YOLOv5 Forward Compatibility Update (#1569)
* YOLOv5 forward compatibility update * add data dir * ci test yolov3 * update build_targets() * update build_targets() * update build_targets() * update yolov3-spp.yaml * add yolov3-tiny.yaml * add yolov3-tiny.yaml * Update yolov3-tiny.yaml * thop bug fix * Detection() device bug fix * Use torchvision.ops.nms() * Remove redundant download mirror * CI tests with yolov3-tiny * Update README.md * Synch train and test iou_thresh * update requirements.txt * Cat apriori autolabels * Confusion matrix * Autosplit * Autosplit * Update README.md * AP no plot * Update caching * Update caching * Caching bug fix * --image-weights bug fix * datasets bug fix * mosaic plots bug fix * plot_study * boxes.max() * boxes.max() * boxes.max() * boxes.max() * boxes.max() * boxes.max() * update * Update README * Update README * Update README.md * Update README.md * results png * Update README * Targets scaling bug fix * update plot_study * update plot_study * update plot_study * update plot_study * Targets scaling bug fix * Finish Readme.md * Finish Readme.md * Finish Readme.md * Update README.md * Creado con Colaboratory
This commit is contained in:
Glenn Jocher
GitHub
parent
98068efebc
commit
76807fae71
@ -1,5 +1,5 @@
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---
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name: "\U0001F41BBug report"
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name: "🐛 Bug report"
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about: Create a report to help us improve
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title: ''
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labels: bug
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@ -9,8 +9,8 @@ assignees: ''
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Before submitting a bug report, please be aware that your issue **must be reproducible** with all of the following, otherwise it is non-actionable, and we can not help you:
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- **Current repo**: run `git fetch && git status -uno` to check and `git pull` to update repo
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- **Common dataset**: coco2017.data or coco64.data
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- **Common environment**: Colab, Google Cloud, or Docker image. See https://github.com/ultralytics/yolov3#reproduce-our-environment
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- **Common dataset**: coco.yaml or coco128.yaml
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- **Common environment**: Colab, Google Cloud, or Docker image. See https://github.com/ultralytics/yolov3#environments
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If this is a custom dataset/training question you **must include** your `train*.jpg`, `test*.jpg` and `results.png` figures, or we can not help you. You can generate these with `utils.plot_results()`.
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@ -1,5 +1,5 @@
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---
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name: "\U0001F680Feature request"
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name: "🚀 Feature request"
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about: Suggest an idea for this project
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title: ''
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labels: enhancement
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76
.github/workflows/ci-testing.yml
vendored
Normal file
76
.github/workflows/ci-testing.yml
vendored
Normal file
@ -0,0 +1,76 @@
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name: CI CPU testing
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on: # https://help.github.com/en/actions/reference/events-that-trigger-workflows
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push:
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pull_request:
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schedule:
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- cron: "0 0 * * *"
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jobs:
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cpu-tests:
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runs-on: ${{ matrix.os }}
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strategy:
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fail-fast: false
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matrix:
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os: [ubuntu-latest, macos-latest, windows-latest]
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python-version: [3.8]
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model: ['yolov3-tiny'] # models to test
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# Timeout: https://stackoverflow.com/a/59076067/4521646
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timeout-minutes: 50
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steps:
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- uses: actions/checkout@v2
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- name: Set up Python ${{ matrix.python-version }}
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uses: actions/setup-python@v2
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with:
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python-version: ${{ matrix.python-version }}
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# Note: This uses an internal pip API and may not always work
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# https://github.com/actions/cache/blob/master/examples.md#multiple-oss-in-a-workflow
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- name: Get pip cache
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id: pip-cache
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run: |
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python -c "from pip._internal.locations import USER_CACHE_DIR; print('::set-output name=dir::' + USER_CACHE_DIR)"
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- name: Cache pip
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uses: actions/cache@v1
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with:
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path: ${{ steps.pip-cache.outputs.dir }}
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key: ${{ runner.os }}-${{ matrix.python-version }}-pip-${{ hashFiles('requirements.txt') }}
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restore-keys: |
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${{ runner.os }}-${{ matrix.python-version }}-pip-
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- name: Install dependencies
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run: |
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python -m pip install --upgrade pip
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pip install -qr requirements.txt -f https://download.pytorch.org/whl/cpu/torch_stable.html
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pip install -q onnx
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python --version
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pip --version
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pip list
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shell: bash
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- name: Download data
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run: |
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# curl -L -o tmp.zip https://github.com/ultralytics/yolov5/releases/download/v1.0/coco128.zip
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# unzip -q tmp.zip -d ../
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# rm tmp.zip
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- name: Tests workflow
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run: |
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# export PYTHONPATH="$PWD" # to run '$ python *.py' files in subdirectories
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di=cpu # inference devices # define device
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# train
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python train.py --img 256 --batch 8 --weights weights/${{ matrix.model }}.pt --cfg models/${{ matrix.model }}.yaml --epochs 1 --device $di
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# detect
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python detect.py --weights weights/${{ matrix.model }}.pt --device $di
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python detect.py --weights runs/train/exp/weights/last.pt --device $di
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# test
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python test.py --img 256 --batch 8 --weights weights/${{ matrix.model }}.pt --device $di
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python test.py --img 256 --batch 8 --weights runs/train/exp/weights/last.pt --device $di
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python models/yolo.py --cfg models/${{ matrix.model }}.yaml # inspect
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python models/export.py --img 256 --batch 1 --weights weights/${{ matrix.model }}.pt # export
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shell: bash
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37
.github/workflows/greetings.yml
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vendored
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- Reduce changes to the absolute **minimum** required for your bug fix or feature addition. _"It is not daily increase but daily decrease, hack away the unessential. The closer to the source, the less wastage there is."_ -Bruce Lee
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issue-message: |
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Hello @${{ github.actor }}, thank you for your interest in our work! Ultralytics has open-sourced YOLOv5 at https://github.com/ultralytics/yolov5, featuring faster, lighter and more accurate object detection. YOLOv5 is recommended for all new projects.
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Hello @${{ github.actor }}, thank you for your interest in 🚀 YOLOv3! Please visit our ⭐️ [Tutorials](https://github.com/ultralytics/yolov3/wiki#tutorials) to get started, where you can find quickstart guides for simple tasks like [Custom Data Training](https://github.com/ultralytics/yolov3/wiki/Train-Custom-Data) all the way to advanced concepts like [Hyperparameter Evolution](https://github.com/ultralytics/yolov5/issues/607).
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<a href="https://apps.apple.com/app/id1452689527" target="_blank">
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<img src="https://user-images.githubusercontent.com/26833433/98699617-a1595a00-2377-11eb-8145-fc674eb9b1a7.jpg" width="800"></a>
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<br/><br/>
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<img src="https://user-images.githubusercontent.com/26833433/90187293-6773ba00-dd6e-11ea-8f90-cd94afc0427f.png" width="800">
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If this is a 🐛 Bug Report, please provide screenshots and **minimum viable code to reproduce your issue**, otherwise we can not help you.
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To continue with this repo, please visit our [Custom Training Tutorial](https://github.com/ultralytics/yolov3/wiki/Train-Custom-Data) to get started, and see our [Google Colab Notebook](https://github.com/ultralytics/yolov3/blob/master/tutorial.ipynb), [Docker Image](https://hub.docker.com/r/ultralytics/yolov3), and [GCP Quickstart Guide](https://github.com/ultralytics/yolov3/wiki/GCP-Quickstart) for example environments.
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If this is a custom training ❓ Question, please provide as much information as possible, including dataset images, training logs, screenshots, and a public link to online [W&B logging](https://github.com/ultralytics/yolov3/wiki/Train-Custom-Data#visualize) if available.
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If this is a bug report, please provide screenshots and **minimum viable code to reproduce your issue**, otherwise we can not help you.
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For business inquiries or professional support requests please visit https://www.ultralytics.com or email Glenn Jocher at glenn.jocher@ultralytics.com.
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If this is a custom model or data training question, please note that Ultralytics does **not** provide free personal support. As a leader in vision ML and AI, we do offer professional consulting, from simple expert advice up to delivery of fully customized, end-to-end production solutions for our clients, such as:
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- **Cloud-based AI** systems operating on **hundreds of HD video streams in realtime.**
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- **Edge AI** integrated into custom iOS and Android apps for realtime **30 FPS video inference.**
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- **Custom data training**, hyperparameter evolution, and model exportation to any destination.
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## Requirements
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For more information please visit https://www.ultralytics.com.
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Python 3.8 or later with all [requirements.txt](https://github.com/ultralytics/yolov3/blob/master/requirements.txt) dependencies installed, including `torch>=1.7`. To install run:
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```bash
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$ pip install -r requirements.txt
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```
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## Environments
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YOLOv3 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):
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- **Google Colab Notebook** with free GPU: <a href="https://colab.research.google.com/github/ultralytics/yolov3/blob/master/tutorial.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>
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- **Kaggle Notebook** with free GPU: [https://www.kaggle.com/ultralytics/yolov3](https://www.kaggle.com/ultralytics/yolov3)
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- **Google Cloud** Deep Learning VM. See [GCP Quickstart Guide](https://github.com/ultralytics/yolov3/wiki/GCP-Quickstart)
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- **Docker Image** https://hub.docker.com/r/ultralytics/yolov3. See [Docker Quickstart Guide](https://github.com/ultralytics/yolov3/wiki/Docker-Quickstart) 
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## Status
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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 YOLOv3 training ([train.py](https://github.com/ultralytics/yolov3/blob/master/train.py)), testing ([test.py](https://github.com/ultralytics/yolov3/blob/master/test.py)), inference ([detect.py](https://github.com/ultralytics/yolov3/blob/master/detect.py)) and export ([export.py](https://github.com/ultralytics/yolov3/blob/master/models/export.py)) on MacOS, Windows, and Ubuntu every 24 hours and on every commit.
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21
.github/workflows/rebase.yml
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21
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name: Automatic Rebase
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# https://github.com/marketplace/actions/automatic-rebase
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on:
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issue_comment:
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types: [created]
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jobs:
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rebase:
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name: Rebase
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if: github.event.issue.pull_request != '' && contains(github.event.comment.body, '/rebase')
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runs-on: ubuntu-latest
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steps:
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- name: Checkout the latest code
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uses: actions/checkout@v2
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with:
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fetch-depth: 0
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- name: Automatic Rebase
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uses: cirrus-actions/rebase@1.3.1
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env:
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GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
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1
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days-before-stale: 30
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days-before-close: 5
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exempt-issue-labels: 'documentation,tutorial'
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operations-per-run: 100 # The maximum number of operations per run, used to control rate limiting.
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62
Dockerfile
62
Dockerfile
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# Start FROM Nvidia PyTorch image https://ngc.nvidia.com/catalog/containers/nvidia:pytorch
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FROM nvcr.io/nvidia/pytorch:20.03-py3
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FROM nvcr.io/nvidia/pytorch:20.10-py3
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# Install dependencies (pip or conda)
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RUN pip install -U gsutil
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# RUN pip install -U -r requirements.txt
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# RUN conda update -n base -c defaults conda
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# RUN conda install -y -c anaconda future numpy opencv matplotlib tqdm pillow
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# RUN conda install -y -c conda-forge scikit-image tensorboard pycocotools
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## Install OpenCV with Gstreamer support
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#WORKDIR /usr/src
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#RUN pip uninstall -y opencv-python
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#RUN apt-get update
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#RUN apt-get install -y gstreamer1.0-tools gstreamer1.0-python3-dbg-plugin-loader libgstreamer1.0-dev libgstreamer-plugins-base1.0-dev
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#RUN git clone https://github.com/opencv/opencv.git && cd opencv && git checkout 4.1.1 && mkdir build
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#RUN git clone https://github.com/opencv/opencv_contrib.git && cd opencv_contrib && git checkout 4.1.1
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#RUN cd opencv/build && cmake ../ \
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# -D OPENCV_EXTRA_MODULES_PATH=../../opencv_contrib/modules \
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# -D BUILD_OPENCV_PYTHON3=ON \
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# -D PYTHON3_EXECUTABLE=/opt/conda/bin/python \
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# -D PYTHON3_INCLUDE_PATH=/opt/conda/include/python3.6m \
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# -D PYTHON3_LIBRARIES=/opt/conda/lib/python3.6/site-packages \
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# -D WITH_GSTREAMER=ON \
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# -D WITH_FFMPEG=OFF \
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# && make && make install && ldconfig
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#RUN cd /usr/local/lib/python3.6/site-packages/cv2/python-3.6/ && mv cv2.cpython-36m-x86_64-linux-gnu.so cv2.so
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#RUN cd /opt/conda/lib/python3.6/site-packages/ && ln -s /usr/local/lib/python3.6/site-packages/cv2/python-3.6/cv2.so cv2.so
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#RUN python3 -c "import cv2; print(cv2.getBuildInformation())"
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# Install dependencies
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RUN pip install --upgrade pip
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# COPY requirements.txt .
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# RUN pip install -r requirements.txt
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RUN pip install gsutil
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# Create working directory
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RUN mkdir -p /usr/src/app
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@ -38,25 +17,36 @@ COPY . /usr/src/app
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# Copy weights
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#RUN python3 -c "from models import *; \
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#attempt_download('weights/yolov3.pt'); \
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#attempt_download('weights/yolov3-spp.pt')"
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#attempt_download('weights/yolov3-spp.pt'); \
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#attempt_download('weights/yolov3-tiny.pt')"
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# --------------------------------------------------- Extras Below ---------------------------------------------------
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# Build and Push
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# t=ultralytics/yolov3:v0 && sudo docker build -t $t . && sudo docker push $t
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# t=ultralytics/yolov3:latest && sudo docker build -t $t . && sudo docker push $t
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# for v in {300..303}; do t=ultralytics/coco:v$v && sudo docker build -t $t . && sudo docker push $t; done
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# Run
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# t=ultralytics/yolov3:v0 && sudo docker pull $t && sudo docker run -it --gpus all --ipc=host $t bash
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# Pull and Run
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# t=ultralytics/yolov3:latest && sudo docker pull $t && sudo docker run -it --ipc=host $t
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# Pull and Run with local directory access
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# t=ultralytics/yolov3:v0 && sudo docker pull $t && sudo docker run -it --gpus all --ipc=host -v "$(pwd)"/coco:/usr/src/coco $t bash
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# t=ultralytics/yolov3:latest && sudo docker pull $t && sudo docker run -it --ipc=host --gpus all -v "$(pwd)"/coco:/usr/src/coco $t
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# Kill all
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# sudo docker kill "$(sudo docker ps -q)"
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# sudo docker kill $(sudo docker ps -q)
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# Kill all image-based
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# sudo docker kill $(sudo docker ps -a -q --filter ancestor=ultralytics/yolov3:v0)
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# sudo docker kill $(sudo docker ps -a -q --filter ancestor=ultralytics/yolov3:latest)
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# Run bash for loop
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# sudo docker run --gpus all --ipc=host ultralytics/yolov3:v0 while true; do python3 train.py --evolve; done
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# Bash into running container
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# sudo docker container exec -it ba65811811ab bash
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# Bash into stopped container
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# sudo docker commit 092b16b25c5b usr/resume && sudo docker run -it --gpus all --ipc=host -v "$(pwd)"/coco:/usr/src/coco --entrypoint=sh usr/resume
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# Send weights to GCP
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# python -c "from utils.general import *; strip_optimizer('runs/train/exp0_*/weights/best.pt', 'tmp.pt')" && gsutil cp tmp.pt gs://*.pt
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# Clean up
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# docker system prune -a --volumes
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219
README.md
219
README.md
@ -1,13 +1,39 @@
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<a href="https://apps.apple.com/app/id1452689527" target="_blank">
|
||||
<img src="https://user-images.githubusercontent.com/26833433/85940594-2d3f7d80-b8d2-11ea-809a-87b3bf6d968b.jpg" width="1000"></a>
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<img src="https://user-images.githubusercontent.com/26833433/99805965-8f2ca800-2b3d-11eb-8fad-13a96b222a23.jpg" width="1000"></a>
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This repo contains Ultralytics inference and training code for YOLOv3 in PyTorch. The code works on Linux, MacOS and Windows. Credit to Joseph Redmon for YOLO https://pjreddie.com/darknet/yolo/.
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BRANCH NOTICE: The [ultralytics/yolov3](https://github.com/ultralytics/yolov3) repository is now divided into two branches:
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* [Master branch](https://github.com/ultralytics/yolov3/tree/master): Forward-compatible with all [YOLOv5](https://github.com/ultralytics/yolov5) models and methods (recommended).
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```bash
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$ git clone https://github.com/ultralytics/yolov3 # master branch (default)
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```
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* [Archive branch](https://github.com/ultralytics/yolov3/tree/archive): Backwards-compatible with original [darknet](https://pjreddie.com/darknet/) *.cfg models (not recommended).
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```bash
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$ git clone -b archive https://github.com/ultralytics/yolov3 # archive branch
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```
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<img src="https://user-images.githubusercontent.com/26833433/100382066-c8bc5200-301a-11eb-907b-799a0301595e.png" width="1000">** GPU Speed measures end-to-end time per image averaged over 5000 COCO val2017 images using a V100 GPU with batch size 32, and includes image preprocessing, PyTorch FP16 inference, postprocessing and NMS. EfficientDet data from [google/automl](https://github.com/google/automl) at batch size 8.
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## Pretrained Checkpoints
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| Model | AP<sup>val</sup> | AP<sup>test</sup> | AP<sub>50</sub> | Speed<sub>GPU</sub> | FPS<sub>GPU</sub> || params | FLOPS |
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|---------- |------ |------ |------ | -------- | ------| ------ |------ | :------: |
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| [YOLOv3](https://github.com/ultralytics/yolov3/releases) | 43.3 | 43.3 | 63.0 | 4.8ms | 208 || 61.9M | 156.4B
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| [YOLOv3-SPP](https://github.com/ultralytics/yolov3/releases) | **44.3** | **44.3** | **64.6** | 4.9ms | 204 || 63.0M | 157.0B
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| [YOLOv3-tiny](https://github.com/ultralytics/yolov3/releases) | 17.6 | 34.9 | 34.9 | **1.7ms** | **588** || 8.9M | 13.3B
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** AP<sup>test</sup> denotes COCO [test-dev2017](http://cocodataset.org/#upload) server results, all other AP results denote val2017 accuracy.
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** All AP numbers are for single-model single-scale without ensemble or TTA. **Reproduce mAP** by `python test.py --data coco.yaml --img 640 --conf 0.001 --iou 0.65`
|
||||
** Speed<sub>GPU</sub> averaged over 5000 COCO val2017 images using a GCP [n1-standard-16](https://cloud.google.com/compute/docs/machine-types#n1_standard_machine_types) V100 instance, and includes image preprocessing, FP16 inference, postprocessing and NMS. NMS is 1-2ms/img. **Reproduce speed** by `python test.py --data coco.yaml --img 640 --conf 0.25 --iou 0.45`
|
||||
** All checkpoints are trained to 300 epochs with default settings and hyperparameters (no autoaugmentation).
|
||||
** Test Time Augmentation ([TTA](https://github.com/ultralytics/yolov5/issues/303)) runs at 3 image sizes. **Reproduce TTA** by `python test.py --data coco.yaml --img 832 --iou 0.65 --augment`
|
||||
|
||||
## Requirements
|
||||
|
||||
Python 3.8 or later with all [requirements.txt](https://github.com/ultralytics/yolov3/blob/master/requirements.txt) dependencies installed, including `torch>=1.6`. To install run:
|
||||
Python 3.8 or later with all [requirements.txt](https://github.com/ultralytics/yolov3/blob/master/requirements.txt) dependencies installed, including `torch>=1.7`. To install run:
|
||||
```bash
|
||||
$ pip install -r requirements.txt
|
||||
```
|
||||
@ -15,149 +41,90 @@ $ pip install -r requirements.txt
|
||||
|
||||
## Tutorials
|
||||
|
||||
* [Notebook](https://github.com/ultralytics/yolov3/blob/master/tutorial.ipynb) <a href="https://colab.research.google.com/github/ultralytics/yolov3/blob/master/tutorial.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>
|
||||
* [Train Custom Data](https://github.com/ultralytics/yolov3/wiki/Train-Custom-Data) << highly recommended
|
||||
* [GCP Quickstart](https://github.com/ultralytics/yolov3/wiki/GCP-Quickstart)
|
||||
* [Docker Quickstart Guide](https://github.com/ultralytics/yolov3/wiki/Docker-Quickstart) 
|
||||
* [A TensorRT Implementation of YOLOv3 and YOLOv4](https://github.com/wang-xinyu/tensorrtx/tree/master/yolov3-spp)
|
||||
* [Train Custom Data](https://github.com/ultralytics/yolov3/wiki/Train-Custom-Data) 🚀 RECOMMENDED
|
||||
* [Weights & Biases Logging](https://github.com/ultralytics/yolov5/issues/1289) 🌟 NEW
|
||||
* [Multi-GPU Training](https://github.com/ultralytics/yolov5/issues/475)
|
||||
* [PyTorch Hub](https://github.com/ultralytics/yolov5/issues/36) ⭐ NEW
|
||||
* [ONNX and TorchScript Export](https://github.com/ultralytics/yolov5/issues/251)
|
||||
* [Test-Time Augmentation (TTA)](https://github.com/ultralytics/yolov5/issues/303)
|
||||
* [Model Ensembling](https://github.com/ultralytics/yolov5/issues/318)
|
||||
* [Model Pruning/Sparsity](https://github.com/ultralytics/yolov5/issues/304)
|
||||
* [Hyperparameter Evolution](https://github.com/ultralytics/yolov5/issues/607)
|
||||
* [Transfer Learning with Frozen Layers](https://github.com/ultralytics/yolov5/issues/1314) ⭐ NEW
|
||||
* [TensorRT Deployment](https://github.com/wang-xinyu/tensorrtx)
|
||||
|
||||
|
||||
## Training
|
||||
## Environments
|
||||
|
||||
**Start Training:** `python3 train.py` to begin training after downloading COCO data with `data/get_coco2017.sh`. Each epoch trains on 117,263 images from the train and validate COCO sets, and tests on 5000 images from the COCO validate set.
|
||||
YOLOv3 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):
|
||||
|
||||
**Resume Training:** `python3 train.py --resume` to resume training from `weights/last.pt`.
|
||||
|
||||
**Plot Training:** `from utils import utils; utils.plot_results()`
|
||||
|
||||
<img src="https://user-images.githubusercontent.com/26833433/78175826-599d4800-7410-11ea-87d4-f629071838f6.png" width="900">
|
||||
|
||||
|
||||
### Image Augmentation
|
||||
|
||||
`datasets.py` applies OpenCV-powered (https://opencv.org/) augmentation to the input image. We use a **mosaic dataloader** to increase image variability during training.
|
||||
|
||||
<img src="https://user-images.githubusercontent.com/26833433/80769557-6e015d00-8b02-11ea-9c4b-69310eb2b962.jpg" width="900">
|
||||
|
||||
|
||||
### Speed
|
||||
|
||||
https://cloud.google.com/deep-learning-vm/
|
||||
**Machine type:** preemptible [n1-standard-8](https://cloud.google.com/compute/docs/machine-types) (8 vCPUs, 30 GB memory)
|
||||
**CPU platform:** Intel Skylake
|
||||
**GPUs:** K80 ($0.14/hr), T4 ($0.11/hr), V100 ($0.74/hr) CUDA with [Nvidia Apex](https://github.com/NVIDIA/apex) FP16/32
|
||||
**HDD:** 300 GB SSD
|
||||
**Dataset:** COCO train 2014 (117,263 images)
|
||||
**Model:** `yolov3-spp.cfg`
|
||||
**Command:** `python3 train.py --data coco2017.data --img 416 --batch 32`
|
||||
|
||||
GPU | n | `--batch-size` | img/s | epoch<br>time | epoch<br>cost
|
||||
--- |--- |--- |--- |--- |---
|
||||
K80 |1| 32 x 2 | 11 | 175 min | $0.41
|
||||
T4 |1<br>2| 32 x 2<br>64 x 1 | 41<br>61 | 48 min<br>32 min | $0.09<br>$0.11
|
||||
V100 |1<br>2| 32 x 2<br>64 x 1 | 122<br>**178** | 16 min<br>**11 min** | **$0.21**<br>$0.28
|
||||
2080Ti |1<br>2| 32 x 2<br>64 x 1 | 81<br>140 | 24 min<br>14 min | -<br>-
|
||||
- **Google Colab Notebook** with free GPU: <a href="https://colab.research.google.com/github/ultralytics/yolov3/blob/master/tutorial.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>
|
||||
- **Kaggle Notebook** with free GPU: [https://www.kaggle.com/ultralytics/yolov3](https://www.kaggle.com/ultralytics/yolov3)
|
||||
- **Google Cloud** Deep Learning VM. See [GCP Quickstart Guide](https://github.com/ultralytics/yolov3/wiki/GCP-Quickstart)
|
||||
- **Docker Image** https://hub.docker.com/r/ultralytics/yolov3. See [Docker Quickstart Guide](https://github.com/ultralytics/yolov3/wiki/Docker-Quickstart) 
|
||||
|
||||
|
||||
## Inference
|
||||
|
||||
detect.py runs inference on a variety of sources, downloading models automatically from the [latest YOLOv3 release](https://github.com/ultralytics/yolov3/releases) and saving results to `runs/detect`.
|
||||
```bash
|
||||
python3 detect.py --source ...
|
||||
$ python detect.py --source 0 # webcam
|
||||
file.jpg # image
|
||||
file.mp4 # video
|
||||
path/ # directory
|
||||
path/*.jpg # glob
|
||||
rtsp://170.93.143.139/rtplive/470011e600ef003a004ee33696235daa # rtsp stream
|
||||
rtmp://192.168.1.105/live/test # rtmp stream
|
||||
http://112.50.243.8/PLTV/88888888/224/3221225900/1.m3u8 # http stream
|
||||
```
|
||||
|
||||
- Image: `--source file.jpg`
|
||||
- Video: `--source file.mp4`
|
||||
- Directory: `--source dir/`
|
||||
- Webcam: `--source 0`
|
||||
- RTSP stream: `--source rtsp://170.93.143.139/rtplive/470011e600ef003a004ee33696235daa`
|
||||
- HTTP stream: `--source http://112.50.243.8/PLTV/88888888/224/3221225900/1.m3u8`
|
||||
|
||||
**YOLOv3:** `python3 detect.py --cfg cfg/yolov3.cfg --weights yolov3.pt`
|
||||
<img src="https://user-images.githubusercontent.com/26833433/64067835-51d5b500-cc2f-11e9-982e-843f7f9a6ea2.jpg" width="500">
|
||||
|
||||
**YOLOv3-tiny:** `python3 detect.py --cfg cfg/yolov3-tiny.cfg --weights yolov3-tiny.pt`
|
||||
<img src="https://user-images.githubusercontent.com/26833433/64067834-51d5b500-cc2f-11e9-9357-c485b159a20b.jpg" width="500">
|
||||
|
||||
**YOLOv3-SPP:** `python3 detect.py --cfg cfg/yolov3-spp.cfg --weights yolov3-spp.pt`
|
||||
<img src="https://user-images.githubusercontent.com/26833433/64067833-51d5b500-cc2f-11e9-8208-6fe197809131.jpg" width="500">
|
||||
|
||||
|
||||
## Pretrained Checkpoints
|
||||
|
||||
Download from: [https://drive.google.com/open?id=1LezFG5g3BCW6iYaV89B2i64cqEUZD7e0](https://drive.google.com/open?id=1LezFG5g3BCW6iYaV89B2i64cqEUZD7e0)
|
||||
|
||||
|
||||
## Darknet Conversion
|
||||
|
||||
To run inference on example images in `data/images`:
|
||||
```bash
|
||||
$ git clone https://github.com/ultralytics/yolov3 && cd yolov3
|
||||
$ python detect.py --source data/images --weights yolov3.pt --conf 0.25
|
||||
|
||||
# convert darknet cfg/weights to pytorch model
|
||||
$ python3 -c "from models import *; convert('cfg/yolov3-spp.cfg', 'weights/yolov3-spp.weights')"
|
||||
Success: converted 'weights/yolov3-spp.weights' to 'weights/yolov3-spp.pt'
|
||||
Namespace(agnostic_nms=False, augment=False, classes=None, conf_thres=0.25, device='', exist_ok=False, img_size=640, iou_thres=0.45, name='exp', project='runs/detect', save_conf=False, save_txt=False, source='data/images/', update=False, view_img=False, weights=['yolov3.pt'])
|
||||
Using torch 1.7.0+cu101 CUDA:0 (Tesla V100-SXM2-16GB, 16130MB)
|
||||
|
||||
# convert cfg/pytorch model to darknet weights
|
||||
$ python3 -c "from models import *; convert('cfg/yolov3-spp.cfg', 'weights/yolov3-spp.pt')"
|
||||
Success: converted 'weights/yolov3-spp.pt' to 'weights/yolov3-spp.weights'
|
||||
Downloading https://github.com/ultralytics/yolov3/releases/download/v1.0/yolov3.pt to yolov3.pt... 100% 118M/118M [00:05<00:00, 24.2MB/s]
|
||||
|
||||
Fusing layers...
|
||||
Model Summary: 261 layers, 61922845 parameters, 0 gradients
|
||||
image 1/2 /content/yolov3/data/images/bus.jpg: 640x480 4 persons, 1 buss, Done. (0.014s)
|
||||
image 2/2 /content/yolov3/data/images/zidane.jpg: 384x640 2 persons, 3 ties, Done. (0.014s)
|
||||
Results saved to runs/detect/exp
|
||||
Done. (0.133s)
|
||||
```
|
||||
<img src="https://user-images.githubusercontent.com/26833433/100375993-06b37900-300f-11eb-8d2d-5fc7b22fbfbd.jpg" width="500">
|
||||
|
||||
### PyTorch Hub
|
||||
|
||||
To run **batched inference** with YOLO3 and [PyTorch Hub](https://github.com/ultralytics/yolov5/issues/36):
|
||||
```python
|
||||
import torch
|
||||
from PIL import Image
|
||||
|
||||
# Model
|
||||
model = torch.hub.load('ultralytics/yolov3', 'yolov3', pretrained=True).autoshape() # for PIL/cv2/np inputs and NMS
|
||||
|
||||
# Images
|
||||
img1 = Image.open('zidane.jpg')
|
||||
img2 = Image.open('bus.jpg')
|
||||
imgs = [img1, img2] # batched list of images
|
||||
|
||||
# Inference
|
||||
prediction = model(imgs, size=640) # includes NMS
|
||||
```
|
||||
|
||||
|
||||
## mAP
|
||||
|
||||
<i></i> |Size |COCO mAP<br>@0.5...0.95 |COCO mAP<br>@0.5
|
||||
--- | --- | --- | ---
|
||||
YOLOv3-tiny<br>YOLOv3<br>YOLOv3-SPP<br>**[YOLOv3-SPP-ultralytics](https://drive.google.com/open?id=1UcR-zVoMs7DH5dj3N1bswkiQTA4dmKF4)** |320 |14.0<br>28.7<br>30.5<br>**37.7** |29.1<br>51.8<br>52.3<br>**56.8**
|
||||
YOLOv3-tiny<br>YOLOv3<br>YOLOv3-SPP<br>**[YOLOv3-SPP-ultralytics](https://drive.google.com/open?id=1UcR-zVoMs7DH5dj3N1bswkiQTA4dmKF4)** |416 |16.0<br>31.2<br>33.9<br>**41.2** |33.0<br>55.4<br>56.9<br>**60.6**
|
||||
YOLOv3-tiny<br>YOLOv3<br>YOLOv3-SPP<br>**[YOLOv3-SPP-ultralytics](https://drive.google.com/open?id=1UcR-zVoMs7DH5dj3N1bswkiQTA4dmKF4)** |512 |16.6<br>32.7<br>35.6<br>**42.6** |34.9<br>57.7<br>59.5<br>**62.4**
|
||||
YOLOv3-tiny<br>YOLOv3<br>YOLOv3-SPP<br>**[YOLOv3-SPP-ultralytics](https://drive.google.com/open?id=1UcR-zVoMs7DH5dj3N1bswkiQTA4dmKF4)** |608 |16.6<br>33.1<br>37.0<br>**43.1** |35.4<br>58.2<br>60.7<br>**62.8**
|
||||
|
||||
- mAP@0.5 run at `--iou-thr 0.5`, mAP@0.5...0.95 run at `--iou-thr 0.7`
|
||||
- Darknet results: https://arxiv.org/abs/1804.02767
|
||||
## Training
|
||||
|
||||
Download [COCO](https://github.com/ultralytics/yolov3/blob/master/data/scripts/get_coco.sh) and run command below. Training times for YOLOv3/YOLOv3-SPP/YOLOv3-tiny are 6/6/2 days on a single V100 (multi-GPU times faster). Use the largest `--batch-size` your GPU allows (batch sizes shown for 16 GB devices).
|
||||
```bash
|
||||
$ python3 test.py --cfg yolov3-spp.cfg --weights yolov3-spp-ultralytics.pt --img 640 --augment
|
||||
|
||||
Namespace(augment=True, batch_size=16, cfg='cfg/yolov3-spp.cfg', conf_thres=0.001, data='coco2014.data', device='', img_size=640, iou_thres=0.6, save_json=True, single_cls=False, task='test', weights='weight
|
||||
Using CUDA device0 _CudaDeviceProperties(name='Tesla V100-SXM2-16GB', total_memory=16130MB)
|
||||
|
||||
Class Images Targets P R mAP@0.5 F1: 100%|█████████| 313/313 [03:00<00:00, 1.74it/s]
|
||||
all 5e+03 3.51e+04 0.375 0.743 0.64 0.492
|
||||
|
||||
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.456
|
||||
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.647
|
||||
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.496
|
||||
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.263
|
||||
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.501
|
||||
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.596
|
||||
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.361
|
||||
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.597
|
||||
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.666
|
||||
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.492
|
||||
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.719
|
||||
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.810
|
||||
|
||||
Speed: 17.5/2.3/19.9 ms inference/NMS/total per 640x640 image at batch-size 16
|
||||
$ python train.py --data coco.yaml --cfg yolov3.yaml --weights '' --batch-size 24
|
||||
yolov3-spp.yaml 24
|
||||
yolov3-tiny.yaml 64
|
||||
```
|
||||
<!-- Speed: 11.4/2.2/13.6 ms inference/NMS/total per 608x608 image at batch-size 1 -->
|
||||
|
||||
|
||||
## Reproduce Our Results
|
||||
|
||||
Run commands below. Training takes about one week on a 2080Ti per model.
|
||||
```bash
|
||||
$ python train.py --data coco2014.data --weights '' --batch-size 16 --cfg yolov3-spp.cfg
|
||||
$ python train.py --data coco2014.data --weights '' --batch-size 32 --cfg yolov3-tiny.cfg
|
||||
```
|
||||
<img src="https://user-images.githubusercontent.com/26833433/80831822-57a9de80-8ba0-11ea-9684-c47afb0432dc.png" width="900">
|
||||
|
||||
|
||||
## Reproduce Our Environment
|
||||
|
||||
To access an up-to-date working environment (with all dependencies including CUDA/CUDNN, Python and PyTorch preinstalled), consider a:
|
||||
|
||||
- **GCP** Deep Learning VM with $300 free credit offer: See our [GCP Quickstart Guide](https://github.com/ultralytics/yolov3/wiki/GCP-Quickstart)
|
||||
- **Google Colab Notebook** with 12 hours of free GPU time. <a href="https://colab.research.google.com/github/ultralytics/yolov3/blob/master/tutorial.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>
|
||||
- **Docker Image** https://hub.docker.com/r/ultralytics/yolov3. See [Docker Quickstart Guide](https://github.com/ultralytics/yolov3/wiki/Docker-Quickstart) 
|
||||
<img src="https://user-images.githubusercontent.com/26833433/100378028-af170c80-3012-11eb-8521-f0d2a8d021bc.png" width="900">
|
||||
|
||||
|
||||
## Citation
|
||||
|
||||
1033
cfg/cd53s-yolov3.cfg
1033
cfg/cd53s-yolov3.cfg
File diff suppressed because it is too large
Load Diff
1155
cfg/cd53s.cfg
1155
cfg/cd53s.cfg
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
@ -1,788 +0,0 @@
|
||||
[net]
|
||||
# Testing
|
||||
#batch=1
|
||||
#subdivisions=1
|
||||
# Training
|
||||
batch=16
|
||||
subdivisions=1
|
||||
width=416
|
||||
height=416
|
||||
channels=3
|
||||
momentum=0.9
|
||||
decay=0.0005
|
||||
angle=0
|
||||
saturation = 1.5
|
||||
exposure = 1.5
|
||||
hue=.1
|
||||
|
||||
learning_rate=0.001
|
||||
burn_in=1000
|
||||
max_batches = 500200
|
||||
policy=steps
|
||||
steps=400000,450000
|
||||
scales=.1,.1
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=32
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
# Downsample
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=64
|
||||
size=3
|
||||
stride=2
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=32
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=64
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
# Downsample
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=3
|
||||
stride=2
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=64
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=64
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
# Downsample
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=3
|
||||
stride=2
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
# Downsample
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=3
|
||||
stride=2
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
# Downsample
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=1024
|
||||
size=3
|
||||
stride=2
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=1024
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=1024
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=1024
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=1024
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
######################
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
filters=1024
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
filters=1024
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
filters=1024
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
filters=18
|
||||
activation=linear
|
||||
|
||||
|
||||
[yolo]
|
||||
mask = 6,7,8
|
||||
anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326
|
||||
classes=1
|
||||
num=9
|
||||
jitter=.3
|
||||
ignore_thresh = .7
|
||||
truth_thresh = 1
|
||||
random=1
|
||||
|
||||
|
||||
[route]
|
||||
layers = -4
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[upsample]
|
||||
stride=2
|
||||
|
||||
[route]
|
||||
layers = -1, 61
|
||||
|
||||
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
filters=512
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
filters=512
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
filters=512
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
filters=18
|
||||
activation=linear
|
||||
|
||||
|
||||
[yolo]
|
||||
mask = 3,4,5
|
||||
anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326
|
||||
classes=1
|
||||
num=9
|
||||
jitter=.3
|
||||
ignore_thresh = .7
|
||||
truth_thresh = 1
|
||||
random=1
|
||||
|
||||
|
||||
|
||||
[route]
|
||||
layers = -4
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[upsample]
|
||||
stride=2
|
||||
|
||||
[route]
|
||||
layers = -1, 36
|
||||
|
||||
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
filters=256
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
filters=256
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
filters=256
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
filters=18
|
||||
activation=linear
|
||||
|
||||
|
||||
[yolo]
|
||||
mask = 0,1,2
|
||||
anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326
|
||||
classes=1
|
||||
num=9
|
||||
jitter=.3
|
||||
ignore_thresh = .7
|
||||
truth_thresh = 1
|
||||
random=1
|
||||
@ -1,804 +0,0 @@
|
||||
# Generated by Glenn Jocher (glenn.jocher@ultralytics.com) for https://github.com/ultralytics/yolov3
|
||||
# def kmean_anchors(path='../coco/train2017.txt', n=12, img_size=(320, 640)): # from utils.utils import *; kmean_anchors()
|
||||
# Evolving anchors: 100%|██████████| 1000/1000 [41:15<00:00, 2.48s/it]
|
||||
# 0.20 iou_thr: 0.992 best possible recall, 4.25 anchors > thr
|
||||
# kmeans anchors (n=12, img_size=(320, 640), IoU=0.005/0.184/0.634-min/mean/best): 6,9, 15,16, 17,35, 37,26, 36,67, 63,42, 57,100, 121,81, 112,169, 241,158, 195,310, 426,359
|
||||
|
||||
[net]
|
||||
# Testing
|
||||
# batch=1
|
||||
# subdivisions=1
|
||||
# Training
|
||||
batch=64
|
||||
subdivisions=16
|
||||
width=608
|
||||
height=608
|
||||
channels=3
|
||||
momentum=0.9
|
||||
decay=0.0005
|
||||
angle=0
|
||||
saturation = 1.5
|
||||
exposure = 1.5
|
||||
hue=.1
|
||||
|
||||
learning_rate=0.001
|
||||
burn_in=1000
|
||||
max_batches = 500200
|
||||
policy=steps
|
||||
steps=400000,450000
|
||||
scales=.1,.1
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=32
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
# Downsample
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=64
|
||||
size=3
|
||||
stride=2
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=32
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=64
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
# Downsample
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=3
|
||||
stride=2
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=64
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=64
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
# Downsample
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=3
|
||||
stride=2
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
# Downsample
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=3
|
||||
stride=2
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
# Downsample
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=1024
|
||||
size=3
|
||||
stride=2
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=1024
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=1024
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=1024
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=1024
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
######################
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
filters=1024
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
# SPP --------------------------------------------------------------------------
|
||||
[maxpool]
|
||||
stride=1
|
||||
size=5
|
||||
|
||||
[route]
|
||||
layers=-2
|
||||
|
||||
[maxpool]
|
||||
stride=1
|
||||
size=9
|
||||
|
||||
[route]
|
||||
layers=-4
|
||||
|
||||
[maxpool]
|
||||
stride=1
|
||||
size=13
|
||||
|
||||
[route]
|
||||
layers=-1,-3,-5,-6
|
||||
# SPP --------------------------------------------------------------------------
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
filters=1024
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
filters=1024
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
filters=258
|
||||
activation=linear
|
||||
|
||||
# YOLO -------------------------------------------------------------------------
|
||||
|
||||
[route]
|
||||
layers = -3
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[upsample]
|
||||
stride=2
|
||||
|
||||
[route]
|
||||
layers = -1, 61
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
filters=512
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
filters=512
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
filters=512
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
filters=258
|
||||
activation=linear
|
||||
|
||||
# YOLO -------------------------------------------------------------------------
|
||||
|
||||
[route]
|
||||
layers = -3
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[upsample]
|
||||
stride=2
|
||||
|
||||
[route]
|
||||
layers = -1, 36
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
filters=256
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
filters=256
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
filters=256
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
filters=258
|
||||
activation=linear
|
||||
|
||||
[yolo]
|
||||
from=88,99,110
|
||||
mask = 6,7,8
|
||||
anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326
|
||||
classes=80
|
||||
num=9
|
||||
|
||||
[yolo]
|
||||
from=88,99,110
|
||||
mask = 3,4,5
|
||||
anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326
|
||||
classes=80
|
||||
num=9
|
||||
|
||||
[yolo]
|
||||
from=88,99,110
|
||||
mask = 0,1,2
|
||||
anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326
|
||||
classes=80
|
||||
num=9
|
||||
@ -1,821 +0,0 @@
|
||||
[net]
|
||||
# Testing
|
||||
# batch=1
|
||||
# subdivisions=1
|
||||
# Training
|
||||
batch=64
|
||||
subdivisions=16
|
||||
width=608
|
||||
height=608
|
||||
channels=3
|
||||
momentum=0.9
|
||||
decay=0.0005
|
||||
angle=0
|
||||
saturation = 1.5
|
||||
exposure = 1.5
|
||||
hue=.1
|
||||
|
||||
learning_rate=0.001
|
||||
burn_in=100
|
||||
max_batches = 5000
|
||||
policy=steps
|
||||
steps=4000,4500
|
||||
scales=.1,.1
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=32
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
# Downsample
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=64
|
||||
size=3
|
||||
stride=2
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=32
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=64
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
# Downsample
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=3
|
||||
stride=2
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=64
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=64
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
# Downsample
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=3
|
||||
stride=2
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
# Downsample
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=3
|
||||
stride=2
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
# Downsample
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=1024
|
||||
size=3
|
||||
stride=2
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=1024
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=1024
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=1024
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=1024
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
######################
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
filters=1024
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
### SPP ###
|
||||
[maxpool]
|
||||
stride=1
|
||||
size=5
|
||||
|
||||
[route]
|
||||
layers=-2
|
||||
|
||||
[maxpool]
|
||||
stride=1
|
||||
size=9
|
||||
|
||||
[route]
|
||||
layers=-4
|
||||
|
||||
[maxpool]
|
||||
stride=1
|
||||
size=13
|
||||
|
||||
[route]
|
||||
layers=-1,-3,-5,-6
|
||||
|
||||
### End SPP ###
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
filters=1024
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
filters=1024
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
filters=18
|
||||
activation=linear
|
||||
|
||||
|
||||
[yolo]
|
||||
mask = 6,7,8
|
||||
anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326
|
||||
classes=1
|
||||
num=9
|
||||
jitter=.3
|
||||
ignore_thresh = .7
|
||||
truth_thresh = 1
|
||||
random=1
|
||||
|
||||
|
||||
[route]
|
||||
layers = -4
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[upsample]
|
||||
stride=2
|
||||
|
||||
[route]
|
||||
layers = -1, 61
|
||||
|
||||
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
filters=512
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
filters=512
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
filters=512
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
filters=18
|
||||
activation=linear
|
||||
|
||||
|
||||
[yolo]
|
||||
mask = 3,4,5
|
||||
anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326
|
||||
classes=1
|
||||
num=9
|
||||
jitter=.3
|
||||
ignore_thresh = .7
|
||||
truth_thresh = 1
|
||||
random=1
|
||||
|
||||
|
||||
|
||||
[route]
|
||||
layers = -4
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[upsample]
|
||||
stride=2
|
||||
|
||||
[route]
|
||||
layers = -1, 36
|
||||
|
||||
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
filters=256
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
filters=256
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
filters=256
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
filters=18
|
||||
activation=linear
|
||||
|
||||
|
||||
[yolo]
|
||||
mask = 0,1,2
|
||||
anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326
|
||||
classes=1
|
||||
num=9
|
||||
jitter=.3
|
||||
ignore_thresh = .7
|
||||
truth_thresh = 1
|
||||
random=1
|
||||
@ -1,821 +0,0 @@
|
||||
[net]
|
||||
# Testing
|
||||
# batch=1
|
||||
# subdivisions=1
|
||||
# Training
|
||||
batch=64
|
||||
subdivisions=16
|
||||
width=608
|
||||
height=608
|
||||
channels=3
|
||||
momentum=0.9
|
||||
decay=0.0005
|
||||
angle=0
|
||||
saturation = 1.5
|
||||
exposure = 1.5
|
||||
hue=.1
|
||||
|
||||
learning_rate=0.001
|
||||
burn_in=100
|
||||
max_batches = 5000
|
||||
policy=steps
|
||||
steps=4000,4500
|
||||
scales=.1,.1
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=32
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
# Downsample
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=64
|
||||
size=3
|
||||
stride=2
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=32
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=64
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
# Downsample
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=3
|
||||
stride=2
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=64
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=64
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
# Downsample
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=3
|
||||
