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ultralytics 8.0.158 add benchmarks to coverage (#4432)
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Yonghye Kwon <developer.0hye@gmail.com>
This commit is contained in:
Glenn Jocher
GitHub
parent
495806565d
commit
87ce15d383
27
.github/workflows/ci.yaml
vendored
27
.github/workflows/ci.yaml
vendored
@ -106,11 +106,7 @@ jobs:
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shell: bash # for Windows compatibility
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run: |
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python -m pip install --upgrade pip wheel
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if [ "${{ matrix.os }}" == "macos-latest" ]; then
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pip install -e ".[export]" --extra-index-url https://download.pytorch.org/whl/cpu
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else
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pip install -e ".[export]" --extra-index-url https://download.pytorch.org/whl/cpu
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fi
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pip install -e ".[export]" coverage --extra-index-url https://download.pytorch.org/whl/cpu
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yolo export format=tflite imgsz=32 || true
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- name: Check environment
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run: |
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@ -125,16 +121,25 @@ jobs:
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pip list
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- name: Benchmark DetectionModel
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shell: bash
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run: yolo benchmark model='path with spaces/${{ matrix.model }}.pt' imgsz=160 verbose=0.26
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run: coverage run -a --source=ultralytics -m ultralytics.cfg.__init__ benchmark model='path with spaces/${{ matrix.model }}.pt' imgsz=160 verbose=0.26
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- name: Benchmark SegmentationModel
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shell: bash
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run: yolo benchmark model='path with spaces/${{ matrix.model }}-seg.pt' imgsz=160 verbose=0.30
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run: coverage run -a --source=ultralytics -m ultralytics.cfg.__init__ benchmark model='path with spaces/${{ matrix.model }}-seg.pt' imgsz=160 verbose=0.30
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- name: Benchmark ClassificationModel
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shell: bash
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run: yolo benchmark model='path with spaces/${{ matrix.model }}-cls.pt' imgsz=160 verbose=0.36
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run: coverage run -a --source=ultralytics -m ultralytics.cfg.__init__ benchmark model='path with spaces/${{ matrix.model }}-cls.pt' imgsz=160 verbose=0.36
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- name: Benchmark PoseModel
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shell: bash
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run: yolo benchmark model='path with spaces/${{ matrix.model }}-pose.pt' imgsz=160 verbose=0.17
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run: coverage run -a --source=ultralytics -m ultralytics.cfg.__init__ benchmark model='path with spaces/${{ matrix.model }}-pose.pt' imgsz=160 verbose=0.17
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- name: Merge Coverage Reports
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run: |
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coverage xml -o coverage-benchmarks.xml
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- name: Upload Coverage Reports to CodeCov
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uses: codecov/codecov-action@v3
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with:
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flags: Benchmarks
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env:
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CODECOV_TOKEN: ${{ secrets.CODECOV_TOKEN }}
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- name: Benchmark Summary
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run: |
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cat benchmarks.log
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@ -183,9 +188,11 @@ jobs:
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- name: Pytest tests
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shell: bash # for Windows compatibility
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run: pytest --cov=ultralytics/ --cov-report xml tests/
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- name: Upload coverage reports to Codecov
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- name: Upload Coverage Reports to CodeCov
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if: github.repository == 'ultralytics/ultralytics' && matrix.os == 'ubuntu-latest' && matrix.python-version == '3.11'
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uses: codecov/codecov-action@v3
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with:
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flags: Tests
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env:
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CODECOV_TOKEN: ${{ secrets.CODECOV_TOKEN }}
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3
.gitignore
vendored
3
.gitignore
vendored
@ -118,6 +118,9 @@ venv.bak/
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.spyderproject
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.spyproject
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# VSCode project settings
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.vscode/
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# Rope project settings
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.ropeproject
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@ -1,6 +1,6 @@
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---
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description: Explore the exporter functionality of Ultralytics. Learn about exporting formats, iOSDetectModel, and try exporting with examples.
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keywords: Ultralytics, Exporter, iOSDetectModel, Export Formats, Try export
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description: Explore the exporter functionality of Ultralytics. Learn about exporting formats, IOSDetectModel, and try exporting with examples.
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keywords: Ultralytics, Exporter, IOSDetectModel, Export Formats, Try export
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---
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# Reference for `ultralytics/engine/exporter.py`
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@ -14,7 +14,7 @@ keywords: Ultralytics, Exporter, iOSDetectModel, Export Formats, Try export
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<br><br>
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---
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## ::: ultralytics.engine.exporter.iOSDetectModel
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## ::: ultralytics.engine.exporter.IOSDetectModel
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<br><br>
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---
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@ -28,7 +28,3 @@ keywords: Ultralytics, Exporter, iOSDetectModel, Export Formats, Try export
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---
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## ::: ultralytics.engine.exporter.try_export
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<br><br>
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---
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## ::: ultralytics.engine.exporter.export
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<br><br>
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@ -12,7 +12,3 @@ keywords: Ultralytics, RTDETRTrainer, model training, Ultralytics models, PyTorc
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---
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## ::: ultralytics.models.rtdetr.train.RTDETRTrainer
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<br><br>
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---
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## ::: ultralytics.models.rtdetr.train.train
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<br><br>
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@ -12,7 +12,3 @@ keywords: Ultralytics, classification predictor, predict, YOLO, AI models, model
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---
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## ::: ultralytics.models.yolo.classify.predict.ClassificationPredictor
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<br><br>
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---
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## ::: ultralytics.models.yolo.classify.predict.predict
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<br><br>
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@ -12,7 +12,3 @@ keywords: Ultralytics, YOLO, Classification Trainer, deep learning, training pro
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---
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## ::: ultralytics.models.yolo.classify.train.ClassificationTrainer
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<br><br>
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---
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## ::: ultralytics.models.yolo.classify.train.train
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<br><br>
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@ -12,7 +12,3 @@ keywords: Ultralytics, YOLO, ClassificationValidator, model validation, model fi
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---
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## ::: ultralytics.models.yolo.classify.val.ClassificationValidator
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<br><br>
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---
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## ::: ultralytics.models.yolo.classify.val.val
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<br><br>
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@ -12,7 +12,3 @@ keywords: Ultralytics, YOLO, DetectionPredictor, detect, predict, object detecti
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---
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## ::: ultralytics.models.yolo.detect.predict.DetectionPredictor
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<br><br>
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---
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## ::: ultralytics.models.yolo.detect.predict.predict
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<br><br>
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@ -12,7 +12,3 @@ keywords: Ultralytics YOLO, YOLO, Detection Trainer, Model Training, Machine Lea
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---
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## ::: ultralytics.models.yolo.detect.train.DetectionTrainer
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<br><br>
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---
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## ::: ultralytics.models.yolo.detect.train.train
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<br><br>
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@ -12,7 +12,3 @@ keywords: Ultralytics, YOLO, Detection Validator, model valuation, precision, re
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---
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## ::: ultralytics.models.yolo.detect.val.DetectionValidator
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<br><br>
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---
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## ::: ultralytics.models.yolo.detect.val.val
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<br><br>
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@ -12,7 +12,3 @@ keywords: Ultralytics, YOLO, PosePredictor, machine learning, AI, predictive mod
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---
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## ::: ultralytics.models.yolo.pose.predict.PosePredictor
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<br><br>
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---
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## ::: ultralytics.models.yolo.pose.predict.predict
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<br><br>
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@ -12,7 +12,3 @@ keywords: Ultralytics, YOLO, PoseTrainer, pose training, AI modeling, custom dat
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---
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## ::: ultralytics.models.yolo.pose.train.PoseTrainer
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<br><br>
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---
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## ::: ultralytics.models.yolo.pose.train.train
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<br><br>
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@ -12,7 +12,3 @@ keywords: PoseValidator, Ultralytics, YOLO, Object detection, Pose validation
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---
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## ::: ultralytics.models.yolo.pose.val.PoseValidator
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<br><br>
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---
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## ::: ultralytics.models.yolo.pose.val.val
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<br><br>
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@ -12,7 +12,3 @@ keywords: YOLO, Ultralytics, object detection, segmentation predictor
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---
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## ::: ultralytics.models.yolo.segment.predict.SegmentationPredictor
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<br><br>
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---
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## ::: ultralytics.models.yolo.segment.predict.predict
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<br><br>
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@ -14,7 +14,7 @@ the required functions or operations as long the as correct formats are followed
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custom model and dataloader by just overriding these functions:
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* `get_model(cfg, weights)` - The function that builds the model to be trained
