("RANK", -1)) WORLD_SIZE = int(os.getenv("WORLD_SIZE", 1)) GIT_INFO = check_git_info() def train(hyp, opt, device, callbacks): # hyp is path/to/hyp.yaml or hyp dictionary """Trains a segmentation model using the provided hyperparameters, options, and callbacks, handling multi-GPU setups, data loading, logging, and validation. """ ( save_dir, epochs, batch_size, weights, single_cls, evolve, data, cfg, resume, noval, nosave, workers, freeze, mask_ratio, ) = ( Path(opt.save_dir), opt.epochs, opt.batch_size, opt.weights, opt.single_cls, opt.evolve, opt.data, opt.cfg, opt.resume, opt.noval, opt.nosave, opt.workers, opt.freeze, opt.mask_ratio, ) # callbacks.run('on_pretrain_routine_start') # Directories w = save_dir / "weights" # weights dir (w.parent if evolve else w).mkdir(parents=True, exist_ok=True) # make dir last, best = w / "last.pt", w / "best.pt" # Hyperparameters if isinstance(hyp, str): with open(hyp, errors="ignore") as f: hyp = yaml.safe_load(f) # load hyps dict LOGGER.info(colorstr("hyperparameters: ") + ", ".join(f"{k}={v}" for k, v in hyp.items())) opt.hyp = hyp.copy() # for saving hyps to checkpoints # Save run settings if not evolve: yaml_save(save_dir / "hyp.yaml", hyp) yaml_save(save_dir / "opt.yaml", vars(opt)) # Loggers data_dict = None if RANK in {-1, 0}: logger = GenericLogger(opt=opt, console_logger=LOGGER) # Config plots = not evolve and not opt.noplots # create plots overlap = not opt.no_overlap cuda = device.type != "cpu" init_seeds(opt.seed + 1 + RANK, deterministic=True) with torch_distributed_zero_first(LOCAL_RANK): data_dict = data_dict or check_dataset(data) # check if None train_path, val_path = data_dict["train"], data_dict["val"] nc = 1 if single_cls else int(data_dict["nc"]) # number of classes names = {0: "item"} if single_cls and len(data_dict["names"]) != 1 else data_dict["names"] # class names is_coco = isinstance(val_path, str) and val_path.endswith("coco/val2017.txt") # COCO dataset # Model check_suffix(weights, ".pt") # check weights pretrained = weights.endswith(".pt") if pretrained: with torch_distributed_zero_first(LOCAL_RANK): weights = attempt_download(weights) # download if not found locally ckpt = torch_load(weights, map_location="cpu") # load checkpoint to CPU to avoid CUDA memory leak model = SegmentationModel(cfg or ckpt["model"].yaml, ch=3, nc=nc, anchors=hyp.get("anchors")).to(device) exclude = ["anchor"] if (cfg or hyp.get("anchors")) and not resume else [] # exclude keys csd = ckpt["model"].float().state_dict() # checkpoint state_dict as FP32 csd = intersect_dicts(csd, model.state_dict(), exclude=exclude) # intersect model.load_state_dict(csd, strict=False) # load LOGGER.info(f"Transferred {len(csd)}/{len(model.state_dict())} items from {weights}") # report else: model = SegmentationModel(cfg, ch=3, nc=nc, anchors=hyp.get("anchors")).to(device) # create amp = check_amp(model) # check AMP # Freeze freeze = [f"model.{x}." for x in (freeze if len(freeze) > 1 else range(freeze[0]))] # layers to freeze for k, v in model.named_parameters(): v.requires_grad = True # train all layers # v.register_hook(lambda x: torch.nan_to_num(x)) # NaN to 0 (commented for erratic training results) if any(x in k for x in freeze): LOGGER.info(f"freezing {k}") v.requires_grad = False # Image size gs = max(int(model.stride.max()), 32) # grid size (max stride) imgsz = check_img_size(opt.imgsz, gs, floor=gs * 2) # verify imgsz is gs-multiple # Batch size if RANK == -1 and batch_size == -1: # single-GPU only, estimate best batch size batch_size = check_train_batch_size(model, imgsz, amp) logger.update_params({"batch_size": batch_size}) # loggers.on_params_update({"batch_size": batch_size}) # Optimizer nbs = 64 # nominal batch size accumulate = max(round(nbs / batch_size), 1) # accumulate loss before optimizing hyp["weight_decay"] *= batch_size * accumulate / nbs # scale weight_decay optimizer = smart_optimizer(model, opt.optimizer, hyp["lr0"], hyp["momentum"], hyp["weight_decay"]) # Scheduler if opt.cos_lr: lf = one_cycle(1, hyp["lrf"], epochs) # cosine 1->hyp['lrf'] else: def lf(x): """Linear learning rate scheduler decreasing from 1 to hyp['lrf'] over the course of given epochs.""" return (1 - x / epochs) * (1.0 - hyp["lrf"]) + hyp["lrf"] # linear scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lf) # plot_lr_scheduler(optimizer, scheduler, epochs) # EMA ema = ModelEMA(model) if RANK in {-1, 0} else None # Resume best_fitness, start_epoch = 0.0, 0 if pretrained: if resume: best_fitness, start_epoch, epochs = smart_resume(ckpt, optimizer, ema, weights, epochs, resume) del ckpt, csd # DP mode if cuda and RANK == -1 and torch.cuda.device_count() > 1: LOGGER.warning( "WARNING ⚠️ DP not recommended, use torch.distributed.run for best DDP Multi-GPU results.\n" "See Multi-GPU Tutorial at https://docs.ultralytics.com/yolov5/tutorials/multi_gpu_training to get started." ) model = torch.nn.DataParallel(model) # SyncBatchNorm if opt.sync_bn and cuda and RANK != -1: model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model).to(device) LOGGER.info("Using SyncBatchNorm()") # Trainloader train_loader, dataset = create_dataloader( train_path, imgsz, batch_size // WORLD_SIZE, gs, single_cls, hyp=hyp, augment=True, cache=None if opt.cache == "val" else opt.cache, rect=opt.rect, rank=LOCAL_RANK, workers=workers, image_weights=opt.image_weights, quad=opt.quad, prefix=colorstr("train: "), shuffle=True, mask_downsample_ratio=mask_ratio, overlap_mask=overlap, ) labels = np.concatenate(dataset.labels, 0) mlc = int(labels[:, 0].max()) # max label class assert mlc < nc, f"Label class {mlc} exceeds nc={nc} in {data}. Possible class labels are 0-{nc - 1}" # Process 0 if RANK in {-1, 0}: val_loader = create_dataloader( val_path, imgsz, batch_size // WORLD_SIZE * 2, gs, single_cls, hyp=hyp, cache=None if noval else opt.cache, rect=True, rank=-1, workers=workers * 2, pad=0.5, mask_downsample_ratio=mask_ratio, overlap_mask=overlap, prefix=colorstr("val: "), )[0] if not resume: if not opt.noautoanchor: check_anchors(dataset, model=model, thr=hyp["anchor_t"], imgsz=imgsz) # run AutoAnchor model.half().float() # pre-reduce anchor precision if plots: plot_labels(labels, names, save_dir) # callbacks.run('on_pretrain_routine_end', labels, names) # DDP mode if cuda and RANK != -1: model = smart_DDP(model) # Model attributes nl = de_parallel(model).model[-1].nl # number of detection layers (to scale hyps) hyp["box"] *= 3 / nl # scale to layers hyp["cls"] *= nc / 80 * 3 / nl # scale to classes and layers hyp["obj"] *= (imgsz / 640) ** 2 * 3 / nl # scale to image size and layers hyp["label_smoothing"] = opt.label_smoothing model.nc = nc # attach number of classes to model model.hyp = hyp # attach hyperparameters to model model.class_weights = labels_to_class_weights(dataset.labels, nc).to(device) * nc # attach class weights model.names = names # Start training t0 = time.time() nb = len(train_loader) # number of batches nw = max(round(hyp["warmup_epochs"] * nb), 100) # number of warmup iterations, max(3 epochs, 100 iterations) # nw = min(nw, (epochs - start_epoch) / 2 * nb) # limit warmup to < 1/2 of training last_opt_step = -1 maps = np.zeros(nc) # mAP per class 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) scheduler.last_epoch = start_epoch - 1 # do not move scaler = torch.cuda.amp.GradScaler(enabled=amp) stopper, stop = EarlyStopping(patience=opt.patience), False compute_loss = ComputeLoss(model, overlap=overlap) # init loss class # callbacks.run('on_train_start') LOGGER.info( f"Image sizes {imgsz} train, {imgsz} val\n" f"Using {train_loader.num_workers * WORLD_SIZE} dataloader workers\n" f"Logging results to {colorstr('bold', save_dir)}\n" f"Starting training for {epochs} epochs..." ) for epoch in range(start_epoch, epochs): # epoch ------------------------------------------------------------------ # callbacks.run('on_train_epoch_start') model.train() # Update image weights (optional, single-GPU only) if opt.image_weights: cw = model.class_weights.cpu().numpy() * (1 - maps) ** 2 / nc # class weights iw = labels_to_image_weights(dataset.labels, nc=nc, class_weights=cw) # image weights dataset.indices = random.choices(range(dataset.n), weights=iw, k=dataset.n) # rand weighted idx # Update mosaic border (optional) # b = int(random.uniform(0.25 * imgsz, 0.75 * imgsz + gs) // gs * gs) # dataset.mosaic_border = [b - imgsz, -b] # height, width borders mloss = torch.zeros(4, device=device) # mean losses if RANK != -1: train_loader.sampler.set_epoch(epoch) pbar = enumerate(train_loader) LOGGER.info( ("\n" + "%11s" * 8) % ("Epoch", "GPU_mem", "box_loss", "seg_loss", "obj_loss", "cls_loss", "Instances", "Size") ) if RANK in {-1, 0}: pbar = tqdm(pbar, total=nb, bar_format=TQDM_BAR_FORMAT) # progress bar optimizer.zero_grad() for i, (imgs, targets, paths, _, masks) in pbar: # batch ------------------------------------------------------ # callbacks.run('on_train_batch_start') ni = i + nb * epoch # number integrated batches (since train start) imgs = imgs.to(device, non_blocking=True).float() / 255 # uint8 to float32, 0-255 to 0.0-1.0 # Warmup if ni <= nw: xi = [0, nw] # x interp # compute_loss.gr = np.interp(ni, xi, [0.0, 1.0]) # iou loss ratio (obj_loss = 1.0 or iou) accumulate = max(1, np.interp(ni, xi, [1, nbs / batch_size]).round()) for j, x in enumerate(optimizer.param_groups): # bias lr falls from 0.1 to lr0, all other lrs rise from 0.0 to lr0 x["lr"] = np.interp(ni, xi, [hyp["warmup_bias_lr"] if j == 0 else 0.0, x["initial_lr"] * lf(epoch)]) if "momentum" in x: x["momentum"] = np.interp(ni, xi, [hyp["warmup_momentum"], hyp["momentum"]]) # Multi-scale if opt.multi_scale: sz = random.randrange(int(imgsz * 0.5), int(imgsz * 1.5) + gs) // gs * gs # size sf = sz / max(imgs.shape[2:]) # scale factor if sf != 1: ns = [math.ceil(x * sf / gs) * gs for x in imgs.shape[2:]] # new shape (stretched to gs-multiple) imgs = nn.functional.interpolate(imgs, size=ns, mode="bilinear", align_corners=False) # Forward with torch.cuda.amp.autocast(amp): pred = model(imgs) # forward loss, loss_items = compute_loss(pred, targets.to(device), masks=masks.to(device).float()) if RANK != -1: loss *= WORLD_SIZE # gradient averaged between devices in DDP mode if opt.quad: loss *= 4.0 # Backward scaler.scale(loss).backward() # Optimize - https://pytorch.org/docs/master/notes/amp_examples.html if ni - last_opt_step >= accumulate: scaler.unscale_(optimizer) # unscale gradients torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=10.0) # clip gradients scaler.step(optimizer) # optimizer.step scaler.update() optimizer.zero_grad() if ema: ema.update(model) last_opt_step = ni # Log if RANK in {-1, 0}: mloss = (mloss * i + loss_items) / (i + 1) # update mean losses mem = f"{torch.cuda.memory_reserved() / 1e9 if torch.cuda.is_available() else 0:.3g}G" # (GB) pbar.set_description( ("%11s" * 2 + "%11.4g" * 6) % (f"{epoch}/{epochs - 1}", mem, *mloss, targets.shape[0], imgs.shape[-1]) ) # callbacks.run('on_train_batch_end', model, ni, imgs, targets, paths) # if callbacks.stop_training: # return # Mosaic plots if plots: if ni < 3: plot_images_and_masks(imgs, targets, masks, paths, save_dir / f"train_batch{ni}.jpg") if ni == 10: files = sorted(save_dir.glob("train*.jpg")) logger.log_images(files, "Mosaics", epoch) # end batch ------------------------------------------------------------------------------------------------ # Scheduler lr = [x["lr"] for x in optimizer.param_groups] # for loggers scheduler.step() if RANK in {-1, 0}: # mAP # callbacks.run('on_train_epoch_end', epoch=epoch) ema.update_attr(model, include=["yaml", "nc", "hyp", "names", "stride", "class_weights"]) final_epoch = (epoch + 1 == epochs) or stopper.possible_stop if not noval or final_epoch: # Calculate mAP results, maps, _ = validate.run( data_dict, batch_size=batch_size // WORLD_SIZE * 2, imgsz=imgsz, half=amp, model=ema.ema, single_cls=single_cls, dataloader=val_loader, save_dir=save_dir, plots=False, callbacks=callbacks, compute_loss=compute_loss, mask_downsample_ratio=mask_ratio, overlap=overlap, ) # Update best mAP fi = fitness(np.array(results).reshape(1, -1)) # weighted combination of [P, R, mAP@.5, mAP@.5-.95] stop = stopper(epoch=epoch, fitness=fi) # early stop check if fi > best_fitness: best_fitness = fi log_vals = list(mloss) + list(results) + lr # callbacks.run('on_fit_epoch_end', log_vals, epoch, best_fitness, fi) # Log val metrics and media metrics_dict = dict(zip(KEYS, log_vals)) logger.log_metrics(metrics_dict, epoch) # Save model if (not nosave) or (final_epoch and not evolve): # if save ckpt = { "epoch": epoch, "best_fitness": best_fitness, "model": deepcopy(de_parallel(model)).half(), "ema": deepcopy(ema.ema).half(), "updates": ema.updates, "optimizer": optimizer.state_dict(), "opt": vars(opt), "git": GIT_INFO, # {remote, branch, commit} if a git repo "date": datetime.now().isoformat(), } # Save last, best and delete torch.save(ckpt, last) if best_fitness == fi: torch.save(ckpt, best) if opt.save_period > 0 and epoch % opt.save_period == 0: torch.save(ckpt, w / f"epoch{epoch}.pt") logger.log_model(w / f"epoch{epoch}.pt") del ckpt # callbacks.run('on_model_save', last, epoch, final_epoch, best_fitness, fi) # EarlyStopping if RANK != -1: # if DDP training broadcast_list = [stop if RANK == 0 else None] dist.broadcast_object_list(broadcast_list, 0) # broadcast 'stop' to all ranks if RANK != 0: stop = broadcast_list[0] if stop: break # must break all DDP ranks # end epoch ---------------------------------------------------------------------------------------------------- # end training ----------------------------------------------------------------------------------------------------- if RANK in {-1, 0}: LOGGER.info(f"\n{epoch - start_epoch + 1} epochs completed in {(time.time() - t0) / 3600:.3f} hours.") for f in last, best: if f.exists(): strip_optimizer(f) # strip optimizers if f is best: LOGGER.info(f"\nValidating {f}...") results, _, _ = validate.run( data_dict, batch_size=batch_size // WORLD_SIZE * 2, imgsz=imgsz, model=attempt_load(f, device).half(), iou_thres=0.65 if is_coco else 0.60, # best pycocotools at iou 0.65 single_cls=single_cls, dataloader=val_loader, save_dir=save_dir, save_json=is_coco, verbose=True, plots=plots, callbacks=callbacks, compute_loss=compute_loss, mask_downsample_ratio=mask_ratio, overlap=overlap, ) # val best model with plots if is_coco: # callbacks.run('on_fit_epoch_end', list(mloss) + list(results) + lr, epoch, best_fitness, fi) metrics_dict = dict(zip(KEYS, list(mloss) + list(results) + lr)) logger.log_metrics(metrics_dict, epoch) # callbacks.run('on_train_end', last, best, epoch, results) # on train end callback using genericLogger logger.log_metrics(dict(zip(KEYS[4:16], results)), epochs) if not opt.evolve: logger.log_model(best, epoch) if plots: plot_results_with_masks(file=save_dir / "results.csv") # save results.png files = ["results.png", "confusion_matrix.png", *(f"{x}_curve.png" for x in ("F1", "PR", "P", "R"))] files = [(save_dir / f) for f in files if (save_dir / f).exists()] # filter LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}") logger.log_images(files, "Results", epoch + 1) logger.log_images(sorted(save_dir.glob("val*.jpg")), "Validation", epoch + 1) torch.cuda.empty_cache() return results def parse_opt(known=False): """Parses command line arguments for training configurations, supporting optional known args parsing.""" parser = argparse.ArgumentParser() parser.add_argument("--weights", type=str, default=ROOT / "yolov5s-seg.pt", help="initial weights path") parser.add_argument("--cfg", type=str, default="", help="model.yaml path") parser.add_argument("--data", type=str, default=ROOT / "data/coco128-seg.yaml", help="dataset.yaml path") parser.add_argument("--hyp", type=str, default=ROOT / "data/hyps/hyp.scratch-low.yaml", help="hyperparameters path") parser.add_argument("--epochs", type=int, default=100, help="total training epochs") parser.add_argument("--batch-size", type=int, default=16, help="total batch size for all GPUs, -1 for autobatch") parser.add_argument("--imgsz", "--img", "--img-size", type=int, default=640, help="train, val image size (pixels)") parser.add_argument("--rect", action="store_true", help="rectangular training") parser.add_argument("--resume", nargs="?", const=True, default=False, help="resume most recent training") parser.add_argument("--nosave", action="store_true", help="only save final checkpoint") parser.add_argument("--noval", action="store_true", help="only validate final epoch") parser.add_argument("--noautoanchor", action="store_true", help="disable AutoAnchor") parser.add_argument("--noplots", action="store_true", help="save no plot files") parser.add_argument("--evolve", type=int, nargs="?", const=300, help="evolve hyperparameters for x generations") parser.add_argument("--bucket", type=str, default="", help="gsutil bucket") parser.add_argument("--cache", type=str, nargs="?", const="ram", help="image --cache ram/disk") parser.add_argument("--image-weights", action="store_true", help="use weighted image selection for training") parser.add_argument("--device", default="", help="cuda device, i.e. 0 or 0,1,2,3 or cpu") parser.add_argument("--multi-scale", action="store_true", help="vary img-size +/- 50%%") parser.add_argument("--single-cls", action="store_true", help="train multi-class data as single-class") parser.add_argument("--optimizer", type=str, choices=["SGD", "Adam", "AdamW"], default="SGD", help="optimizer") parser.add_argument("--sync-bn", action="store_true", help="use SyncBatchNorm, only available in DDP mode") parser.add_argument("--workers", type=int, default=8, help="max dataloader workers (per RANK in DDP mode)") parser.add_argument("--project", default=ROOT / "runs/train-seg", help="save to project/name") parser.add_argument("--name", default="exp", help="save to project/name") parser.add_argument("--exist-ok", action="store_true", help="existing project/name ok, do not increment") parser.add_argument("--quad", action="store_true", help="quad dataloader") parser.add_argument("--cos-lr", action="store_true", help="cosine LR scheduler") parser.add_argument("--label-smoothing", type=float, default=0.0, help="Label smoothing epsilon") parser.add_argument("--patience", type=int, default=100, help="EarlyStopping patience (epochs without improvement)") parser.add_argument("--freeze", nargs="+", type=int, default=[0], help="Freeze layers: backbone=10, first3=0 1 2") parser.add_argument("--save-period", type=int, default=-1, help="Save checkpoint every x epochs (disabled if < 1)") parser.add_argument("--seed", type=int, default=0, help="Global training seed") parser.add_argument("--local_rank", type=int, default=-1, help="Automatic DDP Multi-GPU argument, do not modify") # Instance Segmentation Args parser.add_argument("--mask-ratio", type=int, default=4, help="Downsample the truth masks to saving memory") parser.add_argument("--no-overlap", action="store_true", help="Overlap masks train faster at slightly less mAP") return parser.parse_known_args()[0] if known else parser.parse_args() def main(opt, callbacks=Callbacks()): """Initializes training or evolution of models with given options and callbacks, handling device setup and data preparation. """ if RANK in {-1, 0}: print_args(vars(opt)) check_git_status() check_requirements(ROOT / "requirements.txt") # Resume if opt.resume and not opt.evolve: # resume from specified or most recent last.pt last = Path(check_file(opt.resume) if isinstance(opt.resume, str) else get_latest_run()) opt_yaml = last.parent.parent / "opt.yaml" # train options yaml opt_data = opt.data # original dataset if opt_yaml.is_file(): with open(opt_yaml, errors="ignore") as f: d = yaml.safe_load(f) else: d = torch_load(last, map_location="cpu")["opt"] opt = argparse.Namespace(**d) # replace opt.cfg, opt.weights, opt.resume = "", str(last), True # reinstate if is_url(opt_data): opt.data = check_file(opt_data) # avoid HUB resume auth timeout else: opt.data, opt.cfg, opt.hyp, opt.weights, opt.project = ( check_file(opt.data), check_yaml(opt.cfg), check_yaml(opt.hyp), str(opt.weights), str(opt.project), ) # checks assert len(opt.cfg) or len(opt.weights), "either --cfg or --weights must be specified" if opt.evolve: if opt.project == str(ROOT / "runs/train-seg"): # if default project name, rename to runs/evolve-seg opt.project = str(ROOT / "runs/evolve-seg") opt.exist_ok, opt.resume = opt.resume, False # pass resume to exist_ok and disable resume if opt.name == "cfg": opt.name = Path(opt.cfg).stem # use model.yaml as name opt.save_dir = str(increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok)) # DDP mode device = select_device(opt.device, batch_size=opt.batch_size) if LOCAL_RANK != -1: msg = "is not compatible with YOLOv3 Multi-GPU DDP training" assert not opt.image_weights, f"--image-weights {msg}" assert not opt.evolve, f"--evolve {msg}" assert opt.batch_size != -1, f"AutoBatch with --batch-size -1 {msg}, please pass a valid --batch-size" assert opt.batch_size % WORLD_SIZE == 0, f"--batch-size {opt.batch_size} must be multiple of WORLD_SIZE" assert torch.cuda.device_count() > LOCAL_RANK, "insufficient CUDA devices for DDP command" torch.cuda.set_device(LOCAL_RANK) device = torch.device("cuda", LOCAL_RANK) dist.init_process_group(backend="nccl" if dist.is_nccl_available() else "gloo") # Train if not opt.evolve: train(opt.hyp, opt, device, callbacks) # Evolve hyperparameters (optional) else: # Hyperparameter evolution metadata (mutation scale 0-1, lower_limit, upper_limit) meta = { "lr0": (1, 1e-5, 1e-1), # initial learning rate (SGD=1E-2, Adam=1E-3) "lrf": (1, 0.01, 1.0), # final OneCycleLR learning rate (lr0 * lrf) "momentum": (0.3, 0.6, 0.98), # SGD momentum/Adam beta1 "weight_decay": (1, 0.0, 0.001), # optimizer weight decay "warmup_epochs": (1, 0.0, 5.0), # warmup epochs (fractions ok) "warmup_momentum": (1, 0.0, 0.95), # warmup initial momentum "warmup_bias_lr": (1, 0.0, 0.2), # warmup initial bias lr "box": (1, 0.02, 0.2), # box loss gain "cls": (1, 0.2, 4.0), # cls loss gain "cls_pw": (1, 0.5, 2.0), # cls BCELoss positive_weight "obj": (1, 0.2, 4.0), # obj loss gain (scale with pixels) "obj_pw": (1, 0.5, 2.0), # obj BCELoss positive_weight "iou_t": (0, 0.1, 0.7), # IoU training threshold "anchor_t": (1, 2.0, 8.0), # anchor-multiple threshold "anchors": (2, 2.0, 10.0), # anchors per output grid (0 to ignore) "fl_gamma": (0, 0.0, 2.0), # focal loss gamma (efficientDet default gamma=1.5) "hsv_h": (1, 0.0, 0.1), # image HSV-Hue augmentation (fraction) "hsv_s": (1, 0.0, 0.9), # image HSV-Saturation augmentation (fraction) "hsv_v": (1, 0.0, 0.9), # image HSV-Value augmentation (fraction) "degrees": (1, 0.0, 45.0), # image rotation (+/- deg) "translate": (1, 0.0, 0.9), # image translation (+/- fraction) "scale": (1, 0.0, 0.9), # image scale (+/- gain) "shear": (1, 0.0, 10.0), # image shear (+/- deg) "perspective": (0, 0.0, 0.001), # image perspective (+/- fraction), range 0-0.001 "flipud": (1, 0.0, 1.0), # image flip up-down (probability) "fliplr": (0, 0.0, 1.0), # image flip left-right (probability) "mosaic": (1, 0.0, 1.0), # image mixup (probability) "mixup": (1, 0.0, 1.0), # image mixup (probability) "copy_paste": (1, 0.0, 1.0), } # segment copy-paste (probability) with open(opt.hyp, errors="ignore") as f: hyp = yaml.safe_load(f) # load hyps dict if "anchors" not in hyp: # anchors commented in hyp.yaml hyp["anchors"] = 3 if opt.noautoanchor: del hyp["anchors"], meta["anchors"] opt.noval, opt.nosave, save_dir = True, True, Path(opt.save_dir) # only val/save final epoch # ei = [isinstance(x, (int, float)) for x in hyp.values()] # evolvable indices evolve_yaml, evolve_csv = save_dir / "hyp_evolve.yaml", save_dir / "evolve.csv" if opt.bucket: # download evolve.csv if exists subprocess.run( [ "gsutil", "cp", f"gs://{opt.bucket}/evolve.csv", str(evolve_csv), ] ) for _ in range(opt.evolve): # generations to evolve if evolve_csv.exists(): # if evolve.csv exists: select best hyps and mutate # Select parent(s) parent = "single" # parent selection method: 'single' or 'weighted' x = np.loadtxt(evolve_csv, ndmin=2, delimiter=",", skiprows=1) n = min(5, len(x)) # number of previous results to consider x = x[np.argsort(-fitness(x))][:n] # top n mutations w = fitness(x) - fitness(x).min() + 1e-6 # weights (sum > 0) if parent == "single" or len(x) == 1: # x = x[random.randint(0, n - 1)] # random selection x = x[random.choices(range(n), weights=w)[0]] # weighted selection elif parent == "weighted": x = (x * w.reshape(n, 1)).sum(0) / w.sum() # weighted combination # Mutate mp, s = 0.8, 0.2 # mutation probability, sigma npr = np.random npr.seed(int(time.time())) g = np.array([meta[k][0] for k in hyp.keys()]) # gains 0-1 ng = len(meta) v = np.ones(ng) while all(v == 1): # mutate until a change occurs (prevent duplicates) v = (g * (npr.random(ng) < mp) * npr.randn(ng) * npr.random() * s + 1).clip(0.3, 3.0) for i, k in enumerate(hyp.keys()): # plt.hist(v.ravel(), 300) hyp[k] = float(x[i + 12] * v[i]) # mutate # Constrain to limits for k, v in meta.items(): hyp[k] = max(hyp[k], v[1]) # lower limit hyp[k] = min(hyp[k], v[2]) # upper limit hyp[k] = round(hyp[k], 5) # significant digits # Train mutation results = train(hyp.copy(), opt, device, callbacks) callbacks = Callbacks() # Write mutation results print_mutation(KEYS[4:16], results, hyp.copy(), save_dir, opt.bucket) # Plot results plot_evolve(evolve_csv) LOGGER.info( f"Hyperparameter evolution finished {opt.evolve} generations\n" f"Results saved to {colorstr('bold', save_dir)}\n" f"Usage example: $ python train.py --hyp {evolve_yaml}" ) def run(**kwargs): """Executes model training with specified configurations; see example: `train.run(data='coco128.yaml', imgsz=320, weights='yolov5m.pt')`. """ opt = parse_opt(True) for k, v in kwargs.items(): setattr(opt, k, v) main(opt) return opt if __name__ == "__main__": opt = parse_opt() main(opt) ================================================ FILE: segment/tutorial.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": { "id": "t6MPjfT5NrKQ" }, "source": [ "
\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", " \"Ultralytics\n", " \"Run\n", " \"Open\n", " \"Open\n", "\n", " \"Discord\"\n", " \"Ultralytics\n", " \"Ultralytics\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", "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", "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", "Request an Enterprise License for commercial use at [Ultralytics Licensing](https://www.ultralytics.com/license).\n", "\n", "
\n", "
\n", " \n", " \"Ultralytics\n", " \n", "\n", "

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

\n", "
" ] }, { "cell_type": "markdown", "metadata": { "id": "7mGmQbAO5pQb" }, "source": [ "# Setup\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." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "wbvMlHd_QwMG", "outputId": "171b23f0-71b9-4cbf-b666-6fa2ecef70c8" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "YOLOv5 🚀 v7.0-2-gc9d47ae Python-3.7.15 torch-1.12.1+cu113 CUDA:0 (Tesla T4, 15110MiB)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Setup complete ✅ (2 CPUs, 12.7 GB RAM, 22.6/78.2 GB disk)\n" ] } ], "source": [ "!git clone https://github.com/ultralytics/yolov5 # clone\n", "%cd yolov5\n", "%pip install -qr requirements.txt comet_ml # install\n", "\n", "import torch\n", "\n", "import utils\n", "\n", "display = utils.notebook_init() # checks" ] }, { "cell_type": "markdown", "metadata": { "id": "4JnkELT0cIJg" }, "source": [ "# 1. Predict\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", "```shell\n", "python segment/predict.py --source 0 # webcam\n", " img.jpg # image \n", " vid.mp4 # video\n", " screen # screenshot\n", " path/ # directory\n", " 'path/*.jpg' # glob\n", " 'https://youtu.be/LNwODJXcvt4' # YouTube\n", " 'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream\n", "```" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "zR9ZbuQCH7FX", "outputId": "3f67f1c7-f15e-4fa5-d251-967c3b77eaad" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\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", "YOLOv5 🚀 v7.0-2-gc9d47ae Python-3.7.15 torch-1.12.1+cu113 CUDA:0 (Tesla T4, 15110MiB)\n", "\n", "Downloading https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s-seg.pt to yolov5s-seg.pt...\n", "100% 14.9M/14.9M [00:01<00:00, 12.0MB/s]\n", "\n", "Fusing layers... \n", "YOLOv5s-seg summary: 224 layers, 7611485 parameters, 0 gradients, 26.4 GFLOPs\n", "image 1/2 /content/yolov5/data/images/bus.jpg: 640x480 4 persons, 1 bus, 18.2ms\n", "image 2/2 /content/yolov5/data/images/zidane.jpg: 384x640 2 persons, 1 tie, 13.4ms\n", "Speed: 0.5ms pre-process, 15.8ms inference, 18.5ms NMS per image at shape (1, 3, 640, 640)\n", "Results saved to \u001B[1mruns/predict-seg/exp\u001B[0m\n" ] } ], "source": [ "!python segment/predict.py --weights yolov5s-seg.pt --img 640 --conf 0.25 --source data/images\n", "# display.Image(filename='runs/predict-seg/exp/zidane.jpg', width=600)" ] }, { "cell_type": "markdown", "metadata": { "id": "hkAzDWJ7cWTr" }, "source": [ "        \n", "" ] }, { "cell_type": "markdown", "metadata": { "id": "0eq1SMWl6Sfn" }, "source": [ "# 2. Validate\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." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "WQPtK1QYVaD_", "outputId": "9d751d8c-bee8-4339-cf30-9854ca530449" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Downloading https://github.com/ultralytics/assets/releases/download/v0.0.0/coco2017labels-segments.zip ...\n", "Downloading http://images.cocodataset.org/zips/val2017.zip ...\n", "######################################################################## 100.0%\n", "######################################################################## 100.0%\n" ] } ], "source": [ "# Download COCO val\n", "!bash data/scripts/get_coco.sh --val --segments # download (780M - 5000 images)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "X58w8JLpMnjH", "outputId": "a140d67a-02da-479e-9ddb-7d54bf9e407a" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\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", "YOLOv5 🚀 v7.0-2-gc9d47ae Python-3.7.15 torch-1.12.1+cu113 CUDA:0 (Tesla T4, 15110MiB)\n", "\n", "Fusing layers... \n", "YOLOv5s-seg summary: 224 layers, 7611485 parameters, 0 gradients, 26.4 GFLOPs\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", "\u001B[34m\u001B[1mval: \u001B[0mNew cache created: /content/datasets/coco/val2017.cache\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", " all 5000 36335 0.673 0.517 0.566 0.373 0.672 0.49 0.532 0.319\n", "Speed: 0.6ms pre-process, 4.4ms inference, 2.9ms NMS per image at shape (32, 3, 640, 640)\n", "Results saved to \u001B[1mruns/val-seg/exp\u001B[0m\n" ] } ], "source": [ "# Validate YOLOv5s-seg on COCO val\n", "!python segment/val.py --weights yolov5s-seg.pt --data coco.yaml --img 640 --half" ] }, { "cell_type": "markdown", "metadata": { "id": "ZY2VXXXu74w5" }, "source": [ "# 3. Train\n", "\n", "\n", " \"Ultralytics\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", "- **Pretrained [Models](https://github.com/ultralytics/yolov5/tree/master/models)** are downloaded\n", "automatically from the [latest YOLOv5 release](https://github.com/ultralytics/yolov5/releases)\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", "- **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", "A **Mosaic Dataloader** is used for training which combines 4 images into 1 mosaic." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "i3oKtE4g-aNn" }, "outputs": [], "source": [ "# @title Select YOLOv5 🚀 logger {run: 'auto'}\n", "logger = \"Comet\" # @param ['Comet', 'ClearML', 'TensorBoard']\n", "\n", "if logger == \"Comet\":\n", " %pip install -q comet_ml\n", " import comet_ml\n", "\n", " comet_ml.init()\n", "elif logger == \"ClearML\":\n", " %pip install -q clearml\n", " import clearml\n", "\n", " clearml.browser_login()\n", "elif logger == \"TensorBoard\":\n", " %load_ext tensorboard\n", " %tensorboard --logdir runs/train" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "1NcFxRcFdJ_O", "outputId": "3a3e0cf7-e79c-47a5-c8e7-2d26eeeab988" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\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", "\u001B[34m\u001B[1mgithub: \u001B[0mup to date with https://github.com/ultralytics/yolov5 ✅\n", "YOLOv5 🚀 v7.0-2-gc9d47ae Python-3.7.15 torch-1.12.1+cu113 CUDA:0 (Tesla T4, 15110MiB)\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", "\u001B[34m\u001B[1mTensorBoard: \u001B[0mStart with 'tensorboard --logdir runs/train-seg', view at http://localhost:6006/\n", "\n", "Dataset not found ⚠️, missing paths ['/content/datasets/coco128-seg/images/train2017']\n", "Downloading https://github.com/ultralytics/assets/releases/download/v0.0.0/coco128-seg.zip to coco128-seg.zip...\n", "100% 6.79M/6.79M [00:01<00:00, 6.73MB/s]\n", "Dataset download success ✅ (1.9s), saved to \u001B[1m/content/datasets\u001B[0m\n", "\n", " from n params module arguments \n", " 0 -1 1 3520 models.common.Conv [3, 32, 6, 2, 2] \n", " 1 -1 1 18560 models.common.Conv [32, 64, 3, 2] \n", " 2 -1 1 18816 models.common.C3 [64, 64, 1] \n", " 3 -1 1 73984 models.common.Conv [64, 128, 3, 2] \n", " 4 -1 2 115712 models.common.C3 [128, 128, 2] \n", " 5 -1 1 295424 models.common.Conv [128, 256, 3, 2] \n", " 6 -1 3 625152 models.common.C3 [256, 256, 3] \n", " 7 -1 1 1180672 models.common.Conv [256, 512, 3, 2] \n", " 8 -1 1 1182720 models.common.C3 [512, 512, 1] \n", " 9 -1 1 656896 models.common.SPPF [512, 512, 5] \n", " 10 -1 1 131584 models.common.Conv [512, 256, 1, 1] \n", " 11 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] \n", " 12 [-1, 6] 1 0 models.common.Concat [1] \n", " 13 -1 1 361984 models.common.C3 [512, 256, 1, False] \n", " 14 -1 1 33024 models.common.Conv [256, 128, 1, 1] \n", " 15 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] \n", " 16 [-1, 4] 1 0 models.common.Concat [1] \n", " 17 -1 1 90880 models.common.C3 [256, 128, 1, False] \n", " 18 -1 1 147712 models.common.Conv [128, 128, 3, 2] \n", " 19 [-1, 14] 1 0 models.common.Concat [1] \n", " 20 -1 1 296448 models.common.C3 [256, 256, 1, False] \n", " 21 -1 1 590336 models.common.Conv [256, 256, 3, 2] \n", " 22 [-1, 10] 1 0 models.common.Concat [1] \n", " 23 -1 1 1182720 models.common.C3 [512, 512, 1, False] \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", "Model summary: 225 layers, 7621277 parameters, 7621277 gradients, 26.6 GFLOPs\n", "\n", "Transferred 367/367 items from yolov5s-seg.pt\n", "\u001B[34m\u001B[1mAMP: \u001B[0mchecks passed ✅\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", "\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", "\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", "\u001B[34m\u001B[1mtrain: \u001B[0mNew cache created: /content/datasets/coco128-seg/labels/train2017.cache\n", "\u001B[34m\u001B[1mtrain: \u001B[0mCaching images (0.1GB ram): 100% 128/128 [00:00<00:00, 238.86it/s]\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", "python train.py --img 640 --epochs 3 --data coco128.yaml --weights yolov5s.pt # 3. train\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", "[![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", "\"Comet" ] }, { "cell_type": "markdown", "metadata": { "id": "Lay2WsTjNJzP" }, "source": [ "## ClearML Logging and Automation 🌟 NEW\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", "- `pip install clearml`\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", "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", "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", "\"ClearML" ] }, { "cell_type": "markdown", "metadata": { "id": "-WPvRbS5Swl6" }, "source": [ "## Local Logging\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", "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", "\"Local\n" ] }, { "cell_type": "markdown", "metadata": { "id": "Zelyeqbyt3GD" }, "source": [ "# Environments\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", "- **Notebooks** with free GPU: \"Run \"Open \"Open\n", "- **Google Cloud** Deep Learning VM. See [GCP Quickstart Guide](https://docs.ultralytics.com/yolov5/environments/google_cloud_quickstart_tutorial/)\n", "- **Amazon** Deep Learning AMI. See [AWS Quickstart Guide](https://docs.ultralytics.com/yolov5/environments/aws_quickstart_tutorial/)\n", "- **Docker Image**. See [Docker Quickstart Guide](https://docs.ultralytics.com/yolov5/environments/docker_image_quickstart_tutorial/) \"Docker\n" ] }, { "cell_type": "markdown", "metadata": { "id": "6Qu7Iesl0p54" }, "source": [ "# Status\n", "\n", "![YOLOv5 CI](https://github.com/ultralytics/yolov3/actions/workflows/ci-testing.yml/badge.svg)\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" ] }, { "cell_type": "markdown", "metadata": { "id": "IEijrePND_2I" }, "source": [ "# Appendix\n", "\n", "Additional content below." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "GMusP4OAxFu6" }, "outputs": [], "source": [ "# YOLOv5 PyTorch HUB Inference (DetectionModels only)\n", "\n", "model = torch.hub.load(\"ultralytics/yolov5\", \"yolov5s-seg\") # yolov5n - yolov5x6 or custom\n", "im = \"https://ultralytics.com/images/zidane.jpg\" # file, Path, PIL.Image, OpenCV, nparray, list\n", "results = model(im) # inference\n", "results.print() # or .show(), .save(), .crop(), .pandas(), etc." ] } ], "metadata": { "accelerator": "GPU", "colab": { "name": "YOLOv5 Segmentation Tutorial", "provenance": [], "toc_visible": true }, "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.12" } }, "nbformat": 4, "nbformat_minor": 0 } ================================================ FILE: segment/val.py ================================================ # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license """ Validate a trained YOLOv3 segment model on a segment dataset. Usage: $ bash data/scripts/get_coco.sh --val --segments # download COCO-segments val split (1G, 5000 images) $ python segment/val.py --weights yolov5s-seg.pt --data coco.yaml --img 640 # validate COCO-segments Usage - formats: $ python segment/val.py --weights yolov5s-seg.pt # PyTorch yolov5s-seg.torchscript # TorchScript yolov5s-seg.onnx # ONNX Runtime or OpenCV DNN with --dnn yolov5s-seg_openvino_label # OpenVINO yolov5s-seg.engine # TensorRT yolov5s-seg.mlmodel # CoreML (macOS-only) yolov5s-seg_saved_model # TensorFlow SavedModel yolov5s-seg.pb # TensorFlow GraphDef yolov5s-seg.tflite # TensorFlow Lite yolov5s-seg_edgetpu.tflite # TensorFlow Edge TPU yolov5s-seg_paddle_model # PaddlePaddle """ import argparse import json import os import subprocess import sys from multiprocessing.pool import ThreadPool from pathlib import Path import numpy as np import torch from tqdm import tqdm FILE = Path(__file__).resolve() ROOT = FILE.parents[1] # YOLOv3 root directory if str(ROOT) not in sys.path: sys.path.append(str(ROOT)) # add ROOT to PATH ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative import torch.nn.functional as F from models.common import DetectMultiBackend from models.yolo import SegmentationModel from utils.callbacks import Callbacks from utils.general import ( LOGGER, NUM_THREADS, TQDM_BAR_FORMAT, Profile, check_dataset, check_img_size, check_requirements, check_yaml, coco80_to_coco91_class, colorstr, increment_path, non_max_suppression, print_args, scale_boxes, xywh2xyxy, xyxy2xywh, ) from utils.metrics import ConfusionMatrix, box_iou from utils.plots import output_to_target, plot_val_study from utils.segment.dataloaders import create_dataloader from utils.segment.general import mask_iou, process_mask, process_mask_native, scale_image from utils.segment.metrics import Metrics, ap_per_class_box_and_mask from utils.segment.plots import plot_images_and_masks from utils.torch_utils import de_parallel, select_device, smart_inference_mode def save_one_txt(predn, save_conf, shape, file): """Saves detection results in normalized xywh format (with optional confidence) to a txt file.""" gn = torch.tensor(shape)[[1, 0, 1, 0]] # normalization gain whwh for *xyxy, conf, cls in predn.tolist(): xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() # normalized xywh line = (cls, *xywh, conf) if save_conf else (cls, *xywh) # label format with open(file, "a") as f: f.write(("%g " * len(line)).rstrip() % line + "\n") def save_one_json(predn, jdict, path, class_map, pred_masks): """Saves detection results in COCO JSON format, including bbox, category_id and segmentation if available.""" from pycocotools.mask import encode def single_encode(x): """Encodes a binary mask to COCO RLE format, converting counts to a UTF-8 string for JSON serialization.""" rle = encode(np.asarray(x[:, :, None], order="F", dtype="uint8"))[0] rle["counts"] = rle["counts"].decode("utf-8") return rle image_id = int(path.stem) if path.stem.isnumeric() else path.stem box = xyxy2xywh(predn[:, :4]) # xywh box[:, :2] -= box[:, 2:] / 2 # xy center to top-left corner pred_masks = np.transpose(pred_masks, (2, 0, 1)) with ThreadPool(NUM_THREADS) as pool: rles = pool.map(single_encode, pred_masks) for i, (p, b) in enumerate(zip(predn.tolist(), box.tolist())): jdict.append( { "image_id": image_id, "category_id": class_map[int(p[5])], "bbox": [round(x, 3) for x in b], "score": round(p[4], 5), "segmentation": rles[i], } ) def process_batch(detections, labels, iouv, pred_masks=None, gt_masks=None, overlap=False, masks=False): """Return correct prediction matrix. Args: detections (array[N, 6]), x1, y1, x2, y2, conf, class: labels (array[M, 5]), class, x1, y1, x2, y2 Returns: correct (array[N, 10]), for 10 IoU levels. """ if masks: if overlap: nl = len(labels) index = torch.arange(nl, device=gt_masks.device).view(nl, 1, 1) + 1 gt_masks = gt_masks.repeat(nl, 1, 1) # shape(1,640,640) -> (n,640,640) gt_masks = torch.where(gt_masks == index, 1.0, 0.0) if gt_masks.shape[1:] != pred_masks.shape[1:]: gt_masks = F.interpolate(gt_masks[None], pred_masks.shape[1:], mode="bilinear", align_corners=False)[0] gt_masks = gt_masks.gt_(0.5) iou = mask_iou(gt_masks.view(gt_masks.shape[0], -1), pred_masks.view(pred_masks.shape[0], -1)) else: # boxes iou = box_iou(labels[:, 1:], detections[:, :4]) correct = np.zeros((detections.shape[0], iouv.shape[0])).astype(bool) correct_class = labels[:, 0:1] == detections[:, 5] for i in range(len(iouv)): x = torch.where((iou >= iouv[i]) & correct_class) # IoU > threshold and classes match if x[0].shape[0]: matches = torch.cat((torch.stack(x, 1), iou[x[0], x[1]][:, None]), 1).cpu().numpy() # [label, detect, iou] if x[0].shape[0] > 1: matches = matches[matches[:, 2].argsort()[::-1]] matches = matches[np.unique(matches[:, 1], return_index=True)[1]] # matches = matches[matches[:, 2].argsort()[::-1]] matches = matches[np.unique(matches[:, 0], return_index=True)[1]] correct[matches[:, 1].astype(int), i] = True return torch.tensor(correct, dtype=torch.bool, device=iouv.device) @smart_inference_mode() def run( data, weights=None, # model.pt path(s) batch_size=32, # batch size imgsz=640, # inference size (pixels) conf_thres=0.001, # confidence threshold iou_thres=0.6, # NMS IoU threshold max_det=300, # maximum detections per image task="val", # train, val, test, speed or study device="", # cuda device, i.e. 0 or 0,1,2,3 or cpu workers=8, # max dataloader workers (per RANK in DDP mode) single_cls=False, # treat as single-class dataset augment=False, # augmented inference verbose=False, # verbose output save_txt=False, # save results to *.txt save_hybrid=False, # save label+prediction hybrid results to *.txt save_conf=False, # save confidences in --save-txt labels save_json=False, # save a COCO-JSON results file project=ROOT / "runs/val-seg", # save to project/name name="exp", # save to project/name exist_ok=False, # existing project/name ok, do not increment half=True, # use FP16 half-precision inference dnn=False, # use OpenCV DNN for ONNX inference model=None, dataloader=None, save_dir=Path(""), plots=True, overlap=False, mask_downsample_ratio=1, compute_loss=None, callbacks=Callbacks(), ): """Validates a trained YOLOv3 segmentation model using a specified dataset and evaluation metrics.""" if save_json: check_requirements("pycocotools>=2.0.6") process = process_mask_native # more accurate else: process = process_mask # faster # Initialize/load model and set device training = model is not None if training: # called by train.py device, pt, jit, engine = next(model.parameters()).device, True, False, False # get model device, PyTorch model half &= device.type != "cpu" # half precision only supported on CUDA model.half() if half else model.float() nm = de_parallel(model).model[-1].nm # number of masks else: # called directly device = select_device(device, batch_size=batch_size) # Directories save_dir = increment_path(Path(project) / name, exist_ok=exist_ok) # increment run (save_dir / "labels" if save_txt else save_dir).mkdir(parents=True, exist_ok=True) # make dir # Load model model = DetectMultiBackend(weights, device=device, dnn=dnn, data=data, fp16=half) stride, pt, jit, engine = model.stride, model.pt, model.jit, model.engine imgsz = check_img_size(imgsz, s=stride) # check image size half = model.fp16 # FP16 supported on limited backends with CUDA nm = de_parallel(model).model.model[-1].nm if isinstance(model, SegmentationModel) else 32 # number of masks if engine: batch_size = model.batch_size else: device = model.device if not (pt or jit): batch_size = 1 # export.py models default to batch-size 1 LOGGER.info(f"Forcing --batch-size 1 square inference (1,3,{imgsz},{imgsz}) for non-PyTorch models") # Data data = check_dataset(data) # check # Configure model.eval() cuda = device.type != "cpu" is_coco = isinstance(data.get("val"), str) and data["val"].endswith(f"coco{os.sep}val2017.txt") # COCO dataset nc = 1 if single_cls else int(data["nc"]) # number of classes iouv = torch.linspace(0.5, 0.95, 10, device=device) # iou vector for mAP@0.5:0.95 niou = iouv.numel() # Dataloader if not training: if pt and not single_cls: # check --weights are trained on --data ncm = model.model.nc assert ncm == nc, ( f"{weights} ({ncm} classes) trained on different --data than what you passed ({nc} " f"classes). Pass correct combination of --weights and --data that are trained together." ) model.warmup(imgsz=(1 if pt else batch_size, 3, imgsz, imgsz)) # warmup pad, rect = (0.0, False) if task == "speed" else (0.5, pt) # square inference for benchmarks task = task if task in ("train", "val", "test") else "val" # path to train/val/test images dataloader = create_dataloader( data[task], imgsz, batch_size, stride, single_cls, pad=pad, rect=rect, workers=workers, prefix=colorstr(f"{task}: "), overlap_mask=overlap, mask_downsample_ratio=mask_downsample_ratio, )[0] seen = 0 confusion_matrix = ConfusionMatrix(nc=nc) names = model.names if hasattr(model, "names") else model.module.names # get class names if isinstance(names, (list, tuple)): # old format names = dict(enumerate(names)) class_map = coco80_to_coco91_class() if is_coco else list(range(1000)) s = ("%22s" + "%11s" * 10) % ( "Class", "Images", "Instances", "Box(P", "R", "mAP50", "mAP50-95)", "Mask(P", "R", "mAP50", "mAP50-95)", ) dt = Profile(), Profile(), Profile() metrics = Metrics() loss = torch.zeros(4, device=device) jdict, stats = [], [] # callbacks.run('on_val_start') pbar = tqdm(dataloader, desc=s, bar_format=TQDM_BAR_FORMAT) # progress bar for batch_i, (im, targets, paths, shapes, masks) in enumerate(pbar): # callbacks.run('on_val_batch_start') with dt[0]: if cuda: im = im.to(device, non_blocking=True) targets = targets.to(device) masks = masks.to(device) masks = masks.float() im = im.half() if half else im.float() # uint8 to fp16/32 im /= 255 # 0 - 255 to 0.0 - 1.0 nb, _, height, width = im.shape # batch size, channels, height, width # Inference with dt[1]: preds, protos, train_out = model(im) if compute_loss else (*model(im, augment=augment)[:2], None) # Loss if compute_loss: loss += compute_loss((train_out, protos), targets, masks)[1] # box, obj, cls # NMS targets[:, 2:] *= torch.tensor((width, height, width, height), device=device) # to pixels lb = [targets[targets[:, 0] == i, 1:] for i in range(nb)] if save_hybrid else [] # for autolabelling with dt[2]: preds = non_max_suppression( preds, conf_thres, iou_thres, labels=lb, multi_label=True, agnostic=single_cls, max_det=max_det, nm=nm ) # Metrics plot_masks = [] # masks for plotting for si, (pred, proto) in enumerate(zip(preds, protos)): labels = targets[targets[:, 0] == si, 1:] nl, npr = labels.shape[0], pred.shape[0] # number of labels, predictions path, shape = Path(paths[si]), shapes[si][0] correct_masks = torch.zeros(npr, niou, dtype=torch.bool, device=device) # init correct_bboxes = torch.zeros(npr, niou, dtype=torch.bool, device=device) # init seen += 1 if npr == 0: if nl: stats.append((correct_masks, correct_bboxes, *torch.zeros((2, 0), device=device), labels[:, 0])) if plots: confusion_matrix.process_batch(detections=None, labels=labels[:, 0]) continue # Masks midx = [si] if overlap else targets[:, 0] == si gt_masks = masks[midx] pred_masks = process(proto, pred[:, 6:], pred[:, :4], shape=im[si].shape[1:]) # Predictions if single_cls: pred[:, 5] = 0 predn = pred.clone() scale_boxes(im[si].shape[1:], predn[:, :4], shape, shapes[si][1]) # native-space pred # Evaluate if nl: tbox = xywh2xyxy(labels[:, 1:5]) # target boxes scale_boxes(im[si].shape[1:], tbox, shape, shapes[si][1]) # native-space labels labelsn = torch.cat((labels[:, 0:1], tbox), 1) # native-space labels correct_bboxes = process_batch(predn, labelsn, iouv) correct_masks = process_batch(predn, labelsn, iouv, pred_masks, gt_masks, overlap=overlap, masks=True) if plots: confusion_matrix.process_batch(predn, labelsn) stats.append((correct_masks, correct_bboxes, pred[:, 4], pred[:, 5], labels[:, 0])) # (conf, pcls, tcls) pred_masks = torch.as_tensor(pred_masks, dtype=torch.uint8) if plots and batch_i < 3: plot_masks.append(pred_masks[:15]) # filter top 15 to plot # Save/log if save_txt: save_one_txt(predn, save_conf, shape, file=save_dir / "labels" / f"{path.stem}.txt") if save_json: pred_masks = scale_image( im[si].shape[1:], pred_masks.permute(1, 2, 0).contiguous().cpu().numpy(), shape, shapes[si][1] ) save_one_json(predn, jdict, path, class_map, pred_masks) # append to COCO-JSON dictionary # callbacks.run('on_val_image_end', pred, predn, path, names, im[si]) # Plot images if plots and batch_i < 3: if len(plot_masks): plot_masks = torch.cat(plot_masks, dim=0) plot_images_and_masks(im, targets, masks, paths, save_dir / f"val_batch{batch_i}_labels.jpg", names) plot_images_and_masks( im, output_to_target(preds, max_det=15), plot_masks, paths, save_dir / f"val_batch{batch_i}_pred.jpg", names, ) # pred # callbacks.run('on_val_batch_end') # Compute metrics stats = [torch.cat(x, 0).cpu().numpy() for x in zip(*stats)] # to numpy if len(stats) and stats[0].any(): results = ap_per_class_box_and_mask(*stats, plot=plots, save_dir=save_dir, names=names) metrics.update(results) nt = np.bincount(stats[4].astype(int), minlength=nc) # number of targets per class # Print results pf = "%22s" + "%11i" * 2 + "%11.3g" * 8 # print format LOGGER.info(pf % ("all", seen, nt.sum(), *metrics.mean_results())) if nt.sum() == 0: LOGGER.warning(f"WARNING ⚠️ no labels found in {task} set, can not compute metrics without labels") # Print results per class if (verbose or (nc < 50 and not training)) and nc > 1 and len(stats): for i, c in enumerate(metrics.ap_class_index): LOGGER.info(pf % (names[c], seen, nt[c], *metrics.class_result(i))) # Print speeds t = tuple(x.t / seen * 1e3 for x in dt) # speeds per image if not training: shape = (batch_size, 3, imgsz, imgsz) LOGGER.info(f"Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {shape}" % t) # Plots if plots: confusion_matrix.plot(save_dir=save_dir, names=list(names.values())) # callbacks.run('on_val_end') mp_bbox, mr_bbox, map50_bbox, map_bbox, mp_mask, mr_mask, map50_mask, map_mask = metrics.mean_results() # Save JSON if save_json and len(jdict): w = Path(weights[0] if isinstance(weights, list) else weights).stem if weights is not None else "" # weights anno_json = str(Path("../datasets/coco/annotations/instances_val2017.json")) # annotations pred_json = str(save_dir / f"{w}_predictions.json") # predictions LOGGER.info(f"\nEvaluating pycocotools mAP... saving {pred_json}...") with open(pred_json, "w") as f: json.dump(jdict, f) try: # https://github.com/cocodataset/cocoapi/blob/master/PythonAPI/pycocoEvalDemo.ipynb from pycocotools.coco import COCO from pycocotools.cocoeval import COCOeval anno = COCO(anno_json) # init annotations api pred = anno.loadRes(pred_json) # init predictions api results = [] for eval in COCOeval(anno, pred, "bbox"), COCOeval(anno, pred, "segm"): if is_coco: eval.params.imgIds = [int(Path(x).stem) for x in dataloader.dataset.im_files] # img ID to evaluate eval.evaluate() eval.accumulate() eval.summarize() results.extend(eval.stats[:2]) # update results (mAP@0.5:0.95, mAP@0.5) map_bbox, map50_bbox, map_mask, map50_mask = results except Exception as e: LOGGER.info(f"pycocotools unable to run: {e}") # Return results model.float() # for training if not training: s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else "" LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}{s}") final_metric = mp_bbox, mr_bbox, map50_bbox, map_bbox, mp_mask, mr_mask, map50_mask, map_mask return (*final_metric, *(loss.cpu() / len(dataloader)).tolist()), metrics.get_maps(nc), t def parse_opt(): """Parses and validates command-line arguments for configuring model training or inference.""" parser = argparse.ArgumentParser() parser.add_argument("--data", type=str, default=ROOT / "data/coco128-seg.yaml", help="dataset.yaml path") parser.add_argument("--weights", nargs="+", type=str, default=ROOT / "yolov5s-seg.pt", help="model path(s)") parser.add_argument("--batch-size", type=int, default=32, help="batch size") parser.add_argument("--imgsz", "--img", "--img-size", type=int, default=640, help="inference size (pixels)") parser.add_argument("--conf-thres", type=float, default=0.001, help="confidence threshold") parser.add_argument("--iou-thres", type=float, default=0.6, help="NMS IoU threshold") parser.add_argument("--max-det", type=int, default=300, help="maximum detections per image") parser.add_argument("--task", default="val", help="train, val, test, speed or study") parser.add_argument("--device", default="", help="cuda device, i.e. 0 or 0,1,2,3 or cpu") parser.add_argument("--workers", type=int, default=8, help="max dataloader workers (per RANK in DDP mode)") parser.add_argument("--single-cls", action="store_true", help="treat as single-class dataset") parser.add_argument("--augment", action="store_true", help="augmented inference") parser.add_argument("--verbose", action="store_true", help="report mAP by class") parser.add_argument("--save-txt", action="store_true", help="save results to *.txt") parser.add_argument("--save-hybrid", action="store_true", help="save label+prediction hybrid results to *.txt") parser.add_argument("--save-conf", action="store_true", help="save confidences in --save-txt labels") parser.add_argument("--save-json", action="store_true", help="save a COCO-JSON results file") parser.add_argument("--project", default=ROOT / "runs/val-seg", help="save results to project/name") parser.add_argument("--name", default="exp", help="save to project/name") parser.add_argument("--exist-ok", action="store_true", help="existing project/name ok, do not increment") parser.add_argument("--half", action="store_true", help="use FP16 half-precision inference") parser.add_argument("--dnn", action="store_true", help="use OpenCV DNN for ONNX inference") opt = parser.parse_args() opt.data = check_yaml(opt.data) # check YAML # opt.save_json |= opt.data.endswith('coco.yaml') opt.save_txt |= opt.save_hybrid print_args(vars(opt)) return opt def main(opt): """Executes the primary function based on task, including training, validation, testing, speed, and study benchmarks. """ check_requirements(ROOT / "requirements.txt", exclude=("tensorboard", "thop")) if opt.task in ("train", "val", "test"): # run normally if opt.conf_thres > 0.001: # https://github.com/ultralytics/yolov5/issues/1466 LOGGER.warning(f"WARNING ⚠️ confidence threshold {opt.conf_thres} > 0.001 produces invalid results") if opt.save_hybrid: LOGGER.warning("WARNING ⚠️ --save-hybrid returns high mAP from hybrid labels, not from predictions alone") run(**vars(opt)) else: weights = opt.weights if isinstance(opt.weights, list) else [opt.weights] opt.half = torch.cuda.is_available() and opt.device != "cpu" # FP16 for fastest results if opt.task == "speed": # speed benchmarks # python val.py --task speed --data coco.yaml --batch 1 --weights yolov5n.pt yolov5s.pt... opt.conf_thres, opt.iou_thres, opt.save_json = 0.25, 0.45, False for opt.weights in weights: run(**vars(opt), plots=False) elif opt.task == "study": # speed vs mAP benchmarks # python val.py --task study --data coco.yaml --iou 0.7 --weights yolov5n.pt yolov5s.pt... for opt.weights in weights: f = f"study_{Path(opt.data).stem}_{Path(opt.weights).stem}.txt" # filename to save to x, y = list(range(256, 1536 + 128, 128)), [] # x axis (image sizes), y axis for opt.imgsz in x: # img-size LOGGER.info(f"\nRunning {f} --imgsz {opt.imgsz}...") r, _, t = run(**vars(opt), plots=False) y.append(r + t) # results and times np.savetxt(f, y, fmt="%10.4g") # save subprocess.run(["zip", "-r", "study.zip", "study_*.txt"]) plot_val_study(x=x) # plot else: raise NotImplementedError(f'--task {opt.task} not in ("train", "val", "test", "speed", "study")') if __name__ == "__main__": opt = parse_opt() main(opt) ================================================ FILE: train.py ================================================ # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license """ Train a YOLOv3 model on a custom dataset. Models and datasets download automatically from the latest YOLOv3 release. Usage - Single-GPU training: $ python train.py --data coco128.yaml --weights yolov5s.pt --img 640 # from pretrained (recommended) $ python train.py --data coco128.yaml --weights '' --cfg yolov5s.yaml --img 640 # from scratch Usage - Multi-GPU DDP training: $ 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 Models: https://github.com/ultralytics/yolov5/tree/master/models Datasets: https://github.com/ultralytics/yolov5/tree/master/data Tutorial: https://docs.ultralytics.com/yolov5/tutorials/train_custom_data """ import argparse import math import os import random import subprocess import sys import time from copy import deepcopy from datetime import datetime from pathlib import Path try: import comet_ml # must be imported before torch (if installed) except ImportError: comet_ml = None import numpy as np import torch import torch.distributed as dist import torch.nn as nn import yaml from torch.optim import lr_scheduler from tqdm import tqdm FILE = Path(__file__).resolve() ROOT = FILE.parents[0] # YOLOv3 root directory if str(ROOT) not in sys.path: sys.path.append(str(ROOT)) # add ROOT to PATH ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative from ultralytics.utils.patches import torch_load import val as validate # for end-of-epoch mAP from models.experimental import attempt_load from models.yolo import Model from utils.autoanchor import check_anchors from utils.autobatch import check_train_batch_size from utils.callbacks import Callbacks from utils.dataloaders import create_dataloader from utils.downloads import attempt_download, is_url from utils.general import ( LOGGER, TQDM_BAR_FORMAT, check_amp, check_dataset, check_file, check_git_info, check_git_status, check_img_size, check_requirements, check_suffix, check_yaml, colorstr, get_latest_run, increment_path, init_seeds, intersect_dicts, labels_to_class_weights, labels_to_image_weights, methods, one_cycle, print_args, print_mutation, strip_optimizer, yaml_save, ) from utils.loggers import Loggers from utils.loggers.comet.comet_utils import check_comet_resume from utils.loss import ComputeLoss from utils.metrics import fitness from utils.plots import plot_evolve from utils.torch_utils import ( EarlyStopping, ModelEMA, de_parallel, select_device, smart_DDP, smart_optimizer, smart_resume, torch_distributed_zero_first, ) LOCAL_RANK = int(os.getenv("LOCAL_RANK", -1)) # https://pytorch.org/docs/stable/elastic/run.html RANK = int(os.getenv("RANK", -1)) WORLD_SIZE = int(os.getenv("WORLD_SIZE", 1)) GIT_INFO = check_git_info() def train(hyp, opt, device, callbacks): # hyp is path/to/hyp.yaml or hyp dictionary """Train a YOLOv3 model on a custom dataset and manage the training process. Args: hyp (str | dict): Path to hyperparameters yaml file or hyperparameters dictionary. opt (argparse.Namespace): Parsed command line arguments containing training options. device (torch.device): Device to load and train the model on. callbacks (Callbacks): Callbacks to handle various stages of the training lifecycle. Returns: None Usage - Single-GPU training: $ python train.py --data coco128.yaml --weights yolov5s.pt --img 640 # from pretrained (recommended) $ python train.py --data coco128.yaml --weights '' --cfg yolov5s.yaml --img 640 # from scratch Usage - Multi-GPU DDP training: $ 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 Models: https://github.com/ultralytics/yolov5/tree/master/models Datasets: https://github.com/ultralytics/yolov5/tree/master/data Tutorial: https://docs.ultralytics.com/yolov5/tutorials/train_custom_data Examples: ```python from ultralytics import train import argparse import torch from utils.callbacks import Callbacks # Example usage args = argparse.Namespace( data='coco128.yaml', weights='yolov5s.pt', cfg='yolov5s.yaml', img_size=640, epochs=50, batch_size=16, device='0' ) device = torch.device(f'cuda:{args.device}' if torch.cuda.is_available() else 'cpu') callbacks = Callbacks() train(hyp='hyp.scratch.yaml', opt=args, device=device, callbacks=callbacks) ``` """ save_dir, epochs, batch_size, weights, single_cls, evolve, data, cfg, resume, noval, nosave, workers, freeze = ( Path(opt.save_dir), opt.epochs, opt.batch_size, opt.weights, opt.single_cls, opt.evolve, opt.data, opt.cfg, opt.resume, opt.noval, opt.nosave, opt.workers, opt.freeze, ) callbacks.run("on_pretrain_routine_start") # Directories w = save_dir / "weights" # weights dir (w.parent if evolve else w).mkdir(parents=True, exist_ok=True) # make dir last, best = w / "last.pt", w / "best.pt" # Hyperparameters if isinstance(hyp, str): with open(hyp, errors="ignore") as f: hyp = yaml.safe_load(f) # load hyps dict LOGGER.info(colorstr("hyperparameters: ") + ", ".join(f"{k}={v}" for k, v in hyp.items())) opt.hyp = hyp.copy() # for saving hyps to checkpoints # Save run settings if not evolve: yaml_save(save_dir / "hyp.yaml", hyp) yaml_save(save_dir / "opt.yaml", vars(opt)) # Loggers data_dict = None if RANK in {-1, 0}: loggers = Loggers(save_dir, weights, opt, hyp, LOGGER) # loggers instance # Register actions for k in methods(loggers): callbacks.register_action(k, callback=getattr(loggers, k)) # Process custom dataset artifact link data_dict = loggers.remote_dataset if resume: # If resuming runs from remote artifact weights, epochs, hyp, batch_size = opt.weights, opt.epochs, opt.hyp, opt.batch_size # Config plots = not evolve and not opt.noplots # create plots cuda = device.type != "cpu" init_seeds(opt.seed + 1 + RANK, deterministic=True) with torch_distributed_zero_first(LOCAL_RANK): data_dict = data_dict or check_dataset(data) # check if None train_path, val_path = data_dict["train"], data_dict["val"] nc = 1 if single_cls else int(data_dict["nc"]) # number of classes names = {0: "item"} if single_cls and len(data_dict["names"]) != 1 else data_dict["names"] # class names is_coco = isinstance(val_path, str) and val_path.endswith("coco/val2017.txt") # COCO dataset # Model check_suffix(weights, ".pt") # check weights pretrained = weights.endswith(".pt") if pretrained: with torch_distributed_zero_first(LOCAL_RANK): weights = attempt_download(weights) # download if not found locally ckpt = torch_load(weights, map_location="cpu") # load checkpoint to CPU to avoid CUDA memory leak model = Model(cfg or ckpt["model"].yaml, ch=3, nc=nc, anchors=hyp.get("anchors")).to(device) # create exclude = ["anchor"] if (cfg or hyp.get("anchors")) and not resume else [] # exclude keys csd = ckpt["model"].float().state_dict() # checkpoint state_dict as FP32 csd = intersect_dicts(csd, model.state_dict(), exclude=exclude) # intersect model.load_state_dict(csd, strict=False) # load LOGGER.info(f"Transferred {len(csd)}/{len(model.state_dict())} items from {weights}") # report else: model = Model(cfg, ch=3, nc=nc, anchors=hyp.get("anchors")).to(device) # create amp = check_amp(model) # check AMP # Freeze freeze = [f"model.