stride=2
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
# Downsample
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=3
|
||||
stride=2
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
# Downsample
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=1024
|
||||
size=3
|
||||
stride=2
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=1024
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=1024
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=1024
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=1024
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
######################
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
filters=1024
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
### SPP ###
|
||||
[maxpool]
|
||||
stride=1
|
||||
size=5
|
||||
|
||||
[route]
|
||||
layers=-2
|
||||
|
||||
[maxpool]
|
||||
stride=1
|
||||
size=9
|
||||
|
||||
[route]
|
||||
layers=-4
|
||||
|
||||
[maxpool]
|
||||
stride=1
|
||||
size=13
|
||||
|
||||
[route]
|
||||
layers=-1,-3,-5,-6
|
||||
|
||||
### End SPP ###
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
filters=1024
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
filters=1024
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
filters=24
|
||||
activation=linear
|
||||
|
||||
|
||||
[yolo]
|
||||
mask = 6,7,8
|
||||
anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326
|
||||
classes=3
|
||||
num=9
|
||||
jitter=.3
|
||||
ignore_thresh = .7
|
||||
truth_thresh = 1
|
||||
random=1
|
||||
|
||||
|
||||
[route]
|
||||
layers = -4
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[upsample]
|
||||
stride=2
|
||||
|
||||
[route]
|
||||
layers = -1, 61
|
||||
|
||||
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
filters=512
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
filters=512
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
filters=512
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
filters=24
|
||||
activation=linear
|
||||
|
||||
|
||||
[yolo]
|
||||
mask = 3,4,5
|
||||
anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326
|
||||
classes=3
|
||||
num=9
|
||||
jitter=.3
|
||||
ignore_thresh = .7
|
||||
truth_thresh = 1
|
||||
random=1
|
||||
|
||||
|
||||
|
||||
[route]
|
||||
layers = -4
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[upsample]
|
||||
stride=2
|
||||
|
||||
[route]
|
||||
layers = -1, 36
|
||||
|
||||
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
filters=256
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
filters=256
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
filters=256
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
filters=24
|
||||
activation=linear
|
||||
|
||||
|
||||
[yolo]
|
||||
mask = 0,1,2
|
||||
anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326
|
||||
classes=3
|
||||
num=9
|
||||
jitter=.3
|
||||
ignore_thresh = .7
|
||||
truth_thresh = 1
|
||||
random=1
|
||||
File diff suppressed because it is too large
Load Diff
@ -1,938 +0,0 @@
|
||||
[net]
|
||||
# Testing
|
||||
#batch=1
|
||||
#subdivisions=1
|
||||
# Training
|
||||
batch=64
|
||||
subdivisions=32
|
||||
width=544
|
||||
height=544
|
||||
channels=3
|
||||
momentum=0.9
|
||||
decay=0.0005
|
||||
angle=0
|
||||
saturation = 1.5
|
||||
exposure = 1.5
|
||||
hue=.1
|
||||
|
||||
|
||||
learning_rate=0.001
|
||||
burn_in=1000
|
||||
max_batches = 10000
|
||||
|
||||
policy=steps
|
||||
steps=8000,9000
|
||||
scales=.1,.1
|
||||
|
||||
#policy=sgdr
|
||||
#sgdr_cycle=1000
|
||||
#sgdr_mult=2
|
||||
#steps=4000,6000,8000,9000
|
||||
#scales=1, 1, 0.1, 0.1
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=32
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
# Downsample
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=64
|
||||
size=3
|
||||
stride=2
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=32
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=64
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
# Downsample
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=3
|
||||
stride=2
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=64
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=64
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
# Downsample
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=3
|
||||
stride=2
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
# Downsample
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=3
|
||||
stride=2
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
# Downsample
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=1024
|
||||
size=3
|
||||
stride=2
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=1024
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=1024
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=1024
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=1024
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
######################
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
filters=1024
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
### SPP ###
|
||||
[maxpool]
|
||||
stride=1
|
||||
size=5
|
||||
|
||||
[route]
|
||||
layers=-2
|
||||
|
||||
[maxpool]
|
||||
stride=1
|
||||
size=9
|
||||
|
||||
[route]
|
||||
layers=-4
|
||||
|
||||
[maxpool]
|
||||
stride=1
|
||||
size=13
|
||||
|
||||
[route]
|
||||
layers=-1,-3,-5,-6
|
||||
|
||||
### End SPP ###
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
filters=1024
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
|
||||
|
||||
########### to [yolo-3]
|
||||
|
||||
|
||||
|
||||
[route]
|
||||
layers = -4
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[upsample]
|
||||
stride=2
|
||||
|
||||
[route]
|
||||
layers = -1, 61
|
||||
|
||||
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
filters=512
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
filters=512
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
|
||||
########### to [yolo-2]
|
||||
|
||||
|
||||
|
||||
|
||||
[route]
|
||||
layers = -4
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[upsample]
|
||||
stride=2
|
||||
|
||||
[route]
|
||||
layers = -1, 36
|
||||
|
||||
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
filters=256
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
filters=256
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
|
||||
|
||||
########### to [yolo-1]
|
||||
|
||||
|
||||
########### features of different layers
|
||||
|
||||
|
||||
[route]
|
||||
layers=1
|
||||
|
||||
[reorg3d]
|
||||
stride=2
|
||||
|
||||
[route]
|
||||
layers=5,-1
|
||||
|
||||
[reorg3d]
|
||||
stride=2
|
||||
|
||||
[route]
|
||||
layers=12,-1
|
||||
|
||||
[reorg3d]
|
||||
stride=2
|
||||
|
||||
[route]
|
||||
layers=37,-1
|
||||
|
||||
[reorg3d]
|
||||
stride=2
|
||||
|
||||
[route]
|
||||
layers=62,-1
|
||||
|
||||
|
||||
|
||||
########### [yolo-1]
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[upsample]
|
||||
stride=4
|
||||
|
||||
[route]
|
||||
layers = -1,-12
|
||||
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
filters=256
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
filters=340
|
||||
activation=linear
|
||||
|
||||
|
||||
[yolo]
|
||||
mask = 0,1,2,3
|
||||
anchors = 8,8, 10,13, 16,30, 33,23, 32,32, 30,61, 62,45, 64,64, 59,119, 116,90, 156,198, 373,326
|
||||
classes=80
|
||||
num=12
|
||||
jitter=.3
|
||||
ignore_thresh = .7
|
||||
truth_thresh = 1
|
||||
scale_x_y = 1.05
|
||||
random=0
|
||||
|
||||
|
||||
|
||||
|
||||
########### [yolo-2]
|
||||
|
||||
|
||||
[route]
|
||||
layers = -7
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[upsample]
|
||||
stride=2
|
||||
|
||||
[route]
|
||||
layers = -1,-28
|
||||
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
filters=512
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
filters=340
|
||||
activation=linear
|
||||
|
||||
|
||||
[yolo]
|
||||
mask = 4,5,6,7
|
||||
anchors = 8,8, 10,13, 16,30, 33,23, 32,32, 30,61, 62,45, 64,64, 59,119, 116,90, 156,198, 373,326
|
||||
classes=80
|
||||
num=12
|
||||
jitter=.3
|
||||
ignore_thresh = .7
|
||||
truth_thresh = 1
|
||||
scale_x_y = 1.1
|
||||
random=0
|
||||
|
||||
|
||||
|
||||
########### [yolo-3]
|
||||
|
||||
[route]
|
||||
layers = -14
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[route]
|
||||
layers = -1,-43
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
filters=1024
|
||||
activation=leaky
|
||||
|
||||
|
||||
[convolutional]
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
filters=340
|
||||
activation=linear
|
||||
|
||||
|
||||
[yolo]
|
||||
mask = 8,9,10,11
|
||||
anchors = 8,8, 10,13, 16,30, 33,23, 32,32, 30,61, 62,45, 59,119, 80,80, 116,90, 156,198, 373,326
|
||||
classes=80
|
||||
num=12
|
||||
jitter=.3
|
||||
ignore_thresh = .7
|
||||
truth_thresh = 1
|
||||
scale_x_y = 1.2
|
||||
random=0
|
||||
@ -1,821 +0,0 @@
|
||||
[net]
|
||||
# Testing
|
||||
# batch=1
|
||||
# subdivisions=1
|
||||
# Training
|
||||
batch=64
|
||||
subdivisions=16
|
||||
width=608
|
||||
height=608
|
||||
channels=3
|
||||
momentum=0.9
|
||||
decay=0.0005
|
||||
angle=0
|
||||
saturation = 1.5
|
||||
exposure = 1.5
|
||||
hue=.1
|
||||
|
||||
learning_rate=0.001
|
||||
burn_in=1000
|
||||
max_batches = 500200
|
||||
policy=steps
|
||||
steps=400000,450000
|
||||
scales=.1,.1
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=32
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
# Downsample
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=64
|
||||
size=3
|
||||
stride=2
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=32
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=64
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
# Downsample
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=3
|
||||
stride=2
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=64
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=64
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
# Downsample
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=3
|
||||
stride=2
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
# Downsample
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=3
|
||||
stride=2
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
# Downsample
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=1024
|
||||
size=3
|
||||
stride=2
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=1024
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=1024
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=1024
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=1024
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
######################
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
filters=1024
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
### SPP ###
|
||||
[maxpool]
|
||||
stride=1
|
||||
size=5
|
||||
|
||||
[route]
|
||||
layers=-2
|
||||
|
||||
[maxpool]
|
||||
stride=1
|
||||
size=9
|
||||
|
||||
[route]
|
||||
layers=-4
|
||||
|
||||
[maxpool]
|
||||
stride=1
|
||||
size=13
|
||||
|
||||
[route]
|
||||
layers=-1,-3,-5,-6
|
||||
|
||||
### End SPP ###
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
filters=1024
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
filters=1024
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
filters=255
|
||||
activation=linear
|
||||
|
||||
|
||||
[yolo]
|
||||
mask = 6,7,8
|
||||
anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326
|
||||
classes=80
|
||||
num=9
|
||||
jitter=.3
|
||||
ignore_thresh = .7
|
||||
truth_thresh = 1
|
||||
random=1
|
||||
|
||||
|
||||
[route]
|
||||
layers = -4
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[upsample]
|
||||
stride=2
|
||||
|
||||
[route]
|
||||
layers = -1, 61
|
||||
|
||||
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
filters=512
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
filters=512
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
filters=512
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
filters=255
|
||||
activation=linear
|
||||
|
||||
|
||||
[yolo]
|
||||
mask = 3,4,5
|
||||
anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326
|
||||
classes=80
|
||||
num=9
|
||||
jitter=.3
|
||||
ignore_thresh = .7
|
||||
truth_thresh = 1
|
||||
random=1
|
||||
|
||||
|
||||
|
||||
[route]
|
||||
layers = -4
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[upsample]
|
||||
stride=2
|
||||
|
||||
[route]
|
||||
layers = -1, 36
|
||||
|
||||
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
filters=256
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
filters=256
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
filters=256
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
filters=255
|
||||
activation=linear
|
||||
|
||||
|
||||
[yolo]
|
||||
mask = 0,1,2
|
||||
anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326
|
||||
classes=80
|
||||
num=9
|
||||
jitter=.3
|
||||
ignore_thresh = .7
|
||||
truth_thresh = 1
|
||||
random=1
|
||||
@ -1,870 +0,0 @@
|
||||
[net]
|
||||
# Testing
|
||||
batch=1
|
||||
subdivisions=1
|
||||
# Training
|
||||
# batch=64
|
||||
# subdivisions=16
|
||||
width=608
|
||||
height=608
|
||||
channels=3
|
||||
momentum=0.9
|
||||
decay=0.0005
|
||||
angle=0
|
||||
saturation = 1.5
|
||||
exposure = 1.5
|
||||
hue=.1
|
||||
|
||||
learning_rate=0.001
|
||||
burn_in=1000
|
||||
max_batches = 120200
|
||||
policy=steps
|
||||
steps=70000,100000
|
||||
scales=.1,.1
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=32
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
# Downsample
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=64
|
||||
size=3
|
||||
stride=2
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=32
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=64
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
# Downsample
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=3
|
||||
stride=2
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=64
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=64
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
# Downsample
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=3
|
||||
stride=2
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
# Downsample
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=3
|
||||
stride=2
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
# Downsample
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=1024
|
||||
size=3
|
||||
stride=2
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=1024
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=1024
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=1024
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=1024
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
######################
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
filters=1024
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
### SPP ###
|
||||
[maxpool]
|
||||
stride=1
|
||||
size=5
|
||||
|
||||
[route]
|
||||
layers=-2
|
||||
|
||||
[maxpool]
|
||||
stride=1
|
||||
size=9
|
||||
|
||||
[route]
|
||||
layers=-4
|
||||
|
||||
[maxpool]
|
||||
stride=1
|
||||
size=13
|
||||
|
||||
[route]
|
||||
layers=-1,-3,-5,-6
|
||||
|
||||
### End SPP ###
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
filters=1024
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
filters=1024
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
filters=255
|
||||
activation=linear
|
||||
|
||||
|
||||
[yolo]
|
||||
mask = 6,7,8
|
||||
anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326
|
||||
classes=80
|
||||
num=9
|
||||
jitter=.3
|
||||
ignore_thresh = .7
|
||||
truth_thresh = 1
|
||||
random=1
|
||||
|
||||
|
||||
[route]
|
||||
layers = -4
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[upsample]
|
||||
stride=2
|
||||
|
||||
[route]
|
||||
layers = -1, 61
|
||||
|
||||
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
filters=512
|
||||
activation=leaky
|
||||
|
||||
### SPP ###
|
||||
[maxpool]
|
||||
stride=1
|
||||
size=5
|
||||
|
||||
[route]
|
||||
layers=-2
|
||||
|
||||
[maxpool]
|
||||
stride=1
|
||||
size=9
|
||||
|
||||
[route]
|
||||
layers=-4
|
||||
|
||||
[maxpool]
|
||||
stride=1
|
||||
size=13
|
||||
|
||||
[route]
|
||||
layers=-1,-3,-5,-6
|
||||
|
||||
### End SPP ###
|
||||
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
filters=512
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
filters=512
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
filters=255
|
||||
activation=linear
|
||||
|
||||
|
||||
[yolo]
|
||||
mask = 3,4,5
|
||||
anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326
|
||||
classes=80
|
||||
num=9
|
||||
jitter=.3
|
||||
ignore_thresh = .7
|
||||
truth_thresh = 1
|
||||
random=1
|
||||
|
||||
|
||||
|
||||
[route]
|
||||
layers = -4
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[upsample]
|
||||
stride=2
|
||||
|
||||
[route]
|
||||
layers = -1, 36
|
||||
|
||||
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
filters=256
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
### SPP ###
|
||||
[maxpool]
|
||||
stride=1
|
||||
size=5
|
||||
|
||||
[route]
|
||||
layers=-2
|
||||
|
||||
[maxpool]
|
||||
stride=1
|
||||
size=9
|
||||
|
||||
[route]
|
||||
layers=-4
|
||||
|
||||
[maxpool]
|
||||
stride=1
|
||||
size=13
|
||||
|
||||
[route]
|
||||
layers=-1,-3,-5,-6
|
||||
|
||||
### End SPP ###
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
filters=256
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
filters=256
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
filters=255
|
||||
activation=linear
|
||||
|
||||
|
||||
[yolo]
|
||||
mask = 0,1,2
|
||||
anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326
|
||||
classes=80
|
||||
num=9
|
||||
jitter=.3
|
||||
ignore_thresh = .7
|
||||
truth_thresh = 1
|
||||
random=1
|
||||
@ -1,182 +0,0 @@
|
||||
[net]
|
||||
# Testing
|
||||
batch=1
|
||||
subdivisions=1
|
||||
# Training
|
||||
# batch=64
|
||||
# subdivisions=2
|
||||
width=416
|
||||
height=416
|
||||
channels=3
|
||||
momentum=0.9
|
||||
decay=0.0005
|
||||
angle=0
|
||||
saturation = 1.5
|
||||
exposure = 1.5
|
||||
hue=.1
|
||||
|
||||
learning_rate=0.001
|
||||
burn_in=1000
|
||||
max_batches = 500200
|
||||
policy=steps
|
||||
steps=400000,450000
|
||||
scales=.1,.1
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=16
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[maxpool]
|
||||
size=2
|
||||
stride=2
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=32
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[maxpool]
|
||||
size=2
|
||||
stride=2
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=64
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[maxpool]
|
||||
size=2
|
||||
stride=2
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[maxpool]
|
||||
size=2
|
||||
stride=2
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[maxpool]
|
||||
size=2
|
||||
stride=2
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[maxpool]
|
||||
size=2
|
||||
stride=1
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=1024
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
###########
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
filters=18
|
||||
activation=linear
|
||||
|
||||
|
||||
|
||||
[yolo]
|
||||
mask = 3,4,5
|
||||
anchors = 10,14, 23,27, 37,58, 81,82, 135,169, 344,319
|
||||
classes=1
|
||||
num=6
|
||||
jitter=.3
|
||||
ignore_thresh = .7
|
||||
truth_thresh = 1
|
||||
random=1
|
||||
|
||||
[route]
|
||||
layers = -4
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[upsample]
|
||||
stride=2
|
||||
|
||||
[route]
|
||||
layers = -1, 8
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
filters=18
|
||||
activation=linear
|
||||
|
||||
[yolo]
|
||||
mask = 0,1,2
|
||||
anchors = 10,14, 23,27, 37,58, 81,82, 135,169, 344,319
|
||||
classes=1
|
||||
num=6
|
||||
jitter=.3
|
||||
ignore_thresh = .7
|
||||
truth_thresh = 1
|
||||
random=1
|
||||
@ -1,182 +0,0 @@
|
||||
[net]
|
||||
# Testing
|
||||
batch=1
|
||||
subdivisions=1
|
||||
# Training
|
||||
# batch=64
|
||||
# subdivisions=2
|
||||
width=416
|
||||
height=416
|
||||
channels=3
|
||||
momentum=0.9
|
||||
decay=0.0005
|
||||
angle=0
|
||||
saturation = 1.5
|
||||
exposure = 1.5
|
||||
hue=.1
|
||||
|
||||
learning_rate=0.001
|
||||
burn_in=1000
|
||||
max_batches = 500200
|
||||
policy=steps
|
||||
steps=400000,450000
|
||||
scales=.1,.1
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=16
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[maxpool]
|
||||
size=2
|
||||
stride=2
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=32
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[maxpool]
|
||||
size=2
|
||||
stride=2
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=64
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[maxpool]
|
||||
size=2
|
||||
stride=2
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[maxpool]
|
||||
size=2
|
||||
stride=2
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[maxpool]
|
||||
size=2
|
||||
stride=2
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[maxpool]
|
||||
size=2
|
||||
stride=1
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=1024
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
###########
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
filters=24
|
||||
activation=linear
|
||||
|
||||
|
||||
|
||||
[yolo]
|
||||
mask = 3,4,5
|
||||
anchors = 10,14, 23,27, 37,58, 81,82, 135,169, 344,319
|
||||
classes=3
|
||||
num=6
|
||||
jitter=.3
|
||||
ignore_thresh = .7
|
||||
truth_thresh = 1
|
||||
random=1
|
||||
|
||||
[route]
|
||||
layers = -4
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[upsample]
|
||||
stride=2
|
||||
|
||||
[route]
|
||||
layers = -1, 8
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
filters=24
|
||||
activation=linear
|
||||
|
||||
[yolo]
|
||||
mask = 0,1,2
|
||||
anchors = 10,14, 23,27, 37,58, 81,82, 135,169, 344,319
|
||||
classes=3
|
||||
num=6
|
||||
jitter=.3
|
||||
ignore_thresh = .7
|
||||
truth_thresh = 1
|
||||
random=1
|
||||
@ -1,182 +0,0 @@
|
||||
[net]
|
||||
# Testing
|
||||
batch=1
|
||||
subdivisions=1
|
||||
# Training
|
||||
# batch=64
|
||||
# subdivisions=2
|
||||
width=416
|
||||
height=416
|
||||
channels=3
|
||||
momentum=0.9
|
||||
decay=0.0005
|
||||
angle=0
|
||||
saturation = 1.5
|
||||
exposure = 1.5
|
||||
hue=.1
|
||||
|
||||
learning_rate=0.001
|
||||
burn_in=1000
|
||||
max_batches = 500200
|
||||
policy=steps
|
||||
steps=400000,450000
|
||||
scales=.1,.1
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=16
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[maxpool]
|
||||
size=2
|
||||
stride=2
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=32
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[maxpool]
|
||||
size=2
|
||||
stride=2
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=64
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[maxpool]
|
||||
size=2
|
||||
stride=2
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[maxpool]
|
||||
size=2
|
||||
stride=2
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[maxpool]
|
||||
size=2
|
||||
stride=2
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[maxpool]
|
||||
size=2
|
||||
stride=1
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=1024
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
###########
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
filters=255
|
||||
activation=linear
|
||||
|
||||
|
||||
|
||||
[yolo]
|
||||
mask = 3,4,5
|
||||
anchors = 10,14, 23,27, 37,58, 81,82, 135,169, 344,319
|
||||
classes=80
|
||||
num=6
|
||||
jitter=.3
|
||||
ignore_thresh = .7
|
||||
truth_thresh = 1
|
||||
random=1
|
||||
|
||||
[route]
|
||||
layers = -4
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[upsample]
|
||||
stride=2
|
||||
|
||||
[route]
|
||||
layers = -1, 8
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
filters=255
|
||||
activation=linear
|
||||
|
||||
[yolo]
|
||||
mask = 1,2,3
|
||||
anchors = 10,14, 23,27, 37,58, 81,82, 135,169, 344,319
|
||||
classes=80
|
||||
num=6
|
||||
jitter=.3
|
||||
ignore_thresh = .7
|
||||
truth_thresh = 1
|
||||
random=1
|
||||
@ -1,227 +0,0 @@
|
||||
[net]
|
||||
# Testing
|
||||
# batch=1
|
||||
# subdivisions=1
|
||||
# Training
|
||||
batch=64
|
||||
subdivisions=16
|
||||
width=608
|
||||
height=608
|
||||
channels=3
|
||||
momentum=0.9
|
||||
decay=0.0005
|
||||
angle=0
|
||||
saturation = 1.5
|
||||
exposure = 1.5
|
||||
hue=.1
|
||||
|
||||
learning_rate=0.001
|
||||
burn_in=1000
|
||||
max_batches = 200000
|
||||
policy=steps
|
||||
steps=180000,190000
|
||||
scales=.1,.1
|
||||
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=16
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[maxpool]
|
||||
size=2
|
||||
stride=2
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=32
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[maxpool]
|
||||
size=2
|
||||
stride=2
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=64
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[maxpool]
|
||||
size=2
|
||||
stride=2
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[maxpool]
|
||||
size=2
|
||||
stride=2
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[maxpool]
|
||||
size=2
|
||||
stride=2
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[maxpool]
|
||||
size=2
|
||||
stride=1
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=1024
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
###########
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
filters=18
|
||||
activation=linear
|
||||
|
||||
|
||||
|
||||
[yolo]
|
||||
mask = 6,7,8
|
||||
anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326
|
||||
classes=1
|
||||
num=9
|
||||
jitter=.3
|
||||
ignore_thresh = .7
|
||||
truth_thresh = 1
|
||||
random=1
|
||||
|
||||
[route]
|
||||
layers = -4
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[upsample]
|
||||
stride=2
|
||||
|
||||
[route]
|
||||
layers = -1, 8
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
filters=18
|
||||
activation=linear
|
||||
|
||||
[yolo]
|
||||
mask = 3,4,5
|
||||
anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326
|
||||
classes=1
|
||||
num=9
|
||||
jitter=.3
|
||||
ignore_thresh = .7
|
||||
truth_thresh = 1
|
||||
random=1
|
||||
|
||||
|
||||
|
||||
[route]
|
||||
layers = -3
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[upsample]
|
||||
stride=2
|
||||
|
||||
[route]
|
||||
layers = -1, 6
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
filters=18
|
||||
activation=linear
|
||||
|
||||
[yolo]
|
||||
mask = 0,1,2
|
||||
anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326
|
||||
classes=1
|
||||
num=9
|
||||
jitter=.3
|
||||
ignore_thresh = .7
|
||||
truth_thresh = 1
|
||||
random=1
|
||||
@ -1,227 +0,0 @@
|
||||
[net]
|
||||
# Testing
|
||||
# batch=1
|
||||
# subdivisions=1
|
||||
# Training
|
||||
batch=64
|
||||
subdivisions=16
|
||||
width=608
|
||||
height=608
|
||||
channels=3
|
||||
momentum=0.9
|
||||
decay=0.0005
|
||||
angle=0
|
||||
saturation = 1.5
|
||||
exposure = 1.5
|
||||
hue=.1
|
||||
|
||||
learning_rate=0.001
|
||||
burn_in=1000
|
||||
max_batches = 200000
|
||||
policy=steps
|
||||
steps=180000,190000
|
||||
scales=.1,.1
|
||||
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=16
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[maxpool]
|
||||
size=2
|
||||
stride=2
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=32
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[maxpool]
|
||||
size=2
|
||||
stride=2
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=64
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[maxpool]
|
||||
size=2
|
||||
stride=2
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[maxpool]
|
||||
size=2
|
||||
stride=2
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[maxpool]
|
||||
size=2
|
||||
stride=2
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[maxpool]
|
||||
size=2
|
||||
stride=1
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=1024
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
###########
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
filters=255
|
||||
activation=linear
|
||||
|
||||
|
||||
|
||||
[yolo]
|
||||
mask = 6,7,8
|
||||
anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326
|
||||
classes=80
|
||||
num=9
|
||||
jitter=.3
|
||||
ignore_thresh = .7
|
||||
truth_thresh = 1
|
||||
random=1
|
||||
|
||||
[route]
|
||||
layers = -4
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[upsample]
|
||||
stride=2
|
||||
|
||||
[route]
|
||||
layers = -1, 8
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
filters=255
|
||||
activation=linear
|
||||
|
||||
[yolo]
|
||||
mask = 3,4,5
|
||||
anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326
|
||||
classes=80
|
||||
num=9
|
||||
jitter=.3
|
||||
ignore_thresh = .7
|
||||
truth_thresh = 1
|
||||
random=1
|
||||
|
||||
|
||||
|
||||
[route]
|
||||
layers = -3
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[upsample]
|
||||
stride=2
|
||||
|
||||
[route]
|
||||
layers = -1, 6
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
filters=255
|
||||
activation=linear
|
||||
|
||||
[yolo]
|
||||
mask = 0,1,2
|
||||
anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326
|
||||
classes=80
|
||||
num=9
|
||||
jitter=.3
|
||||
ignore_thresh = .7
|
||||
truth_thresh = 1
|
||||
random=1
|
||||
788
cfg/yolov3.cfg
788
cfg/yolov3.cfg
@ -1,788 +0,0 @@
|
||||
[net]
|
||||
# Testing
|
||||
#batch=1
|
||||
#subdivisions=1
|
||||
# Training
|
||||
batch=16
|
||||
subdivisions=1
|
||||
width=416
|
||||
height=416
|
||||
channels=3
|
||||
momentum=0.9
|
||||
decay=0.0005
|
||||
angle=0
|
||||
saturation = 1.5
|
||||
exposure = 1.5
|
||||
hue=.1
|
||||
|
||||
learning_rate=0.001
|
||||
burn_in=1000
|
||||
max_batches = 500200
|
||||
policy=steps
|
||||
steps=400000,450000
|
||||
scales=.1,.1
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=32
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
# Downsample
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=64
|
||||
size=3
|
||||
stride=2
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=32
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=64
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
# Downsample
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=3
|
||||
stride=2
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=64
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=64
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
# Downsample
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=3
|
||||
stride=2
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
# Downsample
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=3
|
||||
stride=2
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
# Downsample
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=1024
|
||||
size=3
|
||||
stride=2
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=1024
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=1024
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=1024
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=1024
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
######################
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
filters=1024
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
filters=1024
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
filters=1024
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
filters=255
|
||||
activation=linear
|
||||
|
||||
|
||||
[yolo]
|
||||
mask = 6,7,8
|
||||
anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326
|
||||
classes=80
|
||||
num=9
|
||||
jitter=.3
|
||||
ignore_thresh = .7
|
||||
truth_thresh = 1
|
||||
random=1
|
||||
|
||||
|
||||
[route]
|
||||
layers = -4
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[upsample]
|
||||
stride=2
|
||||
|
||||
[route]
|
||||
layers = -1, 61
|
||||
|
||||
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
filters=512
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
filters=512
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
filters=512
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
filters=255
|
||||
activation=linear
|
||||
|
||||
|
||||
[yolo]
|
||||
mask = 3,4,5
|
||||
anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326
|
||||
classes=80
|
||||
num=9
|
||||
jitter=.3
|
||||
ignore_thresh = .7
|
||||
truth_thresh = 1
|
||||
random=1
|
||||
|
||||
|
||||
|
||||
[route]
|
||||
layers = -4
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[upsample]
|
||||
stride=2
|
||||
|
||||
[route]
|
||||
layers = -1, 36
|
||||
|
||||
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
filters=256
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
filters=256
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
filters=256
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
filters=255
|
||||
activation=linear
|
||||
|
||||
|
||||
[yolo]
|
||||
mask = 0,1,2
|
||||
anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326
|
||||
classes=80
|
||||
num=9
|
||||
jitter=.3
|
||||
ignore_thresh = .7
|
||||
truth_thresh = 1
|
||||
random=1
|
||||
1155
cfg/yolov4-relu.cfg
1155
cfg/yolov4-relu.cfg
File diff suppressed because it is too large
Load Diff
@ -1,281 +0,0 @@
|
||||
[net]
|
||||
# Testing
|
||||
#batch=1
|
||||
#subdivisions=1
|
||||
# Training
|
||||
batch=64
|
||||
subdivisions=1
|
||||
width=416
|
||||
height=416
|
||||
channels=3
|
||||
momentum=0.9
|
||||
decay=0.0005
|
||||
angle=0
|
||||
saturation = 1.5
|
||||
exposure = 1.5
|
||||
hue=.1
|
||||
|
||||
learning_rate=0.00261
|
||||
burn_in=1000
|
||||
max_batches = 500200
|
||||
policy=steps
|
||||
steps=400000,450000
|
||||
scales=.1,.1
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=32
|
||||
size=3
|
||||
stride=2
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=64
|
||||
size=3
|
||||
stride=2
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=64
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[route]
|
||||
layers=-1
|
||||
groups=2
|
||||
group_id=1
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=32
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=32
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[route]
|
||||
layers = -1,-2
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=64
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[route]
|
||||
layers = -6,-1
|
||||
|
||||
[maxpool]
|
||||
size=2
|
||||
stride=2
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[route]
|
||||
layers=-1
|
||||
groups=2
|
||||
group_id=1
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=64
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=64
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[route]
|
||||
layers = -1,-2
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[route]
|
||||
layers = -6,-1
|
||||
|
||||
[maxpool]
|
||||
size=2
|
||||
stride=2
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[route]
|
||||
layers=-1
|
||||
groups=2
|
||||
group_id=1
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[route]
|
||||
layers = -1,-2
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[route]
|
||||
layers = -6,-1
|
||||
|
||||
[maxpool]
|
||||
size=2
|
||||
stride=2
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
##################################
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
filters=255
|
||||
activation=linear
|
||||
|
||||
|
||||
|
||||
[yolo]
|
||||
mask = 3,4,5
|
||||
anchors = 10,14, 23,27, 37,58, 81,82, 135,169, 344,319
|
||||
classes=80
|
||||
num=6
|
||||
jitter=.3
|
||||
scale_x_y = 1.05
|
||||
cls_normalizer=1.0
|
||||
iou_normalizer=0.07
|
||||
iou_loss=ciou
|
||||
ignore_thresh = .7
|
||||
truth_thresh = 1
|
||||
random=0
|
||||
resize=1.5
|
||||
nms_kind=greedynms
|
||||
beta_nms=0.6
|
||||
|
||||
[route]
|
||||
layers = -4
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[upsample]
|
||||
stride=2
|
||||
|
||||
[route]
|
||||
layers = -1, 23
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
filters=255
|
||||
activation=linear
|
||||
|
||||
[yolo]
|
||||
mask = 1,2,3
|
||||
anchors = 10,14, 23,27, 37,58, 81,82, 135,169, 344,319
|
||||
classes=80
|
||||
num=6
|
||||
jitter=.3
|
||||
scale_x_y = 1.05
|
||||
cls_normalizer=1.0
|
||||
iou_normalizer=0.07
|
||||
iou_loss=ciou
|
||||
ignore_thresh = .7
|
||||
truth_thresh = 1
|
||||
random=0
|
||||
resize=1.5
|
||||
nms_kind=greedynms
|
||||
beta_nms=0.6
|
||||
1155
cfg/yolov4.cfg
1155
cfg/yolov4.cfg
File diff suppressed because it is too large
Load Diff
@ -1,80 +0,0 @@
|
||||
person
|
||||
bicycle
|
||||
car
|
||||
motorcycle
|
||||
airplane
|
||||
bus
|
||||
train
|
||||
truck
|
||||
boat
|
||||
traffic light
|
||||
fire hydrant
|
||||
stop sign
|
||||
parking meter
|
||||
bench
|
||||
bird
|
||||
cat
|
||||
dog
|
||||
horse
|
||||
sheep
|
||||
cow
|
||||
elephant
|
||||
bear
|
||||
zebra
|
||||
giraffe
|
||||
backpack
|
||||
umbrella
|
||||
handbag
|
||||
tie
|
||||
suitcase
|
||||
frisbee
|
||||
skis
|
||||
snowboard
|
||||
sports ball
|
||||
kite
|
||||
baseball bat
|
||||
baseball glove
|
||||
skateboard
|
||||
surfboard
|
||||
tennis racket
|
||||
bottle
|
||||
wine glass
|
||||
cup
|
||||
fork
|
||||
knife
|
||||
spoon
|
||||
bowl
|
||||
banana
|
||||
apple
|
||||
sandwich
|
||||
orange
|
||||
broccoli
|
||||
carrot
|
||||
hot dog
|
||||
pizza
|
||||
donut
|
||||
cake
|
||||
chair
|
||||
couch
|
||||
potted plant
|
||||
bed
|
||||
dining table
|
||||
toilet
|
||||
tv
|
||||
laptop
|
||||
mouse
|
||||
remote
|
||||
keyboard
|
||||
cell phone
|
||||
microwave
|
||||
oven
|
||||
toaster
|
||||
sink
|
||||
refrigerator
|
||||
book
|
||||
clock
|
||||
vase
|
||||
scissors
|
||||
teddy bear
|
||||
hair drier
|
||||
toothbrush
|
||||
35
data/coco.yaml
Normal file
35
data/coco.yaml
Normal file
@ -0,0 +1,35 @@
|
||||
# COCO 2017 dataset http://cocodataset.org
|
||||
# Train command: python train.py --data coco.yaml
|
||||
# Default dataset location is next to /yolov3:
|
||||
# /parent_folder
|
||||
# /coco
|
||||
# /yolov3
|
||||
|
||||
|
||||
# download command/URL (optional)
|
||||
download: bash data/scripts/get_coco.sh
|
||||
|
||||
# train and val data as 1) directory: path/images/, 2) file: path/images.txt, or 3) list: [path1/images/, path2/images/]
|
||||
train: ../coco/train2017.txt # 118287 images
|
||||
val: ../coco/val2017.txt # 5000 images
|
||||
test: ../coco/test-dev2017.txt # 20288 of 40670 images, submit to https://competitions.codalab.org/competitions/20794
|
||||
|
||||
# number of classes
|
||||
nc: 80
|
||||
|
||||
# class names
|
||||
names: ['person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light',
|
||||
'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow',
|
||||
'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee',
|
||||
'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard',
|
||||
'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple',
|
||||
'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch',
|
||||
'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone',
|
||||
'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear',
|
||||
'hair drier', 'toothbrush']
|
||||
|
||||
# Print classes
|
||||
# with open('data/coco.yaml') as f:
|
||||
# d = yaml.load(f, Loader=yaml.FullLoader) # dict
|
||||
# for i, x in enumerate(d['names']):
|
||||
# print(i, x)
|
||||
@ -1,4 +0,0 @@
|
||||
classes=80
|
||||
train=data/coco1.txt
|
||||
valid=data/coco1.txt
|
||||
names=data/coco.names
|
||||
@ -1 +0,0 @@
|
||||
../coco/images/train2017/000000109622.jpg
|
||||
28
data/coco128.yaml
Normal file
28
data/coco128.yaml
Normal file
@ -0,0 +1,28 @@
|
||||
# COCO 2017 dataset http://cocodataset.org - first 128 training images
|
||||
# Train command: python train.py --data coco128.yaml
|
||||
# Default dataset location is next to /yolov3:
|
||||
# /parent_folder
|
||||
# /coco128
|
||||
# /yolov3
|
||||
|
||||
|
||||
# download command/URL (optional)
|
||||
download: https://github.com/ultralytics/yolov5/releases/download/v1.0/coco128.zip
|
||||
|
||||
# train and val data as 1) directory: path/images/, 2) file: path/images.txt, or 3) list: [path1/images/, path2/images/]
|
||||
train: ../coco128/images/train2017/ # 128 images
|
||||
val: ../coco128/images/train2017/ # 128 images
|
||||
|
||||
# number of classes
|
||||
nc: 80
|
||||
|
||||
# class names
|
||||
names: ['person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light',
|
||||
'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow',
|
||||
'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee',
|
||||
'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard',
|
||||
'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple',
|
||||
'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch',
|
||||
'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone',
|
||||
'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear',
|
||||
'hair drier', 'toothbrush']
|
||||
@ -1,4 +0,0 @@
|
||||
classes=80
|
||||
train=data/coco16.txt
|
||||
valid=data/coco16.txt
|
||||
names=data/coco.names
|
||||
@ -1,16 +0,0 @@
|
||||
../coco/images/train2017/000000109622.jpg
|
||||
../coco/images/train2017/000000160694.jpg
|
||||
../coco/images/train2017/000000308590.jpg
|
||||
../coco/images/train2017/000000327573.jpg
|
||||
../coco/images/train2017/000000062929.jpg
|
||||
../coco/images/train2017/000000512793.jpg
|
||||
../coco/images/train2017/000000371735.jpg
|
||||
../coco/images/train2017/000000148118.jpg
|
||||
../coco/images/train2017/000000309856.jpg
|
||||
../coco/images/train2017/000000141882.jpg
|
||||
../coco/images/train2017/000000318783.jpg
|
||||
../coco/images/train2017/000000337760.jpg
|
||||
../coco/images/train2017/000000298197.jpg
|
||||
../coco/images/train2017/000000042421.jpg
|
||||
../coco/images/train2017/000000328898.jpg
|
||||
../coco/images/train2017/000000458856.jpg
|
||||
@ -1,4 +0,0 @@
|
||||
classes=1
|
||||
train=data/coco1cls.txt
|
||||
valid=data/coco1cls.txt
|
||||
names=data/coco.names
|
||||
@ -1,16 +0,0 @@
|
||||
../coco/images/train2017/000000000901.jpg
|
||||
../coco/images/train2017/000000001464.jpg
|
||||
../coco/images/train2017/000000003220.jpg
|
||||
../coco/images/train2017/000000003365.jpg
|
||||
../coco/images/train2017/000000004772.jpg
|
||||
../coco/images/train2017/000000009987.jpg
|
||||
../coco/images/train2017/000000010498.jpg
|
||||
../coco/images/train2017/000000012455.jpg
|
||||
../coco/images/train2017/000000013992.jpg
|
||||
../coco/images/train2017/000000014125.jpg
|
||||
../coco/images/train2017/000000016314.jpg
|
||||
../coco/images/train2017/000000016670.jpg
|
||||
../coco/images/train2017/000000018412.jpg
|
||||
../coco/images/train2017/000000021212.jpg
|
||||
../coco/images/train2017/000000021826.jpg
|
||||
../coco/images/train2017/000000030566.jpg
|
||||
@ -1,4 +0,0 @@
|
||||
classes=80
|
||||
train=../coco/trainvalno5k.txt
|
||||
valid=../coco/5k.txt
|
||||
names=data/coco.names
|
||||
@ -1,4 +0,0 @@
|
||||
classes=80
|
||||
train=../coco/train2017.txt
|
||||
valid=../coco/val2017.txt
|
||||
names=data/coco.names
|
||||
@ -1,4 +0,0 @@
|
||||
classes=80
|
||||
train=data/coco64.txt
|
||||
valid=data/coco64.txt
|
||||
names=data/coco.names
|
||||
@ -1,64 +0,0 @@
|
||||
../coco/images/train2017/000000109622.jpg
|
||||
../coco/images/train2017/000000160694.jpg
|
||||
../coco/images/train2017/000000308590.jpg
|
||||
../coco/images/train2017/000000327573.jpg
|
||||
../coco/images/train2017/000000062929.jpg
|
||||
../coco/images/train2017/000000512793.jpg
|
||||
../coco/images/train2017/000000371735.jpg
|
||||
../coco/images/train2017/000000148118.jpg
|
||||
../coco/images/train2017/000000309856.jpg
|
||||
../coco/images/train2017/000000141882.jpg
|
||||
../coco/images/train2017/000000318783.jpg
|
||||
../coco/images/train2017/000000337760.jpg
|
||||
../coco/images/train2017/000000298197.jpg
|
||||
../coco/images/train2017/000000042421.jpg
|
||||
../coco/images/train2017/000000328898.jpg
|
||||
../coco/images/train2017/000000458856.jpg
|
||||
../coco/images/train2017/000000073824.jpg
|
||||
../coco/images/train2017/000000252846.jpg
|
||||
../coco/images/train2017/000000459590.jpg
|
||||
../coco/images/train2017/000000273650.jpg
|
||||
../coco/images/train2017/000000331311.jpg
|
||||
../coco/images/train2017/000000156326.jpg
|
||||
../coco/images/train2017/000000262985.jpg
|
||||
../coco/images/train2017/000000253580.jpg
|
||||
../coco/images/train2017/000000447976.jpg
|
||||
../coco/images/train2017/000000378077.jpg
|
||||
../coco/images/train2017/000000259913.jpg
|
||||
../coco/images/train2017/000000424553.jpg
|
||||
../coco/images/train2017/000000000612.jpg
|
||||
../coco/images/train2017/000000267625.jpg
|
||||
../coco/images/train2017/000000566012.jpg
|
||||
../coco/images/train2017/000000196664.jpg
|
||||
../coco/images/train2017/000000363331.jpg
|
||||
../coco/images/train2017/000000057992.jpg
|
||||
../coco/images/train2017/000000520047.jpg
|
||||
../coco/images/train2017/000000453903.jpg
|
||||
../coco/images/train2017/000000162083.jpg
|
||||
../coco/images/train2017/000000268516.jpg
|
||||
../coco/images/train2017/000000277436.jpg
|
||||
../coco/images/train2017/000000189744.jpg
|
||||
../coco/images/train2017/000000041128.jpg
|
||||
../coco/images/train2017/000000527728.jpg
|
||||
../coco/images/train2017/000000465269.jpg
|
||||
../coco/images/train2017/000000246833.jpg
|
||||
../coco/images/train2017/000000076784.jpg
|
||||
../coco/images/train2017/000000323715.jpg
|
||||
../coco/images/train2017/000000560463.jpg
|
||||
../coco/images/train2017/000000006263.jpg
|
||||
../coco/images/train2017/000000094701.jpg
|
||||
../coco/images/train2017/000000521359.jpg
|
||||
../coco/images/train2017/000000302903.jpg
|
||||
../coco/images/train2017/000000047559.jpg
|
||||
../coco/images/train2017/000000480583.jpg
|
||||
../coco/images/train2017/000000050025.jpg
|
||||
../coco/images/train2017/000000084512.jpg
|
||||
../coco/images/train2017/000000508913.jpg
|
||||
../coco/images/train2017/000000093708.jpg
|
||||
../coco/images/train2017/000000070493.jpg
|
||||
../coco/images/train2017/000000539270.jpg
|
||||
../coco/images/train2017/000000474402.jpg
|
||||
../coco/images/train2017/000000209842.jpg
|
||||
../coco/images/train2017/000000028820.jpg
|
||||
../coco/images/train2017/000000154257.jpg
|
||||
../coco/images/train2017/000000342499.jpg
|
||||
@ -1,91 +0,0 @@
|
||||
person
|
||||
bicycle
|
||||
car
|
||||
motorcycle
|
||||
airplane
|
||||
bus
|
||||
train
|
||||
truck
|
||||
boat
|
||||
traffic light
|
||||
fire hydrant
|
||||
street sign
|
||||
stop sign
|
||||
parking meter
|
||||
bench
|
||||
bird
|
||||
cat
|
||||
dog
|
||||
horse
|
||||
sheep
|
||||
cow
|
||||
elephant
|
||||
bear
|
||||
zebra
|
||||
giraffe
|
||||
hat
|
||||
backpack
|
||||
umbrella
|
||||
shoe
|
||||
eye glasses
|
||||
handbag
|
||||
tie
|
||||
suitcase
|
||||
frisbee
|
||||
skis
|
||||
snowboard
|
||||
sports ball
|
||||
kite
|
||||
baseball bat
|
||||
baseball glove
|
||||
skateboard
|
||||
surfboard
|
||||
tennis racket
|
||||
bottle
|
||||
plate
|
||||
wine glass
|
||||
cup
|
||||
fork
|
||||
knife
|
||||
spoon
|
||||
bowl
|
||||
banana
|
||||
apple
|
||||
sandwich
|
||||
orange
|
||||
broccoli
|
||||
carrot
|
||||
hot dog
|
||||
pizza
|
||||
donut
|
||||
cake
|
||||
chair
|
||||
couch
|
||||
potted plant
|
||||
bed
|
||||
mirror
|
||||
dining table
|
||||
window
|
||||
desk
|
||||
toilet
|
||||
door
|
||||
tv
|
||||
laptop
|
||||
mouse
|
||||
remote
|
||||
keyboard
|
||||
cell phone
|
||||
microwave
|
||||
oven
|
||||
toaster
|
||||
sink
|
||||
refrigerator
|
||||
blender
|
||||
book
|
||||
clock
|
||||
vase
|
||||
scissors
|
||||
teddy bear
|
||||
hair drier
|
||||
toothbrush
|
||||
hair brush
|
||||
@ -1,24 +0,0 @@
|
||||
#!/bin/bash
|
||||
# Zip coco folder
|
||||
# zip -r coco.zip coco
|
||||
# tar -czvf coco.tar.gz coco
|
||||
|
||||
# Download labels from Google Drive, accepting presented query
|
||||
filename="coco2014labels.zip"
|
||||
fileid="1s6-CmF5_SElM28r52P1OUrCcuXZN-SFo"
|
||||
curl -c ./cookie -s -L "https://drive.google.com/uc?export=download&id=${fileid}" >/dev/null
|
||||
curl -Lb ./cookie "https://drive.google.com/uc?export=download&confirm=$(awk '/download/ {print $NF}' ./cookie)&id=${fileid}" -o ${filename}
|
||||
rm ./cookie
|
||||
|
||||
# Unzip labels
|
||||
unzip -q ${filename} # for coco.zip
|
||||
# tar -xzf ${filename} # for coco.tar.gz
|
||||
rm ${filename}
|
||||
|
||||
# Download and unzip images
|
||||
cd coco/images
|
||||
f="train2014.zip" && curl http://images.cocodataset.org/zips/$f -o $f && unzip -q $f && rm $f
|
||||
f="val2014.zip" && curl http://images.cocodataset.org/zips/$f -o $f && unzip -q $f && rm $f
|
||||
|
||||
# cd out
|
||||
cd ../..
|
||||
@ -1,24 +0,0 @@
|
||||
#!/bin/bash
|
||||
# Zip coco folder
|
||||
# zip -r coco.zip coco
|
||||
# tar -czvf coco.tar.gz coco
|
||||
|
||||
# Download labels from Google Drive, accepting presented query
|
||||
filename="coco2017labels.zip"
|
||||
fileid="1cXZR_ckHki6nddOmcysCuuJFM--T-Q6L"
|
||||
curl -c ./cookie -s -L "https://drive.google.com/uc?export=download&id=${fileid}" >/dev/null
|
||||
curl -Lb ./cookie "https://drive.google.com/uc?export=download&confirm=$(awk '/download/ {print $NF}' ./cookie)&id=${fileid}" -o ${filename}
|
||||
rm ./cookie
|
||||
|
||||
# Unzip labels
|
||||
unzip -q ${filename} # for coco.zip
|
||||
# tar -xzf ${filename} # for coco.tar.gz
|
||||
rm ${filename}
|
||||
|
||||
# Download and unzip images
|
||||
cd coco/images
|
||||
f="train2017.zip" && curl http://images.cocodataset.org/zips/$f -o $f && unzip -q $f && rm $f
|
||||
f="val2017.zip" && curl http://images.cocodataset.org/zips/$f -o $f && unzip -q $f && rm $f
|
||||
|
||||
# cd out
|
||||
cd ../..
|
||||
38
data/hyp.finetune.yaml
Normal file
38
data/hyp.finetune.yaml
Normal file
@ -0,0 +1,38 @@
|
||||
# Hyperparameters for VOC finetuning
|
||||
# python train.py --batch 64 --weights yolov5m.pt --data voc.yaml --img 512 --epochs 50
|
||||
# See tutorials for hyperparameter evolution https://github.com/ultralytics/yolov5#tutorials
|
||||
|
||||
|
||||
# Hyperparameter Evolution Results
|
||||
# Generations: 306
|
||||
# P R mAP.5 mAP.5:.95 box obj cls
|
||||
# Metrics: 0.6 0.936 0.896 0.684 0.0115 0.00805 0.00146
|
||||
|
||||
lr0: 0.0032
|
||||
lrf: 0.12
|
||||
momentum: 0.843
|
||||
weight_decay: 0.00036
|
||||
warmup_epochs: 2.0
|
||||
warmup_momentum: 0.5
|
||||
warmup_bias_lr: 0.05
|
||||
box: 0.0296
|
||||
cls: 0.243
|
||||
cls_pw: 0.631
|
||||
obj: 0.301
|
||||
obj_pw: 0.911
|
||||
iou_t: 0.2
|
||||
anchor_t: 2.91
|
||||
# anchors: 3.63
|
||||
fl_gamma: 0.0
|
||||
hsv_h: 0.0138
|
||||
hsv_s: 0.664
|
||||
hsv_v: 0.464
|
||||
degrees: 0.373
|
||||
translate: 0.245
|
||||
scale: 0.898
|
||||
shear: 0.602
|
||||
perspective: 0.0
|
||||
flipud: 0.00856
|
||||
fliplr: 0.5
|
||||
mosaic: 1.0
|
||||
mixup: 0.243
|
||||
33
data/hyp.scratch.yaml
Normal file
33
data/hyp.scratch.yaml
Normal file
@ -0,0 +1,33 @@
|
||||
# Hyperparameters for COCO training from scratch
|
||||
# python train.py --batch 40 --cfg yolov5m.yaml --weights '' --data coco.yaml --img 640 --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: 0.2 # 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)
|
||||
|
Before Width: | Height: | Size: 476 KiB After Width: | Height: | Size: 476 KiB |
|
Before Width: | Height: | Size: 165 KiB After Width: | Height: | Size: 165 KiB |
24
data/scripts/get_coco.sh
Executable file
24
data/scripts/get_coco.sh
Executable file
@ -0,0 +1,24 @@
|
||||
#!/bin/bash
|
||||
# COCO 2017 dataset http://cocodataset.org
|
||||
# Download command: bash data/scripts/get_coco.sh
|
||||
# Train command: python train.py --data coco.yaml
|
||||
# Default dataset location is next to /yolov3:
|
||||
# /parent_folder
|
||||
# /coco
|
||||
# /yolov3
|
||||
|
||||
# Download/unzip labels
|
||||
d='../' # unzip directory
|
||||
url=https://github.com/ultralytics/yolov5/releases/download/v1.0/
|
||||
f='coco2017labels.zip' # 68 MB
|
||||
echo 'Downloading' $url$f ' ...' && curl -L $url$f -o $f && unzip -q $f -d $d && rm $f # download, unzip, remove
|
||||
|
||||
# Download/unzip images
|
||||
d='../coco/images' # unzip directory
|
||||
url=http://images.cocodataset.org/zips/
|
||||
f1='train2017.zip' # 19G, 118k images
|
||||
f2='val2017.zip' # 1G, 5k images
|
||||
f3='test2017.zip' # 7G, 41k images (optional)
|
||||
for f in $f1 $f2; do
|
||||
echo 'Downloading' $url$f ' ...' && curl -L $url$f -o $f && unzip -q $f -d $d && rm $f # download, unzip, remove
|
||||
done
|
||||
137
data/scripts/get_voc.sh
Normal file
137
data/scripts/get_voc.sh
Normal file
@ -0,0 +1,137 @@
|
||||
#!/bin/bash
|
||||
# PASCAL VOC dataset http://host.robots.ox.ac.uk/pascal/VOC/
|
||||
# Download command: bash data/scripts/get_voc.sh
|
||||
# Train command: python train.py --data voc.yaml
|
||||
# Default dataset location is next to /yolov5:
|
||||
# /parent_folder
|
||||
# /VOC
|
||||
# /yolov5
|
||||
|
||||
start=$(date +%s)
|
||||
mkdir -p ../tmp
|
||||
cd ../tmp/
|
||||
|
||||
# Download/unzip images and labels
|
||||
d='.' # unzip directory
|
||||
url=https://github.com/ultralytics/yolov5/releases/download/v1.0/
|
||||
f1=VOCtrainval_06-Nov-2007.zip # 446MB, 5012 images
|
||||
f2=VOCtest_06-Nov-2007.zip # 438MB, 4953 images
|
||||
f3=VOCtrainval_11-May-2012.zip # 1.95GB, 17126 images
|
||||
for f in $f1 $f2 $f3; do
|
||||
echo 'Downloading' $url$f ' ...' && curl -L $url$f -o $f && unzip -q $f -d $d && rm $f # download, unzip, remove
|
||||
done
|
||||
|
||||
end=$(date +%s)
|
||||
runtime=$((end - start))
|
||||
echo "Completed in" $runtime "seconds"
|
||||
|
||||
echo "Splitting dataset..."
|
||||
python3 - "$@" <<END
|
||||
import xml.etree.ElementTree as ET
|
||||
import pickle
|
||||
import os
|
||||
from os import listdir, getcwd
|
||||
from os.path import join
|
||||
|
||||
sets=[('2012', 'train'), ('2012', 'val'), ('2007', 'train'), ('2007', 'val'), ('2007', 'test')]
|
||||
|
||||
classes = ["aeroplane", "bicycle", "bird", "boat", "bottle", "bus", "car", "cat", "chair", "cow", "diningtable", "dog", "horse", "motorbike", "person", "pottedplant", "sheep", "sofa", "train", "tvmonitor"]
|
||||
|
||||
|
||||
def convert(size, box):
|
||||
dw = 1./(size[0])
|
||||
dh = 1./(size[1])
|
||||
x = (box[0] + box[1])/2.0 - 1
|
||||
y = (box[2] + box[3])/2.0 - 1
|
||||
w = box[1] - box[0]
|
||||
h = box[3] - box[2]
|
||||
x = x*dw
|
||||
w = w*dw
|
||||
y = y*dh
|
||||
h = h*dh
|
||||
return (x,y,w,h)
|
||||
|
||||
def convert_annotation(year, image_id):
|
||||
in_file = open('VOCdevkit/VOC%s/Annotations/%s.xml'%(year, image_id))
|
||||
out_file = open('VOCdevkit/VOC%s/labels/%s.txt'%(year, image_id), '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)
|
||||
|
||||
for obj in root.iter('object'):
|
||||
difficult = obj.find('difficult').text
|
||||
cls = obj.find('name').text
|
||||
if cls not in classes or int(difficult)==1:
|
||||
continue
|
||||
cls_id = classes.index(cls)
|
||||
xmlbox = obj.find('bndbox')
|
||||
b = (float(xmlbox.find('xmin').text), float(xmlbox.find('xmax').text), float(xmlbox.find('ymin').text), float(xmlbox.find('ymax').text))
|
||||
bb = convert((w,h), b)
|
||||
out_file.write(str(cls_id) + " " + " ".join([str(a) for a in bb]) + '\n')
|
||||
|
||||
wd = getcwd()
|
||||
|
||||
for year, image_set in sets:
|
||||
if not os.path.exists('VOCdevkit/VOC%s/labels/'%(year)):
|
||||
os.makedirs('VOCdevkit/VOC%s/labels/'%(year))
|
||||
image_ids = open('VOCdevkit/VOC%s/ImageSets/Main/%s.txt'%(year, image_set)).read().strip().split()
|
||||
list_file = open('%s_%s.txt'%(year, image_set), 'w')
|
||||
for image_id in image_ids:
|
||||
list_file.write('%s/VOCdevkit/VOC%s/JPEGImages/%s.jpg\n'%(wd, year, image_id))
|
||||
convert_annotation(year, image_id)
|
||||
list_file.close()
|
||||
|
||||
END
|
||||
|
||||
cat 2007_train.txt 2007_val.txt 2012_train.txt 2012_val.txt >train.txt
|
||||
cat 2007_train.txt 2007_val.txt 2007_test.txt 2012_train.txt 2012_val.txt >train.all.txt
|
||||
|
||||
python3 - "$@" <<END
|
||||
|
||||
import shutil
|
||||
import os
|
||||
os.system('mkdir ../VOC/')
|
||||
os.system('mkdir ../VOC/images')
|
||||
os.system('mkdir ../VOC/images/train')
|
||||
os.system('mkdir ../VOC/images/val')
|
||||
|
||||
os.system('mkdir ../VOC/labels')
|
||||
os.system('mkdir ../VOC/labels/train')
|
||||
os.system('mkdir ../VOC/labels/val')
|
||||
|
||||
import os
|
||||
print(os.path.exists('../tmp/train.txt'))
|
||||
f = open('../tmp/train.txt', 'r')
|
||||
lines = f.readlines()
|
||||
|
||||
for line in lines:
|
||||
line = "/".join(line.split('/')[-5:]).strip()
|
||||
if (os.path.exists("../" + line)):
|
||||
os.system("cp ../"+ line + " ../VOC/images/train")
|
||||
|
||||
line = line.replace('JPEGImages', 'labels')
|
||||
line = line.replace('jpg', 'txt')
|
||||
if (os.path.exists("../" + line)):
|
||||
os.system("cp ../"+ line + " ../VOC/labels/train")
|
||||
|
||||
|
||||
print(os.path.exists('../tmp/2007_test.txt'))
|
||||
f = open('../tmp/2007_test.txt', 'r')
|
||||
lines = f.readlines()
|
||||
|
||||
for line in lines:
|
||||
line = "/".join(line.split('/')[-5:]).strip()
|
||||
if (os.path.exists("../" + line)):
|
||||
os.system("cp ../"+ line + " ../VOC/images/val")
|
||||
|
||||
line = line.replace('JPEGImages', 'labels')
|
||||
line = line.replace('jpg', 'txt')
|
||||
if (os.path.exists("../" + line)):
|
||||
os.system("cp ../"+ line + " ../VOC/labels/val")
|
||||
|
||||
END
|
||||
|
||||
rm -rf ../tmp # remove temporary directory
|
||||
echo "VOC download done."