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* `get_dataloder()` - The function that builds the dataloader
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* `get_dataloader()` - The function that builds the dataloader
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More details and source code can be found in [`BaseTrainer` Reference](../reference/engine/trainer.md)
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## DetectionTrainer
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@ -401,6 +401,7 @@ plugins:
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handlers:
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python:
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options:
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docstring_style: google
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show_root_heading: true
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show_source: true
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- ultralytics:
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@ -1,5 +1,5 @@
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# Ultralytics requirements
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# Usage: pip install -r requirements.txt
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# Example: pip install -r requirements.txt
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# Base ----------------------------------------
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matplotlib>=3.2.2
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@ -40,6 +40,14 @@ def test_train(task, model, data):
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@pytest.mark.parametrize('task,model,data', TASK_ARGS)
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def test_val(task, model, data):
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# Download annotations to run pycocotools eval
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# from ultralytics.utils import SETTINGS, Path
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# from ultralytics.utils.downloads import download
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# url = 'https://github.com/ultralytics/assets/releases/download/v0.0.0/'
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# download(f'{url}instances_val2017.json', dir=Path(SETTINGS['datasets_dir']) / 'coco8/annotations')
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# download(f'{url}person_keypoints_val2017.json', dir=Path(SETTINGS['datasets_dir']) / 'coco8-pose/annotations')
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# Validate
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run(f'yolo val {task} model={WEIGHTS_DIR / model}.pt data={data} imgsz=32 save_txt save_json')
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@ -132,13 +132,13 @@ def test_val():
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def test_train_scratch():
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model = YOLO(CFG)
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model.train(data='coco8.yaml', epochs=1, imgsz=32, cache='disk', batch=-1) # test disk caching with AutoBatch
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model.train(data='coco8.yaml', epochs=2, imgsz=32, cache='disk', batch=-1, close_mosaic=1)
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model(SOURCE)
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def test_train_pretrained():
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model = YOLO(WEIGHTS_DIR / 'yolov8n-seg.pt')
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model.train(data='coco8-seg.yaml', epochs=1, imgsz=32, cache='ram', copy_paste=0.5, mixup=0.5) # test RAM caching
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model.train(data='coco8-seg.yaml', epochs=1, imgsz=32, cache='ram', copy_paste=0.5, mixup=0.5)
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model(SOURCE)
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@ -283,6 +283,12 @@ def test_data_converter():
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coco80_to_coco91_class()
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def test_data_annotator():
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from ultralytics.data.annotator import auto_annotate
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auto_annotate(ASSETS, det_model='yolov8n.pt', sam_model='mobile_sam.pt', output_dir=TMP / 'auto_annotate_labels')
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def test_events():
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# Test event sending
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from ultralytics.hub.utils import Events
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@ -304,12 +310,15 @@ def test_utils_init():
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def test_utils_checks():
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from ultralytics.utils.checks import check_requirements, check_yolov5u_filename, git_describe
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from ultralytics.utils.checks import (check_imgsz, check_requirements, check_yolov5u_filename, git_describe,
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print_args)
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check_yolov5u_filename('yolov5n.pt')
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# check_imshow(warn=True)
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git_describe(ROOT)
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check_requirements() # check requirements.txt
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check_imgsz([600, 600], max_dim=1)
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print_args()
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def test_utils_benchmarks():
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@ -1,6 +1,6 @@
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# Ultralytics YOLO 🚀, AGPL-3.0 license
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__version__ = '8.0.157'
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__version__ = '8.0.158'
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from ultralytics.hub import start
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from ultralytics.models import RTDETR, SAM, YOLO
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@ -8,6 +8,7 @@ from ultralytics import SAM, YOLO
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def auto_annotate(data, det_model='yolov8x.pt', sam_model='sam_b.pt', device='', output_dir=None):
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"""
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Automatically annotates images using a YOLO object detection model and a SAM segmentation model.
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Args:
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data (str): Path to a folder containing images to be annotated.
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det_model (str, optional): Pre-trained YOLO detection model. Defaults to 'yolov8x.pt'.
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@ -15,12 +16,20 @@ def auto_annotate(data, det_model='yolov8x.pt', sam_model='sam_b.pt', device='',
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device (str, optional): Device to run the models on. Defaults to an empty string (CPU or GPU, if available).
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output_dir (str | None | optional): Directory to save the annotated results.
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Defaults to a 'labels' folder in the same directory as 'data'.
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Example:
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```python
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from ultralytics.data.annotator import auto_annotate
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auto_annotate(data='ultralytics/assets', det_model='yolov8n.pt', sam_model='mobile_sam.pt')
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```
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"""
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det_model = YOLO(det_model)
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sam_model = SAM(sam_model)
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data = Path(data)
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if not output_dir:
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output_dir = Path(str(data)).parent / 'labels'
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output_dir = data.parent / f'{data.stem}_auto_annotate_labels'
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Path(output_dir).mkdir(exist_ok=True, parents=True)
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det_results = det_model(data, stream=True, device=device)
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@ -402,7 +402,7 @@ class RandomPerspective:
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keypoints (ndarray): keypoints, [N, 17, 3].
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M (ndarray): affine matrix.
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Return:
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Returns:
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new_keypoints (ndarray): keypoints after affine, [N, 17, 3].
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"""
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n, nkpt = keypoints.shape[:2]
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@ -484,7 +484,7 @@ class Exporter:
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classifier_config = ct.ClassifierConfig(list(self.model.names.values())) if self.args.nms else None
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model = self.model
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elif self.model.task == 'detect':
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model = iOSDetectModel(self.model, self.im) if self.args.nms else self.model
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model = IOSDetectModel(self.model, self.im) if self.args.nms else self.model
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else:
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if self.args.nms:
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LOGGER.warning(f"{prefix} WARNING ⚠️ 'nms=True' is only available for Detect models like 'yolov8n.pt'.")
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@ -846,12 +846,11 @@ class Exporter:
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out0, out1 = iter(spec.description.output)
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if MACOS:
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from PIL import Image
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img = Image.new('RGB', (w, h)) # img(192 width, 320 height)
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# img = torch.zeros((*opt.img_size, 3)).numpy() # img size(320,192,3) iDetection
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img = Image.new('RGB', (w, h)) # w=192, h=320
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out = model.predict({'image': img})
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out0_shape = out[out0.name].shape
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out1_shape = out[out1.name].shape
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else: # linux and windows can not run model.predict(), get sizes from pytorch output y
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out0_shape = out[out0.name].shape # (3780, 80)
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out1_shape = out[out1.name].shape # (3780, 4)
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else: # linux and windows can not run model.predict(), get sizes from PyTorch model output y
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out0_shape = self.output_shape[2], self.output_shape[1] - 4 # (3780, 80)
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out1_shape = self.output_shape[2], 4 # (3780, 4)
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@ -963,11 +962,11 @@ class Exporter:
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callback(self)
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class iOSDetectModel(torch.nn.Module):
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"""Wrap an Ultralytics YOLO model for iOS export."""
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class IOSDetectModel(torch.nn.Module):
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"""Wrap an Ultralytics YOLO model for Apple iOS CoreML export."""
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def __init__(self, model, im):
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"""Initialize the iOSDetectModel class with a YOLO model and example image."""
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"""Initialize the IOSDetectModel class with a YOLO model and example image."""
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super().__init__()
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b, c, h, w = im.shape # batch, channel, height, width
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self.model = model
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@ -981,21 +980,3 @@ class iOSDetectModel(torch.nn.Module):
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"""Normalize predictions of object detection model with input size-dependent factors."""
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xywh, cls = self.model(x)[0].transpose(0, 1).split((4, self.nc), 1)
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return cls, xywh * self.normalize # confidence (3780, 80), coordinates (3780, 4)
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def export(cfg=DEFAULT_CFG):
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"""Export a YOLOv model to a specific format."""
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cfg.model = cfg.model or 'yolov8n.yaml'
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cfg.format = cfg.format or 'torchscript'
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from ultralytics import YOLO
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model = YOLO(cfg.model)
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model.export(**vars(cfg))
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if __name__ == '__main__':
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"""
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CLI:
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yolo mode=export model=yolov8n.yaml format=onnx
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"""
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export()
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@ -138,12 +138,14 @@ class BasePredictor:
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return self.model(im, augment=self.args.augment, visualize=visualize)
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def pre_transform(self, im):
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"""Pre-transform input image before inference.