{x}." for x in (freeze if len(freeze) > 1 else range(freeze[0]))] # layers to freeze for k, v in model.named_parameters(): v.requires_grad = True # train all layers # v.register_hook(lambda x: torch.nan_to_num(x)) # NaN to 0 (commented for erratic training results) if any(x in k for x in freeze): LOGGER.info(f"freezing {k}") v.requires_grad = False # Image size gs = max(int(model.stride.max()), 32) # grid size (max stride) imgsz = check_img_size(opt.imgsz, gs, floor=gs * 2) # verify imgsz is gs-multiple # Batch size if RANK == -1 and batch_size == -1: # single-GPU only, estimate best batch size batch_size = check_train_batch_size(model, imgsz, amp) loggers.on_params_update({"batch_size": batch_size}) # Optimizer nbs = 64 # nominal batch size accumulate = max(round(nbs / batch_size), 1) # accumulate loss before optimizing hyp["weight_decay"] *= batch_size * accumulate / nbs # scale weight_decay optimizer = smart_optimizer(model, opt.optimizer, hyp["lr0"], hyp["momentum"], hyp["weight_decay"]) # Scheduler if opt.cos_lr: lf = one_cycle(1, hyp["lrf"], epochs) # cosine 1->hyp['lrf'] else: def lf(x): """Linear learning rate scheduler function with decay calculated by epoch proportion.""" return (1 - x / epochs) * (1.0 - hyp["lrf"]) + hyp["lrf"] # linear scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lf) # plot_lr_scheduler(optimizer, scheduler, epochs) # EMA ema = ModelEMA(model) if RANK in {-1, 0} else None # Resume best_fitness, start_epoch = 0.0, 0 if pretrained: if resume: best_fitness, start_epoch, epochs = smart_resume(ckpt, optimizer, ema, weights, epochs, resume) del ckpt, csd # DP mode if cuda and RANK == -1 and torch.cuda.device_count() > 1: LOGGER.warning( "WARNING ⚠️ DP not recommended, use torch.distributed.run for best DDP Multi-GPU results.\n" "See Multi-GPU Tutorial at https://docs.ultralytics.com/yolov5/tutorials/multi_gpu_training to get started." ) model = torch.nn.DataParallel(model) # SyncBatchNorm if opt.sync_bn and cuda and RANK != -1: model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model).to(device) LOGGER.info("Using SyncBatchNorm()") # Trainloader train_loader, dataset = create_dataloader( train_path, imgsz, batch_size // WORLD_SIZE, gs, single_cls, hyp=hyp, augment=True, cache=None if opt.cache == "val" else opt.cache, rect=opt.rect, rank=LOCAL_RANK, workers=workers, image_weights=opt.image_weights, quad=opt.quad, prefix=colorstr("train: "), shuffle=True, seed=opt.seed, ) labels = np.concatenate(dataset.labels, 0) mlc = int(labels[:, 0].max()) # max label class assert mlc < nc, f"Label class {mlc} exceeds nc={nc} in {data}. Possible class labels are 0-{nc - 1}" # Process 0 if RANK in {-1, 0}: val_loader = create_dataloader( val_path, imgsz, batch_size // WORLD_SIZE * 2, gs, single_cls, hyp=hyp, cache=None if noval else opt.cache, rect=True, rank=-1, workers=workers * 2, pad=0.5, prefix=colorstr("val: "), )[0] if not resume: if not opt.noautoanchor: check_anchors(dataset, model=model, thr=hyp["anchor_t"], imgsz=imgsz) # run AutoAnchor model.half().float() # pre-reduce anchor precision callbacks.run("on_pretrain_routine_end", labels, names) # DDP mode if cuda and RANK != -1: model = smart_DDP(model) # Model attributes nl = de_parallel(model).model[-1].nl # number of detection layers (to scale hyps) hyp["box"] *= 3 / nl # scale to layers hyp["cls"] *= nc / 80 * 3 / nl # scale to classes and layers hyp["obj"] *= (imgsz / 640) ** 2 * 3 / nl # scale to image size and layers hyp["label_smoothing"] = opt.label_smoothing model.nc = nc # attach number of classes to model model.hyp = hyp # attach hyperparameters to model model.class_weights = labels_to_class_weights(dataset.labels, nc).to(device) * nc # attach class weights model.names = names # Start training t0 = time.time() nb = len(train_loader) # number of batches nw = max(round(hyp["warmup_epochs"] * nb), 100) # number of warmup iterations, max(3 epochs, 100 iterations) # nw = min(nw, (epochs - start_epoch) / 2 * nb) # limit warmup to < 1/2 of training last_opt_step = -1 maps = np.zeros(nc) # mAP per class results = (0, 0, 0, 0, 0, 0, 0) # P, R, mAP@.5, mAP@.5-.95, val_loss(box, obj, cls) scheduler.last_epoch = start_epoch - 1 # do not move scaler = torch.cuda.amp.GradScaler(enabled=amp) stopper, stop = EarlyStopping(patience=opt.patience), False compute_loss = ComputeLoss(model) # init loss class callbacks.run("on_train_start") LOGGER.info( f"Image sizes {imgsz} train, {imgsz} val\n" f"Using {train_loader.num_workers * WORLD_SIZE} dataloader workers\n" f"Logging results to {colorstr('bold', save_dir)}\n" f"Starting training for {epochs} epochs..." ) for epoch in range(start_epoch, epochs): # epoch ------------------------------------------------------------------ callbacks.run("on_train_epoch_start") model.train() # Update image weights (optional, single-GPU only) if opt.image_weights: cw = model.class_weights.cpu().numpy() * (1 - maps) ** 2 / nc # class weights iw = labels_to_image_weights(dataset.labels, nc=nc, class_weights=cw) # image weights dataset.indices = random.choices(range(dataset.n), weights=iw, k=dataset.n) # rand weighted idx # Update mosaic border (optional) # b = int(random.uniform(0.25 * imgsz, 0.75 * imgsz + gs) // gs * gs) # dataset.mosaic_border = [b - imgsz, -b] # height, width borders mloss = torch.zeros(3, device=device) # mean losses if RANK != -1: train_loader.sampler.set_epoch(epoch) pbar = enumerate(train_loader) LOGGER.info(("\n" + "%11s" * 7) % ("Epoch", "GPU_mem", "box_loss", "obj_loss", "cls_loss", "Instances", "Size")) if RANK in {-1, 0}: pbar = tqdm(pbar, total=nb, bar_format=TQDM_BAR_FORMAT) # progress bar optimizer.zero_grad() for i, (imgs, targets, paths, _) in pbar: # batch ------------------------------------------------------------- callbacks.run("on_train_batch_start") ni = i + nb * epoch # number integrated batches (since train start) imgs = imgs.to(device, non_blocking=True).float() / 255 # uint8 to float32, 0-255 to 0.0-1.0 # Warmup if ni <= nw: xi = [0, nw] # x interp # compute_loss.gr = np.interp(ni, xi, [0.0, 1.0]) # iou loss ratio (obj_loss = 1.0 or iou) accumulate = max(1, np.interp(ni, xi, [1, nbs / batch_size]).round()) for j, x in enumerate(optimizer.param_groups): # bias lr falls from 0.1 to lr0, all other lrs rise from 0.0 to lr0 x["lr"] = np.interp(ni, xi, [hyp["warmup_bias_lr"] if j == 0 else 0.0, x["initial_lr"] * lf(epoch)]) if "momentum" in x: x["momentum"] = np.interp(ni, xi, [hyp["warmup_momentum"], hyp["momentum"]]) # Multi-scale if opt.multi_scale: sz = random.randrange(int(imgsz * 0.5), int(imgsz * 1.5) + gs) // gs * gs # size sf = sz / max(imgs.shape[2:]) # scale factor if sf != 1: ns = [math.ceil(x * sf / gs) * gs for x in imgs.shape[2:]] # new shape (stretched to gs-multiple) imgs = nn.functional.interpolate(imgs, size=ns, mode="bilinear", align_corners=False) # Forward with torch.cuda.amp.autocast(amp): pred = model(imgs) # forward loss, loss_items = compute_loss(pred, targets.to(device)) # loss scaled by batch_size if RANK != -1: loss *= WORLD_SIZE # gradient averaged between devices in DDP mode if opt.quad: loss *= 4.0 # Backward scaler.scale(loss).backward() # Optimize - https://pytorch.org/docs/master/notes/amp_examples.html if ni - last_opt_step >= accumulate: scaler.unscale_(optimizer) # unscale gradients torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=10.0) # clip gradients scaler.step(optimizer) # optimizer.step scaler.update() optimizer.zero_grad() if ema: ema.update(model) last_opt_step = ni # Log if RANK in {-1, 0}: mloss = (mloss * i + loss_items) / (i + 1) # update mean losses mem = f"{torch.cuda.memory_reserved() / 1e9 if torch.cuda.is_available() else 0:.3g}G" # (GB) pbar.set_description( ("%11s" * 2 + "%11.4g" * 5) % (f"{epoch}/{epochs - 1}", mem, *mloss, targets.shape[0], imgs.shape[-1]) ) callbacks.run("on_train_batch_end", model, ni, imgs, targets, paths, list(mloss)) if callbacks.stop_training: return # end batch ------------------------------------------------------------------------------------------------ # Scheduler lr = [x["lr"] for x in optimizer.param_groups] # for loggers scheduler.step() if RANK in {-1, 0}: # mAP callbacks.run("on_train_epoch_end", epoch=epoch) ema.update_attr(model, include=["yaml", "nc", "hyp", "names", "stride", "class_weights"]) final_epoch = (epoch + 1 == epochs) or stopper.possible_stop if not noval or final_epoch: # Calculate mAP results, maps, _ = validate.run( data_dict, batch_size=batch_size // WORLD_SIZE * 2, imgsz=imgsz, half=amp, model=ema.ema, single_cls=single_cls, dataloader=val_loader, save_dir=save_dir, plots=False, callbacks=callbacks, compute_loss=compute_loss, ) # Update best mAP fi = fitness(np.array(results).reshape(1, -1)) # weighted combination of [P, R, mAP@.5, mAP@.5-.95] stop = stopper(epoch=epoch, fitness=fi) # early stop check if fi > best_fitness: best_fitness = fi log_vals = list(mloss) + list(results) + lr callbacks.run("on_fit_epoch_end", log_vals, epoch, best_fitness, fi) # Save model if (not nosave) or (final_epoch and not evolve): # if save ckpt = { "epoch": epoch, "best_fitness": best_fitness, "model": deepcopy(de_parallel(model)).half(), "ema": deepcopy(ema.ema).half(), "updates": ema.updates, "optimizer": optimizer.state_dict(), "opt": vars(opt), "git": GIT_INFO, # {remote, branch, commit} if a git repo "date": datetime.now().isoformat(), } # Save last, best and delete torch.save(ckpt, last) if best_fitness == fi: torch.save(ckpt, best) if opt.save_period > 0 and epoch % opt.save_period == 0: torch.save(ckpt, w / f"epoch{epoch}.pt") del ckpt callbacks.run("on_model_save", last, epoch, final_epoch, best_fitness, fi) # EarlyStopping if RANK != -1: # if DDP training broadcast_list = [stop if RANK == 0 else None] dist.broadcast_object_list(broadcast_list, 0) # broadcast 'stop' to all ranks if RANK != 0: stop = broadcast_list[0] if stop: break # must break all DDP ranks # end epoch ---------------------------------------------------------------------------------------------------- # end training ----------------------------------------------------------------------------------------------------- if RANK in {-1, 0}: LOGGER.info(f"\n{epoch - start_epoch + 1} epochs completed in {(time.time() - t0) / 3600:.3f} hours.") for f in last, best: if f.exists(): strip_optimizer(f) # strip optimizers if f is best: LOGGER.info(f"\nValidating {f}...") results, _, _ = validate.run( data_dict, batch_size=batch_size // WORLD_SIZE * 2, imgsz=imgsz, model=attempt_load(f, device).half(), iou_thres=0.65 if is_coco else 0.60, # best pycocotools at iou 0.65 single_cls=single_cls, dataloader=val_loader, save_dir=save_dir, save_json=is_coco, verbose=True, plots=plots, callbacks=callbacks, compute_loss=compute_loss, ) # val best model with plots if is_coco: callbacks.run("on_fit_epoch_end", list(mloss) + list(results) + lr, epoch, best_fitness, fi) callbacks.run("on_train_end", last, best, epoch, results) torch.cuda.empty_cache() return results def parse_opt(known=False): """Parse command line arguments for configuring the training of a YOLO model. Args: known (bool): Flag to parse known arguments only, defaults to False. Returns: (argparse.Namespace): Parsed command line arguments. Examples: ```python options = parse_opt() print(options.weights) ``` Notes: * The default weights path is 'yolov3-tiny.pt'. * Set `known` to True for parsing only the known arguments, useful for partial arguments. References: * Models: https://github.com/ultralytics/yolov5/tree/master/models * Datasets: https://github.com/ultralytics/yolov5/tree/master/data * Training Tutorial: https://docs.ultralytics.com/yolov5/tutorials/train_custom_data """ parser = argparse.ArgumentParser() parser.add_argument("--weights", type=str, default=ROOT / "yolov3-tiny.pt", help="initial weights path") parser.add_argument("--cfg", type=str, default="", help="model.yaml path") parser.add_argument("--data", type=str, default=ROOT / "data/coco128.yaml", help="dataset.yaml path") parser.add_argument("--hyp", type=str, default=ROOT / "data/hyps/hyp.scratch-low.yaml", help="hyperparameters path") parser.add_argument("--epochs", type=int, default=100, help="total training epochs") parser.add_argument("--batch-size", type=int, default=16, help="total batch size for all GPUs, -1 for autobatch") parser.add_argument("--imgsz", "--img", "--img-size", type=int, default=640, help="train, val image size (pixels)") parser.add_argument("--rect", action="store_true", help="rectangular training") parser.add_argument("--resume", nargs="?", const=True, default=False, help="resume most recent training") parser.add_argument("--nosave", action="store_true", help="only save final checkpoint") parser.add_argument("--noval", action="store_true", help="only validate final epoch") parser.add_argument("--noautoanchor", action="store_true", help="disable AutoAnchor") parser.add_argument("--noplots", action="store_true", help="save no plot files") parser.add_argument("--evolve", type=int, nargs="?", const=300, help="evolve hyperparameters for x generations") parser.add_argument("--bucket", type=str, default="", help="gsutil bucket") parser.add_argument("--cache", type=str, nargs="?", const="ram", help="image --cache ram/disk") parser.add_argument("--image-weights", action="store_true", help="use weighted image selection for training") parser.add_argument("--device", default="", help="cuda device, i.e. 0 or 0,1,2,3 or cpu") parser.add_argument("--multi-scale", action="store_true", help="vary img-size +/- 50%%") parser.add_argument("--single-cls", action="store_true", help="train multi-class data as single-class") parser.add_argument("--optimizer", type=str, choices=["SGD", "Adam", "AdamW"], default="SGD", help="optimizer") parser.add_argument("--sync-bn", action="store_true", help="use SyncBatchNorm, only available in DDP mode") parser.add_argument("--workers", type=int, default=8, help="max dataloader workers (per RANK in DDP mode)") parser.add_argument("--project", default=ROOT / "runs/train", help="save to project/name") parser.add_argument("--name", default="exp", help="save to project/name") parser.add_argument("--exist-ok", action="store_true", help="existing project/name ok, do not increment") parser.add_argument("--quad", action="store_true", help="quad dataloader") parser.add_argument("--cos-lr", action="store_true", help="cosine LR scheduler") parser.add_argument("--label-smoothing", type=float, default=0.0, help="Label smoothing epsilon") parser.add_argument("--patience", type=int, default=100, help="EarlyStopping patience (epochs without improvement)") parser.add_argument("--freeze", nargs="+", type=int, default=[0], help="Freeze layers: backbone=10, first3=0 1 2") parser.add_argument("--save-period", type=int, default=-1, help="Save checkpoint every x epochs (disabled if < 1)") parser.add_argument("--seed", type=int, default=0, help="Global training seed") parser.add_argument("--local_rank", type=int, default=-1, help="Automatic DDP Multi-GPU argument, do not modify") # Logger arguments parser.add_argument("--entity", default=None, help="Entity") parser.add_argument("--upload_dataset", nargs="?", const=True, default=False, help='Upload data, "val" option') parser.add_argument("--bbox_interval", type=int, default=-1, help="Set bounding-box image logging interval") parser.add_argument("--artifact_alias", type=str, default="latest", help="Version of dataset artifact to use") return parser.parse_known_args()[0] if known else parser.parse_args() def main(opt, callbacks=Callbacks()): """Main training/evolution script handling model checks, DDP setup, training, and hyperparameter evolution. Args: opt (argparse.Namespace): Parsed command-line options. callbacks (Callbacks, optional): Callback object for handling training events. Defaults to Callbacks(). Returns: None Raises: AssertionError: If certain constraints are violated (e.g., when specific options are incompatible with DDP training). Examples: Single-GPU training: ```python $ python train.py --data coco128.yaml --weights yolov5s.pt --img 640 # from pretrained (recommended) $ python train.py --data coco128.yaml --weights '' --cfg yolov5s.yaml --img 640 # from scratch ``` Multi-GPU DDP training: ```python $ 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 ``` Models: https://github.com/ultralytics/yolov5/tree/master/models Datasets: https://github.com/ultralytics/yolov5/tree/master/data Tutorial: https://docs.ultralytics.com/yolov5/tutorials/train_custom_data Notes: - For a tutorial on using Multi-GPU with DDP: https://docs.ultralytics.com/yolov5/tutorials/multi_gpu_training """ if RANK in {-1, 0}: print_args(vars(opt)) check_git_status() check_requirements(ROOT / "requirements.txt") # Resume (from specified or most recent last.pt) if opt.resume and not check_comet_resume(opt) and not opt.evolve: last = Path(check_file(opt.resume) if isinstance(opt.resume, str) else get_latest_run()) opt_yaml = last.parent.parent / "opt.yaml" # train options yaml opt_data = opt.data # original dataset if opt_yaml.is_file(): with open(opt_yaml, errors="ignore") as f: d = yaml.safe_load(f) else: d = torch_load(last, map_location="cpu")["opt"] opt = argparse.Namespace(**d) # replace opt.cfg, opt.weights, opt.resume = "", str(last), True # reinstate if is_url(opt_data): opt.data = check_file(opt_data) # avoid HUB resume auth timeout else: opt.data, opt.cfg, opt.hyp, opt.weights, opt.project = ( check_file(opt.data), check_yaml(opt.cfg), check_yaml(opt.hyp), str(opt.weights), str(opt.project), ) # checks assert len(opt.cfg) or len(opt.weights), "either --cfg or --weights must be specified" if opt.evolve: if opt.project == str(ROOT / "runs/train"): # if default project name, rename to runs/evolve opt.project = str(ROOT / "runs/evolve") opt.exist_ok, opt.resume = opt.resume, False # pass resume to exist_ok and disable resume if opt.name == "cfg": opt.name = Path(opt.cfg).stem # use model.yaml as name opt.save_dir = str(increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok)) # DDP mode device = select_device(opt.device, batch_size=opt.batch_size) if LOCAL_RANK != -1: msg = "is not compatible with YOLOv3 Multi-GPU DDP training" assert not opt.image_weights, f"--image-weights {msg}" assert not opt.evolve, f"--evolve {msg}" assert opt.batch_size != -1, f"AutoBatch with --batch-size -1 {msg}, please pass a valid --batch-size" assert opt.batch_size % WORLD_SIZE == 0, f"--batch-size {opt.batch_size} must be multiple of WORLD_SIZE" assert torch.cuda.device_count() > LOCAL_RANK, "insufficient CUDA devices for DDP command" torch.cuda.set_device(LOCAL_RANK) device = torch.device("cuda", LOCAL_RANK) dist.init_process_group(backend="nccl" if dist.is_nccl_available() else "gloo") # Train if not opt.evolve: train(opt.hyp, opt, device, callbacks) # Evolve hyperparameters (optional) else: # Hyperparameter evolution metadata (mutation scale 0-1, lower_limit, upper_limit) meta = { "lr0": (1, 1e-5, 1e-1), # initial learning rate (SGD=1E-2, Adam=1E-3) "lrf": (1, 0.01, 1.0), # final OneCycleLR learning rate (lr0 * lrf) "momentum": (0.3, 0.6, 0.98), # SGD momentum/Adam beta1 "weight_decay": (1, 0.0, 0.001), # optimizer weight decay "warmup_epochs": (1, 0.0, 5.0), # warmup epochs (fractions ok) "warmup_momentum": (1, 0.0, 0.95), # warmup initial momentum "warmup_bias_lr": (1, 0.0, 0.2), # warmup initial bias lr "box": (1, 0.02, 0.2), # box loss gain "cls": (1, 0.2, 4.0), # cls loss gain "cls_pw": (1, 0.5, 2.0), # cls BCELoss positive_weight "obj": (1, 0.2, 4.0), # obj loss gain (scale with pixels) "obj_pw": (1, 0.5, 2.0), # obj BCELoss positive_weight "iou_t": (0, 0.1, 0.7), # IoU training threshold "anchor_t": (1, 2.0, 8.0), # anchor-multiple threshold "anchors": (2, 2.0, 10.0), # anchors per output grid (0 to ignore) "fl_gamma": (0, 0.0, 2.0), # focal loss gamma (efficientDet default gamma=1.5) "hsv_h": (1, 0.0, 0.1), # image HSV-Hue augmentation (fraction) "hsv_s": (1, 0.0, 0.9), # image HSV-Saturation augmentation (fraction) "hsv_v": (1, 0.0, 0.9), # image HSV-Value augmentation (fraction) "degrees": (1, 0.0, 45.0), # image rotation (+/- deg) "translate": (1, 0.0, 0.9), # image translation (+/- fraction) "scale": (1, 0.0, 0.9), # image scale (+/- gain) "shear": (1, 0.0, 10.0), # image shear (+/- deg) "perspective": (0, 0.0, 0.001), # image perspective (+/- fraction), range 0-0.001 "flipud": (1, 0.0, 1.0), # image flip up-down (probability) "fliplr": (0, 0.0, 1.0), # image flip left-right (probability) "mosaic": (1, 0.0, 1.0), # image mixup (probability) "mixup": (1, 0.0, 1.0), # image mixup (probability) "copy_paste": (1, 0.0, 1.0), } # segment copy-paste (probability) with open(opt.hyp, errors="ignore") as f: hyp = yaml.safe_load(f) # load hyps dict if "anchors" not in hyp: # anchors commented in hyp.yaml hyp["anchors"] = 3 if opt.noautoanchor: del hyp["anchors"], meta["anchors"] opt.noval, opt.nosave, save_dir = True, True, Path(opt.save_dir) # only val/save final epoch # ei = [isinstance(x, (int, float)) for x in hyp.values()] # evolvable indices evolve_yaml, evolve_csv = save_dir / "hyp_evolve.yaml", save_dir / "evolve.csv" if opt.bucket: # download evolve.csv if exists subprocess.run( [ "gsutil", "cp", f"gs://{opt.bucket}/evolve.csv", str(evolve_csv), ] ) for _ in range(opt.evolve): # generations to evolve if evolve_csv.exists(): # if evolve.csv exists: select best hyps and mutate # Select parent(s) parent = "single" # parent selection method: 'single' or 'weighted' x = np.loadtxt(evolve_csv, ndmin=2, delimiter=",", skiprows=1) n = min(5, len(x)) # number of previous results to consider x = x[np.argsort(-fitness(x))][:n] # top n mutations w = fitness(x) - fitness(x).min() + 1e-6 # weights (sum > 0) if parent == "single" or len(x) == 1: # x = x[random.randint(0, n - 1)] # random selection x = x[random.choices(range(n), weights=w)[0]] # weighted selection elif parent == "weighted": x = (x * w.reshape(n, 1)).sum(0) / w.sum() # weighted combination # Mutate mp, s = 0.8, 0.2 # mutation probability, sigma npr = np.random npr.seed(int(time.time())) g = np.array([meta[k][0] for k in hyp.keys()]) # gains 0-1 ng = len(meta) v = np.ones(ng) while all(v == 1): # mutate until a change occurs (prevent duplicates) v = (g * (npr.random(ng) < mp) * npr.randn(ng) * npr.random() * s + 1).clip(0.3, 3.0) for i, k in enumerate(hyp.keys()): # plt.hist(v.ravel(), 300) hyp[k] = float(x[i + 7] * v[i]) # mutate # Constrain to limits for k, v in meta.items(): hyp[k] = max(hyp[k], v[1]) # lower limit hyp[k] = min(hyp[k], v[2]) # upper limit hyp[k] = round(hyp[k], 5) # significant digits # Train mutation results = train(hyp.copy(), opt, device, callbacks) callbacks = Callbacks() # Write mutation results keys = ( "metrics/precision", "metrics/recall", "metrics/mAP_0.5", "metrics/mAP_0.5:0.95", "val/box_loss", "val/obj_loss", "val/cls_loss", ) print_mutation(keys, results, hyp.copy(), save_dir, opt.bucket) # Plot results plot_evolve(evolve_csv) LOGGER.info( f"Hyperparameter evolution finished {opt.evolve} generations\n" f"Results saved to {colorstr('bold', save_dir)}\n" f"Usage example: $ python train.py --hyp {evolve_yaml}" ) def run(**kwargs): """Run the training process for a YOLOv3 model with the specified configurations. Args: data (str): Path to the dataset YAML file. weights (str): Path to the pre-trained weights file or '' to train from scratch. cfg (str): Path to the model configuration file. hyp (str): Path to the hyperparameters YAML file. epochs (int): Total number of training epochs. batch_size (int): Total batch size across all GPUs. imgsz (int): Image size for training and validation (in pixels). rect (bool): Use rectangular training for better aspect ratio preservation. resume (bool | str): Resume most recent training if True, or resume training from a specific checkpoint if a string. nosave (bool): Only save the final checkpoint and not the intermediate ones. noval (bool): Only validate model performance in the final epoch. noautoanchor (bool): Disable automatic anchor generation. noplots (bool): Do not save any plots. evolve (int): Number of generations for hyperparameters evolution. bucket (str): Google Cloud Storage bucket name for saving run artifacts. cache (str | None): Cache images for faster training ('ram' or 'disk'). image_weights (bool): Use weighted image selection for training. device (str): Device to use for training, e.g., '0' for first GPU or 'cpu' for CPU. multi_scale (bool): Use multi-scale training. single_cls (bool): Train a multi-class dataset as a single-class. optimizer (str): Optimizer to use ('SGD', 'Adam', or 'AdamW'). sync_bn (bool): Use synchronized batch normalization (only in DDP mode). workers (int): Maximum number of dataloader workers (per rank in DDP mode). project (str): Location of the output directory. name (str): Unique name for the run. exist_ok (bool): Allow existing output directory. quad (bool): Use quad dataloader. cos_lr (bool): Use cosine learning rate scheduler. label_smoothing (float): Label smoothing epsilon. patience (int): EarlyStopping patience (epochs without improvement). freeze (list[int]): List of layers to freeze, e.g., [0] to freeze only the first layer. save_period (int): Save checkpoint every 'save_period' epochs (disabled if less than 1). seed (int): Global training seed for reproducibility. local_rank (int): For automatic DDP Multi-GPU argument parsing, do not modify. Returns: None Examples: ```python from ultralytics import run run(data='coco128.yaml', weights='yolov5m.pt', imgsz=320, epochs=100, batch_size=16) ``` Notes: - Ensure the dataset YAML file and initial weights are accessible. - Refer to the [Ultralytics YOLOv5 repository](https://github.com/ultralytics/yolov5) for model and data configurations. - Use the [Training Tutorial](https://docs.ultralytics.com/yolov5/tutorials/train_custom_data/) for custom dataset training. """ opt = parse_opt(True) for k, v in kwargs.items(): setattr(opt, k, v) main(opt) return opt if __name__ == "__main__": opt = parse_opt() main(opt) ================================================ FILE: tutorial.ipynb ================================================ { "nbformat": 4, "nbformat_minor": 0, "metadata": { "colab": { "name": "YOLOv5 Tutorial", "provenance": [] }, "kernelspec": { "name": "python3", "display_name": "Python 3" }, "accelerator": "GPU" }, "cells": [ { "cell_type": "markdown", "metadata": { "id": "t6MPjfT5NrKQ" }, "source": [ "
\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", " \"Ultralytics\n", " \"Run\n", " \"Open\n", " \"Open\n", "\n", " \"Discord\"\n", " \"Ultralytics\n", " \"Ultralytics\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", "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", "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", "Request an Enterprise License for commercial use at [Ultralytics Licensing](https://www.ultralytics.com/license).\n", "\n", "
\n", "
\n", " \n", " \"Ultralytics\n", " \n", "\n", "

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

\n", "
" ] }, { "cell_type": "markdown", "metadata": { "id": "7mGmQbAO5pQb" }, "source": [ "# Setup\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." ] }, { "cell_type": "code", "metadata": { "id": "wbvMlHd_QwMG", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "e8225db4-e61d-4640-8b1f-8bfce3331cea" }, "source": [ "!git clone https://github.com/ultralytics/yolov5 # clone\n", "%cd yolov5\n", "%pip install -qr requirements.txt comet_ml # install\n", "\n", "import torch\n", "import utils\n", "display = utils.notebook_init() # checks" ], "execution_count": 1, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "YOLOv5 🚀 v7.0-136-g71244ae Python-3.9.16 torch-2.0.0+cu118 CUDA:0 (Tesla T4, 15102MiB)\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "Setup complete ✅ (2 CPUs, 12.7 GB RAM, 23.3/166.8 GB disk)\n" ] } ] }, { "cell_type": "markdown", "metadata": { "id": "4JnkELT0cIJg" }, "source": [ "# 1. Detect\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", "```shell\n", "python detect.py --source 0 # webcam\n", " img.jpg # image \n", " vid.mp4 # video\n", " screen # screenshot\n", " path/ # directory\n", " 'path/*.jpg' # glob\n", " 'https://youtu.be/LNwODJXcvt4' # YouTube\n", " 'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream\n", "```" ] }, { "cell_type": "code", "metadata": { "id": "zR9ZbuQCH7FX", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "284ef04b-1596-412f-88f6-948828dd2b49" }, "source": [ "!python detect.py --weights yolov5s.pt --img 640 --conf 0.25 --source data/images\n", "# display.Image(filename='runs/detect/exp/zidane.jpg', width=600)" ], "execution_count": 13, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\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", "YOLOv5 🚀 v7.0-136-g71244ae Python-3.9.16 torch-2.0.0+cu118 CUDA:0 (Tesla T4, 15102MiB)\n", "\n", "Downloading https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s.pt to yolov5s.pt...\n", "100% 14.1M/14.1M [00:00<00:00, 24.5MB/s]\n", "\n", "Fusing layers... \n", "YOLOv5s summary: 213 layers, 7225885 parameters, 0 gradients\n", "image 1/2 /content/yolov5/data/images/bus.jpg: 640x480 4 persons, 1 bus, 41.5ms\n", "image 2/2 /content/yolov5/data/images/zidane.jpg: 384x640 2 persons, 2 ties, 60.0ms\n", "Speed: 0.5ms pre-process, 50.8ms inference, 37.7ms NMS per image at shape (1, 3, 640, 640)\n", "Results saved to \u001B[1mruns/detect/exp\u001B[0m\n" ] } ] }, { "cell_type": "markdown", "metadata": { "id": "hkAzDWJ7cWTr" }, "source": [ "        \n", "" ] }, { "cell_type": "markdown", "metadata": { "id": "0eq1SMWl6Sfn" }, "source": [ "# 2. Validate\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." ] }, { "cell_type": "code", "metadata": { "id": "WQPtK1QYVaD_", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "cf7d52f0-281c-4c96-a488-79f5908f8426" }, "source": [ "# Download COCO val\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", "!unzip -q tmp.zip -d ../datasets && rm tmp.zip # unzip" ], "execution_count": 3, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "100%|██████████| 780M/780M [00:12<00:00, 66.6MB/s]\n" ] } ] }, { "cell_type": "code", "metadata": { "id": "X58w8JLpMnjH", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "3e234e05-ee8b-4ad1-b1a4-f6a55d5e4f3d" }, "source": [ "# Validate YOLOv5s on COCO val\n", "!python val.py --weights yolov5s.pt --data coco.yaml --img 640 --half" ], "execution_count": 4, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\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", "YOLOv5 🚀 v7.0-136-g71244ae Python-3.9.16 torch-2.0.0+cu118 CUDA:0 (Tesla T4, 15102MiB)\n", "\n", "Fusing layers... \n", "YOLOv5s summary: 213 layers, 7225885 parameters, 0 gradients\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", "\u001B[34m\u001B[1mval: \u001B[0mNew cache created: /content/datasets/coco/val2017.cache\n", " Class Images Instances P R mAP50 mAP50-95: 100% 157/157 [01:25<00:00, 1.84it/s]\n", " all 5000 36335 0.671 0.519 0.566 0.371\n", "Speed: 0.1ms pre-process, 3.1ms inference, 2.3ms NMS per image at shape (32, 3, 640, 640)\n", "\n", "Evaluating pycocotools mAP... saving runs/val/exp/yolov5s_predictions.json...\n", "loading annotations into memory...\n", "Done (t=0.43s)\n", "creating index...\n", "index created!\n", "Loading and preparing results...\n", "DONE (t=5.32s)\n", "creating index...\n", "index created!\n", "Running per image evaluation...\n", "Evaluate annotation type *bbox*\n", "DONE (t=78.89s).\n", "Accumulating evaluation results...\n", "DONE (t=14.51s).\n", " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.374\n", " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.572\n", " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.402\n", " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.211\n", " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.423\n", " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.489\n", " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.311\n", " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.516\n", " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.566\n", " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.378\n", " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.625\n", " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.722\n", "Results saved to \u001B[1mruns/val/exp\u001B[0m\n" ] } ] }, { "cell_type": "markdown", "metadata": { "id": "ZY2VXXXu74w5" }, "source": [ "# 3. Train\n", "\n", "\n", " \"Ultralytics\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", "- **Pretrained [Models](https://github.com/ultralytics/yolov5/tree/master/models)** are downloaded\n", "automatically from the [latest YOLOv5 release](https://github.com/ultralytics/yolov5/releases)\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", "- **Training Results** are saved to `runs/train/` with incrementing run directories, i.e. `runs/train/exp2`, `runs/train/exp3` etc.\n", "
\n", "\n", "A **Mosaic Dataloader** is used for training which combines 4 images into 1 mosaic.\n", "\n", "## Label a dataset on Roboflow (optional)\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." ] }, { "cell_type": "code", "source": [ "#@title Select YOLOv5 🚀 logger {run: 'auto'}\n", "logger = 'Comet' #@param ['Comet', 'ClearML', 'TensorBoard']\n", "\n", "if logger == 'Comet':\n", " %pip install -q comet_ml\n", " import comet_ml; comet_ml.init()\n", "elif logger == 'ClearML':\n", " %pip install -q clearml\n", " import clearml; clearml.browser_login()\n", "elif logger == 'TensorBoard':\n", " %load_ext tensorboard\n", " %tensorboard --logdir runs/train" ], "metadata": { "id": "i3oKtE4g-aNn" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "1NcFxRcFdJ_O", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "bbeeea2b-04fc-4185-aa64-258690495b5a" }, "source": [ "# Train YOLOv5s on COCO128 for 3 epochs\n", "!python train.py --img 640 --batch 16 --epochs 3 --data coco128.yaml --weights yolov5s.pt --cache" ], "execution_count": 5, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "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", "To enable the following instructions: AVX2 AVX512F FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\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", "\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", "\u001B[34m\u001B[1mgithub: \u001B[0mup to date with https://github.com/ultralytics/yolov5 ✅\n", "YOLOv5 🚀 v7.0-136-g71244ae Python-3.9.16 torch-2.0.0+cu118 CUDA:0 (Tesla T4, 15102MiB)\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", "\u001B[34m\u001B[1mClearML: \u001B[0mrun 'pip install clearml' to automatically track, visualize and remotely train YOLOv5 🚀 in ClearML\n", "\u001B[34m\u001B[1mComet: \u001B[0mrun 'pip install comet_ml' to automatically track and visualize YOLOv5 🚀 runs in Comet\n", "\u001B[34m\u001B[1mTensorBoard: \u001B[0mStart with 'tensorboard --logdir runs/train', view at http://localhost:6006/\n", "\n", "Dataset not found ⚠️, missing paths ['/content/datasets/coco128/images/train2017']\n", "Downloading https://github.com/ultralytics/assets/releases/download/v0.0.0/coco128.zip to coco128.zip...\n", "100% 6.66M/6.66M [00:00<00:00, 75.6MB/s]\n", "Dataset download success ✅ (0.6s), saved to \u001B[1m/content/datasets\u001B[0m\n", "\n", " from n params module arguments \n", " 0 -1 1 3520 models.common.Conv [3, 32, 6, 2, 2] \n", " 1 -1 1 18560 models.common.Conv [32, 64, 3, 2] \n", " 2 -1 1 18816 models.common.C3 [64, 64, 1] \n", " 3 -1 1 73984 models.common.Conv [64, 128, 3, 2] \n", " 4 -1 2 115712 models.common.C3 [128, 128, 2] \n", " 5 -1 1 295424 models.common.Conv [128, 256, 3, 2] \n", " 6 -1 3 625152 models.common.C3 [256, 256, 3] \n", " 7 -1 1 1180672 models.common.Conv [256, 512, 3, 2] \n", " 8 -1 1 1182720 models.common.C3 [512, 512, 1] \n", " 9 -1 1 656896 models.common.SPPF [512, 512, 5] \n", " 10 -1 1 131584 models.common.Conv [512, 256, 1, 1] \n", " 11 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] \n", " 12 [-1, 6] 1 0 models.common.Concat [1] \n", " 13 -1 1 361984 models.common.C3 [512, 256, 1, False] \n", " 14 -1 1 33024 models.common.Conv [256, 128, 1, 1] \n", " 15 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] \n", " 16 [-1, 4] 1 0 models.common.Concat [1] \n", " 17 -1 1 90880 models.common.C3 [256, 128, 1, False] \n", " 18 -1 1 147712 models.common.Conv [128, 128, 3, 2] \n", " 19 [-1, 14] 1 0 models.common.Concat [1] \n", " 20 -1 1 296448 models.common.C3 [256, 256, 1, False] \n", " 21 -1 1 590336 models.common.Conv [256, 256, 3, 2] \n", " 22 [-1, 10] 1 0 models.common.Concat [1] \n", " 23 -1 1 1182720 models.common.C3 [512, 512, 1, False] \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", "Model summary: 214 layers, 7235389 parameters, 7235389 gradients, 16.6 GFLOPs\n", "\n", "Transferred 349/349 items from yolov5s.pt\n", "\u001B[34m\u001B[1mAMP: \u001B[0mchecks passed ✅\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", "\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", "\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", "\u001B[34m\u001B[1mtrain: \u001B[0mNew cache created: /content/datasets/coco128/labels/train2017.cache\n", "\u001B[34m\u001B[1mtrain: \u001B[0mCaching images (0.1GB ram): 100% 128/128 [00:00<00:00, 264.35it/s]\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", "python train.py --img 640 --epochs 3 --data coco128.yaml --weights yolov5s.pt # 3. train\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", "[![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", "\"Comet" ], "metadata": { "id": "nWOsI5wJR1o3" } }, { "cell_type": "markdown", "source": [ "## ClearML Logging and Automation 🌟 NEW\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", "- `pip install clearml`\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", "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", "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", "\"ClearML" ], "metadata": { "id": "Lay2WsTjNJzP" } }, { "cell_type": "markdown", "metadata": { "id": "-WPvRbS5Swl6" }, "source": [ "## Local Logging\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", "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", "\"Local\n" ] }, { "cell_type": "markdown", "metadata": { "id": "Zelyeqbyt3GD" }, "source": [ "# Environments\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", "- **Notebooks** with free GPU: \"Run \"Open \"Open\n", "- **Google Cloud** Deep Learning VM. See [GCP Quickstart Guide](https://docs.ultralytics.com/yolov5/environments/google_cloud_quickstart_tutorial/)\n", "- **Amazon** Deep Learning AMI. See [AWS Quickstart Guide](https://docs.ultralytics.com/yolov5/environments/aws_quickstart_tutorial/)\n", "- **Docker Image**. See [Docker Quickstart Guide](https://docs.ultralytics.com/yolov5/environments/docker_image_quickstart_tutorial/) \"Docker\n" ] }, { "cell_type": "markdown", "metadata": { "id": "6Qu7Iesl0p54" }, "source": [ "# Status\n", "\n", "![YOLOv5 CI](https://github.com/ultralytics/yolov3/actions/workflows/ci-testing.yml/badge.svg)\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" ] }, { "cell_type": "markdown", "metadata": { "id": "IEijrePND_2I" }, "source": [ "# Appendix\n", "\n", "Additional content below." ] }, { "cell_type": "code", "metadata": { "id": "GMusP4OAxFu6" }, "source": [ "# YOLOv5 PyTorch HUB Inference (DetectionModels only)\n", "import torch\n", "\n", "model = torch.hub.load('ultralytics/yolov5', 'yolov5s', force_reload=True) # yolov5n - yolov5x6 or custom\n", "im = 'https://ultralytics.com/images/zidane.jpg' # file, Path, PIL.Image, OpenCV, nparray, list\n", "results = model(im) # inference\n", "results.print() # or .show(), .save(), .crop(), .pandas(), etc." ], "execution_count": null, "outputs": [] } ] } ================================================ FILE: utils/__init__.py ================================================ # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license """utils/initialization.""" import contextlib import platform import threading def emojis(str=""): """Returns platform-dependent emoji-safe version of str; ignores emojis on Windows, else returns original str.""" return str.encode().decode("ascii", "ignore") if platform.system() == "Windows" else str class TryExcept(contextlib.ContextDecorator): """A context manager and decorator for handling exceptions with optional custom messages.""" def __init__(self, msg=""): """Initializes TryExcept with optional custom message, used as decorator or context manager for exception handling. """ self.msg = msg def __enter__(self): """Begin exception-handling block, optionally customizing exception message when used with TryExcept context manager. """ pass def __exit__(self, exc_type, value, traceback): """Ends exception-handling block, optionally prints custom message with exception, suppressing exceptions within context. """ if value: print(emojis(f"{self.msg}{': ' if self.msg else ''}{value}")) return True def threaded(func): """Decorates a function to run in a separate thread, returning the thread object. Usage: @threaded. """ def wrapper(*args, **kwargs): """Runs the decorated function in a separate thread and returns the thread object. Usage: @threaded. """ thread = threading.Thread(target=func, args=args, kwargs=kwargs, daemon=True) thread.start() return thread return wrapper def join_threads(verbose=False): """Joins all daemon threads, excluding the main thread, with an optional verbose flag for logging.""" main_thread = threading.current_thread() for t in threading.enumerate(): if t is not main_thread: if verbose: print(f"Joining thread {t.name}") t.join() def notebook_init(verbose=True): """Initializes notebook environment by checking hardware, software requirements, and cleaning up if in Colab.""" print("Checking setup...") import os import shutil from ultralytics.utils.checks import check_requirements from utils.general import check_font, is_colab from utils.torch_utils import select_device # imports check_font() import psutil if check_requirements("wandb", install=False): os.system("pip uninstall -y wandb") # eliminate unexpected account creation prompt with infinite hang if is_colab(): shutil.rmtree("/content/sample_data", ignore_errors=True) # remove colab /sample_data directory # System info display = None if verbose: gb = 1 << 30 # bytes to GiB (1024 ** 3) ram = psutil.virtual_memory().total total, _used, free = shutil.disk_usage("/") with contextlib.suppress(Exception): # clear display if ipython is installed from IPython import display display.clear_output() s = f"({os.cpu_count()} CPUs, {ram / gb:.1f} GB RAM, {(total - free) / gb:.1f}/{total / gb:.1f} GB disk)" else: s = "" select_device(newline=False) print(emojis(f"Setup complete ✅ {s}")) return display ================================================ FILE: utils/activations.py ================================================ # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license """Activation functions.""" import torch import torch.nn as nn import torch.nn.functional as F class SiLU(nn.Module): """Applies the SiLU activation function to the input tensor as described in https://arxiv.org/pdf/1606.08415.pdf.""" @staticmethod def forward(x): """Applies the SiLU activation function, as detailed in https://arxiv.org/pdf/1606.08415.pdf, on input tensor `x`. """ return x * torch.sigmoid(x) class Hardswish(nn.Module): """Applies the Hardswish activation function to the input tensor `x`.""" @staticmethod def forward(x): """Applies Hardswish activation, suitable for TorchScript, CoreML, ONNX, modifying input `x` as per Hard-SiLU definition. """ return x * F.hardtanh(x + 3, 0.0, 6.0) / 6.0 # for TorchScript, CoreML and ONNX class Mish(nn.Module): """Applies the Mish activation function to improve model performance; see https://github.com/digantamisra98/Mish.""" @staticmethod def forward(x): """Applies the Mish activation function, enhancing model performance and convergence. Reference: https://github.com/digantamisra98/Mish """ return x * F.softplus(x).tanh() class MemoryEfficientMish(nn.Module): """Applies the memory-efficient Mish activation function for improved model performance and reduced memory usage.""" class F(torch.autograd.Function): """Memory-efficient implementation of the Mish activation function for enhanced model performance.""" @staticmethod def forward(ctx, x): """Applies the Mish activation function in a memory-efficient manner, useful for enhancing model performance. """ ctx.save_for_backward(x) return x.mul(torch.tanh(F.softplus(x))) # x * tanh(ln(1 + exp(x))) @staticmethod def backward(ctx, grad_output): """Computes gradient of the Mish activation function for backpropagation, returning the derivative with respect to the input. """ x = ctx.saved_tensors[0] sx = torch.sigmoid(x) fx = F.softplus(x).tanh() return grad_output * (fx + x * sx * (1 - fx * fx)) def forward(self, x): """Applies Mish activation function, useful in neural networks for nonlinear transformation of inputs.""" return self.F.apply(x) class FReLU(nn.Module): """Implements the FReLU activation, combining ReLU and convolution from https://arxiv.org/abs/2007.11824.""" def __init__(self, c1, k=3): # ch_in, kernel """Initializes FReLU with specified channel size and kernel, implementing activation from https://arxiv.org/abs/2007.11824. """ super().__init__() self.conv = nn.Conv2d(c1, c1, k, 1, 1, groups=c1, bias=False) self.bn = nn.BatchNorm2d(c1) def forward(self, x): """Performs FReLU activation on input, returning the max of input and its 2D convolution.""" return torch.max(x, self.bn(self.conv(x))) class AconC(nn.Module): r"""ACON activation (activate or not) AconC: (p1*x-p2*x) * sigmoid(beta*(p1*x-p2*x)) + p2*x, beta is a learnable parameter according to "Activate or Not: Learning Customized Activation" . """ def __init__(self, c1): """Initializes ACON activation with learnable parameters p1, p2, and beta as per https://arxiv.org/pdf/2009.04759.pdf. """ super().__init__() self.p1 = nn.Parameter(torch.randn(1, c1, 1, 1)) self.p2 = nn.Parameter(torch.randn(1, c1, 1, 1)) self.beta = nn.Parameter(torch.ones(1, c1, 1, 1)) def forward(self, x): """Applies a parametric activation function to tensor x; see https://arxiv.org/pdf/2009.04759.pdf for details. """ dpx = (self.p1 - self.p2) * x return dpx * torch.sigmoid(self.beta * dpx) + self.p2 * x class MetaAconC(nn.Module): r"""ACON activation (activate or not) MetaAconC: (p1*x-p2*x) * sigmoid(beta*(p1*x-p2*x)) + p2*x, beta is generated by a small network according to "Activate or Not: Learning Customized Activation" . """ def __init__(self, c1, k=1, s=1, r=16): # ch_in, kernel, stride, r """Initializes MetaAconC activation with params c1, optional k (kernel=1), s (stride=1), r (16), defining activation dynamics. """ super().__init__() c2 = max(r, c1 // r) self.p1 = nn.Parameter(torch.randn(1, c1, 1, 1)) self.p2 = nn.Parameter(torch.randn(1, c1, 1, 1)) self.fc1 = nn.Conv2d(c1, c2, k, s, bias=True) self.fc2 = nn.Conv2d(c2, c1, k, s, bias=True) # self.bn1 = nn.BatchNorm2d(c2) # self.bn2 = nn.BatchNorm2d(c1) def forward(self, x): """Applies a forward pass transforming input `x` using parametric operations and returns the modified tensor.""" y = x.mean(dim=2, keepdims=True).mean(dim=3, keepdims=True) # batch-size 1 bug/instabilities https://github.com/ultralytics/yolov5/issues/2891 # beta = torch.sigmoid(self.bn2(self.fc2(self.bn1(self.fc1(y))))) # bug/unstable beta = torch.sigmoid(self.fc2(self.fc1(y))) # bug patch BN layers removed dpx = (self.p1 - self.p2) * x return dpx * torch.sigmoid(beta * dpx) + self.p2 * x ================================================ FILE: utils/augmentations.py ================================================ # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license """Image augmentation functions.""" import math import random import cv2 import numpy as np import torch import torchvision.transforms as T import torchvision.transforms.functional as TF from utils.general import LOGGER, check_version, colorstr, resample_segments, segment2box, xywhn2xyxy from utils.metrics import bbox_ioa IMAGENET_MEAN = 0.485, 0.456, 0.406 # RGB mean IMAGENET_STD = 0.229, 0.224, 0.225 # RGB standard deviation class Albumentations: """Provides optional image augmentation for YOLOv3 using the Albumentations library if installed.""" def __init__(self, size=640): """Initializes Albumentations class for optional YOLOv3 data augmentation with default size 640.""" self.transform = None prefix = colorstr("albumentations: ") try: import albumentations as A check_version(A.