|
||||
21
data/voc.yaml
Normal file
21
data/voc.yaml
Normal file
@ -0,0 +1,21 @@
|
||||
# PASCAL VOC dataset http://host.robots.ox.ac.uk/pascal/VOC/
|
||||
# Train command: python train.py --data voc.yaml
|
||||
# Default dataset location is next to /yolov3:
|
||||
# /parent_folder
|
||||
# /VOC
|
||||
# /yolov3
|
||||
|
||||
|
||||
# download command/URL (optional)
|
||||
download: bash data/scripts/get_voc.sh
|
||||
|
||||
# train and val data as 1) directory: path/images/, 2) file: path/images.txt, or 3) list: [path1/images/, path2/images/]
|
||||
train: ../VOC/images/train/ # 16551 images
|
||||
val: ../VOC/images/val/ # 4952 images
|
||||
|
||||
# number of classes
|
||||
nc: 20
|
||||
|
||||
# class names
|
||||
names: ['aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car', 'cat', 'chair', 'cow', 'diningtable', 'dog',
|
||||
'horse', 'motorbike', 'person', 'pottedplant', 'sheep', 'sofa', 'train', 'tvmonitor']
|
||||
162
detect.py
162
detect.py
@ -1,82 +1,64 @@
|
||||
import argparse
|
||||
import time
|
||||
from pathlib import Path
|
||||
|
||||
from models import * # set ONNX_EXPORT in models.py
|
||||
from utils.datasets import *
|
||||
from utils.utils import *
|
||||
import cv2
|
||||
import torch
|
||||
import torch.backends.cudnn as cudnn
|
||||
from numpy import random
|
||||
|
||||
from models.experimental import attempt_load
|
||||
from utils.datasets import LoadStreams, LoadImages
|
||||
from utils.general import check_img_size, non_max_suppression, apply_classifier, scale_coords, xyxy2xywh, \
|
||||
strip_optimizer, set_logging, increment_path
|
||||
from utils.plots import plot_one_box
|
||||
from utils.torch_utils import select_device, load_classifier, time_synchronized
|
||||
|
||||
|
||||
def detect(save_img=False):
|
||||
imgsz = (320, 192) if ONNX_EXPORT else opt.img_size # (320, 192) or (416, 256) or (608, 352) for (height, width)
|
||||
out, source, weights, half, view_img, save_txt = opt.output, opt.source, opt.weights, opt.half, opt.view_img, opt.save_txt
|
||||
webcam = source == '0' or source.startswith('rtsp') or source.startswith('http') or source.endswith('.txt')
|
||||
source, weights, view_img, save_txt, imgsz = opt.source, opt.weights, opt.view_img, opt.save_txt, opt.img_size
|
||||
webcam = source.isnumeric() or source.endswith('.txt') or source.lower().startswith(
|
||||
('rtsp://', 'rtmp://', 'http://'))
|
||||
|
||||
# Directories
|
||||
save_dir = Path(increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok)) # increment run
|
||||
(save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True) # make dir
|
||||
|
||||
# Initialize
|
||||
device = torch_utils.select_device(device='cpu' if ONNX_EXPORT else opt.device)
|
||||
if os.path.exists(out):
|
||||
shutil.rmtree(out) # delete output folder
|
||||
os.makedirs(out) # make new output folder
|
||||
set_logging()
|
||||
device = select_device(opt.device)
|
||||
half = device.type != 'cpu' # half precision only supported on CUDA
|
||||
|
||||
# Initialize model
|
||||
model = Darknet(opt.cfg, imgsz)
|
||||
|
||||
# Load weights
|
||||
attempt_download(weights)
|
||||
if weights.endswith('.pt'): # pytorch format
|
||||
model.load_state_dict(torch.load(weights, map_location=device)['model'])
|
||||
else: # darknet format
|
||||
load_darknet_weights(model, weights)
|
||||
# Load model
|
||||
model = attempt_load(weights, map_location=device) # load FP32 model
|
||||
imgsz = check_img_size(imgsz, s=model.stride.max()) # check img_size
|
||||
if half:
|
||||
model.half() # to FP16
|
||||
|
||||
# Second-stage classifier
|
||||
classify = False
|
||||
if classify:
|
||||
modelc = torch_utils.load_classifier(name='resnet101', n=2) # initialize
|
||||
modelc.load_state_dict(torch.load('weights/resnet101.pt', map_location=device)['model']) # load weights
|
||||
modelc.to(device).eval()
|
||||
|
||||
# Eval mode
|
||||
model.to(device).eval()
|
||||
|
||||
# Fuse Conv2d + BatchNorm2d layers
|
||||
# model.fuse()
|
||||
|
||||
# Export mode
|
||||
if ONNX_EXPORT:
|
||||
model.fuse()
|
||||
img = torch.zeros((1, 3) + imgsz) # (1, 3, 320, 192)
|
||||
f = opt.weights.replace(opt.weights.split('.')[-1], 'onnx') # *.onnx filename
|
||||
torch.onnx.export(model, img, f, verbose=False, opset_version=11,
|
||||
input_names=['images'], output_names=['classes', 'boxes'])
|
||||
|
||||
# Validate exported model
|
||||
import onnx
|
||||
model = onnx.load(f) # Load the ONNX model
|
||||
onnx.checker.check_model(model) # Check that the IR is well formed
|
||||
print(onnx.helper.printable_graph(model.graph)) # Print a human readable representation of the graph
|
||||
return
|
||||
|
||||
# Half precision
|
||||
half = half and device.type != 'cpu' # half precision only supported on CUDA
|
||||
if half:
|
||||
model.half()
|
||||
modelc = load_classifier(name='resnet101', n=2) # initialize
|
||||
modelc.load_state_dict(torch.load('weights/resnet101.pt', map_location=device)['model']).to(device).eval()
|
||||
|
||||
# Set Dataloader
|
||||
vid_path, vid_writer = None, None
|
||||
if webcam:
|
||||
view_img = True
|
||||
torch.backends.cudnn.benchmark = True # set True to speed up constant image size inference
|
||||
cudnn.benchmark = True # set True to speed up constant image size inference
|
||||
dataset = LoadStreams(source, img_size=imgsz)
|
||||
else:
|
||||
save_img = True
|
||||
dataset = LoadImages(source, img_size=imgsz)
|
||||
|
||||
# Get names and colors
|
||||
names = load_classes(opt.names)
|
||||
colors = [[random.randint(0, 255) for _ in range(3)] for _ in range(len(names))]
|
||||
names = model.module.names if hasattr(model, 'module') else model.names
|
||||
colors = [[random.randint(0, 255) for _ in range(3)] for _ in names]
|
||||
|
||||
# Run inference
|
||||
t0 = time.time()
|
||||
img = torch.zeros((1, 3, imgsz, imgsz), device=device) # init img
|
||||
_ = model(img.half() if half else img.float()) if device.type != 'cpu' else None # run once
|
||||
_ = model(img.half() if half else img) if device.type != 'cpu' else None # run once
|
||||
for path, img, im0s, vid_cap in dataset:
|
||||
img = torch.from_numpy(img).to(device)
|
||||
img = img.half() if half else img.float() # uint8 to fp16/32
|
||||
@ -85,34 +67,30 @@ def detect(save_img=False):
|
||||
img = img.unsqueeze(0)
|
||||
|
||||
# Inference
|
||||
t1 = torch_utils.time_synchronized()
|
||||
t1 = time_synchronized()
|
||||
pred = model(img, augment=opt.augment)[0]
|
||||
t2 = torch_utils.time_synchronized()
|
||||
|
||||
# to float
|
||||
if half:
|
||||
pred = pred.float()
|
||||
|
||||
# Apply NMS
|
||||
pred = non_max_suppression(pred, opt.conf_thres, opt.iou_thres,
|
||||
multi_label=False, classes=opt.classes, agnostic=opt.agnostic_nms)
|
||||
pred = non_max_suppression(pred, opt.conf_thres, opt.iou_thres, classes=opt.classes, agnostic=opt.agnostic_nms)
|
||||
t2 = time_synchronized()
|
||||
|
||||
# Apply Classifier
|
||||
if classify:
|
||||
pred = apply_classifier(pred, modelc, img, im0s)
|
||||
|
||||
# Process detections
|
||||
for i, det in enumerate(pred): # detections for image i
|
||||
for i, det in enumerate(pred): # detections per image
|
||||
if webcam: # batch_size >= 1
|
||||
p, s, im0 = path[i], '%g: ' % i, im0s[i].copy()
|
||||
p, s, im0 = Path(path[i]), '%g: ' % i, im0s[i].copy()
|
||||
else:
|
||||
p, s, im0 = path, '', im0s
|
||||
p, s, im0 = Path(path), '', im0s
|
||||
|
||||
save_path = str(Path(out) / Path(p).name)
|
||||
save_path = str(save_dir / p.name)
|
||||
txt_path = str(save_dir / 'labels' / p.stem) + ('_%g' % dataset.frame if dataset.mode == 'video' else '')
|
||||
s += '%gx%g ' % img.shape[2:] # print string
|
||||
gn = torch.tensor(im0.shape)[[1, 0, 1, 0]] # normalization gain whwh
|
||||
if det is not None and len(det):
|
||||
# Rescale boxes from imgsz to im0 size
|
||||
gn = torch.tensor(im0.shape)[[1, 0, 1, 0]] # normalization gain whwh
|
||||
if len(det):
|
||||
# Rescale boxes from img_size to im0 size
|
||||
det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round()
|
||||
|
||||
# Print results
|
||||
@ -124,19 +102,20 @@ def detect(save_img=False):
|
||||
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
|
||||
with open(save_path[:save_path.rfind('.')] + '.txt', 'a') as file:
|
||||
file.write(('%g ' * 5 + '\n') % (cls, *xywh)) # label format
|
||||
line = (cls, *xywh, conf) if opt.save_conf else (cls, *xywh) # label format
|
||||
with open(txt_path + '.txt', 'a') as f:
|
||||
f.write(('%g ' * len(line)).rstrip() % line + '\n')
|
||||
|
||||
if save_img or view_img: # Add bbox to image
|
||||
label = '%s %.2f' % (names[int(cls)], conf)
|
||||
plot_one_box(xyxy, im0, label=label, color=colors[int(cls)])
|
||||
plot_one_box(xyxy, im0, label=label, color=colors[int(cls)], line_thickness=3)
|
||||
|
||||
# Print time (inference + NMS)
|
||||
print('%sDone. (%.3fs)' % (s, t2 - t1))
|
||||
|
||||
# Stream results
|
||||
if view_img:
|
||||
cv2.imshow(p, im0)
|
||||
cv2.imshow(str(p), im0)
|
||||
if cv2.waitKey(1) == ord('q'): # q to quit
|
||||
raise StopIteration
|
||||
|
||||
@ -150,42 +129,45 @@ def detect(save_img=False):
|
||||
if isinstance(vid_writer, cv2.VideoWriter):
|
||||
vid_writer.release() # release previous video writer
|
||||
|
||||
fourcc = 'mp4v' # output video codec
|
||||
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))
|
||||
vid_writer = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*opt.fourcc), fps, (w, h))
|
||||
vid_writer = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*fourcc), fps, (w, h))
|
||||
vid_writer.write(im0)
|
||||
|
||||
if save_txt or save_img:
|
||||
print('Results saved to %s' % os.getcwd() + os.sep + out)
|
||||
if platform == 'darwin': # MacOS
|
||||
os.system('open ' + save_path)
|
||||
s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else ''
|
||||
print(f"Results saved to {save_dir}{s}")
|
||||
|
||||
print('Done. (%.3fs)' % (time.time() - t0))
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument('--cfg', type=str, default='cfg/yolov3-spp.cfg', help='*.cfg path')
|
||||
parser.add_argument('--names', type=str, default='data/coco.names', help='*.names path')
|
||||
parser.add_argument('--weights', type=str, default='weights/yolov3-spp-ultralytics.pt', help='weights path')
|
||||
parser.add_argument('--source', type=str, default='data/samples', help='source') # input file/folder, 0 for webcam
|
||||
parser.add_argument('--output', type=str, default='output', help='output folder') # output folder
|
||||
parser.add_argument('--img-size', type=int, default=512, help='inference size (pixels)')
|
||||
parser.add_argument('--conf-thres', type=float, default=0.3, help='object confidence threshold')
|
||||
parser.add_argument('--iou-thres', type=float, default=0.6, help='IOU threshold for NMS')
|
||||
parser.add_argument('--fourcc', type=str, default='mp4v', help='output video codec (verify ffmpeg support)')
|
||||
parser.add_argument('--half', action='store_true', help='half precision FP16 inference')
|
||||
parser.add_argument('--device', default='', help='device id (i.e. 0 or 0,1) or cpu')
|
||||
parser.add_argument('--weights', nargs='+', type=str, default='yolov3.pt', help='model.pt path(s)')
|
||||
parser.add_argument('--source', type=str, default='data/images', help='source') # file/folder, 0 for webcam
|
||||
parser.add_argument('--img-size', type=int, default=640, help='inference size (pixels)')
|
||||
parser.add_argument('--conf-thres', type=float, default=0.25, help='object confidence threshold')
|
||||
parser.add_argument('--iou-thres', type=float, default=0.45, help='IOU threshold for NMS')
|
||||
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='display results')
|
||||
parser.add_argument('--save-txt', action='store_true', help='save results to *.txt')
|
||||
parser.add_argument('--classes', nargs='+', type=int, help='filter by class')
|
||||
parser.add_argument('--save-conf', action='store_true', help='save confidences in --save-txt labels')
|
||||
parser.add_argument('--classes', nargs='+', type=int, help='filter by class: --class 0, or --class 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('--update', action='store_true', help='update all models')
|
||||
parser.add_argument('--project', default='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')
|
||||
opt = parser.parse_args()
|
||||
opt.cfg = check_file(opt.cfg) # check file
|
||||
opt.names = check_file(opt.names) # check file
|
||||
print(opt)
|
||||
|
||||
with torch.no_grad():
|
||||
detect()
|
||||
if opt.update: # update all models (to fix SourceChangeWarning)
|
||||
for opt.weights in ['yolov3.pt', 'yolov3-spp.pt', 'yolov3-tiny.pt']:
|
||||
detect()
|
||||
strip_optimizer(opt.weights)
|
||||
else:
|
||||
detect()
|
||||
|
||||
105
hubconf.py
Normal file
105
hubconf.py
Normal file
@ -0,0 +1,105 @@
|
||||
"""File for accessing YOLOv3 via PyTorch Hub https://pytorch.org/hub/
|
||||
|
||||
Usage:
|
||||
import torch
|
||||
model = torch.hub.load('ultralytics/yolov3', 'yolov3', pretrained=True, channels=3, classes=80)
|
||||
"""
|
||||
|
||||
from pathlib import Path
|
||||
|
||||
import torch
|
||||
|
||||
from models.yolo import Model
|
||||
from utils.general import set_logging
|
||||
from utils.google_utils import attempt_download
|
||||
|
||||
dependencies = ['torch', 'yaml']
|
||||
set_logging()
|
||||
|
||||
|
||||
def create(name, pretrained, channels, classes):
|
||||
"""Creates a specified YOLOv3 model
|
||||
|
||||
Arguments:
|
||||
name (str): name of model, i.e. 'yolov3_spp'
|
||||
pretrained (bool): load pretrained weights into the model
|
||||
channels (int): number of input channels
|
||||
classes (int): number of model classes
|
||||
|
||||
Returns:
|
||||
pytorch model
|
||||
"""
|
||||
config = Path(__file__).parent / 'models' / f'{name}.yaml' # model.yaml path
|
||||
try:
|
||||
model = Model(config, channels, classes)
|
||||
if pretrained:
|
||||
fname = f'{name}.pt' # checkpoint filename
|
||||
attempt_download(fname) # download if not found locally
|
||||
ckpt = torch.load(fname, map_location=torch.device('cpu')) # load
|
||||
state_dict = ckpt['model'].float().state_dict() # to FP32
|
||||
state_dict = {k: v for k, v in state_dict.items() if model.state_dict()[k].shape == v.shape} # filter
|
||||
model.load_state_dict(state_dict, strict=False) # load
|
||||
if len(ckpt['model'].names) == classes:
|
||||
model.names = ckpt['model'].names # set class names attribute
|
||||
# model = model.autoshape() # for PIL/cv2/np inputs and NMS
|
||||
return model
|
||||
|
||||
except Exception as e:
|
||||
help_url = 'https://github.com/ultralytics/yolov5/issues/36'
|
||||
s = 'Cache maybe be out of date, try force_reload=True. See %s for help.' % help_url
|
||||
raise Exception(s) from e
|
||||
|
||||
|
||||
def yolov3(pretrained=False, channels=3, classes=80):
|
||||
"""YOLOv3 model from https://github.com/ultralytics/yolov3
|
||||
|
||||
Arguments:
|
||||
pretrained (bool): load pretrained weights into the model, default=False
|
||||
channels (int): number of input channels, default=3
|
||||
classes (int): number of model classes, default=80
|
||||
|
||||
Returns:
|
||||
pytorch model
|
||||
"""
|
||||
return create('yolov3', pretrained, channels, classes)
|
||||
|
||||
|
||||
def yolov3_spp(pretrained=False, channels=3, classes=80):
|
||||
"""YOLOv3-SPP model from https://github.com/ultralytics/yolov3
|
||||
|
||||
Arguments:
|
||||
pretrained (bool): load pretrained weights into the model, default=False
|
||||
channels (int): number of input channels, default=3
|
||||
classes (int): number of model classes, default=80
|
||||
|
||||
Returns:
|
||||
pytorch model
|
||||
"""
|
||||
return create('yolov3-spp', pretrained, channels, classes)
|
||||
|
||||
|
||||
def yolov3_tiny(pretrained=False, channels=3, classes=80):
|
||||
"""YOLOv3-tiny model from https://github.com/ultralytics/yolov3
|
||||
|
||||
Arguments:
|
||||
pretrained (bool): load pretrained weights into the model, default=False
|
||||
channels (int): number of input channels, default=3
|
||||
classes (int): number of model classes, default=80
|
||||
|
||||
Returns:
|
||||
pytorch model
|
||||
"""
|
||||
return create('yolov3-tiny', pretrained, channels, classes)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
model = create(name='yolov3', pretrained=True, channels=3, classes=80) # example
|
||||
model = model.fuse().autoshape() # for PIL/cv2/np inputs and NMS
|
||||
|
||||
# Verify inference
|
||||
from PIL import Image
|
||||
|
||||
imgs = [Image.open(x) for x in Path('data/images').glob('*.jpg')]
|
||||
results = model(imgs)
|
||||
results.show()
|
||||
results.print()
|
||||
480
models.py
480
models.py
@ -1,480 +0,0 @@
|
||||
from utils.google_utils import *
|
||||
from utils.layers import *
|
||||
from utils.parse_config import *
|
||||
|
||||
ONNX_EXPORT = False
|
||||
|
||||
|
||||
def create_modules(module_defs, img_size, cfg):
|
||||
# Constructs module list of layer blocks from module configuration in module_defs
|
||||
|
||||
img_size = [img_size] * 2 if isinstance(img_size, int) else img_size # expand if necessary
|
||||
_ = module_defs.pop(0) # cfg training hyperparams (unused)
|
||||
output_filters = [3] # input channels
|
||||
module_list = nn.ModuleList()
|
||||
routs = [] # list of layers which rout to deeper layers
|
||||
yolo_index = -1
|
||||
|
||||
for i, mdef in enumerate(module_defs):
|
||||
modules = nn.Sequential()
|
||||
|
||||
if mdef['type'] == 'convolutional':
|
||||
bn = mdef['batch_normalize']
|
||||
filters = mdef['filters']
|
||||
k = mdef['size'] # kernel size
|
||||
stride = mdef['stride'] if 'stride' in mdef else (mdef['stride_y'], mdef['stride_x'])
|
||||
if isinstance(k, int): # single-size conv
|
||||
modules.add_module('Conv2d', nn.Conv2d(in_channels=output_filters[-1],
|
||||
out_channels=filters,
|
||||
kernel_size=k,
|
||||
stride=stride,
|
||||
padding=k // 2 if mdef['pad'] else 0,
|
||||
groups=mdef['groups'] if 'groups' in mdef else 1,
|
||||
bias=not bn))
|
||||
else: # multiple-size conv
|
||||
modules.add_module('MixConv2d', MixConv2d(in_ch=output_filters[-1],
|
||||
out_ch=filters,
|
||||
k=k,
|
||||
stride=stride,
|
||||
bias=not bn))
|
||||
|
||||
if bn:
|
||||
modules.add_module('BatchNorm2d', nn.BatchNorm2d(filters, momentum=0.03, eps=1E-4))
|
||||
else:
|
||||
routs.append(i) # detection output (goes into yolo layer)
|
||||
|
||||
if mdef['activation'] == 'leaky': # activation study https://github.com/ultralytics/yolov3/issues/441
|
||||
modules.add_module('activation', nn.LeakyReLU(0.1, inplace=True))
|
||||
elif mdef['activation'] == 'swish':
|
||||
modules.add_module('activation', Swish())
|
||||
elif mdef['activation'] == 'mish':
|
||||
modules.add_module('activation', Mish())
|
||||
|
||||
elif mdef['type'] == 'BatchNorm2d':
|
||||
filters = output_filters[-1]
|
||||
modules = nn.BatchNorm2d(filters, momentum=0.03, eps=1E-4)
|
||||
if i == 0 and filters == 3: # normalize RGB image
|
||||
# imagenet mean and var https://pytorch.org/docs/stable/torchvision/models.html#classification
|
||||
modules.running_mean = torch.tensor([0.485, 0.456, 0.406])
|
||||
modules.running_var = torch.tensor([0.0524, 0.0502, 0.0506])
|
||||
|
||||
elif mdef['type'] == 'maxpool':
|
||||
k = mdef['size'] # kernel size
|
||||
stride = mdef['stride']
|
||||
maxpool = nn.MaxPool2d(kernel_size=k, stride=stride, padding=(k - 1) // 2)
|
||||
if k == 2 and stride == 1: # yolov3-tiny
|
||||
modules.add_module('ZeroPad2d', nn.ZeroPad2d((0, 1, 0, 1)))
|
||||
modules.add_module('MaxPool2d', maxpool)
|
||||
else:
|
||||
modules = maxpool
|
||||
|
||||
elif mdef['type'] == 'upsample':
|
||||
if ONNX_EXPORT: # explicitly state size, avoid scale_factor
|
||||
g = (yolo_index + 1) * 2 / 32 # gain
|
||||
modules = nn.Upsample(size=tuple(int(x * g) for x in img_size)) # img_size = (320, 192)
|
||||
else:
|
||||
modules = nn.Upsample(scale_factor=mdef['stride'])
|
||||
|
||||
elif mdef['type'] == 'route': # nn.Sequential() placeholder for 'route' layer
|
||||
layers = mdef['layers']
|
||||
filters = sum([output_filters[l + 1 if l > 0 else l] for l in layers])
|
||||
routs.extend([i + l if l < 0 else l for l in layers])
|
||||
modules = FeatureConcat(layers=layers)
|
||||
|
||||
elif mdef['type'] == 'shortcut': # nn.Sequential() placeholder for 'shortcut' layer
|
||||
layers = mdef['from']
|
||||
filters = output_filters[-1]
|
||||
routs.extend([i + l if l < 0 else l for l in layers])
|
||||
modules = WeightedFeatureFusion(layers=layers, weight='weights_type' in mdef)
|
||||
|
||||
elif mdef['type'] == 'reorg3d': # yolov3-spp-pan-scale
|
||||
pass
|
||||
|
||||
elif mdef['type'] == 'yolo':
|
||||
yolo_index += 1
|
||||
stride = [32, 16, 8] # P5, P4, P3 strides
|
||||
if any(x in cfg for x in ['panet', 'yolov4', 'cd53']): # stride order reversed
|
||||
stride = list(reversed(stride))
|
||||
layers = mdef['from'] if 'from' in mdef else []
|
||||
modules = YOLOLayer(anchors=mdef['anchors'][mdef['mask']], # anchor list
|
||||
nc=mdef['classes'], # number of classes
|
||||
img_size=img_size, # (416, 416)
|
||||
yolo_index=yolo_index, # 0, 1, 2...
|
||||
layers=layers, # output layers
|
||||
stride=stride[yolo_index])
|
||||
|
||||
# Initialize preceding Conv2d() bias (https://arxiv.org/pdf/1708.02002.pdf section 3.3)
|
||||
try:
|
||||
j = layers[yolo_index] if 'from' in mdef else -1
|
||||
# If previous layer is a dropout layer, get the one before
|
||||
if module_list[j].__class__.__name__ == 'Dropout':
|
||||
j -= 1
|
||||
bias_ = module_list[j][0].bias # shape(255,)
|
||||
bias = bias_[:modules.no * modules.na].view(modules.na, -1) # shape(3,85)
|
||||
bias[:, 4] += -4.5 # obj
|
||||
bias[:, 5:] += math.log(0.6 / (modules.nc - 0.99)) # cls (sigmoid(p) = 1/nc)
|
||||
module_list[j][0].bias = torch.nn.Parameter(bias_, requires_grad=bias_.requires_grad)
|
||||
except:
|
||||
print('WARNING: smart bias initialization failure.')
|
||||
|
||||
elif mdef['type'] == 'dropout':
|
||||
perc = float(mdef['probability'])
|
||||
modules = nn.Dropout(p=perc)
|
||||
else:
|
||||
print('Warning: Unrecognized Layer Type: ' + mdef['type'])
|
||||
|
||||
# Register module list and number of output filters
|
||||
module_list.append(modules)
|
||||
output_filters.append(filters)
|
||||
|
||||
routs_binary = [False] * (i + 1)
|
||||
for i in routs:
|
||||
routs_binary[i] = True
|
||||
return module_list, routs_binary
|
||||
|
||||
|
||||
class YOLOLayer(nn.Module):
|
||||
def __init__(self, anchors, nc, img_size, yolo_index, layers, stride):
|
||||
super(YOLOLayer, self).__init__()
|
||||
self.anchors = torch.Tensor(anchors)
|
||||
self.index = yolo_index # index of this layer in layers
|
||||
self.layers = layers # model output layer indices
|
||||
self.stride = stride # layer stride
|
||||
self.nl = len(layers) # number of output layers (3)
|
||||
self.na = len(anchors) # number of anchors (3)
|
||||
self.nc = nc # number of classes (80)
|
||||
self.no = nc + 5 # number of outputs (85)
|
||||
self.nx, self.ny, self.ng = 0, 0, 0 # initialize number of x, y gridpoints
|
||||
self.anchor_vec = self.anchors / self.stride
|
||||
self.anchor_wh = self.anchor_vec.view(1, self.na, 1, 1, 2)
|
||||
|
||||
if ONNX_EXPORT:
|
||||
self.training = False
|
||||
self.create_grids((img_size[1] // stride, img_size[0] // stride)) # number x, y grid points
|
||||
|
||||
def create_grids(self, ng=(13, 13), device='cpu'):
|
||||
self.nx, self.ny = ng # x and y grid size
|
||||
self.ng = torch.tensor(ng, dtype=torch.float)
|
||||
|
||||
# build xy offsets
|
||||
if not self.training:
|
||||
yv, xv = torch.meshgrid([torch.arange(self.ny, device=device), torch.arange(self.nx, device=device)])
|
||||
self.grid = torch.stack((xv, yv), 2).view((1, 1, self.ny, self.nx, 2)).float()
|
||||
|
||||
if self.anchor_vec.device != device:
|
||||
self.anchor_vec = self.anchor_vec.to(device)
|
||||
self.anchor_wh = self.anchor_wh.to(device)
|
||||
|
||||
def forward(self, p, out):
|
||||
ASFF = False # https://arxiv.org/abs/1911.09516
|
||||
if ASFF:
|
||||
i, n = self.index, self.nl # index in layers, number of layers
|
||||
p = out[self.layers[i]]
|
||||
bs, _, ny, nx = p.shape # bs, 255, 13, 13
|
||||
if (self.nx, self.ny) != (nx, ny):
|
||||
self.create_grids((nx, ny), p.device)
|
||||
|
||||
# outputs and weights
|
||||
# w = F.softmax(p[:, -n:], 1) # normalized weights
|
||||
w = torch.sigmoid(p[:, -n:]) * (2 / n) # sigmoid weights (faster)
|
||||
# w = w / w.sum(1).unsqueeze(1) # normalize across layer dimension
|
||||
|
||||
# weighted ASFF sum
|
||||
p = out[self.layers[i]][:, :-n] * w[:, i:i + 1]
|
||||
for j in range(n):
|
||||
if j != i:
|
||||
p += w[:, j:j + 1] * \
|
||||
F.interpolate(out[self.layers[j]][:, :-n], size=[ny, nx], mode='bilinear', align_corners=False)
|
||||
|
||||
elif ONNX_EXPORT:
|
||||
bs = 1 # batch size
|
||||
else:
|
||||
bs, _, ny, nx = p.shape # bs, 255, 13, 13
|
||||
if (self.nx, self.ny) != (nx, ny):
|
||||
self.create_grids((nx, ny), p.device)
|
||||
|
||||
# p.view(bs, 255, 13, 13) -- > (bs, 3, 13, 13, 85) # (bs, anchors, grid, grid, classes + xywh)
|
||||
p = p.view(bs, self.na, self.no, self.ny, self.nx).permute(0, 1, 3, 4, 2).contiguous() # prediction
|
||||
|
||||
if self.training:
|
||||
return p
|
||||
|
||||
elif ONNX_EXPORT:
|
||||
# Avoid broadcasting for ANE operations
|
||||
m = self.na * self.nx * self.ny
|
||||
ng = 1. / self.ng.repeat(m, 1)
|
||||
grid = self.grid.repeat(1, self.na, 1, 1, 1).view(m, 2)
|
||||
anchor_wh = self.anchor_wh.repeat(1, 1, self.nx, self.ny, 1).view(m, 2) * ng
|
||||
|
||||
p = p.view(m, self.no)
|
||||
xy = torch.sigmoid(p[:, 0:2]) + grid # x, y
|
||||
wh = torch.exp(p[:, 2:4]) * anchor_wh # width, height
|
||||
p_cls = torch.sigmoid(p[:, 4:5]) if self.nc == 1 else \
|
||||
torch.sigmoid(p[:, 5:self.no]) * torch.sigmoid(p[:, 4:5]) # conf
|
||||
return p_cls, xy * ng, wh
|
||||
|
||||
else: # inference
|
||||
io = p.clone() # inference output
|
||||
io[..., :2] = torch.sigmoid(io[..., :2]) + self.grid # xy
|
||||
io[..., 2:4] = torch.exp(io[..., 2:4]) * self.anchor_wh # wh yolo method
|
||||
io[..., :4] *= self.stride
|
||||
torch.sigmoid_(io[..., 4:])
|
||||
return io.view(bs, -1, self.no), p # view [1, 3, 13, 13, 85] as [1, 507, 85]
|
||||
|
||||
|
||||
class Darknet(nn.Module):
|
||||
# YOLOv3 object detection model
|
||||
|
||||
def __init__(self, cfg, img_size=(416, 416), verbose=False):
|
||||
super(Darknet, self).__init__()
|
||||
|
||||
self.module_defs = parse_model_cfg(cfg)
|
||||
self.module_list, self.routs = create_modules(self.module_defs, img_size, cfg)
|
||||
self.yolo_layers = get_yolo_layers(self)
|
||||
# torch_utils.initialize_weights(self)
|
||||
|
||||
# Darknet Header https://github.com/AlexeyAB/darknet/issues/2914#issuecomment-496675346
|
||||
self.version = np.array([0, 2, 5], dtype=np.int32) # (int32) version info: major, minor, revision
|
||||
self.seen = np.array([0], dtype=np.int64) # (int64) number of images seen during training
|
||||
self.info(verbose) if not ONNX_EXPORT else None # print model description
|
||||
|
||||
def forward(self, x, augment=False, verbose=False):
|
||||
|
||||
if not augment:
|
||||
return self.forward_once(x)
|
||||
else: # Augment images (inference and test only) https://github.com/ultralytics/yolov3/issues/931
|
||||
img_size = x.shape[-2:] # height, width
|
||||
s = [0.83, 0.67] # scales
|
||||
y = []
|
||||
for i, xi in enumerate((x,
|
||||
torch_utils.scale_img(x.flip(3), s[0], same_shape=False), # flip-lr and scale
|
||||
torch_utils.scale_img(x, s[1], same_shape=False), # scale
|
||||
)):
|
||||
# cv2.imwrite('img%g.jpg' % i, 255 * xi[0].numpy().transpose((1, 2, 0))[:, :, ::-1])
|
||||
y.append(self.forward_once(xi)[0])
|
||||
|
||||
y[1][..., :4] /= s[0] # scale
|
||||
y[1][..., 0] = img_size[1] - y[1][..., 0] # flip lr
|
||||
y[2][..., :4] /= s[1] # scale
|
||||
|
||||
# for i, yi in enumerate(y): # coco small, medium, large = < 32**2 < 96**2 <
|
||||
# area = yi[..., 2:4].prod(2)[:, :, None]
|
||||
# if i == 1:
|
||||
# yi *= (area < 96. ** 2).float()
|
||||
# elif i == 2:
|
||||
# yi *= (area > 32. ** 2).float()
|
||||
# y[i] = yi
|
||||
|
||||
y = torch.cat(y, 1)
|
||||
return y, None
|
||||
|
||||
def forward_once(self, x, augment=False, verbose=False):
|
||||
img_size = x.shape[-2:] # height, width
|
||||
yolo_out, out = [], []
|
||||
if verbose:
|
||||
print('0', x.shape)
|
||||
str = ''
|
||||
|
||||
# Augment images (inference and test only)
|
||||
if augment: # https://github.com/ultralytics/yolov3/issues/931
|
||||
nb = x.shape[0] # batch size
|
||||
s = [0.83, 0.67] # scales
|
||||
x = torch.cat((x,
|
||||
torch_utils.scale_img(x.flip(3), s[0]), # flip-lr and scale
|
||||
torch_utils.scale_img(x, s[1]), # scale
|
||||
), 0)
|
||||
|
||||
for i, module in enumerate(self.module_list):
|
||||
name = module.__class__.__name__
|
||||
if name in ['WeightedFeatureFusion', 'FeatureConcat']: # sum, concat
|
||||
if verbose:
|
||||
l = [i - 1] + module.layers # layers
|
||||
sh = [list(x.shape)] + [list(out[i].shape) for i in module.layers] # shapes
|
||||
str = ' >> ' + ' + '.join(['layer %g %s' % x for x in zip(l, sh)])
|
||||
x = module(x, out) # WeightedFeatureFusion(), FeatureConcat()
|
||||
elif name == 'YOLOLayer':
|
||||
yolo_out.append(module(x, out))
|
||||
else: # run module directly, i.e. mtype = 'convolutional', 'upsample', 'maxpool', 'batchnorm2d' etc.
|
||||
x = module(x)
|
||||
|
||||
out.append(x if self.routs[i] else [])
|
||||
if verbose:
|
||||
print('%g/%g %s -' % (i, len(self.module_list), name), list(x.shape), str)
|
||||
str = ''
|
||||
|
||||
if self.training: # train
|
||||
return yolo_out
|
||||
elif ONNX_EXPORT: # export
|
||||
x = [torch.cat(x, 0) for x in zip(*yolo_out)]
|
||||
return x[0], torch.cat(x[1:3], 1) # scores, boxes: 3780x80, 3780x4
|
||||
else: # inference or test
|
||||
x, p = zip(*yolo_out) # inference output, training output
|
||||
x = torch.cat(x, 1) # cat yolo outputs
|
||||
if augment: # de-augment results
|
||||
x = torch.split(x, nb, dim=0)
|
||||
x[1][..., :4] /= s[0] # scale
|
||||
x[1][..., 0] = img_size[1] - x[1][..., 0] # flip lr
|
||||
x[2][..., :4] /= s[1] # scale
|
||||
x = torch.cat(x, 1)
|
||||
return x, p
|
||||
|
||||
def fuse(self):
|
||||
# Fuse Conv2d + BatchNorm2d layers throughout model
|
||||
print('Fusing layers...')
|
||||
fused_list = nn.ModuleList()
|
||||
for a in list(self.children())[0]:
|
||||
if isinstance(a, nn.Sequential):
|
||||
for i, b in enumerate(a):
|
||||
if isinstance(b, nn.modules.batchnorm.BatchNorm2d):
|
||||
# fuse this bn layer with the previous conv2d layer
|
||||
conv = a[i - 1]
|
||||
fused = torch_utils.fuse_conv_and_bn(conv, b)
|
||||
a = nn.Sequential(fused, *list(a.children())[i + 1:])
|
||||
break
|
||||
fused_list.append(a)
|
||||
self.module_list = fused_list
|
||||
self.info() if not ONNX_EXPORT else None # yolov3-spp reduced from 225 to 152 layers
|
||||
|
||||
def info(self, verbose=False):
|
||||
torch_utils.model_info(self, verbose)
|
||||
|
||||
|
||||
def get_yolo_layers(model):
|
||||
return [i for i, m in enumerate(model.module_list) if m.__class__.__name__ == 'YOLOLayer'] # [89, 101, 113]
|
||||
|
||||
|
||||
def load_darknet_weights(self, weights, cutoff=-1):
|
||||
# Parses and loads the weights stored in 'weights'
|
||||
|
||||
# Establish cutoffs (load layers between 0 and cutoff. if cutoff = -1 all are loaded)
|
||||
file = Path(weights).name
|
||||
if file == 'darknet53.conv.74':
|
||||
cutoff = 75
|
||||
elif file == 'yolov3-tiny.conv.15':
|
||||
cutoff = 15
|
||||
|
||||
# Read weights file
|
||||
with open(weights, 'rb') as f:
|
||||
# Read Header https://github.com/AlexeyAB/darknet/issues/2914#issuecomment-496675346
|
||||
self.version = np.fromfile(f, dtype=np.int32, count=3) # (int32) version info: major, minor, revision
|
||||
self.seen = np.fromfile(f, dtype=np.int64, count=1) # (int64) number of images seen during training
|
||||
|
||||
weights = np.fromfile(f, dtype=np.float32) # the rest are weights
|
||||
|
||||
ptr = 0
|
||||
for i, (mdef, module) in enumerate(zip(self.module_defs[:cutoff], self.module_list[:cutoff])):
|
||||
if mdef['type'] == 'convolutional':
|
||||
conv = module[0]
|
||||
if mdef['batch_normalize']:
|
||||
# Load BN bias, weights, running mean and running variance
|
||||
bn = module[1]
|
||||
nb = bn.bias.numel() # number of biases
|
||||
# Bias
|
||||
bn.bias.data.copy_(torch.from_numpy(weights[ptr:ptr + nb]).view_as(bn.bias))
|
||||
ptr += nb
|
||||
# Weight
|
||||
bn.weight.data.copy_(torch.from_numpy(weights[ptr:ptr + nb]).view_as(bn.weight))
|
||||
ptr += nb
|
||||
# Running Mean
|
||||
bn.running_mean.data.copy_(torch.from_numpy(weights[ptr:ptr + nb]).view_as(bn.running_mean))
|
||||
ptr += nb
|
||||
# Running Var
|
||||
bn.running_var.data.copy_(torch.from_numpy(weights[ptr:ptr + nb]).view_as(bn.running_var))
|
||||
ptr += nb
|
||||
else:
|
||||
# Load conv. bias
|
||||
nb = conv.bias.numel()
|
||||
conv_b = torch.from_numpy(weights[ptr:ptr + nb]).view_as(conv.bias)
|
||||
conv.bias.data.copy_(conv_b)
|
||||
ptr += nb
|
||||
# Load conv. weights
|
||||
nw = conv.weight.numel() # number of weights
|
||||
conv.weight.data.copy_(torch.from_numpy(weights[ptr:ptr + nw]).view_as(conv.weight))
|
||||
ptr += nw
|
||||
|
||||
|
||||
def save_weights(self, path='model.weights', cutoff=-1):
|
||||
# Converts a PyTorch model to Darket format (*.pt to *.weights)
|
||||
# Note: Does not work if model.fuse() is applied
|
||||
with open(path, 'wb') as f:
|
||||
# Write Header https://github.com/AlexeyAB/darknet/issues/2914#issuecomment-496675346
|
||||
self.version.tofile(f) # (int32) version info: major, minor, revision
|
||||
self.seen.tofile(f) # (int64) number of images seen during training
|
||||
|
||||
# Iterate through layers
|
||||
for i, (mdef, module) in enumerate(zip(self.module_defs[:cutoff], self.module_list[:cutoff])):
|
||||
if mdef['type'] == 'convolutional':
|
||||
conv_layer = module[0]
|
||||
# If batch norm, load bn first
|
||||
if mdef['batch_normalize']:
|
||||
bn_layer = module[1]
|
||||
bn_layer.bias.data.cpu().numpy().tofile(f)
|
||||
bn_layer.weight.data.cpu().numpy().tofile(f)
|
||||
bn_layer.running_mean.data.cpu().numpy().tofile(f)
|
||||
bn_layer.running_var.data.cpu().numpy().tofile(f)
|
||||
# Load conv bias
|
||||
else:
|
||||
conv_layer.bias.data.cpu().numpy().tofile(f)
|
||||
# Load conv weights
|
||||
conv_layer.weight.data.cpu().numpy().tofile(f)
|
||||
|
||||
|
||||
def convert(cfg='cfg/yolov3-spp.cfg', weights='weights/yolov3-spp.weights'):
|
||||
# Converts between PyTorch and Darknet format per extension (i.e. *.weights convert to *.pt and vice versa)
|
||||
# from models import *; convert('cfg/yolov3-spp.cfg', 'weights/yolov3-spp.weights')
|
||||
|
||||
# Initialize model
|
||||
model = Darknet(cfg)
|
||||
|
||||
# Load weights and save
|
||||
if weights.endswith('.pt'): # if PyTorch format
|
||||
model.load_state_dict(torch.load(weights, map_location='cpu')['model'])
|
||||
target = weights.rsplit('.', 1)[0] + '.weights'
|
||||
save_weights(model, path=target, cutoff=-1)
|
||||
print("Success: converted '%s' to '%s'" % (weights, target))
|
||||
|
||||
elif weights.endswith('.weights'): # darknet format
|
||||
_ = load_darknet_weights(model, weights)
|
||||
|
||||
chkpt = {'epoch': -1,
|
||||
'best_fitness': None,
|
||||
'training_results': None,
|
||||
'model': model.state_dict(),
|
||||
'optimizer': None}
|
||||
|
||||
target = weights.rsplit('.', 1)[0] + '.pt'
|
||||
torch.save(chkpt, target)
|
||||
print("Success: converted '%s' to '%s'" % (weights, target))
|
||||
|
||||
else:
|
||||
print('Error: extension not supported.')