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"""
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Pre-transform input image before inference.
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Args:
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im (List(np.ndarray)): (N, 3, h, w) for tensor, [(h, w, 3) x N] for list.
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Return: A list of transformed imgs.
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Returns:
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(list): A list of transformed images.
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"""
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same_shapes = all(x.shape == im[0].shape for x in im)
|
||||
auto = same_shapes and self.model.pt
|
||||
|
||||
@ -26,7 +26,7 @@ class FastSAMPrompt:
|
||||
import clip # for linear_assignment
|
||||
except ImportError:
|
||||
from ultralytics.utils.checks import check_requirements
|
||||
check_requirements('git+https://github.com/openai/CLIP.git') # required before installing lap from source
|
||||
check_requirements('git+https://github.com/openai/CLIP.git')
|
||||
import clip
|
||||
self.clip = clip
|
||||
|
||||
@ -91,8 +91,6 @@ class FastSAMPrompt:
|
||||
y1 = min(y1, y_t)
|
||||
x2 = max(x2, x_t + w_t)
|
||||
y2 = max(y2, y_t + h_t)
|
||||
h = y2 - y1
|
||||
w = x2 - x1
|
||||
return [x1, y1, x2, y2]
|
||||
|
||||
def plot(self,
|
||||
@ -104,9 +102,11 @@ class FastSAMPrompt:
|
||||
mask_random_color=True,
|
||||
better_quality=True,
|
||||
retina=False,
|
||||
withContours=True):
|
||||
with_countouers=True):
|
||||
if isinstance(annotations[0], dict):
|
||||
annotations = [annotation['segmentation'] for annotation in annotations]
|
||||
if isinstance(annotations, torch.Tensor):
|
||||
annotations = annotations.cpu().numpy()
|
||||
result_name = os.path.basename(self.img_path)
|
||||
image = self.ori_img
|
||||
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
|
||||
@ -123,41 +123,22 @@ class FastSAMPrompt:
|
||||
|
||||
plt.imshow(image)
|
||||
if better_quality:
|
||||
if isinstance(annotations[0], torch.Tensor):
|
||||
annotations = np.array(annotations.cpu())
|
||||
for i, mask in enumerate(annotations):
|
||||
mask = cv2.morphologyEx(mask.astype(np.uint8), cv2.MORPH_CLOSE, np.ones((3, 3), np.uint8))
|
||||
annotations[i] = cv2.morphologyEx(mask.astype(np.uint8), cv2.MORPH_OPEN, np.ones((8, 8), np.uint8))
|
||||
if self.device == 'cpu':
|
||||
annotations = np.array(annotations)
|
||||
self.fast_show_mask(
|
||||
annotations,
|
||||
plt.gca(),
|
||||
random_color=mask_random_color,
|
||||
bbox=bbox,
|
||||
points=points,
|
||||
pointlabel=point_label,
|
||||
retinamask=retina,
|
||||
target_height=original_h,
|
||||
target_width=original_w,
|
||||
)
|
||||
else:
|
||||
if isinstance(annotations[0], np.ndarray):
|
||||
annotations = torch.from_numpy(annotations)
|
||||
self.fast_show_mask_gpu(
|
||||
annotations,
|
||||
plt.gca(),
|
||||
random_color=mask_random_color,
|
||||
bbox=bbox,
|
||||
points=points,
|
||||
pointlabel=point_label,
|
||||
retinamask=retina,
|
||||
target_height=original_h,
|
||||
target_width=original_w,
|
||||
)
|
||||
if isinstance(annotations, torch.Tensor):
|
||||
annotations = annotations.cpu().numpy()
|
||||
if withContours:
|
||||
self.fast_show_mask(
|
||||
annotations,
|
||||
plt.gca(),
|
||||
random_color=mask_random_color,
|
||||
bbox=bbox,
|
||||
points=points,
|
||||
pointlabel=point_label,
|
||||
retinamask=retina,
|
||||
target_height=original_h,
|
||||
target_width=original_w,
|
||||
)
|
||||
|
||||
if with_countouers:
|
||||
contour_all = []
|
||||
temp = np.zeros((original_h, original_w, 1))
|
||||
for i, mask in enumerate(annotations):
|
||||
@ -184,8 +165,8 @@ class FastSAMPrompt:
|
||||
LOGGER.info(f'Saved to {save_path.absolute()}')
|
||||
|
||||
# CPU post process
|
||||
@staticmethod
|
||||
def fast_show_mask(
|
||||
self,
|
||||
annotation,
|
||||
ax,
|
||||
random_color=False,
|
||||
@ -196,32 +177,29 @@ class FastSAMPrompt:
|
||||
target_height=960,
|
||||
target_width=960,
|
||||
):
|
||||
msak_sum = annotation.shape[0]
|
||||
height = annotation.shape[1]
|
||||
weight = annotation.shape[2]
|
||||
# 将annotation 按照面积 排序
|
||||
n, h, w = annotation.shape # batch, height, width
|
||||
|
||||
areas = np.sum(annotation, axis=(1, 2))
|
||||
sorted_indices = np.argsort(areas)
|
||||
annotation = annotation[sorted_indices]
|
||||
annotation = annotation[np.argsort(areas)]
|
||||
|
||||
index = (annotation != 0).argmax(axis=0)
|
||||
if random_color:
|
||||
color = np.random.random((msak_sum, 1, 1, 3))
|
||||
color = np.random.random((n, 1, 1, 3))
|
||||
else:
|
||||
color = np.ones((msak_sum, 1, 1, 3)) * np.array([30 / 255, 144 / 255, 1.0])
|
||||
transparency = np.ones((msak_sum, 1, 1, 1)) * 0.6
|
||||
color = np.ones((n, 1, 1, 3)) * np.array([30 / 255, 144 / 255, 1.0])
|
||||
transparency = np.ones((n, 1, 1, 1)) * 0.6
|
||||
visual = np.concatenate([color, transparency], axis=-1)
|
||||
mask_image = np.expand_dims(annotation, -1) * visual
|
||||
|
||||
show = np.zeros((height, weight, 4))
|
||||
h_indices, w_indices = np.meshgrid(np.arange(height), np.arange(weight), indexing='ij')
|
||||
show = np.zeros((h, w, 4))
|
||||
h_indices, w_indices = np.meshgrid(np.arange(h), np.arange(w), indexing='ij')
|
||||
indices = (index[h_indices, w_indices], h_indices, w_indices, slice(None))
|
||||
# 使用向量化索引更新show的值
|
||||
|
||||
show[h_indices, w_indices, :] = mask_image[indices]
|
||||
if bbox is not None:
|
||||
x1, y1, x2, y2 = bbox
|
||||
ax.add_patch(plt.Rectangle((x1, y1), x2 - x1, y2 - y1, fill=False, edgecolor='b', linewidth=1))
|
||||
# draw point
|
||||
# Draw point
|
||||
if points is not None:
|
||||
plt.scatter(
|
||||
[point[0] for i, point in enumerate(points) if pointlabel[i] == 1],
|
||||
@ -240,63 +218,6 @@ class FastSAMPrompt:
|
||||
show = cv2.resize(show, (target_width, target_height), interpolation=cv2.INTER_NEAREST)
|
||||
ax.imshow(show)
|
||||
|
||||
def fast_show_mask_gpu(
|
||||
self,
|
||||
annotation,
|
||||
ax,
|
||||
random_color=False,
|
||||
bbox=None,
|
||||
points=None,
|
||||
pointlabel=None,
|
||||
retinamask=True,
|
||||
target_height=960,
|
||||
target_width=960,
|
||||
):
|
||||
msak_sum = annotation.shape[0]
|
||||
height = annotation.shape[1]
|
||||
weight = annotation.shape[2]
|
||||
areas = torch.sum(annotation, dim=(1, 2))
|
||||
sorted_indices = torch.argsort(areas, descending=False)
|
||||
annotation = annotation[sorted_indices]
|
||||
# 找每个位置第一个非零值下标
|
||||
index = (annotation != 0).to(torch.long).argmax(dim=0)
|
||||
if random_color:
|
||||
color = torch.rand((msak_sum, 1, 1, 3)).to(annotation.device)
|
||||
else:
|
||||