__version__, "1.0.3", hard=True) # version requirement T = [ A.RandomResizedCrop(height=size, width=size, scale=(0.8, 1.0), ratio=(0.9, 1.11), p=0.0), A.Blur(p=0.01), A.MedianBlur(p=0.01), A.ToGray(p=0.01), A.CLAHE(p=0.01), A.RandomBrightnessContrast(p=0.0), A.RandomGamma(p=0.0), A.ImageCompression(quality_lower=75, p=0.0), ] # transforms self.transform = A.Compose(T, bbox_params=A.BboxParams(format="yolo", label_fields=["class_labels"])) LOGGER.info(prefix + ", ".join(f"{x}".replace("always_apply=False, ", "") for x in T if x.p)) except ImportError: # package not installed, skip pass except Exception as e: LOGGER.info(f"{prefix}{e}") def __call__(self, im, labels, p=1.0): """Applies transformations to an image and its bounding boxes with a probability `p`.""" if self.transform and random.random() < p: new = self.transform(image=im, bboxes=labels[:, 1:], class_labels=labels[:, 0]) # transformed im, labels = new["image"], np.array([[c, *b] for c, b in zip(new["class_labels"], new["bboxes"])]) return im, labels def normalize(x, mean=IMAGENET_MEAN, std=IMAGENET_STD, inplace=False): """Normalizes RGB images in BCHW format using ImageNet stats; use `inplace=True` for in-place normalization.""" return TF.normalize(x, mean, std, inplace=inplace) def denormalize(x, mean=IMAGENET_MEAN, std=IMAGENET_STD): """Converts normalized images back to original form using ImageNet stats; inputs in BCHW format. Example: `denormalize(tensor)`. """ for i in range(3): x[:, i] = x[:, i] * std[i] + mean[i] return x def augment_hsv(im, hgain=0.5, sgain=0.5, vgain=0.5): """Applies HSV color-space augmentation with optional gains; expects BGR image input. Example: `augment_hsv(image)`. """ if hgain or sgain or vgain: r = np.random.uniform(-1, 1, 3) * [hgain, sgain, vgain] + 1 # random gains hue, sat, val = cv2.split(cv2.cvtColor(im, cv2.COLOR_BGR2HSV)) dtype = im.dtype # uint8 x = np.arange(0, 256, dtype=r.dtype) lut_hue = ((x * r[0]) % 180).astype(dtype) lut_sat = np.clip(x * r[1], 0, 255).astype(dtype) lut_val = np.clip(x * r[2], 0, 255).astype(dtype) im_hsv = cv2.merge((cv2.LUT(hue, lut_hue), cv2.LUT(sat, lut_sat), cv2.LUT(val, lut_val))) cv2.cvtColor(im_hsv, cv2.COLOR_HSV2BGR, dst=im) # no return needed def hist_equalize(im, clahe=True, bgr=False): """Equalizes histogram of BGR/RGB image `im` with shape (n,m,3), optionally using CLAHE; returns equalized image.""" yuv = cv2.cvtColor(im, cv2.COLOR_BGR2YUV if bgr else cv2.COLOR_RGB2YUV) if clahe: c = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8)) yuv[:, :, 0] = c.apply(yuv[:, :, 0]) else: yuv[:, :, 0] = cv2.equalizeHist(yuv[:, :, 0]) # equalize Y channel histogram return cv2.cvtColor(yuv, cv2.COLOR_YUV2BGR if bgr else cv2.COLOR_YUV2RGB) # convert YUV image to RGB def replicate(im, labels): """Duplicates half of the smallest bounding boxes in an image to augment dataset; update labels accordingly.""" h, w = im.shape[:2] boxes = labels[:, 1:].astype(int) x1, y1, x2, y2 = boxes.T s = ((x2 - x1) + (y2 - y1)) / 2 # side length (pixels) for i in s.argsort()[: round(s.size * 0.5)]: # smallest indices x1b, y1b, x2b, y2b = boxes[i] bh, bw = y2b - y1b, x2b - x1b yc, xc = int(random.uniform(0, h - bh)), int(random.uniform(0, w - bw)) # offset x, y x1a, y1a, x2a, y2a = [xc, yc, xc + bw, yc + bh] im[y1a:y2a, x1a:x2a] = im[y1b:y2b, x1b:x2b] # im4[ymin:ymax, xmin:xmax] labels = np.append(labels, [[labels[i, 0], x1a, y1a, x2a, y2a]], axis=0) return im, labels def letterbox(im, new_shape=(640, 640), color=(114, 114, 114), auto=True, scaleFill=False, scaleup=True, stride=32): """Resizes and pads an image to a new shape with optional scaling, filling, and stride-multiple constraints.""" shape = im.shape[:2] # current shape [height, width] if isinstance(new_shape, int): new_shape = (new_shape, new_shape) # Scale ratio (new / old) r = min(new_shape[0] / shape[0], new_shape[1] / shape[1]) if not scaleup: # only scale down, do not scale up (for better val mAP) r = min(r, 1.0) # Compute padding ratio = r, r # width, height ratios new_unpad = round(shape[1] * r), round(shape[0] * r) dw, dh = new_shape[1] - new_unpad[0], new_shape[0] - new_unpad[1] # wh padding if auto: # minimum rectangle dw, dh = np.mod(dw, stride), np.mod(dh, stride) # wh padding elif scaleFill: # stretch dw, dh = 0.0, 0.0 new_unpad = (new_shape[1], new_shape[0]) ratio = new_shape[1] / shape[1], new_shape[0] / shape[0] # width, height ratios dw /= 2 # divide padding into 2 sides dh /= 2 if shape[::-1] != new_unpad: # resize im = cv2.resize(im, new_unpad, interpolation=cv2.INTER_LINEAR) top, bottom = round(dh - 0.1), round(dh + 0.1) left, right = round(dw - 0.1), round(dw + 0.1) im = cv2.copyMakeBorder(im, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color) # add border return im, ratio, (dw, dh) def random_perspective( im, targets=(), segments=(), degrees=10, translate=0.1, scale=0.1, shear=10, perspective=0.0, border=(0, 0) ): # torchvision.transforms.RandomAffine(degrees=(-10, 10), translate=(0.1, 0.1), scale=(0.9, 1.1), shear=(-10, 10)) # targets = [cls, xyxy] """Applies a random perspective transformation to an image and its bounding boxes for data augmentation.""" height = im.shape[0] + border[0] * 2 # shape(h,w,c) width = im.shape[1] + border[1] * 2 # Center C = np.eye(3) C[0, 2] = -im.shape[1] / 2 # x translation (pixels) C[1, 2] = -im.shape[0] / 2 # y translation (pixels) # Perspective P = np.eye(3) P[2, 0] = random.uniform(-perspective, perspective) # x perspective (about y) P[2, 1] = random.uniform(-perspective, perspective) # y perspective (about x) # Rotation and Scale R = np.eye(3) a = random.uniform(-degrees, degrees) # a += random.choice([-180, -90, 0, 90]) # add 90deg rotations to small rotations s = random.uniform(1 - scale, 1 + scale) # s = 2 ** random.uniform(-scale, scale) R[:2] = cv2.getRotationMatrix2D(angle=a, center=(0, 0), scale=s) # Shear S = np.eye(3) S[0, 1] = math.tan(random.uniform(-shear, shear) * math.pi / 180) # x shear (deg) S[1, 0] = math.tan(random.uniform(-shear, shear) * math.pi / 180) # y shear (deg) # Translation T = np.eye(3) T[0, 2] = random.uniform(0.5 - translate, 0.5 + translate) * width # x translation (pixels) T[1, 2] = random.uniform(0.5 - translate, 0.5 + translate) * height # y translation (pixels) # Combined rotation matrix M = T @ S @ R @ P @ C # order of operations (right to left) is IMPORTANT if (border[0] != 0) or (border[1] != 0) or (M != np.eye(3)).any(): # image changed if perspective: im = cv2.warpPerspective(im, M, dsize=(width, height), borderValue=(114, 114, 114)) else: # affine im = cv2.warpAffine(im, M[:2], dsize=(width, height), borderValue=(114, 114, 114)) if n := len(targets): use_segments = any(x.any() for x in segments) and len(segments) == n new = np.zeros((n, 4)) if use_segments: # warp segments segments = resample_segments(segments) # upsample for i, segment in enumerate(segments): xy = np.ones((len(segment), 3)) xy[:, :2] = segment xy = xy @ M.T # transform xy = xy[:, :2] / xy[:, 2:3] if perspective else xy[:, :2] # perspective rescale or affine # clip new[i] = segment2box(xy, width, height) else: # warp boxes xy = np.ones((n * 4, 3)) xy[:, :2] = targets[:, [1, 2, 3, 4, 1, 4, 3, 2]].reshape(n * 4, 2) # x1y1, x2y2, x1y2, x2y1 xy = xy @ M.T # transform xy = (xy[:, :2] / xy[:, 2:3] if perspective else xy[:, :2]).reshape(n, 8) # perspective rescale or affine # create new boxes x = xy[:, [0, 2, 4, 6]] y = xy[:, [1, 3, 5, 7]] new = np.concatenate((x.min(1), y.min(1), x.max(1), y.max(1))).reshape(4, n).T # clip new[:, [0, 2]] = new[:, [0, 2]].clip(0, width) new[:, [1, 3]] = new[:, [1, 3]].clip(0, height) # filter candidates i = box_candidates(box1=targets[:, 1:5].T * s, box2=new.T, area_thr=0.01 if use_segments else 0.10) targets = targets[i] targets[:, 1:5] = new[i] return im, targets def copy_paste(im, labels, segments, p=0.5): """Applies Copy-Paste augmentation (https://arxiv.org/abs/2012.07177) on image, labels (nx5 np.array(cls, xyxy)), and segments. """ n = len(segments) if p and n: _h, w, _c = im.shape # height, width, channels im_new = np.zeros(im.shape, np.uint8) for j in random.sample(range(n), k=round(p * n)): l, s = labels[j], segments[j] box = w - l[3], l[2], w - l[1], l[4] ioa = bbox_ioa(box, labels[:, 1:5]) # intersection over area if (ioa < 0.30).all(): # allow 30% obscuration of existing labels labels = np.concatenate((labels, [[l[0], *box]]), 0) segments.append(np.concatenate((w - s[:, 0:1], s[:, 1:2]), 1)) cv2.drawContours(im_new, [segments[j].astype(np.int32)], -1, (1, 1, 1), cv2.FILLED) result = cv2.flip(im, 1) # augment segments (flip left-right) i = cv2.flip(im_new, 1).astype(bool) im[i] = result[i] # cv2.imwrite('debug.jpg', im) # debug return im, labels, segments def cutout(im, labels, p=0.5): """Applies cutout augmentation, potentially removing >60% obscured labels; see https://arxiv.org/abs/1708.04552.""" if random.random() < p: h, w = im.shape[:2] scales = [0.5] * 1 + [0.25] * 2 + [0.125] * 4 + [0.0625] * 8 + [0.03125] * 16 # image size fraction for s in scales: mask_h = random.randint(1, int(h * s)) # create random masks mask_w = random.randint(1, int(w * s)) # box xmin = max(0, random.randint(0, w) - mask_w // 2) ymin = max(0, random.randint(0, h) - mask_h // 2) xmax = min(w, xmin + mask_w) ymax = min(h, ymin + mask_h) # apply random color mask im[ymin:ymax, xmin:xmax] = [random.randint(64, 191) for _ in range(3)] # return unobscured labels if len(labels) and s > 0.03: box = np.array([xmin, ymin, xmax, ymax], dtype=np.float32) ioa = bbox_ioa(box, xywhn2xyxy(labels[:, 1:5], w, h)) # intersection over area labels = labels[ioa < 0.60] # remove >60% obscured labels return labels def mixup(im, labels, im2, labels2): """Applies MixUp augmentation by blending images and labels; see https://arxiv.org/pdf/1710.09412.pdf for details. """ r = np.random.beta(32.0, 32.0) # mixup ratio, alpha=beta=32.0 im = (im * r + im2 * (1 - r)).astype(np.uint8) labels = np.concatenate((labels, labels2), 0) return im, labels def box_candidates(box1, box2, wh_thr=2, ar_thr=100, area_thr=0.1, eps=1e-16): # box1(4,n), box2(4,n) """Evaluates candidate boxes based on width, height, aspect ratio, and area thresholds.""" w1, h1 = box1[2] - box1[0], box1[3] - box1[1] w2, h2 = box2[2] - box2[0], box2[3] - box2[1] ar = np.maximum(w2 / (h2 + eps), h2 / (w2 + eps)) # aspect ratio return (w2 > wh_thr) & (h2 > wh_thr) & (w2 * h2 / (w1 * h1 + eps) > area_thr) & (ar < ar_thr) # candidates def classify_albumentations( augment=True, size=224, scale=(0.08, 1.0), ratio=(0.75, 1.0 / 0.75), # 0.75, 1.33 hflip=0.5, vflip=0.0, jitter=0.4, mean=IMAGENET_MEAN, std=IMAGENET_STD, auto_aug=False, ): # YOLOv3 classification Albumentations (optional, only used if package is installed) """Generates an Albumentations transform pipeline for image classification with optional augmentations.""" prefix = colorstr("albumentations: ") try: import albumentations as A from albumentations.pytorch import ToTensorV2 check_version(A.__version__, "1.0.3", hard=True) # version requirement if augment: # Resize and crop T = [A.RandomResizedCrop(height=size, width=size, scale=scale, ratio=ratio)] if auto_aug: # TODO: implement AugMix, AutoAug & RandAug in albumentation LOGGER.info(f"{prefix}auto augmentations are currently not supported") else: if hflip > 0: T += [A.HorizontalFlip(p=hflip)] if vflip > 0: T += [A.VerticalFlip(p=vflip)] if jitter > 0: color_jitter = (float(jitter),) * 3 # repeat value for brightness, contrast, satuaration, 0 hue T += [A.ColorJitter(*color_jitter, 0)] else: # Use fixed crop for eval set (reproducibility) T = [A.SmallestMaxSize(max_size=size), A.CenterCrop(height=size, width=size)] T += [A.Normalize(mean=mean, std=std), ToTensorV2()] # Normalize and convert to Tensor LOGGER.info(prefix + ", ".join(f"{x}".replace("always_apply=False, ", "") for x in T if x.p)) return A.Compose(T) except ImportError: # package not installed, skip LOGGER.warning(f"{prefix}⚠️ not found, install with `pip install albumentations` (recommended)") except Exception as e: LOGGER.info(f"{prefix}{e}") def classify_transforms(size=224): """Applies classification transforms including center cropping, tensor conversion, and normalization.""" assert isinstance(size, int), f"ERROR: classify_transforms size {size} must be integer, not (list, tuple)" # T.Compose([T.ToTensor(), T.Resize(size), T.CenterCrop(size), T.Normalize(IMAGENET_MEAN, IMAGENET_STD)]) return T.Compose([CenterCrop(size), ToTensor(), T.Normalize(IMAGENET_MEAN, IMAGENET_STD)]) class LetterBox: """Resizes and pads images to a specified size while maintaining aspect ratio.""" def __init__(self, size=(640, 640), auto=False, stride=32): """Initializes LetterBox for YOLOv3 image preprocessing with optional auto-sizing and stride; `size` can be int or tuple. """ super().__init__() self.h, self.w = (size, size) if isinstance(size, int) else size self.auto = auto # pass max size integer, automatically solve for short side using stride self.stride = stride # used with auto def __call__(self, im): # im = np.array HWC """Resizes and pads image `im` (np.array HWC) to specified `size` and `stride`, possibly autosizing for the short side. """ imh, imw = im.shape[:2] r = min(self.h / imh, self.w / imw) # ratio of new/old h, w = round(imh * r), round(imw * r) # resized image hs, ws = (math.ceil(x / self.stride) * self.stride for x in (h, w)) if self.auto else self.h, self.w top, left = round((hs - h) / 2 - 0.1), round((ws - w) / 2 - 0.1) im_out = np.full((self.h, self.w, 3), 114, dtype=im.dtype) im_out[top : top + h, left : left + w] = cv2.resize(im, (w, h), interpolation=cv2.INTER_LINEAR) return im_out class CenterCrop: """Crops the center of an image to a specified size, maintaining aspect ratio.""" def __init__(self, size=640): """Initializes a CenterCrop object for YOLOv3, to crop images to a specified size, with default 640x640.""" super().__init__() self.h, self.w = (size, size) if isinstance(size, int) else size def __call__(self, im): # im = np.array HWC """Crops and resizes an image to specified dimensions, defaulting to 640x640, maintaining aspect ratio.""" imh, imw = im.shape[:2] m = min(imh, imw) # min dimension top, left = (imh - m) // 2, (imw - m) // 2 return cv2.resize(im[top : top + m, left : left + m], (self.w, self.h), interpolation=cv2.INTER_LINEAR) class ToTensor: """Converts a BGR image in numpy format to a PyTorch tensor in RGB format, with optional half precision.""" def __init__(self, half=False): """Initializes ToTensor class for YOLOv3 image preprocessing to convert images to PyTorch tensors, optionally in half precision. """ super().__init__() self.half = half def __call__(self, im): # im = np.array HWC in BGR order """Converts a BGR image in numpy format to a PyTorch tensor in RGB format, with options for half precision and normalization. """ im = np.ascontiguousarray(im.transpose((2, 0, 1))[::-1]) # HWC to CHW -> BGR to RGB -> contiguous im = torch.from_numpy(im) # to torch im = im.half() if self.half else im.float() # uint8 to fp16/32 im /= 255.0 # 0-255 to 0.0-1.0 return im ================================================ FILE: utils/autoanchor.py ================================================ # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license """AutoAnchor utils.""" import random import numpy as np import torch import yaml from tqdm import tqdm from utils import TryExcept from utils.general import LOGGER, TQDM_BAR_FORMAT, colorstr PREFIX = colorstr("AutoAnchor: ") def check_anchor_order(m): """Checks and corrects anchor order in YOLOv3's Detect() module if mismatched with stride order.""" a = m.anchors.prod(-1).mean(-1).view(-1) # mean anchor area per output layer da = a[-1] - a[0] # delta a ds = m.stride[-1] - m.stride[0] # delta s if da and (da.sign() != ds.sign()): # same order LOGGER.info(f"{PREFIX}Reversing anchor order") m.anchors[:] = m.anchors.flip(0) @TryExcept(f"{PREFIX}ERROR") def check_anchors(dataset, model, thr=4.0, imgsz=640): """Evaluates anchor fit to dataset and recomputes if below a threshold, enhancing model performance.""" m = model.module.model[-1] if hasattr(model, "module") else model.model[-1] # Detect() shapes = imgsz * dataset.shapes / dataset.shapes.max(1, keepdims=True) scale = np.random.uniform(0.9, 1.1, size=(shapes.shape[0], 1)) # augment scale wh = torch.tensor(np.concatenate([l[:, 3:5] * s for s, l in zip(shapes * scale, dataset.labels)])).float() # wh def metric(k): # compute metric """Computes and returns best possible recall (bpr) and anchors above threshold (aat) metrics for given anchors. """ r = wh[:, None] / k[None] x = torch.min(r, 1 / r).min(2)[0] # ratio metric best = x.max(1)[0] # best_x aat = (x > 1 / thr).float().sum(1).mean() # anchors above threshold bpr = (best > 1 / thr).float().mean() # best possible recall return bpr, aat stride = m.stride.to(m.anchors.device).view(-1, 1, 1) # model strides anchors = m.anchors.clone() * stride # current anchors bpr, aat = metric(anchors.cpu().view(-1, 2)) s = f"\n{PREFIX}{aat:.2f} anchors/target, {bpr:.3f} Best Possible Recall (BPR). " if bpr > 0.98: # threshold to recompute LOGGER.info(f"{s}Current anchors are a good fit to dataset ✅") else: LOGGER.info(f"{s}Anchors are a poor fit to dataset ⚠️, attempting to improve...") na = m.anchors.numel() // 2 # number of anchors anchors = kmean_anchors(dataset, n=na, img_size=imgsz, thr=thr, gen=1000, verbose=False) new_bpr = metric(anchors)[0] if new_bpr > bpr: # replace anchors anchors = torch.tensor(anchors, device=m.anchors.device).type_as(m.anchors) m.anchors[:] = anchors.clone().view_as(m.anchors) check_anchor_order(m) # must be in pixel-space (not grid-space) m.anchors /= stride s = f"{PREFIX}Done ✅ (optional: update model *.yaml to use these anchors in the future)" else: s = f"{PREFIX}Done ⚠️ (original anchors better than new anchors, proceeding with original anchors)" LOGGER.info(s) def kmean_anchors(dataset="./data/coco128.yaml", n=9, img_size=640, thr=4.0, gen=1000, verbose=True): """Creates kmeans-evolved anchors from training dataset. Args: dataset: path to data.yaml, or a loaded dataset n: number of anchors img_size: image size used for training thr: anchor-label wh ratio threshold hyperparameter hyp['anchor_t'] used for training, default=4.0 gen: generations to evolve anchors using genetic algorithm verbose: print all results Returns: k: kmeans evolved anchors Examples: from utils.autoanchor import *; _ = kmean_anchors() """ from scipy.cluster.vq import kmeans npr = np.random thr = 1 / thr def metric(k, wh): # compute metrics """Computes best possible recall (BPR) and anchors above threshold (AAT) metrics for given anchor boxes.""" r = wh[:, None] / k[None] x = torch.min(r, 1 / r).min(2)[0] # ratio metric # x = wh_iou(wh, torch.tensor(k)) # iou metric return x, x.max(1)[0] # x, best_x def anchor_fitness(k): # mutation fitness """Evaluates the fitness of anchor boxes by computing mean recall weighted by an activation threshold.""" _, best = metric(torch.tensor(k, dtype=torch.float32), wh) return (best * (best > thr).float()).mean() # fitness def print_results(k, verbose=True): """Displays sorted anchors and their metrics including best possible recall and anchors above threshold.""" k = k[np.argsort(k.prod(1))] # sort small to large x, best = metric(k, wh0) bpr, aat = (best > thr).float().mean(), (x > thr).float().mean() * n # best possible recall, anch > thr s = ( f"{PREFIX}thr={thr:.2f}: {bpr:.4f} best possible recall, {aat:.2f} anchors past thr\n" f"{PREFIX}n={n}, img_size={img_size}, metric_all={x.mean():.3f}/{best.mean():.3f}-mean/best, " f"past_thr={x[x > thr].mean():.3f}-mean: " ) for x in k: s += "%i,%i, " % (round(x[0]), round(x[1])) if verbose: LOGGER.info(s[:-2]) return k if isinstance(dataset, str): # *.yaml file with open(dataset, errors="ignore") as f: data_dict = yaml.safe_load(f) # model dict from utils.dataloaders import LoadImagesAndLabels dataset = LoadImagesAndLabels(data_dict["train"], augment=True, rect=True) # Get label wh shapes = img_size * dataset.shapes / dataset.shapes.max(1, keepdims=True) wh0 = np.concatenate([l[:, 3:5] * s for s, l in zip(shapes, dataset.labels)]) # wh # Filter i = (wh0 < 3.0).any(1).sum() if i: LOGGER.info(f"{PREFIX}WARNING ⚠️ Extremely small objects found: {i} of {len(wh0)} labels are <3 pixels in size") wh = wh0[(wh0 >= 2.0).any(1)].astype(np.float32) # filter > 2 pixels # wh = wh * (npr.rand(wh.shape[0], 1) * 0.9 + 0.1) # multiply by random scale 0-1 # Kmeans init try: LOGGER.info(f"{PREFIX}Running kmeans for {n} anchors on {len(wh)} points...") assert n <= len(wh) # apply overdetermined constraint s = wh.std(0) # sigmas for whitening k = kmeans(wh / s, n, iter=30)[0] * s # points assert n == len(k) # kmeans may return fewer points than requested if wh is insufficient or too similar except Exception: LOGGER.warning(f"{PREFIX}WARNING ⚠️ switching strategies from kmeans to random init") k = np.sort(npr.rand(n * 2)).reshape(n, 2) * img_size # random init wh, wh0 = (torch.tensor(x, dtype=torch.float32) for x in (wh, wh0)) k = print_results(k, verbose=False) # Plot # k, d = [None] * 20, [None] * 20 # for i in tqdm(range(1, 21)): # k[i-1], d[i-1] = kmeans(wh / s, i) # points, mean distance # fig, ax = plt.subplots(1, 2, figsize=(14, 7), tight_layout=True) # ax = ax.ravel() # ax[0].plot(np.arange(1, 21), np.array(d) ** 2, marker='.') # fig, ax = plt.subplots(1, 2, figsize=(14, 7)) # plot wh # ax[0].hist(wh[wh[:, 0]<100, 0],400) # ax[1].hist(wh[wh[:, 1]<100, 1],400) # fig.savefig('wh.png', dpi=200) # Evolve f, sh, mp, s = anchor_fitness(k), k.shape, 0.9, 0.1 # fitness, generations, mutation prob, sigma pbar = tqdm(range(gen), bar_format=TQDM_BAR_FORMAT) # progress bar for _ in pbar: v = np.ones(sh) while (v == 1).all(): # mutate until a change occurs (prevent duplicates) v = ((npr.random(sh) < mp) * random.random() * npr.randn(*sh) * s + 1).clip(0.3, 3.0) kg = (k.copy() * v).clip(min=2.0) fg = anchor_fitness(kg) if fg > f: f, k = fg, kg.copy() pbar.desc = f"{PREFIX}Evolving anchors with Genetic Algorithm: fitness = {f:.4f}" if verbose: print_results(k, verbose) return print_results(k).astype(np.float32) ================================================ FILE: utils/autobatch.py ================================================ # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license """Auto-batch utils.""" from copy import deepcopy import numpy as np import torch from utils.general import LOGGER, colorstr from utils.torch_utils import profile def check_train_batch_size(model, imgsz=640, amp=True): """Checks and computes the optimal training batch size for YOLOv3, given model and image size.""" with torch.cuda.amp.autocast(amp): return autobatch(deepcopy(model).train(), imgsz) # compute optimal batch size def autobatch(model, imgsz=640, fraction=0.8, batch_size=16): """Estimates optimal YOLOv3 batch size using available CUDA memory; imgsz:int=640, fraction:float=0.8, batch_size:int=16. """ # Usage: # import torch # from utils.autobatch import autobatch # model = torch.hub.load('ultralytics/yolov5', 'yolov5s', autoshape=False) # print(autobatch(model)) # Check device prefix = colorstr("AutoBatch: ") LOGGER.info(f"{prefix}Computing optimal batch size for --imgsz {imgsz}") device = next(model.parameters()).device # get model device if device.type == "cpu": LOGGER.info(f"{prefix}CUDA not detected, using default CPU batch-size {batch_size}") return batch_size if torch.backends.cudnn.benchmark: LOGGER.info(f"{prefix} ⚠️ Requires torch.backends.cudnn.benchmark=False, using default batch-size {batch_size}") return batch_size # Inspect CUDA memory gb = 1 << 30 # bytes to GiB (1024 ** 3) d = str(device).upper() # 'CUDA:0' properties = torch.cuda.get_device_properties(device) # device properties t = properties.total_memory / gb # GiB total r = torch.cuda.memory_reserved(device) / gb # GiB reserved a = torch.cuda.memory_allocated(device) / gb # GiB allocated f = t - (r + a) # GiB free LOGGER.info(f"{prefix}{d} ({properties.name}) {t:.2f}G total, {r:.2f}G reserved, {a:.2f}G allocated, {f:.2f}G free") # Profile batch sizes batch_sizes = [1, 2, 4, 8, 16] try: img = [torch.empty(b, 3, imgsz, imgsz) for b in batch_sizes] results = profile(img, model, n=3, device=device) except Exception as e: LOGGER.warning(f"{prefix}{e}") # Fit a solution y = [x[2] for x in results if x] # memory [2] p = np.polyfit(batch_sizes[: len(y)], y, deg=1) # first degree polynomial fit b = int((f * fraction - p[1]) / p[0]) # y intercept (optimal batch size) if None in results: # some sizes failed i = results.index(None) # first fail index if b >= batch_sizes[i]: # y intercept above failure point b = batch_sizes[max(i - 1, 0)] # select prior safe point if b < 1 or b > 1024: # b outside of safe range b = batch_size LOGGER.warning(f"{prefix}WARNING ⚠️ CUDA anomaly detected, recommend restart environment and retry command.") fraction = (np.polyval(p, b) + r + a) / t # actual fraction predicted LOGGER.info(f"{prefix}Using batch-size {b} for {d} {t * fraction:.2f}G/{t:.2f}G ({fraction * 100:.0f}%) ✅") return b ================================================ FILE: utils/aws/__init__.py ================================================ # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license ================================================ FILE: utils/aws/mime.sh ================================================ # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license # AWS EC2 instance startup 'MIME' script https://aws.amazon.com/premiumsupport/knowledge-center/execute-user-data-ec2/ # This script will run on every instance restart, not only on first start # --- DO NOT COPY ABOVE COMMENTS WHEN PASTING INTO USERDATA --- Content-Type: multipart/mixed boundary="//" MIME-Version: 1.0 --// Content-Type: text/cloud-config charset="us-ascii" MIME-Version: 1.0 Content-Transfer-Encoding: 7bit Content-Disposition: attachment filename="cloud-config.txt" #cloud-config cloud_final_modules: - [scripts-user, always] --// Content-Type: text/x-shellscript charset="us-ascii" MIME-Version: 1.0 Content-Transfer-Encoding: 7bit Content-Disposition: attachment filename="userdata.txt" #!/bin/bash # --- paste contents of userdata.sh here --- --// ================================================ FILE: utils/aws/resume.py ================================================ # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license # Resume all interrupted trainings in yolov5/ dir including DDP trainings # Usage: $ python utils/aws/resume.py import os import sys from pathlib import Path import torch import yaml from ultralytics.utils.patches import torch_load FILE = Path(__file__).resolve() ROOT = FILE.parents[2] # YOLOv3 root directory if str(ROOT) not in sys.path: sys.path.append(str(ROOT)) # add ROOT to PATH port = 0 # --master_port path = Path("").resolve() for last in path.rglob("*/**/last.pt"): ckpt = torch_load(last) if ckpt["optimizer"] is None: continue # Load opt.yaml with open(last.parent.parent / "opt.yaml", errors="ignore") as f: opt = yaml.safe_load(f) # Get device count d = opt["device"].split(",") # devices nd = len(d) # number of devices ddp = nd > 1 or (nd == 0 and torch.cuda.device_count() > 1) # distributed data parallel if ddp: # multi-GPU port += 1 cmd = f"python -m torch.distributed.run --nproc_per_node {nd} --master_port {port} train.py --resume {last}" else: # single-GPU cmd = f"python train.py --resume {last}" cmd += " > /dev/null 2>&1 &" # redirect output to dev/null and run in daemon thread print(cmd) os.system(cmd) ================================================ FILE: utils/aws/userdata.sh ================================================ #!/bin/bash # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license # AWS EC2 instance startup script https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/user-data.html # This script will run only once on first instance start (for a re-start script see mime.sh) # /home/ubuntu (ubuntu) or /home/ec2-user (amazon-linux) is working dir # Use >300 GB SSD cd home/ubuntu if [ ! -d yolov5 ]; then echo "Running first-time script." # install dependencies, download COCO, pull Docker git clone https://github.com/ultralytics/yolov5 -b master && sudo chmod -R 777 yolov5 cd yolov5 bash data/scripts/get_coco.sh && echo "COCO done." & sudo docker pull ultralytics/yolov5:latest && echo "Docker done." & python -m pip install --upgrade pip && pip install -r requirements.txt && python detect.py && echo "Requirements done." & wait && echo "All tasks done." # finish background tasks else echo "Running re-start script." # resume interrupted runs i=0 list=$(sudo docker ps -qa) # container list i.e. $'one\ntwo\nthree\nfour' while IFS= read -r id; do ((i++)) echo "restarting container $i: $id" sudo docker start $id # sudo docker exec -it $id python train.py --resume # single-GPU sudo docker exec -d $id python utils/aws/resume.py # multi-scenario done <<< "$list" fi ================================================ FILE: utils/callbacks.py ================================================ # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license """Callback utils.""" import threading class Callbacks: """Handles all registered callbacks for YOLOv3 Hooks.""" def __init__(self): """Initializes a Callbacks object to manage YOLOv3 training hooks with various event triggers.""" self._callbacks = { "on_pretrain_routine_start": [], "on_pretrain_routine_end": [], "on_train_start": [], "on_train_epoch_start": [], "on_train_batch_start": [], "optimizer_step": [], "on_before_zero_grad": [], "on_train_batch_end": [], "on_train_epoch_end": [], "on_val_start": [], "on_val_batch_start": [], "on_val_image_end": [], "on_val_batch_end": [], "on_val_end": [], "on_fit_epoch_end": [], # fit = train + val "on_model_save": [], "on_train_end": [], "on_params_update": [], "teardown": [], } self.stop_training = False # set True to interrupt training def register_action(self, hook, name="", callback=None): """Register a new action to a callback hook. Args: hook: The callback hook name to register the action to name: The name of the action for later reference callback: The callback to fire """ assert hook in self._callbacks, f"hook '{hook}' not found in callbacks {self._callbacks}" assert callable(callback), f"callback '{callback}' is not callable" self._callbacks[hook].append({"name": name, "callback": callback}) def get_registered_actions(self, hook=None): """" Returns all the registered actions by callback hook. Args: hook: The name of the hook to check, defaults to all """ return self._callbacks[hook] if hook else self._callbacks def run(self, hook, *args, thread=False, **kwargs): """Loop through the registered actions and fire all callbacks on main thread. Args: hook: The name of the hook to check, defaults to all args: Arguments to receive from YOLOv3 thread: (boolean) Run callbacks in daemon thread kwargs: Keyword Arguments to receive from YOLOv3 """ assert hook in self._callbacks, f"hook '{hook}' not found in callbacks {self._callbacks}" for logger in self._callbacks[hook]: if thread: threading.Thread(target=logger["callback"], args=args, kwargs=kwargs, daemon=True).start() else: logger["callback"](*args, **kwargs) ================================================ FILE: utils/dataloaders.py ================================================ # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license """Dataloaders and dataset utils.""" import contextlib import glob import hashlib import json import math import os import random import shutil import time from itertools import repeat from multiprocessing.pool import Pool, ThreadPool from pathlib import Path from threading import Thread from urllib.parse import urlparse import numpy as np import psutil import torch import torch.nn.functional as F import torchvision import yaml from PIL import ExifTags, Image, ImageOps from torch.utils.data import DataLoader, Dataset, dataloader, distributed from tqdm import tqdm from utils.augmentations import ( Albumentations, augment_hsv, classify_albumentations, classify_transforms, copy_paste, letterbox, mixup, random_perspective, ) from utils.general import ( DATASETS_DIR, LOGGER, NUM_THREADS, TQDM_BAR_FORMAT, check_dataset, check_requirements, check_yaml, clean_str, cv2, is_colab, is_kaggle, segments2boxes, unzip_file, xyn2xy, xywh2xyxy, xywhn2xyxy, xyxy2xywhn, ) from utils.torch_utils import torch_distributed_zero_first # Parameters HELP_URL = "See https://docs.ultralytics.com/yolov5/tutorials/train_custom_data" IMG_FORMATS = "bmp", "dng", "jpeg", "jpg", "mpo", "png", "tif", "tiff", "webp", "pfm" # include image suffixes VID_FORMATS = "asf", "avi", "gif", "m4v", "mkv", "mov", "mp4", "mpeg", "mpg", "ts", "wmv" # include video suffixes LOCAL_RANK = int(os.getenv("LOCAL_RANK", -1)) # https://pytorch.org/docs/stable/elastic/run.html RANK = int(os.getenv("RANK", -1)) PIN_MEMORY = str(os.getenv("PIN_MEMORY", True)).lower() == "true" # global pin_memory for dataloaders # Get orientation exif tag for orientation in ExifTags.TAGS.keys(): if ExifTags.TAGS[orientation] == "Orientation": break def get_hash(paths): """Calculates a SHA256 hash for a list of file or directory paths, combining their total size and path strings.""" size = sum(os.path.getsize(p) for p in paths if os.path.exists(p)) # sizes h = hashlib.sha256(str(size).encode()) # hash sizes h.update("".join(paths).encode()) # hash paths return h.hexdigest() # return hash def exif_size(img): """Returns corrected image size (width, height) considering EXIF rotation metadata.""" s = img.size # (width, height) with contextlib.suppress(Exception): rotation = dict(img._getexif().items())[orientation] if rotation in [6, 8]: # rotation 270 or 90 s = (s[1], s[0]) return s def exif_transpose(image): """ Transpose a PIL image accordingly if it has an EXIF Orientation tag. Inplace version of https://github.com/python-pillow/Pillow/blob/master/src/PIL/ImageOps.py exif_transpose(). :param image: The image to transpose. :return: An image. """ exif = image.getexif() orientation = exif.get(0x0112, 1) # default 1 if orientation > 1: method = { 2: Image.FLIP_LEFT_RIGHT, 3: Image.ROTATE_180, 4: Image.FLIP_TOP_BOTTOM, 5: Image.TRANSPOSE, 6: Image.ROTATE_270, 7: Image.TRANSVERSE, 8: Image.ROTATE_90, }.get(orientation) if method is not None: image = image.transpose(method) del exif[0x0112] image.info["exif"] = exif.tobytes() return image def seed_worker(worker_id): """Sets the seed for a DataLoader worker to ensure reproducibility.""" worker_seed = torch.initial_seed() % 2**32 np.random.seed(worker_seed) random.seed(worker_seed) def create_dataloader( path, imgsz, batch_size, stride, single_cls=False, hyp=None, augment=False, cache=False, pad=0.0, rect=False, rank=-1, workers=8, image_weights=False, quad=False, prefix="", shuffle=False, seed=0, ): """Creates a DataLoader for training, with options for augmentation, caching, and parallelization.""" if rect and shuffle: LOGGER.warning("WARNING ⚠️ --rect is incompatible with DataLoader shuffle, setting shuffle=False") shuffle = False with torch_distributed_zero_first(rank): # init dataset *.cache only once if DDP dataset = LoadImagesAndLabels( path, imgsz, batch_size, augment=augment, # augmentation hyp=hyp, # hyperparameters rect=rect, # rectangular batches cache_images=cache, single_cls=single_cls, stride=int(stride), pad=pad, image_weights=image_weights, prefix=prefix, ) batch_size = min(batch_size, len(dataset)) nd = torch.cuda.device_count() # number of CUDA devices nw = min([os.cpu_count() // max(nd, 1), batch_size if batch_size > 1 else 0, workers]) # number of workers sampler = None if rank == -1 else distributed.DistributedSampler(dataset, shuffle=shuffle) loader = DataLoader if image_weights else InfiniteDataLoader # only DataLoader allows for attribute updates generator = torch.Generator() generator.manual_seed(6148914691236517205 + seed + RANK) return loader( dataset, batch_size=batch_size, shuffle=shuffle and sampler is None, num_workers=nw, sampler=sampler, pin_memory=PIN_MEMORY, collate_fn=LoadImagesAndLabels.collate_fn4 if quad else LoadImagesAndLabels.collate_fn, worker_init_fn=seed_worker, generator=generator, ), dataset class InfiniteDataLoader(dataloader.DataLoader): """Dataloader that reuses workers. Uses same syntax as vanilla DataLoader """ def __init__(self, *args, **kwargs): """Initializes an InfiniteDataLoader that reuses workers with standard DataLoader syntax and a repeating sampler. """ super().__init__(*args, **kwargs) object.__setattr__(self, "batch_sampler", _RepeatSampler(self.batch_sampler)) self.iterator = super().__iter__() def __len__(self): """Returns the length of the batch sampler's sampler.""" return len(self.batch_sampler.sampler) def __iter__(self): """Iterates over the dataset indefinitely, yielding batches from the batch_sampler.""" for _ in range(len(self)): yield next(self.iterator) class _RepeatSampler: """Sampler that repeats forever. Args: sampler (Sampler) """ def __init__(self, sampler): """Initializes an infinitely repeating sampler with a provided `sampler` object.""" self.sampler = sampler def __iter__(self): """Provides an iterator that infinitely repeats over a given `sampler` object.""" while True: yield from iter(self.sampler) class LoadScreenshots: """Loads screenshots as input data for YOLOv3, capturing screen regions specified by coordinates and dimensions.""" def __init__(self, source, img_size=640, stride=32, auto=True, transforms=None): """Initializes a screenshot dataloader for YOLOv3; source format: [screen_number left top width height], default img_size=640, stride=32. """ check_requirements("mss") import mss source, *params = source.split() self.screen, left, top, width, height = 0, None, None, None, None # default to full screen 0 if len(params) == 1: self.screen = int(params[0]) elif len(params) == 4: left, top, width, height = (int(x) for x in params) elif len(params) == 5: self.screen, left, top, width, height = (int(x) for x in params) self.img_size = img_size self.stride = stride self.transforms = transforms self.auto = auto self.mode = "stream" self.frame = 0 self.sct = mss.mss() # Parse monitor shape monitor = self.sct.monitors[self.screen] self.top = monitor["top"] if top is None else (monitor["top"] + top) self.left = monitor["left"] if left is None else (monitor["left"] + left) self.width = width or monitor["width"] self.height = height or monitor["height"] self.monitor = {"left": self.left, "top": self.top, "width": self.width, "height": self.height} def __iter__(self): """Iterates over itself, effectively making the object its own iterator.""" return self def __next__(self): """Captures and returns the next screen image as a NumPy array in BGR format, excluding alpha channel.""" im0 = np.array(self.sct.grab(self.monitor))[:, :, :3] # [:, :, :3] BGRA to BGR s = f"screen {self.screen} (LTWH): {self.left},{self.top},{self.width},{self.height}: " if self.transforms: im = self.transforms(im0) # transforms else: im = letterbox(im0, self.img_size, stride=self.stride, auto=self.auto)[0] # padded resize im = im.transpose((2, 0, 1))[::-1] # HWC to CHW, BGR to RGB im = np.ascontiguousarray(im) # contiguous self.frame += 1 return str(self.screen), im, im0, None, s # screen, img, original img, im0s, s class LoadImages: """Loads images and videos for YOLOv3 from various sources, including directories and '*.txt' path lists.""" def __init__(self, path, img_size=640, stride=32, auto=True, transforms=None, vid_stride=1): """Initializes the data loader for YOLOv3, supporting image, video, directory, and '*.txt' path lists with customizable image sizing. """ if isinstance(path, str) and Path(path).suffix == ".txt": # *.txt file with img/vid/dir on each line path = Path(path).read_text().rsplit() files = [] for p in sorted(path) if isinstance(path, (list, tuple)) else [path]: p = str(Path(p).resolve()) if "*" in p: files.extend(sorted(glob.glob(p, recursive=True))) # glob elif os.path.isdir(p): files.extend(sorted(glob.glob(os.path.join(p, "*.*")))) # dir elif os.path.isfile(p): files.append(p) # files else: raise FileNotFoundError(f"{p} does not exist") images = [x for x in files if x.split(".")[-1].lower() in IMG_FORMATS] videos = [x for x in files if x.split(".")[-1].lower() in VID_FORMATS] ni, nv = len(images), len(videos) self.img_size = img_size self.stride = stride self.files = images + videos self.nf = ni + nv # number of files self.video_flag = [False] * ni + [True] * nv self.mode = "image" self.auto = auto self.transforms = transforms # optional self.vid_stride = vid_stride # video frame-rate stride if any(videos): self._new_video(videos[0]) # new video else: self.cap = None assert self.nf > 0, ( f"No images or videos found in {p}. Supported formats are:\nimages: {IMG_FORMATS}\nvideos: {VID_FORMATS}" ) def __iter__(self): """Initializes the iterator by resetting count to zero and returning the iterator instance itself.""" self.count = 0 return self def __next__(self): """Advances to the next file in the dataset, raising StopIteration when all files are processed.""" if self.count == self.nf: raise StopIteration path = self.files[self.count] if self.video_flag[self.count]: # Read video self.mode = "video" for _ in range(self.vid_stride): self.cap.grab() ret_val, im0 = self.cap.retrieve() while not ret_val: self.count += 1 self.cap.release() if self.count == self.nf: # last video raise StopIteration path = self.files[self.count] self._new_video(path) ret_val, im0 = self.cap.read() self.frame += 1 # im0 = self._cv2_rotate(im0) # for use if cv2 autorotation is False s = f"video {self.count + 1}/{self.nf} ({self.frame}/{self.frames}) {path}: " else: # Read image self.count += 1 im0 = cv2.imread(path) # BGR assert im0 is not None, f"Image Not Found {path}" s = f"image {self.count}/{self.nf} {path}: " if self.transforms: im = self.transforms(im0) # transforms else: im = letterbox(im0, self.img_size, stride=self.stride, auto=self.auto)[0] # padded resize im = im.transpose((2, 0, 1))[::-1] # HWC to CHW, BGR to RGB im = np.ascontiguousarray(im) # contiguous return path, im, im0, self.cap, s def _new_video(self, path): """Initializes a video capture object with frame counting and orientation from a given path.""" self.frame = 0 self.cap = cv2.VideoCapture(path) self.frames = int(self.cap.get(cv2.CAP_PROP_FRAME_COUNT) / self.vid_stride) self.orientation = int(self.cap.get(cv2.CAP_PROP_ORIENTATION_META)) # rotation degrees # self.cap.set(cv2.CAP_PROP_ORIENTATION_AUTO, 0) # disable https://github.com/ultralytics/yolov5/issues/8493 def _cv2_rotate(self, im): """Rotates a cv2 image based on the video's metadata orientation; returns the rotated image.""" if self.orientation == 0: return cv2.rotate(im, cv2.ROTATE_90_CLOCKWISE) elif self.orientation == 180: return cv2.rotate(im, cv2.ROTATE_90_COUNTERCLOCKWISE) elif self.orientation == 90: return cv2.rotate(im, cv2.ROTATE_180) return im def __len__(self): """Returns the number of files in the dataset.""" return self.nf # number of files class LoadStreams: """Loads video streams for YOLOv3 inference, supporting multiple sources and customizable frame sizes.""" def __init__(self, sources="file.streams", img_size=640, stride=32, auto=True, transforms=None, vid_stride=1): """Initializes a stream loader for YOLOv3, handling video sources or files with customizable frame sizes and intervals. """ torch.backends.cudnn.benchmark = True # faster for fixed-size inference self.mode = "stream" self.img_size = img_size self.stride = stride self.vid_stride = vid_stride # video frame-rate stride sources = Path(sources).read_text().rsplit() if os.path.isfile(sources) else [sources] n = len(sources) self.sources = [clean_str(x) for x in sources] # clean source names for later self.imgs, self.fps, self.frames, self.threads = [None] * n, [0] * n, [0] * n, [None] * n for i, s in enumerate(sources): # index, source # Start thread to read frames from video stream st = f"{i + 1}/{n}: {s}... " if urlparse(s).hostname in ("www.youtube.com", "youtube.com", "youtu.be"): # if source is YouTube video # YouTube format i.e. 'https://www.youtube.com/watch?v=Zgi9g1ksQHc' or 'https://youtu.be/LNwODJXcvt4' check_requirements(("pafy", "youtube_dl==2020.12.2")) import pafy s = pafy.new(s).getbest(preftype="mp4").url # YouTube URL s = eval(s) if s.isnumeric() else s # i.e. s = '0' local webcam if s == 0: assert not is_colab(), "--source 0 webcam unsupported on Colab. Rerun command in a local environment." assert not is_kaggle(), "--source 0 webcam unsupported on Kaggle. Rerun command in a local environment." cap = cv2.VideoCapture(s) assert cap.isOpened(), f"{st}Failed to open {s}" w = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)) h = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) fps = cap.get(cv2.CAP_PROP_FPS) # warning: may return 0 or nan self.frames[i] = max(int(cap.get(cv2.CAP_PROP_FRAME_COUNT)), 0) or float("inf") # infinite stream fallback self.fps[i] = max((fps if math.isfinite(fps) else 0) % 100, 0) or 30 # 30 FPS fallback _, self.imgs[i] = cap.read() # guarantee first frame self.threads[i] = Thread(target=self.update, args=([i, cap, s]), daemon=True) LOGGER.info(f"{st} Success ({self.frames[i]} frames {w}x{h} at {self.fps[i]:.2f} FPS)") self.threads[i].start() LOGGER.info("") # newline # check for common shapes s = np.stack([letterbox(x, img_size, stride=stride, auto=auto)[0].shape for x in self.imgs]) self.rect = np.unique(s, axis=0).shape[0] == 1 # rect inference if all shapes equal self.auto = auto and self.rect self.transforms = transforms # optional if not self.rect: LOGGER.warning("WARNING ⚠️ Stream shapes differ. For optimal performance supply similarly-shaped streams.") def update(self, i, cap, stream): """Reads frames from stream `i` into `self.imgs` at intervals defined by `self.vid_stride`, handling reconnection if needed. """ n, f = 0, self.frames[i] # frame number, frame array while cap.isOpened() and n < f: n += 1 cap.grab() # .read() = .grab() followed by .retrieve() if n % self.vid_stride == 0: success, im = cap.retrieve() if success: self.imgs[i] = im else: LOGGER.warning("WARNING ⚠️ Video stream unresponsive, please check your IP camera connection.") self.imgs[i] = np.zeros_like(self.imgs[i]) cap.open(stream) # re-open stream if signal was lost time.sleep(0.0) # wait time def __iter__(self): """Resets and returns an iterator of the current object for iterating through video frames or images.""" self.count = -1 return self def __next__(self): """Iterates video frames or images; halts if all threads are dead or 'q' is pressed.""" self.count += 1 if not all(x.is_alive() for x in self.threads) or cv2.waitKey(1) == ord("q"): # q to quit cv2.destroyAllWindows() raise StopIteration im0 = self.imgs.copy() if self.transforms: im = np.stack([self.transforms(x) for x in im0]) # transforms else: im = np.stack([letterbox(x, self.img_size, stride=self.stride, auto=self.auto)[0] for x in im0]) # resize im = im[..., ::-1].transpose((0, 3, 1, 2)) # BGR to RGB, BHWC to BCHW im = np.ascontiguousarray(im) # contiguous return self.sources, im, im0, None, "" def __len__(self): """Returns the number of sources in the dataset, supporting up to 1E12 frames across streams and scenarios.""" return len(self.sources) # 1E12 frames = 32 streams at 30 FPS for 30 years def img2label_paths(img_paths): """Converts image paths to corresponding label paths by replacing `/images/` with `/labels/` and `.jpg` with `.txt`. """ sa, sb = f"{os.sep}images{os.sep}", f"{os.sep}labels{os.sep}" # /images/, /labels/ substrings return [sb.join(x.rsplit(sa, 1)).rsplit(".", 1)[0] + ".txt" for x in img_paths] class LoadImagesAndLabels(Dataset): """Loads images and labels for YOLOv3 training and validation with support for augmentations and caching.""" cache_version = 0.6 # dataset labels *.cache version rand_interp_methods = [cv2.INTER_NEAREST, cv2.INTER_LINEAR, cv2.INTER_CUBIC, cv2.INTER_AREA, cv2.INTER_LANCZOS4] def __init__( self, path, img_size=640, batch_size=16, augment=False, hyp=None, rect=False, image_weights=False, cache_images=False, single_cls=False, stride=32, pad=0.0, min_items=0, prefix="", ): """Initializes a dataset with images and labels for YOLOv3 training and validation.""" self.img_size = img_size self.augment = augment self.hyp = hyp self.image_weights = image_weights self.rect = False if image_weights else rect self.mosaic = self.augment and not self.rect # load 4 images at a time into a mosaic (only during training) self.mosaic_border = [-img_size // 2, -img_size // 2] self.stride = stride self.path = path self.albumentations = Albumentations(size=img_size) if augment else None try: f = [] # image files for p in path if isinstance(path, list) else [path]: p = Path(p) # os-agnostic if p.is_dir(): # dir f += glob.glob(str(p / "**" / "*.*"), recursive=True) # f = list(p.rglob('*.*')) # pathlib elif p.is_file(): # file with open(p) as t: t = t.read().strip().splitlines() parent = str(p.parent) + os.sep f += [x.replace("./", parent, 1) if x.startswith("./") else x for x in t] # to global path # f += [p.parent / x.lstrip(os.sep) for x in t] # to global path (pathlib) else: raise FileNotFoundError(f"{prefix}{p} does not exist") self.im_files = sorted(x.replace("/", os.sep) for x in f if x.split(".")