|
||||
|
||||
|
||||
def attempt_download(weights):
|
||||
# Attempt to download pretrained weights if not found locally
|
||||
weights = weights.strip().replace("'", '')
|
||||
msg = weights + ' missing, try downloading from https://drive.google.com/open?id=1LezFG5g3BCW6iYaV89B2i64cqEUZD7e0'
|
||||
|
||||
if len(weights) > 0 and not os.path.isfile(weights):
|
||||
d = {'yolov3-spp.weights': '16lYS4bcIdM2HdmyJBVDOvt3Trx6N3W2R',
|
||||
'yolov3.weights': '1uTlyDWlnaqXcsKOktP5aH_zRDbfcDp-y',
|
||||
'yolov3-tiny.weights': '1CCF-iNIIkYesIDzaPvdwlcf7H9zSsKZQ',
|
||||
'yolov3-spp.pt': '1f6Ovy3BSq2wYq4UfvFUpxJFNDFfrIDcR',
|
||||
'yolov3.pt': '1SHNFyoe5Ni8DajDNEqgB2oVKBb_NoEad',
|
||||
'yolov3-tiny.pt': '10m_3MlpQwRtZetQxtksm9jqHrPTHZ6vo',
|
||||
'darknet53.conv.74': '1WUVBid-XuoUBmvzBVUCBl_ELrzqwA8dJ',
|
||||
'yolov3-tiny.conv.15': '1Bw0kCpplxUqyRYAJr9RY9SGnOJbo9nEj',
|
||||
'yolov3-spp-ultralytics.pt': '1UcR-zVoMs7DH5dj3N1bswkiQTA4dmKF4'}
|
||||
|
||||
file = Path(weights).name
|
||||
if file in d:
|
||||
r = gdrive_download(id=d[file], name=weights)
|
||||
else: # download from pjreddie.com
|
||||
url = 'https://pjreddie.com/media/files/' + file
|
||||
print('Downloading ' + url)
|
||||
r = os.system('curl -f ' + url + ' -o ' + weights)
|
||||
|
||||
# Error check
|
||||
if not (r == 0 and os.path.exists(weights) and os.path.getsize(weights) > 1E6): # weights exist and > 1MB
|
||||
os.system('rm ' + weights) # remove partial downloads
|
||||
raise Exception(msg)
|
||||
0
models/__init__.py
Normal file
0
models/__init__.py
Normal file
252
models/common.py
Normal file
252
models/common.py
Normal file
@ -0,0 +1,252 @@
|
||||
# This file contains modules common to various models
|
||||
|
||||
import math
|
||||
|
||||
import numpy as np
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
from PIL import Image, ImageDraw
|
||||
|
||||
from utils.datasets import letterbox
|
||||
from utils.general import non_max_suppression, make_divisible, scale_coords, xyxy2xywh
|
||||
from utils.plots import color_list
|
||||
|
||||
|
||||
def autopad(k, p=None): # kernel, padding
|
||||
# Pad to 'same'
|
||||
if p is None:
|
||||
p = k // 2 if isinstance(k, int) else [x // 2 for x in k] # auto-pad
|
||||
return p
|
||||
|
||||
|
||||
def DWConv(c1, c2, k=1, s=1, act=True):
|
||||
# Depthwise convolution
|
||||
return Conv(c1, c2, k, s, g=math.gcd(c1, c2), act=act)
|
||||
|
||||
|
||||
class Conv(nn.Module):
|
||||
# Standard convolution
|
||||
def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True): # ch_in, ch_out, kernel, stride, padding, groups
|
||||
super(Conv, self).__init__()
|
||||
self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p), groups=g, bias=False)
|
||||
self.bn = nn.BatchNorm2d(c2)
|
||||
self.act = nn.LeakyReLU(0.1) if act else nn.Identity()
|
||||
|
||||
def forward(self, x):
|
||||
return self.act(self.bn(self.conv(x)))
|
||||
|
||||
def fuseforward(self, x):
|
||||
return self.act(self.conv(x))
|
||||
|
||||
|
||||
class Bottleneck(nn.Module):
|
||||
# Standard bottleneck
|
||||
def __init__(self, c1, c2, shortcut=True, g=1, e=0.5): # ch_in, ch_out, shortcut, groups, expansion
|
||||
super(Bottleneck, self).__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):
|
||||
return x + self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x))
|
||||
|
||||
|
||||
class BottleneckCSP(nn.Module):
|
||||
# CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks
|
||||
def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion
|
||||
super(BottleneckCSP, self).__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.LeakyReLU(0.1, inplace=True)
|
||||
self.m = nn.Sequential(*[Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)])
|
||||
|
||||
def forward(self, x):
|
||||
y1 = self.cv3(self.m(self.cv1(x)))
|
||||
y2 = self.cv2(x)
|
||||
return self.cv4(self.act(self.bn(torch.cat((y1, y2), dim=1))))
|
||||
|
||||
|
||||
class SPP(nn.Module):
|
||||
# Spatial pyramid pooling layer used in YOLOv3-SPP
|
||||
def __init__(self, c1, c2, k=(5, 9, 13)):
|
||||
super(SPP, self).__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):
|
||||
x = self.cv1(x)
|
||||
return self.cv2(torch.cat([x] + [m(x) for m in self.m], 1))
|
||||
|
||||
|
||||
class Focus(nn.Module):
|
||||
# Focus wh information into c-space
|
||||
def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True): # ch_in, ch_out, kernel, stride, padding, groups
|
||||
super(Focus, self).__init__()
|
||||
self.conv = Conv(c1 * 4, c2, k, s, p, g, act)
|
||||
|
||||
def forward(self, x): # x(b,c,w,h) -> y(b,4c,w/2,h/2)
|
||||
return self.conv(torch.cat([x[..., ::2, ::2], x[..., 1::2, ::2], x[..., ::2, 1::2], x[..., 1::2, 1::2]], 1))
|
||||
|
||||
|
||||
class Concat(nn.Module):
|
||||
# Concatenate a list of tensors along dimension
|
||||
def __init__(self, dimension=1):
|
||||
super(Concat, self).__init__()
|
||||
self.d = dimension
|
||||
|
||||
def forward(self, x):
|
||||
return torch.cat(x, self.d)
|
||||
|
||||
|
||||
class NMS(nn.Module):
|
||||
# Non-Maximum Suppression (NMS) module
|
||||
conf = 0.25 # confidence threshold
|
||||
iou = 0.45 # IoU threshold
|
||||
classes = None # (optional list) filter by class
|
||||
|
||||
def __init__(self):
|
||||
super(NMS, self).__init__()
|
||||
|
||||
def forward(self, x):
|
||||
return non_max_suppression(x[0], conf_thres=self.conf, iou_thres=self.iou, classes=self.classes)
|
||||
|
||||
|
||||
class autoShape(nn.Module):
|
||||
# input-robust model wrapper for passing cv2/np/PIL/torch inputs. Includes preprocessing, inference and NMS
|
||||
img_size = 640 # inference size (pixels)
|
||||
conf = 0.25 # NMS confidence threshold
|
||||
iou = 0.45 # NMS IoU threshold
|
||||
classes = None # (optional list) filter by class
|
||||
|
||||
def __init__(self, model):
|
||||
super(autoShape, self).__init__()
|
||||
self.model = model.eval()
|
||||
|
||||
def forward(self, imgs, size=640, augment=False, profile=False):
|
||||
# supports inference from various sources. For height=720, width=1280, RGB images example inputs are:
|
||||
# opencv: imgs = cv2.imread('image.jpg')[:,:,::-1] # HWC BGR to RGB x(720,1280,3)
|
||||
# PIL: imgs = Image.open('image.jpg') # HWC x(720,1280,3)
|
||||
# numpy: imgs = np.zeros((720,1280,3)) # HWC
|
||||
# torch: imgs = torch.zeros(16,3,720,1280) # BCHW
|
||||
# multiple: imgs = [Image.open('image1.jpg'), Image.open('image2.jpg'), ...] # list of images
|
||||
|
||||
p = next(self.model.parameters()) # for device and type
|
||||
if isinstance(imgs, torch.Tensor): # torch
|
||||
return self.model(imgs.to(p.device).type_as(p), augment, profile) # inference
|
||||
|
||||
# Pre-process
|
||||
if not isinstance(imgs, list):
|
||||
imgs = [imgs]
|
||||
shape0, shape1 = [], [] # image and inference shapes
|
||||
batch = range(len(imgs)) # batch size
|
||||
for i in batch:
|
||||
imgs[i] = np.array(imgs[i]) # to numpy
|
||||
if imgs[i].shape[0] < 5: # image in CHW
|
||||
imgs[i] = imgs[i].transpose((1, 2, 0)) # reverse dataloader .transpose(2, 0, 1)
|
||||
imgs[i] = imgs[i][:, :, :3] if imgs[i].ndim == 3 else np.tile(imgs[i][:, :, None], 3) # enforce 3ch input
|
||||
s = imgs[i].shape[:2] # HWC
|
||||
shape0.append(s) # image shape
|
||||
g = (size / max(s)) # gain
|
||||
shape1.append([y * g for y in s])
|
||||
shape1 = [make_divisible(x, int(self.stride.max())) for x in np.stack(shape1, 0).max(0)] # inference shape
|
||||
x = [letterbox(imgs[i], new_shape=shape1, auto=False)[0] for i in batch] # pad
|
||||
x = np.stack(x, 0) if batch[-1] else x[0][None] # stack
|
||||
x = np.ascontiguousarray(x.transpose((0, 3, 1, 2))) # BHWC to BCHW
|
||||
x = torch.from_numpy(x).to(p.device).type_as(p) / 255. # uint8 to fp16/32
|
||||
|
||||
# Inference
|
||||
with torch.no_grad():
|
||||
y = self.model(x, augment, profile)[0] # forward
|
||||
y = non_max_suppression(y, conf_thres=self.conf, iou_thres=self.iou, classes=self.classes) # NMS
|
||||
|
||||
# Post-process
|
||||
for i in batch:
|
||||
if y[i] is not None:
|
||||
y[i][:, :4] = scale_coords(shape1, y[i][:, :4], shape0[i])
|
||||
|
||||
return Detections(imgs, y, self.names)
|
||||
|
||||
|
||||
class Detections:
|
||||
# detections class for YOLOv5 inference results
|
||||
def __init__(self, imgs, pred, names=None):
|
||||
super(Detections, self).__init__()
|
||||
self.imgs = imgs # list of images as numpy arrays
|
||||
self.pred = pred # list of tensors pred[0] = (xyxy, conf, cls)
|
||||
self.names = names # class names
|
||||
self.xyxy = pred # xyxy pixels
|
||||
self.xywh = [xyxy2xywh(x) for x in pred] # xywh pixels
|
||||
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 imgs] # normalizations
|
||||
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)
|
||||
|
||||
def display(self, pprint=False, show=False, save=False):
|
||||
colors = color_list()
|
||||
for i, (img, pred) in enumerate(zip(self.imgs, self.pred)):
|
||||
str = f'Image {i + 1}/{len(self.pred)}: {img.shape[0]}x{img.shape[1]} '
|
||||
if pred is not None:
|
||||
for c in pred[:, -1].unique():
|
||||
n = (pred[:, -1] == c).sum() # detections per class
|
||||
str += f'{n} {self.names[int(c)]}s, ' # add to string
|
||||
if show or save:
|
||||
img = Image.fromarray(img.astype(np.uint8)) if isinstance(img, np.ndarray) else img # from np
|
||||
for *box, conf, cls in pred: # xyxy, confidence, class
|
||||
# str += '%s %.2f, ' % (names[int(cls)], conf) # label
|
||||
ImageDraw.Draw(img).rectangle(box, width=4, outline=colors[int(cls) % 10]) # plot
|
||||
if save:
|
||||
f = f'results{i}.jpg'
|
||||
str += f"saved to '{f}'"
|
||||
img.save(f) # save
|
||||
if show:
|
||||
img.show(f'Image {i}') # show
|
||||
if pprint:
|
||||
print(str)
|
||||
|
||||
def print(self):
|
||||
self.display(pprint=True) # print results
|
||||
|
||||
def show(self):
|
||||
self.display(show=True) # show results
|
||||
|
||||
def save(self):
|
||||
self.display(save=True) # save results
|
||||
|
||||
def __len__(self):
|
||||
return self.n
|
||||
|
||||
def tolist(self):
|
||||
# return a list of Detections objects, i.e. 'for result in results.tolist():'
|
||||
x = [Detections([self.imgs[i]], [self.pred[i]], self.names) for i in range(self.n)]
|
||||
for d in x:
|
||||
for k in ['imgs', 'pred', 'xyxy', 'xyxyn', 'xywh', 'xywhn']:
|
||||
setattr(d, k, getattr(d, k)[0]) # pop out of list
|
||||
return x
|
||||
|
||||
|
||||
class Flatten(nn.Module):
|
||||
# Use after nn.AdaptiveAvgPool2d(1) to remove last 2 dimensions
|
||||
@staticmethod
|
||||
def forward(x):
|
||||
return x.view(x.size(0), -1)
|
||||
|
||||
|
||||
class Classify(nn.Module):
|
||||
# Classification head, i.e. x(b,c1,20,20) to x(b,c2)
|
||||
def __init__(self, c1, c2, k=1, s=1, p=None, g=1): # ch_in, ch_out, kernel, stride, padding, groups
|
||||
super(Classify, self).__init__()
|
||||
self.aap = nn.AdaptiveAvgPool2d(1) # to x(b,c1,1,1)
|
||||
self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p), groups=g, bias=False) # to x(b,c2,1,1)
|
||||
self.flat = Flatten()
|
||||
|
||||
def forward(self, x):
|
||||
z = torch.cat([self.aap(y) for y in (x if isinstance(x, list) else [x])], 1) # cat if list
|
||||
return self.flat(self.conv(z)) # flatten to x(b,c2)
|
||||
152
models/experimental.py
Normal file
152
models/experimental.py
Normal file
@ -0,0 +1,152 @@
|
||||
# This file contains experimental modules
|
||||
|
||||
import numpy as np
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
|
||||
from models.common import Conv, DWConv
|
||||
from utils.google_utils import attempt_download
|
||||
|
||||
|
||||
class CrossConv(nn.Module):
|
||||
# Cross Convolution Downsample
|
||||
def __init__(self, c1, c2, k=3, s=1, g=1, e=1.0, shortcut=False):
|
||||
# ch_in, ch_out, kernel, stride, groups, expansion, shortcut
|
||||
super(CrossConv, self).__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):
|
||||
return x + self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x))
|
||||
|
||||
|
||||
class C3(nn.Module):
|
||||
# Cross Convolution CSP
|
||||
def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion
|
||||
super(C3, self).__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.LeakyReLU(0.1, inplace=True)
|
||||
self.m = nn.Sequential(*[CrossConv(c_, c_, 3, 1, g, 1.0, shortcut) for _ in range(n)])
|
||||
|
||||
def forward(self, x):
|
||||
y1 = self.cv3(self.m(self.cv1(x)))
|
||||
y2 = self.cv2(x)
|
||||
return self.cv4(self.act(self.bn(torch.cat((y1, y2), dim=1))))
|
||||
|
||||
|
||||
class Sum(nn.Module):
|
||||
# Weighted sum of 2 or more layers https://arxiv.org/abs/1911.09070
|
||||
def __init__(self, n, weight=False): # n: number of inputs
|
||||
super(Sum, self).__init__()
|
||||
self.weight = weight # apply weights boolean
|
||||
self.iter = range(n - 1) # iter object
|
||||
if weight:
|
||||
self.w = nn.Parameter(-torch.arange(1., n) / 2, requires_grad=True) # layer weights
|
||||
|
||||
def forward(self, x):
|
||||
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 GhostConv(nn.Module):
|
||||
# Ghost Convolution https://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
|
||||
super(GhostConv, self).__init__()
|
||||
c_ = c2 // 2 # hidden channels
|
||||
self.cv1 = Conv(c1, c_, k, s, None, g, act)
|
||||
self.cv2 = Conv(c_, c_, 5, 1, None, c_, act)
|
||||
|
||||
def forward(self, x):
|
||||
y = self.cv1(x)
|
||||
return torch.cat([y, self.cv2(y)], 1)
|
||||
|
||||
|
||||
class GhostBottleneck(nn.Module):
|
||||
# Ghost Bottleneck https://github.com/huawei-noah/ghostnet
|
||||
def __init__(self, c1, c2, k, s):
|
||||
super(GhostBottleneck, self).__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):
|
||||
return self.conv(x) + self.shortcut(x)
|
||||
|
||||
|
||||
class MixConv2d(nn.Module):
|
||||
# Mixed Depthwise Conv https://arxiv.org/abs/1907.09595
|
||||
def __init__(self, c1, c2, k=(1, 3), s=1, equal_ch=True):
|
||||
super(MixConv2d, self).__init__()
|
||||
groups = len(k)
|
||||
if equal_ch: # equal c_ per group
|
||||
i = torch.linspace(0, groups - 1E-6, c2).floor() # c2 indices
|
||||
c_ = [(i == g).sum() for g in range(groups)] # intermediate channels
|
||||
else: # equal weight.numel() per group
|
||||
b = [c2] + [0] * groups
|
||||
a = np.eye(groups + 1, groups, 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_[g]), k[g], s, k[g] // 2, bias=False) for g in range(groups)])
|
||||
self.bn = nn.BatchNorm2d(c2)
|
||||
self.act = nn.LeakyReLU(0.1, inplace=True)
|
||||
|
||||
def forward(self, x):
|
||||
return x + self.act(self.bn(torch.cat([m(x) for m in self.m], 1)))
|
||||
|
||||
|
||||
class Ensemble(nn.ModuleList):
|
||||
# Ensemble of models
|
||||
def __init__(self):
|
||||
super(Ensemble, self).__init__()
|
||||
|
||||
def forward(self, x, augment=False):
|
||||
y = []
|
||||
for module in self:
|
||||
y.append(module(x, augment)[0])
|
||||
# y = torch.stack(y).max(0)[0] # max ensemble
|
||||
# y = torch.cat(y, 1) # nms ensemble
|
||||
y = torch.stack(y).mean(0) # mean ensemble
|
||||
return y, None # inference, train output
|
||||
|
||||
|
||||
def attempt_load(weights, map_location=None):
|
||||
# Loads an ensemble of models weights=[a,b,c] or a single model weights=[a] or weights=a
|
||||
model = Ensemble()
|
||||
for w in weights if isinstance(weights, list) else [weights]:
|
||||
attempt_download(w)
|
||||
model.append(torch.load(w, map_location=map_location)['model'].float().fuse().eval()) # load FP32 model
|
||||
|
||||
# Compatibility updates
|
||||
for m in model.modules():
|
||||
if type(m) in [nn.Hardswish, nn.LeakyReLU, nn.ReLU, nn.ReLU6]:
|
||||
m.inplace = True # pytorch 1.7.0 compatibility
|
||||
elif type(m) is Conv:
|
||||
m._non_persistent_buffers_set = set() # pytorch 1.6.0 compatibility
|
||||
|
||||
if len(model) == 1:
|
||||
return model[-1] # return model
|
||||
else:
|
||||
print('Ensemble created with %s\n' % weights)
|
||||
for k in ['names', 'stride']:
|
||||
setattr(model, k, getattr(model[-1], k))
|
||||
return model # return ensemble
|
||||
94
models/export.py
Normal file
94
models/export.py
Normal file
@ -0,0 +1,94 @@
|
||||
"""Exports a YOLOv5 *.pt model to ONNX and TorchScript formats
|
||||
|
||||
Usage:
|
||||
$ export PYTHONPATH="$PWD" && python models/export.py --weights ./weights/yolov3.pt --img 640 --batch 1
|
||||
"""
|
||||
|
||||
import argparse
|
||||
import sys
|
||||
import time
|
||||
|
||||
sys.path.append('./') # to run '$ python *.py' files in subdirectories
|
||||
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
|
||||
import models
|
||||
from models.experimental import attempt_load
|
||||
from utils.activations import Hardswish
|
||||
from utils.general import set_logging, check_img_size
|
||||
|
||||
if __name__ == '__main__':
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument('--weights', type=str, default='./yolov3.pt', help='weights path') # from yolov3/models/
|
||||
parser.add_argument('--img-size', nargs='+', type=int, default=[640, 640], help='image size') # height, width
|
||||
parser.add_argument('--batch-size', type=int, default=1, help='batch size')
|
||||
opt = parser.parse_args()
|
||||
opt.img_size *= 2 if len(opt.img_size) == 1 else 1 # expand
|
||||
print(opt)
|
||||
set_logging()
|
||||
t = time.time()
|
||||
|
||||
# Load PyTorch model
|
||||
model = attempt_load(opt.weights, map_location=torch.device('cpu')) # load FP32 model
|
||||
labels = model.names
|
||||
|
||||
# Checks
|
||||
gs = int(max(model.stride)) # grid size (max stride)
|
||||
opt.img_size = [check_img_size(x, gs) for x in opt.img_size] # verify img_size are gs-multiples
|
||||
|
||||
# Input
|
||||
img = torch.zeros(opt.batch_size, 3, *opt.img_size) # image size(1,3,320,192) iDetection
|
||||
|
||||
# Update model
|
||||
for k, m in model.named_modules():
|
||||
m._non_persistent_buffers_set = set() # pytorch 1.6.0 compatibility
|
||||
if isinstance(m, models.common.Conv) and isinstance(m.act, nn.Hardswish):
|
||||
m.act = Hardswish() # assign activation
|
||||
# if isinstance(m, models.yolo.Detect):
|
||||
# m.forward = m.forward_export # assign forward (optional)
|
||||
model.model[-1].export = True # set Detect() layer export=True
|
||||
y = model(img) # dry run
|
||||
|
||||
# TorchScript export
|
||||
try:
|
||||
print('\nStarting TorchScript export with torch %s...' % torch.__version__)
|
||||
f = opt.weights.replace('.pt', '.torchscript.pt') # filename
|
||||
ts = torch.jit.trace(model, img)
|
||||
ts.save(f)
|
||||
print('TorchScript export success, saved as %s' % f)
|
||||
except Exception as e:
|
||||
print('TorchScript export failure: %s' % e)
|
||||
|
||||
# ONNX export
|
||||
try:
|
||||
import onnx
|
||||
|
||||
print('\nStarting ONNX export with onnx %s...' % onnx.__version__)
|
||||
f = opt.weights.replace('.pt', '.onnx') # filename
|
||||
torch.onnx.export(model, img, f, verbose=False, opset_version=12, input_names=['images'],
|
||||
output_names=['classes', 'boxes'] if y is None else ['output'])
|
||||
|
||||
# Checks
|
||||
onnx_model = onnx.load(f) # load onnx model
|
||||
onnx.checker.check_model(onnx_model) # check onnx model
|
||||
# print(onnx.helper.printable_graph(onnx_model.graph)) # print a human readable model
|
||||
print('ONNX export success, saved as %s' % f)
|
||||
except Exception as e:
|
||||
print('ONNX export failure: %s' % e)
|
||||
|
||||
# CoreML export
|
||||
try:
|
||||
import coremltools as ct
|
||||
|
||||
print('\nStarting CoreML export with coremltools %s...' % ct.__version__)
|
||||
# convert model from torchscript and apply pixel scaling as per detect.py
|
||||
model = ct.convert(ts, inputs=[ct.ImageType(name='image', shape=img.shape, scale=1 / 255.0, bias=[0, 0, 0])])
|
||||
f = opt.weights.replace('.pt', '.mlmodel') # filename
|
||||
model.save(f)
|
||||
print('CoreML export success, saved as %s' % f)
|
||||
except Exception as e:
|
||||
print('CoreML export failure: %s' % e)
|
||||
|
||||
# Finish
|
||||
print('\nExport complete (%.2fs). Visualize with https://github.com/lutzroeder/netron.' % (time.time() - t))
|
||||
287
models/yolo.py
Normal file
287
models/yolo.py
Normal file
@ -0,0 +1,287 @@
|
||||
import argparse
|
||||
import logging
|
||||
import math
|
||||
import sys
|
||||
from copy import deepcopy
|
||||
from pathlib import Path
|
||||
|
||||
sys.path.append('./') # to run '$ python *.py' files in subdirectories
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
|
||||
from models.common import Conv, Bottleneck, SPP, DWConv, Focus, BottleneckCSP, Concat, NMS, autoShape
|
||||
from models.experimental import MixConv2d, CrossConv, C3
|
||||
from utils.autoanchor import check_anchor_order
|
||||
from utils.general import make_divisible, check_file, set_logging
|
||||
from utils.torch_utils import time_synchronized, fuse_conv_and_bn, model_info, scale_img, initialize_weights, \
|
||||
select_device, copy_attr
|
||||
|
||||
try:
|
||||
import thop # for FLOPS computation
|
||||
except ImportError:
|
||||
thop = None
|
||||
|
||||
|
||||
class Detect(nn.Module):
|
||||
stride = None # strides computed during build
|
||||
export = False # onnx export
|
||||
|
||||
def __init__(self, nc=80, anchors=(), ch=()): # detection layer
|
||||
super(Detect, self).__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.zeros(1)] * self.nl # init grid
|
||||
a = torch.tensor(anchors).float().view(self.nl, -1, 2)
|
||||
self.register_buffer('anchors', a) # shape(nl,na,2)
|
||||
self.register_buffer('anchor_grid', a.clone().view(self.nl, 1, -1, 1, 1, 2)) # shape(nl,1,na,1,1,2)
|
||||
self.m = nn.ModuleList(nn.Conv2d(x, self.no * self.na, 1) for x in ch) # output conv
|
||||
|
||||
def forward(self, x):
|
||||
# x = x.copy() # for profiling
|
||||
z = [] # inference output
|
||||
self.training |= self.export
|
||||
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.grid[i].shape[2:4] != x[i].shape[2:4]:
|
||||
self.grid[i] = self._make_grid(nx, ny).to(x[i].device)
|
||||
|
||||
y = x[i].sigmoid()
|
||||
y[..., 0:2] = (y[..., 0:2] * 2. - 0.5 + self.grid[i].to(x[i].device)) * self.stride[i] # xy
|
||||
y[..., 2:4] = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i] # wh
|
||||
z.append(y.view(bs, -1, self.no))
|
||||
|
||||
return x if self.training else (torch.cat(z, 1), x)
|
||||
|
||||
@staticmethod
|
||||
def _make_grid(nx=20, ny=20):
|
||||
yv, xv = torch.meshgrid([torch.arange(ny), torch.arange(nx)])
|
||||
return torch.stack((xv, yv), 2).view((1, 1, ny, nx, 2)).float()
|
||||
|
||||
|
||||
class Model(nn.Module):
|
||||
def __init__(self, cfg='yolov3.yaml', ch=3, nc=None): # model, input channels, number of classes
|
||||
super(Model, self).__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('Overriding model.yaml nc=%g with nc=%g' % (self.yaml['nc'], nc))
|
||||
self.yaml['nc'] = nc # override yaml value
|
||||
self.model, self.save = parse_model(deepcopy(self.yaml), ch=[ch]) # model, savelist, ch_out
|
||||
# print([x.shape for x in self.forward(torch.zeros(1, ch, 64, 64))])
|
||||
|
||||
# Build strides, anchors
|
||||
m = self.model[-1] # Detect()
|
||||
if isinstance(m, Detect):
|
||||
s = 128 # 2x min stride
|
||||
m.stride = torch.tensor([s / x.shape[-2] for x in self.forward(torch.zeros(1, ch, s, s))]) # forward
|
||||
m.anchors /= m.stride.view(-1, 1, 1)
|
||||
check_anchor_order(m)
|
||||
self.stride = m.stride
|
||||
self._initialize_biases() # only run once
|
||||
# print('Strides: %s' % m.stride.tolist())
|
||||
|
||||
# Init weights, biases
|
||||
initialize_weights(self)
|
||||
self.info()
|
||||
logger.info('')
|
||||
|
||||
def forward(self, x, augment=False, profile=False):
|
||||
if augment:
|
||||
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)
|
||||
yi = self.forward_once(xi)[0] # forward
|
||||
# cv2.imwrite('img%g.jpg' % s, 255 * xi[0].numpy().transpose((1, 2, 0))[:, :, ::-1]) # save
|
||||
yi[..., :4] /= si # de-scale
|
||||
if fi == 2:
|
||||
yi[..., 1] = img_size[0] - yi[..., 1] # de-flip ud
|
||||
elif fi == 3:
|
||||
yi[..., 0] = img_size[1] - yi[..., 0] # de-flip lr
|
||||
y.append(yi)
|
||||
return torch.cat(y, 1), None # augmented inference, train
|
||||
else:
|
||||
return self.forward_once(x, profile) # single-scale inference, train
|
||||
|
||||
def forward_once(self, x, profile=False):
|
||||
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:
|
||||
o = thop.profile(m, inputs=(x,), verbose=False)[0] / 1E9 * 2 if thop else 0 # FLOPS
|
||||
t = time_synchronized()
|
||||
for _ in range(10):
|
||||
_ = m(x)
|
||||
dt.append((time_synchronized() - t) * 100)
|
||||
print('%10.1f%10.0f%10.1fms %-40s' % (o, m.np, dt[-1], m.type))
|
||||
|
||||
x = m(x) # run
|
||||
y.append(x if m.i in self.save else None) # save output
|
||||
|
||||
if profile:
|
||||
print('%.1fms total' % sum(dt))
|
||||
return x
|
||||
|
||||
def _initialize_biases(self, cf=None): # initialize biases into Detect(), cf is class frequency
|
||||
# https://arxiv.org/abs/1708.02002 section 3.3
|
||||
# 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[:, 4] += math.log(8 / (640 / s) ** 2) # obj (8 objects per 640 image)
|
||||
b[:, 5:] += math.log(0.6 / (m.nc - 0.99)) if cf is None else torch.log(cf / cf.sum()) # cls
|
||||
mi.bias = torch.nn.Parameter(b.view(-1), requires_grad=True)
|
||||
|
||||
def _print_biases(self):
|
||||
m = self.model[-1] # Detect() module
|
||||
for mi in m.m: # from
|
||||
b = mi.bias.detach().view(m.na, -1).T # conv.bias(255) to (3,85)
|
||||
print(('%6g Conv2d.bias:' + '%10.3g' * 6) % (mi.weight.shape[1], *b[:5].mean(1).tolist(), b[5:].mean()))
|
||||
|
||||
# def _print_weights(self):
|
||||
# for m in self.model.modules():
|
||||
# if type(m) is Bottleneck:
|
||||
# print('%10.3g' % (m.w.detach().sigmoid() * 2)) # shortcut weights
|
||||
|
||||
def fuse(self): # fuse model Conv2d() + BatchNorm2d() layers
|
||||
print('Fusing layers... ')
|
||||
for m in self.model.modules():
|
||||
if type(m) is Conv and hasattr(m, 'bn'):
|
||||
m.conv = fuse_conv_and_bn(m.conv, m.bn) # update conv
|
||||
delattr(m, 'bn') # remove batchnorm
|
||||
m.forward = m.fuseforward # update forward
|
||||
self.info()
|
||||
return self
|
||||
|
||||
def nms(self, mode=True): # add or remove NMS module
|
||||
present = type(self.model[-1]) is NMS # last layer is NMS
|
||||
if mode and not present:
|
||||
print('Adding NMS... ')
|
||||
m = NMS() # module
|
||||
m.f = -1 # from
|
||||
m.i = self.model[-1].i + 1 # index
|
||||
self.model.add_module(name='%s' % m.i, module=m) # add
|
||||
self.eval()
|
||||
elif not mode and present:
|
||||
print('Removing NMS... ')
|
||||
self.model = self.model[:-1] # remove
|
||||
return self
|
||||
|
||||
def autoshape(self): # add autoShape module
|
||||
print('Adding autoShape... ')
|
||||
m = autoShape(self) # wrap model
|
||||
copy_attr(m, self, include=('yaml', 'nc', 'hyp', 'names', 'stride'), exclude=()) # copy attributes
|
||||
return m
|
||||
|
||||
def info(self, verbose=False, img_size=640): # print model information
|
||||
model_info(self, verbose, img_size)
|
||||
|
||||
|
||||
def parse_model(d, ch): # model_dict, input_channels(3)
|
||||
logger.info('\n%3s%18s%3s%10s %-40s%-30s' % ('', 'from', 'n', 'params', 'module', 'arguments'))
|
||||
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 = 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:
|
||||
pass
|
||||
|
||||
n = max(round(n * gd), 1) if n > 1 else n # depth gain
|
||||
if m in [Conv, Bottleneck, SPP, DWConv, MixConv2d, Focus, CrossConv, BottleneckCSP, C3]:
|
||||
c1, c2 = ch[f], args[0]
|
||||
|
||||
# Normal
|
||||
# if i > 0 and args[0] != no: # channel expansion factor
|
||||
# ex = 1.75 # exponential (default 2.0)
|
||||
# e = math.log(c2 / ch[1]) / math.log(2)
|
||||
# c2 = int(ch[1] * ex ** e)
|
||||
# if m != Focus:
|
||||
|
||||
c2 = make_divisible(c2 * gw, 8) if c2 != no else c2
|
||||
|
||||
# Experimental
|
||||
# if i > 0 and args[0] != no: # channel expansion factor
|
||||
# ex = 1 + gw # exponential (default 2.0)
|
||||
# ch1 = 32 # ch[1]
|
||||
# e = math.log(c2 / ch1) / math.log(2) # level 1-n
|
||||
# c2 = int(ch1 * ex ** e)
|
||||
# if m != Focus:
|
||||
# c2 = make_divisible(c2, 8) if c2 != no else c2
|
||||
|
||||
args = [c1, c2, *args[1:]]
|
||||
if m in [BottleneckCSP, C3]:
|
||||
args.insert(2, n)
|
||||
n = 1
|
||||
elif m is nn.BatchNorm2d:
|
||||
args = [ch[f]]
|
||||
elif m is nn.ZeroPad2d:
|
||||
args = [args]
|
||||
c2 = ch[f]
|
||||
elif m is Concat:
|
||||
c2 = sum([ch[-1 if x == -1 else x + 1] for x in f])
|
||||
elif m is Detect:
|
||||
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)
|
||||
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('%3s%18s%3s%10.0f %-40s%-30s' % (i, f, n, np, t, args)) # 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 nn.Sequential(*layers), sorted(save)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument('--cfg', type=str, default='yolov3.yaml', help='model.yaml')
|
||||
parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
|
||||
opt = parser.parse_args()
|
||||
opt.cfg = check_file(opt.cfg) # check file
|
||||
set_logging()
|
||||
device = select_device(opt.device)
|
||||
|
||||
# Create model
|
||||
model = Model(opt.cfg).to(device)
|
||||
model.train()
|
||||
|
||||
# Profile
|
||||
# img = torch.rand(8 if torch.cuda.is_available() else 1, 3, 640, 640).to(device)
|
||||
# y = model(img, profile=True)
|
||||
|
||||
# Tensorboard
|
||||
# from torch.utils.tensorboard import SummaryWriter
|
||||
# tb_writer = SummaryWriter()
|
||||
# print("Run 'tensorboard --logdir=models/runs' to view tensorboard at http://localhost:6006/")
|
||||
# tb_writer.add_graph(model.model, img) # add model to tensorboard
|
||||
# tb_writer.add_image('test', img[0], dataformats='CWH') # add model to tensorboard
|
||||
51
models/yolov3-spp.yaml
Normal file
51
models/yolov3-spp.yaml
Normal file
@ -0,0 +1,51 @@
|
||||
# parameters
|
||||
nc: 80 # number of classes
|
||||
depth_multiple: 1.0 # model depth multiple
|
||||
width_multiple: 1.0 # layer channel multiple
|
||||
|
||||
# anchors
|
||||
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)
|
||||
]
|
||||
41
models/yolov3-tiny.yaml
Normal file
41
models/yolov3-tiny.yaml
Normal file
@ -0,0 +1,41 @@
|
||||
# parameters
|
||||
nc: 80 # number of classes
|
||||
depth_multiple: 1.0 # model depth multiple
|
||||
width_multiple: 1.0 # layer channel multiple
|
||||
|
||||
# anchors
|
||||
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)
|
||||
]
|
||||
51
models/yolov3.yaml
Normal file
51
models/yolov3.yaml
Normal file
@ -0,0 +1,51 @@
|
||||
# parameters
|
||||
nc: 80 # number of classes
|
||||
depth_multiple: 1.0 # model depth multiple
|
||||
width_multiple: 1.0 # layer channel multiple
|
||||
|
||||
# anchors
|
||||
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)
|
||||
]
|
||||
@ -5,23 +5,26 @@ Cython
|
||||
matplotlib>=3.2.2
|
||||
numpy>=1.18.5
|
||||
opencv-python>=4.1.2
|
||||
pillow
|
||||
Pillow
|
||||
PyYAML>=5.3
|
||||
scipy>=1.4.1
|
||||
tensorboard>=2.2
|
||||
torch>=1.6.0
|
||||
torchvision>=0.7.0
|
||||
torch>=1.7.0
|
||||
torchvision>=0.8.1
|
||||
tqdm>=4.41.0
|
||||
|
||||
# coco ----------------------------------------
|
||||
# pycocotools>=2.0
|
||||
# logging -------------------------------------
|
||||
# wandb
|
||||
|
||||
# plotting ------------------------------------
|
||||
seaborn
|
||||
pandas
|
||||
|
||||
# export --------------------------------------
|
||||
# packaging # for coremltools
|
||||
# coremltools==4.0b3
|
||||
# onnx>=1.7.0
|
||||
# coremltools==4.0
|
||||
# onnx>=1.8.0
|
||||
# scikit-learn==0.19.2 # for coreml quantization
|
||||
|
||||
# extras --------------------------------------
|
||||
# thop # FLOPS computation
|
||||
# seaborn # plotting
|
||||
# pycocotools>=2.0 # COCO mAP
|
||||
|
||||
352
test.py
352
test.py
@ -1,136 +1,170 @@
|
||||
import argparse
|
||||
import glob
|
||||
import json
|
||||
import os
|
||||
from pathlib import Path
|
||||
|
||||
from torch.utils.data import DataLoader
|
||||
import numpy as np
|
||||
import torch
|
||||
import yaml
|
||||
from tqdm import tqdm
|
||||
|
||||
from models import *
|
||||
from utils.datasets import *
|
||||
from utils.utils import *
|
||||
from models.experimental import attempt_load
|
||||
from utils.datasets import create_dataloader
|
||||
from utils.general import coco80_to_coco91_class, check_dataset, check_file, check_img_size, box_iou, \
|
||||
non_max_suppression, scale_coords, xyxy2xywh, xywh2xyxy, set_logging, increment_path
|
||||
from utils.loss import compute_loss
|
||||
from utils.metrics import ap_per_class, ConfusionMatrix
|
||||
from utils.plots import plot_images, output_to_target, plot_study_txt
|
||||
from utils.torch_utils import select_device, time_synchronized
|
||||
|
||||
|
||||
def test(cfg,
|
||||
data,
|
||||
def test(data,
|
||||
weights=None,
|
||||
batch_size=16,
|
||||
imgsz=416,
|
||||
batch_size=32,
|
||||
imgsz=640,
|
||||
conf_thres=0.001,
|
||||
iou_thres=0.6, # for nms
|
||||
iou_thres=0.6, # for NMS
|
||||
save_json=False,
|
||||
single_cls=False,
|
||||
augment=False,
|
||||
verbose=False,
|
||||
model=None,
|
||||
dataloader=None,
|
||||
multi_label=True):
|
||||
save_dir=Path(''), # for saving images
|
||||
save_txt=False, # for auto-labelling
|
||||
save_conf=False,
|
||||
plots=True,
|
||||
log_imgs=0): # number of logged images
|
||||
|
||||
# Initialize/load model and set device
|
||||
if model is None:
|
||||
is_training = False
|
||||
device = torch_utils.select_device(opt.device, batch_size=batch_size)
|
||||
verbose = opt.task == 'test'
|
||||
|
||||
# Remove previous
|
||||
for f in glob.glob('test_batch*.jpg'):
|
||||
os.remove(f)
|
||||
|
||||
# Initialize model
|
||||
model = Darknet(cfg, imgsz)
|
||||
|
||||
# Load weights
|
||||
attempt_download(weights)
|
||||
if weights.endswith('.pt'): # pytorch format
|
||||
model.load_state_dict(torch.load(weights, map_location=device)['model'])
|
||||
else: # darknet format
|
||||
load_darknet_weights(model, weights)
|
||||
|
||||
# Fuse
|
||||
model.fuse()
|
||||
model.to(device)
|
||||
|
||||
if device.type != 'cpu' and torch.cuda.device_count() > 1:
|
||||
model = nn.DataParallel(model)
|
||||
else: # called by train.py
|
||||
is_training = True
|
||||
training = model is not None
|
||||
if training: # called by train.py
|
||||
device = next(model.parameters()).device # get model device
|
||||
verbose = False
|
||||
|
||||
# Configure run
|
||||
data = parse_data_cfg(data)
|
||||
nc = 1 if single_cls else int(data['classes']) # number of classes
|
||||
path = data['valid'] # path to test images
|
||||
names = load_classes(data['names']) # class names
|
||||
else: # called directly
|
||||
set_logging()
|
||||
device = select_device(opt.device, batch_size=batch_size)
|
||||
save_txt = opt.save_txt # save *.txt labels
|
||||
|
||||
# Directories
|
||||
save_dir = Path(increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok)) # increment run
|
||||
(save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True) # make dir
|
||||
|
||||
# Load model
|
||||
model = attempt_load(weights, map_location=device) # load FP32 model
|
||||
imgsz = check_img_size(imgsz, s=model.stride.max()) # check img_size
|
||||
|
||||
# Multi-GPU disabled, incompatible with .half() https://github.com/ultralytics/yolov5/issues/99
|
||||
# if device.type != 'cpu' and torch.cuda.device_count() > 1:
|
||||
# model = nn.DataParallel(model)
|
||||
|
||||
# Half
|
||||
half = device.type != 'cpu' # half precision only supported on CUDA
|
||||
if half:
|
||||
model.half()
|
||||
|
||||
# Configure
|
||||
model.eval()
|
||||
is_coco = data.endswith('coco.yaml') # is COCO dataset
|
||||
with open(data) as f:
|
||||
data = yaml.load(f, Loader=yaml.FullLoader) # model dict
|
||||
check_dataset(data) # check
|
||||
nc = 1 if single_cls else int(data['nc']) # number of classes
|
||||
iouv = torch.linspace(0.5, 0.95, 10).to(device) # iou vector for mAP@0.5:0.95
|
||||
iouv = iouv[0].view(1) # comment for mAP@0.5:0.95
|
||||
niou = iouv.numel()
|
||||
|
||||
# Logging
|
||||
log_imgs, wandb = min(log_imgs, 100), None # ceil
|
||||
try:
|
||||
import wandb # Weights & Biases
|
||||
except ImportError:
|
||||
log_imgs = 0
|
||||
|
||||
# Dataloader
|
||||
if dataloader is None:
|
||||
dataset = LoadImagesAndLabels(path, imgsz, batch_size, rect=True, single_cls=opt.single_cls, pad=0.5)
|
||||
batch_size = min(batch_size, len(dataset))
|
||||
dataloader = DataLoader(dataset,
|
||||
batch_size=batch_size,
|
||||
num_workers=min([os.cpu_count(), batch_size if batch_size > 1 else 0, 8]),
|
||||
pin_memory=True,
|
||||
collate_fn=dataset.collate_fn)
|
||||
if not training:
|
||||
img = torch.zeros((1, 3, imgsz, imgsz), device=device) # init img
|
||||
_ = model(img.half() if half else img) if device.type != 'cpu' else None # run once
|
||||
path = data['test'] if opt.task == 'test' else data['val'] # path to val/test images
|
||||
dataloader = create_dataloader(path, imgsz, batch_size, model.stride.max(), opt, pad=0.5, rect=True)[0]
|
||||
|
||||
seen = 0
|
||||
model.eval()
|
||||
_ = model(torch.zeros((1, 3, imgsz, imgsz), device=device)) if device.type != 'cpu' else None # run once
|
||||
confusion_matrix = ConfusionMatrix(nc=nc)
|
||||
names = {k: v for k, v in enumerate(model.names if hasattr(model, 'names') else model.module.names)}
|
||||
coco91class = coco80_to_coco91_class()
|
||||
s = ('%20s' + '%10s' * 6) % ('Class', 'Images', 'Targets', 'P', 'R', 'mAP@0.5', 'F1')
|
||||
p, r, f1, mp, mr, map, mf1, t0, t1 = 0., 0., 0., 0., 0., 0., 0., 0., 0.
|
||||
s = ('%20s' + '%12s' * 6) % ('Class', 'Images', 'Targets', 'P', 'R', 'mAP@.5', 'mAP@.5:.95')
|
||||
p, r, f1, mp, mr, map50, map, t0, t1 = 0., 0., 0., 0., 0., 0., 0., 0., 0.
|
||||
loss = torch.zeros(3, device=device)
|
||||
jdict, stats, ap, ap_class = [], [], [], []
|
||||
for batch_i, (imgs, targets, paths, shapes) in enumerate(tqdm(dataloader, desc=s)):
|
||||
imgs = imgs.to(device).float() / 255.0 # uint8 to float32, 0 - 255 to 0.0 - 1.0
|
||||
jdict, stats, ap, ap_class, wandb_images = [], [], [], [], []
|
||||
for batch_i, (img, targets, paths, shapes) in enumerate(tqdm(dataloader, desc=s)):
|
||||
img = img.to(device, non_blocking=True)
|
||||
img = img.half() if half else img.float() # uint8 to fp16/32
|
||||
img /= 255.0 # 0 - 255 to 0.0 - 1.0
|
||||
targets = targets.to(device)
|
||||
nb, _, height, width = imgs.shape # batch size, channels, height, width
|
||||
whwh = torch.Tensor([width, height, width, height]).to(device)
|
||||
nb, _, height, width = img.shape # batch size, channels, height, width
|
||||
|
||||
# Disable gradients
|
||||
with torch.no_grad():
|
||||
# Run model
|
||||
t = torch_utils.time_synchronized()
|
||||
inf_out, train_out = model(imgs, augment=augment) # inference and training outputs
|
||||
t0 += torch_utils.time_synchronized() - t
|
||||
t = time_synchronized()
|
||||
inf_out, train_out = model(img, augment=augment) # inference and training outputs
|
||||
t0 += time_synchronized() - t
|
||||
|
||||
# Compute loss
|
||||
if is_training: # if model has loss hyperparameters
|
||||
loss += compute_loss(train_out, targets, model)[1][:3] # GIoU, obj, cls
|
||||
if training:
|
||||
loss += compute_loss([x.float() for x in train_out], targets, model)[1][:3] # box, obj, cls
|
||||
|
||||
# Run NMS
|
||||
t = torch_utils.time_synchronized()
|
||||
output = non_max_suppression(inf_out, conf_thres=conf_thres, iou_thres=iou_thres, multi_label=multi_label)
|
||||
t1 += torch_utils.time_synchronized() - t
|
||||
targets[:, 2:] *= torch.Tensor([width, height, width, height]).to(device) # to pixels
|
||||
lb = [targets[targets[:, 0] == i, 1:] for i in range(nb)] if save_txt else [] # for autolabelling
|
||||
t = time_synchronized()
|
||||
output = non_max_suppression(inf_out, conf_thres=conf_thres, iou_thres=iou_thres, labels=lb)
|
||||
t1 += time_synchronized() - t
|
||||
|
||||
# Statistics per image
|
||||
for si, pred in enumerate(output):
|
||||
labels = targets[targets[:, 0] == si, 1:]
|
||||
nl = len(labels)
|
||||
tcls = labels[:, 0].tolist() if nl else [] # target class
|
||||
path = Path(paths[si])
|
||||
seen += 1
|
||||
|
||||
if pred is None:
|
||||
if len(pred) == 0:
|
||||
if nl:
|
||||
stats.append((torch.zeros(0, niou, dtype=torch.bool), torch.Tensor(), torch.Tensor(), tcls))
|
||||
continue
|
||||
|
||||
# Append to text file
|
||||
# with open('test.txt', 'a') as file:
|
||||
# [file.write('%11.5g' * 7 % tuple(x) + '\n') for x in pred]
|
||||
# Predictions
|
||||
predn = pred.clone()
|
||||
scale_coords(img[si].shape[1:], predn[:, :4], shapes[si][0], shapes[si][1]) # native-space pred
|
||||
|
||||
# Clip boxes to image bounds
|
||||
clip_coords(pred, (height, width))
|
||||
# Append to text file
|
||||
if save_txt:
|
||||
gn = torch.tensor(shapes[si][0])[[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(save_dir / 'labels' / (path.stem + '.txt'), 'a') as f:
|
||||
f.write(('%g ' * len(line)).rstrip() % line + '\n')
|
||||
|
||||
# W&B logging
|
||||
if plots and len(wandb_images) < log_imgs:
|
||||
box_data = [{"position": {"minX": xyxy[0], "minY": xyxy[1], "maxX": xyxy[2], "maxY": xyxy[3]},
|
||||
"class_id": int(cls),
|
||||
"box_caption": "%s %.3f" % (names[cls], conf),
|
||||
"scores": {"class_score": conf},
|
||||
"domain": "pixel"} for *xyxy, conf, cls in pred.tolist()]
|
||||
boxes = {"predictions": {"box_data": box_data, "class_labels": names}} # inference-space
|
||||
wandb_images.append(wandb.Image(img[si], boxes=boxes, caption=path.name))
|
||||
|
||||
# Append to pycocotools JSON dictionary
|
||||
if save_json:
|
||||
# [{"image_id": 42, "category_id": 18, "bbox": [258.15, 41.29, 348.26, 243.78], "score": 0.236}, ...
|
||||
image_id = int(Path(paths[si]).stem.split('_')[-1])
|
||||
box = pred[:, :4].clone() # xyxy
|
||||
scale_coords(imgs[si].shape[1:], box, shapes[si][0], shapes[si][1]) # to original shape
|
||||
box = xyxy2xywh(box) # xywh
|
||||
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(pred.tolist(), box.tolist()):
|
||||
jdict.append({'image_id': image_id,
|
||||
'category_id': coco91class[int(p[5])],
|
||||
'category_id': coco91class[int(p[5])] if is_coco else int(p[5]),
|
||||
'bbox': [round(x, 3) for x in b],
|
||||
'score': round(p[4], 5)})
|
||||
|
||||
@ -141,22 +175,27 @@ def test(cfg,
|
||||
tcls_tensor = labels[:, 0]
|
||||
|
||||
# target boxes
|
||||
tbox = xywh2xyxy(labels[:, 1:5]) * whwh
|
||||
tbox = xywh2xyxy(labels[:, 1:5])
|
||||
scale_coords(img[si].shape[1:], tbox, shapes[si][0], shapes[si][1]) # native-space labels
|
||||
if plots:
|
||||
confusion_matrix.process_batch(pred, torch.cat((labels[:, 0:1], tbox), 1))
|
||||
|
||||
# Per target class
|
||||
for cls in torch.unique(tcls_tensor):
|
||||
ti = (cls == tcls_tensor).nonzero().view(-1) # target indices
|
||||
pi = (cls == pred[:, 5]).nonzero().view(-1) # prediction indices
|
||||
ti = (cls == tcls_tensor).nonzero(as_tuple=False).view(-1) # prediction indices
|
||||
pi = (cls == pred[:, 5]).nonzero(as_tuple=False).view(-1) # target indices
|
||||
|
||||
# Search for detections
|
||||
if pi.shape[0]:
|
||||
# Prediction to target ious
|
||||
ious, i = box_iou(pred[pi, :4], tbox[ti]).max(1) # best ious, indices
|
||||
ious, i = box_iou(predn[pi, :4], tbox[ti]).max(1) # best ious, indices
|
||||
|
||||
# Append detections
|
||||
for j in (ious > iouv[0]).nonzero():
|
||||
detected_set = set()
|
||||
for j in (ious > iouv[0]).nonzero(as_tuple=False):
|
||||
d = ti[i[j]] # detected target
|
||||
if d not in detected:
|
||||
if d.item() not in detected_set:
|
||||
detected_set.add(d.item())
|
||||
detected.append(d)
|
||||
correct[pi[j]] = ious[j] > iouv # iou_thres is 1xn
|
||||
if len(detected) == nl: # all targets already located in image
|
||||
@ -166,93 +205,105 @@ def test(cfg,
|
||||
stats.append((correct.cpu(), pred[:, 4].cpu(), pred[:, 5].cpu(), tcls))
|
||||
|
||||
# Plot images
|
||||
if batch_i < 1:
|
||||
f = 'test_batch%g_gt.jpg' % batch_i # filename
|
||||
plot_images(imgs, targets, paths=paths, names=names, fname=f) # ground truth
|
||||
f = 'test_batch%g_pred.jpg' % batch_i
|
||||
plot_images(imgs, output_to_target(output, width, height), paths=paths, names=names, fname=f) # predictions
|
||||
if plots and batch_i < 3:
|
||||
f = save_dir / f'test_batch{batch_i}_labels.jpg' # filename
|
||||
plot_images(img, targets, paths, f, names) # labels
|
||||
f = save_dir / f'test_batch{batch_i}_pred.jpg'
|
||||
plot_images(img, output_to_target(output), paths, f, names) # predictions
|
||||
|
||||
# Compute statistics
|
||||
stats = [np.concatenate(x, 0) for x in zip(*stats)] # to numpy
|
||||
if len(stats):
|
||||
p, r, ap, f1, ap_class = ap_per_class(*stats)
|
||||
if niou > 1:
|
||||
p, r, ap, f1 = p[:, 0], r[:, 0], ap.mean(1), ap[:, 0] # [P, R, AP@0.5:0.95, AP@0.5]
|
||||
mp, mr, map, mf1 = p.mean(), r.mean(), ap.mean(), f1.mean()
|
||||
if len(stats) and stats[0].any():
|
||||
p, r, ap, f1, ap_class = ap_per_class(*stats, plot=plots, save_dir=save_dir, names=names)
|
||||
p, r, ap50, ap = p[:, 0], r[:, 0], ap[:, 0], ap.mean(1) # [P, R, 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(np.int64), minlength=nc) # number of targets per class
|
||||
else:
|
||||
nt = torch.zeros(1)
|
||||
|
||||
# Plots
|
||||
if plots:
|
||||
confusion_matrix.plot(save_dir=save_dir, names=list(names.values()))
|
||||
if wandb and wandb.run:
|
||||
wandb.log({"Images": wandb_images})
|
||||
wandb.log({"Validation": [wandb.Image(str(f), caption=f.name) for f in sorted(save_dir.glob('test*.jpg'))]})
|
||||
|
||||
# Print results
|
||||
pf = '%20s' + '%10.3g' * 6 # print format
|
||||
print(pf % ('all', seen, nt.sum(), mp, mr, map, mf1))
|
||||
pf = '%20s' + '%12.3g' * 6 # print format
|
||||
print(pf % ('all', seen, nt.sum(), mp, mr, map50, map))
|
||||
|
||||
# Print results per class
|
||||
if verbose and nc > 1 and len(stats):
|
||||
for i, c in enumerate(ap_class):
|
||||
print(pf % (names[c], seen, nt[c], p[i], r[i], ap[i], f1[i]))
|
||||
print(pf % (names[c], seen, nt[c], p[i], r[i], ap50[i], ap[i]))
|
||||
|
||||
# Print speeds
|
||||
if verbose or save_json:
|
||||
t = tuple(x / seen * 1E3 for x in (t0, t1, t0 + t1)) + (imgsz, imgsz, batch_size) # tuple
|
||||
t = tuple(x / seen * 1E3 for x in (t0, t1, t0 + t1)) + (imgsz, imgsz, batch_size) # tuple
|
||||
if not training:
|
||||
print('Speed: %.1f/%.1f/%.1f ms inference/NMS/total per %gx%g image at batch-size %g' % t)
|
||||
|
||||
# Save JSON
|
||||
if save_json and map and len(jdict):
|
||||
print('\nCOCO mAP with pycocotools...')