color = torch.ones((msak_sum, 1, 1, 3)).to(annotation.device) * torch.tensor([30 / 255, 144 / 255, 1.0]).to(
|
||||
annotation.device)
|
||||
transparency = torch.ones((msak_sum, 1, 1, 1)).to(annotation.device) * 0.6
|
||||
visual = torch.cat([color, transparency], dim=-1)
|
||||
mask_image = torch.unsqueeze(annotation, -1) * visual
|
||||
# 按index取数，index指每个位置选哪个batch的数，把mask_image转成一个batch的形式
|
||||
show = torch.zeros((height, weight, 4)).to(annotation.device)
|
||||
h_indices, w_indices = torch.meshgrid(torch.arange(height), torch.arange(weight), indexing='ij')
|
||||
indices = (index[h_indices, w_indices], h_indices, w_indices, slice(None))
|
||||
# 使用向量化索引更新show的值
|
||||
show[h_indices, w_indices, :] = mask_image[indices]
|
||||
show_cpu = show.cpu().numpy()
|
||||
if bbox is not None:
|
||||
x1, y1, x2, y2 = bbox
|
||||
ax.add_patch(plt.Rectangle((x1, y1), x2 - x1, y2 - y1, fill=False, edgecolor='b', linewidth=1))
|
||||
# draw point
|
||||
if points is not None:
|
||||
plt.scatter(
|
||||
[point[0] for i, point in enumerate(points) if pointlabel[i] == 1],
|
||||
[point[1] for i, point in enumerate(points) if pointlabel[i] == 1],
|
||||
s=20,
|
||||
c='y',
|
||||
)
|
||||
plt.scatter(
|
||||
[point[0] for i, point in enumerate(points) if pointlabel[i] == 0],
|
||||
[point[1] for i, point in enumerate(points) if pointlabel[i] == 0],
|
||||
s=20,
|
||||
c='m',
|
||||
)
|
||||
if not retinamask:
|
||||
show_cpu = cv2.resize(show_cpu, (target_width, target_height), interpolation=cv2.INTER_NEAREST)
|
||||
ax.imshow(show_cpu)
|
||||
|
||||
# clip
|
||||
@torch.no_grad()
|
||||
def retrieve(self, model, preprocess, elements, search_text: str, device) -> int:
|
||||
preprocessed_images = [preprocess(image).to(device) for image in elements]
|
||||
|
||||
@ -5,7 +5,6 @@ import torch
|
||||
from ultralytics.engine.predictor import BasePredictor
|
||||
from ultralytics.engine.results import Results
|
||||
from ultralytics.utils import ops
|
||||
from ultralytics.utils.ops import xyxy2xywh
|
||||
|
||||
|
||||
class NASPredictor(BasePredictor):
|
||||
@ -14,7 +13,7 @@ class NASPredictor(BasePredictor):
|
||||
"""Postprocess predictions and returns a list of Results objects."""
|
||||
|
||||
# Cat boxes and class scores
|
||||
boxes = xyxy2xywh(preds_in[0][0])
|
||||
boxes = ops.xyxy2xywh(preds_in[0][0])
|
||||
preds = torch.cat((boxes, preds_in[0][1]), -1).permute(0, 2, 1)
|
||||
|
||||
preds = ops.non_max_suppression(preds,
|
||||
|
||||
@ -4,7 +4,6 @@ import torch
|
||||
|
||||
from ultralytics.models.yolo.detect import DetectionValidator
|
||||
from ultralytics.utils import ops
|
||||
from ultralytics.utils.ops import xyxy2xywh
|
||||
|
||||
__all__ = ['NASValidator']
|
||||
|
||||
@ -13,7 +12,7 @@ class NASValidator(DetectionValidator):
|
||||
|
||||
def postprocess(self, preds_in):
|
||||
"""Apply Non-maximum suppression to prediction outputs."""
|
||||
boxes = xyxy2xywh(preds_in[0][0])
|
||||
boxes = ops.xyxy2xywh(preds_in[0][0])
|
||||
preds = torch.cat((boxes, preds_in[0][1]), -1).permute(0, 2, 1)
|
||||
return ops.non_max_suppression(preds,
|
||||
self.args.conf,
|
||||
|
||||
@ -9,6 +9,19 @@ from ultralytics.utils import ops
|
||||
|
||||
|
||||
class RTDETRPredictor(BasePredictor):
|
||||
"""
|
||||
A class extending the BasePredictor class for prediction based on an RT-DETR detection model.
|
||||
|
||||
Example:
|
||||
```python
|
||||
from ultralytics.utils import ASSETS
|
||||
from ultralytics.models.rtdetr import RTDETRPredictor
|
||||
|
||||
args = dict(model='rtdetr-l.pt', source=ASSETS)
|
||||
predictor = RTDETRPredictor(overrides=args)
|
||||
predictor.predict_cli()
|
||||
```
|
||||
"""
|
||||
|
||||
def postprocess(self, preds, img, orig_imgs):
|
||||
"""Postprocess predictions and returns a list of Results objects."""
|
||||
@ -38,7 +51,9 @@ class RTDETRPredictor(BasePredictor):
|
||||
Args:
|
||||
im (List(np.ndarray)): (N, 3, h, w) for tensor, [(h, w, 3) x N] for list.
|
||||
|
||||
Return: A list of transformed imgs.
|
||||
Notes: The size must be square(640) and scaleFilled.
|
||||
|
||||
Returns:
|
||||
(list): A list of transformed imgs.
|
||||
"""
|
||||
# The size must be square(640) and scaleFilled.
|
||||
return [LetterBox(self.imgsz, auto=False, scaleFill=True)(image=x) for x in im]
|
||||
|
||||
@ -6,12 +6,28 @@ import torch
|
||||
|
||||
from ultralytics.models.yolo.detect import DetectionTrainer
|
||||
from ultralytics.nn.tasks import RTDETRDetectionModel
|
||||
from ultralytics.utils import DEFAULT_CFG, RANK, colorstr
|
||||
from ultralytics.utils import RANK, colorstr
|
||||
|
||||
from .val import RTDETRDataset, RTDETRValidator
|
||||
|
||||
|
||||
class RTDETRTrainer(DetectionTrainer):
|
||||
"""
|
||||
A class extending the DetectionTrainer class for training based on an RT-DETR detection model.
|
||||
|
||||
Notes:
|
||||
- F.grid_sample used in rt-detr does not support the `deterministic=True` argument.
|
||||
- AMP training can lead to NaN outputs and may produce errors during bipartite graph matching.
|
||||
|
||||
Example:
|
||||
```python
|
||||
from ultralytics.models.rtdetr.train import RTDETRTrainer
|
||||
|
||||
args = dict(model='rtdetr-l.yaml', data='coco8.yaml', imgsz=640, epochs=3)
|
||||
trainer = RTDETRTrainer(overrides=args)
|
||||
trainer.train()
|
||||
```
|
||||
"""
|
||||
|
||||
def get_model(self, cfg=None, weights=None, verbose=True):
|
||||
"""Return a YOLO detection model."""
|
||||
@ -54,27 +70,3 @@ class RTDETRTrainer(DetectionTrainer):
|
||||
gt_bbox.append(batch['bboxes'][batch_idx == i].to(batch_idx.device))
|
||||
gt_class.append(batch['cls'][batch_idx == i].to(device=batch_idx.device, dtype=torch.long))
|
||||
return batch
|
||||
|
||||
|
||||
def train(cfg=DEFAULT_CFG, use_python=False):
|
||||
"""Train and optimize RTDETR model given training data and device."""
|
||||
model = 'rtdetr-l.yaml'
|
||||
data = cfg.data or 'coco8.yaml' # or yolo.ClassificationDataset("mnist")
|
||||
device = cfg.device if cfg.device is not None else ''
|
||||
|
||||
# NOTE: F.grid_sample which is in rt-detr does not support deterministic=True
|
||||
# NOTE: amp training causes nan outputs and end with error while doing bipartite graph matching
|
||||
args = dict(model=model,
|
||||
data=data,
|
||||
device=device,
|
||||
imgsz=640,
|
||||
exist_ok=True,
|
||||
batch=4,
|
||||
deterministic=False,
|
||||
amp=False)
|
||||
trainer = RTDETRTrainer(overrides=args)
|
||||
trainer.train()
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
train()
|
||||
|
||||
@ -67,6 +67,18 @@ class RTDETRDataset(YOLODataset):
|
||||
|
||||
|
||||
class RTDETRValidator(DetectionValidator):
|
||||
"""
|
||||
A class extending the DetectionValidator class for validation based on an RT-DETR detection model.