[-1].lower() in IMG_FORMATS) # self.img_files = sorted([x for x in f if x.suffix[1:].lower() in IMG_FORMATS]) # pathlib assert self.im_files, f"{prefix}No images found" except Exception as e: raise Exception(f"{prefix}Error loading data from {path}: {e}\n{HELP_URL}") from e # Check cache self.label_files = img2label_paths(self.im_files) # labels cache_path = (p if p.is_file() else Path(self.label_files[0]).parent).with_suffix(".cache") try: cache, exists = np.load(cache_path, allow_pickle=True).item(), True # load dict assert cache["version"] == self.cache_version # matches current version assert cache["hash"] == get_hash(self.label_files + self.im_files) # identical hash except Exception: cache, exists = self.cache_labels(cache_path, prefix), False # run cache ops # Display cache nf, nm, ne, nc, n = cache.pop("results") # found, missing, empty, corrupt, total if exists and LOCAL_RANK in {-1, 0}: d = f"Scanning {cache_path}... {nf} images, {nm + ne} backgrounds, {nc} corrupt" tqdm(None, desc=prefix + d, total=n, initial=n, bar_format=TQDM_BAR_FORMAT) # display cache results if cache["msgs"]: LOGGER.info("\n".join(cache["msgs"])) # display warnings assert nf > 0 or not augment, f"{prefix}No labels found in {cache_path}, can not start training. {HELP_URL}" # Read cache [cache.pop(k) for k in ("hash", "version", "msgs")] # remove items labels, shapes, self.segments = zip(*cache.values()) nl = len(np.concatenate(labels, 0)) # number of labels assert nl > 0 or not augment, f"{prefix}All labels empty in {cache_path}, can not start training. {HELP_URL}" self.labels = list(labels) self.shapes = np.array(shapes) self.im_files = list(cache.keys()) # update self.label_files = img2label_paths(cache.keys()) # update # Filter images if min_items: include = np.array([len(x) >= min_items for x in self.labels]).nonzero()[0].astype(int) LOGGER.info(f"{prefix}{n - len(include)}/{n} images filtered from dataset") self.im_files = [self.im_files[i] for i in include] self.label_files = [self.label_files[i] for i in include] self.labels = [self.labels[i] for i in include] self.segments = [self.segments[i] for i in include] self.shapes = self.shapes[include] # wh # Create indices n = len(self.shapes) # number of images bi = np.floor(np.arange(n) / batch_size).astype(int) # batch index nb = bi[-1] + 1 # number of batches self.batch = bi # batch index of image self.n = n self.indices = range(n) # Update labels include_class = [] # filter labels to include only these classes (optional) self.segments = list(self.segments) include_class_array = np.array(include_class).reshape(1, -1) for i, (label, segment) in enumerate(zip(self.labels, self.segments)): if include_class: j = (label[:, 0:1] == include_class_array).any(1) self.labels[i] = label[j] if segment: self.segments[i] = [segment[idx] for idx, elem in enumerate(j) if elem] if single_cls: # single-class training, merge all classes into 0 self.labels[i][:, 0] = 0 # Rectangular Training if self.rect: # Sort by aspect ratio s = self.shapes # wh ar = s[:, 1] / s[:, 0] # aspect ratio irect = ar.argsort() self.im_files = [self.im_files[i] for i in irect] self.label_files = [self.label_files[i] for i in irect] self.labels = [self.labels[i] for i in irect] self.segments = [self.segments[i] for i in irect] self.shapes = s[irect] # wh ar = ar[irect] # Set training image shapes shapes = [[1, 1]] * nb for i in range(nb): ari = ar[bi == i] mini, maxi = ari.min(), ari.max() if maxi < 1: shapes[i] = [maxi, 1] elif mini > 1: shapes[i] = [1, 1 / mini] self.batch_shapes = np.ceil(np.array(shapes) * img_size / stride + pad).astype(int) * stride # Cache images into RAM/disk for faster training if cache_images == "ram" and not self.check_cache_ram(prefix=prefix): cache_images = False self.ims = [None] * n self.npy_files = [Path(f).with_suffix(".npy") for f in self.im_files] if cache_images: b, gb = 0, 1 << 30 # bytes of cached images, bytes per gigabytes self.im_hw0, self.im_hw = [None] * n, [None] * n fcn = self.cache_images_to_disk if cache_images == "disk" else self.load_image results = ThreadPool(NUM_THREADS).imap(fcn, range(n)) pbar = tqdm(enumerate(results), total=n, bar_format=TQDM_BAR_FORMAT, disable=LOCAL_RANK > 0) for i, x in pbar: if cache_images == "disk": b += self.npy_files[i].stat().st_size else: # 'ram' self.ims[i], self.im_hw0[i], self.im_hw[i] = x # im, hw_orig, hw_resized = load_image(self, i) b += self.ims[i].nbytes pbar.desc = f"{prefix}Caching images ({b / gb:.1f}GB {cache_images})" pbar.close() def check_cache_ram(self, safety_margin=0.1, prefix=""): """Evaluates if there's enough RAM to cache dataset images, considering a safety margin.""" b, gb = 0, 1 << 30 # bytes of cached images, bytes per gigabytes n = min(self.n, 30) # extrapolate from 30 random images for _ in range(n): im = cv2.imread(random.choice(self.im_files)) # sample image ratio = self.img_size / max(im.shape[0], im.shape[1]) # max(h, w) # ratio b += im.nbytes * ratio**2 mem_required = b * self.n / n # GB required to cache dataset into RAM mem = psutil.virtual_memory() cache = mem_required * (1 + safety_margin) < mem.available # to cache or not to cache, that is the question if not cache: LOGGER.info( f"{prefix}{mem_required / gb:.1f}GB RAM required, " f"{mem.available / gb:.1f}/{mem.total / gb:.1f}GB available, " f"{'caching images ✅' if cache else 'not caching images ⚠️'}" ) return cache def cache_labels(self, path=Path("./labels.cache"), prefix=""): """Caches dataset labels, checks image existence and readability, and records image shapes and segments.""" x = {} # dict nm, nf, ne, nc, msgs = 0, 0, 0, 0, [] # number missing, found, empty, corrupt, messages desc = f"{prefix}Scanning {path.parent / path.stem}..." with Pool(NUM_THREADS) as pool: pbar = tqdm( pool.imap(verify_image_label, zip(self.im_files, self.label_files, repeat(prefix))), desc=desc, total=len(self.im_files), bar_format=TQDM_BAR_FORMAT, ) for im_file, lb, shape, segments, nm_f, nf_f, ne_f, nc_f, msg in pbar: nm += nm_f nf += nf_f ne += ne_f nc += nc_f if im_file: x[im_file] = [lb, shape, segments] if msg: msgs.append(msg) pbar.desc = f"{desc} {nf} images, {nm + ne} backgrounds, {nc} corrupt" pbar.close() if msgs: LOGGER.info("\n".join(msgs)) if nf == 0: LOGGER.warning(f"{prefix}WARNING ⚠️ No labels found in {path}. {HELP_URL}") x["hash"] = get_hash(self.label_files + self.im_files) x["results"] = nf, nm, ne, nc, len(self.im_files) x["msgs"] = msgs # warnings x["version"] = self.cache_version # cache version try: np.save(path, x) # save cache for next time path.with_suffix(".cache.npy").rename(path) # remove .npy suffix LOGGER.info(f"{prefix}New cache created: {path}") except Exception as e: LOGGER.warning(f"{prefix}WARNING ⚠️ Cache directory {path.parent} is not writeable: {e}") # not writeable return x def __len__(self): """Returns the number of image files in the dataset.""" return len(self.im_files) # def __iter__(self): # self.count = -1 # print('ran dataset iter') # #self.shuffled_vector = np.random.permutation(self.nF) if self.augment else np.arange(self.nF) # return self def __getitem__(self, index): """Fetches dataset item at `index` after applying indexing via `self.indices`, supporting linear/shuffled/image_weights modes. """ index = self.indices[index] # linear, shuffled, or image_weights hyp = self.hyp if mosaic := self.mosaic and random.random() < hyp["mosaic"]: # Load mosaic img, labels = self.load_mosaic(index) shapes = None # MixUp augmentation if random.random() < hyp["mixup"]: img, labels = mixup(img, labels, *self.load_mosaic(random.randint(0, self.n - 1))) else: # Load image img, (h0, w0), (h, w) = self.load_image(index) # Letterbox shape = self.batch_shapes[self.batch[index]] if self.rect else self.img_size # final letterboxed shape img, ratio, pad = letterbox(img, shape, auto=False, scaleup=self.augment) shapes = (h0, w0), ((h / h0, w / w0), pad) # for COCO mAP rescaling labels = self.labels[index].copy() if labels.size: # normalized xywh to pixel xyxy format labels[:, 1:] = xywhn2xyxy(labels[:, 1:], ratio[0] * w, ratio[1] * h, padw=pad[0], padh=pad[1]) if self.augment: img, labels = random_perspective( img, labels, degrees=hyp["degrees"], translate=hyp["translate"], scale=hyp["scale"], shear=hyp["shear"], perspective=hyp["perspective"], ) nl = len(labels) # number of labels if nl: labels[:, 1:5] = xyxy2xywhn(labels[:, 1:5], w=img.shape[1], h=img.shape[0], clip=True, eps=1e-3) if self.augment: # Albumentations img, labels = self.albumentations(img, labels) nl = len(labels) # update after albumentations # HSV color-space augment_hsv(img, hgain=hyp["hsv_h"], sgain=hyp["hsv_s"], vgain=hyp["hsv_v"]) # Flip up-down if random.random() < hyp["flipud"]: img = np.flipud(img) if nl: labels[:, 2] = 1 - labels[:, 2] # Flip left-right if random.random() < hyp["fliplr"]: img = np.fliplr(img) if nl: labels[:, 1] = 1 - labels[:, 1] # Cutouts # labels = cutout(img, labels, p=0.5) # nl = len(labels) # update after cutout labels_out = torch.zeros((nl, 6)) if nl: labels_out[:, 1:] = torch.from_numpy(labels) # Convert img = img.transpose((2, 0, 1))[::-1] # HWC to CHW, BGR to RGB img = np.ascontiguousarray(img) return torch.from_numpy(img), labels_out, self.im_files[index], shapes def load_image(self, i): """Loads a single image by index, returning the image, its original dimensions, and resized dimensions.""" im, f, fn = ( self.ims[i], self.im_files[i], self.npy_files[i], ) if im is None: # not cached in RAM if fn.exists(): # load npy im = np.load(fn) else: # read image im = cv2.imread(f) # BGR assert im is not None, f"Image Not Found {f}" h0, w0 = im.shape[:2] # orig hw r = self.img_size / max(h0, w0) # ratio if r != 1: # if sizes are not equal interp = cv2.INTER_LINEAR if (self.augment or r > 1) else cv2.INTER_AREA im = cv2.resize(im, (math.ceil(w0 * r), math.ceil(h0 * r)), interpolation=interp) return im, (h0, w0), im.shape[:2] # im, hw_original, hw_resized return self.ims[i], self.im_hw0[i], self.im_hw[i] # im, hw_original, hw_resized def cache_images_to_disk(self, i): """Saves an image to disk as an *.npy file for faster future loading.""" f = self.npy_files[i] if not f.exists(): np.save(f.as_posix(), cv2.imread(self.im_files[i])) def load_mosaic(self, index): """Loads 4 images into a mosaic for YOLOv3 training, enhancing detection capabilities through data augmentation. """ labels4, segments4 = [], [] s = self.img_size yc, xc = (int(random.uniform(-x, 2 * s + x)) for x in self.mosaic_border) # mosaic center x, y indices = [index] + random.choices(self.indices, k=3) # 3 additional image indices random.shuffle(indices) for i, index in enumerate(indices): # Load image img, _, (h, w) = self.load_image(index) # place img in img4 if i == 0: # top left img4 = np.full((s * 2, s * 2, img.shape[2]), 114, dtype=np.uint8) # base image with 4 tiles x1a, y1a, x2a, y2a = max(xc - w, 0), max(yc - h, 0), xc, yc # xmin, ymin, xmax, ymax (large image) x1b, y1b, x2b, y2b = w - (x2a - x1a), h - (y2a - y1a), w, h # xmin, ymin, xmax, ymax (small image) elif i == 1: # top right x1a, y1a, x2a, y2a = xc, max(yc - h, 0), min(xc + w, s * 2), yc x1b, y1b, x2b, y2b = 0, h - (y2a - y1a), min(w, x2a - x1a), h elif i == 2: # bottom left x1a, y1a, x2a, y2a = max(xc - w, 0), yc, xc, min(s * 2, yc + h) x1b, y1b, x2b, y2b = w - (x2a - x1a), 0, w, min(y2a - y1a, h) elif i == 3: # bottom right x1a, y1a, x2a, y2a = xc, yc, min(xc + w, s * 2), min(s * 2, yc + h) x1b, y1b, x2b, y2b = 0, 0, min(w, x2a - x1a), min(y2a - y1a, h) img4[y1a:y2a, x1a:x2a] = img[y1b:y2b, x1b:x2b] # img4[ymin:ymax, xmin:xmax] padw = x1a - x1b padh = y1a - y1b # Labels labels, segments = self.labels[index].copy(), self.segments[index].copy() if labels.size: labels[:, 1:] = xywhn2xyxy(labels[:, 1:], w, h, padw, padh) # normalized xywh to pixel xyxy format segments = [xyn2xy(x, w, h, padw, padh) for x in segments] labels4.append(labels) segments4.extend(segments) # Concat/clip labels labels4 = np.concatenate(labels4, 0) for x in (labels4[:, 1:], *segments4): np.clip(x, 0, 2 * s, out=x) # clip when using random_perspective() # img4, labels4 = replicate(img4, labels4) # replicate # Augment img4, labels4, segments4 = copy_paste(img4, labels4, segments4, p=self.hyp["copy_paste"]) img4, labels4 = random_perspective( img4, labels4, segments4, degrees=self.hyp["degrees"], translate=self.hyp["translate"], scale=self.hyp["scale"], shear=self.hyp["shear"], perspective=self.hyp["perspective"], border=self.mosaic_border, ) # border to remove return img4, labels4 def load_mosaic9(self, index): """Loads 1 image + 8 random images into a 9-image mosaic for YOLOv3, returning combined image and labels.""" labels9, segments9 = [], [] s = self.img_size indices = [index] + random.choices(self.indices, k=8) # 8 additional image indices random.shuffle(indices) hp, wp = -1, -1 # height, width previous for i, index in enumerate(indices): # Load image img, _, (h, w) = self.load_image(index) # place img in img9 if i == 0: # center img9 = np.full((s * 3, s * 3, img.shape[2]), 114, dtype=np.uint8) # base image with 4 tiles h0, w0 = h, w c = s, s, s + w, s + h # xmin, ymin, xmax, ymax (base) coordinates elif i == 1: # top c = s, s - h, s + w, s elif i == 2: # top right c = s + wp, s - h, s + wp + w, s elif i == 3: # right c = s + w0, s, s + w0 + w, s + h elif i == 4: # bottom right c = s + w0, s + hp, s + w0 + w, s + hp + h elif i == 5: # bottom c = s + w0 - w, s + h0, s + w0, s + h0 + h elif i == 6: # bottom left c = s + w0 - wp - w, s + h0, s + w0 - wp, s + h0 + h elif i == 7: # left c = s - w, s + h0 - h, s, s + h0 elif i == 8: # top left c = s - w, s + h0 - hp - h, s, s + h0 - hp padx, pady = c[:2] x1, y1, x2, y2 = (max(x, 0) for x in c) # allocate coords # Labels labels, segments = self.labels[index].copy(), self.segments[index].copy() if labels.size: labels[:, 1:] = xywhn2xyxy(labels[:, 1:], w, h, padx, pady) # normalized xywh to pixel xyxy format segments = [xyn2xy(x, w, h, padx, pady) for x in segments] labels9.append(labels) segments9.extend(segments) # Image img9[y1:y2, x1:x2] = img[y1 - pady :, x1 - padx :] # img9[ymin:ymax, xmin:xmax] hp, wp = h, w # height, width previous # Offset yc, xc = (int(random.uniform(0, s)) for _ in self.mosaic_border) # mosaic center x, y img9 = img9[yc : yc + 2 * s, xc : xc + 2 * s] # Concat/clip labels labels9 = np.concatenate(labels9, 0) labels9[:, [1, 3]] -= xc labels9[:, [2, 4]] -= yc c = np.array([xc, yc]) # centers segments9 = [x - c for x in segments9] for x in (labels9[:, 1:], *segments9): np.clip(x, 0, 2 * s, out=x) # clip when using random_perspective() # img9, labels9 = replicate(img9, labels9) # replicate # Augment img9, labels9, segments9 = copy_paste(img9, labels9, segments9, p=self.hyp["copy_paste"]) img9, labels9 = random_perspective( img9, labels9, segments9, degrees=self.hyp["degrees"], translate=self.hyp["translate"], scale=self.hyp["scale"], shear=self.hyp["shear"], perspective=self.hyp["perspective"], border=self.mosaic_border, ) # border to remove return img9, labels9 @staticmethod def collate_fn(batch): """Collates batch of images, labels, paths, and shapes, indexing labels for target image identification.""" im, label, path, shapes = zip(*batch) # transposed for i, lb in enumerate(label): lb[:, 0] = i # add target image index for build_targets() return torch.stack(im, 0), torch.cat(label, 0), path, shapes @staticmethod def collate_fn4(batch): """Batches images, labels, paths, and shapes by grouping every 4 items for dataset loading.""" im, label, path, shapes = zip(*batch) # transposed n = len(shapes) // 4 im4, label4, path4, shapes4 = [], [], path[:n], shapes[:n] ho = torch.tensor([[0.0, 0, 0, 1, 0, 0]]) wo = torch.tensor([[0.0, 0, 1, 0, 0, 0]]) s = torch.tensor([[1, 1, 0.5, 0.5, 0.5, 0.5]]) # scale for i in range(n): # zidane torch.zeros(16,3,720,1280) # BCHW i *= 4 if random.random() < 0.5: im1 = F.interpolate(im[i].unsqueeze(0).float(), scale_factor=2.0, mode="bilinear", align_corners=False)[ 0 ].type(im[i].type()) lb = label[i] else: im1 = torch.cat((torch.cat((im[i], im[i + 1]), 1), torch.cat((im[i + 2], im[i + 3]), 1)), 2) lb = torch.cat((label[i], label[i + 1] + ho, label[i + 2] + wo, label[i + 3] + ho + wo), 0) * s im4.append(im1) label4.append(lb) for i, lb in enumerate(label4): lb[:, 0] = i # add target image index for build_targets() return torch.stack(im4, 0), torch.cat(label4, 0), path4, shapes4 # Ancillary functions -------------------------------------------------------------------------------------------------- def flatten_recursive(path=DATASETS_DIR / "coco128"): """Flattens a directory recursively by copying all files to a new top-level directory, given an input path.""" new_path = Path(f"{path!s}_flat") if os.path.exists(new_path): shutil.rmtree(new_path) # delete output folder os.makedirs(new_path) # make new output folder for file in tqdm(glob.glob(f"{Path(path)!s}/**/*.*", recursive=True)): shutil.copyfile(file, new_path / Path(file).name) def extract_boxes(path=DATASETS_DIR / "coco128"): # from utils.dataloaders import *; extract_boxes() """Converts detection dataset to classification dataset, creating one directory per class with images cropped to bounding boxes. """ path = Path(path) # images dir shutil.rmtree(path / "classification") if (path / "classification").is_dir() else None # remove existing files = list(path.rglob("*.*")) n = len(files) # number of files for im_file in tqdm(files, total=n): if im_file.suffix[1:] in IMG_FORMATS: # image im = cv2.imread(str(im_file))[..., ::-1] # BGR to RGB h, w = im.shape[:2] # labels lb_file = Path(img2label_paths([str(im_file)])[0]) if Path(lb_file).exists(): with open(lb_file) as f: lb = np.array([x.split() for x in f.read().strip().splitlines()], dtype=np.float32) # labels for j, x in enumerate(lb): c = int(x[0]) # class f = (path / "classifier") / f"{c}" / f"{path.stem}_{im_file.stem}_{j}.jpg" # new filename if not f.parent.is_dir(): f.parent.mkdir(parents=True) b = x[1:] * [w, h, w, h] # box # b[2:] = b[2:].max() # rectangle to square b[2:] = b[2:] * 1.2 + 3 # pad b = xywh2xyxy(b.reshape(-1, 4)).ravel().astype(int) b[[0, 2]] = np.clip(b[[0, 2]], 0, w) # clip boxes outside of image b[[1, 3]] = np.clip(b[[1, 3]], 0, h) assert cv2.imwrite(str(f), im[b[1] : b[3], b[0] : b[2]]), f"box failure in {f}" def autosplit(path=DATASETS_DIR / "coco128/images", weights=(0.9, 0.1, 0.0), annotated_only=False): """Autosplit a dataset into train/val/test splits and save path/autosplit_*.txt files Usage: from utils.dataloaders import *; autosplit(). Args: path: Path to images directory weights: Train, val, test weights (list, tuple) annotated_only: Only use images with an annotated txt file """ path = Path(path) # images dir files = sorted(x for x in path.rglob("*.*") if x.suffix[1:].lower() in IMG_FORMATS) # image files only n = len(files) # number of files random.seed(0) # for reproducibility indices = random.choices([0, 1, 2], weights=weights, k=n) # assign each image to a split txt = ["autosplit_train.txt", "autosplit_val.txt", "autosplit_test.txt"] # 3 txt files for x in txt: if (path.parent / x).exists(): (path.parent / x).unlink() # remove existing print(f"Autosplitting images from {path}" + ", using *.txt labeled images only" * annotated_only) for i, img in tqdm(zip(indices, files), total=n): if not annotated_only or Path(img2label_paths([str(img)])[0]).exists(): # check label with open(path.parent / txt[i], "a") as f: f.write(f"./{img.relative_to(path.parent).as_posix()}" + "\n") # add image to txt file def verify_image_label(args): """Checks and verifies one image-label pair, fixing common issues and reporting anomalies.""" im_file, lb_file, prefix = args nm, nf, ne, nc, msg, segments = 0, 0, 0, 0, "", [] # number (missing, found, empty, corrupt), message, segments try: # verify images im = Image.open(im_file) im.verify() # PIL verify shape = exif_size(im) # image size assert (shape[0] > 9) & (shape[1] > 9), f"image size {shape} <10 pixels" assert im.format.lower() in IMG_FORMATS, f"invalid image format {im.format}" if im.format.lower() in ("jpg", "jpeg"): with open(im_file, "rb") as f: f.seek(-2, 2) if f.read() != b"\xff\xd9": # corrupt JPEG ImageOps.exif_transpose(Image.open(im_file)).save(im_file, "JPEG", subsampling=0, quality=100) msg = f"{prefix}WARNING ⚠️ {im_file}: corrupt JPEG restored and saved" # verify labels if os.path.isfile(lb_file): nf = 1 # label found with open(lb_file) as f: lb = [x.split() for x in f.read().strip().splitlines() if len(x)] if any(len(x) > 6 for x in lb): # is segment classes = np.array([x[0] for x in lb], dtype=np.float32) segments = [np.array(x[1:], dtype=np.float32).reshape(-1, 2) for x in lb] # (cls, xy1...) lb = np.concatenate((classes.reshape(-1, 1), segments2boxes(segments)), 1) # (cls, xywh) lb = np.array(lb, dtype=np.float32) if nl := len(lb): assert lb.shape[1] == 5, f"labels require 5 columns, {lb.shape[1]} columns detected" assert (lb >= 0).all(), f"negative label values {lb[lb < 0]}" assert (lb[:, 1:] <= 1).all(), f"non-normalized or out of bounds coordinates {lb[:, 1:][lb[:, 1:] > 1]}" _, i = np.unique(lb, axis=0, return_index=True) if len(i) < nl: # duplicate row check lb = lb[i] # remove duplicates if segments: segments = [segments[x] for x in i] msg = f"{prefix}WARNING ⚠️ {im_file}: {nl - len(i)} duplicate labels removed" else: ne = 1 # label empty lb = np.zeros((0, 5), dtype=np.float32) else: nm = 1 # label missing lb = np.zeros((0, 5), dtype=np.float32) return im_file, lb, shape, segments, nm, nf, ne, nc, msg except Exception as e: nc = 1 msg = f"{prefix}WARNING ⚠️ {im_file}: ignoring corrupt image/label: {e}" return [None, None, None, None, nm, nf, ne, nc, msg] class HUBDatasetStats: """Class for generating HUB dataset JSON and `-hub` dataset directory. Args: path: Path to data.yaml or data.zip (with data.yaml inside data.zip) autodownload: Attempt to download dataset if not found locally Usage from utils.dataloaders import HUBDatasetStats stats = HUBDatasetStats('coco128.yaml', autodownload=True) # usage 1 stats = HUBDatasetStats('path/to/coco128.zip') # usage 2 stats.get_json(save=False) stats.process_images() """ def __init__(self, path="coco128.yaml", autodownload=False): """Initializes HUBDatasetStats with dataset path, optionally autodownloads; supports .yaml or .zip formats.""" zipped, data_dir, yaml_path = self._unzip(Path(path)) try: with open(check_yaml(yaml_path), errors="ignore") as f: data = yaml.safe_load(f) # data dict if zipped: data["path"] = data_dir except Exception as e: raise Exception("error/HUB/dataset_stats/yaml_load") from e check_dataset(data, autodownload) # download dataset if missing self.hub_dir = Path(data["path"] + "-hub") self.im_dir = self.hub_dir / "images" self.im_dir.mkdir(parents=True, exist_ok=True) # makes /images self.stats = {"nc": data["nc"], "names": list(data["names"].values())} # statistics dictionary self.data = data @staticmethod def _find_yaml(dir): """Finds a single `data.yaml` file within specified directory, preferring matches to directory name.""" files = list(dir.glob("*.yaml")) or list(dir.rglob("*.yaml")) # try root level first and then recursive assert files, f"No *.yaml file found in {dir}" if len(files) > 1: files = [f for f in files if f.stem == dir.stem] # prefer *.yaml files that match dir name assert files, f"Multiple *.yaml files found in {dir}, only 1 *.yaml file allowed" assert len(files) == 1, f"Multiple *.yaml files found: {files}, only 1 *.yaml file allowed in {dir}" return files[0] def _unzip(self, path): """Unzips a .zip file, verifying its integrity and locating the associated YAML file within the unzipped directory. """ if not str(path).endswith(".zip"): # path is data.yaml return False, None, path assert Path(path).is_file(), f"Error unzipping {path}, file not found" unzip_file(path, path=path.parent) dir = path.with_suffix("") # dataset directory == zip name assert dir.is_dir(), f"Error unzipping {path}, {dir} not found. path/to/abc.zip MUST unzip to path/to/abc/" return True, str(dir), self._find_yaml(dir) # zipped, data_dir, yaml_path def _hub_ops(self, f, max_dim=1920): """Resizes and saves an image at reduced quality for web/app viewing; `f`: path to image, `max_dim`=1920 maximum dimension. """ f_new = self.im_dir / Path(f).name # dataset-hub image filename try: # use PIL im = Image.open(f) r = max_dim / max(im.height, im.width) # ratio if r < 1.0: # image too large im = im.resize((int(im.width * r), int(im.height * r))) im.save(f_new, "JPEG", quality=50, optimize=True) # save except Exception as e: # use OpenCV LOGGER.info(f"WARNING ⚠️ HUB ops PIL failure {f}: {e}") im = cv2.imread(f) im_height, im_width = im.shape[:2] r = max_dim / max(im_height, im_width) # ratio if r < 1.0: # image too large im = cv2.resize(im, (int(im_width * r), int(im_height * r)), interpolation=cv2.INTER_AREA) cv2.imwrite(str(f_new), im) def get_json(self, save=False, verbose=False): """Generates dataset JSON for Ultralytics Platform, with optional saving and verbosity; rounds labels to int class and 6 decimal floats. """ def _round(labels): """Update labels to integer class and 6 decimal place floats.""" return [[int(c), *(round(x, 4) for x in points)] for c, *points in labels] for split in "train", "val", "test": if self.data.get(split) is None: self.stats[split] = None # i.e. no test set continue dataset = LoadImagesAndLabels(self.data[split]) # load dataset x = np.array( [ np.bincount(label[:, 0].astype(int), minlength=self.data["nc"]) for label in tqdm(dataset.labels, total=dataset.n, desc="Statistics") ] ) # shape(128x80) self.stats[split] = { "instance_stats": {"total": int(x.sum()), "per_class": x.sum(0).tolist()}, "image_stats": { "total": dataset.n, "unlabelled": int(np.all(x == 0, 1).sum()), "per_class": (x > 0).sum(0).tolist(), }, "labels": [{str(Path(k).name): _round(v.tolist())} for k, v in zip(dataset.im_files, dataset.labels)], } # Save, print and return if save: stats_path = self.hub_dir / "stats.json" print(f"Saving {stats_path.resolve()}...") with open(stats_path, "w") as f: json.dump(self.stats, f) # save stats.json if verbose: print(json.dumps(self.stats, indent=2, sort_keys=False)) return self.stats def process_images(self): """Compresses images for Ultralytics Platform, saving them to specified directory; supports 'train', 'val', 'test' splits. """ for split in "train", "val", "test": if self.data.get(split) is None: continue dataset = LoadImagesAndLabels(self.data[split]) # load dataset desc = f"{split} images" for _ in tqdm(ThreadPool(NUM_THREADS).imap(self._hub_ops, dataset.im_files), total=dataset.n, desc=desc): pass print(f"Done. All images saved to {self.im_dir}") return self.im_dir # Classification dataloaders ------------------------------------------------------------------------------------------- class ClassificationDataset(torchvision.datasets.ImageFolder): """YOLOv3 Classification Dataset. Args: root: Dataset path transform: torchvision transforms, used by default album_transform: Albumentations transforms, used if installed """ def __init__(self, root, augment, imgsz, cache=False): """Initializes classification dataset with optional augmentation, image resizing, caching, inheriting from ImageFolder. """ super().__init__(root=root) self.torch_transforms = classify_transforms(imgsz) self.album_transforms = classify_albumentations(augment, imgsz) if augment else None self.cache_ram = cache is True or cache == "ram" self.cache_disk = cache == "disk" self.samples = [[*list(x), Path(x[0]).with_suffix(".npy"), None] for x in self.samples] # file, index, npy, im def __getitem__(self, i): """Fetches the item at index `i`, applies caching and transformations, and returns image-sample and index.""" f, j, fn, im = self.samples[i] # filename, index, filename.with_suffix('.npy'), image if self.cache_ram and im is None: im = self.samples[i][3] = cv2.imread(f) elif self.cache_disk: if not fn.exists(): # load npy np.save(fn.as_posix(), cv2.imread(f)) im = np.load(fn) else: # read image im = cv2.imread(f) # BGR if self.album_transforms: sample = self.album_transforms(image=cv2.cvtColor(im, cv2.COLOR_BGR2RGB))["image"] else: sample = self.torch_transforms(im) return sample, j def create_classification_dataloader( path, imgsz=224, batch_size=16, augment=True, cache=False, rank=-1, workers=8, shuffle=True ): # Returns Dataloader object to be used with YOLOv3 Classifier """Creates a DataLoader for image classification tasks with options for augmentation, caching, and distributed training. """ with torch_distributed_zero_first(rank): # init dataset *.cache only once if DDP dataset = ClassificationDataset(root=path, imgsz=imgsz, augment=augment, cache=cache) batch_size = min(batch_size, len(dataset)) nd = torch.cuda.device_count() nw = min([os.cpu_count() // max(nd, 1), batch_size if batch_size > 1 else 0, workers]) sampler = None if rank == -1 else distributed.DistributedSampler(dataset, shuffle=shuffle) generator = torch.Generator() generator.manual_seed(6148914691236517205 + RANK) return InfiniteDataLoader( dataset, batch_size=batch_size, shuffle=shuffle and sampler is None, num_workers=nw, sampler=sampler, pin_memory=PIN_MEMORY, worker_init_fn=seed_worker, generator=generator, ) # or DataLoader(persistent_workers=True) ================================================ FILE: utils/docker/Dockerfile ================================================ # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license # Builds ultralytics/yolov5:latest image on DockerHub https://hub.docker.com/r/ultralytics/yolov3 # Image is CUDA-optimized for YOLOv5 single/multi-GPU training and inference # Start FROM PyTorch image https://hub.docker.com/r/pytorch/pytorch FROM pytorch/pytorch:2.8.0-cuda12.8-cudnn9-runtime # Downloads to user config dir ADD https://ultralytics.com/assets/Arial.ttf https://ultralytics.com/assets/Arial.Unicode.ttf /root/.config/Ultralytics/ # Install linux packages ENV DEBIAN_FRONTEND noninteractive RUN apt update RUN TZ=Etc/UTC apt install -y tzdata RUN apt install --no-install-recommends -y gcc git zip curl htop libgl1 libglib2.0-0 libpython3-dev gnupg # RUN alias python=python3 # Security updates # https://security.snyk.io/vuln/SNYK-UBUNTU1804-OPENSSL-3314796 RUN apt upgrade --no-install-recommends -y openssl # Create working directory RUN rm -rf /usr/src/app && mkdir -p /usr/src/app WORKDIR /usr/src/app # Copy contents # COPY . /usr/src/app (issues as not a .git directory) RUN git clone https://github.com/ultralytics/yolov5 /usr/src/app # Install pip packages COPY requirements.txt . RUN python3 -m pip install --upgrade pip wheel RUN pip install --no-cache -r requirements.txt albumentations comet gsutil notebook \ coremltools onnx onnx-simplifier onnxruntime 'openvino-dev>=2023.0' # tensorflow tensorflowjs \ # Set environment variables ENV OMP_NUM_THREADS=1 # Cleanup ENV DEBIAN_FRONTEND teletype # Usage Examples ------------------------------------------------------------------------------------------------------- # Build and Push # t=ultralytics/yolov5:latest && sudo docker build -f utils/docker/Dockerfile -t $t . && sudo docker push $t # Pull and Run # t=ultralytics/yolov5:latest && sudo docker pull $t && sudo docker run -it --ipc=host --gpus all $t # Pull and Run with local directory access # t=ultralytics/yolov5:latest && sudo docker pull $t && sudo docker run -it --ipc=host --gpus all -v "$(pwd)"/datasets:/usr/src/datasets $t # Kill all # sudo docker kill $(sudo docker ps -q) # Kill all image-based # sudo docker kill $(sudo docker ps -qa --filter ancestor=ultralytics/yolov5:latest) # DockerHub tag update # t=ultralytics/yolov5:latest tnew=ultralytics/yolov5:v6.2 && sudo docker pull $t && sudo docker tag $t $tnew && sudo docker push $tnew # Clean up # sudo docker system prune -a --volumes # Update Ubuntu drivers # https://www.maketecheasier.com/install-nvidia-drivers-ubuntu/ # DDP test # python -m torch.distributed.run --nproc_per_node 2 --master_port 1 train.py --epochs 3 # GCP VM from Image # docker.io/ultralytics/yolov5:latest ================================================ FILE: utils/docker/Dockerfile-arm64 ================================================ # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license # Builds ultralytics/yolov5:latest-arm64 image on DockerHub https://hub.docker.com/r/ultralytics/yolov3 # Image is aarch64-compatible for Apple M1 and other ARM architectures i.e. Jetson Nano and Raspberry Pi # Start FROM Ubuntu image https://hub.docker.com/_/ubuntu FROM arm64v8/ubuntu:22.10 # Downloads to user config dir ADD https://ultralytics.com/assets/Arial.ttf https://ultralytics.com/assets/Arial.Unicode.ttf /root/.config/Ultralytics/ # Install linux packages ENV DEBIAN_FRONTEND noninteractive RUN apt update RUN TZ=Etc/UTC apt install -y tzdata RUN apt install --no-install-recommends -y python3-pip git zip curl htop gcc libgl1 libglib2.0-0 libpython3-dev # RUN alias python=python3 # Install pip packages COPY requirements.txt . RUN python3 -m pip install --upgrade pip wheel RUN pip install --no-cache -r requirements.txt albumentations gsutil notebook \ coremltools onnx onnxruntime # tensorflow-aarch64 tensorflowjs \ # Create working directory RUN mkdir -p /usr/src/app WORKDIR /usr/src/app # Copy contents # COPY . /usr/src/app (issues as not a .git directory) RUN git clone https://github.com/ultralytics/yolov5 /usr/src/app ENV DEBIAN_FRONTEND teletype # Usage Examples ------------------------------------------------------------------------------------------------------- # Build and Push # t=ultralytics/yolov5:latest-arm64 && sudo docker build --platform linux/arm64 -f utils/docker/Dockerfile-arm64 -t $t . && sudo docker push $t # Pull and Run # t=ultralytics/yolov5:latest-arm64 && sudo docker pull $t && sudo docker run -it --ipc=host -v "$(pwd)"/datasets:/usr/src/datasets $t ================================================ FILE: utils/docker/Dockerfile-cpu ================================================ # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license # Builds ultralytics/yolov5:latest-cpu image on DockerHub https://hub.docker.com/r/ultralytics/yolov3 # Image is CPU-optimized for ONNX, OpenVINO and PyTorch YOLOv5 deployments # Start FROM Ubuntu image https://hub.docker.com/_/ubuntu FROM ubuntu:23.10 # Downloads to user config dir ADD https://ultralytics.com/assets/Arial.ttf https://ultralytics.com/assets/Arial.Unicode.ttf /root/.config/Ultralytics/ # Install linux packages # g++ required to build 'tflite_support' and 'lap' packages, libusb-1.0-0 required for 'tflite_support' package RUN apt update \ && apt install --no-install-recommends -y python3-pip git zip curl htop libgl1 libglib2.0-0 libpython3-dev gnupg g++ libusb-1.0-0 # RUN alias python=python3 # Remove python3.11/EXTERNALLY-MANAGED or use 'pip install --break-system-packages' avoid 'externally-managed-environment' Ubuntu nightly error RUN rm -rf /usr/lib/python3.11/EXTERNALLY-MANAGED # Install pip packages COPY requirements.txt . RUN python3 -m pip install --upgrade pip wheel RUN pip install --no-cache -r requirements.txt albumentations gsutil notebook \ coremltools onnx onnx-simplifier onnxruntime 'openvino-dev>=2023.0' \ # tensorflow tensorflowjs \ --extra-index-url https://download.pytorch.org/whl/cpu # Create working directory RUN mkdir -p /usr/src/app WORKDIR /usr/src/app # Copy contents # COPY . /usr/src/app (issues as not a .git directory) RUN git clone https://github.com/ultralytics/yolov5 /usr/src/app # Usage Examples ------------------------------------------------------------------------------------------------------- # Build and Push # t=ultralytics/yolov5:latest-cpu && sudo docker build -f utils/docker/Dockerfile-cpu -t $t . && sudo docker push $t # Pull and Run # t=ultralytics/yolov5:latest-cpu && sudo docker pull $t && sudo docker run -it --ipc=host -v "$(pwd)"/datasets:/usr/src/datasets $t ================================================ FILE: utils/downloads.py ================================================ # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license """Download utils.""" import logging import subprocess import urllib from pathlib import Path import requests import torch def is_url(url, check=True): """Determines if a string is a valid URL and optionally checks its existence online.""" try: url = str(url) result = urllib.parse.urlparse(url) assert all([result.scheme, result.netloc]) # check if is url return (urllib.request.urlopen(url).getcode() == 200) if check else True # check if exists online except (AssertionError, urllib.request.HTTPError): return False def gsutil_getsize(url=""): """Returns the size of a file at a 'gs://' URL using gsutil du command; 0 if file not found or command fails.""" output = subprocess.check_output(["gsutil", "du", url], shell=True, encoding="utf-8") return int(output.split()[0]) if output else 0 def url_getsize(url="https://ultralytics.com/images/bus.jpg"): """Fetches file size in bytes from a URL using an HTTP HEAD request; defaults to -1 if not found.""" response = requests.head(url, allow_redirects=True) return int(response.headers.get("content-length", -1)) def curl_download(url, filename, *, silent: bool = False) -> bool: """Download a file from a url to a filename using curl.""" silent_option = "sS" if silent else "" # silent proc = subprocess.run( [ "curl", "-#", f"-{silent_option}L", url, "--output", filename, "--retry", "9", "-C", "-", ] ) return proc.returncode == 0 def safe_download(file, url, url2=None, min_bytes=1e0, error_msg=""): """Downloads a file from 'url' or 'url2' to 'file', ensuring size > 'min_bytes'; removes incomplete downloads.""" from utils.general import LOGGER file = Path(file) assert_msg = f"Downloaded file '{file}' does not exist or size is < min_bytes={min_bytes}" try: # url1 LOGGER.info(f"Downloading {url} to {file}...") torch.hub.download_url_to_file(url, str(file), progress=LOGGER.level <= logging.INFO) assert file.exists() and file.stat().st_size > min_bytes, assert_msg # check except Exception as e: # url2 if file.exists(): file.unlink() # remove partial downloads LOGGER.info(f"ERROR: {e}\nRe-attempting {url2 or url} to {file}...") # curl download, retry and resume on fail curl_download(url2 or url, file) finally: if not file.exists() or file.stat().st_size < min_bytes: # check if file.exists(): file.unlink() # remove partial downloads LOGGER.info(f"ERROR: {assert_msg}\n{error_msg}") LOGGER.info("") def attempt_download(file, repo="ultralytics/yolov5", release="v7.0"): """Attempts to download a file from a specified URL or GitHub release, ensuring file integrity with a minimum size check. """ from utils.general import LOGGER def github_assets(repository, version="latest"): """Returns GitHub tag and assets for a given repository and version from the GitHub API.""" if version != "latest": version = f"tags/{version}" # i.e. tags/v7.0 response = requests.get(f"https://api.github.com/repos/{repository}/releases/{version}").json() # github api return response["tag_name"], [x["name"] for x in response["assets"]] # tag, assets file = Path(str(file).strip().replace("'", "")) if not file.exists(): # URL specified name = Path(urllib.parse.unquote(str(file))).name # decode '%2F' to '/' etc. if str(file).startswith(("http:/", "https:/")): # download url = str(file).replace(":/", "://") # Pathlib turns :// -> :/ file = name.split("?")[0] # parse authentication https://url.com/file.txt?auth... if Path(file).is_file(): LOGGER.info(f"Found {url} locally at {file}") # file already exists else: safe_download(file=file, url=url, min_bytes=1e5) return file # GitHub assets assets = [f"yolov5{size}{suffix}.pt" for size in "nsmlx" for suffix in ("", "6", "-cls", "-seg")] # default try: tag, assets = github_assets(repo, release) except Exception: try: tag, assets = github_assets(repo) # latest release except Exception: try: tag = subprocess.check_output("git tag", shell=True, stderr=subprocess.STDOUT).decode().split()[-1] except Exception: tag = release if name in assets: file.parent.mkdir(parents=True, exist_ok=True) # make parent dir (if required) safe_download( file, url=f"https://github.com/{repo}/releases/download/{tag}/{name}", min_bytes=1e5, error_msg=f"{file} missing, try downloading from https://github.com/{repo}/releases/{tag}", ) return str(file) ================================================ FILE: utils/flask_rest_api/README.md ================================================ Ultralytics logo # Flask REST API Example for YOLO Models [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. Deploying 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. ## 🔧 Requirements Ensure you have the necessary Python packages installed. The primary requirement is Flask. Install Flask using pip: ```shell pip install Flask torch torchvision ``` _Note: `torch` and `torchvision` are required for loading and running PyTorch-based models like YOLOv3._ ## ▶️ Run the API Once Flask and dependencies are installed, you can start the API server. Execute the Python script: ```shell python restapi.py --port 5000 ``` The API server will start listening on the specified port (default is 5000). ## 🚀 Make a Prediction Request You can send prediction requests to the running API using tools like [`curl`](https://curl.se/) or scripting languages. Send a POST request with an image file (`zidane.jpg` in this example) to the `/v1/object-detection/yolov3` endpoint: ```shell curl -X POST -F image=@zidane.jpg 'http://localhost:5000/v1/object-detection/yolov3' ``` _Ensure `zidane.jpg` (or your test image) is present in the directory where you run the `curl` command._ ## 📄 Understand the Response The 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. Example JSON response: ```json [ { "class": 0, "confidence": 0.8900438547, "height": 0.9318675399, "name": "person", "width": 0.3264600933, "xcenter": 0.7438579798, "ycenter": 0.5207948685 }, { "class": 0, "confidence": 0.8440024257, "height": 0.7155083418, "name": "person", "width": 0.6546785235, "xcenter": 0.427829951, "ycenter": 0.6334488392 }, { "class": 27, "confidence": 0.3771208823, "height": 0.3902671337, "name": "tie", "width": 0.0696444362, "xcenter": 0.3675483763, "ycenter": 0.7991207838 }, { "class": 27, "confidence": 0.3527112305, "height": 0.1540903747, "name": "tie", "width": 0.0336618312, "xcenter": 0.7814827561, "ycenter": 0.5065554976 } ] ``` An 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. ## 🤝 Contributing Contributions 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. ================================================ FILE: utils/flask_rest_api/example_request.py ================================================ # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license """Perform test request.""" import pprint import requests DETECTION_URL = "http://localhost:5000/v1/object-detection/yolov5s" IMAGE = "zidane.jpg" # Read image with open(IMAGE, "rb") as f: image_data = f.read() response = requests.post(DETECTION_URL, files={"image": image_data}).json() pprint.pprint(response) ================================================ FILE: utils/flask_rest_api/restapi.py ================================================ # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license """Run a Flask REST API exposing one or more YOLOv5s models.""" import argparse import io import torch from flask import Flask, request from PIL import Image app = Flask(__name__) models = {} DETECTION_URL = "/v1/object-detection/" @app.route(DETECTION_URL, methods=["POST"]) def predict(model): """Predicts objects in an image using YOLOv5s models exposed via Flask REST API; expects 'image' file in POST request. """ if request.method != "POST": return if request.files.get("image"): # Method 1 # with request.files["image"] as f: # im = Image.open(io.BytesIO(f.read())) # Method 2 im_file = request.files["image"] im_bytes = im_file.read() im = Image.open(io.BytesIO(im_bytes)) if model in models: results = models[model](im, size=640) # reduce size=320 for faster inference return results.pandas().xyxy[0].to_json(orient="records") if __name__ == "__main__": parser = argparse.ArgumentParser(description="Flask API exposing YOLOv3 model") parser.add_argument("--port", default=5000, type=int, help="port number") parser.add_argument("--model", nargs="+", default=["yolov5s"], help="model(s) to run, i.e. --model yolov5n yolov5s") opt = parser.parse_args() for m in opt.model: models[m] = torch.hub.load("ultralytics/yolov5", m, force_reload=True, skip_validation=True) app.run(host="0.0.0.0", port=opt.port) # debug=True causes Restarting with stat ================================================ FILE: utils/general.py ================================================ # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license """General utils.""" from __future__ import annotations import contextlib import glob import inspect import logging import logging.config import math import os import platform import random import re import signal import subprocess import sys import time import urllib from copy import deepcopy from datetime import datetime from itertools import repeat from multiprocessing.pool import ThreadPool from pathlib import Path from subprocess import check_output from tarfile import is_tarfile from zipfile import ZipFile, is_zipfile import cv2 import numpy as np import pandas as pd import torch import torchvision import yaml from packaging.version import parse from ultralytics.utils.checks import check_requirements from ultralytics.utils.patches import torch_load from utils import TryExcept, emojis from utils.downloads import curl_download, gsutil_getsize from utils.metrics import box_iou, fitness FILE = Path(__file__).resolve() ROOT = FILE.parents[1] # YOLOv3 root directory RANK = int(os.getenv("RANK", -1)) # Settings NUM_THREADS = min(8, max(1, os.cpu_count() - 1)) # number of YOLOv3 multiprocessing threads DATASETS_DIR = Path(os.getenv("YOLOv5_DATASETS_DIR", ROOT.parent / "datasets")) # global datasets directory AUTOINSTALL = str(os.getenv("YOLOv5_AUTOINSTALL", True)).lower() == "true" # global auto-install mode VERBOSE = str(os.getenv("YOLOv5_VERBOSE", True)).lower() == "true" # global verbose mode TQDM_BAR_FORMAT = "{l_bar}{bar:10}{r_bar}" # tqdm bar format FONT = "Arial.ttf" # https://github.com/ultralytics/assets/releases/download/v0.0.0/Arial.ttf torch.set_printoptions(linewidth=320, precision=5, profile="long") np.set_printoptions(linewidth=320, formatter={"float_kind": "{:11.5g}".format}) # format short g, %precision=5 pd.options.display.max_columns = 10 cv2.setNumThreads(0) # prevent OpenCV from multithreading (incompatible with PyTorch DataLoader) os.environ["NUMEXPR_MAX_THREADS"] = str(NUM_THREADS) # NumExpr max threads os.environ["OMP_NUM_THREADS"] = "1" if platform.system() == "darwin" else str(NUM_THREADS) # OpenMP (PyTorch and SciPy) os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2" # suppress verbose TF compiler warnings in Colab def is_ascii(s=""): """Checks if input string `s` is composed solely of ASCII characters; compatible with pre-Python 3.7 versions.""" s = str(s) # convert list, tuple, None, etc. to str return len(s.encode().decode("ascii", "ignore")) == len(s) def is_chinese(s="人工智能"): """Determines if a string `s` contains any Chinese characters; returns a boolean.""" return bool(re.search("[\u4e00-\u9fff]", str(s))) def is_colab(): """Checks if the current environment is a Google Colab instance; returns a boolean.""" return "google.colab" in sys.modules def is_jupyter(): """Check if the current script is running inside a Jupyter Notebook. Verified on Colab, Jupyterlab, Kaggle, Paperspace. Returns: bool: True if running inside a Jupyter Notebook, False otherwise. """ with contextlib.suppress(Exception): from IPython import get_ipython return get_ipython() is not None return False def is_kaggle(): """Determines if the environment is a Kaggle Notebook by checking environment variables.""" return os.environ.get("PWD") == "/kaggle/working" and os.environ.get("KAGGLE_URL_BASE") == "https://www.kaggle.com" def is_docker() -> bool: """Check if the process runs inside a docker container.""" if Path("/.dockerenv").exists(): return True try: # check if docker is in control groups with open("/proc/self/cgroup") as file: return any("docker" in line for line in file) except OSError: return False def is_writeable(dir, test=False): """Determines if a directory is writeable, optionally tests by writing a file if `test=True`.""" if not test: return os.access(dir, os.W_OK) # possible issues on Windows file = Path(dir) / "tmp.txt" try: with open(file, "w"): # open file with write permissions pass file.unlink() # remove file return True except OSError: return False LOGGING_NAME = "yolov5" def set_logging(name=LOGGING_NAME, verbose=True): """Configures logging with specified verbosity; 'name' sets logger identity, 'verbose' toggles logging level.""" rank = int(os.getenv("RANK", -1)) # rank in world for Multi-GPU trainings level = logging.INFO if verbose and rank in {-1, 0} else logging.ERROR logging.config.dictConfig( { "version": 1, "disable_existing_loggers": False, "formatters": {name: {"format": "%(message)s"}}, "handlers": { name: { "class": "logging.StreamHandler", "formatter": name, "level": level, } }, "loggers": { name: { "level": level, "handlers": [name], "propagate": False, } }, } ) set_logging(LOGGING_NAME) # run before defining LOGGER LOGGER = logging.getLogger(LOGGING_NAME) # define globally (used in train.py, val.py, detect.py, etc.) if platform.system() == "Windows": for fn in LOGGER.info, LOGGER.warning: setattr(LOGGER, fn.__name__, lambda x: fn(emojis(x))) # emoji safe logging def user_config_dir(dir="Ultralytics", env_var="YOLOV5_CONFIG_DIR"): """Returns user configuration directory path, prefers `env_var` if set, else uses OS-specific path, creates directory if needed. """ if env := os.getenv(env_var): path = Path(env) # use environment variable else: cfg = {"Windows": "AppData/Roaming", "Linux": ".config", "Darwin": "Library/Application Support"} # 3 OS dirs path = Path.home() / cfg.get(platform.system(), "") # OS-specific config dir path = (path if is_writeable(path) else Path("/tmp")) / dir # GCP and AWS lambda fix, only /tmp is writeable path.mkdir(exist_ok=True) # make if required return path CONFIG_DIR = user_config_dir() # Ultralytics settings dir class Profile(contextlib.ContextDecorator): """Profiles code execution time, usable as a context manager or decorator for performance monitoring.""" def __init__(self, t=0.0): """Initializes a profiling context for YOLOv3 with optional timing threshold `t` and checks CUDA availability. """ self.t = t self.cuda = torch.cuda.is_available() def __enter__(self): """Starts the profiling timer, returning the profile instance for use with @Profile() decorator or 'with Profile():' context. """ self.start = self.time() return self def __exit__(self, type, value, traceback): """Ends profiling, calculating time delta and updating total time, for use within 'with Profile():' context.""" self.dt = self.time() - self.start # delta-time self.t += self.dt # accumulate dt def time(self): """Returns current time, ensuring CUDA operations are synchronized if on GPU.""" if self.cuda: torch.cuda.synchronize() return time.time() class Timeout(contextlib.ContextDecorator): """Enforces a timeout on code execution, raising TimeoutError on expiry.""" def __init__(self, seconds, *, timeout_msg="", suppress_timeout_errors=True): """Initializes a timeout context/decorator with specified duration, custom message, and error handling option. """ self.seconds = int(seconds) self.timeout_message = timeout_msg self.suppress = bool(suppress_timeout_errors) def _timeout_handler(self, signum, frame): """Raises a TimeoutError with a custom message upon timeout signal reception.""" raise TimeoutError(self.timeout_message) def __enter__(self): """Starts a countdown for a signal alarm; not supported on Windows.""" if platform.system() != "Windows": # not supported on Windows signal.signal(signal.SIGALRM, self._timeout_handler) # Set handler for SIGALRM signal.alarm(self.seconds) # start countdown for SIGALRM to be raised def __exit__(self, exc_type, exc_val, exc_tb): """Cancels any scheduled SIGALRM on non-Windows platforms, optionally suppressing TimeoutError.""" if platform.system() != "Windows": signal.alarm(0) # Cancel SIGALRM if it's scheduled if self.suppress and exc_type is TimeoutError: # Suppress TimeoutError return True class WorkingDirectory(contextlib.ContextDecorator): """Context manager to temporarily change the working directory, reverting to the original on exit.""" def __init__(self, new_dir): """Initializes context manager to temporarily change working directory, reverting on exit.""" self.dir = new_dir # new dir self.cwd = Path.cwd().resolve() # current dir def __enter__(self): """Temporarily changes the current working directory to `new_dir`, reverting to the original on exit.""" os.chdir(self.dir) def __exit__(self, exc_type, exc_val, exc_tb): """Reverts to the original working directory upon exiting the context manager.""" os.chdir(self.cwd) def methods(instance): """Returns a list of callable class/instance methods, excluding magic methods.""" return [f for f in dir(instance) if callable(getattr(instance, f)) and not f.startswith("__")] def print_args(args: dict | None = None, show_file=True, show_func=False): """Prints function arguments; optionally specify args dict, show file and/or function name.""" x = inspect.currentframe().f_back # previous frame file, _, func, _, _ = inspect.getframeinfo(x) if args is None: # get args automatically args, _, _, frm = inspect.getargvalues(x) args = {k: v for k, v in frm.items() if k in args} try: file = Path(file).resolve().relative_to(ROOT).with_suffix("") except ValueError: file = Path(file).stem s = (f"{file}: " if show_file else "") + (f"{func}: " if show_func else "") LOGGER.info(colorstr(s) + ", ".join(f"{k}={v}" for k, v in args.items())) def init_seeds(seed=0, deterministic=False): """Initializes RNG seeds for reproducibility; `seed`: RNG seed, `deterministic`: enforces deterministic behavior if True. """ random.seed(seed) np.random.seed(seed) torch.manual_seed(seed) torch.cuda.manual_seed(seed) torch.cuda.manual_seed_all(seed) # for Multi-GPU, exception safe # torch.backends.cudnn.benchmark = True # AutoBatch problem https://github.com/ultralytics/yolov5/issues/9287 if deterministic and check_version(torch.__version__, "1.12.0"): # https://github.com/ultralytics/yolov5/pull/8213 torch.use_deterministic_algorithms(True) torch.backends.cudnn.deterministic = True os.environ["CUBLAS_WORKSPACE_CONFIG"] = ":4096:8" os.environ["PYTHONHASHSEED"] = str(seed) def intersect_dicts(da, db, exclude=()): """Intersects two dicts by matching keys and shapes, excluding specified keys, and retains values from the first dict. """ 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} def get_default_args(func): """Returns a dict of `func`'s default arguments using inspection.""" signature = inspect.signature(func) return {k: v.default for k, v in signature.parameters.items() if v.default is not inspect.Parameter.empty} def get_latest_run(search_dir="."): """Returns path to the most recent 'last.pt' file within 'search_dir' for resuming, or an empty string if not found. """ last_list = glob.glob(f"{search_dir}/**/last*.pt", recursive=True) return max(last_list, key=os.path.getctime) if last_list else "" def file_age(path=__file__): """Returns the number of days since the last update of the file specified by 'path'.""" dt = datetime.now() - datetime.fromtimestamp(Path(path).stat().st_mtime) # delta return dt.days # + dt.seconds / 86400 # fractional days def file_date(path=__file__): """Returns file modification date in 'YYYY-M-D' format for the file at 'path'.""" t = datetime.fromtimestamp(Path(path).stat().st_mtime) return f"{t.year}-{t.month}-{t.day}" def file_size(path): """Returns the size of a file or total size of files in a directory at 'path' in MB.""" mb = 1 << 20 # bytes to MiB (1024 ** 2) path = Path(path) if path.is_file(): return path.stat().st_size / mb elif path.is_dir(): return sum(f.stat().st_size for f in path.glob("**/*") if f.is_file()) / mb else: return 0.0 def check_online(): """Checks internet connectivity by attempting to connect to "1.1.1.1" on port 443 twice; returns True if successful. """ import socket def run_once(): """Attempts a single internet connectivity check to '1.1.1.1' on port 443 and returns True if successful.""" try: socket.create_connection(("1.1.1.1", 443), 5) # check host accessibility return True except OSError: return False return run_once() or run_once() # check twice to increase robustness to intermittent connectivity issues def git_describe(path=ROOT): # path must be a directory """Returns human-readable git description of a directory if it's a git repository, otherwise an empty string.""" try: assert (Path(path) / ".git").is_dir() return check_output(f"git -C {path} describe --tags --long --always", shell=True).decode()[:-1] except Exception: return "" @TryExcept() @WorkingDirectory(ROOT) def check_git_status(repo="ultralytics/yolov5", branch="master"): """Checks YOLOv3 code update status against remote, suggests 'git pull' if outdated; requires internet and git repository. """ url = f"https://github.com/{repo}" msg = f", for updates see {url}" s = colorstr("github: ") # string assert Path(".git").exists(), s + "skipping check (not a git repository)" + msg assert check_online(), s + "skipping check (offline)" + msg splits = re.split(pattern=r"\s", string=check_output("git remote -v", shell=True).decode()) matches = [repo in s for s in splits] if any(matches): remote = splits[matches.index(True) - 1] else: remote = "ultralytics" check_output(f"git remote add {remote} {url}", shell=True) check_output(f"git fetch {remote}", shell=True, timeout=5) # git fetch local_branch = check_output("git rev-parse --abbrev-ref HEAD", shell=True).decode().strip() # checked out n = int(check_output(f"git rev-list {local_branch}..{remote}/{branch} --count", shell=True)) # commits behind if n > 0: pull = "git pull" if remote == "origin" else f"git pull {remote} {branch}" s += f"⚠️ YOLOv3 is out of date by {n} commit{'s' * (n > 1)}. Use '{pull}' or 'git clone {url}' to update." else: s += f"up to date with {url} ✅" LOGGER.info(s) @WorkingDirectory(ROOT) def check_git_info(path="."): """Checks YOLOv3 git info (remote, branch, commit) in path, requires 'gitpython'. Returns dict. """ check_requirements("gitpython") import git try: repo = git.Repo(path) remote = repo.remotes.origin.url.replace(".git", "") # i.e. 'https://github.com/ultralytics/yolov5' commit = repo.head.commit.hexsha # i.e. '3134699c73af83aac2a481435550b968d5792c0d' try: branch = repo.active_branch.name # i.e. 'main' except TypeError: # not on any branch branch = None # i.e. 'detached HEAD' state return {"remote": remote, "branch": branch, "commit": commit} except git.exc.InvalidGitRepositoryError: # path is not a git dir return {"remote": None, "branch": None, "commit": None} def check_python(minimum="3.7.0"): """Checks if current Python version meets the specified minimum requirement, raising error if not.""" check_version(platform.python_version(), minimum, name="Python ", hard=True) def check_version(current="0.0.0", minimum="0.0.0", name="version ", pinned=False, hard=False, verbose=False): """Compares current and minimum version requirements, optionally enforcing minimum version and logging warnings.""" current, minimum = (parse(x) for x in (current, minimum)) result = (current == minimum) if pinned else (current >= minimum) # bool s = f"WARNING ⚠️ {name}{minimum} is required by YOLOv3, but {name}{current} is currently installed" # string if hard: assert result, emojis(s) # assert min requirements met if verbose and not result: LOGGER.warning(s) return result def check_img_size(imgsz, s=32, floor=0): """Adjusts image size to be divisible by `s`, ensuring it's above `floor`; returns int for single dim or list for dims. """ if isinstance(imgsz, int): # integer i.e. img_size=640 new_size = max(make_divisible(imgsz, int(s)), floor) else: # list i.e. img_size=[640, 480] imgsz = list(imgsz) # convert to list if tuple new_size = [max(make_divisible(x, int(s)), floor) for x in imgsz] if new_size != imgsz: LOGGER.warning(f"WARNING ⚠️ --img-size {imgsz} must be multiple of max stride {s}, updating to {new_size}") return new_size def check_imshow(warn=False): """Checks if the environment supports image display; warns if `warn=True` and display is unsupported.""" try: assert not is_jupyter() assert not is_docker() cv2.imshow("test", np.zeros((1, 1, 3))) cv2.waitKey(1) cv2.destroyAllWindows() cv2.waitKey(1) return True except Exception as e: if warn: LOGGER.warning(f"WARNING ⚠️ Environment does not support cv2.imshow() or PIL Image.show()\n{e}") return False def check_suffix(file="yolov5s.pt", suffix=(".pt",), msg=""): """Checks for acceptable file suffixes, supports batch checking for lists or tuples of filenames.""" if file and suffix: if isinstance(suffix, str): suffix = [suffix] for f in file if isinstance(file, (list, tuple)) else [file]: s = Path(f).suffix.lower() # file suffix if len(s): assert s in suffix, f"{msg}{f} acceptable suffix is {suffix}" def check_yaml(file, suffix=(".yaml", ".yml")): """Searches/downloads a YAML file and returns its path, ensuring it has a .yaml or .yml suffix.""" return check_file(file, suffix) def check_file(file, suffix=""): """Checks for file's existence locally, downloads if a URL, supports ClearML dataset IDs, and enforces optional suffix. """ check_suffix(file, suffix) # optional file = str(file) # convert to str() if os.path.isfile(file) or not file: # exists return file elif file.startswith(("http:/", "https:/")): # download url = file # warning: Pathlib turns :// -> :/ file = Path(urllib.parse.unquote(file).split("?")