|
||||
imgIds = [int(Path(x).stem.split('_')[-1]) for x in dataloader.dataset.img_files]
|
||||
with open('results.json', 'w') as file:
|
||||
json.dump(jdict, file)
|
||||
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 = glob.glob('../coco/annotations/instances_val*.json')[0] # annotations json
|
||||
pred_json = str(save_dir / f"{w}_predictions.json") # predictions json
|
||||
print('\nEvaluating pycocotools mAP... saving %s...' % pred_json)
|
||||
with open(pred_json, 'w') as f:
|
||||
json.dump(jdict, f)
|
||||
|
||||
try:
|
||||
try: # https://github.com/cocodataset/cocoapi/blob/master/PythonAPI/pycocoEvalDemo.ipynb
|
||||
from pycocotools.coco import COCO
|
||||
from pycocotools.cocoeval import COCOeval
|
||||
|
||||
# https://github.com/cocodataset/cocoapi/blob/master/PythonAPI/pycocoEvalDemo.ipynb
|
||||
cocoGt = COCO(glob.glob('../coco/annotations/instances_val*.json')[0]) # initialize COCO ground truth api
|
||||
cocoDt = cocoGt.loadRes('results.json') # initialize COCO pred api
|
||||
|
||||
cocoEval = COCOeval(cocoGt, cocoDt, 'bbox')
|
||||
cocoEval.params.imgIds = imgIds # [:32] # only evaluate these images
|
||||
cocoEval.evaluate()
|
||||
cocoEval.accumulate()
|
||||
cocoEval.summarize()
|
||||
# mf1, map = cocoEval.stats[:2] # update to pycocotools results (mAP@0.5:0.95, mAP@0.5)
|
||||
except:
|
||||
print('WARNING: pycocotools must be installed with numpy==1.17 to run correctly. '
|
||||
'See https://github.com/cocodataset/cocoapi/issues/356')
|
||||
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.img_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:
|
||||
print('ERROR: pycocotools unable to run: %s' % e)
|
||||
|
||||
# Return results
|
||||
if not training:
|
||||
s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else ''
|
||||
print(f"Results saved to {save_dir}{s}")
|
||||
model.float() # for training
|
||||
maps = np.zeros(nc) + map
|
||||
for i, c in enumerate(ap_class):
|
||||
maps[c] = ap[i]
|
||||
return (mp, mr, map, mf1, *(loss.cpu() / len(dataloader)).tolist()), maps
|
||||
return (mp, mr, map50, map, *(loss.cpu() / len(dataloader)).tolist()), maps, t
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
parser = argparse.ArgumentParser(prog='test.py')
|
||||
parser.add_argument('--cfg', type=str, default='cfg/yolov3-spp.cfg', help='*.cfg path')
|
||||
parser.add_argument('--data', type=str, default='data/coco2014.data', help='*.data path')
|
||||
parser.add_argument('--weights', type=str, default='weights/yolov3-spp-ultralytics.pt', help='weights path')
|
||||
parser.add_argument('--batch-size', type=int, default=16, help='size of each image batch')
|
||||
parser.add_argument('--img-size', type=int, default=512, help='inference size (pixels)')
|
||||
parser.add_argument('--weights', nargs='+', type=str, default='yolov3.pt', help='model.pt path(s)')
|
||||
parser.add_argument('--data', type=str, default='data/coco128.yaml', help='*.data path')
|
||||
parser.add_argument('--batch-size', type=int, default=32, help='size of each image batch')
|
||||
parser.add_argument('--img-size', type=int, default=640, help='inference size (pixels)')
|
||||
parser.add_argument('--conf-thres', type=float, default=0.001, help='object confidence threshold')
|
||||
parser.add_argument('--iou-thres', type=float, default=0.6, help='IOU threshold for NMS')
|
||||
parser.add_argument('--save-json', action='store_true', help='save a cocoapi-compatible JSON results file')
|
||||
parser.add_argument('--task', default='test', help="'test', 'study', 'benchmark'")
|
||||
parser.add_argument('--device', default='', help='device id (i.e. 0 or 0,1) or cpu')
|
||||
parser.add_argument('--single-cls', action='store_true', help='train as single-class dataset')
|
||||
parser.add_argument('--task', default='val', help="'val', 'test', 'study'")
|
||||
parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
|
||||
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-conf', action='store_true', help='save confidences in --save-txt labels')
|
||||
parser.add_argument('--save-json', action='store_true', help='save a cocoapi-compatible JSON results file')
|
||||
parser.add_argument('--project', default='runs/test', 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')
|
||||
opt = parser.parse_args()
|
||||
opt.save_json = opt.save_json or any([x in opt.data for x in ['coco.data', 'coco2014.data', 'coco2017.data']])
|
||||
opt.cfg = check_file(opt.cfg) # check file
|
||||
opt.save_json |= opt.data.endswith('coco.yaml')
|
||||
opt.data = check_file(opt.data) # check file
|
||||
print(opt)
|
||||
|
||||
# task = 'test', 'study', 'benchmark'
|
||||
if opt.task == 'test': # (default) test normally
|
||||
test(opt.cfg,
|
||||
opt.data,
|
||||
if opt.task in ['val', 'test']: # run normally
|
||||
test(opt.data,
|
||||
opt.weights,
|
||||
opt.batch_size,
|
||||
opt.img_size,
|
||||
@ -260,13 +311,22 @@ if __name__ == '__main__':
|
||||
opt.iou_thres,
|
||||
opt.save_json,
|
||||
opt.single_cls,
|
||||
opt.augment)
|
||||
opt.augment,
|
||||
opt.verbose,
|
||||
save_txt=opt.save_txt,
|
||||
save_conf=opt.save_conf,
|
||||
)
|
||||
|
||||
elif opt.task == 'benchmark': # mAPs at 256-640 at conf 0.5 and 0.7
|
||||
y = []
|
||||
for i in list(range(256, 640, 128)): # img-size
|
||||
for j in [0.6, 0.7]: # iou-thres
|
||||
t = time.time()
|
||||
r = test(opt.cfg, opt.data, opt.weights, opt.batch_size, i, opt.conf_thres, j, opt.save_json)[0]
|
||||
y.append(r + (time.time() - t,))
|
||||
np.savetxt('benchmark.txt', y, fmt='%10.4g') # y = np.loadtxt('study.txt')
|
||||
elif opt.task == 'study': # run over a range of settings and save/plot
|
||||
for weights in ['yolov3.pt', 'yolov3-spp.pt', 'yolov3-tiny.pt']:
|
||||
f = 'study_%s_%s.txt' % (Path(opt.data).stem, Path(weights).stem) # filename to save to
|
||||
x = list(range(320, 800, 64)) # x axis
|
||||
y = [] # y axis
|
||||
for i in x: # img-size
|
||||
print('\nRunning %s point %s...' % (f, i))
|
||||
r, _, t = test(opt.data, weights, opt.batch_size, i, opt.conf_thres, opt.iou_thres, opt.save_json,
|
||||
plots=False)
|
||||
y.append(r + t) # results and times
|
||||
np.savetxt(f, y, fmt='%10.4g') # save
|
||||
os.system('zip -r study.zip study_*.txt')
|
||||
plot_study_txt(f, x) # plot
|
||||
|
||||
771
train.py
771
train.py
@ -1,442 +1,537 @@
|
||||
import argparse
|
||||
import logging
|
||||
import math
|
||||
import os
|
||||
import random
|
||||
import time
|
||||
from pathlib import Path
|
||||
from warnings import warn
|
||||
|
||||
import numpy as np
|
||||
import torch.distributed as dist
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
import torch.optim as optim
|
||||
import torch.optim.lr_scheduler as lr_scheduler
|
||||
import torch.utils.data
|
||||
import yaml
|
||||
from torch.cuda import amp
|
||||
from torch.nn.parallel import DistributedDataParallel as DDP
|
||||
from torch.utils.tensorboard import SummaryWriter
|
||||
from tqdm import tqdm
|
||||
|
||||
import test # import test.py to get mAP after each epoch
|
||||
from models import *
|
||||
from utils.datasets import *
|
||||
from utils.utils import *
|
||||
from models.yolo import Model
|
||||
from utils.autoanchor import check_anchors
|
||||
from utils.datasets import create_dataloader
|
||||
from utils.general import labels_to_class_weights, increment_path, labels_to_image_weights, init_seeds, \
|
||||
fitness, strip_optimizer, get_latest_run, check_dataset, check_file, check_git_status, check_img_size, \
|
||||
print_mutation, set_logging
|
||||
from utils.google_utils import attempt_download
|
||||
from utils.loss import compute_loss
|
||||
from utils.plots import plot_images, plot_labels, plot_results, plot_evolution
|
||||
from utils.torch_utils import ModelEMA, select_device, intersect_dicts, torch_distributed_zero_first
|
||||
|
||||
mixed_precision = True
|
||||
try: # Mixed precision training https://github.com/NVIDIA/apex
|
||||
from apex import amp
|
||||
except:
|
||||
print('Apex recommended for faster mixed precision training: https://github.com/NVIDIA/apex')
|
||||
mixed_precision = False # not installed
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
wdir = 'weights' + os.sep # weights dir
|
||||
last = wdir + 'last.pt'
|
||||
best = wdir + 'best.pt'
|
||||
results_file = 'results.txt'
|
||||
|
||||
# Hyperparameters
|
||||
hyp = {'giou': 3.54, # giou loss gain
|
||||
'cls': 37.4, # cls loss gain
|
||||
'cls_pw': 1.0, # cls BCELoss positive_weight
|
||||
'obj': 64.3, # obj loss gain (*=img_size/320 if img_size != 320)
|
||||
'obj_pw': 1.0, # obj BCELoss positive_weight
|
||||
'iou_t': 0.20, # iou training threshold
|
||||
'lr0': 0.01, # initial learning rate (SGD=5E-3, Adam=5E-4)
|
||||
'lrf': 0.0005, # final learning rate (with cos scheduler)
|
||||
'momentum': 0.937, # SGD momentum
|
||||
'weight_decay': 0.0005, # optimizer weight decay
|
||||
'fl_gamma': 0.0, # focal loss gamma (efficientDet default is gamma=1.5)
|
||||
'hsv_h': 0.0138, # image HSV-Hue augmentation (fraction)
|
||||
'hsv_s': 0.678, # image HSV-Saturation augmentation (fraction)
|
||||
'hsv_v': 0.36, # image HSV-Value augmentation (fraction)
|
||||
'degrees': 1.98 * 0, # image rotation (+/- deg)
|
||||
'translate': 0.05 * 0, # image translation (+/- fraction)
|
||||
'scale': 0.05 * 0, # image scale (+/- gain)
|
||||
'shear': 0.641 * 0} # image shear (+/- deg)
|
||||
|
||||
# Overwrite hyp with hyp*.txt (optional)
|
||||
f = glob.glob('hyp*.txt')
|
||||
if f:
|
||||
print('Using %s' % f[0])
|
||||
for k, v in zip(hyp.keys(), np.loadtxt(f[0])):
|
||||
hyp[k] = v
|
||||
|
||||
# Print focal loss if gamma > 0
|
||||
if hyp['fl_gamma']:
|
||||
print('Using FocalLoss(gamma=%g)' % hyp['fl_gamma'])
|
||||
try:
|
||||
import wandb
|
||||
except ImportError:
|
||||
wandb = None
|
||||
logger.info("Install Weights & Biases for experiment logging via 'pip install wandb' (recommended)")
|
||||
|
||||
|
||||
def train(hyp):
|
||||
cfg = opt.cfg
|
||||
data = opt.data
|
||||
epochs = opt.epochs # 500200 batches at bs 64, 117263 images = 273 epochs
|
||||
batch_size = opt.batch_size
|
||||
accumulate = max(round(64 / batch_size), 1) # accumulate n times before optimizer update (bs 64)
|
||||
weights = opt.weights # initial training weights
|
||||
imgsz_min, imgsz_max, imgsz_test = opt.img_size # img sizes (min, max, test)
|
||||
def train(hyp, opt, device, tb_writer=None, wandb=None):
|
||||
logger.info(f'Hyperparameters {hyp}')
|
||||
save_dir, epochs, batch_size, total_batch_size, weights, rank = \
|
||||
Path(opt.save_dir), opt.epochs, opt.batch_size, opt.total_batch_size, opt.weights, opt.global_rank
|
||||
|
||||
# Image Sizes
|
||||
gs = 32 # (pixels) grid size
|
||||
assert math.fmod(imgsz_min, gs) == 0, '--img-size %g must be a %g-multiple' % (imgsz_min, gs)
|
||||
opt.multi_scale |= imgsz_min != imgsz_max # multi if different (min, max)
|
||||
if opt.multi_scale:
|
||||
if imgsz_min == imgsz_max:
|
||||
imgsz_min //= 1.5
|
||||
imgsz_max //= 0.667
|
||||
grid_min, grid_max = imgsz_min // gs, imgsz_max // gs
|
||||
imgsz_min, imgsz_max = int(grid_min * gs), int(grid_max * gs)
|
||||
img_size = imgsz_max # initialize with max size
|
||||
# Directories
|
||||
wdir = save_dir / 'weights'
|
||||
wdir.mkdir(parents=True, exist_ok=True) # make dir
|
||||
last = wdir / 'last.pt'
|
||||
best = wdir / 'best.pt'
|
||||
results_file = save_dir / 'results.txt'
|
||||
|
||||
# Configure run
|
||||
init_seeds()
|
||||
data_dict = parse_data_cfg(data)
|
||||
# Save run settings
|
||||
with open(save_dir / 'hyp.yaml', 'w') as f:
|
||||
yaml.dump(hyp, f, sort_keys=False)
|
||||
with open(save_dir / 'opt.yaml', 'w') as f:
|
||||
yaml.dump(vars(opt), f, sort_keys=False)
|
||||
|
||||
# Configure
|
||||
plots = not opt.evolve # create plots
|
||||
cuda = device.type != 'cpu'
|
||||
init_seeds(2 + rank)
|
||||
with open(opt.data) as f:
|
||||
data_dict = yaml.load(f, Loader=yaml.FullLoader) # data dict
|
||||
with torch_distributed_zero_first(rank):
|
||||
check_dataset(data_dict) # check
|
||||
train_path = data_dict['train']
|
||||
test_path = data_dict['valid']
|
||||
nc = 1 if opt.single_cls else int(data_dict['classes']) # number of classes
|
||||
hyp['cls'] *= nc / 80 # update coco-tuned hyp['cls'] to current dataset
|
||||
test_path = data_dict['val']
|
||||
nc, names = (1, ['item']) if opt.single_cls else (int(data_dict['nc']), data_dict['names']) # number classes, names
|
||||
assert len(names) == nc, '%g names found for nc=%g dataset in %s' % (len(names), nc, opt.data) # check
|
||||
|
||||
# Remove previous results
|
||||
for f in glob.glob('*_batch*.jpg') + glob.glob(results_file):
|
||||
os.remove(f)
|
||||
# Model
|
||||
pretrained = weights.endswith('.pt')
|
||||
if pretrained:
|
||||
with torch_distributed_zero_first(rank):
|
||||
attempt_download(weights) # download if not found locally
|
||||
ckpt = torch.load(weights, map_location=device) # load checkpoint
|
||||
if hyp.get('anchors'):
|
||||
ckpt['model'].yaml['anchors'] = round(hyp['anchors']) # force autoanchor
|
||||
model = Model(opt.cfg or ckpt['model'].yaml, ch=3, nc=nc).to(device) # create
|
||||
exclude = ['anchor'] if opt.cfg or hyp.get('anchors') else [] # exclude keys
|
||||
state_dict = ckpt['model'].float().state_dict() # to FP32
|
||||
state_dict = intersect_dicts(state_dict, model.state_dict(), exclude=exclude) # intersect
|
||||
model.load_state_dict(state_dict, strict=False) # load
|
||||
logger.info('Transferred %g/%g items from %s' % (len(state_dict), len(model.state_dict()), weights)) # report
|
||||
else:
|
||||
model = Model(opt.cfg, ch=3, nc=nc).to(device) # create
|
||||
|
||||
# Initialize model
|
||||
model = Darknet(cfg).to(device)
|
||||
# Freeze
|
||||
freeze = [] # parameter names to freeze (full or partial)
|
||||
for k, v in model.named_parameters():
|
||||
v.requires_grad = True # train all layers
|
||||
if any(x in k for x in freeze):
|
||||
print('freezing %s' % k)
|
||||
v.requires_grad = False
|
||||
|
||||
# Optimizer
|
||||
nbs = 64 # nominal batch size
|
||||
accumulate = max(round(nbs / total_batch_size), 1) # accumulate loss before optimizing
|
||||
hyp['weight_decay'] *= total_batch_size * accumulate / nbs # scale weight_decay
|
||||
|
||||
pg0, pg1, pg2 = [], [], [] # optimizer parameter groups
|
||||
for k, v in dict(model.named_parameters()).items():
|
||||
if '.bias' in k:
|
||||
pg2 += [v] # biases
|
||||
elif 'Conv2d.weight' in k:
|
||||
pg1 += [v] # apply weight_decay
|
||||
else:
|
||||
pg0 += [v] # all else
|
||||
for k, v in model.named_modules():
|
||||
if hasattr(v, 'bias') and isinstance(v.bias, nn.Parameter):
|
||||
pg2.append(v.bias) # biases
|
||||
if isinstance(v, nn.BatchNorm2d):
|
||||
pg0.append(v.weight) # no decay
|
||||
elif hasattr(v, 'weight') and isinstance(v.weight, nn.Parameter):
|
||||
pg1.append(v.weight) # apply decay
|
||||
|
||||
if opt.adam:
|
||||
# hyp['lr0'] *= 0.1 # reduce lr (i.e. SGD=5E-3, Adam=5E-4)
|
||||
optimizer = optim.Adam(pg0, lr=hyp['lr0'])
|
||||
# optimizer = AdaBound(pg0, lr=hyp['lr0'], final_lr=0.1)
|
||||
optimizer = optim.Adam(pg0, lr=hyp['lr0'], betas=(hyp['momentum'], 0.999)) # adjust beta1 to momentum
|
||||
else:
|
||||
optimizer = optim.SGD(pg0, lr=hyp['lr0'], momentum=hyp['momentum'], nesterov=True)
|
||||
|
||||
optimizer.add_param_group({'params': pg1, 'weight_decay': hyp['weight_decay']}) # add pg1 with weight_decay
|
||||
optimizer.add_param_group({'params': pg2}) # add pg2 (biases)
|
||||
print('Optimizer groups: %g .bias, %g Conv2d.weight, %g other' % (len(pg2), len(pg1), len(pg0)))
|
||||
logger.info('Optimizer groups: %g .bias, %g conv.weight, %g other' % (len(pg2), len(pg1), len(pg0)))
|
||||
del pg0, pg1, pg2
|
||||
|
||||
start_epoch = 0
|
||||
best_fitness = 0.0
|
||||
attempt_download(weights)
|
||||
if weights.endswith('.pt'): # pytorch format
|
||||
# possible weights are '*.pt', 'yolov3-spp.pt', 'yolov3-tiny.pt' etc.
|
||||
ckpt = torch.load(weights, map_location=device)
|
||||
# Scheduler https://arxiv.org/pdf/1812.01187.pdf
|
||||
# https://pytorch.org/docs/stable/_modules/torch/optim/lr_scheduler.html#OneCycleLR
|
||||
lf = lambda x: ((1 + math.cos(x * math.pi / epochs)) / 2) * (1 - hyp['lrf']) + hyp['lrf'] # cosine
|
||||
scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lf)
|
||||
# plot_lr_scheduler(optimizer, scheduler, epochs)
|
||||
|
||||
# load model
|
||||
try:
|
||||
ckpt['model'] = {k: v for k, v in ckpt['model'].items() if model.state_dict()[k].numel() == v.numel()}
|
||||
model.load_state_dict(ckpt['model'], strict=False)
|
||||
except KeyError as e:
|
||||
s = "%s is not compatible with %s. Specify --weights '' or specify a --cfg compatible with %s. " \
|
||||
"See https://github.com/ultralytics/yolov3/issues/657" % (opt.weights, opt.cfg, opt.weights)
|
||||
raise KeyError(s) from e
|
||||
# Logging
|
||||
if wandb and wandb.run is None:
|
||||
opt.hyp = hyp # add hyperparameters
|
||||
wandb_run = wandb.init(config=opt, resume="allow",
|
||||
project='YOLOv3' if opt.project == 'runs/train' else Path(opt.project).stem,
|
||||
name=save_dir.stem,
|
||||
id=ckpt.get('wandb_id') if 'ckpt' in locals() else None)
|
||||
|
||||
# load optimizer
|
||||
# Resume
|
||||
start_epoch, best_fitness = 0, 0.0
|
||||
if pretrained:
|
||||
# Optimizer
|
||||
if ckpt['optimizer'] is not None:
|
||||
optimizer.load_state_dict(ckpt['optimizer'])
|
||||
best_fitness = ckpt['best_fitness']
|
||||
|
||||
# load results
|
||||
# Results
|
||||
if ckpt.get('training_results') is not None:
|
||||
with open(results_file, 'w') as file:
|
||||
file.write(ckpt['training_results']) # write results.txt
|
||||
|
||||
# epochs
|
||||
# Epochs
|
||||
start_epoch = ckpt['epoch'] + 1
|
||||
if opt.resume:
|
||||
assert start_epoch > 0, '%s training to %g epochs is finished, nothing to resume.' % (weights, epochs)
|
||||
if epochs < start_epoch:
|
||||
print('%s has been trained for %g epochs. Fine-tuning for %g additional epochs.' %
|
||||
(opt.weights, ckpt['epoch'], epochs))
|
||||
logger.info('%s has been trained for %g epochs. Fine-tuning for %g additional epochs.' %
|
||||
(weights, ckpt['epoch'], epochs))
|
||||
epochs += ckpt['epoch'] # finetune additional epochs
|
||||
|
||||
del ckpt
|
||||
del ckpt, state_dict
|
||||
|
||||
elif len(weights) > 0: # darknet format
|
||||
# possible weights are '*.weights', 'yolov3-tiny.conv.15', 'darknet53.conv.74' etc.
|
||||
load_darknet_weights(model, weights)
|
||||
# Image sizes
|
||||
gs = int(max(model.stride)) # grid size (max stride)
|
||||
imgsz, imgsz_test = [check_img_size(x, gs) for x in opt.img_size] # verify imgsz are gs-multiples
|
||||
|
||||
if opt.freeze_layers:
|
||||
output_layer_indices = [idx - 1 for idx, module in enumerate(model.module_list) if isinstance(module, YOLOLayer)]
|
||||
freeze_layer_indices = [x for x in range(len(model.module_list)) if
|
||||
(x not in output_layer_indices) and
|
||||
(x - 1 not in output_layer_indices)]
|
||||
for idx in freeze_layer_indices:
|
||||
for parameter in model.module_list[idx].parameters():
|
||||
parameter.requires_grad_(False)
|
||||
# DP mode
|
||||
if cuda and rank == -1 and torch.cuda.device_count() > 1:
|
||||
model = torch.nn.DataParallel(model)
|
||||
|
||||
# Mixed precision training https://github.com/NVIDIA/apex
|
||||
if mixed_precision:
|
||||
model, optimizer = amp.initialize(model, optimizer, opt_level='O1', verbosity=0)
|
||||
# SyncBatchNorm
|
||||
if opt.sync_bn and cuda and rank != -1:
|
||||
model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model).to(device)
|
||||
logger.info('Using SyncBatchNorm()')
|
||||
|
||||
# Scheduler https://arxiv.org/pdf/1812.01187.pdf
|
||||
lf = lambda x: (((1 + math.cos(x * math.pi / epochs)) / 2) ** 1.0) * 0.95 + 0.05 # cosine
|
||||
scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lf)
|
||||
scheduler.last_epoch = start_epoch - 1 # see link below
|
||||
# https://discuss.pytorch.org/t/a-problem-occured-when-resuming-an-optimizer/28822
|
||||
# EMA
|
||||
ema = ModelEMA(model) if rank in [-1, 0] else None
|
||||
|
||||
# Plot lr schedule
|
||||
# y = []
|
||||
# for _ in range(epochs):
|
||||
# scheduler.step()
|
||||
# y.append(optimizer.param_groups[0]['lr'])
|
||||
# plt.plot(y, '.-', label='LambdaLR')
|
||||
# plt.xlabel('epoch')
|
||||
# plt.ylabel('LR')
|
||||
# plt.tight_layout()
|
||||
# plt.savefig('LR.png', dpi=300)
|
||||
# DDP mode
|
||||
if cuda and rank != -1:
|
||||
model = DDP(model, device_ids=[opt.local_rank], output_device=opt.local_rank)
|
||||
|
||||
# Initialize distributed training
|
||||
if device.type != 'cpu' and torch.cuda.device_count() > 1 and torch.distributed.is_available():
|
||||
dist.init_process_group(backend='nccl', # 'distributed backend'
|
||||
init_method='tcp://127.0.0.1:9999', # distributed training init method
|
||||
world_size=1, # number of nodes for distributed training
|
||||
rank=0) # distributed training node rank
|
||||
model = torch.nn.parallel.DistributedDataParallel(model, find_unused_parameters=True)
|
||||
model.yolo_layers = model.module.yolo_layers # move yolo layer indices to top level
|
||||
# Trainloader
|
||||
dataloader, dataset = create_dataloader(train_path, imgsz, batch_size, gs, opt,
|
||||
hyp=hyp, augment=True, cache=opt.cache_images, rect=opt.rect, rank=rank,
|
||||
world_size=opt.world_size, workers=opt.workers,
|
||||
image_weights=opt.image_weights)
|
||||
mlc = np.concatenate(dataset.labels, 0)[:, 0].max() # max label class
|
||||
nb = len(dataloader) # number of batches
|
||||
assert mlc < nc, 'Label class %g exceeds nc=%g in %s. Possible class labels are 0-%g' % (mlc, nc, opt.data, nc - 1)
|
||||
|
||||
# Dataset
|
||||
dataset = LoadImagesAndLabels(train_path, img_size, batch_size,
|
||||
augment=True,
|
||||
hyp=hyp, # augmentation hyperparameters
|
||||
rect=opt.rect, # rectangular training
|
||||
cache_images=opt.cache_images,
|
||||
single_cls=opt.single_cls)
|
||||
# Process 0
|
||||
if rank in [-1, 0]:
|
||||
ema.updates = start_epoch * nb // accumulate # set EMA updates
|
||||
testloader = create_dataloader(test_path, imgsz_test, total_batch_size, gs, opt,
|
||||
hyp=hyp, cache=opt.cache_images and not opt.notest, rect=True,
|
||||
rank=-1, world_size=opt.world_size, workers=opt.workers)[0] # testloader
|
||||
|
||||
# Dataloader
|
||||
batch_size = min(batch_size, len(dataset))
|
||||
nw = min([os.cpu_count(), batch_size if batch_size > 1 else 0, 8]) # number of workers
|
||||
dataloader = torch.utils.data.DataLoader(dataset,
|
||||
batch_size=batch_size,
|
||||
num_workers=nw,
|
||||
shuffle=not opt.rect, # Shuffle=True unless rectangular training is used
|
||||
pin_memory=True,
|
||||
collate_fn=dataset.collate_fn)
|
||||
if not opt.resume:
|
||||
labels = np.concatenate(dataset.labels, 0)
|
||||
c = torch.tensor(labels[:, 0]) # classes
|
||||
# cf = torch.bincount(c.long(), minlength=nc) + 1. # frequency
|
||||
# model._initialize_biases(cf.to(device))
|
||||
if plots:
|
||||
plot_labels(labels, save_dir=save_dir)
|
||||
if tb_writer:
|
||||
tb_writer.add_histogram('classes', c, 0)
|
||||
if wandb:
|
||||
wandb.log({"Labels": [wandb.Image(str(x), caption=x.name) for x in save_dir.glob('*labels*.png')]})
|
||||
|
||||
# Testloader
|
||||
testloader = torch.utils.data.DataLoader(LoadImagesAndLabels(test_path, imgsz_test, batch_size,
|
||||
hyp=hyp,
|
||||
rect=True,
|
||||
cache_images=opt.cache_images,
|
||||
single_cls=opt.single_cls),
|
||||
batch_size=batch_size,
|
||||
num_workers=nw,
|
||||
pin_memory=True,
|
||||
collate_fn=dataset.collate_fn)
|
||||
# Anchors
|
||||
if not opt.noautoanchor:
|
||||
check_anchors(dataset, model=model, thr=hyp['anchor_t'], imgsz=imgsz)
|
||||
|
||||
# Model parameters
|
||||
hyp['cls'] *= nc / 80. # scale coco-tuned hyp['cls'] to current dataset
|
||||
model.nc = nc # attach number of classes to model
|
||||
model.hyp = hyp # attach hyperparameters to model
|
||||
model.gr = 1.0 # giou loss ratio (obj_loss = 1.0 or giou)
|
||||
model.gr = 1.0 # iou loss ratio (obj_loss = 1.0 or iou)
|
||||
model.class_weights = labels_to_class_weights(dataset.labels, nc).to(device) # attach class weights
|
||||
|
||||
# Model EMA
|
||||
ema = torch_utils.ModelEMA(model)
|
||||
model.names = names
|
||||
|
||||
# Start training
|
||||
nb = len(dataloader) # number of batches
|
||||
n_burn = max(3 * nb, 500) # burn-in iterations, max(3 epochs, 500 iterations)
|
||||
maps = np.zeros(nc) # mAP per class
|
||||
# torch.autograd.set_detect_anomaly(True)
|
||||
results = (0, 0, 0, 0, 0, 0, 0) # 'P', 'R', 'mAP', 'F1', 'val GIoU', 'val Objectness', 'val Classification'
|
||||
t0 = time.time()
|
||||
print('Image sizes %g - %g train, %g test' % (imgsz_min, imgsz_max, imgsz_test))
|
||||
print('Using %g dataloader workers' % nw)
|
||||
print('Starting training for %g epochs...' % epochs)
|
||||
nw = max(round(hyp['warmup_epochs'] * nb), 1000) # number of warmup iterations, max(3 epochs, 1k iterations)
|
||||
# nw = min(nw, (epochs - start_epoch) / 2 * nb) # limit warmup to < 1/2 of training
|
||||
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 = amp.GradScaler(enabled=cuda)
|
||||
logger.info('Image sizes %g train, %g test\n'
|
||||
'Using %g dataloader workers\nLogging results to %s\n'
|
||||
'Starting training for %g epochs...' % (imgsz, imgsz_test, dataloader.num_workers, save_dir, epochs))
|
||||
for epoch in range(start_epoch, epochs): # epoch ------------------------------------------------------------------
|
||||
model.train()
|
||||
|
||||
# Update image weights (optional)
|
||||
if dataset.image_weights:
|
||||
w = model.class_weights.cpu().numpy() * (1 - maps) ** 2 # class weights
|
||||
image_weights = labels_to_image_weights(dataset.labels, nc=nc, class_weights=w)
|
||||
dataset.indices = random.choices(range(dataset.n), weights=image_weights, k=dataset.n) # rand weighted idx
|
||||
if opt.image_weights:
|
||||
# Generate indices
|
||||
if rank in [-1, 0]:
|
||||
cw = model.class_weights.cpu().numpy() * (1 - maps) ** 2 # 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
|
||||
# Broadcast if DDP
|
||||
if rank != -1:
|
||||
indices = (torch.tensor(dataset.indices) if rank == 0 else torch.zeros(dataset.n)).int()
|
||||
dist.broadcast(indices, 0)
|
||||
if rank != 0:
|
||||
dataset.indices = indices.cpu().numpy()
|
||||
|
||||
mloss = torch.zeros(4).to(device) # mean losses
|
||||
print(('\n' + '%10s' * 8) % ('Epoch', 'gpu_mem', 'GIoU', 'obj', 'cls', 'total', 'targets', 'img_size'))
|
||||
pbar = tqdm(enumerate(dataloader), total=nb) # progress bar
|
||||
# Update mosaic border
|
||||
# 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:
|
||||
dataloader.sampler.set_epoch(epoch)
|
||||
pbar = enumerate(dataloader)
|
||||
logger.info(('\n' + '%10s' * 8) % ('Epoch', 'gpu_mem', 'box', 'obj', 'cls', 'total', 'targets', 'img_size'))
|
||||
if rank in [-1, 0]:
|
||||
pbar = tqdm(pbar, total=nb) # progress bar
|
||||
optimizer.zero_grad()
|
||||
for i, (imgs, targets, paths, _) in pbar: # batch -------------------------------------------------------------
|
||||
ni = i + nb * epoch # number integrated batches (since train start)
|
||||
imgs = imgs.to(device).float() / 255.0 # uint8 to float32, 0 - 255 to 0.0 - 1.0
|
||||
targets = targets.to(device)
|
||||
imgs = imgs.to(device, non_blocking=True).float() / 255.0 # uint8 to float32, 0-255 to 0.0-1.0
|
||||
|
||||
# Burn-in
|
||||
if ni <= n_burn:
|
||||
xi = [0, n_burn] # x interp
|
||||
model.gr = np.interp(ni, xi, [0.0, 1.0]) # giou loss ratio (obj_loss = 1.0 or giou)
|
||||
accumulate = max(1, np.interp(ni, xi, [1, 64 / batch_size]).round())
|
||||
# Warmup
|
||||
if ni <= nw:
|
||||
xi = [0, nw] # x interp
|
||||
# model.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 / total_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, [0.1 if j == 2 else 0.0, x['initial_lr'] * lf(epoch)])
|
||||
x['weight_decay'] = np.interp(ni, xi, [0.0, hyp['weight_decay'] if j == 1 else 0.0])
|
||||
x['lr'] = np.interp(ni, xi, [hyp['warmup_bias_lr'] if j == 2 else 0.0, x['initial_lr'] * lf(epoch)])
|
||||
if 'momentum' in x:
|
||||
x['momentum'] = np.interp(ni, xi, [0.9, hyp['momentum']])
|
||||
x['momentum'] = np.interp(ni, xi, [hyp['warmup_momentum'], hyp['momentum']])
|
||||
|
||||
# Multi-Scale
|
||||
# Multi-scale
|
||||
if opt.multi_scale:
|
||||
if ni / accumulate % 1 == 0: # adjust img_size (67% - 150%) every 1 batch
|
||||
img_size = random.randrange(grid_min, grid_max + 1) * gs
|
||||
sf = img_size / max(imgs.shape[2:]) # scale factor
|
||||
sz = random.randrange(imgsz * 0.5, 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 32-multiple)
|
||||
ns = [math.ceil(x * sf / gs) * gs for x in imgs.shape[2:]] # new shape (stretched to gs-multiple)
|
||||
imgs = F.interpolate(imgs, size=ns, mode='bilinear', align_corners=False)
|
||||
|
||||
# Forward
|
||||
pred = model(imgs)
|
||||
|
||||
# Loss
|
||||
loss, loss_items = compute_loss(pred, targets, model)
|
||||
if not torch.isfinite(loss):
|
||||
print('WARNING: non-finite loss, ending training ', loss_items)
|
||||
return results
|
||||
with amp.autocast(enabled=cuda):
|
||||
pred = model(imgs) # forward
|
||||
loss, loss_items = compute_loss(pred, targets.to(device), model) # loss scaled by batch_size
|
||||
if rank != -1:
|
||||
loss *= opt.world_size # gradient averaged between devices in DDP mode
|
||||
|
||||
# Backward
|
||||
loss *= batch_size / 64 # scale loss
|
||||
if mixed_precision:
|
||||
with amp.scale_loss(loss, optimizer) as scaled_loss:
|
||||
scaled_loss.backward()
|
||||
else:
|
||||
loss.backward()
|
||||
scaler.scale(loss).backward()
|
||||
|
||||
# Optimize
|
||||
if ni % accumulate == 0:
|
||||
optimizer.step()
|
||||
scaler.step(optimizer) # optimizer.step
|
||||
scaler.update()
|
||||
optimizer.zero_grad()
|
||||
ema.update(model)
|
||||
if ema:
|
||||
ema.update(model)
|
||||
|
||||
# Print
|
||||
mloss = (mloss * i + loss_items) / (i + 1) # update mean losses
|
||||
mem = '%.3gG' % (torch.cuda.memory_cached() / 1E9 if torch.cuda.is_available() else 0) # (GB)
|
||||
s = ('%10s' * 2 + '%10.3g' * 6) % ('%g/%g' % (epoch, epochs - 1), mem, *mloss, len(targets), img_size)
|
||||
pbar.set_description(s)
|
||||
if rank in [-1, 0]:
|
||||
mloss = (mloss * i + loss_items) / (i + 1) # update mean losses
|
||||
mem = '%.3gG' % (torch.cuda.memory_reserved() / 1E9 if torch.cuda.is_available() else 0) # (GB)
|
||||
s = ('%10s' * 2 + '%10.4g' * 6) % (
|
||||
'%g/%g' % (epoch, epochs - 1), mem, *mloss, targets.shape[0], imgs.shape[-1])
|
||||
pbar.set_description(s)
|
||||
|
||||
# Plot
|
||||
if ni < 1:
|
||||
f = 'train_batch%g.jpg' % i # filename
|
||||
res = plot_images(images=imgs, targets=targets, paths=paths, fname=f)
|
||||
if tb_writer:
|
||||
tb_writer.add_image(f, res, dataformats='HWC', global_step=epoch)
|
||||
# tb_writer.add_graph(model, imgs) # add model to tensorboard
|
||||
# Plot
|
||||
if plots and ni < 3:
|
||||
f = save_dir / f'train_batch{ni}.jpg' # filename
|
||||
plot_images(images=imgs, targets=targets, paths=paths, fname=f)
|
||||
# if tb_writer:
|
||||
# tb_writer.add_image(f, result, dataformats='HWC', global_step=epoch)
|
||||
# tb_writer.add_graph(model, imgs) # add model to tensorboard
|
||||
elif plots and ni == 3 and wandb:
|
||||
wandb.log({"Mosaics": [wandb.Image(str(x), caption=x.name) for x in save_dir.glob('train*.jpg')]})
|
||||
|
||||
# end batch ------------------------------------------------------------------------------------------------
|
||||
# end epoch ----------------------------------------------------------------------------------------------------
|
||||
|
||||
# Update scheduler
|
||||
# Scheduler
|
||||
lr = [x['lr'] for x in optimizer.param_groups] # for tensorboard
|
||||
scheduler.step()
|
||||
|
||||
# Process epoch results
|
||||
ema.update_attr(model)
|
||||
final_epoch = epoch + 1 == epochs
|
||||
if not opt.notest or final_epoch: # Calculate mAP
|
||||
is_coco = any([x in data for x in ['coco.data', 'coco2014.data', 'coco2017.data']]) and model.nc == 80
|
||||
results, maps = test.test(cfg,
|
||||
data,
|
||||
batch_size=batch_size,
|
||||
imgsz=imgsz_test,
|
||||
model=ema.ema,
|
||||
save_json=final_epoch and is_coco,
|
||||
single_cls=opt.single_cls,
|
||||
dataloader=testloader,
|
||||
multi_label=ni > n_burn)
|
||||
# DDP process 0 or single-GPU
|
||||
if rank in [-1, 0]:
|
||||
# mAP
|
||||
if ema:
|
||||
ema.update_attr(model, include=['yaml', 'nc', 'hyp', 'gr', 'names', 'stride'])
|
||||
final_epoch = epoch + 1 == epochs
|
||||
if not opt.notest or final_epoch: # Calculate mAP
|
||||
results, maps, times = test.test(opt.data,
|
||||
batch_size=total_batch_size,
|
||||
imgsz=imgsz_test,
|
||||
model=ema.ema,
|
||||
single_cls=opt.single_cls,
|
||||
dataloader=testloader,
|
||||
save_dir=save_dir,
|
||||
plots=plots and final_epoch,
|
||||
log_imgs=opt.log_imgs if wandb else 0)
|
||||
|
||||
# Write
|
||||
with open(results_file, 'a') as f:
|
||||
f.write(s + '%10.3g' * 7 % results + '\n') # P, R, mAP, F1, test_losses=(GIoU, obj, cls)
|
||||
if len(opt.name) and opt.bucket:
|
||||
os.system('gsutil cp results.txt gs://%s/results/results%s.txt' % (opt.bucket, opt.name))
|
||||
# Write
|
||||
with open(results_file, 'a') as f:
|
||||
f.write(s + '%10.4g' * 7 % results + '\n') # P, R, mAP@.5, mAP@.5-.95, val_loss(box, obj, cls)
|
||||
if len(opt.name) and opt.bucket:
|
||||
os.system('gsutil cp %s gs://%s/results/results%s.txt' % (results_file, opt.bucket, opt.name))
|
||||
|
||||
# Tensorboard
|
||||
if tb_writer:
|
||||
tags = ['train/giou_loss', 'train/obj_loss', 'train/cls_loss',
|
||||
'metrics/precision', 'metrics/recall', 'metrics/mAP_0.5', 'metrics/F1',
|
||||
'val/giou_loss', 'val/obj_loss', 'val/cls_loss']
|
||||
for x, tag in zip(list(mloss[:-1]) + list(results), tags):
|
||||
tb_writer.add_scalar(tag, x, epoch)
|
||||
# Log
|
||||
tags = ['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',
|
||||
'val/box_loss', 'val/obj_loss', 'val/cls_loss', # val loss
|
||||
'x/lr0', 'x/lr1', 'x/lr2'] # params
|
||||
for x, tag in zip(list(mloss[:-1]) + list(results) + lr, tags):
|
||||
if tb_writer:
|
||||
tb_writer.add_scalar(tag, x, epoch) # tensorboard
|
||||
if wandb:
|
||||
wandb.log({tag: x}) # W&B
|
||||
|
||||
# Update best mAP
|
||||
fi = fitness(np.array(results).reshape(1, -1)) # fitness_i = weighted combination of [P, R, mAP, F1]
|
||||
if fi > best_fitness:
|
||||
best_fitness = fi
|
||||
# Update best mAP
|
||||
fi = fitness(np.array(results).reshape(1, -1)) # weighted combination of [P, R, mAP@.5, mAP@.5-.95]
|
||||
if fi > best_fitness:
|
||||
best_fitness = fi
|
||||
|
||||
# Save model
|
||||
save = (not opt.nosave) or (final_epoch and not opt.evolve)
|
||||
if save:
|
||||
with open(results_file, 'r') as f: # create checkpoint
|
||||
ckpt = {'epoch': epoch,
|
||||
'best_fitness': best_fitness,
|
||||
'training_results': f.read(),
|
||||
'model': ema.ema.module.state_dict() if hasattr(model, 'module') else ema.ema.state_dict(),
|
||||
'optimizer': None if final_epoch else optimizer.state_dict()}
|
||||
|
||||
# Save last, best and delete
|
||||
torch.save(ckpt, last)
|
||||
if (best_fitness == fi) and not final_epoch:
|
||||
torch.save(ckpt, best)
|
||||
del ckpt
|
||||
# Save model
|
||||
save = (not opt.nosave) or (final_epoch and not opt.evolve)
|
||||
if save:
|
||||
with open(results_file, 'r') as f: # create checkpoint
|
||||
ckpt = {'epoch': epoch,
|
||||
'best_fitness': best_fitness,
|
||||
'training_results': f.read(),
|
||||
'model': ema.ema,
|
||||
'optimizer': None if final_epoch else optimizer.state_dict(),
|
||||
'wandb_id': wandb_run.id if wandb else None}
|
||||
|
||||
# Save last, best and delete
|
||||
torch.save(ckpt, last)
|
||||
if best_fitness == fi:
|
||||
torch.save(ckpt, best)
|
||||
del ckpt
|
||||
# end epoch ----------------------------------------------------------------------------------------------------
|
||||
# end training
|
||||
|
||||
n = opt.name
|
||||
if len(n):
|
||||
n = '_' + n if not n.isnumeric() else n
|
||||
fresults, flast, fbest = 'results%s.txt' % n, wdir + 'last%s.pt' % n, wdir + 'best%s.pt' % n
|
||||
for f1, f2 in zip([wdir + 'last.pt', wdir + 'best.pt', 'results.txt'], [flast, fbest, fresults]):
|
||||
if os.path.exists(f1):
|
||||
if rank in [-1, 0]:
|
||||
# Strip optimizers
|
||||
n = opt.name if opt.name.isnumeric() else ''
|
||||
fresults, flast, fbest = save_dir / f'results{n}.txt', wdir / f'last{n}.pt', wdir / f'best{n}.pt'
|
||||
for f1, f2 in zip([wdir / 'last.pt', wdir / 'best.pt', results_file], [flast, fbest, fresults]):
|
||||
if f1.exists():
|
||||
os.rename(f1, f2) # rename
|
||||
ispt = f2.endswith('.pt') # is *.pt
|
||||
strip_optimizer(f2) if ispt else None # strip optimizer
|
||||
os.system('gsutil cp %s gs://%s/weights' % (f2, opt.bucket)) if opt.bucket and ispt else None # upload
|
||||
if str(f2).endswith('.pt'): # is *.pt
|
||||
strip_optimizer(f2) # strip optimizer
|
||||
os.system('gsutil cp %s gs://%s/weights' % (f2, opt.bucket)) if opt.bucket else None # upload
|
||||
# Finish
|
||||
if plots:
|
||||
plot_results(save_dir=save_dir) # save as results.png
|
||||
if wandb:
|
||||
files = ['results.png', 'precision_recall_curve.png', 'confusion_matrix.png']
|
||||
wandb.log({"Results": [wandb.Image(str(save_dir / f), caption=f) for f in files
|
||||
if (save_dir / f).exists()]})
|
||||
logger.info('%g epochs completed in %.3f hours.\n' % (epoch - start_epoch + 1, (time.time() - t0) / 3600))
|
||||
else:
|
||||
dist.destroy_process_group()
|
||||
|
||||
if not opt.evolve:
|
||||
plot_results() # save as results.png
|
||||
print('%g epochs completed in %.3f hours.\n' % (epoch - start_epoch + 1, (time.time() - t0) / 3600))
|
||||
dist.destroy_process_group() if torch.cuda.device_count() > 1 else None
|
||||
wandb.run.finish() if wandb and wandb.run else None
|
||||
torch.cuda.empty_cache()
|
||||
return results
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument('--epochs', type=int, default=300) # 500200 batches at bs 16, 117263 COCO images = 273 epochs
|
||||
parser.add_argument('--batch-size', type=int, default=16) # effective bs = batch_size * accumulate = 16 * 4 = 64
|
||||
parser.add_argument('--cfg', type=str, default='cfg/yolov3-spp.cfg', help='*.cfg path')
|
||||
parser.add_argument('--data', type=str, default='data/coco2017.data', help='*.data path')
|
||||
parser.add_argument('--multi-scale', action='store_true', help='adjust (67%% - 150%%) img_size every 10 batches')
|
||||
parser.add_argument('--img-size', nargs='+', type=int, default=[320, 640], help='[min_train, max-train, test]')
|
||||
parser.add_argument('--weights', type=str, default='yolov3.pt', help='initial weights path')
|
||||
parser.add_argument('--cfg', type=str, default='', help='model.yaml path')
|
||||
parser.add_argument('--data', type=str, default='data/coco128.yaml', help='data.yaml path')
|
||||
parser.add_argument('--hyp', type=str, default='data/hyp.scratch.yaml', help='hyperparameters path')
|
||||
parser.add_argument('--epochs', type=int, default=300)
|
||||
parser.add_argument('--batch-size', type=int, default=16, help='total batch size for all GPUs')
|
||||
parser.add_argument('--img-size', nargs='+', type=int, default=[640, 640], help='[train, test] image sizes')
|
||||
parser.add_argument('--rect', action='store_true', help='rectangular training')
|
||||
parser.add_argument('--resume', action='store_true', help='resume training from last.pt')
|
||||
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('--notest', action='store_true', help='only test final epoch')
|
||||
parser.add_argument('--noautoanchor', action='store_true', help='disable autoanchor check')
|
||||
parser.add_argument('--evolve', action='store_true', help='evolve hyperparameters')
|
||||
parser.add_argument('--bucket', type=str, default='', help='gsutil bucket')
|
||||
parser.add_argument('--cache-images', action='store_true', help='cache images for faster training')
|
||||
parser.add_argument('--weights', type=str, default='weights/yolov3-spp-ultralytics.pt', help='initial weights path')
|
||||
parser.add_argument('--name', default='', help='renames results.txt to results_name.txt if supplied')
|
||||
parser.add_argument('--device', default='', help='device id (i.e. 0 or 0,1 or cpu)')
|
||||
parser.add_argument('--adam', action='store_true', help='use adam optimizer')
|
||||
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 as single-class dataset')
|
||||
parser.add_argument('--freeze-layers', action='store_true', help='Freeze non-output layers')
|
||||
parser.add_argument('--adam', action='store_true', help='use torch.optim.Adam() optimizer')
|
||||
parser.add_argument('--sync-bn', action='store_true', help='use SyncBatchNorm, only available in DDP mode')
|
||||
parser.add_argument('--local_rank', type=int, default=-1, help='DDP parameter, do not modify')
|
||||
parser.add_argument('--log-imgs', type=int, default=16, help='number of images for W&B logging, max 100')
|
||||
parser.add_argument('--workers', type=int, default=8, help='maximum number of dataloader workers')
|
||||
parser.add_argument('--project', default='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')
|
||||
opt = parser.parse_args()
|
||||
opt.weights = last if opt.resume and not opt.weights else opt.weights
|
||||
check_git_status()
|
||||
opt.cfg = check_file(opt.cfg) # check file
|
||||
opt.data = check_file(opt.data) # check file
|
||||
print(opt)
|
||||
opt.img_size.extend([opt.img_size[-1]] * (3 - len(opt.img_size))) # extend to 3 sizes (min, max, test)
|
||||
device = torch_utils.select_device(opt.device, apex=mixed_precision, batch_size=opt.batch_size)
|
||||
if device.type == 'cpu':
|
||||
mixed_precision = False
|
||||
|
||||
# scale hyp['obj'] by img_size (evolved at 320)
|
||||
# hyp['obj'] *= opt.img_size[0] / 320.
|
||||
# Set DDP variables
|
||||
opt.total_batch_size = opt.batch_size
|
||||
opt.world_size = int(os.environ['WORLD_SIZE']) if 'WORLD_SIZE' in os.environ else 1
|
||||
opt.global_rank = int(os.environ['RANK']) if 'RANK' in os.environ else -1
|
||||
set_logging(opt.global_rank)
|
||||
if opt.global_rank in [-1, 0]:
|
||||
check_git_status()
|
||||
|
||||
tb_writer = None
|
||||
if not opt.evolve: # Train normally
|
||||
print('Start Tensorboard with "tensorboard --logdir=runs", view at http://localhost:6006/')
|
||||
tb_writer = SummaryWriter(comment=opt.name)
|
||||
train(hyp) # train normally
|
||||
# Resume
|
||||
if opt.resume: # resume an interrupted run
|
||||
ckpt = opt.resume if isinstance(opt.resume, str) else get_latest_run() # specified or most recent path
|
||||
assert os.path.isfile(ckpt), 'ERROR: --resume checkpoint does not exist'
|
||||
with open(Path(ckpt).parent.parent / 'opt.yaml') as f:
|
||||
opt = argparse.Namespace(**yaml.load(f, Loader=yaml.FullLoader)) # replace
|
||||
opt.cfg, opt.weights, opt.resume = '', ckpt, True
|
||||
logger.info('Resuming training from %s' % ckpt)
|
||||
else:
|
||||
# opt.hyp = opt.hyp or ('hyp.finetune.yaml' if opt.weights else 'hyp.scratch.yaml')
|
||||
opt.data, opt.cfg, opt.hyp = check_file(opt.data), check_file(opt.cfg), check_file(opt.hyp) # check files
|
||||
assert len(opt.cfg) or len(opt.weights), 'either --cfg or --weights must be specified'
|
||||
opt.img_size.extend([opt.img_size[-1]] * (2 - len(opt.img_size))) # extend to 2 sizes (train, test)
|
||||
opt.name = 'evolve' if opt.evolve else opt.name
|
||||
opt.save_dir = increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok | opt.evolve) # increment run
|
||||
|
||||
else: # Evolve hyperparameters (optional)
|
||||
# DDP mode
|
||||
device = select_device(opt.device, batch_size=opt.batch_size)
|
||||
if opt.local_rank != -1:
|
||||
assert torch.cuda.device_count() > opt.local_rank
|
||||
torch.cuda.set_device(opt.local_rank)
|
||||
device = torch.device('cuda', opt.local_rank)
|
||||
dist.init_process_group(backend='nccl', init_method='env://') # distributed backend
|
||||
assert opt.batch_size % opt.world_size == 0, '--batch-size must be multiple of CUDA device count'
|
||||
opt.batch_size = opt.total_batch_size // opt.world_size
|
||||
|
||||
# Hyperparameters
|
||||
with open(opt.hyp) as f:
|
||||
hyp = yaml.load(f, Loader=yaml.FullLoader) # load hyps
|
||||
if 'box' not in hyp:
|
||||
warn('Compatibility: %s missing "box" which was renamed from "giou" in %s' %
|
||||
(opt.hyp, 'https://github.com/ultralytics/yolov5/pull/1120'))
|
||||
hyp['box'] = hyp.pop('giou')
|
||||
|
||||
# Train
|
||||
logger.info(opt)
|
||||
if not opt.evolve:
|
||||
tb_writer = None # init loggers
|
||||
if opt.global_rank in [-1, 0]:
|
||||
logger.info(f'Start Tensorboard with "tensorboard --logdir {opt.project}", view at http://localhost:6006/')
|
||||
tb_writer = SummaryWriter(opt.save_dir) # Tensorboard
|
||||
train(hyp, opt, device, tb_writer, wandb)
|
||||
|
||||
# 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)
|
||||
|
||||
assert opt.local_rank == -1, 'DDP mode not implemented for --evolve'
|
||||
opt.notest, opt.nosave = True, True # only test/save final epoch
|
||||
# ei = [isinstance(x, (int, float)) for x in hyp.values()] # evolvable indices
|
||||
yaml_file = Path(opt.save_dir) / 'hyp_evolved.yaml' # save best result here
|
||||
if opt.bucket:
|
||||
os.system('gsutil cp gs://%s/evolve.txt .' % opt.bucket) # download evolve.txt if exists
|
||||
|
||||
for _ in range(1): # generations to evolve
|
||||
if os.path.exists('evolve.txt'): # if evolve.txt exists: select best hyps and mutate
|
||||
for _ in range(300): # generations to evolve
|
||||
if Path('evolve.txt').exists(): # if evolve.txt exists: select best hyps and mutate
|
||||
# Select parent(s)
|
||||
parent = 'single' # parent selection method: 'single' or 'weighted'
|
||||
x = np.loadtxt('evolve.txt', ndmin=2)
|
||||
@ -450,34 +545,30 @@ if __name__ == '__main__':
|
||||
x = (x * w.reshape(n, 1)).sum(0) / w.sum() # weighted combination
|
||||
|
||||
# Mutate
|
||||
method, mp, s = 3, 0.9, 0.2 # method, mutation probability, sigma
|
||||
mp, s = 0.8, 0.2 # mutation probability, sigma
|
||||
npr = np.random
|
||||
npr.seed(int(time.time()))
|
||||
g = np.array([1, 1, 1, 1, 1, 1, 1, 0, .1, 1, 0, 1, 1, 1, 1, 1, 1, 1]) # gains
|
||||
ng = len(g)
|
||||
if method == 1:
|
||||
v = (npr.randn(ng) * npr.random() * g * s + 1) ** 2.0
|
||||
elif method == 2:
|
||||
v = (npr.randn(ng) * npr.random(ng) * g * s + 1) ** 2.0
|
||||
elif method == 3:
|
||||
v = np.ones(ng)
|
||||
while all(v == 1): # mutate until a change occurs (prevent duplicates)
|
||||
# v = (g * (npr.random(ng) < mp) * npr.randn(ng) * s + 1) ** 2.0
|
||||
v = (g * (npr.random(ng) < mp) * npr.randn(ng) * npr.random() * s + 1).clip(0.3, 3.0)
|
||||
g = np.array([x[0] for x in meta.values()]) # 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] = x[i + 7] * v[i] # mutate
|
||||
hyp[k] = float(x[i + 7] * v[i]) # mutate
|
||||
|
||||
# Clip to limits
|
||||
keys = ['lr0', 'iou_t', 'momentum', 'weight_decay', 'hsv_s', 'hsv_v', 'translate', 'scale', 'fl_gamma']
|
||||
limits = [(1e-5, 1e-2), (0.00, 0.70), (0.60, 0.98), (0, 0.001), (0, .9), (0, .9), (0, .9), (0, .9), (0, 3)]
|
||||
for k, v in zip(keys, limits):
|
||||
hyp[k] = np.clip(hyp[k], v[0], v[1])
|
||||
# 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())
|
||||
results = train(hyp.copy(), opt, device, wandb=wandb)
|
||||
|
||||
# Write mutation results
|
||||
print_mutation(hyp, results, opt.bucket)
|
||||
print_mutation(hyp.copy(), results, yaml_file, opt.bucket)
|
||||
|
||||
# Plot results
|
||||
# plot_evolution_results(hyp)
|
||||
# Plot results
|
||||
plot_evolution(yaml_file)
|
||||
print(f'Hyperparameter evolution complete. Best results saved as: {yaml_file}\n'
|
||||
f'Command to train a new model with these hyperparameters: $ python train.py --hyp {yaml_file}')
|
||||
|
||||
1687
tutorial.ipynb
vendored
1687
tutorial.ipynb
vendored
File diff suppressed because one or more lines are too long
72
utils/activations.py
Normal file
72
utils/activations.py
Normal file
@ -0,0 +1,72 @@
|
||||
# Activation functions
|
||||
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
|
||||
|
||||
# Swish https://arxiv.org/pdf/1905.02244.pdf ---------------------------------------------------------------------------
|
||||
class Swish(nn.Module): #
|
||||
@staticmethod
|
||||
def forward(x):
|
||||
return x * torch.sigmoid(x)
|
||||
|
||||
|
||||
class Hardswish(nn.Module): # export-friendly version of nn.Hardswish()
|
||||
@staticmethod
|
||||
def forward(x):
|
||||
# return x * F.hardsigmoid(x) # for torchscript and CoreML
|
||||
return x * F.hardtanh(x + 3, 0., 6.) / 6. # for torchscript, CoreML and ONNX
|
||||
|
||||
|
||||
class MemoryEfficientSwish(nn.Module):
|
||||
class F(torch.autograd.Function):
|
||||
@staticmethod
|
||||
def forward(ctx, x):
|
||||
ctx.save_for_backward(x)
|
||||
return x * torch.sigmoid(x)
|
||||
|
||||
@staticmethod
|
||||
def backward(ctx, grad_output):
|
||||
x = ctx.saved_tensors[0]
|
||||
sx = torch.sigmoid(x)
|
||||
return grad_output * (sx * (1 + x * (1 - sx)))
|
||||
|
||||
def forward(self, x):
|
||||
return self.F.apply(x)
|
||||
|
||||
|
||||
# Mish https://github.com/digantamisra98/Mish --------------------------------------------------------------------------
|
||||
class Mish(nn.Module):
|
||||
@staticmethod
|
||||
def forward(x):
|
||||
return x * F.softplus(x).tanh()
|
||||
|
||||
|
||||
class MemoryEfficientMish(nn.Module):
|
||||
class F(torch.autograd.Function):
|
||||
@staticmethod
|
||||
def forward(ctx, x):
|
||||
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):
|
||||
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):
|
||||
return self.F.apply(x)
|
||||
|
||||
|
||||
# FReLU https://arxiv.org/abs/2007.11824 -------------------------------------------------------------------------------
|
||||
class FReLU(nn.Module):
|
||||
def __init__(self, c1, k=3): # ch_in, kernel
|
||||
super().__init__()
|
||||
self.conv = nn.Conv2d(c1, c1, k, 1, 1, groups=c1)
|
||||
self.bn = nn.BatchNorm2d(c1)
|
||||
|
||||
def forward(self, x):
|
||||
return torch.max(x, self.bn(self.conv(x)))
|
||||
@ -1,236 +0,0 @@
|
||||
import math
|
||||
|
||||
import torch
|
||||
from torch.optim.optimizer import Optimizer
|
||||
|
||||
|
||||
class AdaBound(Optimizer):
|
||||
"""Implements AdaBound algorithm.