|
||||
|
||||
Example:
|
||||
```python
|
||||
from ultralytics.models.rtdetr import RTDETRValidator
|
||||
|
||||
args = dict(model='rtdetr-l.pt', data='coco8.yaml')
|
||||
validator = RTDETRValidator(args=args)
|
||||
validator(model=args['model'])
|
||||
```
|
||||
"""
|
||||
|
||||
def build_dataset(self, img_path, mode='val', batch=None):
|
||||
"""Build YOLO Dataset
|
||||
|
||||
@ -55,12 +55,14 @@ class Predictor(BasePredictor):
|
||||
return img
|
||||
|
||||
def pre_transform(self, im):
|
||||
"""Pre-transform input image before inference.
|
||||
"""
|
||||
Pre-transform input image before inference.
|
||||
|
||||
Args:
|
||||
im (List(np.ndarray)): (N, 3, h, w) for tensor, [(h, w, 3) x N] for list.
|
||||
|
||||
Return: A list of transformed imgs.
|
||||
Returns:
|
||||
(list): A list of transformed images.
|
||||
"""
|
||||
assert len(im) == 1, 'SAM model has not supported batch inference yet!'
|
||||
return [LetterBox(self.args.imgsz, auto=False, center=False)(image=x) for x in im]
|
||||
|
||||
@ -1,7 +1,7 @@
|
||||
# Ultralytics YOLO 🚀, AGPL-3.0 license
|
||||
|
||||
from ultralytics.models.yolo.classify.predict import ClassificationPredictor, predict
|
||||
from ultralytics.models.yolo.classify.train import ClassificationTrainer, train
|
||||
from ultralytics.models.yolo.classify.val import ClassificationValidator, val
|
||||
from ultralytics.models.yolo.classify.predict import ClassificationPredictor
|
||||
from ultralytics.models.yolo.classify.train import ClassificationTrainer
|
||||
from ultralytics.models.yolo.classify.val import ClassificationValidator
|
||||
|
||||
__all__ = 'ClassificationPredictor', 'predict', 'ClassificationTrainer', 'train', 'ClassificationValidator', 'val'
|
||||
__all__ = 'ClassificationPredictor', 'ClassificationTrainer', 'ClassificationValidator'
|
||||
|
||||
@ -4,10 +4,26 @@ import torch
|
||||
|
||||
from ultralytics.engine.predictor import BasePredictor
|
||||
from ultralytics.engine.results import Results
|
||||
from ultralytics.utils import ASSETS, DEFAULT_CFG
|
||||
from ultralytics.utils import DEFAULT_CFG
|
||||
|
||||
|
||||
class ClassificationPredictor(BasePredictor):
|
||||
"""
|
||||
A class extending the BasePredictor class for prediction based on a classification model.
|
||||
|
||||
Notes:
|
||||
- Torchvision classification models can also be passed to the 'model' argument, i.e. model='resnet18'.
|
||||
|
||||
Example:
|
||||
```python
|
||||
from ultralytics.utils import ASSETS
|
||||
from ultralytics.models.yolo.classify import ClassificationPredictor
|
||||
|
||||
args = dict(model='yolov8n-cls.pt', source=ASSETS)
|
||||
predictor = ClassificationPredictor(overrides=args)
|
||||
predictor.predict_cli()
|
||||
```
|
||||
"""
|
||||
|
||||
def __init__(self, cfg=DEFAULT_CFG, overrides=None, _callbacks=None):
|
||||
super().__init__(cfg, overrides, _callbacks)
|
||||
@ -30,21 +46,3 @@ class ClassificationPredictor(BasePredictor):
|
||||
results.append(Results(orig_img=orig_img, path=img_path, names=self.model.names, probs=pred))
|
||||
|
||||
return results
|
||||
|
||||
|
||||
def predict(cfg=DEFAULT_CFG, use_python=False):
|
||||
"""Run YOLO model predictions on input images/videos."""
|
||||
model = cfg.model or 'yolov8n-cls.pt' # or "resnet18"
|
||||
source = cfg.source or ASSETS
|
||||
|
||||
args = dict(model=model, source=source)
|
||||
if use_python:
|
||||
from ultralytics import YOLO
|
||||
YOLO(model)(**args)
|
||||
else:
|
||||
predictor = ClassificationPredictor(overrides=args)
|
||||
predictor.predict_cli()
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
predict()
|
||||
|
||||
@ -13,6 +13,21 @@ from ultralytics.utils.torch_utils import is_parallel, strip_optimizer, torch_di
|
||||
|
||||
|
||||
class ClassificationTrainer(BaseTrainer):
|
||||
"""
|
||||
A class extending the BaseTrainer class for training based on a classification model.
|
||||
|
||||
Notes:
|
||||
- Torchvision classification models can also be passed to the 'model' argument, i.e. model='resnet18'.
|
||||
|
||||
Example:
|
||||
```python
|
||||
from ultralytics.models.yolo.classify import ClassificationTrainer
|
||||
|
||||
args = dict(model='yolov8n-cls.pt', data='imagenet10', epochs=3)
|
||||
trainer = ClassificationTrainer(overrides=args)
|
||||
trainer.train()
|
||||
```
|
||||
"""
|
||||
|
||||
def __init__(self, cfg=DEFAULT_CFG, overrides=None, _callbacks=None):
|
||||
"""Initialize a ClassificationTrainer object with optional configuration overrides and callbacks."""
|
||||
@ -137,22 +152,3 @@ class ClassificationTrainer(BaseTrainer):
|
||||
cls=batch['cls'].view(-1), # warning: use .view(), not .squeeze() for Classify models
|
||||
fname=self.save_dir / f'train_batch{ni}.jpg',
|
||||
on_plot=self.on_plot)
|
||||
|
||||
|
||||
def train(cfg=DEFAULT_CFG, use_python=False):
|
||||
"""Train a YOLO classification model."""
|
||||
model = cfg.model or 'yolov8n-cls.pt' # or "resnet18"
|
||||
data = cfg.data or 'mnist160' # or yolo.ClassificationDataset("mnist")
|
||||
device = cfg.device if cfg.device is not None else ''
|
||||
|
||||
args = dict(model=model, data=data, device=device)
|
||||
if use_python:
|
||||
from ultralytics import YOLO
|
||||
YOLO(model).train(**args)
|
||||
else:
|
||||
trainer = ClassificationTrainer(overrides=args)
|
||||
trainer.train()
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
train()
|
||||
|
||||
@ -4,12 +4,27 @@ import torch
|
||||
|
||||
from ultralytics.data import ClassificationDataset, build_dataloader
|
||||
from ultralytics.engine.validator import BaseValidator
|
||||
from ultralytics.utils import DEFAULT_CFG, LOGGER
|
||||
from ultralytics.utils import LOGGER
|
||||
from ultralytics.utils.metrics import ClassifyMetrics, ConfusionMatrix
|
||||
from ultralytics.utils.plotting import plot_images
|
||||
|
||||
|
||||
class ClassificationValidator(BaseValidator):
|
||||
"""
|
||||
A class extending the BaseValidator class for validation based on a classification model.
|
||||
|
||||
Notes:
|
||||
- Torchvision classification models can also be passed to the 'model' argument, i.e. model='resnet18'.
|
||||
|
||||
Example:
|
||||
```python
|
||||
from ultralytics.models.yolo.classify import ClassificationValidator
|
||||
|
||||
args = dict(model='yolov8n-cls.pt', data='imagenet10')
|
||||
validator = ClassificationValidator(args=args)
|
||||
validator(model=args['model'])
|
||||
```
|
||||
"""
|
||||
|
||||
def __init__(self, dataloader=None, save_dir=None, pbar=None, args=None, _callbacks=None):
|
||||
"""Initializes ClassificationValidator instance with args, dataloader, save_dir, and progress bar."""