[0]).name # '%2F' to '/', split https://url.com/file.txt?auth if os.path.isfile(file): LOGGER.info(f"Found {url} locally at {file}") # file already exists else: LOGGER.info(f"Downloading {url} to {file}...") torch.hub.download_url_to_file(url, file) assert Path(file).exists() and Path(file).stat().st_size > 0, f"File download failed: {url}" # check return file elif file.startswith("clearml://"): # ClearML Dataset ID assert "clearml" in sys.modules, ( "ClearML is not installed, so cannot use ClearML dataset. Try running 'pip install clearml'." ) return file else: # search files = [] for d in "data", "models", "utils": # search directories files.extend(glob.glob(str(ROOT / d / "**" / file), recursive=True)) # find file assert len(files), f"File not found: {file}" # assert file was found assert len(files) == 1, f"Multiple files match '{file}', specify exact path: {files}" # assert unique return files[0] # return file def check_font(font=FONT, progress=False): """Checks and downloads the specified font to CONFIG_DIR if not present, with optional download progress.""" font = Path(font) file = CONFIG_DIR / font.name if not font.exists() and not file.exists(): url = f"https://github.com/ultralytics/assets/releases/download/v0.0.0/{font.name}" LOGGER.info(f"Downloading {url} to {file}...") torch.hub.download_url_to_file(url, str(file), progress=progress) def check_dataset(data, autodownload=True): """Verifies and prepares dataset by downloading if absent, checking, and unzipping; supports auto-downloading.""" # Download (optional) extract_dir = "" if isinstance(data, (str, Path)) and (is_zipfile(data) or is_tarfile(data)): download(data, dir=f"{DATASETS_DIR}/{Path(data).stem}", unzip=True, delete=False, curl=False, threads=1) data = next((DATASETS_DIR / Path(data).stem).rglob("*.yaml")) extract_dir, autodownload = data.parent, False # Read yaml (optional) if isinstance(data, (str, Path)): data = yaml_load(data) # dictionary # Checks for k in "train", "val", "names": assert k in data, emojis(f"data.yaml '{k}:' field missing ❌") if isinstance(data["names"], (list, tuple)): # old array format data["names"] = dict(enumerate(data["names"])) # convert to dict assert all(isinstance(k, int) for k in data["names"].keys()), "data.yaml names keys must be integers, i.e. 2: car" data["nc"] = len(data["names"]) # Resolve paths path = Path(extract_dir or data.get("path") or "") # optional 'path' default to '.' if not path.is_absolute(): path = (ROOT / path).resolve() data["path"] = path # download scripts for k in "train", "val", "test": if data.get(k): # prepend path if isinstance(data[k], str): x = (path / data[k]).resolve() if not x.exists() and data[k].startswith("../"): x = (path / data[k][3:]).resolve() data[k] = str(x) else: data[k] = [str((path / x).resolve()) for x in data[k]] # Parse yaml _train, val, _test, s = (data.get(x) for x in ("train", "val", "test", "download")) if val: val = [Path(x).resolve() for x in (val if isinstance(val, list) else [val])] # val path if not all(x.exists() for x in val): LOGGER.info("\nDataset not found ⚠️, missing paths %s" % [str(x) for x in val if not x.exists()]) if not s or not autodownload: raise Exception("Dataset not found ❌") t = time.time() if s.startswith("http") and s.endswith(".zip"): # URL f = Path(s).name # filename LOGGER.info(f"Downloading {s} to {f}...") torch.hub.download_url_to_file(s, f) Path(DATASETS_DIR).mkdir(parents=True, exist_ok=True) # create root unzip_file(f, path=DATASETS_DIR) # unzip Path(f).unlink() # remove zip r = None # success elif s.startswith("bash "): # bash script LOGGER.info(f"Running {s} ...") r = subprocess.run(s, shell=True) else: # python script r = exec(s, {"yaml": data}) # return None dt = f"({round(time.time() - t, 1)}s)" s = f"success ✅ {dt}, saved to {colorstr('bold', DATASETS_DIR)}" if r in (0, None) else f"failure {dt} ❌" LOGGER.info(f"Dataset download {s}") check_font("Arial.ttf" if is_ascii(data["names"]) else "Arial.Unicode.ttf", progress=True) # download fonts return data # dictionary def check_amp(model): """Checks PyTorch AMP functionality with model and sample image, returning True if AMP operates correctly.""" from models.common import AutoShape, DetectMultiBackend def amp_allclose(model, im): """Compares FP32 and AMP inference results for a model and image, ensuring outputs are within 10% tolerance.""" m = AutoShape(model, verbose=False) # model a = m(im).xywhn[0] # FP32 inference m.amp = True b = m(im).xywhn[0] # AMP inference return a.shape == b.shape and torch.allclose(a, b, atol=0.1) # close to 10% absolute tolerance prefix = colorstr("AMP: ") device = next(model.parameters()).device # get model device if device.type in ("cpu", "mps"): return False # AMP only used on CUDA devices f = ROOT / "data" / "images" / "bus.jpg" # image to check im = f if f.exists() else "https://ultralytics.com/images/bus.jpg" if check_online() else np.ones((640, 640, 3)) try: assert amp_allclose(deepcopy(model), im) or amp_allclose(DetectMultiBackend("yolov5n.pt", device), im) LOGGER.info(f"{prefix}checks passed ✅") return True except Exception: help_url = "https://github.com/ultralytics/yolov5/issues/7908" LOGGER.warning(f"{prefix}checks failed ❌, disabling Automatic Mixed Precision. See {help_url}") return False def yaml_load(file="data.yaml"): """Safely loads a YAML file, ignoring file errors; default file is 'data.yaml'.""" with open(file, errors="ignore") as f: return yaml.safe_load(f) def yaml_save(file="data.yaml", data=None): """Safely saves data to a YAML file, converting `Path` objects to strings; defaults to 'data.yaml'.""" if data is None: data = {} with open(file, "w") as f: yaml.safe_dump({k: str(v) if isinstance(v, Path) else v for k, v in data.items()}, f, sort_keys=False) def unzip_file(file, path=None, exclude=(".DS_Store", "__MACOSX")): """Unzips '*.zip' to `path` (default: file's parent), excluding files matching `exclude` (`('.DS_Store', '__MACOSX')`). """ if path is None: path = Path(file).parent # default path with ZipFile(file) as zipObj: for f in zipObj.namelist(): # list all archived filenames in the zip if all(x not in f for x in exclude): zipObj.extract(f, path=path) def url2file(url): """Converts a URL to a filename by extracting the last path segment and removing query parameters.""" url = str(Path(url)).replace(":/", "://") # Pathlib turns :// -> :/ return Path(urllib.parse.unquote(url)).name.split("?")[0] # '%2F' to '/', split https://url.com/file.txt?auth def download(url, dir=".", unzip=True, delete=True, curl=False, threads=1, retry=3): """Downloads files from URLs into a specified directory, optionally unzips, and supports multithreading and retries. """ def download_one(url, dir): """Downloads a file from a URL into the specified directory, supporting retries and using curl or torch methods. """ success = True if os.path.isfile(url): f = Path(url) # filename else: # does not exist f = dir / Path(url).name LOGGER.info(f"Downloading {url} to {f}...") for i in range(retry + 1): if curl: success = curl_download(url, f, silent=(threads > 1)) else: torch.hub.download_url_to_file(url, f, progress=threads == 1) # torch download success = f.is_file() if success: break elif i < retry: LOGGER.warning(f"⚠️ Download failure, retrying {i + 1}/{retry} {url}...") else: LOGGER.warning(f"❌ Failed to download {url}...") if unzip and success and (f.suffix == ".gz" or is_zipfile(f) or is_tarfile(f)): LOGGER.info(f"Unzipping {f}...") if is_zipfile(f): unzip_file(f, dir) # unzip elif is_tarfile(f): subprocess.run(["tar", "xf", f, "--directory", f.parent], check=True) # unzip elif f.suffix == ".gz": subprocess.run(["tar", "xfz", f, "--directory", f.parent], check=True) # unzip if delete: f.unlink() # remove zip dir = Path(dir) dir.mkdir(parents=True, exist_ok=True) # make directory if threads > 1: pool = ThreadPool(threads) pool.imap(lambda x: download_one(*x), zip(url, repeat(dir))) # multithreaded pool.close() pool.join() else: for u in [url] if isinstance(url, (str, Path)) else url: download_one(u, dir) def make_divisible(x, divisor): """Adjusts `x` to be nearest and greater than or equal to value divisible by `divisor`.""" if isinstance(divisor, torch.Tensor): divisor = int(divisor.max()) # to int return math.ceil(x / divisor) * divisor def clean_str(s): """Cleans a string by replacing special characters with underscores, e.g., 'test@string!' to 'test_string_'.""" return re.sub(pattern="[|@#!¡·$€%&()=?¿^*;:,¨´><+]", repl="_", string=s) def one_cycle(y1=0.0, y2=1.0, steps=100): """Generates a lambda for a sinusoidal ramp from y1 to y2 over 'steps'; usage: `lambda x: ((1 - math.cos(x * math.pi / steps)) / 2) * (y2 - y1) + y1`. """ return lambda x: ((1 - math.cos(x * math.pi / steps)) / 2) * (y2 - y1) + y1 def colorstr(*input): """Colors strings using ANSI escape codes; see usage example `colorstr('blue', 'hello world')`. [https://en.wikipedia.org/wiki/ANSI_escape_code] """ *args, string = input if len(input) > 1 else ("blue", "bold", input[0]) # color arguments, string colors = { "black": "\033[30m", # basic colors "red": "\033[31m", "green": "\033[32m", "yellow": "\033[33m", "blue": "\033[34m", "magenta": "\033[35m", "cyan": "\033[36m", "white": "\033[37m", "bright_black": "\033[90m", # bright colors "bright_red": "\033[91m", "bright_green": "\033[92m", "bright_yellow": "\033[93m", "bright_blue": "\033[94m", "bright_magenta": "\033[95m", "bright_cyan": "\033[96m", "bright_white": "\033[97m", "end": "\033[0m", # misc "bold": "\033[1m", "underline": "\033[4m", } return "".join(colors[x] for x in args) + f"{string}" + colors["end"] def labels_to_class_weights(labels, nc=80): """Calculates class weights from labels to counteract dataset imbalance; `labels` is a list of numpy arrays with shape `(n, 5)`. """ if labels[0] is None: # no labels loaded return torch.Tensor() labels = np.concatenate(labels, 0) # labels.shape = (866643, 5) for COCO classes = labels[:, 0].astype(int) # labels = [class xywh] weights = np.bincount(classes, minlength=nc) # occurrences per class # Prepend gridpoint count (for uCE training) # gpi = ((320 / 32 * np.array([1, 2, 4])) ** 2 * 3).sum() # gridpoints per image # weights = np.hstack([gpi * len(labels) - weights.sum() * 9, weights * 9]) ** 0.5 # prepend gridpoints to start weights[weights == 0] = 1 # replace empty bins with 1 weights = 1 / weights # number of targets per class weights /= weights.sum() # normalize return torch.from_numpy(weights).float() def labels_to_image_weights(labels, nc=80, class_weights=np.ones(80)): """Calculates image weights from labels using class weights, for balanced sampling.""" # Usage: index = random.choices(range(n), weights=image_weights, k=1) # weighted image sample class_counts = np.array([np.bincount(x[:, 0].astype(int), minlength=nc) for x in labels]) return (class_weights.reshape(1, nc) * class_counts).sum(1) def coco80_to_coco91_class(): # converts 80-index (val2014) to 91-index (paper) """Converts COCO 80-class index to COCO 91-class index. Reference: https://tech.amikelive.com/node-718/what-object-categories-labels-are-in-coco-dataset/ """ # a = np.loadtxt('data/coco.names', dtype='str', delimiter='\n') # b = np.loadtxt('data/coco_paper.names', dtype='str', delimiter='\n') # x1 = [list(a[i] == b).index(True) + 1 for i in range(80)] # darknet to coco # x2 = [list(b[i] == a).index(True) if any(b[i] == a) else None for i in range(91)] # coco to darknet return [ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 27, 28, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 67, 70, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 84, 85, 86, 87, 88, 89, 90, ] def xyxy2xywh(x): """Converts nx4 bounding boxes from corners [x1, y1, x2, y2] to center format [x, y, w, h].""" y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x) y[..., 0] = (x[..., 0] + x[..., 2]) / 2 # x center y[..., 1] = (x[..., 1] + x[..., 3]) / 2 # y center y[..., 2] = x[..., 2] - x[..., 0] # width y[..., 3] = x[..., 3] - x[..., 1] # height return y def xywh2xyxy(x): """Converts bbox format from [x, y, w, h] to [x1, y1, x2, y2], supporting torch.Tensor and np.ndarray.""" y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x) y[..., 0] = x[..., 0] - x[..., 2] / 2 # top left x y[..., 1] = x[..., 1] - x[..., 3] / 2 # top left y y[..., 2] = x[..., 0] + x[..., 2] / 2 # bottom right x y[..., 3] = x[..., 1] + x[..., 3] / 2 # bottom right y return y def xywhn2xyxy(x, w=640, h=640, padw=0, padh=0): """Converts boxes from normalized [x, y, w, h] to [x1, y1, x2, y2] format, applies padding.""" y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x) y[..., 0] = w * (x[..., 0] - x[..., 2] / 2) + padw # top left x y[..., 1] = h * (x[..., 1] - x[..., 3] / 2) + padh # top left y y[..., 2] = w * (x[..., 0] + x[..., 2] / 2) + padw # bottom right x y[..., 3] = h * (x[..., 1] + x[..., 3] / 2) + padh # bottom right y return y def xyxy2xywhn(x, w=640, h=640, clip=False, eps=0.0): """Converts bounding boxes from [x1, y1, x2, y2] format to normalized [x, y, w, h] format.""" if clip: clip_boxes(x, (h - eps, w - eps)) # warning: inplace clip y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x) y[..., 0] = ((x[..., 0] + x[..., 2]) / 2) / w # x center y[..., 1] = ((x[..., 1] + x[..., 3]) / 2) / h # y center y[..., 2] = (x[..., 2] - x[..., 0]) / w # width y[..., 3] = (x[..., 3] - x[..., 1]) / h # height return y def xyn2xy(x, w=640, h=640, padw=0, padh=0): """Converts normalized segments to pixel segments, shape (n,2), adjusting for width `w`, height `h`, and padding.""" y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x) y[..., 0] = w * x[..., 0] + padw # top left x y[..., 1] = h * x[..., 1] + padh # top left y return y def segment2box(segment, width=640, height=640): """Converts a single segment to a bounding box using image dimensions, output shape (4,), ensuring coordinates stay within image boundaries. """ x, y = segment.T # segment xy inside = (x >= 0) & (y >= 0) & (x <= width) & (y <= height) ( x, y, ) = x[inside], y[inside] return np.array([x.min(), y.min(), x.max(), y.max()]) if any(x) else np.zeros((1, 4)) # xyxy def segments2boxes(segments): """Converts segmentation labels to bounding box labels in format (cls, xywh) from (cls, xy1, xy2, ...).""" boxes = [] for s in segments: x, y = s.T # segment xy boxes.append([x.min(), y.min(), x.max(), y.max()]) # cls, xyxy return xyxy2xywh(np.array(boxes)) # cls, xywh def resample_segments(segments, n=1000): """Resamples segments to a fixed number of points (n), returning up-sampled (n,2) segment arrays.""" for i, s in enumerate(segments): s = np.concatenate((s, s[0:1, :]), axis=0) x = np.linspace(0, len(s) - 1, n) xp = np.arange(len(s)) segments[i] = np.concatenate([np.interp(x, xp, s[:, i]) for i in range(2)]).reshape(2, -1).T # segment xy return segments def scale_boxes(img1_shape, boxes, img0_shape, ratio_pad=None): """Rescales bounding boxes from one image shape to another, optionally with ratio and padding adjustments.""" if ratio_pad is None: # calculate from img0_shape gain = min(img1_shape[0] / img0_shape[0], img1_shape[1] / img0_shape[1]) # gain = old / new pad = (img1_shape[1] - img0_shape[1] * gain) / 2, (img1_shape[0] - img0_shape[0] * gain) / 2 # wh padding else: gain = ratio_pad[0][0] pad = ratio_pad[1] boxes[..., [0, 2]] -= pad[0] # x padding boxes[..., [1, 3]] -= pad[1] # y padding boxes[..., :4] /= gain clip_boxes(boxes, img0_shape) return boxes def scale_segments(img1_shape, segments, img0_shape, ratio_pad=None, normalize=False): """Rescales segment coordinates from img1_shape to img0_shape, optionally normalizing, with support for padding adjustments. """ if ratio_pad is None: # calculate from img0_shape gain = min(img1_shape[0] / img0_shape[0], img1_shape[1] / img0_shape[1]) # gain = old / new pad = (img1_shape[1] - img0_shape[1] * gain) / 2, (img1_shape[0] - img0_shape[0] * gain) / 2 # wh padding else: gain = ratio_pad[0][0] pad = ratio_pad[1] segments[:, 0] -= pad[0] # x padding segments[:, 1] -= pad[1] # y padding segments /= gain clip_segments(segments, img0_shape) if normalize: segments[:, 0] /= img0_shape[1] # width segments[:, 1] /= img0_shape[0] # height return segments def clip_boxes(boxes, shape): """Clips bounding boxes to within the specified image shape; supports both torch.Tensor and np.array.""" if isinstance(boxes, torch.Tensor): # faster individually boxes[..., 0].clamp_(0, shape[1]) # x1 boxes[..., 1].clamp_(0, shape[0]) # y1 boxes[..., 2].clamp_(0, shape[1]) # x2 boxes[..., 3].clamp_(0, shape[0]) # y2 else: # np.array (faster grouped) boxes[..., [0, 2]] = boxes[..., [0, 2]].clip(0, shape[1]) # x1, x2 boxes[..., [1, 3]] = boxes[..., [1, 3]].clip(0, shape[0]) # y1, y2 def clip_segments(segments, shape): """Clips segments to within image shape (height, width), supporting torch.Tensor and np.array inputs.""" if isinstance(segments, torch.Tensor): # faster individually segments[:, 0].clamp_(0, shape[1]) # x segments[:, 1].clamp_(0, shape[0]) # y else: # np.array (faster grouped) segments[:, 0] = segments[:, 0].clip(0, shape[1]) # x segments[:, 1] = segments[:, 1].clip(0, shape[0]) # y def non_max_suppression( prediction, conf_thres=0.25, iou_thres=0.45, classes=None, agnostic=False, multi_label=False, labels=(), max_det=300, nm=0, # number of masks ): """Non-Maximum Suppression (NMS) on inference results to reject overlapping detections. Returns: list of detections, on (n,6) tensor per image [xyxy, conf, cls] """ # Checks assert 0 <= conf_thres <= 1, f"Invalid Confidence threshold {conf_thres}, valid values are between 0.0 and 1.0" assert 0 <= iou_thres <= 1, f"Invalid IoU {iou_thres}, valid values are between 0.0 and 1.0" if isinstance(prediction, (list, tuple)): # YOLOv3 model in validation model, output = (inference_out, loss_out) prediction = prediction[0] # select only inference output device = prediction.device mps = "mps" in device.type # Apple MPS if mps: # MPS not fully supported yet, convert tensors to CPU before NMS prediction = prediction.cpu() bs = prediction.shape[0] # batch size nc = prediction.shape[2] - nm - 5 # number of classes xc = prediction[..., 4] > conf_thres # candidates # Settings # min_wh = 2 # (pixels) minimum box width and height max_wh = 7680 # (pixels) maximum box width and height max_nms = 30000 # maximum number of boxes into torchvision.ops.nms() time_limit = 0.5 + 0.05 * bs # seconds to quit after redundant = True # require redundant detections multi_label &= nc > 1 # multiple labels per box (adds 0.5ms/img) merge = False # use merge-NMS t = time.time() mi = 5 + nc # mask start index output = [torch.zeros((0, 6 + nm), device=prediction.device)] * bs for xi, x in enumerate(prediction): # image index, image inference # Apply constraints # x[((x[..., 2:4] < min_wh) | (x[..., 2:4] > max_wh)).any(1), 4] = 0 # width-height x = x[xc[xi]] # confidence # Cat apriori labels if autolabelling if labels and len(labels[xi]): lb = labels[xi] v = torch.zeros((len(lb), nc + nm + 5), device=x.device) v[:, :4] = lb[:, 1:5] # box v[:, 4] = 1.0 # conf v[range(len(lb)), lb[:, 0].long() + 5] = 1.0 # cls x = torch.cat((x, v), 0) # If none remain process next image if not x.shape[0]: continue # Compute conf x[:, 5:] *= x[:, 4:5] # conf = obj_conf * cls_conf # Box/Mask box = xywh2xyxy(x[:, :4]) # center_x, center_y, width, height) to (x1, y1, x2, y2) mask = x[:, mi:] # zero columns if no masks # Detections matrix nx6 (xyxy, conf, cls) if multi_label: i, j = (x[:, 5:mi] > conf_thres).nonzero(as_tuple=False).T x = torch.cat((box[i], x[i, 5 + j, None], j[:, None].float(), mask[i]), 1) else: # best class only conf, j = x[:, 5:mi].max(1, keepdim=True) x = torch.cat((box, conf, j.float(), mask), 1)[conf.view(-1) > conf_thres] # Filter by class if classes is not None: x = x[(x[:, 5:6] == torch.tensor(classes, device=x.device)).any(1)] # Apply finite constraint # if not torch.isfinite(x).all(): # x = x[torch.isfinite(x).all(1)] # Check shape n = x.shape[0] # number of boxes if not n: # no boxes continue x = x[x[:, 4].argsort(descending=True)[:max_nms]] # sort by confidence and remove excess boxes # Batched NMS c = x[:, 5:6] * (0 if agnostic else max_wh) # classes boxes, scores = x[:, :4] + c, x[:, 4] # boxes (offset by class), scores i = torchvision.ops.nms(boxes, scores, iou_thres) # NMS i = i[:max_det] # limit detections if merge and (1 < n < 3e3): # Merge NMS (boxes merged using weighted mean) # update boxes as boxes(i,4) = weights(i,n) * boxes(n,4) iou = box_iou(boxes[i], boxes) > iou_thres # iou matrix weights = iou * scores[None] # box weights x[i, :4] = torch.mm(weights, x[:, :4]).float() / weights.sum(1, keepdim=True) # merged boxes if redundant: i = i[iou.sum(1) > 1] # require redundancy output[xi] = x[i] if mps: output[xi] = output[xi].to(device) if (time.time() - t) > time_limit: LOGGER.warning(f"WARNING ⚠️ NMS time limit {time_limit:.3f}s exceeded") break # time limit exceeded return output def strip_optimizer(f="best.pt", s=""): # from utils.general import *; strip_optimizer() """Strips optimizer from a checkpoint file 'f', optionally saving as 's', to finalize training.""" x = torch_load(f, map_location=torch.device("cpu")) if x.get("ema"): x["model"] = x["ema"] # replace model with ema for k in "optimizer", "best_fitness", "ema", "updates": # keys x[k] = None x["epoch"] = -1 x["model"].half() # to FP16 for p in x["model"].parameters(): p.requires_grad = False torch.save(x, s or f) mb = os.path.getsize(s or f) / 1e6 # filesize LOGGER.info(f"Optimizer stripped from {f},{f' saved as {s},' if s else ''} {mb:.1f}MB") def print_mutation(keys, results, hyp, save_dir, bucket, prefix=colorstr("evolve: ")): """Logs mutation results, updates evolve CSV/YAML, optionally syncs with cloud storage.""" evolve_csv = save_dir / "evolve.csv" evolve_yaml = save_dir / "hyp_evolve.yaml" keys = tuple(keys) + tuple(hyp.keys()) # [results + hyps] keys = tuple(x.strip() for x in keys) vals = results + tuple(hyp.values()) n = len(keys) # Download (optional) if bucket: url = f"gs://{bucket}/evolve.csv" if gsutil_getsize(url) > (evolve_csv.stat().st_size if evolve_csv.exists() else 0): subprocess.run(["gsutil", "cp", f"{url}", f"{save_dir}"]) # download evolve.csv if larger than local # Log to evolve.csv s = "" if evolve_csv.exists() else (("%20s," * n % keys).rstrip(",") + "\n") # add header with open(evolve_csv, "a") as f: f.write(s + ("%20.5g," * n % vals).rstrip(",") + "\n") # Save yaml with open(evolve_yaml, "w") as f: data = pd.read_csv(evolve_csv, skipinitialspace=True) data = data.rename(columns=lambda x: x.strip()) # strip keys i = np.argmax(fitness(data.values[:, :4])) # generations = len(data) f.write( "# YOLOv3 Hyperparameter Evolution Results\n" + f"# Best generation: {i}\n" + f"# Last generation: {generations - 1}\n" + "# " + ", ".join(f"{x.strip():>20s}" for x in keys[:7]) + "\n" + "# " + ", ".join(f"{x:>20.5g}" for x in data.values[i, :7]) + "\n\n" ) yaml.safe_dump(data.loc[i][7:].to_dict(), f, sort_keys=False) # Print to screen LOGGER.info( prefix + f"{generations} generations finished, current result:\n" + prefix + ", ".join(f"{x.strip():>20s}" for x in keys) + "\n" + prefix + ", ".join(f"{x:20.5g}" for x in vals) + "\n\n" ) if bucket: subprocess.run(["gsutil", "cp", f"{evolve_csv}", f"{evolve_yaml}", f"gs://{bucket}"]) # upload def apply_classifier(x, model, img, im0): """Applies a second stage classifier to YOLO outputs, adjusting box shapes and filtering class matches.""" # Example model = torchvision.models.__dict__['efficientnet_b0'](pretrained=True).to(device).eval() im0 = [im0] if isinstance(im0, np.ndarray) else im0 for i, d in enumerate(x): # per image if d is not None and len(d): d = d.clone() # Reshape and pad cutouts b = xyxy2xywh(d[:, :4]) # boxes b[:, 2:] = b[:, 2:].max(1)[0].unsqueeze(1) # rectangle to square b[:, 2:] = b[:, 2:] * 1.3 + 30 # pad d[:, :4] = xywh2xyxy(b).long() # Rescale boxes from img_size to im0 size scale_boxes(img.shape[2:], d[:, :4], im0[i].shape) # Classes pred_cls1 = d[:, 5].long() ims = [] for a in d: cutout = im0[i][int(a[1]) : int(a[3]), int(a[0]) : int(a[2])] im = cv2.resize(cutout, (224, 224)) # BGR im = im[:, :, ::-1].transpose(2, 0, 1) # BGR to RGB, to 3x416x416 im = np.ascontiguousarray(im, dtype=np.float32) # uint8 to float32 im /= 255 # 0 - 255 to 0.0 - 1.0 ims.append(im) pred_cls2 = model(torch.Tensor(ims).to(d.device)).argmax(1) # classifier prediction x[i] = x[i][pred_cls1 == pred_cls2] # retain matching class detections return x def increment_path(path, exist_ok=False, sep="", mkdir=False): """Increments file or directory path, optionally creating the directory, not thread-safe. Args: path (str/Path), exist_ok (bool), sep (str), mkdir (bool). """ path = Path(path) # os-agnostic if path.exists() and not exist_ok: path, suffix = (path.with_suffix(""), path.suffix) if path.is_file() else (path, "") # Method 1 for n in range(2, 9999): p = f"{path}{sep}{n}{suffix}" # increment path if not os.path.exists(p): # break path = Path(p) # Method 2 (deprecated) # dirs = glob.glob(f"{path}{sep}*") # similar paths # matches = [re.search(rf"{path.stem}{sep}(\d+)", d) for d in dirs] # i = [int(m.groups()[0]) for m in matches if m] # indices # n = max(i) + 1 if i else 2 # increment number # path = Path(f"{path}{sep}{n}{suffix}") # increment path if mkdir: path.mkdir(parents=True, exist_ok=True) # make directory return path # OpenCV Multilanguage-friendly functions # ------------------------------------------------------------------------------------ imshow_ = cv2.imshow # copy to avoid recursion errors def imread(filename, flags=cv2.IMREAD_COLOR): """Reads an image from a file, supporting multilanguage paths, and returns it in the specified color scheme.""" return cv2.imdecode(np.fromfile(filename, np.uint8), flags) def imwrite(filename, img): """Writes an image to a file; returns True on success, False on failure. Args: filename (str), img (ndarray). """ try: cv2.imencode(Path(filename).suffix, img)[1].tofile(filename) return True except Exception: return False def imshow(path, im): """Displays an image; accepts a path (str) and image data (ndarray) as arguments.""" imshow_(path.encode("unicode_escape").decode(), im) if Path(inspect.stack()[0].filename).parent.parent.as_posix() in inspect.stack()[-1].filename: cv2.imread, cv2.imwrite, cv2.imshow = imread, imwrite, imshow # redefine # Variables ------------------------------------------------------------------------------------------------------------ ================================================ FILE: utils/google_app_engine/Dockerfile ================================================ FROM gcr.io/google-appengine/python # Create a virtualenv for dependencies. This isolates these packages from # system-level packages. # Use -p python3 or -p python3.7 to select python version. Default is version 2. RUN virtualenv /env -p python3 # Setting these environment variables are the same as running # source /env/bin/activate. ENV VIRTUAL_ENV /env ENV PATH /env/bin:$PATH RUN apt-get update && apt-get install -y python-opencv # Copy the application's requirements.txt and run pip to install all # dependencies into the virtualenv. ADD requirements.txt /app/requirements.txt RUN pip install -r /app/requirements.txt # Add the application source code. ADD . /app # Run a WSGI server to serve the application. gunicorn must be declared as # a dependency in requirements.txt. CMD gunicorn -b :$PORT main:app ================================================ FILE: utils/google_app_engine/additional_requirements.txt ================================================ # add these requirements in your app on top of the existing ones pip==26.0 Flask==3.1.3 gunicorn==23.0.0 werkzeug>=3.0.1 # not directly required, pinned by Snyk to avoid a vulnerability zipp>=3.19.1 # not directly required, pinned by Snyk to avoid a vulnerability ================================================ FILE: utils/google_app_engine/app.yaml ================================================ # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license runtime: custom env: flex service: yolov5app liveness_check: initial_delay_sec: 600 manual_scaling: instances: 1 resources: cpu: 1 memory_gb: 4 disk_size_gb: 20 ================================================ FILE: utils/loggers/__init__.py ================================================ # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license """Logging utils.""" import os import warnings from pathlib import Path import torch from packaging.version import parse from utils.general import LOGGER, colorstr, cv2 from utils.loggers.clearml.clearml_utils import ClearmlLogger from utils.loggers.wandb.wandb_utils import WandbLogger from utils.plots import plot_images, plot_labels, plot_results from utils.torch_utils import de_parallel LOGGERS = ("csv", "tb", "wandb", "clearml", "comet") # *.csv, TensorBoard, Weights & Biases, ClearML RANK = int(os.getenv("RANK", -1)) try: from torch.utils.tensorboard import SummaryWriter except ImportError: def SummaryWriter(*args): """Imports TensorBoard's SummaryWriter for logging, with a fallback returning None if TensorBoard is not installed. """ return None # None = SummaryWriter(str) try: import wandb assert hasattr(wandb, "__version__") # verify package import not local dir if parse(wandb.__version__) >= parse("0.12.2") and RANK in {0, -1}: try: wandb_login_success = wandb.login(timeout=30) except wandb.errors.UsageError: # known non-TTY terminal issue wandb_login_success = False if not wandb_login_success: wandb = None except (ImportError, AssertionError): wandb = None try: import clearml assert hasattr(clearml, "__version__") # verify package import not local dir except (ImportError, AssertionError): clearml = None try: if RANK in {0, -1}: import comet_ml assert hasattr(comet_ml, "__version__") # verify package import not local dir from utils.loggers.comet import CometLogger else: comet_ml = None except (ImportError, AssertionError): comet_ml = None class Loggers: """Manages logging for training and validation using TensorBoard, Weights & Biases, ClearML, and Comet ML.""" def __init__(self, save_dir=None, weights=None, opt=None, hyp=None, logger=None, include=LOGGERS): """Initializes YOLOv3 logging with directory, weights, options, hyperparameters, and includes specified loggers. """ self.save_dir = save_dir self.weights = weights self.opt = opt self.hyp = hyp self.plots = not opt.noplots # plot results self.logger = logger # for printing results to console self.include = include self.keys = [ "train/box_loss", "train/obj_loss", "train/cls_loss", # train loss "metrics/precision", "metrics/recall", "metrics/mAP_0.5", "metrics/mAP_0.5:0.95", # metrics "val/box_loss", "val/obj_loss", "val/cls_loss", # val loss "x/lr0", "x/lr1", "x/lr2", ] # params self.best_keys = ["best/epoch", "best/precision", "best/recall", "best/mAP_0.5", "best/mAP_0.5:0.95"] for k in LOGGERS: setattr(self, k, None) # init empty logger dictionary self.csv = True # always log to csv # Messages if not comet_ml: prefix = colorstr("Comet: ") s = f"{prefix}run 'pip install comet_ml' to automatically track and visualize YOLOv3 🚀 runs in Comet" self.logger.info(s) # TensorBoard s = self.save_dir if "tb" in self.include and not self.opt.evolve: prefix = colorstr("TensorBoard: ") self.logger.info(f"{prefix}Start with 'tensorboard --logdir {s.parent}', view at http://localhost:6006/") self.tb = SummaryWriter(str(s)) # W&B if wandb and "wandb" in self.include: self.opt.hyp = self.hyp # add hyperparameters self.wandb = WandbLogger(self.opt) else: self.wandb = None # ClearML if clearml and "clearml" in self.include: try: self.clearml = ClearmlLogger(self.opt, self.hyp) except Exception: self.clearml = None prefix = colorstr("ClearML: ") LOGGER.warning( f"{prefix}WARNING ⚠️ ClearML is installed but not configured, skipping ClearML logging." f" See https://docs.ultralytics.com/yolov5/tutorials/clearml_logging_integration#readme" ) else: self.clearml = None # Comet if comet_ml and "comet" in self.include: if isinstance(self.opt.resume, str) and self.opt.resume.startswith("comet://"): run_id = self.opt.resume.split("/")[-1] self.comet_logger = CometLogger(self.opt, self.hyp, run_id=run_id) else: self.comet_logger = CometLogger(self.opt, self.hyp) else: self.comet_logger = None @property def remote_dataset(self): """Fetches dataset dictionary from ClearML, W&B, or Comet ML based on the logger instantiated.""" data_dict = None if self.clearml: data_dict = self.clearml.data_dict if self.wandb: data_dict = self.wandb.data_dict if self.comet_logger: data_dict = self.comet_logger.data_dict return data_dict def on_train_start(self): """Calls `on_train_start` method on comet_logger if it's available.""" if self.comet_logger: self.comet_logger.on_train_start() def on_pretrain_routine_start(self): """Initiates pretraining routine on comet_logger if available.""" if self.comet_logger: self.comet_logger.on_pretrain_routine_start() def on_pretrain_routine_end(self, labels, names): """Logs pretrain routine end, plots labels if enabled, updates WandB/Comet with images. Takes `labels` (List of int), `names` (List of str). """ if self.plots: plot_labels(labels, names, self.save_dir) paths = self.save_dir.glob("*labels*.jpg") # training labels if self.wandb: self.wandb.log({"Labels": [wandb.Image(str(x), caption=x.name) for x in paths]}) # if self.clearml: # pass # ClearML saves these images automatically using hooks if self.comet_logger: self.comet_logger.on_pretrain_routine_end(paths) def on_train_batch_end(self, model, ni, imgs, targets, paths, vals): """Logs training batch details, plots initial batches, logs Tensorboard and WandB/ClearML if enabled.""" log_dict = dict(zip(self.keys[:3], vals)) # Callback runs on train batch end # ni: number integrated batches (since train start) if self.plots: if ni < 3: f = self.save_dir / f"train_batch{ni}.jpg" # filename plot_images(imgs, targets, paths, f) if ni == 0 and self.tb and not self.opt.sync_bn: log_tensorboard_graph(self.tb, model, imgsz=(self.opt.imgsz, self.opt.imgsz)) if ni == 10 and (self.wandb or self.clearml): files = sorted(self.save_dir.glob("train*.jpg")) if self.wandb: self.wandb.log({"Mosaics": [wandb.Image(str(f), caption=f.name) for f in files if f.exists()]}) if self.clearml: self.clearml.log_debug_samples(files, title="Mosaics") if self.comet_logger: self.comet_logger.on_train_batch_end(log_dict, step=ni) def on_train_epoch_end(self, epoch): """Callback that updates the current epoch in wandb at the end of each training epoch.""" if self.wandb: self.wandb.current_epoch = epoch + 1 if self.comet_logger: self.comet_logger.on_train_epoch_end(epoch) def on_val_start(self): """Callback that notifies the comet logger at the start of each validation phase.""" if self.comet_logger: self.comet_logger.on_val_start() def on_val_image_end(self, pred, predn, path, names, im): """Callback for logging a single validation image and its predictions to WandB or ClearML at the end of validation. """ if self.wandb: self.wandb.val_one_image(pred, predn, path, names, im) if self.clearml: self.clearml.log_image_with_boxes(path, pred, names, im) def on_val_batch_end(self, batch_i, im, targets, paths, shapes, out): """Logs a single validation batch for Comet ML analytics (batch_i: int, im: tensor, targets: tensor, paths:. list, shapes: list, out: tensor). """ if self.comet_logger: self.comet_logger.on_val_batch_end(batch_i, im, targets, paths, shapes, out) def on_val_end(self, nt, tp, fp, p, r, f1, ap, ap50, ap_class, confusion_matrix): """Logs validation results and images on validation end for visual analytics.""" if self.wandb or self.clearml: files = sorted(self.save_dir.glob("val*.jpg")) if self.wandb: self.wandb.log({"Validation": [wandb.Image(str(f), caption=f.name) for f in files]}) if self.clearml: self.clearml.log_debug_samples(files, title="Validation") if self.comet_logger: self.comet_logger.on_val_end(nt, tp, fp, p, r, f1, ap, ap50, ap_class, confusion_matrix) def on_fit_epoch_end(self, vals, epoch, best_fitness, fi): """Logs epoch results to CSV if enabled, updating with vals, best_fitness, and fi.""" x = dict(zip(self.keys, vals)) if self.csv: file = self.save_dir / "results.csv" n = len(x) + 1 # number of cols s = "" if file.exists() else (("%20s," * n % tuple(["epoch", *self.keys])).rstrip(",") + "\n") # add header with open(file, "a") as f: f.write(s + ("%20.5g," * n % tuple([epoch, *vals])).rstrip(",") + "\n") if self.tb: for k, v in x.items(): self.tb.add_scalar(k, v, epoch) elif self.clearml: # log to ClearML if TensorBoard not used for k, v in x.items(): title, series = k.split("/") self.clearml.task.get_logger().report_scalar(title, series, v, epoch) if self.wandb: if best_fitness == fi: best_results = [epoch, *vals[3:7]] for i, name in enumerate(self.best_keys): self.wandb.wandb_run.summary[name] = best_results[i] # log best results in the summary self.wandb.log(x) self.wandb.end_epoch() if self.clearml: self.clearml.current_epoch_logged_images = set() # reset epoch image limit self.clearml.current_epoch += 1 if self.comet_logger: self.comet_logger.on_fit_epoch_end(x, epoch=epoch) def on_model_save(self, last, epoch, final_epoch, best_fitness, fi): """Logs model to WandB/ClearML, considering save_period and if not final_epoch, also notes if best model so far. """ if (epoch + 1) % self.opt.save_period == 0 and not final_epoch and self.opt.save_period != -1: if self.wandb: self.wandb.log_model(last.parent, self.opt, epoch, fi, best_model=best_fitness == fi) if self.clearml: self.clearml.task.update_output_model( model_path=str(last), model_name="Latest Model", auto_delete_file=False ) if self.comet_logger: self.comet_logger.on_model_save(last, epoch, final_epoch, best_fitness, fi) def on_train_end(self, last, best, epoch, results): """Callback to execute at training end, saving plots of results and relevant metrics to the specified save directory. """ if self.plots: plot_results(file=self.save_dir / "results.csv") # save results.png files = ["results.png", "confusion_matrix.png", *(f"{x}_curve.png" for x in ("F1", "PR", "P", "R"))] files = [(self.save_dir / f) for f in files if (self.save_dir / f).exists()] # filter self.logger.info(f"Results saved to {colorstr('bold', self.save_dir)}") if self.tb and not self.clearml: # These images are already captured by ClearML by now, we don't want doubles for f in files: self.tb.add_image(f.stem, cv2.imread(str(f))[..., ::-1], epoch, dataformats="HWC") if self.wandb: self.wandb.log(dict(zip(self.keys[3:10], results))) self.wandb.log({"Results": [wandb.Image(str(f), caption=f.name) for f in files]}) # Calling wandb.log. TODO: Refactor this into WandbLogger.log_model if not self.opt.evolve: wandb.log_artifact( str(best if best.exists() else last), type="model", name=f"run_{self.wandb.wandb_run.id}_model", aliases=["latest", "best", "stripped"], ) self.wandb.finish_run() if self.clearml and not self.opt.evolve: self.clearml.task.update_output_model( model_path=str(best if best.exists() else last), name="Best Model", auto_delete_file=False ) if self.comet_logger: final_results = dict(zip(self.keys[3:10], results)) self.comet_logger.on_train_end(files, self.save_dir, last, best, epoch, final_results) def on_params_update(self, params: dict): """Updates experiment hyperparameters or configs in WandB and Comet logger with provided params dictionary.""" if self.wandb: self.wandb.wandb_run.config.update(params, allow_val_change=True) if self.comet_logger: self.comet_logger.on_params_update(params) class GenericLogger: """YOLOv3 General purpose logger for non-task specific logging Usage: from utils.loggers import GenericLogger; logger = GenericLogger(...). Args: opt: Run arguments console_logger: Console logger include: loggers to include """ def __init__(self, opt, console_logger, include=("tb", "wandb")): """Initializes a generic logger for YOLOv3, including options for TensorBoard and wandb logging.""" self.save_dir = Path(opt.save_dir) self.include = include self.console_logger = console_logger self.csv = self.save_dir / "results.csv" # CSV logger if "tb" in self.include: prefix = colorstr("TensorBoard: ") self.console_logger.info( f"{prefix}Start with 'tensorboard --logdir {self.save_dir.parent}', view at http://localhost:6006/" ) self.tb = SummaryWriter(str(self.save_dir)) if wandb and "wandb" in self.include: self.wandb = wandb.init( project=web_project_name(str(opt.project)), name=None if opt.name == "exp" else opt.name, config=opt ) else: self.wandb = None def log_metrics(self, metrics, epoch): """Logs metric dictionary to all loggers, including CSV with keys, values, and epoch.""" if self.csv: keys, vals = list(metrics.keys()), list(metrics.values()) n = len(metrics) + 1 # number of cols s = "" if self.csv.exists() else (("%23s," * n % tuple(["epoch", *keys])).rstrip(",") + "\n") # header with open(self.csv, "a") as f: f.write(s + ("%23.5g," * n % tuple([epoch, *vals])).rstrip(",") + "\n") if self.tb: for k, v in metrics.items(): self.tb.add_scalar(k, v, epoch) if self.wandb: self.wandb.log(metrics, step=epoch) def log_images(self, files, name="Images", epoch=0): """Logs images to TensorBoard and Weights & Biases, ensuring file existence and supporting various formats.""" files = [Path(f) for f in (files if isinstance(files, (tuple, list)) else [files])] # to Path files = [f for f in files if f.exists()] # filter by exists if self.tb: for f in files: self.tb.add_image(f.stem, cv2.imread(str(f))[..., ::-1], epoch, dataformats="HWC") if self.wandb: self.wandb.log({name: [wandb.Image(str(f), caption=f.name) for f in files]}, step=epoch) def log_graph(self, model, imgsz=(640, 640)): """Logs model graph to all loggers, accepts `model` and `imgsz` (default (640, 640)) as inputs.""" if self.tb: log_tensorboard_graph(self.tb, model, imgsz) def log_model(self, model_path, epoch=0, metadata=None): """Logs model to all loggers with `model_path`, optional `epoch` (default 0), and `metadata` dictionary.""" if metadata is None: metadata = {} if self.wandb: art = wandb.Artifact(name=f"run_{wandb.run.id}_model", type="model", metadata=metadata) art.add_file(str(model_path)) wandb.log_artifact(art) def update_params(self, params): """Updates logged parameters in wandb; `params`: dictionary to update, requires `wandb` to be initialized.""" if self.wandb: wandb.run.config.update(params, allow_val_change=True) def log_tensorboard_graph(tb, model, imgsz=(640, 640)): """Logs a model graph to TensorBoard using an all-zero input image of shape `(1, 3, imgsz, imgsz)`.""" try: p = next(model.parameters()) # for device, type imgsz = (imgsz, imgsz) if isinstance(imgsz, int) else imgsz # expand im = torch.zeros((1, 3, *imgsz)).to(p.device).type_as(p) # input image (WARNING: must be zeros, not empty) with warnings.catch_warnings(): warnings.simplefilter("ignore") # suppress jit trace warning tb.add_graph(torch.jit.trace(de_parallel(model), im, strict=False), []) except Exception as e: LOGGER.warning(f"WARNING ⚠️ TensorBoard graph visualization failure {e}") def web_project_name(project): """Converts local project name to a web-friendly format by adding a suffix based on its type (classify or segment). """ if not project.startswith("runs/train"): return project suffix = "-Classify" if project.endswith("-cls") else "-Segment" if project.endswith("-seg") else "" return f"YOLOv3{suffix}" ================================================ FILE: utils/loggers/clearml/README.md ================================================ Ultralytics logo # ClearML Integration for Ultralytics YOLO This 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. Clear|MLClear|ML ## ✨ About ClearML [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: - **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/). - **Data Versioning**: Manage and access your custom training datasets with ClearML Data Versioning. See how [Ultralytics datasets](https://docs.ultralytics.com/datasets/) are structured. - **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/). - **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/). - **Model Deployment**: Deploy trained YOLO models as scalable APIs with ClearML Serving in just a few steps. You can leverage any combination of these tools to fit your project requirements. ![ClearML scalars dashboard](https://raw.githubusercontent.com/thepycoder/clearml_screenshots/main/experiment_manager_with_compare.gif) ## 🦾 Setting Up ClearML To use ClearML, connect the SDK to a ClearML Server instance. You have two main options: 1. **ClearML Hosted Service**: Register for a free account at the [ClearML Hosted Service](https://app.clear.ml/). 2. **Self-Hosted Server**: Deploy your own [ClearML Server](https://clear.ml/docs/latest/docs/deploying_clearml/clearml_server) for full control and data privacy. Follow these steps to get started: 1. Install the `clearml` Python package: ```bash pip install clearml ``` 2. Connect the ClearML SDK to your server. Generate credentials in the ClearML Web UI (Settings → Workspace → Create new credentials) and run: ```bash clearml-init ``` Follow the prompts to complete setup. Once configured, ClearML is ready to integrate with your YOLO workflows! 😎 ## 🚀 Training YOLO With ClearML Enabling ClearML experiment tracking for YOLO is simple. Ensure the `clearml` package is installed: ```bash pip install clearml > =1.2.0 ``` With ClearML installed, every YOLO [training run](https://docs.ultralytics.com/modes/train/) is automatically logged. By 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. **Example Training Command:** ```bash # Train with default project/task names python train.py --img 640 --batch 16 --epochs 3 --data coco128.yaml --weights yolov5s.pt --cache ``` **Example with Custom Names:** ```bash # Train with custom project and task names python train.py --project my_yolo_project --name experiment_001 --img 640 --batch 16 --epochs 3 --data coco128.yaml --weights yolov5s.pt --cache ``` ClearML will automatically capture: - Git repository details (URL, commit ID, entry point) and local code changes - Installed Python packages and versions - [Hyperparameters](https://www.ultralytics.com/glossary/hyperparameter-tuning) and script arguments - [Model checkpoints](https://www.ultralytics.com/glossary/model-weights) (use `--save-period n` to save every `n` epochs) - Console output (stdout and stderr) - 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 - Machine details, runtime, and creation date - Generated plots like label correlograms and [confusion matrices](https://www.ultralytics.com/glossary/confusion-matrix) - Debug samples: images with bounding boxes, mosaic visualizations, and validation images per epoch This 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). ## 🔗 Dataset Version Management Versioning 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. ![ClearML Dataset Interface](https://raw.githubusercontent.com/thepycoder/clearml_screenshots/main/clearml_data.gif) ### Prepare Your Dataset YOLO 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: ``` ../ ├── yolov3/ # Your repository └── datasets/ └── coco128/ # Dataset root folder ├── images/ ├── labels/ ├── coco128.yaml # Dataset configuration file <--- IMPORTANT ├── LICENSE └── README.txt ``` Ensure your custom dataset follows a similar structure. ⚠️ **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. ### Upload Your Dataset Navigate to your dataset's root folder and use the `clearml-data` CLI tool to upload and version it: ```bash # Navigate to the dataset directory cd ../datasets/coco128 # Sync the dataset with ClearML (creates a versioned dataset) clearml-data sync --project "YOLO Datasets" --name coco128 --folder . ``` This command creates a new ClearML dataset (or a new version if it exists) named `coco128` within the `YOLO Datasets` project. Alternatively, use granular commands: ```bash # Create a new dataset task clearml-data create --project "YOLO Datasets" --name coco128 # Add files to the dataset (use '.' for current folder) clearml-data add --files . # Finalize and upload the dataset version clearml-data close ``` ### Run Training Using a ClearML Dataset Once 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. ```bash # Replace with the actual ID from ClearML python train.py --img 640 --batch 16 --epochs 3 --data clearml:// yolov5s.pt --cache < your_dataset_id > --weights ``` The dataset ID used will be logged as a parameter in your ClearML experiment, ensuring full traceability. ## 👀 Hyperparameter Optimization ClearML'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. To 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. 1. Locate the HPO script at `utils/loggers/clearml/hpo.py`. 2. Edit the script to include the `template task` ID. 3. Optionally, install [Optuna](https://optuna.org/) (`pip install optuna`) for advanced optimization strategies, or use the default `RandomSearch`. 4. Run the script: ```bash python utils/loggers/clearml/hpo.py ``` This 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. ![HPO in ClearML UI](https://raw.githubusercontent.com/thepycoder/clearml_screenshots/main/hpo.png) ## 🤯 Remote Execution (Advanced) ClearML 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. - **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). Turn any machine into a ClearML Agent by running: ```bash # Replace with your queue(s) name(s) clearml-agent daemon --queue < queues_to_listen_to > [--docker] # Use --docker to run in a Docker container ``` ### Cloning, Editing, and Enqueuing Tasks You can manage remote execution tasks through the ClearML Web UI: 1. **Clone**: Right-click an existing experiment to clone it. 2. **Edit**: Modify hyperparameters or other configurations in the cloned task. 3. **Enqueue**: Right-click the modified task and select "Enqueue" to assign it to a specific queue monitored by your agents. ![Enqueue a task from the ClearML UI](https://raw.githubusercontent.com/thepycoder/clearml_screenshots/main/enqueue.gif) ### Executing a Task Remotely via Code Alternatively, 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`: ```python # ... inside train.py ... # Loggers setup if RANK in {-1, 0}: # Initialize loggers, including ClearML loggers = Loggers(save_dir, weights, opt, hyp, LOGGER) if loggers.clearml: # Add this line to send the task to a queue for remote execution loggers.clearml.task.execute_remotely(queue_name="my_default_queue") # Get dataset dictionary if using ClearML datasets data_dict = loggers.clearml.data_dict # ... rest of the script ... ``` When 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. ### Autoscaling Agents ClearML 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. Learn how to set up autoscalers: [![Watch the Autoscaler setup video](https://img.youtube.com/vi/j4XVMAaUt3E/0.jpg)](https://youtu.be/j4XVMAaUt3E) ## 👋 Contribute Contributions 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! [![Ultralytics open-source contributors](https://raw.githubusercontent.com/ultralytics/assets/main/im/image-contributors.png)](https://github.com/ultralytics/ultralytics/graphs/contributors) ================================================ FILE: utils/loggers/clearml/__init__.py ================================================ # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license ================================================ FILE: utils/loggers/clearml/clearml_utils.py ================================================ # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license """Main Logger class for ClearML experiment tracking.""" import glob import re from pathlib import Path import numpy as np import yaml from ultralytics.utils.plotting import Annotator, colors try: import clearml from clearml import Dataset, Task assert hasattr(clearml, "__version__") # verify package import not local dir except (ImportError, AssertionError): clearml = None def construct_dataset(clearml_info_string): """Load in a clearml dataset and fill the internal data_dict with its contents.""" dataset_id = clearml_info_string.replace("clearml://", "") dataset = Dataset.get(dataset_id=dataset_id) dataset_root_path = Path(dataset.get_local_copy()) # We'll search for the yaml file definition in the dataset yaml_filenames = list(glob.glob(str(dataset_root_path / "*.yaml")) + glob.glob(str(dataset_root_path / "*.yml"))) if len(yaml_filenames) > 1: raise ValueError( "More than one yaml file was found in the dataset root, cannot determine which one contains " "the dataset definition this way." ) elif not yaml_filenames: raise ValueError( "No yaml definition found in dataset root path, check that there is a correct yaml file " "inside the dataset root path." ) with open(yaml_filenames[0]) as f: dataset_definition = yaml.safe_load(f) assert set(dataset_definition.keys()).issuperset({"train", "test", "val", "nc", "names"}), ( "The right keys were not found in the yaml file, make sure it at least has the following keys: ('train', 'test', 'val', 'nc', 'names')" ) data_dict = { "train": ( str((dataset_root_path / dataset_definition["train"]).resolve()) if dataset_definition["train"] else None ) } data_dict["test"] = ( str((dataset_root_path / dataset_definition["test"]).resolve()) if dataset_definition["test"] else None ) data_dict["val"] = ( str((dataset_root_path / dataset_definition["val"]).resolve()) if dataset_definition["val"] else None ) data_dict["nc"] = dataset_definition["nc"] data_dict["names"] = dataset_definition["names"] return data_dict class ClearmlLogger: """Log training runs, datasets, models, and predictions to ClearML. This logger sends information to ClearML at app.clear.ml or to your own hosted server. By default, this information includes hyperparameters, system configuration and metrics, model metrics, code information and basic data metrics and analyses. By providing additional command line arguments to train.py, datasets, models and predictions can also be logged. """ def __init__(self, opt, hyp): """- Initialize ClearML Task, this object will capture the experiment - Upload dataset version to ClearML Data if opt.upload_dataset is True. Args: opt (namespace) -- Commandline arguments for this run: hyp (dict) -- Hyperparameters for this run """ self.current_epoch = 0 # Keep tracked of amount of logged images to enforce a limit self.current_epoch_logged_images = set() # Maximum number of images to log to clearML per epoch self.max_imgs_to_log_per_epoch = 16 # Get the interval of epochs when bounding box images should be logged self.bbox_interval = opt.bbox_interval self.clearml = clearml self.task = None self.data_dict = None if self.clearml: self.task = Task.init( project_name=opt.project if opt.project != "runs/train" else "YOLOv3", task_name=opt.name if opt.name != "exp" else "Training", tags=["YOLOv3"], output_uri=True, reuse_last_task_id=opt.exist_ok, auto_connect_frameworks={"pytorch": False}, # We disconnect pytorch auto-detection, because we added manual model save points in the code ) # ClearML's hooks will already grab all general parameters # Only the hyperparameters coming from the yaml config file # will have to be added manually! self.task.connect(hyp, name="Hyperparameters") self.task.connect(opt, name="Args") # Make sure the code is easily remotely runnable by setting the docker image to use by the remote agent self.task.set_base_docker( "ultralytics/yolov5:latest", docker_arguments='--ipc=host -e="CLEARML_AGENT_SKIP_PYTHON_ENV_INSTALL=1"', docker_setup_bash_script="pip install clearml", ) # Get ClearML Dataset Version if requested if opt.data.startswith("clearml://"): # data_dict should have the following keys: # names, nc (number of classes), test, train, val (all three relative paths to ../datasets) self.data_dict = construct_dataset(opt.data) # Set data to data_dict because wandb will crash without this information and opt is the best way # to give it to them opt.data = self.data_dict def log_debug_samples(self, files, title="Debug Samples"): """Log files (images) as debug samples in the ClearML task. Args: files (List(PosixPath)) a list of file paths in PosixPath format: title (str) A title that groups together images with the same values """ for f in files: if f.exists(): it = re.search(r"_batch(\d+)", f.name) iteration = int(it.groups()[0]) if it else 0 self.task.get_logger().report_image( title=title, series=f.name.replace(it.group(), ""), local_path=str(f), iteration=iteration ) def log_image_with_boxes(self, image_path, boxes, class_names, image, conf_threshold=0.25): """Draw the bounding boxes on a single image and report the result as a ClearML debug sample. Args: image_path (PosixPath) the path the original image file boxes (list): list of scaled predictions in the format - [xmin, ymin, xmax, ymax, confidence, class] class_names (dict): dict containing mapping of class int to class name image (Tensor): A torch tensor containing the actual image data """ if ( len(self.current_epoch_logged_images) < self.max_imgs_to_log_per_epoch and self.current_epoch >= 0 and (self.current_epoch % self.bbox_interval == 0 and image_path not in self.current_epoch_logged_images) ): im = np.ascontiguousarray(np.moveaxis(image.mul(255).clamp(0, 255).byte().cpu().numpy(), 0, 2)) annotator = Annotator(im=im, pil=True) for i, (conf, class_nr, box) in enumerate(zip(boxes[:, 4], boxes[:, 5], boxes[:, :4])): color = colors(i) class_name = class_names[int(class_nr)] confidence_percentage = round(float(conf) * 100, 2) label = f"{class_name}: {confidence_percentage}%" if conf > conf_threshold: annotator.rectangle(box.cpu().numpy(), outline=color) annotator.box_label(box.cpu().numpy(), label=label, color=color) annotated_image = annotator.result() self.task.get_logger().report_image( title="Bounding Boxes", series=image_path.name, iteration=self.current_epoch, image=annotated_image ) self.current_epoch_logged_images.add(image_path) ================================================ FILE: utils/loggers/clearml/hpo.py ================================================ # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license from clearml import Task # Connecting ClearML with the current process, # from here on everything is logged automatically from clearml.automation import HyperParameterOptimizer, UniformParameterRange from clearml.automation.optuna import OptimizerOptuna task = Task.init( project_name="Hyper-Parameter Optimization", task_name="YOLOv3", task_type=Task.TaskTypes.optimizer, reuse_last_task_id=False, ) # Example use case: optimizer = HyperParameterOptimizer( # This is the experiment we want to optimize base_task_id="", # here we define the hyper-parameters to optimize # Notice: The parameter name should exactly match what you see in the UI: / # For Example, here we see in the base experiment a section Named: "General" # under it a parameter named "batch_size", this becomes "General/batch_size" # If you have `argparse` for example, then arguments will appear under the "Args" section, # and you should instead pass "Args/batch_size" hyper_parameters=[ UniformParameterRange("Hyperparameters/lr0", min_value=1e-5, max_value=1e-1), UniformParameterRange("Hyperparameters/lrf", min_value=0.01, max_value=1.0), UniformParameterRange("Hyperparameters/momentum", min_value=0.6, max_value=0.98), UniformParameterRange("Hyperparameters/weight_decay", min_value=0.0, max_value=0.001), UniformParameterRange("Hyperparameters/warmup_epochs", min_value=0.0, max_value=5.0), UniformParameterRange("Hyperparameters/warmup_momentum", min_value=0.0, max_value=0.95), UniformParameterRange("Hyperparameters/warmup_bias_lr", min_value=0.0, max_value=0.2), UniformParameterRange("Hyperparameters/box", min_value=0.02, max_value=0.2), UniformParameterRange("Hyperparameters/cls", min_value=0.2, max_value=4.0), UniformParameterRange("Hyperparameters/cls_pw", min_value=0.5, max_value=2.0), UniformParameterRange("Hyperparameters/obj", min_value=0.2, max_value=4.0), UniformParameterRange("Hyperparameters/obj_pw", min_value=0.5, max_value=2.0), UniformParameterRange("Hyperparameters/iou_t", min_value=0.1, max_value=0.7), UniformParameterRange("Hyperparameters/anchor_t", min_value=2.0, max_value=8.0), UniformParameterRange("Hyperparameters/fl_gamma", min_value=0.0, max_value=4.0), UniformParameterRange("Hyperparameters/hsv_h", min_value=0.0, max_value=0.1), UniformParameterRange("Hyperparameters/hsv_s", min_value=0.0, max_value=0.9), UniformParameterRange("Hyperparameters/hsv_v", min_value=0.0, max_value=0.9), UniformParameterRange("Hyperparameters/degrees", min_value=0.0, max_value=45.0), UniformParameterRange("Hyperparameters/translate", min_value=0.0, max_value=0.9), UniformParameterRange("Hyperparameters/scale", min_value=0.0, max_value=0.9), UniformParameterRange("Hyperparameters/shear", min_value=0.0, max_value=10.0), UniformParameterRange("Hyperparameters/perspective", min_value=0.0, max_value=0.001), UniformParameterRange("Hyperparameters/flipud", min_value=0.0, max_value=1.0), UniformParameterRange("Hyperparameters/fliplr", min_value=0.0, max_value=1.0), UniformParameterRange("Hyperparameters/mosaic", min_value=0.0, max_value=1.0), UniformParameterRange("Hyperparameters/mixup", min_value=0.0, max_value=1.0), UniformParameterRange("Hyperparameters/copy_paste", min_value=0.0, max_value=1.0), ], # this is the objective metric we want to maximize/minimize objective_metric_title="metrics", objective_metric_series="mAP_0.5", # now we decide if we want to maximize it or minimize it (accuracy we maximize) objective_metric_sign="max", # let us limit the number of concurrent experiments, # this in turn will make sure we do dont bombard the scheduler with experiments. # if we have an auto-scaler connected, this, by proxy, will limit the number of machine max_number_of_concurrent_tasks=1, # this is the optimizer class (actually doing the optimization) # Currently, we can choose from GridSearch, RandomSearch or OptimizerBOHB (Bayesian optimization Hyper-Band) optimizer_class=OptimizerOptuna, # If specified only the top K performing Tasks will be kept, the others will be automatically archived save_top_k_tasks_only=5, # 5, compute_time_limit=None, total_max_jobs=20, min_iteration_per_job=None, max_iteration_per_job=None, ) # report every 10 seconds, this is way too often, but we are testing here optimizer.set_report_period(10 / 60) # You can also use the line below instead to run all the optimizer tasks locally, without using queues or agent # an_optimizer.start_locally(job_complete_callback=job_complete_callback) # set the time limit for the optimization process (2 hours) optimizer.set_time_limit(in_minutes=120.0) # Start the optimization process in the local environment optimizer.start_locally() # wait until process is done (notice we are controlling the optimization process in the background) optimizer.wait() # make sure background optimization stopped optimizer.stop() print("We are done, good bye") ================================================ FILE: utils/loggers/comet/README.md ================================================ Ultralytics logo # YOLOv3 Integration with Comet This 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. ## ℹ️ About Comet [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: - Monitor model metrics in real time - Save and version hyperparameters, datasets, and model checkpoints - 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) - Collaborate and share results efficiently Comet ensures your work is always accessible and simplifies team collaboration. ## 🚀 Getting Started ### Install Comet Install Comet using pip: ```shell pip install comet_ml ``` ### Configure Comet Credentials You can set up Comet credentials for YOLOv3 in two ways: 1. **Environment Variables** Set your credentials in your environment: ```shell export COMET_API_KEY=YOUR_COMET_API_KEY export COMET_PROJECT_NAME=YOUR_COMET_PROJECT_NAME # Defaults to 'yolov3' if not set ``` 2. **Comet Configuration File** Create a `.comet.config` file in your working directory: ``` [comet] api_key=YOUR_API_KEY project_name=YOUR_PROJECT_NAME # Defaults to 'yolov3' if not set ``` ### Run the Training Script Run the [Ultralytics training script](https://docs.ultralytics.com/modes/train/) as usual. Comet will automatically integrate with YOLOv3. ```shell # Train YOLOv3 on COCO128 for 5 epochs python train.py --img 640 --batch 16 --epochs 5 --data coco128.yaml --weights yolov3.pt ``` Comet 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/). Comet UI showing YOLO training run ## ✨ Try an Example! Explore 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). Or, try it yourself in Colab: [![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) ## 📊 Automatic Logging By default, Comet logs the following during YOLOv3 training: ### Metrics - Box Loss, Object Loss, Classification Loss (training and validation) - mAP0.5, mAP0.5:0.95 (validation) - Precision and Recall (validation) ### Parameters - All model hyperparameters - All command-line options used during training ### Visualizations - Confusion matrix of model predictions on validation data - PR and F1 curves for all classes - Correlogram of class labels ## ⚙️ Configure Comet Logging You can customize Comet logging using environment variables: ```shell # Comet Logging Configuration export COMET_MODE=online # 'online' or 'offline'. Defaults to online. export COMET_MODEL_NAME=YOUR_MODEL_NAME # Name for the saved model. Defaults to yolov3. export COMET_LOG_CONFUSION_MATRIX=false # Disable confusion matrix logging. Defaults to true. export COMET_MAX_IMAGE_UPLOADS=NUMBER # Max prediction images to log. Defaults to 100. export COMET_LOG_PER_CLASS_METRICS=true # Log per-class metrics. Defaults to false. export COMET_DEFAULT_CHECKPOINT_FILENAME=your_checkpoint.pt # Checkpoint for resuming. Defaults to 'last.pt'. export COMET_LOG_BATCH_LEVEL_METRICS=true # Log batch-level metrics. Defaults to false. export COMET_LOG_PREDICTIONS=true # Set to false to disable prediction logging. Defaults to true. ``` ### Logging Checkpoints with Comet By 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: ```shell python train.py \ --img 640 \ --batch 16 \ --epochs 5 \ --data coco128.yaml \ --weights yolov3.pt \ --save-period 1 # Save checkpoints every epoch ``` ### Logging Model Predictions Model 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. **Note:** The YOLOv3 validation dataloader defaults to a batch size of 32. Adjust logging frequency as needed. See 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). ```shell python train.py \ --img 640 \ --batch 16 \ --epochs 5 \ --data coco128.yaml \ --weights yolov3.pt \ --bbox_interval 2 # Log predictions every 2nd batch per epoch ``` #### Controlling the Number of Prediction Images Logged Comet logs up to 100 validation images by default. Adjust this with the `COMET_MAX_IMAGE_UPLOADS` variable: ```shell env COMET_MAX_IMAGE_UPLOADS=200 python train.py \ --img 640 \ --batch 16 \ --epochs 5 \ --data coco128.yaml \ --weights yolov3.pt \ --bbox_interval 1 ``` #### Logging Class-Level Metrics Enable per-class mAP, precision, recall, and F1-score logging: ```shell env COMET_LOG_PER_CLASS_METRICS=true python train.py \ --img 640 \ --batch 16 \ --epochs 5 \ --data coco128.yaml \ --weights yolov3.pt ``` ## 💾 Uploading a Dataset to Comet Artifacts Store 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`. ```shell python train.py \ --img 640 \ --batch 16 \ --epochs 5 \ --data coco128.yaml \ --weights yolov3.pt \ --upload_dataset # Uploads the dataset specified in coco128.yaml ``` Find uploaded datasets in the Artifacts tab in your Comet Workspace. Comet Artifacts tab showing uploaded dataset Preview data directly in the Comet UI. Comet UI previewing dataset artifact Artifacts are versioned and support metadata. Comet automatically logs metadata from your dataset YAML file. Comet Artifact metadata view ### Using a Saved Artifact To use a dataset stored in Comet Artifacts, update the `path` variable in your dataset YAML file to the Artifact resource URL: ```yaml # contents of artifact.yaml path: "comet:///:" train: images/train # train images (relative to 'path') val: images/val # val images (relative to 'path') # ... other dataset configurations ``` Then, pass this config file to your training script: ```shell python train.py \ --img 640 \ --batch 16 \ --epochs 5 \ --data artifact.yaml \ --weights yolov3.pt ``` Artifacts enable tracking data lineage throughout your workflow. The graph below shows experiments using the uploaded dataset. Comet Artifact lineage graph ## ▶️ Resuming a Training Run If 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. ```shell python train.py \ --resume "comet://YOUR_WORKSPACE/YOUR_WORKSPACE/EXPERIMENT_ID" ``` ## 🔍 Hyperparameter Search with the Comet Optimizer YOLOv3 integrates with Comet's Optimizer for [hyperparameter tuning](https://docs.ultralytics.com/guides/hyperparameter-tuning/) and visualization. ### Configuring an Optimizer Sweep Create a JSON config file for the sweep (e.g., `utils/loggers/comet/optimizer_config.json`): ```json { "spec": { "maxCombo": 10, "objective": "minimize", "metric": "metrics/mAP_0.5", "algorithm": "bayes", "parameters": { "lr0": { "type": "float", "min": 0.001, "max": 0.01 }, "momentum": { "type": "float", "min": 0.85, "max": 0.95 } } }, "name": "YOLOv3 Hyperparameter Sweep", "trials": 1 } ``` Run the sweep with the `hpo.py` script: ```shell python utils/loggers/comet/hpo.py \ --comet_optimizer_config utils/loggers/comet/optimizer_config.json ``` The `hpo.py` script accepts the same arguments as `train.py`. Add any additional arguments as needed: ```shell python utils/loggers/comet/hpo.py \ --comet_optimizer_config utils/loggers/comet/optimizer_config.json \ --save-period 1 \ --bbox_interval 1 ``` ### Running a Sweep in Parallel Use the `comet optimizer` command to run the sweep with multiple workers: ```shell comet optimizer -j \ utils/loggers/comet/hpo.py NUMBER_OF_WORKERS utils/loggers/comet/optimizer_config.json ``` ### Visualizing Results Comet 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). Comet UI showing hyperparameter optimization results ## 🤝 Contributing Contributions 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! ================================================ FILE: utils/loggers/comet/__init__.py ================================================ # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license import glob import json import logging import os import sys from pathlib import Path logger = logging.getLogger(__name__) FILE = Path(__file__).resolve() ROOT = FILE.parents[3] # YOLOv3 root directory if str(ROOT) not in sys.path: sys.path.append(str(ROOT)) # add ROOT to PATH try: import comet_ml # Project Configuration config = comet_ml.config.get_config() COMET_PROJECT_NAME = config.get_string(os.getenv("COMET_PROJECT_NAME"), "comet.project_name", default="yolov5") except ImportError: comet_ml = None COMET_PROJECT_NAME = None import PIL import torch import torchvision.transforms as T import yaml from utils.dataloaders import img2label_paths from utils.general import check_dataset, scale_boxes, xywh2xyxy from utils.metrics import box_iou COMET_PREFIX = "comet://" COMET_MODE = os.getenv("COMET_MODE", "online") # Model Saving Settings COMET_MODEL_NAME = os.getenv("COMET_MODEL_NAME", "yolov5") # Dataset Artifact Settings COMET_UPLOAD_DATASET = os.getenv("COMET_UPLOAD_DATASET", "false").lower() == "true" # Evaluation Settings COMET_LOG_CONFUSION_MATRIX = os.getenv("COMET_LOG_CONFUSION_MATRIX", "true").lower() == "true" COMET_LOG_PREDICTIONS = os.getenv("COMET_LOG_PREDICTIONS", "true").lower() == "true" COMET_MAX_IMAGE_UPLOADS = int(os.getenv("COMET_MAX_IMAGE_UPLOADS", 100)) # Confusion Matrix Settings CONF_THRES = float(os.getenv("CONF_THRES", 0.001)) IOU_THRES = float(os.getenv("IOU_THRES", 0.6)) # Batch Logging Settings COMET_LOG_BATCH_METRICS = os.getenv("COMET_LOG_BATCH_METRICS", "false").lower() == "true" COMET_BATCH_LOGGING_INTERVAL = os.getenv("COMET_BATCH_LOGGING_INTERVAL", 1) COMET_PREDICTION_LOGGING_INTERVAL = os.getenv("COMET_PREDICTION_LOGGING_INTERVAL", 1) COMET_LOG_PER_CLASS_METRICS = os.getenv("COMET_LOG_PER_CLASS_METRICS", "false").lower() == "true" RANK = int(os.getenv("RANK", -1)) to_pil = T.ToPILImage() class CometLogger: """Log metrics, parameters, source code, models and much more with Comet.""" def __init__(self, opt, hyp, run_id=None, job_type="Training", **experiment_kwargs) -> None: """Initialize the CometLogger instance with experiment configurations and hyperparameters for logging.""" self.job_type = job_type self.opt = opt self.hyp = hyp # Comet Flags self.comet_mode = COMET_MODE self.save_model = opt.save_period > -1 self.model_name = COMET_MODEL_NAME # Batch Logging Settings self.log_batch_metrics = COMET_LOG_BATCH_METRICS self.comet_log_batch_interval = COMET_BATCH_LOGGING_INTERVAL # Dataset Artifact Settings self.upload_dataset = self.opt.upload_dataset or COMET_UPLOAD_DATASET self.resume = self.opt.resume self.default_experiment_kwargs = { "log_code": False, "log_env_gpu": True, "log_env_cpu": True, "project_name": COMET_PROJECT_NAME, } | experiment_kwargs self.experiment = self._get_experiment(self.comet_mode, run_id) self.experiment.set_name(self.opt.name) self.data_dict = self.check_dataset(self.opt.data) self.class_names = self.data_dict["names"] self.num_classes = self.data_dict["nc"] self.logged_images_count = 0 self.max_images = COMET_MAX_IMAGE_UPLOADS if run_id is None: self.experiment.log_other("Created from", "YOLOv3") if not isinstance(self.experiment, comet_ml.OfflineExperiment): workspace, project_name, experiment_id = self.experiment.url.split("/")[-3:] self.experiment.log_other( "Run Path", f"{workspace}/{project_name}/{experiment_id}", ) self.log_parameters(vars(opt)) self.log_parameters(self.opt.hyp) self.log_asset_data( self.opt.hyp, name="hyperparameters.json", metadata={"type": "hyp-config-file"}, ) self.log_asset( f"{self.opt.save_dir}/opt.yaml", metadata={"type": "opt-config-file"}, ) self.comet_log_confusion_matrix = COMET_LOG_CONFUSION_MATRIX if hasattr(self.opt, "conf_thres"): self.conf_thres = self.opt.conf_thres else: self.conf_thres = CONF_THRES if hasattr(self.opt, "iou_thres"): self.iou_thres = self.opt.iou_thres else: self.iou_thres = IOU_THRES self.log_parameters({"val_iou_threshold": self.iou_thres, "val_conf_threshold": self.conf_thres}) self.comet_log_predictions = COMET_LOG_PREDICTIONS if self.opt.bbox_interval == -1: self.comet_log_prediction_interval = 1 if self.opt.epochs < 10 else self.opt.epochs // 10 else: self.comet_log_prediction_interval = self.opt.bbox_interval if self.comet_log_predictions: self.metadata_dict = {} self.logged_image_names = [] self.comet_log_per_class_metrics = COMET_LOG_PER_CLASS_METRICS self.experiment.log_others( { "comet_mode": COMET_MODE, "comet_max_image_uploads": COMET_MAX_IMAGE_UPLOADS, "comet_log_per_class_metrics": COMET_LOG_PER_CLASS_METRICS, "comet_log_batch_metrics": COMET_LOG_BATCH_METRICS, "comet_log_confusion_matrix": COMET_LOG_CONFUSION_MATRIX, "comet_model_name": COMET_MODEL_NAME, } ) # Check if running the Experiment with the Comet Optimizer if hasattr(self.opt, "comet_optimizer_id"): self.experiment.log_other("optimizer_id", self.opt.comet_optimizer_id) self.experiment.log_other("optimizer_objective", self.opt.comet_optimizer_objective) self.experiment.log_other("optimizer_metric", self.opt.comet_optimizer_metric) self.experiment.log_other("optimizer_parameters", json.dumps(self.hyp)) def _get_experiment(self, mode, experiment_id=None): """Returns a comet_ml Experiment object, either online or offline, existing or new, based on mode and experiment_id. """ if mode == "offline": return ( comet_ml.ExistingOfflineExperiment( previous_experiment=experiment_id, **self.default_experiment_kwargs, ) if experiment_id is not None else comet_ml.OfflineExperiment( **self.default_experiment_kwargs, ) ) try: if experiment_id is not None: return comet_ml.ExistingExperiment( previous_experiment=experiment_id, **self.default_experiment_kwargs, ) return comet_ml.Experiment(**self.default_experiment_kwargs) except ValueError: logger.warning( "COMET WARNING: " "Comet credentials have not been set. " "Comet will default to offline logging. " "Please set your credentials to enable online logging." ) return self._get_experiment("offline", experiment_id) return def log_metrics(self, log_dict, **kwargs): """Logs metrics to the current experiment using a dictionary of metric names and values.""" self.experiment.log_metrics(log_dict, **kwargs) def log_parameters(self, log_dict, **kwargs): """Logs parameters to the current experiment using a dictionary of parameter names and values.""" self.experiment.log_parameters(log_dict, **kwargs) def log_asset(self, asset_path, **kwargs): """Logs a file or directory at `asset_path` to the current experiment, supporting additional `kwargs`.""" self.experiment.log_asset(asset_path, **kwargs) def log_asset_data(self, asset, **kwargs): """Logs binary asset data to the current experiment, supporting additional `kwargs`.""" self.experiment.log_asset_data(asset, **kwargs) def log_image(self, img, **kwargs): """Logs an image to the current experiment with optional `kwargs` for additional parameters.""" self.experiment.log_image(img, **kwargs) def log_model(self, path, opt, epoch, fitness_score, best_model=False): """Logs a model's state at a given epoch, fitness, and optionality as best, requiring path, options, epoch, and fitness score. """ if not self.save_model: return model_metadata = { "fitness_score": fitness_score[-1], "epochs_trained": epoch + 1, "save_period": opt.save_period, "total_epochs": opt.epochs, } model_files = glob.glob(f"{path}/*.pt") for model_path in model_files: name = Path(model_path).name self.experiment.log_model( self.model_name, file_or_folder=model_path, file_name=name, metadata=model_metadata, overwrite=True, ) def check_dataset(self, data_file): """Loads and validates the dataset configuration from a YAML file.""" with open(data_file) as f: data_config = yaml.safe_load(f) path = data_config.get("path") if path and path.startswith(COMET_PREFIX): path = data_config["path"].replace(COMET_PREFIX, "") return self.download_dataset_artifact(path) self.log_asset(self.opt.data, metadata={"type": "data-config-file"}) return check_dataset(data_file) def log_predictions(self, image, labelsn, path, shape, predn): """Logs filtered predictions with IoU above a threshold, discarding if max image log count reached.""" if self.logged_images_count >= self.max_images: return detections = predn[predn[:, 4] > self.conf_thres] iou = box_iou(labelsn[:, 1:], detections[:, :4]) mask, _ = torch.where(iou > self.iou_thres) if len(mask) == 0: return filtered_detections = detections[mask] filtered_labels = labelsn[mask] image_id = path.split("/")[-1].split(".")[0] image_name = f"{image_id}_curr_epoch_{self.experiment.curr_epoch}" if image_name not in self.logged_image_names: native_scale_image = PIL.Image.open(path) self.log_image(native_scale_image, name=image_name) self.logged_image_names.append(image_name) metadata = [ { "label": f"{self.class_names[int(cls)]}-gt", "score": 100, "box": {"x": xyxy[0], "y": xyxy[1], "x2": xyxy[2], "y2": xyxy[3]}, } for cls, *xyxy in filtered_labels.tolist() ] metadata.extend( { "label": f"{self.class_names[int(cls)]}", "score": conf * 100, "box": {"x": xyxy[0], "y": xyxy[1], "x2": xyxy[2], "y2": xyxy[3]}, } for *xyxy, conf, cls in filtered_detections.tolist() ) self.metadata_dict[image_name] = metadata self.logged_images_count += 1 return def preprocess_prediction(self, image, labels, shape, pred): """Preprocesses predictions by adjusting label and prediction shapes; `image`: input image, `labels`: true labels, `shape`: image shape, `pred`: model predictions. """ nl, _ = labels.shape[0], pred.shape[0] # Predictions if self.opt.single_cls: pred[:, 5] = 0 predn = pred.clone() scale_boxes(image.shape[1:], predn[:, :4], shape[0], shape[1]) labelsn = None if nl: tbox = xywh2xyxy(labels[:, 1:5]) # target boxes scale_boxes(image.shape[1:], tbox, shape[0], shape[1]) # native-space labels labelsn = torch.cat((labels[:, 0:1], tbox), 1) # native-space labels scale_boxes(image.shape[1:], predn[:, :4], shape[0], shape[1]) # native-space pred return predn, labelsn def add_assets_to_artifact(self, artifact, path, asset_path, split): """Adds asset images and labels from `asset_path` to `artifact` by `split`, ensuring paths are sorted.""" img_paths = sorted(glob.glob(f"{asset_path}/*")) label_paths = img2label_paths(img_paths) for image_file, label_file in zip(img_paths, label_paths): image_logical_path, label_logical_path = map(lambda x: os.path.relpath(x, path), [image_file, label_file]) try: artifact.add( image_file, logical_path=image_logical_path, metadata={"split": split}, ) artifact.add( label_file, logical_path=label_logical_path, metadata={"split": split}, ) except ValueError as e: logger.error("COMET ERROR: Error adding file to Artifact. Skipping file.") logger.error(f"COMET ERROR: {e}") continue return artifact def upload_dataset_artifact(self): """Uploads dataset to Comet as an artifact with optional custom dataset name, defaulting to 'yolov5-dataset'.""" dataset_name = self.data_dict.get("dataset_name", "yolov5-dataset") path = str((ROOT / Path(self.data_dict["path"])).resolve()) metadata = self.data_dict.copy() for key in ["train", "val", "test"]: split_path = metadata.get(key) if split_path is not None: metadata[key] = split_path.replace(path, "") artifact = comet_ml.Artifact(name=dataset_name, artifact_type="dataset", metadata=metadata) for key in metadata.keys(): if key in ["train", "val", "test"]: if isinstance(self.upload_dataset, str) and (key != self.upload_dataset): continue asset_path = self.data_dict.get(key) if asset_path is not None: artifact = self.add_assets_to_artifact(artifact, path, asset_path, key) self.experiment.log_artifact(artifact) return def download_dataset_artifact(self, artifact_path): """Downloads a dataset artifact to a specified directory, given its path.""" logged_artifact = self.experiment.get_artifact(artifact_path) artifact_save_dir = str(Path(self.opt.save_dir) / logged_artifact.name) logged_artifact.download(artifact_save_dir) metadata = logged_artifact.metadata data_dict = metadata.copy() data_dict["path"] = artifact_save_dir metadata_names = metadata.get("names") if isinstance(metadata_names, dict): data_dict["names"] = {int(k): v for k, v in metadata.get("names").items()} elif isinstance(metadata_names, list): data_dict["names"] = {int(k): v for k, v in zip(range(len(metadata_names)), metadata_names)} else: raise "Invalid 'names' field in dataset yaml file. Please use a list or dictionary" return self.update_data_paths(data_dict) def update_data_paths(self, data_dict): """Updates 'path' in data_dict with provided path, returning modified data_dict.""" path = data_dict.get("path", "") for split in ["train", "val", "test"]: if data_dict.get(split): split_path = data_dict.get(split) data_dict[split] = ( f"{path}/{split_path}" if isinstance(split, str) else [f"{path}/{x}" for x in split_path] ) return data_dict def on_pretrain_routine_end(self, paths): """Called at the end of the pretraining routine to handle paths modification if `opt.resume` is False.""" if self.opt.resume: return for path in paths: self.log_asset(str(path)) if self.upload_dataset and not self.resume: self.upload_dataset_artifact() return def on_train_start(self): """Logs hyperparameter settings at the start of training.""" self.log_parameters(self.hyp) def on_train_epoch_start(self): """Callback function executed at the start of each training epoch.""" return def on_train_epoch_end(self, epoch): """Callback function executed at the end of each training epoch, updates current epoch in experiment.""" self.experiment.curr_epoch = epoch return def on_train_batch_start(self): """Callback executed at the start of each training batch without inputs or modifications.""" return def on_train_batch_end(self, log_dict, step): """Callback after training batch ends; updates step and logs metrics if conditions met.""" self.experiment.curr_step = step if self.log_batch_metrics and (step % self.comet_log_batch_interval == 0): self.log_metrics(log_dict, step=step) return def on_train_end(self, files, save_dir, last, best, epoch, results): """Callback at training end; logs image metadata to Comet if comet_log_predictions is True.""" if self.comet_log_predictions: curr_epoch = self.experiment.curr_epoch self.experiment.log_asset_data(self.metadata_dict, "image-metadata.json", epoch=curr_epoch) for f in files: self.log_asset(f, metadata={"epoch": epoch}) self.log_asset(f"{save_dir}/results.csv", metadata={"epoch": epoch}) if not self.opt.evolve: model_path = str(best if best.exists() else last) name = Path(model_path).name if self.save_model: self.experiment.log_model( self.model_name, file_or_folder=model_path, file_name=name, overwrite=True, ) # Check if running Experiment with Comet Optimizer if hasattr(self.opt, "comet_optimizer_id"): metric = results.get(self.opt.comet_optimizer_metric) self.experiment.log_other("optimizer_metric_value", metric) self.finish_run() def on_val_start(self): """Prepares environment for validation phase.""" return def on_val_batch_start(self): """Called at the start of each validation batch to prepare the batch environment.""" return def on_val_batch_end(self, batch_i, images, targets, paths, shapes, outputs): """Handles end of validation batch, optionally logs predictions to Comet.ml if conditions met.""" if not (self.comet_log_predictions and ((batch_i + 1) % self.comet_log_prediction_interval == 0)): return for si, pred in enumerate(outputs): if len(pred) == 0: continue image = images[si] labels = targets[targets[:, 0] == si, 1:] shape = shapes[si] path = paths[si] predn, labelsn = self.preprocess_prediction(image, labels, shape, pred) if labelsn is not None: self.log_predictions(image, labelsn, path, shape, predn) return def on_val_end(self, nt, tp, fp, p, r, f1, ap, ap50, ap_class, confusion_matrix): """Logs per-class metric stats to Comet.ml at validation end; requires class-wise tp, fp, nt, p, r, f1, ap, ap50, ap_class, confusion_matrix. """ if self.comet_log_per_class_metrics and self.num_classes > 1: for i, c in enumerate(ap_class): class_name = self.class_names[c] self.experiment.log_metrics( { "mAP@.5": ap50[i], "mAP@.5:.95": ap[i], "precision": p[i], "recall": r[i], "f1": f1[i], "true_positives": tp[i], "false_positives": fp[i], "support": nt[c], }, prefix=class_name, ) if self.comet_log_confusion_matrix: epoch = self.experiment.curr_epoch class_names = list(self.class_names.values()) class_names.append("background") num_classes = len(class_names) self.experiment.log_confusion_matrix( matrix=confusion_matrix.matrix, max_categories=num_classes, labels=class_names, epoch=epoch, column_label="Actual Category", row_label="Predicted Category", file_name=f"confusion-matrix-epoch-{epoch}.json", ) def on_fit_epoch_end(self, result, epoch): """Logs metrics at the end of each training epoch with provided result and epoch number.""" self.log_metrics(result, epoch=epoch) def on_model_save(self, last, epoch, final_epoch, best_fitness, fi): """Logs and saves model periodically if conditions met, excluding final epoch unless best fitness achieved.""" if ((epoch + 1) % self.opt.save_period == 0 and not final_epoch) and self.opt.save_period != -1: self.log_model(last.parent, self.opt, epoch, fi, best_model=best_fitness == fi) def on_params_update(self, params): """Updates and logs model parameters.""" self.log_parameters(params) def finish_run(self): """Terminates the current experiment and performs necessary cleanup operations.""" self.experiment.end() ================================================ FILE: utils/loggers/comet/comet_utils.py ================================================ # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license import logging import os from urllib.parse import urlparse try: import comet_ml except ImportError: comet_ml = None import yaml logger = logging.getLogger(__name__) COMET_PREFIX = "comet://" COMET_MODEL_NAME = os.getenv("COMET_MODEL_NAME", "yolov5") COMET_DEFAULT_CHECKPOINT_FILENAME = os.getenv("COMET_DEFAULT_CHECKPOINT_FILENAME", "last.pt") def download_model_checkpoint(opt, experiment): """Downloads the model checkpoint from Comet ML; updates `opt.weights` with the downloaded file path.""" model_dir = f"{opt.project}/{experiment.name}" os.makedirs(model_dir, exist_ok=True) model_name = COMET_MODEL_NAME model_asset_list = experiment.get_model_asset_list(model_name) if len(model_asset_list) == 0: logger.error(f"COMET ERROR: No checkpoints found for model name : {model_name}") return model_asset_list = sorted( model_asset_list, key=lambda x: x["step"], reverse=True, ) logged_checkpoint_map = {asset["fileName"]: asset["assetId"] for asset in model_asset_list} resource_url = urlparse(opt.weights) checkpoint_filename = resource_url.query if checkpoint_filename: asset_id = logged_checkpoint_map.get(checkpoint_filename) else: asset_id = logged_checkpoint_map.get(COMET_DEFAULT_CHECKPOINT_FILENAME) checkpoint_filename = COMET_DEFAULT_CHECKPOINT_FILENAME if asset_id is None: logger.error(f"COMET ERROR: Checkpoint {checkpoint_filename} not found in the given Experiment") return try: logger.info(f"COMET INFO: Downloading checkpoint {checkpoint_filename}") asset_filename = checkpoint_filename model_binary = experiment.get_asset(asset_id, return_type="binary", stream=False) model_download_path = f"{model_dir}/{asset_filename}" with open(model_download_path, "wb") as f: f.write(model_binary) opt.weights = model_download_path except Exception as e: logger.warning("COMET WARNING: Unable to download checkpoint from Comet") logger.exception(e) def set_opt_parameters(opt, experiment): """Update the opts Namespace with parameters from Comet's ExistingExperiment when resuming a run. Args: opt (argparse.Namespace): Namespace of command line options experiment (comet_ml.APIExperiment): Comet API Experiment object """ asset_list = experiment.get_asset_list() resume_string = opt.resume for asset in asset_list: if asset["fileName"] == "opt.yaml": asset_id = asset["assetId"] asset_binary = experiment.get_asset(asset_id, return_type="binary", stream=False) opt_dict = yaml.safe_load(asset_binary) for key, value in opt_dict.items(): setattr(opt, key, value) opt.resume = resume_string # Save hyperparameters to YAML file # Necessary to pass checks in training script save_dir = f"{opt.project}/{experiment.name}" os.makedirs(save_dir, exist_ok=True) hyp_yaml_path = f"{save_dir}/hyp.yaml" with open(hyp_yaml_path, "w") as f: yaml.dump(opt.hyp, f) opt.hyp = hyp_yaml_path def check_comet_weights(opt): """Downloads model weights from Comet and updates the weights path to point to saved weights location. Args: opt (argparse.Namespace): Command Line arguments passed to YOLOv3 training script Returns: None/bool: Return True if weights are successfully downloaded else return None """ if comet_ml is None: return if isinstance(opt.weights, str) and opt.weights.startswith(COMET_PREFIX): api = comet_ml.API() resource = urlparse(opt.weights) experiment_path = f"{resource.netloc}{resource.path}" experiment = api.get(experiment_path) download_model_checkpoint(opt, experiment) return True return None def check_comet_resume(opt): """Restores run parameters to its original state based on the model checkpoint and logged Experiment parameters. Args: opt (argparse.Namespace): Command Line arguments passed to YOLOv3 training script Returns: None/bool: Return True if the run is restored successfully else return None """ if comet_ml is None: return if isinstance(opt.resume, str) and opt.resume.startswith(COMET_PREFIX): api = comet_ml.API() resource = urlparse(opt.resume) experiment_path = f"{resource.netloc}{resource.path}" experiment = api.get(experiment_path) set_opt_parameters(opt, experiment) download_model_checkpoint(opt, experiment) return True return None ================================================ FILE: utils/loggers/comet/hpo.py ================================================ # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license import argparse import json import logging import os import sys from pathlib import Path import comet_ml logger = logging.getLogger(__name__) FILE = Path(__file__).resolve() ROOT = FILE.parents[3] # YOLOv3 root directory if str(ROOT) not in sys.path: sys.path.append(str(ROOT)) # add ROOT to PATH from train import train from utils.callbacks import Callbacks from utils.general import increment_path from utils.torch_utils import select_device # Project Configuration config = comet_ml.config.get_config() COMET_PROJECT_NAME = config.get_string(os.getenv("COMET_PROJECT_NAME"), "comet.project_name", default="yolov5") def get_args(known=False): """Parses command line arguments for configuring training options, supporting Comet and W&B integrations.""" parser = argparse.ArgumentParser() parser.add_argument("--weights", type=str, default=ROOT / "yolov3-tiny.pt", help="initial weights path") parser.add_argument("--cfg", type=str, default="", help="model.yaml path") parser.add_argument("--data", type=str, default=ROOT / "data/coco128.yaml", help="dataset.yaml path") parser.add_argument("--hyp", type=str, default=ROOT / "data/hyps/hyp.scratch-low.yaml", help="hyperparameters path") parser.add_argument("--epochs", type=int, default=300, help="total training epochs") parser.add_argument("--batch-size", type=int, default=16, help="total batch size for all GPUs, -1 for autobatch") parser.add_argument("--imgsz", "--img", "--img-size", type=int, default=640, help="train, val image size (pixels)") parser.add_argument("--rect", action="store_true", help="rectangular training") parser.add_argument("--resume", nargs="?", const=True, default=False, help="resume most recent training") parser.add_argument("--nosave", action="store_true", help="only save final checkpoint") parser.add_argument("--noval", action="store_true", help="only validate final epoch") parser.add_argument("--noautoanchor", action="store_true", help="disable AutoAnchor") parser.add_argument("--noplots", action="store_true", help="save no plot files") parser.add_argument("--evolve", type=int, nargs="?", const=300, help="evolve hyperparameters for x generations") parser.add_argument("--bucket", type=str, default="", help="gsutil bucket") parser.add_argument("--cache", type=str, nargs="?", const="ram", help='--cache images in "ram" (default) or "disk"') parser.add_argument("--image-weights", action="store_true", help="use weighted image selection for training") parser.add_argument("--device", default="", help="cuda device, i.e. 0 or 0,1,2,3 or cpu") parser.add_argument("--multi-scale", action="store_true", help="vary img-size +/- 50%%") parser.add_argument("--single-cls", action="store_true", help="train multi-class data as single-class") parser.add_argument("--optimizer", type=str, choices=["SGD", "Adam", "AdamW"], default="SGD", help="optimizer") parser.add_argument("--sync-bn", action="store_true", help="use SyncBatchNorm, only available in DDP mode") parser.add_argument("--workers", type=int, default=8, help="max dataloader workers (per RANK in DDP mode)") parser.add_argument("--project", default=ROOT / "runs/train", help="save to project/name") parser.add_argument("--name", default="exp", help="save to project/name") parser.add_argument("--exist-ok", action="store_true", help="existing project/name ok, do not increment") parser.add_argument("--quad", action="store_true", help="quad dataloader") parser.add_argument("--cos-lr", action="store_true", help="cosine LR scheduler") parser.add_argument("--label-smoothing", type=float, default=0.0, help="Label smoothing epsilon") parser.add_argument("--patience", type=int, default=100, help="EarlyStopping patience (epochs without improvement)") parser.add_argument("--freeze", nargs="+", type=int, default=[0], help="Freeze layers: backbone=10, first3=0 1 2") parser.add_argument("--save-period", type=int, default=-1, help="Save checkpoint every x epochs (disabled if < 1)") parser.add_argument("--seed", type=int, default=0, help="Global training seed") parser.add_argument("--local_rank", type=int, default=-1, help="Automatic DDP Multi-GPU argument, do not modify") # Weights & Biases arguments parser.add_argument("--entity", default=None, help="W&B: Entity") parser.add_argument("--upload_dataset", nargs="?", const=True, default=False, help='W&B: Upload data, "val" option') parser.add_argument("--bbox_interval", type=int, default=-1, help="W&B: Set bounding-box image logging interval") parser.add_argument("--artifact_alias", type=str, default="latest", help="W&B: Version of dataset artifact to use") # Comet Arguments parser.add_argument("--comet_optimizer_config", type=str, help="Comet: Path to a Comet Optimizer Config File.") parser.add_argument("--comet_optimizer_id", type=str, help="Comet: ID of the Comet Optimizer sweep.") parser.add_argument("--comet_optimizer_objective", type=str, help="Comet: Set to 'minimize' or 'maximize'.") parser.add_argument("--comet_optimizer_metric", type=str, help="Comet: Metric to Optimize.") parser.add_argument( "--comet_optimizer_workers", type=int, default=1, help="Comet: Number of Parallel Workers to use with the Comet Optimizer.", ) return parser.parse_known_args()[0] if known else parser.parse_args() def run(parameters, opt): """Executes training process with given hyperparameters and options, handling device selection and callback initialization. """ hyp_dict = {k: v for k, v in parameters.items() if k not in ["epochs", "batch_size"]} opt.save_dir = str(increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok or opt.evolve)) opt.batch_size = parameters.get("batch_size") opt.epochs = parameters.get("epochs") device = select_device(opt.device, batch_size=opt.batch_size) train(hyp_dict, opt, device, callbacks=Callbacks()) if __name__ == "__main__": opt = get_args(known=True) opt.weights = str(opt.weights) opt.cfg = str(opt.cfg) opt.data = str(opt.data) opt.project = str(opt.project) optimizer_id = os.getenv("COMET_OPTIMIZER_ID") if optimizer_id is None: with open(opt.comet_optimizer_config) as f: optimizer_config = json.load(f) optimizer = comet_ml.Optimizer(optimizer_config) else: optimizer = comet_ml.Optimizer(optimizer_id) opt.comet_optimizer_id = optimizer.id status = optimizer.status() opt.comet_optimizer_objective = status["spec"]["objective"] opt.comet_optimizer_metric = status["spec"]["metric"] logger.info("COMET INFO: Starting Hyperparameter Sweep") for parameter in optimizer.get_parameters(): run(parameter["parameters"], opt) ================================================ FILE: utils/loggers/wandb/__init__.py ================================================ # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license ================================================ FILE: utils/loggers/wandb/wandb_utils.py ================================================ # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license # WARNING ⚠️ wandb is deprecated and will be removed in future release. # See supported integrations at https://github.com/ultralytics/yolov5#integrations import logging import os import sys from contextlib import contextmanager from pathlib import Path from utils.general import LOGGER, colorstr FILE = Path(__file__).resolve() ROOT = FILE.parents[3] # YOLOv3 root directory if str(ROOT) not in sys.path: sys.path.append(str(ROOT)) # add ROOT to PATH RANK = int(os.getenv("RANK", -1)) DEPRECATION_WARNING = ( f"{colorstr('wandb')}: WARNING ⚠️ wandb is deprecated and will be removed in a future release. " f"See supported integrations at https://github.com/ultralytics/yolov5#integrations." ) try: import wandb assert hasattr(wandb, "__version__") # verify package import not local dir LOGGER.warning(DEPRECATION_WARNING) except (ImportError, AssertionError): wandb = None class WandbLogger: """Log training runs, datasets, models, and predictions to Weights & Biases. This logger sends information to W&B at wandb.ai. By default, this information includes hyperparameters, system configuration and metrics, model metrics, and basic data metrics and analyses. By providing additional command line arguments to train.py, datasets, models and predictions can also be logged. For more on how this logger is used, see the Weights & Biases documentation: https://docs.wandb.com/guides/integrations/yolov5 """ def __init__(self, opt, run_id=None, job_type="Training"): """- Initialize WandbLogger instance - Upload dataset if opt.upload_dataset is True - Setup training processes if job_type is 'Training'. Args: opt (namespace) -- Commandline arguments for this run: run_id (str) -- Run ID of W&B run to be resumed job_type (str) -- To set the job_type for this run """ # Pre-training routine -- self.job_type = job_type self.wandb, self.wandb_run = wandb, wandb.run if wandb else None self.val_artifact, self.train_artifact = None, None self.train_artifact_path, self.val_artifact_path = None, None self.result_artifact = None self.val_table, self.result_table = None, None self.max_imgs_to_log = 16 self.data_dict = None if self.wandb: self.wandb_run = wandb.run or wandb.init( config=opt, resume="allow", project="YOLOv3" if opt.project == "runs/train" else Path(opt.project).stem, entity=opt.entity, name=opt.name if opt.name != "exp" else None, job_type=job_type, id=run_id, allow_val_change=True, ) if self.wandb_run and self.job_type == "Training": if isinstance(opt.data, dict): # This means another dataset manager has already processed the dataset info (e.g. ClearML) # and they will have stored the already processed dict in opt.data self.data_dict = opt.data self.setup_training(opt) def setup_training(self, opt): """Setup the necessary processes for training YOLO models: - Attempt to download model checkpoint and dataset artifacts if opt.resume stats with WANDB_ARTIFACT_PREFIX - Update data_dict, to contain info of previous run if resumed and the paths of dataset artifact if downloaded - Setup log_dict, initialize bbox_interval. Args: opt (namespace) -- commandline arguments for this run """ self.log_dict, self.current_epoch = {}, 0 self.bbox_interval = opt.bbox_interval if isinstance(opt.resume, str): model_dir, _ = self.download_model_artifact(opt) if model_dir: self.weights = Path(model_dir) / "last.pt" config = self.wandb_run.config opt.weights, opt.save_period, opt.batch_size, opt.bbox_interval, opt.epochs, opt.hyp, opt.imgsz = ( str(self.weights), config.save_period, config.batch_size, config.bbox_interval, config.epochs, config.hyp, config.imgsz, ) if opt.bbox_interval == -1: self.bbox_interval = opt.bbox_interval = (opt.epochs // 10) if opt.epochs > 10 else 1 if opt.evolve or opt.noplots: self.bbox_interval = opt.bbox_interval = opt.epochs + 1 # disable bbox_interval def log_model(self, path, opt, epoch, fitness_score, best_model=False): """Log the model checkpoint as W&B artifact. Args: path (Path) -- Path of directory containing the checkpoints: opt (namespace) -- Command line arguments for this run epoch (int) -- Current epoch number fitness_score (float) -- fitness score for current epoch best_model (boolean) -- Boolean representing if the current checkpoint is the best yet. """ model_artifact = wandb.Artifact( f"run_{wandb.run.id}_model", type="model", metadata={ "original_url": str(path), "epochs_trained": epoch + 1, "save period": opt.save_period, "project": opt.project, "total_epochs": opt.epochs, "fitness_score": fitness_score, }, ) model_artifact.add_file(str(path / "last.pt"), name="last.pt") wandb.log_artifact( model_artifact, aliases=[ "latest", "last", f"epoch {self.current_epoch!s}", "best" if best_model else "", ], ) LOGGER.info(f"Saving model artifact on epoch {epoch + 1}") def val_one_image(self, pred, predn, path, names, im): """Evaluates model's prediction for a single image, updating metrics based on comparison with ground truth.""" pass def log(self, log_dict): """Save the metrics to the logging dictionary. Args: log_dict (Dict) -- metrics/media to be logged in current step """ if self.wandb_run: for key, value in log_dict.items(): self.log_dict[key] = value def end_epoch(self): """Commit the log_dict, model artifacts and Tables to W&B and flush the log_dict. Args: best_result (boolean): Boolean representing if the result of this evaluation is best or not """ if self.wandb_run: with all_logging_disabled(): try: wandb.log(self.log_dict) except BaseException as e: LOGGER.info( f"An error occurred in wandb. The training will proceed without interruption. More info\n{e}" ) self.wandb_run.finish() self.wandb_run = None self.log_dict = {} def finish_run(self): """Log metrics if any and finish the current W&B run.""" if self.wandb_run: if self.log_dict: with all_logging_disabled(): wandb.log(self.log_dict) wandb.run.finish() LOGGER.warning(DEPRECATION_WARNING) @contextmanager def all_logging_disabled(highest_level=logging.CRITICAL): """Source - https://gist.github.com/simon-weber/7853144 A context manager that will prevent any logging messages triggered during the body from being processed. :param highest_level: the maximum logging level in use. This would only need to be changed if a custom level greater than CRITICAL is defined. """ previous_level = logging.root.manager.disable logging.disable(highest_level) try: yield finally: logging.disable(previous_level) ================================================ FILE: utils/loss.py ================================================ # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license """Loss functions.""" import torch import torch.nn as nn from utils.metrics import bbox_iou from utils.torch_utils import de_parallel def smooth_BCE(eps=0.1): # https://github.com/ultralytics/yolov3/issues/238#issuecomment-598028441 """Applies label smoothing to BCE targets, returning smoothed positive/negative labels; eps default is 0.1.""" return 1.0 - 0.5 * eps, 0.5 * eps class BCEBlurWithLogitsLoss(nn.Module): """Implements BCEWithLogitsLoss with adjustments to mitigate missing label effects using an alpha parameter.""" def __init__(self, alpha=0.05): """Initializes BCEBlurWithLogitsLoss with alpha to reduce missing label effects; default alpha is 0.05.""" super().__init__() self.loss_fcn = nn.BCEWithLogitsLoss(reduction="none") # must be nn.BCEWithLogitsLoss() self.alpha = alpha def forward(self, pred, true): """Calculates modified BCEWithLogitsLoss factoring in missing labels, taking `pred` logits and `true` labels as inputs. """ loss = self.loss_fcn(pred, true) pred = torch.sigmoid(pred) # prob from logits dx = pred - true # reduce only missing label effects # dx = (pred - true).abs() # reduce missing label and false label effects alpha_factor = 1 - torch.exp((dx - 1) / (self.alpha + 1e-4)) loss *= alpha_factor return loss.mean() class FocalLoss(nn.Module): """Implements Focal Loss to address class imbalance by modulating the loss based on prediction confidence.""" def __init__(self, loss_fcn, gamma=1.5, alpha=0.25): """Initializes FocalLoss with specified loss function, gamma, and alpha for enhanced training on imbalanced datasets. """ super().__init__() self.loss_fcn = loss_fcn # must be nn.BCEWithLogitsLoss() self.gamma = gamma self.alpha = alpha self.reduction = loss_fcn.reduction self.loss_fcn.reduction = "none" # required to apply FL to each element def forward(self, pred, true): """Computes the focal loss between `pred` and `true` using specific alpha and gamma, not applying the modulating factor. """ loss = self.loss_fcn(pred, true) # p_t = torch.exp(-loss) # loss *= self.alpha * (1.000001 - p_t) ** self.gamma # non-zero power for gradient stability # TF implementation https://github.com/tensorflow/addons/blob/v0.7.1/tensorflow_addons/losses/focal_loss.py pred_prob = torch.sigmoid(pred) # prob from logits p_t = true * pred_prob + (1 - true) * (1 - pred_prob) alpha_factor = true * self.alpha + (1 - true) * (1 - self.alpha) modulating_factor = (1.0 - p_t) ** self.gamma loss *= alpha_factor * modulating_factor if self.reduction == "mean": return loss.mean() elif self.reduction == "sum": return loss.sum() else: # 'none' return loss class QFocalLoss(nn.Module): """Implements Quality Focal Loss to handle class imbalance with a modulating factor and alpha.""" def __init__(self, loss_fcn, gamma=1.5, alpha=0.25): """Initializes QFocalLoss with specified loss function, gamma, and alpha for element-wise focal loss application. """ super().