|
||||
It has been proposed in `Adaptive Gradient Methods with Dynamic Bound of Learning Rate`_.
|
||||
Arguments:
|
||||
params (iterable): iterable of parameters to optimize or dicts defining
|
||||
parameter groups
|
||||
lr (float, optional): Adam learning rate (default: 1e-3)
|
||||
betas (Tuple[float, float], optional): coefficients used for computing
|
||||
running averages of gradient and its square (default: (0.9, 0.999))
|
||||
final_lr (float, optional): final (SGD) learning rate (default: 0.1)
|
||||
gamma (float, optional): convergence speed of the bound functions (default: 1e-3)
|
||||
eps (float, optional): term added to the denominator to improve
|
||||
numerical stability (default: 1e-8)
|
||||
weight_decay (float, optional): weight decay (L2 penalty) (default: 0)
|
||||
amsbound (boolean, optional): whether to use the AMSBound variant of this algorithm
|
||||
.. Adaptive Gradient Methods with Dynamic Bound of Learning Rate:
|
||||
https://openreview.net/forum?id=Bkg3g2R9FX
|
||||
"""
|
||||
|
||||
def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), final_lr=0.1, gamma=1e-3,
|
||||
eps=1e-8, weight_decay=0, amsbound=False):
|
||||
if not 0.0 <= lr:
|
||||
raise ValueError("Invalid learning rate: {}".format(lr))
|
||||
if not 0.0 <= eps:
|
||||
raise ValueError("Invalid epsilon value: {}".format(eps))
|
||||
if not 0.0 <= betas[0] < 1.0:
|
||||
raise ValueError("Invalid beta parameter at index 0: {}".format(betas[0]))
|
||||
if not 0.0 <= betas[1] < 1.0:
|
||||
raise ValueError("Invalid beta parameter at index 1: {}".format(betas[1]))
|
||||
if not 0.0 <= final_lr:
|
||||
raise ValueError("Invalid final learning rate: {}".format(final_lr))
|
||||
if not 0.0 <= gamma < 1.0:
|
||||
raise ValueError("Invalid gamma parameter: {}".format(gamma))
|
||||
defaults = dict(lr=lr, betas=betas, final_lr=final_lr, gamma=gamma, eps=eps,
|
||||
weight_decay=weight_decay, amsbound=amsbound)
|
||||
super(AdaBound, self).__init__(params, defaults)
|
||||
|
||||
self.base_lrs = list(map(lambda group: group['lr'], self.param_groups))
|
||||
|
||||
def __setstate__(self, state):
|
||||
super(AdaBound, self).__setstate__(state)
|
||||
for group in self.param_groups:
|
||||
group.setdefault('amsbound', False)
|
||||
|
||||
def step(self, closure=None):
|
||||
"""Performs a single optimization step.
|
||||
Arguments:
|
||||
closure (callable, optional): A closure that reevaluates the model
|
||||
and returns the loss.
|
||||
"""
|
||||
loss = None
|
||||
if closure is not None:
|
||||
loss = closure()
|
||||
|
||||
for group, base_lr in zip(self.param_groups, self.base_lrs):
|
||||
for p in group['params']:
|
||||
if p.grad is None:
|
||||
continue
|
||||
grad = p.grad.data
|
||||
if grad.is_sparse:
|
||||
raise RuntimeError(
|
||||
'Adam does not support sparse gradients, please consider SparseAdam instead')
|
||||
amsbound = group['amsbound']
|
||||
|
||||
state = self.state[p]
|
||||
|
||||
# State initialization
|
||||
if len(state) == 0:
|
||||
state['step'] = 0
|
||||
# Exponential moving average of gradient values
|
||||
state['exp_avg'] = torch.zeros_like(p.data)
|
||||
# Exponential moving average of squared gradient values
|
||||
state['exp_avg_sq'] = torch.zeros_like(p.data)
|
||||
if amsbound:
|
||||
# Maintains max of all exp. moving avg. of sq. grad. values
|
||||
state['max_exp_avg_sq'] = torch.zeros_like(p.data)
|
||||
|
||||
exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq']
|
||||
if amsbound:
|
||||
max_exp_avg_sq = state['max_exp_avg_sq']
|
||||
beta1, beta2 = group['betas']
|
||||
|
||||
state['step'] += 1
|
||||
|
||||
if group['weight_decay'] != 0:
|
||||
grad = grad.add(group['weight_decay'], p.data)
|
||||
|
||||
# Decay the first and second moment running average coefficient
|
||||
exp_avg.mul_(beta1).add_(1 - beta1, grad)
|
||||
exp_avg_sq.mul_(beta2).addcmul_(1 - beta2, grad, grad)
|
||||
if amsbound:
|
||||
# Maintains the maximum of all 2nd moment running avg. till now
|
||||
torch.max(max_exp_avg_sq, exp_avg_sq, out=max_exp_avg_sq)
|
||||
# Use the max. for normalizing running avg. of gradient
|
||||
denom = max_exp_avg_sq.sqrt().add_(group['eps'])
|
||||
else:
|
||||
denom = exp_avg_sq.sqrt().add_(group['eps'])
|
||||
|
||||
bias_correction1 = 1 - beta1 ** state['step']
|
||||
bias_correction2 = 1 - beta2 ** state['step']
|
||||
step_size = group['lr'] * math.sqrt(bias_correction2) / bias_correction1
|
||||
|
||||
# Applies bounds on actual learning rate
|
||||
# lr_scheduler cannot affect final_lr, this is a workaround to apply lr decay
|
||||
final_lr = group['final_lr'] * group['lr'] / base_lr
|
||||
lower_bound = final_lr * (1 - 1 / (group['gamma'] * state['step'] + 1))
|
||||
upper_bound = final_lr * (1 + 1 / (group['gamma'] * state['step']))
|
||||
step_size = torch.full_like(denom, step_size)
|
||||
step_size.div_(denom).clamp_(lower_bound, upper_bound).mul_(exp_avg)
|
||||
|
||||
p.data.add_(-step_size)
|
||||
|
||||
return loss
|
||||
|
||||
|
||||
class AdaBoundW(Optimizer):
|
||||
"""Implements AdaBound algorithm with Decoupled Weight Decay (arxiv.org/abs/1711.05101)
|
||||
It has been proposed in `Adaptive Gradient Methods with Dynamic Bound of Learning Rate`_.
|
||||
Arguments:
|
||||
params (iterable): iterable of parameters to optimize or dicts defining
|
||||
parameter groups
|
||||
lr (float, optional): Adam learning rate (default: 1e-3)
|
||||
betas (Tuple[float, float], optional): coefficients used for computing
|
||||
running averages of gradient and its square (default: (0.9, 0.999))
|
||||
final_lr (float, optional): final (SGD) learning rate (default: 0.1)
|
||||
gamma (float, optional): convergence speed of the bound functions (default: 1e-3)
|
||||
eps (float, optional): term added to the denominator to improve
|
||||
numerical stability (default: 1e-8)
|
||||
weight_decay (float, optional): weight decay (L2 penalty) (default: 0)
|
||||
amsbound (boolean, optional): whether to use the AMSBound variant of this algorithm
|
||||
.. Adaptive Gradient Methods with Dynamic Bound of Learning Rate:
|
||||
https://openreview.net/forum?id=Bkg3g2R9FX
|
||||
"""
|
||||
|
||||
def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), final_lr=0.1, gamma=1e-3,
|
||||
eps=1e-8, weight_decay=0, amsbound=False):
|
||||
if not 0.0 <= lr:
|
||||
raise ValueError("Invalid learning rate: {}".format(lr))
|
||||
if not 0.0 <= eps:
|
||||
raise ValueError("Invalid epsilon value: {}".format(eps))
|
||||
if not 0.0 <= betas[0] < 1.0:
|
||||
raise ValueError("Invalid beta parameter at index 0: {}".format(betas[0]))
|
||||
if not 0.0 <= betas[1] < 1.0:
|
||||
raise ValueError("Invalid beta parameter at index 1: {}".format(betas[1]))
|
||||
if not 0.0 <= final_lr:
|
||||
raise ValueError("Invalid final learning rate: {}".format(final_lr))
|
||||
if not 0.0 <= gamma < 1.0:
|
||||
raise ValueError("Invalid gamma parameter: {}".format(gamma))
|
||||
defaults = dict(lr=lr, betas=betas, final_lr=final_lr, gamma=gamma, eps=eps,
|
||||
weight_decay=weight_decay, amsbound=amsbound)
|
||||
super(AdaBoundW, self).__init__(params, defaults)
|
||||
|
||||
self.base_lrs = list(map(lambda group: group['lr'], self.param_groups))
|
||||
|
||||
def __setstate__(self, state):
|
||||
super(AdaBoundW, self).__setstate__(state)
|
||||
for group in self.param_groups:
|
||||
group.setdefault('amsbound', False)
|
||||
|
||||
def step(self, closure=None):
|
||||
"""Performs a single optimization step.
|
||||
Arguments:
|
||||
closure (callable, optional): A closure that reevaluates the model
|
||||
and returns the loss.
|
||||
"""
|
||||
loss = None
|
||||
if closure is not None:
|
||||
loss = closure()
|
||||
|
||||
for group, base_lr in zip(self.param_groups, self.base_lrs):
|
||||
for p in group['params']:
|
||||
if p.grad is None:
|
||||
continue
|
||||
grad = p.grad.data
|
||||
if grad.is_sparse:
|
||||
raise RuntimeError(
|
||||
'Adam does not support sparse gradients, please consider SparseAdam instead')
|
||||
amsbound = group['amsbound']
|
||||
|
||||
state = self.state[p]
|
||||
|
||||
# State initialization
|
||||
if len(state) == 0:
|
||||
state['step'] = 0
|
||||
# Exponential moving average of gradient values
|
||||
state['exp_avg'] = torch.zeros_like(p.data)
|
||||
# Exponential moving average of squared gradient values
|
||||
state['exp_avg_sq'] = torch.zeros_like(p.data)
|
||||
if amsbound:
|
||||
# Maintains max of all exp. moving avg. of sq. grad. values
|
||||
state['max_exp_avg_sq'] = torch.zeros_like(p.data)
|
||||
|
||||
exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq']
|
||||
if amsbound:
|
||||
max_exp_avg_sq = state['max_exp_avg_sq']
|
||||
beta1, beta2 = group['betas']
|
||||
|
||||
state['step'] += 1
|
||||
|
||||
# Decay the first and second moment running average coefficient
|
||||
exp_avg.mul_(beta1).add_(1 - beta1, grad)
|
||||
exp_avg_sq.mul_(beta2).addcmul_(1 - beta2, grad, grad)
|
||||
if amsbound:
|
||||
# Maintains the maximum of all 2nd moment running avg. till now
|
||||
torch.max(max_exp_avg_sq, exp_avg_sq, out=max_exp_avg_sq)
|
||||
# Use the max. for normalizing running avg. of gradient
|
||||
denom = max_exp_avg_sq.sqrt().add_(group['eps'])
|
||||
else:
|
||||
denom = exp_avg_sq.sqrt().add_(group['eps'])
|
||||
|
||||
bias_correction1 = 1 - beta1 ** state['step']
|
||||
bias_correction2 = 1 - beta2 ** state['step']
|
||||
step_size = group['lr'] * math.sqrt(bias_correction2) / bias_correction1
|
||||
|
||||
# Applies bounds on actual learning rate
|
||||
# lr_scheduler cannot affect final_lr, this is a workaround to apply lr decay
|
||||
final_lr = group['final_lr'] * group['lr'] / base_lr
|
||||
lower_bound = final_lr * (1 - 1 / (group['gamma'] * state['step'] + 1))
|
||||
upper_bound = final_lr * (1 + 1 / (group['gamma'] * state['step']))
|
||||
step_size = torch.full_like(denom, step_size)
|
||||
step_size.div_(denom).clamp_(lower_bound, upper_bound).mul_(exp_avg)
|
||||
|
||||
if group['weight_decay'] != 0:
|
||||
decayed_weights = torch.mul(p.data, group['weight_decay'])
|
||||
p.data.add_(-step_size)
|
||||
p.data.sub_(decayed_weights)
|
||||
else:
|
||||
p.data.add_(-step_size)
|
||||
|
||||
return loss
|
||||
152
utils/autoanchor.py
Normal file
152
utils/autoanchor.py
Normal file
@ -0,0 +1,152 @@
|
||||
# Auto-anchor utils
|
||||
|
||||
import numpy as np
|
||||
import torch
|
||||
import yaml
|
||||
from scipy.cluster.vq import kmeans
|
||||
from tqdm import tqdm
|
||||
|
||||
|
||||
def check_anchor_order(m):
|
||||
# Check anchor order against stride order for YOLOv3 Detect() module m, and correct if necessary
|
||||
a = m.anchor_grid.prod(-1).view(-1) # anchor area
|
||||
da = a[-1] - a[0] # delta a
|
||||
ds = m.stride[-1] - m.stride[0] # delta s
|
||||
if da.sign() != ds.sign(): # same order
|
||||
print('Reversing anchor order')
|
||||
m.anchors[:] = m.anchors.flip(0)
|
||||
m.anchor_grid[:] = m.anchor_grid.flip(0)
|
||||
|
||||
|
||||
def check_anchors(dataset, model, thr=4.0, imgsz=640):
|
||||
# Check anchor fit to data, recompute if necessary
|
||||
print('\nAnalyzing anchors... ', end='')
|
||||
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
|
||||
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
|
||||
|
||||
bpr, aat = metric(m.anchor_grid.clone().cpu().view(-1, 2))
|
||||
print('anchors/target = %.2f, Best Possible Recall (BPR) = %.4f' % (aat, bpr), end='')
|
||||
if bpr < 0.98: # threshold to recompute
|
||||
print('. Attempting to improve anchors, please wait...')
|
||||
na = m.anchor_grid.numel() // 2 # number of anchors
|
||||
new_anchors = kmean_anchors(dataset, n=na, img_size=imgsz, thr=thr, gen=1000, verbose=False)
|
||||
new_bpr = metric(new_anchors.reshape(-1, 2))[0]
|
||||
if new_bpr > bpr: # replace anchors
|
||||
new_anchors = torch.tensor(new_anchors, device=m.anchors.device).type_as(m.anchors)
|
||||
m.anchor_grid[:] = new_anchors.clone().view_as(m.anchor_grid) # for inference
|
||||
m.anchors[:] = new_anchors.clone().view_as(m.anchors) / m.stride.to(m.anchors.device).view(-1, 1, 1) # loss
|
||||
check_anchor_order(m)
|
||||
print('New anchors saved to model. Update model *.yaml to use these anchors in the future.')
|
||||
else:
|
||||
print('Original anchors better than new anchors. Proceeding with original anchors.')
|
||||
print('') # newline
|
||||
|
||||
|
||||
def kmean_anchors(path='./data/coco128.yaml', n=9, img_size=640, thr=4.0, gen=1000, verbose=True):
|
||||
""" Creates kmeans-evolved anchors from training dataset
|
||||
|
||||
Arguments:
|
||||
path: path to dataset *.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
|
||||
|
||||
Return:
|
||||
k: kmeans evolved anchors
|
||||
|
||||
Usage:
|
||||
from utils.autoanchor import *; _ = kmean_anchors()
|
||||
"""
|
||||
thr = 1. / thr
|
||||
|
||||
def metric(k, wh): # compute metrics
|
||||
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
|
||||
_, best = metric(torch.tensor(k, dtype=torch.float32), wh)
|
||||
return (best * (best > thr).float()).mean() # fitness
|
||||
|
||||
def print_results(k):
|
||||
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
|
||||
print('thr=%.2f: %.4f best possible recall, %.2f anchors past thr' % (thr, bpr, aat))
|
||||
print('n=%g, img_size=%s, metric_all=%.3f/%.3f-mean/best, past_thr=%.3f-mean: ' %
|
||||
(n, img_size, x.mean(), best.mean(), x[x > thr].mean()), end='')
|
||||
for i, x in enumerate(k):
|
||||
print('%i,%i' % (round(x[0]), round(x[1])), end=', ' if i < len(k) - 1 else '\n') # use in *.cfg
|
||||
return k
|
||||
|
||||
if isinstance(path, str): # *.yaml file
|
||||
with open(path) as f:
|
||||
data_dict = yaml.load(f, Loader=yaml.FullLoader) # model dict
|
||||
from utils.datasets import LoadImagesAndLabels
|
||||
dataset = LoadImagesAndLabels(data_dict['train'], augment=True, rect=True)
|
||||
else:
|
||||
dataset = path # dataset
|
||||
|
||||
# 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:
|
||||
print('WARNING: Extremely small objects found. '
|
||||
'%g of %g labels are < 3 pixels in width or height.' % (i, len(wh0)))
|
||||
wh = wh0[(wh0 >= 2.0).any(1)] # filter > 2 pixels
|
||||
|
||||
# Kmeans calculation
|
||||
print('Running kmeans for %g anchors on %g points...' % (n, len(wh)))
|
||||
s = wh.std(0) # sigmas for whitening
|
||||
k, dist = kmeans(wh / s, n, iter=30) # points, mean distance
|
||||
k *= s
|
||||
wh = torch.tensor(wh, dtype=torch.float32) # filtered
|
||||
wh0 = torch.tensor(wh0, dtype=torch.float32) # unfiltered
|
||||
k = print_results(k)
|
||||
|
||||
# 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))
|
||||
# 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.tight_layout()
|
||||
# fig.savefig('wh.png', dpi=200)
|
||||
|
||||
# Evolve
|
||||
npr = np.random
|
||||
f, sh, mp, s = anchor_fitness(k), k.shape, 0.9, 0.1 # fitness, generations, mutation prob, sigma
|
||||
pbar = tqdm(range(gen), desc='Evolving anchors with Genetic Algorithm') # 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) * npr.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 = 'Evolving anchors with Genetic Algorithm: fitness = %.4f' % f
|
||||
if verbose:
|
||||
print_results(k)
|
||||
|
||||
return print_results(k)
|
||||
@ -1,9 +1,14 @@
|
||||
# Dataset utils and dataloaders
|
||||
|
||||
import glob
|
||||
import logging
|
||||
import math
|
||||
import os
|
||||
import random
|
||||
import shutil
|
||||
import time
|
||||
from itertools import repeat
|
||||
from multiprocessing.pool import ThreadPool
|
||||
from pathlib import Path
|
||||
from threading import Thread
|
||||
|
||||
@ -14,11 +19,14 @@ from PIL import Image, ExifTags
|
||||
from torch.utils.data import Dataset
|
||||
from tqdm import tqdm
|
||||
|
||||
from utils.utils import xyxy2xywh, xywh2xyxy
|
||||
from utils.general import xyxy2xywh, xywh2xyxy
|
||||
from utils.torch_utils import torch_distributed_zero_first
|
||||
|
||||
# Parameters
|
||||
help_url = 'https://github.com/ultralytics/yolov3/wiki/Train-Custom-Data'
|
||||
img_formats = ['.bmp', '.jpg', '.jpeg', '.png', '.tif', '.tiff', '.dng']
|
||||
vid_formats = ['.mov', '.avi', '.mp4', '.mpg', '.mpeg', '.m4v', '.wmv', '.mkv']
|
||||
img_formats = ['bmp', 'jpg', 'jpeg', 'png', 'tif', 'tiff', 'dng'] # acceptable image suffixes
|
||||
vid_formats = ['mov', 'avi', 'mp4', 'mpg', 'mpeg', 'm4v', 'wmv', 'mkv'] # acceptable video suffixes
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
# Get orientation exif tag
|
||||
for orientation in ExifTags.TAGS.keys():
|
||||
@ -26,6 +34,11 @@ for orientation in ExifTags.TAGS.keys():
|
||||
break
|
||||
|
||||
|
||||
def get_hash(files):
|
||||
# Returns a single hash value of a list of files
|
||||
return sum(os.path.getsize(f) for f in files if os.path.isfile(f))
|
||||
|
||||
|
||||
def exif_size(img):
|
||||
# Returns exif-corrected PIL size
|
||||
s = img.size # (width, height)
|
||||
@ -41,37 +54,104 @@ def exif_size(img):
|
||||
return s
|
||||
|
||||
|
||||
class LoadImages: # for inference
|
||||
def __init__(self, path, img_size=416):
|
||||
path = str(Path(path)) # os-agnostic
|
||||
files = []
|
||||
if os.path.isdir(path):
|
||||
files = sorted(glob.glob(os.path.join(path, '*.*')))
|
||||
elif os.path.isfile(path):
|
||||
files = [path]
|
||||
def create_dataloader(path, imgsz, batch_size, stride, opt, hyp=None, augment=False, cache=False, pad=0.0, rect=False,
|
||||
rank=-1, world_size=1, workers=8, image_weights=False):
|
||||
# Make sure only the first process in DDP process the dataset first, and the following others can use the cache
|
||||
with torch_distributed_zero_first(rank):
|
||||
dataset = LoadImagesAndLabels(path, imgsz, batch_size,
|
||||
augment=augment, # augment images
|
||||
hyp=hyp, # augmentation hyperparameters
|
||||
rect=rect, # rectangular training
|
||||
cache_images=cache,
|
||||
single_cls=opt.single_cls,
|
||||
stride=int(stride),
|
||||
pad=pad,
|
||||
rank=rank,
|
||||
image_weights=image_weights)
|
||||
|
||||
images = [x for x in files if os.path.splitext(x)[-1].lower() in img_formats]
|
||||
videos = [x for x in files if os.path.splitext(x)[-1].lower() in vid_formats]
|
||||
nI, nV = len(images), len(videos)
|
||||
batch_size = min(batch_size, len(dataset))
|
||||
nw = min([os.cpu_count() // world_size, batch_size if batch_size > 1 else 0, workers]) # number of workers
|
||||
sampler = torch.utils.data.distributed.DistributedSampler(dataset) if rank != -1 else None
|
||||
loader = torch.utils.data.DataLoader if image_weights else InfiniteDataLoader
|
||||
# Use torch.utils.data.DataLoader() if dataset.properties will update during training else InfiniteDataLoader()
|
||||
dataloader = loader(dataset,
|
||||
batch_size=batch_size,
|
||||
num_workers=nw,
|
||||
sampler=sampler,
|
||||
pin_memory=True,
|
||||
collate_fn=LoadImagesAndLabels.collate_fn)
|
||||
return dataloader, dataset
|
||||
|
||||
|
||||
class InfiniteDataLoader(torch.utils.data.dataloader.DataLoader):
|
||||
""" Dataloader that reuses workers
|
||||
|
||||
Uses same syntax as vanilla DataLoader
|
||||
"""
|
||||
|
||||
def __init__(self, *args, **kwargs):
|
||||
super().__init__(*args, **kwargs)
|
||||
object.__setattr__(self, 'batch_sampler', _RepeatSampler(self.batch_sampler))
|
||||
self.iterator = super().__iter__()
|
||||
|
||||
def __len__(self):
|
||||
return len(self.batch_sampler.sampler)
|
||||
|
||||
def __iter__(self):
|
||||
for i in range(len(self)):
|
||||
yield next(self.iterator)
|
||||
|
||||
|
||||
class _RepeatSampler(object):
|
||||
""" Sampler that repeats forever
|
||||
|
||||
Args:
|
||||
sampler (Sampler)
|
||||
"""
|
||||
|
||||
def __init__(self, sampler):
|
||||
self.sampler = sampler
|
||||
|
||||
def __iter__(self):
|
||||
while True:
|
||||
yield from iter(self.sampler)
|
||||
|
||||
|
||||
class LoadImages: # for inference
|
||||
def __init__(self, path, img_size=640):
|
||||
p = str(Path(path)) # os-agnostic
|
||||
p = os.path.abspath(p) # absolute path
|
||||
if '*' in p:
|
||||
files = sorted(glob.glob(p, recursive=True)) # glob
|
||||
elif os.path.isdir(p):
|
||||
files = sorted(glob.glob(os.path.join(p, '*.*'))) # dir
|
||||
elif os.path.isfile(p):
|
||||
files = [p] # files
|
||||
else:
|
||||
raise Exception('ERROR: %s does not exist' % p)
|
||||
|
||||
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.files = images + videos
|
||||
self.nF = nI + nV # number of files
|
||||
self.video_flag = [False] * nI + [True] * nV
|
||||
self.nf = ni + nv # number of files
|
||||
self.video_flag = [False] * ni + [True] * nv
|
||||
self.mode = 'images'
|
||||
if any(videos):
|
||||
self.new_video(videos[0]) # new video
|
||||
else:
|
||||
self.cap = None
|
||||
assert self.nF > 0, 'No images or videos found in %s. Supported formats are:\nimages: %s\nvideos: %s' % \
|
||||
(path, img_formats, vid_formats)
|
||||
assert self.nf > 0, 'No images or videos found in %s. Supported formats are:\nimages: %s\nvideos: %s' % \
|
||||
(p, img_formats, vid_formats)
|
||||
|
||||
def __iter__(self):
|
||||
self.count = 0
|
||||
return self
|
||||
|
||||
def __next__(self):
|
||||
if self.count == self.nF:
|
||||
if self.count == self.nf:
|
||||
raise StopIteration
|
||||
path = self.files[self.count]
|
||||
|
||||
@ -82,7 +162,7 @@ class LoadImages: # for inference
|
||||
if not ret_val:
|
||||
self.count += 1
|
||||
self.cap.release()
|
||||
if self.count == self.nF: # last video
|
||||
if self.count == self.nf: # last video
|
||||
raise StopIteration
|
||||
else:
|
||||
path = self.files[self.count]
|
||||
@ -90,14 +170,14 @@ class LoadImages: # for inference
|
||||
ret_val, img0 = self.cap.read()
|
||||
|
||||
self.frame += 1
|
||||
print('video %g/%g (%g/%g) %s: ' % (self.count + 1, self.nF, self.frame, self.nframes, path), end='')
|
||||
print('video %g/%g (%g/%g) %s: ' % (self.count + 1, self.nf, self.frame, self.nframes, path), end='')
|
||||
|
||||
else:
|
||||
# Read image
|
||||
self.count += 1
|
||||
img0 = cv2.imread(path) # BGR
|
||||
assert img0 is not None, 'Image Not Found ' + path
|
||||
print('image %g/%g %s: ' % (self.count, self.nF, path), end='')
|
||||
print('image %g/%g %s: ' % (self.count, self.nf, path), end='')
|
||||
|
||||
# Padded resize
|
||||
img = letterbox(img0, new_shape=self.img_size)[0]
|
||||
@ -106,7 +186,6 @@ class LoadImages: # for inference
|
||||
img = img[:, :, ::-1].transpose(2, 0, 1) # BGR to RGB, to 3x416x416
|
||||
img = np.ascontiguousarray(img)
|
||||
|
||||
# cv2.imwrite(path + '.letterbox.jpg', 255 * img.transpose((1, 2, 0))[:, :, ::-1]) # save letterbox image
|
||||
return path, img, img0, self.cap
|
||||
|
||||
def new_video(self, path):
|
||||
@ -115,27 +194,19 @@ class LoadImages: # for inference
|
||||
self.nframes = int(self.cap.get(cv2.CAP_PROP_FRAME_COUNT))
|
||||
|
||||
def __len__(self):
|
||||
return self.nF # number of files
|
||||
return self.nf # number of files
|
||||
|
||||
|
||||
class LoadWebcam: # for inference
|
||||
def __init__(self, pipe=0, img_size=416):
|
||||
def __init__(self, pipe='0', img_size=640):
|
||||
self.img_size = img_size
|
||||
|
||||
if pipe == '0':
|
||||
pipe = 0 # local camera
|
||||
if pipe.isnumeric():
|
||||
pipe = eval(pipe) # local camera
|
||||
# pipe = 'rtsp://192.168.1.64/1' # IP camera
|
||||
# pipe = 'rtsp://username:password@192.168.1.64/1' # IP camera with login
|
||||
# pipe = 'rtsp://170.93.143.139/rtplive/470011e600ef003a004ee33696235daa' # IP traffic camera
|
||||
# pipe = 'http://wmccpinetop.axiscam.net/mjpg/video.mjpg' # IP golf camera
|
||||
|
||||
# https://answers.opencv.org/question/215996/changing-gstreamer-pipeline-to-opencv-in-pythonsolved/
|
||||
# pipe = '"rtspsrc location="rtsp://username:password@192.168.1.64/1" latency=10 ! appsink' # GStreamer
|
||||
|
||||
# https://answers.opencv.org/question/200787/video-acceleration-gstremer-pipeline-in-videocapture/
|
||||
# https://stackoverflow.com/questions/54095699/install-gstreamer-support-for-opencv-python-package # install help
|
||||
# pipe = "rtspsrc location=rtsp://root:root@192.168.0.91:554/axis-media/media.amp?videocodec=h264&resolution=3840x2160 protocols=GST_RTSP_LOWER_TRANS_TCP ! rtph264depay ! queue ! vaapih264dec ! videoconvert ! appsink" # GStreamer
|
||||
|
||||
self.pipe = pipe
|
||||
self.cap = cv2.VideoCapture(pipe) # video capture object
|
||||
self.cap.set(cv2.CAP_PROP_BUFFERSIZE, 3) # set buffer size
|
||||
@ -184,7 +255,7 @@ class LoadWebcam: # for inference
|
||||
|
||||
|
||||
class LoadStreams: # multiple IP or RTSP cameras
|
||||
def __init__(self, sources='streams.txt', img_size=416):
|
||||
def __init__(self, sources='streams.txt', img_size=640):
|
||||
self.mode = 'images'
|
||||
self.img_size = img_size
|
||||
|
||||
@ -200,7 +271,7 @@ class LoadStreams: # multiple IP or RTSP cameras
|
||||
for i, s in enumerate(sources):
|
||||
# Start the thread to read frames from the video stream
|
||||
print('%g/%g: %s... ' % (i + 1, n, s), end='')
|
||||
cap = cv2.VideoCapture(0 if s == '0' else s)
|
||||
cap = cv2.VideoCapture(eval(s) if s.isnumeric() else s)
|
||||
assert cap.isOpened(), 'Failed to open %s' % s
|
||||
w = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
|
||||
h = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
|
||||
@ -256,55 +327,77 @@ class LoadStreams: # multiple IP or RTSP cameras
|
||||
return 0 # 1E12 frames = 32 streams at 30 FPS for 30 years
|
||||
|
||||
|
||||
def img2label_paths(img_paths):
|
||||
# Define label paths as a function of image paths
|
||||
sa, sb = os.sep + 'images' + os.sep, os.sep + 'labels' + os.sep # /images/, /labels/ substrings
|
||||
return [x.replace(sa, sb, 1).replace('.' + x.split('.')[-1], '.txt') for x in img_paths]
|
||||
|
||||
|
||||
class LoadImagesAndLabels(Dataset): # for training/testing
|
||||
def __init__(self, path, img_size=416, batch_size=16, augment=False, hyp=None, rect=False, image_weights=False,
|
||||
cache_images=False, single_cls=False, pad=0.0):
|
||||
try:
|
||||
path = str(Path(path)) # os-agnostic
|
||||
parent = str(Path(path).parent) + os.sep
|
||||
if os.path.isfile(path): # file
|
||||
with open(path, 'r') as f:
|
||||
f = f.read().splitlines()
|
||||
f = [x.replace('./', parent) if x.startswith('./') else x for x in f] # local to global path
|
||||
elif os.path.isdir(path): # folder
|
||||
f = glob.iglob(path + os.sep + '*.*')
|
||||
else:
|
||||
raise Exception('%s does not exist' % path)
|
||||
self.img_files = [x.replace('/', os.sep) for x in f if os.path.splitext(x)[-1].lower() in img_formats]
|
||||
except:
|
||||
raise Exception('Error loading data from %s. See %s' % (path, help_url))
|
||||
|
||||
n = len(self.img_files)
|
||||
assert n > 0, 'No images found in %s. See %s' % (path, help_url)
|
||||
bi = np.floor(np.arange(n) / batch_size).astype(np.int) # batch index
|
||||
nb = bi[-1] + 1 # number of batches
|
||||
|
||||
self.n = n # number of images
|
||||
self.batch = bi # batch index of image
|
||||
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, rank=-1):
|
||||
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
|
||||
|
||||
# Define labels
|
||||
self.label_files = [x.replace('images', 'labels').replace(os.path.splitext(x)[-1], '.txt')
|
||||
for x in self.img_files]
|
||||
|
||||
# Read image shapes (wh)
|
||||
sp = path.replace('.txt', '') + '.shapes' # shapefile path
|
||||
try:
|
||||
with open(sp, 'r') as f: # read existing shapefile
|
||||
s = [x.split() for x in f.read().splitlines()]
|
||||
assert len(s) == n, 'Shapefile out of sync'
|
||||
except:
|
||||
s = [exif_size(Image.open(f)) for f in tqdm(self.img_files, desc='Reading image shapes')]
|
||||
np.savetxt(sp, s, fmt='%g') # overwrites existing (if any)
|
||||
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)
|
||||
elif p.is_file(): # file
|
||||
with open(p, 'r') as t:
|
||||
t = t.read().splitlines()
|
||||
parent = str(p.parent) + os.sep
|
||||
f += [x.replace('./', parent) if x.startswith('./') else x for x in t] # local to global path
|
||||
else:
|
||||
raise Exception('%s does not exist' % p)
|
||||
self.img_files = sorted([x.replace('/', os.sep) for x in f if x.split('.')[-1].lower() in img_formats])
|
||||
assert self.img_files, 'No images found'
|
||||
except Exception as e:
|
||||
raise Exception('Error loading data from %s: %s\nSee %s' % (path, e, help_url))
|
||||
|
||||
self.shapes = np.array(s, dtype=np.float64)
|
||||
# Check cache
|
||||
self.label_files = img2label_paths(self.img_files) # labels
|
||||
cache_path = Path(self.label_files[0]).parent.with_suffix('.cache') # cached labels
|
||||
if cache_path.is_file():
|
||||
cache = torch.load(cache_path) # load
|
||||
if cache['hash'] != get_hash(self.label_files + self.img_files) or 'results' not in cache: # changed
|
||||
cache = self.cache_labels(cache_path) # re-cache
|
||||
else:
|
||||
cache = self.cache_labels(cache_path) # cache
|
||||
|
||||
# Rectangular Training https://github.com/ultralytics/yolov3/issues/232
|
||||
# Display cache
|
||||
[nf, nm, ne, nc, n] = cache.pop('results') # found, missing, empty, corrupted, total
|
||||
desc = f"Scanning '{cache_path}' for images and labels... {nf} found, {nm} missing, {ne} empty, {nc} corrupted"
|
||||
tqdm(None, desc=desc, total=n, initial=n)
|
||||
assert nf > 0 or not augment, f'No labels found in {cache_path}. Can not train without labels. See {help_url}'
|
||||
|
||||
# Read cache
|
||||
cache.pop('hash') # remove hash
|
||||
labels, shapes = zip(*cache.values())
|
||||
self.labels = list(labels)
|
||||
self.shapes = np.array(shapes, dtype=np.float64)
|
||||
self.img_files = list(cache.keys()) # update
|
||||
self.label_files = img2label_paths(cache.keys()) # update
|
||||
if single_cls:
|
||||
for x in self.labels:
|
||||
x[:, 0] = 0
|
||||
|
||||
n = len(shapes) # number of images
|
||||
bi = np.floor(np.arange(n) / batch_size).astype(np.int) # batch index
|
||||
nb = bi[-1] + 1 # number of batches
|
||||
self.batch = bi # batch index of image
|
||||
self.n = n
|
||||
self.indices = range(n)
|
||||
|
||||
# Rectangular Training
|
||||
if self.rect:
|
||||
# Sort by aspect ratio
|
||||
s = self.shapes # wh
|
||||
@ -312,6 +405,7 @@ class LoadImagesAndLabels(Dataset): # for training/testing
|
||||
irect = ar.argsort()
|
||||
self.img_files = [self.img_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.shapes = s[irect] # wh
|
||||
ar = ar[irect]
|
||||
|
||||
@ -325,107 +419,65 @@ class LoadImagesAndLabels(Dataset): # for training/testing
|
||||
elif mini > 1:
|
||||
shapes[i] = [1, 1 / mini]
|
||||
|
||||
self.batch_shapes = np.ceil(np.array(shapes) * img_size / 32. + pad).astype(np.int) * 32
|
||||
|
||||
# Cache labels
|
||||
self.imgs = [None] * n
|
||||
self.labels = [np.zeros((0, 5), dtype=np.float32)] * n
|
||||
create_datasubset, extract_bounding_boxes, labels_loaded = False, False, False
|
||||
nm, nf, ne, ns, nd = 0, 0, 0, 0, 0 # number missing, found, empty, datasubset, duplicate
|
||||
np_labels_path = str(Path(self.label_files[0]).parent) + '.npy' # saved labels in *.npy file
|
||||
if os.path.isfile(np_labels_path):
|
||||
s = np_labels_path # print string
|
||||
x = np.load(np_labels_path, allow_pickle=True)
|
||||
if len(x) == n:
|
||||
self.labels = x
|
||||
labels_loaded = True
|
||||
else:
|
||||
s = path.replace('images', 'labels')
|
||||
|
||||
pbar = tqdm(self.label_files)
|
||||
for i, file in enumerate(pbar):
|
||||
if labels_loaded:
|
||||
l = self.labels[i]
|
||||
# np.savetxt(file, l, '%g') # save *.txt from *.npy file
|
||||
else:
|
||||
try:
|
||||
with open(file, 'r') as f:
|
||||
l = np.array([x.split() for x in f.read().splitlines()], dtype=np.float32)
|
||||
except:
|
||||
nm += 1 # print('missing labels for image %s' % self.img_files[i]) # file missing
|
||||
continue
|
||||
|
||||
if l.shape[0]:
|
||||
assert l.shape[1] == 5, '> 5 label columns: %s' % file
|
||||
assert (l >= 0).all(), 'negative labels: %s' % file
|
||||
assert (l[:, 1:] <= 1).all(), 'non-normalized or out of bounds coordinate labels: %s' % file
|
||||
if np.unique(l, axis=0).shape[0] < l.shape[0]: # duplicate rows
|
||||
nd += 1 # print('WARNING: duplicate rows in %s' % self.label_files[i]) # duplicate rows
|
||||
if single_cls:
|
||||
l[:, 0] = 0 # force dataset into single-class mode
|
||||
self.labels[i] = l
|
||||
nf += 1 # file found
|
||||
|
||||
# Create subdataset (a smaller dataset)
|
||||
if create_datasubset and ns < 1E4:
|
||||
if ns == 0:
|
||||
create_folder(path='./datasubset')
|
||||
os.makedirs('./datasubset/images')
|
||||
exclude_classes = 43
|
||||
if exclude_classes not in l[:, 0]:
|
||||
ns += 1
|
||||
# shutil.copy(src=self.img_files[i], dst='./datasubset/images/') # copy image
|
||||
with open('./datasubset/images.txt', 'a') as f:
|
||||
f.write(self.img_files[i] + '\n')
|
||||
|
||||
# Extract object detection boxes for a second stage classifier
|
||||
if extract_bounding_boxes:
|
||||
p = Path(self.img_files[i])
|
||||
img = cv2.imread(str(p))
|
||||
h, w = img.shape[:2]
|
||||
for j, x in enumerate(l):
|
||||
f = '%s%sclassifier%s%g_%g_%s' % (p.parent.parent, os.sep, os.sep, x[0], j, p.name)
|
||||
if not os.path.exists(Path(f).parent):
|
||||
os.makedirs(Path(f).parent) # make new output folder
|
||||
|
||||
b = x[1:] * [w, h, w, h] # box
|
||||
b[2:] = b[2:].max() # rectangle to square
|
||||
b[2:] = b[2:] * 1.3 + 30 # pad
|
||||
b = xywh2xyxy(b.reshape(-1, 4)).ravel().astype(np.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(f, img[b[1]:b[3], b[0]:b[2]]), 'Failure extracting classifier boxes'
|
||||
else:
|
||||
ne += 1 # print('empty labels for image %s' % self.img_files[i]) # file empty
|
||||
# os.system("rm '%s' '%s'" % (self.img_files[i], self.label_files[i])) # remove
|
||||
|
||||
pbar.desc = 'Caching labels %s (%g found, %g missing, %g empty, %g duplicate, for %g images)' % (
|
||||
s, nf, nm, ne, nd, n)
|
||||
assert nf > 0 or n == 20288, 'No labels found in %s. See %s' % (os.path.dirname(file) + os.sep, help_url)
|
||||
if not labels_loaded and n > 1000:
|
||||
print('Saving labels to %s for faster future loading' % np_labels_path)
|
||||
np.save(np_labels_path, self.labels) # save for next time
|
||||
self.batch_shapes = np.ceil(np.array(shapes) * img_size / stride + pad).astype(np.int) * stride
|
||||
|
||||
# Cache images into memory for faster training (WARNING: large datasets may exceed system RAM)
|
||||
if cache_images: # if training
|
||||
self.imgs = [None] * n
|
||||
if cache_images:
|
||||
gb = 0 # Gigabytes of cached images
|
||||
pbar = tqdm(range(len(self.img_files)), desc='Caching images')
|
||||
self.img_hw0, self.img_hw = [None] * n, [None] * n
|
||||
for i in pbar: # max 10k images
|
||||
self.imgs[i], self.img_hw0[i], self.img_hw[i] = load_image(self, i) # img, hw_original, hw_resized
|
||||
results = ThreadPool(8).imap(lambda x: load_image(*x), zip(repeat(self), range(n))) # 8 threads
|
||||
pbar = tqdm(enumerate(results), total=n)
|
||||
for i, x in pbar:
|
||||
self.imgs[i], self.img_hw0[i], self.img_hw[i] = x # img, hw_original, hw_resized = load_image(self, i)
|
||||
gb += self.imgs[i].nbytes
|
||||
pbar.desc = 'Caching images (%.1fGB)' % (gb / 1E9)
|
||||
|
||||
# Detect corrupted images https://medium.com/joelthchao/programmatically-detect-corrupted-image-8c1b2006c3d3
|
||||
detect_corrupted_images = False
|
||||
if detect_corrupted_images:
|
||||
from skimage import io # conda install -c conda-forge scikit-image
|
||||
for file in tqdm(self.img_files, desc='Detecting corrupted images'):
|
||||
try:
|
||||
_ = io.imread(file)
|
||||
except:
|
||||
print('Corrupted image detected: %s' % file)
|
||||
def cache_labels(self, path=Path('./labels.cache')):
|
||||
# Cache dataset labels, check images and read shapes
|
||||
x = {} # dict
|
||||
nm, nf, ne, nc = 0, 0, 0, 0 # number missing, found, empty, duplicate
|
||||
pbar = tqdm(zip(self.img_files, self.label_files), desc='Scanning images', total=len(self.img_files))
|
||||
for i, (im_file, lb_file) in enumerate(pbar):
|
||||
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), 'image size <10 pixels'
|
||||
|
||||
# verify labels
|
||||
if os.path.isfile(lb_file):
|
||||
nf += 1 # label found
|
||||
with open(lb_file, 'r') as f:
|
||||
l = np.array([x.split() for x in f.read().splitlines()], dtype=np.float32) # labels
|
||||
if len(l):
|
||||
assert l.shape[1] == 5, 'labels require 5 columns each'
|
||||
assert (l >= 0).all(), 'negative labels'
|
||||
assert (l[:, 1:] <= 1).all(), 'non-normalized or out of bounds coordinate labels'
|
||||
assert np.unique(l, axis=0).shape[0] == l.shape[0], 'duplicate labels'
|
||||
else:
|
||||
ne += 1 # label empty
|
||||
l = np.zeros((0, 5), dtype=np.float32)
|
||||
else:
|
||||
nm += 1 # label missing
|
||||
l = np.zeros((0, 5), dtype=np.float32)
|
||||
x[im_file] = [l, shape]
|
||||
except Exception as e:
|
||||
nc += 1
|
||||
print('WARNING: Ignoring corrupted image and/or label %s: %s' % (im_file, e))
|
||||
|
||||
pbar.desc = f"Scanning '{path.parent / path.stem}' for images and labels... " \
|
||||
f"{nf} found, {nm} missing, {ne} empty, {nc} corrupted"
|
||||
|
||||
if nf == 0:
|
||||
print(f'WARNING: No labels found in {path}. See {help_url}')
|
||||
|
||||
x['hash'] = get_hash(self.label_files + self.img_files)
|
||||
x['results'] = [nf, nm, ne, nc, i + 1]
|
||||
torch.save(x, path) # save for next time
|
||||
logging.info(f"New cache created: {path}")
|
||||
return x
|
||||
|
||||
def __len__(self):
|
||||
return len(self.img_files)
|
||||
@ -437,15 +489,22 @@ class LoadImagesAndLabels(Dataset): # for training/testing
|
||||
# return self
|
||||
|
||||
def __getitem__(self, index):
|
||||
if self.image_weights:
|
||||
index = self.indices[index]
|
||||
index = self.indices[index] # linear, shuffled, or image_weights
|
||||
|
||||
hyp = self.hyp
|
||||
if self.mosaic:
|
||||
mosaic = self.mosaic and random.random() < hyp['mosaic']
|
||||
if mosaic:
|
||||
# Load mosaic
|
||||
img, labels = load_mosaic(self, index)
|
||||
shapes = None
|
||||
|
||||
# MixUp https://arxiv.org/pdf/1710.09412.pdf
|
||||
if random.random() < hyp['mixup']:
|
||||
img2, labels2 = load_mosaic(self, random.randint(0, self.n - 1))
|
||||
r = np.random.beta(8.0, 8.0) # mixup ratio, alpha=beta=8.0
|
||||
img = (img * r + img2 * (1 - r)).astype(np.uint8)
|
||||
labels = np.concatenate((labels, labels2), 0)
|
||||
|
||||
else:
|
||||
# Load image
|
||||
img, (h0, w0), (h, w) = load_image(self, index)
|
||||
@ -468,12 +527,13 @@ class LoadImagesAndLabels(Dataset): # for training/testing
|
||||
|
||||
if self.augment:
|
||||
# Augment imagespace
|
||||
if not self.mosaic:
|
||||
img, labels = random_affine(img, labels,
|
||||
degrees=hyp['degrees'],
|
||||
translate=hyp['translate'],
|
||||
scale=hyp['scale'],
|
||||
shear=hyp['shear'])
|
||||
if not mosaic:
|
||||
img, labels = random_perspective(img, labels,
|
||||
degrees=hyp['degrees'],
|
||||
translate=hyp['translate'],
|
||||
scale=hyp['scale'],
|
||||
shear=hyp['shear'],
|
||||
perspective=hyp['perspective'])
|
||||
|
||||
# Augment colorspace
|
||||
augment_hsv(img, hgain=hyp['hsv_h'], sgain=hyp['hsv_s'], vgain=hyp['hsv_v'])
|
||||
@ -484,28 +544,23 @@ class LoadImagesAndLabels(Dataset): # for training/testing
|
||||
|
||||
nL = len(labels) # number of labels
|
||||
if nL:
|
||||
# convert xyxy to xywh
|
||||
labels[:, 1:5] = xyxy2xywh(labels[:, 1:5])
|
||||
|
||||
# Normalize coordinates 0 - 1
|
||||
labels[:, [2, 4]] /= img.shape[0] # height
|
||||
labels[:, [1, 3]] /= img.shape[1] # width
|
||||
labels[:, 1:5] = xyxy2xywh(labels[:, 1:5]) # convert xyxy to xywh
|
||||
labels[:, [2, 4]] /= img.shape[0] # normalized height 0-1
|
||||
labels[:, [1, 3]] /= img.shape[1] # normalized width 0-1
|
||||
|
||||
if self.augment:
|
||||
# random left-right flip
|
||||
lr_flip = True
|
||||
if lr_flip and random.random() < 0.5:
|
||||
img = np.fliplr(img)
|
||||
if nL:
|
||||
labels[:, 1] = 1 - labels[:, 1]
|
||||
|
||||
# random up-down flip
|
||||
ud_flip = False
|
||||
if ud_flip and random.random() < 0.5:
|
||||
# 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]
|
||||
|
||||
labels_out = torch.zeros((nL, 6))
|
||||
if nL:
|
||||
labels_out[:, 1:] = torch.from_numpy(labels)
|
||||
@ -524,6 +579,7 @@ class LoadImagesAndLabels(Dataset): # for training/testing
|
||||
return torch.stack(img, 0), torch.cat(label, 0), path, shapes
|
||||
|
||||
|
||||
# Ancillary functions --------------------------------------------------------------------------------------------------
|
||||
def load_image(self, index):
|
||||
# loads 1 image from dataset, returns img, original hw, resized hw
|
||||
img = self.imgs[index]
|
||||
@ -565,8 +621,8 @@ def load_mosaic(self, index):
|
||||
|
||||
labels4 = []
|
||||
s = self.img_size
|
||||
xc, yc = [int(random.uniform(s * 0.5, s * 1.5)) for _ in range(2)] # mosaic center x, y
|
||||
indices = [index] + [random.randint(0, len(self.labels) - 1) for _ in range(3)] # 3 additional image indices
|
||||
yc, xc = [int(random.uniform(-x, 2 * s + x)) for x in self.mosaic_border] # mosaic center x, y
|
||||
indices = [index] + [self.indices[random.randint(0, self.n - 1)] for _ in range(3)] # 3 additional image indices
|
||||
for i, index in enumerate(indices):
|
||||
# Load image
|
||||
img, _, (h, w) = load_image(self, index)
|
||||
@ -581,7 +637,7 @@ def load_mosaic(self, index):
|
||||
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, max(xc, w), min(y2a - y1a, 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)
|
||||
@ -603,22 +659,39 @@ def load_mosaic(self, index):
|
||||
# Concat/clip labels
|
||||
if len(labels4):
|
||||
labels4 = np.concatenate(labels4, 0)
|
||||
# np.clip(labels4[:, 1:] - s / 2, 0, s, out=labels4[:, 1:]) # use with center crop
|
||||
np.clip(labels4[:, 1:], 0, 2 * s, out=labels4[:, 1:]) # use with random_affine
|
||||
np.clip(labels4[:, 1:], 0, 2 * s, out=labels4[:, 1:]) # use with random_perspective
|
||||
# img4, labels4 = replicate(img4, labels4) # replicate
|
||||
|
||||
# Augment
|
||||
# img4 = img4[s // 2: int(s * 1.5), s // 2:int(s * 1.5)] # center crop (WARNING, requires box pruning)
|
||||
img4, labels4 = random_affine(img4, labels4,
|
||||
degrees=self.hyp['degrees'],
|
||||
translate=self.hyp['translate'],
|
||||
scale=self.hyp['scale'],
|
||||
shear=self.hyp['shear'],
|
||||
border=-s // 2) # border to remove
|
||||
img4, labels4 = random_perspective(img4, labels4,
|
||||
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 letterbox(img, new_shape=(416, 416), color=(114, 114, 114), auto=True, scaleFill=False, scaleup=True):
|
||||
def replicate(img, labels):
|
||||
# Replicate labels
|
||||
h, w = img.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]
|
||||
img[y1a:y2a, x1a:x2a] = img[y1b:y2b, x1b:x2b] # img4[ymin:ymax, xmin:xmax]
|
||||
labels = np.append(labels, [[labels[i, 0], x1a, y1a, x2a, y2a]], axis=0)
|
||||
|
||||
return img, labels
|
||||
|
||||
|
||||
def letterbox(img, new_shape=(640, 640), color=(114, 114, 114), auto=True, scaleFill=False, scaleup=True):
|
||||
# Resize image to a 32-pixel-multiple rectangle https://github.com/ultralytics/yolov3/issues/232
|
||||
shape = img.shape[:2] # current shape [height, width]
|
||||
if isinstance(new_shape, int):
|
||||
@ -637,8 +710,8 @@ def letterbox(img, new_shape=(416, 416), color=(114, 114, 114), auto=True, scale
|
||||
dw, dh = np.mod(dw, 32), np.mod(dh, 32) # wh padding
|
||||
elif scaleFill: # stretch
|
||||
dw, dh = 0.0, 0.0
|
||||
new_unpad = new_shape
|
||||
ratio = new_shape[0] / shape[1], new_shape[1] / shape[0] # width, height ratios
|
||||
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
|
||||
@ -651,13 +724,22 @@ def letterbox(img, new_shape=(416, 416), color=(114, 114, 114), auto=True, scale
|
||||
return img, ratio, (dw, dh)
|
||||
|
||||
|
||||
def random_affine(img, targets=(), degrees=10, translate=.1, scale=.1, shear=10, border=0):
|
||||
def random_perspective(img, targets=(), degrees=10, translate=.1, scale=.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))
|
||||
# https://medium.com/uruvideo/dataset-augmentation-with-random-homographies-a8f4b44830d4
|
||||
# targets = [cls, xyxy]
|
||||
|
||||
height = img.shape[0] + border * 2
|
||||
width = img.shape[1] + border * 2
|
||||
height = img.shape[0] + border[0] * 2 # shape(h,w,c)
|
||||
width = img.shape[1] + border[1] * 2
|
||||
|
||||
# Center
|
||||
C = np.eye(3)
|
||||
C[0, 2] = -img.shape[1] / 2 # x translation (pixels)
|
||||
C[1, 2] = -img.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)
|
||||
@ -665,22 +747,31 @@ def random_affine(img, targets=(), degrees=10, translate=.1, scale=.1, shear=10,
|
||||
# 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=(img.shape[1] / 2, img.shape[0] / 2), scale=s)
|
||||
|
||||
# Translation
|
||||
T = np.eye(3)
|
||||
T[0, 2] = random.uniform(-translate, translate) * img.shape[0] + border # x translation (pixels)
|
||||
T[1, 2] = random.uniform(-translate, translate) * img.shape[1] + border # y translation (pixels)
|
||||
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 = S @ T @ R # ORDER IS IMPORTANT HERE!!