|
||||
@ -92,21 +107,3 @@ class ClassificationValidator(BaseValidator):
|
||||
fname=self.save_dir / f'val_batch{ni}_pred.jpg',
|
||||
names=self.names,
|
||||
on_plot=self.on_plot) # pred
|
||||
|
||||
|
||||
def val(cfg=DEFAULT_CFG, use_python=False):
|
||||
"""Validate YOLO model using custom data."""
|
||||
model = cfg.model or 'yolov8n-cls.pt' # or "resnet18"
|
||||
data = cfg.data or 'mnist160'
|
||||
|
||||
args = dict(model=model, data=data)
|
||||
if use_python:
|
||||
from ultralytics import YOLO
|
||||
YOLO(model).val(**args)
|
||||
else:
|
||||
validator = ClassificationValidator(args=args)
|
||||
validator(model=args['model'])
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
val()
|
||||
|
||||
@ -1,7 +1,7 @@
|
||||
# Ultralytics YOLO 🚀, AGPL-3.0 license
|
||||
|
||||
from .predict import DetectionPredictor, predict
|
||||
from .train import DetectionTrainer, train
|
||||
from .val import DetectionValidator, val
|
||||
from .predict import DetectionPredictor
|
||||
from .train import DetectionTrainer
|
||||
from .val import DetectionValidator
|
||||
|
||||
__all__ = 'DetectionPredictor', 'predict', 'DetectionTrainer', 'train', 'DetectionValidator', 'val'
|
||||
__all__ = 'DetectionPredictor', 'DetectionTrainer', 'DetectionValidator'
|
||||
|
||||
@ -4,10 +4,23 @@ import torch
|
||||
|
||||
from ultralytics.engine.predictor import BasePredictor
|
||||
from ultralytics.engine.results import Results
|
||||
from ultralytics.utils import ASSETS, DEFAULT_CFG, ops
|
||||
from ultralytics.utils import ops
|
||||
|
||||
|
||||
class DetectionPredictor(BasePredictor):
|
||||
"""
|
||||
A class extending the BasePredictor class for prediction based on a detection model.
|
||||
|
||||
Example:
|
||||
```python
|
||||
from ultralytics.utils import ASSETS
|
||||
from ultralytics.models.yolo.detect import DetectionPredictor
|
||||
|
||||
args = dict(model='yolov8n.pt', source=ASSETS)
|
||||
predictor = DetectionPredictor(overrides=args)
|
||||
predictor.predict_cli()
|
||||
```
|
||||
"""
|
||||
|
||||
def postprocess(self, preds, img, orig_imgs):
|
||||
"""Post-processes predictions and returns a list of Results objects."""
|
||||
@ -27,21 +40,3 @@ class DetectionPredictor(BasePredictor):
|
||||
img_path = path[i] if isinstance(path, list) else path
|
||||
results.append(Results(orig_img=orig_img, path=img_path, names=self.model.names, boxes=pred))
|
||||
return results
|
||||
|
||||
|
||||
def predict(cfg=DEFAULT_CFG, use_python=False):
|
||||
"""Runs YOLO model inference on input image(s)."""
|
||||
model = cfg.model or 'yolov8n.pt'
|
||||
source = cfg.source or ASSETS
|
||||
|
||||
args = dict(model=model, source=source)
|
||||
if use_python:
|
||||
from ultralytics import YOLO
|
||||
YOLO(model)(**args)
|
||||
else:
|
||||
predictor = DetectionPredictor(overrides=args)
|
||||
predictor.predict_cli()
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
predict()
|
||||
|
||||
@ -8,12 +8,24 @@ from ultralytics.data import build_dataloader, build_yolo_dataset
|
||||
from ultralytics.engine.trainer import BaseTrainer
|
||||
from ultralytics.models import yolo
|
||||
from ultralytics.nn.tasks import DetectionModel
|
||||
from ultralytics.utils import DEFAULT_CFG, LOGGER, RANK
|
||||
from ultralytics.utils import LOGGER, RANK
|
||||
from ultralytics.utils.plotting import plot_images, plot_labels, plot_results
|
||||
from ultralytics.utils.torch_utils import de_parallel, torch_distributed_zero_first
|
||||
|
||||
|
||||
class DetectionTrainer(BaseTrainer):
|
||||
"""
|
||||
A class extending the BaseTrainer class for training based on a detection model.
|
||||
|
||||
Example:
|
||||
```python
|
||||
from ultralytics.models.yolo.detect import DetectionTrainer
|
||||
|
||||
args = dict(model='yolov8n.pt', data='coco8.yaml', epochs=3)
|
||||
trainer = DetectionTrainer(overrides=args)
|
||||
trainer.train()
|
||||
```
|
||||
"""
|
||||
|
||||
def build_dataset(self, img_path, mode='train', batch=None):
|
||||
"""
|
||||
@ -102,22 +114,3 @@ class DetectionTrainer(BaseTrainer):
|
||||
boxes = np.concatenate([lb['bboxes'] for lb in self.train_loader.dataset.labels], 0)
|
||||
cls = np.concatenate([lb['cls'] for lb in self.train_loader.dataset.labels], 0)
|
||||
plot_labels(boxes, cls.squeeze(), names=self.data['names'], save_dir=self.save_dir, on_plot=self.on_plot)
|
||||
|
||||
|
||||
def train(cfg=DEFAULT_CFG, use_python=False):
|
||||
"""Train and optimize YOLO model given training data and device."""
|
||||
model = cfg.model or 'yolov8n.pt'
|
||||
data = cfg.data or 'coco8.yaml' # or yolo.ClassificationDataset("mnist")
|
||||
device = cfg.device if cfg.device is not None else ''
|
||||
|
||||
args = dict(model=model, data=data, device=device)
|
||||
if use_python:
|
||||
from ultralytics import YOLO
|
||||
YOLO(model).train(**args)
|
||||
else:
|
||||
trainer = DetectionTrainer(overrides=args)
|
||||
trainer.train()
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
train()
|
||||
|
||||
@ -8,7 +8,7 @@ import torch
|
||||
|
||||
from ultralytics.data import build_dataloader, build_yolo_dataset, converter
|
||||
from ultralytics.engine.validator import BaseValidator
|
||||
from ultralytics.utils import DEFAULT_CFG, LOGGER, ops
|
||||
from ultralytics.utils import LOGGER, ops
|
||||
from ultralytics.utils.checks import check_requirements
|
||||
from ultralytics.utils.metrics import ConfusionMatrix, DetMetrics, box_iou
|
||||
from ultralytics.utils.plotting import output_to_target, plot_images
|
||||
@ -16,6 +16,18 @@ from ultralytics.utils.torch_utils import de_parallel
|
||||
|
||||
|
||||
class DetectionValidator(BaseValidator):
|
||||
"""
|
||||
A class extending the BaseValidator class for validation based on a detection model.
|
||||
|
||||
Example:
|
||||
```python
|
||||
from ultralytics.models.yolo.detect import DetectionValidator
|
||||
|
||||
args = dict(model='yolov8n.pt', data='coco8.yaml')
|
||||
validator = DetectionValidator(args=args)
|
||||
validator(model=args['model'])
|
||||
```
|
||||
"""
|
||||
|
||||
def __init__(self, dataloader=None, save_dir=None, pbar=None, args=None, _callbacks=None):
|
||||
"""Initialize detection model with necessary variables and settings."""
|
||||
@ -254,21 +266,3 @@ class DetectionValidator(BaseValidator):
|
||||
except Exception as e:
|
||||
LOGGER.warning(f'pycocotools unable to run: {e}')
|
||||
return stats
|
||||
|
||||
|
||||
def val(cfg=DEFAULT_CFG, use_python=False):
|
||||
"""Validate trained YOLO model on validation dataset."""