__init__() self.loss_fcn = loss_fcn # must be nn.BCEWithLogitsLoss() self.gamma = gamma self.alpha = alpha self.reduction = loss_fcn.reduction self.loss_fcn.reduction = "none" # required to apply FL to each element def forward(self, pred, true): """Computes focal loss between predictions and true labels using configured loss function, `gamma`, and `alpha`. """ loss = self.loss_fcn(pred, true) pred_prob = torch.sigmoid(pred) # prob from logits alpha_factor = true * self.alpha + (1 - true) * (1 - self.alpha) modulating_factor = torch.abs(true - pred_prob) ** self.gamma loss *= alpha_factor * modulating_factor if self.reduction == "mean": return loss.mean() elif self.reduction == "sum": return loss.sum() else: # 'none' return loss class ComputeLoss: """Computes the total loss for YOLO models by aggregating classification, box regression, and objectness losses.""" sort_obj_iou = False # Compute losses def __init__(self, model, autobalance=False): """Initializes ComputeLoss with model's device and hyperparameters, and sets autobalance.""" device = next(model.parameters()).device # get model device h = model.hyp # hyperparameters # Define criteria BCEcls = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h["cls_pw"]], device=device)) BCEobj = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h["obj_pw"]], device=device)) # Class label smoothing https://arxiv.org/pdf/1902.04103.pdf eqn 3 self.cp, self.cn = smooth_BCE(eps=h.get("label_smoothing", 0.0)) # positive, negative BCE targets # Focal loss g = h["fl_gamma"] # focal loss gamma if g > 0: BCEcls, BCEobj = FocalLoss(BCEcls, g), FocalLoss(BCEobj, g) m = de_parallel(model).model[-1] # Detect() module self.balance = {3: [4.0, 1.0, 0.4]}.get(m.nl, [4.0, 1.0, 0.25, 0.06, 0.02]) # P3-P7 self.ssi = list(m.stride).index(16) if autobalance else 0 # stride 16 index self.BCEcls, self.BCEobj, self.gr, self.hyp, self.autobalance = BCEcls, BCEobj, 1.0, h, autobalance self.na = m.na # number of anchors self.nc = m.nc # number of classes self.nl = m.nl # number of layers self.anchors = m.anchors self.device = device def __call__(self, p, targets): # predictions, targets """Computes loss given predictions and targets, returning class, box, and object loss as tensors.""" lcls = torch.zeros(1, device=self.device) # class loss lbox = torch.zeros(1, device=self.device) # box loss lobj = torch.zeros(1, device=self.device) # object loss tcls, tbox, indices, anchors = self.build_targets(p, targets) # targets # Losses for i, pi in enumerate(p): # layer index, layer predictions b, a, gj, gi = indices[i] # image, anchor, gridy, gridx tobj = torch.zeros(pi.shape[:4], dtype=pi.dtype, device=self.device) # target obj if n := b.shape[0]: # pxy, pwh, _, pcls = pi[b, a, gj, gi].tensor_split((2, 4, 5), dim=1) # faster, requires torch 1.8.0 pxy, pwh, _, pcls = pi[b, a, gj, gi].split((2, 2, 1, self.nc), 1) # target-subset of predictions # Regression pxy = pxy.sigmoid() * 2 - 0.5 pwh = (pwh.sigmoid() * 2) ** 2 * anchors[i] pbox = torch.cat((pxy, pwh), 1) # predicted box iou = bbox_iou(pbox, tbox[i], CIoU=True).squeeze() # iou(prediction, target) lbox += (1.0 - iou).mean() # iou loss # Objectness iou = iou.detach().clamp(0).type(tobj.dtype) if self.sort_obj_iou: j = iou.argsort() b, a, gj, gi, iou = b[j], a[j], gj[j], gi[j], iou[j] if self.gr < 1: iou = (1.0 - self.gr) + self.gr * iou tobj[b, a, gj, gi] = iou # iou ratio # Classification if self.nc > 1: # cls loss (only if multiple classes) t = torch.full_like(pcls, self.cn, device=self.device) # targets t[range(n), tcls[i]] = self.cp lcls += self.BCEcls(pcls, t) # BCE obji = self.BCEobj(pi[..., 4], tobj) lobj += obji * self.balance[i] # obj loss if self.autobalance: self.balance[i] = self.balance[i] * 0.9999 + 0.0001 / obji.detach().item() if self.autobalance: self.balance = [x / self.balance[self.ssi] for x in self.balance] lbox *= self.hyp["box"] lobj *= self.hyp["obj"] lcls *= self.hyp["cls"] bs = tobj.shape[0] # batch size return (lbox + lobj + lcls) * bs, torch.cat((lbox, lobj, lcls)).detach() def build_targets(self, p, targets): """Generates matching anchor targets for compute_loss() from given images and labels in format (image,class,x,y,w,h). """ na, nt = self.na, targets.shape[0] # number of anchors, targets tcls, tbox, indices, anch = [], [], [], [] gain = torch.ones(7, device=self.device) # normalized to gridspace gain ai = torch.arange(na, device=self.device).float().view(na, 1).repeat(1, nt) # same as .repeat_interleave(nt) targets = torch.cat((targets.repeat(na, 1, 1), ai[..., None]), 2) # append anchor indices g = 0.5 # bias off = ( torch.tensor( [ [0, 0], [1, 0], [0, 1], [-1, 0], [0, -1], # j,k,l,m # [1, 1], [1, -1], [-1, 1], [-1, -1], # jk,jm,lk,lm ], device=self.device, ).float() * g ) # offsets for i in range(self.nl): anchors, shape = self.anchors[i], p[i].shape gain[2:6] = torch.tensor(shape)[[3, 2, 3, 2]] # xyxy gain # Match targets to anchors t = targets * gain # shape(3,n,7) if nt: # Matches r = t[..., 4:6] / anchors[:, None] # wh ratio j = torch.max(r, 1 / r).max(2)[0] < self.hyp["anchor_t"] # compare # j = wh_iou(anchors, t[:, 4:6]) > model.hyp['iou_t'] # iou(3,n)=wh_iou(anchors(3,2), gwh(n,2)) t = t[j] # filter # Offsets gxy = t[:, 2:4] # grid xy gxi = gain[[2, 3]] - gxy # inverse j, k = ((gxy % 1 < g) & (gxy > 1)).T l, m = ((gxi % 1 < g) & (gxi > 1)).T j = torch.stack((torch.ones_like(j), j, k, l, m)) t = t.repeat((5, 1, 1))[j] offsets = (torch.zeros_like(gxy)[None] + off[:, None])[j] else: t = targets[0] offsets = 0 # Define bc, gxy, gwh, a = t.chunk(4, 1) # (image, class), grid xy, grid wh, anchors a, (b, c) = a.long().view(-1), bc.long().T # anchors, image, class gij = (gxy - offsets).long() gi, gj = gij.T # grid indices # Append indices.append((b, a, gj.clamp_(0, shape[2] - 1), gi.clamp_(0, shape[3] - 1))) # image, anchor, grid tbox.append(torch.cat((gxy - gij, gwh), 1)) # box anch.append(anchors[a]) # anchors tcls.append(c) # class return tcls, tbox, indices, anch ================================================ FILE: utils/metrics.py ================================================ # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license """Model validation metrics.""" import math import warnings from pathlib import Path import matplotlib.pyplot as plt import numpy as np import torch from utils import TryExcept, threaded def fitness(x): """Calculates model fitness as a weighted sum of metrics [P, R, mAP@0.5, mAP@0.5:0.95] with respective weights.""" w = [0.0, 0.0, 0.1, 0.9] # weights for [P, R, mAP@0.5, mAP@0.5:0.95] return (x[:, :4] * w).sum(1) def smooth(y, f=0.05): """Smooths array `y` using a box filter with fractional size `f`, returning the smoothed array.""" nf = round(len(y) * f * 2) // 2 + 1 # number of filter elements (must be odd) p = np.ones(nf // 2) # ones padding yp = np.concatenate((p * y[0], y, p * y[-1]), 0) # y padded return np.convolve(yp, np.ones(nf) / nf, mode="valid") # y-smoothed def ap_per_class(tp, conf, pred_cls, target_cls, plot=False, save_dir=".", names=(), eps=1e-16, prefix=""): """Compute the average precision, given the recall and precision curves. Source: https://github.com/rafaelpadilla/Object-Detection-Metrics. # Arguments tp: True positives (nparray, nx1 or nx10). conf: Objectness value from 0-1 (nparray). pred_cls: Predicted object classes (nparray). target_cls: True object classes (nparray). plot: Plot precision-recall curve at mAP@0.5 save_dir: Plot save directory # Returns The average precision as computed in py-faster-rcnn. """ # Sort by objectness i = np.argsort(-conf) tp, conf, pred_cls = tp[i], conf[i], pred_cls[i] # Find unique classes unique_classes, nt = np.unique(target_cls, return_counts=True) nc = unique_classes.shape[0] # number of classes, number of detections # Create Precision-Recall curve and compute AP for each class px, py = np.linspace(0, 1, 1000), [] # for plotting ap, p, r = np.zeros((nc, tp.shape[1])), np.zeros((nc, 1000)), np.zeros((nc, 1000)) for ci, c in enumerate(unique_classes): i = pred_cls == c n_l = nt[ci] # number of labels n_p = i.sum() # number of predictions if n_p == 0 or n_l == 0: continue # Accumulate FPs and TPs fpc = (1 - tp[i]).cumsum(0) tpc = tp[i].cumsum(0) # Recall recall = tpc / (n_l + eps) # recall curve r[ci] = np.interp(-px, -conf[i], recall[:, 0], left=0) # negative x, xp because xp decreases # Precision precision = tpc / (tpc + fpc) # precision curve p[ci] = np.interp(-px, -conf[i], precision[:, 0], left=1) # p at pr_score # AP from recall-precision curve for j in range(tp.shape[1]): ap[ci, j], mpre, mrec = compute_ap(recall[:, j], precision[:, j]) if plot and j == 0: py.append(np.interp(px, mrec, mpre)) # precision at mAP@0.5 # Compute F1 (harmonic mean of precision and recall) f1 = 2 * p * r / (p + r + eps) names = [v for k, v in names.items() if k in unique_classes] # list: only classes that have data names = dict(enumerate(names)) # to dict if plot: plot_pr_curve(px, py, ap, Path(save_dir) / f"{prefix}PR_curve.png", names) plot_mc_curve(px, f1, Path(save_dir) / f"{prefix}F1_curve.png", names, ylabel="F1") plot_mc_curve(px, p, Path(save_dir) / f"{prefix}P_curve.png", names, ylabel="Precision") plot_mc_curve(px, r, Path(save_dir) / f"{prefix}R_curve.png", names, ylabel="Recall") i = smooth(f1.mean(0), 0.1).argmax() # max F1 index p, r, f1 = p[:, i], r[:, i], f1[:, i] tp = (r * nt).round() # true positives fp = (tp / (p + eps) - tp).round() # false positives return tp, fp, p, r, f1, ap, unique_classes.astype(int) def compute_ap(recall, precision): """Compute the average precision, given the recall and precision curves # Arguments recall: The recall curve (list) precision: The precision curve (list) # Returns Average precision, precision curve, recall curve. """ # Append sentinel values to beginning and end mrec = np.concatenate(([0.0], recall, [1.0])) mpre = np.concatenate(([1.0], precision, [0.0])) # Compute the precision envelope mpre = np.flip(np.maximum.accumulate(np.flip(mpre))) # Integrate area under curve method = "interp" # methods: 'continuous', 'interp' if method == "interp": x = np.linspace(0, 1, 101) # 101-point interp (COCO) ap = (np.trapezoid if hasattr(np, "trapezoid") else np.trapz)(np.interp(x, mrec, mpre), x) # integrate else: # 'continuous' i = np.where(mrec[1:] != mrec[:-1])[0] # points where x axis (recall) changes ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1]) # area under curve return ap, mpre, mrec class ConfusionMatrix: """Computes and visualizes a confusion matrix for object detection tasks with configurable thresholds.""" def __init__(self, nc, conf=0.25, iou_thres=0.45): """Initializes confusion matrix for object detection with adjustable confidence and IoU thresholds.""" self.matrix = np.zeros((nc + 1, nc + 1)) self.nc = nc # number of classes self.conf = conf self.iou_thres = iou_thres def process_batch(self, detections, labels): """Return intersection-over-union (Jaccard index) of boxes. Both sets of boxes are expected to be in (x1, y1, x2, y2) format. Args: detections (Array[N, 6]), x1, y1, x2, y2, conf, class: labels (Array[M, 5]), class, x1, y1, x2, y2 Returns: None, updates confusion matrix accordingly """ if detections is None: gt_classes = labels.int() for gc in gt_classes: self.matrix[self.nc, gc] += 1 # background FN return detections = detections[detections[:, 4] > self.conf] gt_classes = labels[:, 0].int() detection_classes = detections[:, 5].int() iou = box_iou(labels[:, 1:], detections[:, :4]) x = torch.where(iou > self.iou_thres) if x[0].shape[0]: matches = torch.cat((torch.stack(x, 1), iou[x[0], x[1]][:, None]), 1).cpu().numpy() if x[0].shape[0] > 1: matches = matches[matches[:, 2].argsort()[::-1]] matches = matches[np.unique(matches[:, 1], return_index=True)[1]] matches = matches[matches[:, 2].argsort()[::-1]] matches = matches[np.unique(matches[:, 0], return_index=True)[1]] else: matches = np.zeros((0, 3)) n = matches.shape[0] > 0 m0, m1, _ = matches.transpose().astype(int) for i, gc in enumerate(gt_classes): j = m0 == i if n and sum(j) == 1: self.matrix[detection_classes[m1[j]], gc] += 1 # correct else: self.matrix[self.nc, gc] += 1 # true background if n: for i, dc in enumerate(detection_classes): if not any(m1 == i): self.matrix[dc, self.nc] += 1 # predicted background def tp_fp(self): """Computes true positives and false positives, excluding the background class, from a confusion matrix.""" tp = self.matrix.diagonal() # true positives fp = self.matrix.sum(1) - tp # false positives # fn = self.matrix.sum(0) - tp # false negatives (missed detections) return tp[:-1], fp[:-1] # remove background class @TryExcept("WARNING ⚠️ ConfusionMatrix plot failure") def plot(self, normalize=True, save_dir="", names=()): """Plots confusion matrix as a heatmap; args: normalize(bool), save_dir(str), names(iterable of str).""" import seaborn as sn array = self.matrix / ((self.matrix.sum(0).reshape(1, -1) + 1e-9) if normalize else 1) # normalize columns array[array < 0.005] = np.nan # don't annotate (would appear as 0.00) fig, ax = plt.subplots(1, 1, figsize=(12, 9), tight_layout=True) nc, nn = self.nc, len(names) # number of classes, names sn.set(font_scale=1.0 if nc < 50 else 0.8) # for label size labels = (0 < nn < 99) and (nn == nc) # apply names to ticklabels ticklabels = ([*names, "background"]) if labels else "auto" with warnings.catch_warnings(): warnings.simplefilter("ignore") # suppress empty matrix RuntimeWarning: All-NaN slice encountered sn.heatmap( array, ax=ax, annot=nc < 30, annot_kws={"size": 8}, cmap="Blues", fmt=".2f", square=True, vmin=0.0, xticklabels=ticklabels, yticklabels=ticklabels, ).set_facecolor((1, 1, 1)) ax.set_xlabel("True") ax.set_ylabel("Predicted") ax.set_title("Confusion Matrix") fig.savefig(Path(save_dir) / "confusion_matrix.png", dpi=250) plt.close(fig) def print(self): """Prints each row of the confusion matrix, where matrix elements are separated by spaces.""" for i in range(self.nc + 1): print(" ".join(map(str, self.matrix[i]))) def bbox_iou(box1, box2, xywh=True, GIoU=False, DIoU=False, CIoU=False, eps=1e-7): """Calculates IoU, GIoU, DIoU, CIoU between two bounding boxes, supporting `xywh` and `xyxy` formats.""" # Get the coordinates of bounding boxes if xywh: # transform from xywh to xyxy (x1, y1, w1, h1), (x2, y2, w2, h2) = box1.chunk(4, -1), box2.chunk(4, -1) w1_, h1_, w2_, h2_ = w1 / 2, h1 / 2, w2 / 2, h2 / 2 b1_x1, b1_x2, b1_y1, b1_y2 = x1 - w1_, x1 + w1_, y1 - h1_, y1 + h1_ b2_x1, b2_x2, b2_y1, b2_y2 = x2 - w2_, x2 + w2_, y2 - h2_, y2 + h2_ else: # x1, y1, x2, y2 = box1 b1_x1, b1_y1, b1_x2, b1_y2 = box1.chunk(4, -1) b2_x1, b2_y1, b2_x2, b2_y2 = box2.chunk(4, -1) w1, h1 = b1_x2 - b1_x1, (b1_y2 - b1_y1).clamp(eps) w2, h2 = b2_x2 - b2_x1, (b2_y2 - b2_y1).clamp(eps) # Intersection area inter = (b1_x2.minimum(b2_x2) - b1_x1.maximum(b2_x1)).clamp(0) * ( b1_y2.minimum(b2_y2) - b1_y1.maximum(b2_y1) ).clamp(0) # Union Area union = w1 * h1 + w2 * h2 - inter + eps # IoU iou = inter / union if CIoU or DIoU or GIoU: cw = b1_x2.maximum(b2_x2) - b1_x1.minimum(b2_x1) # convex (smallest enclosing box) width ch = b1_y2.maximum(b2_y2) - b1_y1.minimum(b2_y1) # convex height if CIoU or DIoU: # Distance or Complete IoU https://arxiv.org/abs/1911.08287v1 c2 = cw**2 + ch**2 + eps # convex diagonal squared rho2 = ((b2_x1 + b2_x2 - b1_x1 - b1_x2) ** 2 + (b2_y1 + b2_y2 - b1_y1 - b1_y2) ** 2) / 4 # center dist ** 2 if CIoU: # https://github.com/Zzh-tju/DIoU-SSD-pytorch/blob/master/utils/box/box_utils.py#L47 v = (4 / math.pi**2) * (torch.atan(w2 / h2) - torch.atan(w1 / h1)).pow(2) with torch.no_grad(): alpha = v / (v - iou + (1 + eps)) return iou - (rho2 / c2 + v * alpha) # CIoU return iou - rho2 / c2 # DIoU c_area = cw * ch + eps # convex area return iou - (c_area - union) / c_area # GIoU https://arxiv.org/pdf/1902.09630.pdf return iou # IoU def box_iou(box1, box2, eps=1e-7): # https://github.com/pytorch/vision/blob/master/torchvision/ops/boxes.py """Return intersection-over-union (Jaccard index) of boxes. Both sets of boxes are expected to be in (x1, y1, x2, y2) format. Args: box1 (Tensor[N, 4]): box2 (Tensor[M, 4]) Returns: iou (Tensor[N, M]): the NxM matrix containing the pairwise IoU values for every element in boxes1 and boxes2 """ # inter(N,M) = (rb(N,M,2) - lt(N,M,2)).clamp(0).prod(2) (a1, a2), (b1, b2) = box1.unsqueeze(1).chunk(2, 2), box2.unsqueeze(0).chunk(2, 2) inter = (torch.min(a2, b2) - torch.max(a1, b1)).clamp(0).prod(2) # IoU = inter / (area1 + area2 - inter) return inter / ((a2 - a1).prod(2) + (b2 - b1).prod(2) - inter + eps) def bbox_ioa(box1, box2, eps=1e-7): """Returns the intersection over box2 area given box1, box2. Boxes are x1y1x2y2 box1: np.array of shape(4) box2: np.array of shape(nx4) returns: np.array of shape(n) """ # Get the coordinates of bounding boxes b1_x1, b1_y1, b1_x2, b1_y2 = box1 b2_x1, b2_y1, b2_x2, b2_y2 = box2.T # Intersection area inter_area = (np.minimum(b1_x2, b2_x2) - np.maximum(b1_x1, b2_x1)).clip(0) * ( np.minimum(b1_y2, b2_y2) - np.maximum(b1_y1, b2_y1) ).clip(0) # box2 area box2_area = (b2_x2 - b2_x1) * (b2_y2 - b2_y1) + eps # Intersection over box2 area return inter_area / box2_area def wh_iou(wh1, wh2, eps=1e-7): """Calculates the IoU of width-height pairs, wh1[n,2] and wh2[m,2], returning an nxm IoU matrix.""" wh1 = wh1[:, None] # [N,1,2] wh2 = wh2[None] # [1,M,2] inter = torch.min(wh1, wh2).prod(2) # [N,M] return inter / (wh1.prod(2) + wh2.prod(2) - inter + eps) # iou = inter / (area1 + area2 - inter) # Plots ---------------------------------------------------------------------------------------------------------------- @threaded def plot_pr_curve(px, py, ap, save_dir=Path("pr_curve.png"), names=()): """Plots precision-recall curve, supports per-class curves if < 21 classes; args: px (recall), py (precision list), ap (APs), save_dir, names. """ fig, ax = plt.subplots(1, 1, figsize=(9, 6), tight_layout=True) py = np.stack(py, axis=1) if 0 < len(names) < 21: # display per-class legend if < 21 classes for i, y in enumerate(py.T): ax.plot(px, y, linewidth=1, label=f"{names[i]} {ap[i, 0]:.3f}") # plot(recall, precision) else: ax.plot(px, py, linewidth=1, color="grey") # plot(recall, precision) ax.plot(px, py.mean(1), linewidth=3, color="blue", label=f"all classes {ap[:, 0].mean():.3f} mAP@0.5") ax.set_xlabel("Recall") ax.set_ylabel("Precision") ax.set_xlim(0, 1) ax.set_ylim(0, 1) ax.legend(bbox_to_anchor=(1.04, 1), loc="upper left") ax.set_title("Precision-Recall Curve") fig.savefig(save_dir, dpi=250) plt.close(fig) @threaded def plot_mc_curve(px, py, save_dir=Path("mc_curve.png"), names=(), xlabel="Confidence", ylabel="Metric"): """Plots metric-confidence curve for given classes; px, py shapes (N,), (C, N); save_dir: str or Path; names: tuple of class names. """ fig, ax = plt.subplots(1, 1, figsize=(9, 6), tight_layout=True) if 0 < len(names) < 21: # display per-class legend if < 21 classes for i, y in enumerate(py): ax.plot(px, y, linewidth=1, label=f"{names[i]}") # plot(confidence, metric) else: ax.plot(px, py.T, linewidth=1, color="grey") # plot(confidence, metric) y = smooth(py.mean(0), 0.05) ax.plot(px, y, linewidth=3, color="blue", label=f"all classes {y.max():.2f} at {px[y.argmax()]:.3f}") ax.set_xlabel(xlabel) ax.set_ylabel(ylabel) ax.set_xlim(0, 1) ax.set_ylim(0, 1) ax.legend(bbox_to_anchor=(1.04, 1), loc="upper left") ax.set_title(f"{ylabel}-Confidence Curve") fig.savefig(save_dir, dpi=250) plt.close(fig) ================================================ FILE: utils/plots.py ================================================ # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license """Plotting utils.""" import contextlib import math import os from copy import copy from pathlib import Path import cv2 import matplotlib import matplotlib.pyplot as plt import numpy as np import pandas as pd import seaborn as sn import torch from PIL import Image, ImageDraw from scipy.ndimage.filters import gaussian_filter1d from ultralytics.utils.plotting import Annotator from utils import TryExcept, threaded from utils.general import LOGGER, clip_boxes, increment_path, xywh2xyxy, xyxy2xywh from utils.metrics import fitness # Settings RANK = int(os.getenv("RANK", -1)) matplotlib.rc("font", **{"size": 11}) matplotlib.use("Agg") # for writing to files only class Colors: """Provides a color palette and methods to convert indices to RGB or BGR color tuples.""" def __init__(self): """Initializes the Colors class with a palette from the Ultralytics color palette.""" hexs = ( "FF3838", "FF9D97", "FF701F", "FFB21D", "CFD231", "48F90A", "92CC17", "3DDB86", "1A9334", "00D4BB", "2C99A8", "00C2FF", "344593", "6473FF", "0018EC", "8438FF", "520085", "CB38FF", "FF95C8", "FF37C7", ) self.palette = [self.hex2rgb(f"#{c}") for c in hexs] self.n = len(self.palette) def __call__(self, i, bgr=False): """Converts index `i` to a color from predefined palette, returning in BGR format if `bgr` is True, else RGB.""" c = self.palette[int(i) % self.n] return (c[2], c[1], c[0]) if bgr else c @staticmethod def hex2rgb(h): # rgb order (PIL) """Converts hexadecimal color `h` to RGB tuple; `h` format should be '#RRGGBB'.""" return tuple(int(h[1 + i : 1 + i + 2], 16) for i in (0, 2, 4)) colors = Colors() # create instance for 'from utils.plots import colors' def feature_visualization(x, module_type, stage, n=32, save_dir=Path("runs/detect/exp")): """x: Features to be visualized module_type: Module type stage: Module stage within model n: Maximum number of feature maps to plot save_dir: Directory to save results. """ if "Detect" not in module_type: _batch, channels, height, width = x.shape # batch, channels, height, width if height > 1 and width > 1: f = save_dir / f"stage{stage}_{module_type.split('.')[-1]}_features.png" # filename blocks = torch.chunk(x[0].cpu(), channels, dim=0) # select batch index 0, block by channels n = min(n, channels) # number of plots _fig, ax = plt.subplots(math.ceil(n / 8), 8, tight_layout=True) # 8 rows x n/8 cols ax = ax.ravel() plt.subplots_adjust(wspace=0.05, hspace=0.05) for i in range(n): ax[i].imshow(blocks[i].squeeze()) # cmap='gray' ax[i].axis("off") LOGGER.info(f"Saving {f}... ({n}/{channels})") plt.savefig(f, dpi=300, bbox_inches="tight") plt.close() np.save(str(f.with_suffix(".npy")), x[0].cpu().numpy()) # npy save def hist2d(x, y, n=100): """Generates a 2D log-scaled histogram from input arrays `x` and `y`, with `n` bins for each axis.""" xedges, yedges = np.linspace(x.min(), x.max(), n), np.linspace(y.min(), y.max(), n) hist, xedges, yedges = np.histogram2d(x, y, (xedges, yedges)) xidx = np.clip(np.digitize(x, xedges) - 1, 0, hist.shape[0] - 1) yidx = np.clip(np.digitize(y, yedges) - 1, 0, hist.shape[1] - 1) return np.log(hist[xidx, yidx]) def butter_lowpass_filtfilt(data, cutoff=1500, fs=50000, order=5): """Applies a low-pass Butterworth filter using forward-backward method. See: https://stackoverflow.com/questions/28536191/how-to-filter-smooth-with-scipy-numpy """ from scipy.signal import butter, filtfilt # https://stackoverflow.com/questions/28536191/how-to-filter-smooth-with-scipy-numpy def butter_lowpass(cutoff, fs, order): """Applies a low-pass Butterworth filter to input data using forward-backward method; see https://stackoverflow.com/questions/28536191/how-to-filter-smooth-with-scipy-numpy. """ nyq = 0.5 * fs normal_cutoff = cutoff / nyq return butter(order, normal_cutoff, btype="low", analog=False) b, a = butter_lowpass(cutoff, fs, order=order) return filtfilt(b, a, data) # forward-backward filter def output_to_target(output, max_det=300): """Converts model output to [batch_id, class_id, x, y, w, h, conf] format for plotting, handling up to `max_det` detections. """ targets = [] for i, o in enumerate(output): box, conf, cls = o[:max_det, :6].cpu().split((4, 1, 1), 1) j = torch.full((conf.shape[0], 1), i) targets.append(torch.cat((j, cls, xyxy2xywh(box), conf), 1)) return torch.cat(targets, 0).numpy() @threaded def plot_images(images, targets, paths=None, fname="images.jpg", names=None): """Plots a grid of images with labels, optionally resizing and annotating with target boxes and names.""" if isinstance(images, torch.Tensor): images = images.cpu().float().numpy() if isinstance(targets, torch.Tensor): targets = targets.cpu().numpy() max_size = 1920 # max image size max_subplots = 16 # max image subplots, i.e. 4x4 bs, _, h, w = images.shape # batch size, _, height, width bs = min(bs, max_subplots) # limit plot images ns = np.ceil(bs**0.5) # number of subplots (square) if np.max(images[0]) <= 1: images *= 255 # de-normalise (optional) # Build Image mosaic = np.full((int(ns * h), int(ns * w), 3), 255, dtype=np.uint8) # init for i, im in enumerate(images): if i == max_subplots: # if last batch has fewer images than we expect break x, y = int(w * (i // ns)), int(h * (i % ns)) # block origin im = im.transpose(1, 2, 0) mosaic[y : y + h, x : x + w, :] = im # Resize (optional) scale = max_size / ns / max(h, w) if scale < 1: h = math.ceil(scale * h) w = math.ceil(scale * w) mosaic = cv2.resize(mosaic, tuple(int(x * ns) for x in (w, h))) # Annotate fs = int((h + w) * ns * 0.01) # font size annotator = Annotator(mosaic, line_width=round(fs / 10), font_size=fs, pil=True, example=names) for i in range(i + 1): x, y = int(w * (i // ns)), int(h * (i % ns)) # block origin annotator.rectangle([x, y, x + w, y + h], None, (255, 255, 255), width=2) # borders if paths: annotator.text([x + 5, y + 5], text=Path(paths[i]).name[:40], txt_color=(220, 220, 220)) # filenames if len(targets) > 0: ti = targets[targets[:, 0] == i] # image targets boxes = xywh2xyxy(ti[:, 2:6]).T classes = ti[:, 1].astype("int") labels = ti.shape[1] == 6 # labels if no conf column conf = None if labels else ti[:, 6] # check for confidence presence (label vs pred) if boxes.shape[1]: if boxes.max() <= 1.01: # if normalized with tolerance 0.01 boxes[[0, 2]] *= w # scale to pixels boxes[[1, 3]] *= h elif scale < 1: # absolute coords need scale if image scales boxes *= scale boxes[[0, 2]] += x boxes[[1, 3]] += y for j, box in enumerate(boxes.T.tolist()): cls = classes[j] color = colors(cls) cls = names[cls] if names else cls if labels or conf[j] > 0.25: # 0.25 conf thresh label = f"{cls}" if labels else f"{cls} {conf[j]:.1f}" annotator.box_label(box, label, color=color) annotator.im.save(fname) # save def plot_lr_scheduler(optimizer, scheduler, epochs=300, save_dir=""): """Simulates and plots LR schedule over epochs, saving figure to `save_dir`.""" optimizer, scheduler = copy(optimizer), copy(scheduler) # do not modify originals y = [] for _ in range(epochs): scheduler.step() y.append(optimizer.param_groups[0]["lr"]) plt.plot(y, ".-", label="LR") plt.xlabel("epoch") plt.ylabel("LR") plt.grid() plt.xlim(0, epochs) plt.ylim(0) plt.savefig(Path(save_dir) / "LR.png", dpi=200) plt.close() def plot_val_txt(): # from utils.plots import *; plot_val() """Plots 2D and 1D histograms of object center locations from 'val.txt', saving as 'hist2d.png' and 'hist1d.png'.""" x = np.loadtxt("val.txt", dtype=np.float32) box = xyxy2xywh(x[:, :4]) cx, cy = box[:, 0], box[:, 1] fig, ax = plt.subplots(1, 1, figsize=(6, 6), tight_layout=True) ax.hist2d(cx, cy, bins=600, cmax=10, cmin=0) ax.set_aspect("equal") plt.savefig("hist2d.png", dpi=300) _fig, ax = plt.subplots(1, 2, figsize=(12, 6), tight_layout=True) ax[0].hist(cx, bins=600) ax[1].hist(cy, bins=600) plt.savefig("hist1d.png", dpi=200) def plot_targets_txt(): # from utils.plots import *; plot_targets_txt() """Plots histograms for target attributes from 'targets.txt' and saves as 'targets.jpg'.""" x = np.loadtxt("targets.txt", dtype=np.float32).T s = ["x targets", "y targets", "width targets", "height targets"] _fig, ax = plt.subplots(2, 2, figsize=(8, 8), tight_layout=True) ax = ax.ravel() for i in range(4): ax[i].hist(x[i], bins=100, label=f"{x[i].mean():.3g} +/- {x[i].std():.3g}") ax[i].legend() ax[i].set_title(s[i]) plt.savefig("targets.jpg", dpi=200) def plot_val_study(file="", dir="", x=None): # from utils.plots import *; plot_val_study() """Plots validation study results from 'study*.txt' files, comparing model performance and speed.""" save_dir = Path(file).parent if file else Path(dir) plot2 = False # plot additional results if plot2: ax = plt.subplots(2, 4, figsize=(10, 6), tight_layout=True)[1].ravel() _fig2, ax2 = plt.subplots(1, 1, figsize=(8, 4), tight_layout=True) # for f in [save_dir / f'study_coco_{x}.txt' for x in ['yolov5n6', 'yolov5s6', 'yolov5m6', 'yolov5l6', 'yolov5x6']]: for f in sorted(save_dir.glob("study*.txt")): y = np.loadtxt(f, dtype=np.float32, usecols=[0, 1, 2, 3, 7, 8, 9], ndmin=2).T x = np.arange(y.shape[1]) if x is None else np.array(x) if plot2: s = ["P", "R", "mAP@.5", "mAP@.5:.95", "t_preprocess (ms/img)", "t_inference (ms/img)", "t_NMS (ms/img)"] for i in range(7): ax[i].plot(x, y[i], ".-", linewidth=2, markersize=8) ax[i].set_title(s[i]) j = y[3].argmax() + 1 ax2.plot( y[5, 1:j], y[3, 1:j] * 1e2, ".-", linewidth=2, markersize=8, label=f.stem.replace("study_coco_", "").replace("yolo", "YOLO"), ) ax2.plot( 1e3 / np.array([209, 140, 97, 58, 35, 18]), [34.6, 40.5, 43.0, 47.5, 49.7, 51.5], "k.-", linewidth=2, markersize=8, alpha=0.25, label="EfficientDet", ) ax2.grid(alpha=0.2) ax2.set_yticks(np.arange(20, 60, 5)) ax2.set_xlim(0, 57) ax2.set_ylim(25, 55) ax2.set_xlabel("GPU Speed (ms/img)") ax2.set_ylabel("COCO AP val") ax2.legend(loc="lower right") f = save_dir / "study.png" print(f"Saving {f}...") plt.savefig(f, dpi=300) @TryExcept() # known issue https://github.com/ultralytics/yolov5/issues/5395 def plot_labels(labels, names=(), save_dir=Path("")): """Plots dataset labels correlogram, class distribution, and label geometry; saves to `save_dir`.""" LOGGER.info(f"Plotting labels to {save_dir / 'labels.jpg'}... ") c, b = labels[:, 0], labels[:, 1:].transpose() # classes, boxes nc = int(c.max() + 1) # number of classes x = pd.DataFrame(b.transpose(), columns=["x", "y", "width", "height"]) # seaborn correlogram sn.pairplot(x, corner=True, diag_kind="auto", kind="hist", diag_kws=dict(bins=50), plot_kws=dict(pmax=0.9)) plt.savefig(save_dir / "labels_correlogram.jpg", dpi=200) plt.close() # matplotlib labels matplotlib.use("svg") # faster ax = plt.subplots(2, 2, figsize=(8, 8), tight_layout=True)[1].ravel() y = ax[0].hist(c, bins=np.linspace(0, nc, nc + 1) - 0.5, rwidth=0.8) with contextlib.suppress(Exception): # color histogram bars by class [y[2].patches[i].set_color([x / 255 for x in colors(i)]) for i in range(nc)] # known issue #3195 ax[0].set_ylabel("instances") if 0 < len(names) < 30: ax[0].set_xticks(range(len(names))) ax[0].set_xticklabels(list(names.values()), rotation=90, fontsize=10) else: ax[0].set_xlabel("classes") sn.histplot(x, x="x", y="y", ax=ax[2], bins=50, pmax=0.9) sn.histplot(x, x="width", y="height", ax=ax[3], bins=50, pmax=0.9) # rectangles labels[:, 1:3] = 0.5 # center labels[:, 1:] = xywh2xyxy(labels[:, 1:]) * 2000 img = Image.fromarray(np.ones((2000, 2000, 3), dtype=np.uint8) * 255) for cls, *box in labels[:1000]: ImageDraw.Draw(img).rectangle(box, width=1, outline=colors(cls)) # plot ax[1].imshow(img) ax[1].axis("off") for a in [0, 1, 2, 3]: for s in ["top", "right", "left", "bottom"]: ax[a].spines[s].set_visible(False) plt.savefig(save_dir / "labels.jpg", dpi=200) matplotlib.use("Agg") plt.close() def imshow_cls(im, labels=None, pred=None, names=None, nmax=25, verbose=False, f=Path("images.jpg")): """Displays a grid of classification images with optional labels and predictions, saving to file.""" from utils.augmentations import denormalize names = names or [f"class{i}" for i in range(1000)] blocks = torch.chunk( denormalize(im.clone()).cpu().float(), len(im), dim=0 ) # select batch index 0, block by channels n = min(len(blocks), nmax) # number of plots m = min(8, round(n**0.5)) # 8 x 8 default _fig, ax = plt.subplots(math.ceil(n / m), m) # 8 rows x n/8 cols ax = ax.ravel() if m > 1 else [ax] # plt.subplots_adjust(wspace=0.05, hspace=0.05) for i in range(n): ax[i].imshow(blocks[i].squeeze().permute((1, 2, 0)).numpy().clip(0.0, 1.0)) ax[i].axis("off") if labels is not None: s = names[labels[i]] + (f"—{names[pred[i]]}" if pred is not None else "") ax[i].set_title(s, fontsize=8, verticalalignment="top") plt.savefig(f, dpi=300, bbox_inches="tight") plt.close() if verbose: LOGGER.info(f"Saving {f}") if labels is not None: LOGGER.info("True: " + " ".join(f"{names[i]:3s}" for i in labels[:nmax])) if pred is not None: LOGGER.info("Predicted:" + " ".join(f"{names[i]:3s}" for i in pred[:nmax])) return f def plot_evolve(evolve_csv="path/to/evolve.csv"): # from utils.plots import *; plot_evolve() """Plots evolution of hyperparameters from a CSV file, highlighting best results.""" evolve_csv = Path(evolve_csv) data = pd.read_csv(evolve_csv) keys = [x.strip() for x in data.columns] x = data.values f = fitness(x) j = np.argmax(f) # max fitness index plt.figure(figsize=(10, 12), tight_layout=True) matplotlib.rc("font", **{"size": 8}) print(f"Best results from row {j} of {evolve_csv}:") for i, k in enumerate(keys[7:]): v = x[:, 7 + i] mu = v[j] # best single result plt.subplot(6, 5, i + 1) plt.scatter(v, f, c=hist2d(v, f, 20), cmap="viridis", alpha=0.8, edgecolors="none") plt.plot(mu, f.max(), "k+", markersize=15) plt.title(f"{k} = {mu:.3g}", fontdict={"size": 9}) # limit to 40 characters if i % 5 != 0: plt.yticks([]) print(f"{k:>15}: {mu:.3g}") f = evolve_csv.with_suffix(".png") # filename plt.savefig(f, dpi=200) plt.close() print(f"Saved {f}") def plot_results(file="path/to/results.csv", dir=""): """Plots training results from 'results.csv'; usage: `plot_results('path/to/results.csv')`.""" save_dir = Path(file).parent if file else Path(dir) fig, ax = plt.subplots(2, 5, figsize=(12, 6), tight_layout=True) ax = ax.ravel() files = list(save_dir.glob("results*.csv")) assert len(files), f"No results.csv files found in {save_dir.resolve()}, nothing to plot." for f in files: try: data = pd.read_csv(f) s = [x.strip() for x in data.columns] x = data.values[:, 0] for i, j in enumerate([1, 2, 3, 4, 5, 8, 9, 10, 6, 7]): y = data.values[:, j].astype("float") # y[y == 0] = np.nan # don't show zero values ax[i].plot(x, y, marker=".", label=f.stem, linewidth=2, markersize=8) # actual results ax[i].plot(x, gaussian_filter1d(y, sigma=3), ":", label="smooth", linewidth=2) # smoothing line ax[i].set_title(s[j], fontsize=12) # if j in [8, 9, 10]: # share train and val loss y axes # ax[i].get_shared_y_axes().join(ax[i], ax[i - 5]) except Exception as e: LOGGER.info(f"Warning: Plotting error for {f}: {e}") ax[1].legend() fig.savefig(save_dir / "results.png", dpi=200) plt.close() def profile_idetection(start=0, stop=0, labels=(), save_dir=""): """Plots iDetection per-image logs from '*.txt', including metrics like storage and FPS; pass start, stop times, labels, and save_dir. """ ax = plt.subplots(2, 4, figsize=(12, 6), tight_layout=True)[1].ravel() s = ["Images", "Free Storage (GB)", "RAM Usage (GB)", "Battery", "dt_raw (ms)", "dt_smooth (ms)", "real-world FPS"] files = list(Path(save_dir).glob("frames*.txt")) for fi, f in enumerate(files): try: results = np.loadtxt(f, ndmin=2).T[:, 90:-30] # clip first and last rows n = results.shape[1] # number of rows x = np.arange(start, min(stop, n) if stop else n) results = results[:, x] t = results[0] - results[0].min() # set t0=0s results[0] = x for i, a in enumerate(ax): if i < len(results): label = labels[fi] if len(labels) else f.stem.replace("frames_", "") a.plot(t, results[i], marker=".", label=label, linewidth=1, markersize=5) a.set_title(s[i]) a.set_xlabel("time (s)") # if fi == len(files) - 1: # a.set_ylim(bottom=0) for side in ["top", "right"]: a.spines[side].set_visible(False) else: a.remove() except Exception as e: print(f"Warning: Plotting error for {f}; {e}") ax[1].legend() plt.savefig(Path(save_dir) / "idetection_profile.png", dpi=200) def save_one_box(xyxy, im, file=Path("im.jpg"), gain=1.02, pad=10, square=False, BGR=False, save=True): """Saves/enhances a crop from `im` defined by `xyxy` to `file` or returns it; customizable with `gain`, `pad`, `square`, `BGR`. """ xyxy = torch.tensor(xyxy).view(-1, 4) b = xyxy2xywh(xyxy) # boxes if square: b[:, 2:] = b[:, 2:].max(1)[0].unsqueeze(1) # attempt rectangle to square b[:, 2:] = b[:, 2:] * gain + pad # box wh * gain + pad xyxy = xywh2xyxy(b).long() clip_boxes(xyxy, im.shape) crop = im[int(xyxy[0, 1]) : int(xyxy[0, 3]), int(xyxy[0, 0]) : int(xyxy[0, 2]), :: (1 if BGR else -1)] if save: file.parent.mkdir(parents=True, exist_ok=True) # make directory f = str(increment_path(file).with_suffix(".jpg")) # cv2.imwrite(f, crop) # save BGR, https://github.com/ultralytics/yolov5/issues/7007 chroma subsampling issue Image.fromarray(crop[..., ::-1]).save(f, quality=95, subsampling=0) # save RGB return crop ================================================ FILE: utils/segment/__init__.py ================================================ # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license ================================================ FILE: utils/segment/augmentations.py ================================================ # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license """Image augmentation functions.""" import math import random import cv2 import numpy as np from ..augmentations import box_candidates from ..general import resample_segments, segment2box def mixup(im, labels, segments, im2, labels2, segments2): """Applies MixUp augmentation by blending pairs of images, labels, and segments; see https://arxiv.org/pdf/1710.09412.pdf. """ r = np.random.beta(32.0, 32.0) # mixup ratio, alpha=beta=32.0 im = (im * r + im2 * (1 - r)).astype(np.uint8) labels = np.concatenate((labels, labels2), 0) segments = np.concatenate((segments, segments2), 0) return im, labels, segments def random_perspective( im, targets=(), segments=(), degrees=10, translate=0.1, scale=0.1, shear=10, perspective=0.0, border=(0, 0) ): # torchvision.transforms.RandomAffine(degrees=(-10, 10), translate=(.1, .1), scale=(.9, 1.1), shear=(-10, 10)) # targets = [cls, xyxy] """Applies random perspective augmentation including rotation, translation, scale, and shear transformations.""" height = im.shape[0] + border[0] * 2 # shape(h,w,c) width = im.shape[1] + border[1] * 2 # Center C = np.eye(3) C[0, 2] = -im.shape[1] / 2 # x translation (pixels) C[1, 2] = -im.shape[0] / 2 # y translation (pixels) # Perspective P = np.eye(3) P[2, 0] = random.uniform(-perspective, perspective) # x perspective (about y) P[2, 1] = random.uniform(-perspective, perspective) # y perspective (about x) # Rotation and Scale R = np.eye(3) a = random.uniform(-degrees, degrees) # a += random.choice([-180, -90, 0, 90]) # add 90deg rotations to small rotations s = random.uniform(1 - scale, 1 + scale) # s = 2 ** random.uniform(-scale, scale) R[:2] = cv2.getRotationMatrix2D(angle=a, center=(0, 0), scale=s) # Shear S = np.eye(3) S[0, 1] = math.tan(random.uniform(-shear, shear) * math.pi / 180) # x shear (deg) S[1, 0] = math.tan(random.uniform(-shear, shear) * math.pi / 180) # y shear (deg) # Translation T = np.eye(3) T[0, 2] = random.uniform(0.5 - translate, 0.5 + translate) * width # x translation (pixels) T[1, 2] = random.uniform(0.5 - translate, 0.5 + translate) * height # y translation (pixels) # Combined rotation matrix M = T @ S @ R @ P @ C # order of operations (right to left) is IMPORTANT if (border[0] != 0) or (border[1] != 0) or (M != np.eye(3)).any(): # image changed if perspective: im = cv2.warpPerspective(im, M, dsize=(width, height), borderValue=(114, 114, 114)) else: # affine im = cv2.warpAffine(im, M[:2], dsize=(width, height), borderValue=(114, 114, 114)) new_segments = [] if n := len(targets): new = np.zeros((n, 4)) segments = resample_segments(segments) # upsample for i, segment in enumerate(segments): xy = np.ones((len(segment), 3)) xy[:, :2] = segment xy = xy @ M.T # transform xy = xy[:, :2] / xy[:, 2:3] if perspective else xy[:, :2] # perspective rescale or affine # clip new[i] = segment2box(xy, width, height) new_segments.append(xy) # filter candidates i = box_candidates(box1=targets[:, 1:5].T * s, box2=new.T, area_thr=0.01) targets = targets[i] targets[:, 1:5] = new[i] new_segments = np.array(new_segments)[i] return im, targets, new_segments ================================================ FILE: utils/segment/dataloaders.py ================================================ # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license """Dataloaders.""" import os import random import cv2 import numpy as np import torch from torch.utils.data import DataLoader, distributed from ..augmentations import augment_hsv, copy_paste, letterbox from ..dataloaders import InfiniteDataLoader, LoadImagesAndLabels, seed_worker from ..general import LOGGER, xyn2xy, xywhn2xyxy, xyxy2xywhn from ..torch_utils import torch_distributed_zero_first from .augmentations import mixup, random_perspective RANK = int(os.getenv("RANK", -1)) def create_dataloader( path, imgsz, batch_size, stride, single_cls=False, hyp=None, augment=False, cache=False, pad=0.0, rect=False, rank=-1, workers=8, image_weights=False, quad=False, prefix="", shuffle=False, mask_downsample_ratio=1, overlap_mask=False, seed=0, ): """Creates a DataLoader for images and labels with optional augmentations and distributed sampling.""" if rect and shuffle: LOGGER.warning("WARNING ⚠️ --rect is incompatible with DataLoader shuffle, setting shuffle=False") shuffle = False with torch_distributed_zero_first(rank): # init dataset *.cache only once if DDP dataset = LoadImagesAndLabelsAndMasks( path, imgsz, batch_size, augment=augment, # augmentation hyp=hyp, # hyperparameters rect=rect, # rectangular batches cache_images=cache, single_cls=single_cls, stride=int(stride), pad=pad, image_weights=image_weights, prefix=prefix, downsample_ratio=mask_downsample_ratio, overlap=overlap_mask, ) batch_size = min(batch_size, len(dataset)) nd = torch.cuda.device_count() # number of CUDA devices nw = min([os.cpu_count() // max(nd, 1), batch_size if batch_size > 1 else 0, workers]) # number of workers sampler = None if rank == -1 else distributed.DistributedSampler(dataset, shuffle=shuffle) loader = DataLoader if image_weights else InfiniteDataLoader # only DataLoader allows for attribute updates generator = torch.Generator() generator.manual_seed(6148914691236517205 + seed + RANK) return loader( dataset, batch_size=batch_size, shuffle=shuffle and sampler is None, num_workers=nw, sampler=sampler, pin_memory=True, collate_fn=LoadImagesAndLabelsAndMasks.collate_fn4 if quad else LoadImagesAndLabelsAndMasks.collate_fn, worker_init_fn=seed_worker, generator=generator, ), dataset class LoadImagesAndLabelsAndMasks(LoadImagesAndLabels): # for training/testing """Loads images, labels, and masks for training/testing with optional augmentations including mosaic and mixup.""" def __init__( self, path, img_size=640, batch_size=16, augment=False, hyp=None, rect=False, image_weights=False, cache_images=False, single_cls=False, stride=32, pad=0, min_items=0, prefix="", downsample_ratio=1, overlap=False, ): """Initializes image, label, and mask loading for training/testing with optional augmentations.""" super().__init__( path, img_size, batch_size, augment, hyp, rect, image_weights, cache_images, single_cls, stride, pad, min_items, prefix, ) self.downsample_ratio = downsample_ratio self.overlap = overlap def __getitem__(self, index): """Fetches the dataset item at a given index, handling linear, shuffled, or image-weighted indexing.""" index = self.indices[index] # linear, shuffled, or image_weights hyp = self.hyp if mosaic := self.mosaic and random.random() < hyp["mosaic"]: # Load mosaic img, labels, segments = self.load_mosaic(index) shapes = None # MixUp augmentation if random.random() < hyp["mixup"]: img, labels, segments = mixup(img, labels, segments, *self.load_mosaic(random.randint(0, self.n - 1))) else: # Load image img, (h0, w0), (h, w) = self.load_image(index) # Letterbox shape = self.batch_shapes[self.batch[index]] if self.rect else self.img_size # final letterboxed shape img, ratio, pad = letterbox(img, shape, auto=False, scaleup=self.augment) shapes = (h0, w0), ((h / h0, w / w0), pad) # for COCO mAP rescaling labels = self.labels[index].copy() # [array, array, ....], array.shape=(num_points, 2), xyxyxyxy segments = self.segments[index].copy() if len(segments): for i_s in range(len(segments)): segments[i_s] = xyn2xy( segments[i_s], ratio[0] * w, ratio[1] * h, padw=pad[0], padh=pad[1], ) if labels.size: # normalized xywh to pixel xyxy format labels[:, 1:] = xywhn2xyxy(labels[:, 1:], ratio[0] * w, ratio[1] * h, padw=pad[0], padh=pad[1]) if self.augment: img, labels, segments = random_perspective( img, labels, segments=segments, degrees=hyp["degrees"], translate=hyp["translate"], scale=hyp["scale"], shear=hyp["shear"], perspective=hyp["perspective"], ) nl = len(labels) # number of labels masks = [] if nl: labels[:, 1:5] = xyxy2xywhn(labels[:, 1:5], w=img.shape[1], h=img.shape[0], clip=True, eps=1e-3) if self.overlap: masks, sorted_idx = polygons2masks_overlap( img.shape[:2], segments, downsample_ratio=self.downsample_ratio ) masks = masks[None] # (640, 640) -> (1, 640, 640) labels = labels[sorted_idx] else: masks = polygons2masks(img.shape[:2], segments, color=1, downsample_ratio=self.downsample_ratio) masks = ( torch.from_numpy(masks) if len(masks) else torch.zeros( 1 if self.overlap else nl, img.shape[0] // self.downsample_ratio, img.shape[1] // self.downsample_ratio ) ) # TODO: albumentations support if self.augment: # Albumentations # there are some augmentation that won't change boxes and masks, # so just be it for now. img, labels = self.albumentations(img, labels) nl = len(labels) # update after albumentations # HSV color-space augment_hsv(img, hgain=hyp["hsv_h"], sgain=hyp["hsv_s"], vgain=hyp["hsv_v"]) # Flip up-down if random.random() < hyp["flipud"]: img = np.flipud(img) if nl: labels[:, 2] = 1 - labels[:, 2] masks = torch.flip(masks, dims=[1]) # Flip left-right if random.random() < hyp["fliplr"]: img = np.fliplr(img) if nl: labels[:, 1] = 1 - labels[:, 1] masks = torch.flip(masks, dims=[2]) # Cutouts # labels = cutout(img, labels, p=0.5) labels_out = torch.zeros((nl, 6)) if nl: labels_out[:, 1:] = torch.from_numpy(labels) # Convert img = img.transpose((2, 0, 1))[::-1] # HWC to CHW, BGR to RGB img = np.ascontiguousarray(img) return (torch.from_numpy(img), labels_out, self.im_files[index], shapes, masks) def load_mosaic(self, index): """Loads 4-image mosaic for YOLOv3 training, combining 1 target image with 3 random images within specified border constraints. """ labels4, segments4 = [], [] s = self.img_size yc, xc = (int(random.uniform(-x, 2 * s + x)) for x in self.mosaic_border) # mosaic center x, y # 3 additional image indices indices = [index] + random.choices(self.indices, k=3) # 3 additional image indices for i, index in enumerate(indices): # Load image img, _, (h, w) = self.load_image(index) # place img in img4 if i == 0: # top left img4 = np.full((s * 2, s * 2, img.shape[2]), 114, dtype=np.uint8) # base image with 4 tiles x1a, y1a, x2a, y2a = max(xc - w, 0), max(yc - h, 0), xc, yc # xmin, ymin, xmax, ymax (large image) x1b, y1b, x2b, y2b = w - (x2a - x1a), h - (y2a - y1a), w, h # xmin, ymin, xmax, ymax (small image) elif i == 1: # top right x1a, y1a, x2a, y2a = xc, max(yc - h, 0), min(xc + w, s * 2), yc x1b, y1b, x2b, y2b = 0, h - (y2a - y1a), min(w, x2a - x1a), h elif i == 2: # bottom left x1a, y1a, x2a, y2a = max(xc - w, 0), yc, xc, min(s * 2, yc + h) x1b, y1b, x2b, y2b = w - (x2a - x1a), 0, w, min(y2a - y1a, h) elif i == 3: # bottom right x1a, y1a, x2a, y2a = xc, yc, min(xc + w, s * 2), min(s * 2, yc + h) x1b, y1b, x2b, y2b = 0, 0, min(w, x2a - x1a), min(y2a - y1a, h) img4[y1a:y2a, x1a:x2a] = img[y1b:y2b, x1b:x2b] # img4[ymin:ymax, xmin:xmax] padw = x1a - x1b padh = y1a - y1b labels, segments = self.labels[index].copy(), self.segments[index].copy() if labels.size: labels[:, 1:] = xywhn2xyxy(labels[:, 1:], w, h, padw, padh) # normalized xywh to pixel xyxy format segments = [xyn2xy(x, w, h, padw, padh) for x in segments] labels4.append(labels) segments4.extend(segments) # Concat/clip labels labels4 = np.concatenate(labels4, 0) for x in (labels4[:, 1:], *segments4): np.clip(x, 0, 2 * s, out=x) # clip when using random_perspective() # img4, labels4 = replicate(img4, labels4) # replicate # Augment img4, labels4, segments4 = copy_paste(img4, labels4, segments4, p=self.hyp["copy_paste"]) img4, labels4, segments4 = random_perspective( img4, labels4, segments4, degrees=self.hyp["degrees"], translate=self.hyp["translate"], scale=self.hyp["scale"], shear=self.hyp["shear"], perspective=self.hyp["perspective"], border=self.mosaic_border, ) # border to remove return img4, labels4, segments4 @staticmethod def collate_fn(batch): """Batches images, labels, paths, shapes, and masks; modifies label indices for target image association.""" img, label, path, shapes, masks = zip(*batch) # transposed batched_masks = torch.cat(masks, 0) for i, l in enumerate(label): l[:, 0] = i # add target image index for build_targets() return torch.stack(img, 0), torch.cat(label, 0), path, shapes, batched_masks def polygon2mask(img_size, polygons, color=1, downsample_ratio=1): """ Args: img_size (tuple): The image size. polygons (np.ndarray): [N, M], N is the number of polygons, M is the number of points(Be divided by 2). """ mask = np.zeros(img_size, dtype=np.uint8) polygons = np.asarray(polygons) polygons = polygons.astype(np.int32) shape = polygons.shape polygons = polygons.reshape(shape[0], -1, 2) cv2.fillPoly(mask, polygons, color=color) nh, nw = (img_size[0] // downsample_ratio, img_size[1] // downsample_ratio) # NOTE: fillPoly firstly then resize is trying the keep the same way # of loss calculation when mask-ratio=1. mask = cv2.resize(mask, (nw, nh)) return mask def polygons2masks(img_size, polygons, color, downsample_ratio=1): """ Args: img_size (tuple): The image size. polygons (list[np.ndarray]): each polygon is [N, M], N is the number of polygons, M is the number of points(Be divided by 2). """ masks = [] for si in range(len(polygons)): mask = polygon2mask(img_size, [polygons[si].reshape(-1)], color, downsample_ratio) masks.append(mask) return np.array(masks) def polygons2masks_overlap(img_size, segments, downsample_ratio=1): """Return a (640, 640) overlap mask.""" masks = np.zeros( (img_size[0] // downsample_ratio, img_size[1] // downsample_ratio), dtype=np.int32 if len(segments) > 255 else np.uint8, ) areas = [] ms = [] for si in range(len(segments)): mask = polygon2mask( img_size, [segments[si].reshape(-1)], downsample_ratio=downsample_ratio, color=1, ) ms.append(mask) areas.append(mask.sum()) areas = np.asarray(areas) index = np.argsort(-areas) ms = np.array(ms)[index] for i in range(len(segments)): mask = ms[i] * (i + 1) masks = masks + mask masks = np.clip(masks, a_min=0, a_max=i + 1) return masks, index ================================================ FILE: utils/segment/general.py ================================================ # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license import cv2 import numpy as np import torch import torch.nn.functional as F def crop_mask(masks, boxes): """"Crop" predicted masks by zeroing out everything not in the predicted bbox. Vectorized by Chong (thanks Chong). Args: - masks should be a size [n, h, w] tensor of masks - boxes should be a size [n, 4] tensor of bbox coords in relative point form """ _n, h, w = masks.shape x1, y1, x2, y2 = torch.chunk(boxes[:, :, None], 4, 1) # x1 shape(1,1,n) r = torch.arange(w, device=masks.device, dtype=x1.dtype)[None, None, :] # rows shape(1,w,1) c = torch.arange(h, device=masks.device, dtype=x1.dtype)[None, :, None] # cols shape(h,1,1) return masks * ((r >= x1) * (r < x2) * (c >= y1) * (c < y2)) def process_mask_upsample(protos, masks_in, bboxes, shape): """Crop after upsample. protos: [mask_dim, mask_h, mask_w] masks_in: [n, mask_dim], n is number of masks after nms bboxes: [n, 4], n is number of masks after nms shape: input_image_size, (h, w). return: h, w, n """ c, mh, mw = protos.shape # CHW masks = (masks_in @ protos.float().view(c, -1)).sigmoid().view(-1, mh, mw) masks = F.interpolate(masks[None], shape, mode="bilinear", align_corners=False)[0] # CHW masks = crop_mask(masks, bboxes) # CHW return masks.gt_(0.5) def process_mask(protos, masks_in, bboxes, shape, upsample=False): """Crop before upsample. proto_out: [mask_dim, mask_h, mask_w] out_masks: [n, mask_dim], n is number of masks after nms bboxes: [n, 4], n is number of masks after nms shape:input_image_size, (h, w). return: h, w, n """ c, mh, mw = protos.shape # CHW ih, iw = shape masks = (masks_in @ protos.float().view(c, -1)).sigmoid().view(-1, mh, mw) # CHW downsampled_bboxes = bboxes.clone() downsampled_bboxes[:, 0] *= mw / iw downsampled_bboxes[:, 2] *= mw / iw downsampled_bboxes[:, 3] *= mh / ih downsampled_bboxes[:, 1] *= mh / ih masks = crop_mask(masks, downsampled_bboxes) # CHW if upsample: masks = F.interpolate(masks[None], shape, mode="bilinear", align_corners=False)[0] # CHW return masks.gt_(0.5) def process_mask_native(protos, masks_in, bboxes, shape): """Crop after upsample. protos: [mask_dim, mask_h, mask_w] masks_in: [n, mask_dim], n is number of masks after nms bboxes: [n, 4], n is number of masks after nms shape: input_image_size, (h, w). return: h, w, n """ c, mh, mw = protos.shape # CHW masks = (masks_in @ protos.float().view(c, -1)).sigmoid().view(-1, mh, mw) gain = min(mh / shape[0], mw / shape[1]) # gain = old / new pad = (mw - shape[1] * gain) / 2, (mh - shape[0] * gain) / 2 # wh padding top, left = int(pad[1]), int(pad[0]) # y, x bottom, right = int(mh - pad[1]), int(mw - pad[0]) masks = masks[:, top:bottom, left:right] masks = F.interpolate(masks[None], shape, mode="bilinear", align_corners=False)[0] # CHW masks = crop_mask(masks, bboxes) # CHW return masks.gt_(0.5) def scale_image(im1_shape, masks, im0_shape, ratio_pad=None): """Img1_shape: model input shape, [h, w] img0_shape: origin pic shape, [h, w, 3] masks: [h, w, num].""" # Rescale coordinates (xyxy) from im1_shape to im0_shape if ratio_pad is None: # calculate from im0_shape gain = min(im1_shape[0] / im0_shape[0], im1_shape[1] / im0_shape[1]) # gain = old / new pad = (im1_shape[1] - im0_shape[1] * gain) / 2, (im1_shape[0] - im0_shape[0] * gain) / 2 # wh padding else: pad = ratio_pad[1] top, left = int(pad[1]), int(pad[0]) # y, x bottom, right = int(im1_shape[0] - pad[1]), int(im1_shape[1] - pad[0]) if len(masks.shape) < 2: raise ValueError(f'"len of masks shape" should be 2 or 3, but got {len(masks.shape)}') masks = masks[top:bottom, left:right] # masks = masks.permute(2, 0, 1).contiguous() # masks = F.interpolate(masks[None], im0_shape[:2], mode='bilinear', align_corners=False)[0] # masks = masks.permute(1, 2, 0).contiguous() masks = cv2.resize(masks, (im0_shape[1], im0_shape[0])) if len(masks.shape) == 2: masks = masks[:, :, None] return masks def mask_iou(mask1, mask2, eps=1e-7): """mask1: [N, n] m1 means number of predicted objects mask2: [M, n] m2 means number of gt objects Note: n means image_w x image_h. return: masks iou, [N, M] """ intersection = torch.matmul(mask1, mask2.t()).clamp(0) union = (mask1.sum(1)[:, None] + mask2.sum(1)[None]) - intersection # (area1 + area2) - intersection return intersection / (union + eps) def masks_iou(mask1, mask2, eps=1e-7): """mask1: [N, n] m1 means number of predicted objects mask2: [N, n] m2 means number of gt objects Note: n means image_w x image_h. return: masks iou, (N, ) """ intersection = (mask1 * mask2).sum(1).clamp(0) # (N, ) union = (mask1.sum(1) + mask2.sum(1))[None] - intersection # (area1 + area2) - intersection return intersection / (union + eps) def masks2segments(masks, strategy="largest"): """Converts binary masks to polygon segments with 'largest' or 'concat' strategies, returning lists of (n,xy) coordinates. """ segments = [] for x in masks.int().cpu().numpy().astype("uint8"): c = cv2.findContours(x, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)[0] if c: if strategy == "concat": # concatenate all segments c = np.concatenate([x.reshape(-1, 2) for x in c]) elif strategy == "largest": # select largest segment c = np.array(c[np.array([len(x) for x in c]).argmax()]).reshape(-1, 2) else: c = np.zeros((0, 2)) # no segments found segments.append(c.astype("float32")) return segments ================================================ FILE: utils/segment/loss.py ================================================ # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license import torch import torch.nn as nn import torch.nn.functional as F from ..general import xywh2xyxy from ..loss import FocalLoss, smooth_BCE from ..metrics import bbox_iou from ..torch_utils import de_parallel from .general import crop_mask class ComputeLoss: """Computes classification, box regression, objectness, and segmentation losses for YOLOv3 model predictions.""" def __init__(self, model, autobalance=False, overlap=False): """Initializes ComputeLoss with model settings, optional autobalancing, and overlap handling.""" self.sort_obj_iou = False self.overlap = overlap device = next(model.parameters()).device # get model device h = model.hyp # hyperparameters # Define criteria BCEcls = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h["cls_pw"]], device=device)) BCEobj = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h["obj_pw"]], device=device)) # Class label smoothing https://arxiv.org/pdf/1902.04103.pdf eqn 3 self.cp, self.cn = smooth_BCE(eps=h.get("label_smoothing", 0.0)) # positive, negative BCE targets # Focal loss g = h["fl_gamma"] # focal loss gamma if g > 0: BCEcls, BCEobj = FocalLoss(BCEcls, g), FocalLoss(BCEobj, g) m = de_parallel(model).model[-1] # Detect() module self.balance = {3: [4.0, 1.0, 0.4]}.get(m.nl, [4.0, 1.0, 0.25, 0.06, 0.02]) # P3-P7 self.ssi = list(m.stride).index(16) if autobalance else 0 # stride 16 index self.BCEcls, self.BCEobj, self.gr, self.hyp, self.autobalance = BCEcls, BCEobj, 1.0, h, autobalance self.na = m.na # number of anchors self.nc = m.nc # number of classes self.nl = m.nl # number of layers self.nm = m.nm # number of masks self.anchors = m.anchors self.device = device def __call__(self, preds, targets, masks): # predictions, targets, model """Computes losses given predictions, targets, and masks; returns tuple of class, box, object, and segmentation losses. """ p, proto = preds bs, nm, mask_h, mask_w = proto.shape # batch size, number of masks, mask height, mask width lcls = torch.zeros(1, device=self.device) lbox = torch.zeros(1, device=self.device) lobj = torch.zeros(1, device=self.device) lseg = torch.zeros(1, device=self.device) tcls, tbox, indices, anchors, tidxs, xywhn = self.build_targets(p, targets) # targets # Losses for i, pi in enumerate(p): # layer index, layer predictions b, a, gj, gi = indices[i] # image, anchor, gridy, gridx tobj = torch.zeros(pi.shape[:4], dtype=pi.dtype, device=self.device) # target obj if n := b.shape[0]: pxy, pwh, _, pcls, pmask = pi[b, a, gj, gi].split((2, 2, 1, self.nc, nm), 1) # subset of predictions # Box regression pxy = pxy.sigmoid() * 2 - 0.5 pwh = (pwh.sigmoid() * 2) ** 2 * anchors[i] pbox = torch.cat((pxy, pwh), 1) # predicted box iou = bbox_iou(pbox, tbox[i], CIoU=True).squeeze() # iou(prediction, target) lbox += (1.0 - iou).mean() # iou loss # Objectness iou = iou.detach().clamp(0).type(tobj.dtype) if self.sort_obj_iou: j = iou.argsort() b, a, gj, gi, iou = b[j], a[j], gj[j], gi[j], iou[j] if self.gr < 1: iou = (1.0 - self.gr) + self.gr * iou tobj[b, a, gj, gi] = iou # iou ratio # Classification if self.nc > 1: # cls loss (only if multiple classes) t = torch.full_like(pcls, self.cn, device=self.device) # targets t[range(n), tcls[i]] = self.cp lcls += self.BCEcls(pcls, t) # BCE # Mask regression if tuple(masks.shape[-2:]) != (mask_h, mask_w): # downsample masks = F.interpolate(masks[None], (mask_h, mask_w), mode="nearest")[0] marea = xywhn[i][:, 2:].prod(1) # mask width, height normalized mxyxy = xywh2xyxy(xywhn[i] * torch.tensor([mask_w, mask_h, mask_w, mask_h], device=self.device)) for bi in b.unique(): j = b == bi # matching index if self.overlap: mask_gti = torch.where(masks[bi][None] == tidxs[i][j].view(-1, 1, 1), 1.0, 0.0) else: mask_gti = masks[tidxs[i]][j] lseg += self.single_mask_loss(mask_gti, pmask[j], proto[bi], mxyxy[j], marea[j]) obji = self.BCEobj(pi[..., 4], tobj) lobj += obji * self.balance[i] # obj loss if self.autobalance: self.balance[i] = self.balance[i] * 0.9999 + 0.0001 / obji.detach().item() if self.autobalance: self.balance = [x / self.balance[self.ssi] for x in self.balance] lbox *= self.hyp["box"] lobj *= self.hyp["obj"] lcls *= self.hyp["cls"] lseg *= self.hyp["box"] / bs loss = lbox + lobj + lcls + lseg return loss * bs, torch.cat((lbox, lseg, lobj, lcls)).detach() def single_mask_loss(self, gt_mask, pred, proto, xyxy, area): """Computes single image mask loss using BCE, cropping based on bbox. Args: gt_mask[n,h,w], pred[n,nm], proto[nm,h,w], xyxy[n,4], area[n]. """ pred_mask = (pred @ proto.view(self.nm, -1)).view(-1, *proto.shape[1:]) # (n,32) @ (32,80,80) -> (n,80,80) loss = F.binary_cross_entropy_with_logits(pred_mask, gt_mask, reduction="none") return (crop_mask(loss, xyxy).mean(dim=(1, 2)) / area).mean() def build_targets(self, p, targets): """Prepares targets for loss computation by appending anchor indices; supports optional target overlap handling. """ na, nt = self.na, targets.shape[0] # number of anchors, targets tcls, tbox, indices, anch, tidxs, xywhn = [], [], [], [], [], [] gain = torch.ones(8, device=self.device) # normalized to gridspace gain ai = torch.arange(na, device=self.device).float().view(na, 1).repeat(1, nt) # same as .repeat_interleave(nt) if self.overlap: batch = p[0].shape[0] ti = [] for i in range(batch): num = (targets[:, 0] == i).sum() # find number of targets of each image ti.append(torch.arange(num, device=self.device).float().view(1, num).repeat(na, 1) + 1) # (na, num) ti = torch.cat(ti, 1) # (na, nt) else: ti = torch.arange(nt, device=self.device).float().view(1, nt).repeat(na, 1) targets = torch.cat((targets.repeat(na, 1, 1), ai[..., None], ti[..., None]), 2) # append anchor indices g = 0.5 # bias off = ( torch.tensor( [ [0, 0], [1, 0], [0, 1], [-1, 0], [0, -1], # j,k,l,m # [1, 1], [1, -1], [-1, 1], [-1, -1], # jk,jm,lk,lm ], device=self.device, ).float() * g ) # offsets for i in range(self.nl): anchors, shape = self.anchors[i], p[i].shape gain[2:6] = torch.tensor(shape)[[3, 2, 3, 2]] # xyxy gain # Match targets to anchors t = targets * gain # shape(3,n,7) if nt: # Matches r = t[..., 4:6] / anchors[:, None] # wh ratio j = torch.max(r, 1 / r).max(2)[0] < self.hyp["anchor_t"] # compare # j = wh_iou(anchors, t[:, 4:6]) > model.hyp['iou_t'] # iou(3,n)=wh_iou(anchors(3,2), gwh(n,2)) t = t[j] # filter # Offsets gxy = t[:, 2:4] # grid xy gxi = gain[[2, 3]] - gxy # inverse j, k = ((gxy % 1 < g) & (gxy > 1)).T l, m = ((gxi % 1 < g) & (gxi > 1)).T j = torch.stack((torch.ones_like(j), j, k, l, m)) t = t.repeat((5, 1, 1))[j] offsets = (torch.zeros_like(gxy)[None] + off[:, None])[j] else: t = targets[0] offsets = 0 # Define bc, gxy, gwh, at = t.chunk(4, 1) # (image, class), grid xy, grid wh, anchors (a, tidx), (b, c) = at.long().T, bc.long().T # anchors, image, class gij = (gxy - offsets).long() gi, gj = gij.T # grid indices # Append indices.append((b, a, gj.clamp_(0, shape[2] - 1), gi.clamp_(0, shape[3] - 1))) # image, anchor, grid tbox.append(torch.cat((gxy - gij, gwh), 1)) # box anch.append(anchors[a]) # anchors tcls.append(c) # class tidxs.append(tidx) xywhn.append(torch.cat((gxy, gwh), 1) / gain[2:6]) # xywh normalized return tcls, tbox, indices, anch, tidxs, xywhn ================================================ FILE: utils/segment/metrics.py ================================================ # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license """Model validation metrics.""" import numpy as np from ..metrics import ap_per_class def fitness(x): """Calculates model fitness as a weighted sum of 8 metrics, where `x` is an array of shape [N, 8].""" w = [0.0, 0.0, 0.1, 0.9, 0.0, 0.0, 0.1, 0.9] return (x[:, :8] * w).sum(1) def ap_per_class_box_and_mask( tp_m, tp_b, conf, pred_cls, target_cls, plot=False, save_dir=".", names=(), ): """ Args: tp_b: tp of boxes. tp_m: tp of masks. other arguments see `func: ap_per_class`. """ results_boxes = ap_per_class( tp_b, conf, pred_cls, target_cls, plot=plot, save_dir=save_dir, names=names, prefix="Box" )[2:] results_masks = ap_per_class( tp_m, conf, pred_cls, target_cls, plot=plot, save_dir=save_dir, names=names, prefix="Mask" )[2:] return { "boxes": { "p": results_boxes[0], "r": results_boxes[1], "ap": results_boxes[3], "f1": results_boxes[2], "ap_class": results_boxes[4], }, "masks": { "p": results_masks[0], "r": results_masks[1], "ap": results_masks[3], "f1": results_masks[2], "ap_class": results_masks[4], }, } class