|
||||
if (border != 0) or (M != np.eye(3)).any(): # image changed
|
||||
img = cv2.warpAffine(img, M[:2], dsize=(width, height), flags=cv2.INTER_LINEAR, borderValue=(114, 114, 114))
|
||||
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:
|
||||
img = cv2.warpPerspective(img, M, dsize=(width, height), borderValue=(114, 114, 114))
|
||||
else: # affine
|
||||
img = cv2.warpAffine(img, M[:2], dsize=(width, height), borderValue=(114, 114, 114))
|
||||
|
||||
# Visualize
|
||||
# import matplotlib.pyplot as plt
|
||||
# ax = plt.subplots(1, 2, figsize=(12, 6))[1].ravel()
|
||||
# ax[0].imshow(img[:, :, ::-1]) # base
|
||||
# ax[1].imshow(img2[:, :, ::-1]) # warped
|
||||
|
||||
# Transform label coordinates
|
||||
n = len(targets)
|
||||
@ -688,7 +779,11 @@ def random_affine(img, targets=(), degrees=10, translate=.1, scale=.1, shear=10,
|
||||
# warp points
|
||||
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)[:, :2].reshape(n, 8)
|
||||
xy = xy @ M.T # transform
|
||||
if perspective:
|
||||
xy = (xy[:, :2] / xy[:, 2:3]).reshape(n, 8) # rescale
|
||||
else: # affine
|
||||
xy = xy[:, :2].reshape(n, 8)
|
||||
|
||||
# create new boxes
|
||||
x = xy[:, [0, 2, 4, 6]]
|
||||
@ -704,26 +799,28 @@ def random_affine(img, targets=(), degrees=10, translate=.1, scale=.1, shear=10,
|
||||
# h = (xy[:, 3] - xy[:, 1]) * reduction
|
||||
# xy = np.concatenate((x - w / 2, y - h / 2, x + w / 2, y + h / 2)).reshape(4, n).T
|
||||
|
||||
# reject warped points outside of image
|
||||
# clip boxes
|
||||
xy[:, [0, 2]] = xy[:, [0, 2]].clip(0, width)
|
||||
xy[:, [1, 3]] = xy[:, [1, 3]].clip(0, height)
|
||||
w = xy[:, 2] - xy[:, 0]
|
||||
h = xy[:, 3] - xy[:, 1]
|
||||
area = w * h
|
||||
area0 = (targets[:, 3] - targets[:, 1]) * (targets[:, 4] - targets[:, 2])
|
||||
ar = np.maximum(w / (h + 1e-16), h / (w + 1e-16)) # aspect ratio
|
||||
i = (w > 4) & (h > 4) & (area / (area0 * s + 1e-16) > 0.2) & (ar < 10)
|
||||
|
||||
# filter candidates
|
||||
i = box_candidates(box1=targets[:, 1:5].T * s, box2=xy.T)
|
||||
targets = targets[i]
|
||||
targets[:, 1:5] = xy[i]
|
||||
|
||||
return img, targets
|
||||
|
||||
|
||||
def box_candidates(box1, box2, wh_thr=2, ar_thr=20, area_thr=0.1): # box1(4,n), box2(4,n)
|
||||
# Compute candidate boxes: box1 before augment, box2 after augment, wh_thr (pixels), aspect_ratio_thr, area_ratio
|
||||
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 + 1e-16), h2 / (w2 + 1e-16)) # aspect ratio
|
||||
return (w2 > wh_thr) & (h2 > wh_thr) & (w2 * h2 / (w1 * h1 + 1e-16) > area_thr) & (ar < ar_thr) # candidates
|
||||
|
||||
|
||||
def cutout(image, labels):
|
||||
# https://arxiv.org/abs/1708.04552
|
||||
# https://github.com/hysts/pytorch_cutout/blob/master/dataloader.py
|
||||
# https://towardsdatascience.com/when-conventional-wisdom-fails-revisiting-data-augmentation-for-self-driving-cars-4831998c5509
|
||||
# Applies image cutout augmentation https://arxiv.org/abs/1708.04552
|
||||
h, w = image.shape[:2]
|
||||
|
||||
def bbox_ioa(box1, box2):
|
||||
@ -768,78 +865,69 @@ def cutout(image, labels):
|
||||
return labels
|
||||
|
||||
|
||||
def reduce_img_size(path='../data/sm4/images', img_size=1024): # from utils.datasets import *; reduce_img_size()
|
||||
# creates a new ./images_reduced folder with reduced size images of maximum size img_size
|
||||
path_new = path + '_reduced' # reduced images path
|
||||
create_folder(path_new)
|
||||
for f in tqdm(glob.glob('%s/*.*' % path)):
|
||||
try:
|
||||
img = cv2.imread(f)
|
||||
h, w = img.shape[:2]
|
||||
r = img_size / max(h, w) # size ratio
|
||||
if r < 1.0:
|
||||
img = cv2.resize(img, (int(w * r), int(h * r)), interpolation=cv2.INTER_AREA) # _LINEAR fastest
|
||||
fnew = f.replace(path, path_new) # .replace(Path(f).suffix, '.jpg')
|
||||
cv2.imwrite(fnew, img)
|
||||
except:
|
||||
print('WARNING: image failure %s' % f)
|
||||
|
||||
|
||||
def convert_images2bmp(): # from utils.datasets import *; convert_images2bmp()
|
||||
# Save images
|
||||
formats = [x.lower() for x in img_formats] + [x.upper() for x in img_formats]
|
||||
# for path in ['../coco/images/val2014', '../coco/images/train2014']:
|
||||
for path in ['../data/sm4/images', '../data/sm4/background']:
|
||||
create_folder(path + 'bmp')
|
||||
for ext in formats: # ['.bmp', '.jpg', '.jpeg', '.png', '.tif', '.dng']
|
||||
for f in tqdm(glob.glob('%s/*%s' % (path, ext)), desc='Converting %s' % ext):
|
||||
cv2.imwrite(f.replace(ext.lower(), '.bmp').replace(path, path + 'bmp'), cv2.imread(f))
|
||||
|
||||
# Save labels
|
||||
# for path in ['../coco/trainvalno5k.txt', '../coco/5k.txt']:
|
||||
for file in ['../data/sm4/out_train.txt', '../data/sm4/out_test.txt']:
|
||||
with open(file, 'r') as f:
|
||||
lines = f.read()
|
||||
# lines = f.read().replace('2014/', '2014bmp/') # coco
|
||||
lines = lines.replace('/images', '/imagesbmp')
|
||||
lines = lines.replace('/background', '/backgroundbmp')
|
||||
for ext in formats:
|
||||
lines = lines.replace(ext, '.bmp')
|
||||
with open(file.replace('.txt', 'bmp.txt'), 'w') as f:
|
||||
f.write(lines)
|
||||
|
||||
|
||||
def recursive_dataset2bmp(dataset='../data/sm4_bmp'): # from utils.datasets import *; recursive_dataset2bmp()
|
||||
# Converts dataset to bmp (for faster training)
|
||||
formats = [x.lower() for x in img_formats] + [x.upper() for x in img_formats]
|
||||
for a, b, files in os.walk(dataset):
|
||||
for file in tqdm(files, desc=a):
|
||||
p = a + '/' + file
|
||||
s = Path(file).suffix
|
||||
if s == '.txt': # replace text
|
||||
with open(p, 'r') as f:
|
||||
lines = f.read()
|
||||
for f in formats:
|
||||
lines = lines.replace(f, '.bmp')
|
||||
with open(p, 'w') as f:
|
||||
f.write(lines)
|
||||
elif s in formats: # replace image
|
||||
cv2.imwrite(p.replace(s, '.bmp'), cv2.imread(p))
|
||||
if s != '.bmp':
|
||||
os.system("rm '%s'" % p)
|
||||
|
||||
|
||||
def imagelist2folder(path='data/coco_64img.txt'): # from utils.datasets import *; imagelist2folder()
|
||||
# Copies all the images in a text file (list of images) into a folder
|
||||
create_folder(path[:-4])
|
||||
with open(path, 'r') as f:
|
||||
for line in f.read().splitlines():
|
||||
os.system('cp "%s" %s' % (line, path[:-4]))
|
||||
print(line)
|
||||
|
||||
|
||||
def create_folder(path='./new_folder'):
|
||||
def create_folder(path='./new'):
|
||||
# Create folder
|
||||
if os.path.exists(path):
|
||||
shutil.rmtree(path) # delete output folder
|
||||
os.makedirs(path) # make new output folder
|
||||
|
||||
|
||||
def flatten_recursive(path='../coco128'):
|
||||
# Flatten a recursive directory by bringing all files to top level
|
||||
new_path = Path(path + '_flat')
|
||||
create_folder(new_path)
|
||||
for file in tqdm(glob.glob(str(Path(path)) + '/**/*.*', recursive=True)):
|
||||
shutil.copyfile(file, new_path / Path(file).name)
|
||||
|
||||
|
||||
def extract_boxes(path='../coco128/'): # from utils.datasets import *; extract_boxes('../coco128')
|
||||
# Convert detection dataset into classification dataset, with one directory per class
|
||||
|
||||
path = Path(path) # images dir
|
||||
shutil.rmtree(path / 'classifier') if (path / 'classifier').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, 'r') as f:
|
||||
lb = np.array([x.split() for x in f.read().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(np.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='../coco128', weights=(0.9, 0.1, 0.0)): # from utils.datasets import *; autosplit('../coco128')
|
||||
""" Autosplit a dataset into train/val/test splits and save path/autosplit_*.txt files
|
||||
# Arguments
|
||||
path: Path to images directory
|
||||
weights: Train, val, test weights (list)
|
||||
"""
|
||||
path = Path(path) # images dir
|
||||
files = list(path.rglob('*.*'))
|
||||
n = len(files) # number of files
|
||||
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
|
||||
[(path / x).unlink() for x in txt if (path / x).exists()] # remove existing
|
||||
for i, img in tqdm(zip(indices, files), total=n):
|
||||
if img.suffix[1:] in img_formats:
|
||||
with open(path / txt[i], 'a') as f:
|
||||
f.write(str(img) + '\n') # add image to txt file
|
||||
|
||||
@ -1,18 +0,0 @@
|
||||
#!/bin/bash
|
||||
#for i in 0 1 2 3
|
||||
#do
|
||||
# t=ultralytics/yolov3:v139 && sudo docker pull $t && sudo nvidia-docker run -d --ipc=host -v "$(pwd)"/coco:/usr/src/coco $t utils/evolve.sh $i
|
||||
# sleep 30
|
||||
#done
|
||||
|
||||
while true; do
|
||||
# python3 train.py --data ../data/sm4/out.data --img-size 320 --epochs 100 --batch 64 --accum 1 --weights yolov3-tiny.conv.15 --multi --bucket ult/wer --evolve --cache --device $1 --cfg yolov3-tiny3-1cls.cfg --single --adam
|
||||
# python3 train.py --data ../out/data.data --img-size 608 --epochs 10 --batch 8 --accum 8 --weights ultralytics68.pt --multi --bucket ult/athena --evolve --device $1 --cfg yolov3-spp-1cls.cfg
|
||||
|
||||
python3 train.py --data coco2014.data --img-size 512 608 --epochs 27 --batch 8 --accum 8 --evolve --weights '' --bucket ult/coco/sppa_512 --device $1 --cfg yolov3-sppa.cfg --multi
|
||||
done
|
||||
|
||||
# coco epoch times --img-size 416 608 --epochs 27 --batch 16 --accum 4
|
||||
# 36:34 2080ti
|
||||
# 21:58 V100
|
||||
# 63:00 T4
|
||||
39
utils/gcp.sh
39
utils/gcp.sh
@ -1,39 +0,0 @@
|
||||
#!/usr/bin/env bash
|
||||
|
||||
# New VM
|
||||
rm -rf sample_data yolov3
|
||||
git clone https://github.com/ultralytics/yolov3
|
||||
# git clone -b test --depth 1 https://github.com/ultralytics/yolov3 test # branch
|
||||
# sudo apt-get install zip
|
||||
#git clone https://github.com/NVIDIA/apex && cd apex && pip install -v --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" . --user && cd .. && rm -rf apex
|
||||
sudo conda install -yc conda-forge scikit-image pycocotools
|
||||
# python3 -c "from yolov3.utils.google_utils import gdrive_download; gdrive_download('193Zp_ye-3qXMonR1nZj3YyxMtQkMy50k','coco2014.zip')"
|
||||
python3 -c "from yolov3.utils.google_utils import gdrive_download; gdrive_download('1WQT6SOktSe8Uw6r10-2JhbEhMY5DJaph','coco2017.zip')"
|
||||
python3 -c "from yolov3.utils.google_utils import gdrive_download; gdrive_download('1C3HewOG9akA3y456SZLBJZfNDPkBwAto','knife.zip')"
|
||||
python3 -c "from yolov3.utils.google_utils import gdrive_download; gdrive_download('13g3LqdpkNE8sPosVJT6KFXlfoMypzRP4','sm4.zip')"
|
||||
sudo shutdown
|
||||
|
||||
# Mount local SSD
|
||||
lsblk
|
||||
sudo mkfs.ext4 -F /dev/nvme0n1
|
||||
sudo mkdir -p /mnt/disks/nvme0n1
|
||||
sudo mount /dev/nvme0n1 /mnt/disks/nvme0n1
|
||||
sudo chmod a+w /mnt/disks/nvme0n1
|
||||
cp -r coco /mnt/disks/nvme0n1
|
||||
|
||||
# Kill All
|
||||
t=ultralytics/yolov3:v1
|
||||
docker kill $(docker ps -a -q --filter ancestor=$t)
|
||||
|
||||
# Evolve coco
|
||||
sudo -s
|
||||
t=ultralytics/yolov3:evolve
|
||||
# docker kill $(docker ps -a -q --filter ancestor=$t)
|
||||
for i in 0 1 6 7; do
|
||||
docker pull $t && docker run --gpus all -d --ipc=host -v "$(pwd)"/coco:/usr/src/coco $t bash utils/evolve.sh $i
|
||||
sleep 30
|
||||
done
|
||||
|
||||
#COCO training
|
||||
n=131 && t=ultralytics/coco:v131 && sudo docker pull $t && sudo docker run -it --gpus all --ipc=host -v "$(pwd)"/coco:/usr/src/coco $t python3 train.py --data coco2014.data --img-size 320 640 --epochs 300 --batch 16 --weights '' --device 0 --cfg yolov3-spp.cfg --bucket ult/coco --name $n && sudo shutdown
|
||||
n=132 && t=ultralytics/coco:v131 && sudo docker pull $t && sudo docker run -it --gpus all --ipc=host -v "$(pwd)"/coco:/usr/src/coco $t python3 train.py --data coco2014.data --img-size 320 640 --epochs 300 --batch 64 --weights '' --device 0 --cfg yolov3-tiny.cfg --bucket ult/coco --name $n && sudo shutdown
|
||||
445
utils/general.py
Executable file
445
utils/general.py
Executable file
@ -0,0 +1,445 @@
|
||||
# General utils
|
||||
|
||||
import glob
|
||||
import logging
|
||||
import math
|
||||
import os
|
||||
import platform
|
||||
import random
|
||||
import re
|
||||
import subprocess
|
||||
import time
|
||||
from pathlib import Path
|
||||
|
||||
import cv2
|
||||
import matplotlib
|
||||
import numpy as np
|
||||
import torch
|
||||
import torchvision
|
||||
import yaml
|
||||
|
||||
from utils.google_utils import gsutil_getsize
|
||||
from utils.metrics import fitness
|
||||
from utils.torch_utils import init_torch_seeds
|
||||
|
||||
# Set printoptions
|
||||
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
|
||||
matplotlib.rc('font', **{'size': 11})
|
||||
|
||||
# Prevent OpenCV from multithreading (to use PyTorch DataLoader)
|
||||
cv2.setNumThreads(0)
|
||||
|
||||
|
||||
def set_logging(rank=-1):
|
||||
logging.basicConfig(
|
||||
format="%(message)s",
|
||||
level=logging.INFO if rank in [-1, 0] else logging.WARN)
|
||||
|
||||
|
||||
def init_seeds(seed=0):
|
||||
random.seed(seed)
|
||||
np.random.seed(seed)
|
||||
init_torch_seeds(seed)
|
||||
|
||||
|
||||
def get_latest_run(search_dir='.'):
|
||||
# Return path to most recent 'last.pt' in /runs (i.e. to --resume from)
|
||||
last_list = glob.glob(f'{search_dir}/**/last*.pt', recursive=True)
|
||||
return max(last_list, key=os.path.getctime) if last_list else ''
|
||||
|
||||
|
||||
def check_git_status():
|
||||
# Suggest 'git pull' if repo is out of date
|
||||
if platform.system() in ['Linux', 'Darwin'] and not os.path.isfile('/.dockerenv'):
|
||||
s = subprocess.check_output('if [ -d .git ]; then git fetch && git status -uno; fi', shell=True).decode('utf-8')
|
||||
if 'Your branch is behind' in s:
|
||||
print(s[s.find('Your branch is behind'):s.find('\n\n')] + '\n')
|
||||
|
||||
|
||||
def check_img_size(img_size, s=32):
|
||||
# Verify img_size is a multiple of stride s
|
||||
new_size = make_divisible(img_size, int(s)) # ceil gs-multiple
|
||||
if new_size != img_size:
|
||||
print('WARNING: --img-size %g must be multiple of max stride %g, updating to %g' % (img_size, s, new_size))
|
||||
return new_size
|
||||
|
||||
|
||||
def check_file(file):
|
||||
# Search for file if not found
|
||||
if os.path.isfile(file) or file == '':
|
||||
return file
|
||||
else:
|
||||
files = glob.glob('./**/' + file, recursive=True) # find file
|
||||
assert len(files), 'File Not Found: %s' % file # assert file was found
|
||||
assert len(files) == 1, "Multiple files match '%s', specify exact path: %s" % (file, files) # assert unique
|
||||
return files[0] # return file
|
||||
|
||||
|
||||
def check_dataset(dict):
|
||||
# Download dataset if not found locally
|
||||
val, s = dict.get('val'), dict.get('download')
|
||||
if val and len(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):
|
||||
print('\nWARNING: Dataset not found, nonexistent paths: %s' % [str(x) for x in val if not x.exists()])
|
||||
if s and len(s): # download script
|
||||
print('Downloading %s ...' % s)
|
||||
if s.startswith('http') and s.endswith('.zip'): # URL
|
||||
f = Path(s).name # filename
|
||||
torch.hub.download_url_to_file(s, f)
|
||||
r = os.system('unzip -q %s -d ../ && rm %s' % (f, f)) # unzip
|
||||
else: # bash script
|
||||
r = os.system(s)
|
||||
print('Dataset autodownload %s\n' % ('success' if r == 0 else 'failure')) # analyze return value
|
||||
else:
|
||||
raise Exception('Dataset not found.')
|
||||
|
||||
|
||||
def make_divisible(x, divisor):
|
||||
# Returns x evenly divisible by divisor
|
||||
return math.ceil(x / divisor) * divisor
|
||||
|
||||
|
||||
def labels_to_class_weights(labels, nc=80):
|
||||
# Get class weights (inverse frequency) from training labels
|
||||
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(np.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)
|
||||
|
||||
|
||||
def labels_to_image_weights(labels, nc=80, class_weights=np.ones(80)):
|
||||
# Produces image weights based on class mAPs
|
||||
n = len(labels)
|
||||
class_counts = np.array([np.bincount(labels[i][:, 0].astype(np.int), minlength=nc) for i in range(n)])
|
||||
image_weights = (class_weights.reshape(1, nc) * class_counts).sum(1)
|
||||
# index = random.choices(range(n), weights=image_weights, k=1) # weight image sample
|
||||
return image_weights
|
||||
|
||||
|
||||
def coco80_to_coco91_class(): # converts 80-index (val2014) to 91-index (paper)
|
||||
# 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
|
||||
x = [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]
|
||||
return x
|
||||
|
||||
|
||||
def xyxy2xywh(x):
|
||||
# Convert nx4 boxes from [x1, y1, x2, y2] to [x, y, w, h] where xy1=top-left, xy2=bottom-right
|
||||
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):
|
||||
# Convert nx4 boxes from [x, y, w, h] to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right
|
||||
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 scale_coords(img1_shape, coords, img0_shape, ratio_pad=None):
|
||||
# Rescale coords (xyxy) from img1_shape to img0_shape
|
||||
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]
|
||||
|
||||
coords[:, [0, 2]] -= pad[0] # x padding
|
||||
coords[:, [1, 3]] -= pad[1] # y padding
|
||||
coords[:, :4] /= gain
|
||||
clip_coords(coords, img0_shape)
|
||||
return coords
|
||||
|
||||
|
||||
def clip_coords(boxes, img_shape):
|
||||
# Clip bounding xyxy bounding boxes to image shape (height, width)
|
||||
boxes[:, 0].clamp_(0, img_shape[1]) # x1
|
||||
boxes[:, 1].clamp_(0, img_shape[0]) # y1
|
||||
boxes[:, 2].clamp_(0, img_shape[1]) # x2
|
||||
boxes[:, 3].clamp_(0, img_shape[0]) # y2
|
||||
|
||||
|
||||
def bbox_iou(box1, box2, x1y1x2y2=True, GIoU=False, DIoU=False, CIoU=False, eps=1e-9):
|
||||
# Returns the IoU of box1 to box2. box1 is 4, box2 is nx4
|
||||
box2 = box2.T
|
||||
|
||||
# Get the coordinates of bounding boxes
|
||||
if x1y1x2y2: # x1, y1, x2, y2 = box1
|
||||
b1_x1, b1_y1, b1_x2, b1_y2 = box1[0], box1[1], box1[2], box1[3]
|
||||
b2_x1, b2_y1, b2_x2, b2_y2 = box2[0], box2[1], box2[2], box2[3]
|
||||
else: # transform from xywh to xyxy
|
||||
b1_x1, b1_x2 = box1[0] - box1[2] / 2, box1[0] + box1[2] / 2
|
||||
b1_y1, b1_y2 = box1[1] - box1[3] / 2, box1[1] + box1[3] / 2
|
||||
b2_x1, b2_x2 = box2[0] - box2[2] / 2, box2[0] + box2[2] / 2
|
||||
b2_y1, b2_y2 = box2[1] - box2[3] / 2, box2[1] + box2[3] / 2
|
||||
|
||||
# Intersection area
|
||||
inter = (torch.min(b1_x2, b2_x2) - torch.max(b1_x1, b2_x1)).clamp(0) * \
|
||||
(torch.min(b1_y2, b2_y2) - torch.max(b1_y1, b2_y1)).clamp(0)
|
||||
|
||||
# Union Area
|
||||
w1, h1 = b1_x2 - b1_x1, b1_y2 - b1_y1 + eps
|
||||
w2, h2 = b2_x2 - b2_x1, b2_y2 - b2_y1 + eps
|
||||
union = w1 * h1 + w2 * h2 - inter + eps
|
||||
|
||||
iou = inter / union
|
||||
if GIoU or DIoU or CIoU:
|
||||
cw = torch.max(b1_x2, b2_x2) - torch.min(b1_x1, b2_x1) # convex (smallest enclosing box) width
|
||||
ch = torch.max(b1_y2, b2_y2) - torch.min(b1_y1, 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 distance squared
|
||||
if DIoU:
|
||||
return iou - rho2 / c2 # DIoU
|
||||
elif CIoU: # https://github.com/Zzh-tju/DIoU-SSD-pytorch/blob/master/utils/box/box_utils.py#L47
|
||||
v = (4 / math.pi ** 2) * torch.pow(torch.atan(w2 / h2) - torch.atan(w1 / h1), 2)
|
||||
with torch.no_grad():
|
||||
alpha = v / ((1 + eps) - iou + v)
|
||||
return iou - (rho2 / c2 + v * alpha) # CIoU
|
||||
else: # GIoU https://arxiv.org/pdf/1902.09630.pdf
|
||||
c_area = cw * ch + eps # convex area
|
||||
return iou - (c_area - union) / c_area # GIoU
|
||||
else:
|
||||
return iou # IoU
|
||||
|
||||
|
||||
def box_iou(box1, box2):
|
||||
# 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.
|
||||
Arguments:
|
||||
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
|
||||
"""
|
||||
|
||||
def box_area(box):
|
||||
# box = 4xn
|
||||
return (box[2] - box[0]) * (box[3] - box[1])
|
||||
|
||||
area1 = box_area(box1.T)
|
||||
area2 = box_area(box2.T)
|
||||
|
||||
# inter(N,M) = (rb(N,M,2) - lt(N,M,2)).clamp(0).prod(2)
|
||||
inter = (torch.min(box1[:, None, 2:], box2[:, 2:]) - torch.max(box1[:, None, :2], box2[:, :2])).clamp(0).prod(2)
|
||||
return inter / (area1[:, None] + area2 - inter) # iou = inter / (area1 + area2 - inter)
|
||||
|
||||
|
||||
def wh_iou(wh1, wh2):
|
||||
# Returns the nxm IoU matrix. wh1 is nx2, wh2 is mx2
|
||||
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) # iou = inter / (area1 + area2 - inter)
|
||||
|
||||
|
||||
def non_max_suppression(prediction, conf_thres=0.1, iou_thres=0.6, classes=None, agnostic=False, labels=()):
|
||||
"""Performs Non-Maximum Suppression (NMS) on inference results
|
||||
|
||||
Returns:
|
||||
detections with shape: nx6 (x1, y1, x2, y2, conf, cls)
|
||||
"""
|
||||
|
||||
nc = prediction[0].shape[1] - 5 # number of classes
|
||||
xc = prediction[..., 4] > conf_thres # candidates
|
||||
|
||||
# Settings
|
||||
min_wh, max_wh = 2, 4096 # (pixels) minimum and maximum box width and height
|
||||
max_det = 300 # maximum number of detections per image
|
||||
time_limit = 10.0 # 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()
|
||||
output = [torch.zeros(0, 6)] * prediction.shape[0]
|
||||
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]):
|
||||
l = labels[xi]
|
||||
v = torch.zeros((len(l), nc + 5), device=x.device)
|
||||
v[:, :4] = l[:, 1:5] # box
|
||||
v[:, 4] = 1.0 # conf
|
||||
v[range(len(l)), l[:, 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 (center x, center y, width, height) to (x1, y1, x2, y2)
|
||||
box = xywh2xyxy(x[:, :4])
|
||||
|
||||
# Detections matrix nx6 (xyxy, conf, cls)
|
||||
if multi_label:
|
||||
i, j = (x[:, 5:] > conf_thres).nonzero(as_tuple=False).T
|
||||
x = torch.cat((box[i], x[i, j + 5, None], j[:, None].float()), 1)
|
||||
else: # best class only
|
||||
conf, j = x[:, 5:].max(1, keepdim=True)
|
||||
x = torch.cat((box, conf, j.float()), 1)[conf.view(-1) > conf_thres]
|
||||
|
||||
# Filter by class
|
||||
if classes:
|
||||
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)]
|
||||
|
||||
# If none remain process next image
|
||||
n = x.shape[0] # number of boxes
|
||||
if not n:
|
||||
continue
|
||||
|
||||
# Sort by confidence
|
||||
# x = x[x[:, 4].argsort(descending=True)]
|
||||
|
||||
# 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
|
||||
if i.shape[0] > max_det: # limit detections
|
||||
i = i[:max_det]
|
||||
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 (time.time() - t) > time_limit:
|
||||
break # time limit exceeded
|
||||
|
||||
return output
|
||||
|
||||
|
||||
def strip_optimizer(f='weights/best.pt', s=''): # from utils.general import *; strip_optimizer()
|
||||
# Strip optimizer from 'f' to finalize training, optionally save as 's'
|
||||
x = torch.load(f, map_location=torch.device('cpu'))
|
||||
x['optimizer'] = None
|
||||
x['training_results'] = 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
|
||||
print('Optimizer stripped from %s,%s %.1fMB' % (f, (' saved as %s,' % s) if s else '', mb))
|
||||
|
||||
|
||||
def print_mutation(hyp, results, yaml_file='hyp_evolved.yaml', bucket=''):
|
||||
# Print mutation results to evolve.txt (for use with train.py --evolve)
|
||||
a = '%10s' * len(hyp) % tuple(hyp.keys()) # hyperparam keys
|
||||
b = '%10.3g' * len(hyp) % tuple(hyp.values()) # hyperparam values
|
||||
c = '%10.4g' * len(results) % results # results (P, R, mAP@0.5, mAP@0.5:0.95, val_losses x 3)
|
||||
print('\n%s\n%s\nEvolved fitness: %s\n' % (a, b, c))
|
||||
|
||||
if bucket:
|
||||
url = 'gs://%s/evolve.txt' % bucket
|
||||
if gsutil_getsize(url) > (os.path.getsize('evolve.txt') if os.path.exists('evolve.txt') else 0):
|
||||
os.system('gsutil cp %s .' % url) # download evolve.txt if larger than local
|
||||
|
||||
with open('evolve.txt', 'a') as f: # append result
|
||||
f.write(c + b + '\n')
|
||||
x = np.unique(np.loadtxt('evolve.txt', ndmin=2), axis=0) # load unique rows
|
||||
x = x[np.argsort(-fitness(x))] # sort
|
||||
np.savetxt('evolve.txt', x, '%10.3g') # save sort by fitness
|
||||
|
||||
# Save yaml
|
||||
for i, k in enumerate(hyp.keys()):
|
||||
hyp[k] = float(x[0, i + 7])
|
||||
with open(yaml_file, 'w') as f:
|
||||
results = tuple(x[0, :7])
|
||||
c = '%10.4g' * len(results) % results # results (P, R, mAP@0.5, mAP@0.5:0.95, val_losses x 3)
|
||||
f.write('# Hyperparameter Evolution Results\n# Generations: %g\n# Metrics: ' % len(x) + c + '\n\n')
|
||||
yaml.dump(hyp, f, sort_keys=False)
|
||||
|
||||
if bucket:
|
||||
os.system('gsutil cp evolve.txt %s gs://%s' % (yaml_file, bucket)) # upload
|
||||
|
||||
|
||||
def apply_classifier(x, model, img, im0):
|
||||
# applies a second stage classifier to yolo outputs
|
||||
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_coords(img.shape[2:], d[:, :4], im0[i].shape)
|
||||
|
||||
# Classes
|
||||
pred_cls1 = d[:, 5].long()
|
||||
ims = []
|
||||
for j, a in enumerate(d): # per item
|
||||
cutout = im0[i][int(a[1]):int(a[3]), int(a[0]):int(a[2])]
|
||||
im = cv2.resize(cutout, (224, 224)) # BGR
|
||||
# cv2.imwrite('test%i.jpg' % j, cutout)
|
||||
|
||||
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 # 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=True, sep=''):
|
||||
# Increment path, i.e. runs/exp --> runs/exp{sep}0, runs/exp{sep}1 etc.
|
||||
path = Path(path) # os-agnostic
|
||||
if (path.exists() and exist_ok) or (not path.exists()):
|
||||
return str(path)
|
||||
else:
|
||||
dirs = glob.glob(f"{path}{sep}*") # similar paths
|
||||
matches = [re.search(rf"%s{sep}(\d+)" % path.stem, 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
|
||||
return f"{path}{sep}{n}" # update path
|
||||
25
utils/google_app_engine/Dockerfile
Normal file
25
utils/google_app_engine/Dockerfile
Normal file
@ -0,0 +1,25 @@
|
||||
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
|
||||
4
utils/google_app_engine/additional_requirements.txt
Normal file
4
utils/google_app_engine/additional_requirements.txt
Normal file
@ -0,0 +1,4 @@
|
||||
# add these requirements in your app on top of the existing ones
|
||||
pip==18.1
|
||||
Flask==1.0.2
|
||||
gunicorn==19.9.0
|
||||
14
utils/google_app_engine/app.yaml
Normal file
14
utils/google_app_engine/app.yaml
Normal file
@ -0,0 +1,14 @@
|
||||
runtime: custom
|
||||
env: flex
|
||||
|
||||
service: yolov3app
|
||||
|
||||
liveness_check:
|
||||
initial_delay_sec: 600
|
||||
|
||||
manual_scaling:
|
||||
instances: 1
|
||||
resources:
|
||||
cpu: 1
|
||||
memory_gb: 4
|
||||
disk_size_gb: 20
|
||||
@ -1,17 +1,60 @@
|
||||
# This file contains google utils: https://cloud.google.com/storage/docs/reference/libraries
|
||||
# pip install --upgrade google-cloud-storage
|
||||
# Google utils: https://cloud.google.com/storage/docs/reference/libraries
|
||||
|
||||
import os
|
||||
import platform
|
||||
import subprocess
|
||||
import time
|
||||
from pathlib import Path
|
||||
|
||||
import torch
|
||||
|
||||
|
||||
# from google.cloud import storage
|
||||
def gsutil_getsize(url=''):
|
||||
# gs://bucket/file size https://cloud.google.com/storage/docs/gsutil/commands/du
|
||||
s = subprocess.check_output('gsutil du %s' % url, shell=True).decode('utf-8')
|
||||
return eval(s.split(' ')[0]) if len(s) else 0 # bytes
|
||||
|
||||
|
||||
def gdrive_download(id='1HaXkef9z6y5l4vUnCYgdmEAj61c6bfWO', name='coco.zip'):
|
||||
# https://gist.github.com/tanaikech/f0f2d122e05bf5f971611258c22c110f
|
||||
# Downloads a file from Google Drive, accepting presented query
|
||||
# from utils.google_utils import *; gdrive_download()
|
||||
def attempt_download(weights):
|
||||
# Attempt to download pretrained weights if not found locally
|
||||
weights = weights.strip().replace("'", '')
|
||||
file = Path(weights).name.lower()
|
||||
|
||||
msg = weights + ' missing, try downloading from https://github.com/ultralytics/yolov3/releases/'
|
||||
models = ['yolov3.pt', 'yolov3-spp.pt', 'yolov3-tiny.pt'] # available models
|
||||
redundant = False # offer second download option
|
||||
|
||||
if file in models and not os.path.isfile(weights):
|
||||
# Google Drive
|
||||
# d = {'yolov5s.pt': '1R5T6rIyy3lLwgFXNms8whc-387H0tMQO',
|
||||
# 'yolov5m.pt': '1vobuEExpWQVpXExsJ2w-Mbf3HJjWkQJr',
|
||||
# 'yolov5l.pt': '1hrlqD1Wdei7UT4OgT785BEk1JwnSvNEV',
|
||||
# 'yolov5x.pt': '1mM8aZJlWTxOg7BZJvNUMrTnA2AbeCVzS'}
|
||||
# r = gdrive_download(id=d[file], name=weights) if file in d else 1
|
||||
# if r == 0 and os.path.exists(weights) and os.path.getsize(weights) > 1E6: # check
|
||||
# return
|
||||
|
||||
try: # GitHub
|
||||
url = 'https://github.com/ultralytics/yolov3/releases/download/v1.0/' + file
|
||||
print('Downloading %s to %s...' % (url, weights))
|
||||
torch.hub.download_url_to_file(url, weights)
|
||||
assert os.path.exists(weights) and os.path.getsize(weights) > 1E6 # check
|
||||
except Exception as e: # GCP
|
||||
print('Download error: %s' % e)
|
||||
assert redundant, 'No secondary mirror'
|
||||
url = 'https://storage.googleapis.com/ultralytics/yolov3/ckpt/' + file
|
||||
print('Downloading %s to %s...' % (url, weights))
|
||||
r = os.system('curl -L %s -o %s' % (url, weights)) # torch.hub.download_url_to_file(url, weights)
|
||||
finally:
|
||||
if not (os.path.exists(weights) and os.path.getsize(weights) > 1E6): # check
|
||||
os.remove(weights) if os.path.exists(weights) else None # remove partial downloads
|
||||
print('ERROR: Download failure: %s' % msg)
|
||||
print('')
|
||||
return
|
||||
|
||||
|
||||
def gdrive_download(id='1n_oKgR81BJtqk75b00eAjdv03qVCQn2f', name='coco128.zip'):
|
||||
# Downloads a file from Google Drive. from utils.google_utils import *; gdrive_download()
|
||||
t = time.time()
|
||||
|
||||
print('Downloading https://drive.google.com/uc?export=download&id=%s as %s... ' % (id, name), end='')
|
||||
@ -19,13 +62,13 @@ def gdrive_download(id='1HaXkef9z6y5l4vUnCYgdmEAj61c6bfWO', name='coco.zip'):
|
||||
os.remove('cookie') if os.path.exists('cookie') else None
|
||||
|
||||
# Attempt file download
|
||||
os.system("curl -c ./cookie -s -L \"https://drive.google.com/uc?export=download&id=%s\" > /dev/null" % id)
|
||||
out = "NUL" if platform.system() == "Windows" else "/dev/null"
|
||||
os.system('curl -c ./cookie -s -L "drive.google.com/uc?export=download&id=%s" > %s ' % (id, out))
|
||||
if os.path.exists('cookie'): # large file
|
||||
s = "curl -Lb ./cookie \"https://drive.google.com/uc?export=download&confirm=`awk '/download/ {print $NF}' ./cookie`&id=%s\" -o %s" % (
|
||||
id, name)
|
||||
s = 'curl -Lb ./cookie "drive.google.com/uc?export=download&confirm=%s&id=%s" -o %s' % (get_token(), id, name)
|
||||
else: # small file
|
||||
s = "curl -s -L -o %s 'https://drive.google.com/uc?export=download&id=%s'" % (name, id)
|
||||
r = os.system(s) # execute, capture return values
|
||||
s = 'curl -s -L -o %s "drive.google.com/uc?export=download&id=%s"' % (name, id)
|
||||
r = os.system(s) # execute, capture return
|
||||
os.remove('cookie') if os.path.exists('cookie') else None
|
||||
|
||||
# Error check
|
||||
@ -44,29 +87,36 @@ def gdrive_download(id='1HaXkef9z6y5l4vUnCYgdmEAj61c6bfWO', name='coco.zip'):
|
||||
return r
|
||||
|
||||
|
||||
def upload_blob(bucket_name, source_file_name, destination_blob_name):
|
||||
# Uploads a file to a bucket
|
||||
# https://cloud.google.com/storage/docs/uploading-objects#storage-upload-object-python
|
||||
def get_token(cookie="./cookie"):
|
||||
with open(cookie) as f:
|
||||
for line in f:
|
||||
if "download" in line:
|
||||
return line.split()[-1]
|
||||
return ""
|
||||
|
||||
storage_client = storage.Client()
|
||||
bucket = storage_client.get_bucket(bucket_name)
|
||||
blob = bucket.blob(destination_blob_name)
|
||||
|
||||
blob.upload_from_filename(source_file_name)
|
||||
|
||||
print('File {} uploaded to {}.'.format(
|
||||
source_file_name,
|
||||
destination_blob_name))
|
||||
|
||||
|
||||
def download_blob(bucket_name, source_blob_name, destination_file_name):
|
||||
# Uploads a blob from a bucket
|
||||
storage_client = storage.Client()
|
||||
bucket = storage_client.get_bucket(bucket_name)
|
||||
blob = bucket.blob(source_blob_name)
|
||||
|
||||
blob.download_to_filename(destination_file_name)
|
||||
|
||||
print('Blob {} downloaded to {}.'.format(
|
||||
source_blob_name,
|
||||
destination_file_name))
|
||||
# def upload_blob(bucket_name, source_file_name, destination_blob_name):
|
||||
# # Uploads a file to a bucket
|
||||
# # https://cloud.google.com/storage/docs/uploading-objects#storage-upload-object-python
|
||||
#
|
||||
# storage_client = storage.Client()
|
||||
# bucket = storage_client.get_bucket(bucket_name)
|
||||
# blob = bucket.blob(destination_blob_name)
|
||||
#
|
||||
# blob.upload_from_filename(source_file_name)
|
||||
#
|
||||
# print('File {} uploaded to {}.'.format(
|
||||
# source_file_name,
|
||||
# destination_blob_name))
|
||||
#
|
||||
#
|
||||
# def download_blob(bucket_name, source_blob_name, destination_file_name):
|
||||
# # Uploads a blob from a bucket
|
||||
# storage_client = storage.Client()
|
||||
# bucket = storage_client.get_bucket(bucket_name)
|
||||
# blob = bucket.blob(source_blob_name)
|
||||
#
|
||||
# blob.download_to_filename(destination_file_name)
|
||||
#
|
||||
# print('Blob {} downloaded to {}.'.format(
|
||||
# source_blob_name,
|
||||
# destination_file_name))
|
||||
|
||||
148
utils/layers.py
148
utils/layers.py
@ -1,148 +0,0 @@
|
||||
import torch.nn.functional as F
|
||||
|
||||
from utils.utils import *
|
||||
|
||||
|
||||
def make_divisible(v, divisor):
|
||||
# Function ensures all layers have a channel number that is divisible by 8
|
||||
# https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet/mobilenet.py
|
||||
return math.ceil(v / divisor) * divisor
|
||||
|
||||
|
||||
class Flatten(nn.Module):
|
||||
# Use after nn.AdaptiveAvgPool2d(1) to remove last 2 dimensions
|
||||
def forward(self, x):
|
||||
return x.view(x.size(0), -1)
|
||||
|
||||
|
||||
class Concat(nn.Module):
|
||||
# Concatenate a list of tensors along dimension
|
||||
def __init__(self, dimension=1):
|
||||
super(Concat, self).__init__()
|
||||
self.d = dimension
|
||||
|
||||
def forward(self, x):
|
||||
return torch.cat(x, self.d)
|
||||
|
||||
|
||||
class FeatureConcat(nn.Module):
|
||||
def __init__(self, layers):
|
||||
super(FeatureConcat, self).__init__()
|
||||
self.layers = layers # layer indices
|
||||
self.multiple = len(layers) > 1 # multiple layers flag
|
||||
|
||||
def forward(self, x, outputs):
|
||||
return torch.cat([outputs[i] for i in self.layers], 1) if self.multiple else outputs[self.layers[0]]
|
||||
|
||||
|
||||
class WeightedFeatureFusion(nn.Module): # weighted sum of 2 or more layers https://arxiv.org/abs/1911.09070
|
||||
def __init__(self, layers, weight=False):
|
||||
super(WeightedFeatureFusion, self).__init__()
|
||||
self.layers = layers # layer indices
|
||||
self.weight = weight # apply weights boolean
|
||||
self.n = len(layers) + 1 # number of layers
|
||||
if weight:
|
||||
self.w = nn.Parameter(torch.zeros(self.n), requires_grad=True) # layer weights
|
||||
|
||||
def forward(self, x, outputs):
|
||||
# Weights
|
||||
if self.weight:
|
||||
w = torch.sigmoid(self.w) * (2 / self.n) # sigmoid weights (0-1)
|
||||
x = x * w[0]
|
||||
|
||||
# Fusion
|
||||
nx = x.shape[1] # input channels
|
||||
for i in range(self.n - 1):
|
||||
a = outputs[self.layers[i]] * w[i + 1] if self.weight else outputs[self.layers[i]] # feature to add
|
||||
na = a.shape[1] # feature channels
|
||||
|
||||
# Adjust channels
|
||||
if nx == na: # same shape
|
||||
x = x + a
|
||||
elif nx > na: # slice input
|
||||
x[:, :na] = x[:, :na] + a # or a = nn.ZeroPad2d((0, 0, 0, 0, 0, dc))(a); x = x + a
|
||||
else: # slice feature
|
||||
x = x + a[:, :nx]
|
||||
|
||||
return x
|
||||
|
||||
|
||||
class MixConv2d(nn.Module): # MixConv: Mixed Depthwise Convolutional Kernels https://arxiv.org/abs/1907.09595
|
||||
def __init__(self, in_ch, out_ch, k=(3, 5, 7), stride=1, dilation=1, bias=True, method='equal_params'):
|
||||
super(MixConv2d, self).__init__()
|
||||
|
||||
groups = len(k)
|
||||
if method == 'equal_ch': # equal channels per group
|
||||
i = torch.linspace(0, groups - 1E-6, out_ch).floor() # out_ch indices
|
||||
ch = [(i == g).sum() for g in range(groups)]
|
||||
else: # 'equal_params': equal parameter count per group
|
||||
b = [out_ch] + [0] * groups
|
||||
a = np.eye(groups + 1, groups, k=-1)
|
||||
a -= np.roll(a, 1, axis=1)
|
||||
a *= np.array(k) ** 2
|
||||
a[0] = 1
|
||||
ch = np.linalg.lstsq(a, b, rcond=None)[0].round().astype(int) # solve for equal weight indices, ax = b
|
||||
|
||||
self.m = nn.ModuleList([nn.Conv2d(in_channels=in_ch,
|
||||
out_channels=ch[g],
|
||||
kernel_size=k[g],
|
||||
stride=stride,
|
||||
padding=k[g] // 2, # 'same' pad
|
||||
dilation=dilation,
|
||||
bias=bias) for g in range(groups)])
|
||||
|
||||
def forward(self, x):
|
||||
return torch.cat([m(x) for m in self.m], 1)
|
||||
|
||||
|
||||
# Activation functions below -------------------------------------------------------------------------------------------
|
||||
class SwishImplementation(torch.autograd.Function):
|
||||
@staticmethod
|
||||
def forward(ctx, x):
|
||||
ctx.save_for_backward(x)
|
||||
return x * torch.sigmoid(x)
|
||||
|
||||
@staticmethod
|
||||
def backward(ctx, grad_output):
|
||||
x = ctx.saved_tensors[0]
|
||||
sx = torch.sigmoid(x) # sigmoid(ctx)
|
||||
return grad_output * (sx * (1 + x * (1 - sx)))
|
||||
|
||||
|
||||
class MishImplementation(torch.autograd.Function):
|
||||
@staticmethod
|
||||
def forward(ctx, x):
|
||||
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):
|
||||
x = ctx.saved_tensors[0]
|
||||
sx = torch.sigmoid(x)
|
||||
fx = F.softplus(x).tanh()
|
||||
return grad_output * (fx + x * sx * (1 - fx * fx))
|
||||
|
||||
|
||||
class MemoryEfficientSwish(nn.Module):
|
||||
def forward(self, x):
|
||||
return SwishImplementation.apply(x)
|
||||
|
||||
|
||||
class MemoryEfficientMish(nn.Module):
|
||||
def forward(self, x):
|
||||
return MishImplementation.apply(x)
|
||||
|
||||
|
||||
class Swish(nn.Module):
|
||||
def forward(self, x):
|
||||
return x * torch.sigmoid(x)
|
||||
|
||||
|
||||
class HardSwish(nn.Module): # https://arxiv.org/pdf/1905.02244.pdf
|
||||
def forward(self, x):
|
||||
return x * F.hardtanh(x + 3, 0., 6., True) / 6.