|
||||
model = cfg.model or 'yolov8n.pt'
|
||||
data = cfg.data or 'coco8.yaml'
|
||||
|
||||
args = dict(model=model, data=data)
|
||||
if use_python:
|
||||
from ultralytics import YOLO
|
||||
YOLO(model).val(**args)
|
||||
else:
|
||||
validator = DetectionValidator(args=args)
|
||||
validator(model=args['model'])
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
val()
|
||||
|
||||
@ -1,7 +1,7 @@
|
||||
# Ultralytics YOLO 🚀, AGPL-3.0 license
|
||||
|
||||
from .predict import PosePredictor, predict
|
||||
from .train import PoseTrainer, train
|
||||
from .val import PoseValidator, val
|
||||
from .predict import PosePredictor
|
||||
from .train import PoseTrainer
|
||||
from .val import PoseValidator
|
||||
|
||||
__all__ = 'PoseTrainer', 'train', 'PoseValidator', 'val', 'PosePredictor', 'predict'
|
||||
__all__ = 'PoseTrainer', 'PoseValidator', 'PosePredictor'
|
||||
|
||||
@ -2,10 +2,23 @@
|
||||
|
||||
from ultralytics.engine.results import Results
|
||||
from ultralytics.models.yolo.detect.predict import DetectionPredictor
|
||||
from ultralytics.utils import ASSETS, DEFAULT_CFG, LOGGER, ops
|
||||
from ultralytics.utils import DEFAULT_CFG, LOGGER, ops
|
||||
|
||||
|
||||
class PosePredictor(DetectionPredictor):
|
||||
"""
|
||||
A class extending the DetectionPredictor class for prediction based on a pose model.
|
||||
|
||||
Example:
|
||||
```python
|
||||
from ultralytics.utils import ASSETS
|
||||
from ultralytics.models.yolo.pose import PosePredictor
|
||||
|
||||
args = dict(model='yolov8n-pose.pt', source=ASSETS)
|
||||
predictor = PosePredictor(overrides=args)
|
||||
predictor.predict_cli()
|
||||
```
|
||||
"""
|
||||
|
||||
def __init__(self, cfg=DEFAULT_CFG, overrides=None, _callbacks=None):
|
||||
super().__init__(cfg, overrides, _callbacks)
|
||||
@ -40,21 +53,3 @@ class PosePredictor(DetectionPredictor):
|
||||
boxes=pred[:, :6],
|
||||
keypoints=pred_kpts))
|
||||
return results
|
||||
|
||||
|
||||
def predict(cfg=DEFAULT_CFG, use_python=False):
|
||||
"""Runs YOLO to predict objects in an image or video."""
|
||||
model = cfg.model or 'yolov8n-pose.pt'
|
||||
source = cfg.source or ASSETS
|
||||
|
||||
args = dict(model=model, source=source)
|
||||
if use_python:
|
||||
from ultralytics import YOLO
|
||||
YOLO(model)(**args)
|
||||
else:
|
||||
predictor = PosePredictor(overrides=args)
|
||||
predictor.predict_cli()
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
predict()
|
||||
|
||||
@ -9,6 +9,18 @@ from ultralytics.utils.plotting import plot_images, plot_results
|
||||
|
||||
|
||||
class PoseTrainer(yolo.detect.DetectionTrainer):
|
||||
"""
|
||||
A class extending the DetectionTrainer class for training based on a pose model.
|
||||
|
||||
Example:
|
||||
```python
|
||||
from ultralytics.models.yolo.pose import PoseTrainer
|
||||
|
||||
args = dict(model='yolov8n-pose.pt', data='coco8-pose.yaml', epochs=3)
|
||||
trainer = PoseTrainer(overrides=args)
|
||||
trainer.train()
|
||||
```
|
||||
"""
|
||||
|
||||
def __init__(self, cfg=DEFAULT_CFG, overrides=None, _callbacks=None):
|
||||
"""Initialize a PoseTrainer object with specified configurations and overrides."""
|
||||
@ -59,22 +71,3 @@ class PoseTrainer(yolo.detect.DetectionTrainer):
|
||||
def plot_metrics(self):
|
||||
"""Plots training/val metrics."""
|
||||
plot_results(file=self.csv, pose=True, on_plot=self.on_plot) # save results.png
|
||||
|
||||
|
||||
def train(cfg=DEFAULT_CFG, use_python=False):
|
||||
"""Train the YOLO model on the given data and device."""
|
||||
model = cfg.model or 'yolov8n-pose.yaml'
|
||||
data = cfg.data or 'coco8-pose.yaml'
|
||||
device = cfg.device if cfg.device is not None else ''
|
||||
|
||||
args = dict(model=model, data=data, device=device)
|
||||
if use_python:
|
||||
from ultralytics import YOLO
|
||||
YOLO(model).train(**args)
|
||||
else:
|
||||
trainer = PoseTrainer(overrides=args)
|
||||
trainer.train()
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
train()
|
||||
|
||||
@ -6,13 +6,25 @@ import numpy as np
|
||||
import torch
|
||||
|
||||
from ultralytics.models.yolo.detect import DetectionValidator
|
||||
from ultralytics.utils import DEFAULT_CFG, LOGGER, ops
|
||||
from ultralytics.utils import LOGGER, ops
|
||||
from ultralytics.utils.checks import check_requirements
|
||||
from ultralytics.utils.metrics import OKS_SIGMA, PoseMetrics, box_iou, kpt_iou
|
||||
from ultralytics.utils.plotting import output_to_target, plot_images
|
||||
|
||||
|
||||
class PoseValidator(DetectionValidator):
|
||||
"""
|
||||
A class extending the DetectionValidator class for validation based on a pose model.
|
||||
|
||||
Example:
|
||||
```python
|
||||
from ultralytics.models.yolo.pose import PoseValidator
|
||||
|
||||
args = dict(model='yolov8n-pose.pt', data='coco8-pose.yaml')
|
||||
validator = PoseValidator(args=args)
|
||||
validator(model=args['model'])
|
||||
```
|
||||
"""
|
||||
|
||||
def __init__(self, dataloader=None, save_dir=None, pbar=None, args=None, _callbacks=None):
|
||||
"""Initialize a 'PoseValidator' object with custom parameters and assigned attributes."""
|
||||
@ -201,21 +213,3 @@ class PoseValidator(DetectionValidator):
|
||||
except Exception as e:
|
||||
LOGGER.warning(f'pycocotools unable to run: {e}')
|
||||
return stats
|
||||
|
||||
|
||||
def val(cfg=DEFAULT_CFG, use_python=False):
|
||||
"""Performs validation on YOLO model using given data."""
|
||||
model = cfg.model or 'yolov8n-pose.pt'
|
||||
data = cfg.data or 'coco8-pose.yaml'
|
||||
|
||||
args = dict(model=model, data=data)
|
||||
if use_python:
|
||||
from ultralytics import YOLO
|
||||
YOLO(model).val(**args)
|
||||
else:
|
||||
validator = PoseValidator(args=args)
|
||||
validator(model=args['model'])
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
val()
|
||||
|
||||
@ -1,7 +1,7 @@
|
||||
# Ultralytics YOLO 🚀, AGPL-3.0 license
|
||||
|
||||
from .predict import SegmentationPredictor, predict
|
||||
from .train import SegmentationTrainer, train
|
||||
from .val import SegmentationValidator, val
|
||||
from .predict import SegmentationPredictor
|
||||
from .train import SegmentationTrainer
|
||||
from .val import SegmentationValidator
|
||||
|
||||
__all__ = 'SegmentationPredictor', 'predict', 'SegmentationTrainer', 'train', 'SegmentationValidator', 'val'
|
||||
__all__ = 'SegmentationPredictor', 'SegmentationTrainer', 'SegmentationValidator'
|
||||
|
||||
@ -4,10 +4,23 @@ import torch
|
||||
|
||||
from ultralytics.engine.results import Results
|
||||
from ultralytics.models.yolo.detect.predict import DetectionPredictor
|
||||
from ultralytics.utils import ASSETS, DEFAULT_CFG, ops
|
||||
from ultralytics.utils import DEFAULT_CFG, ops
|
||||
|
||||
|
||||
class SegmentationPredictor(DetectionPredictor):
|
||||
"""
|
||||
A class extending the DetectionPredictor class for prediction based on a segmentation model.
|
||||
|
||||
Example:
|
||||
```python
|
||||
from ultralytics.utils import ASSETS
|
||||
from ultralytics.models.yolo.segment import SegmentationPredictor
|
||||
|
||||
args = dict(model='yolov8n-seg.pt', source=ASSETS)
|
||||
predictor = SegmentationPredictor(overrides=args)
|
||||
predictor.predict_cli()
|
||||
```
|
||||
"""
|
||||
|
||||
def __init__(self, cfg=DEFAULT_CFG, overrides=None, _callbacks=None):
|
||||
super().__init__(cfg, overrides, _callbacks)
|
||||
@ -42,21 +55,3 @@ class SegmentationPredictor(DetectionPredictor):
|
||||
results.append(
|
||||
Results(orig_img=orig_img, path=img_path, names=self.model.names, boxes=pred[:, :6], masks=masks))
|
||||
return results
|
||||
|
||||
|
||||
def predict(cfg=DEFAULT_CFG, use_python=False):
|
||||
"""Runs YOLO object detection on an image or video source."""