Metric: """Represents model evaluation metrics including precision, recall, F1 score, and average precision (AP) values.""" def __init__(self) -> None: """Initializes Metric class attributes for precision, recall, F1 score, AP values, and AP class indices.""" self.p = [] # (nc, ) self.r = [] # (nc, ) self.f1 = [] # (nc, ) self.all_ap = [] # (nc, 10) self.ap_class_index = [] # (nc, ) @property def ap50(self): """AP@0.5 of all classes. Returns: (nc, ) or []. """ return self.all_ap[:, 0] if len(self.all_ap) else [] @property def ap(self): """AP@0.5:0.95. Returns: (nc, ) or []. """ return self.all_ap.mean(1) if len(self.all_ap) else [] @property def mp(self): """Mean precision of all classes. Returns: float. """ return self.p.mean() if len(self.p) else 0.0 @property def mr(self): """Mean recall of all classes. Returns: float. """ return self.r.mean() if len(self.r) else 0.0 @property def map50(self): """Mean AP@0.5 of all classes. Returns: float. """ return self.all_ap[:, 0].mean() if len(self.all_ap) else 0.0 @property def map(self): """Mean AP@0.5:0.95 of all classes. Returns: float. """ return self.all_ap.mean() if len(self.all_ap) else 0.0 def mean_results(self): """Mean of results, return mp, mr, map50, map.""" return (self.mp, self.mr, self.map50, self.map) def class_result(self, i): """Class-aware result, return p[i], r[i], ap50[i], ap[i].""" return (self.p[i], self.r[i], self.ap50[i], self.ap[i]) def get_maps(self, nc): """Calculates mean average precisions (mAPs) for each class; `nc`: num of classes; returns array of mAPs per class. """ maps = np.zeros(nc) + self.map for i, c in enumerate(self.ap_class_index): maps[c] = self.ap[i] return maps def update(self, results): """ Args: results: tuple(p, r, ap, f1, ap_class). """ p, r, all_ap, f1, ap_class_index = results self.p = p self.r = r self.all_ap = all_ap self.f1 = f1 self.ap_class_index = ap_class_index class Metrics: """Metric for boxes and masks.""" def __init__(self) -> None: """Initializes the Metrics class with separate Metric instances for boxes and masks.""" self.metric_box = Metric() self.metric_mask = Metric() def update(self, results): """ Args: results: Dict{'boxes': Dict{}, 'masks': Dict{}}. """ self.metric_box.update(list(results["boxes"].values())) self.metric_mask.update(list(results["masks"].values())) def mean_results(self): """Calculates and returns the sum of mean results from 'metric_box' and 'metric_mask'.""" return self.metric_box.mean_results() + self.metric_mask.mean_results() def class_result(self, i): """Combines and returns class-specific results from 'metric_box' and 'metric_mask' for class index 'i'.""" return self.metric_box.class_result(i) + self.metric_mask.class_result(i) def get_maps(self, nc): """Returns combined mean Average Precision (mAP) scores for bounding boxes and masks for `nc` classes.""" return self.metric_box.get_maps(nc) + self.metric_mask.get_maps(nc) @property def ap_class_index(self): """Returns the AP class index, identical for both boxes and masks.""" return self.metric_box.ap_class_index KEYS = [ "train/box_loss", "train/seg_loss", # train loss "train/obj_loss", "train/cls_loss", "metrics/precision(B)", "metrics/recall(B)", "metrics/mAP_0.5(B)", "metrics/mAP_0.5:0.95(B)", # metrics "metrics/precision(M)", "metrics/recall(M)", "metrics/mAP_0.5(M)", "metrics/mAP_0.5:0.95(M)", # metrics "val/box_loss", "val/seg_loss", # val loss "val/obj_loss", "val/cls_loss", "x/lr0", "x/lr1", "x/lr2", ] BEST_KEYS = [ "best/epoch", "best/precision(B)", "best/recall(B)", "best/mAP_0.5(B)", "best/mAP_0.5:0.95(B)", "best/precision(M)", "best/recall(M)", "best/mAP_0.5(M)", "best/mAP_0.5:0.95(M)", ] ================================================ FILE: utils/segment/plots.py ================================================ # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license import contextlib import math from pathlib import Path import cv2 import matplotlib.pyplot as plt import numpy as np import pandas as pd import torch from .. import threaded from ..general import xywh2xyxy from ..plots import Annotator, colors @threaded def plot_images_and_masks(images, targets, masks, paths=None, fname="images.jpg", names=None): """Plots a grid of images with annotations and masks, optionally resizing and saving the result.""" if isinstance(images, torch.Tensor): images = images.cpu().float().numpy() if isinstance(targets, torch.Tensor): targets = targets.cpu().numpy() if isinstance(masks, torch.Tensor): masks = masks.cpu().numpy().astype(int) max_size = 1920 # max image size max_subplots = 16 # max image subplots, i.e. 4x4 bs, _, h, w = images.shape # batch size, _, height, width bs = min(bs, max_subplots) # limit plot images ns = np.ceil(bs**0.5) # number of subplots (square) if np.max(images[0]) <= 1: images *= 255 # de-normalise (optional) # Build Image mosaic = np.full((int(ns * h), int(ns * w), 3), 255, dtype=np.uint8) # init for i, im in enumerate(images): if i == max_subplots: # if last batch has fewer images than we expect break x, y = int(w * (i // ns)), int(h * (i % ns)) # block origin im = im.transpose(1, 2, 0) mosaic[y : y + h, x : x + w, :] = im # Resize (optional) scale = max_size / ns / max(h, w) if scale < 1: h = math.ceil(scale * h) w = math.ceil(scale * w) mosaic = cv2.resize(mosaic, tuple(int(x * ns) for x in (w, h))) # Annotate fs = int((h + w) * ns * 0.01) # font size annotator = Annotator(mosaic, line_width=round(fs / 10), font_size=fs, pil=True, example=names) for i in range(i + 1): x, y = int(w * (i // ns)), int(h * (i % ns)) # block origin annotator.rectangle([x, y, x + w, y + h], None, (255, 255, 255), width=2) # borders if paths: annotator.text([x + 5, y + 5], text=Path(paths[i]).name[:40], txt_color=(220, 220, 220)) # filenames if len(targets) > 0: idx = targets[:, 0] == i ti = targets[idx] # image targets boxes = xywh2xyxy(ti[:, 2:6]).T classes = ti[:, 1].astype("int") labels = ti.shape[1] == 6 # labels if no conf column conf = None if labels else ti[:, 6] # check for confidence presence (label vs pred) if boxes.shape[1]: if boxes.max() <= 1.01: # if normalized with tolerance 0.01 boxes[[0, 2]] *= w # scale to pixels boxes[[1, 3]] *= h elif scale < 1: # absolute coords need scale if image scales boxes *= scale boxes[[0, 2]] += x boxes[[1, 3]] += y for j, box in enumerate(boxes.T.tolist()): cls = classes[j] color = colors(cls) cls = names[cls] if names else cls if labels or conf[j] > 0.25: # 0.25 conf thresh label = f"{cls}" if labels else f"{cls} {conf[j]:.1f}" annotator.box_label(box, label, color=color) # Plot masks if len(masks): if masks.max() > 1.0: # mean that masks are overlap image_masks = masks[[i]] # (1, 640, 640) nl = len(ti) index = np.arange(nl).reshape(nl, 1, 1) + 1 image_masks = np.repeat(image_masks, nl, axis=0) image_masks = np.where(image_masks == index, 1.0, 0.0) else: image_masks = masks[idx] im = np.asarray(annotator.im).copy() for j, box in enumerate(boxes.T.tolist()): if labels or conf[j] > 0.25: # 0.25 conf thresh color = colors(classes[j]) mh, mw = image_masks[j].shape if mh != h or mw != w: mask = image_masks[j].astype(np.uint8) mask = cv2.resize(mask, (w, h)) mask = mask.astype(bool) else: mask = image_masks[j].astype(bool) with contextlib.suppress(Exception): im[y : y + h, x : x + w, :][mask] = ( im[y : y + h, x : x + w, :][mask] * 0.4 + np.array(color) * 0.6 ) annotator.fromarray(im) annotator.im.save(fname) # save def plot_results_with_masks(file="path/to/results.csv", dir="", best=True): """Plots training results from CSV, highlighting best/last metrics; supports custom file paths and directory saving. """ save_dir = Path(file).parent if file else Path(dir) fig, ax = plt.subplots(2, 8, figsize=(18, 6), tight_layout=True) ax = ax.ravel() files = list(save_dir.glob("results*.csv")) assert len(files), f"No results.csv files found in {save_dir.resolve()}, nothing to plot." for f in files: try: data = pd.read_csv(f) index = np.argmax( 0.9 * data.values[:, 8] + 0.1 * data.values[:, 7] + 0.9 * data.values[:, 12] + 0.1 * data.values[:, 11] ) s = [x.strip() for x in data.columns] x = data.values[:, 0] for i, j in enumerate([1, 2, 3, 4, 5, 6, 9, 10, 13, 14, 15, 16, 7, 8, 11, 12]): y = data.values[:, j] # y[y == 0] = np.nan # don't show zero values ax[i].plot(x, y, marker=".", label=f.stem, linewidth=2, markersize=2) if best: # best ax[i].scatter(index, y[index], color="r", label=f"best:{index}", marker="*", linewidth=3) ax[i].set_title(s[j] + f"\n{round(y[index], 5)}") else: # last ax[i].scatter(x[-1], y[-1], color="r", label="last", marker="*", linewidth=3) ax[i].set_title(s[j] + f"\n{round(y[-1], 5)}") # if j in [8, 9, 10]: # share train and val loss y axes # ax[i].get_shared_y_axes().join(ax[i], ax[i - 5]) except Exception as e: print(f"Warning: Plotting error for {f}: {e}") ax[1].legend() fig.savefig(save_dir / "results.png", dpi=200) plt.close() ================================================ FILE: utils/torch_utils.py ================================================ # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license """PyTorch utils.""" import math import os import platform import subprocess import time import warnings from contextlib import contextmanager from copy import deepcopy from pathlib import Path import torch import torch.distributed as dist import torch.nn as nn import torch.nn.functional as F from torch.nn.parallel import DistributedDataParallel as DDP from utils.general import LOGGER, check_version, colorstr, file_date, git_describe LOCAL_RANK = int(os.getenv("LOCAL_RANK", -1)) # https://pytorch.org/docs/stable/elastic/run.html RANK = int(os.getenv("RANK", -1)) WORLD_SIZE = int(os.getenv("WORLD_SIZE", 1)) try: import thop # for FLOPs computation except ImportError: thop = None # Suppress PyTorch warnings warnings.filterwarnings("ignore", message="User provided device_type of 'cuda', but CUDA is not available. Disabling") warnings.filterwarnings("ignore", category=UserWarning) def smart_inference_mode(torch_1_9=check_version(torch.__version__, "1.9.0")): """Applies torch.inference_mode() if torch>=1.9.0 or torch.no_grad() otherwise as a decorator to functions.""" def decorate(fn): """Applies torch.inference_mode() if torch>=1.9.0, otherwise torch.no_grad(), as a decorator to functions.""" return (torch.inference_mode if torch_1_9 else torch.no_grad)()(fn) return decorate def smartCrossEntropyLoss(label_smoothing=0.0): """Returns CrossEntropyLoss with optional label smoothing for torch>=1.10.0; warns if label smoothing used with older versions. """ if check_version(torch.__version__, "1.10.0"): return nn.CrossEntropyLoss(label_smoothing=label_smoothing) if label_smoothing > 0: LOGGER.warning(f"WARNING ⚠️ label smoothing {label_smoothing} requires torch>=1.10.0") return nn.CrossEntropyLoss() def smart_DDP(model): """Initializes DDP for a model with version checks; fails for torch==1.12.0 due to known issues. See https://github.com/ultralytics/yolov5/issues/8395. """ assert not check_version(torch.__version__, "1.12.0", pinned=True), ( "torch==1.12.0 torchvision==0.13.0 DDP training is not supported due to a known issue. " "Please upgrade or downgrade torch to use DDP. See https://github.com/ultralytics/yolov5/issues/8395" ) if check_version(torch.__version__, "1.11.0"): return DDP(model, device_ids=[LOCAL_RANK], output_device=LOCAL_RANK, static_graph=True) else: return DDP(model, device_ids=[LOCAL_RANK], output_device=LOCAL_RANK) def reshape_classifier_output(model, n=1000): """Reshapes the last layer of a model to have 'n' outputs; supports YOLOv3, ResNet, EfficientNet, adjusting Linear and Conv2d layers. """ from models.common import Classify name, m = list((model.model if hasattr(model, "model") else model).named_children())[-1] # last module if isinstance(m, Classify): # YOLOv3 Classify() head if m.linear.out_features != n: m.linear = nn.Linear(m.linear.in_features, n) elif isinstance(m, nn.Linear): # ResNet, EfficientNet if m.out_features != n: setattr(model, name, nn.Linear(m.in_features, n)) elif isinstance(m, nn.Sequential): types = [type(x) for x in m] if nn.Linear in types: i = types.index(nn.Linear) # nn.Linear index if m[i].out_features != n: m[i] = nn.Linear(m[i].in_features, n) elif nn.Conv2d in types: i = types.index(nn.Conv2d) # nn.Conv2d index if m[i].out_channels != n: m[i] = nn.Conv2d(m[i].in_channels, n, m[i].kernel_size, m[i].stride, bias=m[i].bias is not None) @contextmanager def torch_distributed_zero_first(local_rank: int): """Context manager ensuring ordered execution in distributed training by synchronizing local masters first.""" if local_rank not in [-1, 0]: dist.barrier(device_ids=[local_rank]) yield if local_rank == 0: dist.barrier(device_ids=[0]) def device_count(): """Returns the count of available CUDA devices; supports Linux and Windows, using nvidia-smi.""" assert platform.system() in ("Linux", "Windows"), "device_count() only supported on Linux or Windows" try: cmd = "nvidia-smi -L | wc -l" if platform.system() == "Linux" else 'nvidia-smi -L | find /c /v ""' # Windows return int(subprocess.run(cmd, shell=True, capture_output=True, check=True).stdout.decode().split()[-1]) except Exception: return 0 def select_device(device="", batch_size=0, newline=True): """Selects the device for running models, handling CPU, GPU, and MPS with optional batch size divisibility check.""" s = f"YOLOv3 🚀 {git_describe() or file_date()} Python-{platform.python_version()} torch-{torch.__version__} " device = str(device).strip().lower().replace("cuda:", "").replace("none", "") # to string, 'cuda:0' to '0' cpu = device == "cpu" mps = device == "mps" # Apple Metal Performance Shaders (MPS) if cpu or mps: os.environ["CUDA_VISIBLE_DEVICES"] = "-1" # force torch.cuda.is_available() = False elif device: # non-cpu device requested os.environ["CUDA_VISIBLE_DEVICES"] = device # set environment variable - must be before assert is_available() assert torch.cuda.is_available() and torch.cuda.device_count() >= len(device.replace(",", "")), ( f"Invalid CUDA '--device {device}' requested, use '--device cpu' or pass valid CUDA device(s)" ) if not cpu and not mps and torch.cuda.is_available(): # prefer GPU if available devices = device.split(",") if device else "0" # range(torch.cuda.device_count()) # i.e. 0,1,6,7 n = len(devices) # device count if n > 1 and batch_size > 0: # check batch_size is divisible by device_count assert batch_size % n == 0, f"batch-size {batch_size} not multiple of GPU count {n}" space = " " * (len(s) + 1) for i, d in enumerate(devices): p = torch.cuda.get_device_properties(i) s += f"{'' if i == 0 else space}CUDA:{d} ({p.name}, {p.total_memory / (1 << 20):.0f}MiB)\n" # bytes to MB arg = "cuda:0" elif mps and getattr(torch, "has_mps", False) and torch.backends.mps.is_available(): # prefer MPS if available s += "MPS\n" arg = "mps" else: # revert to CPU s += "CPU\n" arg = "cpu" if not newline: s = s.rstrip() LOGGER.info(s) return torch.device(arg) def time_sync(): """Synchronizes PyTorch across available CUDA devices and returns current time in seconds.""" if torch.cuda.is_available(): torch.cuda.synchronize() return time.time() def profile(input, ops, n=10, device=None): """YOLOv3 speed/memory/FLOPs profiler. Examples: input = torch.randn(16, 3, 640, 640) m1 = lambda x: x * torch.sigmoid(x) m2 = nn.SiLU() profile(input, [m1, m2], n=100) # profile over 100 iterations. """ results = [] if not isinstance(device, torch.device): device = select_device(device) print( f"{'Params':>12s}{'GFLOPs':>12s}{'GPU_mem (GB)':>14s}{'forward (ms)':>14s}{'backward (ms)':>14s}" f"{'input':>24s}{'output':>24s}" ) for x in input if isinstance(input, list) else [input]: x = x.to(device) x.requires_grad = True for m in ops if isinstance(ops, list) else [ops]: m = m.to(device) if hasattr(m, "to") else m # device m = m.half() if hasattr(m, "half") and isinstance(x, torch.Tensor) and x.dtype is torch.float16 else m tf, tb, t = 0, 0, [0, 0, 0] # dt forward, backward try: flops = thop.profile(m, inputs=(x,), verbose=False)[0] / 1e9 * 2 # GFLOPs except Exception: flops = 0 try: for _ in range(n): t[0] = time_sync() y = m(x) t[1] = time_sync() try: _ = (sum(yi.sum() for yi in y) if isinstance(y, list) else y).sum().backward() t[2] = time_sync() except Exception: # no backward method # print(e) # for debug t[2] = float("nan") tf += (t[1] - t[0]) * 1000 / n # ms per op forward tb += (t[2] - t[1]) * 1000 / n # ms per op backward mem = torch.cuda.memory_reserved() / 1e9 if torch.cuda.is_available() else 0 # (GB) s_in, s_out = (tuple(x.shape) if isinstance(x, torch.Tensor) else "list" for x in (x, y)) # shapes p = sum(x.numel() for x in m.parameters()) if isinstance(m, nn.Module) else 0 # parameters print(f"{p:12}{flops:12.4g}{mem:>14.3f}{tf:14.4g}{tb:14.4g}{s_in!s:>24s}{s_out!s:>24s}") results.append([p, flops, mem, tf, tb, s_in, s_out]) except Exception as e: print(e) results.append(None) torch.cuda.empty_cache() return results def is_parallel(model): """Checks if a model is using DataParallel (DP) or DistributedDataParallel (DDP).""" return type(model) in (nn.parallel.DataParallel, nn.parallel.DistributedDataParallel) def de_parallel(model): """Returns a single-GPU model if input model is using DataParallel (DP) or DistributedDataParallel (DDP).""" return model.module if is_parallel(model) else model def initialize_weights(model): """Initializes weights for Conv2D, BatchNorm2d, and activation layers (Hardswish, LeakyReLU, ReLU, ReLU6, SiLU) in a model. """ for m in model.modules(): t = type(m) if t is nn.Conv2d: pass # nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') elif t is nn.BatchNorm2d: m.eps = 1e-3 m.momentum = 0.03 elif t in [nn.Hardswish, nn.LeakyReLU, nn.ReLU, nn.ReLU6, nn.SiLU]: m.inplace = True def find_modules(model, mclass=nn.Conv2d): """Finds indices of layers in 'model' matching 'mclass'; default searches for 'nn.Conv2d'.""" return [i for i, m in enumerate(model.module_list) if isinstance(m, mclass)] def sparsity(model): """Calculates and returns the global sparsity of a model as the ratio of zero-valued parameters to total parameters. """ a, b = 0, 0 for p in model.parameters(): a += p.numel() b += (p == 0).sum() return b / a def prune(model, amount=0.3): """Prunes Conv2d layers in a model to a specified global sparsity using l1 unstructured pruning.""" import torch.nn.utils.prune as prune for name, m in model.named_modules(): if isinstance(m, nn.Conv2d): prune.l1_unstructured(m, name="weight", amount=amount) # prune prune.remove(m, "weight") # make permanent LOGGER.info(f"Model pruned to {sparsity(model):.3g} global sparsity") def fuse_conv_and_bn(conv, bn): """Fuses Conv2d and BatchNorm2d layers for efficiency; see https://tehnokv.com/posts/fusing-batchnorm-and-conv/.""" fusedconv = ( nn.Conv2d( conv.in_channels, conv.out_channels, kernel_size=conv.kernel_size, stride=conv.stride, padding=conv.padding, dilation=conv.dilation, groups=conv.groups, bias=True, ) .requires_grad_(False) .to(conv.weight.device) ) # Prepare filters w_conv = conv.weight.clone().view(conv.out_channels, -1) w_bn = torch.diag(bn.weight.div(torch.sqrt(bn.eps + bn.running_var))) fusedconv.weight.copy_(torch.mm(w_bn, w_conv).view(fusedconv.weight.shape)) # Prepare spatial bias b_conv = torch.zeros(conv.weight.size(0), device=conv.weight.device) if conv.bias is None else conv.bias b_bn = bn.bias - bn.weight.mul(bn.running_mean).div(torch.sqrt(bn.running_var + bn.eps)) fusedconv.bias.copy_(torch.mm(w_bn, b_conv.reshape(-1, 1)).reshape(-1) + b_bn) return fusedconv def model_info(model, verbose=False, imgsz=640): """Prints model layers, parameters, gradients, and GFLOPs if verbose; handles various `imgsz`. Usage: model_info(model). """ n_p = sum(x.numel() for x in model.parameters()) # number parameters n_g = sum(x.numel() for x in model.parameters() if x.requires_grad) # number gradients if verbose: print(f"{'layer':>5} {'name':>40} {'gradient':>9} {'parameters':>12} {'shape':>20} {'mu':>10} {'sigma':>10}") for i, (name, p) in enumerate(model.named_parameters()): name = name.replace("module_list.", "") print( "%5g %40s %9s %12g %20s %10.3g %10.3g" % (i, name, p.requires_grad, p.numel(), list(p.shape), p.mean(), p.std()) ) try: # FLOPs p = next(model.parameters()) stride = max(int(model.stride.max()), 32) if hasattr(model, "stride") else 32 # max stride im = torch.empty((1, p.shape[1], stride, stride), device=p.device) # input image in BCHW format flops = thop.profile(deepcopy(model), inputs=(im,), verbose=False)[0] / 1e9 * 2 # stride GFLOPs imgsz = imgsz if isinstance(imgsz, list) else [imgsz, imgsz] # expand if int/float fs = f", {flops * imgsz[0] / stride * imgsz[1] / stride:.1f} GFLOPs" # 640x640 GFLOPs except Exception: fs = "" name = Path(model.yaml_file).stem.replace("yolov5", "YOLOv3") if hasattr(model, "yaml_file") else "Model" LOGGER.info(f"{name} summary: {len(list(model.modules()))} layers, {n_p} parameters, {n_g} gradients{fs}") def scale_img(img, ratio=1.0, same_shape=False, gs=32): # img(16,3,256,416) """Scales and optionally pads an image tensor to a specified ratio, maintaining its aspect ratio constrained by `gs`. """ if ratio == 1.0: return img h, w = img.shape[2:] s = (int(h * ratio), int(w * ratio)) # new size img = F.interpolate(img, size=s, mode="bilinear", align_corners=False) # resize if not same_shape: # pad/crop img h, w = (math.ceil(x * ratio / gs) * gs for x in (h, w)) return F.pad(img, [0, w - s[1], 0, h - s[0]], value=0.447) # value = imagenet mean def copy_attr(a, b, include=(), exclude=()): """Copies attributes from object b to a, with options to include or exclude specific attributes.""" for k, v in b.__dict__.items(): if (len(include) and k not in include) or k.startswith("_") or k in exclude: continue else: setattr(a, k, v) def smart_optimizer(model, name="Adam", lr=0.001, momentum=0.9, decay=1e-5): """Initializes a smart optimizer for YOLOv3 with custom parameter groups for different weight decays and biases.""" g = [], [], [] # optimizer parameter groups bn = tuple(v for k, v in nn.__dict__.items() if "Norm" in k) # normalization layers, i.e. BatchNorm2d() for v in model.modules(): for p_name, p in v.named_parameters(recurse=0): if p_name == "bias": # bias (no decay) g[2].append(p) elif p_name == "weight" and isinstance(v, bn): # weight (no decay) g[1].append(p) else: g[0].append(p) # weight (with decay) if name == "Adam": optimizer = torch.optim.Adam(g[2], lr=lr, betas=(momentum, 0.999)) # adjust beta1 to momentum elif name == "AdamW": optimizer = torch.optim.AdamW(g[2], lr=lr, betas=(momentum, 0.999), weight_decay=0.0) elif name == "RMSProp": optimizer = torch.optim.RMSprop(g[2], lr=lr, momentum=momentum) elif name == "SGD": optimizer = torch.optim.SGD(g[2], lr=lr, momentum=momentum, nesterov=True) else: raise NotImplementedError(f"Optimizer {name} not implemented.") optimizer.add_param_group({"params": g[0], "weight_decay": decay}) # add g0 with weight_decay optimizer.add_param_group({"params": g[1], "weight_decay": 0.0}) # add g1 (BatchNorm2d weights) LOGGER.info( f"{colorstr('optimizer:')} {type(optimizer).__name__}(lr={lr}) with parameter groups " f"{len(g[1])} weight(decay=0.0), {len(g[0])} weight(decay={decay}), {len(g[2])} bias" ) return optimizer def smart_hub_load(repo="ultralytics/yolov5", model="yolov5s", **kwargs): """Loads YOLO model from Ultralytics repo with smart error handling, supports `force_reload` on failure. See https://github.com/ultralytics/yolov5 """ if check_version(torch.__version__, "1.9.1"): kwargs["skip_validation"] = True # validation causes GitHub API rate limit errors if check_version(torch.__version__, "1.12.0"): kwargs["trust_repo"] = True # argument required starting in torch 0.12 try: return torch.hub.load(repo, model, **kwargs) except Exception: return torch.hub.load(repo, model, force_reload=True, **kwargs) def smart_resume(ckpt, optimizer, ema=None, weights="yolov5s.pt", epochs=300, resume=True): """Resumes or fine-tunes training from a checkpoint with optimizer and EMA support; updates epochs based on progress. """ best_fitness = 0.0 start_epoch = ckpt["epoch"] + 1 if ckpt["optimizer"] is not None: optimizer.load_state_dict(ckpt["optimizer"]) # optimizer best_fitness = ckpt["best_fitness"] if ema and ckpt.get("ema"): ema.ema.load_state_dict(ckpt["ema"].float().state_dict()) # EMA ema.updates = ckpt["updates"] if resume: assert start_epoch > 0, ( f"{weights} training to {epochs} epochs is finished, nothing to resume.\n" f"Start a new training without --resume, i.e. 'python train.py --weights {weights}'" ) LOGGER.info(f"Resuming training from {weights} from epoch {start_epoch} to {epochs} total epochs") if epochs < start_epoch: LOGGER.info(f"{weights} has been trained for {ckpt['epoch']} epochs. Fine-tuning for {epochs} more epochs.") epochs += ckpt["epoch"] # finetune additional epochs return best_fitness, start_epoch, epochs class EarlyStopping: """Monitors training to halt if no improvement in fitness metric is observed for a specified number of epochs.""" def __init__(self, patience=30): """Initializes EarlyStopping to monitor training, halting if no improvement in 'patience' epochs, defaulting to 30. """ self.best_fitness = 0.0 # i.e. mAP self.best_epoch = 0 self.patience = patience or float("inf") # epochs to wait after fitness stops improving to stop self.possible_stop = False # possible stop may occur next epoch def __call__(self, epoch, fitness): """Updates stopping criteria based on fitness; returns True to stop if no improvement in 'patience' epochs.""" if fitness >= self.best_fitness: # >= 0 to allow for early zero-fitness stage of training self.best_epoch = epoch self.best_fitness = fitness delta = epoch - self.best_epoch # epochs without improvement self.possible_stop = delta >= (self.patience - 1) # possible stop may occur next epoch stop = delta >= self.patience # stop training if patience exceeded if stop: LOGGER.info( f"Stopping training early as no improvement observed in last {self.patience} epochs. " f"Best results observed at epoch {self.best_epoch}, best model saved as best.pt.\n" f"To update EarlyStopping(patience={self.patience}) pass a new patience value, " f"i.e. `python train.py --patience 300` or use `--patience 0` to disable EarlyStopping." ) return stop class ModelEMA: """Updated Exponential Moving Average (EMA) from https://github.com/rwightman/pytorch-image-models Keeps a moving average of everything in the model state_dict (parameters and buffers) For EMA details see https://www.tensorflow.org/api_docs/python/tf/train/ExponentialMovingAverage. """ def __init__(self, model, decay=0.9999, tau=2000, updates=0): """Initializes EMA with model, optional decay (default 0.9999), tau (2000), and updates count, setting model to eval mode. """ self.ema = deepcopy(de_parallel(model)).eval() # FP32 EMA self.updates = updates # number of EMA updates self.decay = lambda x: decay * (1 - math.exp(-x / tau)) # decay exponential ramp (to help early epochs) for p in self.ema.parameters(): p.requires_grad_(False) def update(self, model): """Updates EMA parameters based on model weights, decay factor, and increment update count.""" self.updates += 1 d = self.decay(self.updates) msd = de_parallel(model).state_dict() # model state_dict for k, v in self.ema.state_dict().items(): if v.dtype.is_floating_point: # true for FP16 and FP32 v *= d v += (1 - d) * msd[k].detach() # assert v.dtype == msd[k].dtype == torch.float32, f'{k}: EMA {v.dtype} and model {msd[k].dtype} must be FP32' def update_attr(self, model, include=(), exclude=("process_group", "reducer")): """Updates EMA attributes by copying from model, excluding 'process_group' and 'reducer' by default.""" copy_attr(self.ema, model, include, exclude) ================================================ FILE: utils/triton.py ================================================ # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license """Utils to interact with the Triton Inference Server.""" from __future__ import annotations from urllib.parse import urlparse import torch class TritonRemoteModel: """A wrapper over a model served by the Triton Inference Server. It can be configured to communicate over GRPC or HTTP. It accepts Torch Tensors as input and returns them as outputs. """ def __init__(self, url: str): """Keyword Arguments: url: Fully qualified address of the Triton server - for e.g. grpc://localhost:8000.""" parsed_url = urlparse(url) if parsed_url.scheme == "grpc": from tritonclient.grpc import InferenceServerClient, InferInput self.client = InferenceServerClient(parsed_url.netloc) # Triton GRPC client model_repository = self.client.get_model_repository_index() self.model_name = model_repository.models[0].name self.metadata = self.client.get_model_metadata(self.model_name, as_json=True) def create_input_placeholders() -> list[InferInput]: return [ InferInput(i["name"], [int(s) for s in i["shape"]], i["datatype"]) for i in self.metadata["inputs"] ] else: from tritonclient.http import InferenceServerClient, InferInput self.client = InferenceServerClient(parsed_url.netloc) # Triton HTTP client model_repository = self.client.get_model_repository_index() self.model_name = model_repository[0]["name"] self.metadata = self.client.get_model_metadata(self.model_name) def create_input_placeholders() -> list[InferInput]: return [ InferInput(i["name"], [int(s) for s in i["shape"]], i["datatype"]) for i in self.metadata["inputs"] ] self._create_input_placeholders_fn = create_input_placeholders @property def runtime(self): """Returns the model runtime.""" return self.metadata.get("backend", self.metadata.get("platform")) def __call__(self, *args, **kwargs) -> torch.Tensor | tuple[torch.Tensor, ...]: """Invokes the model. Parameters can be provided via args or kwargs. args, if provided, are assumed to match the order of inputs of the model. kwargs are matched with the model input names. """ inputs = self._create_inputs(*args, **kwargs) response = self.client.infer(model_name=self.model_name, inputs=inputs) result = [] for output in self.metadata["outputs"]: tensor = torch.as_tensor(response.as_numpy(output["name"])) result.append(tensor) return result[0] if len(result) == 1 else result def _create_inputs(self, *args, **kwargs): """Generates model inputs from args or kwargs, not allowing both; raises error if neither or both are provided. """ args_len, kwargs_len = len(args), len(kwargs) if not args_len and not kwargs_len: raise RuntimeError("No inputs provided.") if args_len and kwargs_len: raise RuntimeError("Cannot specify args and kwargs at the same time") placeholders = self._create_input_placeholders_fn() if args_len: if args_len != len(placeholders): raise RuntimeError(f"Expected {len(placeholders)} inputs, got {args_len}.") for input, value in zip(placeholders, args): input.set_data_from_numpy(value.cpu().numpy()) else: for input in placeholders: value = kwargs[input.name] input.set_data_from_numpy(value.cpu().numpy()) return placeholders ================================================ FILE: val.py ================================================ # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license """ Validate a trained YOLOv3 detection model on a detection dataset. Usage: $ python val.py --weights yolov5s.pt --data coco128.yaml --img 640 Usage - formats: $ python val.py --weights yolov5s.pt # PyTorch yolov5s.torchscript # TorchScript yolov5s.onnx # ONNX Runtime or OpenCV DNN with --dnn yolov5s_openvino_model # OpenVINO yolov5s.engine # TensorRT yolov5s.mlmodel # CoreML (macOS-only) yolov5s_saved_model # TensorFlow SavedModel yolov5s.pb # TensorFlow GraphDef yolov5s.tflite # TensorFlow Lite yolov5s_edgetpu.tflite # TensorFlow Edge TPU yolov5s_paddle_model # PaddlePaddle """ import argparse import json import os import subprocess import sys from pathlib import Path import numpy as np import torch from tqdm import tqdm FILE = Path(__file__).resolve() ROOT = FILE.parents[0] # YOLOv3 root directory if str(ROOT) not in sys.path: sys.path.append(str(ROOT)) # add ROOT to PATH ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative from models.common import DetectMultiBackend from utils.callbacks import Callbacks from utils.dataloaders import create_dataloader from utils.general import ( LOGGER, TQDM_BAR_FORMAT, Profile, check_dataset, check_img_size, check_requirements, check_yaml, coco80_to_coco91_class, colorstr, increment_path, non_max_suppression, print_args, scale_boxes, xywh2xyxy, xyxy2xywh, ) from utils.metrics import ConfusionMatrix, ap_per_class, box_iou from utils.plots import output_to_target, plot_images, plot_val_study from utils.torch_utils import select_device, smart_inference_mode def save_one_txt(predn, save_conf, shape, file): """Saves detection results in a text format, including labels and optionally confidence scores. Args: predn (torch.Tensor): A tensor containing normalized prediction results in the format (x1, y1, x2, y2, conf, cls). save_conf (bool): A flag indicating whether to save confidence scores. shape (tuple[int, int]): Original image shape in the format (height, width). file (str | Path): Path to the file where the results will be saved. Returns: None Examples: ```python from pathlib import Path import torch predn = torch.tensor([ [10, 20, 100, 200, 0.9, 1], [30, 40, 150, 250, 0.8, 0], ]) save_conf = True shape = (416, 416) file = Path("results.txt") save_one_txt(predn, save_conf, shape, file) ``` Notes: - The function normalizes bounding box coordinates before saving. - Each line in the output file will contain class, x-center, y-center, width, height and optionally confidence score. - The format is compatible with YOLO training dataset format. """ gn = torch.tensor(shape)[[1, 0, 1, 0]] # normalization gain whwh for *xyxy, conf, cls in predn.tolist(): xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() # normalized xywh line = (cls, *xywh, conf) if save_conf else (cls, *xywh) # label format with open(file, "a") as f: f.write(("%g " * len(line)).rstrip() % line + "\n") def save_one_json(predn, jdict, path, class_map): """Save detection results in JSON format containing image_id, category_id, bbox, and score per detection. Args: predn (torch.Tensor): Normalized prediction tensor of shape (N, 6) where N is the number of detections. Each detection should contain (x1, y1, x2, y2, confidence, class). jdict (list): List to store the JSON serializable detections. path (Path): Path object representing the image file path. class_map (dict[int, int]): Dictionary mapping class indices to their respective category IDs. Returns: None Examples: ```python predn = torch.tensor([[50, 30, 200, 150, 0.9, 0], [30, 20, 180, 150, 0.8, 1]]) jdict = [] path = Path('images/000001.jpg') class_map = {0: 1, 1: 2} save_one_json(predn, jdict, path, class_map) ``` Notes: - The image_id is extracted from the image file path. - Bounding boxes are converted from xyxy format to xywh format. - The JSON output format is compatible with COCO dataset evaluation. """ image_id = int(path.stem) if path.stem.isnumeric() else path.stem box = xyxy2xywh(predn[:, :4]) # xywh box[:, :2] -= box[:, 2:] / 2 # xy center to top-left corner for p, b in zip(predn.tolist(), box.tolist()): jdict.append( { "image_id": image_id, "category_id": class_map[int(p[5])], "bbox": [round(x, 3) for x in b], "score": round(p[4], 5), } ) def process_batch(detections, labels, iouv): """Computes correct prediction matrix for detections against ground truth labels at various IoU thresholds. Args: detections (np.ndarray): Array of detections with shape (N, 6), where each detection contains [x1, y1, x2, y2, confidence, class]. labels (np.ndarray): Array of ground truth labels with shape (M, 5), where each label contains [class, x1, y1, x2, y2]. iouv (np.ndarray): Array of IoU thresholds to use for evaluation. Returns: np.ndarray: Boolean array of shape (N, len(iouv)), indicating correct predictions at each IoU threshold. Examples: ```python detections = np.array([[50, 50, 150, 150, 0.8, 0], [30, 30, 120, 120, 0.7, 1]]) labels = np.array([[0, 50, 50, 150, 150], [1, 30, 30, 120, 120]]) iouv = np.array([0.5, 0.6, 0.7]) correct = process_batch(detections, labels, iouv) ``` Notes: - This function compares detections and ground truth labels to establish matches based on IoU and class. - It supports multiple IoU thresholds to evaluate prediction accuracy flexibly. """ correct = np.zeros((detections.shape[0], iouv.shape[0])).astype(bool) iou = box_iou(labels[:, 1:], detections[:, :4]) correct_class = labels[:, 0:1] == detections[:, 5] for i in range(len(iouv)): x = torch.where((iou >= iouv[i]) & correct_class) # IoU > threshold and classes match if x[0].shape[0]: matches = torch.cat((torch.stack(x, 1), iou[x[0], x[1]][:, None]), 1).cpu().numpy() # [label, detect, iou] if x[0].shape[0] > 1: matches = matches[matches[:, 2].argsort()[::-1]] matches = matches[np.unique(matches[:, 1], return_index=True)[1]] # matches = matches[matches[:, 2].argsort()[::-1]] matches = matches[np.unique(matches[:, 0], return_index=True)[1]] correct[matches[:, 1].astype(int), i] = True return torch.tensor(correct, dtype=torch.bool, device=iouv.device) @smart_inference_mode() def run( data, weights=None, # model.pt path(s) batch_size=32, # batch size imgsz=640, # inference size (pixels) conf_thres=0.001, # confidence threshold iou_thres=0.6, # NMS IoU threshold max_det=300, # maximum detections per image task="val", # train, val, test, speed or study device="", # cuda device, i.e. 0 or 0,1,2,3 or cpu workers=8, # max dataloader workers (per RANK in DDP mode) single_cls=False, # treat as single-class dataset augment=False, # augmented inference verbose=False, # verbose output save_txt=False, # save results to *.txt save_hybrid=False, # save label+prediction hybrid results to *.txt save_conf=False, # save confidences in --save-txt labels save_json=False, # save a COCO-JSON results file project=ROOT / "runs/val", # save to project/name name="exp", # save to project/name exist_ok=False, # existing project/name ok, do not increment half=True, # use FP16 half-precision inference dnn=False, # use OpenCV DNN for ONNX inference model=None, dataloader=None, save_dir=Path(""), plots=True, callbacks=Callbacks(), compute_loss=None, ): """Validates a trained YOLO model on a dataset and saves detection results in specified formats. Args: data (str | dict): Path to the dataset configuration file (.yaml) or a dictionary containing the dataset paths. weights (str | list, optional): Path to the trained model weights file(s). Default is None. batch_size (int, optional): Batch size for inference. Default is 32. imgsz (int, optional): Input image size for inference in pixels. Default is 640. conf_thres (float, optional): Confidence threshold for object detection. Default is 0.001. iou_thres (float, optional): IoU threshold for Non-Maximum Suppression (NMS). Default is 0.6. max_det (int, optional): Maximum number of detections per image. Default is 300. task (str, optional): Task type, can be 'train', 'val', 'test', 'speed', or 'study'. Default is 'val'. device (str, optional): Device for computation, e.g., '0' for GPU or 'cpu' for CPU. Default is "". workers (int, optional): Number of dataloader workers. Default is 8. single_cls (bool, optional): Whether to treat the dataset as a single-class dataset. Default is False. augment (bool, optional): Whether to apply augmented inference. Default is False. verbose (bool, optional): Whether to output verbose information. Default is False. save_txt (bool, optional): Whether to save detection results in text format (*.txt). Default is False. save_hybrid (bool, optional): Whether to save hybrid results (labels+predictions) in text format (*.txt). Default is False. save_conf (bool, optional): Whether to save confidence scores in text format labels. Default is False. save_json (bool, optional): Whether to save detection results in COCO JSON format. Default is False. project (str | Path, optional): Directory path to save validation results. Default is ROOT / 'runs/val'. name (str, optional): Directory name to save validation results. Default is 'exp'. exist_ok (bool, optional): Whether to overwrite existing project/name directory. Default is False. half (bool, optional): Whether to use half-precision (FP16) for inference. Default is True. dnn (bool, optional): Whether to use OpenCV DNN for ONNX inference. Default is False. model (torch.nn.Module, optional): Existing model instance. Default is None. dataloader (torch.utils.data.DataLoader, optional): Existing dataloader instance. Default is None. save_dir (Path, optional): Path to directory to save results. Default is Path(""). plots (bool, optional): Whether to generate plots for visual results. Default is True. callbacks (Callbacks, optional): Callbacks instance for event handling. Default is Callbacks(). compute_loss (Callable, optional): Loss function for computing training loss. Default is None. Returns: (tuple): A tuple containing: - metrics (torch.Tensor): Dictionary containing metrics such as precision, recall, mAP, F1 score, etc. - times (dict): Dictionary containing times for different parts of the pipeline (e.g., preprocessing, inference, NMS). - samples (torch.Tensor): Torch tensor containing validation samples. Examples: ```python metrics, times, samples = run( data='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='cpu' ) ``` """ # Initialize/load model and set device training = model is not None if training: # called by train.py device, pt, jit, engine = next(model.parameters()).device, True, False, False # get model device, PyTorch model half &= device.type != "cpu" # half precision only supported on CUDA model.half() if half else model.float() else: # called directly device = select_device(device, batch_size=batch_size) # Directories save_dir = increment_path(Path(project) / name, exist_ok=exist_ok) # increment run (save_dir / "labels" if save_txt else save_dir).mkdir(parents=True, exist_ok=True) # make dir # Load model model = DetectMultiBackend(weights, device=device, dnn=dnn, data=data, fp16=half) stride, pt, jit, engine = model.stride, model.pt, model.jit, model.engine imgsz = check_img_size(imgsz, s=stride) # check image size half = model.fp16 # FP16 supported on limited backends with CUDA if engine: batch_size = model.batch_size else: device = model.device if not (pt or jit): batch_size = 1 # export.py models default to batch-size 1 LOGGER.info(f"Forcing --batch-size 1 square inference (1,3,{imgsz},{imgsz}) for non-PyTorch models") # Data data = check_dataset(data) # check # Configure model.eval() cuda = device.type != "cpu" is_coco = isinstance(data.get("val"), str) and data["val"].endswith(f"coco{os.sep}val2017.txt") # COCO dataset nc = 1 if single_cls else int(data["nc"]) # number of classes iouv = torch.linspace(0.5, 0.95, 10, device=device) # iou vector for mAP@0.5:0.95 niou = iouv.numel() # Dataloader if not training: if pt and not single_cls: # check --weights are trained on --data ncm = model.model.nc assert ncm == nc, ( f"{weights} ({ncm} classes) trained on different --data than what you passed ({nc} " f"classes). Pass correct combination of --weights and --data that are trained together." ) model.warmup(imgsz=(1 if pt else batch_size, 3, imgsz, imgsz)) # warmup pad, rect = (0.0, False) if task == "speed" else (0.5, pt) # square inference for benchmarks task = task if task in ("train", "val", "test") else "val" # path to train/val/test images dataloader = create_dataloader( data[task], imgsz, batch_size, stride, single_cls, pad=pad, rect=rect, workers=workers, prefix=colorstr(f"{task}: "), )[0] seen = 0 confusion_matrix = ConfusionMatrix(nc=nc) names = model.names if hasattr(model, "names") else model.module.names # get class names if isinstance(names, (list, tuple)): # old format names = dict(enumerate(names)) class_map = coco80_to_coco91_class() if is_coco else list(range(1000)) s = ("%22s" + "%11s" * 6) % ("Class", "Images", "Instances", "P", "R", "mAP50", "mAP50-95") 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 dt = Profile(), Profile(), Profile() # profiling times loss = torch.zeros(3, device=device) jdict, stats, ap, ap_class = [], [], [], [] callbacks.run("on_val_start") pbar = tqdm(dataloader, desc=s, bar_format=TQDM_BAR_FORMAT) # progress bar for batch_i, (im, targets, paths, shapes) in enumerate(pbar): callbacks.run("on_val_batch_start") with dt[0]: if cuda: im = im.to(device, non_blocking=True) targets = targets.to(device) im = im.half() if half else im.float() # uint8 to fp16/32 im /= 255 # 0 - 255 to 0.0 - 1.0 nb, _, height, width = im.shape # batch size, channels, height, width # Inference with dt[1]: preds, train_out = model(im) if compute_loss else (model(im, augment=augment), None) # Loss if compute_loss: loss += compute_loss(train_out, targets)[1] # box, obj, cls # NMS targets[:, 2:] *= torch.tensor((width, height, width, height), device=device) # to pixels lb = [targets[targets[:, 0] == i, 1:] for i in range(nb)] if save_hybrid else [] # for autolabelling with dt[2]: preds = non_max_suppression( preds, conf_thres, iou_thres, labels=lb, multi_label=True, agnostic=single_cls, max_det=max_det ) # Metrics for si, pred in enumerate(preds): labels = targets[targets[:, 0] == si, 1:] nl, npr = labels.shape[0], pred.shape[0] # number of labels, predictions path, shape = Path(paths[si]), shapes[si][0] correct = torch.zeros(npr, niou, dtype=torch.bool, device=device) # init seen += 1 if npr == 0: if nl: stats.append((correct, *torch.zeros((2, 0), device=device), labels[:, 0])) if plots: confusion_matrix.process_batch(detections=None, labels=labels[:, 0]) continue # Predictions if single_cls: pred[:, 5] = 0 predn = pred.clone() scale_boxes(im[si].shape[1:], predn[:, :4], shape, shapes[si][1]) # native-space pred # Evaluate if nl: tbox = xywh2xyxy(labels[:, 1:5]) # target boxes scale_boxes(im[si].shape[1:], tbox, shape, shapes[si][1]) # native-space labels labelsn = torch.cat((labels[:, 0:1], tbox), 1) # native-space labels correct = process_batch(predn, labelsn, iouv) if plots: confusion_matrix.process_batch(predn, labelsn) stats.append((correct, pred[:, 4], pred[:, 5], labels[:, 0])) # (correct, conf, pcls, tcls) # Save/log if save_txt: save_one_txt(predn, save_conf, shape, file=save_dir / "labels" / f"{path.stem}.txt") if save_json: save_one_json(predn, jdict, path, class_map) # append to COCO-JSON dictionary callbacks.run("on_val_image_end", pred, predn, path, names, im[si]) # Plot images if plots and batch_i < 3: plot_images(im, targets, paths, save_dir / f"val_batch{batch_i}_labels.jpg", names) # labels plot_images(im, output_to_target(preds), paths, save_dir / f"val_batch{batch_i}_pred.jpg", names) # pred callbacks.run("on_val_batch_end", batch_i, im, targets, paths, shapes, preds) # Compute metrics stats = [torch.cat(x, 0).cpu().numpy() for x in zip(*stats)] # to numpy if len(stats) and stats[0].any(): tp, fp, p, r, f1, ap, ap_class = ap_per_class(*stats, plot=plots, save_dir=save_dir, names=names) ap50, ap = ap[:, 0], ap.mean(1) # AP@0.5, AP@0.5:0.95 mp, mr, map50, map = p.mean(), r.mean(), ap50.mean(), ap.mean() nt = np.bincount(stats[3].astype(int), minlength=nc) # number of targets per class # Print results pf = "%22s" + "%11i" * 2 + "%11.3g" * 4 # print format LOGGER.info(pf % ("all", seen, nt.sum(), mp, mr, map50, map)) if nt.sum() == 0: LOGGER.warning(f"WARNING ⚠️ no labels found in {task} set, can not compute metrics without labels") # Print results per class if (verbose or (nc < 50 and not training)) and nc > 1 and len(stats): for i, c in enumerate(ap_class): LOGGER.info(pf % (names[c], seen, nt[c], p[i], r[i], ap50[i], ap[i])) # Print speeds t = tuple(x.t / seen * 1e3 for x in dt) # speeds per image if not training: shape = (batch_size, 3, imgsz, imgsz) LOGGER.info(f"Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {shape}" % t) # Plots if plots: confusion_matrix.plot(save_dir=save_dir, names=list(names.values())) callbacks.run("on_val_end", nt, tp, fp, p, r, f1, ap, ap50, ap_class, confusion_matrix) # Save JSON if save_json and len(jdict): w = Path(weights[0] if isinstance(weights, list) else weights).stem if weights is not None else "" # weights anno_json = str(Path("../datasets/coco/annotations/instances_val2017.json")) # annotations if not os.path.exists(anno_json): anno_json = os.path.join(data["path"], "annotations", "instances_val2017.json") pred_json = str(save_dir / f"{w}_predictions.json") # predictions LOGGER.info(f"\nEvaluating pycocotools mAP... saving {pred_json}...") with open(pred_json, "w") as f: json.dump(jdict, f) try: # https://github.com/cocodataset/cocoapi/blob/master/PythonAPI/pycocoEvalDemo.ipynb check_requirements("pycocotools>=2.0.6") from pycocotools.coco import COCO from pycocotools.cocoeval import COCOeval anno = COCO(anno_json) # init annotations api pred = anno.loadRes(pred_json) # init predictions api eval = COCOeval(anno, pred, "bbox") if is_coco: eval.params.imgIds = [int(Path(x).stem) for x in dataloader.dataset.im_files] # image IDs to evaluate eval.evaluate() eval.accumulate() eval.summarize() map, map50 = eval.stats[:2] # update results (mAP@0.5:0.95, mAP@0.5) except Exception as e: LOGGER.info(f"pycocotools unable to run: {e}") # Return results model.float() # for training if not training: s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else "" LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}{s}") maps = np.zeros(nc) + map for i, c in enumerate(ap_class): maps[c] = ap[i] return (mp, mr, map50, map, *(loss.cpu() / len(dataloader)).tolist()), maps, t def parse_opt(): """Parses and returns command-line options for dataset paths, model parameters, and inference settings. Args: --data (str): Path to the dataset YAML file. Default is 'data/coco128.yaml'. --weights (list[str]): Paths to one or more model files. Default is 'yolov3-tiny.pt'. --batch-size (int): Number of images per batch during inference. Default is 32. --imgsz (int): Inference size (pixels). Default is 640. --conf-thres (float): Confidence threshold for object detection. Default is 0.001. --iou-thres (float): IoU threshold for non-max suppression (NMS). Default is 0.6. --max-det (int): Maximum number of detections per image. Default is 300. --task (str): Task to perform: 'train', 'val', 'test', 'speed', or 'study'. Default is 'val'. --device (str): CUDA device identifier (e.g., '0' or '0,1,2,3') or 'cpu' for using CPU. Default is "". --workers (int): Maximum number of dataloader workers (per RANK in DDP mode). Default is 8. --single-cls (bool): Treat the dataset as a single-class dataset. Default is False. --augment (bool): Apply test-time augmentation during inference. Default is False. --verbose (bool): Print mAP by class. Default is False. --save-txt (bool): Save detection results in '.txt' format. Default is False. --save-hybrid (bool): Save hybrid results containing both label and prediction in '.txt' format. Default is False. --save-conf (bool): Save confidence scores in the '--save-txt' labels. Default is False. --save-json (bool): Save detection results in COCO JSON format. Default is False. --project (str): Project directory to save results. Default is 'runs/val'. --name (str): Name of the experiment to save results. Default is 'exp'. --exist-ok (bool): Whether to overwrite existing project/name without incrementing. Default is False. --half (bool): Use FP16 half-precision during inference. Default is False. --dnn (bool): Use OpenCV DNN backend for ONNX inference. Default is False. Returns: opt (argparse.Namespace): Parsed command-line options. Examples: Use the following command to run validation with custom settings: ```python $ python val.py --weights yolov5s.pt --data coco128.yaml --img 640 ``` Notes: - The function uses `argparse` to handle command-line options. - It also modifies some options based on specific conditions, such as appending additional flags for saving in JSON format and checking for the `coco.yaml` dataset. """ parser = argparse.ArgumentParser() parser.add_argument("--data", type=str, default=ROOT / "data/coco128.yaml", help="dataset.yaml path") parser.add_argument("--weights", nargs="+", type=str, default=ROOT / "yolov3-tiny.pt", help="model path(s)") parser.add_argument("--batch-size", type=int, default=32, help="batch size") parser.add_argument("--imgsz", "--img", "--img-size", type=int, default=640, help="inference size (pixels)") parser.add_argument("--conf-thres", type=float, default=0.001, help="confidence threshold") parser.add_argument("--iou-thres", type=float, default=0.6, help="NMS IoU threshold") parser.add_argument("--max-det", type=int, default=300, help="maximum detections per image") parser.add_argument("--task", default="val", help="train, val, test, speed or study") parser.add_argument("--device", default="", help="cuda device, i.e. 0 or 0,1,2,3 or cpu") parser.add_argument("--workers", type=int, default=8, help="max dataloader workers (per RANK in DDP mode)") parser.add_argument("--single-cls", action="store_true", help="treat as single-class dataset") parser.add_argument("--augment", action="store_true", help="augmented inference") parser.add_argument("--verbose", action="store_true", help="report mAP by class") parser.add_argument("--save-txt", action="store_true", help="save results to *.txt") parser.add_argument("--save-hybrid", action="store_true", help="save label+prediction hybrid results to *.txt") parser.add_argument("--save-conf", action="store_true", help="save confidences in --save-txt labels") parser.add_argument("--save-json", action="store_true", help="save a COCO-JSON results file") parser.add_argument("--project", default=ROOT / "runs/val", help="save to project/name") parser.add_argument("--name", default="exp", help="save to project/name") parser.add_argument("--exist-ok", action="store_true", help="existing project/name ok, do not increment") parser.add_argument("--half", action="store_true", help="use FP16 half-precision inference") parser.add_argument("--dnn", action="store_true", help="use OpenCV DNN for ONNX inference") opt = parser.parse_args() opt.data = check_yaml(opt.data) # check YAML opt.save_json |= opt.data.endswith("coco.yaml") opt.save_txt |= opt.save_hybrid print_args(vars(opt)) return opt def main(opt): """Executes model tasks including training, validation, and speed or study benchmarks based on specified options. Args: opt (argparse.Namespace): Parsed command-line options for dataset paths, model parameters, and inference settings. Returns: None Examples: To validate a trained YOLOv3 model: ```bash $ python val.py --weights yolov3.pt --data coco.yaml --img 640 --task val ``` For running speed benchmarks: ```bash $ python val.py --task speed --data coco.yaml --weights yolov3.pt --batch-size 1 ``` Links: For more information, visit the official repository: https://github.com/ultralytics/ultralytics Notes: This function orchestrates different tasks based on the user input provided through command-line arguments. It supports tasks like `train`, `val`, `test`, `speed`, and `study`. Depending on the task, it validates the model on a dataset, performs speed benchmarks, or runs mAP benchmarks. """ check_requirements(ROOT / "requirements.txt", exclude=("tensorboard", "thop")) if opt.task in ("train", "val", "test"): # run normally if opt.conf_thres > 0.001: # https://github.com/ultralytics/yolov5/issues/1466 LOGGER.info(f"WARNING ⚠️ confidence threshold {opt.conf_thres} > 0.001 produces invalid results") if opt.save_hybrid: LOGGER.info("WARNING ⚠️ --save-hybrid will return high mAP from hybrid labels, not from predictions alone") run(**vars(opt)) else: weights = opt.weights if isinstance(opt.weights, list) else [opt.weights] opt.half = torch.cuda.is_available() and opt.device != "cpu" # FP16 for fastest results if opt.task == "speed": # speed benchmarks # python val.py --task speed --data coco.yaml --batch 1 --weights yolov5n.pt yolov5s.pt... opt.conf_thres, opt.iou_thres, opt.save_json = 0.25, 0.45, False for opt.weights in weights: run(**vars(opt), plots=False) elif opt.task == "study": # speed vs mAP benchmarks # python val.py --task study --data coco.yaml --iou 0.7 --weights yolov5n.pt yolov5s.pt... for opt.weights in weights: f = f"study_{Path(opt.data).stem}_{Path(opt.weights).stem}.txt" # filename to save to x, y = list(range(256, 1536 + 128, 128)), [] # x axis (image sizes), y axis for opt.imgsz in x: # img-size LOGGER.info(f"\nRunning {f} --imgsz {opt.imgsz}...") r, _, t = run(**vars(opt), plots=False) y.append(r + t) # results and times np.savetxt(f, y, fmt="%10.4g") # save subprocess.run(["zip", "-r", "study.zip", "study_*.txt"]) plot_val_study(x=x) # plot else: raise NotImplementedError(f'--task {opt.task} not in ("train", "val", "test", "speed", "study")') if __name__ == "__main__": opt = parse_opt() main(opt)