|
||||
|
||||
|
||||
class Mish(nn.Module): # https://github.com/digantamisra98/Mish
|
||||
def forward(self, x):
|
||||
return x * F.softplus(x).tanh()
|
||||
179
utils/loss.py
Normal file
179
utils/loss.py
Normal file
@ -0,0 +1,179 @@
|
||||
# Loss functions
|
||||
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
|
||||
from utils.general import bbox_iou
|
||||
from utils.torch_utils import is_parallel
|
||||
|
||||
|
||||
def smooth_BCE(eps=0.1): # https://github.com/ultralytics/yolov3/issues/238#issuecomment-598028441
|
||||
# return positive, negative label smoothing BCE targets
|
||||
return 1.0 - 0.5 * eps, 0.5 * eps
|
||||
|
||||
|
||||
class BCEBlurWithLogitsLoss(nn.Module):
|
||||
# BCEwithLogitLoss() with reduced missing label effects.
|
||||
def __init__(self, alpha=0.05):
|
||||
super(BCEBlurWithLogitsLoss, self).__init__()
|
||||
self.loss_fcn = nn.BCEWithLogitsLoss(reduction='none') # must be nn.BCEWithLogitsLoss()
|
||||
self.alpha = alpha
|
||||
|
||||
def forward(self, pred, true):
|
||||
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):
|
||||
# Wraps focal loss around existing loss_fcn(), i.e. criteria = FocalLoss(nn.BCEWithLogitsLoss(), gamma=1.5)
|
||||
def __init__(self, loss_fcn, gamma=1.5, alpha=0.25):
|
||||
super(FocalLoss, self).__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):
|
||||
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
|
||||
|
||||
|
||||
def compute_loss(p, targets, model): # predictions, targets, model
|
||||
device = targets.device
|
||||
lcls, lbox, lobj = torch.zeros(1, device=device), torch.zeros(1, device=device), torch.zeros(1, device=device)
|
||||
tcls, tbox, indices, anchors = build_targets(p, targets, model) # targets
|
||||
h = model.hyp # hyperparameters
|
||||
|
||||
# Define criteria
|
||||
BCEcls = nn.BCEWithLogitsLoss(pos_weight=torch.Tensor([h['cls_pw']])).to(device)
|
||||
BCEobj = nn.BCEWithLogitsLoss(pos_weight=torch.Tensor([h['obj_pw']])).to(device)
|
||||
|
||||
# Class label smoothing https://arxiv.org/pdf/1902.04103.pdf eqn 3
|
||||
cp, cn = smooth_BCE(eps=0.0)
|
||||
|
||||
# Focal loss
|
||||
g = h['fl_gamma'] # focal loss gamma
|
||||
if g > 0:
|
||||
BCEcls, BCEobj = FocalLoss(BCEcls, g), FocalLoss(BCEobj, g)
|
||||
|
||||
# Losses
|
||||
nt = 0 # number of targets
|
||||
no = len(p) # number of outputs
|
||||
balance = [4.0, 1.0, 0.4] if no == 3 else [4.0, 1.0, 0.4, 0.1] # P3-5 or P3-6
|
||||
for i, pi in enumerate(p): # layer index, layer predictions
|
||||
b, a, gj, gi = indices[i] # image, anchor, gridy, gridx
|
||||
tobj = torch.zeros_like(pi[..., 0], device=device) # target obj
|
||||
|
||||
n = b.shape[0] # number of targets
|
||||
if n:
|
||||
nt += n # cumulative targets
|
||||
ps = pi[b, a, gj, gi] # prediction subset corresponding to targets
|
||||
|
||||
# Regression
|
||||
pxy = ps[:, :2].sigmoid() * 2. - 0.5
|
||||
pwh = (ps[:, 2:4].sigmoid() * 2) ** 2 * anchors[i]
|
||||
pbox = torch.cat((pxy, pwh), 1).to(device) # predicted box
|
||||
iou = bbox_iou(pbox.T, tbox[i], x1y1x2y2=False, CIoU=True) # iou(prediction, target)
|
||||
lbox += (1.0 - iou).mean() # iou loss
|
||||
|
||||
# Objectness
|
||||
tobj[b, a, gj, gi] = (1.0 - model.gr) + model.gr * iou.detach().clamp(0).type(tobj.dtype) # iou ratio
|
||||
|
||||
# Classification
|
||||
if model.nc > 1: # cls loss (only if multiple classes)
|
||||
t = torch.full_like(ps[:, 5:], cn, device=device) # targets
|
||||
t[range(n), tcls[i]] = cp
|
||||
lcls += BCEcls(ps[:, 5:], t) # BCE
|
||||
|
||||
# Append targets to text file
|
||||
# with open('targets.txt', 'a') as file:
|
||||
# [file.write('%11.5g ' * 4 % tuple(x) + '\n') for x in torch.cat((txy[i], twh[i]), 1)]
|
||||
|
||||
lobj += BCEobj(pi[..., 4], tobj) * balance[i] # obj loss
|
||||
|
||||
s = 3 / no # output count scaling
|
||||
lbox *= h['box'] * s
|
||||
lobj *= h['obj'] * s * (1.4 if no == 4 else 1.)
|
||||
lcls *= h['cls'] * s
|
||||
bs = tobj.shape[0] # batch size
|
||||
|
||||
loss = lbox + lobj + lcls
|
||||
return loss * bs, torch.cat((lbox, lobj, lcls, loss)).detach()
|
||||
|
||||
|
||||
def build_targets(p, targets, model):
|
||||
# Build targets for compute_loss(), input targets(image,class,x,y,w,h)
|
||||
det = model.module.model[-1] if is_parallel(model) else model.model[-1] # Detect() module
|
||||
na, nt = det.na, targets.shape[0] # number of anchors, targets
|
||||
tcls, tbox, indices, anch = [], [], [], []
|
||||
gain = torch.ones(7, device=targets.device) # normalized to gridspace gain
|
||||
ai = torch.arange(na, device=targets.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=targets.device).float() * g # offsets
|
||||
|
||||
for i in range(det.nl):
|
||||
anchors = det.anchors[i]
|
||||
gain[2:6] = torch.tensor(p[i].shape)[[3, 2, 3, 2]] # xyxy gain
|
||||
|
||||
# Match targets to anchors
|
||||
t = targets * gain
|
||||
if nt:
|
||||
# Matches
|
||||
r = t[:, :, 4:6] / anchors[:, None] # wh ratio
|
||||
j = torch.max(r, 1. / r).max(2)[0] < model.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),))
|
||||
t = t.repeat((off.shape[0], 1, 1))[j]
|
||||
offsets = (torch.zeros_like(gxy)[None] + off[:, None])[j]
|
||||
else:
|
||||
t = targets[0]
|
||||
offsets = 0
|
||||
|
||||
# Define
|
||||
b, c = t[:, :2].long().T # image, class
|
||||
gxy = t[:, 2:4] # grid xy
|
||||
gwh = t[:, 4:6] # grid wh
|
||||
gij = (gxy - offsets).long()
|
||||
gi, gj = gij.T # grid xy indices
|
||||
|
||||
# Append
|
||||
a = t[:, 6].long() # anchor indices
|
||||
indices.append((b, a, gj.clamp_(0, gain[3] - 1), gi.clamp_(0, gain[2] - 1))) # image, anchor, grid indices
|
||||
tbox.append(torch.cat((gxy - gij, gwh), 1)) # box
|
||||
anch.append(anchors[a]) # anchors
|
||||
tcls.append(c) # class
|
||||
|
||||
return tcls, tbox, indices, anch
|
||||
203
utils/metrics.py
Normal file
203
utils/metrics.py
Normal file
@ -0,0 +1,203 @@
|
||||
# Model validation metrics
|
||||
|
||||
from pathlib import Path
|
||||
|
||||
import matplotlib.pyplot as plt
|
||||
import numpy as np
|
||||
import torch
|
||||
|
||||
from . import general
|
||||
|
||||
|
||||
def fitness(x):
|
||||
# Model fitness as a weighted combination of metrics
|
||||
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 ap_per_class(tp, conf, pred_cls, target_cls, plot=False, save_dir='precision-recall_curve.png', names=[]):
|
||||
""" 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 = np.unique(target_cls)
|
||||
|
||||
# Create Precision-Recall curve and compute AP for each class
|
||||
px, py = np.linspace(0, 1, 1000), [] # for plotting
|
||||
pr_score = 0.1 # score to evaluate P and R https://github.com/ultralytics/yolov3/issues/898
|
||||
s = [unique_classes.shape[0], tp.shape[1]] # number class, number iou thresholds (i.e. 10 for mAP0.5...0.95)
|
||||
ap, p, r = np.zeros(s), np.zeros(s), np.zeros(s)
|
||||
for ci, c in enumerate(unique_classes):
|
||||
i = pred_cls == c
|
||||
n_l = (target_cls == c).sum() # number of labels
|
||||
n_p = i.sum() # number of predictions
|
||||
|
||||
if n_p == 0 or n_l == 0:
|
||||
continue
|
||||
else:
|
||||
# Accumulate FPs and TPs
|
||||
fpc = (1 - tp[i]).cumsum(0)
|
||||
tpc = tp[i].cumsum(0)
|
||||
|
||||
# Recall
|
||||
recall = tpc / (n_l + 1e-16) # recall curve
|
||||
r[ci] = np.interp(-pr_score, -conf[i], recall[:, 0]) # r at pr_score, negative x, xp because xp decreases
|
||||
|
||||
# Precision
|
||||
precision = tpc / (tpc + fpc) # precision curve
|
||||
p[ci] = np.interp(-pr_score, -conf[i], precision[:, 0]) # 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 score (harmonic mean of precision and recall)
|
||||
f1 = 2 * p * r / (p + r + 1e-16)
|
||||
|
||||
if plot:
|
||||
plot_pr_curve(px, py, ap, save_dir, names)
|
||||
|
||||
return p, r, ap, f1, unique_classes.astype('int32')
|
||||
|
||||
|
||||
def compute_ap(recall, precision):
|
||||
""" Compute the average precision, given the recall and precision curves.
|
||||
Source: https://github.com/rbgirshick/py-faster-rcnn.
|
||||
# Arguments
|
||||
recall: The recall curve (list).
|
||||
precision: The precision curve (list).
|
||||
# Returns
|
||||
The average precision as computed in py-faster-rcnn.
|
||||
"""
|
||||
|
||||
# Append sentinel values to beginning and end
|
||||
mrec = recall # np.concatenate(([0.], recall, [recall[-1] + 1E-3]))
|
||||
mpre = precision # np.concatenate(([0.], precision, [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.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:
|
||||
# Updated version of https://github.com/kaanakan/object_detection_confusion_matrix
|
||||
def __init__(self, nc, conf=0.25, iou_thres=0.45):
|
||||
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.
|
||||
Arguments:
|
||||
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
|
||||
"""
|
||||
detections = detections[detections[:, 4] > self.conf]
|
||||
gt_classes = labels[:, 0].int()
|
||||
detection_classes = detections[:, 5].int()
|
||||
iou = general.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(np.int16)
|
||||
for i, gc in enumerate(gt_classes):
|
||||
j = m0 == i
|
||||
if n and sum(j) == 1:
|
||||
self.matrix[gc, detection_classes[m1[j]]] += 1 # correct
|
||||
else:
|
||||
self.matrix[gc, self.nc] += 1 # background FP
|
||||
|
||||
if n:
|
||||
for i, dc in enumerate(detection_classes):
|
||||
if not any(m1 == i):
|
||||
self.matrix[self.nc, dc] += 1 # background FN
|
||||
|
||||
def matrix(self):
|
||||
return self.matrix
|
||||
|
||||
def plot(self, save_dir='', names=()):
|
||||
try:
|
||||
import seaborn as sn
|
||||
|
||||
array = self.matrix / (self.matrix.sum(0).reshape(1, self.nc + 1) + 1E-6) # normalize
|
||||
array[array < 0.005] = np.nan # don't annotate (would appear as 0.00)
|
||||
|
||||
fig = plt.figure(figsize=(12, 9))
|
||||
sn.set(font_scale=1.0 if self.nc < 50 else 0.8) # for label size
|
||||
labels = (0 < len(names) < 99) and len(names) == self.nc # apply names to ticklabels
|
||||
sn.heatmap(array, annot=self.nc < 30, annot_kws={"size": 8}, cmap='Blues', fmt='.2f', square=True,
|
||||
xticklabels=names + ['background FN'] if labels else "auto",
|
||||
yticklabels=names + ['background FP'] if labels else "auto").set_facecolor((1, 1, 1))
|
||||
fig.axes[0].set_xlabel('True')
|
||||
fig.axes[0].set_ylabel('Predicted')
|
||||
fig.tight_layout()
|
||||
fig.savefig(Path(save_dir) / 'confusion_matrix.png', dpi=250)
|
||||
except Exception as e:
|
||||
pass
|
||||
|
||||
def print(self):
|
||||
for i in range(self.nc + 1):
|
||||
print(' '.join(map(str, self.matrix[i])))
|
||||
|
||||
|
||||
# Plots ----------------------------------------------------------------------------------------------------------------
|
||||
|
||||
def plot_pr_curve(px, py, ap, save_dir='.', names=()):
|
||||
fig, ax = plt.subplots(1, 1, figsize=(9, 6))
|
||||
py = np.stack(py, axis=1)
|
||||
|
||||
if 0 < len(names) < 21: # show mAP in legend if < 10 classes
|
||||
for i, y in enumerate(py.T):
|
||||
ax.plot(px, y, linewidth=1, label=f'{names[i]} %.3f' % ap[i, 0]) # 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='all classes %.3f mAP@0.5' % ap[:, 0].mean())
|
||||
ax.set_xlabel('Recall')
|
||||
ax.set_ylabel('Precision')
|
||||
ax.set_xlim(0, 1)
|
||||
ax.set_ylim(0, 1)
|
||||
plt.legend(bbox_to_anchor=(1.04, 1), loc="upper left")
|
||||
fig.tight_layout()
|
||||
fig.savefig(Path(save_dir) / 'precision_recall_curve.png', dpi=250)
|
||||
@ -1,71 +0,0 @@
|
||||
import os
|
||||
|
||||
import numpy as np
|
||||
|
||||
|
||||
def parse_model_cfg(path):
|
||||
# Parse the yolo *.cfg file and return module definitions path may be 'cfg/yolov3.cfg', 'yolov3.cfg', or 'yolov3'
|
||||
if not path.endswith('.cfg'): # add .cfg suffix if omitted
|
||||
path += '.cfg'
|
||||
if not os.path.exists(path) and os.path.exists('cfg' + os.sep + path): # add cfg/ prefix if omitted
|
||||
path = 'cfg' + os.sep + path
|
||||
|
||||
with open(path, 'r') as f:
|
||||
lines = f.read().split('\n')
|
||||
lines = [x for x in lines if x and not x.startswith('#')]
|
||||
lines = [x.rstrip().lstrip() for x in lines] # get rid of fringe whitespaces
|
||||
mdefs = [] # module definitions
|
||||
for line in lines:
|
||||
if line.startswith('['): # This marks the start of a new block
|
||||
mdefs.append({})
|
||||
mdefs[-1]['type'] = line[1:-1].rstrip()
|
||||
if mdefs[-1]['type'] == 'convolutional':
|
||||
mdefs[-1]['batch_normalize'] = 0 # pre-populate with zeros (may be overwritten later)
|
||||
else:
|
||||
key, val = line.split("=")
|
||||
key = key.rstrip()
|
||||
|
||||
if key == 'anchors': # return nparray
|
||||
mdefs[-1][key] = np.array([float(x) for x in val.split(',')]).reshape((-1, 2)) # np anchors
|
||||
elif (key in ['from', 'layers', 'mask']) or (key == 'size' and ',' in val): # return array
|
||||
mdefs[-1][key] = [int(x) for x in val.split(',')]
|
||||
else:
|
||||
val = val.strip()
|
||||
# TODO: .isnumeric() actually fails to get the float case
|
||||
if val.isnumeric(): # return int or float
|
||||
mdefs[-1][key] = int(val) if (int(val) - float(val)) == 0 else float(val)
|
||||
else:
|
||||
mdefs[-1][key] = val # return string
|
||||
|
||||
# Check all fields are supported
|
||||
supported = ['type', 'batch_normalize', 'filters', 'size', 'stride', 'pad', 'activation', 'layers', 'groups',
|
||||
'from', 'mask', 'anchors', 'classes', 'num', 'jitter', 'ignore_thresh', 'truth_thresh', 'random',
|
||||
'stride_x', 'stride_y', 'weights_type', 'weights_normalization', 'scale_x_y', 'beta_nms', 'nms_kind',
|
||||
'iou_loss', 'iou_normalizer', 'cls_normalizer', 'iou_thresh', 'probability']
|
||||
|
||||
f = [] # fields
|
||||
for x in mdefs[1:]:
|
||||
[f.append(k) for k in x if k not in f]
|
||||
u = [x for x in f if x not in supported] # unsupported fields
|
||||
assert not any(u), "Unsupported fields %s in %s. See https://github.com/ultralytics/yolov3/issues/631" % (u, path)
|
||||
|
||||
return mdefs
|
||||
|
||||
|
||||
def parse_data_cfg(path):
|
||||
# Parses the data configuration file
|
||||
if not os.path.exists(path) and os.path.exists('data' + os.sep + path): # add data/ prefix if omitted
|
||||
path = 'data' + os.sep + path
|
||||
|
||||
with open(path, 'r') as f:
|
||||
lines = f.readlines()
|
||||
|
||||
options = dict()
|
||||
for line in lines:
|
||||
line = line.strip()
|
||||
if line == '' or line.startswith('#'):
|
||||
continue
|
||||
key, val = line.split('=')
|
||||
options[key.strip()] = val.strip()
|
||||
|
||||
return options
|
||||
379
utils/plots.py
Normal file
379
utils/plots.py
Normal file
@ -0,0 +1,379 @@
|
||||
# Plotting utils
|
||||
|
||||
import glob
|
||||
import os
|
||||
import random
|
||||
from copy import copy
|
||||
from pathlib import Path
|
||||
|
||||
import cv2
|
||||
import math
|
||||
import matplotlib
|
||||
import matplotlib.pyplot as plt
|
||||
import numpy as np
|
||||
import torch
|
||||
import yaml
|
||||
from PIL import Image, ImageDraw
|
||||
from scipy.signal import butter, filtfilt
|
||||
|
||||
from utils.general import xywh2xyxy, xyxy2xywh
|
||||
from utils.metrics import fitness
|
||||
|
||||
# Settings
|
||||
matplotlib.use('Agg') # for writing to files only
|
||||
|
||||
|
||||
def color_list():
|
||||
# Return first 10 plt colors as (r,g,b) https://stackoverflow.com/questions/51350872/python-from-color-name-to-rgb
|
||||
def hex2rgb(h):
|
||||
return tuple(int(h[1 + i:1 + i + 2], 16) for i in (0, 2, 4))
|
||||
|
||||
return [hex2rgb(h) for h in plt.rcParams['axes.prop_cycle'].by_key()['color']]
|
||||
|
||||
|
||||
def hist2d(x, y, n=100):
|
||||
# 2d histogram used in labels.png and evolve.png
|
||||
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):
|
||||
# https://stackoverflow.com/questions/28536191/how-to-filter-smooth-with-scipy-numpy
|
||||
def butter_lowpass(cutoff, fs, order):
|
||||
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 plot_one_box(x, img, color=None, label=None, line_thickness=None):
|
||||
# Plots one bounding box on image img
|
||||
tl = line_thickness or round(0.002 * (img.shape[0] + img.shape[1]) / 2) + 1 # line/font thickness
|
||||
color = color or [random.randint(0, 255) for _ in range(3)]
|
||||
c1, c2 = (int(x[0]), int(x[1])), (int(x[2]), int(x[3]))
|
||||
cv2.rectangle(img, c1, c2, color, thickness=tl, lineType=cv2.LINE_AA)
|
||||
if label:
|
||||
tf = max(tl - 1, 1) # font thickness
|
||||
t_size = cv2.getTextSize(label, 0, fontScale=tl / 3, thickness=tf)[0]
|
||||
c2 = c1[0] + t_size[0], c1[1] - t_size[1] - 3
|
||||
cv2.rectangle(img, c1, c2, color, -1, cv2.LINE_AA) # filled
|
||||
cv2.putText(img, label, (c1[0], c1[1] - 2), 0, tl / 3, [225, 255, 255], thickness=tf, lineType=cv2.LINE_AA)
|
||||
|
||||
|
||||
def plot_wh_methods(): # from utils.plots import *; plot_wh_methods()
|
||||
# Compares the two methods for width-height anchor multiplication
|
||||
# https://github.com/ultralytics/yolov3/issues/168
|
||||
x = np.arange(-4.0, 4.0, .1)
|
||||
ya = np.exp(x)
|
||||
yb = torch.sigmoid(torch.from_numpy(x)).numpy() * 2
|
||||
|
||||
fig = plt.figure(figsize=(6, 3), dpi=150)
|
||||
plt.plot(x, ya, '.-', label='YOLOv3')
|
||||
plt.plot(x, yb ** 2, '.-', label='YOLOv5 ^2')
|
||||
plt.plot(x, yb ** 1.6, '.-', label='YOLOv5 ^1.6')
|
||||
plt.xlim(left=-4, right=4)
|
||||
plt.ylim(bottom=0, top=6)
|
||||
plt.xlabel('input')
|
||||
plt.ylabel('output')
|
||||
plt.grid()
|
||||
plt.legend()
|
||||
fig.tight_layout()
|
||||
fig.savefig('comparison.png', dpi=200)
|
||||
|
||||
|
||||
def output_to_target(output):
|
||||
# Convert model output to target format [batch_id, class_id, x, y, w, h, conf]
|
||||
targets = []
|
||||
for i, o in enumerate(output):
|
||||
for *box, conf, cls in o.cpu().numpy():
|
||||
targets.append([i, cls, *list(*xyxy2xywh(np.array(box)[None])), conf])
|
||||
return np.array(targets)
|
||||
|
||||
|
||||
def plot_images(images, targets, paths=None, fname='images.jpg', names=None, max_size=640, max_subplots=16):
|
||||
# Plot image grid with labels
|
||||
|
||||
if isinstance(images, torch.Tensor):
|
||||
images = images.cpu().float().numpy()
|
||||
if isinstance(targets, torch.Tensor):
|
||||
targets = targets.cpu().numpy()
|
||||
|
||||
# un-normalise
|
||||
if np.max(images[0]) <= 1:
|
||||
images *= 255
|
||||
|
||||
tl = 3 # line thickness
|
||||
tf = max(tl - 1, 1) # font thickness
|
||||
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)
|
||||
|
||||
# Check if we should resize
|
||||
scale_factor = max_size / max(h, w)
|
||||
if scale_factor < 1:
|
||||
h = math.ceil(scale_factor * h)
|
||||
w = math.ceil(scale_factor * w)
|
||||
|
||||
colors = color_list() # list of colors
|
||||
mosaic = np.full((int(ns * h), int(ns * w), 3), 255, dtype=np.uint8) # init
|
||||
for i, img in enumerate(images):
|
||||
if i == max_subplots: # if last batch has fewer images than we expect
|
||||
break
|
||||
|
||||
block_x = int(w * (i // ns))
|
||||
block_y = int(h * (i % ns))
|
||||
|
||||
img = img.transpose(1, 2, 0)
|
||||
if scale_factor < 1:
|
||||
img = cv2.resize(img, (w, h))
|
||||
|
||||
mosaic[block_y:block_y + h, block_x:block_x + w, :] = img
|
||||
if len(targets) > 0:
|
||||
image_targets = targets[targets[:, 0] == i]
|
||||
boxes = xywh2xyxy(image_targets[:, 2:6]).T
|
||||
classes = image_targets[:, 1].astype('int')
|
||||
labels = image_targets.shape[1] == 6 # labels if no conf column
|
||||
conf = None if labels else image_targets[:, 6] # check for confidence presence (label vs pred)
|
||||
|
||||
if boxes.shape[1] and boxes.max() <= 1: # if normalized
|
||||
boxes[[0, 2]] *= w # scale to pixels
|
||||
boxes[[1, 3]] *= h
|
||||
boxes[[0, 2]] += block_x
|
||||
boxes[[1, 3]] += block_y
|
||||
for j, box in enumerate(boxes.T):
|
||||
cls = int(classes[j])
|
||||
color = colors[cls % len(colors)]
|
||||
cls = names[cls] if names else cls
|
||||
if labels or conf[j] > 0.25: # 0.25 conf thresh
|
||||
label = '%s' % cls if labels else '%s %.1f' % (cls, conf[j])
|
||||
plot_one_box(box, mosaic, label=label, color=color, line_thickness=tl)
|
||||
|
||||
# Draw image filename labels
|
||||
if paths:
|
||||
label = Path(paths[i]).name[:40] # trim to 40 char
|
||||
t_size = cv2.getTextSize(label, 0, fontScale=tl / 3, thickness=tf)[0]
|
||||
cv2.putText(mosaic, label, (block_x + 5, block_y + t_size[1] + 5), 0, tl / 3, [220, 220, 220], thickness=tf,
|
||||
lineType=cv2.LINE_AA)
|
||||
|
||||
# Image border
|
||||
cv2.rectangle(mosaic, (block_x, block_y), (block_x + w, block_y + h), (255, 255, 255), thickness=3)
|
||||
|
||||
if fname:
|
||||
r = min(1280. / max(h, w) / ns, 1.0) # ratio to limit image size
|
||||
mosaic = cv2.resize(mosaic, (int(ns * w * r), int(ns * h * r)), interpolation=cv2.INTER_AREA)
|
||||
# cv2.imwrite(fname, cv2.cvtColor(mosaic, cv2.COLOR_BGR2RGB)) # cv2 save
|
||||
Image.fromarray(mosaic).save(fname) # PIL save
|
||||
return mosaic
|
||||
|
||||
|
||||
def plot_lr_scheduler(optimizer, scheduler, epochs=300, save_dir=''):
|
||||
# Plot LR simulating training for full epochs
|
||||
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.tight_layout()
|
||||
plt.savefig(Path(save_dir) / 'LR.png', dpi=200)
|
||||
|
||||
|
||||
def plot_test_txt(): # from utils.plots import *; plot_test()
|
||||
# Plot test.txt histograms
|
||||
x = np.loadtxt('test.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()
|
||||
# Plot targets.txt histograms
|
||||
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='%.3g +/- %.3g' % (x[i].mean(), x[i].std()))
|
||||
ax[i].legend()
|
||||
ax[i].set_title(s[i])
|
||||
plt.savefig('targets.jpg', dpi=200)
|
||||
|
||||
|
||||
def plot_study_txt(path='', x=None): # from utils.plots import *; plot_study_txt()
|
||||
# Plot study.txt generated by test.py
|
||||
fig, ax = plt.subplots(2, 4, figsize=(10, 6), tight_layout=True)
|
||||
ax = ax.ravel()
|
||||
|
||||
fig2, ax2 = plt.subplots(1, 1, figsize=(8, 4), tight_layout=True)
|
||||
for f in [Path(path) / f'study_coco_{x}.txt' for x in ['yolov3', 'yolov3-spp', 'yolov3-tiny']]:
|
||||
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)
|
||||
s = ['P', 'R', 'mAP@.5', 'mAP@.5:.95', 't_inference (ms/img)', 't_NMS (ms/img)', 't_total (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[6, :j], y[3, :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=.25, label='EfficientDet')
|
||||
|
||||
ax2.grid()
|
||||
ax2.set_xlim(0, 30)
|
||||
ax2.set_ylim(15, 50)
|
||||
ax2.set_yticks(np.arange(15, 55, 5))
|
||||
ax2.set_xlabel('GPU Speed (ms/img)')
|
||||
ax2.set_ylabel('COCO AP val')
|
||||
ax2.legend(loc='lower right')
|
||||
plt.savefig('test_study.png', dpi=300)
|
||||
|
||||
|
||||
def plot_labels(labels, save_dir=''):
|
||||
# plot dataset labels
|
||||
c, b = labels[:, 0], labels[:, 1:].transpose() # classes, boxes
|
||||
nc = int(c.max() + 1) # number of classes
|
||||
colors = color_list()
|
||||
|
||||
# seaborn correlogram
|
||||
try:
|
||||
import seaborn as sns
|
||||
import pandas as pd
|
||||
x = pd.DataFrame(b.transpose(), columns=['x', 'y', 'width', 'height'])
|
||||
sns.pairplot(x, corner=True, diag_kind='hist', kind='scatter', markers='o',
|
||||
plot_kws=dict(s=3, edgecolor=None, linewidth=1, alpha=0.02),
|
||||
diag_kws=dict(bins=50))
|
||||
plt.savefig(Path(save_dir) / 'labels_correlogram.png', dpi=200)
|
||||
plt.close()
|
||||
except Exception as e:
|
||||
pass
|
||||
|
||||
# matplotlib labels
|
||||
ax = plt.subplots(2, 2, figsize=(8, 8), tight_layout=True)[1].ravel()
|
||||
ax[0].hist(c, bins=np.linspace(0, nc, nc + 1) - 0.5, rwidth=0.8)
|
||||
ax[0].set_xlabel('classes')
|
||||
ax[2].scatter(b[0], b[1], c=hist2d(b[0], b[1], 90), cmap='jet')
|
||||
ax[2].set_xlabel('x')
|
||||
ax[2].set_ylabel('y')
|
||||
ax[3].scatter(b[2], b[3], c=hist2d(b[2], b[3], 90), cmap='jet')
|
||||
ax[3].set_xlabel('width')
|
||||
ax[3].set_ylabel('height')
|
||||
|
||||
# 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[int(cls) % 10]) # 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(Path(save_dir) / 'labels.png', dpi=200)
|
||||
plt.close()
|
||||
|
||||
|
||||
def plot_evolution(yaml_file='data/hyp.finetune.yaml'): # from utils.plots import *; plot_evolution()
|
||||
# Plot hyperparameter evolution results in evolve.txt
|
||||
with open(yaml_file) as f:
|
||||
hyp = yaml.load(f, Loader=yaml.FullLoader)
|
||||
x = np.loadtxt('evolve.txt', ndmin=2)
|
||||
f = fitness(x)
|
||||
# weights = (f - f.min()) ** 2 # for weighted results
|
||||
plt.figure(figsize=(10, 12), tight_layout=True)
|
||||
matplotlib.rc('font', **{'size': 8})
|
||||
for i, (k, v) in enumerate(hyp.items()):
|
||||
y = x[:, i + 7]
|
||||
# mu = (y * weights).sum() / weights.sum() # best weighted result
|
||||
mu = y[f.argmax()] # best single result
|
||||
plt.subplot(6, 5, i + 1)
|
||||
plt.scatter(y, f, c=hist2d(y, f, 20), cmap='viridis', alpha=.8, edgecolors='none')
|
||||
plt.plot(mu, f.max(), 'k+', markersize=15)
|
||||
plt.title('%s = %.3g' % (k, mu), fontdict={'size': 9}) # limit to 40 characters
|
||||
if i % 5 != 0:
|
||||
plt.yticks([])
|
||||
print('%15s: %.3g' % (k, mu))
|
||||
plt.savefig('evolve.png', dpi=200)
|
||||
print('\nPlot saved as evolve.png')
|
||||
|
||||
|
||||
def plot_results_overlay(start=0, stop=0): # from utils.plots import *; plot_results_overlay()
|
||||
# Plot training 'results*.txt', overlaying train and val losses
|
||||
s = ['train', 'train', 'train', 'Precision', 'mAP@0.5', 'val', 'val', 'val', 'Recall', 'mAP@0.5:0.95'] # legends
|
||||
t = ['Box', 'Objectness', 'Classification', 'P-R', 'mAP-F1'] # titles
|
||||
for f in sorted(glob.glob('results*.txt') + glob.glob('../../Downloads/results*.txt')):
|
||||
results = np.loadtxt(f, usecols=[2, 3, 4, 8, 9, 12, 13, 14, 10, 11], ndmin=2).T
|
||||
n = results.shape[1] # number of rows
|
||||
x = range(start, min(stop, n) if stop else n)
|
||||
fig, ax = plt.subplots(1, 5, figsize=(14, 3.5), tight_layout=True)
|
||||
ax = ax.ravel()
|
||||
for i in range(5):
|
||||
for j in [i, i + 5]:
|
||||
y = results[j, x]
|
||||
ax[i].plot(x, y, marker='.', label=s[j])
|
||||
# y_smooth = butter_lowpass_filtfilt(y)
|
||||
# ax[i].plot(x, np.gradient(y_smooth), marker='.', label=s[j])
|
||||
|
||||
ax[i].set_title(t[i])
|
||||
ax[i].legend()
|
||||
ax[i].set_ylabel(f) if i == 0 else None # add filename
|
||||
fig.savefig(f.replace('.txt', '.png'), dpi=200)
|
||||
|
||||
|
||||
def plot_results(start=0, stop=0, bucket='', id=(), labels=(), save_dir=''):
|
||||
# Plot training 'results*.txt'. from utils.plots import *; plot_results(save_dir='runs/train/exp')
|
||||
fig, ax = plt.subplots(2, 5, figsize=(12, 6))
|
||||
ax = ax.ravel()
|
||||
s = ['Box', 'Objectness', 'Classification', 'Precision', 'Recall',
|
||||
'val Box', 'val Objectness', 'val Classification', 'mAP@0.5', 'mAP@0.5:0.95']
|
||||
if bucket:
|
||||
# files = ['https://storage.googleapis.com/%s/results%g.txt' % (bucket, x) for x in id]
|
||||
files = ['results%g.txt' % x for x in id]
|
||||
c = ('gsutil cp ' + '%s ' * len(files) + '.') % tuple('gs://%s/results%g.txt' % (bucket, x) for x in id)
|
||||
os.system(c)
|
||||
else:
|
||||
files = list(Path(save_dir).glob('results*.txt'))
|
||||
assert len(files), 'No results.txt files found in %s, nothing to plot.' % os.path.abspath(save_dir)
|
||||
for fi, f in enumerate(files):
|
||||
try:
|
||||
results = np.loadtxt(f, usecols=[2, 3, 4, 8, 9, 12, 13, 14, 10, 11], ndmin=2).T
|
||||
n = results.shape[1] # number of rows
|
||||
x = range(start, min(stop, n) if stop else n)
|
||||
for i in range(10):
|
||||
y = results[i, x]
|
||||
if i in [0, 1, 2, 5, 6, 7]:
|
||||
y[y == 0] = np.nan # don't show zero loss values
|
||||
# y /= y[0] # normalize
|
||||
label = labels[fi] if len(labels) else f.stem
|
||||
ax[i].plot(x, y, marker='.', label=label, linewidth=2, markersize=8)
|
||||
ax[i].set_title(s[i])
|
||||
# if i in [5, 6, 7]: # share train and val loss y axes
|
||||
# ax[i].get_shared_y_axes().join(ax[i], ax[i - 5])
|
||||
except Exception as e:
|
||||
print('Warning: Plotting error for %s; %s' % (f, e))
|
||||
|
||||
fig.tight_layout()
|
||||
ax[1].legend()
|
||||
fig.savefig(Path(save_dir) / 'results.png', dpi=200)
|
||||
@ -1,24 +1,45 @@
|
||||
# PyTorch utils
|
||||
|
||||
import logging
|
||||
import math
|
||||
import os
|
||||
import time
|
||||
from contextlib import contextmanager
|
||||
from copy import deepcopy
|
||||
|
||||
import torch
|
||||
import torch.backends.cudnn as cudnn
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
import torchvision
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
def init_seeds(seed=0):
|
||||
@contextmanager
|
||||
def torch_distributed_zero_first(local_rank: int):
|
||||
"""
|
||||
Decorator to make all processes in distributed training wait for each local_master to do something.
|
||||
"""
|
||||
if local_rank not in [-1, 0]:
|
||||
torch.distributed.barrier()
|
||||
yield
|
||||
if local_rank == 0:
|
||||
torch.distributed.barrier()
|
||||
|
||||
|
||||
def init_torch_seeds(seed=0):
|
||||
# Speed-reproducibility tradeoff https://pytorch.org/docs/stable/notes/randomness.html
|
||||
torch.manual_seed(seed)
|
||||
|
||||
# Reduce randomness (may be slower on Tesla GPUs) # https://pytorch.org/docs/stable/notes/randomness.html
|
||||
if seed == 0:
|
||||
if seed == 0: # slower, more reproducible
|
||||
cudnn.deterministic = True
|
||||
cudnn.benchmark = False
|
||||
else: # faster, less reproducible
|
||||
cudnn.deterministic = False
|
||||
cudnn.benchmark = True
|
||||
|
||||
|
||||
def select_device(device='', apex=False, batch_size=None):
|
||||
def select_device(device='', batch_size=None):
|
||||
# device = 'cpu' or '0' or '0,1,2,3'
|
||||
cpu_request = device.lower() == 'cpu'
|
||||
if device and not cpu_request: # if device requested other than 'cpu'
|
||||
@ -32,16 +53,15 @@ def select_device(device='', apex=False, batch_size=None):
|
||||
if ng > 1 and batch_size: # check that batch_size is compatible with device_count
|
||||
assert batch_size % ng == 0, 'batch-size %g not multiple of GPU count %g' % (batch_size, ng)
|
||||
x = [torch.cuda.get_device_properties(i) for i in range(ng)]
|
||||
s = 'Using CUDA ' + ('Apex ' if apex else '') # apex for mixed precision https://github.com/NVIDIA/apex
|
||||
s = f'Using torch {torch.__version__} '
|
||||
for i in range(0, ng):
|
||||
if i == 1:
|
||||
s = ' ' * len(s)
|
||||
print("%sdevice%g _CudaDeviceProperties(name='%s', total_memory=%dMB)" %
|
||||
(s, i, x[i].name, x[i].total_memory / c))
|
||||
logger.info("%sCUDA:%g (%s, %dMB)" % (s, i, x[i].name, x[i].total_memory / c))
|
||||
else:
|
||||
print('Using CPU')
|
||||
logger.info(f'Using torch {torch.__version__} CPU')
|
||||
|
||||
print('') # skip a line
|
||||
logger.info('') # skip a line
|
||||
return torch.device('cuda:0' if cuda else 'cpu')
|
||||
|
||||
|
||||
@ -50,52 +70,77 @@ def time_synchronized():
|
||||
return time.time()
|
||||
|
||||
|
||||
def is_parallel(model):
|
||||
return type(model) in (nn.parallel.DataParallel, nn.parallel.DistributedDataParallel)
|
||||
|
||||
|
||||
def intersect_dicts(da, db, exclude=()):
|
||||
# Dictionary intersection of matching keys and shapes, omitting 'exclude' keys, using da values
|
||||
return {k: v for k, v in da.items() if k in db and not any(x in k for x in exclude) and v.shape == db[k].shape}
|
||||
|
||||
|
||||
def initialize_weights(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-4
|
||||
m.eps = 1e-3
|
||||
m.momentum = 0.03
|
||||
elif t in [nn.LeakyReLU, nn.ReLU, nn.ReLU6]:
|
||||
elif t in [nn.Hardswish, nn.LeakyReLU, nn.ReLU, nn.ReLU6]:
|
||||
m.inplace = True
|
||||
|
||||
|
||||
def find_modules(model, mclass=nn.Conv2d):
|
||||
# finds layer indices matching module class 'mclass'
|
||||
# Finds layer indices matching module class 'mclass'
|
||||
return [i for i, m in enumerate(model.module_list) if isinstance(m, mclass)]
|
||||
|
||||
|
||||
def sparsity(model):
|
||||
# Return global model sparsity
|
||||
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):
|
||||
# Prune model to requested global sparsity
|
||||
import torch.nn.utils.prune as prune
|
||||
print('Pruning model... ', end='')
|
||||
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
|
||||
print(' %.3g global sparsity' % sparsity(model))
|
||||
|
||||
|
||||
def fuse_conv_and_bn(conv, bn):
|
||||
# https://tehnokv.com/posts/fusing-batchnorm-and-conv/
|
||||
with torch.no_grad():
|
||||
# init
|
||||
fusedconv = torch.nn.Conv2d(conv.in_channels,
|
||||
conv.out_channels,
|
||||
kernel_size=conv.kernel_size,
|
||||
stride=conv.stride,
|
||||
padding=conv.padding,
|
||||
bias=True)
|
||||
# Fuse convolution and batchnorm layers 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,
|
||||
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.size()))
|
||||
# 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.size()))
|
||||
|
||||
# prepare spatial bias
|
||||
if conv.bias is not None:
|
||||
b_conv = conv.bias
|
||||
else:
|
||||
b_conv = torch.zeros(conv.weight.size(0))
|
||||
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)
|
||||
# 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
|
||||
return fusedconv
|
||||
|
||||
|
||||
def model_info(model, verbose=False):
|
||||
# Plots a line-by-line description of a PyTorch model
|
||||
def model_info(model, verbose=False, img_size=640):
|
||||
# Model information. img_size may be int or list, i.e. img_size=640 or img_size=[640, 320]
|
||||
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:
|
||||
@ -107,40 +152,57 @@ def model_info(model, verbose=False):
|
||||
|
||||
try: # FLOPS
|
||||
from thop import profile
|
||||
macs, _ = profile(model, inputs=(torch.zeros(1, 3, 480, 640),), verbose=False)
|
||||
fs = ', %.1f GFLOPS' % (macs / 1E9 * 2)
|
||||
except:
|
||||
stride = int(model.stride.max())
|
||||
img = torch.zeros((1, 3, stride, stride), device=next(model.parameters()).device) # input
|
||||
flops = profile(deepcopy(model), inputs=(img,), verbose=False)[0] / 1E9 * 2 # stride FLOPS
|
||||
img_size = img_size if isinstance(img_size, list) else [img_size, img_size] # expand if int/float
|
||||
fs = ', %.1f GFLOPS' % (flops * img_size[0] / stride * img_size[1] / stride) # 640x640 FLOPS
|
||||
except (ImportError, Exception):
|
||||
fs = ''
|
||||
|
||||
print('Model Summary: %g layers, %g parameters, %g gradients%s' % (len(list(model.parameters())), n_p, n_g, fs))
|
||||
logger.info(f"Model Summary: {len(list(model.modules()))} layers, {n_p} parameters, {n_g} gradients{fs}")
|
||||
|
||||
|
||||
def load_classifier(name='resnet101', n=2):
|
||||
# Loads a pretrained model reshaped to n-class output
|
||||
import pretrainedmodels # https://github.com/Cadene/pretrained-models.pytorch#torchvision
|
||||
model = pretrainedmodels.__dict__[name](num_classes=1000, pretrained='imagenet')
|
||||
model = torchvision.models.__dict__[name](pretrained=True)
|
||||
|
||||
# Display model properties
|
||||
for x in ['model.input_size', 'model.input_space', 'model.input_range', 'model.mean', 'model.std']:
|
||||
print(x + ' =', eval(x))
|
||||
# ResNet model properties
|
||||
# input_size = [3, 224, 224]
|
||||
# input_space = 'RGB'
|
||||
# input_range = [0, 1]
|
||||
# mean = [0.485, 0.456, 0.406]
|
||||
# std = [0.229, 0.224, 0.225]
|
||||
|
||||
# Reshape output to n classes
|
||||
filters = model.last_linear.weight.shape[1]
|
||||
model.last_linear.bias = torch.nn.Parameter(torch.zeros(n))
|
||||
model.last_linear.weight = torch.nn.Parameter(torch.zeros(n, filters))
|
||||
model.last_linear.out_features = n
|
||||
filters = model.fc.weight.shape[1]
|
||||
model.fc.bias = nn.Parameter(torch.zeros(n), requires_grad=True)
|
||||
model.fc.weight = nn.Parameter(torch.zeros(n, filters), requires_grad=True)
|
||||
model.fc.out_features = n
|
||||
return model
|
||||
|
||||
|
||||
def scale_img(img, ratio=1.0, same_shape=True): # img(16,3,256,416), r=ratio
|
||||
def scale_img(img, ratio=1.0, same_shape=False): # img(16,3,256,416), r=ratio
|
||||
# scales img(bs,3,y,x) by ratio
|
||||
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
|
||||
gs = 64 # (pixels) grid size
|
||||
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
|
||||
if ratio == 1.0:
|
||||
return img
|
||||
else:
|
||||
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
|
||||
gs = 32 # (pixels) grid size
|
||||
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=()):
|
||||
# Copy attributes from b to a, options to only include [...] and to exclude [...]
|
||||
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)
|
||||
|
||||
|
||||
class ModelEMA:
|
||||
@ -149,46 +211,32 @@ class ModelEMA:
|
||||
This is intended to allow functionality like
|
||||
https://www.tensorflow.org/api_docs/python/tf/train/ExponentialMovingAverage
|
||||
A smoothed version of the weights is necessary for some training schemes to perform well.
|
||||
E.g. Google's hyper-params for training MNASNet, MobileNet-V3, EfficientNet, etc that use
|
||||
RMSprop with a short 2.4-3 epoch decay period and slow LR decay rate of .96-.99 requires EMA
|
||||
smoothing of weights to match results. Pay attention to the decay constant you are using
|
||||
relative to your update count per epoch.
|
||||
To keep EMA from using GPU resources, set device='cpu'. This will save a bit of memory but
|
||||
disable validation of the EMA weights. Validation will have to be done manually in a separate
|
||||
process, or after the training stops converging.
|
||||
This class is sensitive where it is initialized in the sequence of model init,
|
||||
GPU assignment and distributed training wrappers.
|
||||
I've tested with the sequence in my own train.py for torch.DataParallel, apex.DDP, and single-GPU.
|
||||
"""
|
||||
|
||||
def __init__(self, model, decay=0.9999, device=''):
|
||||
# make a copy of the model for accumulating moving average of weights
|
||||
self.ema = deepcopy(model)
|
||||
self.ema.eval()
|
||||
self.updates = 0 # number of EMA updates
|
||||
def __init__(self, model, decay=0.9999, updates=0):
|
||||
# Create EMA
|
||||
self.ema = deepcopy(model.module if is_parallel(model) else model).eval() # FP32 EMA
|
||||
# if next(model.parameters()).device.type != 'cpu':
|
||||
# self.ema.half() # FP16 EMA
|
||||
self.updates = updates # number of EMA updates
|
||||
self.decay = lambda x: decay * (1 - math.exp(-x / 2000)) # decay exponential ramp (to help early epochs)
|
||||
self.device = device # perform ema on different device from model if set
|
||||
if device:
|
||||
self.ema.to(device=device)
|
||||
for p in self.ema.parameters():
|
||||
p.requires_grad_(False)
|
||||
|
||||
def update(self, model):
|
||||
self.updates += 1
|
||||
d = self.decay(self.updates)
|
||||
# Update EMA parameters
|
||||
with torch.no_grad():
|
||||
if type(model) in (nn.parallel.DataParallel, nn.parallel.DistributedDataParallel):
|
||||
msd, esd = model.module.state_dict(), self.ema.module.state_dict()
|
||||
else:
|
||||
msd, esd = model.state_dict(), self.ema.state_dict()
|
||||
self.updates += 1
|
||||
d = self.decay(self.updates)
|
||||
|
||||
for k, v in esd.items():
|
||||
msd = model.module.state_dict() if is_parallel(model) else model.state_dict() # model state_dict
|
||||
for k, v in self.ema.state_dict().items():
|
||||
if v.dtype.is_floating_point:
|
||||
v *= d
|
||||
v += (1. - d) * msd[k].detach()
|
||||
|
||||
def update_attr(self, model):
|
||||
# Assign attributes (which may change during training)
|
||||
for k in model.__dict__.keys():
|
||||
if not k.startswith('_'):
|
||||
setattr(self.ema, k, getattr(model, k))
|
||||
def update_attr(self, model, include=(), exclude=('process_group', 'reducer')):
|
||||
# Update EMA attributes
|
||||
copy_attr(self.ema, model, include, exclude)
|
||||
|
||||
1080
utils/utils.py
1080
utils/utils.py
File diff suppressed because it is too large
Load Diff
10
weights/download_weights.sh
Executable file
10
weights/download_weights.sh
Executable file
@ -0,0 +1,10 @@
|
||||
#!/bin/bash
|
||||
# Download latest models from https://github.com/ultralytics/yolov3/releases
|
||||
|
||||
python - <<EOF
|
||||
from utils.google_utils import attempt_download
|
||||
|
||||
for x in ['yolov3', 'yolov3-spp', 'yolov3-tiny']:
|
||||
attempt_download(f'{x}.pt')
|
||||
|
||||
EOF
|
||||
@ -1,24 +0,0 @@
|
||||
#!/bin/bash
|
||||
|
||||
# make '/weights' directory if it does not exist and cd into it
|
||||
# mkdir -p weights && cd weights
|
||||
|
||||
# copy darknet weight files, continue '-c' if partially downloaded
|
||||
# wget -c https://pjreddie.com/media/files/yolov3.weights
|
||||
# wget -c https://pjreddie.com/media/files/yolov3-tiny.weights
|
||||
# wget -c https://pjreddie.com/media/files/yolov3-spp.weights
|
||||
|
||||
# yolov3 pytorch weights
|
||||
# download from Google Drive: https://drive.google.com/drive/folders/1uxgUBemJVw9wZsdpboYbzUN4bcRhsuAI
|
||||
|
||||
# darknet53 weights (first 75 layers only)
|
||||
# wget -c https://pjreddie.com/media/files/darknet53.conv.74
|
||||
|
||||
# yolov3-tiny weights from darknet (first 16 layers only)
|
||||
# ./darknet partial cfg/yolov3-tiny.cfg yolov3-tiny.weights yolov3-tiny.conv.15 15
|
||||
# mv yolov3-tiny.conv.15 ../
|
||||
|
||||
# new method
|
||||
python3 -c "from models import *;
|
||||
attempt_download('weights/yolov3.pt');
|
||||
attempt_download('weights/yolov3-spp.pt')"
|
||||
Loading…
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Reference in New Issue
Block a user