|
||||
model = cfg.model or 'yolov8n-seg.pt'
|
||||
source = cfg.source or ASSETS
|
||||
|
||||
args = dict(model=model, source=source)
|
||||
if use_python:
|
||||
from ultralytics import YOLO
|
||||
YOLO(model)(**args)
|
||||
else:
|
||||
predictor = SegmentationPredictor(overrides=args)
|
||||
predictor.predict_cli()
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
predict()
|
||||
|
||||
@ -9,6 +9,18 @@ from ultralytics.utils.plotting import plot_images, plot_results
|
||||
|
||||
|
||||
class SegmentationTrainer(yolo.detect.DetectionTrainer):
|
||||
"""
|
||||
A class extending the DetectionTrainer class for training based on a segmentation model.
|
||||
|
||||
Example:
|
||||
```python
|
||||
from ultralytics.models.yolo.segment import SegmentationTrainer
|
||||
|
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args = dict(model='yolov8n-seg.pt', data='coco8-seg.yaml', epochs=3)
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trainer = SegmentationTrainer(overrides=args)
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trainer.train()
|
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```
|
||||
"""
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||||
|
||||
def __init__(self, cfg=DEFAULT_CFG, overrides=None, _callbacks=None):
|
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"""Initialize a SegmentationTrainer object with given arguments."""
|
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@ -46,19 +58,11 @@ class SegmentationTrainer(yolo.detect.DetectionTrainer):
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plot_results(file=self.csv, segment=True, on_plot=self.on_plot) # save results.png
|
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|
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|
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def train(cfg=DEFAULT_CFG, use_python=False):
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def train(cfg=DEFAULT_CFG):
|
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"""Train a YOLO segmentation model based on passed arguments."""
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model = cfg.model or 'yolov8n-seg.pt'
|
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data = cfg.data or 'coco8-seg.yaml'
|
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device = cfg.device if cfg.device is not None else ''
|
||||
|
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args = dict(model=model, data=data, device=device)
|
||||
if use_python:
|
||||
from ultralytics import YOLO
|
||||
YOLO(model).train(**args)
|
||||
else:
|
||||
trainer = SegmentationTrainer(overrides=args)
|
||||
trainer.train()
|
||||
args = dict(model=cfg.model or 'yolov8n-seg.pt', data=cfg.data or 'coco8-seg.yaml')
|
||||
trainer = SegmentationTrainer(overrides=args)
|
||||
trainer.train()
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
|
||||
@ -15,6 +15,18 @@ from ultralytics.utils.plotting import output_to_target, plot_images
|
||||
|
||||
|
||||
class SegmentationValidator(DetectionValidator):
|
||||
"""
|
||||
A class extending the DetectionValidator class for validation based on a segmentation model.
|
||||
|
||||
Example:
|
||||
```python
|
||||
from ultralytics.models.yolo.segment import SegmentationValidator
|
||||
|
||||
args = dict(model='yolov8n-seg.pt', data='coco8-seg.yaml')
|
||||
validator = SegmentationValidator(args=args)
|
||||
validator(model=args['model'])
|
||||
```
|
||||
"""
|
||||
|
||||
def __init__(self, dataloader=None, save_dir=None, pbar=None, args=None, _callbacks=None):
|
||||
"""Initialize SegmentationValidator and set task to 'segment', metrics to SegmentMetrics."""
|
||||
@ -233,18 +245,11 @@ class SegmentationValidator(DetectionValidator):
|
||||
return stats
|
||||
|
||||
|
||||
def val(cfg=DEFAULT_CFG, use_python=False):
|
||||
def val(cfg=DEFAULT_CFG):
|
||||
"""Validate trained YOLO model on validation data."""
|
||||
model = cfg.model or 'yolov8n-seg.pt'
|
||||
data = cfg.data or 'coco8-seg.yaml'
|
||||
|
||||
args = dict(model=model, data=data)
|
||||
if use_python:
|
||||
from ultralytics import YOLO
|
||||
YOLO(model).val(**args)
|
||||
else:
|
||||
validator = SegmentationValidator(args=args)
|
||||
validator(model=args['model'])
|
||||
args = dict(model=cfg.model or 'yolov8n-seg.pt', data=cfg.data or 'coco8-seg.yaml')
|
||||
validator = SegmentationValidator(args=args)
|
||||
validator(model=args['model'])
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
|
||||
@ -414,13 +414,10 @@ class AutoBackend(nn.Module):
|
||||
scale, zero_point = output['quantization']
|
||||
x = (x.astype(np.float32) - zero_point) * scale # re-scale
|
||||
if x.ndim > 2: # if task is not classification
|
||||
# Denormalize xywh with input image size
|
||||
# Denormalize xywh by image size. See https://github.com/ultralytics/ultralytics/pull/1695
|
||||
# xywh are normalized in TFLite/EdgeTPU to mitigate quantization error of integer models
|
||||
# See this PR for details: https://github.com/ultralytics/ultralytics/pull/1695
|
||||
x[:, 0] *= w
|
||||
x[:, 1] *= h
|
||||
x[:, 2] *= w
|
||||
x[:, 3] *= h
|
||||
x[:, [0, 2]] *= w
|
||||
x[:, [1, 3]] *= h
|
||||
y.append(x)
|
||||
# TF segment fixes: export is reversed vs ONNX export and protos are transposed
|
||||
if len(y) == 2: # segment with (det, proto) output order reversed
|
||||
|
||||
@ -169,7 +169,7 @@ def plt_settings(rcparams=None, backend='Agg'):
|
||||
"""
|
||||
Decorator to temporarily set rc parameters and the backend for a plotting function.
|
||||
|
||||
Usage:
|
||||
Example:
|
||||
decorator: @plt_settings({"font.size": 12})
|
||||
context manager: with plt_settings({"font.size": 12}):
|
||||
|
||||
|
||||
@ -18,8 +18,7 @@ from .metrics import box_iou
|
||||
|
||||
class Profile(contextlib.ContextDecorator):
|
||||
"""
|
||||
YOLOv8 Profile class.
|
||||
Usage: as a decorator with @Profile() or as a context manager with 'with Profile():'
|
||||
YOLOv8 Profile class. Use as a decorator with @Profile() or as a context manager with 'with Profile():'.
|
||||
"""
|
||||
|
||||
def __init__(self, t=0.0):
|
||||
|
||||
@ -10,12 +10,14 @@ TORCH_1_10 = check_version(torch.__version__, '1.10.0')
|
||||
|
||||
|
||||
def select_candidates_in_gts(xy_centers, gt_bboxes, eps=1e-9):
|
||||
"""select the positive anchor center in gt
|
||||
"""
|
||||
Select the positive anchor center in gt.
|
||||
|
||||
Args:
|
||||
xy_centers (Tensor): shape(h*w, 4)
|
||||
gt_bboxes (Tensor): shape(b, n_boxes, 4)
|
||||
Return:
|
||||
|
||||
Returns:
|
||||
(Tensor): shape(b, n_boxes, h*w)
|
||||
"""
|
||||
n_anchors = xy_centers.shape[0]
|
||||
@ -27,13 +29,14 @@ def select_candidates_in_gts(xy_centers, gt_bboxes, eps=1e-9):
|
||||
|
||||
|
||||
def select_highest_overlaps(mask_pos, overlaps, n_max_boxes):
|
||||
"""if an anchor box is assigned to multiple gts,
|
||||
the one with the highest iou will be selected.
|
||||
"""
|
||||
If an anchor box is assigned to multiple gts, the one with the highest IoI will be selected.
|
||||
|
||||
Args:
|
||||
mask_pos (Tensor): shape(b, n_max_boxes, h*w)
|
||||
overlaps (Tensor): shape(b, n_max_boxes, h*w)
|
||||
Return:
|
||||
|
||||
Returns:
|
||||
target_gt_idx (Tensor): shape(b, h*w)
|
||||
fg_mask (Tensor): shape(b, h*w)
|
||||
mask_pos (Tensor): shape(b, n_max_boxes, h*w)
|
||||
|
||||
Loading…
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Reference in New Issue
Block a user